\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|ccc}
 \toprule
Method & Size (M/MB) & PESQ1 & SI-SDR & STOI \\
\midrule
Noisy & - & 1.83 & -2.73 & 73.00 \\
NSnet & 1.27/4.84 & 1.91 & 0.34 & 73.02 \\
DTLN & 0.99/3.78 & 2.23 & 2.12 & 80.40 \\
\midrule
TRU-Net (FP32) & 0.38/1.45 & \bf{2.51} & \bf{3.51} & \bf{81.22} \\
TRU-Net (INT8) & 0.38/0.36 & 2.49 & 3.03 & 80.56 \\
\bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Hyeong-Seok Choi
Sungjin Park
Jie Hwan Lee
Hoon Heo
Dongsuk Jeon
Kyogu Lee

"Real-time Denoising and Dereverberation with Tiny Recurrent U-Net" by Hyeong-Seok Choi, Sungjin Park, Jie Hwan Lee, Hoon Heo, Dongsuk Jeon, and Kyogu Lee, arXiv:2102.03207v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Transit-Length Distribution for Particle Transport in Binary Markovian Mixed Media" by Brian C. Kiedrowski and Emily H. Vu, arXiv:2412.19359v1, 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}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c|ccc||}
 \hline
 $M$ & $G$ & $K$ & $H$\\
 \hline\hline
 $\mathbb{S}^3 \times \mathbb{R}$ & $\mathrm{SU}(2)$ & - & $1$\\
 \hline
 $\mathbb{S}^2\times \mathbb{R}^2$ & $\mathrm{SO}(3) \times \mathrm{SO}(2)$ & $\mathrm{SO}(2)\times \mathrm{SO}(2)$ & $\mathrm{SO}(2)\times 1$\\
 \hline
 $\mathbb{R}^4$ & $\mathrm{SU}(2)$ & $\mathrm{SU}(2)$ & $1$\\
 \hline
 $\mathcal{O}(-n)$ & $\mathrm{SU}(2)$ & $\mathrm{U}(1)$ & $\mathbb{Z}_n$, $n$ \text{odd}\\
 \hline
 $\mathcal{O}(-2n)$ & $\mathrm{SO}(3)$ & $\mathrm{SO}(2)$ & $\mathbb{Z}_n$\\
 \hline
 \end{tabular}
\caption{Simply-connected non-compact $4$-manifolds that admit a cohomogeneity one compact group action and their effective irreducible group diagrams.}
\end{table}
\end{document}

math

"Cohomogeneity one 4-dimensional gradient Ricci solitons" by Patrick Donovan, arXiv:2503.15033v1, 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
\begin{tabular}{c|l}
 $\mathbb{E}[M]$ & $10.29$ \\
 $\text{Var}[M]$ & $181.189$ \\
 Median & $6$ \\
 Mode & $1$ \\
 Min & $1$ \\
 Max & $800$ \\
 \end{tabular}
\caption{Descriptive statistics for empirical out-degree $M$}
\end{table}
\end{document}

cs

Lourens Touwen
Doina Bucur
Remco van der Hofstad
Alessandro Garavaglia
Nelly Litvak

"Learning the mechanisms of network growth" by Lourens Touwen, Doina Bucur, Remco van der Hofstad, Alessandro Garavaglia, and Nelly Litvak, arXiv:2404.00793v3, 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
\begin{tabular}{l|c|c|ccc}
\hline
\multirow{2}{*}{\textbf{Methods}} & \multirow{2}{*}{\shortstack{\textbf{Mean}\\ \textbf{RR}} } & \multirow{2}{*}{\textbf{F1}} & \multicolumn{3}{c}{\textbf{Hits} @ } \\
 & & & \textbf{K=1} & \textbf{K=3} & \textbf{K=5} \\ \hline \hline
Coarse & 97.00 & 85.51 & 94.69 & 99.33 & 99.79 \\ \hline
Middle & 97.85 & 87.67 & 96.24 & 99.58 & 99.83 \\ \hline
Fine & \textbf{98.58} & \textbf{89.39} & \textbf{97.49} & \textbf{99.68} & \textbf{99.90} \\ \hline
\end{tabular}
\caption{\textbf{Evaluation on node matching with different levels of point geometric feature.}}
\end{table}
\end{document}

cs

"SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks" by Yaxu Xie, Alain Pagani, and Didier Stricker, arXiv:2403.19474v1, 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{Ranks of the models based on the MSE. The three best average ranks are in bold. The acronym ``WMSE'' is ``weighted MSE'', ``FR'' is ``focal-R'', ``BMSE'' is ``balanced MSE'', and ``SWMSE'' is ``scaling-weighted MSE''. The numbers ``1'', ``2'', and ``3'' behind ``SWMSE'' indicate the hyperparameters $\alpha=1.5$, $\beta=0.5$, $\alpha=2$, $\beta=0.5$, and $\alpha=2$, $\beta=1$ respectively, applied to the other tables in this study.}
\begin{tabular}{lllllllllll}
\textbf{Location} & \textbf{Persist} & \textbf{MSE} & \textbf{MAE} & \textbf{Huber} & \textbf{WMSE} & \textbf{FR} & \textbf{BMSE} & \textbf{SWMSE1} & \textbf{SWMSE2} & \textbf{SWMSE3} \\ \hline
BOP & 5 & 3 & 4 & 8 & 2 & 1 & 9 & 6 & 7 & 10 \\
BP & 6 & 3 & 4 & 5 & 1 & 2 & 9 & 7 & 8 & 10 \\
CI & 6 & 3 & 1 & 9 & 4 & 2 & 8 & 5 & 7 & 10 \\
CR & 5 & 2 & 4 & 7 & 3 & 1 & 9 & 6 & 8 & 10 \\
CS & 4 & 2 & 5 & 9 & 1 & 3 & 7 & 6 & 8 & 10 \\
F & 5 & 3 & 4 & 8 & 2 & 1 & 7 & 6 & 9 & 10 \\
HG & 5 & 2 & 4 & 8 & 3 & 1 & 9 & 6 & 7 & 10 \\
OP & 5 & 1.5 & 3 & 6 & 4 & 1.5 & 9 & 7 & 8 & 10 \\
R & 5 & 3 & 4 & 9 & 2 & 1 & 8 & 6 & 7 & 10 \\
SI & 5 & 2 & 4 & 7 & 3 & 1 & 9 & 6 & 8 & 10 \\
T & 1 & 5 & 2.5 & 9 & 4 & 2.5 & 7 & 6 & 8 & 10 \\
W & 1 & 5 & 2 & 9 & 4 & 6 & 8 & 3 & 7 & 10 \\ \hline
Average & 4.4167 & \textbf{2.875} & 3.4583 & 7.8333 & \textbf{2.75} & \textbf{1.9167} & 8.25 & 5.8333 & 7.6667 & 10
\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[T1]{fontenc}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{PICP evaluation for he constant coefficient Poisson equation}
\begin{tabular}{|c|c|}
		\hline
		Kernel function & PICP \\ \hline
		Gaussian+Laplacian & 72.7 \% \\ \hline
		Gaussian+Exponential & 81.8 \% \\ \hline
		Gaussian & 72.7 \% \\ \hline
		Rational Quadratic+Laplacian & 90.9 \% \\ \hline
		Matérn+Laplacian & 72.7 \% \\ \hline
		Rational Quadratic+Gaussian & 63.6 \% \\ \hline
		Matérn+Gaussian+Laplacian & 90.9 \% \\ \hline
	\end{tabular}
\end{table}
\end{document}

math

Juan Zhang
Junyue Luo
Fangfang Zhang
Xiaoqiang Yue

"Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs" by Juan Zhang, Junyue Luo, Fangfang Zhang, and Xiaoqiang Yue, arXiv:2504.14930v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

On the Last Kervaire Invariant Problem

"On the Last Kervaire Invariant Problem" by Weinan Lin, Guozhen Wang, and Zhouli Xu, arXiv:2412.10879v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Properties and Deviations of Random Sums of Densely Dependent Random Variables" by Shane Sparkes and Lu Zhang, arXiv:2310.11554v1, 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}{lccc}
		\toprule
		Setup & Adam & K-FAC & Ours \\
		\midrule
		MLP & 2848 & 2953 & 3315 \\
		LeNet & 2944 & 3022 & 3369 \\
		BigMLP & 1777 & 2989 & 4365 \\
		VGG & 1696 & 3117 & 4613 \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

math

"Adapting Newton's Method to Neural Networks through a Summary of Higher-Order Derivatives" by Pierre Wolinski, arXiv:2312.03885v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Binbin Huang
Zehao Yu
Anpei Chen
Andreas Geiger
Shenghua Gao

"2D Gaussian Splatting for Geometrically Accurate Radiance Fields" by Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao, arXiv:2403.17888v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Silvana M. Pesenti
Sebastian Jaimungal
Yuri F. Saporito
Rodrigo S. Targino

"Risk Budgeting Allocation for Dynamic Risk Measures" by Silvana M. Pesenti, Sebastian Jaimungal, Yuri F. Saporito, and Rodrigo S. Targino, arXiv:2305.11319v5, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Hang Zhang
Rongguang Wang
Jinwei Zhang
Chao Li
Gufeng Yang
Pascal Spincemaille
Thanh Nguyen
Yi Wang

"NeRD: Neural Representation of Distribution for Medical Image Segmentation" by Hang Zhang, Rongguang Wang, Jinwei Zhang, Chao Li, Gufeng Yang, Pascal Spincemaille, Thanh Nguyen, and Yi Wang, arXiv:2103.04020v1, 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 of the percentages of free-text responses mentioning each of the eight top-level aspects in the Codebook of Critical CP Factors. Bold text in each column indicates the top two categories for reported useful/important and lacking/desired aspects.}
\begin{tabular}{lcc}
 \toprule
 CP Aspect & Useful/Important & Lacking/Desired \\
 \midrule
 1. Content & 26\% & 16\% \\
 2. Discovery & 13\% & 3\% \\
 3. Social & 13\% & 13\% \\
 4. Platform & \textbf{31\%} & \textbf{44\%} \\
 5. Consumption & 9\% & 11\% \\
 6. Editing & \textbf{47\%} & 16\% \\
 7. Visibility & 20\% & \textbf{31\%} \\
 8. Engagement & 6\% & 21\% \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

"User perspectives on critical factors for collaborative playlists" by So Yeon Park and Blair Kaneshiro, arXiv:2101.03648v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Kentaro Ohno
Tatsuhiko Shirai
Nozomu Togawa

"Toward Practical Benchmarks of Ising Machines: A Case Study on the Quadratic Knapsack Problem" by Kentaro Ohno, Tatsuhiko Shirai, and Nozomu Togawa, arXiv:2403.19175v2, 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|c|}
 \hline
 $\Delta x$ & $N$ & CPUtime & GPUtime & Speed-up\\
 \hline\hline
 0.1 & 394 & 1.35 & 0.07 & 20x \\
 \hline
 0.05 & 549 & 9.38 & 0.13 & 73x\\
 \hline
 0.025 & 766 & 66.91 & 0.26 & 277x \\
 \hline
 0.0125 & 1067 & 484.76 & 1.02 & 474x \\
 \hline
 \end{tabular}
\caption{Value iteration method for test 4, CPU vs GPU}
\end{table}
\end{document}

math

Simone Cacace
Roberto Ferretti
Giulia Tatafiore

"Numerical Hopf-Lax formulae for Hamilton-Jacobi equations on unstructured geometries" by Simone Cacace, Roberto Ferretti, and Giulia Tatafiore, arXiv:2503.13311v1, 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||c|c|c|}
\hline
Round of DFP & 0 to 500 & 501 to 1000 & 1001 to 1700 \\
\hline
Learning rate & 1e-2 & 1e-3 & 1e-4 \\
\hline
\end{tabular}
\caption{Training schedule for CARA case.}
\end{table}
\end{document}

q-fin

Robert Balkin
Hector D. Ceniceros
Ruimeng Hu

"Stochastic Delay Differential Games: Financial Modeling and Machine Learning Algorithms" by Robert Balkin, Hector D. Ceniceros, and Ruimeng Hu, arXiv:2307.06450v1, 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}{lcccc}
 \toprule
	&		&		&	Simplified	&	Traditional	\\
	&	Japanese	&	Korean	&	Chinese	&	Chinese	\\
\cmidrule{1-5}
Homoglyphic 	&	\textbf{0.456}	&	\textbf{0.292}	&	\textbf{0.476}	&	\textbf{0.465}	\\
\ \ distance \\
Levenshtein 	&	0.396	&	0.188	&	0.375	&	0.407	\\
\ \ distance \\
Simstring 	&	0.376	&	0.247	&	0.425	&	0.383	\\
\ \ (cosine) \\
Simstring 	&	0.380	&	0.248	&	0.426	&	0.385	\\
\ \ (dice) \\
Simstring 	&	0.380	&	0.248	&	0.426	&	0.385	\\
\ \ (jaccard) \\
FuzzyChinese	&	0.168	&	0.000	&	0.473	&	0.372	\\
\ \ (stroke) \\
FuzzyChinese 	&	0.230	&	0.110	&	0.137	&	0.197	\\
\ \ (character) \\
 \bottomrule
 \end{tabular}
\caption{\textbf{Matching Results: Synthetic Data}. This table reports accuracy linking synthetic paired data generated by OCR'ing location and firm names - rendered with augmented digital fonts - with two different OCR engines.}
\end{table}
\end{document}

q-fin

Xinmei Yang
Abhishek Arora
Shao-Yu Jheng
Melissa Dell

"Quantifying Character Similarity with Vision Transformers" by Xinmei Yang, Abhishek Arora, Shao-Yu Jheng, and Melissa Dell, arXiv:2305.14672v1, 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|c|c}
		Mean log-relative wealth of & growth-optimal portfolio & signature portfolio\\ \hline
		Black-Scholes market: & 9.0115 & 9.0122 \\
		Volatility stabilized market: & 8.7619 & 8.7417 \\
		Signature market: & 0.4399 & 0.4398 \\
	\end{tabular}
\caption{ Mean logarithmic relative wealth of the theoretical growth-optimal and signature portfolios, evaluated on 100'000 test-samples, in each market respectively.}
\end{table}
\end{document}

q-fin

"Signature Methods in Stochastic Portfolio Theory" by Christa Cuchiero and Janka Möller, arXiv:2310.02322v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

