\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ll}
		Parameters & values\\
		\toprule
		$\delta$ & 0.01\\
		$(\alpha_d, \phi_d, \sigma_d)$ & ( -0.02, 8, 0.016)\\
		$(\alpha_g, \phi_g, \sigma_g)$ & ( -0.02, 8, 0.016) \\
		$(\alpha_\lambda, \varphi, \sigma_\lambda)$ & (0, 0.01, 0.016)\\
		$A_d$ & 0.12\\
		$(A_g, A_g')$ & (0.10, 0.15)\\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

q-fin

Michael Barnett
William Brock
Lars Peter Hansen
Ruimeng Hu
Joseph Huang

"A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty" by Michael Barnett, William Brock, Lars Peter Hansen, Ruimeng Hu, and Joseph Huang, arXiv:2310.13200v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

""Medium-n studies" in computing education conferences" by Michael Guerzhoy, arXiv:2311.14679v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lll}
\hline
\textbf{Output} & \textbf{natbib command} & \textbf{Old command}\\
\hline
 & \verb|\citep| & \verb|\cite| \\
 & \verb|\citealp| & no equivalent \\
 & \verb|\citet| & \verb|\newcite| \\
 & \verb|\citeyearpar| & \verb|\shortcite| \\
\hline
\end{tabular}
\caption{ Citation commands supported by the style file. The style is based on the natbib package and supports all natbib citation commands. It also supports commands defined in previous style files for compatibility.}
\end{table}
\end{document}

cs

Yingfa Chen
Zhengyan Zhang
Xu Han
Chaojun Xiao
Zhiyuan Liu
Chen Chen
Kuai Li
Tao Yang
Maosong Sun

"Robust and Scalable Model Editing for Large Language Models" by Yingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Chen Chen, Kuai Li, Tao Yang, and Maosong Sun, arXiv:2403.17431v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Robust Functional Ward's Linkages with Applications in EEG data Clustering" by Tianbo Chen, arXiv:2501.11081v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccccc}
\toprule[1.5pt]
\multirow{2}{*}{Model} & \multirow{2}{*}{Utility} & \multicolumn{4}{c}{Terminal PnLs} \\
& & Path 1 & Path 2 & Path 3 & Path 4 \\ \midrule[1.5pt]
LSTM (Classical) & $-2.173$ & $-2.578$ & $-1.225$ & $-1.420$ & $-2.671$ \\
LSTM (Butterfly – Simulation) & $-2.176$ & $-2.586$ & $-1.194$ & $-1.439$ & $-2.671$ \\
LSTM (Butterfly – Hardware) & $-2.194$ & $-2.610$ & $-1.284$ & $-1.488$ & $-2.658$ \\
\midrule
Transformer (Classical) & $-2.167$ & $-2.563$ & $-1.219$ & $-1.411$ & $-2.673$ \\
Transformer (Butterfly – Simulation) & $-2.195$ & $-2.639$ & $-1.242$ & $-1.388$ & $-2.672$ \\
Transformer (Butterfly – Hardware) & $-2.539$ & $-3.341$ & $-1.355$ & $-1.247$ & $-2.713$ \\
\bottomrule[1.5pt]
\end{tabular}
\caption{Comparison of exact simulation and Quantinuum H1-1 hardware results for orthogonal layer models, evaluating expected utilities and terminal PnLs with transaction costs over $4$ paths and $5$ trading days, evaluating the performance under hardware conditions.}
\end{table}
\end{document}

q-fin

Quantum Deep Hedging

El Amine Cherrat
Snehal Raj
Iordanis Kerenidis
Abhishek Shekhar
Ben Wood
Jon Dee
Shouvanik Chakrabarti
Richard Chen
Dylan Herman
Shaohan Hu
Pierre Minssen
Ruslan Shaydulin
Yue Sun
Romina Yalovetzky
Marco Pistoia

"Quantum Deep Hedging" by El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky, and Marco Pistoia, arXiv:2303.16585v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccccccc} \toprule
\multirow{2}[2]{*}{Metric} & \multicolumn{4}{c}{$n=75$} & \multicolumn{4}{c}{$n=300$} \\
 \cmidrule(lr){2-5} \cmidrule(lr){6-9}
 & EB	& BS	& Ens	& IPB	& EB	& BS	& Ens	& IPB
 \\ \midrule
 RMSE 	& $3.1$	& $2.7$	& $2.4$	& $\mathbf{1.4}$
 & $2.9$	& $3.0$	& $2.0$	& $\mathbf{1.1}$ \\
 NLPD	& $3.0$	& $2.0$	& $2.6$	& $\mathbf{1.6}$
 & $2.7$	& $3.0$	& $2.2$	& $\mathbf{1.1}$ \\
 CRPS	& $3.0$	& $2.3$	& $2.6$	& $\mathbf{1.4}$
 & $2.7$	& $3.3$	& $2.1$	& $\mathbf{1.1}$ \\
\bottomrule\end{tabular}
\end{table}
\end{document}

cs

"On Uncertainty Quantification for Near-Bayes Optimal Algorithms" by Ziyu Wang and Chris Holmes, arXiv:2403.19381v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Few-Shot Website Fingerprinting Attack

Mantun Chen
Yongjun Wang
Zhiquan Qin
Xiatian Zhu

"Few-Shot Website Fingerprinting Attack" by Mantun Chen, Yongjun Wang, Zhiquan Qin, and Xiatian Zhu, arXiv:2101.10063v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Aparna Bansal
Nicolás Barnafi
Rodolfo Araya
Dwijendra Narain Pandey

"Equal order stabilized finite elements with Nitsche for stationary Navier-Stokes problem with slip boundary conditions : a priori and a posteriori error analysis" by Aparna Bansal, Nicolás Barnafi, Rodolfo Araya, and Dwijendra Narain Pandey, arXiv:2501.14715v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|ccc}
 \hline
 Method & AUC@$5^{\circ}$ & AUC@$10^{\circ}$ & AUC@$20^{\circ}$ \\
 \hline
 PointCN~ & 12.38 & 28.15 & 48.04 \\
 NM-Net~ & 12.59 & 30.62 & 52.07 \\
 OANet~ & 16.86 & 36.74 & 57.40 \\
 PointACN~ & 18.64 & 38.76 & 59.56 \\
 \hline
 Our CLNet & \bf{26.19} & \bf{46.33} & \bf{65.48} \\
 \hline
 \end{tabular}
\caption{\textbf{Pose estimation without a robust estimator on YFCC100M.} For these results, we used the weighted 8-point algorithm~ instead of a robust estimator, e.g., RANSAC, to estimate the essential matrices.}
\end{table}
\end{document}

cs

Chen Zhao
Yixiao Ge
Feng Zhu
Rui Zhao
Hongsheng Li
Mathieu Salzmann

"Progressive Correspondence Pruning by Consensus Learning" by Chen Zhao, Yixiao Ge, Feng Zhu, Rui Zhao, Hongsheng Li, and Mathieu Salzmann, arXiv:2101.00591v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|l|l|l|l|}
 \hline
 Model & $Rank_{M}$ & $v_{M}$ & $MCS_{M}$ & $Rank_{R}$ & $v_{R}$ & $MCS_{R}$ \\ \hline
 LWNL & 1 & -1.40 & 1 & 1 & -0.21 & 1 \\ \hline
 BDL & 2 & -1.25 & 1 & 2 & -0.05 & 0.95 \\ \hline
 LWL & 3 & 0.52 & 1 & 3 & 0.08 & 0.87 \\ \hline
 RBLW & 4 & 0.63 & 0.69 & 4 & 1.29 & 0.81 \\ \hline
 RIE & 5 & 0.82 & 0.55 & 5 & 3.09 & 0.70 \\ \hline
 \end{tabular}
\caption{Out-of-Sample MCS Test for Daily Estimation Horizon (Normal Period) - for Covariance Estimation Methods}
\end{table}
\end{document}

q-fin

"Precision versus Shrinkage: A Comparative Analysis of Covariance Estimation Methods for Portfolio Allocation" by Sumanjay Dutta and Shashi Jain, arXiv:2305.11298v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|cc}
& Predicted 0 & Predicted 1\\\hline
Actual 0 & 21 & 1\\
Actual 1 & 2 & 21
\end{tabular}
\caption{Confusion out-of-sample matrix for GAM model applied to the iris dataset }
\end{table}
\end{document}

stat

"Kolmogorov GAM Networks are all you need!" by Sarah Polson and Vadim Sokolov, arXiv:2501.00704v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
\textbf{Data detection strategy} & \textbf{Computational complexity} \\
\hline
RML with NN search & $\mathcal{O}(N_{\mathrm{NN}}K \max\{{N_{\mathrm{NN}},M}\})$ \\
\hline
JD & $\mathcal{O}(L^K)$ \\
\hline
$N$-JD & $\mathcal{O}(N^K)$ \\
\hline
H-SUD & $\mathcal{O}(L)$ \\
\hline
E-SUD & $\mathcal{O}(L^K)$ \\
\hline
Genie-aided data detection & $\mathcal{O}(L)$ \\
\hline
\end{tabular}
\caption{Computational complexity.}
\end{table}
\end{document}

eess

"Enhanced Uplink Data Detection for Massive MIMO with 1-Bit ADCs: Analysis and Joint Detection" by Amin Radbord, Italo Atzeni, and Antti Tolli, arXiv:2312.04183v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

María González-Calderón
María Jaenada
Leandro Pardo

"Robust statistical inference for accelerated life-tests with one-shot devices under log-logistic distributions" by María González-Calderón, María Jaenada, and Leandro Pardo, arXiv:2502.20467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The MSE values resulted by different network models.}
\begin{tabular}{|c|c|c|c|c|}
\hline
Model & FC & FC-CNN & FC-LSTM & FC-CNN-LSTM \\ \hline
MSE (Training) & 0.06 & 0.0497 & 0.0438 & 0.019\\ \hline
MSE (Validation) & 0.0589 & 0.0485 & 0.0429 & 0.017\\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Praveen Damacharla
Hamid Rajabalipanah
Mohammad Hosein Fakheri

"LSTM-CNN Network for Audio Signature Analysis in Noisy Environments" by Praveen Damacharla, Hamid Rajabalipanah, and Mohammad Hosein Fakheri, arXiv:2312.07059v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of predicate term relationships in test-set of GazeVQA questions: Note that ``nom.'', ``acc.'' and ``dat.'' denote numbers of nominative, accusative and dative cases, AQ and CQ refer to caption of Fig.~.}
\begin{tabular}{c|r|r|r}
 \hline
 Types & \textit{ga} (nom.) & \textit{wo} (acc.) & \textit{ni} (dat.) \\\hline
 AQ & 2,044 & 1,028 & 440 \\
 CQ & 2,912 & 1,584 & 569 \\\hline
 \end{tabular}
\end{table}
\end{document}

cs

Shun Inadumi
Seiya Kawano
Akishige Yuguchi
Yasutomo Kawanishi
Koichiro Yoshino

"A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions" by Shun Inadumi, Seiya Kawano, Akishige Yuguchi, Yasutomo Kawanishi, and Koichiro Yoshino, arXiv:2403.17545v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Lina M. Montoya
Elvin H. Geng
Harriet F. Adhiambo
Eliud Akama
Starley B. Shade
Assurah Elly
Thomas Odeny
Maya L. Petersen

"Cost Effectiveness Analyses for Sequential Multiple Assignment Randomized Trials" by Lina M. Montoya, Elvin H. Geng, Harriet F. Adhiambo, Eliud Akama, Starley B. Shade, Assurah Elly, Thomas Odeny, and Maya L. Petersen, arXiv:2502.07973v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Outcome Regression Methods for Analyzing Hybrid Control Studies: Balancing Bias and Variability" by Zhiwei Zhang, Jialuo Liu, and Wei Liu, arXiv:2501.17358v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccccc}
 \toprule
 & \ \ HP\ \ \ & NORM & \multicolumn{2}{c}{TA} & \multicolumn{2}{c}{TVA} & \textbf{Total}\\
 & & & HG & LG & HG & LG & \\
 \midrule
 Slides & 41 & 21 & 26 & 146 & 20 & 38 & {\bfseries 292}\\
 \midrule
 $\sigma$ = 7000 & 59 & 74 & 98 & 411 & 93 & 132 & \textbf{867} \\
 $\sigma$ = 800 & 545 & 950 & 454 & 3618 & 916 & 2186 & \textbf{8699} \\
 Total & 604 & 1024 & 552 & 4029 & 1009 & 2318 & \textbf{9536} \\
 \bottomrule
 \end{tabular}
\caption{\textit{UniToPatho} class distribution for whole image slides (top) and the two patch scales made available (bottom).}
\end{table}
\end{document}

eess

Carlo Alberto Barbano
Daniele Perlo
Enzo Tartaglione
Attilio Fiandrotti
Luca Bertero
Paola Cassoni
Marco Grangetto

"UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading" by Carlo Alberto Barbano, Daniele Perlo, Enzo Tartaglione, Attilio Fiandrotti, Luca Bertero, Paola Cassoni, and Marco Grangetto, arXiv:2101.09991v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance with SimCLR on various datasets (mean $\pm$ 99\% confidence interval).}
\begin{tabular}{|l|l|l|l|}
\hline
 & CIFAR10 & CIFAR100 & SVHN \\ \hline
SimCLR & 63.34 $\pm$ 0.0016 & 28.53 $\pm$ 0.0017 & 83.19 $\pm$ 0.0017 \\ \hline
SimCLR + H & 64.04 $\pm$ 0.0029 & 29.10 $\pm$ 0.0025 & 82.37 $\pm$ 0.0024 \\ \hline
SimCLR + A & \textbf{64.71 $\pm$ 0.0023} & \textbf{31.33 $\pm$ 0.0024} & \textbf{83.85 $\pm$ 0.0017} \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

"Explicit homography estimation improves contrastive self-supervised learning" by David Torpey and Richard Klein, arXiv:2101.04713v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of datasets.}
\begin{tabular}{|c|c|c|c|}
 \hline
 Datasets & \#Category & \#Training & \#Test \\
 \hline
 \hline
 CUB-$200$-$2011$ & $200$ & $5994$ & $5794$ \\
 FGVC-Aircraft & $100$ & $6667$ & $3333$ \\
 Stanford Cars & $196$ & $8144$ & $8041$ \\
 Flowers-$102$ & $102$ & $2040$ & $6149$ \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Dongliang Chang
Yixiao Zheng
Zhanyu Ma
Ruoyi Du
Kongming Liang

"Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained Features" by Dongliang Chang, Yixiao Zheng, Zhanyu Ma, Ruoyi Du, and Kongming Liang, arXiv:2102.00367v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Precision/recall pairs of the two GCN models' classification tasks during training with different datasets. The $\beta$-skeleton graph is used for all tasks.}
\begin{tabular}{lccc}
\hline \hline\noalign{\smallskip}
Dataset & Line start & Line end & Edge clustering \\
\noalign{\smallskip}\hline\noalign{\smallskip}
PubLayNet & 0.998/0.992 & 0.992/0.990 & 0.994/0.997 \\
\noalign{\smallskip}\noalign{\smallskip}
Web synthetic & 0.995/0.996 & 0.994/0.997 & 0.978/0.980 \\
\noalign{\smallskip}\noalign{\smallskip}
Augmented web & 0.988/0.986 & 0.990/0.987 & 0.958/0.966 \\
synthetic \\
\noalign{\smallskip}\noalign{\smallskip}
Human annotated & -/- & -/- & 0.901/0.912 \\
\noalign{\smallskip}\hline
\end{tabular}
\end{table}
\end{document}

cs

Renshen Wang
Yasuhisa Fujii
Ashok C. Popat

"Post-OCR Paragraph Recognition by Graph Convolutional Networks" by Renshen Wang, Yasuhisa Fujii, and Ashok C. Popat, arXiv:2101.12741v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c||cccc|}
 & $x_1$ & $x_2$ & $\cdots$ & $x_s$ \\ \hline\hline
 $y_1$ & $\alpha + 1$ & $\alpha + 2$ & $\cdots$ & $\alpha + s$ \\
 $y_2$ & $\alpha + 2$ & $\alpha + 3$ & $\cdots$ & $\alpha + s + 1$ \\
 $\vdots$ & $\vdots$ & & & $\vdots$ \\
 $y_t$ & $\alpha + t$ & $\alpha + t + 1$ & $\cdots$ & $\alpha + s + t - 1$ \\ \hline
 \end{tabular}
\caption{The sequential coloring with shift $\alpha$ in matrix form.}
\end{table}
\end{document}

math

MacKenzie Carr
Eun-Kyung Cho
Nicholas Crawford
Vesna Iršič
Leilani Pai
Rebecca Robinson

"On the interval coloring impropriety of graphs" by MacKenzie Carr, Eun-Kyung Cho, Nicholas Crawford, Vesna Iršič, Leilani Pai, and Rebecca Robinson, arXiv:2312.14881v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Randomized directional search for nonconvex optimization" by Yuxuan Zhang and Wenxun Xing, arXiv:2501.00469v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Sample selection with noise rate estimation in noise learning of medical image analysis" by Maolin Li and Giacomo Tarroni, arXiv:2312.15233v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Deep incremental learning models for financial temporal tabular datasets with distribution shifts" by Thomas Wong and Mauricio Barahona, arXiv:2303.07925v10, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Samrat Acharya
Hafiz Anwar Ullah Khan
Ramesh Karri
Yury Dvorkin

"MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid Operation" by Samrat Acharya, Hafiz Anwar Ullah Khan, Ramesh Karri, and Yury Dvorkin, arXiv:2311.06226v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average drowsiness classification performance (\%) according to the type of normalization}
\begin{tabular}{lccccc} \toprule
 Normalization & Accuracy & \textit{F}1-score & Precision & Recall & AUROC \\ \midrule
 BN & \textbf{72.93} & 69.09 & 72.28 & 72.97 & \textbf{72.74} \\
 IN & 47.41 & 41.27 & 34.29 & 57.83 & 46.97 \\
 IBN & 71.93 & \textbf{69.25} & \textbf{72.54} & \textbf{74.68} & 72.28 \\ \midrule
 DSBN & \textbf{73.27} & \textbf{69.63} & \textbf{71.98} & 74.17 & \textbf{74.02} \\
 DSIN & 47.40 & 55.24 & 44.39 & \textbf{78.31} & 47.87 \\
 DSON & 71.08 & 65.80 & 70.64 & 70.35 & 69.56 \\\bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Dong-Young Kim
Dong-Kyun Han
Seo-Hyeon Park
Geun-Deok Jang
Seong-Whan Lee

"Improving Generalization of Drowsiness State Classification by Domain-Specific Normalization" by Dong-Young Kim, Dong-Kyun Han, Seo-Hyeon Park, Geun-Deok Jang, and Seong-Whan Lee, arXiv:2312.09461v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Computation time in minutes to fit the AI-REML algorithm as a function of the modelling strategy for the simulation model with no causal predictor (0\% heritability) and for the simulation model with 100 causal predictors explaining 2\% of heritability for both continuous and binary phenotypes. We present the median value with IQR in brackets.}
\begin{tabular}{ll|cccc}
\hline
& & \multicolumn{2}{c}{Binary phenotypes} & \multicolumn{2}{c}{Continuous phenotypes} \\
Number of PCs & GRM & 0\% & 2\% & 0\% & 2\% \\
 \hline
 0 & full & 9.9 (7.0) & 8.3 (7.0) & 7.7 (0.4) & 6.9 (1.0) \\
 & sparse & 2.7 (1.9) & 2.5 (0.9) & 1.3 (0.2) & 1.4 (0.1) \\
 10 & full & 7.3 (7.6) & 8.3 (2.6) & 7.4 (0.3) & 6.7 (1.1) \\
 & sparse & 1.6 (1.0) & 1.2 (1.2) & 1.1 (0.2) & 1.2 (0.1) \\
 20 & full & 9.0 (8.1) & 8.5 (4.4) & 7.5 (1) & 8.2 (1.2) \\
 & sparse & 1.6 (0.9) & 1.7 (1.2) & 1.3 (0.1) & 1.7 (0.1) \\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

Julien St-Pierre
Sahir Rai Bhatnagar
Massimiliano Orri
Michel Boivin
Josée Dupuis
Karim Oualkacha

"Penalized generalized linear mixed models for longitudinal outcomes in genetic association studies" by Julien St-Pierre, Sahir Rai Bhatnagar, Massimiliano Orri, Michel Boivin, Josée Dupuis, and Karim Oualkacha, arXiv:2501.11083v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c||c|c}
 Configuration & Coordinates &
 Configuration & Coordinates \\
 \hline
 $P_{i+2} \in U_{2,3}$ & $x_{2i} = I$ &
 $P_{i+2} \in V_{2,3}$ & $x_{2i+1} = I$ \\
 $P_{i+2} \in U_{1,3}$ & $x_{2i} = J$ &
 $P_{i+2} \in V_{2,4}$ & $x_{2i+1} = J$ \\
 $P_{i+2} \in U_{1,2}$ & $x_{2i} = K$ &
 $P_{i+2} \in V_{3,4}$ & $x_{2i+1} = K $
 \end{tabular}
\end{table}
\end{document}

math

"Spirals, Tic-Tac-Toe Partition, and Deep Diagonal Maps" by Zhengyu Zou, arXiv:2412.15561v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Quantile and expectile copula-based hidden Markov regression models for the analysis of the cryptocurrency market" by Beatrice Foroni, Luca Merlo, and Lea Petrella, arXiv:2307.06400v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Strategic Effort and Bandwagon Effects in Finite Multi-Stage Games with Non-Linear Externalities: Evidence from Triathlon" by Felix Reichel, arXiv:2505.03247v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems" by Grzegorz Filcek and Janusz Miroforidis, arXiv:2401.00292v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance Metrics. The first column denotes the allocation strategy. The second and third columns show the Mean Square Error~(MSE) and Mean Absolute Error~(MAE) of the difference between the in-sample and out-sample risk, respectively. The last column shows the Mean Sum of Absolute Weights~(MSAW). The average is computed over the m=121 data window setting on a minimum variance portfolio scenario~($\mathbf{g}=1$) at a fixed level $\mathcal{G}=1$ and dimensional factor $q=1/2$.}
\begin{tabular}{|l|l|l|l|}
 \hline
 Case & MSE & MAE & MSAW \\ \hline
 $M(E)$ & 0.025005 & 0.149937 & 1.909886 \\ \hline
 $M(\Xi^{linear})$ & {\bf 0.000238} & {\bf 0.013284} & 1.049836 \\ \hline
 $M(\Xi^{TW})$ & 0.000360 & 0.014970 & 1.050949 \\ \hline
 $NCO(E)$ & 0.001302 & 0.027702 & 1.083757 \\ \hline
 $NCO(\Xi^{linear})$ & 0.000255 & 0.013803 & {\bf 1.000924} \\ \hline
 $NCO(\Xi^{TW})$ & 0.000353 & 0.014983 & 1.070408 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

Andrés García-Medina
Benito Rodriguéz-Camejo

"Random matrix theory and nested clustered portfolios on Mexican markets" by Andrés García-Medina and Benito Rodriguéz-Camejo, arXiv:2306.05667v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simplified illustration of the Metarule matrix. Rows represent data instances, and columns represent rules.}
\begin{tabular}{rcccccrc}
 \toprule
 & \textbf{R\textsubscript{1}} & \textbf{R\textsubscript{2}} & \textbf{R\textsubscript{3}} & \textbf{R\textsubscript{4}} & \textbf{R\textsubscript{5}} & \textbf{R\textsubscript{6}} & \textbf{R\textsubscript{7}} \\
 \midrule
 \textbf{1} & $R_1$ & $R_2$ & & $R_4$ & & & \\
 \textbf{2} & & $R_2$ & & $R_4$ & & & \\
 \textbf{3} & & $R_2$ & $R_3$ & & $R_5$ & & \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

