diff --git a/app/code/utils.py b/app/code/utils.py
index b5bc586a..1e53953e 100755
--- a/app/code/utils.py
+++ b/app/code/utils.py
@@ -15,29 +15,22 @@
 FIELDS += 'publicationTypes,publicationDate,citationCount,'
 FIELDS += 'publicationVenue,externalIds,abstract'
 
-def add_negative_articles(topic_obj, dic, max_num_articles=10):
+def add_negative_articles(topic_obj, dic):
     """
     Add the negative articles to the topic object
     """
     if 'negative' not in topic_obj.paper_ids:
         topic_obj.paper_ids['negative'] = {}
-    num_topics = len(dic) - 1
-    while len(topic_obj.paper_ids["negative"]) < max_num_articles:
-        for topic in dic:
-            if topic == topic_obj.topic:
+    for topic in dic:
+        # Skip the current topic
+        if topic == topic_obj.topic:
+            continue
+        # Add the articles from the other topics
+        # as negative articles to the current topic
+        for paper_id in dic[topic].paper_ids['positive']:
+            if paper_id in topic_obj.paper_ids['negative']:
                 continue
-            articles_per_topic = max_num_articles // num_topics
-            for paper_id in dic[topic].paper_ids['positive']:
-                if paper_id in topic_obj.paper_ids['negative']:
-                    continue
-                topic_obj.paper_ids['negative'][paper_id]=dic[topic].paper_ids['positive'][paper_id]
-                articles_per_topic -= 1
-                if articles_per_topic == 0:
-                    break
-                if len(topic_obj.paper_ids["negative"]) == max_num_articles:
-                    break
-            if len(topic_obj.paper_ids["negative"]) == max_num_articles:
-                break
+            topic_obj.paper_ids['negative'][paper_id]=dic[topic].paper_ids['positive'][paper_id]
     print (f'Added {len(topic_obj.paper_ids["negative"])} negative articles for {topic_obj.topic}.')
 
 def update_paper_details(topic_obj):
diff --git a/docs/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378.md b/docs/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378.md
index 37319a7d..2373fca1 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 2024-08-05 09:00:07 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:20 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
diff --git a/docs/recommendations/0acd117521ef5aafb09fed02ab415523b330b058.md b/docs/recommendations/0acd117521ef5aafb09fed02ab415523b330b058.md
index 27c6a07a..51c0d3d7 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 2024-08-05 09:00:11 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:27 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -74,7 +74,7 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3159</td>
+          <td>3168</td>
           <td>63</td>
         </tr>
     
diff --git a/docs/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b.md b/docs/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b.md
index d7d81558..2a769002 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 2024-08-05 08:59:44 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:41 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -109,7 +109,7 @@ hide:
             Hongyuan Yu, Ting Li, Weichen Yu, Jianguo Li, Yan Huang, Liang Wang, A. Liu
           </td>
           <td>2022-07-01</td>
-          <td>DBLP, ArXiv</td>
+          <td>ArXiv, DBLP</td>
           <td>37</td>
           <td>33</td>
         </tr>
diff --git a/docs/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b.md b/docs/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b.md
index f90d56f3..4a831c24 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 2024-08-05 08:59:46 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:44 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -122,7 +122,7 @@ hide:
           </td>
           <td>2023-10-03</td>
           <td>ArXiv</td>
-          <td>109</td>
+          <td>111</td>
           <td>9</td>
         </tr>
     
@@ -146,7 +146,7 @@ hide:
           </td>
           <td>2023-06-19</td>
           <td>ArXiv</td>
-          <td>32</td>
+          <td>34</td>
           <td>8</td>
         </tr>
     
diff --git a/docs/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277.md b/docs/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277.md
index f98dccec..b4dbc7cb 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 2024-08-05 08:59:49 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:50 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>55</td>
+          <td>56</td>
           <td>17</td>
         </tr>
     
