\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}{cccccc}
\hline
Copula & Distribution $C(u, v ; \cdot)$& Range of $\theta$ & $\lambda_L$ & $\lambda_U$ & Generator $\psi(\cdot)$\\
\hline
Guassian & $\Phi_{\theta}(\Phi^{-1}(u), \Phi^{-1}(v))$ & $(-1, 1)$ & 0 & 0 & $\times$\\
Student $t$& $T_{\nu, \theta}(t_{\nu}^{-1}(u), t_{\nu}^{-1}(v))$ & $(-1, 1)$ & \multicolumn{2}{c}{$2t_{\nu+1}\left(-\frac{\sqrt{\nu+1}\sqrt{1-\theta}}{\sqrt{1+\theta}}\right)$} &$\times$ \\
Gumbel & $\exp\left(-[(\ln u)^{\theta} + (\ln v)^{\theta}]^{1/\theta}\right)$ & $[1,\infty)$ & 0 & $2 - 2^{1/\theta}$& $(- \ln t)^{\theta}$\\
Clayton & $\left(u^{-\theta} + v^{-\theta} - 1\right)^{-\frac{1}{\theta}}$ & $(0,\infty)$ & $2 - 2^{1/\theta}$ & 0& $(t^{-\theta} - 1)/\theta$\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Bivariate copula models with correlation parameter $\theta$, upper tail dependence $\lambda_L$, lower tail $\lambda_U$ dependence parameters}
\end{table}
\end{document}

stat

"Systemic Risk in Market Microstructure of Crude Oil and Gasoline Futures Prices: A Hawkes Flocking Model Approach" by Hyun Jin Jang, Kiseop Lee, and Kyungsub Lee, arXiv:2012.04181v1, 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{The chi-square test for years vs hours averages the results of 1000 tests.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr} \toprule
 & All Type & Rear-end & Sideswipe \\ \midrule
Statstic & 12.49404 & 16.72444 & 9.186534 \\
P-value & 0.275962 & 0.195133 & 0.41611 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xiwen Chen
Hao Wang
Abolfazl Razi
Brendan Russo
Jason Pacheco
John Roberts
Jeffrey Wishart
Larry Head
Alonso Granados Baca

"Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study" by Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head, and Alonso Granados Baca, arXiv:2201.13229v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters for adaptive localized neuron strategy used in Section .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
 \hline
 Cases & $S_1$ & $S_2$ & $N_L$ & $R_L$\\
 \hline
 $\varepsilon = 120^{-1}$ & 8 & 3 & 1500 & 10\\
 $\varepsilon = 500^{-1}$ & 8 & 5 & 1500 & 10\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Deep collocation method: A framework for solving PDEs using neural networks with error control" by Mingxing Weng, Zhiping Mao, and Jie Shen, arXiv:2502.17203v2, 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|c|c|}
\hline
 \textbf{Lower or increase subjects payoff given that:} & \textbf{Amount} \\ \hline
 Subject used own work -- outcome: success & *enter amount* \\
 Subject used own work -- outcome: failure & *enter amount* \\
 Subject delegated (machine/human) -- outcome: success & *enter amount* \\
 Subject delegated (machine/human) -- outcome: failure & *enter amount* \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Decision to increase or decrease the delegator's pay-off}
\end{table}
\end{document}

cs

"Hiding Behind Machines: When Blame Is Shifted to Artificial Agents" by Till Feier, Jan Gogoll, and Matthias Uhl, arXiv:2101.11465v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Automating the Design of Multigrid Methods with Evolutionary Program Synthesis" by Jonas Schmitt, arXiv:2312.14875v1, 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|ccccccccc} \hline
{\bf Network} & {\bf $N$} & {\bf $M$} & {\bf $k_{avg}$} & {\bf $k_{min}$}
& {\bf $k_{max}$} & {\bf $L_{avg}$} & {\bf $D$} & {\bf $R$} & {\bf $E$} \\ \hline
OpenFlight & 2905 & 15645 & 10.77 & 1 & 242 & 4.097 & 14 & 0.080912 & 0.266934\\
AS Oregon & 6474 & 12572 & 3.883 & 1 & 1458 & 3.705 & 9 & 0.012500 & 0.290399\\
PowerGrid & 4941 & 6594 & 2.669 & 1 & 19 & 18.989 & 46 & 0.052428 & 0.062878\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yukio Hayashi
Atsushi Tanaka
Jun Matsukubo

"More Tolerant Reconstructed Networks by Self-Healing against Attacks in Saving Resource" by Yukio Hayashi, Atsushi Tanaka, and Jun Matsukubo, arXiv:2101.02814v2, 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}{cccc}
 \hline
 \textbf{\# Dimensions} & \textbf{SG Level} & \textbf{\# of Points} & \textbf{Average TI time (ms)}\\
 \hline
 4 & 3 & 137 & 21 \\
 8 & 3 & 849 & 1021 \\
 16 & 3 & 6,049 & 480,430 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{SG performance on the benchmark IRBC model.}
\end{table}
\end{document}

q-fin

Aryan Eftekhari
Michel Juillard
Normann Rion
Simon Scheidegger

"Scalable Global Solution Techniques for High-Dimensional Models in Dynare" by Aryan Eftekhari, Michel Juillard, Normann Rion, and Simon Scheidegger, arXiv:2503.11464v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Víctor Blanco
José-Fernando Camacho-Vallejo
Carlos Corpus

"A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology" by Víctor Blanco, José-Fernando Camacho-Vallejo, and Carlos Corpus, arXiv:2503.00561v1, 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
\caption{Selected Greeks for an at–the–money call ($S=K=4200$)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\hline
Scenario & $\Delta$ & $\Gamma$ & $\nu_{0}$ & $\mathcal V$ (Vanna)\\\hline
Baseline (BS) & 0.527 & $1.15\times10^{-4}$ & 239.8 & 0.014\\
Memory–only & 0.525 & $1.09\times10^{-4}$ & 268.6 & 0.013\\
Full (smfBm–J) & 0.522 & $0.98\times10^{-4}$ & 268.9 & 0.019\\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Pricing Fractal Derivatives under Sub-Mixed Fractional Brownian Motion with Jumps" by Nader Karimi, arXiv:2506.24111v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Jean-Philippe Boussemart
Walter Briec
Raluca Parvulescu
Paola Ravelojaona

"$Λ$-Returns to Scale and Individual Minimum Extrapolation Principle" by Jean-Philippe Boussemart, Walter Briec, Raluca Parvulescu, and Paola Ravelojaona, arXiv:2212.04724v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccccc}
 \toprule
 & \multicolumn{6}{c}{Prompt year} & & \multicolumn{2}{c}{Accuracy}\\
 \cmidrule{2-7} \cmidrule{9-10}
 & 1993 & 2001 & 2009 & 2017 & 2021 & 2025 & & Pre-cutoff & Post-cutoff \\
 \midrule
 Correct output & Clinton & Bush & Obama & Trump & Biden & Trump \\
 \midrule
 BERT & \textcolor{blue}{\textbf{Clinton}} & Clinton & \textcolor{blue}{\textbf{Obama}} & Obama &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Obama & & $2/4$ & \cellcolor{lightgray}$0/2$\\
 ModernBERT & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \textcolor{blue}{\textbf{Obama}} & \textcolor{blue}{\textbf{Trump}} & \textcolor{blue}{\textbf{Biden}} & \cellcolor{lightgray}\textcolor{blue}{\textbf{Trump}} & & $5/5$ & \cellcolor{lightgray}$1/1$\\
 \midrule
 $\text{ChronoBERT}_{1999}$ & Roosevelt & \cellcolor{lightgray}Hoover & \cellcolor{lightgray}Roosevelt & \cellcolor{lightgray}Hoover & \cellcolor{lightgray}Hoover & \cellcolor{lightgray}Washington & & $0/1$ & \cellcolor{lightgray} $0/5$\\
 $\text{ChronoBERT}_{2000}$ & \textcolor{blue}{\textbf{Clinton}} & \cellcolor{lightgray}Clinton & \cellcolor{lightgray}Clinton & \cellcolor{lightgray}Clinton & \cellcolor{lightgray}Clinton & \cellcolor{lightgray}Wilson & & $1/1$ & \cellcolor{lightgray} $0/5$\\
 $\text{ChronoBERT}_{2001}$ & \textcolor{blue}{\textbf{Clinton}} & Clinton & \cellcolor{lightgray}Clinton & \cellcolor{lightgray}Clinton & \cellcolor{lightgray}Clinton & \cellcolor{lightgray}Washington & & $1/2$ & \cellcolor{lightgray} $0/4$\\
 $\text{ChronoBERT}_{2002}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Clinton & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Clinton & & $2/2$ & \cellcolor{lightgray} $0/4$\\
 $\text{ChronoBERT}_{2003}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Clinton & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Clinton & & $2/2$ & \cellcolor{lightgray} $0/4$\\
 $\text{ChronoBERT}_{2004}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Clinton & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Clinton & & $2/2$ & \cellcolor{lightgray} $0/4$\\
 $\text{ChronoBERT}_{2005}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Clinton & & $2/2$ & \cellcolor{lightgray} $0/4$\\
 $\text{ChronoBERT}_{2006}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Clinton & & $2/2$ & \cellcolor{lightgray} $0/4$\\
 $\text{ChronoBERT}_{2007}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Bush & \cellcolor{lightgray}Monroe & & $2/2$ & \cellcolor{lightgray} $0/4$\\
 $\text{ChronoBERT}_{2008}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} &\cellcolor{lightgray}Bush &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Bush &\cellcolor{lightgray}Wilson & & $2/2$ & \cellcolor{lightgray} $0/4$\\
 $\text{ChronoBERT}_{2009}$ & \textcolor{blue}{\textbf{Clinton}} & Clinton & \textcolor{blue}{\textbf{Obama}} &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Wilson & & $2/3$ & \cellcolor{lightgray} $0/3$\\
 $\text{ChronoBERT}_{2010}$ & \textcolor{blue}{\textbf{Clinton}} & Obama & \textcolor{blue}{\textbf{Obama}} &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Wilson & & $2/3$ & \cellcolor{lightgray} $0/3$\\
 $\text{ChronoBERT}_{2011}$ & \textcolor{blue}{\textbf{Clinton}} & Clinton & \textcolor{blue}{\textbf{Obama}} &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Wilson & & $2/3$ & \cellcolor{lightgray} $0/3$\\
 $\text{ChronoBERT}_{2012}$ & Obama & Obama & \textcolor{blue}{\textbf{Obama}} &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Obama & & $1/3$ & \cellcolor{lightgray} $0/3$\\
 $\text{ChronoBERT}_{2013}$ & \textcolor{blue}{\textbf{Clinton}} & Obama & \textcolor{blue}{\textbf{Obama}} &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Monroe & & $2/3$ & \cellcolor{lightgray} $0/3$\\
 $\text{ChronoBERT}_{2014}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \textcolor{blue}{\textbf{Obama}} &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Monroe & & $3/3$ & \cellcolor{lightgray} $0/3$\\
 $\text{ChronoBERT}_{2015}$ & \textcolor{blue}{\textbf{Clinton}} & Clinton & \textcolor{blue}{\textbf{Obama}} &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Monroe & & $2/3$ & \cellcolor{lightgray} $0/3$\\
 $\text{ChronoBERT}_{2016}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \textcolor{blue}{\textbf{Obama}} &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Obama &\cellcolor{lightgray}Obama & & $3/3$ & \cellcolor{lightgray} $0/3$\\
 $\text{ChronoBERT}_{2017}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \textcolor{blue}{\textbf{Obama}} & \textcolor{blue}{\textbf{Trump}} &\cellcolor{lightgray}Trump &\cellcolor{lightgray}Monroe & & $4/4$ & \cellcolor{lightgray} $0/2$\\
 $\text{ChronoBERT}_{2018}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \textcolor{blue}{\textbf{Obama}} & \textcolor{blue}{\textbf{Trump}} &\cellcolor{lightgray}Trump &\cellcolor{lightgray}Obama & & $4/4$ & \cellcolor{lightgray} $0/2$\\
 $\text{ChronoBERT}_{2019}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \textcolor{blue}{\textbf{Obama}} & \textcolor{blue}{\textbf{Trump}} &\cellcolor{lightgray}Trump &\cellcolor{lightgray}Obama & & $4/4$ & \cellcolor{lightgray} $0/2$\\
 $\text{ChronoBERT}_{2020}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \textcolor{blue}{\textbf{Obama}} & \textcolor{blue}{\textbf{Trump}} &\cellcolor{lightgray}Trump &\cellcolor{lightgray}\textcolor{blue}{\textbf{Trump}} & & $4/4$ & \cellcolor{lightgray} $1/2$\\
 $\text{ChronoBERT}_{2021}$ & \textcolor{blue}{\textbf{Clinton}} & Clinton & \textcolor{blue}{\textbf{Obama}} & \textcolor{blue}{\textbf{Trump}} & \textcolor{blue}{\textbf{Biden}} &\cellcolor{lightgray}Biden & & $4/5$ & \cellcolor{lightgray} $0/1$\\
 $\text{ChronoBERT}_{2022}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \textcolor{blue}{\textbf{Obama}} & \textcolor{blue}{\textbf{Trump}} & \textcolor{blue}{\textbf{Biden}} &\cellcolor{lightgray}Biden & & $5/5$ & \cellcolor{lightgray} $0/1$\\
 $\text{ChronoBERT}_{2023}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \textcolor{blue}{\textbf{Obama}} & \textcolor{blue}{\textbf{Trump}} & \textcolor{blue}{\textbf{Biden}} &\cellcolor{lightgray}Biden & & $5/5$ & \cellcolor{lightgray} $0/1$\\
 $\text{ChronoBERT}_{2024}$ & \textcolor{blue}{\textbf{Clinton}} & \textcolor{blue}{\textbf{Bush}} & \textcolor{blue}{\textbf{Obama}} & \textcolor{blue}{\textbf{Trump}} & \textcolor{blue}{\textbf{Biden}} &\cellcolor{lightgray}Biden & & $5/5$ & \cellcolor{lightgray} $0/1$\\
 \hline
 \multicolumn{8}{l}{$\text{ChronoBERT}_{1999}$ through $\text{ChronoBERT}_{2024}$} & $68/78$ & \cellcolor{lightgray}$1/78$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Masked-Token Predictions of U.S. Presidents using ChronoBERT}
\end{table}
\end{document}