"Forest-ORE: Mining Optimal Rule Ensemble to interpret Random Forest models" by Haddouchi Maissae and Berrado Abdelaziz, arXiv:2403.17588v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"High Resolution Optimized High-Order Schemes for Discretization of Non-Linear Straight and Mixed Second Derivative Terms" by Hemanth Chandravamsi and Steven H. Frankel, arXiv:2312.12069v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Baseline algorithms, their target graph structure, operational assumption, and computational complexity.}
\begin{tabular}{lllc}
 \hline
 Algorithm & Graph Structure & Assumption & Complexity\\ \hline
 $\mathsf{GL}$-$\mathsf{SigRep}$~ & connected & smooth signals & $O(np^2)$ \\
 $\mathsf{SSGL}$~ & connected & smooth signals & $O(p^2)$\\
 $\mathsf{GLE}$-$\mathsf{ADMM}$~ & connected & LGMRF & $O(p^3)$\\
 $\mathsf{NGL}$-$\mathsf{MCP}$~ & connected & LGMRF & $O(p^3)$\\
 $\mathsf{SGL}$~ & $k$-component & LGMRF & $O(p^3)$\\
 $\mathsf{CLR}$~ & $k$-component & smooth signals & $O(p^3)$ \\
 \end{tabular}
\end{table}
\end{document}

eess

José Vinícius de Miranda Cardoso
Jiaxi Ying
Daniel Perez Palomar

"Algorithms for Learning Graphs in Financial Markets" by José Vinícius de Miranda Cardoso, Jiaxi Ying, and Daniel Perez Palomar, arXiv:2012.15410v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc} \hline
			 	& 		& X (10) 	& Y (4) & Z (9)	\\ \hline
						& Budget	& 		& 		&		\\ \hline
			 Agent 1 	& 8 		& 	& + 	& +		\\ \hline
			 Agent 2 	& 1	 	& 	& + 	& +		\\ \hline
			 Agent 3 	& 10		& + 		& 		& +
			\end{tabular}
\caption{Instance where Agent 3 is misrepresenting her preferences.}
\end{table}
\end{document}

cs

"Participatory Funding Coordination: Model, Axioms and Rules" by Haris Aziz and Aditya Ganguly, arXiv:2101.11722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Arithmetic mean reduction results. Here, $K$ is the instance reduced by the different configurations, $\tilde{K}$ the instance reduced by \textit{Fast - no gnn red} and $G$ the original graph. We refer to the number of nodes $n$, and edges $m$ of the different graphs in the index. Furthermore, we give the offset and reduction time. In the column Time exp., we give the time which is used only for expensive reductions and the reduction screening. }
\begin{tabular}{lcrrrrrrr}
 \textbf{Config} & \textbf{GNN} & $\bf n_K/n_{\tilde{K}}$ & $\bf n_K/n_G$ & $\bf m_K/m_G$ & \textbf{Offset} & \textbf{Time} & \textbf{Time exp.} & $\bf \# n_K=0$ \\ \midrule
 \textit{Fast} & no gnn red & 100.00 & 5.50 & 51.13 & 13\,179\,456 & \textbf{36.55} & 0.00 & 49 / 83 \\
 \textit{Fast} & never & 86.78 & 4.77 & 50.85 & 13\,270\,743 & 74.74 & 38.19 & 52 / 83 \\
 \textit{Fast} & always & 87.11 & 4.79 & 50.90 & 13\,269\,029 & 65.51 & 28.96 & 52 / 83 \\
 \textit{Fast} & initial & 87.13 & 4.79 & 50.88 & 13\,268\,824 & 58.59 & 22.04 & 52 / 83 \\
 \textit{Fast} & initial tight & 87.41 & 4.81 & 50.91 & 13\,267\,958 & 45.12 & 8.57 & 51 / 83 \\
 \textit{Strong} & no gnn red & 98.74 & 5.43 & 50.81 & 13\,188\,267 & 68.86 & 0.00 & 50 / 83 \\
 \textit{Strong} & never & \textbf{85.06} & \textbf{4.68} & \textbf{50.51} & \textbf{13\,280\,038} & 101.56 & 32.70 & 53 / 83 \\
 \textit{Strong} & always & 85.56 & 4.71 & 50.55 & 13\,278\,028 & 107.17 & 38.31 & 53 / 83 \\
 \textit{Strong} & initial & 85.66 & 4.71 & 50.56 & 13\,277\,789 & 88.24 & 19.38 & \textbf{54 / 83} \\
 \textit{Strong} & initial tight & 85.65 & 4.71 & 50.58 & 13\,277\,974 & 85.20 & 16.34 & 53 / 83
 \end{tabular}
\end{table}
\end{document}

math

Ernestine Großmann
Kenneth Langedal
Christian Schulz

"Accelerating Reductions Using Graph Neural Networks and a New Concurrent Local Search for the Maximum Weight Independent Set Problem" by Ernestine Großmann, Kenneth Langedal, and Christian Schulz, arXiv:2412.14198v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{siunitx}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccc}\toprule
		Parameter & Value \\ \midrule \midrule
		$L_1$ & \SI{8.78}{\milli\henry} \\
		$L_2$ & \SI{8.55}{\milli\henry} \\
		$C_1$ & \SI{2.19}{\milli\farad} \\
		$C_2$ & \SI{7.6}{\milli\farad} \\
		$R_1$ & \SI{1.58}{\ohm} \\ %\SI{1.57}{\ohm} \\
		$R_2$ & \SI{1.69}{\ohm} \\
		$G_{sc}$ & \SI{200}{\micro\siemens} \\
		$G$ & \SI{50}{\micro\siemens} \\
		$f_{sw}$ & \SI{10}{\kilo\hertz} \\ \bottomrule
	\end{tabular}
\caption{Parameter values used in the test platform.}
\end{table}
\end{document}

cs

Felipe Morales
Rafael Cisneros
Romeo Ortega
Antonio Sanchez-Squella

"Nonlinear Voltage Regulation of an Auxiliary Energy Storage of a Multiport Interconnection" by Felipe Morales, Rafael Cisneros, Romeo Ortega, and Antonio Sanchez-Squella, arXiv:2403.19901v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Maria Frasca
Davide La Torre
Gabriella Pravettoni
Ilaria Cutica

"Predicting Parkinson's disease evolution using deep learning" by Maria Frasca, Davide La Torre, Gabriella Pravettoni, and Ilaria Cutica, arXiv:2312.17290v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|cc|}
\hline
I am a [BLANK]\\
I work as a [BLANK]\\
My occupation is [BLANK]\\
My dream job is [BLANK]\\
works as a [BLANK]\\
wants to become a [BLANK]\\
Overall i am quite happy with being a [BLANK] \\
I am depressed working as a [BLANK] \\
In conclusion i am neutral towards my job as a [BLANK] \\
I am working as a [BLANK] \\
This is my job: [BLANK]\\
I am conflicted about being a [BLANK]\\
\hline
\end{tabular}
\caption{ List of neutral sentences used in the experiments.} %TODO cite bolukbasi implementation as source
\end{table}
\end{document}

cs

Philip Kenneweg
Alexander Schulz
Sarah Schröder
Barbara Hammer

"Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in Transformers" by Philip Kenneweg, Alexander Schulz, Sarah Schröder, and Barbara Hammer, arXiv:2404.01317v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Relative errors of the problem with a piecewise smooth solution}
\begin{tabular}{|c|c|c|}
\hline
Time & $\frac{\|u(\cdot, t_k)-u^{(k)}_{N}\|_{L^2(\Omega)}}{\|u(\cdot, t_k)\|_{L^2(\Omega)}}$ \\ \hline
0.00 & $8.2421\times 10^{-4}$ \\ \hline
0.25 & $6.86119\times 10^{-4}$ \\ \hline
0.50 & $8.8717\times 10^{-4}$ \\ \hline
0.75 & $6.0632\times 10^{-4}$ \\ \hline
1.00 & $5.2592\times 10^{-4}$ \\ \hline
\end{tabular}
\end{table}
\end{document}

math

"Evolving Neural Network (ENN) Method for One-Dimensional Scalar Hyperbolic Conservation Laws: I Linear and Quadratic Fluxes" by Zhiqiang Cai and Brooke Hejnal, arXiv:2312.06919v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{An example of a contingency table illustrating the detection of two sentiments (joy and admiration) in our corpus.}
\begin{tabular}{l|cc|r}
 & No Joy & Joy & Total\\
 \midrule
 No Admiration & 4274 & 112 & 4386 \\
 Admiration & 205 & 22 & 227 \\
 \midrule
 Total & 4479 & 134 & 4613 \\
 \end{tabular}
\end{table}
\end{document}

stat

Henrique S. Xavier
Diogo Cortiz
Mateus Silvestrin
Ana Luísa Freitas
Letícia Yumi Nakao Morello
Fernanda Naomi Pantaleão
Gabriel Gaudencio do Rêgo

"A Bayesian framework for measuring association and its application to emotional dynamics in Web discourse" by Henrique S. Xavier, Diogo Cortiz, Mateus Silvestrin, Ana Luísa Freitas, Letícia Yumi Nakao Morello, Fernanda Naomi Pantaleão, and Gabriel Gaudencio do Rêgo, arXiv:2311.05330v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
\toprule
\textbf{Model } & \textbf{TU } & \textbf{$\%$ Unique Words} & $\#$ \textbf{FT } \\
\hline
TBIP & 0.7629 & 0.615 & 7/30 \\
BTM & \textbf{0.7870} & \textbf{0.642} & \textbf{2/30} \\
\bottomrule
\end{tabular}
\caption{Topic Uniqueness \textbf{[remove this table]}}
\end{table}
\end{document}

cs

Runcong Zhao
Lin Gui
Gabriele Pergola
Yulan He

"Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews" by Runcong Zhao, Lin Gui, Gabriele Pergola, and Yulan He, arXiv:2101.10150v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Smell detection performance}
\begin{tabular}{llll}
 \hline
 Requirement smell & Precision & Recall & F1 \\ \hline
 Subjective language & 0.3421 & 0.4885 & 0.4024 \\
 Ambiguous adv./ adj. & 0.4898 & 0.3380 & 0.4000 \\
 Non-verifiable term & 0.2598 & 0.2994 & 0.2782 \\
 Superlative & 0.3500 & 0.8750 & 0.5000 \\
 Comparative & 0.4746 & 0.8750 & 0.6154 \\
 Negative & 0.2897 & 0.9333 & 0.4421 \\
 Vague pron. & 0.1363 & 0.6966 & 0.2279 \\
 Uncertain-verb & 0.7766 & 0.9815 & 0.8671 \\
 Polysemy & 0.6688 & 0.8322 & 0.7416 \\
 \textbf{Average} & \textbf{0.4209} & \textbf{0.7022} & \textbf{0.4972} \\ \hline
 \end{tabular}
\end{table}
\end{document}

cs

"Natural Language Requirements Testability Measurement Based on Requirement Smells" by Morteza Zakeri-Nasrabadi and Saeed Parsa, arXiv:2403.17479v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Data density of human versus artificial tactile sensing.}
\begin{tabular}{lll}
\toprule
\textbf{Data rate (KB/s)} & \textbf{Human } & \textbf{Artificial} \\
\midrule
\textbf{Body} & 2,500 & 630 \\
\textbf{Hand} & 212.5 & 0.5 \\
\textbf{Fingertip} & 25 & 2,125 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Sonja Groß
Amartya Ganguly
Hendrik Dietz
Sami Haddadin

"The Next Evolution of Artificial Sense of Touch" by Sonja Groß, Amartya Ganguly, Hendrik Dietz, and Sami Haddadin, arXiv:2310.16641v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{rlrr}
 \hline
 & name & mean & sd \\
 \hline
1 & alwaysA & -0.0040 & 0.0784 \\
 2 & alwaysB & 0.4965 & 0.0873 \\
 3 & alwaysC & 0.2439 & 0.0795 \\
 4 & estimated & 0.7085 & 0.0873 \\
 5 & known & 0.7073 & 0.0892 \\
 6 & random & 0.2459 & 0.0777 \\
 \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Achim Ahrens
Alessandra Stampi-Bombelli
Selina Kurer
Dominik Hangartner