@@ -146,7 +146,7 @@ hide:
           </td>
           <td>2023-10-14</td>
           <td>ArXiv</td>
-          <td>44</td>
+          <td>45</td>
           <td>14</td>
         </tr>
     
diff --git a/docs/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f.md b/docs/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f.md
index 041defa1..7c28247c 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 2024-08-05 08:59:47 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:45 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -86,7 +86,7 @@ hide:
           </td>
           <td>2024-06-09</td>
           <td>ArXiv</td>
-          <td>0</td>
+          <td>1</td>
           <td>2</td>
         </tr>
     
@@ -122,7 +122,7 @@ hide:
           </td>
           <td>2022-10-08</td>
           <td>ArXiv</td>
-          <td>56</td>
+          <td>57</td>
           <td>17</td>
         </tr>
     
diff --git a/docs/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68.md b/docs/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68.md
index 27c1d303..69be0b1b 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 2024-08-05 08:59:53 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:58 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>DBLP, ArXiv, MAG</td>
+          <td>MAG, ArXiv, DBLP</td>
           <td>110</td>
           <td>93</td>
         </tr>
@@ -90,30 +90,6 @@ hide:
           <td>49</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>56</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>0</td>
-          <td>10</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.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/a7d68b1702af08ce4dbbf2cd0b083e744ae5c6be" target='_blank'>Effectively Modeling Time Series with Simple Discrete State Spaces</a></td>
@@ -137,11 +113,35 @@ hide:
             R. G. Krishnan, Uri Shalit, D. Sontag
           </td>
           <td>2016-09-30</td>
-          <td>DBLP, ArXiv, MAG</td>
-          <td>424</td>
+          <td>MAG, ArXiv, DBLP</td>
+          <td>427</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 5b9e771e..0787df0a 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 2024-08-05 09:00:08 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:23 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -122,7 +122,7 @@ hide:
           </td>
           <td>2020-07-02</td>
           <td>ArXiv</td>
-          <td>126</td>
+          <td>127</td>
           <td>22</td>
         </tr>
     
diff --git a/docs/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220.md b/docs/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220.md
index 57114c6c..65c5db8c 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 2024-08-05 09:00:10 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:26 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -109,7 +109,7 @@ hide:
             Philipp Holl, V. Koltun, Nils Thuerey
           </td>
           <td>2021-09-30</td>
-          <td>DBLP, ArXiv</td>
+          <td>ArXiv, DBLP</td>
           <td>5</td>
           <td>103</td>
         </tr>
@@ -134,7 +134,7 @@ hide:
           </td>
           <td>2021-05-06</td>
           <td>Physical review letters</td>
-          <td>81</td>
+          <td>82</td>
           <td>81</td>
         </tr>
     
diff --git a/docs/recommendations/3c9961153493370500020c81527b3548c96f81e0.md b/docs/recommendations/3c9961153493370500020c81527b3548c96f81e0.md
index a31e229c..4d703246 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 2024-08-05 09:00:04 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:15 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,7 +50,7 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3159</td>
+          <td>3168</td>
           <td>63</td>
         </tr>
     
diff --git a/docs/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2.md b/docs/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2.md
index ec67fd55..2edff6bc 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 2024-08-05 09:00:05 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:17 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -74,7 +74,7 @@ hide:
           </td>
           <td>2022-01-13</td>
           <td>Proceedings of the Royal Society A</td>
-          <td>67</td>
+          <td>68</td>
           <td>63</td>
         </tr>
     
