\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overview of Privacy Threats and Defense Types.}
\begin{tabular}{|c|c|c|}
\hline
\multirow{2}{*}{\textbf{Privacy Threats}}
& Model Extraction Attack & \\
\cline{2-3}
& Membership Inference Attack & \\
\hline
\hline
\multirow{4}{*}{\textbf{Defense Types}}
& Differential Privacy & \\
\cline{2-3}
& Homomorphic Encryption & \\
\cline{2-3}
& Secure Multi-Party Computation & \\
\cline{2-3}
& Trust Execution Environment & \\ %ramer2018slalom
\hline
\end{tabular}
\end{table}
\end{document}

eess

Alexander D. Zemskov
Yao Fu
Runchao Li
Xufei Wang
Vispi Karkaria
Ying-Kuan Tsai
Wei Chen
Jianjing Zhang
Robert Gao
Jian Cao
Kenneth A. Loparo
Pan Li

"Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey" by Alexander D. Zemskov, Yao Fu, Runchao Li, Xufei Wang, Vispi Karkaria, Ying-Kuan Tsai, Wei Chen, Jianjing Zhang, Robert Gao, Jian Cao, Kenneth A. Loparo, and Pan Li, arXiv:2412.13939v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Value of Bermudan Swaption}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll|lll}
\hline
 Time & \multicolumn{3}{c|}{$T_2$=year 2} & \multicolumn{3}{c}{$t_1$=day 10}\\
 & Mean & Median & SD
 & Mean & Median & SD \\
 \hline
 SLSM & 72.975 &72.946 & 0.85212 & 69.517 & 69.489 & 0.81175 \\
 GLSM & 75.391 &72.946 & 0.86740 & 71.819 & 71.707 & 0.82638 \\
 LLSM & 73.452 &73.426 & 0.87146 & 69.971 & 69.946 & 0.83018 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Simulation-based Value-at-Risk for Nonlinear Portfolios" by Junyao Chen, Tony Sit, and Hoi Ying Wong, arXiv:1904.09088v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
\hline
 Full Form & Abbreviation\\
\hline\hline
Ornstein-Uhlenbeck &OU\\
Partial differential equation & PDE \\
Probability distribution function & PDF\\
Stochastic differential equation &SDE\\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Lists of abbreviations.}
\end{table}
\end{document}

math

Jared C. Bronski
Lingyun Ding
Richard M. McLaughlin

"Correlation function of a random scalar field evolving with a rapidly fluctuating Gaussian process" by Jared C. Bronski, Lingyun Ding, and Richard M. McLaughlin, arXiv:2202.11223v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Konstantin Burlachenko
Samuel Horváth
Peter Richtárik

"FL_PyTorch: optimization research simulator for federated learning" by Konstantin Burlachenko, Samuel Horváth, and Peter Richtárik, arXiv:2202.03099v2, 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{Summary statistics of the computation time (seconds) for calculating the joint GQMLE and the stepwise GQMLE ($N = 1000$) for $1000$ iterations; SD means Standard deviation.}
\begin{tabular}{lcccccc}
\toprule
 & Min & Q1 & Mean (SD) & Median &Q3 & Max
\\
\hline
Joint GQMLE & 395.19 & 628.88 & 763.71 (1027.20) & 714.53 & 828.17 & 32547.95 \\
Stepwise GQMLE & 105.13 & 173.47 & 204.99 (243.49) & 196.23 & 219.97 & 7679.54 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Gaussian quasi-likelihood analysis for non-Gaussian linear mixed-effects model with system noise" by Takumi Imamura and Hiroki Masuda, arXiv:2412.00796v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
\multicolumn{4}{c}{\textbf{NS-CliffWalking Single Change without Notification}} \\ % Centered
\hline
 & \textbf{0.8} & \textbf{0.6} & \textbf{0.4} \\
\hline
\textbf{MCTS} & -773.62 $\pm$ 54.67 & -1216.72 $\pm$ 63.68 & -1593.89 $\pm$ 68.9 \\
\textbf{AlphaZero} & 64.41 $\pm$ 3.44 & 6.97 $\pm$ 8.2 & -543.94 $\pm$ 45.98 \\
\textbf{DDQN} & -287.17 $\pm$ 40.55 & -1018.27 $\pm$ 96.95 & -1742.54 $\pm$ 91.29 \\
\textbf{PAMCTS - 0.25} & -790.60 $\pm$ 46.66 & -1159.77 $\pm$ 53.85 & -1572.21 $\pm$ 60.82 \\
\textbf{PAMCTS - 0.5} & -54.22 $\pm$ 14.25 & -374.64 $\pm$ 44.31 & -477.50 $\pm$ 54.66 \\
\textbf{PAMCTS - 0.75} & -109.08 $\pm$ 25.99 & -477.50 $\pm$ 54.65 & -1382.04 $\pm$ 77.88 \\
\textbf{ADA-MCTS} & N/A & N/A & N/A \\
\textbf{RATS} & -- & -- & -- \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Nathaniel S. Keplinger
Baiting Luo
Iliyas Bektas
Yunuo Zhang
Kyle Hollins Wray
Aron Laszka
Abhishek Dubey
Ayan Mukhopadhyay

"NS-Gym: Open-Source Simulation Environments and Benchmarks for Non-Stationary Markov Decision Processes" by Nathaniel S. Keplinger, Baiting Luo, Iliyas Bektas, Yunuo Zhang, Kyle Hollins Wray, Aron Laszka, Abhishek Dubey, and Ayan Mukhopadhyay, arXiv:2501.09646v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{siunitx}
\usepackage{graphicx}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc|ccccc}
 \multicolumn{3}{c|}{\multirow{2}{*}{Method}}
 & \multicolumn{3}{c}{Error factor}
 & Memory
 & Speed \\
 & &
 & mean
 & 99th per.
 & max
 & (\SI{}{\mega\byte})
 & (\SI{}{\milli\second}) \\
 \hline
 \multirow{10}{*}{\rotatebox[origin=c]{90}{No outlier removal}}
 & \multicolumn{2}{c|}{near. neighbor}
 & $\times$13.1
 & $\times$11.3
 & $\times$3e5
 & $1\,650$
 & 62 \\
 & \multicolumn{2}{c|}{linear}
 & 15.7
 & $\times$2.3
 & $\times$143
 & $1\,650$
 & 1.5e3 \\
 \cline{2-8}
 & \multirow{3}{0.5em}{\rotatebox[origin=c]{90}{spline}}
 & linear
 & 15.7
 & $\times$2.3
 & $\times$144
 & $1\,650$
 & \ldots \\
 & & cubic
 & 11.2
 & $\times$2.2
 & $\times$122
 & $1\,650$
 & \ldots \\
 & & quintic
 & 19.1
 & $\times$2.9
 & $\times$304
 & $1\,650$
 & \ldots \\
 \cline{2-8}
 & \multirow{3}{0.5em}{\rotatebox[origin=c]{90}{RBF}}
 & linear
 & 10.2
 & 96.8
 & $\times$99
 & $1\,650$
 & 1.1e4 \\
 & & cubic
 & 10.4
 & $\times$2.1
 & $\times$112
 & $1\,650$
 & 1.1e4 \\
 & & quintic
 & 10.9
 & $\times$2.1
 & $\times$118
 & $1\,650$
 & 1.1e4 \\
 \cline{2-8}
 & \multirow{2}{0.5em}{\rotatebox[origin=c]{90}{ANN}}
 & R
 & 7.3
 & 64.8
 & \textbf{$\times$81}
 & \textbf{118}
 & \textbf{12} \\
 & & R+P
 & \textbf{6.2}
 & \textbf{49.7}
 & $\times$84
 & \textbf{118}
 & 13 \\
 \hline
 \hline
 \multirow{13}{*}{\rotatebox[origin=c]{90}{Outlier removal on training set}}
 & \multicolumn{2}{c|}{near. neighbor}
 & $\times$13.1
 & $\times$11.6
 & $\times$3e5
 & $1\,650$
 & 62 \\
 & \multicolumn{2}{c|}{linear}
 & 15.9
 & $\times$2.4
 & $\times$143
 & $1\,650$
 & 1.5e3 \\
 \cline{2-8}
 & \multirow{3}{0.5em}{\rotatebox[origin=c]{90}{spline}}
 & linear
 & 15.9
 & $\times$2.4
 & $\times$144
 & $1\,650$
 & \ldots \\
 & & cubic
 & 11.1
 & $\times$2.2
 & $\times$120
 & $1\,650$
 & \ldots \\
 & & quintic
 & 20.0
 & $\times$2.7
 & $\times$285
 & $1\,650$
 & \ldots \\
 \cline{2-8}
 & \multirow{3}{0.5em}{\rotatebox[origin=c]{90}{RBF}}
 & linear
 & 10.3
 & 97.3
 & $\times$97.5
 & $1\,650$
 & 1.1e4 \\
 & & cubic
 & 10.5
 & $\times$2.0
 & $\times$106
 & $1\,650$
 & 1.1e4\\
 & & quintic
 & 10.9
 & $\times$2.0
 & $\times$114
 & $1\,650$
 & 1.1e4\\
 \cline{2-8}
 & \multirow{5}{0.5em}{\rotatebox[origin=c]{90}{ANN}}
 & R
 & 5.1
 & 42.0
 & \textbf{$\times$32.8}
 & 118
 & 12 \\
 & & R+P
 & 5.5
 & {42.3}
 & {$\times$41}
 & 118
 & 13 \\
 \cline{3-8}
 & & R+P+C
 & {4.9}
 & 44.5
 & $\times$44
 & 51
 & 14 \\
 & & R+P+D
 & \textbf{4.5}
 & \textbf{33.1}
 & $\times$33.8
 & 125
 & \textbf{11} \\
 & & R+P+C+D
 & 4.8
 & 37.9
 & $\times$37.6
 & \textbf{43}
 & 14
 \end{tabular}
\end{table}
\end{document}

stat

Pierre Palud
Lucas Einig
Franck Le Petit
Emeric Bron
Pierre Chainais
Jocelyn Chanussot
Jérôme Pety
Pierre-Antoine Thouvenin
David Languignon
Ivana Bešlić
Miriam G. Santa-Maria
Jan H. Orkisz
Léontine E. Ségal
Antoine Zakardjian
Sébastien Bardeau
Maryvonne Gerin
Javier R. Goicoechea
Pierre Gratier
Viviana V. Guzman
Annie Hughes
François Levrier
Harvey S. Liszt
Jacques Le Bourlot
Antoine Roueff
Albrecht Sievers

"Neural network-based emulation of interstellar medium models" by Pierre Palud, Lucas Einig, Franck Le Petit, Emeric Bron, Pierre Chainais, Jocelyn Chanussot, Jérôme Pety, Pierre-Antoine Thouvenin, David Languignon, Ivana Bešlić, Miriam G. Santa-Maria, Jan H. Orkisz, Léontine E. Ségal, Antoine Zakardjian, Sébastien Bardeau, Maryvonne Gerin, Javier R. Goicoechea, Pierre Gratier, Viviana V. Guzman, Annie Hughes, François Levrier, Harvey S. Liszt, Jacques Le Bourlot, Antoine Roueff, and Albrecht Sievers, arXiv:2309.01724v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Performance evaluation of DMUs using hybrid fuzzy multi-objective data envelopment analysis" by Awadh Pratap Singh and Shiv Prasad Yadav, arXiv:2202.01585v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllllllll}
		\hline
		& & & OM& LDA & SVM & NN\\ \hline
		$N$ & $\sigma$ & $n_1$ & $CCR$ \vline& $CCR$ \vline& $CCR$ \vline& $CCR$ \\ \hline
		\multirow{1}{*}{5}
		& .05 &60 &95	&	65&		79&	\textbf{96}&	\\\
		& $-$ &80 &95 	&	74&		82&	\textbf{97}&	\\\
		& $-$ &100 &96 	&	73&		82&	\textbf{97}&	\\\
		\multirow{1}{*}{10}
		& .05 &60 &\textbf{99}	&	65&		79&	96&	\\\
		& $-$ &80 &\textbf{97} 	&	74&		82&	97&	\\\
		& $-$ &100 & \textbf{98}	&	73&		82&	97&	\\\		\multirow{1}{*}{5}
		& .15 &60 &93 &76&		84&	\textbf{95}&	\\\
		& $-$ &80 &93&83&		90&	\textbf{97}&	\\\
		& $-$ &100 &93 	&	72&		81&	\textbf{94}&	\\\
		\multirow{1}{*}{10}
		& .15 &60 &\textbf{95} &76&		84&	95&	\\\
		& $-$ &80 &94&83&		90&	\textbf{97}&	\\\
		& $-$ &100 &\textbf{95} 	&	72&		81&	94&	\\
			 \\ \hline
		\end{tabular}
\end{adjustbox}
\caption{Prediction results on a testing sample of size $80\%$ of the sample size of data generated using Example 3. The correct classification rate (CCR) in $\%$ for the proposed method (OM), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and Neural Network (NN). The bold values highlight the best classification rates}
\end{table}
\end{document}

math

Asma BenSaber
Sophie Dabo-Niang
Abderrazek Karoui

"Multivariate nonparametric regression by least squares Jacobi polynomials approximations" by Asma BenSaber, Sophie Dabo-Niang, and Abderrazek Karoui, arXiv:2202.01283v1, 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{Household-level non-response rates by region.}
\begin{tabular}{lrr}
 Region & Rate \\
 \hline
 Central & 0.783 \\
 East Central & 0.841 \\
 Northeast & 0.840 \\
 Northwest & 0.832 \\
 Southeast & 0.752 \\
 Southeast Central & 0.790 \\
 Southwest & 0.810 \\
 West Central & 0.818 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

