\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{hhline}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 & imp DG$^{\Delta t = 10^{-3}}_{\delta = 0.05}$
 & bwe DG$^{\Delta t = 10^{-3}}$
 & fwe DG$^{\Delta t = 1.5 \times 10^{-5}}_{\delta = 0.5}$
 & emp DG$^{\Delta t = 3 \times 10^{-5}}_{\delta = 0.05}$
 & RK4 DG$^{\Delta t = 6 \times 10^{-5}}_{\delta = 0.05}$ \\
 \hhline{======}
 time & 2425 s & 2861 s & 28694 s & 27291 s & 25612 s \\
 {\bf factor} & {\bf 1.0} & {\bf 1.2} & {\bf 11.0} & {\bf 11.3} & {\bf 10.6}
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Milan Holec
Ben Zhu
Ilon Joseph
Christopher J. Vogl
Ben S. Southworth
Alejandro Campos
Andris M. Dimits
Will E. Pazner

"Arbitrary Order Energy and Enstrophy Conserving Finite Element Methods for 2D Incompressible Fluid Dynamics and Drift-Reduced Magnetohydrodynamics" by Milan Holec, Ben Zhu, Ilon Joseph, Christopher J. Vogl, Ben S. Southworth, Alejandro Campos, Andris M. Dimits, and Will E. Pazner, arXiv:2202.13022v1, 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|}
\hline
Layer & Number of neurons & Activation function \\ \hline
input & 50 & ReLU \\ \hline
hidden layer 1 & 50 & ReLU \\ \hline
hidden layer 2 & 50 & ReLU \\ \hline
output & 1 & none \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Summary of the layers and activation functions of the PINN used to directly approximate the heat transfer solution. ReLU stands for Rectified Linear Unit.}
\end{table}
\end{document}

math

Kamil Doległo
Anna Paszyńska
Maciej Paszyński
Leszek Demkowicz

"Deep neural networks for smooth approximation of physics with higher order and continuity B-spline base functions" by Kamil Doległo, Anna Paszyńska, Maciej Paszyński, and Leszek Demkowicz, arXiv:2201.00904v1, 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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Value Matching effectiveness of different models in Auto-Join Benchmark. The best score along each column is in bold; the second best score is \underline{underlined}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|rrr|}
 \toprule
 \textbf{Model}&{\textbf{Precision}}&{\textbf{Recall}}&{$\mathbf{F_1}$\textbf{-Score}}\\
 \hline
 FastText&0.70&0.67&0.66\\
 BERT&0.72&0.76&0.73\\
 RoBERTa&0.73&0.77&0.74\\
 Llama3&\underline{0.81}&\underline{0.85}&\underline{0.81}\\
 Mistral&\textbf{0.81}&\textbf{0.86}&\textbf{0.82}\\

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

cs

Fuzzy Integration of Data Lake Tables

Aamod Khatiwada
Roee Shraga
Renée J. Miller

"Fuzzy Integration of Data Lake Tables" by Aamod Khatiwada, Roee Shraga, and Renée J. Miller, arXiv:2501.09211v1, 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{Experimental Results on NLP Tasks}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
Task & Method & Accuracy (\%) & FLOPs (G) & Memory (MB) \\
\midrule
Text Classification (AG News)& Transformer-Base & 92.5 & 2.8 & 150 \\
 & Early-Exit & 92.1 & 2.4 & 130 \\
 & Dynamic-Depth & 92.3 & 2.1 & 120 \\
 & \textbf{Ours (Transformer$^{-1}$)} & \textbf{92.4} & \textbf{1.6} & \textbf{100} \\
\midrule
Sentiment Analysis (SST-2) & Transformer-Base & 91.2 & 1.5 & 80 \\
 & Early-Exit & 90.8 & 1.3 & 70 \\
 & Dynamic-Depth & 91.0 & 1.1 & 65 \\
 & \textbf{Ours (Transformer$^{-1}$)} & \textbf{91.1} & \textbf{0.8} & \textbf{50} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Lumen AI
Tengzhou No. 1 Middle School
Shihao Ji
Zihui Song
Fucheng Zhong
Jisen Jia
Zhaobo Wu
Zheyi Cao
Xu Tianhao

"Transformer^-1: Input-Adaptive Computation for Resource-Constrained Deployment" by Lumen AI, Tengzhou No. 1 Middle School, Shihao Ji, Zihui Song, Fucheng Zhong, Jisen Jia, Zhaobo Wu, Zheyi Cao, and Xu Tianhao, arXiv:2501.16394v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Dehua Bi
Tianjian Zhou
Wei Zhong
Yuan Ji

"PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data" by Dehua Bi, Tianjian Zhou, Wei Zhong, and Yuan Ji, arXiv:2310.20087v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Builder subsidy summary.}
\begin{tabular}{lccccccc}
\toprule
\textbf{Builder} & \textbf{Subsidized Blocks} & \textbf{Subsidized Blocks} & \textbf{Builder Subsidy} & \textbf{Builder Subsidy} \\
& Before Correction & After Correction & Before Correction & After Correction \\
& [\#] & [\#] & [USD] & [USD] \\
\midrule
\midrule
beaverbuild & 498,388 & 426,335 & -2.31M & -2.13M \\
rsync & 179,925 & 153,713 & -3.89M & -3.77M \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Fei Wu
Danning Sui
Thomas Thiery
Mallesh Pai

"Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest" by Fei Wu, Danning Sui, Thomas Thiery, and Mallesh Pai, arXiv:2507.13023v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Nicolas Floquet
Joseph Le Roux
Nadi Tomeh
Thierry Charnois

"Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement" by Nicolas Floquet, Joseph Le Roux, Nadi Tomeh, and Thierry Charnois, arXiv:2501.09451v1, 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
Prompt Lang. $\downarrow$ & German & Czech & Russian \\ \midrule
$\emptyset$ & 57.4 & 52.3 & 52.1 \\
English & 57.0 & 51.2 & 51.6 \\
German & 54.8 & 50.6 & 51.1 \\
Czech & 57.5 & 49.9 & 51.8 \\
All Above & 55.5 & 48.1 & 51.1 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Tomasz Limisiewicz
David Mareček
Tomáš Musil

"Dual Debiasing: Remove Stereotypes and Keep Factual Gender for Fair Language Modeling and Translation" by Tomasz Limisiewicz, David Mareček, and Tomáš Musil, arXiv:2501.10150v2, 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{Mean Opinion Score (MOS) with $95\%$ confidence intervals for different models.}
\begin{tabular}{cc}
\toprule
 & {\textbf{MOS on speech quality}} \\
\midrule
MelGAN & 3.95 $\pm$ 0.05 \\
WaveRNN & 4.34 $\pm$ 0.04 \\
\textbf{TFGAN} & \textbf{4.35 $\pm$ 0.04} \\
Ground Truth & 4.42 $\pm$ 0.03 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Qiao Tian
Yi Chen
Zewang Zhang
Heng Lu
Linghui Chen
Lei Xie
Shan Liu

"TFGAN: Time and Frequency Domain Based Generative Adversarial Network for High-fidelity Speech Synthesis" by Qiao Tian, Yi Chen, Zewang Zhang, Heng Lu, Linghui Chen, Lei Xie, and Shan Liu, arXiv:2011.12206v1, 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{Finetuning accuracy results}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 & rephrased & question\_as\_answer & simple\_for\_medium \\
 \midrule
 GPT-3.5-Turbo & 0.60 & 0.18 & 0.36 \\
 GPT-4o-mini & 0.62 & 0 & 0.25 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Qiang Sun
Sirui Li
Du Huynh
Mark Reynolds
Wei Liu

"TimelineKGQA: A Comprehensive Question-Answer Pair Generator for Temporal Knowledge Graphs" by Qiang Sun, Sirui Li, Du Huynh, Mark Reynolds, and Wei Liu, arXiv:2501.04343v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\hline
\small Variable & \small DAX &
\small SMI & \small CAC &
\small FTSE \\ \hline\hline
\small DAX & ${\small -}$ &
${\small 21.5009}$ & ${\small %
21.0786}$ & ${\small 21.9731}$ \\ \hline
\small SMI & ${\small 14.2812}$ &
${\small -}$ & ${\small 14.4703}$
& ${\small 14.3737}$ \\ \hline
\small CAC & ${\small 18.8362}$ &
${\small 19.4915}$ & ${\small -}$
& ${\small 19.1671}$ \\ \hline
\small FTSE & ${\small 13.9075}$ &
${\small 13.7593}$ & ${\small %
13.6576}$ & ${\small -}$ \\ \hline\hline
& & & &
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Involving copula functions in Conditional Tail Expectation" by Brahim Brahimi, arXiv:1205.4345v4, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrr}
\toprule
Data & $n$ & $|\mathcal{E}|$&density&$|\mathcal{E}^r|$&$\frac{|\mathcal{E}^r|}{|\mathcal{E}|}$(\%)&$K$&$d$ \\ \midrule
\textit{HeadToHead}&602&5010&1.38e-02&464&9.26&48&32\\
\textit{Finance}&1315&1729225&1.00e+00&1729225&100&20&64\\
\textit{Animal}&21&193&4.60e-01&64&33.16&3&8\\
\textit{Faculty:Business}&113&1787&1.41e-01&0&0.00&5&16\\
\textit{Faculty:CS}&206&1407&3.33e-02&0&0.00&9&16\\
\textit{Faculty:History}&145&1204&5.77e-02&0&0.00&12&16\\
\textit{Football(avg)}&20&201&5.29e-01&71&32.17&9&8\\
\textit{Basketball(avg)}&316&3506&3.51e-02&986&28.57&20&16\\
\textit{Football finer (avg)}&20&367&9.65e-01&367&100&9&8\\
\textit{Basketball finer (avg)}&316&6139&6.12e-02&6139&100&20&16\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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

"GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks" by Yixuan He, Quan Gan, David Wipf, Gesine Reinert, Junchi Yan, and Mihai Cucuringu, arXiv:2202.00211v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Adversarial robustness of models pre-trained and linearly finetuned on ImageNet-1K (100 epochs) against PGD attacks. For an attack: $\epsilon$ defines the perturbation distance, $\gamma$ the step size and ``Iterations" the number of steps.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c|c|c|c}
 \toprule
 $\epsilon$ & $0.003$ &$0.01$ & $0.03$& $0.1$ &$0.003$&$0.01$ & $0.003$& $0.01$\\
 $\gamma$ & $\epsilon/40$ &$\epsilon/40$ & $\epsilon/40$& $\epsilon/40$ &$\epsilon/40$&$\epsilon/40$ & $\epsilon/10$& $\epsilon/10$\\
 Iterations & $1$ & $1$ & $1$ & $1$ & $5$ & $5$ & $1$ & $1$ \\
 \midrule
 BYOL & $64.7$ & $54.1$& $34.7$& $8$ & $45.1$& $14.4$& $51.7$& $27.4$\\
 BYOL-mc & $64.4$ & $50.8$& $26.4$& $3.3$&$39.9$& $7.7$& $47.5$& $18.6$ \\
 CO-BYOL (R50) & $\mathbf{67.4}$ & $\mathbf{59.4}$& $\mathbf{43.9}$& $\mathbf{15.4}$& $\mathbf{52.0}$& $\mathbf{23.1}$& $\mathbf{57.2}$& $\mathbf{36.3}$\\
 CO-BYOL (RF99-R50) & $63.8$ & $49.2$& $27.0$& $4.3$ & $37.1$& $7.5$& $18.6$ & $3.8$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Arthur Aubret
Céline Teulière
Jochen Triesch