@@ -102,6 +102,18 @@ hide:
           <td>78</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.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/9fb357e57624525f27224822502061cc8fb831e1" target='_blank'>Detecting chaos in lineage-trees: A deep learning approach</a></td>
+          <td>
+            H. Rappeport, Irit Levin Reisman, Naftali Tishby, N. Balaban
+          </td>
+          <td>2021-06-08</td>
+          <td>ArXiv</td>
+          <td>2</td>
+          <td>56</td>
+        </tr>
+    
         <tr id="A generally applicable robust data-driven network modeling strategy offers rapid means to predict and control complex systems. We propose a universal method for data-driven modeling of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge. Complex nonlinear dynamics govern many fields of science and engineering. Data-driven dynamic modeling often assumes a low-dimensional subspace or manifold for the state. We liberate ourselves from this assumption by proposing cluster-based network modeling (CNM) bridging machine learning, network science, and statistical physics. CNM describes short- and long-term behavior and is fully automatable, as it does not rely on application-specific knowledge. CNM is demonstrated for the Lorenz attractor, ECG heartbeat signals, Kolmogorov flow, and a high-dimensional actuated turbulent boundary layer. Even the notoriously difficult modeling benchmark of rare events in the Kolmogorov flow is solved. This automatable universal data-driven representation of complex nonlinear dynamics complements and expands network connectivity science and promises new fast-track avenues to understand, estimate, predict, and control complex systems in all scientific fields.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/b0c3d159c14b0488299269c3fd62da3b9f608709" target='_blank'>Cluster-based network modeling—From snapshots to complex dynamical systems</a></td>
@@ -126,30 +138,6 @@ hide:
           <td>49</td>
         </tr>
     
-        <tr id="Chaotic dynamical systems (DS) are ubiquitous in nature and society. Often we are interested in reconstructing such systems from observed time series for prediction or mechanistic insight, where by reconstruction we mean learning geometrical and invariant temporal properties of the system in question (like attractors). However, training reconstruction algorithms like recurrent neural networks (RNNs) on such systems by gradient-descent based techniques faces severe challenges. This is mainly due to exploding gradients caused by the exponential divergence of trajectories in chaotic systems. Moreover, for (scientific) interpretability we wish to have as low dimensional reconstructions as possible, preferably in a model which is mathematically tractable. Here we report that a surprisingly simple modification of teacher forcing leads to provably strictly all-time bounded gradients in training on chaotic systems, and, when paired with a simple architectural rearrangement of a tractable RNN design, piecewise-linear RNNs (PLRNNs), allows for faithful reconstruction in spaces of at most the dimensionality of the observed system. We show on several DS that with these amendments we can reconstruct DS better than current SOTA algorithms, in much lower dimensions. Performance differences were particularly compelling on real world data with which most other methods severely struggled. This work thus led to a simple yet powerful DS reconstruction algorithm which is highly interpretable at the same time.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6ca393ee5afbe0e876baae5e428724ffd090efc5" target='_blank'>Generalized Teacher Forcing for Learning Chaotic Dynamics</a></td>
-          <td>
-            F. Hess, Z. Monfared, Manuela Brenner, D. Durstewitz
-          </td>
-          <td>2023-06-07</td>
-          <td>DBLP, ArXiv</td>
-          <td>15</td>
-          <td>36</td>
-        </tr>
-    
-        <tr id="Complex systems are commonly modeled using nonlinear dynamical systems. These models are often high-dimensional and chaotic. An important goal in studying physical systems through the lens of mathematical models is to determine when the system undergoes changes in qualitative behavior. A detailed description of the dynamics can be difficult or impossible to obtain for high-dimensional and chaotic systems. Therefore, a more sensible goal is to recognize and mark transitions of a system between qualitatively different regimes of behavior. In practice, one is interested in developing techniques for detection of such transitions from sparse observations, possibly contaminated by noise. In this paper we develop a framework to accurately tag different regimes of complex systems based on topological features. In particular, our framework works with a high degree of success in picking out a cyclically orbiting regime from a stationary equilibrium regime in high-dimensional stochastic dynamical systems.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4201cd896bbab7c98602be04bb744b6c470b0dfb" target='_blank'>Automatic recognition and tagging of topologically different regimes in dynamical systems</a></td>
-          <td>
-            Jesse Berwald, M. Gidea, Mikael Vejdemo-Johansson
-          </td>
-          <td>2013-12-09</td>
-          <td>ArXiv</td>
-          <td>34</td>
-          <td>22</td>
-        </tr>
-    
   </tbody>
   <tfoot>
     <tr>
diff --git a/docs/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba.md b/docs/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba.md
index 9ddec945..d8311235 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 2024-08-05 08:59:42 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:38 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -121,7 +121,7 @@ hide:
             Steven Cheng-Xian Li, Benjamin M Marlin
           </td>
           <td>2020-07-12</td>
-          <td>DBLP, ArXiv, MAG</td>
+          <td>MAG, ArXiv, DBLP</td>
           <td>47</td>
           <td>32</td>
         </tr>
diff --git a/docs/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01.md b/docs/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01.md
index 45a8af01..010a684c 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 2024-08-05 09:00:07 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:21 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -74,7 +74,7 @@ hide:
           </td>
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
-          <td>108</td>
+          <td>109</td>
           <td>63</td>
         </tr>
     
