\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model Parameters of Research Vehicle}
\begin{tabular}{|cc|c|c|}
\hline
\multicolumn{2}{|c|}{Description} & Unit & Value \\ \hline
\multicolumn{2}{|c|}{Vehicle mass ($m$)} & $kg$ & 30.5 \\ \hline
\multicolumn{2}{|c|}{Seawater Density ($\rho$)} & $kg/m^3$ & 1030 \\ \hline
\multicolumn{2}{|c|}{Vehicle Buoyancy ($B$)} & $N$ & 306 \\ \hline
\multicolumn{2}{|c|}{MOI about x-axis ($I_{xx}$)} & $kg.m^2$ & 0.177 \\ \hline
\multicolumn{2}{|c|}{MOI about z-axis ($I_{zz}$)} & $kg.m^2$ & 3.45 \\ \hline
\multirow{3}{*}{Center of Bouancy (CB)} & $x_{cg}$ & $m$ & 0 \\ \cline{2-4}
 & $y_{cg}$ & $m$ & 0 \\ \cline{2-4}
 & $z_{cg}$ & $m$ & 0.0192 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Rajini Makam
Pruthviraj Mane
Suresh Sundaram
P. B. Sujit

"A Comprehensive Study on Modelling and Control of Autonomous Underwater Vehicle" by Rajini Makam, Pruthviraj Mane, Suresh Sundaram, and P. B. Sujit, arXiv:2312.02690v3, 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{Optimal CIM encoding parameters for accelerometer signals.}
\begin{tabular}{lc}
 \toprule
 Parameter&Value\\
 \midrule
 X quantization levels& 59\\
 X $d_{max}$&1.0\\
 Y quantization levels& 55\\
 Y $d_{max}$&0.5\\
 Z quantization levels& 14\\
 Z $d_{max}$&0.6\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"Memory-Efficient, Limb Position-Aware Hand Gesture Recognition using Hyperdimensional Computing" by Andy Zhou, Rikky Muller, and Jan Rabaey, arXiv:2103.05267v1, licensed under CC BY-SA 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|c|c|c}
 \hline
 Method & Train Accuracy & Test Accuracy & OOD Accuracy & Difference \\
 \hline
 Standard & $85.20 \pm 2.0$ & $82.75 \pm 1.9$ & $70.2 \pm 1.6$ & $12.55$ \\
 difFOCI with 75\% feats. & $82.66 \pm 1.2$ & $81.7 \pm 2.7$ & $68.95 \pm 0.8$ & $12.22$ \\
 difFOCI with 50\% feats. & $80.19 \pm 2.4$ & $79.4 \pm 1.0$ & $67.9 \pm 1.2$ & $11.5$ \\
 difFOCI with 25\% feats. & $79.55 \pm 2.1$ & $78.72 \pm 1.1$ & $65.40 \pm 2.8$ & $13.32$ \\
 \hline
 \end{tabular}
\caption{Difference between standard predictive accuracy using ResNet-50 on CIFAR10 and CIFAR10.1}
\end{table}
\end{document}

stat

Krunoslav Lehman Pavasovic
David Lopez-Paz
Giulio Biroli
Levent Sagun

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Evaluation of unconditional generation.} LTMs achieve comparable performance on Gen PPL and Entropy while offering substantially faster generation speed. }
\begin{tabular}{lcccc}
\toprule
Model &Gen PPL($\downarrow$) &Entropy($\uparrow$) &Samples$/s$($\uparrow$)\\
\midrule
GPT-2-Medium & 229.7 & 6.02 &0.053\\
GPT-2-Large & 60.4 & 5.71 &0.014\\
LTM-Small &178.7	&5.67 &0.23 \\
LTM-Medium & 104.5	& 5.62 &0.14 \\
LTM-Large &	87.1& 5.61 &0.08 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Deqian Kong
Minglu Zhao
Dehong Xu
Bo Pang
Shu Wang
Edouardo Honig
Zhangzhang Si
Chuan Li
Jianwen Xie
Sirui Xie
Ying Nian Wu

"Scalable Language Models with Posterior Inference of Latent Thought Vectors" by Deqian Kong, Minglu Zhao, Dehong Xu, Bo Pang, Shu Wang, Edouardo Honig, Zhangzhang Si, Chuan Li, Jianwen Xie, Sirui Xie, and Ying Nian Wu, arXiv:2502.01567v1, 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{GNR vs Factor Shares: Elasticity Distributions Statistics}
\begin{tabular}{lcccccccc}
 \toprule
 & \multicolumn{2}{c}{$elas^K$} & \multicolumn{2}{c}{$elas^L$} & \multicolumn{2}{c}{$elas^M$} & \multicolumn{2}{c}{\textit{Returns to Scale}}\\
 \midrule
 \midrule
Statistic & GNR & Factor Shares & GNR & Factor Shares & GNR & Factor Shares & GNR & Factor Shares \\
 \midrule
Mean & 0.18 & 0.32 & 0.45 & 0.26 & 0.31 & 0.42 & 0.95 & 1.00 \\
 Median & 0.16 & 0.30 & 0.47 & 0.23 & 0.30 & 0.42 & 0.97 & 1.00 \\
 SD & 0.12 & 0.16 & 0.21 & 0.15 & 0.17 & 0.20 & 0.17 & 0.00 \\
 Skewness & 0.98 & 0.74 & -0.38 & 0.82 & 0.62 & 0.07 & -0.30 & \\
 Kurtosis & 3.07 & 0.69 & 2.22 & 0.62 & 1.56 & -0.56 & 10.20 & \\
 N & 3,168,792 & 3,031,226 & 3,168,792 & 3,031,226 & 3,168,792 & 3,031,226 & 3,168,792 & 3,168,792 \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Do Productivity Shocks Cause Inputs Misallocation?" by Davide Luparello, arXiv:2306.08760v6, 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}{r|rrrrr}
$\tau^2$ & $2^0$ & $2^1$ & $2^2$ & $2^3$ & $2^4$ \\
\hline
$\sigma(2^\tau)$ & 0.881 & 0.944 & 0.982 & 0.997 & 1.000 \\
$\sigma(-2^\tau)$ & 0.119 & 0.056 & 0.018 & 0.003 & 0.000 \\
\end{tabular}
\end{table}
\end{document}

stat

Andrew Cooper
Annie S. Booth
Robert B. Gramacy

"Modernizing full posterior inference for surrogate modeling of categorical-output simulation experiments" by Andrew Cooper, Annie S. Booth, and Robert B. Gramacy, arXiv:2501.14946v1, 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}
 mean($h_t$) = 8.99 \\
 min($h_t$) = 7.69 \\
 max($h_t$) = 10.57 \\
 var($h_t$) = 0.63 \\
 CV($h_t$) = 0.0881
 \end{tabular}
\caption{ Statistical key figures of the $h_t$ noted in the left table of XXXref{table:2}.}
\end{table}
\end{document}

q-fin

Florian Gach
Simon Hochgerner
Eva Kienbacher
Gabriel Schachinger

"Mean-field Libor market model and valuation of long term guarantees" by Florian Gach, Simon Hochgerner, Eva Kienbacher, and Gabriel Schachinger, arXiv:2310.09022v3, 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{Lossless rate (bpppc) on HySpecNet-11k hard test set.}
\begin{tabular}{cccccc}
\textbf{Model size} & \textbf{LineRWKV} & \textbf{CCSDS} & \textbf{RWA} & \textbf{Diff. CCSDS} & \textbf{Diff. RWA} \\
\hline
\hline
XS & \textbf{5.647} & 5.801 & 5.772 & -0.154 & -0.125 \\
S & \textbf{5.521} & 5.801 & 5.772 & -0.280 & -0.251 \\
M & \textbf{5.510} & 5.801 & 5.772 & -0.291 & -0.262 \\
L & \textbf{5.370} & 5.801 & 5.772 & -0.431 & -0.402 \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Diego Valsesia
Tiziano Bianchi
Enrico Magli

"Onboard deep lossless and near-lossless predictive coding of hyperspectral images with line-based attention" by Diego Valsesia, Tiziano Bianchi, and Enrico Magli, arXiv:2403.17677v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yiwei Shi
Jingyu Hu
Yu Zhang
Mengyue Yang
Weinan Zhang
Cunjia Liu
Weiru Liu

"Beyond Prior Limits: Addressing Distribution Misalignment in Particle Filtering" by Yiwei Shi, Jingyu Hu, Yu Zhang, Mengyue Yang, Weinan Zhang, Cunjia Liu, and Weiru Liu, arXiv:2501.18501v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Peter Greenstreet
Thomas Jaki
Alun Bedding
Pavel Mozgunov

"A preplanned multi-stage platform trial for discovering multiple superior treatments with control of FWER and power" by Peter Greenstreet, Thomas Jaki, Alun Bedding, and Pavel Mozgunov, arXiv:2308.12798v1, 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
 Method & Frobnability \\
 \midrule
 Theirs & Frumpy \\
 Yours & Frobbly \\
 Ours & Makes one's heart Frob\\
 \bottomrule
 \end{tabular}
\caption{Results. Ours is better.}
\end{table}
\end{document}

eess

Raghavv Goel
Cecilia Morales
Manpreet Singh
Artur Dubrawski
John Galeotti
Howie Choset

"Motion Informed Needle Segmentation in Ultrasound Images" by Raghavv Goel, Cecilia Morales, Manpreet Singh, Artur Dubrawski, John Galeotti, and Howie Choset, arXiv:2312.01239v3, 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.


stat

"Optimal design of experiments with quantitative-sequence factors" by Yaping Wang, Sixu Liu, and Qian Xiao, arXiv:2502.03241v1, 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
 Coefficient Decay & Volatility Model \\
 \midrule
 Exponential & EWMA,ARCH,GARCH,SV \\
 Power law & PWMA,IGARCH,FIGARCH \\
 MAXFLAT & Nonlin State Space Vol\\

 \bottomrule
 \end{tabular}
\caption{Vol Model by decay speed}
\end{table}
\end{document}

q-fin

"Adjust factor with volatility model using MAXFLAT low-pass filter and construct portfolio in China A share market" by Ke Zhang, arXiv:2304.04676v2, 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{Partial BD-Rate results for different variants on Kodak~. The baseline is the full model.}
\begin{tabular}{c|c|c|c|c}
 \hline
 $\textbf{Variant}$&$\textbf{BD-Rate}_L$&$\textbf{BD-Rate}_F$&$\textbf{BD-Rate}_K$&$\textbf{BD-Rate}_I$\\
 \hline
 w/o D text&$4.02\%$&$5.22\%$&$16.40\%$&$9.41\%$ \\
 w/o G text&$5.89\%$&$9.77\%$&$19.50\%$&$15.69\%$ \\
 w/o text&$11.51\%$&$18.70\%$&$29.59\%$&$25.81\%$ \\
 \hline
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Shiyu Qin
Bin Chen
Yujun Huang
Baoyi An
Tao Dai
Shu-Tao Xia

"Perceptual Image Compression with Cooperative Cross-Modal Side Information" by Shiyu Qin, Bin Chen, Yujun Huang, Baoyi An, Tao Dai, and Shu-Tao Xia, arXiv:2311.13847v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Eigenvector fluctuations and limit results for random graphs with infinite rank kernels" by Minh Tang and Joshua R. Cape, arXiv:2501.15725v1, 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}{llllll} \toprule
 & \multicolumn{5}{c}{Active set no.}\\
& 1 & 2& 3 & 4 & 5 \\
\midrule
MSE & 0.046 & 0.050 & 0.054 & .059 & 0.061\\
PE & 0.152 & 0.165 & 0.192 & 0.240 & 0.279\\
\bottomrule
\end{tabular}
\caption{The in-sample and out-of-sample prediction errors for a sequence of cross-validated Lasso estimates.}
\end{table}
\end{document}

stat

Andrea Bratsberg
Magne Thoresen
Jelle J. Goeman

"Bad estimation, good prediction: the Lasso in dense regimes" by Andrea Bratsberg, Magne Thoresen, and Jelle J. Goeman, arXiv:2502.07959v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Arthur Brack
Daniel Uwe Müller
Anett Hoppe
Ralph Ewerth