"Optimal multi-action treatment allocation: A two-phase field experiment to boost immigrant naturalization" by Achim Ahrens, Alessandra Stampi-Bombelli, Selina Kurer, and Dominik Hangartner, arXiv:2305.00545v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lllll}
\toprule
 & $\phi_0$ & $\phi_1$ & $\phi_2$ & $\phi_3$ \\ \midrule
Kendall & 32.080 (2.345) & 0.274 (0.021) & 0.377 (0.020) & 0.063 (0.018) \\
Hamming & 6.955 (0.847) & 0.070 (0.038) & 0.320 (0.038) & 0.061 (0.041) \\ \bottomrule
\end{tabular}
\caption{Maximum likelihood estimates and their respective standard errors (in parentheses) of Kendall and Hamming R-GARCH(3,0) models fitted to the tennis rankings data. }
\end{table}
\end{document}

stat

"Time Series Analysis of Rankings: A GARCH-Type Approach" by Luiza Piancastelli and Wagner Barreto-Souza, arXiv:2502.05102v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc}
\toprule
\textbf{Metric} & \textbf{B\&H} & \textbf{Top 1} & \textbf{Top 3} & \textbf{Top 5} &\textbf{Top 10} \\
\midrule
\textbf{Cumulative Return (\%)} & -66.9497 & 52.0936 & 29.4430 & 54.6958 & \textbf{79.4448} \\
\midrule
\textbf{Sharpe Ratio} & -2.0845 & 1.8642 & 1.1214 & 2.4960 & \textbf{2.7469} \\
\midrule
\textbf{Daily Volatility (\%)} & 3.8050 & 3.3105 & 3.1105 & \textbf{2.5960} & 3.4262 \\
\midrule
\textbf{Annualized Volatility (\%)} & 60.4020 & 52.5529 & 49.3779 & \textbf{41.2100} & 54.3891 \\
\midrule
\textbf{Max Drawdown (\%)} & 67.3269 & 25.2355 & 27.0972 & \textbf{12.5734} & 17.1360 \\
\bottomrule
\end{tabular}
\caption{Comparison of overall trading performance during the testing period with different configurations of working memory capacity.}
\end{table}
\end{document}

q-fin

Yangyang Yu
Haohang Li
Zhi Chen
Yuechen Jiang
Yang Li
Denghui Zhang
Rong Liu
Jordan W. Suchow
Khaldoun Khashanah

"FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design" by Yangyang Yu, Haohang Li, Zhi Chen, Yuechen Jiang, Yang Li, Denghui Zhang, Rong Liu, Jordan W. Suchow, and Khaldoun Khashanah, arXiv:2311.13743v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Prasanna Vijayaraghavan
Jeffrey Frederic Queisser
Sergio Verduzco Flores
Jun Tani

"Development of Compositionality and Generalization through Interactive Learning of Language and Action of Robots" by Prasanna Vijayaraghavan, Jeffrey Frederic Queisser, Sergio Verduzco Flores, and Jun Tani, arXiv:2403.19995v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llllll}
\hline
\textbf{Sub Sample} & \textbf{a} & \textbf{s} & \textbf{h} & \textbf{$h_{dummy}$} & \textbf{hs} \\ \hline
Value & 5.58 & -0.37 & -0.06 & 0.00 & 3.94 \\
 & 4.68 & -4.35 & -0.33 & 1.48 & 2.58 \\
 & & & & & \\
Growth & 2.24 & -0.11 & 0.40 & 0.00 & 0.96 \\
 & 1.97 & -1.49 & 2.43 & -0.89 & 0.79 \\
 & & & & & \\ \hline
\end{tabular}
\caption{Value and Growth subsample of firms are determined by yearly median of BE/ME value (including negative BE/ME). If the firm's BE/ME at year $t$ is bigger than the BE/ME median at time $t$, is classified as big firm, and vise versa. On each month, we conducted the following regression. \\ $R_i-RF=a + s \cdot log(ME_i) + h \cdot log(BE/ME)_i^{+} + h_{dummy} \cdot BE_{Dummy, i} + hs \cdot HSE_i + \epsilon_i $.\\ $log(BE/ME)^{+}_i$ is BE/ME value that fills BE/ME with BE less than 0. $BE_{Dummy, i}$ is 1 when BE is less than 0, else 1. Then we calculated time-series average and its t-value of coefficients and intercept. Returns are observed at July, year $t$ to June, year $t+1$, and BE/ME are observed at December, year $t-1$ and HSE, ME is observed at June, year $t$.}
\end{table}
\end{document}

q-fin

"Do We Price Happiness? Evidence from Korean Stock Market" by HyeonJun Kim, arXiv:2308.10039v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{``Science''--The Causal Estimand.}
\begin{tabular}{lcccc}
				\toprule
				\toprule
				& Covariates & Potential & Unit-Level &Summary \\
				Units & $\boldsymbol{X}$ & Outcome Functions & Causal Effects & Causal Effects \\
				\midrule
				1 & $\boldsymbol{X}_1$ & $w_1\in\mathcal{W}\mapsto Y_1(w_1)$ & $Y_1(w_1)$ v.s. $Y_1(w_1^{\prime})$ &\multirow{2}{3cm}{Comparison of $Y_i(w_i)$ v.s. $Y_i(w_i^{\prime})$ for a common set of units}\\
				$\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & \\
				$i$ & $\boldsymbol{X}_i$ & $w_i\in\mathcal{W}\mapsto Y_i(w_i)$ & $Y_i(w_i)$ v.s. $Y_i(w_i^{\prime})$ &\\
				$\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & \\
				$n$ & $\boldsymbol{X}_n$ & $w_n\in\mathcal{W}\mapsto Y_n(w_n)$ & $Y_n(w_n)$ v.s. $Y_n(w_n^{\prime})$ &\\
				\bottomrule
			\end{tabular}
\end{table}
\end{document}

stat

"Fisher's Randomization Test for Causality with General Types of Treatments" by Zhen Zhong, Shan Huang, and Donald B. Rubin, arXiv:2501.06864v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Covariate adjustment and estimation of difference in proportions in randomized clinical trials" by Jialuo Liu and Dong Xi, arXiv:2308.15688v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|cc|c|}
\hline
 \multirow{3}{*}{Methods} & \multicolumn{3}{c|}{Places ($256 \times 256$)} \\ \cline{2-4}
 & \multicolumn{2}{c|}{FID$\downarrow$} & Diversity$\uparrow$ \\ \cline{2-4}
 & Small Mask & Large Mask & Box \\ \hline
Restrictive & 1.02 & 2.82 & 0.29$\pm$0.06 \\ \hline
Miracle & 0.93 & 2.71 & 0.29$\pm$0.06 \\ %& \textbf{93.0}
 \hline
\end{tabular}
\caption{Comparisons of FID and diversity scores between the restrictive encoder and the miracle encoder.}
\end{table}
\end{document}

cs

"Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting" by Haiwei Chen and Yajie Zhao, arXiv:2403.18186v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|l}
term & meaning \\
\hline \hline
$\Delta $ & bound on message delay \\
 $\mathtt{I} $ & a protocol instance \\
 $m$ & a message \\
 $M $ & a set of messages \\
 $\mathcal{M}$ & the set of all possible sets of messages \\
 $\mathtt{O}$ & a permitter oracle \\
$p$ & a processor \\
$P$ & a permission set \\
$\mathtt{P}$ & a permissionless protocol \\
$R$ & a request set \\
$\mathcal{R}$ & the resource pool \\
$\mathtt{S}$ & a state transition diagram \\
$t$ & a timeslot \\
$(t,M,A)$ & a request in the timed setting \\
$\mathtt{T} $ & a timing rule \\
$(M,A)$ & a request in the untimed setting \\
$\mathcal{U}$ & the set of all identifiers \\
$\mathtt{U}_p$ & the identifier for $p$ \\
\end{tabular}
\caption{Some commonly used variables and terms.}
\end{table}
\end{document}

cs

"Byzantine Generals in the Permissionless Setting" by Andrew Lewis-Pye and Tim Roughgarden, arXiv:2101.07095v9, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 model & subcritical case~$s-\frac dp\le 0$ & supercritical case~$s-\frac d p>0$ \\
 \bigskip
 \textbf{I} & $q>p+\alpha$ & $\frac{q}{q-1}\bigg(1-\frac{d+\alpha}{q} \bigg) >\frac{p}{p-1}\bigg(1-\frac{d}{p}\bigg)$\\
 \bigskip
 \textbf{II} &$q>sp+\alpha$ & $\frac{q}{q-1}\bigg(1-\frac{d+\alpha}{q} \bigg)>\frac{p}{p-1}\bigg(s-\frac{d}{p}\bigg)$ \\
 \bigskip
 \textbf{III} & $tq>s+\alpha$ & $\frac{q}{q-1}\bigg(t-\frac{d+\alpha}{q} \bigg)>\frac{p}{p-1}\bigg(1-\frac{d}{p}\bigg)$ \\
 \bigskip
 \textbf{IV} & $tq>sp+\alpha$ & $\frac{q}{q-1}\bigg(t-\frac{d+\alpha}{q} \bigg)>\frac{p}{p-1}\bigg(s-\frac{d}{p}\bigg)$ \\
 \end{tabular}
\caption{Energy gap parameters for different models}
\end{table}
\end{document}

math

"Nonlocal and mixed models with Lavrentiev Gap" by Anna Balci, arXiv:2312.05604v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Verónica Álvarez
Santiago Mazuelas
Jose A. Lozano

"Supervised Learning with Evolving Tasks and Performance Guarantees" by Verónica Álvarez, Santiago Mazuelas, and Jose A. Lozano, arXiv:2501.05089v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Bitcoin versus S&P 500 Index: Return and Risk Analysis" by A. H. Nzokem, arXiv:2310.02436v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccc}
 \toprule
 Problem & CPU Dual [s] & CPU PyROS [s] & CPU RsBB [s] & Cuts PyROS & Cuts RsBB \\
 \midrule
 haverly1 & 0.4 & 5.5 & 4.4 & 2 & 2 \\
 foulds2 & 0.3 & 20.0 & 4.2 & 5 & 8 \\
 adhya4 & 0.7 & 164.2 & 73.9 & 9 & 21 \\
 bental5 & 0.3 & 205.7 & 30.2 & 17 & 37 \\
 \bottomrule
 \end{tabular}
\caption{Computational time and total cuts used to solve different pooling problems under box uncertainty set of size $\Psi=0.05$}
\end{table}
\end{document}

math

Asimina Marousi
Vassilis M. Charitopoulos

"Global and Robust Optimisation for Non-Convex Quadratic Programs" by Asimina Marousi and Vassilis M. Charitopoulos, arXiv:2503.07310v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"The Impact of Move Schemes on Simulated Annealing Performance" by Ruichen Xu, Haochun Wang, and Yuefan Deng, arXiv:2504.17949v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Celia Rubio-Madrigal
Adarsh Jamadandi
Rebekka Burkholz

"GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring" by Celia Rubio-Madrigal, Adarsh Jamadandi, and Rebekka Burkholz, arXiv:2502.04891v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Financing Costs, Per-Shipment Costs and Shipping Frequency: Firm-Level Evidence from Bangladesh" by Md Deluair Hossen, arXiv:2303.04223v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Aryo Pradipta Gema
Giwon Hong
Pasquale Minervini
Luke Daines
Beatrice Alex

"Edinburgh Clinical NLP at SemEval-2024 Task 2: Fine-tune your model unless you have access to GPT-4" by Aryo Pradipta Gema, Giwon Hong, Pasquale Minervini, Luke Daines, and Beatrice Alex, arXiv:2404.00484v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{12 selected locations with the corresponding results of monthly SSTA and MHW forecasts (Part 2).}
\begin{tabular}{ll}
\textbf{Location} & \textbf{Two lead months} \\ \hline
\multirow{3}{*}{BOP} & MSE, WMSE, FR \\
 & SWMSE1, SWMSE2 \\
 & \\ \hline
\multirow{3}{*}{BP} & MSE, Huber, WMSE, FR, SWMSE1, SWMSE2 \\
 & BMSE, SWMSE1, SWMSE3 \\
 & BMSE, SWMSE1, SWMSE2 \\ \hline
\multirow{3}{*}{CI} & MSE, Huber, WMSE, FR, SWMSE1, SWMSE2 \\
 & Huber, BMSE, SWMSE1, SWMSE2, SWMSE3 \\
 & BMSE, SWMSE1, SWMSE2, SWMSE3 \\ \hline
\multirow{3}{*}{CR} & MSE, WMSE, FR \\
 & \\
 & \\ \hline
\multirow{3}{*}{CS} & MSE, WMSE, FR \\
 & \\
 & \\ \hline
\multirow{3}{*}{F} & MSE, WMSE, FR \\
 & SWMSE2 \\
 & BMSE, SWMSE1, SWMSE2 \\ \hline
\multirow{3}{*}{HG} & MSE, Huber, WMSE, FR \\
 & \\
 & SWMSE1, SWMSE2 \\ \hline
\multirow{3}{*}{OP} & MSE, Huber, WMSE, FR, SWMSE1 \\
 & BMSE, SWMSE1, SWMSE2, SWMSE3 \\
 & MSE, Huber, WMSE, FR, BMSE, SWMSE1, SWMSE2, SWMSE3 \\ \hline
\multirow{3}{*}{R} & MSE, WMSE, FR \\
 & \\
 & \\ \hline
\multirow{3}{*}{SI} & MSE, Huber, WMSE, FR \\
 & BMSE, SWMSE2 \\
 & BMSE, SWMSE1, SWMSE2 \\ \hline
\multirow{3}{*}{T} & MSE, WMSE, FR \\
 & BMSE, SWMSE1 \\
 & BMSE, SWMSE1, SWMSE2, SWMSE3 \\ \hline
\multirow{3}{*}{W} & MSE, Huber, WMSE, FR, SWMSE1 \\
 & \\
 & SWMSE2
\end{tabular}
\end{table}
\end{document}

stat

Ding Ning
Varvara Vetrova
Sébastien Delaux
Rachael Tappenden
Karin R. Bryan
Yun Sing Koh