diff --git a/docs/recommendations/4a111f7a3b56d0468f13104999844885157ef17d.md b/docs/recommendations/4a111f7a3b56d0468f13104999844885157ef17d.md
index 6e1446b4..f7c67325 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 2024-08-05 08:59:48 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:48 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -74,10 +74,22 @@ hide:
           </td>
           <td>2022-11-27</td>
           <td>ArXiv</td>
-          <td>469</td>
+          <td>479</td>
           <td>34</td>
         </tr>
     
+        <tr id="Time series forecasting is a critical and challenging task in practical application. Recent advancements in pre-trained foundation models for time series forecasting have gained significant interest. However, current methods often overlook the multi-scale nature of time series, which is essential for accurate forecasting. To address this, we propose HiMTM, a hierarchical multi-scale masked time series modeling with self-distillation for long-term forecasting. HiMTM integrates four key components: (1) hierarchical multi-scale transformer (HMT) to capture temporal information at different scales; (2) decoupled encoder-decoder (DED) that directs the encoder towards feature extraction while the decoder focuses on pretext tasks; (3) hierarchical self-distillation (HSD) for multi-stage feature-level supervision signals during pre-training; and (4) cross-scale attention fine-tuning (CSA-FT) to capture dependencies between different scales for downstream tasks. These components collectively enhance multi-scale feature extraction in masked time series modeling, improving forecasting accuracy. Extensive experiments on seven mainstream datasets show that HiMTM surpasses state-of-the-art self-supervised and end-to-end learning methods by a considerable margin of 3.16-68.54\%. Additionally, HiMTM outperforms the latest robust self-supervised learning method, PatchTST, in cross-domain forecasting by a significant margin of 2.3\%. The effectiveness of HiMTM is further demonstrated through its application in natural gas demand forecasting.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/d945eae22457648e191742c37bcad88a114ce703" target='_blank'>HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting</a></td>
+          <td>
+            Shubao Zhao, Ming Jin, Zhaoxiang Hou, Che-Sheng Yang, Zengxiang Li, Qingsong Wen, Yi Wang
+          </td>
+          <td>2024-01-10</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>5</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.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/b98bc7d48357932537fc9ba6035e3b6bd1018ae6" target='_blank'>Two Steps Forward and One Behind: Rethinking Time Series Forecasting with Deep Learning</a></td>
@@ -85,7 +97,7 @@ hide:
             Riccardo Ughi, Eugenio Lomurno, Matteo Matteucci
           </td>
           <td>2023-04-10</td>
-          <td>DBLP, ArXiv</td>
+          <td>ArXiv, DBLP</td>
           <td>1</td>
           <td>5</td>
         </tr>
@@ -114,18 +126,6 @@ hide:
           <td>2</td>
         </tr>
     
