\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Fail rate of different models on instruction-following tasks (standard errors in parentheses).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 & Budget & Color & Brand \\
 & Constrained & Based & Based \\
 \midrule
 \midrule
 Claude Sonnet 3.5 & $4.0\%$ ($0.5\%$) & $13.5\%$ ($1.4\%$) & \textcolor{gray}{$0.0\%$}\\
 Claude Sonnet 3.7 & \textcolor{gray}{$0.0\%$} & $4.4\%$ ($0.5\%$) & \textcolor{gray}{$0.0\%$} \\
 Claude Sonnet 4.0 & \textcolor{gray}{$0.0\%$} & $3.8\%$ ($0.5\%$) & \textcolor{gray}{$0.0\%$} \\
 GPT\textendash4o & \textcolor{gray}{$0.0\%$} & \textcolor{gray}{$0.0\%$} & \textcolor{gray}{$0.0\%$}\\
 GPT\textendash4.1 & \textcolor{gray}{$0.0\%$} & \textcolor{gray}{$0.0\%$} & \textcolor{gray}{$0.0\%$} \\
 Gemini 2.0 Flash & \textcolor{gray}{$0.0\%$} & \textcolor{gray}{$0.0\%$} & \textcolor{gray}{$0.0\%$} \\
 Gemini 2.5 Flash & \textcolor{gray}{$0.0\%$} & \textcolor{gray}{$0.0\%$} & \textcolor{gray}{$0.0\%$} \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Amine Allouah
Omar Besbes
Josué D Figueroa
Yash Kanoria
Akshit Kumar

"What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce" by Amine Allouah, Omar Besbes, Josué D Figueroa, Yash Kanoria, and Akshit Kumar, arXiv:2508.02630v1, 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 our results with two weakly-supervised methods (ASMO and WSS using image-tag supervision) and a fully-supervised method SOSD additionally using subitizing information in terms of S-measure (larger is better).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
	\hline
		Methods & ASMO & WSS & SOSD & Ours \\\hline
		ECSSD & 0.827 & 0.829 & 0.837 & \textbf{0.860} \\\hline
		DUT & 0.736 & 0.803 & 0.816 & \textbf{0.832} \\\hline
		Pascal-S & 0.702 & 0.815 & 0.742 & \textbf{0.854} \\\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xiaoyang Zheng
Xin Tan
Jie Zhou
Lizhuang Ma
Rynson W. H. Lau

"Weakly-Supervised Saliency Detection via Salient Object Subitizing" by Xiaoyang Zheng, Xin Tan, Jie Zhou, Lizhuang Ma, and Rynson W. H. Lau, arXiv:2101.00932v1, 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.


q-fin

"Do Temporary Workers Face Higher Wage Markdowns? Evidence from India's Automotive Sector" by Davide Luparello, arXiv:2507.08222v1, 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}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Performance comparison on LIDC dataset. Number of parameters, computation complexity for forward propagation, computation time per training epoch (t) and area under the receiver operating characteristics (AUROC) curve (higher is better) averaged over 5-fold cross validation for all methods are reported. All three tensor network models use $\beta=5$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccrc}
 \toprule
Models & \# Param. ($M$) & Complexity &time (s) & AUROC\\
 \midrule
 {MLTN (ours)} &0.42 &$
	 \left ( \frac{\log N}{\log k^{2\cdot L}} + {L-1} \right ) \cdot k^2 \cdot d \cdot \beta^2$&2.8& $0.88 \pm 0.01$ \\

	 {LoTeNet } &0.49&$ \left ( \frac{\log N}{\log k^{2\cdot L}} + \sum_{l=1}^{L-1}\frac{N}{k^{2 \cdot l}} \right ) \cdot k^2 \cdot d \cdot \beta^2 $ & 18.5 & $0.87 \pm 0.01$ \\
 {Tenet-X} & 0.82 &$\mathcal{O}\left( N \cdot d \cdot \beta^2\right)$ &30.2& $0.82 \pm 0.01$\\
 {MLP (L=4)} &0.52 & $\mathcal{O}\left(N \cdot L\right)$ &3.2& $0.86 \pm 0.01$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Raghavendra Selvan
Silas Ørting
Erik B Dam

"Multi-layered tensor networks for image classification" by Raghavendra Selvan, Silas Ørting, and Erik B Dam, arXiv:2011.06982v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \multirow{2.5}{*}{Location} & \multicolumn{4}{c}{Languages}\\
 \cmidrule(lr){2-5}
 & En. & Fr. & Sp. & Port. \\
 \midrule
 Africa* & 6 & 4 & 0 & 1 \\
 Americas$^\dag$ & 0 & 1 & 2 & 1 \\
 Asia & 10 & 0 & 0 & 1 \\
 Europe & 13 & 13 & 11 & 5 \\
 Latin America and the Carib. & 3 & 0 & 15 & 2 \\
 North America & 13 & 1 & 2 & 1 \\
 Oceania & 5 & 0 & 0 & 0 \\
 World-wide & 16 & 11 & 10 & 11 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Distribution of entries in English, French, Spanish and Portuguese across continents.}
\end{table}
\end{document}

cs

Angelina McMillan-Major
Zaid Alyafeai
Stella Biderman
Kimbo Chen
Francesco De Toni
Gérard Dupont
Hady Elsahar
Chris Emezue
Alham Fikri Aji
Suzana Ilić
Nurulaqilla Khamis
Colin Leong
Maraim Masoud
Aitor Soroa
Pedro Ortiz Suarez
Zeerak Talat
Daniel van Strien
Yacine Jernite

"Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources" by Angelina McMillan-Major, Zaid Alyafeai, Stella Biderman, Kimbo Chen, Francesco De Toni, Gérard Dupont, Hady Elsahar, Chris Emezue, Alham Fikri Aji, Suzana Ilić, Nurulaqilla Khamis, Colin Leong, Maraim Masoud, Aitor Soroa, Pedro Ortiz Suarez, Zeerak Talat, Daniel van Strien, and Yacine Jernite, arXiv:2201.10066v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\toprule
\textbf{Assumptions} & \textbf{Ours} & \textbf{Proxy Domain Adaptation} \\ \midrule
Task & Out-of-distribution & Domain Adaptation \\
Observed variables & $X$, $Y$ and ($U$ or $S$) & $X$, $Y$, $U$ and $S$ \\
Number of labeled sources & 1 & multiple ($\geq n_z$) \\
Conditional independence & $S \perp X,Y \mid Z$ (or $U \perp X,Y \mid Z$) & $U \perp X,Y,S \mid Z$ and $S \perp X \mid Z$ \\
Causality between $X$ and $Y$ & $X \leftrightarrow Y$ & $X \rightarrow Y$\\
Full rank & $P(S \mid Z)$ (or $P(U \mid Z)$) must be full rank & $P(S \mid Z)$ must be full rank and $P(Z \mid U,X)$ must be full rank for X \\
Discrete unobserved confounder & Yes & Yes \\
Informative variable & No & Yes \\
Existence of high-likelihood sample & Yes & No \\
Proxy available at test time & No & Yes \\
$ns \geq n_z$ & Yes & Yes \\
$n_z$ known & No & No \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Comparison of Assumptions between Ours and }
\end{table}
\end{document}

stat

Parjanya Prashant
Seyedeh Baharan Khatami
Bruno Ribeiro
Babak Salimi

"Scalable Out-of-distribution Robustness in the Presence of Unobserved Confounders" by Parjanya Prashant, Seyedeh Baharan Khatami, Bruno Ribeiro, and Babak Salimi, arXiv:2411.19923v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Mixed Precision GMRES-based Iterative Refinement with Recycling" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, 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}{lccccccccc} \hline
 & \multicolumn{3}{c}{G--N} & \multicolumn{3}{c}{LP} & \multicolumn{3}{c}{MCT}\\ %first row
 Dataset & \multicolumn{2}{c}{Metric} &\#DC &\multicolumn{2}{c}{Metric} &\#DC &\multicolumn{2}{c}{Metric} &\#DC \\ %second row (inner)
 & Q & NMI & & Q & NMI & & Q & NMI & \\%third row (most inner)
 \hline
 G-pTie &.908& .794 & 308 &.77& .602 & 1319 & .915 & .791 & 263 \\
 ego-Twitter &.334& .197 & 1431 &.215& .131 & 2131 &.307 & .230 & 1131 \\
 ego-Facebook &.522 & .590 & 1037 &.421 & .304 & 1780 &.503 & .372 & 1845 \\
 P-pTie & .473 & .311 & 1107 & .360 & .267 & 2071 &.601 & .472 & 985 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Isa Inuwa-Dutse
Mark Liptrott
Yannis Korkontzelos

"A multilevel clustering technique for community detection" by Isa Inuwa-Dutse, Mark Liptrott, and Yannis Korkontzelos, arXiv:2101.06551v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Input-Output Analysis: New Results From Markov Chain Theory" by Nizar Riane and Claire David, arXiv:2301.08136v2, 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|}
\hline
Iteration \( k \) & Step Size \( \alpha_k \) & Noise \( \xi_k \) & Update \( x_k \) & Function Value \( f(x_k) \) \\ \hline
0 & - & - & 10 & 50 \\ \hline
1 & 1 & 0.5 & -0.5 & 0.125 \\ \hline
2 & 0.574 & -0.2 & 0.4 & 0.08 \\ \hline
3 & 0.494 & 0.1 & 0.154 & 0.012 \\ \hline
4 & 0.445 & -0.3 & 0.13 & 0.00845 \\ \hline
5 & 0.398 & 0.2 & -0.053 & 0.0014 \\ \hline
6 & 0.364 & -0.1 & -0.069 & 0.00238 \\ \hline
7 & 0.334 & 0.15 & -0.036 & 0.000648 \\ \hline
8 & 0.308 & -0.05 & -0.049 & 0.0012 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Stochastic Steepest Descent Iterations}
\end{table}
\end{document}

math

"Generalized Steepest Descent Methods on Riemannian Manifolds and Hilbert Spaces: Convergence Analysis and Stochastic Extensions" by Rashid A. and Amal A Samad, arXiv:2502.19929v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Spearman's rho for bivariate zero-inflated data" by Jasper Arends and Elisa Perrone, arXiv:2503.13148v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc|lc}
 \toprule
 \multirow{1}{*}{Custodian location} & \multirow{1}{*}{\#} & \multirow{1}{*}{Custodian Location} & \multirow{1}{*}{\#}\\
 \midrule
 Spain & 27 & France & 9 \\
 USA & 22 & South Africa & 6 \\
 Vietnam & 14 & UK & 5 \\
 Indonesia & 14 & Australia & 4 \\
 India & 11 & Germany & 4 \\
 Colombia & 10 & China & 3 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Top 12 most frequent custodian locations.}
\end{table}
\end{document}

cs


stat

Wei-Ning Chen
Graham Cormode
Akash Bharadwaj
Peter Romov
Ayfer Özgür

"Federated Experiment Design under Distributed Differential Privacy" by Wei-Ning Chen, Graham Cormode, Akash Bharadwaj, Peter Romov, and Ayfer Özgür, arXiv:2311.04375v1, 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{{\small Scheduling actions of storage units}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \hline
 \toprule
 Action $c$ & $-2$ & $-1$ & 0 & 1 & 2 \\ \hline
 Storage discharging power/MW & $-2$ & $-1$ & 0 & $+1$ & $+2$ \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games" by Junkai Hu and Li Xia, arXiv:2503.22779v2, 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}{lcccccccc}
\hline
 & \multicolumn{4}{c}{\textbf{Human Advisor}} & \multicolumn{4}{c}{\textbf{LLM-advisor}} \\
\cline{2-5} \cline{6-9}
 & \textbf{All} & \multicolumn{3}{c}{\textbf{Narrative}} & \textbf{All} & \multicolumn{3}{c}{\textbf{Narrative}} \\
 & & 1 & 2 & 3 & & 1 & 2 & 3 \\
\hline
\textbf{PC} & 0.85 & 0.78 & 0.89 & 0.89 & 0.69 & 0.80 & 0.83 & 0.41 \\
\textbf{MCR} & 0.89 & 0.67 & 1.00 & 1.00 & 0.76 & 0.90 & 0.90 & 0.44 \\
\hline
\end{tabular}
\caption{Comparison of Preference Coverage (PC) and Majority Capture Rate (MCR) between Human and LLM-advisors.}
\end{table}
\end{document}

q-fin

Takehiro Takayanagi
Kiyoshi Izumi
Javier Sanz-Cruzado
Richard McCreadie
Iadh Ounis

