From da39c8afc0af3971056e4d5e94317383aeceeaaf Mon Sep 17 00:00:00 2001
From: GitHub Action <action@github.com>
Date: Mon, 19 Aug 2024 06:05:52 +0000
Subject: [PATCH] update data

---
 ...6a0ba437d41a7c82c08a9636a4438c1b5031378.md |   12 +-
 ...acd117521ef5aafb09fed02ab415523b330b058.md |   12 +-
 ...d01d21137a5af9f04e4b16a55a0f732cb8a540b.md |   12 +-
 ...23acfbccca0460171b6b06a4012dbb991cde55b.md |   16 +-
 ...6f01c1b3ddd0b2abd5ddfe4fdb3f74767607277.md |    8 +-
 ...df04f33a8ef313cc2067147dbb79c3ca7c5c99f.md |   16 +-
 ...79cd637b7e38bba1dd8915b5ce68cbcacecbe68.md |   58 +-
 ...b2aa13d4959073f61ad70555bc8c7da7d116196.md |    8 +-
 ...5e2571c17246577e0bc1b9de57a314c3b60e220.md |   10 +-
 ...c9961153493370500020c81527b3548c96f81e0.md |    8 +-
 ...df50e9b73cc2937dfd651f4c3344bc99b7ed3f2.md |   12 +-
 ...55bfc515eb279cc09023faa1f78c6efb61224ba.md |    8 +-
 ...971f9abd024e40fbbdff2e9492745b68a6bca01.md |   12 +-
 ...a111f7a3b56d0468f13104999844885157ef17d.md |   14 +-
 ...bd2c0acaf58c25f71617db2396188c74d29bf14.md |    4 +-
 ...d150cec2775f9bc863760448f14104cc8f42368.md |   14 +-
 ...0d0d998fa038182b3b69a57adb9b2f82d40589c.md |    6 +-
 ...48d90b713997a771e2c49f02cd771e8b7b10b37.md |   10 +-
 ...3dd9c49f205280991826b2ea4b50344203916b4.md |   12 +-
 ...83547fdbd88552328a6615ec620f96e39c57018.md |   18 +-
 ...2eb064f432557c59ce99834d7dc7817e4687271.md |   26 +-
 ...0fc3882a9976f6a9cdc3a724bce184b786503da.md |    8 +-
 ...3c94ccc094dcf546e8e31c9a42506302e837524.md |   16 +-
 ...cea298edb788edf821aef58f0952c3e8debc25a.md |   12 +-
 ...3dbbd0f0de51b421a6220bd6480b8d2e99a88e9.md |   14 +-
 ...2a83369383aff37224170c1ae3d3870d5d9e419.md |   14 +-
 ...2e1f1b8e6c1b7f4f166e15b7c674945856a51b6.md |    8 +-
 ...6f0a85009481dcfd93aaa43ed3f980e5033b0d8.md |   28 +-
 ...45f85fa1beaa795c24c4ff86f1f2deece72252f.md |    6 +-
 ...f1f1cf9df8c413ec7345da7604ba28597da5b90.md |   14 +-
 site/Symbolic regression/index.html           | 3121 +++++++++--------
 site/Time-series forecasting/index.html       | 2897 ++++++++-------
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 .../index.html                                |   28 +-
 .../index.html                                |    6 +-
 .../index.html                                |   14 +-
 site/search/search_index.json                 |    2 +-
 site/sitemap.xml.gz                           |  Bin 127 -> 127 bytes
 64 files changed, 3414 insertions(+), 3438 deletions(-)

diff --git a/docs/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378.md b/docs/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378.md
index 126f3ef8..d03f0075 100644
--- a/docs/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378.md
+++ b/docs/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:45 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:28 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -51,7 +51,7 @@ hide:
           <td>2023-03-15</td>
           <td>PLOS Computational Biology</td>
           <td>3</td>
-          <td>54</td>
+          <td>55</td>
         </tr>
     
         <tr id="High-throughput data acquisition in synthetic biology leads to an abundance of data that need to be processed and aggregated into useful biological models. Building dynamical models based on this wealth of data is of paramount importance to understand and optimize designs of synthetic biology constructs. However, building models manually for each data set is inconvenient and might become infeasible for highly complex synthetic systems. In this paper, we present state-of-the-art system identification techniques and combine them with chemical reaction network theory (CRNT) to generate dynamic models automatically. On the system identification side, Sparse Bayesian Learning offers methods to learn from data the sparsest set of dictionary functions necessary to capture the dynamics of the system into ODE models; on the CRNT side, building on such sparse ODE models, all possible network structures within a given parameter uncertainty region can be computed. Additionally, the system identification process can be complemented with constraints on the parameters to, for example, enforce stability or non-negativity-thus offering relevant physical constraints over the possible network structures. In this way, the wealth of data can be translated into biologically relevant network structures, which then steers the data acquisition, thereby providing a vital step for closed-loop system identification.">
@@ -63,7 +63,7 @@ hide:
           <td>2018-09-24</td>
           <td>2018 IEEE Conference on Decision and Control (CDC)</td>
           <td>3</td>
-          <td>34</td>
+          <td>35</td>
         </tr>
     
         <tr id="Reconstruction of biochemical reaction networks (BRN) and genetic regulatory networks (GRN) in particular is a central topic in systems biology which raises crucial theoretical challenges in system identification. Nonlinear Ordinary Differential Equations (ODEs) that involve polynomial and rational functions are typically used to model biochemical reaction networks. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data quite difficult. In this paper, we present a network reconstruction algorithm that can deal with ODE model descriptions containing polynomial and rational functions. Rather than identifying the parameters of linear or nonlinear ODEs characterised by pre-defined equation structures, our methodology allows us to determine the nonlinear ODEs structure together with their associated parameters. To solve the network reconstruction problem, we cast it as a compressive sensing (CS) problem and use sparse Bayesian learning (SBL) algorithms as a computationally efficient and robust way to obtain its solution.">
@@ -75,7 +75,7 @@ hide:
           <td>2012-05-08</td>
           <td>2012 IEEE 51st IEEE Conference on Decision and Control (CDC)</td>
           <td>33</td>
-          <td>34</td>
+          <td>35</td>
         </tr>
     
         <tr id="None">
@@ -110,8 +110,8 @@ hide:
           </td>
           <td>2022-06-01</td>
           <td>Nonlinear Dynamics</td>
-          <td>27</td>
-          <td>90</td>
+          <td>28</td>
+          <td>91</td>
         </tr>
     
         <tr id="Systems biology is a new discipline built upon the premise that an understanding of how cells and organisms carry out their functions cannot be gained by looking at cellular components in isolation. Instead, consideration of the interplay between the parts of systems is indispensable for analyzing, modeling, and predicting systems' behavior. Studying biological processes under this premise, systems biology combines experimental techniques and computational methods in order to construct predictive models. Both in building and utilizing models of biological systems, inverse problems arise at several occasions, for example, (i) when experimental time series and steady state data are used to construct biochemical reaction networks, (ii) when model parameters are identified that capture underlying mechanisms or (iii) when desired qualitative behavior such as bistability or limit cycle oscillations is engineered by proper choices of parameter combinations. In this paper we review principles of the modeling process in systems biology and illustrate the ill-posedness and regularization of parameter identification problems in that context. Furthermore, we discuss the methodology of qualitative inverse problems and demonstrate how sparsity enforcing regularization allows the determination of key reaction mechanisms underlying the qualitative behavior.">
diff --git a/docs/recommendations/0acd117521ef5aafb09fed02ab415523b330b058.md b/docs/recommendations/0acd117521ef5aafb09fed02ab415523b330b058.md
index 30558b13..7285350c 100644
--- a/docs/recommendations/0acd117521ef5aafb09fed02ab415523b330b058.md
+++ b/docs/recommendations/0acd117521ef5aafb09fed02ab415523b330b058.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:57 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:36 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,7 +50,7 @@ hide:
           </td>
           <td>2023-11-01</td>
           <td>Chaos</td>
-          <td>2</td>
+          <td>3</td>
           <td>11</td>
         </tr>
     
@@ -74,8 +74,8 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3181</td>
-          <td>63</td>
+          <td>3190</td>
+          <td>65</td>
         </tr>
     
         <tr id="None">
@@ -98,7 +98,7 @@ hide:
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>237</td>
+          <td>238</td>
           <td>12</td>
         </tr>
     
@@ -135,7 +135,7 @@ hide:
           <td>2017-12-01</td>
           <td>2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)</td>
           <td>12</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b.md b/docs/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b.md
index 86ce3664..cda25a8d 100644
--- a/docs/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b.md
+++ b/docs/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:12 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:04:58 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -51,7 +51,7 @@ hide:
           <td>2023-10-24</td>
           <td>ArXiv</td>
           <td>4</td>
-          <td>49</td>
+          <td>50</td>
         </tr>
     
         <tr id="Graph neural networks (GNNs) have been widely applied in multi-variate time-series forecasting (MTSF) tasks because of their capability in capturing the correlations among different time-series. These graph-based learning approaches improve the forecasting performance by discovering and understanding the underlying graph structures, which represent the data correlation. When the explicit prior graph structures are not available, most existing works cannot guarantee the sparsity of the generated graphs that make the overall model computational expensive and less interpretable. In this work, we propose a decoupled training method, which includes a graph generating module and a GNNs forecasting module. First, we use Graphical Lasso (or GraphLASSO) to directly exploit the sparsity pattern from data to build graph structures in both static and time-varying cases. Second, we fit these graph structures and the input data into a Graph Convolutional Recurrent Network (GCRN) to train a forecasting model. The experimental results on three real-world datasets show that our novel approach has competitive performance against existing state-of-the-art forecasting algorithms while providing sparse, meaningful and explainable graph structures and reducing training time by approximately 40%. Our PyTorch implementation is publicly available at https://github.com/HySonLab/GraphLASSO">
@@ -99,7 +99,7 @@ hide:
           <td>2021-09-10</td>
           <td>ArXiv</td>
           <td>12</td>
-          <td>45</td>
+          <td>46</td>
         </tr>
     
         <tr id="Multivariate time-series forecasting is a critical task for many applications, and graph time-series network is widely studied due to its capability to capture the spatial-temporal correlation simultaneously. However, most existing works focus more on learning with the explicit prior graph structure, while ignoring potential information from the implicit graph structure, yielding incomplete structure modeling. Some recent works attempts to learn the intrinsic or implicit graph structure directly, while lacking a way to combine explicit prior structure with implicit structure together. In this paper, we propose Regularized Graph Structure Learning (RGSL) model to incorporate both explicit prior structure and implicit structure together, and learn the forecasting deep networks along with the graph structure. RGSL consists of two innovative modules. First, we derive an implicit dense similarity matrix through node embedding, and learn the sparse graph structure using the Regularized Graph Generation (RGG) based on the Gumbel Softmax trick. Second, we propose a Laplacian Matrix Mixed-up Module (LM3) to fuse the explicit graph and implicit graph together. We conduct experiments on three real-word datasets. Results show that the proposed RGSL model outperforms existing graph forecasting algorithms with a notable margin, while learning meaningful graph structure simultaneously. Our code and models are made publicly available at https://github.com/alipay/RGSL.git.">
@@ -111,7 +111,7 @@ hide:
           <td>2022-07-01</td>
           <td>ArXiv, DBLP</td>
           <td>37</td>
-          <td>33</td>
+          <td>34</td>
         </tr>
     
         <tr id="Time series forecasting is an extensively studied subject in statistics, economics, and computer science. Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the performance of a time series model. When using deep neural networks as forecasting models, we hypothesize that exploiting the pairwise information among multiple (multivariate) time series also improves their forecast. If an explicit graph structure is known, graph neural networks (GNNs) have been demonstrated as powerful tools to exploit the structure. In this work, we propose learning the structure simultaneously with the GNN if the graph is unknown. We cast the problem as learning a probabilistic graph model through optimizing the mean performance over the graph distribution. The distribution is parameterized by a neural network so that discrete graphs can be sampled differentiably through reparameterization. Empirical evaluations show that our method is simpler, more efficient, and better performing than a recently proposed bilevel learning approach for graph structure learning, as well as a broad array of forecasting models, either deep or non-deep learning based, and graph or non-graph based.">
@@ -122,8 +122,8 @@ hide:
           </td>
           <td>2021-01-18</td>
           <td>ArXiv</td>
-          <td>175</td>
-          <td>36</td>
+          <td>177</td>
+          <td>37</td>
         </tr>
     
         <tr id="Multi-variate time series forecasting is an important problem with a wide range of applications. Recent works model the relations between time-series as graphs and have shown that propagating information over the relation graph can improve time series forecasting. However, in many cases, relational information is not available or is noisy and reliable. Moreover, most works ignore the underlying uncertainty of time-series both for structure learning and deriving the forecasts resulting in the structure not capturing the uncertainty resulting in forecast distributions with poor uncertainty estimates. We tackle this challenge and introduce STOIC, that leverages stochastic correlations between time-series to learn underlying structure between time-series and to provide well-calibrated and accurate forecasts. Over a wide-range of benchmark datasets STOIC provides around 16% more accurate and 14% better-calibrated forecasts. STOIC also shows better adaptation to noise in data during inference and captures important and useful relational information in various benchmarks.">
diff --git a/docs/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b.md b/docs/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b.md
index b0891585..aa65d353 100644
--- a/docs/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b.md
+++ b/docs/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:13 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:04:59 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,7 +50,7 @@ hide:
           </td>
           <td>2024-03-12</td>
           <td>ArXiv</td>
-          <td>24</td>
+          <td>27</td>
           <td>18</td>
         </tr>
     
@@ -63,7 +63,7 @@ hide:
           <td>2024-02-04</td>
           <td>ArXiv</td>
           <td>3</td>
-          <td>65</td>
+          <td>66</td>
         </tr>
     
         <tr id="Large language models (LLMs) are being applied to time series tasks, particularly time series forecasting. However, are language models actually useful for time series? After a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade the forecasting results -- in most cases the results even improved. We also find that despite their significant computational cost, pretrained LLMs do no better than models trained from scratch, do not represent the sequential dependencies in time series, and do not assist in few-shot settings. Additionally, we explore time series encoders and reveal that patching and attention structures perform similarly to state-of-the-art LLM-based forecasters.">
@@ -86,8 +86,8 @@ hide:
           </td>
           <td>2024-02-04</td>
           <td>ArXiv</td>
-          <td>6</td>
-          <td>65</td>
+          <td>5</td>
+          <td>66</td>
         </tr>
     
         <tr id="In this paper, we introduce TimeGPT, the first foundation model for time series, capable of generating accurate predictions for diverse datasets not seen during training. We evaluate our pre-trained model against established statistical, machine learning, and deep learning methods, demonstrating that TimeGPT zero-shot inference excels in performance, efficiency, and simplicity. Our study provides compelling evidence that insights from other domains of artificial intelligence can be effectively applied to time series analysis. We conclude that large-scale time series models offer an exciting opportunity to democratize access to precise predictions and reduce uncertainty by leveraging the capabilities of contemporary advancements in deep learning.">
@@ -98,7 +98,7 @@ hide:
           </td>
           <td>2023-10-05</td>
           <td>ArXiv</td>
-          <td>39</td>
+          <td>40</td>
           <td>1</td>
         </tr>
     
@@ -122,7 +122,7 @@ hide:
           </td>
           <td>2023-10-03</td>
           <td>ArXiv</td>
-          <td>113</td>
+          <td>120</td>
           <td>9</td>
         </tr>
     
@@ -146,7 +146,7 @@ hide:
           </td>
           <td>2023-06-19</td>
           <td>ArXiv</td>
-          <td>34</td>
+          <td>35</td>
           <td>8</td>
         </tr>
     
diff --git a/docs/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277.md b/docs/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277.md
index 47cfce78..048d1e2e 100644
--- a/docs/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277.md
+++ b/docs/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:17 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:02 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -74,7 +74,7 @@ hide:
           </td>
           <td>2022-09-20</td>
           <td>IEEE Transactions on Knowledge and Data Engineering</td>
-          <td>56</td>
+          <td>58</td>
           <td>17</td>
         </tr>
     
@@ -146,7 +146,7 @@ hide:
           </td>
           <td>2023-10-14</td>
           <td>ArXiv</td>
-          <td>45</td>
+          <td>48</td>
           <td>14</td>
         </tr>
     
@@ -159,7 +159,7 @@ hide:
           <td>2024-02-04</td>
           <td>ArXiv</td>
           <td>3</td>
-          <td>65</td>
+          <td>66</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f.md b/docs/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f.md
index 34da21bd..d123de5d 100644
--- a/docs/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f.md
+++ b/docs/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:14 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:00 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -62,8 +62,8 @@ hide:
           </td>
           <td>2024-02-13</td>
           <td>ArXiv</td>
-          <td>30</td>
-          <td>8</td>
+          <td>32</td>
+          <td>9</td>
         </tr>
     
         <tr id="Message-passing graph neural networks (GNNs), while excelling at capturing local relationships, often struggle with long-range dependencies on graphs. Conversely, graph transformers (GTs) enable information exchange between all nodes but oversimplify the graph structure by treating them as a set of fixed-length vectors. This work proposes a novel architecture, NeuralWalker, that overcomes the limitations of both methods by combining random walks with message passing. NeuralWalker achieves this by treating random walks as sequences, allowing for the application of recent advances in sequence models in order to capture long-range dependencies within these walks. Based on this concept, we propose a framework that offers (1) more expressive graph representations through random walk sequences, (2) the ability to utilize any sequence model for capturing long-range dependencies, and (3) the flexibility by integrating various GNN and GT architectures. Our experimental evaluations demonstrate that NeuralWalker achieves significant performance improvements on 19 graph and node benchmark datasets, notably outperforming existing methods by up to 13% on the PascalVoc-SP and COCO-SP datasets. Code is available at https://github.com/BorgwardtLab/NeuralWalker.">
@@ -87,7 +87,7 @@ hide:
           <td>2024-06-09</td>
           <td>ArXiv</td>
           <td>1</td>
-          <td>2</td>
+          <td>3</td>
         </tr>
     
         <tr id="Graph representation learning (GRL) is fundamental in multi-graph applications like molecular property prediction. Graph neural networks (GNNs) have emerged as a popular method for GRL. However, existing GRL methods primarily focus on designing GNN models with enhanced expressiveness while overlooking the memory efficiency of algorithms during training. The memory inefficiency problem is caused by a contextual constraint imposed on node representations, which requires each node to be context-dependent on its input graph. In this paper, we propose a novel method, called context sketching (COS), for memory-efficient graph representation learning in multi-graph scenarios. We first formally define the contextual constraint based on the enclosed $\infty-$hop subgraphs of nodes. Subsequently, we propose to relax the original contextual constraint by requiring each node to be context-dependent on its enclosed $k-$hop subgraph $(k \ll \infty)$ which is a contextual sketch of the enclosed $\infty$-hop subgraph. Lastly, we prove that COS constructs an optimal solution to a memory-related objective associated with graph coarsening. Experiments on four widely used benchmark datasets demonstrate that COS can reduce the memory footprint of baselines by a large margin with almost no accuracy loss.">
@@ -111,7 +111,7 @@ hide:
           <td>2024-06-17</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
+          <td>7</td>
         </tr>
     
         <tr id="The Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision. However, when it comes to graph-structured data, transformers have not achieved competitive performance, especially on large graphs. In this paper, we identify the main deficiencies of current graph transformers:(1) Existing node sampling strategies in Graph Transformers are agnostic to the graph characteristics and the training process. (2) Most sampling strategies only focus on local neighbors and neglect the long-range dependencies in the graph. We conduct experimental investigations on synthetic datasets to show that existing sampling strategies are sub-optimal. To tackle the aforementioned problems, we formulate the optimization strategies of node sampling in Graph Transformer as an adversary bandit problem, where the rewards are related to the attention weights and can vary in the training procedure. Meanwhile, we propose a hierarchical attention scheme with graph coarsening to capture the long-range interactions while reducing computational complexity. Finally, we conduct extensive experiments on real-world datasets to demonstrate the superiority of our method over existing graph transformers and popular GNNs.">
@@ -122,8 +122,8 @@ hide:
           </td>
           <td>2022-10-08</td>
           <td>ArXiv</td>
-          <td>58</td>
-          <td>17</td>
+          <td>59</td>
+          <td>19</td>
         </tr>
     
         <tr id="Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the success is limited to small-scale graphs due to the drawbacks of full dot-product attention on graphs such as the quadratic complexity with respect to the number of nodes and message aggregation from enormous irrelevant nodes. To address these issues, we propose Deformable Graph Transformer (DGT) that performs sparse attention via dynamically sampled relevant nodes for efficiently handling large-scale graphs with a linear complexity in the number of nodes. Specifically, our framework first constructs multiple node sequences with various criteria to consider both structural and semantic proximity. Then, combining with our learnable Katz Positional Encodings, the sparse attention is applied to the node sequences for learning node representations with a significantly reduced computational cost. Extensive experiments demonstrate that our DGT achieves state-of-the-art performance on 7 graph benchmark datasets with 2.5 - 449 times less computational cost compared to transformer-based graph models with full attention.">
@@ -135,7 +135,7 @@ hide:
           <td>2022-06-29</td>
           <td>ArXiv</td>
           <td>6</td>
-          <td>27</td>
+          <td>28</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68.md b/docs/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68.md
index 71d538b8..7c3e7d49 100644
--- a/docs/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68.md
+++ b/docs/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:27 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:09 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -49,7 +49,7 @@ hide:
             Andreas Doerr, Christian Daniel, Martin Schiegg, D. Nguyen-Tuong, S. Schaal, Marc Toussaint, Sebastian Trimpe
           </td>
           <td>2018-01-31</td>
-          <td>MAG, ArXiv, DBLP</td>
+          <td>ArXiv, MAG, DBLP</td>
           <td>110</td>
           <td>93</td>
         </tr>
@@ -87,7 +87,31 @@ hide:
           <td>2022-05-26</td>
           <td>J. Mach. Learn. Res.</td>
           <td>11</td>
-          <td>49</td>
+          <td>50</td>
+        </tr>
+    
+        <tr id="Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other. Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents. This paper introduces the relational state-space model (R-SSM), a sequential hierarchical latent variable model that makes use of graph neural networks (GNNs) to simulate the joint state transitions of multiple correlated objects. By letting GNNs cooperate with SSM, R-SSM provides a flexible way to incorporate relational information into the modeling of multi-object dynamics. We further suggest augmenting the model with normalizing flows instantiated for vertex-indexed random variables and propose two auxiliary contrastive objectives to facilitate the learning. The utility of R-SSM is empirically evaluated on synthetic and real time series datasets.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/7a1e5377b08489c2969f73c56efc557e34f578e1" target='_blank'>Relational State-Space Model for Stochastic Multi-Object Systems</a></td>
+          <td>
+            Fan Yang, Ling Chen, Fan Zhou, Yusong Gao, Wei Cao
+          </td>
+          <td>2020-01-13</td>
+          <td>ArXiv</td>
+          <td>8</td>
+          <td>57</td>
+        </tr>
+    
+        <tr id="Over the past few years, research on deep graph learning has shifted from static graphs to temporal graphs in response to real-world complex systems that exhibit dynamic behaviors. In practice, temporal graphs are formalized as an ordered sequence of static graph snapshots observed at discrete time points. Sequence models such as RNNs or Transformers have long been the predominant backbone networks for modeling such temporal graphs. Yet, despite the promising results, RNNs struggle with long-range dependencies, while transformers are burdened by quadratic computational complexity. Recently, state space models (SSMs), which are framed as discretized representations of an underlying continuous-time linear dynamical system, have garnered substantial attention and achieved breakthrough advancements in independent sequence modeling. In this work, we undertake a principled investigation that extends SSM theory to temporal graphs by integrating structural information into the online approximation objective via the adoption of a Laplacian regularization term. The emergent continuous-time system introduces novel algorithmic challenges, thereby necessitating our development of GraphSSM, a graph state space model for modeling the dynamics of temporal graphs. Extensive experimental results demonstrate the effectiveness of our GraphSSM framework across various temporal graph benchmarks.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/919e5db29c7b7be4468b975eb4c0fa4a543165fc" target='_blank'>State Space Models on Temporal Graphs: A First-Principles Study</a></td>
+          <td>
+            Jintang Li, Ruofan Wu, Xinzhou Jin, Boqun Ma, Liang Chen, Zibin Zheng
+          </td>
+          <td>2024-06-03</td>
+          <td>ArXiv</td>
+          <td>1</td>
+          <td>11</td>
         </tr>
     
         <tr id="Time series modeling is a well-established problem, which often requires that methods (1) expressively represent complicated dependencies, (2) forecast long horizons, and (3) efficiently train over long sequences. State-space models (SSMs) are classical models for time series, and prior works combine SSMs with deep learning layers for efficient sequence modeling. However, we find fundamental limitations with these prior approaches, proving their SSM representations cannot express autoregressive time series processes. We thus introduce SpaceTime, a new state-space time series architecture that improves all three criteria. For expressivity, we propose a new SSM parameterization based on the companion matrix -- a canonical representation for discrete-time processes -- which enables SpaceTime's SSM layers to learn desirable autoregressive processes. For long horizon forecasting, we introduce a"closed-loop"variation of the companion SSM, which enables SpaceTime to predict many future time-steps by generating its own layer-wise inputs. For efficient training and inference, we introduce an algorithm that reduces the memory and compute of a forward pass with the companion matrix. With sequence length $\ell$ and state-space size $d$, we go from $\tilde{O}(d \ell)$ na\"ively to $\tilde{O}(d + \ell)$. In experiments, our contributions lead to state-of-the-art results on extensive and diverse benchmarks, with best or second-best AUROC on 6 / 7 ECG and speech time series classification, and best MSE on 14 / 16 Informer forecasting tasks. Furthermore, we find SpaceTime (1) fits AR($p$) processes that prior deep SSMs fail on, (2) forecasts notably more accurately on longer horizons than prior state-of-the-art, and (3) speeds up training on real-world ETTh1 data by 73% and 80% relative wall-clock time over Transformers and LSTMs.">
@@ -99,7 +123,7 @@ hide:
           <td>2023-03-16</td>
           <td>ArXiv</td>
           <td>28</td>
-          <td>43</td>
+          <td>44</td>
         </tr>
     
         <tr id="
@@ -113,35 +137,11 @@ hide:
             R. G. Krishnan, Uri Shalit, D. Sontag
           </td>
           <td>2016-09-30</td>
-          <td>MAG, ArXiv, DBLP</td>
+          <td>ArXiv, MAG, DBLP</td>
           <td>428</td>
           <td>48</td>
         </tr>
     
-        <tr id="Probabilistic inference in high-dimensional state-space models is computationally challenging. For many spatiotemporal systems, however, prior knowledge about the dependency structure of state variables is available. We leverage this structure to develop a computationally efficient approach to state estimation and learning in graph-structured state-space models with (partially) unknown dynamics and limited historical data. Building on recent methods that combine ideas from deep learning with principled inference in Gaussian Markov random fields (GMRF), we reformulate graph-structured state-space models as Deep GMRFs defined by simple spatial and temporal graph layers. This results in a flexible spatiotemporal prior that can be learned efficiently from a single time sequence via variational inference. Under linear Gaussian assumptions, we retain a closed-form posterior, which can be sampled efficiently using the conjugate gradient method, scaling favourably compared to classical Kalman filter based approaches">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/66173ff04bc062987a4395181001bf9b7c3eb21b" target='_blank'>Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems</a></td>
-          <td>
-            Fiona Lippert, B. Kranstauber, E. E. V. Loon, Patrick Forr'e
-          </td>
-          <td>2023-06-14</td>
-          <td>ArXiv</td>
-          <td>1</td>
-          <td>23</td>
-        </tr>
-    
-        <tr id="Many applications, e.g., healthcare, education, call for effective methods methods for constructing predictive models from high dimensional time series data where the relationship between variables can be complex and vary over time. In such settings, the underlying system undergoes a sequence of unobserved transitions among a finite set of hidden states. Furthermore, the relationships between the observed variables and their temporal dynamics may depend on the hidden state of the system. To further complicate matters, the hidden state sequences underlying the observed data from different individuals may not be aligned relative to a common frame of reference. Against this background, we consider the novel problem of jointly learning the state-dependent inter-variable relationships as well as the pattern of transitions between hidden states from multi-variate time series data. To solve this problem, we introduce the State-Regularized Vector Autoregressive Model (SrVARM) which combines a state-regularized recurrent neural network to learn the dynamics of transitions between discrete hidden states with an augmented autoregressive model which models the inter-variable dependencies in each state using a state-dependent directed acyclic graph (DAG). We propose an efficient algorithm for training SrVARM by leveraging a recently introduced reformulation of the combinatorial problem of optimizing the DAG structure with respect to a scoring function into a continuous optimization problem. We report results of extensive experiments with simulated data as well as a real-world benchmark that show that SrVARM outperforms state-of-the-art baselines in recovering the unobserved state transitions and discovering the state-dependent relationships among variables.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4ddaa0ff15691ba148dd88c82b03547e9d2aa013" target='_blank'>SrVARM: State Regularized Vector Autoregressive Model for Joint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Multi-variate Time Series</a></td>
-          <td>
-            Tsung-Yu Hsieh, Yiwei Sun, Xianfeng Tang, Suhang Wang, Vasant G Honavar
-          </td>
-          <td>2021-04-19</td>
-          <td>Proceedings of the Web Conference 2021</td>
-          <td>8</td>
-          <td>54</td>
-        </tr>
-    
   </tbody>
   <tfoot>
     <tr>
diff --git a/docs/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196.md b/docs/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196.md
index c8315d31..5b4344c7 100644
--- a/docs/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196.md
+++ b/docs/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:50 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:29 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -122,7 +122,7 @@ hide:
           </td>
           <td>2020-07-02</td>
           <td>ArXiv</td>
-          <td>128</td>
+          <td>129</td>
           <td>22</td>
         </tr>
     
@@ -147,7 +147,7 @@ hide:
           <td>2019-06-10</td>
           <td>J. Comput. Phys.</td>
           <td>21</td>
-          <td>18</td>
+          <td>19</td>
         </tr>
     
         <tr id="In recent years, we have witnessed a significant shift toward ever-more complex and ever-larger-scale systems in the majority of the grand societal challenges tackled in applied sciences. The need to comprehend and predict the dynamics of complex systems have spurred developments in large-scale simulations and a multitude of methods across several disciplines. The goals of understanding and prediction in complex dynamical systems, however, have been hindered by high dimensionality, complexity and chaotic behaviours. Recent advances in data-driven techniques and machine-learning approaches have revolutionized how we model and analyse complex systems. The integration of these techniques with dynamical systems theory opens up opportunities to tackle previously unattainable challenges in modelling and prediction of dynamical systems. While data-driven prediction methods have made great strides in recent years, it is still necessary to develop new techniques to improve their applicability to a wider range of complex systems in science and engineering. This focus issue shares recent developments in the field of complex dynamical systems with emphasis on data-driven, data-assisted and artificial intelligence-based discovery of dynamical systems. This article is part of the theme issue 'Data-driven prediction in dynamical systems'.">
@@ -158,7 +158,7 @@ hide:
           </td>
           <td>2022-06-20</td>
           <td>Philosophical transactions. Series A, Mathematical, physical, and engineering sciences</td>
-          <td>41</td>
+          <td>42</td>
           <td>33</td>
         </tr>
     
diff --git a/docs/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220.md b/docs/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220.md
index 2b4cade2..0a647737 100644
--- a/docs/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220.md
+++ b/docs/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:56 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:34 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -51,7 +51,7 @@ hide:
           <td>2019-06-25</td>
           <td>IEEE Access</td>
           <td>95</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
     
         <tr id="Energy conservation is a basic physics principle, the breakdown of which often implies new physics. This paper presents a method for data-driven "new physics" discovery. Specifically, given a trajectory governed by unknown forces, our neural new-physics detector (NNPhD) aims to detect new physics by decomposing the force field into conservative and nonconservative components, which are represented by a Lagrangian neural network (LNN) and an unconstrained neural network, respectively, trained to minimize the force recovery error plus a constant λ times the magnitude of the predicted nonconservative force. We show that a phase transition occurs at λ=1, universally for arbitrary forces. We demonstrate that NNPhD successfully discovers new physics in toy numerical experiments, rediscovering friction (1493) from a damped double pendulum, Neptune from Uranus' orbit (1846), and gravitational waves (2017) from an inspiraling orbit. We also show how NNPhD coupled with an integrator outperforms both an LNN and an unconstrained neural network for predicting the future of a damped double pendulum.">
@@ -98,7 +98,7 @@ hide:
           </td>
           <td>2023-05-31</td>
           <td>2023 American Control Conference (ACC)</td>
-          <td>15</td>
+          <td>16</td>
           <td>37</td>
         </tr>
     
@@ -111,7 +111,7 @@ hide:
           <td>2021-09-30</td>
           <td>ArXiv, DBLP</td>
           <td>5</td>
-          <td>103</td>
+          <td>105</td>
         </tr>
     
         <tr id="None">
@@ -135,7 +135,7 @@ hide:
           <td>2021-05-06</td>
           <td>Physical review letters</td>
           <td>82</td>
-          <td>81</td>
+          <td>82</td>
         </tr>
     
         <tr id="None">
diff --git a/docs/recommendations/3c9961153493370500020c81527b3548c96f81e0.md b/docs/recommendations/3c9961153493370500020c81527b3548c96f81e0.md
index 5eb2cee5..dfe63b15 100644
--- a/docs/recommendations/3c9961153493370500020c81527b3548c96f81e0.md
+++ b/docs/recommendations/3c9961153493370500020c81527b3548c96f81e0.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:39 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:22 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,8 +50,8 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3181</td>
-          <td>63</td>
+          <td>3190</td>
+          <td>65</td>
         </tr>
     
         <tr id="None">
@@ -62,7 +62,7 @@ hide:
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>237</td>
+          <td>238</td>
           <td>12</td>
         </tr>
     
diff --git a/docs/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2.md b/docs/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2.md
index 5c358491..0393d28c 100644
--- a/docs/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2.md
+++ b/docs/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:40 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:24 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -63,7 +63,7 @@ hide:
           <td>2022-11-20</td>
           <td>Physical review. E</td>
           <td>8</td>
-          <td>50</td>
+          <td>51</td>
         </tr>
     
         <tr id="A central challenge in data-driven model discovery is the presence of hidden, or latent, variables that are not directly measured but are dynamically important. Takens’ theorem provides conditions for when it is possible to augment partial measurements with time delayed information, resulting in an attractor that is diffeomorphic to that of the original full-state system. This diffeomorphism is typically unknown, and learning the dynamics in the embedding space has remained an open challenge for decades. Here, we design a deep autoencoder network to learn a coordinate transformation from the delay embedded space into a new space, where it is possible to represent the dynamics in a sparse, closed form. We demonstrate this approach on the Lorenz, Rössler and Lotka–Volterra systems, as well as a Lorenz analogue from a video of a chaotic waterwheel experiment. This framework combines deep learning and the sparse identification of nonlinear dynamics methods to uncover interpretable models within effective coordinates.">
@@ -74,8 +74,8 @@ hide:
           </td>
           <td>2022-01-13</td>
           <td>Proceedings of the Royal Society A</td>
-          <td>68</td>
-          <td>63</td>
+          <td>69</td>
+          <td>65</td>
         </tr>
     
         <tr id="Making accurate forecasts for a complex system is a challenge in various practical applications. The major difficulty in solving such a problem concerns nonlinear spatiotemporal dynamics with time-varying characteristics. Takens' delay embedding theory provides a way to transform high-dimensional spatial information into temporal information. In this work, by combining delay embedding theory and deep learning techniques, we propose a novel framework, delay-embedding-based forecast Machine (DEFM), to predict the future values of a target variable in a self-supervised and multistep-ahead manner based on high-dimensional observations. With a three-module spatiotemporal architecture, the DEFM leverages deep neural networks to effectively extract both the spatially and temporally associated information from the observed time series even with time-varying parameters or additive noise. The DEFM can accurately predict future information by transforming spatiotemporal information to the delay embeddings of a target variable. The efficacy and precision of the DEFM are substantiated through applications in three spatiotemporally chaotic systems: a 90-dimensional (90D) coupled Lorenz system, the Lorenz 96 system, and the Kuramoto-Sivashinsky equation with inhomogeneity. Additionally, the performance of the DEFM is evaluated on six real-world datasets spanning various fields. Comparative experiments with five prediction methods illustrate the superiority and robustness of the DEFM and show the great potential of the DEFM in temporal information mining and forecasting.">
@@ -87,7 +87,7 @@ hide:
           <td>2020-05-16</td>
           <td>Chaos</td>
           <td>1</td>
-          <td>78</td>
+          <td>79</td>
         </tr>
     
         <tr id="Making accurate forecasts for a complex system is a challenge in various practical applications. The major difficulty in solving such a problem concerns nonlinear spatiotemporal dynamics with time-varying characteristics. Takens' delay embedding theory provides a way to transform high-dimensional spatial information into temporal information. In this work, by combining delay embedding theory and deep learning techniques, we propose a novel framework, delay-embedding-based forecast Machine (DEFM), to predict the future values of a target variable in a self-supervised and multistep-ahead manner based on high-dimensional observations. With a three-module spatiotemporal architecture, the DEFM leverages deep neural networks to effectively extract both the spatially and temporally associated information from the observed time series even with time-varying parameters or additive noise. The DEFM can accurately predict future information by transforming spatiotemporal information to the delay embeddings of a target variable. The efficacy and precision of the DEFM are substantiated through applications in three spatiotemporally chaotic systems: a 90-dimensional (90D) coupled Lorenz system, the Lorenz 96 system, and the Kuramoto-Sivashinsky equation with inhomogeneity. Additionally, the performance of the DEFM is evaluated on six real-world datasets spanning various fields. Comparative experiments with five prediction methods illustrate the superiority and robustness of the DEFM and show the great potential of the DEFM in temporal information mining and forecasting.">
@@ -99,7 +99,7 @@ hide:
           <td>2020-05-16</td>
           <td>Chaos</td>
           <td>1</td>
-          <td>78</td>
+          <td>79</td>
         </tr>
     
         <tr id="Many complex phenomena, from weather systems to heartbeat rhythm patterns, are effectively modeled as low-dimensional dynamical systems. Such systems may behave chaotically under certain conditions, and so the ability to detect chaos based on empirical measurement is an important step in characterizing and predicting these processes. Classifying a system as chaotic usually requires estimating its largest Lyapunov exponent, which quantifies the average rate of convergence or divergence of initially close trajectories in state space, and for which a positive value is generally accepted as an operational definition of chaos. Estimating the largest Lyapunov exponent from observations of a process is especially challenging in systems affected by dynamical noise, which is the case for many models of real-world processes, in particular models of biological systems. We describe a novel method for estimating the largest Lyapunov exponent from data, based on training Deep Learning models on synthetically generated trajectories, and demonstrate that this method yields accurate and noise-robust predictions given relatively short inputs and across a range of different dynamical systems. Our method is unique in that it can analyze tree-shaped data, a ubiquitous topology in biological settings, and specifically in dynamics over lineages of cells or organisms. We also characterize the types of input information extracted by our models for their predictions, allowing for a deeper understanding into the different ways by which chaos can be analyzed in different topologies.">
diff --git a/docs/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba.md b/docs/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba.md
index 31ed9d81..958d5abb 100644
--- a/docs/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba.md
+++ b/docs/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:10 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:04:56 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -111,7 +111,7 @@ hide:
           <td>2023-10-17</td>
           <td>2023 IEEE International Conference on Data Mining (ICDM)</td>
           <td>0</td>
-          <td>8</td>
+          <td>9</td>
         </tr>
     
         <tr id="Irregularly-sampled time series occur in many domains including healthcare. They can be challenging to model because they do not naturally yield a fixed-dimensional representation as required by many standard machine learning models. In this paper, we consider irregular sampling from the perspective of missing data. We model observed irregularly-sampled time series data as a sequence of index-value pairs sampled from a continuous but unobserved function. We introduce an encoder-decoder framework for learning from such generic indexed sequences. We propose learning methods for this framework based on variational autoencoders and generative adversarial networks. For continuous irregularly-sampled time series, we introduce continuous convolutional layers that can efficiently interface with existing neural network architectures. Experiments show that our models are able to achieve competitive or better classification results on irregularly-sampled multivariate time series compared to recent RNN models while offering significantly faster training times.">
@@ -121,9 +121,9 @@ hide:
             Steven Cheng-Xian Li, Benjamin M Marlin
           </td>
           <td>2020-07-12</td>
-          <td>MAG, ArXiv, DBLP</td>
+          <td>ArXiv, MAG, DBLP</td>
           <td>47</td>
-          <td>32</td>
+          <td>33</td>
         </tr>
     
         <tr id="We consider a sequence of related multivariate time series learning tasks, such as predicting failures for different instances of a machine from time series of multi-sensor data, or activity recognition tasks over different individuals from multiple wearable sensors. We focus on two under-explored practical challenges arising in such settings: (i) Each task may have a different subset of sensors, i.e., providing different partial observations of the underlying ‘system’. This restriction can be due to different manufacturers in the former case, and people wearing more or less measurement devices in the latter (ii) We are not allowed to store or re-access data from a task once it has been observed at the task level. This may be due to privacy considerations in the case of people, or legal restrictions placed by machine owners. Nevertheless, we would like to (a) improve performance on subsequent tasks using experience from completed tasks as well as (b) continue to perform better on past tasks, e.g., update the model and improve predictions on even the first machine after learning from subsequently observed ones. We note that existing continual learning methods do not take into account variability in input dimensions arising due to different subsets of sensors being available across tasks, and struggle to adapt to such variable input dimensions (VID) tasks. In this work, we address this shortcoming of existing methods. To this end, we learn task-specific generative models and classifiers, and use these to augment data for target tasks. Since the input dimensions across tasks vary, we propose a novel conditioning module based on graph neural networks to aid a standard recurrent neural network. We evaluate the efficacy of the proposed approach on three publicly available datasets corresponding to two activity recognition tasks (classification) and one prognostics task (regression). We demonstrate that it is possible to significantly enhance the performance on future and previous tasks while learning continuously from VID tasks without storing data.">
diff --git a/docs/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01.md b/docs/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01.md
index 63c67d6a..fc38a08b 100644
--- a/docs/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01.md
+++ b/docs/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:48 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:29 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -75,7 +75,7 @@ hide:
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
           <td>109</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
     
         <tr id="Data-driven modeling frameworks that adopt sparse regression techniques, such as sparse identification of nonlinear dynamics (SINDy) and its modifications, are developed to resolve difficulties in extracting underlying dynamics from experimental data. In contrast to neural-network-based methods, these methods are designed to obtain white-box analytical models. In this work, we incorporate the concept of SINDy and knowledge in the field of classical mechanics to identify interpretable and sparse expressions of total energy and the Lagrangian that shelters the hidden dynamics. Moreover, our method (hereafter referred as Lagrangian-SINDy) is developed to use knowledge of simple systems that form the system being analyzed to ensure the likelihood of correct results and to improve the learning pace. Lagrangian-SINDy is highly accurate in discovering interpretable dynamics via energy-related physical quantities. Its performance is validated with three popular multi-DOF nonlinear dynamical systems, namely the spherical pendulum, double pendulum and cart-pendulum system. Comparisons with other SINDy-based methods are made and Lagrangian-SINDy is found to provide the most compact analytical models.">
@@ -135,7 +135,7 @@ hide:
           <td>2024-04-24</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
+          <td>7</td>
         </tr>
     
         <tr id="In this work we analyze the effectiveness of the Sparse Identification of Nonlinear Dynamics (SINDy) technique on three benchmark datasets for nonlinear identification, to provide a better understanding of its suitability when tackling real dynamical systems. While SINDy can be an appealing strategy for pursuing physics-based learning, our analysis highlights difficulties in dealing with unobserved states and non-smooth dynamics. Due to the ubiquity of these features in real systems in general, and control applications in particular, we complement our analysis with hands-on approaches to tackle these issues in order to exploit SINDy also in these challenging contexts.">
@@ -146,7 +146,7 @@ hide:
           </td>
           <td>2024-03-01</td>
           <td>ArXiv</td>
-          <td>0</td>
+          <td>1</td>
           <td>1</td>
         </tr>
     
@@ -158,8 +158,8 @@ hide:
           </td>
           <td>2020-07-06</td>
           <td>Journal of computational physics</td>
-          <td>120</td>
-          <td>19</td>
+          <td>121</td>
+          <td>20</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/4a111f7a3b56d0468f13104999844885157ef17d.md b/docs/recommendations/4a111f7a3b56d0468f13104999844885157ef17d.md
index cfe9e987..a2c1bccc 100644
--- a/docs/recommendations/4a111f7a3b56d0468f13104999844885157ef17d.md
+++ b/docs/recommendations/4a111f7a3b56d0468f13104999844885157ef17d.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:16 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:02 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,7 +50,7 @@ hide:
           </td>
           <td>2024-03-12</td>
           <td>ArXiv</td>
-          <td>24</td>
+          <td>27</td>
           <td>18</td>
         </tr>
     
@@ -62,8 +62,8 @@ hide:
           </td>
           <td>2024-02-04</td>
           <td>ArXiv</td>
-          <td>6</td>
-          <td>65</td>
+          <td>5</td>
+          <td>66</td>
         </tr>
     
         <tr id="We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are served as input tokens to Transformer; (ii) channel-independence where each channel contains a single univariate time series that shares the same embedding and Transformer weights across all the series. Patching design naturally has three-fold benefit: local semantic information is retained in the embedding; computation and memory usage of the attention maps are quadratically reduced given the same look-back window; and the model can attend longer history. Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models. We also apply our model to self-supervised pre-training tasks and attain excellent fine-tuning performance, which outperforms supervised training on large datasets. Transferring of masked pre-trained representation on one dataset to others also produces SOTA forecasting accuracy. Code is available at: https://github.com/yuqinie98/PatchTST.">
@@ -74,7 +74,7 @@ hide:
           </td>
           <td>2022-11-27</td>
           <td>ArXiv</td>
-          <td>487</td>
+          <td>494</td>
           <td>34</td>
         </tr>
     
@@ -87,7 +87,7 @@ hide:
           <td>2024-01-10</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>5</td>
+          <td>6</td>
         </tr>
     
         <tr id="The Transformer is a highly successful deep learning model that has revolutionised the world of artificial neural networks, first in natural language processing and later in computer vision. This model is based on the attention mechanism and is able to capture complex semantic relationships between a variety of patterns present in the input data. Precisely because of these characteristics, the Transformer has recently been exploited for time series forecasting problems, assuming a natural adaptability to the domain of continuous numerical series. Despite the acclaimed results in the literature, some works have raised doubts about the robustness and effectiveness of this approach. In this paper, we further investigate the effectiveness of Transformer-based models applied to the domain of time series forecasting, demonstrate their limitations, and propose a set of alternative models that are better performing and significantly less complex. In particular, we empirically show how simplifying Transformer-based forecasting models almost always leads to an improvement, reaching state of the art performance. We also propose shallow models without the attention mechanism, which compete with the overall state of the art in long time series forecasting, and demonstrate their ability to accurately predict time series over extremely long windows. From a methodological perspective, we show how it is always necessary to use a simple baseline to verify the effectiveness of proposed models, and finally, we conclude the paper with a reflection on recent research paths and the opportunity to follow trends and hypes even where it may not be necessary.">
@@ -99,7 +99,7 @@ hide:
           <td>2023-04-10</td>
           <td>ArXiv, DBLP</td>
           <td>1</td>
-          <td>5</td>
+          <td>6</td>
         </tr>
     
         <tr id="Time series has been left behind in the era of pre-training and transfer learning. While research in the fields of natural language processing and computer vision are enjoying progressively larger datasets to train massive models, the most popular time series datasets consist of only tens of thousands of time steps, limiting our ability to study the effectiveness of pre-training and scaling. Recent studies have also cast doubt on the need for expressive models and scale. To alleviate these issues, we introduce three large-scale time series forecasting datasets from the cloud operations (CloudOps) domain, the largest having billions of observations, enabling further study into pre-training and scaling of time series models. We build the empirical groundwork for studying pre-training and scaling of time series models and pave the way for future research by identifying a promising candidate architecture. We show that it is a strong zero-shot baseline and benefits from further scaling, both in model and dataset size. Accompanying these datasets and results is a suite of comprehensive benchmark results comparing classical and deep learning baselines to our pre-trained method - achieving a 27% reduction in error on the largest dataset. Code and datasets can be found https://github.com/SalesforceAIResearch/pretrain-time-series-cloudops.">
diff --git a/docs/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14.md b/docs/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14.md
index 7dfe43e2..02a46c93 100644
--- a/docs/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14.md
+++ b/docs/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:23 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:06 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -63,7 +63,7 @@ hide:
           <td>2022-03-15</td>
           <td>ACM Transactions on Knowledge Discovery from Data</td>
           <td>38</td>
-          <td>36</td>
+          <td>37</td>
         </tr>
     
         <tr id="Domain generalization methods aim to learn models robust to domain shift with data from a limited number of source domains and without access to target domain samples during training. Popular domain alignment methods for domain generalization seek to extract domain-invariant features by minimizing the discrepancy between feature distributions across all domains, disregarding inter-domain relationships. In this paper, we instead propose a novel representation learning methodology that selectively enforces prediction consistency between source domains estimated to be closely-related. Specifically, we hypothesize that domains share different class-informative representations, so instead of aligning all domains which can cause negative transfer, we only regularize the discrepancy between closely-related domains. We apply our method to time-series classification tasks and conduct comprehensive experiments on three public real-world datasets. Our method significantly improves over the baseline and achieves better or competitive performance in comparison with state-of-the-art methods in terms of both accuracy and model calibration.">
diff --git a/docs/recommendations/5d150cec2775f9bc863760448f14104cc8f42368.md b/docs/recommendations/5d150cec2775f9bc863760448f14104cc8f42368.md
index 930d85eb..26676580 100644
--- a/docs/recommendations/5d150cec2775f9bc863760448f14104cc8f42368.md
+++ b/docs/recommendations/5d150cec2775f9bc863760448f14104cc8f42368.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:38 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:21 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,8 +50,8 @@ hide:
           </td>
           <td>2019-03-29</td>
           <td>Proceedings of the National Academy of Sciences of the United States of America</td>
-          <td>599</td>
-          <td>63</td>
+          <td>603</td>
+          <td>65</td>
         </tr>
     
         <tr id="None">
@@ -86,7 +86,7 @@ hide:
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>237</td>
+          <td>238</td>
           <td>12</td>
         </tr>
     
@@ -123,7 +123,7 @@ hide:
           <td>2021-05-11</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
           <td>37</td>
-          <td>53</td>
+          <td>52</td>
         </tr>
     
         <tr id="None">
@@ -135,7 +135,7 @@ hide:
           <td>2014-12-10</td>
           <td>The European Physical Journal Special Topics</td>
           <td>64</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
     
         <tr id="None">
@@ -147,7 +147,7 @@ hide:
           <td>2014-12-01</td>
           <td>The European Physical Journal Special Topics</td>
           <td>7</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c.md b/docs/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c.md
index f378d1c3..50c223de 100644
--- a/docs/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c.md
+++ b/docs/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:39 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:21 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -63,7 +63,7 @@ hide:
           <td>2024-05-07</td>
           <td>ArXiv</td>
           <td>1</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
     
         <tr id="None">
@@ -110,7 +110,7 @@ hide:
           </td>
           <td>2021-12-20</td>
           <td>ArXiv</td>
-          <td>12</td>
+          <td>13</td>
           <td>72</td>
         </tr>
     
diff --git a/docs/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37.md b/docs/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37.md
index e48adb9e..cf7003ea 100644
--- a/docs/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37.md
+++ b/docs/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:21 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:04 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -87,7 +87,7 @@ hide:
           <td>2023-06-26</td>
           <td>DBLP</td>
           <td>2</td>
-          <td>139</td>
+          <td>140</td>
         </tr>
     
         <tr id="Contrastive representation learning is crucial in medical time series analysis as it alleviates dependency on labor-intensive, domain-specific, and scarce expert annotations. However, existing contrastive learning methods primarily focus on one single data level, which fails to fully exploit the intricate nature of medical time series. To address this issue, we present COMET, an innovative hierarchical framework that leverages data consistencies at all inherent levels in medical time series. Our meticulously designed model systematically captures data consistency from four potential levels: observation, sample, trial, and patient levels. By developing contrastive loss at multiple levels, we can learn effective representations that preserve comprehensive data consistency, maximizing information utilization in a self-supervised manner. We conduct experiments in the challenging patient-independent setting. We compare COMET against six baselines using three diverse datasets, which include ECG signals for myocardial infarction and EEG signals for Alzheimer's and Parkinson's diseases. The results demonstrate that COMET consistently outperforms all baselines, particularly in setup with 10% and 1% labeled data fractions across all datasets. These results underscore the significant impact of our framework in advancing contrastive representation learning techniques for medical time series. The source code is available at https://github.com/DL4mHealth/COMET.">
@@ -99,7 +99,7 @@ hide:
           <td>2023-10-21</td>
           <td>ArXiv</td>
           <td>19</td>
-          <td>2</td>
+          <td>3</td>
         </tr>
     
         <tr id="Unsupervised domain adaptation (UDA) provides a strategy for improving machine learning performance in data-rich (target) domains where ground truth labels are inaccessible but can be found in related (source) domains. In cases where meta-domain information such as label distributions is available, weak supervision can further boost performance. We propose a novel framework, CALDA, to tackle these two problems. CALDA synergistically combines the principles of contrastive learning and adversarial learning to robustly support multi-source UDA (MS-UDA) for time series data. Similar to prior methods, CALDA utilizes adversarial learning to align source and target feature representations. Unlike prior approaches, CALDA additionally leverages cross-source label information across domains. CALDA pulls examples with the same label close to each other, while pushing apart examples with different labels, reshaping the space through contrastive learning. Unlike prior contrastive adaptation methods, CALDA requires neither data augmentation nor pseudo labeling, which may be more challenging for time series. We empirically validate our proposed approach. Based on results from human activity recognition, electromyography, and synthetic datasets, we find utilizing cross-source information improves performance over prior time series and contrastive methods. Weak supervision further improves performance, even in the presence of noise, allowing CALDA to offer generalizable strategies for MS-UDA.">
@@ -111,7 +111,7 @@ hide:
           <td>2021-09-30</td>
           <td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td>
           <td>14</td>
-          <td>72</td>
+          <td>71</td>
         </tr>
     
         <tr id="To improve the accuracy of clinical multivariate time series (MTS) classification (such as EEG and ECG) by a novel self-supervised paradigm that directly captures the dynamics between the different time series learned together to optimize the classification task. Labels in clinical datasets are very often insufficient. One way to address this challenge is leveraging self-supervision. This paradigm attempts to identify a supervisory signal inherent within a dataset to serve as a surrogate label. We present a novel form of self-supervision: dynamics of clinical MTS. Unlike other self-supervision methods, such as masking, that are intuitive but still heuristic, we suggest to learn a representation justified by Koopman theory. The latter was shown useful for representing clinical time series and can be used as a form of surrogate task to improve the clinical MTS classification. In the ECG task, we show that our proposed framework achieved higher sensitivity and specificity than the state-of-the-art (SOTA) baseline over numerous common diagnoses. For EEG abnormality classification, our proposed framework also achieved higher sensitivity and specificity than the SOTA baseline. All results are statistically significant. Our technique yields reliable clinical diagnosis in an empirical study employing signals from thousands of patients in multiple clinical tasks employing two types of clinical-grade sensors (ECG and EEG) as compared to the state-of-the-art machine learning. Leveraging time-series-dynamics self-supervision can help mitigate the lack of labels in clinical datasets used for training machine learning algorithms and significantly improve their performance. Specifically, the ECG system presented in this work is being trialed in hospitals, used by top cardiologists for patient diagnosis and treatment. We believe that the deployment of such cutting-edge technology will significantly improve the accuracy and speed of cardiac assessments.">
@@ -159,7 +159,7 @@ hide:
           <td>2022-04-24</td>
           <td>IEEE transactions on neural networks and learning systems</td>
           <td>4</td>
-          <td>5</td>
+          <td>6</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/73dd9c49f205280991826b2ea4b50344203916b4.md b/docs/recommendations/73dd9c49f205280991826b2ea4b50344203916b4.md
index 04631f90..568e4a02 100644
--- a/docs/recommendations/73dd9c49f205280991826b2ea4b50344203916b4.md
+++ b/docs/recommendations/73dd9c49f205280991826b2ea4b50344203916b4.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:51 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:33 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -74,7 +74,7 @@ hide:
           </td>
           <td>2024-03-01</td>
           <td>ArXiv</td>
-          <td>0</td>
+          <td>1</td>
           <td>1</td>
         </tr>
     
@@ -110,8 +110,8 @@ hide:
           </td>
           <td>2017-11-15</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>427</td>
-          <td>63</td>
+          <td>428</td>
+          <td>65</td>
         </tr>
     
         <tr id="None">
@@ -134,8 +134,8 @@ hide:
           </td>
           <td>2020-04-05</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>191</td>
-          <td>63</td>
+          <td>194</td>
+          <td>65</td>
         </tr>
     
         <tr id="We consider the problem of learning the dynamics of a linear system when one has access to data generated by an auxiliary system that shares similar (but not identical) dynamics, in addition to data from the true system. We use a weighted least squares approach, and provide finite sample error bounds of the learned model as a function of the number of samples and various system parameters from the two systems as well as the weight assigned to the auxiliary data. We show that the auxiliary data can help to reduce the intrinsic system identification error due to noise, at the price of adding a portion of error that is due to the differences between the two system models. We further provide a data-dependent bound that is computable when some prior knowledge about the systems, such as upper bounds on noise levels and model difference, is available. This bound can also be used to determine the weight that should be assigned to the auxiliary data during the model training stage.">
diff --git a/docs/recommendations/883547fdbd88552328a6615ec620f96e39c57018.md b/docs/recommendations/883547fdbd88552328a6615ec620f96e39c57018.md
index 03568b01..4598b6ff 100644
--- a/docs/recommendations/883547fdbd88552328a6615ec620f96e39c57018.md
+++ b/docs/recommendations/883547fdbd88552328a6615ec620f96e39c57018.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:58 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:38 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,7 +50,7 @@ hide:
           </td>
           <td>2021-07-05</td>
           <td>Royal Society Open Science</td>
-          <td>52</td>
+          <td>54</td>
           <td>31</td>
         </tr>
     
@@ -63,7 +63,7 @@ hide:
           <td>2023-01-30</td>
           <td>ArXiv</td>
           <td>11</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
     
         <tr id="None">
@@ -86,8 +86,8 @@ hide:
           </td>
           <td>2020-04-05</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>191</td>
-          <td>63</td>
+          <td>194</td>
+          <td>65</td>
         </tr>
     
         <tr id="We propose a fast probabilistic framework for identifying differential equations governing the dynamics of observed data. We recast the SINDy method within a Bayesian framework and use Gaussian approximations for the prior and likelihood to speed up computation. The resulting method, Bayesian-SINDy, not only quantifies uncertainty in the parameters estimated but also is more robust when learning the correct model from limited and noisy data. Using both synthetic and real-life examples such as Lynx-Hare population dynamics, we demonstrate the effectiveness of the new framework in learning correct model equations and compare its computational and data efficiency with existing methods. Because Bayesian-SINDy can quickly assimilate data and is robust against noise, it is particularly suitable for biological data and real-time system identification in control. Its probabilistic framework also enables the calculation of information entropy, laying the foundation for an active learning strategy.">
@@ -98,8 +98,8 @@ hide:
           </td>
           <td>2024-02-23</td>
           <td>ArXiv</td>
-          <td>0</td>
-          <td>37</td>
+          <td>1</td>
+          <td>38</td>
         </tr>
     
         <tr id="Significance Understanding dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled technology, including aircraft, combustion engines, satellites, and electrical power. This work develops a novel framework to discover governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity techniques and machine learning. The resulting models are parsimonious, balancing model complexity with descriptive ability while avoiding overfitting. There are many critical data-driven problems, such as understanding cognition from neural recordings, inferring climate patterns, determining stability of financial markets, predicting and suppressing the spread of disease, and controlling turbulence for greener transportation and energy. With abundant data and elusive laws, data-driven discovery of dynamics will continue to play an important role in these efforts. Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.">
@@ -110,8 +110,8 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3181</td>
-          <td>63</td>
+          <td>3190</td>
+          <td>65</td>
         </tr>
     
         <tr id="The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. However, SINDy assumes the user has prior knowledge of the variables in the system and of a function library that can act as a basis for the system. In this paper, we demonstrate on real world data how the Augmented SINDy algorithm outperforms SINDy in the presence of system variable uncertainty. We then show SINDy can be further augmented to perform robustly when both kinds of uncertainty are present.">
diff --git a/docs/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271.md b/docs/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271.md
index beffe4a9..1418fb16 100644
--- a/docs/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271.md
+++ b/docs/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:44 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:26 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -42,18 +42,6 @@ hide:
   </thead>
   <tbody>
     
-        <tr id="Many dynamical systems of interest are nonlinear, with examples in turbulence, epidemiology, neuroscience, and finance, making them difficult to control using linear approaches. Model predictive control (MPC) is a powerful model-based optimization technique that enables the control of such nonlinear systems with constraints. However, modern systems often lack computationally tractable models, motivating the use of system identification techniques to learn accurate and efficient models for real-time control. In this tutorial article, we review emerging data-driven methods for model discovery and how they are used for nonlinear MPC. In particular, we focus on the sparse identification of nonlinear dynamics (SINDy) algorithm and show how it may be used with MPC on an infectious disease control example. We compare the performance against MPC based on a linear dynamic mode decomposition (DMD) model. Code is provided to run the tutorial examples and may be modified to extend this data-driven control framework to arbitrary nonlinear systems.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/700552dde778bd1f6b76f7237e9b561597b859ba" target='_blank'>SINDy with Control: A Tutorial</a></td>
-          <td>
-            Urban Fasel, E. Kaiser, J. Kutz, Bingni W. Brunton, S. Brunton
-          </td>
-          <td>2021-08-30</td>
-          <td>2021 60th IEEE Conference on Decision and Control (CDC)</td>
-          <td>45</td>
-          <td>63</td>
-        </tr>
-    
         <tr id="In this work we analyze the effectiveness of the Sparse Identification of Nonlinear Dynamics (SINDy) technique on three benchmark datasets for nonlinear identification, to provide a better understanding of its suitability when tackling real dynamical systems. While SINDy can be an appealing strategy for pursuing physics-based learning, our analysis highlights difficulties in dealing with unobserved states and non-smooth dynamics. Due to the ubiquity of these features in real systems in general, and control applications in particular, we complement our analysis with hands-on approaches to tackle these issues in order to exploit SINDy also in these challenging contexts.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/99877062e70fae24c531d58aa880fa443ec10c9b" target='_blank'>SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study</a></td>
@@ -62,7 +50,7 @@ hide:
           </td>
           <td>2024-03-01</td>
           <td>ArXiv</td>
-          <td>0</td>
+          <td>1</td>
           <td>1</td>
         </tr>
     
@@ -86,8 +74,8 @@ hide:
           </td>
           <td>2020-04-05</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>191</td>
-          <td>63</td>
+          <td>194</td>
+          <td>65</td>
         </tr>
     
         <tr id="Modern societies have an abundance of data yet good system models are rare. Unfortunately, many of the current system identification and machine learning techniques fail to generalize outside of the training set, producing models that violate basic physical laws. This work proposes a novel method for the Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI). SINDy-SI is an iterative method that uses Sum-of-Squares (SOS) programming to learn optimally fitted models while guaranteeing that the learned model satisfies side information, such as symmetry's and physical laws. Guided by the principle of Occam's razor, that the simplest or most regularized best fitted model is typically the superior choice, during each iteration SINDy-SI prunes the basis functions associated with small coefficients, yielding a sparse dynamical model upon termination. Through several numerical experiments we will show how the combination of side information constraints and sparse polynomial representation cultivates dynamical models that obey known physical laws while displaying impressive generalized performance beyond the training set.">
@@ -122,8 +110,8 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3181</td>
-          <td>63</td>
+          <td>3190</td>
+          <td>65</td>
         </tr>
     
         <tr id="Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community. PySINDy is a Python package that provides tools for applying the sparse identification of nonlinear dynamics (SINDy) approach to data-driven model discovery. In this major update to PySINDy, we implement several advanced features that enable the discovery of more general differential equations from noisy and limited data. The library of candidate terms is extended for the identification of actuated systems, partial differential equations (PDEs), and implicit differential equations. Robust formulations, including the integral form of SINDy and ensembling techniques, are also implemented to improve performance for real-world data. Finally, we provide a range of new optimization algorithms, including several sparse regression techniques and algorithms to enforce and promote inequality constraints and stability. Together, these updates enable entirely new SINDy model discovery capabilities that have not been reported in the literature, such as constrained PDE identification and ensembling with different sparse regression optimizers.">
@@ -135,7 +123,7 @@ hide:
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
           <td>109</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
     
         <tr id="We develop a principled mathematical framework for controlling nonlinear, networked dynamical systems. Our method integrates dimensionality reduction, bifurcation theory, and emerging model discovery tools to find low-dimensional subspaces where feed-forward control can be used to manipulate a system to a desired outcome. The method leverages the fact that many high-dimensional networked systems have many fixed points, allowing for the computation of control signals that will move the system between any pair of fixed points. The sparse identification of nonlinear dynamics (SINDy) algorithm is used to fit a nonlinear dynamical system to the evolution on the dominant, low-rank subspace. This then allows us to use bifurcation theory to find collections of constant control signals that will produce the desired objective path for a prescribed outcome. Specifically, we can destabilize a given fixed point while making the target fixed point an attractor. The discovered control signals can be easily projected back to the original high-dimensional state and control space. We illustrate our nonlinear control procedure on established bistable, low-dimensional biological systems, showing how control signals are found that generate switches between the fixed points. We then demonstrate our control procedure for high-dimensional systems on random high-dimensional networks and Hopfield memory networks.">
diff --git a/docs/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da.md b/docs/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da.md
index 4e8ea72a..a94ed2ec 100644
--- a/docs/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da.md
+++ b/docs/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:06:00 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:42 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -51,7 +51,7 @@ hide:
           <td>2018-07-14</td>
           <td>ArXiv</td>
           <td>9</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
     
         <tr id="Sparse regression models are increasingly prevalent due to their ease of interpretability and superior out-of-sample performance. However, the exact model of sparse regression with an $\ell_0$ constraint restricting the support of the estimators is a challenging (\NP-hard) non-convex optimization problem. In this paper, we derive new strong convex relaxations for sparse regression. These relaxations are based on the ideal (convex-hull) formulations for rank-one quadratic terms with indicator variables. The new relaxations can be formulated as semidefinite optimization problems in an extended space and are stronger and more general than the state-of-the-art formulations, including the perspective reformulation and formulations with the reverse Huber penalty and the minimax concave penalty functions. Furthermore, the proposed rank-one strengthening can be interpreted as a \textit{non-separable, non-convex, unbiased} sparsity-inducing regularizer, which dynamically adjusts its penalty according to the shape of the error function without inducing bias for the sparse solutions. In our computational experiments with benchmark datasets, the proposed conic formulations are solved within seconds and result in near-optimal solutions (with 0.4\% optimality gap) for non-convex $\ell_0$-problems. Moreover, the resulting estimators also outperform alternative convex approaches from a statistical perspective, achieving high prediction accuracy and good interpretability.">
@@ -87,7 +87,7 @@ hide:
           <td>2014-01-03</td>
           <td></td>
           <td>58</td>
-          <td>94</td>
+          <td>95</td>
         </tr>
     
         <tr id="High-dimensional sparse linear regression is a basic problem in machine learning and statistics. Consider a linear model $y = X\theta^\star + w$, where $y \in \mathbb{R}^n$ is the vector of observations, $X \in \mathbb{R}^{n \times d}$ is the covariate matrix and $w \in \mathbb{R}^n$ is an unknown noise vector. In many applications, the linear regression model is high-dimensional in nature, meaning that the number of observations $n$ may be substantially smaller than the number of covariates $d$. In these cases, it is common to assume that $\theta^\star$ is sparse, and the goal in sparse linear regression is to estimate this sparse $\theta^\star$, given $(X,y)$. 
@@ -101,7 +101,7 @@ As a simple consequence, our approach also provides a new way for estimating $\t
           <td>2017-07-25</td>
           <td>ArXiv</td>
           <td>1</td>
-          <td>29</td>
+          <td>30</td>
         </tr>
     
         <tr id="Reconstruction of signals from undersampled and noisy measurements is a topic of considerable interest. Sharpness conditions directly control the recovery performance of restart schemes for first-order methods without the need for restrictive assumptions such as strong convexity. However, they are challenging to apply in the presence of noise or approximate model classes (e.g., approximate sparsity). We provide a first-order method: Weighted, Accelerated and Restarted Primal-dual (WARPd), based on primal-dual iterations and a novel restart-reweight scheme. Under a generic approximate sharpness condition, WARPd achieves stable linear convergence to the desired vector. Many problems of interest fit into this framework. For example, we analyze sparse recovery in compressed sensing, low-rank matrix recovery, matrix completion, TV regularization, minimization of $\|Bx\|_{l^1}$ under constraints ($l^1$-analysis problems for general $B$), and mixed regularization problems. We show how several quantities controlling recovery performance also provide explicit approximate sharpness constants. Numerical experiments show that WARPd compares favorably with specialized state-of-the-art methods and is ideally suited for solving large-scale problems. We also present a noise-blind variant based on the Square-Root LASSO decoder. Finally, we show how to unroll WARPd as neural networks. This approximation theory result provides lower bounds for stable and accurate neural networks for inverse problems and sheds light on architecture choices. Code and a gallery of examples are made available online as a MATLAB package.">
diff --git a/docs/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524.md b/docs/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524.md
index 323a108b..91be4443 100644
--- a/docs/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524.md
+++ b/docs/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524.md
@@ -11,7 +11,7 @@ hide:
 
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   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:24 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:08 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -51,7 +51,7 @@ hide:
           <td>2022-04-23</td>
           <td>ArXiv</td>
           <td>5</td>
-          <td>49</td>
+          <td>50</td>
         </tr>
     
         <tr id="Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has been a surge in GNN-based approaches for time series analysis. These approaches can explicitly model inter-temporal and inter-variable relationships, which traditional and other deep neural network-based methods struggle to do. In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation. Our aim is to guide designers and practitioners to understand, build applications, and advance research of GNN4TS. At first, we provide a comprehensive task-oriented taxonomy of GNN4TS. Then, we present and discuss representative research works and introduce mainstream applications of GNN4TS. A comprehensive discussion of potential future research directions completes the survey. This survey, for the first time, brings together a vast array of knowledge on GNN-based time series research, highlighting foundations, practical applications, and opportunities of graph neural networks for time series analysis.">
@@ -61,9 +61,9 @@ hide:
             Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, C. Alippi, G. I. Webb, Irwin King, Shirui Pan
           </td>
           <td>2023-07-07</td>
-          <td>ArXiv</td>
+          <td>IEEE transactions on pattern analysis and machine intelligence</td>
           <td>61</td>
-          <td>49</td>
+          <td>50</td>
         </tr>
     
         <tr id="Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS). Recent studies offer evidence that the use of graph neural networks to capture latent correlations between time series holds a potential for enhanced forecasting. However, most existing methods rely on predefined or self-learning graphs, which are either static or unintentionally dynamic, and thus cannot model the time-varying correlations that exhibit trends and periodicities caused by the regularity of the underlying processes in CPS. To tackle such limitation, we propose Time-aware Graph Structure Learning (TagSL), which extracts time-aware correlations among time series by measuring the interaction of node and time representations in high-dimensional spaces. Notably, we introduce time discrepancy learning that utilizes contrastive learning with distance-based regularization terms to constrain learned spatial correlations to a trend sequence. Additionally, we propose a periodic discriminant function to enable the capture of periodic changes from the state of nodes. Next, we present a Graph Convolution-based Gated Recurrent Unit (GCGRU) that jointly captures spatial and temporal dependencies while learning time-aware and node-specific patterns. Finally, we introduce a unified framework named Time-aware Graph Convolutional Recurrent Network (TGCRN), combining TagSL, and GCGRU in an encoder-decoder architecture for multi-step spatiotemporal forecasting. We report on experiments with TGCRN and popular existing approaches on five real-world datasets, thus providing evidence that TGCRN is capable of advancing the state-of-the-art. We also cover a detailed ablation study and visualization analysis, offering detailed insight into the effectiveness of time-aware structure learning.">
@@ -98,7 +98,7 @@ hide:
           </td>
           <td>2023-02-22</td>
           <td>ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</td>
-          <td>4</td>
+          <td>5</td>
           <td>42</td>
         </tr>
     
@@ -122,8 +122,8 @@ hide:
           </td>
           <td>2022-02-17</td>
           <td>IEEE Transactions on Knowledge and Data Engineering</td>
-          <td>56</td>
-          <td>42</td>
+          <td>58</td>
+          <td>43</td>
         </tr>
     
         <tr id="Existing multivariate time series anomaly detection methods utilize graph neural networks to model inter-sensor dependencies and achieve satisfactory results. However, these methods often employ a predefined or self-learning static adjacency matrix for graph information aggregation, disregarding the dynamic characteristics of inter-sensor dependencies in the real world, which change over time and are observable at different time scales as dynamic changing graph structure information. Furthermore, existing techniques struggle to capture long-term temporal contextual relationships. To address these issues, we propose a Dynamic Graph Learning with Long and Short-term time model (DGL-LS) for multivariate time series anomaly detection. The network integrates a dynamic graph structure learning method among sensors in multi-time mode and long- and short-term temporal context information to enhance the prediction of multivariate time series anomalies. We propose a strategy for learning dynamic graphs that aggregates sensor node representations at various time intervals, and then uses an attention-based gating unit to evolve the node dynamic characterization and construct a series of adjacency matrices that describe the dynamic changes in graph structure over time. Moreover, we formulate a long-short time convolution to capture the long-term and short-term temporal dependencies within the time series. We demonstrate the superiority of our approach over other state-of-the-art methods by conducting extensive experiments on three publicly available datasets.">
@@ -135,7 +135,7 @@ hide:
           <td>2023-09-07</td>
           <td>2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)</td>
           <td>0</td>
-          <td>3</td>
+          <td>4</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a.md b/docs/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a.md
index fedc18d7..e914db92 100644
--- a/docs/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a.md
+++ b/docs/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a.md
@@ -11,7 +11,7 @@ hide:
 
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   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:13 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:04:58 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -75,7 +75,7 @@ hide:
           <td>2022-05-26</td>
           <td>J. Mach. Learn. Res.</td>
           <td>11</td>
-          <td>49</td>
+          <td>50</td>
         </tr>
     
         <tr id="The challenge of effectively learning inter-series correlations for multivariate time series forecasting remains a substantial and unresolved problem. Traditional deep learning models, which are largely dependent on the Transformer paradigm for modeling long sequences, often fail to integrate information from multiple time series into a coherent and universally applicable model. To bridge this gap, our paper presents ForecastGrapher, a framework reconceptualizes multivariate time series forecasting as a node regression task, providing a unique avenue for capturing the intricate temporal dynamics and inter-series correlations. Our approach is underpinned by three pivotal steps: firstly, generating custom node embeddings to reflect the temporal variations within each series; secondly, constructing an adaptive adjacency matrix to encode the inter-series correlations; and thirdly, augmenting the GNNs' expressive power by diversifying the node feature distribution. To enhance this expressive power, we introduce the Group Feature Convolution GNN (GFC-GNN). This model employs a learnable scaler to segment node features into multiple groups and applies one-dimensional convolutions with different kernel lengths to each group prior to the aggregation phase. Consequently, the GFC-GNN method enriches the diversity of node feature distribution in a fully end-to-end fashion. Through extensive experiments and ablation studies, we show that ForecastGrapher surpasses strong baselines and leading published techniques in the domain of multivariate time series forecasting.">
@@ -98,8 +98,8 @@ hide:
           </td>
           <td>2020-05-24</td>
           <td>Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining</td>
-          <td>945</td>
-          <td>54</td>
+          <td>950</td>
+          <td>55</td>
         </tr>
     
         <tr id="Graph Neural Networks (GNN) have gained significant traction in the forecasting domain, especially for their capacity to simultaneously account for intra-series temporal correlations and inter-series relationships. This paper introduces a novel Hierarchical GNN (DeepHGNN) framework, explicitly designed for forecasting in complex hierarchical structures. The uniqueness of DeepHGNN lies in its innovative graph-based hierarchical interpolation and an end-to-end reconciliation mechanism. This approach ensures forecast accuracy and coherence across various hierarchical levels while sharing signals across them, addressing a key challenge in hierarchical forecasting. A critical insight in hierarchical time series is the variance in forecastability across levels, with upper levels typically presenting more predictable components. DeepHGNN capitalizes on this insight by pooling and leveraging knowledge from all hierarchy levels, thereby enhancing the overall forecast accuracy. Our comprehensive evaluation set against several state-of-the-art models confirm the superior performance of DeepHGNN. This research not only demonstrates DeepHGNN's effectiveness in achieving significantly improved forecast accuracy but also contributes to the understanding of graph-based methods in hierarchical time series forecasting.">
@@ -122,8 +122,8 @@ hide:
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>29</td>
-          <td>4</td>
+          <td>30</td>
+          <td>5</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9.md b/docs/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9.md
index 00d74caf..cd58e490 100644
--- a/docs/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9.md
+++ b/docs/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9.md
@@ -11,7 +11,7 @@ hide:
 
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   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:09 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:04:55 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -75,7 +75,7 @@ hide:
           <td>2023-10-24</td>
           <td>ArXiv</td>
           <td>4</td>
-          <td>49</td>
+          <td>50</td>
         </tr>
     
         <tr id="With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time series. Time-Series Anomaly Detection (TSAD) is an important task in various time-series applications such as e-commerce, cybersecurity, vehicle maintenance, and healthcare monitoring. However, this task is very challenging as it requires considering both the intra-variable dependency and the inter-variable dependency, where a variable can be defined as an observation in time-series data. Recent graph-based approaches have made impressive progress in tackling the challenges of this field. In this survey, we conduct a comprehensive and up-to-date review of TSAD using graphs, referred to as G-TSAD. First, we explore the significant potential of graph representation learning for time-series data. Then, we review state-of-the-art graph anomaly detection techniques in the context of time series and discuss their strengths and drawbacks. Finally, we discuss the technical challenges and potential future directions for possible improvements in this research field.">
@@ -86,7 +86,7 @@ hide:
           </td>
           <td>2023-01-31</td>
           <td>ArXiv</td>
-          <td>5</td>
+          <td>4</td>
           <td>15</td>
         </tr>
     
@@ -98,8 +98,8 @@ hide:
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>29</td>
-          <td>4</td>
+          <td>30</td>
+          <td>5</td>
         </tr>
     
         <tr id="Anomaly detection of time series, especially multivariate time series(time series with multiple sensors), has been focused on for several years. Though existing method has achieved great progress, there are several challenging problems to be solved. Firstly, existing method including neural network only concentrate on the relationship in terms of timestamp. To be exact, they only want to know how does the data in the past influence which in the future. However, one sensor sometimes intervenes in other sensor such as the speed of wind may cause decrease of temperature. Secondly, there exist two categories of model for time series anomaly detection: prediction model and reconstruction model. Prediction model is adept at learning timely representation while short of capability when faced with sparse anomaly. Conversely, reconstruction model is opposite. Therefore, how can we efficiently get the relationship both in terms of both timestamp and sensors becomes our main topic. Our approach uses GAT, which is originated from graph neural network, to obtain connection between sensors. And LSTM is used to obtain relationships timely. Our approach is also designed to be double headed to calculate both prediction loss and reconstruction loss via VAE(Variational Auto-Encoder). In order to take advantage of two sorts of model, multi-task optimization algorithm is used in this model.">
@@ -122,7 +122,7 @@ hide:
           </td>
           <td>2023-02-04</td>
           <td>ArXiv</td>
-          <td>3</td>
+          <td>4</td>
           <td>22</td>
         </tr>
     
@@ -159,7 +159,7 @@ hide:
           <td>2023-07-17</td>
           <td>IEEE transactions on neural networks and learning systems</td>
           <td>8</td>
-          <td>18</td>
+          <td>19</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419.md b/docs/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419.md
index c068b1a5..2f9c04b0 100644
--- a/docs/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419.md
+++ b/docs/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:11 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:04:57 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -51,7 +51,7 @@ hide:
           <td>2023-10-24</td>
           <td>ArXiv</td>
           <td>4</td>
-          <td>49</td>
+          <td>50</td>
         </tr>
     
         <tr id="Neural forecasting of spatiotemporal time series drives both research and industrial innovation in several relevant application domains. Graph neural networks (GNNs) are often the core component of the forecasting architecture. However, in most spatiotemporal GNNs, the computational complexity scales up to a quadratic factor with the length of the sequence times the number of links in the graph, hence hindering the application of these models to large graphs and long temporal sequences. While methods to improve scalability have been proposed in the context of static graphs, few research efforts have been devoted to the spatiotemporal case. To fill this gap, we propose a scalable architecture that exploits an efficient encoding of both temporal and spatial dynamics. In particular, we use a randomized recurrent neural network to embed the history of the input time series into high-dimensional state representations encompassing multi-scale temporal dynamics. Such representations are then propagated along the spatial dimension using different powers of the graph adjacency matrix to generate node embeddings characterized by a rich pool of spatiotemporal features. The resulting node embeddings can be efficiently pre-computed in an unsupervised manner, before being fed to a feed-forward decoder that learns to map the multi-scale spatiotemporal representations to predictions. The training procedure can then be parallelized node-wise by sampling the node embeddings without breaking any dependency, thus enabling scalability to large networks. Empirical results on relevant datasets show that our approach achieves results competitive with the state of the art, while dramatically reducing the computational burden.">
@@ -62,8 +62,8 @@ hide:
           </td>
           <td>2022-09-14</td>
           <td>ArXiv</td>
-          <td>28</td>
-          <td>49</td>
+          <td>29</td>
+          <td>50</td>
         </tr>
     
         <tr id="Outstanding achievements of graph neural networks for spatiotemporal time series analysis show that relational constraints introduce an effective inductive bias into neural forecasting architectures. Often, however, the relational information characterizing the underlying data-generating process is unavailable and the practitioner is left with the problem of inferring from data which relational graph to use in the subsequent processing stages. We propose novel, principled - yet practical - probabilistic score-based methods that learn the relational dependencies as distributions over graphs while maximizing end-to-end the performance at task. The proposed graph learning framework is based on consolidated variance reduction techniques for Monte Carlo score-based gradient estimation, is theoretically grounded, and, as we show, effective in practice. In this paper, we focus on the time series forecasting problem and show that, by tailoring the gradient estimators to the graph learning problem, we are able to achieve state-of-the-art performance while controlling the sparsity of the learned graph and the computational scalability. We empirically assess the effectiveness of the proposed method on synthetic and real-world benchmarks, showing that the proposed solution can be used as a stand-alone graph identification procedure as well as a graph learning component of an end-to-end forecasting architecture.">
@@ -75,7 +75,7 @@ hide:
           <td>2022-05-26</td>
           <td>J. Mach. Learn. Res.</td>
           <td>11</td>
-          <td>49</td>
+          <td>50</td>
         </tr>
     
         <tr id="Time series forecasting with additional spatial de-pendencies has attracted a tremendous amount of research interest in social sciences, due to its importance in modern real-world applications. The Graph Neural Networks (GNN) is one of the most exciting deep learning techniques among these spatio-temporal modeling approaches. Most existing spatio-temporal GNN frameworks are based on a two-step modeling process. In such scenario, spatial and temporal dependencies are modeled in separate steps, which lead to problems such as complex architecture design, hard to scale, etc. Targeting the shortcomings of existing studies, we take both spatial and temporal dependencies from another perspective, and consider them as two heterogeneous types of edges in the graph. We propose a unified spatio-temporal GNN framework that captures both dependencies in a single step. More specifically, for each node in the graph, a unified neural network component is designed to simultaneously extract information from its sur-rounding neighbors (spatial) and its past records (temporal), which enables much easier dependency aggregation with faster execution. Experiment results demonstrate the superiority of the proposed framework over state-of-the-art (SOTA) baselines on various applications, including modeling smart cities and data-driven political science research.">
@@ -110,8 +110,8 @@ hide:
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>29</td>
-          <td>4</td>
+          <td>30</td>
+          <td>5</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6.md b/docs/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6.md
index 3bf4c7c1..3a5d5948 100644
--- a/docs/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6.md
+++ b/docs/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:18 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:03 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -63,7 +63,7 @@ hide:
           <td>2024-06-04</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
+          <td>4</td>
         </tr>
     
         <tr id="We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.">
@@ -74,7 +74,7 @@ hide:
           </td>
           <td>2024-03-12</td>
           <td>ArXiv</td>
-          <td>24</td>
+          <td>27</td>
           <td>18</td>
         </tr>
     
@@ -86,7 +86,7 @@ hide:
           </td>
           <td>2023-10-12</td>
           <td>ArXiv</td>
-          <td>13</td>
+          <td>14</td>
           <td>40</td>
         </tr>
     
diff --git a/docs/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8.md b/docs/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8.md
index 8f29fb4b..ba7ccdff 100644
--- a/docs/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8.md
+++ b/docs/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:59 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:40 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -62,8 +62,8 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3181</td>
-          <td>63</td>
+          <td>3190</td>
+          <td>65</td>
         </tr>
     
         <tr id="Modern societies have an abundance of data yet good system models are rare. Unfortunately, many of the current system identification and machine learning techniques fail to generalize outside of the training set, producing models that violate basic physical laws. This work proposes a novel method for the Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI). SINDy-SI is an iterative method that uses Sum-of-Squares (SOS) programming to learn optimally fitted models while guaranteeing that the learned model satisfies side information, such as symmetry's and physical laws. Guided by the principle of Occam's razor, that the simplest or most regularized best fitted model is typically the superior choice, during each iteration SINDy-SI prunes the basis functions associated with small coefficients, yielding a sparse dynamical model upon termination. Through several numerical experiments we will show how the combination of side information constraints and sparse polynomial representation cultivates dynamical models that obey known physical laws while displaying impressive generalized performance beyond the training set.">
@@ -87,7 +87,7 @@ hide:
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
           <td>109</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
     
         <tr id="Sparse regression has emerged as a popular technique for learning dynamical systems from temporal data, beginning with the SINDy (Sparse Identification of Nonlinear Dynamics) framework proposed by arXiv:1509.03580. Quantifying the uncertainty inherent in differential equations learned from data remains an open problem, thus we propose leveraging recent advances in statistical inference for sparse regression to address this issue. Focusing on systems of ordinary differential equations (ODEs), SINDy assumes that each equation is a parsimonious linear combination of a few candidate functions, such as polynomials, and uses methods such as sequentially-thresholded least squares or the Lasso to identify a small subset of these functions that govern the system's dynamics. We instead employ bias-corrected versions of the Lasso and ridge regression estimators, as well as an empirical Bayes variable selection technique known as SEMMS, to estimate each ODE as a linear combination of terms that are statistically significant. We demonstrate through simulations that this approach allows us to recover the functional terms that correctly describe the dynamics more often than existing methods that do not account for uncertainty.">
@@ -110,7 +110,7 @@ hide:
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>237</td>
+          <td>238</td>
           <td>12</td>
         </tr>
     
@@ -122,8 +122,8 @@ hide:
           </td>
           <td>2020-04-17</td>
           <td>arXiv: Dynamical Systems</td>
-          <td>46</td>
-          <td>63</td>
+          <td>47</td>
+          <td>65</td>
         </tr>
     
         <tr id="Data-driven modeling frameworks that adopt sparse regression techniques, such as sparse identification of nonlinear dynamics (SINDy) and its modifications, are developed to resolve difficulties in extracting underlying dynamics from experimental data. In contrast to neural-network-based methods, these methods are designed to obtain white-box analytical models. In this work, we incorporate the concept of SINDy and knowledge in the field of classical mechanics to identify interpretable and sparse expressions of total energy and the Lagrangian that shelters the hidden dynamics. Moreover, our method (hereafter referred as Lagrangian-SINDy) is developed to use knowledge of simple systems that form the system being analyzed to ensure the likelihood of correct results and to improve the learning pace. Lagrangian-SINDy is highly accurate in discovering interpretable dynamics via energy-related physical quantities. Its performance is validated with three popular multi-DOF nonlinear dynamical systems, namely the spherical pendulum, double pendulum and cart-pendulum system. Comparisons with other SINDy-based methods are made and Lagrangian-SINDy is found to provide the most compact analytical models.">
@@ -138,18 +138,6 @@ hide:
           <td>23</td>
         </tr>
     
-        <tr id="Many dynamical systems of interest are nonlinear, with examples in turbulence, epidemiology, neuroscience, and finance, making them difficult to control using linear approaches. Model predictive control (MPC) is a powerful model-based optimization technique that enables the control of such nonlinear systems with constraints. However, modern systems often lack computationally tractable models, motivating the use of system identification techniques to learn accurate and efficient models for real-time control. In this tutorial article, we review emerging data-driven methods for model discovery and how they are used for nonlinear MPC. In particular, we focus on the sparse identification of nonlinear dynamics (SINDy) algorithm and show how it may be used with MPC on an infectious disease control example. We compare the performance against MPC based on a linear dynamic mode decomposition (DMD) model. Code is provided to run the tutorial examples and may be modified to extend this data-driven control framework to arbitrary nonlinear systems.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/700552dde778bd1f6b76f7237e9b561597b859ba" target='_blank'>SINDy with Control: A Tutorial</a></td>
-          <td>
-            Urban Fasel, E. Kaiser, J. Kutz, Bingni W. Brunton, S. Brunton
-          </td>
-          <td>2021-08-30</td>
-          <td>2021 60th IEEE Conference on Decision and Control (CDC)</td>
-          <td>45</td>
-          <td>63</td>
-        </tr>
-    
         <tr id="With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets. Recently, a framework called Sparse Identification of Nonlinear Dynamics (SINDy) has been introduced to identify the governing equations of dynamical systems from simulation data. In this study, we extend SINDy to stochastic dynamical systems which are frequently used to model biophysical processes. We prove the asymptotic correctness of stochastic SINDy in the infinite data limit, both in the original and projected variables. We discuss algorithms to solve the sparse regression problem arising from the practical implementation of SINDy and show that cross validation is an essential tool to determine the right level of sparsity. We demonstrate the proposed methodology on two test systems, namely, the diffusion in a one-dimensional potential and the projected dynamics of a two-dimensional diffusion process.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/8787d66296c0337aeb54d87885d29a63af17711b" target='_blank'>Sparse learning of stochastic dynamical equations.</a></td>
@@ -159,7 +147,7 @@ hide:
           <td>2017-12-06</td>
           <td>The Journal of chemical physics</td>
           <td>191</td>
-          <td>44</td>
+          <td>45</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f.md b/docs/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f.md
index 64c44287..892a1997 100644
--- a/docs/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f.md
+++ b/docs/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:16 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:01 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,7 +50,7 @@ hide:
           </td>
           <td>2023-10-12</td>
           <td>ArXiv</td>
-          <td>13</td>
+          <td>14</td>
           <td>40</td>
         </tr>
     
@@ -74,7 +74,7 @@ hide:
           </td>
           <td>2023-10-03</td>
           <td>ArXiv</td>
-          <td>113</td>
+          <td>120</td>
           <td>9</td>
         </tr>
     
diff --git a/docs/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90.md b/docs/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90.md
index da099fc0..33d2eba4 100644
--- a/docs/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90.md
+++ b/docs/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:29 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:12 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,8 +50,8 @@ hide:
           </td>
           <td>2024-02-04</td>
           <td>ArXiv</td>
-          <td>6</td>
-          <td>65</td>
+          <td>5</td>
+          <td>66</td>
         </tr>
     
         <tr id="Large pre-trained models have been vital in recent advancements in domains like language and vision, making model training for individual downstream tasks more efficient and provide superior performance. However, tackling time-series analysis tasks usually involves designing and training a separate model from scratch leveraging training data and domain expertise specific to the task. We tackle a significant challenge for pre-training a foundational time-series model from multi-domain time-series datasets: extracting semantically useful tokenized inputs to the model across heterogenous time-series from different domains. We propose Large Pre-trained Time-series Models (LPTM) that introduces a novel method of \textit{adaptive segmentation} that automatically identifies optimal dataset-specific segmentation strategy during pre-training. This enables LPTM to perform similar to or better than domain-specific state-of-art model when fine-tuned to different downstream time-series analysis tasks and under zero-shot settings. LPTM achieves superior forecasting and time-series classification results taking up to 40% less data and 50% less training time compared to state-of-art baselines.">
@@ -74,7 +74,7 @@ hide:
           </td>
           <td>2024-02-26</td>
           <td>ArXiv</td>
-          <td>3</td>
+          <td>4</td>
           <td>3</td>
         </tr>
     
@@ -110,7 +110,7 @@ hide:
           </td>
           <td>2024-03-12</td>
           <td>ArXiv</td>
-          <td>24</td>
+          <td>27</td>
           <td>18</td>
         </tr>
     
@@ -122,11 +122,11 @@ hide:
           </td>
           <td>2024-02-04</td>
           <td>ArXiv</td>
-          <td>26</td>
+          <td>28</td>
           <td>22</td>
         </tr>
     
-        <tr id="Learning time-series representations for discriminative tasks has been a long-standing challenge. Current pre-training methods are limited in either unidirectional next-token prediction or randomly masked token prediction. We propose a novel architecture called Bidirectional Timely Generative Pre-trained Transformer (BiTimelyGPT), which pre-trains on time-series data by both next-token and previous-token predictions in alternating transformer layers. This pre-training task preserves original distribution and data shapes of the time-series. Additionally, the full-rank forward and backward attention matrices exhibit more expressive representation capabilities. Using biosignal data, BiTimelyGPT demonstrates superior performance in predicting neurological functionality, disease diagnosis, and physiological signs. By visualizing the attention heatmap, we observe that the pre-trained BiTimelyGPT can identify discriminative segments from time-series sequences, even more so after fine-tuning on the task.">
+        <tr id="Learning time-series representations for discriminative tasks, such as classification and regression, has been a long-standing challenge in the healthcare domain. Current pre-training methods are limited in either unidirectional next-token prediction or randomly masked token prediction. We propose a novel architecture called Bidirectional Timely Generative Pre-trained Transformer (BiTimelyGPT), which pre-trains on biosignals and longitudinal clinical records by both next-token and previous-token prediction in alternating transformer layers. This pre-training task preserves original distribution and data shapes of the time-series. Additionally, the full-rank forward and backward attention matrices exhibit more expressive representation capabilities. Using biosignals and longitudinal clinical records, BiTimelyGPT demonstrates superior performance in predicting neurological functionality, disease diagnosis, and physiological signs. By visualizing the attention heatmap, we observe that the pre-trained BiTimelyGPT can identify discriminative segments from biosignal time-series sequences, even more so after fine-tuning on the task.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/1d0328153f4136635b7df336fb958c92d066ac29" target='_blank'>Bidirectional Generative Pre-training for Improving Time Series Representation Learning</a></td>
           <td>
diff --git a/site/Symbolic regression/index.html b/site/Symbolic regression/index.html
index e3bc1f4a..b29d0005 100644
--- a/site/Symbolic regression/index.html	
+++ b/site/Symbolic regression/index.html	
@@ -451,7 +451,7 @@ <h1>Symbolic regression</h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:06:06 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:48 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -516,8 +516,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences of the United States of America</td>
-          <td>3181</td>
-          <td>63</td>
+          <td>3190</td>
+          <td>65</td>
 
             <td><a href='../recommendations/5d150cec2775f9bc863760448f14104cc8f42368' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -556,8 +556,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2019-03-29</td>
           <td>Proceedings of the National Academy of Sciences of the United States of America</td>
-          <td>599</td>
-          <td>63</td>
+          <td>603</td>
+          <td>65</td>
 
             <td><a href='../recommendations/3c9961153493370500020c81527b3548c96f81e0' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -576,8 +576,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2016-08-18</td>
           <td>Nature Communications</td>
-          <td>446</td>
-          <td>63</td>
+          <td>448</td>
+          <td>65</td>
 
             <td><a href='../recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -596,8 +596,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2017-11-15</td>
           <td>Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>427</td>
-          <td>63</td>
+          <td>428</td>
+          <td>65</td>
 
             <td><a href='../recommendations/b2eb064f432557c59ce99834d7dc7817e4687271' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -615,9 +615,9 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
             N. Mangan, S. Brunton, J. Proctor, J. Kutz
           </td>
           <td>2016-05-26</td>
-          <td>IEEE Transactions on Molecular Biological and Multi-Scale Communications, IEEE Transactions on Molecular, Biological and Multi-Scale Communications</td>
-          <td>315</td>
-          <td>63</td>
+          <td>IEEE Transactions on Molecular, Biological and Multi-Scale Communications, IEEE Transactions on Molecular Biological and Multi-Scale Communications</td>
+          <td>317</td>
+          <td>65</td>
 
             <td><a href='../recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -636,8 +636,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2020-04-05</td>
           <td>Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>191</td>
-          <td>63</td>
+          <td>194</td>
+          <td>65</td>
 
             <td><a href='../recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -675,9 +675,9 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
             Kadierdan Kaheman, E. Kaiser, B. Strom, J. Kutz, S. Brunton
           </td>
           <td>2019-09-18</td>
-          <td>ArXiv, arXiv.org</td>
-          <td>31</td>
-          <td>63</td>
+          <td>arXiv.org, ArXiv</td>
+          <td>32</td>
+          <td>65</td>
 
             <td><a href='../recommendations/73dd9c49f205280991826b2ea4b50344203916b4' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -696,8 +696,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2019-06-19</td>
           <td>Frontiers in Artificial Intelligence</td>
-          <td>66</td>
-          <td>63</td>
+          <td>67</td>
+          <td>65</td>
 
             <td><a href='../recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -716,8 +716,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2016-09-21</td>
           <td>Science Advances</td>
-          <td>1171</td>
-          <td>63</td>
+          <td>1172</td>
+          <td>65</td>
 
             <td><a href='../recommendations/0acd117521ef5aafb09fed02ab415523b330b058' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -736,8 +736,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2021-11-22</td>
           <td>Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>157</td>
-          <td>63</td>
+          <td>158</td>
+          <td>65</td>
 
             <td><a href='../recommendations/883547fdbd88552328a6615ec620f96e39c57018' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -756,8 +756,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2022-06-01</td>
           <td>Nonlinear Dynamics</td>
-          <td>27</td>
-          <td>90</td>
+          <td>28</td>
+          <td>91</td>
 
             <td><a href='../recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -776,8 +776,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2018-07-14</td>
           <td>IEEE Access</td>
-          <td>114</td>
-          <td>63</td>
+          <td>115</td>
+          <td>65</td>
 
             <td><a href='../recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -835,33 +835,33 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>0</td>
         </tr>
 
-        <tr id="Enhancing the sparsity of data-driven reduced-order models (ROMs) has gained increasing attention in recent years. In this work, we analyze an efficient approach to identifying skillful ROMs with a sparse structure using an information-theoretic indicator called causation entropy. The causation entropy quantifies in a statistical way the additional contribution of each term to the underlying dynamics beyond the information already captured by all the other terms in the ansatz. By doing so, the causation entropy assesses the importance of each term to the dynamics before a parameter estimation procedure is performed. Thus, the approach can be utilized to eliminate terms with little dynamic impact, leading to a parsimonious structure that retains the essential physics. To circumvent the difficulty of estimating high-dimensional probability density functions (PDFs) involved in the causation entropy computation, we leverage Gaussian approximations for such PDFs, which are demonstrated to be sufficient even in the presence of highly non-Gaussian dynamics. The effectiveness of the approach is illustrated by the Kuramoto-Sivashinsky equation by building sparse causation-based ROMs for various purposes, such as recovering long-term statistics and inferring unobserved dynamics via data assimilation with partial observations.">
+        <tr id="This paper focuses on the application of experimental data-based system identification of unknown systems utilising sparse identification of nonlinear dynamics (SINDy). SINDy is used to detect the system dynamics in the three well-known nonlinear systems. Analyzed are SINDy’s abilities to accurately represent transient/steady-state behaviour, noise effect, and model structure. A sparse set of basis functions can effectively capture the dynamics of a system, according to the data-driven approach known as SINDy. The coefficients of these basis functions are determined via methods of sparse regression, and the final model is made up of a number of sparse ordinary differential equations. The findings demonstrate that SINDy, with sufficient time-series data, can capture both transient and steady-state phenomena. According to the analysis of the noise effect, SINDy’s performance declines as the system’s noise level rises. The feature library must contain the appropriate model structure in order for SINDy to function effectively. SINDy has the potential to extract unknown system dynamics from experimental data.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/51343d94516402e35ba4c9c52a62e0d4dd4986ba" target='_blank'>
-              Minimum Reduced-Order Models via Causal Inference
+          <td><a href="https://www.semanticscholar.org/paper/315b7c145c5a86b92e656ad174cede9f67bf3f34" target='_blank'>
+              Data-driven system identification of unknown systems utilising sparse identification of nonlinear dynamics (SINDy)
               </a>
             </td>
           <td>
-            Nan Chen, Honghu Liu
+            P. Pandey, H. Haddad Khodaparast, M. Friswell, T. Chatterjee, N. Jamia, T. Deighan
           </td>
-          <td>2024-06-29</td>
-          <td>ArXiv</td>
-          <td>0</td>
+          <td>2024-06-01</td>
+          <td>Journal of Physics: Conference Series</td>
           <td>0</td>
+          <td>2</td>
         </tr>
 
-        <tr id="This paper focuses on the application of experimental data-based system identification of unknown systems utilising sparse identification of nonlinear dynamics (SINDy). SINDy is used to detect the system dynamics in the three well-known nonlinear systems. Analyzed are SINDy’s abilities to accurately represent transient/steady-state behaviour, noise effect, and model structure. A sparse set of basis functions can effectively capture the dynamics of a system, according to the data-driven approach known as SINDy. The coefficients of these basis functions are determined via methods of sparse regression, and the final model is made up of a number of sparse ordinary differential equations. The findings demonstrate that SINDy, with sufficient time-series data, can capture both transient and steady-state phenomena. According to the analysis of the noise effect, SINDy’s performance declines as the system’s noise level rises. The feature library must contain the appropriate model structure in order for SINDy to function effectively. SINDy has the potential to extract unknown system dynamics from experimental data.">
+        <tr id="Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are first-principled, explainable, and sample-efficient. However, they often rely on strong modeling assumptions and expensive numerical integration, requiring significant computational resources and domain expertise. While deep learning (DL) provides efficient alternatives for modeling complex dynamics, they require a large amount of labeled training data. Furthermore, its predictions may disobey the governing physical laws and are difficult to interpret. Physics-guided DL aims to integrate first-principled physical knowledge into data-driven methods. It has the best of both worlds and is well equipped to better solve scientific problems. Recently, this field has gained great progress and has drawn considerable interest across discipline Here, we introduce the framework of physics-guided DL with a special emphasis on learning dynamical systems. We describe the learning pipeline and categorize state-of-the-art methods under this framework. We also offer our perspectives on the open challenges and emerging opportunities.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/315b7c145c5a86b92e656ad174cede9f67bf3f34" target='_blank'>
-              Data-driven system identification of unknown systems utilising sparse identification of nonlinear dynamics (SINDy)
+          <td><a href="https://www.semanticscholar.org/paper/60d721e89c2f9c549241a4982b77f9c752b34460" target='_blank'>
+              Learning dynamical systems from data: An introduction to physics-guided deep learning
               </a>
             </td>
           <td>
-            P. Pandey, H. Haddad Khodaparast, M. Friswell, T. Chatterjee, N. Jamia, T. Deighan
+            Rose Yu, Rui Wang
           </td>
-          <td>2024-06-01</td>
-          <td>Journal of Physics: Conference Series</td>
-          <td>0</td>
+          <td>2024-06-24</td>
+          <td>Proceedings of the National Academy of Sciences of the United States of America</td>
+          <td>1</td>
           <td>1</td>
         </tr>
 
@@ -880,21 +880,6 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>49</td>
         </tr>
 
-        <tr id="Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are first-principled, explainable, and sample-efficient. However, they often rely on strong modeling assumptions and expensive numerical integration, requiring significant computational resources and domain expertise. While deep learning (DL) provides efficient alternatives for modeling complex dynamics, they require a large amount of labeled training data. Furthermore, its predictions may disobey the governing physical laws and are difficult to interpret. Physics-guided DL aims to integrate first-principled physical knowledge into data-driven methods. It has the best of both worlds and is well equipped to better solve scientific problems. Recently, this field has gained great progress and has drawn considerable interest across discipline Here, we introduce the framework of physics-guided DL with a special emphasis on learning dynamical systems. We describe the learning pipeline and categorize state-of-the-art methods under this framework. We also offer our perspectives on the open challenges and emerging opportunities.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/60d721e89c2f9c549241a4982b77f9c752b34460" target='_blank'>
-              Learning dynamical systems from data: An introduction to physics-guided deep learning
-              </a>
-            </td>
-          <td>
-            Rose Yu, Rui Wang
-          </td>
-          <td>2024-06-24</td>
-          <td>Proceedings of the National Academy of Sciences of the United States of America</td>
-          <td>1</td>
-          <td>1</td>
-        </tr>
-
         <tr id="We present a computational technique for modeling the evolution of dynamical systems in a reduced basis, with a focus on the challenging problem of modeling partially-observed partial differential equations (PDEs) on high-dimensional non-uniform grids. We address limitations of previous work on data-driven flow map learning in the sense that we focus on noisy and limited data to move toward data collection scenarios in real-world applications. Leveraging recent work on modeling PDEs in modal and nodal spaces, we present a neural network structure that is suitable for PDE modeling with noisy and limited data available only on a subset of the state variables or computational domain. In particular, spatial grid-point measurements are reduced using a learned linear transformation, after which the dynamics are learned in this reduced basis before being transformed back out to the nodal space. This approach yields a drastically reduced parameterization of the neural network compared with previous flow map models for nodal space learning. This primarily allows for smaller training data sets, but also enables reduced training times.">
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           <td><a href="https://www.semanticscholar.org/paper/33821b7c0acd5258ca0c60a09ffdc55439ac7ac2" target='_blank'>
@@ -951,10 +936,25 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           </td>
           <td>2024-07-05</td>
           <td>ArXiv</td>
-          <td>2</td>
+          <td>3</td>
           <td>22</td>
         </tr>
 
+        <tr id="Model parsimony is an important \emph{cognitive bias} in data-driven modelling that aids interpretability and helps to prevent over-fitting. Sparse identification of nonlinear dynamics (SINDy) methods are able to learn sparse representations of complex dynamics directly from data, given a basis of library functions. In this work, a novel Bayesian treatment of dictionary learning system identification, as an alternative to SINDy, is envisaged. The proposed method -- Bayesian identification of nonlinear dynamics (BINDy) -- is distinct from previous approaches in that it targets the full joint posterior distribution over both the terms in the library and their parameterisation in the model. This formulation confers the advantage that an arbitrary prior may be placed over the model structure to produce models that are sparse in the model space rather than in parameter space. Because this posterior is defined over parameter vectors that can change in dimension, the inference cannot be performed by standard techniques. Instead, a Gibbs sampler based on reversible-jump Markov-chain Monte-Carlo is proposed. BINDy is shown to compare favourably to ensemble SINDy in three benchmark case-studies. In particular, it is seen that the proposed method is better able to assign high probability to correct model terms.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/04666a9e47b5508a0c0f966265fe51da2b2c634a" target='_blank'>
+              BINDy -- Bayesian identification of nonlinear dynamics with reversible-jump Markov-chain Monte-Carlo
+              </a>
+            </td>
+          <td>
+            M.D. Champneys, T. J. Rogers
+          </td>
+          <td>2024-08-15</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>1</td>
+        </tr>
+
         <tr id="The parametric greedy latent space dynamics identification (gLaSDI) framework has demonstrated promising potential for accurate and efficient modeling of high-dimensional nonlinear physical systems. However, it remains challenging to handle noisy data. To enhance robustness against noise, we incorporate the weak-form estimation of nonlinear dynamics (WENDy) into gLaSDI. In the proposed weak-form gLaSDI (WgLaSDI) framework, an autoencoder and WENDy are trained simultaneously to discover intrinsic nonlinear latent-space dynamics of high-dimensional data. Compared to the standard sparse identification of nonlinear dynamics (SINDy) employed in gLaSDI, WENDy enables variance reduction and robust latent space discovery, therefore leading to more accurate and efficient reduced-order modeling. Furthermore, the greedy physics-informed active learning in WgLaSDI enables adaptive sampling of optimal training data on the fly for enhanced modeling accuracy. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including viscous and inviscid Burgers' equations, time-dependent radial advection, and the Vlasov equation for plasma physics. With data that contains 5-10% Gaussian white noise, WgLaSDI outperforms gLaSDI by orders of magnitude, achieving 1-7% relative errors. Compared with the high-fidelity models, WgLaSDI achieves 121 to 1,779x speed-up.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/15fe2e82d4161103b69fe83f184552b9e9774633" target='_blank'>
@@ -985,6 +985,21 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>11</td>
         </tr>
 
+        <tr id="It has been found recently that more data can, counter-intuitively, hurt the performance of deep neural networks. Here, we show that a more extreme version of the phenomenon occurs in data-driven models of dynamical systems. To elucidate the underlying mechanism, we focus on next-generation reservoir computing (NGRC) -- a popular framework for learning dynamics from data. We find that, despite learning a better representation of the flow map with more training data, NGRC can adopt an ill-conditioned ``integrator'' and lose stability. We link this data-induced instability to the auxiliary dimensions created by the delayed states in NGRC. Based on these findings, we propose simple strategies to mitigate the instability, either by increasing regularization strength in tandem with data size, or by carefully introducing noise during training. Our results highlight the importance of proper regularization in data-driven modeling of dynamical systems.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/b882f72ec6688e53edbf612e32d2b97c2a4c8236" target='_blank'>
+              How more data can hurt: Instability and regularization in next-generation reservoir computing
+              </a>
+            </td>
+          <td>
+            Yuanzhao Zhang, Sean P. Cornelius
+          </td>
+          <td>2024-07-11</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>1</td>
+        </tr>
+
         <tr id="Stochastic collocation (SC) is a well‐known non‐intrusive method of constructing surrogate models for uncertainty quantification. In dynamical systems, SC is especially suited for full‐field uncertainty propagation that characterizes the distributions of the high‐dimensional solution fields of a model with stochastic input parameters. However, due to the highly nonlinear nature of the parameter‐to‐solution map in even the simplest dynamical systems, the constructed SC surrogates are often inaccurate. This work presents an alternative approach, where we apply the SC approximation over the dynamics of the model, rather than the solution. By combining the data‐driven sparse identification of nonlinear dynamics framework with SC, we construct dynamics surrogates and integrate them through time to construct the surrogate solutions. We demonstrate that the SC‐over‐dynamics framework leads to smaller errors, both in terms of the approximated system trajectories as well as the model state distributions, when compared against full‐field SC applied to the solutions directly. We present numerical evidence of this improvement using three test problems: a chaotic ordinary differential equation, and two partial differential equations from solid mechanics.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/dc0b58e254bb512300814c52b4a101046377d6e4" target='_blank'>
@@ -1000,19 +1015,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>30</td>
         </tr>
 
-        <tr id="It has been found recently that more data can, counter-intuitively, hurt the performance of deep neural networks. Here, we show that a more extreme version of the phenomenon occurs in data-driven models of dynamical systems. To elucidate the underlying mechanism, we focus on next-generation reservoir computing (NGRC) -- a popular framework for learning dynamics from data. We find that, despite learning a better representation of the flow map with more training data, NGRC can adopt an ill-conditioned ``integrator'' and lose stability. We link this data-induced instability to the auxiliary dimensions created by the delayed states in NGRC. Based on these findings, we propose simple strategies to mitigate the instability, either by increasing regularization strength in tandem with data size, or by carefully introducing noise during training. Our results highlight the importance of proper regularization in data-driven modeling of dynamical systems.">
+        <tr id="We propose a novel learning framework for Koopman operator of nonlinear dynamical systems that is informed by the governing equation and guarantees long-time stability and robustness to noise. In contrast to existing frameworks where either ad-hoc observables or blackbox neural networks are used to construct observables in the extended dynamic mode decomposition (EDMD), our observables are informed by governing equations via Polyflow. To improve the noise robustness and guarantee long-term stability, we designed a stable parameterization of the Koopman operator together with a progressive learning strategy for roll-out recurrent loss. To further improve model performance in the phase space, a simple iterative strategy of data augmentation was developed. Numerical experiments of prediction and control of classic nonlinear systems with ablation study showed the effectiveness of the proposed techniques over several state-of-the-art practices.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/b882f72ec6688e53edbf612e32d2b97c2a4c8236" target='_blank'>
-              How more data can hurt: Instability and regularization in next-generation reservoir computing
+          <td><a href="https://www.semanticscholar.org/paper/33d038a0e7775662768898c432284e433922c891" target='_blank'>
+              Learning Noise-Robust Stable Koopman Operator for Control with Physics-Informed Observables
               </a>
             </td>
           <td>
-            Yuanzhao Zhang, Sean P. Cornelius
+            Shahriar Akbar Sakib, Shaowu Pan
           </td>
-          <td>2024-07-11</td>
+          <td>2024-08-13</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>0</td>
         </tr>
 
         <tr id="Dynamic Mode Decomposition (DMD) and its variants, such as extended DMD (EDMD), are broadly used to fit simple linear models to dynamical systems known from observable data. As DMD methods work well in several situations but perform poorly in others, a clarification of the assumptions under which DMD is applicable is desirable. Upon closer inspection, existing interpretations of DMD methods based on the Koopman operator are not quite satisfactory: they justify DMD under assumptions that hold only with probability zero for generic observables. Here, we give a justification for DMD as a local, leading-order reduced model for the dominant system dynamics under conditions that hold with probability one for generic observables and non-degenerate observational data. We achieve this for autonomous and for periodically forced systems of finite or infinite dimensions by constructing linearizing transformations for their dominant dynamics within attracting slow spectral submanifolds (SSMs). Our arguments also lead to a new algorithm, data-driven linearization (DDL), which is a higher-order, systematic linearization of the observable dynamics within slow SSMs. We show by examples how DDL outperforms DMD and EDMD on numerical and experimental data.">
@@ -1027,94 +1042,94 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2024-07-11</td>
           <td>ArXiv</td>
           <td>1</td>
-          <td>0</td>
+          <td>1</td>
         </tr>
 
         <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/cabb9ef9934cc450e8873d693c8dd8dfc73087ab" target='_blank'>
-              MBD-NODE: physics-informed data-driven modeling and simulation of constrained multibody systems
+              MBD-NODE: Physics-informed data-driven modeling and simulation of constrained multibody systems
               </a>
             </td>
           <td>
             Jingquan Wang, Shu Wang, H. Unjhawala, Jinlong Wu, D. Negrut
           </td>
           <td>2024-07-11</td>
-          <td>Multibody System Dynamics</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>28</td>
+          <td>29</td>
         </tr>
 
-        <tr id="While linear systems are well-understood, no explicit solution exists for general nonlinear systems. Thus, it is desirable to make the understanding of linear system available in the nonlinear setting. This motivates the search for linearization techniques which are able to represent the nonlinear system by an equivalent, linear system. While much progress has been made extending various linearization techniques to larger domains and more delicate attractor geometries, the limitations of such techniques for truly nonlinear dynamics, such as dynamics with coexisting attractors, have been pointed out recently. In this work, we show that genuinely nonlinear dynamics can be globally linearized. To this end, we investigate systems with a continuous spectrum, a limit cycle, and coexisting solutions and explicitly construct linear systems mimicking these nonlinear behaviors as close as possible. We approximate the transformations between linear and nonlinear system with deep neural networks. The obtained linearizations are finite dimensional exceeding the phase space dimension of the underlying linear system by one at most.">
+        <tr id="Data-driven discovery of partial differential equations (PDEs) has emerged as a promising approach for deriving governing physics when domain knowledge about observed data is limited. Despite recent progress, the identification of governing equations and their parametric dependencies using conventional information criteria remains challenging in noisy situations, as the criteria tend to select overly complex PDEs. In this paper, we introduce an extension of the uncertainty-penalized Bayesian information criterion (UBIC), which is adapted to solve parametric PDE discovery problems efficiently without requiring computationally expensive PDE simulations. This extended UBIC uses quantified PDE uncertainty over different temporal or spatial points to prevent overfitting in model selection. The UBIC is computed with data transformation based on power spectral densities to discover the governing parametric PDE that truly captures qualitative features in frequency space with a few significant terms and their parametric dependencies (i.e., the varying PDE coefficients), evaluated with confidence intervals. Numerical experiments on canonical PDEs demonstrate that our extended UBIC can identify the true number of terms and their varying coefficients accurately, even in the presence of noise. The code is available at \url{https://github.com/Pongpisit-Thanasutives/parametric-discovery}.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/2196ed95f5d349e07c7af94fd99240eec34dfdb2" target='_blank'>
-              Learning Global Linear Representations of Truly Nonlinear Dynamics
+          <td><a href="https://www.semanticscholar.org/paper/d605721a21ed5e6e69cc14a1ab3eb9a559099ea9" target='_blank'>
+              Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discovery
               </a>
             </td>
           <td>
-            Thomas Breunung, F. Kogelbauer
+            Pongpisit Thanasutives, Ken-ichi Fukui
           </td>
-          <td>2024-08-06</td>
+          <td>2024-08-15</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
+          <td>3</td>
         </tr>
 
-        <tr id="In a landscape where scientific discovery is increasingly driven by data, the integration of machine learning (ML) with traditional scientific methodologies has emerged as a transformative approach. This paper introduces a novel, data-driven framework that synergizes physics-based priors with advanced ML techniques to address the computational and practical limitations inherent in first-principle-based methods and brute-force machine learning methods. Our framework showcases four algorithms, each embedding a specific physics-based prior tailored to a particular class of nonlinear systems, including separable and nonseparable Hamiltonian systems, hyperbolic partial differential equations, and incompressible fluid dynamics. The intrinsic incorporation of physical laws preserves the system's intrinsic symmetries and conservation laws, ensuring solutions are physically plausible and computationally efficient. The integration of these priors also enhances the expressive power of neural networks, enabling them to capture complex patterns typical in physical phenomena that conventional methods often miss. As a result, our models outperform existing data-driven techniques in terms of prediction accuracy, robustness, and predictive capability, particularly in recognizing features absent from the training set, despite relying on small datasets, short training periods, and small sample sizes.">
+        <tr id="The identification of self-similarity is an indispensable tool for understanding and modelling physical phenomena. Unfortunately, this is not always possible to perform formally in highly complex problems. We propose a methodology to extract the similarity variables of a self-similar physical process directly from data, without prior knowledge of the governing equations or boundary conditions, based on an optimization problem and symbolic regression. We analyze the accuracy and robustness of our method in four problems which have been influential in fluid mechanics research: a laminar boundary layer, Burger's equation, a turbulent wake, and a collapsing cavity. Our analysis considers datasets acquired via both numerical and wind-tunnel experiments.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6ba424643899decf4b48792c380cc0c66abe63c7" target='_blank'>
-              Data-Driven Computing Methods for Nonlinear Physics Systems with Geometric Constraints
+          <td><a href="https://www.semanticscholar.org/paper/b4e59a9e878c505fc222908a8279de60c32a29db" target='_blank'>
+              Extracting self-similarity from data
               </a>
             </td>
           <td>
-            Yunjin Tong
+            Nikos Bempedelis, Luca Magri, Konstantinos Steiros
           </td>
-          <td>2024-06-20</td>
+          <td>2024-07-15</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
-        <tr id="The Green's function, serving as a kernel function that delineates the interaction relationships of physical quantities within a field, holds significant research implications across various disciplines. It forms the foundational basis for the renowned Biot-Savart formula in fluid dynamics, the theoretical solution of the pressure Poisson equation, and et al. Despite their importance, the theoretical derivation of the Green's function is both time-consuming and labor-intensive. In this study, we employed DISCOVER, an advanced symbolic regression method leveraging symbolic binary trees and reinforcement learning, to identify unknown Green's functions for several elementary partial differential operators, including Laplace operators, Helmholtz operators, and second-order differential operators with jump conditions. The Laplace and Helmholtz operators are particularly vital for resolving the pressure Poisson equation, while second-order differential operators with jump conditions are essential for analyzing multiphase flows and shock waves. By incorporating physical hard constraints, specifically symmetry properties inherent to these self-adjoint operators, we significantly enhanced the performance of the DISCOVER framework, potentially doubling its efficacy. Notably, the Green's functions discovered for the Laplace and Helmholtz operators precisely matched the true Green's functions. Furthermore, for operators without known exact Green's functions, such as the periodic Helmholtz operator and second-order differential operators with jump conditions, we identified potential Green's functions with solution error on the order of 10^(-10). This application of symbolic regression to the discovery of Green's functions represents a pivotal advancement in leveraging artificial intelligence to accelerate scientific discoveries, particularly in fluid dynamics and related fields.">
+        <tr id="While linear systems are well-understood, no explicit solution exists for general nonlinear systems. Thus, it is desirable to make the understanding of linear system available in the nonlinear setting. This motivates the search for linearization techniques which are able to represent the nonlinear system by an equivalent, linear system. While much progress has been made extending various linearization techniques to larger domains and more delicate attractor geometries, the limitations of such techniques for truly nonlinear dynamics, such as dynamics with coexisting attractors, have been pointed out recently. In this work, we show that genuinely nonlinear dynamics can be globally linearized. To this end, we investigate systems with a continuous spectrum, a limit cycle, and coexisting solutions and explicitly construct linear systems mimicking these nonlinear behaviors as close as possible. We approximate the transformations between linear and nonlinear system with deep neural networks. The obtained linearizations are finite dimensional exceeding the phase space dimension of the underlying linear system by one at most.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/724df8871efc6ccb1cbb6f3d3883d97c3ec801ba" target='_blank'>
-              Discovery of Green's function based on symbolic regression with physical hard constraints
+          <td><a href="https://www.semanticscholar.org/paper/2196ed95f5d349e07c7af94fd99240eec34dfdb2" target='_blank'>
+              Learning Global Linear Representations of Truly Nonlinear Dynamics
               </a>
             </td>
           <td>
-            Jianghang Gu, Mengge Du, Yuntian Chen, Shiyi Chen
+            Thomas Breunung, F. Kogelbauer
           </td>
-          <td>2024-08-01</td>
+          <td>2024-08-06</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>4</td>
+          <td>6</td>
         </tr>
 
-        <tr id="Machine learning techniques have recently been of great interest for solving differential equations. Training these models is classically a data-fitting task, but knowledge of the expression of the differential equation can be used to supplement the training objective, leading to the development of physics-informed scientific machine learning. In this article, we focus on one class of models called nonlinear vector autoregression (NVAR) to solve ordinary differential equations (ODEs). Motivated by connections to numerical integration and physics-informed neural networks, we explicitly derive the physics-informed NVAR (piNVAR) which enforces the right-hand side of the underlying differential equation regardless of NVAR construction. Because NVAR and piNVAR completely share their learned parameters, we propose an augmented procedure to jointly train the two models. Then, using both data-driven and ODE-driven metrics, we evaluate the ability of the piNVAR model to predict solutions to various ODE systems, such as the undamped spring, a Lotka-Volterra predator-prey nonlinear model, and the chaotic Lorenz system.">
+        <tr id="The identification of a mathematical dynamics model is a crucial step in the designing process of a controller. However, it is often very difficult to identify the system's governing equations, especially in complex environments that combine physical laws of different disciplines. In this paper, we present a new approach that allows identifying an ordinary differential equation by means of a physics-informed machine learning algorithm. Our method introduces a special neural network that allows exploiting prior human knowledge to a certain degree and extends it autonomously, so that the resulting differential equations describe the system as accurately as possible. We validate the method on a Duffing oscillator with simulation data and, additionally, on a cascaded tank example with real-world data. Subsequently, we use the developed algorithm in a model-based reinforcement learning framework by alternately identifying and controlling a system to a target state. We test the performance by swinging-up an inverted pendulum on a cart.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/59096640a40ed6b70acbbfdaca5a505cd1330bb4" target='_blank'>
-              Physics-informed nonlinear vector autoregressive models for the prediction of dynamical systems
+          <td><a href="https://www.semanticscholar.org/paper/9fb84f2c4de6cf8d43f1207b6c58095273d711f1" target='_blank'>
+              Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning
               </a>
             </td>
           <td>
-            James H. Adler, Samuel Hocking, Xiaozhe Hu, Shafiqul Islam
+            Tobias Nagel, Marco F. Huber
           </td>
-          <td>2024-07-25</td>
+          <td>2024-06-28</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>2</td>
         </tr>
 
-        <tr id="The identification of a mathematical dynamics model is a crucial step in the designing process of a controller. However, it is often very difficult to identify the system's governing equations, especially in complex environments that combine physical laws of different disciplines. In this paper, we present a new approach that allows identifying an ordinary differential equation by means of a physics-informed machine learning algorithm. Our method introduces a special neural network that allows exploiting prior human knowledge to a certain degree and extends it autonomously, so that the resulting differential equations describe the system as accurately as possible. We validate the method on a Duffing oscillator with simulation data and, additionally, on a cascaded tank example with real-world data. Subsequently, we use the developed algorithm in a model-based reinforcement learning framework by alternately identifying and controlling a system to a target state. We test the performance by swinging-up an inverted pendulum on a cart.">
+        <tr id="Machine learning techniques have recently been of great interest for solving differential equations. Training these models is classically a data-fitting task, but knowledge of the expression of the differential equation can be used to supplement the training objective, leading to the development of physics-informed scientific machine learning. In this article, we focus on one class of models called nonlinear vector autoregression (NVAR) to solve ordinary differential equations (ODEs). Motivated by connections to numerical integration and physics-informed neural networks, we explicitly derive the physics-informed NVAR (piNVAR) which enforces the right-hand side of the underlying differential equation regardless of NVAR construction. Because NVAR and piNVAR completely share their learned parameters, we propose an augmented procedure to jointly train the two models. Then, using both data-driven and ODE-driven metrics, we evaluate the ability of the piNVAR model to predict solutions to various ODE systems, such as the undamped spring, a Lotka-Volterra predator-prey nonlinear model, and the chaotic Lorenz system.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/9fb84f2c4de6cf8d43f1207b6c58095273d711f1" target='_blank'>
-              Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning
+          <td><a href="https://www.semanticscholar.org/paper/59096640a40ed6b70acbbfdaca5a505cd1330bb4" target='_blank'>
+              Physics-informed nonlinear vector autoregressive models for the prediction of dynamical systems
               </a>
             </td>
           <td>
-            Tobias Nagel, Marco F. Huber
+            James H. Adler, Samuel Hocking, Xiaozhe Hu, Shafiqul Islam
           </td>
-          <td>2024-06-28</td>
+          <td>2024-07-25</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>2</td>
@@ -1150,34 +1165,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>47</td>
         </tr>
 
-        <tr id="We present a pragmatic approach to the sparse identification of nonlinear dynamics for systems with discrete delays. It relies on approximating the underlying delay model with a system of ordinary differential equations via pseudospectral collocation. To minimize the reconstruction error, the new strategy avoids optimizing all possible multiple unknown delays, identifying only the maximum one. The computational burden is thus greatly reduced, improving the performance of recent implementations that work directly on the delay system.">
+        <tr id="Physics-Informed Neural Networks (PINNs) have revolutionized solving differential equations by integrating physical laws into neural network training. This paper explores PINNs for open-loop optimal control problems (OCPs) with incomplete information, such as unknown initial conditions and sparse boundary data. We derive optimality conditions from the Lagrangian multipliers and use a neural network to predict the state, adjoint, and control variables. In contrast with previous methods, our approach integrates these elements into a single neural network and addresses scenarios with consistently limited data. Specifically, we address the study of partially unknown equations identifying underlying parameters online by searching for the optimal solution. Numerical examples show the effectiveness of the proposed method even in scenarios characterized by a considerable lack of information.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/af6a7301b4680a556da5635cdd6aedbb64f047b0" target='_blank'>
-              Sparse identification of time delay systems via pseudospectral collocation
+          <td><a href="https://www.semanticscholar.org/paper/1de965a4fc10f47113e0b8b25e5ae6627f2c9cbb" target='_blank'>
+              A PINN approach for the online identification and control of unknown PDEs
               </a>
             </td>
           <td>
-            Enrico Bozzo, Dimitri Breda, Muhammad Tanveer
+            Alessandro Alla, Giulia Bertaglia, Elisa Calzola
           </td>
-          <td>2024-08-04</td>
+          <td>2024-08-06</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Physics-Informed Neural Networks (PINNs) have revolutionized solving differential equations by integrating physical laws into neural network training. This paper explores PINNs for open-loop optimal control problems (OCPs) with incomplete information, such as unknown initial conditions and sparse boundary data. We derive optimality conditions from the Lagrangian multipliers and use a neural network to predict the state, adjoint, and control variables. In contrast with previous methods, our approach integrates these elements into a single neural network and addresses scenarios with consistently limited data. Specifically, we address the study of partially unknown equations identifying underlying parameters online by searching for the optimal solution. Numerical examples show the effectiveness of the proposed method even in scenarios characterized by a considerable lack of information.">
+        <tr id="We present a pragmatic approach to the sparse identification of nonlinear dynamics for systems with discrete delays. It relies on approximating the underlying delay model with a system of ordinary differential equations via pseudospectral collocation. To minimize the reconstruction error, the new strategy avoids optimizing all possible multiple unknown delays, identifying only the maximum one. The computational burden is thus greatly reduced, improving the performance of recent implementations that work directly on the delay system.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1de965a4fc10f47113e0b8b25e5ae6627f2c9cbb" target='_blank'>
-              A PINN approach for the online identification and control of unknown PDEs
+          <td><a href="https://www.semanticscholar.org/paper/af6a7301b4680a556da5635cdd6aedbb64f047b0" target='_blank'>
+              Sparse identification of time delay systems via pseudospectral collocation
               </a>
             </td>
           <td>
-            Alessandro Alla, Giulia Bertaglia, Elisa Calzola
+            Enrico Bozzo, Dimitri Breda, Muhammad Tanveer
           </td>
-          <td>2024-08-06</td>
+          <td>2024-08-04</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>0</td>
         </tr>
 
         <tr id="In order to accurately model the dynamics of non-linear electro-mechanical systems, it is imperative to consider the contributions of coupling terms and dissipation. The Lagrangian formulation alone is insufficient to fully capture the holistic behavior of the system. Coupling and dissipation mechanisms play a pivotal role in shaping the system's response. Consequently, to effectively capture the dynamics of inter-coupled electro-mechanical systems with dissipation, we propose an extended Lagrangian-informed deep neural network framework in this paper. Our approach leverages the underlying physics-based knowledge of the system, incorporating it into the neural network architecture. By employing the Euler-Lagrange equations as constraints in the training process, we ensure that the learned dynamics conform to the true behavior of the system. To validate the theoretical framework, we conduct simulation experiments on a DC motor with a cart system, which serves as a representative model of dissipative nonlinear electro-mechanical systems. The experimental results demonstrate the efficacy of our approach in accurately capturing and integrating the dynamics to solve the reference tracking model predictive control design.">
@@ -1195,19 +1210,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>4</td>
         </tr>
 
-        <tr id="We use parsimonious diffusion maps (PDMs) to discover the latent dynamics of high-fidelity Navier-Stokes simulations with a focus on the 2D fluidic pinball problem. By varying the Reynolds number, different flow regimes emerge, ranging from steady symmetric flows to quasi-periodic asymmetric and turbulence. We show, that the proposed non-linear manifold learning scheme, identifies in a crisp manner the expected intrinsic dimension of the underlying emerging dynamics over the parameter space. In particular, PDMs, estimate that the emergent dynamics in the oscillatory regime can be captured by just two variables, while in the chaotic regime, the dominant modes are three as anticipated by the normal form theory. On the other hand, proper orthogonal decomposition (POD)/PCA, most commonly used for dimensionality reduction in fluid mechanics, does not provide such a crisp separation between the dominant modes. To validate the performance of PDMs, we also computed the reconstruction error, by constructing a decoder using Geometric Harmonics. We show that the proposed scheme outperforms the POD/PCA over the whole Reynolds number range. Thus, we believe that the proposed scheme will allow for the development of more accurate reduced order models for high-fidelity fluid dynamics simulators, thus relaxing the curse of dimensionality in numerical analysis tasks such as bifurcation analysis, optimization and control.">
+        <tr id="Conventional physics-based modeling techniques involve high effort, e.g., time and expert knowledge, while data-driven methods often lack interpretability, structure, and sometimes reliability. To mitigate this, we present a data-driven system identification framework that derives models in the port-Hamiltonian (pH) formulation. This formulation is suitable for multi-physical systems while guaranteeing the useful system theoretical properties of passivity and stability. Our framework combines linear and nonlinear reduction with structured, physics-motivated system identification. In this process, high-dimensional state data obtained from possibly nonlinear systems serves as input for an autoencoder, which then performs two tasks: (i) nonlinearly transforming and (ii) reducing this data onto a low-dimensional latent space. In this space, a linear pH system, that satisfies the pH properties per construction, is parameterized by the weights of a neural network. The mathematical requirements are met by defining the pH matrices through Cholesky factorizations. The neural networks that define the coordinate transformation and the pH system are identified in a joint optimization process to match the dynamics observed in the data while defining a linear pH system in the latent space. The learned, low-dimensional pH system can describe even nonlinear systems and is rapidly computable due to its small size. The method is exemplified by a parametric mass-spring-damper and a nonlinear pendulum example, as well as the high-dimensional model of a disc brake with linear thermoelastic behavior.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/62e263f73e87d937004633a6c97b19b9217c4c1e" target='_blank'>
-              Learning the Latent dynamics of Fluid flows from High-Fidelity Numerical Simulations using Parsimonious Diffusion Maps
+          <td><a href="https://www.semanticscholar.org/paper/8c88f9ec4b5bd522ac8e312f90d2801489785951" target='_blank'>
+              Data-driven identification of latent port-Hamiltonian systems
               </a>
             </td>
           <td>
-            Alessandro Della Pia, Dimitris G. Patsatzis, Lucia Russo, C. Siettos
+            J. Rettberg, Jonas Kneifl, Julius Herb, Patrick Buchfink, J. Fehr, B. Haasdonk
           </td>
-          <td>2024-08-05</td>
+          <td>2024-08-15</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>23</td>
+          <td>32</td>
         </tr>
 
         <tr id="Closure models are widely used in simulating complex multiscale dynamical systems such as turbulence and the earth system, for which direct numerical simulation that resolves all scales is often too expensive. For those systems without a clear scale separation, deterministic and local closure models often lack enough generalization capability, which limits their performance in many real-world applications. In this work, we propose a data-driven modeling framework for constructing stochastic and non-local closure models via conditional diffusion model and neural operator. Specifically, the Fourier neural operator is incorporated into a score-based diffusion model, which serves as a data-driven stochastic closure model for complex dynamical systems governed by partial differential equations (PDEs). We also demonstrate how accelerated sampling methods can improve the efficiency of the data-driven stochastic closure model. The results show that the proposed methodology provides a systematic approach via generative machine learning techniques to construct data-driven stochastic closure models for multiscale dynamical systems with continuous spatiotemporal fields.">
@@ -1221,23 +1236,8 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           </td>
           <td>2024-08-06</td>
           <td>ArXiv</td>
-          <td>0</td>
-          <td>1</td>
-        </tr>
-
-        <tr id="Modeling the dynamics of flexible objects has become an emerging topic in the community as these objects become more present in many applications, e.g., soft robotics. Due to the properties of flexible materials, the movements of soft objects are often highly nonlinear and, thus, complex to predict. Data-driven approaches seem promising for modeling those complex dynamics but often neglect basic physical principles, which consequently makes them untrustworthy and limits generalization. To address this problem, we propose a physics-constrained learning method that combines powerful learning tools and reliable physical models. Our method leverages the data collected from observations by sending them into a Gaussian process that is physically constrained by a distributed Port-Hamiltonian model. Based on the Bayesian nature of the Gaussian process, we not only learn the dynamics of the system, but also enable uncertainty quantification. Furthermore, the proposed approach preserves the compositional nature of Port-Hamiltonian systems.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/020e63b8b17cdec54c056d8a1edc98770c8fa7ab" target='_blank'>
-              Physics-constrained learning for PDE systems with uncertainty quantified port-Hamiltonian models
-              </a>
-            </td>
-          <td>
-            Kaiyuan Tan, Peilun Li, Thomas Beckers
-          </td>
-          <td>2024-06-17</td>
-          <td>ArXiv, DBLP</td>
-          <td>0</td>
           <td>1</td>
+          <td>2</td>
         </tr>
 
         <tr id="Forecasting dynamical systems is of importance to numerous real-world applications. When possible, dynamical systems forecasts are constructed based on first-principles-based models such as through the use of differential equations. When these equations are unknown, non-intrusive techniques must be utilized to build predictive models from data alone. Machine learning (ML) methods have recently been used for such tasks. Moreover, ML methods provide the added advantage of significant reductions in time-to-solution for predictions in contrast with first-principle based models. However, many state-of-the-art ML-based methods for forecasting rely on neural networks, which may be expensive to train and necessitate requirements for large amounts of memory. In this work, we propose a quantum mechanics inspired ML modeling strategy for learning nonlinear dynamical systems that provides data-driven forecasts for complex dynamical systems with reduced training time and memory costs. This approach, denoted the quantum reservoir computing technique (QRC), is a hybrid quantum-classical framework employing an ensemble of interconnected small quantum systems via classical linear feedback connections. By mapping the dynamical state to a suitable quantum representation amenable to unitary operations, QRC is able to predict complex nonlinear dynamical systems in a stable and accurate manner. We demonstrate the efficacy of this framework through benchmark forecasts of the NOAA Optimal Interpolation Sea Surface Temperature dataset and compare the performance of QRC to other ML methods.">
@@ -1267,7 +1267,7 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2024-06-28</td>
           <td>Nature computational science</td>
           <td>3</td>
-          <td>1</td>
+          <td>2</td>
         </tr>
 
         <tr id="Neural simulators for modeling complex dynamical systems have been extensively studied for various real-world applications, such as weather forecasting, ocean current prediction, and computational fluid dynamics simulation. Although they have demonstrated powerful fitting and predicting, most existing models are only built to learn single-system dynamics. Several advanced researches have considered learning dynamics across environments, which can exploit the potential commonalities among the dynamics across environments and adapt to new environments. However, these methods still are prone to scarcity problems where per-environment data is sparse or limited. Therefore, we propose a novel CoNDP (Context-Informed Neural ODE Processes) to achieve learning system dynamics from sparse observations across environments. It can fully use contextual information of each environment to better capture the intrinsic commonalities across environments and distinguishable differences among environments while modeling uncertainty of system evolution, producing more accurate predictions. Intensive experiments are conducted on five complex dynamical systems in various fields. Results show that the proposed CoNDP can achieve optimal results compared with common neural simulators and state-of-the-art cross-environmental models.">
@@ -1285,49 +1285,49 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>5</td>
         </tr>
 
-        <tr id="None">
+        <tr id="Simulating spatiotemporal turbulence with high fidelity remains a cornerstone challenge in computational fluid dynamics (CFD) due to its intricate multiscale nature and prohibitive computational demands. Traditional approaches typically employ closure models, which attempt to represent small-scale features in an unresolved manner. However, these methods often sacrifice accuracy and lose high-frequency/wavenumber information, especially in scenarios involving complex flow physics. In this paper, we introduce an innovative neural differentiable modeling framework designed to enhance the predictability and efficiency of spatiotemporal turbulence simulations. Our approach features differentiable hybrid modeling techniques that seamlessly integrate deep neural networks with numerical PDE solvers within a differentiable programming framework, synergizing deep learning with physics-based CFD modeling. Specifically, a hybrid differentiable neural solver is constructed on a coarser grid to capture large-scale turbulent phenomena, followed by the application of a Bayesian conditional diffusion model that generates small-scale turbulence conditioned on large-scale flow predictions. Two innovative hybrid architecture designs are studied, and their performance is evaluated through comparative analysis against conventional large eddy simulation techniques with physics-based subgrid-scale closures and purely data-driven neural solvers. The findings underscore the potential of the neural differentiable modeling framework to significantly enhance the accuracy and computational efficiency of turbulence simulations. This study not only demonstrates the efficacy of merging deep learning with physics-based numerical solvers but also sets a new precedent for advanced CFD modeling techniques, highlighting the transformative impact of differentiable programming in scientific computing.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1491d337e12daab70edf38ee62d8f8ee586b8e98" target='_blank'>
-              Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems
+          <td><a href="https://www.semanticscholar.org/paper/28636be802bd2c5ddbd07e3772c8b3a7822e64fa" target='_blank'>
+              Neural Differentiable Modeling with Diffusion-Based Super-resolution for Two-Dimensional Spatiotemporal Turbulence
               </a>
             </td>
           <td>
-            Katiana Kontolati, S. Goswami, G. Em Karniadakis, Michael D Shields
+            Xiantao Fan, Deepak Akhare, Jian-Xun Wang
           </td>
-          <td>2024-06-14</td>
-          <td>Nature Communications</td>
-          <td>6</td>
-          <td>19</td>
+          <td>2024-06-28</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>2</td>
         </tr>
 
-        <tr id="When neural networks are trained from data to simulate the dynamics of physical systems, they encounter a persistent challenge: the long-time dynamics they produce are often unphysical or unstable. We analyze the origin of such instabilities when learning linear dynamical systems, focusing on the training dynamics. We make several analytical findings which empirical observations suggest extend to nonlinear dynamical systems. First, the rate of convergence of the training dynamics is uneven and depends on the distribution of energy in the data. As a special case, the dynamics in directions where the data have no energy cannot be learned. Second, in the unlearnable directions, the dynamics produced by the neural network depend on the weight initialization, and common weight initialization schemes can produce unstable dynamics. Third, injecting synthetic noise into the data during training adds damping to the training dynamics and can stabilize the learned simulator, though doing so undesirably biases the learned dynamics. For each contributor to instability, we suggest mitigative strategies. We also highlight important differences between learning discrete-time and continuous-time dynamics, and discuss extensions to nonlinear systems.">
+        <tr id="Understanding how the collective activity of neural populations relates to computation and ultimately behavior is a key goal in neuroscience. To this end, statistical methods which describe high-dimensional neural time series in terms of low-dimensional latent dynamics have played a fundamental role in characterizing neural systems. Yet, what constitutes a successful method involves two opposing criteria: (1) methods should be expressive enough to capture complex nonlinear dynamics, and (2) they should maintain a notion of interpretability often only warranted by simpler linear models. In this paper, we develop an approach that balances these two objectives: the Gaussian Process Switching Linear Dynamical System (gpSLDS). Our method builds on previous work modeling the latent state evolution via a stochastic differential equation whose nonlinear dynamics are described by a Gaussian process (GP-SDEs). We propose a novel kernel function which enforces smoothly interpolated locally linear dynamics, and therefore expresses flexible -- yet interpretable -- dynamics akin to those of recurrent switching linear dynamical systems (rSLDS). Our approach resolves key limitations of the rSLDS such as artifactual oscillations in dynamics near discrete state boundaries, while also providing posterior uncertainty estimates of the dynamics. To fit our models, we leverage a modified learning objective which improves the estimation accuracy of kernel hyperparameters compared to previous GP-SDE fitting approaches. We apply our method to synthetic data and data recorded in two neuroscience experiments and demonstrate favorable performance in comparison to the rSLDS.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/2a7fe97785b117217a2f1064f6983b137fe02e06" target='_blank'>
-              On instabilities in neural network-based physics simulators
+          <td><a href="https://www.semanticscholar.org/paper/b38dffa6030cac42cce7564c8fe38875646c5f4f" target='_blank'>
+              Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems
               </a>
             </td>
           <td>
-            Daniel Floryan
+            Amber Hu, D. Zoltowski, Aditya Nair, David Anderson, Lea Duncker, Scott W. Linderman
           </td>
-          <td>2024-06-18</td>
+          <td>2024-07-19</td>
           <td>ArXiv</td>
-          <td>1</td>
           <td>0</td>
+          <td>27</td>
         </tr>
 
-        <tr id="Understanding how the collective activity of neural populations relates to computation and ultimately behavior is a key goal in neuroscience. To this end, statistical methods which describe high-dimensional neural time series in terms of low-dimensional latent dynamics have played a fundamental role in characterizing neural systems. Yet, what constitutes a successful method involves two opposing criteria: (1) methods should be expressive enough to capture complex nonlinear dynamics, and (2) they should maintain a notion of interpretability often only warranted by simpler linear models. In this paper, we develop an approach that balances these two objectives: the Gaussian Process Switching Linear Dynamical System (gpSLDS). Our method builds on previous work modeling the latent state evolution via a stochastic differential equation whose nonlinear dynamics are described by a Gaussian process (GP-SDEs). We propose a novel kernel function which enforces smoothly interpolated locally linear dynamics, and therefore expresses flexible -- yet interpretable -- dynamics akin to those of recurrent switching linear dynamical systems (rSLDS). Our approach resolves key limitations of the rSLDS such as artifactual oscillations in dynamics near discrete state boundaries, while also providing posterior uncertainty estimates of the dynamics. To fit our models, we leverage a modified learning objective which improves the estimation accuracy of kernel hyperparameters compared to previous GP-SDE fitting approaches. We apply our method to synthetic data and data recorded in two neuroscience experiments and demonstrate favorable performance in comparison to the rSLDS.">
+        <tr id="Accurately predicting the long-term behavior of chaotic systems is crucial for various applications such as climate modeling. However, achieving such predictions typically requires iterative computations over a dense spatiotemporal grid to account for the unstable nature of chaotic systems, which is expensive and impractical in many real-world situations. An alternative approach to such a full-resolved simulation is using a coarse grid and then correcting its errors through a \textit{closure model}, which approximates the overall information from fine scales not captured in the coarse-grid simulation. Recently, ML approaches have been used for closure modeling, but they typically require a large number of training samples from expensive fully-resolved simulations (FRS). In this work, we prove an even more fundamental limitation, i.e., the standard approach to learning closure models suffers from a large approximation error for generic problems, no matter how large the model is, and it stems from the non-uniqueness of the mapping. We propose an alternative end-to-end learning approach using a physics-informed neural operator (PINO) that overcomes this limitation by not using a closure model or a coarse-grid solver. We first train the PINO model on data from a coarse-grid solver and then fine-tune it with (a small amount of) FRS and physics-based losses on a fine grid. The discretization-free nature of neural operators means that they do not suffer from the restriction of a coarse grid that closure models face, and they can provably approximate the long-term statistics of chaotic systems. In our experiments, our PINO model achieves a 120x speedup compared to FRS with a relative error $\sim 5\%$. In contrast, the closure model coupled with a coarse-grid solver is $58$x slower than PINO while having a much higher error $\sim205\%$ when the closure model is trained on the same FRS dataset.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/b38dffa6030cac42cce7564c8fe38875646c5f4f" target='_blank'>
-              Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems
+          <td><a href="https://www.semanticscholar.org/paper/ba56bfb99a735364b70e9f185e5b2a87b7ff7d3f" target='_blank'>
+              Beyond Closure Models: Learning Chaotic-Systems via Physics-Informed Neural Operators
               </a>
             </td>
           <td>
-            Amber Hu, D. Zoltowski, Aditya Nair, David Anderson, Lea Duncker, Scott W. Linderman
+            Chuwei Wang, Julius Berner, Zong-Yi Li, Di Zhou, Jiayun Wang, Jane Bae, A. Anandkumar
           </td>
-          <td>2024-07-19</td>
+          <td>2024-08-09</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>26</td>
+          <td>18</td>
         </tr>
 
         <tr id="In this paper, we propose a physics-informed learning-based Koopman modeling approach and present a Koopman-based self-tuning moving horizon estimation design for a class of nonlinear systems. Specifically, we train Koopman operators and two neural networks - the state lifting network and the noise characterization network - using both data and available physical information. The two neural networks account for the nonlinear lifting functions for Koopman modeling and describing system noise distributions, respectively. Accordingly, a stochastic linear Koopman model is established in the lifted space to forecast the dynamic behavior of the nonlinear system. Based on the Koopman model, a self-tuning linear moving horizon estimation (MHE) scheme is developed. The weighting matrices of the MHE design are updated using the pre-trained noise characterization network at each sampling instant. The proposed estimation scheme is computationally efficient because only convex optimization is involved during online implementation, and updating the weighting matrices of the MHE scheme does not require re-training the neural networks. We verify the effectiveness and evaluate the performance of the proposed method via the application to a simulated chemical process.">
@@ -1342,7 +1342,7 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2024-08-07</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>34</td>
+          <td>35</td>
         </tr>
 
         <tr id="Reconstructing a nonlinear dynamical system from empirical time series is a fundamental task in data-driven analysis. One of the main challenges is the existence of hidden variables; we only have records for some variables, and those for hidden variables are unavailable. In this work, the techniques for Carleman linearization, phase-space embedding, and dynamic mode decomposition are integrated to rebuild an optimal dynamical system from time series for one specific variable. Using the Takens theorem, the embedding dimension is determined, which is adopted as the dynamical system's dimension. The Carleman linearization is then used to transform this finite nonlinear system into an infinite linear system, which is further truncated into a finite linear system using the dynamic mode decomposition technique. We illustrate the performance of this integrated technique using data generated by the well-known Lorenz model, the Duffing oscillator, and empirical records of electrocardiogram, electroencephalogram, and measles outbreaks. The results show that this solution accurately estimates the operators of the nonlinear dynamical systems. This work provides a new data-driven method to estimate the Carleman operator of nonlinear dynamical systems.">
@@ -1357,37 +1357,52 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2024-08-01</td>
           <td>Chaos</td>
           <td>0</td>
-          <td>17</td>
+          <td>18</td>
         </tr>
 
-        <tr id="Lifted linear predictor (LLP) is an artificial linear dynamical system designed to predict trajectories of a generally nonlinear dynamical system based on the current state (or measurements) and the input. The main benefit of the LLP is its potential ability to capture the nonlinear system's dynamics with precision superior to other linearization techniques, such as local linearization about the operation point. The idea of lifting is supported by the theory of Koopman Operators. For LLP identification, we focus on the data-driven method based on the extended dynamic mode decomposition (EDMD) algorithm. However, while the EDMD algorithm presents an extremely simple and efficient way to obtain the LLP, it can also yield poor results. In this paper, we present some less intuitive practical guidelines for data-driven identification of the LLPs, aiming at improving usability of LLPs for designing control. We support the guidelines with two motivating examples. The implementation of the examples are shared on a public repository.">
+        <tr id="Neural manifolds are an attractive theoretical framework for characterizing the complex behaviors of neural populations. However, many of the tools for identifying these low-dimensional subspaces are correlational and provide limited insight into the underlying dynamics. The ability to precisely control this latent activity would allow researchers to investigate the structure and function of neural manifolds. Employing techniques from the field of optimal control, we simulate controlling the latent dynamics of a neural population using closed-loop, dynamically generated sensory inputs. Using a spiking neural network (SNN) as a model of a neural circuit, we find low-dimensional representations of both the network activity (the neural manifold) and a set of salient visual stimuli. With a data-driven latent dynamics model, we apply model predictive control (MPC) to provide anticipatory, optimal control over the trajectory of the circuit in a latent space. We are able to control the latent dynamics of the SNN to follow several reference trajectories despite observing only a subset of neurons and with a substantial amount of unknown noise injected into the network. These results provide a framework to experimentally test for causal relationships between manifold dynamics and other variables of interest such as organismal behavior and BCI performance.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/bf1ce9a8c8346f1264e7cecb5aea44d98aa65af7" target='_blank'>
-              Practical Guidelines for Data-driven Identification of Lifted Linear Predictors for Control
+          <td><a href="https://www.semanticscholar.org/paper/e2f3319a9926e88582124c34d6157bfd48e1d637" target='_blank'>
+              Model Predictive Control of the Neural Manifold
               </a>
             </td>
           <td>
-            Loi Do, Adam Uchytil, Zdenvek Hur'ak
+            Christof Fehrman, C. D. Meliza
           </td>
-          <td>2024-08-02</td>
+          <td>2024-06-21</td>
+          <td>ArXiv</td>
+          <td>1</td>
+          <td>6</td>
+        </tr>
+
+        <tr id="A physics-informed neural network (PINN) is used to produce a variety of self-trapped necklace solutions of the (2+1)-dimensional nonlinear Schr\"{o}dinger/Gross-Pitaevskii equation. We elaborate the analysis for the existence and evolution of necklace patterns with integer, half-integer, and fractional reduced orbital angular momenta by means of PINN. The patterns exhibit phenomena similar to rotation of rigid bodies and centrifugal force. Even though the necklaces slowly expand (or shrink), they preserve their structure in the course of the quasi-stable propagation over several diffraction lengths, which is completely different from the ordinary fast diffraction-dominated dynamics. By comparing different ingredients, including the training time, loss value and $\mathbb{L}_{2}$ error, PINN accurately predicts specific nonlinear dynamical properties of the evolving necklace patterns. Furthermore, we perform the data-driven discovery of parameters for both clean and perturbed training data, adding $1\%$ random noise in the latter case. The results reveal that PINN not only effectively emulates the solution of partial differential equations, but also offers applications for predicting the nonlinear dynamics of physically relevant types of patterns.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/27df54bec46e632334278c89b601027aa8a3cf9e" target='_blank'>
+              Physics-informed neural network for nonlinear dynamics of self-trapped necklace beams
+              </a>
+            </td>
+          <td>
+            Dongshuai Liu, Wen Zhang, Yanxia Gao, Dianyuan Fan, B. Malomed, Lifu Zhang
+          </td>
+          <td>2024-08-10</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>2</td>
+          <td>79</td>
         </tr>
 
-        <tr id="Neural manifolds are an attractive theoretical framework for characterizing the complex behaviors of neural populations. However, many of the tools for identifying these low-dimensional subspaces are correlational and provide limited insight into the underlying dynamics. The ability to precisely control this latent activity would allow researchers to investigate the structure and function of neural manifolds. Employing techniques from the field of optimal control, we simulate controlling the latent dynamics of a neural population using closed-loop, dynamically generated sensory inputs. Using a spiking neural network (SNN) as a model of a neural circuit, we find low-dimensional representations of both the network activity (the neural manifold) and a set of salient visual stimuli. With a data-driven latent dynamics model, we apply model predictive control (MPC) to provide anticipatory, optimal control over the trajectory of the circuit in a latent space. We are able to control the latent dynamics of the SNN to follow several reference trajectories despite observing only a subset of neurons and with a substantial amount of unknown noise injected into the network. These results provide a framework to experimentally test for causal relationships between manifold dynamics and other variables of interest such as organismal behavior and BCI performance.">
+        <tr id="Lifted linear predictor (LLP) is an artificial linear dynamical system designed to predict trajectories of a generally nonlinear dynamical system based on the current state (or measurements) and the input. The main benefit of the LLP is its potential ability to capture the nonlinear system's dynamics with precision superior to other linearization techniques, such as local linearization about the operation point. The idea of lifting is supported by the theory of Koopman Operators. For LLP identification, we focus on the data-driven method based on the extended dynamic mode decomposition (EDMD) algorithm. However, while the EDMD algorithm presents an extremely simple and efficient way to obtain the LLP, it can also yield poor results. In this paper, we present some less intuitive practical guidelines for data-driven identification of the LLPs, aiming at improving usability of LLPs for designing control. We support the guidelines with two motivating examples. The implementation of the examples are shared on a public repository.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e2f3319a9926e88582124c34d6157bfd48e1d637" target='_blank'>
-              Model Predictive Control of the Neural Manifold
+          <td><a href="https://www.semanticscholar.org/paper/bf1ce9a8c8346f1264e7cecb5aea44d98aa65af7" target='_blank'>
+              Practical Guidelines for Data-driven Identification of Lifted Linear Predictors for Control
               </a>
             </td>
           <td>
-            Christof Fehrman, C. D. Meliza
+            Loi Do, Adam Uchytil, Zdenvek Hur'ak
           </td>
-          <td>2024-06-21</td>
+          <td>2024-08-02</td>
           <td>ArXiv</td>
           <td>1</td>
-          <td>6</td>
+          <td>2</td>
         </tr>
 
         <tr id="Modeling the non-linear dynamics of a system from measurement data accurately is an open challenge. Over the past few years, various tools such as SINDy and DySMHO have emerged as approaches to distill dynamics from data. However, challenges persist in accurately capturing dynamics of a system especially when the physical knowledge about the system is unknown. A promising solution is to use a hybrid paradigm, that combines mechanistic and black-box models to leverage their respective strengths. In this study, we combine a hybrid modeling paradigm with sparse regression, to develop and identify models simultaneously. Two methods are explored, considering varying complexities, data quality, and availability and by comparing different case studies. In the first approach, we integrate SINDy-discovered models with neural ODE structures, to model unknown physics. In the second approach, we employ Multifidelity Surrogate Models (MFSMs) to construct composite models comprised of SINDy-discovered models and error-correction models.">
@@ -1405,6 +1420,21 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>25</td>
         </tr>
 
+        <tr id="Neural Ordinary Differential Equations (ODEs) represent a significant advancement at the intersection of machine learning and dynamical systems, offering a continuous-time analog to discrete neural networks. Despite their promise, deploying neural ODEs in practical applications often encounters the challenge of stiffness, a condition where rapid variations in some components of the solution demand prohibitively small time steps for explicit solvers. This work addresses the stiffness issue when employing neural ODEs for model order reduction by introducing a suitable reparametrization in time. The considered map is data-driven and it is induced by the adaptive time-stepping of an implicit solver on a reference solution. We show the map produces a nonstiff system that can be cheaply solved with an explicit time integration scheme. The original, stiff, time dynamic is recovered by means of a map learnt by a neural network that connects the state space to the time reparametrization. We validate our method through extensive experiments, demonstrating improvements in efficiency for the neural ODE inference while maintaining robustness and accuracy. The neural network model also showcases good generalization properties for times beyond the training data.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/fc642cd2dc03d50f310399a91145bd5a14a7f646" target='_blank'>
+              Neural Ordinary Differential Equations for Model Order Reduction of Stiff Systems
+              </a>
+            </td>
+          <td>
+            Matteo Caldana, J. Hesthaven
+          </td>
+          <td>2024-08-12</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>63</td>
+        </tr>
+
         <tr id="We present a numerical method for learning unknown nonautonomous stochastic dynamical system, i.e., stochastic system subject to time dependent excitation or control signals. Our basic assumption is that the governing equations for the stochastic system are unavailable. However, short bursts of input/output (I/O) data consisting of certain known excitation signals and their corresponding system responses are available. When a sufficient amount of such I/O data are available, our method is capable of learning the unknown dynamics and producing an accurate predictive model for the stochastic responses of the system subject to arbitrary excitation signals not in the training data. Our method has two key components: (1) a local approximation of the training I/O data to transfer the learning into a parameterized form; and (2) a generative model to approximate the underlying unknown stochastic flow map in distribution. After presenting the method in detail, we present a comprehensive set of numerical examples to demonstrate the performance of the proposed method, especially for long-term system predictions.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/4edbecee7f2d1381939691be911b0026cab616bb" target='_blank'>
@@ -1450,55 +1480,41 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>18</td>
         </tr>
 
-        <tr id="This article is devoted to providing a review of mathematical formulations in which Polynomial Chaos Theory (PCT) has been incorporated into stochastic model predictive control (SMPC). In the past decade, PCT has been shown to provide a computationally tractable way to perform complete and accurate uncertainty propagation through (smooth) nonlinear dynamic systems. As such, it represents a very useful computational tool for accelerating the computations needed in SMPC with time invariant uncertainties. It turns out that it can also be used to reduce complexity of chance constraints, which are an important component of SMPC. In this paper, we provide an overview of PCT and discuss how it can be applied in such time invariant settings.">
+        <tr id="
+ We introduce a novel neural network structure called Strongly Constrained Theory-Guided Neural Network (SCTgNN), to investigate the behaviours of the localized solutions of the generalized nonlinear Schrödinger (NLS) equation. This equation comprises four physically significant nonlinear evolution equations, namely, (i) NLS equation, Hirota equation Lakshmanan-Porsezian-Daniel (LPD) equation and fifth-order NLS equation. The generalized NLS equation demonstrates nonlinear effects up to quintic order, indicating rich and complex dynamics in various fields of physics. By combining concepts from the Physics-Informed Neural Network (PINN) and Theory-Guided Neural Network (TgNN) models, SCTgNN aims to enhance our understanding of complex phenomena, particularly within nonlinear systems that defy conventional patterns. To begin, we employ the TgNN method to predict the behaviours of localized waves, including solitons, rogue waves, and breathers, within the generalized NLS equation. We then use SCTgNN to predict the aforementioned localized solutions and calculate the mean square errors in both SCTgNN and TgNN in predicting these three localized solutions. Our findings reveal that both models excel in understanding complex behaviours and provide predictions across a wide variety of situations.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/97793bc13282813dbb41b1713188675eb03584d1" target='_blank'>
-              Polynomial Chaos-based Stochastic Model Predictive Control: An Overview and Future Research Directions
+          <td><a href="https://www.semanticscholar.org/paper/e405cc588c01c3af8b8d5bde5f1526dd7d964ae3" target='_blank'>
+              On examining the predictive capabilities of two variants of PINN in validating localised wave solutions in the generalized nonlinear Schrödinger equation
               </a>
             </td>
           <td>
-            Prabhat Kumar Mishra, J. Paulson, R. Braatz
+            Thulasidharan K, Sinthuja N, Vishnu Priya N, Senthilvelan M
           </td>
-          <td>2024-06-15</td>
-          <td>ArXiv</td>
+          <td>2024-07-10</td>
+          <td>Communications in Theoretical Physics</td>
+          <td>0</td>
           <td>0</td>
-          <td>73</td>
         </tr>
 
