\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison between VRoC and baselines on the rumor veracity classification task. Lc represents that the news related to Charlie Hebdo is left out while trained under L principle. }
\begin{tabular}{lccccc}
			\toprule
			& & & True & False & Unverified \\
			& Accuracy & Macro-F1 & F1 & F1 & F1 \\\hline
			MT-UA* & $0.483$ & $0.418$ & - & - & -\\
			VAE-LSTM & $0.628$ & $0.627$ & $0.691$ & $0.576$ & $0.615$ \\
			VRoC & $\textbf{0.667}$ & $\textbf{0.667}$ & $\textbf{0.745}$ & $\textbf{0.632}$ & $\textbf{0.624}$\\\hline
			MTL2* (Lc) & $0.441$ & $0.376$ & - & - & - \\
			MTL3* (Lc) & $0.492$ & $0.396$ & $\textbf{0.681}$ & $0.232$ & $0.351$ \\
			VAE-LSTM (Lc) & $0.507$ & $0.503$ & $0.545$ & $\textbf{0.449}$ & $\textbf{0.515}$ \\
			VRoC (Lc)& $\textbf{0.531}$ & $\textbf{0.513}$ & $0.564$ & $0.434$ & $0.480$\\\hline
			TreeLSTM* (L) & $0.500$ & $0.379$ & $0.396$ & $\textbf{0.563}$ & $0.506$ \\
			VAE-LSTM (L) & $0.494$ & $0.475$ & $0.429$ & $0.472$ & $\textbf{0.523}$ \\
			VRoC (L) & $\textbf{0.521}$ & $\textbf{0.484}$ & $\textbf{0.480}$ & $0.504$ & $0.465$\\

 \bottomrule
		\end{tabular}
\end{table}
\end{document}

cs

Mingxi Cheng
Shahin Nazarian
Paul Bogdan

"VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text" by Mingxi Cheng, Shahin Nazarian, and Paul Bogdan, arXiv:2102.00816v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Graph Neural Network for Traffic Forecasting: A Survey" by Weiwei Jiang and Jiayun Luo, arXiv:2101.11174v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Empirical power of $12$ two-sample tests on the example data with $\alpha = 0.05$.}
\begin{tabular}{|c|cccccccccccc|}
 \hline
 Method & CM & GET & BD & GED & RF & MT & GPK & RISE & MMD & xMMD & aMMD & mMMD \\\hline
 Power & 0.05 & 0.06 & 0.05 & 0.09 & 0.07 & 0.06 & 0.04 & 0.06 & 0.05 & 0.06 & 0.06 & 0.01
 \\\hline
 \end{tabular}
\end{table}
\end{document}

math

"MATES: Multi-view Aggregated Two-Sample Test" by Zexi Cai, Wenbo Fei, and Doudou Zhou, arXiv:2412.16684v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cc|cc}
\hline
\multirow{2}{*}{Model} & \multicolumn{2}{c|}{RF Question} & \multicolumn{2}{c}{DF Question} \\ \cline{2-5}
 & \# answerable & \# ill-structured & \# answerable & \# ill-structured \\ \hline
RWKV & 277.32 & 240.04 & 397.08 & 325.98 \\
Llama-2 & 138.40 & 112.25 & 157.98 & 98.0 \\
Claude-1 & 184.34 & 3.88 & 244.92 & 4.66 \\
Claude-2 & 184.34 & 6.28 & 244.92 & 1.86 \\
GPT-3.5 & 184.34 & 30.81 & 244.92 & 65.60 \\
GPT-4 & 184.34 & 2.49 & 244.92 & 5.74 \\
\hline
\end{tabular}
\caption{Average (per-maze) numbers of answerable questions and ill-structured answers for each model.}
\end{table}
\end{document}

cs

Peng Ding
Jiading Fang
Peng Li
Kangrui Wang
Xiaochen Zhou
Mo Yu
Jing Li
Matthew R. Walter
Hongyuan Mei

"MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models" by Peng Ding, Jiading Fang, Peng Li, Kangrui Wang, Xiaochen Zhou, Mo Yu, Jing Li, Matthew R. Walter, and Hongyuan Mei, arXiv:2403.19913v2, 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{Training set} & \textbf{Test set} \\ \hline \hline
 Ankylosaurus\_\&\_Stegosaurus & Friends\_1 \\ \hline
 Ceiling\_Light & Bikes \\ \hline
 ISO\_Chart\_16 & Flowers\\ \hline
 Perforated\_Metal\_1 & Ankylosaurur \& Diplodocus 1 \\ \hline
 Sophie\_\&\_Vincent\_3 & \\ \hline
 Yan\_\&\_Krios\_standing & \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

Yung-Hsuan Chao
Haoran Hong
Gene Cheung
Antonio Ortega

"Pre-demosaic Graph-based Light Field Image Compression" by Yung-Hsuan Chao, Haoran Hong, Gene Cheung, and Antonio Ortega, arXiv:2102.07883v2, 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{Top 5 ASNs and Countries in Probe Campaign by Number of /48 Prefixes Probed}
\begin{tabular}{|c|r||c|r|}\hline
 \textbf{ASN} & \textbf{\# /48} & \textbf{Country} & \textbf{\# /48} \\\hline\hline
 8881 & 5,149 & DE & 5,985 \\ \hline
 6799 & 3,386 & GR & 4,063 \\\hline
 1241 & 635 & CN & 1,126 \\\hline
 9808 & 608 & BR & 561\\\hline
 3320 & 530 & BO & 264\\\hline
 96 Other ASNs & 2,577 & 20 Other Countries & 886 \\\hline
 \textbf{Total} & 12,885 & \textbf{Total} & 12,885\\\hline
 \end{tabular}
\end{table}
\end{document}

cs

"Follow the Scent: Defeating IPv6 Prefix Rotation Privacy" by Erik C. Rye, Robert Beverly, and kc claffy, arXiv:2102.00542v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Benign applications spawning multiple processes.}
\begin{tabular}{clrc}
 \toprule
 \textbf{Type} & \textbf{Application} & \textbf{Version} & \textbf{spawn processes?}\\
 \midrule
 \multirow{5}{*}{Office} & MS Word & 16.0.11929.20436& $\times$ \\
 & MS Powerpoint & 16.0.11929.20436 & $\times$\\
 & MS Excel & 16.0.11929.20436 & $\times$\\
 & MS Outlook & 16.0.11929.20436 & \checkmark\\
 & Trio Office: Word, Slide, Spreadsheet & - & \checkmark\\
 \midrule
 \multirow{4}{*}{Development} & Pycharm & 11.0.3+12-b304.56 amd64 & \checkmark\\
 & Matlab & R2019a & \checkmark\\
 & Visual Studio C++ & 2019 community version & \checkmark\\
 & Android Studio & 191.6010548 & \checkmark \\
 \midrule
 \multirow{6}{*}{Tools} & Adobe Acrobat Reader & 20.006.20034 & \checkmark \\
 & Adobe Photoshop Express & 3.0.316 &$\times$\\
 & PhotoScape & 3.7 &$\times$\\
 & Cool File Viewer & - & $\times$\\
 & PicArt Photo Studio & - &$\times$\\
 & Paint 3D & - &$\times$\\
 \midrule
 \multirow{5}{*}{Cloud and Internet} & Dropbox & - &$\times$\\
 & Googledrive & - &$\times$\\
 & Internet Explorer & 11.1039.17763 & \checkmark\\
 & Google Chrome & 80.0.3987.132 & \checkmark\\
 & Remote Desktop & - & $\times$\\
 \midrule
 \multirow{4}{*}{Messenger} & Telegram & 1.9.7 &$\times$\\
 & WhatApp & 0.4.930 & \checkmark\\
 & Skype & 1.9.7 & $\times$\\
 & Facebook Messenger & - &\checkmark\\
 \midrule
 \multirow{3}{*}{Document} & Wordpad & - &$\times$ \\
 & Notepad & - & $\times$\\
 & OneNote & 16001.12527.20128.0 &$\times$ \\
 \midrule
 \multirow{3 }{*}{Media player} & VLC & 3.0.8 & $\times$\\
 & Netflix & 6.95.602 & $\times$\\
 & GOM Player & 2.3.49.5312 & $\times$\\
 \midrule
 \multirow{4}{*}{Miscellaneous} & Spotify & - & \checkmark \\
 & KeePass Password manager & 1.38 & $\times$\\
 & Discord & - & $\times$\\
 & Facebook & - & $\times$\\
 \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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|ccc} \hline
 \textbf{Methods} & \textbf{Prec.} & \textbf{Recall} & \textbf{F1} \\ \hline \hline
 SGA~ & 92.03 & 90.94 & 91.48\\ \hline
 SGA* & 93.29 & 90.34 & 91.79\\ \hline
 SG-PGM \small{(ours)} & \underline{94.59} & \underline{92.03} & \underline{93.29}\\ \hline
 SG-PGM@3 \small{(ours)} & \textbf{95.41} & \textbf{95.01} & \textbf{95.21} \\ \hline
 \end{tabular}
\caption{\textbf{Overlap check for point cloud registration.} $T=I_{4}$ between fragments. All metrics are the-higher-the-better.}
\end{table}
\end{document}

cs

"SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks" by Yaxu Xie, Alain Pagani, and Didier Stricker, arXiv:2403.19474v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l||r|r|r|r|r|r|r|}
			\hline
				&	all	&	2017	&	2018	&	2019	\\
			\hline
 MSE	&	4948990.80	&	2272350.07	&	4575637.58	&	3133176.51	\\
 RMSE	&	2224.63	&	1507.43	&	2139.07	&	1770.08	\\
 MAE	&	1691.37	&	1132.86	&	1462.48	&	1377.82	\\
			\hline

		\end{tabular}
\caption{Error measures (MSE, RMSE, MAE) for the the improved day ahead-load forecast with a rolling window length of three month. MSE is given in [$MWh^2$], RMSE and MAE in [$MWh$].}
\end{table}
\end{document}

q-fin

Thomas Möbius
Mira Watermeyer
Oliver Grothe
Felix Müsgens

"Enhancing Energy System Models Using Better Load Forecasts" by Thomas Möbius, Mira Watermeyer, Oliver Grothe, and Felix Müsgens, arXiv:2302.11017v1, 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{Top Co-occurring Terms with \emph{child} Based on Log-Likelihood Ratio}
\begin{tabular}{lr} \\
\hline
\textbf{Co-occurring Term} & \textbf{LLR} \\
\hline
family & 47.51 \\
at home & 36.31 \\
parent & 35.17 \\
mother & 33.05 \\
as a & 32.15 \\
raise & 32.08 \\
think about & 26.30 \\
sick & 24.08 \\
much & 23.99 \\
one & 23.22 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Text Data Analysis of Maternal Narratives: Albanian Women in Italy" by Eleonora Miaci and Emiliano Seri, arXiv:2501.05181v1, 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{Parameters for the case II of .}
\begin{tabular}{cccccccccc}\toprule
		\multicolumn{5}{c}{Heston} & \multicolumn{3}{c}{Market} & \multicolumn{2}{c}{Option}\\\cmidrule(lr){1-5}\cmidrule(lr){6-8}\cmidrule(lr){9-10}
		$V(0)$ & $\theta$ & $\kappa$ & $\sigma$ & $\rho$ & $r$ & $q$ & $X(0)$ & $K$ & $T$ \\\midrule
		0.12 & 0.12 & 3.0 & 0.04 & 0.6 & 0.01 & 0.04 & 100 & 100 & 1\\\bottomrule
	\end{tabular}
\end{table}
\end{document}

q-fin

"Instabilities of Super-Time-Stepping Methods on the Heston Stochastic Volatility Model" by Fabien Le Floc'h, arXiv:2309.00540v1, 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{Backstepping and sliding-mode controller parameters}
\begin{tabular}{cc}
		\hline
		Parameters & Value \\
		\hline
		$[k_{bx1}$ $k_{bx2}]$ & [1 1]\\
 $[k_{by1}$ $k_{by2}]$ & [1 1]\\
 $[k_{bz1}$ $k_{bz2}]$ & [5 1]\\
		$[k_{sx1}$ $k_{sx2}]$ & [2 1]\\
 $[k_{sy1}$ $k_{sy2}]$ & [2 1]\\
 $[k_{sz1}$ $k_{sz2}]$ & [3 2.5]\\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Bailun Jiang
Boyang Li
Ching-Wei Chang
Chih-Yung Wen