"Are Generative AI Agents Effective Personalized Financial Advisors?" by Takehiro Takayanagi, Kiyoshi Izumi, Javier Sanz-Cruzado, Richard McCreadie, and Iadh Ounis, arXiv:2504.05862v2, 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{Comparison of per-organ mean F1 scores on the OCELOT test set for tissue segmentation. The tissue segmentation model is denoted as TSM. The highest score for each organ is marked in \textbf{bold}.}
\begin{tabular}{lccccccc}
 \midrule
 & all & kidney & endometrium & bladder & prostate & stomach & head-neck \\
 & n=130 & n=41 & n=25 & n=26 & n=16 & n=12 & n=10 \\
 \midrule
 TSM & .8927 & .9241 & .9062 & .8358 & .8119 & .9014 & \textbf{.8863} \\
 TSM + TTA & \textbf{.8951} & \textbf{.9282} & \textbf{.9088} & \textbf{.8383} & \textbf{.8178} & \textbf{.9035} & .8774 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"SoftCTM: Cell detection by soft instance segmentation and consideration of cell-tissue interaction" by Lydia A. Schoenpflug and Viktor H. Koelzer, arXiv:2312.12151v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|cc|cc|}
\toprule
 & & \multicolumn{2}{|c|}{\textbf{LW}} & \multicolumn{2}{|c|}{\textbf{CS-LW}} \\
\midrule
 \textbf{BN} & \textbf{N} & \textbf{MAE $\pm$ Std.} & \textbf{Time} & \textbf{MAE $\pm$ Std.} & \textbf{Time} \\
\midrule
\midrule
\multirow{4}{*}{\textit{Alarm}} & 100 & 0.2105 $\pm$ 0.1372	& 0.09 & 	\textbf{0.0721} $\pm$	\textbf{0.0983} & \textbf{0.06}\\ \cline{2-6}
 & 1000 & 0.0766 $\pm$ 0.0608 &	0.86 & \textbf{0.0240}	$\pm$ \textbf{0.0182}	& \textbf{0.53}
 \\
 \cline{2-6}
 & 10000 & 0.0282 $\pm$	0.0181 & 8.64 & \textbf{0.0091} $\pm$ \textbf{0.0069}	& \textbf{5.53} \\ \cline{2-6}
 & 100000 & 0.0086 $\pm$ 0.0067 & 89.93	& \textbf{0.0034} $\pm$ \textbf{0.0027} & \textbf{57.64}\\
\midrule
\midrule
\multirow{4}{*}{\textit{Andes}} & 100 & 0.0821 $\pm$ 0.0477 & 1.07 &	\textbf{0.0619} $\pm$ \textbf{0.0453} & \textbf{0.22} \\ \cline{2-6}
 & 1000 & 0.0257 $\pm$ 0.0184 & 10.62 & \textbf{0.0163} $\pm$ \textbf{0.0139} & \textbf{2.20} \\ \cline{2-6}
 & 10000 & 0.0087 $\pm$	0.0069 & 106.55 & \textbf{0.0058} $\pm$ \textbf{0.0042} &	\textbf{22.62}\\ \cline{2-6}
 & 100000 & 0.0025 $\pm$ \textbf{0.0015} & 1074.93 & \textbf{0.0020} $\pm$ 0.0016 & \textbf{233.72}\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{The mean absolute error (MAE), the standard deviation of the error (Std.), and the average execution time (in seconds) versus the number of samples (N). For each case, LW and CS-LW were executed 30 times.}
\end{table}
\end{document}

cs

Nitesh Kumar
Ondrej Kuzelka
Luc De Raedt

"First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs" by Nitesh Kumar, Ondrej Kuzelka, and Luc De Raedt, arXiv:2201.11165v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Efficiency of our method compared against two-stage pretraining methods.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}\toprule
\textbf{Method} &\textbf{\# of Data} &\textbf{Total \# of Data} &\textbf{Time (min.)} &\textbf{Total Time (min.)} \\\midrule
1st Stage &2,513,435 &\multirow{2}{*}{2,913,435} &$\sim$1258.50 &\multirow{2}{*}{$\sim$1727.75} \\
2nd Stage &400,000 & &$\sim$469.25 & \\
\midrule
\textbf{ECG-Byte} A &1,000,000 &\multirow{2}{*}{1,400,000} & $\sim$152.47&\multirow{2}{*}{$\sim$\textbf{572.79}} \\
end-to-end A &400,000 & &$\sim$420.32 & \\
\midrule
\textbf{ECG-Byte} B &2,500,000 &\multirow{2}{*}{2,900,000} &$\sim$304.27 &\multirow{2}{*}{$\sim$\textbf{724.59}} \\
end-to-end B &400,000 & &$\sim$420.32 & \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

William Han
Chaojing Duan
Michael A. Rosenberg
Emerson Liu
Ding Zhao

"ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling" by William Han, Chaojing Duan, Michael A. Rosenberg, Emerson Liu, and Ding Zhao, arXiv:2412.14373v3, 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{Quantitative evaluation of the results in Fig. for AT , MSTV , HLO , and our method TGV. For each result, we list mean angular difference $\theta$ (in degrees), vertex-based mesh-to-mesh error $E_v \ (\times 10^{-2})$ and the execution time (in seconds).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
 \toprule
 \footnotesize Mesh &
 \multicolumn{2}{c}{\footnotesize AT}&
 \multicolumn{2}{c}{\footnotesize MSTV}&
 \multicolumn{2}{c}{\footnotesize HLO} &
 \multicolumn{2}{c}{\footnotesize TGV}\\
 \midrule
 \footnotesize Child& \multicolumn{2}{c|}{7.61,\,0.62;\,3.80} & \multicolumn{2}{c|}{7.22,\,0.55;\,7.06} & \multicolumn{2}{c|}{7.93,\,0.69;\,1.31} & \multicolumn{2}{c}{\textbf{6.29},\,\textbf{0.48};\,25.9}\\
 \footnotesize Joint& \multicolumn{2}{c|}{1.89,\,0.79;\,2.84} & \multicolumn{2}{c|}{1.99,\,0.81;\,2.75} &
 \multicolumn{2}{c|}{6.06,\,2.15;\,0.34} & \multicolumn{2}{c}{\textbf{1.45},\,\textbf{0.70};\,12.4}\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zheng Liu
YanLei Li
Weina Wang
Ligang Liu
Renjie Chen

"Mesh Total Generalized Variation for Denoising" by Zheng Liu, YanLei Li, Weina Wang, Ligang Liu, and Renjie Chen, arXiv:2101.02322v2, 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{Classification results for three binary tasks: H vs. BD, H vs. BPD and BD vs. BDP, using logistic regression. Results shown are macro-averaged $\mathrm{F_1}$ and AUROC scores across all interviews. $p$-value used for feature selection: `*'$<$ 0.005, or else $<$ 0.001 is used.}
\begin{tabular}{c|cccccc}
\hline
 & \multicolumn{2}{c}{\bf H/BD} & \multicolumn{2}{c}{\bf H/BPD} & \multicolumn{2}{c}{\bf BD/BPD} \\
\bf Subject & $\mathbf{F_1}$ & \bf AUC & $\mathbf{F_1}$ & \bf AUC & $\mathbf{F_1}$ & \bf AUC \\ \hline
Participant & \textbf{0.738} & \textbf{0.738} & \textbf{0.827} & \textbf{0.841} & \textbf{0.710} & \textbf{0.716}\\\
Interviewer & 0.581 & 0.583 & 0.102* & 0.100* & 0.552* & 0.556*\\
Both & 0.477 & 0.488 & 0.512* & 0.515* & 0.683 & 0.686\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Bo Wang
Yue Wu
Nemanja Vaci
Maria Liakata
Terry Lyons
Kate E A Saunders

"Modelling Paralinguistic Properties in Conversational Speech to Detect Bipolar Disorder and Borderline Personality Disorder" by Bo Wang, Yue Wu, Nemanja Vaci, Maria Liakata, Terry Lyons, and Kate E A Saunders, arXiv:2102.09607v1, 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}{|l|l|}
\hline
Sector & Company (ticker)\\\hline
Metal and Mining & Mechel (MLTR), TMK-Group (TRMK) \\ \hline
Oil and Gas & Surgutneftegas (SNGS), Gaspromneft (SIBN) \\ \hline
Consumer sector & Magnit (MGNT), Lenta (LENT) \\\hline
Construction & PIK (PIKK), Samolet (SMLT)\\\hline
Telecommunications & MTS (MTSS), Rostelecom (RTKMP)\\\hline
Transport & AEROFLOT (AFLT), Sovcomflot (FLOT) \\\hline
Finance & Bank Saint-Petersburg (BSPB), SFI (SFIN) \\\hline
Chemical Industry & Phosagro (PHOR), Kazanorgsintez (KZOSP) \\\hline
Power Engineering & Rushydro (HYDR), Rosseti Center (MRKC) \\\hline
\end{tabular}
\end{adjustbox}
\caption{Economic sectors and companies (tickers) included into the dataset}
\end{table}
\end{document}

q-fin

"Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2503.08696v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study of $\beta$ in Softplus with respect to integral calibration error $\overline{\text{ECE}}$ and FID. The classifier is the official Pytorch ResNet checkpoint. The diffusion model is from . We generate 10,000 ImageNet 128x128 samples with 250 DDPM steps for evaluation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
 & ReLU & SoftPlus ($\beta$=8) & SoftPlus ($\beta$=5) & SoftPlus ($\beta$=4) & SoftPlus ($\beta$=3) \\
\midrule
$\overline{\text{ECE}}$ & 0.34 & 0.31 & 0.26 & 0.21 & 0.07 \\
FID & 7.17 & 6.99 & 6.89 & 6.73 & 6.61 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jiajun Ma
Tianyang Hu
Wenjia Wang
Jiacheng Sun

"Elucidating The Design Space of Classifier-Guided Diffusion Generation" by Jiajun Ma, Tianyang Hu, Wenjia Wang, and Jiacheng Sun, arXiv:2310.11311v1, 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{Comparing different multi-step loss configuration on real-world (50).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\hline
 & 1-step & 2-step & 3x1-step & 3-step & ours \\
\hline
PSNR & 24.52 & 24.71 & 23.70 & \underline{24.85} & \textbf{25.18} \\
SSIM & 0.904 & 0.904 & 0.901 & \underline{0.906} & \textbf{0.909} \\
LPIPS & 0.133 & 0.123 & 0.132 & \underline{0.120} & \textbf{0.118} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Utilizing Multi-step Loss for Single Image Reflection Removal" by Abdelrahman Elnenaey and Marwan Torki, arXiv:2412.08582v2, licensed under CC0 1.0. 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{Statistics number of people using antidepressants pill in Taiwan.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|c|c|c|c|}
 \hline
 \textbf{Country} & \textbf{2018} & \textbf{2017} & \textbf{2016} & \textbf{2015} & \textbf{2014} \\
 \hline
 Taiwan & 1,397,197 & 1,330,204 & 1,273,561 & 1,121,659 & 1,194,395 \\
 \hline
 Under 30 & 162,402 & 143,024 & 127,508 & 118,186 & 115,740 \\
 \hline
 \textbf{Ratio} & \textbf{11.62\%} & \textbf{10.57\%} & \textbf{10.01\%} & \textbf{9.75\%} & \textbf{9.69\%} \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Quang-Anh N. D.
Manh-Hung Ha
Thai Kim Dinh
Minh-Duc Pham
Ninh Nguyen Van