q-fin

Songrun He
Linying Lv
Asaf Manela
Jimmy Wu

"Chronologically Consistent Large Language Models" by Songrun He, Linying Lv, Asaf Manela, and Jimmy Wu, arXiv:2502.21206v3, licensed under CC BY 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.


stat

Taban Baghfalaki
Reza Hashemi
Christophe Tzourio
Catherine Helmer
Helene Jacqmin-Gadda

"A Two-stage Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers and Competing Risk Outcomes" by Taban Baghfalaki, Reza Hashemi, Christophe Tzourio, Catherine Helmer, and Helene Jacqmin-Gadda, arXiv:2412.03797v1, 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{FPGA utilization of cache configs (f = 50 MHz)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 \multirow{2}{*}{\textbf{Config}} & \multicolumn{2}{c|}{\textbf{L1 Data Cache}} & \multirow{2}{*}{\textbf{Total LUTs}} \\ \cline{2-3}
 & \textbf{LUTs} & \textbf{BRAM} & \\ \hline
 BASE & 3065 & 4 & 131026 \\
 C-64-8 & 3579 ($+16.8\%$) & 8 ($+100\%$) & 133271 ($+1.7\%$) \\
 C-64-16 & 4248 ($+38.6\%$) & 16 ($+300\%$) & 136078 ($+3.9\%$) \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Farhad Merchant
Dominik Sisejkovic
Lennart M. Reimann
Kirthihan Yasotharan
Thomas Grass
Rainer Leupers

"ANDROMEDA: An FPGA Based RISC-V MPSoC Exploration Framework" by Farhad Merchant, Dominik Sisejkovic, Lennart M. Reimann, Kirthihan Yasotharan, Thomas Grass, and Rainer Leupers, arXiv:2101.05591v1, licensed under CC0 1.0. 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}{cccc}
 \toprule
 Relative $L_2$ error & PINNs & IR-PINNs1 & IR-PINNs2 \\
 \midrule
 $u$ & 8.1022e-04 & 5.9748e-04 & 5.3934e-04 \\
 \midrule
 $v$ & 5.0101e-02 & 3.0561e-02 & 3.2439e-02 \\
 \midrule
 Running time (hours) & 0.3288 & 0.3693 & 0.4173 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\textit{Boussinesq-Burgers equations:} Relative $L_2$ errors and running time of different methods.}
\end{table}
\end{document}

math

Xiaodong Feng
Haojiong Shangguan
Tao Tang
Xiaoliang Wan

"Integral regularization PINNs for evolution equations" by Xiaodong Feng, Haojiong Shangguan, Tao Tang, and Xiaoliang Wan, arXiv:2503.23729v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Antonio Cabrales
Manu García
David Ramos Muñoz
Angel Sánchez

"The Interactions of Social Norms about Climate Change: Science, Institutions and Economics" by Antonio Cabrales, Manu García, David Ramos Muñoz, and Angel Sánchez, arXiv:2208.09239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Multiscale detection of practically significant changes in a gradually varying time series" by Patrick Bastian and Holger Dette, arXiv:2504.15872v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Julia Herbinger
Susanne Dandl
Fiona K. Ewald
Sofia Loibl
Giuseppe Casalicchio

"Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation" by Julia Herbinger, Susanne Dandl, Fiona K. Ewald, Sofia Loibl, and Giuseppe Casalicchio, arXiv:2310.03112v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{diagbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Absolute Performance gap between SNN-SC-HARQ and Multi-rate SNN-SC under the same bandwidth}
\begin{tabular}{c||c|c|c|c|c|c|c}
\hline
\hline
\diagbox{$\theta$}{BER}&0.00&0.05 &0.10&0.15 &0.20&0.25 &0.30 \\
\hline
\hline
0.635 & 0.02 & 0.06 & 0.09 & 0.01 & 0.03 & 0.11 & 0.07 \\
0.645 & 0.02 & 0.01 & 0.07 & 0.09 & 0.06 & 0.11 & 0.01 \\
0.655 & 0.00 & 0.03 & 0.01 & 0.06 & 0.08 & 0.05 & 0.05 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Mengyang Wang
Jiahui Li
Mengyao Ma
Xiaopeng Fan

"Spiking Semantic Communication for Feature Transmission with HARQ" by Mengyang Wang, Jiahui Li, Mengyao Ma, and Xiaopeng Fan, arXiv:2310.08804v1, 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{\textbf{The best classification rates obtained with different SVM kernels}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
 \textbf{Linaer SVM} & \textbf{Polynomial SVM} & \textbf{RBF SVM} \\

 \hline
 81.1\% & 84.7\% & 92.9\% \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Naveenkumar G Venkataswamy
Masudul H Imtiaz

"Support Vector Machine for Person Classification Using the EEG Signals" by Naveenkumar G Venkataswamy and Masudul H Imtiaz, arXiv:2411.17446v1, 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{Results on the ASRU200 and ASRU700+LibriSpeech460 datasets, both tested on the ASRU\_Test set, in terms of English WER, Mandarin CER, and Mandarin-English MER (\%):}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc|cccc}
 \toprule
 \multirow{2}{*}{System} & \multicolumn{4}{c|}{ASRU700+LibriSpeech460} & \multicolumn{4}{c}{ASRU200} \\
 \cline{2-5} \cline{6-9} ~ & Params. & MER & CER & WER & Params. & MER & CER & WER \\
 \hline
 CTC-LPB~ & - & - & - & - & - & 11.8 & - & - \\
 LAL~ & - & - & - & - & 48.3M & 11.7 & 9.2 & 35.1 \\
 BA-MoE~ & 43M & 10.28 & 8.16 & 27.48 & - & - & - & - \\
 SC-MoE~ & 50.2M & 8.66 & 6.50 & 26.36 & - & - & - & -\\
 \midrule
 Baseline & 44.1M & 8.78 & 6.68 & 25.71 & 41.5M & 11.35 & 8.68 & 33.09 \\
 S1 & 44.5M & 8.65 & 6.64 & 25.01 & 41.9M & 11.18 & 8.52 & 32.87 \\
 S2 & 44.5M & 8.60 & 6.56 & 25.25 & 41.9M & 11.10 & 8.42 & 32.92 \\
 S3 & 50.6M & 8.26 & 6.32 & 24.06 & 47.9M & 10.93 & 8.38 & 31.64 \\
 CAMEL & 59.6M & \textbf{8.10} & 6.22 & 23.41 & 56.9M & \textbf{10.55} & 8.04 & 31.00 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

He Wang
Xucheng Wan
Naijun Zheng
Kai Liu
Huan Zhou
Guojian Li
Lei Xie

"CAMEL: Cross-Attention Enhanced Mixture-of-Experts and Language Bias for Code-Switching Speech Recognition" by He Wang, Xucheng Wan, Naijun Zheng, Kai Liu, Huan Zhou, Guojian Li, and Lei Xie, arXiv:2412.12760v2, 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}{|c|ccccc|}
\hline
Entry time & Link 1 & Link 2 & Link 3 & Link 4 & Link 5 \\
\hline
0 & 5 & 1 & 3 & 1 & 1 \\
1 & 6 & 1 & 3 & 6 & 1 \\
2 & 4 & 2 & 3 & 5 & 2 \\
3 & 5 & 3 & 3 & 4 & 4 \\
4 & 7 & 5 & 3 & 3 & 2 \\
5 & 8 & 8 & 3 & 2 & 1 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Stephen D. Boyles
Nicholas E. Lownes
Avinash Unnikrishnan

"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, 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{Dataset Distribution}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
Class & Instances & Percentage \\
\midrule
Factual & 1,980 & 40.8\% \\
Persuasive & 1,479 & 30.5\% \\
Misleading & 1,389 & 28.7\% \\
\midrule
Total & 4,848 & 100\% \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Sayem Hossen
Monalisa Moon Joti
Md. Golam Rashed

"Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach" by Sayem Hossen, Monalisa Moon Joti, and Md. Golam Rashed, arXiv:2508.09935v1, 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{Comparison of inference time and model size.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
Benchmarked method & Inference time (s) & Trainable params. \\
\midrule
MRSegmentator & 57.95 $\pm$ 53.15 & 31M \\
MRISegmentator-Abdomen & 98.19 $\pm$ 69.83 & 31M \\
TotalSegmentator MRI & 39.60 $\pm$ 10.34 & 31M \\
ABDSynth & 21.17 $\pm$ 19.30 & 13M \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Deepa Krishnaswamy
Cosmin Ciausu
Steve Pieper
Ron Kikinis
Benjamin Billot
Andrey Fedorov

"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation" by Deepa Krishnaswamy, Cosmin Ciausu, Steve Pieper, Ron Kikinis, Benjamin Billot, and Andrey Fedorov, arXiv:2507.17971v2, 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}{lllll}
\hline
\multicolumn{5}{c}{MSE} \\ \hline
Method & $\beta_{00}=0$ & $\beta_{01}=1$ & $\beta_{10}=0$ & $\beta_{11}=-1$ \\ \hline
MixT &3.6119&0.1376&2.4279&0.1662 \\
MixEP & 0.0411& 0.02742& 0.06486&0.04627 \\ \hline
\multicolumn{5}{c}{bias} \\ \hline
Method & $\beta_{00}=0$ & $\beta_{01}=1$ & $\beta_{10}=0$ & $\beta_{11}=-1$\\ \hline
MixT &0.8222&-0.1153& 0.6159&0.1979 \\
MixEP & 0.08276& -0.02965& 0.1790& 0.05284 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{CaseIV:The mean squared errors (MSE)and the bias of the parameter estimates for each estimation method.}
\end{table}
\end{document}

stat

"Robust mixture regression with Exponential Power distribution" by Xiao Chen, arXiv:2012.10637v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}\hline
 \bf{Model} & \bf{Resolution} & \bf{Seconds / 1k images $\downarrow$} \\\hline
 StyleGAN2 & 1024 & 184.97 \\
 SWAGAN-Bi & 1024 & 95.06 \\ \hline
 StyleGAN2 & 256 & 143.04 \\
 SWAGAN-Bi & 256 & 80.07 \\
 SWAGAN-Bi w/ NWD & 256 & 129.65 \\
 SWAGAN-NU & 256 & 70.06 \\
 Wavelet at final layer & 256 & 116.06 \\ \hline
 \end{tabular}
\caption{Average time (in seconds) for the network to process 1000 real images during training. Wavelet-based generators consistently outperform the alternatives. The reported times are based on the calculation provided in StyleGAN2's original implementation and do not include any of the (significant) time spent on benchmark calculations or sample generation.}
\end{table}
\end{document}

eess

Rinon Gal
Dana Cohen
Amit Bermano
Daniel Cohen-Or

"SWAGAN: A Style-based Wavelet-driven Generative Model" by Rinon Gal, Dana Cohen, Amit Bermano, and Daniel Cohen-Or, arXiv:2102.06108v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Elena Bortolato
Francesco Bertolino
Monica Musio
Laura Ventura

"Bayesian Discrepancy Measure: Higher-order and Skewed approximations" by Elena Bortolato, Francesco Bertolino, Monica Musio, and Laura Ventura, arXiv:2505.00185v1, 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{OLS Regression Results: RRE as Predictor of Annualized Turnover}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Variable} & \textbf{Coefficient} & \textbf{Std. Error} \\
\midrule
Intercept & 4.494*** & 0.104 \\
RRE & -4.361*** & 0.113 \\
\midrule
\textbf{Model Statistics} & & \\
$R^2$ & 0.815 & \\
\bottomrule
\multicolumn{3}{l}{\footnotesize{*** p $<$ 0.001}}
\end{tabular}
\end{adjustbox}
\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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cll|}
			\hline
			\textbf{Case} & \textbf{Conditions} & \textbf{Singular points} \\
			\hline
			\hline
			A& $c_0=0$, $c_3=0$. & $S_0$. \\
			\hline
			B& $c_0=0$, $c_3\neq0$. & $S_0$, $S_4$. \\
			\hline
			C& $c_0\neq0$, $c_3^2<4c_0c_2$. & $S_0$. \\
			\hline
			D& $c_0\neq0$, $c_3^2=4c_0c_2$. & $S_0$, $S_3$. \\
			\hline
			E& $c_0\neq0$, $c_3^2>4c_0c_2$. & $S_0$, $S_1$, $S_2$. \\
			\hline
		\end{tabular}
\end{adjustbox}
\caption{Cases with the singular points on the exceptional divisor of system .}
\end{table}
\end{document}

math

Érika Diz-Pita
Jaume Llibre
M. Victoria Otero-Espinar

"Phase portraits of a family of Kolmogorov systems depending on six parameters" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.13714v1, 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|ccccc}
 $j \setminus i$ & $0$ & $1$ & $\cdots$ & $c-1$ & $c$ \\ \hline
$0$ & $1$ & . & $\cdots$ & . & . \\
$q$ & . & $\beta_{1,q}$ & $\cdots$ & $\beta_{c-1,q}$ & . \\
$2q$ & . & . & $\cdots$ & . & $1$
\end{tabular}
\end{adjustbox}
\caption{Del Pezzo $q$-secant varieties and complexes}
\end{table}
\end{document}

math

"A Fröberg type theorem for higher secant complexes" by Junho Choe and Jaewoo Jung, arXiv:2502.20987v2, 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}{lllllll}
		\hline
		& \multicolumn{2}{c}{$t$-fusion} & \multicolumn{2}{c}{Laplace} & \multicolumn{2}{c}{L1 fusion} \\
		Signal strength & adj.MSE & se.adj.MSE & adj.MSE & se.adj.MSE & adj.MSE & se.adj.MSE \\
		\hline
		weak & 0.038685 & 0.012287 & 0.13113 & 0.008525 & 0.014461 & 0.006232 \\
		medium & 0.009272 & 0.002897 & 0.100271 & 0.003018 & 0.002314 & 0.000997 \\
		strong & 0.001384 & 0.001635 & 0.094502 & 0.001528 & 0.000575 & 0.000226 \\
		\hline
	\end{tabular}
\end{adjustbox}
\caption{2-d lattice Graphs: Comparison of performance wrt adjusted MSE for $t$-fusion and Laplace fusion methods along with $L_1$ fusion method.}
\end{table}
\end{document}

stat

"Graph signal denoising using $t$-shrinkage priors" by Sayantan Banerjee and Weining Shen, arXiv:2012.13696v1, 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}{lrrr}
\hline\hline
 Model & Probability & Mean & STD\\
\hline
 Period & 2003-2007\\
\hline
Unconditional (one normal) & 100.00\% & 0.03\% & 0.77\%\\
Conditional on quiet & 58.68\% & 0.10\% & 0.43\% \\
Conditional on hectic & 41.32\% & -0.08\% & 1.07\%\\
\hline
 Period & 2004-2008\\
\hline
Unconditional (one normal) & 100.00\% & 0.03\% & 0.83\%\\
Conditional on quiet & 56.77\% & 0.10\% & 0.38\% \\
Conditional on hectic & 43.23\% & -0.06\% & 1.17\%\\
\hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Meng-Jou Lu
Cathy Yi-Hsuan Chen
Wolfgang Karl Härdle

"Copula-Based Factor Model for Credit Risk Analysis" by Meng-Jou Lu, Cathy Yi-Hsuan Chen, and Wolfgang Karl Härdle, arXiv:2009.12092v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\centering Alternative measures of the difficulty of item \#11898}
\begin{tabular}{ccccc}
				\midrule
				Position effect & Average fraction & Fatigue effect (in pp) & Question \\
				estimation method & correct responses & $\times$ average position & difficulty \\
				(1) & (2) & (3) & (4) \\ \midrule
				None &	0.36&	0 $\times$ 64 = 0&	0.36	\\
				Pooling all items &	0.36&	-0.08 $\times$ 64 = -5.1&	0.41	\\
				Item-specific effect &	0.36&	-0.24 $\times$ 64 = -15.3&	0.51	\\
				Shrinkage estimator &	0.36&	-0.24 $\times$ 64 = -15.3&	0.51	\\
				By fraction corr. resp.&	0.36&	-0.15 $\times$ 64 = -9.6&	0.45	\\
				By academic subject &	0.36&	-0.03 $\times$ 64 = -1.6&	0.37	\\

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

q-fin

"Cognitive Endurance, Talent Selection, and the Labor Market Returns to Human Capital" by Germán Reyes, arXiv:2301.02575v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Modeling Count Data via Copulas

Hadi Safari-Katesari
S. Yaser Samadi
Samira Zaroudi

"Modeling Count Data via Copulas" by Hadi Safari-Katesari, S. Yaser Samadi, and Samira Zaroudi, arXiv:2012.02021v1, 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}{l|ccccc}
 \toprule
 $j$ & $\mu_{{\rm circ}}$ & $\kappa_{{\rm circ}}$ & $\mu_{{\rm axial}}$ & $\kappa_{{\rm axial}}$ & $\rho$\\
 \midrule
 1 & -1.92 (-2.05, -1.80) & 5.60 (4.59, 10.06) & 0.68 (0.62, 0.75) & 18.96 (14.8, 36.8) & 0.24 (0.09, 0.42) \\
 2 & -2.32 (-2.45, -2.20) & 5.00 (4.26, 9.02) & 0.14 (0.05, 0.23) & 10.01 (8.44, 19.2) & 0.35 (0.12, 0.50) \\
 3 & -1.16 (-1.25, -1.05) & 5.99 (5.33, 10.20) & -1.25 (-1.39, -1.09) & 3.13 (2.60, 5.30) & 0.07 (-0.16, 0.22) \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Point estimates (ET confidence intervals) for the parameters of the density.}
\end{table}
\end{document}

stat

Marco Mingione
Francesco Lagona
Priyanka Nagar
Francois von Holtzhausen
Andriette Bekker
Janine Schoombie
Peter C. le Roux

"Does wind affect the orientation of vegetation stripes? A copula-based mixture model for axial and circular data" by Marco Mingione, Francesco Lagona, Priyanka Nagar, Francois von Holtzhausen, Andriette Bekker, Janine Schoombie, and Peter C. le Roux, arXiv:2411.11461v1, 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{Statistics of the three datasets - NASDAQ100, S\&P500 and NIFTY500.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccr}
\toprule
- & NASDAQ100 & S\&P500 & NIFTY500 \\
\midrule
\#Assets & 83 & 442 & 336 \\
\#Days & 2800 & 2800 & 2800 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Ambedkar Dukkipati
Kawin Mayilvaghanan
Naveen Kumar Pallekonda
Sai Prakash Hadnoor
Ranga Shaarad Ayyagari