"Seeing the Whole in the Parts in Self-Supervised Representation Learning" by Arthur Aubret, Céline Teulière, and Jochen Triesch, arXiv:2501.02860v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Running time of the closed-form expression of (28) using the parameters in Fig. 4.}
\begin{tabular}{l|c|c|c}
 \hline
Value of $l_0$ & $20$ & $30$ & $40$\\
 \hline
 Running time (s) & 1.0523 & 2.1142 & 3.1765\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

He Huang
Zeping Sui
Zilong Liu
Wei Huang
Md. Noor-A-Rahim
Haishi Wang
Zhiheng Hu

"Energy Detection over Composite $κ-μ$ Shadowed Fading Channels with Inverse Gaussian Distribution in Ultra mMTC Networks" by He Huang, Zeping Sui, Zilong Liu, Wei Huang, Md. Noor-A-Rahim, Haishi Wang, and Zhiheng Hu, arXiv:2508.21614v1, 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{CySecBench Dataset Format Overview}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 \textbf{Format} & \textbf{Prompts per Category} & \textbf{Total Prompts} \\
 \hline
 Small Sample Set & 50 & 500 \\ \hline
 Medium Sample Set & 200 & 2000 \\ \hline
 Large Sample Set & 600 & 6000 \\ \hline
 Individual Categories & Varies & Category-specific \\ \hline
 Full Set & - & 12662\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Johan Wahréus
Ahmed Mohamed Hussain
Panos Papadimitratos

"CySecBench: Generative AI-based CyberSecurity-focused Prompt Dataset for Benchmarking Large Language Models" by Johan Wahréus, Ahmed Mohamed Hussain, and Panos Papadimitratos, arXiv:2501.01335v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\hline
\small Variable & \small DAX &
\small SMI & \small CAC &
\small FTSE \\ \hline\hline
\small DAX & ${\small 1}$ &
${\small 0.4052}$ & ${\small 0.4374}
$ & ${\small 0.3706}$ \\ \hline
\small SMI & ${\small 0.4052}$ &
${\small 1}$ & ${\small 0.3791}$ &
${\small 0.3924}$ \\ \hline
\small CAC & ${\small 0.4374}$ &
${\small 0.3791}$ & ${\small 1}$ &
${\small 0.4076}$ \\ \hline
\small FTSE & ${\small 0.3706}$ &
${\small 0.3924}$ & ${\small 0.4076}
$ & ${\small 1}$ \\ \hline\hline
& & & &
\end{tabular}
\end{adjustbox}
\caption{Kendall's tau matrix estimates from four European stock indices returns.}%
\end{table}
\end{document}

q-fin


stat

"Survey calibration for causal inference: a simple method to balance covariate distributions" by Maciej Beręsewicz, arXiv:2310.11969v2, 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}{ccccccc}
			\toprule
			 & \multicolumn{2}{c}{Poly} & \multicolumn{2}{c}{CC} \\
			\cmidrule(lr){2-3}\cmidrule(lr){4-5}
			 & Real Noise & Translated Noise & Real Noise & Translated Noise \\
			\midrule
			Spatial domain & 0.082225 & 0.004478 & 0.040635 & 0.005701 \\
			\midrule
			Frequency domain & 0.999470 & 0.006094 & 0.853160 & 0.008451 \\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