"Emotional Vietnamese Speech-Based Depression Diagnosis Using Dynamic Attention Mechanism" by Quang-Anh N. D., Manh-Hung Ha, Thai Kim Dinh, Minh-Duc Pham, and Ninh Nguyen Van, arXiv:2412.08683v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Projected Langevin Monte Carlo algorithms in non-convex and super-linear setting" by Chenxu Pang, Xiaojie Wang, and Yue Wu, arXiv:2312.17077v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Workplace Breastfeeding Legislation and Female Labor Force Participation in the United States" by Julia Hatamyar, arXiv:2209.05916v2, 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}{ll}
 $\Dot{}$ & time derivative \\
 $*$ & indicates optimality \\
 $\hat{}$ & denotes model prediction
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Daniel Mayfrank
Mehmet Velioglu
Alexander Mitsos
Manuel Dahmen

"Sample-Efficient Reinforcement Learning of Koopman eNMPC" by Daniel Mayfrank, Mehmet Velioglu, Alexander Mitsos, and Manuel Dahmen, arXiv:2503.18787v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The correlation coefficients and the correlation test results between the complexity and the disagreement metrics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{crlrl}\hline
 \multirow{2}{*}{Complexity metric} &\multicolumn{2}{c}{obscurity} &\multicolumn{2}{c}{discrepancy}\\\cline{2-5}
 & $r$ & $p$-value & $r$ & $p$-value \\\hline
 T2 & -0.019 & 0.9332 & -0.070 & 0.7614\\
 T3 & -0.109 & 0.6377 & -0.172 & 0.4546\\
 T4 & -0.215 & 0.3485 & -0.292 & 0.1986\\\hline
 \cellcolor{yellow}L1 & \cellcolor{yellow}0.767 & \cellcolor{yellow}0.0001*** & \cellcolor{yellow}0.560 & \cellcolor{yellow}0.0082**\\
 \cellcolor{yellow}L2 & \cellcolor{yellow}0.754 & \cellcolor{yellow}0.0001*** & \cellcolor{yellow}0.577 & \cellcolor{yellow}0.0061**\\
 \cellcolor{yellow}L3 & \cellcolor{yellow}0.740 & \cellcolor{yellow}0.0001*** & \cellcolor{yellow}0.568 & \cellcolor{yellow}0.0071**\\\hline
 F1 & -0.420 & 0.0587 & -0.334 & 0.1383\\
 F1v & -0.081 & 0.7241 & 0.195 & 0.3957\\
 F2 & 0.094 & 0.6823 & 0.039 & 0.8644\\
 F3 & 0.136 & 0.5542 & 0.013 & 0.9532\\
 F4 & 0.302 & 0.1820 & 0.220 & 0.3360\\\hline
 N1 & 0.619 & 0.0056** & 0.447 & 0.0546\\
 N2 & -0.019 & 0.9397 & 0.143 & 0.5591\\
 N3 & 0.542 & 0.0180* & 0.407 & 0.0835\\
 \cellcolor{yellow}N4 & \cellcolor{yellow}0.512 & \cellcolor{yellow}0.0266* & \cellcolor{yellow}0.564 & \cellcolor{yellow}0.0118*\\
 T1 & -0.147 & 0.5458 & -0.227 & 0.3494\\
 LSC & 0.438 & 0.0618 & 0.369 & 0.1195\\\hline
 \multicolumn{5}{r}{* $p$-value $<0.05$, ** $p$-value $<0.01$, *** $p$-value $<0.001$}
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective" by Mustafa Cavus and Przemyslaw Biecek, arXiv:2412.09712v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
 \toprule
 & \multicolumn{2}{c}{Spearman's $\rho$} & \multicolumn{2}{c}{Kendall's $\tau$}\\
 LLM Judge & MMAU & MMAR & MMAU & MMAR \\
 \midrule
 Qwen 2.5 7B & \textbf{0.733} & \textbf{0.746} & \textbf{0.629} & \textbf{0.652} \\
 Llama 3.1 8B & 0.692 & 0.664 & 0.585 & 0.574 \\
 Prometheus 2 7B & 0.542 & 0.606 & 0.448 & 0.514 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Correlation between human and LLM-as-a-Judge for evaluating open-ended responses for answer correctness.}
\end{table}
\end{document}

eess

Sonal Kumar
Šimon Sedláček
Vaibhavi Lokegaonkar
Fernando López
Wenyi Yu
Nishit Anand
Hyeonggon Ryu
Lichang Chen
Maxim Plička
Miroslav Hlaváček
William Fineas Ellingwood
Sathvik Udupa
Siyuan Hou
Allison Ferner
Sara Barahona
Cecilia Bolaños
Satish Rahi
Laura Herrera-Alarcón
Satvik Dixit
Siddhi Patil
Soham Deshmukh
Lasha Koroshinadze
Yao Liu
Leibny Paola Garcia Perera
Eleni Zanou
Themos Stafylakis
Joon Son Chung
David Harwath
Chao Zhang
Dinesh Manocha
Alicia Lozano-Diez
Santosh Kesiraju
Sreyan Ghosh
Ramani Duraiswami

"MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence" by Sonal Kumar, Šimon Sedláček, Vaibhavi Lokegaonkar, Fernando López, Wenyi Yu, Nishit Anand, Hyeonggon Ryu, Lichang Chen, Maxim Plička, Miroslav Hlaváček, William Fineas Ellingwood, Sathvik Udupa, Siyuan Hou, Allison Ferner, Sara Barahona, Cecilia Bolaños, Satish Rahi, Laura Herrera-Alarcón, Satvik Dixit, Siddhi Patil, Soham Deshmukh, Lasha Koroshinadze, Yao Liu, Leibny Paola Garcia Perera, Eleni Zanou, Themos Stafylakis, Joon Son Chung, David Harwath, Chao Zhang, Dinesh Manocha, Alicia Lozano-Diez, Santosh Kesiraju, Sreyan Ghosh, and Ramani Duraiswami, arXiv:2508.13992v1, 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{Overview of the Dataset per Market Capitalization}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrrrr}
 & \textbf{LargeCap} & \textbf{MidCap} & \textbf{SmallCap} & \textbf{None} \\ \hline
\textbf{\# of companies} & 537 & 818 & 1502 & 28 \\ \hline
\textbf{\# of articles per company} & & & & \\
\textit{\textbf{average}} & 2189 & 1445 & 822 & 309 \\
\textit{\textbf{min}} & 5 & 5 & 5 & 13 \\
\textit{\textbf{max}} & 2005 & 1789 & 1984 & 1122 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, 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}
\usepackage{booktabs}
\usepackage{soul}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
\toprule
\cline{4-5}
 & & & \multicolumn{2}{c}{\textbf{VMI (ours)}} \\ \cline{4-5}
 & General MI & Generative MI & \multicolumn{2}{c}{\textbf{DCGAN}} \\ \cline{4-5}
 & & & \textbf{Gaussian} & \textbf{Flow} \\\cline{4-5}
 \midrule
\textbf{Accuracy} & 0.00{\tiny$\pm$ 0.00 } & 0.92{\tiny$\pm$ 0.02 } & 0.93{\tiny$\pm$ 0.06 } & \textbf{0.95}{\tiny$\pm$ 0.02 } \\
\textbf{Precision} & 0.00{\tiny$\pm$ 0.00 } & \ul{0.25}{\tiny$\pm$ 0.14 } & \ul{0.26}{\tiny$\pm$ 0.13 } & \textbf{ 0.35}{\tiny$\pm$ 0.15 } \\
\textbf{Density} & 0.00{\tiny$\pm$ 0.00 } & \ul{ 0.09}{\tiny$\pm$ 0.07 } & \ul{ 0.11}{\tiny$\pm$ 0.06 } & {\bf 0.14}{\tiny$\pm$ 0.09 } \\
\midrule
\textbf{Recall} & 0.00{\tiny$\pm$ 0.00 } & 0.39{\tiny$\pm$ 0.12 } & {\bf 0.54}{\tiny$\pm$ 0.12 } & 0.25{\tiny$\pm$ 0.10 } \\
\textbf{Coverage} & 0.00{\tiny$\pm$ 0.00 } & \ul{ 0.20}{\tiny$\pm$ 0.12 } & \ul{ 0.17}{\tiny$\pm$ 0.08 } & {\bf 0.24}{\tiny$\pm$ 0.12 } \\
\textbf{Diversity} & 0.00{\tiny$\pm$ 0.00 } & \ul{ 0.29}{\tiny$\pm$ 0.17 } & {\bf 0.36}{\tiny$\pm$ 0.15 } & \ul{ 0.24}{\tiny$\pm$ 0.16 } \\
\midrule
\textbf{FID} & 376.7 & 88.91 & 82.52 & \textbf{77.73 } \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{MNIST: comparing baseline and our attacks.}
\end{table}
\end{document}

cs

Variational Model Inversion Attacks

Kuan-Chieh Wang
Yan Fu
Ke Li
Ashish Khisti
Richard Zemel
Alireza Makhzani

"Variational Model Inversion Attacks" by Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, and Alireza Makhzani, arXiv:2201.10787v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Online learning techniques for prediction of temporal tabular datasets with regime changes" by Thomas Wong and Mauricio Barahona, arXiv:2301.00790v4, 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{{Results for different values of $\epsilon$. Reference value for $Y_0$ computed with the FFT (with $\epsilon=0$) is given by $0.1375$.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
$\epsilon$ &	$\widetilde{Y}_0^\Theta\ (Y_0=1375)$	& Relative error & Time (s) \\
\midrule\midrule
0.005	&	0.295	&	115\%	&	1698		\\
0.001	&	0.149	&	8\%	&	1778			\\
0.0005	&	0.155	&	13\%	&	1932			\\
0.0001	&	0.134	&	2.4\%	&	2329			\\
0.00005	&	0.138	&	0.7\%	&	3526			\\
0.00001	 &	0.137	&	0.4\%	&	9332			\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

A deep solver for BSDEs with jumps

Kristoffer Andersson
Alessandro Gnoatto
Marco Patacca
Athena Picarelli

"A deep solver for BSDEs with jumps" by Kristoffer Andersson, Alessandro Gnoatto, Marco Patacca, and Athena Picarelli, arXiv:2211.04349v3, 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{Summary Statistics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
 \toprule
 \textbf{Variable} & \textbf{N} & \textbf{Mean} & \textbf{Std. Dev.} & \textbf{Min} & \textbf{P25} & \textbf{P50} & \textbf{P75} & \textbf{Max} \\
 \midrule
 \multicolumn{9}{l}{\textit{Panel A: Survey skills sample}} \\
 AI Teaching Impact & 90 & 0.05 & 1.13 & -2.00 & -1.00 & 0.00 & 1.00 & 2.00 \\
 AI Workplace Skill Disruption Index & 90 & -0.42 & 2.34 & -4.54 & -2.55 & -0.06 & 1.51 & 3.36 \\
 Open Skill & 88 & 0.53 & 0.50 & 0.00 & 0.00 & 1.00 & 1.00 & 1.00 \\
 \midrule
 \multicolumn{9}{l}{\textit{Panel B: Full sample with imputed data}} \\
 AI Teaching Impact (imputed) & 194 & 0.12 & 0.83 & -2.00 & -0.44 & 0.21 & 0.60 & 2.00 \\
 AI Workplace Skill Disruption Index & 194 & 0.00 & 2.26 & -4.54 & -1.77 & 0.43 & 1.71 & 4.45 \\
 Open Skill & 147 & 0.60 & 0.49 & 0.00 & 0.00 & 1.00 & 1.00 & 1.00 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Training for Obsolescence? The AI-Driven Education Trap" by Andrew J. Peterson, arXiv:2508.19625v1, 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{Corpus specification}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
 Term & Value \\ \hline
 \# of speakers & 39 \\
 \# of books & 24 \\
 \# of audiobooks & 74 \\
 \# of book-authors & 7 \\
 Duration [hour] & 31.5 \\
 Sampling rate [Hz] & 44.1
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Shinnosuke Takamichi
Wataru Nakata
Naoko Tanji
Hiroshi Saruwatari