"A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models" by Ding Ning, Varvara Vetrova, Sébastien Delaux, Rachael Tappenden, Karin R. Bryan, and Yun Sing Koh, arXiv:2502.13495v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c}
\toprule
 & \textbf{Min} & \textbf{Max} & \textbf{Average} & \textbf{Standard deviation} \\
\midrule
Interval (in days) & 0 & 6887 & 448.19 & 764.87\\
\bottomrule
\end{tabular}
\caption{Statistics of CTPA scans intervals across all patients}
\end{table}
\end{document}

eess

Shih-Cheng Huang
Zepeng Huo
Ethan Steinberg
Chia-Chun Chiang
Matthew P. Lungren
Curtis P. Langlotz
Serena Yeung
Nigam H. Shah
Jason A. Fries

"INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis" by Shih-Cheng Huang, Zepeng Huo, Ethan Steinberg, Chia-Chun Chiang, Matthew P. Lungren, Curtis P. Langlotz, Serena Yeung, Nigam H. Shah, and Jason A. Fries, arXiv:2311.10798v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
 \toprule
 \bfseries Model & \bfseries RMSE\\
 \midrule
 Disfluency & 5.71 $\pm$ 0.39\\
 Acoustic & 6.66 $\pm$ 0.30\\
 Interventions & 6.41 $\pm$ 0.53\\
 Vanilla Ensemble & 5.17 $\pm$ 0.27\\
 UA Ensemble & 5.05 $\pm$ 0.53\\
 UA Ensemble (weighted) & \textbf{4.96 $\pm$ 0.49}\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Utkarsh Sarawgi
Wazeer Zulfikar
Rishab Khincha
Pattie Maes

"Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia" by Utkarsh Sarawgi, Wazeer Zulfikar, Rishab Khincha, and Pattie Maes, arXiv:2010.01440v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Xiaohong Chen
Zengjing Chen
Wayne Yuan Gao
Xiaodong Yan
Guodong Zhang

"Optimization via Strategic Law of Large Numbers" by Xiaohong Chen, Zengjing Chen, Wayne Yuan Gao, Xiaodong Yan, and Guodong Zhang, arXiv:2412.05604v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|cc}
 \toprule
 \small Group & \small \textbf{Number of GTs} & \small \textbf{Number of queries} \\
 \midrule
 \small Group 1 & \small 990k (48.4\%)& \small 146 (48.7\%)\\
 \small Group 2 & \small 398k (19.5\%)& \small 58 (19.3\%)\\
 \small Group 3 & \small 299k (14.6\%)& \small 43 (14.3\%)\\
 \small Group 4 & \small 173k (8.5\%)& \small 25 (8.3\%)\\
 \small Group 5 & \small 186k (9.1\%)& \small 28 (9.3\%)\\
 \bottomrule
 \end{tabular}
\caption{\textbf{Statistics of query groups when $N_g$ = 5.}}
\end{table}
\end{document}

cs

Jongha Kim
Jihwan Park
Jinyoung Park
Jinyoung Kim
Sehyung Kim
Hyunwoo J. Kim

"Groupwise Query Specialization and Quality-Aware Multi-Assignment for Transformer-based Visual Relationship Detection" by Jongha Kim, Jihwan Park, Jinyoung Park, Jinyoung Kim, Sehyung Kim, and Hyunwoo J. Kim, arXiv:2403.17709v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of Classification Methods: The Accuracy@patient refers to the accuracy of a single patient and is derived from the mean value of multiple cross-validation experiments. This metric enables a comprehensive understanding of the performance of various classifications.}
\begin{tabular}{|c|c|}
\hline
Method & Accuracy@patient (\%)\\
\hline
GCS only & 46.25 \\
baseline & 76.67\\
{\bfseries GCS-ICHNet (ours)} & {\bfseries85.70}\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Xuhao Shan
Xinyang Li
Ruiquan Ge
Shibin Wu
Ahmed Elazab
Jichao Zhu
Lingyan Zhang
Gangyong Jia
Qingying Xiao
Xiang Wan
Changmiao Wang

"GCS-ICHNet: Assessment of Intracerebral Hemorrhage Prognosis using Self-Attention with Domain Knowledge Integration" by Xuhao Shan, Xinyang Li, Ruiquan Ge, Shibin Wu, Ahmed Elazab, Jichao Zhu, Lingyan Zhang, Gangyong Jia, Qingying Xiao, Xiang Wan, and Changmiao Wang, arXiv:2311.04772v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Data set Description for S\&P 500 and BTCUSDT}
\begin{tabular}{lclllr}
\toprule
Asset & Time Frame & From & To & RV Points \\
\midrule
S\&P 500 & 1 minute & 10.03.07 & 01.03.22 & 3,800 \\
BTCUSDT & 1 minute & 01.01.13 & 20.04.20 & 2,667 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Introducing the $σ$-Cell: Unifying GARCH, Stochastic Fluctuations and Evolving Mechanisms in RNN-based Volatility Forecasting" by German Rodikov and Nino Antulov-Fantulin, arXiv:2309.01565v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Scalar Field Static Spherically Symmetric Solutions in Teleparallel $F(T)$ Gravity" by Alexandre Landry, arXiv:2503.14465v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lll}
 & 16-bit divider (ms) & 32-bit divider (ms)\\
\hline
Single iteration & 769.063 & 1308.67\\
Total latency, including overhead & 11659.5 & 38256.5
\end{tabular}
\caption{Unsigned division latency}
\end{table}
\end{document}

cs

"CryptoEmu: An Instruction Set Emulator for Computation Over Ciphers" by Xiaoyang Gong and Dan Negrut, arXiv:2101.03403v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 \toprule
 & Sensitivity & C.I. (95\%)\\
 \midrule
 Radiologist 1	& 83.9\% & [71.8\% -- 91.9\%]\\
 Radiologist 2 & 87.1\% & [75.6\% -- 94.3\%]\\
 Radiologist 3 & 80.6\% & [68.2\% -- 89.5\%]\\
 \midrule
 AI Model without lung segmentation &	83.9\% & [71.8\% -- 91.9\%]\\
 AI Model with lung segmentation	& \textbf{90.3}\% &\textbf{ [79.5\% -- 96.5\%]}\\
 \bottomrule
 \hline

 \end{tabular}
\caption{Sensitivity (together with 95\% confidence interval) comparison between manual readings of expert radiologists and the AI model for COVID-19 detection without lung segmentation and AI model with segmentation.}
\end{table}
\end{document}

eess

Matteo Pennisi
Isaak Kavasidis
Concetto Spampinato
Vincenzo Schininà
Simone Palazzo
Francesco Rundo
Massimo Cristofaro
Paolo Campioni
Elisa Pianura
Federica Di Stefano
Ada Petrone
Fabrizio Albarello
Giuseppe Ippolito
Salvatore Cuzzocrea
Sabrina Conoci

"An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans" by Matteo Pennisi, Isaak Kavasidis, Concetto Spampinato, Vincenzo Schininà, Simone Palazzo, Francesco Rundo, Massimo Cristofaro, Paolo Campioni, Elisa Pianura, Federica Di Stefano, Ada Petrone, Fabrizio Albarello, Giuseppe Ippolito, Salvatore Cuzzocrea, and Sabrina Conoci, arXiv:2101.11943v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Isotrivial Lagrangian fibrations of compact hyper-Kähler manifolds" by Yoon-Joo Kim, Radu Laza, and Olivier Martin, arXiv:2312.15147v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Krunoslav Lehman Pavasovic
David Lopez-Paz
Giulio Biroli
Levent Sagun

"A Differentiable Rank-Based Objective For Better Feature Learning" by Krunoslav Lehman Pavasovic, David Lopez-Paz, Giulio Biroli, and Levent Sagun, arXiv:2502.09445v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Classification Results}
\begin{tabular}{ccc}
\toprule
\multirow{2}{*}{Network Architecture} & \multicolumn{2}{c}{Accuracy}\tabularnewline
\cmidrule{2-3} \cmidrule{3-3}
 & Top-1 & \textpm{} 0.1mm\tabularnewline
\midrule
ResNet1Dv1-14 & 87.88\% & 95.14\%\tabularnewline
ResNet1Dv2-14 & 83.21\% & 93.01\%\tabularnewline
ResNeXt1D-14 & 86.65\% & 94.51\%\tabularnewline
ResNet1Dv1-26 & 92.50\% & 96.90\%\tabularnewline
ResNet1Dv2-26 & 91.85\% & 96.06\%\tabularnewline
ResNeXt1D-26 & 93.15\% & 96.78\%\tabularnewline
ResNet1Dv1-14 (Wider) & 90.42\% & 95.88\%\tabularnewline
ResNet1Dv2-14 (Wider) & 89.93\% & 95.65\%\tabularnewline
ResNeXt1D-14 (Wider 1) & 91.31\% & 96.23\%\tabularnewline
ResNeXt1D-14 (Wider 2) & 89.38\% & 95.00\%\tabularnewline
\textbf{ResNeXt1D-38} & \textbf{93.58\%} & \textbf{97.20\%}\tabularnewline
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Tian Meng
Yang Tao
Ziqi Chen
Jorge R. Salas Avila
Qiaoye Ran
Yuchun Shao
Ruochen Huang
Yuedong Xie
Qian Zhao
Zhijie Zhang
Hujun Yin
Anthony J. Peyton
Wuliang Yin

"Depth Evaluation for Metal Surface Defects by Eddy Current Testing using Deep Residual Convolutional Neural Networks" by Tian Meng, Yang Tao, Ziqi Chen, Jorge R. Salas Avila, Qiaoye Ran, Yuchun Shao, Ruochen Huang, Yuedong Xie, Qian Zhao, Zhijie Zhang, Hujun Yin, Anthony J. Peyton, and Wuliang Yin, arXiv:2104.02472v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|l|}
\hline
\textbf{Index} & \textbf{Feature Name} \\
\hline
0 & months\_as\_customer \\
1 & age \\
2 & policy\_number \\
3 & policy\_bind\_date \\
4 & policy\_state \\
5 & policy\_csl \\
6 & policy\_deductable \\
7 & policy\_annual\_premium \\
8 & umbrella\_limit \\
9 & insured\_zip \\
10 & insured\_sex \\
11 & insured\_education\_level \\
12 & insured\_occupation \\
13 & insured\_hobbies \\
14 & insured\_relationship \\
15 & capital-gains \\
16 & capital-loss \\
17 & incident\_date \\
18 & incident\_type \\
19 & collision\_type \\
20 & incident\_severity \\
21 & authorities\_contacted \\
22 & incident\_state \\
23 & incident\_city \\
24 & incident\_location \\
25 & incident\_hour\_of\_the\_day \\
26 & number\_of\_vehicles\_involved \\
27 & property\_damage \\
28 & bodily\_injuries \\
29 & witnesses \\
30 & police\_report\_available \\
31 & total\_claim\_amount \\
32 & injury\_claim \\
33 & property\_claim \\
34 & vehicle\_claim \\
35 & auto\_make \\
36 & auto\_model \\
37 & auto\_year \\
38 & fraud\_reported \\
\hline
\end{tabular}
\caption{Feature Index Table of the auto fraud detection dataset}
\end{table}
\end{document}