eess

Inju Ha
Donghun Ryou
Seonguk Seo
Bohyung Han

"Learning to Translate Noise for Robust Image Denoising" by Inju Ha, Donghun Ryou, Seonguk Seo, and Bohyung Han, arXiv:2412.04727v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Correct Frequency and running Time$\times10^{2}~$(second) of different optimization methods in finding the global maximum of different functions in Subsection , implemented using \texttt{R} and \texttt{Matlab}. SMCOtree has highest Correct Frequency with low running time in all cases. }
\begin{tabular}{ccccccccccccccccccc}
\hline
\multicolumn{3}{c}{Case} && \multicolumn{3}{c}{Method} && \multicolumn{3}{c}{Frequency (\texttt{R} | \texttt{Matlab})} && \multicolumn{3}{c}{Time$\times10^{2}~$(\texttt{R} | \texttt{Matlab})} && \multicolumn{3}{c}{RMSE (\texttt{R} | \texttt{Matlab})}\\
\hline
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMBS} && \multicolumn{3}{c}{1.00 | } && \multicolumn{3}{c}{9.95 | } && \multicolumn{3}{c}{1.65$\times10^{-6}$ | }\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{1.00 | } && \multicolumn{3}{c}{12.48 | } && \multicolumn{3}{c}{1.39$\times10^{-6}$ | }\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{GD} && \multicolumn{3}{c}{0.09 | 0.26} && \multicolumn{3}{c}{4.44 | 2.51} && \multicolumn{3}{c}{1.11 | }\\
\multicolumn{3}{c}{Case 1, $d=1$} && \multicolumn{3}{c}{SA$_1$} && \multicolumn{3}{c}{1.00 | 0.87} && \multicolumn{3}{c}{23.80 | 6.29} && \multicolumn{3}{c}{4.11$\times10^{-5}$ | }\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_2$} && \multicolumn{3}{c}{1.00 | 0.83} && \multicolumn{3}{c}{31.73 | 6.79} && \multicolumn{3}{c}{3.31$\times10^{-5}$ | }\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_1$} && \multicolumn{3}{c}{1.00 | 0.88} && \multicolumn{3}{c}{23.93 | 2.07} && \multicolumn{3}{c}{1.30$\times10^{-4}$ | }\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_2$} && \multicolumn{3}{c}{1.00 | 0.92} && \multicolumn{3}{c}{50.49 | 2.76} && \multicolumn{3}{c}{6.09$\times10^{-5}$ | }\\
\hline
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCO} && \multicolumn{3}{c}{\textbf{0.99} | \textbf{1.00}} && \multicolumn{3}{c}{\textbf{1.29} | \textbf{0.21}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{0.99} | \textbf{1.00}} && \multicolumn{3}{c}{2.51 | 3.38}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.90 | 0.88} && \multicolumn{3}{c}{10.39 | 10.55}\\
\multicolumn{3}{c}{Case 2, $d=2$} && \multicolumn{3}{c}{SA$_3$} && \multicolumn{3}{c}{0.31 | \textbf{0.97}} && \multicolumn{3}{c}{10.05 | 20.35}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_4$} && \multicolumn{3}{c}{0.29 | \textbf{0.98}} && \multicolumn{3}{c}{12.54 | 20.48}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_1$} && \multicolumn{3}{c}{0.38 | \textbf{1.00}} && \multicolumn{3}{c}{8.76 | 3.97}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_2$} && \multicolumn{3}{c}{0.47 | \textbf{1.00}} && \multicolumn{3}{c}{12.70 | 4.62}\\
\hline
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCO} && \multicolumn{3}{c}{\textbf{1.00} | \textbf{1.00}} && \multicolumn{3}{c}{\textbf{0.92} | \textbf{0.11}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{1.00} | \textbf{1.00}} && \multicolumn{3}{c}{2.33 | 1.32}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.81 | 0.78} && \multicolumn{3}{c}{9.86 | 11.46}\\
\multicolumn{3}{c}{Case 3, Griewank, $d=2$} && \multicolumn{3}{c}{SA$_3$} && \multicolumn{3}{c}{0.01 | 0.43} && \multicolumn{3}{c}{8.91 | 13.13}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_4$} && \multicolumn{3}{c}{0.04 | 0.40} && \multicolumn{3}{c}{11.08 | 13.21}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_1$} && \multicolumn{3}{c}{\textbf{0.99} | \textbf{1.00}} && \multicolumn{3}{c}{8.04 | 1.99}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_2$} && \multicolumn{3}{c}{\textbf{1.00} | \textbf{1.00}} && \multicolumn{3}{c}{11.60 | 2.37}\\
\hline
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCO} && \multicolumn{3}{c}{0.88 | 0.88} && \multicolumn{3}{c}{6.44 | 0.07}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{1.00} | \textbf{0.93}} && \multicolumn{3}{c}{\textbf{8.47} | \textbf{2.58}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.73 | 0.75} && \multicolumn{3}{c}{10.04 | 11.30}\\
\multicolumn{3}{c}{Case 4, Rastrigin, $d=2$} && \multicolumn{3}{c}{SA$_3$} && \multicolumn{3}{c}{0.16 | 0.65} && \multicolumn{3}{c}{11.57 | 14.07}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_4$} && \multicolumn{3}{c}{0.18 | 0.58} && \multicolumn{3}{c}{14.42 | 14.48}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_1$} && \multicolumn{3}{c}{\textbf{0.99} | 0.62} && \multicolumn{3}{c}{9.07 | 4.33}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_2$} && \multicolumn{3}{c}{\textbf{1.00} | 0.68} && \multicolumn{3}{c}{13.15 | 5.94}\\
\hline
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{1.00} | \textbf{0.99}} && \multicolumn{3}{c}{\textbf{74.07} | \textbf{80.57}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.00 | 0.01} && \multicolumn{3}{c}{51.17 | 62.14}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_5$} && \multicolumn{3}{c}{0.00 | 0.00} && \multicolumn{3}{c}{98.67 | 71.20}\\
\multicolumn{3}{c}{Case 5, Rastrigin, $d=10$} && \multicolumn{3}{c}{SA$_6$} && \multicolumn{3}{c}{0.00 | 0.00} && \multicolumn{3}{c}{127.31 | 76.06}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_3$} && \multicolumn{3}{c}{0.64 | 0.47} && \multicolumn{3}{c}{101.20 | 98.84}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_4$} && \multicolumn{3}{c}{0.91 | 0.60} && \multicolumn{3}{c}{158.94 | 207.17}\\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Xiaohong Chen
Zengjing Chen
Wayne Yuan Gao
Xiaodong Yan
Guodong Zhang