David Kline
Zehang Li
Yue Chu
Jon Wakefield
William C. Miller
Abigail Norris Turner
Samuel J Clark

"Estimating Seroprevalence of SARS-CoV-2 in Ohio: A Bayesian Multilevel Poststratification Approach with Multiple Diagnostic Tests" by David Kline, Zehang Li, Yue Chu, Jon Wakefield, William C. Miller, Abigail Norris Turner, and Samuel J Clark, arXiv:2011.09033v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
 \toprule
 Method & Frobnability \\
 \midrule
 Theirs & Frumpy \\
 Yours & Frobbly \\
 Ours & Makes one's heart Frob\\
 \bottomrule
 \end{tabular}
\caption{Results. Ours is better.}
\end{table}
\end{document}

eess

August Leander Høeg
Sophia W. Bardenfleth
Hans Martin Kjer
Tim B. Dyrby
Vedrana Andersen Dahl
Anders Dahl

"MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions" by August Leander Høeg, Sophia W. Bardenfleth, Hans Martin Kjer, Tim B. Dyrby, Vedrana Andersen Dahl, and Anders Dahl, arXiv:2412.03379v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
\hline
\textbf{Style} & \textbf{N} & \textbf{Confidence} & \textbf{Polarity} & \textbf{Subjectivity} & \textbf{Sarcasm (\%)} \\
\hline
Affiliative & 49 & 0.748 & 0.145 & 0.406 & 18.4 \\
Self-enhancing & 61 & 0.889 & 0.227 & 0.430 & 32.8 \\
Self-deprecating & 46 & 0.811 & -0.002 & 0.474 & 8.7 \\
Aggressive & 57 & 0.780 & -0.039 & 0.404 & 29.8 \\
Neutral & 80 & 0.887 & 0.089 & 0.273 & 48.8 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Distribution of Confidence Scores and Affective Metrics Across Humour Styles}
\end{table}
\end{document}

cs

Mary Ogbuka Kenneth
Foaad Khosmood
Abbas Edalat

"Explaining Humour Style Classifications: An XAI Approach to Understanding Computational Humour Analysis" by Mary Ogbuka Kenneth, Foaad Khosmood, and Abbas Edalat, arXiv:2501.02891v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{booktabs}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{xcolor}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrrlrrr}
 \toprule
 \toprule
 & & & & \multicolumn{3}{c}{Selected by } \\
 \multicolumn{2}{c}{Variable category} & Name & Importance order in boosting & Boosting & AIC & lasso \\
 \midrule
 \midrule
 \multirow{17}[16]{*}{Evolutions} & \multirow{4}[2]{*}{Balance} & \cellcolor[rgb]{ .325, 1, .341} var1 & \cellcolor[rgb]{ .325, 1, .341} 28 & \cellcolor[rgb]{ .325, 1, .341} & \cellcolor[rgb]{ .325, 1, .341} & \cellcolor[rgb]{ .325, 1, .341} \\
 & & \cellcolor[rgb]{ .325, 1, .341} var3 & \cellcolor[rgb]{ .325, 1, .341} 19 & \cellcolor[rgb]{ .325, 1, .341} & \cellcolor[rgb]{ .325, 1, .341} & \cellcolor[rgb]{ .325, 1, .341} \\
 & & \cellcolor[rgb]{ .325, 1, .341} var5 & \cellcolor[rgb]{ .325, 1, .341} 22 & \cellcolor[rgb]{ .325, 1, .341} & \cellcolor[rgb]{ .325, 1, .341} & \cellcolor[rgb]{ .325, 1, .341} \\
 & & \cellcolor[rgb]{ .325, 1, .341} var7 & \cellcolor[rgb]{ .325, 1, .341} 24 & \cellcolor[rgb]{ .325, 1, .341} & \cellcolor[rgb]{ .325, 1, .341} & \cellcolor[rgb]{ .325, 1, .341} \\
 \cmidrule{2-7} & \multirow{6}[6]{*}{Violations} & \cellcolor[rgb]{ .573, .804, .863} var9 & \cellcolor[rgb]{ .573, .804, .863} 3 & \cellcolor[rgb]{ .573, .804, .863} YES & \cellcolor[rgb]{ .573, .804, .863} YES & \cellcolor[rgb]{ .573, .804, .863} YES \\
 & & \cellcolor[rgb]{ .922, .945, .871} var10 & \cellcolor[rgb]{ .922, .945, .871} 29 & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} \\
 \cmidrule{3-7} & & \cellcolor[rgb]{ .573, .804, .863} var11 & \cellcolor[rgb]{ .573, .804, .863} 1 & \cellcolor[rgb]{ .573, .804, .863} YES & \cellcolor[rgb]{ .573, .804, .863} YES & \cellcolor[rgb]{ .573, .804, .863} \\
 & & \cellcolor[rgb]{ .922, .945, .871} var12 & \cellcolor[rgb]{ .922, .945, .871} 26 & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} \\
 \cmidrule{3-7} & & \cellcolor[rgb]{ .573, .804, .863} var13 & \cellcolor[rgb]{ .573, .804, .863} 2 & \cellcolor[rgb]{ .573, .804, .863} YES & \cellcolor[rgb]{ .573, .804, .863} YES & \cellcolor[rgb]{ .573, .804, .863} YES \\
 & & \cellcolor[rgb]{ .922, .945, .871} var14 & \cellcolor[rgb]{ .922, .945, .871} 27 & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} YES & \cellcolor[rgb]{ .922, .945, .871} YES \\
 \cmidrule{2-7} & Balance Vitality & \cellcolor[rgb]{ .573, .804, .863} var15 & \cellcolor[rgb]{ .573, .804, .863} 13 & \cellcolor[rgb]{ .573, .804, .863} & \cellcolor[rgb]{ .573, .804, .863} & \cellcolor[rgb]{ .573, .804, .863} \\
 \cmidrule{2-7} & \multirow{6}[6]{*}{Credits \& Debits} & \cellcolor[rgb]{ .573, .804, .863} var16 & \cellcolor[rgb]{ .573, .804, .863} 15 & \cellcolor[rgb]{ .573, .804, .863} & \cellcolor[rgb]{ .573, .804, .863} & \cellcolor[rgb]{ .573, .804, .863} \\
 & & \cellcolor[rgb]{ .922, .945, .871} var17 & \cellcolor[rgb]{ .922, .945, .871} 25 & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} \\
 \cmidrule{3-7} & & \cellcolor[rgb]{ .573, .804, .863} var18 & \cellcolor[rgb]{ .573, .804, .863} 12 & \cellcolor[rgb]{ .573, .804, .863} & \cellcolor[rgb]{ .573, .804, .863} & \cellcolor[rgb]{ .573, .804, .863} \\
 & & \cellcolor[rgb]{ .922, .945, .871} var19 & \cellcolor[rgb]{ .922, .945, .871} 20 & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} \\
 \cmidrule{3-7} & & \cellcolor[rgb]{ .573, .804, .863} var20 & \cellcolor[rgb]{ .573, .804, .863} 17 & \cellcolor[rgb]{ .573, .804, .863} & \cellcolor[rgb]{ .573, .804, .863} & \cellcolor[rgb]{ .573, .804, .863} \\
 & & \cellcolor[rgb]{ .922, .945, .871} var21 & \cellcolor[rgb]{ .922, .945, .871} 21 & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} \\
 \midrule
 \multirow{4}[4]{*}{Risk} & \multirow{2}[2]{*}{Balance stability} & \cellcolor[rgb]{ .573, .804, .863} var22 & \cellcolor[rgb]{ .573, .804, .863} 14 & \cellcolor[rgb]{ .573, .804, .863} & \cellcolor[rgb]{ .573, .804, .863} & \cellcolor[rgb]{ .573, .804, .863} \\
 & & \cellcolor[rgb]{ .922, .945, .871} var23 & \cellcolor[rgb]{ .922, .945, .871} 23 & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} YES \\
 \cmidrule{2-7} & \multirow{2}[2]{*}{Credits stability} & \cellcolor[rgb]{ .573, .804, .863} var24 & \cellcolor[rgb]{ .573, .804, .863} 7 & \cellcolor[rgb]{ .573, .804, .863} YES & \cellcolor[rgb]{ .573, .804, .863} YES & \cellcolor[rgb]{ .573, .804, .863} YES \\
 & & \cellcolor[rgb]{ .922, .945, .871} var25 & \cellcolor[rgb]{ .922, .945, .871} 11 & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} & \cellcolor[rgb]{ .922, .945, .871} \\
 \midrule
 \multirow{7}[2]{*}{Actual} & \multirow{7}[2]{*}{} & var26 & 10 & & & \\
 & & var27 & 4 & YES & & \\
 & & var28 & 30 & & & \\
 & & var31 & 18 & & YES & YES \\
 & & var32 & 6 & YES & & \\
 & & var33 & 8 & YES & & \\
 & & var34 & 5 & YES & & \\
 \cmidrule{1-2} \multirow{2}[2]{*}{Attributes} & \multirow{2}[2]{*}{} & var29 & 16 & & YES & YES \\
 & & var30 & 9 & & YES & YES \\
 \midrule
 & & & & & & \\
 & & \cellcolor[rgb]{ .325, 1, .341} & Information on [t-23, t] & & & \\
 & & \cellcolor[rgb]{ .573, .804, .863} & Information on [t-11, t] & & & \\
 & & \cellcolor[rgb]{ .855, .933, .953} \textcolor[rgb]{ .573, .816, .314}{} & Information on [t-23, t-12] & & & \\
 \end{tabular}
\end{adjustbox}
\caption{Variable selection among 30 variables of Definition 1 according to boosting, stepwise selection and lasso. Stepwise selection with AIC criterion (both forward and backward) has yielded 8 variables. For the purpose of comparison, we have chosen the 8 best variables in boosting. For lasso, we have adjusted the parameter $\lambda$ so as to yield exactly 8 non-zero coefficients.}
\end{table}
\end{document}

q-fin

Jinglun Yao
Maxime Levy-Chapira
Mamikon Margaryan

"Checking account activity and credit default risk of enterprises: An application of statistical learning methods" by Jinglun Yao, Maxime Levy-Chapira, and Mamikon Margaryan, arXiv:1707.00757v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf{Standardised Residuals Tests for BAC stock exchange}} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} % centered columns (4 columns)
		\hline\hline %inserts double horizontal lines
		 & Residuals & Tests & Statistics & p-value \\ % inserts table
		\hline
		Jarque-Bera Test & $R$ & $\chi^2$ & 4918340 & 0 \\ %inserting body of the table
		Shapiro-Wilk Test & $R$ & W & 0.649712 & 0 \\
	 Ljung-Box Test & $R$ & Q(10) & 14.71311 & 0.1429 \\
		Ljung-Box Test & $R$ & Q(15) & 17.82156 & 0.2722\\
		Ljung-Box Test & $R$ & Q(20) & 18.77450 & 0.5365 \\
		Ljung-Box Test & $R^2$ & Q(10) & 0.107465 & 1\\
		Ljung-Box Test & $R^2$ & Q(15) & 0.130187 & 1 \\
		Ljung-Box Test & $R^2$ & Q(20) & 0.147051 & 1 \\
		LM-Arch Test & $R$ & T$R^2$ & 0.126933 & 1 \\
		\hline \hline%inserts single line
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Maria C Mariani
Md Al Masum Bhuiyan
Osei K Tweneboah
Hector Gonzalez-Huizar
Ionut Florescu