"Predictive AI with External Knowledge Infusion for Stocks" by Ambedkar Dukkipati, Kawin Mayilvaghanan, Naveen Kumar Pallekonda, Sai Prakash Hadnoor, and Ranga Shaarad Ayyagari, arXiv:2504.20058v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|ccc|ccc}
 \toprule & \multicolumn{3}{c|}{CoNLL03} & \multicolumn{3}{c}{WikiGold} \\ %\cline{2-7}
 & ORG & LOC & MISC & ORG & LOC & MISC \\
 \midrule PER & 72 & 37 & 15 & 13 & 5 & 1 \\
 ORG & - & 180 & 59 & - & 44 & 41 \\
 LOC & - & - & 158 & - & - & 5 \\
 \bottomrule
 \end{tabular}
\caption{The number of entities overlapping among classes in the CoNLL03 and WikiGold dataset.}
\end{table}
\end{document}

cs

Yuji Naraki
Ryosuke Yamaki
Yoshikazu Ikeda
Takafumi Horie
Kotaro Yoshida
Ryotaro Shimizu
Hiroki Naganuma

"Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation" by Yuji Naraki, Ryosuke Yamaki, Yoshikazu Ikeda, Takafumi Horie, Kotaro Yoshida, Ryotaro Shimizu, and Hiroki Naganuma, arXiv:2404.01334v2, 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{Three types of mixing-related data augmentation methods we propose here, and the corresponding parameter settings}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|ll}
\toprule
\textbf{Mixing} & Description & Scale & Range \\
\midrule
 & Contrast & Amount & 1$-$70 \\
 & Equalizer 1 & Frequency & 32$-$4,096 \\
 & Equalizer 2 & Frequency & 32$-$4,096 \\
 & Reverb & Reverb & 1$-$70 \\
 & Pink noise & Volume & 0.01$-$0.04 \\
\toprule
\textbf{Paring} & Description & Threshold & Notes\\
\midrule
 & Chroma distance & 0.48 & Mean within songs: 0.45\\
 & & & Mean across songs: 0.51\\
 & Correlation & 20 & Within songs: 2$-$30\\
 & & & Across songs: 0$-$10\\
\toprule
\textbf{Silence}& Description & Threshold & Notes\\
\midrule
 & Reduce silence & 20 & top db\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ching-Yu Chiu
Wen-Yi Hsiao
Yin-Cheng Yeh
Yi-Hsuan Yang
Alvin Wen-Yu Su

"Mixing-Specific Data Augmentation Techniques for Improved Blind Violin/Piano Source Separation" by Ching-Yu Chiu, Wen-Yi Hsiao, Yin-Cheng Yeh, Yi-Hsuan Yang, and Alvin Wen-Yu Su, arXiv:2008.02480v1, 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}{lc} \hline
$\mu$ & 0.0818\\
 $\sigma$ & .1849 \\
 LETF leverage & $\beta = 2$\\
30 day T-bill return $r$ & 0.0032\\
LETF expense ratio $c_\ell$ & .0089 \\
VETF expense ratio $c_v$ & 0.0 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Peter Forsyth
Pieter van Staden
Yuying Li

"Making Leveraged Exchange-Traded Funds Work for your Portfolio" by Peter Forsyth, Pieter van Staden, and Yuying Li, arXiv:2506.19200v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Differential Trend in the Share of Offline Spend by Age -- \textit{Covid Adopters} vs. \textit{Organic Adopters}}
\begin{tabular}{lcc}
 \midrule \midrule
 DV: & \multicolumn{2}{c}{Share of Offline Spend}\\
 Model: & (1) Senior Adopters & (2) Young Adopters \\
 \midrule
 \emph{Variables}\\
 Organic\_Adopter & 0.4947 (0.0092) & 0.4728 (0.0081)\\
 & [0.000] & [0.000]\\
 Covid\_Adopter & 0.0706 (0.0130) & 0.0882 (0.0114)\\
 & [0.000] & [0.000]\\
 t & -0.0019 (0.0005) & -0.0022 (0.0004)\\
 & [0.001] & [0.000]\\
 Covid\_Adopter * t & 0.0020 (0.0006) & 0.0004 (0.0006)\\
 & [0.002] & [0.523]\\
 \midrule
 \emph{Fit statistics}\\
 Observations & 72 & 72\\
 R$^2$ & 0.79964 & 0.82429\\
 Adjusted R$^2$ & 0.79080 & 0.81654\\
 \midrule \midrule
 \multicolumn{3}{l}{\emph{Standard errors are shown in parentheses; p-values are shown in square brackets.}}\\
 \end{tabular}
\end{table}
\end{document}

q-fin

Channel Choice and Customer Value

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Example table.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|} \hline
 Species & \bf Mean & \bf Std.~Dev. \\ \hline
 1 & 3.4 & 1.2 \\
 2 & 5.4 & 0.6 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Patrick E. Farrell
Tim van Beeck
Umberto Zerbinati

"Analysis and numerical analysis of the Helmholtz-Korteweg equation" by Patrick E. Farrell, Tim van Beeck, and Umberto Zerbinati, arXiv:2503.10771v1, 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{Dice Scores for 10-Fold Cross-Validation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\hline
\textbf{Model} & \textbf{Average Dice Score} \\ \hline
MLP & 0.55 \\ \hline
KAN & 0.57 \\ \hline
proKAN & 0.58 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Bhavesh Gyanchandani
Aditya Oza
Abhinav Roy

"ProKAN: Progressive Stacking of Kolmogorov-Arnold Networks for Efficient Liver Segmentation" by Bhavesh Gyanchandani, Aditya Oza, and Abhinav Roy, arXiv:2412.19713v1, 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{Sampling error}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|}
\hline
 $k$ & $3$ & $4$ & $5$ & $6$ & $7$ & $8$ & $9$ & $10$ & $B_Y$\\
\hline\hline
\emph{$\epsilon$} & $1.55$ & $3.07$ & $267$ & $318$ & $18.8$ & $1.54$ & $1.56$ & $169$ & $1.46$\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Signal processing with a distribution of graph operators" by Feng Ji and Wee Peng Tay, arXiv:2012.06296v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Asian Basket Spread Options: A New Approximation Based on Stochastic Taylor Expansions" by Fabien Le Floc'h, arXiv:2504.16011v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\centering
Timestamp & SACR\_SUNN\_in(GB) & SACR\_SUNN\_out(GB) \\ \hline

1514822400 & 14110930202 & 1025131246 \\ \hline
1514826000 & 13453619303 & 9191557943 \\ \hline

1514829600 & 12168879944 & 7793842045 \\ \hline
1514833200 & 11231198033 & 7097237528 \\ \hline
1514836800 & 10780847622 & 8048293939 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Sample traffic trace with time stamp collected from one router interface across a link.}
\end{table}
\end{document}

eess

Tanwi Mallick
Mariam Kiran
Bashir Mohammed
Prasanna Balaprakash

"Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks" by Tanwi Mallick, Mariam Kiran, Bashir Mohammed, and Prasanna Balaprakash, arXiv:2008.12767v1, 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||c|c||c|c||} \hline
\textbf{Methods} &\multicolumn{4}{|c||}{\textbf{Datasets}} \\ \cline{2-5}
 &\multicolumn{2}{|c||}{\textbf{SPaSe}} &\multicolumn{2}{|c||}{\textbf{WiSe}} \\ \cline{2-5}
 &\textbf{mean IoU} &\textbf{PA}&\textbf{mean IoU} &\textbf{PA} \\ \hline \hline
Haurilet \emph{et al.}~ &35.80 &77.40 &- &- \\
Haurilet \emph{et al.}~ &- &- &37.20 &88.50 \\
HANet~ &32.37 &76.54 &39.35 &88.72 \\
DANet~ &33.12 &77.39 &44.85 &88.80 \\
RANet~ &35.77 &78.64 &43.31 &88.77 \\
Ours &36.17 &79.11 &46.74 &89.80 \\ \hline \hline
\end{tabular}
\end{adjustbox}
\caption{illustrates the comparison of the proposed method with state-of-the-art techniques on benchmark datasets WiSe and SPaSe datasets. }
\end{table}
\end{document}

cs

Classroom Slide Narration System

"Classroom Slide Narration System" by Jobin K. V., Ajoy Mondal, and C. V. Jawahar, arXiv:2201.08574v1, licensed under CC BY 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{Summary of Training Hyperparameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lr}
 \Xhline{1px}
 \textbf{Hyperparameters of SUDO-DRL and benchmarks} & Value \\
 \hline
 Critic NN learning rate & 0.001 \\
 Actor NN learning rate & 0.0001 \\
 Decay rate of learning rate & 0.001 \\
 Discount factor, $\gamma$ & 0.99 \\
 GAE parameter, $\lambda$ & 0.99 \\
 Clipping parameter, $\epsilon$ & 0.2 \\
 Policy entropy loss weight, $\omega$ & 0.01 \\
 Decay rate of sampling priority, $\varrho$ & 0.95 \\
 Unified on-off policy loss function hyperparameter, $\beta_{1}, \beta_{2}$ & 0.9 \\
 On-policy and off-policy batch size, $B_{1}, B_2$ & 128 \\
 Number of sampled states for score scheme, $K$ & 50 \\
 Number of tested AoI and channel state, $\Xi$ & 4 \\
 Number of past trajectories for average score computing, $\bar{u}$ & 50 \\
 Time horizon of each episode, $T$ & 128 \\
 Size of replay buffer, $R$ & $200$ \\
 Number of episodes for pre-training, $I_{1}$ & $10 \times N$ \\
 Total number of episodes for training, $I$ & $10000$ \\
 Optimizer during training & Adam \\
 \Xhline{1px}
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

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

Mostafa Shabani
Martin Magris
George Tzagkarakis
Juho Kanniainen
Alexandros Iosifidis

"Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots" by Mostafa Shabani, Martin Magris, George Tzagkarakis, Juho Kanniainen, and Alexandros Iosifidis, arXiv:2210.14605v2, 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{Experimental evaluation setup}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \hline
 Component & Type & CPU & RAM & Disk\\
 \hline
 Caliper Benchmark & N/A & 8 vCPU & 30 GB & 288 GB\\
 Broker & Fabric V2.2 & 8 vCPU & 30 GB & 288 GB\\
 Fabric Subscriber & Fabric V1.4 & 2 vCPU & 7.5 GB & 36 GB\\
 Besu Subscriber & Besu & 2 vCPU & 7.5 GB & 36 GB\\
 Publisher & Fabric V2.2 & 2 vCPU & 7.5 GB & 36 GB\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Sara Ghaemi
Sara Rouhani
Rafael Belchior
Rui S. Cruz
Hamzeh Khazaei
Petr Musilek