"Optimization via Strategic Law of Large Numbers" by Xiaohong Chen, Zengjing Chen, Wayne Yuan Gao, Xiaodong Yan, and Guodong Zhang, arXiv:2412.05604v3, 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}
 \toprule
 \textbf{Question}: \\
 At a school in Boston, USA, the average height of John\\
 and Mike is exactly the same as Tom's height, and John\\
 is taller than Tom. Then:\\
 A. Mike is taller than Tom \\
 B. Tom is taller than Mike \\
 C. Mike and Tom are the same height \\
 \textbf{Ground Truth}: \\
 Let's say John's height is $J$, Mike's height is $M$, and \\
 Tom's height is $T$. We are given that the average height\\
 of John and Mike is the same as Tom's height. \\
 So, $(J + M)/2 = T$.\\
 We are also given that John is taller than Tom, meaning\\
 $J > T$. Since $J > T$, and $(J+M)/2 = T$, then $J +$\\
 $M = 2T$. Because $J$ is greater than $T$, $M$ must be \\
 smaller than $T$ to balance the equation. Therefore, $T$\\
 $ > M$. The answer is $B$.\\
 \midrule
 \textbf{ChatGPT's reasoning step:} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{An example }
\end{table}
\end{document}

cs

Zhan Ling
Kang Liu
Kai Yan
Yifan Yang
Weijian Lin
Ting-Han Fan
Lingfeng Shen
Zhengyin Du
Jiecao Chen

"LongReason: A Synthetic Long-Context Reasoning Benchmark via Context Expansion" by Zhan Ling, Kang Liu, Kai Yan, Yifan Yang, Weijian Lin, Ting-Han Fan, Lingfeng Shen, Zhengyin Du, and Jiecao Chen, arXiv:2501.15089v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrr}
\toprule
\multirow{2}{*}{Trajectory Smell} & \multicolumn{2}{c}{Human} & \multicolumn{2}{c}{SAN} & \multicolumn{2}{c}{TOD} \\ \cmidrule(l){2-7}
 & Failing & Passing & Failing & Passing & Failing & Passing \\
\midrule
NO\_OP\_CAT\_SMELL & 0.44 & 0.22 & 0.70 & 0.53 & 0.60 & 0.54 \\
NO\_TEST\_SMELL & 0.66 & 0.13 & 0.01 & 0.00 & 0.01 & 0.00 \\
CONSECUTIVE\_SEARCH & 0.67 & 0.33 & 0.41 & 0.10 & 0.31 & 0.18 \\
CONSECUTIVE\_EDIT & 0.21 & 0.18 & 0.63 & 0.29 & 0.68 & 0.32 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Smell incidence for Passerine trajectories varies across bug type and resulting patch plausibility.}
\end{table}
\end{document}

cs

Pat Rondon
Renyao Wei
José Cambronero
Jürgen Cito
Aaron Sun
Siddhant Sanyam
Michele Tufano
Satish Chandra

"Evaluating Agent-based Program Repair at Google" by Pat Rondon, Renyao Wei, José Cambronero, Jürgen Cito, Aaron Sun, Siddhant Sanyam, Michele Tufano, and Satish Chandra, arXiv:2501.07531v1, 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|cc|cc|}
\hline
method & $\beta$ & $j$ (Bosons) & $\lambda_j^h$ & $j$ (Fermions) & $\lambda_j^h$ \\ \hline
& & 0 & -0.2476034576 & 1 & -0.1825896533 \\
IGA &0.344595351 & 2 & -0.1412793292 & 3 & -0.1182591543 \\
& & 4 & -0.1060931444 & 5 & -0.1005294105 \\ \hline
& & 0 & -0.247603458 & 1 & -0.182589653 \\
BO in &0.34459535 & 2 & -0.141279329 & 3 & -0.118259157 \\
& & 4 & -0.106093864 & 5 & -0.102845702 \\ \hline

\end{tabular}
\end{adjustbox}
\caption{Eigenenergies of the three-body problem with a mass ratio 20 and an exponentially decaying potential when using IGA and BO approximation in .}
\end{table}
\end{document}

math

"Isogeometric Analysis of Bound States of a Quantum Three-Body Problem in 1D" by Quanling Deng, arXiv:2202.09986v2, 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|}
\hline
Heading level & Example & Font size and style\\
\hline
Title (centered) & {\Large\bfseries Lecture Notes} & 14 point, bold\\
1st-level heading & {\large\bfseries 1 Introduction} & 12 point, bold\\
2nd-level heading & {\bfseries 2.1 Printing Area} & 10 point, bold\\
3rd-level heading & {\bfseries Run-in Heading in Bold.} Text follows & 10 point, bold\\
4th-level heading & {\itshape Lowest Level Heading.} Text follows & 10 point, italic\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mohit Tekriwal
Avi Tachna-Fram
Jean-Baptiste Jeannin
Manos Kapritsos
Dimitra Panagou

"Formally verified asymptotic consensus in robust networks" by Mohit Tekriwal, Avi Tachna-Fram, Jean-Baptiste Jeannin, Manos Kapritsos, and Dimitra Panagou, arXiv:2202.13833v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Ahmed K. Kadhim
Lei Jiao
Rishad Shafik
Ole-Christoffer Granmo