-        <tr id="In this work, we present a method which determines optimal multi-step dynamic mode decomposition (DMD) models via entropic regression, which is a nonlinear information flow detection algorithm. Motivated by the higher-order DMD (HODMD) method of \cite{clainche}, and the entropic regression (ER) technique for network detection and model construction found in \cite{bollt, bollt2}, we develop a method that we call ERDMD that produces high fidelity time-delay DMD models that allow for nonuniform time space, and the time spacing is discovered by consider most informativity based on ER. These models are shown to be highly efficient and robust. We test our method over several data sets generated by chaotic attractors and show that we are able to build excellent reconstructions using relatively minimal models. We likewise are able to better identify multiscale features via our models which enhances the utility of dynamic mode decomposition.">
+        <tr id="We introduce a method based on Gaussian process regression to identify discrete variational principles from observed solutions of a field theory. The method is based on the data-based identification of a discrete Lagrangian density. It is a geometric machine learning technique in the sense that the variational structure of the true field theory is reflected in the data-driven model by design. We provide a rigorous convergence statement of the method. The proof circumvents challenges posed by the ambiguity of discrete Lagrangian densities in the inverse problem of variational calculus. Moreover, our method can be used to quantify model uncertainty in the equations of motions and any linear observable of the discrete field theory. This is illustrated on the example of the discrete wave equation and Schr\"odinger equation. The article constitutes an extension of our previous article arXiv:2404.19626 for the data-driven identification of (discrete) Lagrangians for variational dynamics from an ode setting to the setting of discrete pdes.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/3bfefac0bfd82ec97ab7068cba3a9ad9a039f752" target='_blank'>
-              Entropic Regression DMD (ERDMD) Discovers Informative Sparse and Nonuniformly Time Delayed Models
+          <td><a href="https://www.semanticscholar.org/paper/e38b7de6c7022b28628627bbf045123b665619b4" target='_blank'>
+              Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification
               </a>
             </td>
           <td>
-            Christopher W. Curtis, Erik Bollt, D. J. Alford-Lago
+            Christian Offen
           </td>
-          <td>2024-06-17</td>
+          <td>2024-07-10</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>2</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Nonlinear and non-stationary processes are prevalent in various natural and physical phenomena, where system dynamics can change qualitatively due to bifurcation phenomena. Traditional machine learning methods have advanced our ability to learn and predict such systems from observed time series data. However, predicting the behavior of systems with temporal parameter variations without knowledge of true parameter values remains a significant challenge. This study leverages the reservoir computing framework to address this problem by unsupervised extraction of slowly varying system parameters from time series data. We propose a model architecture consisting of a slow reservoir with long timescale internal dynamics and a fast reservoir with short timescale dynamics. The slow reservoir extracts the temporal variation of system parameters, which are then used to predict unknown bifurcations in the fast dynamics. Through experiments using data generated from chaotic dynamical systems, we demonstrate the ability to predict bifurcations not present in the training data. Our approach shows potential for applications in fields such as neuroscience, material science, and weather prediction, where slow dynamics influencing qualitative changes are often unobservable.">
+        <tr id="Machine learning presents opportunities to improve the scale-specific accuracy of mechanistic models in a data-driven manner. Here we demonstrate the use of a machine learning technique called Sparse Identification of Nonlinear Dynamics (SINDy) to improve a simple mechanistic model of algal growth. Time-series measurements of the microalga Chlorella Vulgaris were generated under controlled photobioreactor conditions at the University of Technology Sydney. A simple mechanistic growth model based on intensity of light and temperature was integrated over time and compared to the time-series data. While the mechanistic model broadly captured the overall growth trend, discrepancies remained between the model and data due to the model's simplicity and non-ideal behavior of real-world measurement. SINDy was applied to model the residual error by identifying an error derivative correction term. Addition of this SINDy-informed error dynamics term shows improvement to model accuracy while maintaining interpretability of the underlying mechanistic framework. This work demonstrates the potential for machine learning techniques like SINDy to aid simple mechanistic models in scale-specific predictive accuracy.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1f7ba13092aa22ef11ae95641fe8961d09c8fcd7" target='_blank'>
-              Prediction of Unobserved Bifurcation by Unsupervised Extraction of Slowly Time-Varying System Parameter Dynamics from Time Series Using Reservoir Computing
-              </a>
-            </td>
-          <td>
-            Keita Tokuda, Yuichi Katori
-          </td>
-          <td>2024-06-20</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>10</td>
-        </tr>
-
-        <tr id="Machine learning presents opportunities to improve the scale-specific accuracy of mechanistic models in a data-driven manner. Here we demonstrate the use of a machine learning technique called Sparse Identification of Nonlinear Dynamics (SINDy) to improve a simple mechanistic model of algal growth. Time-series measurements of the microalga Chlorella Vulgaris were generated under controlled photobioreactor conditions at the University of Technology Sydney. A simple mechanistic growth model based on intensity of light and temperature was integrated over time and compared to the time-series data. While the mechanistic model broadly captured the overall growth trend, discrepancies remained between the model and data due to the model's simplicity and non-ideal behavior of real-world measurement. SINDy was applied to model the residual error by identifying an error derivative correction term. Addition of this SINDy-informed error dynamics term shows improvement to model accuracy while maintaining interpretability of the underlying mechanistic framework. This work demonstrates the potential for machine learning techniques like SINDy to aid simple mechanistic models in scale-specific predictive accuracy.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/0efd19b71be3ac13e47f098d9a6e63082477a540" target='_blank'>
-              Improving Mechanistic Model Accuracy with Machine Learning Informed Physics
+          <td><a href="https://www.semanticscholar.org/paper/0efd19b71be3ac13e47f098d9a6e63082477a540" target='_blank'>
+              Improving Mechanistic Model Accuracy with Machine Learning Informed Physics
               </a>
             </td>
           <td>
@@ -1510,50 +1526,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>1</td>
         </tr>
 
-        <tr id="We introduce a method based on Gaussian process regression to identify discrete variational principles from observed solutions of a field theory. The method is based on the data-based identification of a discrete Lagrangian density. It is a geometric machine learning technique in the sense that the variational structure of the true field theory is reflected in the data-driven model by design. We provide a rigorous convergence statement of the method. The proof circumvents challenges posed by the ambiguity of discrete Lagrangian densities in the inverse problem of variational calculus. Moreover, our method can be used to quantify model uncertainty in the equations of motions and any linear observable of the discrete field theory. This is illustrated on the example of the discrete wave equation and Schr\"odinger equation. The article constitutes an extension of our previous article arXiv:2404.19626 for the data-driven identification of (discrete) Lagrangians for variational dynamics from an ode setting to the setting of discrete pdes.">
+        <tr id="Data assimilation is a central problem in many geophysical applications, such as weather forecasting. It aims to estimate the state of a potentially large system, such as the atmosphere, from sparse observations, supplemented by prior physical knowledge. The size of the systems involved and the complexity of the underlying physical equations make it a challenging task from a computational point of view. Neural networks represent a promising method of emulating the physics at low cost, and therefore have the potential to considerably improve and accelerate data assimilation. In this work, we introduce a deep learning approach where the physical system is modeled as a sequence of coarse-to-fine Gaussian prior distributions parametrized by a neural network. This allows us to define an assimilation operator, which is trained in an end-to-end fashion to minimize the reconstruction error on a dataset with different observation processes. We illustrate our approach on chaotic dynamical physical systems with sparse observations, and compare it to traditional variational data assimilation methods.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e38b7de6c7022b28628627bbf045123b665619b4" target='_blank'>
-              Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification
+          <td><a href="https://www.semanticscholar.org/paper/2950eebe3fe16f882003d5d9945c97a8517a5f89" target='_blank'>
+              Neural Incremental Data Assimilation
               </a>
             </td>
           <td>
-            Christian Offen
+            Matthieu Blanke, R. Fablet, Marc Lelarge
           </td>
-          <td>2024-07-10</td>
+          <td>2024-06-21</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
-        </tr>
-
-        <tr id="
- We introduce a novel neural network structure called Strongly Constrained Theory-Guided Neural Network (SCTgNN), to investigate the behaviours of the localized solutions of the generalized nonlinear Schrödinger (NLS) equation. This equation comprises four physically significant nonlinear evolution equations, namely, (i) NLS equation, Hirota equation Lakshmanan-Porsezian-Daniel (LPD) equation and fifth-order NLS equation. The generalized NLS equation demonstrates nonlinear effects up to quintic order, indicating rich and complex dynamics in various fields of physics. By combining concepts from the Physics-Informed Neural Network (PINN) and Theory-Guided Neural Network (TgNN) models, SCTgNN aims to enhance our understanding of complex phenomena, particularly within nonlinear systems that defy conventional patterns. To begin, we employ the TgNN method to predict the behaviours of localized waves, including solitons, rogue waves, and breathers, within the generalized NLS equation. We then use SCTgNN to predict the aforementioned localized solutions and calculate the mean square errors in both SCTgNN and TgNN in predicting these three localized solutions. Our findings reveal that both models excel in understanding complex behaviours and provide predictions across a wide variety of situations.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e405cc588c01c3af8b8d5bde5f1526dd7d964ae3" target='_blank'>
-              On examining the predictive capabilities of two variants of PINN in validating localised wave solutions in the generalized nonlinear Schrödinger equation
-              </a>
-            </td>
-          <td>
-            Thulasidharan K, Sinthuja N, Vishnu Priya N, Senthilvelan M
-          </td>
-          <td>2024-07-10</td>
-          <td>Communications in Theoretical Physics</td>
-          <td>0</td>
-          <td>0</td>
+          <td>16</td>
         </tr>
 
-        <tr id="Data assimilation is a central problem in many geophysical applications, such as weather forecasting. It aims to estimate the state of a potentially large system, such as the atmosphere, from sparse observations, supplemented by prior physical knowledge. The size of the systems involved and the complexity of the underlying physical equations make it a challenging task from a computational point of view. Neural networks represent a promising method of emulating the physics at low cost, and therefore have the potential to considerably improve and accelerate data assimilation. In this work, we introduce a deep learning approach where the physical system is modeled as a sequence of coarse-to-fine Gaussian prior distributions parametrized by a neural network. This allows us to define an assimilation operator, which is trained in an end-to-end fashion to minimize the reconstruction error on a dataset with different observation processes. We illustrate our approach on chaotic dynamical physical systems with sparse observations, and compare it to traditional variational data assimilation methods.">
+        <tr id="Non-stationary systems are found throughout the world, from climate patterns under the influence of variation in carbon dioxide concentration, to brain dynamics driven by ascending neuromodulation. Accordingly, there is a need for methods to analyze non-stationary processes, and yet most time-series analysis methods that are used in practice, on important problems across science and industry, make the simplifying assumption of stationarity. One important problem in the analysis of non-stationary systems is the problem class that we refer to as Parameter Inference from a Non-stationary Unknown Process (PINUP). Given an observed time series, this involves inferring the parameters that drive non-stationarity of the time series, without requiring knowledge or inference of a mathematical model of the underlying system. Here we review and unify a diverse literature of algorithms for PINUP. We formulate the problem, and categorize the various algorithmic contributions. This synthesis will allow researchers to identify gaps in the literature and will enable systematic comparisons of different methods. We also demonstrate that the most common systems that existing methods are tested on - notably the non-stationary Lorenz process and logistic map - are surprisingly easy to perform well on using simple statistical features like windowed mean and variance, undermining the practice of using good performance on these systems as evidence of algorithmic performance. We then identify more challenging problems that many existing methods perform poorly on and which can be used to drive methodological advances in the field. Our results unify disjoint scientific contributions to analyzing non-stationary systems and suggest new directions for progress on the PINUP problem and the broader study of non-stationary phenomena.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/2950eebe3fe16f882003d5d9945c97a8517a5f89" target='_blank'>
-              Neural Incremental Data Assimilation
+          <td><a href="https://www.semanticscholar.org/paper/585c3c4f3b30a814bc05e6f9e38a5a28b702ee79" target='_blank'>
+              Parameter inference from a non-stationary unknown process
               </a>
             </td>
           <td>
-            Matthieu Blanke, R. Fablet, Marc Lelarge
+            Kieran S. Owens, Ben D. Fulcher
           </td>
-          <td>2024-06-21</td>
+          <td>2024-07-12</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>16</td>
+          <td>1</td>
         </tr>
 
         <tr id="In this study, we investigate the effect of reservoir computing training data on the reconstruction of chaotic dynamics. Our findings indicate that a training time series comprising a few periodic orbits of low periods can successfully reconstruct the Lorenz attractor. We also demonstrate that biased training data does not negatively impact reconstruction success. Our method's ability to reconstruct a physical measure is much better than the so-called cycle expansion approach, which relies on weighted averaging. Additionally, we demonstrate that fixed point attractors and chaotic transients can be accurately reconstructed by a model trained from a few periodic orbits, even when using different parameters.">
@@ -1593,27 +1593,12 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
               </a>
             </td>
           <td>
-            C. Ghnatios, Joel Mouterde, Jerome Tomezyk, Joaquim Da Silva, Francisco Chinesta
+            C. Ghnatios, Joel Mouterde, Jerome Tomezyk, Joaquim Da Silva, F. Chinesta
           </td>
           <td>2024-07-19</td>
           <td>Mathematics</td>
           <td>0</td>
-          <td>13</td>
-        </tr>
-
-        <tr id="Non-stationary systems are found throughout the world, from climate patterns under the influence of variation in carbon dioxide concentration, to brain dynamics driven by ascending neuromodulation. Accordingly, there is a need for methods to analyze non-stationary processes, and yet most time-series analysis methods that are used in practice, on important problems across science and industry, make the simplifying assumption of stationarity. One important problem in the analysis of non-stationary systems is the problem class that we refer to as Parameter Inference from a Non-stationary Unknown Process (PINUP). Given an observed time series, this involves inferring the parameters that drive non-stationarity of the time series, without requiring knowledge or inference of a mathematical model of the underlying system. Here we review and unify a diverse literature of algorithms for PINUP. We formulate the problem, and categorize the various algorithmic contributions. This synthesis will allow researchers to identify gaps in the literature and will enable systematic comparisons of different methods. We also demonstrate that the most common systems that existing methods are tested on - notably the non-stationary Lorenz process and logistic map - are surprisingly easy to perform well on using simple statistical features like windowed mean and variance, undermining the practice of using good performance on these systems as evidence of algorithmic performance. We then identify more challenging problems that many existing methods perform poorly on and which can be used to drive methodological advances in the field. Our results unify disjoint scientific contributions to analyzing non-stationary systems and suggest new directions for progress on the PINUP problem and the broader study of non-stationary phenomena.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/585c3c4f3b30a814bc05e6f9e38a5a28b702ee79" target='_blank'>
-              Parameter inference from a non-stationary unknown process
-              </a>
-            </td>
-          <td>
-            Kieran S. Owens, Ben D. Fulcher
-          </td>
-          <td>2024-07-12</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>1</td>
+          <td>14</td>
         </tr>
 
         <tr id="Dynamical systems provide a comprehensive way to study complex and changing behaviors across various sciences. Many modern systems are too complicated to analyze directly or we do not have access to models, driving significant interest in learning methods. Koopman operators have emerged as a dominant approach because they allow the study of nonlinear dynamics using linear techniques by solving an infinite-dimensional spectral problem. However, current algorithms face challenges such as lack of convergence, hindering practical progress. This paper addresses a fundamental open question: \textit{When can we robustly learn the spectral properties of Koopman operators from trajectory data of dynamical systems, and when can we not?} Understanding these boundaries is crucial for analysis, applications, and designing algorithms. We establish a foundational approach that combines computational analysis and ergodic theory, revealing the first fundamental barriers -- universal for any algorithm -- associated with system geometry and complexity, regardless of data quality and quantity. For instance, we demonstrate well-behaved smooth dynamical systems on tori where non-trivial eigenfunctions of the Koopman operator cannot be determined by any sequence of (even randomized) algorithms, even with unlimited training data. Additionally, we identify when learning is possible and introduce optimal algorithms with verification that overcome issues in standard methods. These results pave the way for a sharp classification theory of data-driven dynamical systems based on how many limits are needed to solve a problem. These limits characterize all previous methods, presenting a unified view. Our framework systematically determines when and how Koopman spectral properties can be learned.">
@@ -1627,38 +1612,38 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           </td>
           <td>2024-07-08</td>
           <td>ArXiv</td>
-          <td>1</td>
+          <td>2</td>
           <td>16</td>
         </tr>
 
-        <tr id="Evolutional deep neural networks (EDNN) solve partial differential equations (PDEs) by marching the network representation of the solution fields, using the governing equations. Use of a single network to solve coupled PDEs on large domains requires a large number of network parameters and incurs a significant computational cost. We introduce coupled EDNN (C-EDNN) to solve systems of PDEs by using independent networks for each state variable, which are only coupled through the governing equations. We also introduce distributed EDNN (D-EDNN) by spatially partitioning the global domain into several elements and assigning individual EDNNs to each element to solve the local evolution of the PDE. The networks then exchange the solution and fluxes at their interfaces, similar to flux-reconstruction methods, and ensure that the PDE dynamics are accurately preserved between neighboring elements. Together C-EDNN and D-EDNN form the general class of Multi-EDNN methods. We demonstrate these methods with aid of canonical problems including linear advection, the heat equation, and the compressible Navier-Stokes equations in Couette and Taylor-Green flows.">
+        <tr id="The manipulation of deformable linear objects (DLOs) via model-based control requires an accurate and computationally efficient dynamics model. Yet, data-driven DLO dynamics models require large training data sets while their predictions often do not generalize, whereas physics-based models rely on good approximations of physical phenomena and often lack accuracy. To address these challenges, we propose a physics-informed neural ODE capable of predicting agile movements with significantly less data and hyper-parameter tuning. In particular, we model DLOs as serial chains of rigid bodies interconnected by passive elastic joints in which interaction forces are predicted by neural networks. The proposed model accurately predicts the motion of an robotically-actuated aluminium rod and an elastic foam cylinder after being trained on only thirty seconds of data. The project code and data are available at: \url{https://tinyurl.com/neuralprba}">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/fd0f0c8cff01c50a1d909bdecf5745289c495bfd" target='_blank'>
-              Multi evolutional deep neural networks (Multi-EDNN)
+          <td><a href="https://www.semanticscholar.org/paper/815ff430d40660092c67ed599422c4c980a0db8d" target='_blank'>
+              Learning deformable linear object dynamics from a single trajectory
               </a>
             </td>
           <td>
-            Hadden Kim, T. Zaki
+            Shamil Mamedov, A. R. Geist, Ruan Viljoen, Sebastian Trimpe, Jan Swevers
           </td>
-          <td>2024-07-17</td>
+          <td>2024-07-03</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>35</td>
+          <td>6</td>
         </tr>
 
-        <tr id="The manipulation of deformable linear objects (DLOs) via model-based control requires an accurate and computationally efficient dynamics model. Yet, data-driven DLO dynamics models require large training data sets while their predictions often do not generalize, whereas physics-based models rely on good approximations of physical phenomena and often lack accuracy. To address these challenges, we propose a physics-informed neural ODE capable of predicting agile movements with significantly less data and hyper-parameter tuning. In particular, we model DLOs as serial chains of rigid bodies interconnected by passive elastic joints in which interaction forces are predicted by neural networks. The proposed model accurately predicts the motion of an robotically-actuated aluminium rod and an elastic foam cylinder after being trained on only thirty seconds of data. The project code and data are available at: \url{https://tinyurl.com/neuralprba}">
+        <tr id="Physics-informed neural networks have proven to be a powerful tool for solving differential equations, leveraging the principles of physics to inform the learning process. However, traditional deep neural networks often face challenges in achieving high accuracy without incurring significant computational costs. In this work, we implement the Physics-Informed Kolmogorov-Arnold Neural Networks (PIKAN) through efficient-KAN and WAV-KAN, which utilize the Kolmogorov-Arnold representation theorem. PIKAN demonstrates superior performance compared to conventional deep neural networks, achieving the same level of accuracy with fewer layers and reduced computational overhead. We explore both B-spline and wavelet-based implementations of PIKAN and benchmark their performance across various ordinary and partial differential equations using unsupervised (data-free) and supervised (data-driven) techniques. For certain differential equations, the data-free approach suffices to find accurate solutions, while in more complex scenarios, the data-driven method enhances the PIKAN's ability to converge to the correct solution. We validate our results against numerical solutions and achieve $99 \%$ accuracy in most scenarios.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/815ff430d40660092c67ed599422c4c980a0db8d" target='_blank'>
-              Learning deformable linear object dynamics from a single trajectory
+          <td><a href="https://www.semanticscholar.org/paper/785706911b444c085e5f732a9fef3bb0159349db" target='_blank'>
+              Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficent-KAN and WAV-KAN
               </a>
             </td>
           <td>
-            Shamil Mamedov, A. R. Geist, Ruan Viljoen, Sebastian Trimpe, Jan Swevers
+            Subhajit Patra, Sonali Panda, B. K. Parida, Mahima Arya, Kurt Jacobs, Denys I. Bondar, Abhijit Sen
           </td>
-          <td>2024-07-03</td>
+          <td>2024-07-25</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
+          <td>3</td>
         </tr>
 
         <tr id="Trained neural networks (NN) have attractive features for closing governing equations, but in the absence of additional constraints, they can stray from physical reality. A NN formulation is introduced to preclude spurious oscillations that violate solution boundedness or positivity. It is embedded in the discretized equations as a machine learning closure and strictly constrained, inspired by total variation diminishing (TVD) methods for hyperbolic conservation laws. The constraint is exactly enforced during gradient-descent training by rescaling the NN parameters, which maps them onto an explicit feasible set. Demonstrations show that the constrained NN closure model usefully recovers linear and nonlinear hyperbolic phenomena and anti-diffusion while enforcing the non-oscillatory property. Finally, the model is applied to subgrid-scale (SGS) modeling of a turbulent reacting flow, for which it suppresses spurious oscillations in scalar fields that otherwise violate the solution boundedness. It outperforms a simple penalization of oscillations in the loss function.">
@@ -1676,34 +1661,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>9</td>
         </tr>
 
-        <tr id="Physics-informed neural networks have proven to be a powerful tool for solving differential equations, leveraging the principles of physics to inform the learning process. However, traditional deep neural networks often face challenges in achieving high accuracy without incurring significant computational costs. In this work, we implement the Physics-Informed Kolmogorov-Arnold Neural Networks (PIKAN) through efficient-KAN and WAV-KAN, which utilize the Kolmogorov-Arnold representation theorem. PIKAN demonstrates superior performance compared to conventional deep neural networks, achieving the same level of accuracy with fewer layers and reduced computational overhead. We explore both B-spline and wavelet-based implementations of PIKAN and benchmark their performance across various ordinary and partial differential equations using unsupervised (data-free) and supervised (data-driven) techniques. For certain differential equations, the data-free approach suffices to find accurate solutions, while in more complex scenarios, the data-driven method enhances the PIKAN's ability to converge to the correct solution. We validate our results against numerical solutions and achieve $99 \%$ accuracy in most scenarios.">
+        <tr id="Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of their function as surrogates for computationally intensive digital twinning simulators. Although purely data-driven methods are available for ROM construction, schemes that allow to retain a portion of the physics tend to enhance the interpretability and generalization of ROMs. However, physics-based techniques can adversely scale when dealing with nonlinear systems that feature parametric dependencies. This study introduces a generative physics-based ROM that is suited for nonlinear systems with parametric dependencies and is additionally able to quantify the confidence associated with the respective estimates. A main contribution of this work is the conditioning of these parametric ROMs to features that can be derived from monitoring measurements, feasibly in an online fashion. This is contrary to most existing ROM schemes, which remain restricted to the prescription of the physics-based, and usually a priori unknown, system parameters. Our work utilizes conditional Variational Autoencoders to continuously map the required reduction bases to a feature vector extracted from limited output measurements, while additionally allowing for a probabilistic assessment of the ROM-estimated Quantities of Interest. An auxiliary task using a neural network-based parametrization of suitable probability distributions is introduced to re-establish the link with physical model parameters. We verify the proposed scheme on a series of simulated case studies incorporating effects of geometric and material nonlinearity under parametric dependencies related to system properties and input load characteristics.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/785706911b444c085e5f732a9fef3bb0159349db" target='_blank'>
-              Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficent-KAN and WAV-KAN
+          <td><a href="https://www.semanticscholar.org/paper/eb8cb80fb7c1708ef89c5e655f1cf2a1a98b8f0a" target='_blank'>
+              A Reduced Order Model conditioned on monitoring features for estimation and uncertainty quantification in engineered systems
               </a>
             </td>
           <td>
-            Subhajit Patra, Sonali Panda, B. K. Parida, Mahima Arya, Kurt Jacobs, Denys I. Bondar, Abhijit Sen
+            Konstantinos Vlachas, Thomas Simpson, Anthony Garland, D. Quinn, Charbel Farhat, Eleni Chatzi
           </td>
-          <td>2024-07-25</td>
+          <td>2024-07-24</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>3</td>
         </tr>
 
-        <tr id="We introduce a general framework for solving partial differential equations (PDEs) using generative diffusion models. In particular, we focus on the scenarios where we do not have the full knowledge of the scene necessary to apply classical solvers. Most existing forward or inverse PDE approaches perform poorly when the observations on the data or the underlying coefficients are incomplete, which is a common assumption for real-world measurements. In this work, we propose DiffusionPDE that can simultaneously fill in the missing information and solve a PDE by modeling the joint distribution of the solution and coefficient spaces. We show that the learned generative priors lead to a versatile framework for accurately solving a wide range of PDEs under partial observation, significantly outperforming the state-of-the-art methods for both forward and inverse directions.">
+        <tr id="The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution for the first issue using a data-driven surrogate model in the form of a convolutional Long-Short Term Memory network with actuation. We demonstrate that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. Furthermore, we show that iteratively updating the model is of major importance to avoid biases in the RL training. Detailed ablation studies reveal the most important ingredients of the modeling process. We use the chaotic Kuramoto-Sivashinsky equation do demonstrate our findings.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/5c821763f76272b6deee95f9729107969ce0ae44" target='_blank'>
-              DiffusionPDE: Generative PDE-Solving Under Partial Observation
+          <td><a href="https://www.semanticscholar.org/paper/73d91c3ba7ba5d34e084bcd3bd2c0c397052d50a" target='_blank'>
+              Numerical Evidence for Sample Efficiency of Model-Based Over Model-Free Reinforcement Learning Control of Partial Differential Equations
               </a>
             </td>
           <td>
-            Jiahe Huang, Guandao Yang, Zichen Wang, Jeong Joon Park
+            Stefan Werner, Sebastian Peitz
           </td>
           <td>2024-06-25</td>
-          <td>ArXiv</td>
+          <td>2024 European Control Conference (ECC)</td>
+          <td>0</td>
           <td>0</td>
-          <td>1</td>
         </tr>
 
         <tr id="
@@ -1722,17 +1707,17 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>10</td>
         </tr>
 
-        <tr id="The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution for the first issue using a data-driven surrogate model in the form of a convolutional Long-Short Term Memory network with actuation. We demonstrate that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. Furthermore, we show that iteratively updating the model is of major importance to avoid biases in the RL training. Detailed ablation studies reveal the most important ingredients of the modeling process. We use the chaotic Kuramoto-Sivashinsky equation do demonstrate our findings.">
+        <tr id="Complex dynamic systems are typically either modeled using expert knowledge in the form of differential equations, or via data-driven universal approximation models such as artificial neural networks (ANN). While the first approach has advantages with respect to interpretability, transparency, data-efficiency, and extrapolation, the second approach is able to learn completely unknown functional relations from data and may result in models that can be evaluated more efficiently. To combine the complementary advantages, universal differential equations (UDE) have been suggested, which replace unknown terms in the differential equations with ANN. These hybrid models allow to both encode prior domain knowledge such as first principles and to learn unknown mechanisms from data. Often, data for the training of UDE can only be obtained via costly experiments. We consider optimal experimental design (OED) for the planning of experiments and generation of data needed to train UDE. The number of weights in the embedded ANN usually leads to an overfitting of the regression problem. To make the OED problem tractable for optimization, we propose and compare dimension reduction methods that are based on lumping of weights and singular value decomposition of the Fisher information matrix (FIM), respectively. They result in lower-dimensional variational differential equations which are easier to solve and which yield regular FIM. Our numerical results showcase the advantages of OED for UDE, such as an increased data-efficiency and better extrapolation properties.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/73d91c3ba7ba5d34e084bcd3bd2c0c397052d50a" target='_blank'>
-              Numerical Evidence for Sample Efficiency of Model-Based Over Model-Free Reinforcement Learning Control of Partial Differential Equations
+          <td><a href="https://www.semanticscholar.org/paper/2230fd0495ec13469526bd77cb385acbdb4bddfa" target='_blank'>
+              Optimal Experimental Design for Universal Differential Equations
               </a>
             </td>
           <td>
-            Stefan Werner, Sebastian Peitz
+            Christoph Plate, Carl Julius Martensen, Sebastian Sager
           </td>
-          <td>2024-06-25</td>
-          <td>2024 European Control Conference (ECC)</td>
+          <td>2024-08-13</td>
+          <td>ArXiv</td>
           <td>0</td>
           <td>0</td>
         </tr>
@@ -1749,112 +1734,97 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2024-07-30</td>
           <td>Entropy</td>
           <td>0</td>
-          <td>0</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of their function as surrogates for computationally intensive digital twinning simulators. Although purely data-driven methods are available for ROM construction, schemes that allow to retain a portion of the physics tend to enhance the interpretability and generalization of ROMs. However, physics-based techniques can adversely scale when dealing with nonlinear systems that feature parametric dependencies. This study introduces a generative physics-based ROM that is suited for nonlinear systems with parametric dependencies and is additionally able to quantify the confidence associated with the respective estimates. A main contribution of this work is the conditioning of these parametric ROMs to features that can be derived from monitoring measurements, feasibly in an online fashion. This is contrary to most existing ROM schemes, which remain restricted to the prescription of the physics-based, and usually a priori unknown, system parameters. Our work utilizes conditional Variational Autoencoders to continuously map the required reduction bases to a feature vector extracted from limited output measurements, while additionally allowing for a probabilistic assessment of the ROM-estimated Quantities of Interest. An auxiliary task using a neural network-based parametrization of suitable probability distributions is introduced to re-establish the link with physical model parameters. We verify the proposed scheme on a series of simulated case studies incorporating effects of geometric and material nonlinearity under parametric dependencies related to system properties and input load characteristics.">
+        <tr id="We introduce new machine-learning techniques for analyzing chaotic dynamical systems. The primary objectives of the study include the development of a new and simple method for calculating the Lyapunov exponent using only two trajectory data points unlike traditional methods that require an averaging procedure, the exploration of phase transition graphs from regular periodic to chaotic dynamics to identify"almost integrable"trajectories where conserved quantities deviate from whole numbers, and the identification of"integrable regions"within chaotic trajectories. These methods are applied and tested on two dynamical systems:"Two objects moving on a rod"and the"Henon-Heiles"systems.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/eb8cb80fb7c1708ef89c5e655f1cf2a1a98b8f0a" target='_blank'>
-              A Reduced Order Model conditioned on monitoring features for estimation and uncertainty quantification in engineered systems
+          <td><a href="https://www.semanticscholar.org/paper/8beba1025021fc988f71772e30840ce6845fdf65" target='_blank'>
+              Deciphering Complexity: Machine Learning Insights into Chaotic Dynamical Systems
               </a>
             </td>
           <td>
-            Konstantinos Vlachas, Thomas Simpson, Anthony Garland, D. Quinn, Charbel Farhat, Eleni Chatzi
+            Lazare Osmanov
           </td>
-          <td>2024-07-24</td>
+          <td>2024-08-04</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
-        </tr>
-
-        <tr id="The temporal analysis of products (TAP) technique produces extensive transient kinetic data sets, but it is challenging to translate the large quantity of raw data into physically interpretable kinetic models, largely due to the computational scaling of existing numerical methods for fitting TAP data. In this work, we utilize kinetics-informed neural networks (KINNs), which are artificial feedforward neural networks designed to solve ordinary differential equations constrained by micro-kinetic models, to model the TAP data. We demonstrate that, under the assumption that all concentrations are known in the thin catalyst zone, KINNs can simultaneously fit the transient data, retrieve the kinetic model parameters, and interpolate unseen pulse behavior for multi-pulse experiments. We further demonstrate that, by modifying the loss function, KINNs maintain these capabilities even when precise thin-zone information is unavailable, as would be the case with real experimental TAP data. We also compare the approach to existing optimization techniques, which reveals improved noise tolerance and performance in extracting kinetic parameters. The KINNs approach offers an efficient alternative for TAP analysis and can assist in interpreting transient kinetics in complex systems over long timescales.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e1f3493d7eb9b985ff3ea227fb05f7db3343654b" target='_blank'>
-              Fitting micro-kinetic models to transient kinetics of temporal analysis of product reactors using kinetics-informed neural networks
-              </a>
-            </td>
-          <td>
-            Dingqi Nai, G. S. Gusmão, Zachary Kilwein, Fani Boukouvala, A. Medford
-          </td>
-          <td>2024-06-20</td>
-          <td>ArXiv</td>
           <td>0</td>
-          <td>40</td>
         </tr>
 
-        <tr id="Multiphysics problems that are characterized by complex interactions among fluid dynamics, heat transfer, structural mechanics, and electromagnetics, are inherently challenging due to their coupled nature. While experimental data on certain state variables may be available, integrating these data with numerical solvers remains a significant challenge. Physics-informed neural networks (PINNs) have shown promising results in various engineering disciplines, particularly in handling noisy data and solving inverse problems. However, their effectiveness in forecasting nonlinear phenomena in multiphysics regimes is yet to be fully established. This study introduces NeuroSEM, a hybrid framework integrating PINNs with the high-fidelity Spectral Element Method (SEM) solver, Nektar++. NeuroSEM leverages strengths of both PINNs and SEM, providing robust solutions for multiphysics problems. PINNs are trained to assimilate data and model physical phenomena in specific subdomains, which are then integrated into Nektar++. We demonstrate the efficiency and accuracy of NeuroSEM for thermal convection in cavity flow and flow past a cylinder. The framework effectively handles data assimilation by addressing those subdomains and state variables where data are available. We applied NeuroSEM to the Rayleigh-B\'enard convection system, including cases with missing thermal boundary conditions. Our results indicate that NeuroSEM accurately models the physical phenomena and assimilates the data within the specified subdomains. The framework's plug-and-play nature facilitates its extension to other multiphysics or multiscale problems. Furthermore, NeuroSEM is optimized for an efficient execution on emerging integrated GPU-CPU architectures. This hybrid approach enhances the accuracy and efficiency of simulations, making it a powerful tool for tackling complex engineering challenges in various scientific domains.">
+        <tr id="Many physical systems exhibit translational invariance, meaning that the underlying physical laws are independent of the position in space. Data driven approximations of the infinite dimensional but linear Koopman operator of non-linear dynamical systems need to be physically informed in order to respect such physical symmetries. In the current work, we introduce a translation invariant extended dynamic mode decomposition (tieDMD) for coupled non-linear systems on periodic domains. This is achieved by exploiting a block-diagonal structure of the Koopman operator in Fourier space. Variants of tieDMD are applied to data obtained on one-dimensional periodic domains from the non-linear phase-diffusion equation, the Burgers equation, the Korteweg-de Vries equation, and a coupled FitzHugh-Nagumo system of partial differential equations. The reconstruction capability of tieDMD is compared to existing linear and non-linear variants of the dynamic mode decomposition applied to the same data. For the regarded data, tieDMD consistently shows superior capabilities in data reconstruction.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/bf355890cfebb6682f601bfaea0ed245cda232dc" target='_blank'>
-              NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements
+          <td><a href="https://www.semanticscholar.org/paper/c3bb71d64de3bbe99545b75cc623bacfad3e1e76" target='_blank'>
+              Approximation of translation invariant Koopman operators for coupled non-linear systems.
               </a>
             </td>
           <td>
-            K. Shukla, Zongren Zou, Chi Hin Chan, Additi Pandey, Zhicheng Wang, G. Karniadakis
+            Thomas Hochrainer, Gurudas Kar
           </td>
-          <td>2024-07-30</td>
-          <td>ArXiv</td>
+          <td>2024-08-01</td>
+          <td>Chaos</td>
           <td>0</td>
-          <td>127</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Bifurcations mark qualitative changes of long-term behavior in dynamical systems and can often signal sudden ("hard") transitions or catastrophic events (divergences). Accurately locating them is critical not just for deeper understanding of observed dynamic behavior, but also for designing efficient interventions. When the dynamical system at hand is complex, possibly noisy, and expensive to sample, standard (e.g. continuation based) numerical methods may become impractical. We propose an active learning framework, where Bayesian Optimization is leveraged to discover saddle-node or Hopf bifurcations, from a judiciously chosen small number of vector field observations. Such an approach becomes especially attractive in systems whose state x parameter space exploration is resource-limited. It also naturally provides a framework for uncertainty quantification (aleatoric and epistemic), useful in systems with inherent stochasticity.">
+        <tr id="Koopman operators and transfer operators represent nonlinear dynamics in state space through its induced action on linear spaces of observables and measures, respectively. This framework enables the use of linear operator theory for supervised and unsupervised learning of nonlinear dynamical systems, and has received considerable interest in recent years. Here, we propose a data-driven technique for spectral approximation of Koopman operators of continuous-time, measure-preserving ergodic systems that is asymptotically consistent and makes direct use of known equations of motion (physics). Our approach is based on a bounded transformation of the Koopman generator (an operator implementing directional derivatives of observables along the dynamical flow), followed by smoothing by a Markov semigroup of kernel integral operators. This results in a skew-adjoint, compact operator whose eigendecomposition is expressible as a variational generalized eigenvalue problem. We develop Galerkin methods to solve this eigenvalue problem and study their asymptotic consistency in the large-data limit. A key aspect of these methods is that they are physics-informed, in the sense of making direct use of dynamical vector field information through automatic differentiation of kernel functions. Solutions of the eigenvalue problem reconstruct evolution operators that preserve unitarity of the underlying Koopman group while spectrally converging to it in a suitable limit. In addition, the computed eigenfunctions have representatives in a reproducing kernel Hilbert space, enabling out-of-sample evaluation of learned dynamical features. Numerical experiments performed with this method on integrable and chaotic low-dimensional systems demonstrate its efficacy in extracting dynamically coherent observables under complex dynamics.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/8580056dd0caf69a81d8730639e0828479823e8a" target='_blank'>
-              Active search for Bifurcations
+          <td><a href="https://www.semanticscholar.org/paper/7bfdb15c2217fa4993fd812bd48a56f8b7c2df5e" target='_blank'>
+              Physics-informed spectral approximation of Koopman operators
               </a>
             </td>
           <td>
-            Y. M. Psarellis, T. Sapsis, I. G. Kevrekidis
+            C. Valva, Dimitrios Giannakis
           </td>
-          <td>2024-06-17</td>
+          <td>2024-08-11</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>37</td>
+          <td>3</td>
         </tr>
 
-        <tr id="We study interacting particle systems driven by noise, modeling phenomena such as opinion dynamics. We are interested in systems that exhibit phase transitions i.e. non-uniqueness of stationary states for the corresponding McKean-Vlasov PDE, in the mean field limit. We develop an efficient numerical scheme for identifying all steady states (both stable and unstable) of the mean field McKean-Vlasov PDE, based on a spectral Galerkin approximation combined with a deflated Newton's method to handle the multiplicity of solutions. Having found all possible equilibra, we formulate an optimal control strategy for steering the dynamics towards a chosen unstable steady state. The control is computed using iterated open-loop solvers in a receding horizon fashion. We demonstrate the effectiveness of the proposed steady state computation and stabilization methodology on several examples, including the noisy Hegselmann-Krause model for opinion dynamics and the Haken-Kelso-Bunz model from biophysics. The numerical experiments validate the ability of the approach to capture the rich self-organization landscape of these systems and to stabilize unstable configurations of interest. The proposed computational framework opens up new possibilities for understanding and controlling the collective behavior of noise-driven interacting particle systems, with potential applications in various fields such as social dynamics, biological synchronization, and collective behavior in physical and social systems.">
+        <tr id="This proposed work introduces a data-assimilation-assisted approach to train neural networks, aimed at effectively reducing epistemic uncertainty in state estimates of separated flows. This method, referred to as model-consistent training, ensures that input features are derived directly from physics-based models, such as Reynolds Averaged Navier Stokes (RANS) turbulence models, to accurately represent the current state of the flow. Autoencoders have been selected for this task due to their capability to capture essential information from large datasets, making them particularly suitable for handling high-dimensional data with numerous discretization points in both spatial and temporal dimensions. This innovative approach integrates the ensemble Kalman method to enhance the training process, providing a robust framework for improving model accuracy and performance in turbulent flow predictions.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/8cb185f3e81fd7f2446362e4cda24e70bab5a5c9" target='_blank'>
-              Computation and Control of Unstable Steady States for Mean Field Multiagent Systems
+          <td><a href="https://www.semanticscholar.org/paper/8c44941f2e715c2b0202ef4e5d7ba0fe96659fe0" target='_blank'>
+              Developing a Model-Consistent Reduced-Dimensionality training approach to quantify and reduce epistemic uncertainty in separated flows
               </a>
             </td>
           <td>
-            Sara Bicego, D. Kalise, G. Pavliotis
+            Minghan Chu
           </td>
-          <td>2024-06-17</td>
+          <td>2024-07-31</td>
           <td>ArXiv</td>
-          <td>1</td>
-          <td>32</td>
+          <td>0</td>
+          <td>0</td>
         </tr>
 
-        <tr id="We consider solving complex spatiotemporal dynamical systems governed by partial differential equations (PDEs) using frequency domain-based discrete learning approaches, such as Fourier neural operators. Despite their widespread use for approximating nonlinear PDEs, the majority of these methods neglect fundamental physical laws and lack interpretability. We address these shortcomings by introducing Physics-embedded Fourier Neural Networks (PeFNN) with flexible and explainable error control. PeFNN is designed to enforce momentum conservation and yields interpretable nonlinear expressions by utilizing unique multi-scale momentum-conserving Fourier (MC-Fourier) layers and an element-wise product operation. The MC-Fourier layer is by design translation- and rotation-invariant in the frequency domain, serving as a plug-and-play module that adheres to the laws of momentum conservation. PeFNN establishes a new state-of-the-art in solving widely employed spatiotemporal PDEs and generalizes well across input resolutions. Further, we demonstrate its outstanding performance for challenging real-world applications such as large-scale flood simulations.">
+        <tr id="This paper presents, for the first time, a framework for Kolmogorov-Arnold Networks (KANs) in power system applications. Inspired by the recently proposed KAN architecture, this paper proposes physics-informed Kolmogorov-Arnold Networks (PIKANs), a novel KAN-based physics-informed neural network (PINN) tailored to efficiently and accurately learn dynamics within power systems. The PIKANs present a promising alternative to conventional Multi-Layer Perceptrons (MLPs) based PINNs, achieving superior accuracy in predicting power system dynamics while employing a smaller network size. Simulation results on a single-machine infinite bus system and a 4-bus 2- generator system underscore the accuracy of the PIKANs in predicting rotor angle and frequency with fewer learnable parameters than conventional PINNs. Furthermore, the simulation results demonstrate PIKANs capability to accurately identify uncertain inertia and damping coefficients. This work opens up a range of opportunities for the application of KANs in power systems, enabling efficient determination of grid dynamics and precise parameter identification.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/b1987fcbdf3f85b47d4df2efa239c0c5050a329a" target='_blank'>
-              Physics-embedded Fourier Neural Network for Partial Differential Equations
+          <td><a href="https://www.semanticscholar.org/paper/ba0e7f4ba7daa48bc5af967eba3f7af6be45476e" target='_blank'>
+              Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics
               </a>
             </td>
           <td>
-            Qingsong Xu, Nils Thuerey, Yilei Shi, Jonathan Bamber, Chaojun Ouyang, Xiao Xiang Zhu
+            H. Shuai, Fangxing Li
           </td>
-          <td>2024-07-15</td>
+          <td>2024-08-13</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>19</td>
+          <td>3</td>
         </tr>
 
-        <tr id="This proposed work introduces a data-assimilation-assisted approach to train neural networks, aimed at effectively reducing epistemic uncertainty in state estimates of separated flows. This method, referred to as model-consistent training, ensures that input features are derived directly from physics-based models, such as Reynolds Averaged Navier Stokes (RANS) turbulence models, to accurately represent the current state of the flow. Autoencoders have been selected for this task due to their capability to capture essential information from large datasets, making them particularly suitable for handling high-dimensional data with numerous discretization points in both spatial and temporal dimensions. This innovative approach integrates the ensemble Kalman method to enhance the training process, providing a robust framework for improving model accuracy and performance in turbulent flow predictions.">
+        <tr id="Reservoir computing (RC) is known as a powerful machine learning approach for learning complex dynamics from limited data. Here, we use RC to predict highly stochastic dynamics of cell shapes. We find that RC is able to predict the steady state climate from very limited data. Furthermore, the RC learns the timescale of transients from only four observations. We find that these capabilities of the RC to act as a dynamic twin allows us to also infer important statistics of cell shape dynamics of unobserved conditions.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/8c44941f2e715c2b0202ef4e5d7ba0fe96659fe0" target='_blank'>
-              Developing a Model-Consistent Reduced-Dimensionality training approach to quantify and reduce epistemic uncertainty in separated flows
+          <td><a href="https://www.semanticscholar.org/paper/20f5cfa59cf3363d794a2715ae08d7e092121b0f" target='_blank'>
+              Machine learning from limited data: Predicting biological dynamics under a time-varying external input
               </a>
             </td>
           <td>
-            Minghan Chu
+            Hoony Kang, Keshav Srinivasan, Wolfgang Losert
           </td>
-          <td>2024-07-31</td>
+          <td>2024-08-15</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>2</td>
         </tr>
 
         <tr id="We propose CoNSAL (Combining Neural networks and Symbolic regression for Analytical Lyapunov function) to construct analytical Lyapunov functions for nonlinear dynamic systems. This framework contains a neural Lyapunov function and a symbolic regression component, where symbolic regression is applied to distill the neural network to precise analytical forms. Our approach utilizes symbolic regression not only as a tool for translation but also as a means to uncover counterexamples. This procedure terminates when no counterexamples are found in the analytical formulation. Compared with previous results, CoNSAL directly produces an analytical form of the Lyapunov function with improved interpretability in both the learning process and the final results. We apply CoNSAL to 2-D inverted pendulum, path following, Van Der Pol Oscillator, 3-D trig dynamics, 4-D rotating wheel pendulum, 6-D 3-bus power system, and demonstrate that our algorithm successfully finds their valid Lyapunov functions. Code examples are available at https://github.com/HaohanZou/CoNSAL.">
@@ -1869,7 +1839,7 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2024-06-21</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>1</td>
         </tr>
 
         <tr id="Port-Hamiltonian systems (pHS) allow for a structure-preserving modeling of dynamical systems. Coupling pHS via linear relations between input and output defines an overall pHS, which is structure preserving. However, in multiphysics applications, some subsystems do not allow for a physical pHS description, as (a) this is not available or (b) too expensive. Here, data-driven approaches can be used to deliver a pHS for such subsystems, which can then be coupled to the other subsystems in a structure-preserving way. In this work, we derive a data-driven identification approach for port-Hamiltonian differential algebraic equation (DAE) systems. The approach uses input and state space data to estimate nonlinear effort functions of pH-DAEs. As underlying technique, we us (multi-task) Gaussian processes. This work thereby extends over the current state of the art, in which only port-Hamiltonian ordinary differential equation systems could be identified via Gaussian processes. We apply this approach successfully to two applications from network design and constrained multibody system dynamics, based on pH-DAE system of index one and three, respectively.">
@@ -1883,7 +1853,7 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           </td>
           <td>2024-06-26</td>
           <td>ArXiv</td>
-          <td>0</td>
+          <td>1</td>
           <td>2</td>
         </tr>
 
@@ -1902,6 +1872,21 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>9</td>
         </tr>
 
+        <tr id="A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system. Models of these neural dynamics should ideally be both interpretable and fit the observed data well. Low-rank recurrent neural networks (RNNs) exhibit such interpretability by having tractable dynamics. However, it is unclear how to best fit low-rank RNNs to data consisting of noisy observations of an underlying stochastic system. Here, we propose to fit stochastic low-rank RNNs with variational sequential Monte Carlo methods. We validate our method on several datasets consisting of both continuous and spiking neural data, where we obtain lower dimensional latent dynamics than current state of the art methods. Additionally, for low-rank models with piecewise linear nonlinearities, we show how to efficiently identify all fixed points in polynomial rather than exponential cost in the number of units, making analysis of the inferred dynamics tractable for large RNNs. Our method both elucidates the dynamical systems underlying experimental recordings and provides a generative model whose trajectories match observed trial-to-trial variability.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/579f56813a03517163b86fa89044dc78505bf2cb" target='_blank'>
+              Inferring stochastic low-rank recurrent neural networks from neural data
+              </a>
+            </td>
+          <td>
+            Matthijs Pals, A. E. Saugtekin, Felix Pei, Manuel Gloeckler, J. H. Macke
+          </td>
+          <td>2024-06-24</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>4</td>
+        </tr>
+
         <tr id="The effective inclusion of a priori knowledge when embedding known data in physics‐based models of dynamical systems can ensure that the reconstructed model respects physical principles, while simultaneously improving the accuracy of the solution in the previously unseen regions of state space. This paper presents a physics‐constrained data‐driven discrepancy modeling method that variationally embeds known data in the modeling framework. The hierarchical structure of the method yields fine scale variational equations that facilitate the derivation of residuals which are comprised of the first‐principles theory and sensor‐based data from the dynamical system. The embedding of the sensor data via residual terms leads to discrepancy‐informed closure models that yield a method which is driven not only by boundary and initial conditions, but also by measurements that are taken at only a few observation points in the target system. Specifically, the data‐embedding term serves as residual‐based least‐squares loss function, thus retaining variational consistency. Another important relation arises from the interpretation of the stabilization tensor as a kernel function, thereby incorporating a priori knowledge of the problem and adding computational intelligence to the modeling framework. Numerical test cases show that when known data is taken into account, the data driven variational (DDV) method can correctly predict the system response in the presence of several types of discrepancies. Specifically, the damped solution and correct energy time histories are recovered by including known data in the undamped situation. Morlet wavelet analyses reveal that the surrogate problem with embedded data recovers the fundamental frequency band of the target system. The enhanced stability and accuracy of the DDV method is manifested via reconstructed displacement and velocity fields that yield time histories of strain and kinetic energies which match the target systems. The proposed DDV method also serves as a procedure for restoring eigenvalues and eigenvectors of a deficient dynamical system when known data is taken into account, as shown in the numerical test cases presented here.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/dda505dc42399a19a75eff263166e84931a95cf6" target='_blank'>
@@ -1932,21 +1917,6 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>12</td>
         </tr>
 
-        <tr id="We introduce a simple, stochastic, a-posteriori, turbulence closure model based on a reduced subgrid scale term. This subgrid scale term is tailor-made to capture the statistics of a small set of spatially-integrate quantities of interest (QoIs), with only one unresolved scalar time series per QoI. In contrast to other data-driven surrogates the dimension of the ``learning problem"is reduced from an evolving field to one scalar time series per QoI. We use an a-posteriori, nudging approach to find the distribution of the scalar series over time. This approach has the advantage of taking the interaction between the solver and the surrogate into account. A stochastic surrogate parametrization is obtained by random sampling from the found distribution for the scalar time series. Compared to an a-priori trained convolutional neural network, evaluating the new method is computationally much cheaper and gives similar long-term statistics.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/b0ca16a5e3ed50325feff6ca9949ff71eb89bbdb" target='_blank'>
-              Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics
-              </a>
-            </td>
-          <td>
-            Rik Hoekstra, D. Crommelin, W. Edeling
-          </td>
-          <td>2024-07-19</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>22</td>
-        </tr>
-
         <tr id="The continuously increasing amount of noisy data demands the development of accurate and efficient models for analysis, modeling, and control. In this article, we propose a novel data-driven moment matching method which employs Tikhonov regularization in the Reproducing Kernel Hilbert Spaces (RKHSs). Specifically, considering a realistic scenario in which the system's plant is unknown and only noisy measured data are available, we provide an estimation of the moment of the unknown plant by solving a regularized optimization problem on RKHS. For, we first demonstrate that the estimation of the moment can be improved via tuning the regularization term, and further, we show under which condition the effect of the transient improves the performance of the estimation. Then, we construct a parameterized model characterized by a kernel-based output mapping. Finally, the proposed data-driven approach is validated and discussed by means of a DC-to-DC Cuk converter driven by a Van der Pol oscillator.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/970212021b4134e841e09f425053018067ffcdbb" target='_blank'>
@@ -1962,21 +1932,6 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2</td>
         </tr>
 
-        <tr id="In this paper, we present a data-driven model predictive control (DDMPC) framework specifically designed for constrained single-input single-output (SISO) nonlinear systems. Our approach involves customizing a set-theoretic receding horizon controller within a data-driven context. To achieve this, we translate model-based conditions into data series of available input and output signals. This translation process leverages recent advances in data-driven control theory, enabling the controller to operate effectively without relying on explicit system models. The proposed framework incorporates a robust methodology for managing system constraints, ensuring that the control actions remain within predefined bounds. By means of time sequences, the controller learns the underlying system dynamics and adapts to changes in real time, providing enhanced performance and reliability. The integration of set-theoretic methods allows for the systematic handling of uncertainties and disturbances, which are common when the trajectory of a nonlinear system is embedded inside a linear trajectory state tube. To validate the effectiveness of our DDMPC framework, we conduct extensive simulations on a nonlinear DC motor system. The results demonstrate significant improvements in control performance, highlighting the robustness and adaptability of our approach compared to traditional model-based MPC techniques.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/557a1699a30747c83bf5827911be2a7cb58cbe44" target='_blank'>
-              A Data-Driven Approach to Set-Theoretic Model Predictive Control for Nonlinear Systems
-              </a>
-            </td>
-          <td>
-            Francesco Giannini, D. Famularo
-          </td>
-          <td>2024-06-23</td>
-          <td>Information</td>
-          <td>0</td>
-          <td>18</td>
-        </tr>
-
         <tr id="This paper presents an algorithm for developing sparse surrogate models that satisfy mass/energy conservation even when the training data are noisy and violate the conservation laws. In the first step, we employ the Bayesian Identification of Dynamic Sparse Algebraic Model (BIDSAM) algorithm proposed in our previous work to obtain a set of hierarchically ranked sparse models which approximate system behaviors with linear combinations of a set of well-defined basis functions. Although the model building algorithm was shown to be robust to noisy data, conservation laws may not be satisfied by the surrogate models. In this work we propose an algorithm that augments a data reconciliation step with the BIDSAM model for satisfaction of conservation laws. This method relies only on known boundary conditions and hence is generic for any chemical system. Two case studies are considered-one focused on mass conservation and another on energy conservation. Results show that models with minimum bias are built by using the developed algorithm while exactly satisfying the conservation laws for all data.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/a3b228eccbfc381c1bf8429fb1e6ed55fe3fb34c" target='_blank'>
@@ -1992,36 +1947,6 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>29</td>
         </tr>
 
-        <tr id="We propose the Artificial Intelligence Velocimetry-Thermometry (AIVT) method to infer hidden temperature fields from experimental turbulent velocity data. This physics-informed machine learning method enables us to infer continuous temperature fields using only sparse velocity data, hence eliminating the need for direct temperature measurements. Specifically, AIVT is based on physics-informed Kolmogorov-Arnold Networks (not neural networks) and is trained by optimizing a combined loss function that minimizes the residuals of the velocity data, boundary conditions, and the governing equations. We apply AIVT to a unique set of experimental volumetric and simultaneous temperature and velocity data of Rayleigh-B\'enard convection (RBC) that we acquired by combining Particle Image Thermometry and Lagrangian Particle Tracking. This allows us to compare AIVT predictions and measurements directly. We demonstrate that we can reconstruct and infer continuous and instantaneous velocity and temperature fields from sparse experimental data at a fidelity comparable to direct numerical simulations (DNS) of turbulence. This, in turn, enables us to compute important quantities for quantifying turbulence, such as fluctuations, viscous and thermal dissipation, and QR distribution. This paradigm shift in processing experimental data using AIVT to infer turbulent fields at DNS-level fidelity is a promising avenue in breaking the current deadlock of quantitative understanding of turbulence at high Reynolds numbers, where DNS is computationally infeasible.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/eb6d8afffcc7bf5a551a6c5a5b9a454bde425d85" target='_blank'>
-              Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks
-              </a>
-            </td>
-          <td>
-            Juan Diego Toscano, Theo Kaufer, Zhibo Wang, Martin Maxey, Christian Cierpka, G. Karniadakis
-          </td>
-          <td>2024-07-22</td>
-          <td>ArXiv</td>
-          <td>2</td>
-          <td>127</td>
-        </tr>
-
-        <tr id="Classical model reduction techniques project the governing equations onto a linear subspace of the original state space. More recent data-driven techniques use neural networks to enable nonlinear projections. Whilst those often enable stronger compression, they may have redundant parameters and lead to suboptimal latent dimensionality. To overcome these, we propose a multistep algorithm that induces sparsity in the encoder-decoder networks for effective reduction in the number of parameters and additional compression of the latent space. This algorithm starts with sparsely initialized a network and training it using linearized Bregman iterations. These iterations have been very successful in computer vision and compressed sensing tasks, but have not yet been used for reduced-order modelling. After the training, we further compress the latent space dimensionality by using a form of proper orthogonal decomposition. Last, we use a bias propagation technique to change the induced sparsity into an effective reduction of parameters. We apply this algorithm to three representative PDE models: 1D diffusion, 1D advection, and 2D reaction-diffusion. Compared to conventional training methods like Adam, the proposed method achieves similar accuracy with 30% less parameters and a significantly smaller latent space.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6d0716bb98b1bad42ed563160283dce3e8413da6" target='_blank'>
-              Sparsifying dimensionality reduction of PDE solution data with Bregman learning
-              </a>
-            </td>
-          <td>
-            T. J. Heeringa, Christoph Brune, Mengwu Guo
-          </td>
-          <td>2024-06-18</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>1</td>
-        </tr>
-
         <tr id="
  The growing time-series data make it possible to glimpse the hidden dynamics in various fields. However, developing a computational toolbox with high interpretability to unveil the interaction dynamics from data remains a crucial challenge. Here, we propose a new computational approach called Automated Dynamical Model Inference based on Expression Trees (ADMIET), in which the machine learning algorithm, the numerical integration of ordinary differential equations and the interpretability from prior knowledge are embedded into the symbolic learning scheme to establish a general framework for revealing the hidden dynamics in time-series data. ADMIET takes full advantage of both machine learning algorithm and expression tree. Firstly, we translate the prior knowledge into constraints on the structure of expression tree, reducing the search space and enhancing the interpretability. Secondly, we utilize the proposed adaptive penalty function to ensure the convergence of gradient descent algorithm and the selection of the symbols. Compared to gene expression programming, ADMIET exhibits its remarkable capability in function fitting with higher accuracy and broader applicability. Moreover, ADMIET can better fit parameters in nonlinear forms compared to regression methods. Furthermore, we apply ADMIET to two typical biological systems and one real data with different prior knowledge to infer the dynamical equations. The results indicate that ADMIET can not only discover the interaction relationships but also provide accurate estimates of the parameters in the equations. These results demonstrate ADMIET's superiority in revealing interpretable dynamics from time-series biological data.">
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@@ -2068,142 +1993,97 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>6</td>
         </tr>
 
-        <tr id="After decades of attention, emergence continues to lack a centralized mathematical definition that leads to a rigorous emergence test applicable to physical flocks and swarms, particularly those containing both deterministic elements (eg, interactions) and stochastic perturbations like measurement noise. This study develops a heuristic test based on singular value curve analysis of data matrices containing deterministic and Gaussian noise signals. The minimum detection criteria are identified, and statistical and matrix space analysis developed to determine upper and lower bounds. This study applies the analysis to representative examples by using recorded trajectories of mixed deterministic and stochastic trajectories for multi-agent, cellular automata, and biological video. Examples include Cucker Smale and Vicsek flocking, Gaussian noise and its integration, recorded observations of bird flocking, and 1D cellular automata. Ensemble simulations including measurement noise are performed to compute statistical variation and discussed relative to random matrix theory noise bounds. The results indicate singular knee analysis of recorded trajectories can detect gradated levels on a continuum of structure and noise. Across the eight singular value decay metrics considered, the angle subtended at the singular value knee emerges with the most potential for supporting cross-embodiment emergence detection, the size of noise bounds is used as an indication of required sample size, and the presence of a large fraction of singular values inside noise bounds as an indication of noise.">
+        <tr id="Constraining a numerical weather prediction (NWP) model with observations via 4D variational (4D-Var) data assimilation is often difficult to implement in practice due to the need to develop and maintain a software-based tangent linear model and adjoint model. One of the most common 4D-Var algorithms uses an incremental update procedure, which has been shown to be an approximation of the Gauss-Newton method. Here we demonstrate that when using a forecast model that supports automatic differentiation, an efficient and in some cases more accurate alternative approximation of the Gauss-Newton method can be applied by combining backpropagation of errors with Hessian approximation. This approach can be used with either a conventional numerical model implemented within a software framework that supports automatic differentiation, or a machine learning (ML) based surrogate model. We test the new approach on a variety of Lorenz-96 and quasi-geostrophic models. The results indicate potential for a deeper integration of modeling, data assimilation, and new technologies in a next-generation of operational forecast systems that leverage weather models designed to support automatic differentiation.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/95edd6bd909e45c28aaee6e5647dbbba81fb0ca9" target='_blank'>
-              Singular knee identification to support emergence recognition in physical swarm and cellular automata trajectories
+          <td><a href="https://www.semanticscholar.org/paper/4b43d025345ba51dc1b8aa4bb1314e91f7a7174b" target='_blank'>
+              4D-Var using Hessian approximation and backpropagation applied to automatically-differentiable numerical and machine learning models
               </a>
             </td>
           <td>
-            Imraan A. Faruque, Ishriak Ahmed
+            Kylen Solvik, Stephen G. Penny, Stephan Hoyer
           </td>
-          <td>2024-06-20</td>
+          <td>2024-08-05</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>10</td>
         </tr>
 
-        <tr id="Universal differential equations (UDEs) leverage the respective advantages of mechanistic models and artificial neural networks and combine them into one dynamic model. However, these hybrid models can suffer from unrealistic solutions, such as negative values for biochemical quantities. We present non-negative UDE (nUDEs), a constrained UDE variant that guarantees non-negative values. Furthermore, we explore regularisation techniques to improve generalisation and interpretability of UDEs.">
+        <tr id="For mathematical and experimental ease, models with time varying parameters are often simplified to assume constant parameters. However, this simplification can potentially lead to identifiability issues (lack of uniqueness of parameter estimates). Methods have been developed to algebraically and numerically determine the identifiability of a model, as well as resolve identifiability issues. This specific type of simplification presents an alternate opportunity to instead use this information to resolve the unidentifiability. Given that re-parameterizing, collecting more data, and adding inputs can be potentially costly or impractical, this could present new alternatives. We present a method for resolving unidentifiability in a system by introducing a new data stream correlated with a parameter of interest. First, we demonstrate how and when non-constant input data can be introduced into any rational function ODE system without worsening the model identifiability. Then, we prove when these input functions improve structural and potentially also practical identifiability for a given model and relevant data. By utilizing pre-existing data streams, these methods can potentially reduce experimental costs, while still answering key questions. By connecting mathematical proofs to application, our framework removes guesswork from when, where, and how researchers can best introduce new data to improve model outcomes.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/44abab70f51501595f3b1ac64da11aea862e8c1e" target='_blank'>
-              Non-Negative Universal Differential Equations With Applications in Systems Biology
+          <td><a href="https://www.semanticscholar.org/paper/fbc38cc84b188d260525dcc553f64371eca970c3" target='_blank'>
+              Examining the impact of forcing function inputs on structural identifiability
               </a>
             </td>
           <td>
-            Maren Philipps, Antonia Körner, Jakob Vanhoefer, Dilan Pathirana, Jan Hasenauer
+            Jessica R Conrad, Marisa C Eisenberg
           </td>
-          <td>2024-06-20</td>
+          <td>2024-07-03</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
+          <td>0</td>
         </tr>
 
-        <tr id="None">
+        <tr id="This paper examines the application of the Kernel Sum of Squares (KSOS) method for enhancing kernel learning from data, particularly in the context of dynamical systems. Traditional kernel-based methods, despite their theoretical soundness and numerical efficiency, frequently struggle with selecting optimal base kernels and parameter tuning, especially with gradient-based methods prone to local optima. KSOS mitigates these issues by leveraging a global optimization framework with kernel-based surrogate functions, thereby achieving more reliable and precise learning of dynamical systems. Through comprehensive numerical experiments on the Logistic Map, Henon Map, and Lorentz System, KSOS is shown to consistently outperform gradient descent in minimizing the relative-$\rho$ metric and improving kernel accuracy. These results highlight KSOS's effectiveness in predicting the behavior of chaotic dynamical systems, demonstrating its capability to adapt kernels to underlying dynamics and enhance the robustness and predictive power of kernel-based approaches, making it a valuable asset for time series analysis in various scientific fields.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c33ede3f4d08a8ab945870db595f8bca4b91e35e" target='_blank'>
-              Toward quantitative characterization of simulated earthquake-cycle complexities
+          <td><a href="https://www.semanticscholar.org/paper/be7befd5338268cfdfa3b26e66bece3caef3b91d" target='_blank'>
+              Kernel Sum of Squares for Data Adapted Kernel Learning of Dynamical Systems from Data: A global optimization approach
               </a>
             </td>
           <td>
-            Shiqi Wang
+            Daniel Lengyel, P. Parpas, B. Hamzi, H. Owhadi
           </td>
-          <td>2024-07-22</td>
-          <td>Scientific Reports</td>
-          <td>0</td>
+          <td>2024-08-12</td>
+          <td>ArXiv</td>
           <td>0</td>
+          <td>36</td>
         </tr>
 
-        <tr id="Constraining a numerical weather prediction (NWP) model with observations via 4D variational (4D-Var) data assimilation is often difficult to implement in practice due to the need to develop and maintain a software-based tangent linear model and adjoint model. One of the most common 4D-Var algorithms uses an incremental update procedure, which has been shown to be an approximation of the Gauss-Newton method. Here we demonstrate that when using a forecast model that supports automatic differentiation, an efficient and in some cases more accurate alternative approximation of the Gauss-Newton method can be applied by combining backpropagation of errors with Hessian approximation. This approach can be used with either a conventional numerical model implemented within a software framework that supports automatic differentiation, or a machine learning (ML) based surrogate model. We test the new approach on a variety of Lorenz-96 and quasi-geostrophic models. The results indicate potential for a deeper integration of modeling, data assimilation, and new technologies in a next-generation of operational forecast systems that leverage weather models designed to support automatic differentiation.">
+        <tr id="Traditional partial differential equations with constant coefficients often struggle to capture abrupt changes in real-world phenomena, leading to the development of variable coefficient PDEs and Markovian switching models. Recently, research has introduced the concept of PDEs with Markov switching models, established their well-posedness and presented numerical methods. However, there has been limited discussion on parameter estimation for the jump coefficients in these models. This paper addresses this gap by focusing on parameter inference for the wave equation with Markovian switching. We propose a Bayesian statistical framework using discrete sparse Bayesian learning to establish its convergence and a uniform error bound. Our method requires fewer assumptions and enables independent parameter inference for each segment by allowing different underlying structures for the parameter estimation problem within each segmented time interval. The effectiveness of our approach is demonstrated through three numerical cases, which involve noisy spatiotemporal data from different wave equations with Markovian switching. The results show strong performance in parameter estimation for variable coefficient PDEs.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4b43d025345ba51dc1b8aa4bb1314e91f7a7174b" target='_blank'>
-              4D-Var using Hessian approximation and backpropagation applied to automatically-differentiable numerical and machine learning models
+          <td><a href="https://www.semanticscholar.org/paper/5eda7824c0ecf7f45c2be3abef5f95f30fc1aafe" target='_blank'>
+              Parameters Inference for Nonlinear Wave Equations with Markovian Switching
               </a>
             </td>
           <td>
-            Kylen Solvik, Stephen G. Penny, Stephan Hoyer
+            Yi Zhang, Zhikun Zhang, Xiangjun Wang
           </td>
-          <td>2024-08-05</td>
+          <td>2024-08-12</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>10</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Identification of an accurate and simple model of a complex underactuated crane dynamics for varying operational conditions is a crucial step towards designing and implementation of real-time monitoring and control systems to enhance crane safety and operational efficiency. This paper considers a non-parametric data-driven identification of an overhead crane dynamics using symbolic regression techniques to find compromise between model complexity and predicted output accuracy. A grammar-guided genetic programming (G3P) combined with l 0 sparse regression is applied with two different variants of grammar to automatically construct a nonlinear autoregressive exogenous (NARX) model of different forms, termed extended and polynomial models. The proposed method is compared with a linear parameter-varying ARX (LPV-ARX) model. Identification is performed on experimental data ob - tained from a laboratory-scale overhead crane. The identified models are compared in terms of prediction accuracy, model’s complexity measured using number of model terms, and execution time. The regularized G3P method outperformed the LPV-ARX model in terms of model predictive output accuracy. The G3P with the extended gram - mar resulted in more accurate crane velocity prediction models than the models with the polynomial grammar. The payload sway prediction model with the polynomial grammar was less complex in all measured metrics while there was no statistical significance in the accuracy when compared to the models with extended grammar.">
+        <tr id="Predicting the dynamics of turbulent fluid flows has long been a central goal of science and engineering. Yet, even with modern computing technology, accurate simulation of all but the simplest turbulent flow-fields remains impossible: the fields are too chaotic and multi-scaled to directly store them in memory and perform time-evolution. An alternative is to treat turbulence $\textit{probabilistically}$, viewing flow properties as random variables distributed according to joint probability density functions (PDFs). Turbulence PDFs are neither chaotic nor multi-scale, but are still challenging to simulate due to their high dimensionality. Here we show how to overcome the dimensionality problem by parameterising turbulence PDFs into an extremely compressed format known as a"tensor network"(TN). The TN paradigm enables simulations on single CPU cores that would otherwise be impractical even with supercomputers: for a $5+1$ dimensional PDF of a chemically reactive turbulent flow, we achieve reductions in memory and computational costs by factors of $\mathcal{O}(10^6)$ and $\mathcal{O}(10^3)$, respectively, compared to standard finite difference algorithms. A future path is opened towards something heretofore regarded as infeasible: directly simulating high-dimensional PDFs of both turbulent flows and other chaotic systems that are useful to describe probabilistically.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/a0fd7459dec2b37c265c59ee912f91d33416fd37" target='_blank'>
-              Evolutionary and Sparse Regression Approach for Data-Driven Modelling of an Overhead Crane Dynamics
+          <td><a href="https://www.semanticscholar.org/paper/241b90969989bdb43a42587945b6b357630bfd72" target='_blank'>
+              Tensor networks enable the calculation of turbulence probability distributions
               </a>
             </td>
           <td>
-            Tom Kusznir, Jarosław Smoczek, Bolesław Karwat
+            Nikita Gourianov, P. Givi, Dieter Jaksch, Stephen B. Pope
           </td>
-          <td>2024-08-01</td>
-          <td>Advances in Science and Technology Research Journal</td>
+          <td>2024-07-12</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
+          <td>31</td>
         </tr>
 
-        <tr id="In this paper we explore the performance of deep hidden physics model (M. Raissi 2018) for autonomous systems. These systems are described by set of ordinary differential equations which do not explicitly depend on time. Such systems can be found in nature and have applications in modeling chemical concentrations, population dynamics, n-body problems in physics etc. In this work we consider dynamics of states, which explain how the states will evolve are unknown to us. We approximate state and dynamics both using neural networks. We have considered examples of 2D linear/nonlinear and Lorenz systems. We observe that even without knowing all the states information, we can estimate dynamics of certain states whose state information are known.">
+        <tr id="This paper presents the open-source stochastic model predictive control framework GRAMPC-S for nonlinear uncertain systems with chance constraints. It provides several uncertainty propagation methods to predict stochastic moments of the system state and can consider unknown parts of the system dynamics using Gaussian process regression. These methods are used to reformulate a stochastic MPC formulation as a deterministic one that is solved with GRAMPC. The performance of the presented framework is evaluated using examples from a wide range of technical areas. The experimental evaluation shows that GRAMPC-S can be used in practice for the control of nonlinear uncertain systems with sampling times in the millisecond range.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/51aacec8ca8c47d33eaf94dfcad19ac7e9735c07" target='_blank'>
-              Recovering the state and dynamics of autonomous system with partial states solution using neural networks
+          <td><a href="https://www.semanticscholar.org/paper/7c62a7baf784d003dda62b0480d24a61f0d5beb4" target='_blank'>
+              A software framework for stochastic model predictive control of nonlinear continuous-time systems (GRAMPC-S)
               </a>
             </td>
           <td>
-            Vijay Kag
+            D. Landgraf, Andreas Völz, Knut Graichen
           </td>
-          <td>2024-08-04</td>
+          <td>2024-07-12</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>9</td>
         </tr>
 
-        <tr id="For mathematical and experimental ease, models with time varying parameters are often simplified to assume constant parameters. However, this simplification can potentially lead to identifiability issues (lack of uniqueness of parameter estimates). Methods have been developed to algebraically and numerically determine the identifiability of a model, as well as resolve identifiability issues. This specific type of simplification presents an alternate opportunity to instead use this information to resolve the unidentifiability. Given that re-parameterizing, collecting more data, and adding inputs can be potentially costly or impractical, this could present new alternatives. We present a method for resolving unidentifiability in a system by introducing a new data stream correlated with a parameter of interest. First, we demonstrate how and when non-constant input data can be introduced into any rational function ODE system without worsening the model identifiability. Then, we prove when these input functions improve structural and potentially also practical identifiability for a given model and relevant data. By utilizing pre-existing data streams, these methods can potentially reduce experimental costs, while still answering key questions. By connecting mathematical proofs to application, our framework removes guesswork from when, where, and how researchers can best introduce new data to improve model outcomes.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/fbc38cc84b188d260525dcc553f64371eca970c3" target='_blank'>
-              Examining the impact of forcing function inputs on structural identifiability
-              </a>
-            </td>
-          <td>
-            Jessica R Conrad, Marisa C Eisenberg
-          </td>
-          <td>2024-07-03</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-
-        <tr id="Deep Operator Networks (DeepONets) and their physics-informed variants have shown significant promise in learning mappings between function spaces of partial differential equations, enhancing the generalization of traditional neural networks. However, for highly nonlinear real-world applications like aerospace composites processing, existing models often fail to capture underlying solutions accurately and are typically limited to single input functions, constraining rapid process design development. This paper introduces an advanced physics-informed DeepONet tailored for such complex systems with multiple input functions. Equipped with architectural enhancements like nonlinear decoders and effective training strategies such as curriculum learning and domain decomposition, the proposed model handles high-dimensional design spaces with significantly improved accuracy, outperforming the vanilla physics-informed DeepONet by two orders of magnitude. Its zero-shot prediction capability across a broad design space makes it a powerful tool for accelerating composites process design and optimization, with potential applications in other engineering fields characterized by strong nonlinearity.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f126a96d65c71da6172a1c8961580b6248f14d65" target='_blank'>
-              An Advanced Physics-Informed Neural Operator for Comprehensive Design Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing Case Study
-              </a>
-            </td>
-          <td>
-            Milad Ramezankhani, A. Deodhar, Rishi Parekh, Dagnachew Birru
-          </td>
-          <td>2024-06-20</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>7</td>
-        </tr>
-
-        <tr id="This paper presents the open-source stochastic model predictive control framework GRAMPC-S for nonlinear uncertain systems with chance constraints. It provides several uncertainty propagation methods to predict stochastic moments of the system state and can consider unknown parts of the system dynamics using Gaussian process regression. These methods are used to reformulate a stochastic MPC formulation as a deterministic one that is solved with GRAMPC. The performance of the presented framework is evaluated using examples from a wide range of technical areas. The experimental evaluation shows that GRAMPC-S can be used in practice for the control of nonlinear uncertain systems with sampling times in the millisecond range.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/7c62a7baf784d003dda62b0480d24a61f0d5beb4" target='_blank'>
-              A software framework for stochastic model predictive control of nonlinear continuous-time systems (GRAMPC-S)
-              </a>
-            </td>
-          <td>
-            D. Landgraf, Andreas Volz, Knut Graichen
-          </td>
-          <td>2024-07-12</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>2</td>
-        </tr>
-
-        <tr id="In this study, we present a novel non-intrusive reduced-order model (ROM) for solving time-dependent stochastic partial differential equations (SPDEs). Utilizing proper orthogonal decomposition (POD), we extract spatial modes from high-fidelity solutions. A dynamic mode decomposition (DMD) method is then applied to vertically stacked matrices of projection coefficients for future prediction of coefficient fields. Polynomial chaos expansion (PCE) is employed to construct a mapping from random parameter inputs to the DMD-predicted coefficient field. These lead to the POD-DMD-PCE method. The innovation lies in vertically stacking projection coefficients, ensuring time-dimensional consistency in the coefficient matrix for DMD and facilitating parameter integration for PCE analysis. This method combines the model reduction of POD with the time extrapolation strengths of DMD, effectively recovering field solutions both within and beyond the training time interval. The efficiency and time extrapolation capabilities of the proposed method are validated through various nonlinear SPDEs. These include a reaction-diffusion equation with 19 parameters, a two-dimensional heat equation with two parameters, and a one-dimensional Burgers equation with three parameters.">
+        <tr id="In this study, we present a novel non-intrusive reduced-order model (ROM) for solving time-dependent stochastic partial differential equations (SPDEs). Utilizing proper orthogonal decomposition (POD), we extract spatial modes from high-fidelity solutions. A dynamic mode decomposition (DMD) method is then applied to vertically stacked matrices of projection coefficients for future prediction of coefficient fields. Polynomial chaos expansion (PCE) is employed to construct a mapping from random parameter inputs to the DMD-predicted coefficient field. These lead to the POD-DMD-PCE method. The innovation lies in vertically stacking projection coefficients, ensuring time-dimensional consistency in the coefficient matrix for DMD and facilitating parameter integration for PCE analysis. This method combines the model reduction of POD with the time extrapolation strengths of DMD, effectively recovering field solutions both within and beyond the training time interval. The efficiency and time extrapolation capabilities of the proposed method are validated through various nonlinear SPDEs. These include a reaction-diffusion equation with 19 parameters, a two-dimensional heat equation with two parameters, and a one-dimensional Burgers equation with three parameters.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/f03b1ec02154fef08c7c119096855e273290c56c" target='_blank'>
               Non-intrusive reduced-order model for time-dependent stochastic partial differential equations utilizing dynamic mode decomposition and polynomial chaos expansion.
@@ -2218,19 +2098,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>7</td>
         </tr>
 
-        <tr id="Forecasting hypersonic glide vehicle (HGV) trajectories accurately is crucial for defense, but traditional methods face challenges due to the scarce real-world data and the intricate dynamics of these vehicles. Data-driven approaches based on deep learning, while having emerged in recent years, often exhibit limitations in predictive accuracy and long-term forecasting. Whereas, physics-informed neural networks (PINNs) offer a solution by incorporating physical laws, but they treat these laws as constraints rather than fully integrating them into the learning process. This paper presents PhysNODE, a novel physics-embedded neural ODE model for the precise forecasting of HGV trajectories, which directly integrates the equations of HGV motion into a neural ODE. PhysNODE leverages a neural network to estimate the hidden aerodynamic parameters within these equations. These parameters are then combined with observable physical quantities to form a derivative function, which is fed into an ODE solver to predict the future trajectory. Comprehensive experiments using simulated datasets of HGV trajectories demonstrate that PhysNODE outperforms the state-of-the-art data-driven and physics-informed methods, particularly when training data is limited. The results highlight the benefit of embedding the physics of the HGV motion into the neural ODE for improved accuracy and stability in trajectory predicting.">
+        <tr id="We investigate the ability to discover data assimilation (DA) schemes meant for chaotic dynamics with deep learning (DL). The focus is on learning the analysis step of sequential DA, from state trajectories and their observations, using a simple residual convolutional neural network, while assuming the dynamics to be known. Experiments are performed with the Lorenz 96 dynamics, which display spatiotemporal chaos and for which solid benchmarks for DA performance exist. The accuracy of the states obtained from the learned analysis approaches that of the best possibly tuned ensemble Kalman filter (EnKF), and is far better than that of variational DA alternatives. Critically, this can be achieved while propagating even just a single state in the forecast step. We investigate the reason for achieving ensemble filtering accuracy without an ensemble. We diagnose that the analysis scheme actually identifies key dynamical perturbations, mildly aligned with the unstable subspace, from the forecast state alone, without any ensemble-based covariances representation. This reveals that the analysis scheme has learned some multiplicative ergodic theorem associated to the DA process seen as a non-autonomous random dynamical system.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e7307968fbc209c2ec3e1870bf2b222a5f3a9c10" target='_blank'>
-              Enhanced Trajectory Forecasting for Hypersonic Glide Vehicle via Physics-Embedded Neural ODE
+          <td><a href="https://www.semanticscholar.org/paper/049cb29c29c44d7bcd0b2872f2cc68742751159c" target='_blank'>
+              Deep learning-based sequential data assimilation for chaotic dynamics identifies local instabilities from single state forecasts
               </a>
             </td>
           <td>
-            Shaoning Lu, Yue Qian
+            Marc Bocquet, A. Farchi, Tobias S. Finn, Charlotte Durand, Sibo Cheng, Yumeng Chen, I. Pasmans, A. Carrassi
           </td>
-          <td>2024-08-06</td>
-          <td>Drones</td>
-          <td>0</td>
+          <td>2024-08-08</td>
+          <td>ArXiv</td>
           <td>0</td>
+          <td>28</td>
         </tr>
 
         <tr id="We provide an algorithm for the simultaneous system identification and model predictive control of nonlinear systems. The algorithm has finite-time near-optimality guarantees and asymptotically converges to the optimal (non-causal) controller. Particularly, the algorithm enjoys sublinear dynamic regret, defined herein as the suboptimality against an optimal clairvoyant controller that knows how the unknown disturbances and system dynamics will adapt to its actions. The algorithm is self-supervised and applies to control-affine systems with unknown dynamics and disturbances that can be expressed in reproducing kernel Hilbert spaces. Such spaces can model external disturbances and modeling errors that can even be adaptive to the system's state and control input. For example, they can model wind and wave disturbances to aerial and marine vehicles, or inaccurate model parameters such as inertia of mechanical systems. The algorithm first generates random Fourier features that are used to approximate the unknown dynamics or disturbances. Then, it employs model predictive control based on the current learned model of the unknown dynamics (or disturbances). The model of the unknown dynamics is updated online using least squares based on the data collected while controlling the system. We validate our algorithm in both hardware experiments and physics-based simulations. The simulations include (i) a cart-pole aiming to maintain the pole upright despite inaccurate model parameters, and (ii) a quadrotor aiming to track reference trajectories despite unmodeled aerodynamic drag effects. The hardware experiments include a quadrotor aiming to track a circular trajectory despite unmodeled aerodynamic drag effects, ground effects, and wind disturbances.">
@@ -2248,21 +2128,6 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>19</td>
         </tr>
 
-        <tr id="Wildland fires pose terrifying natural hazards, underscoring the urgent need to develop data-driven and physics-informed digital twins for wildfire prevention, monitoring, intervention, and response. In this direction of research, this work introduces a physics-informed neural network (PiNN) to learn the unknown parameters of an interpretable wildfire spreading model. The considered wildfire spreading model integrates fundamental physical laws articulated by key model parameters, essential for capturing the complex behavior of wildfires. The proposed machine learning approach leverages the theory of artificial neural networks with the physical constraints governing wildfire dynamics, such as the first principles of mass and energy conservation. Training of the PiNN for physics-informed parameter identification is realized using data of the temporal evolution of one- and two-dimensional (plane surface) fire fronts that have been obtained from a high-fidelity simulator of the wildfire spreading model under consideration. The parameter learning results demonstrate the remarkable predictive ability of the proposed PiNN in uncovering the unknown coefficients in both the one- and two-dimensional fire spreading scenarios. Additionally, this methodology exhibits robustness by identifying the same parameters in the presence of noisy data. The proposed framework is envisioned to be incorporated in a physics-informed digital twin for intelligent wildfire management and risk assessment.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/62a95332da195a72205da26346da59e96afa55ce" target='_blank'>
-              Physics-informed neural networks for parameter learning of wildfire spreading
-              </a>
-            </td>
-          <td>
-            K. Vogiatzoglou, C. Papadimitriou, V. Bontozoglou, Konstantinos Ampountolas
-          </td>
-          <td>2024-06-20</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>45</td>
-        </tr>
-
         <tr id="We consider the problem of data-driven stochastic optimal control of an unknown LTI dynamical system. Assuming the process noise is normally distributed, we pose the problem of steering the state's mean and covariance to a target normal distribution, under noisy data collected from the underlying system, a problem commonly referred to as covariance steering (CS). A novel framework for Data-driven Uncertainty quantification and density STeering (DUST) is presented that simultaneously characterizes the noise affecting the measured data and designs an optimal affine-feedback controller to steer the density of the state to a prescribed terminal value. We use both indirect and direct data-driven design approaches based on the notions of persistency of excitation and subspace predictors to exactly represent the mean and covariance dynamics of the state in terms of the data and noise realizations. Since both the mean and the covariance steering sub-problems are plagued with distributional uncertainty arising from noisy data collection, we first estimate the noise realization from this dataset and subsequently compute tractable upper bounds on the estimation errors. The moment steering problems are then solved to optimality using techniques from robust control and robust optimization. Lastly, we present an alternative control design approach based on the certainty equivalence principle and interpret the problem as one of CS under multiplicative uncertainties. We analyze the performance and efficacy of each of these data-driven approaches using a case study and compare them with their model-based counterparts.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/63a1f296701b24baf2d77272d401b9179d8d8312" target='_blank'>
@@ -2278,185 +2143,169 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>5</td>
         </tr>
 
-        <tr id="The Koopman operator theory is a powerful tool for the linear analysis and control of nonlinear systems that lifts the nonlinear states into a higher dimensional linear space known as the Koopman space. The linear Koopman space provides an attractive approach for designing linear control strategies for nonlinear systems. However, a significant challenge arises because the Koopman states cannot be directly related to the actual states of the system. This discrepancy can complicate the design of cost functions and constraints for a model predictive controller. In this work, we introduce a novel Koopman representation combined with a training scheme that resolves this issue by defining auxiliary states that are injective and monotonic to the original states. We evaluate the effectiveness of the proposed scheme through numerical experiments.">
+        <tr id="Pedestrian crowds encompass a complex interplay of intentional movements aimed at reaching specific destinations, fluctuations due to personal and interpersonal variability, and interactions with each other and the environment. Previous work showed the effectiveness of Langevin-like equations in capturing the statistical properties of pedestrian dynamics in simple settings, such as almost straight trajectories. However, modeling more complex dynamics, e.g. when multiple routes and origin-destinations are involved, remains a significant challenge. In this work, we introduce a novel and generic framework to describe the dynamics of pedestrians in any geometric setting, significantly extending previous works. Our model is based on Langevin dynamics with two timescales. The fast timescale corresponds to the stochastic fluctuations present when a pedestrian is walking. The slow timescale is associated with the dynamics that a pedestrian plans to follow, thus a smoother path. Employing a data-driven approach inspired by statistical field theories, we learn the complex potentials directly from the data, namely a high-statistics database of real-life pedestrian trajectories. This approach makes the model generic as the potentials can be read from any trajectory data set and the underlying Langevin structure enables physics-based insights. We validate our model through a comprehensive statistical analysis, comparing simulated trajectories with actual pedestrian measurements across five complementary settings, including a real-life train platform scenario, underscoring its practical societal relevance. We show that our model effectively captures fluctuation statistics in pedestrian motion. Beyond providing fundamental insights and predictive capabilities in pedestrian dynamics, our model could be used to investigate generic active dynamics such as vehicular traffic and collective animal behavior.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c8db596cd32649e058785f5df77360f7b2b12672" target='_blank'>
-              A Novel Koopman Representation for Efficient Linear Model Predictive Control of Nonlinear Systems
+          <td><a href="https://www.semanticscholar.org/paper/c76fb4ec34bb7b21a1b522e6fe51864181436102" target='_blank'>
+              Data-driven physics-based modeling of pedestrian dynamics
               </a>
             </td>
           <td>
-            Omar Sayed, Sergio Lucia
+            C. Pouw, Geert G.M. van der Vleuten, Alessandro Corbetta, Federico Toschi
           </td>
-          <td>2024-06-25</td>
-          <td>2024 European Control Conference (ECC)</td>
+          <td>2024-07-30</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>18</td>
         </tr>
 
-        <tr id="This paper aims to construct a representation to approximate the behavior of linear time-invariant systems from a large data set whose input contains unmeasured uncertainty and output is subject to measurement noise. Using big data combined with the statistical properties of the input uncertainty and measurement noise, the covariance of the input uncertainty and the output can be approximated. This enables the construction of an approximate covariance of the trajectories in the data set, through which a representation for the approximation of system behavior is obtained. The behavior of this representation is shown to converge in probability to the true behavior. An illustrative example is provided to show that the proposed representation is able to predict the system trajectory to a satisfactory accuracy. The result of this paper provides a potential basis for the development of data-based trajectory estimation and predictive control algorithm when the system input uncertainty is unmeasured.">
+        <tr id="Understanding the dynamics of nonequilibrium quantum many-body systems is an important research topic in a wide range of fields across condensed matter physics, quantum optics, and high-energy physics. However, numerical studies of large-scale nonequilibrium phenomena in realistic materials face serious challenges due to intrinsic high-dimensionality of quantum many-body problems. The nonequilibrium properties of many-body systems can be described by the dynamics of the Green's function of the system, whose time evolution is given by a high-dimensional system of integro-differential equations, known as the Kadanoff-Baym equations (KBEs). The time-convolution term in KBEs, which needs to be recalculated at each time step, makes it difficult to perform long-time simulations. In this paper, we develop an operator-learning framework based on Recurrent Neural Networks (RNNs) to address this challenge. The proposed framework utilizes RNNs to learn the nonlinear mapping between Green's functions and convolution integrals in KBEs. By using the learned operators as a surrogate model in the KBE solver, we obtain a general machine-learning scheme for predicting the dynamics of nonequilibrium Green's functions. This new methodology reduces the temporal computational complexity from $O(N_t^3)$ to $O(N_t)$, where $N_t$ is the total time steps taken in a simulation, thereby making it possible to study large many-body problems which are currently infeasible with conventional KBE solvers. Through different numerical examples, we demonstrate the effectiveness of the operator-learning based approach in providing accurate predictions of physical observables such as the reduced density matrix and time-resolved photoemission spectra. Moreover, our framework exhibits clear numerical convergence and can be easily parallelized, thereby facilitating many possible further developments and applications.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/963fe53a6065bfdb118e3128347e8c5bfc500a1d" target='_blank'>
-              Approximating the System Behavior with Input Uncertainty Using Big Data
+          <td><a href="https://www.semanticscholar.org/paper/526a9cb7b29055f6d28d2a654e064ac8a68bb587" target='_blank'>
+              Predicting nonequilibrium Green's function dynamics and photoemission spectra via nonlinear integral operator learning
               </a>
             </td>
           <td>
-            Yitao Yan, Jie Bao, Biao Huang
+            Yuanran Zhu, Jia Yin, Cian C. Reeves, Chao Yang, Vojtěch Vlček
           </td>
-          <td>2024-06-18</td>
-          <td>2024 IEEE 18th International Conference on Control & Automation (ICCA)</td>
+          <td>2024-07-13</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
+          <td>4</td>
         </tr>
 
-        <tr id="In this paper, we introduce semi-autonomous neural ordinary differential equations (SA-NODEs), a variation of the vanilla NODEs, employing fewer parameters. We investigate the universal approximation properties of SA-NODEs for dynamical systems from both a theoretical and a numerical perspective. Within the assumption of a finite-time horizon, under general hypotheses we establish an asymptotic approximation result, demonstrating that the error vanishes as the number of parameters goes to infinity. Under additional regularity assumptions, we further specify this convergence rate in relation to the number of parameters, utilizing quantitative approximation results in the Barron space. Based on the previous result, we prove an approximation rate for transport equations by their neural counterparts. Our numerical experiments validate the effectiveness of SA-NODEs in capturing the dynamics of various ODE systems and transport equations. Additionally, we compare SA-NODEs with vanilla NODEs, highlighting the superior performance and reduced complexity of our approach.">
+        <tr id="Predicting chaotic systems is crucial for understanding complex behaviors, yet challenging due to their sensitivity to initial conditions and inherent unpredictability. Probabilistic Reservoir Computing (RC) is well-suited for long-term chaotic predictions by handling complex dynamic systems. Spin-Orbit Torque (SOT) devices in spintronics, with their nonlinear and probabilistic operations, can enhance performance in these tasks. This study proposes an RC system utilizing SOT devices for predicting chaotic dynamics. By simulating the reservoir in an RC network with SOT devices that achieve nonlinear resistance changes with random distribution, we enhance the robustness for the predictive capability of the model. The RC network predicted the behaviors of the Mackey-Glass and Lorenz chaotic systems, demonstrating that stochastic SOT devices significantly improve long-term prediction accuracy.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/0c428906ea1c6ad6b5b45bc8a6ac5d654dad8749" target='_blank'>
-              Universal Approximation of Dynamical Systems by Semi-Autonomous Neural ODEs and Applications
+          <td><a href="https://www.semanticscholar.org/paper/c63d0c8c5a8e26832c065f07e28872c0828b13c7" target='_blank'>
+              Improved Long-Term Prediction of Chaos Using Reservoir Computing Based on Stochastic Spin-Orbit Torque Devices
               </a>
             </td>
           <td>
-            Ziqian Li, Kang Liu, Lorenzo Liverani, Enrique Zuazua
+            Cen Wang, Xinyao Lei, Kaiming Cai, Xiaofei Yang, Yue Zhang
           </td>
-          <td>2024-07-24</td>
+          <td>2024-07-02</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>1</td>
         </tr>
 
-        <tr id="Given the complexity and nonlinearity inherent in traffic dynamics within vehicular platoons, there exists a critical need for a modeling methodology with high accuracy while concurrently achieving physical analyzability. Currently, there are two predominant approaches: the physics model-based approach and the Artificial Intelligence (AI)--based approach. Knowing the facts that the physical-based model usually lacks sufficient modeling accuracy and potential function mismatches and the pure-AI-based method lacks analyzability, this paper innovatively proposes an AI-based Koopman approach to model the unknown nonlinear platoon dynamics harnessing the power of AI and simultaneously maintain physical analyzability, with a particular focus on periods of traffic oscillation. Specifically, this research first employs a deep learning framework to generate the embedding function that lifts the original space into the embedding space. Given the embedding space descriptiveness, the platoon dynamics can be expressed as a linear dynamical system founded by the Koopman theory. Based on that, the routine of linear dynamical system analysis can be conducted on the learned traffic linear dynamics in the embedding space. By that, the physical interpretability and analyzability of model-based methods with the heightened precision inherent in data-driven approaches can be synergized. Comparative experiments have been conducted with existing modeling approaches, which suggests our method's superiority in accuracy. Additionally, a phase plane analysis is performed, further evidencing our approach's effectiveness in replicating the complex dynamic patterns. Moreover, the proposed methodology is proven to feature the capability of analyzing the stability, attesting to the physical analyzability.">
+        <tr id="With the rapid development of articial intelligence, especially deep learning technology, scientic researchers have begun to explore its application in the eld of traditional scientic computing. Traditional scientic computing relies on mathematical equations to describe and predict the scientic laws of nature, while deep learning provides a new perspective to solve complex mathematical problems by learning patterns in data. The introduction of the Physical Information Neural Network (PINN) and the Ordinary Dierential Equation (ODENet) network layer enables deep learning technology to more accurately simulate and predict scientic phenomena. This study shows that by embedding an ODE-Net network layer in a physical information neural network (PINN), the tting accuracy and generalization performance of the model can be signicantly improved. Experimental results show that compared with traditional numerical methods and fully connected neural networks, this model combined with deep learning technology not only shows higher accuracy when solving partial dierential equations, but also exhibits faster convergence speed and stronger adaptability. These ndings not only promote the integration of scientic computing and deep learning, but also provide new research directions and practical strategies for using deep learning technology to solve complex scientic problems.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/61de6c4570b16ba2cf34ebf49465dfa322f09fda" target='_blank'>
-              Physically Analyzable AI-Based Nonlinear Platoon Dynamics Modeling During Traffic Oscillation: A Koopman Approach
+          <td><a href="https://www.semanticscholar.org/paper/dedb6a263e8acff267028f948e0fbc5cc501e50c" target='_blank'>
+              A numerical method to solve PDE through PINN based on ODENet
               </a>
             </td>
           <td>
-            Kexin Tian, Haotian Shi, Yang Zhou, Sixu Li
+            Ziyi Wang
           </td>
-          <td>2024-06-20</td>
-          <td>ArXiv</td>
+          <td>2024-06-24</td>
+          <td>Applied and Computational Engineering</td>
           <td>0</td>
-          <td>6</td>
-        </tr>
-
-        <tr id="
- Sparse identification of nonlinear dynamics is a popular approach to system identification. In this approach system identification is reformulated as a sparse regression problem, and the use of a good sparse regression method is crucial. Sparse Bayesian learning based on collaborative neurodynamic optimization is a recent method that consistently produces high-quality solutions. In this article, we extensively assess how this method performs for ordinary differential equation identification. We find that it works very well compared with sparse regression algorithms currently used for this task in terms of the tradeoff between the approximation accuracy and the complexity of the identified system. We also propose a way to substantially reduce the computational complexity of this algorithm compared with its original implementation, thus making it even more practical.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/47eded588aeaefa1d9b8730359939cd75cf7620e" target='_blank'>
-              Nonlinear system identification via sparse Bayesian regression based on collaborative neurodynamic optimization
-              </a>
-            </td>
-          <td>
-            Alexey Okunev, Evgeny Burnaev
-          </td>
-          <td>2024-08-04</td>
-          <td>Journal of Inverse and Ill-posed Problems</td>
           <td>0</td>
-          <td>1</td>
         </tr>
 
-        <tr id="This paper considers the data-driven stabilization of linear boundary controlled parabolic PDEs by making use of the Koopman operator. For this, a Koopman eigenstructure assignment problem is solved, which amounts to determine a feedback of the Koopman open-loop eigenfunctionals assigning a desired finite set of closed-loop Koopman eigenvalues and eigenfunctionals to the closed-loop system. It is shown that the designed controller only needs a finite number of open-loop Koopman eigenvalues and modes of the state. They are determined by extending the classical Krylov-DMD to parabolic systems. For this, only a finite number of pointlike outputs and their temporal samples as well as temporal samples of the inputs are required resulting in a data-driven solution of the eigenstructure assignment problem. Exponential stability of the closed-loop system in the presence of small Krylov-DMD errors is verified. An unstable diffusion-reaction system demonstrates the new data-driven controller design technique for distributed-parameter systems.">
+        <tr id="Chaotic systems, such as turbulent flows, are ubiquitous in science and engineering. However, their study remains a challenge due to the large range scales, and the strong interaction with other, often not fully understood, physics. As a consequence, the spatiotemporal resolution required for accurate simulation of these systems is typically computationally infeasible, particularly for applications of long-term risk assessment, such as the quantification of extreme weather risk due to climate change. While data-driven modeling offers some promise of alleviating these obstacles, the scarcity of high-quality simulations results in limited available data to train such models, which is often compounded by the lack of stability for long-horizon simulations. As such, the computational, algorithmic, and data restrictions generally imply that the probability of rare extreme events is not accurately captured. In this work we present a general strategy for training neural network models to non-intrusively correct under-resolved long-time simulations of chaotic systems. The approach is based on training a post-processing correction operator on under-resolved simulations nudged towards a high-fidelity reference. This enables us to learn the dynamics of the underlying system directly, which allows us to use very little training data, even when the statistics thereof are far from converged. Additionally, through the use of probabilistic network architectures we are able to leverage the uncertainty due to the limited training data to further improve extrapolation capabilities. We apply our framework to severely under-resolved simulations of quasi-geostrophic flow and demonstrate its ability to accurately predict the anisotropic statistics over time horizons more than 30 times longer than the data seen in training.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/03f995f29cd6086138895c91c94cd523bfb83873" target='_blank'>
-              Data-Driven Control of Linear Parabolic Systems using Koopman Eigenstructure Assignment
+          <td><a href="https://www.semanticscholar.org/paper/04ef0e9cecd284e7e1805b26a448f7108f474e9b" target='_blank'>
+              A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data
               </a>
             </td>
           <td>
-            Member Ieee Joachim Deutscher
+            Benedikt Barthel Sorensen, Leonardo Zepeda-N'unez, Ignacio Lopez-Gomez, Zhong Yi Wan, Rob Carver, Fei Sha, T. Sapsis
           </td>
-          <td>2024-06-29</td>
+          <td>2024-08-02</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>39</td>
         </tr>
 
-        <tr id="Understanding the dynamics of nonequilibrium quantum many-body systems is an important research topic in a wide range of fields across condensed matter physics, quantum optics, and high-energy physics. However, numerical studies of large-scale nonequilibrium phenomena in realistic materials face serious challenges due to intrinsic high-dimensionality of quantum many-body problems. The nonequilibrium properties of many-body systems can be described by the dynamics of the Green's function of the system, whose time evolution is given by a high-dimensional system of integro-differential equations, known as the Kadanoff-Baym equations (KBEs). The time-convolution term in KBEs, which needs to be recalculated at each time step, makes it difficult to perform long-time simulations. In this paper, we develop an operator-learning framework based on Recurrent Neural Networks (RNNs) to address this challenge. The proposed framework utilizes RNNs to learn the nonlinear mapping between Green's functions and convolution integrals in KBEs. By using the learned operators as a surrogate model in the KBE solver, we obtain a general machine-learning scheme for predicting the dynamics of nonequilibrium Green's functions. This new methodology reduces the temporal computational complexity from $O(N_t^3)$ to $O(N_t)$, where $N_t$ is the total time steps taken in a simulation, thereby making it possible to study large many-body problems which are currently infeasible with conventional KBE solvers. Through different numerical examples, we demonstrate the effectiveness of the operator-learning based approach in providing accurate predictions of physical observables such as the reduced density matrix and time-resolved photoemission spectra. Moreover, our framework exhibits clear numerical convergence and can be easily parallelized, thereby facilitating many possible further developments and applications.">
+        <tr id="There is overwhelming evidence that cognition, perception, and action rely on feedback control. However, if and how neural population dynamics are amenable to different control strategies is poorly understood, in large part because machine learning methods to directly assess controllability in neural population dynamics are lacking. To address this gap, we developed a novel dimensionality reduction method, Feedback Controllability Components Analysis (FCCA), that identifies subspaces of linear dynamical systems that are most feedback controllable based on a new measure of feedback controllability. We further show that PCA identifies subspaces of linear dynamical systems that maximize a measure of feedforward controllability. As such, FCCA and PCA are data-driven methods to identify subspaces of neural population data (approximated as linear dynamical systems) that are most feedback and feedforward controllable respectively, and are thus natural contrasts for hypothesis testing. We developed new theory that proves that non-normality of underlying dynamics determines the divergence between FCCA and PCA solutions, and confirmed this in numerical simulations. Applying FCCA to diverse neural population recordings, we find that feedback controllable dynamics are geometrically distinct from PCA subspaces and are better predictors of animal behavior. Our methods provide a novel approach towards analyzing neural population dynamics from a control theoretic perspective, and indicate that feedback controllable subspaces are important for behavior.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/526a9cb7b29055f6d28d2a654e064ac8a68bb587" target='_blank'>
-              Predicting nonequilibrium Green's function dynamics and photoemission spectra via nonlinear integral operator learning
+          <td><a href="https://www.semanticscholar.org/paper/b12cb2ef2eca3c8ee46436c8d7fddcc5787df892" target='_blank'>
+              Identifying Feedforward and Feedback Controllable Subspaces of Neural Population Dynamics
               </a>
             </td>
           <td>
-            Yuanran Zhu, Jia Yin, Cian C. Reeves, Chao Yang, Vojtěch Vlček
+            Ankit Kumar, Loren M. Frank, Kristofer E. Bouchard
           </td>
-          <td>2024-07-13</td>
+          <td>2024-08-11</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
+          <td>5</td>
         </tr>
 
-        <tr id="Matrix evolution equations occur in many applications, such as dynamical Lyapunov/Sylvester systems or Riccati equations in optimization and stochastic control, machine learning or data assimilation. In many cases, their tightest stability condition is coming from a linear term. Exponential time differencing (ETD) is known to produce highly stable numerical schemes by treating the linear term in an exact fashion. In particular, for stiff problems, ETD methods are a method of choice. We propose an extension of the class of ETD algorithms to matrix-valued dynamical equations. This allows us to produce highly efficient and stable integration schemes. We show their efficiency and applicability for a variety of real-world problems, from geophysical applications to dynamical problems in machine learning.">
+        <tr id="Studies show that artificial intelligence (AI) with embedded physics solvers has improved the accuracy of predictions on various physics problems, especially those associated with fluid dynamics. The crucial element in optimizing weight training for estimating flow fields within the AI network lies in the choice of the loss function. In addressing regression-type problems, particularly those involving the temporal evolution of flow fields, the mean square error (MSE) loss function is commonly employed at the current and single time step. However, an issue arises in existing methodologies that utilize MSE-based loss functions with single-time step information for predicting unsteady flow. Most of these approaches overlook the significance of incorporating the temporal history of the flow, a factor that cannot be disregarded in the context of numerical solvers. Hence, in this work, a physics-based AI (PbAI) method with higher-order loss functions is applied to unsteady scenarios, in particular to two distinct turbulent flows where a multitude of fine structures is present, namely, forced and decaying turbulence. Direct numerical simulations on uniform Cartesian grids are conducted to simulate these scenarios, generating two distinct datasets for training and inference. Each dataset comprises 32 randomly initialized conditions spanning 4, 848 time steps for each turbulent flow type. Five distinct models are devised, incorporating features such as rollouts from coarse numerical solvers and temporal considerations in the loss function calculation. The constructed PbAI models demonstrate consistent improvements in predictive performance over the entire temporal domain. These findings are further corroborated through vorticity correlation analyses. The empirical result demonstrates that the accuracy of the baseline case improves by up to 48% and 30% for forced and decaying turbulence, respectively. These results significantly underscore the importance of the temporal histories of flow in the loss function in enhancing predictive capabilities for complex and unsteady turbulent flows.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f314941ad68915d7af43429006a76cffcf6ab2db" target='_blank'>
-              Exponential time differencing for matrix-valued dynamical systems
+          <td><a href="https://www.semanticscholar.org/paper/e6a508269bfb08f5c8c66413efa4d444da72838f" target='_blank'>
+              Multi-Order Loss Functions For Accelerating Unsteady Flow Simulations with Physics-Based AI
               </a>
             </td>
           <td>
-            Nayef Shkeir, Tobias Schäfer, T. Grafke
+            Wei Xian Lim, Naheed Anjum Arafat, Wai Lee Chan, Adams Kong
           </td>
-          <td>2024-06-19</td>
-          <td>ArXiv</td>
+          <td>2024-06-25</td>
+          <td>2024 IEEE Conference on Artificial Intelligence (CAI)</td>
           <td>0</td>
-          <td>14</td>
+          <td>4</td>
         </tr>
 
-        <tr id="With the rapid development of articial intelligence, especially deep learning technology, scientic researchers have begun to explore its application in the eld of traditional scientic computing. Traditional scientic computing relies on mathematical equations to describe and predict the scientic laws of nature, while deep learning provides a new perspective to solve complex mathematical problems by learning patterns in data. The introduction of the Physical Information Neural Network (PINN) and the Ordinary Dierential Equation (ODENet) network layer enables deep learning technology to more accurately simulate and predict scientic phenomena. This study shows that by embedding an ODE-Net network layer in a physical information neural network (PINN), the tting accuracy and generalization performance of the model can be signicantly improved. Experimental results show that compared with traditional numerical methods and fully connected neural networks, this model combined with deep learning technology not only shows higher accuracy when solving partial dierential equations, but also exhibits faster convergence speed and stronger adaptability. These ndings not only promote the integration of scientic computing and deep learning, but also provide new research directions and practical strategies for using deep learning technology to solve complex scientic problems.">
+        <tr id="There is a growing interest in methods for detecting and interpreting changes in experimental time evolution data. Based on measured time series, the quantitative characterization of dynamical phase transitions at bifurcation points of the underlying chaotic systems is a notoriously difficult task. Building on prior theoretical studies that focus on the discontinuities at $q=1$ in the order-$q$ R\'enyi-entropy of the trajectory space, we measure the derivative of the spectrum. We derive within the general context of Markov processes a computationally efficient closed-form expression for this measure. We investigate its properties through well-known dynamical systems exploring its scope and limitations. The proposed mathematical instrument can serve as a predictor of dynamical phase transitions in time series.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/dedb6a263e8acff267028f948e0fbc5cc501e50c" target='_blank'>
-              A numerical method to solve PDE through PINN based on ODENet
+          <td><a href="https://www.semanticscholar.org/paper/68dbc71449fe5142fd6c84de4af2b46f1f3412b9" target='_blank'>
+              Measuring dynamical phase transitions in time series
               </a>
             </td>
           <td>
-            Ziyi Wang
+            Bulcs'u S'andor, Andr'as Rusu, K'aroly D'enes, M'aria Ercsey-Ravasz, Zs.I. L'az'ar
           </td>
-          <td>2024-06-24</td>
-          <td>Applied and Computational Engineering</td>
-          <td>0</td>
+          <td>2024-07-18</td>
+          <td>ArXiv</td>
           <td>0</td>
+          <td>2</td>
         </tr>
 
-        <tr id="Studies show that artificial intelligence (AI) with embedded physics solvers has improved the accuracy of predictions on various physics problems, especially those associated with fluid dynamics. The crucial element in optimizing weight training for estimating flow fields within the AI network lies in the choice of the loss function. In addressing regression-type problems, particularly those involving the temporal evolution of flow fields, the mean square error (MSE) loss function is commonly employed at the current and single time step. However, an issue arises in existing methodologies that utilize MSE-based loss functions with single-time step information for predicting unsteady flow. Most of these approaches overlook the significance of incorporating the temporal history of the flow, a factor that cannot be disregarded in the context of numerical solvers. Hence, in this work, a physics-based AI (PbAI) method with higher-order loss functions is applied to unsteady scenarios, in particular to two distinct turbulent flows where a multitude of fine structures is present, namely, forced and decaying turbulence. Direct numerical simulations on uniform Cartesian grids are conducted to simulate these scenarios, generating two distinct datasets for training and inference. Each dataset comprises 32 randomly initialized conditions spanning 4, 848 time steps for each turbulent flow type. Five distinct models are devised, incorporating features such as rollouts from coarse numerical solvers and temporal considerations in the loss function calculation. The constructed PbAI models demonstrate consistent improvements in predictive performance over the entire temporal domain. These findings are further corroborated through vorticity correlation analyses. The empirical result demonstrates that the accuracy of the baseline case improves by up to 48% and 30% for forced and decaying turbulence, respectively. These results significantly underscore the importance of the temporal histories of flow in the loss function in enhancing predictive capabilities for complex and unsteady turbulent flows.">
+        <tr id=""AI for Science"aims to solve fundamental scientific problems using AI techniques. As most physical phenomena can be described as Partial Differential Equations (PDEs) , approximating their solutions using neural networks has evolved as a central component of scientific-ML. Physics-Informed Neural Networks (PINNs) is the general method that has evolved for this task but its training is well-known to be very unstable. In this work we explore the possibility of changing the model being trained from being just a neural network to being a non-linear transformation of it - one that algebraically includes the boundary/initial conditions. This reduces the number of terms in the loss function than the standard PINN losses. We demonstrate that our modification leads to significant performance gains across a range of benchmark tasks, in various dimensions and without having to tweak the training algorithm. Our conclusions are based on conducting hundreds of experiments, in the fully unsupervised setting, over multiple linear and non-linear PDEs set to exactly solvable scenarios, which lends to a concrete measurement of our performance gains in terms of order(s) of magnitude lower fractional errors being achieved, than by standard PINNs. The code accompanying this manuscript is publicly available at, https://github.com/MorganREN/Improving-PINNs-By-Algebraic-Inclusion-of-Boundary-and-Initial-Conditions">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e6a508269bfb08f5c8c66413efa4d444da72838f" target='_blank'>
-              Multi-Order Loss Functions For Accelerating Unsteady Flow Simulations with Physics-Based AI
+          <td><a href="https://www.semanticscholar.org/paper/50ec057bbac038765a71bbcf35743418ac73a1d3" target='_blank'>
+              Improving PINNs By Algebraic Inclusion of Boundary and Initial Conditions
               </a>
             </td>
           <td>
-            Wei Xian Lim, Naheed Anjum Arafat, Wai Lee Chan, Adams Kong
+            Mohan Ren, Zhihao Fang, Keren Li, Anirbit Mukherjee
           </td>
-          <td>2024-06-25</td>
-          <td>2024 IEEE Conference on Artificial Intelligence (CAI)</td>
+          <td>2024-07-30</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>4</td>
+          <td>5</td>
         </tr>
 
-        <tr id="Stress and material deformation field predictions are among the most important tasks in computational mechanics. These predictions are typically made by solving the governing equations of continuum mechanics using finite element analysis, which can become computationally prohibitive considering complex microstructures and material behaviors. Machine learning (ML) methods offer potentially cost effective surrogates for these applications. However, existing ML surrogates are either limited to low-dimensional problems and/or do not provide uncertainty estimates in the predictions. This work proposes an ML surrogate framework for stress field prediction and uncertainty quantification for diverse materials microstructures. A modified Bayesian U-net architecture is employed to provide a data-driven image-to-image mapping from initial microstructure to stress field with prediction (epistemic) uncertainty estimates. The Bayesian posterior distributions for the U-net parameters are estimated using three state-of-the-art inference algorithms: the posterior sampling-based Hamiltonian Monte Carlo method and two variational approaches, the Monte-Carlo Dropout method and the Bayes by Backprop algorithm. A systematic comparison of the predictive accuracy and uncertainty estimates for these methods is performed for a fiber reinforced composite material and polycrystalline microstructure application. It is shown that the proposed methods yield predictions of high accuracy compared to the FEA solution, while uncertainty estimates depend on the inference approach. Generally, the Hamiltonian Monte Carlo and Bayes by Backprop methods provide consistent uncertainty estimates. Uncertainty estimates from Monte Carlo Dropout, on the other hand, are more difficult to interpret and depend strongly on the method's design.">
+        <tr id="Abstract The advent of machine learning has led to innovative approaches in dealing with clinical data. Among these, Neural Ordinary Differential Equations (Neural ODEs), hybrid models merging mechanistic with deep learning models have shown promise in accurately modeling continuous dynamical systems. Although initial applications of Neural ODEs in the field of model‐informed drug development and clinical pharmacology are becoming evident, applying these models to actual clinical trial datasets—characterized by sparse and irregularly timed measurements—poses several challenges. Traditional models often have limitations with sparse data, highlighting the urgent need to address this issue, potentially through the use of assumptions. This review examines the fundamentals of Neural ODEs, their ability to handle sparse and irregular data, and their applications in model‐informed drug development.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/314729f380c4d02b7c0fadec49d7899822cb4c18" target='_blank'>
-              Bayesian neural networks for predicting uncertainty in full-field material response
+          <td><a href="https://www.semanticscholar.org/paper/d55a1a4acc93d905399f2126e7beb524fd64ec38" target='_blank'>
+              Bridging pharmacology and neural networks: A deep dive into neural ordinary differential equations
               </a>
             </td>
           <td>
-            G. Pasparakis, Lori Graham-Brady, Michael D. Shields
+            Idris Bachali Losada, N. Terranova
           </td>
-          <td>2024-06-21</td>
-          <td>ArXiv</td>
+          <td>2024-07-11</td>
+          <td>CPT: Pharmacometrics & Systems Pharmacology</td>
           <td>0</td>
-          <td>3</td>
+          <td>12</td>
         </tr>
 
-        <tr id=""AI for Science"aims to solve fundamental scientific problems using AI techniques. As most physical phenomena can be described as Partial Differential Equations (PDEs) , approximating their solutions using neural networks has evolved as a central component of scientific-ML. Physics-Informed Neural Networks (PINNs) is the general method that has evolved for this task but its training is well-known to be very unstable. In this work we explore the possibility of changing the model being trained from being just a neural network to being a non-linear transformation of it - one that algebraically includes the boundary/initial conditions. This reduces the number of terms in the loss function than the standard PINN losses. We demonstrate that our modification leads to significant performance gains across a range of benchmark tasks, in various dimensions and without having to tweak the training algorithm. Our conclusions are based on conducting hundreds of experiments, in the fully unsupervised setting, over multiple linear and non-linear PDEs set to exactly solvable scenarios, which lends to a concrete measurement of our performance gains in terms of order(s) of magnitude lower fractional errors being achieved, than by standard PINNs. The code accompanying this manuscript is publicly available at, https://github.com/MorganREN/Improving-PINNs-By-Algebraic-Inclusion-of-Boundary-and-Initial-Conditions">
+        <tr id="The treatment of Hall-effect thrusters as nonlinear, dynamical systems has emerged as a new perspective to understand and analyze data acquired from the thrusters. The acquisition of high-speed data that can resolve the characteristic high-frequency oscillations of these thruster enables additional levels of classification in these thrusters. Notably, these signals may serve as unique indicators for the full state of the system that can aid digital representations of thrusters and predictions of thruster dynamics. In this work, a Reservoir Computing framework is explored to build surrogate models from experimental time-series measurements of a Hall-effect thruster. Such a framework has shown immense promise for predicting the behavior of low-dimensional yet chaotic dynamical systems. In particular, the surrogates created by the Reservoir Computing framework are capable of both predicting the observed behavior of the thruster and estimating the values of certain measurements from others, known as inference.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/50ec057bbac038765a71bbcf35743418ac73a1d3" target='_blank'>
-              Improving PINNs By Algebraic Inclusion of Boundary and Initial Conditions
+          <td><a href="https://www.semanticscholar.org/paper/408d323572dab3c6a6dc96e645703d0f78eee449" target='_blank'>
+              Time-Resolved Data-Driven Surrogates of Hall-effect Thrusters
               </a>
             </td>
           <td>
-            Mohan Ren, Zhihao Fang, Keren Li, Anirbit Mukherjee
+            Adrian S. Wong, Christine M. Greve, Daniel Q. Eckhardt
           </td>
-          <td>2024-07-30</td>
+          <td>2024-08-12</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>5</td>
+          <td>0</td>
         </tr>
 
         <tr id="We introduce neural information field filter, a Bayesian state and parameter estimation method for high-dimensional nonlinear dynamical systems given large measurement datasets. Solving such a problem using traditional methods, such as Kalman and particle filters, is computationally expensive. Information field theory is a Bayesian approach that can efficiently reconstruct dynamical model state paths and calibrate model parameters from noisy measurement data. To apply the method, we parameterize the time evolution state path using the span of a finite linear basis. The existing method has to reparameterize the state path by initial states to satisfy the initial condition. Designing an expressive yet simple linear basis before knowing the true state path is crucial for inference accuracy but challenging. Moreover, reparameterizing the state path using the initial state is easy to perform for a linear basis, but is nontrivial for more complex and expressive function parameterizations, such as neural networks. The objective of this paper is to simplify and enrich the class of state path parameterizations using neural networks for the information field theory approach. To this end, we propose a generalized physics-informed conditional prior using an auxiliary initial state. We show the existing reparameterization is a special case. We parameterize the state path using a residual neural network that consists of a linear basis function and a Fourier encoding fully connected neural network residual function. The residual function aims to correct the error of the linear basis function. To sample from the intractable posterior distribution, we develop an optimization algorithm, nested stochastic variational inference, and a sampling algorithm, nested preconditioned stochastic gradient Langevin dynamics. A series of numerical and experimental examples verify and validate the proposed method.">
@@ -2489,21 +2338,6 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>13</td>
         </tr>
 
-        <tr id="Continuous attractors offer a unique class of solutions for storing continuous-valued variables in recurrent system states for indefinitely long time intervals. Unfortunately, continuous attractors suffer from severe structural instability in general--they are destroyed by most infinitesimal changes of the dynamical law that defines them. This fragility limits their utility especially in biological systems as their recurrent dynamics are subject to constant perturbations. We observe that the bifurcations from continuous attractors in theoretical neuroscience models display various structurally stable forms. Although their asymptotic behaviors to maintain memory are categorically distinct, their finite-time behaviors are similar. We build on the persistent manifold theory to explain the commonalities between bifurcations from and approximations of continuous attractors. Fast-slow decomposition analysis uncovers the persistent manifold that survives the seemingly destructive bifurcation. Moreover, recurrent neural networks trained on analog memory tasks display approximate continuous attractors with predicted slow manifold structures. Therefore, continuous attractors are functionally robust and remain useful as a universal analogy for understanding analog memory.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/61ff7bb39a7ceddbbe33620a5adbb79b520c5f87" target='_blank'>
-              Back to the Continuous Attractor
-              </a>
-            </td>
-          <td>
-            'Abel S'agodi, Guillermo Mart'in-S'anchez, Piotr Sok'ol, Il Memming Park
-          </td>
-          <td>2024-07-31</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>1</td>
-        </tr>
-
         <tr id="Multi-fidelity machine learning methods address the accuracy-efficiency trade-off by integrating scarce, resource-intensive high-fidelity data with abundant but less accurate low-fidelity data. We propose a practical multi-fidelity strategy for problems spanning low- and high-dimensional domains, integrating a non-probabilistic regression model for the low-fidelity with a Bayesian model for the high-fidelity. The models are trained in a staggered scheme, where the low-fidelity model is transfer-learned to the high-fidelity data and a Bayesian model is trained for the residual. This three-model strategy -- deterministic low-fidelity, transfer learning, and Bayesian residual -- leads to a prediction that includes uncertainty quantification both for noisy and noiseless multi-fidelity data. The strategy is general and unifies the topic, highlighting the expressivity trade-off between the transfer-learning and Bayesian models (a complex transfer-learning model leads to a simpler Bayesian model, and vice versa). We propose modeling choices for two scenarios, and argue in favor of using a linear transfer-learning model that fuses 1) kernel ridge regression for low-fidelity with Gaussian processes for high-fidelity; or 2) deep neural network for low-fidelity with a Bayesian neural network for high-fidelity. We demonstrate the effectiveness and efficiency of the proposed strategies and contrast them with the state-of-the-art based on various numerical examples. The simplicity of these formulations makes them practical for a broad scope of future engineering applications.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/f0aac32171f95be4080bfb1caf4a77b613f4b14d" target='_blank'>
@@ -2519,21 +2353,6 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>0</td>
         </tr>
 
-        <tr id="We present a neural operator architecture to simulate Lagrangian dynamics, such as fluid flow, granular flows, and elastoplasticity. Traditional numerical methods, such as the finite element method (FEM), suffer from long run times and large memory consumption. On the other hand, approaches based on graph neural networks are faster but still suffer from long computation times on dense graphs, which are often required for high-fidelity simulations. Our model, GIOROM or Graph Interaction Operator for Reduced-Order Modeling, learns temporal dynamics within a reduced-order setting, capturing spatial features from a highly sparse graph representation of the input and generalizing to arbitrary spatial locations during inference. The model is geometry-aware and discretization-agnostic and can generalize to different initial conditions, velocities, and geometries after training. We show that point clouds of the order of 100,000 points can be inferred from sparse graphs with $\sim$1000 points, with negligible change in computation time. We empirically evaluate our model on elastic solids, Newtonian fluids, Non-Newtonian fluids, Drucker-Prager granular flows, and von Mises elastoplasticity. On these benchmarks, our approach results in a 25$\times$ speedup compared to other neural network-based physics simulators while delivering high-fidelity predictions of complex physical systems and showing better performance on most benchmarks. The code and the demos are provided at https://github.com/HrishikeshVish/GIOROM.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/b0931d26d7d6b637fece82e4653a754d6a28f5dc" target='_blank'>
-              Reduced-Order Neural Operators: Learning Lagrangian Dynamics on Highly Sparse Graphs
-              </a>
-            </td>
-          <td>
-            Hrishikesh Viswanath, Yue Chang, Julius Berner, Peter Yichen Chen, Aniket Bera
-          </td>
-          <td>2024-07-04</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>28</td>
-        </tr>
-
         <tr id="This paper proposes a generalized algorithmic approach for learning linear model representations for nonlinear systems within the Koopman framework. We focus on schemes that rely on learning the nonlinear transformation functions using deep neural networks. Beyond achieving dynamical accuracy, our primary objective is to develop models capable of simulating nonlinear systems across multiple time steps in the linear space. An algorithm that is based on recursive least squares is proposed to address the optimization complexities inherent in learning such models. In addition, we leverage the learned linear representation to design a linear quadratic regulator to control the original nonlinear system. The effectiveness of the proposed algorithm is demonstrated in two numerical examples.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/1dc9bf823159462d30a9a2199b18f6959ef1ccd3" target='_blank'>
@@ -2561,158 +2380,142 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2024-08-07</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>24</td>
+          <td>25</td>
         </tr>
 
-        <tr id="Physics-informed neural networks (PINNs) have shown promising potential for solving partial differential equations (PDEs) using deep learning. 
-
-However, PINNs face training difficulties for evolutionary PDEs, particularly for dynamical systems whose solutions exhibit multi-scale or turbulent behavior over time.
-
-The reason is that PINNs may violate the temporal causality property since all the temporal features in the PINNs loss are trained simultaneously. 
-
-This paper proposes to use implicit time differencing schemes to enforce temporal causality, and use transfer learning to sequentially update the PINNs in space as surrogates for PDE solutions in different time frames.
-
-The evolving PINNs are better able to capture the varying complexities of the evolutionary equations, while only requiring minor updates between adjacent time frames.
-
-Our method is theoretically proven to be convergent if the time step is small and each PINN in different time frames is well-trained.
-
-In addition, we provide state-of-the-art (SOTA) numerical results for a variety of benchmarks for which existing PINNs formulations may fail or be inefficient.
-
-We demonstrate that the proposed method improves the accuracy of PINNs approximation for evolutionary PDEs and improves efficiency by a factor of 4–40x.
-
-The code is available at https://github.com/SiqiChen9/TL-DPINNs.">
+        <tr id="One of the most promising applications of machine learning (ML) in computational physics is to accelerate the solution of partial differential equations (PDEs). The key objective of ML-based PDE solvers is to output a sufficiently accurate solution faster than standard numerical methods, which are used as a baseline comparison. We first perform a systematic review of the ML-for-PDE solving literature. Of articles that use ML to solve a fluid-related PDE and claim to outperform a standard numerical method, we determine that 79% (60/76) compare to a weak baseline. Second, we find evidence that reporting biases, especially outcome reporting bias and publication bias, are widespread. We conclude that ML-for-PDE solving research is overoptimistic: weak baselines lead to overly positive results, while reporting biases lead to underreporting of negative results. To a large extent, these issues appear to be caused by factors similar to those of past reproducibility crises: researcher degrees of freedom and a bias towards positive results. We call for bottom-up cultural changes to minimize biased reporting as well as top-down structural reforms intended to reduce perverse incentives for doing so.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/2baf92352b6cdc0a229e693600511b572a32085f" target='_blank'>
-              Causality-enhanced Discreted Physics-informed Neural Networks for Predicting Evolutionary Equations
+          <td><a href="https://www.semanticscholar.org/paper/fda0812099547cd3b91031851f644e1929b4b77c" target='_blank'>
+              Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
               </a>
             </td>
           <td>
-            Ye Li, Siqi Chen, Bin Shan, Sheng-Jun Huang
+            N. McGreivy, Ammar Hakim
           </td>
-          <td>2024-08-01</td>
-          <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
+          <td>2024-07-09</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>5</td>
         </tr>
 
-        <tr id="A machine-learning strategy for investigating the stability of fluid flow problems is proposed herein. The computational procedure is demonstrably robust and does not require parameter tuning. The essential feature of the strategy is that the computational solution of the Navier--Stokes equations is a reliable proxy for laboratory experiments investigating sensitivity to flow parameters. The applicability of our bifurcation detection strategy is demonstrated by an investigation of two classical examples of flow instability associated with thermal convection. The codes used to generate the numerical results are available online.">
+        <tr id="Tipping points occur in many real-world systems, at which the system shifts suddenly from one state to another. The ability to predict the occurrence of tipping points from time series data remains an outstanding challenge and a major interest in a broad range of research fields. Particularly, the widely used methods based on bifurcation theory are neither reliable in prediction accuracy nor applicable for irregularly-sampled time series which are commonly observed from real-world systems. Here we address this challenge by developing a deep learning algorithm for predicting the occurrence of tipping points in untrained systems, by exploiting information about normal forms. Our algorithm not only outperforms traditional methods for regularly-sampled model time series but also achieves accurate predictions for irregularly-sampled model time series and empirical time series. Our ability to predict tipping points for complex systems paves the way for mitigation risks, prevention of catastrophic failures, and restoration of degraded systems, with broad applications in social science, engineering, and biology.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ada3b0ba98c9f07f4ec32419fa6e2cd617f9cbce" target='_blank'>
-              Machine learning for hydrodynamic stability
+          <td><a href="https://www.semanticscholar.org/paper/6885f6f695d68eb9294efe8cb408c1d39a6fbe58" target='_blank'>
+              Deep learning for predicting the occurrence of tipping points
               </a>
             </td>
           <td>
-            David J. Silvester
+            Chengzuo Zhuge, Jiawei Li, Wei Chen
           </td>
-          <td>2024-07-11</td>
+          <td>2024-07-26</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
-        <tr id="One of the most promising applications of machine learning (ML) in computational physics is to accelerate the solution of partial differential equations (PDEs). The key objective of ML-based PDE solvers is to output a sufficiently accurate solution faster than standard numerical methods, which are used as a baseline comparison. We first perform a systematic review of the ML-for-PDE solving literature. Of articles that use ML to solve a fluid-related PDE and claim to outperform a standard numerical method, we determine that 79% (60/76) compare to a weak baseline. Second, we find evidence that reporting biases, especially outcome reporting bias and publication bias, are widespread. We conclude that ML-for-PDE solving research is overoptimistic: weak baselines lead to overly positive results, while reporting biases lead to underreporting of negative results. To a large extent, these issues appear to be caused by factors similar to those of past reproducibility crises: researcher degrees of freedom and a bias towards positive results. We call for bottom-up cultural changes to minimize biased reporting as well as top-down structural reforms intended to reduce perverse incentives for doing so.">
+        <tr id="Most scientific machine learning (SciML) applications of neural networks involve hundreds to thousands of parameters, and hence, uncertainty quantification for such models is plagued by the curse of dimensionality. Using physical applications, we show that $L_0$ sparsification prior to Stein variational gradient descent ($L_0$+SVGD) is a more robust and efficient means of uncertainty quantification, in terms of computational cost and performance than the direct application of SGVD or projected SGVD methods. Specifically, $L_0$+SVGD demonstrates superior resilience to noise, the ability to perform well in extrapolated regions, and a faster convergence rate to an optimal solution.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/fda0812099547cd3b91031851f644e1929b4b77c" target='_blank'>
-              Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
+          <td><a href="https://www.semanticscholar.org/paper/2ef84e51598bc76cbb9e56bd3fdb600206e852c2" target='_blank'>
+              Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
               </a>
             </td>
           <td>
-            N. McGreivy, Ammar Hakim
+            G. A. Padmanabha, J. Fuhg, C. Safta, Reese E. Jones, N. Bouklas
           </td>
-          <td>2024-07-09</td>
+          <td>2024-06-30</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>4</td>
+          <td>19</td>
         </tr>
 
-        <tr id="In this letter we investigate an information theoretic analogue of the classic two masses on spring system, arising from a physical interpretation of Friston's free energy principle in the theory of learning in a system of agents. Using methods from classical mechanics on manifolds, we define a kinetic energy term using the Fisher metric on distributions and a potential energy function defined in terms of stress on the agents' beliefs. The resulting Lagrangian (Hamiltonian) produces a variation of the classic DeGroot dynamics. In the two agent case, the potential function is defined using the Jeffrey's divergence and the resulting dynamics are characterized by a non-linear spring. These dynamics produce trajectories that resemble flows on tori but are shown numerically to produce chaos near the boundary of the space. We then investigate persuasion as an information theoretic control problem where analysis indicates that manipulating peer pressure with a fixed target is a more stable approach to altering an agent's belief than providing a slowly changing belief state that approaches the target.">
+        <tr id="Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional regression or learning problems to increase the confidence of a given predictor. We develop a new data-driven approach for UQ in regression that applies both to classical regression approaches such as the LASSO as well as to neural networks. One of the most notable UQ techniques is the debiased LASSO, which modifies the LASSO to allow for the construction of asymptotic confidence intervals by decomposing the estimation error into a Gaussian and an asymptotically vanishing bias component. However, in real-world problems with finite-dimensional data, the bias term is often too significant to be neglected, resulting in overly narrow confidence intervals. Our work rigorously addresses this issue and derives a data-driven adjustment that corrects the confidence intervals for a large class of predictors by estimating the means and variances of the bias terms from training data, exploiting high-dimensional concentration phenomena. This gives rise to non-asymptotic confidence intervals, which can help avoid overestimating uncertainty in critical applications such as MRI diagnosis. Importantly, our analysis extends beyond sparse regression to data-driven predictors like neural networks, enhancing the reliability of model-based deep learning. Our findings bridge the gap between established theory and the practical applicability of such debiased methods.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/52c8e6266ddf04fc931ce8e2677ea1562dda36dc" target='_blank'>
-              Dynamics of An Information Theoretic Analog of Two Masses on a Spring
+          <td><a href="https://www.semanticscholar.org/paper/6b74e7097e6bf5e51f1391656df98c0c04de0d93" target='_blank'>
+              Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
               </a>
             </td>
           <td>
-            Geoff Goehle, Christopher Griffin
+            Frederik Hoppe, C. M. Verdun, Hannah Laus, Felix Krahmer, Holger Rauhut
           </td>
-          <td>2024-07-03</td>
+          <td>2024-07-18</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>5</td>
         </tr>
 
-        <tr id="In this work, we propose a novel variational quantum approach for solving a class of nonlinear optimal control problems. Our approach integrates Dirac's canonical quantization of dynamical systems with the solution of the ground state of the resulting non-Hermitian Hamiltonian via a variational quantum eigensolver (VQE). We introduce a new perspective on the Dirac bracket formulation for generalized Hamiltonian dynamics in the presence of constraints, providing a clear motivation and illustrative examples. Additionally, we explore the structural properties of Dirac brackets within the context of multidimensional constrained optimization problems. Our approach for solving a class of nonlinear optimal control problems employs a VQE-based approach to determine the eigenstate and corresponding eigenvalue associated with the ground state energy of a non-Hermitian Hamiltonian. Assuming access to an ideal VQE, our formulation demonstrates excellent results, as evidenced by selected computational examples. Furthermore, our method performs well when combined with a VQE-based approach for non-Hermitian Hamiltonian systems. Our VQE-based formulation effectively addresses challenges associated with a wide range of optimal control problems, particularly in high-dimensional scenarios. Compared to standard classical approaches, our quantum-based method shows significant promise and offers a compelling alternative for tackling complex, high-dimensional optimization challenges.">
+        <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/5e1359db07127c2dadc8e7e0253647078bc4c0c0" target='_blank'>
-              A quantum approach for optimal control
+          <td><a href="https://www.semanticscholar.org/paper/dcaa44f8b49302272e5cab62125e644f57aefae1" target='_blank'>
+              Characterizing the dynamics of multi-scale global high impact weather events
               </a>
             </td>
           <td>
-            Hiram Sandesara, Alok Shukla, P. Vedula
+            L. R. Frank, V. Galinsky, Zhenhai Zhang, F. M. Ralph
           </td>
-          <td>2024-07-03</td>
-          <td>ArXiv</td>
+          <td>2024-08-15</td>
+          <td>Scientific Reports</td>
           <td>0</td>
-          <td>17</td>
+          <td>13</td>
         </tr>
 
-        <tr id="Deep neural networks (DNNs) have achieved exceptional performance across various fields by learning complex nonlinear mappings from large-scale datasets. However, they encounter challenges such as high computational costs and limited interpretability. To address these issues, hybrid approaches that integrate physics with AI are gaining interest. This paper introduces a novel physics-based AI model called the"Nonlinear Schr\"odinger Network", which treats the Nonlinear Schr\"odinger Equation (NLSE) as a general-purpose trainable model for learning complex patterns including nonlinear mappings and memory effects from data. Existing physics-informed machine learning methods use neural networks to approximate the solutions of partial differential equations (PDEs). In contrast, our approach directly treats the PDE as a trainable model to obtain general nonlinear mappings that would otherwise require neural networks. As a type of physics-AI symbiosis, it offers a more interpretable and parameter-efficient alternative to traditional black-box neural networks, achieving comparable or better accuracy in some time series classification tasks while significantly reducing the number of required parameters. Notably, the trained Nonlinear Schr\"odinger Network is interpretable, with all parameters having physical meanings as properties of a virtual physical system that transforms the data to a more separable space. This interpretability allows for insight into the underlying dynamics of the data transformation process. Applications to time series forecasting have also been explored. While our current implementation utilizes the NLSE, the proposed method of using physics equations as trainable models to learn nonlinear mappings from data is not limited to the NLSE and may be extended to other master equations of physics.">
+        <tr id="In the field of machine learning, comprehending the intricate training dynamics of neural networks poses a significant challenge. This paper explores the training dynamics of neural networks, particularly whether these dynamics can be expressed in a general closed-form solution. We demonstrate that the dynamics of the gradient flow in two-layer narrow networks is not an integrable system. Integrable systems are characterized by trajectories confined to submanifolds defined by level sets of first integrals (invariants), facilitating predictable and reducible dynamics. In contrast, non-integrable systems exhibit complex behaviors that are difficult to predict. To establish the non-integrability, we employ differential Galois theory, which focuses on the solvability of linear differential equations. We demonstrate that under mild conditions, the identity component of the differential Galois group of the variational equations of the gradient flow is non-solvable. This result confirms the system's non-integrability and implies that the training dynamics cannot be represented by Liouvillian functions, precluding a closed-form solution for describing these dynamics. Our findings highlight the necessity of employing numerical methods to tackle optimization problems within neural networks. The results contribute to a deeper understanding of neural network training dynamics and their implications for machine learning optimization strategies.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/86e993b6260a5161e12c46644b62f8d53d34dba1" target='_blank'>
-              Nonlinear Schr\"odinger Network
+          <td><a href="https://www.semanticscholar.org/paper/0ddef324ecc792db4cfe809ea02647e7e5e7d877" target='_blank'>
+              Absence of Closed-Form Descriptions for Gradient Flow in Two-Layer Narrow Networks
               </a>
             </td>
           <td>
-            Yiming Zhou, Callen MacPhee, Tingyi Zhou, Bahram Jalali
+            Yeachan Park
           </td>
-          <td>2024-07-19</td>
+          <td>2024-08-15</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>2</td>
+          <td>0</td>
         </tr>
 
-        <tr id="Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional regression or learning problems to increase the confidence of a given predictor. We develop a new data-driven approach for UQ in regression that applies both to classical regression approaches such as the LASSO as well as to neural networks. One of the most notable UQ techniques is the debiased LASSO, which modifies the LASSO to allow for the construction of asymptotic confidence intervals by decomposing the estimation error into a Gaussian and an asymptotically vanishing bias component. However, in real-world problems with finite-dimensional data, the bias term is often too significant to be neglected, resulting in overly narrow confidence intervals. Our work rigorously addresses this issue and derives a data-driven adjustment that corrects the confidence intervals for a large class of predictors by estimating the means and variances of the bias terms from training data, exploiting high-dimensional concentration phenomena. This gives rise to non-asymptotic confidence intervals, which can help avoid overestimating uncertainty in critical applications such as MRI diagnosis. Importantly, our analysis extends beyond sparse regression to data-driven predictors like neural networks, enhancing the reliability of model-based deep learning. Our findings bridge the gap between established theory and the practical applicability of such debiased methods.">
+        <tr id="Many dynamical systems are subjected to stochastic influences, such as random excitations, noise, and unmodeled behavior. Tracking the system's state and parameters based on a physical model is a common task for which filtering algorithms, such as Kalman filters and their non-linear extensions, are typically used. However, many of these filters use assumptions on the transition probabilities or the covariance model, which can lead to inaccuracies in non-linear systems. We will show the application of a stochastic coupling filter that can approximate arbitrary transition densities under non-Gaussian noise. The filter is based on transport maps, which couple the approximation densities to a user-chosen reference density, allowing for straightforward sampling and evaluation of probabilities.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6b74e7097e6bf5e51f1391656df98c0c04de0d93" target='_blank'>
-              Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
+          <td><a href="https://www.semanticscholar.org/paper/674a1134430bb9d26de4b9e60e39258373cc6b87" target='_blank'>
+              Transport Map Coupling Filter for State-Parameter Estimation
               </a>
             </td>
           <td>
-            Frederik Hoppe, C. M. Verdun, Hannah Laus, Felix Krahmer, Holger Rauhut
+            J. Grashorn, M. Broggi, Ludovic Chamoin, Michael Beer
           </td>
-          <td>2024-07-18</td>
+          <td>2024-07-02</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>4</td>
+          <td>21</td>
         </tr>
 
-        <tr id="This paper addresses the need for deep learning models to integrate well-defined constraints into their outputs, driven by their application in surrogate models, learning with limited data and partial information, and scenarios requiring flexible model behavior to incorporate non-data sample information. We introduce Bayesian Entropy Neural Networks (BENN), a framework grounded in Maximum Entropy (MaxEnt) principles, designed to impose constraints on Bayesian Neural Network (BNN) predictions. BENN is capable of constraining not only the predicted values but also their derivatives and variances, ensuring a more robust and reliable model output. To achieve simultaneous uncertainty quantification and constraint satisfaction, we employ the method of multipliers approach. This allows for the concurrent estimation of neural network parameters and the Lagrangian multipliers associated with the constraints. Our experiments, spanning diverse applications such as beam deflection modeling and microstructure generation, demonstrate the effectiveness of BENN. The results highlight significant improvements over traditional BNNs and showcase competitive performance relative to contemporary constrained deep learning methods.">
+        <tr id="Simulating Darcy flows in porous media is fundamental to understand the future flow behavior of fluids in hydrocarbon and carbon storage reservoirs. Geological models of reservoirs are often associated with high uncertainly leading to many numerical simulations for history matching and production optimization. Machine learning models trained with simulation data can provide a faster alternative to traditional simulators. In this paper we present a single Fourier Neural Operator (FNO) surrogate that outperforms traditional reservoir simulators by the ability to predict pressures and saturations on varying permeability fields, well locations, well controls, and number of wells. The maximum-mean relative error of 95\% of pressure and saturation predictions is less than 5\%. This is achieved by employing a simple yet very effective data augmentation technique that reduces the dataset size by 75\% and reduces overfitting. Also, constructing the input tensor in a binary fashion enables predictions on unseen well locations, well controls, and number of wells. Such model can accelerate history matching and reservoir characterization procedures by several orders of magnitude. The ability to predict on new well locations, well controls, and number of wells enables highly efficient reservoir management and optimization.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4d74383a8ad720e106440e126defbad7231d316d" target='_blank'>
-              Bayesian Entropy Neural Networks for Physics-Aware Prediction
+          <td><a href="https://www.semanticscholar.org/paper/7d7a6b2e3b8fbcb3a223ec6dc61bbed982599799" target='_blank'>
+              Neural Operator-Based Proxy for Reservoir Simulations Considering Varying Well Settings, Locations, and Permeability Fields
               </a>
             </td>
           <td>
-            R. Rathnakumar, Jiayu Huang, Hao Yan, Yongming Liu
+            Daniel Badawi, Eduardo Gildin
           </td>
-          <td>2024-07-01</td>
+          <td>2024-07-13</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Many dynamical systems are subjected to stochastic influences, such as random excitations, noise, and unmodeled behavior. Tracking the system's state and parameters based on a physical model is a common task for which filtering algorithms, such as Kalman filters and their non-linear extensions, are typically used. However, many of these filters use assumptions on the transition probabilities or the covariance model, which can lead to inaccuracies in non-linear systems. We will show the application of a stochastic coupling filter that can approximate arbitrary transition densities under non-Gaussian noise. The filter is based on transport maps, which couple the approximation densities to a user-chosen reference density, allowing for straightforward sampling and evaluation of probabilities.">
+        <tr id="We provide an approach enabling one to employ physics-informed neural networks (PINNs) for uncertainty quantification. Our approach is applicable to systems where observations are scarce (or even lacking), these being typical situations associated with subsurface water bodies. Our novel physics-informed neural network under uncertainty (PINN-UU) integrates the space-time domain across which processes take place and uncertain parameter spaces within a unique computational domain. PINN-UU is then trained to satisfy the relevant physical principles (e.g., mass conservation) in the defined input domain. We employ a stage training approach via transfer learning to accommodate high-dimensional solution spaces. We demonstrate the effectiveness of PINN-UU in a scenario associated with reactive transport in porous media, showcasing its reliability, efficiency, and applicability to sensitivity analysis. PINN-UU emerges as a promising tool for robust uncertainty quantification, with broad applicability to groundwater systems. As such, it can be considered as a valuable alternative to traditional methods such as multi-realization Monte Carlo simulations based on direct solvers or black-box surrogate models.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/674a1134430bb9d26de4b9e60e39258373cc6b87" target='_blank'>
-              Transport Map Coupling Filter for State-Parameter Estimation
+          <td><a href="https://www.semanticscholar.org/paper/9eb1778f55d55df5b6fa3d3b473be94da31915f8" target='_blank'>
+              Modelling parametric uncertainty in PDEs models via Physics-Informed Neural Networks
               </a>
             </td>
           <td>
-            J. Grashorn, M. Broggi, Ludovic Chamoin, Michael Beer
+            Milad Panahi, G. Porta, M. Riva, A. Guadagnini
           </td>
-          <td>2024-07-02</td>
+          <td>2024-08-08</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>21</td>
+          <td>43</td>
         </tr>
 
         <tr id="A dynamical system may be defined by a simple transition law - such as a map or a vector field. Most learning techniques primarily try to recreate the dynamic evolution law. This is a major shortcoming, as most dynamic properties of interest are asymptotic properties such as an attractor or invariant measure. One of the major theoretical challenges for numerical methods is that approximating the dynamical law may not be sufficient to approximate these asymptotic properties. This article presents a method of representing a discrete-time deterministic dynamical system as a Markov process. The procedure is completely data-driven. The technique is proved to be convergent -- the stationary density of the Markov process has a support that converges to the targeted invariant set. Thus invariant sets of arbitrary dynamical systems, even with complicated non-smooth topology, can be approximated by this technique. Under further assumptions of stochastic stability of the targeted system, the technique is also shown to provide a convergent statistical approximation.">
@@ -2730,139 +2533,94 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>0</td>
         </tr>
 
-        <tr id="Mathematical models are indispensable to the system biology toolkit for studying the structure and behavior of intracellular signaling networks. A common approach to modeling is to develop a system of equations that encode the known biology using approximations and simplifying assumptions. As a result, the same signaling pathway can be represented by multiple models, each with its set of underlying assumptions, which opens up challenges for model selection and decreases certainty in model predictions. Here, we use Bayesian multimodel inference to develop a framework to increase certainty in systems biology models. Using models of the extracellular regulated kinase (ERK) pathway, we first show that multimodel inference increases predictive certainty and yields predictors that are robust to changes in the set of available models. We then show that predictions made with multi-model inference are robust to data uncertainties introduced by decreasing the measurement duration and reducing the sample size. Finally, we use multimodel inference to identify a new model to explain experimentally measured sub-cellular location-specific ERK activity dynamics. In summary, our framework highlights multimodel inference as a disciplined approach to increasing the certainty of intracellular signaling activity predictions.">
+        <tr id="Echo state network (ESN) implements an alternative paradigm called reservoir computing to train recurrent neural networks (RNNs), where internal weights are randomly generated and kept fixed, and only readout weights need to be trained, which greatly reduces the training complexity of RNNs. ESN not only facilitates the practical implementation of RNNs but also shows superior performance over fully trained RNNs across a range of applications. However, the conventional ESN suffers from the drawbacks of stringent conditions for weight convergence and slow convergence speed. This paper proposes a memory regressor extended learning method to update the readout weights of ESNs. By constructing and incorporating a generalized prediction error based on regressor extension and filtering, the capacity of ESN to utilize historical data can be greatly improved. In the discrete-time domain, it is proven that exponential convergence of readout weights is achieved under a condition termed interval excitation that is strictly weaker than the classical condition of persistent excitation. Simulation results on modeling a 10th-order nonlinear autoregressive moving-average (NARMA) system have revealed that the proposed approach accelerates weight convergence speed almost ten times higher compared to the conventional ESN.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/492e6724b6c05e22b755836f73b25e0ce04843d4" target='_blank'>
-              Increasing certainty in systems biology models using Bayesian multimodel inference
+          <td><a href="https://www.semanticscholar.org/paper/4e0fa7b7dfe357970b3e72acb8d86266f028d299" target='_blank'>
+              Memory Regressor Extended Echo State Networks for Nonlinear Dynamics Modeling
               </a>
             </td>
           <td>
-            Nathaniel Linden-Santangeli, Jin Zhang, Boris Kramer, P. Rangamani
+            Kai Hu, Qian Wang, Tian Shi, Kohei Nakajima, Yongping Pan
           </td>
-          <td>2024-06-17</td>
-          <td>bioRxiv</td>
+          <td>2024-06-25</td>
+          <td>2024 European Control Conference (ECC)</td>
           <td>0</td>
-          <td>4</td>
+          <td>2</td>
         </tr>
 
-        <tr id="Quantum many-body systems are of great interest for many research areas, including physics, biology and chemistry. However, their simulation is extremely challenging, due to the exponential growth of the Hilbert space with the system size, making it exceedingly difficult to parameterize the wave functions of large systems by using exact methods. Neural networks and machine learning in general are a way to face this challenge. For instance, methods like Tensor networks and Neural Quantum States are being investigated as promising tools to obtain the wave function of a quantum mechanical system. In this tutorial, we focus on a particularly promising class of deep learning algorithms. We explain how to construct a Physics-Informed Neural Network (PINN) able to solve the Schr\"odinger equation for a given potential, by finding its eigenvalues and eigenfunctions. This technique is unsupervised, and utilizes a novel computational method in a manner that is barely explored. PINNs are a deep learning method that exploits Automatic Differentiation to solve Integro-Differential Equations in a mesh-free way. We show how to find both the ground and the excited states. The method discovers the states progressively by starting from the ground state. We explain how to introduce inductive biases in the loss to exploit further knowledge of the physical system. Such additional constraints allow for a faster and more accurate convergence. This technique can then be enhanced by a smart choice of collocation points in order to take advantage of the mesh-free nature of the PINN. The methods are made explicit by applying them to the infinite potential well and the particle in a ring, a challenging problem to be learned by an AI agent due to the presence of complex-valued eigenfunctions and degenerate states.">
+        <tr id="This work proposes a data‐driven state observation algorithm for nonlinear dynamical systems, when the true state trajectory is not measurable and hence the states information needs to be reconstructed from input and output measurements. Such a reduction is formed by kernel canonical correlation analysis (KCCA), which (i) implicitly maps the available input–output data into a higher‐dimensional feature space, namely the reproducing kernel Hilbert space (RKHS); (ii) finds a projection of the past input–output data and a projection of the future input–output data with maximal correlation; and (iii) identifies the projected inputs and outputs, namely the canonical variates, as the observed states. We adopt a least squares support vector machine (LS‐SVM) formulation for KCCA, which imposes regularization on the vectors that specify the projections and is amenable to convex optimization. We prove theoretically that, based on the statistical consistency of KCCA, the observed states determined by the proposed state observer has a guaranteed correlativity with the actual states (when properly transformed). Furthermore, such observed states, when supplemented with the information of succeeding inputs, can be used to predict the succeeding outputs with guaranteed upper bound on the prediction error. Case studies are performed on two numerical examples and an exothermic continuously stirred tank reactor (CSTR).">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1d46911358218ce97ee8b811df02cf98e36f257d" target='_blank'>
-              A Tutorial on the Use of Physics-Informed Neural Networks to Compute the Spectrum of Quantum Systems
+          <td><a href="https://www.semanticscholar.org/paper/afc53bd0e86a21cb48431210cc83dc9943ac1742" target='_blank'>
+              Data‐driven nonlinear state observation for controlled systems: A kernel method and its analysis
               </a>
             </td>
           <td>
-            Lorenzo Brevi, Antonio Mandarino, Enrico Prati
+            Moritz Woelk, Wentao Tang
           </td>
-          <td>2024-07-30</td>
-          <td>ArXiv</td>
-          <td>0</td>
+          <td>2024-07-08</td>
+          <td>The Canadian Journal of Chemical Engineering</td>
           <td>0</td>
+          <td>1</td>
         </tr>
 
-        <tr id="In this paper, we evaluate the effectiveness of deep operator networks (DeepONets) in solving both forward and inverse problems of partial differential equations (PDEs) on unknown manifolds. By unknown manifolds, we identify the manifold by a set of randomly sampled data point clouds that are assumed to lie on or close to the manifold. When the loss function incorporates the physics, resulting in the so-called physics-informed DeepONets (PI-DeepONets), we approximate the differentiation terms in the PDE by an appropriate operator approximation scheme. For the second-order elliptic PDE with a nontrivial diffusion coefficient, we approximate the differentiation term with one of these methods: the Diffusion Maps (DM), the Radial Basis Functions (RBF), and the Generalized Moving Least Squares (GMLS) methods. For the GMLS approximation, which is more flexible for problems with boundary conditions, we derive the theoretical error bound induced by the approximate differentiation. Numerically, we found that DeepONet is accurate for various types of diffusion coefficients, including linear, exponential, piecewise linear, and quadratic functions, for linear and semi-linear PDEs with/without boundaries. When the number of observations is small, PI-DeepONet trained with sufficiently large samples of PDE constraints produces more accurate approximations than DeepONet. For the inverse problem, we incorporate PI-DeepONet in a Bayesian Markov Chain Monte Carlo (MCMC) framework to estimate the diffusion coefficient from noisy solutions of the PDEs measured at a finite number of point cloud data. Numerically, we found that PI-DeepONet provides accurate approximations comparable to those obtained by a more expensive method that directly solves the PDE on the proposed diffusion coefficient in each MCMC iteration.">
+        <tr id="Accurate estimate of long-term risk is critical for safe decision-making, but sampling from rare risk events and long-term trajectories can be prohibitively costly. Risk gradient can be used in many first-order techniques for learning and control methods, but gradient estimate is difficult to obtain using Monte Carlo (MC) methods because the infinitesimal divisor may significantly amplify sampling noise. Motivated by this gap, we propose an efficient method to evaluate long-term risk probabilities and their gradients using short-term samples without sufficient risk events. We first derive that four types of long-term risk probability are solutions of certain partial differential equations (PDEs). Then, we propose a physics-informed learning technique that integrates data and physics information (aforementioned PDEs). The physics information helps propagate information beyond available data and obtain provable generalization beyond available data, which in turn enables long-term risk to be estimated using short-term samples of safe events. Finally, we demonstrate in simulation that the proposed technique has improved sample efficiency, generalizes well to unseen regions, and adapts to changing system parameters.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f1a2db1eb2d9fce75d3ec1e4b8ba8bfdab80a365" target='_blank'>
-              Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators
+          <td><a href="https://www.semanticscholar.org/paper/b4b5d11c4861e28deddce08df1d92f4a1c23b924" target='_blank'>
+              Generalizable Physics-Informed Learning for Stochastic Safety-Critical Systems
               </a>
             </td>
           <td>
-            Anran Jiao, Qile Yan, Jhn Harlim, Lu Lu
+            Zhuoyuan Wang, Albert Chern, Yorie Nakahira
           </td>
-          <td>2024-07-07</td>
+          <td>2024-07-11</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>2</td>
+          <td>10</td>
         </tr>
 
-        <tr id="This paper introduces a data-driven operator learning method for multiscale partial differential equations, with a particular emphasis on preserving high-frequency information. Drawing inspiration from the representation of multiscale parameterized solutions as a combination of low-rank global bases (such as low-frequency Fourier modes) and localized bases over coarse patches (analogous to dilated convolution), we propose the Dilated Convolutional Neural Operator (DCNO). The DCNO architecture effectively captures both high-frequency and low-frequency features while maintaining a low computational cost through a combination of convolution and Fourier layers. We conduct experiments to evaluate the performance of DCNO on various datasets, including the multiscale elliptic equation, its inverse problem, Navier-Stokes equation, and Helmholtz equation. We show that DCNO strikes an optimal balance between accuracy and computational cost and offers a promising solution for multiscale operator learning.">
+        <tr id="Cyclostationary linear inverse models (CS-LIMs), generalized versions of the classical (stationary) LIM, are advanced data-driven techniques for extracting the first-order time-dependent dynamics and random forcing relevant information from complex non-linear stochastic processes. Though CS-LIMs lead to a breakthrough in climate sciences, their mathematical background and properties are worth further exploration. This study focuses on the mathematical perspective of CS-LIMs and introduces two variants: e-CS-LIM and l-CS-LIM. The former refines the original CS-LIM using the interval-wise linear Markov approximation, while the latter serves as an analytic inverse model for the linear periodic stochastic systems. Although relying on approximation, e-CS-LIM converges to l-CS-LIM under specific conditions and shows noise-robust performance. Numerical experiments demonstrate that each CS-LIM reveals the temporal structure of the system. The e-CS-LIM optimizes the original model for better dynamics performance, while l-CS-LIM excels in diffusion estimation due to reduced approximation reliance. Moreover, CS-LIMs are applied to real-world ENSO data, yielding a consistent result aligning with observations and current ENSO understanding.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e338e15dbb3267fc12cb16cf06497f5e097ca722" target='_blank'>
-              Dilated convolution neural operator for multiscale partial differential equations
+          <td><a href="https://www.semanticscholar.org/paper/7f20f17be02b5ed8df3e9de809737832dc15d7e7" target='_blank'>
+              On the Cyclostationary Linear Inverse Models: A Mathematical Insight and Implication
               </a>
             </td>
           <td>
-            Bo Xu, Xinliang Liu, Lei Zhang
+            Justin Lien, Yan-Ning Kuo, Hiroyasu Ando
           </td>
-          <td>2024-07-16</td>
+          <td>2024-07-15</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>8</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Echo state network (ESN) implements an alternative paradigm called reservoir computing to train recurrent neural networks (RNNs), where internal weights are randomly generated and kept fixed, and only readout weights need to be trained, which greatly reduces the training complexity of RNNs. ESN not only facilitates the practical implementation of RNNs but also shows superior performance over fully trained RNNs across a range of applications. However, the conventional ESN suffers from the drawbacks of stringent conditions for weight convergence and slow convergence speed. This paper proposes a memory regressor extended learning method to update the readout weights of ESNs. By constructing and incorporating a generalized prediction error based on regressor extension and filtering, the capacity of ESN to utilize historical data can be greatly improved. In the discrete-time domain, it is proven that exponential convergence of readout weights is achieved under a condition termed interval excitation that is strictly weaker than the classical condition of persistent excitation. Simulation results on modeling a 10th-order nonlinear autoregressive moving-average (NARMA) system have revealed that the proposed approach accelerates weight convergence speed almost ten times higher compared to the conventional ESN.">
+        <tr id="The Koopman operator framework is a promising direction of analysis and synthesis of systems with nonlinear dynamics based on (linear) Koopman operators. In this paper, we address the resolvent of a Koopman operator for a nonlinear autonomous discrete-time system, which we call the Koopman resolvent, and its identification problem. First, we show that for the nonlinear system with a scalar-valued output, the $z$- transform of the output is represented by the action of Koop-man resolvent. Second, we describe an identification method of the Koopman resolvent directly from time-series data of the output, in which we estimate parameters of the resolvent as well as poles and residues of the z-transform of the output. By combining the so-called frequency-domain Prony method with the Vandermonde-Cauchy form in the Dynamic Mode Decomposition (DMD), we propose the method which we call the frequency-domain DMD, in which all the unknowns can be estimated in the frequency domain.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4e0fa7b7dfe357970b3e72acb8d86266f028d299" target='_blank'>
-              Memory Regressor Extended Echo State Networks for Nonlinear Dynamics Modeling
+          <td><a href="https://www.semanticscholar.org/paper/df691892a89d4f0c1e7b43d373b04818f75bcc0b" target='_blank'>
+              Koopman Resolvents of Nonlinear Discrete-Time Systems: Formulation and Identification
               </a>
             </td>
           <td>
-            Kai Hu, Qian Wang, Tian Shi, Kohei Nakajima, Yongping Pan
+            Yoshihiko Susuki, A. Mauroy, Z. Drmač
           </td>
           <td>2024-06-25</td>
           <td>2024 European Control Conference (ECC)</td>
           <td>0</td>
-          <td>2</td>
-        </tr>
-
-        <tr id="This work proposes a data‐driven state observation algorithm for nonlinear dynamical systems, when the true state trajectory is not measurable and hence the states information needs to be reconstructed from input and output measurements. Such a reduction is formed by kernel canonical correlation analysis (KCCA), which (i) implicitly maps the available input–output data into a higher‐dimensional feature space, namely the reproducing kernel Hilbert space (RKHS); (ii) finds a projection of the past input–output data and a projection of the future input–output data with maximal correlation; and (iii) identifies the projected inputs and outputs, namely the canonical variates, as the observed states. We adopt a least squares support vector machine (LS‐SVM) formulation for KCCA, which imposes regularization on the vectors that specify the projections and is amenable to convex optimization. We prove theoretically that, based on the statistical consistency of KCCA, the observed states determined by the proposed state observer has a guaranteed correlativity with the actual states (when properly transformed). Furthermore, such observed states, when supplemented with the information of succeeding inputs, can be used to predict the succeeding outputs with guaranteed upper bound on the prediction error. Case studies are performed on two numerical examples and an exothermic continuously stirred tank reactor (CSTR).">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/afc53bd0e86a21cb48431210cc83dc9943ac1742" target='_blank'>
-              Data‐driven nonlinear state observation for controlled systems: A kernel method and its analysis
-              </a>
-            </td>
-          <td>
-            Moritz Woelk, Wentao Tang
-          </td>
-          <td>2024-07-08</td>
-          <td>The Canadian Journal of Chemical Engineering</td>
-          <td>0</td>
-          <td>1</td>
-        </tr>
-
-        <tr id="Accurate estimate of long-term risk is critical for safe decision-making, but sampling from rare risk events and long-term trajectories can be prohibitively costly. Risk gradient can be used in many first-order techniques for learning and control methods, but gradient estimate is difficult to obtain using Monte Carlo (MC) methods because the infinitesimal divisor may significantly amplify sampling noise. Motivated by this gap, we propose an efficient method to evaluate long-term risk probabilities and their gradients using short-term samples without sufficient risk events. We first derive that four types of long-term risk probability are solutions of certain partial differential equations (PDEs). Then, we propose a physics-informed learning technique that integrates data and physics information (aforementioned PDEs). The physics information helps propagate information beyond available data and obtain provable generalization beyond available data, which in turn enables long-term risk to be estimated using short-term samples of safe events. Finally, we demonstrate in simulation that the proposed technique has improved sample efficiency, generalizes well to unseen regions, and adapts to changing system parameters.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/b4b5d11c4861e28deddce08df1d92f4a1c23b924" target='_blank'>
-              Generalizable Physics-Informed Learning for Stochastic Safety-Critical Systems
-              </a>
-            </td>
-          <td>
-            Zhuoyuan Wang, Albert Chern, Yorie Nakahira
-          </td>
-          <td>2024-07-11</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>10</td>
+          <td>20</td>
         </tr>
 
-        <tr id="AI for partial differential equations (PDEs) has garnered significant attention, particularly with the emergence of Physics-informed neural networks (PINNs). The recent advent of Kolmogorov-Arnold Network (KAN) indicates that there is potential to revisit and enhance the previously MLP-based PINNs. Compared to MLPs, KANs offer interpretability and require fewer parameters. PDEs can be described in various forms, such as strong form, energy form, and inverse form. While mathematically equivalent, these forms are not computationally equivalent, making the exploration of different PDE formulations significant in computational physics. Thus, we propose different PDE forms based on KAN instead of MLP, termed Kolmogorov-Arnold-Informed Neural Network (KINN) for solving forward and inverse problems. We systematically compare MLP and KAN in various numerical examples of PDEs, including multi-scale, singularity, stress concentration, nonlinear hyperelasticity, heterogeneous, and complex geometry problems. Our results demonstrate that KINN significantly outperforms MLP regarding accuracy and convergence speed for numerous PDEs in computational solid mechanics, except for the complex geometry problem. This highlights KINN's potential for more efficient and accurate PDE solutions in AI for PDEs.">
+        <tr id="We propose an $\alpha$-separable graph Hamiltonian network ($\alpha$-SGHN) that reveals complex interaction patterns between particles in lattice systems. Utilizing trajectory data, $\alpha$-SGHN infers potential interactions without prior knowledge about particle coupling, overcoming the limitations of traditional graph neural networks that require predefined links. Furthermore, $\alpha$-SGHN preserves all conservation laws during trajectory prediction. Experimental results demonstrate that our model, incorporating structural information, outperforms baseline models based on conventional neural networks in predicting lattice systems. We anticipate that the results presented will be applicable beyond the specific onsite and inter-site interaction lattices studied, including the Frenkel-Kontorova model, the rotator lattice, and the Toda lattice.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/80aada58d34200a752fa45d6612bdb75745b47c0" target='_blank'>
-              Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
+          <td><a href="https://www.semanticscholar.org/paper/fa79f8d7dee50b48f0bb37e6b9aef968f5166f29" target='_blank'>
+              $\alpha$-SGHN: A Robust Model for Learning Particle Interactions in Lattice Systems
               </a>
             </td>
           <td>
-            Yizheng Wang, Jia Sun, Jinshuai Bai, C. Anitescu, M. Eshaghi, X. Zhuang, T. Rabczuk, Yinghua Liu
+            Yixian Gao, R. Geng, Panayotis Kevrekidis, Hong-Kun Zhang, Jian Zu
           </td>
-          <td>2024-06-16</td>
+          <td>2024-07-16</td>
           <td>ArXiv</td>
-          <td>8</td>
-          <td>69</td>
-        </tr>
-
-        <tr id="The Koopman operator framework is a promising direction of analysis and synthesis of systems with nonlinear dynamics based on (linear) Koopman operators. In this paper, we address the resolvent of a Koopman operator for a nonlinear autonomous discrete-time system, which we call the Koopman resolvent, and its identification problem. First, we show that for the nonlinear system with a scalar-valued output, the $z$- transform of the output is represented by the action of Koop-man resolvent. Second, we describe an identification method of the Koopman resolvent directly from time-series data of the output, in which we estimate parameters of the resolvent as well as poles and residues of the z-transform of the output. By combining the so-called frequency-domain Prony method with the Vandermonde-Cauchy form in the Dynamic Mode Decomposition (DMD), we propose the method which we call the frequency-domain DMD, in which all the unknowns can be estimated in the frequency domain.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/df691892a89d4f0c1e7b43d373b04818f75bcc0b" target='_blank'>
-              Koopman Resolvents of Nonlinear Discrete-Time Systems: Formulation and Identification
-              </a>
-            </td>
-          <td>
-            Yoshihiko Susuki, A. Mauroy, Z. Drmač
-          </td>
-          <td>2024-06-25</td>
-          <td>2024 European Control Conference (ECC)</td>
           <td>0</td>
-          <td>20</td>
+          <td>2</td>
         </tr>
 