On higher-dimensional symmetric designs

"On higher-dimensional symmetric designs" by Vedran Krčadinac and Mario Osvin Pavčević, arXiv:2412.09067v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Juan Andres Espinosa-Torres
Jaime Ramirez-Cuellar

"The Effects of the Pandemic on Market Power and Profitability" by Juan Andres Espinosa-Torres and Jaime Ramirez-Cuellar, arXiv:2303.08765v1, 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{Quantitative comparison of different DR algorithms in terms of OA, AA, and $\kappa$ using the NN classifier on the Indian Pines dataset. The best one is shown in bold.}
\begin{tabular}{c||c|ccc}
\toprule[1.5pt] Methods & dimension & OA (\%) & AA (\%) & $\kappa$ \\
\hline \hline
OSF & 220 & 65.89 & 75.71 & 0.6148\\
OTVCA & 16 & 74.18 & 77.61 & 0.7228\\
RLMR & 20 & 83.75 & 86.90 & 0.8147\\
FSDA & 15 & 64.14 & 74.52 & 0.5964\\
JPlay & 20 & 83.92 & 89.35 & 0.8169\\
IMR & 20 & 82.80 & 86.27 & 0.8033\\
JPSA & 20 & \bf 92.98 & \bf 95.40 & \bf 0.9197\\
\bottomrule[1.5pt]
\end{tabular}
\end{table}
\end{document}

eess

Danfeng Hong
Wei He
Naoto Yokoya
Jing Yao
Lianru Gao
Liangpei Zhang
Jocelyn Chanussot
Xiao Xiang Zhu

"Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing" by Danfeng Hong, Wei He, Naoto Yokoya, Jing Yao, Lianru Gao, Liangpei Zhang, Jocelyn Chanussot, and Xiao Xiang Zhu, arXiv:2103.01449v1, 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
\caption{Results of defending against temporal corruptions with the help of our proposed training strategy on THUMOS14-C. Our method consistently improves the robustness of various TAD models with different features.}
\begin{tabular}{l|l|l|l}
\hline
\multirow{2}{*}{Backbone(feature)} & \multirow{2}{*}{\shortstack{Clean \\ mAP}} & \multirow{2}{*}{\shortstack{Corrupted \\ mAP}} & \multirow{2}{*}{\shortstack{Relative \\ Robutness}}\\
& & & \\
\hline
TemporalMaxer(I3D) & 60.72 & 47.82 & 78.76 \\
 + Ours & \textbf{61.04 (0.32 $\uparrow$)}& \textbf{51.95 (4.13 $\uparrow$)} & \textbf{85.10 (6.34 $\uparrow$)} \\ \hline
TriDet(I3D) & 61.33 & 51.71 & 84.31 \\
 + Ours & \textbf{62.63 (1.30 $\uparrow$)}& \textbf{54.07 (2.36 $\uparrow$)} & \textbf{86.32 (2.01 $\uparrow$)} \\ \hline
TriDet(VideoMAEv2) & 75.16 & 61.10 & 81.29 \\
 + Ours & \textbf{75.60 (0.44 $\uparrow$)}& \textbf{68.28 (7.18 $\uparrow$)} & \textbf{90.32 (9.03 $\uparrow$)} \\ \hline
ActionFormer(I3D) & 61.53 & 50.61 & 82.25 \\
 + Ours & \textbf{61.63 (0.10 $\uparrow$)}& \textbf{53.95 (3.34 $\uparrow$)} & \textbf{87.54 (5.29 $\uparrow$)} \\ \hline
ActionFormer(VideoMAEv2) & 73.84 & 58.33 & 78.99 \\
 + Ours & \textbf{74.06 (0.22 $\uparrow$)}& \textbf{68.29 (9.96 $\uparrow$)} & \textbf{92.21 (13.22 $\uparrow$)} \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

Runhao Zeng
Xiaoyong Chen
Jiaming Liang
Huisi Wu
Guangzhong Cao
Yong Guo

"Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions" by Runhao Zeng, Xiaoyong Chen, Jiaming Liang, Huisi Wu, Guangzhong Cao, and Yong Guo, arXiv:2403.20254v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Two sample test for covariance matrices in ultra-high dimension" by Xiucai Ding, Yichen Hu, and Zhenggang Wang, arXiv:2312.10796v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Local Eviction Moratoria and the Spread of COVID-19" by Julia Hatamyar and Christopher F. Parmeter, arXiv:2307.00251v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc} \hline
		 		& $\{X, Y\}$	& $\{X, Z\}$	& $\{Y, Z\}$		\\ \hline
	Egalitarian Welfare 	& (2, 2, 5)	& (1, 1, 3)	& (2, 3, 3)		\\ \hline
		\end{tabular}
\caption{Egalitarian welfares of certain project sets to be funded.}
\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{Average classification accuracies, for the fullsize datasets. Best performing method is marked in \textbf{bold}. }
\begin{tabular}{ccccc}
 \toprule
method & ADAM & SGDSLS & ADAMSLS & PLASLS \\
 \cmidrule(r){1-1} \cmidrule(r){2-5}
$accuracy$ & 0.8745 & 0.8714 & \textbf{0.8830} & 0.8779 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Philip Kenneweg
Leonardo Galli
Tristan Kenneweg
Barbara Hammer

"Faster Convergence for Transformer Fine-tuning with Line Search Methods" by Philip Kenneweg, Leonardo Galli, Tristan Kenneweg, and Barbara Hammer, arXiv:2403.18506v1, 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{Parameter values for all three models which are chosen as constant if the mechanism is included. }
\begin{tabular}{cccccccccccccccccccc}
 $a_1$ & $C_1$ & $\varepsilon$ & $\kappa$ & $d_1^\mathrm{ctc}$ & $a_{2,h}$ & $C_{T_h}$ & $a_{6}$ & $d_{T_h}^\mathrm{diff}$ & $C_{T_c}$ & $a_3$ & $a_\mathrm{nd}$ & $d_3^\mathrm{diff}$\\ \hline
 1 & 1 & 0.05& 0.01 & 0.6 &2 &8 & 0.2 & 0.9 &15 &0.8 &0.6 &0.5
\end{tabular}
\end{table}
\end{document}

math

Cordula Reisch
Sandra Nickel
Hans-Michael Tautenhahn

"Building up a model family for inflammations" by Cordula Reisch, Sandra Nickel, and Hans-Michael Tautenhahn, arXiv:2312.05982v1, 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{Data communication requirements and computational complexity for different fusion methods}
\begin{tabular}{cccc}
 {\bf Method} & {\bf Data [bytes] }& {\bf Data [\%CF] } & {\bf Complexity}\\
\hline
 {CF} & 801216 & --- & $O(104^3)$ \\
\hline
 {BDF-CF} & 273216 & 34.1 & $O(104^3)$ \\
\hline
 {Approximate BDF-CF} & 3456 & 0.43 & $O(104^3)$ \\
\hline
 {HS-CF} & 3456 & 0.43 & $O(18^3)$ \\
\end{tabular}
\end{table}
\end{document}

cs

"Exact and Approximate Heterogeneous Bayesian Decentralized Data Fusion" by Ofer Dagan and Nisar R. Ahmed, arXiv:2101.11116v4, 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{Summary of Core 4 (Purple) from Fig. .}
\begin{tabular}{ll}
\hline
\textbf{Category} & \textbf{Details} \\ \hline
\textbf{Source Set} & atp\_c, s7p\_c \\ \hline
\textbf{Sink Set} & adp\_c, h\_c, e4p\_c \\ \hline
\textbf{Autocatalytic Set} & f6p\_c, fdp\_c, g3p\_c, dhap\_c \\ \hline
\textbf{Growth Factor} & 1.2207340620350884 \\ \hline
\textbf{Reactions} & \\ \hline
R1 & atp\_c + f6p\_c $\to$ adp\_c + fdp\_c + h\_c \\
R6 & g3p\_c + s7p\_c $\to$ f6p\_c + e4p\_c \\
R8 & dhap\_c $\to$ g3p\_c \\
R9 & fdp\_c $\to$ g3p\_c + dhap\_c \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Víctor Blanco
Gabriel González
Praful Gagrani

"Identifying Self-Amplifying Hypergraph Structures through Mathematical Optimization" by Víctor Blanco, Gabriel González, and Praful Gagrani, arXiv:2412.15776v2, 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}{|l|l|l|l|l|l|l|}
 \hline
 Method & CV2 Error & Non-zeros & Condition No. & Delta WS \\ \hline
 Glasso2 & 0.92 & 5.8E+4 & 47.56 & 7.15E-15 \\ \hline
 Clime2 & 1.01 & 2.5E+4 & 19.32 & 8.19E-15 \\ \hline
 Greedy Prune2 & 1.11 & 1.8E+4 & 34.42 & 3.16E-15 \\ \hline
 MB2 & 0.96 & 4.1E+4 & 21.69 & 6.72E-15 \\ \hline
 HybridMB2 & 1.08 & 3.1E+4 & 41.85 & 3.87E-14 \\ \hline
 \end{tabular}
\caption{Results for precision matrix selected via 5-fold CV2 on the Nifty500 constituents}
\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}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
 \hline
 & ODE & Sinkhorn \\ \hline
 Computed regularized primal value & 0.5033 & 0.5080\\ \hline
 Optimal unregularized primal value & 0.5024 & 0.5024\\ \hline
 Optimal unregularized primal value + Entropy & 0.5117 & 0.5117 \\ \hline
 Iterations & 100 & 684 \\ \hline
 CPU time (sec) & 1.49 & 0.15 \\ \hline
 \end{tabular}
\caption{Comparison of the performance between 4-th order Runge-Kutta ODE method and Sinkhorn algorithm for two marginal optimal transport with repulsive cost $c(x, y) = -\log(0.1 + |x - y|)$.}
\end{table}
\end{document}

cs

Joshua Zoen-Git Hiew
Luca Nenna
Brendan Pass

"An ordinary differential equation for entropic optimal transport and its linearly constrained variants" by Joshua Zoen-Git Hiew, Luca Nenna, and Brendan Pass, arXiv:2403.20238v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{makecell}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{The performance of OV-Uni3DETR on the KITTI and nuScenes dataset for open-vocabulary 3D object detection.} For KITTI, the car and cyclist classes are seen during training while the pedestrian class is novel. We report AP$_{25}$ with 11 recall positions on the moderate difficulty. For nuScenes, ``Car, trailer, construction vehicle, motorcycle, bicycle'' are seen and the rest five are unseen ones. }
\begin{tabular}{c|c|ccc|ccc|ccc}
\Xhline{1.1pt}
\multirow{2}*{Method}& \multirow{2}*{Inputs}& \multicolumn{3}{c|}{KITTI} & \multicolumn{6}{c}{nuScenes} \\
 & &AP$_{Ped.}$ & AP$_{Car}$ & AP$_{Cyc.}$ & AP$_{novel}$ & AP$_{base}$ & \multicolumn{1}{c@{}}{AP$_{all}$\ } & NDS$_{novel}$ & NDS$_{base}$ & NDS$_{all}$ \\
\hline
 Det-PointCLIP & P & 0.32 & 3.67 & 1.32 & 0.59 & 2.13 & 1.36 & 1.92 & 5.86 & 3.89\\
 Det-PointCLIPv2 & P & 0.32 & 3.58 & 1.22 & 0.61 & 2.05 & 1.33 & 1.97 & 5.74 & 3.86\\
 3D-CLIP & P+I & 1.28 & 42.28 & 21.99 & 2.74 & 12.60 & 7.67 & 8.98 & 23.81 & 16.39\\
\hline
\multirow[c]{3}{*}{OV-Uni3DETR (ours)} & P & 19.57 & 92.44 & 56.67 & 15.48 & 61.28 & 38.39 & 15.61 & 44.71 & 30.16 \\
 & I & 9.98 & 75.14 & 18.44 & 12.54 & 55.30 & 33.93 & 14.67 & 39.43 & 27.05\\
 & P+I & \textbf{23.04} & \textbf{92.55} & \textbf{58.21} & \textbf{18.96} & \textbf{63.34} & \textbf{41.15} & \textbf{17.05} & \textbf{46.69} & \textbf{31.87}\\
\Xhline{1.1pt}
\end{tabular}
\end{table}
\end{document}