-        <tr id="Long-term time-series forecasting (LTSF) plays a crucial role in various practical applications. Transformer and its variants have become the de facto backbone for LTSF, offering exceptional capabilities in processing long sequence data. However, existing Transformer-based models, such as Fedformer and Informer, often achieve their best performances on validation sets after just a few epochs, indicating potential underutilization of the Transformer's capacity. One of the reasons that contribute to this overfitting is data redundancy arising from the rolling forecasting settings in the data augmentation process, particularly evident in longer sequences with highly similar adjacent data. In this paper, we propose a novel approach to address this issue by employing curriculum learning and introducing a memory-driven decoder. Specifically, we progressively introduce Bernoulli noise to the training samples, which effectively breaks the high similarity between adjacent data points. To further enhance forecasting accuracy, we introduce a memory-driven decoder. This component enables the model to capture seasonal tendencies and dependencies in the time-series data and leverages temporal relationships to facilitate the forecasting process. The experimental results on six real-life LTSF benchmarks demonstrate that our approach can be seamlessly plugged into varying Transformer-based models, with our approach enhancing the LTSF performances of various Transformer-based models by maximally 30%.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e4e01b1059fa20b8fc5817f292fe38466381dc26" target='_blank'>CLMFormer: Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System</a></td>
-          <td>
-            Mingjie Li, Xiaoyun Zhao, R. Liu, Changlin Li, Xiaohan Wang, Xiaojun Chang
-          </td>
-          <td>2022-07-16</td>
-          <td>ArXiv</td>
-          <td>1</td>
-          <td>78</td>
-        </tr>
-    
   </tbody>
   <tfoot>
     <tr>
diff --git a/docs/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14.md b/docs/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14.md
index d72270f7..cf55310e 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 2024-08-05 08:59:52 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:55 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -85,7 +85,7 @@ hide:
             Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Bernie Wang, Xifeng Yan
           </td>
           <td>2021-02-13</td>
-          <td>DBLP, ArXiv</td>
+          <td>ArXiv, DBLP</td>
           <td>56</td>
           <td>65</td>
         </tr>
diff --git a/docs/recommendations/5d150cec2775f9bc863760448f14104cc8f42368.md b/docs/recommendations/5d150cec2775f9bc863760448f14104cc8f42368.md
index 01b5dd94..c41737b5 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 2024-08-05 09:00:02 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:12 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
diff --git a/docs/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c.md b/docs/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c.md
index 2b5a37af..092a1d5b 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 2024-08-05 09:00:03 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:14 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -114,6 +114,18 @@ hide:
           <td>72</td>
         </tr>
     
+        <tr id="High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy. Deep-learning approaches have been shown suitable for such super-resolution tasks. However, a high number of high-resolution examples is needed, which may not be available for many cases. Moreover, the obtained predictions may lack in complying with the physical principles, e.g. mass and momentum conservation. Physics-informed deep learning provides frameworks for integrating data and physical laws for learning. In this study, we apply physics-informed neural networks (PINNs) for super-resolution of flow-field data both in time and space from a limited set of noisy measurements without having any high-resolution reference data. Our objective is to obtain a continuous solution of the problem, providing a physically-consistent prediction at any point in the solution domain. We demonstrate the applicability of PINNs for the super-resolution of flow-field data in time and space through three canonical cases: Burgers’ equation, two-dimensional vortex shedding behind a circular cylinder and the minimal turbulent channel flow. The robustness of the models is also investigated by adding synthetic Gaussian noise. Furthermore, we show the capabilities of PINNs to improve the resolution and reduce the noise in a real experimental dataset consisting of hot-wire-anemometry measurements. Our results show the adequate capabilities of PINNs in the context of data augmentation for experiments in fluid mechanics.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/ecf61b26615fc12f9edc4826dd81a23ac0d72f37" target='_blank'>Physics-informed deep-learning applications to experimental fluid mechanics</a></td>
+          <td>
+            Hamidreza Eivazi, Yuning Wang, R. Vinuesa
+          </td>
+          <td>2022-03-29</td>
+          <td>Measurement Science and Technology</td>
+          <td>21</td>
+          <td>38</td>
+        </tr>
+    
         <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/606824bebf4e444fca9b988823883f1ec99f19ec" target='_blank'>A physics-informed operator regression framework for extracting data-driven continuum models</a></td>
@@ -138,19 +150,6 @@ hide:
           <td>47</td>
         </tr>
     