"J-MAC: Japanese multi-speaker audiobook corpus for speech synthesis" by Shinnosuke Takamichi, Wataru Nakata, Naoko Tanji, and Hiroshi Saruwatari, arXiv:2201.10896v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"The Lax-Friedrichs method in one-dimensional hemodynamics" by Anika Beckers and Niklas Kolbe, arXiv:2501.16115v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
\textbf{RN-AdS$_4$ black brane} & \textbf{Plane-Poiseulle flow}\\
\hline
radial direction, $z$ & direction perpendicular to plates, $y$\\
$U(1)$ symmetry & translations in $x$\\
chemical potential deformation, $\mu$ & applied pressure gradient, $\partial_x P$\\
radial electric field, $E_z = 2 \mu$ & mean flow in $x$-direction, $U = 1-y^2$\\
non-normal Hamiltonian, $\mathcal{H}$ & non-normal Hamiltonian, $O_\text{OS}$ \\
charged scalar $\psi$ QNMs (fig. ) & no-slip stream function perturbations $\Phi$ (fig. )\\
superradiance (spontaneously broken $U(1)$) & turbulence (spontaneously broken $\partial_x$)\\
$\psi$ charge, $q$ & $\Phi$ wave number, $\alpha$\\
net energy loss through $\mathcal{H}^+$ & net energy loss via kinematic viscosity $\nu$
\end{tabular}
\end{adjustbox}
\caption{ Summary of the coarse analogy between transient superradiance phenomena discussed in this work, and transient effects in perturbations to plane-Poiseulle flow.}
\end{table}
\end{document}

math

Javier Carballo
Christiana Pantelidou
Benjamin Withers

"Non-modal effects in black hole perturbation theory: Transient Superradiance" by Javier Carballo, Christiana Pantelidou, and Benjamin Withers, arXiv:2503.05871v2, 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{Algorithm results for a basket call option example of dimension $100$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrrr}
\toprule
Step	&	Loss		& $Y_0$		& Time$^*$ \\
\midrule\midrule
0	&	274.500	&	-0.068	&	57	\\
100	&	40.219	&	1.590	&	242	\\
1000	&	31.378	&	12.241	&	444	\\
2000	&	23.488	&	12.820	&	648	\\
3000	&	35.340	&	11.852	&	854	\\
4000	&	25.627	&	12.367	&	1065	\\
5000	&	21.274	&	12.493	&	1270	\\
8000	&	29.688	&	13.299	&	1900	\\
10000	&	20.917	&	13.028	&	2319	\\
15000	&	18.102	&	13.510	&	3372	\\
20000	&	17.951	&	13.479	&	4438	\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

A deep solver for BSDEs with jumps


\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}{cccc}
 & $\sigma$ $(s^{-1})\times 10^{6}$ & $\theta$ $(^\circ)$ & $\zeta (s^{-1})\times 10^{6}$ \\ \cline{1-4}
 Strain-only Simulation \\ \cline{1-4}
Simulated & $1.17$ & $6.68$ & N/A \\
Bootstrap & $1.32 \pm 0.365$ & $6.29 \pm 2.47$ & N/A \\ \cline{1-4}
Strain-dominated Simulation \\ \cline{1-4}
Simulated & $1.22$ & $6.78$ & $1.61$ \\
Bootstrap & $1.55 \pm 0.459$ & $8.08 \pm 3.43$ & $1.94 \pm 0.572$
\end{tabular}
\end{adjustbox}
\caption{Observed standard errors from simulation, and average bootstrap standard error estimates from equation~ (where $B=100$), over 100 repeated simulations, for both the strain-only and strain-dominated simulations of Figure~ over 1 day. We also provide the standard deviation of bootstrap standard error estimates across the 100 simulations, as indicated after the $\pm$ symbol.}
\end{table}
\end{document}

stat

Sarah Oscroft
Adam M. Sykulski
Jeffrey J. Early

"Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories" by Sarah Oscroft, Adam M. Sykulski, and Jeffrey J. Early, arXiv:2012.00789v2, 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{Summary of the original datasets used in . All attributes are numeric except for InternetAds which has 1555 binary attributes.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllllll}
\toprule
Dataset & $n$ & Outliers & Attributes & Dataset & $n$ & Outliers & Attributes \\ \midrule
Annthyroid & 7200 & 534 & 21 & InternetAds & 3264 & 454 & 1555* \\
Arrhythmia & 450 & 206 & 259 & PageBlocks & 5473 & 560 & 10 \\
Cardiotocography & 2126 & 471 & 21 & Parkinson & 195 & 147 & 22 \\
HeartDisease & 270 & 120 & 13 & Pima & 768 & 268 & 8 \\
Hepatitis & 80 & 13 & 19 & SpamBase & 4601 & 1813 & 57 \\
Stamps & 340 & 31 & 9 & Wilt & 4839 & 261 & 5 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Sheikh Arafat
Na Sun
Maria L. Weese
Waldyn G. Martinez

"Boundary Peeling: Outlier Detection Method Using One-Class Peeling" by Sheikh Arafat, Na Sun, Maria L. Weese, and Waldyn G. Martinez, arXiv:2309.05630v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Decision Confidence for Asia network}
\begin{tabular}{llllll}
 & States & Minimum & Maximum & Mean & Standard Deviation \\ \hline
 \multirow{2}{*}{No evidence} & False & 100.00\% & 100.00\% & 100.00\% & 0.00\% \\
 & True & 0.00\% & 0.00\% & 0.00\% & 0.00\% \\ \hline
 \multirow{2}{*}{Evidence} & False & 0.00\% & 100.00\% & 16.12\% & 36.77\% \\
 & True & 0.00\% & 100.00\% & 83.88\% & 36.77\%
 \end{tabular}
\end{table}
\end{document}

cs

"A Taxonomy of Explainable Bayesian Networks" by Iena Petronella Derks and Alta de Waal, arXiv:2101.11844v1, 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 clustering performance of k-means on original continuous data, binary SBM, multiplex SBM and multi-DirSBM, evaluated on synthetic data with varying settings: number of nodes $n$, numbers of clusters $K$, numbers of layers $S$, network densities (high or low) and cluster overlap (yes and no). The results are presented as mean adjusted Rand index values, with standard deviation in parentheses, computed across 20 synthetic data sets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc||cccc}
 \multicolumn{5}{c||}{Scenario settings} & \multicolumn{3}{c}{Model} \\
 \hline \hline
 $n$ & $K$ & $S$ & Density & Overlap & k-means & SBM & Multiplex SBM & multi-DirSBM \\
 \hline
 50 & 2 & 2 & High & No & 0.949 (0.227) & 1 (0) & 1 (0) & 1 (0) \\
 50 & 2 & 4 & Low & Yes & 0.937 (0.226) & 0.384 (0.358) & 0.905 (0.295) & 1 (0) \\
 50 & 3 & 2 & High & Yes & 0.629 (0.201) & 0.627 (0.217) & 0.979 (0.0408) & 1 (0) \\
 50 & 3 & 4 & Low & No & 0.671 (0.207) & 0.0739 (0.130) & 0.922 (0.191) & 1 (0) \\
 100 & 3 & 2 & High & No & 0.785 (0.248) & 1 (0) & 1 (0) & 1 (0) \\
 100 & 3 & 4 & High & Yes & 0.789 (0.265) & 1 (0) & 1 (0) & 1 (0) \\
 100 & 5 & 2 & High & No & 0.755 (0.151) & 1 (0) & 1 (0) & 0.993 (0.0323) \\
 100 & 5 & 4 & Low & Yes & 0.696 (0.198) & 0.0510 (0.0745) & 0.977 (0.0697) & 0.987 (0.0562) \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Iuliia Promskaia
Adrian O'Hagan
Michael Fop

"Multiplex Dirichlet stochastic block model for clustering multidimensional compositional networks" by Iuliia Promskaia, Adrian O'Hagan, and Michael Fop, arXiv:2412.11971v1, 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{Sample Sizes for the Counterfactual Study - By Category}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\toprule
 & Dry Dog Food & Dog Hygiene & Cat Hygiene & Total \\
\midrule
Adopters & 765 & 838 & 450 & 2425\\
Non-Adopters & 2879 & 1400 & 1165 & 6314 \\ \midrule
Total & 3644 & 2238 & 1615 & 8739 \\ \midrule
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"How does online shopping affect offline price sensitivity?" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, 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{Performance of sentences ranked by confidence for the different NER classifiers. Total sentences in training and development data: 1331, out of which 207 (15.6\%) contain discrepancies with adjudicated ground truth.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|llllll|}
 \hline
 \textbf{NER} & \textbf{Threshold} & \textbf{Sentences} & \textbf{Precision}& \textbf{Recall}&\textbf{F-score}\\
&&\textbf{to check}&&&\\
\hline
Baseline&All&1331 (100\%)&15.6&\textbf{100.0}&26.9\\
\hline
 &Top-100&7.5\%&\textbf{34.0}&16.4&22.1\\
 Bilstm-crf&Top-200&15.0\%&32.0&30.9&31.4\\
 &Top-500&37.6\%&28.2&68.1&\textbf{39.9}\\
\hline
&Top-100&7.5\%& 32.0 & 15.5 & 20.8\\
BioBERT&Top-200&15\%& 27.5 & 26.6 & 27.0\\
&Top-500&37.6\%& 22.4 & 54.1 & 31.7\\
\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

David Martinez Iraola
Antonio Jimeno Yepes

"Single versus Multiple Annotation for Named Entity Recognition of Mutations" by David Martinez Iraola and Antonio Jimeno Yepes, arXiv:2101.07450v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Off-Balance Sheet Activities and Scope Economies in U.S. Banking" by Jingfang Zhang and Emir Malikov, arXiv:2302.14603v1, 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}{ccccccc}
 Regimen / c & 0 & 0.5 & 1 & 1.5 & 2 \\
 \hline
 T1 (n = 33) vs C1 (n = 8) & 40 & 40 & 40 & 40 & 31 \\
 T2 (n = 32) vs C2 (n = 8) & 27 & 17 & 3 & 1 & 0 \\
 T3 (n = 24) vs C3 (n = 8) & 15 & 4 & 3 & 0 & 0 \\
 T4 (n = 23) vs C4 (n = 8) & 29 & 29 & 24 & 23 & 17 \\
\end{tabular}
\end{adjustbox}
\caption{Pointwise $95\%$ lower confidence limits of $N(c)$ for four vaccine regimens, with $c=0, 0.5, 1, 1.5$ and $2$.}
\end{table}
\end{document}

stat

"The role of randomization inference in unraveling individual treatment effects in early phase vaccine trials" by Zhe Chen, Xinran Li, and Bo Zhang, arXiv:2310.14399v2, 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}{lcc}
		\textbf{Month} & \textbf{Dead people} & \textbf{Population of Florence} \\ \hline
		May	& 600	& 59400 \\
		June &	700 &	58700 \\
		July &	2700 &	56000 \\
		August	& 5000	& 51000 \\
		September &	2000	& 49000 \\
		October	& 600	& 48400 \\
		November	& 200	& 48200 \\
		December	& 100 &	48100 \\ \hline
	\end{tabular}
\end{adjustbox}
\caption{Temporal distribution of plague victims and population in Florence during the year 1417 }
\end{table}
\end{document}

stat

Giorgio Kaniadakis
Mauro M. Baldi
Thomas S. Deisboeck
Giulia Grisolia
Dionissios T. Hristopulos
Antonio M. Scarfone
Amelia Sparavigna
Tatsuaki Wada
Umberto Lucia