cs

Zhenyu Wang
Yali Li
Taichi Liu
Hengshuang Zhao
Shengjin Wang

"OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality Propagation" by Zhenyu Wang, Yali Li, Taichi Liu, Hengshuang Zhao, and Shengjin Wang, arXiv:2403.19580v2, 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{Kolmogorov-Smirnov test on actual and modelled returns of SBU after COVID 19 (significance level$=0.01$)}
\begin{tabular}{ccccc}
			\hline
			& \text{MM-gBm} & \text{gBm} & \text{MM-XOU} & \text{XOU} \\ \hline
			p-value & 0.53552 & $4.22e^{-13}$ & 0.0103 & $5.33e^{-21}$ \\
			Decision & Similar & Different & Similar & Different \\ \hline
	\end{tabular}
\end{table}
\end{document}

q-fin

Erina Nanyonga
Juma Kasozi
Fred Mayambala
Hassan W. Kayondo
Matt Davison

"A Stochastic Model for Illiquid Stock Prices and its Conclusion about Correlation Measurement" by Erina Nanyonga, Juma Kasozi, Fred Mayambala, Hassan W. Kayondo, and Matt Davison, arXiv:2509.10553v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Generative weather for improved crop model simulations" by Yuji Saikai, arXiv:2404.00528v1, 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{Fixed Effects Linear Regression Results - Blitzes Outcomes}
\begin{tabular}{lccc}
					\tabularnewline \midrule \midrule
					Dependent Variables: & Vehicles Stopped & Traffic Tickets & Seized Vehicles\\
					& (1) & (2)\\
					\midrule
					Blitz Duration & 31.84$^{***}$ & & \\
					& (8.081) & & \\
					Blitz Duration$^2$ & -2.636$^{***}$ & & \\
					& (0.8516) & & \\
					Number of Officers & 1.218$^{***}$ & 0.3056$^{***}$ & 0.2457$^{**}$\\
					& (0.3715) & (0.0986) & (0.1172)\\
					Blitz Type Mobile & 2.153 & 1.767$^{***}$ & 0.7897$^{***}$\\
					& (2.271) & (0.5541) & (0.2473)\\
					Vehicles Stopped & & 0.1553$^{***}$ & 0.0447$^{***}$\\
					& & (0.0159) & (0.0066)\\
					Vehicles Stopped$^2$ & & -0.0003$^{***}$ & $-8.63\times 10^{-5 ***}$\\
					& & ($7.14\times 10^{-5}$) & ($2.96\times 10^{-5}$)\\
					\midrule
					\emph{Fixed-effects}\\
					Cell & Yes & Yes & Yes\\
					Day & Yes & Yes & Yes\\
					Period of the Day & Yes & Yes & Yes\\
					Day of the Week & Yes & Yes & Yes\\
					\midrule
					Outcome Mean & 95.9 & 16.5 & 4.79\\
					Observations & 3,409 & 3,409 & 3,409\\
					R$^2$ & 0.48693 & 0.65483 & 0.60094\\
					\midrule \midrule
					\multicolumn{4}{l}{\emph{Standard-errors clustered at cell in parentheses}}\\
					\multicolumn{4}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
				\end{tabular}
\end{table}
\end{document}

q-fin

"Is Crime Displacement Inevitable? Evidence from Police Crackdowns in Fortaleza, Brazil" by José Raimundo Carvalho and Marcelino Guerra, arXiv:2503.13571v1, 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}

math

Dinesh Parthasarathy
Tommaso Bevilacqua
Martin Lanser
Axel Klawonn
Harald Köstler

"Towards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations" by Dinesh Parthasarathy, Tommaso Bevilacqua, Martin Lanser, Axel Klawonn, and Harald Köstler, arXiv:2412.08186v1, 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}{|l|lccc|c|}
\hline
\small{g \textbackslash Q}&0&2&4&6\\
\hline
0& 5& -15 & 15&-5 \\
1& 5 & -20& 20&-5 \\
2& 1&- 8 & 8&- 1 \\
3& 0& -1 & 1& 0 \\
\hline
\end{tabular}
\end{table}
\end{document}

math

"The HOMFLY-PT polynomial and HZ factorisation" by Andreani Petrou and Shinobu Hikami, arXiv:2412.04933v3, 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{Dataset specifications.}
\begin{tabular}{lrrrr}
\toprule
\midrule
Dataset & \# Train & \# Test & \# Dim & \# Class \\ \midrule
Adult & 36,139 & 9,034 & 87 & 2 \\
CreditInfo & 105,000 & 45,000 & 10 & 2 \\
SUSY & 2,500,000 & 2,500,000 & 18 & 2\\
HIGGS & 5,500,000 & 5,500,000 & 28 & 2 \\
Optdigits & 3,822 & 1,796 & 64 & 10 \\
Pendigits & 7,493 & 3,497 & 16 & 10 \\
Letter & 15,000 & 5,000 & 16 & 26 \\
Covtype & 290,506 & 290,506 & 54 & 7 \\
{Abalone} & {2,785} & {1,392} & {8} & {Reg.} \\
{WineQuality} & {4,332} & {2,165} & {12} & {Reg.} \\
\midrule
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Huawei Lin
Jun Woo Chung
Yingjie Lao
Weijie Zhao

"Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data" by Huawei Lin, Jun Woo Chung, Yingjie Lao, and Weijie Zhao, arXiv:2502.01634v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games?" by Steve Phelps and Yvan I. Russell, arXiv:2305.07970v2, 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}{lrrr}
 \toprule
 $\alpha$ & $\beta$ & $\gamma$ & $\delta$ \\
 \midrule
 A & 1 & a & 3 \\
 B & 2 & b & 2 \\
 C & 3 & c & 1 \\
 \bottomrule
 \end{tabular}
\caption{This is a dummy table to be replaced.}
\end{table}
\end{document}

eess

Chuang Niu
Ge Wang
Pingkun Yan
Juergen Hahn
Youfang Lai
Xun Jia
Arjun Krishna
Klaus Mueller
Andreu Badal
KyleJ. Myers
Rongping Zeng

"Noise Entangled GAN For Low-Dose CT Simulation" by Chuang Niu, Ge Wang, Pingkun Yan, Juergen Hahn, Youfang Lai, Xun Jia, Arjun Krishna, Klaus Mueller, Andreu Badal, KyleJ. Myers, and Rongping Zeng, arXiv:2102.09615v1, 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}{l|c|c|c|c}
\hline
\hline
 & Adversarial Attack & Auto Manufacturer & City Planning & Platatable Diet \\
\hline
\hline
ReLU SOS & 6 & 2 & 0 & 14* \\
ReLU Big-M & 6 & 5 & 0 & 13* \\
GBDT & - & 15+ & 2 & 15+ \\
\hline
\hline
 & Tree Planting & Water Potability & Wine Manufacturer & Workload Dispatching \\
\hline
\hline
ReLU SOS & 0 & 8 & 3 & 0 \\
ReLU Big-M & 2* & 8 & 3 & 0 \\
GBDT & - & 6 & 4 & - \\
\hline
\hline
\end{tabular}
\end{table}
\end{document}

math

Mark Turner
Antonia Chmiela
Thorsten Koch
Michael Winkler

"PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs" by Mark Turner, Antonia Chmiela, Thorsten Koch, and Michael Winkler, arXiv:2312.08074v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Robust Trading in a Generalized Lattice Market" by Chung-Han Hsieh and Xin-Yu Wang, arXiv:2310.11023v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Irina Maliukov
Gera Weiss
Oded Margalit
Achiya Elyasaf

"Evolving Assembly Code in an Adversarial Environment" by Irina Maliukov, Gera Weiss, Oded Margalit, and Achiya Elyasaf, arXiv:2403.19489v2, 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{Number of tetrahedra and points generated by each method when processing the CBC3D mesh converted from the segmented image of the first aneurysm and the surface mesh of the second aneurysm. `Tets' is short for tetrahedra. K means thousand and M means million.}
\begin{tabular}{lc|cc}
\hline
Methods & & Aneurysm 1 & Aneurysm 2 \\
\hline
\multirow{2}{*}{TetGen (iso-sizing)} & Tets & 120K & - \\
 & Points & 37K & - \\
\hline
\multirow{2}{*}{TetGen (iso)} & Tets & 118K & 12K \\
 & Points & 36K & 3K \\
\hline
\multirow{2}{*}{AFLR (bl)} & Tets & 805K & 105K \\
 & Points & 144K & 18K \\
\hline
\multirow{2}{*}{CDT3D (iso)} & Tets & 631K & 31K \\
 & Points & 122K & 7K \\
\hline
\multirow{2}{*}{CDT3D (aniso)} & Tets & 1.59M & 80K \\
 & Points & 284K & 15K \\
\hline
\end{tabular}
\end{table}
\end{document}

math

Kevin Garner
Fotis Drakopoulos
Chander Sadasivan
Nikos Chrisochoides

"Towards Real-time Adaptive Anisotropic Image-to-mesh Conversion for Vascular Flow Simulations" by Kevin Garner, Fotis Drakopoulos, Chander Sadasivan, and Nikos Chrisochoides, arXiv:2412.13222v1, 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{Time stamp features}
\begin{tabular}{|l|c|}
 \hline
 \hline
 \textbf{Feature Name} & \textbf{Range of Values}\\
 \hline
 \hline
 Weekday (Monday$\rightarrow$Sunday) & $[0;6]$\\
 \hline
 Day of the month & $[1;31]$\\
 \hline
 Day of the year & $[1;365]$\\
 \hline
 Month of the year & $[1;12]$\\
 \hline
 Week of the year & $[1;53]$\\
 \hline
 Week of the month & $[1;4]$\\
 \hline
 Quarter of the year & $[1;4]$\\
 \hline
 Recorded year & $[2018;2022]$\\
 \hline
 Working Day & [$0$: Weekend/Holiday; $1$: Business day]\\
 \hline
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Nassr Al-Dahabreh
Mohammad Ali Sayed
Khaled Sarieddine
Mohamed Elhattab
Maurice Khabbaz
Ribal Atallah
Chadi Assi

"A Data-Driven Framework for Improving Public EV Charging Infrastructure: Modeling and Forecasting" by Nassr Al-Dahabreh, Mohammad Ali Sayed, Khaled Sarieddine, Mohamed Elhattab, Maurice Khabbaz, Ribal Atallah, and Chadi Assi, arXiv:2312.05333v1, 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{Margin settings}
\begin{tabular}{cccc}
Page & Top & Bottom & Left/Right \\\hline
First & 3.5 & 2.5 & 1.5 \\
Rest & 2.5 & 2.5 & 1.5 \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Mohamed Abdalmoaty
Jared Miller
Mingzhou Yin
Roy S. Smith