-        <tr id="
- In the context of science, the well-known adage “a picture is worth a thousand words” might well be “a model is worth a thousand datasets.” Scientific models, such as Newtonian physics or biological gene regulatory networks, are human-driven simplifications of complex phenomena that serve as surrogates for the countless experiments that validated the models. Recently, machine learning has been able to overcome the inaccuracies of approximate modeling by directly learning the entire set of nonlinear interactions from data. However, without any predetermined structure from the scientific basis behind the problem, machine learning approaches are flexible but data-expensive, requiring large databases of homogeneous labeled training data. A central challenge is reconciling data that is at odds with simplified models without requiring "big data". In this work demonstrate how a mathematical object, which we denote universal differential equations (UDEs), can be utilized as a theoretical underpinning to a diverse array of problems in scientific machine learning to yield efficient algorithms and generalized approaches. The UDE model augments scientific models with machine-learnable structures for scientifically-based learning. We show how UDEs can be utilized to discover previously unknown governing equations, accurately extrapolate beyond the original data, and accelerate model simulation, all in a time and data-efficient manner. This advance is coupled with open-source software that allows for training UDEs which incorporate physical constraints, delayed interactions, implicitly-defined events, and intrinsic stochasticity in the model. Our examples show how a diverse set of computationally-difficult modeling issues across scientific disciplines, from automatically discovering biological mechanisms to accelerating the training of physics-informed neural networks and large-eddy simulations, can all be transformed into UDE training problems that are efficiently solved by a single software methodology.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/696b388ee6221c6dbcfd647a06883b2bfee773d9" target='_blank'>Universal Differential Equations for Scientific Machine Learning</a></td>
-          <td>
-            Christopher Rackauckas, Yingbo Ma, Julius Martensen, Collin Warner, K. Zubov, R. Supekar, Dominic J. Skinner, A. Ramadhan
-          </td>
-          <td>2020-01-13</td>
-          <td>ArXiv</td>
-          <td>483</td>
-          <td>25</td>
-        </tr>
-    
   </tbody>
   <tfoot>
     <tr>
diff --git a/docs/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37.md b/docs/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37.md
index 9e9019b8..41bca1bc 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 2024-08-05 08:59:51 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:54 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -145,7 +145,7 @@ hide:
             Konstantinos Kallidromitis, Denis A. Gudovskiy, Kozuka Kazuki, Ohama Iku, Luca Rigazio
           </td>
           <td>2021-10-24</td>
-          <td>DBLP, ArXiv</td>
+          <td>ArXiv, DBLP</td>
           <td>10</td>
           <td>13</td>
         </tr>
diff --git a/docs/recommendations/73dd9c49f205280991826b2ea4b50344203916b4.md b/docs/recommendations/73dd9c49f205280991826b2ea4b50344203916b4.md
index ea89564d..4ed6398c 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 2024-08-05 09:00:09 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:24 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -102,6 +102,18 @@ hide:
           <td>5</td>
         </tr>
     