"The k-statistics approach to epidemiology" by Giorgio Kaniadakis, Mauro M. Baldi, Thomas S. Deisboeck, Giulia Grisolia, Dionissios T. Hristopulos, Antonio M. Scarfone, Amelia Sparavigna, Tatsuaki Wada, and Umberto Lucia, arXiv:2012.00629v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\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|c|c|}
\hline
\multirow{2}{*}{\textbf{Time Slice}} & \multicolumn{3}{c|}{\textbf{STM Method}} & \multicolumn{3}{c|}{\textbf{LDA Method}} & \multicolumn{3}{c|}{\textbf{Tensor LDA Method}} \\ \cline{2-10}
 & \textbf{[,1]} & \textbf{[,2]} & \textbf{[,3]} & \textbf{[,1]} & \textbf{[,2]} & \textbf{[,3]} & \textbf{[,1]} & \textbf{[,2]} & \textbf{[,3]} \\ \hline
\multicolumn{10}{|c|}{\textbf{Slice 1 (Statistics)}} \\ \hline
Theoretical Group & 0.36 & 0.34 & 0.27 & 0.00 & 0.26 & 0.25 & 0.00 & 0.00 & 0.00 \\ \hline
Application Group & 0.21 & 0.18 & 0.19 & 0.25 & 0.25 & 0.25 & 0.00 & 0.00 & 0.00 \\ \hline
\textbf{Total} & \textbf{0.57} & \textbf{0.52} & \textbf{0.46} & \textbf{0.25} & \textbf{0.51} & \textbf{0.50} & \textbf{0.00} & \textbf{0.00} & \textbf{0.00} \\ \hline
\multicolumn{10}{|c|}{\textbf{Slice 2 (Math)}} \\ \hline
Theoretical Group & 0.52 & 0.54 & 0.52 & 0.00 & 0.23 & 0.25 & 0.00 & 0.00 & 0.00 \\ \hline
Application Group & 0.00 & 0.00 & 0.00 & 0.26 & 0.25 & 0.25 & 0.00 & 0.00 & 0.00 \\ \hline
\textbf{Total} & \textbf{0.52} & \textbf{0.54} & \textbf{0.52} & \textbf{0.26} & \textbf{0.49} & \textbf{0.50} & \textbf{0.00} & \textbf{0.00} & \textbf{0.00} \\ \hline
\multicolumn{10}{|c|}{\textbf{Slice 3 (Computer Science)}} \\ \hline
Theoretical Group & 0.00 & 0.00 & 0.00 & 0.00 & 0.25 & 0.26 & 0.00 & 0.00 & 0.00 \\ \hline
Application Group & 0.45 & 0.50 & 0.49 & 0.25 & 0.25 & 0.25 & 1.00 & 1.00 & 1.00 \\ \hline
\textbf{Total} & \textbf{0.45} & \textbf{0.50} & \textbf{0.49} & \textbf{0.25} & \textbf{0.50} & \textbf{0.51} & \textbf{1.00} & \textbf{1.00} & \textbf{1.00} \\ \hline
\multicolumn{10}{|c|}{\textbf{Slice 4 (Physics)}} \\ \hline
Theoretical Group & 0.12 & 0.13 & 0.21 & 0.00 & 0.25 & 0.24 & 1.00 & 1.00 & 1.00 \\ \hline
Application Group & 0.35 & 0.32 & 0.33 & 0.25 & 0.25 & 0.25 & 0.00 & 0.00 & 0.00 \\ \hline
\textbf{Total} & \textbf{0.47} & \textbf{0.45} & \textbf{0.54} & \textbf{0.25} & \textbf{0.50} & \textbf{0.50} & \textbf{1.00} & \textbf{1.00} & \textbf{1.00} \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Comparison of Core-Tensor Data (Rounded to 2 Decimal Places) for STM, LDA, and Tensor LDA Methods}
\end{table}
\end{document}

stat

Tensor Topic Modeling Via HOSVD

"Tensor Topic Modeling Via HOSVD" by Yating Liu and Claire Donnat, arXiv:2501.00535v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Prakrati Kushwah
Anupama Ghorai
Jitraj Saha

"Convergence-rate and error analysis of sectional-volume average method for the collisional breakage equation with multi-dimensional modelling" by Prakrati Kushwah, Anupama Ghorai, and Jitraj Saha, arXiv:2504.15365v1, 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{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
$ M$ & 1 & 2 & 3 \\
 \hline
 $(r_1)_M$ by () & $-1.28324$ & $-1.33149$ & $-1.32556$ \\
 \hline
 $\eta=\left|\frac{(r_1)_{M}-(r_1)_{ex}}{(r_1)_{ex}}\right|$ & 0.0291 & 0.0073 & 0.0028
\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Sergii Skurativskyi
Sergiy Mykulyak
Inna Skurativska

"Approximation of Invariant Solutions to the Nonlinear Filtration Equation by Modified Pade Approximants" by Sergii Skurativskyi, Sergiy Mykulyak, and Inna Skurativska, arXiv:2504.16001v1, 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|}
 \hline
 \textbf{Index} & \textbf{Test 1 Eigenvalues} & \textbf{Test 2 Eigenvalues} \\ \hline
 $\lambda_1$ & \(0.999999135\) & \(1.00000083\) \\ \hline
 $\lambda_2$ & \(0.993172638\) & \(0.99359029\) \\ \hline
 $\lambda_3$ & \(0.892089359\) & \(0.86772991\) \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Triple-well system: First three eigenvalues of the Koopman generator estimated using SDMD.}
\end{table}
\end{document}

math

Yuanchao Xu
Kaidi Shao
Isao Ishikawa
Yuka Hashimoto
Nikos Logothetis
Zhongwei Shen

"A Data-Driven Framework for Koopman Semigroup Estimation in Stochastic Dynamical Systems" by Yuanchao Xu, Kaidi Shao, Isao Ishikawa, Yuka Hashimoto, Nikos Logothetis, and Zhongwei Shen, arXiv:2501.13301v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"An Exact SIR Series Solution and an Exploration of the Related Parameter Space" by Daniel P Hobbs, arXiv:2504.17026v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
\hline
 Years & Absolute price change in per cent \\\hline
 2021--2022 & 432.41\\\hline
 2022--2023 & 45.20\\\hline
 2023--2024 & 1.06\\\hline
 2024-2025 & 90.00\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Coins median annual percentage price change.}
\end{table}
\end{document}

q-fin

Kasymkhan Khubiev
Mikhail Semenov
Irina Podlipnova

"Finance-Grounded Optimization For Algorithmic Trading" by Kasymkhan Khubiev, Mikhail Semenov, and Irina Podlipnova, arXiv:2509.04541v1, 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{DCI Formats in NR for PDSCH scheduling}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c||}
		\hline
		Format & Usage \\
		\hline\hline
		Format 1$$0 & used for the scheduling of PDSCH in one DL cell \\
		Format 1$$1 & used for the scheduling of PDSCH in one cell\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Hongzhi Chen
De Mi
Manuel Fuentes
Eduardo Garro
Jose Luis Carcel
Belkacem Mouhouche
Pei Xiao
Rahim Tafazolli

"On the Performance of PDCCH in LTE and 5G New Radio" by Hongzhi Chen, De Mi, Manuel Fuentes, Eduardo Garro, Jose Luis Carcel, Belkacem Mouhouche, Pei Xiao, and Rahim Tafazolli, arXiv:2001.02757v1, licensed under CC0 1.0. 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}{|l|c|}
 \hline
 \bfseries Static Metric & \bfseries Correlation Coefficient \\
 \hline
 Number of Bytecode Instructions (NBI) &	-0.61652431 \\
 \hline
 Response for a Class (RFC)&	-0.603928734 \\ \hline
 Lines of Code (LOC)&	-0.58334397 \\ \hline
 Weighted Methods per Class (WMC)&	-0.56027992 \\ \hline
 Number of Method Calls (NMC)&	-0.55492337 \\ \hline
 Number of Method Calls External (NMCE)&	-0.512557727 \\ \hline
 Lines of Comment (LOCCOM)&	-0.50032704 \\ \hline
 Cohesion Among Methods in class (CAM)&	0.521969871 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{\fontsize{10pt}{11pt}\selectfont{\itshape{The Spearman correlation of static metrics with the mutation score}}}
\end{table}
\end{document}

cs

"Investigating Software Testability and Test cases Effectiveness" by Mamdouh Alenezi, arXiv:2201.10090v1, 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{Estimation performance by benchmark algorithms under the default scenario}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 & \multicolumn{3}{c}{\textbf{Algorithms}} \\
\hline
\textbf{Metrics} & \textbf{Binary} & \textbf{TCS} & \textbf{SNN} \\
\hline
\textbf{AUROC} & \textbf{0.96 (0.816,1.106)} & 0.82 (0.729,0.914) & 0.85 (0.725,0.983) \\
\textbf{Concordance} & 0.76 (0.730,0.799) & \textbf{0.90 (0.852,0.947)} & 0.86 (0.798,0.913) \\
\hline
\textbf{Bias (IPW)} & 0.65 (0.625,0.675) & \textbf{-} & 0.15 (0.133,0.167) \\
\textbf{Bias (TMLE)} & 0.63 (0.576,0.684) & \textbf{-} & 0.14 (0.129,0.151) \\
\textbf{Bias (ATE)} & - & \textbf{0.10 (0.061,0.136)} & - \\
\textbf{Bias (ITE)} & 0.75 (0.706,0.794) & \textbf{0.10 (0.064,0.137)} & 0.43 (0.401,0.459)\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Causal inference for observational longitudinal studies using deep survival models" by Jie Zhu and Blanca Gallego, arXiv:2101.10643v12, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
Model & Metric & $t=0$ & $t=1$ & $t=2$ & $t=3$ \\
\hline
\midrule
\multirow{2}{*}{FlowKac} & $D_{\text{KL}}$ & 1.60e-2 & \textbf{8.90e-3} & \textbf{5.20e-3} & \textbf{3.19e-2} \\
& $L^2$ & 6.07e-2 & \textbf{4.70e-2} & \textbf{4.74e-2} & \textbf{1.37e-1} \\
\midrule
\multirow{2}{*}{PINN} & $D_{\text{KL}}$ & \textbf{1.00e-4} & 4.94e-2 & 2.94e-1 & 1.68 \\
& $L^2$ & \textbf{5.70e-3} & 2.10e-1 & 5.18e-1 & 1.11 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Performance comparison of FlowKac and PINN models on the 2d Ornstein-Uhlenbeck process using KL divergence ($D_{\text{KL}}$) and $L^2$ error metrics.}
\end{table}
\end{document}

math

Naoufal El Bekri
Lucas Drumetz
Franck Vermet

"FlowKac: An Efficient Neural Fokker-Planck solver using Temporal Normalizing Flows and the Feynman-Kac Formula" by Naoufal El Bekri, Lucas Drumetz, and Franck Vermet, arXiv:2503.11427v2, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
 \toprule[1pt]
 Axial Attention& + SE & + Our Channelization \\
 \midrule[0.5pt]
		\midrule[0.5pt]
 50.27($\pm$0.2) & 50.37($\pm$0.2)& 51.06($\pm$0.2) \\
 \bottomrule[1pt]
 \end{tabular}
\end{adjustbox}
\caption{ Result comparison between axial attention, axial attention + SE and channelized axial attention. }
\end{table}
\end{document}