"Frequency-Domain Identification of Discrete-Time Systems using Sum-of-Rational Optimization" by Mohamed Abdalmoaty, Jared Miller, Mingzhou Yin, and Roy S. Smith, arXiv:2312.15722v1, 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|cccccccc}
 &\multicolumn{8}{c}{Back-off counter value} \\
 & 0 & 1 & 2 & 3 & 4 & 5 & 6 &$\dots$\\ \hline
 retry 0 & 0 & 0 & 2 & 0 & 0 & 0 & 0 &$\dots$\\
 retry 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 &$\dots$\\
 $\vdots$ & & & & & & & \\
 retry $max retries$ & 0 & 0 & 0 & 0 & 0 & 0 & 0 &$\dots$\\
 \end{tabular}
\caption{The state representation for the 802.11 model}
\end{table}
\end{document}

cs

Bjørn Ivar Teigen
Neil Davies
Kai Olav Ellefsen
Tor Skeie
Jim Torresen

"A Model of WiFi Performance With Bounded Latency" by Bjørn Ivar Teigen, Neil Davies, Kai Olav Ellefsen, Tor Skeie, and Jim Torresen, arXiv:2101.12512v1, 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{Accuracy and running time of different optimization methods in finding the global maximum of different functions in Subsection . SMCOtree demonstrated higher accuracy with lower time costs.}
\begin{tabular}{ccccccccccccccc}
\cline{1-15}
\multicolumn{3}{c}{Case} && \multicolumn{3}{c}{Method } && \multicolumn{3}{c}{Accuracy} && \multicolumn{3}{c}{Time$\times10^{2}$ (second)} \\
\cline{1-15}
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{\textbf{1.02}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA($T =$ 80)} && \multicolumn{3}{c}{0.99} && \multicolumn{3}{c}{6.87}\\
\multicolumn{3}{c}{Case 1} && \multicolumn{3}{c}{SA($T =$ 100)} && \multicolumn{3}{c}{0.98} && \multicolumn{3}{c}{7.17}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S =$ 100)} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{2.21}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S =$ 150)} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{2.69}\\
\cline{1-15}
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{0.97}} && \multicolumn{3}{c}{\textbf{1.20}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA($T$= 80)} && \multicolumn{3}{c}{0.87} && \multicolumn{3}{c}{6.29}\\
\multicolumn{3}{c}{Case 2} && \multicolumn{3}{c}{SA($T$= 100)} && \multicolumn{3}{c}{0.83} && \multicolumn{3}{c}{6.79}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 100)} && \multicolumn{3}{c}{0.88} && \multicolumn{3}{c}{2.07}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 150)} && \multicolumn{3}{c}{0.92} && \multicolumn{3}{c}{2.76}\\
\cline{1-15}
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{\textbf{3.38}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA($T$= 80)} && \multicolumn{3}{c}{0.97} && \multicolumn{3}{c}{20.35}\\
\multicolumn{3}{c}{Case 3} && \multicolumn{3}{c}{SA($T$= 100)} && \multicolumn{3}{c}{0.98} && \multicolumn{3}{c}{20.48}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 100)} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{3.97}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 150)} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{4.62}\\
\cline{1-15}
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{0.93}} && \multicolumn{3}{c}{\textbf{2.85}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA($T$= 80)} && \multicolumn{3}{c}{0.65} && \multicolumn{3}{c}{14.07}\\
\multicolumn{3}{c}{Case 4, Rastrigin} && \multicolumn{3}{c}{SA($T$= 100)} && \multicolumn{3}{c}{0.58} && \multicolumn{3}{c}{14.48}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 100)} && \multicolumn{3}{c}{0.62} && \multicolumn{3}{c}{4.33}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 150)} && \multicolumn{3}{c}{0.68} && \multicolumn{3}{c}{5.94}\\
\cline{1-15}
\end{tabular}
\end{table}
\end{document}

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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Descriptive statistics for income-consumption data.}
\begin{tabular}{crrrrrrrrr}
			\hline
			n & Min. & 1st Qu. & Median & Mean & 3rd Qu. & Max. & Std. dv. & CS & CK\\
			\hline
			7,957 & 0.004
			& 0.514
			& 0.553
			& 0.557
			& 0.608
			& 0.935
			& 0.086
			& -0.419
			& 3.343\\
			\hline
	\end{tabular}
\end{table}
\end{document}

stat

"A novel unit-asymmetric distribution based on correlated Fréchet random variables" by Roberto Vila and Felipe Quintino, arXiv:2501.00970v1, 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}{lrl}
\hline \textbf{Type of Text} & \textbf{Font Size} & \textbf{Style} \\ \hline
paper title & 15 pt & bold \\
author names & 12 pt & bold \\
author affiliation & 12 pt & \\
the word ``Abstract'' & 12 pt & bold \\
section titles & 12 pt & bold \\
subsection titles & 11 pt & bold \\
document text & 11 pt &\\
captions & 10 pt & \\
abstract text & 10 pt & \\
bibliography & 10 pt & \\
footnotes & 9 pt & \\
\hline
\end{tabular}
\caption{ Font guide. }
\end{table}
\end{document}

cs

"On Explaining Your Explanations of BERT: An Empirical Study with Sequence Classification" by Zhengxuan Wu and Desmond C. Ong, arXiv:2101.00196v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Linghan Cai
Shenjin Huang
Ye Zhang
Jinpeng Lu
Yongbing Zhang

"Rethinking Attention-Based Multiple Instance Learning for Whole-Slide Pathological Image Classification: An Instance Attribute Viewpoint" by Linghan Cai, Shenjin Huang, Ye Zhang, Jinpeng Lu, and Yongbing Zhang, arXiv:2404.00351v1, 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{Detailed information of the non-restricted output network architecture.}
\begin{tabular}{c|c|c|c|c}
			\hline
			Layer number & Layer & output shape & number of parameter & activation function \\ \hline
			1 & dense\_1 (Dense) & (None, 24) & 600 & relu \\ \hline
			2 & dropout\_1 (Dropout) & (None, 24) & 0 & - \\ \hline
			3 & dense\_2 (Dense) & (None, 300) & 90300 & relu \\ \hline
			4 & dropout\_2 (Dropout) & (None, 300) & 0 & - \\ \hline
			5 & dense\_3 (Dense) & (None, 300) & 90300 & relu \\ \hline
			6 & dropout\_3 (Dropout) & (None, 300) & 0 & - \\ \hline
			7 & dense\_4 (Dense) & (None, 300) & 90300 & relu \\ \hline
			8 & dropout\_4 (Dropout) & (None, 300) & 0 & - \\ \hline
			9 & dense\_5 (Dense) & (None, 300) & 90300 & relu \\ \hline
			10 & dropout\_5 (Dropout) & (None, 300) & 0 & - \\ \hline
			11 & dense\_6 (Dense) & (None, 1024) & 308224 & sigmoid \\ \hline
	\end{tabular}
\end{table}
\end{document}

eess

Fardin Ghorbani
Sina Beyraghi
Javad Shabanpour
Homayoon Oraizi
Hossein Soleimani
Mohammad Soleimani

"Deep neural network-based automatic metasurface design with a wide frequency range" by Fardin Ghorbani, Sina Beyraghi, Javad Shabanpour, Homayoon Oraizi, Hossein Soleimani, and Mohammad Soleimani, arXiv:2101.10866v1, 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{Parameters for Wi-Fi networks}
\begin{tabular}{|c|c|}
\hline
\textbf{Parameters} &\textbf{Value} \\ \hline
Carrier frequency & 2.4 GHz, 5 GHz, 6 GHz \\ \hline
Bandwidth & $40$ MHz, $80$ MHz, $160$ MHz \\ \hline
Channel Type & IEEE channel model B \\ \hline
slot time ($\mu$s) & $9$ \\ \hline
SIFS ($\mu$s) & 16 \\ \hline
DIFS ($\mu$s) & 34 \\ \hline
EIFS ($\mu$s) & SIFS + ACK + DIFS \\ \hline
PHY preamble \& Header ($\mu$s) & 20 \\ \hline
Payload Size & 1500 Bytes \\ \hline
ACK\_Timeout ($\mu$s) & 300
 \\ \hline
ACK (Bytes) & 14 + PHY Header \\ \hline
$CW_{min}$ & $16$ \\ \hline
$CW_{max}$ & $1024$ \\ \hline
$m$ & $6$ \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Lyutianyang Zhang
Hao Yin
Sumit Roy
Liu Cao
Xiangyu Gao
Vanlin Sathya

"IEEE 802.11be Network Throughput Optimization with Multi-Link Operation and AP Coordination" by Lyutianyang Zhang, Hao Yin, Sumit Roy, Liu Cao, Xiangyu Gao, and Vanlin Sathya, arXiv:2312.00345v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Machine Proofs for Adams Differentials and Extension Problems among CW Spectra" by Weinan Lin, Guozhen Wang, and Zhouli Xu, arXiv:2412.10876v2, 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{US cotton Production From 2013-2014 to 2019- 2020}
\begin{tabular}{cc}
		\toprule
		\textbf{Year} & \textbf{Production} \\
		\midrule
		2013-14 & 2.81 \\
		2014-15 & 3.55 \\
		2015-16 & 2.81 \\
		2016-17 & 3.74 \\
		2017-18 & 4.56 \\
		2018-19 & 4 \\
		2019-20 & 4.34 \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

stat

Qazi J. Azhad
Abdul Nasir Khan
Bhagwati Devi
Jahangir Sabbir Khan
Ayush Tripathi

"Bayesian estimation of Unit-Weibull distribution based on dual generalized order statistics with application to the Cotton Production Data" by Qazi J. Azhad, Abdul Nasir Khan, Bhagwati Devi, Jahangir Sabbir Khan, and Ayush Tripathi, arXiv:2502.02927v1, 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{Model Accuracy on Validation Sets}
\begin{tabular}{llll}
\toprule
{\textbf{2-class}} & &\\
& Baidu Baike & \hspace{1em}90.29\\
& Wikipedia & \hspace{1em}89.65\\
& People's Daily & \hspace{1em}92.64\\
\midrule
{\textbf{3-class}} & &\\
& Baidu Baike & \hspace{1em}67.44\\
& Wikipedia & \hspace{1em}66.07\\
& People's Daily & \hspace{1em}67.80\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"Censorship of Online Encyclopedias: Implications for NLP Models" by Eddie Yang and Margaret E. Roberts, arXiv:2101.09294v1, 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.


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.


\documentclass{article}
\usepackage{arydshln}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|c|c|c|}
\hline
Method &Pitts. &San. &Stlu. &Eyn. &MSLS \\ %& Avg\\
\hline \hline
Superpoint &85.1 &53.2 &76.4 &67.5 &36.9\\ %&63.8\\
DELF &86.0 &86.6 &85.3 &78.3 &\textbf{80.2}\\ %&83.3\\
L2-distance &75.7 &57.3 &56.2 &67.7 &36.8\\ %&58.7\\
STUN &74.0 &54.0 &58.0 &67.6 &37.4\\ %&58.2\\
\textit{SUE} &78.9 &70.7 &72.8 &77.3 &46.0\\ %&69.1\\
\hdashline
DELF+L2-di. &85.7 &86.1 &82.3 &77.3 &72.0\\ %&80.7\\
DELF+STUN &85.4 &81.6 &80.1 &75.0 &68.2\\ %&78.1\\
DELF+SUE &\textbf{87.1} &\textbf{89.6} &\textbf{88.7} &\textbf{82.1} &\textbf{73.4}\\ %&\textbf{84.1}\\
\hline
\end{tabular}
\caption{Binary classification accuracy given the uncertainty estimates of various methods, using a linear SVM trained \textit{only} on the Pittsburgh dataset. The combination \textit{DELF + SUE} generalizes better than baseline combinations, except on the MSLS dataset where although \textit{DELF+SUE} is better than the other combinations, the SVM boundaries learned from Pittsburgh are not the best.}
\end{table}
\end{document}

cs

Mubariz Zaffar
Liangliang Nan
Julian F. P. Kooij

"On the Estimation of Image-matching Uncertainty in Visual Place Recognition" by Mubariz Zaffar, Liangliang Nan, and Julian F. P. Kooij, arXiv:2404.00546v1, 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{M-FACTS dispatch ($\beta_{i,j}$) for 5 bus system in different strategies}
\begin{tabular}{c|c|c|c}
\hline
FACTS location ($i-j$) & $c_2$ & $c_3$ & $c_4$ \\ \hline
2-1	&	-0.1499	&	0.0520	&	-0.0965	\\
4-1	&		&	0.2000	&	0.2000	\\
5-1	&		&	0.2000	&	0.2000	\\
3-2	&	-0.0760	&	0.1196	&	0.1171	\\
4-3	&	-0.1057	&	0.1054	&	-0.0561	\\
5-4	&	-0.2000	&	 	&	-0.2000	\\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

"Controllable Transmission Networks under Demand Uncertainty with Modular FACTS" by Alireza Soroudi, arXiv:2105.10402v1, 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}
(n,m)&$ (2000,10)$& $(5000,20)$ & $(10000,50)$\\ \hline
 BM& 1.00 & 1.00 & 1.00\\
 M1& 0.49 & 0.25 & 0.00 \\
 M2& 0.98 & 1.00 & 1.00\\
 M3& 0.45 & 0.00 & 0.00\\
 M4& 0.96 & 1.00 & 1.00\\
\end{tabular}
\caption{\scriptsize Empirical (MMLE) Bayes rescaling of the Mat\'ern Gaussian process prior. Proportion of experiments when the true function $f_0$ was inside in the $L_2$-credible ball.}
\end{table}
\end{document}

math

Botond Szabo
Amine Hadji
Aad van der Vaart

"Adaptation using spatially distributed Gaussian Processes" by Botond Szabo, Amine Hadji, and Aad van der Vaart, arXiv:2312.14130v2, 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{Comparison when simulating 500 samples of the target distribution with density $S'$. }
\begin{tabular}{lrrrrc}
\hline
 & HMC & RWMH & t-walk \\
\hline
Acceptance rate & 92.49\% & 24.77\% & 18.25\% \\
Execution time (s)& 166 & 16861 & 11766 \\
Simulations required & 17,600 & 1,150,200 & 5,500,200 \\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