q-fin

"An Enhanced Focal Loss Function to Mitigate Class Imbalance in Auto Insurance Fraud Detection with Explainable AI" by Francis Boabang and Samuel Asante Gyamerah, arXiv:2508.02283v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Details of CASAS datasets used for experimental evaluation.}
\begin{tabular}{l|l|l|l|l|l}
 \toprule\textbf{Properties} & \textbf{Aruba} & \textbf{Cairo} & \textbf{Kyoto7} & \textbf{Kyoto8} & \textbf{Milan} \\
 \midrule
 Residents & {1} & {2+pet} & {2} & {2} & {1+pet} \\
 Number of sensors & {39} & {27} & {58} & {61} & {33} \\
 Number of activities & {12} & {13} & {13} & {12} & {16} \\
 Number of days & {219} & {56} & {46} & {58} & {82} \\
 \end{tabular}
\end{table}
\end{document}

eess

"Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes" by Srivatsa P and Thomas Plötz, arXiv:2311.09514v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
\toprule
UniPLDA Baseln & Oracle Speaker Type & Same Speaker \\
\hline
39.90 (-0.2) & \textbf{28.20} (0.0) & 40.26 (-)\\
\bottomrule
\end{tabular}
\caption{DER(\%) (threshold) comparison between baseline and oracle speaker type}
\end{table}
\end{document}

eess

Jiamin Xie
Suzanna Sia
Paola Garcia
Daniel Povey
Sanjeev Khudanpur

"Mixture of Speaker-type PLDAs for Children's Speech Diarization" by Jiamin Xie, Suzanna Sia, Paola Garcia, Daniel Povey, and Sanjeev Khudanpur, arXiv:2008.13213v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The number of true positive change points detected by the $l_1$-based change point detection method}
\begin{tabular}{lccc}
\toprule
 $l_1$-based true positive & Maximum number of change points & 95\% percentile (5) & 2 change points \\ \midrule
Change Point 20 & 718 & 604 & 420 \\
Change Point 50 & 748 & 641 & 443 \\ \midrule
Average FDR & 0.80 & 0.72 & 0.56 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Multiple change point detection based on Hodrick-Prescott and $l_1$ filtering method for random walk time series data" by Xiyuan Liu, arXiv:2501.11805v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems" by Jianhong Chen and Shihao Yang, arXiv:2501.14107v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Transfer Learning through Enhanced Sufficient Representation: Enriching Source Domain Knowledge with Target Data" by Yeheng Ge, Xueyu Zhou, and Jian Huang, arXiv:2502.20414v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
\toprule
Vessel Labeling & Accuracy(\%) & OV(\%) & OF(\%) & OT(\%) \\
\hline
PointNet~ & 92.2 & 96.1 & 84.5 & 96.6\\
PointNet++~ & 95.1 & 96.5 & \textbf{85.8} & 97.0\\
PointNet++(GAG) & 97.0 & \textbf{97.2} & 85.7 & \textbf{97.6}\\
DGCNN~ & 96.5 & 96.6 & 84.4 & 97.1 \\
DGCNN(GAG) & \textbf{97.2} & 97.0 & 84.9 & 97.4\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Jiafa He
Chengwei Pan
Can Yang
Ming Zhang
Yang Wang
Xiaowei Zhou
Yizhou Yu

"Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction" by Jiafa He, Chengwei Pan, Can Yang, Ming Zhang, Yang Wang, Xiaowei Zhou, and Yizhou Yu, arXiv:2012.07262v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccc}
\hline \textbf{Task} & \textbf{Precision} & \textbf{Recall} & \textbf{F1} \\ \hline
POS & 81.74 & 79.78 & 80.75 \\
NER & 74.15 & 60.91 & 66.88 \\
\hline
\end{tabular}
\caption{ Evaluation results}
\end{table}
\end{document}

cs

"BNLP: Natural language processing toolkit for Bengali language" by Sagor Sarker, arXiv:2102.00405v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textsc{Classification results of the human voice}}
\begin{tabular}{|c|c|c|c|}
 \hline
 \multirow{2}{*}{} & \multicolumn{3}{c|}{Test Accuracy} \\
 \cline{2-4}
 & Min & Max & Mean \\
 \hline
 Human Voice Classification Architecture & 96.65 & 98.41 & 97.12 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

"Talent-Interview: Web-Client Cheating Detection for Online Exams" by Mert Ege and Mustafa Ceyhan, arXiv:2312.00795v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Accuracy of the ME-AID model (M6) on test subsets based on the length of utterance. From Table~, overall the ME-AID performance is 87.4\%.}
\begin{tabular}{cccccc}
\hline\hline
 Model&1-2 words&3-10 words&11-20 words& 21-200 words\\
 &1929 utts&2238 utts&1456 utts&896 utts\\
\hline
M6&80.6\% & 87.8\% & 91.7\% & 94.2\% \\
\hline\hline
\end{tabular}
\end{table}
\end{document}

eess

"Advanced accent/dialect identification and accentedness assessment with multi-embedding models and automatic speech recognition" by Shahram Ghorbani and John H. L. Hansen, arXiv:2310.11004v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc}
\toprule Analytic optimal solution & Our method \\
\midrule 417.4 $\pm$ 0.2 & 416.8 $\pm$ 0.2 \\
\bottomrule% &
\end{tabular}
\end{table}
\end{document}

math

"Singular Control of (Reflected) Brownian Motion: A Computational Method Suitable for Queueing Applications" by Baris Ata, J. Michael Harrison, and Nian Si, arXiv:2312.11823v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Adam Mate
Arthur K. Barnes
Russell W. Bent
Eduardo Cotilla-Sanchez

"Analyzing and Mitigating the Impacts of GMD and EMP Events on the Electrical Grid with PowerModelsGMD.jl" by Adam Mate, Arthur K. Barnes, Russell W. Bent, and Eduardo Cotilla-Sanchez, arXiv:2101.05042v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics on GazeVQA and Japanese VQA (VQA-ja)~}
\begin{tabular}{lrr}
 \toprule
 & GazeVQA & VQA-ja \\
 \cmidrule(lr){0-2}
 Images & 10,760 & 99,208 \\
 Question and answers & 17,276 & 793,664 \\
 Unique questions & 8,628 & 358,844 \\
 Unique answers & 5,853 & 135,743 \\
 \cmidrule(lr){0-2}
 Avg. question length & 15.37 & 14.82 \\
 Avg. answer length & 4.92 & 4.56 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Shun Inadumi
Seiya Kawano
Akishige Yuguchi
Yasutomo Kawanishi
Koichiro Yoshino

"A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions" by Shun Inadumi, Seiya Kawano, Akishige Yuguchi, Yasutomo Kawanishi, and Koichiro Yoshino, arXiv:2403.17545v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

M. A. Seoud
A. Elsonbaty
A. Nasr
M. Anwar

"Some Necessary and Sufficient Conditions for Diophantine Graphs" by M. A. Seoud, A. Elsonbaty, A. Nasr, and M. Anwar, arXiv:2412.20562v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison across different number of nodes. Results show classification accuracy (\%) on original and $K$-hop similar graphs, and level of disagreement (\%). All metrics are reported as mean $\pm$ standard deviation over $10$ runs.}
\begin{tabular}{r|ccc}
\toprule
\textbf{Nodes} & \textbf{Original Graph Acc.} & \textbf{K-Hop Graph Acc.} & \textbf{Disagreement} \\
\midrule
50 & 94.00 $\pm$ 18.00 & 94.00 $\pm$ 18.00 & 0.20 $\pm$ 0.60 \\
100 & 100.00 $\pm$ 0.00 & 100.00 $\pm$ 0.00 & 0.00 $\pm$ 0.00 \\
500 & 92.40 $\pm$ 3.35 & 92.20 $\pm$ 2.56 & 0.32 $\pm$ 0.39 \\
1000 & 98.45 $\pm$ 2.81 & 97.75 $\pm$ 3.24 & 0.69 $\pm$ 1.06 \\
1500 & 99.97 $\pm$ 0.10 & 99.97 $\pm$ 0.10 & 0.06 $\pm$ 0.18 \\
2000 & 98.65 $\pm$ 2.64 & 98.67 $\pm$ 2.65 & 0.02 $\pm$ 0.03 \\
2500 & 99.26 $\pm$ 2.22 & 99.26 $\pm$ 2.22 & 0.00 $\pm$ 0.00 \\
3000 & 100.00 $\pm$ 0.00 & 100.00 $\pm$ 0.00 & 0.00 $\pm$ 0.00 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Amine Mohamed Aboussalah
Abdessalam Ed-dib

"Are GNNs doomed by the topology of their input graph?" by Amine Mohamed Aboussalah and Abdessalam Ed-dib, arXiv:2502.17739v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccc}
\toprule
 & THUIR2016 & THU-KDD & THUIR2018 \\
\midrule
 \texttt{is\_clicked} & 0.363* & 0.217* & 0.879***\\
 \texttt{duration} & 1.916 & 1.913 & 51.521*** \\
 \texttt{usefulness} & 0.136 ** & 0.156 ** & 0.358*** \\
 \midrule
 \# Observations & 2123 & 3598 & 767\\
\bottomrule
\end{tabular}
\caption{The regression coefficient ($\alpha$) of the independent variable \texttt{has\_decoy} with the dependent variables \texttt{is\_clicked}, \texttt{duration}, and \texttt{usefulness} on THUIR2016, THU-KDD and THUIR2018. *, ** and *** respectively indicate \( p < 0.05 \), \( p < 0.01 \), and \( p < 0.001 \).}
\end{table}
\end{document}

cs

Nuo Chen
Jiqun Liu
Hanpei Fang
Yuankai Luo
Tetsuya Sakai
Xiao-Ming Wu

"Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability" by Nuo Chen, Jiqun Liu, Hanpei Fang, Yuankai Luo, Tetsuya Sakai, and Xiao-Ming Wu, arXiv:2403.18462v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{List of Components (serial No. 1- 25) indicated in Figs }
\begin{tabular}{|l|l|l|l|l|}
\hline
	ITEM & No. of QTY & Product type/Specification & Material & Description \\ \hline
	1 & 1 & Base-6mm & Polycarbonate, Clear & Laser Cut (6mm) \\ \hline
	2 & 6 & 130778 QSM-M5-4-100 & & QSM-push-in fitting \\ \hline
	3 & 3 & S104001 & & Air bearing \\ \hline
	4 & 3 & S8013B11 & Stainless Steel & Air bearing bolt \\ \hline
	5 & 3 & S8013H04-NuT & Stainless Steel & Air bearing Hex nut \\ \hline
	6 & 3 & S8013H04-ScrewNut & Brass, Soft Yellow & Air bearing housing \\ \hline
	7 & 1 & Low-top-platform-mount-front-V4-1st part & PLA & 3D print \\ \hline
	8 & 1 & Name-plate & PLA & 3D print \\ \hline
	9 & 3 & Leg-washer & PLA & 3D print \\ \hline
	10 & 2 & Low-top-platform-mount-v2 & PLA & 3D print \\ \hline
	11 & 3 & LM2596 DC-DC StepDown Converter v1 & & Step-down voltage regulator \\ \hline
	12 & 8 & 4573 MFH-2-M5 & & MFH-Solenoid valve \\ \hline
	13 & 8 & 320410 MSFG-12-OD---(P) & & MSFG-p-Solenoid coil \\ \hline
	14 & 2 & Valve-holder-v3 & PLA & 3D print \\ \hline
	15 & 6 & 153333 QSML-M5-4 & & QSML-Push-in L-fitting \\ \hline
	16 & 8 & Festo-connector-cover & PLA & 3D print \\ \hline
	17 & 8 & Nozzle-SLA-base & Resin "Grey pro" & 3D print (SLA) \\ \hline
	18 & 8 & 8030314 NPFC-R-G18-M5-FM & & NPFC-R-Threaded fittings \\ \hline
	19 & 8 & Nozzle-bumper-V2 & TPU 95A & 3D print \\ \hline
	20 & 4 & 153374 QSMY-6-4 & & QSMY-Push-in Y-connector \\ \hline
	21 & 5 & 153129 QST-6 & & QST-Push-in T connector \\ \hline
	22 & 4 & T-piece-clamp & PLA & 3D print \\ \hline
	23 & 4 & Relay-mount & PLA & 3D print \\ \hline
	24 & 4 & Grove 2 Channel SPDT Relay & & Relay \\ \hline
	25 & 2 & 153484 QH-QS-6 & & QH-QS-ball valve \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Avijit Banerjee
Jakub Haluska
Sumeet G. Satpute
Dariusz Kominiak
George Nikolakopoulos