"Coreference Resolution in Research Papers from Multiple Domains" by Arthur Brack, Daniel Uwe Müller, Anett Hoppe, and Ralph Ewerth, arXiv:2101.00884v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{arydshln}
\usepackage{amsfonts}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|r|r|cc|}
\hline
\multirow{2}{*}{Repre.} & \multirow{2}{*}{Token type} & Voc. size &Embed. & \multicolumn{2}{|c|}{$\text{Sample}_k(\cdot)$} \\
 & & $|\mathcal{V}_k|$ &size ($d_k$) &$\tau$ &$\rho$\\
\hline\hline
\multirow{9}{*}{CP} & [track] &2 (+1) &3 &1.0 & 0.90 \\
 \cdashline{2-6}[1pt/1pt]
 & [tempo] &58 (+2) &128 &1.2 & 0.90 \\
 & [position/bar] &17 (+1) &64 &1.2 & 1.00 \\
 & [chord] &133 (+2) &256 &1.0 & 0.99 \\
 \cdashline{2-6}[1pt/1pt]
 & [pitch] &86 (+1) &512 &1.0 & 0.90 \\
 & [duration] &17 (+1) &128 &2.0 & 0.90 \\
 & [velocity] &24 (+1) &128 &5.0 & 1.00 \\
 \cdashline{2-6}[1pt/1pt]
 & [family] &4 ~~~~~~~ &32 &1.0 &0.90 \\
 \cdashline{2-6}[1pt/1pt]
 &total &341 (+9) & ---& --- & ---\\
\hline\hline
REMI &total &338 ~~~~~~~ &512 &1.2 &0.90 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Wen-Yi Hsiao
Jen-Yu Liu
Yin-Cheng Yeh
Yi-Hsuan Yang

"Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs" by Wen-Yi Hsiao, Jen-Yu Liu, Yin-Cheng Yeh, and Yi-Hsuan Yang, arXiv:2101.02402v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|c|c|c|}
 \hline
 hyperparameter & symbol & CartPole-v0 & Acrobot-v1 & value range & log. scale \\
 \hline
 NES step size & $\alpha$ & $0.148$ & $0.727$ & $0.1-1$ & True \\
 NES std. dev. & $\sigma$ & $0.0124$ & $0.0114$ & $0.01-1$ & True \\
 NES mirrored sampling & - & True & True & False/True & - \\
 NES score transformation & - & better avg. & better avg. & (rank transform, linear transform, etc.) & - \\
 NES SE number of hidden layers & - & $1$ & $1$ & $1-2$ & False \\
 NES SE hidden layer size & - & $83$ & $167$ & $48-192$ & True \\
 NES SE activation function & - & LReLU & PReLU & Tanh/ReLU/LReLU/PRelu & - \\
 DDQN initial episodes & - & $1$ & $20$ & $1-20$ & True \\
 DDQN batch size & - & $199$ & $149$ & $64-256$ & False \\
 DDQN learning rate & - & $0.000304$ & $0.00222$ & $0.0001-0.005$ & True \\
 DDQN target network update rate & - & $0.00848$ & $0.0209$ & $0.005-0.05$ & True \\
 DDQN discount factor & - & $0.988$ & $0.991$ & $0.9-0.999$ & True (inv.) \\
 DDQN initial epsilon & - & $0.809$ & $0.904$ & $0.8-1$ & True \\
 DDQN minimal epsilon & - & $0.0371$ & $0.0471$ & $0.005-0.05$ & True \\
 DDQN epsilon decay factor & - & $0.961$ & $0.899$ & $0.8-0.99$ & True (inv.) \\
 DDQN number of hidden layers & - & $1$ & $1$ & $1-2$ & False \\
 DDQN hidden layer size & - & $57$ & $112$ & $48-192$ & True \\
 DDQN activation function & - & Tanh & LReLU & Tanh/ReLU/LReLU/PRelu & - \\
 \hline
\end{tabular}
\caption{Optimized hyperparameters for experiment depicted in Figure }
\end{table}
\end{document}

cs

Fabio Ferreira
Thomas Nierhoff
Frank Hutter

"Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies" by Fabio Ferreira, Thomas Nierhoff, and Frank Hutter, arXiv:2101.09721v3, 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}{lcrrr}
\hline
\textbf{Family} & \textbf{Abbrv.} & \textbf{Languages} & \textbf{Concepts} & \textbf{Words} \\ \hline
Afrasian & AfA & 21 & 39 & 770 \\
Dravidian & Drav & 4 & 183 & 716 \\
Indo-European & IE & 12 & 185 & 2209 \\
Kartvelian & Kart & 1 & 180 & 180 \\
Lolo-Burmese & LoBur & 15 & 39 & 565 \\
Mayan & May & 30 & 94 & 2667 \\
Mixe-Zoque & MZ & 10 & 94 & 905 \\
Mon-Khmer & MKh & 9 & 199 & 1701 \\
Mon-Khmer & MKh & 16 & 94 & 1332 \\
Munda & Mun & 4 & 199 & 759 \\
Uto-Aztecan & UAz & 9 & 94 & 803 \\ \hline
\end{tabular}
\caption{Language families considered in this study.}
\end{table}
\end{document}

cs

V. S. D. S. Mahesh Akavarapu
Arnab Bhattacharya

"A Likelihood Ratio Test of Genetic Relationship among Languages" by V. S. D. S. Mahesh Akavarapu and Arnab Bhattacharya, arXiv:2404.00284v1, 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}{cccc}
 \toprule
 Cross-fit LR test & \multicolumn{3}{c}{Moment-based test} \\
 \cmidrule(lr){2-4}
 & 4 cores & 8 cores & 16 cores \\
 \midrule
 13.75 & 111.65 & 56.64 & 41.84 \\
 \bottomrule
 \end{tabular}
\caption{Computation Time (in seconds)}
\end{table}
\end{document}

stat

"Universal Inference for Incomplete Discrete Choice Models" by Hiroaki Kaido and Yi Zhang, arXiv:2501.17973v1, licensed under CC BY-SA 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
 & LJ Speech & VCTK \\
 \midrule
 Insertion Errors & 61.89 \% & 36.36 \% \\
 Substitution Errors & 35.13 \% & 28.28 \% \\
 Deletion Errors & 2.98 \% & 35.35 \% \\
 \bottomrule
 \end{tabular}
\caption{Percentage of Different Types of Errors Across datasets}
\end{table}
\end{document}

eess

Anirudh S. Sundar
Chao-Han Huck Yang
David M. Chan
Shalini Ghosh
Venkatesh Ravichandran
Phani Sankar Nidadavolu

"Multimodal Attention Merging for Improved Speech Recognition and Audio Event Classification" by Anirudh S. Sundar, Chao-Han Huck Yang, David M. Chan, Shalini Ghosh, Venkatesh Ravichandran, and Phani Sankar Nidadavolu, arXiv:2312.14378v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccccc}\toprule
 time (s) && $\mathbb{P}^1$ & $\mathbb{P}^2$ & $\mathbb{P}^3$ & $\mathbb{P}^4$ & $\mathbb{P}^5$\\
\midrule
Runge-Kutta && 4.60E-01 & 3.73E+01 & 1.12E+03 & 1.51E+04 & 2.00E+05\\
SDG/SDC && 1.93E+00 & 6.56E+00 & 1.83E+01 & 3.67E+01 & 6.94E+01\\
Ratio && 0.24 & 5.7 & 61 & 411 & 2882 \\
\midrule
\bottomrule
\end{tabular}
\caption{The ratio of the computational time (s) of using the third order Runge-Kutta method, the SDG/SDC method and the adaptive SDG/SDC method when the desired accuracy is achieved (spatial error dominate). For the linear hyperbolic equation~, with $160$ elements in space.}
\end{table}
\end{document}

math

"Efficient Time Discretization for Exploring Spatial Superconvergence of Discontinuous Galerkin Methods" by Xiaozhou Li, arXiv:2312.09592v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of different portfolio strategies on different datasets. Bold represents the maximum (best) value, and underline represents the minimum (worst) value.}
\begin{tabular}{l|cc|cc|cc}
\toprule
 & \multicolumn{2}{c|}{CS}&\multicolumn{2}{c|}{EGD} &\multicolumn{2}{c}{B\&H} \\
\midrule
Dataset & APY & Sharpe & APY & Sharpe & APY & Sharpe\\
\midrule
NYSE & 0.162 & \textbf{14.360} & \textbf{0.162} & 14.310& \underline{0.129} & \underline{9.529} \\
DJIA & \textbf{-0.099} & \textbf{-8.714} & -0.101 & -8.848& \underline{-0.126} & \underline{-10.812} \\
SP500 & \textbf{0.104} & \textbf{4.595} & 0.101 & 4.395& \underline{0.061} & \underline{1.347} \\
TSE & 0.124 & 10.225& \underline{0.123} & \underline{10.204} & \textbf{0.127} & \textbf{10.629} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Convex optimization over a probability simplex" by James Chok and Geoffrey M. Vasil, arXiv:2305.09046v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{D1-BDBR savings of LSRN-PCGC against V-PCC v22~}
\begin{tabular}{lccc}
\toprule
 \multirow{3}{*}{Point Cloud Sequence} & {LSRN-PCGC} & {LSRN-PCGC} \\
 & with G-PCC & with OctAttention \\
 & {vs. V-PCC} & {vs. V-PCC} \\
\midrule
loot & $-33.8\%$ & $-52.2\%$ \\
redandblack & $-46.2\%$ & $-61.3\%$ \\
soldier & $-45.0\%$ & $-58.7\%$ \\
queen & $-44.6\%$ & $-59.0\%$ \\
longdress & $-35.3\%$ & $-52.7\%$ \\
basketball\_player\_vox11 & $-31.0\%$ & $-42.5\%$ \\
dancer\_player\_vox11 & $-35.1\%$ & $-46.7\%$ \\
\midrule
\textbf{8iVFB Average} & {$-40.1\%$} & {$-56.2\%$}\\
\midrule
\textbf{MPEG Cat2 Average} & {$-38.7\%$} & {$-53.3\%$}\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"Lightweight super resolution network for point cloud geometry compression" by Wei Zhang, Dingquan Li, Ge Li, and Wen Gao, arXiv:2311.00970v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Jakwang Kim
Jiyoung Park
Anirban Bhattacharya

"Robust Estimation in metric spaces: Achieving Exponential Concentration with a Fréchet Median" by Jakwang Kim, Jiyoung Park, and Anirban Bhattacharya, arXiv:2504.14161v1, 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|cc}
 Parameter & Value & Description\\
 \hline
$\beta$ & 0.0052 & strength of feedback from tension to GTPase activation\\
$b$ & 0.2530 & basal activation rate \\
$\gamma$ & 1.6 & scaled rate of feedback activation \\
$G_T$ & 2 & mechanical activation constant \\
$\ell_0$ & 1 & rest length \\
$\phi_1$ & 0.9 & Hill function amplitude \\
$\phi_2$ & 2 & Hill function amplitude \\
$G_h$ & 0.4 & half-maximum GTPase activity \\
$\epsilon$ & 0.1 & rate of contraction \\
$n$, $p$, $m$ & 4 & Hill coefficients \\
 \end{tabular}
\caption{Model parameters for the GTPase activation model .}
\end{table}
\end{document}

math

Kateryna Nechyporenko
Peter Ashwin
Krasimira Tsaneva-Atanasova

"A Novel Route to Oscillations via non-central SNICeroclinic Bifurcation: unfolding the separatrix loop between a saddle-node and a saddle" by Kateryna Nechyporenko, Peter Ashwin, and Krasimira Tsaneva-Atanasova, arXiv:2412.12298v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters for the proposed method}
\begin{tabular}{ll} % {lccc} ±íÊ¾¸÷ÁÐÔªËØ¶ÔÆë·½Ê½£¬left-l,right-r,center-c
		\hline
		Variables&Value\\\hline
		$\gamma$&-2\\
		$R$&4\\
		$u_{\max}$&15\\
		$M$&25\\
		$Q^+$&$\text{diag}\{[0.01,\cdots,0.01,0.02,0.02]\}$\\
		$\Gamma$&$[1,10,5,1]$\\
		$G_r$&$[100,120,140,160,160,150,140,140]^\top$\\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