"Adversarial Attacks on AI-Generated Text Detection Models: A Token Probability-Based Approach Using Embeddings" by Ahmed K. Kadhim, Lei Jiao, Rishad Shafik, and Ole-Christoffer Granmo, arXiv:2501.18998v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of MSE in Scenario 2.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
\hline
$n$ &RPackage & CVTF &SURE & Traditional CV \\
\hline
300 & 0.029 (0.021) & 0.029 (0.017) & 0.032 (0.023) & 0.028 (0.016)\\
600 & 0.015 (0.010) & 0.013 (0.006) & 0.016 (0.010) & 0.013 (0.006)\\
1200 & 0.007 (0.004) & 0.006 (0.003) & 0.008 (0.004) & 0.007 (0.004)\\
2400 & 0.004 (0.003) & 0.003 (0.002) & 0.004 (0.004) & 0.003 (0.002)\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Anamitra Chaudhuri
Sabyasachi Chatterjee

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Effect of Vertical Expansions (Levels) and Horizontal Expansions (Stages) on the Dice Score (Mean$\pm$Standard Deviation) in Dataset-1}}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{Level}} & \multicolumn{3}{c|}{\textbf{CovSegNet2D}} & \multicolumn{3}{c|}{\textbf{CovSegNet3D}} \\ \cline{2-7}
 & \textbf{1-stage} & \textbf{2-stage} & \textbf{3-stage} & \textbf{1-stage} & \textbf{2-stage} & \textbf{3-stage} \\ \hline
\textbf{2} & 49.9$\pm$0.37 & 75.3$\pm$0.13 & 78.12$\pm$0.21 & 57.3$\pm$0.18 & 79.8$\pm$0.18 & 82.1$\pm$0.19 \\ \hline
\textbf{3} & 64.8$\pm$0.23 & 85.8$\pm$0.32 & 88.5$\pm$0.15 & 69.3$\pm$0.35 & 89.2$\pm$0.26 & 90.2$\pm$0.25 \\ \hline
\textbf{4} & 75.2$\pm$0.32 & 89.6$\pm$0.27 & 90.8$\pm$0.22 & 79.8$\pm$0.29 & \textbf{92.3$\pm$0.15} & 91.8$\pm$0.17 \\ \hline
\textbf{5} & 83.5$\pm$0.19 & \textbf{91.1$\pm$0.25} & 89.9$\pm$0.12 & 84.5$\pm$0.43 & 90.2$\pm$0.34 & 89.7$\pm$0.28 \\ \hline
\textbf{6} & 86.7$\pm$0.27 & 90.9$\pm$0.21 & 89.1$\pm$0.11 & 89.3$\pm$0.21 & 89.8$\pm$0.41 & 87.9$\pm$0.36 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Tanvir Mahmud
Md Awsafur Rahman
Shaikh Anowarul Fattah
Sun-Yuan Kung

"CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans" by Tanvir Mahmud, Md Awsafur Rahman, Shaikh Anowarul Fattah, and Sun-Yuan Kung, arXiv:2012.01473v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The upper bound of a simple network for the classification task with or without residual structure. The channels setting represents where main differences in architectures are (see details in Appendix ). "p16" means that there is a pooling layer before the convolutional layer with 16 channels. And "r16" represents that a residual structure is added in the convolutional layer with $16$ channels. The last three columns in this table is similar to Table . In this part, the input size in all the experiments is $(24 \times 24)$.}
\begin{tabular}{ccccc}
 	\hline
		No. & Channels setting & w/ res-structure & w/o res-structure & ratio \\
 	\hline
		1 & 4-p16-p16-r16-r16-r16& $7.313 \times 10^{1930}$ & $3.720 \times 10^{1930}$ & $1.966$\\
 2 & 4-p4-4-r4-r4-r4 & $5.052 \times 10^{1542}$ & $3.393 \times 10^{1542}$ & $1.489$\\
 3 & 4-p16-p16-r16-16-16 & $5.232\times 10^{1930}$ & $3.720 \times 10^{1930}$ & $1.406$\\
 4 & 4-p16-p16-r16-r16-r16-r16-r16 & $3.012 \times 10^{2277}$ & $5.882 \times 10^{2276}$ & $5.121$\\
 5 & 4-p16-p32-r32-r32-r32 & $7.180 \times 10^{2623}$ & $7.166\times 10^{2623}$ & $1.002$\\
		\hline
 	\end{tabular}
\end{table}
\end{document}

stat

"A General Computational Framework to Measure the Expressiveness of Complex Networks Using a Tighter Upper Bound of Linear Regions" by Yutong Xie, Gaoxiang Chen, and Quanzheng Li, arXiv:2012.04428v1, 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|}
 \hline
 \textbf{Examples of Regulation} & \textbf{Classification} \\
 \hline
 outright ban on algorithms (e.g., ) & per se \\
 \hline
 setting up formal non-collusive specifications and requiring proof of verification & per se \\
 \hline
 inspecting source code to judge the degree of (non-)competitiveness within context & rule of reason \\
 \hline
 requiring passing dynamic unit tests with specific protocols & per se \\
 \hline
 use context-specific input to dynamically test and analyze pricing behavior & rule of reason \\
 \hline
 requiring monitor wrapper \; & per se \\
 \hline
 requiring passing data audit ( and this paper)
 & per se\\
 \hline
 analyzing cost under context and audit ( and this paper) & rule of reason \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Comparison of different proposed regulations of algorithmic collusion}
\end{table}
\end{document}

cs

Jason D. Hartline
Chang Wang
Chenhao Zhang

"Regulation of Algorithmic Collusion, Refined: Testing Pessimistic Calibrated Regret" by Jason D. Hartline, Chang Wang, and Chenhao Zhang, arXiv:2501.09740v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of results for 3D}
\begin{tabular}{llllllll}
 \hline
 Model & Fusion Type & Fusion Pos & SSIM & SSIM \% Gain & PSNR & \% PSNR Gain \\ \hline
 Baseline SRGAN & - & - & 0.94 & - & 29.95 & - \\
 \textbf{Deterministic} & \textbf{Concat} & \textbf{Late} & \textbf{0.98} & \textbf{4.25} & \textbf{33.22} & \textbf{10.91} \\
 Deterministic & Concat & Early & 0.95 & 1.06 & 30.40 & 1.5 \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