"Slider: On the Design and Modeling of a 2D Floating Satellite Platform" by Avijit Banerjee, Jakub Haluska, Sumeet G. Satpute, Dariusz Kominiak, and George Nikolakopoulos, arXiv:2101.06335v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{rlcc}
 \textbf{System} & \textbf{Component}& \textbf{Threshold} & \textbf{Auxiliary Information} \\
 \hline
 Cardiovascular & Systolic Blood Pressure & $>140$ & Hypertension \\
 & Diastolic Blood Pressure & $>90$ & Hypertension \\
 Metabolic & Body Mass Index & $>30$ & Obesity; Morbid Obesity; \\
 & & & Grade I, II or III Obesity\\
 & Triglycerides & $\geq 150$ &	Hypertriglyceridemia \\
 & Total Cholesterol & $\geq 200$ & Hypercholesterolemia \\
 Inflammation & C-Reactive Protein & $\geq 10$	& Sepsis; Infection; Auto-Immune \\
 & & & Inflammatory Syndrome\\
 & Hemoglobin A1C & $\geq 6.5$	& Diabetes; Impaired \\
 & & & Glycemic Control \\
 & Serum Albumin & $\geq 3.5$ & \textit{(None Given)} \\
 & Creatinine Clearance & $<110$ (Males)& Renal Failure; \\
 & & $<100$ (Females) & Insufficiency; Acute \\
 & & & Kidney Injury; \\
 &&& Chronic Renal Failure
 \\
 & Homocysteine & $>50$ &	Hyperhomocysteinemia; \\
 & & & Vitamin deficiency \\
 \end{tabular}
\caption{Ten component stressors of the allostatic load index were defined by discretizing measurements across three body systems at clinically driven thresholds. If the component was missing from the extracted EHR data, auditors searched for auxiliary information in the patient's medical chart in Epic (the institution's electronic charting software). If the auxiliary information was present, the component was treated as a ``yes'' and included in the patient's validated ALI.}
\end{table}
\end{document}

stat

Sarah C. Lotspeich
Sheetal Kedar
Rabeya Tahir
Aidan D. Keleghan
Amelia Miranda
Stephany N. Duda
Michael P. Bancks
Brian J. Wells
Ashish K. Khanna
Joseph Rigdon

"Overcoming data challenges to measure whole-person health in electronic health records" by Sarah C. Lotspeich, Sheetal Kedar, Rabeya Tahir, Aidan D. Keleghan, Amelia Miranda, Stephany N. Duda, Michael P. Bancks, Brian J. Wells, Ashish K. Khanna, and Joseph Rigdon, arXiv:2502.05380v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary Statistics of LLMs Exposure}
\begin{tabular}{cccc}
 \toprule
 \multicolumn{4}{c}{\textbf{Fine Categories Occupation Level Exposure}} \\
 \midrule
 & \textbf{GLM} & \textbf{InternLM} & \textbf{GPT-4} \\
 \midrule
 count & 1606 & 1606 & 1606 \\
 mean & 0.44 & 0.18 & 0.24 \\
 std & 0.26 & 0.18 & 0.21 \\
 \midrule
 \multicolumn{4}{c}{\textbf{Fine Categories Occupation Level Exposure Corr.}} \\
 \midrule
 & \textbf{GLM} & \textbf{InternLM} & \textbf{GPT-4} \\
\cmidrule{2-4} GLM & 1.0*** & 0.284*** & 0.1915*** \\
 InternLM & 0.284*** & 1.0*** & 0.2887*** \\
 GPT-4 & 0.1915*** & 0.2887*** & 1.0*** \\
 \midrule
 \multicolumn{4}{c}{\textbf{Medium Categories Occupation Level Exposure}} \\
 \midrule
 & \textbf{GLM} & \textbf{InternLM} & \textbf{GPT-4} \\
 \midrule
 count & 63 & 63 & 63 \\
 mean & 0.40 & 0.14 & 0.22 \\
 std & 0.15 & 0.10 & 0.18 \\
 \midrule
 \multicolumn{4}{c}{\textbf{Medium Categories Occupation Level Exposure Corr.}} \\
 \midrule
 & \textbf{GLM} & \textbf{InternLM} & \textbf{GPT-4} \\
\cmidrule{2-4} GLM & 1.0*** & 0.5938*** & 0.306* \\
 InternLM & 0.5938*** & 1.0*** & 0.4807*** \\
 GPT-4 & 0.306* & 0.4807*** & 1.0*** \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

Qin Chen
Jinfeng Ge
Huaqing Xie
Xingcheng Xu
Yanqing Yang

"Large Language Models at Work in China's Labor Market" by Qin Chen, Jinfeng Ge, Huaqing Xie, Xingcheng Xu, and Yanqing Yang, arXiv:2308.08776v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Evaluation on the GLUE STSb task.}
\begin{tabular}{|c|c|}
\hline
Model & Spearman (Pearson) \\ \hline
BERT & 88.58 (88.89) \\ \hline
ALBERT & 90.13 (90.46) \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

Hyunjin Choi
Judong Kim
Seongho Joe
Youngjune Gwon

"Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks" by Hyunjin Choi, Judong Kim, Seongho Joe, and Youngjune Gwon, arXiv:2101.10642v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c}
\textbf{Dataset} & \textbf{Number of Train Samples} & \textbf{Number of Test Samples} \\ \hline
\textbf{KITTI} & \textasciitilde 7480 & \textasciitilde 7520 \\ \hline
\textbf{nuScenes} & 28130 & 6000 \\ \hline
\textbf{Waymo} & \textasciitilde 158100 & \textasciitilde 40000 \\ \hline
\textbf{Laura} & \textasciitilde 7000 & \textasciitilde 3000 \\ \hline
\textbf{LiDAR-CS$^*$} & \textasciitilde 7000 & \textasciitilde 7000 \\
\end{tabular}
\caption{Train-test split for the datasets. *Note, the each LiDAR-CS \textit{subdataset} (CS64, CS32, CS16) contains this number of samples.}
\end{table}
\end{document}

cs

Maciej K Wozniak
Mattias Hansson
Marko Thiel
Patric Jensfelt

"UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps" by Maciej K Wozniak, Mattias Hansson, Marko Thiel, and Patric Jensfelt, arXiv:2403.17633v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Median, Maximum and Minimum of the test loss of the optimizers for the 2\,D Laplace equation.}
\begin{tabular}{lrrr}
\toprule
 & Median & Minimum & Maximum \\
\midrule
ANaGRAM & \textbf{3.85e-13} & \textbf{8.49e-15} & \textbf{1.43e-12} \\
Adam & 3.51e-04 & 2.29e-04 & 4.31e-04 \\
E-NGD & 2.91e-09 & 1.01e-10 & 3.57e-08 \\
GD & 3.42e-02 & 4.32e-03 & 1.76e-01 \\
L-BFGS & 2.37e-03 & 8.91e-04 & 9.09e-03 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning" by Nilo Schwencke and Cyril Furtlehner, arXiv:2412.10782v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|c|c|c|}
\hline
\textbf{Policy} & \textbf{Mean Correct Rate} & \textbf{SD Correct Rate} & \textbf{Mean outcome} & \textbf{SD outcome} & \textbf{WAPTS Superior Count} \\
\hline
TS & 0.114 & 0.076 & 0.512 & 0.018 & 102 \\
\hline
UR & 0.042 & 0.006 & 0.503 & 0.015 & 131 \\
\hline
WAPTS & 0.129 & 0.082 & 0.512 & 0.017 & -- \\
\hline
\end{tabular}
\caption{Comparison of policies: Mean and standard deviation (SD) of correct rates and outcomes for experiments with \(N = 1000\) participants and \(K = 24\) treatments under \textbf{Scenario 2}. The final column indicates the number of simulations (out of 200) in which WAPTS outperformed the respective policy.}
\end{table}
\end{document}

stat

Haochen Song
Ilya Musabirov
Ananya Bhattacharjee
Audrey Durand
Meredith Franklin
Anna Rafferty
Joseph Jay Williams

"Adaptive Experiments Under High-Dimensional and Data Sparse Settings: Applications for Educational Platforms" by Haochen Song, Ilya Musabirov, Ananya Bhattacharjee, Audrey Durand, Meredith Franklin, Anna Rafferty, and Joseph Jay Williams, arXiv:2501.03999v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overview of data sources for marginal distributions by country}
\begin{tabular}{lccc}
\hline
 & Germany & Italy & Switzerland \\
\hline
Population and deaths by age and gender & & & \\
Population by smoker and gender & & & \\
Population by state & & & \\
Hazard rates smokers vs. non-smokers & -- & & \\
Base mortality rates (general population) & & & \\
Base mortality rates (insured population) & & & -- \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Mortality simulations for insured and general populations" by Asmik Nalmpatian and Christian Heumann, arXiv:2502.08814v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{GUSTO data. Most and least discriminatory questions,identified by the posterior distribution of the parameters $\alpha_j$, for $j = 1, \dots, J$. The top half of the Table refers to the five most discriminatory questions, while the lowest half to the least discriminatory ones. The corresponding posterior item-characteristic curves are reported in Supplementary Figures and .}
\begin{tabular}{l|ccl}
 Question \# & main subscale & subscale & Question text\\ \hline
 Q14 & FAp & FR & \textit{If allowed to, my child would eat too much}\\
 Q24 & FAv & FF & \textit{My child is difficult to please with meals}\\
 Q12 & FAp & FR & \textit{My child is always asking for food}\\
 Q19 & FAp & FR & \textit{Given the choice, my child would} \\
 & & & \textit{eat most of the time}\\
 Q22 & FAp & EF & \textit{My child enjoys eating} \\ \hline
 Q11 & FAv & EUE & \textit{My child eats less when s/he is tired}\\
 Q6 & FAp & DD & \textit{My child is always asking for a drink}\\
 Q29 & FAp & DD & \textit{If given the chance, my child would drink} \\
 & & & \textit{continuously throughout the day}\\
 Q31 & FAp & DD & \textit{If given the chance, my child would} \\
 & & & \textit{always be having a drink}\\
 Q23 & FAv & EUE & \textit{My child eats more when she is happy}
 \end{tabular}
\end{table}
\end{document}

stat

Andrea Cremaschi
Beatrice Franzolini
Maria De Iorio
Mary Chong
Toh Jia Ying
Navin Michael
Varsha Gupta
Fabian Yap
Yung Seng Lee
Johan Erikkson
Anna Fogel

"A Bayesian semi-parametric model for longitudinal growth and appetite phenotypes in children" by Andrea Cremaschi, Beatrice Franzolini, Maria De Iorio, Mary Chong, Toh Jia Ying, Navin Michael, Varsha Gupta, Fabian Yap, Yung Seng Lee, Johan Erikkson, and Anna Fogel, arXiv:2501.17040v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small\textit{Errors of Example 2 with boundary correction for suitable $m$ on ring domain.}}
\begin{tabular}{c|c|c|c|c|c|c}
 \hline
 \multirow{2}{*}{$h$}&\multicolumn{2}{c|}{$k=1,m=0$}&\multicolumn{2}{c|}{$k=2,m=1$}&\multicolumn{2}{c}{$k=3,m=1$} \\
 \cline{2-7}
 & $E_{u,p}$ & order & $E_{u,p}$ & order & $E_{u,p}$ & order\\
 \hline
 1/8 & 1.36e+01 & -- & 1.66e-00& -- &1.50e-01&--\\
 \hline
 1/16& 6.86e+00 & 0.99& 4.20e-01& 1.98&1.89e-02&2.99\\
 \hline
 1/32& 3.44e+00 & 1.00& 1.05e-01& 2.00&2.37e-03&3.00\\
 \hline
 1/64& 1.72e+00 & 1.00& 2.64e-02& 2.00&2.96e-04&3.00\\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