"Bayesian Optimization Assisted Meal Bolus Decision Based on Gaussian Processes Learning and Risk-Sensitive Control" by Deheng Cai, Wei Liu, Linong Ji, and Dawei Shi, arXiv:2101.08044v1, 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{Dataset Summary}
\begin{tabular}{l|c}
 \hline
 Earthquakes & 155 \\\hline
 Temporal window & 2018-2021 \\\hline
 Image channels & 2 (VV and VH) \\\hline
 Area size & $20km \times 20km$ \\\hline
 Temporal difference & 1-13 days \\\hline
 Time series length & 3 \\\hline
 Patch size & $512 \times 512$ \\\hline
 Magnitudes & $> 4$ mb \\\hline
 \end{tabular}
\end{table}
\end{document}

cs

"QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1" by Daniele Rege Cambrin and Paolo Garza, arXiv:2403.18116v1, 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{\textbf{Accuracy analysis of the supplement experiment on \textit{Lost and Found}} (\%).}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Group} & \multicolumn{2}{c|}{\textbf{SwiftNet-RGB}} &\multicolumn{2}{c|}{\textbf{EAFNet-RGBD}}\\
\hline
\textbf{Class} & Road & Obstacle & Road & Obstacle\\
\hline
\rule[-1ex]{0pt}{3.5ex} \textbf{Precision} & 85.4& 26.5&\textbf{88.2}& \textbf{76.2}\\
\hline
\rule[-1ex]{0pt}{3.5ex} \textbf{Recall} & 63.9&49.8& \textbf{75.9}& \textbf{63.0}\\
\hline
\rule[-1ex]{0pt}{3.5ex} \textbf{IoU} & 56.2& 20.9& \textbf{68.9}& \textbf{52.7}\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"Polarization-driven Semantic Segmentation via Efficient Attention-bridged Fusion" by Kaite Xiang, Kailun Yang, and Kaiwei Wang, arXiv:2011.13313v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Jin Yuan
Xuelan Qiu
Jinran Wu
Jiesi Guo
Weide Li
You-Gan Wang

"Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study" by Jin Yuan, Xuelan Qiu, Jinran Wu, Jiesi Guo, Weide Li, and You-Gan Wang, arXiv:2406.11847v1, 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.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Word error rates (WERs) of the child ASR experiment using a BLSTM-based acoustic model adapted from adult speech. The left two columns indicate whether $f_o$ normalization (``Norm?'') and data augmentation using $f_o$ perturbation (``Aug?'') were used. WERs for both CMU Kids and OGI Kids' are reported in the latter columns.}
\begin{tabular}{cc|cc}
 \toprule
 \textbf{Norm?} & \textbf{Aug?} & \textbf{CMU Kids} & \textbf{OGI Kids'} \\
 \midrule \midrule
 No & No & 16.88 & 6.84 \\
 Yes & No & 16.93 & 6.50 \\
 No & Yes & 16.63 & 5.85 \\
 Yes & Yes & \textbf{16.47} & \textbf{5.52} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition" by Gary Yeung, Ruchao Fan, and Abeer Alwan, arXiv:2102.09106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

AI exposure predicts unemployment risk

"AI exposure predicts unemployment risk" by Morgan Frank, Yong-Yeol Ahn, and Esteban Moro, arXiv:2308.02624v1, 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}{lll}
\hline
Sample-size & Newton's iterations & Fixed point iterations \\ \hline
$m = 801$ &
\begin{minipage}{5.5cm}{\begin{equation*}
\begin{split}
\dot{x}_{1}(t) = -0.100 x_{1}(t) + 2.000 x_{2}(t)\\
\dot{x}_{2}(t) = -2.001 x_{1}(t) - 0.100 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} &
\begin{minipage}{5.5cm}{\begin{equation*}
\begin{split}
\dot{x}_{1}(t) = -0.100 x_{1}(t) + 2.000 x_{2}(t)\\
\dot{x}_{2}(t) = -2.001 x_{1}(t) - 0.100 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} \\ \hline
$m = 201$ &
\begin{minipage}{5.5cm}{\begin{equation*}
\begin{split}
\dot{x}_{1}(t) = -0.100 x_{1}(t) + 2.000 x_{2}(t)\\
\dot{x}_{2}(t) = -2.001 x_{1}(t) - 0.100 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} &
\begin{minipage}{5.5cm}{\begin{equation*}
\begin{split}
\dot{x}_{1}(t) = -0.100 x_{1}(t) + 2.000 x_{2}(t)\\
\dot{x}_{2}(t) = -2.001 x_{1}(t) - 0.100 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} \\ \hline
$m = 41$ &
\begin{minipage}{5.5cm}{\begin{equation*}
\begin{split}
\dot{x}_{1}(t) = -0.101 x_{1}(t) + 2.003 x_{2}(t)\\
\dot{x}_{2}(t) = -2.003 x_{1}(t) - 0.101 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} &
\begin{minipage}{5.5cm}{\begin{equation*}
\begin{split}
&\dot{x}_{1}(t) = -0.101 x_{1}(t) + 2.004 x_{2}(t)\\
&\dot{x}_{2}(t) = -2.004 x_{1}(t) - 0.101 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} \\ \hline
$m = 31$ &
\begin{minipage}{5.5cm}{\begin{equation*}
\begin{split}
\dot{x}_{1}(t) = -0.102 x_{1}(t) + 2.009 x_{2}(t)\\
\dot{x}_{2}(t) = -2.008 x_{1}(t) - 0.103 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} &
\begin{minipage}{5.5cm}{\begin{equation*}
\begin{split}
\dot{x}_{1}(t) = -0.100 x_{1}(t) + 2.015 x_{2}(t)\\
\dot{x}_{2}(t) = -2.014 x_{1}(t) - 0.100 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} \\ \hline
\end{tabular}
\caption{Linear damped oscillator: the discovered governing equations using IRK-SINDy in the approach of Newton's iterations and fixed point iterations for various sample-size $m$.}
\end{table}
\end{document}

math

Mehrdad Anvari
Hamidreza Marasi
Hossein Kheiri

"Impilict Runge-Kutta based sparse identification of governing equations in biologically motivated systems" by Mehrdad Anvari, Hamidreza Marasi, and Hossein Kheiri, arXiv:2502.20319v1, 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{Summary Statistics of Customer-Level Demographic Variables}
\begin{tabular}{lllllllll}
 \hline
 Variable & Mean & SD & 25th & 50th & 75th & (Min, Max) & Count & Missing (\%) \\ \hline
 \textit{Age} & 40.96 & 12.75 & 31 & 39 & 49 & (7, 99) & 4,302,771 & 27.49 \\
 \textit{HouseholdIncome} & 1854.37 & 1471.55 & 824.54 & 1358.21 & 2448.73 & (8, 30826) & 4,377,957 & 26.23 \\
 \textit{PanelLength} & 19.76 & 19.39 & 1 & 14 & 34 & (1, 63) & 5,934,248 & 0.00 \\ \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

Channel Choice and Customer Value

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"Channel Choice and Customer Value" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2508.00208v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c}
 Feature & Interpretation & Functional Form \\
 \hline
 $Z_j$ & Primitive Feature& $Z_j \sim U[0,1]$, $j=1,...m$ \\
 $X_i$ & Secondary Feature & $X_i = \sum_{k=1}^{h_k} c_i X_i^k, i=1,...p$\\
 $h_k$ & Degree & $h_k \sim U\{0,1,2,3\}$\\
 $c_i$ & Coefficients & $c_i \sim U[0,1]$, $i=1,...p$ \\
 $b$ & Probability Black & $b=\max\{0,\min\{1,\tilde{b}\}\}$, \\
 & & $\tilde{b} \sim \begin{cases}
 \mathcal{N}(0.1,.04) & \frac{1}{m}\sum_{j=1}^{m} Z_j \leq \tau_b\\
 \mathcal{N}(0.9,.04) & \frac{1}{m}\sum_{j=1}^{m} Z_j > \tau_b
 \end{cases}$\\
 $\tau_b$ & Threshold on $b$ & $\frac{1}{2} +1.2\sqrt{1/(12m)}$
 \\
 & (based Irwin-Hall distribution) & \\
 $B$ & Indicator for Black & $B \sim \text{Bernoulli}(b)$ \\
 $\tilde{P}(Y)$ & Score of Outcome & $\tilde{P}(Y) = \sum_{i} \left[d_i X_i^k + d_{iB} B\right]$ \\
 $P(Y)$ & Normalized Score of Outcome & $P(Y) = \frac{\tilde{P}(Y) - \min(\tilde{P}(Y))}{\max(\tilde{P}(Y)) - \min(\tilde{P}(Y))}$\\
 $Y$ & Realized Outcome & $Y \sim \begin{cases} \text{Bernoulli}(0.1) & P(Y) \leq \tau \\\text{Bernoulli} (0.9) & P(Y)> \tau \\\end{cases}$
 \\
 $d_i$ & Coefficients for features $X$ & $d_i \sim U[0,1]$\\
 $d_{iB}$ & Coefficients for indicator for Black & $d_{iB} \sim U[0,u_B]$\\

 \end{tabular}
\caption{Description of several variables we use in our simulation study and their functional forms. For ease of notation, we omit the index denoting individuals in the dataset. Unspecified constants were selected by inspection to match key indicators across scenario and are specified in Table 8.}
\end{table}
\end{document}

stat

Hadi Elzayn
Emily Black
Patrick Vossler
Nathanael Jo
Jacob Goldin
Daniel E. Ho

"Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features" by Hadi Elzayn, Emily Black, Patrick Vossler, Nathanael Jo, Jacob Goldin, and Daniel E. Ho, arXiv:2310.01679v1, 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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c}
\toprule
\textbf{Notation} & \textbf{Description} \\ \midrule
$S$ & RCT indicator \\
$W$ & Patient baseline characteristics \\
$A$ & Treatment indicator \\
$Y$ & Outcome \\ \midrule
$Q_P(S,W,A)=E_P(Y\mid S,W,A)$ & Outcome regression \\
$\bar{Q}_P(W,A)=E_P(Y\mid W,A)$ & Outcome regression, marginalized over $S$ \\
$\theta_P(W)=E_P(Y\mid W)$ & Outcome regression, marginalized over $S$ and $A$ \\
$g_P(a\mid W)=P(A=a\mid W)$ & Treatment mechanism \\
$\Pi_P(s\mid W,A)=P(S=s\mid W,A)$ & RCT enrollment mechanism \\
$\tau_{S,P}(W,A)=E_P(Y\mid S=1,W,A)-E_P(Y\mid S=0,W,A)$ & Conditional average RCT-enrollment effect \\
$\tau_{A,P}(W)=E_P(Y\mid W,A=1)-E_P(Y\mid W,A=0)$ & Conditional average treatment effect (CATE) \\ \midrule
$\Psi^F(P_{O,U})=E_W[E(Y_1-Y_0\mid S=1,W)]$ & Covariate-pooled ATE full-data parameter \\
$\Psi^F_2(P_{O,U})=E_W[E(Y_1-Y_0\mid S=1,W)\mid S=1]$ & RCT-only ATE full-data parameter \\
$\tilde{\Psi}(P_0)=E_0[\tau_{A,0}(W)]$ & Pooled-ATE estimand \\
$\tilde{\Psi}_{\mathcal{M}_{A,w}}(P_0)=E_0[\tau_{A,\beta_0}(W)]$ & Pooled-ATE projection estimand \\
$\Psi^\#(P_0)=E_0[\Pi_0(0\mid W,0)\tau_{S,0}(W,0)-\Pi_0(0\mid W,1)\tau_{S,0}(W,1)]$ & Bias estimand \\
$\Psi^\#_{\mathcal{M}_{S,w}}(P_0)=E_0[\Pi_0(0\mid W,0)\tau_{S,\beta_0}(W,0)-\Pi_0(0\mid W,1)\tau_{S,\beta_0}(W,1)]$ & Bias projection estimand \\
\bottomrule
\end{tabular}
\caption{Notations and their descriptions.}
\end{table}
\end{document}