"Hybrid Aerodynamics-Based Model Predictive Control for a Tail-Sitter UAV" by Bailun Jiang, Boyang Li, Ching-Wei Chang, and Chih-Yung Wen, arXiv:2312.14453v1, 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|c|c|}
 \hline
 $m$ &1 & 2 & 3 & 4 & 5 & 6 \\
 \hline
 $h(m)$ & 0 & 0.25 & 0.333 & 0.375 & 0.375 & 0.380 \\
 \hline
 $h(m)+\frac{2}{3}\frac1{2^m}$ & 0.333 & 0.417 & 0.417 & 0.417 & 0.396 & 0.391 \\
 \hline
\end{tabular}
\end{table}
\end{document}

math

"An intermediate case of exponential multivalued forbidden matrix configuration" by Wallace Peaslee, Attila Sali, and Jun Yan, arXiv:2312.11446v1, 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|}
 \hline
 & \textbf{1.0s} & \textbf{0.5s} & \textbf{0.2s} & \textbf{0.1s} \\
 \hline
 EF21 & 486.1s & 360.6s & 284.2s & 258.0s \\
 \hline
 Kimad & 385.2s & 285.2s & 225.2s & 205.2s \\
 \hline
 \end{tabular}
\caption{Average step time across T\textsubscript{comm}. $M=4$ workers.}
\end{table}
\end{document}

math

Jihao Xin
Ivan Ilin
Shunkang Zhang
Marco Canini
Peter Richtárik

"Kimad: Adaptive Gradient Compression with Bandwidth Awareness" by Jihao Xin, Ivan Ilin, Shunkang Zhang, Marco Canini, and Peter Richtárik, arXiv:2312.08053v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"High Order in Space and Time Schemes Through an Approximate Lax-Wendroff Procedure" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2502.07798v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Yan-Hong Yang
Ying-Lin Liu
Ying-Hui Shao

"Visibility graph analysis of crude oil futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict" by Yan-Hong Yang, Ying-Lin Liu, and Ying-Hui Shao, arXiv:2310.18903v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Risk-Adaptive Local Decision Rules

"Risk-Adaptive Local Decision Rules" by Johannes O. Royset and Miguel A. Lejeune, arXiv:2310.09844v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Quasi-rectifiable Lie algebras for partial differential equations" by A. M. Grundland and J. de Lucas, arXiv:2312.05238v2, 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}{lcc}
\hline
Models & Mean IoU(\%) & Pixel Acc(\%) \\
\hline\hline
DenseASPP & 58.31 & 89.31 \\
PSPNet+ResNet-101 & 63.24 & 91.92 \\
DeepLabv3Plus + ResNet-101 & 63.74 & 92.51 \\
DeepLabv3Plus +ResNeXt-101 & 64.64 & 93.14 \\
DeepLabv3Plus + Xception & 64.12 & 94.08 \\
\hline
DeepLabv3Plus + Xception+SE & 65.49 & 94.12 \\
DeepLabv3Plus + Xception+baseline-c & 65.52 & 94.21 \\
\hline
RethNet + baseline-c & 62.11 & 92.44 \\
RethNet + Rethinker-d & \textbf{76.56} & \textbf{96.45} \\
RethNet + Rethinker-e & \textbf{79.46} & \textbf{96.11} \\
\hline
\end{tabular}
\caption{ Experimental results on test samples of our MSLD dataset. }
\end{table}
\end{document}

cs

Shohrukh Bekmirzaev
Seoyoung Oh
Sangwook Yoo

"RethNet: Object-by-Object Learning for Detecting Facial Skin Problems" by Shohrukh Bekmirzaev, Seoyoung Oh, and Sangwook Yoo, arXiv:2101.02127v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

PowerMLP: An Efficient Version of KAN

Ruichen Qiu
Yibo Miao
Shiwen Wang
Lijia Yu
Yifan Zhu
Xiao-Shan Gao

"PowerMLP: An Efficient Version of KAN" by Ruichen Qiu, Yibo Miao, Shiwen Wang, Lijia Yu, Yifan Zhu, and Xiao-Shan Gao, arXiv:2412.13571v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Data augmentation configurations for initial cross domain experiments. The table is thought so complement Fig.~ and give quantitative information about DA methods. Settings for are presented as baseline \textit{BL}. We extend DA by \textit{inv. gamma}: inverse gamma , \textit{gaussian}: Gaussian blur and noise, \textit{multi./additive br}.: multiplicative/additive brightness, \textit{all}: all of the aforementioned, \textit{BL enhanced}: ranges of $BL$ enhanced, \textit{BL enhanced + br}.: ranges of $BL$ enhanced and brightness enhanced, heavy data augmentation: combination of different DA settings.}
\begin{tabular}{|l|l|l|l|l|l|l|l|}
\hline
experiment & rotation & gamma & inv. gamma & Gaussian & add. brightness & multi. brightness & contrast \\ \hline
default nnU-Net & $\pm 30$ & $(0.7, 1.5)$ & - & yes & - & $\mu = 0$, $\sigma = 0.1$ & no\\
BL &$\pm 30$ & - & - & no & - & - & no \\
BL enhanced & $\pm 60$ & - & - & no & - & - & no\\
BL enhanced + br. & $\pm 60$ & - & - & no & (0.6, 1.5) & $\mu = 0$, $\sigma = 0.2$ & no\\
BL + all & $\pm 30$ & (0.7, 1.5) & (0.7, 1.5) & yes & (0.7, 1.3) & $\mu = 0$, $\sigma = 0.1$ & yes\\
heavy DA & $\pm 180$ & (0.6, 1.6) & (0.6, 1.6) & yes & (0.7, 1.3) & $\mu = 0$, $\sigma = 0.3$ & yes \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Peter M. Full
Fabian Isensee
Paul F. Jäger
Klaus Maier-Hein

"Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI" by Peter M. Full, Fabian Isensee, Paul F. Jäger, and Klaus Maier-Hein, arXiv:2011.07592v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\footnotesize The IQA performance of pixel-wise and structure-wise methods.}
\begin{tabular}{ccccccc}%
			\hline
			\multirow{2}{*}{Metrics} & \multicolumn{3}{c|}{Pixel-wise IQA} & \multicolumn{3}{c|}{Structure-wise IQA} \\
			\cline{2-7} & PSNR & S-PSNR & CPP-PSNR & SSIM & FSIM & IWSSIM\\
			\hline
			PLCC &0.485 & 0.542 & 0.512 & 0.571 & 0.925 & 0.883 \\
			SROCC & 0.397 &0.429 & 0.401 & 0.518 & 0.886 & 0.880 \\
			MAE &9.491 &8.987 & 9.222 & 8.743 & 3.926 & 4.938 \\ \hline
		\end{tabular}
\end{table}
\end{document}

eess

"Spatial Attention-based Non-reference Perceptual Quality Prediction Network for Omnidirectional Images" by Li Yang, Mai Xu, Deng Xin, and Bo Feng, arXiv:2103.06116v1, 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{Number of parameters in Student Models}
\begin{tabular}{llll}
\hline
Model & Parameters \\\hline
FS2 & 3,52,402 \\
FS4 & 88,266 \\
FS8 & 22,150 \\
FS16 & 5,580 \\
FS32 & 1417 \\\hline
\end{tabular}
\end{table}
\end{document}

eess

Soumava Paul
Gurunath Reddy M
K Sreenivasa Rao
Partha Pratim Das

"Knowledge Distillation for Singing Voice Detection" by Soumava Paul, Gurunath Reddy M, K Sreenivasa Rao, and Partha Pratim Das, arXiv:2011.04297v2, 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}{ccl}
		\textbf{Random Variable} & \textbf{Realization} & \textbf{Description} \\
		$K^{(n_1,n_2)}_{m_1,m_2}$ & $k$ & $\#$ of new global distinct species \\
		$S^{(n_1,n_2)}_{m_1,m_2}$ & $s$ & $\#$ of new shared species \\
		$K^{(n_j)}_{j,m_j}$ & $k_j$ & $\#$ of new local distinct species in group $j$\\
		$K^{*(n)}_{j,m}$ & $k^*_j$ & $\#$ of new distinct species in group $j$\\
		\empty & \empty & but missing in group $j^\prime$ \\
		$S^*_{m}$ & $s^*$ & $\#$ of new shared species among \\
		\empty & \empty & the $k$ new distinct species \\
		$S_{j^\prime,j}$ & $s_{j^\prime,j}$ & $\#$ of species which were first \textit{only} observed in \\
		\empty & \empty & group $j\prime$ and that are then observed in group $j$ \\
	\end{tabular}
\caption{Ouf-of-sample statistics.}
\end{table}
\end{document}

stat

Alessandro Colombi
Raffaele Argiento
Federico Camerlenghi
Lucia Paci

"How many unseen species are in multiple areas?" by Alessandro Colombi, Raffaele Argiento, Federico Camerlenghi, and Lucia Paci, arXiv:2502.04122v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{}
& (A) & (B) & (C) & (D) & (E) \\ \hline
 & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} & Geometric & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{green}{\checkmark} & \textcolor{green}{\checkmark} & Geometric & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{green}{\checkmark} & \textcolor{green}{\checkmark} & Linear & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{green}{\checkmark} & \textcolor{green}{\checkmark} & Linear & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} & Linear & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{green}{\checkmark} & \textcolor{green}{\checkmark} & Q-linear & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{green}{\checkmark} & \textcolor{green}{\checkmark} & Linear & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{green}{\checkmark} & \textcolor{green}{\checkmark} & $\mathcal{O}\left(\frac{\ln k}{\sqrt{k}}\right)$ & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} & $\mathcal{O}(1 / nk)$ & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{red}{\text{\sffamily X}} & \textcolor{green}{\checkmark} & (n/a) & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{red}{\text{\sffamily X}} & \textcolor{green}{\checkmark} & Linear & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} & $\mathcal{O}\left(\frac{\ln k}{\sqrt{k}}\right)$ & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} & Linear & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} & $\mathcal{O}(\frac{1}{\log k})$ & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} & Linear & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} & $\mathcal{O}\left(\frac{\ln k}{\sqrt{k}}\right)$ & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} & $\mathcal{O}\left(\frac{1}{\sqrt{k}}\right)$ & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} & Linear & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{red}{\text{\sffamily X}} & \textcolor{red}{\text{\sffamily X}} & Linear & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} & Linear & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} & $\mathcal{O}\left(\frac{1}{{k}^{1/2}}\right)$ & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} & Linear & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
 & \textcolor{red}{\text{\sffamily X}} & \textcolor{green}{\checkmark} & Linear & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
Algorithm~ & \textcolor{green}{\checkmark} & \textcolor{green}{\checkmark} & Linear & \textcolor{green}{\checkmark} & \textcolor{red}{\text{\sffamily X}} \\
\hline
Algorithm~ & \textcolor{green}{\checkmark} & \textcolor{green}{\checkmark} & Linear & \textcolor{green}{\checkmark} & \textcolor{green}{\checkmark} \\
\hline
Algorithm~ & \textcolor{green}{\checkmark} & \textcolor{green}{\checkmark} & Linear & \textcolor{green}{\checkmark} & \textcolor{green}{\checkmark} \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Apostolos I. Rikos
Wei Jiang
Themistoklis Charalambous
Karl H. Johansson

"Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping" by Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous, and Karl H. Johansson, arXiv:2504.16477v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
\hline
 & \multicolumn{2}{c}{(1). No random coef.} & \multicolumn{2}{c}{(2). Random coef.}\tabularnewline
 & Est. & SE & Est. & SE\tabularnewline
\hline
\hline
$\overline{\alpha}:$ Median of price coef. (yen/1000) & 2.562 & 0.346 & 2.932 & 0.487\tabularnewline
$\sigma_{\alpha}$: Shape parameter of $\alpha_{i}$ & - & - & 0.096 & 0.269\tabularnewline
$\rho_{Inc}:$ nest parameter (incandescent) & 0.961 & 0.011 & 0.956 & 0.011\tabularnewline
$\rho_{CFL}:$ nest parameter (CFL) & 0.700 & 0.035 & 0.658 & 0.04\tabularnewline
$\theta_{2000h,40W}$ & 1.356 & 0.035 & 1.383 & 0.036\tabularnewline
$\theta_{2000h,60W}$ & 1.357 & 0.034 & 1.385 & 0.035\tabularnewline
$\theta_{2000h,100W}$ & 1.387 & 0.038 & 1.418 & 0.04\tabularnewline
\hline
Number of iterations (Proposed) & \multicolumn{2}{c}{33} & \multicolumn{2}{c}{313}\tabularnewline
Number of iterations (BLP-based) & \multicolumn{2}{c}{5804} & \multicolumn{2}{c}{6237}\tabularnewline
\hline
Comp. time (sec; Proposed) & \multicolumn{2}{c}{0.835} & \multicolumn{2}{c}{4.931}\tabularnewline
Comp. time (sec; BLP-based) & \multicolumn{2}{c}{74.684} & \multicolumn{2}{c}{87.608}\tabularnewline
\hline
\end{tabular}
\caption{Results of demand parameter estimates}
\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}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccccc}
		\toprule[1pt]
		\multirow{2}{*}{~~Databases~~} & \multirow{2}{*}{~~~~~~Metrics~~~~~~} & \multicolumn{5}{c}{~~Methods~~} \\ \cline{3-7}
		 & & ~~AE~~ & ~~VAE~~ & ~~GMVAE~~ & ~~fAnoGAN~~ & ~~Ours~~ \\ \hline \hline
		\multirow{2}{*}{BraTS} & AUPRC & 0.229 & 0.331 & 0.253 & 0.373 & \textbf{0.511} \\
		& {[}DICE{]} & 0.378 & 0.440 & 0.408 & 0.453 & \textbf{0.544} \\ \hline
		\multirow{2}{*}{ISLES} & AUPRC & 0.043 & 0.050 & 0.057 & 0.076 & \textbf{0.110} \\
		& {[}DICE{]} & 0.112 & 0.117 & 0.116 & 0.164 & \textbf{0.178} \\
		\bottomrule[1pt]
	\end{tabular}
\end{table}
\end{document}

eess

Chenxin Li
Yunlong Zhang
Jiongcheng Li
Yue Huang
Xinghao Ding

"Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation" by Chenxin Li, Yunlong Zhang, Jiongcheng Li, Yue Huang, and Xinghao Ding, arXiv:2103.09094v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"$k$-Neighbor Based Curriculum Sampling for Sequence Prediction" by James O' Neill and Danushka Bollegala, arXiv:2101.09313v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c||c|c|}
\hline
Parameter & Value & Parameter & Value \\
\hline
$a$ & $1 \times 10^{-2}\ \$ $ & $a^B$ & $1 \times 10^{-2} \ \$ $ \\
$\eta$ & $1 \times 10^{-2}\ \$ \cdot \text{day}$ & $\eta^B$ & $5 \times 10^{-3}\ \$ \cdot \text{day}$ \\
$T$ & $2\ \text{days}$ & $\mathbb E [\mu]$ & $0\ \$ \cdot \text{day}^{-1}$ \\
 & & $\mu$ (realized) & $5\ \$ \cdot \text{day}^{-1}$\\
\hline
\end{tabular}
\caption{Parameter values.}
\end{table}
\end{document}

q-fin

Philippe Bergault
Pierre Cardaliaguet
Wenbin Yan

"Optimal hedging of an informed broker facing many traders" by Philippe Bergault, Pierre Cardaliaguet, and Wenbin Yan, arXiv:2506.08992v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Janie Coulombe
Dany El-Riachi
Fanxing Du
Tianze Jiao

"Constructing optimal dynamic monitoring and treatment regimes: An application to hypertension care" by Janie Coulombe, Dany El-Riachi, Fanxing Du, and Tianze Jiao, arXiv:2501.08274v1, 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}{l|lcr|lcr|lcr}%{l|lr|lr|lr}
\toprule
{} & Lang. & \texttt{ts} & +(\%) & Lang. & \texttt{ts} & +(\%) & Lang. & \texttt{ts} & +(\%) \\
\midrule
& \multicolumn{3}{c}{\textbf{Parsing}} & \multicolumn{3}{c}{\textbf{POS Tagging}} & \multicolumn{3}{c}{\textbf{NER}} \\
1 & mya & 0.33 & 40.4 & \textbf{kin} & 0.41 & 35.1 & zho & 0.16 & 49.6 \\
2 & ell & 0.15 & 31.6 & \textbf{kmr} & 0.36 & 36.9 & tel & 0.08 & 32.8 \\
3 & \textbf{kmr} & 0.14 & 35.9 & \textbf{mos} & 0.27 & 34.2 & hun & 0.08 & 40.8 \\
4 & yor & 0.14 & 33.3 & hye & 0.27 & 36.9 & heb & 0.04 & 34.4 \\
5 & \textbf{pcm} & 0.13 & 31.6 & cym & 0.22 & 37.7 & est & 0.03 & 36.8 \\
\toprule
& \multicolumn{3}{c}{\textbf{XNLI}} & \multicolumn{3}{c}{\textbf{ANLI}} & \multicolumn{3}{c}{\textbf{TyDiQA}} \\
1 & \textbf{hau} & -34.4 & 0.0 & \textbf{bam} & -15.0 & 0.0 & zho & 0.7 & 77.8 \\
2 & \textbf{bam} & -34.9 & 0.0 & \textbf{hau} & -17.8 & 0.0 & jpn & 0.1 & 44.4 \\
3 & \textbf{gub} & -36.4 & 0.0 & \textbf{gub} & -18.4 & 0.0 & gle & -0.1 & 44.4 \\
4 & \textbf{ewe} & -36.7 & 0.0 & deu & -19.8 & 0.0 & \textbf{wol} & -0.1 & 44.4 \\
5 & hin & -37.1 & 0.0 & fin & -19.9 & 0.0 & cym & -0.1 & 33.3 \\
\bottomrule
\end{tabular}
\caption{Top 5 transfer languages per task ranked using the aggregated transfer score (\texttt{ts} columns; see App.~ for computation). Unseen ones are \textbf{bolded}. \textit{+(\%)} is the percentage of languages receiving positive transfer. No transfer language helps all target languages. (Complete rank with transfer scores: Table~-).}
\end{table}
\end{document}

cs

"An Efficient Approach for Studying Cross-Lingual Transfer in Multilingual Language Models" by Fahim Faisal and Antonios Anastasopoulos, arXiv:2403.20088v1, 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{Example of 48-ary mapping table for $m=3$ using a greedy algorithm }
\begin{tabular}{|c|c|c|c|c|c|}
\hline
DNA & Symbol & DNA & Symbol & DNA & Symbol \\ \hline
AAC & 0 & TCT & 27 & GTG & 40 \\ \hline
AAT & 1 & CCT & 26 & ATG & 42 \\ \hline
GAT & 3 & CCA & 30 & TTG & 43 \\ \hline
TAT & 2 & CCG & 31 & CTG & 41 \\ \hline
TGT & 6 & CAG & 29 & CTA & 45 \\ \hline
CGT & 7 & CAT & 28 & CGA & 47 \\ \hline
AGT & 5 & CAC & 20 & AGA & 46 \\ \hline
AGC & 4 & TAC & 21 & GGA & 44 \\ \hline
ATC & 12 & TGC & 23 & TGA & 36 \\ \hline
ATA & 13 & TTC & 22 & CGC & 37 \\ \hline
GTA & 15 & TTA & 18 & CTC & 39 \\ \hline
GCA & 14 & TCA & 19 & GTC & 38 \\ \hline
ACA & 10 & TCG & 17 & GAC & 34 \\ \hline
ACG & 11 & TAG & 16 & GGC & 35 \\ \hline
ACT & 9 & AAG & 24 & GGT & 33 \\ \hline
GCT & 25 & GAG & 8 & GCG & 32 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Seong-Joon Park
Yongwoo Lee
Jong-Seon No

"Iterative DNA Coding Scheme With GC Balance and Run-Length Constraints Using a Greedy Algorithm" by Seong-Joon Park, Yongwoo Lee, and Jong-Seon No, arXiv:2103.03540v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc} \hline
Strategy & Median[$W_T$] & E[$W_T$] & std[$W_T$] & 5th Percentile & Median IRR (annual) \\ \hline
 Neural network & 362.7 & 401.3 & 212.6 & 133.9 & 0.078\\
 Benchmark & 308.5 & 342.9 & 165.0 & 149.0 & 0.056 \\ \hline
 Neural network (zero premium) & 364.2 & 403.4 & 211.8 & 136.3 & 0.078\\\hline
\end{tabular}
\caption{Statistic of strategies. Annualized borrowing premium is 3\%. Results are based on the evaluation of the learned neural network model (from high-inflation data) on the testing data set (low-inflation data).}
\end{table}
\end{document}

q-fin

"Neural Network Approach to Portfolio Optimization with Leverage Constraints:a Case Study on High Inflation Investment" by Chendi Ni, Yuying Li, and Peter A. Forsyth, arXiv:2304.05297v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"A Hamiltonian Approach to Barrier Option Pricing Under Vasicek Model" by Chao Guo and Ning Yao, arXiv:2307.07103v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Some Remarks on the Riesz and reverse Riesz transforms on Broken Line" by Dangyang He, arXiv:2503.14885v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Dibyendu Adak
Felipe Lepe
Gonzalo Rivera

"A noncoforming virtual element approximation for the Oseen eigenvalue problem" by Dibyendu Adak, Felipe Lepe, and Gonzalo Rivera, arXiv:2412.16813v1, 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{\footnotesize \textbf{Feature descriptions of \textsc{Calhousing} dataset.}}
\begin{tabular}{c|c|c|c}
\hline
Feature name & Index &Description & Feature type \\ \hline
\hline
MedInc & 1&Median income in block & Numerical \\ \hline
HouseAge & 2&Median house age in block & Numerical \\ \hline
AveRooms & 3&Average number of rooms & Numerical \\ \hline
AveBedrms & 4 &Average number of bedrooms & Numerical \\ \hline
Population & 5 &Population in block & Numerical \\ \hline
AveOccup & 6 &Average house occupancy & Numerical \\ \hline
Latitude & 7 &Latitude of house block & Numerical \\ \hline
Longitude & 8 &Longitude of house block & Numerical \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Seokhun Park
Insung Kong
Yongchan Choi
Chanmoo Park
Yongdai Kim

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|lllll}
 \toprule
 Class & \bfseries 1 & \bfseries 2 & \bfseries 3 & \bfseries 4 & \bfseries 5 \\
 \midrule
 \bfseries 1 & 2.383 & 2.719 & 3.533 & 17.334 & 9.011 \\
 \bfseries 2 & & 0.914 & 4.115 & 19.439 & 11.038 \\
 \bfseries 3 & & & 2.536 & 15.492 & 7.750 \\
 \bfseries 4 & & & & 4.077 & 9.535 \\
 \bfseries 5 & & & & & 5.043 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Jens Agerberg
Andrea Guidolin
Andrea Martinelli
Pepijn Roos Hoefgeest
David Eklund
Martina Scolamiero

"Certifying Robustness via Topological Representations" by Jens Agerberg, Andrea Guidolin, Andrea Martinelli, Pepijn Roos Hoefgeest, David Eklund, and Martina Scolamiero, arXiv:2501.10876v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Attack succes rates (ASRs) achieved by the proposed anti-forensic GAN attack in the training data and CNN architecture mismatch scenerio. }
\begin{tabular}{|lcccc|}
		\hline
		 \multicolumn{5}{|c|}{\textbf{Proposed Anti-Forensic GAN Attack}}\\
		\hline
 \textbf{CNN Architect. } & \textbf{Detection} & \textbf{Classification} &\textbf{ Parameterization} &\textbf{Avg.}\\
		MISLnet& 0.99&0.72&0.95&0.88\\
		TransferNet& 0.99 & 0.80 & 0.91 &0.90\\
		PHNet & 0.64& 0.98 &0.78&0.80\\
		SRNet& 0.85& 0.03 & 0.45&0.44\\
		DenseNet\textunderscore BC & 0.94& 0.77&0.09 &0.60\\
		VGG-19& 0.94&0.98 &0.92&0.95\\
		\textcolor{blue}{\textbf{Avg.}} & \textcolor{blue}{\textbf{0.89}}& \textcolor{blue}{\textbf{0.71}} &\textcolor{blue}{ \textbf{0.68}}&\textcolor{blue}{\textbf{0.76}}\\\hline
	\end{tabular}
\end{table}
\end{document}

cs

"A Transferable Anti-Forensic Attack on Forensic CNNs Using A Generative Adversarial Network" by Xinwei Zhao, Chen Chen, and Matthew C. Stamm, arXiv:2101.09568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{World cup 2022 match: Argentina (goalkeeper {\bf{Emiliano Martinez}}) versus France (goalkeeper {\bf{Hugo Lloris}}).}
\begin{tabular}{lllll}
\hline
\hline
 shoot &Argentina & true &France goalkeeper &goal \\
 number &penalty kicker & cluster &clustering &result \\
\hline
 1 &Lionel Messi &1 &1 &allowed \\
 2 &Paulo Dybala &2 &3 &allowed \\
 3 &Leandro Paredes &1 &1 &allowed \\
 4 &Gonzalo Montiel &1 &3 &allowed \\
\hline
 shoot &Germany& true & Argentina goalkeeper &goal \\
 number &penalty kicker & cluster&clustering &result \\
\hline
 1 &Kylian Mbapp{\'e} &2 &2 &allowed \\
 2 &Kingsley Coman &1 &1 &saved \\
 3 &Aur{\'e}lien Tchouam{\'e}ni&{\bf{\color{red}{1}}}&1 &saved \\
 4 &Randal Muani &2 &1 &allowed \\
 \hline
 goalkeeper stats & & & &\\
 Martinez:& \multicolumn{4}{l}{RI=1, DDI=1.000, MRDI=1.000, SV=0.500, GSI=0.450}\\
 Lloris: & \multicolumn{4}{l}{RI=1, DDI=0.693, MRDI=0.693, SV=0.000, GSI=0.250}\\
\hline
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Soccer Goalkeeper Performance Evaluation: Clustering Approach" by Mahdi Teimouri, arXiv:2502.05548v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccc}
\toprule\addlinespace
$\gamma$ & Low-fidelity & Without preconditioning & With
preconditioning\tabularnewline
\midrule
$3$ & $6.13$ & $4.88$ & $4.66$\tabularnewline
$2$ & $8.43$ & $5.60$ & $5.26$\tabularnewline
$1$ & $8.84$ & $6.26$ & $6.51$\tabularnewline
$0$ & $14.73$ & $8.41$ & $8.45$\tabularnewline
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Ali Siahkoohi
Gabrio Rizzuti
Mathias Louboutin
Philipp A. Witte
Felix J. Herrmann

