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fix: #47
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gurdeep330 committed Aug 8, 2024
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27 changes: 10 additions & 17 deletions app/code/utils.py
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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):
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2 changes: 1 addition & 1 deletion docs/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378.md
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<body>
<p>
<i class="footer">This page was last updated on 2024-08-05 09:00:07 UTC</i>
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4 changes: 2 additions & 2 deletions docs/recommendations/0acd117521ef5aafb09fed02ab415523b330b058.md
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<body>
<p>
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</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>

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4 changes: 2 additions & 2 deletions docs/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b.md
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<body>
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<i class="footer">This page was last updated on 2024-08-05 08:59:44 UTC</i>
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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>
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6 changes: 3 additions & 3 deletions docs/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b.md
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<body>
<p>
<i class="footer">This page was last updated on 2024-08-05 08:59:46 UTC</i>
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</td>
<td>2023-10-03</td>
<td>ArXiv</td>
<td>109</td>
<td>111</td>
<td>9</td>
</tr>

Expand All @@ -146,7 +146,7 @@ hide:
</td>
<td>2023-06-19</td>
<td>ArXiv</td>
<td>32</td>
<td>34</td>
<td>8</td>
</tr>

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6 changes: 3 additions & 3 deletions docs/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277.md
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<body>
<p>
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</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>

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</td>
<td>2023-10-14</td>
<td>ArXiv</td>
<td>44</td>
<td>45</td>
<td>14</td>
</tr>

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6 changes: 3 additions & 3 deletions docs/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f.md
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<body>
<p>
<i class="footer">This page was last updated on 2024-08-05 08:59:47 UTC</i>
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</td>
<td>2024-06-09</td>
<td>ArXiv</td>
<td>0</td>
<td>1</td>
<td>2</td>
</tr>

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</td>
<td>2022-10-08</td>
<td>ArXiv</td>
<td>56</td>
<td>57</td>
<td>17</td>
</tr>

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56 changes: 28 additions & 28 deletions docs/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68.md
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<body>
<p>
<i class="footer">This page was last updated on 2024-08-05 08:59:53 UTC</i>
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<div class="note info" onclick="startIntro()">
Expand Down Expand Up @@ -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>
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<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>
Expand All @@ -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>
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4 changes: 2 additions & 2 deletions docs/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196.md
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<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()">
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</td>
<td>2020-07-02</td>
<td>ArXiv</td>
<td>126</td>
<td>127</td>
<td>22</td>
</tr>

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6 changes: 3 additions & 3 deletions docs/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220.md
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<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>
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<div class="note info" onclick="startIntro()">
Expand Down Expand Up @@ -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>
Expand All @@ -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>

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4 changes: 2 additions & 2 deletions docs/recommendations/3c9961153493370500020c81527b3548c96f81e0.md
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<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>
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<div class="note info" onclick="startIntro()">
Expand Down Expand Up @@ -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>

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