stat

Sky Qiu
Jens Tarp
Andrew Mertens
Mark van der Laan

"An Estimator-Robust Design for Augmenting Randomized Controlled Trial with External Real-World Data" by Sky Qiu, Jens Tarp, Andrew Mertens, and Mark van der Laan, arXiv:2501.17835v1, 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{ Estimates of propensity parameter ${\beta}_{c1}$ and population mean ${\mu}$ for $S_r=U$, non-probability sampling fraction $f_c = 0.5$, population size $N=1,000$ (scenario S7) }
\begin{tabular}{c|cccc}
 & $Mean$ & $SE$ & $\overline{\widehat{SE}}$ & $95\%CI$ \\ %& $Mean$ & $SE$ & $\overline{\widehat{SE}}$ & $SE_U$ & $95\%CI$ \\
 \hline
 & \multicolumn{4}{c}{} \\
$\hat{\beta}_{ILR/PILR}$ & 1.01 & 0.11 & 0.11 & 0.95 \\
$\hat{\beta}_{CLW}$ & 1.01 & 0.09 & 0.09 & 0.94 \\
 & \multicolumn{4}{c}{} \\
$\hat{\mu}_{ILR/PILR}$ & 1.00 & 0.07 & 0.07 & 0.94 \\
$\hat{\mu}_{CLW}$ & 1.00 & 0.08 & 0.08 & 0.94
\end{tabular}
\end{table}
\end{document}

stat

Vladislav Beresovsky
Julie Gershunskaya
Terrance D. Savitsky

"Review of Quasi-Randomization Approaches for Estimation from Non-probability Samples" by Vladislav Beresovsky, Julie Gershunskaya, and Terrance D. Savitsky, arXiv:2312.05383v3, 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{Irreducible representations of $S_3$ group elements}
\begin{tabular}{lllc}
 \toprule
 $S_3$ & $I$ & $S$ & $V$ \\
 \midrule
 $e$ & $1$ & $1$ & $\begin{bmatrix}1&0 \\ 0&1\end{bmatrix}$ \\
 $r$ & $1$ & $1$ & $\begin{bmatrix}e^{i\frac{2\pi}{3}} & 0 \\ 0 & e^{-i\frac{2\pi}{3}}\end{bmatrix}$ \\
 $r^2$ & $1$ & $1$ & $\begin{bmatrix}e^{-i\frac{2\pi}{3}} & 0 \\ 0 & e^{i\frac{2\pi}{3}}\end{bmatrix}$ \\
 $x$ & $1$ & $-1$ & $\begin{bmatrix}0 & 1 \\ 1 & 0\end{bmatrix}$ \\
 $xr$ & $1$ & $-1$ & $\begin{bmatrix}0 & e^{-i\frac{2\pi}{3}} \\ e^{i\frac{2\pi}{3}} & 0\end{bmatrix}$ \\
 $xr^2$ & $1$ & $-1$ & $\begin{bmatrix}0 & e^{i\frac{2\pi}{3}} \\ e^{-i\frac{2\pi}{3}} & 0\end{bmatrix}$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

"Gapped Boundaries of Kitaev's Quantum Double Models: A Lattice Realization of Anyon Condensation from Lagrangian Algebras" by Mu Li, Xiao-Han Yang, and Xiao-Yu Dong, arXiv:2504.19512v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Prior distributions, whether or not they have a singularity at 0, Point of Maximum Curvature (PMCurv) and $P(|\theta| < \text{PMCurv})$. }
\begin{tabular}{lccc}
\toprule
Prior & Singularity? & PMCurv & $P(|\theta| < \text{PMCurv})$ \\ \midrule
Horseshoe $(q=2, a = 0.5, b = 0.5, \xi = 1)$ & Yes & 0.195 & 0.27 \\
Gambel 1 $(q=2, a = 0.5, b = 0.52, \xi = 1)$ & No & 0.183 & 0.25 \\
Gambel 2 $(q=2, a = 0.3, b = 1.6, \xi = 1)$ & No & 0.98 & 0.27 \\
Laplace $(\lambda = 1)$ & No & 0.34 & 0.29 \\
Spike and slab & Yes & - & - \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Maria De Iorio
Andreas Heinecke
Beatrice Franzolini
Rafael Cabral

"Singularities in Bayesian Inference: Crucial or Overstated?" by Maria De Iorio, Andreas Heinecke, Beatrice Franzolini, and Rafael Cabral, arXiv:2501.06618v1, 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|}

			\hline
			\textbf{Previous Methods} & \textbf{Accuracy\%} \\\hline

			Chen et al.~ & 96.7 \\\hline
			Z.Ahmad et al.~ & 96.7 \\\hline
			\textbf{Proposed Method} & \textbf{98.3} \\
			\hline
		\end{tabular}
\caption{ Comparison of Accuracies of proposed method with previous methods on inertial component of Kinect V2 dataset}
\end{table}
\end{document}

eess

"Multidomain Multimodal Fusion For Human Action Recognition Using Inertial Sensors" by Zeeshan Ahmad and Naimul Khan, arXiv:2008.09748v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyper-parameters}
\begin{tabular}{ll}
 \toprule
 Name & Value \\
 \midrule
 Batch size & 1024 \\
 Replay memory size & 16384 ($2^{14}$) \\
 $\lambda$ & 0.9 \\
 Rollout length & 32 \\
 Burn-in samples & 1024 \\
 Learning rate & 0.001 to 0 \\
 $\bar{c}_D$ (Formula ) & 0.5 \\
 $\bar{\rho}_D$ (Formula ) & 1.0 \\
 $\bar{c}_V$ (Formula ) & 1.0 \\
 $\bar{\rho}_V$ (Formula ) & 1.0 \\
 $\epsilon$ (Formula ) & 0.2 \\
 $1/\eta$ & 0.5 \\
 $b$ (Formula ) & 0.5 \\
 Optimizer & Adam \\
 \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.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccccccc}
\hline
& Pruning & Global & Global Int & Two-Step & Semi\\
\hline
Rectangular & 0.57 & 0.48 & 0.38 & 0.47 & 0.47 \\
Circular & 0.58 & 0.57 & 0.47 & 0.53 & 0.55 \\
Sine cosine & 0.44 & 0.34 & 0.29 & 0.37 & 0.37 \\
Elliptical & 2.60 & 2.59 & 2.59 & 2.60 & 2.58 \\
\hline
\end{tabular}
\caption{Median RMSE for $n=100$ and $M=500$ Monte Carlo iterations.}
\end{table}
\end{document}

stat

Early Stopping for Regression Trees

"Early Stopping for Regression Trees" by Ratmir Miftachov and Markus Reiß, arXiv:2502.04709v2, 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
Country & Alpha \\ \midrule
Brazil & -0.636 \\
UK & -0.309 \\
US & -0.191 \\
France & -0.007 \\
Canada & 0.769 \\
Germany & 1.095 \\
Japan & 1.181 \\
India & 1.616 \\
China & 2.646 \\ \bottomrule
\end{tabular}
\caption{Jensen's alpha (CAPM) for each country's index}
\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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sub-period net outperformance (\% per year)}
\begin{tabular}{lcccc}
\toprule
Period & 1994–1999 & 2000–2009 & 2010–2019 & 2020–2024 \\ \midrule
Entropy FGP & $+2.7$ & $+3.4$ & $+2.9$ & $+1.1$ \\
Diversity FGP ($p=0.7$) & $+3.1$ & $+4.6$ & $+3.2$ & $+0.6$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Functionally Generated Portfolios Under Stochastic Transaction Costs: Theory and Empirical Evidence" by Nader Karimi and Erfan Salavati, arXiv:2507.09196v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Information and statistics of the 12 label and feature time series of the locations in New Zealand. The test for normal distribution is the Shapiro-Wilk Test at a significance level of 0.05, which was applied to each of the 12 complete time series that contains 1680 data points. ``Abbr.'' is the abbreviation and ``Std.'' is the standard deviation, applied to the other tables in this study.}
\begin{tabular}{llllll}
\textbf{Location} & \textbf{Abbr.} & \textbf{Coordinates} & \textbf{Mean} & \textbf{Std.} & \textbf{Is normally distributed} \\ \hline
Bay of Plenty & BOP & 37.25°S, 176.75°E & 0.0539 & 0.8376 & Yes \\
Bank Peninsula & BP & 43.75°S, 173.25°E & -0.0291 & 0.8573 & No \\
Chatham Island & CI & 43.75°S, 176.75°W & -0.0170 & 0.9278 & No \\
Cape Reinga & CR & 34.25°S, 172.25°E & 0.0652 & 0.8237 & No \\
Cook Strait & CS & 40.75°S, 174.25°E & 0.0286 & 0.6657 & Yes \\
Fiordland & F & 44.25°S, 167.25°E & 0.0519 & 0.9631 & No \\
Hauraki Gulf & HG & 36.75°S, 175.25°E & 0.0490 & 0.8536 & Yes \\
Otago Peninsula & OP & 45.75°S, 170.75°E & -0.0091 & 0.7822 & No \\
Raglan & R & 37.75°S, 174.75°E & 0.0792 & 0.8447 & No \\
Stewart Island & SI & 47.25°S, 167.75°E & 0.0053 & 0.8000 & No \\
Taranaki & T & 38.75°S, 174.25°E & 0.1074 & 0.9864 & Yes \\
Wairarapa & W & 41.25°S, 176.25°E & 0.1052 & 1.0462 & No
\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.


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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Possible Pulses and Their Analytical Expressions for PIM.}
\begin{tabular}{|c|c|}\hline
			Possible Pulses for BPSK & Analytical Expressions\\ \hline
			$\pm \psi_0(t) $ & $ \pm 2^{1/4} e^{-\pi t^2} $ \\ \hline
			$\pm \psi_1(t)$ & $ \pm 2^{1/4}(2 \sqrt{\pi})t e^{-\pi t^2}$\\ \hline
			$\pm \psi_2(t) $ & $ \pm \frac{2^{1/4}}{2\sqrt{2}} (8 \pi t^2 - 2) e^{- \pi t^2}$\\ \hline
			$\pm \psi_3(t)$ & $ \pm 2^{1/4}\left( \frac{4 \pi \sqrt{2 \pi} t^3 - 3\sqrt{2 \pi}t }{\sqrt{3}} \right) e^{-\pi t^2}$\\ \hline
	\end{tabular}
\end{table}
\end{document}

eess

Pulse Index Modulation

Sultan Aldirmaz-Colak
Erdogan Aydin
Yasin Celik
Yusuf Acar
Ertugrul Basar

"Pulse Index Modulation" by Sultan Aldirmaz-Colak, Erdogan Aydin, Yasin Celik, Yusuf Acar, and Ertugrul Basar, arXiv:2101.09340v1, 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}
\hline
 & Highway & Generalized Highway & DGM \\
\hline
Layers & $4$ & $3$ & $2$ \\
Nodes per layer & $50$ & $50$ & $50$ \\
Total arameters & $20,901$ & $23,451$ & $24,467$ \\
\hline
\end{tabular}
\caption{Layer configurations for the highway and DGM network, in order to have comparable amount of parameters.}
\end{table}
\end{document}

q-fin

Laurens Van Mieghem
Antonis Papapantoleon
Jonas Papazoglou-Hennig

"Machine learning for option pricing: an empirical investigation of network architectures" by Laurens Van Mieghem, Antonis Papapantoleon, and Jonas Papazoglou-Hennig, arXiv:2307.07657v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of processed sequences with and without applying feature detection and SVD in the measurement fusion~process.}
\begin{tabular}{cccccc}
\toprule
\multirow{2}{*}{\textbf{Sequence}} & \multirow{2}{*}{\textbf{Scene} }& \multicolumn{2}{c}{\textbf{Fusion with 3 Measures}} & \multicolumn{2}{c}{\textbf{Fusion with 2 Measures}} \\\cmidrule{3-6}
& & \textbf{Translational [\%]} & \textbf{Angular [deg/m]} & \textbf{Translational [\%]} & \textbf{Angular [deg/m]}\\
\midrule
00 & Urban & 1.28 & 0.0051 & 1.31 & 0.0052 \\
01 & Highway & 2.36 & 0.0135 & 7.08 & 0.0122 \\
02 & Urban/Country & 1.15 & 0.0028 & 1.21 & 0.0030 \\
03 & Country & 0.93 & 0.0024 & 0.97 & 0.0022 \\
04 & Country & 0.98 & 0.0033 & 0.69 & 0.0031 \\
05 & Urban & 0.45 & 0.0018 & 0.91 & 0.0052 \\
07 & Urban & 0.44 & 0.0034 & 0.63 & 0.0022 \\
09 & Urban/Country & 0.64 & 0.0013 & 0.93 & 0.0014 \\
10 & Urban/Country & 0.83 & 0.0017 & 0.84 & 0.0017 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Iván García Daza
Monica Rentero
Carlota Salinas Maldonado
Rubén Izquierdo Gonzalo
Noelia Hernández Parra
Augusto Luis Ballardini
David Fernández Llorca