"A Pub-Sub Architecture to Promote Blockchain Interoperability" by Sara Ghaemi, Sara Rouhani, Rafael Belchior, Rui S. Cruz, Hamzeh Khazaei, and Petr Musilek, arXiv:2101.12331v1, 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}{ccccccccccc} \toprule
 & & & & \multicolumn{3}{c}{AUC} & & \multicolumn{3}{c}{Average Precision} \\ \cmidrule{5-7} \cmidrule{9-11}
 $\sigma$ & $w$ & $\alpha$ & & Mixed & Mean & Last & & Mixed & Mean & Last \\ \midrule
 0.01 & 2.75 & 0.2 & & 0.89 & 0.81 & 0.74 & & 0.17 & 0.09 & 0.08 \\
 & & 0.5 & & 0.89 & 0.81 & 0.73 & & 0.20 & 0.12 & 0.09 \\
 & & 0.7 & & 0.89 & 0.81 & 0.73 & & 0.25 & 0.14 & 0.11 \\
 & & 1.0 & & 0.89 & 0.81 & 0.73 & & 0.33 & 0.20 & 0.15 \\
 & & & & & & & & & & \\
 0.1 & 4 & 0.2 & & 0.95 & 0.95 & 0.90 & & 0.09 & 0.09 & 0.07 \\
 & & 0.5 & & 0.95 & 0.94 & 0.90 & & 0.11 & 0.11 & 0.09 \\
 & & 0.7 & & 0.96 & 0.96 & 0.91 & & 0.15 & 0.15 & 0.12 \\
 & & 1.0 & & 0.96 & 0.95 & 0.91 & & 0.20 & 0.20 & 0.16 \\
 & & & & & & & & & & \\
 1 & 500 & 0.2 & & 0.94 & 0.95 & 0.90 & & 0.08 & 0.09 & 0.07 \\
 & & 0.5 & & 0.94 & 0.95 & 0.90 & & 0.10 & 0.11 & 0.09 \\
 & & 0.7 & & 0.94 & 0.95 & 0.90 & & 0.13 & 0.14 & 0.12 \\
 & & 1.0 & & 0.95 & 0.96 & 0.92 & & 0.18 & 0.21 & 0.16 \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Accuracy of our model of user behavior (Mixed) compared against two single-point benchmarks (Mean and Last). The area under the ROC curve (AUC) and average precision are reported for these models, for a variety choices of the multinomial logit tuning parameters ($\sigma,w$) and sampling exponent ($\alpha$). Results are aggregated over the entire set of test users, replicated 20 times each.}
\end{table}
\end{document}

stat

Vivek F. Farias
Andrew A. Li
Deeksha Sinha

"Optimizing Offer Sets in Sub-Linear Time" by Vivek F. Farias, Andrew A. Li, and Deeksha Sinha, arXiv:2011.08606v1, 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|ccc|ccc}
\hline\hline
 & \multicolumn{3}{c}{$ r = 0.8 $} \vline & \multicolumn{3}{c}{$ r = 1.0 $} \\
 & DAX & FTSE 100 & S\&P 500 & DAX & FTSE 100 & S\&P 500 \\
\hline
 $\tau = 0.05$ & 85 & 83 & 90 & 89 & 88 & 95 \\
 $\tau = 0.01$ & 93 & 95 & 95 & 100 & 102 & 101 \\
 $\tau = 0.0025$ & 101 & 97 & 101 & 108 & 99 & 104 \\
\hline
\hline
\end{tabular}
\end{adjustbox}
\caption{Mean value of the adaptively selected intervals. Note: the average number of trading days of the adaptive interval length is provided for the DAX, FTSE 100 and S\&P 500 market indices at three expectile levels, $\tau = 0.05$, $\tau = 0.01$ and $\tau = 0.0025$, and the modest $\left(r = 0.50\right)$ and the conservative $\left(r = 1.00\right)$ risk case.}
\end{table}
\end{document}

q-fin

Xiu Xu
Andrija Mihoci
Wolfgang Karl Härdle

"lCARE -- localizing Conditional AutoRegressive Expectiles" by Xiu Xu, Andrija Mihoci, and Wolfgang Karl Härdle, arXiv:2009.13215v1, 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{ICC scores of the FTD dataset, and mean measurement differences ± confidence interval (CI) of the HC18 dataset achieved by all the algorithms.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
Network Name & FTD ICC {\hskip 0.1in} & HC18 Mean Difference ± CI (mm) {\hskip 0.1in}\\
\hline
Vanilla BiometryNet & 0.684 & 4.56 ± 7.41\\
BiometryNet\_SAF\_a1 & 0.737 & 4.02 ± 6.70\\
BiometryNet\_SAF\_a4 & 0.724 & 4.03 ± 7.73\\
BiometryNet\_SAF\_a8 & 0.719 & 3.86 ± 7.74\\
EfficientNet & 0.688 & 32.76 ± 21.01\\
EfficientNet\_SAF\_a4 & 0.725 & 4.87 ± 5.79\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Shijia Zhou
Euijoon Ahn
Hao Wang
Ann Quinton
Narelle Kennedy
Pradeeba Sridar
Ralph Nanan
Jinman Kim

"Improving Automatic Fetal Biometry Measurement with Swoosh Activation Function" by Shijia Zhou, Euijoon Ahn, Hao Wang, Ann Quinton, Narelle Kennedy, Pradeeba Sridar, Ralph Nanan, and Jinman Kim, arXiv:2412.11377v1, 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}{cccc}
\hline
Dataset & Train & Dev & Test\\
\hline
\multicolumn{4}{c}{\textit{Unlabeled}} \\
\hline
NIH & 40365 & NA & NA \\
MIMIC-CXR & 57696 & NA & NA \\
\hline
\multicolumn{4}{c}{\textit{Labeled}} \\
\hline
MIMIC-CXR E & 3660 & 1182 & 12125 \\
MIMIC-CXR Co & 1120 & 375 & 11031 \\
MIMIC-CXR Ca & 11724 & 3876 & 12791 \\
MIMIC-CXR A & 2164 & 3992 & 12129 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Dataset statistics for all datasets. The training and development sets of MIMIC CXR Edema, Consolidation, Cardiomegaly and Atelecatasis were filtered to contain one negative control for each positive sample. Guides of abbreviations for medical diagnosis: (E)dema, (Co)nsolidation, (Ca)rdiomegaly and (A)telectasis.}
\end{table}
\end{document}

cs

Adam Yala
Victor Quach
Homa Esfahanizadeh
Rafael G. L. D'Oliveira
Ken R. Duffy
Muriel Médard
Tommi S. Jaakkola
Regina Barzilay

"Syfer: Neural Obfuscation for Private Data Release" by Adam Yala, Victor Quach, Homa Esfahanizadeh, Rafael G. L. D'Oliveira, Ken R. Duffy, Muriel Médard, Tommi S. Jaakkola, and Regina Barzilay, arXiv:2201.12406v1, 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
\multicolumn{3}{|c|}{(a) 2-class phase classification}\\
\hline
Label &
$h_2/J$&
Phase\\
\hline
0 & $-1.15<h_2/J<0$ & SPT \\
\hline
1 & $h_2/J<-1.15$ or $h_2/J>0$ & Other phases\\
\hline
\multicolumn{3}{|c|}{(b) 3-class phase classification} \\
\hline
Label &
$h_2/J$&
Phase\\
\hline
0 & $-1.15<h_2/J<0$ & SPT \\
\hline
1 & N/A & Nothing\\
\hline
2& $h_2/J>0$& Paramagnetic\\
\hline
3& $h_2/J<-1.15$& Antiferromagnetic\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Quantum Inception Score

"Quantum Inception Score" by Akira Sone, Akira Tanji, and Naoki Yamamoto, arXiv:2311.12163v4, 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
 $n$ & Error $\|\cdot\|_1$ & Order $\|\cdot\|_1$ & Error
 $\|\cdot\|_{\infty}$ & Order $\|\cdot\|_{\infty}$ \\
 \hline
 40 & 7.80E$-4$ & $-$ & 2.61E$-2$ & $-$ \\
 \hline
 80 & 2.82E$-6$ & 8.11 & 8.14E$-5$ & 8.32 \\
 \hline
 160 & 1.11E$-7$ & 4.66 & 6.31E$-6$ & 3.69 \\
 \hline
 320 & 2.40E$-9$ & 5.53 & 2.28E$-7$ & 4.79 \\
 \hline
 640 & 4.48E$-11$ & 5.74 & 6.73E$-9$ & 5.08 \\
 \hline
 1280 & 9.61E$-13$ & 5.54 & 1.92E$-10$ & 5.13 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Error table for Burgers equation, $t=0.3$. WLS-GAW.}
\end{table}
\end{document}

math

"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, 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{Clean downstream accuracy (CDA).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccc}
\toprule
Downstream Task & SimCLR & MoCo v2 & BYOL \\
\midrule
STL-10 & 0.783 & 0.889 & 0.948 \\
CIFAR-10 & 0.766 & 0.712 & 0.855 \\
MNIST & 0.974 & 0.940 & 0.974 \\
F-MNIST & 0.874 & 0.852 & 0.894 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders" by Tianshuo Cong, Xinlei He, and Yang Zhang, arXiv:2201.11692v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\hline\hline
Model & Log Likelihood & AIC & BIC \\
\hline
ARCH(1) & 1281.7 & -2567.4 & -2558.6 \\
ARCH(2) & 1283.4 & -2560.8 & -2547.6 \\
ARCH(3) & \textcolor{blue}{1291.6} & \textcolor{blue}{-2575.2} & \textcolor{blue}{-2557.5}\\
ARCH(4) & 1288.8 & -2567.5 & -2545.4\\
\hline\hline
\end{tabular}
\end{adjustbox}
\caption{Estimation result of ARIMA-ARCH models.}
\end{table}
\end{document}

q-fin

Shi Chen
Cathy Yi-Hsuan Chen
Wolfgang Karl Härdle

"A first econometric analysis of the CRIX family" by Shi Chen, Cathy Yi-Hsuan Chen, and Wolfgang Karl Härdle, arXiv:2009.12129v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Guozhong Zheng
Jiqiang Zhang
Jing Zhang
Weiran Cai
Li Chen

"Decoding trust: A reinforcement learning perspective" by Guozhong Zheng, Jiqiang Zhang, Jing Zhang, Weiran Cai, and Li Chen, arXiv:2309.14598v2, 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{Spearman's correlation between learned parameters and computed normalized student delays. p$<$0.001\text{***} p$<$0.01 \text{**} p$<$0.05\text{*}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 \multicolumn{2}{|c|}{} & $\alpha$ & $\mu$ & delay \\ \hline
 \multirow{3}{*}{CANVAS}
 &$\alpha$ & 1 & & \\\cline{2-5}
 & $\mu$ & 0.284\text{***}& 1 & \\\cline{2-5}
 & delay & 0.345\text{***} & 0.144\text{***}&1\\\hline
 \multirow{3}{*}{MORF}
 &$\alpha$ & 1 & & \\\cline{2-5}
 &$\mu$ & 0.243\text{***}& 1 & \\\cline{2-5}
 &delay & 0.264\text{***} & 0.412\text{***}&1\\
\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mengfan Yao
Siqian Zhao
Shaghayegh Sahebi
Reza Feyzi Behnagh

"Relaxed Clustered Hawkes Process for Procrastination Modeling in MOOCs" by Mengfan Yao, Siqian Zhao, Shaghayegh Sahebi, and Reza Feyzi Behnagh, arXiv:2102.00093v1, 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{Heterogeneity in treatment effects}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
Variable & Above median & Below median & Difference & $p$-value \\ \hline
Metaculus & 0.053*** & 0.077*** & 0.024* & 0.096 \\
24 hour volume & 0.049*** & 0.081*** & 0.032** & 0.041 \\
Total volume & 0.069*** & 0.081*** & 0.012 & 0.191 \\
Comments & 0.069*** & 0.084*** & 0.015* & 0.071 \\
Total traders & 0.067*** & 0.083*** & 0.017* & 0.056 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

How manipulable are prediction markets?