Praneet Dutta
Bruce Power
Adam Halpert
Carlos Ezequiel
Aravind Subramanian
Chanchal Chatterjee
Sindhu Hari
Kenton Prindle
Vishal Vaddina
Andrew Leach
Raj Domala
Laura Bandura
Massimo Mascaro

"3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement" by Praneet Dutta, Bruce Power, Adam Halpert, Carlos Ezequiel, Aravind Subramanian, Chanchal Chatterjee, Sindhu Hari, Kenton Prindle, Vishal Vaddina, Andrew Leach, Raj Domala, Laura Bandura, and Massimo Mascaro, arXiv:1911.06932v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Typology of screening cases in the Dutch CRC EHR with observed proportion per type (n=810; note: the data example uses up to four visits for illustration, but the real CRC EHR contained up to seven visits, see Table ).}
\begin{tabular}{llcccccc}
		\multicolumn{3}{c}{} & \multicolumn{5}{c}{Data example} \\
		\cline{4-8}
		Type & Description & Proportion (\%) & $v_1$ & $v_2$ & $v_3$ & $v_4$ & $r_i$ \\
		\hline
		1 & Incident in follow-up with baseline test & 17.7 & 0 & 3.0 & 6.0 & & 1 \\
		2 & Right censored with baseline test & 46.5 & 0 & 2.9 & 6.3 & $\infty$ & 1 \\
		3 & Observed prevalent at baseline test & 20.4 & 0 & & & & 1 \\
		4 & Right censored with only a baseline test & 8.9 & 0 & $\infty$ & & & 1 \\
		5 & Incident in follow-up without a baseline test & 2.7 & 0 & 6.2 & 8.3 & & 0 \\
		6 & Right censored without a baseline test & 3.8 & 0 & 5.9 & $\infty$ & & 0 \\
		\hline
	\end{tabular}
\end{table}
\end{document}

stat

Thomas Klausch
Birgit I. Lissenberg-Witte
Veerle M. Coupé

"A Bayesian prevalence-incidence mixture model for screening outcomes with misclassification" by Thomas Klausch, Birgit I. Lissenberg-Witte, and Veerle M. Coupé, arXiv:2412.16065v2, 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{List of Movie Genres}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\hline
\multicolumn{7}{c}{\textbf{Movie Genres}} \\ \hline
Action & Comedy & Family & Sports & Mystery & Sci-Fi & Horror \\
Adventure & Crime & Fantasy & War & Romance & Thriller & Musical (Music, Musical) \\
Animation & Documentary & Film-Noir & Western & Biography & Drama & History \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Falguni Roy
Yiduo Shen
Na Zhao
Xiaofeng Ding
Md. Omar Faruk

"A Closer Look on Gender Stereotypes in Movie Recommender Systems and Their Implications with Privacy" by Falguni Roy, Yiduo Shen, Na Zhao, Xiaofeng Ding, and Md. Omar Faruk, arXiv:2501.04420v1, 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{Average relevant products returned per query on the ESCI dataset using different methods.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Method} & \textbf{$cov(\tilde{Q})$} & \textbf{Gain ($\%$)} \\ \midrule
Supervised & 111 & 0 \\
RLQR & 145 & 30.6 \\
CLOVER & 132 & 18.9 \\
DRQR & 130 & 17.1 \\
Task-Oriented QR & 114 & 2.7 \\ \midrule
\textbf{MiniELM (Our)} & \textbf{171} & \textbf{54.1} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Duy A. Nguyen
Rishi Kesav Mohan
Van Yang
Pritom Saha Akash
Kevin Chen-Chuan Chang