"Fail-Aware LIDAR-Based Odometry for Autonomous Vehicles" by Iván García Daza, Monica Rentero, Carlota Salinas Maldonado, Rubén Izquierdo Gonzalo, Noelia Hernández Parra, Augusto Luis Ballardini, and David Fernández Llorca, arXiv:2103.03626v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c}
 \hline
 Criteria & Avg. (STD) \\ \hline
 Enjoyment & 6.1 (1.02) \\ \hline
 Exploration & 6.1 (1.05) \\ \hline
 Expressiveness & 4.8 (1.36) \\ \hline
 Immersion & 4.6 (1.67) \\ \hline
 Results Worth Effort & 5.0 (1.59) \\ \hline
 \end{tabular}
\caption{The table shows the Creativity Support Index scores for Enjoyment, Exploration, Expressiveness, Immersion, and Results Worth Effort in terms of a 7-point Likert-scale.}
\end{table}
\end{document}

cs

Bekzat Tilekbay
Saelyne Yang
Michal Lewkowicz
Alex Suryapranata
Juho Kim

"ExpressEdit: Video Editing with Natural Language and Sketching" by Bekzat Tilekbay, Saelyne Yang, Michal Lewkowicz, Alex Suryapranata, and Juho Kim, arXiv:2403.17693v1, 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
Test Set & \multicolumn{2}{l|}{Internal Test set} & \multicolumn{2}{l|}{External Test Set} \\ \hline
Augmentation & 30\% & 50\% & 30\% & 50\% \\ \hline
Baseline on 100\% & 0.8569 & 0.8569 & 0.9298 & 0.9298 \\ \hline
None & 0.8251 & 0.854 & 0.859 & 0.9058 \\ \hline
Translate & 0.8615 & 0.8515 & 0.9106 & 0.9232 \\ \hline
Rotate & 0.8626 & 0.8587 & 0.9373 & 0.9251 \\ \hline
Gamma & 0.8464 & 0.868 & 0.8954 & 0.9428 \\ \hline
Mixup & 0.8574 & 0.8638 & 0.9042 & 0.9329 \\ \hline
Flip & 0.8584 & 0.8483 & 0.9249 & 0.9135 \\ \hline
\end{tabular}
\caption{The AUC ROC of the different augmentations calculated on 30\% and 50\% data for both internal and external test sets.}
\end{table}
\end{document}

eess

Jitesh Seth
Rohit Lokwani
Viraj Kulkarni
Aniruddha Pant
Amit Kharat

"Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations" by Jitesh Seth, Rohit Lokwani, Viraj Kulkarni, Aniruddha Pant, and Amit Kharat, arXiv:2102.12764v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccc}
 \toprule
 \textsc{Dataset}& \textsc{Poison Rate} (\%) & \textsc{Eval Clean} (\%) & \textsc{Success Rate} (\%)\\
 \midrule
 \rowcolor{orange!20}
 CIFAR10 & 0.0 & 93.35 & 10.0 \\
 CIFAR10 & 0.002 & 93.57 & 68.95 \\
 CIFAR10 & 0.01 & 93.35 & 97.57 \\
 CIFAR10 & 0.1 & 94.08 & 99.81 \\
 CIFAR10 & 0.2 & 94.01 & 100.00 \\
 CIFAR10 & 1 & 93.28 & 100.00 \\
 \hline
 \rowcolor{orange!20}
 GTSRB & 0.0 & 97.43 & 1.02 \\
 \rowcolor{orange!20}
 GTSRB & 0.002 & 97.65 & 1.54 \\
 GTSRB & 0.01 & 96.79 & 87.77 \\
 GTSRB & 0.1 & 97.78 & 99.57 \\
 GTSRB & 0.2 & 97.34 & 99.44 \\
 GTSRB & 1 & 97.92 & 99.98 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Jonathan Gallagher
Yasaman Esfandiari
Callen MacPhee
Michael Warren

"Provably effective detection of effective data poisoning attacks" by Jonathan Gallagher, Yasaman Esfandiari, Callen MacPhee, and Michael Warren, arXiv:2501.11795v1, 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|c}
Dataset
& Model
& $\substack{ \text{Training} \\ \text{set size }n}$
& $\substack{\text{Step sizes } \eta\\ \text{range}}$
& $\substack{\text{Batch size } b\\ \text{range}}$
& $\substack{c \ = \ \eta \cdot k\\ \text{range}}$
\\
\hline
 MNIST[1] & MLP & 60k & 0.1 - 0.01 & 32-64 & 190 - 9.4 \\
 Cifar10(0)[2] & DenseNet-BC-190[4] & 50k & 0.1 - 0.001 & 64 & 78 - 0.8\\
 Cifar100 [2] & ResNet-BiT[5] & 50k & 0.03 - 0.0003 & 4096 & 0.36 - 0.004\\
 ImageNet[3] & ResNet152[6] & 1.2M & 0.1 - 0.001 & 256 & 470 - 4.7 \\
 \hline
 \hline
\end{tabular}
\end{table}
\end{document}

math

"On the Trajectories of SGD Without Replacement" by Pierfrancesco Beneventano, arXiv:2312.16143v2, 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{PSNRs under different ISO settings}
\begin{tabular}{lccccc}
	\toprule
	 & ISO-800 & ISO-1600 & ISO-3200 & ISO-4800 & ISO-6400 \\
	\midrule
	k-Sigma Transform & 43.21 & \textbf{41.48} & \textbf{39.49} & 38.17 & \textbf{36.94} \\
	single-iso-1600 & 42.96 & \textbf{41.48} & 35.01 & 31.33 & 28.86 \\
	single-iso-3200 & 41.79 & 40.87 & \textbf{39.51} & 36.97 & 34.08 \\
	single-iso-6400 & 39.59 & 38.59 & 38.11 & 37.80 & \textbf{36.91} \\
	\bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

Yuzhi Wang
Haibin Huang
Qin Xu
Jiaming Liu
Yiqun Liu
Jue Wang

"Practical Deep Raw Image Denoising on Mobile Devices" by Yuzhi Wang, Haibin Huang, Qin Xu, Jiaming Liu, Yiqun Liu, and Jue Wang, arXiv:2010.06935v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Antonio Guerriero
Roberto Pietrantuono
Stefano Russo

"DeepSample: DNN sampling-based testing for operational accuracy assessment" by Antonio Guerriero, Roberto Pietrantuono, and Stefano Russo, arXiv:2403.19271v1, 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{The average selected numbers for CCNet-generated motion sequences. Baseline vs. CCNet: a group of 16 pairs of motion sequences generated by a baseline model and CCNet. Mean±std: mean and variance of the numbers of CCNet-generated motion sequences selected by all the participants.}
\begin{tabular}{ccc}
 \toprule
 Groups & numbers for CCNet (mean±std) \\ \midrule
 DAE-LSTM vs. CCNet & 12.31±2.34 \\
 ERD-4LR vs. CCNet & 11.63±2.87 \\
 PFNN vs. CCNet & 11.45±1.87 \\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Shuaiying Hou
Congyi Wang
Wenlin Zhuang
Yu Chen
Yangang Wang
Hujun Bao
Jinxiang Chai
Weiwei Xu

"A causal convolutional neural network for multi-subject motion modeling and generation" by Shuaiying Hou, Congyi Wang, Wenlin Zhuang, Yu Chen, Yangang Wang, Hujun Bao, Jinxiang Chai, and Weiwei Xu, arXiv:2101.12276v2, 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|ccc|ccc|ccc|}
			\hline
			& \multicolumn{3}{c|}{Size (\%)} & \multicolumn{3}{c|}{Global Power (\%)} & \multicolumn{3}{c|}{Local Power (\%)}
			& \multicolumn{3}{c|}{$\hat{k}_{LR}$} \\
			$T$ & 6 & 12 & 24 & 6 & 12 & 24 & 6 & 12 & 24 & 6 & 12 & 24 \\ \hline
			$n=500$ & 6.0 & 5.2 & 6.7 & 100 & 100 & 100 & 80 & 100 & 100 & 2.0 & 2.0 & 2.1 \\
			& (2.8) & (0.3) & (0.4) & (0.1) & (0.0) & (0.0) & (20.5)& (0.0) & (0.0) & (0.1)& (0.1) & (0.2) \\
			$n=1000$ & 5.6 & 4.9 & 5.5 & 100 & 100 & 100 & 81 & 100 & 100 & 2.0 & 2.0 & 2.0 \\
			& (2.3) & (0.3) & (0.3) & (0.0) & (0.0) & (0.0) & (21.1) & (0.0) & (0.0) & (0.0) & (0.0) & (0.1) \\
			$n=5000$ & 5.3 & 5.0 & 4.9 & 100 & 100 & 100 & 85 & 100 & 100 & 2.0 & 2.0 & 2.0 \\
			& (0.9) & (0.3) & (0.3) & (0.0) & (0.0) & (0.0) & (20.4) &(0.0) & (0.0) & (0.0) &(0.0) & (0.1) \\ \hline
		\end{tabular}
\caption{For each sample size combination $(n,T)$, we provide the average size and power in \% for the statistic $LR(2)$ (first three panels), and the average of the estimated number $\hat{k}_{LR}$ of factors obtained by sequential testing (last panel). Nominal size is $5\%$ for the first three panels, and $\alpha_n = 10/n$ for the last panel. Global power refers to the global alternative $\bar{\kappa}=0$, and local power refers to the local alternative $\bar{\kappa}=0.5$. In parentheses, we report the standard deviations for size, power, and $\hat{k}_{LR}$ across $100$ different draws of the factor path.}
\end{table}
\end{document}

q-fin

Latent Factor Analysis in Short Panels

Alain-Philippe Fortin
Patrick Gagliardini
Olivier Scaillet

"Latent Factor Analysis in Short Panels" by Alain-Philippe Fortin, Patrick Gagliardini, and Olivier Scaillet, arXiv:2306.14004v2, 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{sbiGRu layers Parameters}
\begin{tabular}{|c|c|c|}
		\hline
			\textbf{Layer (type)} & \textbf{Output Shape} & \textbf{Param} \\
			\hline
			GRU & [(None, None, 256)] & 296448 \\
			\hline
			GRU & [(None, 256)] & 296448 \\
			\hline
 Dense & [(None, 1024)] & 263168 \\
			\hline
 Dropout & [(None, 1024)] & 0 \\
			\hline
 Dense & [(None, 512)] & 524800 \\
			\hline
 Dropout & [(None, 512)] & 0 \\
			\hline
 Dense & [(None, 128)] & 65664 \\
			\hline
 Dropout & [(None, 128)] & 0 \\
			\hline
 Dense & [(None, 64)] & 8256 \\
			\hline
 Dense & [(None, 4)] & 260 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

eess

Maria Frasca
Davide La Torre
Gabriella Pravettoni
Ilaria Cutica

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Objective evaluation results on the impact of noise weight $\gamma$ in Appendix .}
\begin{tabular}{lccc}
 \toprule
 \textbf{Noise Weight} $\gamma$& \textbf{Faithfulness (stats.)} $\uparrow$ & \textbf{Faithfulness (latent)} $\uparrow$ & \textbf{DOA} $\uparrow$ \\
 \midrule
 $\gamma=0$ & $\mathbf{0.946}\pm0.001^a$ & $\mathbf{0.228}\pm0.005^a$ & $0.300\pm0.005^c$\\
 $\gamma=0.25$ & $0.945\pm0.001^{ab}$ & $0.215\pm0.005^b$ & $0.308\pm0.005^{bc}$ \\
 $\gamma=0.5$ & $0.944\pm0.001^b$ & $0.187\pm0.004^c$ & $0.320\pm0.006^{ab}$ \\
 $\gamma=1$ & $0.936\pm0.002^c$ & $0.127\pm0.003^d$ & $\mathbf{0.325}\pm0.007^a$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling" by Jingwei Zhao, Gus Xia, Ziyu Wang, and Ye Wang, arXiv:2310.16334v4, 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{Iteration number of our trained solver over different coefficient distributions. \footnotesize Re$10$ and Re$10^5$ refer to $Re=10,10^5$ with coefficients generated from white noise. The solver is trained only on coefficients from white noise with $Re = 1000$}
\begin{tabular}{|c|l|l|l|l|l|l|l|}
 \hline
 grid & noise & CIFAR10 & FMNIST & MNIST & mldata & Re$10$ & Re$10^5$ \\ \hline
 31x31 & 7 & 8.2 & 13.6 & 15.1 & 7.7 & 8.1 & 7 \\
 63x63 & 7.1 & 9.8 & 17.3 & 18.4 & 8.1 & 9 & 7.8 \\
 127x127 & 8 & 11.5 & 19.2 & 19.6 & 9.3 & 9 & 8 \\
 255x255 & 8 & 11.1 & 20.6 & 20.9 & 10.5 & 10 & 8 \\
 511x511 & 9 & 13.4 & 22.5 & 24 & 11.8 & 12 & 9.9 \\
 1023x1023 & 12 & 15.9 & 25.2 & 31 & 13.8 & 15 & 12 \\
 2047x2047 & 15 & 19.3 & 39.7 & 45.2 & 16.9 & 21 & 16 \\
 4095x4095 & 22 & 25.1 & 62.3 & 105.8 & 23.2 & 31.7 & 22 \\ \hline
 \end{tabular}
\end{table}
\end{document}