"Preconditioned training of normalizing flows for variational inference in inverse problems" by Ali Siahkoohi, Gabrio Rizzuti, Mathias Louboutin, Philipp A. Witte, and Felix J. Herrmann, arXiv:2101.03709v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Accuracy, Precision, Recall and F1-Score using mentioned classification based modeling approaches trained and tested on combined datset-1 and dataset-2.}
\begin{tabular}{|l|l|l|l|l|}
\hline
\rule[-1ex]{0pt}{3.5ex} Model Name & Accuracy & Precision & Recall & F1-score \\
\hline
\rule[-1ex]{0pt}{3.5ex} Logistic Regression & 82.4\% & 0.822 & 0.828 & 0.828\\
\hline
\rule[-1ex]{0pt}{3.5ex} Convolutional Neural Network & 90.2\% & 0.912 & 0.901 & 0.904 \\
\hline
\rule[-1ex]{0pt}{3.5ex} Transfer Learning(VGG16) & 93.3\% & 0.931 & 0.932 & 0.928 \\
\hline
\rule[-1ex]{0pt}{3.5ex} Siamese Networks & 94.6\% & 0.945 & 0.941 & 0.947 \\
\hline
\rule[-1ex]{0pt}{3.5ex} Siamese Networks(Transfer Learning) & \textbf{96.4\%} & \textbf{0.965} & \textbf{0.962} & \textbf{0.959} \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach" by Shruti Jadon, arXiv:2102.06285v2, 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}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the data representations applied in this paper. We use notation $[a..b]$ to denote the integer interval $\{x \mid a \leq x \leq b, x \in \mathbb{Z}\}$ including both endpoints.}
\begin{tabular}{llll}
 \toprule
 & \textbf{Multi-Track Arrangement} & \textbf{Piano Reduction} & \textbf{Orchestral Function} \\
 \midrule
 \textbf{Data Representation} & $\mathbf{x} \in [0..32]^{T\times K \times 32 \times 128}$ & $\mathrm{pn}[\mathbf{x}]\in [0..32]^{T \times 32 \times 128}$ & $\mathrm{fn}[\mathbf{x}] \in [0, 1]^{T\times K \times 32}$ \\
 \midrule
 \textbf{Latent Dimension} & $\mathbf{z} \in \mathbb{R}^{T \times K \times 256}$ & $\mathbf{c} \in \mathbb{R}^{T \times 256}$ & $s \in [0..127]^{8T\times K}$ \\
 \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.


q-fin

Marc Chen
Mohammad Shirazi
Peter A. Forsyth
Yuying Li

"Machine Learning and Hamilton-Jacobi-Bellman Equation for Optimal Decumulation: a Comparison Study" by Marc Chen, Mohammad Shirazi, Peter A. Forsyth, and Yuying Li, arXiv:2306.10582v1, 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{Comparison of the mean absolute error (MAE) and standard error of the mean (SEM) of the estimated $\tau(X_i)$ estimated by CSF, MISTR, IPCW-IV, and MISTR-IV on a random test set with $n_{test}=5000$ observations for the IV case. Results are based on 100 replications and are multiplied by a readability factor of 100. }
\begin{tabular}{|l|cc|cc|cc|cc|}
\toprule
 & \multicolumn{2}{|c|}{CSF} & \multicolumn{2}{|c|}{MISTR} & \multicolumn{2}{|c|}{IPCW-IV} & \multicolumn{2}{|c|}{MISTR-IV} \\
Setting & MAE & SEM & MAE & SEM & MAE & SEM & MAE & SEM \\
\midrule
200 & 21.29 & 0.50 & 20.91 & 0.49 & 26.28 & 0.47 & \textbf{14.33} & 0.32 \\
201 & 27.61 & 0.56 & 27.28 & 0.55 & 35.84 & 0.66 & \textbf{18.24} & 0.42 \\
202 & 29.03 & 0.57 & 28.70 & 0.57 & 33.27 & 0.56 & \textbf{17.50} & 0.40 \\
203 & 18.25 & 0.40 & 16.30 & 0.38 & 27.70 & 0.55 & \textbf{12.82} & 0.29 \\
204 & 16.14 & 0.46 & 13.37 & 0.44 & 32.10 & 1.12 & \textbf{11.40} & 0.37 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees" by Tomer Meir, Uri Shalit, and Malka Gorfine, arXiv:2502.01575v1, 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{Mean Squared Error (MSE) and standard error of the mean (SEM) for HIV datasets with additional censoring. Results are based on 10 replications of censoring sampling.}
\begin{tabular}{|l|cc|cc|}
\toprule
 & \multicolumn{2}{|c|}{CSF} & \multicolumn{2}{|c|}{MISTR} \\
Setting & MSE & SEM & MSE & SEM \\
\midrule
HIV-1 & 1.388 & 0.165 & \textbf{1.207} & 0.251 \\
HIV-2 & 1.875 & 0.266 & \textbf{1.209} & 0.249 \\
HIV-3 & 1.320 & 0.238 & \textbf{0.777} & 0.190 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of different sampling strategies in TRC loss, measured by the performance of TriDet on THUMOS14-C. Our action-centric sampling leads to the best results on both clean and corrupted data.}
\begin{tabular}{c|c|c}
\hline
\multirow{2}{*}{Sampling Strategy} & \multirow{2}{*}{\shortstack{Clean \\ mAP}} & \multirow{2}{*}{\shortstack{Corrupted \\ mAP}} \\
 & & \\
\cline{1-3}
Without TRC & 75.16 & 61.10 \\
Full Video & 74.21 (0.95 $\downarrow$) & 67.21 (6.11 $\uparrow$) \\
Full Action & 75.04 (0.12 $\downarrow$) & 67.23 (6.13 $\uparrow$) \\
Action Center (Ours) & \textbf{75.60 (0.44 $\uparrow$)} & \textbf{68.28 (7.18 $\uparrow$)} \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The runtime in seconds of the color set by the K-means method and human specified (including the time taken in deciding the number of colors).}
\begin{tabular}{c|c|c}
			\hline
			Images & K-means & Human \\ \hline
			Figure (a) & 8.36 & 157.67 \\
			Figure (b) & 8.36 & 159.54 \\
			Figure (c) & 8.39 & 159.93 \\
			Figure (d) & 6.18 & 275.27 \\\hline
	\end{tabular}
\end{table}
\end{document}

eess

Tingting Wu
Xiaoyu Gu
Jinbo Shao
Ruoxuan Zhou
Zhi Li

"Color image segmentation based on a convex K-means approach" by Tingting Wu, Xiaoyu Gu, Jinbo Shao, Ruoxuan Zhou, and Zhi Li, arXiv:2103.09565v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Chao-Han Huck Yang
I-Te Danny Hung
Yi Ouyang
Pin-Yu Chen

"Training a Resilient Q-Network against Observational Interference" by Chao-Han Huck Yang, I-Te Danny Hung, Yi Ouyang, and Pin-Yu Chen, arXiv:2102.09677v3, 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
\caption{Summary of Regression Variables}
\begin{tabular}{lccccl}
 \toprule
 Variable & Mean & Std. Dev. & Min & Max & Data Sources\\
 \midrule
 \textbf{Export} & & & & & \textbf{Bangladesh Customs}\\
 Ln shipping frequency & 1.05 & 1.23 & 0 & 7.57 & \\
 Ln per-shipment value & 9.42 & 1.58 & -1.73 & 20.33 &\\
 Ln export value & 10.48 & 2.13 & -1.73 & 20.33 &\\
 Ln export weight & 8.09 & 2.29 & -4.61 & 19.79 &\\

 & & & & &\\
 \textbf{Distance and time} & & & & &\\
 Ln per-shipment costs & 7.08 & 0.39 & 5.90 & 9.89 & Doing Business Survey database (WB)\\
 Time to import (days) & 9.35 & 5.61 & 4 & 117 & Doing Business Survey database (WB)\\
 Port to port transport time (days) & 22.67 & 6.69 & 0 & 37 & searates.com\\
 Ln distance & 8.88 & 0.51 & 6.06 & 9.81 & CEPII distance dataset\\
 Ln Sea-distance & 9.45 & 0.38 & 7.81 & 9.98 & CERDI sea-distance database\\
 Logistics performance index & 2.87 & 0.57 & 1.34 & 4.11 & LPI database (WB) \\
 & & & & &\\

 \textbf{Financing costs (\%)} & & & & &\\
 Exporter's interest rates & 12.69 & 0.93 & 11.30 & 13.77 & Bangladesh Bank\\
 Importer's interest rates & 5.15 & 3.52 & 0.5 & 58.98 & International Financial Statistics (IMF)\\
 Exporter's net interest margin & 4.29 & 0.99 & 2.77 & 5.57 & \\

 Importer's net interest margin & 2.30 & 1.45 & 0.12 & 20.48 &\\
 Debt-GDP ratio & 123.04 & 50.21 & 2.17 & 906.38 &\\

 & & & & &\\
 \textbf{Control variables} & & & & & \textbf{CEPII gravity dataset}\\
 Ln GDP & 27.87 & 1.49 & 18.46 & 30.45 &\\
 Ln GDP per capita & 10.23 & 0.96 & 5.09 & 11.63 &\\
 Island & 0.13 & 0.34 & 0 & 1 &\\
 Landlocked & 0.03 & 0.17 & 0 & 1 &\\
 Common religion & 0.09 & 0.23 & 0 & 0.86 &\\
 Common colony& 0.07 & 0.26 & 0 & 1 &\\
 \midrule
 N = & & & &544,486 &\\
 \bottomrule
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Comparison of the Sobol' indices obtained by the Saltelli implementation () and simple binning approach on a toy model.}
\begin{tabular}{lccc}
\hline
Effect & Saltelli implementation () & Simple binning method & Delta \\ \hline
\multicolumn{4}{l}{First-order effects} \\
$P_s$ & 36 \% & 35 \% & $-1$ \% \\
$C_s$ & 0 \% & 1 \% & 1 \% \\
$P_t$ & 22 \% & 20 \% & $-2$ \% \\
$C_t$ & 0 \% & 1 \% & 1 \% \\
$P_j$ & 8 \% & 8 \% & 0 \% \\
$C_j$ & 0 \% & 2 \% & 2 \% \\
Sum of first-order effects & 66 \% & 67 \% & 1 \% \\ \hline
\multicolumn{4}{l}{Second-order effects (of selected pairs of variables)} \\
$P_sC_s$ & 18 \% & 16 \% & $-2$ \% \\
$P_tC_t$ & 11 \% & 10 \% & $-1$ \% \\
$P_jC_j$ & 5 \% & 6 \% & 1 \% \\
Sum of second-order effects & 34 \% & 32 \% & $-2$ \% \\ \hline
Sum of all effects & 100 \% & 99 \% & $-1$ \% \\ \hline
Model evaluations & 21000 & 1000 & \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Mariia Kozlova
Antti Ahola
Pamphile T. Roy
Julian Scott Yeomans