"How manipulable are prediction markets?" by Itzhak Rasooly and Roberto Rozzi, arXiv:2503.03312v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Hongfei Wang
Binghui Liu
Long Feng
Yanyuan Ma

"Fisher's combined probability test for cross-sectional independence in panel data models with serial correlation" by Hongfei Wang, Binghui Liu, Long Feng, and Yanyuan Ma, arXiv:2309.08543v1, 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{Overlap detection results on AMI mix-headset data, in terms of Precision (\%) and Recall (\%).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\multirow{2}{*}{\textbf{Model (feature type)}} & \multicolumn{2}{c}{\textbf{Dev}} & \multicolumn{2}{c}{\textbf{Eval}} \\
\cmidrule(r{4pt}){2-3} \cmidrule(l){4-5}
 & \textbf{Precision} & \textbf{Recall} & \textbf{Precision} & \textbf{Recall} \\
\midrule
ConvNet (Spectogram)~ & 80.5 & 50.2 & 75.8 & 44.6 \\
E2E BLSTM (MFCC)~ & 90.0 & 52.5 & 91.9 & 48.4 \\
E2E BLSTM (SincNet)~ & 90.0 & 63.8 & 86.8 & 65.8 \\
\midrule
Our method (MFCC) & 83.9 & 68.5 & 86.4 & 65.2 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Multi-class Spectral Clustering with Overlaps for Speaker Diarization" by Desh Raj, Zili Huang, and Sanjeev Khudanpur, arXiv:2011.02900v1, 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}{ccccc}
 \hline
 \textbf{\# Dimensions} & \textbf{SG Level} & \textbf{\# of Points} & \textbf{Avg. Error} & \textbf{Max. error (99.9\%)} \\
 \hline
 4 & 3 & 137 & -3.62 & -2.61 \\
 4 & 5 & 1105 & -4.21 & -3.10 \\
 4 & 7 & 7,537 & -4.64 & -3.31 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Average (Avg.) and maximum (Max.) Euler equation residuals (i.e., errors) for the $4$-dimensional IRBC model across increasing SG refinement levels (SG Level), with corresponding grid point counts. All errors are reported in $\log_{10}$ scale.}
\end{table}
\end{document}

q-fin

Aryan Eftekhari
Michel Juillard
Normann Rion
Simon Scheidegger

"Scalable Global Solution Techniques for High-Dimensional Models in Dynare" by Aryan Eftekhari, Michel Juillard, Normann Rion, and Simon Scheidegger, arXiv:2503.11464v1, 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|c|c|}
\hline
Stock/bond & Years & $P$ & $T$ & Average Wealth & 90\% Wealth \\
\hline
90/10 & 20 & 10.15\% & 15.54 & 4.8 & 11.5 \\
\hline
90/10 & 40 & 30.66\% & 24.76 & 48.7 & 122.6 \\
\hline
60/40 & 20 & 4.35\% & 17.12 & 2.8 & 5.8 \\
\hline
60/40 & 40 & 28.2\% & 28.46 & 14.4 & 37.8 \\
\hline
40/60 & 20 & 1.51\% & 18.16 & 2.0 & 3.6\\
\hline
40/60 & 40 & 32.3\% & 31.1 & 5.2 & 14.0\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Ruin probability $P$, ruin average time $T$, average final wealth, and final wealth corresponding to the 90\%-path.}
\end{table}
\end{document}

q-fin

Andrey Sarantsev
Angel Piotrowski
Ian Anderson

"A Time Series Model for Three Asset Classes used in Financial Simulator" by Andrey Sarantsev, Angel Piotrowski, and Ian Anderson, arXiv:2508.06010v1, 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}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\emph{Ablation analysis.} Mean and standard deviation of accuracy (\%) on CLEVR and CLOSURE across three repetitions. We add imitations of properties of TMN, i.e., Variable number of layers (VL) and Split tokens (ST) to the standard Transformer w/ PR. VL is an imitation of the question-specific composition of modules in TMN and changes the number of layers depending on a program length (up to $26$), but the each layer (module in TMN) is not specialized for specific sub-tasks. SoM stands for the specialization of the modules.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|c|c}
\toprule
\multirow{2}{*}{Methods} &\multicolumn{3}{c|}{Properties} & CLEVR & CLOSURE \\ %\cmidrule{1-3}
 & VL & ST & SoM & (In-Distribution) & (Syst. Generalization) \\ \midrule
Transformer w/ PR & & & & 97.1 $\pm$ 0.14 & 64.5 $\pm$ 2.5 \\
Transformer w/ PR + VL & $\checkmark$ & & &94.0 $\pm$ 1.8 & 60.2 $\pm$ 2.9 \\
Transformer w/ PR + VL + ST& $\checkmark$ & $\checkmark$ & & 56.8 $\pm$ 0.042 & 43.6 $\pm$ 0.59 \\
TMN-Stack & $\checkmark$ & $\checkmark$ & $\checkmark$ & 98.0 $\pm$ 0.030 & \textbf{90.9 $\pm$ 0.49} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Moyuru Yamada
Vanessa D'Amario
Kentaro Takemoto
Xavier Boix
Tomotake Sasaki

"Transformer Module Networks for Systematic Generalization in Visual Question Answering" by Moyuru Yamada, Vanessa D'Amario, Kentaro Takemoto, Xavier Boix, and Tomotake Sasaki, arXiv:2201.11316v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|cccc}
 \toprule
 Method& T\_1& T\_2& T\_3& T\_4\\
 \midrule
 CoW& 0.39& 0.22& 0.08& 0.03\\
 CLIP Map& 0.33& 0.19& 0.06& 0.0\\
 VLMap& 0.56& 0.39& 0.19& 0.08\\
 \textbf{IVLMap (Ours)}& \textbf{0.64}& \textbf{0.44}& \textbf{0.25}& \textbf{0.14}\\
 \bottomrule
 \end{tabular}
\caption{Outcome of Zero-Shot Instance Level Object Goal Navigation from Natural Language. T\_k for achieving the kth subgoal out of the total 4 subgoals in each task. }
\end{table}
\end{document}

cs

Jiacui Huang
Hongtao Zhang
Mingbo Zhao
Zhou Wu

"IVLMap: Instance-Aware Visual Language Grounding for Consumer Robot Navigation" by Jiacui Huang, Hongtao Zhang, Mingbo Zhao, and Zhou Wu, arXiv:2403.19336v1, 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}{rrr}
\hline
\textbf{Obs} & \textbf{SITEID} & \textbf{one} \\ \hline
\textbf{1} & 1008 & 1 \\
\textbf{2} & 1009 & 1 \\
\textbf{3} & 1010 & 1 \\
\textbf{4} & 1012 & 1 \\
\textbf{5} & 1015 & 1 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Tracie L. Shing
John S. Preisser
Richard C. Zink

"GEECORR: A SAS macro for regression models of correlated binary responses and within-cluster correlation using generalized estimating equations" by Tracie L. Shing, John S. Preisser, and Richard C. Zink, arXiv:2011.11592v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Szymon Kubiak
Tillman Weyde
Oleksandr Galkin
Dan Philps
Ram Gopal

"Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe" by Szymon Kubiak, Tillman Weyde, Oleksandr Galkin, Dan Philps, and Ram Gopal, arXiv:2311.16004v1, 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}{cccccc}
 & Success \% & Distance \% \\
 \hline
 Without Initialisation & 38.8 & 73.2 \\
 Without Extended Reward & 12.3 & 58.3 \\
 Full Method & \textbf{82.2} & \textbf{96.1}\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Ablation study for initialising setup policies from a default walk policy, and receiving an extended reward, for switching from a walking policy to a jump policy on a single terrain sample.}
\end{table}
\end{document}

cs

Brendan Tidd
Nicolas Hudson
Akansel Cosgun
Jurgen Leitner

"Learning Setup Policies: Reliable Transition Between Locomotion Behaviours" by Brendan Tidd, Nicolas Hudson, Akansel Cosgun, and Jurgen Leitner, arXiv:2101.09391v2, 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
 $n=7500$ & Predicted: Default & Predicted: Not default \\ \hline
Actual: Default & 500 & 1095 \\ \hline
Actual: Not default & 234 & 5671 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Confusion matrix of the neural network for the Taiwan credit score dataset. }
\end{table}
\end{document}

q-fin

"Interpretable Selective Learning in Credit Risk" by Dangxing Chen, Weicheng Ye, and Jiahui Ye, arXiv:2209.10127v1, 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{Statistics of the ASVspoof19 dataset (logical access) for training spoofing countermeasure systems, with the number of speakers by gender.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Partition}& \textbf{Male} & \textbf{Female} & \textbf{Bonafide} & \textbf{Spoof} \\ \midrule
 Train & 8 & 12 & 2,580 & 22,800 \\
 Development & 8 & 12 & 2,548 & 22,296 \\
 Evaluation & 30 & 37 & 7,355 & 63,882 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Anssi Kanervisto
Ville Hautamäki
Tomi Kinnunen
Junichi Yamagishi

"Optimizing Tandem Speaker Verification and Anti-Spoofing Systems" by Anssi Kanervisto, Ville Hautamäki, Tomi Kinnunen, and Junichi Yamagishi, arXiv:2201.09709v1, 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{Subjects' Self- and Group Assessments Before and After the Test \& Questionnaire Control Summary Statistics, by Treatment}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccccc}
 \hline
 \hline
 & & \multicolumn{2}{c}{Replication} & & \multicolumn{2}{c}{Adaptation} & & \multicolumn{2}{c}{Mann-Whitney U}\\\cline{3-4} \cline{6-7} \cline{9-10}
 Variable & & Mean & Std. Dev. & & Mean & Std. Dev. & & z & p-value\\
 \hline
 \textbf{Before} & & & & & & & & \\
 Predicted own performance & & 13.54 & 3.20 & & 14.28 & 3.60 & & 1.77 & 0.0766\\
 Predicted group performance & & 12.27 & 2.40 & & 13.47 & 2.27 & & 3.18 & 0.0014 \\
 Better & & 1.118 & 0.25 & & 1.079 & 0.28 & & -0.32 & 0.7530 \\
 \hline
 \textbf{Before} & & & & & & & & \\
 Sample performance & & 2.36 & 0.85 & & 2.41 & 0.74 & & 0.18 & 0.8593\\
 Test performance & & 13.6 & 3.1 & & 13.1 & 3.3 & & -0.83 & 0.4074 \\
 \textbf{After} & & & & & & & & & \\
 Estimated group performance & & 12.6 & 2.6 & & 12.6 & 2.7 & & -0.13 & 0.8988\\
 Estimation accuracy & & -0.1 & 3.5 & & 1.2 & 4.3 & & 1.93 & 0.0530\\
 \hline
 \textbf{Questionnaire Controls} & & & & & & & & \\
 INCOM & & 3.56 & 0.66 & & 3.54 & 0.69 & & 0.22 & 0.8297 \\
 GSE & & 3.84 & 0.77 & & 3.91 & 0.57 & & 0.03 & 0.9810\\
 Altruism & & 3.77 & 0.72 & & 3.84 & 0.70 & & -0.79 & 0.4308\\
 Risk (MPL) & & 15.35 & 6.43 & & 15.39 & 5.63 & & -0.22 & 0.8269 \\
 Risk (11-point) & & 6.13 & 2.47 & & 5.58 & 2.33 & & 1.489 & 0.1368\\
 Ambiguity & & 12.58 & 5.86 & & 12.16 & 5.43 & & 0.416 & 0.6788\\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)" by Marius Protte, arXiv:2507.15568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A Differential Index Measuring Rater's Capability in Educational Assessment" by Y. -G. Wang, J. Wu, and X. Qiu, arXiv:2502.09099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Sourav Medya
Mohammad Rasoolinejad
Yang Yang
Brian Uzzi