         <tr id="We propose a meta-learning method for modeling Hamiltonian dynamics from a limited number of data. Although Hamiltonian neural networks have been successfully used for modeling dynamics that obey the energy conservation law, they require many data to achieve high performance. The proposed method meta-learns our neural network-based model using datasets in various dynamical systems, such that our model can predict vector fields of unseen systems. In our model, a system representation is inferred from given small data using an encoder network. Then, the system-specific vector field is predicted by modeling the Hamiltonian using a Gaussian process (GP) with neural network-based mean and kernel functions that depend on the inferred system representation. This GP-based Hamiltonian allows us to analytically obtain predictions that are adapted to small data while imposing the constraint of the conservation law. The neural networks are shared across systems, which enables us to learn knowledge from multiple systems, and use it for unseen systems. In our experiments, we demonstrate that the proposed method outperforms existing methods for predicting dynamics from a small number of observations in target systems.">
@@ -2877,37 +2635,22 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2024-08-01</td>
           <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
           <td>0</td>
-          <td>7</td>
+          <td>8</td>
         </tr>
 
-        <tr id="Within the context of machine learning-based closure mappings for RANS turbulence modelling, physical realizability is often enforced using ad-hoc postprocessing of the predicted anisotropy tensor. In this study, we address the realizability issue via a new physics-based loss function that penalizes non-realizable results during training, thereby embedding a preference for realizable predictions into the model. Additionally, we propose a new framework for data-driven turbulence modelling which retains the stability and conditioning of optimal eddy viscosity-based approaches while embedding equivariance. Several modifications to the tensor basis neural network to enhance training and testing stability are proposed. We demonstrate the conditioning, stability, and generalization of the new framework and model architecture on three flows: flow over a flat plate, flow over periodic hills, and flow through a square duct. The realizability-informed loss function is demonstrated to significantly increase the number of realizable predictions made by the model when generalizing to a new flow configuration. Altogether, the proposed framework enables the training of stable and equivariant anisotropy mappings, with more physically realizable predictions on new data. We make our code available for use and modification by others.">
+        <tr id="This paper is aimed at using the newly developing field of physics informed machine learning (PIML) to develop models for predicting the remaining useful lifetime (RUL) aircraft engines. We consider the well-known benchmark NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data as the main data for this paper, which consists of sensor outputs in a variety of different operating modes. C-MAPSS is a well-studied dataset with much existing work in the literature that address RUL prediction with classical and deep learning methods. In the absence of published empirical physical laws governing the C-MAPSS data, our approach first uses stochastic methods to estimate the governing physics models from the noisy time series data. In our approach, we model the various sensor readings as being governed by stochastic differential equations, and we estimate the corresponding transition density mean and variance functions of the underlying processes. We then augment LSTM (long-short term memory) models with the learned mean and variance functions during training and inferencing. Our PIML based approach is different from previous methods, and we use the data to first learn the physics. Our results indicate that PIML discovery and solutions methods are well suited for this problem and outperform previous data-only deep learning methods for this data set and task. Moreover, the framework developed herein is flexible, and can be adapted to other situations (other sensor modalities or combined multi-physics environments), including cases where the underlying physics is only partially observed or known.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/d49d700d5797f8d7145ca83fa89f5c1491a5d703" target='_blank'>
-              Realizability-Informed Machine Learning for Turbulence Anisotropy Mappings
+          <td><a href="https://www.semanticscholar.org/paper/47c68f22e1c160e3cdac30be6f54af8a48a8eccd" target='_blank'>
+              Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines
               </a>
             </td>
           <td>
-            R. McConkey, Eugene Yee, F. Lien
+            Sriram Nagaraj, Truman Hickok
           </td>
-          <td>2024-06-17</td>
+          <td>2024-06-21</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>36</td>
-        </tr>
-
-        <tr id="Nonlinear optimization (NOPT) is a meaningful tool for solving complex tasks in fields like engineering, economics, and operations research, among others. However, NOPT has problems when it comes to dealing with data variability and noisy input measurements that lead to incorrect solutions. Furthermore, nonlinear constraints may result in outcomes that are either infeasible or suboptimal, such as nonconvex optimization. This paper introduces a novel regularized physics-informed neural network (RPINN) framework as a new NOPT tool for both supervised and unsupervised data-driven scenarios. Our RPINN is threefold: By using custom activation functions and regularization penalties in an artificial neural network (ANN), RPINN can handle data variability and noisy inputs. Furthermore, it employs physics principles to construct the network architecture, computing the optimization variables based on network weights and learned features. In addition, it uses automatic differentiation training to make the system scalable and cut down on computation time through batch-based back-propagation. The test results for both supervised and unsupervised NOPT tasks show that our RPINN can provide solutions that are competitive compared to state-of-the-art solvers. In turn, the robustness of RPINN against noisy input measurements makes it particularly valuable in environments with fluctuating information. Specifically, we test a uniform mixture model and a gas-powered system as NOPT scenarios. Overall, with RPINN, its ANN-based foundation offers significant flexibility and scalability.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/516f48e222fdad35b92113ecdbf8a459d37c95aa" target='_blank'>
-              A Regularized Physics-Informed Neural Network to Support Data-Driven Nonlinear Constrained Optimization
-              </a>
-            </td>
-          <td>
-            Diego Armando Perez-Rosero, A. Álvarez-Meza, G. Castellanos-Domínguez
-          </td>
-          <td>2024-07-18</td>
-          <td>Comput.</td>
           <td>0</td>
-          <td>24</td>
         </tr>
 
         <tr id="Physics informed neural networks (PINNs) have effectively demonstrated the ability to approximate the solutions of a system of partial differential equations (PDEs) by embedding the governing equations and auxiliary conditions directly into the loss function using automatic differentiation. Despite demonstrating potential across diverse applications, PINNs have encountered challenges in accurately predicting solutions for time-dependent problems. In response, this study presents a novel methodology aimed at enhancing the predictive capability of PINNs for time-dependent scenarios. Our approach involves dividing the temporal domain into multiple subdomains and employing an adaptive weighting strategy at the initial condition and at the interfaces between these subdomains. By employing such interfacial conditioning in physics informed neural networks (IcPINN), we have solved several unsteady PDEs (e.g., Allen–Cahn equation, advection equation, Korteweg–De Vries equation, Cahn–Hilliard equation, and Navier–Stokes equations) and conducted a comparative analysis with numerical results. The results have demonstrated that IcPINN was successful in obtaining highly accurate results in each case without the need for using any labeled data.">
@@ -2925,21 +2668,6 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>1</td>
         </tr>
 
-        <tr id="The task of sampling from a probability density can be approached as transporting a tractable density function to the target, known as dynamical measure transport. In this work, we tackle it through a principled unified framework using deterministic or stochastic evolutions described by partial differential equations (PDEs). This framework incorporates prior trajectory-based sampling methods, such as diffusion models or Schr\"odinger bridges, without relying on the concept of time-reversals. Moreover, it allows us to propose novel numerical methods for solving the transport task and thus sampling from complicated targets without the need for the normalization constant or data samples. We employ physics-informed neural networks (PINNs) to approximate the respective PDE solutions, implying both conceptional and computational advantages. In particular, PINNs allow for simulation- and discretization-free optimization and can be trained very efficiently, leading to significantly better mode coverage in the sampling task compared to alternative methods. Moreover, they can readily be fine-tuned with Gauss-Newton methods to achieve high accuracy in sampling.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/be6b71f84d3c641029effa69aa23e0c32eb9de03" target='_blank'>
-              Dynamical Measure Transport and Neural PDE Solvers for Sampling
-              </a>
-            </td>
-          <td>
-            Jingtong Sun, Julius Berner, Lorenz Richter, Marius Zeinhofer, Johannes Muller, K. Azizzadenesheli, A. Anandkumar
-          </td>
-          <td>2024-07-10</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>31</td>
-        </tr>
-
         <tr id="The objective of system identification is to derive models from input/output data. To extend advancements in regularization techniques for linear finite impulse response (FIR) models to the nonlinear domain, we employ local model networks (LMNs) with locally regularized FIR models to identify nonlinear processes. The training of the LMN is performed using the local linear model tree (LOLIMOT) algorithm, resulting in both the partitioning of the model space and the estimation of the corresponding linear local models for each region. One key advantage of this algorithm lies in its ability to create separate input spaces for the linear models (x-space) and the validity functions (z-space) comprising the partitioning. While the most straightforward choice (x-space = z-space) results in an extremely high-dimensional z-space for local FIR models, we ad-dress this drawback by proposing different z-spaces spanned by the input spaces of autoregressive with exogenous inputs (ARX) models or Laguerre filter models, respectively. The theoretical capabilities for the proposed z-spaces are characterized. The superiority in terms of computation time, as well as comparable performance for Laguerre z-spaces and FIR z-spaces, is demon-strated through numerical examples. Additionally, the limitations for the utilization of ARX z-spaces are highlighted. Finally, all z-spaces have been evaluated on real-world data of a Wiener-Hammerstein benchmark. The FIR and Laguerre z- space showed comparable performance, while the ARX z-space performed worse.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/9d7f2bb8d2c64f0e73154c706dc8921696b583cb" target='_blank'>
@@ -2955,34 +2683,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2</td>
         </tr>
 
-        <tr id="The advent of machine learning has led to innovative approaches in dealing with clinical data. Among these, Neural Ordinary Differential Equations (Neural ODEs), hybrid models merging mechanistic with deep learning models have shown promise in accurately modeling continuous dynamical systems. Although initial applications of Neural ODEs in the field of model-informed drug development and clinical pharmacology are becoming evident, applying these models to actual clinical trial datasets-characterized by sparse and irregularly timed measurements-poses several challenges. Traditional models often have limitations with sparse data, highlighting the urgent need to address this issue, potentially through the use of assumptions. This review examines the fundamentals of Neural ODEs, their ability to handle sparse and irregular data, and their applications in model-informed drug development.">
+        <tr id="The task of sampling from a probability density can be approached as transporting a tractable density function to the target, known as dynamical measure transport. In this work, we tackle it through a principled unified framework using deterministic or stochastic evolutions described by partial differential equations (PDEs). This framework incorporates prior trajectory-based sampling methods, such as diffusion models or Schr\"odinger bridges, without relying on the concept of time-reversals. Moreover, it allows us to propose novel numerical methods for solving the transport task and thus sampling from complicated targets without the need for the normalization constant or data samples. We employ physics-informed neural networks (PINNs) to approximate the respective PDE solutions, implying both conceptional and computational advantages. In particular, PINNs allow for simulation- and discretization-free optimization and can be trained very efficiently, leading to significantly better mode coverage in the sampling task compared to alternative methods. Moreover, they can readily be fine-tuned with Gauss-Newton methods to achieve high accuracy in sampling.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/d55a1a4acc93d905399f2126e7beb524fd64ec38" target='_blank'>
-              Bridging pharmacology and neural networks: A deep dive into neural ordinary differential equations.
+          <td><a href="https://www.semanticscholar.org/paper/be6b71f84d3c641029effa69aa23e0c32eb9de03" target='_blank'>
+              Dynamical Measure Transport and Neural PDE Solvers for Sampling
               </a>
             </td>
           <td>
-            Idris Bachali Losada, N. Terranova
+            Jingtong Sun, Julius Berner, Lorenz Richter, Marius Zeinhofer, Johannes Müller, K. Azizzadenesheli, A. Anandkumar
           </td>
-          <td>2024-07-11</td>
-          <td>CPT: pharmacometrics & systems pharmacology</td>
+          <td>2024-07-10</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>12</td>
+          <td>32</td>
         </tr>
 
-        <tr id="This paper is aimed at using the newly developing field of physics informed machine learning (PIML) to develop models for predicting the remaining useful lifetime (RUL) aircraft engines. We consider the well-known benchmark NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data as the main data for this paper, which consists of sensor outputs in a variety of different operating modes. C-MAPSS is a well-studied dataset with much existing work in the literature that address RUL prediction with classical and deep learning methods. In the absence of published empirical physical laws governing the C-MAPSS data, our approach first uses stochastic methods to estimate the governing physics models from the noisy time series data. In our approach, we model the various sensor readings as being governed by stochastic differential equations, and we estimate the corresponding transition density mean and variance functions of the underlying processes. We then augment LSTM (long-short term memory) models with the learned mean and variance functions during training and inferencing. Our PIML based approach is different from previous methods, and we use the data to first learn the physics. Our results indicate that PIML discovery and solutions methods are well suited for this problem and outperform previous data-only deep learning methods for this data set and task. Moreover, the framework developed herein is flexible, and can be adapted to other situations (other sensor modalities or combined multi-physics environments), including cases where the underlying physics is only partially observed or known.">
+        <tr id="Latent dynamical systems have been widely used to characterize the dynamics of neural population activity in the brain. However, these models typically ignore the fact that the brain contains multiple cell types. This limits their ability to capture the functional roles of distinct cell classes, or to accurately predict the effects of cell-specific optogenetic perturbations on neural activity or behavior. To overcome these limitations, we introduce the “cell-type dynamical systems” (CTDS) model. This model extends latent linear dynamical systems to contain distinct latent variables for each cell class, with biologically inspired constraints on both dynamics and emissions. To illustrate our approach, we consider neural recordings with distinct excitatory (E) and inhibitory (I) populations. The CTDS model defines separate latents for E and I cells, and constrains the dynamics so that E (I) latents have a strictly positive (negative) effects on other latents. We applied CTDS to recordings from rat frontal orienting fields (FOF) and anterior dorsal striatum (ADS) during an auditory decision-making task. The model achieved higher accuracy than a standard linear dynamical system (LDS), and revealed that both E and I latents could be used to decode the animal’s choice, showing that choice-related information is not restricted to a single cell class. We also performed in-silico optogenetic perturbation experiments in the FOF and ADS, and found that CTDS was able to replicate the causal effects of different perturbations on behavior, whereas a standard LDS model—which lacks the ability to capture cell-specific perturbations—did not. Crucially, our model allowed us to understand the effects of these perturbations by revealing the dynamics of different cell-specific latents. Finally, CTDS can also be used to identify cell types for neurons whose class labels are unknown in electrophysiological recordings. These results illustrate the power of the CTDS model to provide more accurate and more biologically interpretable descriptions of neural population dynamics and their relationship to behavior.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/47c68f22e1c160e3cdac30be6f54af8a48a8eccd" target='_blank'>
-              Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines
+          <td><a href="https://www.semanticscholar.org/paper/31c1cd3d2f0ffab7d6fe212ee7ca7fdd655d8dfa" target='_blank'>
+              Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems
               </a>
             </td>
           <td>
-            Sriram Nagaraj, Truman Hickok
+            Aditi Jha, Diksha Gupta, C. Brody, Jonathan W. Pillow
           </td>
-          <td>2024-06-21</td>
-          <td>ArXiv</td>
-          <td>0</td>
+          <td>2024-07-11</td>
+          <td>bioRxiv</td>
           <td>0</td>
+          <td>45</td>
         </tr>
 
         <tr id="Due to their flexibility and ease of use, Dynamical Movement Primitives (DMPs) are widely used in robotics applications and research. DMPs combine linear dynamical systems to achieve robustness to perturbations and adaptation to moving targets with non-linear function approximators to fit a wide range of demonstrated trajectories.We propose a novel DMP formulation with a generalized logistic function as a delayed goal system. This formulation inherently has low initial jerk, and generates the bell-shaped velocity profiles that are typical of human movement. As the novel formulation is more expressive, it is able to fit a wide range of human demonstrations well, also without a non-linear forcing term. We exploit this increased expressiveness by automating the fitting of the dynamical system parameters through opti-mization. Our experimental evaluation demonstrates that this optimization regularizes the forcing term, and improves the interpolation accuracy of parametric DMPs.">
@@ -3000,19 +2728,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>32</td>
         </tr>
 
-        <tr id="The research topic is: data-driven Bayesian state estimation with compressed measurement (BSCM) of model-free process, say for a (causal) tracking application. The dimension of the temporal measurement vector is lower than the dimension of the temporal state vector to be estimated. Hence the state estimation problem is an underdetermined inverse problem. The state-space-model (SSM) of the underlying dynamical process is assumed to be unknown and hence, we use the terminology 'model-free process'. In absence of the SSM, we can not employ traditional model-driven methods like Kalman Filter (KF) and Particle Filter (PF) and instead require data-driven methods. We first experimentally show that two existing unsupervised learning-based data-driven methods fail to address the BSCM problem for model-free process; they are data-driven nonlinear state estimation (DANSE) method and deep Markov model (DMM) method. The unsupervised learning uses unlabelled data comprised of only noisy measurements. While DANSE provides a good predictive performance to model the temporal measurement data as time-series, its unsupervised learning lacks a regularization for state estimation. We then investigate use of a semi-supervised learning approach, and develop a semi-supervised learning-based DANSE method, referred to as SemiDANSE. In the semi-supervised learning, we use a limited amount of labelled data along-with a large amount of unlabelled data, and that helps to bring the desired regularization for BSCM problem in the absence of SSM. The labelled data means pairwise measurement-and-state data. Using three chaotic dynamical systems (or processes) with nonlinear SSMs as benchmark, we show that the data-driven SemiDANSE provides competitive performance for BSCM against three SSM-informed methods - a hybrid method called KalmanNet, and two traditional model-driven methods called extended KF and unscented KF.">
+        <tr id="Solving partial differential equations (PDEs) is a fundamental problem in engineering and science. While neural PDE solvers can be more efficient than established numerical solvers, they often require large amounts of training data that is costly to obtain. Active Learning (AL) could help surrogate models reach the same accuracy with smaller training sets by querying classical solvers with more informative initial conditions and PDE parameters. While AL is more common in other domains, it has yet to be studied extensively for neural PDE solvers. To bridge this gap, we introduce AL4PDE, a modular and extensible active learning benchmark. It provides multiple parametric PDEs and state-of-the-art surrogate models for the solver-in-the-loop setting, enabling the evaluation of existing and the development of new AL methods for PDE solving. We use the benchmark to evaluate batch active learning algorithms such as uncertainty- and feature-based methods. We show that AL reduces the average error by up to 71% compared to random sampling and significantly reduces worst-case errors. Moreover, AL generates similar datasets across repeated runs, with consistent distributions over the PDE parameters and initial conditions. The acquired datasets are reusable, providing benefits for surrogate models not involved in the data generation.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f2671168e70fb8ea79bbef366bd1d59fc9ae9415" target='_blank'>
-              Data-driven Bayesian State Estimation with Compressed Measurement of Model-free Process using Semi-supervised Learning
+          <td><a href="https://www.semanticscholar.org/paper/1dd92c53455982680c81f73c1708da2a22eaa764" target='_blank'>
+              Active Learning for Neural PDE Solvers
               </a>
             </td>
           <td>
-            Anubhab Ghosh, Y. Eldar, Saikat Chatterjee
+            Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller, Makoto Takamoto, Mathias Niepert
           </td>
-          <td>2024-07-10</td>
+          <td>2024-08-02</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>26</td>
+          <td>8</td>
         </tr>
 
         <tr id="We study nonequilibrium quantum dynamics of spin chains by employing principal component analysis (PCA) on data sets of wave function snapshots and examine how information propagates within these data sets. The quantities we employ are derived from the spectrum of the sample second moment matrix, built directly from data sets. Our investigations on several interacting spin chains featuring distinct spin or energy transport reveal that the growth of data information spreading follows the same dynamical exponents as that of the underlying quantum transport of spin or energy. Specifically, our approach enables an easy, data-driven, and importantly interpretable diagnostic to track energy transport with a limited number of samples, which is usually challenging without any assumption on the Hamiltonian form. These observations are obtained at a modest finite size and evolution time, which aligns with experimental and numerical constraints. Our framework directly applies to experimental quantum simulator data sets of dynamics in higher-dimensional systems, where classical simulation methods usually face significant limitations and apply equally to both near- and far-from-equilibrium quenches.">
@@ -3030,64 +2758,64 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>10</td>
         </tr>
 
-        <tr id="Physics-Informed Neural Networks (PINNs) have emerged as a robust framework for solving Partial Differential Equations (PDEs) by approximating their solutions via neural networks and imposing physics-based constraints on the loss function. Traditionally, Multilayer Perceptrons (MLPs) are the neural network of choice, and significant progress has been made in optimizing their training. Recently, Kolmogorov-Arnold Networks (KANs) were introduced as a viable alternative, with the potential of offering better interpretability and efficiency while requiring fewer parameters. In this paper, we present a fast JAX-based implementation of grid-dependent Physics-Informed Kolmogorov-Arnold Networks (PIKANs) for solving PDEs. We propose an adaptive training scheme for PIKANs, incorporating known MLP-based PINN techniques, introducing an adaptive state transition scheme to avoid loss function peaks between grid updates, and proposing a methodology for designing PIKANs with alternative basis functions. Through comparative experiments we demonstrate that these adaptive features significantly enhance training efficiency and solution accuracy. Our results illustrate the effectiveness of PIKANs in improving performance for PDE solutions, highlighting their potential as a superior alternative in scientific and engineering applications.">
+        <tr id="To understand and control the dynamics of coupled oscillators, it is important to reveal the structure of the interaction network from observed data. While various techniques have been developed for inferring the network of asynchronous systems, it remains challenging to infer the network of synchronized oscillators without external stimulations. In this study, we develop a method for non-invasively inferring the network of synchronized and/or de-synchronized oscillators. An approach to network inference would be to fit the data to a set of differential equations describing the dynamics of phase oscillators. However, we show that this method fails to infer the true network due to the problems that arise when we use short-time phase differences. Therefore, we propose a method based on the circle map, which describes the phase change in one oscillatory cycle. We demonstrate the efficacy of the proposed method through the successful inference of the network structure from simulated data of limit cycle oscillator models. Our method provides a unified and concise framework for network estimation for a wide class of oscillator systems.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c1aaa25c69658448f0abd4e2430cf6925f39fdd5" target='_blank'>
-              Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks
+          <td><a href="https://www.semanticscholar.org/paper/a85617c5e51b81ee7f45457c35d92af54f040aed" target='_blank'>
+              Network inference from oscillatory signals based on circle map
               </a>
             </td>
           <td>
-            Spyros Rigas, M. Papachristou, Theofilos Papadopoulos, Fotios Anagnostopoulos, Georgios Alexandridis
+            Akari Matsuki, Hiroshi Kori, Ryota Kobayashi
           </td>
-          <td>2024-07-24</td>
+          <td>2024-07-10</td>
           <td>ArXiv</td>
           <td>1</td>
-          <td>1</td>
+          <td>2</td>
         </tr>
 
-        <tr id="Latent dynamical systems have been widely used to characterize the dynamics of neural population activity in the brain. However, these models typically ignore the fact that the brain contains multiple cell types. This limits their ability to capture the functional roles of distinct cell classes, or to accurately predict the effects of cell-specific optogenetic perturbations on neural activity or behavior. To overcome these limitations, we introduce the “cell-type dynamical systems” (CTDS) model. This model extends latent linear dynamical systems to contain distinct latent variables for each cell class, with biologically inspired constraints on both dynamics and emissions. To illustrate our approach, we consider neural recordings with distinct excitatory (E) and inhibitory (I) populations. The CTDS model defines separate latents for E and I cells, and constrains the dynamics so that E (I) latents have a strictly positive (negative) effects on other latents. We applied CTDS to recordings from rat frontal orienting fields (FOF) and anterior dorsal striatum (ADS) during an auditory decision-making task. The model achieved higher accuracy than a standard linear dynamical system (LDS), and revealed that both E and I latents could be used to decode the animal’s choice, showing that choice-related information is not restricted to a single cell class. We also performed in-silico optogenetic perturbation experiments in the FOF and ADS, and found that CTDS was able to replicate the causal effects of different perturbations on behavior, whereas a standard LDS model—which lacks the ability to capture cell-specific perturbations—did not. Crucially, our model allowed us to understand the effects of these perturbations by revealing the dynamics of different cell-specific latents. Finally, CTDS can also be used to identify cell types for neurons whose class labels are unknown in electrophysiological recordings. These results illustrate the power of the CTDS model to provide more accurate and more biologically interpretable descriptions of neural population dynamics and their relationship to behavior.">
+        <tr id="Physics-Informed Neural Networks (PINNs) have emerged as a robust framework for solving Partial Differential Equations (PDEs) by approximating their solutions via neural networks and imposing physics-based constraints on the loss function. Traditionally, Multilayer Perceptrons (MLPs) are the neural network of choice, and significant progress has been made in optimizing their training. Recently, Kolmogorov-Arnold Networks (KANs) were introduced as a viable alternative, with the potential of offering better interpretability and efficiency while requiring fewer parameters. In this paper, we present a fast JAX-based implementation of grid-dependent Physics-Informed Kolmogorov-Arnold Networks (PIKANs) for solving PDEs. We propose an adaptive training scheme for PIKANs, incorporating known MLP-based PINN techniques, introducing an adaptive state transition scheme to avoid loss function peaks between grid updates, and proposing a methodology for designing PIKANs with alternative basis functions. Through comparative experiments we demonstrate that these adaptive features significantly enhance training efficiency and solution accuracy. Our results illustrate the effectiveness of PIKANs in improving performance for PDE solutions, highlighting their potential as a superior alternative in scientific and engineering applications.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/31c1cd3d2f0ffab7d6fe212ee7ca7fdd655d8dfa" target='_blank'>
-              Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems
+          <td><a href="https://www.semanticscholar.org/paper/c1aaa25c69658448f0abd4e2430cf6925f39fdd5" target='_blank'>
+              Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks
               </a>
             </td>
           <td>
-            Aditi Jha, Diksha Gupta, C. Brody, Jonathan W. Pillow
+            Spyros Rigas, M. Papachristou, Theofilos Papadopoulos, Fotios Anagnostopoulos, Georgios Alexandridis
           </td>
-          <td>2024-07-11</td>
-          <td>bioRxiv</td>
-          <td>0</td>
-          <td>45</td>
+          <td>2024-07-24</td>
+          <td>ArXiv</td>
+          <td>1</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Solving partial differential equations (PDEs) is a fundamental problem in engineering and science. While neural PDE solvers can be more efficient than established numerical solvers, they often require large amounts of training data that is costly to obtain. Active Learning (AL) could help surrogate models reach the same accuracy with smaller training sets by querying classical solvers with more informative initial conditions and PDE parameters. While AL is more common in other domains, it has yet to be studied extensively for neural PDE solvers. To bridge this gap, we introduce AL4PDE, a modular and extensible active learning benchmark. It provides multiple parametric PDEs and state-of-the-art surrogate models for the solver-in-the-loop setting, enabling the evaluation of existing and the development of new AL methods for PDE solving. We use the benchmark to evaluate batch active learning algorithms such as uncertainty- and feature-based methods. We show that AL reduces the average error by up to 71% compared to random sampling and significantly reduces worst-case errors. Moreover, AL generates similar datasets across repeated runs, with consistent distributions over the PDE parameters and initial conditions. The acquired datasets are reusable, providing benefits for surrogate models not involved in the data generation.">
+        <tr id="The research topic is: data-driven Bayesian state estimation with compressed measurement (BSCM) of model-free process, say for a (causal) tracking application. The dimension of the temporal measurement vector is lower than the dimension of the temporal state vector to be estimated. Hence the state estimation problem is an underdetermined inverse problem. The state-space-model (SSM) of the underlying dynamical process is assumed to be unknown and hence, we use the terminology 'model-free process'. In absence of the SSM, we can not employ traditional model-driven methods like Kalman Filter (KF) and Particle Filter (PF) and instead require data-driven methods. We first experimentally show that two existing unsupervised learning-based data-driven methods fail to address the BSCM problem for model-free process; they are data-driven nonlinear state estimation (DANSE) method and deep Markov model (DMM) method. The unsupervised learning uses unlabelled data comprised of only noisy measurements. While DANSE provides a good predictive performance to model the temporal measurement data as time-series, its unsupervised learning lacks a regularization for state estimation. We then investigate use of a semi-supervised learning approach, and develop a semi-supervised learning-based DANSE method, referred to as SemiDANSE. In the semi-supervised learning, we use a limited amount of labelled data along-with a large amount of unlabelled data, and that helps to bring the desired regularization for BSCM problem in the absence of SSM. The labelled data means pairwise measurement-and-state data. Using three chaotic dynamical systems (or processes) with nonlinear SSMs as benchmark, we show that the data-driven SemiDANSE provides competitive performance for BSCM against three SSM-informed methods - a hybrid method called KalmanNet, and two traditional model-driven methods called extended KF and unscented KF.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1dd92c53455982680c81f73c1708da2a22eaa764" target='_blank'>
-              Active Learning for Neural PDE Solvers
+          <td><a href="https://www.semanticscholar.org/paper/f2671168e70fb8ea79bbef366bd1d59fc9ae9415" target='_blank'>
+              Data-driven Bayesian State Estimation with Compressed Measurement of Model-free Process using Semi-supervised Learning
               </a>
             </td>
           <td>
-            Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller, Makoto Takamoto, Mathias Niepert
+            Anubhab Ghosh, Y. Eldar, Saikat Chatterjee
           </td>
-          <td>2024-08-02</td>
+          <td>2024-07-10</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>8</td>
+          <td>27</td>
         </tr>
 
-        <tr id="We introduce data to predictive control, D2PC, a framework to facilitate the design of robust and predictive controllers from data. The proposed framework is designed for discrete-time stochastic linear systems with output measurements and provides a principled design of a predictive controller based on data. The framework starts with a parameter identification method based on the Expectation-Maximization algorithm, which incorporates pre-defined structural constraints. Additionally, we provide an asymptotically correct method to quantify uncertainty in parameter estimates. Next, we develop a strategy to synthesize robust dynamic output-feedback controllers tailored to the derived uncertainty characterization. Finally, we introduce a predictive control scheme that guarantees recursive feasibility and satisfaction of chance constraints. This framework marks a significant advancement in integrating data into robust and predictive control schemes. We demonstrate the efficacy of D2PC through a numerical example involving a $10$-dimensional spring-mass-damper system.">
+        <tr id="Modern datasets in neuroscience enable unprecedented inquiries into the relationship between complex behaviors and the activity of many simultaneously recorded neurons. While latent variable models can successfully extract low-dimensional embeddings from such recordings, using them to generate realistic spiking data, especially in a behavior-dependent manner, still poses a challenge. Here, we present Latent Diffusion for Neural Spiking data (LDNS), a diffusion-based generative model with a low-dimensional latent space: LDNS employs an autoencoder with structured state-space (S4) layers to project discrete high-dimensional spiking data into continuous time-aligned latents. On these inferred latents, we train expressive (conditional) diffusion models, enabling us to sample neural activity with realistic single-neuron and population spiking statistics. We validate LDNS on synthetic data, accurately recovering latent structure, firing rates, and spiking statistics. Next, we demonstrate its flexibility by generating variable-length data that mimics human cortical activity during attempted speech. We show how to equip LDNS with an expressive observation model that accounts for single-neuron dynamics not mediated by the latent state, further increasing the realism of generated samples. Finally, conditional LDNS trained on motor cortical activity during diverse reaching behaviors can generate realistic spiking data given reach direction or unseen reach trajectories. In summary, LDNS simultaneously enables inference of low-dimensional latents and realistic conditional generation of neural spiking datasets, opening up further possibilities for simulating experimentally testable hypotheses.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/2ea52a361e1eddc6fae9770357307a43f124b006" target='_blank'>
-              From Data to Predictive Control: A Framework for Stochastic Linear Systems with Output Measurements
+          <td><a href="https://www.semanticscholar.org/paper/4063dac20e1d0f5e11b6e07992c712df9558080d" target='_blank'>
+              Latent Diffusion for Neural Spiking Data
               </a>
             </td>
           <td>
-            Haldun Balim, Andrea Carron, M. Zeilinger, Johannes Kohler
+            J. Kapoor, Auguste Schulz, Julius Vetter, Felix Pei, Richard Gao, J. H. Macke
           </td>
-          <td>2024-07-24</td>
+          <td>2024-06-27</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>35</td>
+          <td>3</td>
         </tr>
 
         <tr id="This paper proposes a fully data-driven approach for optimal control of nonlinear control-affine systems represented by a stochastic diffusion. The focus is on the scenario where both the nonlinear dynamics and stage cost functions are unknown, while only control penalty function and constraints are provided. Leveraging the theory of reproducing kernel Hilbert spaces, we introduce novel kernel mean embeddings (KMEs) to identify the Markov transition operators associated with controlled diffusion processes. The KME learning approach seamlessly integrates with modern convex operator-theoretic Hamilton-Jacobi-Bellman recursions. Thus, unlike traditional dynamic programming methods, our approach exploits the ``kernel trick'' to break the curse of dimensionality. We demonstrate the effectiveness of our method through numerical examples, highlighting its ability to solve a large class of nonlinear optimal control problems.">
@@ -3105,19 +2833,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>15</td>
         </tr>
 
-        <tr id="The aim of this paper is to explore the relationship between invariant cones and nonlinear normal modes in piecewise linear mechanical systems. As a key result, we extend the invariant cone concept, originally established for homogeneous piecewise linear systems, to a class of inhomogeneous continuous piecewise linear systems. The inhomogeneous terms can be constant and/or time-dependent, modeling nonsmooth mechanical systems with a clearance gap and external harmonic forcing, respectively. Using an augmented state vector, a modified invariant cone problem is formulated and solved to compute the nonlinear normal modes, understood as periodic solutions of the underlying conservative dynamics. An important contribution is that the invariant cones of the underlying homogeneous system can be regarded as a singularity in the theory of nonlinear normal modes of continuous piecewise linear systems. In addition, we use a similar methodology to take external harmonic forcing into account. We illustrate our approach using numerical examples. The resulting backbone curves and frequency response diagrams are compared to the results obtained using the shooting method and brute force time integration.">
+        <tr id="We introduce data to predictive control, D2PC, a framework to facilitate the design of robust and predictive controllers from data. The proposed framework is designed for discrete-time stochastic linear systems with output measurements and provides a principled design of a predictive controller based on data. The framework starts with a parameter identification method based on the Expectation-Maximization algorithm, which incorporates pre-defined structural constraints. Additionally, we provide an asymptotically correct method to quantify uncertainty in parameter estimates. Next, we develop a strategy to synthesize robust dynamic output-feedback controllers tailored to the derived uncertainty characterization. Finally, we introduce a predictive control scheme that guarantees recursive feasibility and satisfaction of chance constraints. This framework marks a significant advancement in integrating data into robust and predictive control schemes. We demonstrate the efficacy of D2PC through a numerical example involving a $10$-dimensional spring-mass-damper system.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/7d0e9619ac1a08f3a2a807303a2f2a6fae6be411" target='_blank'>
-              Extended invariant cones as Nonlinear Normal Modes of inhomogeneous piecewise linear systems
+          <td><a href="https://www.semanticscholar.org/paper/2ea52a361e1eddc6fae9770357307a43f124b006" target='_blank'>
+              From Data to Predictive Control: A Framework for Stochastic Linear Systems with Output Measurements
               </a>
             </td>
           <td>
-            A. Karoui, R. Leine
+            Haldun Balim, Andrea Carron, M. Zeilinger, Johannes Kohler
           </td>
-          <td>2024-07-22</td>
+          <td>2024-07-24</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>32</td>
+          <td>36</td>
         </tr>
 
         <tr id="We study the model reduction by moment matching problem for linear systems in a data-driven framework. We show that reduced-order models can be directly computed from data without knowledge of the structure of the signal generator or of its internal state. The reduced-order models thus obtained match the moments of the unknown underlying system asymptotically. Our construction provides a simple way to enforce additional constraints in the reduced-order model. We demonstrate the applicability of the results using data from a high-dimensional model of a building.">
@@ -3132,49 +2860,49 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2024-06-25</td>
           <td>2024 European Control Conference (ECC)</td>
           <td>0</td>
-          <td>1</td>
+          <td>2</td>
         </tr>
 
-        <tr id="Using equilibrium fluctuations to understand the response of a physical system to an externally imposed perturbation is the basis for linear response theory, which is widely used to interpret experiments and shed light on microscopic dynamics. For nonequilibrium systems, perturbations cannot be interpreted simply by monitoring fluctuations in a conjugate observable -- additional dynamical information is needed. The theory of linear response around nonequilibrium steady states relies on path ensemble averaging, which makes this theory inapplicable to perturbations that affect the diffusion constant or temperature in a stochastic system. Here, we show that a separate, ``effective'' physical process can be used to describe the perturbed dynamics and that this dynamics in turn allows us to accurately calculate the response to a change in the diffusion. Interestingly, the effective dynamics contains an additional drift that is proportional to the ``score'' of the instantaneous probability density of the system -- this object has also been studied extensively in recent years in the context of denoising diffusion models in the machine learning literature. Exploiting recently developed algorithms for learning the score, we show that we can carry out nonequilibrium response calculations on systems for which the exact score cannot be obtained.">
+        <tr id="Computer simulations based on partial differential equations (PDEs) describing physical phenomena, are widely used for analyzing the performance of high voltage insulation. Such simulation models require access to physical model parameters, which are often hard to obtain. The purpose of the present paper is to explore how Physics-Informed Neural Network (PINN) tailored for specific PDEs, can be used to extract relevant physical parameters from synthetic data. Electric potential or charge distributions in 1-dimensional coaxial and Cartesian domains were used as training data for PINN models. The following physical parameters were successfully extracted: (i) space charge profiles, (ii) mobilities of ions, and (iii) the radius of the inner electrode in the coaxial geometry. The impact of noisy training data on the accuracy was also studied for the different cases. The main finding is that PINN models can successfully be used to extract model parameters for electrical charge transport problems using synthetic data as input. This approach has large potential to strengthen the research on charge dynamics in gas, liquid, and solid insulation as well as other topics related to high voltage insulation systems.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f45af9914ab526c24b8c0bd202775dddc280a5c4" target='_blank'>
-              Computing Nonequilibrium Responses with Score-shifted Stochastic Differential Equations
+          <td><a href="https://www.semanticscholar.org/paper/f9d44af663a8e4f7e16a2dffe2c289b99726e746" target='_blank'>
+              Usage of Physics-Informed Neural Network to Extract Physical Parameters From High Voltage Experiments
               </a>
             </td>
           <td>
-            J'er'emie Klinger, Grant M. Rotskoff
+            O. Hjortstam, C. Björnson, F. Ågren, T. Hammarström, Y. V. Serdyuk, C. Häger
           </td>
-          <td>2024-06-20</td>
-          <td>ArXiv</td>
+          <td>2024-06-30</td>
+          <td>2024 IEEE 5th International Conference on Dielectrics (ICD)</td>
+          <td>0</td>
           <td>0</td>
-          <td>24</td>
         </tr>
 
-        <tr id="Recently, machine learning potentials (MLP) largely enhances the reliability of molecular dynamics, but its accuracy is limited by the underlying $\textit{ab initio}$ methods. A viable approach to overcome this limitation is to refine the potential by learning from experimental data, which now can be done efficiently using modern automatic differentiation technique. However, potential refinement is mostly performed using thermodynamic properties, leaving the most accessible and informative dynamical data (like spectroscopy) unexploited. In this work, through a comprehensive application of adjoint and gradient truncation methods, we show that both memory and gradient explosion issues can be circumvented in many situations, so the dynamical property differentiation is well-behaved. Consequently, both transport coefficients and spectroscopic data can be used to improve the density functional theory based MLP towards higher accuracy. Essentially, this work contributes to the solution of the inverse problem of spectroscopy by extracting microscopic interactions from vibrational spectroscopic data.">
+        <tr id="We apply two data assimilation (DA) methods, a smoother and a filter, and a model-free machine learning (ML) shallow network to forecast two weakly turbulent systems. We analyse the effect of the spatial sparsity of observations on accuracy of the predictions obtained from these data-driven methods. Based on the results, we divide the spatial sparsity levels in three zones. First is the good-predictions zone in which both DA and ML methods work. We find that in the good-predictions zone the observations remain dense enough to accurately capture the fractal manifold of the system's dynamics, which is measured using the correlation dimension. The accuracy of the DA methods in this zone remains almost as good as for full-resolution observations. Second is the reasonable-predictions zone in which the DA methods still work but at reduced prediction accuracy. Third is the bad-predictions zone in which even the DA methods fail. We find that the sparsity level up to which the DA methods work is almost the same up to which chaos synchronisation of these systems can be achieved. The main implications of these results are that they (i) firmly establish the spatial resolution up to which the data-driven methods can be utilised, (ii) provide measures to determine if adding more sensors will improve the predictions, and (iii) quantify the advantage (in terms of the required measurement resolution) of using the governing equations within data-driven methods. We also discuss the applicability of these results to fully developed turbulence.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/b907da66c5c83725b4a28f0615aa0dec6d876592" target='_blank'>
-              Refining Potential Energy Surface through Dynamical Properties via Differentiable Molecular Simulation
+          <td><a href="https://www.semanticscholar.org/paper/f37955fcf738b892c9cedb2952e279d466fad9b8" target='_blank'>
+              Predictability of weakly turbulent systems from spatially sparse observations using data assimilation and machine learning
               </a>
             </td>
           <td>
-            Bin Han, Kuang Yu
+            Vikrant Gupta, Yuanqing Chen, Minping Wan
           </td>
-          <td>2024-06-26</td>
+          <td>2024-07-14</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>1</td>
         </tr>
 
-        <tr id="In science and engineering, there is often a need to repeatedly solve large-scale and high-resolution partial differential equations (PDEs). Neural operators are a new type of models that can map between function spaces, allowing trained models to emulate the solution operators of PDEs. This paper introduces a novel Fourier neural operator with a multigrid architecture (MgFNO). The MgFNO combines the frequency principle of deep neural networks (DNNs) with the multigrid idea for solving linear systems. To speed up the training process of the FNO, a three-layer V-cycle multigrid architecture is used. This architecture involves training the model multiple times on a coarse grid and then transferring it to a fine grid to accelerate the training of the model. The DNN-based solver learns the solution from low to high frequency, while the multigrid method acquires the solution from high to low frequency. Note that the FNO is a resolution-invariant solution operator, therefore the corresponding calculations are greatly simplified. Finally, experiments are conducted on Burgers' equation, Darcy flow, and Navier-Stokes equation. The results demonstrate that the proposed MgFNO outperforms the traditional Fourier neural operator.">
+        <tr id="Macroscopic features of dynamical systems such as almost-invariant sets and coherent sets provide crucial high-level information on how the dynamics organises phase space. We introduce a method to identify time-parameterised families of almost-invariant sets in time-dependent dynamical systems, as well as the families' emergence and disappearance. In contrast to coherent sets, which may freely move about in phase space over time, our technique focuses on families of metastable sets that are quasi-stationary in space. Our straightforward approach extends successful transfer operator methods for almost-invariant sets to time-dependent dynamics and utilises the Ulam scheme for the generator of the transfer operator on a time-expanded domain. The new methodology is illustrated with an idealised fluid flow and with atmospheric velocity data. We identify atmospheric blocking events in the 2003 European heatwave and compare our technique to existing geophysical methods of blocking diagnosis.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/fdb67e73d3dca54bf98c02624335ca91b76b8cbf" target='_blank'>
-              MgFNO: Multi-grid Architecture Fourier Neural Operator for Parametric Partial Differential Equations
+          <td><a href="https://www.semanticscholar.org/paper/85cdef684e76e594fd24e0e2863fe622d0a8314d" target='_blank'>
+              Identifying the onset and decay of quasi-stationary families of almost-invariant sets with an application to atmospheric blocking events
               </a>
             </td>
           <td>
-            Zi-Hao Guo, Hou-Biao Li
+            Aleksandar Badza, Gary Froyland School of Mathematics, Statistics Unsw Sydney Nsw Australia
           </td>
-          <td>2024-07-11</td>
+          <td>2024-07-09</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>0</td>
@@ -3192,25 +2920,10 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2024-07-18</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>16</td>
-        </tr>
-
-        <tr id="Macroscopic features of dynamical systems such as almost-invariant sets and coherent sets provide crucial high-level information on how the dynamics organises phase space. We introduce a method to identify time-parameterised families of almost-invariant sets in time-dependent dynamical systems, as well as the families' emergence and disappearance. In contrast to coherent sets, which may freely move about in phase space over time, our technique focuses on families of metastable sets that are quasi-stationary in space. Our straightforward approach extends successful transfer operator methods for almost-invariant sets to time-dependent dynamics and utilises the Ulam scheme for the generator of the transfer operator on a time-expanded domain. The new methodology is illustrated with an idealised fluid flow and with atmospheric velocity data. We identify atmospheric blocking events in the 2003 European heatwave and compare our technique to existing geophysical methods of blocking diagnosis.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/85cdef684e76e594fd24e0e2863fe622d0a8314d" target='_blank'>
-              Identifying the onset and decay of quasi-stationary families of almost-invariant sets with an application to atmospheric blocking events
-              </a>
-            </td>
-          <td>
-            Aleksandar Badza, Gary Froyland School of Mathematics, Statistics Unsw Sydney Nsw Australia
-          </td>
-          <td>2024-07-09</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>0</td>
+          <td>17</td>
         </tr>
 
-        <tr id="Filtering-the task of estimating the conditional distribution of states of a dynamical system given partial, noisy, observations-is important in many areas of science and engineering, including weather and climate prediction. However, the filtering distribution is generally intractable to obtain for high-dimensional, nonlinear systems. Filters used in practice, such as the ensemble Kalman filter (EnKF), are biased for nonlinear systems and have numerous tuning parameters. Here, we present a framework for learning a parameterized analysis map-the map that takes a forecast distribution and observations to the filtering distribution-using variational inference. We show that this methodology can be used to learn gain matrices for filtering linear and nonlinear dynamical systems, as well as inflation and localization parameters for an EnKF. Future work will apply this framework to learn new filtering algorithms.">
+        <tr id="Filtering - the task of estimating the conditional distribution of states of a dynamical system given partial, noisy, observations - is important in many areas of science and engineering, including weather and climate prediction. However, the filtering distribution is generally intractable to obtain for high-dimensional, nonlinear systems. Filters used in practice, such as the ensemble Kalman filter (EnKF), are biased for nonlinear systems and have numerous tuning parameters. Here, we present a framework for learning a parameterized analysis map - the map that takes a forecast distribution and observations to the filtering distribution - using variational inference. We show that this methodology can be used to learn gain matrices for filtering linear and nonlinear dynamical systems, as well as inflation and localization parameters for an EnKF. Future work will apply this framework to learn new filtering algorithms.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/0caedb45ee3429ec36e56edd056c7ae412f7b6b8" target='_blank'>
               Learning Optimal Filters Using Variational Inference
@@ -3221,23 +2934,8 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           </td>
           <td>2024-06-26</td>
           <td>ArXiv</td>
-          <td>0</td>
-          <td>9</td>
-        </tr>
-
-        <tr id="In this work, the Einstein notation is utilized to synthesize state and parameter transition matrices, by solving a set of ordinary differential equations. Additionally, for the system identification problem, it has been demonstrated that the gradient and Hessian of a cost function can be analytically constructed using the same matrix and tensor metrics. A general gradientbased optimization problem is then posed to identify unknown system parameters and unknown initial conditions. Here, the analytical gradient and Hessian of the cost function are derived using these state and parameter transition matrices. The more robust performance of the proposed method for identifying unknown system parameters and unknown initial conditions over an existing conventional quasi-Newton method-based system identification toolbox (available in MATLAB) is demonstrated by using two widely used benchmark datasets from real dynamic systems. In the existing toolbox, gradient and Hessian information, which are derived using a finite difference method, are more susceptible to numerical errors compared to the analytical approach presented. Keywords: Gradient-based Optimization, Transition matrix and tensors, Gradient and Hessian, System identification.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/d6a58acbda8ea18ff7cffd5f6ddaf4b09ce3e029" target='_blank'>
-              Analytical Gradient and Hessian Evaluation for System Identification using State-Parameter Transition Tensors
-              </a>
-            </td>
-          <td>
-            Premjit Saha, Tarunraj Singh
-          </td>
-          <td>2024-07-03</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>0</td>
+          <td>1</td>
+          <td>10</td>
         </tr>
 
         <tr id="A data-driven approach to calculating tight-binding models for discrete coupled-mode systems is presented. Specifically, spectral and topological data is used to build an appropriate discrete model that accurately replicates these properties. This work is motivated by topological insulator systems that are often described by tight-binding models. The problem is formulated as the minimization of an appropriate residual (objective) function. Given bulk spectral data and a topological index (e.g. winding number), an appropriate discrete model is obtained to arbitrary precision. A nonlinear least squares method is used to determine the coefficients. The effectiveness of the scheme is highlighted against a Schr\"odinger equation with a periodic potential that can be described by the Su-Schrieffer-Heeger model.">
@@ -3255,429 +2953,415 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>0</td>
         </tr>
 
-        <tr id="Although neural operator networks theoretically approximate any operator mapping, the limited generalization capability prevents them from learning correct physical dynamics when potential data biases exist, particularly in the practical PDE solving scenario where the available data amount is restricted or the resolution is extremely low. To address this issue, we propose and formulate the Physical Trajectory Residual Learning (DeltaPhi), which learns to predict the physical residuals between the pending solved trajectory and a known similar auxiliary trajectory. First, we transform the direct operator mapping between input-output function fields in original training data to residual operator mapping between input function pairs and output function residuals. Next, we learn the surrogate model for the residual operator mapping based on existing neural operator networks. Additionally, we design helpful customized auxiliary inputs for efficient optimization. Through extensive experiments, we conclude that, compared to direct learning, physical residual learning is preferred for PDE solving.">
+        <tr id="
+Fusing known physics into data-driven learners allows modelling practitioners to combine the expressive power of traditional machine learning with known mechanistic laws, where the objective is to enhance predictive performance, interpretability, and model generalisation. A core consideration that must be made when implementing a physics-informed learning architecture is how relevant knowledge will be embedded into the model structure, which, generally, is informed by the type of physics that is available. Frequently this knowledge may not be complete, with only a partial understanding of the governing physics available. 
+
+In this work, possible paths for deriving Gaussian process kernels that are representative of partial knowledge will be considered. How the type of knowledge that is possessed influences the derivation will be explored, particularly when there is the potential for some aspect of misspecified physics. An example of deriving partially structured kernels will be investigated for modelling the decoupled response of a GARTEUR laboratory aircraft structure, where the derived kernels are used to decompose the dynamics of the aircraft into modal contributions. 
+
+">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/75ea87f76067c9b62442cad18c16c45c61d35489" target='_blank'>
-              DeltaPhi: Learning Physical Trajectory Residual for PDE Solving
+          <td><a href="https://www.semanticscholar.org/paper/da9bbf07470b8cc6d6606c4b51a040b8e45ce3d6" target='_blank'>
+              Gaussian process kernels for partial physical insight
               </a>
             </td>
           <td>
-            Xihang Yue, Linchao Zhu, Yi Yang
+            Matthew R. Jones, D. J. Pitchforth, E. J. Cross
           </td>
-          <td>2024-06-14</td>
-          <td>ArXiv</td>
+          <td>2024-07-01</td>
+          <td>e-Journal of Nondestructive Testing</td>
           <td>0</td>
-          <td>41</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Fractional and tempered fractional partial differential equations (PDEs) are effective models of long-range interactions, anomalous diffusion, and non-local effects. Traditional numerical methods for these problems are mesh-based, thus struggling with the curse of dimensionality (CoD). Physics-informed neural networks (PINNs) offer a promising solution due to their universal approximation, generalization ability, and mesh-free training. In principle, Monte Carlo fractional PINN (MC-fPINN) estimates fractional derivatives using Monte Carlo methods and thus could lift CoD. However, this may cause significant variance and errors, hence affecting convergence; in addition, MC-fPINN is sensitive to hyperparameters. In general, numerical methods and specifically PINNs for tempered fractional PDEs are under-developed. Herein, we extend MC-fPINN to tempered fractional PDEs to address these issues, resulting in the Monte Carlo tempered fractional PINN (MC-tfPINN). To reduce possible high variance and errors from Monte Carlo sampling, we replace the one-dimensional (1D) Monte Carlo with 1D Gaussian quadrature, applicable to both MC-fPINN and MC-tfPINN. We validate our methods on various forward and inverse problems of fractional and tempered fractional PDEs, scaling up to 100,000 dimensions. Our improved MC-fPINN/MC-tfPINN using quadrature consistently outperforms the original versions in accuracy and convergence speed in very high dimensions.">
+        <tr id="In recent decades, the utilization of machine learning (ML) and artificial intelligence (AI) approaches have been explored for process modelling applications. However, different types of ML models may have contrasting advantages and disadvantages, which become critical during the optimal selection of a specific data‐driven model for a particular application as well as estimation of parameters during model training. This paper compares and contrasts two different types of data‐driven modelling approaches, namely the series/parallel all‐nonlinear static‐dynamic neural network models and models from a Bayesian ML approach. Both types of AI modelling approaches considered in this work have shown to significantly outperform several state‐of‐the‐art steady‐state and dynamic data‐driven modelling techniques for various performance measures, specifically, model sparsity, predictive capabilities, and computational expense. The performances of the proposed model structures and algorithms have been evaluated for two nonlinear dynamic chemical engineering systems—a plug‐flow reactor for vapour phase cracking of acetone for production of acetic anhydride and a pilot‐plant for post‐combustion CO2 capture using monoethanolamine as the solvent. For the validation data from the CO2 capture pilot plant, root mean squared error (RMSE) for flue gas outlet temperature, flowrate and CO2 concentration is 0.05%, 1.07%, and 5.0%, respectively, for the all‐nonlinear static‐dynamic neural networks and 0.1%, 1.75%, and 14.14%, respectively, for the Bayesian ML models. For the plug flow reactor data, the Bayesian ML models yield superior RMSE compared to the all‐nonlinear static‐dynamic neural networks when the measurement data are corrupted with Gaussian, auto‐correlated, or cross‐correlated noise.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6f554b1c8b8360b3842290f4ce5cc7ca4106693d" target='_blank'>
-              Tackling the Curse of Dimensionality in Fractional and Tempered Fractional PDEs with Physics-Informed Neural Networks
+          <td><a href="https://www.semanticscholar.org/paper/056ceb214531414b85db491a54767e185fb5c38a" target='_blank'>
+              All‐nonlinear static‐dynamic neural networks versus Bayesian machine learning for data‐driven modelling of chemical processes
               </a>
             </td>
           <td>
-            Zheyuan Hu, Kenji Kawaguchi, Zhongqiang Zhang, G. Karniadakis
+            Angan Mukherjee, Samuel Adeyemo, D. Bhattacharyya
           </td>
-          <td>2024-06-17</td>
-          <td>ArXiv</td>
+          <td>2024-06-24</td>
+          <td>The Canadian Journal of Chemical Engineering</td>
           <td>0</td>
-          <td>127</td>
+          <td>29</td>
         </tr>
 
-        <tr id="This paper introduces PDEformer-1, a versatile neural solver capable of simultaneously addressing various partial differential equations (PDEs). With the PDE represented as a computational graph, we facilitate the seamless integration of symbolic and numeric information inherent in a PDE. A graph Transformer and an implicit neural representation (INR) are employed subsequently to generate mesh-free predicted solutions. We generated a dataset with up to three million samples involving diverse one-dimensional PDEs to pretrain our model. Compared with baseline models trained specifically on benchmark datasets, our pretrained model achieves comparable accuracy via zero-shot inference, and the advantage expands after finetuning. For PDEs new or unseen in the pretraining stage, our model can adapt quickly by finetuning on a relatively small set of examples from the target equation. Additionally, PDEformer-1 demonstrates promising results in the inverse problem of PDE scalar coefficient recovery and coefficient field recovery.">
+        <tr id="Equations of State model relations between thermodynamic variables and are ubiquitous in scientific modelling, appearing in modern day applications ranging from Astrophysics to Climate Science. The three desired properties of a general Equation of State model are adherence to the Laws of Thermodynamics, incorporation of phase transitions, and multiscale accuracy. Analytic models that adhere to all three are hard to develop and cumbersome to work with, often resulting in sacrificing one of these elements for the sake of efficiency. In this work, two deep-learning methods are proposed that provably satisfy the first and second conditions on a large-enough region of thermodynamic variable space. The first is based on learning the generating function (thermodynamic potential) while the second is based on structure-preserving, symplectic neural networks, respectively allowing modifications near or on phase transition regions. They can be used either"from scratch"to learn a full Equation of State, or in conjunction with a pre-existing consistent model, functioning as a modification that better adheres to experimental data. We formulate the theory and provide several computational examples to justify both approaches, and highlight their advantages and shortcomings.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/40c21c8b077c4c5aea793ff9f0cb0cf1332acd86" target='_blank'>
-              PDEformer-1: A Foundation Model for One-Dimensional Partial Differential Equations
+          <td><a href="https://www.semanticscholar.org/paper/6515ce69075cc8fe93c06073e12dcf389d87c37b" target='_blank'>
+              Neural Network Representations of Multiphase Equations of State
               </a>
             </td>
           <td>
-            Zhanhong Ye, Xiang Huang, Leheng Chen, Zining Liu, Bingyang Wu, Hongsheng Liu, Zidong Wang, Bin Dong
+            George A. Kevrekidis, Daniel A. Serino, Alexander Kaltenborn, J. Gammel, J. Burby, Marc L. Klasky
           </td>
-          <td>2024-07-09</td>
+          <td>2024-06-28</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>5</td>
+          <td>16</td>
         </tr>
 
-        <tr id="Physics-informed neural networks (PINNs) represent a significant advancement in scientific machine learning by integrating fundamental physical laws into their architecture through loss functions. PINNs have been successfully applied to solve various forward and inverse problems in partial differential equations (PDEs). However, a notable challenge can emerge during the early training stages when solving inverse problems. Specifically, data losses remain high while PDE residual losses are minimized rapidly, thereby exacerbating the imbalance between loss terms and impeding the overall efficiency of PINNs. To address this challenge, this study proposes a novel framework termed data-guided physics-informed neural networks (DG-PINNs). The DG-PINNs framework is structured into two distinct phases: a pre-training phase and a fine-tuning phase. In the pre-training phase, a loss function with only the data loss is minimized in a neural network. In the fine-tuning phase, a composite loss function, which consists of the data loss, PDE residual loss, and, if available, initial and boundary condition losses, is minimized in the same neural network. Notably, the pre-training phase ensures that the data loss is already at a low value before the fine-tuning phase commences. This approach enables the fine-tuning phase to converge to a minimal composite loss function with fewer iterations compared to existing PINNs. To validate the effectiveness, noise-robustness, and efficiency of DG-PINNs, extensive numerical investigations are conducted on inverse problems related to several classical PDEs, including the heat equation, wave equation, Euler--Bernoulli beam equation, and Navier--Stokes equation. The numerical results demonstrate that DG-PINNs can accurately solve these inverse problems and exhibit robustness against noise in training data.">
+        <tr id="The paper presents a framework for online learning of the Koopman operator using streaming data. Many complex systems for which data-driven modeling and control are sought provide streaming sensor data, the abundance of which can present computational challenges but cannot be ignored. Streaming data can intermittently sample dynamically different regimes or rare events which could be critical to model and control. Using ideas from subspace identification, we present a method where the Grassmannian distance between the subspace of an extended observability matrix and the streaming segment of data is used to assess the `novelty' of the data. If this distance is above a threshold, it is added to an archive and the Koopman operator is updated if not it is discarded. Therefore, our method identifies data from segments of trajectories of a dynamical system that are from different dynamical regimes, prioritizes minimizing the amount of data needed in updating the Koopman model and furthermore reduces the number of basis functions by learning them adaptively. Therefore, by dynamically adjusting the amount of data used and learning basis functions, our method optimizes the model's accuracy and the system order.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e8631f5379ee6bc7f5986ad7bd980d846ff5079c" target='_blank'>
-              Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations
+          <td><a href="https://www.semanticscholar.org/paper/a7ee04d648defa5ecd02e1e1b2368c710c5df6c2" target='_blank'>
+              Online learning of Koopman operator using streaming data from different dynamical regimes
               </a>
             </td>
           <td>
-            Wei Zhou, Y. F. Xu
+            Kartik Loya, Phanindra Tallapragada
           </td>
-          <td>2024-07-15</td>
+          <td>2024-07-18</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
+          <td>16</td>
         </tr>
 
-        <tr id="The deep operator network (DeepONet) is a popular neural operator architecture that has shown promise in solving partial differential equations (PDEs) by using deep neural networks to map between infinite-dimensional function spaces. In the absence of labeled datasets, we utilize the PDE residual loss to learn the physical system, an approach known as physics-informed DeepONet. This method faces significant computational challenges, primarily due to the curse of dimensionality, as the computational cost increases exponentially with finer discretization. In this paper, we introduce the Separable DeepONet framework to address these challenges and improve scalability for high-dimensional PDEs. Our approach involves a factorization technique where sub-networks handle individual one-dimensional coordinates, thereby reducing the number of forward passes and the size of the Jacobian matrix. By using forward-mode automatic differentiation, we further optimize the computational cost related to the Jacobian matrix. As a result, our modifications lead to a linear scaling of computational cost with discretization density, making Separable DeepONet suitable for high-dimensional PDEs. We validate the effectiveness of the separable architecture through three benchmark PDE models: the viscous Burgers equation, Biot's consolidation theory, and a parametrized heat equation. In all cases, our proposed framework achieves comparable or improved accuracy while significantly reducing computational time compared to conventional DeepONet. These results demonstrate the potential of Separable DeepONet in efficiently solving complex, high-dimensional PDEs, advancing the field of physics-informed machine learning.">
+        <tr id="Dynamic systems described by differential equations often involve feedback among system components. When there are time delays for components to sense and respond to feedback, delay differential equation (DDE) models are commonly used. This paper considers the problem of inferring unknown system parameters, including the time delays, from noisy and sparse experimental data observed from the system. We propose an extension of manifold-constrained Gaussian processes to conduct parameter inference for DDEs, whereas the time delay parameters have posed a challenge for existing methods that bypass numerical solvers. Our method uses a Bayesian framework to impose a Gaussian process model over the system trajectory, conditioned on the manifold constraint that satisfies the DDEs. For efficient computation, a linear interpolation scheme is developed to approximate the values of the time-delayed system outputs, along with corresponding theoretical error bounds on the approximated derivatives. Two simulation examples, based on Hutchinson's equation and the lac operon system, together with a real-world application using Ontario COVID-19 data, are used to illustrate the efficacy of our method.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6c6d4d267faeeac3657a5fc9f403c2070de6ac02" target='_blank'>
-              Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning
+          <td><a href="https://www.semanticscholar.org/paper/59505531dc6e56a864c5a883b0860d09309620d3" target='_blank'>
+              Inference for Delay Differential Equations Using Manifold-Constrained Gaussian Processes
               </a>
             </td>
           <td>
-            Luis Mandl, S. Goswami, L. Lambers, Tim Ricken
+            Yuxuan Zhao, Samuel W. K. Wong
           </td>
-          <td>2024-07-21</td>
+          <td>2024-06-21</td>
           <td>ArXiv</td>
+          <td>0</td>
           <td>1</td>
-          <td>19</td>
         </tr>
 
-        <tr id="
-Fusing known physics into data-driven learners allows modelling practitioners to combine the expressive power of traditional machine learning with known mechanistic laws, where the objective is to enhance predictive performance, interpretability, and model generalisation. A core consideration that must be made when implementing a physics-informed learning architecture is how relevant knowledge will be embedded into the model structure, which, generally, is informed by the type of physics that is available. Frequently this knowledge may not be complete, with only a partial understanding of the governing physics available. 
-
-In this work, possible paths for deriving Gaussian process kernels that are representative of partial knowledge will be considered. How the type of knowledge that is possessed influences the derivation will be explored, particularly when there is the potential for some aspect of misspecified physics. An example of deriving partially structured kernels will be investigated for modelling the decoupled response of a GARTEUR laboratory aircraft structure, where the derived kernels are used to decompose the dynamics of the aircraft into modal contributions. 
-
-">
+        <tr id="In this paper, we develop the Asymptotic-Preserving Neural Networks (APNNs) approach to study the forward and inverse problem for the semiconductor Boltzmann equation. The goal of the neural network is to resolve the computational challenges of conventional numerical methods and multiple scales of the model. To guarantee the network can operate uniformly in different regimes, it is desirable to carry the Asymptotic-Preservation (AP) property in the learning process. In a micro-macro decomposition framework, we design such an AP formulation of loss function. The convergence analysis of both the loss function and its neural network is shown, based on the Universal Approximation Theorem and hypocoercivity theory of the model equation. We show a series of numerical tests for forward and inverse problems of both the semiconductor Boltzmann and the Boltzmann-Poisson system to validate the effectiveness of our proposed method, which addresses the significance of the AP property when dealing with inverse problems of multiscale Boltzmann equations especially when only sparse or partially observed data are available.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/da9bbf07470b8cc6d6606c4b51a040b8e45ce3d6" target='_blank'>
-              Gaussian process kernels for partial physical insight
+          <td><a href="https://www.semanticscholar.org/paper/bb3329e7d90a830059df34e4bc6454db16ae0c60" target='_blank'>
+              Asymptotic-preserving neural networks for the semiconductor Boltzmann equation and its application on inverse problems
               </a>
             </td>
           <td>
-            Matthew R. Jones, D. J. Pitchforth, E. J. Cross
+            Liu Liu, Yating Wang, Xueyu Zhu, Zhenyi Zhu
           </td>
-          <td>2024-07-01</td>
-          <td>e-Journal of Nondestructive Testing</td>
-          <td>0</td>
-          <td>3</td>
+          <td>2024-07-23</td>
+          <td>ArXiv</td>
+          <td>1</td>
+          <td>1</td>
         </tr>
 
-        <tr id="In recent decades, the utilization of machine learning (ML) and artificial intelligence (AI) approaches have been explored for process modelling applications. However, different types of ML models may have contrasting advantages and disadvantages, which become critical during the optimal selection of a specific data‐driven model for a particular application as well as estimation of parameters during model training. This paper compares and contrasts two different types of data‐driven modelling approaches, namely the series/parallel all‐nonlinear static‐dynamic neural network models and models from a Bayesian ML approach. Both types of AI modelling approaches considered in this work have shown to significantly outperform several state‐of‐the‐art steady‐state and dynamic data‐driven modelling techniques for various performance measures, specifically, model sparsity, predictive capabilities, and computational expense. The performances of the proposed model structures and algorithms have been evaluated for two nonlinear dynamic chemical engineering systems—a plug‐flow reactor for vapour phase cracking of acetone for production of acetic anhydride and a pilot‐plant for post‐combustion CO2 capture using monoethanolamine as the solvent. For the validation data from the CO2 capture pilot plant, root mean squared error (RMSE) for flue gas outlet temperature, flowrate and CO2 concentration is 0.05%, 1.07%, and 5.0%, respectively, for the all‐nonlinear static‐dynamic neural networks and 0.1%, 1.75%, and 14.14%, respectively, for the Bayesian ML models. For the plug flow reactor data, the Bayesian ML models yield superior RMSE compared to the all‐nonlinear static‐dynamic neural networks when the measurement data are corrupted with Gaussian, auto‐correlated, or cross‐correlated noise.">
+        <tr id="We introduce mochi_class, an extension of the Einstein-Boltzmann solver hi_class, designed to unlock the full phenomenological potential of Horndeski gravity. This extension allows for general input functions of time without the need for hard-coded parametrisations or covariant Lagrangians. By replacing the traditional $\alpha$-parametrisation with a set of stable basis functions, mochi_class ensures that the resulting effective theories are inherently free from gradient and ghost instabilities. Additionally, mochi_class features a quasi-static approximation implemented at the level of modified metric potentials, enhancing prediction accuracy, especially for models transitioning between a super- and sub-Compton regime. mochi_class can robustly handle a wide range of models without fine-tuning, and introduces a new approximation scheme that activates modifications to the standard cosmology deep in the matter-dominated era. Furthermore, it incorporates viability conditions on the equation of motion for the scalar field fluctuations, aiding in the identification of numerical instabilities. Through comprehensive validation against other Einstein-Boltzmann solvers, mochi_class demonstrates excellent performance and accuracy, broadening the scope of hi_class by facilitating the study of specific modified gravity models and enabling exploration of previously inaccessible regions of the Horndeski landscape. The code is publicly available at https://github.com/mcataneo/mochi_class_public">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/056ceb214531414b85db491a54767e185fb5c38a" target='_blank'>
-              All‐nonlinear static‐dynamic neural networks versus Bayesian machine learning for data‐driven modelling of chemical processes
+          <td><a href="https://www.semanticscholar.org/paper/45c44ba92da169b2eb987805f3b6ad66528aeee4" target='_blank'>
+              mochi_class: Modelling Optimisation to Compute Horndeski In class
               </a>
             </td>
           <td>
-            Angan Mukherjee, Samuel Adeyemo, D. Bhattacharyya
+            Matteo Cataneo, Emilio Bellini
           </td>
-          <td>2024-06-24</td>
-          <td>The Canadian Journal of Chemical Engineering</td>
+          <td>2024-07-16</td>
+          <td>ArXiv</td>
+          <td>0</td>
           <td>0</td>
-          <td>29</td>
         </tr>
 
-        <tr id="Interatomic potentials are essential to go beyond ab initio size limitations, but simulation results depend sensitively on potential parameters. Forward propagation of parameter variation is key for uncertainty quantification, whilst backpropagation has found application for emerging inverse problems such as fine-tuning or targeted design. Here, the implicit derivative of functions defined as a fixed point is used to Taylor expand the energy and structure of atomic minima in potential parameters, evaluating terms via automatic differentiation, dense linear algebra or a novel sparse operator approach. The latter allows efficient forward and backpropagation through relaxed structures of arbitrarily large systems. The implicit expansion accurately predicts lattice distortion and defect formation energies and volumes with classical and machine-learning potentials, enabling high-dimensional uncertainty propagation without prohibitive overhead. We then show how the implicit derivative can be used to solve challenging inverse problems, minimizing an implicit loss to fine-tune potentials and stabilize solute-induced structural rearrangements at dislocations in tungsten.">
+        <tr id="In recent years, surrogate models based on deep neural networks have been widely used to solve partial differential equations for fluid flow physics. This kind of model focuses on global interpolation of the training data and thus requires a large network structure. The process is both time consuming and computationally costly. In the present study, we develop a neural network with local converging input (NNLCI) for high-fidelity prediction using unstructured data. The framework uses the local domain of dependence with converging coarse solutions as input, thereby greatly reducing computational resource and training time. As a validation case, the NNLCI method is applied to study two-dimensional inviscid supersonic flows in channels with bumps. Different bump geometries and locations are examined to benchmark the effectiveness and versatility of this new approach. The NNLCI method can accurately and efficiently capture the structure and dynamics of the entire flowfield, including regions with shock discontinuities. For a new bump configuration, the method can perform prediction with only one neural network, eliminating the need for repeated training of multiple networks for different geometries. A saving of computing wall time is achieved by several orders of magnitude against the high-fidelity simulation with the same level of accuracy. The demand on training data is modest, and the training data can be allocated sparsely. These features are especially advantageous compared with conventional global-to-global deep learning methods and physics-informed methods.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/35b1023f8fbb2a5d8ac7969cbeeb6f4a14bc27f2" target='_blank'>
-              Exploring parameter dependence of atomic minima with implicit differentiation
+          <td><a href="https://www.semanticscholar.org/paper/88415c3817a7058d9678f49a393b306a231a9953" target='_blank'>
+              Neural Network with Local Converging Input for Unstructured-Grid Computational Fluid Dynamics
               </a>
             </td>
           <td>
-            I. Maliyov, P. Grigorev, T. Swinburne
+            Weiming Ding, Haoxiang Huang, T. Lee, Yingjie Liu, Vigor Yang
           </td>
-          <td>2024-07-02</td>
-          <td>ArXiv</td>
+          <td>2024-07-01</td>
+          <td>AIAA Journal</td>
           <td>0</td>
-          <td>17</td>
+          <td>1</td>
         </tr>
 
-        <tr id="The paper presents a framework for online learning of the Koopman operator using streaming data. Many complex systems for which data-driven modeling and control are sought provide streaming sensor data, the abundance of which can present computational challenges but cannot be ignored. Streaming data can intermittently sample dynamically different regimes or rare events which could be critical to model and control. Using ideas from subspace identification, we present a method where the Grassmannian distance between the subspace of an extended observability matrix and the streaming segment of data is used to assess the `novelty' of the data. If this distance is above a threshold, it is added to an archive and the Koopman operator is updated if not it is discarded. Therefore, our method identifies data from segments of trajectories of a dynamical system that are from different dynamical regimes, prioritizes minimizing the amount of data needed in updating the Koopman model and furthermore reduces the number of basis functions by learning them adaptively. Therefore, by dynamically adjusting the amount of data used and learning basis functions, our method optimizes the model's accuracy and the system order.">
+        <tr id="Accurate quantum dynamics simulations of nonadiabatic processes are important for studies of electron transfer, energy transfer, and photochemical reactions in complex systems. In this comparative study, we benchmark various approximate nonadiabatic dynamics methods with mapping variables against numerically exact calculations based on the tensor-train (TT) representation of high-dimensional arrays, including TT-KSL for zero-temperature dynamics and TT-thermofield dynamics for finite-temperature dynamics. The approximate nonadiabatic dynamics methods investigated include mixed quantum-classical Ehrenfest mean-field and fewest-switches surface hopping, linearized semiclassical mapping dynamics, symmetrized quasiclassical dynamics, the spin-mapping method, and extended classical mapping models. Different model systems were evaluated, including the spin-boson model for nonadiabatic dynamics in the condensed phase, the linear vibronic coupling model for electronic transition through conical intersections, the photoisomerization model of retinal, and Tully's one-dimensional scattering models. Our calculations show that the optimal choice of approximate dynamical method is system-specific, and the accuracy is sensitively dependent on the zero-point-energy parameter and the initial sampling strategy for the mapping variables.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/a7ee04d648defa5ecd02e1e1b2368c710c5df6c2" target='_blank'>
-              Online learning of Koopman operator using streaming data from different dynamical regimes
+          <td><a href="https://www.semanticscholar.org/paper/328d4dede1c70e1ea2e979a03782edbe7fbd9ab1" target='_blank'>
+              Benchmarking various nonadiabatic semiclassical mapping dynamics methods with tensor-train thermo-field dynamics.
               </a>
             </td>
           <td>
-            Kartik Loya, Phanindra Tallapragada
+            Zengkui Liu, Ningyi Lyu, Zhubin Hu, Hao Zeng, Victor S. Batista, Xiang Sun
           </td>
-          <td>2024-07-18</td>
-          <td>ArXiv</td>
+          <td>2024-07-09</td>
+          <td>The Journal of chemical physics</td>
           <td>0</td>
-          <td>16</td>
+          <td>18</td>
         </tr>
 
-        <tr id="Equations of State model relations between thermodynamic variables and are ubiquitous in scientific modelling, appearing in modern day applications ranging from Astrophysics to Climate Science. The three desired properties of a general Equation of State model are adherence to the Laws of Thermodynamics, incorporation of phase transitions, and multiscale accuracy. Analytic models that adhere to all three are hard to develop and cumbersome to work with, often resulting in sacrificing one of these elements for the sake of efficiency. In this work, two deep-learning methods are proposed that provably satisfy the first and second conditions on a large-enough region of thermodynamic variable space. The first is based on learning the generating function (thermodynamic potential) while the second is based on structure-preserving, symplectic neural networks, respectively allowing modifications near or on phase transition regions. They can be used either"from scratch"to learn a full Equation of State, or in conjunction with a pre-existing consistent model, functioning as a modification that better adheres to experimental data. We formulate the theory and provide several computational examples to justify both approaches, and highlight their advantages and shortcomings.">
+        <tr id="Reservoir computing has been shown to be a useful framework for predicting critical transitions of a dynamical system if the bifurcation parameter is also provided as an input. Its utility is significant because in real-world scenarios, the exact model equations are unknown. This Letter shows how the theory of dynamical system provides the underlying mechanism behind the prediction. Using numerical methods, by considering dynamical systems which show Hopf bifurcation, we demonstrate that the map produced by the reservoir after a successful training undergoes a Neimark-Sacker bifurcation such that the critical point of the map is in immediate proximity to that of the original dynamical system. In addition, we have compared and analyzed different structures in the phase space. Our findings provide insight into the functioning of machine learning algorithms for predicting critical transitions.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6515ce69075cc8fe93c06073e12dcf389d87c37b" target='_blank'>
-              Neural Network Representations of Multiphase Equations of State
+          <td><a href="https://www.semanticscholar.org/paper/3e38c7638dd9a0b907d25cf396095bd4a2d25e12" target='_blank'>
+              Dynamical analysis of a parameter-aware reservoir computer
               </a>
             </td>
           <td>
-            George A. Kevrekidis, Daniel A. Serino, Alexander Kaltenborn, J. Gammel, J. Burby, Marc L. Klasky
+            Dishant Sisodia, S. Jalan
           </td>
-          <td>2024-06-28</td>
+          <td>2024-07-20</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>16</td>
+          <td>26</td>
         </tr>
 
-        <tr id="Physics-informed neural network (PINN) is a neural network that combines machine learning methods with the physics of the problem often expressed in terms of differential equations along with boundary/initial conditions. In this paper, we employed unsupervised PINNs to solve steady-state incompressible laminar periodic flow problems without using any data. First, the PINN code for periodic flows was verified using flow between parallel plates. Further, two geometries were considered in this paper: periodic flow over cylinders between parallel plates and periodic flows through wavy channels, up to a maximum Reynolds number of 400. The proposed approach showed excellent results when compared to grid-independent computational fluid dynamics results with maximum L2-norm error of O(10−2) and O(10−1) for streamwise and cross-stream velocity, respectively.">
+        <tr id="In this work, we present a novel methodology for performing the supervised classification of time-ordered noisy data; we call this methodology Entropic Sparse Probabilistic Approximation with Markov regularization (eSPA-Markov). It is an extension of entropic learning methodologies, allowing the simultaneous learning of segmentation patterns, entropy-optimal feature space discretizations, and Bayesian classification rules. We prove the conditions for the existence and uniqueness of the learning problem solution and propose a one-shot numerical learning algorithm that—in the leading order—scales linearly in dimension. We show how this technique can be used for the computationally scalable identification of persistent (metastable) regime affiliations and regime switches from high-dimensional non-stationary and noisy time series, i.e., when the size of the data statistics is small compared to their dimensionality and when the noise variance is larger than the variance in the signal. We demonstrate its performance on a set of toy learning problems, comparing eSPA-Markov to state-of-the-art techniques, including deep learning and random forests. We show how this technique can be used for the analysis of noisy time series from DNA and RNA Nanopore sequencing.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/37b64b38129afd029fc5be9c28e7cd56bf22a48a" target='_blank'>
-              Physics-informed neural networks for periodic flows
+          <td><a href="https://www.semanticscholar.org/paper/eadfee466307a1a3b8e63299cb3e600477c1f902" target='_blank'>
+              On Entropic Learning from Noisy Time Series in the Small Data Regime
               </a>
             </td>
           <td>
-            Smruti Shah, N. K. Anand
+            Davide Bassetti, Lukáš Pospíšil, I. Horenko
           </td>
-          <td>2024-07-01</td>
-          <td>Physics of Fluids</td>
+          <td>2024-06-28</td>
+          <td>Entropy</td>
           <td>0</td>
-          <td>1</td>
-        </tr>
-
-        <tr id="We introduce a method that combines neural operators, physics-informed machine learning, and standard numerical methods for solving PDEs. The proposed approach extends each of the aforementioned methods and unifies them within a single framework. We can parametrically solve partial differential equations in a data-free manner and provide accurate sensitivities, meaning the derivatives of the solution space with respect to the design space. These capabilities enable gradient-based optimization without the typical sensitivity analysis costs, unlike adjoint methods that scale directly with the number of response functions. Our Finite Operator Learning (FOL) approach uses an uncomplicated feed-forward neural network model to directly map the discrete design space (i.e. parametric input space) to the discrete solution space (i.e. finite number of sensor points in the arbitrary shape domain) ensuring compliance with physical laws by designing them into loss functions. The discretized governing equations, as well as the design and solution spaces, can be derived from any well-established numerical techniques. In this work, we employ the Finite Element Method (FEM) to approximate fields and their spatial derivatives. Subsequently, we conduct Sobolev training to minimize a multi-objective loss function, which includes the discretized weak form of the energy functional, boundary conditions violations, and the stationarity of the residuals with respect to the design variables. Our study focuses on the steady-state heat equation within heterogeneous materials that exhibits significant phase contrast and possibly temperature-dependent conductivity. The network's tangent matrix is directly used for gradient-based optimization to improve the microstructure's heat transfer characteristics. ...">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6248ca7a394e7da69cd6d0cb638b974e5ed8f57e" target='_blank'>
-              Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEs
-              </a>
-            </td>
-          <td>
-            Shahed Rezaei, Reza Najian Asl, Kianoosh Taghikhani, Ahmad Moeineddin, Michael Kaliske, Markus Apel
-          </td>
-          <td>2024-07-04</td>
-          <td>ArXiv</td>
-          <td>1</td>
-          <td>18</td>
+          <td>26</td>
         </tr>
 
-        <tr id="Stochastic dynamics on sparse graphs and disordered systems often lead to complex behaviors characterized by heterogeneity in time and spatial scales, slow relaxation, localization, and aging phenomena. The mathematical tools and approximation techniques required to analyze these complex systems are still under development, posing significant technical challenges and resulting in a reliance on numerical simulations. We introduce a novel computational framework for investigating the dynamics of sparse disordered systems with continuous degrees of freedom. Starting with a graphical model representation of the dynamic partition function for a system of linearly-coupled stochastic differential equations, we use dynamic cavity equations on locally tree-like factor graphs to approximate the stochastic measure. Here, cavity marginals are identified with local functionals of single-site trajectories. Our primary approximation involves a second-order truncation of a small-coupling expansion, leading to a Gaussian form for the cavity marginals. For linear dynamics with additive noise, this method yields a closed set of causal integro-differential equations for cavity versions of one-time and two-time averages. These equations provide an exact dynamical description within the local tree-like approximation, retrieving classical results for the spectral density of sparse random matrices. Global constraints, non-linear forces, and state-dependent noise terms can be addressed using a self-consistent perturbative closure technique. The resulting equations resemble those of dynamical mean-field theory in the mode-coupling approximation used for fully-connected models. However, due to their cavity formulation, the present method can also be applied to ensembles of sparse random graphs and employed as a message-passing algorithm on specific graph instances.">
+        <tr id="
+ Turbulence closure modeling using machine learning is at an early crossroads. The extraordinary success of machine learning (ML) in a variety of challenging fields had given rise to an expectation of similar transformative advances in the area of turbulence closure modeling. However, by most accounts, the current rate of progress toward accurate and predictive ML-RANS (Reynolds Averaged Navier-Stokes) closure models has been very slow. Upon retrospection, the absence of rapid transformative progress can be attributed to two factors: the underestimation of the intricacies of turbulence modeling and the overestimation of ML’s ability to capture all features without employing targeted strategies. To pave the way for more meaningful ML closures tailored to address the nuances of turbulence, this article seeks to review the foundational flow physics to assess the challenges in the context of data-driven approaches. Revisiting analogies with statistical mechanics and stochastic systems, the key physical complexities and mathematical limitations are explicated. It is noted that the current ML approaches do not systematically address the inherent limitations of a statistical approach or the inadequacies of the mathematical forms of closure expressions. The study underscores the drawbacks of supervised learning-based closures and stresses the importance of a more discerning ML modeling framework. As ML methods evolve (which is happening at a rapid pace) and our understanding of the turbulence phenomenon improves, the inferences expressed here should be suitably modified.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1409599021b5a3a43a70ea04c2d775f534dd885c" target='_blank'>
-              Gaussian approximation of dynamic cavity equations for linearly-coupled stochastic dynamics
+          <td><a href="https://www.semanticscholar.org/paper/d5cf3709be3b3ab9de17733558b062f9c6b30ac8" target='_blank'>
+              Turbulence Closure Modeling with Machine Learning: A Foundational Physics Perspective
               </a>
             </td>
           <td>
-            Mattia Tarabolo, Luca Dall'Asta
+            S. Girimaji
           </td>
-          <td>2024-06-20</td>
-          <td>ArXiv</td>
+          <td>2024-07-23</td>
+          <td>New Journal of Physics</td>
           <td>0</td>
-          <td>1</td>
+          <td>42</td>
         </tr>
 
-        <tr id="Dynamic systems described by differential equations often involve feedback among system components. When there are time delays for components to sense and respond to feedback, delay differential equation (DDE) models are commonly used. This paper considers the problem of inferring unknown system parameters, including the time delays, from noisy and sparse experimental data observed from the system. We propose an extension of manifold-constrained Gaussian processes to conduct parameter inference for DDEs, whereas the time delay parameters have posed a challenge for existing methods that bypass numerical solvers. Our method uses a Bayesian framework to impose a Gaussian process model over the system trajectory, conditioned on the manifold constraint that satisfies the DDEs. For efficient computation, a linear interpolation scheme is developed to approximate the values of the time-delayed system outputs, along with corresponding theoretical error bounds on the approximated derivatives. Two simulation examples, based on Hutchinson's equation and the lac operon system, together with a real-world application using Ontario COVID-19 data, are used to illustrate the efficacy of our method.">
+        <tr id="Gaussian Processes (GPs) and Linear Dynamical Systems (LDSs) are essential time series and dynamic system modeling tools. GPs can handle complex, nonlinear dynamics but are computationally demanding, while LDSs offer efficient computation but lack the expressive power of GPs. To combine their benefits, we introduce a universal method that allows an LDS to mirror stationary temporal GPs. This state-space representation, known as the Markovian Gaussian Process (Markovian GP), leverages the flexibility of kernel functions while maintaining efficient linear computation. Unlike existing GP-LDS conversion methods, which require separability for most multi-output kernels, our approach works universally for single- and multi-output stationary temporal kernels. We evaluate our method by computing covariance, performing regression tasks, and applying it to a neuroscience application, demonstrating that our method provides an accurate state-space representation for stationary temporal GPs.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/59505531dc6e56a864c5a883b0860d09309620d3" target='_blank'>
-              Inference for Delay Differential Equations Using Manifold-Constrained Gaussian Processes
+          <td><a href="https://www.semanticscholar.org/paper/f45b6ab8336d143d48c626e23d36a99a22707758" target='_blank'>
+              Markovian Gaussian Process: A Universal State-Space Representation for Stationary Temporal Gaussian Process
               </a>
             </td>
           <td>
-            Yuxuan Zhao, Samuel W. K. Wong
+            Weihan Li, Yule Wang, Chengrui Li, Anqi Wu
           </td>
-          <td>2024-06-21</td>
+          <td>2024-06-29</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>1</td>
         </tr>
 
-        <tr id="Diffusion regulates a phenomenal number of natural processes and the dynamics of many successful generative models. Existing models to learn the diffusion terms from observational data rely on complex bilevel optimization problems and properly model only the drift of the system. We propose a new simple model, JKOnet*, which bypasses altogether the complexity of existing architectures while presenting significantly enhanced representational capacity: JKOnet* recovers the potential, interaction, and internal energy components of the underlying diffusion process. JKOnet* minimizes a simple quadratic loss, runs at lightspeed, and drastically outperforms other baselines in practice. Additionally, JKOnet* provides a closed-form optimal solution for linearly parametrized functionals. Our methodology is based on the interpretation of diffusion processes as energy-minimizing trajectories in the probability space via the so-called JKO scheme, which we study via its first-order optimality conditions, in light of few-weeks-old advancements in optimization in the probability space.">
+        <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e136b8e7261e1835acc89bf0402e956bd4ee5225" target='_blank'>
-              Learning Diffusion at Lightspeed
+          <td><a href="https://www.semanticscholar.org/paper/1e47886cf1fed1baf5e2839789863c0355296cdc" target='_blank'>
+              The Distance Between: An Algorithmic Approach to Comparing Stochastic Models to Time-Series Data
               </a>
             </td>
           <td>
-            Antonio Terpin, Nicolas Lanzetti, Florian Dörfler
+            Brock D. Sherlock, Marko A. A. Boon, Maria Vlasiou, A. Coster
           </td>
-          <td>2024-06-18</td>
-          <td>ArXiv</td>
+          <td>2024-07-26</td>
+          <td>Bulletin of Mathematical Biology</td>
           <td>0</td>
-          <td>10</td>
+          <td>25</td>
         </tr>
 
-        <tr id="We introduce an innovative approach for solving high-dimensional Fokker-Planck-L\'evy (FPL) equations in modeling non-Brownian processes across disciplines such as physics, finance, and ecology. We utilize a fractional score function and Physical-informed neural networks (PINN) to lift the curse of dimensionality (CoD) and alleviate numerical overflow from exponentially decaying solutions with dimensions. The introduction of a fractional score function allows us to transform the FPL equation into a second-order partial differential equation without fractional Laplacian and thus can be readily solved with standard physics-informed neural networks (PINNs). We propose two methods to obtain a fractional score function: fractional score matching (FSM) and score-fPINN for fitting the fractional score function. While FSM is more cost-effective, it relies on known conditional distributions. On the other hand, score-fPINN is independent of specific stochastic differential equations (SDEs) but requires evaluating the PINN model's derivatives, which may be more costly. We conduct our experiments on various SDEs and demonstrate numerical stability and effectiveness of our method in dealing with high-dimensional problems, marking a significant advancement in addressing the CoD in FPL equations.">
+        <tr id="For many industrial processes, a digital twin is available, which is essentially a highly complex model whose parameters may not be properly tuned for the specific process. By relying on the availability of such a digital twin, this paper introduces a novel approach to data-driven control, where the digital twin is used to generate samples and suitable controllers for various perturbed versions of its parameters. A supervised learning algorithm is then employed to estimate a direct mapping from the data to the best controller to use. This map consists of a model reduction step, followed by a neural network architecture whose output provides the parameters of the controller. The data-to-controller map is pre-computed based on artificially generated data, but its execution once deployed is computationally very efficient, thus providing a simple and inexpensive way to tune and re-calibrate controllers directly from data. The benefits of this novel approach are illustrated via numerical simulations.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/8627a1dd31e82891b19eb525d8d99ebc327fe094" target='_blank'>
-              Score-fPINN: Fractional Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck-Levy Equations
+          <td><a href="https://www.semanticscholar.org/paper/6916b9be2764f075e341e143b9e58edc122152ad" target='_blank'>
+              From Data to Control: A Two-Stage Simulation-Based Approach
               </a>
             </td>
           <td>
-            Zheyuan Hu, Zhongqiang Zhang, G. Karniadakis, Kenji Kawaguchi
+            Federico Dettù, Braghadeesh Lakshminarayanan, Simone Formentin, Cristian R. Rojas
           </td>
-          <td>2024-06-17</td>
-          <td>ArXiv</td>
+          <td>2024-06-25</td>
+          <td>2024 European Control Conference (ECC)</td>
           <td>0</td>
-          <td>127</td>
+          <td>3</td>
         </tr>
 
-        <tr id="We introduce mochi_class, an extension of the Einstein-Boltzmann solver hi_class, designed to unlock the full phenomenological potential of Horndeski gravity. This extension allows for general input functions of time without the need for hard-coded parametrisations or covariant Lagrangians. By replacing the traditional $\alpha$-parametrisation with a set of stable basis functions, mochi_class ensures that the resulting effective theories are inherently free from gradient and ghost instabilities. Additionally, mochi_class features a quasi-static approximation implemented at the level of modified metric potentials, enhancing prediction accuracy, especially for models transitioning between a super- and sub-Compton regime. mochi_class can robustly handle a wide range of models without fine-tuning, and introduces a new approximation scheme that activates modifications to the standard cosmology deep in the matter-dominated era. Furthermore, it incorporates viability conditions on the equation of motion for the scalar field fluctuations, aiding in the identification of numerical instabilities. Through comprehensive validation against other Einstein-Boltzmann solvers, mochi_class demonstrates excellent performance and accuracy, broadening the scope of hi_class by facilitating the study of specific modified gravity models and enabling exploration of previously inaccessible regions of the Horndeski landscape. The code is publicly available at https://github.com/mcataneo/mochi_class_public">
+        <tr id="Model predictive control (MPC) is a powerful control method for handling complex nonlinear systems that are subject to constraints. However, the real-time application of this approach can be severely limited by the need to solve constrained nonlinear optimization problems at each sampling time. To this end, this work introduces a novel learning-based iterative solver that provides highly accurate predictions, optimality certification, and fast evaluation of the MPC solution at each sampling time. To learn this iterative solver, we propose an unsupervised training algorithm that builds on the Karush-Kuhn-Tucker optimality conditions, modified by a Fischer-Burmeister formulation, and eliminates the need for prior sampling of exact optimizer solutions. By exploiting efficient vector-Jacobian and Jacobian-vector products via automatic differentiation, the proposed training algorithm can be efficiently executed. We demonstrate the potential of the proposed learning-based iterative solver on the example of nonlinear model predictive control of a nonlinear double integrator. We show its advantages when compared to exact optimizer solutions and with an imitation learning-based approach that directly obtains a data-based approximation of the MPC control law.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/45c44ba92da169b2eb987805f3b6ad66528aeee4" target='_blank'>
-              mochi_class: Modelling Optimisation to Compute Horndeski In class
+          <td><a href="https://www.semanticscholar.org/paper/04655a12d07dee491135aaae2f055b737e275291" target='_blank'>
+              Learning Iterative Solvers for Accurate and Fast Nonlinear Model Predictive Control via Unsupervised Training
               </a>
             </td>
           <td>
-            Matteo Cataneo, Emilio Bellini
+            Lukas Lüken, Sergio Lucia
           </td>
-          <td>2024-07-16</td>
-          <td>ArXiv</td>
-          <td>0</td>
+          <td>2024-06-25</td>
+          <td>2024 European Control Conference (ECC)</td>
           <td>0</td>
+          <td>2</td>
         </tr>
 
-        <tr id="In recent years, surrogate models based on deep neural networks have been widely used to solve partial differential equations for fluid flow physics. This kind of model focuses on global interpolation of the training data and thus requires a large network structure. The process is both time consuming and computationally costly. In the present study, we develop a neural network with local converging input (NNLCI) for high-fidelity prediction using unstructured data. The framework uses the local domain of dependence with converging coarse solutions as input, thereby greatly reducing computational resource and training time. As a validation case, the NNLCI method is applied to study two-dimensional inviscid supersonic flows in channels with bumps. Different bump geometries and locations are examined to benchmark the effectiveness and versatility of this new approach. The NNLCI method can accurately and efficiently capture the structure and dynamics of the entire flowfield, including regions with shock discontinuities. For a new bump configuration, the method can perform prediction with only one neural network, eliminating the need for repeated training of multiple networks for different geometries. A saving of computing wall time is achieved by several orders of magnitude against the high-fidelity simulation with the same level of accuracy. The demand on training data is modest, and the training data can be allocated sparsely. These features are especially advantageous compared with conventional global-to-global deep learning methods and physics-informed methods.">
+        <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/88415c3817a7058d9678f49a393b306a231a9953" target='_blank'>
-              Neural Network with Local Converging Input for Unstructured-Grid Computational Fluid Dynamics
+          <td><a href="https://www.semanticscholar.org/paper/a94495dae3c918bd50c5f83ca6c9f29d9b4dba12" target='_blank'>
+              Active learning for adaptive surrogate model improvement in high-dimensional problems
               </a>
             </td>
           <td>
-            Weiming Ding, Haoxiang Huang, T. Lee, Yingjie Liu, Vigor Yang
+            Yulin Guo, Paromita Nath, Sankaran Mahadevan, Paul Witherell
           </td>
           <td>2024-07-01</td>
-          <td>AIAA Journal</td>
+          <td>Structural and Multidisciplinary Optimization</td>
           <td>0</td>
-          <td>1</td>
+          <td>7</td>
         </tr>
 
-        <tr id="The classical Fokker-Planck equation (FPE) is a key tool in physics for describing systems influenced by drag forces and Gaussian noise, with applications spanning multiple fields. We consider the fractional Fokker-Planck equation (FFPE), which models the time evolution of probability densities for systems driven by L\'evy processes, relevant in scenarios where Gaussian assumptions fail. The paper presents an efficient and accurate numerical approach for the free-space FFPE with constant coefficients and Dirac-delta initial conditions. This method utilizes the integral representation of the solutions and enables the efficient handling of very high-dimensional problems using fast algorithms. Our work is the first to present a high-precision numerical solver for the free-space FFPE with Dirac-delta initial conditions. This opens the door for future research on more complex scenarios, including those with variable coefficients and other types of initial conditions.">
+        <tr id="Inferring parameters of models of biochemical kinetics from single-cell data remains challenging because of the uncertainty arising from the intractability of the likelihood function of stochastic reaction networks. Such uncertainty falls beyond current error quantification measures, which focus on the effects of finite sample size and identifiability but lack theoretical guarantees when likelihood approximations are needed. Here, we propose an inference method for stochastic reaction networks with nonlinear and rational propensities at steady state that provides bounds on the parameters via convex optimisation over sets constrained by moment equations and moment matrices. Our approach takes observations from the stochastic reaction network and forms moment intervals, which are then used to constrain parameters through convex sets. The bounds on the parameters contain the true parameters under the condition that the moment intervals contain the true stationary moments, thus providing uncertainty quantification and error guarantees. Our approach does not need to predict moments and distributions for given parameters (i.e., it avoids solving or simulating the forward problem), and hence circumvents intractable likelihood computations or computationally expensive simulations. We demonstrate its use for uncertainty quantification, data integration and prediction of latent species statistics through synthetic data from common nonlinear biochemical models including the Schl\"ogl model, the toggle switch and post-transcriptional regulation.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ab459a31b5b4688b589a082fdb2f5572eff4cc8c" target='_blank'>
-              A Fast and Accurate Solver for the Fractional Fokker-Planck Equation with Dirac-Delta Initial Conditions
+          <td><a href="https://www.semanticscholar.org/paper/de4b88b799eae872be620beb74ef0b6049b65d90" target='_blank'>
+              Moment-based parameter inference with error guarantees for stochastic reaction networks
               </a>
             </td>
           <td>
-            Qihao Ye, Xiaochuan Tian, Dong Wang
+            Zekai Li, Mauricio Barahona, Philipp Thomas
           </td>
-          <td>2024-07-22</td>
+          <td>2024-06-25</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
-        <tr id="In a sequence of papers, the author examined several statistical affinity measures for selecting the coarse degrees of freedom (CDOFs) or coarse nodes (Cnodes) in algebraic multigrid (AMG) for systems of elliptic partial differential equations (PDEs). These measures were applied to a set of relaxed vectors that exposes the problematic error components. Once the CDOFs are determined using any one of these measures, the interpolation operator is constructed in a bootstrap AMG (BAMG) procedure. However, in a recent paper of Kahl and Rottmann, the statistical least angle regression (LARS) method was utilized in the coarsening procedure and shown to be promising in the CDOF selection. This method is generally used in the statistics community to select the most relevant variables in constructing a parsimonious model for a very complicated and high‐dimensional model or data set (i.e., variable selection for a “reduced” model). As pointed out by Kahl and Rottmann, the LARS procedure has the ability to detect group relations between variables, which can be more useful than binary relations that are derived from strength‐of‐connection, or affinity measures, between pairs of variables. Moreover, by using an updated Cholesky factorization approach in the regression computation, the LARS procedure can be performed efficiently even when the original set of variables is large; and due to the LARS formulation itself (i.e., its ‐norm constraint), sparse interpolation operators can be generated. In this article, we extend the LARS coarsening approach to systems of PDEs. Furthermore, we incorporate some modifications to the LARS approach based on the so‐called elastic net and relaxed lasso methods, which are well known and thoroughly analyzed in the statistics community for ameliorating several major issues with LARS as a variable selection procedure. We note that the original LARS coarsening approach may have addressed some of these issues in similar or other ways but due to the limited details provided there, it is difficult to determine the extent of their similarities. Incorporating these modifications (or effecting them in similar ways) leads to improved robustness in the LARS coarsening procedure, and numerical experiments indicate that the changes lead to faster convergence in the multigrid method. Moreover, the relaxed lasso modification permits an indirect BAMG (iBAMG) extension to the interpolation operator. This iBAMG extension applied in an intra‐ or inter‐variable interpolation setting (i.e., nodal‐based coarsening), as well as in variable‐based coarsening, which will not preserve the nodal structure of a finest‐level discretization on the lower levels of the multilevel hierarchy, will be examined. For the variable‐based coarsening, because of the parsimonious feature of LARS, the performance is reasonably good when applied to systems of PDEs albeit at a substantial additional cost over a nodal‐based procedure.">
+        <tr id="In this study, we delve into the Structured State Space Model (S4), Change Point Detection methodologies, and the Switching Non-linear Dynamics System (SNLDS). Our central proposition is an enhanced inference technique and long-range dependency method for SNLDS. The cornerstone of our approach is the fusion of S4 and SNLDS, leveraging the strengths of both models to effectively address the intricacies of long-range dependencies in switching time series. Through rigorous testing, we demonstrate that our proposed methodology adeptly segments and reproduces long-range dependencies in both the 1-D Lorenz dataset and the 2-D bouncing ball dataset. Notably, our integrated approach outperforms the standalone SNLDS in these tasks.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/9cd541114204fbe8a6e43f7aea58c521464d1e1f" target='_blank'>
-              Least angle regression, relaxed lasso, and elastic net for algebraic multigrid of systems of elliptic partial differential equations
+          <td><a href="https://www.semanticscholar.org/paper/737fccae30a52b719925aa3bdefbccf7758f66f5" target='_blank'>
+              Long Range Switching Time Series Prediction via State Space Model
               </a>
             </td>
           <td>
-            Barry Lee
+            Jiaming Zhang, Yang Ding, Yunfeng Gao
           </td>
-          <td>2024-06-18</td>
-          <td>Numerical Linear Algebra with Applications</td>
-          <td>1</td>
-          <td>1</td>
+          <td>2024-07-27</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>0</td>
         </tr>
 
-        <tr id="The differentiable programming paradigm is a cornerstone of modern scientific computing. It refers to numerical methods for computing the gradient of a numerical model's output. Many scientific models are based on differential equations, where differentiable programming plays a crucial role in calculating model sensitivities, inverting model parameters, and training hybrid models that combine differential equations with data-driven approaches. Furthermore, recognizing the strong synergies between inverse methods and machine learning offers the opportunity to establish a coherent framework applicable to both fields. Differentiating functions based on the numerical solution of differential equations is non-trivial. Numerous methods based on a wide variety of paradigms have been proposed in the literature, each with pros and cons specific to the type of problem investigated. Here, we provide a comprehensive review of existing techniques to compute derivatives of numerical solutions of differential equations. We first discuss the importance of gradients of solutions of differential equations in a variety of scientific domains. Second, we lay out the mathematical foundations of the various approaches and compare them with each other. Third, we cover the computational considerations and explore the solutions available in modern scientific software. Last but not least, we provide best-practices and recommendations for practitioners. We hope that this work accelerates the fusion of scientific models and data, and fosters a modern approach to scientific modelling.">
+        <tr id="This study presents a novel approach that leverages Neural Ordinary Differential Equations (Neural ODEs) to unravel the intricate relationships between inputs and outputs in Large Language Models (LLMs), and employs robust control to fine-tune outputs to meet predefined standards. Central to our methodology is the transformation of LLM inputs and outputs into a lower-dimensional latent space, facilitating a detailed examination of the information processing pathways within LLMs. Neural ODEs play a pivotal role in this investigation by providing a dynamic model that captures the continuous evolution of data within the LLMs. Additionally, robust control mechanisms are applied to strategically adjust the model's outputs, ensuring they not only maintain high quality and reliability but also adhere to specific performance criteria. This fusion of Neural ODEs and robust control represents a significant advancement in LLM interpretability, offering a comprehensive framework that elucidates the previously opaque mechanisms of these complex models. Our empirical results validate the effectiveness of this integrated approach, making a substantial contribution to the field of explainable AI by merging advanced machine learning techniques with the critical need for transparency and control in AI outputs.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e3ef6261d72acb3b8f584e24594af1af686836c8" target='_blank'>
-              Differentiable Programming for Differential Equations: A Review
+          <td><a href="https://www.semanticscholar.org/paper/e11388eb181a61495a9fb75dd0509dda44b5e3b3" target='_blank'>
+              Unveiling LLM Mechanisms Through Neural ODEs and Control Theory
               </a>
             </td>
           <td>
-            Facundo Sapienza, Jordi Bolibar, Frank Schäfer, Brian Groenke, Avik Pal, Victor Boussange, Patrick Heimbach, Giles Hooker, Fernando P'erez, Per-Olof Persson, Christopher Rackauckas
+            Yukun Zhang
           </td>
-          <td>2024-06-14</td>
+          <td>2024-06-23</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>7</td>
+          <td>0</td>
         </tr>
 
-        <tr id="We introduce and analyze generalised polynomial chaos (gPC), considering both intrusive and non-intrusive approaches, as an uncertainty quantification method in studies of probabilistic robustness. The considered gPC methods are complementary to Monte Carlo (MC) methods and are shown to be fast and scalable, allowing for comprehensive and efficient exploration of parameter spaces. These properties enable robustness analysis of a wider set of models, compared to computationally expensive MC methods, while retaining desired levels of accuracy. We discuss the application of gPC methods to systems in biology and neuroscience, notably subject to multiple parametric uncertainties, and we examine a well-known model of neural dynamics as a case study.">
+        <tr id="Natural and technological networks exhibit dynamics that can lead to complex cooperative behaviors, such as synchronization in coupled oscillators and rhythmic activity in neuronal networks. Understanding these collective dynamics is crucial for deciphering a range of phenomena from brain activity to power grid stability. Recent interest in co-evolutionary networks has highlighted the intricate interplay between dynamics on and of the network with mixed time scales. Here, we explore the collective behavior of excitable oscillators in a simple networks of two Theta neurons with adaptive coupling without self-interaction. Through a combination of bifurcation analysis and numerical simulations, we seek to understand how the level of adaptivity in the coupling strength, $a$, influences the dynamics. We first investigate the dynamics possible in the non-adaptive limit; our bifurcation analysis reveals stability regions of quiescence and spiking behaviors, where the spiking frequencies mode-lock in a variety of configurations. Second, as we increase the adaptivity $a$, we observe a widening of the associated Arnol'd tongues, which may overlap and give room for multi-stable configurations. For larger adaptivity, the mode-locked regions may further undergo a period-doubling cascade into chaos. Our findings contribute to the mathematical theory of adaptive networks and offer insights into the potential mechanisms underlying neuronal communication and synchronization.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/abb89db7124ec1925a107b3eb959ec0287242359" target='_blank'>
-              Efficient gPC-based quantification of probabilistic robustness for systems in neuroscience
+          <td><a href="https://www.semanticscholar.org/paper/7ddfebfd3c68f11d067d5fd26480c3939e159e0a" target='_blank'>
+              Co-evolutionary dynamics for two adaptively coupled Theta neurons
               </a>
             </td>
           <td>
-            Uros Sutulovic, Daniele Proverbio, Rami Katz, Giulia Giordano
+            Felix Augustsson, E. A. Martens
           </td>
-          <td>2024-06-19</td>
+          <td>2024-07-01</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>21</td>
         </tr>
 
-        <tr id="Gaussian Processes (GPs) and Linear Dynamical Systems (LDSs) are essential time series and dynamic system modeling tools. GPs can handle complex, nonlinear dynamics but are computationally demanding, while LDSs offer efficient computation but lack the expressive power of GPs. To combine their benefits, we introduce a universal method that allows an LDS to mirror stationary temporal GPs. This state-space representation, known as the Markovian Gaussian Process (Markovian GP), leverages the flexibility of kernel functions while maintaining efficient linear computation. Unlike existing GP-LDS conversion methods, which require separability for most multi-output kernels, our approach works universally for single- and multi-output stationary temporal kernels. We evaluate our method by computing covariance, performing regression tasks, and applying it to a neuroscience application, demonstrating that our method provides an accurate state-space representation for stationary temporal GPs.">
+        <tr id="Solving complex Partial Differential Equations (PDEs) accurately and efficiently is an essential and challenging problem in all scientific and engineering disciplines. Mesh movement methods provide the capability to improve the accuracy of the numerical solution without increasing the overall mesh degree of freedom count. Conventional sophisticated mesh movement methods are extremely expensive and struggle to handle scenarios with complex boundary geometries. However, existing learning-based methods require re-training from scratch given a different PDE type or boundary geometry, which limits their applicability, and also often suffer from robustness issues in the form of inverted elements. In this paper, we introduce the Universal Mesh Movement Network (UM2N), which -- once trained -- can be applied in a non-intrusive, zero-shot manner to move meshes with different size distributions and structures, for solvers applicable to different PDE types and boundary geometries. UM2N consists of a Graph Transformer (GT) encoder for extracting features and a Graph Attention Network (GAT) based decoder for moving the mesh. We evaluate our method on advection and Navier-Stokes based examples, as well as a real-world tsunami simulation case. Our method outperforms existing learning-based mesh movement methods in terms of the benchmarks described above. In comparison to the conventional sophisticated Monge-Amp\`ere PDE-solver based method, our approach not only significantly accelerates mesh movement, but also proves effective in scenarios where the conventional method fails. Our project page is at https://erizmr.github.io/UM2N/.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f45b6ab8336d143d48c626e23d36a99a22707758" target='_blank'>
-              Markovian Gaussian Process: A Universal State-Space Representation for Stationary Temporal Gaussian Process
+          <td><a href="https://www.semanticscholar.org/paper/569f07b5381b1df88a804a15614ac2ddef7715f2" target='_blank'>
+              Towards Universal Mesh Movement Networks
               </a>
             </td>
           <td>
-            Weihan Li, Yule Wang, Chengrui Li, Anqi Wu
+            Mingrui Zhang, Chunyang Wang, Stephan Kramer, Joseph G. Wallwork, Siyi Li, Jiancheng Liu, Xiang Chen, M. Piggott
           </td>
           <td>2024-06-29</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>38</td>
         </tr>
 
-        <tr id="None">
+        <tr id="In this article, a distributed neural network modeling framework including a novel neural hybrid system model is proposed for enhancing the scalability of neural network models in modeling dynamical systems. First, high-dimensional training data samples will be mapped to a low-dimensional feature space through the principal component analysis (PCA) featuring process. Following that, the feature space is bisected into multiple partitions based on the variation of the Shannon entropy under the maximum entropy (ME) bisecting process. The behavior of subsystems in the prespecified state space partitions will then be approximated using a group of shallow neural networks (SNNs) known as extreme learning machines (ELMs), and then it can further simplify the model by merging the redundant lattices based on their training error performance. The proposed modeling framework can handle high-dimensional dynamical system modeling problems with the advantages of reducing model complexity and improving model performance in training and verification. To demonstrate the effectiveness of the proposed modeling framework, examples of modeling the LASA dataset and an industrial robot are presented.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/a94495dae3c918bd50c5f83ca6c9f29d9b4dba12" target='_blank'>
-              Active learning for adaptive surrogate model improvement in high-dimensional problems
+          <td><a href="https://www.semanticscholar.org/paper/7a2a2b97f2bbf0735400cc24119a8d6645252543" target='_blank'>
+              A Distributed Neural Hybrid System Learning Framework in Modeling Complex Dynamical Systems.
               </a>
             </td>
           <td>
-            Yulin Guo, Paromita Nath, Sankaran Mahadevan, Paul Witherell
+            Yejiang Yang, Tao Wang, Weiming Xiang
           </td>
-          <td>2024-07-01</td>
-          <td>Structural and Multidisciplinary Optimization</td>
+          <td>2024-06-28</td>
+          <td>IEEE transactions on neural networks and learning systems</td>
           <td>0</td>
-          <td>7</td>
+          <td>2</td>
         </tr>
 
-        <tr id="Inferring parameters of models of biochemical kinetics from single-cell data remains challenging because of the uncertainty arising from the intractability of the likelihood function of stochastic reaction networks. Such uncertainty falls beyond current error quantification measures, which focus on the effects of finite sample size and identifiability but lack theoretical guarantees when likelihood approximations are needed. Here, we propose an inference method for stochastic reaction networks with nonlinear and rational propensities at steady state that provides bounds on the parameters via convex optimisation over sets constrained by moment equations and moment matrices. Our approach takes observations from the stochastic reaction network and forms moment intervals, which are then used to constrain parameters through convex sets. The bounds on the parameters contain the true parameters under the condition that the moment intervals contain the true stationary moments, thus providing uncertainty quantification and error guarantees. Our approach does not need to predict moments and distributions for given parameters (i.e., it avoids solving or simulating the forward problem), and hence circumvents intractable likelihood computations or computationally expensive simulations. We demonstrate its use for uncertainty quantification, data integration and prediction of latent species statistics through synthetic data from common nonlinear biochemical models including the Schl\"ogl model, the toggle switch and post-transcriptional regulation.">
+        <tr id="Motivated by important applications to the analysis of complex noise-induced phenomena, we consider a problem of the constructive description of randomly forced equilibria for nonlinear systems with multiplicative noise. Using the apparatus of the first approximation systems, we construct an approximation of mean square deviations that explicitly takes into account the presence of multiplicative noises, depending on the current system state. A spectral criterion of existence and exponential stability of the stationary second moments for the solution of the first approximation system is presented. For mean square deviation, we derive an expansion in powers of the small parameter of noise intensity. Based on this theory, we derive a new, more accurate approximation of mean square deviations in a general nonlinear system with multiplicative noises. This approximation is compared with the widely used approximation based on the stochastic sensitivity technique. The general mathematical results are illustrated with examples of the model of climate dynamics and the van der Pol oscillator with hard excitement.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/de4b88b799eae872be620beb74ef0b6049b65d90" target='_blank'>
-              Moment-based parameter inference with error guarantees for stochastic reaction networks
+          <td><a href="https://www.semanticscholar.org/paper/f8b4c1e986ba88d48cf2674010a03c5713b44f90" target='_blank'>
+              Approximations in Mean Square Analysis of Stochastically Forced Equilibria for Nonlinear Dynamical Systems
               </a>
             </td>
           <td>
-            Zekai Li, Mauricio Barahona, Philipp Thomas
+            I. Bashkirtseva
           </td>
-          <td>2024-06-25</td>
-          <td>ArXiv</td>
-          <td>0</td>
+          <td>2024-07-13</td>
+          <td>Mathematics</td>
           <td>0</td>
+          <td>26</td>
         </tr>
 
-        <tr id="Natural and technological networks exhibit dynamics that can lead to complex cooperative behaviors, such as synchronization in coupled oscillators and rhythmic activity in neuronal networks. Understanding these collective dynamics is crucial for deciphering a range of phenomena from brain activity to power grid stability. Recent interest in co-evolutionary networks has highlighted the intricate interplay between dynamics on and of the network with mixed time scales. Here, we explore the collective behavior of excitable oscillators in a simple networks of two Theta neurons with adaptive coupling without self-interaction. Through a combination of bifurcation analysis and numerical simulations, we seek to understand how the level of adaptivity in the coupling strength, $a$, influences the dynamics. We first investigate the dynamics possible in the non-adaptive limit; our bifurcation analysis reveals stability regions of quiescence and spiking behaviors, where the spiking frequencies mode-lock in a variety of configurations. Second, as we increase the adaptivity $a$, we observe a widening of the associated Arnol'd tongues, which may overlap and give room for multi-stable configurations. For larger adaptivity, the mode-locked regions may further undergo a period-doubling cascade into chaos. Our findings contribute to the mathematical theory of adaptive networks and offer insights into the potential mechanisms underlying neuronal communication and synchronization.">
+        <tr id="A surrogate model approximates the outputs of a solver of Partial Differential Equations (PDEs) with a low computational cost. In this article, we propose a method to build learning-based surrogates in the context of parameterized PDEs, which are PDEs that depend on a set of parameters but are also temporal and spatial processes. Our contribution is a method hybridizing the Proper Orthogonal Decomposition and several Support Vector Regression machines. This method is conceived to work in real-time, thus aimed for being used in the context of digital twins, where a user can perform an interactive analysis of results based on the proposed surrogate. We present promising results on two use cases concerning electrical machines. These use cases are not toy examples but are produced an industrial computational code, they use meshes representing non-trivial geometries and contain non-linearities.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/7ddfebfd3c68f11d067d5fd26480c3939e159e0a" target='_blank'>
-              Co-evolutionary dynamics for two adaptively coupled Theta neurons
+          <td><a href="https://www.semanticscholar.org/paper/e63bc34c8490fd304b6cbb27c3dc5e2405d617e2" target='_blank'>
+              A Fast Learning-Based Surrogate of Electrical Machines using a Reduced Basis
               </a>
             </td>
           <td>
-            Felix Augustsson, E. A. Martens
+            Alejandro Rib'es, Nawfal Benchekroun, Théo Delagnes
           </td>
-          <td>2024-07-01</td>
+          <td>2024-06-27</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>21</td>
+          <td>0</td>
         </tr>
 
-        <tr id="Model predictive control (MPC) is a powerful control method for handling complex nonlinear systems that are subject to constraints. However, the real-time application of this approach can be severely limited by the need to solve constrained nonlinear optimization problems at each sampling time. To this end, this work introduces a novel learning-based iterative solver that provides highly accurate predictions, optimality certification, and fast evaluation of the MPC solution at each sampling time. To learn this iterative solver, we propose an unsupervised training algorithm that builds on the Karush-Kuhn-Tucker optimality conditions, modified by a Fischer-Burmeister formulation, and eliminates the need for prior sampling of exact optimizer solutions. By exploiting efficient vector-Jacobian and Jacobian-vector products via automatic differentiation, the proposed training algorithm can be efficiently executed. We demonstrate the potential of the proposed learning-based iterative solver on the example of nonlinear model predictive control of a nonlinear double integrator. We show its advantages when compared to exact optimizer solutions and with an imitation learning-based approach that directly obtains a data-based approximation of the MPC control law.">
+        <tr id="The intersection of artificial intelligence (AI) and partial differential equations (PDEs), emphasizing how AI techniques can revolutionize the analysis and solution of PDEs in various scientific and engineering applications. Traditional methods for solving PDEs often face challenges related to computational complexity, high-dimensionality, and nonlinearity. By leveraging advanced AI algorithms, particularly deep learning and neural networks, we propose novel approaches to approximate solutions, reduce computational costs, and handle complex boundary conditions more effectively. The study highlights the advantages of AI-driven methods in terms of accuracy, efficiency, and scalability, presenting case studies from fluid dynamics, quantum mechanics, and financial mathematics. Our findings suggest that AI has the potential to significantly enhance the analytical capabilities and practical applications of PDEs, paving the way for new advancements in both theoretical research and real-world problem solving">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/04655a12d07dee491135aaae2f055b737e275291" target='_blank'>
-              Learning Iterative Solvers for Accurate and Fast Nonlinear Model Predictive Control via Unsupervised Training
+          <td><a href="https://www.semanticscholar.org/paper/c86e95ee6e33bf3061f0363c446c469fe71a1b5c" target='_blank'>
+              AI Based Analysis and Partial Differential Equations
               </a>
             </td>
           <td>
-            Lukas Lüken, Sergio Lucia
+            M. Krishna Reddy, N. Vijayabhaskar Reddy
           </td>
-          <td>2024-06-25</td>
-          <td>2024 European Control Conference (ECC)</td>
+          <td>2024-07-21</td>
+          <td>International Journal of Advanced Research in Science, Communication and Technology</td>
+          <td>0</td>
           <td>0</td>
-          <td>2</td>
         </tr>
 
-        <tr id="Motivated by important applications to the analysis of complex noise-induced phenomena, we consider a problem of the constructive description of randomly forced equilibria for nonlinear systems with multiplicative noise. Using the apparatus of the first approximation systems, we construct an approximation of mean square deviations that explicitly takes into account the presence of multiplicative noises, depending on the current system state. A spectral criterion of existence and exponential stability of the stationary second moments for the solution of the first approximation system is presented. For mean square deviation, we derive an expansion in powers of the small parameter of noise intensity. Based on this theory, we derive a new, more accurate approximation of mean square deviations in a general nonlinear system with multiplicative noises. This approximation is compared with the widely used approximation based on the stochastic sensitivity technique. The general mathematical results are illustrated with examples of the model of climate dynamics and the van der Pol oscillator with hard excitement.">
+        <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f8b4c1e986ba88d48cf2674010a03c5713b44f90" target='_blank'>
-              Approximations in Mean Square Analysis of Stochastically Forced Equilibria for Nonlinear Dynamical Systems
+          <td><a href="https://www.semanticscholar.org/paper/65f70fe6d698072acfae68cb9be3a90281df6d72" target='_blank'>
+              Application of physics encoded neural networks to improve predictability of properties of complex multi-scale systems
               </a>
             </td>
           <td>
-            I. Bashkirtseva
+            M. Meinders, Jack Yang, Erik van der Linden
           </td>
-          <td>2024-07-13</td>
-          <td>Mathematics</td>
+          <td>2024-07-01</td>
+          <td>Scientific Reports</td>
           <td>0</td>
-          <td>26</td>
+          <td>30</td>
         </tr>
 
         <tr id="The purpose of this paper is to propose a novel perspective, based on Willems'"behavior theory", on the design of an unknown-input observer for a given linear time-invariant discrete-time state-space model, with unknown disturbances affecting both the state and the output equations. The problem is first addressed assuming that the original system model is known, and later assuming that the model is unknown but historical data satisfying a certain assumption are available. In both cases, fundamental concepts in behavior theory, as the projection of a behavior, the inclusion of a behavior in another one, and the use of kernel and image representations, provide quite powerful tools to determine necessary and sufficient conditions for the existence of an unknown-input observer (UIO), as well as algorithms to design one of them, if it exists.">
@@ -3695,79 +3379,88 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>33</td>
         </tr>
 
-        <tr id="None">
+        <tr id="Many real-world applications demand accurate and fast predictions, as well as reliable uncertainty estimates. However, quantifying uncertainty on high-dimensional predictions is still a severely under-invested problem, especially when input-output relationships are non-linear. To handle this problem, the present work introduces an innovative approach that combines autoencoder deep neural networks with the probabilistic regression capabilities of Gaussian processes. The autoencoder provides a low-dimensional representation of the solution space, while the Gaussian process is a Bayesian method that provides a probabilistic mapping between the low-dimensional inputs and outputs. We validate the proposed framework for its application to surrogate modeling of non-linear finite element simulations. Our findings highlight that the proposed framework is computationally efficient as well as accurate in predicting non-linear deformations of solid bodies subjected to external forces, all the while providing insightful uncertainty assessments.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/65f70fe6d698072acfae68cb9be3a90281df6d72" target='_blank'>
-              Application of physics encoded neural networks to improve predictability of properties of complex multi-scale systems
+          <td><a href="https://www.semanticscholar.org/paper/c5ebe3e35ead48c1cec6afd16f55a4873eabdbd1" target='_blank'>
+              Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids
               </a>
             </td>
           <td>
-            M. Meinders, Jack Yang, Erik van der Linden
+            Saurabh Deshpande, Hussein Rappel, Mark Hobbs, Stéphane P. A. Bordas, Jakub Lengiewicz
           </td>
-          <td>2024-07-01</td>
-          <td>Scientific Reports</td>
+          <td>2024-07-15</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>29</td>
+          <td>4</td>
         </tr>
 
-        <tr id="A surrogate model approximates the outputs of a solver of Partial Differential Equations (PDEs) with a low computational cost. In this article, we propose a method to build learning-based surrogates in the context of parameterized PDEs, which are PDEs that depend on a set of parameters but are also temporal and spatial processes. Our contribution is a method hybridizing the Proper Orthogonal Decomposition and several Support Vector Regression machines. This method is conceived to work in real-time, thus aimed for being used in the context of digital twins, where a user can perform an interactive analysis of results based on the proposed surrogate. We present promising results on two use cases concerning electrical machines. These use cases are not toy examples but are produced an industrial computational code, they use meshes representing non-trivial geometries and contain non-linearities.">
+        <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e63bc34c8490fd304b6cbb27c3dc5e2405d617e2" target='_blank'>
-              A Fast Learning-Based Surrogate of Electrical Machines using a Reduced Basis
+          <td><a href="https://www.semanticscholar.org/paper/4eb1f5b104b75cee5eba224ac4724ead85e4c238" target='_blank'>
+              Physics-informed neural network simulation of thermal cavity flow
               </a>
             </td>
           <td>
-            Alejandro Rib'es, Nawfal Benchekroun, Th'eo Delagnes
+            Eric Fowler, Christopher J McDevitt, Subrata Roy
           </td>
-          <td>2024-06-27</td>
-          <td>ArXiv</td>
-          <td>0</td>
+          <td>2024-07-02</td>
+          <td>Scientific Reports</td>
           <td>0</td>
+          <td>2</td>
         </tr>
 
-        <tr id="In this work, we describe a novel approach to building a neural PDE solver leveraging recent advances in transformer based neural network architectures. Our model can provide solutions for different values of PDE parameters without any need for retraining the network. The training is carried out in a self-supervised manner, similar to pretraining approaches applied in language and vision tasks. We hypothesize that the model is in effect learning a family of operators (for multiple parameters) mapping the initial condition to the solution of the PDE at any future time step t. We compare this approach with the Fourier Neural Operator (FNO), and demonstrate that it can generalize over the space of PDE parameters, despite having a higher prediction error for individual parameter values compared to the FNO. We show that performance on a specific parameter can be improved by finetuning the model with very small amounts of data. We also demonstrate that the model scales with data as well as model size.">
+        <tr id="Partial differential equations (PDEs) are extensively utilized for modeling various physical phenomena. These equations often depend on certain parameters, necessitating either the identification of optimal parameters or solving the equation across multiple parameters to understand how a structure might react under different conditions. Performing an exhaustive search over the parameter space requires solving the PDE multiple times, which is generally impractical. To address this challenge, Reduced Order Models (ROMs) are constructed from a snapshot dataset comprising parameter-solution pairs. ROMs facilitate the rapid solving of PDEs for new parameters. Recently, Deep Learning ROMs (DL-ROMs) have been introduced as a new method to obtain ROM. Additionally, the PDE of interest may depend on the domain, which can be characterized by parameters or measurements and may evolve with the system, requiring parametrization for ROM construction. In this paper, we develop a Deep-ROM capable of extracting and efficiently utilizing domain parametrization. Unlike traditional domain parametrization methods, our approach does not require user-defined control points and can effectively handle domains with varying numbers of components. Moreover, our model can derive meaningful parametrization even when a domain mesh is unavailable, a common scenario in biomedical applications. Our work leverages Deep Neural Networks to effectively reduce the dimensionality of the PDE and the domain characteristic function.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/629ea9c7440dd696fb2129b9994297e01c3d883e" target='_blank'>
-              Self-supervised Pretraining for Partial Differential Equations
+          <td><a href="https://www.semanticscholar.org/paper/90e23c188396aefabed7701142b8c0a910c3d228" target='_blank'>
+              Deep Learning Reduced Order Modelling on Parametric and Data driven domains
               </a>
             </td>
           <td>
-            Varun Madhavan, Amal S Sebastian, Bharath Ramsundar, Venkat Viswanathan
+            Martina Bukavc, Iva Manojlovi'c, B. Muha, Domagoj Vlah University of Notre Dame, Notre Dame, In, University of Utah Department of Biomedical Engineering, Computing, Departmento Mathematics, Faculty of Computer Science, U. Zagreb
           </td>
-          <td>2024-07-03</td>
+          <td>2024-07-24</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>19</td>
+          <td>24</td>
         </tr>
 
-        <tr id="In this article, a distributed neural network modeling framework including a novel neural hybrid system model is proposed for enhancing the scalability of neural network models in modeling dynamical systems. First, high-dimensional training data samples will be mapped to a low-dimensional feature space through the principal component analysis (PCA) featuring process. Following that, the feature space is bisected into multiple partitions based on the variation of the Shannon entropy under the maximum entropy (ME) bisecting process. The behavior of subsystems in the prespecified state space partitions will then be approximated using a group of shallow neural networks (SNNs) known as extreme learning machines (ELMs), and then it can further simplify the model by merging the redundant lattices based on their training error performance. The proposed modeling framework can handle high-dimensional dynamical system modeling problems with the advantages of reducing model complexity and improving model performance in training and verification. To demonstrate the effectiveness of the proposed modeling framework, examples of modeling the LASA dataset and an industrial robot are presented.">
+        <tr id="We consider a typical learning problem of point estimations for modeling of nonlinear functions or dynamical systems in which generalization, i.e., verifying a given learned model, can be embedded as an integral part of the learning process or dynamics. In particular, we consider an empirical risk minimization based learning problem that exploits gradient methods from continuous-time perspective with small random perturbations, which is guided by the training dataset loss. Here, we provide an asymptotic probability estimate in the small noise limit based-on the Freidlin-Wentzell theory of large deviations, when the sample path of the random process corresponding to the randomly perturbed gradient dynamical system hits a certain target set, i.e., a rare event, when the latter is specified by the testing dataset loss landscape. Interestingly, the proposed framework can be viewed as one way of improving generalization and robustness in learning problems that provides new insights leading to optimal point estimates which is guided by training data loss, while, at the same time, the learning dynamics has an access to the testing dataset loss landscape in some form of future achievable or anticipated target goal. Moreover, as a by-product, we establish a connection with optimal control problem, where the target set, i.e., the rare event, is considered as the desired outcome or achievable target goal for a certain optimal control problem, for which we also provide a verification result reinforcing the rationale behind the proposed framework. Finally, we present a computational algorithm that solves the corresponding variational problem leading to an optimal point estimates and, as part of this work, we also present some numerical results for a typical case of nonlinear regression problem.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/7a2a2b97f2bbf0735400cc24119a8d6645252543" target='_blank'>
-              A Distributed Neural Hybrid System Learning Framework in Modeling Complex Dynamical Systems.
+          <td><a href="https://www.semanticscholar.org/paper/3eb8a6a20e76ca76075c140c11561ca482a74778" target='_blank'>
+              Embedding generalization within the learning dynamics: An approach based-on sample path large deviation theory
               </a>
             </td>
           <td>
-            Yejiang Yang, Tao Wang, Weiming Xiang
+            G. Befekadu
           </td>
-          <td>2024-06-28</td>
-          <td>IEEE transactions on neural networks and learning systems</td>
+          <td>2024-08-04</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>2</td>
+          <td>10</td>
         </tr>
 
-        <tr id="None">
+        <tr id="Networks in machine learning offer examples of complex high-dimensional dynamical systems inspired by and reminiscent of biological systems. Here, we study the learning dynamics of generalized Hopfield networks, which permit visualization of internal memories. These networks have been shown to proceed through a “feature-to-prototype” transition, as the strength of network nonlinearity is increased, wherein the learned, or terminal, states of internal memories transition from mixed to pure states. Focusing on the prototype learning dynamics of the internal memories, we observe stereotypical dynamics of memories wherein similar subgroups of memories sequentially split at well-defined saddles. The splitting order is interpretable and reproducible from one simulation to the other. The dynamics prior to splits are robust to variations in many features of the system. To develop a more rigorous understanding of these global dynamics, we study smaller subsystems that exhibit similar properties to the full system. Within these smaller systems, we combine analytical calculations with numerical simulations to study the dynamics of the feature-to-prototype transition, and the emergence of saddle points in the learning landscape. We exhibit regimes where saddles appear and disappear through saddle-node bifurcations, qualitatively changing the distribution of learned memories as the strength of the nonlinearity is varied—allowing us to systematically investigate the mechanisms that underlie the emergence of the learning dynamics. Several features of the learning dynamics are reminiscent of the Waddington's caricature of cellular differentiation, and we attempt to make this analogy more precise. Memories can thus differentiate in a predictive and controlled way, revealing bridges between experimental biology, dynamical systems theory, and machine learning.
+
+
+
+
+ Published by the American Physical Society
+ 2024
+
+
+">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4eb1f5b104b75cee5eba224ac4724ead85e4c238" target='_blank'>
-              Physics-informed neural network simulation of thermal cavity flow
+          <td><a href="https://www.semanticscholar.org/paper/acfb0996b8a2dd52af629b0fc803ccf66a9f85a9" target='_blank'>
+              Waddington landscape for prototype learning in generalized Hopfield networks
               </a>
             </td>
           <td>
-            Eric Fowler, Christopher J McDevitt, Subrata Roy
+            Nacer Eddine Boukacem, Allen Leary, Robin Thériault, Felix Gottlieb, Madhav Mani, Paul François
           </td>
-          <td>2024-07-02</td>
-          <td>Scientific Reports</td>
+          <td>2024-07-23</td>
+          <td>Physical Review Research</td>
           <td>0</td>
-          <td>2</td>
+          <td>1</td>
         </tr>
 
         <tr id="The formation of dynamical patterns is one of the most striking features of non-equilibrium physical systems. Recent work has shown that such patterns arise generically from forces that violate Newton's third law, known as non-reciprocal interactions. These non-equilibrium phenomena are challenging for modern theories. Here, we introduce a model mixture of active (self-propelled) and passive (diffusive) particles with non-reciprocal effective interactions, which is amenable to exact mathematical analysis. We exploit state-of-the-art methods to derive exact hydrodynamic equations for the particle densities. We study the resulting collective behavior, including the linear stability of homogeneous states and phase coexistence in large systems. This reveals a novel phase diagram with the spinodal associated with active phase separation protruding through the associated binodal, heralding the emergence of dynamical steady states. We analyze these states in the thermodynamic limit of large system size, showing, for example, that sharp interfaces may travel at finite velocities, but traveling phase-separated states are forbidden. The model's mathematical tractability enables precise new conclusions beyond those available by numerical simulation of particle models or field theories.">
@@ -3800,898 +3493,1292 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2</td>
         </tr>
 
-        <tr id=". Time delay error is a significant error source in adaptive optics (AO) systems. It arises from the latency between sensing the wavefront and applying the correction. Predictive control algorithms reduce the time delay error, providing significant performance gains, especially for high-contrast imaging. However, the predictive controller ’ s performance depends on factors such as the wavefront sensor (WFS) type, the measurement noise level, the AO system ’ s geometry, and the atmospheric conditions. We study the limits of prediction under different imaging conditions through spatiotemporal Gaussian process models. The method provides a predictive recon-structor that is optimal in the least-squares sense, conditioned on the fixed times series of WFS data and our knowledge of the atmospheric conditions. We demonstrate that knowledge is power in predictive AO control. With a Shack – Hartmann sensor-based extreme AO instrument, perfect knowledge of the wind and atmospheric profile and exact frozen flow evolution lead to a reduction of the residual wavefront phase variance up to a factor of 3.5 compared with a non-predictive approach. If there is uncertainty in the profile or evolution models, the gain is more modest. Still, assuming that only effective wind speed is available (without direction) led to reductions in variance by a factor of ∼ 2 . 3 . We also study the value of data for predictive filters by computing the experimental utility for different scenarios to answer questions such as how many past telemetry frames should the prediction filter consider and whether is it always most advantageous to use the most recent data. We show that within the scenarios considered, more data provide a consistent increase in prediction accuracy. Furthermore, we demonstrate that given a computational limitation on how many past frames, we can use an optimized selection of n past frames, which leads to a 10% to 15% additional improvement in root mean square over using the n latest consecutive frames of data.">
+        <tr id="Neural operators such as the Fourier Neural Operator (FNO) have been shown to provide resolution-independent deep learning models that can learn mappings between function spaces. For example, an initial condition can be mapped to the solution of a partial differential equation (PDE) at a future time-step using a neural operator. Despite the popularity of neural operators, their use to predict solution functions over a domain given only data over the boundary (such as a spatially varying Dirichlet boundary condition) remains unexplored. In this paper, we refer to such problems as boundary-to-domain problems; they have a wide range of applications in areas such as fluid mechanics, solid mechanics, heat transfer etc. We present a novel FNO-based architecture, named Lifting Product FNO (or LP-FNO) which can map arbitrary boundary functions defined on the lower-dimensional boundary to a solution in the entire domain. Specifically, two FNOs defined on the lower-dimensional boundary are lifted into the higher dimensional domain using our proposed lifting product layer. We demonstrate the efficacy and resolution independence of the proposed LP-FNO for the 2D Poisson equation.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f1389784d499e34266870d5dbf7e2781f8e4b6cd" target='_blank'>
-              Power of prediction: spatiotemporal Gaussian process modeling for predictive control in slope-based wavefront sensing
+          <td><a href="https://www.semanticscholar.org/paper/f352a2d81715807d302b4b1be51ece5c849052d2" target='_blank'>
+              Learning the boundary-to-domain mapping using Lifting Product Fourier Neural Operators for partial differential equations
               </a>
             </td>
           <td>
-            J. Nousiainen, Juha-Pekka Puska, T. Helin, Nuutti Hyvönen, Markus Kasper
+            Aditya Kashi, Arka Daw, Muralikrishnan Gopalakrishnan Meena, Hao Lu
           </td>
-          <td>2024-07-12</td>
-          <td>Journal of Astronomical Telescopes, Instruments, and Systems</td>
+          <td>2024-06-24</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>18</td>
+          <td>4</td>
         </tr>
 
-        <tr id="Networks in machine learning offer examples of complex high-dimensional dynamical systems inspired by and reminiscent of biological systems. Here, we study the learning dynamics of generalized Hopfield networks, which permit visualization of internal memories. These networks have been shown to proceed through a “feature-to-prototype” transition, as the strength of network nonlinearity is increased, wherein the learned, or terminal, states of internal memories transition from mixed to pure states. Focusing on the prototype learning dynamics of the internal memories, we observe stereotypical dynamics of memories wherein similar subgroups of memories sequentially split at well-defined saddles. The splitting order is interpretable and reproducible from one simulation to the other. The dynamics prior to splits are robust to variations in many features of the system. To develop a more rigorous understanding of these global dynamics, we study smaller subsystems that exhibit similar properties to the full system. Within these smaller systems, we combine analytical calculations with numerical simulations to study the dynamics of the feature-to-prototype transition, and the emergence of saddle points in the learning landscape. We exhibit regimes where saddles appear and disappear through saddle-node bifurcations, qualitatively changing the distribution of learned memories as the strength of the nonlinearity is varied—allowing us to systematically investigate the mechanisms that underlie the emergence of the learning dynamics. Several features of the learning dynamics are reminiscent of the Waddington's caricature of cellular differentiation, and we attempt to make this analogy more precise. Memories can thus differentiate in a predictive and controlled way, revealing bridges between experimental biology, dynamical systems theory, and machine learning.
-
-
-
-
- Published by the American Physical Society
- 2024
-
-
-">
+        <tr id="PurposeSimulation-based digital twins represent an effort to provide high-accuracy real-time insights into operational physical processes. However, the computation time of many multi-physical simulation models is far from real-time. It might even exceed sensible time frames to produce sufficient data for training data-driven reduced-order models. This study presents TwinLab, a framework for data-efficient, yet accurate training of neural-ODE type reduced-order models with only two data sets.Design/methodology/approachCorrelations between test errors of reduced-order models and distinct features of corresponding training data are investigated. Having found the single best data sets for training, a second data set is sought with the help of similarity and error measures to enrich the training process effectively.FindingsAdding a suitable second training data set in the training process reduces the test error by up to 49% compared to the best base reduced-order model trained only with one data set. Such a second training data set should at least yield a good reduced-order model on its own and exhibit higher levels of dissimilarity to the base training data set regarding the respective excitation signal. Moreover, the base reduced-order model should have elevated test errors on the second data set. The relative error of the time series ranges from 0.18% to 0.49%. Prediction speed-ups of up to a factor of 36,000 are observed.Originality/valueThe proposed computational framework facilitates the automated, data-efficient extraction of non-intrusive reduced-order models for digital twins from existing simulation models, independent of the simulation software.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/acfb0996b8a2dd52af629b0fc803ccf66a9f85a9" target='_blank'>
-              Waddington landscape for prototype learning in generalized Hopfield networks
+          <td><a href="https://www.semanticscholar.org/paper/4aeb7a00d93947f8e222188bb9f980ac834448d1" target='_blank'>
+              TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins
               </a>
             </td>
           <td>
-            Nacer Eddine Boukacem, Allen Leary, Robin Thériault, Felix Gottlieb, Madhav Mani, Paul François
+            Maximilian Kannapinn, M. Schäfer, Oliver Weeger
           </td>
-          <td>2024-07-23</td>
-          <td>Physical Review Research</td>
+          <td>2024-07-04</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>2</td>
         </tr>
 
-        <tr id="Partial differential equations (PDEs) are extensively utilized for modeling various physical phenomena. These equations often depend on certain parameters, necessitating either the identification of optimal parameters or solving the equation across multiple parameters to understand how a structure might react under different conditions. Performing an exhaustive search over the parameter space requires solving the PDE multiple times, which is generally impractical. To address this challenge, Reduced Order Models (ROMs) are constructed from a snapshot dataset comprising parameter-solution pairs. ROMs facilitate the rapid solving of PDEs for new parameters. Recently, Deep Learning ROMs (DL-ROMs) have been introduced as a new method to obtain ROM. Additionally, the PDE of interest may depend on the domain, which can be characterized by parameters or measurements and may evolve with the system, requiring parametrization for ROM construction. In this paper, we develop a Deep-ROM capable of extracting and efficiently utilizing domain parametrization. Unlike traditional domain parametrization methods, our approach does not require user-defined control points and can effectively handle domains with varying numbers of components. Moreover, our model can derive meaningful parametrization even when a domain mesh is unavailable, a common scenario in biomedical applications. Our work leverages Deep Neural Networks to effectively reduce the dimensionality of the PDE and the domain characteristic function.">
+        <tr id="This work addresses stochastic optimal control problems where the unknown state evolves in continuous time while partial, noisy, and possibly controllable measurements are only available in discrete time. We develop a framework for controlling such systems, focusing on the measure-valued process of the system's state and the control actions that depend on noisy and incomplete data. Our approach uses a stochastic optimal control framework with a probability measure-valued state, which accommodates noisy measurements and integrates them into control decisions through a Bayesian update mechanism. We characterize the control optimality in terms of a sequence of interlaced Hamilton Jacobi Bellman (HJB) equations coupled with controlled impulse steps at the measurement times. For the case of Gaussian-controlled processes, we derive an equivalent HJB equation whose state variable is finite-dimensional, namely the state's mean and covariance. We demonstrate the effectiveness of our methods through numerical examples. These include control under perfect observations, control under no observations, and control under noisy observations. Our numerical results highlight significant differences in the control strategies and their performance, emphasizing the challenges and computational demands of dealing with uncertainty in state observation.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/90e23c188396aefabed7701142b8c0a910c3d228" target='_blank'>
-              Deep Learning Reduced Order Modelling on Parametric and Data driven domains
+          <td><a href="https://www.semanticscholar.org/paper/e6ba24031ebf3376cb6db64dd547b84c989cf92e" target='_blank'>
+              Continuous time Stochastic optimal control under discrete time partial observations
               </a>
             </td>
           <td>
-            Martina Bukavc, Iva Manojlovi'c, B. Muha, Domagoj Vlah University of Notre Dame, Notre Dame, In, University of Utah Department of Biomedical Engineering, Computing, Departmento Mathematics, Faculty of Computer Science, U. Zagreb
+            Christian Bayer, Boualem Djehiche, Eliza Rezvanova, Raúl Tempone
           </td>
-          <td>2024-07-24</td>
+          <td>2024-07-25</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>24</td>
         </tr>
 
-        <tr id="We consider a typical learning problem of point estimations for modeling of nonlinear functions or dynamical systems in which generalization, i.e., verifying a given learned model, can be embedded as an integral part of the learning process or dynamics. In particular, we consider an empirical risk minimization based learning problem that exploits gradient methods from continuous-time perspective with small random perturbations, which is guided by the training dataset loss. Here, we provide an asymptotic probability estimate in the small noise limit based-on the Freidlin-Wentzell theory of large deviations, when the sample path of the random process corresponding to the randomly perturbed gradient dynamical system hits a certain target set, i.e., a rare event, when the latter is specified by the testing dataset loss landscape. Interestingly, the proposed framework can be viewed as one way of improving generalization and robustness in learning problems that provides new insights leading to optimal point estimates which is guided by training data loss, while, at the same time, the learning dynamics has an access to the testing dataset loss landscape in some form of future achievable or anticipated target goal. Moreover, as a by-product, we establish a connection with optimal control problem, where the target set, i.e., the rare event, is considered as the desired outcome or achievable target goal for a certain optimal control problem, for which we also provide a verification result reinforcing the rationale behind the proposed framework. Finally, we present a computational algorithm that solves the corresponding variational problem leading to an optimal point estimates and, as part of this work, we also present some numerical results for a typical case of nonlinear regression problem.">
+        <tr id="Biological neural networks can perform complex computations to predict their environment, far above the limited predictive capabilities of individual neurons. While conventional approaches to understanding these computations often focus on isolating the contributions of single neurons, here we argue that a deeper understanding requires considering emergent dynamics - dynamics that make the whole system"more than the sum of its parts". Specifically, we examine the relationship between prediction performance and emergence by leveraging recent quantitative metrics of emergence, derived from Partial Information Decomposition, and by modelling the prediction of environmental dynamics in a bio-inspired computational framework known as reservoir computing. Notably, we reveal a bidirectional coupling between prediction performance and emergence, which generalises across task environments and reservoir network topologies, and is recapitulated by three key results: 1) Optimising hyperparameters for performance enhances emergent dynamics, and vice versa; 2) Emergent dynamics represent a near sufficient criterion for prediction success in all task environments, and an almost necessary criterion in most environments; 3) Training reservoir computers on larger datasets results in stronger emergent dynamics, which contain task-relevant information crucial for performance. Overall, our study points to a pivotal role of emergence in facilitating environmental predictions in a bio-inspired computational architecture.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/3eb8a6a20e76ca76075c140c11561ca482a74778" target='_blank'>
-              Embedding generalization within the learning dynamics: An approach based-on sample path large deviation theory
+          <td><a href="https://www.semanticscholar.org/paper/2572feb06e7ea060ecb305043a5d4b589af5886d" target='_blank'>
+              Evolving reservoir computers reveals bidirectional coupling between predictive power and emergent dynamics
               </a>
             </td>
           <td>
-            G. Befekadu
+            Hanna M. Tolle, A. Luppi, Anil K. Seth, P. Mediano
           </td>
-          <td>2024-08-04</td>
+          <td>2024-06-27</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>10</td>
+          <td>26</td>
         </tr>
 
-        <tr id="Many real-world applications demand accurate and fast predictions, as well as reliable uncertainty estimates. However, quantifying uncertainty on high-dimensional predictions is still a severely under-invested problem, especially when input-output relationships are non-linear. To handle this problem, the present work introduces an innovative approach that combines autoencoder deep neural networks with the probabilistic regression capabilities of Gaussian processes. The autoencoder provides a low-dimensional representation of the solution space, while the Gaussian process is a Bayesian method that provides a probabilistic mapping between the low-dimensional inputs and outputs. We validate the proposed framework for its application to surrogate modeling of non-linear finite element simulations. Our findings highlight that the proposed framework is computationally efficient as well as accurate in predicting non-linear deformations of solid bodies subjected to external forces, all the while providing insightful uncertainty assessments.">
+        <tr id="This paper introduces a novel nonlinear stochastic model predictive control path integral (MPPI) method, which considers chance constraints on system states. The proposed belief-space stochastic MPPI (BSS-MPPI) applies Monte-Carlo sampling to evaluate state distributions resulting from underlying systematic disturbances, and utilizes a Control Barrier Function (CBF) inspired heuristic in belief space to fulfill the specified chance constraints. Compared to several previous stochastic predictive control methods, our approach applies to general nonlinear dynamics without requiring the computationally expensive system linearization step. Moreover, the BSS-MPPI controller can solve optimization problems without limiting the form of the objective function and chance constraints. By multi-threading the sampling process using a GPU, we can achieve fast real-time planning for time- and safety-critical tasks such as autonomous racing. Our results on a realistic race-car simulation study show significant reductions in constraint violation compared to some of the prior MPPI approaches, while being comparable in computation times.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c5ebe3e35ead48c1cec6afd16f55a4873eabdbd1" target='_blank'>
-              Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids
+          <td><a href="https://www.semanticscholar.org/paper/ec0a95b01ae133b323176ef995b8490fff8991be" target='_blank'>
+              Chance-Constrained Information-Theoretic Stochastic Model Predictive Control with Safety Shielding
               </a>
             </td>
           <td>
-            Saurabh Deshpande, Hussein Rappel, Mark Hobbs, Stéphane P. A. Bordas, Jakub Lengiewicz
+            Ji Yin, P. Tsiotras, K. Berntorp
           </td>
-          <td>2024-07-15</td>
+          <td>2024-08-01</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>4</td>
+          <td>49</td>
         </tr>
 
-        <tr id="This work addresses stochastic optimal control problems where the unknown state evolves in continuous time while partial, noisy, and possibly controllable measurements are only available in discrete time. We develop a framework for controlling such systems, focusing on the measure-valued process of the system's state and the control actions that depend on noisy and incomplete data. Our approach uses a stochastic optimal control framework with a probability measure-valued state, which accommodates noisy measurements and integrates them into control decisions through a Bayesian update mechanism. We characterize the control optimality in terms of a sequence of interlaced Hamilton Jacobi Bellman (HJB) equations coupled with controlled impulse steps at the measurement times. For the case of Gaussian-controlled processes, we derive an equivalent HJB equation whose state variable is finite-dimensional, namely the state's mean and covariance. We demonstrate the effectiveness of our methods through numerical examples. These include control under perfect observations, control under no observations, and control under noisy observations. Our numerical results highlight significant differences in the control strategies and their performance, emphasizing the challenges and computational demands of dealing with uncertainty in state observation.">
+        <tr id="The development of novel materials in recent years has been accelerated greatly by the use of computational modelling techniques aimed at elucidating the complex physics controlling microstructure formation in materials, the properties of which control material function. One such technique is the phase field method, a field theoretic approach that couples various thermophysical fields to microscopic order parameter fields that track the phases of microstructure. Phase field models are framed as multiple, non-linear, partial differential equations, which are extremely challenging to compute efficiently. Recent years have seen an explosion of computational algorithms aimed at enhancing the efficiency of phase field simulations. One such technique, adaptive mesh refinement (AMR), dynamically adapts numerical meshes to be highly refined around steep spatial gradients of the PDE fields and coarser where the fields are smooth. This reduces the number of computations per time step significantly, thus reducing the total time of computation. What AMR doesn't do is allow for adaptive time stepping. This work combines AMR with a neural network algorithm that uses a U-Net with a Convolutional Long-Short Term Memory (CLSTM) base to accelerate phase field simulations. Our neural network algorithm is described in detail and tested in on simulations of directional solidification of a dilute binary alloy, a paradigm that is highly practical for its relevance to the solidification of alloys.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e6ba24031ebf3376cb6db64dd547b84c989cf92e" target='_blank'>
-              Continuous time Stochastic optimal control under discrete time partial observations
+          <td><a href="https://www.semanticscholar.org/paper/f7c3b439cab9bc1574b9ee846128f87f45347f16" target='_blank'>
+              LeapFrog: Getting the Jump on Multi-Scale Materials Simulations Using Machine Learning
               </a>
             </td>
           <td>
-            Christian Bayer, Boualem Djehiche, Eliza Rezvanova, Raúl Tempone
+            Damien Pinto, M. Greenwood, N. Provatas
           </td>
-          <td>2024-07-25</td>
+          <td>2024-06-21</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>24</td>
+          <td>36</td>
         </tr>
 
-        <tr id="Deep learning with physics-informed neural networks (PINNs) has emerged as a highly popular and effective approach for solving partial differential equations(PDEs). In this paper, we first investigate the extrapolation capability of the PINN method for time-dependent PDEs. Taking advantage of this extrapolation property, we can generalize the training result obtained in the time subinterval to the large interval by adding a correction term to the network parameters of the subinterval. The correction term is determined by further training with the sample points in the added subinterval. Secondly, by designing an extrapolation control function with special characteristics and combining it with the correction term, we construct a new neural network architecture whose network parameters are coupled with the time variable, which we call the extrapolation-driven network architecture. Based on this architecture, using a single neural network, we can obtain the overall PINN solution of the whole domain with the following two characteristics: (1) it completely inherits the local solution of the interval obtained from the previous training, (2) at the interval node, it strictly maintains the continuity and smoothness that the true solution has. The extrapolation-driven network architecture allows us to divide a large time domain into multiple subintervals and solve the time-dependent PDEs one by one in chronological order. This training scheme respects the causality principle and effectively overcomes the difficulties of the conventional PINN method in solving the evolution equation on a large time domain. Numerical experiments verify the performance of our proposed method.">
+        <tr id="Temporal data modelling techniques with neural networks are useful in many domain applications, including time-series forecasting and control engineering. This paper aims at developing a recurrent version of stochastic configuration networks (RSCNs) for problem solving, where we have no underlying assumption on the dynamic orders of the input variables. Given a collection of historical data, we first build an initial RSCN model in the light of a supervisory mechanism, followed by an online update of the output weights by using a projection algorithm. Some theoretical results are established, including the echo state property, the universal approximation property of RSCNs for both the offline and online learnings, and the convergence of the output weights. The proposed RSCN model is remarkably distinguished from the well-known echo state networks (ESNs) in terms of the way of assigning the input random weight matrix and a special structure of the random feedback matrix. A comprehensive comparison study among the long short-term memory (LSTM) network, the original ESN, and several state-of-the-art ESN methods such as the simple cycle reservoir (SCR), the polynomial ESN (PESN), the leaky-integrator ESN (LIESN) and RSCN is carried out. Numerical results clearly indicate that the proposed RSCN performs favourably over all of the datasets.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e0e945aa09792681fe44ec2d98a12595cf885296" target='_blank'>
-              An extrapolation-driven network architecture for physics-informed deep learning
+          <td><a href="https://www.semanticscholar.org/paper/46e3c55f0b0e77192fb6cc422ecf599a47334d73" target='_blank'>
+              Recurrent Stochastic Configuration Networks for Temporal Data Analytics
               </a>
             </td>
           <td>
-            Yong Wang, Yanzhong Yao, Zhiming Gao
+            Dianhui Wang, Gang Dang
           </td>
-          <td>2024-06-18</td>
+          <td>2024-06-21</td>
           <td>ArXiv</td>
-          <td>0</td>
+          <td>1</td>
           <td>1</td>
         </tr>
 
-        <tr id="Diffusion models excel at creating visually-convincing images, but they often struggle to meet subtle constraints inherent in the training data. Such constraints could be physics-based (e.g., satisfying a PDE), geometric (e.g., respecting symmetry), or semantic (e.g., including a particular number of objects). When the training data all satisfy a certain constraint, enforcing this constraint on a diffusion model not only improves its distribution-matching accuracy but also makes it more reliable for generating valid synthetic data and solving constrained inverse problems. However, existing methods for constrained diffusion models are inflexible with different types of constraints. Recent work proposed to learn mirror diffusion models (MDMs) in an unconstrained space defined by a mirror map and to impose the constraint with an inverse mirror map, but analytical mirror maps are challenging to derive for complex constraints. We propose neural approximate mirror maps (NAMMs) for general constraints. Our approach only requires a differentiable distance function from the constraint set. We learn an approximate mirror map that pushes data into an unconstrained space and a corresponding approximate inverse that maps data back to the constraint set. A generative model, such as an MDM, can then be trained in the learned mirror space and its samples restored to the constraint set by the inverse map. We validate our approach on a variety of constraints, showing that compared to an unconstrained diffusion model, a NAMM-based MDM substantially improves constraint satisfaction. We also demonstrate how existing diffusion-based inverse-problem solvers can be easily applied in the learned mirror space to solve constrained inverse problems.">
+        <tr id="Reliable uncertainty measures are required when using data based machine learning interatomic potentials (MLIPs) for atomistic simulations. In this work, we propose for sparse Gaussian Process Regression type MLIP a stochastic uncertainty measure akin to the query-by-committee approach often used in conjunction with neural network based MLIPs. The uncertainty measure is coined \textit{"label noise"} ensemble uncertainty as it emerges from adding noise to the energy labels in the training data. We find that this method of calculating an ensemble uncertainty is as well calibrated as the one obtained from the closed-form expression for the posterior variance when the sparse GPR is treated as a projected process. Comparing the two methods, our proposed ensemble uncertainty is, however, faster to evaluate than the closed-form expression. Finally, we demonstrate that the proposed uncertainty measure acts better to support a Bayesian search for optimal structure of Au$_{20}$ clusters.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/41e376b129ad93d8ea21f2780e8a91014ca3759f" target='_blank'>
-              Neural Approximate Mirror Maps for Constrained Diffusion Models
+          <td><a href="https://www.semanticscholar.org/paper/cbe74de7bdeef59e212d49ccb746e4deaac70d5f" target='_blank'>
+              Efficient ensemble uncertainty estimation in Gaussian Processes Regression
               </a>
             </td>
           <td>
-            Berthy T. Feng, Ricardo Baptista, Katherine L. Bouman
+            Mads-Peter V. Christiansen, Nikolaj Rønne, Bjork Hammer
           </td>
-          <td>2024-06-18</td>
+          <td>2024-07-17</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>5</td>
+          <td>6</td>
         </tr>
 
-        <tr id="In neurological networks, the emergence of various causal interactions and information flows among nodes is governed by the structural connectivity in conjunction with the node dynamics. The information flow describes the direction and the magnitude of an excitatory neuron’s influence to the neighbouring neurons. However, the intricate relationship between network dynamics and information flows is not well understood. Here, we address this challenge by first identifying a generic mechanism that defines the evolution of various information routing patterns in response to modifications in the underlying network dynamics. Moreover, with emerging techniques in brain stimulation, designing optimal stimulation directed towards a target region with an acceptable magnitude remains an ongoing and significant challenge. In this work, we also introduce techniques for computing optimal inputs that follow a desired stimulation routing path towards the target brain region. This optimization problem can be efficiently resolved using non-linear programming tools and permits the simultaneous assignment of multiple desired patterns at different instances. We establish the algebraic and graph-theoretic conditions necessary to ensure the feasibility and stability of information routing patterns (IRPs). We illustrate the routing mechanisms and control methods for attaining desired patterns in biological oscillatory dynamics. Author Summary A complex network is described by collection of subsystems or nodes, often exchanging information among themselves via fixed interconnection pattern or structure of the network. This combination of nodes, interconnection structure and the information exchange enables the overall network system to function. These information exchange patterns change over time and switch patterns whenever a node or set of nodes are subject to external perturbations or stimulations. In many cases one would want to drive the system to desired information patterns, resulting in desired network system behaviour, by appropriately designing the perturbating signals. We present mathematical framework to design perturbation signals that drive the system to the desired behaviour. We demonstrate the applicability of our framework in the context of brain stimulation and in modifying causal interactions in gene regulatory networks.">
+        <tr id="Predictive estimation, which comprises model calibration, model prediction, and validation, is a common objective when performing inverse uncertainty quantification (UQ) in diverse scientific applications. These techniques typically require thousands to millions of realisations of the forward model, leading to high computational costs. Surrogate models are often used to approximate these simulations. However, many surrogate models suffer from the fundamental limitation of being unable to estimate plausible high-dimensional outputs, inevitably compromising their use in the UQ framework. To address this challenge, this study introduces an efficient surrogate modelling workflow tailored for high-dimensional outputs. Specifically, a two-step approach is developed: (1) a dimensionality reduction technique is used for extracting data features and mapping the original output space into a reduced space; and (2) a multivariate surrogate model is constructed directly on the reduced space. The combined approach is shown to improve the accuracy of the surrogate model while retaining the computational efficiency required for UQ inversion. The proposed surrogate method, combined with Bayesian inference, is evaluated for a civil engineering application by performing inverse analyses on a laterally loaded pile problem. The results demonstrate the superiority of the proposed framework over traditional surrogate methods in dealing with high-dimensional outputs for sequential inversion analysis.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ed590d96ecaf348aa0a2b4eec0b2b12f116b9d73" target='_blank'>
-              Functional Control of Network Dynamical Systems: An Information Theoretic Approach
+          <td><a href="https://www.semanticscholar.org/paper/dace1d7c26c300e5c21c5561b92782d077d84947" target='_blank'>
+              A two-step surrogate method for sequential uncertainty quantification in high-dimensional inverse problems
               </a>
             </td>
           <td>
-            Moirangthem Sailash Singh, R. Pasumarthy, Umesh Vaidya, Steffen Leonhardt
+            Ningxin Yang, Truong Le, Lidija Zdravkovi'c, David M. Potts
           </td>
-          <td>2024-06-17</td>
-          <td>bioRxiv</td>
+          <td>2024-07-16</td>
+          <td>ArXiv</td>
+          <td>0</td>
           <td>0</td>
-          <td>14</td>
         </tr>
 
-        <tr id="Partial Differential Equations (PDEs) serve as the cornerstone for a wide range of scientific endeavours, their solutions weaving through the core of diverse fields such as structural engineering, fluid dynamics, and financial modelling. PDEs are notoriously hard to solve, due to their the intricate nature, and finding solutions to PDEs often exceeds the capabilities of traditional computational approaches. Recent advances in quantum computing have triggered a growing interest from researchers for the design of quantum algorithms for solving PDEs. In this work, we introduce two different architectures of a novel variational quantum algorithm (VQA) with Lagrange polynomial encoding in combination with derivative quantum circuits using the Hadamard test differentiation to approximate the solution of PDEs. To demonstrate the potential of our new VQA, two well-known PDEs are used: the damped mass-spring system from a given initial value and the Poisson equation for periodic, Dirichlet and Neumann boundary conditions. It is shown that the proposed new VQA has a reduced gate complexity compared to previous variational quantum algorithms, for a similar or better quality of the solution.">
+        <tr id="The probability that a configuration of a physical system reacts, or transitions from one metastable state to another, is quantified by the committor function. This function contains richly detailed mechanistic information about transition pathways, but a full parameterization of the committor requires building representing a high-dimensional function, a generically challenging task. Recent efforts to leverage neural networks as a means to solve high-dimensional partial differential equations, often called"physics-informed"machine learning, have brought the committor into computational reach. Here, we build on the semigroup approach to learning the committor and assess its utility for predicting dynamical quantities such as transition rates. We show that a careful reframing of the objective function and improved adaptive sampling strategies provide highly accurate representations of the committor. Furthermore, by directly applying the Hill relation, we show that these committors provide accurate transition rates for molecular system.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/43f25f6546a5a52c8783c7a55d78b150c3546742" target='_blank'>
-              A New Variational Quantum Algorithm Based on Lagrange Polynomial Encoding to Solve Partial Differential Equations
+          <td><a href="https://www.semanticscholar.org/paper/61781a644a9abeb378998d8505d6add714bb238b" target='_blank'>
+              Committor guided estimates of molecular transition rates
               </a>
             </td>
           <td>
-            Josephine Hunout, Sylvain Laizet, Lorenzo Iannucci
+            Andrew R. Mitchell, Grant M. Rotskoff
           </td>
-          <td>2024-07-23</td>
+          <td>2024-08-12</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>24</td>
         </tr>
 