"Volatility Models Applied to Geophysics and High Frequency Financial Market Data" by Maria C Mariani, Md Al Masum Bhuiyan, Osei K Tweneboah, Hector Gonzalez-Huizar, and Ionut Florescu, arXiv:1901.09145v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll} \toprule
			Parameter & Definition\\ \midrule
			SCV & Single Customer Value\\
			$AC_i$ & Acquisition Path through Channel $i$\\
			Tr & Path to Trial Post Acquisition\\
			$C_{Tr}$ & Path to failure of Trial (month 0)\\
			$C_i$ & Path to failure at month $i$, post success of month $i-1$\\
			$S_{Tr}$ & Path to success of Trial\\
			$S_i$ & Path to success at month $i$, post success of month $i-1$\\
			$P_{i}$ & Overall probability of path $i$\\
			$p_{i}$ & Conditional probability of path $i$ given success at $i-1$ \\
			$P_{Tr}$ & Probability of entering trial after acquisition\\
			$P_{C_{Tr}}$ & Probability of churn during trial\\
			$P_{S_{Tr}}$ & Probability of continuation from Trial\\
			$P_{C_t}$ & Overall probability of churn after $t$ time periods\\
			$P_{S_t}$ & Overall probability of continuing after $t$ time periods\\
			$CAC_j$ & Cost of Acquiring Customer through channel $j$\\
			$CAC_{mean}$ & Average Cost of Acquiring Customer through all channels\\
			$CC$ & Cost of retaining a customer beyond the trial period\\
			$R_{i}$ & Overall contribution to revenue from path $i$\\
			$r_{i}$ & Conditional revenue given path $i$\\
			$r_{pt}$ & Revenue per unit time net of costs\\
			$R_{C_{Tr}}$ & Contribution to overall net revenue given churn at Trial\\
			$R_{C_t}$ & Contribution to overall net Revenue given churn at time $t$\\
			$r_{C_{Tr}}$ & Net Revenue on the path churning out during trial\\
			$r_{C_t}$ & Net Revenue on the path churning at time $t$\\
			$F$ & Factor variable external the process\\ \bottomrule
		\end{tabular}
\end{adjustbox}
\caption{Variable definitions used in this work}
\end{table}
\end{document}

q-fin

"A generalized Bayesian framework for the analysis of subscription based businesses" by Rahul Madhavan and Ankit Baraskar, arXiv:1704.05729v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c||c|c|c|} \hline
 \textbf{Magnif.}& \textbf{Balanced Accuracy}& \textbf{AUC}& \textbf{F1 Score}\\ \hline
 40x& 54.0\% & \textbf{0.860} & 0.477 \\ \hline
 20x& 55.0\% & 0.829 & 0.485 \\ \hline
 10x& \textbf{62.0\%} & 0.850 & \textbf{0.549} \\ \hline
 5x& 61.0\% & 0.858 & 0.545 \\ \hline
 2.5x& 58.1\% & 0.857 & 0.516 \\ \hline
 1.25x& 58.0\% & 0.855 & 0.529 \\ \hline
 \end{tabular}
\caption{External validation results. Best results in \textbf{bold}.}
\end{table}
\end{document}

eess

Jack Breen
Katie Allen
Kieran Zucker
Nicolas M. Orsi
Nishant Ravikumar

"Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping" by Jack Breen, Katie Allen, Kieran Zucker, Nicolas M. Orsi, and Nishant Ravikumar, arXiv:2311.13956v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the COS, Haar wavelet and SFP methods in terms of absolute error for pricing a call option under the Heston model. The reference value is computed via the COS method using 50000 terms. The SFP method is more accurate than the others with fewer summation terms required. The parameters are taken from \textbf{Heston--Para1}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cc|cc|cc||cc|cc|cc|} % centered columns (4 columns)
\hline
\multicolumn{6}{|c||}{\textbf{T=30}}&\multicolumn{6}{c|}{\textbf{T=45}}\\
\hline
\multicolumn{2}{|c|}{\textbf{Haar}}&\multicolumn{2}{c|}{\textbf{COS}}&\multicolumn{2}{c||}{\textbf{SFP}}&\multicolumn{2}{c|}{\textbf{Haar}}&\multicolumn{2}{c|}{\textbf{COS}}&\multicolumn{2}{c|}{\textbf{SFP}}\\
\hline
scale&error&N&error&U&error&scale&error&N&error&U&error\\
\hline
3&2.58e+02& 8& 1.72e+06 &8&3.783&3& 3.92e+02& 8& 3.19e+07&8&2.584\\
4& 2.72e+00& 16& 2.75e+05&16&2.259e-01&4& 1.09e+01& 16& 5.45e+06&16&2.953e-01\\
5& 7.94e-01& 32& 2.19e+03&32&3.981e-04&5& 5.99e-01& 32& 3.10e+04&32&9.438e-04\\
6& 2.46e-03& 64& 3.37e-01&64&1.353e-06&6& 1.05e-02& 64& 9.68e-01&64&3.049e-06	\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Singular Fourier-Padé Series Expansion of European Option Prices" by Tat Lung Chan, arXiv:1706.06709v3, 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}{ccccc}
 & \multicolumn{2}{c}{\textbf{Cross-Validation}} & \multicolumn{2}{c}{\textbf{Test Set Cohort}}\tabularnewline
 & AUC & Brier score & AUC & Brier score\tabularnewline
\hline
\hline
Rigid BART & 0.677 & 0.166 & 0.653 & 0.160\tabularnewline
Flexible BART & 0.676 & 0.173 & 0.556 & 0.162\tabularnewline
cv-BART & 0.678 & 0.168 & 0.557 & 0.162\tabularnewline
Rigid EB-coBART 1 & 0.688 & 0.154 & 0.692 & \textbf{0.153}\tabularnewline
Rigid EB-coBART 2 & 0.713 & 0.155 & 0.703 & \textbf{0.153}\tabularnewline
Flexible EB-coBART 1 & 0.715 & 0.155 & 0.693 & 0.154\tabularnewline
Flexible EB-coBART 2 & 0.709 & 0.156 & \textbf{0.712} & \textbf{0.153}\tabularnewline
IPI-BART & 0.704 & \textbf{0.153} & 0.669 & 0.154\tabularnewline
DART & 0.70 & 0.171 & 0.552 & 0.163\tabularnewline
Random Forest & 0.688 & 0.183 & 0.637 & 0.158\tabularnewline
Corf & 0.711 & 0.157 & 0.656 & 0.159\tabularnewline
ecpc & \textbf{0.728} & 0.159 & 0.701 & 0.154\tabularnewline
Ridge & 0.723 & 0.161 & 0.689 & 0.154\tabularnewline
\end{tabular}
\end{table}
\end{document}

stat

Jeroen M. Goedhart
Thomas Klausch
Jurriaan Janssen
Mark A. van de Wiel

"Co-data Learning for Bayesian Additive Regression Trees" by Jeroen M. Goedhart, Thomas Klausch, Jurriaan Janssen, and Mark A. van de Wiel, arXiv:2311.09997v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Dalila Failli
Bruno Arpino
Maria Francesca Marino

"Multilevel mixtures of latent trait analyzers for clustering multi-layer bipartite networks" by Dalila Failli, Bruno Arpino, and Maria Francesca Marino, arXiv:2311.03829v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Episodic Memories Generation and Evaluation Benchmark for Large Language Models" by Alexis Huet, Zied Ben Houidi, and Dario Rossi, arXiv:2501.13121v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Adaptive Sequential SAA for Solving Two-stage Stochastic Linear Programs" by Raghu Pasupathy and Yongjia Song, arXiv:2012.03761v1, 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{{\bf Sample loan of 100 florins.} New money is shown in blue. The loan is for F100. F10 is given as gold coin. F90 is given as paper. The debtor takes that out into the world with him and when he comes back he has made a profit over his loan (profit not shown). From that money he pays F100.38 to the bank in gold. Before the reader bounces up and down saying, “But you can’t do that!” think about it. The Medici make a loan that is 90\% paper – all the paper is created money. They get back all of that as hard-money coin, plus hard-money coin as interest. How could Giovanni cancel the hard-money coin away? Looking at the other 90 florin gold coins of principal payment, how would he cancel the latter -- throw it in the Arno River?}
\begin{tabular}{r|rrrrrr}
\textbf{} & \textbf{Reserves/Working} & & & & \textbf{Debtor} & \textbf{Debtor} \\
\textbf{} & \textbf{ capital} & \textbf{Liability} & \textbf{Asset} & \textbf{Income} & \textbf{Coin} & \textbf{paper} \\
\textit{} & \textit{Capital} & \textit{Bank } & \textit{ } & \textit{} & \textit{} & \textit{} \\
\textit{} & \textit{account} & \textit{borrowed} & \textit{Suspense} & \textit{Loan} & \textit{} & \textit{} \\
\textit{} & \textit{(Tier 1 \& 2 )} & \textit{funds} & \textit{Account} & \textit{payment} & \textit{} & \textit{} \\ \hline
Initial Conditions & 100 & & & & 10 & \\
Loan origination & -10 & {\color[HTML]{CC0000} } & {\color[HTML]{CC0000} 100} & {\color[HTML]{CC0000} } & {\color[HTML]{800000} 10} & {\color[HTML]{0000FF} 90} \\
Interest & & {\color[HTML]{CC0000} } & {\color[HTML]{CC0000} 0.38} & {\color[HTML]{CC0000} } & {\color[HTML]{CC0000} } & {\color[HTML]{0000FF} } \\
Interest Payment \#1. & {\color[HTML]{000099} 0.38} & {\color[HTML]{CC0000} } & {\color[HTML]{CC0000} -0.38} & {\color[HTML]{0000FF} 0.38} & {\color[HTML]{0000FF} -0.38} & \\
Loan payoff & {\color[HTML]{0000FF} 100} & {\color[HTML]{CC0000} } & {\color[HTML]{CC0000} -100} & {\color[HTML]{000099} 100} & {\color[HTML]{000099} -100} & \\
Totals & 190.38 & & 0 & {\color[HTML]{000099} 100.38} & {\color[HTML]{000099} 9.62} & 0
\end{tabular}
\end{table}
\end{document}

q-fin

"Cancellation of principal in banking: Four radical ideas emerge from deep examination of double entry bookkeeping in banking" by Brian P. Hanley, arXiv:2010.10703v4, 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}{lc|cc|cc}
\toprule
\multicolumn{2}{c}{} & \multicolumn{2}{c}{Majority voting} & \multicolumn{2}{c}{Averaging} \\
\midrule
Training method & Test Set & EIR States & EIR Actions & EIR States & EIR Actions \\
\midrule
\multirow{2}{*}{SABLAS}
& IND & 19.06\% & 10.7\% & 37.6\% & 23.1\% \\
& OOD & 9.68\% & 9.37\% & 35.00\% & 22.97\% \\
\midrule
\multirow{2}{*}{iDBF}
& IND & 11.5\% & 2.4\% & 18.8\% & 12.9\% \\
& OOD & 5.2\% & 0.87\% & 15.2\% & 8.3\% \\
\bottomrule
\end{tabular}
\caption{IND and OOD EIR for ensembles with a VC1 backbone.}
\end{table}
\end{document}

eess

"Learning Ensembles of Vision-based Safety Control Filters" by Ihab Tabbara and Hussein Sibai, arXiv:2412.02029v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
				\hline
				Graph & $\alpha$ & Inertia bound & (Floor of) ratio bound \\
				\hline\hline
				Complete $K_n$ & 1 & 1 & 1\\
				\hline
				Halved Cube $K_n$ & 1 & 1 & 1\\
				\hline
				Cycle $C_5$ & 2 & 2 & 2\\
				\hline
				Halved Cube 16-cell & 2 & 3 & 2\\
				\hline
				Frankl–R\"odl $FR_{1/2}^4$ & 2 & 5 & 2\\
				\hline
				Circulant $(10;1,2)$ & 3 & 4 & 3\\
				\hline
				Petersen & 4 & 4 & 4\\
				\hline
				Prism$(5)=C_5\square P_2$ & 4 & 4 & 4\\
				\hline
				Shrikhande & 4 & 7 & 4\\
				\hline
				Hoffman & 8 & 11 & 8\\
				\hline
				Dodecahedron & 8 & 11 & 8\\
				\hline
				Middle Cube $MQ_3$ & 10 & 10 & 10\\
				\hline
				Desargues & 10 & 10 & 10\\
				\hline
				Coxeter & 12 & 12 & 12\\
				\hline
				Odd $O_3$ & 15 & 15 & 15\\
				\hline
				Hoffman-Singleton & 15 & 21 & 15\\
				\hline
				Hypercube $Q_5$ & 16 & 16 & 16\\
				\hline
			\end{tabular}
\end{adjustbox}
\caption{Some examples of the independence number, the inertia bound, and the ratio bound in some graphs.}
\end{table}
\end{document}