"An Exploratory Study of Stock Price Movements from Earnings Calls" by Sourav Medya, Mohammad Rasoolinejad, Yang Yang, and Brian Uzzi, arXiv:2203.12460v1, 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|l|l|}
\hline
\textbf{Transaction} & \textbf{Gas usage} & \textbf{Cost (USD)} & \textbf{Impact (kgCO\textsubscript{2}eq)} \\ \hline
Minting & 450k & 88.03 & 114.90 \\ \hline
Buying & 300k & 58.69 & 76.60 \\ \hline
Transferring & 80k & 15.65 & 20.42 \\ \hline
Bidding & 100k & 19.56 & 25.54 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Impact of different transactions of a hypothetical cryptoart piece. We use the average gas price from April 29, 2021 to May 5, 2021 of 61 Gwei , as well as the average Ether price of 3207 USD in the same period .}
\end{table}
\end{document}

cs

"Green NFTs: A Study on the Environmental Impact of Cryptoart Technologies" by Samuele Marro and Luca Donno, arXiv:2202.00003v3, 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}{|l|c|c|c|c|c|c|c|c|c|}
 \toprule
 \# OTM opt. & JUN20 & SEP20 & DEC20 & MAR21 & JUN21 & SEP21 & DEC21 & JUN22 & DEC22 \\
 \bottomrule
 \toprule
 Mean & 92 & 116 & 105 & 58 & 49 & 13 & 49 & 10 & 24 \\
 Median & 92 & 120 & 119 & 51 & 46 & 13 & 54 & 9 & 26 \\
 Std & 9 & 7 & 27 & 25 & 7 & 3 & 9 & 1 & 6 \\
 $q_{0.05}$ & 76 & 101 & 30 & 26 & 42 & 7 & 26 & 8 & 7 \\
 $q_{0.95}$ & 103 & 120 & 120 & 94 & 63 & 17 & 56 & 11 & 28 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\small{Number of OTM options considered in the Volatility surface calibration. We report the number of OTM call ($K>F_0$) and put ($K<F_0$) in the moneyness range $[-30\,\$,30\,\$]$: mean, median, standard deviation (std), and quantiles (q) 5\%, 95\% considering each value date.}}
\end{table}
\end{document}

q-fin

Roberto Baviera
Michele Domenico Massaria

"The Additive Bachelier model with an application to the oil option market in the Covid period" by Roberto Baviera and Michele Domenico Massaria, arXiv:2506.09760v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Attention vs Choice in Incomplete Welfare Take-Up: What Works for WIC?" by Lei Bill Wang and Sooa Ahn, arXiv:2506.03457v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

A. Felipe
M. Jaenada
P. Miranda
L. Pardo

"Robust parameter estimation of the log-logistic distribution based on density power divergence estimators" by A. Felipe, M. Jaenada, P. Miranda, and L. Pardo, arXiv:2312.02662v1, 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}{r|lll|lllll}
&\multicolumn{3}{c}{New Scheme} &\multicolumn{5}{c}{ILUTP($\tau$)} \\
\# & $\tau_{large}$ & $\tau_{mix}$ & $\tau_{small}$ & $10^{-5}$ & $10^{-4}$ & $10^{-3}$ &$10^{-2}$ & $10^{-1}$ \\ \hline
1 & 3 & 3 & 2& 3 & 5 &$\dagger$ &$\dagger$ &$\dagger$ \\
2 & 10 & 9 & 2 & 4 & 9 &$\dagger$ &$\dagger$ &$\dagger$ \\
3 & 2 & 2 & 1 & 1 & 2 & 2 & 3 & 4 \\
4 & 7 & 2 & 1& 1 & 2 & 2 & 3 & 4 \\
5 & 34 & 5 & 4 & 1 & 2 & 2 & 3 &4 \\
6 & 27 & 11 & 11 & 1 & 2 & 2 & 3 & 4 \\
7 & 4 & 17 & 1 & 2 & 2 & 4 & 10 &$\dagger$ \\
8 & $\ddagger$ & 53 & 5 & $\dagger$ & $\dagger$ & $\dagger$ & $\dagger$ &$\dagger$ \\
9 & 9 & 2 & 2 & 1 & 1 & $\dagger$ & $\dagger$ &$\dagger$ \\
\end{tabular}
\end{adjustbox}
\caption{Number of outer fGMRES/GMRES iterations for the general case}
\end{table}
\end{document}

math

"Robust iterative methods for linear systems with saddle point structure" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Birthweight Declined During the Pandemic and It Is Falling Further Post-pandemic" by Maysam Rabbani and Elijah Gervais, arXiv:2505.14735v1, 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}{||cc||}
 \hline
 Embedding Dimensions & 512 \\
 Crysformer MLP Dimensions & 512 \\
 Attention Head Count & 8 \\
 Patch Size & 4 \\
 Attention Block Depth & 6 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Default hyperparameter values for tuning on dipeptide dataset.}
\end{table}
\end{document}

math

Tom Pan
Evan Dramko
Mitchell D. Miller
George N. Phillips
Anastasios Kyrillidis

"RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runs" by Tom Pan, Evan Dramko, Mitchell D. Miller, George N. Phillips, and Anastasios Kyrillidis, arXiv:2503.00143v1, 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{Simulation exercises} \medskip
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
				\hline \hline
				Parameter & Exercise 1 & Exercise 2 & Exercise 3 ($p_4=0.4$, $T=50$)\\
				 & $T(W_B=W_0)$ & $p_3(W_D=W_0)$ & $p_3(W_E=W_0)$ \\
				 \hline
				 Baseline & 98.11 & 58.30\% & 30.09\% \\
				 $\rho=0.03$ & 47.23 & 37.33\% & 27.13\% \\
				 $\rho=0.05$ & 33.72 & 27.38\% & 23.10\% \\
				 $g^{AI}=0.4$ & 79.31 & 67.25\% & 51.74\% \\
				 $g^{AI}=0.1$ & 150.31 & 36.05\% & 9.26\% \\
				\hline \hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The Economics of p(doom): Scenarios of Existential Risk and Economic Growth in the Age of Transformative AI" by Jakub Growiec and Klaus Prettner, arXiv:2503.07341v1, 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|}
 \hline
 Functional Response & Formulation & $a$ & $b$ \\
 \hline
 Holling & $\displaystyle g_h(y) = \frac{a_h}{1 + b_h y}$ & 0.385 & 0.265 \\ & & &\\
 Ivlev & $\displaystyle g_i(y) = \frac{a_i}{b_i(1 - \exp(-b_i y))}$ & 0.21 & 0.15\\ & & & \\
 Trigonometric & $\displaystyle g_t(y) = \frac{a_t}{b_t}\tanh(b_t y)$ & 0.27 & 0.18 \\ & & & \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{ Estimated parameters for different functional form}
\end{table}
\end{document}

math

Suvranil Chowdhury
Susmita Sarkar
Joydev Chattopadhyay

"Modeling Cost-Associated Cooperation: A Dilemma of Species Interaction Unveiling New Aspects of Fear Effect" by Suvranil Chowdhury, Susmita Sarkar, and Joydev Chattopadhyay, arXiv:2501.16522v1, 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{Inference gaps on trees in the 95\% credible set of DS1. The \textsc{All} columns refer to the average gaps over all trees in the credible set.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccccc}
\toprule
\multirow{2}{*}{Gap} & \multicolumn{2}{c}{PSP} & \multicolumn{2}{c}{Planar (16)} & \multicolumn{2}{c}{Planar (32)} & \multicolumn{2}{c}{RealNVP (5)} & \multicolumn{2}{c}{RealNVP (10)}\\
\cmidrule(lr){2-3}\cmidrule(lr){4-5}\cmidrule(lr){6-7}\cmidrule(lr){8-9}\cmidrule(lr){10-11}
& Tree 36 & All & Tree 36 & All & Tree 36 & All& Tree 36 & All & Tree 36 & All \\
\midrule
Approximation & 1.29 & 1.21 & 1.12 & 1.08 & 1.07 & 1.02 & 0.65 & 0.62 & {\bfseries 0.43} & {\bfseries 0.40} \\
Amortization & 3.37 & 0.84 & 2.80 & 0.82 & {\bfseries 1.33} & {\bfseries 0.72} & 3.10 & 0.98 & 1.83 & 0.93 \\
Inference & 4.66 & 2.05 & 3.92 & 1.90 & 2.40 & 1.74 & 3.75 & 1.60 & {\bfseries 2.26} & {\bfseries 1.33} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows" by Cheng Zhang, arXiv:2012.00459v1, 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{Two-Way Fixed Effects Estimate - Offline Elasticity}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \tabularnewline \midrule \midrule
 Dependent Variable: & \multicolumn{4}{c}{Offline Price Elasticity}\\
 & Dry Dog Food & Dry Cat Food & Dog Hygiene & Cat Hygiene \\
 Model: & (1) & (2) & (3) & (4)\\
 \midrule
 \emph{Variables}\\
 PostOnlineAdoption & -0.2145$^{**}$ & -0.0206$^{***}$ & -1.544$^{***}$ & -0.6368$^{***}$\\
 & (0.0697) & (0.0038) & (0.3308) & (0.1605)\\
 \midrule
 \emph{Fixed-effects}\\
 Customer & Yes & Yes & Yes & Yes\\
 YearMonth & Yes & Yes & Yes & Yes\\
 \midrule
 \emph{Fit statistics}\\
 Observations & 136,411 & 45,443 & 78,253 & 55,410\\
 R$^2$ & 0.91126 & 0.95578 & 0.76395 & 0.82317\\
 Within R$^2$ & 0.24557 & 0.01260 & 0.32091 & 0.32582\\
 \midrule \midrule
 \multicolumn{5}{l}{\emph{Bootstrapped standard-errors in parentheses via 500 replications}}\\
 \multicolumn{5}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05}}\\
 \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.