-        <tr id="This paper introduces a novel nonlinear stochastic model predictive control path integral (MPPI) method, which considers chance constraints on system states. The proposed belief-space stochastic MPPI (BSS-MPPI) applies Monte-Carlo sampling to evaluate state distributions resulting from underlying systematic disturbances, and utilizes a Control Barrier Function (CBF) inspired heuristic in belief space to fulfill the specified chance constraints. Compared to several previous stochastic predictive control methods, our approach applies to general nonlinear dynamics without requiring the computationally expensive system linearization step. Moreover, the BSS-MPPI controller can solve optimization problems without limiting the form of the objective function and chance constraints. By multi-threading the sampling process using a GPU, we can achieve fast real-time planning for time- and safety-critical tasks such as autonomous racing. Our results on a realistic race-car simulation study show significant reductions in constraint violation compared to some of the prior MPPI approaches, while being comparable in computation times.">
+        <tr id="In functional data analysis, replicate observations of a smooth functional process and its derivatives offer a unique opportunity to flexibly estimate continuous-time ordinary differential equation models. Ramsay (1996) first proposed to estimate a linear ordinary differential equation from functional data in a technique called Principal Differential Analysis, by formulating a functional regression in which the highest-order derivative of a function is modelled as a time-varying linear combination of its lower-order derivatives. Principal Differential Analysis was introduced as a technique for data reduction and representation, using solutions of the estimated differential equation as a basis to represent the functional data. In this work, we re-formulate PDA as a generative statistical model in which functional observations arise as solutions of a deterministic ODE that is forced by a smooth random error process. This viewpoint defines a flexible class of functional models based on differential equations and leads to an improved understanding and characterisation of the sources of variability in Principal Differential Analysis. It does, however, result in parameter estimates that can be heavily biased under the standard estimation approach of PDA. Therefore, we introduce an iterative bias-reduction algorithm that can be applied to improve parameter estimates. We also examine the utility of our approach when the form of the deterministic part of the differential equation is unknown and possibly non-linear, where Principal Differential Analysis is treated as an approximate model based on time-varying linearisation. We demonstrate our approach on simulated data from linear and non-linear differential equations and on real data from human movement biomechanics. Supplementary R code for this manuscript is available at \url{https://github.com/edwardgunning/UnderstandingOfPDAManuscript}.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ec0a95b01ae133b323176ef995b8490fff8991be" target='_blank'>
-              Chance-Constrained Information-Theoretic Stochastic Model Predictive Control with Safety Shielding
+          <td><a href="https://www.semanticscholar.org/paper/8d0be3d0b8344a99cb8ce9f30ed01a1653af4baa" target='_blank'>
+              An Understanding of Principal Differential Analysis
               </a>
             </td>
           <td>
-            Ji Yin, P. Tsiotras, K. Berntorp
+            Edward Gunning, Giles Hooker
           </td>
-          <td>2024-08-01</td>
+          <td>2024-06-26</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>49</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Reliable uncertainty measures are required when using data based machine learning interatomic potentials (MLIPs) for atomistic simulations. In this work, we propose for sparse Gaussian Process Regression type MLIP a stochastic uncertainty measure akin to the query-by-committee approach often used in conjunction with neural network based MLIPs. The uncertainty measure is coined \textit{"label noise"} ensemble uncertainty as it emerges from adding noise to the energy labels in the training data. We find that this method of calculating an ensemble uncertainty is as well calibrated as the one obtained from the closed-form expression for the posterior variance when the sparse GPR is treated as a projected process. Comparing the two methods, our proposed ensemble uncertainty is, however, faster to evaluate than the closed-form expression. Finally, we demonstrate that the proposed uncertainty measure acts better to support a Bayesian search for optimal structure of Au$_{20}$ clusters.">
+        <tr id="
+Of great relevance to climate engineering is the systematic relationship between the radiative forcing to the climate system and the response of the system, a relationship often represented by the linear response function (LRF) of the system. However, estimating the LRF often becomes an ill-posed inverse problem due to high-dimensionality and non-unique relationships between the forcing and response. Recent advances in machine learning make it possible to address the ill-posed inverse problem through regularization and sparse system fitting. Here we develop a convolutional neural network (CNN) for regularized inversion. The CNN is trained using the surface temperature responses from a set of Green’s function perturbation experiments as imagery input data together with data sample densification. The resulting CNN model can infer the forcing pattern responsible for the temperature response from out-of-sample forcing scenarios. This promising proof-of-concept suggests a possible strategy for estimating the optimal forcing to negate certain undesirable effects of climate change. The limited success of this effort underscores the challenges of solving an inverse problem for a climate system with inherent nonlinearity.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/cbe74de7bdeef59e212d49ccb746e4deaac70d5f" target='_blank'>
-              Efficient ensemble uncertainty estimation in Gaussian Processes Regression
+          <td><a href="https://www.semanticscholar.org/paper/f4dd739f36233f85b623bc7abb71b0aaaed422e9" target='_blank'>
+              Neural networks to find the optimal forcing for offsetting the anthropogenic climate change effects
               </a>
             </td>
           <td>
-            Mads-Peter V. Christiansen, Nikolaj Rønne, Bjork Hammer
+            Huiying Ren, Jian Lu, Z. J. Hou, Tse-Chun Chen, L. R. Leung, Fukai Liu
           </td>
-          <td>2024-07-17</td>
+          <td>2024-07-03</td>
+          <td>Artificial Intelligence for the Earth Systems</td>
+          <td>0</td>
+          <td>11</td>
+        </tr>
+
+        <tr id="Modeling thermal states for complex space missions, such as the surface exploration of airless bodies, requires high computation, whether used in ground-based analysis for spacecraft design or during onboard reasoning for autonomous operations. For example, a finite-element thermal model with hundreds of elements can take significant time to simulate, which makes it unsuitable for onboard reasoning during time-sensitive scenarios such as descent and landing, proximity operations, or in-space assembly. Further, the lack of fast and accurate thermal modeling drives thermal designs to be more conservative and leads to spacecraft with larger mass and higher power budgets. The emerging paradigm of physics-informed machine learning (PIML) presents a class of hybrid modeling architectures that address this challenge by combining simplified physics models with machine learning (ML) models resulting in models which maintain both interpretability and robustness. Such techniques enable designs with reduced mass and power through onboard thermal-state estimation and control and may lead to improved onboard handling of off-nominal states, including unplanned down-time. The PIML model or hybrid model presented here consists of a neural network which predicts reduced nodalizations (distribution and size of coarse mesh) given on-orbit thermal load conditions, and subsequently a (relatively coarse) finite-difference model operates on this mesh to predict thermal states. We compare the computational performance and accuracy of the hybrid model to a data-driven neural net model, and a high-fidelity finite-difference model of a prototype Earth-orbiting small spacecraft. The PIML based active nodalization approach provides significantly better generalization than the neural net model and coarse mesh model, while reducing computing cost by up to 1.7x compared to the high-fidelity model.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/04894c31eb0dcaa0ede3881b219f3bfbb359c4b6" target='_blank'>
+              Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator
+              </a>
+            </td>
+          <td>
+            Manaswin Oddiraju, Zaki Hasnain, Saptarshi Bandyopadhyay, Eric Sunada, Souma Chowdhury
+          </td>
+          <td>2024-07-08</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
+          <td>9</td>
         </tr>
 
-        <tr id="The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (i) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct E. coli promoters and (ii) design nonequilibrium promoter architectures with desired input-output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.">
+        <tr id="In this paper, we propose and study several inverse problems of determining unknown parameters in nonlocal nonlinear coupled PDE systems, including the potentials, nonlinear interaction functions and time-fractional orders. In these coupled systems, we enforce non-negativity of the solutions, aligning with realistic scenarios in biology and ecology. There are several salient features of our inverse problem study: the drastic reduction in measurement/observation data due to averaging effects, the nonlinear coupling between multiple equations, and the nonlocality arising from fractional-type derivatives. These factors present significant challenges to our inverse problem, and such inverse problems have never been explored in previous literature. To address these challenges, we develop new and effective schemes. Our approach involves properly controlling the injection of different source terms to obtain multiple sets of mean flux data. This allows us to achieve unique identifiability results and accurately determine the unknown parameters. Finally, we establish a connection between our study and practical applications in biology, further highlighting the relevance of our work in real-world contexts.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/cb8df262d95f461b20aad0ae54899068bcc11044" target='_blank'>
-              A differentiable Gillespie algorithm for simulating chemical kinetics, parameter estimation, and designing synthetic biological circuits
+          <td><a href="https://www.semanticscholar.org/paper/6630d9bfe19de762af36c38b8a48daf0ad1ea2eb" target='_blank'>
+              Inverse problems for coupled nonlocal nonlinear systems arising in mathematical biology
               </a>
             </td>
           <td>
-            K. Rijal, Pankaj Mehta
+            Ming-Hui Ding, Hongyu Liu, Catharine W. K. Lo
           </td>
-          <td>2024-07-05</td>
-          <td>bioRxiv</td>
+          <td>2024-07-22</td>
+          <td>ArXiv</td>
           <td>0</td>
           <td>4</td>
         </tr>
 
-        <tr id="The development of novel materials in recent years has been accelerated greatly by the use of computational modelling techniques aimed at elucidating the complex physics controlling microstructure formation in materials, the properties of which control material function. One such technique is the phase field method, a field theoretic approach that couples various thermophysical fields to microscopic order parameter fields that track the phases of microstructure. Phase field models are framed as multiple, non-linear, partial differential equations, which are extremely challenging to compute efficiently. Recent years have seen an explosion of computational algorithms aimed at enhancing the efficiency of phase field simulations. One such technique, adaptive mesh refinement (AMR), dynamically adapts numerical meshes to be highly refined around steep spatial gradients of the PDE fields and coarser where the fields are smooth. This reduces the number of computations per time step significantly, thus reducing the total time of computation. What AMR doesn't do is allow for adaptive time stepping. This work combines AMR with a neural network algorithm that uses a U-Net with a Convolutional Long-Short Term Memory (CLSTM) base to accelerate phase field simulations. Our neural network algorithm is described in detail and tested in on simulations of directional solidification of a dilute binary alloy, a paradigm that is highly practical for its relevance to the solidification of alloys.">
+        <tr id="The integration of particle or Kalman filters with machine learning tools like support vector machines, Gaussian processes, or neural networks has seen extensive exploration in the context of prognostic and health management, particularly in model-based applications. This paper focuses on the Multi-Layer Perceptron Particle Filter (MLP-PF), a data-driven approach that harnesses the non-linearity of MLP to describe degradation trajectories without relying on a physical model. The Bayesian nature of the particle filter is utilized to update MLP parameters, providing flexibility to the method and accommodating unexpected changes in the degradation behavior. To showcase the versatility of MLP-PF, this work demonstrates its seamless integration into diverse use cases, such as lithium-ion battery analysis, virtual health monitoring for turbofans, and the assessment of fatigue crack growth. We illustrate how it effortlessly accommodates various contexts through slight parameter modifications. Adjustment includes variation in the number of neurons or layers in the MLP, threshold adjustments, initial training refinements and the adaptation of the process noise. Addressing different degradation processes across these applications, MLP-PF proves its adaptability and utility in various contexts. These findings highlight the method’s versatility in adapting to diverse use cases and its potential as a robust prognostic tool across various industries. MLP-PF offers a practical and efficient means of estimating remaining useful life and predicting degradation in complex systems, with implications for advancing prognostic tools in diverse applications.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f7c3b439cab9bc1574b9ee846128f87f45347f16" target='_blank'>
-              LeapFrog: Getting the Jump on Multi-Scale Materials Simulations Using Machine Learning
+          <td><a href="https://www.semanticscholar.org/paper/eb6fdd4461741278755a0c986fba0e2f5cd89de3" target='_blank'>
+              Data-Driven Prognostics with Multi-Layer Perceptron Particle Filter: a Cross-Industry Exploration
               </a>
             </td>
           <td>
-            Damien Pinto, M. Greenwood, N. Provatas
+            Francesco Canceliere, Sylvain Girard, Jean-Marc Bourinet
+          </td>
+          <td>2024-06-27</td>
+          <td>PHM Society European Conference</td>
+          <td>0</td>
+          <td>15</td>
+        </tr>
+
+        <tr id="Solving partial differential equations (PDEs) and their inverse problems using Physics-informed neural networks (PINNs) is a rapidly growing approach in the physics and machine learning community. Although several architectures exist for PINNs that work remarkably in practice, our theoretical understanding of their performances is somewhat limited. In this work, we study the behavior of a Bayesian PINN estimator of the solution of a PDE from $n$ independent noisy measurement of the solution. We focus on a class of equations that are linear in their parameters (with unknown coefficients $\theta_\star$). We show that when the partial differential equation admits a classical solution (say $u_\star$), differentiable to order $\beta$, the mean square error of the Bayesian posterior mean is at least of order $n^{-2\beta/(2\beta + d)}$. Furthermore, we establish a convergence rate of the linear coefficients of $\theta_\star$ depending on the order of the underlying differential operator. Last but not least, our theoretical results are validated through extensive simulations.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/d332b278e945ecc01ec826948d1f3554cba5312b" target='_blank'>
+              On the estimation rate of Bayesian PINN for inverse problems
+              </a>
+            </td>
+          <td>
+            Yi Sun, Debarghya Mukherjee, Yves Atchadé
           </td>
           <td>2024-06-21</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>36</td>
+          <td>7</td>
         </tr>
 
-        <tr id="We introduce a real-time identification method for discrete-time state-dependent switching systems in both the input--output and state-space domains. In particular, we design a system of adaptive algorithms running in two timescales; a stochastic approximation algorithm implements an online deterministic annealing scheme at a slow timescale and estimates the mode-switching signal, and an recursive identification algorithm runs at a faster timescale and updates the parameters of the local models based on the estimate of the switching signal. We first focus on piece-wise affine systems and discuss identifiability conditions and convergence properties based on the theory of two-timescale stochastic approximation. In contrast to standard identification algorithms for switched systems, the proposed approach gradually estimates the number of modes and is appropriate for real-time system identification using sequential data acquisition. The progressive nature of the algorithm improves computational efficiency and provides real-time control over the performance-complexity trade-off. Finally, we address specific challenges that arise in the application of the proposed methodology in identification of more general switching systems. Simulation results validate the efficacy of the proposed methodology.">
+        <tr id="Neural networks are popular data-driven modeling tools that come with high data collection costs. This paper proposes a residual-based multipeaks adaptive sampling (RMAS) algorithm, which can reduce the demand for a large number of samples in the identification of stochastic dynamical systems. Compared to classical residual-based sampling algorithms, the RMAS algorithm achieves higher system identification accuracy without relying on any hyperparameters. Subsequently, combining the RMAS algorithm and neural network, a few-shot identification (FSI) method for stochastic dynamical systems is proposed, which is applied to the identification of a vegetation biomass change model and the Rayleigh-Van der Pol impact vibration model. We show that the RMAS algorithm modifies residual-based sampling algorithms and, in particular, reduces the system identification error by 76% with the same sample sizes. Moreover, the surrogate model accurately predicts the first escape probability density function and the P bifurcation behavior in the systems, with the error of less than 1.59×10-2. Finally, the robustness of the FSI method is validated.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1b89639e4c9af642397cc1de2b3ac52dbf06fea3" target='_blank'>
-              Real-time Hybrid System Identification with Online Deterministic Annealing
+          <td><a href="https://www.semanticscholar.org/paper/295396f31e547b6c81f17b2bb53b2116ac400a8e" target='_blank'>
+              A few-shot identification method for stochastic dynamical systems based on residual multipeaks adaptive sampling.
               </a>
             </td>
           <td>
-            Christos N. Mavridis, K. H. Johansson
+            Xiao-Kai An, Lin Du, Feng Jiang, Yu-jia Zhang, Zi-Chen Deng, Jürgen Kurths
           </td>
-          <td>2024-08-03</td>
+          <td>2024-07-01</td>
+          <td>Chaos</td>
+          <td>0</td>
+          <td>7</td>
+        </tr>
+
+        <tr id="The paper considers the observer synthesis for nonlinear, time-varying plants with uncertain parameters under multiharmonic disturbance. It is assumed that the relative degree of the plant is known, the regressor linearly depends on the state vector and may have a nonlinear relationship with the output signal. The proposed solution consists of three steps. Initially, an unknown input state observer is synthesized. This observer, however, necessitates the measurement of output derivatives equal to the plant's relative degree. To relax this limitation, an alternative representation of the observer is introduced. Further, based on this observer, the unknown parameters and disturbances are reconstructed using an autoregression model and the dynamic regressor extension and mixing (DREM) approach. This approach allows the estimates to be obtained in a finite time. Finally, based on these estimates, an observer has been constructed that does not require measurements of the output derivatives. The effectiveness and efficiency of this solution are demonstrated through a computer simulation.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/f3f630f0776c6b0ed2b88908f39e12f1e0579daa" target='_blank'>
+              State estimation for a class of nonlinear time-varying uncertain system under multiharmonic disturbance
+              </a>
+            </td>
+          <td>
+            A. A. Margun, V. Bui, A. A. Bobtsov, Denis V. Efimov
+          </td>
+          <td>2024-07-25</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>2</td>
+          <td>6</td>
         </tr>
 
-        <tr id="Predictive estimation, which comprises model calibration, model prediction, and validation, is a common objective when performing inverse uncertainty quantification (UQ) in diverse scientific applications. These techniques typically require thousands to millions of realisations of the forward model, leading to high computational costs. Surrogate models are often used to approximate these simulations. However, many surrogate models suffer from the fundamental limitation of being unable to estimate plausible high-dimensional outputs, inevitably compromising their use in the UQ framework. To address this challenge, this study introduces an efficient surrogate modelling workflow tailored for high-dimensional outputs. Specifically, a two-step approach is developed: (1) a dimensionality reduction technique is used for extracting data features and mapping the original output space into a reduced space; and (2) a multivariate surrogate model is constructed directly on the reduced space. The combined approach is shown to improve the accuracy of the surrogate model while retaining the computational efficiency required for UQ inversion. The proposed surrogate method, combined with Bayesian inference, is evaluated for a civil engineering application by performing inverse analyses on a laterally loaded pile problem. The results demonstrate the superiority of the proposed framework over traditional surrogate methods in dealing with high-dimensional outputs for sequential inversion analysis.">
+        <tr id="Advances in experimental techniques allow the collection of high-space-and-time resolution data that track individual motile entities over time. This poses the question of how to use these data to efficiently and effectively calibrate motion models. However, typical mathematical models often overlook the inherent aspects of data collection, such as the discreteness and the experimental noise of the measured locations. In this paper, we focus on velocity-jump models suitable to describe single-agent motion in one spatial dimension, characterised by successive Markovian transitions between a finite network of $n$ states, each with a specified velocity and a fixed rate of switching to every other state. Since the problem of finding the exact distributions of discrete-time noisy data is generally intractable, we derive a series of approximations for the data distributions and compare them to in-silico data generated by the models using four example network structures. These comparisons suggest that the approximations are accurate given sufficiently infrequent state switching, or equivalently, a sufficiently high data collection frequency. Moreover, for infrequent switching, the PDFs comparisons highlight the importance of accounting for the correlation between subsequent measured locations, due to the likely permanence in the state visited in the previous measurement. The approximate distributions computed can be used for fast parameter inference and model selection between a range of velocity-jump models using single-agent tracking data.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/dace1d7c26c300e5c21c5561b92782d077d84947" target='_blank'>
-              A two-step surrogate method for sequential uncertainty quantification in high-dimensional inverse problems
+          <td><a href="https://www.semanticscholar.org/paper/8f4862ab7ce4151b9c0569c8c9b73d89ab08ca0e" target='_blank'>
+              Approximate solutions of a general stochastic velocity-jump process subject to discrete-time noisy observations
               </a>
             </td>
           <td>
-            Ningxin Yang, Truong Le, Lidija Zdravkovi'c, David M. Potts
+            Arianna Ceccarelli, Alexander P. Browning, Ruth E. Baker
           </td>
-          <td>2024-07-16</td>
+          <td>2024-06-28</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
-        <tr id="The gamma process is a natural model for monotonic degradation processes. In practice, it is desirable to extend the single gamma process to incorporate measurement error and to construct models for the degradation of several nominally identical units. In this paper, we show how these extensions are easily facilitated through the Bayesian hierarchical modelling framework. Following the precepts of the Bayesian statistical workflow, we show the principled construction of a noisy gamma process model. We also reparameterise the gamma process to simplify the specification of priors and make it obvious how the single gamma process model can be extended to include unit-to-unit variability or covariates. We first fit the noisy gamma process model to a single simulated degradation trace. In doing so, we find an identifiability problem between the volatility of the gamma process and the measurement error when there are only a few noisy degradation observations. However, this lack of identifiability can be resolved by including extra information in the analysis through a stronger prior or extra data that informs one of the non-identifiable parameters, or by borrowing information from multiple units. We then explore extensions of the model to account for unit-to-unit variability and demonstrate them using a crack-propagation data set with added measurement error. Lastly, we perform model selection in a fully Bayesian framework by using cross-validation to approximate the expected log probability density of new observation. We also show how failure time distributions with uncertainty intervals can be calculated for new units or units that are currently under test but are yet to fail.">
+        <tr id="The chaotic nature of fluid flow and the uncertainties in initial conditions limit predictability. Small errors that occur in the initial condition can grow exponentially until they saturate at $\mathcal{O}$(1). Ensemble forecasting averages multiple runs with slightly different initial conditions and other data to produce more accurate results and extend the predictability horizon. However, they can be computationally expensive. We develop a penalty-based ensemble method with a shared coefficient matrix to reduce required memory and computational cost and thereby allow larger ensemble sizes. Penalty methods relax the incompressibility condition to decouple the pressure and velocity, reducing memory requirements. This report gives stability proof and an error estimate of the penalty-based ensemble method, extends it to the Navier-Stokes equations with random variables using Monte Carlo sampling, and validates the method's accuracy and efficiency with three numerical experiments.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/316ccb89df995256d931317923452a0aa87a5a44" target='_blank'>
-              Bayesian Hierarchical Modelling of Noisy Gamma Processes: Model Formulation, Identifiability, Model Fitting, and Extensions to Unit-to-Unit Variability
+          <td><a href="https://www.semanticscholar.org/paper/52cdf874930613b2823b442be8bdfe4e5d17d2e9" target='_blank'>
+              Numerical Analysis of Penalty-based Ensemble Methods
               </a>
             </td>
           <td>
-            Ryan Leadbetter, Gabriel Gonzalez Caceres, A. Phatak
+            Rui Fang
           </td>
-          <td>2024-06-17</td>
+          <td>2024-07-13</td>
           <td>ArXiv</td>
-          <td>0</td>
-          <td>15</td>
+          <td>2</td>
+          <td>2</td>
         </tr>
 
-        <tr id="Modeling thermal states for complex space missions, such as the surface exploration of airless bodies, requires high computation, whether used in ground-based analysis for spacecraft design or during onboard reasoning for autonomous operations. For example, a finite-element thermal model with hundreds of elements can take significant time to simulate, which makes it unsuitable for onboard reasoning during time-sensitive scenarios such as descent and landing, proximity operations, or in-space assembly. Further, the lack of fast and accurate thermal modeling drives thermal designs to be more conservative and leads to spacecraft with larger mass and higher power budgets. The emerging paradigm of physics-informed machine learning (PIML) presents a class of hybrid modeling architectures that address this challenge by combining simplified physics models with machine learning (ML) models resulting in models which maintain both interpretability and robustness. Such techniques enable designs with reduced mass and power through onboard thermal-state estimation and control and may lead to improved onboard handling of off-nominal states, including unplanned down-time. The PIML model or hybrid model presented here consists of a neural network which predicts reduced nodalizations (distribution and size of coarse mesh) given on-orbit thermal load conditions, and subsequently a (relatively coarse) finite-difference model operates on this mesh to predict thermal states. We compare the computational performance and accuracy of the hybrid model to a data-driven neural net model, and a high-fidelity finite-difference model of a prototype Earth-orbiting small spacecraft. The PIML based active nodalization approach provides significantly better generalization than the neural net model and coarse mesh model, while reducing computing cost by up to 1.7x compared to the high-fidelity model.">
+        <tr id="Beyond estimating parameters of interest from data, one of the key goals of statistical inference is to properly quantify uncertainty in these estimates. In Bayesian inference, this uncertainty is provided by the posterior distribution, the computation of which typically involves an intractable high-dimensional integral. Among available approximation methods, sampling-based approaches come with strong theoretical guarantees but scale poorly to large problems, while variational approaches scale well but offer few theoretical guarantees. In particular, variational methods are known to produce overconfident estimates of posterior uncertainty and are typically non-identifiable, with many latent variable configurations generating equivalent predictions. Here, we address these challenges by showing how diffusion-based models (DBMs), which have recently produced state-of-the-art performance in generative modeling tasks, can be repurposed for performing calibrated, identifiable Bayesian inference. By exploiting a previously established connection between the stochastic and probability flow ordinary differential equations (pfODEs) underlying DBMs, we derive a class of models, inflationary flows, that uniquely and deterministically map high-dimensional data to a lower-dimensional Gaussian distribution via ODE integration. This map is both invertible and neighborhood-preserving, with controllable numerical error, with the result that uncertainties in the data are correctly propagated to the latent space. We demonstrate how such maps can be learned via standard DBM training using a novel noise schedule and are effective at both preserving and reducing intrinsic data dimensionality. The result is a class of highly expressive generative models, uniquely defined on a low-dimensional latent space, that afford principled Bayesian inference.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/04894c31eb0dcaa0ede3881b219f3bfbb359c4b6" target='_blank'>
-              Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator
+          <td><a href="https://www.semanticscholar.org/paper/17907a21b0ea22f5a5548076e21700dea63c3931" target='_blank'>
+              Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models
               </a>
             </td>
           <td>
-            Manaswin Oddiraju, Zaki Hasnain, Saptarshi Bandyopadhyay, Eric Sunada, Souma Chowdhury
+            Daniela de Albuquerque, John Pearson
           </td>
-          <td>2024-07-08</td>
+          <td>2024-07-11</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>9</td>
+          <td>0</td>
         </tr>
 
-        <tr id="Solving partial differential equations (PDEs) and their inverse problems using Physics-informed neural networks (PINNs) is a rapidly growing approach in the physics and machine learning community. Although several architectures exist for PINNs that work remarkably in practice, our theoretical understanding of their performances is somewhat limited. In this work, we study the behavior of a Bayesian PINN estimator of the solution of a PDE from $n$ independent noisy measurement of the solution. We focus on a class of equations that are linear in their parameters (with unknown coefficients $\theta_\star$). We show that when the partial differential equation admits a classical solution (say $u_\star$), differentiable to order $\beta$, the mean square error of the Bayesian posterior mean is at least of order $n^{-2\beta/(2\beta + d)}$. Furthermore, we establish a convergence rate of the linear coefficients of $\theta_\star$ depending on the order of the underlying differential operator. Last but not least, our theoretical results are validated through extensive simulations.">
+        <tr id="Many application areas rely on models that can be readily simulated but lack a closed-form likelihood, or an accurate approximation under arbitrary parameter values. Existing parameter estimation approaches in this setting are generally approximate. Recent work on using neural network models to reconstruct the mapping from the data space to the parameters from a set of synthetic parameter-data pairs suffers from the curse of dimensionality, resulting in inaccurate estimation as the data size grows. We propose a dimension-reduced approach to likelihood-free estimation which combines the ideas of reconstruction map estimation with dimension-reduction approaches based on subject-specific knowledge. We examine the properties of reconstruction map estimation with and without dimension reduction and explore the trade-off between approximation error due to information loss from reducing the data dimension and approximation error. Numerical examples show that the proposed approach compares favorably with reconstruction map estimation, approximate Bayesian computation, and synthetic likelihood estimation.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/d332b278e945ecc01ec826948d1f3554cba5312b" target='_blank'>
-              On the estimation rate of Bayesian PINN for inverse problems
+          <td><a href="https://www.semanticscholar.org/paper/e928447f6c322a7beac72466a09947bc31240ec4" target='_blank'>
+              Dimension-reduced Reconstruction Map Learning for Parameter Estimation in Likelihood-Free Inference Problems
               </a>
             </td>
           <td>
-            Yi Sun, Debarghya Mukherjee, Yves Atchadé
+            Rui Zhang, O. Chkrebtii, Dongbin Xiu
           </td>
-          <td>2024-06-21</td>
+          <td>2024-07-19</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>9</td>
+        </tr>
+
+        <tr id="Several forms for constructing novel physics-informed neural-networks (PINN) for the solution of partial-differential-algebraic equations based on derivative operator splitting are proposed, using the nonlinear Kirchhoff rod as a prototype for demonstration. The open-source DeepXDE is likely the most well documented framework with many examples. Yet, we encountered some pathological problems and proposed novel methods to resolve them. Among these novel methods are the PDE forms, which evolve from the lower-level form with fewer unknown dependent variables to higher-level form with more dependent variables, in addition to those from lower-level forms. Traditionally, the highest-level form, the balance-of-momenta form, is the starting point for (hand) deriving the lowest-level form through a tedious (and error prone) process of successive substitutions. The next step in a finite element method is to discretize the lowest-level form upon forming a weak form and linearization with appropriate interpolation functions, followed by their implementation in a code and testing. The time-consuming tedium in all of these steps could be bypassed by applying the proposed novel PINN directly to the highest-level form. We developed a script based on JAX. While our JAX script did not show the pathological problems of DDE-T (DDE with TensorFlow backend), it is slower than DDE-T. That DDE-T itself being more efficient in higher-level form than in lower-level form makes working directly with higher-level form even more attractive in addition to the advantages mentioned further above. Since coming up with an appropriate learning-rate schedule for a good solution is more art than science, we systematically codified in detail our experience running optimization through a normalization/standardization of the network-training process so readers can reproduce our results.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/ea66520b54fdb2078af780a8675f7d6bbe935817" target='_blank'>
+              Partial-differential-algebraic equations of nonlinear dynamics by Physics-Informed Neural-Network: (I) Operator splitting and framework assessment
+              </a>
+            </td>
+          <td>
+            L. Vu-Quoc, A. Humer
+          </td>
+          <td>2024-07-13</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>32</td>
+        </tr>
+
+        <tr id="Machine Learning (ML) is extensively used for predicting transfer times for general purpose Wide Area Networks (WANs) or public Internet applications, but for Research and Education Networks (RENs) two major gaps exist in literature. First, RENs i.e. networks carrying large data flows have received limited attention by the networking community. RENs behave differently compared to the general purpose Internet applications and other network types. Hence, ML models from other network types cannot be used interchangeably for large data transfers. Second, the ML models are used as blackboxes to train on measured network values and then used to predict transfer times or other runtime network parameters. In this paper, we present a dynamical systems model of the large data transfers typical of RENs in the form of a system of Ordinary Differential Equations (ODEs) inspired by the Lotka-Volterra competition model. We present a transfer time prediction component called Dynamic Transfer Time Predictor (DTTP) which solves the ODEs and predicts the future transfer times. Second we formulate a loss function based on Lyapunov function called Lyapunov Drift Correction (LDC) that self-corrects the transfer time prediction errors dynamically.To design and develop our model, we studied real-world datasets consisting of over 100 million transfer records collected from platforms such as Open Science Grid (OSG), Large Hadron Collider Optical Private Network (LHCOPN), Worldwide LHC Grid (WLCG), as well as the RENs of Internet2 and ESNet. We integrate our model into well-known neural network models and regressors and present evaluation results.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/4cf5d71336cf90025f7d641a600628cc1d15b0d7" target='_blank'>
+              Improving Transfer Time Prediction of ML Models via Auto-correcting Dynamical Systems Modeling
+              </a>
+            </td>
+          <td>
+            Venkat Sai Suman Lamba Karanam, B. Ramamurthy
+          </td>
+          <td>2024-06-24</td>
+          <td>2024 IEEE 10th International Conference on Network Softwarization (NetSoft)</td>
+          <td>0</td>
+          <td>38</td>
+        </tr>
+
+        <tr id="Scenario-based optimization and control has proven to be an efficient approach to account for system uncertainty. In particular, the performance of scenario-based model predictive control (MPC) schemes depends on the accuracy of uncertainty quantification. However, current learning- and scenario-based MPC (sMPC) approaches employ a single timeinvariant probabilistic model (learned offline), which may not accurately describe time-varying uncertainties. Instead, this paper presents a model-agnostic meta-learning (MAML) of Bayesian neural networks (BNN) for adaptive uncertainty quantification that would be subsequently used for adaptive-scenario-tree model predictive control design of nonlinear systems with unknown dynamics to enhance control performance. In particular, the proposed approach learns both a global BNN model and an updating law to refine the BNN model. At each time step, the updating law transforms the global BNN model into more precise local BNN models in real time. The adapted local model is then used to generate scenarios for sMPC design at each time step. A probabilistic safety certificate is incorporated in the scenario generation to ensure that the trajectories of the generated scenarios contain the real trajectory of the system and that all the scenarios adhere to the constraints with a high probability. Experiments using closed-loop simulations of a numerical example demonstrate that the proposed approach can improve the performance of scenario-based MPC compared to using only one BNN model learned offline for all time steps.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/473eca6073be70a1728a3138bc7e859f877dc3e3" target='_blank'>
+              Adaptive Uncertainty Quantification for Scenario-based Control Using Meta-learning of Bayesian Neural Networks
+              </a>
+            </td>
+          <td>
+            Yajie Bao, Javad Mohammadpour Velni
+          </td>
+          <td>2024-07-19</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>13</td>
+        </tr>
+
+        <tr id="The efficient solution (fast and accurate) of parametric partial differential equations (pPDE) is of major interest in many domains of science and engineering, enabling evaluations of the quantities of interest, optimization, control, and uncertainty propagation—all them under stringent real-time constraints. Different methodologies have been proposed in the past within the model order reduction (MOR) community, based on the use of reduced bases (RB) or the separated representation at the heart of the so-called proper generalized decompositions (PGD). In PGD, an alternate-direction strategy is employed to circumvent the integration issues of operating in multi-dimensional domains. Recently, physics informed neural networks (PINNs), a particular collocation schema where the unknown field is approximated by a neural network (NN), have emerged in the domain of scientific machine learning. PNNs combine the versatility of NN-based approximation with the ease of collocating pPDE. The present paper proposes a combination of both procedures to find an efficient solution for pPDE, that can either be viewed as an efficient collocation procedure for PINN, or as a monolithic PGD that bypasses the use of the fixed-point alternated directions.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/73ebcfa6347c0ff167a1d0bd01e8827132585776" target='_blank'>
+              A Parsimonious Separated Representation Empowering PINN–PGD-Based Solutions for Parametrized Partial Differential Equations
+              </a>
+            </td>
+          <td>
+            C. Ghnatios, F. Chinesta
+          </td>
+          <td>2024-07-29</td>
+          <td>Mathematics</td>
+          <td>0</td>
+          <td>14</td>
+        </tr>
+
+        <tr id="The interdependence and high dimensionality of multivariate signals present significant challenges for denoising, as conventional univariate methods often struggle to capture the complex interactions between variables. A successful approach must consider not only the multivariate dependencies of the desired signal but also the multivariate dependencies of the interfering noise. In our previous research, we introduced a method using machine learning to extract the maximum portion of ``predictable information"from univariate signal. We extend this approach to multivariate signals, with the key idea being to properly incorporate the interdependencies of the noise back into the interdependent reconstruction of the signal. The method works successfully for various multivariate signals, including chaotic signals and highly oscillating sinusoidal signals which are corrupted by spatially correlated intensive noise. It consistently outperforms other existing multivariate denoising methods across a wide range of scenarios.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/53d763f53bd7322558de4eef640fa8ea0e0002cc" target='_blank'>
+              Unsupervised Reservoir Computing for Multivariate Denoising of Severely Contaminated Signals
+              </a>
+            </td>
+          <td>
+            Jaesung Choi, Pilwon Kim
+          </td>
+          <td>2024-07-26</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>1</td>
+        </tr>
+
+        <tr id="This paper establishes a method for solving partial differential equations using a multi-step physics-informed deep operator neural network. The network is trained by embedding physics-informed constraints. Different from traditional neural networks for solving partial differential equations, the proposed method uses a deep neural operator network to indirectly construct the mapping relationship between the variable functions and solution functions. This approach makes full use of the hidden information between the variable functions and independent variables. The process whereby the model captures incredibly complex and highly nonlinear relationships is simplified, thereby making network learning easier and enhancing the extraction of information about the independent variables in partial differential systems. In terms of solving partial differential equations, we verify that the multi-step physics-informed deep operator neural network markedly improves the solution accuracy compared with a traditional physics-informed deep neural operator network, especially when the problem involves complex physical phenomena with large gradient changes.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/ac5d801ec2ae339a109eb310769fe243cbff7025" target='_blank'>
+              Multi-Step Physics-Informed Deep Operator Neural Network for Directly Solving Partial Differential Equations
+              </a>
+            </td>
+          <td>
+            Jing Wang, Yubo Li, Anping Wu, Zheng Chen, Jun Huang, Qingfeng Wang, Feng Liu
+          </td>
+          <td>2024-06-25</td>
+          <td>Applied Sciences</td>
+          <td>0</td>
+          <td>6</td>
+        </tr>
+
+        <tr id="Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properties of matter at the atomic scale. Both established and state-of-the-art approaches, with almost no exceptions, are built to be exactly equivariant to translations, permutations, and rotations of the atoms. Incorporating symmetries -- rotations in particular -- constrains the model design space and implies more complicated architectures that are often also computationally demanding. There are indications that non-symmetric models can easily learn symmetries from data, and that doing so can even be beneficial for the accuracy of the model. We put a model that obeys rotational invariance only approximately to the test, in realistic scenarios involving simulations of gas-phase, liquid, and solid water. We focus specifically on physical observables that are likely to be affected -- directly or indirectly -- by symmetry breaking, finding negligible consequences when the model is used in an interpolative, bulk, regime. Even for extrapolative gas-phase predictions, the model remains very stable, even though symmetry artifacts are noticeable. We also discuss strategies that can be used to systematically reduce the magnitude of symmetry breaking when it occurs, and assess their impact on the convergence of observables.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/5dc964683c6ce6be616e96982ba7cf5172752d68" target='_blank'>
+              Probing the effects of broken symmetries in machine learning
+              </a>
+            </td>
+          <td>
+            Marcel F. Langer, S. Pozdnyakov, Michele Ceriotti
+          </td>
+          <td>2024-06-25</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>9</td>
+        </tr>
+
+        <tr id="Physics‐Informed Neural Networks (PINNs) have solved numerous mechanics problems by training to minimize the loss functions of governing partial differential equations (PDEs). Despite successful development of PINNs in various systems, computational efficiency and fidelity prediction have remained profound challenges. To fill such gaps, this study proposed a Physics‐Informed Neural Operator Solver (PINOS) to achieve accurate and fast simulations without any required data set. The training of PINOS adopts a weak form based on the principle of least work for static simulations and a storng form for dynamic systems in solid mechanics. Results from numerical examples indicated that PINOS is capable of approximating solutions notably faster than the benchmarks of PINNs in both static an dynamic systems. The comparisons also showed that PINOS reached a convergence speed of over 20 times faster than finite element software in two‐dimensional and three‐dimensional static problems. Furthermore, this study examined the zero‐shot super‐resolution capability by developing Super‐Resolution PINOS (SR‐PINOS) that was trained on a coarse mesh and validated on fine mesh. The numerical results demonstrate the great performance of the model to obtain accurate solutions with a speed up, suggesting effectiveness in increasing sampling points and scaling a simulation. This study also discusses the differentiation methods of PINOS and SR‐PINOS and suggests potential implementations related to forward applications for promising machine learning methods for structural designs and optimization.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/bebd3feaac10c5ad88eafaf917f41d32f2aa051e" target='_blank'>
+              Physics‐informed neural operator solver and super‐resolution for solid mechanics
+              </a>
+            </td>
+          <td>
+            Chawit Kaewnuratchadasorn, Jiaji Wang, Chul‐Woo Kim
+          </td>
+          <td>2024-07-11</td>
+          <td>Computer-Aided Civil and Infrastructure Engineering</td>
+          <td>0</td>
+          <td>1</td>
+        </tr>
+
+        <tr id="Variational Physics-Informed Neural Networks often suffer from poor convergence when using stochastic gradient-descent-based optimizers. By introducing a Least Squares solver for the weights of the last layer of the neural network, we improve the convergence of the loss during training in most practical scenarios. This work analyzes the computational cost of the resulting hybrid Least-Squares/Gradient-Descent optimizer and explains how to implement it efficiently. In particular, we show that a traditional implementation based on backward-mode automatic differentiation leads to a prohibitively expensive algorithm. To remedy this, we propose using either forward-mode automatic differentiation or an ultraweak-type scheme that avoids the differentiation of trial functions in the discrete weak formulation. The proposed alternatives are up to 100 times faster than the traditional one, recovering a computational cost-per-iteration similar to that of a conventional gradient-descent-based optimizer alone. To support our analysis, we derive computational estimates and conduct numerical experiments in one- and two-dimensional problems.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/17eda77a3cdea99de9f795c5bb3e5312a9f5c667" target='_blank'>
+              Optimizing Variational Physics-Informed Neural Networks Using Least Squares
+              </a>
+            </td>
+          <td>
+            C. Uriarte, Manuela Bastidas, David Pardo, Jamie M. Taylor, Sergio Rojas
+          </td>
+          <td>2024-07-29</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>3</td>
+        </tr>
+
+        <tr id="Time-varying optimization problems are central to many engineering applications, where performance metrics and system constraints evolve dynamically with time. A number of algorithms have been proposed in recent years to solve such problems; a common feature of all these methods is that they implicitly require precise knowledge of the temporal variability of the solutions in order to exactly track the optimizers. In this paper, we seek to lift these stringent assumptions. Our main result is a fundamental characterization, showing that an algorithm can track an optimal trajectory if and only if it contains a model of the temporal variability of the problem. We refer to this concept to as the internal model principle of time-varying optimization. By recasting the optimization objective as a nonlinear regulation problem and using tools from center manifold theory, we provide necessary and sufficient conditions both for an optimization algorithm to achieve exact asymptotic tracking and for such an algorithm to exist. We illustrate the applicability of the approach numerically on both synthetic problems as well as practical problems in transportation.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/8aee1c4d1d9bfe7e68a03ee2eb770d81f18d112d" target='_blank'>
+              The Internal Model Principle of Time-Varying Optimization
+              </a>
+            </td>
+          <td>
+            G. Bianchin, Bryan Van Scoy
+          </td>
+          <td>2024-07-10</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>11</td>
+        </tr>
+
+        <tr id="Modeling atmospheric chemistry is complex and computationally intense. Given the recent success of Deep neural networks in digital signal processing, we propose a Neural Network Emulator for fast chemical concentration modeling. We consider atmospheric chemistry as a time-dependent Ordinary Differential Equation. To extract the hidden correlations between initial states and future time evolution, we propose ChemNNE, an Attention based Neural Network Emulator (NNE) that can model the atmospheric chemistry as a neural ODE process. To efficiently simulate the chemical changes, we propose the sinusoidal time embedding to estimate the oscillating tendency over time. More importantly, we use the Fourier neural operator to model the ODE process for efficient computation. We also propose three physical-informed losses to supervise the training optimization. To evaluate our model, we propose a large-scale chemical dataset that can be used for neural network training and evaluation. The extensive experiments show that our approach achieves state-of-the-art performance in modeling accuracy and computational speed.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/803f6e09980225170873259886d0fd771d2085f8" target='_blank'>
+              Neural Network Emulator for Atmospheric Chemical ODE
+              </a>
+            </td>
+          <td>
+            Zhi-Song Liu, Petri S. Clusius, Michael Boy
+          </td>
+          <td>2024-08-03</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>6</td>
+        </tr>
+
+        <tr id="In this work, we discuss the use of a recently introduced machine learning (ML) technique known as Fourier neural operators (FNO) as an efficient alternative to the traditional solution of the time-dependent Schrödinger equation (TDSE). FNOs are ML models which are employed in the approximated solution of partial differential equations. For a wavepacket propagating in an anharmonic potential and for a tunneling system, we show that the FNO approach can accurately and faithfully model wavepacket propagation via the density. Additionally, we demonstrate that FNOs can be a suitable replacement for traditional TDSE solvers in cases where the results of the quantum dynamical simulation are required repeatedly such as in the case of parameter optimization problems (e.g., control). The speed-up from the FNO method allows for its combination with the Markov-chain Monte Carlo approach in applications that involve solving inverse problems such as optimal and coherent laser control of the outcome of dynamical processes.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/e9adc38ac192d36f40f2d38abb968ebc1c04c08b" target='_blank'>
+              Accelerating wavepacket propagation with machine learning.
+              </a>
+            </td>
+          <td>
+            Kanishka Singh, Ka Hei Lee, Daniel Peláez, A. Bande
+          </td>
+          <td>2024-06-21</td>
+          <td>Journal of computational chemistry</td>
+          <td>0</td>
+          <td>12</td>
+        </tr>
+
+        <tr id="Inverse stochastic resonance (ISR) is a phenomenon where noise reduces rather than increases the firing rate of a neuron, sometimes leading to complete quiescence. ISR was first experimentally verified with cerebellar Purkinje neurons. These experiments showed that ISR enables optimal information transfer between the input and output spike train of neurons. Subsequent studies demonstrated the efficiency of information processing and transfer in neural networks with small-world topology. We conducted a numerical investigation into the impact of adaptivity on ISR in a small-world network of noisy FitzHugh-Nagumo (FHN) neurons, operating in a bistable regime with a stable fixed point and a limit cycle -- a prerequisite for ISR. Our results show that the degree of ISR is highly dependent on the FHN model's timescale separation parameter $\epsilon$. The network structure undergoes dynamic adaptation via mechanisms of either spike-time-dependent plasticity (STDP) with potentiation-/depression-domination parameter $P$, or homeostatic structural plasticity (HSP) with rewiring frequency $F$. We demonstrate that both STDP and HSP amplify ISR when $\epsilon$ lies within the bistability region of FHN neurons. Specifically, at larger values of $\epsilon$ within the bistability regime, higher rewiring frequencies $F$ enhance ISR at intermediate (weak) synaptic noise intensities, while values of $P$ consistent with depression-domination (potentiation-domination) enhance (deteriorate) ISR. Moreover, although STDP and HSP parameters may jointly enhance ISR, $P$ has a greater impact on ISR compared to $F$. Our findings inform future ISR enhancement strategies in noisy artificial neural circuits, aiming to optimize information transfer between input and output spike trains in neuromorphic systems, and prompt venues for experiments in neural networks.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/d7c658287ef239a4055ba2bf2436639fd02518af" target='_blank'>
+              Inverse stochastic resonance in adaptive small-world neural networks
+              </a>
+            </td>
+          <td>
+            Marius E. Yamakou, Jinjie Zhu, Erik A. Martens
+          </td>
+          <td>2024-07-03</td>
+          <td>ArXiv</td>
+          <td>1</td>
+          <td>9</td>
+        </tr>
+
+        <tr id="Transformations are a key tool in the qualitative study of dynamical systems: transformations to a normal form, for example, underpin the study of instabilities and bifurcations. In this work, we test, and when possible establish, an equivalence between two different artificial neural networks by attempting to construct a data-driven transformation between them, using diffusion maps with a Mahalanobis-like metric. If the construction succeeds, the two networks can be thought of as belonging to the same equivalence class. We first discuss transformation functions between only the outputs of the two networks; we then also consider transformations that take into account outputs (activations) of a number of internal neurons from each network. Whitney's theorem dictates the number of (generic) measurements from one of the networks required to reconstruct each and every feature of the second network. The construction of the transformation function relies on a consistent, intrinsic representation of the network input space. We illustrate our algorithm by matching neural network pairs trained to learn (a) observations of scalar functions, (b) observations of two-dimensional vector fields, and (c) representations of images of a moving three-dimensional object (a rotating horse). We also demonstrate reconstruction of a network's input (and output) from minimal partial observations of intermediate neuron activations. The construction of equivalences across different network instantiations clearly relates to transfer learning and will also be valuable in establishing equivalence between different machine learning-based tools.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/e0effe6704619a849e7613b7f6a123884ce613df" target='_blank'>
+              Transformations establishing equivalence across neural networks: When have two networks learned the same task?
+              </a>
+            </td>
+          <td>
+            Tom S. Bertalan, Felix Dietrich, I. Kevrekidis
+          </td>
+          <td>2024-07-01</td>
+          <td>Chaos</td>
+          <td>0</td>
+          <td>15</td>
+        </tr>
+
+        <tr id="Areas of computational mechanics such as uncertainty quantification and optimization usually involve repeated evaluation of numerical models that represent the behavior of engineering systems. In the case of complex nonlinear systems however, these models tend to be expensive to evaluate, making surrogate models quite valuable. Artificial neural networks approximate systems very well by taking advantage of the inherent information of its given training data. In this context, this paper investigates the improvement of the training process by including sensitivity information, which are partial derivatives w.r.t. inputs, as outlined by Sobolev training. In computational mechanics, sensitivities can be applied to neural networks by expanding the training loss function with additional loss terms, thereby improving training convergence resulting in lower generalisation error. This improvement is shown in two examples of linear and non-linear material behavior. More specifically, the Sobolev designed loss function is expanded with residual weights adjusting the effect of each loss on the training step. Residual weighting is the given scaling to the different training data, which in this case are response and sensitivities. These residual weights are optimized by an adaptive scheme, whereby varying objective functions are explored, with some showing improvements in accuracy and precision of the general training convergence.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/bc84ce2dba59d576dd4d3f10a9b0f7b62826171a" target='_blank'>
+              Sobolev neural network with residual weighting as a surrogate in linear and non-linear mechanics
+              </a>
+            </td>
+          <td>
+            A.O.M. Kilicsoy, J. Liedmann, M. Valdebenito, F. Barthold, M. Faes
+          </td>
+          <td>2024-07-23</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>24</td>
+        </tr>
+
+        <tr id="Skilful Machine Learned weather forecasts have challenged our approach to numerical weather prediction, demonstrating competitive performance compared to traditional physics-based approaches. Data-driven systems have been trained to forecast future weather by learning from long historical records of past weather such as the ECMWF ERA5. These datasets have been made freely available to the wider research community, including the commercial sector, which has been a major factor in the rapid rise of ML forecast systems and the levels of accuracy they have achieved. However, historical reanalyses used for training and real-time analyses used for initial conditions are produced by data assimilation, an optimal blending of observations with a physics-based forecast model. As such, many ML forecast systems have an implicit and unquantified dependence on the physics-based models they seek to challenge. Here we propose a new approach, training a neural network to predict future weather purely from historical observations with no dependence on reanalyses. We use raw observations to initialise a model of the atmosphere (in observation space) learned directly from the observations themselves. Forecasts of crucial weather parameters (such as surface temperature and wind) are obtained by predicting weather parameter observations (e.g. SYNOP surface data) at future times and arbitrary locations. We present preliminary results on forecasting observations 12-hours into the future. These already demonstrate successful learning of time evolutions of the physical processes captured in real observations. We argue that this new approach, by staying purely in observation space, avoids many of the challenges of traditional data assimilation, can exploit a wider range of observations and is readily expanded to simultaneous forecasting of the full Earth system (atmosphere, land, ocean and composition).">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/f12110407568e11614a4bec8a9d4a303c2c72458" target='_blank'>
+              Data driven weather forecasts trained and initialised directly from observations
+              </a>
+            </td>
+          <td>
+            Anthony McNally, Christian Lessig, Peter Lean, Eulalie Boucher, Mihai Alexe, E. Pinnington, M. Chantry, Simon Lang, Chris Burrows, Marcin Chrust, F. Pinault, Ethel Villeneuve, Niels Bormann, Sean Healy
+          </td>
+          <td>2024-07-22</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>15</td>
+        </tr>
+
+        <tr id="The Path-dependent Neural Jump ODE (PD-NJ-ODE) is a model for online prediction of generic (possibly non-Markovian) stochastic processes with irregular (in time) and potentially incomplete (with respect to coordinates) observations. It is a model for which convergence to the $L^2$-optimal predictor, which is given by the conditional expectation, is established theoretically. Thereby, the training of the model is solely based on a dataset of realizations of the underlying stochastic process, without the need of knowledge of the law of the process. In the case where the underlying process is deterministic, the conditional expectation coincides with the process itself. Therefore, this framework can equivalently be used to learn the dynamics of ODE or PDE systems solely from realizations of the dynamical system with different initial conditions. We showcase the potential of our method by applying it to the chaotic system of a double pendulum. When training the standard PD-NJ-ODE method, we see that the prediction starts to diverge from the true path after about half of the evaluation time. In this work we enhance the model with two novel ideas, which independently of each other improve the performance of our modelling setup. The resulting dynamics match the true dynamics of the chaotic system very closely. The same enhancements can be used to provably enable the PD-NJ-ODE to learn long-term predictions for general stochastic datasets, where the standard model fails. This is verified in several experiments.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/ba851d147a33cf0c6fcf639b5d4990df283a0b23" target='_blank'>
+              Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs
+              </a>
+            </td>
+          <td>
+            F. Krach, Josef Teichmann
+          </td>
+          <td>2024-07-26</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>13</td>
+        </tr>
+
+        <tr id="The interdisciplinary time-series analysis literature encompasses thousands of statistical features for quantifying interpretable properties of dynamical data. But for any given application, it is likely that just a small subset of informative time-series features is required to capture the dynamical quantities of interest. So, while comprehensive libraries of time-series features have been developed, it is useful to construct reduced and computationally efficient subsets for specific applications. In this work, we demonstrate a systematic process to deduce such a reduced set, focused on the problem of distinguishing changes to functional Magnetic Resonance Imaging (fMRI) time series caused by a range of experimental manipulations of excitatory and inhibitory neural activity in mouse cortical circuits. We reduce a comprehensive library of over 7000 candidate time-series features down to a subset of 16 features, which we call catchaMouse16, that aims to both: (i) accurately characterize biologically relevant properties of fMRI time series; and (ii) minimize inter-feature redundancy. The catchaMouse16 feature set accurately classifies experimental perturbations of neuronal activity from fMRI recordings, and also shows strong generalization performance on an unseen mouse and human resting-state fMRI data where it tracks spatial variations in excitatory and inhibitory cortical cell densities, often with greater statistical power than the full hctsa feature set. We provide an efficient, open-source implementation of the catchaMouse16 feature set in C (achieving an approximately 60 times speed-up relative to the native Matlab code of the same features), with wrappers for Python and Matlab. This work demonstrates a procedure to reduce a large candidate time-series feature set down to the key statistical properties of mouse fMRI dynamics that can be used to efficiently quantify and interpret informative dynamical patterns in neural time series.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/e685be5f1377cb0acf6421ed0e2a179e1e4372df" target='_blank'>
+              Canonical time-series features for characterizing biologically informative dynamical patterns in fMRI
+              </a>
+            </td>
+          <td>
+            Imran Alam, Brendan Harris, Patrick Cahill, Oliver Cliff, M. Markicevic, Valerio Zerbi, BD Fulcher
+          </td>
+          <td>2024-07-17</td>
+          <td>bioRxiv</td>
+          <td>0</td>
+          <td>10</td>
+        </tr>
+
+        <tr id="Deep Gaussian Processes (DGPs) leverage a compositional structure to model non-stationary processes. DGPs typically rely on local inducing point approximations across intermediate GP layers. Recent advances in DGP inference have shown that incorporating global Fourier features from Reproducing Kernel Hilbert Space (RKHS) can enhance the DGPs' capability to capture complex non-stationary patterns. This paper extends the use of these features to compositional GPs involving linear transformations. In particular, we introduce Ordinary Differential Equation (ODE) -based RKHS Fourier features that allow for adaptive amplitude and phase modulation through convolution operations. This convolutional formulation relates our work to recently proposed deep latent force models, a multi-layer structure designed for modelling nonlinear dynamical systems. By embedding these adjustable RKHS Fourier features within a doubly stochastic variational inference framework, our model exhibits improved predictive performance across various regression tasks.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/6828a61a888094de685450ee2ce84e8f6bcfb2a8" target='_blank'>
+              Adaptive RKHS Fourier Features for Compositional Gaussian Process Models
+              </a>
+            </td>
+          <td>
+            Xinxing Shi, Thomas Baldwin-McDonald, Mauricio A. 'Alvarez
+          </td>
+          <td>2024-07-01</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>1</td>
+        </tr>
+
+        <tr id="Understanding the dynamics of open quantum systems in strong coupling and non-Markovian regimes remains a formidable theoretical challenge. One popular and well-established method of approximation in these circumstances is provided by the polaron master equation (PME). In this work we reevaluate and extend the validity of the PME to capture the impact of non-Markovian polaron dressing, induced by non-equilibrium open system dynamics. By comparing with numerically exact techniques, we confirm that while the standard PME successfully predicts the dynamics of system observables that commute with the polaron transformation (e.g. populations in the Pauli z-basis), it can struggle to fully capture those that do not (e.g. coherences). This limitation stems from the mixing of system and environment degrees of freedom inherent to the polaron transformation, which affects the accuracy of calculated expectation values within the polaron frame. Employing the Nakajima-Zwanzig projection operator formalism, we introduce correction terms that provide an accurate description of observables that do not commute with the transformation. We demonstrate the significance of the correction terms in two cases, the canonical spin-boson model and a dissipative time-dependent Landau-Zener protocol, where they are shown to impact the system dynamics on both short and long timescales.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/97eb69ef1b908a5b349cac016b071bc744ed7afd" target='_blank'>
+              Capturing non-Markovian polaron dressing with the master equation formalism
+              </a>
+            </td>
+          <td>
+            Jake Iles-Smith, Owen Diba, A. Nazir
+          </td>
+          <td>2024-07-15</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>30</td>
+        </tr>
+
+        <tr id="Following the discovery of the least squares method in 1805 by Legendre and later in 1809 by Gauss, surrogate modeling and machine learning have come a long way. From identifying patterns and trends in process data to predictive modeling, optimization, fault detection, reaction network discovery, and process operations, machine learning became an integral part of all aspects of process design and process systems engineering. This is enabled, at the same time necessitated, by the vast amounts of data that are readily available from processes, increased digitalization, automation, increasing computation power, and simulation software that can model complex phenomena that span over several temporal and spatial scales. Although this paper is not a comprehensive review, it gives an overview of the recent history of machine learning models that we use every day and how they shaped process design problems from the recent advances to the exploration of their prospects.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/4ca2456c9116fb14b2abeebf2076d63a64574d2d" target='_blank'>
+              From Then to Now and Beyond: Exploring How Machine Learning Shapes Process Design Problems
+              </a>
+            </td>
+          <td>
+            Burcu Beykal
+          </td>
+          <td>2024-07-09</td>
+          <td>Systems and Control Transactions</td>
+          <td>0</td>
+          <td>0</td>
+        </tr>
+
+        <tr id="Controlling the evolution of complex physical systems is a fundamental task across science and engineering. Classical techniques suffer from limited applicability or huge computational costs. On the other hand, recent deep learning and reinforcement learning-based approaches often struggle to optimize long-term control sequences under the constraints of system dynamics. In this work, we introduce Diffusion Physical systems Control (DiffPhyCon), a new class of method to address the physical systems control problem. DiffPhyCon excels by simultaneously minimizing both the learned generative energy function and the predefined control objectives across the entire trajectory and control sequence. Thus, it can explore globally and identify near-optimal control sequences. Moreover, we enhance DiffPhyCon with prior reweighting, enabling the discovery of control sequences that significantly deviate from the training distribution. We test our method in 1D Burgers' equation and 2D jellyfish movement control in a fluid environment. Our method outperforms widely applied classical approaches and state-of-the-art deep learning and reinforcement learning methods. Notably, DiffPhyCon unveils an intriguing fast-close-slow-open pattern observed in the jellyfish, aligning with established findings in the field of fluid dynamics.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/2ed151b432bd4c9d4bafa7399dadc48d42ebe3d6" target='_blank'>
+              A Generative Approach to Control Complex Physical Systems
+              </a>
+            </td>
+          <td>
+            Long Wei, Peiyan Hu, Ruiqi Feng, Haodong Feng, Yixuan Du, Tao Zhang, Rui Wang, Yue Wang, Zhi-Ming Ma, Tailin Wu
+          </td>
+          <td>2024-07-09</td>
+          <td>ArXiv</td>
+          <td>1</td>
+          <td>3</td>
+        </tr>
+
+        <tr id="Verification of uncertain, complex dynamical systems is crucial in the modern day world. An increasingly common method to verify complex logic specifications for dynamical systems involves symbolic abstractions: simpler, finite-state models whose behaviour mimics the one of the systems of interest. By sampling trajectories of the concrete, unknown system and via robust analysis, we build a data-driven abstraction, related to the underlying model through a probabilistic behavioural inclusion relation. As the distribution from which the trajectories are drawn is unknown, we adopt two distinct distribution-free theories, namely scenario optimization and conformal prediction. We compare and discuss the differences between the two approaches in terms of the type of guarantees that they are able to provide. Furthermore, via experimental benchmarks we outline the efficiency of the two methods with respect to the number of samples available and the tightness of the guarantees.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/a4bc6fac276ef549b5a06e10e0b424eaf5ed1fd7" target='_blank'>
+              Scenario Approach and Conformal Prediction for Verification of Unknown Systems via Data-Driven Abstractions
+              </a>
+            </td>
+          <td>
+            Rudi Coppola, Andrea Peruffo, Lars Lindemann, Manuel Mazo
+          </td>
+          <td>2024-06-25</td>
+          <td>2024 European Control Conference (ECC)</td>
+          <td>0</td>
+          <td>9</td>
+        </tr>
+
+        <tr id="Encoding frequency stability constraints in the operation problem is challenging due to its complex dynamics. Recently, data-driven approaches have been proposed to learn the stability criteria offline with the trained model embedded as a constraint of online optimization. However, random sampling of stationary operation points is less efficient in generating balanced stable and unstable samples. Meanwhile, the performance of such a model is strongly dependent on the quality of the training dataset. Observing this research gap, we propose a gradient-based data generation method via forward-mode automatic differentiation. In this method, the original dynamic system is augmented with new states that represent the dynamic of sensitivities of the original states, which can be solved by invoking any ODE solver for a single time. To compensate for the contradiction between the gradient of various frequency stability criteria, gradient surgery is proposed by projecting the gradient on the normal plane of the other. In the end, we demonstrate the superior performance of the proposed sampling algorithm, compared with the unrolling differentiation and finite difference. All codes are available at https://github.com/xuwkk/frequency_sample_ad.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/c2a8a10d25fc8962321b615ea83477e1fb4d6310" target='_blank'>
+              Efficient Sampling for Data-Driven Frequency Stability Constraint via Forward-Mode Automatic Differentiation
+              </a>
+            </td>
+          <td>
+            Wangkun Xu, Qian Chen, Pudong Ge, Zhongda Chu, Fei Teng
+          </td>
+          <td>2024-07-21</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>7</td>
         </tr>
 
-        <tr id="Advances in experimental techniques allow the collection of high-space-and-time resolution data that track individual motile entities over time. This poses the question of how to use these data to efficiently and effectively calibrate motion models. However, typical mathematical models often overlook the inherent aspects of data collection, such as the discreteness and the experimental noise of the measured locations. In this paper, we focus on velocity-jump models suitable to describe single-agent motion in one spatial dimension, characterised by successive Markovian transitions between a finite network of $n$ states, each with a specified velocity and a fixed rate of switching to every other state. Since the problem of finding the exact distributions of discrete-time noisy data is generally intractable, we derive a series of approximations for the data distributions and compare them to in-silico data generated by the models using four example network structures. These comparisons suggest that the approximations are accurate given sufficiently infrequent state switching, or equivalently, a sufficiently high data collection frequency. Moreover, for infrequent switching, the PDFs comparisons highlight the importance of accounting for the correlation between subsequent measured locations, due to the likely permanence in the state visited in the previous measurement. The approximate distributions computed can be used for fast parameter inference and model selection between a range of velocity-jump models using single-agent tracking data.">
+        <tr id="Deep learning-based partial differential equation(PDE) solvers have received much attention in the past few years. Methods of this category can solve a wide range of PDEs with high accuracy, typically by transforming the problems into highly nonlinear optimization problems of neural network parameters. This work reviews several deep learning solvers proposed a few years ago, including PINN, WAN, DRM, and VPINN. Numerical results are provided to make comparisons amongst them and address the importance of loss formulation and the optimization method. A rigorous error analysis for PINN is also presented. Finally, we discuss the current limitations and bottlenecks of these methods.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/8f4862ab7ce4151b9c0569c8c9b73d89ab08ca0e" target='_blank'>
-              Approximate solutions of a general stochastic velocity-jump process subject to discrete-time noisy observations
+          <td><a href="https://www.semanticscholar.org/paper/e8ab3da28291cf15c3b44c4f064373f4597eb015" target='_blank'>
+              A Review of Neural Network Solvers for Second-order Boundary Value Problems
               </a>
             </td>
           <td>
-            Arianna Ceccarelli, Alexander P. Browning, Ruth E. Baker
+            Ramesh Chandra Sau, Luowei Yin
           </td>
-          <td>2024-06-28</td>
+          <td>2024-06-29</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
-        <tr id="In this paper, we propose and study several inverse problems of determining unknown parameters in nonlocal nonlinear coupled PDE systems, including the potentials, nonlinear interaction functions and time-fractional orders. In these coupled systems, we enforce non-negativity of the solutions, aligning with realistic scenarios in biology and ecology. There are several salient features of our inverse problem study: the drastic reduction in measurement/observation data due to averaging effects, the nonlinear coupling between multiple equations, and the nonlocality arising from fractional-type derivatives. These factors present significant challenges to our inverse problem, and such inverse problems have never been explored in previous literature. To address these challenges, we develop new and effective schemes. Our approach involves properly controlling the injection of different source terms to obtain multiple sets of mean flux data. This allows us to achieve unique identifiability results and accurately determine the unknown parameters. Finally, we establish a connection between our study and practical applications in biology, further highlighting the relevance of our work in real-world contexts.">
+        <tr id="Symmetries play a pivotal role in our understanding of the properties of quantum many-body systems. While there are theorems and a well-established toolbox for systems in thermal equilibrium, much less is known about the role of symmetries and their connection to dynamics out of equilibrium. This arises due to the direct link between a system's thermal state and its Hamiltonian, which is generally not the case for nonequilibrium dynamics. Here we present a pathway to identify the effective symmetries and to extract them from data in nonequilibrium quantum many-body systems. Our approach is based on exact relations between correlation functions involving different numbers of spatial points, which can be viewed as nonequilibrium versions of (equal-time) Ward identities encoding the symmetries of the system. We derive symmetry witnesses, which are particularly suitable for the analysis of measured or simulated data at different snapshots in time. To demonstrate the potential of the approach, we apply our method to numerical and experimental data for a spinor Bose gas. We investigate the important question of a dynamical restoration of an explicitly broken symmetry of the Hamiltonian by the initial state. Remarkably, it is found that effective symmetry restoration can occur long before the system equilibrates. We also use the approach to define and identify spontaneous symmetry breaking far from equilibrium, which is of great relevance for applications to nonequilibrium phase transitions. Our work opens new avenues for the classification and analysis of quantum as well as classical many-body dynamics in a large variety of systems, ranging from ultracold quantum gases to cosmology.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6630d9bfe19de762af36c38b8a48daf0ad1ea2eb" target='_blank'>
-              Inverse problems for coupled nonlocal nonlinear systems arising in mathematical biology
+          <td><a href="https://www.semanticscholar.org/paper/8a8950d1ce041653ba3716007a86405364e34167" target='_blank'>
+              Extracting the symmetries of nonequilibrium quantum many-body systems
               </a>
             </td>
           <td>
-            Ming-Hui Ding, Hongyu Liu, Catharine W. K. Lo
+            A. Mikheev, Viktoria Noel, Ido Siovitz, H. Strobel, M. Oberthaler, Jurgen Berges
           </td>
-          <td>2024-07-22</td>
+          <td>2024-07-25</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>4</td>
+          <td>48</td>
         </tr>
 
-        <tr id="In functional data analysis, replicate observations of a smooth functional process and its derivatives offer a unique opportunity to flexibly estimate continuous-time ordinary differential equation models. Ramsay (1996) first proposed to estimate a linear ordinary differential equation from functional data in a technique called Principal Differential Analysis, by formulating a functional regression in which the highest-order derivative of a function is modelled as a time-varying linear combination of its lower-order derivatives. Principal Differential Analysis was introduced as a technique for data reduction and representation, using solutions of the estimated differential equation as a basis to represent the functional data. In this work, we re-formulate PDA as a generative statistical model in which functional observations arise as solutions of a deterministic ODE that is forced by a smooth random error process. This viewpoint defines a flexible class of functional models based on differential equations and leads to an improved understanding and characterisation of the sources of variability in Principal Differential Analysis. It does, however, result in parameter estimates that can be heavily biased under the standard estimation approach of PDA. Therefore, we introduce an iterative bias-reduction algorithm that can be applied to improve parameter estimates. We also examine the utility of our approach when the form of the deterministic part of the differential equation is unknown and possibly non-linear, where Principal Differential Analysis is treated as an approximate model based on time-varying linearisation. We demonstrate our approach on simulated data from linear and non-linear differential equations and on real data from human movement biomechanics. Supplementary R code for this manuscript is available at \url{https://github.com/edwardgunning/UnderstandingOfPDAManuscript}.">
+        <tr id="We consider quantum or classical many-body Hamiltonian systems, whose dynamics is given by an integrable, contact interactions, plus another, possibly long-range, generic two-body potential. We show how the dynamics of local observables is given in terms of a generalised version of Bogoliubov-Born-Green-Kirkwood-Yvon hierarchy, which we denote as gBBGKY, which is built for the densities, and their correlations, of the quasiparticles of the underlying integrable model. Unlike the usual cases of perturbation theory from free gases, the presence of local interactions in the integrable model"lifts"the so-called kinetic blocking, and the second layer of the hierarchy reproduces the dynamics at all time-scales. The latter consists of a fast pre-equilibration to a non-thermal steady state, and its subsequent thermalisation to a Gibbs ensemble. We show how the final relaxation is encoded into a Boltzmann scattering integral involving three or higher body-scatterings, and which, remarkably, is entirely determined by the diffusion constants of the underlying integrable model. We check our results with exact molecular dynamics simulations, finding perfect agreement. Our results show how gBBGKY can be successfully employed in quantum systems to compute scattering integrals and Fermi's golden rule transition rates.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/8d0be3d0b8344a99cb8ce9f30ed01a1653af4baa" target='_blank'>
-              An Understanding of Principal Differential Analysis
+          <td><a href="https://www.semanticscholar.org/paper/e0ea32035f5732ebc13f1df5f3d70be5820746ea" target='_blank'>
+              Generalised BBGKY hierarchy for near-integrable dynamics
               </a>
             </td>
           <td>
-            Edward Gunning, Giles Hooker
+            Leonardo Biagetti, Maciej Lebek, M. Panfil, J. D. Nardis
           </td>
-          <td>2024-06-26</td>
+          <td>2024-08-01</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
+          <td>19</td>
         </tr>
 
-        <tr id="The paper considers the observer synthesis for nonlinear, time-varying plants with uncertain parameters under multiharmonic disturbance. It is assumed that the relative degree of the plant is known, the regressor linearly depends on the state vector and may have a nonlinear relationship with the output signal. The proposed solution consists of three steps. Initially, an unknown input state observer is synthesized. This observer, however, necessitates the measurement of output derivatives equal to the plant's relative degree. To relax this limitation, an alternative representation of the observer is introduced. Further, based on this observer, the unknown parameters and disturbances are reconstructed using an autoregression model and the dynamic regressor extension and mixing (DREM) approach. This approach allows the estimates to be obtained in a finite time. Finally, based on these estimates, an observer has been constructed that does not require measurements of the output derivatives. The effectiveness and efficiency of this solution are demonstrated through a computer simulation.">
+        <tr id="We propose physics-informed holomorphic neural networks (PIHNNs) as a method to solve boundary value problems where the solution can be represented via holomorphic functions. Specifically, we consider the case of plane linear elasticity and, by leveraging the Kolosov-Muskhelishvili representation of the solution in terms of holomorphic potentials, we train a complex-valued neural network to fulfill stress and displacement boundary conditions while automatically satisfying the governing equations. This is achieved by designing the network to return only approximations that inherently satisfy the Cauchy-Riemann conditions through specific choices of layers and activation functions. To ensure generality, we provide a universal approximation theorem guaranteeing that, under basic assumptions, the proposed holomorphic neural networks can approximate any holomorphic function. Furthermore, we suggest a new tailored weight initialization technique to mitigate the issue of vanishing/exploding gradients. Compared to the standard PINN approach, noteworthy benefits of the proposed method for the linear elasticity problem include a more efficient training, as evaluations are needed solely on the boundary of the domain, lower memory requirements, due to the reduced number of training points, and $C^\infty$ regularity of the learned solution. Several benchmark examples are used to verify the correctness of the obtained PIHNN approximations, the substantial benefits over traditional PINNs, and the possibility to deal with non-trivial, multiply-connected geometries via a domain-decomposition strategy.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f3f630f0776c6b0ed2b88908f39e12f1e0579daa" target='_blank'>
-              State estimation for a class of nonlinear time-varying uncertain system under multiharmonic disturbance
+          <td><a href="https://www.semanticscholar.org/paper/1e71714f3dda61e6e50ace4788915467f7d4f810" target='_blank'>
+              Physics-Informed Holomorphic Neural Networks (PIHNNs): Solving Linear Elasticity Problems
               </a>
             </td>
           <td>
-            A. A. Margun, V. Bui, A. A. Bobtsov, Denis V. Efimov
+            Matteo Calafa, Emil Hovad, A. Engsig-Karup, T. Andriollo
           </td>
-          <td>2024-07-25</td>
+          <td>2024-07-01</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
+          <td>20</td>
         </tr>
 
-        <tr id="From pedestrians to Kuramoto oscillators, interactions between agents govern how a multitude of dynamical systems evolve in space and time. Discovering how these agents relate to each other can improve our understanding of the often complex dynamics that underlie these systems. Recent works learn to categorize relationships between agents based on observations of their physical behavior. These approaches are limited in that the relationship categories are modelled as independent and mutually exclusive, when in real world systems categories are often interacting. In this work, we introduce a level of abstraction between the physical behavior of agents and the categories that define their behavior. To do this, we learn a mapping from the agents' states to their affinities for each category in a graph neural network. We integrate the physical proximity of agents and their affinities in a nonlinear opinion dynamics model which provides a mechanism to identify mutually exclusive categories, predict an agent's evolution in time, and control an agent's behavior. We demonstrate the utility of our model for learning interpretable categories for mechanical systems, and demonstrate its efficacy on several long-horizon trajectory prediction benchmarks where we consistently out perform existing methods.">
+        <tr id="We present an expansion of a many-body correlation function in a sum of pseudomodes - exponents with complex frequencies that encompass both decay and oscillations. We demonstrate that, typically, it is enough to take a few first terms of this infinite sum to obtain an excellent approximation to the correlation function at any time, with the large time behavior being determined solely by the first pseudomode. The pseudomode expansion emerges in the framework of the Heisenberg version of the recursion method. This method essentially solves Heisenberg equations in a Lanczos tridiagonal basis constructed in the Krylov space of a given observable. To obtain pseudomodes, we first add artificial dissipation satisfying the dissipative generalization of the universal operator growth hypothesis, and then take the limit of the vanishing dissipation strength. Fast convergence of the pseudomode expansion is facilitated by the localization in the Krylov space, which is generic in the presence of dissipation and can survive the limit of the vanishing dissipation strength. As an illustration, we apply the pseudomode expansion to calculate infinite-temperature autocorrelation functions in the quantum Ising and $XX$ spin-$1/2$ models on the square lattice.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/667ae92834a3c09ccd46dc0cf9ad000cab20dfe8" target='_blank'>
-              Relational Reasoning On Graphs Using Opinion Dynamics
+          <td><a href="https://www.semanticscholar.org/paper/8c0e1c45eedb9a16b0066dded4a252c6974da710" target='_blank'>
+              Pseudomode expansion of many-body correlation functions
               </a>
             </td>
           <td>
-            Yulong Yang, Bowen Feng, Keqin Wang, Naomi Leonard, Adji B. Dieng, Christine Allen-Blanchette
+            Alexander Teretenkov, F. Uskov, Oleg Lychkovskiy
           </td>
-          <td>2024-06-20</td>
+          <td>2024-07-17</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>13</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Inverse problems describe the process of estimating the causal factors from a set of measurements or data. Mapping of often incomplete or degraded data to parameters is ill-posed, thus data-driven iterative solutions are required, for example when reconstructing clean images from poor signals. Diffusion models have shown promise as potent generative tools for solving inverse problems due to their superior reconstruction quality and their compatibility with iterative solvers. However, most existing approaches are limited to linear inverse problems represented as Stochastic Differential Equations (SDEs). This simplification falls short of addressing the challenging nature of real-world problems, leading to amplified cumulative errors and biases. We provide an explanation for this gap through the lens of measure-preserving dynamics of Random Dynamical Systems (RDS) with which we analyse Temporal Distribution Discrepancy and thus introduce a theoretical framework based on RDS for SDE diffusion models. We uncover several strategies that inherently enhance the stability and generalizability of diffusion models for inverse problems and introduce a novel score-based diffusion framework, the \textbf{D}ynamics-aware S\textbf{D}E \textbf{D}iffusion \textbf{G}enerative \textbf{M}odel (D$^3$GM). The \textit{Measure-preserving property} can return the degraded measurement to the original state despite complex degradation with the RDS concept of \textit{stability}. Our extensive experimental results corroborate the effectiveness of D$^3$GM across multiple benchmarks including a prominent application for inverse problems, magnetic resonance imaging. Code and data will be publicly available.">
+        <tr id="Studying the temporal evolution of complex systems requires tools able to detect the presence and quantify the strength of predictable dynamics within their output signals. Information theory aids in such a description, particularly through information storage (IS), which reflects the regularity of system dynamics by measuring the information shared between the present and the past system states. While the conventional IS computation provides an overall measure of predictable information, transient behaviors of predictability occurring during system transitions can be assessed by time-resolved measures such as the local information storage (L-IS), assuming stationarity, and the time-varying information storage (TV-IS), without stationarity assumptions. In this work, through a comparative analysis in simulated and real contexts, we aim to demonstrate how these methods complement each other and reveal dynamic changes of the system behavior associated to state transitions. In simulations, we show that the TV-IS can effectively track sudden changes of the information stored in the system, which is reflected in its average value computed over specific time intervals; on the other hand, the surprise originated by the emergence of a change in the predictability of the system is reflected in the variance of the L-IS computed within specific time intervals. In neurophysiological applications, the distinct phenomena of respiratory activity in sleep apnea and brain activity during somatosensory stimulation both reveal a significant decrease of IS evoked by state transitions, highlighting how such transitions can inject new information in physiological systems, affecting significantly their internal dynamics. Overall, TV-IS and L-IS appear to provide different and complementary information about the behavior of the systems under investigation, thereby offering valuable tools for the study of complex physiological systems where both stationary and non-stationary conditions may be present.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/bf17cd0dcbb94d9ebc02ca488860c685bf255511" target='_blank'>
-              Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamics
+          <td><a href="https://www.semanticscholar.org/paper/7c3b648f9e9d48cea5dda06ced2a7fa4a474d58a" target='_blank'>
+              Exploring transient neurophysiological states through local and time-varying measures of Information Dynamics
               </a>
             </td>
           <td>
-            Weitong Zhang, Chengqi Zang, Liu Li, Sarah Cechnicka, Ouyang Cheng, Bernhard Kainz
+            Y. Antonacci, C. Barà, G. De Felice, A. Sferlazza, R. Pernice, L. Faes
           </td>
-          <td>2024-06-19</td>
-          <td>ArXiv</td>
+          <td>2024-06-24</td>
+          <td>bioRxiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>21</td>
         </tr>
 
-        <tr id="We are interested in the computational study of shock hydrodynamics, i.e. problems involving compressible solids, liquids, and gases that undergo large deformation. These problems are dynamic and nonlinear and can exhibit complex instabilities. Due to advances in high performance computing it is possible to parameterize a hydrodynamic problem and perform a computational study yielding $\mathcal{O}\left({\rm TB}\right)$ of simulation state data. We present an interactive machine learning tool that can be used to compress, browse, and interpolate these large simulation datasets. This tool allows computational scientists and researchers to quickly visualize"what-if"situations, perform sensitivity analyses, and optimize complex hydrodynamic experiments.">
+        <tr id="In the central nervous system, sequences of neural activity form trajectories on low dimensional neural manifolds. The neural computation underlying flexible cognition and behavior relies on dynamic control of these structures. For example different tasks or behaviors are represented on different subspaces, requiring fast timescale subspace rotation to move from one behavior to the next. For flexibility in a particular behavior, the neural trajectory must be dynamically controllable within that behaviorally determined subspace. To understand how dynamic control of neural trajectories and their underlying subspaces may be implemented in neural circuits, we first characterized the relationship between features of neural activity sequences and aspects of the low dimensional projection. Based on this, we propose neural mechanisms that can act within local circuits to modulate activity sequences thereby controlling neural trajectories in low dimensional subspaces. In particular, we show that gain modulation and transient synaptic currents control the speed and path of neural trajectories and clustered inhibition determines manifold orientation. Together, these neural mechanisms may enable a substrate for fast timescale computation on neural manifolds.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/7a3d102318eac008b4f999d4fed788b9945f727e" target='_blank'>
-              Machine Learning Visualization Tool for Exploring Parameterized Hydrodynamics
+          <td><a href="https://www.semanticscholar.org/paper/0a6120a5f2c70336b63e76de5fc920ea9b4af59d" target='_blank'>
+              Dynamic control of neural manifolds
               </a>
             </td>
           <td>
-            C. Jekel, D. Sterbentz, T. M. Stitt, P. Mocz, R. Rieben, D. A. White, J. Belof
+            Andrew B. Lehr, Arvind Kumar, Christian Tetzlaff
           </td>
-          <td>2024-06-20</td>
-          <td>ArXiv</td>
+          <td>2024-07-11</td>
+          <td>bioRxiv</td>
           <td>0</td>
-          <td>21</td>
+          <td>6</td>
         </tr>
 
-        <tr id="Beyond estimating parameters of interest from data, one of the key goals of statistical inference is to properly quantify uncertainty in these estimates. In Bayesian inference, this uncertainty is provided by the posterior distribution, the computation of which typically involves an intractable high-dimensional integral. Among available approximation methods, sampling-based approaches come with strong theoretical guarantees but scale poorly to large problems, while variational approaches scale well but offer few theoretical guarantees. In particular, variational methods are known to produce overconfident estimates of posterior uncertainty and are typically non-identifiable, with many latent variable configurations generating equivalent predictions. Here, we address these challenges by showing how diffusion-based models (DBMs), which have recently produced state-of-the-art performance in generative modeling tasks, can be repurposed for performing calibrated, identifiable Bayesian inference. By exploiting a previously established connection between the stochastic and probability flow ordinary differential equations (pfODEs) underlying DBMs, we derive a class of models, inflationary flows, that uniquely and deterministically map high-dimensional data to a lower-dimensional Gaussian distribution via ODE integration. This map is both invertible and neighborhood-preserving, with controllable numerical error, with the result that uncertainties in the data are correctly propagated to the latent space. We demonstrate how such maps can be learned via standard DBM training using a novel noise schedule and are effective at both preserving and reducing intrinsic data dimensionality. The result is a class of highly expressive generative models, uniquely defined on a low-dimensional latent space, that afford principled Bayesian inference.">
+        <tr id="Piecewise-deterministic Markov processes (PDMPs) are often used to model abrupt changes in the global environment or capabilities of a controlled system. This is typically done by considering a set of"operating modes"(each with its own system dynamics and performance metrics) and assuming that the mode can switch stochastically while the system state evolves. Such models have a broad range of applications in engineering, economics, manufacturing, robotics, and biological sciences. Here, we introduce and analyze an"occasionally observed"version of mode-switching PDMPs. We show how such systems can be controlled optimally if the planner is not alerted to mode-switches as they occur but may instead have access to infrequent mode observations. We first develop a general framework for handling this through dynamic programming on a higher-dimensional mode-belief space. While quite general, this method is rarely practical due to the curse of dimensionality. We then discuss assumptions that allow for solving the same problem much more efficiently, with the computational costs growing linearly (rather than exponentially) with the number of modes. We use this approach to derive Hamilton-Jacobi-Bellman PDEs and quasi-variational inequalities encoding the optimal behavior for a variety of planning horizons (fixed, infinite, indefinite, random) and mode-observation schemes (at fixed times or on-demand). We discuss the computational challenges associated with each version and illustrate the resulting methods on test problems from surveillance-evading path planning. We also include an example based on robotic navigation: a Mars rover that minimizes the expected time to target while accounting for the possibility of unobserved/incremental damages and dynamics-altering breakdowns.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/17907a21b0ea22f5a5548076e21700dea63c3931" target='_blank'>
-              Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models
+          <td><a href="https://www.semanticscholar.org/paper/96c947b88c4459fcab4ac11d8b03e77f99a42182" target='_blank'>
+              Occasionally Observed Piecewise-deterministic Markov Processes
               </a>
             </td>
           <td>
-            Daniela de Albuquerque, John Pearson
+            Marissa Gee, Alexander Vladimirsky
           </td>
-          <td>2024-07-11</td>
+          <td>2024-08-02</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
-        <tr id="Several forms for constructing novel physics-informed neural-networks (PINN) for the solution of partial-differential-algebraic equations based on derivative operator splitting are proposed, using the nonlinear Kirchhoff rod as a prototype for demonstration. The open-source DeepXDE is likely the most well documented framework with many examples. Yet, we encountered some pathological problems and proposed novel methods to resolve them. Among these novel methods are the PDE forms, which evolve from the lower-level form with fewer unknown dependent variables to higher-level form with more dependent variables, in addition to those from lower-level forms. Traditionally, the highest-level form, the balance-of-momenta form, is the starting point for (hand) deriving the lowest-level form through a tedious (and error prone) process of successive substitutions. The next step in a finite element method is to discretize the lowest-level form upon forming a weak form and linearization with appropriate interpolation functions, followed by their implementation in a code and testing. The time-consuming tedium in all of these steps could be bypassed by applying the proposed novel PINN directly to the highest-level form. We developed a script based on JAX. While our JAX script did not show the pathological problems of DDE-T (DDE with TensorFlow backend), it is slower than DDE-T. That DDE-T itself being more efficient in higher-level form than in lower-level form makes working directly with higher-level form even more attractive in addition to the advantages mentioned further above. Since coming up with an appropriate learning-rate schedule for a good solution is more art than science, we systematically codified in detail our experience running optimization through a normalization/standardization of the network-training process so readers can reproduce our results.">
+        <tr id="In this article we study if a Deep Learning technique can be used to obtain an approximated value of the Lyapunov exponents of a dynamical system. Moreover, we want to know if Machine Learning techniques are able, once trained, to provide the complete Lyapunov exponents spectrum with just single-variable time series. We train a Convolutional Neural Network and we use the resulting network to approximate the complete spectrum using the time series of just one variable from the studied systems (Lorenz system and coupled Lorenz system). The results are quite stunning as all the values are well approximated with only partial data. This strategy permits to speed up the complete analysis of the systems and also to study the hyperchaotic dynamics in the coupled Lorenz system.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ea66520b54fdb2078af780a8675f7d6bbe935817" target='_blank'>
-              Partial-differential-algebraic equations of nonlinear dynamics by Physics-Informed Neural-Network: (I) Operator splitting and framework assessment
+          <td><a href="https://www.semanticscholar.org/paper/09290aec6c4160820790c56611a3f3eca51095dc" target='_blank'>
+              Full Lyapunov Exponents spectrum with Deep Learning from single-variable time series
               </a>
             </td>
           <td>
-            L. Vu-Quoc, A. Humer
+            C. Mayora-Cebollero, A. Mayora-Cebollero, 'Alvaro Lozano, Roberto Barrio
           </td>
-          <td>2024-07-13</td>
+          <td>2024-06-23</td>
           <td>ArXiv</td>
-          <td>0</td>
-          <td>32</td>
+          <td>1</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Scenario-based optimization and control has proven to be an efficient approach to account for system uncertainty. In particular, the performance of scenario-based model predictive control (MPC) schemes depends on the accuracy of uncertainty quantification. However, current learning- and scenario-based MPC (sMPC) approaches employ a single timeinvariant probabilistic model (learned offline), which may not accurately describe time-varying uncertainties. Instead, this paper presents a model-agnostic meta-learning (MAML) of Bayesian neural networks (BNN) for adaptive uncertainty quantification that would be subsequently used for adaptive-scenario-tree model predictive control design of nonlinear systems with unknown dynamics to enhance control performance. In particular, the proposed approach learns both a global BNN model and an updating law to refine the BNN model. At each time step, the updating law transforms the global BNN model into more precise local BNN models in real time. The adapted local model is then used to generate scenarios for sMPC design at each time step. A probabilistic safety certificate is incorporated in the scenario generation to ensure that the trajectories of the generated scenarios contain the real trajectory of the system and that all the scenarios adhere to the constraints with a high probability. Experiments using closed-loop simulations of a numerical example demonstrate that the proposed approach can improve the performance of scenario-based MPC compared to using only one BNN model learned offline for all time steps.">
+        <tr id="
+ Reservoir computing is an efficient artificial neural network for model-free prediction and analysis of dynamical systems time series. As a data-based method, the capacity of reservoir computing is stongly affected by the time sampling interval of training data. In this paper, taking Lorenz system as an example, we explore the influence of this sampling interval on the performance of reservoir computing in predicting chaotic sequences. When the sampling interval increases, the prediction capacity of reservoir computing is first enhanced then weakened, presenting a bell-shaped curve. By slightly revising the calculation method of the output matrix, the prediction performance of reservoir computing with small sampling interval can be improved. Furthermore, reservoir computing can learn and reproduce the state of chaotic system with a large time interval, which is almost five times larger than that of the classic fourth-order Runge-Kutta method. Our results show the capacity of reservoir computing in the applications where the time sampling intervals are constrained and laid the foundation for building a fast algorithm with larger time iteration steps.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/473eca6073be70a1728a3138bc7e859f877dc3e3" target='_blank'>
-              Adaptive Uncertainty Quantification for Scenario-based Control Using Meta-learning of Bayesian Neural Networks
+          <td><a href="https://www.semanticscholar.org/paper/2299dd61e3d42dbb604ec45370f99d03bbd6e6f9" target='_blank'>
+              Large sampling intervals for learning and predicting chaotic systems with reservoir computing
               </a>
             </td>
           <td>
-            Yajie Bao, Javad Mohammadpour Velni
+            Qingyan Xie, Zixiang Yan, Hui Zhao, Jian Gao, Jinghua Xiao
           </td>
-          <td>2024-07-19</td>
-          <td>ArXiv</td>
+          <td>2024-06-28</td>
+          <td>Journal of Physics A: Mathematical and Theoretical</td>
           <td>0</td>
-          <td>13</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Energy based models (EBMs) are appealing for their generality and simplicity in data likelihood modeling, but have conventionally been difficult to train due to the unstable and time-consuming implicit MCMC sampling during contrastive divergence training. In this paper, we present a novel energy-based generative framework, Variational Potential Flow (VAPO), that entirely dispenses with implicit MCMC sampling and does not rely on complementary latent models or cooperative training. The VAPO framework aims to learn a potential energy function whose gradient (flow) guides the prior samples, so that their density evolution closely follows an approximate data likelihood homotopy. An energy loss function is then formulated to minimize the Kullback-Leibler divergence between density evolution of the flow-driven prior and the data likelihood homotopy. Images can be generated after training the potential energy, by initializing the samples from Gaussian prior and solving the ODE governing the potential flow on a fixed time interval using generic ODE solvers. Experiment results show that the proposed VAPO framework is capable of generating realistic images on various image datasets. In particular, our proposed framework achieves competitive FID scores for unconditional image generation on the CIFAR-10 and CelebA datasets.">
+        <tr id="Many problems in Physics and Chemistry are formulated as the minimization of a functional. Therefore, methods for solving these problems typically require differentiating maps whose input and/or output are functions -- commonly referred to as variational differentiation. Such maps are not addressed at the mathematical level by the chain rule, which underlies modern symbolic and algorithmic differentiation (AD) systems. Although there are algorithmic solutions such as tracing and reverse accumulation, they do not provide human readability and introduce strict programming constraints that bottleneck performance, especially in high-performance computing (HPC) environments. In this manuscript, we propose a new computer theoretic model of differentiation by combining the pullback of the $\mathbf{B}$ and $\mathbf{C}$ combinators from the combinatory logic. Unlike frameworks based on the chain rule, this model differentiates a minimal complete basis for the space of computable functions. Consequently, the model is capable of analytic backpropagation and variational differentiation while supporting complex numbers. To demonstrate the generality of this approach we build a system named CombDiff, which can differentiate nontrivial variational problems such as Hartree-Fock (HF) theory and multilayer perceptrons.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6bc60701775b0cc308c3beaf6e494e8920626217" target='_blank'>
-              Variational Potential Flow: A Novel Probabilistic Framework for Energy-Based Generative Modelling
+          <td><a href="https://www.semanticscholar.org/paper/4c640b0ba0b06a2a81e8f349b2681297670ab04e" target='_blank'>
+              Automating Variational Differentiation
               </a>
             </td>
           <td>
-            Junn Yong Loo, Michelle Adeline, Arghya Pal, Vishnu Monn Baskaran, Chee-Ming Ting, Raphaël C.-W. Phan
+            Kangbo Li, Anil Damle
           </td>
-          <td>2024-07-21</td>
+          <td>2024-06-23</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>10</td>
+          <td>0</td>
         </tr>
 
-        <tr id="The efficient solution (fast and accurate) of parametric partial differential equations (pPDE) is of major interest in many domains of science and engineering, enabling evaluations of the quantities of interest, optimization, control, and uncertainty propagation—all them under stringent real-time constraints. Different methodologies have been proposed in the past within the model order reduction (MOR) community, based on the use of reduced bases (RB) or the separated representation at the heart of the so-called proper generalized decompositions (PGD). In PGD, an alternate-direction strategy is employed to circumvent the integration issues of operating in multi-dimensional domains. Recently, physics informed neural networks (PINNs), a particular collocation schema where the unknown field is approximated by a neural network (NN), have emerged in the domain of scientific machine learning. PNNs combine the versatility of NN-based approximation with the ease of collocating pPDE. The present paper proposes a combination of both procedures to find an efficient solution for pPDE, that can either be viewed as an efficient collocation procedure for PINN, or as a monolithic PGD that bypasses the use of the fixed-point alternated directions.">
+        <tr id="Stochastic systems are used to model a variety of phenomena in which noise plays an essential role. In these models, one potential goal is to determine if noise can induce transitions between states, and if so, to calculate the most probable escape path from an attractor. In the small noise limit, the Freidlin-Wentzell theory of large deviations provides a variational framework to calculate these paths. This work focuses on using large deviation theory to calculate such paths for stochastic gradient systems with non-gradient perturbations. While for gradient systems the most probable escape paths consist of time-reversed heteroclinic orbits, for general systems it can be a challenging calculation. By applying Melnikov theory to the resulting Euler-Lagrange equations recast in Hamiltonian form, we determine a condition for when the optimal escape path is the heteroclinic orbit for the perturbed system. We provide a numerical example to illustrate how the computed most probable escape path compares with the theoretical result.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/73ebcfa6347c0ff167a1d0bd01e8827132585776" target='_blank'>
-              A Parsimonious Separated Representation Empowering PINN–PGD-Based Solutions for Parametrized Partial Differential Equations
+          <td><a href="https://www.semanticscholar.org/paper/330a794bfa885f5551f29fba1960f92ebc6ccf55" target='_blank'>
+              Most probable escape paths in perturbed gradient systems
               </a>
             </td>
           <td>
-            C. Ghnatios, Francisco Chinesta
+            Katherine Slyman, Mackenzie Simper, John A Gemmer, Bjorn Sandstede
           </td>
-          <td>2024-07-29</td>
-          <td>Mathematics</td>
+          <td>2024-07-25</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>13</td>
+          <td>7</td>
         </tr>
 
-        <tr id="Many industrial processes can be described by distributed parameter systems (DPSs) governed by partial differential equations (PDEs). In this research, a spatiotemporal network is proposed for DPS modeling without any process knowledge. Since traditional linear modeling methods may not work well for nonlinear DPSs, the proposed method considers the nonlinear space-time separation, which is transformed into a Lagrange dual optimization problem under the orthogonal constraint. The optimization problem can be solved by the proposed neural network with good structural interpretability. The spatial construction method is employed to derive the continuous spatial basis functions (SBFs) based on the discrete spatial features. The nonlinear temporal model is derived by the Gaussian process regression (GPR). Benefiting from spatial construction and GPR, the proposed method enables spatially continuous modeling and provides a reliable output range under the given confidence level. Experiments on a catalytic reaction process and a battery thermal process demonstrate the effectiveness and superiority of the proposed method.">
+        <tr id="Overparameterized stochastic differential equation (SDE) models have achieved remarkable success in various complex environments, such as PDE-constrained optimization, stochastic control and reinforcement learning, financial engineering, and neural SDEs. These models often feature system evolution coefficients that are parameterized by a high-dimensional vector $\theta \in \mathbb{R}^n$, aiming to optimize expectations of the SDE, such as a value function, through stochastic gradient ascent. Consequently, designing efficient gradient estimators for which the computational complexity scales well with $n$ is of significant interest. This paper introduces a novel unbiased stochastic gradient estimator--the generator gradient estimator--for which the computation time remains stable in $n$. In addition to establishing the validity of our methodology for general SDEs with jumps, we also perform numerical experiments that test our estimator in linear-quadratic control problems parameterized by high-dimensional neural networks. The results show a significant improvement in efficiency compared to the widely used pathwise differentiation method: Our estimator achieves near-constant computation times, increasingly outperforms its counterpart as $n$ increases, and does so without compromising estimation variance. These empirical findings highlight the potential of our proposed methodology for optimizing SDEs in contemporary applications.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6bcfa839d7defe2cde7d0acf69e5a592bdf81de3" target='_blank'>
-              Spatiotemporal Transformation-Based Neural Network With Interpretable Structure for Modeling Distributed Parameter Systems.
+          <td><a href="https://www.semanticscholar.org/paper/6fc1545e20fb78c9271560d34a3512c20ad26286" target='_blank'>
+              An Efficient High-dimensional Gradient Estimator for Stochastic Differential Equations
               </a>
             </td>
           <td>
-            Peng Wei, Han-Xiong Li
+            Shengbo Wang, Jose Blanchet, Peter Glynn
           </td>
-          <td>2024-07-25</td>
-          <td>IEEE transactions on neural networks and learning systems</td>
+          <td>2024-07-14</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>22</td>
+          <td>4</td>
         </tr>
 
-        <tr id="In this paper, we study efficient approximate sampling for probability distributions known up to normalization constants. We specifically focus on a problem class arising in Bayesian inference for large-scale inverse problems in science and engineering applications. The computational challenges we address with the proposed methodology are: (i) the need for repeated evaluations of expensive forward models; (ii) the potential existence of multiple modes; and (iii) the fact that gradient of, or adjoint solver for, the forward model might not be feasible. While existing Bayesian inference methods meet some of these challenges individually, we propose a framework that tackles all three systematically. Our approach builds upon the Fisher-Rao gradient flow in probability space, yielding a dynamical system for probability densities that converges towards the target distribution at a uniform exponential rate. This rapid convergence is advantageous for the computational burden outlined in (i). We apply Gaussian mixture approximations with operator splitting techniques to simulate the flow numerically; the resulting approximation can capture multiple modes thus addressing (ii). Furthermore, we employ the Kalman methodology to facilitate a derivative-free update of these Gaussian components and their respective weights, addressing the issue in (iii). The proposed methodology results in an efficient derivative-free sampler flexible enough to handle multi-modal distributions: Gaussian Mixture Kalman Inversion (GMKI). The effectiveness of GMKI is demonstrated both theoretically and numerically in several experiments with multimodal target distributions, including proof-of-concept and two-dimensional examples, as well as a large-scale application: recovering the Navier-Stokes initial condition from solution data at positive times.">
+        <tr id="Diffusion models (DMs) are a class of generative machine learning methods that sample a target distribution by transforming samples of a trivial (often Gaussian) distribution using a learned stochastic differential equation. In standard DMs, this is done by learning a ``score function'' that reverses the effect of adding diffusive noise to the distribution of interest. Here we consider the generalisation of DMs to lattice systems with discrete degrees of freedom, and where noise is added via Markov chain jump dynamics. We show how to use tensor networks (TNs) to efficiently define and sample such ``discrete diffusion models'' (DDMs) without explicitly having to solve a stochastic differential equation. We show the following: (i) by parametrising the data and evolution operators as TNs, the denoising dynamics can be represented exactly; (ii) the auto-regressive nature of TNs allows to generate samples efficiently and without bias; (iii) for sampling Boltzmann-like distributions, TNs allow to construct an efficient learning scheme that integrates well with Monte Carlo. We illustrate this approach to study the equilibrium of two models with non-trivial thermodynamics, the $d=1$ constrained Fredkin chain and the $d=2$ Ising model.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/0d77575c529cb455ba8ac5289fe0e14615a7c4f1" target='_blank'>
-              Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows
+          <td><a href="https://www.semanticscholar.org/paper/4033d59c9ab7aac9bd3ba446669a55ca00e0800c" target='_blank'>
+              Discrete generative diffusion models without stochastic differential equations: a tensor network approach
               </a>
             </td>
           <td>
-            Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
+            Luke Causer, Grant M. Rotskoff, J. P. Garrahan
           </td>
-          <td>2024-06-25</td>
+          <td>2024-07-15</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>9</td>
+          <td>54</td>
         </tr>
 
-        <tr id="Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properties of matter at the atomic scale. Both established and state-of-the-art approaches, with almost no exceptions, are built to be exactly equivariant to translations, permutations, and rotations of the atoms. Incorporating symmetries -- rotations in particular -- constrains the model design space and implies more complicated architectures that are often also computationally demanding. There are indications that non-symmetric models can easily learn symmetries from data, and that doing so can even be beneficial for the accuracy of the model. We put a model that obeys rotational invariance only approximately to the test, in realistic scenarios involving simulations of gas-phase, liquid, and solid water. We focus specifically on physical observables that are likely to be affected -- directly or indirectly -- by symmetry breaking, finding negligible consequences when the model is used in an interpolative, bulk, regime. Even for extrapolative gas-phase predictions, the model remains very stable, even though symmetry artifacts are noticeable. We also discuss strategies that can be used to systematically reduce the magnitude of symmetry breaking when it occurs, and assess their impact on the convergence of observables.">
+        <tr id="In this study, we considered the problem of estimating epidemiological parameters based on physics-informed neural networks (PINNs). In practice, not all trajectory data corresponding to the population estimated by epidemic models can be obtained, and some observed trajectories are noisy. Learning PINNs to estimate unknown epidemiological parameters using such partial observations is challenging. Accordingly, we introduce the concept of algebraic observability into PINNs. The validity of the proposed PINN, named as an algebraically observable PINNs, in terms of estimation parameters and prediction of unobserved variables, is demonstrated through numerical experiments.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/5dc964683c6ce6be616e96982ba7cf5172752d68" target='_blank'>
-              Probing the effects of broken symmetries in machine learning
+          <td><a href="https://www.semanticscholar.org/paper/0f7cf742ff57aaaa10202e43c7d6149e073d6568" target='_blank'>
+              Estimate Epidemiological Parameters given Partial Observations based on Algebraically Observable PINNs
               </a>
             </td>
           <td>
-            Marcel F. Langer, S. Pozdnyakov, Michele Ceriotti
+            Mizuka Komatsu
           </td>
-          <td>2024-06-25</td>
+          <td>2024-07-17</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>9</td>
+          <td>0</td>
         </tr>
 
-        <tr id="In this work, we propose a set of physics-informed geometric operators (GOs) to enrich the geometric data provided for training surrogate/discriminative models, dimension reduction, and generative models, typically employed for performance prediction, dimension reduction, and creating data-driven parameterisations, respectively. However, as both the input and output streams of these models consist of low-level shape representations, they often fail to capture shape characteristics essential for performance analyses. Therefore, the proposed GOs exploit the differential and integral properties of shapes--accessed through Fourier descriptors, curvature integrals, geometric moments, and their invariants--to infuse high-level intrinsic geometric information and physics into the feature vector used for training, even when employing simple model architectures or low-level parametric descriptions. We showed that for surrogate modelling, along with the inclusion of the notion of physics, GOs enact regularisation to reduce over-fitting and enhance generalisation to new, unseen designs. Furthermore, through extensive experimentation, we demonstrate that for dimension reduction and generative models, incorporating the proposed GOs enriches the training data with compact global and local geometric features. This significantly enhances the quality of the resulting latent space, thereby facilitating the generation of valid and diverse designs. Lastly, we also show that GOs can enable learning parametric sensitivities to a great extent. Consequently, these enhancements accelerate the convergence rate of shape optimisers towards optimal solutions.">
+        <tr id="Identifying the causal relations between interested variables plays a pivotal role in representation learning as it provides deep insights into the dataset. Identifiability, as the central theme of this approach, normally hinges on leveraging data from multiple distributions (intervention, distribution shift, time series, etc.). Despite the exciting development in this field, a practical but often overlooked problem is: what if those distribution shifts happen sequentially? In contrast, any intelligence possesses the capacity to abstract and refine learned knowledge sequentially -- lifelong learning. In this paper, with a particular focus on the nonlinear independent component analysis (ICA) framework, we move one step forward toward the question of enabling models to learn meaningful (identifiable) representations in a sequential manner, termed continual causal representation learning. We theoretically demonstrate that model identifiability progresses from a subspace level to a component-wise level as the number of distributions increases. Empirically, we show that our method achieves performance comparable to nonlinear ICA methods trained jointly on multiple offline distributions and, surprisingly, the incoming new distribution does not necessarily benefit the identification of all latent variables.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e89b2b8bdad2c98d764f8c828e40b1065ff7464d" target='_blank'>
-              Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design
+          <td><a href="https://www.semanticscholar.org/paper/d9dd025ff21a417d50c5564f864be35cbb2325c6" target='_blank'>
+              Continual Learning of Nonlinear Independent Representations
               </a>
             </td>
           <td>
-            Shahroz Khan, Zahid Masood, Muhammad Usama, Konstantinos V. Kostas, P. Kaklis, Wei Chen
+            Boyang Sun, Ignavier Ng, Guan-Hong Chen, Yifan Shen, Qirong Ho, Kun Zhang
           </td>
-          <td>2024-07-10</td>
+          <td>2024-08-11</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>21</td>
+          <td>12</td>
         </tr>
 
-        <tr id="We discuss an approach to mathematically modelling systems made of objects that are coupled together, using generative models of the dependence relationships between states (or trajectories) of the things comprising such systems. This broad class includes open or non-equilibrium systems and is especially relevant to self-organising systems. The ensuing variational free energy principle (FEP) has certain advantages over using random dynamical systems explicitly, notably, by being more tractable and offering a parsimonious explanation of why the joint system evolves in the way that it does, based on the properties of the coupling between system components. Using the FEP allows us to model the dynamics of an object as if it were a process of variational inference, because variational free energy (or surprisal) is a Lyapunov function for its dynamics. In short, we argue that using generative models to represent and track relations among subsystems leads us to a particular statistical theory of interacting systems. Conversely, this theory enables us to construct nested models that respect the known relations among subsystems. We point out that the fact that a physical object conforms to the FEP does not necessarily imply that this object performs inference in the literal sense; rather, it is a useful explanatory fiction which replaces the 'explicit' dynamics of the object with an 'implicit' flow on free energy gradients - a fiction that may or may not be entertained by the object itself.">
+        <tr id="In this paper we introduce a Meshfree Variational Physics Informed Neural Network. It is a Variational Physics Informed Neural Network that does not require the generation of a triangulation of the entire domain and that can be trained with an adaptive set of test functions. In order to generate the test space we exploit an a posteriori error indicator and add test functions only where the error is higher. Four training strategies are proposed and compared. Numerical results show that the accuracy is higher than the one of a Variational Physics Informed Neural Network trained with the same number of test functions but defined on a quasi-uniform mesh.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/7d9000095cab2d2187f9dc3eb5aeba6fe5752a46" target='_blank'>
-              An approach to non-equilibrium statistical physics using variational Bayesian inference
+          <td><a href="https://www.semanticscholar.org/paper/8a9a2ceba8c6c319fce3b13aa5f1c315e14309ac" target='_blank'>
+              Meshfree Variational Physics Informed Neural Networks (MF-VPINN): an adaptive training strategy
               </a>
             </td>
           <td>
-            M. Ramstead, D. A. R. Sakthivadivel, K. Friston
+            S. Berrone, Moreno Pintore
           </td>
-          <td>2024-06-17</td>
+          <td>2024-06-28</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>23</td>
         </tr>
 
-        <tr id="Variational Physics-Informed Neural Networks often suffer from poor convergence when using stochastic gradient-descent-based optimizers. By introducing a Least Squares solver for the weights of the last layer of the neural network, we improve the convergence of the loss during training in most practical scenarios. This work analyzes the computational cost of the resulting hybrid Least-Squares/Gradient-Descent optimizer and explains how to implement it efficiently. In particular, we show that a traditional implementation based on backward-mode automatic differentiation leads to a prohibitively expensive algorithm. To remedy this, we propose using either forward-mode automatic differentiation or an ultraweak-type scheme that avoids the differentiation of trial functions in the discrete weak formulation. The proposed alternatives are up to 100 times faster than the traditional one, recovering a computational cost-per-iteration similar to that of a conventional gradient-descent-based optimizer alone. To support our analysis, we derive computational estimates and conduct numerical experiments in one- and two-dimensional problems.">
+        <tr id="Over the course of four decades, model predictive control (MPC) has become one of the great success stories in systems and control. It has grown from its native habitat (chemical process control) into all domains of control applications—power and energy systems, mechatronics and robotics, as well as aerospace and aeronautics. Hence, in a modern systems and control curriculum, MPC triggers not so much the question of if it should be taught. In fact, industrial demand for and the continued research potential of MPC suggest that one should rather ask the Aristotelian 5W1H (what, when, where, why, who, and how?) about teaching MPC. This article presents insights into the 5Ws distilled from the results of a survey on teaching MPC conducted in the systems and control community. Moreover, the how is approached through blueprint suggestions for curricula for an undergraduate discrete-time linear-quadratic MPC course and for graduate courses covering the continuous-time nonlinear avenue and the learning-based route.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/17eda77a3cdea99de9f795c5bb3e5312a9f5c667" target='_blank'>
-              Optimizing Variational Physics-Informed Neural Networks Using Least Squares
+          <td><a href="https://www.semanticscholar.org/paper/95cbb4bf5304c328ffa01fa4a5e73c270ccce7a8" target='_blank'>
+              Teaching Model Predictive Control: What, When, Where, Why, Who, and How? [Focus on Education]
               </a>
             </td>
           <td>
-            C. Uriarte, Manuela Bastidas, David Pardo, Jamie M. Taylor, Sergio Rojas
+            T. Faulwasser, Eric C. Kerrigan, Filip Logist, Sergio Lucia, M. Mönnigmann, Alessandra Parisio, M. S. Darup
           </td>
-          <td>2024-07-29</td>
-          <td>ArXiv</td>
+          <td>2024-08-01</td>
+          <td>IEEE Control Systems</td>
           <td>0</td>
-          <td>3</td>
+          <td>29</td>
         </tr>
 
-        <tr id="In this work, we discuss the use of a recently introduced machine learning (ML) technique known as Fourier neural operators (FNO) as an efficient alternative to the traditional solution of the time-dependent Schrödinger equation (TDSE). FNOs are ML models which are employed in the approximated solution of partial differential equations. For a wavepacket propagating in an anharmonic potential and for a tunneling system, we show that the FNO approach can accurately and faithfully model wavepacket propagation via the density. Additionally, we demonstrate that FNOs can be a suitable replacement for traditional TDSE solvers in cases where the results of the quantum dynamical simulation are required repeatedly such as in the case of parameter optimization problems (e.g., control). The speed-up from the FNO method allows for its combination with the Markov-chain Monte Carlo approach in applications that involve solving inverse problems such as optimal and coherent laser control of the outcome of dynamical processes.">
+        <tr id="Wild animals are commonly fitted with trackers that record their position through time, to learn about their behaviour. Broadly, statistical models for tracking data often fall into two categories: local models focus on describing small-scale movement decisions, and global models capture large-scale spatial distributions. Due to this dichotomy, it is challenging to describe mathematically how animals' distributions arise from their short-term movement patterns, and to combine data sets collected at different scales. We propose a multiscale model of animal movement and space use based on the underdamped Langevin process, widely used in statistical physics. The model is convenient to describe animal movement for three reasons: it is specified in continuous time (such that its parameters are not dependent on an arbitrary time scale), its speed and direction are autocorrelated (similarly to real animal trajectories), and it has a closed form stationary distribution that we can view as a model of long-term space use. We use the common form of a resource selection function for the stationary distribution, to model the environmental drivers behind the animal's movement decisions. We further increase flexibility by allowing movement parameters to be time-varying, e.g., to account for daily cycles in an animal's activity. We formulate the model as a state-space model and present a method of inference based on the Kalman filter. The approach requires discretising the continuous-time process, and we use simulations to investigate performance for various time resolutions of observation. The approach works well at fine resolutions, though the estimated stationary distribution tends to be too flat when time intervals between observations are very long.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e9adc38ac192d36f40f2d38abb968ebc1c04c08b" target='_blank'>
-              Accelerating wavepacket propagation with machine learning.
+          <td><a href="https://www.semanticscholar.org/paper/9df6a9afdb8773bfa193d6bae41f91e4a44b70ca" target='_blank'>
+              Multiscale modelling of animal movement with persistent dynamics
               </a>
             </td>
           <td>
-            Kanishka Singh, Ka Hei Lee, Daniel Peláez, A. Bande
+            T. Michelot
           </td>
           <td>2024-06-21</td>
-          <td>Journal of computational chemistry</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>12</td>
+          <td>14</td>
         </tr>
 
-        <tr id="In this paper, we introduce an efficient method for identifying fractional dynamic systems using extended sparse regression and cross-validation techniques. The former identifies equations that fit the data with varying candidate functions, while the latter determines the optimal equation with the fewest terms yet ensuring accuracy. The identified optimal equation is expected to share the same dynamic properties as the original fractional system. Unlike previous studies focusing on efficiently computing fractional terms, this strategy addresses dynamic analysis from a data perspective. Importantly, in the proposed method, we treat the fractional order as a variable to account for its impact on the dynamic properties of the identified equation. This treatment enables the identified equation to successfully capture dynamic behaviors when the fractional order changes. We validate the effectiveness of the method using three classical fractional-order systems as well as an energy harvesting system. Interestingly, we find that, although the identified equations do not contain non-local terms like the original fractional-order systems, they exhibit the same stochastic P-bifurcation phenomena. In other words, we construct an equivalent equation without memory properties, sharing the dynamic properties with the original system.">
+        <tr id="The ubiquity of multiscale interactions in complex systems is well-recognized, with development and heredity serving as a prime example of how processes at different temporal scales influence one another. This work introduces a novel multiscale state-space model to explore the dynamic interplay between systems interacting across different time scales, with feedback between each scale. We propose a Bayesian learning framework to estimate unknown states by learning the unknown process noise covariances within this multiscale model. We develop a Particle Gibbs with Ancestor Sampling (PGAS) algorithm for inference and demonstrate through simulations the efficacy of our approach.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/13dd436d0243839bfd4433de2c44addeac9d1f62" target='_blank'>
-              Identification method for a fractional-order system in terms of equivalent dynamic properties.
+          <td><a href="https://www.semanticscholar.org/paper/6a4108fef8d82f9c5401baeb3eaafc732ca1c2a3" target='_blank'>
+              Bayesian Learning in a Nonlinear Multiscale State-Space Model
               </a>
             </td>
           <td>
-            Minjuan Yuan, Wei Xu, Fawang Liu, Liang Wang, Yisha Lu
+            Nayely V'elez-Cruz, Manfred D. Laubichler
           </td>
-          <td>2024-07-01</td>
-          <td>Chaos</td>
+          <td>2024-08-12</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>4</td>
+          <td>1</td>
         </tr>
 
-        <tr id="This paper investigates the use of the Gaussian Process Convolution Model (GPCM) as an output only system identification tool for structural systems. The form of the model assumes a priori that the observed data arise as the result of a convolution between an unknown linear filter and an unobserved white noise process, where each of these are modelled as a GP. The GPCM infers both the linear time filter (which is the impulse response function, i.e. Green’s function, of the system) and driving white noise process in a Bayesian probabilistic fashion with an approximate variational posterior over both signals. It will be shown that although the model structure is intuitive and sensible priors are applied, the GPCM falls short in recovering the linear impulse response of interest response due to the problem of identifiability. This is an interesting result indicating that physically informed kernel structures alone are not enough to recover the true impulse response in similar non-parametric probabilistic models. Despite this, the avenue of research remains highly promising, and several ideas are proposed to improve the model as a system identification tool.">
+        <tr id="
+
+
+Ecosystems contain numerous species interacting with each other and the environment. However, data on all relevant state variables is rarely available, hampering inference and prediction. Empirical dynamic modelling (EDM) is a valuable tool for prediction, inference and control in such partially observed systems.
+
+However, EDM typically assumes that the available time series are observed without error. Failing to account for observation noise strongly biases estimates of Lyapunov exponents and reduces forecast accuracy. To address this limitation, we propose incorporating EDM into a hidden Markov framework and using an iterative scheme based on the expectation maximization (EM) algorithm to obtain filtered state and parameter estimates.
+
+We evaluate the performance of this approach on several simulated dynamical systems with a range of additive noise levels, as well as on insect population time series.
+
+Accounting for observation noise improved accuracy of population forecasts and estimates of Lyapunov exponents (LE) over a wide range of noise levels relevant to ecological time series.
+
+">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/310430f0e85c7e78bf920da254b97d3d29a49049" target='_blank'>
-              Convolution models for output only linear structural system identification and the problem of identifiability.
+          <td><a href="https://www.semanticscholar.org/paper/4e9c4d6519c0328c66d02635359f84c4d6cf2f4d" target='_blank'>
+              Accounting for observation noise in equation‐free forecasting: The hidden‐Markov S‐map
               </a>
             </td>
           <td>
-            J. H. Mclean, N. Dervilis, T. J. Rogers
+            Dylan Esguerra, Stephan B. Munch
           </td>
-          <td>2024-06-01</td>
-          <td>Journal of Physics: Conference Series</td>
+          <td>2024-07-01</td>
+          <td>Methods in Ecology and Evolution</td>
           <td>0</td>
-          <td>26</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Time-varying optimization problems are central to many engineering applications, where performance metrics and system constraints evolve dynamically with time. A number of algorithms have been proposed in recent years to solve such problems; a common feature of all these methods is that they implicitly require precise knowledge of the temporal variability of the solutions in order to exactly track the optimizers. In this paper, we seek to lift these stringent assumptions. Our main result is a fundamental characterization, showing that an algorithm can track an optimal trajectory if and only if it contains a model of the temporal variability of the problem. We refer to this concept to as the internal model principle of time-varying optimization. By recasting the optimization objective as a nonlinear regulation problem and using tools from center manifold theory, we provide necessary and sufficient conditions both for an optimization algorithm to achieve exact asymptotic tracking and for such an algorithm to exist. We illustrate the applicability of the approach numerically on both synthetic problems as well as practical problems in transportation.">
+        <tr id="In this work, we introduce a novel learning-based controller tailored for autonomous control of a batch-type precipitation process involving calcium and magnesium carbonates. The process takes in fluid containing valuable materials such as Ca+2and Mg+2 ions, along with impurities and seed particles, to facilitate the sequential precipitation of these ions into their respective carbonates. The controller's goal is to attain a specified size of the precipitated particles under different process uncertainties. Here the residence time, i.e. the time allowed for the ions to remain in fluid phase, is used as the manipulation variable. The controller is designed as a solution to a stochastic optimal control problem and implemented using machine learning techniques. For the prediction model, we use convolutional neural networks (CNN) and for the control synthesis, we use a type of recurrent neural networks (RNNs). The designed control is learnable, adaptable to varying process dynamics and robust to random disturbances in the process, thus resulting in a learnable adaptive, and robust controller (LARC). The effectiveness of LARC is validated through different simulation-based tests.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/8aee1c4d1d9bfe7e68a03ee2eb770d81f18d112d" target='_blank'>
-              The Internal Model Principle of Time-Varying Optimization
+          <td><a href="https://www.semanticscholar.org/paper/2ca52c12e0ce9a97c03f851037abfed99e3a3847" target='_blank'>
+              Learnable Adaptive and Robust Controller for a Two Particle Carbonate Precipitation Process
               </a>
             </td>
           <td>
-            G. Bianchin, Bryan Van Scoy
+            Mikhail Kakanov, S. Hiremath, Andreas Voigt, Kai Sundmacher, N. Bajçinca
           </td>
-          <td>2024-07-10</td>
-          <td>ArXiv</td>
+          <td>2024-06-25</td>
+          <td>2024 European Control Conference (ECC)</td>
           <td>0</td>
-          <td>11</td>
+          <td>15</td>
         </tr>
 
-        <tr id="The use of implicit time-stepping schemes for the numerical approximation of solutions to stiff nonlinear time-evolution equations brings well-known advantages including, typically, better stability behaviour and corresponding support of larger time steps, and better structure preservation properties. However, this comes at the price of having to solve a nonlinear equation at every time step of the numerical scheme. In this work, we propose a novel operator learning based hybrid Newton's method to accelerate this solution of the nonlinear time step system for stiff time-evolution nonlinear equations. We propose a targeted learning strategy which facilitates robust unsupervised learning in an offline phase and provides a highly efficient initialisation for the Newton iteration leading to consistent acceleration of Newton's method. A quantifiable rate of improvement in Newton's method achieved by improved initialisation is provided and we analyse the upper bound of the generalisation error of our unsupervised learning strategy. These theoretical results are supported by extensive numerical results, demonstrating the efficiency of our proposed neural hybrid solver both in one- and two-dimensional cases.">
+        <tr id="Compartment models with delay terms are widely used across a range of disciplines. The motivation to include delay terms varies across different contexts. In epidemiological and pharmacokinetic models, the delays are often used to represent an incubation period. In this work, we derive a compartment model with delay terms from an underlying non-Markov stochastic process. Delay terms arise when waiting times are drawn from a delay exponential distribution. This stochastic process approach allows us to preserve the physicality of the model, gaining understanding into the conditions under which delay terms can arise. By providing the conditions under which the delay exponential function is a probability distribution, we establish a critical value for the delay terms. An exact stochastic simulation method is introduced for the generalized model, enabling us to utilize the simulation in scenarios where intrinsic stochasticity is significant, such as when the population size is small. We illustrate the applications of the model and validate our simulation algorithm on examples drawn from epidemiology and pharmacokinetics.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/40b5bc74deb4b809b58e9a9532752462a89bc6be" target='_blank'>
-              A fast neural hybrid Newton solver adapted to implicit methods for nonlinear dynamics
+          <td><a href="https://www.semanticscholar.org/paper/15bad9e29f43321e2682f7822900b1143d36ab9c" target='_blank'>
+              Delay compartment models from a stochastic process
               </a>
             </td>
           <td>
-            Tianyu Jin, G. Maierhofer, Katharina Schratz, Yang Xiang
+            C. Angstmann, A. V. McGann, Zhuang Xu
           </td>
-          <td>2024-07-04</td>
+          <td>2024-06-25</td>
           <td>ArXiv</td>
           <td>1</td>
-          <td>5</td>
+          <td>16</td>
         </tr>
 
-        <tr id="Areas of computational mechanics such as uncertainty quantification and optimization usually involve repeated evaluation of numerical models that represent the behavior of engineering systems. In the case of complex nonlinear systems however, these models tend to be expensive to evaluate, making surrogate models quite valuable. Artificial neural networks approximate systems very well by taking advantage of the inherent information of its given training data. In this context, this paper investigates the improvement of the training process by including sensitivity information, which are partial derivatives w.r.t. inputs, as outlined by Sobolev training. In computational mechanics, sensitivities can be applied to neural networks by expanding the training loss function with additional loss terms, thereby improving training convergence resulting in lower generalisation error. This improvement is shown in two examples of linear and non-linear material behavior. More specifically, the Sobolev designed loss function is expanded with residual weights adjusting the effect of each loss on the training step. Residual weighting is the given scaling to the different training data, which in this case are response and sensitivities. These residual weights are optimized by an adaptive scheme, whereby varying objective functions are explored, with some showing improvements in accuracy and precision of the general training convergence.">
+        <tr id="Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the Multiscale Modeling Framework (MMF), which embeds a kilometer-resolution cloud-resolving model within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning (ML) offers a unique opportunity to make MMF more accessible by emulating the embedded cloud-resolving model and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including real geography, seasonality, explicit cloud condensate predictions, and land coupling. Our model demonstrates skillful online performance in metrics such as 5-year zonal mean biases compared to previous MMF emulation studies. The monthly error against reference MMF simulations with the same initial condition approaches the fundamental predictability limit. Key factors contributing to our online performance include an expressive U-Net architecture, additional input features that include large-scale forcings and convection memory, and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Our work showcases ML parameterization's potential for operational-level climate simulations.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/bc84ce2dba59d576dd4d3f10a9b0f7b62826171a" target='_blank'>
-              Sobolev neural network with residual weighting as a surrogate in linear and non-linear mechanics
+          <td><a href="https://www.semanticscholar.org/paper/71dfebd2303c30bf8e52df7f42375cda87b276cf" target='_blank'>
+              Stable Machine-Learning Parameterization of Subgrid Processes with Real Geography and Full-physics Emulation
               </a>
             </td>
           <td>
-            A.O.M. Kilicsoy, J. Liedmann, M. Valdebenito, F. Barthold, M. Faes
+            Zeyuan Hu, Akshay Subramaniam, Zhiming Kuang, Jerry Lin, Sungduk Yu, Walter M. Hannah, Noah D. Brenowitz, Josh Romero, Michael S. Pritchard
           </td>
-          <td>2024-07-23</td>
+          <td>2024-06-28</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>23</td>
+          <td>6</td>
         </tr>
 
-        <tr id="The brain needs to perform time-critical computations to ensure survival. A potential solution lies in the non-local, distributed computation at the whole-brain level made possible by criticality and amplified by the rare long-range connections found in the brain’s unique anatomical structure. This non-locality can be captured by the mathematical structure of Schrödinger’s wave equation, which is at the heart of the novel CHARM (Complex Harmonics decomposition) framework that performs the necessary dimensional manifold reduction able to extract non-locality in critical spacetime brain dynamics. Using a large neuroimaging dataset of over 1,000 people, CHARM captured the critical, non-local/long-range nature of brain dynamics and the underlying mechanisms were established using a precise whole-brain model. Equally, CHARM revealed the significantly different critical dynamics of wakefulness and sleep. Overall, CHARM is a promising theoretical framework for capturing the low-dimensionality of the complex network dynamics observed in neuroscience and provides evidence that networks of brain regions rather than individual brain regions are the key computational engines of critical brain dynamics.">
+        <tr id="This paper studies the stability and convergence properties of a class of multi-agent concurrent learning (CL) algorithms with momentum and restart. Such algorithms can be integrated as part of the estimation pipelines of data-enabled multi-agent control systems to enhance transient performance while maintaining stability guarantees. However, characterizing restarting policies that yield stable behaviors in decentralized CL systems, especially when the network topology of the communication graph is directed, has remained an open problem. In this paper, we provide an answer to this problem by synergistically leveraging tools from graph theory and hybrid dynamical systems theory. Specifically, we show that under a cooperative richness condition on the overall multi-agent system's data, and by employing coordinated periodic restart with a frequency that is tempered by the level of asymmetry of the communication graph, the resulting decentralized dynamics exhibit robust asymptotic stability properties, characterized in terms of input-to-state stability bounds, and also achieve a desirable transient performance. To demonstrate the practical implications of the theoretical findings, three applications are also presented: cooperative parameter estimation over networks with private data sets, cooperative model-reference adaptive control, and cooperative data-enabled feedback optimization of nonlinear plants.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/79c92bde562ae0356acda295ead7128e8c11928b" target='_blank'>
-              Complex harmonics reveal low-dimensional manifolds of critical brain dynamics
+          <td><a href="https://www.semanticscholar.org/paper/567d4ebaa08c236655c7c5193532c8e7f8713fc7" target='_blank'>
+              Decentralized Concurrent Learning with Coordinated Momentum and Restart
               </a>
             </td>
           <td>
-            G. Deco, Y. Perl, M. Kringelbach
+            Daniel E. Ochoa, Muhammad U. Javed, Xudong Chen, Jorge I. Poveda
           </td>
-          <td>2024-06-16</td>
-          <td>bioRxiv</td>
+          <td>2024-06-21</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>13</td>
+          <td>6</td>
         </tr>
 
-        <tr id="A new particle-based sampling and approximate inference method, based on electrostatics and Newton mechanics principles, is introduced with theoretical ground, algorithm design and experimental validation. This method simulates an interacting particle system (IPS) where particles, i.e. the freely-moving negative charges and spatially-fixed positive charges with magnitudes proportional to the target distribution, interact with each other via attraction and repulsion induced by the resulting electric fields described by Poisson's equation. The IPS evolves towards a steady-state where the distribution of negative charges conforms to the target distribution. This physics-inspired method offers deterministic, gradient-free sampling and inference, achieving comparable performance as other particle-based and MCMC methods in benchmark tasks of inferring complex densities, Bayesian logistic regression and dynamical system identification. A discrete-time, discrete-space algorithmic design, readily extendable to continuous time and space, is provided for usage in more general inference problems occurring in probabilistic machine learning scenarios such as Bayesian inference, generative modelling, and beyond.">
+        <tr id="This paper is concerned with the fundamental limits of nonlinear dynamical system learning from input-output traces. Specifically, we show that recurrent neural networks (RNNs) are capable of learning nonlinear systems that satisfy a Lipschitz property and forget past inputs fast enough in a metric-entropy optimal manner. As the sets of sequence-to-sequence maps realized by the dynamical systems we consider are significantly more massive than function classes generally considered in deep neural network approximation theory, a refined metric-entropy characterization is needed, namely in terms of order, type, and generalized dimension. We compute these quantities for the classes of exponentially-decaying and polynomially-decaying Lipschitz fading-memory systems and show that RNNs can achieve them.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f084ff68562acff79ea7b538f61a3d7bacd36009" target='_blank'>
-              Electrostatics-based particle sampling and approximate inference
+          <td><a href="https://www.semanticscholar.org/paper/14056322161789dd48312016e9d0e76682856a25" target='_blank'>
+              Metric-Entropy Limits on Nonlinear Dynamical System Learning
               </a>
             </td>
           <td>
-            Yongchao Huang
+            Yang Pan, Clemens Hutter, Helmut Bölcskei
           </td>
-          <td>2024-06-28</td>
+          <td>2024-07-01</td>
           <td>ArXiv</td>
-          <td>2</td>
           <td>0</td>
+          <td>2</td>
         </tr>
 
-        <tr id="In this work, we first show that the problem of parameter identification is often ill-conditioned and lacks the persistence of excitation required for the convergence of online learning schemes. To tackle these challenges, we introduce the notion of optimal and greedy excitation sets which contain data points with sufficient richness to aid in the identification task. We then present the greedy excitation set-based recursive least squares algorithm to alleviate the problem of the lack of persistent excitation, and prove that the iterates generated by the proposed algorithm minimize an auxiliary weighted least squares cost function. When data points are generated from time-varying parameters, online estimators tend to underfit the true parameter trajectory, and their predictability deteriorates. To tackle this problem, we propose a memory resetting scheme leveraging change point detection techniques. Finally, we illustrate the performance of the proposed algorithms via several numerical case studies to learn the (time-varying) parameters of networked epidemic dynamics, and compare it with results obtained using conventional approaches.">
+        <tr id="Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For such liquid-metal dealloying (LMD) process, phase field models have been developed. However, the governing equations often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, stiffness in the PDEs requires an extremely small time steps (e.g. $10^{-12}$ or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose U-Shaped Adaptive Fourier Neural Operators (U-AFNO), a machine learning (ML) model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g. the deformation of the liquid-metal interface) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in-spite of the chaotic nature of LMD. Our model reproduces the key micro-structure statistics and QoIs with a level of accuracy on-par with the high-fidelity numerical solver. We also investigate the opportunity of using hybrid simulations, in which we alternate forward leap in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f77ed1877cc1126478cecdbf4ca3316c240db17e" target='_blank'>
-              Online Identification of Time-Varying Systems Using Excitation Sets and Change Point Detection
+          <td><a href="https://www.semanticscholar.org/paper/868bad8571187ae2f328db235d6feb8d496140d9" target='_blank'>
+              Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone
               </a>
             </td>
           <td>
-            Chi Ho Leung, Ashish R. Hota, Philip E. Par'e
+            Christophe Bonneville, N. Bieberdorf, Arun Hegde, Mark Asta, H. Najm, Laurent Capolungo, C. Safta
           </td>
-          <td>2024-06-14</td>
+          <td>2024-06-24</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>44</td>
         </tr>
 
-        <tr id="We develop an algorithm to approximate the time evolution of a probability measure without explicitly learning an operator that governs the evolution. A particular application of interest is discrete measures $\mu_t^N$ that arise from particle systems. In many such situations, the individual particles move chaotically on short time scales, making it difficult to learn the dynamics of a governing operator, but the bulk distribution $\mu_t^N$ approximates an absolutely continuous measure $\mu_t$ that evolves ``smoothly.'' If $\mu_t$ is known on some time interval, then linearized optimal transport theory provides an Euler-like scheme for approximating the evolution of $\mu_t$ using its ``tangent vector field'' (represented as a time-dependent vector field on $\mathbb R^d$), which can be computed as a limit of optimal transport maps. We propose an analog of this Euler approximation to predict the evolution of the discrete measure $\mu_t^N$ (without knowing $\mu_t$). To approximate the analogous tangent vector field, we use a finite difference over a time step that sits between the two time scales of the system -- long enough for the large-$N$ evolution ($\mu_t$) to emerge but short enough to satisfactorily approximate the derivative object used in the Euler scheme. By allowing the limiting behavior to emerge, the optimal transport maps closely approximate the vector field describing the bulk distribution's smooth evolution instead of the individual particles' more chaotic movements. We demonstrate the efficacy of this approach with two illustrative examples, Gaussian diffusion and a cell chemotaxis model, and show that our method succeeds in predicting the bulk behavior over relatively large steps.">
+        <tr id="Data-driven deep learning models are on the verge of transforming global weather forecasting. It is an open question if this success can extend to climate modeling, where long inference rollouts and data complexity pose significant challenges. Here, we present the first conditional generative model able to produce global climate ensemble simulations that are accurate and physically consistent. Our model runs at 6-hourly time steps and is shown to be stable for 10-year-long simulations. Our approach beats relevant baselines and nearly reaches a gold standard for successful climate model emulation. We discuss the key design choices behind our dynamics-informed diffusion model-based approach which enables this significant step towards efficient, data-driven climate simulations that can help us better understand the Earth and adapt to a changing climate.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/91e3852e9601d6a2130fe9f07f6ceddf1cf18e5f" target='_blank'>
-              Using Linearized Optimal Transport to Predict the Evolution of Stochastic Particle Systems
+          <td><a href="https://www.semanticscholar.org/paper/d369ffa4e13e39e6b7c4b96ff3ad94b4aa9a3c23" target='_blank'>
+              Probabilistic Emulation of a Global Climate Model with Spherical DYffusion
               </a>
             </td>
           <td>
-            Nicholas Karris, Evangelos A. Nikitopoulos, Ioannis Kevrekidis, Seungjoon Lee, Alexander Cloninger
+            Salva Rühling Cachay, Brian Henn, Oliver Watt‐Meyer, Christopher S. Bretherton, Rose Yu
           </td>
-          <td>2024-08-03</td>
+          <td>2024-06-21</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>2</td>
+          <td>11</td>
         </tr>
 
-        <tr id="We present a method to approximately solve general instances of combinatorial optimization problems using the physical dynamics of 3d rotors obeying Landau-Lifshitz-Gilbert dynamics. Conventional techniques to solve discrete optimization problems that use simple continuous relaxation of the objective function followed by gradient descent minimization are inherently unable to avoid local optima, thus producing poor-quality solutions. Our method considers the physical dynamics of macrospins capable of escaping from local minima, thus facilitating the discovery of high-quality, nearly optimal solutions, as supported by extensive numerical simulations on a prototypical quadratic unconstrained binary optimization (QUBO) problem. Our method produces solutions that compare favorably with those obtained using state-of-the-art minimization algorithms (such as simulated annealing) while offering the advantage of being physically realizable by means of arrays of stochastic magnetic tunnel junction devices.">
+        <tr id="Analyzing complex experimental data with multiple parameters is challenging. We propose using Singular Value Decomposition (SVD) as an effective solution. This method, demonstrated through real experimental data analysis, surpasses conventional approaches in understanding complex physics data. Singular values and vectors distinguish and highlight various physical mechanisms and scales, revealing previously challenging elements. SVD emerges as a powerful tool for navigating complex experimental landscapes, showing promise for diverse experimental measurements.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/33e0bfbbf320ac6d9c59add6984bce2ee8a38966" target='_blank'>
-              Solving combinatorial optimization problems through stochastic Landau-Lifshitz-Gilbert dynamical systems
+          <td><a href="https://www.semanticscholar.org/paper/24e7da06892289a90ca7ca4e5171ea48a7012501" target='_blank'>
+              Unraveling Complexity: Singular Value Decomposition in Complex Experimental Data Analysis
               </a>
             </td>
           <td>
-            Dairong Chen, Andrew D. Kent, Dries Sels, F. Morone
+            Judith F. Stein, Aviad Frydman, R. Berkovits
           </td>
-          <td>2024-06-29</td>
+          <td>2024-07-23</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>23</td>
         </tr>
 
-        <tr id="The Path-dependent Neural Jump ODE (PD-NJ-ODE) is a model for online prediction of generic (possibly non-Markovian) stochastic processes with irregular (in time) and potentially incomplete (with respect to coordinates) observations. It is a model for which convergence to the $L^2$-optimal predictor, which is given by the conditional expectation, is established theoretically. Thereby, the training of the model is solely based on a dataset of realizations of the underlying stochastic process, without the need of knowledge of the law of the process. In the case where the underlying process is deterministic, the conditional expectation coincides with the process itself. Therefore, this framework can equivalently be used to learn the dynamics of ODE or PDE systems solely from realizations of the dynamical system with different initial conditions. We showcase the potential of our method by applying it to the chaotic system of a double pendulum. When training the standard PD-NJ-ODE method, we see that the prediction starts to diverge from the true path after about half of the evaluation time. In this work we enhance the model with two novel ideas, which independently of each other improve the performance of our modelling setup. The resulting dynamics match the true dynamics of the chaotic system very closely. The same enhancements can be used to provably enable the PD-NJ-ODE to learn long-term predictions for general stochastic datasets, where the standard model fails. This is verified in several experiments.">
+        <tr id="The spin-vector Monte Carlo model is widely used as a benchmark for the classicality of quantum annealers but severely restricts the time evolution. The spin-vector Langevin (SVL) model has been proposed and tested as an alternative, closely reproducing the real-time dynamics of physical quantum annealers such as D-Wave machines in the dissipative regime. We investigate the SVL annealing dynamics of classical O(2) rotors on regular graphs, identifying universal features in the nonequilibrium dynamics when changing the range of interactions and the topology of the graph. Regular graphs with low connectance or edge density exhibit universal scaling dynamics consistent with the Kibble-Zurek mechanism, which leads to a power-law dependence of the density of defects and the residual energy as a function of the annealing time. As the interaction range is increased, the power-law scaling is suppressed, and an exponential scaling with the annealing time sets in. Our results establish a universal breakdown of the Kibble-Zurek mechanism in classical systems characterized by long-range interactions, in sharp contrast with previous findings in the quantum domain.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ba851d147a33cf0c6fcf639b5d4990df283a0b23" target='_blank'>
-              Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs
+          <td><a href="https://www.semanticscholar.org/paper/e358e334c41ef5944c2b2f71d0941cafc762ea6d" target='_blank'>
+              Annealing Dynamics of Regular Rotor Networks: Universality and Its Breakdown
               </a>
             </td>
           <td>
-            F. Krach, Josef Teichmann
+            Andr'as Grabarits, Gaetano Sammartino, Adolfo del Campo
           </td>
-          <td>2024-07-26</td>
+          <td>2024-07-12</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>13</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Predictive models are a crucial component of many robotic systems. Yet, constructing accurate predictive models for a variety of deformable objects, especially those with unknown physical properties, remains a significant challenge. This paper introduces AdaptiGraph, a learning-based dynamics modeling approach that enables robots to predict, adapt to, and control a wide array of challenging deformable materials with unknown physical properties. AdaptiGraph leverages the highly flexible graph-based neural dynamics (GBND) framework, which represents material bits as particles and employs a graph neural network (GNN) to predict particle motion. Its key innovation is a unified physical property-conditioned GBND model capable of predicting the motions of diverse materials with varying physical properties without retraining. Upon encountering new materials during online deployment, AdaptiGraph utilizes a physical property optimization process for a few-shot adaptation of the model, enhancing its fit to the observed interaction data. The adapted models can precisely simulate the dynamics and predict the motion of various deformable materials, such as ropes, granular media, rigid boxes, and cloth, while adapting to different physical properties, including stiffness, granular size, and center of pressure. On prediction and manipulation tasks involving a diverse set of real-world deformable objects, our method exhibits superior prediction accuracy and task proficiency over non-material-conditioned and non-adaptive models. The project page is available at https://robopil.github.io/adaptigraph/ .">
+        <tr id="This work addresses the synthesis of optimal feedback control laws via machine learning. In particular, the Averaged Feedback Learning Scheme (AFLS) and a data driven method are considered. Hypotheses for each method ensuring the convergence of the evaluation of the objective function of the underlying control problem at the obtained feedback-laws towards the optimal value function are provided. These hypotheses are connected to the regularity of the value function and the stability of the dynamics. In the case of AFLS these hypotheses only require H\"older continuity of the value function, whereas for the data driven method the value function must be at least $C^2$. It is demonstrated that these methods are connected via their optimality conditions. Additionally, numerical experiments are provided by applying both methods to a family control problems, parameterized by a positive real number which controls the regularity of the value function. For small parameters the value function is smooth and in contrast for large parameters it is non-differentiable, but semi-concave. The results of the experiments indicate that both methods have a similar performance for the case that the value function is smooth. On the other hand, if the value function is not differentiable, AFLS has a better performance which is consistent with the obtained convergence results.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/06d79953e85f7981f8a2cabad2165ca739af35af" target='_blank'>
-              AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation
+          <td><a href="https://www.semanticscholar.org/paper/307b109ee8daf5810e48f9fba58490e24a131f80" target='_blank'>
+              Convergence of machine learning methods for feedback control laws: averaged feedback learning scheme and data driven method
               </a>
             </td>
           <td>
-            Kaifeng Zhang, Baoyu Li, Kris Hauser, Yunzhu Li
+            Karl Kunisch, Donato V'asquez-Varas
           </td>
-          <td>2024-07-10</td>
+          <td>2024-07-25</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
-        </tr>
-
-        <tr id="Physics-informed neural networks (PINNs) are emerging as a promising artificial intelligence approach for solving complex two-phase flow simulations. A critical challenge in these simulations is an accurate representation of the gas–liquid interface using interface tracking methods. While numerous studies in conventional computational fluid dynamics (CFD) have addressed this issue, there remains a notable absence of research within the context of PINNs-based two-phase flow simulations. Therefore, this study aims to develop a robust and generic PINNs for two-phase flow by incorporating the governing equations with three advanced interface tracking methods—specifically, the Volume of Fluid, Level Set, and Phase-Field method—into an improved PINN framework that has been previously proposed and validated. To further enhance the performance of the PINNs in simulating two-phase flow, the phase field constraints, residual connection and the time divide-and-conquer strategies are employed for restricting neural network training within the scope of physical laws. This self-adaptive and time divide-and-conquer (AT) PINNs is then optimized by minimizing both the residual and loss terms of partial differential equation. By incorporating the three different interface tracking methods, it efficiently handles high-order derivative terms and captures the phase interface. The case of single rising bubble in two-phase flow is simulated to validate the robustness and accuracy of the AT PINNs. The simulation's accuracy is evaluated by comparing its performance in terms of velocity, pressure, phase field, center of mass, and rising velocity with that of conventional PINNs and CFD benchmarks. The results indicate that the AT PINNs coupled with these interface tracking methods offers a satisfactory performance in simulating rising bubble phenomenon.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c5f2cb080d39451d218ad284af4230b961cb02e7" target='_blank'>
-              Self-adaptive and time divide-and-conquer physics-informed neural networks for two-phase flow simulations using interface tracking methods
-              </a>
-            </td>
-          <td>
-            Wen Zhou, Shuichiro Miwa, Koji Okamoto
-          </td>
-          <td>2024-07-01</td>
-          <td>Physics of Fluids</td>
-          <td>0</td>
-          <td>1</td>
+          <td>2</td>
         </tr>
 
-        <tr id="The interdisciplinary time-series analysis literature encompasses thousands of statistical features for quantifying interpretable properties of dynamical data. But for any given application, it is likely that just a small subset of informative time-series features is required to capture the dynamical quantities of interest. So, while comprehensive libraries of time-series features have been developed, it is useful to construct reduced and computationally efficient subsets for specific applications. In this work, we demonstrate a systematic process to deduce such a reduced set, focused on the problem of distinguishing changes to functional Magnetic Resonance Imaging (fMRI) time series caused by a range of experimental manipulations of excitatory and inhibitory neural activity in mouse cortical circuits. We reduce a comprehensive library of over 7000 candidate time-series features down to a subset of 16 features, which we call catchaMouse16, that aims to both: (i) accurately characterize biologically relevant properties of fMRI time series; and (ii) minimize inter-feature redundancy. The catchaMouse16 feature set accurately classifies experimental perturbations of neuronal activity from fMRI recordings, and also shows strong generalization performance on an unseen mouse and human resting-state fMRI data where it tracks spatial variations in excitatory and inhibitory cortical cell densities, often with greater statistical power than the full hctsa feature set. We provide an efficient, open-source implementation of the catchaMouse16 feature set in C (achieving an approximately 60 times speed-up relative to the native Matlab code of the same features), with wrappers for Python and Matlab. This work demonstrates a procedure to reduce a large candidate time-series feature set down to the key statistical properties of mouse fMRI dynamics that can be used to efficiently quantify and interpret informative dynamical patterns in neural time series.">
+        <tr id="Predicting the trajectories of systems with unknown dynamics (\textit{i.e.} the governing rules) is crucial in various research fields, including physics and biology. This challenge has gathered significant attention from diverse communities. Most existing works focus on learning fixed system dynamics within one single system. However, real-world applications often involve multiple systems with different types of dynamics or evolving systems with non-stationary dynamics (dynamics shifts). When data from those systems are continuously collected and sequentially fed to machine learning models for training, these models tend to be biased toward the most recently learned dynamics, leading to catastrophic forgetting of previously observed/learned system dynamics. To this end, we aim to learn system dynamics via continual learning. Specifically, we present a novel framework of Mode-switching Graph ODE (MS-GODE), which can continually learn varying dynamics and encode the system-specific dynamics into binary masks over the model parameters. During the inference stage, the model can select the most confident mask based on the observational data to identify the system and predict future trajectories accordingly. Empirically, we systematically investigate the task configurations and compare the proposed MS-GODE with state-of-the-art techniques. More importantly, we construct a novel benchmark of biological dynamic systems, featuring diverse systems with disparate dynamics and significantly enriching the research field of machine learning for dynamic systems.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e685be5f1377cb0acf6421ed0e2a179e1e4372df" target='_blank'>
-              Canonical time-series features for characterizing biologically informative dynamical patterns in fMRI
+          <td><a href="https://www.semanticscholar.org/paper/cb4045f3e6f4dfdaceedc6d8a70fc7a232722b3e" target='_blank'>
+              Learning System Dynamics without Forgetting
               </a>
             </td>
           <td>
-            Imran Alam, Brendan Harris, Patrick Cahill, Oliver Cliff, M. Markicevic, Valerio Zerbi, BD Fulcher
+            Xikun Zhang, Dongjin Song, Yushan Jiang, Yixin Chen, Dacheng Tao
           </td>
-          <td>2024-07-17</td>
-          <td>bioRxiv</td>
+          <td>2024-06-30</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>10</td>
+          <td>5</td>
         </tr>
 
-        <tr id="Understanding the dynamics of open quantum systems in strong coupling and non-Markovian regimes remains a formidable theoretical challenge. One popular and well-established method of approximation in these circumstances is provided by the polaron master equation (PME). In this work we reevaluate and extend the validity of the PME to capture the impact of non-Markovian polaron dressing, induced by non-equilibrium open system dynamics. By comparing with numerically exact techniques, we confirm that while the standard PME successfully predicts the dynamics of system observables that commute with the polaron transformation (e.g. populations in the Pauli z-basis), it can struggle to fully capture those that do not (e.g. coherences). This limitation stems from the mixing of system and environment degrees of freedom inherent to the polaron transformation, which affects the accuracy of calculated expectation values within the polaron frame. Employing the Nakajima-Zwanzig projection operator formalism, we introduce correction terms that provide an accurate description of observables that do not commute with the transformation. We demonstrate the significance of the correction terms in two cases, the canonical spin-boson model and a dissipative time-dependent Landau-Zener protocol, where they are shown to impact the system dynamics on both short and long timescales.">
+        <tr id="Detecting and quantifying causality is a focal topic in the fields of science, engineering, and interdisciplinary studies. However, causal studies on non-intervention systems attract much attention but remain extremely challenging. To address this challenge, we propose a framework named Interventional Dynamical Causality (IntDC) for such non-intervention systems, along with its computational criterion, Interventional Embedding Entropy (IEE), to quantify causality. The IEE criterion theoretically and numerically enables the deciphering of IntDC solely from observational (non-interventional) time-series data, without requiring any knowledge of dynamical models or real interventions in the considered system. Demonstrations of performance showed the accuracy and robustness of IEE on benchmark simulated systems as well as real-world systems, including the neural connectomes of C. elegans, COVID-19 transmission networks in Japan, and regulatory networks surrounding key circadian genes.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/97eb69ef1b908a5b349cac016b071bc744ed7afd" target='_blank'>
-              Capturing non-Markovian polaron dressing with the master equation formalism
+          <td><a href="https://www.semanticscholar.org/paper/731fd0ad43f607161c56e7dddae19516d57b53d3" target='_blank'>
+              Deciphering interventional dynamical causality from non-intervention systems
               </a>
             </td>
           <td>
-            Jake Iles-Smith, Owen Diba, A. Nazir
+            Jifan Shi, Yang Li, Juan Zhao, Siyang Leng, Kazuyuki Aihara, Luonan Chen, Wei Lin
           </td>
-          <td>2024-07-15</td>
+          <td>2024-06-29</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>30</td>
+          <td>11</td>
         </tr>
 
-        <tr id="A recent study in turbulent flow simulation demonstrated the potential of generative diffusion models for fast 3D surrogate modeling. This approach eliminates the need for specifying initial states or performing lengthy simulations, significantly accelerating the process. While adept at sampling individual frames from the learned manifold of turbulent flow states, the previous model lacks the capability to generate sequences, hindering analysis of dynamic phenomena. This work addresses this limitation by introducing a 4D generative diffusion model and a physics-informed guidance technique that enables the generation of realistic sequences of flow states. Our findings indicate that the proposed method can successfully sample entire subsequences from the turbulent manifold, even though generalizing from individual frames to sequences remains a challenging task. This advancement opens doors for the application of generative modeling in analyzing the temporal evolution of turbulent flows, providing valuable insights into their complex dynamics.">
+        <tr id="We propose a novel reduced-order methodology to describe complex multi-frequency fluid dynamics from time-resolved snapshot data. Starting point is the Cluster-based Network Model (CNM) thanks to its fully automatable development and human interpretability. Our key innovation is to model the transitions from cluster to cluster much more accurately by replacing snapshot states with short-term trajectories ("orbits") over multiple clusters, thus avoiding nonphysical intra-cluster diffusion in the dynamic reconstruction. The proposed orbital CNM (oCNM) employs functional clustering to coarse-grain the short-term trajectories. Specifically, different filtering techniques, resulting in different temporal basis expansions, demonstrate the versatility and capability of the oCNM to adapt to diverse flow phenomena. The oCNM is illustrated on the Stuart-Landau oscillator and its post-transient solution with time-varying parameters to test its ability to capture the amplitude selection mechanism and multi-frequency behaviours. Then, the oCNM is applied to the fluidic pinball across varying flow regimes at different Reynolds numbers, including the periodic, quasi-periodic, and chaotic dynamics. This orbital-focused perspective enhances the understanding of complex temporal behaviours by incorporating high-frequency behaviour into the kinematics of short-time trajectories while modelling the dynamics of the lower frequencies. In analogy to Spectral Proper Orthogonal Decomposition, which marked the transition from spatial-only modes to spatio-temporal ones, this work advances from analysing temporal local states to examining piecewise short-term trajectories, or orbits. By merging advanced analytical methods, such as the functional representation of short-time trajectories with CNM, this study paves the way for new approaches to dissect the complex dynamics characterising turbulent systems.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/dc78763ae5036e70c9aea5f2b3c74d7a5cb9b6e2" target='_blank'>
-              Unfolding Time: Generative Modeling for Turbulent Flows in 4D
+          <td><a href="https://www.semanticscholar.org/paper/413941f904c49031a4ce7ee2d45cc551a5cabb12" target='_blank'>
+              Orbital cluster-based network modelling
               </a>
             </td>
           <td>
-            Abdullah Saydemir, Marten Lienen, Stephan Gunnemann
+            A. Colanera, Nan Deng, M. Chiatto, Luigi De Luca, Bernd R. Noack
           </td>
-          <td>2024-06-17</td>
+          <td>2024-07-01</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>5</td>
+          <td>12</td>
         </tr>
 
-        <tr id="Background: Deep learning techniques, particularly neural networks, have revolutionized computational physics, offering powerful tools for solving complex partial differential equations (PDEs). However, ensuring stability and efficiency remains a challenge, especially in scenarios involving nonlinear and time-dependent equations. Methodology: This paper introduces novel residual-based architectures, namely the Simple Highway Network and the Squared Residual Network, designed to enhance stability and accuracy in physics-informed neural networks (PINNs). These architectures augment traditional neural networks by incorporating residual connections, which facilitate smoother weight updates and improve backpropagation efficiency. Results: Through extensive numerical experiments across various examples including linear and nonlinear, time-dependent and independent PDEs we demonstrate the efficacy of the proposed architectures. The Squared Residual Network, in particular, exhibits robust performance, achieving enhanced stability and accuracy compared to conventional neural networks. These findings underscore the potential of residual-based architectures in advancing deep learning for PDEs and computational physics applications.">
+        <tr id="In this paper, we apply the Fourier neural operator (FNO) paradigm to ocean circulation and prediction problems. We aim to show that the complicated non-linear dynamics of an ocean circulation can be captured by a flexible, efficient, and expressive structure of the FNO networks. The machine learning model (FNO3D and the recurrent FNO2D networks) trained by simulated data as well as real data takes spatiotemporal input and predicts future ocean states (sea surface current and sea surface height). For this, the double gyre ocean circulation model driven by stochastic wind stress is considered to represent an ideal ocean circulation. In order to generate the training and test data that exhibits rich spatiotemporal variability, the initial states are perturbed by Gaussian random fields. Experimental results confirm that the trained models yield satisfactory prediction accuracy for both types of FNO models in this case. Second, as the training set, we used the HYCOM reanalysis data in a regional ocean. FNO2D experiments demonstrated that the 5-day input to 5-day prediction yields the averaged root mean square errors (RMSEs) of 5.0 cm/s, 6.7 cm/s, 7.9 cm/s, 8.9 cm/s, and 9.4 cm/s in surface current, calculated consecutively for each day, in a regional ocean circulation of the East/Japan Sea. Similarly, the RMSEs for sea surface height were 2.3 cm, 3.5 cm, 4.2 cm, 4.6 cm, and 4.9 cm, for each day. We also trained the model with 15-day input and 10-day prediction, resulting in comparable performance. Extensive numerical tests show that, once learned, the resolution-free FNO model instantly forecasts the ocean states and can be used as an alternative fast solver in various inference algorithms.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/8a982200ec2b52eae770a77e49ba08579a14c4c4" target='_blank'>
-              Stable Weight Updating: A Key to Reliable PDE Solutions Using Deep Learning
+          <td><a href="https://www.semanticscholar.org/paper/66940e0cf48566a2e9e8617081952275da1fa484" target='_blank'>
+              Applications of the Fourier neural operator in a regional ocean modeling and prediction
               </a>
             </td>
           <td>
-            A. Noorizadegan, R. Cavoretto, D. Young, C. S. Chen
+            Byoung-Ju Choi, H. Jin, Bataa Lkhagvasuren
           </td>
-          <td>2024-07-10</td>
-          <td>ArXiv</td>
+          <td>2024-07-02</td>
+          <td>Frontiers in Marine Science</td>
           <td>0</td>
-          <td>4</td>
+          <td>5</td>
         </tr>
 
-        <tr id="Scientific computing has been an indispensable tool in applied sciences and engineering, where traditional numerical methods are often employed due to their superior accuracy guarantees. However, these methods often encounter challenges when dealing with problems involving complex geometries. Machine learning-based methods, on the other hand, are mesh-free, thus providing a promising alternative. In particular, operator learning methods have been proposed to learn the mapping from the input space to the solution space, enabling rapid inference of solutions to partial differential equations (PDEs) once trained. In this work, we address the parametric elliptic interface problem. Building upon the deep operator network (DeepONet), we propose an extended interface deep operator network (XI-DeepONet). XI-DeepONet exhibits three unique features: (1) The interface geometry is incorporated into the neural network as an additional input, enabling the network to infer solutions for new interface geometries once trained; (2) The level set function associated with the interface geometry is treated as the input, on which the solution mapping is continuous and can be effectively approximated by the deep operator network; (3) The network can be trained without any input-output data pairs, thus completely avoiding the need for meshes of any kind, directly or indirectly. We conduct a comprehensive series of numerical experiments to demonstrate the accuracy and robustness of the proposed method.">
+        <tr id="In this paper, we design a transfer sparse identification algorithm under the Bayesian framework through introducing other system knowledge into the system to be identified. This method provides a new identification solution for a nonlinear autoregressive model with exogenous inputs (NARX). The estimates of the transferred parameters are calculated by adding the transfer correction term to the un‐transferred estimates. To achieve this, a joint prior distribution is devised for the parameters, ultimately enhancing the efficient utilization of existing data, reducing the reliance on new data, and achieving more accurate identification. The maximized marginal likelihood method is used to find the transfer gain and the transfer information matrix in the transfer correction term. Meanwhile, in order to make the algorithm automatically adapt to different data, we design an automatic structure detection method based on the transfer framework. The method automatically determines the sparsity threshold based on the maximum inter‐class variance. Two examples are provided to demonstrate the advantages of our algorithm.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/649bca2a52c735c23a301cc270501ada4e4462c2" target='_blank'>
-              XI-DeepONet: An operator learning method for elliptic interface problems
+          <td><a href="https://www.semanticscholar.org/paper/275895802c6290128f34f9549d5e7ee7a46c6781" target='_blank'>
+              A Bayesian transfer sparse identification method for nonlinear ARX systems
               </a>
             </td>
           <td>
-            Ran Bi, Jingrun Chen, Weibing Deng
+            Kang Zhang, Xiaoli Luan, Feng Ding, Fei Liu
           </td>
-          <td>2024-07-21</td>
-          <td>ArXiv</td>
-          <td>0</td>
+          <td>2024-08-08</td>
+          <td>International Journal of Adaptive Control and Signal Processing</td>
           <td>0</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Verification of uncertain, complex dynamical systems is crucial in the modern day world. An increasingly common method to verify complex logic specifications for dynamical systems involves symbolic abstractions: simpler, finite-state models whose behaviour mimics the one of the systems of interest. By sampling trajectories of the concrete, unknown system and via robust analysis, we build a data-driven abstraction, related to the underlying model through a probabilistic behavioural inclusion relation. As the distribution from which the trajectories are drawn is unknown, we adopt two distinct distribution-free theories, namely scenario optimization and conformal prediction. We compare and discuss the differences between the two approaches in terms of the type of guarantees that they are able to provide. Furthermore, via experimental benchmarks we outline the efficiency of the two methods with respect to the number of samples available and the tightness of the guarantees.">
+        <tr id="Engineers often utilize data-driven surrogate models in optimization to partially replace the costly computational simulations of physics-based models. However, different aspects affect the accuracy and prediction capability of the surrogate models, e.g., the dimensionality of the data and nonlinearities in the mapping between input and output. Learning well-performing models by selecting appropriate techniques to fit the data benefits from the machine learning and data science expertise of the engineer which may vary depending on the application domain. Recently, large language models (LLMs) have shown promising capabilities to support humans through natural language-based interfaces in approaching technical problems, as well as to co-develop software and to democratize domain knowledge. In this paper, we utilize ChatGPT 4 to co-develop a framework to select and train surrogate models for engineering optimization tasks. More specifically, we interact with ChatGPT to outline a process and software to support the selection and application of regression techniques based on characteristics of the available data and target application. We evaluate the developed methodology on synthetic and realistic engineering optimization data and problems. In our experiments, we demonstrate that the models obtained through the methodology developed with ChatGPT achieve comparable performance in regression and optimization tasks than observed in existing works in the literature. Hence, despite some limitations, such as missing updates of available software libraries, LLMs can support less experienced engineers to solve surrogate-assisted optimization problems more efficiently by providing insights on the application data and software for deploying surrogate models.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/a4bc6fac276ef549b5a06e10e0b424eaf5ed1fd7" target='_blank'>
-              Scenario Approach and Conformal Prediction for Verification of Unknown Systems via Data-Driven Abstractions
+          <td><a href="https://www.semanticscholar.org/paper/b199732a0ef844fff7389cc7d27737b52409f2df" target='_blank'>
+              Large Language Model-assisted Surrogate Modelling for Engineering Optimization
               </a>
             </td>
           <td>
-            Rudi Coppola, Andrea Peruffo, Lars Lindemann, Manuel Mazo
+            Thiago Rios, Felix Lanfermann, Stefan Menzel
           </td>
           <td>2024-06-25</td>
-          <td>2024 European Control Conference (ECC)</td>
-          <td>0</td>
-          <td>9</td>
+          <td>2024 IEEE Conference on Artificial Intelligence (CAI)</td>
+          <td>1</td>
+          <td>7</td>
         </tr>
 
-        <tr id="Controlling the evolution of complex physical systems is a fundamental task across science and engineering. Classical techniques suffer from limited applicability or huge computational costs. On the other hand, recent deep learning and reinforcement learning-based approaches often struggle to optimize long-term control sequences under the constraints of system dynamics. In this work, we introduce Diffusion Physical systems Control (DiffPhyCon), a new class of method to address the physical systems control problem. DiffPhyCon excels by simultaneously minimizing both the learned generative energy function and the predefined control objectives across the entire trajectory and control sequence. Thus, it can explore globally and identify near-optimal control sequences. Moreover, we enhance DiffPhyCon with prior reweighting, enabling the discovery of control sequences that significantly deviate from the training distribution. We test our method in 1D Burgers' equation and 2D jellyfish movement control in a fluid environment. Our method outperforms widely applied classical approaches and state-of-the-art deep learning and reinforcement learning methods. Notably, DiffPhyCon unveils an intriguing fast-close-slow-open pattern observed in the jellyfish, aligning with established findings in the field of fluid dynamics.">
+        <tr id="We study the dynamics of a continuous-time model of the Stochastic Gradient Descent (SGD) for the least-square problem. Indeed, pursuing the work of Li et al. (2019), we analyze Stochastic Differential Equations (SDEs) that model SGD either in the case of the training loss (finite samples) or the population one (online setting). A key qualitative feature of the dynamics is the existence of a perfect interpolator of the data, irrespective of the sample size. In both scenarios, we provide precise, non-asymptotic rates of convergence to the (possibly degenerate) stationary distribution. Additionally, we describe this asymptotic distribution, offering estimates of its mean, deviations from it, and a proof of the emergence of heavy-tails related to the step-size magnitude. Numerical simulations supporting our findings are also presented.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/2ed151b432bd4c9d4bafa7399dadc48d42ebe3d6" target='_blank'>
-              A Generative Approach to Control Complex Physical Systems
+          <td><a href="https://www.semanticscholar.org/paper/ecfeb578d6cbf8848e1912ad8e32e903a645ea7a" target='_blank'>
+              Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent
               </a>
             </td>
           <td>
-            Long Wei, Peiyan Hu, Ruiqi Feng, Haodong Feng, Yixuan Du, Tao Zhang, Rui Wang, Yue Wang, Zhi-Ming Ma, Tailin Wu
+            Adrien Schertzer, Loucas Pillaud-Vivien
           </td>
-          <td>2024-07-09</td>
+          <td>2024-07-02</td>
           <td>ArXiv</td>
-          <td>1</td>
-          <td>3</td>
+          <td>0</td>
+          <td>10</td>
         </tr>
 