"Understanding the Hamiltonian Monte Carlo through its Physics Fundamentals and Examples" by Abraham Granados and Isaías Bañales, arXiv:2501.13932v1, 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
 Model & AAN & OC & S2orc & PAN \\ \toprule
 \textsc{SMASH} & 80.8 & - & - & - \\
 \textsc{SMITH} \textsuperscript{} & 85.4 & - & - & - \\
 \textsc{BERT-HAN} & 65.0 & 86.3 & 90.8 & \textbf{87.4} \\
 \textsc{GRU-HAN+CDA} & 75.1 & 89.9 & 91.6 & 78.2 \\
 \textsc{BERT-HAN+CDA} & 82.1 & 87.8 & 92.1 & 86.2 \\
 \midrule
 Longformer &85.4& 93.4 & 95.8 & 80.4 \\
 Local \textsc{CDLM} & 83.8 & 92.1 & 94.5 & 80.9 \\
 Rand \textsc{CDLM} & 85.7& 93.5 & 94.6 & 79.4 \\
 Prefix \textsc{CDLM} & 87.3 & 94.8 & 94.7 & 81.7 \\
 \textsc{CDLM} & \textbf{88.8} & \textbf{95.3} & \textbf{96.5} & 82.9\\
 \bottomrule
 \end{tabular}
\caption{$F_1$ scores over the document matching benchmarks' test sets.}
\end{table}
\end{document}

cs

CDLM: Cross-Document Language Modeling

Avi Caciularu
Arman Cohan
Iz Beltagy
Matthew E. Peters
Arie Cattan
Ido Dagan

"CDLM: Cross-Document Language Modeling" by Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew E. Peters, Arie Cattan, and Ido Dagan, arXiv:2101.00406v2, 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{\textbf{Test Accuracy on CIFAR-100 over 190 Epochs} It represents the mean accuracy and standard deviation for five seeds across each of the five models. The content in parentheses indicates the number of last layers used for the reset; if not specified, one layer is assumed. The meanings of each abbreviation in the table's headline are as follows: KL refers to self-distillation. K represents the number of teachers, where a value of zero indicates that self-distillation is not utilized. Regarding the reset, R indicates whether it is used; thus, when R is zero, reset is not utilized. $T_{\text{cycle}}$ signifies the duration of the cycle used for the teacher update and the student reset, expressed in epochs. M indicates the re-initialization method used for reset, where a value of one means that re-initialization was done using the mean of the teachers' weights. A value of zero signifies that random weights were utilized for re-initialization.}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
\toprule
 KL & \multicolumn{3}{|c|}{RESET} & \multicolumn{6}{|c|}{Model} \\
\cmidrule(lr){1-1} \cmidrule(lr){2-4} \cmidrule(lr){5-10}
K & R & $T_{\text{cycle}}$ & M & googlenet & mobilenetv2 & shufflenet & squeezenet & vgg(3) & vgg(1) \\
\midrule
0 & 0 & 0 & 0 & 76.52 ± 0.28 & 70.19 ± 0.36 & 70.48 ± 0.39 & 69.46 ± 0.33 & 71.73 ± 0.28 & 72.01 ± 0.19 \\
 & 1 & 1 & 0 & 73.30 ± 0.80 & 68.35 ± 0.31 & 66.51 ± 0.29 & 41.27 ± 2.25 & 15.89 ± 8.86 & 71.81 ± 0.31 \\
 & & & 1 & 76.85 ± 0.29 & 70.20 ± 0.24 & 70.93 ± 0.32 & 68.99 ± 0.45 & 72.38 ± 0.28 & 71.82 ± 0.27 \\
 & & 20 & 0 & 76.66 ± 0.26 & 69.71 ± 0.22 & 70.04 ± 0.29 & 68.26 ± 0.79 & 68.31 ± 2.75 & 71.77 ± 0.38 \\
 & & & 1 & 76.59 ± 0.20 & 69.56 ± 0.33 & 70.14 ± 0.24 & 68.64 ± 0.41 & 71.62 ± 0.29 & 71.82 ± 0.35 \\
 & & 50 & 0 & 76.93 ± 0.37 & 69.92 ± 0.23 & 70.23 ± 0.44 & 69.42 ± 0.38 & 72.96 ± 0.24 & 71.90 ± 0.09 \\
 & & & 1 & 76.60 ± 0.17 & 69.91 ± 0.29 & 70.69 ± 0.35 & 69.63 ± 0.26 & 71.66 ± 0.20 & 71.97 ± 0.18 \\
 & & 100 & 0 & 76.79 ± 0.22 & 69.84 ± 0.23 & 70.65 ± 0.33 & 69.39 ± 0.47 & 72.82 ± 0.05 & 71.83 ± 0.22 \\
 & & & 1 & 76.45 ± 0.17 & 69.96 ± 0.46 & 70.44 ± 0.38 & 69.86 ± 0.18 & 71.72 ± 0.32 & 71.83 ± 0.22 \\
1 & 0 & 0 & 0 & 76.30 ± 0.24 & 70.74 ± 0.29 & 71.31 ± 0.33 & 70.37 ± 0.31 & 72.23 ± 0.31 & 72.45 ± 0.18 \\
 & 1 & 1 & 0 & 73.61 ± 0.39 & 67.03 ± 2.21 & 63.67 ± 1.57 & 37.06 ± 5.21 & 21.11 ± 3.54 & 71.83 ± 0.16 \\
 & & & 1 & 77.08 ± 0.36 & 70.24 ± 0.48 & 70.90 ± 0.46 & 68.89 ± 0.42 & 72.05 ± 0.35 & 71.94 ± 0.14 \\
 & & 20 & 0 & 75.43 ± 0.54 & 70.50 ± 0.42 & 70.45 ± 0.28 & 68.58 ± 0.23 & 68.67 ± 1.68 & 71.94 ± 0.42 \\
 & & & 1 & 75.89 ± 0.80 & 69.50 ± 0.61 & 70.53 ± 0.38 & 69.37 ± 0.36 & 71.81 ± 0.22 & 71.94 ± 0.42 \\
 & & 50 & 0 & 76.03 ± 0.28 & 70.41 ± 0.27 & 70.87 ± 0.73 & 69.39 ± 0.29 & 72.86 ± 0.39 & 72.03 ± 0.44 \\
 & & & 1 & 75.83 ± 1.03 & 69.84 ± 0.38 & 71.06 ± 0.42 & 69.29 ± 0.40 & 72.13 ± 0.19 & 72.11 ± 0.43 \\
 & & 100 & 0 & 75.16 ± 1.21 & 70.51 ± 0.40 & 70.36 ± 0.24 & 67.97 ± 0.40 & 72.62 ± 0.11 & 72.06 ± 0.29 \\
 & & & 1 & 75.03 ± 1.40 & 70.98 ± 0.45 & 71.95 ± 0.75 & 70.59 ± 0.28 & 72.41 ± 0.35 & 72.07 ± 0.30 \\
2 & 0 & 0 & 0 & 75.86 ± 1.12 & 70.88 ± 0.18 & 71.11 ± 0.47 & 70.54 ± 0.34 & 72.37 ± 0.18 & 72.50 ± 0.25 \\
 & 1 & 1 & 0 & 73.98 ± 0.28 & 66.85 ± 1.14 & 64.74 ± 1.03 & 42.51 ± 2.54 & 17.18 ± 5.40 & 71.86 ± 0.23 \\
 & & & 1 & 76.82 ± 0.27 & 70.04 ± 0.26 & 70.98 ± 0.11 & 68.79 ± 0.31 & 71.81 ± 0.27 & 71.77 ± 0.39 \\
 & & 20 & 0 & 75.13 ± 0.26 & 70.51 ± 0.26 & 70.48 ± 0.33 & 68.35 ± 0.26 & 69.46 ± 4.21 & 72.11 ± 0.22 \\
 & & & 1 & 75.57 ± 1.28 & 70.25 ± 0.39 & 71.06 ± 0.25 & 69.55 ± 0.23 & 72.16 ± 0.21 & 72.09 ± 0.14 \\
 & & 50 & 0 & 75.96 ± 0.50 & 70.71 ± 0.36 & 70.92 ± 0.50 & 69.55 ± 0.18 & 72.82 ± 0.10 & 72.00 ± 0.30 \\
 & & & 1 & 76.04 ± 0.39 & 70.38 ± 0.36 & 70.70 ± 0.69 & 70.02 ± 0.05 & 72.67 ± 0.23 & 71.96 ± 0.17 \\
 & & 100 & 0 & 76.08 ± 0.33 & 70.62 ± 0.25 & 70.63 ± 0.22 & 69.30 ± 0.35 & 72.87 ± 0.11 & 72.38 ± 0.22 \\
 & & & 1 & 76.27 ± 0.44 & 70.92 ± 0.33 & 71.21 ± 0.35 & 70.19 ± 0.42 & 73.06 ± 0.16 & 72.29 ± 0.26 \\
4 & 0 & 0 & 0 & 75.71 ± 1.52 & 71.21 ± 0.26 & 71.28 ± 0.48 & 70.31 ± 0.34 & 72.25 ± 0.32 & 72.34 ± 0.35 \\
 & 1 & 1 & 0 & 74.03 ± 0.32 & 66.51 ± 1.64 & 64.79 ± 1.88 & 41.74 ± 2.26 & 21.09 ± 7.56 & 71.98 ± 0.45 \\
 & & & 1 & 76.28 ± 0.12 & 70.00 ± 0.26 & 70.60 ± 0.41 & 68.62 ± 0.36 & 72.16 ± 0.37 & 71.95 ± 0.34 \\
 & & 20 & 0 & 75.41 ± 0.42 & 70.45 ± 0.19 & 70.32 ± 0.59 & 68.54 ± 0.25 & 69.24 ± 3.97 & 71.97 ± 0.35 \\
 & & & 1 & 76.57 ± 0.27 & 70.11 ± 0.22 & 71.01 ± 0.21 & 69.46 ± 0.36 & 71.64 ± 0.29 & 72.11 ± 0.34 \\
 & & 50 & 0 & 75.01 ± 2.00 & 70.70 ± 0.43 & 70.42 ± 0.16 & 69.40 ± 0.49 & 72.59 ± 0.18 & 72.02 ± 0.33 \\
 & & & 1 & 75.59 ± 0.91 & 70.40 ± 0.19 & 71.25 ± 0.26 & 69.92 ± 0.38 & 72.47 ± 0.24 & 72.18 ± 0.07 \\
 & & 100 & 0 & 76.20 ± 0.18 & 70.42 ± 0.34 & 70.89 ± 0.47 & 69.50 ± 0.36 & 72.98 ± 0.18 & 72.31 ± 0.33 \\
 & & & 1 & 75.63 ± 0.81 & 70.46 ± 0.40 & 71.20 ± 0.51 & 70.35 ± 0.17 & 72.92 ± 0.41 & 72.42 ± 0.24 \\
8 & 0 & 0 & 0 & 76.06 ± 0.25 & 70.69 ± 0.31 & 71.27 ± 0.27 & 70.21 ± 0.19 & 72.41 ± 0.22 & 72.57 ± 0.21 \\
 & 1 & 1 & 0 & 74.20 ± 0.33 & 67.82 ± 1.40 & 64.04 ± 1.67 & 44.47 ± 3.51 & 22.50 ± 3.62 & 72.14 ± 0.50 \\
 & & & 1 & 75.49 ± 0.49 & 70.20 ± 0.15 & 70.29 ± 0.41 & 68.81 ± 0.43 & 71.88 ± 0.24 & 71.71 ± 0.31 \\
 & & 20 & 0 & 75.03 ± 0.62 & 70.58 ± 0.15 & 70.80 ± 0.44 & 68.58 ± 0.56 & 67.50 ± 1.86 & 71.99 ± 0.10 \\
 & & & 1 & 75.41 ± 1.26 & 70.29 ± 0.28 & 70.61 ± 0.25 & 69.15 ± 0.16 & 69.37 ± 1.30 & 72.03 ± 0.24 \\
 & & 50 & 0 & 75.48 ± 0.93 & 70.59 ± 0.21 & 70.55 ± 0.44 & 69.59 ± 0.26 & 72.87 ± 0.30 & 72.34 ± 0.26 \\
 & & & 1 & 75.75 ± 0.61 & 70.34 ± 0.33 & 70.60 ± 0.65 & 69.33 ± 0.30 & 67.87 ± 1.50 & 72.71 ± 0.42 \\
 & & 100 & 0 & 76.40 ± 0.31 & 70.65 ± 0.18 & 70.37 ± 0.40 & 69.60 ± 0.26 & 72.84 ± 0.12 & 72.62 ± 0.37 \\
 & & & 1 & 75.47 ± 1.37 & 70.43 ± 0.39 & 70.93 ± 0.51 & 69.96 ± 0.41 & 67.91 ± 2.05 & 72.22 ± 0.30 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"Diverse Feature Learning by Self-distillation and Reset" by Sejik Park, arXiv:2403.19941v1, 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}{lcc}
 \toprule
 Method & mIoU & Accuracy \\
 \midrule
 SLIC (RGB features) & 0.137 & 0.416 \\
 SLIC (DINOv2 features) & 0.258 & 0.280 \\
 DFC & 0.398 & 0.505 \\
 DoubleDIP & 0.356 & 0.423 \\
 IIC \dag & 0.172 & \\
 PiCIE & 0.325 & 0.405 \\
 SegSort & 0.480 & 0.505 \\
 W-Net & 0.428 & 0.531 \\
 GraPL (proposed) & \textbf{0.527} & \textbf{0.569} \\
 \bottomrule
 \end{tabular}
\caption{BSDS500 performance comparison of GraPL with other unsupervised deep-learning methods and baselines. \\\dag The value listed for IIC is sourced from .}
\end{table}
\end{document}