cs

Ye Huang
Di Kang
Wenjing Jia
Xiangjian He
Liu Liu

"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccccclccccccccc}
\cmidrule{1-10} \cmidrule{12-21}
\multicolumn{10}{c}{Stage 1 Regression} & & \multicolumn{10}{c}{Stage 2 Regression} \\ \cmidrule{1-10} \cmidrule{12-21}
 & \multicolumn{3}{c}{Supervised} & & \multicolumn{3}{c}{Semi-Supervised} & & & & \multicolumn{4}{c}{Supervised} & & \multicolumn{3}{c}{Semi-Supervised} & & \\ \cmidrule{2-4} \cmidrule{6-8} \cmidrule{12-15} \cmidrule{17-19}
Parameter & Estimate & SE & P-val & & Estimate & SE & P-val & & RE & & Parameter & Estimate & SE & P-val & & Estimate & SE & P-val & & RE \\ \cmidrule{1-4} \cmidrule{6-8} \cmidrule{10-10} \cmidrule{12-15} \cmidrule{17-19} \cmidrule{21-21}
Intercept & \textbf{0.424} & \textbf{0.082} & \textbf{0.00} & & \textbf{0.518} & \textbf{0.028} & \textbf{0.00} & & 2.937 & & $Y_1$ & \textbf{0.37} & \textbf{0.11} & \textbf{0.00} & & \textbf{0.55} & \textbf{0.05} & \textbf{0.00} & & 2.08 \\
 Female & -0.237 & 0.167 & 0.16 & & \textbf{-0.184} & \textbf{0.067} & \textbf{0.007} & & 2.514 & & Intercept & 0.08 & 0.06 & 0.17 & & 0.04 & 0.02 & 0.14 & & 2.40 \\
 Age & 0.155 & 0.088 & 0.081 & & \textbf{0.18} & \textbf{0.034} & \textbf{0.00} & & 2.588 & & Female & -0.01 & 0.10 & 0.92 & & -0.00 & 0.05 & 0.98 & & 2.21 \\
 Charlson Score & 0.006 & 0.072 & 0.929 & & -0.047 & 0.026 & 0.075 & & 2.776 & & Age & 0.05 & 0.06 & 0.35 & & \textbf{0.07} & \textbf{0.02} & \textbf{0.00} & & 2.33 \\
 Prior anti-TNF & -0.038 & 0.06 & 0.524 & &\textbf{ -0.085} & \textbf{0.019} & \textbf{0.00} & & 3.177 & & Charlson Score & 0.04 & 0.04 & 0.33 & & \textbf{0.06} & \textbf{0.02} & \textbf{0.01} & & 2.06 \\
 Perianal & \textbf{0.138} & \textbf{0.06} & \textbf{0.022} & & \textbf{0.179} & \textbf{0.022} & \textbf{0.00} & & 2.688 & & Prior anti-TNF & -0.05 & 0.05 & 0.29 & & \textbf{-0.09} & \textbf{0.02} & \textbf{0.00} & & 2.39 \\
 Bleeding & 0.049 & 0.08 & 0.54 & & 0.058 & 0.03 & 0.055 & & 2.675 & & Perianal & -0.01 & 0.04 & 0.80 & & \textbf{-0.03} & \textbf{0.02} & \textbf{0.06} & & 2.31 \\
 A1 & 0.163 & 0.488 & 0.739 & & 0.148 & 0.206 & 0.473 & & 2.374 & & Bleeding & -0.04 & 0.05 & 0.49 & & -0.03 & 0.03 & 0.29 & & 2.14 \\
 Female$\times A_1$ & 0.168 & 0.696 & 0.81 & & -0.042 & 0.287 & 0.886 & & 2.424 & & A1 & 0.11 & 0.25 & 0.67 & & 0.03 & 0.10 & 0.74 & & 2.60 \\
 Age$\times A_1$ & -0.177 & 0.264 & 0.503 & & \textbf{-0.278} & \textbf{0.109} & \textbf{0.013} & & 2.418 & & Abscess$_2$ & 0.06 & 0.04 & 0.16 & & \textbf{0.05} & \textbf{0.01} & \textbf{0.00} & & 2.68 \\
 Charlson Score$\times A_1$ & 0.136 & 0.391 & 0.728 & & 0.195 & 0.178 & 0.276 & & 2.194 & & Fistula$_2$ & 0.02 & 0.05 & 0.67 & & 0.01 & 0.02 & 0.62 & & 2.33 \\
 Perianal$\times A_1$ & -0.113 & 0.226 & 0.618 & & -0.019 & 0.08 & 0.808 & & 2.838 & & Female$\times A_1$ & 0.13 & 0.38 & 0.74 & & 0.17 & 0.16 & 0.30 & & 2.37 \\
 Bleeding$\times A_1$ & 0.262 & 0.364 & 0.474 & & 0.127 & 0.161 & 0.431 & & 2.267 & & Age$\times A_1$ & -0.02 & 0.12 & 0.88 & & -0.09 & 0.06 & 0.17 & & 1.94 \\
 & & & & & & & & & & & Charlson Score$\times A_1$ & -0.02 & 0.16 & 0.89 & & 0.04 & 0.07 & 0.55 & & 2.19 \\
 & & & & & & & & & & & Perianal$\times A_1$ & -0.14 & 0.09 & 0.15 & & \textbf{-0.17} & \textbf{0.04} & \textbf{0.00} & & 2.34 \\
 & & & & & & & & & & & Bleeding$\times A_1$ & 0.13 & 0.20 & 0.51 & & 0.03 & 0.09 & 0.76 & & 2.17 \\
 & & & & & & & & & & & A2 & 0.07 & 0.17 & 0.69 & & \textbf{0.22} & \textbf{0.07} & \textbf{0.00} & & 2.55 \\
 & & & & & & & & & & & Female$\times A_2$ & -0.39 & 0.28 & 0.16 & & \textbf{-0.51} & \textbf{0.11} & \textbf{0.00} & & 2.53 \\
 & & & & & & & & & & & Age$\times A_2$ & 0.09 & 0.10 & 0.40 & & \textbf{0.15} & \textbf{0.04} & \textbf{0.00} & & 2.27 \\
 & & & & & & & & & & & Charlson Score$\times A_2$ & 0.01 & 0.07 & 0.84 & & -0.03 & 0.03 & 0.42 & & 2.08 \\
 & & & & & & & & & & & Perianal$\times A_2$ & \textbf{0.20} & \textbf{0.09} &\textbf{ 0.04} & & \textbf{0.23} & \textbf{0.04} &\textbf{ 0.00} & & 2.23 \\
 & & & & & & & & & & & Bleeding$\times A_2$ & 0.03 & 0.08 & 0.77 & & 0.02 & 0.04 & 0.49 & & 2.34 \\
 & & & & & & & & & & & Abscess$_2\times A_2$ & -0.13 & 0.07 & 0.06 & & \textbf{-0.09} & \textbf{0.03} & \textbf{0.00} & & 2.31 \\
 & & & & & & & & & & & Fistula$_2\times A_2$ & -0.04 & 0.06 & 0.56 & & -0.03 & 0.03 & 0.36 & & 2.17 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Results of Inflammatory Bowel Disease data set, for first and second stage regressions. Fully supervised $Q$-learning is shown on the left and semi-supervised is shown on the right. Last columns in the panels show relative efficiency (RE) defined as the ratio of standard errors of the semi-supervised vs. supervised method, RE greater than one favors semi-supervised. Significant coefficients at the 0.05 level are in bold.}
\end{table}
\end{document}

stat

Aaron Sonabend-W
Nilanjana Laha
Ashwin N. Ananthakrishnan
Tianxi Cai
Rajarshi Mukherjee

"Semi-Supervised Off Policy Reinforcement Learning" by Aaron Sonabend-W, Nilanjana Laha, Ashwin N. Ananthakrishnan, Tianxi Cai, and Rajarshi Mukherjee, arXiv:2012.04809v5, 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{STT Performance Test Results with VOTE400}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
\hline
Region(\texttt{G}) & Gender & Acc. \texttt{M} (\%) & Acc. \texttt{C} (\%) \\ \hline \hline
SE & M & 90 & 90 \\ \hline
SE & F & 90 & 80 \\ \hline
GW & M & 80 & 90 \\ \hline
GW & F & 90 & 80 \\ \hline
DG & M & 70 & 80 \\ \hline
DG & F & 90 & 80 \\ \hline
GN & M & 90 & 80 \\ \hline
GN & F & 80 & 80 \\ \hline
JN & M & 70 & 50 \\ \hline
JN & F & 80 & 60 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Minsu Jang
Sangwon Seo
Dohyung Kim
Jaeyeon Lee
Jaehong Kim
Jun-Hwan Ahn

"VOTE400(Voide Of The Elderly 400 Hours): A Speech Dataset to Study Voice Interface for Elderly-Care" by Minsu Jang, Sangwon Seo, Dohyung Kim, Jaeyeon Lee, Jaehong Kim, and Jun-Hwan Ahn, arXiv:2101.11469v1, 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}{rccc}
		\toprule
		& MOTA & IDF1 & HOTA \\
		\midrule
		FRCNN detector & 0.016 & 0.017 & 0.119 \\
		Tracktor++v2 tracker & 0.918 & 0.774 & 0.789 \\
		BoT\_SORT tracker & 1 & 1 & 0.921 \\
		GMPHDOGM17 tracker & 1 & 1 & 0.942 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jan Krejčí
Oliver Kost
Ondřej Straka
Yuxuan Xia
Lennart Svensson
Ángel F. García-Fernández

"TGOSPA Metric Parameters Selection and Evaluation for Visual Multi-object Tracking" by Jan Krejčí, Oliver Kost, Ondřej Straka, Yuxuan Xia, Lennart Svensson, and Ángel F. García-Fernández, arXiv:2412.08321v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Jon Asier Bárcena-Petisco
Luis Martínez
María Merino
Juan Manuel Montoya
Antonio Vera-López

"Fibonacci-like partitions and their associated piecewise-defined permutations" by Jon Asier Bárcena-Petisco, Luis Martínez, María Merino, Juan Manuel Montoya, and Antonio Vera-López, arXiv:2503.19696v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Reagent prices in € mol$^{-1}$ (for reactants and reagents) or € L$^{-1}$ (for solvents) used in this study, as obtained from \textit{Sigma-Aldrich} and \textit{ABCR} on July 15, 2024.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
 \toprule
 Reagent & Price \\ \midrule \midrule
 $\alpha$-Methylbenzylamine & 31 € mol$^{-1}$ \\
 Benzyl bromide & 58 € mol$^{-1}$ \\
 {[1,1$'$-Bis(di-tert-butylphosphino)ferrocen]}dichlorpalladium(II) (Pd(dtbpf)Cl$_2$) & 131,700 € mol$^{-1}$ \\
 Chloroform & 104 € L$^{-1}$ \\
 2,4-Difluoronitrobenzene & 164 € mol$^{-1}$ \\
 Ethanol & 55 € L$^{-1}$ \\
 Morpholine & 21 € mol$^{-1}$ \\
 Potassium phosphate & 20 € mol$^{-1}$ \\
 4-Trifluoromethylphenylboronic acid pinacol ester (CF$_3$-Ph-Bpin) & 940 € mol$^{-1}$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mohammad Haddadnia
Leonie Grashoff
Felix Strieth-Kalthoff

"BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives" by Mohammad Haddadnia, Leonie Grashoff, and Felix Strieth-Kalthoff, arXiv:2501.15554v1, 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}{ll} \hline
 Scaled variables & Unit\\ \hline
 $r_\pm=r^*_\pm/R_\pm$ & dimensionless \\
 $x=x^*/L$ & dimensionless\\
 $c_\pm=c^*_\pm/c_{\pm,\mathrm{max}}$ & dimensionless \\
 $c_\mathrm{e}=c_\mathrm{e}^*/c_{\mathrm{e},0}$ & dimensionless \\$N_\mathrm{e}=N_\mathrm{e}^*/(c_{\mathrm{e},0}L)$ & 1/s \\
 $j_\pm=j^*_\pm/(R_\pm c_{\pm,\mathrm{max}})$ & 1/s\\
 $i_{0,\pm}=i^*_{0,\pm}/(F R_\pm c_{\pm,\mathrm{max}})$ & 1/s\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Scaling of the variables of the full model. The variables of the full models are denoted with an asterisk in superscript and the new variables without. Other variables are kept the same as in the full dimensional model.}
\end{table}
\end{document}

eess

Noël Hallemans
Nicola E. Courtier
Colin P. Please
Brady Planden
Rishit Dhoot
Robert Timms
S. Jon chapman
David Howey
Stephen R. Duncan