-        <tr id="Encoding frequency stability constraints in the operation problem is challenging due to its complex dynamics. Recently, data-driven approaches have been proposed to learn the stability criteria offline with the trained model embedded as a constraint of online optimization. However, random sampling of stationary operation points is less efficient in generating balanced stable and unstable samples. Meanwhile, the performance of such a model is strongly dependent on the quality of the training dataset. Observing this research gap, we propose a gradient-based data generation method via forward-mode automatic differentiation. In this method, the original dynamic system is augmented with new states that represent the dynamic of sensitivities of the original states, which can be solved by invoking any ODE solver for a single time. To compensate for the contradiction between the gradient of various frequency stability criteria, gradient surgery is proposed by projecting the gradient on the normal plane of the other. In the end, we demonstrate the superior performance of the proposed sampling algorithm, compared with the unrolling differentiation and finite difference. All codes are available at https://github.com/xuwkk/frequency_sample_ad.">
+        <tr id="This paper presents a novel approach for propagating uncertainties in dynamical systems building on high-order Taylor expansions of the flow and moment-generating functions (MGFs). Unlike prior methods that focus on Gaussian distributions, our approach leverages the relationship between MGFs and distribution moments to extend high-order uncertainty propagation techniques to non-Gaussian scenarios. This significantly broadens the applicability of these methods to a wider range of problems and uncertainty types. High-order moment computations are performed one-off and symbolically, reducing the computational burden of the technique to the calculation of Taylor series coefficients around a nominal trajectory, achieved by efficiently integrating the system's variational equations. Furthermore, the use of the proposed approach in combination with event transition tensors, allows for accurate propagation of uncertainties at specific events, such as the landing surface of a celestial body, the crossing of a predefined Poincar\'e section, or the trigger of an arbitrary event during the propagation. Via numerical simulations we demonstrate the effectiveness of our method in various astrodynamics applications, including the unperturbed and perturbed two-body problem, and the circular restricted three-body problem, showing that it accurately propagates non-Gaussian uncertainties both at future times and at event manifolds.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c2a8a10d25fc8962321b615ea83477e1fb4d6310" target='_blank'>
-              Efficient Sampling for Data-Driven Frequency Stability Constraint via Forward-Mode Automatic Differentiation
+          <td><a href="https://www.semanticscholar.org/paper/6afd2423f192d0672dfad436cf9f3cd283ac1177" target='_blank'>
+              Nonlinear Propagation of Non-Gaussian Uncertainties: Theory and Applications to Spacecraft Trajectory Design
               </a>
             </td>
           <td>
-            Wangkun Xu, Qian Chen, Pudong Ge, Zhongda Chu, Fei Teng
+            Giacomo Acciarini, Nicola Baresi, David Lloyd, Dario Izzo
           </td>
-          <td>2024-07-21</td>
+          <td>2024-08-09</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>7</td>
+          <td>3</td>
         </tr>
 
     </tbody>
@@ -4724,7 +4811,7 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
 
   var trace1 = {
     x: ['2024'],
-    y: [41],
+    y: [32],
     name: 'Num of citations',
     yaxis: 'y1',
     type: 'scatter'
diff --git a/site/Time-series forecasting/index.html b/site/Time-series forecasting/index.html
index 073b7f18..ef377bb0 100644
--- a/site/Time-series forecasting/index.html	
+++ b/site/Time-series forecasting/index.html	
@@ -453,7 +453,7 @@ <h1>Time-series forecasting</h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:37 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:19 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -517,9 +517,9 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
             Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, C. Alippi, G. I. Webb, Irwin King, Shirui Pan
           </td>
           <td>2023-07-07</td>
-          <td>ArXiv, arXiv.org</td>
+          <td>IEEE transactions on pattern analysis and machine intelligence</td>
           <td>61</td>
-          <td>49</td>
+          <td>50</td>
 
             <td><a href='../recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -538,8 +538,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           </td>
           <td>2021-10-11</td>
           <td>ArXiv, International Conference on Learning Representations</td>
-          <td>68</td>
-          <td>46</td>
+          <td>70</td>
+          <td>47</td>
 
             <td><a href='../recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -559,7 +559,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           <td>2023-02-08</td>
           <td>Neural Information Processing Systems, ArXiv</td>
           <td>15</td>
-          <td>49</td>
+          <td>50</td>
 
             <td><a href='../recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -579,7 +579,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           <td>2022-05-26</td>
           <td>J. Mach. Learn. Res., Journal of machine learning research</td>
           <td>11</td>
-          <td>49</td>
+          <td>50</td>
 
             <td><a href='../recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -597,9 +597,9 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
             Andrea Cini, Ivan Marisca, Daniele Zambon, C. Alippi
           </td>
           <td>2023-10-24</td>
-          <td>ArXiv, arXiv.org</td>
+          <td>arXiv.org, ArXiv</td>
           <td>4</td>
-          <td>49</td>
+          <td>50</td>
 
             <td><a href='../recommendations/ccea298edb788edf821aef58f0952c3e8debc25a' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -618,7 +618,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           </td>
           <td>2023-10-11</td>
           <td>Neural Information Processing Systems, ArXiv</td>
-          <td>119</td>
+          <td>123</td>
           <td>14</td>
 
             <td><a href='../recommendations/123acfbccca0460171b6b06a4012dbb991cde55b' target='_blank'>
@@ -637,9 +637,9 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
             Chloe X. Wang, Oleksii Tsepa, Jun Ma, Bo Wang
           </td>
           <td>2024-02-01</td>
-          <td>ArXiv, arXiv.org</td>
-          <td>46</td>
-          <td>7</td>
+          <td>arXiv.org, ArXiv</td>
+          <td>47</td>
+          <td>5</td>
 
             <td><a href='../recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -657,8 +657,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
             Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou
           </td>
           <td>2023-10-14</td>
-          <td>ArXiv, arXiv.org</td>
-          <td>45</td>
+          <td>arXiv.org, ArXiv</td>
+          <td>48</td>
           <td>14</td>
 
             <td><a href='../recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f' target='_blank'>
@@ -677,8 +677,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
             Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo
           </td>
           <td>2024-02-04</td>
-          <td>ArXiv, arXiv.org</td>
-          <td>26</td>
+          <td>arXiv.org, ArXiv</td>
+          <td>28</td>
           <td>22</td>
 
             <td><a href='../recommendations/4a111f7a3b56d0468f13104999844885157ef17d' target='_blank'>
@@ -698,7 +698,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           </td>
           <td>2023-10-03</td>
           <td>ArXiv, International Conference on Learning Representations</td>
-          <td>113</td>
+          <td>120</td>
           <td>9</td>
 
             <td><a href='../recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277' target='_blank'>
@@ -717,7 +717,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
             Vijay Ekambaram, Arindam Jati, Nam H. Nguyen, Pankaj Dayama, Chandra Reddy, Wesley M. Gifford, Jayant Kalagnanam
           </td>
           <td>2024-01-08</td>
-          <td>ArXiv, arXiv.org</td>
+          <td>arXiv.org, ArXiv</td>
           <td>2</td>
           <td>2</td>
 
@@ -738,8 +738,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           </td>
           <td>2022-06-17</td>
           <td>Neural Information Processing Systems, ArXiv</td>
-          <td>158</td>
-          <td>46</td>
+          <td>162</td>
+          <td>47</td>
 
             <td><a href='../recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -759,7 +759,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           <td>2023-02-06</td>
           <td>ArXiv, DBLP</td>
           <td>23</td>
-          <td>46</td>
+          <td>47</td>
 
             <td><a href='../recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -779,7 +779,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           <td>None</td>
           <td>DBLP</td>
           <td>6</td>
-          <td>49</td>
+          <td>50</td>
 
             <td><a href='../recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -797,9 +797,9 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
             Daniele Zambon, Andrea Cini, L. Livi, C. Alippi
           </td>
           <td>2023-01-04</td>
-          <td>ArXiv, arXiv.org</td>
+          <td>arXiv.org, ArXiv</td>
           <td>3</td>
-          <td>49</td>
+          <td>50</td>
 
             <td><a href='../recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -819,7 +819,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           <td>2024-02-29</td>
           <td>ArXiv</td>
           <td>2</td>
-          <td>46</td>
+          <td>47</td>
 
             <td><a href='../recommendations/bcbcc2e1af8bcf6b07edf866be95116a8ed0bf91' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -874,37 +874,7 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2024-08-01</td>
           <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
           <td>0</td>
-          <td>2</td>
-        </tr>
-
-        <tr id="Time series forecasting (TSF) is crucial in fields like economic forecasting, weather prediction, traffic flow analysis, and public health surveillance. Real-world time series data often include noise, outliers, and missing values, making accurate forecasting challenging. Traditional methods model point-to-point relationships, which limits their ability to capture complex temporal patterns and increases their susceptibility to noise.To address these issues, we introduce the WindowMixer model, built on an all-MLP framework. WindowMixer leverages the continuous nature of time series by examining temporal variations from a window-based perspective. It decomposes time series into trend and seasonal components, handling them individually. For trends, a fully connected (FC) layer makes predictions. For seasonal components, time windows are projected to produce window tokens, processed by Intra-Window-Mixer and Inter-Window-Mixer modules. The Intra-Window-Mixer models relationships within each window, while the Inter-Window-Mixer models relationships between windows. This approach captures intricate patterns and long-range dependencies in the data.Experiments show WindowMixer consistently outperforms existing methods in both long-term and short-term forecasting tasks.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/0b5e7cfba3feae0a37aa3ce200bec06808907851" target='_blank'>
-              WindowMixer: Intra-Window and Inter-Window Modeling for Time Series Forecasting
-              </a>
-            </td>
-          <td>
-            Quangao Liu, Ruiqi Li, Maowei Jiang, Wei Yang, Chen Liang, Longlong Pang, Zhuozhang Zou
-          </td>
-          <td>2024-06-14</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>1</td>
-        </tr>
-
-        <tr id="Time series, characterized by a sequence of data points arranged in a discrete-time order, are ubiquitous in real-world applications. Different from other modalities, time series present unique challenges due to their complex and dynamic nature, including the entanglement of nonlinear patterns and time-variant trends. Analyzing time series data is of great significance in real-world scenarios and has been widely studied over centuries. Recent years have witnessed remarkable breakthroughs in the time series community, with techniques shifting from traditional statistical methods to advanced deep learning models. In this paper, we delve into the design of deep time series models across various analysis tasks and review the existing literature from two perspectives: basic modules and model architectures. Further, we develop and release Time Series Library (TSLib) as a fair benchmark of deep time series models for diverse analysis tasks, which implements 24 mainstream models, covers 30 datasets from different domains, and supports five prevalent analysis tasks. Based on TSLib, we thoroughly evaluate 12 advanced deep time series models on different tasks. Empirical results indicate that models with specific structures are well-suited for distinct analytical tasks, which offers insights for research and adoption of deep time series models. Code is available at https://github.com/thuml/Time-Series-Library.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/425182f5c96c1d239da5cbe3a24371b2bab6319b" target='_blank'>
-              Deep Time Series Models: A Comprehensive Survey and Benchmark
-              </a>
-            </td>
-          <td>
-            Yuxuan Wang, Haixu Wu, Jiaxiang Dong, Yong Liu, Mingsheng Long, Jianmin Wang
-          </td>
-          <td>2024-07-18</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>65</td>
+          <td>3</td>
         </tr>
 
         <tr id="Long time series forecasting aims to utilize historical information to forecast future states over extended horizons. Traditional RNN-based series forecasting methods struggle to effectively address long-term dependencies and gradient issues in long time series problems. Recently, SegRNN has emerged as a leading RNN-based model tailored for long-term series forecasting, demonstrating state-of-the-art performance while maintaining a streamlined architecture through innovative segmentation and parallel decoding techniques. Nevertheless, SegRNN has several limitations: its fixed segmentation disrupts data continuity and fails to effectively leverage information across different segments, the segmentation strategy employed by SegRNN does not fundamentally address the issue of information loss within the recurrent structure. To address these issues, we propose the ISMRNN method with three key enhancements: we introduce an implicit segmentation structure to decompose the time series and map it to segmented hidden states, resulting in denser information exchange during the segmentation phase. Additionally, we incorporate residual structures in the encoding layer to mitigate information loss within the recurrent structure. To extract information more effectively, we further integrate the Mamba architecture to enhance time series information extraction. Experiments on several real-world long time series forecasting datasets demonstrate that our model surpasses the performance of current state-of-the-art models.">
@@ -937,34 +907,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>4</td>
         </tr>
 
-        <tr id="Multi-variate time series forecasting is an important problem with a wide range of applications. Recent works model the relations between time-series as graphs and have shown that propagating information over the relation graph can improve time series forecasting. However, in many cases, relational information is not available or is noisy and reliable. Moreover, most works ignore the underlying uncertainty of time-series both for structure learning and deriving the forecasts resulting in the structure not capturing the uncertainty resulting in forecast distributions with poor uncertainty estimates. We tackle this challenge and introduce STOIC, that leverages stochastic correlations between time-series to learn underlying structure between time-series and to provide well-calibrated and accurate forecasts. Over a wide-range of benchmark datasets STOIC provides around 16% more accurate and 14% better-calibrated forecasts. STOIC also shows better adaptation to noise in data during inference and captures important and useful relational information in various benchmarks.">
+        <tr id="Time series, characterized by a sequence of data points arranged in a discrete-time order, are ubiquitous in real-world applications. Different from other modalities, time series present unique challenges due to their complex and dynamic nature, including the entanglement of nonlinear patterns and time-variant trends. Analyzing time series data is of great significance in real-world scenarios and has been widely studied over centuries. Recent years have witnessed remarkable breakthroughs in the time series community, with techniques shifting from traditional statistical methods to advanced deep learning models. In this paper, we delve into the design of deep time series models across various analysis tasks and review the existing literature from two perspectives: basic modules and model architectures. Further, we develop and release Time Series Library (TSLib) as a fair benchmark of deep time series models for diverse analysis tasks, which implements 24 mainstream models, covers 30 datasets from different domains, and supports five prevalent analysis tasks. Based on TSLib, we thoroughly evaluate 12 advanced deep time series models on different tasks. Empirical results indicate that models with specific structures are well-suited for distinct analytical tasks, which offers insights for research and adoption of deep time series models. Code is available at https://github.com/thuml/Time-Series-Library.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/7d4b412399f89c9cd66cffc4ab811b74172dcc63" target='_blank'>
-              Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/425182f5c96c1d239da5cbe3a24371b2bab6319b" target='_blank'>
+              Deep Time Series Models: A Comprehensive Survey and Benchmark
               </a>
             </td>
           <td>
-            Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. A. Prakash
+            Yuxuan Wang, Haixu Wu, Jiaxiang Dong, Yong Liu, Mingsheng Long, Jianmin Wang
           </td>
-          <td>2024-07-02</td>
+          <td>2024-07-18</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>9</td>
+          <td>66</td>
         </tr>
 
-        <tr id="Long-term time series forecasting in centralized environments poses unique challenges regarding data privacy, communication overhead, and scalability. To address these challenges, we propose FedTime, a federated large language model (LLM) tailored for long-range time series prediction. Specifically, we introduce a federated pre-trained LLM with fine-tuning and alignment strategies. Prior to the learning process, we employ K-means clustering to partition edge devices or clients into distinct clusters, thereby facilitating more focused model training. We also incorporate channel independence and patching to better preserve local semantic information, ensuring that important contextual details are retained while minimizing the risk of information loss. We demonstrate the effectiveness of our FedTime model through extensive experiments on various real-world forecasting benchmarks, showcasing substantial improvements over recent approaches. In addition, we demonstrate the efficiency of FedTime in streamlining resource usage, resulting in reduced communication overhead.">
+        <tr id="Multi-variate time series forecasting is an important problem with a wide range of applications. Recent works model the relations between time-series as graphs and have shown that propagating information over the relation graph can improve time series forecasting. However, in many cases, relational information is not available or is noisy and reliable. Moreover, most works ignore the underlying uncertainty of time-series both for structure learning and deriving the forecasts resulting in the structure not capturing the uncertainty resulting in forecast distributions with poor uncertainty estimates. We tackle this challenge and introduce STOIC, that leverages stochastic correlations between time-series to learn underlying structure between time-series and to provide well-calibrated and accurate forecasts. Over a wide-range of benchmark datasets STOIC provides around 16% more accurate and 14% better-calibrated forecasts. STOIC also shows better adaptation to noise in data during inference and captures important and useful relational information in various benchmarks.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/cfbf583c88970300ba4a74619b02be88524899df" target='_blank'>
-              A federated large language model for long-term time series forecasting
+          <td><a href="https://www.semanticscholar.org/paper/7d4b412399f89c9cd66cffc4ab811b74172dcc63" target='_blank'>
+              Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting
               </a>
             </td>
           <td>
-            Raed Abdel-Sater, A. B. Hamza
+            Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. A. Prakash
           </td>
-          <td>2024-07-30</td>
+          <td>2024-07-02</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>9</td>
         </tr>
 
         <tr id="To achieve more accurate prediction results in Time Series Forecasting (TSF), it is essential to distinguish between the valuable patterns (invariant patterns) of the spatial-temporal relationship and the patterns that are prone to generate distribution shift (variant patterns), then combine them for forecasting.The existing works, such as transformer-based models and GNN-based models, focus on capturing main forecasting dependencies whether it is stable or not, and they tend to overlook patterns that carry both useful information and distribution shift. In this paper, we propose a model for better forecasting time series: Decoupled Invariant Attention Network (DIAN), which contains two modules to learn spatial and temporal relationships respectively: 1) Spatial Decoupled Invariant-Variant Learning (SDIVL) to decouple the spatial invariant and variant attention scores, and then leverage convolutional networks to effectively integrate them for subsequent layers; 2) Temporal Augmented Invariant-Variant Learning (TAIVL) to decouple temporal invariant and variant patterns and combine them for further forecasting.In this module, we also design Temporal Intervention Mechanism to create multiple intervened samples by reassembling variant patterns across time stamps to eliminate the spurious impacts of variant patterns.In addition, we propose Joint Optimization to minimize the loss function considering all invariant patterns, variant patterns and intervened patterns so that our model can gain a more stable predictive ability.Extensive experiments on five datasets have demonstrated our superior performance with higher efficiency compared with state-of-the-art methods.">
@@ -979,19 +949,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2024-08-01</td>
           <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
           <td>0</td>
-          <td>3</td>
+          <td>4</td>
         </tr>
 
-        <tr id="Spatio-temporal graph (STG) forecasting is a critical task with extensive applications in the real world, including traffic and weather forecasting. Although several recent methods have been proposed to model complex dynamics in STGs, addressing long-range spatio-temporal dependencies remains a significant challenge, leading to limited performance gains. Inspired by a recently proposed state space model named Mamba, which has shown remarkable capability of capturing long-range dependency, we propose a new STG forecasting framework named SpoT-Mamba. SpoT-Mamba generates node embeddings by scanning various node-specific walk sequences. Based on the node embeddings, it conducts temporal scans to capture long-range spatio-temporal dependencies. Experimental results on the real-world traffic forecasting dataset demonstrate the effectiveness of SpoT-Mamba.">
+        <tr id="Long-term time series forecasting in centralized environments poses unique challenges regarding data privacy, communication overhead, and scalability. To address these challenges, we propose FedTime, a federated large language model (LLM) tailored for long-range time series prediction. Specifically, we introduce a federated pre-trained LLM with fine-tuning and alignment strategies. Prior to the learning process, we employ K-means clustering to partition edge devices or clients into distinct clusters, thereby facilitating more focused model training. We also incorporate channel independence and patching to better preserve local semantic information, ensuring that important contextual details are retained while minimizing the risk of information loss. We demonstrate the effectiveness of our FedTime model through extensive experiments on various real-world forecasting benchmarks, showcasing substantial improvements over recent approaches. In addition, we demonstrate the efficiency of FedTime in streamlining resource usage, resulting in reduced communication overhead.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/249b1ae3616ef24f7cea210b7e049f39f2586206" target='_blank'>
-              SpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs with Selective State Spaces
+          <td><a href="https://www.semanticscholar.org/paper/cfbf583c88970300ba4a74619b02be88524899df" target='_blank'>
+              A federated large language model for long-term time series forecasting
               </a>
             </td>
           <td>
-            Jinhyeok Choi, Heehyeon Kim, Minhyeong An, Joyce Jiyoung Whang
+            Raed Abdel-Sater, A. B. Hamza
           </td>
-          <td>2024-06-17</td>
+          <td>2024-07-30</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>1</td>
@@ -1054,22 +1024,22 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2024-07-14</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Graph Neural Networks (GNNs) have shown impressive performance in graph representation learning, but they face challenges in capturing long-range dependencies due to their limited expressive power. To address this, Graph Transformers (GTs) were introduced, utilizing self-attention mechanism to effectively model pairwise node relationships. Despite their advantages, GTs suffer from quadratic complexity w.r.t. the number of nodes in the graph, hindering their applicability to large graphs. In this work, we present Graph-Enhanced Contextual Operator (GECO), a scalable and effective alternative to GTs that leverages neighborhood propagation and global convolutions to effectively capture local and global dependencies in quasilinear time. Our study on synthetic datasets reveals that GECO reaches 169x speedup on a graph with 2M nodes w.r.t. optimized attention. Further evaluations on diverse range of benchmarks showcase that GECO scales to large graphs where traditional GTs often face memory and time limitations. Notably, GECO consistently achieves comparable or superior quality compared to baselines, improving the SOTA up to 4.5%, and offering a scalable and effective solution for large-scale graph learning.">
+        <tr id="In multivariate time-series forecasting (MTSF), extracting the temporal correlations of the input sequences is crucial. While popular Transformer-based predictive models can perform well, their quadratic computational complexity results in inefficiency and high overhead. The recently emerged Mamba, a selective state space model, has shown promising results in many fields due to its strong temporal feature extraction capabilities and linear computational complexity. However, due to the unilateral nature of Mamba, channel-independent predictive models based on Mamba cannot attend to the relationships among all variables in the manner of Transformer-based models. To address this issue, we combine fast-attention with Mamba to introduce a novel framework named FMamba for MTSF. Technically, we first extract the temporal features of the input variables through an embedding layer, then compute the dependencies among input variables via the fast-attention module. Subsequently, we use Mamba to selectively deal with the input features and further extract the temporal dependencies of the variables through the multi-layer perceptron block (MLP-block). Finally, FMamba obtains the predictive results through the projector, a linear layer. Experimental results on eight public datasets demonstrate that FMamba can achieve state-of-the-art performance while maintaining low computational overhead.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/48052c9ebe066b9fcd653897dabf18582ec7e7fb" target='_blank'>
-              A Scalable and Effective Alternative to Graph Transformers
+          <td><a href="https://www.semanticscholar.org/paper/3c5ced872aa5d16bf3aa59c40931ed85b54cb3e5" target='_blank'>
+              FMamba: Mamba based on Fast-attention for Multivariate Time-series Forecasting
               </a>
             </td>
           <td>
-            Kaan Sancak, Zhigang Hua, Jin Fang, Yan Xie, Andrey Malevich, Bo Long, M. F. Balin, Ümit V. Çatalyürek
+            Shusen Ma, Yu Kang, Peng Bai, Yunan Zhao
           </td>
-          <td>2024-06-17</td>
+          <td>2024-07-20</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
+          <td>1</td>
         </tr>
 
         <tr id="None">
@@ -1087,49 +1057,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>18</td>
         </tr>
 
-        <tr id="In multivariate time-series forecasting (MTSF), extracting the temporal correlations of the input sequences is crucial. While popular Transformer-based predictive models can perform well, their quadratic computational complexity results in inefficiency and high overhead. The recently emerged Mamba, a selective state space model, has shown promising results in many fields due to its strong temporal feature extraction capabilities and linear computational complexity. However, due to the unilateral nature of Mamba, channel-independent predictive models based on Mamba cannot attend to the relationships among all variables in the manner of Transformer-based models. To address this issue, we combine fast-attention with Mamba to introduce a novel framework named FMamba for MTSF. Technically, we first extract the temporal features of the input variables through an embedding layer, then compute the dependencies among input variables via the fast-attention module. Subsequently, we use Mamba to selectively deal with the input features and further extract the temporal dependencies of the variables through the multi-layer perceptron block (MLP-block). Finally, FMamba obtains the predictive results through the projector, a linear layer. Experimental results on eight public datasets demonstrate that FMamba can achieve state-of-the-art performance while maintaining low computational overhead.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/3c5ced872aa5d16bf3aa59c40931ed85b54cb3e5" target='_blank'>
-              FMamba: Mamba based on Fast-attention for Multivariate Time-series Forecasting
-              </a>
-            </td>
-          <td>
-            Shusen Ma, Yu Kang, Peng Bai, Yunan Zhao
-          </td>
-          <td>2024-07-20</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>1</td>
-        </tr>
-
-        <tr id="Traffic prediction is a challenging spatio-temporal forecasting problem that involves highly complex spatio-temporal correlations. This paper proposes a Multi-level Multi-view Augmented Spatio-temporal Transformer (LVSTformer) for traffic prediction. The model aims to capture spatial dependencies from three different levels: local geographic, global semantic, and pivotal nodes, along with long- and short-term temporal dependencies. Specifically, we design three spatial augmented views to delve into the spatial information from the perspectives of local, global, and pivotal nodes. By combining three spatial augmented views with three parallel spatial self-attention mechanisms, the model can comprehensively captures spatial dependencies at different levels. We design a gated temporal self-attention mechanism to effectively capture long- and short-term temporal dependencies. Furthermore, a spatio-temporal context broadcasting module is introduced between two spatio-temporal layers to ensure a well-distributed allocation of attention scores, alleviating overfitting and information loss, and enhancing the generalization ability and robustness of the model. A comprehensive set of experiments is conducted on six well-known traffic benchmarks, the experimental results demonstrate that LVSTformer achieves state-of-the-art performance compared to competing baselines, with the maximum improvement reaching up to 4.32%.">
+        <tr id="This technical report describes the Time Series Optimized Transformer for Observability (Toto), a new state of the art foundation model for time series forecasting developed by Datadog. In addition to advancing the state of the art on generalized time series benchmarks in domains such as electricity and weather, this model is the first general-purpose time series forecasting foundation model to be specifically tuned for observability metrics. Toto was trained on a dataset of one trillion time series data points, the largest among all currently published time series foundation models. Alongside publicly available time series datasets, 75% of the data used to train Toto consists of fully anonymous numerical metric data points from the Datadog platform. In our experiments, Toto outperforms existing time series foundation models on observability data. It does this while also excelling at general-purpose forecasting tasks, achieving state-of-the-art zero-shot performance on multiple open benchmark datasets.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/22af70b0b268da0f71549bad07d5b3f24528fb16" target='_blank'>
-              Rethinking Spatio-Temporal Transformer for Traffic Prediction:Multi-level Multi-view Augmented Learning Framework
+          <td><a href="https://www.semanticscholar.org/paper/325e7ce8ff734f133cfa379bd8ac1021604c68aa" target='_blank'>
+              Toto: Time Series Optimized Transformer for Observability
               </a>
             </td>
           <td>
-            Jiaqi Lin, Qianqian Ren
+            Ben Cohen, E. Khwaja, Kan Wang, Charles Masson, Elise Ram'e, Youssef Doubli, Othmane Abou-Amal
           </td>
-          <td>2024-06-17</td>
+          <td>2024-07-10</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>4</td>
         </tr>
 
-        <tr id="This technical report describes the Time Series Optimized Transformer for Observability (Toto), a new state of the art foundation model for time series forecasting developed by Datadog. In addition to advancing the state of the art on generalized time series benchmarks in domains such as electricity and weather, this model is the first general-purpose time series forecasting foundation model to be specifically tuned for observability metrics. Toto was trained on a dataset of one trillion time series data points, the largest among all currently published time series foundation models. Alongside publicly available time series datasets, 75% of the data used to train Toto consists of fully anonymous numerical metric data points from the Datadog platform. In our experiments, Toto outperforms existing time series foundation models on observability data. It does this while also excelling at general-purpose forecasting tasks, achieving state-of-the-art zero-shot performance on multiple open benchmark datasets.">
+        <tr id="It is challenging to scale time series forecasting models such that they forecast accurately for multiple distinct domains and datasets, all with potentially different underlying collection procedures (e.g., sample resolution), patterns (e.g., periodicity), and prediction requirements (e.g., reconstruction vs. forecasting). We call this general task universal forecasting. Existing methods usually assume that input data is regularly sampled, and they forecast to pre-determined horizons, resulting in failure to generalise outside of the scope of their training. We propose the DAM - a neural model that takes randomly sampled histories and outputs an adjustable basis composition as a continuous function of time for forecasting to non-fixed horizons. It involves three key components: (1) a flexible approach for using randomly sampled histories from a long-tail distribution, that enables an efficient global perspective of the underlying temporal dynamics while retaining focus on the recent history; (2) a transformer backbone that is trained on these actively sampled histories to produce, as representational output, (3) the basis coefficients of a continuous function of time. We show that a single univariate DAM, trained on 25 time series datasets, either outperformed or closely matched existing SoTA models at multivariate long-term forecasting across 18 datasets, including 8 held-out for zero-shot transfer, even though these models were trained to specialise for each dataset-horizon combination. This single DAM excels at zero-shot transfer and very-long-term forecasting, performs well at imputation, is interpretable via basis function composition and attention, can be tuned for different inference-cost requirements, is robust to missing and irregularly sampled data {by design}.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/325e7ce8ff734f133cfa379bd8ac1021604c68aa" target='_blank'>
-              Toto: Time Series Optimized Transformer for Observability
+          <td><a href="https://www.semanticscholar.org/paper/e177ce13d5e3ca4673b2fff9481408cd98bba0d3" target='_blank'>
+              DAM: Towards A Foundation Model for Time Series Forecasting
               </a>
             </td>
           <td>
-            Ben Cohen, E. Khwaja, Kan Wang, Charles Masson, Elise Ram'e, Youssef Doubli, Othmane Abou-Amal
+            Luke Darlow, Qiwen Deng, Ahmed Hassan, Martin Asenov, Rajkarn Singh, Artjom Joosen, Adam Barker, A. Storkey
           </td>
-          <td>2024-07-10</td>
+          <td>2024-07-25</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>4</td>
+          <td>43</td>
         </tr>
 
         <tr id="Long-term time series forecasting (LTSF) has been widely applied in finance, traffic prediction, and other domains. Recently, patch-based transformers have emerged as a promising approach, segmenting data into sub-level patches that serve as input tokens. However, existing methods mostly rely on predetermined patch lengths, necessitating expert knowledge and posing challenges in capturing diverse characteristics across various scales. Moreover, time series data exhibit diverse variations and fluctuations across different temporal scales, which traditional approaches struggle to model effectively. In this paper, we propose a dynamic tokenizer with a dynamic sparse learning algorithm to capture diverse receptive fields and sparse patterns of time series data. In order to build hierarchical receptive fields, we develop a multi-scale Transformer model, coupled with multi-scale sequence extraction, capable of capturing multi-resolution features. Additionally, we introduce a group-aware rotary position encoding technique to enhance intra- and inter-group position awareness among representations across different temporal scales. Our proposed model, named DRFormer, is evaluated on various real-world datasets, and experimental results demonstrate its superiority compared to existing methods. Our code is available at: https://github.com/ruixindingECNU/DRFormer.">
@@ -1159,37 +1114,22 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2024-07-12</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>17</td>
-        </tr>
-
-        <tr id="Inspired by Large Language Models (LLMs), Time Series Forecasting (TSF), a long-standing task in time series analysis, is undergoing a transition towards Large Time Series Models (LTSMs), aiming to train universal transformer-based models for TSF. However, training LTSMs on heterogeneous time series data poses unique challenges, including diverse frequencies, dimensions, and patterns across datasets. Recent endeavors have studied and evaluated various design choices aimed at enhancing LTSM training and generalization capabilities, spanning pre-processing techniques, model configurations, and dataset configurations. In this work, we comprehensively analyze these design choices and aim to identify the best practices for training LTSM. Moreover, we propose \emph{time series prompt}, a novel statistical prompting strategy tailored to time series data. Furthermore, based on the observations in our analysis, we introduce \texttt{LTSM-bundle}, which bundles the best design choices we have identified. Empirical results demonstrate that \texttt{LTSM-bundle} achieves superior zero-shot and few-shot performances compared to state-of-the-art LSTMs and traditional TSF methods on benchmark datasets.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/631c05be8806b43fac7aaf4d041de59562ac17f9" target='_blank'>
-              Understanding Different Design Choices in Training Large Time Series Models
-              </a>
-            </td>
-          <td>
-            Yu-Neng Chuang, Songchen Li, Jiayi Yuan, Guanchu Wang, Kwei-Herng Lai, Leisheng Yu, Sirui Ding, Chia-yuan Chang, Qiaoyu Tan, D. Zha, Xia Hu
-          </td>
-          <td>2024-06-20</td>
-          <td>ArXiv</td>
-          <td>1</td>
-          <td>22</td>
+          <td>18</td>
         </tr>
 
-        <tr id="It is challenging to scale time series forecasting models such that they forecast accurately for multiple distinct domains and datasets, all with potentially different underlying collection procedures (e.g., sample resolution), patterns (e.g., periodicity), and prediction requirements (e.g., reconstruction vs. forecasting). We call this general task universal forecasting. Existing methods usually assume that input data is regularly sampled, and they forecast to pre-determined horizons, resulting in failure to generalise outside of the scope of their training. We propose the DAM - a neural model that takes randomly sampled histories and outputs an adjustable basis composition as a continuous function of time for forecasting to non-fixed horizons. It involves three key components: (1) a flexible approach for using randomly sampled histories from a long-tail distribution, that enables an efficient global perspective of the underlying temporal dynamics while retaining focus on the recent history; (2) a transformer backbone that is trained on these actively sampled histories to produce, as representational output, (3) the basis coefficients of a continuous function of time. We show that a single univariate DAM, trained on 25 time series datasets, either outperformed or closely matched existing SoTA models at multivariate long-term forecasting across 18 datasets, including 8 held-out for zero-shot transfer, even though these models were trained to specialise for each dataset-horizon combination. This single DAM excels at zero-shot transfer and very-long-term forecasting, performs well at imputation, is interpretable via basis function composition and attention, can be tuned for different inference-cost requirements, is robust to missing and irregularly sampled data {by design}.">
+        <tr id="We propose a transformer architecture for time series forecasting with a focus on time series tokenisation and apply it to a real-world prediction problem from the pricing domain. Our architecture aims to learn effective representations at many scales across all available data simultaneously. The model contains a number of novel modules: a differentiated form of time series patching which employs multiple resolutions, a multiple-resolution module for time-varying known variables, a mixer-based module for capturing cross-series information, and a novel output head with favourable scaling to account for the increased number of tokens. We present an application of this model to a real world prediction problem faced by the markdown team at a very large retailer. On the experiments conducted our model outperforms in-house models and the selected existing deep learning architectures.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e177ce13d5e3ca4673b2fff9481408cd98bba0d3" target='_blank'>
-              DAM: Towards A Foundation Model for Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/d7954d6328e25d15549188da2570cc67407bbd56" target='_blank'>
+              Multiple-Resolution Tokenization for Time Series Forecasting with an Application to Pricing
               </a>
             </td>
           <td>
-            Luke Darlow, Qiwen Deng, Ahmed Hassan, Martin Asenov, Rajkarn Singh, Artjom Joosen, Adam Barker, A. Storkey
+            Egon Persak, Miguel F. Anjos, Sebastian Lautz, Aleksandar Kolev
           </td>
-          <td>2024-07-25</td>
+          <td>2024-07-03</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>43</td>
+          <td>1</td>
         </tr>
 
         <tr id="Recent advancements in language modeling have shown promising results when applied to time series data. In particular, fine-tuning pre-trained large language models (LLMs) for time series classification tasks has achieved state-of-the-art (SOTA) performance on standard benchmarks. However, these LLM-based models have a significant drawback due to the large model size, with the number of trainable parameters in the millions. In this paper, we propose an alternative approach to leveraging the success of language modeling in the time series domain. Instead of fine-tuning LLMs, we utilize a language embedding model to embed time series and then pair the embeddings with a simple classification head composed of convolutional neural networks (CNN) and multilayer perceptron (MLP). We conducted extensive experiments on well-established time series classification benchmark datasets. We demonstrated LETS-C not only outperforms the current SOTA in classification accuracy but also offers a lightweight solution, using only 14.5% of the trainable parameters on average compared to the SOTA model. Our findings suggest that leveraging language encoders to embed time series data, combined with a simple yet effective classification head, offers a promising direction for achieving high-performance time series classification while maintaining a lightweight model architecture.">
@@ -1207,34 +1147,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>12</td>
         </tr>
 
-        <tr id="We propose a transformer architecture for time series forecasting with a focus on time series tokenisation and apply it to a real-world prediction problem from the pricing domain. Our architecture aims to learn effective representations at many scales across all available data simultaneously. The model contains a number of novel modules: a differentiated form of time series patching which employs multiple resolutions, a multiple-resolution module for time-varying known variables, a mixer-based module for capturing cross-series information, and a novel output head with favourable scaling to account for the increased number of tokens. We present an application of this model to a real world prediction problem faced by the markdown team at a very large retailer. On the experiments conducted our model outperforms in-house models and the selected existing deep learning architectures.">
+        <tr id="Discrete-Time Dynamic Graphs (DTDGs), which are prevalent in real-world implementations and notable for their ease of data acquisition, have garnered considerable attention from both academic researchers and industry practitioners. The representation learning of DTDGs has been extensively applied to model the dynamics of temporally changing entities and their evolving connections. Currently, DTDG representation learning predominantly relies on GNN+RNN architectures, which manifest the inherent limitations of both Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs). GNNs suffer from the over-smoothing issue as the models architecture goes deeper, while RNNs struggle to capture long-term dependencies effectively. GNN+RNN architectures also grapple with scaling to large graph sizes and long sequences. Additionally, these methods often compute node representations separately and focus solely on individual node characteristics, thereby overlooking the behavior intersections between the two nodes whose link is being predicted, such as instances where the two nodes appear together in the same context or share common neighbors. This paper introduces a novel representation learning method DTFormer for DTDGs, pivoting from the traditional GNN+RNN framework to a Transformer-based architecture. Our approach exploits the attention mechanism to concurrently process topological information within the graph at each timestamp and temporal dynamics of graphs along the timestamps, circumventing the aforementioned fundamental weakness of both GNNs and RNNs. Moreover, we enhance the model's expressive capability by incorporating the intersection relationships among nodes and integrating a multi-patching module. Extensive experiments conducted on six public dynamic graph benchmark datasets confirm our model's efficacy, achieving the SOTA performance.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/d7954d6328e25d15549188da2570cc67407bbd56" target='_blank'>
-              Multiple-Resolution Tokenization for Time Series Forecasting with an Application to Pricing
+          <td><a href="https://www.semanticscholar.org/paper/d0d562378884600e23eb9392361a2aa22e512b2e" target='_blank'>
+              DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning
               </a>
             </td>
           <td>
-            Egon Persak, Miguel F. Anjos, Sebastian Lautz, Aleksandar Kolev
+            Xi Chen, Yun Xiong, Siwei Zhang, Jiawei Zhang, Yao Zhang, Shiyang Zhou, Xixi Wu, Mingyang Zhang, Tengfei Liu, Weiqiang Wang
           </td>
-          <td>2024-07-03</td>
+          <td>2024-07-26</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>4</td>
         </tr>
 
-        <tr id="Discrete-Time Dynamic Graphs (DTDGs), which are prevalent in real-world implementations and notable for their ease of data acquisition, have garnered considerable attention from both academic researchers and industry practitioners. The representation learning of DTDGs has been extensively applied to model the dynamics of temporally changing entities and their evolving connections. Currently, DTDG representation learning predominantly relies on GNN+RNN architectures, which manifest the inherent limitations of both Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs). GNNs suffer from the over-smoothing issue as the models architecture goes deeper, while RNNs struggle to capture long-term dependencies effectively. GNN+RNN architectures also grapple with scaling to large graph sizes and long sequences. Additionally, these methods often compute node representations separately and focus solely on individual node characteristics, thereby overlooking the behavior intersections between the two nodes whose link is being predicted, such as instances where the two nodes appear together in the same context or share common neighbors. This paper introduces a novel representation learning method DTFormer for DTDGs, pivoting from the traditional GNN+RNN framework to a Transformer-based architecture. Our approach exploits the attention mechanism to concurrently process topological information within the graph at each timestamp and temporal dynamics of graphs along the timestamps, circumventing the aforementioned fundamental weakness of both GNNs and RNNs. Moreover, we enhance the model's expressive capability by incorporating the intersection relationships among nodes and integrating a multi-patching module. Extensive experiments conducted on six public dynamic graph benchmark datasets confirm our model's efficacy, achieving the SOTA performance.">
+        <tr id="Transformer has gained widespread adoption in modeling time series due to the exceptional ability of its self-attention mechanism in capturing long-range dependencies. However, when processing time series data with numerous variates, the vanilla self-attention mechanism tends to distribute attention weights evenly and smoothly, causing row-homogenization in attention maps and further hampering time series forecasting. To tackle this issue, we propose an advanced Transformer architecture entitled SDformer, which designs two novel modules, Spectral-Filter-Transform (SFT) and Dynamic-Directional-Attention (DDA), and integrates them into the encoder of Transformer to achieve more intensive attention allocation. Specifically, the SFT module utilizes the Fast Fourier Transform to select the most prominent frequencies, along with a Hamming Window to smooth and denoise the filtered series data; The DDA module applies a specialized kernel function to the query and key vectors projected from the denoised data, concentrating this innovative attention mechanism more effectively on the most informative variates to obtain a sharper attention distribution. These two modules jointly enable attention weights to be more salient among numerous variates, which in turn enhances the attention's ability to capture multivariate correlations, improving the performance in forecasting. Extensive experiments on public datasets demonstrate its superior performance over other state-of-the-art models. Code is available at https://github.com/zhouziyu02/SDformer.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/d0d562378884600e23eb9392361a2aa22e512b2e" target='_blank'>
-              DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning
+          <td><a href="https://www.semanticscholar.org/paper/2059f2f21fb401de3268af0691764082ba5fdfbd" target='_blank'>
+              SDformer: Transformer with Spectral Filter and Dynamic Attention for Multivariate Time Series Long-term Forecasting
               </a>
             </td>
           <td>
-            Xi Chen, Yun Xiong, Siwei Zhang, Jiawei Zhang, Yao Zhang, Shiyang Zhou, Xixi Wu, Mingyang Zhang, Tengfei Liu, Weiqiang Wang
+            Ziyu Zhou, Gengyu Lyu, Yiming Huang, Zihao Wang, Ziyu Jia, Zhen Yang
           </td>
-          <td>2024-07-26</td>
-          <td>ArXiv</td>
+          <td>2024-08-01</td>
+          <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
           <td>0</td>
-          <td>4</td>
+          <td>11</td>
         </tr>
 
         <tr id="Exploiting self-supervised learning (SSL) to extract the universal representations of time series could not only capture the natural properties of time series but also offer huge help to the downstream tasks. Nevertheless, existing time series representation learning (TSRL) methods face challenges in attaining universality. Indeed, existing methods relying solely on one SSL strategy (either contrastive learning (CL) or generative) often fall short in capturing rich semantic information for various downstream tasks. Moreover, time series exhibit diverse distributions and inherent characteristics, particularly with the common occurrence of missing values, posing a notable challenge for existing backbones in effectively handling such diverse time series data. To bridge these gaps, we propose CTRL, a framework for universal TSRL. For the first time, we employ Neural Controlled Differential Equation (NCDE) as the backbone for TSRL, which captures the continuous processes and exhibits robustness to missing data. Additionally, a dual-task SSL strategy, integrating both reconstruction and contrasting tasks, is proposed to enrich the semantic information of the learned representations. Furthermore, novel hard negative construction and false negative elimination mechanisms are proposed to improve sampling efficiency and reduce sampling bias in CL. Finally, extensive experiments demonstrate the superiority of CTRL in forecasting, classification, and imputation tasks, particularly its outstanding robustness to missing data.">
@@ -1252,19 +1192,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>25</td>
         </tr>
 
-        <tr id="Transformer has gained widespread adoption in modeling time series due to the exceptional ability of its self-attention mechanism in capturing long-range dependencies. However, when processing time series data with numerous variates, the vanilla self-attention mechanism tends to distribute attention weights evenly and smoothly, causing row-homogenization in attention maps and further hampering time series forecasting. To tackle this issue, we propose an advanced Transformer architecture entitled SDformer, which designs two novel modules, Spectral-Filter-Transform (SFT) and Dynamic-Directional-Attention (DDA), and integrates them into the encoder of Transformer to achieve more intensive attention allocation. Specifically, the SFT module utilizes the Fast Fourier Transform to select the most prominent frequencies, along with a Hamming Window to smooth and denoise the filtered series data; The DDA module applies a specialized kernel function to the query and key vectors projected from the denoised data, concentrating this innovative attention mechanism more effectively on the most informative variates to obtain a sharper attention distribution. These two modules jointly enable attention weights to be more salient among numerous variates, which in turn enhances the attention's ability to capture multivariate correlations, improving the performance in forecasting. Extensive experiments on public datasets demonstrate its superior performance over other state-of-the-art models. Code is available at https://github.com/zhouziyu02/SDformer.">
+        <tr id="Accurate workload forecasting is critical for efficient resource management in cloud computing systems, enabling effective scheduling and autoscaling. Despite recent advances with transformer-based forecasting models, challenges remain due to the non-stationary, nonlinear characteristics of workload time series and the long-term dependencies. In particular, inconsistent performance between long-term history and near-term forecasts hinders long-range predictions. This paper proposes a novel framework leveraging self-supervised multiscale representation learning to capture both long-term and near-term workload patterns. The long-term history is encoded through multiscale representations while the near-term observations are modeled via temporal flow fusion. These representations of different scales are fused using an attention mechanism and characterized with normalizing flows to handle non-Gaussian/non-linear distributions of time series. Extensive experiments on 9 benchmarks demonstrate superiority over existing methods.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/2059f2f21fb401de3268af0691764082ba5fdfbd" target='_blank'>
-              SDformer: Transformer with Spectral Filter and Dynamic Attention for Multivariate Time Series Long-term Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/690e772c64d7f67b330d55aa96ffa09ecc7d2ae2" target='_blank'>
+              Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting
               </a>
             </td>
           <td>
-            Ziyu Zhou, Gengyu Lyu, Yiming Huang, Zihao Wang, Ziyu Jia, Zhen Yang
+            Shiyu Wang, Zhixuan Chu, Yinbo Sun, Yu Liu, Yuliang Guo, Yang Chen, Huiyang Jian, Lintao Ma, Xingyu Lu, Jun Zhou
           </td>
-          <td>2024-08-01</td>
-          <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
+          <td>2024-07-29</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>11</td>
+          <td>4</td>
         </tr>
 
         <tr id="Spatio-temporal forecasting of traffic flow data represents a typical problem in the field of machine learning, impacting urban traffic management systems. Traditional statistical and machine learning methods cannot adequately handle both the temporal and spatial dependencies in these complex traffic flow datasets. A prevalent approach in the field is to combine graph convolutional networks and multi-head attention mechanisms for spatio-temporal processing. This paper proposes a wavelet-based temporal attention model, namely a wavelet-based dynamic spatio-temporal aware graph neural network (W-DSTAGNN), for tackling the traffic forecasting problem. Benchmark experiments using several statistical metrics confirm that our proposal efficiently captures spatio-temporal correlations and outperforms ten state-of-the-art models on three different real-world traffic datasets. Our proposed ensemble data-driven method can handle dynamic temporal and spatial dependencies and make long-term forecasts in an efficient manner.">
@@ -1279,24 +1219,39 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2024-07-05</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>11</td>
         </tr>
 
-        <tr id="Frequency domain representation of time series feature offers a concise representation for handling real-world time series data with inherent complexity and dynamic nature. However, current frequency-based methods with complex operations still fall short of state-of-the-art time domain methods for general time series analysis. In this work, we present Omni-Dimensional Frequency Learner (ODFL) model based on a in depth analysis among all the three aspects of the spectrum feature: channel redundancy property among the frequency dimension, the sparse and un-salient frequency energy distribution among the frequency dimension, and the semantic diversity among the variable dimension. Technically, our method is composed of a semantic-adaptive global filter with attention to the un-salient frequency bands and partial operation among the channel dimension. Empirical results show that ODFL achieves consistent state-of-the-art in five mainstream time series analysis tasks, including short- and long-term forecasting, imputation, classification, and anomaly detection, offering a promising foundation for time series analysis.">
+        <tr id="This research examines the use of Large Language Models (LLMs) in predicting time series, with a specific focus on the LLMTIME model. Despite the established effectiveness of LLMs in tasks such as text generation, language translation, and sentiment analysis, this study highlights the key challenges that large language models encounter in the context of time series prediction. We assess the performance of LLMTIME across multiple datasets and introduce classical almost periodic functions as time series to gauge its effectiveness. The empirical results indicate that while large language models can perform well in zero-shot forecasting for certain datasets, their predictive accuracy diminishes notably when confronted with diverse time series data and traditional signals. The primary finding of this study is that the predictive capacity of LLMTIME, similar to other LLMs, significantly deteriorates when dealing with time series data that contain both periodic and trend components, as well as when the signal comprises complex frequency components.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/de75ddcb9fb13372a31ee08af8dd80b5913d70f3" target='_blank'>
-              Omni-Dimensional Frequency Learner for General Time Series Analysis
+          <td><a href="https://www.semanticscholar.org/paper/11e794835a1f7d46560666d90c8eab3c24d84c05" target='_blank'>
+              An Evaluation of Standard Statistical Models and LLMs on Time Series Forecasting
               </a>
             </td>
           <td>
-            Xianing Chen.Hanting Chen, Hanting Chen, Hailin Hu
+            Rui Cao, Qiaohua Wang
           </td>
-          <td>2024-07-15</td>
+          <td>2024-08-09</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
+        <tr id="The complexity and heterogeneity of data in many real-world applications pose significant challenges for traditional machine learning and signal processing techniques. For instance, in medicine, effective analysis of diverse physiological signals is crucial for patient monitoring and clinical decision-making and yet highly challenging. We introduce MedTsLLM, a general multimodal large language model (LLM) framework that effectively integrates time series data and rich contextual information in the form of text to analyze physiological signals, performing three tasks with clinical relevance: semantic segmentation, boundary detection, and anomaly detection in time series. These critical tasks enable deeper analysis of physiological signals and can provide actionable insights for clinicians. We utilize a reprogramming layer to align embeddings of time series patches with a pretrained LLM's embedding space and make effective use of raw time series, in conjunction with textual context. Given the multivariate nature of medical datasets, we develop methods to handle multiple covariates. We additionally tailor the text prompt to include patient-specific information. Our model outperforms state-of-the-art baselines, including deep learning models, other LLMs, and clinical methods across multiple medical domains, specifically electrocardiograms and respiratory waveforms. MedTsLLM presents a promising step towards harnessing the power of LLMs for medical time series analysis that can elevate data-driven tools for clinicians and improve patient outcomes.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/6ac9e9c8bdd4eebe3cf6adf7187b9ffb16ff5b64" target='_blank'>
+              MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis
+              </a>
+            </td>
+          <td>
+            Nimeesha Chan, Felix Parker, William Bennett, Tianyi Wu, Mung Yao Jia, James Fackler, Kimia Ghobadi
+          </td>
+          <td>2024-08-14</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>1</td>
+        </tr>
+
         <tr id="Current Transformer methods for Multivariate Time-Series Forecasting (MTSF) are all based on the conventional attention mechanism. They involve sequence embedding and performing a linear projection of Q, K, and V, and then computing attention within this latent space. We have never delved into the attention mechanism to explore whether such a mapping space is optimal for MTSF. To investigate this issue, this study first proposes Frequency Spectrum attention (FSatten), a novel attention mechanism based on the frequency domain space. It employs the Fourier transform for embedding and introduces Multi-head Spectrum Scaling (MSS) to replace the conventional linear mapping of Q and K. FSatten can accurately capture the periodic dependencies between sequences and outperform the conventional attention without changing mainstream architectures. We further design a more general method dubbed Scaled Orthogonal attention (SOatten). We propose an orthogonal embedding and a Head-Coupling Convolution (HCC) based on the neighboring similarity bias to guide the model in learning comprehensive dependency patterns. Experiments show that FSatten and SOatten surpass the SOTA which uses conventional attention, making it a good alternative as a basic attention mechanism for MTSF. The codes and log files will be released at: https://github.com/Joeland4/FSatten-SOatten.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/0b4ec3cddfc06c1f060d66a3123f8a9a0b157d23" target='_blank'>
@@ -1312,6 +1267,21 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>0</td>
         </tr>
 
+        <tr id="Tabular data is ubiquitous in many real-life systems. In particular, time-dependent tabular data, where rows are chronologically related, is typically used for recording historical events, e.g., financial transactions, healthcare records, or stock history. Recently, hierarchical variants of the attention mechanism of transformer architectures have been used to model tabular time-series data. At first, rows (or columns) are encoded separately by computing attention between their fields. Subsequently, encoded rows (or columns) are attended to one another to model the entire tabular time-series. While efficient, this approach constrains the attention granularity and limits its ability to learn patterns at the field-level across separate rows, or columns. We take a first step to address this gap by proposing Fieldy, a fine-grained hierarchical model that contextualizes fields at both the row and column levels. We compare our proposal against state of the art models on regression and classification tasks using public tabular time-series datasets. Our results show that combining row-wise and column-wise attention improves performance without increasing model size. Code and data are available at https://github.com/raphaaal/fieldy.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/a9e5c4649872cb3a4b417acdce4fae0ea8d72ebf" target='_blank'>
+              Fine-grained Attention in Hierarchical Transformers for Tabular Time-series
+              </a>
+            </td>
+          <td>
+            Raphaël Azorin, Zied Ben-Houidi, Massimo Gallo, A. Finamore, Pietro Michiardi
+          </td>
+          <td>2024-06-21</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>24</td>
+        </tr>
+
         <tr id="Short fixed-length inputs are the main bottleneck of deep learning methods in long time-series forecasting tasks. Prolonging input length causes overfitting, rapidly deteriorating accuracy. Our research indicates that the overfitting is a combination reaction of the multi-scale pattern coupling in time series and the fixed focusing scale of current models. First, we find that the patterns exhibited by a time series across various scales are reflective of its multi-periodic nature, where each scale corresponds to specific period length. Second, We find that the token size predominantly dictates model behavior, as it determines the scale at which the model focuses and the context size it can accommodate. Our idea is to decouple the multi-scale temporal patterns of time series and to model each pattern with its corresponding period length as token size. We introduced a novel series-decomposition module(MPSD), and a Multi-Token Pattern Recognition neural network(MTPR), enabling the model to handle \textit{inputs up to $10\times$ longer}. Sufficient context enhances performance(\textit{38% maximum precision improvement}), and the decoupling approach offers \textit{Low complexity($0.22\times$ cost)} and \textit{high interpretability}.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/380b86c3909fe3e098a4ec9618f09d59bb9ef626" target='_blank'>
@@ -1339,52 +1309,52 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2024-07-18</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>4</td>
+          <td>5</td>
         </tr>
 
-        <tr id="Tabular data is ubiquitous in many real-life systems. In particular, time-dependent tabular data, where rows are chronologically related, is typically used for recording historical events, e.g., financial transactions, healthcare records, or stock history. Recently, hierarchical variants of the attention mechanism of transformer architectures have been used to model tabular time-series data. At first, rows (or columns) are encoded separately by computing attention between their fields. Subsequently, encoded rows (or columns) are attended to one another to model the entire tabular time-series. While efficient, this approach constrains the attention granularity and limits its ability to learn patterns at the field-level across separate rows, or columns. We take a first step to address this gap by proposing Fieldy, a fine-grained hierarchical model that contextualizes fields at both the row and column levels. We compare our proposal against state of the art models on regression and classification tasks using public tabular time-series datasets. Our results show that combining row-wise and column-wise attention improves performance without increasing model size. Code and data are available at https://github.com/raphaaal/fieldy.">
+        <tr id="Learning useful representations for continuous-time dynamic graphs (CTDGs) is challenging, due to the concurrent need to span long node interaction histories and grasp nuanced temporal details. In particular, two problems emerge: (1) Encoding longer histories requires more computational resources, making it crucial for CTDG models to maintain low computational complexity to ensure efficiency; (2) Meanwhile, more powerful models are needed to identify and select the most critical temporal information within the extended context provided by longer histories. To address these problems, we propose a CTDG representation learning model named DyGMamba, originating from the popular Mamba state space model (SSM). DyGMamba first leverages a node-level SSM to encode the sequence of historical node interactions. Another time-level SSM is then employed to exploit the temporal patterns hidden in the historical graph, where its output is used to dynamically select the critical information from the interaction history. We validate DyGMamba experimentally on the dynamic link prediction task. The results show that our model achieves state-of-the-art in most cases. DyGMamba also maintains high efficiency in terms of computational resources, making it possible to capture long temporal dependencies with a limited computation budget.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/a9e5c4649872cb3a4b417acdce4fae0ea8d72ebf" target='_blank'>
-              Fine-grained Attention in Hierarchical Transformers for Tabular Time-series
+          <td><a href="https://www.semanticscholar.org/paper/aeb6fbef9a2f455a71773488e09f1c4f6686ac55" target='_blank'>
+              DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
               </a>
             </td>
           <td>
-            Raphaël Azorin, Zied Ben-Houidi, Massimo Gallo, A. Finamore, Pietro Michiardi
+            Zifeng Ding, Yifeng Li, Yuan He, Antonio Norelli, Jingcheng Wu, Volker Tresp, Yunpu Ma, Michael Bronstein
           </td>
-          <td>2024-06-21</td>
+          <td>2024-08-08</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>24</td>
+          <td>22</td>
         </tr>
 
-        <tr id="The COVID-19 pandemic and influenza outbreaks have underscored the critical need for predictive models that can effectively integrate spatial and temporal dynamics to enable accurate epidemic forecasting. Traditional time-series analysis approaches have fallen short in capturing the intricate interplay between these factors. Recent advancements have witnessed the incorporation of graph neural networks and machine learning techniques to bridge this gap, enhancing predictive accuracy and providing novel insights into disease spread mechanisms. Notable endeavors include leveraging human mobility data, employing transfer learning, and integrating advanced models such as Transformers and Graph Convolutional Networks (GCNs) to improve forecasting performance across diverse geographies for both influenza and COVID-19. However, these models often face challenges related to data quality, model transferability, and potential overfitting, highlighting the necessity for more adaptable and robust approaches. This paper introduces the Graph Attention-based Spatial Temporal (GAST) model, which employs graph attention networks (GATs) to overcome these limitations by providing a nuanced understanding of epidemic dynamics through a sophisticated spatio-temporal analysis framework. Our contributions include the development and validation of the GAST model, demonstrating its superior forecasting capabilities for influenza and COVID-19 spread, with a particular focus on short-term, daily predictions. The model’s application to both influenza and COVID-19 datasets showcases its versatility and potential to inform public health interventions across a range of infectious diseases.">
+        <tr id="Intelligent transportation systems based on sensor signals are crucial in addressing contemporary transportation issues, accomplishing dynamic traffic management, and facilitating route planning. However, the highly dynamic and intricate nature of traffic sensor signals presents difficulties for traffic prediction, with current models for traffic prediction inadequate in meeting the requirements of both long-term and short-term prediction tasks. In this paper, we propose a novel deep-learning framework called dynamic spatial-temporal correlation learning network (DSTLNet) that jointly leverages dynamical spatial and temporal features of traffic sensor signals to further improve the accuracy of long-and short-term traffic modeling and route planning. Specifically, we leverage the temporal convolutional network to capture long-term correlations. In addition, a spatial graph convolutional network is developed to dynamically model spatial features, and long-and short-term fusion layers are used to fuse the extracted long-and short-term temporal features, respectively. Experimental results on real-world datasets show that DSTLNet is competitive with the state-of-the-art, especially for long-term traffic prediction.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/628bd28abb35127fc3d4e794dfc8e0860b738427" target='_blank'>
-              Modeling epidemic dynamics using Graph Attention based Spatial Temporal networks
+          <td><a href="https://www.semanticscholar.org/paper/c2ed14c7ddd2366e53463d2401366cc0c6036604" target='_blank'>
+              DSTLNet: Dynamic Spatial-Temporal Correlation Learning Network for Traffic Sensor Signal Prediction
               </a>
             </td>
           <td>
-            Xiaofeng Zhu, Yi Zhang, Haoru Ying, Huanning Chi, Guanqun Sun, Lingxia Zeng
+            Yuxiang Shan, Hailiang Lu, Weidong Lou
           </td>
-          <td>2024-07-15</td>
-          <td>PLOS ONE</td>
-          <td>0</td>
+          <td>2024-08-08</td>
+          <td>Sensors and Materials</td>
           <td>0</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Time series models, typically trained on numerical data, are designed to forecast future values. These models often rely on weighted averaging techniques over time intervals. However, real-world time series data is seldom isolated and is frequently influenced by non-numeric factors. For instance, stock price fluctuations are impacted by daily random events in the broader world, with each event exerting a unique influence on price signals. Previously, forecasts in financial markets have been approached in two main ways: either as time-series problems over price sequence or sentiment analysis tasks. The sentiment analysis tasks aim to determine whether news events will have a positive or negative impact on stock prices, often categorizing them into discrete labels. Recognizing the need for a more comprehensive approach to accurately model time series prediction, we propose a collaborative modeling framework that incorporates textual information about relevant events for predictions. Specifically, we leverage the intuition of large language models about future changes to update real number time series predictions. We evaluated the effectiveness of our approach on financial market data.">
+        <tr id="Long-term traffic flow forecasting plays a crucial role in intelligent transportation as it allows traffic managers to adjust their decisions in advance. However, the problem is challenging due to spatio-temporal correlations and complex dynamic patterns in continuous-time stream data. Neural Differential Equations (NDEs) are among the state-of-the-art methods for learning continuous-time traffic dynamics. However, the traditional NDE models face issues in long-term traffic forecasting due to failures in capturing delayed traffic patterns, dynamic edge (location-to-location correlation) patterns, and abrupt trend patterns. To fill this gap, we propose a new NDE architecture called Multi-View Neural Differential Equations. Our model captures current states, delayed states, and trends in different state variables (views) by learning latent multiple representations within Neural Differential Equations. Extensive experiments conducted on several real-world traffic datasets demonstrate that our proposed method outperforms the state-of-the-art and achieves superior prediction accuracy for long-term forecasting and robustness with noisy or missing inputs.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/7c2fd193f7d8f4fb40c5f865884bd2b528a475a4" target='_blank'>
-              Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models
+          <td><a href="https://www.semanticscholar.org/paper/713b5e17e000120673eebab412e9b9ae591db136" target='_blank'>
+              Multi-View Neural Differential Equations for Continuous-Time Stream Data in Long-Term Traffic Forecasting
               </a>
             </td>
           <td>
-            Litton J. Kurisinkel, Pruthwik Mishra, Yue Zhang
+            Zibo Liu, Zhe Jiang, Shigang Chen
           </td>
-          <td>2024-07-04</td>
+          <td>2024-08-12</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>5</td>
+          <td>2</td>
         </tr>
 
         <tr id="Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems. Traditional methods for extracting knowledge from these graphs often depend on feature engineering or deep learning. Feature engineering is limited by the manual and time-intensive nature of crafting features, while deep learning approaches suffer from high inference latency, making them impractical for real-time applications. This paper introduces Deep-Graph-Sprints (DGS), a novel deep learning architecture designed for efficient representation learning on CTDGs with low-latency inference requirements. We benchmark DGS against state-of-the-art feature engineering and graph neural network methods using five diverse datasets. The results indicate that DGS achieves competitive performance while improving inference speed up to 12x compared to other deep learning approaches on our tested benchmarks. Our method effectively bridges the gap between deep representation learning and low-latency application requirements for CTDGs.">
@@ -1399,112 +1369,97 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2024-07-10</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>17</td>
+          <td>18</td>
         </tr>
 
-        <tr id="Urban traffic speed prediction aims to estimate the future traffic speed for improving urban transportation services. Enormous efforts have been made to exploit Graph Neural Networks (GNNs) for modeling spatial correlations and temporal dependencies of traffic speed evolving patterns, regularized by graph topology. While achieving promising results, current traffic speed prediction methods still suffer from ignoring topology-free patterns, which cannot be captured by GNNs. To tackle this challenge, we propose a generic model for enabling the current GNN-based methods to preserve topology-free patterns. Specifically, we first develop a Dual Cross-Scale Transformer (DCST) architecture, including a Spatial Transformer and a Temporal Transformer, to preserve the cross-scale topology-free patterns and associated dynamics, respectively. Then, to further integrate both topology-regularized/-free patterns, we propose a distillation-style learning framework, in which the existing GNN-based methods are considered as the teacher model, and the proposed DCST architecture is considered as the student model. The teacher model would inject the learned topology-regularized patterns into the student model for integrating topology-free patterns. The extensive experimental results demonstrated the effectiveness of our methods.">
+        <tr id="Time series models, typically trained on numerical data, are designed to forecast future values. These models often rely on weighted averaging techniques over time intervals. However, real-world time series data is seldom isolated and is frequently influenced by non-numeric factors. For instance, stock price fluctuations are impacted by daily random events in the broader world, with each event exerting a unique influence on price signals. Previously, forecasts in financial markets have been approached in two main ways: either as time-series problems over price sequence or sentiment analysis tasks. The sentiment analysis tasks aim to determine whether news events will have a positive or negative impact on stock prices, often categorizing them into discrete labels. Recognizing the need for a more comprehensive approach to accurately model time series prediction, we propose a collaborative modeling framework that incorporates textual information about relevant events for predictions. Specifically, we leverage the intuition of large language models about future changes to update real number time series predictions. We evaluated the effectiveness of our approach on financial market data.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/3911024df853ccf11138d35835572ce863df51bf" target='_blank'>
-              Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction
+          <td><a href="https://www.semanticscholar.org/paper/7c2fd193f7d8f4fb40c5f865884bd2b528a475a4" target='_blank'>
+              Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models
               </a>
             </td>
           <td>
-            Yicheng Zhou, P. Wang, Hao Dong, Denghui Zhang, Dingqi Yang, Yanjie Fu, Pengyang Wang
+            Litton J. Kurisinkel, Pruthwik Mishra, Yue Zhang
           </td>
-          <td>2024-06-24</td>
+          <td>2024-07-04</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>17</td>
+          <td>5</td>
         </tr>
 
-        <tr id="Representing temporal-structured samples is essential for effective time series analysis tasks. So far, recurrent networks, convolution networks and transformer-style models have been successively applied in temporal data representation, yielding notable results. However, most existing methods primarily focus on modeling and representing the variation patterns within time series in the time domain. As a highly abstracted information entity, 1D time series couples various patterns such as trends, seasonality, and dramatic changes (instantaneous high dynamic), it is difficult to exploit these highly coupled properties merely by analysis tools on purely time domain. To this end, we present Spectrogram Analysis and Representation Network (SpecAR-Net). SpecAR-Net aims at learning more comprehensive representations by modeling raw time series in both time and frequency domain, where an efficient joint extraction of time-frequency features is achieved through a group of learnable 2D multi-scale parallel complex convolution blocks. Experimental results show that the SpecAR-Net achieves excellent performance on 5 major downstream tasks i.e., classification, anomaly detection, imputation, long- and short-term forecasting. Code and appendix are available at https://github.com/Dongyi2go/SpecAR_Net.">
+        <tr id="The COVID-19 pandemic and influenza outbreaks have underscored the critical need for predictive models that can effectively integrate spatial and temporal dynamics to enable accurate epidemic forecasting. Traditional time-series analysis approaches have fallen short in capturing the intricate interplay between these factors. Recent advancements have witnessed the incorporation of graph neural networks and machine learning techniques to bridge this gap, enhancing predictive accuracy and providing novel insights into disease spread mechanisms. Notable endeavors include leveraging human mobility data, employing transfer learning, and integrating advanced models such as Transformers and Graph Convolutional Networks (GCNs) to improve forecasting performance across diverse geographies for both influenza and COVID-19. However, these models often face challenges related to data quality, model transferability, and potential overfitting, highlighting the necessity for more adaptable and robust approaches. This paper introduces the Graph Attention-based Spatial Temporal (GAST) model, which employs graph attention networks (GATs) to overcome these limitations by providing a nuanced understanding of epidemic dynamics through a sophisticated spatio-temporal analysis framework. Our contributions include the development and validation of the GAST model, demonstrating its superior forecasting capabilities for influenza and COVID-19 spread, with a particular focus on short-term, daily predictions. The model’s application to both influenza and COVID-19 datasets showcases its versatility and potential to inform public health interventions across a range of infectious diseases.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1dfaff572dd20e45c52824bd90981982d242c6dc" target='_blank'>
-              SpecAR-Net: Spectrogram Analysis and Representation Network for Time Series
+          <td><a href="https://www.semanticscholar.org/paper/628bd28abb35127fc3d4e794dfc8e0860b738427" target='_blank'>
+              Modeling epidemic dynamics using Graph Attention based Spatial Temporal networks
               </a>
             </td>
           <td>
-            Yi Dong, Liwen Zhang, Youcheng Zhang, Shi Peng, Wen Chen, Zhe Ma
+            Xiaofeng Zhu, Yi Zhang, Haoru Ying, Huanning Chi, Guanqun Sun, Lingxia Zeng
           </td>
-          <td>2024-08-01</td>
-          <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
+          <td>2024-07-15</td>
+          <td>PLOS ONE</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
-        <tr id="The field of temporal graph learning aims to learn from evolving network data to forecast future interactions. Given a collection of observed temporal graphs, is it possible to predict the evolution of an unseen network from the same domain? To answer this question, we first present the Temporal Graph Scaling (TGS) dataset, a large collection of temporal graphs consisting of eighty-four ERC20 token transaction networks collected from 2017 to 2023. Next, we evaluate the transferability of Temporal Graph Neural Networks (TGNNs) for the temporal graph property prediction task by pre-training on a collection of up to sixty-four token transaction networks and then evaluating the downstream performance on twenty unseen token networks. We find that the neural scaling law observed in NLP and Computer Vision also applies in temporal graph learning, where pre-training on greater number of networks leads to improved downstream performance. To the best of our knowledge, this is the first empirical demonstration of the transferability of temporal graphs learning. On downstream token networks, the largest pre-trained model outperforms single model TGNNs on thirteen unseen test networks. Therefore, we believe that this is a promising first step towards building foundation models for temporal graphs.">
+        <tr id="Urban traffic speed prediction aims to estimate the future traffic speed for improving urban transportation services. Enormous efforts have been made to exploit Graph Neural Networks (GNNs) for modeling spatial correlations and temporal dependencies of traffic speed evolving patterns, regularized by graph topology. While achieving promising results, current traffic speed prediction methods still suffer from ignoring topology-free patterns, which cannot be captured by GNNs. To tackle this challenge, we propose a generic model for enabling the current GNN-based methods to preserve topology-free patterns. Specifically, we first develop a Dual Cross-Scale Transformer (DCST) architecture, including a Spatial Transformer and a Temporal Transformer, to preserve the cross-scale topology-free patterns and associated dynamics, respectively. Then, to further integrate both topology-regularized/-free patterns, we propose a distillation-style learning framework, in which the existing GNN-based methods are considered as the teacher model, and the proposed DCST architecture is considered as the student model. The teacher model would inject the learned topology-regularized patterns into the student model for integrating topology-free patterns. The extensive experimental results demonstrated the effectiveness of our methods.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/7296550cef5154a13eafc3eb00a54296113d8a8a" target='_blank'>
-              Towards Neural Scaling Laws for Foundation Models on Temporal Graphs
+          <td><a href="https://www.semanticscholar.org/paper/3911024df853ccf11138d35835572ce863df51bf" target='_blank'>
+              Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction
               </a>
             </td>
           <td>
-            Razieh Shirzadkhani, Tran Gia Bao Ngo, Kiarash Shamsi, Shenyang Huang, Farimah Poursafaei, Poupak Azad, Reihaneh Rabbany, Baris Coskunuzer, Guillaume Rabusseau, C. Akcora
+            Yicheng Zhou, P. Wang, Hao Dong, Denghui Zhang, Dingqi Yang, Yanjie Fu, Pengyang Wang
           </td>
-          <td>2024-06-14</td>
+          <td>2024-06-24</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>19</td>
+          <td>17</td>
         </tr>
 
-        <tr id="Time series forecasting has essential applications across various domains. For instance, forecasting power time series can optimize energy usage and bolster grid stability and reliability. Existing models based on transformer architecture are limited to classical design, ignoring the impact of spatial information and noise on model architecture design. Therefore, we propose an atypical design of Transformer-based models for multivariate time series forecasting. This design consists of two critical components: (i) spectral clustering center of time series employed as the focal point for attention computation; (ii) alternating attention mechanism wherein each query transformer is compatible with spectral clustering centers, executing attention at the sequence level instead of the token level. The alternating design has a two-fold benefit: firstly, it eliminates the uncertainty noise present in the dependent variable sequence of the channel input, and secondly, it incorporates the Euclidean distance to mitigate the impact of extreme values on the attention matrix, thereby aligning predictions more closely to the sequence's natural progression. Experiments on ten real-world datasets, encompassing Wind, Electricity, Weather, and others, demonstrate that our Spectral Central Alternating Transformer (SCAT) outperforms state-of-the-art methods (SOTA) by an average of 42.8% in prediction performance in power time series forecasting.">
+        <tr id="Representing temporal-structured samples is essential for effective time series analysis tasks. So far, recurrent networks, convolution networks and transformer-style models have been successively applied in temporal data representation, yielding notable results. However, most existing methods primarily focus on modeling and representing the variation patterns within time series in the time domain. As a highly abstracted information entity, 1D time series couples various patterns such as trends, seasonality, and dramatic changes (instantaneous high dynamic), it is difficult to exploit these highly coupled properties merely by analysis tools on purely time domain. To this end, we present Spectrogram Analysis and Representation Network (SpecAR-Net). SpecAR-Net aims at learning more comprehensive representations by modeling raw time series in both time and frequency domain, where an efficient joint extraction of time-frequency features is achieved through a group of learnable 2D multi-scale parallel complex convolution blocks. Experimental results show that the SpecAR-Net achieves excellent performance on 5 major downstream tasks i.e., classification, anomaly detection, imputation, long- and short-term forecasting. Code and appendix are available at https://github.com/Dongyi2go/SpecAR_Net.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f0478468dc09d88ac952d50786c548b2bee25e6a" target='_blank'>
-              SCAT: A Time Series Forecasting with Spectral Central Alternating Transformers
+          <td><a href="https://www.semanticscholar.org/paper/1dfaff572dd20e45c52824bd90981982d242c6dc" target='_blank'>
+              SpecAR-Net: Spectrogram Analysis and Representation Network for Time Series
               </a>
             </td>
           <td>
-            Chengjie Zhou, Chao Che, Pengfei Wang, Qiang Zhang
+            Yi Dong, Liwen Zhang, Youcheng Zhang, Shi Peng, Wen Chen, Zhe Ma
           </td>
           <td>2024-08-01</td>
           <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
           <td>0</td>
-          <td>7</td>
-        </tr>
-
-        <tr id="Occupational skill demand (OSD) forecasting seeks to predict dynamic skill demand specific to occupations, beneficial for employees and employers to grasp occupational nature and maintain a competitive edge in the rapidly evolving labor market. Although recent research has proposed data-driven techniques for forecasting skill demand, the focus has remained predominantly on overall trends rather than occupational granularity. In this paper, we propose a novel Pre-training Enhanced Dynamic Graph Autoencoder (Pre-DyGAE), forecasting skill demand from an occupational perspective. Specifically, we aggregate job descriptions (JDs) by occupation and segment them into several timestamps. Subsequently, in the initial timestamps, we pre-train a graph autoencoder (GAE), consisting of a semantically-aware cross-attention enhanced uncertainty-aware encoder and decoders for link prediction and edge regression to achieve graph reconstruction. In particular, we utilize contrastive learning on skill cooccurrence clusters to solve the data sparsity and a unified Tweedie and ranking loss for predicting the imbalanced distribution. Afterward, we incorporate an adaptive temporal encoding unit and a temporal shift module into GAE to achieve a dynamic GAE (DyGAE). Furthermore, we fine-tune the DyGAE with a two-stage optimization strategy and infer future representations. Extensive experiments on four real-world datasets validate the effectiveness of Pre-DyGAE compared with state-of-the-art baselines.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1cdd74aa1df255a4731d9b190f68e9d885a98f04" target='_blank'>
-              Pre-DyGAE: Pre-training Enhanced Dynamic Graph Autoencoder for Occupational Skill Demand Forecasting
-              </a>
-            </td>
-          <td>
-            Xi Chen, Chuan Qin, Zhigaoyuan Wang, Yihang Cheng, Chao Wang, Hengshu Zhu, Hui Xiong
-          </td>
-          <td>2024-08-01</td>
-          <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
-          <td>1</td>
-          <td>3</td>
+          <td>0</td>
         </tr>
 
-        <tr id="Key elements of human events are extracted as quadruples that consist of subject, relation, object, and timestamp. This representation can be extended to a quintuple by adding a fifth element: a textual summary that briefly describes the event. These quadruples or quintuples, when organized within a specific domain, form a temporal knowledge graph (TKG). Current learning frameworks focus on a few TKG-related tasks, such as predicting an object given a subject and a relation or forecasting the occurrences of multiple types of events (i.e., relation) in the next time window. They typically rely on complex structural and sequential models like graph neural networks (GNNs) and recurrent neural networks (RNNs) to update intermediate embeddings. However, these methods often neglect the contextual information inherent in each quintuple, which can be effectively captured through concise textual descriptions. In this paper, we investigate how large language models (LLMs) can streamline the design of TKG learning frameworks while maintaining competitive accuracy in prediction and forecasting tasks. We develop multiple prompt templates to frame the object prediction (OP) task as a standard question-answering (QA) task, suitable for instruction fine-tuning with an encoder-decoder generative LLM. For multi-event forecasting (MEF), we design simple yet effective prompt templates for each TKG quintuple. This novel approach removes the need for GNNs and RNNs, instead utilizing an encoder-only LLM to generate fixed intermediate embeddings, which are subsequently processed by a prediction head with a self-attention mechanism to forecast potential future relations. Extensive experiments on multiple real-world datasets using various evaluation metrics validate the effectiveness and robustness of our approach.">
+        <tr id="The scaling of large language models (LLMs) has revolutionized their capabilities in various tasks, yet this growth must be matched with efficient computational strategies. The Mixture-of-Experts (MoE) architecture stands out for its ability to scale model size without significantly increasing training costs. Despite their advantages, current MoE models often display parameter inefficiency. For instance, a pre-trained MoE-based LLM with 52 billion parameters might perform comparably to a standard model with 6.7 billion parameters. Being a crucial part of MoE, current routers in different layers independently assign tokens without leveraging historical routing information, potentially leading to suboptimal token-expert combinations and the parameter inefficiency problem. To alleviate this issue, we introduce the Layerwise Recurrent Router for Mixture-of-Experts (RMoE). RMoE leverages a Gated Recurrent Unit (GRU) to establish dependencies between routing decisions across consecutive layers. Such layerwise recurrence can be efficiently parallelly computed for input tokens and introduces negotiable costs. Our extensive empirical evaluations demonstrate that RMoE-based language models consistently outperform a spectrum of baseline models. Furthermore, RMoE integrates a novel computation stage orthogonal to existing methods, allowing seamless compatibility with other MoE architectures. Our analyses attribute RMoE's gains to its effective cross-layer information sharing, which also improves expert selection and diversity. Our code is at https://github.com/qiuzh20/RMoE">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/173bd3b71792bf891ab5af4a6454e6ba07576bcd" target='_blank'>
-              Large Language Models as Event Forecasters
+          <td><a href="https://www.semanticscholar.org/paper/541773c4cbb30bcdcbc82b8d02eba15af811f739" target='_blank'>
+              Layerwise Recurrent Router for Mixture-of-Experts
               </a>
             </td>
           <td>
-            Libo Zhang, Yue Ning
+            Zihan Qiu, Zeyu Huang, Shuang Cheng, Yizhi Zhou, Zili Wang, Ivan Titov, Jie Fu
           </td>
-          <td>2024-06-15</td>
+          <td>2024-08-13</td>
           <td>ArXiv</td>
-          <td>1</td>
           <td>0</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Time series forecasting is one of the crucial tasks in many industrial fields and bears financial, resource, and control benefits. The leading solutions in this domain are often associated with machine learning approaches. The drawback in many approaches is the missing interpretability of the lookback and the predicted time series. Therefore, we propose a novel method named Bag-of-Future, to introduce interpretability in a backcast as well as forecast of the given series. Our approach aims to select and predict multiple patterns for the input series, based on continuous functions in a preselected bag of functions. Furthermore, stacking multiple encodings results in a continuous parameterization of the given dataset. The predicted bags are used to give precise forecasts and can unveil black box systems. Among synthetic as well as multiple industrial energy datasets we present competitive results in terms of performance, interpretability and pattern detection of the forecast as well as the given time series. Lastly, we present ablations on the main parameters of our approach, evaluated on multiple criterions to provide parameter recommendations.">
+        <tr id="Battery life estimation is critical for optimizing battery performance and guaranteeing minimal degradation for better efficiency and reliability of battery-powered systems. The existing methods to predict the Remaining Useful Life(RUL) of Lithium-ion Batteries (LiBs) neglect the relational dependencies of the battery parameters to model the nonlinear degradation trajectories. We present the Battery GraphNets framework that jointly learns to incorporate a discrete dependency graph structure between battery parameters to capture the complex interactions and the graph-learning algorithm to model the intrinsic battery degradation for RUL prognosis. The proposed method outperforms several popular methods by a significant margin on publicly available battery datasets and achieves SOTA performance. We report the ablation studies to support the efficacy of our approach.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/354ecda4d141b4bec61e3c359ef1cf109f3f29c3" target='_blank'>
-              Bag-of-Future: Time Series Forecasting by Continuous Function Candidates
+          <td><a href="https://www.semanticscholar.org/paper/cb566703033116a6d7a3066560ba85a02a8f83cf" target='_blank'>
+              Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation
               </a>
             </td>
           <td>
-            Hendrik Klopries, Vignesh Chandramouli, Andreas Schwung
+            Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana
           </td>
-          <td>2024-06-18</td>
-          <td>2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE)</td>
+          <td>2024-08-14</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>13</td>
+          <td>3</td>
         </tr>
 
         <tr id="None">
@@ -1537,6 +1492,21 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>0</td>
         </tr>
 
+        <tr id="In the AIOps (Artificial Intelligence for IT Operations) era, accurately forecasting system states is crucial. In microservices systems, this task encounters the challenge of dynamic and complex spatio-temporal relationships among microservice instances, primarily due to dynamic deployments, diverse call paths, and cascading effects among instances. Current time-series forecasting methods, which focus mainly on intrinsic patterns, are insufficient in environments where spatial relationships are critical. Similarly, spatio-temporal graph approaches often neglect the nature of temporal trend, concentrating mostly on message passing between nodes. Moreover, current research in microservices domain frequently underestimates the importance of network metrics and topological structures in capturing the evolving dynamics of systems. This paper introduces STMformer, a model tailored for forecasting system states in microservices environments, capable of handling multi-node and multivariate time series. Our method leverages dynamic network connection data and topological information to assist in modeling the intricate spatio-temporal relationships within the system. Additionally, we integrate the PatchCrossAttention module to compute the impact of cascading effects globally. We have developed a dataset based on a microservices system and conducted comprehensive experiments with STMformer against leading methods. In both short-term and long-term forecasting tasks, our model consistently achieved a 8.6% reduction in MAE(Mean Absolute Error) and a 2.2% reduction in MSE (Mean Squared Error). The source code is available at https://github.com/xuyifeiiie/STMformer.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/d0bdfae99d444ac389fd3590ebcd41ac91a7b2f4" target='_blank'>
+              System States Forecasting of Microservices with Dynamic Spatio-Temporal Data
+              </a>
+            </td>
+          <td>
+            Yifei Xu, Jingguo Ge, Haina Tang, Shuai Ding, Tong Li, Hui Li
+          </td>
+          <td>2024-08-15</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>1</td>
+        </tr>
+
         <tr id="Temporal graphs have gained increasing importance due to their ability to model dynamically evolving relationships. These graphs can be represented through either a stream of edge events or a sequence of graph snapshots. Until now, the development of machine learning methods for both types has occurred largely in isolation, resulting in limited experimental comparison and theoretical crosspollination between the two. In this paper, we introduce Unified Temporal Graph (UTG), a framework that unifies snapshot-based and event-based machine learning models under a single umbrella, enabling models developed for one representation to be applied effectively to datasets of the other. We also propose a novel UTG training procedure to boost the performance of snapshot-based models in the streaming setting. We comprehensively evaluate both snapshot and event-based models across both types of temporal graphs on the temporal link prediction task. Our main findings are threefold: first, when combined with UTG training, snapshotbased models can perform competitively with event-based models such as TGN and GraphMixer even on event datasets. Second, snapshot-based models are at least an order of magnitude faster than most event-based models during inference. Third, while event-based methods such as NAT and DyGFormer outperforms snapshotbased methods on both types of temporal graphs, this is because they leverage joint neighborhood structural features thus emphasizing the potential to incorporate these features into snapshot-based models as well. These findings highlight the importance of comparing model architectures independent of the data format and suggest the potential of combining the efficiency of snapshot-based models with the performance of event-based models in the future.">
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           <td><a href="https://www.semanticscholar.org/paper/7fff2ad251ff12c30edda0c89831123380698940" target='_blank'>
@@ -1563,38 +1533,38 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           </td>
           <td>2024-06-23</td>
           <td>ArXiv</td>
-          <td>0</td>
           <td>1</td>
+          <td>2</td>
         </tr>
 
-        <tr id="Recent State Space Models (SSMs) such as S4, S5, and Mamba have shown remarkable computational benefits in long-range temporal dependency modeling. However, in many sequence modeling problems, the underlying process is inherently modular and it is of interest to have inductive biases that mimic this modular structure. In this paper, we introduce SlotSSMs, a novel framework for incorporating independent mechanisms into SSMs to preserve or encourage separation of information. Unlike conventional SSMs that maintain a monolithic state vector, SlotSSMs maintains the state as a collection of multiple vectors called slots. Crucially, the state transitions are performed independently per slot with sparse interactions across slots implemented via the bottleneck of self-attention. In experiments, we evaluate our model in object-centric video understanding, 3D visual reasoning, and video prediction tasks, which involve modeling multiple objects and their long-range temporal dependencies. We find that our proposed design offers substantial performance gains over existing sequence modeling methods.">
+        <tr id="In the field of machine learning and artificial intelligence, time series forecasting plays a pivotal role across various domains such as finance, healthcare, and weather. However, the task of selecting the most suitable forecasting method for a given dataset is a complex task due to the diversity of data patterns and characteristics. This research aims to address this challenge by proposing a comprehensive benchmark for evaluating and ranking time series forecasting methods across a wide range of datasets. This study investigates the comparative performance of many methods from two prominent time series forecasting frameworks, AutoGluon-Timeseries, and sktime to shed light on their applicability in different real-world scenarios. This research contributes to the field of time series forecasting by providing a robust benchmarking methodology and facilitating informed decision-making when choosing forecasting methods for achieving optimal prediction.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4cfbf0d0740e94e6b1aeeddbb5fbaae03a9029cc" target='_blank'>
-              Slot State Space Models
+          <td><a href="https://www.semanticscholar.org/paper/52869870f9e404f4e69a10b76ca410b00d19088d" target='_blank'>
+              Can time series forecasting be automated? A benchmark and analysis
               </a>
             </td>
           <td>
-            Jindong Jiang, Fei Deng, Gautam Singh, Minseung Lee, Sungjin Ahn
+            Anvitha Thirthapura Sreedhara, Joaquin Vanschoren
           </td>
-          <td>2024-06-18</td>
+          <td>2024-07-23</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>10</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Analyzing sequential data is crucial in many domains, particularly due to the abundance of data collected from the Internet of Things paradigm. Time series classification, the task of categorizing sequential data, has gained prominence, with machine learning approaches demonstrating remarkable performance on public benchmark datasets. However, progress has primarily been in designing architectures for learning representations from raw data at fixed (or ideal) time scales, which can fail to generalize to longer sequences. This work introduces a \textit{compositional representation learning} approach trained on statistically coherent components extracted from sequential data. Based on a multi-scale change space, an unsupervised approach is proposed to segment the sequential data into chunks with similar statistical properties. A sequence-based encoder model is trained in a multi-task setting to learn compositional representations from these temporal components for time series classification. We demonstrate its effectiveness through extensive experiments on publicly available time series classification benchmarks. Evaluating the coherence of segmented components shows its competitive performance on the unsupervised segmentation task.">
+        <tr id="The Variable Subset Forecasting (VSF) problem, where the majority of variables are unavailable in the inference stage of multivariate forecasting, has been an important but under-explored task with broad impacts in many real-world applications. Missing values, absent inter-correlation, and the impracticality of retraining largely hinder the ability of multivariate forecasting models to capture inherent relationships among variables, impacting their performance. However, existing approaches towards these issues either heavily rely on local temporal correlation or face limitations in fully recovering missing information from the unavailable subset, accompanied by notable computational expenses. To address these problems, we propose a novel density estimation solution to recover the information of missing variables via flows-based generative framework. In particular, a novel generative network for time series, namely Time-series Reconstruction Flows (TRF), is proposed to estimate and reconstruct the missing variable subset. In addition, a novel meta-training framework, Variable-Agnostic Meta Learning, has been developed to enhance the generalization ability of TRF, enabling it to adapt to diverse missing variables situations. Finally, extensive experiments are conducted to demonstrate the superiority of our proposed method compared with baseline methods.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/0f550adb5e176a948a8df78a459210a01fa137a7" target='_blank'>
-              Capturing Temporal Components for Time Series Classification
+          <td><a href="https://www.semanticscholar.org/paper/5dcee67fc3a8091cc9cc24057f5c0b9c933f4044" target='_blank'>
+              Reconstructing Missing Variables for Multivariate Time Series Forecasting via Conditional Generative Flows
               </a>
             </td>
           <td>
-            Venkata Ragavendra Vavilthota, Ranjith Ramanathan, Sathyanarayanan N. Aakur
+            Xuanming Hu, Wei Fan, Haifeng Chen, Pengyang Wang, Yanjie Fu
           </td>
-          <td>2024-06-20</td>
-          <td>ArXiv</td>
+          <td>2024-08-01</td>
+          <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
           <td>0</td>
-          <td>7</td>
+          <td>3</td>
         </tr>
 
         <tr id="Transformer, a deep neural network architecture, has long dominated the field of natural language processing and beyond. Nevertheless, the recent introduction of Mamba challenges its supremacy, sparks considerable interest among researchers, and gives rise to a series of Mamba-based models that have exhibited notable potential. This survey paper orchestrates a comprehensive discussion, diving into essential research dimensions, covering: (i) the functioning of the Mamba mechanism and its foundation on the principles of structured state space models; (ii) the proposed improvements and the integration of Mamba with various networks, exploring its potential as a substitute for Transformers; (iii) the combination of Transformers and Mamba to compensate for each other's shortcomings. We have also made efforts to interpret Mamba and Transformer in the framework of kernel functions, allowing for a comparison of their mathematical nature within a unified context. Our paper encompasses the vast majority of improvements related to Mamba to date.">
@@ -1624,7 +1594,7 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2024-07-07</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>2</td>
         </tr>
 
         <tr id="Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that are related to each other via a hierarchical relation. Recent works, however, do not address two important challenges that are typically observed in many demand forecasting applications at large companies. First, many time-series at lower levels of the hierarchy have high sparsity i.e., they have a significant number of zeros. Most HTSF methods do not address this varying sparsity across the hierarchy. Further, they do not scale well to the large size of the real-world hierarchy typically unseen in benchmarks used in literature. We resolve both these challenges by proposing HAILS, a novel probabilistic hierarchical model that enables accurate and calibrated probabilistic forecasts across the hierarchy by adaptively modeling sparse and dense time-series with different distributional assumptions and reconciling them to adhere to hierarchical constraints. We show the scalability and effectiveness of our methods by evaluating them against real-world demand forecasting datasets. We deploy HAILS at a large chemical manufacturing company for a product demand forecasting application with over ten thousand products and observe a significant 8.5\% improvement in forecast accuracy and 23% better improvement for sparse time-series. The enhanced accuracy and scalability make HAILS a valuable tool for improved business planning and customer experience.">
@@ -1642,36 +1612,51 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>7</td>
         </tr>
 
-        <tr id="The Variable Subset Forecasting (VSF) problem, where the majority of variables are unavailable in the inference stage of multivariate forecasting, has been an important but under-explored task with broad impacts in many real-world applications. Missing values, absent inter-correlation, and the impracticality of retraining largely hinder the ability of multivariate forecasting models to capture inherent relationships among variables, impacting their performance. However, existing approaches towards these issues either heavily rely on local temporal correlation or face limitations in fully recovering missing information from the unavailable subset, accompanied by notable computational expenses. To address these problems, we propose a novel density estimation solution to recover the information of missing variables via flows-based generative framework. In particular, a novel generative network for time series, namely Time-series Reconstruction Flows (TRF), is proposed to estimate and reconstruct the missing variable subset. In addition, a novel meta-training framework, Variable-Agnostic Meta Learning, has been developed to enhance the generalization ability of TRF, enabling it to adapt to diverse missing variables situations. Finally, extensive experiments are conducted to demonstrate the superiority of our proposed method compared with baseline methods.">
+        <tr id="Human mobility is intricately influenced by urban contexts spatially and temporally, constituting essential domain knowledge in understanding traffic systems. While existing traffic forecasting models primarily rely on raw traffic data and advanced deep learning techniques, incorporating contextual information remains underexplored due to the lack of effective integration frameworks and the complexity of urban contexts. This study proposes a novel context-aware knowledge graph (CKG) framework to enhance traffic speed forecasting by effectively modeling spatial and temporal contexts. Employing a relation-dependent integration strategy, the framework generates context-aware representations from the spatial and temporal units of CKG to capture spatio-temporal dependencies of urban contexts. A CKG-GNN model, combining the CKG, dual-view multi-head self-attention (MHSA), and graph neural network (GNN), is then designed to predict traffic speed using these context-aware representations. Our experiments demonstrate that CKG's configuration significantly influences embedding performance, with ComplEx and KG2E emerging as optimal for embedding spatial and temporal units, respectively. The CKG-GNN model surpasses benchmark models, achieving an average MAE of $3.46\pm0.01$ and a MAPE of $14.76\pm0.09\%$ for traffic speed predictions from 10 to 120 minutes. The dual-view MHSA analysis reveals the crucial role of relation-dependent features from the context-based view and the model's ability to prioritize recent time slots in prediction from the sequence-based view. The CKG framework's model-agnostic nature suggests its potential applicability in various applications of intelligent transportation systems. Overall, this study underscores the importance of incorporating domain-specific contexts into traffic forecasting and merging context-aware knowledge graphs with neural networks to enhance accuracy.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/5dcee67fc3a8091cc9cc24057f5c0b9c933f4044" target='_blank'>
-              Reconstructing Missing Variables for Multivariate Time Series Forecasting via Conditional Generative Flows
+          <td><a href="https://www.semanticscholar.org/paper/0865c15082b62b4f18ab6dbdb8fae8daa0b14da6" target='_blank'>
+              Context-aware knowledge graph framework for traffic speed forecasting using graph neural network
               </a>
             </td>
           <td>
-            Xuanming Hu, Wei Fan, Haifeng Chen, Pengyang Wang, Yanjie Fu
+            Yatao Zhang, Yi Wang, Song Gao, Martin Raubal
           </td>
-          <td>2024-08-01</td>
-          <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
+          <td>2024-07-25</td>
+          <td>ArXiv</td>
           <td>0</td>
           <td>2</td>
         </tr>
 
-        <tr id="In the field of machine learning and artificial intelligence, time series forecasting plays a pivotal role across various domains such as finance, healthcare, and weather. However, the task of selecting the most suitable forecasting method for a given dataset is a complex task due to the diversity of data patterns and characteristics. This research aims to address this challenge by proposing a comprehensive benchmark for evaluating and ranking time series forecasting methods across a wide range of datasets. This study investigates the comparative performance of many methods from two prominent time series forecasting frameworks, AutoGluon-Timeseries, and sktime to shed light on their applicability in different real-world scenarios. This research contributes to the field of time series forecasting by providing a robust benchmarking methodology and facilitating informed decision-making when choosing forecasting methods for achieving optimal prediction.">
+        <tr id="Recently, the emerging graph Transformers have made significant advancements for node classification on graphs. In most graph Transformers, a crucial step involves transforming the input graph into token sequences as the model input, enabling Transformer to effectively learn the node representations. However, we observe that existing methods only express partial graph information of nodes through single-type token generation. Consequently, they require tailored strategies to encode additional graph-specific features into the Transformer to ensure the quality of node representation learning, limiting the model flexibility to handle diverse graphs. To this end, we propose a new graph Transformer called NTFormer to address this issue. NTFormer introduces a novel token generator called Node2Par, which constructs various token sequences using different token elements for each node. This flexibility allows Node2Par to generate valuable token sequences from different perspectives, ensuring comprehensive expression of rich graph features. Benefiting from the merits of Node2Par, NTFormer only leverages a Transformer-based backbone without graph-specific modifications to learn node representations, eliminating the need for graph-specific modifications. Extensive experiments conducted on various benchmark datasets containing homophily and heterophily graphs with different scales demonstrate the superiority of NTFormer over representative graph Transformers and graph neural networks for node classification.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/52869870f9e404f4e69a10b76ca410b00d19088d" target='_blank'>
-              Can time series forecasting be automated? A benchmark and analysis
+          <td><a href="https://www.semanticscholar.org/paper/012b888fe3db6a2cd56d30404db3dd58e423b8bd" target='_blank'>
+              NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification
               </a>
             </td>
           <td>
-            Anvitha Thirthapura Sreedhara, Joaquin Vanschoren
+            Jinsong Chen, Siyu Jiang, Kun He
           </td>
-          <td>2024-07-23</td>
+          <td>2024-06-27</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>1</td>
         </tr>
 
+        <tr id="While most time series are non-stationary, it is inevitable for models to face the distribution shift issue in time series forecasting. Existing solutions manipulate statistical measures (usually mean and std.) to adjust time series distribution. However, these operations can be theoretically seen as the transformation towards zero frequency component of the spectrum which cannot reveal full distribution information and would further lead to information utilization bottleneck in normalization, thus hindering forecasting performance. To address this problem, we propose to utilize the whole frequency spectrum to transform time series to make full use of data distribution from the frequency perspective. We present a deep frequency derivative learning framework, DERITS, for non-stationary time series forecasting. Specifically, DERITS is built upon a novel reversible transformation, namely Frequency Derivative Transformation (FDT) that makes signals derived in the frequency domain to acquire more stationary frequency representations. Then, we propose the Order-adaptive Fourier Convolution Network to conduct adaptive frequency filtering and learning. Furthermore, we organize DERITS as a parallel-stacked architecture for the multi-order derivation and fusion for forecasting. Finally, we conduct extensive experiments on several datasets which show the consistent superiority in both time series forecasting and shift alleviation.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/fa3605455e65d9ad0e7e7cb1252d54167b777e50" target='_blank'>
+              Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
+              </a>
+            </td>
+          <td>
+            Wei Fan, Kun Yi, Hangting Ye, Zhiyuan Ning, Qi Zhang, Ning An
+          </td>
+          <td>2024-06-29</td>
+          <td>ArXiv</td>
+          <td>1</td>
+          <td>4</td>
+        </tr>
+
         <tr id="Implicit graph neural networks have gained popularity in recent years as they capture long-range dependencies while improving predictive performance in static graphs. Despite the tussle between performance degradation due to the oversmoothing of learned embeddings and long-range dependency being more pronounced in dynamic graphs, as features are aggregated both across neighborhood and time, no prior work has proposed an implicit graph neural model in a dynamic setting. In this paper, we present Implicit Dynamic Graph Neural Network (IDGNN) a novel implicit neural network for dynamic graphs which is the first of its kind. A key characteristic of IDGNN is that it demonstrably is well-posed, i.e., it is theoretically guaranteed to have a fixed-point representation. We then demonstrate that the standard iterative algorithm often used to train implicit models is computationally expensive in our dynamic setting as it involves computing gradients, which themselves have to be estimated in an iterative manner. To overcome this, we pose an equivalent bilevel optimization problem and propose an efficient single-loop training algorithm that avoids iterative computation by maintaining moving averages of key components of the gradients. We conduct extensive experiments on real-world datasets on both classification and regression tasks to demonstrate the superiority of our approach over the state-of-the-art baselines. We also demonstrate that our bi-level optimization framework maintains the performance of the expensive iterative algorithm while obtaining up to \textbf{1600x} speed-up.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/3093322772df05295e0448968c691f9fa9b30d06" target='_blank'>
@@ -1687,79 +1672,79 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>4</td>
         </tr>
 
-        <tr id="This paper introduces BiLSTM-MLAM, a novel multi-scale time series prediction model. Initially, the approach utilizes bidirectional long short-term memory to capture information from both forward and backward directions in time series data. Subsequently, a multi-scale patch segmentation module generates various long sequences composed of equal-length segments, enabling the model to capture data patterns across multiple time scales by adjusting segment lengths. Finally, the local attention mechanism enhances feature extraction by accurately identifying and weighting important time segments, thereby strengthening the model’s understanding of the local features of the time series, followed by feature fusion. The model demonstrates outstanding performance in time series prediction tasks by effectively capturing sequence information across various time scales. Experimental validation illustrates the superior performance of BiLSTM-MLAM compared to six baseline methods across multiple datasets. When predicting the remaining life of aircraft engines, BiLSTM-MLAM outperforms the best baseline model by 6.66% in RMSE and 11.50% in MAE. In the LTE dataset, it achieves RMSE improvements of 12.77% and MAE enhancements of 3.06%, while in the load dataset, it demonstrates RMSE enhancements of 17.96% and MAE improvements of 30.39%. Additionally, ablation experiments confirm the positive impact of each module on prediction accuracy. Through segment length parameter tuning experiments, combining different segment lengths has resulted in lower prediction errors, affirming the effectiveness of the multi-scale fusion strategy in enhancing prediction accuracy by integrating information from multiple time scales.">
+        <tr id="Accurate evaluation of forecasting models is essential for ensuring reliable predictions. Current practices for evaluating and comparing forecasting models focus on summarising performance into a single score, using metrics such as SMAPE. We hypothesize that averaging performance over all samples dilutes relevant information about the relative performance of models. Particularly, conditions in which this relative performance is different than the overall accuracy. We address this limitation by proposing a novel framework for evaluating univariate time series forecasting models from multiple perspectives, such as one-step ahead forecasting versus multi-step ahead forecasting. We show the advantages of this framework by comparing a state-of-the-art deep learning approach with classical forecasting techniques. While classical methods (e.g. ARIMA) are long-standing approaches to forecasting, deep neural networks (e.g. NHITS) have recently shown state-of-the-art forecasting performance in benchmark datasets. We conducted extensive experiments that show NHITS generally performs best, but its superiority varies with forecasting conditions. For instance, concerning the forecasting horizon, NHITS only outperforms classical approaches for multi-step ahead forecasting. Another relevant insight is that, when dealing with anomalies, NHITS is outperformed by methods such as Theta. These findings highlight the importance of aspect-based model evaluation.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ea6da7e837a4e1fc91974188bf6ea513a85abf72" target='_blank'>
-              BiLSTM-MLAM: A Multi-Scale Time Series Prediction Model for Sensor Data Based on Bi-LSTM and Local Attention Mechanisms
+          <td><a href="https://www.semanticscholar.org/paper/11c0631ee5b9502a277f7561c809f6a420e0df60" target='_blank'>
+              Forecasting with Deep Learning: Beyond Average of Average of Average Performance
               </a>
             </td>
           <td>
-            Yongxin Fan, Qian Tang, Yangming Guo, Yifei Wei
+            Vítor Cerqueira, Luis Roque, Carlos Soares
           </td>
-          <td>2024-06-01</td>
-          <td>Sensors (Basel, Switzerland)</td>
+          <td>2024-06-24</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>14</td>
         </tr>
 
-        <tr id="While most time series are non-stationary, it is inevitable for models to face the distribution shift issue in time series forecasting. Existing solutions manipulate statistical measures (usually mean and std.) to adjust time series distribution. However, these operations can be theoretically seen as the transformation towards zero frequency component of the spectrum which cannot reveal full distribution information and would further lead to information utilization bottleneck in normalization, thus hindering forecasting performance. To address this problem, we propose to utilize the whole frequency spectrum to transform time series to make full use of data distribution from the frequency perspective. We present a deep frequency derivative learning framework, DERITS, for non-stationary time series forecasting. Specifically, DERITS is built upon a novel reversible transformation, namely Frequency Derivative Transformation (FDT) that makes signals derived in the frequency domain to acquire more stationary frequency representations. Then, we propose the Order-adaptive Fourier Convolution Network to conduct adaptive frequency filtering and learning. Furthermore, we organize DERITS as a parallel-stacked architecture for the multi-order derivation and fusion for forecasting. Finally, we conduct extensive experiments on several datasets which show the consistent superiority in both time series forecasting and shift alleviation.">
+        <tr id="While tokenized graph Transformers have demonstrated strong performance in node classification tasks, their reliance on a limited subset of nodes with high similarity scores for constructing token sequences overlooks valuable information from other nodes, hindering their ability to fully harness graph information for learning optimal node representations. To address this limitation, we propose a novel graph Transformer called GCFormer. Unlike previous approaches, GCFormer develops a hybrid token generator to create two types of token sequences, positive and negative, to capture diverse graph information. And a tailored Transformer-based backbone is adopted to learn meaningful node representations from these generated token sequences. Additionally, GCFormer introduces contrastive learning to extract valuable information from both positive and negative token sequences, enhancing the quality of learned node representations. Extensive experimental results across various datasets, including homophily and heterophily graphs, demonstrate the superiority of GCFormer in node classification, when compared to representative graph neural networks (GNNs) and graph Transformers.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/fa3605455e65d9ad0e7e7cb1252d54167b777e50" target='_blank'>
-              Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/e5e10bd5b694099c88e649d9502adecd0acd52f7" target='_blank'>
+              Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers
               </a>
             </td>
           <td>
-            Wei Fan, Kun Yi, Hangting Ye, Zhiyuan Ning, Qi Zhang, Ning An
+            Jinsong Chen, Hanpeng Liu, J. Hopcroft, Kun He
           </td>
-          <td>2024-06-29</td>
+          <td>2024-06-27</td>
           <td>ArXiv</td>
-          <td>1</td>
-          <td>3</td>
+          <td>0</td>
+          <td>75</td>
         </tr>
 
-        <tr id="Traffic forecasting is crucial for smart cities and intelligent transportation initiatives, where deep learning has made significant progress in modeling complex spatio-temporal patterns in recent years. However, current public datasets have limitations in reflecting the ultra-dynamic nature of real-world scenarios, characterized by continuously evolving infrastructures, varying temporal distributions, and temporal gaps due to sensor downtimes or changes in traffic patterns. These limitations inevitably restrict the practical applicability of existing traffic forecasting datasets. To bridge this gap, we present XXLTraffic, the largest available public traffic dataset with the longest timespan and increasing number of sensor nodes over the multiple years observed in the data, curated to support research in ultra-dynamic forecasting. Our benchmark includes both typical time-series forecasting settings with hourly and daily aggregated data and novel configurations that introduce gaps and down-sample the training size to better simulate practical constraints. We anticipate the new XXLTraffic will provide a fresh perspective for the time-series and traffic forecasting communities. It would also offer a robust platform for developing and evaluating models designed to tackle ultra-dynamic and extremely long forecasting problems. Our dataset supplements existing spatio-temporal data resources and leads to new research directions in this domain.">
+        <tr id="Link prediction models can benefit from incorporating textual descriptions of entities and relations, enabling fully inductive learning and flexibility in dynamic graphs. We address the challenge of also capturing rich structured information about the local neighbourhood of entities and their relations, by introducing a Transformer-based approach that effectively integrates textual descriptions with graph structure, reducing the reliance on resource-intensive text encoders. Our experiments on three challenging datasets show that our Fast-and-Frugal Text-Graph (FnF-TG) Transformers achieve superior performance compared to the previous state-of-the-art methods, while maintaining efficiency and scalability.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/11aaf60abe4ce0825c53f16f0d34e6f3d8d20624" target='_blank'>
-              XXLTraffic: Expanding and Extremely Long Traffic Dataset for Ultra-Dynamic Forecasting Challenges
+          <td><a href="https://www.semanticscholar.org/paper/749af11854db6b02422c6073e34d6bcae5781e0d" target='_blank'>
+              Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors
               </a>
             </td>
           <td>
-            Du Yin, Hao Xue, Arian Prabowo, Shuang Ao, Flora D. Salim
+            Andrei Catalin Coman, Christos Theodoropoulos, M. Moens, James Henderson
           </td>
-          <td>2024-06-18</td>
+          <td>2024-08-13</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>6</td>
         </tr>
 
-        <tr id="Stock prices, as an economic indicator, reflect changes in economic development and market conditions. Traditional stock price prediction models often only consider time-series data and are limited by the mechanisms of the models themselves. Some deep learning models have high computational costs, depend on a large amount of high-quality data, and have poor interpretations, making it difficult to intuitively understand the driving factors behind the predictions. Some studies have used deep learning models to extract text features and combine them with price data to make joint predictions, but there are issues with dealing with information noise, accurate extraction of text sentiment, and how to efficiently fuse text and numerical data. To address these issues in this paper, we propose a background-aware multi-source fusion financial trend forecasting mechanism. The system leverages a large language model to extract key information from policy and stock review texts, utilizing the MacBERT model to generate feature vectors. These vectors are then integrated with stock price data to form comprehensive feature representations. These integrated features are input into a neural network comprising various deep learning architectures. By integrating multiple data sources, the system offers a holistic view of market dynamics. It harnesses the comprehensive analytical and interpretative capabilities of large language models, retaining deep semantic and sentiment information from policy texts to provide richer input features for stock trend prediction. Additionally, we compare the accuracy of six models (LSTM, BiLSTM, MogrifierLSTM, GRU, ST-LSTM, SwinLSTM). The results demonstrate that our system achieves generally better accuracy in predicting stock movements, attributed to the incorporation of large language model processing, policy information, and other influential features.">
+        <tr id="Analyzing time-series data that may contain personal information, particularly in the medical field, presents serious privacy concerns. Sensitive health data from patients is often used to train machine-learning models for diagnostics and ongoing care. Assessing the privacy risk of such models is crucial to making knowledgeable decisions on whether to use a model in production, share it with third parties, or deploy it in patients homes. Membership Inference Attacks (MIA) are a key method for this kind of evaluation, however time-series prediction models have not been thoroughly studied in this context. We explore existing MIA techniques on time-series models, and introduce new features, focusing on the seasonality and trend components of the data. Seasonality is estimated using a multivariate Fourier transform, and a low-degree polynomial is used to approximate trends. We applied these techniques to various types of time-series models, using datasets from the health domain. Our results demonstrate that these new features enhance the effectiveness of MIAs in identifying membership, improving the understanding of privacy risks in medical data applications.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/71bb0a6dfcebd80314575c81e752deb73c312df2" target='_blank'>
-              Background-aware Multi-source Fusion Financial Trend Forecasting Mechanism
+          <td><a href="https://www.semanticscholar.org/paper/fc26ca37aef313f1b2c96b71c1f95662bfd7a6e2" target='_blank'>
+              Membership Inference Attacks Against Time-Series Models
               </a>
             </td>
           <td>
-            Fengting Mo, Shanshan Yan, Yinhao Xiao
+            Noam Koren, Abigail Goldsteen, Ariel Farkash, Guy Amit
           </td>
-          <td>2024-07-01</td>
+          <td>2024-07-03</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>8</td>
         </tr>
 
-        <tr id="Accurate evaluation of forecasting models is essential for ensuring reliable predictions. Current practices for evaluating and comparing forecasting models focus on summarising performance into a single score, using metrics such as SMAPE. We hypothesize that averaging performance over all samples dilutes relevant information about the relative performance of models. Particularly, conditions in which this relative performance is different than the overall accuracy. We address this limitation by proposing a novel framework for evaluating univariate time series forecasting models from multiple perspectives, such as one-step ahead forecasting versus multi-step ahead forecasting. We show the advantages of this framework by comparing a state-of-the-art deep learning approach with classical forecasting techniques. While classical methods (e.g. ARIMA) are long-standing approaches to forecasting, deep neural networks (e.g. NHITS) have recently shown state-of-the-art forecasting performance in benchmark datasets. We conducted extensive experiments that show NHITS generally performs best, but its superiority varies with forecasting conditions. For instance, concerning the forecasting horizon, NHITS only outperforms classical approaches for multi-step ahead forecasting. Another relevant insight is that, when dealing with anomalies, NHITS is outperformed by methods such as Theta. These findings highlight the importance of aspect-based model evaluation.">
+        <tr id="Large language models (LLMs) and their fine-tuning techniques have demonstrated superior performance in various language understanding and generation tasks. This paper explores fine-tuning LLMs for stock return forecasting with financial newsflow. In quantitative investing, return forecasting is fundamental for subsequent tasks like stock picking, portfolio optimization, etc. We formulate the model to include text representation and forecasting modules. We propose to compare the encoder-only and decoder-only LLMs, considering they generate text representations in distinct ways. The impact of these different representations on forecasting performance remains an open question. Meanwhile, we compare two simple methods of integrating LLMs' token-level representations into the forecasting module. The experiments on real news and investment universes reveal that: (1) aggregated representations from LLMs' token-level embeddings generally produce return predictions that enhance the performance of long-only and long-short portfolios; (2) in the relatively large investment universe, the decoder LLMs-based prediction model leads to stronger portfolios, whereas in the small universes, there are no consistent winners. Among the three LLMs studied (DeBERTa, Mistral, Llama), Mistral performs more robustly across different universes; (3) return predictions derived from LLMs' text representations are a strong signal for portfolio construction, outperforming conventional sentiment scores.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/11c0631ee5b9502a277f7561c809f6a420e0df60" target='_blank'>
-              Forecasting with Deep Learning: Beyond Average of Average of Average Performance
+          <td><a href="https://www.semanticscholar.org/paper/f6c0a84ac1f1fe79cfde96f4e163d0d69f9c06cb" target='_blank'>
+              Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow
               </a>
             </td>
           <td>
-            Vítor Cerqueira, Luis Roque, Carlos Soares
+            Tian Guo, E. Hauptmann
           </td>
-          <td>2024-06-24</td>
+          <td>2024-07-25</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>13</td>
+          <td>3</td>
         </tr>
 
         <tr id="Despite the success of Heterogeneous Graph Neural Networks (HGNNs) in modeling real-world Heterogeneous Information Networks (HINs), challenges such as expressiveness limitations and over-smoothing have prompted researchers to explore Graph Transformers (GTs) for enhanced HIN representation learning. However, research on GT in HINs remains limited, with two key shortcomings in existing work: (1) A node's neighbors at different distances in HINs convey diverse semantics. Unfortunately, existing methods ignore such differences and uniformly treat neighbors within a given distance in a coarse manner, which results in semantic confusion. (2) Nodes in HINs have various types, each with unique semantics. Nevertheless, existing methods mix nodes of different types during neighbor aggregation, hindering the capture of proper correlations between nodes of diverse types. To bridge these gaps, we design an innovative structure named (k,t)-ring neighborhood, where nodes are initially organized by their distance, forming different non-overlapping k-ring neighborhoods for each distance. Within each k-ring structure, nodes are further categorized into different groups according to their types, thus emphasizing the heterogeneity of both distances and types in HINs naturally. Based on this structure, we propose a novel Hierarchical Heterogeneous Graph Transformer (HHGT) model, which seamlessly integrates a Type-level Transformer for aggregating nodes of different types within each k-ring neighborhood, followed by a Ring-level Transformer for aggregating different k-ring neighborhoods in a hierarchical manner. Extensive experiments are conducted on downstream tasks to verify HHGT's superiority over 14 baselines, with a notable improvement of up to 24.75% in NMI and 29.25% in ARI for node clustering task on the ACM dataset compared to the best baseline.">
@@ -1777,19 +1762,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>4</td>
         </tr>
 
-        <tr id="The most fundamental capability of modern AI methods such as Large Language Models (LLMs) is the ability to predict the next token in a long sequence of tokens, known as ``sequence modeling."Although the Transformers model is the current dominant approach to sequence modeling, its quadratic computational cost with respect to sequence length is a significant drawback. State-space models (SSMs) offer a promising alternative due to their linear decoding efficiency and high parallelizability during training. However, existing SSMs often rely on seemingly ad hoc linear recurrence designs. In this work, we explore SSM design through the lens of online learning, conceptualizing SSMs as meta-modules for specific online learning problems. This approach links SSM design to formulating precise online learning objectives, with state transition rules derived from optimizing these objectives. Based on this insight, we introduce a novel deep SSM architecture based on the implicit update for optimizing an online regression objective. Our experimental results show that our models outperform state-of-the-art SSMs, including the Mamba model, on standard sequence modeling benchmarks and language modeling tasks.">
+        <tr id="Time series classification with missing data is a prevalent issue in time series analysis, as temporal data often contain missing values in practical applications. The traditional two-stage approach, which handles imputation and classification separately, can result in sub-optimal performance as label information is not utilized in the imputation process. On the other hand, a one-stage approach can learn features under missing information, but feature representation is limited as imputed errors are propagated in the classification process. To overcome these challenges, this study proposes an end-to-end neural network that unifies data imputation and representation learning within a single framework, allowing the imputation process to take advantage of label information. Differing from previous methods, our approach places less emphasis on the accuracy of imputation data and instead prioritizes classification performance. A specifically designed multi-scale feature learning module is implemented to extract useful information from the noise-imputation data. The proposed model is evaluated on 68 univariate time series datasets from the UCR archive, as well as a multivariate time series dataset with various missing data ratios and 4 real-world datasets with missing information. The results indicate that the proposed model outperforms state-of-the-art approaches for incomplete time series classification, particularly in scenarios with high levels of missing data.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/af4e8a4bf3effaf04a141e1048276c77ea8585a9" target='_blank'>
-              Longhorn: State Space Models are Amortized Online Learners
+          <td><a href="https://www.semanticscholar.org/paper/5eecace8149eba147315a0809afa377389f6ba24" target='_blank'>
+              An End-to-End Model for Time Series Classification In the Presence of Missing Values
               </a>
             </td>
           <td>
-            Bo Liu, Rui Wang, Lemeng Wu, Yihao Feng, Peter Stone, Qian Liu
+            Pengshuai Yao, Mengna Liu, Xu Cheng, Fan Shi, Huan Li, Xiufeng Liu, Shengyong Chen
           </td>
-          <td>2024-07-19</td>
+          <td>2024-08-11</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>16</td>
+          <td>1</td>
         </tr>
 
         <tr id="Message-Passing Neural Networks (MPNNs) are extensively employed in graph learning tasks but suffer from limitations such as the restricted scope of information exchange, by being confined to neighboring nodes during each round of message passing. Various strategies have been proposed to address these limitations, including incorporating virtual nodes to facilitate global information exchange. In this study, we introduce the Hierarchical Support Graph (HSG), an extension of the virtual node concept created through recursive coarsening of the original graph. This approach provides a flexible framework for enhancing information flow in graphs, independent of the specific MPNN layers utilized. We present a theoretical analysis of HSGs, investigate their empirical performance, and demonstrate that HSGs can surpass other methods augmented with virtual nodes, achieving state-of-the-art results across multiple datasets.">
@@ -1804,22 +1789,7 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2024-06-22</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>18</td>
-        </tr>
-
-        <tr id="Time series generation is a crucial research topic in the area of deep learning, which can be used for data augmentation, imputing missing values, and forecasting. Currently, latent diffusion models are ascending to the forefront of generative modeling for many important data representations. Being the most pivotal in the computer vision domain, latent diffusion models have also recently attracted interest in other communities, including NLP, Speech, and Geometric Space. In this work, we propose TimeLDM, a novel latent diffusion model for high-quality time series generation. TimeLDM is composed of a variational autoencoder that encodes time series into an informative and smoothed latent content and a latent diffusion model operating in the latent space to generate latent information. We evaluate the ability of our method to generate synthetic time series with simulated and realistic datasets, benchmark the performance against existing state-of-the-art methods. Qualitatively and quantitatively, we find that the proposed TimeLDM persistently delivers high-quality generated time series. Sores from Context-FID and Discriminative indicate that TimeLDM consistently and significantly outperforms current state-of-the-art benchmarks with an average improvement of 3.4$\times$ and 3.8$\times$, respectively. Further studies demonstrate that our method presents better performance on different lengths of time series data generation. To the best of our knowledge, this is the first study to explore the potential of the latent diffusion model for unconditional time series generation and establish a new baseline for synthetic time series.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c127c4b179c7e5d61f7a4347e0003557a02536f1" target='_blank'>
-              TimeLDM: Latent Diffusion Model for Unconditional Time Series Generation
-              </a>
-            </td>
-          <td>
-            Jian Qian, Miao Sun, Sifan Zhou, Biao Wan, Minhao Li, Patrick Chiang
-          </td>
-          <td>2024-07-05</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>0</td>
+          <td>19</td>
         </tr>
 
         <tr id="The success of large pretrained models in natural language processing (NLP) and computer vision (CV) has opened new avenues for constructing foundation models for time series forecasting (TSF). Traditional TSF foundation models rely heavily on numerical data fitting. In contrast, the human brain is inherently skilled at processing visual information, prefer predicting future trends by observing visualized sequences. From a biomimetic perspective, utilizing models to directly process numerical sequences might not be the most effective route to achieving Artificial General Intelligence (AGI). This paper proposes ViTime, a novel Visual Intelligence-based foundation model for TSF. ViTime overcomes the limitations of numerical time series data fitting by utilizing visual data processing paradigms and employs a innovative data synthesis method during training, called Real Time Series (RealTS). Experiments on a diverse set of previously unseen forecasting datasets demonstrate that ViTime achieves state-of-the-art zero-shot performance, even surpassing the best individually trained supervised models in some situations. These findings suggest that visual intelligence can significantly enhance time series analysis and forecasting, paving the way for more advanced and versatile models in the field. The code for our framework is accessible at https://github.com/IkeYang/ViTime.">
@@ -1837,34 +1807,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>8</td>
         </tr>
 
-        <tr id="Large language models (LLMs) and their fine-tuning techniques have demonstrated superior performance in various language understanding and generation tasks. This paper explores fine-tuning LLMs for stock return forecasting with financial newsflow. In quantitative investing, return forecasting is fundamental for subsequent tasks like stock picking, portfolio optimization, etc. We formulate the model to include text representation and forecasting modules. We propose to compare the encoder-only and decoder-only LLMs, considering they generate text representations in distinct ways. The impact of these different representations on forecasting performance remains an open question. Meanwhile, we compare two simple methods of integrating LLMs' token-level representations into the forecasting module. The experiments on real news and investment universes reveal that: (1) aggregated representations from LLMs' token-level embeddings generally produce return predictions that enhance the performance of long-only and long-short portfolios; (2) in the relatively large investment universe, the decoder LLMs-based prediction model leads to stronger portfolios, whereas in the small universes, there are no consistent winners. Among the three LLMs studied (DeBERTa, Mistral, Llama), Mistral performs more robustly across different universes; (3) return predictions derived from LLMs' text representations are a strong signal for portfolio construction, outperforming conventional sentiment scores.">
+        <tr id="Time series generation is a crucial research topic in the area of deep learning, which can be used for data augmentation, imputing missing values, and forecasting. Currently, latent diffusion models are ascending to the forefront of generative modeling for many important data representations. Being the most pivotal in the computer vision domain, latent diffusion models have also recently attracted interest in other communities, including NLP, Speech, and Geometric Space. In this work, we propose TimeLDM, a novel latent diffusion model for high-quality time series generation. TimeLDM is composed of a variational autoencoder that encodes time series into an informative and smoothed latent content and a latent diffusion model operating in the latent space to generate latent information. We evaluate the ability of our method to generate synthetic time series with simulated and realistic datasets, benchmark the performance against existing state-of-the-art methods. Qualitatively and quantitatively, we find that the proposed TimeLDM persistently delivers high-quality generated time series. Sores from Context-FID and Discriminative indicate that TimeLDM consistently and significantly outperforms current state-of-the-art benchmarks with an average improvement of 3.4$\times$ and 3.8$\times$, respectively. Further studies demonstrate that our method presents better performance on different lengths of time series data generation. To the best of our knowledge, this is the first study to explore the potential of the latent diffusion model for unconditional time series generation and establish a new baseline for synthetic time series.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f6c0a84ac1f1fe79cfde96f4e163d0d69f9c06cb" target='_blank'>
-              Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow
+          <td><a href="https://www.semanticscholar.org/paper/c127c4b179c7e5d61f7a4347e0003557a02536f1" target='_blank'>
+              TimeLDM: Latent Diffusion Model for Unconditional Time Series Generation
               </a>
             </td>
           <td>
-            Tian Guo, E. Hauptmann
+            Jian Qian, Miao Sun, Sifan Zhou, Biao Wan, Minhao Li, Patrick Chiang
           </td>
-          <td>2024-07-25</td>
+          <td>2024-07-05</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Multi-agent trajectory prediction is crucial to autonomous driving and understanding the surrounding environment. Learning-based approaches for multi-agent trajectory prediction, such as primarily relying on graph neural networks, graph transformers, and hypergraph neural networks, have demonstrated outstanding performance on real-world datasets in recent years. However, the hypergraph transformer-based method for trajectory prediction is yet to be explored. Therefore, we present a MultiscAle Relational Transformer (MART) network for multi-agent trajectory prediction. MART is a hypergraph transformer architecture to consider individual and group behaviors in transformer machinery. The core module of MART is the encoder, which comprises a Pair-wise Relational Transformer (PRT) and a Hyper Relational Transformer (HRT). The encoder extends the capabilities of a relational transformer by introducing HRT, which integrates hyperedge features into the transformer mechanism, promoting attention weights to focus on group-wise relations. In addition, we propose an Adaptive Group Estimator (AGE) designed to infer complex group relations in real-world environments. Extensive experiments on three real-world datasets (NBA, SDD, and ETH-UCY) demonstrate that our method achieves state-of-the-art performance, enhancing ADE/FDE by 3.9%/11.8% on the NBA dataset. Code is available at https://github.com/gist-ailab/MART.">
+        <tr id="Causal discovery from time-series data aims to capture both intra-slice (contemporaneous) and inter-slice (time-lagged) causality between variables within the temporal chain, which is crucial for various scientific disciplines. Compared to causal discovery from non-time-series data, causal discovery from time-series data necessitates more serialized samples with a larger amount of observed time steps. To address the challenges, we propose a novel gradient-based causal discovery approach STIC, which focuses on \textbf{S}hort-\textbf{T}erm \textbf{I}nvariance using \textbf{C}onvolutional neural networks to uncover the causal relationships from time-series data. Specifically, STIC leverages both the short-term time and mechanism invariance of causality within each window observation, which possesses the property of independence, to enhance sample efficiency. Furthermore, we construct two causal convolution kernels, which correspond to the short-term time and mechanism invariance respectively, to estimate the window causal graph. To demonstrate the necessity of convolutional neural networks for causal discovery from time-series data, we theoretically derive the equivalence between convolution and the underlying generative principle of time-series data under the assumption that the additive noise model is identifiable. Experimental evaluations conducted on both synthetic and FMRI benchmark datasets demonstrate that our STIC outperforms baselines significantly and achieves the state-of-the-art performance, particularly when the datasets contain a limited number of observed time steps. Code is available at \url{https://github.com/HITshenrj/STIC}.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6d30fe4d8196a1c8f984e6dc8d8f3783c98168cb" target='_blank'>
-              MART: MultiscAle Relational Transformer Networks for Multi-agent Trajectory Prediction
+          <td><a href="https://www.semanticscholar.org/paper/801fe20484b5bf27c8c2899d947656d8aa4dff2e" target='_blank'>
+              Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks
               </a>
             </td>
           <td>
-            Seongju Lee, Junseok Lee, Yeonguk Yu, Taeri Kim, Kyoobin Lee
+            Rujia Shen, Boran Wang, Chao Zhao, Yi Guan, Jingchi Jiang
           </td>
-          <td>2024-07-31</td>
+          <td>2024-08-15</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>16</td>
+          <td>3</td>
         </tr>
 
         <tr id="Time series forecasting has played an important role in different industries, including economics, energy, weather, and healthcare. RNN-based methods have shown promising potential due to their strong ability to model the interaction of time and variables. However, they are prone to gradient issues like gradient explosion and vanishing gradients. And the prediction accuracy is not high. To address the above issues, this paper proposes a Fractional-order Lipschitz Recurrent Neural Network with a Frequency-domain Gated Attention mechanism (FLRNN-FGA). There are three major components: the Fractional-order Lipschitz Recurrent Neural Network (FLRNN), frequency module, and gated attention mechanism. In the FLRNN, fractional-order integration is employed to describe the dynamic systems accurately. It can capture long-term dependencies and improve prediction accuracy. Lipschitz weight matrices are applied to alleviate the gradient issues. In the frequency module, temporal data are transformed into the frequency domain by Fourier transform. Frequency domain processing can reduce the computational complexity of the model. In the gated attention mechanism, the gated structure can regulate attention information transmission to reduce the number of model parameters. Extensive experimental results on five real-world benchmark datasets demonstrate the effectiveness of FLRNN-FGA compared with the state-of-the-art methods.">
@@ -1882,19 +1852,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>7</td>
         </tr>
 
-        <tr id="Evaluating the contribution of individual data points to a model's prediction is critical for interpreting model predictions and improving model performance. Existing data contribution methods have been applied to various data types, including tabular data, images, and texts; however, their primary focus has been on i.i.d. settings. Despite the pressing need for principled approaches tailored to time series datasets, the problem of estimating data contribution in such settings remains unexplored, possibly due to challenges associated with handling inherent temporal dependencies. This paper introduces TimeInf, a data contribution estimation method for time-series datasets. TimeInf uses influence functions to attribute model predictions to individual time points while preserving temporal structures. Our extensive empirical results demonstrate that TimeInf outperforms state-of-the-art methods in identifying harmful anomalies and helpful time points for forecasting. Additionally, TimeInf offers intuitive and interpretable attributions of data values, allowing us to easily distinguish diverse anomaly patterns through visualizations.">
+        <tr id="Multi-agent trajectory prediction is crucial to autonomous driving and understanding the surrounding environment. Learning-based approaches for multi-agent trajectory prediction, such as primarily relying on graph neural networks, graph transformers, and hypergraph neural networks, have demonstrated outstanding performance on real-world datasets in recent years. However, the hypergraph transformer-based method for trajectory prediction is yet to be explored. Therefore, we present a MultiscAle Relational Transformer (MART) network for multi-agent trajectory prediction. MART is a hypergraph transformer architecture to consider individual and group behaviors in transformer machinery. The core module of MART is the encoder, which comprises a Pair-wise Relational Transformer (PRT) and a Hyper Relational Transformer (HRT). The encoder extends the capabilities of a relational transformer by introducing HRT, which integrates hyperedge features into the transformer mechanism, promoting attention weights to focus on group-wise relations. In addition, we propose an Adaptive Group Estimator (AGE) designed to infer complex group relations in real-world environments. Extensive experiments on three real-world datasets (NBA, SDD, and ETH-UCY) demonstrate that our method achieves state-of-the-art performance, enhancing ADE/FDE by 3.9%/11.8% on the NBA dataset. Code is available at https://github.com/gist-ailab/MART.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/3cf948c645144f6d8565608c19f9779114df6df2" target='_blank'>
-              TimeInf: Time Series Data Contribution via Influence Functions
+          <td><a href="https://www.semanticscholar.org/paper/6d30fe4d8196a1c8f984e6dc8d8f3783c98168cb" target='_blank'>
+              MART: MultiscAle Relational Transformer Networks for Multi-agent Trajectory Prediction
               </a>
             </td>
           <td>
-            Yizi Zhang, Jingyan Shen, Xiaoxue Xiong, Yongchan Kwon
+            Seongju Lee, Junseok Lee, Yeonguk Yu, Taeri Kim, Kyoobin Lee
           </td>
-          <td>2024-07-21</td>
+          <td>2024-07-31</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>16</td>
         </tr>
 
         <tr id="Graphs have emerged as critical data structures for content analysis in various domains, such as social network analysis, bioinformatics, and recommendation systems. Node classification, a fundamental task in this context, is typically tackled using graph neural networks (GNNs). Unfortunately, conventional GNNs still face challenges in scenarios with few labeled nodes, despite the prevalence of few-shot node classification tasks in real-world applications. To address this challenge, various approaches have been proposed, including graph meta-learning, transfer learning, and methods based on Large Language Models (LLMs). However, traditional meta-learning and transfer learning methods often require prior knowledge from base classes or fail to exploit the potential advantages of unlabeled nodes. Meanwhile, LLM-based methods may overlook the zero-shot capabilities of LLMs and rely heavily on the quality of generated contexts. In this paper, we propose a novel approach that integrates LLMs and GNNs, leveraging the zero-shot inference and reasoning capabilities of LLMs and employing a Graph-LLM-based active learning paradigm to enhance GNNs' performance. Extensive experiments demonstrate the effectiveness of our model in improving node classification accuracy with considerably limited labeled data, surpassing state-of-the-art baselines by significant margins.">
@@ -1912,19 +1882,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>11</td>
         </tr>
 
-        <tr id="Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is to learn the normal patterns of time series data, thereby identifying deviations in test samples. Most existing TSAD methods focus on modeling data from the temporal dimension, while ignoring the semantic information in the spatial dimension. To address this issue, we introduce a novel approach, called Spatial-Temporal Normality learning (STEN). STEN is composed of a sequence Order prediction-based Temporal Normality learning (OTN) module that captures the temporal correlations within sequences, and a Distance prediction-based Spatial Normality learning (DSN) module that learns the relative spatial relations between sequences in a feature space. By synthesizing these two modules, STEN learns expressive spatial-temporal representations for the normal patterns hidden in the time series data. Extensive experiments on five popular TSAD benchmarks show that STEN substantially outperforms state-of-the-art competing methods. Our code is available at https://github.com/mala-lab/STEN.">
+        <tr id="Causal discovery on event sequences holds a pivotal significance across domains such as healthcare, finance, and industrial systems. The crux of this endeavor lies in unraveling causal structures among event types, typically portrayed as directed acyclic graphs (DAGs). Nonetheless, prevailing methodologies often grapple with untenable assumptions and intricate optimization hurdles. To address these challenges, we present a novel model named CausalNET. At the heart of CausalNET is a special prediction module based on the Transformer architecture, which prognosticates forthcoming events by leveraging historical occurrences, with its predictive prowess amplified by a trainable causal graph engineered to fathom causal relationships among event types. Further, to augment the predictive paradigm, we devise a causal decay matrix to encapsulate the reciprocal influence of events upon each other within the topological network. During training, we alternatively refine the prediction module and fine-tune the causal graph. Comprehensive evaluation on a spectrum of real-world and synthetic datasets underscores the superior performance and scalability of CausalNET, which marks a promising step forward in the realm of causal discovery. Code and Appendix are available at https://github.com/CGCL-codes/CausalNET.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6136ae3b84b2b1b9e6215a5c67a3153abd0eedc2" target='_blank'>
-              Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection
+          <td><a href="https://www.semanticscholar.org/paper/7b393ee640bb697584ae62c868cc019ed098b961" target='_blank'>
+              CausalNET: Unveiling Causal Structures on Event Sequences by Topology-Informed Causal Attention
               </a>
             </td>
           <td>
-            Yutong Chen, Hongzuo Xu, Guansong Pang, Hezhe Qiao, Yuan Zhou, Mingsheng Shang
+            Hua Zhu, Hong Huang, Kehan Yin, Zejun Fan, Hai Jin, Bang Liu
           </td>
-          <td>2024-06-28</td>
-          <td>ArXiv</td>
+          <td>2024-08-01</td>
+          <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
           <td>0</td>
-          <td>5</td>
+          <td>1</td>
         </tr>
 
         <tr id="In this paper, we leverage the power of latent diffusion models to generate synthetic time series tabular data. Along with the temporal and feature correlations, the heterogeneous nature of the feature in the table has been one of the main obstacles in time series tabular data modeling. We tackle this problem by combining the ideas of the variational auto-encoder (VAE) and the denoising diffusion probabilistic model (DDPM). Our model named as \texttt{TimeAutoDiff} has several key advantages including (1) Generality: the ability to handle the broad spectrum of time series tabular data from single to multi-sequence datasets; (2) Good fidelity and utility guarantees: numerical experiments on six publicly available datasets demonstrating significant improvements over state-of-the-art models in generating time series tabular data, across four metrics measuring fidelity and utility; (3) Fast sampling speed: entire time series data generation as opposed to the sequential data sampling schemes implemented in the existing diffusion-based models, eventually leading to significant improvements in sampling speed, (4) Entity conditional generation: the first implementation of conditional generation of multi-sequence time series tabular data with heterogenous features in the literature, enabling scenario exploration across multiple scientific and engineering domains. Codes are in preparation for release to the public, but available upon request.">
@@ -1942,34 +1912,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>3</td>
         </tr>
 
-        <tr id="Continuous-Time Dynamic Graph (CTDG) precisely models evolving real-world relationships, drawing heightened interest in dynamic graph learning across academia and industry. However, existing CTDG models encounter challenges stemming from noise and limited historical data. Graph Data Augmentation (GDA) emerges as a critical solution, yet current approaches primarily focus on static graphs and struggle to effectively address the dynamics inherent in CTDGs. Moreover, these methods often demand substantial domain expertise for parameter tuning and lack theoretical guarantees for augmentation efficacy. To address these issues, we propose Conda, a novel latent diffusion-based GDA method tailored for CTDGs. Conda features a sandwich-like architecture, incorporating a Variational Auto-Encoder (VAE) and a conditional diffusion model, aimed at generating enhanced historical neighbor embeddings for target nodes. Unlike conventional diffusion models trained on entire graphs via pre-training, Conda requires historical neighbor sequence embeddings of target nodes for training, thus facilitating more targeted augmentation. We integrate Conda into the CTDG model and adopt an alternating training strategy to optimize performance. Extensive experimentation across six widely used real-world datasets showcases the consistent performance improvement of our approach, particularly in scenarios with limited historical data.">
+        <tr id="Evaluating the contribution of individual data points to a model's prediction is critical for interpreting model predictions and improving model performance. Existing data contribution methods have been applied to various data types, including tabular data, images, and texts; however, their primary focus has been on i.i.d. settings. Despite the pressing need for principled approaches tailored to time series datasets, the problem of estimating data contribution in such settings remains unexplored, possibly due to challenges associated with handling inherent temporal dependencies. This paper introduces TimeInf, a data contribution estimation method for time-series datasets. TimeInf uses influence functions to attribute model predictions to individual time points while preserving temporal structures. Our extensive empirical results demonstrate that TimeInf outperforms state-of-the-art methods in identifying harmful anomalies and helpful time points for forecasting. Additionally, TimeInf offers intuitive and interpretable attributions of data values, allowing us to easily distinguish diverse anomaly patterns through visualizations.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/eb15255bdb2698993e5ad0223454abd6a732d2e7" target='_blank'>
-              Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model
+          <td><a href="https://www.semanticscholar.org/paper/3cf948c645144f6d8565608c19f9779114df6df2" target='_blank'>
+              TimeInf: Time Series Data Contribution via Influence Functions
               </a>
             </td>
           <td>
-            Yuxing Tian, Yiyan Qi, Aiwen Jiang, Qi Huang, Jian Guo
+            Yizi Zhang, Jingyan Shen, Xiaoxue Xiong, Yongchan Kwon
           </td>
-          <td>2024-07-11</td>
+          <td>2024-07-21</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>2</td>
-        </tr>
-
-        <tr id="Graph neural networks (GNNs) excel in learning from network-like data but often lack interpretability, making their application challenging in domains requiring transparent decision-making. We propose the Graph Kolmogorov-Arnold Network (GKAN), a novel GNN model leveraging spline-based activation functions on edges to enhance both accuracy and interpretability. Our experiments on five benchmark datasets demonstrate that GKAN outperforms state-of-the-art GNN models in node classification, link prediction, and graph classification tasks. In addition to the improved accuracy, GKAN's design inherently provides clear insights into the model's decision-making process, eliminating the need for post-hoc explainability techniques. This paper discusses the methodology, performance, and interpretability of GKAN, highlighting its potential for applications in domains where interpretability is crucial.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ce82880f5527b26fdb12cd1fa13d4cbc45c9e012" target='_blank'>
-              Kolmogorov-Arnold Graph Neural Networks
-              </a>
-            </td>
-          <td>
-            Gianluca De Carlo, A. Mastropietro, Aris Anagnostopoulos
-          </td>
-          <td>2024-06-26</td>
-          <td>ArXiv</td>
-          <td>4</td>
-          <td>4</td>
+          <td>0</td>
         </tr>
 
         <tr id="Temporal causal discovery is a crucial task aimed at uncovering the causal relations within time series data. The latest temporal causal discovery methods usually train deep learning models on prediction tasks to uncover the causality between time series. They capture causal relations by analyzing the parameters of some components of the trained models, e.g., attention weights and convolution weights. However, this is an incomplete mapping process from the model parameters to the causality and fails to investigate the other components, e.g., fully connected layers and activation functions, that are also significant for causal discovery. To facilitate the utilization of the whole deep learning models in temporal causal discovery, we proposed an interpretable transformer-based causal discovery model termed CausalFormer, which consists of the causality-aware transformer and the decomposition-based causality detector. The causality-aware transformer learns the causal representation of time series data using a prediction task with the designed multi-kernel causal convolution which aggregates each input time series along the temporal dimension under the temporal priority constraint. Then, the decomposition-based causality detector interprets the global structure of the trained causality-aware transformer with the proposed regression relevance propagation to identify potential causal relations and finally construct the causal graph. Experiments on synthetic, simulated, and real datasets demonstrate the state-of-the-art performance of CausalFormer on discovering temporal causality. Our code is available at https://github.com/lingbai-kong/CausalFormer.">
@@ -1987,64 +1942,79 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>49</td>
         </tr>
 
-        <tr id="The increasing number of vehicles highlights the need for efficient parking space management. Predicting real-time Parking Availability (PA) can help mitigate traffic congestion and the corresponding social problems, which is a pressing issue in densely populated cities like Singapore. In this study, we aim to collectively predict future PA across Singapore with complex factors from various domains. The contributions in this paper are listed as follows: (1) A New Dataset: We introduce the SINPA dataset, containing a year's worth of PA data from 1,687 parking lots in Singapore, enriched with various spatial and temporal factors. (2) A Data-Driven Approach: We present DeepPA, a novel deep-learning framework, to collectively and efficiently predict future PA across thousands of parking lots. (3) Extensive Experiments and Deployment: DeepPA demonstrates a 9.2% reduction in prediction error for up to 3-hour forecasts compared to existing advanced models. Furthermore, we implement DeepPA in a practical web-based platform to provide real-time PA predictions to aid drivers and inform urban planning for the governors in Singapore. We release the dataset and source code at https://github.com/yoshall/SINPA.">
+        <tr id="The application of deep learning techniques for predicting stock market prices is a prominent and widely researched topic in the field of data science. To effectively predict market trends, it is essential to utilize a diversified dataset. In this paper, we give a graph neural network based convolutional neural network (CNN) model, that can be applied on diverse source of data, in the attempt to extract features to predict the trends of indices of \text{S}\&\text{P} 500, NASDAQ, DJI, NYSE, and RUSSEL. The experiments show that the associated models improve the performance of prediction in all indices over the baseline algorithms by about $4\% \text{ to } 15\%$, in terms of F-measure. A trading simulation is generated from predictions and gained a Sharpe ratio of over 3.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/11f318de84519ab95af7443ff95d5d21fc56fdd1" target='_blank'>
-              Predicting Carpark Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach
+          <td><a href="https://www.semanticscholar.org/paper/db9f0abbc8601da414d831f4eef6626e555a4457" target='_blank'>
+              GraphCNNpred: A stock market indices prediction using a Graph based deep learning system
               </a>
             </td>
           <td>
-            Huaiwu Zhang, Yutong Xia, Siru Zhong, Kun Wang, Zekun Tong, Qingsong Wen, Roger Zimmermann, Yuxuan Liang
+            Yuhui Jin
           </td>
-          <td>2024-08-01</td>
-          <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
-          <td>0</td>
-          <td>5</td>
+          <td>2024-07-04</td>
+          <td>ArXiv</td>
+          <td>2</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Time-series classification is vital in health monitoring and human activity recognition, as well as in areas such as financial forecasting, process control, and a wide array of forecasting tasks. Traditional time-series models segment data into windows and assign one label per window, often missing label transitions within those windows. This paper presents a novel many-to-many time-series model and post-processing using hybrid recurrent neural networks with attention mechanisms, which more effectively captures label transitions over traditional many-to-one models. Further, unlike typical other many-to-many models, our approach doesn’t require a decoder. Instead, it employs an RNN, generating a label for every input time step. During inference, a weighted voting scheme consolidates overlapping predictions into one label per time step. Experiments show our model remains effective on time-series with sparse label shifts, but particularly excels in detecting frequent transitions. This model is ideal for tasks demanding accurate pinpointing of rapid label changes in time-series data, such as gesture recognition, making it ideal for fast-paced human activity recognition. 1">
+        <tr id="Continuous-Time Dynamic Graph (CTDG) precisely models evolving real-world relationships, drawing heightened interest in dynamic graph learning across academia and industry. However, existing CTDG models encounter challenges stemming from noise and limited historical data. Graph Data Augmentation (GDA) emerges as a critical solution, yet current approaches primarily focus on static graphs and struggle to effectively address the dynamics inherent in CTDGs. Moreover, these methods often demand substantial domain expertise for parameter tuning and lack theoretical guarantees for augmentation efficacy. To address these issues, we propose Conda, a novel latent diffusion-based GDA method tailored for CTDGs. Conda features a sandwich-like architecture, incorporating a Variational Auto-Encoder (VAE) and a conditional diffusion model, aimed at generating enhanced historical neighbor embeddings for target nodes. Unlike conventional diffusion models trained on entire graphs via pre-training, Conda requires historical neighbor sequence embeddings of target nodes for training, thus facilitating more targeted augmentation. We integrate Conda into the CTDG model and adopt an alternating training strategy to optimize performance. Extensive experimentation across six widely used real-world datasets showcases the consistent performance improvement of our approach, particularly in scenarios with limited historical data.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/d206e88758c3992f96eaf77f9ddd4f0e63779b32" target='_blank'>
-              Many-to-Many Prediction for Effective Modeling of Frequent Label Transitions in Time Series
+          <td><a href="https://www.semanticscholar.org/paper/eb15255bdb2698993e5ad0223454abd6a732d2e7" target='_blank'>
+              Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model
               </a>
             </td>
           <td>
-            Alexander Katrompas, V. Metsis
+            Yuxing Tian, Yiyan Qi, Aiwen Jiang, Qi Huang, Jian Guo
           </td>
-          <td>2024-06-26</td>
-          <td>Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments</td>
+          <td>2024-07-11</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>17</td>
+          <td>2</td>
         </tr>
 
-        <tr id="Recurrent neural network-based sequence-to-sequence models have been extensively applied for multi-step-ahead time series forecasting. These models typically involve a decoder trained using either its previous forecasts or the actual observed values as the decoder inputs. However, relying on self-generated predictions can lead to the rapid accumulation of errors over multiple steps, while using the actual observations introduces exposure bias as these values are unavailable during the extrapolation stage. In this regard, this study proposes a novel training approach called reinforced decoder, which introduces auxiliary models to generate alternative decoder inputs that remain accessible when extrapolating. Additionally, a reinforcement learning algorithm is utilized to dynamically select the optimal inputs to improve accuracy. Comprehensive experiments demonstrate that our approach outperforms representative training methods over several datasets. Furthermore, the proposed approach also exhibits promising performance when generalized to self-attention-based sequence-to-sequence forecasting models.">
+        <tr id="Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is to learn the normal patterns of time series data, thereby identifying deviations in test samples. Most existing TSAD methods focus on modeling data from the temporal dimension, while ignoring the semantic information in the spatial dimension. To address this issue, we introduce a novel approach, called Spatial-Temporal Normality learning (STEN). STEN is composed of a sequence Order prediction-based Temporal Normality learning (OTN) module that captures the temporal correlations within sequences, and a Distance prediction-based Spatial Normality learning (DSN) module that learns the relative spatial relations between sequences in a feature space. By synthesizing these two modules, STEN learns expressive spatial-temporal representations for the normal patterns hidden in the time series data. Extensive experiments on five popular TSAD benchmarks show that STEN substantially outperforms state-of-the-art competing methods. Our code is available at https://github.com/mala-lab/STEN.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ecdcef5ef150ad893f080cafe211f505c559753a" target='_blank'>
-              Reinforced Decoder: Towards Training Recurrent Neural Networks for Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/6136ae3b84b2b1b9e6215a5c67a3153abd0eedc2" target='_blank'>
+              Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection
               </a>
             </td>
           <td>
-            Qi Sima, Xinze Zhang, Yukun Bao, Siyue Yang, Liang Shen
+            Yutong Chen, Hongzuo Xu, Guansong Pang, Hezhe Qiao, Yuan Zhou, Mingsheng Shang
           </td>
-          <td>2024-06-14</td>
+          <td>2024-06-28</td>
           <td>ArXiv</td>
           <td>0</td>
+          <td>5</td>
+        </tr>
+
+        <tr id="How to capture dynamic spatial-temporal dependencies remains an open question in multivariate time series (MTS) forecasting. Although recent advanced spatial-temporal graph neural networks (STGNNs) achieve superior forecasting performance, they either consider pre-defined spatial correlations or simply learn static graphs. Some research has tried to learn many adjacent matrices to reveal time-varying spatial correlations, but they generate discrete graphs which cannot encode evolutionary information and also face computational complexity problem. In this paper, we propose two significant plugins to help automatically learn enhanced dynamic spatial-temporal embedding of MTS data: (1) a novel neural conditional random field (CRF) layer. We find that the implicit time-varying spatial dependencies are reflected by the explicit changeable links between edges, and we propose the neural CRF to encode such pairwise changeable evolutionary inter-dependencies; (2) a structure adaptive graph convolution (SAGC) that does not require pre-defined graphs to capture semantically richer spatial correlations. Then, we integrate the neural CRF, SAGC with recurrent neural network to develop a new STGNN paradigm termed Adaptive Spatial-Temporal graph neural network with Conditional Random Field (ASTCRF), which can be trained in an end-to-end fashion. We validate the effectiveness, efficiency and scalability of ASTCRF on five public benchmark MTS datasets.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/667940911f90cfd6980e5e1731731f7c2af49b08" target='_blank'>
+              Dynamic Spatial-Temporal Embedding via Neural Conditional Random Field for Multivariate Time Series Forecasting
+              </a>
+            </td>
+          <td>
+            Peiyu Yi, Feihu Huang, Jian Peng, Zhifeng Bao
+          </td>
+          <td>2024-06-27</td>
+          <td>ACM Transactions on Spatial Algorithms and Systems</td>
           <td>0</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Recent advances in large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. These models have demonstrated remarkable capabilities in understanding context, processing vast amounts of data, and generating human-preferred contents. In this survey, we explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We provide a discussion of the progress and advantages of LLMs in financial contexts, analyzing their advanced technologies as well as prospective capabilities in contextual understanding, transfer learning flexibility, complex emotion detection, etc. We then highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications. For each application area, we delve into specific methodologies, such as textual analysis, knowledge-based analysis, forecasting, data augmentation, planning, decision support, and simulations. Furthermore, a comprehensive collection of datasets, model assets, and useful codes associated with mainstream applications are presented as resources for the researchers and practitioners. Finally, we outline the challenges and opportunities for future research, particularly emphasizing a number of distinctive aspects in this field. We hope our work can help facilitate the adoption and further development of LLMs in the financial sector.">
+        <tr id="Modern recurrent layers are emerging as a promising path toward edge deployment of foundation models, especially in the context of large language models (LLMs). Compressing the whole input sequence in a finite-dimensional representation enables recurrent layers to model long-range dependencies while maintaining a constant inference cost for each token and a fixed memory requirement. However, the practical deployment of LLMs in resource-limited environments often requires further model compression, such as quantization and pruning. While these techniques are well-established for attention-based models, their effects on recurrent layers remain underexplored. In this preliminary work, we focus on post-training quantization for recurrent LLMs and show that Mamba models exhibit the same pattern of outlier channels observed in attention-based LLMs. We show that the reason for the difficulty of quantizing SSMs is caused by activation outliers, similar to those observed in transformer-based LLMs. We report baseline results for post-training quantization of Mamba that do not take into account the activation outliers and suggest first steps for outlier-aware quantization.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/8663871588271b4f20645fefcc7d25d9bd1547cf" target='_blank'>
-              A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges
+          <td><a href="https://www.semanticscholar.org/paper/3d553478d06f751f872a82c774c8262ce66d47fd" target='_blank'>
+              Mamba-PTQ: Outlier Channels in Recurrent Large Language Models
               </a>
             </td>
           <td>
-            Yuqi Nie, Yaxuan Kong, Xiaowen Dong, John M. Mulvey, H. Poor, Qingsong Wen, Stefan Zohren
+            Alessandro Pierro, Steven Abreu
           </td>
-          <td>2024-06-15</td>
+          <td>2024-07-17</td>
           <td>ArXiv</td>
+          <td>0</td>
           <td>1</td>
-          <td>3</td>
         </tr>
 
         <tr id="The modelling of temporal patterns in dynamic graphs is an important current research issue in the development of time-aware GNNs. Whether or not a specific sequence of events in a temporal graph constitutes a temporal pattern not only depends on the frequency of its occurrence. We consider whether it deviates from what is expected in a temporal graph where timestamps are randomly shuffled. While accounting for such a random baseline is important to model temporal patterns, it has mostly been ignored by current temporal graph neural networks. To address this issue we propose HYPA-DBGNN, a novel two-step approach that combines (i) the inference of anomalous sequential patterns in time series data on graphs based on a statistically principled null model, with (ii) a neural message passing approach that utilizes a higher-order De Bruijn graph whose edges capture overrepresented sequential patterns. Our method leverages hypergeometric graph ensembles to identify anomalous edges within both first- and higher-order De Bruijn graphs, which encode the temporal ordering of events. The model introduces an inductive bias that enhances model interpretability. We evaluate our approach for static node classification using benchmark datasets and a synthetic dataset that showcases its ability to incorporate the observed inductive bias regarding over- and under-represented temporal edges. We demonstrate the framework's effectiveness in detecting similar patterns within empirical datasets, resulting in superior performance compared to baseline methods in node classification tasks. To the best of our knowledge, our work is the first to introduce statistically informed GNNs that leverage temporal and causal sequence anomalies. HYPA-DBGNN represents a path for bridging the gap between statistical graph inference and neural graph representation learning, with potential applications to static GNNs.">
@@ -2062,19 +2032,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>8</td>
         </tr>
 
-        <tr id="None">
+        <tr id="Sequential recommendation systems aim to provide personalized recommendations by analyzing dynamic preferences and dependencies within user behavior sequences. Recently, Transformer models can effectively capture user preferences. However, their quadratic computational complexity limits recommendation performance on long interaction sequence data. Inspired by the State Space Model (SSM)representative model, Mamba, which efficiently captures user preferences in long interaction sequences with linear complexity, we find that Mamba's recommendation effectiveness is limited in short interaction sequences, with failing to recall items of actual interest to users and exacerbating the data sparsity cold start problem. To address this issue, we innovatively propose a new model, MaTrRec, which combines the strengths of Mamba and Transformer. This model fully leverages Mamba's advantages in handling long-term dependencies and Transformer's global attention advantages in short-term dependencies, thereby enhances predictive capabilities on both long and short interaction sequence datasets while balancing model efficiency. Notably, our model significantly improves the data sparsity cold start problem, with an improvement of up to 33% on the highly sparse Amazon Musical Instruments dataset. We conducted extensive experimental evaluations on five widely used public datasets. The experimental results show that our model outperforms the current state-of-the-art sequential recommendation models on all five datasets. The code is available at https://github.com/Unintelligentmumu/MaTrRec.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c6b30fb9799602bf4dfd68fdcf16333cb2b7ff18" target='_blank'>
-              CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks
+          <td><a href="https://www.semanticscholar.org/paper/412e6c60b2d5426ebf3724e770876d116c398984" target='_blank'>
+              MaTrRec: Uniting Mamba and Transformer for Sequential Recommendation
               </a>
             </td>
           <td>
-            Soheila Molaei, Nima Ghanbari Bousejin, Ghadeer O. Ghosheh, Anshul Thakur, V. Chauhan, Tingting Zhu, David A. Clifton
+            Shun Zhang, Runsen Zhang, Zhirong Yang
           </td>
-          <td>2024-08-01</td>
-          <td>Journal of Healthcare Informatics Research</td>
+          <td>2024-07-27</td>
+          <td>ArXiv</td>
+          <td>0</td>
           <td>0</td>
-          <td>11</td>
         </tr>
 
         <tr id="Recently, Large Language Models (LLMs) have demonstrated great potential in various data mining tasks, such as knowledge question answering, mathematical reasoning, and commonsense reasoning. However, the reasoning capability of LLMs on temporal event forecasting has been under-explored. To systematically investigate their abilities in temporal event forecasting, we conduct a comprehensive evaluation of LLM-based methods for temporal event forecasting. Due to the lack of a high-quality dataset that involves both graph and textual data, we first construct a benchmark dataset, named MidEast-TE-mini. Based on this dataset, we design a series of baseline methods, characterized by various input formats and retrieval augmented generation(RAG) modules. From extensive experiments, we find that directly integrating raw texts into the input of LLMs does not enhance zero-shot extrapolation performance. In contrast, incorporating raw texts in specific complex events and fine-tuning LLMs significantly improves performance. Moreover, enhanced with retrieval modules, LLM can effectively capture temporal relational patterns hidden in historical events. Meanwhile, issues such as popularity bias and the long-tail problem still persist in LLMs, particularly in the RAG-based method. These findings not only deepen our understanding of LLM-based event forecasting methods but also highlight several promising research directions.We consider that this comprehensive evaluation, along with the identified research opportunities, will significantly contribute to future research on temporal event forecasting through LLMs.">
@@ -2092,14 +2062,44 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>10</td>
         </tr>
 
-        <tr id="Modern recurrent layers are emerging as a promising path toward edge deployment of foundation models, especially in the context of large language models (LLMs). Compressing the whole input sequence in a finite-dimensional representation enables recurrent layers to model long-range dependencies while maintaining a constant inference cost for each token and a fixed memory requirement. However, the practical deployment of LLMs in resource-limited environments often requires further model compression, such as quantization and pruning. While these techniques are well-established for attention-based models, their effects on recurrent layers remain underexplored. In this preliminary work, we focus on post-training quantization for recurrent LLMs and show that Mamba models exhibit the same pattern of outlier channels observed in attention-based LLMs. We show that the reason for the difficulty of quantizing SSMs is caused by activation outliers, similar to those observed in transformer-based LLMs. We report baseline results for post-training quantization of Mamba that do not take into account the activation outliers and suggest first steps for outlier-aware quantization.">
+        <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/3d553478d06f751f872a82c774c8262ce66d47fd" target='_blank'>
-              Mamba-PTQ: Outlier Channels in Recurrent Large Language Models
+          <td><a href="https://www.semanticscholar.org/paper/c6b30fb9799602bf4dfd68fdcf16333cb2b7ff18" target='_blank'>
+              CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks
               </a>
             </td>
           <td>
-            Alessandro Pierro, Steven Abreu
+            Soheila Molaei, Nima Ghanbari Bousejin, Ghadeer O. Ghosheh, Anshul Thakur, V. Chauhan, Tingting Zhu, David A. Clifton
+          </td>
+          <td>2024-08-01</td>
+          <td>Journal of Healthcare Informatics Research</td>
+          <td>0</td>
+          <td>11</td>
+        </tr>
+
+        <tr id="Time-series classification is vital in health monitoring and human activity recognition, as well as in areas such as financial forecasting, process control, and a wide array of forecasting tasks. Traditional time-series models segment data into windows and assign one label per window, often missing label transitions within those windows. This paper presents a novel many-to-many time-series model and post-processing using hybrid recurrent neural networks with attention mechanisms, which more effectively captures label transitions over traditional many-to-one models. Further, unlike typical other many-to-many models, our approach doesn’t require a decoder. Instead, it employs an RNN, generating a label for every input time step. During inference, a weighted voting scheme consolidates overlapping predictions into one label per time step. Experiments show our model remains effective on time-series with sparse label shifts, but particularly excels in detecting frequent transitions. This model is ideal for tasks demanding accurate pinpointing of rapid label changes in time-series data, such as gesture recognition, making it ideal for fast-paced human activity recognition. 1">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/d206e88758c3992f96eaf77f9ddd4f0e63779b32" target='_blank'>
+              Many-to-Many Prediction for Effective Modeling of Frequent Label Transitions in Time Series
+              </a>
+            </td>
+          <td>
+            Alexander Katrompas, V. Metsis
+          </td>
+          <td>2024-06-26</td>
+          <td>Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments</td>
+          <td>0</td>
+          <td>18</td>
+        </tr>
+
+        <tr id="Dynamic link prediction is a critical task in the analysis of evolving networks, with applications ranging from recommender systems to economic exchanges. However, the concept of the temporal receptive field, which refers to the temporal context that models use for making predictions, has been largely overlooked and insufficiently analyzed in existing research. In this study, we present a comprehensive analysis of the temporal receptive field in dynamic graph learning. By examining multiple datasets and models, we formalize the role of temporal receptive field and highlight their crucial influence on predictive accuracy. Our results demonstrate that appropriately chosen temporal receptive field can significantly enhance model performance, while for some models, overly large windows may introduce noise and reduce accuracy. We conduct extensive benchmarking to validate our findings, ensuring that all experiments are fully reproducible. Code is available at https://github.com/ykrmm/BenchmarkTW .">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/31f09be25a90f0bff93ee2f78d85164a62f24094" target='_blank'>
+              Temporal receptive field in dynamic graph learning: A comprehensive analysis
+              </a>
+            </td>
+          <td>
+            Yannis Karmim, Leshanshui Yang, Raphael Fournier S'Niehotta, Cl'ement Chatelain, S'ebastien Adam, Nicolas Thome
           </td>
           <td>2024-07-17</td>
           <td>ArXiv</td>
@@ -2107,94 +2107,94 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>1</td>
         </tr>
 