"Simple binning algorithm and SimDec visualization for comprehensive sensitivity analysis of complex computational models" by Mariia Kozlova, Antti Ahola, Pamphile T. Roy, and Julian Scott Yeomans, arXiv:2310.13446v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Adil Rengim Cetingoz
Charles-Albert Lehalle

"Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chance" by Adil Rengim Cetingoz and Charles-Albert Lehalle, arXiv:2501.03993v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Example of mutation operators}
\begin{tabular}{|l|c|c|c|c|c|c|c|c|}
 		\hline
 		Patients & 5 (2) & 2 (1) & \textbf{\color{blue}1 	 (2)}& 3 (1) &\textbf{\color{blue}6 (2)} &4 (3) & 1 (3) \\
 		\hline

 		Caregivers & \textbf{\color{blue}1 } & 1 & 1 & 2 & 1 & 2 & 2\\

 		\hline

 	\end{tabular}
\end{table}
\end{document}

math

"Mathematical models and heuristics for the home health care routing and scheduling problem" by Mohammed Bazirha, arXiv:2312.08032v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of dataset sizes for the unimodal dataset and the combined multimodal dataset. We note that the size of the multimodal dataset decreases when the two modalities are paired.}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Dataset} & \textbf{Training} & \textbf{Validation} & \textbf{Testing} & \textbf{Context} \\
 \hline
 Clinical time series data & 124,671 & 8,813 & 20,747 & 124,671 \\
 Chest X-rays & 42,628 & 4,802 & 11,914 & 42,628 \\
 Multimodal & 7,756 & 877 & 2,161 & 7,756 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

L. Julian Lechuga Lopez
Tim G. J. Rudner
Farah E. Shamout

"Informative Priors Improve the Reliability of Multimodal Clinical Data Classification" by L. Julian Lechuga Lopez, Tim G. J. Rudner, and Farah E. Shamout, arXiv:2312.00794v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports" by Xinyu Zhao, Hao Yan, and Yongming Liu, arXiv:2403.17914v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The impact of surplus sharing on the outcomes of specific investments under negotiated transfer pricing: An agent-based simulation with fuzzy Q-learning agents" by Christian Mitsch, arXiv:2301.12255v1, 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}{ccccc}
\toprule
& SN & ONI & OCNN & ConvNorm \\
\midrule[0.7pt]
$\rho$ & 2.724 & 0.001 & 2.288 & 3.332\\
\bottomrule
\end{tabular}
\caption{\textbf{Average layer-wise condition number ratio of vanilla method on top of other methods} The experiments are conducted on natural settings with the same set of hyperparameter of .}
\end{table}
\end{document}

eess

Sheng Liu
Xiao Li
Yuexiang Zhai
Chong You
Zhihui Zhu
Carlos Fernandez-Granda
Qing Qu

"Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training" by Sheng Liu, Xiao Li, Yuexiang Zhai, Chong You, Zhihui Zhu, Carlos Fernandez-Granda, and Qing Qu, arXiv:2103.00673v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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.


q-fin

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textsc{CPU time for $60$ iterations of Algorithm 3.}}
\begin{tabular}{lllllll}
		\hline \hline
		Mean of $\eta_k$ & $1$ Mnats/s & $2$ Mnats/s & $3$ Mnats/s & $4$ Mnats/s & \\
		CPU time & $0.051$ s & $0.082$ s & $0.104$ s & $0.121$ s \\
		Mean of $\eta_k$ & & $5$ Mnats/s & $6$ Mnats/s & \\
		CPU time & & $0.144$ s & $0.158$ s \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Navid Reyhanian
Hamid Farmanbar
Zhi-Quan Luo

"Resource Reservation in Backhaul and Radio Access Network with Uncertain User Demands" by Navid Reyhanian, Hamid Farmanbar, and Zhi-Quan Luo, arXiv:2102.11844v1, 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|ccccccccc|}
\hline
 & \multicolumn{9}{c|}{$n$} \\
$(v,k,\lambda)$ & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\
\hline
$(3,2,1)$ & 1 & 2 & 1 & 1 & 0 & & & & \\
$(7,3,1)$ & 1 & 13 & 20 & 4 & 3 & 2 & 0 & 0 & 0 \\
$(7,4,2)$ & 1 & 877 & 884 & 74 & 19 & 9 & 6 & 5 & 0 \\
\hline
\end{tabular}
\caption{Numbers of $\P^n(v,k,\lambda)$-cubes up to equivalence.}
\end{table}
\end{document}

math

On higher-dimensional symmetric designs

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameter values}
\begin{tabular}{|c|c|} \hline
 $T$& 1\\ \hline
 $\Delta t$& 0.001\\ \hline
 $M$& 3000\\ \hline
 $\mu_0$& Uniform(0,1)\\\hline
 \end{tabular}
\end{table}
\end{document}

math

"Learning Stochastic Dynamical Systems with Structured Noise" by Ziheng Guo, James Greene, and Ming Zhong, arXiv:2503.01077v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Tinggang Zhao
Zhenyu Zhao
Changpin Li
Dongxia Li

"Spectral approximation of $ψ$-fractional differential equation based on mapped Jacobi functions" by Tinggang Zhao, Zhenyu Zhao, Changpin Li, and Dongxia Li, arXiv:2312.16426v1, 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}{rrrrr}
\toprule
$d$ & $n$ & Runtime & Generations & Time/generation \\ \midrule
$2$ & $6$ & $0.6 \, {\rm s}$ & $5$ & $0.12 \, {\rm s}$ \\
$2$ & $24$ & $99.9 \, {\rm s}$ & $15$ & $6.6 \, {\rm s}$ \\
$4$ & $16$ & $28.7 \, {\rm s}$ & $7$ & $4.1 \, {\rm s}$ \\
$4$ & $40$ & $9 \, {\rm min} \, 6 \, {\rm s}$ & $9$ & $1 \, {\rm min} \, 7 \, {\rm s}$ \\ \bottomrule
\end{tabular}
\caption{Runtimes of the genetic algorithm for different configurations. The number of generations was capped at $15$.}
\end{table}
\end{document}

math

"A genetic algorithm to generate maximally orthogonal frames in complex space" by Sebastián Roca-Jerat and Juan Román-Roche, arXiv:2504.21084v2, 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{Overlap ratios and the search results on 128 TSP-1000 instances in using different embedding dimension $m$. We select top 20 elements from each row in the heat maps.}
\begin{tabular}{lcccr}
\toprule
$m$& Overlap Ratio(\%)& Performance Gap(\%) \\
\midrule
500 & 99.99 & 1.1995 $\pm$ 0.1849\\
1000 & 100.00 & \textbf{1.1608} $\pm$ 0.1844 \\
1500 & 100.00 & 1.1616 $\pm$ 0.1743\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"On Size and Hardness Generalization in Unsupervised Learning for the Travelling Salesman Problem" by Yimeng Min and Carla P. Gomes, arXiv:2403.20212v2, 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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccc}
 \toprule [0.2em]
 \multirow{2}{*}{\textbf{\textsc{Base Models}}} & \multicolumn{2}{c}{\bf WMT'14 EN-DE} & \multicolumn{2}{c}{\bf WMT'16 EN-RO}\\
 \cmidrule[0.15em](r){2-3} \cmidrule[0.15em](r){4-5}
 	& \textbf{Param.} & \textbf{BLEU} & \textbf{Param.} & \textbf{BLEU} \\ \midrule[0.1em]
 	DeLighT & 37M & 27.6 & 22M & 34.3\\
 	Evolved Transformer & 48M & 27.7 & --- & --- \\
 	DeLighT & 54M & 28.0 & 52M &\textbf{34.7} \\
 	Evolved Transformer& 64M & 28.2 & --- & --- \\
 	\midrule[0.07em]
 	\midrule[0.07em]
 	Transformer-base (orig) & 65M & 27.3& 62M & 34.2$^\dagger$\\
 	Transformer-base (ours) & 61M & 27.7 & 62M & 34.1 \\
 \midrule[0.1em]
 	Sandwich-base & 38M & 27.3 & --- & --- \\
 	Only SAFE, $d_e = 256$ & 54M & 27.7 & --- & --- \\
 \midrule[0.1em]
 \textsc{Subformer-small} & 38M & 27.7 & 20M & 34.1 \\
 \textsc{Subformer-base} & 52M & 28.1 & 48M & \textbf{34.7}\\
 \textsc{Subformer-mid} & 63M & \textbf{28.5} & --- & --- \\
 \bottomrule[0.15em]
 \end{tabular}
\caption{Results on WMT'14 EN-DE and WMT'16 EN-RO task, for our base models. The $^\dagger$ superscript indicates results from .}
\end{table}
\end{document}

cs

Machel Reid
Edison Marrese-Taylor
Yutaka Matsuo

"Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers" by Machel Reid, Edison Marrese-Taylor, and Yutaka Matsuo, arXiv:2101.00234v3, 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{Architecture details of the predictor network.}
\begin{tabular}{ccc}
 \toprule
 \textbf{Input} & \textbf{Output} & \textbf{Layer} \\
 \midrule
 8000 & 1024 & Linear \\
 1024 & 1024 & LeakyReLU (slope: 0.01) \\
 1024 & 10 & Linear \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Guillaume Quétant
Pavlo Molchanov
Slava Voloshynovskiy

"TwinTURBO: Semi-Supervised Fine-Tuning of Foundation Models via Mutual Information Decompositions for Downstream Task and Latent Spaces" by Guillaume Quétant, Pavlo Molchanov, and Slava Voloshynovskiy, arXiv:2503.07851v2, 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{Squared error loss for different prior distributions in the simulation study 2.}
\begin{tabular}{lcccccc}
 \toprule
 & \multicolumn{3}{c}{$\epsilon = 2$} & \multicolumn{3}{c}{$\epsilon = 10$} \\
 \cmidrule(lr){2-4}\cmidrule(lr){5-7}
 & $w=0.05$ & $w=0.2$ & $w=0.5$ & $w=0.05$ & $w=0.2$ & $w=0.5$ \\ \midrule
Horseshoe & 0.519 & 0.535 & 0.572 & 0.530 & 0.535 & \textbf{0.589} \\
Gambel 1 & 0.510 & 0.529 & \textbf{0.571} & 0.522 & 0.527 & 0.590 \\
Gambel 2 & \textbf{0.211} & 0.334 & 1.513 & 0.197 & 0.350 & 1.189 \\
Laplace & 1.133 & 0.670 & 1.632 & 0.734 & 0.678 & 0.919 \\
Spike and slab & 0.408 & \textbf{0.282} & 0.950 & \textbf{0.167} & \textbf{0.324} & 1.124 \\
\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}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The naturalness MOSs and PERs on the Small TTS dataset}
\begin{tabular}{c|c|ccc}
\hline
\multirow{2}{*}{System} & \multirow{2}{*}{MOS} & \multicolumn{3}{c}{PER (\%)}\tabularnewline\cline{3-5}
 & & long & short & overall \\\hline\hline
Transformer-S & 2.71 $\pm$ 0.091 & 62.03 & 12.66 & 40.88 \\\hline
P-Transformer-S & 3.75 $\pm$ 0.071 & 47.77 & 6.46 & 30.08\\\hline
\end{tabular}
\end{table}
\end{document}

eess

Yeunju Choi
Youngmoon Jung
Youngjoo Suh
Hoirin Kim

"Learning to Maximize Speech Quality Directly Using MOS Prediction for Neural Text-to-Speech" by Yeunju Choi, Youngmoon Jung, Youngjoo Suh, and Hoirin Kim, arXiv:2011.01174v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
 \hline
 & ODE & Sinkhorn \\ \hline
 Computed regularized primal value & 0.0050 & 0.0052\\ \hline
 Optimal unregularized primal value & 0 & 0\\ \hline
 Optimal unregularized primal value + Entropy & 0.0092 & 0.0092 \\ \hline
 Iterations & 100 & 324 \\ \hline
 CPU time (sec) & 1.40 & 0.04 \\ \hline
 \end{tabular}
\caption{Comparison of the performance between 4-th order Runge-Kutta ODE method and Sinkhorn algorithm for two marginal optimal transport with attractive cost $c(x, y) = (y - x)^2$.}
\end{table}
\end{document}

cs

Joshua Zoen-Git Hiew
Luca Nenna
Brendan Pass

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Attributes of EPRI J1 feeder}
\begin{tabular}{|l|c|l|}
\hline
 \textbf{Parameter} & \textbf{Value} & \textbf{Comments} \\\hline
Primary Voltage & 12.47 kV & Substation transformer at 69 kV \\\hline
 Secondary Voltage & 240 V & \\\hline
 Total Customers & 1384 & 363/375/643/3 (Phase A/B/C/3-ph) \\\hline
 Total Nodes & 4245 & 2037 Primary/2208 Secondary \\\hline