math

Aida Abiad
Cristina Dalfó
Miquel Àngel Fiol
Sjanne Zeijlemaker

"On inertia and ratio type bounds for the $k$-independence number of a graph and their relationship" by Aida Abiad, Cristina Dalfó, Miquel Àngel Fiol, and Sjanne Zeijlemaker, arXiv:2201.04901v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameters for \textsc{Agent Laboratory}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\toprule
\textbf{Category} & \textbf{Hyperparameter} & \textbf{Value} \\ \midrule
\textbf{Literature Review} & Number of Paper Summaries & 5 \\
& Full Text History Decay Steps & 3 \\
& Agent temperature & 0.8 \\ \midrule
\textbf{Data Preparation} &
 Experiment Timeout & 120s \\ \midrule
\textbf{Running Experiments} & mle-solver steps & 3 \\
& Code repair attempts & 2 \\
& Maximum top codes & 2 \\
& Error history length & 5 \\
& Code history length & 2 \\
& Number of comparison trials & 2 \\
& Experiment Timeout & 600s \\
& Score generation temperature & 0.6 \\
& Repair temperature & 0.8 \\
& Initial code temperature & 1.0 \\
& Solver temperature & 1.0 \\ \midrule
\textbf{Paper Writing} & paper-solver steps & 5 \\ & Maximum top papers & 1 \\
& Paper history length & 10 \\
& Number of Reviewers & 1 \\
& Number of comparison trials & 2 \\
& Solver temperature & 1.0 \\
& Initial paper temperature & 0.8 \\ \midrule
\textbf{Paper Refinement}
& Number of Reviewers & 3 \\ \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Samuel Schmidgall
Yusheng Su
Ze Wang
Ximeng Sun
Jialian Wu
Xiaodong Yu
Jiang Liu
Michael Moor
Zicheng Liu
Emad Barsoum

"Agent Laboratory: Using LLM Agents as Research Assistants" by Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Michael Moor, Zicheng Liu, and Emad Barsoum, arXiv:2501.04227v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Risk in Network Economies

"Risk in Network Economies" by Victor Sellemi, arXiv:2208.01467v1, 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
 \quad & Baseline & $G = 4$ & $G = 8$ & $G=16$ \\
 \midrule
 Memory usage (Mb) & $1171.44$ & $\mathbf{1048.44}$ & $1065.89$ & $1087.93 $ \\
 Compared to baseline (\%) & $-0$ & $-\mathbf{10.50}$ & $-9.01 $ & $-7.13$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Anzhu Yu
Wenyue Guo
Bing Liu
Xin Chen
Xin Wang
Xuefeng Cao
Bingchuan Jiang

"Attention Aware Cost Volume Pyramid Based Multi-view Stereo Network for 3D Reconstruction" by Anzhu Yu, Wenyue Guo, Bing Liu, Xin Chen, Xin Wang, Xuefeng Cao, and Bingchuan Jiang, arXiv:2011.12722v1, 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{Ablation study results on training phases.}
\begin{tabular}{cccccccccc}
 \toprule
 \textbf{Model} & \textbf{MME} & \textbf{MMBench} & \textbf{OCRBench} & \textbf{DocVQA} & \textbf{ChartQA} & \textbf{InfoVQA} & \textbf{Overall} \\
 \midrule
 MindVL-4Phase & 80.8 & 82.3 & 83.6 & 92.7 & 86.4 & 76.9 & 83.8 \\
 MindVL-2Phase* & 79.4 & 81.8 & 84.9 & 93.5 & 85.2 & 75.8 & 83.4 \\
 MindVL-2Phase & 81.6 & 82.4 & 87.0 & 94.2 & 86.8 & 79.6 & 85.3 \\
 MindVL-3Phase & 84.1 & 84.3 & 87.6 & 94.7 & 87.2 & 81.1 & 86.5 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Feilong Chen
Yijiang Liu
Yi Huang
Hao Wang
Miren Tian
Ya-Qi Yu
Minghui Liao
Jihao Wu

"MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs" by Feilong Chen, Yijiang Liu, Yi Huang, Hao Wang, Miren Tian, Ya-Qi Yu, Minghui Liao, and Jihao Wu, arXiv:2509.11662v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Causality test results}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \toprule
Null Hypothesis & F-statistic & Probability\\
 \hline\hline
 8 Hour price does not Granger Cause FundingRate & 4.28999 & 5.E-08 \\
 \hline
 Funding Rate does not Granger Cause 8 hour price & 7.11106 & 1.E-15 \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Sai Srikar Nimmagadda
Pawan Sasanka Ammanamanchi

"BitMEX Funding Correlation with Bitcoin Exchange Rate" by Sai Srikar Nimmagadda and Pawan Sasanka Ammanamanchi, arXiv:1912.03270v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|}
 		\hline
 		Event & Magnitude & Date & Time (UTC) & Latitude & Longitude\\
 		 \hline
 		Earthquake & 3.0 & 7/12/14 & 7:12:53 & $32.58^{\circ}$ & $-109.08^{\circ}$ \\
 		 \hline
 		Explosion & 3.2 & 12/23/99 & 21:15:48 & $32.65^{\circ}$ & $-109.08^{\circ}$ \\ \hline
 	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Descriptions of datasets used in the experiments}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
		\hline
		Dataset & \# Classes & \# Features & \# Samples \\
		\hline
		Heart & $2$ & $44$ & $267$ \\\hline
		Ionosphere & $2$ & $32$ & $351$\\\hline
 Parkinson & $2$ & $754$ & $756$ \\\hline
		Micromass & $10$ & $1087$ & $360$ \\\hline
 HTAD & $7$& $54$& $1386$\\\hline
 MNIST & $10$ & $784$ & $60000$ \\\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Nhan Phan
Thu Nguyen
Uyen Dang
Pål Halvorsen
Michael A. Riegler

"Principal Components for Neural Network Initialization" by Nhan Phan, Thu Nguyen, Uyen Dang, Pål Halvorsen, and Michael A. Riegler, arXiv:2501.19114v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
			\toprule
			\textbf{Depth} & $1$ & $3$ & \color{blue} $5$ & $10$ & $15$ & $25$ & $50$ \\
			\textbf{RMSE} & $17.994$ & $16.711$ & \color{blue} $16.027$ & $18.764$ & $19.941$ & $20.235$ & $20.219$ \\
			\bottomrule \smallskip
		\end{tabular}
\end{adjustbox}
\caption{Decision Tree errors (RMSE) for different tree depths, ranging from $1$ to $50$. The model with the lowest error is highlighted in blue.}
\end{table}
\end{document}

cs

S. Sanjuan
D. A. Méndez
R. Arnau
J. M. Calabuig
X. Díaz de Otálora Aguirre
F. Estellés

"Soft Computing Approaches for Predicting Shade-Seeking Behaviour in Dairy Cattle under Heat Stress: A Comparative Study of Random Forests and Neural Networks" by S. Sanjuan, D. A. Méndez, R. Arnau, J. M. Calabuig, X. Díaz de Otálora Aguirre, and F. Estellés, arXiv:2501.05494v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Sparse Fréchet Sufficient Dimension Reduction with Graphical Structure Among Predictors" by Jiaying Weng, Kai Tan, Cheng Wang, and Zhou Yu, arXiv:2310.19114v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
			\hline
			Symmetric random walk in & Non-symmetric random walk in this paper\\
			\hline
			$\mathcal{S}_1$ and $\mathcal{S}_2$ are non-self-intersecting.&$\mathcal{S}_1$ and $\mathcal{S}_2$ can be self-intersecting.\\
			 & (requires Assumption to exclude\\
			 &the cases of self-intersecting curves)\\
			 \hline
			$X(y)\in\mathcal{S}_1$ and $y\in\mathcal{S}_2$ & $X(y)\in\mathcal{S}_1$ and $y\in\mathcal{S}_2$\\
			have different orientations. & can have the same orientations.\\
			 & (requires Assumption to exclude\\
			 &the cases of the same orientations)\\
			\hline
			Angles at $1$ of $\mathcal{S}_1$ and $\mathcal{S}_2$ are equal & Angles at $1$ of $\mathcal{S}_1$ and $\mathcal{S}_2$ can be different\\
			and different from $0$& or equal to $0$.\\
			 & (requires assumption to exclude\\
			 &the cases of the cusps at $1$)\\
			\hline
			\multicolumn{2}{|c|}{Case $p_{1,1}=0$: $\mathcal{S}_1^+$ and $\mathcal{S}_2^+$ contain $0$.}\\
			\multicolumn{2}{|c|}{Case $p_{1,1}=0$, $p_{1,0}=p_{0,1}$: $\mathcal{S}_1$ and $\mathcal{S}_2$ contain $0$.}\\
			\multicolumn{2}{|c|}{(same proof of Proposition~)}\\
			\hline
			& Case $p_{1,1}=0$, $p_{1,0}\not= p_{0,1}$:\\
			(Case $p_{1,1}=0$, $p_{1,0}\not= p_{0,1}$&$0$ lies in the interior of one curve\\
			does not exist)&and the exterior of the other curve.\\
			 &(requires analytic continuations\\
			 &to properly define functions at $0$)\\
			\hline
			$\mathcal{S}_1$ and $\mathcal{S}_2$ coincide, & $\mathcal{S}_1$ and $\mathcal{S}_2$ do not coincide.\\
			$X(y)=\overline{y}$ for all $y\in\mathcal{S}_2$.&\\
			(Conformal welding is naturally&(Conformal welding is obtained\\
			obtained on the unit circle)&by solving a BVP with quasisymmetric shift\\
			&under Assumption~)\\
			\hline
		\end{tabular}
\end{adjustbox}
\caption{Comparison between symmetric and non-symmetric cases}
\end{table}
\end{document}

math

"Discrete harmonic functions for non-symmetric Laplace operators in the quarter plane" by Viet Hung Hoang, arXiv:2201.09985v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Sparse grid implementation of a fixed-point fast sweeping WENO scheme for Eikonal equations" by Zachary M. Miksis and Yong-Tao Zhang, arXiv:2201.08912v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Beatrice Ghitti
Eleuterio F. Toro
Lucas O. Müller

"Nonlinear lumped-parameter models for blood flow simulations in networks of vessels" by Beatrice Ghitti, Eleuterio F. Toro, and Lucas O. Müller, arXiv:2201.01617v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sample table title}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 \multicolumn{2}{c}{Part} \\
 \cmidrule(r){1-2}
 Name & Description & Size ($\mu$m) \\
 \midrule
 Dendrite & Input terminal & $\sim$100 \\
 Axon & Output terminal & $\sim$10 \\
 Soma & Cell body & up to $10^6$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Steffen Schotthöfer
Tianbai Xiao
Martin Frank
Cory D. Hauck