+        <tr id="Data-driven discovery of dynamics via machine learning is pushing the frontiers of modelling and control efforts, providing a tremendous opportunity to extend the reach of model predictive control (MPC). However, many leading methods in machine learning, such as neural networks (NN), require large volumes of training data, may not be interpretable, do not easily include known constraints and symmetries, and may not generalize beyond the attractor where models are trained. These factors limit their use for the online identification of a model in the low-data limit, for example following an abrupt change to the system dynamics. In this work, we extend the recent sparse identification of nonlinear dynamics (SINDY) modelling procedure to include the effects of actuation and demonstrate the ability of these models to enhance the performance of MPC, based on limited, noisy data. SINDY models are parsimonious, identifying the fewest terms in the model needed to explain the data, making them interpretable and generalizable. We show that the resulting SINDY-MPC framework has higher performance, requires significantly less data, and is more computationally efficient and robust to noise than NN models, making it viable for online training and execution in response to rapid system changes. SINDY-MPC also shows improved performance over linear data-driven models, although linear models may provide a stopgap until enough data is available for SINDY. SINDY-MPC is demonstrated on a variety of dynamical systems with different challenges, including the chaotic Lorenz system, a simple model for flight control of an F8 aircraft, and an HIV model incorporating drug treatment.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/b2eb064f432557c59ce99834d7dc7817e4687271" target='_blank'>Sparse identification of nonlinear dynamics for model predictive control in the low-data limit</a></td>
+          <td>
+            E. Kaiser, J. Kutz, S. Brunton
+          </td>
+          <td>2017-11-15</td>
+          <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
+          <td>425</td>
+          <td>63</td>
+        </tr>
+    
         <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/acd85b948087067938680356a0cb7597de12a25d" target='_blank'>Data-based modeling and control of nonlinear process systems using sparse identification: An overview of recent results</a></td>
@@ -138,18 +150,6 @@ hide:
           <td>36</td>
         </tr>
     
-        <tr id="Physics-based and first-principles models pervade the engineering and physical sciences, allowing for the ability to model the dynamics of complex systems with a prescribed accuracy. The approximations used in deriving governing equations often result in discrepancies between the model and sensor-based measurements of the system, revealing the approximate nature of the equations and/or the signal-to-noise ratio of the sensor itself. In modern dynamical systems, such discrepancies between model and measurement can lead to poor quantification, often undermining the ability to produce accurate and precise control algorithms. We introduce a discrepancy modeling framework to identify the missing physics and resolve the model-measurement mismatch with two distinct approaches: (i) by learning a model for the evolution of systematic state-space residual, and (ii) by discovering a model for the deterministic dynamical error. Regardless of approach, a common suite of data-driven model discovery methods can be used. The choice of method depends on one's intent (e.g., mechanistic interpretability) for discrepancy modeling, sensor measurement characteristics (e.g., quantity, quality, resolution), and constraints imposed by practical applications (e.g., modeling approaches using the suite of data-driven modeling methods on three continuous dynamical systems under varying signal-to-noise ratios. Finally, we emphasize structural shortcomings of each discrepancy modeling approach depending on error type. In summary, if the true dynamics are unknown (i.e., an imperfect model), one should learn a discrepancy model of the missing physics in the dynamical space. Yet, if the true dynamics are known yet model-measurement mismatch still exists, one should learn a discrepancy model in the state space.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/c4a38c94d23389326f5043d63b9f5f6f0d8c0c50" target='_blank'>Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects</a></td>
-          <td>
-            Megan R. Ebers, K. Steele, J. Kutz
-          </td>
-          <td>2022-03-10</td>
-          <td>SIAM J. Appl. Dyn. Syst.</td>
-          <td>5</td>
-          <td>31</td>
-        </tr>
-    
   </tbody>
   <tfoot>
     <tr>
diff --git a/docs/recommendations/883547fdbd88552328a6615ec620f96e39c57018.md b/docs/recommendations/883547fdbd88552328a6615ec620f96e39c57018.md
index 7992783d..db4e5dc3 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 2024-08-05 09:00:11 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:29 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -110,7 +110,7 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3159</td>
+          <td>3168</td>
           <td>63</td>
         </tr>
     
diff --git a/docs/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271.md b/docs/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271.md
index e9e624c3..2af11485 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 2024-08-05 09:00:06 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:18 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -122,7 +122,7 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3159</td>
+          <td>3168</td>
           <td>63</td>
         </tr>
     