 Total Load & 10.95 MW & Includes 5 MW aggregated load \\\hline
 Transformers & 819 & 218/225/372/4 (Phase A/B/C/3-ph) \\\hline
 SVRs & 9 & 3/3/2/1 (Phase A/B/C/Substation) \\\hline
 Capacitors & 3900 kVAR & 5 Capacitors \\\hline
 Existing PV & 1813.6 kW & 1.71 MW commercial, 103.6 kW residential \\\hline
\end{tabular}
\end{table}
\end{document}

eess

"Analyzing Cross-Phase Effects of Reactive Power Intervention on Distribution Voltage Control" by Dhaval Dalal, Anamitra Pal, and Raja Ayyanar, arXiv:2311.14553v2, 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
		$h^{-1}$ & sharp & EOC & sharp & EOC & sharp & EOC\\
		& $p=1$ & & $p=2$ & & $p=3$ & \\
		\hline
		32 & 6.51e-04 & & 9.92e-07 & & 6.05e-09 & \\
		64 & 1.70e-04 & 1.94 & 1.24e-07 & 3.00 & 3.86e-10 & 3.97 \\
		128 & 4.35e-05 & 1.97 & 1.56e-08 & 2.99 & 2.45e-11 & 3.99 \\
		256 & 1.10e-05 & 1.98 & 1.95e-09 & 3.00 & & \\
		512 & 2.77e-06 & 1.99 & & & & \\
		\hline
	\end{tabular}
\caption{Quasi-1D test with quartic solution, $L^2$ convergence history of our stabilized sharp interface method with $\delta = 0$ on uniform meshes for different polynomial approximations.}
\end{table}
\end{document}

math

Maxim Olshanskii
Jan-Phillip Bäcker
Dmitri Kuzmin

"Projected gradient stabilization of sharp and diffuse interface formulations in unfitted Nitsche finite element methods" by Maxim Olshanskii, Jan-Phillip Bäcker, and Dmitri Kuzmin, arXiv:2501.16594v2, 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{Average time taken to reach a target region for different exploration strategies.}
\begin{tabular}{ccccc}
			\toprule[0.12em]&
			{\multirow{2}{*}{\textbf{Strategy}}} & \multicolumn{2}{c}{\textbf{Success rate}} & \multirow{2}{*}{\textbf{Time taken}}\\
			\cmidrule{3-4}
			& & \# Trials & \# Success & \textbf{(seconds)} \\
			\midrule
			\multirow{3}{*}{\textbf{2D}}
			& Strategy 1 & $100$ & $100$ & $\mathbf{66.9}$ \\
			& Strategy 2 & $100$ & $96$ & $106.8$ 	\\
			& Strategy 3 & $100$ & $100$ & $122.9$ \\
			& Strategy 4 & $100$ & $98$ & $155.4$ 	\\

			\cmidrule{2-5}
			\multirow{3}{*}{\textbf{3D}}
			& Strategy 1 & $100$ & $100$ & $\mathbf{84.7}$ \\
			& Strategy 2 & $100$ & $92$ & $141.4$ \\
			& Strategy 3 & $100$ & $98$ & $292.3$\\
			& Strategy 4 & $100$ & $95$ & $247.5$\\

			\bottomrule[0.12em]
		\end{tabular}
\end{table}
\end{document}

eess

Suhan Shetty
João Silvério
Sylvain Calinon

"Ergodic Exploration using Tensor Train: Applications in Insertion Tasks" by Suhan Shetty, João Silvério, and Sylvain Calinon, arXiv:2101.04428v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|}
\hline
$B$ & $q$ & \textbf{CPU Time} & \textbf{MIPGap} \\
\hline
\multirow{3}{*}{$0.1 \widehat{B}$} & {0.5} & 2595.82 & 0\% \\
 & {0.7} & 18093.03 & 0\% \\
 & {0.9} & 21602.8 & 0.1\% \\\hline
\multirow{3}{*}{$0.2 \widehat{B}$} & {0.5} & 84.67 & 0\% \\
 & {0.7} & 473.81 & 0\% \\
 & {0.9} & 3106.91 & 0\% \\\hline
\end{tabular}
\caption{CPU times (in seconds) and MIP gaps for solving the instances in the case study.}
\end{table}
\end{document}

math

Víctor Blanco
Yolanda Hinojosa
Victor Zavala

"The Waste-to-Biomethane Logistic Problem: A mathematical optimization approach" by Víctor Blanco, Yolanda Hinojosa, and Victor Zavala, arXiv:2312.01995v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Privilege Scores

Ludwig Bothmann
Philip A. Boustani
Jose M. Alvarez
Giuseppe Casalicchio
Bernd Bischl
Susanne Dandl

"Privilege Scores" by Ludwig Bothmann, Philip A. Boustani, Jose M. Alvarez, Giuseppe Casalicchio, Bernd Bischl, and Susanne Dandl, arXiv:2502.01211v1, 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}{llllllll}
		\multicolumn{2}{l}{subcase} & $\pi_0$ & $\pi_f(0)$ & $\pi_f(1)$ & CATE(0) & CATE(1) & self-fulfilling \\ \cline{1-8}
		 0 & $=$ & 0 & 1 & 0 & 0 & & yes \\
		 0 & $<$ & 0 & 1 & 0 & - & & no \\
		 0 & $>$ & 0 & 1 & 0 & + & & yes \\
		 1 & $=$ & 1 & 1 & 0 & & 0 & yes \\
		 1 & $<$ & 1 & 1 & 0 & & + & yes \\
		 1 & $>$ & 1 & 1 & 0 & & - & no
	\end{tabular}
\end{table}
\end{document}

stat

Wouter A. C. van Amsterdam
Nan van Geloven
Jesse H. Krijthe
Rajesh Ranganath
Giovanni Ciná

"When accurate prediction models yield harmful self-fulfilling prophecies" by Wouter A. C. van Amsterdam, Nan van Geloven, Jesse H. Krijthe, Rajesh Ranganath, and Giovanni Ciná, arXiv:2312.01210v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
Coadjoint orbit type & Dynamical system on torus & Hill equation monodromy \\ \hline
$T_{\Delta,n}$ & phase-locking, $\rho=2n$ & hyperbolic \\ \hline
$T_{0,n}$ & roots of Arnold tongues & degenerate \\ \hline
$T_{\pm,n}$ & boundaries of Arnold tongues & parabolic \\ \hline
$T_{\alpha,0}$ & no phase-locking, $\rho=\alpha/\pi$ & elliptic \\ \hline
\end{tabular}
\caption{Correspondence between Virasoro coadjoint orbits and the phase-locking}
\end{table}
\end{document}

math

Artem Alexandrov
Alexey Glutsyuk
Alexander Gorsky

"Phase-locking in dynamical systems and quantum mechanics" by Artem Alexandrov, Alexey Glutsyuk, and Alexander Gorsky, arXiv:2504.20181v1, 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{WER for Isolated spoken digit recognition task for training and testing phase.}
\begin{tabular}{|c|c|c|}
\hline
& \textbf{Training WER} & \textbf{Testing WER} \\ \hline
\textbf{Mean} & $0.05\%$ & $0.34\%$ \\ \hline
\textbf{Std} & $0.13\%$ & $0.51\%$\\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Prajnesh Kumar
Mingwei Jin
Ting Bu
Santosh Kumar
Yu-Ping Huang

"Efficient Reservoir Computing using Field Programmable Gate Array and Electro-optic Modulation" by Prajnesh Kumar, Mingwei Jin, Ting Bu, Santosh Kumar, and Yu-Ping Huang, arXiv:2102.09049v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{booktabs}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lllll}
 \toprule
 \multicolumn{5}{c}{No-regret learning algorithms} \\
 \cmidrule(r){1-5}
 Algorithm & Setting & Regret bound $R^T$ & Constraint bound $\mathcal{V}_m^T$ & Contexts\\
 \midrule
 c.GPMW & Repeated game & ${\mathcal{O}}(1/\varepsilon^2)$ & $\mathcal{O}(\varepsilon)$
 & \checkmark\\
 AdaNormalHedge & Sleeping expert problem & ${\mathcal{O}}(1/\varepsilon^6)$ &${\mathcal{O}}(\varepsilon)$ &\\
 RECCO & Online convex optimization & ${\mathcal{O}}(1/\varepsilon^6)$ & ${\mathcal{O}}(\varepsilon)$ &\\
 CONFIG & Black-box optimization & $\Tilde{\mathcal{O}}(1/\varepsilon^4)$ & Averaged zero &\\
 \textbf{AdaNormalGP} & Repeated game & $\Tilde{\mathcal{O}}(1/\varepsilon^6)$ & Zero all the time & \checkmark\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"Multi-Agent Learning in Contextual Games under Unknown Constraints" by Anna M. Maddux and Maryam Kamgarpour, arXiv:2310.14685v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccccc}
\toprule
Sample size of external arms & Strategy & $|\text{Bias}|$ & Variance & 95\% CI widths & 95\% CI coverage & Folds pooled (\%) \\
\midrule
\multirow{1}{*}{0} & RCT data only & 0.002 & 0.081 & 1.169 & 0.97 & - \\
\midrule
\multirow{2}{*}{500}
& Random & 0.241 & 0.157 & 1.166 & 0.75 & 0.358 \\
& TES + PS matching & \textbf{0.077} & \textbf{0.075} & \textbf{1.067} & \textbf{0.95} & \textbf{0.588} \\
\midrule
\multirow{2}{*}{600}
& Random & 0.289 & 0.203 & 1.179 & 0.72 & 0.390 \\
& TES + PS matching & \textbf{0.080} & \textbf{0.072} & \textbf{1.068} & \textbf{0.95} & \textbf{0.566} \\
\midrule
\multirow{2}{*}{700}
& Random & 0.284 & 0.186 & 1.175 & 0.68 & 0.376 \\
& TES + PS matching & \textbf{0.089} & \textbf{0.078} & \textbf{1.081} & \textbf{0.95} & \textbf{0.514} \\
\midrule
\multirow{2}{*}{800}
& Random & 0.270 & 0.174 & 1.177 & 0.73 & 0.354 \\
& TES + PS matching & \textbf{0.100} & \textbf{0.077} & \textbf{1.084} & \textbf{0.95} & \textbf{0.502} \\
\midrule
\multirow{2}{*}{900}
& Random & 0.281 & 0.180 & 1.182 & 0.73 & 0.366 \\
& TES + PS matching & \textbf{0.104} & \textbf{0.075} & \textbf{1.084} & \textbf{0.95} & \textbf{0.484} \\
\midrule
\multirow{2}{*}{1000}
& Random & 0.251 & 0.167 & 1.188 & 0.75 & 0.312 \\
& TES + PS matching & \textbf{0.103} & \textbf{0.077} & \textbf{1.096} & \textbf{0.95} & \textbf{0.460} \\
\bottomrule
\end{tabular}
\caption{Absolute bias, variance, average 95\% confidence interval widths, coverage, and proportion of folds that selected the pooled-ATE estimand averaged across simulation runs by external data sample size and sampling strategy. ``RCT data only" denotes the RCT-only design, analyzed with the TMLE estimator. ``Random" denotes a randomly sampled external cohort of the specified size, analyzed with the ES-CVTMLE estimator. ``TES + PS matching" denotes our proposed trial enrollment score and propensity score matching strategy for sampling the external cohort of the specified size, also analyzed with the ES-CVTMLE estimator.}
\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.


stat

Emily N Peterson
Rachel C. Nethery
Jarvis T. Chen
Loni P. Tabb
Brent A. Coull
Frederic B. Piel
Lance A Waller