eess

Isaac Wasserman
Jeova Farias Sales Rocha Neto

"Patch-Based Deep Unsupervised Image Segmentation using Graph Cuts" by Isaac Wasserman and Jeova Farias Sales Rocha Neto, arXiv:2311.01475v2, 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|}
\hline
\textbf{Algorithm} & \texttt{Enumeration} & \texttt{ULO}\\
 \hline
 \hline
\textbf{Running time} & $\underbrace{\sum_{i\, \in \,[n]}\,T^{(i)}(m)}_{\text{full enumeration}}$ & $\underbrace{\sum_{i \in H_K}\,T^{(i)}(m)}_{\text{phase (a)}} + \underbrace{\sum_{k=1}^K\,\sum_{i\,\in\, [n]\backslash H_k}\,T^{(i)}(|S_k|)}_{\text{phase (b)}}$\\
\hline
\textbf{Guarantees} & \emph{global} optimality & $\epsilon$ \emph{global} or $\tilde{\epsilon}$ \emph{relative} optimality gap \\
\hline
\end{tabular}
\caption{General time complexities of \texttt{ULO} and the baseline \texttt{enumeration strategy}.}
\end{table}
\end{document}

math

"Global minimization of a minimum of a finite collection of functions" by Guillaume Van Dessel and François Glineur, arXiv:2412.04625v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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.


stat

"Balancing Flexibility and Interpretability: A Conditional Linear Model Estimation via Random Forest" by Ricardo Masini and Marcelo Medeiros, arXiv:2502.13438v1, 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{CPU time (in seconds) on the simulated datasets COIL20-$t$}
\begin{tabular}{|c|c|c|c|c|c|c|}
		\hline
		method & $t=1$ &$t=2$ &$t=3$ &$t=4$ &$t=5$ & $t=6$\\
		\hline
		NEPv & $8.2 \cdot 10^3$ & $1.7 \cdot 10^4$ & $3.4\cdot 10^4$ & $6.2\cdot 10^4$ & $1.0\cdot 10^5$ & $1.5 \cdot 10^5$\\
		AccNEPv & $4.2\cdot 10^3$ &$5.6\cdot 10^3$ &$5.7\cdot 10^3$ &$5.9\cdot 10^3$ &$5.5\cdot 10^3$ &$4.6\cdot 10^3$\\
		\hline
	\end{tabular}
\end{table}
\end{document}

math

"An NEPv Approach for Feature Selection via Orthogonal OCCA with the (2,1)-norm Regularization" by Li Wang, Lei-Hong Zhang, and Ren-Cang Li, arXiv:2502.18633v1, 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}{lc}
\toprule
\textbf{Parameter} & \textbf{Value}\\
\midrule
FC1 size & 768 \\
FC2 size & 600 \\
Number of epochs & 5 \\
Learning rate & 1E-03 \\
Optimizer & AdamW \\
Dropout probability & 0.1 \\
Batch size & 1 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Vipula Rawte
S. M Towhidul Islam Tonmoy
S M Mehedi Zaman
Prachi Priya
Aman Chadha
Amit P. Sheth
Amitava Das

""Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing" by Vipula Rawte, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman, Prachi Priya, Aman Chadha, Amit P. Sheth, and Amitava Das, arXiv:2403.18976v1, 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{Mean Dice score \& Hausdorff value comparison on BraTS-2017 validation set. ET, WT, TC denote as Enhancing Tumor, Whole Tumor, Tumor Core, respectively.}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{Model} & \multicolumn{3}{c|}{Dice} & \multicolumn{3}{c|}{Hausdorff} \\ \cline{2-7} & ET & WT & TC & ET & WT & TC \\ \hline
Ours &\textbf{0.7471} & 0.8991 &\textbf{0.7991} &\textbf{4.30} &5.08 &6.78\\\hline
UNet scSE &0.7288 &0.8819 &0.7701 &5.71 & 6.37 &8.16 \\\hline
UNet &0.7243 &0.8776 &0.7615 &5.64 & 6.70 &8.23 \\\hline
EMMA &{0.738} & \textbf{0.901} &0.797 &{4.50} &\textbf{4.23} &\textbf{6.56}\\ \hline
DeepLab v3 & 0.6631 & 0.8863 & 0.7834 & 4.39 & 5.62 & 8.51\\ \hline
GCN &0.7017 &0.8785 &0.7794 &5.22 &6.54 &8.37 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Mobarakol Islam
Navodini Wijethilake
Hongliang Ren

"Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival Prediction" by Mobarakol Islam, Navodini Wijethilake, and Hongliang Ren, arXiv:2104.01149v2, 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{Velocity RMSE [mph] results of the Koopman-based Model with and without online update.}
\begin{tabular}{ccccc}
 \toprule
 Prediction Horizon [s] & 50s & 20s & 10s & 5s \\
 \midrule
 Offline Model & 12.57 & 11.58 & 10.78 & 7.22\\
 \hline
 Online Model& 10.45 & 7.98 & 5.93 & 3.69 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Mehmet Fatih Ozkan
Jeff Chrstos
Marcello Canova
Stephanie Stockar

"Modeling Driver Behavior in Speed Advisory Systems: Koopman-based Approach with Online Update" by Mehmet Fatih Ozkan, Jeff Chrstos, Marcello Canova, and Stephanie Stockar, arXiv:2502.20347v2, 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}{ccccc}
\hline
 & \multicolumn{2}{c}{Static Est.} & \multicolumn{2}{c}{Dynamic est.}\tabularnewline
 & Est. & SE & Est. & SE\tabularnewline
\hline
\hline
$\alpha:$ price coef. (yen/1000) & 2.558 & 0.346 & 2.562 & 0.346\tabularnewline
$\rho_{Inc}:$ nest parameter (incandescent) & 0.961 & 0.011 & 0.961 & 0.011\tabularnewline
$\rho_{CFL}:$ nest parameter (CFL) & 0.701 & 0.035 & 0.700 & 0.035\tabularnewline
\hline
\end{tabular}
\caption{Parameter Estimates (The case without random coefficients)}
\end{table}
\end{document}

q-fin

"When do firms sell high durability products? The case of light bulb industry" by Takeshi Fukasawa, arXiv:2503.23792v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Edson Pindza
Jules Clement Mba
Sutene Mwambi
Nneka Umeorah

"Neural Network for valuing Bitcoin options under jump-diffusion and market sentiment model" by Edson Pindza, Jules Clement Mba, Sutene Mwambi, and Nneka Umeorah, arXiv:2310.09622v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Soodeh Habibi
Michal Kocvara
Michael Stingl

"On the numerical solution of Lasserre relaxations of unconstrained binary quadratic optimization problem" by Soodeh Habibi, Michal Kocvara, and Michael Stingl, arXiv:2412.19776v1, 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}

cs

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.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Approximate number of computations in each iteration of MAP-ISD}
\begin{tabular}{ll}
\hline\noalign{\smallskip}
 Steps involved in MAP-ISD & Computational complexity \\
\noalign{\smallskip}\hline\noalign{\smallskip}
Finding low-complexity matrix inverse (required only once) & $(2N_r-1)MU$ \\
Finding the most favorable MAP & $2MN_rU$ \\
Finding reliability of the solution & $(4N_r-1)U$\\
\noalign{\smallskip}\hline
\end{tabular}
\end{table}
\end{document}

eess

"Low-Complexity Interference Cancellation Algorithms for Detection in Media-based Modulated Uplink Massive-MIMO Systems" by Manish Mandloi and Devendra Singh Gurjar, arXiv:2101.01905v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Detecting Mild Traumatic Brain Injury with MEG Scan Data: One-vs-K-Sample Tests" by Jian Zhang and Gary Green, arXiv:2502.04258v1, 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{ANOVA analysis for the imbalanced dataset}
\begin{tabular}{lrrrrr}
		\hline
		& Df & Sum Sq & Mean Sq & F value & Pr($>$F) \\
		\hline
		Models & 6 & 8947.34 & 1491.22 & 60.10 & 0.0000 \\
		feat\_extr & 4 & 18879.49 & 4719.87 & 190.23 & 0.0000 \\
		Models:feat\_extr & 24 & 28916.51 & 1204.85 & 48.56 & 0.0000 \\
		Residuals & 1715 & 42552.50 & 24.81 & & \\
		\hline
	\end{tabular}
\end{table}
\end{document}

cs

Cyrille Feudjio
Victoire Djimna Noyum
Younous Perieukeu Mofendjou
Rockefeller
Ernest Fokoué

"A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG Data" by Cyrille Feudjio, Victoire Djimna Noyum, Younous Perieukeu Mofendjou, Rockefeller, and Ernest Fokoué, arXiv:2102.01647v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"ADPBA: Efficiently generating Lagrangian cuts for two-stage stochastic integer programs" by Xiaoyu Luo, Mingming Xu, and Chuanhou Gao, arXiv:2312.16445v1, 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|}
 \hline
 Knot & Static Reference Frame Word Code \\
 \hline
 $9_{35}$ & $FFRDDLFDDDDFLFFURRDFFDLLLLLLBBBBBB$\\
 \hline
 $9_{40}$ & $FRUUUUUBBBBBDDDDFFFLLLUU$\\
 \hline
 $9_{41}$ & $UFFFFDDDDDDDDDRRRRUBBBUUUUBBBRRRRDDBBBUU$\\
 \hline
 \end{tabular}
\caption{The code for the pieces of $9_{35}, 9_{40}$ and $9_{41}$.}
\end{table}
\end{document}

math

Knots and Coxeter Groups

Dylan Burke
Geoffrey Cuff-Chartrand
Malors Espinosa
Mateusz Kazimierczak
Mohammadamin Mobedi

"Knots and Coxeter Groups" by Dylan Burke, Geoffrey Cuff-Chartrand, Malors Espinosa, Mateusz Kazimierczak, and Mohammadamin Mobedi, arXiv:2503.10785v1, 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{Descriptive statistics of the graph classification datasets taken from used for the evaluation of \textit{Troupe}.} %These datasets are fairly balanced, while the graphs are hetereogeneous with respect to size, density and diameter.}
\begin{tabular}{ccccccccc}
 &\multicolumn{2}{c}{\textbf{Classes}} & \multicolumn{2}{c}{\textbf{Nodes}} & \multicolumn{2}{c}{\textbf{Density}} & \multicolumn{2}{c}{\textbf{Diameter}} \\
 \cline{2-3} \cline{4-5}\cline{6-7}\cline{8-9}
\textbf{Dataset} & \textbf{Positive} &\textbf{Negative} & \textbf{Min} & \textbf{Max} & \textbf{Min} & \textbf{Max} & \textbf{Min}& \textbf{Max} \\\hline
\textbf{Reddit} & 521 &479 & 11 &93 &0.023 &0.027 &2 &18 \\
\textbf{Twitch} &520&480&14&52&0.039&0.714&2&2 \\
\textbf{GitHub} &552&448 & 10 & 942 & 0.004 &0.509 & 2 & 15 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