-        <tr id="How to capture dynamic spatial-temporal dependencies remains an open question in multivariate time series (MTS) forecasting. Although recent advanced spatial-temporal graph neural networks (STGNNs) achieve superior forecasting performance, they either consider pre-defined spatial correlations or simply learn static graphs. Some research has tried to learn many adjacent matrices to reveal time-varying spatial correlations, but they generate discrete graphs which cannot encode evolutionary information and also face computational complexity problem. In this paper, we propose two significant plugins to help automatically learn enhanced dynamic spatial-temporal embedding of MTS data: (1) a novel neural conditional random field (CRF) layer. We find that the implicit time-varying spatial dependencies are reflected by the explicit changeable links between edges, and we propose the neural CRF to encode such pairwise changeable evolutionary inter-dependencies; (2) a structure adaptive graph convolution (SAGC) that does not require pre-defined graphs to capture semantically richer spatial correlations. Then, we integrate the neural CRF, SAGC with recurrent neural network to develop a new STGNN paradigm termed Adaptive Spatial-Temporal graph neural network with Conditional Random Field (ASTCRF), which can be trained in an end-to-end fashion. We validate the effectiveness, efficiency and scalability of ASTCRF on five public benchmark MTS datasets.">
+        <tr id="Accurate traffic flow forecasting is a crucial component of intelligent transportation systems, playing a pivotal role in enhancing transportation intelligence. The integration of Graph Neural Networks (GNNs) and Transformers in traffic flow forecasting has gained significant adoption for enhancing prediction accuracy. Yet, the complex spatial and temporal dependencies present in traffic data continue to pose substantial challenges: (1) Most GNN-based methods assume that the graph structure reflects the actual dependencies between nodes, overlooking the complex dependencies present in the real-world context. (2) Standard time-series models are unable to effectively model complex temporal dependencies, hindering prediction accuracy. To tackle these challenges, the authors propose a novel Transformer-based Spatiotemporal Graph Diffusion Convolution Network (TSGDC) for Traffic Flow Forecasting, which leverages graph diffusion and transformer to capture the complexity and dynamics of spatial and temporal patterns, thereby enhancing prediction performance. The authors designed an Efficient Channel Attention (ECA) that learns separately from the feature dimensions collected by traffic sensors and the temporal dimensions of traffic data, aiding in spatiotemporal modeling. Chebyshev Graph Diffusion Convolution (GDC) is used to capture the complex dependencies within the spatial distribution. Sequence decomposition blocks, as internal operations of transformers, are employed to gradually extract long-term stable trends from hidden complex variables. Additionally, by integrating multi-scale dependencies, including recent, daily, and weekly patterns, accurate traffic flow predictions are achieved. Experimental results on various public datasets show that TSGDC outperforms conventional traffic forecasting models, particularly in accuracy and robustness.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/667940911f90cfd6980e5e1731731f7c2af49b08" target='_blank'>
-              Dynamic Spatial-Temporal Embedding via Neural Conditional Random Field for Multivariate Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/f28ffc0b410cb4e7b7c33a98e7941875942be67a" target='_blank'>
+              Transformer-Based Spatiotemporal Graph Diffusion Convolution Network for Traffic Flow Forecasting
               </a>
             </td>
           <td>
-            Peiyu Yi, Feihu Huang, Jian Peng, Zhifeng Bao
+            Siwei Wei, Yang Yang, Donghua Liu, Ke Deng, Chunzhi Wang
           </td>
-          <td>2024-06-27</td>
-          <td>ACM Transactions on Spatial Algorithms and Systems</td>
+          <td>2024-08-09</td>
+          <td>Electronics</td>
           <td>0</td>
-          <td>3</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Time Series Classification (TSC) encompasses two settings: classifying entire sequences or classifying segmented subsequences. The raw time series for segmented TSC usually contain Multiple classes with Varying Duration of each class (MVD). Therefore, the characteristics of MVD pose unique challenges for segmented TSC, yet have been largely overlooked by existing works. Specifically, there exists a natural temporal dependency between consecutive instances (segments) to be classified within MVD. However, mainstream TSC models rely on the assumption of independent and identically distributed (i.i.d.), focusing on independently modeling each segment. Additionally, annotators with varying expertise may provide inconsistent boundary labels, leading to unstable performance of noise-free TSC models. To address these challenges, we first formally demonstrate that valuable contextual information enhances the discriminative power of classification instances. Leveraging the contextual priors of MVD at both the data and label levels, we propose a novel consistency learning framework Con4m, which effectively utilizes contextual information more conducive to discriminating consecutive segments in segmented TSC tasks, while harmonizing inconsistent boundary labels for training. Extensive experiments across multiple datasets validate the effectiveness of Con4m in handling segmented TSC tasks on MVD.">
+        <tr id="Many empirical studies of labor market questions rely on estimating relatively simple predictive models using small, carefully constructed longitudinal survey datasets based on hand-engineered features. Large Language Models (LLMs), trained on massive datasets, encode vast quantities of world knowledge and can be used for the next job prediction problem. However, while an off-the-shelf LLM produces plausible career trajectories when prompted, the probability with which an LLM predicts a particular job transition conditional on career history will not, in general, align with the true conditional probability in a given population. Recently, Vafa et al. (2024) introduced a transformer-based"foundation model", CAREER, trained using a large, unrepresentative resume dataset, that predicts transitions between jobs; it further demonstrated how transfer learning techniques can be used to leverage the foundation model to build better predictive models of both transitions and wages that reflect conditional transition probabilities found in nationally representative survey datasets. This paper considers an alternative where the fine-tuning of the CAREER foundation model is replaced by fine-tuning LLMs. For the task of next job prediction, we demonstrate that models trained with our approach outperform several alternatives in terms of predictive performance on the survey data, including traditional econometric models, CAREER, and LLMs with in-context learning, even though the LLM can in principle predict job titles that are not allowed in the survey data. Further, we show that our fine-tuned LLM-based models' predictions are more representative of the career trajectories of various workforce subpopulations than off-the-shelf LLM models and CAREER. We conduct experiments and analyses that highlight the sources of the gains in the performance of our models for representative predictions.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/bf2d1773723caebef0ace7589e223775b9178a15" target='_blank'>
-              Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification
+          <td><a href="https://www.semanticscholar.org/paper/8553495cea575792c19bf8e77ac26d3a92afd7c5" target='_blank'>
+              LABOR-LLM: Language-Based Occupational Representations with Large Language Models
               </a>
             </td>
           <td>
-            Junru Chen, Tianyu Cao, Jing Xu, Jiahe Li, Zhilong Chen, Tao Xiao, Yang Yang
+            Tianyu Du, Ayush Kanodia, Herman Brunborg, Keyon Vafa, Susan Athey
           </td>
-          <td>2024-07-31</td>
+          <td>2024-06-25</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>2</td>
+          <td>13</td>
         </tr>
 
-        <tr id="Stock price forecasting is a challenging area of research, particularly due to the complexity and unpredictability of financial markets. The accuracy of prediction models is influenced by various factors, including nonlinearity, seasonality, and economic shocks. Deep learning has demonstrated better forecasts of stock prices than traditional approaches. This study, therefore, proposed a new approach to improve forecasting system based on an end-to-end convolutional recurrent neural network (CRNN) with attention mechanism. Our approach first investigates local stock price features using 1D convolutional neural network, and then employs a bidirectional long short-term memory (Bi-LSTM) network for forecasting. This model stands out by effectively utilizing contextual data and representing the temporal character of data. The Bi-LSTM is helpful for understanding the history and future contextual information since it uncovers both past and future contexts of stock data. Furthermore, integrating attention mechanism within the CRNN represents a significant improvement. This allows our model to handle long input sequences more effectively and capture the inherent stochasticity in stock prices, which is often missed by traditional models. The effectiveness of our approach is investigated using data on 10 stock indexes from Yahoo Finance. The results show that our method outperforms ARIMA, LSTM, and conventional methods. ">
+        <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/d308e58a2b17825f4c41dbf390403ee62eb1a34c" target='_blank'>
-              An improved convolutional recurrent neural network for stock price forecasting
+          <td><a href="https://www.semanticscholar.org/paper/0084b07c9c1f87776c162af58347aa0ba6767f3d" target='_blank'>
+              DyHANE: dynamic heterogeneous attributed network embedding through experience node replay
               </a>
             </td>
           <td>
-            Hoang Vuong Pham, Hung Phu Lam, Le Nhat Duy, T. Pham, T. Trinh
+            Liliana Martirano, D. Ienco, R. Interdonato, Andrea Tagarelli
           </td>
-          <td>2024-09-01</td>
-          <td>IAES International Journal of Artificial Intelligence (IJ-AI)</td>
+          <td>2024-07-05</td>
+          <td>Appl. Netw. Sci.</td>
           <td>0</td>
-          <td>4</td>
+          <td>29</td>
         </tr>
 
-        <tr id="Accurate load forecasting is essential for maintaining the power balance between generators and consumers, especially with the increasing integration of renewable energy sources, which introduce significant intermittent volatility. With the development of data-driven methods, machine learning and deep learning-based models have become the predominant approach for load forecasting tasks. In recent years, pre-trained language models (PLMs) have made significant advancements, demonstrating superior performance in various fields. This paper proposes a load forecasting method based on PLMs, which offers not only accurate predictive ability but also general and flexible applicability. Additionally, a data modeling method is proposed to effectively transform load sequence data into natural language for PLM training. Furthermore, we introduce a data enhancement strategy that eliminate the impact of PLM hallucinations on forecasting results. The effectiveness of the proposed method has been validated on two real-world datasets. Compared with existing methods, our approach shows state-of-the-art performance across all validation metrics.">
+        <tr id="Stock price forecasting is a challenging area of research, particularly due to the complexity and unpredictability of financial markets. The accuracy of prediction models is influenced by various factors, including nonlinearity, seasonality, and economic shocks. Deep learning has demonstrated better forecasts of stock prices than traditional approaches. This study, therefore, proposed a new approach to improve forecasting system based on an end-to-end convolutional recurrent neural network (CRNN) with attention mechanism. Our approach first investigates local stock price features using 1D convolutional neural network, and then employs a bidirectional long short-term memory (Bi-LSTM) network for forecasting. This model stands out by effectively utilizing contextual data and representing the temporal character of data. The Bi-LSTM is helpful for understanding the history and future contextual information since it uncovers both past and future contexts of stock data. Furthermore, integrating attention mechanism within the CRNN represents a significant improvement. This allows our model to handle long input sequences more effectively and capture the inherent stochasticity in stock prices, which is often missed by traditional models. The effectiveness of our approach is investigated using data on 10 stock indexes from Yahoo Finance. The results show that our method outperforms ARIMA, LSTM, and conventional methods. ">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/01f84016326ad53ac67ec2299719090300765b73" target='_blank'>
-              LFPLM: A General and Flexible Load Forecasting Framework based on Pre-trained Language Model
+          <td><a href="https://www.semanticscholar.org/paper/d308e58a2b17825f4c41dbf390403ee62eb1a34c" target='_blank'>
+              An improved convolutional recurrent neural network for stock price forecasting
               </a>
             </td>
           <td>
-            Mingyang Gao, Suyang Zhou, Wei Gu, Zhi Wu, Zijian Hu, Hong Zhu, Haiquan Liu
+            Hoang Vuong Pham, Hung Phu Lam, Le Nhat Duy, T. Pham, T. Trinh
           </td>
-          <td>2024-06-17</td>
-          <td>ArXiv</td>
+          <td>2024-09-01</td>
+          <td>IAES International Journal of Artificial Intelligence (IJ-AI)</td>
           <td>0</td>
-          <td>33</td>
+          <td>4</td>
         </tr>
 
-        <tr id="Although traffic prediction has been receiving considerable attention with a number of successes in the context of intelligent transportation systems, the prediction of traffic states over a complex transportation network that contains different road types has remained a challenge. This study proposes a multi-scale graph wavelet temporal convolution network (MSGWTCN) to predict the traffic states in complex transportation networks. Specifically, a multi-scale spatial block is designed to simultaneously capture the spatial information at different levels, and the gated temporal convolution network is employed to extract the temporal dependencies of the data. The model jointly learns to mount multiple levels of the spatial interactions by stacking graph wavelets with different scales. Two real-world datasets are used in this study to investigate the model performance, including a highway network in Seattle and a dense road network of Manhattan in New York City. Experiment results show that the proposed model outperforms other baseline models. Furthermore, different scales of graph wavelets are found to be effective in extracting local, intermediate and global information at the same time and thus enable the model to learn a complex transportation network topology with various types of road segments. By carefully customizing the scales of wavelets, the model is able to improve the prediction performance and better adapt to different network configurations.">
+        <tr id="An emerging topic in large language models (LLMs) is their application to time series forecasting, characterizing mainstream and patternable characteristics of time series. A relevant but rarely explored and more challenging question is whether LLMs can detect and explain time series anomalies, a critical task across various real-world applications. In this paper, we investigate the capabilities of LLMs, specifically GPT-4 and LLaMA3, in detecting and explaining anomalies in time series. Our studies reveal that: 1) LLMs cannot be directly used for time series anomaly detection. 2) By designing prompt strategies such as in-context learning and chain-of-thought prompting, GPT-4 can detect time series anomalies with results competitive to baseline methods. 3) We propose a synthesized dataset to automatically generate time series anomalies with corresponding explanations. By applying instruction fine-tuning on this dataset, LLaMA3 demonstrates improved performance in time series anomaly detection tasks. In summary, our exploration shows the promising potential of LLMs as time series anomaly detectors.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4d5e11bf997ee82ceae16dc66b7c5ab1148908d6" target='_blank'>
-              Traffic Prediction considering Multiple Levels of Spatial-temporal Information: A Multi-scale Graph Wavelet-based Approach
+          <td><a href="https://www.semanticscholar.org/paper/12b073694ad8cf460d1e74a34b68d07536e0db14" target='_blank'>
+              Can LLMs Serve As Time Series Anomaly Detectors?
               </a>
             </td>
           <td>
-            Zilin Bian, Jingqin Gao, K. Ozbay, Zhenning Li
+            Manqing Dong, Hao Huang, Longbing Cao
           </td>
-          <td>2024-06-18</td>
+          <td>2024-08-06</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>44</td>
+          <td>0</td>
         </tr>
 
-        <tr id="Fluctuations in the financial market are influenced by various driving forces and numerous factors. Traditional financial research aims to identify the factors influencing stock prices, and existing works construct a common neural network learning framework that learns temporal dependency using a fixed time window of historical information, such as RNN and LSTM models. However, these models only consider the short-term and point-to-point relationships within stock series. The financial market is a complex and dynamic system with many unobservable temporal patterns. Therefore, we propose an adaptive period-aggregation model called the Latent Period-Aggregated Stock Transformer (LPAST). The model integrates a variational autoencoder (VAE) with a period-to-period attention mechanism for multistep prediction in the financial time series. Additionally, we introduce a self-correlation learning method and routing mechanism to handle complex multi-period aggregations and information distribution. Main contributions include proposing a novel period-aggregation representation scheme, introducing a new attention mechanism, and validating the model’s superiority in long-horizon prediction tasks. The LPAST model demonstrates its potential and effectiveness in financial market prediction, highlighting its relevance in financial research and predictive analytics.">
+        <tr id="Crime forecasting is a critical component of urban analysis and essential for stabilizing society today. Unlike other time series forecasting problems, crime incidents are sparse, particularly in small regions and within specific time periods. Traditional spatial-temporal deep learning models often struggle with this sparsity, as they typically cannot effectively handle the non-Gaussian nature of crime data, which is characterized by numerous zeros and over-dispersed patterns. To address these challenges, we introduce a novel approach termed Spatial Temporal Multivariate Zero-Inflated Negative Binomial Graph Neural Networks (STMGNN-ZINB). This framework leverages diffusion and convolution networks to analyze spatial, temporal, and multivariate correlations, enabling the parameterization of probabilistic distributions of crime incidents. By incorporating a Zero-Inflated Negative Binomial model, STMGNN-ZINB effectively manages the sparse nature of crime data, enhancing prediction accuracy and the precision of confidence intervals. Our evaluation on real-world datasets confirms that STMGNN-ZINB outperforms existing models, providing a more reliable tool for predicting and understanding crime dynamics.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c8b56ca8a213a228fe44d00254f0cbca7f9209bd" target='_blank'>
-              Period-aggregated transformer for learning latent seasonalities in long-horizon financial time series
+          <td><a href="https://www.semanticscholar.org/paper/24c426aeaad4d19ac16a0dbe2914166f1ee8d9da" target='_blank'>
+              Uncertainty-Aware Crime Prediction With Spatial Temporal Multivariate Graph Neural Networks
               </a>
             </td>
           <td>
-            Zhenyang Tang, Jinshui Huang, Denisa Rinprasertmeechai
+            Zepu Wang, Xiaobo Ma, Huajie Yang, Weimin Lvu, Peng Sun, Sharath Chandra Guntuku
           </td>
           <td>2024-08-08</td>
-          <td>PLOS ONE</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>27</td>
         </tr>
 
         <tr id="In accordance with the findings presented in [34], this study examines the applicability of Machine Learning (ML) models and training strategies from the Natural Language Processing (NLP) domain in addressing time series problems, emphasizing the structural and operational aspects of these models and strategies. Recognizing the structural congruence within the data, we opt for Stock Price Prediction (SPP) as the designated domain to assess the transferability of NLP models and strategies. Building upon initial positive outcomes derived from quantitative SPP models in our ongoing research endeavors, we provide a rationale for exploring a range of additional methods and conducting subsequent research experiments. The outlined research aims to elucidate the efficacy of leveraging NLP models and techniques for addressing time series problems exemplified as SPP.">
@@ -2212,34 +2212,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>12</td>
         </tr>
 
-        <tr id="Many of today's data is time-series data originating from various sources, such as sensors, transaction systems, or production systems. Major challenges with such data include privacy and business sensitivity. Generative time-series models have the potential to overcome these problems, allowing representative synthetic data, such as people's movement in cities, to be shared openly and be used to the benefit of society at large. However, contemporary approaches are limited to prohibitively short sequences and small scales. Aside from major memory limitations, the models generate less accurate and less representative samples the longer the sequences are. This issue is further exacerbated by the lack of a comprehensive and accessible benchmark. Furthermore, a common need in practical applications is what-if analysis and dynamic adaptation to data distribution changes, for usage in decision making and to manage a changing world: What if this road is temporarily blocked or another road is added? The focus of this paper is on mobility data, such as people's movement in cities, requiring all these issues to be addressed. To this end, we propose a transformer-based diffusion model, TDDPM, for time-series which outperforms and scales substantially better than state-of-the-art. This is evaluated in a new comprehensive benchmark across several sequence lengths, standard datasets, and evaluation measures. We also demonstrate how the model can be conditioned on a prior over spatial occupancy frequency information, allowing the model to generate mobility data for previously unseen environments and for hypothetical scenarios where the underlying road network and its usage changes. This is evaluated by training on mobility data from part of a city. Then, using only aggregate spatial information as prior, we demonstrate out-of-distribution generalization to the unobserved remainder of the city.">
+        <tr id="Graphs are crucial for representing interrelated data and aiding predictive modeling by capturing complex relationships. Achieving high-quality graph representation is important for identifying linked patterns, leading to improvements in Graph Neural Networks (GNNs) to better capture data structures. However, challenges such as data scarcity, high collection costs, and ethical concerns limit progress. As a result, generative models and data augmentation have become more and more popular. This study explores using generated graphs for data augmentation, comparing the performance of combining generated graphs with real graphs, and examining the effect of different quantities of generated graphs on graph classification tasks. The experiments show that balancing scalability and quality requires different generators based on graph size. Our results introduce a new approach to graph data augmentation, ensuring consistent labels and enhancing classification performance.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1d7a486a59e6327be349015918f17ee84f6c4776" target='_blank'>
-              Deep Temporal Deaggregation: Large-Scale Spatio-Temporal Generative Models
+          <td><a href="https://www.semanticscholar.org/paper/f4641fc657af2ab1a0c304535fd1f2caa9ccc97f" target='_blank'>
+              Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs
               </a>
             </td>
           <td>
-            David Bergstrom, Mattias Tiger, Fredrik Heintz
+            Sumeyye Bas, Kıymet Kaya, Resul Tugay, S. G. Oguducu
           </td>
-          <td>2024-06-18</td>
+          <td>2024-07-20</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
+          <td>4</td>
         </tr>
 
-        <tr id="An emerging topic in large language models (LLMs) is their application to time series forecasting, characterizing mainstream and patternable characteristics of time series. A relevant but rarely explored and more challenging question is whether LLMs can detect and explain time series anomalies, a critical task across various real-world applications. In this paper, we investigate the capabilities of LLMs, specifically GPT-4 and LLaMA3, in detecting and explaining anomalies in time series. Our studies reveal that: 1) LLMs cannot be directly used for time series anomaly detection. 2) By designing prompt strategies such as in-context learning and chain-of-thought prompting, GPT-4 can detect time series anomalies with results competitive to baseline methods. 3) We propose a synthesized dataset to automatically generate time series anomalies with corresponding explanations. By applying instruction fine-tuning on this dataset, LLaMA3 demonstrates improved performance in time series anomaly detection tasks. In summary, our exploration shows the promising potential of LLMs as time series anomaly detectors.">
+        <tr id="Fluctuations in the financial market are influenced by various driving forces and numerous factors. Traditional financial research aims to identify the factors influencing stock prices, and existing works construct a common neural network learning framework that learns temporal dependency using a fixed time window of historical information, such as RNN and LSTM models. However, these models only consider the short-term and point-to-point relationships within stock series. The financial market is a complex and dynamic system with many unobservable temporal patterns. Therefore, we propose an adaptive period-aggregation model called the Latent Period-Aggregated Stock Transformer (LPAST). The model integrates a variational autoencoder (VAE) with a period-to-period attention mechanism for multistep prediction in the financial time series. Additionally, we introduce a self-correlation learning method and routing mechanism to handle complex multi-period aggregations and information distribution. Main contributions include proposing a novel period-aggregation representation scheme, introducing a new attention mechanism, and validating the model’s superiority in long-horizon prediction tasks. The LPAST model demonstrates its potential and effectiveness in financial market prediction, highlighting its relevance in financial research and predictive analytics.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/12b073694ad8cf460d1e74a34b68d07536e0db14" target='_blank'>
-              Can LLMs Serve As Time Series Anomaly Detectors?
+          <td><a href="https://www.semanticscholar.org/paper/c8b56ca8a213a228fe44d00254f0cbca7f9209bd" target='_blank'>
+              Period-aggregated transformer for learning latent seasonalities in long-horizon financial time series
               </a>
             </td>
           <td>
-            Manqing Dong, Hao Huang, Longbing Cao
+            Zhenyang Tang, Jinshui Huang, Denisa Rinprasertmeechai
           </td>
-          <td>2024-08-06</td>
-          <td>ArXiv</td>
-          <td>0</td>
+          <td>2024-08-08</td>
+          <td>PLOS ONE</td>
           <td>0</td>
+          <td>1</td>
         </tr>
 
         <tr id="None">
@@ -2257,36 +2257,6 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>1</td>
         </tr>
 
-        <tr id="Crime forecasting is a critical component of urban analysis and essential for stabilizing society today. Unlike other time series forecasting problems, crime incidents are sparse, particularly in small regions and within specific time periods. Traditional spatial-temporal deep learning models often struggle with this sparsity, as they typically cannot effectively handle the non-Gaussian nature of crime data, which is characterized by numerous zeros and over-dispersed patterns. To address these challenges, we introduce a novel approach termed Spatial Temporal Multivariate Zero-Inflated Negative Binomial Graph Neural Networks (STMGNN-ZINB). This framework leverages diffusion and convolution networks to analyze spatial, temporal, and multivariate correlations, enabling the parameterization of probabilistic distributions of crime incidents. By incorporating a Zero-Inflated Negative Binomial model, STMGNN-ZINB effectively manages the sparse nature of crime data, enhancing prediction accuracy and the precision of confidence intervals. Our evaluation on real-world datasets confirms that STMGNN-ZINB outperforms existing models, providing a more reliable tool for predicting and understanding crime dynamics.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/24c426aeaad4d19ac16a0dbe2914166f1ee8d9da" target='_blank'>
-              Uncertainty-Aware Crime Prediction With Spatial Temporal Multivariate Graph Neural Networks
-              </a>
-            </td>
-          <td>
-            Zepu Wang, Xiaobo Ma, Huajie Yang, Weimin Lvu, Peng Sun, Sharath Chandra Guntuku
-          </td>
-          <td>2024-08-08</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>28</td>
-        </tr>
-
-        <tr id="To generate coherent responses, language models infer unobserved meaning from their input text sequence. One potential explanation for this capability arises from theories of delay embeddings in dynamical systems, which prove that unobserved variables can be recovered from the history of only a handful of observed variables. To test whether language models are effectively constructing delay embeddings, we measure the capacities of sequence models to reconstruct unobserved dynamics. We trained 1-layer transformer decoders and state-space sequence models on next-step prediction from noisy, partially-observed time series data. We found that each sequence layer can learn a viable embedding of the underlying system. However, state-space models have a stronger inductive bias than transformers-in particular, they more effectively reconstruct unobserved information at initialization, leading to more parameter-efficient models and lower error on dynamics tasks. Our work thus forges a novel connection between dynamical systems and deep learning sequence models via delay embedding theory.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/de9997857d06f20ea1332b1569e9abd7f837abca" target='_blank'>
-              Delay Embedding Theory of Neural Sequence Models
-              </a>
-            </td>
-          <td>
-            Mitchell Ostrow, Adam J. Eisen, I. Fiete
-          </td>
-          <td>2024-06-17</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>30</td>
-        </tr>
-
         <tr id="Ensemble forecasts from numerical weather prediction models show systematic errors that require correction via post-processing. While there has been substantial progress in flexible neural network-based post-processing methods over the past years, most station-based approaches still treat every input data point separately which limits the capabilities for leveraging spatial structures in the forecast errors. In order to improve information sharing across locations, we propose a graph neural network architecture for ensemble post-processing, which represents the station locations as nodes on a graph and utilizes an attention mechanism to identify relevant predictive information from neighboring locations. In a case study on 2-m temperature forecasts over Europe, the graph neural network model shows substantial improvements over a highly competitive neural network-based post-processing method.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/1fe4b1eeac90174d97c76b0869c2c1e6f138febe" target='_blank'>
@@ -2302,49 +2272,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>0</td>
         </tr>
 
-        <tr id="Recently, Large Language Models (LLMs) have attracted significant attention for their exceptional performance across a broad range of tasks, particularly in text analysis. However, the finance sector presents a distinct challenge due to its dependence on time-series data for complex forecasting tasks. In this study, we introduce a novel framework called LLMFactor, which employs Sequential Knowledge-Guided Prompting (SKGP) to identify factors that influence stock movements using LLMs. Unlike previous methods that relied on keyphrases or sentiment analysis, this approach focuses on extracting factors more directly related to stock market dynamics, providing clear explanations for complex temporal changes. Our framework directs the LLMs to create background knowledge through a fill-in-the-blank strategy and then discerns potential factors affecting stock prices from related news. Guided by background knowledge and identified factors, we leverage historical stock prices in textual format to predict stock movement. An extensive evaluation of the LLMFactor framework across four benchmark datasets from both the U.S. and Chinese stock markets demonstrates its superiority over existing state-of-the-art methods and its effectiveness in financial time-series forecasting.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/7185f24f33de547954baa0c71cafaf41786e81ba" target='_blank'>
-              LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
-              </a>
-            </td>
-          <td>
-            Meiyun Wang, Kiyoshi Izumi, Hiroki Sakaji
-          </td>
-          <td>2024-06-16</td>
-          <td>ArXiv</td>
-          <td>1</td>
-          <td>12</td>
-        </tr>
-
-        <tr id="In this study, we delve into the Structured State Space Model (S4), Change Point Detection methodologies, and the Switching Non-linear Dynamics System (SNLDS). Our central proposition is an enhanced inference technique and long-range dependency method for SNLDS. The cornerstone of our approach is the fusion of S4 and SNLDS, leveraging the strengths of both models to effectively address the intricacies of long-range dependencies in switching time series. Through rigorous testing, we demonstrate that our proposed methodology adeptly segments and reproduces long-range dependencies in both the 1-D Lorenz dataset and the 2-D bouncing ball dataset. Notably, our integrated approach outperforms the standalone SNLDS in these tasks.">
+        <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/737fccae30a52b719925aa3bdefbccf7758f66f5" target='_blank'>
-              Long Range Switching Time Series Prediction via State Space Model
+          <td><a href="https://www.semanticscholar.org/paper/a8f0842a1540b91ce53047bb74813c3d07827909" target='_blank'>
+              Fine-scale deep learning model for time series forecasting
               </a>
             </td>
           <td>
-            Jiaming Zhang, Yang Ding, Yunfeng Gao
+            Yuwei Chen, Wenjing Jia, Qiang Wu
           </td>
-          <td>2024-07-27</td>
-          <td>ArXiv</td>
-          <td>0</td>
+          <td>2024-08-06</td>
+          <td>Applied Intelligence</td>
           <td>0</td>
+          <td>1</td>
         </tr>
 
-        <tr id="None">
+        <tr id="We introduce NodeFlow, a flexible framework for probabilistic regression on tabular data that combines Neural Oblivious Decision Ensembles (NODEs) and Conditional Continuous Normalizing Flows (CNFs). It offers improved modeling capabilities for arbitrary probabilistic distributions, addressing the limitations of traditional parametric approaches. In NodeFlow, the NODE captures complex relationships in tabular data through a tree-like structure, while the conditional CNF utilizes the NODE’s output space as a conditioning factor. The training process of NodeFlow employs standard gradient-based learning, facilitating the end-to-end optimization of the NODEs and CNF-based density estimation. This approach ensures outstanding performance, ease of implementation, and scalability, making NodeFlow an appealing choice for practitioners and researchers. Comprehensive assessments on benchmark datasets underscore NodeFlow’s efficacy, revealing its achievement of state-of-the-art outcomes in multivariate probabilistic regression setup and its strong performance in univariate regression tasks. Furthermore, ablation studies are conducted to justify the design choices of NodeFlow. In conclusion, NodeFlow’s end-to-end training process and strong performance make it a compelling solution for practitioners and researchers. Additionally, it opens new avenues for research and application in the field of probabilistic regression on tabular data.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/a8f0842a1540b91ce53047bb74813c3d07827909" target='_blank'>
-              Fine-scale deep learning model for time series forecasting
+          <td><a href="https://www.semanticscholar.org/paper/7b7ef3bf708e12014b3094d7f3efb36a9d8eb2f4" target='_blank'>
+              NodeFlow: Towards End-to-End Flexible Probabilistic Regression on Tabular Data
               </a>
             </td>
           <td>
-            Yuwei Chen, Wenjing Jia, Qiang Wu
+            Patryk Wielopolski, Oleksii Furman, Maciej Zięba
           </td>
-          <td>2024-08-06</td>
-          <td>Applied Intelligence</td>
-          <td>0</td>
+          <td>2024-07-01</td>
+          <td>Entropy</td>
           <td>0</td>
+          <td>2</td>
         </tr>
 
         <tr id="The prediction of time series is a challenging task relevant in such diverse applications as analyzing financial data, forecasting flow dynamics or understanding biological processes. Especially chaotic time series that depend on a long history pose an exceptionally difficult problem. While machine learning has shown to be a promising approach for predicting such time series, it either demands long training time and much training data when using deep recurrent neural networks. Alternative, when using a reservoir computing approach it comes with high uncertainty and typically a high number of random initializations and extensive hyper-parameter tuning when using a reservoir computing approach. In this paper, we focus on the reservoir computing approach and propose a new mapping of input data into the reservoir's state space. Furthermore, we incorporate this method in two novel network architectures increasing parallelizability, depth and predictive capabilities of the neural network while reducing the dependence on randomness. For the evaluation, we approximate a set of time series from the Mackey-Glass equation, inhabiting non-chaotic as well as chaotic behavior and compare our approaches in regard to their predictive capabilities to echo state networks and gated recurrent units. For the chaotic time series, we observe an error reduction of up to $85.45\%$ and up to $87.90\%$ in contrast to echo state networks and gated recurrent units respectively. Furthermore, we also observe tremendous improvements for non-chaotic time series of up to $99.99\%$ in contrast to existing approaches.">
@@ -2354,7 +2309,7 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
               </a>
             </td>
           <td>
-            Johannes Viehweg, Dominik Walther, Prof. Dr.-Ing. Patrick Mader
+            Johannes Viehweg, Dominik Walther, Patrick Mäder
           </td>
           <td>2024-07-09</td>
           <td>ArXiv</td>
@@ -2362,19 +2317,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2</td>
         </tr>
 
-        <tr id="Temporal networks are effective in capturing the evolving interactions of networks over time, such as social networks and e-commerce networks. In recent years, researchers have primarily concentrated on developing specific model architectures for Temporal Graph Neural Networks (TGNNs) in order to improve the representation quality of temporal nodes and edges. However, limited attention has been given to the quality of negative samples during the training of TGNNs. When compared with static networks, temporal networks present two specific challenges for negative sampling: positive sparsity and positive shift. Positive sparsity refers to the presence of a single positive sample amidst numerous negative samples at each timestamp, while positive shift relates to the variations in positive samples across different timestamps. To robustly address these challenges in training TGNNs, we introduce Curriculum Negative Mining (CurNM), a model-aware curriculum learning framework that adaptively adjusts the difficulty of negative samples. Within this framework, we first establish a dynamically updated negative pool that balances random, historical, and hard negatives to address the challenges posed by positive sparsity. Secondly, we implement a temporal-aware negative selection module that focuses on learning from the disentangled factors of recently active edges, thus accurately capturing shifting preferences. Extensive experiments on 12 datasets and 3 TGNNs demonstrate that our method outperforms baseline methods by a significant margin. Additionally, thorough ablation studies and parameter sensitivity experiments verify the usefulness and robustness of our approach. Our code is available at https://github.com/zziyue83/CurNM.">
+        <tr id="Graph-level anomaly detection (GLAD) has already gained significant importance and has become a popular field of study, attracting considerable attention across numerous downstream works. The core focus of this domain is to capture and highlight the anomalous information within given graph datasets. In most existing studies, anomalies are often the instances of few. The stark imbalance misleads current GLAD methods to focus on learning the patterns of normal graphs more, further impacting anomaly detection performance. Moreover, existing methods predominantly utilize the inherent features of nodes to identify anomalous graph patterns which is approved suboptimal according to our experiments. In this work, we propose an imbalanced GLAD method via counterfactual augmentation and feature learning. Specifically, we first construct anomalous samples based on counterfactual learning, aiming to expand and balance the datasets. Additionally, we construct a module based on Graph Neural Networks (GNNs), which allows us to utilize degree attributes to complement the inherent attribute features of nodes. Then, we design an adaptive weight learning module to integrate features tailored to different datasets effectively to avoid indiscriminately treating all features as equivalent. Furthermore, extensive baseline experiments conducted on public datasets substantiate the robustness and effectiveness. Besides, we apply the model to brain disease datasets, which can prove the generalization capability of our work. The source code of our work is available online.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/56efc5b54a9beda670261028d60fe6056619863a" target='_blank'>
-              Curriculum Negative Mining For Temporal Networks
+          <td><a href="https://www.semanticscholar.org/paper/f75be7b79dc5b02dfc1845486489956beb059786" target='_blank'>
+              Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learning
               </a>
             </td>
           <td>
-            Ziyue Chen, Tongya Zheng, Mingli Song
+            Zitong Wang, Xuexiong Luo, Enfeng Song, Qiuqing Bai, Fu Lin
           </td>
-          <td>2024-07-24</td>
+          <td>2024-07-13</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>5</td>
+          <td>4</td>
         </tr>
 
         <tr id="Time series forecasting is an important task in many fields ranging from supply chain management to weather forecasting. Recently, Transformer neural network architectures have shown promising results in forecasting on common time series benchmark datasets. However, application to supply chain demand forecasting, which can have challenging characteristics such as sparsity and cross-series effects, has been limited. In this work, we explore the application of Transformer-based models to supply chain demand forecasting. In particular, we develop a new Transformer-based forecasting approach using a shared, multi-task per-time series network with an initial component applying attention across time series, to capture interactions and help address sparsity. We provide a case study applying our approach to successfully improve demand prediction for a medical device manufacturing company. To further validate our approach, we also apply it to public demand forecasting datasets as well and demonstrate competitive to superior performance compared to a variety of baseline and state-of-the-art forecast methods across the private and public datasets.">
@@ -2392,230 +2347,214 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>12</td>
         </tr>
 
-        <tr id="With the remarkable success of generative models like ChatGPT, Artificial Intelligence Generated Content (AIGC) is undergoing explosive development. Not limited to text and images, generative models can generate industrial time series data, addressing challenges such as the difficulty of data collection and data annotation. Due to their outstanding generation ability, they have been widely used in Internet of Things, metaverse, and cyber-physical-social systems to enhance the efficiency of industrial production. In this paper, we present a comprehensive overview of generative models for industrial time series from deep generative models (DGMs) to large generative models (LGMs). First, a DGM-based AIGC framework is proposed for industrial time series generation. Within this framework, we survey advanced industrial DGMs and present a multi-perspective categorization. Furthermore, we systematically analyze the critical technologies required to construct industrial LGMs from four aspects: large-scale industrial dataset, LGMs architecture for complex industrial characteristics, self-supervised training for industrial time series, and fine-tuning of industrial downstream tasks. Finally, we conclude the challenges and future directions to enable the development of generative models in industry.">
+        <tr id="Temporal networks are effective in capturing the evolving interactions of networks over time, such as social networks and e-commerce networks. In recent years, researchers have primarily concentrated on developing specific model architectures for Temporal Graph Neural Networks (TGNNs) in order to improve the representation quality of temporal nodes and edges. However, limited attention has been given to the quality of negative samples during the training of TGNNs. When compared with static networks, temporal networks present two specific challenges for negative sampling: positive sparsity and positive shift. Positive sparsity refers to the presence of a single positive sample amidst numerous negative samples at each timestamp, while positive shift relates to the variations in positive samples across different timestamps. To robustly address these challenges in training TGNNs, we introduce Curriculum Negative Mining (CurNM), a model-aware curriculum learning framework that adaptively adjusts the difficulty of negative samples. Within this framework, we first establish a dynamically updated negative pool that balances random, historical, and hard negatives to address the challenges posed by positive sparsity. Secondly, we implement a temporal-aware negative selection module that focuses on learning from the disentangled factors of recently active edges, thus accurately capturing shifting preferences. Extensive experiments on 12 datasets and 3 TGNNs demonstrate that our method outperforms baseline methods by a significant margin. Additionally, thorough ablation studies and parameter sensitivity experiments verify the usefulness and robustness of our approach. Our code is available at https://github.com/zziyue83/CurNM.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/dc1ce4a6d24e82a1c9c05d41f69b1e606efa4115" target='_blank'>
-              AIGC for Industrial Time Series: From Deep Generative Models to Large Generative Models
+          <td><a href="https://www.semanticscholar.org/paper/56efc5b54a9beda670261028d60fe6056619863a" target='_blank'>
+              Curriculum Negative Mining For Temporal Networks
               </a>
             </td>
           <td>
-            Lei Ren, Haiteng Wang, Yang Tang, Chunhua Yang
+            Ziyue Chen, Tongya Zheng, Mingli Song
           </td>
-          <td>2024-07-16</td>
+          <td>2024-07-24</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>5</td>
         </tr>
 
-        <tr id="Training models on spatio-temporal (ST) data poses an open problem due to the complicated and diverse nature of the data itself, and it is challenging to ensure the model's performance directly trained on the original ST data. While limiting the variety of training data can make training easier, it can also lead to a lack of knowledge and information for the model, resulting in a decrease in performance. To address this challenge, we presented an innovative paradigm that incorporates three separate forms of curriculum learning specifically targeting from spatial, temporal, and quantile perspectives. Furthermore, our framework incorporates a stacking fusion module to combine diverse information from three types of curriculum learning, resulting in a strong and thorough learning process. We demonstrated the effectiveness of this framework with extensive empirical evaluations, highlighting its better performance in addressing complex ST challenges. We provided thorough ablation studies to investigate the effectiveness of our curriculum and to explain how it contributes to the improvement of learning efficiency on ST data.">
+        <tr id="We propose a novel data-driven network framework for forecasting problems related to E-mini S\&P 500 and CBOE Volatility Index futures, in which products with different expirations act as distinct nodes. We provide visual demonstrations of the correlation structures of these products in terms of their returns, realized volatility, and trading volume. The resulting networks offer insights into the contemporaneous movements across the different products, illustrating how inherently connected the movements of the future products belonging to these two classes are. These networks are further utilized by a multi-channel Graph Convolutional Network to enhance the predictive power of a Long Short-Term Memory network, allowing for the propagation of forecasts of highly correlated quantities, combining the temporal with the spatial aspect of the term structure.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6785d8ecb5eb658efde7e06f9afec1ed69bfadb7" target='_blank'>
-              Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
+          <td><a href="https://www.semanticscholar.org/paper/250fe7baf3fd0aa1f684eedd3e76aeb1b17f89aa" target='_blank'>
+              A GCN-LSTM Approach for ES-mini and VX Futures Forecasting
               </a>
             </td>
           <td>
-            Du Yin, Jinliang Deng, Shuang Ao, Zechen Li, Hao Xue, Arian Prabowo, Renhe Jiang, Xuan Song, Flora D. Salim
+            N. Michael, Mihai Cucuringu, Sam Howison
           </td>
-          <td>2024-06-18</td>
+          <td>2024-08-10</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>18</td>
+          <td>8</td>
         </tr>
 
-        <tr id="Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to model mismatch errors or network generalization issues. Large language models (LLMs) have demonstrated powerful modeling and generalization abilities, and have been successfully applied to cross-modal tasks, including the time series analysis. Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered channel prediction method (LLM4CP) to predict the future downlink channel state information (CSI) sequence based on the historical uplink CSI sequence. We fine-tune the network while freezing most of the parameters of the pre-trained LLM for better cross-modality knowledge transfer. To bridge the gap between the channel data and the feature space of the LLM, preprocessor, embedding, and output modules are specifically tailored by taking into account unique channel characteristics. Simulations validate that the proposed method achieves SOTA prediction performance on full-sample, few-shot, and generalization tests with low training and inference costs.">
+        <tr id="In the realm of event prediction, temporal knowledge graph forecasting (TKGF) stands as a pivotal technique. Previous approaches face the challenges of not utilizing experience during testing and relying on a single short-term history, which limits adaptation to evolving data. In this paper, we introduce the Online Neural-Symbolic Event Prediction (ONSEP) framework, which innovates by integrating dynamic causal rule mining (DCRM) and dual history augmented generation (DHAG). DCRM dynamically constructs causal rules from real-time data, allowing for swift adaptation to new causal relationships. In parallel, DHAG merges short-term and long-term historical contexts, leveraging a bi-branch approach to enrich event prediction. Our framework demonstrates notable performance enhancements across diverse datasets, with significant Hit@k (k=1,3,10) improvements, showcasing its ability to augment large language models (LLMs) for event prediction without necessitating extensive retraining. The ONSEP framework not only advances the field of TKGF but also underscores the potential of neural-symbolic approaches in adapting to dynamic data environments.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/3266981f8129e839d04c0a6524f1e81a2fea54d6" target='_blank'>
-              LLM4CP: Adapting Large Language Models for Channel Prediction
+          <td><a href="https://www.semanticscholar.org/paper/0f4e7c0661406c5e4a78a9aa8c2f70648662b630" target='_blank'>
+              ONSEP: A Novel Online Neural-Symbolic Framework for Event Prediction Based on Large Language Model
               </a>
             </td>
           <td>
-            Boxun Liu, Xuanyu Liu, Shijian Gao, Xiang Cheng, Liuqing Yang
+            Xuanqing Yu, Wangtao Sun, Jingwei Li, Kang Liu, Chengbao Liu, JieFu Tan
           </td>
-          <td>2024-06-01</td>
-          <td>Journal of Communications and Information Networks</td>
+          <td>2024-08-14</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>40</td>
+          <td>8</td>
         </tr>
 
-        <tr id="Conformal prediction provides machine learning models with prediction sets that offer theoretical guarantees, but the underlying assumption of exchangeability limits its applicability to time series data. Furthermore, existing approaches struggle to handle multi-step ahead prediction tasks, where uncertainty estimates across multiple future time points are crucial. We propose JANET (Joint Adaptive predictioN-region Estimation for Time-series), a novel framework for constructing conformal prediction regions that are valid for both univariate and multivariate time series. JANET generalises the inductive conformal framework and efficiently produces joint prediction regions with controlled K-familywise error rates, enabling flexible adaptation to specific application needs. Our empirical evaluation demonstrates JANET's superior performance in multi-step prediction tasks across diverse time series datasets, highlighting its potential for reliable and interpretable uncertainty quantification in sequential data.">
+        <tr id="Accurately predicting PM2.5 is a crucial task for protecting public health and making policy decisions. In the meanwhile, it is also a challenging task, given the complex spatio-temporal patterns of PM2.5 concentrations. Recently, the utilization of graph neural network (GNN) models has emerged as a promising approach, demonstrating significant advantages in capturing the spatial and temporal dependencies associated with PM2.5 concentrations. In this work, we collected a comprehensive dataset spanning 308 cities in China, encompassing data on seven pollutants as well as meteorological variables from January 2015 to September 2022. To effectively predict the PM2.5 concentrations, we propose a graph attention recurrent neural network (GARNN) model by taking into account both meteorological and geographical information. Extensive experiments validated the efficiency of the proposed GARNN model, revealing its superior performance compared to other existing methods in terms of predictive capabilities. This study contributes to advancing the understanding and prediction of PM2.5 concentrations, providing a valuable tool for addressing environmental challenges.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/b21605b67376e0377590d3cdfb99e3c43495b865" target='_blank'>
-              JANET: Joint Adaptive predictioN-region Estimation for Time-series
+          <td><a href="https://www.semanticscholar.org/paper/f0d3185a50e22682b8c650ceac52e70dd3a9849f" target='_blank'>
+              A Graph Attention Recurrent Neural Network Model for PM2.5 Prediction: A Case Study in China from 2015 to 2022
               </a>
             </td>
           <td>
-            Eshant English, Eliot Wong-Toi, Matteo Fontana, Stephan Mandt, P. Smyth, Christoph Lippert
+            Rui Pan, Tuozhen Liu, Lingfei Ma
           </td>
-          <td>2024-07-08</td>
-          <td>ArXiv</td>
+          <td>2024-07-03</td>
+          <td>Atmosphere</td>
           <td>0</td>
-          <td>8</td>
+          <td>19</td>
         </tr>
 
-        <tr id="Transformer-based methods have recently demonstrated their potential in time series forecasting problems. However, the mainstream approach, primarily utilizing attention to model inter-step correlation in the time domain, is constrained by two significant issues that lead to ineffective and inefficient multivariate forecasting. The first is that key representations in the time domain are scattered and sparse, resulting in parameter bloat and increased difficulty in capturing time dependencies. The second is that treating time step points as uniformly embedded tokens leads to the erasure of inter-variate correlations. To address these challenges, we propose a frequency-wise and variables-oriented transformer-based method. This method leverages the intrinsic conjugate symmetry in the frequency domain, enabling compact frequency domain representations that naturally mix information across time points while reducing spatio-temporal costs. Multivariate inter-correlations can also be captured from similar frequency domain components, which enhances the variables-oriented attention mechanism modeling capability. Further, we employ both polar and complex domain perspectives to enrich the frequency domain representations and decode complicated temporal patterns. We propose frequency-enhanced independent representation multi-head attention (FIR-Attention) to leverage these advantages for improved multivariate interaction. Techniques such as cutting-off frequency and equivalent mapping are used to ensure the model’s lightweight nature. Extensive experiments on eight mainstream datasets show that our approach achieves first-rate satisfactory results and, importantly, requires only one percent of the spatio-temporal cost of mainstream methods.">
+        <tr id="In this study, we delve into the Structured State Space Model (S4), Change Point Detection methodologies, and the Switching Non-linear Dynamics System (SNLDS). Our central proposition is an enhanced inference technique and long-range dependency method for SNLDS. The cornerstone of our approach is the fusion of S4 and SNLDS, leveraging the strengths of both models to effectively address the intricacies of long-range dependencies in switching time series. Through rigorous testing, we demonstrate that our proposed methodology adeptly segments and reproduces long-range dependencies in both the 1-D Lorenz dataset and the 2-D bouncing ball dataset. Notably, our integrated approach outperforms the standalone SNLDS in these tasks.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/36f87cbd5187e521184ddb319b132292cb8ae608" target='_blank'>
-              Frequency-Enhanced Transformer with Symmetry-Based Lightweight Multi-Representation for Multivariate Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/737fccae30a52b719925aa3bdefbccf7758f66f5" target='_blank'>
+              Long Range Switching Time Series Prediction via State Space Model
               </a>
             </td>
           <td>
-            Chenyue Wang, Zhouyuan Zhang, Xin Wang, Mingyang Liu, Lin Chen, Jiatian Pi
+            Jiaming Zhang, Yang Ding, Yunfeng Gao
           </td>
-          <td>2024-06-25</td>
-          <td>Symmetry</td>
+          <td>2024-07-27</td>
+          <td>ArXiv</td>
+          <td>0</td>
           <td>0</td>
-          <td>1</td>
         </tr>
 
-        <tr id="We introduce the Robustness of Hierarchically Organized Time Series (RHiOTS) framework, designed to assess the robustness of hierarchical time series forecasting models and algorithms on real-world datasets. Hierarchical time series, where lower-level forecasts must sum to upper-level ones, are prevalent in various contexts, such as retail sales across countries. Current empirical evaluations of forecasting methods are often limited to a small set of benchmark datasets, offering a narrow view of algorithm behavior. RHiOTS addresses this gap by systematically altering existing datasets and modifying the characteristics of individual series and their interrelations. It uses a set of parameterizable transformations to simulate those changes in the data distribution. Additionally, RHiOTS incorporates an innovative visualization component, turning complex, multidimensional robustness evaluation results into intuitive, easily interpretable visuals. This approach allows an in-depth analysis of algorithm and model behavior under diverse conditions. We illustrate the use of RHiOTS by analyzing the predictive performance of several algorithms. Our findings show that traditional statistical methods are more robust than state-of-the-art deep learning algorithms, except when the transformation effect is highly disruptive. Furthermore, we found no significant differences in the robustness of the algorithms when applying specific reconciliation methods, such as MinT. RHiOTS provides researchers with a comprehensive tool for understanding the nuanced behavior of forecasting algorithms, offering a more reliable basis for selecting the most appropriate method for a given problem.">
+        <tr id="Deep neural networks (DNNs) are prominent in predictive analytics for accurately forecasting target variables. However, inherent uncertainties necessitate constructing prediction intervals for reliability. The existing literature often lacks practical methodologies for creating predictive intervals, especially for time series with trends and seasonal patterns. This paper explicitly details a practical approach integrating dual-output Monte Carlo Dropout (MCDO) with DNNs to approximate predictive means and variances within a Bayesian framework, enabling forecast interval construction. The dual-output architecture employs a custom loss function, combining mean squared error with Softplus-derived predictive variance, ensuring non-negative variance values. Hyperparameter optimization is performed through a grid search exploring activation functions, dropout rates, epochs, and batch sizes. Empirical distributions of predictive means and variances from the MCDO demonstrate the results of the dual-output MCDO DNNs. The proposed method achieves a significant improvement in forecast accuracy, with an RMSE reduction of about 10% compared to the seasonal autoregressive integrated moving average model. Additionally, the method provides more reliable forecast intervals, as evidenced by a higher coverage proportion and narrower interval widths. A case study on Thailand’s durian export data showcases the method’s utility and applicability to other datasets with trends and/or seasonal components.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/525cf42ca049ecd3089189612e74d0df68dbcae0" target='_blank'>
-              RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms
+          <td><a href="https://www.semanticscholar.org/paper/48e9c93416cccc0d5c4d1f0646a11cd372d3ec5b" target='_blank'>
+              Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian Exports
               </a>
             </td>
           <td>
-            Luis Roque, Carlos Soares, Luís Torgo
+            Unyamanee Kummaraka, Patchanok Srisuradetchai
           </td>
-          <td>2024-08-06</td>
-          <td>ArXiv</td>
+          <td>2024-08-02</td>
+          <td>Forecasting</td>
           <td>0</td>
-          <td>36</td>
+          <td>3</td>
         </tr>
 
-        <tr id="This paper introduces scour physics-informed neural networks (SPINNs), a hybrid physics-data-driven framework for bridge scour prediction using deep learning. SPINNs are developed based on historical scour monitoring data and integrate physics-based empirical equations into neural networks as supplementary loss components. We incorporated three architectures: LSTM, CNN, and NLinear as the base data-driven model. Despite varying performance across different base models and bridges, SPINNs overall outperformed pure data-driven models. In some bridge cases, SPINN reduced forecasting errors by up to 50 percent. In this study, we also explored general models for bridge clusters, trained by aggregating datasets across multiple bridges in a region. The pure data-driven models mostly benefited from this approach, in particular bridges with limited data. However, bridge-specific SPINNs provided more accurate predictions than general SPINNs for almost all case studies. Also, the time-dependent empirical equations derived from SPINNs showed reasonable accuracy in estimating maximum scour depth, providing more accurate predictions compared to HEC-18. Comparing both SPINNs and pure deep learning models with traditional HEC-18 equation indicates substantial improvements in scour prediction accuracy. This study can pave the way for hybrid physics-machine learning methodologies to be implemented for bridge scour design and maintenance.">
+        <tr id="With the remarkable success of generative models like ChatGPT, Artificial Intelligence Generated Content (AIGC) is undergoing explosive development. Not limited to text and images, generative models can generate industrial time series data, addressing challenges such as the difficulty of data collection and data annotation. Due to their outstanding generation ability, they have been widely used in Internet of Things, metaverse, and cyber-physical-social systems to enhance the efficiency of industrial production. In this paper, we present a comprehensive overview of generative models for industrial time series from deep generative models (DGMs) to large generative models (LGMs). First, a DGM-based AIGC framework is proposed for industrial time series generation. Within this framework, we survey advanced industrial DGMs and present a multi-perspective categorization. Furthermore, we systematically analyze the critical technologies required to construct industrial LGMs from four aspects: large-scale industrial dataset, LGMs architecture for complex industrial characteristics, self-supervised training for industrial time series, and fine-tuning of industrial downstream tasks. Finally, we conclude the challenges and future directions to enable the development of generative models in industry.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/15c070cd7b0c585c482566941ee6901cf985b1cd" target='_blank'>
-              Introducing a Physics-informed Deep Learning Framework for Bridge Scour Prediction
+          <td><a href="https://www.semanticscholar.org/paper/dc1ce4a6d24e82a1c9c05d41f69b1e606efa4115" target='_blank'>
+              AIGC for Industrial Time Series: From Deep Generative Models to Large Generative Models
               </a>
             </td>
           <td>
-            N. Yousefpour, Bo Wang
+            Lei Ren, Haiteng Wang, Yang Tang, Chunhua Yang
           </td>
-          <td>2024-07-01</td>
+          <td>2024-07-16</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Forecasting multivariate time series is a computationally intensive task challenged by extreme or redundant samples. Recent resampling methods aim to increase training efficiency by reweighting samples based on their running losses. However, these methods do not solve the problems caused by heavy-tailed distribution losses, such as overfitting to outliers. To tackle these issues, we introduce a novel approach: a Gaussian loss-weighted sampler that multiplies their running losses with a Gaussian distribution weight. It reduces the probability of selecting samples with very low or very high losses while favoring those close to average losses. As it creates a weighted loss distribution that is not heavy-tailed theoretically, there are several advantages to highlight compared to existing methods: 1) it relieves the inefficiency in learning redundant easy samples and overfitting to outliers, 2) It improves training efficiency by preferentially learning samples close to the average loss. Application on real-world time series forecasting datasets demonstrate improvements in prediction quality for 1%-4% using mean square error measurements in channel-independent settings. The code will be available online after 1 the review.">
+        <tr id="Conformal prediction provides machine learning models with prediction sets that offer theoretical guarantees, but the underlying assumption of exchangeability limits its applicability to time series data. Furthermore, existing approaches struggle to handle multi-step ahead prediction tasks, where uncertainty estimates across multiple future time points are crucial. We propose JANET (Joint Adaptive predictioN-region Estimation for Time-series), a novel framework for constructing conformal prediction regions that are valid for both univariate and multivariate time series. JANET generalises the inductive conformal framework and efficiently produces joint prediction regions with controlled K-familywise error rates, enabling flexible adaptation to specific application needs. Our empirical evaluation demonstrates JANET's superior performance in multi-step prediction tasks across diverse time series datasets, highlighting its potential for reliable and interpretable uncertainty quantification in sequential data.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ad56f787b7bab71041c092d434e98cae941c807c" target='_blank'>
-              Robust Time Series Forecasting with Non-Heavy-Tailed Gaussian Loss-Weighted Sampler
+          <td><a href="https://www.semanticscholar.org/paper/b21605b67376e0377590d3cdfb99e3c43495b865" target='_blank'>
+              JANET: Joint Adaptive predictioN-region Estimation for Time-series
               </a>
             </td>
           <td>
-            Jiang You, A. Çela, Ren'e Natowicz, Jacob Ouanounou, Patrick Siarry
+            Eshant English, Eliot Wong-Toi, Matteo Fontana, Stephan Mandt, P. Smyth, Christoph Lippert
           </td>
-          <td>2024-06-19</td>
+          <td>2024-07-08</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>20</td>
+          <td>8</td>
         </tr>
 
-        <tr id="
- Transformer models have recently become very successful in the natural language domain. Their value as sequence-to-sequence translators there, also makes them a highly interesting technique for learning relationships between astrophysical time series. Our aim is investigate how well such a transformer neural network can establish causal temporal relations between different channels of a single-source signal. We thus apply a transformer model to the two phases of Gamma-Ray Bursts (GRBs), reconstructing one phase from the other. GRBs are unique instances where a single process and event produces two distinct time variable phenomena: the prompt emission and the afterglow. We here investigate if a transformer model can predict the afterglow flux from the prompt emission. If successful, such a predictive scheme might then be distilled to the most important underlying physics drivers in the future. We combine the transformer model with a novel dense neural network setup to directly estimate the starting value of the prediction. We find that the transformer model can, in some instances, successfully predict different phases of canonical afterglows, including the plateau phase. Hence it is a useful and promising new astrophysical analysis technique. For the GRB test case, the method marginally exceeds the baseline model overall, but still achieves accurate recovery of the prompt-afterglow fluence-fluence correlation in reconstructed light curves. Despite this progress, we conclude that consistent improvement over the baseline model is not yet achieved for the GRB case. We discuss the future improvements in data and modeling that are required to identify new physical-relation parameters or new insights into the single process driving both GRB phases.">
+        <tr id="Time series forecasting has been an essential field in many different application areas, including economic analysis, meteorology, and so forth. The majority of time series forecasting models are trained using the mean squared error (MSE). However, this training based on MSE causes a limitation known as prediction delay. The prediction delay, which implies the ground-truth precedes the prediction, can cause serious problems in a variety of fields, e.g., finance and weather forecasting -- as a matter of fact, predictions succeeding ground-truth observations are not practically meaningful although their MSEs can be low. This paper proposes a new perspective on traditional time series forecasting tasks and introduces a new solution to mitigate the prediction delay. We introduce a continuous-time gated recurrent unit (GRU) based on the neural ordinary differential equation (NODE) which can supervise explicit time-derivatives. We generalize the GRU architecture in a continuous-time manner and minimize the prediction delay through our time-derivative regularization. Our method outperforms in metrics such as MSE, Dynamic Time Warping (DTW) and Time Distortion Index (TDI). In addition, we demonstrate the low prediction delay of our method in a variety of datasets.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/02b8a26ec634127e2135486c8cdd15a32f134075" target='_blank'>
-              Transformer models for astrophysical time series and the GRB prompt-afterglow relation
+          <td><a href="https://www.semanticscholar.org/paper/cb1274dd33e80fab3c983829e9b94abcebe9e2a8" target='_blank'>
+              Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization
               </a>
             </td>
           <td>
-            O. Boersma, Eliot H. Ayache, J. van Leeuwen
+            Sheo Yon Jhin, Seojin Kim, Noseong Park
           </td>
-          <td>2024-06-18</td>
-          <td>RAS Techniques and Instruments</td>
+          <td>2024-06-29</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>12</td>
-        </tr>
-
-        <tr id="We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. Compared to using text or graph modalities, the investigation of utilizing images for temporal event forecasting has not been fully explored, especially in the era of large language models (LLMs). To bridge this gap, we are particularly interested in two key questions of: 1) why images will help in temporal event forecasting, and 2) how to integrate images into the LLM-based forecasting framework. To answer these research questions, we propose to identify two essential functions that images play in the scenario of temporal event forecasting, i.e., highlighting and complementary. Then, we develop a novel framework, named MM-Forecast. It employs an Image Function Identification module to recognize these functions as verbal descriptions using multimodal large language models (MLLMs), and subsequently incorporates these function descriptions into LLM-based forecasting models. To evaluate our approach, we construct a new multimodal dataset, MidEast-TE-mm, by extending an existing event dataset MidEast-TE-mini with images. Empirical studies demonstrate that our MM-Forecast can correctly identify the image functions, and further more, incorporating these verbal function descriptions significantly improves the forecasting performance. The dataset, code, and prompts are available at https://github.com/LuminosityX/MM-Forecast.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/881bcfa9a1a54c64991dd08db8ba6dea916b5ef1" target='_blank'>
-              MM-Forecast: A Multimodal Approach to Temporal Event Forecasting with Large Language Models
-              </a>
-            </td>
-          <td>
-            Haoxuan Li, Zhengmao Yang, Yunshan Ma, Yi Bin, Yang Yang, Tat-Seng Chua
-          </td>
-          <td>2024-08-08</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>7</td>
+          <td>1</td>
         </tr>
 
-        <tr id="In this paper, we introduce a novel theoretical framework for multi-task regression, applying random matrix theory to provide precise performance estimations, under high-dimensional, non-Gaussian data distributions. We formulate a multi-task optimization problem as a regularization technique to enable single-task models to leverage multi-task learning information. We derive a closed-form solution for multi-task optimization in the context of linear models. Our analysis provides valuable insights by linking the multi-task learning performance to various model statistics such as raw data covariances, signal-generating hyperplanes, noise levels, as well as the size and number of datasets. We finally propose a consistent estimation of training and testing errors, thereby offering a robust foundation for hyperparameter optimization in multi-task regression scenarios. Experimental validations on both synthetic and real-world datasets in regression and multivariate time series forecasting demonstrate improvements on univariate models, incorporating our method into the training loss and thus leveraging multivariate information.">
+        <tr id="Self-supervised Pretrained Models (PTMs) have demonstrated remarkable performance in computer vision and natural language processing tasks. These successes have prompted researchers to design PTMs for time series data. In our experiments, most self-supervised time series PTMs were surpassed by simple supervised models. We hypothesize this undesired phenomenon may be caused by data scarcity. In response, we test six time series generation methods, use the generated data in pretraining in lieu of the real data, and examine the effects on classification performance. Our results indicate that replacing a real-data pretraining set with a greater volume of only generated samples produces noticeable improvement.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/83734272d78baa3517416ebc962bcbb5accc3346" target='_blank'>
-              Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/bcb109299f76471af99ce2c8c40fc510cec2c1b0" target='_blank'>
+              A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining
               </a>
             </td>
           <td>
-            Romain Ilbert, Malik Tiomoko, Cosme Louart, Ambroise Odonnat, Vasilii Feofanov, Themis Palpanas, I. Redko
+            Audrey Der, Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Zhongfang Zhuang, Vivian Lai, Junpeng Wang, Liang Wang, Wei Zhang, Eamonn J. Keogh
           </td>
-          <td>2024-06-14</td>
+          <td>2024-08-15</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>49</td>
+          <td>22</td>
         </tr>
 
-        <tr id="Time series forecasting has been an essential field in many different application areas, including economic analysis, meteorology, and so forth. The majority of time series forecasting models are trained using the mean squared error (MSE). However, this training based on MSE causes a limitation known as prediction delay. The prediction delay, which implies the ground-truth precedes the prediction, can cause serious problems in a variety of fields, e.g., finance and weather forecasting -- as a matter of fact, predictions succeeding ground-truth observations are not practically meaningful although their MSEs can be low. This paper proposes a new perspective on traditional time series forecasting tasks and introduces a new solution to mitigate the prediction delay. We introduce a continuous-time gated recurrent unit (GRU) based on the neural ordinary differential equation (NODE) which can supervise explicit time-derivatives. We generalize the GRU architecture in a continuous-time manner and minimize the prediction delay through our time-derivative regularization. Our method outperforms in metrics such as MSE, Dynamic Time Warping (DTW) and Time Distortion Index (TDI). In addition, we demonstrate the low prediction delay of our method in a variety of datasets.">
+        <tr id="Transformer-based methods have recently demonstrated their potential in time series forecasting problems. However, the mainstream approach, primarily utilizing attention to model inter-step correlation in the time domain, is constrained by two significant issues that lead to ineffective and inefficient multivariate forecasting. The first is that key representations in the time domain are scattered and sparse, resulting in parameter bloat and increased difficulty in capturing time dependencies. The second is that treating time step points as uniformly embedded tokens leads to the erasure of inter-variate correlations. To address these challenges, we propose a frequency-wise and variables-oriented transformer-based method. This method leverages the intrinsic conjugate symmetry in the frequency domain, enabling compact frequency domain representations that naturally mix information across time points while reducing spatio-temporal costs. Multivariate inter-correlations can also be captured from similar frequency domain components, which enhances the variables-oriented attention mechanism modeling capability. Further, we employ both polar and complex domain perspectives to enrich the frequency domain representations and decode complicated temporal patterns. We propose frequency-enhanced independent representation multi-head attention (FIR-Attention) to leverage these advantages for improved multivariate interaction. Techniques such as cutting-off frequency and equivalent mapping are used to ensure the model’s lightweight nature. Extensive experiments on eight mainstream datasets show that our approach achieves first-rate satisfactory results and, importantly, requires only one percent of the spatio-temporal cost of mainstream methods.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/cb1274dd33e80fab3c983829e9b94abcebe9e2a8" target='_blank'>
-              Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization
+          <td><a href="https://www.semanticscholar.org/paper/36f87cbd5187e521184ddb319b132292cb8ae608" target='_blank'>
+              Frequency-Enhanced Transformer with Symmetry-Based Lightweight Multi-Representation for Multivariate Time Series Forecasting
               </a>
             </td>
           <td>
-            Sheo Yon Jhin, Seojin Kim, Noseong Park
+            Chenyue Wang, Zhouyuan Zhang, Xin Wang, Mingyang Liu, Lin Chen, Jiatian Pi
           </td>
-          <td>2024-06-29</td>
-          <td>ArXiv</td>
+          <td>2024-06-25</td>
+          <td>Symmetry</td>
           <td>0</td>
           <td>1</td>
         </tr>
 
-        <tr id="Message passing graph neural networks (GNNs) would appear to be powerful tools to learn distributed algorithms via gradient descent, but generate catastrophically incorrect predictions when nodes update asynchronously during inference. This failure under asynchrony effectively excludes these architectures from many potential applications, such as learning local communication policies between resource-constrained agents in, e.g., robotic swarms or sensor networks. In this work we explore why this failure occurs in common GNN architectures, and identify"implicitly-defined"GNNs as a class of architectures which is provably robust to partially asynchronous"hogwild"inference, adapting convergence guarantees from work in asynchronous and distributed optimization, e.g., Bertsekas (1982); Niu et al. (2011). We then propose a novel implicitly-defined GNN architecture, which we call an energy GNN. We show that this architecture outperforms other GNNs from this class on a variety of synthetic tasks inspired by multi-agent systems, and achieves competitive performance on real-world datasets.">
+        <tr id="We introduce the Robustness of Hierarchically Organized Time Series (RHiOTS) framework, designed to assess the robustness of hierarchical time series forecasting models and algorithms on real-world datasets. Hierarchical time series, where lower-level forecasts must sum to upper-level ones, are prevalent in various contexts, such as retail sales across countries. Current empirical evaluations of forecasting methods are often limited to a small set of benchmark datasets, offering a narrow view of algorithm behavior. RHiOTS addresses this gap by systematically altering existing datasets and modifying the characteristics of individual series and their interrelations. It uses a set of parameterizable transformations to simulate those changes in the data distribution. Additionally, RHiOTS incorporates an innovative visualization component, turning complex, multidimensional robustness evaluation results into intuitive, easily interpretable visuals. This approach allows an in-depth analysis of algorithm and model behavior under diverse conditions. We illustrate the use of RHiOTS by analyzing the predictive performance of several algorithms. Our findings show that traditional statistical methods are more robust than state-of-the-art deep learning algorithms, except when the transformation effect is highly disruptive. Furthermore, we found no significant differences in the robustness of the algorithms when applying specific reconciliation methods, such as MinT. RHiOTS provides researchers with a comprehensive tool for understanding the nuanced behavior of forecasting algorithms, offering a more reliable basis for selecting the most appropriate method for a given problem.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/3d41f9e0bdc43fc19966e67aef4422bfb59a0315" target='_blank'>
-              Graph Neural Networks Gone Hogwild
+          <td><a href="https://www.semanticscholar.org/paper/525cf42ca049ecd3089189612e74d0df68dbcae0" target='_blank'>
+              RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms
               </a>
             </td>
           <td>
-            Olga Solodova, Nick Richardson, Deniz Oktay, Ryan P. Adams
+            Luis Roque, Carlos Soares, Luís Torgo
           </td>
-          <td>2024-06-29</td>
+          <td>2024-08-06</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>5</td>
+          <td>36</td>
         </tr>
 
-        <tr id="Self-supervised contrastive learning has become a key technique in deep learning, particularly in time series analysis, due to its ability to learn meaningful representations without explicit supervision. Augmentation is a critical component in contrastive learning, where different augmentations can dramatically impact performance, sometimes influencing accuracy by over 30%. However, the selection of augmentations is predominantly empirical which can be suboptimal, or grid searching that is time-consuming. In this paper, we establish a principled framework for selecting augmentations based on dataset characteristics such as trend and seasonality. Specifically, we construct 12 synthetic datasets incorporating trend, seasonality, and integration weights. We then evaluate the effectiveness of 8 different augmentations across these synthetic datasets, thereby inducing generalizable associations between time series characteristics and augmentation efficiency. Additionally, we evaluated the induced associations across 6 real-world datasets encompassing domains such as activity recognition, disease diagnosis, traffic monitoring, electricity usage, mechanical fault prognosis, and finance. These real-world datasets are diverse, covering a range from 1 to 12 channels, 2 to 10 classes, sequence lengths of 14 to 1280, and data frequencies from 250 Hz to daily intervals. The experimental results show that our proposed trend-seasonality-based augmentation recommendation algorithm can accurately identify the effective augmentations for a given time series dataset, achieving an average Recall@3 of 0.667, outperforming baselines. Our work provides guidance for studies employing contrastive learning in time series analysis, with wide-ranging applications. All the code, datasets, and analysis results will be released at https://github.com/DL4mHealth/TS-Contrastive-Augmentation-Recommendation.">
+        <tr id="We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. Compared to using text or graph modalities, the investigation of utilizing images for temporal event forecasting has not been fully explored, especially in the era of large language models (LLMs). To bridge this gap, we are particularly interested in two key questions of: 1) why images will help in temporal event forecasting, and 2) how to integrate images into the LLM-based forecasting framework. To answer these research questions, we propose to identify two essential functions that images play in the scenario of temporal event forecasting, i.e., highlighting and complementary. Then, we develop a novel framework, named MM-Forecast. It employs an Image Function Identification module to recognize these functions as verbal descriptions using multimodal large language models (MLLMs), and subsequently incorporates these function descriptions into LLM-based forecasting models. To evaluate our approach, we construct a new multimodal dataset, MidEast-TE-mm, by extending an existing event dataset MidEast-TE-mini with images. Empirical studies demonstrate that our MM-Forecast can correctly identify the image functions, and further more, incorporating these verbal function descriptions significantly improves the forecasting performance. The dataset, code, and prompts are available at https://github.com/LuminosityX/MM-Forecast.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/8f81988b1b5f7abf716ef2eabd113a123b5df9c7" target='_blank'>
-              Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification
+          <td><a href="https://www.semanticscholar.org/paper/881bcfa9a1a54c64991dd08db8ba6dea916b5ef1" target='_blank'>
+              MM-Forecast: A Multimodal Approach to Temporal Event Forecasting with Large Language Models
               </a>
             </td>
           <td>
-            Ziyu Liu, Azadeh Alavi, Minyi Li, Xiang Zhang
+            Haoxuan Li, Zhengmao Yang, Yunshan Ma, Yi Bin, Yang Yang, Tat-Seng Chua
           </td>
-          <td>2024-07-12</td>
+          <td>2024-08-08</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>7</td>
         </tr>
 
-        <tr id="This study assesses the efficacy of a variety of neural network models in predicting gold prices, with a particular emphasis on GRU, N-BEATS, N-BEATS-GRU, N-BEATS-LSTM, and LSTM. We evaluated the models using a comprehensive dataset and metrics such as MSE, MAE, MAPE, and RMSE. The GRU model demonstrated the highest MAPE of 3.10%, as indicated by the results. N-BEATS was the second-best-performing model. In stark contrast to expectations, hybrid models, notably N-BEATS-LSTM, underperformed. Compared to LSTM-based models, visual analysis demonstrated that GRU and N-BEATS more effectively captured short-term and long-term trends. The results indicate that simplified models, such as GRU, may be more effective than complex hybrids in predicting gold prices.">
+        <tr id="Message passing graph neural networks (GNNs) would appear to be powerful tools to learn distributed algorithms via gradient descent, but generate catastrophically incorrect predictions when nodes update asynchronously during inference. This failure under asynchrony effectively excludes these architectures from many potential applications, such as learning local communication policies between resource-constrained agents in, e.g., robotic swarms or sensor networks. In this work we explore why this failure occurs in common GNN architectures, and identify"implicitly-defined"GNNs as a class of architectures which is provably robust to partially asynchronous"hogwild"inference, adapting convergence guarantees from work in asynchronous and distributed optimization, e.g., Bertsekas (1982); Niu et al. (2011). We then propose a novel implicitly-defined GNN architecture, which we call an energy GNN. We show that this architecture outperforms other GNNs from this class on a variety of synthetic tasks inspired by multi-agent systems, and achieves competitive performance on real-world datasets.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/9e3c45aea06050813b31f6650f6bff25d76c7c1f" target='_blank'>
-              Comparative Performance of Neural Network Architectures in Gold Price Prediction: a Study of GRU, N-BEATS, LSTM and Hybrid Models
+          <td><a href="https://www.semanticscholar.org/paper/3d41f9e0bdc43fc19966e67aef4422bfb59a0315" target='_blank'>
+              Graph Neural Networks Gone Hogwild
               </a>
             </td>
           <td>
-            Gonese Tapiwa Anthony, Nana Adi Appiah, Dongxiao Ren
+            Olga Solodova, Nick Richardson, Deniz Oktay, Ryan P. Adams
           </td>
-          <td>2024-07-06</td>
-          <td>International Journal For Multidisciplinary Research</td>
-          <td>0</td>
+          <td>2024-06-29</td>
+          <td>ArXiv</td>
           <td>0</td>
+          <td>5</td>
         </tr>
 
         <tr id="Attention-based models such as Transformers and recurrent models like state space models (SSMs) have emerged as successful methods for autoregressive sequence modeling. Although both enable parallel training, none enable parallel generation due to their autoregressiveness. We propose the variational SSM (VSSM), a variational autoencoder (VAE) where both the encoder and decoder are SSMs. Since sampling the latent variables and decoding them with the SSM can be parallelized, both training and generation can be conducted in parallel. Moreover, the decoder recurrence allows generation to be resumed without reprocessing the whole sequence. Finally, we propose the autoregressive VSSM that can be conditioned on a partial realization of the sequence, as is common in language generation tasks. Interestingly, the autoregressive VSSM still enables parallel generation. We highlight on toy problems (MNIST, CIFAR) the empirical gains in speed-up and show that it competes with traditional models in terms of generation quality (Transformer, Mamba SSM).">
@@ -2633,36 +2572,6 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2</td>
         </tr>
 
-        <tr id="In sequential event prediction, which finds applications in finance, retail, social networks, and healthcare, a crucial task is forecasting multiple future events within a specified time horizon. Traditionally, this has been addressed through autoregressive generation using next-event prediction models, such as Marked Temporal Point Processes. However, autoregressive methods use their own output for future predictions, potentially reducing quality as the prediction horizon extends. In this paper, we challenge traditional approaches by introducing a novel benchmark, HoTPP, specifically designed to evaluate a model's ability to predict event sequences over a horizon. This benchmark features a new metric inspired by object detection in computer vision, addressing the limitations of existing metrics in assessing models with imprecise time-step predictions. Our evaluations on established datasets employing various models demonstrate that high accuracy in next-event prediction does not necessarily translate to superior horizon prediction, and vice versa. HoTPP aims to serve as a valuable tool for developing more robust event sequence prediction methods, ultimately paving the way for further advancements in the field.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/5c0701f646c8eaac8073e437a46b0dddf2f8bc4f" target='_blank'>
-              HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?
-              </a>
-            </td>
-          <td>
-            Ivan Karpukhin, F. Shipilov, Andrey Savchenko
-          </td>
-          <td>2024-06-20</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-
-        <tr id="Real-time lightweight time series anomaly detection has become increasingly crucial in cybersecurity and many other domains. Its ability to adapt to unforeseen pattern changes and swiftly identify anomalies enables prompt responses and critical decision-making. While several such anomaly detection approaches have been introduced in recent years, they primarily utilize a single type of recurrent neural networks (RNNs) and have been implemented in only one deep learning framework. It is unclear how the use of different types of RNNs available in various deep learning frameworks affects the performance of these anomaly detection approaches due to the absence of comprehensive evaluations. Arbitrarily choosing a RNN variant and a deep learning framework to implement an anomaly detection approach may not reflect its true performance and could potentially mislead users into favoring one approach over another. In this paper, we aim to study the influence of various types of RNNs available in popular deep learning frameworks on real-time lightweight time series anomaly detection. We reviewed several state-of-the-art approaches and implemented a representative anomaly detection approach using well-known RNN variants supported by three widely recognized deep learning frameworks. A comprehensive evaluation is then conducted to analyze the performance of each implementation across real-world, open-source time series datasets. The evaluation results provide valuable guidance for selecting the appropriate RNN variant and deep learning framework for real-time, lightweight time series anomaly detection.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4e9a7be2c4b74758dc6223627d57183497a8fda5" target='_blank'>
-              Impact of Recurrent Neural Networks and Deep Learning Frameworks on Real-time Lightweight Time Series Anomaly Detection
-              </a>
-            </td>
-          <td>
-            Ming-Chang Lee, Jia-Chun Lin, Sokratis Katsikas
-          </td>
-          <td>2024-07-26</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>11</td>
-        </tr>
-
         <tr id="Modeling time series data has become a very at tractive research topic due to its wide application, such as human activity recognition, financial forecasting and sensor-based automatic system monitoring. Recently deep learning models have shown great advances in modeling the time series data but they heavily depend on a large amount of labeled data. To avoid costly labeling, this paper explores domain adaptation from a labeled source domain to the unlabeled target domain on time series data. To achieve the goal, we propose a disentangled representation learning framework named CADT to disentangle the domain-invariant features from the domain-specific ones. Particularly, CADT is injected with a novel class-wise hypersphere loss to improve the generalization of the classifier from the source domain to the target domain. Intuitively, it restricts the source data of the same class within the same hypersphere and minimizes the radius of it, which in turn enlarges the margin between different classes and makes the decision boundary of both domains easier. We further devise several kinds of domain-preserving data augmentation methods to better capture the domain-specific patterns. Extensive experiments on two public datasets and two real-world applications demonstrate the effectiveness of the proposed model against several state-of-the-art baselines.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/a21870156817614d3af47fda7b03e99e6181a852" target='_blank'>
@@ -2678,64 +2587,70 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>6</td>
         </tr>
 
-        <tr id="Point forecasting in univariate random walks is an important but challenging research topic that has attracted numerous researchers. Unfortunately, traditional regression methods for this task often fail to surpass naive benchmarks due to data unpredictability. From a decision fusion perspective, this study proposes a novel forecasting method, which is derived from a variant definition of random walks, where the random error term for the future value is expressed as a positive random error multiplied by a direction sign. This method, based on the fusion of movement and naive predictions, does not require a loss function for optimization and can be optimized by estimating movement prediction accuracy on the validation set. This characteristic prevents the fusion method from reverting to traditional regression methods and allows it to integrate various machine learning and deep learning models for movement prediction. The method's efficacy is demonstrated through simulations and real-world data experiments. It reliably outperforms naive forecasts with moderate movement prediction accuracies, such as 0.55, and is superior to baseline models such as the ARIMA, linear regression, MLP, and LSTM networks in forecasting the S&P 500 index and Bitcoin prices. This method is particularly advantageous when accurate point predictions are challenging but accurate movement predictions are attainable, translating movement predictions into point forecasts in random walk contexts.">
+        <tr id="Long-separated research has been conducted on two highly correlated tracks: traffic and incidents. Traffic track witnesses complicating deep learning models, e.g., to push the prediction a few percent more accurate, and the incident track only studies the incidents alone, e.g., to infer the incident risk. We, for the first time, spatiotemporally aligned the two tracks in a large-scale region (16,972 traffic nodes) over the whole year of 2023: our XTraffic dataset includes traffic, i.e., time-series indexes on traffic flow, lane occupancy, and average vehicle speed, and incidents, whose records are spatiotemporally-aligned with traffic data, with seven different incident classes. Additionally, each node includes detailed physical and policy-level meta-attributes of lanes. Our data can revolutionalize traditional traffic-related tasks towards higher interpretability and practice: instead of traditional prediction or classification tasks, we conduct: (1) post-incident traffic forecasting to quantify the impact of different incidents on traffic indexes; (2) incident classification using traffic indexes to determine the incidents types for precautions measures; (3) global causal analysis among the traffic indexes, meta-attributes, and incidents to give high-level guidance of the interrelations of various factors; (4) local causal analysis within road nodes to examine how different incidents affect the road segments' relations. The dataset is available at http://xaitraffic.github.io.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/0b9059265922cee433928426de5698fef6247b1c" target='_blank'>
-              Fusion of Movement and Naive Predictions for Point Forecasting in Univariate Random Walks
+          <td><a href="https://www.semanticscholar.org/paper/5b7ebd41f1ed8a8a78309e7df817ed2ca8c6f1fa" target='_blank'>
+              XTraffic: A Dataset Where Traffic Meets Incidents with Explainability and More
               </a>
             </td>
           <td>
-            Cheng Zhang
+            Xiaochuan Gou, Ziyue Li, Tian Lan, Junpeng Lin, Zhishuai Li, Bingyu Zhao, Chen Zhang, Di Wang, Xiangliang Zhang
           </td>
-          <td>2024-06-20</td>
+          <td>2024-07-16</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>5</td>
         </tr>
 
-        <tr id="The goal of stock trend prediction is to forecast future market movements for informed investment decisions. Existing methods mostly focus on predicting stock trends with supervised models trained on extensive annotated data. However, human annotation can be resource-intensive and the annotated data are not readily available. Inspired by the impressive few-shot capability of Large Language Models (LLMs), we propose using LLMs in a few-shot setting to overcome the scarcity of labeled data and make prediction more feasible to investors. Previous works typically merge multiple financial news for predicting stock trends, causing two significant problems when using LLMs: (1) Merged news contains noise, and (2) it may exceed LLMs' input limits, leading to performance degradation. To overcome these issues, we propose a two-step method 'denoising-then-voting'. Specifically, we introduce an `Irrelevant' category, and predict stock trends for individual news instead of merged news. Then we aggregate these predictions using majority voting. The proposed method offers two advantages: (1) Classifying noisy news as irrelevant removes its impact on the final prediction. (2) Predicting for individual news mitigates LLMs' input length limits. Our method achieves 66.59% accuracy in S&P 500, 62.17% in CSI-100, and 61.17% in HK stock prediction, outperforming the standard few-shot counterparts by around 7%, 4%, and 4%. Furthermore, our proposed method performs on par with state-of-the-art supervised methods.">
+        <tr id="In multivariate and multistep time series prediction research, we often face the problems of insufficient spatial feature extraction and insufficient time-dependent mining of historical series data, which also brings great challenges to multivariate time series analysis and prediction. Inspired by the attention mechanism and residual module, this study proposes a multivariate time series prediction method based on a convolutional-residual gated recurrent hybrid model (CNN-DA-RGRU) with a two-layer attention mechanism to solve the multivariate time series prediction problem in these two stages. Specifically, the convolution module of the proposed model is used to extract the relational features among the sequences, and the two-layer attention mechanism can pay more attention to the relevant variables and give them higher weights to eliminate the irrelevant features, while the residual gated loop module is used to extract the time-varying features of the sequences, in which the residual block is used to achieve the direct connectivity to enhance the expressive power of the model, to solve the gradient explosion and vanishing scenarios, and to facilitate gradient propagation. Experiments were conducted on two public datasets using the proposed model to determine the model hyperparameters, and ablation experiments were conducted to verify the effectiveness of the model; by comparing it with several models, the proposed model was found to achieve good results in multivariate time series-forecasting tasks.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/69ec9e81b1d4ced8ceb0214bdf33e38627aa5f5c" target='_blank'>
-              Enhancing Few-Shot Stock Trend Prediction with Large Language Models
+          <td><a href="https://www.semanticscholar.org/paper/5b50ae3b720af720b3168b475e54d37b97e5e669" target='_blank'>
+              A Multivariate Time Series Prediction Method Based on Convolution-Residual Gated Recurrent Neural Network and Double-Layer Attention
               </a>
             </td>
           <td>
-            Yiqi Deng, Xingwei He, Jiahao Hu, S.M. Yiu
+            Chuxin Cao, Jianhong Huang, Man Wu, Zhizhe Lin, Yan Sun
           </td>
-          <td>2024-07-12</td>
-          <td>ArXiv</td>
+          <td>2024-07-18</td>
+          <td>Electronics</td>
           <td>0</td>
-          <td>1</td>
+          <td>6</td>
         </tr>
 
-        <tr id="The modern autoregressive Large Language Models (LLMs) have achieved outstanding performance on NLP benchmarks, and they are deployed in the real world. However, they still suffer from limitations of the autoregressive training paradigm. For example, autoregressive token generation is notably slow and can be prone to \textit{exposure bias}. The diffusion-based language models were proposed as an alternative to autoregressive generation to address some of these limitations. We evaluate the recently proposed Score Entropy Discrete Diffusion (SEDD) approach and show it is a promising alternative to autoregressive generation but it has some short-comings too. We empirically demonstrate the advantages and challenges of SEDD, and observe that SEDD generally matches autoregressive models in perplexity and on benchmarks such as HellaSwag, Arc or WinoGrande. Additionally, we show that in terms of inference latency, SEDD can be up to 4.5$\times$ more efficient than GPT-2. While SEDD allows conditioning on tokens at abitrary positions, SEDD appears slightly weaker than GPT-2 for conditional generation given short prompts. Finally, we reproduced the main results from the original SEDD paper.">
+        <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c80e4529318d9cbfb6d8f8dd80b74cb69d3129b3" target='_blank'>
-              Promises, Outlooks and Challenges of Diffusion Language Modeling
+          <td><a href="https://www.semanticscholar.org/paper/debe0ee50d9dc6388d2e21ad87cbf672e2865ff3" target='_blank'>
+              Randomnet: clustering time series using untrained deep neural networks
               </a>
             </td>
           <td>
-            Justin Deschenaux, Caglar Gulcehre
+            Xiaosheng Li, Wenjie Xi, Jessica Lin
           </td>
-          <td>2024-06-17</td>
-          <td>ArXiv</td>
+          <td>2024-06-22</td>
+          <td>Data Mining and Knowledge Discovery</td>
           <td>0</td>
-          <td>3</td>
+          <td>5</td>
         </tr>
 
-        <tr id="With the advent of large models based on the Transformer architecture, researchers have observed an anomalous phenomenon in the Attention mechanism--there is a very high attention on the first element, which is prevalent across Transformer-based models. It is crucial to understand it for the development of techniques focusing on attention distribution, such as Key-Value (KV) Cache compression and infinite extrapolation; however, the latent cause leaves to be unknown. In this paper, we analyze such a phenomenon from the perspective of waiver phenomenon, which involves reducing the internal values of certain elements in the sequence, allowing them to absorb excess attention without affecting their contribution to information. In specific models, due to differences in positional encoding and attention patterns, we have found that the selection of waiver elements by the model can be categorized into two methods: positional-encoding-based and feature-distribution-within-elements-based.">
+        <tr id="
+ Traffic forecasting problems involve jointly modeling the non-linear spatio-temporal dependencies at different scales. While Graph Neural Network models have been effectively used to capture the non-linear spatial dependencies, capturing the dynamic spatial dependencies between the locations remains a major challenge. The errors in capturing such dependencies propagate in modeling the temporal dependencies between the locations, thereby severely affecting the performance of long-term predictions. While transformer-based mechanisms have been recently proposed for capturing the dynamic spatial dependencies, these methods are susceptible to fluctuations in data brought on by unforeseen events like traffic congestion and accidents. To mitigate these issues we propose an improvised Spatio-temporal parallel transformer (STPT) based model for traffic prediction that uses multiple adjacency graphs passed through a pair of coupled graph transformer-convolution network units, operating in parallel, to generate more noise-resilient embeddings. We conduct extensive experiments on 4 real-world traffic datasets and compare the performance of STPT with several state-of-the-art baselines, in terms of measures like RMSE, MAE, and MAPE. We find that using STPT improves the performance by around
+
+ \(10-34\%\)
+
+ as compared to the baselines. We also investigate the applicability of the model on other spatio-temporal data in other domains. We use a covid-19 dataset to predict the number of future occurrences in different regions from a given set of historical occurrences. The results demonstrate the superiority of our model for such datasets.
+">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/cb15472f8041a122fac2d0004d34596990719a33" target='_blank'>
-              Unveiling and Controlling Anomalous Attention Distribution in Transformers
+          <td><a href="https://www.semanticscholar.org/paper/d890dbf3502df2b73e7403abca6b1585a2a7bdd4" target='_blank'>
+              Spatio-Temporal Parallel Transformer based model for Traffic Prediction
               </a>
             </td>
           <td>
-            Ruiqing Yan, Xingbo Du, Haoyu Deng, Linghan Zheng, Qiuzhuang Sun, Jifang Hu, Yuhang Shao, Penghao Jiang, Jinrong Jiang, Lian Zhao
+            Rahul Kumar, João Mendes‐Moreira, Joydeep Chandra
           </td>
-          <td>2024-06-26</td>
-          <td>ArXiv</td>
-          <td>0</td>
+          <td>2024-07-19</td>
+          <td>ACM Transactions on Knowledge Discovery from Data</td>
           <td>0</td>
+          <td>5</td>
         </tr>
 
         <tr id="Large Language Models (LLMs) have been shown to perform well for many downstream tasks. Transfer learning can enable LLMs to acquire skills that were not targeted during pre-training. In financial contexts, LLMs can sometimes beat well-established benchmarks. This paper investigates how well LLMs perform in the task of forecasting corporate credit ratings. We show that while LLMs are very good at encoding textual information, traditional methods are still very competitive when it comes to encoding numeric and multimodal data. For our task, current LLMs perform worse than a more traditional XGBoost architecture that combines fundamental and macroeconomic data with high-density text-based embedding features.">
@@ -2753,6 +2668,21 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>45</td>
         </tr>
 
+        <tr id=": Sales forecasting is crucial for business planning and resource allocation. Data-driven approaches have become popular in this field. This study compares the performance of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and Prophet within the context of specific sales categories derived from acquiring data provided by a bank. This study uses a time series dataset provided by Tink off data, which covers various sales categories and time intervals. These categories, including pharmacies, railway tickets, books, sporting goods and fuel stations, present unique forecasting challenges because of their distinct demand patterns and potential for high volatility. Through a comparative analysis focusing on accuracy, robustness and computational efficiency, the study reveals that while all models demonstrate efficacy in certain scenarios, their performance varies depending on the specific category and forecasting horizon. ARIMA exhibits consistent accuracy across categories, particularly for daily predictions, aligning with its strength in capturing trends and seasonality. LSTM, on the other hand, shows promise for hourly predictions in categories like fuel stations, leveraging its ability to learn long-term dependencies. However, the LSTM model shows inconsistent results, sometimes outperforming others, but with varying performance across runs. This study provides insights for practitioners within the banking and financial sectors seeking to select the most appropriate forecasting model based on their specific sales categories and forecasting needs.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/d18133f1dde33c5c7df061aac195fad8e9eac107" target='_blank'>
+              Sales Forecasting Models: Comparison between ARIMA, LSTM and Prophet
+              </a>
+            </td>
+          <td>
+            Dmitry Brykin
+          </td>
+          <td>2024-10-01</td>
+          <td>Journal of Computer Science</td>
+          <td>0</td>
+          <td>0</td>
+        </tr>
+
         <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/f5525624998a69eda4aa0ec2d507973f15dabde2" target='_blank'>
@@ -2768,70 +2698,64 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>15</td>
         </tr>
 
-        <tr id="
- Traffic forecasting problems involve jointly modeling the non-linear spatio-temporal dependencies at different scales. While Graph Neural Network models have been effectively used to capture the non-linear spatial dependencies, capturing the dynamic spatial dependencies between the locations remains a major challenge. The errors in capturing such dependencies propagate in modeling the temporal dependencies between the locations, thereby severely affecting the performance of long-term predictions. While transformer-based mechanisms have been recently proposed for capturing the dynamic spatial dependencies, these methods are susceptible to fluctuations in data brought on by unforeseen events like traffic congestion and accidents. To mitigate these issues we propose an improvised Spatio-temporal parallel transformer (STPT) based model for traffic prediction that uses multiple adjacency graphs passed through a pair of coupled graph transformer-convolution network units, operating in parallel, to generate more noise-resilient embeddings. We conduct extensive experiments on 4 real-world traffic datasets and compare the performance of STPT with several state-of-the-art baselines, in terms of measures like RMSE, MAE, and MAPE. We find that using STPT improves the performance by around
-
- \(10-34\%\)
-
- as compared to the baselines. We also investigate the applicability of the model on other spatio-temporal data in other domains. We use a covid-19 dataset to predict the number of future occurrences in different regions from a given set of historical occurrences. The results demonstrate the superiority of our model for such datasets.
-">
+        <tr id="Accurate stock market predictions following earnings reports are crucial for investors. Traditional methods, particularly classical machine learning models, struggle with these predictions because they cannot effectively process and interpret extensive textual data contained in earnings reports and often overlook nuances that influence market movements. This paper introduces an advanced approach by employing Large Language Models (LLMs) instruction fine-tuned with a novel combination of instruction-based techniques and quantized low-rank adaptation (QLoRA) compression. Our methodology integrates 'base factors', such as financial metric growth and earnings transcripts, with 'external factors', including recent market indices performances and analyst grades, to create a rich, supervised dataset. This comprehensive dataset enables our models to achieve superior predictive performance in terms of accuracy, weighted F1, and Matthews correlation coefficient (MCC), especially evident in the comparison with benchmarks such as GPT-4. We specifically highlight the efficacy of the llama-3-8b-Instruct-4bit model, which showcases significant improvements over baseline models. The paper also discusses the potential of expanding the output capabilities to include a 'Hold' option and extending the prediction horizon, aiming to accommodate various investment styles and time frames. This study not only demonstrates the power of integrating cutting-edge AI with fine-tuned financial data but also paves the way for future research in enhancing AI-driven financial analysis tools.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/d890dbf3502df2b73e7403abca6b1585a2a7bdd4" target='_blank'>
-              Spatio-Temporal Parallel Transformer based model for Traffic Prediction
+          <td><a href="https://www.semanticscholar.org/paper/31e6b91ed8eb6f5fe8703301fc439fd1af160132" target='_blank'>
+              Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach
               </a>
             </td>
           <td>
-            Rahul Kumar, João Mendes‐Moreira, Joydeep Chandra
+            Haowei Ni, Shuchen Meng, Xupeng Chen, Ziqing Zhao, Andi Chen, Panfeng Li, Shiyao Zhang, Qifu Yin, Yuanqing Wang, Yuxi Chan
           </td>
-          <td>2024-07-19</td>
-          <td>ACM Transactions on Knowledge Discovery from Data</td>
+          <td>2024-08-13</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>5</td>
+          <td>7</td>
         </tr>
 
-        <tr id="Efficient representation of complex infrastructure systems is crucial for system‐level management tasks, such as edge prediction, component classification, and decision‐making. However, the complex interactions between the infrastructure systems and their spatial environments increased the complexity of network representation learning. This study introduces a novel geometric‐based multimodal deep learning model for spatially embedded network representation learning, namely the regional spatial graph convolutional network (RSGCN). The developed RSGCN model simultaneously learns from the node's multimodal spatial features. To evaluate the network representation performance, the introduced RSGCN model is used to embed different infrastructure networks into latent spaces and then reconstruct the networks. A synthetic network dataset, a California Highway Network, and a New Jersey Power Network were used as testbeds. The performance of the developed model is compared with two other state‐of‐the‐art geometric deep learning models, GraphSAGE and Spatial Graph Convolutional Network. The results demonstrate the importance of considering regional information and the effectiveness of using novel graph convolutional neural networks for a more accurate representation of complex infrastructure systems.">
+        <tr id="Distribution shift over time occurs in many settings. Leveraging historical data is necessary to learn a model for the last time point when limited data is available in the final period, yet few methods have been developed specifically for this purpose. In this work, we construct a benchmark with different sequences of synthetic shifts to evaluate the effectiveness of 3 classes of methods that 1) learn from all data without adapting to the final period, 2) learn from historical data with no regard to the sequential nature and then adapt to the final period, and 3) leverage the sequential nature of historical data when tailoring a model to the final period. We call this benchmark Seq-to-Final to highlight the focus on using a sequence of time periods to learn a model for the final time point. Our synthetic benchmark allows users to construct sequences with different types of shift and compare different methods. We focus on image classification tasks using CIFAR-10 and CIFAR-100 as the base images for the synthetic sequences. We also evaluate the same methods on the Portraits dataset to explore the relevance to real-world shifts over time. Finally, we create a visualization to contrast the initializations and updates from different methods at the final time step. Our results suggest that, for the sequences in our benchmark, methods that disregard the sequential structure and adapt to the final time point tend to perform well. The approaches we evaluate that leverage the sequential nature do not offer any improvement. We hope that this benchmark will inspire the development of new algorithms that are better at leveraging sequential historical data or a deeper understanding of why methods that disregard the sequential nature are able to perform well.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6eefffcdebbdf1dd6cb24f14d87797386c0c6010" target='_blank'>
-              Modeling of spatially embedded networks via regional spatial graph convolutional networks
+          <td><a href="https://www.semanticscholar.org/paper/dff5247f9c46cb7745eb5c8b20a4225f15cd7d8a" target='_blank'>
+              Seq-to-Final: A Benchmark for Tuning from Sequential Distributions to a Final Time Point
               </a>
             </td>
           <td>
-            Xudong Fan, Jürgen Hackl
+            Christina X. Ji, Ahmed M. Alaa, David Sontag
           </td>
-          <td>2024-06-20</td>
-          <td>Computer-Aided Civil and Infrastructure Engineering</td>
+          <td>2024-07-12</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>12</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Short-Term Load Forecasting (STLF) plays a crucial role in energy management for planning and managing the operation strategy of hybrid energy sources. A common STLF approach involves making predictions based on multiple input variables which affect the load. However, additional data besides historical load data often are not available, making STLF a challenging univariate task. Two approaches are discussed in this paper for the univariate day-ahead STLF: the first integrates temporal information into the input regression vector, with Seasonal Auto-Regressive Moving Average (SARMA) and Prophet being the best representatives for the application considered; the second integrates temporal information directly into its architecture through the feedback of the internal state, with Long Short-Term Memory (LSTM) and vanilla Recurrent Neural Network (RNN) chosen for this purpose. The models were evaluated on a real case study involving a university building in Italy. The results validate the effectiveness of the LSTM model. LSTM performed significantly better than SARIMA, and slightly better than RNN and Prophet, achieving a Mean Absolute Error (MAE) equal to 13.12 kW, a Root Mean Square Error (RMSE) equal to 25.09 kW, a Mean Absolute Percentage Error (MAPE) equal to 10.97% and an R2 score equal to 0.85. This work provides a foundation for the improvement and deployment of such time series forecasting techniques in the emerging field of STLF for energy management.">
+        <tr id="In the rapidly evolving field of real estate economics, the prediction of house prices continues to be a complex challenge, intricately tied to a multitude of socio-economic factors. Traditional predictive models often overlook spatial interdependencies that significantly influence housing prices. The objective of this study is to leverage Graph Neural Networks (GNNs) on open statistics knowledge graphs to model these spatial dependencies and predict house prices across Scotland’s 2011 data zones. The methodology involves retrieving integrated statistical indicators from the official Scottish Open Government Data portal and applying three representative GNN algorithms: ChebNet, GCN, and GraphSAGE. These GNNs are compared against traditional models, including the tabular-based XGBoost and a simple Multi-Layer Perceptron (MLP), demonstrating superior prediction accuracy. Innovative contributions of this study include the use of GNNs to model spatial dependencies in real estate economics and the application of local and global explainability techniques to enhance transparency and trust in the predictions. The global feature importance is determined by a logistic regression surrogate model while the local, region-level understanding of the GNN predictions is achieved through the use of GNNExplainer. Explainability results are compared with those from a previous work that applied the XGBoost machine learning algorithm and the SHapley Additive exPlanations (SHAP) explainability framework on the same dataset. Interestingly, both the global surrogate model and the SHAP approach underscored the comparative illness factor, a health indicator, and the ratio of detached dwellings as the most crucial features in the global explainability. In the case of local explanations, while both methods showed similar results, the GNN approach provided a richer, more comprehensive understanding of the predictions for two specific data zones.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e087a93b258393dffd6c5f3ffccb50ae4ffd55ee" target='_blank'>
-              A Comparison of Univariate Methods for Day-Ahead Short-Term Load Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/615cab34abada51059e7bf812011aa18b664bad3" target='_blank'>
+              Explainable Graph Neural Networks: An Application to Open Statistics Knowledge Graphs for Estimating House Prices
               </a>
             </td>
           <td>
-            Giorgia Ghione, Malik Ali Judge, Marco Badami, E. Pasero, Vincenzo Franzitta, G. Cirrincione
+            Areti Karamanou, Petros Brimos, E. Kalampokis, K. Tarabanis
           </td>
-          <td>2024-06-25</td>
-          <td>2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)</td>
+          <td>2024-08-06</td>
+          <td>Technologies</td>
           <td>0</td>
-          <td>23</td>
+          <td>36</td>
         </tr>
 
-        <tr id="Distribution shift over time occurs in many settings. Leveraging historical data is necessary to learn a model for the last time point when limited data is available in the final period, yet few methods have been developed specifically for this purpose. In this work, we construct a benchmark with different sequences of synthetic shifts to evaluate the effectiveness of 3 classes of methods that 1) learn from all data without adapting to the final period, 2) learn from historical data with no regard to the sequential nature and then adapt to the final period, and 3) leverage the sequential nature of historical data when tailoring a model to the final period. We call this benchmark Seq-to-Final to highlight the focus on using a sequence of time periods to learn a model for the final time point. Our synthetic benchmark allows users to construct sequences with different types of shift and compare different methods. We focus on image classification tasks using CIFAR-10 and CIFAR-100 as the base images for the synthetic sequences. We also evaluate the same methods on the Portraits dataset to explore the relevance to real-world shifts over time. Finally, we create a visualization to contrast the initializations and updates from different methods at the final time step. Our results suggest that, for the sequences in our benchmark, methods that disregard the sequential structure and adapt to the final time point tend to perform well. The approaches we evaluate that leverage the sequential nature do not offer any improvement. We hope that this benchmark will inspire the development of new algorithms that are better at leveraging sequential historical data or a deeper understanding of why methods that disregard the sequential nature are able to perform well.">
+        <tr id="Multivariate time series modeling has been essential in sensor-based data mining tasks. However, capturing complex dynamics caused by intra-variable (temporal) and inter-variable (spatial) relationships while simultaneously taking into account evolving data distributions is a non-trivial task, which faces accumulated computational overhead and multiple temporal patterns or distribution modes. Most existing methods focus on the former direction without adaptive task-specific learning ability. To this end, we developed a holistic spatial-temporal meta-learning probabilistic inference framework, entitled ST-MeLaPI, for the efficient and versatile learning of complex dynamics. Specifically, first, a multivariate relationship recognition module is utilized to learn task-specific inter-variable dependencies. Then, a multiview meta-learning and probabilistic inference strategy was designed to learn shared parameters while enabling the fast and flexible learning of task-specific parameters for different batches. At the core are spatial dependency-oriented and temporal pattern-oriented meta-learning approximate probabilistic inference modules, which can quickly adapt to changing environments via stochastic neurons at each timestamp. Finally, a gated aggregation scheme is leveraged to realize appropriate information selection for the generative style prediction. We benchmarked our approach against state-of-the-art methods with real-world data. The experimental results demonstrate the superiority of our approach over the baselines.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/dff5247f9c46cb7745eb5c8b20a4225f15cd7d8a" target='_blank'>
-              Seq-to-Final: A Benchmark for Tuning from Sequential Distributions to a Final Time Point
+          <td><a href="https://www.semanticscholar.org/paper/4dea55e0f4c4c2f82b1128047a54ec3d4fccaa7f" target='_blank'>
+              Multiview Spatial-Temporal Meta-Learning for Multivariate Time Series Forecasting
               </a>
             </td>
           <td>
-            Christina X. Ji, Ahmed M. Alaa, David Sontag
+            Liang Zhang, Jianping Zhu, Bo Jin, Xiaopeng Wei
           </td>
-          <td>2024-07-12</td>
-          <td>ArXiv</td>
+          <td>2024-07-01</td>
+          <td>Sensors (Basel, Switzerland)</td>
           <td>0</td>
-          <td>3</td>
+          <td>8</td>
         </tr>
 
         <tr id="Conformal prediction offers a practical framework for distribution-free uncertainty quantification, providing finite-sample coverage guarantees under relatively mild assumptions on data exchangeability. However, these assumptions cease to hold for time series due to their temporally correlated nature. In this work, we present a novel use of conformal prediction for time series forecasting that incorporates time series decomposition. This approach allows us to model different temporal components individually. By applying specific conformal algorithms to each component and then merging the obtained prediction intervals, we customize our methods to account for the different exchangeability regimes underlying each component. Our decomposition-based approach is thoroughly discussed and empirically evaluated on synthetic and real-world data. We find that the method provides promising results on well-structured time series, but can be limited by factors such as the decomposition step for more complex data.">
@@ -2849,19 +2773,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>0</td>
         </tr>
 
-        <tr id="In response to the insufficient consideration of spatio-temporal dependencies and traffic pattern similarity in traffic flow prediction methods based on federated learning, as well as the neglect of model heterogeneity and objective heterogeneity, a traffic flow prediction model based on federated learning and spatio-temporal graph neural networks is proposed. The model is divided into two stages. In the road network division stage, the traffic road network is divided into subnetworks by the dynamic time warping algorithm and the K-means algorithm, to ensure the same subnetwork has the similar traffic flow pattern. The federated learning stage is divided into two sub-stages. In the local training phase, the spatio-temporal graph neural network with an attention mechanism is utilized to create personalized models and meme models to capture the spatio-temporal dependencies of each subnetwork. At the same time, deep mutual learning is utilized to address model heterogeneity and objective heterogeneity through knowledge distillation. In the global aggregation phase, a multi-factor weighted aggregation strategy is designed to measure the contribution of each local model to the global model, to enhance the fairness of aggregation. Three sets of experiments were conducted on two real datasets, and the experimental results demonstrate that the proposed model outperforms the baseline models in three common evaluation metrics.">
+        <tr id="Summarizing web graphs is challenging due to the heterogeneity of the modeled information and its changes over time. We investigate the use of neural networks for lifelong graph summarization. Assuming we observe the web graph at a certain time, we train the networks to summarize graph vertices. We apply this trained network to summarize the vertices of the changed graph at the next point in time. Subsequently, we continue training and evaluating the network to perform lifelong graph summarization. We use the GNNs Graph-MLP and GraphSAINT, as well as an MLP baseline, to summarize the temporal graphs. We compare $1$-hop and $2$-hop summaries. We investigate the impact of reusing parameters from a previous snapshot by measuring the backward and forward transfer and the forgetting rate of the neural networks. Our extensive experiments on ten weekly snapshots of a web graph with over $100$M edges, sampled in 2012 and 2022, show that all networks predominantly use $1$-hop information to determine the summary, even when performing $2$-hop summarization. Due to the heterogeneity of web graphs, in some snapshots, the $2$-hop summary produces over ten times more vertex summaries than the $1$-hop summary. When using the network trained on the last snapshot from 2012 and applying it to the first snapshot of 2022, we observe a strong drop in accuracy. We attribute this drop over the ten-year time warp to the strongly increased heterogeneity of the web graph in 2022.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/2e28c42dd920b2051aed0b2541a2f1897a0594b3" target='_blank'>
-              Traffic Flow Prediction Based on Federated Learning and Spatio-Temporal Graph Neural Networks
+          <td><a href="https://www.semanticscholar.org/paper/39a9e812343d7550486a8b525adf2fcc87cd162e" target='_blank'>
+              Lifelong Graph Summarization with Neural Networks: 2012, 2022, and a Time Warp
               </a>
             </td>
           <td>
-            Jian Feng, Cailing Du, Qi Mu
+            Jonatan Frank, Marcel Hoffmann, N. Lell, David Richerby, A. Scherp
           </td>
-          <td>2024-06-18</td>
-          <td>ISPRS Int. J. Geo Inf.</td>
-          <td>0</td>
+          <td>2024-07-25</td>
+          <td>ArXiv</td>
           <td>0</td>
+          <td>28</td>
         </tr>
 
         <tr id="We explore various neural network architectures for modeling the dynamics of the cryptocurrency market. Traditional linear models often fall short in accurately capturing the unique and complex dynamics of this market. In contrast, Deep Neural Networks (DNNs) have demonstrated considerable proficiency in time series forecasting. This papers introduces novel neural network framework that blend the principles of econometric state space models with the dynamic capabilities of Recurrent Neural Networks (RNNs). We propose state space models using Long Short Term Memory (LSTM), Gated Residual Units (GRU) and Temporal Kolmogorov-Arnold Networks (TKANs). According to the results, TKANs, inspired by Kolmogorov-Arnold Networks (KANs) and LSTM, demonstrate promising outcomes.">
@@ -2876,67 +2800,82 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2024-07-21</td>
           <td>ArXiv</td>
           <td>1</td>
+          <td>1</td>
+        </tr>
+
+        <tr id="Despite the massive attention given to time-series explanations due to their extensive applications, a notable limitation in existing approaches is their primary reliance on the time-domain. This overlooks the inherent characteristic of time-series data containing both time and frequency features. In this work, we present Spectral eXplanation (SpectralX), an XAI framework that provides time-frequency explanations for time-series black-box classifiers. This easily adaptable framework enables users to"plug-in"various perturbation-based XAI methods for any pre-trained time-series classification models to assess their impact on the explanation quality without having to modify the framework architecture. Additionally, we introduce Feature Importance Approximations (FIA), a new perturbation-based XAI method. These methods consist of feature insertion, deletion, and combination techniques to enhance computational efficiency and class-specific explanations in time-series classification tasks. We conduct extensive experiments in the generated synthetic dataset and various UCR Time-Series datasets to first compare the explanation performance of FIA and other existing perturbation-based XAI methods in both time-domain and time-frequency domain, and then show the superiority of our FIA in the time-frequency domain with the SpectralX framework. Finally, we conduct a user study to confirm the practicality of our FIA in SpectralX framework for class-specific time-frequency based time-series explanations. The source code is available in https://github.com/gustmd0121/Time_is_not_Enough">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/a9a442393d869ce8529cda8dfa179e2d54425503" target='_blank'>
+              Time is Not Enough: Time-Frequency based Explanation for Time-Series Black-Box Models
+              </a>
+            </td>
+          <td>
+            Hyunseung Chung, Sumin Jo, Yeonsu Kwon, Edward Choi
+          </td>
+          <td>2024-08-07</td>
+          <td>ArXiv</td>
           <td>0</td>
+          <td>4</td>
         </tr>
 
-        <tr id="Multivariate time series modeling has been essential in sensor-based data mining tasks. However, capturing complex dynamics caused by intra-variable (temporal) and inter-variable (spatial) relationships while simultaneously taking into account evolving data distributions is a non-trivial task, which faces accumulated computational overhead and multiple temporal patterns or distribution modes. Most existing methods focus on the former direction without adaptive task-specific learning ability. To this end, we developed a holistic spatial-temporal meta-learning probabilistic inference framework, entitled ST-MeLaPI, for the efficient and versatile learning of complex dynamics. Specifically, first, a multivariate relationship recognition module is utilized to learn task-specific inter-variable dependencies. Then, a multiview meta-learning and probabilistic inference strategy was designed to learn shared parameters while enabling the fast and flexible learning of task-specific parameters for different batches. At the core are spatial dependency-oriented and temporal pattern-oriented meta-learning approximate probabilistic inference modules, which can quickly adapt to changing environments via stochastic neurons at each timestamp. Finally, a gated aggregation scheme is leveraged to realize appropriate information selection for the generative style prediction. We benchmarked our approach against state-of-the-art methods with real-world data. The experimental results demonstrate the superiority of our approach over the baselines.">
+        <tr id="Dynamic graphs (DG) are often used to describe evolving interactions between nodes in real-world applications. Temporal patterns are a natural feature of DGs and are also key to representation learning. However, existing dynamic GCN models are mostly composed of static GCNs and sequence modules, which results in the separation of spatiotemporal information and cannot effectively capture complex temporal patterns in DGs. To address this problem, this study proposes a spatial-temporal graph convolutional networks with diversified transformation (STGCNDT), which includes three aspects: a) constructing a unified graph tensor convolutional network (GTCN) using tensor M-products without the need to represent spatiotemporal information separately; b) introducing three transformation schemes in GTCN to model complex temporal patterns to aggregate temporal information; and c) constructing an ensemble of diversified transformation schemes to obtain higher representation capabilities. Empirical studies on four DGs that appear in communication networks show that the proposed STGCNDT significantly outperforms state-of-the-art models in solving link weight estimation tasks due to the diversified transformations.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4dea55e0f4c4c2f82b1128047a54ec3d4fccaa7f" target='_blank'>
-              Multiview Spatial-Temporal Meta-Learning for Multivariate Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/645c7ccb4c62510922512fdba7088d9493b9fd62" target='_blank'>
+              Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning
               </a>
             </td>
           <td>
-            Liang Zhang, Jianping Zhu, Bo Jin, Xiaopeng Wei
+            Ling Wang, Yixiang Huang, Hao Wu
           </td>
-          <td>2024-07-01</td>
-          <td>Sensors (Basel, Switzerland)</td>
+          <td>2024-08-05</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>8</td>
+          <td>1</td>
         </tr>
 
-        <tr id="The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era, the quick development of telecommunications services, the implementation of the number portability policy, and the intensifying competition among operators. At the same time, users' consumption preferences and choices are evolving. Excellent churn prediction models must be created in order to accurately predict the churn tendency, since keeping existing customers is far less expensive than acquiring new ones. But conventional or learning-based algorithms can only go so far into a single subscriber's data; they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features. Additionally, the current churn prediction models have a high computational burden, a fuzzy weight distribution, and significant resource economic costs. The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures, ignoring the reference value supplied by other users with the same package. This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network (GAT-CNN) to address the aforementioned issues. The main contributions of this paper are as follows: Firstly, we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device, edge, and cloud layers. Second, we extend the use of users' own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously. Lastly, we build an integrated offline-online system for churn prediction based on the strengths of the two models, and we experimentally validate the efficacy of cloud-side collaborative training and inference. In summary, the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses.">
+        <tr id="Recently, the incorporation of both temporal features and the correlation across time series has become an effective approach in time series prediction. Spatio-Temporal Graph Neural Networks (STGNNs) demonstrate good performance on many Temporal-correlation Forecasting Problem. However, when applied to tasks lacking periodicity, such as stock data prediction, the effectiveness and robustness of STGNNs are found to be unsatisfactory. And STGNNs are limited by memory savings so that cannot handle problems with a large number of nodes. In this paper, we propose a novel approach called the Temporal-Correlation Graph Pre-trained Network (TCGPN) to address these limitations. TCGPN utilize Temporal-correlation fusion encoder to get a mixed representation and pre-training method with carefully designed temporal and correlation pre-training tasks. Entire structure is independent of the number and order of nodes, so better results can be obtained through various data enhancements. And memory consumption during training can be significantly reduced through multiple sampling. Experiments are conducted on real stock market data sets CSI300 and CSI500 that exhibit minimal periodicity. We fine-tune a simple MLP in downstream tasks and achieve state-of-the-art results, validating the capability to capture more robust temporal correlation patterns.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/389c1f686431f303fa0095054fe8c8ecdb8f4c61" target='_blank'>
-              User churn prediction hierarchical model based on graph attention convolutional neural networks
+          <td><a href="https://www.semanticscholar.org/paper/1184b61fe584f056e3f7156ed3fe740a346b75a1" target='_blank'>
+              TCGPN: Temporal-Correlation Graph Pre-trained Network for Stock Forecasting
               </a>
             </td>
           <td>
-            Mei Miao, Tang Miao, Zhou Long
+            Wenbo Yan, Ying Tan
           </td>
-          <td>2024-07-01</td>
-          <td>China Communications</td>
+          <td>2024-07-26</td>
+          <td>ArXiv</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
-        <tr id="Effective imputation is a crucial preprocessing step for time series analysis. Despite the development of numerous deep learning algorithms for time series imputation, the community lacks standardized and comprehensive benchmark platforms to effectively evaluate imputation performance across different settings. Moreover, although many deep learning forecasting algorithms have demonstrated excellent performance, whether their modeling achievements can be transferred to time series imputation tasks remains unexplored. To bridge these gaps, we develop TSI-Bench, the first (to our knowledge) comprehensive benchmark suite for time series imputation utilizing deep learning techniques. The TSI-Bench pipeline standardizes experimental settings to enable fair evaluation of imputation algorithms and identification of meaningful insights into the influence of domain-appropriate missingness ratios and patterns on model performance. Furthermore, TSI-Bench innovatively provides a systematic paradigm to tailor time series forecasting algorithms for imputation purposes. Our extensive study across 34,804 experiments, 28 algorithms, and 8 datasets with diverse missingness scenarios demonstrates TSI-Bench's effectiveness in diverse downstream tasks and potential to unlock future directions in time series imputation research and analysis. The source code and experiment logs are available at https://github.com/WenjieDu/AwesomeImputation.">
+        <tr id="Weather forecasting refers to learning evolutionary patterns of some key upper-air and surface variables which is of great significance. Recently, deep learning-based methods have been increasingly applied in the field of weather forecasting due to their powerful feature learning capabilities. However, prediction methods based on the original space iteration struggle to effectively and efficiently utilize large number of weather variables. Therefore, we propose an 'encoding-prediction-decoding' prediction network. This network can efficiently benefit to more related input variables with key variables, that is, it can adaptively extract key variable-related low-dimensional latent feature from much more input atmospheric variables for iterative prediction. And we construct a loss function to guide the iteration of latent feature by utilizing multiple atmospheric variables in corresponding lead times. The obtained latent features through iterative prediction are then decoded to obtain the predicted values of key variables in multiple lead times. In addition, we improve the HTA algorithm in \cite{bi2023accurate} by inputting more time steps to enhance the temporal correlation between the prediction results and input variables. Both qualitative and quantitative prediction results on ERA5 dataset validate the superiority of our method over other methods. (The code will be available at https://github.com/rs-lsl/Kvp-lsi)">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/74049a723c1c52b4f2dc01b28bef137771cb1148" target='_blank'>
-              TSI-Bench: Benchmarking Time Series Imputation
+          <td><a href="https://www.semanticscholar.org/paper/11cc8cb84dcb1157d6fac95012b48b1ab2b7d05d" target='_blank'>
+              Efficiently improving key weather variables forecasting by performing the guided iterative prediction in latent space
               </a>
             </td>
           <td>
-            Wenjie Du, Jun Wang, Linglong Qian, Yiyuan Yang, Fanxing Liu, Zepu Wang, Zina Ibrahim, Haoxin Liu, Zhiyuan Zhao, Yingjie Zhou, Wenjia Wang, Kaize Ding, Yuxuan Liang, B. A. Prakash, Qingsong Wen
+            Shuangliang Li, Siwei Li
           </td>
-          <td>2024-06-18</td>
+          <td>2024-07-27</td>
           <td>ArXiv</td>
-          <td>2</td>
-          <td>4</td>
+          <td>0</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Recently, the incorporation of both temporal features and the correlation across time series has become an effective approach in time series prediction. Spatio-Temporal Graph Neural Networks (STGNNs) demonstrate good performance on many Temporal-correlation Forecasting Problem. However, when applied to tasks lacking periodicity, such as stock data prediction, the effectiveness and robustness of STGNNs are found to be unsatisfactory. And STGNNs are limited by memory savings so that cannot handle problems with a large number of nodes. In this paper, we propose a novel approach called the Temporal-Correlation Graph Pre-trained Network (TCGPN) to address these limitations. TCGPN utilize Temporal-correlation fusion encoder to get a mixed representation and pre-training method with carefully designed temporal and correlation pre-training tasks. Entire structure is independent of the number and order of nodes, so better results can be obtained through various data enhancements. And memory consumption during training can be significantly reduced through multiple sampling. Experiments are conducted on real stock market data sets CSI300 and CSI500 that exhibit minimal periodicity. We fine-tune a simple MLP in downstream tasks and achieve state-of-the-art results, validating the capability to capture more robust temporal correlation patterns.">
+        <tr id="This paper presents Diffusion Forcing, a new training paradigm where a diffusion model is trained to denoise a set of tokens with independent per-token noise levels. We apply Diffusion Forcing to sequence generative modeling by training a causal next-token prediction model to generate one or several future tokens without fully diffusing past ones. Our approach is shown to combine the strengths of next-token prediction models, such as variable-length generation, with the strengths of full-sequence diffusion models, such as the ability to guide sampling to desirable trajectories. Our method offers a range of additional capabilities, such as (1) rolling-out sequences of continuous tokens, such as video, with lengths past the training horizon, where baselines diverge and (2) new sampling and guiding schemes that uniquely profit from Diffusion Forcing's variable-horizon and causal architecture, and which lead to marked performance gains in decision-making and planning tasks. In addition to its empirical success, our method is proven to optimize a variational lower bound on the likelihoods of all subsequences of tokens drawn from the true joint distribution. Project website: https://boyuan.space/diffusion-forcing">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1184b61fe584f056e3f7156ed3fe740a346b75a1" target='_blank'>
-              TCGPN: Temporal-Correlation Graph Pre-trained Network for Stock Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/40d63dc2b465c9081e4efc5a19514da151e97fe7" target='_blank'>
+              Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
               </a>
             </td>
           <td>
-            Wenbo Yan, Ying Tan
+            Boyuan Chen, Diego Marti Monso, Yilun Du, Max Simchowitz, Russ Tedrake, Vincent Sitzmann
           </td>
-          <td>2024-07-26</td>
+          <td>2024-07-01</td>
           <td>ArXiv</td>
-          <td>0</td>
-          <td>0</td>
+          <td>1</td>
+          <td>3</td>
         </tr>
 
         <tr id="We present a knowledge-guided machine learning (KGML) framework for modeling multi-scale processes, and study its performance in the context of streamflow forecasting in hydrology. Specifically, we propose a novel hierarchical recurrent neural architecture that factorizes the system dynamics at multiple temporal scales and captures their interactions. This framework consists of an inverse and a forward model. The inverse model is used to empirically resolve the system's temporal modes from data (physical model simulations, observed data, or a combination of them from the past), and these states are then used in the forward model to predict streamflow. In a hydrological system, these modes can represent different processes, evolving at different temporal scales (e.g., slow: groundwater recharge and baseflow vs. fast: surface runoff due to extreme rainfall). A key advantage of our framework is that once trained, it can incorporate new observations into the model's context (internal state) without expensive optimization approaches (e.g., EnKF) that are traditionally used in physical sciences for data assimilation. Experiments with several river catchments from the NWS NCRFC region show the efficacy of this ML-based data assimilation framework compared to standard baselines, especially for basins that have a long history of observations. Even for basins that have a shorter observation history, we present two orthogonal strategies of training our FHNN framework: (a) using simulation data from imperfect simulations and (b) using observation data from multiple basins to build a global model. We show that both of these strategies (that can be used individually or together) are highly effective in mitigating the lack of training data. The improvement in forecast accuracy is particularly noteworthy for basins where local models perform poorly because of data sparsity.">
@@ -2954,169 +2893,169 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>44</td>
         </tr>
 
-        <tr id="This study proposes a novel hybrid model, called ICE2DE-MDL, integrating secondary decomposition, entropy, machine and deep learning methods to predict a stock closing price. In this context, first of all, the noise contained in the financial time series was eliminated. A denoising method, which utilizes entropy and the two-level ICEEMDAN methodology, is suggested to achieve this. Subsequently, we applied many deep learning and machine learning methods, including long-short term memory (LSTM), LSTM-BN, gated recurrent unit (GRU), and SVR, to the IMFs obtained from the decomposition, classifying them as noiseless. Afterward, the best training method was determined for each IMF. Finally, the proposed model’s forecast was obtained by hierarchically combining the prediction results of each IMF. The ICE2DE-MDL model was applied to eight stock market indices and three stock data sets, and the next day’s closing price of these stock items was predicted. The results indicate that RMSE values ranged from 0.031 to 0.244, MAE values ranged from 0.026 to 0.144, MAPE values ranged from 0.128 to 0.594, and R-squared values ranged from 0.905 to 0.998 for stock indices and stock forecasts. Furthermore, comparisons were made with various hybrid models proposed within the scope of stock forecasting to evaluate the performance of the ICE2DE-MDL model. Upon comparison, The ICE2DE-MDL model demonstrated superior performance relative to existing models in the literature for both forecasting stock market indices and individual stocks. Additionally, to our knowledge, this study is the first to effectively eliminate noise in stock item data using the concepts of entropy and ICEEMDAN. It is also the second study to apply ICEEMDAN to a financial time series prediction problem.">
+        <tr id="Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method whose final prediction is based on intermediate, human-understandable concepts rather than the raw input. Through time-consuming manual interventions, a user can correct wrongly predicted concept values to enhance the model's downstream performance. We propose Stochastic Concept Bottleneck Models (SCBMs), a novel approach that models concept dependencies. In SCBMs, a single-concept intervention affects all correlated concepts, thereby improving intervention effectiveness. Unlike previous approaches that model the concept relations via an autoregressive structure, we introduce an explicit, distributional parameterization that allows SCBMs to retain the CBMs' efficient training and inference procedure. Additionally, we leverage the parameterization to derive an effective intervention strategy based on the confidence region. We show empirically on synthetic tabular and natural image datasets that our approach improves intervention effectiveness significantly. Notably, we showcase the versatility and usability of SCBMs by examining a setting with CLIP-inferred concepts, alleviating the need for manual concept annotations.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/47edf39aa7db2d8e2b70434db719f87000ddd194" target='_blank'>
-              Multi level perspectives in stock price forecasting: ICE2DE-MDL
+          <td><a href="https://www.semanticscholar.org/paper/aeef1c8046f93d178a091e733fe0d2c8c8c0ca3a" target='_blank'>
+              Stochastic Concept Bottleneck Models
               </a>
             </td>
           <td>
-            Zinnet Duygu Aksehir, Erdal Kiliç
+            Moritz Vandenhirtz, Sonia Laguna, Ricards Marcinkevics, Julia E. Vogt
           </td>
-          <td>2024-06-24</td>
-          <td>PeerJ Computer Science</td>
+          <td>2024-06-27</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>2</td>
+          <td>8</td>
         </tr>
 
-        <tr id="Summarizing web graphs is challenging due to the heterogeneity of the modeled information and its changes over time. We investigate the use of neural networks for lifelong graph summarization. Assuming we observe the web graph at a certain time, we train the networks to summarize graph vertices. We apply this trained network to summarize the vertices of the changed graph at the next point in time. Subsequently, we continue training and evaluating the network to perform lifelong graph summarization. We use the GNNs Graph-MLP and GraphSAINT, as well as an MLP baseline, to summarize the temporal graphs. We compare $1$-hop and $2$-hop summaries. We investigate the impact of reusing parameters from a previous snapshot by measuring the backward and forward transfer and the forgetting rate of the neural networks. Our extensive experiments on ten weekly snapshots of a web graph with over $100$M edges, sampled in 2012 and 2022, show that all networks predominantly use $1$-hop information to determine the summary, even when performing $2$-hop summarization. Due to the heterogeneity of web graphs, in some snapshots, the $2$-hop summary produces over ten times more vertex summaries than the $1$-hop summary. When using the network trained on the last snapshot from 2012 and applying it to the first snapshot of 2022, we observe a strong drop in accuracy. We attribute this drop over the ten-year time warp to the strongly increased heterogeneity of the web graph in 2022.">
+        <tr id="Accurate short-term forecasting of power load is essential for the reliable operation of the comprehensive energy systems of ports and for effectively reducing energy consumption. Owing to the complexity of port systems, traditional load forecasting methods often struggle to capture the non-linearity and multifactorial interactions within the factors creating power load. To address these challenges, this study combines variational mode decomposition (VMD), temporal convolutional network (TCN), and long short-term memory (LSTM) network to develop a multi-feature-based VMD-TCN-LSTM model for the short-term forecasting of the power load of ports. VMD is first used to decompose the power load series of ports into multiple, relatively stable components to mitigate volatility. Furthermore, meteorological and temporal features are introduced into the TCN-LSTM model, which combines the temporal feature extraction capability of the TCN and the long term-dependent learning capability of the LSTM. Comparative analyses with other common forecasting models using the observed power load data from a coastal port in China demonstrate that the proposed forecasting model achieves a higher prediction accuracy, with an R-squared value of 0.94, mean squared error of 3.59 MW, and a mean absolute percentage error of 2.36%.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/39a9e812343d7550486a8b525adf2fcc87cd162e" target='_blank'>
-              Lifelong Graph Summarization with Neural Networks: 2012, 2022, and a Time Warp
+          <td><a href="https://www.semanticscholar.org/paper/0ede2faa5546cfbefa083cdef9bfe4e7663ea5a1" target='_blank'>
+              Multifeature-Based Variational Mode Decomposition–Temporal Convolutional Network–Long Short-Term Memory for Short-Term Forecasting of the Load of Port Power Systems
               </a>
             </td>
           <td>
-            Jonatan Frank, Marcel Hoffmann, N. Lell, David Richerby, A. Scherp
+            Guang Chen, Xiaofeng Ma, Lin Wei
           </td>
-          <td>2024-07-25</td>
-          <td>ArXiv</td>
+          <td>2024-06-22</td>
+          <td>Sustainability</td>
+          <td>0</td>
           <td>0</td>
-          <td>27</td>
         </tr>
 
-        <tr id="This study introduces a novel approach that integrates dynamic Bayesian network with attention based spatio-temporal graph convolutional network to forecast railway train delays, capturing the intricate operation interactions between train events and the dynamic evolution of train delays. Initially, train delay patterns are identified using the K-means clustering algorithm and incorporated as additional variables into the prediction model. To capture dynamic causality in delay propagation, we utilize a dynamic Bayesian network-based dynamic causality graph, incorporating train delay data and domain knowledge to effectively model the train delay propagation. Leveraging these insights on delay dynamic propagation, we propose an attention based spatio-temporal graph convolutional network that effectively models the dynamic spatio-temporal dependency among train events and enhances the accuracy of delay predictions. The proposed method is assessed using operational data from the Wuhan-Guangzhou high-speed railway. Results show that the proposed model outperforms the baseline models, particularly with the expansion of the prediction horizon. The learned dynamic causality of the train delay propagation enhances interpretability and results in a 6.97% reduction in mean absolute error. Furthermore, train delay patterns and weather variables contribute to a respective 12.85% and 4.37% reduction in mean absolute error. The statistical tests further validate the efficacy of the proposed model.">
+        <tr id="Graph Neural Networks (GNNs)-based recommendation algorithms typically assume that training and testing data are drawn from independent and identically distributed (IID) spaces. However, this assumption often fails in the presence of out-of-distribution (OOD) data, resulting in significant performance degradation. In this study, we construct a Structural Causal Model (SCM) to analyze interaction data, revealing that environmental confounders (e.g., the COVID-19 pandemic) lead to unstable correlations in GNN-based models, thus impairing their generalization to OOD data. To address this issue, we propose a novel approach, graph representation learning via causal diffusion (CausalDiffRec) for OOD recommendation. This method enhances the model's generalization on OOD data by eliminating environmental confounding factors and learning invariant graph representations. Specifically, we use backdoor adjustment and variational inference to infer the real environmental distribution, thereby eliminating the impact of environmental confounders. This inferred distribution is then used as prior knowledge to guide the representation learning in the reverse phase of the diffusion process to learn the invariant representation. In addition, we provide a theoretical derivation that proves optimizing the objective function of CausalDiffRec can encourage the model to learn environment-invariant graph representations, thereby achieving excellent generalization performance in recommendations under distribution shifts. Our extensive experiments validate the effectiveness of CausalDiffRec in improving the generalization of OOD data, and the average improvement is up to 10.69% on Food, 18.83% on KuaiRec, 22.41% on Yelp2018, and 11.65% on Douban datasets.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/31fbcd9b82bde6579a24f9baa5171614e4509e9c" target='_blank'>
-              Bayesian Spatio-Temporal Graph Convolutional Network for Railway Train Delay Prediction
+          <td><a href="https://www.semanticscholar.org/paper/ce6c02c791a420b29ba47ebbf6ece6010fd1a289" target='_blank'>
+              Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation
               </a>
             </td>
           <td>
-            Jianmin Li, Xinyue Xu, Xin Ding, Jun Liu, Bin Ran
+            Chu Zhao, Enneng Yang, Yuliang Liang, Pengxiang Lan, Yuting Liu, Jianzhe Zhao, Guibing Guo, Xingwei Wang
           </td>
-          <td>2024-07-01</td>
-          <td>IEEE Transactions on Intelligent Transportation Systems</td>
+          <td>2024-08-01</td>
+          <td>ArXiv</td>
           <td>0</td>
           <td>6</td>
         </tr>
 
-        <tr id="Dynamic graphs (DG) are often used to describe evolving interactions between nodes in real-world applications. Temporal patterns are a natural feature of DGs and are also key to representation learning. However, existing dynamic GCN models are mostly composed of static GCNs and sequence modules, which results in the separation of spatiotemporal information and cannot effectively capture complex temporal patterns in DGs. To address this problem, this study proposes a spatial-temporal graph convolutional networks with diversified transformation (STGCNDT), which includes three aspects: a) constructing a unified graph tensor convolutional network (GTCN) using tensor M-products without the need to represent spatiotemporal information separately; b) introducing three transformation schemes in GTCN to model complex temporal patterns to aggregate temporal information; and c) constructing an ensemble of diversified transformation schemes to obtain higher representation capabilities. Empirical studies on four DGs that appear in communication networks show that the proposed STGCNDT significantly outperforms state-of-the-art models in solving link weight estimation tasks due to the diversified transformations.">
+        <tr id="Accurate traffic forecasting is more necessary than ever for transportation departments, especially given its significant role in traffic planning, management, and control. However, most existing methods struggle to address complex spatial correlations on road networks, nonlinear temporal dynamics, and difficult long‐term prediction. This article proposes a novel spatial temporal graph gated transformer (STGGT) to overcome these challenges. The suggested model differs from Google's transformer because it uses a hybrid architecture that integrates graph convolutional networks (GCNs), attention, and gated recurrent units (GRUs) instead of solely relying on attention. Specifically, STGGT uses GCNs to extract spatial dependencies, utilizes attention and GRUs to extract temporal dependencies, and handle long‐term prediction. Experiments indicate that STGGT outperforms the state‐of‐the‐art baseline models on two real‐world traffic datasets of 9%–40%. The proposed model offers a promising solution for accurate traffic forecasting, simultaneously addressing the challenges of complex spatial correlations, nonlinear temporal dynamics, and long‐term prediction.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/645c7ccb4c62510922512fdba7088d9493b9fd62" target='_blank'>
-              Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning
+          <td><a href="https://www.semanticscholar.org/paper/64e802ce9b30e768f218018adb39b56cd14ee339" target='_blank'>
+              A spatial-temporal graph gated transformer for traffic forecasting
               </a>
             </td>
           <td>
-            Ling Wang, Yixiang Huang, Hao Wu
+            Haroun Bouchemoukha, M. Zennir, Ahmed Alioua
           </td>
-          <td>2024-08-05</td>
-          <td>ArXiv</td>
-          <td>0</td>
+          <td>2024-06-26</td>
+          <td>Trans. Emerg. Telecommun. Technol.</td>
           <td>0</td>
+          <td>4</td>
         </tr>
 
-        <tr id="Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, FMs can find uses in electric power grids, challenged by the energy transition and climate change. In this paper, we call for the development of, and state why we believe in, the potential of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities, pioneering a new approach in leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit.">
+        <tr id="Dynamic Quality-of-Service (QoS) data capturing temporal variations in user-service interactions, are essential source for service selection and user behavior understanding. Approaches based on Latent Feature Analysis (LFA) have shown to be beneficial for discovering effective temporal patterns in QoS data. However, existing methods cannot well model the spatiality and temporality implied in dynamic interactions in a unified form, causing abundant accuracy loss for missing QoS estimation. To address the problem, this paper presents a novel Graph Convolutional Networks (GCNs)-based dynamic QoS estimator namely Spatiotemporal Coupling GCN (SCG) model with the three-fold ideas as below. First, SCG builds its dynamic graph convolution rules by incorporating generalized tensor product framework, for unified modeling of spatial and temporal patterns. Second, SCG combines the heterogeneous GCN layer with tensor factorization, for effective representation learning on bipartite user-service graphs. Third, it further simplifies the dynamic GCN structure to lower the training difficulties. Extensive experiments have been conducted on two large-scale widely-adopted QoS datasets describing throughput and response time. The results demonstrate that SCG realizes higher QoS estimation accuracy compared with the state-of-the-arts, illustrating it can learn powerful representations to users and cloud services.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ccdce57aa37af5fab8e15c5c8f5d8a9b8a8675a2" target='_blank'>
-              A Perspective on Foundation Models for the Electric Power Grid
+          <td><a href="https://www.semanticscholar.org/paper/53de573b22bccc67f4c62494a24ad6245cd7246a" target='_blank'>
+              A Novel Spatiotemporal Coupling Graph Convolutional Network
               </a>
             </td>
           <td>
-            Hendrik Hamann, Thomas Brunschwiler, B. Gjorgiev, Leonardo S. A. Martins, Alban Puech, Anna Varbella, Jonas Weiss, Juan Bernabé-Moreno, Alexandre Blondin Mass'e, Seong Choi, Ian Foster, B. Hodge, Rishabh Jain, Kibaek Kim, Vincent Mai, Franccois Miralles, M. D. Montigny, Octavio Ramos-Leaños, Hussein Suprême, Le Xie, El-Nasser S. Youssef, Arnaud Zinflou, Alexander J. Belvi, Ricardo J. Bessa, Bishnu Prasad Bhattari, J. Schmude, Stanislav Sobolevsky
+            Fanghui Bi
           </td>
-          <td>2024-07-12</td>
+          <td>2024-08-09</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>58</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Accurate short-term forecasting of power load is essential for the reliable operation of the comprehensive energy systems of ports and for effectively reducing energy consumption. Owing to the complexity of port systems, traditional load forecasting methods often struggle to capture the non-linearity and multifactorial interactions within the factors creating power load. To address these challenges, this study combines variational mode decomposition (VMD), temporal convolutional network (TCN), and long short-term memory (LSTM) network to develop a multi-feature-based VMD-TCN-LSTM model for the short-term forecasting of the power load of ports. VMD is first used to decompose the power load series of ports into multiple, relatively stable components to mitigate volatility. Furthermore, meteorological and temporal features are introduced into the TCN-LSTM model, which combines the temporal feature extraction capability of the TCN and the long term-dependent learning capability of the LSTM. Comparative analyses with other common forecasting models using the observed power load data from a coastal port in China demonstrate that the proposed forecasting model achieves a higher prediction accuracy, with an R-squared value of 0.94, mean squared error of 3.59 MW, and a mean absolute percentage error of 2.36%.">
+        <tr id="Industrial time-series, as a structural data responds to production process information, can be utilized to perform data-driven decision-making for effective monitoring of industrial production process. However, there are some challenges for time-series forecasting in industry, e.g., predicting few-shot caused by data shortage, and decision-confusing caused by unknown treatment policy. To cope with the problems, we propose a novel causal domain adaptation framework, Causal Domain Adaptation (CDA) forecaster to improve the performance on the interested domain with limited data (target). Firstly, we analyze the causality existing along with treatments, and thus ensure the shared causality over time. Subsequently, we propose an answer-based attention mechanism to achieve domain-invariant representation by the shared causality in both domains. Then, a novel domain-adaptation is built to model treatments and outcomes jointly training on source and target domain. The main insights are that our designed answer-based attention mechanism allows the target domain to leverage the existed causality in source time-series even with different treatments, and our forecaster can predict the counterfactual outcome of industrial time-series, meaning a guidance in production process. Compared with commonly baselines, our method on real-world and synthetic oilfield datasets demonstrates the effectiveness in across-domain prediction and the practicality in guiding production process">
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-          <td><a href="https://www.semanticscholar.org/paper/0ede2faa5546cfbefa083cdef9bfe4e7663ea5a1" target='_blank'>
-              Multifeature-Based Variational Mode Decomposition–Temporal Convolutional Network–Long Short-Term Memory for Short-Term Forecasting of the Load of Port Power Systems
+          <td><a href="https://www.semanticscholar.org/paper/156edecae87bbcc79ee4253c37525b426f87a42a" target='_blank'>
+              Domain Adaptation for Industrial Time-series Forecasting via Counterfactual Inference
               </a>
             </td>
           <td>
-            Guang Chen, Xiaofeng Ma, Lin Wei
+            Chao Min, Guo-quan Wen, Jiangru Yuan, Jun Yi, Xing Guo
           </td>
-          <td>2024-06-22</td>
-          <td>Sustainability</td>
-          <td>0</td>
+          <td>2024-07-19</td>
+          <td>ArXiv</td>
           <td>0</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Weather forecasting refers to learning evolutionary patterns of some key upper-air and surface variables which is of great significance. Recently, deep learning-based methods have been increasingly applied in the field of weather forecasting due to their powerful feature learning capabilities. However, prediction methods based on the original space iteration struggle to effectively and efficiently utilize large number of weather variables. Therefore, we propose an 'encoding-prediction-decoding' prediction network. This network can efficiently benefit to more related input variables with key variables, that is, it can adaptively extract key variable-related low-dimensional latent feature from much more input atmospheric variables for iterative prediction. And we construct a loss function to guide the iteration of latent feature by utilizing multiple atmospheric variables in corresponding lead times. The obtained latent features through iterative prediction are then decoded to obtain the predicted values of key variables in multiple lead times. In addition, we improve the HTA algorithm in \cite{bi2023accurate} by inputting more time steps to enhance the temporal correlation between the prediction results and input variables. Both qualitative and quantitative prediction results on ERA5 dataset validate the superiority of our method over other methods. (The code will be available at https://github.com/rs-lsl/Kvp-lsi)">
+        <tr id="Graph Neural Networks (GNNs) have emerged as powerful tools for supervised machine learning over graph-structured data, while sampling-based node representation learning is widely utilized in unsupervised learning. However, scalability remains a major challenge in both supervised and unsupervised learning for large graphs (e.g., those with over 1 billion nodes). The scalability bottleneck largely stems from the mini-batch sampling phase in GNNs and the random walk sampling phase in unsupervised methods. These processes often require storing features or embeddings in memory. In the context of distributed training, they require frequent, inefficient random access to data stored across different workers. Such repeated inter-worker communication for each mini-batch leads to high communication overhead and computational inefficiency. We propose GraphScale, a unified framework for both supervised and unsupervised learning to store and process large graph data distributedly. The key insight in our design is the separation of workers who store data and those who perform the training. This separation allows us to decouple computing and storage in graph training, thus effectively building a pipeline where data fetching and data computation can overlap asynchronously. Our experiments show that GraphScale outperforms state-of-the-art methods for distributed training of both GNNs and node embeddings. We evaluate GraphScale both on public and proprietary graph datasets and observe a reduction of at least 40% in end-to-end training times compared to popular distributed frameworks, without any loss in performance. While most existing methods don't support billion-node graphs for training node embeddings, GraphScale is currently deployed in production at TikTok enabling efficient learning over such large graphs.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/11cc8cb84dcb1157d6fac95012b48b1ab2b7d05d" target='_blank'>
-              Efficiently improving key weather variables forecasting by performing the guided iterative prediction in latent space
+          <td><a href="https://www.semanticscholar.org/paper/e5115bc6bf426026833b4f7fb72ae6282f678143" target='_blank'>
+              GraphScale: A Framework to Enable Machine Learning over Billion-node Graphs
               </a>
             </td>
           <td>
-            Shuangliang Li, Siwei Li
+            Vipul Gupta, Xin Chen, Ruoyun Huang, Fanlong Meng, Jianjun Chen, Yujun Yan
           </td>
-          <td>2024-07-27</td>
+          <td>2024-07-22</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>1</td>
         </tr>
 
-        <tr id="This paper presents Diffusion Forcing, a new training paradigm where a diffusion model is trained to denoise a set of tokens with independent per-token noise levels. We apply Diffusion Forcing to sequence generative modeling by training a causal next-token prediction model to generate one or several future tokens without fully diffusing past ones. Our approach is shown to combine the strengths of next-token prediction models, such as variable-length generation, with the strengths of full-sequence diffusion models, such as the ability to guide sampling to desirable trajectories. Our method offers a range of additional capabilities, such as (1) rolling-out sequences of continuous tokens, such as video, with lengths past the training horizon, where baselines diverge and (2) new sampling and guiding schemes that uniquely profit from Diffusion Forcing's variable-horizon and causal architecture, and which lead to marked performance gains in decision-making and planning tasks. In addition to its empirical success, our method is proven to optimize a variational lower bound on the likelihoods of all subsequences of tokens drawn from the true joint distribution. Project website: https://boyuan.space/diffusion-forcing">
+        <tr id="Deep learning models are widely used in multivariate time series forecasting, yet, they have high computational costs. One way to reduce this cost is by reducing data dimensionality, which involves removing unimportant or low importance information with the proper method. This work presents a study on an explainability feature selection framework composed of four methods (IMV‐LSTM Tensor, LIME‐LSTM, Average SHAP‐LSTM, and Instance SHAP‐LSTM) aimed at using the LSTM black‐box model complexity to its favour, with the end goal of improving the error metrics and reducing the computational cost on a forecast task. To test the framework, three datasets with a total of 101 multivariate time series were used, with the explainability methods outperforming the baseline methods in most of the data, be it in error metrics or computation time for the LSTM model training.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/40d63dc2b465c9081e4efc5a19514da151e97fe7" target='_blank'>
-              Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
+          <td><a href="https://www.semanticscholar.org/paper/c38a287f4c56c37be6a1916bb90ba43b4504c73a" target='_blank'>
+              KDBI special issue: Explainability feature selection framework application for LSTM multivariate time‐series forecast self optimization
               </a>
             </td>
           <td>
-            Boyuan Chen, Diego Marti Monso, Yilun Du, Max Simchowitz, Russ Tedrake, Vincent Sitzmann
+            Eduardo M. Rodrigues, Yassine Baghoussi, João Mendes‐Moreira
           </td>
-          <td>2024-07-01</td>
-          <td>ArXiv</td>
-          <td>1</td>
-          <td>3</td>
+          <td>2024-07-04</td>
+          <td>Expert Systems</td>
+          <td>0</td>
+          <td>2</td>
         </tr>
 
-        <tr id="Distribution shifts between training and test data are all but inevitable over the lifecycle of a deployed model and lead to performance decay. Adapting the model can hopefully mitigate this drop in performance. Yet, adaptation is challenging since it must be unsupervised: we usually do not have access to any labeled data at test time. In this paper, we propose a probabilistic state-space model that can adapt a deployed model subjected to distribution drift. Our model learns the dynamics induced by distribution shifts on the last set of hidden features. Without requiring labels, we infer time-evolving class prototypes that serve as a dynamic classification head. Moreover, our approach is lightweight, modifying only the model's last linear layer. In experiments on real-world distribution shifts and synthetic corruptions, we demonstrate that our approach performs competitively with methods that require back-propagation and access to the model backbone. Our model especially excels in the case of small test batches - the most difficult setting.">
+        <tr id="Time series forecasting (TSF) is immensely important in extensive applications, such as electricity transformation, financial trade, medical monitoring, and smart agriculture. Although Transformer-based methods can handle time series data, their ability to predict long-term time series is limited due to the ``anti-order"nature of the self-attention mechanism. To address this problem, we focus on frequency domain to weaken the impact of order in TSF and propose the FreqBlock, where we first obtain frequency representations through the Frequency Transform Module. Subsequently, a newly designed Frequency Cross Attention is used to obtian enhanced frequency representations between the real and imaginary parts, thus establishing a link between the attention mechanism and the inherent Kramer-Kronig relations (KKRs). Our backbone network, FreqTSF, adopts a residual structure by concatenating multiple FreqBlocks to simulate KKRs in the frequency domain and avoid degradation problems. On a theoretical level, we demonstrate that the proposed two modules can significantly reduce the time and memory complexity from $\mathcal{O}(L^2)$ to $\mathcal{O}(L)$ for each FreqBlock computation. Empirical studies on four benchmark datasets show that FreqTSF achieves an overall relative MSE reduction of 15\% and an overall relative MAE reduction of 11\% compared to the state-of-the-art methods. The code will be available soon.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/2b1d8333834155ef8196c5d42306122cf3b18d7f" target='_blank'>
-              Test-Time Adaptation with State-Space Models
+          <td><a href="https://www.semanticscholar.org/paper/c7a597bc1188e94cf4e9583a7acbceb292b7f71f" target='_blank'>
+              FreqTSF: Time Series Forecasting Via Simulating Frequency Kramer-Kronig Relations
               </a>
             </td>
           <td>
-            Mona Schirmer, Dan Zhang, Eric Nalisnick
+            Rujia Shen, Liangliang Liu, Boran Wang, Yi Guan, Yang Yang, Jingchi Jiang
           </td>
-          <td>2024-07-17</td>
+          <td>2024-07-31</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>11</td>
         </tr>
 
-        <tr id="In Activities of Daily Living (ADL) research, which has gained prominence due to the burgeoning aging population, the challenge of acquiring sufficient ground truth data for model training is a significant bottleneck. This obstacle necessitates a pivot towards unsupervised representation learning methodologies, which do not require many labeled datasets. The existing research focused on the tradeoff between the fully supervised model and the unsupervised pre-trained model and found that the unsupervised version outperformed in most cases. However, their investigation did not use large enough Human Activity Recognition (HAR) datasets, both datasets resulting in 3 dimensions. This poster extends the investigation by employing a large multivariate time series HAR dataset and experimenting with the models with different combinations of critical training parameters such as batch size and learning rate to observe the performance tradeoff. Our findings reveal that the pre-trained model is comparable to the fully supervised classification with a larger multivariate time series HAR dataset. This discovery underscores the potential of unsupervised representation learning in ADL extractions and highlights the importance of model configuration in optimizing performance.">
+        <tr id="Track reconstruction is a crucial task in particle experiments and is traditionally very computationally expensive due to its combinatorial nature. Recently, graph neural networks (GNNs) have emerged as a promising approach that can improve scalability. Most of these GNN-based methods, including the edge classification (EC) and the object condensation (OC) approach, require an input graph that needs to be constructed beforehand. In this work, we consider a one-shot OC approach that reconstructs particle tracks directly from a set of hits (point cloud) by recursively applying graph attention networks with an evolving graph structure. This approach iteratively updates the graphs and can better facilitate the message passing across each graph. Preliminary studies on the TrackML dataset show better track performance compared to the methods that require a fixed input graph.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/35a259468f150d476f6c25c9be7e1988af7676ae" target='_blank'>
-              Poster: Comparative Study of Transformer Models on a Large Multivariate Time Series HAR Dataset
+          <td><a href="https://www.semanticscholar.org/paper/398108b585c483e0611513c7746467f7cd38df17" target='_blank'>
+              EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction
               </a>
             </td>
           <td>
-            Hyungtaek Kwon, Zongxing Xie, Mengjing Liu, Fan Ye
+            P. Calafiura, Jay Chan, Loic Delabrouille, Brandon Wang
           </td>
-          <td>2024-06-19</td>
-          <td>2024 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)</td>
+          <td>2024-07-18</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>10</td>
+          <td>48</td>
         </tr>
 
-        <tr id="Accurate traffic forecasting is more necessary than ever for transportation departments, especially given its significant role in traffic planning, management, and control. However, most existing methods struggle to address complex spatial correlations on road networks, nonlinear temporal dynamics, and difficult long‐term prediction. This article proposes a novel spatial temporal graph gated transformer (STGGT) to overcome these challenges. The suggested model differs from Google's transformer because it uses a hybrid architecture that integrates graph convolutional networks (GCNs), attention, and gated recurrent units (GRUs) instead of solely relying on attention. Specifically, STGGT uses GCNs to extract spatial dependencies, utilizes attention and GRUs to extract temporal dependencies, and handle long‐term prediction. Experiments indicate that STGGT outperforms the state‐of‐the‐art baseline models on two real‐world traffic datasets of 9%–40%. The proposed model offers a promising solution for accurate traffic forecasting, simultaneously addressing the challenges of complex spatial correlations, nonlinear temporal dynamics, and long‐term prediction.">
+        <tr id="The effectiveness of univariate forecasting models is often hampered by conditions that cause them stress. A model is considered to be under stress if it shows a negative behaviour, such as higher-than-usual errors or increased uncertainty. Understanding the factors that cause stress to forecasting models is important to improve their reliability, transparency, and utility. This paper addresses this problem by contributing with a novel framework called MAST (Meta-learning and data Augmentation for Stress Testing). The proposed approach aims to model and characterize stress in univariate time series forecasting models, focusing on conditions where they exhibit large errors. In particular, MAST is a meta-learning approach that predicts the probability that a given model will perform poorly on a given time series based on a set of statistical time series features. MAST also encompasses a novel data augmentation technique based on oversampling to improve the metadata concerning stress. We conducted experiments using three benchmark datasets that contain a total of 49.794 time series to validate the performance of MAST. The results suggest that the proposed approach is able to identify conditions that lead to large errors. The method and experiments are publicly available in a repository.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/64e802ce9b30e768f218018adb39b56cd14ee339" target='_blank'>
-              A spatial-temporal graph gated transformer for traffic forecasting
+          <td><a href="https://www.semanticscholar.org/paper/21df316c9190406997a7ddcf7668a9d6a38d85cf" target='_blank'>
+              Meta-learning and Data Augmentation for Stress Testing Forecasting Models
               </a>
             </td>
           <td>
-            Haroun Bouchemoukha, M. Zennir, Ahmed Alioua
+            Ricardo In'acio, Vítor Cerqueira, Mar'ilia Barandas, Carlos Soares
           </td>
-          <td>2024-06-26</td>
-          <td>Trans. Emerg. Telecommun. Technol.</td>
+          <td>2024-06-24</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>4</td>
+          <td>14</td>
         </tr>
 
         <tr id="Multivariate time series anomaly detection is a crucial data mining technique with a wide range of applications in areas such as IT applications. Currently, the majority of anomaly detection methods for time series data rely on unsupervised approaches due to the rarity of anomaly labels. However, in real-world scenarios, obtaining a limited number of anomaly labels is feasible and affordable. Effective usage of these labels can offer valuable insights into the temporal characteristics of anomalies and play a pivotal role in guiding anomaly detection efforts. To improve the performance of multivariate time series anomaly detection, we proposed a novel deep learning model named EDD (Encoder-Decoder-Discriminator) that leverages limited anomaly samples. The EDD model innovatively integrates a graph attention network with long short term memory (LSTM) to extract spatial and temporal features from multivariate time series data. This integrated approach enables the model to capture complex patterns and dependencies within the data. Additionally, the model skillfully maps series data into a latent space, utilizing a carefully crafted loss function to cluster normal data tightly in the latent space while dispersing abnormal data randomly. This innovative design results in distinct probability distributions for normal and abnormal data in the latent space, enabling precise identification of anomalous data. To evaluate the performance of our EDD model, we conducted extensive experimental validation across three diverse datasets. The results demonstrate the significant superiority of our model in multivariate time series anomaly detection. Specifically, the average F1-Score of our model outperformed the second-best method by 2.7% and 73.4% in both evaluation approaches, respectively, highlighting its superior detection capabilities. These findings validate the effectiveness of our proposed EDD model in leveraging limited anomaly samples for accurate and robust anomaly detection in multivariate time series data.">
@@ -3129,204 +3068,174 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
             Dong Wei, Wu Sun, Xiaofeng Zou, Dan Ma, Huarong Xu, Panfeng Chen, Chaoshu Yang, Mei Chen, Hui Li
           </td>
           <td>2024-07-31</td>
-          <td>PeerJ Computer Science</td>
+          <td>PeerJ Comput. Sci.</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
-        <tr id="Motion forecasting transforms sequences of past movements and environment context into future motion. Recent methods rely on learned representations, resulting in hidden states that are difficult to interpret. In this work, we use natural language to quantize motion features in a human-interpretable way, and measure the degree to which they are embedded in hidden states. Our experiments reveal that hidden states of motion sequences are arranged with respect to our discrete sets of motion features. Following these insights, we fit control vectors to motion features, which allow for controlling motion forecasts at inference. Consequently, our method enables controlling transformer-based motion forecasting models with textual inputs, providing a unique interface to interact with and understand these models. Our implementation is available at https://github.com/kit-mrt/future-motion">
+        <tr id="The increasing adoption of rooftop photovoltaic (PV) power generation systems in residential areas necessitates accurate monitoring and disaggregation of behind-the-meter (BTM) load and PV power. Despite recent advancements, existing BTM disaggregation approaches suffer from three major drawbacks: neglecting task-relevant spatiotemporal features, overfitting, and lack of a sparse neural architecture which leads to high sample complexity. This paper addresses them by introducing a deep sparse attention graph recurrent framework. This framework conceptualizes a set of neighboring residential units as a graph where the nodes are the net load values of the units and the edges show the mutual information (MI) of these measurements. We develop an Attention Gated Recurrent Unit (AGRU) to capture enhanced temporal characteristics of the net load. We employ a novel low-rank Dictionary Learning (DL) method to discern spatiotemporal features of these measurements and further utilize a Rectified Linear Unit (ReLU) neural network that incorporates an MI-based dropout to provide a sparse model for the estimation of the BTM load and PV. Experimental results validate the effectiveness of our proposed model, exhibiting superior performance on the Ausgrid dataset in BTM load and PV power estimation compared to state-of-the-art methods.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/532aa80ce98765e362c195552115b28f1d833bd5" target='_blank'>
-              Words in Motion: Representation Engineering for Motion Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/ba52197a4d7f5071efd214b6546a45314b15bb13" target='_blank'>
+              Sparse Attention Graph Gated Recurrent Unit for Spatiotemporal Behind-The-Meter Load and PV Disaggregation
               </a>
             </td>
           <td>
-            Ömer Sahin Tas, Royden Wagner
+            Mahdi Khodayar, Ali Farajzadeh Bavil, M. Saffari
           </td>
-          <td>2024-06-17</td>
-          <td>ArXiv</td>
-          <td>1</td>
-          <td>3</td>
+          <td>2024-06-27</td>
+          <td>2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)</td>
+          <td>0</td>
+          <td>14</td>
         </tr>
 
-        <tr id="Graph Neural Networks (GNNs) have emerged as powerful tools for supervised machine learning over graph-structured data, while sampling-based node representation learning is widely utilized in unsupervised learning. However, scalability remains a major challenge in both supervised and unsupervised learning for large graphs (e.g., those with over 1 billion nodes). The scalability bottleneck largely stems from the mini-batch sampling phase in GNNs and the random walk sampling phase in unsupervised methods. These processes often require storing features or embeddings in memory. In the context of distributed training, they require frequent, inefficient random access to data stored across different workers. Such repeated inter-worker communication for each mini-batch leads to high communication overhead and computational inefficiency. We propose GraphScale, a unified framework for both supervised and unsupervised learning to store and process large graph data distributedly. The key insight in our design is the separation of workers who store data and those who perform the training. This separation allows us to decouple computing and storage in graph training, thus effectively building a pipeline where data fetching and data computation can overlap asynchronously. Our experiments show that GraphScale outperforms state-of-the-art methods for distributed training of both GNNs and node embeddings. We evaluate GraphScale both on public and proprietary graph datasets and observe a reduction of at least 40% in end-to-end training times compared to popular distributed frameworks, without any loss in performance. While most existing methods don't support billion-node graphs for training node embeddings, GraphScale is currently deployed in production at TikTok enabling efficient learning over such large graphs.">
+        <tr id="In the era of rapid globalization and digitalization, accurate identification of similar stocks has become increasingly challenging due to the non-stationary nature of financial markets and the ambiguity in conventional regional and sector classifications. To address these challenges, we examine SimStock, a novel temporal self-supervised learning framework that combines techniques from self-supervised learning (SSL) and temporal domain generalization to learn robust and informative representations of financial time series data. The primary focus of our study is to understand the similarities between stocks from a broader perspective, considering the complex dynamics of the global financial landscape. We conduct extensive experiments on four real-world datasets with thousands of stocks and demonstrate the effectiveness of SimStock in finding similar stocks, outperforming existing methods. The practical utility of SimStock is showcased through its application to various investment strategies, such as pairs trading, index tracking, and portfolio optimization, where it leads to superior performance compared to conventional methods. Our findings empirically examine the potential of data-driven approach to enhance investment decision-making and risk management practices by leveraging the power of temporal self-supervised learning in the face of the ever-changing global financial landscape.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e5115bc6bf426026833b4f7fb72ae6282f678143" target='_blank'>
-              GraphScale: A Framework to Enable Machine Learning over Billion-node Graphs
+          <td><a href="https://www.semanticscholar.org/paper/c259bc9adf3019870613cbb7888ea4fac4e1929f" target='_blank'>
+              Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management
               </a>
             </td>
           <td>
-            Vipul Gupta, Xin Chen, Ruoyun Huang, Fanlong Meng, Jianjun Chen, Yujun Yan
+            Yoon-Jeong Hwang, Stefan Zohren, Yongjae Lee
           </td>
-          <td>2024-07-22</td>
+          <td>2024-07-18</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Industrial time-series, as a structural data responds to production process information, can be utilized to perform data-driven decision-making for effective monitoring of industrial production process. However, there are some challenges for time-series forecasting in industry, e.g., predicting few-shot caused by data shortage, and decision-confusing caused by unknown treatment policy. To cope with the problems, we propose a novel causal domain adaptation framework, Causal Domain Adaptation (CDA) forecaster to improve the performance on the interested domain with limited data (target). Firstly, we analyze the causality existing along with treatments, and thus ensure the shared causality over time. Subsequently, we propose an answer-based attention mechanism to achieve domain-invariant representation by the shared causality in both domains. Then, a novel domain-adaptation is built to model treatments and outcomes jointly training on source and target domain. The main insights are that our designed answer-based attention mechanism allows the target domain to leverage the existed causality in source time-series even with different treatments, and our forecaster can predict the counterfactual outcome of industrial time-series, meaning a guidance in production process. Compared with commonly baselines, our method on real-world and synthetic oilfield datasets demonstrates the effectiveness in across-domain prediction and the practicality in guiding production process">
+        <tr id="This article describes the methods that achieved 4th and 6th place in the forecasting and investment challenges, respectively, of the M6 competition, ultimately securing the 1st place in the overall duathlon ranking. In the forecasting challenge, we tested a novel meta-learning model that utilizes hypernetworks to design a parametric model tailored to a specific family of forecasting tasks. This approach allowed us to leverage similarities observed across individual forecasting tasks while also acknowledging potential heterogeneity in their data generating processes. The model's training can be directly performed with backpropagation, eliminating the need for reliance on higher-order derivatives and is equivalent to a simultaneous search over the space of parametric functions and their optimal parameter values. The proposed model's capabilities extend beyond M6, demonstrating superiority over state-of-the-art meta-learning methods in the sinusoidal regression task and outperforming conventional parametric models on time-series from the M4 competition. In the investment challenge, we adjusted portfolio weights to induce greater or smaller correlation between our submission and that of other participants, depending on the current ranking, aiming to maximize the probability of achieving a good rank.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/156edecae87bbcc79ee4253c37525b426f87a42a" target='_blank'>
-              Domain Adaptation for Industrial Time-series Forecasting via Counterfactual Inference
+          <td><a href="https://www.semanticscholar.org/paper/79c059ff368b1797f8ebcd428512691d3f6bcd34" target='_blank'>
+              Designing Time-Series Models With Hypernetworks&Adversarial Portfolios
               </a>
             </td>
           <td>
-            Chao Min, Guo-quan Wen, Jiangru Yuan, Jun Yi, Xing Guo
+            Filip Stanvek
           </td>
-          <td>2024-07-19</td>
+          <td>2024-07-29</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
+          <td>0</td>
         </tr>
 