math

"MGCNN: a learnable multigrid solver for sparse linear systems from PDEs on structured grids" by Yan Xie, Minrui Lv, and Chensong Zhang, arXiv:2312.11093v2, 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|c|c}
 $U$ & 5 & 10 & 15 & 20 & 25 & 30 \\
 \hline
 \hline
 Analytical & 59 & 138 & & & & \\
 \hline
 Super-user & 60 & 142 & 229 & 336 & 443 & 533 \\
 \hline
 RGA & 63 & 147 & 236 & 347 & 455 & 551
 \end{tabular}
\caption{$\mathrm{round}(|f|)$ vs average users per helper $U$, $L=5$.}
\end{table}
\end{document}

math

Kagan Akcay
MohammadJavad Salehi
Antti Tölli
Giuseppe Caire

"Optimal Fairness Scheduling for Coded Caching in Multi-AP Multi-antenna WLAN" by Kagan Akcay, MohammadJavad Salehi, Antti Tölli, and Giuseppe Caire, arXiv:2312.02595v2, 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}{cccc}
		$\beta_t$ & MSE & Average $L^p$ & Average $L^*$ \\
		\midrule
		$[0,0.4)$ & 0.000000000021 & 0.479730599078 & 0.287987013094 \\
		$[0.7,1)$ & 0.000000000063 & 0.479651389605 & 0.240905881121 \\
	\end{tabular}
\end{table}
\end{document}

q-fin

"The QLBS Model within the presence of feedback loops through the impacts of a large trader" by Ahmet Umur Özsoy and Ömür Uğur, arXiv:2311.06790v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Superpixel-based Refinement for Object Proposal Generation" by Christian Wilms and Simone Frintrop, arXiv:2101.04574v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Sana Ahmadi
Pierre Bellec
Tristan Glatard

"Scaling up ridge regression for brain encoding in a massive individual fMRI dataset" by Sana Ahmadi, Pierre Bellec, and Tristan Glatard, arXiv:2403.19421v1, 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 statistics of the datasets.}
\begin{tabular}{l|r|r|r}
\hline
 & Amazon-book & Last-FM & Yelp2018 \\
\hline
\#Users & 70,679 & 23,566 & 45,919 \\
\#Items & 24,915 & 48,123 & 45,538 \\
\#Interactions & 847,733 & 3,034,796 & 1,185,068 \\
\hline
\#Entities & 88,572 & 58,266 & 90,961 \\
\#Relations & 39 & 9 & 42 \\
\#Triplets & 2,557,746 & 464,567 & 1,853,704 \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Chen Ma
Liheng Ma
Yingxue Zhang
Haolun Wu
Xue Liu
Mark Coates

"Knowledge-Enhanced Top-K Recommendation in Poincaré Ball" by Chen Ma, Liheng Ma, Yingxue Zhang, Haolun Wu, Xue Liu, and Mark Coates, arXiv:2101.04852v2, 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}{ccccccc}
\hline
 & \multicolumn{3}{c}{{\small{}Panasonic}} & \multicolumn{3}{c}{{\small{}Toshiba}}\tabularnewline
\cline{2-7} \cline{3-7} \cline{4-7} \cline{5-7} \cline{6-7} \cline{7-7}
 & {\small{}(A-1)} & {\small{}(A-2)} & {\small{}(A-3)} & {\small{}(B-1)} & {\small{}(B-2)} & {\small{}(B-3)}\tabularnewline
 & {\small{}1000h \& 2000h} & {\small{}1000h only} & {\small{}2000h only} & {\small{}1000h \& 2000h} & {\small{}1000h only} & {\small{}2000h only}\tabularnewline
\hline
\hline
{\small{}Joint profit} & {\small{}24.67} & {\small{}25.77} & {\small{}23.41} & {\small{}24.67} & {\small{}25.02} & {\small{}25.29}\tabularnewline
{\small{}Profit (Panasonic)} & {\small{}10.77} & {\small{}10.68} & {\small{}9.62} & {\small{}10.77} & {\small{}11.11} & {\small{}12.26}\tabularnewline
{\small{}Profit (Toshiba)} & {\small{}13.9} & {\small{}15.09} & {\small{}13.79} & {\small{}13.9} & {\small{}13.91} & {\small{}13.02}\tabularnewline
\hline
{\small{}No inventory consumers (\%)} & {\small{}18.61} & {\small{}19.08} & {\small{}18} & {\small{}18.61} & {\small{}18.74} & {\small{}17.78}\tabularnewline
{\small{}Average price (1000h Inc.; yen)} & {\small{}94.73} & {\small{}98.08} & {\small{}89.43} & {\small{}94.73} & {\small{}94.59} & {\small{}112.39}\tabularnewline
{\small{}Average price (2000h Inc.; yen)} & {\small{}172.57} & {\small{}178.26} & {\small{}167.33} & {\small{}172.57} & {\small{}174.36} & {\small{}173.92}\tabularnewline
{\small{}Average price (CFL; yen)} & {\small{}796.53} & {\small{}798.87} & {\small{}796.41} & {\small{}796.53} & {\small{}796.9} & {\small{}798.09}\tabularnewline
{\small{}Disposal (million)} & {\small{}3.04} & {\small{}3.11} & {\small{}2.94} & {\small{}3.04} & {\small{}3.06} & {\small{}2.89}\tabularnewline
\hline
{\small{}$\Delta$CS} & {\small{}-} & {\small{}-1.09} & {\small{}-0.41} & {\small{}-} & {\small{}-0.34} & {\small{}-2.03}\tabularnewline
{\small{}$\Delta$PS (excluding fixed cost)} & {\small{}-} & {\small{}1.2} & {\small{}-1.26} & {\small{}-} & {\small{}0.37} & {\small{}0.74}\tabularnewline
{\small{}$\Delta$TS (excluding Ext. / fixed costs)} & {\small{}-} & {\small{}0.11} & {\small{}-1.67} & {\small{}-} & {\small{}0.03} & {\small{}-1.29}\tabularnewline
{\small{}$\Delta$Ext. (electricity usage)} & {\small{}-} & {\small{}-1.01} & {\small{}0.92} & {\small{}-} & {\small{}-0.31} & {\small{}0.77}\tabularnewline
{\small{}$\Delta$Ext. (waste disposal)} & {\small{}-} & {\small{}0.03} & {\small{}-0.02} & {\small{}-} & {\small{}0.01} & {\small{}-0.02}\tabularnewline
{\small{}$\Delta$TS (excluding fixed costs)} & {\small{}-} & {\small{}1.08} & {\small{}-2.57} & {\small{}-} & {\small{}0.33} & {\small{}-2.04}\tabularnewline
{\small{}Upper bound of Fixed cost savings} & {\small{}-} & {\small{}0.06} & {\small{}0.83} & {\small{}-} & {\small{}0.06} & {\small{}0.83}\tabularnewline
\hline
\end{tabular}
\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.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|c|}
\hline
\bf Split & \bf \#Samples & \bf \#Fake & \bf \#Real\\
\hline
Train & 6420 & 3060 & 3360\\
Validation & 2140 & 1020 & 1120\\
Test & 2140 & 1021 & 1120\\
\hline
\hline
\end{tabular}
\caption{Results on validation set for COVID-19 Fake news identification task for English language}
\end{table}
\end{document}

cs

Tathagata Raha
Vijayasaradhi Indurthi
Aayush Upadhyaya
Jeevesh Kataria
Pramud Bommakanti
Vikram Keswani
Vasudeva Varma

"Identifying COVID-19 Fake News in Social Media" by Tathagata Raha, Vijayasaradhi Indurthi, Aayush Upadhyaya, Jeevesh Kataria, Pramud Bommakanti, Vikram Keswani, and Vasudeva Varma, arXiv:2101.11954v2, 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{Ablations on DPMamba (S).}
\begin{tabular}{c|ccc}
 \toprule
 Configuration & SI-SNRi (dB) & SDRi (dB) & \#Params (M) \\ \hline \hline
 Unidirectional & 16.9 & 17.2 & 7.4 \\
 LayerNorm & 20.6 & 20.8 & 8.1 \\ \hline
 H=8 & 20.6 & 20.8 & 7.7 \\
 H=32 & 20.6 & 20.8 & 8.9 \\ \hline
 Without DM & 20.0 & 20.2 & 8.1 \\ \hline
 Default (S, 100 epochs) & 20.6 & 20.8 & 8.1 \\ \hline
 \end{tabular}
\end{table}
\end{document}

cs

"Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation" by Xilin Jiang, Cong Han, and Nima Mesgarani, arXiv:2403.18257v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Various fit statistics of the final BR-models across transition types, having deleted influential observations. Information criteria include the \textit{Akaike Information Criterion} (AIC). The $R_\mathrm{F}^2$ refers to the pseudo coefficient of determination. Skewness refers to the Fisher-Pearson skewness coefficient in summarising the distribution of Pearson residuals of each BR-model. The $p$-values are those originating from a KS-test in testing the residual distribution for normality.}
\begin{tabular}{llllll}
\toprule
\multirow{2}{*}{\textbf{BR-model $kl$}} & \multicolumn{5}{c}{\textbf{Fit statistics}} \\
 & Sample size & AIC & $R^2_{\mathrm{F}}$ & Skewness & KS $p$-values \\ \midrule
PP & 190 & -2,039 & 69.96\% & 0.476 & 13.56\% \\
PD & 189 & -2,445 & 87.21\% & 0.710 & 16.12\% \\
PS & 189 & -2,031 & 60.08\% & -0.849 & 3.24\% \\
DD & 190 & -1,269 & 33.27\% & -0.198 & 91.67\% \\
DS & 190 & -1,535 & 63.47\% & 1.405 & 10.67\% \\
DW & 190 & -1,529 & 39.74\% & 0.872 & 38.84\% \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework" by Arno Botha, Tanja Verster, and Roland Breedt, arXiv:2502.14479v1, 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}
\hline
{\bf Require}
\\
{1.} A dataset $D$ with labels ``meritorious'' and ``non-meritorious''. \\
{2.} A specific classification method.
\\ \hline
{\bf Compute a value of the $I$-index}
\\
{1.} Randomly assign $(1-r)100\%$ of the dataset $D$ as a training set, and
the remaining \\ \quad $r100\%$ as a testing set.
\\
{2.} Train a classifier $f$ using the specific classification method based on the training set.
\\
{3.} Apply $f$ on the testing set, leading to a meritorious subset with the indices
$d_m\subseteq D$ of \\ \quad the testing set.
\\
{4.} Compute the $I$-index using $(x_i,y_i)$, $i\in d_m$.
\\ \hline
\end{tabular}
\caption{Calculating the $I$-index.}
\end{table}
\end{document}