cs

"The Shapley Value of Classifiers in Ensemble Games" by Benedek Rozemberczki and Rik Sarkar, arXiv:2101.02153v2, 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}{llllllll}
 \toprule
 & Mean & Std & Q5 & Q50 & Q95 & N & T \\
 \midrule
 \multicolumn{8}{c}{\textbf{Panel A: main market indices}} \\
Insurance Equity Index & 0.002 & 1.731 & -2.609 & 0.023 & 2.487 & 1 & 6472 \\
 Bank Equity Index & -0.012 & 1.899 & -2.927 & 0.007 & 2.777 & 1 & 6472 \\
 Stocks ex Fin. Equity Index & 0.009 & 1.119 & -1.798 & 0.031 & 1.658 & 1 & 6472 \\
 Govt Bond Index & 0.006 & 0.370 & -0.596 & 0.007 & 0.560 & 1 & 6472 \\
 \midrule
 \multicolumn{8}{c}{\textbf{Panel B: insurance stock-level data}} \\
 Insurance Brokers & 0.047 & 1.882 & -2.319 & 0.000 & 2.493 & 7 & 6472 \\
 Life Health & 0.010 & 1.528 & -2.380 & 0.039 & 2.160 & 20 & 6472 \\
 Multiline & 0.013 & 1.412 & -2.097 & 0.039 & 1.961 & 38 & 6472 \\
 Property and Casualty & 0.026 & 1.564 & -2.295 & 0.053 & 2.175 & 26 & 6472 \\
 Reinsurance & 0.012 & 1.626 & -2.342 & 0.045 & 2.207 & 7 & 6472 \\
 All & 0.012 & 2.375 & -3.253 & 0.004 & 3.295 & 70 & 5584 \\
 \bottomrule
\end{tabular}
\caption{This table presents summary statistics for the logged return series (in percentages). Panel A refers to the main market indices: the equity indices for insurance, banking, non-financial stocks, and government bonds. Panel B refers to insurance stock-level data. Specifically, the table reports the mean, standard deviation, and the 5th, 50th, and 95th percentiles. For the insurance subsectors we consider value-weighted indices, while ``All'' refers to simple average across all the individual insurance stocks. The number of individual assets (N) and daily observations (T) are also reported. The sample period spans from January 3, 2000, to October 22, 2024. Data source: Datastream.}
\end{table}
\end{document}

q-fin

Giovanni Bonaccolto
Nicola Borri
Andrea Consiglio
Giorgio Di Giorgio

"Systemic Risk in the European Insurance Sector" by Giovanni Bonaccolto, Nicola Borri, Andrea Consiglio, and Giorgio Di Giorgio, arXiv:2505.02635v1, 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{\textbf{Efficiency analysis}. Comparison of training and inference times and parameters between SAM, DualNet and TwF.}
\begin{tabular}{l|rrr}
\toprule
\textbf{Metric} & \textbf{DualNet~} & \textbf{TwF~} & \textbf{SAM} \\
\midrule
Train params & 16 M & 58 M & 23 M \\
Train time & $\sim$ 6.5 h & $\sim$ 3.0 h & $\sim$ 1.0 h \\
Inference params & 16 M & 11 M & 22 M \\
Inference time & 3.45 ms & 3.15 ms & 7.50 ms \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Giovanni Bellitto
Federica Proietto Salanitri
Matteo Pennisi
Matteo Boschini
Angelo Porrello
Simone Calderara
Simone Palazzo
Concetto Spampinato

"Selective Attention-based Modulation for Continual Learning" by Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi, Matteo Boschini, Angelo Porrello, Simone Calderara, Simone Palazzo, and Concetto Spampinato, arXiv:2403.20086v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cc|cc}
			\multirow{2}{*}{Case number} & \multicolumn{2}{c|}{Constant viscosity} & \multicolumn{2}{c}{Varying viscosity} \\
			& Onset time & $n_x$ & Onset time & $n_x$ \\
			\hline
			Base & 11 654 s & 1 & 11 671 s & 1\\ \hline
			Case 1 & 10 489 s & 1 & 10 504 s & 1 \\
			Case 2 & 8414 s & 1 & 8426 s & 1 \\
			Case 3 & 5998 s & 1 & 6007 s & 1\\ \hline
			Case 4 & 17 804 s & 1 & 17 850 s & 1 \\
			Case 5 & 43 081 s & 1 & 43 294 s & 1 \\
			Case 6 & 193 211 s & 1 & 195 669 s & 1\\ \hline
			Case 7 & 7317 s & 1 & 7332 s & 1 \\
			Case 8 & 3098 s & 1 & 3105 s & 1\\
			Case 9 & 856 s & 3 & 858 s & 3 \\
			\hline
		\end{tabular}
\caption{Onset times and preferred number of waves $n_x$ for partially saturated porous domain.}
\end{table}
\end{document}

math

C. Bringedal
S. Kiemle
C. J. van Duijn
R. Helmig

"Impact of saturation on evaporation-driven density instabilities in porous media: mathematical and numerical analysis" by C. Bringedal, S. Kiemle, C. J. van Duijn, and R. Helmig, arXiv:2501.12784v1, 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}{lrrrrrrrrr}
\hline\hline
DEA model & \multicolumn{9}{c}{Number of DMUs by number of improvement items} \\ \cline{2-10}
& &&0 item & & 1 item & & 2 items & & 3 items \\ \hline
SBM & &&12 {\small DMUs} & & - & & 6 {\small DMUs} & & 5 {\small DMUs} \\
max SBM & &&12 {\small DMUs} & & 10 {\small DMUs} & & 1 {\small DMUs} & & - \\
max RM &&& 12 {\small DMUs} & & 11 {\small DMUs} & & - & & - \\ \hline \hline
\end{tabular}
\end{table}
\end{document}

math

"Closest targets in Russell graph measure of strongly monotonic efficiency for an extended facet production possibility set" by Kazuyuki Sekitani and Yu Zhao, arXiv:2312.15422v1, 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|c|c|c|c}\toprule
 Strategy& $n=50$& $n=200$ & $n=750$ &$n=1000$ \\\midrule
 Greedy & 117.97& 351.52& 511.33& 664.55\\
 RLMCS & 69.37& 228.37& 356.67& 432.50\\
 FFCG& 77.99& 250.63& 377.40& 462.73\\\bottomrule
 \end{tabular}
\caption{Experimental results on CSP with different size $n$. It reports the average number of columns added, which is only compared between multiple-column selection strategies.}
\end{table}
\end{document}

math

Yi-Xiang Hu
Feng Wu
Shaoang Li
Yifang Zhao
Xiang-Yang Li

"FFCG: Effective and Fast Family Column Generation for Solving Large-Scale Linear Program" by Yi-Xiang Hu, Feng Wu, Shaoang Li, Yifang Zhao, and Xiang-Yang Li, arXiv:2412.19066v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Effect of Recommendation on Investment Return}
\begin{tabular}{lcccc}
 \midrule
 Dependent Variable: & (1) & (2) & (3) & (4) \\
 InvestReturn & All Investors & Low SES & Med SES & High SES\\
 \midrule
 Recommended & -0.895** & -1.127** & -1.105 & 1.597 \\
 & (0.452) & (0.463) & (0.793) & (1.979) \\
 Polynomial Degree & 2 & 2 & 2 & 2 \\
 Investor Group Dummies & Yes & No & No & No \\
 Fund Type Dummies & Yes & Yes & Yes & Yes \\
 Month Dummies & Yes & Yes & Yes & Yes \\
 Day of Week Dummies & Yes & Yes & Yes & Yes \\
 Control Variables & Yes & Yes & Yes & Yes \\
 Number of Observations & 4,618 & 3,221 & 886 & 511 \\
 \textcolor[rgb]{ .133, .133, .133}{\textit{$R^2$}} & 0.135 & 0.118 & 0.193 & 0.491\\
 \midrule
 \multicolumn{5}{p{36em}}{\scriptsize Notes: {***} $p<0.01$, {**} $p<0.05$, {*} $p<0.1$. The dependent variable in Columns (1) - (4) is InvestReturn. Control variables include management fees, purchase fees, custodial fees, asset size, dummy for valuable fund brand, fund past returns, fund risk level, and fund tenure. Columns (2)-(4) show the estimation results for the sub-samples of investors with low, medium, and high SES respectively. Robust standard errors are in parentheses.} \\
 \end{tabular}
\end{table}
\end{document}

q-fin

"The Effect of Product Recommendations on Online Investor Behaviors" by Ruiqi Rich Zhu, Cheng He, and Yu Jeffrey Hu, arXiv:2303.14263v2, 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}{lc}
\toprule
 Country & Sortino ratio \\ \midrule
China & 111.53 \\
India & 2.1476 \\
Australia & 0.6212 \\
Germany & 0.4071 \\
US & 0.3679 \\
Brazil & 0.3346 \\
France & 0.0861 \\
Canada & 0.0048 \\
Japan & 0.0003 \\
UK & -0.0224 \\ \bottomrule
\end{tabular}
\caption{Sortino ratios of the DEIs of different countries}
\end{table}
\end{document}

q-fin

Thisari K. Mahanama
Abootaleb Shirvani
Svetlozar Rachev
Frank J. Fabozzi

"The Financial Market of Environmental Indices" by Thisari K. Mahanama, Abootaleb Shirvani, Svetlozar Rachev, and Frank J. Fabozzi, arXiv:2308.15661v1, 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{Analysis of the effect of incoming order size on the time required to complete the matching operation. As the size increases, more standing orders need to be considered to fully match the incoming order. The capacity for the book is set to $N=100$. Testing is done on an Nvidia 2080 Ti GPU.}
\begin{tabular}{|c|c|}
 \hline
 Market $Q_a$ & Time to match (ms) \\
 \hline
 0 & 0.132 \\
 10 & 0.206 \\
 500 & 0.271 \\
 1,000 & 0.336 \\
 10,000 & 2.326 \\
 \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Sascha Frey
Kang Li
Peer Nagy
Silvia Sapora
Chris Lu
Stefan Zohren
Jakob Foerster
Anisoara Calinescu

"JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading" by Sascha Frey, Kang Li, Peer Nagy, Silvia Sapora, Chris Lu, Stefan Zohren, Jakob Foerster, and Anisoara Calinescu, arXiv:2308.13289v1, 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{Training set, test set, and retraining set for using GPR to predict the optimal parameters for AMG solving the Helmholtz equation with variable coefficient $5x$}
\begin{tabular}{|c|c|c|c|c|c|}
			\hline
			Training set & $n:128\sim512, \Delta n=16$
			&Test set & $n:150\sim600, \Delta n=32$
			&	Retraining set & $n:150\sim600, \Delta n=32$ \\ \hline
		\end{tabular}
\end{table}
\end{document}

math

Juan Zhang
Junyue Luo
Fangfang Zhang
Xiaoqiang Yue

"Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs" by Juan Zhang, Junyue Luo, Fangfang Zhang, and Xiaoqiang Yue, arXiv:2504.14930v1, 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{Clustering performance with different variance-covariance structures: AR(1), Independent, and Compound Symmetry. The performance metrics are shown by the average Rand Index (Rand), average Adjusted Rand Index (ARI), and average Jaccard Index (Jaccard). Oracle is the benchmark, PCME-ME-avg is the point clustered mixed effect model with averaged clusters as the initial value, Average is the averaging of all replicates of data, PCME-ME-naive is the point clustered mixed effect model with naive clusters as the initial value, and Naive is one replicate of data. The parameters are set as $n = 90$, $\sigma_X = 1.5$, and $\rho = 0.5$.}
\begin{tabular}{r|rrrrr|rrrrr|rrrrr}
 \toprule
 & \multicolumn{5}{c|}{Rand} & \multicolumn{5}{c|}{ARI} & \multicolumn{5}{c}{Jaccard} \\
 \midrule
 $\sigma_U$ & Oracle & PCME-ME-avg & Average & PCME-ME-naive & Naive & Oracle & PCME-ME-avg & Average & PCME-ME-naive & Naive & Oracle & PCME-ME-avg & Average & PCME-ME-naive & Naive \\
 \midrule
 \multicolumn{16}{c}{\textbf{AR(1)}} \\
 1.0 & 0.928 & 0.928 & 0.879 & 0.897 & 0.502 & 0.848 & 0.840 & 0.753 & 0.783 & 0.210 & 0.832 & 0.812 & 0.746 & 0.766 & 0.417 \\
 1.5 & 0.928 & 0.905 & 0.813 & 0.843 & 0.340 & 0.849 & 0.785 & 0.638 & 0.675 & 0.016 & 0.833 & 0.756 & 0.657 & 0.672 & 0.332 \\
 2.0 & 0.931 & 0.877 & 0.738 & 0.780 & 0.330 & 0.854 & 0.722 & 0.519 & 0.555 & 0.004 & 0.838 & 0.693 & 0.574 & 0.576 & 0.327 \\
 3.0 & 0.929 & 0.798 & 0.481 & 0.662 & 0.327 & 0.850 & 0.549 & 0.178 & 0.337 & 0.001 & 0.834 & 0.545 & 0.398 & 0.433 & 0.326 \\
 \midrule
 \multicolumn{16}{c}{\textbf{Independent}} \\
 1.0 & 0.999 & 0.998 & 0.998 & 0.997 & 0.983 & 0.998 & 0.996 & 0.996 & 0.994 & 0.962 & 0.998 & 0.995 & 0.995 & 0.992 & 0.952 \\
 1.5 & 0.999 & 0.997 & 0.997 & 0.989 & 0.891 & 0.998 & 0.992 & 0.992 & 0.975 & 0.778 & 0.998 & 0.990 & 0.990 & 0.967 & 0.770 \\
 2.0 & 0.999 & 0.993 & 0.993 & 0.966 & 0.600 & 0.998 & 0.984 & 0.984 & 0.923 & 0.337 & 0.998 & 0.979 & 0.979 & 0.903 & 0.482 \\
 3.0 & 0.999 & 0.975 & 0.972 & 0.887 & 0.342 & 0.998 & 0.943 & 0.938 & 0.742 & 0.017 & 0.998 & 0.928 & 0.923 & 0.712 & 0.332 \\
 \midrule
 \multicolumn{16}{c}{\textbf{Compound Symmetry}} \\
 1.0 & 0.992 & 0.991 & 0.990 & 0.992 & 0.963 & 0.981 & 0.978 & 0.977 & 0.982 & 0.915 & 0.976 & 0.972 & 0.971 & 0.977 & 0.898 \\
 1.5 & 0.992 & 0.983 & 0.987 & 0.986 & 0.798 & 0.981 & 0.960 & 0.971 & 0.967 & 0.639 & 0.976 & 0.952 & 0.963 & 0.958 & 0.678 \\
 2.0 & 0.992 & 0.966 & 0.981 & 0.967 & 0.484 & 0.981 & 0.918 & 0.955 & 0.922 & 0.200 & 0.976 & 0.907 & 0.944 & 0.904 & 0.423 \\
 3.0 & 0.992 & 0.904 & 0.948 & 0.888 & 0.335 & 0.982 & 0.784 & 0.884 & 0.738 & 0.010 & 0.976 & 0.785 & 0.867 & 0.712 & 0.329 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