"A Bayesian Spatial Berkson error approach to estimate small area opioid mortality rates accounting for population-at-risk uncertainty" by Emily N Peterson, Rachel C. Nethery, Jarvis T. Chen, Loni P. Tabb, Brent A. Coull, Frederic B. Piel, and Lance A Waller, arXiv:2312.13331v1, 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
Method & Kinetics & SSv2$^\dagger$ & SSv2$^*$ & HMDB & UCF \\ \midrule
CMN & 78.9 & - & - & - & - \\
CMN-J & 78.9 & 48.8 & - & - & - \\
TARN & 78.5 & - & - & - & - \\
ARN & 82.4 & - & - & 60.6 & 83.1 \\
OTAM & 85.8 & - & 52.3 & - & - \\
TRX (Ours) & \bf{85.9} & \bf{59.1} & \bf{64.6} & \bf{75.6} & \bf{96.1} \\
\bottomrule
\end{tabular}
\caption{Results on 5-way 5-shot benchmarks of Kinetics~(split from ), SSv2 ($^\dagger$: split from , $^*$: split from~), HMDB51 and UCF101 (both splits from ).}
\end{table}
\end{document}

cs

Toby Perrett
Alessandro Masullo
Tilo Burghardt
Majid Mirmehdi
Dima Damen

"Temporal-Relational CrossTransformers for Few-Shot Action Recognition" by Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmehdi, and Dima Damen, arXiv:2101.06184v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|ll|ll}
\toprule
\multirow{2.5}{*}{\textbf{Test Modalities}} & \multicolumn{2}{c}{\textbf{YouCook2}} & \multicolumn{2}{|c}{\textbf{ActivityNet}} \\ \cmidrule(lr){2-3} \cmidrule(lr){4-5}
 & \textbf{METEOR} & \textbf{CIDEr} & \textbf{METEOR} & \textbf{CIDEr} \\ \midrule
V+E+A & 23.11 & 74.13 & 14.09 & 42.29 \\ \midrule
V+A & 21.05 (\textcolor{red}{-2.06}) & 59.55 (\textcolor{red}{-14.58}) & 12.24 (\textcolor{red}{-1.85}) & 29.71 (\textcolor{red}{-12.58}) \\
V+E & 12.46 (\textcolor{red}{-10.65}) & 8.77 (\textcolor{red}{-65.36}) & 12.91 (\textcolor{red}{-1.18}) & 43.14 (\textcolor{green}{+0.85}) \\
V & 6.79 (\textcolor{red}{-16.32}) & 3.42 (\textcolor{red}{-70.71}) & 11.64 (\textcolor{red}{-2.45}) & 26.08 (\textcolor{red}{-16.21}) \\ \bottomrule
\end{tabular}
\caption{The performance of the vanilla MVPC model on YouCook2 and ActicityNet Captions in different modality missing settings.}
\end{table}
\end{document}

cs

Sishuo Chen
Lei Li
Shuhuai Ren
Rundong Gao
Yuanxin Liu
Xiaohan Bi
Xu Sun
Lu Hou

"Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality" by Sishuo Chen, Lei Li, Shuhuai Ren, Rundong Gao, Yuanxin Liu, Xiaohan Bi, Xu Sun, and Lu Hou, arXiv:2403.19221v1, 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{Optimality gap distribution for 10-minute runs}
\begin{tabular}{lccc}
\toprule
{Optimality gap} & {GRB} & {SCIP} \\
\midrule
Below 0.1\% & 442 & 273 \\
Between 0.1\% and 10\% & 92 & 112 \\
Above 10\% & 171 & 258 \\
No solution & 86 & 148 \\
Total & 791 & 791 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

Charly Robinson La Rocca
Jean-François Cordeau
Emma Frejinger

"Supervised Large Neighbourhood Search for MIPs" by Charly Robinson La Rocca, Jean-François Cordeau, and Emma Frejinger, arXiv:2501.10778v1, 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{Value of the stopping rule $\tau_{\iota} = \inf \{ t\in [\iota,T] \ : \ X_t = X^*_\iota\}$, $X^*_\iota = \text{argmax}_{x}L_\iota^x$ for varying inspection date $\iota$ and parameters $T=1$, $N=400$, $\varepsilon =0.05$. }
\begin{tabular}{ccc}
 Inspection date & Value & Monte Carlo Error\\ \hline\hline
 $\iota= 0.5$ & 1.0404 & 0.0035 \\
 $\iota= 0.6$ & 1.0897 & 0.0040\\
 $\iota= 0.7$ & 1.1116 & 0.0046\\

 $\iota= 0.8$ & 1.0892 & 0.0052\\
 $\iota= 0.9$ & 1.0182 & 0.0056\\
 \end{tabular}
\end{table}
\end{document}

q-fin

Occupied Processes: Going with the Flow

"Occupied Processes: Going with the Flow" by Valentin Tissot-Daguette, arXiv:2311.07936v2, 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{Classification datasets used in this paper.}
\begin{tabular}{lccccc}
\toprule
\textbf{Dataset} & \#Classes & Size (Train/Val) & Acc. Metric \\
\midrule
Aircraft~ & 100 & 6,667/3,333 & Mean Per-Class\\
Caltech101~ & 101 & 3,030/5,647 & Mean Per-Class\\
Cars~ & 120 & 8,144/8,041 & Top-1 \\
CUB-200~ & 200 & 5,994/5,794 & Top-1 \\
DTD~ & 47 & 3,760/1,880 & Top-1\\
Dogs~ & 120 & 12,000/8,580 & Top-1 \\
Flowers~ & 102 & 2,040/6,149 & Mean Per-Class\\
Food~ & 101 & 75,750/25,250 & Top-1\\
Pets~ & 37 & 3,680/3,669 & Mean Per-Class\\
SUN397~ & 397 & 19,850/19,850 & Top-1\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Yuhang Li
Xin Dong
Chen Chen
Jingtao Li
Yuxin Wen
Michael Spranger
Lingjuan Lyu

"Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization" by Yuhang Li, Xin Dong, Chen Chen, Jingtao Li, Yuxin Wen, Michael Spranger, and Lingjuan Lyu, arXiv:2403.19866v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Effects of Air Pollution on Teenagers' Cognitive Performance: Evidence from School Leaving Examination in Poland" by Agata Galkiewicz, arXiv:2506.19801v1, 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}{|l|r|r|r|r|}
 \toprule
 Altered Difference & Original & Exponential & Normal & Normal \\
 & & (Mean=10) & (prob=0.75) & (Mean=100) \\
 \midrule
 Min & 63.300 & 46.682 & 0.000 & 0.000 \\
 First Quartile & 1372.900 & 1353.362 & 1339.904 & 1179.467 \\
 Median & 2739.500 & 2719.508 & 2705.926 & 2546.122 \\
 Third Quartile & 4743.150 & 4722.762 & 4707.969 & 4543.598 \\
 Max & 8990.000 & 8989.290 & 8959.826 & 8802.135 \\
 Mean & 3186.568 & 3167.498 & 3154.230 & 2996.864 \\
 Standard Dev. & 2138.397 & 2138.558 & 2138.973 & 2137.692 \\
 Coeff. of Var. & 0.671 & 0.675 & 0.678 & 0.713 \\
 Autocorr. Lag: 24 & 0.437 & 0.437 & 0.436 & 0.436 \\
 Days Below 95\% & 0 & 4 & 16 & 237 \\
 Days Above 105\% & 0 & 0 & 0 & 0 \\
 \bottomrule
 \end{tabular}
\caption{Statistics on altering PJM's 2021 wind generation data using the altered difference method with three altered series using the incremental selection method with $\alpha = 0.9$.}
\end{table}
\end{document}

stat

"Resampling Methods that Generate Time Series Data to Enable Sensitivity and Model Analysis in Energy Modeling" by Kelly Wang and Steven O. Kimbrough, arXiv:2502.08102v1, 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{Control mechanism for each flight segment.}
\begin{tabular}{lc}
 \hline
 Flight Segment & Control Mechanism \\\hline
 Segment 1 & Bank angle modulation \\
 Segment 2 & None \\
 Segment 3 & Retrograde thrust \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

"Indirect Optimization of Multi-Phase Trajectories Involving Arbitrary Discrete Logic" by Harish Saranathan, arXiv:2412.07960v1, 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}{rrrrr}
 \hline
 & Odds ratio & Std. Err. & z & Pr($>$$|$z$|$) \\
 \hline
X.Intercept. & 0.62 & 1.13 & -3.88 & 0.00 \\
 Participant\_groupAltruistic & 1.53 & 1.19 & 2.47 & 0.01 \\
 Participant\_groupCompetitive & 0.44 & 1.19 & -4.67 & 0.00 \\
 Participant\_groupSelfish & 0.49 & 1.19 & -4.11 & 0.00 \\
 Modelgpt.3.5.turbo.0301 & 2.15 & 1.11 & 7.55 & 0.00 \\
 Modelgpt.3.5.turbo.1106 & 1.41 & 1.12 & 3.13 & 0.00 \\
 Participant\_groupControl.Partner\_conditionT4TC & 3.65 & 1.10 & 14.22 & 0.00 \\
 Participant\_groupAltruistic.Partner\_conditionT4TC & 6.19 & 1.11 & 17.69 & 0.00 \\
 Participant\_groupCompetitive.Partner\_conditionT4TC & 2.22 & 1.10 & 8.10 & 0.00 \\
 Participant\_groupCooperative.Partner\_conditionT4TC & 6.82 & 1.10 & 19.81 & 0.00 \\
 Participant\_groupSelfish.Partner\_conditionT4TC & 2.79 & 1.10 & 10.71 & 0.00 \\
 Participant\_groupControl.Partner\_conditionC & 3.18 & 1.10 & 12.64 & 0.00 \\
 Participant\_groupAltruistic.Partner\_conditionC & 5.18 & 1.11 & 16.00 & 0.00 \\
 Participant\_groupCooperative.Partner\_conditionC & 6.33 & 1.10 & 19.00 & 0.00 \\
 Participant\_groupSelfish.Partner\_conditionC & 1.78 & 1.10 & 5.90 & 0.00 \\
 Participant\_groupControl.Modelgpt.3.5.turbo.0301.Partner\_conditionD & 0.60 & 1.15 & -3.81 & 0.00 \\
 Participant\_groupAltruistic.Modelgpt.3.5.turbo.0301.Partner\_conditionD & 0.52 & 1.15 & -4.83 & 0.00 \\
 Participant\_groupCompetitive.Modelgpt.3.5.turbo.0301.Partner\_conditionD & 0.61 & 1.15 & -3.48 & 0.00 \\
 Participant\_groupCooperative.Modelgpt.3.5.turbo.0301.Partner\_conditionD & 0.56 & 1.14 & -4.39 & 0.00 \\
 Participant\_groupSelfish.Modelgpt.3.5.turbo.0301.Partner\_conditionD & 0.72 & 1.15 & -2.42 & 0.02 \\
 Participant\_groupAltruistic.Modelgpt.3.5.turbo.1106.Partner\_conditionD & 3.33 & 1.17 & 7.78 & 0.00 \\
 Participant\_groupCooperative.Modelgpt.3.5.turbo.1106.Partner\_conditionD & 1.89 & 1.15 & 4.47 & 0.00 \\
 Participant\_groupControl.Modelgpt.3.5.turbo.0301.Partner\_conditionT4TD & 0.59 & 1.14 & -3.92 & 0.00 \\
 Participant\_groupAltruistic.Modelgpt.3.5.turbo.0301.Partner\_conditionT4TD & 0.56 & 1.15 & -4.19 & 0.00 \\
 Participant\_groupCompetitive.Modelgpt.3.5.turbo.0301.Partner\_conditionT4TD & 0.67 & 1.15 & -2.90 & 0.00 \\
 Participant\_groupCooperative.Modelgpt.3.5.turbo.0301.Partner\_conditionT4TD & 0.55 & 1.14 & -4.46 & 0.00 \\
 Participant\_groupSelfish.Modelgpt.3.5.turbo.0301.Partner\_conditionT4TD & 0.71 & 1.15 & -2.54 & 0.01 \\
 Participant\_groupAltruistic.Modelgpt.3.5.turbo.1106.Partner\_conditionT4TD & 2.61 & 1.16 & 6.41 & 0.00 \\
 Participant\_groupCooperative.Modelgpt.3.5.turbo.1106.Partner\_conditionT4TD & 1.90 & 1.15 & 4.54 & 0.00 \\
 Participant\_groupControl.Modelgpt.3.5.turbo.0301.Partner\_conditionT4TC & 0.51 & 1.15 & -4.68 & 0.00 \\
 Participant\_groupAltruistic.Modelgpt.3.5.turbo.0301.Partner\_conditionT4TC & 0.18 & 1.16 & -11.60 & 0.00 \\
 Participant\_groupCompetitive.Modelgpt.3.5.turbo.0301.Partner\_conditionT4TC & 0.32 & 1.15 & -7.92 & 0.00 \\
 Participant\_groupCooperative.Modelgpt.3.5.turbo.0301.Partner\_conditionT4TC & 0.31 & 1.16 & -8.09 & 0.00 \\
 Participant\_groupSelfish.Modelgpt.3.5.turbo.0301.Partner\_conditionT4TC & 0.74 & 1.15 & -2.16 & 0.03 \\
 Participant\_groupCompetitive.Modelgpt.3.5.turbo.1106.Partner\_conditionT4TC & 0.59 & 1.17 & -3.42 & 0.00 \\
 Participant\_groupSelfish.Modelgpt.3.5.turbo.1106.Partner\_conditionT4TC & 0.62 & 1.16 & -3.23 & 0.00 \\
 Participant\_groupControl.Modelgpt.3.5.turbo.0301.Partner\_conditionC & 0.66 & 1.16 & -2.89 & 0.00 \\
 Participant\_groupAltruistic.Modelgpt.3.5.turbo.0301.Partner\_conditionC & 0.22 & 1.16 & -10.42 & 0.00 \\
 Participant\_groupCompetitive.Modelgpt.3.5.turbo.0301.Partner\_conditionC & 0.48 & 1.16 & -4.94 & 0.00 \\
 Participant\_groupCooperative.Modelgpt.3.5.turbo.0301.Partner\_conditionC & 0.36 & 1.16 & -6.93 & 0.00 \\
 \hline
\end{tabular}
\caption{Model estimates for Prisoners Dilemma. These are shown for significant coefficients only ($p<0.05$) on an odds ratio scale rounded to 2 decimal places.}
\end{table}
\end{document}