"Physics-based battery model parametrisation from impedance data" by Noël Hallemans, Nicola E. Courtier, Colin P. Please, Brady Planden, Rishit Dhoot, Robert Timms, S. Jon chapman, David Howey, and Stephen R. Duncan, arXiv:2412.10896v4, 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|}\hline
 Setting & $a_j = a_k$ & $a_j \neq a_k$ \\ \hline
 $\nu_j = \nu_k = 1/2$ & Laplace & asymmetric Laplace \\ %\hline
 $\nu_j = \nu_k$ & Bessel function & Bessel function \\ %\hline
 $\nu_j \neq \nu_k$ & gamma difference & gamma difference \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Multivariate Matérn Models -- A Spectral Approach" by Drew Yarger, Stilian Stoev, and Tailen Hsing, arXiv:2309.02584v2, 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{Methods used for comparison. Eq. refers to the equation in the paper describing the model; Tier classifies the model with respect tiers; EF specifies the source of the emission factor.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
			\toprule
			& Method 1 & Method 2 & Method 3 & Method 4 & Method 5 & Method 6 \\ \hline
			Eq. & () & () & () & () & () & ()\\
			Tier & 1 & 2 & 1 & 1 & 2 & 3 \\
			EF & IPCC & ISPRA & IPCC & IPCC & ISPRA & IPSRA\\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Marina Bertolini
Pierdomenico Duttilo
Francesco Lisi

"Accounting carbon emissions from electricity generation: a review and comparison of emission factor-based methods" by Marina Bertolini, Pierdomenico Duttilo, and Francesco Lisi, arXiv:2411.13663v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Dice scores for WM, CSF, and GM segmentations in masked areas on the HCP dataset, evaluated using FSL FAST and FastSurfer.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccc}
\toprule
\textbf{Tools} & \textbf{Models} & \textbf{WM} & \textbf{CSF} & \textbf{GM} \\
\midrule
\multirow{5}{*}{\textbf{FAST}}
& Autoencoder 3D & $0.755\pm0.066$ & $0.444\pm0.079$ & $0.656\pm0.055$ \\
 & Pix2pix 3D & $0.787\pm0.053$ & $0.538\pm0.060$ & $0.689\pm0.053$ \\
 & DDPM 3D & $0.776\pm0.066$ &
 $0.475\pm0.070$ & $0.656\pm0.046$ \\
 & Make-A-Volume & $0.780\pm0.053$ & $0.537\pm0.066$ & $0.692\pm0.043$ \\
 & Ours & \textbf{0.827$\pm$0.047} & \textbf{0.627$\pm$0.059} & \textbf{0.742$\pm$0.038} \\
\midrule
\multirow{5}{*}{\textbf{FastSurfer}}
& Autoencoder 3D & ${0.742\pm0.069}$ & ${0.412\pm0.178}$ & ${0.531\pm0.096}$ \\
 & Pix2pix 3D & ${0.773\pm0.056}$ & ${0.617\pm0.169}$ & ${0.587\pm0.094}$ \\

 & DDPM 3D & ${0.769\pm0.064}$ & ${0.640\pm0.140}$ & ${0.653\pm0.066}$ \\
 & Make-A-Volume & ${0.773\pm0.061}$ & ${0.676\pm0.136}$ & ${0.672\pm0.055}$ \\
 & Ours & \textbf{0.829$\pm$0.051} & \textbf{0.780$\pm$0.103} & \textbf{0.737$\pm$0.055} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Dou Hoon Kwark
Shirui Luo
Xiyue Zhu
Yudu Li
Zhi-Pei Liang
Volodymyr Kindratenko

"Hierarchical Diffusion Framework for Pseudo-Healthy Brain MRI Inpainting with Enhanced 3D Consistency" by Dou Hoon Kwark, Shirui Luo, Xiyue Zhu, Yudu Li, Zhi-Pei Liang, and Volodymyr Kindratenko, arXiv:2507.17911v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Markus Brueckner
Tomoo Kikuchi
George Vachadze

"Transitional Dynamics of the Saving Rate and Economic Growth" by Markus Brueckner, Tomoo Kikuchi, and George Vachadze, arXiv:2012.15435v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Han Liu
Yubo Fan
Can Cui
Dingjie Su
Andrew McNeil
Benoit M. Dawant

"Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion" by Han Liu, Yubo Fan, Can Cui, Dingjie Su, Andrew McNeil, and Benoit M. Dawant, arXiv:2201.10647v1, 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{\small{Average PMSE across data sets for several simulation settings for BART and EB-coBART in the rigid tree setting ($\alpha=0.1,$ $\beta=4,$ $k=1$) and the flexible tree setting ($\alpha=0.95,$ $\beta=2,$ $k=2$). Also included are four competitors: ridge regression, random forest, and co-data learners CoRF and ecpc.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 & $N=100$ & $N=200$\tabularnewline
\hline
\hline
Flexible BART & 836.4 & 787.5\tabularnewline
Flexible EB-coBART & 744.5 & 688.6\tabularnewline
Rigid BART & 883.4 & 863.0\tabularnewline
Rigid EB-coBART & 847.8 & 822.2\tabularnewline
DART & 836 & 786\tabularnewline
Ridge & 733.5 & 550.2\tabularnewline
Ecpc & 493.4 & 241.3\tabularnewline
Random forest & 857.5 & 826.6\tabularnewline
CoRF & 792.4 & 749.9\tabularnewline
\end{tabular}
\end{adjustbox}
\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.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Estimation performance of the quantile coefficient function at $\tau = 0.5$ for \textbf{Scenario }, based on 500 simulation runs.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\hline\hline
&Method & $\beta_0$ & $\beta_1$ &$\beta_0$ & $\beta_1$\\
\hline
 && \multicolumn{2}{c}{$N_1 = 500$}&\multicolumn{2}{c}{$N_1 = 1000$}\\
\cline{3-6}
\multirow{3}{*}{Relative MSE}&HCQRF &1.9e-04&0.001&1.5e-04&6.1e-05\\
&CQRF&9.6e-05&0.018&7.1e-05&0.008\\
&HCQRF-c &1.7e-04&0.000&1.4e-04&4.9e-05\\
&grf-c &5.0e-05&0.028&3.4e-05&0.025\\
\cline{2-6}
\multirow{3}{*}{Relative MAE}&HCQRF &0.011&0.011&0.010&0.006\\
&CQRF&0.008&0.067&0.007&0.033\\
&HCQRF-c &0.011&0.007&0.009&0.006\\
&grf-c & 0.006&0.097&0.005&0.089\\
\hline\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Hybrid Censored Quantile Regression Forest to Assess the Heterogeneous Effects" by Huichen Zhu, Yifei Sun, and Ying Wei, arXiv:2212.05672v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Goal-oriented Transmission Scheduling: Structure-guided DRL with a Unified Dual On-policy and Off-policy Approach" by Jiazheng Chen and Wanchun Liu, arXiv:2501.11921v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Saizhuo Wang
Hao Kong
Jiadong Guo
Fengrui Hua
Yiyan Qi
Wanyun Zhou
Jiahao Zheng
Xinyu Wang
Lionel M. Ni
Jian Guo

"QuantBench: Benchmarking AI Methods for Quantitative Investment" by Saizhuo Wang, Hao Kong, Jiadong Guo, Fengrui Hua, Yiyan Qi, Wanyun Zhou, Jiahao Zheng, Xinyu Wang, Lionel M. Ni, and Jian Guo, arXiv:2504.18600v1, 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}

cs

Bart van Oort
Luís Cruz
Babak Loni
Arie van Deursen

""Project smells" -- Experiences in Analysing the Software Quality of ML Projects with mllint" by Bart van Oort, Luís Cruz, Babak Loni, and Arie van Deursen, arXiv:2201.08246v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc} \toprule
\multicolumn{3}{c}{$\rho^\Phi$}\\ \midrule
 \quad\quad\quad 0.1\quad\quad\quad &\quad\quad\quad 0.5\quad\quad\quad & \quad\quad\quad 0.9 \quad\quad\quad \\ \toprule
\quad\quad\quad 0.407 \quad\quad\quad & \quad\quad\quad 2.035 \quad\quad\quad & \quad\quad\quad 3.663 \quad\quad\quad\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Microscopic Traffic Models, Accidents, and Insurance Losses" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, 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{RMSE of Covariance Estimate between Neighborhood Lasso and MMHC}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\hline
\textbf{$\Sigma$-Structure} &\textbf{(n,p)} &\textbf{Neighborhood Lasso RMSE} &\textbf{MMHC RMSE}\\
\hline
\textbf{Toeplitz} & (100,100) & 0.122 & 0.130 \\
 & (100,100) & 0.056 & 0.060 \\
 & (500,100) & 0.112 & 0.109 \\
 & (500,1000) & 0.055 & 0.053 \\
\textbf{Equal} & (100,100) & 0.115 & 0.108\\
 & (100,1000) & 0.039 & 0.039 \\
 & (500,100) & 0.108 & 0.106 \\
 & (500,1000) & 0.041 & 0.041 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Causal Discovery on Dependent Binary Data" by Alex Chen and Qing Zhou, arXiv:2412.20289v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{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|}
\hline
\textbf{Tested}&\multicolumn{6}{|c|}{\textbf{\phantom{joker} \; Mean Values \, \& \, Standard Deviations}}\\%&\multicolumn{6}{|c|}{\textbf{Standard deviations}} \\
\cline{2-7}
\textbf{ Algorithms } & \textbf{\text{ CPU(Sec) }}& \textbf{\text{ nIter }}& \textbf{\text{ nFun }}& \textbf{\text{ CPU(Sec) }}& \textbf{\text{ nIter }}& \textbf{\text{ nFun }}\\
\hline
\tt{FISTA}& ${\mathrm{10.9}}$& ${\mathrm{2527.2}}$ & ${\mathrm{2528.2 }}$& ${\mathrm{30.1}}$& ${\mathrm{2141.6}}$ & ${\mathrm{2141.6}}$\\
\hline
\tt{FPC-BB}& ${\mathrm{2.3}}$& ${\mathrm{592.7}}$ & ${\mathrm{1129.7}}$& ${\mathrm{5.7}}$& ${\mathrm{471.6}}$ & ${\mathrm{971.1}}$\\
\hline
\tt{SpaRSA}& ${\mathrm{2.5}}$& ${\mathrm{659.9}}$ & ${\mathrm{659.9}}$& ${\mathrm{6.4}}$& ${\mathrm{527.3}}$ & ${\mathrm{527.3}}$\\
\hline
\tt{TwIST}& ${\mathrm{1.3}}$& ${\mathrm{387.0}}$ &
${\mathrm{443.4}}$& ${\mathrm{2.9}}$&
${\mathrm{324.2 }}$ & ${\mathrm{352.5}}$\\
\hline
\tt{ISGA}&
${\mathrm{0.9}}$&
${\mathrm{209.7}}$ &
${\mathrm{291.5}}$& ${\mathrm{1.6}}$& ${\mathrm{37.3}}$ & ${\mathrm{36.1}}$ \\
\hline
\tt{smISGA}& %\cellcolor{gray!20}
${\mathrm{ 0.9}}$&
${\mathrm{216.6}}$ &
${\mathrm{492.4}}$& ${\mathrm{1.8}}$& ${\mathrm{67.7}}$ & ${\mathrm{138.8}}$\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Semi-Monotone Goldstein Line Search Strategy with Application in Sparse Recovery" by Shima Shabani and Michael Breuß, arXiv:2503.13099v1, 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{Performance percentage of two methods on the VML-MOC test set.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
&Mean pixel IU&Mean line IU\\
\midrule
Single-oriented Gaussian~&37.88&12.89\\ \\
Multi-oriented Gaussian&80.96&60.99\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Berat Kurar Barakat
Rafi Cohen
Irina Rabaev
Jihad El-Sana

"VML-MOC: Segmenting a multiply oriented and curved handwritten text lines dataset" by Berat Kurar Barakat, Rafi Cohen, Irina Rabaev, and Jihad El-Sana, arXiv:2101.07542v1, 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{F1-scores for different methods of vectorization on the TDT data split.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 Vectorization & Train F1-score & DEV F1-score & Test F1-score \\ \hline
 distilBERT-tokenizer & \textbf{0.9933} & \textbf{0.9807} & \textbf{0.9708} \\ \hline
 neural stacking & 0.9645 & 0.9377 & 0.9461 \\ \hline
 linear stacking & 0.9695 & 0.9445 & 0.9425 \\ \hline
 tax2vec & 0.9895 & 0.9415 & 0.9407 \\ \hline
 linear stacking\_probs& 0.9710 & 0.9380 & 0.9390 \\ \hline
 LSA& 0.9658 & 0.9302 & 0.9281 \\ \hline
 roberta-large-nli-stsb-mean-tokens & 0.9623 & 0.9184 & 0.9142 \\ \hline
 xlm-r-large-en-ko-nli-ststb &0.9376 & 0.9226 & 0.9124 \\ \hline
 distilbert-base-nli-mean-tokens & 0.9365 & 0.9124 & 0.9113 \\ \hline

 tax2vec(kg) & 0.8830 & 0.8842 & 0.8892 \\ \hline
 Hand crafted features & 0.7861 & 0.7903 & 0.7805 \\ \hline