"Neural network-based, structure-preserving entropy closures for the Boltzmann moment system" by Steffen Schotthöfer, Tianbai Xiao, Martin Frank, and Cory D. Hauck, arXiv:2201.10364v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}\hline
		Nb& Dim&$T_\text{basis}$&$T_\text{ass}$&$T_\text{solve}$& $e_{L^2}$& $e_{H^1}$ \tabularnewline\hline
		/&417,911&/& 246.3& 3264.5&/&/ \tabularnewline\hline
		4+0 & 2576 & 19.2 & 185.1 & 92.4 & 2.89e-04 & 2.32e-01	 \tabularnewline\hline
		6+0 & 3864 & 23.5 & 194.0 & 172.7 & 2.03e-04 & 1.54e-01 \tabularnewline\hline
		8+0 & 5152 & 26.6 & 194.7 & 271.8 & 1.53e-04 & 1.16e-01 \tabularnewline\hline
		2+1 & 1932 & 23.3 & 197.1 & 64.2 & 2.53e-04 & 1.16e-01 \tabularnewline\hline
		3+1 & 2576 & 26.6 & 194.4 & 97.8 & 1.86e-04 & 9.33e-02 \tabularnewline\hline
		3+2 & 3220 & 35.3 & 187.5 & 140.0 & 1.45e-04 & 5.55e-02 \tabularnewline\hline
		4+1 & 3220 & 28.9 & 197.7 & 139.3 & 1.48e-04 & 7.67e-02 \tabularnewline\hline
		4+2 & 3864 & 40.3 & 199.4 & 189.6 & 1.25e-04 & 4.62e-02 \tabularnewline\hline
		5+1 & 3864 & 33.0 & 195.1 & 183.8 & 1.14e-04 & 6.42e-02 \tabularnewline\hline
		4+1(1 update) & 3220 & 56.6 & 194.1 & 138.5 & 9.75e-05 & 3.83e-02 \tabularnewline\hline
		4+1(3 updates)& 3220 & 38.3 & 198.8 & 138.5 & 1.10e-04 & 5.62e-02 \tabularnewline\hline
		5+1(1 update)& 3864 & 43.5 & 195.6 & 181.1 & 8.72e-05 & 4.72e-02 \tabularnewline\hline
		5+1(3 updates) & 3864 & 63.9 & 203.2 & 190.0 & 7.68e-05 & 3.30e-02 \tabularnewline\hline
	\end{tabular}
\end{adjustbox}
\caption{Computational performance comparison between the reference solution and GMsFEM solution, $K_3$, mixed boundary condition.}
\end{table}
\end{document}

math

"Generalized multiscale finite element method for highly heterogeneous compressible flow" by Shubin Fu, Eric Chung, and Lina Zhao, arXiv:2201.07371v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|ccccc|cc}
\hline
 & Time period & $T$ & $\lambda_{min}^{ran}$ & $\lambda_{max}^{ran}$ & $\lambda_{min}^{real}$ & $\lambda_{max}^{real}$ & $\gamma_{p}$ & $\gamma_{v}$\tabularnewline
\hline
SH & 2003.1-2011.7 & 2067 & 0.42 & 1.83 & 0.02 & 98.0 & 3.0 & 4.2\tabularnewline
SZ & 2003.1-2011.4 & 2000 & 0.41 & 1.85 & 0.12 & 98.0 & 3.1 & 4.3\tabularnewline
TW & 2003.1-2011.11 & 2206 & 0.43 & 1.80 & 0.14 & 72.3 & 3.2 & 4.7\tabularnewline
HK & 2003.1-2011.9 & 2146 & 0.43 & 1.82 & 0.12 & 35.5 & 3.2 & 3.7\tabularnewline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Spatial and temporal structures of four financial markets in Greater China" by F. Y. Ouyang, B. Zheng, and X. F. Jiang, arXiv:1402.1046v1, licensed under CC BY 3.0. 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}{lccccc}
\hline
Model & Corr $\uparrow$ & RMSE $\downarrow$ & UTMOS $\uparrow$ & WER $\downarrow$ & SECS $\uparrow$ \\ \hline
StableVC & 0.75 & 12.87 & 4.06 & 2.12 & 0.64 \\
\quad w/o $\text{vp} \& \text{multi}$ & 0.59 & 18.78 & 3.51 &22.16 & 0.41 \\
\quad w/o $\alpha$ & 0.73 & 12.85 & 4.01 &2.21 & 0.36 \\
\quad w/o $\mathcal{L_{\text{GRL}}}$ & 0.68 & 14.61 & 3.62 &2.63 & 0.51 \\
\quad w/o \text{dur} & 0.66 & 16.51 & 4.05 &2.27 & 0.63 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Jixun Yao
Yuguang Yang
Yu Pan
Ziqian Ning
Jiaohao Ye
Hongbin Zhou
Lei Xie

"StableVC: Style Controllable Zero-Shot Voice Conversion with Conditional Flow Matching" by Jixun Yao, Yuguang Yang, Yu Pan, Ziqian Ning, Jiaohao Ye, Hongbin Zhou, and Lei Xie, arXiv:2412.04724v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Occupational Immobility: Share of Stayers by Segment}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cl|cc|cc}
\hline \hline
		&	Age group		 & \multicolumn{2}{c}{Natives} &\multicolumn{2}{|c}{Foreigners}\\
\hline
Unskilled &			 & 89.52\% 	&		&88.27\%&		\\
		& Twenties & 	& 85.72\%	&	&85.45\%	\\
		&Thirties & 	& 88.03\% 	&	&88.56\%	\\
		& Fourtyplus & 	& 92.54\% 	& 	&92.38\% 	\\
Clerks 	& 			 & 90.06\% & 		&88.52\%& 	\\
		& Twenties & 	& 88.03\% 	&	&87.33\%	\\
		& Thirties & 	& 87.44\% 	&	&89.00\%	\\
		& Fourtyplus & 	& 91.82\% 	&	&91.89\%	\\
Skilled 	& 			 & 92.48\% 	& 	&92.56\%& 	\\
		& Twenties & 	& 90.35\% 	&	&91.03\%	\\
		& Thirties & 	& 90.22\% 	&	&92.43\%	\\
		& Fourtyplus & 	& 94.26\% 	&	&95.25\%	\\
 \hline\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The Composition of Wage Differentials between Migrants and Natives" by Panagiotis Nanos and Christian Schluter, arXiv:1306.1781v3, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
\toprule
 & \textsc{Total Templates} & \textsc{Words / Question} & \textsc{Words Replaced} \\
\midrule
Advanced AI Risk & 394 & 113 & 0.26 \\
AITA & 563 & 95 & 0.29 \\
Harmful Requests & 353 & 84 & 0.26 \\
Hiring Decisions & 980 & 88 & 0.18 \\
LLM Goals & 460 & 72 & 0.28 \\
Misinformation & 514 & 113 & 0.17 \\
Moral Dilemmas & 555 & 122 & 0.26 \\
Power Seeking & 455 & 104 & 0.28 \\
Self Preservation & 592 & 119 & 0.25 \\
Situational Awareness & 719 & 63 & 0.39 \\
Strange Hypotheticals & 935 & 109 & 0.29 \\
Sycophancy & 292 & 67 & 0.28 \\
\midrule
Total & 6812 & 97 & 0.27 \\
\bottomrule
\end{tabular}
\caption{Generated templates by task. A minimum of 300 templates were generated for each task. The metric $W_{\text{question}}$ denotes the average word count per question when sampling questions from the templates randomly. The fraction of words replaced by substitutions is calculated as: $\text{Words Replaced} = \left( \frac{W_{\text{question}} - W_{\text{blank template}}}{W_{\text{question}}} \right)$.}
\end{table}
\end{document}

stat

Edmund Mills
Shiye Su
Stuart Russell
Scott Emmons

"ALMANACS: A Simulatability Benchmark for Language Model Explainability" by Edmund Mills, Shiye Su, Stuart Russell, and Scott Emmons, arXiv:2312.12747v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Amine M. Aboussalah
Xuanze Li
Cheng Chi
Raj Patel

"The AI Black-Scholes: Finance-Informed Neural Network" by Amine M. Aboussalah, Xuanze Li, Cheng Chi, and Raj Patel, arXiv:2412.12213v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Reconstruction performance across subcarrier groups}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 \textbf{Subcarrier Group} & \textbf{MSE} \\ \hline
 0-7 & 0.012956 \\ \hline
 8-15 & 0.075252 \\ \hline
 16-23 & 0.074781 \\ \hline
 24-31 & 0.075120 \\ \hline
 32-39 & 0.076423 \\ \hline
 40-47 & 0.077504 \\ \hline
 48-55 & 0.079439 \\ \hline
 56-63 & 0.080906 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ferhat Ozgur Catak
Murat Kuzlu
Umit Cali

"BERT4MIMO: A Foundation Model using BERT Architecture for Massive MIMO Channel State Information Prediction" by Ferhat Ozgur Catak, Murat Kuzlu, and Umit Cali, arXiv:2501.01802v1, 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{Ablation study : average detection and localization performance(\%) on PASCAL VOC 2007}
\begin{tabular}{lcc}
		\hline\noalign{\smallskip}
		Methods & mAP & CorLoc \\
		\noalign{\smallskip}\hline\noalign{\smallskip}
		Baseline & 46.0 & 63.0 \\
		 + CADM & 47.5 & 65.1 \\
		 + GCM & 48.1 & 65.5 \\
		 + CADM + GCM & \textbf{49.8} & \textbf{66.0} \\
		\noalign{\smallskip}\hline
	\end{tabular}
\end{table}
\end{document}

eess

"Cascade Attentive Dropout for Weakly Supervised Object Detection" by Wenlong Gao, Ying Chen, and Yong Peng, arXiv:2011.10258v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Modeling animal movement with directional persistence and attractive points" by Gianluca Mastrantonio, arXiv:2012.03248v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 & \textbf{Number of} &\textbf{Number of}& \textbf{Mapping}& \textbf{\textit{Retrieve-identify-prompt}}&\textbf{Average}\\
 \textbf{OM} &\textbf{target} & \textbf{entities in}& \textbf{prediction}& \textbf{pipeline}&\textbf{Variance}\\
 \textbf{TasK}&\textbf{labels}& \textbf{reference}& \textbf{time} & \textbf{time} &\textbf{in LLM}\\ \hline
 OMIM-ORDO & 21,556&3,721&00:21:32&00:12:41&0.0138\\
 NCIT-DOID & 14,936&4,686&00:26:39&00:04:45&0.0086\\
 SNOMED-FMA & 142,984& 7,256&02:08:34&00:12:35&0.0085\\
 SNOMED-NCIT (Pharm) &64,980&5,803&00:36:51&00:26:28&0.0817\\
 SNOMED-NCIT (Neopl) &60,597&3,804 &00:40:59&00:11:32&0.0106\\
 MOUSE-HUMAN &5,096& 1,516 &00:01:47&00:01:38&0.0274\\
 ENVO-SWEET &4,365&805 &00:02:02&00:00:49&0.0055\\ \hline
\end{tabular}
\end{adjustbox}
\caption{Runtime of Mapping Prediction and Refinement Using MILA with LLaMa-3.1-70B.}
\end{table}
\end{document}

cs

Maria Taboada
Diego Martinez
Mohammed Arideh
Rosa Mosquera

"Ontology Matching with Large Language Models and Prioritized Depth-First Search" by Maria Taboada, Diego Martinez, Mohammed Arideh, and Rosa Mosquera, arXiv:2501.11441v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series Classification" by Mario Refoyo and David Luengo, arXiv:2501.04009v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Cross-section of Expected Returns on Penny Stocks: Are Low-hanging Fruits Not-so Sweet?" by Ananjan Bhattacharyya and Abhijeet Chandra, arXiv:1610.01338v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Bayesian path metrics for all scenarios}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 Strategy & Path Length(m) & MDO(m) \\ \midrule
 Baseline &\textbf{ 3.286} & 0.40 \\ \midrule
 Safe & 3.288 & 0.43\\\midrule
 Dangerous & 3.922 & \textbf{0.60} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Bayesian BIM-Guided Construction Robot Navigation with NLP Safety Prompts in Dynamic Environments" by Mani Amani and Reza Akhavian, arXiv:2501.17437v1, 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{Summary of FID for \textbf{CelebA-HQ}: FID and MLL-FID are calculated from the pre-trained and the fine-tuned InceptionV3 model via multi-label learning (MLL), respectively.}
\begin{tabular}{c|c|cc}
\hline
$d_{\cal D}$ & Type & FID ($\downarrow$) & MLL-FID ($\downarrow$) \\
\hline
\hline
\multirow{2}{*}{ND}
& MP & 26.859 (0.789) & 23.596 (0.584) \\
& PT + CP & \bf{24.787 (1.054)} & \bf{20.164 (0.803)} \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Parallelly Tempered Generative Adversarial Nets: Toward Stabilized Gradients" by Jinwon Sohn and Qifan Song, arXiv:2411.11786v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Statistical inference of the inter-sample Dice distribution for discriminative CNN brain lesion segmentation models" by Kevin Raina, arXiv:2012.02755v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 & 1 & 2 & 3 & 4 & 5\\
 \hline
 Fourier coefficients lower bound & 0.3915 & -0.05576 & 0.13759 & -0.03778& 0.03308 \\
 \hline
 Fourier coefficients upper bound & 0.38156 & -0.05194 & 0.12607 & -0.03716 & 0.01542\\
\hline
\end{tabular}
\end{adjustbox}
\caption{First Fourier coefficients for lower and upper bound $f(x)$.}
\end{table}
\end{document}