@@ -134,7 +134,7 @@ hide:
           </td>
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
-          <td>108</td>
+          <td>109</td>
           <td>63</td>
         </tr>
     
diff --git a/docs/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da.md b/docs/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da.md
index 61727cc7..e8ef3be1 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 2024-08-05 09:00:13 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:32 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
diff --git a/docs/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524.md b/docs/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524.md
index df517f26..bc795129 100644
--- a/docs/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524.md
+++ b/docs/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-05 08:59:53 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:57 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -62,7 +62,7 @@ hide:
           </td>
           <td>2023-07-07</td>
           <td>ArXiv</td>
-          <td>59</td>
+          <td>61</td>
           <td>49</td>
         </tr>
     
diff --git a/docs/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a.md b/docs/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a.md
index 9b3fe348..77d69946 100644
--- a/docs/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a.md
+++ b/docs/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-05 08:59:45 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:42 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -98,7 +98,7 @@ hide:
           </td>
           <td>2020-05-24</td>
           <td>Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining</td>
-          <td>934</td>
+          <td>938</td>
           <td>54</td>
         </tr>
     
@@ -122,7 +122,7 @@ hide:
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>24</td>
+          <td>25</td>
           <td>4</td>
         </tr>
     
diff --git a/docs/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9.md b/docs/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9.md
index adb46d95..65de8c14 100644
--- a/docs/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9.md
+++ b/docs/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-08-05 08:59:41 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:36 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -98,7 +98,7 @@ hide:
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>24</td>
+          <td>25</td>
           <td>4</td>
         </tr>
     
diff --git a/docs/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419.md b/docs/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419.md
index 13c101b9..79cda625 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 2024-08-05 08:59:43 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:39 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -62,7 +62,7 @@ hide:
           </td>
           <td>2022-09-14</td>
           <td>ArXiv</td>
-          <td>27</td>
+          <td>28</td>
           <td>49</td>
         </tr>
     
@@ -110,7 +110,7 @@ hide:
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>24</td>
+          <td>25</td>
           <td>4</td>
         </tr>
     
diff --git a/docs/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6.md b/docs/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6.md
index 06b1db68..31769f01 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 2024-08-05 08:59:50 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:52 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -58,7 +58,7 @@ hide:
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/818793961396bbe4c744932676f0ff9cc8385bed" target='_blank'>Generative Pre-Trained Diffusion Paradigm for Zero-Shot Time Series Forecasting</a></td>
           <td>
-            Jiarui Yang, Tao Dai, Naiqi Li, Junxi Wu, Peiyuan Liu, Jinmin Li, Jigang Bao, Haigang Zhang, Shutao Xia
+            Jiarui Yang, Tao Dai, Naiqi Li, Junxi Wu, Peiyuan Liu, Jinmin Li, Jigang Bao, Haigang Zhang, Shu-Tao Xia
           </td>
           <td>2024-06-04</td>
           <td>ArXiv</td>
diff --git a/docs/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8.md b/docs/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8.md
index c7641424..a16e4992 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 2024-08-05 09:00:12 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:30 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -62,7 +62,7 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3159</td>
+          <td>3168</td>
           <td>63</td>
         </tr>
     
@@ -86,7 +86,7 @@ hide:
           </td>
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
-          <td>108</td>
+          <td>109</td>
           <td>63</td>
         </tr>
     
@@ -158,7 +158,7 @@ hide:
           </td>
           <td>2017-12-06</td>
           <td>The Journal of chemical physics</td>
-          <td>189</td>
+          <td>190</td>
           <td>44</td>
         </tr>
     
diff --git a/docs/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f.md b/docs/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f.md
index 863ce75b..b087071c 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 2024-08-05 08:59:48 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:16:47 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -74,7 +74,7 @@ hide:
           </td>
           <td>2023-10-03</td>
           <td>ArXiv</td>
-          <td>109</td>
+          <td>111</td>
           <td>9</td>
         </tr>
     
diff --git a/docs/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90.md b/docs/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90.md
index 62c1e455..692e6334 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 2024-08-05 08:59:54 UTC</i>
+  <i class="footer">This page was last updated on 2024-08-08 13:17:00 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