"Clustering of functional data prone to complex heteroscedastic measurement error" by Andi Mai, Lan Xue, Roger Zoh, and Carmen Tekwe, arXiv:2501.14919v2, 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|ccc}
 \hline
 & Bonus ratio & Regret upper bound ratio & Empirical regret ratio\\ \hline
 \texttt{CH} & $7/2$ & $10$ & - \\ \hline
 \texttt{BF} & $\sqrt{2}$ & $5/4$ & $1.87 \pm 0.03$ \\ \hline
 \end{tabular}
\caption{Improvement ratios in the bonuses, regret upper bounds, and empirical regret between our analysis and the original of~.}
\end{table}
\end{document}

stat

A Refined Analysis of UCBVI

Simone Drago
Marco Mussi
Alberto Maria Metelli

"A Refined Analysis of UCBVI" by Simone Drago, Marco Mussi, and Alberto Maria Metelli, arXiv:2502.17370v1, 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 analysis parameters of the STFT losses in our work. Hanning window is used in the signal analysis.}
\begin{tabular}{ccc}
\textbf{Frame shift} & \textbf{Frame length} & \textbf{FFT size} \\ \hline
50 & 240 & 512 \\ \hline
120 & 600 & 1024 \\ \hline
240 & 1200 & 2048 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Haohan Guo
Heng Lu
Na Hu
Chunlei Zhang
Shan Yang
Lei Xie
Dan Su
Dong Yu

"Phonetic Posteriorgrams based Many-to-Many Singing Voice Conversion via Adversarial Training" by Haohan Guo, Heng Lu, Na Hu, Chunlei Zhang, Shan Yang, Lei Xie, Dan Su, and Dong Yu, arXiv:2012.01837v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Water Cells in Compositions of 1s and 2s" by Brian Hopkins and Aram Tangboonduangjit, arXiv:2412.11528v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

General Social Agents

"General Social Agents" by Benjamin S. Manning and John J. Horton, arXiv:2508.17407v3, 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{hhline}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
Transition & $e_\mu \, [\SI{}{\milli\second}]$ & $e_\sigma \, [\SI{}{\milli\second}]$ \\ \hhline{|=|=|=|}
Build-up to Jetting & $-0.26$ & $0.12$ \\ \hline
Jetting to Relaxation & $-0.04$ & $0.05$ \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"Hybrid Systems, Iterative Learning Control, and Non-minimum Phase" by Isaac A. Spiegel, arXiv:2102.13070v3, 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{Sample table title}
\begin{tabular}{lll}
 \toprule
 \multicolumn{2}{c}{Part} \\
 \cmidrule(r){1-2}
 Name & Description & Size ($\mu$m) \\
 \midrule
 Dendrite & Input terminal & $\sim$100 \\
 Axon & Output terminal & $\sim$10 \\
 Soma & Cell body & up to $10^6$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Qing Wang
Yingru Li
Jiechao Xiong
Tong Zhang

"Divergence-Augmented Policy Optimization" by Qing Wang, Yingru Li, Jiechao Xiong, and Tong Zhang, arXiv:2501.15034v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Arbaz Khan
Felipe Lepe
David Mora
Jesus Vellojin

"Finite element analysis of the nearly incompressible linear elasticity eigenvalue problem with variable coefficients" by Arbaz Khan, Felipe Lepe, David Mora, and Jesus Vellojin, arXiv:2312.10715v1, 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{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Spearman Correlation Analysis between Aesthetic Measures and Model Confidence: the hypotheses of image noise (\textbf{H1}), font-size contrast (\textbf{H2}), misalignment (\textbf{H3}), and layout complexity (\textbf{H4}) are tested; \textbf{bold} values denote significance (p-value < 0.05). T/L/I stands for Text/Layout/Image, respectively, indicating the modalities that are used for model input.}
\begin{tabular}{cccccc}
 \toprule
 \textbf{Hypo.} & \textbf{Dataset} & \textbf{T+L+I} & \textbf{T+L} & \textbf{T+I} & \textbf{T} \\
 \midrule
 \multirow{1}{*}{\textbf{H1}}
 & IDL & \textbf{-0.19} & -0.02 & \textbf{-0.14} & +0.03 \\
 \midrule
 \multirow{1}{*}{\textbf{H2}}
 & FUNSD & \textbf{-0.44} & -0.01 & -0.15 & -0.20 \\
 \midrule
 \multirow{2}{*}{\textbf{H3}}
 & FUNSD & \textbf{-0.16} & \textbf{-0.11} & -0.00 & -0.02 \\
 & IDL & +0.03 & -0.04 & \textbf{+0.11} & \textbf{-0.16} \\
 \midrule
 \multirow{1}{*}{\textbf{H4}}
 & IDL & \textbf{-0.17} & \textbf{-0.08} & \textbf{-0.39} & +0.04 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Hsiu-Wei Yang
Abhinav Agrawal
Pavlos Fragkogiannis
Shubham Nitin Mulay

"Can AI Models Appreciate Document Aesthetics? An Exploration of Legibility and Layout Quality in Relation to Prediction Confidence" by Hsiu-Wei Yang, Abhinav Agrawal, Pavlos Fragkogiannis, and Shubham Nitin Mulay, arXiv:2403.18183v1, 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{Noise variance lower bounds used for comparisons.}
\begin{tabular}{|c|c|}
		\hline
		Composition & Lower Bound of \\
		Method & $\sigma_k^2$ \\
		\hline\hline
		Proposed & \\
		\hline
		MA &$\displaystyle \frac{4q_k^2T}{1-q_k}\!\Bigl( \frac{2}{\epsilon_k^2}\log{\frac{1}{\delta_k}} \!+\! \frac{1}{\epsilon_k}+\mathcal{O}(\log\delta_k^{-1})\Bigr) $ \\
		\hline
		AC1 & \\
		\hline
		AC2 & $\displaystyle\frac{4q_k^2}{1-q_k}\frac{8T\log(e+\frac{\epsilon_k}{\delta_k})}{\epsilon_k^2}$ \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

"Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication" by Muah Kim, Onur Günlü, and Rafael F. Schaefer, arXiv:2102.04737v1, 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|}
			\toprule
			Method & AUC Score & \% Improvement \\
			\midrule
			MLP No Teacher Forcing & 1375.48 (34.57) & 4.07 (1.01)\\
			MLP Teacher Forcing & 1387.49 (25.96) & 3.22 (1.25) \\
			MLP Scheduled Sampling & 1377.01 (31.92) & 3.96 (1.20) \\
			LSTM No Teacher Forcing & 1323.92 (25.34) & 7.66 (0.96) \\
			LSTM Teacher Forcing & 1340.43 (20.24) & 6.49 (1.24)\\
 LSTM Scheduled Sampling & 1324.83 (27.04) & 7.60 (0.98)\\
 TokenwiseSoftmax & 1354.78 (31.87) & 5.51 (0.94)\\
			TokenwiseEntropy & 1360.41 (31.50) & 5.12 (0.62)\\
 \bottomrule
		\end{tabular}
\caption{AUC Scores for various Token-level Rejector architecturs and training}
\end{table}
\end{document}

stat

"Learning to Partially Defer for Sequences" by Sahana Rayan and Ambuj Tewari, arXiv:2502.01459v1, 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{Evaluation of Methods on Synthetic Data }
\begin{tabular}{|l|c|c|c|}
\hline
\textbf{Methods} & \textbf{LE}& \textbf{SD}& \textbf{AUC} \\
\hline
\textbf{ESBN Supervised $^*$}& \textbf{14.98(10.63)}& \textbf{21.96(8.02)} & \textbf{0.91(0.11)} \\
\hline
\textbf{ESBN Unsupervised}& 46.83(32.51)& 75.54(7.4) & 0.72(0.23) \\
\hline
\textbf{MNE}& 49.04(31.36) & 64.34(13.82)& 0.81(0.17) \\
\hline
\textbf{dSPM}&35.42(12.98) &48.48(7.87) & 0.88(0.11) \\
\hline
\textbf{sLORETA} &34.84(23.95) &66.39(11.44) & 0.89(0.11) \\
\hline
\textbf{eLORETA} &38.58(26.35) &67.37(11.25) & 0.88(0.12) \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Chen Wei
Kexin Lou
Zhengyang Wang
Mingqi Zhao
Dante Mantini
Quanying Liu

"Edge Sparse Basis Network: A Deep Learning Framework for EEG Source Localization" by Chen Wei, Kexin Lou, Zhengyang Wang, Mingqi Zhao, Dante Mantini, and Quanying Liu, arXiv:2102.09188v3, licensed under CC BY-SA 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{Results for Problem ().}
\begin{tabular}{|r|r|}
\hline
$\ell$ & $\rho_\ell$\hspace{1.5cm} \\
\hline
0& 2.500000000000000e-01\\
1& -6.974105632991501e-03\\
2& -6.267686473630449e-06\\
3& -5.040632537594832e-12\\
4& -2.508583045846617e-15\\
\hline
\end{tabular}
\end{table}
\end{document}

math

Luigi Brugnano
Gianmarco Gurioli
Felice Iavernaro

"A shooting-Newton procedure for solving fractional terminal value problems" by Luigi Brugnano, Gianmarco Gurioli, and Felice Iavernaro, arXiv:2312.08516v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Quantitative Comparison on PlanIGN}. Our model shows a remarkably better performance than other existing models.}
\begin{tabular}{cccccc}
\toprule
Method & RMSE$\downarrow$ & Acc($\sigma_1$)$\uparrow$ & Acc($\sigma_2$)$\uparrow$ & FID$\downarrow$ & KID$\downarrow$ \\
\midrule
CUT & 30.5 & 46.7 & 55.8 & 68.4 & 2.8 \\
\rowcolor{gray!25}
CycleGAN & 27.0 & 15.1 & 57.6 & 97.5 & 6.6 \\
DRIT & 34.8 & 33.6 & 36.9 & 76.4 & 3.8 \\
\rowcolor{gray!25}
GcGAN & 32.7 & 54.5 & 56.9 & 110.8 & 8.2 \\
SRUNIT & 32.3 & 48.8 & 52.8 & 60.2 & \textbf{2.2} \\
\rowcolor{gray!25}
\bf StegoGAN (ours) & \textbf{22.5} & \textbf{66.1} & \textbf{74.8} & \textbf{58.4} & 2.4 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Sidi Wu
Yizi Chen
Samuel Mermet
Lorenz Hurni
Konrad Schindler
Nicolas Gonthier
Loic Landrieu

"StegoGAN: Leveraging Steganography for Non-Bijective Image-to-Image Translation" by Sidi Wu, Yizi Chen, Samuel Mermet, Lorenz Hurni, Konrad Schindler, Nicolas Gonthier, and Loic Landrieu, arXiv:2403.20142v1, 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}{|cl||cc|c|}
\hline
& &gamma deviance &RMSE &average\\
\hline
(0)& null model &2.085& 35,311&24,641\\\hline
(1a)& gamma FFNN &1.704& 32,562&24,932\\
 (1b)& gamma FFNN recalibrated &1.640& 32,005&24,641\\\hline
 (2)& binary regression tree &1.761& 32,706&24,641\\\hline
\end{tabular}
\caption{Loss figures in the Swedish motorcycle example only considering {\tt RiskClass} and {\tt VehAge} as covariates. }
\end{table}
\end{document}

q-fin

"Isotonic Recalibration under a Low Signal-to-Noise Ratio" by Mario V. Wüthrich and Johanna Ziegel, arXiv:2301.02692v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.