q-fin

Peiheng Gao
Ning Sun
Xuefeng Wang
Chen Yang
Ričardas Zitikis

"NLP-based detection of systematic anomalies among the narratives of consumer complaints" by Peiheng Gao, Ning Sun, Xuefeng Wang, Chen Yang, and Ričardas Zitikis, arXiv:2308.11138v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lllll}
 & & CryptoEmu & CryptoEmu& \\
Operation & HELib & (single core) & (multi-core) & Speedup\\
\hline
Addition & 10484.1ms & 4400.67ms & 566.149ms &18.518x\\
Subtraction & 10962.2ms & 5088.57ms & 599.396ms & 18.289x\\
Multiplication (unsigned) & 69988.9ms & 86389.8ms & 10396.1ms & 6.732x\\
Multiplication (signed) & 81707.2ms & 92408.4ms & 10985.4ms & 7.438x\\
LLS (immediate)& 0.534724ms & 0.0040335ms & 0.0040335ms & 132.571x\\
Bitwise XOR & 1.52444ms & 416.013 ms & 47.1986ms & -30.9613x\\
Bitwise OR & 771.12ms & 416.146 ms & 47.6171ms & 16.194x\\
Bitwise AND & 756.641ms & 411.014ms & 47.6001ms & 15.90x\\
Bitwise NOT & 1.8975ms & 0.012508 ms & 0.012508 ms & 151.703x\\
Comparison & 4706.07ms & 519.757ms & 110.416ms & 42.62x\\
\end{tabular}
\caption{HELib vs CryptoEmu}
\end{table}
\end{document}

cs

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


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}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The empirical comparison of estimated time-lagged weight matrix $\mathbf{W}_t$ in the dynamic SVAR model setup. The number in parenthesis denotes the standard deviation. }
\begin{tabular}{ccccccc}
\toprule
Methods & Metric & F1 & F2 & & \\
\midrule
\multirow{4}{*}{DYNOTEARS} & FDR & 0.40 (0.01) & 0.36(0.01) & \\
& TPR & 0.27(0.01) & 0.27(0.01) & \\
& SHD & 3.64(0.02) & 3.75(0.02) & \\
\midrule
\multirow{4}{*}{Proposed} & FDR & \textbf{0.28(0.02)} & \textbf{0.26(0.01)} & \\
& TPR & \textbf{0.95(0.03)} & \textbf{0.98(0.00)} & & \\
 & SHD & \textbf{2.20(0.01)} & \textbf{1.93(0.08)} & \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Dynamic Causal Structure Discovery and Causal Effect Estimation" by Jianian Wang and Rui Song, arXiv:2501.06534v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Mirco Ciallella
Stephane Clain
Elena Gaburro
Mario Ricchiuto

"Very high order treatment of embedded curved boundaries in compressible flows: ADER discontinuous Galerkin with a space-time Reconstruction for Off-site data" by Mirco Ciallella, Stephane Clain, Elena Gaburro, and Mario Ricchiuto, arXiv:2312.07170v1, 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|}
			\hline
			\hline
		$\nu$ & $\mu$ & $\lambda$ & $r$ \\
		\hline
		0.35 & 7.2000e+10 & 1.6800e+11 & 0.6797\\
		0.49 & 7.2000e+10 & 3.5280e+12 & 0.5999 \\
		0.49999 & 7.2000e+10 & 3.5999e+15 & 0.5947 \\
		0.5 & 7.2000e+10 & $\infty$ & 0.5649 \\
		\hline
		\hline
		\end{tabular}
\caption{Test . Different values of $\nu$, together with the corresponding computed values of $\mu$, $\lambda$ and the Sobolev exponent.}
\end{table}
\end{document}

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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 Model & Active Parameters & One-Step SPE \\
\hline
GNAR(1, [1]) & 2 & 5.088 \\
 VAR(1) & $140^2$ & 12.504 \\
 Res. VAR(1) & 3830 & 9.976 \\
 Sparse VAR(1) & 3609 & 10.169 \\
 AR(1) & $140$ & 88.181 \\
 GNAR(1, [1])* & 141 & 4.989
\end{tabular}
\caption{One-step prediction error, $\hat{\boldsymbol{X}}_{448}$ is predicted using the previous 447 observations.}
\end{table}
\end{document}

stat

Guy Nason
Daniel Salnikov
Mario Cortina-Borja

"New tools for network time series with an application to COVID-19 hospitalisations" by Guy Nason, Daniel Salnikov, and Mario Cortina-Borja, arXiv:2312.00530v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Hyperparameter search space for Random Forest, XGBoost, SVM, and other SVM-based methods (RFSVM, COSSVM, LMNNSVM, and DWD).}
\begin{tabular}{|lc|}
 \hline
 \multicolumn{2}{|c|}{\textbf{Random Forest}} \\
 \hline
 Max. Depth & $\{10^i \vert i=1,\ldots,10\}$ and \texttt{None} \\
 \hline
 Max. Features & \{1\%, 5\%, 10\%, 20\%, 30\%\} \\
 \hline
 Min. Samples Leaf & \{1, 2, 4\} \\
 \hline
 Min. Samples Split & \{2, 5, 10\} \\
 \hline
 Number of trees & 500 \\
 \hline\hline
 \multicolumn{2}{|c|}{\textbf{XGBoost}} \\
 \hline
 Max. Depth &	$\{x \vert x \in \mathbb{N}, 4 \leq x \leq 15\}$\\
 \hline
 Subsample & $[0.8,1]$ \\
 \hline
 Column sample by tree & $[0.5,1]$ \\
 \hline
 Regularization Lambda & $[0,1]$\\
 \hline
 Max. number of iterations ~~~& 500\\
 \hline\hline
 \multicolumn{2}{|c|}{\textbf{SVM}} \\
 \hline
 C & $\{10^i \vert~ i=-2,\dots,4 \}$\\
 \hline
 $\gamma$ & $\{10^i \vert~ i=-4,\dots,2 \}$ \\
 \hline\hline
 \multicolumn{2}{|c|}{\textbf{RFSVM, COSSVM, LMNNSVM, and DWD}} \\
 \hline
 C & $\{10^i \vert~ i=-2,\dots,4 \}$\\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Lucca Portes Cavalheiro
Simon Bernard
Jean Paul Barddal
Laurent Heutte

"Random Forest Kernel for High-Dimension Low Sample Size Classification" by Lucca Portes Cavalheiro, Simon Bernard, Jean Paul Barddal, and Laurent Heutte, arXiv:2310.14710v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Network log-ARCH models for forecasting stock market volatility" by Raffaele Mattera and Philipp Otto, arXiv:2303.11064v1, 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{Quantitative comparison on C-60 dataset using UIQM, UCIQE, and NIQE metrics.}
\begin{tabular}{|l|l|l|l|}
\hline
 & UIQM$\uparrow$ & UCIQE$\uparrow$ & NIQE$\downarrow$ \\ \hline
Raw\_Images & 1.99 & 0.478 & 5.18 \\ \hline
Fusion & 2.78 & 0.512 & 4.74 \\ \hline
IBLA & 1.81 & 0.574 & 5.02 \\ \hline
U-Transformer& 2.65 & 0.534 & 4.94\\ \hline
Funie-GAN & \underline{3.10} & 0.572 & \underline{4.73} \\ \hline
Water-Net & 2.57 & \underline{0.578} & 4.88 \\ \hline
UWCNN & 2.25 & 0.466 & 4.89 \\ \hline
Ours & \textbf{3.12} & \textbf{0.591} & \textbf{4.67} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Ahsan Baidar Bakht
Zikai Jia
Muhayy ud Din
Waseem Akram
Lyes Saad Soud
Lakmal Seneviratne
Defu Lin
Shaoming He
Irfan Hussain

"MuLA-GAN: Multi-Level Attention GAN for Enhanced Underwater Visibility" by Ahsan Baidar Bakht, Zikai Jia, Muhayy ud Din, Waseem Akram, Lyes Saad Soud, Lakmal Seneviratne, Defu Lin, Shaoming He, and Irfan Hussain, arXiv:2312.15633v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Total number of points contained within each AVTIS2 point cloud and the percentage overlap with the short-range TLS point cloud which contained 25,414,521 points.}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Sensor} & \textbf{Site} & \textbf{Points} & \textbf{Overlap} \\
\hline
AVTIS2 (Single) & Site 1 (3.3 km) & 8,019 & 2,025 (25.25\%) \\
\hline
AVTIS2 (Multiple) & Site 1 (3.3 km) & 14,671 & 2,846 (19.40\%) \\
\hline
AVTIS2 (Single) & Site 2 (1.4 km) & 25,774 & 11,772 (45.67\%) \\
\hline
AVTIS2 (Multiple) & Site 2 (1.4 km) & 41,364 & 15,806 (38.21\%) \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

William D. Harcourt
David G. Macfarlane
Duncan A. Robertson

"3D terrain mapping and filtering from coarse resolution data cubes extracted from real-aperture 94 GHz radar" by William D. Harcourt, David G. Macfarlane, and Duncan A. Robertson, arXiv:2310.08120v1, 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{File I/O pattern matcher's performance.}
\begin{tabular}{lrrrrrr}
 \toprule
 \textbf{Ransomware} & \textbf{TPR (\%)} &\textbf{FPR (\%)} &\textbf{FNR (\%)} & \textbf{Prec. (\%)} & \textbf{Rec. (\%)} &\textbf{F1 score (\%)} \\ \midrule
 Crypto & 95.19 & 0 & 4.81 & 100 & 95.19 & 97.53\\
 Screen-locker &33.33 & 0 & 66.66 & 100 & 33.33 & 50.00 \\ \midrule
 All ransomware & 65.50 & 0 & 34.5 & 100 & 65.50 & 79.15 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Muhammad Ejaz Ahmed
Hyoungshick Kim
Seyit Camtepe
Surya Nepal

"Peeler: Profiling Kernel-Level Events to Detect Ransomware" by Muhammad Ejaz Ahmed, Hyoungshick Kim, Seyit Camtepe, and Surya Nepal, arXiv:2101.12434v1, 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}{lccccc}
 \toprule
 MLE parameters & $\alpha$ & $\beta$ & $\delta$ & $\epsilon$ & $q$\\
 \midrule
 Standard MLE density (Green) & $1.00$ & $-191$ & $0.130$ & $-195$ & -\\
 Flexible MLE density (Red) & $1.00$ & $-3000$ & $0.235$ & $-3000$ & $0.0339$\\
 \bottomrule
 \end{tabular}
\caption{MLE parameters of the power exponentially linked exposure example rounded to three significant digits (Section ).}
\end{table}
\end{document}

stat

"Modeling lower-truncated and right-censored insurance claims with an extension of the MBBEFD class" by Selim Gatti and Mario V. Wüthrich, arXiv:2310.11471v2, 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.


\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}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|cc|}
\hline
$\delta$ & & \textsc{Smart-ME} \\
\hline
0.001 & 0.06 & 0.05 \\
0.01 & 0.05 & 0.05 \\
0.1 & 0.23 & 0.44 \\
0.5 & 6.52 & 26.47 \\
1 & 23.63 & 50.74 \\
1.5 & 70.61 & 149.80 \\
\hline
\end{tabular}
\caption{Relative cost difference (in \%) versus \textsc{OFF}, for $\delta \in \{0.001, 0.01, 0.1, 0.5, 1, 1.5\}$, $\gamma = 2$, $T = 501$.}
\end{table}
\end{document}

math

Chamsi Hssaine
Huseyin Topaloglu
Garrett van Ryzin

"Target-Following Online Resource Allocation Using Proxy Assignments" by Chamsi Hssaine, Huseyin Topaloglu, and Garrett van Ryzin, arXiv:2412.12321v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Revisiting Stochastic Collocation with Exponential Splines for an Arbitrage-Free Interpolation of Option Prices" by Fabien Le Floc'h, arXiv:2508.12419v1, licensed under CC BY-SA 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{Mean PSNR / SSIM on the validation volumes of the different approaches for both contrasts. The best results are in bold font.}
\begin{tabular}{|l|c|c|c|}
\hline
\textbf{Model} & \textbf{Radial} & \textbf{Spiral} & \textbf{\# Parameters}\\ \hline
\textbf{PDNet no DC} & 27.02 / 0.6747 & 28.02 / 0.6946 & 156k \\ \hline
\textbf{Adjoint + DC} & 27.11 / 0.6471 & 31.70 / 0.7213 & 0 \\ \hline
\textbf{U-net on Adjoint + DC} & 32.26 / 0.7224 & 32.82 / 0.7460 & 481k \\ \hline
\textbf{PDNet w DC} & \textbf{32.66 / 0.7327} & \textbf{33.08 / 0.7534} & 156k \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Zaccharie Ramzi
Jean-Luc Starck
Philippe Ciuciu

"Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction" by Zaccharie Ramzi, Jean-Luc Starck, and Philippe Ciuciu, arXiv:2101.01570v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|}
\hline
 & $\alpha$ \\ \hline
Stratified + active learning sets & 0.68 \\ \hline
Top-scored evaluation set & 0.43 \\ \hline
Random set & 0.4 \\ \hline
Full dataset & \\ \hline
\end{tabular}
\caption{Inter-annotator agreement in terms of Krippendorff $\alpha$ across datasets}
\end{table}
\end{document}

cs

Manuel Tonneau
Pedro Vitor Quinta de Castro
Karim Lasri
Ibrahim Farouq
Lakshminarayanan Subramanian
Victor Orozco-Olvera
Samuel P. Fraiberger

"NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data" by Manuel Tonneau, Pedro Vitor Quinta de Castro, Karim Lasri, Ibrahim Farouq, Lakshminarayanan Subramanian, Victor Orozco-Olvera, and Samuel P. Fraiberger, arXiv:2403.19260v3, 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}{lcccccc} %\hline
 & Gaussian & ICAR+ & Cubic & VC-BART & GAM & GAM \\
 & prior & prior & B-splines& & 12 knots & 24 knots\\ %\hline
n=100 & 0.173 & 0.166 & 0.168 & 0.297 & 0.159 & 0.173 \\
n=1000 & 0.063 & 0.063 & 0.078 & 0.285 & 0.055 & 0.058 \\
\end{tabular}
\caption{Independent errors simulation. Root Mean Squared Error $E_F\left[ \hat{E}(y_i) - E_F(y_i) \right]^2$ for 0-degree cut splines, cubic B-splines, VC-BART and GAM (R package mgcv) with $k=12$ and $k=24$ knots }
\end{table}
\end{document}

stat

David Rossell
Arnold Kisuk Kseung
Ignacio Saez
Michele Guindani

"Semi-parametric local variable selection under misspecification" by David Rossell, Arnold Kisuk Kseung, Ignacio Saez, and Michele Guindani, arXiv:2401.10235v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Illustration of some well-known vision-based techniques employed in wildfire detection}
\begin{tabular}{llll}
 \toprule
 \textbf{Algorithm}& \textbf{Reference(s)}	 & \textbf{Pros}& \textbf{Cons}\\
 \midrule
\multirow{2}{*}{Feature Detection} &	~ &	\multirow{2}{*}{Easy to deploy} & Cannot detect temporal \\
&~ & & smoke pattern \\
Time Series Detection & \multirow{2}{*}{} & Temporal pattern & Longer\\
(Temporal Detection) & & can be extracted &processing time \\
\multirow{2}{*}{DL Neural Networks} & \multirow{2}{*}{}& Highly accurate & Resource requirement \\
& & and well-established & and delay response \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Chiu Chun Chan
Sheeraz A. Alvi
Xiangyun Zhou
Salman Durrani
Nicholas Wilson
Marta Yebra

"A Survey on IoT Ground Sensing Systems for Early Wildfire Detection: Technologies, Challenges and Opportunities" by Chiu Chun Chan, Sheeraz A. Alvi, Xiangyun Zhou, Salman Durrani, Nicholas Wilson, and Marta Yebra, arXiv:2312.10919v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Early Stopping for Regression Trees

"Early Stopping for Regression Trees" by Ratmir Miftachov and Markus Reiß, arXiv:2502.04709v2, 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{ Results for KMO test in Factor Analysis by two data imputation methods}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Sub-Index} & \textbf{MICE} & \textbf{Random Forest} \\
\hline
Economic Index & 0.72 & 0.71 \\
Institutional Index & 0.90 & 0.90 \\
Quality of Life Index & 0.89 & 0.85 \\
Sustainability Index & 0.62 & 0.49 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies" by Tanay Panat and Rohitash Chandra, arXiv:2502.06866v2, 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}{|r||r|r|r|r|r|}
 \hline
 \multirow{2}{*}{POVI-BNN} & \multicolumn{5}{c|}{APOVI-BNN} \\
 \cline{2-6}
 & $|\boldsymbol{\Xi}| = 0$ & $|\boldsymbol{\Xi}| = 1$ & $|\boldsymbol{\Xi}| = 2$ & $|\boldsymbol{\Xi}| = 5$ & $|\boldsymbol{\Xi}| = 10$ \\
 \hline \hline
 $3.13\pm0.37$ & $5.38\pm0.59$ & $-0.24\pm6.12$ & $3.22\pm4.05$ & $4.81\pm0.49$ & $4.48\pm0.88$ \\
 \hline
 \end{tabular}
\caption{Obtained ELBO value for POVI-BNN and APOVI-BNN on test datasets generated from SE covariance GP prior samples.}
\end{table}
\end{document}

stat

"Amortised Inference in Bayesian Neural Networks" by Tommy Rochussen, arXiv:2309.03018v1, licensed under CC BY-NC-SA 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{NDTM outperforms existing methods for Non-linear deblur on distortion metrics like PSNR and SSIM. \textbf{Bold}: best.}
\begin{tabular}{c|cc|cc}
\toprule
 & \multicolumn{2}{c|}{FFHQ (256 × 256)} & \multicolumn{2}{c}{ImageNet (256 × 256)} \\ \midrule
Method & PSNR↑ & SSIM↑ & PSNR↑ & SSIM↑ \\ \midrule
DPS & 8.12 & 0.262 & 6.67 & 0.156 \\
RED-diff & 24.88 & 0.717 & 21.88 & 0.623 \\ \midrule
NDTM (ours) & \textbf{30.64} & \textbf{0.874} & \textbf{24.41} & \textbf{0.732} \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Kushagra Pandey
Farrin Marouf Sofian
Felix Draxler
Theofanis Karaletsos
Stephan Mandt

"Variational Control for Guidance in Diffusion Models" by Kushagra Pandey, Farrin Marouf Sofian, Felix Draxler, Theofanis Karaletsos, and Stephan Mandt, arXiv:2502.03686v1, 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{The experimental results for various methods under different metrics are presented for the FOP defined as $\min \{ -(\theta + u)^{\top} x \mid \parallel x \parallel_2^2 \leq a^2 \} = \min \left\{-\sum_{k=1}^{p} (\theta_k + u_k) x_k \mid \parallel x \parallel_2^2 \leq a^2 \right\}$ in the \textbf{Noiseless} setting. For the FY loss, we set $\lambda=0.1$ and $\Omega(x) = 1/2\|x\|_2^2$}
\begin{tabular}{c|cccc|cccc|cccc}
\hline
\multirow{2}{*}{Sample size} & \multicolumn{4}{c|}{Parameter Error} & \multicolumn{4}{c|}{Decision Error} & \multicolumn{4}{c}{Regret} \\ \cline{2-13}
 & FY & SPA & KKA & VIA & FY & SPA & KKA & VIA & FY & SPA & KKA & VIA \\ \hline
50 & \textbf{0.04} & - & 4.59 & 0.00 & \textbf{0.00} & - & 1.08 & 0.00 & \textbf{0.00} & - & 0.38 & 0.00 \\
100 & \textbf{0.04} & - & 4.80 & - & \textbf{0.00} & - & 1.13 & - & \textbf{0.00} & - & 0.40 & - \\
300 & \textbf{0.04} & - & 4.93 & - & \textbf{0.00} & - & 1.16 & - & \textbf{0.00} & - & 0.41 & - \\
500 & \textbf{0.04} & - & 4.96 & - & \textbf{0.00} & - & 1.17 & - & \textbf{0.00} & - & 0.42 & - \\
1000 & \textbf{0.03} & - & 4.98 & - & \textbf{0.00} & - & 1.18 & - & \textbf{0.00} & - & 0.42 & - \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

"A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization" by Zhehao Li, Yanchen Wu, and Xiaojie Mao, arXiv:2502.16120v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccc}
\hline \hline
 \multicolumn{5}{r}{\hspace{3cm} \underline{Dependent variable: budget share}} \\
& $\omega_{c^{f}}$ & $\omega_{\ell^{m}}$ & $\omega_{\ell^{f}}$ & $\omega_{C}$ \\ \hline
\multirow{2}{*}{$\ln(\frac{PC1^f}{PC1^m})$} & -.031 & .055 &.043 & -.067 \\
	 							& (.034)& (.047) &(.064)&(.073) \\
\hline
Additional covariates & Yes & Yes & Yes & Yes \\
Time dummies & Yes& Yes& Yes& Yes \\ \hline
Collective test & \multicolumn{4}{c}{$\chi^2$(4) = 3.476 ($p-$value = .502)} \\ \hline \hline
\end{tabular}
\end{table}
\end{document}

q-fin

"Does personality affect the allocation of resources within households?" by Gastón P. Fernández, arXiv:2307.02918v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zero-Leverage Puzzle

"Zero-Leverage Puzzle" by Mykola Pinchuk, arXiv:2302.00761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Neural Network Element Method for Partial Differential Equations" by Yifan Wang, Zhongshuo Lin, and Hehu Xie, arXiv:2504.16862v1, 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
\begin{tabular}{lrr}
\hline\hline
Consecutive years&Number of firms&Fraction, \%\tabularnewline
\hline
1&$6061$&$31.231$\tabularnewline
2&$3799$&$19.575$\tabularnewline
3&$2513$&$12.949$\tabularnewline
4&$1764$&$ 9.090$\tabularnewline
5&$1273$&$ 6.559$\tabularnewline
6&$ 949$&$ 4.890$\tabularnewline
7&$ 727$&$ 3.746$\tabularnewline
8&$ 528$&$ 2.721$\tabularnewline
9&$ 403$&$ 2.077$\tabularnewline
10&$ 313$&$ 1.613$\tabularnewline
11&$ 256$&$ 1.319$\tabularnewline
12&$ 189$&$ 0.974$\tabularnewline
13&$ 151$&$ 0.778$\tabularnewline
14&$ 121$&$ 0.623$\tabularnewline
15&$ 96$&$ 0.495$\tabularnewline
16&$ 72$&$ 0.371$\tabularnewline
17&$ 61$&$ 0.314$\tabularnewline
18&$ 55$&$ 0.281$\tabularnewline
19&$ 51$&$ 0.260$\tabularnewline
20&$ 41$&$ 0.209$\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Zero-Leverage Puzzle


\documentclass{article}
\usepackage{booktabs}
\usepackage{siunitx}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The evaluation results using the baseline i-vector method~ with random data split and simple thresholding on "dev-clean" and "test-clean" datasets. Each positive sample is tested against 20 negative samples. Furthermore, 20 different positive samples are tested per speaker. Columns one and two show the i-vector dimensionality and number of GMM elements, respectively.}
\begin{tabular}{cccc}
\toprule
\textbf{i-Vec dimension} & \textbf{GMM elements} & \textbf{dev-clean EER} & \textbf{test-clean EER} \\
\midrule
\num{600} & \num{1024} & \SI{16.65}{\percent} & \SI{18.58}{\percent} \\
\num{400} & \num{512} &\SI{17.80}{\percent} & \SI{17.70}{\percent} \\
\num{300} & \num{256} & \SI{18.86}{\percent} & \SI{16.90}{\percent} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"An Empirical Study on Text-Independent Speaker Verification based on the GE2E Method" by Soroosh Tayebi Arasteh, arXiv:2011.04896v4, 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{Neural network approach using two shallow networks of width 32 for both $u$ and $f$ in . In both cases $\operatorname{tanh}$ is used as an activation function, hence the Laplacian of $u_\theta$ is not representative by the network $f_\psi$. We report relative $H^2$ errors.}
\begin{tabular}{|c||c|c|c|}
 \hline
 $\alpha^2 \;\backslash\; \gamma^2$ & $10^0$ & $10^{2}$ & $10^{4}$ \\ \hline \hline
 $1$ & 1.32e-05 & 1.08e-04 & 9.27e-04 \\ \hline
 $10^{-2}$ & 2.79e-05 & 4.11e-05 & 5.95e-04 \\ \hline
 $10^{-4}$ & 5.63e-05 & 3.07e-04 & 4.38e-03 \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Kent-Andre Mardal
Jarle Sogn
Marius Zeinhofer

"Variational Formulations of the Strong Formulation -- Forward and Inverse Modeling using Isogeometric Analysis and Physics-Informed Networks" by Kent-Andre Mardal, Jarle Sogn, and Marius Zeinhofer, arXiv:2312.03496v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.