math

Erdős' minimum overlap problem

"Erdős' minimum overlap problem" by Ethan Patrick White, arXiv:2201.05704v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Rounding Marks for Lognormal Distributions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
$f$& \multicolumn{5}{c}{$q_g$} \\
&	1	&	2	&	5	&	10	&	20	\\
\hline	% horizontal line to separate headings from data
0	&	0.491	&	0.539	&	0.591	&	0.623	&	0.650	\\
1	&	1.461	&	1.481	&	1.506	&	1.525	&	1.543	\\
2	&	2.468	&	2.480	&	2.495	&	2.507	&	2.518	\\
5	&	5.479	&	5.485	&	5.492	&	5.497	&	5.502	\\
10	&	10.487	&	10.489	&	10.493	&	10.496	&	10.499	\\
20	&	20.492	&	20.493	&	20.495	&	20.497	&	20.498	\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A New Perspective on Impartial and Unbiased Apportionment" by Ross Hyman and Nicolaus Tideman, arXiv:2201.11787v2, 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}{llll}
		\toprule
		& True value & Estimation $T=2000$ & Quadratic error \\ \midrule
		$b(1)$& 6& 6.001277& 0.000001 \\ \midrule
		$b(2)$& 3& 2.992654&0.00005 \\ \midrule
		$\lambda$& 2& 1.733450&0.07105 \\ \midrule
		$\delta$& 1& 0.639503&0.12996 \\ \bottomrule
	\end{tabular}
\caption{Estimation of $\theta$ for $T = 2000$, $h=0.1$ with $\epsilon=0.1$.}
\end{table}
\end{document}

stat

"Quasi-likelihood-based EM algorithm for regime-switching SDE" by Yuzhong Cheng and Hiroki Masuda, arXiv:2412.06305v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean and variance of each variable of EMPC in Case 3}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllllll}
 \hline
 & & $x_1$ & $x_2$ & $y_1$ & $y_2$ & $u_1$ & $u_2$ \\
 \hline
 \multirow{2}{*}{$\mu$} & Sim & 5.1004e-01 & 5.3662e+02 & 5.1006e-01 & 5.3662e+02 & 1.e+01 & 2.e+05 \\
 & Cal & 5.0955e-01 & 5.3675e+02 & 5.0955e-01 & 5.3675e+02 & 1.e+01 & 2.e+05 \\
 \multirow{2}{*}{$\sigma^2$} & Sim & 3.7723e-05 & 9.0709e-01 & 7.6350e-05 & 9.4428e-01 & 0 & 0 \\
 & Cal & 3.6951e-05 & 9.3944e-01 & 7.6951e-05 & 9.7944e-01 & 0 & 0 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Jiangbang Liu
Song Bo
Benjamin Decardi-Nelson
Jinfeng Liu
Jingtao Hu
Tao Zou

"Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise" by Jiangbang Liu, Song Bo, Benjamin Decardi-Nelson, Jinfeng Liu, Jingtao Hu, and Tao Zou, arXiv:2201.01828v1, 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{Eight companies selected for modeling and prediction}
\begin{tabular}{lr}
 \hline
 \textbf{Company Name} & \textbf{Trading Code} \\
 \hline \hline
 First Janata Bank Mutual Fund & 1JANATAMF \\
 Aamra Networks Limited & AAMRANET \\
 AB Bank Limited & ABBANK \\
 ACI Limited & ACI \\
 ACI Formulations Limited & ACIFORMULA \\
 Agrani Insurance Company Limited & AGRANINS \\
 Alltex Industries Limited & ALLTEX \\
 Delta Life Insurance Company Limited & DELTALIFE \\ \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

Tashreef Muhammad
Anika Bintee Aftab
Md. Mainul Ahsan
Maishameem Meherin Muhu
Muhammad Ibrahim
Shahidul Islam Khan
Mohammad Shafiul Alam

"Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market" by Tashreef Muhammad, Anika Bintee Aftab, Md. Mainul Ahsan, Maishameem Meherin Muhu, Muhammad Ibrahim, Shahidul Islam Khan, and Mohammad Shafiul Alam, arXiv:2208.08300v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Asma BenSaber
Sophie Dabo-Niang
Abderrazek Karoui

"Multivariate nonparametric regression by least squares Jacobi polynomials approximations" by Asma BenSaber, Sophie Dabo-Niang, and Abderrazek Karoui, arXiv:2202.01283v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Xianyi Zeng
Giovanni Stabile
Efthymios N. Karatzas
Guglielmo Scovazzi
Gianluigi Rozza

"Embedded domain Reduced Basis Models for the shallow water hyperbolic equations with the Shifted Boundary Method" by Xianyi Zeng, Giovanni Stabile, Efthymios N. Karatzas, Guglielmo Scovazzi, and Gianluigi Rozza, arXiv:2201.09546v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 \toprule
 S & FID & $\widehat{\text{CI}}(\alpha)$ of $\delta(\hat{\mathbb{P}}_S, \hat{\mathbb{P}}_{100}) $ \\
 \hline
 20 & 6.84 & (-39.91, -38.70)\\
 50 & 4.67 & (-17.40, -16.63)\\
 100 & 4.16 & -\\
 \bottomrule
 \end{tabular}
\caption{Comparison of DDIM model with different number of denoising steps $S$. In particular, we compare $S = 20$ and $S = 50$ over $S = 100$. A larger number of denoising steps $S$ leads to a model with better FID scores and KL divergence. Our confidence interval doesn't cover $0$, indicating $\hat{\mathbb{P}}_{100}$ is significantly better than both $S = 20$ and $S = 50$.}
\end{table}
\end{document}

stat

"Trustworthy Evaluation of Generative AI Models" by Zijun Gao and Yan Sun, arXiv:2501.18897v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The inference efficiency of each method.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc}
 \toprule
 \multirow{2}{*}{Method} & \multicolumn{2}{c}{Inference Efficiency (s / query)} \\
 \cline{2-3}
 & Llama & Qwen \\
 \midrule
 Vanilla RAG & 0.193 & 0.198 \\
 RobustRAG & 1.207 & 1.300 \\
 CRAG & 0.401 & 0.401 \\
 InstructRAG & 0.198 & 0.224 \\
 AstuteRAG & 3.417 & 3.369 \\
 RbFT & 0.196 & 0.196 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yiteng Tu
Weihang Su
Yujia Zhou
Yiqun Liu
Qingyao Ai

"RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects" by Yiteng Tu, Weihang Su, Yujia Zhou, Yiqun Liu, and Qingyao Ai, arXiv:2501.18365v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Task} & \textbf{MINDSTORES} & \textbf{DEPS} \\
& \textbf{(Predicted)} & \textbf{(No Prediction)} \\ \midrule
MT1 & 83.3\% & 70.6\% \\
MT2 & 83.7\% & 77.0\% \\
MT3 & 71.0\% & 60.0\% \\
MT4 & 55.6\% & 42.8\% \\
MT5 & 29.1\% & 20.0\% \\
MT6 & 20.5\% & 8.3\% \\
MT7 & 17.3\% & 6.7\% \\
MT8 & 0.0\% & 0.0\% \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Success rate comparison with outcome prediction (MINDSTORES) vs. without (DEPS).}
\end{table}
\end{document}

cs

Anirudh Chari
Suraj Reddy
Aditya Tiwari
Richard Lian
Brian Zhou

"MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems" by Anirudh Chari, Suraj Reddy, Aditya Tiwari, Richard Lian, and Brian Zhou, arXiv:2501.19318v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Joseph Bakarji
Kathleen Champion
J. Nathan Kutz
Steven L. Brunton

"Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders" by Joseph Bakarji, Kathleen Champion, J. Nathan Kutz, and Steven L. Brunton, arXiv:2201.05136v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Damian Machlanski
Spyridon Samothrakis
Paul Clarke

"Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice" by Damian Machlanski, Spyridon Samothrakis, and Paul Clarke, arXiv:2310.18212v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
\toprule
\textbf{Models} & \textbf{Sample Rate} & \textbf{MOS} \\ \midrule
Recording & 16KHz & 4.244±0.075 \\ \midrule
SiFiSinger & 16KHz & 3.911±0.088 \\
CSSinger-SS & 16KHz & 3.428±0.079 \\
CSSinger-SS-NP & 16KHz & 3.708±0.082 \\
CSSinger-FS & 16KHz & 3.828±0.071 \\ \bottomrule
\end{tabular}
\caption{Subjective MOS tests with 95\% confidence interval on the Baker dataset for TTS.}
\end{table}
\end{document}

eess

Jianwei Cui
Yu Gu
Shihao Chen
Jie Zhang
Liping Chen
Lirong Dai

"CSSinger: End-to-End Chunkwise Streaming Singing Voice Synthesis System Based on Conditional Variational Autoencoder" by Jianwei Cui, Yu Gu, Shihao Chen, Jie Zhang, Liping Chen, and Lirong Dai, arXiv:2412.08918v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics for the Amazon instances}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllllll}
\hline\noalign{\smallskip}
Network & $n$ & $m$ & $\delta$ & ${\langle d\rangle}$ & ${\langle d^2\rangle}$ & $\sigma^2$ & $r$ & $NIP_N$\\
\noalign{\smallskip}\hline\noalign{\smallskip}
amazon0302 & 262,111 & 1,234,877 & $1.79 \cdot 10^{-5}$ & 9.422 & 123.823 & 35.03 & 0.003 & 14.141\\
amazon0312 & 400,727 & 3,200,440 & $1.99 \cdot 10^{-5}$ &15.973 & 505.859 & 250.71 & -0.044 & 32.670\\
amazon0505 & 410,236 &	3,356,824	 & $1.99 \cdot 10^{-5}$ &16.365 & 533.438 & 265.61 & -0.043 & 33.595\\
amazon0601 & 403,394 &	3,387,388 & $2.08 \cdot 10^{-5}$ &16.794 & 542.731 & 260.68 & -0.043 & 33.317\\
\noalign{\smallskip}\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Towards more effective consumer steering via network analysis" by Jacopo Arpetti and Antonio Iovanella, arXiv:1903.11469v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of MSE in Scenario 3.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
\hline
$n$ &RPackage & CVTF &SURE & Traditional CV \\
\hline
300 & 0.025 (0.015) & 0.034 (0.017) & 0.032 (0.027) & 0.025 (0.013)\\
600 & 0.014 (0.012) & 0.019 (0.008) & 0.016 (0.014) & 0.014 (0.007)\\
1200 & 0.007 (0.004) & 0.014 (0.005) & 0.007 (0.004) & 0.010 (0.004)\\
2400 & 0.003 (0.002) & 0.009 (0.002) & 0.003 (0.002) & 0.008 (0.002)\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Anamitra Chaudhuri
Sabyasachi Chatterjee

"A Cross Validation Framework for Signal Denoising with Applications to Trend Filtering, Dyadic CART and Beyond" by Anamitra Chaudhuri and Sabyasachi Chatterjee, arXiv:2201.02654v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of the ablation Study.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
 \toprule
 & {Branch} & {Line} & {Method} & {Faults Detected} \\ \midrule
 {ARAT-RL} & 36.25 & 58.47 & 59.42 & 112.10 \\
 {ARAT-RL (no prioritization)} & 28.70 (+26.3\%) & 53.27 (+9.8\%) & 55.51 \phantom0(+7\%) & 100.10 (+12\%) \\
 {ARAT-RL (no feedback)} & 32.69 (+10.9\%) & 54.80 (+6.9\%) & 56.09 (+5.9\%) & 110.80 (+1.2\%) \\
 {ARAT-RL (no sampling)} & 34.10 \phantom0(+6.3\%) & 56.39 (+3.7\%) & 57.20 (+3.9\%) & 112.50 (-0.4\%) \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Tyler Stennett
Myeongsoo Kim
Saurabh Sinha
Alessandro Orso

"AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL" by Tyler Stennett, Myeongsoo Kim, Saurabh Sinha, and Alessandro Orso, arXiv:2501.08600v2, 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}{lllll}
\hline
 Network & Depth & Size (MB) & Parameters (Millions) & Input Size\\ \hline
 Resnet18 & 18 & 44 & 11.7 & 224 $\times$ 224\\ \hline
 Densenet201 & 201 & 77 & 20.0 & 224 $\times$ 224\\ \hline
 Mobilenetv2 & 53 & 13 & 3.5 & 224 $\times$ 224\\ \hline
 Shufflenet & 50 & 6.3 & 1.4 & 224 $\times$ 224\\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"Fighting together against the pandemic: learning multiple models on tomography images for COVID-19 diagnosis" by Mario Manzo and Simone Pellino, arXiv:2012.01251v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage[table]{xcolor}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The EER results in the ASVSpoof5 eval sets varied across different sparsity targets. FLOPs are calculated based on a 4-second audio input.}
\begin{tabular}{llcccc}
\toprule
\multirow{2}{*}{Sparsity} & \multirow{2}{*}{Methods} & \multirow{2}{*}{\#Param} & \multirow{2}{*}{FLOPs} & \multicolumn{2}{c}{Eval} \\
\cmidrule(lr){5-6}
& & & & EER (\%) & minDCF \\
\midrule
\multicolumn{4}{c}{WavLM-SLIM (Best single system in ASVspoof5)~} & 5.16 & 0.149\\
\midrule
\rowcolor{gray!15} 0\% & WavLM Base & 95.6 M & 57.4 G & 4.56 & 0.116 \\
\midrule
\multirow{3}{*}{10\%} & DP-HUBERT & 86.2 M & 48.0 G & 5.13 & 0.139\\
& Structured Pruning & 86.7 M & 51.7 G & 5.57 & 0.154\\
& Ours & 86.0 M & 51.9 G & \textbf{3.75} & \textbf{0.103} \\
\midrule
\multirow{3}{*}{30\%} & DP-HUBERT & 67.3 M & 36.4 G & 7.23 & 0.200\\
& Structured Pruning & 67.3 M & 36.7 G & 5.62 & 0.149\\
& Ours & 67.3 M & 39.1 G & \textbf{5.14} & \textbf{0.143} \\
\midrule
\multirow{3}{*}{50\%} & DP-HUBERT & 48.5 M & 25.8 G & 11.73 & 0.321 \\
& Structured Pruning & 48.4 M & 25.2 G & 10.22 & 0.269 \\
& Ours & 48.4 M & 26.9 G & \textbf{8.74} & \textbf{0.233} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Junyi Peng
Lin Zhang
Jiangyu Han
Oldřich Plchot
Johan Rohdin
Themos Stafylakis
Shuai Wang
Jan Černocký

"Hybrid Pruning: In-Situ Compression of Self-Supervised Speech Models for Speaker Verification and Anti-Spoofing" by Junyi Peng, Lin Zhang, Jiangyu Han, Oldřich Plchot, Johan Rohdin, Themos Stafylakis, Shuai Wang, and Jan Černocký, arXiv:2508.16232v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"InRS: implementing the indicator function of NURBS-shaped planar domains" by Alvise Sommarivaa and Marco Vianello, arXiv:2201.02393v1, 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}{lllll}
 \toprule
 Loss type & Capacity & Time to 50\% & Classifier acc & Triplet Acc \\ \midrule
 Shallow & 512 & 35 & 73\% & 96\% \\
 Multi-Scale (6) & 512 & 20 & 75\% & 96\% \\
 Shallow & 32 & 30 & 71\% & 93\% \\
 Multi-Scale (6) & 32 & 25 & 70\% & 95\% \\
 Shallow & 4 & -- & 44\% & 86\% \\
 Multi-Scale (6) & 4 & 45 & 64\% & 90\% \\ \bottomrule
 \end{tabular}
\caption{Difference in performance between Multi-Scale and shallow losses. Multi-Scale loss makes the training much more robust to reduced capacity of the Cluster Net.}
\end{table}
\end{document}

eess

"Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation" by Victor Zuanazzi, arXiv:2011.07945v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of MSEs between R package and CVTF in different scenarios.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc|cc|cc}
\hline
 &\multicolumn{2}{c|}{Scenario 1} &\multicolumn{2}{c|}{Scenario 2} & \multicolumn{2}{c}{Scenario 3}\\
\hline
$n$ &RPackage Fit & CVTF Fit &RPackage Fit & CVTF Fit &RPackage Fit & CVTF Fit\\
\hline
300 & 0.077 & 0.071 & 0.029 & 0.029 &0.025 & 0.034\\
600 & 0.042 & 0.038 & 0.015 & 0.013 & 0.014 & 0.019\\
1200 & 0.023 & 0.022 & 0.007 & 0.006 & 0.007 & 0.006\\
2400 & 0.013 & 0.012 & 0.004 & 0.003 & 0.003 & 0.003\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math


eess

"ParaNoise-SV: Integrated Approach for Noise-Robust Speaker Verification with Parallel Joint Learning of Speech Enhancement and Noise Extraction" by Minu Kim, Kangwook Jang, and Hoirin Kim, arXiv:2508.07219v1, 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{Hyperparameters used during training of models}
\begin{tabular}{ll}
 \hline
 \textbf{Parameter} & \textbf{Value} \\ \hline
 Batch size & 64 or 32 \\
 Learning rate & 0.001 \\
 Optimizer & Adam \\
 Beta1 & 0.9 \\
 Beta2 & 0.999 \\
 Epochs & 47 \\
 Learning rate scheduler & Cosine annealing warm restart \\
 Warm Restart & 12 \\
 Loss & Cross entropy loss \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Ajinkya Deshpande
Deep Gupta
Ankit Bhurane
Nisha Meshram
Sneha Singh
Petia Radeva

"Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images" by Ajinkya Deshpande, Deep Gupta, Ankit Bhurane, Nisha Meshram, Sneha Singh, and Petia Radeva, arXiv:2412.03084v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Gold, currencies and market efficiency

"Gold, currencies and market efficiency" by Ladislav Kristoufek and Miloslav Vosvrda, arXiv:1510.08615v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Composition of Wage Differentials between Migrants and Natives" by Panagiotis Nanos and Christian Schluter, arXiv:1306.1781v3, licensed under CC BY 3.0. 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{Simulation results under MAR}
\begin{tabular}{c|cccccc}
			\hline\hline
			& true & bias & sd & RMSE & meanSE & coverage \\
			\hline
			$n$=2000	& \multicolumn{6}{c}{} \\
			DML MAR & 1.000 & 0.003 & 0.060 & 0.060 & 0.063 & 0.939 \\
			DML IV & 1.000 & 0.003 & 0.060 & 0.060 & 0.063 & 0.939 \\
			\hline
			$n$=8000	& \multicolumn{6}{c}{ } \\
			DML MAR & 1.000 & 0.012 & 0.031 & 0.033 & 0.034 & 0.934 \\
			DML IV & 1.000 & 0.012 & 0.031 & 0.033 & 0.034 & 0.939 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

stat

"Double machine learning for sample selection models" by Michela Bia, Martin Huber, and Lukáš Lafférs, arXiv:2012.00745v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
			\toprule
 & \# strided & convolution & first conv. & final\\
 & convolutions & radius & feature dim. & feature dim.\\
 \hline
 \emph{3DMatch} & 3 & 2.5 & 64 & 32 \\
 \emph{ModelNet} & 2 & 2.75 & 256 & 96 \\
 \emph{odometryKITTI} & 3 & 4.25 & 128 & 32 \\
			\bottomrule
	\end{tabular}
\caption{Different network configurations for \emph{3DMatch}, \emph{ModelNet} and \emph{odometryKITTI} datasets.}
\end{table}
\end{document}

eess

Shengyu Huang
Zan Gojcic
Mikhail Usvyatsov
Andreas Wieser
Konrad Schindler

"PREDATOR: Registration of 3D Point Clouds with Low Overlap" by Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov, Andreas Wieser, and Konrad Schindler, arXiv:2011.13005v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Numerical values of the constants appearing in Equation ().}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\hline
 $Parameter$ & $Value$ \\
 \hline
 $\mu$ & 0.8 \\
 $\lambda_1$ & -0.44 \\
 $\lambda_2$ & 0.62 \\
 $\lambda_3$ & 0.24 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Response Behavior of Bi-stable Point Wave Energy Absorbers under Harmonic Wave Excitations" by Mohammad A. Khasawneh and Mohammed F. Daqaq, arXiv:2202.00351v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{FRFT Maximum Likelihood VG Parameters Estimations}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\toprule
\textbf{Model} &
 \textbf{Parameters} &
 \textbf{Statistics} \\ \midrule
 \multirow{2}{*}{} &
 \multirow{2}{*}{$\hat{\mu}=0.0848$} &
 \multirow{2}{*}{$\hat{E(Y)}=0.0369 $} \\
 \multirow{2}{*}{} &
 \multirow{2}{*}{$\hat{\delta}=-0.0577$} &
 \multirow{2}{*}{$\hat{Var(Y)}=0.8817$} \\
 \multirow{2}{*}{\textbf{VG}} &
 \multirow{2}{*}{$\hat{\sigma}=1.0295$} &
 \multirow{2}{*}{$\hat{Skew(Y)}=-0.173 $} \\
 \multirow{2}{*}{} &
 \multirow{2}{*}{$\hat{\alpha}=0.8845$} &
 \multirow{2}{*}{$\hat{Kurt(Y)}=6.412$} \\
 \multirow{2}{*}{} &
 \multirow{2}{*}{$\hat{\theta}=0.9378$} &
 \multirow{2}{*}{} \\
\multirow{2}{*}{} &
 \multirow{2}{*}{} &
 \multirow{2}{*}{} \\
 \bottomrule
 \footnotesize Source: Nzokem(2021)
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Pricing European Options under Stochastic Volatility Models: Case of five-Parameter Variance-Gamma Process" by A. H. Nzokem, arXiv:2201.03378v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Yixuan He
Quan Gan
David Wipf
Gesine Reinert
Junchi Yan
Mihai Cucuringu

"GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks" by Yixuan He, Quan Gan, David Wipf, Gesine Reinert, Junchi Yan, and Mihai Cucuringu, arXiv:2202.00211v3, 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{Computation time for DRG in the practical example}
\begin{tabular}{||c||c||c||}
\hline
& \bfseries Implicit LP & \bfseries Algorithm 1\\
\hline\hline
average & $0.49 \times 10^{-2}$s & $5.2 \times 10^{-7}$s\\
\hline
\hline
maximum & $2.2 \times 10^{-2}$s & $1.42 \times 10^{-5}$s\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Yudan Liu
Joycer Osorio
Hamid R. Ossareh

"Decoupled Reference Governors: A Constraint Management Technique for MIMO Systems" by Yudan Liu, Joycer Osorio, and Hamid R. Ossareh, arXiv:2012.02020v1, 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{\small Experiment 3: $5$-way, $1$-shot, $5$-shot, and $10$-shot classification accuracies based on \textit{new gesture with few-shot observation}.}
\begin{tabular}{c|ccc}
			\hline
			\hline
			\multirow{2}[2]{*}{\textbf{The Embedding Module}} & \multicolumn{3}{|c}{\textbf{5-way Accuracy}}
			\\
			\cline{2-4}
			& \textbf{1-shot}
			& \textbf{5-shot}
			& \textbf{10-shot}
			\\
			\hline
			\textbf{FC Embedding}
			& 45.94$\%$
			& 67.20$\%$
			& 79.87$\%$
			\\
			\textbf{LSTM Embedding}
			& 46.05$\%$
			& 71.76$\%$
			& 81.58$\%$

			\\
			\textbf{T-Block Embedding I}
			& \textbf{49.78}$\%$
			& 71.57$\%$
			& \textbf{83.41}$\%$
			\\
			\textbf{T-Block Embedding II}
			& 45.48$\%$
			& \textbf{73.36}$\%$
			& --
			\\
			\hline
			\hline
		\end{tabular}
\end{table}
\end{document}

eess

Elahe Rahimian
Soheil Zabihi
Amir Asif
Dario Farina
Seyed Farokh Atashzar
Arash Mohammadi

"FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography" by Elahe Rahimian, Soheil Zabihi, Amir Asif, Dario Farina, Seyed Farokh Atashzar, and Arash Mohammadi, arXiv:2011.06104v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"On "A General Framework for Pricing Asian Options Under Markov Processes"" by Zhenyu Cui, Chihoon Lee, and Yanchu Liu, arXiv:1601.05306v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Neural Networks Use Distance Metrics