q-fin

"The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games?" by Steve Phelps and Yvan I. Russell, arXiv:2305.07970v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Using Internal Bar Strength as a Key Indicator for Trading Country ETFs" by Aditya Pandey and Kunal Joshi, arXiv:2306.12434v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
		\hline
		Algorithm & setup & Single iteration \\
		\hline
		Standard MHE & $N=30,N_{T_s}=20,\alpha_{\cdot,0-3}$ s & 2.77 s \\
		Filtered MHE & $N=20,N_{T_s}=20,\alpha_{\cdot,0-3}$ & 2.18 s \\
		Filtered MHE & $N=10,N_{T_s}=20,\alpha_{\cdot,0-3}$ & 1.09 s \\

		\hline
	\end{tabular}
\caption{ \textit{Standard} MHE computation times for different configurations.}
\end{table}
\end{document}

eess

Tushar Desai
Federico Oliva
Riccardo M. G. Ferrari
Daniele Carnevale

"Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator" by Tushar Desai, Federico Oliva, Riccardo M. G. Ferrari, and Daniele Carnevale, arXiv:2310.14332v1, 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 parameters for achieving reliability and imperceptibility at a distance of 0.5 m between the microphone and user in different environments: (a) conference room, (b) dining hall, and (c) in a car parked at a gas station.}
\begin{tabular}{lllll}
 \toprule[1.5pt]
 Location & \parbox[t]{1.5cm}{SPL (dB-A)}&\parbox[t]{1.8cm}{Frequency \\($\pm$ 200 Hz)} & \parbox[t]{1.8cm}{Amplitude \\ (normalized)} & \parbox[t]{1.1cm}{Bitrate \\ (bits/sec)}\\
 \midrule
 \parbox[t]{2.5cm}{Conference Room} & 41 & 4000 Hz & 0.52& 35\\
 Dining Hall & 58 & 5200 Hz & 1 & 25 \\
 Gas Station (Car) & 47 & 4800 Hz & 0.61 & 33 \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Yangyong Zhang
Maliheh Shirvanian
Sunpreet S. Arora
Jianwei Huang
Guofei Gu

"Practical Speech Re-use Prevention in Voice-driven Services" by Yangyong Zhang, Maliheh Shirvanian, Sunpreet S. Arora, Jianwei Huang, and Guofei Gu, arXiv:2101.04773v1, 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|c|}
\hline
Model & DQN & CIQ & DQN-CF & DQN-SA & DVRLQ & DQN-VAE & DQN-CEVAE\\ \hline
Env$_1$ Markov & 112.3 & \textbf{195.0} & 181.4 & 131.4 & 112.1& 163.7& 155.6 \\ \hline
Env$_2$ Markov & 9.4 & \textbf{12.1} & 11.7 & 9.1 & 11.5 & 11.2 & 11.2\\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Chao-Han Huck Yang
I-Te Danny Hung
Yi Ouyang
Pin-Yu Chen

"Training a Resilient Q-Network against Observational Interference" by Chao-Han Huck Yang, I-Te Danny Hung, Yi Ouyang, and Pin-Yu Chen, arXiv:2102.09677v3, 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}{lrrrrrrr}
 \toprule
 &\multicolumn{2}{c}{5-fold CV} &\multicolumn{2}{c}{Test Set} &\multicolumn{2}{c}{Environment Set} \\\cmidrule(lr){2-3}\cmidrule(lr){4-5}\cmidrule(lr){6-7}
 Model &Mean Ave. Precision &Std. Deviation &Mean Ave. Precision &Std. Deviation &Mean Ave. Precision &Std. Deviation \\\cmidrule(lr){1-1}\cmidrule(lr){2-3}\cmidrule(lr){4-5}\cmidrule(lr){6-7}
 Logistic Regression &98.24 &0.77 &98.64 &0.21 &84.61 &1.35 \\
 MLP &98.83 &0.86 &99.39 &0.19 &89.50 &1.50 \\
 CNN &99.14 &0.63 &98.60 &0.51 &91.32 &1.82 \\
 \bottomrule
\end{tabular}
\caption{Average precision from CV, Test and Environment evaluation.}
\end{table}
\end{document}

eess

"The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft" by Blake Downward and Jon Nordby, arXiv:2311.06368v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{makecell}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc|c}
\toprule
& \multicolumn{3}{c}{Our experiments} & Literature \\
\midrule
 \textbf{Task} & \textbf{ \thead{Multi-class \\ 2D U-Net} } &\textbf{ \thead{Single-class \\ 2D U-Net} } & \textbf{ \thead{Multi-class \\ FiLMed U-Net }} & \textbf{\thead{2D U-Net \\ (On whole challenge dataset)}}\\
\midrule
Liver & $50.3 \pm 18.3$ & $95.1 \pm 1.4$ & $94.1 \pm 1.6$ & \thead{ $94.37 \pm N/A$ } \\
Spleen & $35.6 \pm 14.2$ & $91.7 \pm 6.3$ & $92.2 \pm 5.3$ & \thead{$94.2 \pm N/A$ } \\
Kidney & $39.2 \pm 13.1$ & $90.4 \pm 9.3$ & $90.7 \pm 8.1$ & \thead {$93.0 \pm 1.2$ } \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Andreanne Lemay
Charley Gros
Olivier Vincent
Yaou Liu
Joseph Paul Cohen
Julien Cohen-Adad

"Benefits of Linear Conditioning with Metadata for Image Segmentation" by Andreanne Lemay, Charley Gros, Olivier Vincent, Yaou Liu, Joseph Paul Cohen, and Julien Cohen-Adad, arXiv:2102.09582v2, 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{Shape of weights estimation accuracy of the three different methods: LASSO-U, LASSO-M, sg-LASSO-M. Entries are the average mean squared error. $s = 6$ and $t = \{t_1 = 10\%, t_2 = 30\%, t_3 = 50\%\}$ percentile of the set $\{T_i: T_i \text{ is uncensored }, i \in [N]\}$.}
\begin{tabular}{cccccccc}
 \toprule
 & \multicolumn{7}{c}{Scenario 2} \\
\cmidrule{2-8} & \multicolumn{3}{c}{$N = 800$} & & \multicolumn{3}{c}{$N = 1200$} \\
\cmidrule{2-8} & \multicolumn{7}{c}{$t = t_1$} \\
\cmidrule{2-8} & LASSO-U & LASSO-M & sg-LASSO-M & & LASSO-U & LASSO-M & sg-LASSO-M \\
\cmidrule{2-8} $(1+log(t-s))\boldsymbol{\operatorname{Beta}}(1,3)$ & 0.762 & 0.587 & 0.559 & & 0.731 & 0.593 & 0.545 \\
 $(-1+log(t-s))\boldsymbol{\operatorname{Beta}}(2,3)$ & 2.211 & 1.844 & 1.870 & & 2.164 & 1.785 & 1.743 \\
\cmidrule{2-8} & \multicolumn{7}{c}{$t = t_2$} \\
\cmidrule{2-8} & LASSO-U & LASSO-M & sg-LASSO-M & & LASSO-U & LASSO-M & sg-LASSO-M \\
\cmidrule{2-8} $(1+log(t-s))\boldsymbol{\operatorname{Beta}}(1,3)$ & 1.125 & 0.943 & 0.908 & & 1.061 & 0.870 & 0.842 \\
 $(-1+log(t-s))\boldsymbol{\operatorname{Beta}}(2,3)$ & 1.645 & 1.376 & 1.384 & & 1.590 & 1.308 & 1.313 \\
\cmidrule{2-8} & \multicolumn{7}{c}{$t = t_3$} \\
\cmidrule{2-8} & LASSO-U & LASSO-M & sg-LASSO-M & & LASSO-U & LASSO-M & sg-LASSO-M \\
\cmidrule{2-8} $(1+log(t-s))\boldsymbol{\operatorname{Beta}}(1,3)$ & 1.398 & 1.218 & 1.172 & & 1.320 & 1.122 & 1.096 \\
 $(-1+log(t-s))\boldsymbol{\operatorname{Beta}}(2,3)$ & 1.406 & 1.209 & 1.216 & & 1.350 & 1.122 & 1.134 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Wei Miao
Jad Beyhum
Jonas Striaukas
Ingrid Van Keilegom

"High-dimensional censored MIDAS logistic regression for corporate survival forecasting" by Wei Miao, Jad Beyhum, Jonas Striaukas, and Ingrid Van Keilegom, arXiv:2502.09740v1, 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{Johansen Cointegration Test using Trace Test Statistic (10\% Significance Level)}
\begin{tabular}{cccc}
\hline
\textbf{Rank (r)} & \textbf{Cointegrating Relations} & \textbf{Test Statistic} & \textbf{Critical Value} \\
\hline
0 & 2 & 51.38 & 13.43 \\
1 & 2 & 2.606 & 2.705 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Juan Plazuelo Pascual
Carlos Tardon Rubio
Juan Toro Cebada
Angel Hernando Veciana

"Price Discovery in Cryptocurrency Markets" by Juan Plazuelo Pascual, Carlos Tardon Rubio, Juan Toro Cebada, and Angel Hernando Veciana, arXiv:2506.08718v1, 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
\caption{Variance of the distance of 2D BBox groups and distance groups for KITTI dataset}
\begin{tabular}{cccc}
				& & & (unit : $m^2$)\\
				\hline
				\hline
				\# of predictors & \multirow{2}{*}{order} & 2D BBox & distance \\
				(groups) & & grouping & grouping \\
				\hline
				\hline
				\multirow{2}{*}{2} & 1 & 14.84 & 25.69 \\
				& 2 & 220.12 & 31.76 \\
				\hline
				\multirow{3}{*}{3} & 1 & 9.07 & 12.26 \\
				& 2 & 33.26 & 8.60 \\
				& 3 & 186.27 & 20.30 \\
				\hline
				\multirow{5}{*}{5} & 1 & 7.08 & 5.36 \\
				& 2 & 18.68 & 4.27 \\
				& 3 & 49.57 & 3.10 \\
				& 4 & 91.14 & 9.28 \\
				& 5 & 98.21 & 3.55 \\
				\hline
				\hline
			\end{tabular}
\end{table}
\end{document}

cs

"Anchor Distance for 3D Multi-Object Distance Estimation from 2D Single Shot" by Hyeonwoo Yu and Jean Oh, arXiv:2101.10399v2, 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|r|r|}
\hline
\textbf{Statistic} & \textbf{Value Uniswap v2} & \textbf{Value Binance} \\ \hline
Price Change During Event (\%) & 4.78 & 6.78 \\ \hline
Event Maximum Price(USD) & 3371.73
 & 3431.15 \\ \hline
Event Minimum Price (USD) & 	3217.91 & 3213.05 \\ \hline
\end{tabular}
\caption{Descriptive statistics for the event on May 20th: Uniswap v2 pool WETH/USDT and ETH on Binance}
\end{table}
\end{document}

q-fin


math

Tim Bürchner
Simon Schmid
Lukas Bergbreiter
Ernst Rank
Stefan Kollmannsberger
Christian U. Grosse

"Quantitative Comparison of the Total Focusing Method, Reverse Time Migration, and Full Waveform Inversion for Ultrasonic Imaging" by Tim Bürchner, Simon Schmid, Lukas Bergbreiter, Ernst Rank, Stefan Kollmannsberger, and Christian U. Grosse, arXiv:2412.07347v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.