-        <tr id="Time series forecasting (TSF) is immensely important in extensive applications, such as electricity transformation, financial trade, medical monitoring, and smart agriculture. Although Transformer-based methods can handle time series data, their ability to predict long-term time series is limited due to the ``anti-order"nature of the self-attention mechanism. To address this problem, we focus on frequency domain to weaken the impact of order in TSF and propose the FreqBlock, where we first obtain frequency representations through the Frequency Transform Module. Subsequently, a newly designed Frequency Cross Attention is used to obtian enhanced frequency representations between the real and imaginary parts, thus establishing a link between the attention mechanism and the inherent Kramer-Kronig relations (KKRs). Our backbone network, FreqTSF, adopts a residual structure by concatenating multiple FreqBlocks to simulate KKRs in the frequency domain and avoid degradation problems. On a theoretical level, we demonstrate that the proposed two modules can significantly reduce the time and memory complexity from $\mathcal{O}(L^2)$ to $\mathcal{O}(L)$ for each FreqBlock computation. Empirical studies on four benchmark datasets show that FreqTSF achieves an overall relative MSE reduction of 15\% and an overall relative MAE reduction of 11\% compared to the state-of-the-art methods. The code will be available soon.">
+        <tr id="This study introduces an innovative deep learning approach for predicting the remaining useful life (RUL) of industrial machinery. Accurate RUL forecasts play a crucial role in enabling proactive maintenance and enhancing operational efficiency within the contexts of Industry 4.0 and 5.0. However, current data-driven models face challenges when dealing with complex, high-dimensional time-series sensor data. To address these obstacles, we present a framework for predicting RUL of industrial equipment, employing variational recurrent neural networks (VRNNs). By harnessing the expressive power of recurrent neural networks and variational inference, VRNNs demonstrate proficiency in modeling temporal relationships and managing intricate, high-dimensional time-series sensor data. This deep learning model represents a significant advancement in data-driven prognostics, providing more robust and interpretable RUL forecasts derived from intricate time-series sensor data. Potential applications encompass predictive maintenance and asset management in industries embracing Industry 4.0 and 5.0 paradigms.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c7a597bc1188e94cf4e9583a7acbceb292b7f71f" target='_blank'>
-              FreqTSF: Time Series Forecasting Via Simulating Frequency Kramer-Kronig Relations
+          <td><a href="https://www.semanticscholar.org/paper/4411fb5e68889eaebadb3fe36a16107e207404ff" target='_blank'>
+              Variational Recurrent Neural Networks (VRNN) for RUL Estimation
               </a>
             </td>
           <td>
-            Rujia Shen, Liangliang Liu, Boran Wang, Yi Guan, Yang Yang, Jingchi Jiang
+            Amal Ayadi, M. Benatia, Ramzi Chaieb, Anne Louis
           </td>
-          <td>2024-07-31</td>
-          <td>ArXiv</td>
+          <td>2024-07-08</td>
+          <td>2024 16th International Conference on Human System Interaction (HSI)</td>
           <td>0</td>
-          <td>11</td>
+          <td>6</td>
         </tr>
 
-        <tr id="The effectiveness of univariate forecasting models is often hampered by conditions that cause them stress. A model is considered to be under stress if it shows a negative behaviour, such as higher-than-usual errors or increased uncertainty. Understanding the factors that cause stress to forecasting models is important to improve their reliability, transparency, and utility. This paper addresses this problem by contributing with a novel framework called MAST (Meta-learning and data Augmentation for Stress Testing). The proposed approach aims to model and characterize stress in univariate time series forecasting models, focusing on conditions where they exhibit large errors. In particular, MAST is a meta-learning approach that predicts the probability that a given model will perform poorly on a given time series based on a set of statistical time series features. MAST also encompasses a novel data augmentation technique based on oversampling to improve the metadata concerning stress. We conducted experiments using three benchmark datasets that contain a total of 49.794 time series to validate the performance of MAST. The results suggest that the proposed approach is able to identify conditions that lead to large errors. The method and experiments are publicly available in a repository.">
+        <tr id="Identification of critical nodes is a prominent topic in the study of complex networks. Numerous methods have been proposed, yet most exhibit inherent limitations. Traditional approaches primarily analyze specific structural features of the network; however, node influence is typically the result of a combination of multiple factors. Machine learning-based methods struggle to effectively represent the complex characteristics of network structures through suitable embedding techniques and require substantial data for training, rendering them prohibitively costly for large-scale networks. To address these challenges, this paper presents an active learning model based on GraphSAGE and Transformer, named GNNTAL. This model is initially pre-trained on random or synthetic networks and subsequently fine-tuned on real-world networks by selecting a few representative nodes using K-Means clustering and uncertainty sampling. This approach offers two main advantages: (1) it significantly reduces training costs; (2) it simultaneously incorporates both local and global features. A series of comparative experiments conducted on twelve real-world networks demonstrate that GNNTAL achieves superior performance. Additionally, this paper proposes an influence maximization method based on the predictions of the GNNTAL model, which achieves optimal performance without the need for complex computations. Finally, the paper analyses certain limitations of the GNNTAL model and suggests potential solutions.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/21df316c9190406997a7ddcf7668a9d6a38d85cf" target='_blank'>
-              Meta-learning and Data Augmentation for Stress Testing Forecasting Models
+          <td><a href="https://www.semanticscholar.org/paper/f2c2569fd1bd2320230e824c982a501344641e9c" target='_blank'>
+              GNNTAL:A Novel Model for Identifying Critical Nodes in Complex Networks
               </a>
             </td>
           <td>
-            Ricardo In'acio, Vítor Cerqueira, Mar'ilia Barandas, Carlos Soares
+            Hao Wang, Ting Luo, Shuang-ping Yang, Ming Jing, Jian Wang, Na Zhao
           </td>
           <td>2024-06-24</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>13</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Deep learning models are widely used in multivariate time series forecasting, yet, they have high computational costs. One way to reduce this cost is by reducing data dimensionality, which involves removing unimportant or low importance information with the proper method. This work presents a study on an explainability feature selection framework composed of four methods (IMV‐LSTM Tensor, LIME‐LSTM, Average SHAP‐LSTM, and Instance SHAP‐LSTM) aimed at using the LSTM black‐box model complexity to its favour, with the end goal of improving the error metrics and reducing the computational cost on a forecast task. To test the framework, three datasets with a total of 101 multivariate time series were used, with the explainability methods outperforming the baseline methods in most of the data, be it in error metrics or computation time for the LSTM model training.">
+        <tr id="Time series analysis is pivotal for business and financial decision making, especially with the increasing integration of the Internet of Things (IoT). However, leveraging time series data for forecasting requires extensive preprocessing to address challenges such as missing values, heteroscedasticity, seasonality, outliers, and noise. Different approaches are necessary for univariate and multivariate time series, Gaussian and non-Gaussian time series, and stationary versus non-stationary time series. Handling missing data alone is complex, demanding unique solutions for each type. Extracting statistical features, identifying data quality issues, and selecting appropriate cleaning and forecasting techniques require significant effort, time, and expertise. To streamline this process, we propose an automated strategy called Preptimize, which integrates statistical and machine learning techniques and recommends prediction model blueprints, suggesting the most suitable approaches for a given dataset as an initial step towards further analysis. Preptimize reads a sample from a large dataset and recommends the blueprint model based on optimization, making it easy to use even for non-experts. The results of various experiments indicated that Preptimize either outperformed or had comparable performance to benchmark models across multiple sectors, including stock prices, cryptocurrency, and power consumption prediction. This demonstrates the framework’s effectiveness in recommending suitable prediction models for various time series datasets, highlighting its broad applicability across different domains in time series forecasting.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c38a287f4c56c37be6a1916bb90ba43b4504c73a" target='_blank'>
-              KDBI special issue: Explainability feature selection framework application for LSTM multivariate time‐series forecast self optimization
+          <td><a href="https://www.semanticscholar.org/paper/228a2e2a0f2984f0be41b8a4718860d9d8b020d5" target='_blank'>
+              Preptimize: Automation of Time Series Data Preprocessing and Forecasting
               </a>
             </td>
           <td>
-            Eduardo M. Rodrigues, Yassine Baghoussi, João Mendes‐Moreira
+            Mehak Usmani, Z. Memon, Adil Zulfiqar, Rizwan Qureshi
           </td>
-          <td>2024-07-04</td>
-          <td>Expert Systems</td>
+          <td>2024-08-01</td>
+          <td>Algorithms</td>
           <td>0</td>
-          <td>2</td>
+          <td>18</td>
         </tr>
 
-        <tr id="In the era of rapid globalization and digitalization, accurate identification of similar stocks has become increasingly challenging due to the non-stationary nature of financial markets and the ambiguity in conventional regional and sector classifications. To address these challenges, we examine SimStock, a novel temporal self-supervised learning framework that combines techniques from self-supervised learning (SSL) and temporal domain generalization to learn robust and informative representations of financial time series data. The primary focus of our study is to understand the similarities between stocks from a broader perspective, considering the complex dynamics of the global financial landscape. We conduct extensive experiments on four real-world datasets with thousands of stocks and demonstrate the effectiveness of SimStock in finding similar stocks, outperforming existing methods. The practical utility of SimStock is showcased through its application to various investment strategies, such as pairs trading, index tracking, and portfolio optimization, where it leads to superior performance compared to conventional methods. Our findings empirically examine the potential of data-driven approach to enhance investment decision-making and risk management practices by leveraging the power of temporal self-supervised learning in the face of the ever-changing global financial landscape.">
+        <tr id="We conducted extensive experiments on domain adaptation of the Meta-Llama-3-70B-Instruct model on SEC data, exploring its performance on both general and domain-specific benchmarks. Our focus included continual pre-training (CPT) and model merging, aiming to enhance the model's domain-specific capabilities while mitigating catastrophic forgetting. Through this study, we evaluated the impact of integrating financial regulatory data into a robust language model and examined the effectiveness of our model merging techniques in preserving and improving the model's instructive abilities. The model is accessible at hugging face: https://huggingface.co/arcee-ai/Llama-3-SEC-Base, arcee-ai/Llama-3-SEC-Base. This is an intermediate checkpoint of our final model, which has seen 20B tokens so far. The full model is still in the process of training. This is a preprint technical report with thorough evaluations to understand the entire process.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c259bc9adf3019870613cbb7888ea4fac4e1929f" target='_blank'>
-              Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management
+          <td><a href="https://www.semanticscholar.org/paper/c6cc3ff91538d584ddfd0c07506ebaf195e5f5ca" target='_blank'>
+              Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation
               </a>
             </td>
           <td>
-            Yoon-Jeong Hwang, Stefan Zohren, Yongjae Lee
+            Shamane Siriwardhana, Mark McQuade, Thomas Gauthier, Lucas Atkins, Fernando Fernandes Neto, Luke Meyers, Anneketh Vij, Tyler Odenthal, Charles Goddard, Mary MacCarthy, Jacob Solawetz
           </td>
-          <td>2024-07-18</td>
+          <td>2024-06-21</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>6</td>
         </tr>
 
-        <tr id="Identification of critical nodes is a prominent topic in the study of complex networks. Numerous methods have been proposed, yet most exhibit inherent limitations. Traditional approaches primarily analyze specific structural features of the network; however, node influence is typically the result of a combination of multiple factors. Machine learning-based methods struggle to effectively represent the complex characteristics of network structures through suitable embedding techniques and require substantial data for training, rendering them prohibitively costly for large-scale networks. To address these challenges, this paper presents an active learning model based on GraphSAGE and Transformer, named GNNTAL. This model is initially pre-trained on random or synthetic networks and subsequently fine-tuned on real-world networks by selecting a few representative nodes using K-Means clustering and uncertainty sampling. This approach offers two main advantages: (1) it significantly reduces training costs; (2) it simultaneously incorporates both local and global features. A series of comparative experiments conducted on twelve real-world networks demonstrate that GNNTAL achieves superior performance. Additionally, this paper proposes an influence maximization method based on the predictions of the GNNTAL model, which achieves optimal performance without the need for complex computations. Finally, the paper analyses certain limitations of the GNNTAL model and suggests potential solutions.">
+        <tr id="With the advancement of deep learning techniques, the performance of Automatic Program Repair(APR) techniques has reached a new level. Previous deep learning-based APR techniques essentially modified program sentences in the Autoregressive(AR) manner, which predicts future values based on past values. Due to the manner of word-by-word generation, the AR-based APR technique has a huge time delay. This negative consequence overshadows the widespread adoption of APR techniques in real-life software development. To address the issue, we aim to apply the Non-Autoregressive(NAR) method to the APR task, which can output target code in a parallel manner to avoid huge inference delays. To effectively adapt the NAR manner for the APR task, we in this paper propose NARRepair, the first customized NAR code generation model for the APR task. The NARRepair features three major novelties, including 1) using repair actions to alleviate the over-correction issue, 2) extracting dependency information from AST to alleviate the issue of lacking inter-word dependency information, 3) employing two-stage decoding to alleviate the issue of lacking contextual information. We evaluated NARRepair on three widely used datasets in the APR community, and the results show that our technique can significantly improve the inference speed while maintaining high repair accuracy.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f2c2569fd1bd2320230e824c982a501344641e9c" target='_blank'>
-              GNNTAL:A Novel Model for Identifying Critical Nodes in Complex Networks
+          <td><a href="https://www.semanticscholar.org/paper/385934bd633b36326faa9d91a3be7c7bdd4ef956" target='_blank'>
+              NARRepair: Non-Autoregressive Code Generation Model for Automatic Program Repair
               </a>
             </td>
           <td>
-            Hao Wang, Ting Luo, Shuang-ping Yang, Ming Jing, Jian Wang, Na Zhao
+            Zhen Yang, Zhen Yang, Zhongxing Yu
           </td>
           <td>2024-06-24</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
-        </tr>
-
-        <tr id="The increasing adoption of rooftop photovoltaic (PV) power generation systems in residential areas necessitates accurate monitoring and disaggregation of behind-the-meter (BTM) load and PV power. Despite recent advancements, existing BTM disaggregation approaches suffer from three major drawbacks: neglecting task-relevant spatiotemporal features, overfitting, and lack of a sparse neural architecture which leads to high sample complexity. This paper addresses them by introducing a deep sparse attention graph recurrent framework. This framework conceptualizes a set of neighboring residential units as a graph where the nodes are the net load values of the units and the edges show the mutual information (MI) of these measurements. We develop an Attention Gated Recurrent Unit (AGRU) to capture enhanced temporal characteristics of the net load. We employ a novel low-rank Dictionary Learning (DL) method to discern spatiotemporal features of these measurements and further utilize a Rectified Linear Unit (ReLU) neural network that incorporates an MI-based dropout to provide a sparse model for the estimation of the BTM load and PV. Experimental results validate the effectiveness of our proposed model, exhibiting superior performance on the Ausgrid dataset in BTM load and PV power estimation compared to state-of-the-art methods.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ba52197a4d7f5071efd214b6546a45314b15bb13" target='_blank'>
-              Sparse Attention Graph Gated Recurrent Unit for Spatiotemporal Behind-The-Meter Load and PV Disaggregation
-              </a>
-            </td>
-          <td>
-            Mahdi Khodayar, Ali Farajzadeh Bavil, M. Saffari
-          </td>
-          <td>2024-06-27</td>
-          <td>2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)</td>
-          <td>0</td>
-          <td>14</td>
+          <td>1</td>
         </tr>
 
-        <tr id="This article describes the methods that achieved 4th and 6th place in the forecasting and investment challenges, respectively, of the M6 competition, ultimately securing the 1st place in the overall duathlon ranking. In the forecasting challenge, we tested a novel meta-learning model that utilizes hypernetworks to design a parametric model tailored to a specific family of forecasting tasks. This approach allowed us to leverage similarities observed across individual forecasting tasks while also acknowledging potential heterogeneity in their data generating processes. The model's training can be directly performed with backpropagation, eliminating the need for reliance on higher-order derivatives and is equivalent to a simultaneous search over the space of parametric functions and their optimal parameter values. The proposed model's capabilities extend beyond M6, demonstrating superiority over state-of-the-art meta-learning methods in the sinusoidal regression task and outperforming conventional parametric models on time-series from the M4 competition. In the investment challenge, we adjusted portfolio weights to induce greater or smaller correlation between our submission and that of other participants, depending on the current ranking, aiming to maximize the probability of achieving a good rank.">
+        <tr id="The growing demand for air travel requires technological advancements in air traffic management as well as mechanisms for monitoring and ensuring safe and efficient operations. In terminal airspaces, predictive models of future movements and traffic flows can help with proactive planning and efficient coordination; however, varying airport topologies, and interactions with other agents, among other factors, make accurate predictions challenging. Data-driven predictive models have shown promise for handling numerous variables to enable various downstream tasks, including collision risk assessment, taxi-out time prediction, departure metering, and emission estimations. While data-driven methods have shown improvements in these tasks, prior works lack large-scale curated surface movement datasets within the public domain and the development of generalizable trajectory forecasting models. In response to this, we propose two contributions: (1) Amelia-48, a large surface movement dataset collected using the System Wide Information Management (SWIM) Surface Movement Event Service (SMES). With data collection beginning in Dec 2022, the dataset provides more than a year's worth of SMES data (~30TB) and covers 48 airports within the US National Airspace System. In addition to releasing this data in the public domain, we also provide post-processing scripts and associated airport maps to enable research in the forecasting domain and beyond. (2) Amelia-TF model, a transformer-based next-token-prediction large multi-agent multi-airport trajectory forecasting model trained on 292 days or 9.4 billion tokens of position data encompassing 10 different airports with varying topology. The open-sourced model is validated on unseen airports with experiments showcasing the different prediction horizon lengths, ego-agent selection strategies, and training recipes to demonstrate the generalization capabilities.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/79c059ff368b1797f8ebcd428512691d3f6bcd34" target='_blank'>
-              Designing Time-Series Models With Hypernetworks&Adversarial Portfolios
+          <td><a href="https://www.semanticscholar.org/paper/fe29091a5f28ae9c53d07479551f034ed3c2e387" target='_blank'>
+              Amelia: A Large Model and Dataset for Airport Surface Movement Forecasting
               </a>
             </td>
           <td>
-            Filip Stanvek
+            Ingrid Navarro, Pablo Ortega-Kral, Jay Patrikar, Haichuan Wang, Zelin Ye, Jong Hoon Park, Jean Oh, Sebastian Scherer
           </td>
-          <td>2024-07-29</td>
+          <td>2024-07-30</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>8</td>
         </tr>
 
-        <tr id="The reward model has become increasingly important in alignment, assessment, and data construction for large language models (LLMs). Most existing researchers focus on enhancing reward models through data improvements, following the conventional training framework for reward models that directly optimizes the predicted rewards. In this paper, we propose a hybrid alignment framework HaF-RM for reward model training by introducing an additional constraint on token-level policy probabilities in addition to the reward score. It can simultaneously supervise the internal preference model at the token level and optimize the mapping layer of the reward model at the sequence level. Theoretical justifications and experiment results on five datasets show the validity and effectiveness of our proposed hybrid framework for training a high-quality reward model. By decoupling the reward modeling procedure and incorporating hybrid supervision, our HaF-RM framework offers a principled and effective approach to enhancing the performance and alignment of reward models, a critical component in the responsible development of powerful language models. We release our code at https://haf-rm.github.io.">
+        <tr id="Dynamic graph learning aims to uncover evolutionary laws in real-world systems, enabling accurate social recommendation (link prediction) or early detection of cancer cells (classification). Inspired by the success of state space models, e.g., Mamba, for efficiently capturing long-term dependencies in language modeling, we propose DyG-Mamba, a new continuous state space model (SSM) for dynamic graph learning. Specifically, we first found that using inputs as control signals for SSM is not suitable for continuous-time dynamic network data with irregular sampling intervals, resulting in models being insensitive to time information and lacking generalization properties. Drawing inspiration from the Ebbinghaus forgetting curve, which suggests that memory of past events is strongly correlated with time intervals rather than specific details of the events themselves, we directly utilize irregular time spans as control signals for SSM to achieve significant robustness and generalization. Through exhaustive experiments on 12 datasets for dynamic link prediction and dynamic node classification tasks, we found that DyG-Mamba achieves state-of-the-art performance on most of the datasets, while also demonstrating significantly improved computation and memory efficiency.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/581d831fcea84501bec33161c716d5bf94a8a345" target='_blank'>
-              HAF-RM: A Hybrid Alignment Framework for Reward Model Training
+          <td><a href="https://www.semanticscholar.org/paper/3cc84a5a1e02661fc3681d8be4134205b0b09954" target='_blank'>
+              DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs
               </a>
             </td>
           <td>
-            Shujun Liu, Xiaoyu Shen, Yuhang Lai, Siyuan Wang, Shengbin Yue, Zengfeng Huang, Xuanjing Huang, Zhongyu Wei
+            Dongyuan Li, Shiyin Tan, Ying Zhang, Ming Jin, Shirui Pan, Manabu Okumura, Renhe Jiang
           </td>
-          <td>2024-07-04</td>
+          <td>2024-08-13</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>14</td>
-        </tr>
-
-        <tr id="Time series analysis is pivotal for business and financial decision making, especially with the increasing integration of the Internet of Things (IoT). However, leveraging time series data for forecasting requires extensive preprocessing to address challenges such as missing values, heteroscedasticity, seasonality, outliers, and noise. Different approaches are necessary for univariate and multivariate time series, Gaussian and non-Gaussian time series, and stationary versus non-stationary time series. Handling missing data alone is complex, demanding unique solutions for each type. Extracting statistical features, identifying data quality issues, and selecting appropriate cleaning and forecasting techniques require significant effort, time, and expertise. To streamline this process, we propose an automated strategy called Preptimize, which integrates statistical and machine learning techniques and recommends prediction model blueprints, suggesting the most suitable approaches for a given dataset as an initial step towards further analysis. Preptimize reads a sample from a large dataset and recommends the blueprint model based on optimization, making it easy to use even for non-experts. The results of various experiments indicated that Preptimize either outperformed or had comparable performance to benchmark models across multiple sectors, including stock prices, cryptocurrency, and power consumption prediction. This demonstrates the framework’s effectiveness in recommending suitable prediction models for various time series datasets, highlighting its broad applicability across different domains in time series forecasting.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/228a2e2a0f2984f0be41b8a4718860d9d8b020d5" target='_blank'>
-              Preptimize: Automation of Time Series Data Preprocessing and Forecasting
-              </a>
-            </td>
-          <td>
-            Mehak Usmani, Z. Memon, Adil Zulfiqar, Rizwan Qureshi
-          </td>
-          <td>2024-08-01</td>
-          <td>Algorithms</td>
-          <td>0</td>
-          <td>18</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Financial market simulation (FMS) serves as a promising tool for understanding market anomalies and the underlying trading behaviors. To ensure high-fidelity simulations, it is crucial to calibrate the FMS model for generating data closely resembling the observed market data. Previous efforts primarily focused on calibrating the mid-price data, leading to essential information loss of the market activities and thus biasing the calibrated model. The Limit Order Book (LOB) data is the fundamental data fully capturing the market micro-structure and is adopted by worldwide exchanges. However, LOB is not applicable to existing calibration objective functions due to its tabular structure not suitable for the vectorized input requirement. This paper proposes to explicitly learn the vectorized representations of LOB with a Transformer-based autoencoder. Then the latent vector, which captures the major information of LOB, can be applied for calibration. Extensive experiments show that the learned latent representation not only preserves the non-linear auto-correlation in the temporal axis, but the precedence between successive price levels of LOB. Besides, it is verified that the performance of the representation learning stage is consistent with the downstream calibration tasks. Thus, this work also progresses the FMS on LOB data, for the first time.">
+        <tr id="Reliable gas price forecasts are an essential information for gas and energy traders, for risk managers and also economists. However, ahead of the war in Ukraine Europe began to suffer from substantially increased and volatile gas prices which culminated in the aftermath of the North Stream 1 explosion. This shock changed both trend and volatility structure of the prices and has considerable effects on forecasting models. In this study we investigate whether modern machine learning methods such as neural networks are more resilient against such changes than statistical models such as autoregressive moving average (ARMA) models with conditional heteroskedasticity, or copula-based time series models. Thereby the focus lies on interval forecasting and applying respective evaluation measures. As data, the Front Month prices from the Dutch Title Transfer Facility, currently the predominant European exchange, are used. We see that, during the shock period, most models underestimate the variance while overestimating the variance in the after-shock period. Furthermore, we recognize that, during the shock, the simpler models, i.e. an ARMA model with conditional heteroskedasticity and the multilayer perceptron (a neural network), perform best with regards to prediction interval coverage. Interestingly, the widely-used long-short term neural network is outperformed by its competitors.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/df3a27179dd89dd0a9a9b62d5ac1b218eae8f825" target='_blank'>
-              SimLOB: Learning Representations of Limited Order Book for Financial Market Simulation
+          <td><a href="https://www.semanticscholar.org/paper/85754bf41e34f87a3356a090d97ccc44867749ef" target='_blank'>
+              Interval Forecasts for Gas Prices in the Face of Structural Breaks -- Statistical Models vs. Neural Networks
               </a>
             </td>
           <td>
-            Yuanzhe Li, Yue Wu, Peng Yang
+            Stephan Schluter, Sven Pappert, Martin Neumann
           </td>
-          <td>2024-06-27</td>
+          <td>2024-07-23</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>2</td>
         </tr>
 
         <tr id="In this paper, we apply a supervised machine-learning approach to solve a fundamental problem in queueing theory: estimating the transient distribution of the number in the system for a G(t)/GI/1. We develop a neural network mechanism that provides a fast and accurate predictor of these distributions for moderate horizon lengths and practical settings. It is based on using a Recurrent Neural Network (RNN) architecture based on the first several moments of the time-dependant inter-arrival and the stationary service time distributions; we call it the Moment-Based Recurrent Neural Network (RNN) method (MBRNN ). Our empirical study suggests MBRNN requires only the first four inter-arrival and service time moments. We use simulation to generate a substantial training dataset and present a thorough performance evaluation to examine the accuracy of our method using two different test sets. We show that even under the configuration with the worst performance errors, the mean number of customers over the entire timeline has an error of less than 3%. While simulation modeling can achieve high accuracy, the advantage of the MBRNN over simulation is runtime, while the MBRNN analyzes hundreds of systems within a fraction of a second. This paper focuses on a G(t)/GI/1; however, the MBRNN approach demonstrated here can be extended to other queueing systems, as the training data labeling is based on simulations (which can be applied to more complex systems) and the training is based on deep learning, which can capture very complex time sequence tasks. In summary, the MBRNN can potentially revolutionize our ability to perform transient analyses of queueing systems.">
@@ -3344,21 +3253,6 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>14</td>
         </tr>
 
-        <tr id="Reliable gas price forecasts are an essential information for gas and energy traders, for risk managers and also economists. However, ahead of the war in Ukraine Europe began to suffer from substantially increased and volatile gas prices which culminated in the aftermath of the North Stream 1 explosion. This shock changed both trend and volatility structure of the prices and has considerable effects on forecasting models. In this study we investigate whether modern machine learning methods such as neural networks are more resilient against such changes than statistical models such as autoregressive moving average (ARMA) models with conditional heteroskedasticity, or copula-based time series models. Thereby the focus lies on interval forecasting and applying respective evaluation measures. As data, the Front Month prices from the Dutch Title Transfer Facility, currently the predominant European exchange, are used. We see that, during the shock period, most models underestimate the variance while overestimating the variance in the after-shock period. Furthermore, we recognize that, during the shock, the simpler models, i.e. an ARMA model with conditional heteroskedasticity and the multilayer perceptron (a neural network), perform best with regards to prediction interval coverage. Interestingly, the widely-used long-short term neural network is outperformed by its competitors.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/85754bf41e34f87a3356a090d97ccc44867749ef" target='_blank'>
-              Interval Forecasts for Gas Prices in the Face of Structural Breaks -- Statistical Models vs. Neural Networks
-              </a>
-            </td>
-          <td>
-            Stephan Schluter, Sven Pappert, Martin Neumann
-          </td>
-          <td>2024-07-23</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>1</td>
-        </tr>
-
         <tr id="Generating synthetic financial time series data that accurately reflects real-world market dynamics holds tremendous potential for various applications, including portfolio optimization, risk management, and large scale machine learning. We present an approach for training generative models for financial time series using the maximum mean discrepancy (MMD) with a signature kernel. Our method leverages the expressive power of the signature transform to capture the complex dependencies and temporal structures inherent in financial data. We employ a moving average model to model the variance of the noise input, enhancing the model's ability to reproduce stylized facts such as volatility clustering. Through empirical experiments on S&P 500 index data, we demonstrate that our model effectively captures key characteristics of financial time series and outperforms a comparable GAN-based approach. In addition, we explore the application of the synthetic data generated to train a reinforcement learning agent for portfolio management, achieving promising results. Finally, we propose a method to add robustness to the generative model by tweaking the noise input so that the generated sequences can be adjusted to different market environments with minimal data.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/492d244da8647554a7439883786cfe931ec4c342" target='_blank'>
@@ -3389,109 +3283,94 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>20</td>
         </tr>
 
-        <tr id="Time-series anomaly detection plays an important role in engineering processes, like development, manufacturing and other operations involving dynamic systems. These processes can greatly benefit from advances in the field, as state-of-the-art approaches may aid in cases involving, for example, highly dimensional data. To provide the reader with understanding of the terminology, this survey introduces a novel taxonomy where a distinction between online and offline, and training and inference is made. Additionally, it presents the most popular data sets and evaluation metrics used in the literature, as well as a detailed analysis. Furthermore, this survey provides an extensive overview of the state-of-the-art model-based online semi- and unsupervised anomaly detection approaches for multivariate time-series data, categorising them into different model families and other properties. The biggest research challenge revolves around benchmarking, as currently there is no reliable way to compare different approaches against one another. This problem is two-fold: on the one hand, public data sets suffers from at least one fundamental flaw, while on the other hand, there is a lack of intuitive and representative evaluation metrics in the field. Moreover, the way most publications choose a detection threshold disregards real-world conditions, which hinders the application in the real world. To allow for tangible advances in the field, these issues must be addressed in future work.">
+        <tr id="Clinical trials need to recruit a sufficient number of volunteer patients to demonstrate the statistical power of the treatment (e.g., a new drug) in curing a certain disease. Clinical trial recruitment has a significant impact on trial success. Forecasting whether the recruitment process would be successful before we run the trial would save many resources and time. This paper develops a novel deep&cross network with large language model (LLM)-augmented text feature that learns semantic information from trial eligibility criteria and predicts enrollment success. The proposed method enables interpretability by understanding which sentence/word in eligibility criteria contributes heavily to prediction. We also demonstrate the empirical superiority of the proposed method (0.7002 PR-AUC) over a bunch of well-established machine learning methods. The code and curated dataset are publicly available at https://anonymous.4open.science/r/TrialEnroll-7E12.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/a67a6d566bea9cce5824feccf14aaa87581e3871" target='_blank'>
-              Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions
+          <td><a href="https://www.semanticscholar.org/paper/8b0084ee93fc8c700058b3d5d2d7c58de05f753a" target='_blank'>
+              TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep&Cross Network and Large Language Models
               </a>
             </td>
           <td>
-            Lucas Correia, Jan-Christoph Goos, Philipp Klein, Thomas Back, Anna V. Kononova
+            Ling Yue, Sixue Xing, Jintai Chen, Tianfan Fu
           </td>
-          <td>2024-08-07</td>
+          <td>2024-07-18</td>
           <td>ArXiv</td>
-          <td>0</td>
-          <td>13</td>
+          <td>1</td>
+          <td>3</td>
         </tr>
 
-        <tr id="Accurate modeling of car-following behaviors is essential for various applications in traffic management and autonomous driving systems. However, current approaches often suffer from limitations like high sensitivity to data quality and lack of interpretability. In this study, we propose GenFollower, a novel zero-shot prompting approach that leverages large language models (LLMs) to address these challenges. We reframe car-following behavior as a language modeling problem and integrate heterogeneous inputs into structured prompts for LLMs. This approach achieves improved prediction performance and interpretability compared to traditional baseline models. Experiments on the Waymo Open datasets demonstrate GenFollower's superior performance and ability to provide interpretable insights into factors influencing car-following behavior. This work contributes to advancing the understanding and prediction of car-following behaviors, paving the way for enhanced traffic management and autonomous driving systems.">
+        <tr id="Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophisticated cyberattacks, particularly masquerade attacks, which inject false data that mimic legitimate messages at the expected frequency. These attacks pose severe risks such as unintended acceleration, brake deactivation, and rogue steering. Traditional intrusion detection systems (IDS) often struggle to detect these subtle intrusions due to their seamless integration into normal traffic. This paper introduces a novel framework for detecting masquerade attacks in the CAN bus using graph machine learning (ML). We hypothesize that the integration of shallow graph embeddings with time series features derived from CAN frames enhances the detection of masquerade attacks. We show that by representing CAN bus frames as message sequence graphs (MSGs) and enriching each node with contextual statistical attributes from time series, we can enhance detection capabilities across various attack patterns compared to using only graph-based features. Our method ensures a comprehensive and dynamic analysis of CAN frame interactions, improving robustness and efficiency. Extensive experiments on the ROAD dataset validate the effectiveness of our approach, demonstrating statistically significant improvements in the detection rates of masquerade attacks compared to a baseline that uses only graph-based features, as confirmed by Mann-Whitney U and Kolmogorov-Smirnov tests (p<0.05).">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/3c2dc1a2b947f863938dfbf2e05352e7bff43498" target='_blank'>
-              GenFollower: Enhancing Car-Following Prediction with Large Language Models
+          <td><a href="https://www.semanticscholar.org/paper/31a90fb5c4f3760898c538549095797acfa7f429" target='_blank'>
+              Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning
               </a>
             </td>
           <td>
-            Xianda Chen, Mingxing Peng, PakHin Tiu, Yuanfei Wu, Junjie Chen, Meixin Zhu, Xinhu Zheng
+            William Marfo, P. Moriano, Deepak K. Tosh, Shirley V. Moore
           </td>
-          <td>2024-07-08</td>
+          <td>2024-08-10</td>
           <td>ArXiv</td>
-          <td>2</td>
-          <td>4</td>
+          <td>0</td>
+          <td>8</td>
         </tr>
 
-        <tr id="Flood prediction is a critical challenge in the context of climate change, with significant implications for ecosystem preservation, human safety, and infrastructure protection. In this study, we tackle this problem by applying the Quantum-Train (QT) technique to a forecasting Long Short-Term Memory (LSTM) model trained by Quantum Machine Learning (QML) with significant parameter reduction. The QT technique, originally successful in the A Matter of Taste challenge at QHack 2024, leverages QML to reduce the number of trainable parameters to a polylogarithmic function of the number of parameters in a classical neural network (NN). This innovative framework maps classical NN weights to a Hilbert space, altering quantum state probability distributions to adjust NN parameters. Our approach directly processes classical data without the need for quantum embedding and operates independently of quantum computing resources post-training, making it highly practical and accessible for real-world flood prediction applications. This model aims to improve the efficiency of flood forecasts, ultimately contributing to better disaster preparedness and response.">
+        <tr id="Accurate modeling of car-following behaviors is essential for various applications in traffic management and autonomous driving systems. However, current approaches often suffer from limitations like high sensitivity to data quality and lack of interpretability. In this study, we propose GenFollower, a novel zero-shot prompting approach that leverages large language models (LLMs) to address these challenges. We reframe car-following behavior as a language modeling problem and integrate heterogeneous inputs into structured prompts for LLMs. This approach achieves improved prediction performance and interpretability compared to traditional baseline models. Experiments on the Waymo Open datasets demonstrate GenFollower's superior performance and ability to provide interpretable insights into factors influencing car-following behavior. This work contributes to advancing the understanding and prediction of car-following behaviors, paving the way for enhanced traffic management and autonomous driving systems.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c6070c93b2e55353e37efc3cde9139fec8b0debb" target='_blank'>
-              Quantum-Train Long Short-Term Memory: Application on Flood Prediction Problem
+          <td><a href="https://www.semanticscholar.org/paper/3c2dc1a2b947f863938dfbf2e05352e7bff43498" target='_blank'>
+              GenFollower: Enhancing Car-Following Prediction with Large Language Models
               </a>
             </td>
           <td>
-            Chu-Hsuan Abraham Lin, Chen-Yu Liu, Kuan-Cheng Chen
+            Xianda Chen, Mingxing Peng, PakHin Tiu, Yuanfei Wu, Junjie Chen, Meixin Zhu, Xinhu Zheng
           </td>
-          <td>2024-07-11</td>
+          <td>2024-07-08</td>
           <td>ArXiv</td>
-          <td>1</td>
           <td>2</td>
+          <td>5</td>
         </tr>
 
-        <tr id="Travel choice analysis is crucial for understanding individual travel behavior to develop appropriate transport policies and recommendation systems in Intelligent Transportation Systems (ITS). Despite extensive research, this domain faces two critical challenges: a) modeling with limited survey data, and b) simultaneously achieving high model explainability and accuracy. In this paper, we introduce a novel prompt-learning-based Large Language Model(LLM) framework that significantly improves prediction accuracy and provides explicit explanations for individual predictions. This framework involves three main steps: transforming input variables into textual form; building of demonstrations similar to the object, and applying these to a well-trained LLM. We tested the framework's efficacy using two widely used choice datasets: London Passenger Mode Choice (LPMC) and Optima-Mode collected in Switzerland. The results indicate that the LLM significantly outperforms state-of-the-art deep learning methods and discrete choice models in predicting people's choices. Additionally, we present a case of explanation illustrating how the LLM framework generates understandable and explicit explanations at the individual level.">
+        <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/d6c15845a955d6a89c45684ba61801c976943999" target='_blank'>
-              Enhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approach
+          <td><a href="https://www.semanticscholar.org/paper/24a196d9ee7fda6754365bee42a89aa1d9add80b" target='_blank'>
+              Generalized spatial–temporal regression graph convolutional transformer for traffic forecasting
               </a>
             </td>
           <td>
-            Xuehao Zhai, Hanlin Tian, Lintong Li, Tianyu Zhao
+            Lang Xiong, Liyun Su, Shiyi Zeng, Xiangjing Li, Tong Wang, Feng Zhao
           </td>
-          <td>2024-06-19</td>
-          <td>ArXiv</td>
+          <td>2024-08-10</td>
+          <td>Complex &amp; Intelligent Systems</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
         <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6aee1f022642bde0a16f9beb44cbc7c9b67c2d34" target='_blank'>
-              Stacking for Probabilistic Short-Term Load Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/4ccc940a891f74aa82e4a858d918d21d59774433" target='_blank'>
+              A binary-domain recurrent-like architecture-based dynamic graph neural network
               </a>
             </td>
           <td>
-            Grzegorz Dudek
+            Zi-chao Chen, Sui Lin
           </td>
-          <td>2024-06-15</td>
-          <td>ArXiv</td>
+          <td>2024-06-25</td>
+          <td>Auton. Intell. Syst.</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
-        <tr id="The significance of modeling long-term user interests for CTR prediction tasks in large-scale recommendation systems is progressively gaining attention among researchers and practitioners. Existing work, such as SIM and TWIN, typically employs a two-stage approach to model long-term user behavior sequences for efficiency concerns. The first stage rapidly retrieves a subset of sequences related to the target item from a long sequence using a search-based mechanism namely the General Search Unit (GSU), while the second stage calculates the interest scores using the Exact Search Unit (ESU) on the retrieved results. Given the extensive length of user behavior sequences spanning the entire life cycle, potentially reaching up to 10^6 in scale, there is currently no effective solution for fully modeling such expansive user interests. To overcome this issue, we introduced TWIN-V2, an enhancement of TWIN, where a divide-and-conquer approach is applied to compress life-cycle behaviors and uncover more accurate and diverse user interests. Specifically, a hierarchical clustering method groups items with similar characteristics in life-cycle behaviors into a single cluster during the offline phase. By limiting the size of clusters, we can compress behavior sequences well beyond the magnitude of 10^5 to a length manageable for online inference in GSU retrieval. Cluster-aware target attention extracts comprehensive and multi-faceted long-term interests of users, thereby making the final recommendation results more accurate and diverse. Extensive offline experiments on a multi-billion-scale industrial dataset and online A/B tests have demonstrated the effectiveness of TWIN-V2. Under an efficient deployment framework, TWIN-V2 has been successfully deployed to the primary traffic that serves hundreds of millions of daily active users at Kuaishou.">
+        <tr id="The dynamic characteristics of multiphase industrial processes present significant challenges in the field of industrial big data modeling. Traditional soft sensing models frequently neglect the process dynamics and have difficulty in capturing transient phenomena like phase transitions. To address this issue, this article introduces a causality-driven sequence segmentation (CDSS) model. This model first identifies the local dynamic properties of the causal relationships between variables, which are also referred to as causal mechanisms. It then segments the sequence into different phases based on the sudden shifts in causal mechanisms that occur during phase transitions. Additionally, a novel metric, similarity distance, is designed to evaluate the temporal consistency of causal mechanisms, which includes both causal similarity distance and stable similarity distance. The discovered causal relationships in each phase are represented as a temporal causal graph (TCG). Furthermore, a soft sensing model called temporal-causal graph convolutional network (TC-GCN) is trained for each phase, by using the time-extended data and the adjacency matrix of TCG. The numerical examples are utilized to validate the proposed CDSS model, and the segmentation results demonstrate that CDSS has excellent performance on segmenting both stable and unstable multiphase series. Especially, it has higher accuracy in separating non-stationary time series compared to other methods. The effectiveness of the proposed CDSS model and the TC-GCN model is also verified through a penicillin fermentation process. Experimental results indicate that the breakpoints discovered by CDSS align well with the reaction mechanisms and TC-GCN significantly has excellent predictive accuracy.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c5859754c1f6c099199d42b488415b04a7bfd527" target='_blank'>
-              TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou
+          <td><a href="https://www.semanticscholar.org/paper/f4b2b1a8bce30a394b4fb8eac0b13365c9a406b2" target='_blank'>
+              Causality-driven Sequence Segmentation for Enhancing Multiphase Industrial Process Data Analysis and Soft Sensing
               </a>
             </td>
           <td>
-            Zihua Si, Lin Guan, ZhongXiang Sun, Xiaoxue Zang, Jing Lu, Yiqun Hui, Xingchao Cao, Zeyu Yang, Yichen Zheng, Dewei Leng, Kai Zheng, Chenbin Zhang, Ya-Ling Niu, Yang Song, Kun Gai
+            Yimeng He, Le Yao, Xinmin Zhang, Xiangyin Kong, Zhihuan Song
           </td>
-          <td>2024-07-23</td>
+          <td>2024-06-30</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>10</td>
-        </tr>
-
-        <tr id="None">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4ccc940a891f74aa82e4a858d918d21d59774433" target='_blank'>
-              A binary-domain recurrent-like architecture-based dynamic graph neural network
-              </a>
-            </td>
-          <td>
-            Zi-chao Chen, Sui Lin
-          </td>
-          <td>2024-06-25</td>
-          <td>Auton. Intell. Syst.</td>
-          <td>0</td>
-          <td>0</td>
+          <td>7</td>
         </tr>
 
         <tr id="
@@ -3508,67 +3387,67 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2024-07-01</td>
           <td>SPE Journal</td>
           <td>0</td>
-          <td>1</td>
+          <td>2</td>
         </tr>
 
-        <tr id="The advent of the"pre-train, prompt"paradigm has recently extended its generalization ability and data efficiency to graph representation learning, following its achievements in Natural Language Processing (NLP). Initial graph prompt tuning approaches tailored specialized prompting functions for Graph Neural Network (GNN) models pre-trained with specific strategies, such as edge prediction, thus limiting their applicability. In contrast, another pioneering line of research has explored universal prompting via adding prompts to the input graph's feature space, thereby removing the reliance on specific pre-training strategies. However, the necessity to add feature prompts to all nodes remains an open question. Motivated by findings from prompt tuning research in the NLP domain, which suggest that highly capable pre-trained models need less conditioning signal to achieve desired behaviors, we advocate for strategically incorporating necessary and lightweight feature prompts to certain graph nodes to enhance downstream task performance. This introduces a combinatorial optimization problem, requiring a policy to decide 1) which nodes to prompt and 2) what specific feature prompts to attach. We then address the problem by framing the prompt incorporation process as a sequential decision-making problem and propose our method, RELIEF, which employs Reinforcement Learning (RL) to optimize it. At each step, the RL agent selects a node (discrete action) and determines the prompt content (continuous action), aiming to maximize cumulative performance gain. Extensive experiments on graph and node-level tasks with various pre-training strategies in few-shot scenarios demonstrate that our RELIEF outperforms fine-tuning and other prompt-based approaches in classification performance and data efficiency.">
+        <tr id="Understanding causal relationships between machines is crucial for fault diagnosis and optimization in manufacturing processes. Real-world datasets frequently exhibit up to 90% missing data and high dimensionality from hundreds of sensors. These datasets also include domain-specific expert knowledge and chronological order information, reflecting the recording order across different machines, which is pivotal for discerning causal relationships within the manufacturing data. However, previous methods for handling missing data in scenarios akin to real-world conditions have not been able to effectively utilize expert knowledge. Conversely, prior methods that can incorporate expert knowledge struggle with datasets that exhibit missing values. Therefore, we propose COKE to construct causal graphs in manufacturing datasets by leveraging expert knowledge and chronological order among sensors without imputing missing data. Utilizing the characteristics of the recipe, we maximize the use of samples with missing values, derive embeddings from intersections with an initial graph that incorporates expert knowledge and chronological order, and create a sensor ordering graph. The graph-generating process has been optimized by an actor-critic architecture to obtain a final graph that has a maximum reward. Experimental evaluations in diverse settings of sensor quantities and missing proportions demonstrate that our approach compared with the benchmark methods shows an average improvement of 39.9% in the F1-score. Moreover, the F1-score improvement can reach 62.6% when considering the configuration similar to real-world datasets, and 85.0% in real-world semiconductor datasets. The source code is available at https://github.com/OuTingYun/COKE.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/aa8fa1d37cc736e2e0e468c570031c30e0505a54" target='_blank'>
-              RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning
+          <td><a href="https://www.semanticscholar.org/paper/01c7381bbb5f5ffdc2ffb14208944e6202b5a8e9" target='_blank'>
+              COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Data
               </a>
             </td>
           <td>
-            Jiapeng Zhu, Zichen Ding, Jianxiang Yu, Jiaqi Tan, Xiang Li, Weining Qian
+            Ting-Yun Ou, Ching Chang, Wen-Chih Peng
           </td>
-          <td>2024-08-06</td>
+          <td>2024-07-17</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>2</td>
         </tr>
 
-        <tr id="We present working notes for DS@GT team in the eRisk 2024 for Tasks 1 and 3. We propose a ranking system for Task 1 that predicts symptoms of depression based on the Beck Depression Inventory (BDI-II) questionnaire using binary classifiers trained on question relevancy as a proxy for ranking. We find that binary classifiers are not well calibrated for ranking, and perform poorly during evaluation. For Task 3, we use embeddings from BERT to predict the severity of eating disorder symptoms based on user post history. We find that classical machine learning models perform well on the task, and end up competitive with the baseline models. Representation of text data is crucial in both tasks, and we find that sentence transformers are a powerful tool for downstream modeling. Source code and models are available at \url{https://github.com/dsgt-kaggle-clef/erisk-2024}.">
+        <tr id="Long-term time series forecasting is a long-standing challenge in various applications. A central issue in time series forecasting is that methods should expressively capture long-term dependency. Furthermore, time series forecasting methods should be flexible when applied to different scenarios. Although Fourier analysis offers an alternative to effectively capture reusable and periodic patterns to achieve long-term forecasting in different scenarios, existing methods often assume high-frequency components represent noise and should be discarded in time series forecasting. However, we conduct a series of motivation experiments and discover that the role of certain frequencies varies depending on the scenarios. In some scenarios, removing high-frequency components from the original time series can improve the forecasting performance, while in others scenarios, removing them is harmful to forecasting performance. Therefore, it is necessary to treat the frequencies differently according to specific scenarios. To achieve this, we first reformulate the time series forecasting problem as learning a transfer function of each frequency in the Fourier domain. Further, we design Frequency Dynamic Fusion (FreDF), which individually predicts each Fourier component, and dynamically fuses the output of different frequencies. Moreover, we provide a novel insight into the generalization ability of time series forecasting and propose the generalization bound of time series forecasting. Then we prove FreDF has a lower bound, indicating that FreDF has better generalization ability. Extensive experiments conducted on multiple benchmark datasets and ablation studies demonstrate the effectiveness of FreDF.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ca43005a97c3f1e83533ca7844c05d8888da1f70" target='_blank'>
-              DS@GT eRisk 2024: Sentence Transformers for Social Media Risk Assessment
+          <td><a href="https://www.semanticscholar.org/paper/180860f60e6655bebc9757b695b4fbc18a0c39ee" target='_blank'>
+              Not All Frequencies Are Created Equal:Towards a Dynamic Fusion of Frequencies in Time-Series Forecasting
               </a>
             </td>
           <td>
-            David Guecha, Aaryan Potdar, Anthony Miyaguchi
+            Xingyu Zhang, Siyu Zhao, Zeen Song, Huijie Guo, Jianqi Zhang, Changwen Zheng, Wenwen Qiang
           </td>
-          <td>2024-07-10</td>
+          <td>2024-07-17</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>8</td>
         </tr>
 
-        <tr id="Time series data is fundamentally important for describing many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision-making. To develop eXplainable AI (XAI) in these domains therefore implies explaining salient information in the time series. Current methods for obtaining saliency maps assumes localized information in the raw input space. In this paper, we argue that the salient information of a number of time series is more likely to be localized in the frequency domain. We propose FreqRISE, which uses masking based methods to produce explanations in the frequency and time-frequency domain, which shows the best performance across a number of tasks.">
+        <tr id="We present working notes for DS@GT team in the eRisk 2024 for Tasks 1 and 3. We propose a ranking system for Task 1 that predicts symptoms of depression based on the Beck Depression Inventory (BDI-II) questionnaire using binary classifiers trained on question relevancy as a proxy for ranking. We find that binary classifiers are not well calibrated for ranking, and perform poorly during evaluation. For Task 3, we use embeddings from BERT to predict the severity of eating disorder symptoms based on user post history. We find that classical machine learning models perform well on the task, and end up competitive with the baseline models. Representation of text data is crucial in both tasks, and we find that sentence transformers are a powerful tool for downstream modeling. Source code and models are available at \url{https://github.com/dsgt-kaggle-clef/erisk-2024}.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/96f14090fbcfee008626ce8a8d563732fe1519ce" target='_blank'>
-              Explaining time series models using frequency masking
+          <td><a href="https://www.semanticscholar.org/paper/ca43005a97c3f1e83533ca7844c05d8888da1f70" target='_blank'>
+              DS@GT eRisk 2024: Sentence Transformers for Social Media Risk Assessment
               </a>
             </td>
           <td>
-            Thea Brusch, Kristoffer Wickstrøm, Mikkel N. Schmidt, T. S. Alstrøm, Robert Jenssen
+            David Guecha, Aaryan Potdar, Anthony Miyaguchi
           </td>
-          <td>2024-06-19</td>
-          <td>ArXiv</td>
+          <td>2024-07-10</td>
+          <td>ArXiv, DBLP</td>
+          <td>0</td>
           <td>0</td>
-          <td>12</td>
         </tr>
 
-        <tr id="In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of zero-trust architectures has become increasingly crucial to protect security. However, provisioning zero-trust services in NGNs poses significant challenges, primarily due to the environmental complexity and dynamics. Motivated by these challenges, this paper explores efficient zero-trust service provisioning using hierarchical micro-segmentations. Specifically, we model zero-trust networks via hierarchical graphs, thereby jointly considering the resource- and trust-level features to optimize service efficiency. We organize such zero-trust networks through micro-segmentations, which support granular zero-trust policies efficiently. To generate the optimal micro-segmentation, we present the Large Language Model-Enhanced Graph Diffusion (LEGD) algorithm, which leverages the diffusion process to realize a high-quality generation paradigm. Additionally, we utilize policy boosting and Large Language Models (LLM) to enable LEGD to optimize the generation policy and understand complicated graphical features. Moreover, realizing the unique trustworthiness updates or service upgrades in zero-trust NGN, we further present LEGD-Adaptive Maintenance (LEGD-AM), providing an adaptive way to perform task-oriented fine-tuning on LEGD. Extensive experiments demonstrate that the proposed LEGD achieves 90% higher efficiency in provisioning services compared with other baselines. Moreover, the LEGD-AM can reduce the service outage time by over 50%.">
+        <tr id="Visual analytics is essential for studying large time series due to its ability to reveal trends, anomalies, and insights. DeepVATS is a tool that merges Deep Learning (Deep) with Visual Analytics (VA) for the analysis of large time series data (TS). It has three interconnected modules. The Deep Learning module, developed in R, manages the load of datasets and Deep Learning models from and to the Storage module. This module also supports models training and the acquisition of the embeddings from the latent space of the trained model. The Storage module operates using the Weights and Biases system. Subsequently, these embeddings can be analyzed in the Visual Analytics module. This module, based on an R Shiny application, allows the adjustment of the parameters related to the projection and clustering of the embeddings space. Once these parameters are set, interactive plots representing both the embeddings, and the time series are shown. This paper introduces the tool and examines its scalability through log analytics. The execution time evolution is examined while the length of the time series is varied. This is achieved by resampling a large data series into smaller subsets and logging the main execution and rendering times for later analysis of scalability.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/9c8c139f33667170dc0fa673b2932966b3d4af49" target='_blank'>
-              Hierarchical Micro-Segmentations for Zero-Trust Services via Large Language Model (LLM)-enhanced Graph Diffusion
+          <td><a href="https://www.semanticscholar.org/paper/4512438def79546a16c0581fc3e2b73339d52b93" target='_blank'>
+              Exploring Scalability in Large-Scale Time Series in DeepVATS framework
               </a>
             </td>
           <td>
-            Yinqiu Liu, Guangyuan Liu, Hongyang Du, D. Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim, X. Shen
+            Inmaculada Santamaria-Valenzuela, V. Rodríguez-Fernández, David Camacho
           </td>
-          <td>2024-06-20</td>
+          <td>2024-08-08</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>112</td>
+          <td>10</td>
         </tr>
 
         <tr id="Macroeconomic data are crucial for monitoring countries' performance and driving policy. However, traditional data acquisition processes are slow, subject to delays, and performed at a low frequency. We address this 'ragged-edge' problem with a two-step framework. The first step is a supervised learning model predicting observed low-frequency figures. We propose a neural-network-based nowcasting model that exploits mixed-frequency, high-dimensional data. The second step uses the elasticities derived from the previous step to interpolate unobserved high-frequency figures. We apply our method to nowcast countries' yearly research and development (R&D) expenditure series. These series are collected through infrequent surveys, making them ideal candidates for this task. We exploit a range of predictors, chiefly Internet search volume data, and document the relevance of these data in improving out-of-sample predictions. Furthermore, we leverage the high frequency of our data to derive monthly estimates of R&D expenditures, which are currently unobserved. We compare our results with those obtained from the classical regression-based and the sparse temporal disaggregation methods. Finally, we validate our results by reporting a strong correlation with monthly R&D employment data.">
@@ -3583,116 +3462,54 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2024-07-16</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>22</td>
-        </tr>
-
-        <tr id="aeon is a unified Python 3 library for all machine learning tasks involving time series. The package contains modules for time series forecasting, classification, extrinsic regression and clustering, as well as a variety of utilities, transformations and distance measures designed for time series data. aeon also has a number of experimental modules for tasks such as anomaly detection, similarity search and segmentation. aeon follows the scikit-learn API as much as possible to help new users and enable easy integration of aeon estimators with useful tools such as model selection and pipelines. It provides a broad library of time series algorithms, including efficient implementations of the very latest advances in research. Using a system of optional dependencies, aeon integrates a wide variety of packages into a single interface while keeping the core framework with minimal dependencies. The package is distributed under the 3-Clause BSD license and is available at https://github.com/ aeon-toolkit/aeon. This version was submitted to the JMLR journal on 02 Nov 2023 for v0.5.0 of aeon. At the time of this preprint aeon has released v0.9.0, and has had substantial changes.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6664110bc4fee7c79dd1928d2a37dd9222295260" target='_blank'>
-              aeon: a Python toolkit for learning from time series
-              </a>
-            </td>
-          <td>
-            Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume, Christopher Holder, David Guijo Rubio, Guzal Bulatova, Leonidas Tsaprounis, Lukasz Mentel, Martin Walter, Patrick Schäfer, Anthony J. Bagnall
-          </td>
-          <td>2024-06-20</td>
-          <td>ArXiv</td>
-          <td>2</td>
-          <td>8</td>
+          <td>23</td>
         </tr>
 
-        <tr id="Despite recent advancements in Self-Supervised Learning (SSL) for Time Series analysis, a noticeable gap persists between the anticipated achievements and actual performance. While these methods have demonstrated formidable generalization capabilities with minimal labels in various domains, their effectiveness in distinguishing between different classes based on a limited number of annotated records is notably lacking. Our hypothesis attributes this bottleneck to the prevalent use of Contrastive Learning, a shared training objective in previous state-of-the-art (SOTA) methods. 
-
-By mandating distinctiveness between representations for negative pairs drawn from separate records, this approach compels the model to encode unique record-based patterns but simultaneously neglects changes occurring across the entire record. To overcome this challenge, we introduce Distilled Embedding for Almost-Periodic Time Series (DEAPS) in this paper, offering a non-contrastive method tailored for quasiperiodic time series, such as electrocardiogram (ECG) data. By avoiding the use of negative pairs, we not only mitigate the model's blindness to temporal changes but also enable the integration of a "Gradual Loss (L_gra)" function. This function guides the model to effectively capture dynamic patterns evolving throughout the record. The outcomes are promising, as DEAPS demonstrates a notable improvement of +10% over existing SOTA methods when just a few annotated records are presented to fit a Machine Learning (ML) model based on the learned representation.">
+        <tr id="Modeling count-valued time sequences has been receiving growing interests because count time sequences naturally arise in physical and social domains. Poisson gamma dynamical systems (PGDSs) are newly-developed methods, which can well capture the expressive latent transition structure and bursty dynamics behind count sequences. In particular, PGDSs demonstrate superior performance in terms of data imputation and prediction, compared with canonical linear dynamical system (LDS) based methods. Despite these advantages, PGDS cannot capture the heterogeneous overdispersed behaviours of the underlying dynamic processes. To mitigate this defect, we propose a negative-binomial-randomized gamma Markov process, which not only significantly improves the predictive performance of the proposed dynamical system, but also facilitates the fast convergence of the inference algorithm. Moreover, we develop methods to estimate both factor-structured and graph-structured transition dynamics, which enable us to infer more explainable latent structure, compared with PGDSs. Finally, we demonstrate the explainable latent structure learned by the proposed method, and show its superior performance in imputing missing data and forecasting future observations, compared with the related models.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6322ab84125e948e9fb4ccfe259303f93b26f226" target='_blank'>
-              Contrastive Learning Is Not Optimal for Quasiperiodic Time Series
+          <td><a href="https://www.semanticscholar.org/paper/d6fa589fdaa2383d6e9edb62b53fd8d71ec793f5" target='_blank'>
+              Negative-Binomial Randomized Gamma Dynamical Systems for Heterogeneous Overdispersed Count Time Sequences
               </a>
             </td>
           <td>
-            A. Atienza, J. Bardram, S. Puthusserypady
+            Rui Huang, Sikun Yang, Heinz Koeppl
           </td>
-          <td>2024-07-24</td>
+          <td>2024-08-01</td>
           <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
           <td>0</td>
-          <td>2</td>
+          <td>4</td>
         </tr>
 
-        <tr id="Long-term time series forecasting is a long-standing challenge in various applications. A central issue in time series forecasting is that methods should expressively capture long-term dependency. Furthermore, time series forecasting methods should be flexible when applied to different scenarios. Although Fourier analysis offers an alternative to effectively capture reusable and periodic patterns to achieve long-term forecasting in different scenarios, existing methods often assume high-frequency components represent noise and should be discarded in time series forecasting. However, we conduct a series of motivation experiments and discover that the role of certain frequencies varies depending on the scenarios. In some scenarios, removing high-frequency components from the original time series can improve the forecasting performance, while in others scenarios, removing them is harmful to forecasting performance. Therefore, it is necessary to treat the frequencies differently according to specific scenarios. To achieve this, we first reformulate the time series forecasting problem as learning a transfer function of each frequency in the Fourier domain. Further, we design Frequency Dynamic Fusion (FreDF), which individually predicts each Fourier component, and dynamically fuses the output of different frequencies. Moreover, we provide a novel insight into the generalization ability of time series forecasting and propose the generalization bound of time series forecasting. Then we prove FreDF has a lower bound, indicating that FreDF has better generalization ability. Extensive experiments conducted on multiple benchmark datasets and ablation studies demonstrate the effectiveness of FreDF.">
+        <tr id="An overview of various open-source Python libraries for time series analysis and forecasting is presented. It covers such tools as Prophet, Kats, Merlion, as well as ARIMA, LSTM algorithms, which allow to study seasonality, trends and anomalies in time series data. The capabilities of each library, their advantages and applications in time series data analysis are discussed in detail.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/180860f60e6655bebc9757b695b4fbc18a0c39ee" target='_blank'>
-              Not All Frequencies Are Created Equal:Towards a Dynamic Fusion of Frequencies in Time-Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/7bdeb61d1e26117d5e63a3cb3e86b8b03e7303b1" target='_blank'>
+              Review of Open-Source Libraries for Solving Time Series Forecasting Problems
               </a>
             </td>
           <td>
-            Xingyu Zhang, Siyu Zhao, Zeen Song, Huijie Guo, Jianqi Zhang, Changwen Zheng, Wenwen Qiang
+            E.A. Svekolnikova, V.N. Panovskiy
           </td>
-          <td>2024-07-17</td>
-          <td>ArXiv</td>
+          <td>2024-07-01</td>
+          <td>Моделирование и анализ данных</td>
           <td>0</td>
-          <td>8</td>
-        </tr>
-
-        <tr id="Accurate driver attention prediction can serve as a critical reference for intelligent vehicles in understanding traffic scenes and making informed driving decisions. Though existing studies on driver attention prediction improved performance by incorporating advanced saliency detection techniques, they overlooked the opportunity to achieve human-inspired prediction by analyzing driving tasks from a cognitive science perspective. During driving, drivers' working memory and long-term memory play crucial roles in scene comprehension and experience retrieval, respectively. Together, they form situational awareness, facilitating drivers to quickly understand the current traffic situation and make optimal decisions based on past driving experiences. To explicitly integrate these two types of memory, this paper proposes an Adaptive Hybrid-Memory-Fusion (AHMF) driver attention prediction model to achieve more human-like predictions. Specifically, the model first encodes information about specific hazardous stimuli in the current scene to form working memories. Then, it adaptively retrieves similar situational experiences from the long-term memory for final prediction. Utilizing domain adaptation techniques, the model performs parallel training across multiple datasets, thereby enriching the accumulated driving experience within the long-term memory module. Compared to existing models, our model demonstrates significant improvements across various metrics on multiple public datasets, proving the effectiveness of integrating hybrid memories in driver attention prediction.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/83a7e0a70b4a6946f4139722bd3355104544778e" target='_blank'>
-              AHMF: Adaptive Hybrid-Memory-Fusion Model for Driver Attention Prediction
-              </a>
-            </td>
-          <td>
-            Dongyang Xu, Qingfan Wang, Ji Ma, Xiangyun Zeng, Lei Chen
-          </td>
-          <td>2024-07-24</td>
-          <td>ArXiv</td>
           <td>0</td>
-          <td>4</td>
         </tr>
 
-        <tr id="None">
+        <tr id="Stock price forecasting has been an important area of interest for economists and computer scientists. In addition to traditional statistical methods, advanced artificial intelligence techniques such as machine learning can stand out with their ability to process complex data sets and adapt to historical data. In recent years, hybrid models combining deep learning and time series methods have demonstrated superior performance in stock selection and portfolio optimisation. This study comparatively analyses the performance of LSTM and ARIMA models in time series forecasting. In the study, the stock prices of Oracle company are predicted using two different models, LSTM and ARIMA. Model performance is evaluated using metrics like MSE, MAE, RMSE, and MAPE. Both models have been found to be successful in different metrics. The LSTM model has lower error values; meanwhile, the ARIMA model produced proportionally more accurate forecasts. The study concludes that given the potential offered by deep learning, models such as LSTM are essential for time series forecasting. The flexibility of deep learning allows the development of customized models for different data types and time series problems.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/85961a97dd0769d04e19fd1588cb93092c4d8280" target='_blank'>
-              Multi-view Heterogeneous Graph Neural Networks for Node Classification
+          <td><a href="https://www.semanticscholar.org/paper/7fa13189dabbbc094e6e28d8bc9e13d6f6bccac1" target='_blank'>
+              Comparative Analysis of LSTM and ARIMA Models in Stock Price Prediction: A Technology Company Example
               </a>
             </td>
           <td>
-            Xi Zeng, Fangyuan Lei, Changdong Wang, Qing-Yun Dai
+            Y. Kırelli
           </td>
-          <td>2024-06-24</td>
-          <td>Data Science and Engineering</td>
+          <td>2024-07-30</td>
+          <td>Black Sea Journal of Engineering and Science</td>
           <td>0</td>
           <td>2</td>
         </tr>
 
-        <tr id="The integration of Graph Neural Networks (GNNs) and Neural Ordinary and Partial Differential Equations has been extensively studied in recent years. GNN architectures powered by neural differential equations allow us to reason about their behavior, and develop GNNs with desired properties such as controlled smoothing or energy conservation. In this paper we take inspiration from Turing instabilities in a Reaction Diffusion (RD) system of partial differential equations, and propose a novel family of GNNs based on neural RD systems. We \textcolor{black}{demonstrate} that our RDGNN is powerful for the modeling of various data types, from homophilic, to heterophilic, and spatio-temporal datasets. We discuss the theoretical properties of our RDGNN, its implementation, and show that it improves or offers competitive performance to state-of-the-art methods.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/57254c8083a6fe4f39ea4b61b190c97bb047ab60" target='_blank'>
-              Graph Neural Reaction Diffusion Models
-              </a>
-            </td>
-          <td>
-            Moshe Eliasof, Eldad Haber, Eran Treister
-          </td>
-          <td>2024-06-16</td>
-          <td>ArXiv</td>
-          <td>1</td>
-          <td>15</td>
-        </tr>
-
-        <tr id="Language models (LMs) are known to suffer from forgetting of previously learned examples when fine-tuned, breaking stability of deployed LM systems. Despite efforts on mitigating forgetting, few have investigated whether, and how forgotten upstream examples are associated with newly learned tasks. Insights on such associations enable efficient and targeted mitigation of forgetting. In this paper, we empirically analyze forgetting that occurs in $N$ upstream examples while the model learns $M$ new tasks and visualize their associations with a $M \times N$ matrix. We empirically demonstrate that the degree of forgetting can often be approximated by simple multiplicative contributions of the upstream examples and newly learned tasks. We also reveal more complicated patterns where specific subsets of examples are forgotten with statistics and visualization. Following our analysis, we predict forgetting that happens on upstream examples when learning a new task with matrix completion over the empirical associations, outperforming prior approaches that rely on trainable LMs. Project website: https://inklab.usc.edu/lm-forgetting-prediction/">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/7e34d7ceff31597694fb9b44cd8a72849ce8a4ad" target='_blank'>
-              Demystifying Forgetting in Language Model Fine-Tuning with Statistical Analysis of Example Associations
-              </a>
-            </td>
-          <td>
-            Xisen Jin, Xiang Ren
-          </td>
-          <td>2024-06-20</td>
-          <td>ArXiv</td>
-          <td>2</td>
-          <td>12</td>
-        </tr>
-
         <tr id="Local natural gas distribution companies (LDCs) require accurate demand forecasts across various time periods, geographic regions, and customer class hierarchies. Achieving coherent forecasts across these hierarchies is challenging but crucial for optimal decision making, resource allocation, and operational efficiency. This work introduces a method that structures the gas distribution system into cross-temporal hierarchies to produce accurate and coherent forecasts. We apply our method to a case study involving three operational regions, forecasting at different geographical levels and analyzing both hourly and daily frequencies. Trained on five years of data and tested on one year, our model achieves a 10% reduction in hourly mean absolute scaled error and a 3% reduction in daily mean absolute scaled error.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/737511869f437a80c5594b5ce945f660699fd7a2" target='_blank'>
@@ -3708,49 +3525,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>27</td>
         </tr>
 
-        <tr id="Accurate traffic flow forecasting is vital for intelligent transportation systems, especially with urbanization worsening traffic congestion, which affects daily life, economic growth, and the environment. Precise forecasts aid in managing and optimizing transportation systems, reducing congestion, and improving air quality by cutting emissions. However, predicting outcomes is difficult due to intricate spatial relationships, nonlinear temporal patterns, and the challenges associated with long-term forecasting. Current research often uses static graph structures, overlooking dynamic and long-range dependencies. To tackle these issues, we introduce the spatiotemporal dynamic multi-hop network (ST-DMN), a Seq2Seq framework. This model incorporates spatiotemporal convolutional blocks (ST-Blocks) with residual connections in the encoder to condense historical traffic data into a fixed-dimensional vector. A dynamic graph represents time-varying inter-segment relationships, and multi-hop operation in the encoder’s spatial convolutional layer and the decoder’s diffusion multi-hop graph convolutional gated recurrent units (DMGCGRUs) capture long-range dependencies. Experiments on two real-world datasets METR-LA and PEMS-BAY show that ST-DMN surpasses existing models in three metrics.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/a95e1e501ec2f4452c161c53c744ae576c08979c" target='_blank'>
-              Spatiotemporal Dynamic Multi-Hop Network for Traffic Flow Forecasting
-              </a>
-            </td>
-          <td>
-            Wenguang Chai, Qingfeng Luo, Zhizhe Lin, Jingwen Yan, Jinglin Zhou, Teng Zhou
-          </td>
-          <td>2024-07-09</td>
-          <td>Sustainability</td>
-          <td>0</td>
-          <td>11</td>
-        </tr>
-
-        <tr id="Score-based diffusion models have recently emerged as state-of-the-art generative models for a variety of data modalities. Nonetheless, it remains unclear how to adapt these models to generate long multivariate time series. Viewing a time series as the discretization of an underlying continuous process, we introduce SigDiffusion, a novel diffusion model operating on log-signature embeddings of the data. The forward and backward processes gradually perturb and denoise log-signatures preserving their algebraic structure. To recover a signal from its log-signature, we provide new closed-form inversion formulae expressing the coefficients obtained by expanding the signal in a given basis (e.g. Fourier or orthogonal polynomials) as explicit polynomial functions of the log-signature. Finally, we show that combining SigDiffusion with these inversion formulae results in highly realistic time series generation, competitive with the current state-of-the-art on various datasets of synthetic and real-world examples.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ece5bfacaf676cb0effa18100fdf73cff5cb6a11" target='_blank'>
-              SigDiffusions: Score-Based Diffusion Models for Long Time Series via Log-Signature Embeddings
-              </a>
-            </td>
-          <td>
-            Barbora Barancikova, Zhuoyue Huang, Cristopher Salvi
-          </td>
-          <td>2024-06-14</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>1</td>
-        </tr>
-
-        <tr id="Previous work has demonstrated that MLPs within ReLU Transformers exhibit high levels of sparsity, with many of their activations equal to zero for any given token. We build on that work to more deeply explore how token-level sparsity evolves over the course of training, and how it connects to broader sparsity patterns over the course of a sequence or batch, demonstrating that the different layers within small transformers exhibit distinctly layer-specific patterns on both of these fronts. In particular, we demonstrate that the first and last layer of the network have distinctive and in many ways inverted relationships to sparsity, and explore implications for the structure of feature representations being learned at different depths of the model. We additionally explore the phenomenon of ReLU dimensions"turning off", and show evidence suggesting that"neuron death"is being primarily driven by the dynamics of training, rather than simply occurring randomly or accidentally as a result of outliers.">
+        <tr id="Accurate transfer time prediction and future transfer time information are important for both public transport operators and passengers. However, existing studies cannot effectively manage high-dimensional transfer time data, capture the complex nonlinearity of transfer time, or provide accurate transfer time information. This study provides a reliable prediction model called low-rank Bayesian temporal matrix factorization (LBTMF) to address these challenges. First, on the basis of a high-dimensional spatiotemporal matrix of transfer time data, we develop a low-rank temporal-regularized matrix factorization-based imputation module to capture spatial and temporal characteristics to replace missing transfer time data. Second, to further predict the transfer time with the imputation of missing data, we propose the spatiotemporal-based Bayesian temporal matrix factorization prediction module to recover hourly and daily regular characteristics to predict the transfer time at different metro stations during various periods. Finally, the comprehensive experimental findings suggest that the LBTMF model outperforms other excellent approaches in terms of imputation efficiency, prediction accuracy, and robustness.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1aa9d0e983103d88f245c886b665063318695a7a" target='_blank'>
-              Uncovering Layer-Dependent Activation Sparsity Patterns in ReLU Transformers
+          <td><a href="https://www.semanticscholar.org/paper/3d330ce1319f0da72418d383026426b3a3674268" target='_blank'>
+              A Low-Rank Bayesian Temporal Matrix Factorization for the Transfer Time Prediction Between Metro and Bus Systems
               </a>
             </td>
           <td>
-            Cody Wild, Jesper Anderson
+            Pan Wu, Mingyang Pei, Tao Wang, Yang Liu, Zhiyuan Liu, Lingshu Zhong
           </td>
-          <td>2024-07-10</td>
-          <td>ArXiv</td>
-          <td>0</td>
+          <td>2024-07-01</td>
+          <td>IEEE Transactions on Intelligent Transportation Systems</td>
           <td>0</td>
+          <td>8</td>
         </tr>
 
         <tr id="The recurring neural network is a deep learning algorithm that is commonly used to develop prediction systems. There are many variants of RNN such as RNN itself, long-short-term memory (LSTM), and gated recurring unit, so it is frequently debatable which algorithm from the RNN family has the most optimal efficiency and computation time. When developing a prediction system, sequential or time series data is required so that an accurate prediction can be made. Sequential or time series data involve data arranged in a time sequence, such as weather data, financial data, carbon emission data, and traffic data recorded over time. This research will be carried out by predicting the three RNN models against historical Bitcoin value data. The research method used is Experimental Design by comparing the performance between the three models on bitcoin value time series data, testing is done by involving hyperparameters such as Tanh, Sigmoid, and ReLU activation functions, batch size, and epochs. The aim of this research is to find out which RNN model can produce the most optimal performance and find out what performance measures can be used to evaluate and compare the performance between the three models. The results of the study show that LSTM is the most effective model with RMSE 0.012441 and MSE 0.000155 but inefficient because it takes 3 minutes 24 seconds to run the computation; in the meantime, the Tanh activation function gives the most optimal prediction than Sigmoid and RelU and therefore should be the main candidate to be used with RNN models when predicting Bitcoin prices.">
@@ -3768,34 +3555,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>9</td>
         </tr>
 
-        <tr id="Stock price forecasting has been an important area of interest for economists and computer scientists. In addition to traditional statistical methods, advanced artificial intelligence techniques such as machine learning can stand out with their ability to process complex data sets and adapt to historical data. In recent years, hybrid models combining deep learning and time series methods have demonstrated superior performance in stock selection and portfolio optimisation. This study comparatively analyses the performance of LSTM and ARIMA models in time series forecasting. In the study, the stock prices of Oracle company are predicted using two different models, LSTM and ARIMA. Model performance is evaluated using metrics like MSE, MAE, RMSE, and MAPE. Both models have been found to be successful in different metrics. The LSTM model has lower error values; meanwhile, the ARIMA model produced proportionally more accurate forecasts. The study concludes that given the potential offered by deep learning, models such as LSTM are essential for time series forecasting. The flexibility of deep learning allows the development of customized models for different data types and time series problems.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/7fa13189dabbbc094e6e28d8bc9e13d6f6bccac1" target='_blank'>
-              Comparative Analysis of LSTM and ARIMA Models in Stock Price Prediction: A Technology Company Example
-              </a>
-            </td>
-          <td>
-            Y. Kırelli
-          </td>
-          <td>2024-07-30</td>
-          <td>Black Sea Journal of Engineering and Science</td>
-          <td>0</td>
-          <td>2</td>
-        </tr>
-
-        <tr id="A dynamic weighted directed graph (DWDG) is commonly encountered in various application scenarios. It involves extensive dynamic interactions among numerous nodes. Most existing approaches explore the intricate temporal patterns hidden in a DWDG from the purely data-driven perspective, which suffers from accuracy loss when a DWDG exhibits strong fluctuations over time. To address this issue, this study proposes a novel Extended-Kalman-Filter-Incorporated Latent Feature (EKLF) model to represent a DWDG from the model-driven perspective. Its main idea is divided into the following two-fold ideas: a) adopting a control model, i.e., the Extended Kalman Filter (EKF), to track the complex temporal patterns precisely with its nonlinear state-transition and observation functions; and b) introducing an alternating least squares (ALS) algorithm to train the latent features (LFs) alternatively for precisely representing a DWDG. Empirical studies on DWDG datasets demonstrate that the proposed EKLF model outperforms state-of-the-art models in prediction accuracy and computational efficiency for missing edge weights of a DWDG. It unveils the potential for precisely representing a DWDG by incorporating a control model.">
+        <tr id="Accurate traffic flow forecasting is vital for intelligent transportation systems, especially with urbanization worsening traffic congestion, which affects daily life, economic growth, and the environment. Precise forecasts aid in managing and optimizing transportation systems, reducing congestion, and improving air quality by cutting emissions. However, predicting outcomes is difficult due to intricate spatial relationships, nonlinear temporal patterns, and the challenges associated with long-term forecasting. Current research often uses static graph structures, overlooking dynamic and long-range dependencies. To tackle these issues, we introduce the spatiotemporal dynamic multi-hop network (ST-DMN), a Seq2Seq framework. This model incorporates spatiotemporal convolutional blocks (ST-Blocks) with residual connections in the encoder to condense historical traffic data into a fixed-dimensional vector. A dynamic graph represents time-varying inter-segment relationships, and multi-hop operation in the encoder’s spatial convolutional layer and the decoder’s diffusion multi-hop graph convolutional gated recurrent units (DMGCGRUs) capture long-range dependencies. Experiments on two real-world datasets METR-LA and PEMS-BAY show that ST-DMN surpasses existing models in three metrics.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/8a67cf8783fcc0ce6aba4d219506d923562461ab" target='_blank'>
-              An Extended Kalman Filter Integrated Latent Feature Model on Dynamic Weighted Directed Graphs
+          <td><a href="https://www.semanticscholar.org/paper/a95e1e501ec2f4452c161c53c744ae576c08979c" target='_blank'>
+              Spatiotemporal Dynamic Multi-Hop Network for Traffic Flow Forecasting
               </a>
             </td>
           <td>
-            Hongxun Zhou, Xiangyu Chen, Ye Yuan
+            Wenguang Chai, Qingfeng Luo, Zhizhe Lin, Jingwen Yan, Jinglin Zhou, Teng Zhou
           </td>
-          <td>2024-07-31</td>
-          <td>ArXiv</td>
-          <td>0</td>
+          <td>2024-07-09</td>
+          <td>Sustainability</td>
           <td>0</td>
+          <td>12</td>
         </tr>
 
         <tr id="Objectives. To build neural network models of time series (LSTM, GRU, RNN) and compare the results of forecasting with their mutual help and the results of standard models (ARIMA, ETS), in order to ascertain in which cases a certain group of models should be used.Methods. The paper provides a review of neural network models and considers the structure of RNN, LSTM, and GRU models. They are used for modeling time series in Russian macroeconomic statistics. The quality of model adjustment to the data and the quality of forecasts are compared experimentally. Neural network and standard models can be used both for the entire series and for its parts (trend and seasonality). When building a forecast for several time intervals in the future, two approaches are considered: building a forecast for the entire interval at once, and step-by-step forecasting. In this way there are several combinations of models that can be used for forecasting. These approaches are analyzed in the computational experiment.Results. Several experiments have been conducted in which standard (ARIMA, ETS, LOESS) and neural network models (LSTM, GRU, RNN) are built and compared in terms of proximity of the forecast to the series data in the test period.Conclusions. In the case of seasonal time series, models based on neural networks surpassed the standard ARIMA and ETS models in terms of forecast accuracy for the test period. The single-step forecast is computationally less efficient than the integral forecast for the entire target period. However, it is not possible to accurately indicate which approach is the best in terms of quality for a given series. Combined models (neural networks for trend, ARIMA for seasonality) almost always give good results. When forecasting a non-seasonal heteroskedastic series of share price, the standard approaches (LOESS method and ETS model) showed the best results.">
@@ -3813,64 +3585,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2</td>
         </tr>
 
-        <tr id="We demonstrate the use of Conditional Variational Encoder (CVAE) to improve the forecasts of daily stock volume time series in both short and long term forecasting tasks, with the use of advanced information of input variables such as rebalancing dates. CVAE generates non-linear time series as out-of-sample forecasts, which have better accuracy and closer fit of correlation to the actual data, compared to traditional linear models. These generative forecasts can also be used for scenario generation, which aids interpretation. We further discuss correlations in non-stationary time series and other potential extensions from the CVAE forecasts.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ea52e1dae9f11eaac9d89cadce3583bf129d01b4" target='_blank'>
-              Stock Volume Forecasting with Advanced Information by Conditional Variational Auto-Encoder
-              </a>
-            </td>
-          <td>
-            Parley R Yang, Alexander Y. Shestopaloff
-          </td>
-          <td>2024-06-19</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>7</td>
-        </tr>
-
-        <tr id="Learning spatio-temporal patterns of polar ice layers is crucial for monitoring the change in ice sheet balance and evaluating ice dynamic processes. While a few researchers focus on learning ice layer patterns from echogram images captured by airborne snow radar sensors via different convolutional neural networks, the noise in the echogram images proves to be a major obstacle. Instead, we focus on geometric deep learning based on graph neural networks to learn the spatio-temporal patterns from thickness information of shallow ice layers and make predictions for deep layers. In this paper, we propose a physics-informed hybrid graph neural network that combines the GraphSAGE framework for graph feature learning with the long short-term memory (LSTM) structure for learning temporal changes, and introduce measurements of physical ice properties from Model Atmospheric Regional (MAR) weather model as physical node features. We found that our proposed network can consistently outperform the current non-inductive or non-physical model in predicting deep ice layer thickness.">
+        <tr id="A dynamic weighted directed graph (DWDG) is commonly encountered in various application scenarios. It involves extensive dynamic interactions among numerous nodes. Most existing approaches explore the intricate temporal patterns hidden in a DWDG from the purely data-driven perspective, which suffers from accuracy loss when a DWDG exhibits strong fluctuations over time. To address this issue, this study proposes a novel Extended-Kalman-Filter-Incorporated Latent Feature (EKLF) model to represent a DWDG from the model-driven perspective. Its main idea is divided into the following two-fold ideas: a) adopting a control model, i.e., the Extended Kalman Filter (EKF), to track the complex temporal patterns precisely with its nonlinear state-transition and observation functions; and b) introducing an alternating least squares (ALS) algorithm to train the latent features (LFs) alternatively for precisely representing a DWDG. Empirical studies on DWDG datasets demonstrate that the proposed EKLF model outperforms state-of-the-art models in prediction accuracy and computational efficiency for missing edge weights of a DWDG. It unveils the potential for precisely representing a DWDG by incorporating a control model.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/14283719423d1b3a94963bf3dac91b3889050624" target='_blank'>
-              Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Network
+          <td><a href="https://www.semanticscholar.org/paper/8a67cf8783fcc0ce6aba4d219506d923562461ab" target='_blank'>
+              An Extended Kalman Filter Integrated Latent Feature Model on Dynamic Weighted Directed Graphs
               </a>
             </td>
           <td>
-            Zesheng Liu, Maryam Rahnemoonfar
+            Hongxun Zhou, Xiangyu Chen, Ye Yuan
           </td>
-          <td>2024-06-21</td>
+          <td>2024-07-31</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
-        <tr id="Data valuation quantifies the value of training data, and is used for data attribution (i.e., determining the contribution of training data towards model predictions), and data selection; both of which are important for curating high-quality datasets to train large language models. In our paper, we show that data valuation through in-context probing (i.e., prompting a LLM) approximates influence functions for selecting training data. We provide a theoretical sketch on this connection based on transformer models performing"implicit"gradient descent on its in-context inputs. Our empirical findings show that in-context probing and gradient-based influence frameworks are similar in how they rank training data. Furthermore, fine-tuning experiments on data selected by either method reveal similar model performance.">
+        <tr id="Time series forecasting plays a crucial role in various fields, including economics, finance, and weather prediction. This study explores the application of time series forecasting techniques to predict future values based on historical data patterns. Different models, such as ARIMA, SARIMA, and exponential smoothing, are commonly employed to capture the underlying trends, seasonality, and irregularities in time series data. The accuracy of the forecasts depends on selecting the appropriate model and fine-tuning its parameters. Regular validation and refinement of the forecasting models are essential to ensure their reliability and adaptability to changing conditions. Overall, time series forecasting serves as a valuable tool for decision-making, resource allocation, and planning in numerous domains. The Autoregressive Integrated Moving Average (ARIMA) model was used in this research to predict Albanian exports. The ARIMA models provided reliable forecasts for the near future and placed more importance on recent observations rather than distant past data. However, a limitation of this study is the lack of using multiple models on new data to improve future forecasts. Considering the significance of exports in Albania, it is crucial to periodically validate and refine the model-building exercises. Based on the findings, the study recommends using the ARIMA (3,2,3) model for forecasting. Furthermore, the forecast errors were statistically tested and validated, confirming the model's strong predictive ability. It is important to note that a forecasting technique that performs well in one situation may not be suitable for another. Therefore, the validation of a specific model should be regularly assessed as time progresses. For the purpose of forecasting the annual exports of Albania, researchers can employ these models.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/cf9eb15317f8bdfea21ed3f865944e3fbf4a177b" target='_blank'>
-              In-Context Probing Approximates Influence Function for Data Valuation
+          <td><a href="https://www.semanticscholar.org/paper/0d8ae00de95edb0226fad574129969bcb6e56842" target='_blank'>
+              FORECASTING EXPORTS IN ALBANIA
               </a>
             </td>
           <td>
-            Cathy Jiao, Gary Gao, Chenyan Xiong
+            Brikena Sulejmani
           </td>
-          <td>2024-07-17</td>
-          <td>ArXiv</td>
-          <td>0</td>
+          <td>2024-07-30</td>
+          <td>PEOPLE: International Journal of Social Sciences</td>
           <td>0</td>
-        </tr>
-
-        <tr id="Through integrating the evolutionary correlations across global states in the bidirectional recursion, an explainable Bayesian recurrent neural smoother (EBRNS) is proposed for offline data-assisted fixed-interval state smoothing. At first, the proposed model, containing global states in the evolutionary interval, is transformed into an equivalent model with bidirectional memory. This transformation incorporates crucial global state information with support for bi-directional recursive computation. For the transformed model, the joint state-memory-trend Bayesian filtering and smoothing frameworks are derived by introducing the bidirectional memory iteration mechanism and offline data into Bayesian estimation theory. The derived frameworks are implemented using the Gaussian approximation to ensure analytical properties and computational efficiency. Finally, the neural network modules within EBRNS and its two-stage training scheme are designed. Unlike most existing approaches that artificially combine deep learning and model-based estimation, the bidirectional recursion and internal gated structures of EBRNS are naturally derived from Bayesian estimation theory, explainably integrating prior model knowledge, online measurement, and offline data. Experiments on representative real-world datasets demonstrate that the high smoothing accuracy of EBRNS is accompanied by data efficiency and a lightweight parameter scale.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/57349533774d9106325c9d199314485305e9ade6" target='_blank'>
-              Explainable Bayesian Recurrent Neural Smoother to Capture Global State Evolutionary Correlations
-              </a>
-            </td>
-          <td>
-            Shi Yan, Yan Liang, Huayu Zhang, Le Zheng, Difan Zou, Binglu Wang
-          </td>
-          <td>2024-06-17</td>
-          <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
         </tr>
 
         <tr id="Large Language Models (LLMs) excel in diverse tasks but often underperform in specialized fields due to limited domain-specific or proprietary corpus. Continual pre-training (CPT) enhances LLM capabilities by imbuing new domain-specific or proprietary knowledge while replaying general corpus to prevent catastrophic forgetting. The data mixture ratio of general corpus and domain-specific corpus, however, has been chosen heuristically, leading to sub-optimal training efficiency in practice. In this context, we attempt to re-visit the scaling behavior of LLMs under the hood of CPT, and discover a power-law relationship between loss, mixture ratio, and training tokens scale. We formalize the trade-off between general and domain-specific capabilities, leading to a well-defined Critical Mixture Ratio (CMR) of general and domain data. By striking the balance, CMR maintains the model's general ability and achieves the desired domain transfer, ensuring the highest utilization of available resources. Therefore, if we value the balance between efficiency and effectiveness, CMR can be consider as the optimal mixture ratio.Through extensive experiments, we ascertain the predictability of CMR, and propose CMR scaling law and have substantiated its generalization. These findings offer practical guidelines for optimizing LLM training in specialized domains, ensuring both general and domain-specific performance while efficiently managing training resources.">
@@ -3888,49 +3630,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>5</td>
         </tr>
 
-        <tr id="Time series forecasting plays a crucial role in various fields, including economics, finance, and weather prediction. This study explores the application of time series forecasting techniques to predict future values based on historical data patterns. Different models, such as ARIMA, SARIMA, and exponential smoothing, are commonly employed to capture the underlying trends, seasonality, and irregularities in time series data. The accuracy of the forecasts depends on selecting the appropriate model and fine-tuning its parameters. Regular validation and refinement of the forecasting models are essential to ensure their reliability and adaptability to changing conditions. Overall, time series forecasting serves as a valuable tool for decision-making, resource allocation, and planning in numerous domains. The Autoregressive Integrated Moving Average (ARIMA) model was used in this research to predict Albanian exports. The ARIMA models provided reliable forecasts for the near future and placed more importance on recent observations rather than distant past data. However, a limitation of this study is the lack of using multiple models on new data to improve future forecasts. Considering the significance of exports in Albania, it is crucial to periodically validate and refine the model-building exercises. Based on the findings, the study recommends using the ARIMA (3,2,3) model for forecasting. Furthermore, the forecast errors were statistically tested and validated, confirming the model's strong predictive ability. It is important to note that a forecasting technique that performs well in one situation may not be suitable for another. Therefore, the validation of a specific model should be regularly assessed as time progresses. For the purpose of forecasting the annual exports of Albania, researchers can employ these models.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/0d8ae00de95edb0226fad574129969bcb6e56842" target='_blank'>
-              FORECASTING EXPORTS IN ALBANIA
-              </a>
-            </td>
-          <td>
-            Brikena Sulejmani
-          </td>
-          <td>2024-07-30</td>
-          <td>PEOPLE: International Journal of Social Sciences</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-
-        <tr id="This paper introduces a novel integration of Gaussian Mixture Models (GMMs) with Long Short-Term Memory (LSTM) networks to enhance predictive maintenance strategies for industrial machinery. The contribution lies in using GMM for the initial classification of bearing states and LSTM to predict the precise location of potential bearing failures. This hybrid model leverages Fast Fourier Transform (FFT) and Wavelet Packet Decomposition (WPD) for robust feature extraction, creating a nuanced input representation for the LSTM. Our approach delineates a state-of-the-art method for real-time health monitoring and failure prognosis in rotating machinery, offering substantial advancements in predictive maintenance. The model is rigorously validated on distinct datasets, confirming its ability to generalize with high precision and recall rates while effectively circumventing overfitting. It successfully determines not only the condition of bearings but also identifies the exact bearing that may fail, providing a critical tool for preemptive maintenance actions. This integrated methodology of GMMs for state determination combined with the temporal prediction capabilities of LSTM sets a new precedent in the predictive maintenance domain, showcasing the potential for significant reductions in unplanned downtime and maintenance costs. The findings presented herein are instrumental for the field, proposing a practical and effective solution to a long-standing challenge in the maintenance of industrial rotating equipment.">
+        <tr id="Learning spatio-temporal patterns of polar ice layers is crucial for monitoring the change in ice sheet balance and evaluating ice dynamic processes. While a few researchers focus on learning ice layer patterns from echogram images captured by airborne snow radar sensors via different convolutional neural networks, the noise in the echogram images proves to be a major obstacle. Instead, we focus on geometric deep learning based on graph neural networks to learn the spatio-temporal patterns from thickness information of shallow ice layers and make predictions for deep layers. In this paper, we propose a physics-informed hybrid graph neural network that combines the GraphSAGE framework for graph feature learning with the long short-term memory (LSTM) structure for learning temporal changes, and introduce measurements of physical ice properties from Model Atmospheric Regional (MAR) weather model as physical node features. We found that our proposed network can consistently outperform the current non-inductive or non-physical model in predicting deep ice layer thickness.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/31effb29fc784a7490583774cf5a7b3a0e163899" target='_blank'>
-              A New Predictive Maintenance Approach: novel integration of GMM-LSTM for prediction of latent state and failure location of Rotating Machinery
+          <td><a href="https://www.semanticscholar.org/paper/14283719423d1b3a94963bf3dac91b3889050624" target='_blank'>
+              Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Network
               </a>
             </td>
           <td>
-            Arman Ghavidel, H. J. Park, Samuel F. Kovacic, Andres Sousa-Poza
+            Zesheng Liu, Maryam Rahnemoonfar
           </td>
-          <td>2024-06-23</td>
-          <td>2024 19th Annual System of Systems Engineering Conference (SoSE)</td>
+          <td>2024-06-21</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>12</td>
-        </tr>
-
-        <tr id="None">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/debe0ee50d9dc6388d2e21ad87cbf672e2865ff3" target='_blank'>
-              Randomnet: clustering time series using untrained deep neural networks
-              </a>
-            </td>
-          <td>
-            Xiaosheng Li, Wenjie Xi, Jessica Lin
-          </td>
-          <td>2024-06-22</td>
-          <td>Data Mining and Knowledge Discovery</td>
           <td>0</td>
-          <td>5</td>
         </tr>
 
         <tr id="Temporal data modelling techniques with neural networks are useful in many domain applications, including time-series forecasting and control engineering. This paper aims at developing a recurrent version of stochastic configuration networks (RSCNs) for problem solving, where we have no underlying assumption on the dynamic orders of the input variables. Given a collection of historical data, we first build an initial RSCN model in the light of a supervisory mechanism, followed by an online update of the output weights by using a projection algorithm. Some theoretical results are established, including the echo state property, the universal approximation property of RSCNs for both the offline and online learnings, and the convergence of the output weights. The proposed RSCN model is remarkably distinguished from the well-known echo state networks (ESNs) in terms of the way of assigning the input random weight matrix and a special structure of the random feedback matrix. A comprehensive comparison study among the long short-term memory (LSTM) network, the original ESN, and several state-of-the-art ESN methods such as the simple cycle reservoir (SCR), the polynomial ESN (PESN), the leaky-integrator ESN (LIESN) and RSCN is carried out. Numerical results clearly indicate that the proposed RSCN performs favourably over all of the datasets.">
@@ -3948,19 +3660,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>1</td>
         </tr>
 
-        <tr id="None">
+        <tr id="Abstract Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine learning or deep learning methods have shown some success in this task, but their outcomes were not satisfactory enough, primarily due to the neglect of evolutionary information. In this paper, we introduce the Chaos-Attention net for Promoter Evolution (CAPE) to address the limitations of existing methods. We comprehensively extract evolutionary information within promoters using merged chaos game representation and process the overall information with modified DenseNet and Transformer structures. Our model achieves state-of-the-art results on two kinds of distinct tasks related to prokaryotic promoter strength prediction. The incorporation of evolutionary information enhances the model’s accuracy, with transfer learning further extending its adaptability. Furthermore, experimental results confirm CAPE’s efficacy in simulating in silico directed evolution of promoters, marking a significant advancement in predictive modeling for prokaryotic promoter strength. Our paper also presents a user-friendly website for the practical implementation of in silico directed evolution on promoters. The source code implemented in this study and the instructions on accessing the website can be found in our GitHub repository https://github.com/BobYHY/CAPE.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ca70c38270fe1f47a4e82ae81496c70c0e541ccf" target='_blank'>
-              Multivariate Bayesian Time-Series Model with Multi-temporal Convolution Network for Forecasting Stock Market During COVID-19 Pandemic
+          <td><a href="https://www.semanticscholar.org/paper/04f78ff241635d6b984404fb6a2df4fcea90938f" target='_blank'>
+              CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution
               </a>
             </td>
           <td>
-            Paramita Ray, B. Ganguli, Amlan Chakrabarti
+            Ruohan Ren, Hongyu Yu, Jiahao Teng, Sihui Mao, Zixuan Bian, Yangtianze Tao, Stephen S.-T. Yau
           </td>
-          <td>2024-06-27</td>
-          <td>Int. J. Comput. Intell. Syst.</td>
+          <td>2024-07-25</td>
+          <td>Briefings in Bioinformatics</td>
           <td>0</td>
-          <td>13</td>
+          <td>4</td>
         </tr>
 
         <tr id="
@@ -3994,19 +3706,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>10</td>
         </tr>
 
-        <tr id="Time-series analysis plays a crucial role in numerous real-world applications, ranging from financial forecasting to environmental monitoring and beyond. Traditional classification techniques often struggle to effectively handle the uncertainties and imprecision inherent in time series data. To address this challenge, fuzzy time series models have emerged as a promising alternative, offering a flexible framework capable of capturing the uncertainties and vagueness intrinsic to temporal patterns. In this paper, we propose an Optimized Time Series Interval Valued Fuzzy System (OTS-IVFS) that leverages the power of type-2 fuzzy logic to handle temporal data effectively and provide fully explainable models. We have performed experiments on five data sets from a diverse set of use cases. Our system significantly outperforms the best-in-class algorithms for the Earthquake dataset by 16.67% increase in accuracy while giving comparable results in 3 other datasets, whilst maintaining full interpretability.">
+        <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/a7c44baa43d3adf4e6fe37951cb548cb1eb0045c" target='_blank'>
-              A Type-2 Fuzzy Time Series Classification System with Optimized Time Period Selection
+          <td><a href="https://www.semanticscholar.org/paper/ca70c38270fe1f47a4e82ae81496c70c0e541ccf" target='_blank'>
+              Multivariate Bayesian Time-Series Model with Multi-temporal Convolution Network for Forecasting Stock Market During COVID-19 Pandemic
               </a>
             </td>
           <td>
-            Ashishsingh Bhatia, Hani Hagras
+            Paramita Ray, B. Ganguli, Amlan Chakrabarti
           </td>
-          <td>2024-06-30</td>
-          <td>2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)</td>
+          <td>2024-06-27</td>
+          <td>Int. J. Comput. Intell. Syst.</td>
           <td>0</td>
-          <td>2</td>
+          <td>13</td>
         </tr>
 
         <tr id="Evaluating the performance of causal discovery algorithms that aim to find causal relationships between time-dependent processes remains a challenging topic. In this paper, we show that certain characteristics of datasets, such as varsortability (Reisach et al. 2021) and $R^2$-sortability (Reisach et al. 2023), also occur in datasets for autocorrelated stationary time series. We illustrate this empirically using four types of data: simulated data based on SVAR models and Erd\H{o}s-R\'enyi graphs, the data used in the 2019 causality-for-climate challenge (Runge et al. 2019), real-world river stream datasets, and real-world data generated by the Causal Chamber of (Gamella et al. 2024). To do this, we adapt var- and $R^2$-sortability to time series data. We also investigate the extent to which the performance of score-based causal discovery methods goes hand in hand with high sortability. Arguably, our most surprising finding is that the investigated real-world datasets exhibit high varsortability and low $R^2$-sortability indicating that scales may carry a significant amount of causal information.">
@@ -4024,49 +3736,64 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>0</td>
         </tr>
 
+        <tr id="This paper proposes a new self-organizing interval type-2 fuzzy neural network with multiple outputs (SOIT2FNN-MO) for multi-step time series prediction. Differing from the traditional six-layer IT2FNN, a nine-layer network is developed to improve prediction accuracy, uncertainty handling and model interpretability. First, a new co-antecedent layer and a modified consequent layer are devised to improve the interpretability of the fuzzy model for multi-step predictions. Second, a new transformation layer is designed to address the potential issues in the vanished rule firing strength caused by highdimensional inputs. Third, a new link layer is proposed to build temporal connections between multi-step predictions. Furthermore, a two-stage self-organizing mechanism is developed to automatically generate the fuzzy rules, in which the first stage is used to create the rule base from empty and perform the initial optimization, while the second stage is to fine-tune all network parameters. Finally, various simulations are carried out on chaotic and microgrid time series prediction problems, demonstrating the superiority of our approach in terms of prediction accuracy, uncertainty handling and model interpretability.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/966cd74254d10e689fc476d8fb5dfec5e7e234b6" target='_blank'>
+              A New Self-organizing Interval Type-2 Fuzzy Neural Network for Multi-Step Time Series Prediction
+              </a>
+            </td>
+          <td>
+            Fulong Yao, Wanqing Zhao, Matthew Forshaw, Yang Song
+          </td>
+          <td>2024-07-10</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>1</td>
+        </tr>
+
         <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/d42310c3c0cb0b37adead8fdeac11e5ad83e66bf" target='_blank'>
-              VaryMinions: leveraging RNNs to identify variants in variability-intensive systems' logs
+          <td><a href="https://www.semanticscholar.org/paper/c6f317b12417c129221d4d1696f5c5b193bef61a" target='_blank'>
+              Discrete-time graph neural networks for transaction prediction in Web3 social platforms
               </a>
             </td>
           <td>
-            Sophie Fortz, Paul Temple, Xavier Devroey, Patrick Heymans, Gilles Perrouin
+            Manuel Dileo, Matteo Zignani
           </td>
-          <td>2024-06-15</td>
-          <td>Empir. Softw. Eng.</td>
+          <td>2024-06-25</td>
+          <td>Mach. Learn.</td>
           <td>0</td>
-          <td>18</td>
+          <td>15</td>
         </tr>
 
-        <tr id="We show how to create Artificial Neural Network based models for performing time series exponential-like smoothing and the well-known Holt time series analysis. Our work fares well compared to the well known Holt time series analysis and prediction method, while avoiding the burden of searching for the parameters of the model. We present the theoretical justification of the connection between the two models and experimental results showing the similarities of these models.">
+        <tr id="Generative pretrained models represent a significant advancement in natural language processing and computer vision, which can generate coherent and contextually relevant content based on the pre-training on large general datasets and fine-tune for specific tasks. Building foundation models using large scale omic data is promising to decode and understand the complex signaling language patterns within cells. Different from existing foundation models of omic data, we build a foundation model, mosGraphGPT, for multi-omic signaling (mos) graphs, in which the multi-omic data was integrated and interpreted using a multi-level signaling graph. The model was pretrained using multi-omic data of cancers in The Cancer Genome Atlas (TCGA), and fine-turned for multi-omic data of Alzheimer’s Disease (AD). The experimental evaluation results showed that the model can not only improve the disease classification accuracy, but also is interpretable by uncovering disease targets and signaling interactions. And the model code are uploaded via GitHub with link: https://github.com/mosGraph/mosGraphGPT">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f8e83c1ae737c5762111a8369b4addb7dc4a7e19" target='_blank'>
-              Artificial Neural Network Models For Time Series Smoothing And Holt Trend Analysis
+          <td><a href="https://www.semanticscholar.org/paper/921061abb7058296f902de2f988608493d126a30" target='_blank'>
+              mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI
               </a>
             </td>
           <td>
-            Kazeem Bankole, Felix Hamza-Lup, I. Iacob
+            Heming Zhang, Di Huang, Emily Chen, Dekang Cao, Tim Xu, Ben Dizdar, Guangfu Li, Yixin Chen, Philip R. O. Payne, Michael Province, Fuhai Li
           </td>
-          <td>2024-06-27</td>
-          <td>2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)</td>
+          <td>2024-08-06</td>
+          <td>bioRxiv</td>
           <td>0</td>
-          <td>8</td>
+          <td>6</td>
         </tr>
 
-        <tr id="In this paper, we explore advanced modifications to the Tweedie regression model in order to address its limitations in modeling aggregate claims for various types of insurance such as automobile, health, and liability. Traditional Tweedie models, while effective in capturing the probability and magnitude of claims, usually fall short in accurately representing the large incidence of zero claims. Our recommended approach involves a refined modeling of the zero-claim process, together with the integration of boosting methods in order to help leverage an iterative process to enhance predictive accuracy. Despite the inherent slowdown in learning algorithms due to this iteration, several efficient implementation techniques that also help precise tuning of parameter like XGBoost, LightGBM, and CatBoost have emerged. Nonetheless, we chose to utilize CatBoost, a efficient boosting approach that effectively handles categorical and other special types of data. The core contribution of our paper is the assembly of separate modeling for zero claims and the application of tree-based boosting ensemble methods within a CatBoost framework, assuming that the inflated probability of zero is a function of the mean parameter. The efficacy of our enhanced Tweedie model is demonstrated through the application of an insurance telematics dataset, which presents the additional complexity of compositional feature variables. Our modeling results reveal a marked improvement in model performance, showcasing its potential to deliver more accurate predictions suitable for insurance claim analytics.">
+        <tr id="The balance between model capacity and generalization has been a key focus of recent discussions in long-term time series forecasting. Two representative channel strategies are closely associated with model expressivity and robustness, including channel independence (CI) and channel dependence (CD). The former adopts individual channel treatment and has been shown to be more robust to distribution shifts, but lacks sufficient capacity to model meaningful channel interactions. The latter is more expressive for representing complex cross-channel dependencies, but is prone to overfitting. To balance the two strategies, we present a channel-aware low-rank adaptation method to condition CD models on identity-aware individual components. As a plug-in solution, it is adaptable for a wide range of backbone architectures. Extensive experiments show that it can consistently and significantly improve the performance of both CI and CD models with demonstrated efficiency and flexibility. The code is available at https://github.com/tongnie/C-LoRA.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/5af74d76d784bacfe974c20112cdcad54eedeb64" target='_blank'>
-              Zero-Inflated Tweedie Boosted Trees with CatBoost for Insurance Loss Analytics
+          <td><a href="https://www.semanticscholar.org/paper/99eb0dde01b9d4215864beae7111853843550b8a" target='_blank'>
+              Channel-Aware Low-Rank Adaptation in Time Series Forecasting
               </a>
             </td>
           <td>
-            Banghee So, Emiliano A. Valdez
+            Tong Nie, Yuewen Mei, Guoyang Qin, Jiangming Sun, Wei Ma
           </td>
-          <td>2024-06-23</td>
+          <td>2024-07-24</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>27</td>
+          <td>3</td>
         </tr>
 
         <tr id="In many situations, the measurements of a studied phenomenon are provided sequentially, and the prediction of its class needs to be made as early as possible so as not to incur too high a time penalty, but not too early and risk paying the cost of misclassification. This problem has been particularly studied in the case of time series, and is known as Early Classification of Time Series (ECTS). Although it has been the subject of a growing body of literature, there is still a lack of a systematic, shared evaluation protocol to compare the relative merits of the various existing methods. This document begins by situating these methods within a principle-based taxonomy. It defines dimensions for organizing their evaluation, and then reports the results of a very extensive set of experiments along these dimensions involving nine state-of-the art ECTS algorithms. In addition, these and other experiments can be carried out using an open-source library in which most of the existing ECTS algorithms have been implemented (see \url{https://github.com/ML-EDM/ml_edm}).">
@@ -4084,64 +3811,79 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>10</td>
         </tr>
 
-        <tr id="We devise a novel method for implied volatility smoothing based on neural operators. The goal of implied volatility smoothing is to construct a smooth surface that links the collection of prices observed at a specific instant on a given option market. Such price data arises highly dynamically in ever-changing spatial configurations, which poses a major limitation to foundational machine learning approaches using classical neural networks. While large models in language and image processing deliver breakthrough results on vast corpora of raw data, in financial engineering the generalization from big historical datasets has been hindered by the need for considerable data pre-processing. In particular, implied volatility smoothing has remained an instance-by-instance, hands-on process both for neural network-based and traditional parametric strategies. Our general operator deep smoothing approach, instead, directly maps observed data to smoothed surfaces. We adapt the graph neural operator architecture to do so with high accuracy on ten years of raw intraday S&P 500 options data, using a single set of weights. The trained operator adheres to critical no-arbitrage constraints and is robust with respect to subsampling of inputs (occurring in practice in the context of outlier removal). We provide extensive historical benchmarks and showcase the generalization capability of our approach in a comparison with SVI, an industry standard parametrization for implied volatility. The operator deep smoothing approach thus opens up the use of neural networks on large historical datasets in financial engineering.">
+        <tr id="This paper presents a new method to determine the optimal batch size for applying deep learning models with time series data. A set of batch sizes is determined by considering the length of the repetition pattern of the data using the Fast Fourier Transform (FFT). A comparative analysis is conducted to identify the impact of varying batch sizes on prediction errors for the three deep learning models. The results show that the RNN model has the optimal batch size that produces the minimum prediction error. In the DNN and CNN models, the optimal batch size is not correlated with the repetition pattern of time series data. Therefore, it is not recommended to apply CNN and DNN models of time series data. However, if used, a small batch size can be selected to reduce training time. In addition, the range of prediction error according to batch size is significantly larger for RNN models compared to DNN and CNN models.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/9ba8ee0b732411bf71bcc071a076ad227505ac9c" target='_blank'>
-              Operator Deep Smoothing for Implied Volatility
+          <td><a href="https://www.semanticscholar.org/paper/d1d9bac2cd18ff1641bc3ec0c44bcff2270a3253" target='_blank'>
+              Determination of Optimal Batch Size of Deep Learning Models with Time Series Data
               </a>
             </td>
           <td>
-            Lukas Gonon, Antoine Jacquier, Ruben Wiedemann
+            J. Hwang, Sang-Soo Lee, Jeong-Won Gil, Choul-Ki Lee
           </td>
-          <td>2024-06-17</td>
-          <td>ArXiv</td>
+          <td>2024-07-12</td>
+          <td>Sustainability</td>
           <td>0</td>
-          <td>15</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Despite the massive attention given to time-series explanations due to their extensive applications, a notable limitation in existing approaches is their primary reliance on the time-domain. This overlooks the inherent characteristic of time-series data containing both time and frequency features. In this work, we present Spectral eXplanation (SpectralX), an XAI framework that provides time-frequency explanations for time-series black-box classifiers. This easily adaptable framework enables users to"plug-in"various perturbation-based XAI methods for any pre-trained time-series classification models to assess their impact on the explanation quality without having to modify the framework architecture. Additionally, we introduce Feature Importance Approximations (FIA), a new perturbation-based XAI method. These methods consist of feature insertion, deletion, and combination techniques to enhance computational efficiency and class-specific explanations in time-series classification tasks. We conduct extensive experiments in the generated synthetic dataset and various UCR Time-Series datasets to first compare the explanation performance of FIA and other existing perturbation-based XAI methods in both time-domain and time-frequency domain, and then show the superiority of our FIA in the time-frequency domain with the SpectralX framework. Finally, we conduct a user study to confirm the practicality of our FIA in SpectralX framework for class-specific time-frequency based time-series explanations. The source code is available in https://github.com/gustmd0121/Time_is_not_Enough">
+        <tr id="In the financial industry, the ability to predict financial metrics accurately and in a timely manner can significantly impact investment decisions, risk management, and strategic planning. In recent years, machine learning has emerged as a powerful tool for such predictions. This study aims to explore the heretofore underexplored predictive potential of ChatGPT by predicting positive or negative changes in quarterly financial metrics, such as revenue and sales numbers, using textual data from social media. The performance of ChatGPT is compared against Long Short-Term Memory (LSTM) neural network models developed as part of this research. The methodology involves preprocessing large datasets from Twitter concerning major companies such as Amazon, Google, and Tesla, training LSTM models, and prompt engineering for ChatGPT-based predictions. Initial findings indicate that LSTM models can predict quarterly financial metric changes with up to 87% accuracy, significantly outperforming ChatGPT, which achieves a maximum accuracy of 67%. Therefore, at the current time, ChatGPT cannot be considered a reliable predictive tool for changes in quarterly financial metrics.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/9801e236174959fcc7a0915d023fa9b450328420" target='_blank'>
-              Time is Not Enough: Time-Frequency based Explanation for Time-Series Black-Box Models
+          <td><a href="https://www.semanticscholar.org/paper/8b4e2a6ac5ac0110e5991ee0a5cd2260ead16e09" target='_blank'>
+              Comparing ChatGPT And LSTM In Predicting Changes In Quarterly Financial Metrics
               </a>
             </td>
           <td>
-            Hyunseung Chung, Sumin Jo, Yeonsu Kwon, Edward Choi
+            Vitali Chaiko
           </td>
-          <td>2024-08-07</td>
-          <td>ArXiv</td>
+          <td>2024-06-30</td>
+          <td>Business &amp; Management Compass</td>
+          <td>0</td>
           <td>0</td>
-          <td>4</td>
         </tr>
 
-        <tr id="The balance between model capacity and generalization has been a key focus of recent discussions in long-term time series forecasting. Two representative channel strategies are closely associated with model expressivity and robustness, including channel independence (CI) and channel dependence (CD). The former adopts individual channel treatment and has been shown to be more robust to distribution shifts, but lacks sufficient capacity to model meaningful channel interactions. The latter is more expressive for representing complex cross-channel dependencies, but is prone to overfitting. To balance the two strategies, we present a channel-aware low-rank adaptation method to condition CD models on identity-aware individual components. As a plug-in solution, it is adaptable for a wide range of backbone architectures. Extensive experiments show that it can consistently and significantly improve the performance of both CI and CD models with demonstrated efficiency and flexibility. The code is available at https://github.com/tongnie/C-LoRA.">
+        <tr id="Capturing long-term dependency from historical behaviors is the key to the success of sequential recommendation; however, existing methods focus on extracting global sequential information while neglecting to obtain deep representations from subsequences. Previous research has revealed that the restricted inter-item transfer is fundamental to sequential modeling, and some potential substructures of sequences can help models learn more effective long-term dependency compared to the whole sequence. To automatically find better subsequences and perform efficient learning, we propose a sequential recommendation model with a gated recurrent unit and Transformers, abbreviated as GAT4Rec, which employs Transformers with shared parameters across layers to model users’ historical interaction sequences. The representation learned by the gated recurrent unit is used as the gating signal to identify the optimal substructure in user sequences. The fused representation of the subsequence and edge information is extracted by the encoding layer to make the corresponding recommendations. Experimental results on four well-known publicly available datasets demonstrate that our GAT4Rec model outperforms other recommendation models, achieving performance improvements of 5.77%, 1.35%, 11.58%, and 1.79% in the normalized discounted cumulative gain metric (NDCG@10), respectively.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/99eb0dde01b9d4215864beae7111853843550b8a" target='_blank'>
-              Channel-Aware Low-Rank Adaptation in Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/79a52d00fb976862ea5711ed640577ec503068bd" target='_blank'>
+              GAT4Rec: Sequential Recommendation with a Gated Recurrent Unit and Transformers
               </a>
             </td>
           <td>
-            Tong Nie, Yuewen Mei, Guoyang Qin, Jiangming Sun, Wei Ma
+            Huaiwen He, Xiangdong Yang, Feng Huang, Feng Yi, Shangsong Liang
           </td>
-          <td>2024-07-24</td>
-          <td>ArXiv</td>
+          <td>2024-07-12</td>
+          <td>Mathematics</td>
+          <td>0</td>
+          <td>1</td>
+        </tr>
+
+        <tr id="The prediction of vacant parking spaces (VPSs) can reduce the time drivers spend searching for parking, thus alleviating traffic congestion. However, previous studies have mostly focused on modeling the temporal features of VPSs using historical data, neglecting the complex and extensive spatial characteristics of different parking lots within the transportation network. This is mainly due to the lack of direct physical connections between parking lots, making it challenging to quantify the spatio-temporal features among them. To address this issue, we propose a dynamic spatio-temporal adaptive graph convolutional recursive network (DSTAGCRN) for VPS prediction. Specifically, DSTAGCRN divides VPS data into seasonal and periodic trend components and combines daily and weekly information with node embeddings using the dynamic parameter-learning module (DPLM) to generate dynamic graphs. Then, by integrating gated recurrent units (GRUs) with the parameter-learning graph convolutional recursive module (PLGCRM) of DPLM, we infer the spatio-temporal dependencies for each time step. Furthermore, we introduce a multihead attention mechanism to effectively capture and fuse the spatio-temporal dependencies and dynamic changes in the VPS data, thereby enhancing the prediction performance. Finally, we evaluate the proposed DSTAGCRN on three real parking datasets. Extensive experiments and analyses demonstrate that the DSTAGCRN model proposed in this study not only improves the prediction accuracy but can also better extract the dynamic spatio-temporal characteristics of available parking space data in multiple parking lots.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/eca0d5f95593149c00ab5b7ac8b7112a781351ba" target='_blank'>
+              Dynamic Spatio-Temporal Adaptive Graph Convolutional Recurrent Networks for Vacant Parking Space Prediction
+              </a>
+            </td>
+          <td>
+            Liangpeng Gao, Wenli Fan, Wenliang Jian
+          </td>
+          <td>2024-07-07</td>
+          <td>Applied Sciences</td>
           <td>0</td>
           <td>3</td>
         </tr>
 
-        <tr id="None">
+        <tr id="In this paper, we explore advanced modifications to the Tweedie regression model in order to address its limitations in modeling aggregate claims for various types of insurance such as automobile, health, and liability. Traditional Tweedie models, while effective in capturing the probability and magnitude of claims, usually fall short in accurately representing the large incidence of zero claims. Our recommended approach involves a refined modeling of the zero-claim process, together with the integration of boosting methods in order to help leverage an iterative process to enhance predictive accuracy. Despite the inherent slowdown in learning algorithms due to this iteration, several efficient implementation techniques that also help precise tuning of parameter like XGBoost, LightGBM, and CatBoost have emerged. Nonetheless, we chose to utilize CatBoost, a efficient boosting approach that effectively handles categorical and other special types of data. The core contribution of our paper is the assembly of separate modeling for zero claims and the application of tree-based boosting ensemble methods within a CatBoost framework, assuming that the inflated probability of zero is a function of the mean parameter. The efficacy of our enhanced Tweedie model is demonstrated through the application of an insurance telematics dataset, which presents the additional complexity of compositional feature variables. Our modeling results reveal a marked improvement in model performance, showcasing its potential to deliver more accurate predictions suitable for insurance claim analytics.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/55679dc2295465efbfdc86b6413e01e067432aa1" target='_blank'>
-              Eigen-entropy based time series signatures to support multivariate time series classification
+          <td><a href="https://www.semanticscholar.org/paper/5af74d76d784bacfe974c20112cdcad54eedeb64" target='_blank'>
+              Zero-Inflated Tweedie Boosted Trees with CatBoost for Insurance Loss Analytics
               </a>
             </td>
           <td>
-            Abhidnya Patharkar, Jiajing Huang, Teresa Wu, Erica Forzani, Leslie Thomas, Marylaura Lind, Naomi Gades
+            Banghee So, Emiliano A. Valdez
           </td>
-          <td>2024-07-12</td>
-          <td>Scientific Reports</td>
+          <td>2024-06-23</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>27</td>
         </tr>
 
         <tr id="Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for automated knowledge graph learning from time series data, specifically tailored for industrial applications. Our framework addresses the complexities inherent in industrial datasets, transforming them into knowledge graphs that improve decision-making, process optimization, and knowledge discovery. Additionally, it employs Granger causality to identify key attributes that can inform the design of predictive models. To illustrate the practical utility of our approach, we also present a motivating use case demonstrating the benefits of our framework in a real-world industrial scenario. Further, we demonstrate how the automated conversion of time series data into knowledge graphs can identify causal influences or dependencies between important process parameters.">
@@ -4159,64 +3901,64 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>12</td>
         </tr>
 
-        <tr id="Deep neural networks (DNNs) are prominent in predictive analytics for accurately forecasting target variables. However, inherent uncertainties necessitate constructing prediction intervals for reliability. The existing literature often lacks practical methodologies for creating predictive intervals, especially for time series with trends and seasonal patterns. This paper explicitly details a practical approach integrating dual-output Monte Carlo Dropout (MCDO) with DNNs to approximate predictive means and variances within a Bayesian framework, enabling forecast interval construction. The dual-output architecture employs a custom loss function, combining mean squared error with Softplus-derived predictive variance, ensuring non-negative variance values. Hyperparameter optimization is performed through a grid search exploring activation functions, dropout rates, epochs, and batch sizes. Empirical distributions of predictive means and variances from the MCDO demonstrate the results of the dual-output MCDO DNNs. The proposed method achieves a significant improvement in forecast accuracy, with an RMSE reduction of about 10% compared to the seasonal autoregressive integrated moving average model. Additionally, the method provides more reliable forecast intervals, as evidenced by a higher coverage proportion and narrower interval widths. A case study on Thailand’s durian export data showcases the method’s utility and applicability to other datasets with trends and/or seasonal components.">
+        <tr id="Time series data is widely available in a variety of industries. By forecasting time series, decision-makers can better grasp future trends and make more effective decisions. Financial time series data exhibit non-stationarity and high volatility. High-frequency fluctuations in financial products such as exchange rates, bonds and equities may reflect external shocks and risks in global financial markets, which are potentially dangerous and may threaten national economic security or even trigger financial crises. For financial time series data, a deep recurrent neural network first progressively processes each data point in the time series through its recurrent unit. Each recurring unit can adjust its own weights to better predict or analyze future values. Over time, these recurrent units continuously update their internal state, resulting in a comprehensive understanding of the characteristics of the entire data sequence. In addition, we add a gating mechanism to further improve the network's ability to control the flow of information, so that the model is more effective when retaining long-term dependencies, so as to improve the accuracy of prediction and the stability of the model. Experimental results show that our recurrent neural network model shows higher prediction accuracy and stability than other baseline models on financial time series datasets.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/48e9c93416cccc0d5c4d1f0646a11cd372d3ec5b" target='_blank'>
-              Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian Exports
+          <td><a href="https://www.semanticscholar.org/paper/6f5f866e6e62fae9396ebd199496590a68d45ac8" target='_blank'>
+              Research on time-series financial data prediction and analysis based on deep recurrent neural network
               </a>
             </td>
           <td>
-            Unyamanee Kummaraka, Patchanok Srisuradetchai
+            Feng Yuan
           </td>
-          <td>2024-08-02</td>
-          <td>Forecasting</td>
+          <td>2024-07-25</td>
+          <td>Applied and Computational Engineering</td>
+          <td>0</td>
           <td>0</td>
-          <td>2</td>
         </tr>
 
-        <tr id="Capturing long-term dependency from historical behaviors is the key to the success of sequential recommendation; however, existing methods focus on extracting global sequential information while neglecting to obtain deep representations from subsequences. Previous research has revealed that the restricted inter-item transfer is fundamental to sequential modeling, and some potential substructures of sequences can help models learn more effective long-term dependency compared to the whole sequence. To automatically find better subsequences and perform efficient learning, we propose a sequential recommendation model with a gated recurrent unit and Transformers, abbreviated as GAT4Rec, which employs Transformers with shared parameters across layers to model users’ historical interaction sequences. The representation learned by the gated recurrent unit is used as the gating signal to identify the optimal substructure in user sequences. The fused representation of the subsequence and edge information is extracted by the encoding layer to make the corresponding recommendations. Experimental results on four well-known publicly available datasets demonstrate that our GAT4Rec model outperforms other recommendation models, achieving performance improvements of 5.77%, 1.35%, 11.58%, and 1.79% in the normalized discounted cumulative gain metric (NDCG@10), respectively.">
+        <tr id="Graph data structures offer a versatile and powerful means to model relationships and interconnections in various domains, promising substantial advantages in data representation, analysis, and visualization. In games, graph-based data structures are omnipresent and represent, for example, game economies, skill trees or complex, branching quest lines. With this paper, we propose G-PCGRL, a novel and controllable method for the procedural generation of graph data using reinforcement learning. Therefore, we frame this problem as manipulating a graph's adjacency matrix to fulfill a given set of constraints. Our method adapts and extends the Procedural Content Generation via Reinforcement Learning (PCGRL) framework and introduces new representations to frame the problem of graph data generation as a Markov decision process. We compare the performance of our method with the original PCGRL, the run time with a random search and evolutionary algorithm, and evaluate G-PCGRL on two graph data domains in games: game economies and skill trees. The results show that our method is capable of generating graph-based content quickly and reliably to support and inspire designers in the game creation process. In addition, trained models are controllable in terms of the type and number of nodes to be generated.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/79a52d00fb976862ea5711ed640577ec503068bd" target='_blank'>
-              GAT4Rec: Sequential Recommendation with a Gated Recurrent Unit and Transformers
+          <td><a href="https://www.semanticscholar.org/paper/da8186d9c837b3b5140555e1d9904967ed9b8441" target='_blank'>
+              G-PCGRL: Procedural Graph Data Generation via Reinforcement Learning
               </a>
             </td>
           <td>
-            Huaiwen He, Xiangdong Yang, Feng Huang, Feng Yi, Shangsong Liang
+            Florian Rupp, Kai Eckert
           </td>
-          <td>2024-07-12</td>
-          <td>Mathematics</td>
+          <td>2024-07-15</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>2</td>
         </tr>
 
-        <tr id="This paper presents a new method to determine the optimal batch size for applying deep learning models with time series data. A set of batch sizes is determined by considering the length of the repetition pattern of the data using the Fast Fourier Transform (FFT). A comparative analysis is conducted to identify the impact of varying batch sizes on prediction errors for the three deep learning models. The results show that the RNN model has the optimal batch size that produces the minimum prediction error. In the DNN and CNN models, the optimal batch size is not correlated with the repetition pattern of time series data. Therefore, it is not recommended to apply CNN and DNN models of time series data. However, if used, a small batch size can be selected to reduce training time. In addition, the range of prediction error according to batch size is significantly larger for RNN models compared to DNN and CNN models.">
+        <tr id="This paper introduces a new addition to the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family, tailored specifically for time series and forecasting analysis. This new algorithm leverages the concept of similarity and higher-order temporal interactions across multiple time scales to enhance predictive accuracy and interpretability in forecasting. To evaluate the effectiveness of SPINEX, we present comprehensive benchmarking experiments comparing it against 18 algorithms and across 49 synthetic and real datasets characterized by varying trends, seasonality, and noise levels. Our performance assessment focused on forecasting accuracy and computational efficiency. Our findings reveal that SPINEX consistently ranks among the top 5 performers in forecasting precision and has a superior ability to handle complex temporal dynamics compared to commonly adopted algorithms. Moreover, the algorithm's explainability features, Pareto efficiency, and medium complexity (on the order of O(log n)) are demonstrated through detailed visualizations to enhance the prediction and decision-making process. We note that integrating similarity-based concepts opens new avenues for research in predictive analytics, promising more accurate and transparent decision making.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/d1d9bac2cd18ff1641bc3ec0c44bcff2270a3253" target='_blank'>
-              Determination of Optimal Batch Size of Deep Learning Models with Time Series Data
+          <td><a href="https://www.semanticscholar.org/paper/eb8311e78efe8332c0928c44bc76694e1d771836" target='_blank'>
+              SPINEX-TimeSeries: Similarity-based Predictions with Explainable Neighbors Exploration for Time Series and Forecasting Problems
               </a>
             </td>
           <td>
-            J. Hwang, Sang-Soo Lee, Jeong-Won Gil, Choul-Ki Lee
+            Ahmed Z Naser, MZ Naser
           </td>
-          <td>2024-07-12</td>
-          <td>Sustainability</td>
+          <td>2024-08-04</td>
+          <td>ArXiv</td>
+          <td>0</td>
           <td>0</td>
-          <td>1</td>
         </tr>
 
-        <tr id="Time series data is widely available in a variety of industries. By forecasting time series, decision-makers can better grasp future trends and make more effective decisions. Financial time series data exhibit non-stationarity and high volatility. High-frequency fluctuations in financial products such as exchange rates, bonds and equities may reflect external shocks and risks in global financial markets, which are potentially dangerous and may threaten national economic security or even trigger financial crises. For financial time series data, a deep recurrent neural network first progressively processes each data point in the time series through its recurrent unit. Each recurring unit can adjust its own weights to better predict or analyze future values. Over time, these recurrent units continuously update their internal state, resulting in a comprehensive understanding of the characteristics of the entire data sequence. In addition, we add a gating mechanism to further improve the network's ability to control the flow of information, so that the model is more effective when retaining long-term dependencies, so as to improve the accuracy of prediction and the stability of the model. Experimental results show that our recurrent neural network model shows higher prediction accuracy and stability than other baseline models on financial time series datasets.">
+        <tr id="Inventory prediction is concerned with the forecasting of future demand for products in order to optimize inventory levels and supply chain management. The challenges include demand volatility, data quality, multi-dimensional interactions, lead time variability, seasonal trends, and dynamic pricing. Nevertheless, these models suffer from numerous shortcomings, and in this research, we propose a new model, MMCW-BiLSTM (modified multi-dimensional collaboratively wrapped BiLSTM), for inventory prediction. The MMCW-BiLSTM model reflects a considerable leap in inventory forecasting by combining a number of components in order to consider intricate temporal dependencies and incorporate feature interactions. The MMCW-BiLSTM makes use of BiLSTM layers, collaborative attention mechanisms, and a multi-dimensional attention approach to learn from augmented datasets consisting of the original features and the extracted time series data. Moreover, adding a Taylor series transformation allows for a more precise description of the features in the model, thus improving the prediction precision. The results show that the models make the least mistakes when they use the AV demand forecasting dataset, with MAE values of 1.75, MAPE values of 2.89, MSE values of 6.76, and RMSE values of 2.6. Similarly, when utilizing the product demand dataset, the model also achieves the lowest error values for these metrics at 1.97, 3.91, 8.76, and 2.96. Likewise, when utilizing the dairy goods sales dataset, the model also achieves the lowest error values for these metrics at 2.54, 3.69, 10.39, and 3.22.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6f5f866e6e62fae9396ebd199496590a68d45ac8" target='_blank'>
-              Research on time-series financial data prediction and analysis based on deep recurrent neural network
+          <td><a href="https://www.semanticscholar.org/paper/cccb2c220a6df8c844c4317f0b7f000d6fa902b8" target='_blank'>
+              Inventory Prediction Using a Modified Multi-Dimensional Collaborative Wrapped Bi-Directional Long Short-Term Memory Model
               </a>
             </td>
           <td>
-            Feng Yuan
+            Said Abualuroug, A. Alzubi, Kolawole Iyiola
           </td>
-          <td>2024-07-25</td>
-          <td>Applied and Computational Engineering</td>
-          <td>0</td>
+          <td>2024-07-03</td>
+          <td>Applied Sciences</td>
           <td>0</td>
+          <td>4</td>
         </tr>
 
         <tr id="Dynamic multi-objective optimization problems (DMOPs) exhibit variations in constraints, decision parameter quantities, and the number of objectives over time, leading to changes in their optimal solutions. Prediction-based methods currently stand as the prevailing paradigm for handling DMOPs. Implicit correlations among solutions ob-tained at consecutive time steps while solving DMOPs con-tinuously can guide predictions for future time increments in the time series. Most existing methods rely on a single linear or nonlinear model to capture the correlations of historical optimal solutions. However, a linear model struggles with the nonlinear scenario, and using a nonlinear model to fit the linear relationship of the historical optimal solutions increases the training time because the nonlinear model has more parameters. This paper proposes a novel framework for time series ensemble prediction based on correlation type detection TSEPFCTD. The framework leverages correlation type detection strategy to identify the autocorrelation types in the time series of historical solutions. Subsequently, distinct prediction models are applied for different types, enhancing prediction accuracy. We implement this prediction framework on MOEA/D-DE, constructing a novel algorithm for solving DMOPs. Experimental results on a series of test suites demonstrate the effectiveness of our algorithm in providing robust solution outcomes.">
@@ -4231,22 +3973,22 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2024-06-30</td>
           <td>2024 IEEE Congress on Evolutionary Computation (CEC)</td>
           <td>0</td>
-          <td>0</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Time series classification stands as a pivotal and intricate challenge across various domains, including finance, healthcare, and industrial systems. In contemporary research, there has been a notable upsurge in exploring feature extraction through random sampling. Unlike deep convolutional networks, these methods sidestep elaborate training procedures, yet they often necessitate generating a surplus of features to comprehensively encapsulate time series nuances. Consequently, some features may lack relevance to labels or exhibit multi-collinearity with others. In this paper, we propose a novel hierarchical feature selection method aided by ANOVA variance analysis to address this challenge. Through meticulous experimentation, we demonstrate that our method substantially reduces features by over 94% while preserving accuracy -- a significant advancement in the field of time series analysis and feature selection.">
+        <tr id="Unsupervised Domain Adaptation (UDA) has emerged as a key solution in data-driven fault diagnosis, addressing domain shift where models underperform in changing environments. However, under the realm of continually changing environments, UDA tends to underperform on previously seen domains when adapting to new ones - a problem known as catastrophic forgetting. To address this limitation, we introduce the EverAdapt framework, specifically designed for continuous model adaptation in dynamic environments. Central to EverAdapt is a novel Continual Batch Normalization (CBN), which leverages source domain statistics as a reference point to standardize feature representations across domains. EverAdapt not only retains statistical information from previous domains but also adapts effectively to new scenarios. Complementing CBN, we design a class-conditional domain alignment module for effective integration of target domains, and a Sample-efficient Replay strategy to reinforce memory retention. Experiments on real-world datasets demonstrate EverAdapt superiority in maintaining robust fault diagnosis in dynamic environments. Our code is available: https://github.com/mohamedr002/EverAdapt">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/5c07ba99c8d7ae5a1590ce850bbbbc596fbc4609" target='_blank'>
-              HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis
+          <td><a href="https://www.semanticscholar.org/paper/0ac47f70a5e647f647faa7b42263e7b8c29cd85a" target='_blank'>
+              EverAdapt: Continuous Adaptation for Dynamic Machine Fault Diagnosis Environments
               </a>
             </td>
           <td>
-            Alireza Keshavarzian, S. Valaee
+            Edward, Mohamed Ragab, Yuecong Xu, Min Wu, Zhenghua Chen, Abdulla Alseiari, Xiaoli Li
           </td>
-          <td>2024-07-22</td>
+          <td>2024-07-24</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>44</td>
+          <td>37</td>
         </tr>
 
         <tr id="As a pivotal component of the global economic system, the stock market is subject to a multitude of influences, including the macroeconomic environment, market sentiment, and policy changes. Consequently, the ability to forecast stock prices is of paramount importance. Conventional time series forecasting techniques, such asARIMA and GARCH, are ill-equipped to handle complex nonlinear relationships. In contrast, recurrent neural networks, particularly Long Short-Term Memory (LSTM) networks, are particularly adept at handling time-dependent data. In light of recent advances in machine learning and deep learning, this study aims to assess and compare the efficacy of LSTM neural networks and Informer models in stock price forecasting. The objectives of this research are twofold: first, to compare the prediction accuracy using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R²; and second, to explore fusion strategies to enhance overall prediction performance and computational efficiency. The methodology includes the following steps: data collection and preprocessing, model construction, feature engineering, and model training and evaluation. This study presents a systematic comparison of the effectiveness of LSTM and Informer models in stock price prediction. The findings indicate that a fusion strategy combining the advantages of both models is expected to enhance prediction accuracy and computational efficiency.">
@@ -4264,49 +4006,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>0</td>
         </tr>
 
-        <tr id="Effective hospital resource management is crucial for service cost and workforce efficiency, especially during abnormal situations with sudden changes in patient volumes. Hospitals often face challenges in resource management and find it difficult to predict patient numbers due to uncertain and volatile data. If a hospital can accurately predict the number of patients during abnormal situations, it can allocate resources to align with the expected patient influx. This helps the hospital maintain appropriate costs and optimize workforce efficiency. In this research, we propose a solution to predict the number of daily outpatient visits from a private hospital in Thailand during the COVID-19 outbreak period as a case study. We use the Ensemble Empirical Mode Decomposition (EEMD) to preprocess the data by decomposing it into intrinsic mode functions, reducing the impact of data volatility and predict these intrinsic mode functions using a Long Short-Term Memory (LSTM) model with the service utilization pattern (seasonal value) as a lookback period to enhance prediction efficiency. This approach is referred to as the EEMD-LSTM model. Experimental results indicate that, compared to LSTM, ARIMA, and AutoML models evaluated for prediction periods of 7, 14, 21, and 28 days using metrics including MAE, RMSE, MAPE, and MAE Ratio, the EEMD-LSTM model demonstrates superior performance in predicting patient numbers during abnormal events. Unlike other models, the EEMD-LSTM model exhibits minimal changes in error when the prediction period is adjusted, highlighting its robustness. Overall, the EEMD-LSTM model consistently outperforms all other models assessed. When applied to data from the orthopedic department, although the MAE values are similar, noticeable differences in trends are observed. Moreover, when predicting data from the pediatric department, the EEMD-LSTM model proves to be more effective than all other models. This model can be effectively utilized by hospitals for resource management and planning, including medication and staffing, ensuring optimal service delivery to patients during abnormal situations.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4abf6281e7c8d5246cec887ba8f53218343c3d06" target='_blank'>
-              Prediction of Daily Outpatient Volume Using Ensemble Empirical Mode Decomposition and Long-Short Term Memory During the Incident
-              </a>
-            </td>
-          <td>
-            Teeraphat Buengbongkoch, N. Nupairoj
-          </td>
-          <td>2024-06-19</td>
-          <td>2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE)</td>
-          <td>0</td>
-          <td>7</td>
-        </tr>
-
-        <tr id="This paper introduces a new addition to the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family, tailored specifically for time series and forecasting analysis. This new algorithm leverages the concept of similarity and higher-order temporal interactions across multiple time scales to enhance predictive accuracy and interpretability in forecasting. To evaluate the effectiveness of SPINEX, we present comprehensive benchmarking experiments comparing it against 18 algorithms and across 49 synthetic and real datasets characterized by varying trends, seasonality, and noise levels. Our performance assessment focused on forecasting accuracy and computational efficiency. Our findings reveal that SPINEX consistently ranks among the top 5 performers in forecasting precision and has a superior ability to handle complex temporal dynamics compared to commonly adopted algorithms. Moreover, the algorithm's explainability features, Pareto efficiency, and medium complexity (on the order of O(log n)) are demonstrated through detailed visualizations to enhance the prediction and decision-making process. We note that integrating similarity-based concepts opens new avenues for research in predictive analytics, promising more accurate and transparent decision making.">
+        <tr id="Time series classification stands as a pivotal and intricate challenge across various domains, including finance, healthcare, and industrial systems. In contemporary research, there has been a notable upsurge in exploring feature extraction through random sampling. Unlike deep convolutional networks, these methods sidestep elaborate training procedures, yet they often necessitate generating a surplus of features to comprehensively encapsulate time series nuances. Consequently, some features may lack relevance to labels or exhibit multi-collinearity with others. In this paper, we propose a novel hierarchical feature selection method aided by ANOVA variance analysis to address this challenge. Through meticulous experimentation, we demonstrate that our method substantially reduces features by over 94% while preserving accuracy -- a significant advancement in the field of time series analysis and feature selection.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/eb8311e78efe8332c0928c44bc76694e1d771836" target='_blank'>
-              SPINEX-TimeSeries: Similarity-based Predictions with Explainable Neighbors Exploration for Time Series and Forecasting Problems
+          <td><a href="https://www.semanticscholar.org/paper/5c07ba99c8d7ae5a1590ce850bbbbc596fbc4609" target='_blank'>
+              HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis
               </a>
             </td>
           <td>
-            Ahmed Z Naser, MZ Naser
+            Alireza Keshavarzian, S. Valaee
           </td>
-          <td>2024-08-04</td>
+          <td>2024-07-22</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>44</td>
         </tr>
 
-        <tr id="Fuzzy neural network (FNN) is regarded as a prominent approach in application of time-series modeling. With the capability of fuzzy reasoning, FNN can capture temporal patterns from the time-series samples. However, the existing FNNs may suffer from the temporal pattern distortion because possibly multiscale features cannot be explored sufficiently. To address this problem, a time-aware fuzzy neural network, based on the frequency-enhanced modulation mechanism (FEM-TAFNN), is developed for time-series prediction in this article. First, a Fourier-based decoder is established to extract the multiscale features. This decoder employs the frequency-domain model to orthogonally separate the time-scale features with different frequencies into independent temporal patterns based on the Fourier basis, which prevents the overlap of temporal patterns using time-domain analysis. Second, a frequency-enhanced modulation mechanism is designed to shape fuzzy rules of FNN based on the contribution of different temporal patterns in the frequency spectrum. It enables FEM-TAFNN to modulate out the realistic multiscale temporal patterns. Finally, the proposed FEM-TAFNN is tested on four multiscale time-series datasets. The empirical results confirm its superior prediction performance than other methods.">
+        <tr id="We developed DyGETViz, a novel framework for effectively visualizing dynamic graphs (DGs) that are ubiquitous across diverse real-world systems. This framework leverages recent advancements in discrete-time dynamic graph (DTDG) models to adeptly handle the temporal dynamics inherent in dynamic graphs. DyGETViz effectively captures both micro- and macro-level structural shifts within these graphs, offering a robust method for representing complex and massive dynamic graphs. The application of DyGETViz extends to a diverse array of domains, including ethology, epidemiology, finance, genetics, linguistics, communication studies, social studies, and international relations. Through its implementation, DyGETViz has revealed or confirmed various critical insights. These include the diversity of content sharing patterns and the degree of specialization within online communities, the chronological evolution of lexicons across decades, and the distinct trajectories exhibited by aging-related and non-related genes. Importantly, DyGETViz enhances the accessibility of scientific findings to non-domain experts by simplifying the complexities of dynamic graphs. Our framework is released as an open-source Python package for use across diverse disciplines. Our work not only addresses the ongoing challenges in visualizing and analyzing DTDG models but also establishes a foundational framework for future investigations into dynamic graph representation and analysis across various disciplines.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ce803c3df8b377a910569d38f5c6288c1fad84ce" target='_blank'>
-              Time-Aware Fuzzy Neural Network Based on Frequency-Enhanced Modulation Mechanism
+          <td><a href="https://www.semanticscholar.org/paper/09cb3de3028ed37e10c48ae40a5a59f3c2e8d14c" target='_blank'>
+              Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories
               </a>
             </td>
           <td>
-            Hong-gui Han, Zecheng Tang, Xiaolong Wu, Hongyan Yang, Jun-Li Qiao
+            Yiqiao Jin, Andrew Zhao, Yeon-Chang Lee, Meng Ye, Ajay Divakaran, Srijan Kumar
           </td>
-          <td>2024-08-01</td>
-          <td>IEEE Transactions on Fuzzy Systems</td>
+          <td>2024-06-25</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>38</td>
+          <td>4</td>
         </tr>
 
         <tr id="Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs) has garnered significant research interest, largely due to its powerful capabilities in modeling complex interactions between nodes. A fundamental and crucial requirement for representation learning in CTDGs is the appropriate estimation and preservation of proximity. However, due to the sparse and evolving characteristics of CTDGs, the spatial-temporal properties inherent in high-order proximity remain largely unexplored. Despite its importance, this property presents significant challenges due to the computationally intensive nature of personalized interaction intensity estimation and the dynamic attributes of CTDGs. To this end, we propose a novel Correlated Spatial-Temporal Positional encoding that incorporates a parameter-free personalized interaction intensity estimation under the weak assumption of the Poisson Point Process. Building on this, we introduce the Dynamic Graph Transformer with \Correlated Spatial-Temporal Positional Encoding (CorDGT), which efficiently retains the evolving spatial-temporal high-order proximity for effective node representation learning in CTDGs. Extensive experiments on seven small and two large-scale datasets demonstrate the superior performance and scalability of the proposed CorDGT.">
@@ -4339,21 +4066,6 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>1</td>
         </tr>
 
-        <tr id="The variability and low signal-to-noise ratio in financial data, combined with the necessity for interpretability, make the alpha factor mining workflow a crucial component of quantitative investment. Transitioning from early manual extraction to genetic programming, the most advanced approach in this domain currently employs reinforcement learning to mine a set of combination factors with fixed weights. However, the performance of resultant alpha factors exhibits inconsistency, and the inflexibility of fixed factor weights proves insufficient in adapting to the dynamic nature of financial markets. To address this issue, this paper proposes a two-stage formulaic alpha generating framework AlphaForge, for alpha factor mining and factor combination. This framework employs a generative-predictive neural network to generate factors, leveraging the robust spatial exploration capabilities inherent in deep learning while concurrently preserving diversity. The combination model within the framework incorporates the temporal performance of factors for selection and dynamically adjusts the weights assigned to each component alpha factor. Experiments conducted on real-world datasets demonstrate that our proposed model outperforms contemporary benchmarks in formulaic alpha factor mining. Furthermore, our model exhibits a notable enhancement in portfolio returns within the realm of quantitative investment.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/67b68385fb304e7c75297c481c403fdc0a7da70e" target='_blank'>
-              AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors
-              </a>
-            </td>
-          <td>
-            Hao Shi, Cuicui Luo, Weili Song, Xinting Zhang, Xiang Ao
-          </td>
-          <td>2024-06-26</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-
         <tr id="Predictive analytics with machine learning approaches has widely penetrated and shown great success in system health management over the decade. However, how to convert the prediction to an actionable plan for maintenance is still far from mature. This study investigates how to narrow the gapbetween predictive outcomes and prescriptive descriptions for system maintenance using an agentic approach based on the large language model (LLM). Additionally, with the retrieval-augmented generation (RAG) technique and tool usage capability, the LLM can be context-aware when making decisions in maintenance strategy proposals considering predictions from machine learning. In this way, the proposed method can push forward the boundary of current machine-learning methods from a predictor to an advisor for decision-making workload offload. For verification, a case study on linear actuator fault diagnosis is conducted with the GPT-4 model. The result demonstrates that the proposed method can perform fault detection without extra training or fine-tuning with comparable performance to baseline methods and deliver more informatic diagnosis analysis and suggestions. This research can shed light on the application of large language models in the construction of versatile and flexible artificial intelligence agents for maintenance tasks.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/40776e2aba7dd99d2dadc993e370a0592a750cb3" target='_blank'>
@@ -4384,34 +4096,49 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>1</td>
         </tr>
 
-        <tr id="None">
+        <tr id="In recent years, deep learning has increasingly gained attention in the field of traffic prediction. Existing traffic prediction models often rely on GCNs or attention mechanisms with O(N^2) complexity to dynamically extract traffic node features, which lack efficiency and are not lightweight. Additionally, these models typically only utilize historical data for prediction, without considering the impact of the target information on the prediction. To address these issues, we propose a Pattern-Matching Dynamic Memory Network (PM-DMNet). PM-DMNet employs a novel dynamic memory network to capture traffic pattern features with only O(N) complexity, significantly reducing computational overhead while achieving excellent performance. The PM-DMNet also introduces two prediction methods: Recursive Multi-step Prediction (RMP) and Parallel Multi-step Prediction (PMP), which leverage the time features of the prediction targets to assist in the forecasting process. Furthermore, a transfer attention mechanism is integrated into PMP, transforming historical data features to better align with the predicted target states, thereby capturing trend changes more accurately and reducing errors. Extensive experiments demonstrate the superiority of the proposed model over existing benchmarks. The source codes are available at: https://github.com/wengwenchao123/PM-DMNet.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/b0b0a5a85891a45e2599f55130e1ac5892eeb99e" target='_blank'>
-              Spatial-temporal memory enhanced multi-level attention network for origin-destination demand prediction
+          <td><a href="https://www.semanticscholar.org/paper/27093b45b377c53bb3e2360dcde0bd4cae7da8df" target='_blank'>
+              Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction
               </a>
             </td>
           <td>
-            Jiawei Lu, Lin Pan, Qian-nan Ren
+            Wenchao Weng, Mei Wu, Hanyu Jiang, Wanzeng Kong, Xiangjie Kong, Feng Xia
           </td>
-          <td>2024-06-14</td>
-          <td>Complex &amp; Intelligent Systems</td>
+          <td>2024-08-12</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
+          <td>42</td>
         </tr>
 
-        <tr id="Predicting earthquakes with precision remains an ongoing challenge in earthquake early warning systems (EEWS), that struggle with accuracy and fail to provide timely warnings for impending earthquakes. Recent efforts employing deep learning techniques have shown promise in overcoming these limitations. However, current methods lack the ability to capture subtle frequency changes indicative of seismic activity in real-time, limiting their effectiveness in EEWS. To address this gap, we propose REAVER, a novel approach for real-time prediction of P- and S-waves of earthquakes using attention-based sliding-window spectrograms. REAVER leverages Mel-Spectrogram signal representations to capture temporal frequency changes in seismic signals effectively. By employing an encoder-decoder architecture with attention mechanisms, REAVER accurately predicts the onset of P- and S-waves moments when an earthquake occurs. We benchmark the effectiveness of REAVER, showing its performance in terms of both accuracy and real-time prediction capabilities compared to existing methods. Additionally, we provide a web-based implementation of REAVER, allowing users to monitor seismic activity in real-time and analyze historical earthquake waveforms.">
+        <tr id="Fuzzy neural network (FNN) is regarded as a prominent approach in application of time-series modeling. With the capability of fuzzy reasoning, FNN can capture temporal patterns from the time-series samples. However, the existing FNNs may suffer from the temporal pattern distortion because possibly multiscale features cannot be explored sufficiently. To address this problem, a time-aware fuzzy neural network, based on the frequency-enhanced modulation mechanism (FEM-TAFNN), is developed for time-series prediction in this article. First, a Fourier-based decoder is established to extract the multiscale features. This decoder employs the frequency-domain model to orthogonally separate the time-scale features with different frequencies into independent temporal patterns based on the Fourier basis, which prevents the overlap of temporal patterns using time-domain analysis. Second, a frequency-enhanced modulation mechanism is designed to shape fuzzy rules of FNN based on the contribution of different temporal patterns in the frequency spectrum. It enables FEM-TAFNN to modulate out the realistic multiscale temporal patterns. Finally, the proposed FEM-TAFNN is tested on four multiscale time-series datasets. The empirical results confirm its superior prediction performance than other methods.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c009ac27441aaf679db4b33826270416482f927a" target='_blank'>
-              REAVER: Real-time Earthquake Prediction with Attention-based Sliding-Window Spectrograms
+          <td><a href="https://www.semanticscholar.org/paper/ce803c3df8b377a910569d38f5c6288c1fad84ce" target='_blank'>
+              Time-Aware Fuzzy Neural Network Based on Frequency-Enhanced Modulation Mechanism
               </a>
             </td>
           <td>
-            Lotfy Abdel Khaliq, Sabine Janzen, Wolfgang Maass
+            Hong-gui Han, Zecheng Tang, Xiaolong Wu, Hongyan Yang, Jun-Li Qiao
           </td>
           <td>2024-08-01</td>
-          <td>Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence</td>
+          <td>IEEE Transactions on Fuzzy Systems</td>
+          <td>0</td>
+          <td>40</td>
+        </tr>
+
+        <tr id="Forecast reconciliation has become a prominent topic in recent forecasting literature, with a primary distinction made between cross-sectional and temporal hierarchies. This work focuses on temporal hierarchies, such as aggregating monthly time series data to annual data. We explore the impact of various forecast reconciliation methods on temporally aggregated ARIMA models, thereby bridging the fields of hierarchical forecast reconciliation and temporal aggregation both theoretically and experimentally. Our paper is the first to theoretically examine the effects of temporal hierarchical forecast reconciliation, demonstrating that the optimal method aligns with a bottom-up aggregation approach. To assess the practical implications and performance of the reconciled forecasts, we conduct a series of simulation studies, confirming that the findings extend to more complex models. This result helps explain the strong performance of the bottom-up approach observed in many prior studies. Finally, we apply our methods to real data examples, where we observe similar results.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/985e5918331ffab3f0ab26045d8e9e749f6655c6" target='_blank'>
+              Rediscovering Bottom-Up: Effective Forecasting in Temporal Hierarchies
+              </a>
+            </td>
+          <td>
+            Lukas Neubauer, Peter Filzmoser
+          </td>
+          <td>2024-07-02</td>
+          <td>ArXiv</td>
+          <td>0</td>
           <td>0</td>
-          <td>6</td>
         </tr>
 
         <tr id="In an era where machine learning permeates every facet of human existence, and data evolves incessantly, the application of machine learning models transcends mere data processing. It involves navigating constant changes exemplified by the phenomenon of concept drift, which often affects model performance.
@@ -4435,21 +4162,6 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>50</td>
         </tr>
 
-        <tr id="With the rapid digitization of the world, an increasing number of real-world applications are turning to non-Euclidean data, modeled as graphs. Due to their intrinsic high complexity and irregularity, learning from graph data demands tremendous computational power. Recently, CMOS-compatible Ising machines, i.e., dynamical systems fabricated with CMOS technologies, have emerged as a new approach that harnesses the inherent power of nature within dynamical systems to efficiently resolve binary optimization problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL) tasks, Ising machines face significant hurdles: (i) they are binary and thus ill-suited for real-valued problems; (ii) their expensive all-to-all coupling network that guarantees generality for optimization problems poses daunting scalability concerns.To address these challenges, this paper proposes a nature-powered graph learning framework dubbed DS-GL, which is the first effort to transform the process of solving graph learning problems into the natural annealing process within a parameterized dynamical system embodied as a CMOS chip. To tackle the two major hurdles, DS-GL first augments the Ising machine architecture to modify the self-reaction term of its Hamiltonian function from linear to quadratic, effectively serving as an energy regulator. This adjustment maintains the system’s original physical interpretation while enabling it to process continuous, real-valued data. Second, to address the scaling issue, DS-GL further upgrades the real-valued dense Ising machine by decomposing it into a mesh-based multi-PE dynamical system that supports efficient distributed spatial-temporal co-annealing across different PEs through sparse interconnects. By exploiting the inherent sparsity and community structures in real-world graphs, DS-GL is able to map complex graph learning tasks onto the scalable dynamical system while maintaining high accuracy. Evaluations with four diverse GL applications across seven real-world datasets, including traffic flow and COVID-19 prediction, show that DS-GL can deliver from 103 × to 105 × speedups over Graph Neural Networks on GPUs while operating at a power 2 orders of magnitude lower than GPUs, with 5% – 30% accuracy enhancement.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f5c8f2bb9d71e3bd5704dfdfc3c6c83338cf48fc" target='_blank'>
-              DS-GL: Advancing Graph Learning via Harnessing Nature’s Power within Scalable Dynamical Systems
-              </a>
-            </td>
-          <td>
-            Ruibing Song, Chunshu Wu, Chuan Liu, Ang Li, Michael C. Huang, Tony Tong Geng
-          </td>
-          <td>2024-06-29</td>
-          <td>2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)</td>
-          <td>0</td>
-          <td>8</td>
-        </tr>
-
         <tr id="Deep learning (DL) has been widely applied to forecast the sales volume of household appliances with high accuracy. Unfortunately, in small towns, due to the limited amount of historical sales data, it is difficult to forecast household appliance sales accurately. To overcome the above-mentioned challenge, we propose a novel household appliance sales forecasting algorithm based on transfer learning, temporal convolutional network (TCN), long short-term memory (LSTM), and attention mechanism (called “TransTLA”). Firstly, we combine TCN and LSTM to exploit the spatiotemporal correlation of sales data. Secondly, we utilize the attention mechanism to make full use of the features of sales data. Finally, in order to mitigate the impact of data scarcity and regional differences, a transfer learning technique is used to improve the predictive performance in small towns, with the help of the learning experience from the megacity. The experimental outcomes reveal that the proposed TransTLA model significantly outperforms traditional forecasting methods in predicting small town household appliance sales volumes. Specifically, TransTLA achieves an average mean absolute error (MAE) improvement of 27.60% over LSTM, 9.23% over convolutional neural networks (CNN), and 11.00% over the CNN-LSTM-Attention model across one to four step-ahead predictions. This study addresses the data scarcity problem in small town sales forecasting, helping businesses improve inventory management, enhance customer satisfaction, and contribute to a more efficient supply chain, benefiting the overall economy.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/0b9ca38d56c22558ee1fb94ba33b348585bf6d14" target='_blank'>
@@ -4465,34 +4177,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>0</td>
         </tr>
 
-        <tr id="Solar energy has become a promising renewable energy source, offering significant opportunities for photovoltaic (PV) systems. Accurate and reliable PV generation forecasts are crucial for efficient grid integration and optimized system planning. However, the complexity of environmental factors, including seasonal and daily patterns, as well as social behaviors and user habits, presents significant challenges. Traditional prediction models often struggle with capturing the complex nonlinear dynamics in multivariate time series, leading to low prediction accuracy. To address this issue, this paper proposes a new PV power prediction method that considers factors such as light, air pressure, wind direction, and social behavior, assigning different weights to them to accurately extract nonlinear feature relationships. The framework integrates long short-term memory (LSTM) and gated recurrent units (GRU) to capture local time features, while bidirectional LSTM (BiLSTM) and an attention mechanism extract global spatiotemporal relationships, effectively capturing key features related to historical output. This improves the accuracy of multi-step predictions. To verify the feasibility of the method for multivariate time series, we conducted experiments using PV power prediction as a scenario and compared the results with LSTM, CNN, BiLSTM, CNN-LSTM and GRU models. The experimental results show that the proposed method outperforms these models, with a mean absolute error (MAE) of 12.133, root mean square error (RMSE) of 14.234, mean absolute percentage error (MAPE) of 2.1%, and a coefficient of determination (R2) of 0.895. These results indicate the effectiveness and potential of the method in PV prediction tasks.">
+        <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/5cd0c75b48f548d60683a1e8ab48e8b00609e962" target='_blank'>
-              A Photovoltaic Prediction Model with Integrated Attention Mechanism
+          <td><a href="https://www.semanticscholar.org/paper/1e7d935a1fd72ec0c78f76e9366ac22a96bd7a77" target='_blank'>
+              A Meta-learner approach to multistep-ahead time series prediction
               </a>
             </td>
           <td>
-            Xiangshu Lei
+            Fouad Bahrpeyma, V. M. Ngo, M. Roantree, A. Mccarren
           </td>
-          <td>2024-07-04</td>
-          <td>Mathematics</td>
-          <td>0</td>
+          <td>2024-07-09</td>
+          <td>International Journal of Data Science and Analytics</td>
           <td>0</td>
+          <td>17</td>
         </tr>
 
-        <tr id="None">
+        <tr id="Solar energy has become a promising renewable energy source, offering significant opportunities for photovoltaic (PV) systems. Accurate and reliable PV generation forecasts are crucial for efficient grid integration and optimized system planning. However, the complexity of environmental factors, including seasonal and daily patterns, as well as social behaviors and user habits, presents significant challenges. Traditional prediction models often struggle with capturing the complex nonlinear dynamics in multivariate time series, leading to low prediction accuracy. To address this issue, this paper proposes a new PV power prediction method that considers factors such as light, air pressure, wind direction, and social behavior, assigning different weights to them to accurately extract nonlinear feature relationships. The framework integrates long short-term memory (LSTM) and gated recurrent units (GRU) to capture local time features, while bidirectional LSTM (BiLSTM) and an attention mechanism extract global spatiotemporal relationships, effectively capturing key features related to historical output. This improves the accuracy of multi-step predictions. To verify the feasibility of the method for multivariate time series, we conducted experiments using PV power prediction as a scenario and compared the results with LSTM, CNN, BiLSTM, CNN-LSTM and GRU models. The experimental results show that the proposed method outperforms these models, with a mean absolute error (MAE) of 12.133, root mean square error (RMSE) of 14.234, mean absolute percentage error (MAPE) of 2.1%, and a coefficient of determination (R2) of 0.895. These results indicate the effectiveness and potential of the method in PV prediction tasks.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1e7d935a1fd72ec0c78f76e9366ac22a96bd7a77" target='_blank'>
-              A Meta-learner approach to multistep-ahead time series prediction
+          <td><a href="https://www.semanticscholar.org/paper/5cd0c75b48f548d60683a1e8ab48e8b00609e962" target='_blank'>
+              A Photovoltaic Prediction Model with Integrated Attention Mechanism
               </a>
             </td>
           <td>
-            Fouad Bahrpeyma, V. M. Ngo, M. Roantree, A. Mccarren
+            Xiangshu Lei
           </td>
-          <td>2024-07-09</td>
-          <td>International Journal of Data Science and Analytics</td>
+          <td>2024-07-04</td>
+          <td>Mathematics</td>
+          <td>0</td>
           <td>0</td>
-          <td>17</td>
         </tr>
 
         <tr id="
@@ -4511,34 +4223,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>10</td>
         </tr>
 
-        <tr id="In the modern transport industry, vast and diverse information arrays, particularly those including time series data, are rapidly expanding. This growth presents an opportunity to improve the quality of forecasting. Researchers and practitioners are continuously developing innovative tools to predict their future values. The goal of the research is to improve the performance of automated forecasting environments in a systematic and structured way. This paper investigates the effect of substituting the initial time series with another of a similar nature, during the training phase of the model’s development. A financial data set and the Prophet model are employed for this objective. It is observed that the impact on the accuracy of the predicted future values is promising, albeit not significant. Based on the obtained results, valuable conclusions are drawn, and recommendations for further improvements are provided. By highlighting the importance of diverse data incorporation, this research assists in making informed choices and leveraging the full potential of available information for more precise forecasting outcomes.">
+        <tr id="With the rapid digitization of the world, an increasing number of real-world applications are turning to non-Euclidean data, modeled as graphs. Due to their intrinsic high complexity and irregularity, learning from graph data demands tremendous computational power. Recently, CMOS-compatible Ising machines, i.e., dynamical systems fabricated with CMOS technologies, have emerged as a new approach that harnesses the inherent power of nature within dynamical systems to efficiently resolve binary optimization problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL) tasks, Ising machines face significant hurdles: (i) they are binary and thus ill-suited for real-valued problems; (ii) their expensive all-to-all coupling network that guarantees generality for optimization problems poses daunting scalability concerns.To address these challenges, this paper proposes a nature-powered graph learning framework dubbed DS-GL, which is the first effort to transform the process of solving graph learning problems into the natural annealing process within a parameterized dynamical system embodied as a CMOS chip. To tackle the two major hurdles, DS-GL first augments the Ising machine architecture to modify the self-reaction term of its Hamiltonian function from linear to quadratic, effectively serving as an energy regulator. This adjustment maintains the system’s original physical interpretation while enabling it to process continuous, real-valued data. Second, to address the scaling issue, DS-GL further upgrades the real-valued dense Ising machine by decomposing it into a mesh-based multi-PE dynamical system that supports efficient distributed spatial-temporal co-annealing across different PEs through sparse interconnects. By exploiting the inherent sparsity and community structures in real-world graphs, DS-GL is able to map complex graph learning tasks onto the scalable dynamical system while maintaining high accuracy. Evaluations with four diverse GL applications across seven real-world datasets, including traffic flow and COVID-19 prediction, show that DS-GL can deliver from 103 × to 105 × speedups over Graph Neural Networks on GPUs while operating at a power 2 orders of magnitude lower than GPUs, with 5% – 30% accuracy enhancement.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c8cdf9821562b0563c9797fb5821a5a46f40c681" target='_blank'>
-              Time Series Cross-Sequence Prediction
+          <td><a href="https://www.semanticscholar.org/paper/f5c8f2bb9d71e3bd5704dfdfc3c6c83338cf48fc" target='_blank'>
+              DS-GL: Advancing Graph Learning via Harnessing Nature’s Power within Scalable Dynamical Systems
               </a>
             </td>
           <td>
-            K. Koparanov, E. Antonova, D. Minkovska, K. Georgiev
+            Ruibing Song, Chunshu Wu, Chuan Liu, Ang Li, Michael C. Huang, Tong Geng
           </td>
-          <td>2024-07-19</td>
-          <td>WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS</td>
+          <td>2024-06-29</td>
+          <td>2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)</td>
           <td>0</td>
-          <td>3</td>
+          <td>8</td>
         </tr>
 