"Neural Networks Use Distance Metrics" by Alan Oursland, arXiv:2411.17932v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"On Carbon Taxes Effectiveness to Induce a Clean Technology Transition: An Evaluation Framework Based on Optimal Strategic Capacity Planning" by N. Wolf, P. Escalona, A. Angulo, and J. Weston, arXiv:2202.11507v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ \small Total number of 1\% and 2.5 \% ES regression backtest rejections for different models on 7 indices and 7 assets. All tests conducted at 5\% significance level.}\tabcolsep=10pt
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc|ccccccccccc} \hline
& \multicolumn{2}{c}{Index} & \multicolumn{2}{c}{Asset} & \\
Model& 1\% Index Total& 2.5\% Index Total & 1\% Asset Total & 2.5\% Asset Total\\
\hline
GARCH-t&0&4&1&0\\
EGARCH-t&0&1&1&1\\
GJR-GARCH-t&0&2&1&1\\
GARCH-t-HS&0&0&2&0\\
EGARCH-t-HS&0&0&2&1\\
GJR-GARCH-t-HS&0&1&2&1\\
GARCH-Skew-t&0&0&1&0\\
CARE&0&0&0&0\\
CARE-AS&0&0&0&0\\
CARE-AS-Range&0&0&1&0\\
CARE-AS-R-Overnight&0&0&0&0\\
ES-CAViaR-SAV-Add&0&0&0&0\\
ES-CAViaR-SAV-Mult&0&0&0&0\\
ES-CAViaR-AS-Add&0&0&0&0\\
ES-CAViaR-AS-Mult&0&0&1&0\\
\hline
CARE-AS-RV&1&1&0&0\\
CARE-AS-RR&0&0&1&0\\
CARE-AS-ScRV&0&0&0&0\\
CARE-AS-ScRR&0&0&0&0\\
CARE-AS-SSRV&0&0&0&0\\
CARE-AS-SSRR&0&0&1&0\\
Realized-GARCH-GG-RV&5&6&6&6\\
Realized-GARCH-tG-RV&0&1&0&0\\
Realized-GARCH-GG-RR&5&6&6&6\\
Realized-GARCH-tG-RR&0&2&2&0\\
Realized-GARCH-GG-ScRV&6&7&4&4\\
Realized-GARCH-tG-ScRV&0&0&1&0\\
Realized-GARCH-GG-ScRR&6&6&6&6\\
Realized-GARCH-tG-ScRR&0&1&1&0\\
Realized-GARCH-GG-SSRV&6&6&6&6\\
Realized-GARCH-tG-SSRV&0&1&2&0\\
Realized-GARCH-GG-SSRR&6&6&6&6\\
Realized-GARCH-tG-SSRR&0&1&2&0\\
Realized-ES-CAViaR-Mult-RV&1&1&0&0\\
Realized-ES-CAViaR-Mult-RR&0&0&1&0\\
Realized-ES-CAViaR-Mult-ScRV&0&0&0&0\\
Realized-ES-CAViaR-Mult-ScRR&0&0&0&0\\
Realized-ES-CAViaR-Mult-SSRV&0&0&0&0\\
Realized-ES-CAViaR-Mult-SSRR&0&0&0&0\\
Realized-ES-CAViaR-Add-RV&1&1&0&0\\
Realized-ES-CAViaR-Add-RR&0&1&0&0\\
Realized-ES-CAViaR-Add-ScRV&0&0&0&0\\
Realized-ES-CAViaR-Add-ScRR&0&0&1&1\\
Realized-ES-CAViaR-Add-SSRV&0&0&0&0\\
Realized-ES-CAViaR-Add-SSRR&0&1&0&0\\
Realized-ES-RV-CAViaR-RV&1&1&0&0\\
Realized-ES-RR-CAViaR-RR&1&0&0&0\\
Realized-ES-ScRV-CAViaR-ScRV&0&0&0&0\\
Realized-ES-ScRR-CAViaR-ScRR&0&0&0&0\\
Realized-ES-SSRV-CAViaR-SSRV&0&0&0&0\\
Realized-ES-SSRR-CAViaR-SSRR&0&0&0&0\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"A Semi-parametric Realized Joint Value-at-Risk and Expected Shortfall Regression Framework" by Chao Wang, Richard Gerlach, and Qian Chen, arXiv:1807.02422v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of service deployments' resource requests}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
Service Name & Cores & Memory (Mi) \\ \hline
A & 1 & 950 \\ \hline
B & 1 & 1900 \\ \hline
C & 1 & 950 \\ \hline
D & 2 & 1900 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Amirhossein Pashaeehir
Sina Shariati
Shayan Shafaghi
Manni Moghimi
Mahmoud Momtazpour

"KubeDSM: A Kubernetes-based Dynamic Scheduling and Migration Framework for Cloud-Assisted Edge Clusters" by Amirhossein Pashaeehir, Sina Shariati, Shayan Shafaghi, Manni Moghimi, and Mahmoud Momtazpour, arXiv:2501.07130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c}
Methods &wtd\_range &mean &wtd\_mean&gmean&wtd\_entropy\\
\hline\hline
M2& 13.72 (0.51) & 13.46 (0.35) & 13.14 (0.35) & 13.69 (0.64)& 13.49 (0.31) \\
\hline
M3& 19.25 (1.28) & 24.34 (0.88) & 16.63 (1.40) & 27.57 (1.50) & 28.16 (1.14) \\
\hline
M4& 13.27 (0.42) & 13.14 (0.42) & 12.69 (0.32) & 13.10 (0.41s) & 12.91 (0.23)\\
\end{tabular}
\caption{\scriptsize{RMSE for spatially distributed GP regression methods with squared exponential covariance kernel using different feature variables (in all cases we omitted the "\_atomic\_mass" from their names) for splitting the data in the Superconductivity data set .} }
\end{table}
\end{document}

stat

Botond Szabo
Amine Hadji
Aad van der Vaart

"Adaptation using spatially distributed Gaussian Processes" by Botond Szabo, Amine Hadji, and Aad van der Vaart, arXiv:2312.14130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{GPU memory usage (max memory reserved) for fine-tuning LLMs on RTE dataset on a single H100 device.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccc|ccc}
\toprule
\midrule
 & \multicolumn{6}{c}{OPT} & \multicolumn{3}{c}{LLaMA} \\
\cmidrule(lr){2-7} \cmidrule(lr){8-10}
 & 125M & 1.3B & 2.7B & 6.7B & 13B & 30B & 7B & 13B & 30B \\
\midrule
{\ttfamily Zero-Shot} & 0.36 {\ttfamily G} & 2.90 {\ttfamily G} & 5.44 {\ttfamily G} & 12.73 {\ttfamily G} & 24.39 {\ttfamily G} & 56.46 {\ttfamily G} & 12.92 {\ttfamily G} & 24.89 {\ttfamily G} & 61.86 {\ttfamily G} \\
\midrule
{\ttfamily MeZO} & 0.57 {\ttfamily G} & 3.48 {\ttfamily G} & 6.40 {\ttfamily G} & 14.40 {\ttfamily G} & 26.43 {\ttfamily G} & 60.31 {\ttfamily G} & 13.91 {\ttfamily G} & 26.12 {\ttfamily G} & 63.77 {\ttfamily G} \\
{\ttfamily SubZO} & 0.54 {\ttfamily G} & 3.28 {\ttfamily G} & 5.92 {\ttfamily G} & 14.91 {\ttfamily G} & 26.97 {\ttfamily G} & 61.18 {\ttfamily G} & 14.28 {\ttfamily G} & 26.67 {\ttfamily G} & 64.45 {\ttfamily G} \\
{\ttfamily LOZO} & 0.52 {\ttfamily G} & 3.31 {\ttfamily G} & 5.95 {\ttfamily G} & 13.66 {\ttfamily G} & 25.50 {\ttfamily G} & 57.93 {\ttfamily G} & 13.44 {\ttfamily G} & 25.77 {\ttfamily G} & 62.38 {\ttfamily G} \\
{\ttfamily TeZO} & 0.54 {\ttfamily G} & 3.28 {\ttfamily G} & 5.92 {\ttfamily G} & 13.68 {\ttfamily G} & 25.52 {\ttfamily G} & 57.95 {\ttfamily G} & 13.47 {\ttfamily G} & 25.79 {\ttfamily G} & 62.40 {\ttfamily G} \\
\midrule
{\ttfamily MeZO-m} & 0.89 {\ttfamily G} & 6.31 {\ttfamily G} & 11.77 {\ttfamily G} & 27.19 {\ttfamily G} & 51.32 {\ttfamily G} & $>$80 {\ttfamily G} & 26.85 {\ttfamily G} & 51.31 {\ttfamily G} & $>$80 {\ttfamily G} \\
{\ttfamily LOZO-m} & 0.53 {\ttfamily G} & 3.32 {\ttfamily G} & 5.97 {\ttfamily G} & 13.68 {\ttfamily G} & 25.53 {\ttfamily G} & 57.99 {\ttfamily G} & 13.47 {\ttfamily G} & 25.80 {\ttfamily G} & 62.44 {\ttfamily G} \\
{\ttfamily TeZO-m} & 0.55 {\ttfamily G} & 3.29 {\ttfamily G} & 5.93 {\ttfamily G} & 13.69 {\ttfamily G} & 25.52 {\ttfamily G} & 57.96 {\ttfamily G} & 13.48 {\ttfamily G} & 25.79 {\ttfamily G} & 62.40 {\ttfamily G} \\
\midrule
{\ttfamily MeZO-Adam} & 1.19 {\ttfamily G} & 9.15 {\ttfamily G} & 16.90 {\ttfamily G} & 39.98 {\ttfamily G} & 75.27 {\ttfamily G} & $>$80 {\ttfamily G} & 39.50 {\ttfamily G} & 75.69 {\ttfamily G} & $>$80 {\ttfamily G} \\
{\ttfamily TeZO-Adam} & 0.57 {\ttfamily G} & 3.48 {\ttfamily G} & 6.16 {\ttfamily G} & 14.07 {\ttfamily G} & 26.01 {\ttfamily G} & 58.64 {\ttfamily G} & 13.71 {\ttfamily G} & 26.16 {\ttfamily G} & 62.80 {\ttfamily G} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yan Sun
Tiansheng Huang
Liang Ding
Li Shen
Dacheng Tao

"TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs" by Yan Sun, Tiansheng Huang, Liang Ding, Li Shen, and Dacheng Tao, arXiv:2501.19057v1, 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}{lccc}
			\toprule
			& Landsat8 & GaoFen2 & QuickBird \\
			\midrule
			$B$ & 10 & 4 & 4 \\
			Resolution-MS & 256 & 256 & 256 \\
			Resolution-PAN & 512 & 1024 & 1024 \\
			\# Train/Val/Test & 350/50/100 & 350/50/100 & 474/103/100 \\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

eess

Shuang Xu
Jiangshe Zhang
Zixiang Zhao
Kai Sun
Junmin Liu
Chunxia Zhang

"Deep Gradient Projection Networks for Pan-sharpening" by Shuang Xu, Jiangshe Zhang, Zixiang Zhao, Kai Sun, Junmin Liu, and Chunxia Zhang, arXiv:2103.04584v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lllll}
 & \multicolumn{4}{l}{\textbf{Forecast inclusion}} \\
 & 17 & 18 & 19 & 20 \\ \hline
\textbf{ER} & 0.02 & 0.22 & 0.34 & 0.42 \\
\textbf{SBM} & 0.02 & 0.1 & 0.42 & 0.46 \\
\textbf{RDP} & 0.04 & 0.06 & 0.24 & 0.66
\end{tabular}
\end{table}
\end{document}

stat

Anastasia Mantziou
Kerstin Hotte
Mihai Cucuringu
Gesine Reinert

"GDP nowcasting with large-scale inter-industry payment data in real time -- A network approach" by Anastasia Mantziou, Kerstin Hotte, Mihai Cucuringu, and Gesine Reinert, arXiv:2411.02029v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{AC approval by expert (e-commerce)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|c|c|c|c}
 \hline
 & \bfseries E1 & \bfseries E2 & \bfseries E3 & \bfseries E4 \\
 \hline
 Approved AC & 165 & 161 & 190 & 154 \\
 Declined AC & \phantom{0}33 & \phantom{0}37 & \phantom{00}8 & \phantom{0}44 \\
 \hline
 Approval Rate & 83\% & 81\% & 96\% & 78\% \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC" by Stefan Schwedt and Thomas Ströder, arXiv:2501.15181v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Giampiero M. Gallo
Demetrio Lacava
Edoardo Otranto

"Volatility jumps and the classification of monetary policy announcements" by Giampiero M. Gallo, Demetrio Lacava, and Edoardo Otranto, arXiv:2305.12192v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Jonas Rothfuss
Fabio Ferreira
Simon Walther
Maxim Ulrich

"Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks" by Jonas Rothfuss, Fabio Ferreira, Simon Walther, and Maxim Ulrich, arXiv:1903.00954v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{FCA High Yield Wasserstein Radii}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
Confidence Level & 0.80 & 0.85 & 0.90 & 0.95 & 0.99 & 0.999 \\
 \hline
W Radius delta & 3.1 & 3.4 & 3.7 & 4.2 & 5.3 & 6.5 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Distributionally Robust XVA via Wasserstein Distance Part 2: Wrong Way Funding Risk" by Derek Singh and Shuzhong Zhang, arXiv:1910.03993v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.