q-fin

Early Impacts of M365 Copilot

Eleanor Wiske Dillon
Sonia Jaffe
Sida Peng
Alexia Cambon

"Early Impacts of M365 Copilot" by Eleanor Wiske Dillon, Sonia Jaffe, Sida Peng, and Alexia Cambon, arXiv:2504.11443v1, 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{Cluster and Sector Average Hype Indices}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Cluster} & \textbf{Cluster Avg.} & \textbf{Sector} & \textbf{Sector Avg.} \\
 & \textbf{Hype Index} & & \textbf{Hype Index} \\
\hline
Over-Hyped & 0.19354 & Financials & 0.19395 \\
 & & Information Technology & 0.19313 \\
\hline
Neutral-Hyped & 0.11352 & Communication & 0.12236 \\
 & & Consumer Discretionary & 0.11554 \\
 & & Health Care & 0.11534 \\
 & & Industrials & 0.10253 \\
 & & Consumer Staples & 0.09190 \\
\hline
Under-Hyped & 0.01681 & Energy & 0.02678 \\
 & & Utilities & 0.01842 \\
 & & Real Estate & 0.01371 \\
 & & Materials & 0.00633 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Zheng Cao
Wanchaloem Wunkaew
Helyette Geman

"The Hype Index: an NLP-driven Measure of Market News Attention" by Zheng Cao, Wanchaloem Wunkaew, and Helyette Geman, arXiv:2506.06329v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Jason A. Platt
Stephen G. Penny
Timothy A. Smith
Tse-Chun Chen
Henry D. I. Abarbanel

"A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics" by Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen, and Henry D. I. Abarbanel, arXiv:2201.08910v1, 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}{llllll}
 \toprule
 & & \multicolumn{4}{c}{\textbf{Placement}} \\
 \cmidrule(r){3-6}
 &&\multicolumn{2}{c}{\textbf{DR}}& \multicolumn{2}{c}{\textbf{Random}} \\
 \textbf{Measure}&\textbf{Label}& \textbf{$\overline{B} -\overline{L}$} &\textbf{\textit{p-val.}}&\textbf{$\overline{B} -\overline{L}$} &\textbf{\textit{p-val.}}\\ \midrule
 Time &&&&\\
 & Shape & -0.1 & 1.0 & -0.4 & 0.97 \\
 & Color & -2.5 & \textbf{0.05} & 0.8 & 0.82 \\
 & Texture & -1.1 & 0.56 & -3.0 & \textbf{0.05}\\

 Hovered&&&&\\
 samples& Shape & 1.0 & 0.96 & 3.1 & 0.50 \\
 & Color & -1.4 & 0.92 & 5.0 & \textbf{0.05} \\
 & Texture & -1.3 & 0.93 & -2.7 & 0.71\\

 Distance &&&&\\
 & Shape & 71 & 0.99 & -131& 0.98 \\
 & Color & -333 & 0.72 & 272 & 0.82 \\
 & Texture & -150 & 0.95 & -1136 & \textbf{0.02} \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"A proposal and evaluation of new timbre visualisation methods for audio sample browsers" by Etienne Richan and Jean Rouat, arXiv:2011.15096v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Beyond Volatility: Common Factors in Idiosyncratic Quantile Risks" by Jozef Barunik and Matej Nevrla, arXiv:2208.14267v4, 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{API-based LLMs considered in this paper via \emph{FinEval}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 \toprule
 Type & Models & Version & Provider \\
 \midrule
 \multirow{7}{*}{Online}
 & GPT-4o-mini & gpt-4o-mini-2024-07-18 &OpenAI \\
 & GPT-4.1 &gpt-4.1-2025-04-14 &OpenAI \\
 & GPT-4.1-mini &gpt-4.1-mini-2025-04-14 &OpenAI \\
 & GPT-4.1-nano &gpt-4.1-nano-2025-04-14 &OpenAI \\
 & o3-mini & o3-mini-2025-01-31 & OpenAI \\
 & DeepSeek-V3 & deepseek-v3-250324 & Huoshan \\
 & Claude-3.7-Sonnet &claude-3-7-sonnet-20250219 & Anthropic \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Junzhe Jiang
Chang Yang
Aixin Cui
Sihan Jin
Ruiyu Wang
Bo Li
Xiao Huang
Dongning Sun
Xinrun Wang

"FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs" by Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Ruiyu Wang, Bo Li, Xiao Huang, Dongning Sun, and Xinrun Wang, arXiv:2505.13533v1, 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{Accuracy Improvements with 1-Layer GCN for SBM Datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
 Dataset & Clustering &Intra & $\widehat{H}$ & Improvement \\ \hline
 SBM\_PATTERN & \textbf{0.427} & \textbf{0.589} & \textbf{0.811} & \textbf{34.78} \\
 \hline
 SBM\_CLUSTER & 0.317 &0.33 & 0.954 & 27.68 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Lavender Yao Jiang
John Shi
Mark Cheung
Oren Wright
José M. F. Moura

"Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification" by Lavender Yao Jiang, John Shi, Mark Cheung, Oren Wright, and José M. F. Moura, arXiv:2012.08698v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Recognition accuracy (\%) of competing approaches on the AR database when only one neutral face image per subject from Session 1 is used as training sample, the dimension that leads to the best result for IGO and SOIGO based approaches is given in parentheses.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\hline
Methods & \multicolumn{2}{c}{Sunglasses} & \multicolumn{2}{c}{Scarf} & Overall \\ \hline
 & Session1 & Session2 & Session1 & Session2 & \\
HQPAMI & 56.67 & 38.00 & 38.00 & 22.33 & 3875 \\
NR & 28.33 & 16.67 & 29.67 & 17.33 & 23.00 \\
ProCRC & 53.07 & 31.00 & 18.67 & 7.33 & 27.52 \\
F-LR-IRNNLS & 88.67 & 60.33 & 67.00 & 49.67 & 66.42 \\
EGSNR & 84.00 & 54.00 & 70.33 & 48.33 & 64.16 \\
LDMR & 68.33 & 45.67 & 59.67 & 34.00 & 51.92 \\
GD-HASLR & 92.00 & 66.67 & 82.67 & 58.67 & 75.00 \\
IGO-PCA-NNC & 89.00 (99) & 69.00 (99) & 73.33 (97) & 53.33 (96)& 71.17\\
IGO-PCA-CRC & \textbf{93.00 (85)} & 74.33 (92) & 81.67 (88) & 58.33 (95) & 76.83 \\
SOIGO-PCA-NNC & 88.67 (92) & 73.33 (96) & 80.33 (99) & 61.00 (88) & 75.83 \\
CSOIGO & 92.67 (89) & \textbf{76.67 (93)} & \textbf{83.33 (75)} & \textbf{65.33 (99)} & \textbf{79.50} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Face recognition via compact second order image gradient orientations" by He-Feng Yin, Xiao-Jun Wu, and Xiaoning Song, arXiv:2201.09246v2, 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{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The LibriAugmented dataset. $\mathcal{D}_{LLM}$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\hline
\textbf{Ratio} & \textbf{Impairment (Parameters)} \\
\hline
$0.050$ & Identity \\
$0.050$ & AddBackgroundNoise (snr=$-10\sim 15$ dB) \\
$0.050$ & ClippingImpairment (percentile=$10\sim40\%$) \\
$0.050$ & GainTransition (gain=$-60\sim20$ dB) \\
$0.050$ & LowPassFilter (cutoff\_freq=$0.5\sim1$ kHz) \\
$0.050$ & Mp3Compression (bit\_rate=$8\sim14$) \\
$0.050$ & PitchShift (semitones=$-4\sim4$ kHz) \\
$0.050$ & RoomSimulator (rt60=$0.8\sim1.5$ s) \\
$0.050$ & TimeMask (band\_part=$0.2\sim0.5$) \\
$0.050$ & TimeStretch (rate=$0.5\sim2$) \\
$0.083$ & AddBackgroundNoise + RoomSimulator \\
$0.083$ & AddBackgroundNoise + LowPassFilter \\
$0.083$ & AddBackgroundNoise + TimeStretch \\
$0.083$ & RoomSimulator + Mp3Compression \\
$0.083$ & PitchShift + LowPassFilter \\
$0.083$ & GainTransition + TimeMask \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Fredrik Cumlin
Xinyu Liang
Anubhab Ghosh
Saikat Chatterjee

"Leveraging LLMs for Scalable Non-intrusive Speech Quality Assessment" by Fredrik Cumlin, Xinyu Liang, Anubhab Ghosh, and Saikat Chatterjee, arXiv:2508.06284v1, 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{Solution Number under Different Resolution}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
\toprule
Resolution (step increment) & 8 & 16 & 32 & 64 \\ \midrule
Size of binary solution & 19 & 17 & 15 & 13 \\
Solution number & 524288 & 131072 & 32768 & 8192 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yingfang Yuan
Wenjun Wang
George M. Coghill
Wei Pang

"A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks" by Yingfang Yuan, Wenjun Wang, George M. Coghill, and Wei Pang, arXiv:2101.09300v2, 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}{cccc}
 \toprule
 $n$ & \textit{largest} $m$ & $n(n-1)/2$ & \textit{density} \\
 \midrule
 11 & 25 & 55 & 0.45\\
 12 & 31 & 66 & 0.47\\
 13 & 34 & 78 & 0.43\\
 14 & 40 & 91 & 0.44\\
 15 & 45 & 105 & 0.43\\
 16 & 48 & 120 & 0.40\\
 20 & 82 & 190 & 0.43\\
 30 & 95 & 435 & 0.22\\
 40 & 133 & 780 & 0.17\\
 50 & 163 & 1225 & 0.13\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\small Additional results about the $G(n,m)$ model. For each $n$, the density of the found 1-metric antidimensional graph having the maximum number of edges is reported.}
\end{table}
\end{document}

math

Serafino Cicerone
Gabriele Di Stefano
Sandi Klavžar
Ismael G. Yero

"Burning some myths on privacy properties of social networks against active attacks" by Serafino Cicerone, Gabriele Di Stefano, Sandi Klavžar, and Ismael G. Yero, arXiv:2504.16944v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc}
\hline
\(n\) & Min Gap of $TU_n$ \\ \hline
1 & 1 \\
2 & 0.585786 \\
3 & 0.381966 \\
4 & 0.267949 \\
5 & 0.198062 \\
6 & 0.152241 \\
7 & 0.120615 \\
8 & 0.097887 \\
9 & 0.0810141 \\
10 & 0.0681483 \\ \hline
\end{tabular}
\caption{The minimum spectral gap of twisted unknots $TU_n$ for different values of \(n\). All of these minimal gaps occurred in bidgree $(0,3-n)$. }
\end{table}
\end{document}

math

Alexander Schmidhuber
Michele Reilly
Paolo Zanardi
Seth Lloyd
Aaron Lauda

"A quantum algorithm for Khovanov homology" by Alexander Schmidhuber, Michele Reilly, Paolo Zanardi, Seth Lloyd, and Aaron Lauda, arXiv:2501.12378v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.