-        <tr id="In this study, a long short-term memory network (LSTM) model is used to predict the population size and risk level of each country in 2030 by combining global population data from 1950 to 2021 and country risk rating indices from 1954 to 2023.The core of the LSTM model is its three gating units: forgetting gates, input gates, and output gates, which enable the model to effectively deal with the long-term dependence problems and avoid the problems of gradient vanishing and gradient explosion in traditional recurrent neural networks. Through correlation analysis of the input data and prediction errors, as well as an autocorrelation study of the errors, the model shows prediction accuracy at certain time delays, although the prediction errors of the model are not observed to be completely random on certain delay terms, implying that the model may need further optimisation in these areas. In addition, this study applies the entropy method to calculate the construction suitability evaluation index for each country in 2030, which provides a scientific basis for the development of future construction strategies. By analysing the mean square error (MSE) during the training process, the study shows that the performance of the model on the training and validation sets gradually improves as the number of training rounds increases, although the MSE on the test set increases at some points, showing signs of overfitting. Finally, countries were scored and ranked for construction suitability based on predicted population and risk class scores. Based on the composite suitability score index, we categorised the countries into three building suitability classes, each corresponding to a different building strategy. In areas of low suitability, robust materials and designs that are resistant to natural hazards are recommended; in areas of moderate suitability, sustainable building design and green building materials are recommended; and in areas of high suitability, investment in high-quality and high-end design building projects should be emphasised to meet the needs of higher-income groups. These strategies will contribute to the needs of different regions and populations and improve the accuracy and efficiency of real estate decisions. Through the application of this deep learning model, we have not only improved our ability to predict global population dynamics and construction risks, but also provided important data support for urban planning and development strategies on a global scale.">
+        <tr id="In the modern transport industry, vast and diverse information arrays, particularly those including time series data, are rapidly expanding. This growth presents an opportunity to improve the quality of forecasting. Researchers and practitioners are continuously developing innovative tools to predict their future values. The goal of the research is to improve the performance of automated forecasting environments in a systematic and structured way. This paper investigates the effect of substituting the initial time series with another of a similar nature, during the training phase of the model’s development. A financial data set and the Prophet model are employed for this objective. It is observed that the impact on the accuracy of the predicted future values is promising, albeit not significant. Based on the obtained results, valuable conclusions are drawn, and recommendations for further improvements are provided. By highlighting the importance of diverse data incorporation, this research assists in making informed choices and leveraging the full potential of available information for more precise forecasting outcomes.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/84d6d751134c919f51e6eaeaceb49a82cdec6192" target='_blank'>
-              Using Deep Learning to Predict Global Population Dynamics and Construction Risks
+          <td><a href="https://www.semanticscholar.org/paper/c8cdf9821562b0563c9797fb5821a5a46f40c681" target='_blank'>
+              Time Series Cross-Sequence Prediction
               </a>
             </td>
           <td>
-            Kai Jin, Xingxin Yang, Yonghao Zhou, Yongzheng Wu
+            K. Koparanov, E. Antonova, D. Minkovska, K. Georgiev
           </td>
-          <td>2024-06-20</td>
-          <td>Transactions on Computer Science and Intelligent Systems Research</td>
-          <td>0</td>
+          <td>2024-07-19</td>
+          <td>WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS</td>
           <td>0</td>
+          <td>3</td>
         </tr>
 
         <tr id="None">
@@ -4556,19 +4268,64 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>21</td>
         </tr>
 
-        <tr id="Accurately predicting PM2.5 is a crucial task for protecting public health and making policy decisions. In the meanwhile, it is also a challenging task, given the complex spatio-temporal patterns of PM2.5 concentrations. Recently, the utilization of graph neural network (GNN) models has emerged as a promising approach, demonstrating significant advantages in capturing the spatial and temporal dependencies associated with PM2.5 concentrations. In this work, we collected a comprehensive dataset spanning 308 cities in China, encompassing data on seven pollutants as well as meteorological variables from January 2015 to September 2022. To effectively predict the PM2.5 concentrations, we propose a graph attention recurrent neural network (GARNN) model by taking into account both meteorological and geographical information. Extensive experiments validated the efficiency of the proposed GARNN model, revealing its superior performance compared to other existing methods in terms of predictive capabilities. This study contributes to advancing the understanding and prediction of PM2.5 concentrations, providing a valuable tool for addressing environmental challenges.">
+        <tr id="Change point detection in time series seeks to identify times when the probability distribution of time series changes. It is widely applied in many areas, such as human-activity sensing and medical science. In the context of multivariate time series, this typically involves examining the joint distribution of high-dimensional data: If any one variable changes, the whole time series is assumed to have changed. However, in practical applications, we may be interested only in certain components of the time series, exploring abrupt changes in their distributions in the presence of other time series. Here, assuming an underlying structural causal model that governs the time-series data generation, we address this problem by proposing a two-stage non-parametric algorithm that first learns parts of the causal structure through constraint-based discovery methods. The algorithm then uses conditional relative Pearson divergence estimation to identify the change points. The conditional relative Pearson divergence quantifies the distribution disparity between consecutive segments in the time series, while the causal discovery method enables a focus on the causal mechanism, facilitating access to independent and identically distributed (IID) samples. Theoretically, the typical assumption of samples being IID in conventional change point detection methods can be relaxed based on the Causal Markov Condition. Through experiments on both synthetic and real-world datasets, we validate the correctness and utility of our approach.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f0d3185a50e22682b8c650ceac52e70dd3a9849f" target='_blank'>
-              A Graph Attention Recurrent Neural Network Model for PM2.5 Prediction: A Case Study in China from 2015 to 2022
+          <td><a href="https://www.semanticscholar.org/paper/010ab97fc0f4c786a8c27a35f6bd53cf9efb60a2" target='_blank'>
+              Causal Discovery-Driven Change Point Detection in Time Series
               </a>
             </td>
           <td>
-            Rui Pan, Tuozhen Liu, Lingfei Ma
+            Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan Rossi, Murat Kocaoglu
+          </td>
+          <td>2024-07-10</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>7</td>
+        </tr>
+
+        <tr id="Background: Biomedical data are usually collections of longitudinal data assessed at certain points in time. Clinical observations assess the presences and severity of symptoms, which are the basis for the description and modeling of disease progression. Deciphering potential underlying unknowns from the distinct observation would substantially improve the understanding of pathological cascades. Hidden Markov Models (HMMs) have been successfully applied to the processing of possibly noisy continuous signals. We apply ensembles of HMMs to categorically distributed multivariate time series data, leaving space for expert domain knowledge in the prediction process. Methods: We use an ensemble of HMMs to predict the loss of free walking ability as one major clinical deterioration in the most common autosomal dominantly inherited ataxia disorder worldwide. Results: We present a prediction pipeline that processes data paired with a configuration file, enabling us to train, validate and query an ensemble of HMMs. In particular, we provide a theoretical and practical framework for multivariate time-series inference based on HMMs that includes constructing multiple HMMs, each to predict a particular observable variable. Our analysis is conducted on pseudo-data, but also on biomedical data based on Spinocerebellar ataxia type 3 disease. Conclusions: We find that the model shows promising results for the data we tested. The strength of this approach is that HMMs are well understood, probabilistic and interpretable models, setting it apart from most Deep Learning approaches. We publish all code and evaluation pseudo-data in an open-source repository.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/75eb137d735a965284db79b60d1396e83205c3ac" target='_blank'>
+              Ensemble of HMMs for Sequence Prediction on Multivariate Biomedical Data
+              </a>
+            </td>
+          <td>
+            Richard Fechner, Jens Dörpinghaus, R. Rockenfeller, J. Faber
           </td>
           <td>2024-07-03</td>
-          <td>Atmosphere</td>
+          <td>BioMedInformatics</td>
           <td>0</td>
-          <td>18</td>
+          <td>9</td>
+        </tr>
+
+        <tr id="Constructing accurate spatiotemporal correlations is a challenging task in joint prediction of multiple photovoltaic sites. Some advanced algorithms for incorporating other surrounding site information have been proposed, such as graph neural network-based methods, which are usually based on static or dynamic graphs to build spatial dependencies between sites. However, the possibility of the simultaneous existence of multiple spatial dependencies is not considered. This paper establishes a spatiotemporal prediction model based on hybrid spatiotemporal graph neural network. In this model, we apply adaptive hybrid graph learning to learn composite spatial correlations among multiple sites. A temporal convolution module with multi-subsequence temporal data input is used to extract local semantic information to better predict future nonlinear temporal dependencies. A spatiotemporal adaptive fusion module is added to address the issue of integrating diverse spatiotemporal trends among multiple sites. To assess the model's predictive performance, nine solar radiation observation stations were selected in two different climatic environments. The average root mean square error (RMSE) of the constructed model was 38.51 and 49.90 W/m2, with average mean absolute error (MAE) of 14.72 and 23.06 W/m2, respectively. Single-site and multi-site prediction models were selected as baseline models. Compared with the baseline models, the RMSE and MAE reduce by 3.1%–20.8% and 8.9%–32.8%, respectively, across all sites. The proposed model demonstrates the effectiveness of improving accuracy in forecasting solar irradiance through multi-site predictions.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/9f5975f5f85d9f8b58520410f9d2fb8ae33faa1d" target='_blank'>
+              Multi-site solar irradiance prediction based on hybrid spatiotemporal graph neural network
+              </a>
+            </td>
+          <td>
+            Yunjun Yu, Zejie Cheng, Biao Xiong, Qian Li
+          </td>
+          <td>2024-07-01</td>
+          <td>Journal of Renewable and Sustainable Energy</td>
+          <td>0</td>
+          <td>10</td>
+        </tr>
+
+        <tr id="Studying low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1,000-10,000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part I, we construct an ensemble weather forecasting system based on Spherical Fourier Neural Operators (SFNO), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. Using large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states, and the ML ensemble thus passes a crucial spectral test in the literature. The IFS and ML ensembles have similar Extreme Forecast Indices, and we show that the ML extreme weather forecasts are reliable and discriminating.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/de58dea585a06935529ae1f31353338f254df5af" target='_blank'>
+              Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators
+              </a>
+            </td>
+          <td>
+            Ankur Mahesh, William Collins, B. Bonev, Noah D. Brenowitz, Y. Cohen, Joshua Elms, Peter Harrington, K. Kashinath, Thorsten Kurth, Joshua North, Travis OBrien, Michael S. Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, Jared Willard
+          </td>
+          <td>2024-08-06</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>25</td>
         </tr>
 
         <tr id="Graph neural networks (GNNs) have gained significant attention in diverse domains, ranging from urban planning to pandemic management. Ensuring both accuracy and robustness in GNNs remains a challenge due to insufficient quality data that contains sufficient features. With sufficient training data where all spatiotemporal patterns are well-represented, existing GNN models can make reasonably accurate predictions. However, existing methods fail when the training data are drawn from different circumstances (e.g., traffic patterns on regular days) than test data (e.g., traffic patterns after a natural disaster). Such challenges are usually classified under domain generalization. In this work, we show that one way to address this challenge in the context of spatiotemporal prediction is by incorporating domain differential equations into graph convolutional networks (GCNs). We theoretically derive conditions where GCNs incorporating such domain differential equations are robust to mismatched training and testing data compared to baseline domain agnostic models. To support our theory, we propose two domain-differential-equation-informed networks: Reaction-Diffusion Graph Convolutional Network (RDGCN), which incorporates differential equations for traffic speed evolution, and the Susceptible-Infectious-Recovered Graph Convolutional Network (SIRGCN), which incorporates a disease propagation model. Both RDGCN and SIRGCN are based on reliable and interpretable domain differential equations that allow the models to generalize to unseen patterns. We experimentally show that RDGCN and SIRGCN are more robust with mismatched testing data than state-of-the-art deep learning methods.">
@@ -4586,6 +4343,21 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>1</td>
         </tr>
 
+        <tr id="With the vigorous development of e-commerce, accurately modeling and predicting user behavior has become a key factor in improving business efficiency. Precisely understanding user behavior not only enables companies to provide personalized services but also allows them to stand out in the intense market competition. This study aims to explore the effectiveness of applying end-to-end models, Long Short-Term Memory (LSTM), and attention mechanisms in time series modeling to enhance the performance of modeling and predicting user behavior in e-commerce. In the methodology section, we first introduce the basic principles of the end-to-end model, which extracts features directly from raw data for prediction, avoiding the need for intricate feature engineering. Simultaneously, we introduce Long Short-Term Memory (LSTM) to better capture long-term dependencies in time series data.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/0da5e0a0f8f43013d568bfc235ed44504eef00ce" target='_blank'>
+              End-to-End Modeling and Long Short-Term Memory Application in Time Series Modeling
+              </a>
+            </td>
+          <td>
+            Guodong Yang, Kuangyu Li, Yunfan Zhang, Qindi Feng
+          </td>
+          <td>2024-07-30</td>
+          <td>Journal of Organizational and End User Computing</td>
+          <td>0</td>
+          <td>0</td>
+        </tr>
+
         <tr id="The COVID-19 pandemic has posed significant chal-lenges to global health, economies, and social structures since its emergence in late 2019. Southern California, a region with unique characteristics and diverse communities, has experienced difficul-ties in controlling the virus's spread. Understanding the factors that influence COVID-19 transmission in Southern California is crucial for informing public health policies and mitigating the virus's impact on the communities. In this study, we collected weekly zip code level data from March to December 2020 in Southern California and applied various machine learning algorithms, such as graph neural networks (GNN), multi-layer perceptrons (MLP), and gradient boosting trees (XGBoost), to model the spread of COVID-19. Our main contributions include the development of various COVID-19 forecasting models that incorporate socioeconomically significant variables and zip code level mobility data, coupled with variable impact assessments to provide valuable insights for informing future public health policies and strategies. Our predictive model not only attains high levels of accuracy in forecasting but also facilitates the understanding of how each variable contributes to the final outcome. These findings have the potential to enhance our understanding of COVID-19 transmission patterns and inform targeted mitigation strategies in the region.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/e3a3a205cede5dc9ee960274ff3f51815bbca1ea" target='_blank'>
@@ -4601,109 +4373,124 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>6</td>
         </tr>
 
-        <tr id="In oil and gas exploration, accurate fluid prediction is crucial for strategic decisions. Our study introduces the BiGRU–Transformer, a deep learning model merging bidirectional gated recurrent units (BiGRUs) with the Transformer. The BiGRU interprets well logging data in a bidirectional manner, ensuring a comprehensive understanding of temporal factors at different depths and time intervals, enhancing fluid prediction precision. The model's sensitivity to local temporal dynamics is heightened by its recurrent neural network structure and gating mechanisms. This feature is particularly beneficial in identifying essential details during brief geological occurrences and minor temporal shifts. The bidirectional approach of the model is tailored to accommodate geological variations across assorted depths and timelines, thus boosting its capability to adapt to diverse geological formations. In the Transformer, the self-attention mechanism dynamically adjusts information importance in geological sequences, enabling flexible handling of diverse relationships and a deeper understanding of intricate structures. Primary data come from key well log curves, providing essential features for analysis. Fed into the BiGRU–Transformer, it links fluid attributes with logging parameters. Benchmarking against state-of-the-art models shows our BiGRU–Transformer's superior accuracy and impressive generalization in fluid prediction across various scenarios. This innovation marks a significant advancement in machine learning for well logging, offering a precise tool for geologists and engineers, enhancing exploration and development quality. The BiGRU–Transformer highlights the transformative impact of advanced machine learning in geosciences, paving the way for novel approaches in oil and gas resource exploration and utilization.">
+        <tr id="Most state-of-the-art AI applications in atmospheric science are based on classic deep learning approaches. However, such approaches cannot automatically integrate multiple complicated procedures to construct an intelligent agent, since each functionality is enabled by a separate model learned from independent climate datasets. The emergence of foundation models, especially multimodal foundation models, with their ability to process heterogeneous input data and execute complex tasks, offers a substantial opportunity to overcome this challenge. In this report, we want to explore a central question - how the state-of-the-art foundation model, i.e., GPT-4o, performs various atmospheric scientific tasks. Toward this end, we conduct a case study by categorizing the tasks into four main classes, including climate data processing, physical diagnosis, forecast and prediction, and adaptation and mitigation. For each task, we comprehensively evaluate the GPT-4o's performance along with a concrete discussion. We hope that this report may shed new light on future AI applications and research in atmospheric science.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/0cba1284a3a852f82463472d3a606d4e2182fbe4" target='_blank'>
-              Enhancing fluid identification via an innovative transformer model with bidirectional recurrent units network leveraging well logging data
+          <td><a href="https://www.semanticscholar.org/paper/18a501f9bccb6fcfbb0d37e1ac9f7d535942b340" target='_blank'>
+              On the Opportunities of (Re)-Exploring Atmospheric Science by Foundation Models: A Case Study
               </a>
             </td>
           <td>
-            Youzhuang Sun, Shanchen Pang, Yongan Zhang
+            Lujia Zhang, Hanzhe Cui, Yurong Song, Chenyue Li, Binhang Yuan, Mengqian Lu
           </td>
-          <td>2024-07-01</td>
-          <td>Physics of Fluids</td>
+          <td>2024-07-25</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>4</td>
+          <td>2</td>
         </tr>
 
-        <tr id="Constructing accurate spatiotemporal correlations is a challenging task in joint prediction of multiple photovoltaic sites. Some advanced algorithms for incorporating other surrounding site information have been proposed, such as graph neural network-based methods, which are usually based on static or dynamic graphs to build spatial dependencies between sites. However, the possibility of the simultaneous existence of multiple spatial dependencies is not considered. This paper establishes a spatiotemporal prediction model based on hybrid spatiotemporal graph neural network. In this model, we apply adaptive hybrid graph learning to learn composite spatial correlations among multiple sites. A temporal convolution module with multi-subsequence temporal data input is used to extract local semantic information to better predict future nonlinear temporal dependencies. A spatiotemporal adaptive fusion module is added to address the issue of integrating diverse spatiotemporal trends among multiple sites. To assess the model's predictive performance, nine solar radiation observation stations were selected in two different climatic environments. The average root mean square error (RMSE) of the constructed model was 38.51 and 49.90 W/m2, with average mean absolute error (MAE) of 14.72 and 23.06 W/m2, respectively. Single-site and multi-site prediction models were selected as baseline models. Compared with the baseline models, the RMSE and MAE reduce by 3.1%–20.8% and 8.9%–32.8%, respectively, across all sites. The proposed model demonstrates the effectiveness of improving accuracy in forecasting solar irradiance through multi-site predictions.">
+        <tr id="The cinematic metaverse aims to create a virtual space with the context of a film. Users can enter this space in the form of avatars, experiencing the cinematic plot firsthand in an immersive manner. This requires us to design a rational computation resource allocation and synchronization algorithm to meet the demands of multi‐objective joint optimization, such as low latency and high throughput, which ensures that users can seamlessly switch between virtual and real worlds and acquire immersive experiences. Unfortunately, the explosive growth in the number of users makes it difficult to jointly optimize multiple objectives. Predicting traffic generated by the users' avatars in the cinematic metaverse is significant for the optimization process. Although graph neural networks‐based traffic prediction models achieve superior prediction accuracy, these methods rely only on physical distances‐based topological graph information, while failing to comprehensively reflect the real relationships between avatars in the cinematic metaverse. To address this issue, we present a novel Multi‐Graph Representation Spatio‐Temporal Attention Networks (MGRSTANet) for traffic prediction in the cinematic metaverse. Specifically, based on multiple topological graph information (e.g., physical distances, centerity, and similarity), we first design Multi‐Graph Embedding (MGE) module to generate multiple graph representations, thus reflecting on the real relationships between avatars more comprehensively. The Spatio‐Temporal Attention (STAtt) module is then proposed to extract spatio‐temporal correlations in each graph representations, thus improving prediction accuracy. We conduct simulation experiments to evaluate the effectiveness of MGRSTANet. The experimental results demonstrate that our proposed model outperforms the state‐of‐the‐art baselines in terms of prediction accuracy, making it appropriate for traffic forecasting in the cinematic metaverse.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/9f5975f5f85d9f8b58520410f9d2fb8ae33faa1d" target='_blank'>
-              Multi-site solar irradiance prediction based on hybrid spatiotemporal graph neural network
+          <td><a href="https://www.semanticscholar.org/paper/7a17e82d53c5d1ce27349fc52790d7c9df247a47" target='_blank'>
+              Multi-graph representation spatio-temporal attention networks for traffic forecasting in the cinematic metaverse
               </a>
             </td>
           <td>
-            Yunjun Yu, Zejie Cheng, Biao Xiong, Qian Li
+            Ke Li, Xiaoming He, Yinqiu Liu, Meng Chen
           </td>
           <td>2024-07-01</td>
-          <td>Journal of Renewable and Sustainable Energy</td>
+          <td>Trans. Emerg. Telecommun. Technol.</td>
           <td>0</td>
-          <td>10</td>
+          <td>1</td>
         </tr>
 
-        <tr id="Studying low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1,000-10,000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part I, we construct an ensemble weather forecasting system based on Spherical Fourier Neural Operators (SFNO), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. Using large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states, and the ML ensemble thus passes a crucial spectral test in the literature. The IFS and ML ensembles have similar Extreme Forecast Indices, and we show that the ML extreme weather forecasts are reliable and discriminating.">
+        <tr id="Diffusion Transformer, the backbone of Sora for video generation, successfully scales the capacity of diffusion models, pioneering new avenues for high-fidelity sequential data generation. Unlike static data such as images, sequential data consists of consecutive data frames indexed by time, exhibiting rich spatial and temporal dependencies. These dependencies represent the underlying dynamic model and are critical to validate the generated data. In this paper, we make the first theoretical step towards bridging diffusion transformers for capturing spatial-temporal dependencies. Specifically, we establish score approximation and distribution estimation guarantees of diffusion transformers for learning Gaussian process data with covariance functions of various decay patterns. We highlight how the spatial-temporal dependencies are captured and affect learning efficiency. Our study proposes a novel transformer approximation theory, where the transformer acts to unroll an algorithm. We support our theoretical results by numerical experiments, providing strong evidence that spatial-temporal dependencies are captured within attention layers, aligning with our approximation theory.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/de58dea585a06935529ae1f31353338f254df5af" target='_blank'>
-              Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators
+          <td><a href="https://www.semanticscholar.org/paper/29e9034e1e9a82b3bffe33d3460d002e29d5958c" target='_blank'>
+              Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data
               </a>
             </td>
           <td>
-            Ankur Mahesh, William Collins, B. Bonev, Noah D. Brenowitz, Y. Cohen, Joshua Elms, Peter Harrington, K. Kashinath, Thorsten Kurth, Joshua North, Travis OBrien, Michael S. Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, Jared Willard
+            Hengyu Fu, Zehao Dou, Jiawei Guo, Mengdi Wang, Minshuo Chen
           </td>
-          <td>2024-08-06</td>
+          <td>2024-07-23</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>25</td>
+          <td>4</td>
         </tr>
 
-        <tr id="Neural networks are becoming increasingly widely used in various fields, especially for academic risk forecasts. Academic risk prediction is a hot topic in the field of big data in education that aims to identify and help students who experience great academic difficulties. In recent years, the use of machine learning algorithms and deep learning algorithms to achieve academic risk prediction has garnered increased attention and development. However, most of these studies use nontime series data as features for prediction, which are slightly insufficient in terms of timeliness. Therefore, this article focuses on time series data features that are more expressive of changes in students' learning status and uses multivariate time series data as predictive features. This article proposes a method based on multivariate time series features and a neural network to predict academic risk. The method includes three steps: first, the multivariate time series feature is extracted from the interaction records of the students' online learning platforms; second, the multivariate time series feature transformation model ROCKET is applied to convert the multivariate time series feature into a new feature; third, the new feature is converted into a final prediction result using a linear classification model. Comparative tests show that the proposed method has high effectiveness.">
+        <tr id="Continual graph learning (CGL) is an important and challenging task that aims to extend static GNNs to dynamic task flow scenarios. As one of the mainstream CGL methods, the experience replay (ER) method receives widespread attention due to its superior performance. However, existing ER methods focus on identifying samples by feature significance or topological relevance, which limits their utilization of comprehensive graph data. In addition, the topology-based ER methods only consider local topological information and add neighboring nodes to the buffer, which ignores the global topological information and increases memory overhead. To bridge these gaps, we propose a novel method called Feature-Topology Fusion-based Experience Replay (FTF-ER) to effectively mitigate the catastrophic forgetting issue with enhanced efficiency. Specifically, from an overall perspective to maximize the utilization of the entire graph data, we propose a highly complementary approach including both feature and global topological information, which can significantly improve the effectiveness of the sampled nodes. Moreover, to further utilize global topological information, we propose Hodge Potential Score (HPS) as a novel module to calculate the topological importance of nodes. HPS derives a global node ranking via Hodge decomposition on graphs, providing more accurate global topological information compared to neighbor sampling. By excluding neighbor sampling, HPS significantly reduces buffer storage costs for acquiring topological information and simultaneously decreases training time. Compared with state-of-the-art methods, FTF-ER achieves a significant improvement of 3.6% in AA and 7.1% in AF on the OGB-Arxiv dataset, demonstrating its superior performance in the class-incremental learning setting.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/b266183a4b1b25ac12ccc8026d8fabfe9d72f507" target='_blank'>
-              A study of online academic risk prediction based on neural network multivariate time series features
+          <td><a href="https://www.semanticscholar.org/paper/5a0f6d19e56c93eb398b62ebfdf9a94a5c5b4bed" target='_blank'>
+              FTF-ER: Feature-Topology Fusion-Based Experience Replay Method for Continual Graph Learning
               </a>
             </td>
           <td>
-            Yang Wu, Mengping Yu, Huan Huang, Rui Hou
+            Jinhui Pang, Changqing Lin, Xiaoshuai Hao, Rong Yin, Zixuan Wang, Zhihui Zhang, Jinglin He, Huang Tai Sheng
           </td>
-          <td>2024-08-06</td>
-          <td>Concurrency and Computation: Practice and Experience</td>
+          <td>2024-07-28</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>2</td>
+          <td>1</td>
         </tr>
 
-        <tr id="None">
+        <tr id="Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/315baf76729f0231ac3eaabcfa04e94b0886a269" target='_blank'>
-              Fractional-order state space reconstruction: a new frontier in multivariate complex time series
+          <td><a href="https://www.semanticscholar.org/paper/82ecf0c70aa07f89d48d8aa7c5d4ec1674f0475d" target='_blank'>
+              Scalable and Interpretable Forecasting of Hydrological Time Series Based on Variational Gaussian Processes
               </a>
             </td>
           <td>
-            Jieren Xie, Guanghua Xu, Xiaobi Chen, Xun Zhang, Ruiquan Chen, Zengyao Yang, Churui Fang, Peiyuan Tian, Qingqiang Wu, Sicong Zhang
+            J. D. Pastrana-Cortés, J. Gil-González, A. Álvarez-Meza, D. Cárdenas-Peña, Á. Orozco-Gutiérrez
           </td>
-          <td>2024-08-05</td>
-          <td>Scientific Reports</td>
+          <td>2024-07-15</td>
+          <td>Water</td>
           <td>0</td>
-          <td>17</td>
+          <td>15</td>
         </tr>
 
-        <tr id="Gold is a form of investment known as a safe haven asset because of its stability in unstable market conditions. Gold price forecasting is important for investors as decisions making tool. This study aims to study the best long short--term memory (LSTM) hyperparameters (optimizer, learning rate, and epoch) from cross--validation for forecasting. LSTM, as part of deep learning methods, is developed based on a RNN widely used in time series forecasting. LSTM is superior compared to other methods for its ability to minimize errors and forecast for long--term periods. Walk--forward validation with sliding and extending window scenarios as a form of cross--validation is used to see the method's accuracy. The used data is sourced from the World Gold Council with daily data periods for January 1, 2003, to December 31, 2023. The optimizer used is Adam and RMSProp, each with learning rate values of 0.01, 0.001, 0.0001, and epoch values of 100, 500, 1000. The best model uses the Adam optimizer, a learning rate of 0.01, and an epoch value of 100 with a MAPE value of 0.4867\% in the validation process. Forecasting results show a tendency for gold prices to increase in the next eight years.">
+        <tr id="In recent years, sequential recommendation has received widespread attention for its role in enhancing user experience and driving personalized content recommendations. However, it also encounters challenges, including the limitations of modeling information and the variability of user preferences. A novel time-aware Long-Short Term Transformer (TLSTSRec) for sequential recommendation is introduced in this paper to address these challenges. TLSTSRec has two major innovative features. (1) Accurate modeling of users is achieved by fully leveraging temporal information. Time information is modeled by creating a trainable timestamp matrix from both the perspectives of time duration and time spectrum. (2) A novel time-aware Transformer model is proposed. To address the inherent variability of user preferences over time, the model combines long-term and short-term temporal information and adjusts the personalized trade-offs between long-term and short-term sequences using adaptive fusion layers. Subsequently, newly designed encoders and decoders are employed to model timestamps and interaction items. Finally, extensive experiments substantiate the effectiveness of TLSTSRec relative to various state-of-the-art sequential recommendation models based on MC/RNN/GNN/SA across a spectrum of widely used metrics. Furthermore, experiments are conducted to validate the rationality of the TLSTSRec structure.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/de63dd1bcbcef959e9467cc4e0bf1ade3346c078" target='_blank'>
-              Implementation of Long Short-Term Memory for Gold Prices Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/417f89b6992377e3e5d706672bfa4da837f011df" target='_blank'>
+              TLSTSRec: Time-aware long short-term attention neural network for sequential recommendation
               </a>
             </td>
           <td>
-            M. R. Nurhambali, Y. Angraini, A. Fitrianto
+            Hongwei Chen, Luanxuan Liu, Zexi Chen
           </td>
-          <td>2024-06-27</td>
-          <td>Malaysian Journal of Mathematical Sciences</td>
+          <td>2024-08-01</td>
+          <td>Intelligent Data Analysis</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
-        <tr id="With the vigorous development of e-commerce, accurately modeling and predicting user behavior has become a key factor in improving business efficiency. Precisely understanding user behavior not only enables companies to provide personalized services but also allows them to stand out in the intense market competition. This study aims to explore the effectiveness of applying end-to-end models, Long Short-Term Memory (LSTM), and attention mechanisms in time series modeling to enhance the performance of modeling and predicting user behavior in e-commerce. In the methodology section, we first introduce the basic principles of the end-to-end model, which extracts features directly from raw data for prediction, avoiding the need for intricate feature engineering. Simultaneously, we introduce Long Short-Term Memory (LSTM) to better capture long-term dependencies in time series data.">
+        <tr id="Literature abounds with various statistical and machine learning techniques for stock market forecasting. However, Reinforcement Learning (RL) is conspicuous by its absence in this field and is little explored despite its potential to address the dynamic and uncertain nature of the stock market. In a first-of-its-kind study, this research precisely bridges this gap, by forecasting stock prices using RL, in the static as well as streaming contexts using deep RL techniques. In the static context, we employed three deep RL algorithms for forecasting the stock prices: Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimisation (PPO) and Recurrent Deterministic Policy Gradient (RDPG) and compared their performance with Multi-Layer Perceptron (MLP), Support Vector Regression (SVR) and General Regression Neural Network (GRNN). In addition, we proposed a generic streaming analytics-based forecasting approach leveraging the real-time processing capabilities of Spark streaming for all six methods. This approach employs a sliding window technique for real-time forecasting or nowcasting using the above-mentioned algorithms. We demonstrated the effectiveness of the proposed approach on the daily closing prices of four different financial time series dataset as well as the Mackey–Glass time series, a benchmark chaotic time series dataset. We evaluated the performance of these methods using three metrics: Symmetric Mean Absolute Percentage (SMAPE), Directional Symmetry statistic (DS) and Theil’s U Coefficient. The results are promising for DDPG in the static context and GRNN turned out to be the best in streaming context. We performed the Diebold–Mariano (DM) test to assess the statistical significance of the best-performing models.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/0da5e0a0f8f43013d568bfc235ed44504eef00ce" target='_blank'>
-              End-to-End Modeling and Long Short-Term Memory Application in Time Series Modeling
+          <td><a href="https://www.semanticscholar.org/paper/399a8108ea6cff24099de6a2388ecbb9069a00fa" target='_blank'>
+              Deep Reinforcement Learning for Financial Forecasting in Static and Streaming Cases
               </a>
             </td>
           <td>
-            Guodong Yang, Kuangyu Li, Yunfan Zhang, Qindi Feng
+            Aravilli Atchuta Ram, Sandarbh Yadav, Yelleti Vivek, V. Ravi
           </td>
-          <td>2024-07-30</td>
-          <td>Journal of Organizational and End User Computing</td>
+          <td>2024-06-27</td>
+          <td>Journal of Information &amp; Knowledge Management</td>
           <td>0</td>
+          <td>40</td>
+        </tr>
+
+        <tr id="High precision in forecasting travel demand for online car-hailing is crucial for traffic management to schedule vehicles, hence reducing energy consumption and achieving sustainable development. Netflix demand forecasting relies on the capture of spatiotemporal correlations. To extract the spatiotemporal information more fully, this study designs and develops a novel spatiotemporal prediction model with multidimensional inputs (MSACL) by embedding a self-attention memory (SAM) module into a convolutional long short-term memory neural network (ConvLSTM). The SAM module can extract features with long-range spatiotemporal dependencies. The experimental data are derived from the Chengdu City online car-hailing trajectory data set and the external factors data set. Comparative experiments demonstrate that the proposed model has higher accuracy. The proposed model outperforms the Sa-ConvLSTM model and has the highest prediction accuracy, shows a reduction in the mean absolute error (MAE) by 1.72, a reduction in the mean squared error (MSE) by 0.43, and an increase in the R-squared (R2) by 4%. In addition, ablation experiments illustrate the effectiveness of each component, where the external factor inputs have the least impact on the model accuracy, but the removal of the SAM module results in the most significant decrease in model accuracy.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/ea2c249a41fa5eff75101cd3187bb7f3b33e1f26" target='_blank'>
+              Prediction on Demand for Regional Online Car-Hailing Travel Based on Self-Attention Memory and ConvLSTM
+              </a>
+            </td>
+          <td>
+            Jianqi Li, Wenbao Zeng, Weiqi Liu, Rongjun Cheng
+          </td>
+          <td>2024-07-04</td>
+          <td>Sustainability</td>
           <td>0</td>
+          <td>1</td>
         </tr>
 
         <tr id="Echo state network (ESN) implements an alternative paradigm called reservoir computing to train recurrent neural networks (RNNs), where internal weights are randomly generated and kept fixed, and only readout weights need to be trained, which greatly reduces the training complexity of RNNs. ESN not only facilitates the practical implementation of RNNs but also shows superior performance over fully trained RNNs across a range of applications. However, the conventional ESN suffers from the drawbacks of stringent conditions for weight convergence and slow convergence speed. This paper proposes a memory regressor extended learning method to update the readout weights of ESNs. By constructing and incorporating a generalized prediction error based on regressor extension and filtering, the capacity of ESN to utilize historical data can be greatly improved. In the discrete-time domain, it is proven that exponential convergence of readout weights is achieved under a condition termed interval excitation that is strictly weaker than the classical condition of persistent excitation. Simulation results on modeling a 10th-order nonlinear autoregressive moving-average (NARMA) system have revealed that the proposed approach accelerates weight convergence speed almost ten times higher compared to the conventional ESN.">
@@ -4721,17 +4508,17 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2</td>
         </tr>
 
-        <tr id="Time series data are a crucial information source for various natural and societal processes. Short time series can exhibit long-range correlations that reveal significant features not easily discernible in longer ones. Such short time series find utility in AI applications for training models to recognize patterns, make predictions, and perform classification tasks. However, traditional methods like DFA fail as classifiers for monofractal short time series, especially when the series are very short. In this study, we evaluate the performance of the traditional DFA method against the CNN-SVM approach of neural networks as classifiers for different monofractal models. We examine their performance as a function of the decreasing length of synthetic samples. The results demonstrate that CNN-SVM achieves superior classification rates compared to DFA. The overall accuracy rate of CNN-SVM ranges between 64% and 98%, whereas DFA’s accuracy rate ranges between 16% and 64%.">
+        <tr id="With technological advancements, much data is being captured by sensors, smartphones, wearable devices, and so forth. These vast datasets are stored in data centres and utilized to forge data‐driven models for the condition monitoring of infrastructures and systems through future data mining tasks. However, these datasets often surpass the processing capabilities of traditional information systems and methodologies due to their significant size. Additionally, not all samples within these datasets contribute valuable information during the model training phase, leading to inefficiencies. The processing and training of Machine Learning algorithms become time‐consuming, and storing all the data demands excessive space, contributing to the Big Data challenge. In this paper, we propose two novel techniques to reduce large time‐series datasets into more compact versions without undermining the predictive performance of the resulting models. These methods also aim to decrease the time required for training the models and the storage space needed for the condensed datasets. We evaluated our techniques on five public datasets, employing three Machine Learning algorithms: Holt‐Winters, SARIMA, and LSTM. The outcomes indicate that for most of the datasets examined, our techniques maintain, and in several instances enhance, the forecasting accuracy of the models. Moreover, we significantly reduced the time required to train the Machine Learning algorithms employed.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/5feb342e8be3450c12f6ffb26decb8d36426da88" target='_blank'>
-              Analyzing Monofractal Short and Very Short Time Series: A Comparison of Detrended Fluctuation Analysis and Convolutional Neural Networks as Classifiers
+          <td><a href="https://www.semanticscholar.org/paper/33d3e765763fbda62c139a833d2484281eec1ade" target='_blank'>
+              Sampling approaches to reduce very frequent seasonal time series
               </a>
             </td>
           <td>
-            Juan L. López, José A. Vásquez-Coronel
+            Afonso Baldo, Paulo J. S. Ferreira, João Mendes‐Moreira
           </td>
-          <td>2024-08-06</td>
-          <td>Fractal and Fractional</td>
+          <td>2024-07-25</td>
+          <td>Expert Systems</td>
           <td>0</td>
           <td>2</td>
         </tr>
@@ -4751,19 +4538,169 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2</td>
         </tr>
 
-        <tr id="In this work, we present a novel methodology for performing the supervised classification of time-ordered noisy data; we call this methodology Entropic Sparse Probabilistic Approximation with Markov regularization (eSPA-Markov). It is an extension of entropic learning methodologies, allowing the simultaneous learning of segmentation patterns, entropy-optimal feature space discretizations, and Bayesian classification rules. We prove the conditions for the existence and uniqueness of the learning problem solution and propose a one-shot numerical learning algorithm that—in the leading order—scales linearly in dimension. We show how this technique can be used for the computationally scalable identification of persistent (metastable) regime affiliations and regime switches from high-dimensional non-stationary and noisy time series, i.e., when the size of the data statistics is small compared to their dimensionality and when the noise variance is larger than the variance in the signal. We demonstrate its performance on a set of toy learning problems, comparing eSPA-Markov to state-of-the-art techniques, including deep learning and random forests. We show how this technique can be used for the analysis of noisy time series from DNA and RNA Nanopore sequencing.">
+        <tr id="Out-of-stock prediction refers to the activity of forecasting the time when a product will not be available for purchase because of an inventory deficiency. Due to difficulties, out-of-stock forecasting models now face certain challenges. Incorrect demand forecasting may result in a lack or excess of goods in stock, a factor that affects client satisfaction and the profitability of companies. Accordingly, the new approach BCHO-TCN LightGBM, which is based on Buzzard Coney Hawk Optimization with a deep temporal convolutional neural network and the Light Gradient-Boosting Machine framework, is developed to deal with all challenges in the existing models in the field of out-of-stock prediction. The role that BCHO plays in the LightGBM-based deep temporal CNNis rooted in modifying the classifier to improve both accuracy and speed. Integrating BCHO into the model training process allows us to optimize and adjust the hyperparameters and the weights of the CNN linked with the temporal DNN, which, in turn, makes the model perform better in the extraction of temporal features from time-series data. This optimization strategy, which derives from the cooperative behaviors and evasion tactics of BCHO, is a powerful source of information for the computational optimization agent. This leads to a faster convergence of the model towards optimal solutions and therefore improves the overall accuracy and predictive abilities of the temporal CNN with the LightGBM algorithm. The results indicate that when using data from Amazon India’s product listings, the model shows a high degree of accuracy, as well as excellent net present value (NPV), present discounted value (PDV), and threat scores, with values reaching 94.52%, 95.16%, 94.81%, and 95.76%, respectively. Likewise, in a k-fold 10 scenario, the model achieves values of 94.81%, 95.60%, 96.28%, and 95.86% for the same metrics.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/eadfee466307a1a3b8e63299cb3e600477c1f902" target='_blank'>
-              On Entropic Learning from Noisy Time Series in the Small Data Regime
+          <td><a href="https://www.semanticscholar.org/paper/96cb7b1001e9b5ba05e22ee7aaa6f6de94c16dcd" target='_blank'>
+              Out-of-Stock Prediction Model Using Buzzard Coney Hawk Optimization-Based LightGBM-Enabled Deep Temporal Convolutional Neural Network
               </a>
             </td>
           <td>
-            Davide Bassetti, Lukáš Pospíšil, I. Horenko
+            Ahmed Elghadghad, A. Alzubi, Kolawole Iyiola
           </td>
-          <td>2024-06-28</td>
-          <td>Entropy</td>
+          <td>2024-07-05</td>
+          <td>Applied Sciences</td>
+          <td>0</td>
+          <td>4</td>
+        </tr>
+
+        <tr id="This paper presents a novel neuro-fuzzy model, termed fuzzy recurrent stochastic configuration networks (F-RSCNs), for industrial data analytics. Unlike the original recurrent stochastic configuration network (RSCN), the proposed F-RSCN is constructed by multiple sub-reservoirs, and each sub-reservoir is associated with a Takagi-Sugeno-Kang (TSK) fuzzy rule. Through this hybrid framework, first, the interpretability of the model is enhanced by incorporating fuzzy reasoning to embed the prior knowledge into the network. Then, the parameters of the neuro-fuzzy model are determined by the recurrent stochastic configuration (RSC) algorithm. This scheme not only ensures the universal approximation property and fast learning speed of the built model but also overcomes uncertain problems, such as unknown dynamic orders, arbitrary structure determination, and the sensitivity of learning parameters in modelling nonlinear dynamics. Finally, an online update of the output weights is performed using the projection algorithm, and the convergence analysis of the learning parameters is given. By integrating TSK fuzzy inference systems into RSCNs, F-RSCNs have strong fuzzy inference capability and can achieve sound performance for both learning and generalization. Comprehensive experiments show that the proposed F-RSCNs outperform other classical neuro-fuzzy and non-fuzzy models, demonstrating great potential for modelling complex industrial systems.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/011103f067ff8221c76b025cca9c174b3d93244b" target='_blank'>
+              Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analytics
+              </a>
+            </td>
+          <td>
+            Dianhui Wang, Gang Dang
+          </td>
+          <td>2024-07-06</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>1</td>
+        </tr>
+
+        <tr id="PurposeTo optimize train operations, dispatchers currently rely on experience for quick adjustments when delays occur. However, delay predictions often involve imprecise shifts based on known delay times. Real-time and accurate train delay predictions, facilitated by data-driven neural network models, can significantly reduce dispatcher stress and improve adjustment plans. Leveraging current train operation data, these models enable swift and precise predictions, addressing challenges posed by train delays in high-speed rail networks during unforeseen events.Design/methodology/approachThis paper proposes CBLA-net, a neural network architecture for predicting late arrival times. It combines CNN, Bi-LSTM, and attention mechanisms to extract features, handle time series data, and enhance information utilization. Trained on operational data from the Beijing-Tianjin line, it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.FindingsThis study evaluates our model's predictive performance using two data approaches: one considering full data and another focusing only on late arrivals. Results show precise and rapid predictions. Training with full data achieves a MAE of approximately 0.54 minutes and a RMSE of 0.65 minutes, surpassing the model trained solely on delay data (MAE: is about 1.02 min, RMSE: is about 1.52 min). Despite superior overall performance with full data, the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals. For enhanced adaptability to real-world train operations, training with full data is recommended.Originality/valueThis paper introduces a novel neural network model, CBLA-net, for predicting train delay times. It innovatively compares and analyzes the model's performance using both full data and delay data formats. Additionally, the evaluation of the network's predictive capabilities considers different scenarios, providing a comprehensive demonstration of the model's predictive performance.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/e57a492ae484333d79b26e0ab06d19a0ae256575" target='_blank'>
+              Short-term train arrival delay prediction: a data-driven approach
+              </a>
+            </td>
+          <td>
+            Qingyun Fu, Shuxin Ding, Tao Zhang, Rongsheng Wang, Ping Hu, Cunlai Pu
+          </td>
+          <td>2024-07-02</td>
+          <td>Railway Sciences</td>
+          <td>0</td>
+          <td>14</td>
+        </tr>
+
+        <tr id="This paper proposes a deep recurrent neural network (DRNN) approach to model the one-hour-ahead wind speed forecasting by using various meteorological sensory data from the North Wyke farm platform (NWFP). To refine model input, mutual information analysis is applied to eliminate irrelevant sensory data. The DRNN architecture employs three recurrent layers Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and simple Recurrent Neural Network (RNN) to capture temporal relationships. The proposed networks are tested using real-life, one-year data from the NWFP. The results showed a strong correlation between the actual and predicted wind speed for LSTM, GRU, and RNN layers-based DRNN, however, simple RNN slightly outperformed the other two recurrent layers. The distribution of the network errors over the year is also analyzed. Although the observed meteorological data between the years was from different distributions, the proposed network generalized well even though these data were altered due to global warming.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/85208f740e1c9925156ec820fb1175b1cb13df77" target='_blank'>
+              Wind Speed Prediction Using Deep Recurrent Neural Networks and Farm Platform Features for One-Hour-Ahead Forecast
+              </a>
+            </td>
+          <td>
+            Emre Özbilge, Yonal Kırsal
+          </td>
+          <td>2024-06-27</td>
+          <td>Çukurova Üniversitesi Mühendislik Fakültesi Dergisi</td>
+          <td>0</td>
+          <td>9</td>
+        </tr>
+
+        <tr id="The Path-dependent Neural Jump ODE (PD-NJ-ODE) is a model for online prediction of generic (possibly non-Markovian) stochastic processes with irregular (in time) and potentially incomplete (with respect to coordinates) observations. It is a model for which convergence to the $L^2$-optimal predictor, which is given by the conditional expectation, is established theoretically. Thereby, the training of the model is solely based on a dataset of realizations of the underlying stochastic process, without the need of knowledge of the law of the process. In the case where the underlying process is deterministic, the conditional expectation coincides with the process itself. Therefore, this framework can equivalently be used to learn the dynamics of ODE or PDE systems solely from realizations of the dynamical system with different initial conditions. We showcase the potential of our method by applying it to the chaotic system of a double pendulum. When training the standard PD-NJ-ODE method, we see that the prediction starts to diverge from the true path after about half of the evaluation time. In this work we enhance the model with two novel ideas, which independently of each other improve the performance of our modelling setup. The resulting dynamics match the true dynamics of the chaotic system very closely. The same enhancements can be used to provably enable the PD-NJ-ODE to learn long-term predictions for general stochastic datasets, where the standard model fails. This is verified in several experiments.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/ba851d147a33cf0c6fcf639b5d4990df283a0b23" target='_blank'>
+              Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs
+              </a>
+            </td>
+          <td>
+            F. Krach, Josef Teichmann
+          </td>
+          <td>2024-07-26</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>13</td>
+        </tr>
+
+        <tr id="This paper explores the prediction of the dynamics of piecewise smooth maps using various deep learning models. We have shown various novel ways of predicting the dynamics of piecewise smooth maps using deep learning models. Moreover, we have used machine learning models such as Decision Tree Classifier, Logistic Regression, K-Nearest Neighbor, Random Forest, and Support Vector Machine for predicting the border collision bifurcation in the 1D normal form map and the 1D tent map. Further, we classified the regular and chaotic behaviour of the 1D tent map and the 2D Lozi map using deep learning models like Convolutional Neural Network (CNN), ResNet50, and ConvLSTM via cobweb diagram and phase portraits. We also classified the chaotic and hyperchaotic behaviour of the 3D piecewise smooth map using deep learning models such as the Feed Forward Neural Network (FNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Finally, deep learning models such as Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) are used for reconstructing the two parametric charts of 2D border collision bifurcation normal form map.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/aac95c6cb783f8093374db2d5a3d6f429f49bbfd" target='_blank'>
+              Deep Learning for Prediction and Classifying the Dynamical behaviour of Piecewise Smooth Maps
+              </a>
+            </td>
+          <td>
+            S. VismayaV, Bharath V Nair, S. S. Muni
+          </td>
+          <td>2024-06-24</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>10</td>
+        </tr>
+
+        <tr id="None">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/d1bae805ccd830107d647947cbe64aa0294f9638" target='_blank'>
+              A Kernel Attention-based Transformer Model for Survival Prediction of Heart Disease Patients.
+              </a>
+            </td>
+          <td>
+            Palak Kaushal, Shailendra Singh, Rajesh Vijayvergiya
+          </td>
+          <td>2024-08-05</td>
+          <td>Journal of cardiovascular translational research</td>
+          <td>0</td>
+          <td>1</td>
+        </tr>
+
+        <tr id="Graph Neural Networks (GNNs) excel in diverse tasks, yet their applications in high-stakes domains are often hampered by unreliable predictions. Although numerous uncertainty quantification methods have been proposed to address this limitation, they often lack \textit{rigorous} uncertainty estimates. This work makes the first attempt to introduce a distribution-free and model-agnostic uncertainty quantification approach to construct a predictive interval with a statistical guarantee for GNN-based link prediction. We term it as \textit{conformalized link prediction.} Our approach builds upon conformal prediction (CP), a framework that promises to construct statistically robust prediction sets or intervals. We first theoretically and empirically establish a permutation invariance condition for the application of CP in link prediction tasks, along with an exact test-time coverage. Leveraging the important structural information in graphs, we then identify a novel and crucial connection between a graph's adherence to the power law distribution and the efficiency of CP. This insight leads to the development of a simple yet effective sampling-based method to align the graph structure with a power law distribution prior to the standard CP procedure. Extensive experiments demonstrate that for conformalized link prediction, our approach achieves the desired marginal coverage while significantly improving the efficiency of CP compared to baseline methods.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/046f6abdbf63fbb80d831102e7889c6801ad3545" target='_blank'>
+              Conformalized Link Prediction on Graph Neural Networks
+              </a>
+            </td>
+          <td>
+            Tianyi Zhao, Jian Kang, Lu Cheng
+          </td>
+          <td>2024-06-26</td>
+          <td>ArXiv</td>
+          <td>1</td>
+          <td>1</td>
+        </tr>
+
+        <tr id="End-of-life vehicle (ELV) forecasting constitutes a crucial aspect of sustainable waste management and resource allocation strategies. While the existing literature predominantly employs time-series forecasting and machine learning methodologies, a dearth of studies leveraging deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, is evident. Moreover, the focus on localized contexts within national or municipal boundaries overlooks the imperative of addressing ELV generation dynamics at an international scale, particularly within entities such as the EU-27. Furthermore, the absence of methodologies to reconcile missing historical data presents a significant limitation in forecasting accuracy. In response to these critical gaps, this study proposes a pioneering framework that integrates grey systems theory (GST)-based backcasting with LSTM-based deep learning methodologies for forecasting ELV generation within the EU until 2040. By introducing this innovative approach, this study not only extends the methodological repertoire within the field but also enhances the applicability of findings to supranational regulatory frameworks. Moreover, the incorporation of backcasting techniques addresses data limitations, ensuring more robust and accurate forecasting outcomes. The results indicate an anticipated decline in the recovery and recycling of ELVs, underscoring the urgent need for intervention by policymakers and stakeholders in the waste management sector. Through these contributions, this study enriches our understanding of ELV generation dynamics and facilitates informed decision-making processes in environmental sustainability and resource management domains.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/f77631c5d497447eda2ed5efa6e1eb40e0a25f2e" target='_blank'>
+              Forecasting End-of-Life Vehicle Generation in the EU-27: A Hybrid LSTM-Based Forecasting and Grey Systems Theory-Based Backcasting Approach
+              </a>
+            </td>
+          <td>
+            Selman Karagoz
+          </td>
+          <td>2024-07-02</td>
+          <td>Pollutants</td>
+          <td>0</td>
+          <td>0</td>
+        </tr>
+
+        <tr id="In this paper we introduce a method for significantly improving the signal to noise ratio in financial data. The approach relies on combining a target variable with different context variables and use auto-encoders (AEs) to learn reconstructions of the combined inputs. The objective is to obtain agreement among pairs of AEs which are trained on related but different inputs and for which they are forced to find common ground. The training process is set up as a"conversation"where the models take turns at producing a prediction (speaking) and reconciling own predictions with the output of the other AE (listening), until an agreement is reached. This leads to a new way of constraining the complexity of the data representation generated by the AE. Unlike standard regularization whose strength needs to be decided by the designer, the proposed mutual regularization uses the partner network to detect and amend the lack of generality of the learned representation of the data. The integration of alternative perspectives enhances the de-noising capacity of a single AE and allows us to discover new regularities in financial time-series which can be converted into profitable trading strategies.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/88138df593fb8ebbfe08e9779a37bd6bdc468272" target='_blank'>
+              Strong denoising of financial time-series
+              </a>
+            </td>
+          <td>
+            Matthias J. Feiler
+          </td>
+          <td>2024-08-11</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>0</td>
+        </tr>
+
+        <tr id="To predict the future movements of stock markets, numerous studies concentrate on daily data and employ various machine learning (ML) models as benchmarks that often vary and lack standardization across different research works. This paper tries to solve the problem from a fresh standpoint by aiming to predict the weekly movements, and introducing a novel benchmark of random traders. This benchmark is independent of any ML model, thus making it more objective and potentially serving as a commonly recognized standard. During training process, apart from the basic features such as technical indicators, scaling laws and directional changes are introduced as additional features, furthermore, the training datasets are also adjusted by assigning varying weights to different samples, the weighting approach allows the models to emphasize specific samples. On back-testing, several trained models show good performance, with the multi-layer perception (MLP) demonstrating stability and robustness across extensive and comprehensive data that include upward, downward and cyclic trends. The unique perspective of this work that focuses on weekly movements, incorporates new features and creates an objective benchmark, contributes to the existing literature on stock market prediction.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/7a0bb044a3d1045d39d6d28a519d37fb7ac7b36d" target='_blank'>
+              Machine learning in weekly movement prediction
+              </a>
+            </td>
+          <td>
+            Han Gui
+          </td>
+          <td>2024-07-13</td>
+          <td>ArXiv</td>
+          <td>0</td>
           <td>0</td>
-          <td>26</td>
         </tr>
 
     </tbody>
@@ -4796,7 +4733,7 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
 
   var trace1 = {
     x: ['2023', '2024'],
-    y: [0, 26],
+    y: [0, 13],
     name: 'Num of citations',
     yaxis: 'y1',
     type: 'scatter'
diff --git a/site/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378/index.html b/site/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378/index.html
index 116401ae..f729d0a4 100644
--- a/site/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378/index.html
+++ b/site/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:45 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:28 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -485,7 +485,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2023-03-15</td>
           <td>PLOS Computational Biology</td>
           <td>3</td>
-          <td>54</td>
+          <td>55</td>
         </tr>
 
         <tr id="High-throughput data acquisition in synthetic biology leads to an abundance of data that need to be processed and aggregated into useful biological models. Building dynamical models based on this wealth of data is of paramount importance to understand and optimize designs of synthetic biology constructs. However, building models manually for each data set is inconvenient and might become infeasible for highly complex synthetic systems. In this paper, we present state-of-the-art system identification techniques and combine them with chemical reaction network theory (CRNT) to generate dynamic models automatically. On the system identification side, Sparse Bayesian Learning offers methods to learn from data the sparsest set of dictionary functions necessary to capture the dynamics of the system into ODE models; on the CRNT side, building on such sparse ODE models, all possible network structures within a given parameter uncertainty region can be computed. Additionally, the system identification process can be complemented with constraints on the parameters to, for example, enforce stability or non-negativity-thus offering relevant physical constraints over the possible network structures. In this way, the wealth of data can be translated into biologically relevant network structures, which then steers the data acquisition, thereby providing a vital step for closed-loop system identification.">
@@ -497,7 +497,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2018-09-24</td>
           <td>2018 IEEE Conference on Decision and Control (CDC)</td>
           <td>3</td>
-          <td>34</td>
+          <td>35</td>
         </tr>
 
         <tr id="Reconstruction of biochemical reaction networks (BRN) and genetic regulatory networks (GRN) in particular is a central topic in systems biology which raises crucial theoretical challenges in system identification. Nonlinear Ordinary Differential Equations (ODEs) that involve polynomial and rational functions are typically used to model biochemical reaction networks. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data quite difficult. In this paper, we present a network reconstruction algorithm that can deal with ODE model descriptions containing polynomial and rational functions. Rather than identifying the parameters of linear or nonlinear ODEs characterised by pre-defined equation structures, our methodology allows us to determine the nonlinear ODEs structure together with their associated parameters. To solve the network reconstruction problem, we cast it as a compressive sensing (CS) problem and use sparse Bayesian learning (SBL) algorithms as a computationally efficient and robust way to obtain its solution.">
@@ -509,7 +509,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2012-05-08</td>
           <td>2012 IEEE 51st IEEE Conference on Decision and Control (CDC)</td>
           <td>33</td>
-          <td>34</td>
+          <td>35</td>
         </tr>
 
         <tr id="None">
@@ -544,8 +544,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-06-01</td>
           <td>Nonlinear Dynamics</td>
-          <td>27</td>
-          <td>90</td>
+          <td>28</td>
+          <td>91</td>
         </tr>
 
         <tr id="Systems biology is a new discipline built upon the premise that an understanding of how cells and organisms carry out their functions cannot be gained by looking at cellular components in isolation. Instead, consideration of the interplay between the parts of systems is indispensable for analyzing, modeling, and predicting systems' behavior. Studying biological processes under this premise, systems biology combines experimental techniques and computational methods in order to construct predictive models. Both in building and utilizing models of biological systems, inverse problems arise at several occasions, for example, (i) when experimental time series and steady state data are used to construct biochemical reaction networks, (ii) when model parameters are identified that capture underlying mechanisms or (iii) when desired qualitative behavior such as bistability or limit cycle oscillations is engineered by proper choices of parameter combinations. In this paper we review principles of the modeling process in systems biology and illustrate the ill-posedness and regularization of parameter identification problems in that context. Furthermore, we discuss the methodology of qualitative inverse problems and demonstrate how sparsity enforcing regularization allows the determination of key reaction mechanisms underlying the qualitative behavior.">
diff --git a/site/recommendations/0acd117521ef5aafb09fed02ab415523b330b058/index.html b/site/recommendations/0acd117521ef5aafb09fed02ab415523b330b058/index.html
index cf5d24b5..1231c0f3 100644
--- a/site/recommendations/0acd117521ef5aafb09fed02ab415523b330b058/index.html
+++ b/site/recommendations/0acd117521ef5aafb09fed02ab415523b330b058/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:57 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:36 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,7 +484,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-11-01</td>
           <td>Chaos</td>
-          <td>2</td>
+          <td>3</td>
           <td>11</td>
         </tr>
 
@@ -508,8 +508,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3181</td>
-          <td>63</td>
+          <td>3190</td>
+          <td>65</td>
         </tr>
 
         <tr id="None">
@@ -532,7 +532,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>237</td>
+          <td>238</td>
           <td>12</td>
         </tr>
 
@@ -569,7 +569,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2017-12-01</td>
           <td>2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)</td>
           <td>12</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b/index.html b/site/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b/index.html
index f40c022b..6248cb3a 100644
--- a/site/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b/index.html
+++ b/site/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:12 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:04:58 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -485,7 +485,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2023-10-24</td>
           <td>ArXiv</td>
           <td>4</td>
-          <td>49</td>
+          <td>50</td>
         </tr>
 
         <tr id="Graph neural networks (GNNs) have been widely applied in multi-variate time-series forecasting (MTSF) tasks because of their capability in capturing the correlations among different time-series. These graph-based learning approaches improve the forecasting performance by discovering and understanding the underlying graph structures, which represent the data correlation. When the explicit prior graph structures are not available, most existing works cannot guarantee the sparsity of the generated graphs that make the overall model computational expensive and less interpretable. In this work, we propose a decoupled training method, which includes a graph generating module and a GNNs forecasting module. First, we use Graphical Lasso (or GraphLASSO) to directly exploit the sparsity pattern from data to build graph structures in both static and time-varying cases. Second, we fit these graph structures and the input data into a Graph Convolutional Recurrent Network (GCRN) to train a forecasting model. The experimental results on three real-world datasets show that our novel approach has competitive performance against existing state-of-the-art forecasting algorithms while providing sparse, meaningful and explainable graph structures and reducing training time by approximately 40%. Our PyTorch implementation is publicly available at https://github.com/HySonLab/GraphLASSO">
@@ -533,7 +533,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2021-09-10</td>
           <td>ArXiv</td>
           <td>12</td>
-          <td>45</td>
+          <td>46</td>
         </tr>
 
         <tr id="Multivariate time-series forecasting is a critical task for many applications, and graph time-series network is widely studied due to its capability to capture the spatial-temporal correlation simultaneously. However, most existing works focus more on learning with the explicit prior graph structure, while ignoring potential information from the implicit graph structure, yielding incomplete structure modeling. Some recent works attempts to learn the intrinsic or implicit graph structure directly, while lacking a way to combine explicit prior structure with implicit structure together. In this paper, we propose Regularized Graph Structure Learning (RGSL) model to incorporate both explicit prior structure and implicit structure together, and learn the forecasting deep networks along with the graph structure. RGSL consists of two innovative modules. First, we derive an implicit dense similarity matrix through node embedding, and learn the sparse graph structure using the Regularized Graph Generation (RGG) based on the Gumbel Softmax trick. Second, we propose a Laplacian Matrix Mixed-up Module (LM3) to fuse the explicit graph and implicit graph together. We conduct experiments on three real-word datasets. Results show that the proposed RGSL model outperforms existing graph forecasting algorithms with a notable margin, while learning meaningful graph structure simultaneously. Our code and models are made publicly available at https://github.com/alipay/RGSL.git.">
@@ -545,7 +545,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2022-07-01</td>
           <td>ArXiv, DBLP</td>
           <td>37</td>
-          <td>33</td>
+          <td>34</td>
         </tr>
 
         <tr id="Time series forecasting is an extensively studied subject in statistics, economics, and computer science. Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the performance of a time series model. When using deep neural networks as forecasting models, we hypothesize that exploiting the pairwise information among multiple (multivariate) time series also improves their forecast. If an explicit graph structure is known, graph neural networks (GNNs) have been demonstrated as powerful tools to exploit the structure. In this work, we propose learning the structure simultaneously with the GNN if the graph is unknown. We cast the problem as learning a probabilistic graph model through optimizing the mean performance over the graph distribution. The distribution is parameterized by a neural network so that discrete graphs can be sampled differentiably through reparameterization. Empirical evaluations show that our method is simpler, more efficient, and better performing than a recently proposed bilevel learning approach for graph structure learning, as well as a broad array of forecasting models, either deep or non-deep learning based, and graph or non-graph based.">
@@ -556,8 +556,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2021-01-18</td>
           <td>ArXiv</td>
-          <td>175</td>
-          <td>36</td>
+          <td>177</td>
+          <td>37</td>
         </tr>
 
         <tr id="Multi-variate time series forecasting is an important problem with a wide range of applications. Recent works model the relations between time-series as graphs and have shown that propagating information over the relation graph can improve time series forecasting. However, in many cases, relational information is not available or is noisy and reliable. Moreover, most works ignore the underlying uncertainty of time-series both for structure learning and deriving the forecasts resulting in the structure not capturing the uncertainty resulting in forecast distributions with poor uncertainty estimates. We tackle this challenge and introduce STOIC, that leverages stochastic correlations between time-series to learn underlying structure between time-series and to provide well-calibrated and accurate forecasts. Over a wide-range of benchmark datasets STOIC provides around 16% more accurate and 14% better-calibrated forecasts. STOIC also shows better adaptation to noise in data during inference and captures important and useful relational information in various benchmarks.">
diff --git a/site/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b/index.html b/site/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b/index.html
index a22c7319..3dd2f8c8 100644
--- a/site/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b/index.html
+++ b/site/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:13 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:04:59 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,7 +484,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-03-12</td>
           <td>ArXiv</td>
-          <td>24</td>
+          <td>27</td>
           <td>18</td>
         </tr>
 
@@ -497,7 +497,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2024-02-04</td>
           <td>ArXiv</td>
           <td>3</td>
-          <td>65</td>
+          <td>66</td>
         </tr>
 
         <tr id="Large language models (LLMs) are being applied to time series tasks, particularly time series forecasting. However, are language models actually useful for time series? After a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade the forecasting results -- in most cases the results even improved. We also find that despite their significant computational cost, pretrained LLMs do no better than models trained from scratch, do not represent the sequential dependencies in time series, and do not assist in few-shot settings. Additionally, we explore time series encoders and reveal that patching and attention structures perform similarly to state-of-the-art LLM-based forecasters.">
@@ -520,8 +520,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-02-04</td>
           <td>ArXiv</td>
-          <td>6</td>
-          <td>65</td>
+          <td>5</td>
+          <td>66</td>
         </tr>
 
         <tr id="In this paper, we introduce TimeGPT, the first foundation model for time series, capable of generating accurate predictions for diverse datasets not seen during training. We evaluate our pre-trained model against established statistical, machine learning, and deep learning methods, demonstrating that TimeGPT zero-shot inference excels in performance, efficiency, and simplicity. Our study provides compelling evidence that insights from other domains of artificial intelligence can be effectively applied to time series analysis. We conclude that large-scale time series models offer an exciting opportunity to democratize access to precise predictions and reduce uncertainty by leveraging the capabilities of contemporary advancements in deep learning.">
@@ -532,7 +532,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-10-05</td>
           <td>ArXiv</td>
-          <td>39</td>
+          <td>40</td>
           <td>1</td>
         </tr>
 
@@ -556,7 +556,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-10-03</td>
           <td>ArXiv</td>
-          <td>113</td>
+          <td>120</td>
           <td>9</td>
         </tr>
 
@@ -580,7 +580,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-06-19</td>
           <td>ArXiv</td>
-          <td>34</td>
+          <td>35</td>
           <td>8</td>
         </tr>
 
diff --git a/site/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277/index.html b/site/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277/index.html
index ae0391b9..014594c2 100644
--- a/site/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277/index.html
+++ b/site/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:17 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:02 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -508,7 +508,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-09-20</td>
           <td>IEEE Transactions on Knowledge and Data Engineering</td>
-          <td>56</td>
+          <td>58</td>
           <td>17</td>
         </tr>
 
@@ -580,7 +580,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-10-14</td>
           <td>ArXiv</td>
-          <td>45</td>
+          <td>48</td>
           <td>14</td>
         </tr>
 
@@ -593,7 +593,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2024-02-04</td>
           <td>ArXiv</td>
           <td>3</td>
-          <td>65</td>
+          <td>66</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f/index.html b/site/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f/index.html
index 8868875a..b88c0836 100644
--- a/site/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f/index.html
+++ b/site/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:14 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:00 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -496,8 +496,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-02-13</td>
           <td>ArXiv</td>
-          <td>30</td>
-          <td>8</td>
+          <td>32</td>
+          <td>9</td>
         </tr>
 
         <tr id="Message-passing graph neural networks (GNNs), while excelling at capturing local relationships, often struggle with long-range dependencies on graphs. Conversely, graph transformers (GTs) enable information exchange between all nodes but oversimplify the graph structure by treating them as a set of fixed-length vectors. This work proposes a novel architecture, NeuralWalker, that overcomes the limitations of both methods by combining random walks with message passing. NeuralWalker achieves this by treating random walks as sequences, allowing for the application of recent advances in sequence models in order to capture long-range dependencies within these walks. Based on this concept, we propose a framework that offers (1) more expressive graph representations through random walk sequences, (2) the ability to utilize any sequence model for capturing long-range dependencies, and (3) the flexibility by integrating various GNN and GT architectures. Our experimental evaluations demonstrate that NeuralWalker achieves significant performance improvements on 19 graph and node benchmark datasets, notably outperforming existing methods by up to 13% on the PascalVoc-SP and COCO-SP datasets. Code is available at https://github.com/BorgwardtLab/NeuralWalker.">
@@ -521,7 +521,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2024-06-09</td>
           <td>ArXiv</td>
           <td>1</td>
-          <td>2</td>
+          <td>3</td>
         </tr>
 
         <tr id="Graph representation learning (GRL) is fundamental in multi-graph applications like molecular property prediction. Graph neural networks (GNNs) have emerged as a popular method for GRL. However, existing GRL methods primarily focus on designing GNN models with enhanced expressiveness while overlooking the memory efficiency of algorithms during training. The memory inefficiency problem is caused by a contextual constraint imposed on node representations, which requires each node to be context-dependent on its input graph. In this paper, we propose a novel method, called context sketching (COS), for memory-efficient graph representation learning in multi-graph scenarios. We first formally define the contextual constraint based on the enclosed $\infty-$hop subgraphs of nodes. Subsequently, we propose to relax the original contextual constraint by requiring each node to be context-dependent on its enclosed $k-$hop subgraph $(k \ll \infty)$ which is a contextual sketch of the enclosed $\infty$-hop subgraph. Lastly, we prove that COS constructs an optimal solution to a memory-related objective associated with graph coarsening. Experiments on four widely used benchmark datasets demonstrate that COS can reduce the memory footprint of baselines by a large margin with almost no accuracy loss.">
@@ -545,7 +545,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2024-06-17</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
+          <td>7</td>
         </tr>
 
         <tr id="The Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision. However, when it comes to graph-structured data, transformers have not achieved competitive performance, especially on large graphs. In this paper, we identify the main deficiencies of current graph transformers:(1) Existing node sampling strategies in Graph Transformers are agnostic to the graph characteristics and the training process. (2) Most sampling strategies only focus on local neighbors and neglect the long-range dependencies in the graph. We conduct experimental investigations on synthetic datasets to show that existing sampling strategies are sub-optimal. To tackle the aforementioned problems, we formulate the optimization strategies of node sampling in Graph Transformer as an adversary bandit problem, where the rewards are related to the attention weights and can vary in the training procedure. Meanwhile, we propose a hierarchical attention scheme with graph coarsening to capture the long-range interactions while reducing computational complexity. Finally, we conduct extensive experiments on real-world datasets to demonstrate the superiority of our method over existing graph transformers and popular GNNs.">
@@ -556,8 +556,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-10-08</td>
           <td>ArXiv</td>
-          <td>58</td>
-          <td>17</td>
+          <td>59</td>
+          <td>19</td>
         </tr>
 
         <tr id="Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the success is limited to small-scale graphs due to the drawbacks of full dot-product attention on graphs such as the quadratic complexity with respect to the number of nodes and message aggregation from enormous irrelevant nodes. To address these issues, we propose Deformable Graph Transformer (DGT) that performs sparse attention via dynamically sampled relevant nodes for efficiently handling large-scale graphs with a linear complexity in the number of nodes. Specifically, our framework first constructs multiple node sequences with various criteria to consider both structural and semantic proximity. Then, combining with our learnable Katz Positional Encodings, the sparse attention is applied to the node sequences for learning node representations with a significantly reduced computational cost. Extensive experiments demonstrate that our DGT achieves state-of-the-art performance on 7 graph benchmark datasets with 2.5 - 449 times less computational cost compared to transformer-based graph models with full attention.">
@@ -569,7 +569,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2022-06-29</td>
           <td>ArXiv</td>
           <td>6</td>
-          <td>27</td>
+          <td>28</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68/index.html b/site/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68/index.html
index 8c3a1f79..ae046141 100644
--- a/site/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68/index.html
+++ b/site/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:27 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:09 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -483,7 +483,7 @@ <h3 data-intro='Recommendations for the article'>
             Andreas Doerr, Christian Daniel, Martin Schiegg, D. Nguyen-Tuong, S. Schaal, Marc Toussaint, Sebastian Trimpe
           </td>
           <td>2018-01-31</td>
-          <td>MAG, ArXiv, DBLP</td>
+          <td>ArXiv, MAG, DBLP</td>
           <td>110</td>
           <td>93</td>
         </tr>
@@ -521,7 +521,31 @@ <h3 data-intro='Recommendations for the article'>
           <td>2022-05-26</td>
           <td>J. Mach. Learn. Res.</td>
           <td>11</td>
-          <td>49</td>
+          <td>50</td>
+        </tr>
+
+        <tr id="Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other. Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents. This paper introduces the relational state-space model (R-SSM), a sequential hierarchical latent variable model that makes use of graph neural networks (GNNs) to simulate the joint state transitions of multiple correlated objects. By letting GNNs cooperate with SSM, R-SSM provides a flexible way to incorporate relational information into the modeling of multi-object dynamics. We further suggest augmenting the model with normalizing flows instantiated for vertex-indexed random variables and propose two auxiliary contrastive objectives to facilitate the learning. The utility of R-SSM is empirically evaluated on synthetic and real time series datasets.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/7a1e5377b08489c2969f73c56efc557e34f578e1" target='_blank'>Relational State-Space Model for Stochastic Multi-Object Systems</a></td>
+          <td>
+            Fan Yang, Ling Chen, Fan Zhou, Yusong Gao, Wei Cao
+          </td>
+          <td>2020-01-13</td>
+          <td>ArXiv</td>
+          <td>8</td>
+          <td>57</td>
+        </tr>
+
+        <tr id="Over the past few years, research on deep graph learning has shifted from static graphs to temporal graphs in response to real-world complex systems that exhibit dynamic behaviors. In practice, temporal graphs are formalized as an ordered sequence of static graph snapshots observed at discrete time points. Sequence models such as RNNs or Transformers have long been the predominant backbone networks for modeling such temporal graphs. Yet, despite the promising results, RNNs struggle with long-range dependencies, while transformers are burdened by quadratic computational complexity. Recently, state space models (SSMs), which are framed as discretized representations of an underlying continuous-time linear dynamical system, have garnered substantial attention and achieved breakthrough advancements in independent sequence modeling. In this work, we undertake a principled investigation that extends SSM theory to temporal graphs by integrating structural information into the online approximation objective via the adoption of a Laplacian regularization term. The emergent continuous-time system introduces novel algorithmic challenges, thereby necessitating our development of GraphSSM, a graph state space model for modeling the dynamics of temporal graphs. Extensive experimental results demonstrate the effectiveness of our GraphSSM framework across various temporal graph benchmarks.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/919e5db29c7b7be4468b975eb4c0fa4a543165fc" target='_blank'>State Space Models on Temporal Graphs: A First-Principles Study</a></td>
+          <td>
+            Jintang Li, Ruofan Wu, Xinzhou Jin, Boqun Ma, Liang Chen, Zibin Zheng
+          </td>
+          <td>2024-06-03</td>
+          <td>ArXiv</td>
+          <td>1</td>
+          <td>11</td>
         </tr>
 
         <tr id="Time series modeling is a well-established problem, which often requires that methods (1) expressively represent complicated dependencies, (2) forecast long horizons, and (3) efficiently train over long sequences. State-space models (SSMs) are classical models for time series, and prior works combine SSMs with deep learning layers for efficient sequence modeling. However, we find fundamental limitations with these prior approaches, proving their SSM representations cannot express autoregressive time series processes. We thus introduce SpaceTime, a new state-space time series architecture that improves all three criteria. For expressivity, we propose a new SSM parameterization based on the companion matrix -- a canonical representation for discrete-time processes -- which enables SpaceTime's SSM layers to learn desirable autoregressive processes. For long horizon forecasting, we introduce a"closed-loop"variation of the companion SSM, which enables SpaceTime to predict many future time-steps by generating its own layer-wise inputs. For efficient training and inference, we introduce an algorithm that reduces the memory and compute of a forward pass with the companion matrix. With sequence length $\ell$ and state-space size $d$, we go from $\tilde{O}(d \ell)$ na\"ively to $\tilde{O}(d + \ell)$. In experiments, our contributions lead to state-of-the-art results on extensive and diverse benchmarks, with best or second-best AUROC on 6 / 7 ECG and speech time series classification, and best MSE on 14 / 16 Informer forecasting tasks. Furthermore, we find SpaceTime (1) fits AR($p$) processes that prior deep SSMs fail on, (2) forecasts notably more accurately on longer horizons than prior state-of-the-art, and (3) speeds up training on real-world ETTh1 data by 73% and 80% relative wall-clock time over Transformers and LSTMs.">
@@ -533,7 +557,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2023-03-16</td>
           <td>ArXiv</td>
           <td>28</td>
-          <td>43</td>
+          <td>44</td>
         </tr>
 
         <tr id="
@@ -547,35 +571,11 @@ <h3 data-intro='Recommendations for the article'>
             R. G. Krishnan, Uri Shalit, D. Sontag
           </td>
           <td>2016-09-30</td>
-          <td>MAG, ArXiv, DBLP</td>
+          <td>ArXiv, MAG, DBLP</td>
           <td>428</td>
           <td>48</td>
         </tr>
 
-        <tr id="Probabilistic inference in high-dimensional state-space models is computationally challenging. For many spatiotemporal systems, however, prior knowledge about the dependency structure of state variables is available. We leverage this structure to develop a computationally efficient approach to state estimation and learning in graph-structured state-space models with (partially) unknown dynamics and limited historical data. Building on recent methods that combine ideas from deep learning with principled inference in Gaussian Markov random fields (GMRF), we reformulate graph-structured state-space models as Deep GMRFs defined by simple spatial and temporal graph layers. This results in a flexible spatiotemporal prior that can be learned efficiently from a single time sequence via variational inference. Under linear Gaussian assumptions, we retain a closed-form posterior, which can be sampled efficiently using the conjugate gradient method, scaling favourably compared to classical Kalman filter based approaches">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/66173ff04bc062987a4395181001bf9b7c3eb21b" target='_blank'>Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems</a></td>
-          <td>
-            Fiona Lippert, B. Kranstauber, E. E. V. Loon, Patrick Forr'e
-          </td>
-          <td>2023-06-14</td>
-          <td>ArXiv</td>
-          <td>1</td>
-          <td>23</td>
-        </tr>
-
-        <tr id="Many applications, e.g., healthcare, education, call for effective methods methods for constructing predictive models from high dimensional time series data where the relationship between variables can be complex and vary over time. In such settings, the underlying system undergoes a sequence of unobserved transitions among a finite set of hidden states. Furthermore, the relationships between the observed variables and their temporal dynamics may depend on the hidden state of the system. To further complicate matters, the hidden state sequences underlying the observed data from different individuals may not be aligned relative to a common frame of reference. Against this background, we consider the novel problem of jointly learning the state-dependent inter-variable relationships as well as the pattern of transitions between hidden states from multi-variate time series data. To solve this problem, we introduce the State-Regularized Vector Autoregressive Model (SrVARM) which combines a state-regularized recurrent neural network to learn the dynamics of transitions between discrete hidden states with an augmented autoregressive model which models the inter-variable dependencies in each state using a state-dependent directed acyclic graph (DAG). We propose an efficient algorithm for training SrVARM by leveraging a recently introduced reformulation of the combinatorial problem of optimizing the DAG structure with respect to a scoring function into a continuous optimization problem. We report results of extensive experiments with simulated data as well as a real-world benchmark that show that SrVARM outperforms state-of-the-art baselines in recovering the unobserved state transitions and discovering the state-dependent relationships among variables.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4ddaa0ff15691ba148dd88c82b03547e9d2aa013" target='_blank'>SrVARM: State Regularized Vector Autoregressive Model for Joint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Multi-variate Time Series</a></td>
-          <td>
-            Tsung-Yu Hsieh, Yiwei Sun, Xianfeng Tang, Suhang Wang, Vasant G Honavar
-          </td>
-          <td>2021-04-19</td>
-          <td>Proceedings of the Web Conference 2021</td>
-          <td>8</td>
-          <td>54</td>
-        </tr>
-
   </tbody>
   <tfoot>
     <tr>
diff --git a/site/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196/index.html b/site/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196/index.html
index 5586a3ae..97dbad3f 100644
--- a/site/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196/index.html
+++ b/site/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:50 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:29 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -556,7 +556,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-07-02</td>
           <td>ArXiv</td>
-          <td>128</td>
+          <td>129</td>
           <td>22</td>
         </tr>
 
@@ -581,7 +581,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2019-06-10</td>
           <td>J. Comput. Phys.</td>
           <td>21</td>
-          <td>18</td>
+          <td>19</td>
         </tr>
 
         <tr id="In recent years, we have witnessed a significant shift toward ever-more complex and ever-larger-scale systems in the majority of the grand societal challenges tackled in applied sciences. The need to comprehend and predict the dynamics of complex systems have spurred developments in large-scale simulations and a multitude of methods across several disciplines. The goals of understanding and prediction in complex dynamical systems, however, have been hindered by high dimensionality, complexity and chaotic behaviours. Recent advances in data-driven techniques and machine-learning approaches have revolutionized how we model and analyse complex systems. The integration of these techniques with dynamical systems theory opens up opportunities to tackle previously unattainable challenges in modelling and prediction of dynamical systems. While data-driven prediction methods have made great strides in recent years, it is still necessary to develop new techniques to improve their applicability to a wider range of complex systems in science and engineering. This focus issue shares recent developments in the field of complex dynamical systems with emphasis on data-driven, data-assisted and artificial intelligence-based discovery of dynamical systems. This article is part of the theme issue 'Data-driven prediction in dynamical systems'.">
@@ -592,7 +592,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-06-20</td>
           <td>Philosophical transactions. Series A, Mathematical, physical, and engineering sciences</td>
-          <td>41</td>
+          <td>42</td>
           <td>33</td>
         </tr>
 
diff --git a/site/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220/index.html b/site/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220/index.html
index 34a27d6e..8f5437df 100644
--- a/site/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220/index.html
+++ b/site/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:56 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:34 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -485,7 +485,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2019-06-25</td>
           <td>IEEE Access</td>
           <td>95</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
 
         <tr id="Energy conservation is a basic physics principle, the breakdown of which often implies new physics. This paper presents a method for data-driven "new physics" discovery. Specifically, given a trajectory governed by unknown forces, our neural new-physics detector (NNPhD) aims to detect new physics by decomposing the force field into conservative and nonconservative components, which are represented by a Lagrangian neural network (LNN) and an unconstrained neural network, respectively, trained to minimize the force recovery error plus a constant λ times the magnitude of the predicted nonconservative force. We show that a phase transition occurs at λ=1, universally for arbitrary forces. We demonstrate that NNPhD successfully discovers new physics in toy numerical experiments, rediscovering friction (1493) from a damped double pendulum, Neptune from Uranus' orbit (1846), and gravitational waves (2017) from an inspiraling orbit. We also show how NNPhD coupled with an integrator outperforms both an LNN and an unconstrained neural network for predicting the future of a damped double pendulum.">
@@ -532,7 +532,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-05-31</td>
           <td>2023 American Control Conference (ACC)</td>
-          <td>15</td>
+          <td>16</td>
           <td>37</td>
         </tr>
 
@@ -545,7 +545,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2021-09-30</td>
           <td>ArXiv, DBLP</td>
           <td>5</td>
-          <td>103</td>
+          <td>105</td>
         </tr>
 
         <tr id="None">
@@ -569,7 +569,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2021-05-06</td>
           <td>Physical review letters</td>
           <td>82</td>
-          <td>81</td>
+          <td>82</td>
         </tr>
 
         <tr id="None">
diff --git a/site/recommendations/3c9961153493370500020c81527b3548c96f81e0/index.html b/site/recommendations/3c9961153493370500020c81527b3548c96f81e0/index.html
index 26404441..0af8d0fa 100644
--- a/site/recommendations/3c9961153493370500020c81527b3548c96f81e0/index.html
+++ b/site/recommendations/3c9961153493370500020c81527b3548c96f81e0/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:39 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:22 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,8 +484,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3181</td>
-          <td>63</td>
+          <td>3190</td>
+          <td>65</td>
         </tr>
 
         <tr id="None">
@@ -496,7 +496,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>237</td>
+          <td>238</td>
           <td>12</td>
         </tr>
 
diff --git a/site/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2/index.html b/site/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2/index.html
index 20a9000e..ed241a7b 100644
--- a/site/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2/index.html
+++ b/site/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:40 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:24 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -497,7 +497,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2022-11-20</td>
           <td>Physical review. E</td>
           <td>8</td>
-          <td>50</td>
+          <td>51</td>
         </tr>
 
         <tr id="A central challenge in data-driven model discovery is the presence of hidden, or latent, variables that are not directly measured but are dynamically important. Takens’ theorem provides conditions for when it is possible to augment partial measurements with time delayed information, resulting in an attractor that is diffeomorphic to that of the original full-state system. This diffeomorphism is typically unknown, and learning the dynamics in the embedding space has remained an open challenge for decades. Here, we design a deep autoencoder network to learn a coordinate transformation from the delay embedded space into a new space, where it is possible to represent the dynamics in a sparse, closed form. We demonstrate this approach on the Lorenz, Rössler and Lotka–Volterra systems, as well as a Lorenz analogue from a video of a chaotic waterwheel experiment. This framework combines deep learning and the sparse identification of nonlinear dynamics methods to uncover interpretable models within effective coordinates.">
@@ -508,8 +508,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-01-13</td>
           <td>Proceedings of the Royal Society A</td>
-          <td>68</td>
-          <td>63</td>
+          <td>69</td>
+          <td>65</td>
         </tr>
 
         <tr id="Making accurate forecasts for a complex system is a challenge in various practical applications. The major difficulty in solving such a problem concerns nonlinear spatiotemporal dynamics with time-varying characteristics. Takens' delay embedding theory provides a way to transform high-dimensional spatial information into temporal information. In this work, by combining delay embedding theory and deep learning techniques, we propose a novel framework, delay-embedding-based forecast Machine (DEFM), to predict the future values of a target variable in a self-supervised and multistep-ahead manner based on high-dimensional observations. With a three-module spatiotemporal architecture, the DEFM leverages deep neural networks to effectively extract both the spatially and temporally associated information from the observed time series even with time-varying parameters or additive noise. The DEFM can accurately predict future information by transforming spatiotemporal information to the delay embeddings of a target variable. The efficacy and precision of the DEFM are substantiated through applications in three spatiotemporally chaotic systems: a 90-dimensional (90D) coupled Lorenz system, the Lorenz 96 system, and the Kuramoto-Sivashinsky equation with inhomogeneity. Additionally, the performance of the DEFM is evaluated on six real-world datasets spanning various fields. Comparative experiments with five prediction methods illustrate the superiority and robustness of the DEFM and show the great potential of the DEFM in temporal information mining and forecasting.">
@@ -521,7 +521,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2020-05-16</td>
           <td>Chaos</td>
           <td>1</td>
-          <td>78</td>
+          <td>79</td>
         </tr>
 
         <tr id="Making accurate forecasts for a complex system is a challenge in various practical applications. The major difficulty in solving such a problem concerns nonlinear spatiotemporal dynamics with time-varying characteristics. Takens' delay embedding theory provides a way to transform high-dimensional spatial information into temporal information. In this work, by combining delay embedding theory and deep learning techniques, we propose a novel framework, delay-embedding-based forecast Machine (DEFM), to predict the future values of a target variable in a self-supervised and multistep-ahead manner based on high-dimensional observations. With a three-module spatiotemporal architecture, the DEFM leverages deep neural networks to effectively extract both the spatially and temporally associated information from the observed time series even with time-varying parameters or additive noise. The DEFM can accurately predict future information by transforming spatiotemporal information to the delay embeddings of a target variable. The efficacy and precision of the DEFM are substantiated through applications in three spatiotemporally chaotic systems: a 90-dimensional (90D) coupled Lorenz system, the Lorenz 96 system, and the Kuramoto-Sivashinsky equation with inhomogeneity. Additionally, the performance of the DEFM is evaluated on six real-world datasets spanning various fields. Comparative experiments with five prediction methods illustrate the superiority and robustness of the DEFM and show the great potential of the DEFM in temporal information mining and forecasting.">
@@ -533,7 +533,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2020-05-16</td>
           <td>Chaos</td>
           <td>1</td>
-          <td>78</td>
+          <td>79</td>
         </tr>
 
         <tr id="Many complex phenomena, from weather systems to heartbeat rhythm patterns, are effectively modeled as low-dimensional dynamical systems. Such systems may behave chaotically under certain conditions, and so the ability to detect chaos based on empirical measurement is an important step in characterizing and predicting these processes. Classifying a system as chaotic usually requires estimating its largest Lyapunov exponent, which quantifies the average rate of convergence or divergence of initially close trajectories in state space, and for which a positive value is generally accepted as an operational definition of chaos. Estimating the largest Lyapunov exponent from observations of a process is especially challenging in systems affected by dynamical noise, which is the case for many models of real-world processes, in particular models of biological systems. We describe a novel method for estimating the largest Lyapunov exponent from data, based on training Deep Learning models on synthetically generated trajectories, and demonstrate that this method yields accurate and noise-robust predictions given relatively short inputs and across a range of different dynamical systems. Our method is unique in that it can analyze tree-shaped data, a ubiquitous topology in biological settings, and specifically in dynamics over lineages of cells or organisms. We also characterize the types of input information extracted by our models for their predictions, allowing for a deeper understanding into the different ways by which chaos can be analyzed in different topologies.">
diff --git a/site/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba/index.html b/site/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba/index.html
index 51d0228d..09369a8d 100644
--- a/site/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba/index.html
+++ b/site/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:10 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:04:56 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -545,7 +545,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2023-10-17</td>
           <td>2023 IEEE International Conference on Data Mining (ICDM)</td>
           <td>0</td>
-          <td>8</td>
+          <td>9</td>
         </tr>
 
         <tr id="Irregularly-sampled time series occur in many domains including healthcare. They can be challenging to model because they do not naturally yield a fixed-dimensional representation as required by many standard machine learning models. In this paper, we consider irregular sampling from the perspective of missing data. We model observed irregularly-sampled time series data as a sequence of index-value pairs sampled from a continuous but unobserved function. We introduce an encoder-decoder framework for learning from such generic indexed sequences. We propose learning methods for this framework based on variational autoencoders and generative adversarial networks. For continuous irregularly-sampled time series, we introduce continuous convolutional layers that can efficiently interface with existing neural network architectures. Experiments show that our models are able to achieve competitive or better classification results on irregularly-sampled multivariate time series compared to recent RNN models while offering significantly faster training times.">
@@ -555,9 +555,9 @@ <h3 data-intro='Recommendations for the article'>
             Steven Cheng-Xian Li, Benjamin M Marlin
           </td>
           <td>2020-07-12</td>
-          <td>MAG, ArXiv, DBLP</td>
+          <td>ArXiv, MAG, DBLP</td>
           <td>47</td>
-          <td>32</td>
+          <td>33</td>
         </tr>
 
         <tr id="We consider a sequence of related multivariate time series learning tasks, such as predicting failures for different instances of a machine from time series of multi-sensor data, or activity recognition tasks over different individuals from multiple wearable sensors. We focus on two under-explored practical challenges arising in such settings: (i) Each task may have a different subset of sensors, i.e., providing different partial observations of the underlying ‘system’. This restriction can be due to different manufacturers in the former case, and people wearing more or less measurement devices in the latter (ii) We are not allowed to store or re-access data from a task once it has been observed at the task level. This may be due to privacy considerations in the case of people, or legal restrictions placed by machine owners. Nevertheless, we would like to (a) improve performance on subsequent tasks using experience from completed tasks as well as (b) continue to perform better on past tasks, e.g., update the model and improve predictions on even the first machine after learning from subsequently observed ones. We note that existing continual learning methods do not take into account variability in input dimensions arising due to different subsets of sensors being available across tasks, and struggle to adapt to such variable input dimensions (VID) tasks. In this work, we address this shortcoming of existing methods. To this end, we learn task-specific generative models and classifiers, and use these to augment data for target tasks. Since the input dimensions across tasks vary, we propose a novel conditioning module based on graph neural networks to aid a standard recurrent neural network. We evaluate the efficacy of the proposed approach on three publicly available datasets corresponding to two activity recognition tasks (classification) and one prognostics task (regression). We demonstrate that it is possible to significantly enhance the performance on future and previous tasks while learning continuously from VID tasks without storing data.">
diff --git a/site/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01/index.html b/site/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01/index.html
index 5c8a7c52..219b052c 100644
--- a/site/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01/index.html
+++ b/site/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:48 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:29 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -509,7 +509,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
           <td>109</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
 
         <tr id="Data-driven modeling frameworks that adopt sparse regression techniques, such as sparse identification of nonlinear dynamics (SINDy) and its modifications, are developed to resolve difficulties in extracting underlying dynamics from experimental data. In contrast to neural-network-based methods, these methods are designed to obtain white-box analytical models. In this work, we incorporate the concept of SINDy and knowledge in the field of classical mechanics to identify interpretable and sparse expressions of total energy and the Lagrangian that shelters the hidden dynamics. Moreover, our method (hereafter referred as Lagrangian-SINDy) is developed to use knowledge of simple systems that form the system being analyzed to ensure the likelihood of correct results and to improve the learning pace. Lagrangian-SINDy is highly accurate in discovering interpretable dynamics via energy-related physical quantities. Its performance is validated with three popular multi-DOF nonlinear dynamical systems, namely the spherical pendulum, double pendulum and cart-pendulum system. Comparisons with other SINDy-based methods are made and Lagrangian-SINDy is found to provide the most compact analytical models.">
@@ -569,7 +569,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2024-04-24</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
+          <td>7</td>
         </tr>
 
         <tr id="In this work we analyze the effectiveness of the Sparse Identification of Nonlinear Dynamics (SINDy) technique on three benchmark datasets for nonlinear identification, to provide a better understanding of its suitability when tackling real dynamical systems. While SINDy can be an appealing strategy for pursuing physics-based learning, our analysis highlights difficulties in dealing with unobserved states and non-smooth dynamics. Due to the ubiquity of these features in real systems in general, and control applications in particular, we complement our analysis with hands-on approaches to tackle these issues in order to exploit SINDy also in these challenging contexts.">
@@ -580,7 +580,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-03-01</td>
           <td>ArXiv</td>
-          <td>0</td>
+          <td>1</td>
           <td>1</td>
         </tr>
 
@@ -592,8 +592,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-07-06</td>
           <td>Journal of computational physics</td>
-          <td>120</td>
-          <td>19</td>
+          <td>121</td>
+          <td>20</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/4a111f7a3b56d0468f13104999844885157ef17d/index.html b/site/recommendations/4a111f7a3b56d0468f13104999844885157ef17d/index.html
index 4a9f2be5..a5f35a4c 100644
--- a/site/recommendations/4a111f7a3b56d0468f13104999844885157ef17d/index.html
+++ b/site/recommendations/4a111f7a3b56d0468f13104999844885157ef17d/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:16 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:02 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,7 +484,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-03-12</td>
           <td>ArXiv</td>
-          <td>24</td>
+          <td>27</td>
           <td>18</td>
         </tr>
 
@@ -496,8 +496,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-02-04</td>
           <td>ArXiv</td>
-          <td>6</td>
-          <td>65</td>
+          <td>5</td>
+          <td>66</td>
         </tr>
 
         <tr id="We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are served as input tokens to Transformer; (ii) channel-independence where each channel contains a single univariate time series that shares the same embedding and Transformer weights across all the series. Patching design naturally has three-fold benefit: local semantic information is retained in the embedding; computation and memory usage of the attention maps are quadratically reduced given the same look-back window; and the model can attend longer history. Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models. We also apply our model to self-supervised pre-training tasks and attain excellent fine-tuning performance, which outperforms supervised training on large datasets. Transferring of masked pre-trained representation on one dataset to others also produces SOTA forecasting accuracy. Code is available at: https://github.com/yuqinie98/PatchTST.">
@@ -508,7 +508,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-11-27</td>
           <td>ArXiv</td>
-          <td>487</td>
+          <td>494</td>
           <td>34</td>
         </tr>
 
@@ -521,7 +521,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2024-01-10</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>5</td>
+          <td>6</td>
         </tr>
 
         <tr id="The Transformer is a highly successful deep learning model that has revolutionised the world of artificial neural networks, first in natural language processing and later in computer vision. This model is based on the attention mechanism and is able to capture complex semantic relationships between a variety of patterns present in the input data. Precisely because of these characteristics, the Transformer has recently been exploited for time series forecasting problems, assuming a natural adaptability to the domain of continuous numerical series. Despite the acclaimed results in the literature, some works have raised doubts about the robustness and effectiveness of this approach. In this paper, we further investigate the effectiveness of Transformer-based models applied to the domain of time series forecasting, demonstrate their limitations, and propose a set of alternative models that are better performing and significantly less complex. In particular, we empirically show how simplifying Transformer-based forecasting models almost always leads to an improvement, reaching state of the art performance. We also propose shallow models without the attention mechanism, which compete with the overall state of the art in long time series forecasting, and demonstrate their ability to accurately predict time series over extremely long windows. From a methodological perspective, we show how it is always necessary to use a simple baseline to verify the effectiveness of proposed models, and finally, we conclude the paper with a reflection on recent research paths and the opportunity to follow trends and hypes even where it may not be necessary.">
@@ -533,7 +533,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2023-04-10</td>
           <td>ArXiv, DBLP</td>
           <td>1</td>
-          <td>5</td>
+          <td>6</td>
         </tr>
 
         <tr id="Time series has been left behind in the era of pre-training and transfer learning. While research in the fields of natural language processing and computer vision are enjoying progressively larger datasets to train massive models, the most popular time series datasets consist of only tens of thousands of time steps, limiting our ability to study the effectiveness of pre-training and scaling. Recent studies have also cast doubt on the need for expressive models and scale. To alleviate these issues, we introduce three large-scale time series forecasting datasets from the cloud operations (CloudOps) domain, the largest having billions of observations, enabling further study into pre-training and scaling of time series models. We build the empirical groundwork for studying pre-training and scaling of time series models and pave the way for future research by identifying a promising candidate architecture. We show that it is a strong zero-shot baseline and benefits from further scaling, both in model and dataset size. Accompanying these datasets and results is a suite of comprehensive benchmark results comparing classical and deep learning baselines to our pre-trained method - achieving a 27% reduction in error on the largest dataset. Code and datasets can be found https://github.com/SalesforceAIResearch/pretrain-time-series-cloudops.">
diff --git a/site/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14/index.html b/site/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14/index.html
index f703da84..2ad64683 100644
--- a/site/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14/index.html
+++ b/site/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:23 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:06 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -497,7 +497,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2022-03-15</td>
           <td>ACM Transactions on Knowledge Discovery from Data</td>
           <td>38</td>
-          <td>36</td>
+          <td>37</td>
         </tr>
 
         <tr id="Domain generalization methods aim to learn models robust to domain shift with data from a limited number of source domains and without access to target domain samples during training. Popular domain alignment methods for domain generalization seek to extract domain-invariant features by minimizing the discrepancy between feature distributions across all domains, disregarding inter-domain relationships. In this paper, we instead propose a novel representation learning methodology that selectively enforces prediction consistency between source domains estimated to be closely-related. Specifically, we hypothesize that domains share different class-informative representations, so instead of aligning all domains which can cause negative transfer, we only regularize the discrepancy between closely-related domains. We apply our method to time-series classification tasks and conduct comprehensive experiments on three public real-world datasets. Our method significantly improves over the baseline and achieves better or competitive performance in comparison with state-of-the-art methods in terms of both accuracy and model calibration.">
diff --git a/site/recommendations/5d150cec2775f9bc863760448f14104cc8f42368/index.html b/site/recommendations/5d150cec2775f9bc863760448f14104cc8f42368/index.html
index d09473ce..9516f1b8 100644
--- a/site/recommendations/5d150cec2775f9bc863760448f14104cc8f42368/index.html
+++ b/site/recommendations/5d150cec2775f9bc863760448f14104cc8f42368/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:38 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:21 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,8 +484,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2019-03-29</td>
           <td>Proceedings of the National Academy of Sciences of the United States of America</td>
-          <td>599</td>
-          <td>63</td>
+          <td>603</td>
+          <td>65</td>
         </tr>
 
         <tr id="None">
@@ -520,7 +520,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>237</td>
+          <td>238</td>
           <td>12</td>
         </tr>
 
@@ -557,7 +557,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2021-05-11</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
           <td>37</td>
-          <td>53</td>
+          <td>52</td>
         </tr>
 
         <tr id="None">
@@ -569,7 +569,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2014-12-10</td>
           <td>The European Physical Journal Special Topics</td>
           <td>64</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
 
         <tr id="None">
@@ -581,7 +581,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2014-12-01</td>
           <td>The European Physical Journal Special Topics</td>
           <td>7</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c/index.html b/site/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c/index.html
index c2f5504c..cc566db0 100644
--- a/site/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c/index.html
+++ b/site/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:39 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:21 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -497,7 +497,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2024-05-07</td>
           <td>ArXiv</td>
           <td>1</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
 
         <tr id="None">
@@ -544,7 +544,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2021-12-20</td>
           <td>ArXiv</td>
-          <td>12</td>
+          <td>13</td>
           <td>72</td>
         </tr>
 
diff --git a/site/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37/index.html b/site/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37/index.html
index 3c01f847..5305f20e 100644
--- a/site/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37/index.html
+++ b/site/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:21 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:04 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -521,7 +521,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2023-06-26</td>
           <td>DBLP</td>
           <td>2</td>
-          <td>139</td>
+          <td>140</td>
         </tr>
 
         <tr id="Contrastive representation learning is crucial in medical time series analysis as it alleviates dependency on labor-intensive, domain-specific, and scarce expert annotations. However, existing contrastive learning methods primarily focus on one single data level, which fails to fully exploit the intricate nature of medical time series. To address this issue, we present COMET, an innovative hierarchical framework that leverages data consistencies at all inherent levels in medical time series. Our meticulously designed model systematically captures data consistency from four potential levels: observation, sample, trial, and patient levels. By developing contrastive loss at multiple levels, we can learn effective representations that preserve comprehensive data consistency, maximizing information utilization in a self-supervised manner. We conduct experiments in the challenging patient-independent setting. We compare COMET against six baselines using three diverse datasets, which include ECG signals for myocardial infarction and EEG signals for Alzheimer's and Parkinson's diseases. The results demonstrate that COMET consistently outperforms all baselines, particularly in setup with 10% and 1% labeled data fractions across all datasets. These results underscore the significant impact of our framework in advancing contrastive representation learning techniques for medical time series. The source code is available at https://github.com/DL4mHealth/COMET.">
@@ -533,7 +533,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2023-10-21</td>
           <td>ArXiv</td>
           <td>19</td>
-          <td>2</td>
+          <td>3</td>
         </tr>
 
         <tr id="Unsupervised domain adaptation (UDA) provides a strategy for improving machine learning performance in data-rich (target) domains where ground truth labels are inaccessible but can be found in related (source) domains. In cases where meta-domain information such as label distributions is available, weak supervision can further boost performance. We propose a novel framework, CALDA, to tackle these two problems. CALDA synergistically combines the principles of contrastive learning and adversarial learning to robustly support multi-source UDA (MS-UDA) for time series data. Similar to prior methods, CALDA utilizes adversarial learning to align source and target feature representations. Unlike prior approaches, CALDA additionally leverages cross-source label information across domains. CALDA pulls examples with the same label close to each other, while pushing apart examples with different labels, reshaping the space through contrastive learning. Unlike prior contrastive adaptation methods, CALDA requires neither data augmentation nor pseudo labeling, which may be more challenging for time series. We empirically validate our proposed approach. Based on results from human activity recognition, electromyography, and synthetic datasets, we find utilizing cross-source information improves performance over prior time series and contrastive methods. Weak supervision further improves performance, even in the presence of noise, allowing CALDA to offer generalizable strategies for MS-UDA.">
@@ -545,7 +545,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2021-09-30</td>
           <td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td>
           <td>14</td>
-          <td>72</td>
+          <td>71</td>
         </tr>
 
         <tr id="To improve the accuracy of clinical multivariate time series (MTS) classification (such as EEG and ECG) by a novel self-supervised paradigm that directly captures the dynamics between the different time series learned together to optimize the classification task. Labels in clinical datasets are very often insufficient. One way to address this challenge is leveraging self-supervision. This paradigm attempts to identify a supervisory signal inherent within a dataset to serve as a surrogate label. We present a novel form of self-supervision: dynamics of clinical MTS. Unlike other self-supervision methods, such as masking, that are intuitive but still heuristic, we suggest to learn a representation justified by Koopman theory. The latter was shown useful for representing clinical time series and can be used as a form of surrogate task to improve the clinical MTS classification. In the ECG task, we show that our proposed framework achieved higher sensitivity and specificity than the state-of-the-art (SOTA) baseline over numerous common diagnoses. For EEG abnormality classification, our proposed framework also achieved higher sensitivity and specificity than the SOTA baseline. All results are statistically significant. Our technique yields reliable clinical diagnosis in an empirical study employing signals from thousands of patients in multiple clinical tasks employing two types of clinical-grade sensors (ECG and EEG) as compared to the state-of-the-art machine learning. Leveraging time-series-dynamics self-supervision can help mitigate the lack of labels in clinical datasets used for training machine learning algorithms and significantly improve their performance. Specifically, the ECG system presented in this work is being trialed in hospitals, used by top cardiologists for patient diagnosis and treatment. We believe that the deployment of such cutting-edge technology will significantly improve the accuracy and speed of cardiac assessments.">
@@ -593,7 +593,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2022-04-24</td>
           <td>IEEE transactions on neural networks and learning systems</td>
           <td>4</td>
-          <td>5</td>
+          <td>6</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/73dd9c49f205280991826b2ea4b50344203916b4/index.html b/site/recommendations/73dd9c49f205280991826b2ea4b50344203916b4/index.html
index 2f354f9c..f9570a62 100644
--- a/site/recommendations/73dd9c49f205280991826b2ea4b50344203916b4/index.html
+++ b/site/recommendations/73dd9c49f205280991826b2ea4b50344203916b4/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:51 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:33 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -508,7 +508,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-03-01</td>
           <td>ArXiv</td>
-          <td>0</td>
+          <td>1</td>
           <td>1</td>
         </tr>
 
@@ -544,8 +544,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2017-11-15</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>427</td>
-          <td>63</td>
+          <td>428</td>
+          <td>65</td>
         </tr>
 
         <tr id="None">
@@ -568,8 +568,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-04-05</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>191</td>
-          <td>63</td>
+          <td>194</td>
+          <td>65</td>
         </tr>
 
         <tr id="We consider the problem of learning the dynamics of a linear system when one has access to data generated by an auxiliary system that shares similar (but not identical) dynamics, in addition to data from the true system. We use a weighted least squares approach, and provide finite sample error bounds of the learned model as a function of the number of samples and various system parameters from the two systems as well as the weight assigned to the auxiliary data. We show that the auxiliary data can help to reduce the intrinsic system identification error due to noise, at the price of adding a portion of error that is due to the differences between the two system models. We further provide a data-dependent bound that is computable when some prior knowledge about the systems, such as upper bounds on noise levels and model difference, is available. This bound can also be used to determine the weight that should be assigned to the auxiliary data during the model training stage.">
diff --git a/site/recommendations/883547fdbd88552328a6615ec620f96e39c57018/index.html b/site/recommendations/883547fdbd88552328a6615ec620f96e39c57018/index.html
index 973dd86b..5c3030a5 100644
--- a/site/recommendations/883547fdbd88552328a6615ec620f96e39c57018/index.html
+++ b/site/recommendations/883547fdbd88552328a6615ec620f96e39c57018/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:58 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:38 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,7 +484,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2021-07-05</td>
           <td>Royal Society Open Science</td>
-          <td>52</td>
+          <td>54</td>
           <td>31</td>
         </tr>
 
@@ -497,7 +497,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2023-01-30</td>
           <td>ArXiv</td>
           <td>11</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
 
         <tr id="None">
@@ -520,8 +520,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-04-05</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>191</td>
-          <td>63</td>
+          <td>194</td>
+          <td>65</td>
         </tr>
 
         <tr id="We propose a fast probabilistic framework for identifying differential equations governing the dynamics of observed data. We recast the SINDy method within a Bayesian framework and use Gaussian approximations for the prior and likelihood to speed up computation. The resulting method, Bayesian-SINDy, not only quantifies uncertainty in the parameters estimated but also is more robust when learning the correct model from limited and noisy data. Using both synthetic and real-life examples such as Lynx-Hare population dynamics, we demonstrate the effectiveness of the new framework in learning correct model equations and compare its computational and data efficiency with existing methods. Because Bayesian-SINDy can quickly assimilate data and is robust against noise, it is particularly suitable for biological data and real-time system identification in control. Its probabilistic framework also enables the calculation of information entropy, laying the foundation for an active learning strategy.">
@@ -532,8 +532,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-02-23</td>
           <td>ArXiv</td>
-          <td>0</td>
-          <td>37</td>
+          <td>1</td>
+          <td>38</td>
         </tr>
 
         <tr id="Significance Understanding dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled technology, including aircraft, combustion engines, satellites, and electrical power. This work develops a novel framework to discover governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity techniques and machine learning. The resulting models are parsimonious, balancing model complexity with descriptive ability while avoiding overfitting. There are many critical data-driven problems, such as understanding cognition from neural recordings, inferring climate patterns, determining stability of financial markets, predicting and suppressing the spread of disease, and controlling turbulence for greener transportation and energy. With abundant data and elusive laws, data-driven discovery of dynamics will continue to play an important role in these efforts. Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.">
@@ -544,8 +544,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3181</td>
-          <td>63</td>
+          <td>3190</td>
+          <td>65</td>
         </tr>
 
         <tr id="The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. However, SINDy assumes the user has prior knowledge of the variables in the system and of a function library that can act as a basis for the system. In this paper, we demonstrate on real world data how the Augmented SINDy algorithm outperforms SINDy in the presence of system variable uncertainty. We then show SINDy can be further augmented to perform robustly when both kinds of uncertainty are present.">
diff --git a/site/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271/index.html b/site/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271/index.html
index 1a6d7294..85aa7bd5 100644
--- a/site/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271/index.html
+++ b/site/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:44 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:26 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -476,18 +476,6 @@ <h3 data-intro='Recommendations for the article'>
   </thead>
   <tbody>
 
-        <tr id="Many dynamical systems of interest are nonlinear, with examples in turbulence, epidemiology, neuroscience, and finance, making them difficult to control using linear approaches. Model predictive control (MPC) is a powerful model-based optimization technique that enables the control of such nonlinear systems with constraints. However, modern systems often lack computationally tractable models, motivating the use of system identification techniques to learn accurate and efficient models for real-time control. In this tutorial article, we review emerging data-driven methods for model discovery and how they are used for nonlinear MPC. In particular, we focus on the sparse identification of nonlinear dynamics (SINDy) algorithm and show how it may be used with MPC on an infectious disease control example. We compare the performance against MPC based on a linear dynamic mode decomposition (DMD) model. Code is provided to run the tutorial examples and may be modified to extend this data-driven control framework to arbitrary nonlinear systems.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/700552dde778bd1f6b76f7237e9b561597b859ba" target='_blank'>SINDy with Control: A Tutorial</a></td>
-          <td>
-            Urban Fasel, E. Kaiser, J. Kutz, Bingni W. Brunton, S. Brunton
-          </td>
-          <td>2021-08-30</td>
-          <td>2021 60th IEEE Conference on Decision and Control (CDC)</td>
-          <td>45</td>
-          <td>63</td>
-        </tr>
-
         <tr id="In this work we analyze the effectiveness of the Sparse Identification of Nonlinear Dynamics (SINDy) technique on three benchmark datasets for nonlinear identification, to provide a better understanding of its suitability when tackling real dynamical systems. While SINDy can be an appealing strategy for pursuing physics-based learning, our analysis highlights difficulties in dealing with unobserved states and non-smooth dynamics. Due to the ubiquity of these features in real systems in general, and control applications in particular, we complement our analysis with hands-on approaches to tackle these issues in order to exploit SINDy also in these challenging contexts.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/99877062e70fae24c531d58aa880fa443ec10c9b" target='_blank'>SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study</a></td>
@@ -496,7 +484,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-03-01</td>
           <td>ArXiv</td>
-          <td>0</td>
+          <td>1</td>
           <td>1</td>
         </tr>
 
@@ -520,8 +508,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-04-05</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>191</td>
-          <td>63</td>
+          <td>194</td>
+          <td>65</td>
         </tr>
 
         <tr id="Modern societies have an abundance of data yet good system models are rare. Unfortunately, many of the current system identification and machine learning techniques fail to generalize outside of the training set, producing models that violate basic physical laws. This work proposes a novel method for the Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI). SINDy-SI is an iterative method that uses Sum-of-Squares (SOS) programming to learn optimally fitted models while guaranteeing that the learned model satisfies side information, such as symmetry's and physical laws. Guided by the principle of Occam's razor, that the simplest or most regularized best fitted model is typically the superior choice, during each iteration SINDy-SI prunes the basis functions associated with small coefficients, yielding a sparse dynamical model upon termination. Through several numerical experiments we will show how the combination of side information constraints and sparse polynomial representation cultivates dynamical models that obey known physical laws while displaying impressive generalized performance beyond the training set.">
@@ -556,8 +544,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3181</td>
-          <td>63</td>
+          <td>3190</td>
+          <td>65</td>
         </tr>
 
         <tr id="Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community. PySINDy is a Python package that provides tools for applying the sparse identification of nonlinear dynamics (SINDy) approach to data-driven model discovery. In this major update to PySINDy, we implement several advanced features that enable the discovery of more general differential equations from noisy and limited data. The library of candidate terms is extended for the identification of actuated systems, partial differential equations (PDEs), and implicit differential equations. Robust formulations, including the integral form of SINDy and ensembling techniques, are also implemented to improve performance for real-world data. Finally, we provide a range of new optimization algorithms, including several sparse regression techniques and algorithms to enforce and promote inequality constraints and stability. Together, these updates enable entirely new SINDy model discovery capabilities that have not been reported in the literature, such as constrained PDE identification and ensembling with different sparse regression optimizers.">
@@ -569,7 +557,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
           <td>109</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
 
         <tr id="We develop a principled mathematical framework for controlling nonlinear, networked dynamical systems. Our method integrates dimensionality reduction, bifurcation theory, and emerging model discovery tools to find low-dimensional subspaces where feed-forward control can be used to manipulate a system to a desired outcome. The method leverages the fact that many high-dimensional networked systems have many fixed points, allowing for the computation of control signals that will move the system between any pair of fixed points. The sparse identification of nonlinear dynamics (SINDy) algorithm is used to fit a nonlinear dynamical system to the evolution on the dominant, low-rank subspace. This then allows us to use bifurcation theory to find collections of constant control signals that will produce the desired objective path for a prescribed outcome. Specifically, we can destabilize a given fixed point while making the target fixed point an attractor. The discovered control signals can be easily projected back to the original high-dimensional state and control space. We illustrate our nonlinear control procedure on established bistable, low-dimensional biological systems, showing how control signals are found that generate switches between the fixed points. We then demonstrate our control procedure for high-dimensional systems on random high-dimensional networks and Hopfield memory networks.">
diff --git a/site/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da/index.html b/site/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da/index.html
index 10d14f7d..4169ed58 100644
--- a/site/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da/index.html
+++ b/site/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:06:00 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:42 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -485,7 +485,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2018-07-14</td>
           <td>ArXiv</td>
           <td>9</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
 
         <tr id="Sparse regression models are increasingly prevalent due to their ease of interpretability and superior out-of-sample performance. However, the exact model of sparse regression with an $\ell_0$ constraint restricting the support of the estimators is a challenging (\NP-hard) non-convex optimization problem. In this paper, we derive new strong convex relaxations for sparse regression. These relaxations are based on the ideal (convex-hull) formulations for rank-one quadratic terms with indicator variables. The new relaxations can be formulated as semidefinite optimization problems in an extended space and are stronger and more general than the state-of-the-art formulations, including the perspective reformulation and formulations with the reverse Huber penalty and the minimax concave penalty functions. Furthermore, the proposed rank-one strengthening can be interpreted as a \textit{non-separable, non-convex, unbiased} sparsity-inducing regularizer, which dynamically adjusts its penalty according to the shape of the error function without inducing bias for the sparse solutions. In our computational experiments with benchmark datasets, the proposed conic formulations are solved within seconds and result in near-optimal solutions (with 0.4\% optimality gap) for non-convex $\ell_0$-problems. Moreover, the resulting estimators also outperform alternative convex approaches from a statistical perspective, achieving high prediction accuracy and good interpretability.">
@@ -521,7 +521,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2014-01-03</td>
           <td></td>
           <td>58</td>
-          <td>94</td>
+          <td>95</td>
         </tr>
 
         <tr id="High-dimensional sparse linear regression is a basic problem in machine learning and statistics. Consider a linear model $y = X\theta^\star + w$, where $y \in \mathbb{R}^n$ is the vector of observations, $X \in \mathbb{R}^{n \times d}$ is the covariate matrix and $w \in \mathbb{R}^n$ is an unknown noise vector. In many applications, the linear regression model is high-dimensional in nature, meaning that the number of observations $n$ may be substantially smaller than the number of covariates $d$. In these cases, it is common to assume that $\theta^\star$ is sparse, and the goal in sparse linear regression is to estimate this sparse $\theta^\star$, given $(X,y)$. 
@@ -535,7 +535,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2017-07-25</td>
           <td>ArXiv</td>
           <td>1</td>
-          <td>29</td>
+          <td>30</td>
         </tr>
 
         <tr id="Reconstruction of signals from undersampled and noisy measurements is a topic of considerable interest. Sharpness conditions directly control the recovery performance of restart schemes for first-order methods without the need for restrictive assumptions such as strong convexity. However, they are challenging to apply in the presence of noise or approximate model classes (e.g., approximate sparsity). We provide a first-order method: Weighted, Accelerated and Restarted Primal-dual (WARPd), based on primal-dual iterations and a novel restart-reweight scheme. Under a generic approximate sharpness condition, WARPd achieves stable linear convergence to the desired vector. Many problems of interest fit into this framework. For example, we analyze sparse recovery in compressed sensing, low-rank matrix recovery, matrix completion, TV regularization, minimization of $\|Bx\|_{l^1}$ under constraints ($l^1$-analysis problems for general $B$), and mixed regularization problems. We show how several quantities controlling recovery performance also provide explicit approximate sharpness constants. Numerical experiments show that WARPd compares favorably with specialized state-of-the-art methods and is ideally suited for solving large-scale problems. We also present a noise-blind variant based on the Square-Root LASSO decoder. Finally, we show how to unroll WARPd as neural networks. This approximation theory result provides lower bounds for stable and accurate neural networks for inverse problems and sheds light on architecture choices. Code and a gallery of examples are made available online as a MATLAB package.">
diff --git a/site/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524/index.html b/site/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524/index.html
index fd48ab6f..4f634880 100644
--- a/site/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524/index.html
+++ b/site/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:24 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:08 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -485,7 +485,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2022-04-23</td>
           <td>ArXiv</td>
           <td>5</td>
-          <td>49</td>
+          <td>50</td>
         </tr>
 
         <tr id="Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has been a surge in GNN-based approaches for time series analysis. These approaches can explicitly model inter-temporal and inter-variable relationships, which traditional and other deep neural network-based methods struggle to do. In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation. Our aim is to guide designers and practitioners to understand, build applications, and advance research of GNN4TS. At first, we provide a comprehensive task-oriented taxonomy of GNN4TS. Then, we present and discuss representative research works and introduce mainstream applications of GNN4TS. A comprehensive discussion of potential future research directions completes the survey. This survey, for the first time, brings together a vast array of knowledge on GNN-based time series research, highlighting foundations, practical applications, and opportunities of graph neural networks for time series analysis.">
@@ -495,9 +495,9 @@ <h3 data-intro='Recommendations for the article'>
             Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, C. Alippi, G. I. Webb, Irwin King, Shirui Pan
           </td>
           <td>2023-07-07</td>
-          <td>ArXiv</td>
+          <td>IEEE transactions on pattern analysis and machine intelligence</td>
           <td>61</td>
-          <td>49</td>
+          <td>50</td>
         </tr>
 
         <tr id="Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS). Recent studies offer evidence that the use of graph neural networks to capture latent correlations between time series holds a potential for enhanced forecasting. However, most existing methods rely on predefined or self-learning graphs, which are either static or unintentionally dynamic, and thus cannot model the time-varying correlations that exhibit trends and periodicities caused by the regularity of the underlying processes in CPS. To tackle such limitation, we propose Time-aware Graph Structure Learning (TagSL), which extracts time-aware correlations among time series by measuring the interaction of node and time representations in high-dimensional spaces. Notably, we introduce time discrepancy learning that utilizes contrastive learning with distance-based regularization terms to constrain learned spatial correlations to a trend sequence. Additionally, we propose a periodic discriminant function to enable the capture of periodic changes from the state of nodes. Next, we present a Graph Convolution-based Gated Recurrent Unit (GCGRU) that jointly captures spatial and temporal dependencies while learning time-aware and node-specific patterns. Finally, we introduce a unified framework named Time-aware Graph Convolutional Recurrent Network (TGCRN), combining TagSL, and GCGRU in an encoder-decoder architecture for multi-step spatiotemporal forecasting. We report on experiments with TGCRN and popular existing approaches on five real-world datasets, thus providing evidence that TGCRN is capable of advancing the state-of-the-art. We also cover a detailed ablation study and visualization analysis, offering detailed insight into the effectiveness of time-aware structure learning.">
@@ -532,7 +532,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-02-22</td>
           <td>ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</td>
-          <td>4</td>
+          <td>5</td>
           <td>42</td>
         </tr>
 
@@ -556,8 +556,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-02-17</td>
           <td>IEEE Transactions on Knowledge and Data Engineering</td>
-          <td>56</td>
-          <td>42</td>
+          <td>58</td>
+          <td>43</td>
         </tr>
 
         <tr id="Existing multivariate time series anomaly detection methods utilize graph neural networks to model inter-sensor dependencies and achieve satisfactory results. However, these methods often employ a predefined or self-learning static adjacency matrix for graph information aggregation, disregarding the dynamic characteristics of inter-sensor dependencies in the real world, which change over time and are observable at different time scales as dynamic changing graph structure information. Furthermore, existing techniques struggle to capture long-term temporal contextual relationships. To address these issues, we propose a Dynamic Graph Learning with Long and Short-term time model (DGL-LS) for multivariate time series anomaly detection. The network integrates a dynamic graph structure learning method among sensors in multi-time mode and long- and short-term temporal context information to enhance the prediction of multivariate time series anomalies. We propose a strategy for learning dynamic graphs that aggregates sensor node representations at various time intervals, and then uses an attention-based gating unit to evolve the node dynamic characterization and construct a series of adjacency matrices that describe the dynamic changes in graph structure over time. Moreover, we formulate a long-short time convolution to capture the long-term and short-term temporal dependencies within the time series. We demonstrate the superiority of our approach over other state-of-the-art methods by conducting extensive experiments on three publicly available datasets.">
@@ -569,7 +569,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2023-09-07</td>
           <td>2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)</td>
           <td>0</td>
-          <td>3</td>
+          <td>4</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a/index.html b/site/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a/index.html
index 3d9d79d3..eadf0816 100644
--- a/site/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a/index.html
+++ b/site/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:13 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:04:58 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -509,7 +509,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2022-05-26</td>
           <td>J. Mach. Learn. Res.</td>
           <td>11</td>
-          <td>49</td>
+          <td>50</td>
         </tr>
 
         <tr id="The challenge of effectively learning inter-series correlations for multivariate time series forecasting remains a substantial and unresolved problem. Traditional deep learning models, which are largely dependent on the Transformer paradigm for modeling long sequences, often fail to integrate information from multiple time series into a coherent and universally applicable model. To bridge this gap, our paper presents ForecastGrapher, a framework reconceptualizes multivariate time series forecasting as a node regression task, providing a unique avenue for capturing the intricate temporal dynamics and inter-series correlations. Our approach is underpinned by three pivotal steps: firstly, generating custom node embeddings to reflect the temporal variations within each series; secondly, constructing an adaptive adjacency matrix to encode the inter-series correlations; and thirdly, augmenting the GNNs' expressive power by diversifying the node feature distribution. To enhance this expressive power, we introduce the Group Feature Convolution GNN (GFC-GNN). This model employs a learnable scaler to segment node features into multiple groups and applies one-dimensional convolutions with different kernel lengths to each group prior to the aggregation phase. Consequently, the GFC-GNN method enriches the diversity of node feature distribution in a fully end-to-end fashion. Through extensive experiments and ablation studies, we show that ForecastGrapher surpasses strong baselines and leading published techniques in the domain of multivariate time series forecasting.">
@@ -532,8 +532,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-05-24</td>
           <td>Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining</td>
-          <td>945</td>
-          <td>54</td>
+          <td>950</td>
+          <td>55</td>
         </tr>
 
         <tr id="Graph Neural Networks (GNN) have gained significant traction in the forecasting domain, especially for their capacity to simultaneously account for intra-series temporal correlations and inter-series relationships. This paper introduces a novel Hierarchical GNN (DeepHGNN) framework, explicitly designed for forecasting in complex hierarchical structures. The uniqueness of DeepHGNN lies in its innovative graph-based hierarchical interpolation and an end-to-end reconciliation mechanism. This approach ensures forecast accuracy and coherence across various hierarchical levels while sharing signals across them, addressing a key challenge in hierarchical forecasting. A critical insight in hierarchical time series is the variance in forecastability across levels, with upper levels typically presenting more predictable components. DeepHGNN capitalizes on this insight by pooling and leveraging knowledge from all hierarchy levels, thereby enhancing the overall forecast accuracy. Our comprehensive evaluation set against several state-of-the-art models confirm the superior performance of DeepHGNN. This research not only demonstrates DeepHGNN's effectiveness in achieving significantly improved forecast accuracy but also contributes to the understanding of graph-based methods in hierarchical time series forecasting.">
@@ -556,8 +556,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>29</td>
-          <td>4</td>
+          <td>30</td>
+          <td>5</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9/index.html b/site/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9/index.html
index f3035185..da4b9cff 100644
--- a/site/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9/index.html
+++ b/site/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:09 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:04:55 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -509,7 +509,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2023-10-24</td>
           <td>ArXiv</td>
           <td>4</td>
-          <td>49</td>
+          <td>50</td>
         </tr>
 
         <tr id="With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time series. Time-Series Anomaly Detection (TSAD) is an important task in various time-series applications such as e-commerce, cybersecurity, vehicle maintenance, and healthcare monitoring. However, this task is very challenging as it requires considering both the intra-variable dependency and the inter-variable dependency, where a variable can be defined as an observation in time-series data. Recent graph-based approaches have made impressive progress in tackling the challenges of this field. In this survey, we conduct a comprehensive and up-to-date review of TSAD using graphs, referred to as G-TSAD. First, we explore the significant potential of graph representation learning for time-series data. Then, we review state-of-the-art graph anomaly detection techniques in the context of time series and discuss their strengths and drawbacks. Finally, we discuss the technical challenges and potential future directions for possible improvements in this research field.">
@@ -520,7 +520,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-01-31</td>
           <td>ArXiv</td>
-          <td>5</td>
+          <td>4</td>
           <td>15</td>
         </tr>
 
@@ -532,8 +532,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>29</td>
-          <td>4</td>
+          <td>30</td>
+          <td>5</td>
         </tr>
 
         <tr id="Anomaly detection of time series, especially multivariate time series(time series with multiple sensors), has been focused on for several years. Though existing method has achieved great progress, there are several challenging problems to be solved. Firstly, existing method including neural network only concentrate on the relationship in terms of timestamp. To be exact, they only want to know how does the data in the past influence which in the future. However, one sensor sometimes intervenes in other sensor such as the speed of wind may cause decrease of temperature. Secondly, there exist two categories of model for time series anomaly detection: prediction model and reconstruction model. Prediction model is adept at learning timely representation while short of capability when faced with sparse anomaly. Conversely, reconstruction model is opposite. Therefore, how can we efficiently get the relationship both in terms of both timestamp and sensors becomes our main topic. Our approach uses GAT, which is originated from graph neural network, to obtain connection between sensors. And LSTM is used to obtain relationships timely. Our approach is also designed to be double headed to calculate both prediction loss and reconstruction loss via VAE(Variational Auto-Encoder). In order to take advantage of two sorts of model, multi-task optimization algorithm is used in this model.">
@@ -556,7 +556,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-02-04</td>
           <td>ArXiv</td>
-          <td>3</td>
+          <td>4</td>
           <td>22</td>
         </tr>
 
@@ -593,7 +593,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2023-07-17</td>
           <td>IEEE transactions on neural networks and learning systems</td>
           <td>8</td>
-          <td>18</td>
+          <td>19</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419/index.html b/site/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419/index.html
index 52cb614f..c0ff51f0 100644
--- a/site/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419/index.html
+++ b/site/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:11 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:04:57 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -485,7 +485,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2023-10-24</td>
           <td>ArXiv</td>
           <td>4</td>
-          <td>49</td>
+          <td>50</td>
         </tr>
 
         <tr id="Neural forecasting of spatiotemporal time series drives both research and industrial innovation in several relevant application domains. Graph neural networks (GNNs) are often the core component of the forecasting architecture. However, in most spatiotemporal GNNs, the computational complexity scales up to a quadratic factor with the length of the sequence times the number of links in the graph, hence hindering the application of these models to large graphs and long temporal sequences. While methods to improve scalability have been proposed in the context of static graphs, few research efforts have been devoted to the spatiotemporal case. To fill this gap, we propose a scalable architecture that exploits an efficient encoding of both temporal and spatial dynamics. In particular, we use a randomized recurrent neural network to embed the history of the input time series into high-dimensional state representations encompassing multi-scale temporal dynamics. Such representations are then propagated along the spatial dimension using different powers of the graph adjacency matrix to generate node embeddings characterized by a rich pool of spatiotemporal features. The resulting node embeddings can be efficiently pre-computed in an unsupervised manner, before being fed to a feed-forward decoder that learns to map the multi-scale spatiotemporal representations to predictions. The training procedure can then be parallelized node-wise by sampling the node embeddings without breaking any dependency, thus enabling scalability to large networks. Empirical results on relevant datasets show that our approach achieves results competitive with the state of the art, while dramatically reducing the computational burden.">
@@ -496,8 +496,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-09-14</td>
           <td>ArXiv</td>
-          <td>28</td>
-          <td>49</td>
+          <td>29</td>
+          <td>50</td>
         </tr>
 
         <tr id="Outstanding achievements of graph neural networks for spatiotemporal time series analysis show that relational constraints introduce an effective inductive bias into neural forecasting architectures. Often, however, the relational information characterizing the underlying data-generating process is unavailable and the practitioner is left with the problem of inferring from data which relational graph to use in the subsequent processing stages. We propose novel, principled - yet practical - probabilistic score-based methods that learn the relational dependencies as distributions over graphs while maximizing end-to-end the performance at task. The proposed graph learning framework is based on consolidated variance reduction techniques for Monte Carlo score-based gradient estimation, is theoretically grounded, and, as we show, effective in practice. In this paper, we focus on the time series forecasting problem and show that, by tailoring the gradient estimators to the graph learning problem, we are able to achieve state-of-the-art performance while controlling the sparsity of the learned graph and the computational scalability. We empirically assess the effectiveness of the proposed method on synthetic and real-world benchmarks, showing that the proposed solution can be used as a stand-alone graph identification procedure as well as a graph learning component of an end-to-end forecasting architecture.">
@@ -509,7 +509,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2022-05-26</td>
           <td>J. Mach. Learn. Res.</td>
           <td>11</td>
-          <td>49</td>
+          <td>50</td>
         </tr>
 
         <tr id="Time series forecasting with additional spatial de-pendencies has attracted a tremendous amount of research interest in social sciences, due to its importance in modern real-world applications. The Graph Neural Networks (GNN) is one of the most exciting deep learning techniques among these spatio-temporal modeling approaches. Most existing spatio-temporal GNN frameworks are based on a two-step modeling process. In such scenario, spatial and temporal dependencies are modeled in separate steps, which lead to problems such as complex architecture design, hard to scale, etc. Targeting the shortcomings of existing studies, we take both spatial and temporal dependencies from another perspective, and consider them as two heterogeneous types of edges in the graph. We propose a unified spatio-temporal GNN framework that captures both dependencies in a single step. More specifically, for each node in the graph, a unified neural network component is designed to simultaneously extract information from its sur-rounding neighbors (spatial) and its past records (temporal), which enables much easier dependency aggregation with faster execution. Experiment results demonstrate the superiority of the proposed framework over state-of-the-art (SOTA) baselines on various applications, including modeling smart cities and data-driven political science research.">
@@ -544,8 +544,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>29</td>
-          <td>4</td>
+          <td>30</td>
+          <td>5</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6/index.html b/site/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6/index.html
index 9f71dd70..e04ea48b 100644
--- a/site/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6/index.html
+++ b/site/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:18 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:03 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -497,7 +497,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2024-06-04</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
+          <td>4</td>
         </tr>
 
         <tr id="We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.">
@@ -508,7 +508,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-03-12</td>
           <td>ArXiv</td>
-          <td>24</td>
+          <td>27</td>
           <td>18</td>
         </tr>
 
@@ -520,7 +520,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-10-12</td>
           <td>ArXiv</td>
-          <td>13</td>
+          <td>14</td>
           <td>40</td>
         </tr>
 
diff --git a/site/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8/index.html b/site/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8/index.html
index e45528c8..9b031606 100644
--- a/site/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8/index.html
+++ b/site/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:59 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:40 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -496,8 +496,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3181</td>
-          <td>63</td>
+          <td>3190</td>
+          <td>65</td>
         </tr>
 
         <tr id="Modern societies have an abundance of data yet good system models are rare. Unfortunately, many of the current system identification and machine learning techniques fail to generalize outside of the training set, producing models that violate basic physical laws. This work proposes a novel method for the Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI). SINDy-SI is an iterative method that uses Sum-of-Squares (SOS) programming to learn optimally fitted models while guaranteeing that the learned model satisfies side information, such as symmetry's and physical laws. Guided by the principle of Occam's razor, that the simplest or most regularized best fitted model is typically the superior choice, during each iteration SINDy-SI prunes the basis functions associated with small coefficients, yielding a sparse dynamical model upon termination. Through several numerical experiments we will show how the combination of side information constraints and sparse polynomial representation cultivates dynamical models that obey known physical laws while displaying impressive generalized performance beyond the training set.">
@@ -521,7 +521,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
           <td>109</td>
-          <td>63</td>
+          <td>65</td>
         </tr>
 
         <tr id="Sparse regression has emerged as a popular technique for learning dynamical systems from temporal data, beginning with the SINDy (Sparse Identification of Nonlinear Dynamics) framework proposed by arXiv:1509.03580. Quantifying the uncertainty inherent in differential equations learned from data remains an open problem, thus we propose leveraging recent advances in statistical inference for sparse regression to address this issue. Focusing on systems of ordinary differential equations (ODEs), SINDy assumes that each equation is a parsimonious linear combination of a few candidate functions, such as polynomials, and uses methods such as sequentially-thresholded least squares or the Lasso to identify a small subset of these functions that govern the system's dynamics. We instead employ bias-corrected versions of the Lasso and ridge regression estimators, as well as an empirical Bayes variable selection technique known as SEMMS, to estimate each ODE as a linear combination of terms that are statistically significant. We demonstrate through simulations that this approach allows us to recover the functional terms that correctly describe the dynamics more often than existing methods that do not account for uncertainty.">
@@ -544,7 +544,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>237</td>
+          <td>238</td>
           <td>12</td>
         </tr>
 
@@ -556,8 +556,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-04-17</td>
           <td>arXiv: Dynamical Systems</td>
-          <td>46</td>
-          <td>63</td>
+          <td>47</td>
+          <td>65</td>
         </tr>
 
         <tr id="Data-driven modeling frameworks that adopt sparse regression techniques, such as sparse identification of nonlinear dynamics (SINDy) and its modifications, are developed to resolve difficulties in extracting underlying dynamics from experimental data. In contrast to neural-network-based methods, these methods are designed to obtain white-box analytical models. In this work, we incorporate the concept of SINDy and knowledge in the field of classical mechanics to identify interpretable and sparse expressions of total energy and the Lagrangian that shelters the hidden dynamics. Moreover, our method (hereafter referred as Lagrangian-SINDy) is developed to use knowledge of simple systems that form the system being analyzed to ensure the likelihood of correct results and to improve the learning pace. Lagrangian-SINDy is highly accurate in discovering interpretable dynamics via energy-related physical quantities. Its performance is validated with three popular multi-DOF nonlinear dynamical systems, namely the spherical pendulum, double pendulum and cart-pendulum system. Comparisons with other SINDy-based methods are made and Lagrangian-SINDy is found to provide the most compact analytical models.">
@@ -572,18 +572,6 @@ <h3 data-intro='Recommendations for the article'>
           <td>23</td>
         </tr>
 
-        <tr id="Many dynamical systems of interest are nonlinear, with examples in turbulence, epidemiology, neuroscience, and finance, making them difficult to control using linear approaches. Model predictive control (MPC) is a powerful model-based optimization technique that enables the control of such nonlinear systems with constraints. However, modern systems often lack computationally tractable models, motivating the use of system identification techniques to learn accurate and efficient models for real-time control. In this tutorial article, we review emerging data-driven methods for model discovery and how they are used for nonlinear MPC. In particular, we focus on the sparse identification of nonlinear dynamics (SINDy) algorithm and show how it may be used with MPC on an infectious disease control example. We compare the performance against MPC based on a linear dynamic mode decomposition (DMD) model. Code is provided to run the tutorial examples and may be modified to extend this data-driven control framework to arbitrary nonlinear systems.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/700552dde778bd1f6b76f7237e9b561597b859ba" target='_blank'>SINDy with Control: A Tutorial</a></td>
-          <td>
-            Urban Fasel, E. Kaiser, J. Kutz, Bingni W. Brunton, S. Brunton
-          </td>
-          <td>2021-08-30</td>
-          <td>2021 60th IEEE Conference on Decision and Control (CDC)</td>
-          <td>45</td>
-          <td>63</td>
-        </tr>
-
         <tr id="With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets. Recently, a framework called Sparse Identification of Nonlinear Dynamics (SINDy) has been introduced to identify the governing equations of dynamical systems from simulation data. In this study, we extend SINDy to stochastic dynamical systems which are frequently used to model biophysical processes. We prove the asymptotic correctness of stochastic SINDy in the infinite data limit, both in the original and projected variables. We discuss algorithms to solve the sparse regression problem arising from the practical implementation of SINDy and show that cross validation is an essential tool to determine the right level of sparsity. We demonstrate the proposed methodology on two test systems, namely, the diffusion in a one-dimensional potential and the projected dynamics of a two-dimensional diffusion process.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/8787d66296c0337aeb54d87885d29a63af17711b" target='_blank'>Sparse learning of stochastic dynamical equations.</a></td>
@@ -593,7 +581,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2017-12-06</td>
           <td>The Journal of chemical physics</td>
           <td>191</td>
-          <td>44</td>
+          <td>45</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f/index.html b/site/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f/index.html
index 52e66230..1df9551e 100644
--- a/site/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f/index.html
+++ b/site/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:16 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:01 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,7 +484,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-10-12</td>
           <td>ArXiv</td>
-          <td>13</td>
+          <td>14</td>
           <td>40</td>
         </tr>
 
@@ -508,7 +508,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-10-03</td>
           <td>ArXiv</td>
-          <td>113</td>
+          <td>120</td>
           <td>9</td>
         </tr>
 
diff --git a/site/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90/index.html b/site/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90/index.html
index 785ea536..d65bfb41 100644
--- a/site/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90/index.html
+++ b/site/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-12 06:05:29 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-19 06:05:12 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,8 +484,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-02-04</td>
           <td>ArXiv</td>
-          <td>6</td>
-          <td>65</td>
+          <td>5</td>
+          <td>66</td>
         </tr>
 
         <tr id="Large pre-trained models have been vital in recent advancements in domains like language and vision, making model training for individual downstream tasks more efficient and provide superior performance. However, tackling time-series analysis tasks usually involves designing and training a separate model from scratch leveraging training data and domain expertise specific to the task. We tackle a significant challenge for pre-training a foundational time-series model from multi-domain time-series datasets: extracting semantically useful tokenized inputs to the model across heterogenous time-series from different domains. We propose Large Pre-trained Time-series Models (LPTM) that introduces a novel method of \textit{adaptive segmentation} that automatically identifies optimal dataset-specific segmentation strategy during pre-training. This enables LPTM to perform similar to or better than domain-specific state-of-art model when fine-tuned to different downstream time-series analysis tasks and under zero-shot settings. LPTM achieves superior forecasting and time-series classification results taking up to 40% less data and 50% less training time compared to state-of-art baselines.">
@@ -508,7 +508,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-02-26</td>
           <td>ArXiv</td>
-          <td>3</td>
+          <td>4</td>
           <td>3</td>
         </tr>
 
@@ -544,7 +544,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-03-12</td>
           <td>ArXiv</td>
-          <td>24</td>
+          <td>27</td>
           <td>18</td>
         </tr>
 
@@ -556,11 +556,11 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-02-04</td>
           <td>ArXiv</td>
-          <td>26</td>
+          <td>28</td>
           <td>22</td>
         </tr>
 
-        <tr id="Learning time-series representations for discriminative tasks has been a long-standing challenge. Current pre-training methods are limited in either unidirectional next-token prediction or randomly masked token prediction. We propose a novel architecture called Bidirectional Timely Generative Pre-trained Transformer (BiTimelyGPT), which pre-trains on time-series data by both next-token and previous-token predictions in alternating transformer layers. This pre-training task preserves original distribution and data shapes of the time-series. Additionally, the full-rank forward and backward attention matrices exhibit more expressive representation capabilities. Using biosignal data, BiTimelyGPT demonstrates superior performance in predicting neurological functionality, disease diagnosis, and physiological signs. By visualizing the attention heatmap, we observe that the pre-trained BiTimelyGPT can identify discriminative segments from time-series sequences, even more so after fine-tuning on the task.">
+        <tr id="Learning time-series representations for discriminative tasks, such as classification and regression, has been a long-standing challenge in the healthcare domain. Current pre-training methods are limited in either unidirectional next-token prediction or randomly masked token prediction. We propose a novel architecture called Bidirectional Timely Generative Pre-trained Transformer (BiTimelyGPT), which pre-trains on biosignals and longitudinal clinical records by both next-token and previous-token prediction in alternating transformer layers. This pre-training task preserves original distribution and data shapes of the time-series. Additionally, the full-rank forward and backward attention matrices exhibit more expressive representation capabilities. Using biosignals and longitudinal clinical records, BiTimelyGPT demonstrates superior performance in predicting neurological functionality, disease diagnosis, and physiological signs. By visualizing the attention heatmap, we observe that the pre-trained BiTimelyGPT can identify discriminative segments from biosignal time-series sequences, even more so after fine-tuning on the task.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/1d0328153f4136635b7df336fb958c92d066ac29" target='_blank'>Bidirectional Generative Pre-training for Improving Time Series Representation Learning</a></td>
           <td>
diff --git a/site/search/search_index.json b/site/search/search_index.json
index 3b6db8a0..86e569f6 100644
--- a/site/search/search_index.json
+++ b/site/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Home","text":"Introduction <p>Welcome to Team VPE's Literature Survey System! This project leverages the powerful Semantic Scholar's Recommendation API to provide you with highly relevant research article recommendations based on your curated lists of articles.</p> <p>Moreover, this website contains curated collection of references on differentiable and learned simulator algorithms for developing digital twins with applications in drug discovery and development.</p> Features <ul> <li>List-based recommendations: For each topic, we use a manually curated list of positive articles. We then select an equal number of negative articles from other topics. Semantics Scholar's recommendation system uses the set of positive and negative to provide up to 300 recommendations per topic.</li> <li>Single-paper recommendations: For each article in our curated lists, Semantics Scholar's recommendation system returns up to 10 related articles.</li> </ul> How it works? <ul> <li>Scroll to the top of this page and click one of the tabs to open the relevant section.</li> <li>Each topic contains two tables. The first table lists the manually curated articles, and the second table lists the articles recommended for that topic by the recommendation system. The last column of the first table contains a hyperlink that opens a new tab with articles recommended for that specific article.</li> <li>Click on the help icon at the top of the page to learn more about that section.</li> </ul>         Check out our GitHub open_in_new repository to learn how to build one for yourself."},{"location":"Symbolic%20regression/","title":"Symbolic regression","text":"<p> This page was last updated on 2024-08-12 06:06:06 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p> </p> <p> </p>"},{"location":"Symbolic%20regression/#manually_curated_articles","title":"Manually curated articles on Symbolic regression","text":"Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index                View recommendations                visibility_off                  Discovering governing equations from data by sparse identification of nonlinear dynamical systems                               S. Brunton, J. Proctor, J. Kutz            2015-09-11 Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences of the United States of America 3181 63 open_in_new visibility_off                  Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression                               Patrick A. K. Reinbold, Logan Kageorge, M. Schatz, R. Grigoriev            2021-02-24 Nature Communications 85 23 open_in_new visibility_off                  Data-driven discovery of coordinates and governing equations                               Kathleen P. Champion, Bethany Lusch, J. Kutz, S. Brunton            2019-03-29 Proceedings of the National Academy of Sciences of the United States of America 599 63 open_in_new visibility_off                  Chaos as an intermittently forced linear system                               S. Brunton, Bingni W. Brunton, J. Proctor, E. Kaiser, J. Kutz            2016-08-18 Nature Communications 446 63 open_in_new visibility_off                  Sparse identification of nonlinear dynamics for model predictive control in the low-data limit                               E. Kaiser, J. Kutz, S. Brunton            2017-11-15 Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences 427 63 open_in_new visibility_off                  Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics                               N. Mangan, S. Brunton, J. Proctor, J. Kutz            2016-05-26 IEEE Transactions on Molecular Biological and Multi-Scale Communications, IEEE Transactions on Molecular, Biological and Multi-Scale Communications 315 63 open_in_new visibility_off                  SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics                               Kadierdan Kaheman, J. Kutz, S. Brunton            2020-04-05 Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences 191 63 open_in_new visibility_off                  Multidimensional Approximation of Nonlinear Dynamical Systems                               Patrick Gel\u00df, Stefan Klus, J. Eisert, Christof Schutte            2018-09-07 Journal of Computational and Nonlinear Dynamics 61 76 open_in_new visibility_off                  Learning Discrepancy Models From Experimental Data                               Kadierdan Kaheman, E. Kaiser, B. Strom, J. Kutz, S. Brunton            2019-09-18 ArXiv, arXiv.org 31 63 open_in_new visibility_off                  Discovery of Physics From Data: Universal Laws and Discrepancies                               Brian M. de Silva, D. Higdon, S. Brunton, J. Kutz            2019-06-19 Frontiers in Artificial Intelligence 66 63 open_in_new visibility_off                  Data-driven discovery of partial differential equations                               S. Rudy, S. Brunton, J. Proctor, J. Kutz            2016-09-21 Science Advances 1171 63 open_in_new visibility_off                  Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control                               Urban Fasel, J. Kutz, Bingni W. Brunton, S. Brunton            2021-11-22 Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences 157 63 open_in_new visibility_off                  Learning sparse nonlinear dynamics via mixed-integer optimization                               D. Bertsimas, Wes Gurnee            2022-06-01 Nonlinear Dynamics 27 90 open_in_new visibility_off                  A Unified Framework for Sparse Relaxed Regularized Regression: SR3                               P. Zheng, T. Askham, S. Brunton, J. Kutz, A. Aravkin            2018-07-14 IEEE Access 114 63 open_in_new Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index View recommendations"},{"location":"Symbolic%20regression/#recommended_articles","title":"Recommended articles on Symbolic regression","text":"Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index visibility_off                Discovering governing equation in structural dynamics from acceleration-only measurements                             Calvin Alvares, Souvik Chakraborty            2024-07-18 ArXiv 0 0 visibility_off                Minimum Reduced-Order Models via Causal Inference                             Nan Chen, Honghu Liu            2024-06-29 ArXiv 0 0 visibility_off                Data-driven system identification of unknown systems utilising sparse identification of nonlinear dynamics (SINDy)                             P. Pandey, H. Haddad Khodaparast, M. Friswell, T. Chatterjee, N. Jamia, T. Deighan            2024-06-01 Journal of Physics: Conference Series 0 1 visibility_off                Data-driven Discovery of Delay Differential Equations with Discrete Delays                             Alessandro Pecile, N. Demo, M. Tezzele, G. Rozza, Dimitri Breda            2024-07-29 ArXiv 1 49 visibility_off                Learning dynamical systems from data: An introduction to physics-guided deep learning                             Rose Yu, Rui Wang            2024-06-24 Proceedings of the National Academy of Sciences of the United States of America 1 1 visibility_off                Principal Component Flow Map Learning of PDEs from Incomplete, Limited, and Noisy Data                             Victor Churchill            2024-07-15 ArXiv 0 0 visibility_off                Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems                             Shane A. 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Davis            2022-11-15 ArXiv, DBLP 2 6 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/6bd28606fbae3449f831248804264c9885e992f9/","title":"6bd28606fbae3449f831248804264c9885e992f9","text":""},{"location":"recommendations/6bd28606fbae3449f831248804264c9885e992f9/#_1","title":"6bd28606fbae3449f831248804264c9885e992f9","text":"<p> This page was last updated on 2024-07-22 06:06:49 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy. Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Interpretable Machine Learning for Perturbation Biology              Bo Yuan, Ciyue Shen, Augustin Luna, Anil Korkut, D. Marks, John Ingraham, C. 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Sundaram            2023-02-08 ArXiv 7 36 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/80744010d90c8ede052c7ac6ba8c38c9de959c6e/","title":"80744010d90c8ede052c7ac6ba8c38c9de959c6e","text":""},{"location":"recommendations/80744010d90c8ede052c7ac6ba8c38c9de959c6e/#_1","title":"80744010d90c8ede052c7ac6ba8c38c9de959c6e","text":"<p> This page was last updated on 2024-07-22 06:06:35 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator. Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Two methods to approximate the Koopman operator with a reservoir computer.              Marvyn Gulina, A. 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Johnson, Shara Balakrishnan, Enoch Yeung            2022-12-14 ArXiv 1 17 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/8540780e6b9422f7a1264edb70f39d3ff79bb8c1/","title":"8540780e6b9422f7a1264edb70f39d3ff79bb8c1","text":""},{"location":"recommendations/8540780e6b9422f7a1264edb70f39d3ff79bb8c1/#_1","title":"8540780e6b9422f7a1264edb70f39d3ff79bb8c1","text":"<p> This page was last updated on 2024-07-22 06:05:55 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Graph Neural Ordinary Differential Equations Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Continuous-Depth Neural Models for Dynamic Graph Prediction              Michael Poli, Stefano Massaroli, Clayton M. Rabideau, Junyoung Park, A. Yamashita, H. 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Sterck, Omer Ege Kara            2024-04-03 ArXiv 0 25 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/883547fdbd88552328a6615ec620f96e39c57018/","title":"883547fdbd88552328a6615ec620f96e39c57018","text":""},{"location":"recommendations/883547fdbd88552328a6615ec620f96e39c57018/#_1","title":"883547fdbd88552328a6615ec620f96e39c57018","text":"<p> This page was last updated on 2024-08-12 06:05:58 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Sparsifying priors for Bayesian uncertainty quantification in model discovery              Seth M. Hirsh, D. Barajas-Solano, J. 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The enabling modules in the toolkit are combined into a single method, but the individual modules can be tactically applied in other equation discovery methods (SINDy or not) to improve results on high-noise data. Second, we propose a technique, applicable to any model discovery method based on  $\\dot { \\boldsymbol x} = { \\boldsymbol f} ({ \\boldsymbol x})$ , to assess the accuracy of a discovered model in the context of non-unique solutions due to noisy data. Currently, this non-uniqueness can obscure a discovered model\u2019s accuracy and thus a discovery method\u2019s effectiveness. We describe a technique that uses linear dependencies among functionals to transform a discovered model into an equivalent form that is closest to the true model, enabling more accurate assessment of a discovered model\u2019s correctness.\"&gt;           visibility_off A Toolkit for Data-Driven Discovery of Governing Equations in High-Noise Regimes              Charles B. Delahunt, J. 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Syed, Vuong Van Pham            2022-12-05 Artificial Intelligence Review 17 16 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/90cc86274f947b15ec3cc8c1dcfe1fc8db608e03/","title":"90cc86274f947b15ec3cc8c1dcfe1fc8db608e03","text":""},{"location":"recommendations/90cc86274f947b15ec3cc8c1dcfe1fc8db608e03/#_1","title":"90cc86274f947b15ec3cc8c1dcfe1fc8db608e03","text":"<p> This page was last updated on 2024-07-22 06:06:14 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Physical Design using Differentiable Learned Simulators Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Neural Fluidic System Design and Control with Differentiable Simulation              Yifei Li, Yuchen Sun, Pingchuan Ma, Eftychios Sifakis, Tao Du, Bo Zhu, Wojciech Matusik            2024-05-22 ArXiv 0 33 visibility_off Accelerating Particle and Fluid Simulations with Differentiable Graph Networks for Solving Forward and Inverse Problems              Krishna Kumar, Yonjin Choi            2023-09-23 Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis 3 1 visibility_off FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation              Zhou Xian, Bo Zhu, Zhenjia Xu, H. 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Ferguson            2019-06-02 ArXiv 31 35 visibility_off Autoencoders for dimensionality reduction in molecular dynamics: Collective variable dimension, biasing, and transition states.              Zineb Belkacemi, M. Bianciotto, H. Minoux, T. Leli\u00e8vre, G. Stoltz, P. Gkeka            2023-06-05 The Journal of chemical physics 3 37 visibility_off Operator Autoencoders: Learning Physical Operations on Encoded Molecular Graphs              Willis Hoke, D. Shea, S. Casey            2021-05-26 ArXiv 0 8 visibility_off Nonlinear Discovery of Slow Molecular Modes using Hierarchical Dynamics Encoders              Wei Chen, Hythem Sidky, Andrew L. Ferguson            2019-02-09 The Journal of chemical physics 77 35 visibility_off Using an Autoencoder for Dimensionality Reduction in Quantum Dynamics              S. Reiter, T. Schnappinger, R. Vivie-Riedle            2019-09-17 MAG, DBLP 2 27 visibility_off VAMPnets for deep learning of molecular kinetics              Andreas Mardt, Luca Pasquali, Hao Wu, F. No\u00e9            2017-10-16 Nature Communications 463 61 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/e0ee02a573b3d83fec55ed5d7c80f1afa055a7b4/","title":"E0ee02a573b3d83fec55ed5d7c80f1afa055a7b4","text":""},{"location":"recommendations/e0ee02a573b3d83fec55ed5d7c80f1afa055a7b4/#_1","title":"E0ee02a573b3d83fec55ed5d7c80f1afa055a7b4","text":"<p> This page was last updated on 2024-07-22 06:06:15 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Constraint-based graph network simulator Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Hamiltonian Graph Networks with ODE Integrators              Alvaro Sanchez-Gonzalez, V. 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Yu            2024-04-01 Neural networks : the official journal of the International Neural Network Society 0 3 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419/","title":"E2a83369383aff37224170c1ae3d3870d5d9e419","text":""},{"location":"recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419/#_1","title":"E2a83369383aff37224170c1ae3d3870d5d9e419","text":"<p> This page was last updated on 2024-08-12 06:05:11 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Taming Local Effects in Graph-based Spatiotemporal Forecasting Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Graph Deep Learning for Time Series Forecasting              Andrea Cini, Ivan Marisca, Daniele Zambon, C. Alippi            2023-10-24 ArXiv 4 49 visibility_off Scalable Spatiotemporal Graph Neural Networks              Andrea Cini, Ivan Marisca, F. Bianchi, C. Alippi            2022-09-14 ArXiv 28 49 visibility_off Sparse Graph Learning from Spatiotemporal Time Series              Andrea Cini, Daniele Zambon, C. Alippi            2022-05-26 J. Mach. Learn. Res. 11 49 visibility_off Unified Spatio-Temporal Graph Neural Networks: Data-Driven Modeling for Social Science              Yifan Li, Yu Lin, Y. Gao, L. 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Tsung            2024-05-03 ArXiv 2 47 visibility_off Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning              Yuxuan Bian, Xu Ju, Jiangtong Li, Zhijian Xu, Dawei Cheng, Qiang Xu            2024-02-07 ArXiv 5 5 visibility_off Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain              Gerald Woo, Chenghao Liu, Akshat Kumar, Doyen Sahoo            2023-10-08 ArXiv 7 22 visibility_off One Fits All: Universal Time Series Analysis by Pretrained LM and Specially Designed Adaptors              Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin            2023-11-24 ArXiv 2 7 visibility_off MultiCast: Zero-Shot Multivariate Time Series Forecasting Using LLMs              Georgios Chatzigeorgakidis, Konstantinos Lentzos, Dimitrios Skoutas            2024-05-13 2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW) 0 7 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8/","title":"E6f0a85009481dcfd93aaa43ed3f980e5033b0d8","text":""},{"location":"recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8/#_1","title":"E6f0a85009481dcfd93aaa43ed3f980e5033b0d8","text":"<p> This page was last updated on 2024-08-12 06:05:59 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Learning sparse nonlinear dynamics via mixed-integer optimization Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Automatically discovering ordinary differential equations from data with sparse regression              Kevin Egan, Weizhen Li, Rui Carvalho            2024-01-09 Communications Physics 7 1 visibility_off Discovering governing equations from data by sparse identification of nonlinear dynamical systems              S. 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Wells            2023-08-17 ArXiv 0 40 visibility_off Physics-informed learning of governing equations from scarce data              Zhao Chen, Yang Liu, Hao Sun            2020-05-05 Nature Communications 237 12 visibility_off PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data.              Brian M. de Silva, Kathleen P. Champion, M. Quade, Jean-Christophe Loiseau, J. Kutz, S. Brunton            2020-04-17 arXiv: Dynamical Systems 46 63 visibility_off Discovering Interpretable Dynamics by Sparsity Promotion on Energy and the Lagrangian              H. Chu, M. Hayashibe            2020-01-31 IEEE Robotics and Automation Letters 23 23 visibility_off SINDy with Control: A Tutorial              Urban Fasel, E. Kaiser, J. Kutz, Bingni W. Brunton, S. Brunton            2021-08-30 2021 60th IEEE Conference on Decision and Control (CDC) 45 63 visibility_off Sparse learning of stochastic dynamical equations.              L. Boninsegna, F. N\u00fcske, C. Clementi            2017-12-06 The Journal of chemical physics 191 44 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f/","title":"F45f85fa1beaa795c24c4ff86f1f2deece72252f","text":""},{"location":"recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f/#_1","title":"F45f85fa1beaa795c24c4ff86f1f2deece72252f","text":"<p> This page was last updated on 2024-08-12 06:05:16 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article A decoder-only foundation model for time-series forecasting Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting              Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Bilovs, Hena Ghonia, N. 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Res. 658 24 visibility_off Solving differential equations using physics informed deep learning: a hand-on tutorial with benchmark tests              H. Baty, L. Baty            2023-02-23 ArXiv 3 14 visibility_off Understanding on Physics-Informed DeepONet              Sang-Min Lee            2024-01-31 Journal of the Korea Academia-Industrial cooperation Society 0 0 visibility_off Physics Informed RNN-DCT Networks for Time-Dependent Partial Differential Equations              Benwei Wu, O. Hennigh, J. Kautz, S. Choudhry, Wonmin Byeon            2022-02-24 ArXiv 4 91 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90/","title":"Ff1f1cf9df8c413ec7345da7604ba28597da5b90","text":""},{"location":"recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90/#_1","title":"Ff1f1cf9df8c413ec7345da7604ba28597da5b90","text":"<p> This page was last updated on 2024-08-12 06:05:29 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article UNITS: A Unified Multi-Task Time Series Model Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Timer: Generative Pre-trained Transformers Are Large Time Series Models              Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long            2024-02-04 ArXiv 6 65 visibility_off Large Pre-trained time series models for cross-domain Time series analysis tasks              Harshavardhan Kamarthi, B. 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+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Home","text":"Introduction <p>Welcome to Team VPE's Literature Survey System! This project leverages the powerful Semantic Scholar's Recommendation API to provide you with highly relevant research article recommendations based on your curated lists of articles.</p> <p>Moreover, this website contains curated collection of references on differentiable and learned simulator algorithms for developing digital twins with applications in drug discovery and development.</p> Features <ul> <li>List-based recommendations: For each topic, we use a manually curated list of positive articles. We then select an equal number of negative articles from other topics. Semantics Scholar's recommendation system uses the set of positive and negative to provide up to 300 recommendations per topic.</li> <li>Single-paper recommendations: For each article in our curated lists, Semantics Scholar's recommendation system returns up to 10 related articles.</li> </ul> How it works? <ul> <li>Scroll to the top of this page and click one of the tabs to open the relevant section.</li> <li>Each topic contains two tables. The first table lists the manually curated articles, and the second table lists the articles recommended for that topic by the recommendation system. The last column of the first table contains a hyperlink that opens a new tab with articles recommended for that specific article.</li> <li>Click on the help icon at the top of the page to learn more about that section.</li> </ul>         Check out our GitHub open_in_new repository to learn how to build one for yourself."},{"location":"Symbolic%20regression/","title":"Symbolic regression","text":"<p> This page was last updated on 2024-08-19 06:05:48 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p> </p> <p> </p>"},{"location":"Symbolic%20regression/#manually_curated_articles","title":"Manually curated articles on Symbolic regression","text":"Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index                View recommendations                visibility_off                  Discovering governing equations from data by sparse identification of nonlinear dynamical systems                               S. Brunton, J. Proctor, J. Kutz            2015-09-11 Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences of the United States of America 3190 65 open_in_new visibility_off                  Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression                               Patrick A. K. Reinbold, Logan Kageorge, M. Schatz, R. Grigoriev            2021-02-24 Nature Communications 85 23 open_in_new visibility_off                  Data-driven discovery of coordinates and governing equations                               Kathleen P. Champion, Bethany Lusch, J. Kutz, S. Brunton            2019-03-29 Proceedings of the National Academy of Sciences of the United States of America 603 65 open_in_new visibility_off                  Chaos as an intermittently forced linear system                               S. Brunton, Bingni W. Brunton, J. Proctor, E. Kaiser, J. Kutz            2016-08-18 Nature Communications 448 65 open_in_new visibility_off                  Sparse identification of nonlinear dynamics for model predictive control in the low-data limit                               E. Kaiser, J. Kutz, S. Brunton            2017-11-15 Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences 428 65 open_in_new visibility_off                  Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics                               N. Mangan, S. Brunton, J. Proctor, J. Kutz            2016-05-26 IEEE Transactions on Molecular, Biological and Multi-Scale Communications, IEEE Transactions on Molecular Biological and Multi-Scale Communications 317 65 open_in_new visibility_off                  SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics                               Kadierdan Kaheman, J. Kutz, S. Brunton            2020-04-05 Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences 194 65 open_in_new visibility_off                  Multidimensional Approximation of Nonlinear Dynamical Systems                               Patrick Gel\u00df, Stefan Klus, J. Eisert, Christof Schutte            2018-09-07 Journal of Computational and Nonlinear Dynamics 61 76 open_in_new visibility_off                  Learning Discrepancy Models From Experimental Data                               Kadierdan Kaheman, E. Kaiser, B. Strom, J. Kutz, S. Brunton            2019-09-18 arXiv.org, ArXiv 32 65 open_in_new visibility_off                  Discovery of Physics From Data: Universal Laws and Discrepancies                               Brian M. de Silva, D. Higdon, S. Brunton, J. Kutz            2019-06-19 Frontiers in Artificial Intelligence 67 65 open_in_new visibility_off                  Data-driven discovery of partial differential equations                               S. Rudy, S. Brunton, J. Proctor, J. Kutz            2016-09-21 Science Advances 1172 65 open_in_new visibility_off                  Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control                               Urban Fasel, J. Kutz, Bingni W. Brunton, S. Brunton            2021-11-22 Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences 158 65 open_in_new visibility_off                  Learning sparse nonlinear dynamics via mixed-integer optimization                               D. Bertsimas, Wes Gurnee            2022-06-01 Nonlinear Dynamics 28 91 open_in_new visibility_off                  A Unified Framework for Sparse Relaxed Regularized Regression: SR3                               P. Zheng, T. Askham, S. Brunton, J. Kutz, A. Aravkin            2018-07-14 IEEE Access 115 65 open_in_new Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index View recommendations"},{"location":"Symbolic%20regression/#recommended_articles","title":"Recommended articles on Symbolic regression","text":"Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index visibility_off                Discovering governing equation in structural dynamics from acceleration-only measurements                             Calvin Alvares, Souvik Chakraborty            2024-07-18 ArXiv 0 0 visibility_off                Data-driven system identification of unknown systems utilising sparse identification of nonlinear dynamics (SINDy)                             P. Pandey, H. Haddad Khodaparast, M. Friswell, T. Chatterjee, N. Jamia, T. Deighan            2024-06-01 Journal of Physics: Conference Series 0 2 visibility_off                Learning dynamical systems from data: An introduction to physics-guided deep learning                             Rose Yu, Rui Wang            2024-06-24 Proceedings of the National Academy of Sciences of the United States of America 1 1 visibility_off                Data-driven Discovery of Delay Differential Equations with Discrete Delays                             Alessandro Pecile, N. Demo, M. Tezzele, G. Rozza, Dimitri Breda            2024-07-29 ArXiv 1 49 visibility_off                Principal Component Flow Map Learning of PDEs from Incomplete, Limited, and Noisy Data                             Victor Churchill            2024-07-15 ArXiv 0 0 visibility_off                Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems                             Shane A. 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