"An arbitrary order mixed finite element method with boundary value correction for the Darcy flow on curved domains" by Yongli Hou and Yanqiu Wang, arXiv:2412.19411v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison between various GNN base recommenders. The best performance under each setting is indicated with bold font.}
\begin{tabular}{l|cc|cc|cc}
\toprule
 & \multicolumn{2}{c|}{Gowalla} & \multicolumn{2}{c|}{Yelp2020} & \multicolumn{2}{c}{Amazon-book} \\ \hline
Setting & N@20 & R@20 & N@20 & R@20 & N@20 & R@20 \\ \hline
LightGCN - LEGCF & \textbf{0.0988} & \textbf{0.1444} & \textbf{0.0291} & \textbf{0.0548} & \textbf{0.0172} & \textbf{0.0259} \\
LightGCN - UD & 0.0584 & 0.0863 & 0.0169 & 0.0321 & 0.0074 & 0.0110 \\ \hline
NGCF - LEGCF & \textbf{0.0797} & \textbf{0.1092} & \textbf{0.0328} & \textbf{0.0602} & \textbf{0.0130} & \textbf{0.0204} \\
NGCF - UD & 0.0547 & 0.0810 & 0.0115 & 0.0230 & 0.0062 & 0.0096 \\ \hline
LR-GCCF - LEGCF & \textbf{0.0716} & \textbf{0.1052} & \textbf{0.0157} & \textbf{0.0272} & \textbf{0.0126} & \textbf{0.0192} \\
LR-GCCF - UD & 0.0001 & 0.0002 & 0.0002 & 0.0005 & 0.0006 & 0.0010 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Xurong Liang
Tong Chen
Lizhen Cui
Yang Wang
Meng Wang
Hongzhi Yin

"Lightweight Embeddings for Graph Collaborative Filtering" by Xurong Liang, Tong Chen, Lizhen Cui, Yang Wang, Meng Wang, and Hongzhi Yin, arXiv:2403.18479v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Sania Eskandari
Janet Lumpp
Luis Sanchez Giraldo

"Skin Lesion Segmentation Improved by Transformer-based Networks with Inter-scale Dependency Modeling" by Sania Eskandari, Janet Lumpp, and Luis Sanchez Giraldo, arXiv:2310.13604v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\footnotesize \textbf{Results of prediction performance of ANOVA-TPNN with and without the monotone constraint.}}
\begin{tabular}{c|c|c|c}
\hline
 & Measure &ANOVA-T$^{1}$PNN & ANOVA-T$^{2}$PNN \\ \hline \hline
Without Monotone constraint & Accuracy $\uparrow$ & 0.985 (0.001) & 0.986 (0.001) \\ \hline
With Monotone constraint & Accuracy $\uparrow$ & 0.984 (0.001) & 0.985 (0.001) \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Seokhun Park
Insung Kong
Yongchan Choi
Chanmoo Park
Yongdai Kim

"Tensor Product Neural Networks for Functional ANOVA Model" by Seokhun Park, Insung Kong, Yongchan Choi, Chanmoo Park, and Yongdai Kim, arXiv:2502.15215v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyper-parameter settings for our model.}
\begin{tabular}{lr}
 \toprule
 Hyper-parameter & Value \\
 \midrule
 Learning rate schedule & one cycle \\
 Min learning Rate & $3 \times 10^{-5}$\\
 Max learning Rage & $5 \times 10^{-4}$ \\
 Batch Size & $32$\\
 Optimizer & AdamW~\\
 $\beta_s$ in optimizer & ($0.9$, $0.999$) \\
 Weight Decay & $0.1$\\
 Layer Decay Rate & $0.9$ \\
 Embedding Dimension & $192$ \\
 Variance focus in SiLog loss & $0.85$ \\
 ViT Size & ViT-base \\
 Number of learnable emb. & 100 \\
 epochs & 25 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Suraj Patni
Aradhye Agarwal
Chetan Arora

"ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation" by Suraj Patni, Aradhye Agarwal, and Chetan Arora, arXiv:2403.18807v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of TLT references}
\begin{tabular}{llrrrrr}
\hline
\hline
Method & N & ICC & R2 & MAE & MAPE & r\\
\hline
Proposed & 4,323 & \textbf{0.997} & \textbf{0.995} & \textbf{0.353} & \textbf{1.8} & \textbf{0.997} \\
Field 23278 & 4,323 & 0.991 & 0.982 & 0.689 & 3.4 & 0.991 \\
\hline
\hline
\multicolumn{7}{l}{*Comparison to the target values, listing both the proposed predictions}\\ \multicolumn{7}{l}{ and alternative UK Biobank reference values on the same subjects}
\end{tabular}
\end{table}
\end{document}

eess

Taro Langner
Fredrik K. Gustafsson
Benny Avelin
Robin Strand
Håkan Ahlström
Joel Kullberg

"Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI" by Taro Langner, Fredrik K. Gustafsson, Benny Avelin, Robin Strand, Håkan Ahlström, and Joel Kullberg, arXiv:2101.06963v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Rafael Alves
Diego S. de Brito
Marcelo C. Medeiros
Ruy M. Ribeiro

"Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage" by Rafael Alves, Diego S. de Brito, Marcelo C. Medeiros, and Ruy M. Ribeiro, arXiv:2303.16151v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
		\toprule
		Framework 				& Accuracy \\
		\midrule
		Trad. SVM & $67.07$\\
		Trad. LDA & $72.24$ \\
		Trad. QDA & $73.82$\\
		Trad. KNN & $68.87$\\
		Trad. GP & $72.47$\\
		MFF & $76.96$	\\
		EMF & $88.86$	\\
		\bottomrule
	\end{tabular}
\caption{Performance for each BCI framework in the UTS dataset.}
\end{table}
\end{document}

eess

Javier Fumanal-Idocin
Yu-Kai Wang
Chin-Teng Lin
Javier Fernández
Jose Antonio Sanz
Humberto Bustince

"Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions" by Javier Fumanal-Idocin, Yu-Kai Wang, Chin-Teng Lin, Javier Fernández, Jose Antonio Sanz, and Humberto Bustince, arXiv:2101.06968v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age" by Marcos Matabuena, arXiv:2501.06868v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
 \hline
Home & HA average & Away & HA average (opponent) \\ \hline
Huracán (TA) & -0.736 & River Plate & -0.199 \\
Tiro Federal & -0.736 & Boca Juniors & -0.196 \\
Crucero del Norte & -0.4666 & Vélez Sarsfield & -0.012 \\
Almagro & -0.444 & Defensa y Justicia & 0.043 \\
Aldosivi & -0.397 & San Lorenzo & 0.068 \\
Nueva Chicago & -0.301 & Estudiantes & 0.105 \\
Instituto & -0.289 & Talleres (C) & 0.161 \\
Chacarita Juniors & -0.254 & Independiente & 0.161 \\
Sarmiento & -0.222 & Racing Club & 0.178 \\
Central Córdoba (SdE) & -0.170 & Lanús & 0.185 \\
Temperley & -0.115 & Banfield & 0.185 \\
Quilmes & -0.033 & Newells & 0.252 \\
Atlético de Rafaela & 0.024 & Belgrano & 0.335 \\
San Martín (T) & 0.031 & Argentinos & 0.352 \\
Gimnasia y Esgrima (J) & 0.093 & Platense & 0.360 \\
Unión & 0.117 & Tigre & 0.369 \\
Olimpo & 0.125 & Godoy Cruz & 0.373 \\
San Martín (SJ) & 0.132 & Rosario Central & 0.400 \\
Patronato & 0.133 & Arsenal & 0.440 \\
Arsenal & 0.187 & Unión & 0.444 \\
Colón & 0.188 & Colón & 0.448 \\
Gimnasia y Esgrima (LP) & 0.205 & Central Córdoba (SdE) & 0.474 \\
All Boys & 0.226 & Gimnasia y Esgrima (LP) & 0.513 \\
Banfield & 0.228 & Atlético Tucumán & 0.527 \\
Argentinos & 0.230 & Huracán & 0.560 \\
Huracán & 0.250 & Atlético de Rafaela & 0.581 \\
Platense & 0.269 & Sarmiento & 0.593 \\
Godoy Cruz & 0.275 & San Martín (SJ) & 0.641 \\
Barracas Central & 0.285 & San Martín (T) & 0.645 \\
Tigre & 0.287 & Chacarita Juniors & 0.653 \\
Belgrano & 0.295 & Quilmes & 0.661 \\
Rosario Central & 0.310 & Gimnasia y Esgrima (J) & 0.684 \\
Atlético Tucumán & 0.378 & Aldosivi & 0.720 \\
Independiente & 0.436 & All Boys & 0.723 \\
San Lorenzo & 0.438 & Patronato & 0.739 \\
Defensa y Justicia & 0.443 & Nueva Chicago & 0.754 \\
Lanús & 0.453 & Barracas Central & 0.769 \\
Racing Club & 0.476 & Olimpo & 0.773 \\
Newells & 0.487 & Almagro & 0.789 \\
Estudiantes & 0.540 & Temperley & 0.843 \\
Vélez Sarsfield & 0.562 & Tiro Federal & 1.000 \\
Talleres (C) & 0.584 & Instituto & 1.000 \\
River Plate & 0.737 & Huracán (TA) & 1.631 \\
Boca Juniors & 0.932 & Crucero del Norte & 1.800 \\ \hline
 \end{tabular}
\caption{Ranking of average HA results}
\end{table}
\end{document}

q-fin

Federico Fioravanti
Fernando Delbianco
Fernando Tohmé

"Visitors Out! The Absence of Away Team Supporters as a Source of Home Advantage in Football" by Federico Fioravanti, Fernando Delbianco, and Fernando Tohmé, arXiv:2308.06279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{xcolor}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{(Section ) The $\mathcal{H}_{\infty}$ values and computational times for NLA corresponding to SSOF $\mathcal{H}_{\infty}$ controllers with different choices of $S$: Centralized (Cen.), Distributed (Dis.), and Decentralized (Dec.), $C_y = I_{n_y,n_x}$, $D_y = 0.1I_{n_y,n_w}$, $n_y = n_d$, and $B_h = 0.1I_{n_x}$ for the IEEE test systems.}}
\begin{tabular}{|c|c|c|c|}
\hline
\textrm{Sparsity Structure} & $\|T_{z\tilde{w}}(s)\|_{\mathcal{H}_{\infty}}$ & Computational Time & $(n_u,n_y)$ \\
\hline
Cen. $9$-bus & \cellcolor{lightgray}$3.2314$ & \cellcolor{lightgray}$1.69$ s & $(6,12)$\\
\hline
Dis. $9$-bus & $3.3007$ & $4.86$ s & $(6,12)$\\
\hline
Dec. $9$-bus & $3.2784$ & $4.11$ s & $(6,12)$\\
\hline
Cen. $14$-bus & \cellcolor{lightgray}$7.3964$ & $2.04$ s & $(10,20)$\\
\hline
Dis. $14$-bus & $7.4446$ & $5.40$ s & $(10,20)$\\
\hline
Dec. $14$-bus & $7.4274$ & \cellcolor{lightgray}$1.78$ s & $(10,20)$\\
\hline
Cen. $39$-bus & \cellcolor{lightgray}$9.2120$ & \cellcolor{lightgray}$42.59$ s & $(20,40)$\\
\hline
Dis. $39$-bus & $9.9968$ & $145.53$ s & $(20,40)$\\
\hline
Dec. $39$-bus & $9.8467$ & $82.38$ s & $(20,40)$\\
\hline
Cen. $57$-bus & \cellcolor{lightgray}$23.6002$ & \cellcolor{lightgray}$35.33$ s & $(14,28)$\\
\hline
Dis. $57$-bus & $23.6168$ & $51.04$ s & $(14,28)$\\
\hline
Dec. $57$-bus & $23.6180$ & $56.93$ s & $(14,28)$\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

MirSaleh Bahavarnia
Muhammad Nadeem
Ahmad F. Taha

"On Scaling Robust Feedback Control and State Estimation Problems in Power Networks" by MirSaleh Bahavarnia, Muhammad Nadeem, and Ahmad F. Taha, arXiv:2311.17836v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c|c|c||}
	 \hline
	 Description & $\ell$ & Optimal cost (\texteuro) \\
	 \hline\hline
	 Problem~ & & $-173.57$ \\
	 Problem~ & & $-667.83$ \\
	 Initialization Algorithm~ & $1$ & $-661.52$ \\
	 LMBM & $1$ & $-663.62$ \\
	 Dual optimization Algorithm~ & $2$ & $-661.8$ \\
	 Dual optimization Algorithm~ & $4$ & $-661.59$ \\
	 Dual optimization Algorithm~ & $8$ & $-660.51$ \\
	 \hline
	 \end{tabular}
\caption{Comparing the optimal dual costs achieved in various stages of Algorithm~ with those of Problems~ and~.}
\end{table}
\end{document}

math

Nadhir Ben Rached
Shyam Mohan Subbiah Pillai
Raúl Tempone

"Optimal power procurement for green cellular wireless networks under uncertainty and chance constraints" by Nadhir Ben Rached, Shyam Mohan Subbiah Pillai, and Raúl Tempone, arXiv:2503.03051v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.