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

cs

Boshko Koloski
Timen Stepišnik Perdih
Senja Pollak
Blaž Škrlj

"Identification of COVID-19 related Fake News via Neural Stacking" by Boshko Koloski, Timen Stepišnik Perdih, Senja Pollak, and Blaž Škrlj, arXiv:2101.03988v2, 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{Nodes on the designed BNs with assumed metrics for CACC}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 \hline
 Node & Description & Confidentiality & Integrity & Availability \\
 \hline
 RSE & connected vehicle roadside equipment & M & M & M \\
 ITS & ITS roadway equipment & M & H & M \\
 TMC & traffic management center & M & H & M \\
 OBE & vehicle OBE & L & M & M \\
 BV & basic vehicle & L & M & L \\
 OV & other vehicle OBEs & L & H & M \\
 DII & driver input information & M & H & H \\
 DI & driver input & M & H & H \\
 VCC & vehicle cluster coordination & L & H & M \\
 VVV & vehicle path prediction & NA & H & M \\
 VP & vehicle profile & L & M & M \\
 DRV & driver update information & L & M & M \\
 VCA & vehicle control automation & M & H & H \\
 DR & driver updates & NA & M & M \\
 BSC & basic safety communication & L & M & M \\
 HVS & host vehicle status & L & M & H \\
 DI & driver input & M & H & H \\
 VED & vehicle environmental data & L & M & M \\
 VLM & vehicle location and motion & L & H & M \\
 VCW & vehicle control warning & L & H & H \\
 VCP & vehicle cooperative cruise control & L & H & M \\
 BSM & basic safety message & L & L & H \\
 DD & detection by driver & L & M & H \\
 DS & detection by system & L & M & H \\
 RDD & roadside detection by system driver & L & M & H \\
 RDS & roadside detection by system & L & M & H \\
 SA & certificate (security) authority & L & M & H \\
 WSA & wrong safety warning and signal phasing & L & M & H \\
 CB & sending channel busy & L & M & H \\
 DCC & distribution congestion conrol mechanism & L & M & H \\
 LC & location tracking & L & M & H \\
 RDS & detection by system & L & M & H \\
 LTC & fake long term certificate & L & M & H \\
 CRL & fake certificate revocation list & L & M & H \\
 PC & pseudonym certificate & L & M & H \\
 DB & database map poisining & L & M & H \\
 MP & map poisining (Web) & L & M & H \\
 PC & pseudonym certificate & L & M & H \\
 DOSR & denial of service (RSE) & L & M & H \\
 SD & device shutdown & L & M & H \\
 BLK & block pseudonym change & L & M & H \\
 RB & remote reboot & L & M & H \\
 DOS & denial of service (vehs,pedes) & L & M & H \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Gurcan Comert
Mashrur Chowdhury
David M. Nicol

"Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks" by Gurcan Comert, Mashrur Chowdhury, and David M. Nicol, arXiv:2011.09436v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overview of Experiment Configurations}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 \textbf{Category} & \textbf{Variation} & \textbf{Abbreviation} \\
 \midrule
 \multirow{3}{*}{Performance}
 & Cumulative Return & CR \\
 & Sharpe Ratio & SH \\
 & Sortino Ratio & SO \\
 \midrule
 \multirow{3}{*}{Benchmark}
 & Equal-weighted & EW \\
 & Value-weighted & VW \\
 & Portfolio Optimization & mSSRM \\
 \midrule
 \multirow{2}{*}{Objective}
 & Maximum Sharpe Ratio & MSH \\
 & Maximum Sortino Ratio & MSO \\
 \midrule
 \multirow{3}{*}{Model}
 & Mamba & M \\
 & LSTM & L \\
 & Transformer & T \\
 \midrule
 \multirow{3}{*}{Method}
 & Prediction-Focused Learning & PFL \\
 & End-to-End Learning & E2E \\
 & Decision by Supervised Learning & DSL (Ours) \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Juhyeong Kim
Sungyoon Choi
Youngbin Lee
Yejin Kim
Yongmin Choi
Yongjae Lee

"Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio Optimization" by Juhyeong Kim, Sungyoon Choi, Youngbin Lee, Yejin Kim, Yongmin Choi, and Yongjae Lee, arXiv:2503.13544v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, 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{Validity for the point-wise convolution of GMDC on CVIU dataset. Note that the SubEC is not involved in these variants. The proposed GMDC uses grouped kernels denoted as $[K_1, K_2, \dots, K_N]$. The best performance is highlighted in bold.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc|cccc}
\toprule
Model & \multicolumn{4}{c|}{w/o Point-wise Conv} & \multicolumn{4}{c}{with Point-wise Conv} \\
\midrule
GMDC Groups & SRCC $\uparrow$ & KRCC $\uparrow$ & PLCC $\uparrow$& RMSE $\downarrow$& SRCC $\uparrow$& KRCC $\uparrow$& PLCC $\uparrow$& RMSE $\downarrow$\\
\midrule
$[3,7]$ & 0.945 & 0.800 & 0.951 & 0.884 & 0.974 & 0.866 & 0.977 & 0.605 \\
$[3,5,7]$ & 0.967 & 0.850 & 0.968 & 0.713 & 0.972 & 0.857 & 0.972 & 0.642 \\
$[3,5,7,9]$ & 0.973 & 0.864 & 0.976 & \textbf{0.601} & \textbf{0.975} & \textbf{0.868} & \textbf{0.978} & 0.632\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yixiao Li
Xiaoyuan Yang
Guanghui Yue
Jun Fu
Qiuping Jiang
Xu Jia
Paul L. Rosin
Hantao Liu
Wei Zhou

"Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment" by Yixiao Li, Xiaoyuan Yang, Guanghui Yue, Jun Fu, Qiuping Jiang, Xu Jia, Paul L. Rosin, Hantao Liu, and Wei Zhou, arXiv:2509.06442v1, 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}{|ccc|}
\hline
 \textbf{Kernel} & \textbf{Number of points tested} & \textbf{Test error} \\
 \hline
Epanechnikov & 468 & $6.41025\%$\\
Cosine & 468 & $7.2649\%$\\
Uniform & 468 & $8.5470\%$\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Calculated EPE for Epanechnikov, cosine and uniform kernel run on infant 5}
\end{table}
\end{document}

eess

"Computational Challenges in Non-parametric Prediction of Bradycardia in Preterm Infants" by Sinjini Mitra, arXiv:2011.09307v1, 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|}
\hline
\textbf{Conditional Variable} & \textbf{Probability} \\ \hline
$X_1=0$ & $0.45$ \\ \hline
$X_1=1$ & $0.55$ \\ \hline
$X_2=0 | X_1 = 0$ & $0.55$ \\ \hline
$X_2=1 | X_1 = 0$ & $0.45$ \\ \hline
$X_2=0 | X_1 = 1$ & $0.45$ \\ \hline
$X_2=1 | X_1 = 1$ & $0.55$ \\ \hline
$W_1=0 | X_1 = 0$ & $0.46$ \\ \hline
$W_1=1 | X_1 = 0$ & $0.54$ \\ \hline
$W_1=0 | X_1 = 1$ & $0.54$ \\ \hline
$W_1=1 | X_1 = 1$ & $0.46$ \\ \hline
$W_2=0 | X_2 = 0$ & $0.52$ \\ \hline
$W_2=1 | X_2 = 0$ & $0.48$ \\ \hline
$W_2=0 | X_2 = 1$ & $0.48$ \\ \hline
$W_2=1 | X_2 = 1$ & $0.52$ \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Conditional Probability Distributions}
\end{table}
\end{document}

stat

"Budgeted and Non-budgeted Causal Bandits" by Vineet Nair, Vishakha Patil, and Gaurav Sinha, arXiv:2012.07058v1, 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{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Selected indicators for the different methods}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c||c|c||c|c|c|c}
\thead{Approximation \\ formula} & \thead{Arrival \\ process} & \thead{Service \\ process} & \thead{Mean \\ number of \\ iterations} & \thead{Mean total \\ computation \\ time} & \thead{Maximum \\ capacity \\ $\hat{n}_{max}$} & \thead{Traffic share \\ at max. \\ capacity} \\ \hline \hline
None & M & M & $9$ & $2.22$ s & $11.70$ & $0.5$ \\ \hline
None & PH & M & $9$ & $17.04$ s & $12.97$ & $0.5$ \\ \hline
None & M & PH & $8.44$ & $78.86$ s & $14.53$ & $0.5$ \\ \hline
None & PH & PH & $9$ & $1432.20$ s & $16.90$ & $0.1$ \\ \hline \hline
Kingman & M & M & $8.89$ & $2.35$ s & $16.80$ & $0.5$ \\ \hline
Kingman & PH & M & $8$ & $15.76$ s & $15.91$ & $0.5$ \\ \hline
Kingman & M & PH & $7.78$ & $74.65$ s & $15.55$ & $0.5$ \\ \hline \hline
Hertel & M & M & $9.33$ & $2.31$ s & $17.29$ & $0.5$ \\ \hline
Hertel & PH & M & $8$ & $15.70$ s & $15.91$ & $0.5$ \\ \hline
Hertel & M & PH & $9.22$ & $87.33$ s & $18.17$ & $0.5$
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Utilizing phase-type distributions for queueing-based railway junction performance determination" by Tamme Emunds and Nils Nießen, arXiv:2412.04188v1, 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}{cc}
 \toprule
 Name & Meaning \\
 \midrule
 adjclose & Adjusted closing price \\
 amount & Total trading amount \\
 change & Price change compared to previous close \\
 close & Closing price of the trading day \\
 factor & Adjustment factor for price normalization \\
 high & Highest price during the trading day \\
 low & Lowest price during the trading day \\
 open & Opening price of the trading day \\
 volume & Number of shares traded \\
 vwap & Volume-weighted average price \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Feature names and their corresponding meanings.}
\end{table}
\end{document}

q-fin

Hongjun Ding
Binqi Chen
Jinsheng Huang
Taian Guo
Zhengyang Mao
Guoyi Shao
Lutong Zou
Luchen Liu
Ming Zhang

"AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining" by Hongjun Ding, Binqi Chen, Jinsheng Huang, Taian Guo, Zhengyang Mao, Guoyi Shao, Lutong Zou, Luchen Liu, and Ming Zhang, arXiv:2508.13174v1, 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{Predicted plane error $e$ vs evaluation score}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccccc}
\hline
$e = |p - t|$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\
\hline
\textbf{Score} & 1.00 & 0.74 & 0.50 & 0.32 & 0.18 & 0.10 & 0.05 & 0.02 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Nikolay Burlutskiy
Marija Kekic
Jordi de la Torre
Philipp Plewa
Mehdi Boroumand
Julia Jurkowska
Borjan Venovski
Maria Chiara Biagi
Yeman Brhane Hagos
Roksana Malinowska-Traczyk
Yibo Wang
Jacek Zalewski
Paula Sawczuk
Karlo Pintarić
Fariba Yousefi
Leif Hultin

"MiceBoneChallenge: Micro-CT public dataset and six solutions for automatic growth plate detection in micro-CT mice bone scans" by Nikolay Burlutskiy, Marija Kekic, Jordi de la Torre, Philipp Plewa, Mehdi Boroumand, Julia Jurkowska, Borjan Venovski, Maria Chiara Biagi, Yeman Brhane Hagos, Roksana Malinowska-Traczyk, Yibo Wang, Jacek Zalewski, Paula Sawczuk, Karlo Pintarić, Fariba Yousefi, and Leif Hultin, arXiv:2411.17260v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.