"RL-based Query Rewriting with Distilled LLM for online E-Commerce Systems" by Duy A. Nguyen, Rishi Kesav Mohan, Van Yang, Pritom Saha Akash, and Kevin Chen-Chuan Chang, arXiv:2501.18056v2, 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{The relative space-time Frobenius errors of the projected FOM solutions and ROM computations in the case $R=0.08$ of Test 1. Note that SBM-ROM fails to deliver any solution.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccccccc}
 \toprule[.5mm]
 & \multicolumn{3}{l}{FOM projection} & & \multicolumn{3}{l}{SBM-iROM} & & \multicolumn{3}{l}{SBM-ROM} \\ \cmidrule[.2mm](lr){2-4} \cmidrule[.2mm](lr){6-8} \cmidrule[.2mm](l){10-12}
 $\mu_{\textrm{pod}}$ & $h$ & $hv_1$ & $hv_2$ & & $h$ & $hv_1$ & $hv_2$ & & $h$ & $hv_1$ & $hv_2$ \\ \cmidrule[.3mm](l){2-12}
 $1-10^{-5}$ & 1.53e-3 & 2.28e-2 & 2.12e-1 & & 1.83e-3 & 3.21e-2 & 3.02e-1 & & -- & -- & -- \\
 $1-10^{-6}$ & 6.46e-4 & 1.44e-2 & 1.69e-1 & & 1.30e-3 & 2.45e-2 & 2.14e-1 & & -- & -- & -- \\
 $1-10^{-7}$ & 3.88e-4 & 1.23e-2 & 1.54e-1 & & 5.81e-4 & 1.84e-2 & 1.98e-1 & & -- & -- & -- \\
 $1-10^{-8}$ & 3.67e-4 & 1.19e-2 & 1.51e-1 & & 5.39e-4 & 1.81e-2 & 1.98e-1 & & -- & -- & -- \\
 $1-10^{-9}$ & 3.59e-4 & 1.19e-2 & 1.50e-1 & & 5.44e-4 & 1.81e-2 & 1.97e-1 & & -- & -- & -- \\
 \bottomrule[.4mm]
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameters of ligand and proteins graphs in joint models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
 & \multicolumn{2}{c}{CrossDocked} & \multicolumn{2}{c}{Binding Moad} \\ \midrule
 & joint $C_{\alpha}$ & joint full-& joint $C_{\alpha}$ & joint full- \\
 & model & atom model & model & atom model \\ \midrule
Edges (ligand-ligand) & fully connected & fully connected & fully connected & fully connected \\ \midrule
Edges (ligand-pocket) & $<5$ \,\AA & $<5$ \,\AA & $<8$ \,\AA & $<7$ \,\AA \\ \midrule
 Edges (pocket-pocket) & $<5$ \,\AA & $<5$ \,\AA & $<8$ \,\AA & $<4$ \,\AA \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Sirine Ayadi
Leon Hetzel
Johanna Sommer
Fabian Theis
Stephan Günnemann

"Unified Guidance for Geometry-Conditioned Molecular Generation" by Sirine Ayadi, Leon Hetzel, Johanna Sommer, Fabian Theis, and Stephan Günnemann, arXiv:2501.02526v1, 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{Details of datasets and experimental set up}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccc}
\hline
Dataset & Data distribution & Model & Number & Number of & Training & Testing \\
& & & of clients & training rounds & batch size & batch size \\
\hline\hline
Caltech101 & Pathological & ViT-B16 & 10 & 100 & 32 & 100 \\
OxfordPets & Pathological & ViT-B16 & 10 & 100 & 32 & 100 \\
OxfordFlowers & Pathological & ViT-B16 & 10 & 100 & 32 & 100 \\
Food101 & Pathological & ViT-B16 & 10 & 100 & 32 & 100 \\
CIFAR-100 & Dirichlet & ResNet50 & 25, 50 & 200 & 32 & 100 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Linh Tran
Wei Sun
Stacy Patterson
Ana Milanova

"Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models" by Linh Tran, Wei Sun, Stacy Patterson, and Ana Milanova, arXiv:2501.13904v3, 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.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameters for experiments with Non-Peaky object distributions in Kitchen Environment 2.}
\begin{tabular}{|l|cc|cc|}
\hline
 & \multicolumn{2}{c|}{\textbf{Gen-Lin}} & \multicolumn{2}{c|}{\textbf{Neural}} \\ \hline
\textbf{Hyperparameter} & Greedy & CP-SAT & Greedy & CP-SAT \\ \hline
Learning Rate ($\eta$) & $16.62$ & $10.44$ & $0.01$ & $0.01$ \\ \hline
Exploration parameter ($\alpha$) & $10$ & $0.1$ & $0.1$ & $2.04$ \\ \hline
Number of vantage points ($k$) & $50$ & $50$ & $50$ & $50$ \\ \hline
Alpha planner ($\alpha_p$) & $0.49$ & - & $0.38$ & - \\ \hline
Map Resolution & $37$ & $37$ & $75$ & $75$ \\ \hline
Positional Embedding Size & $20$ & $50$ & $50$ & $15$ \\ \hline
Feature Vector Normalization & mean-var & mean-var & mean-var & mean-var \\ \hline
Sigmoid Scale ($s$) & - & - & $10.00$ & $17.56$ \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Sohan Rudra
Saksham Goel
Anirban Santara
Claudio Gentile
Laurent Perron
Fei Xia
Vikas Sindhwani
Carolina Parada
Gaurav Aggarwal

"A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations" by Sohan Rudra, Saksham Goel, Anirban Santara, Claudio Gentile, Laurent Perron, Fei Xia, Vikas Sindhwani, Carolina Parada, and Gaurav Aggarwal, arXiv:2211.16309v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Richness estimation with species identity error" by Jai-Hua Yen and Chun-Huo Chiu, arXiv:2012.07485v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Classification of ALS patients based on acoustic analysis of sustained vowel phonations" by Maxim Vashkevich and Yulia Rushkevich, arXiv:2012.07347v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Errors and convergence orders of finite element pressure $p^n_h$ with $P^1$-$P^1$ pair.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c|c}
 \toprule
 \multirow{2}{*}{1/h} & \multicolumn{2}{c}{$\max\limits_{n}\|p^n - p^n_h\|_{L^2}$} & \multicolumn{2}{c}{$\left\|p - p_h\right\|_{\ell^2(L^2)}$} & \multicolumn{2}{c}{$\sqrt{\Delta t}\left\|\nabla(p-p_h)\right\|_{\ell^2(L^2)}$} \cr
 \cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7}
 & error & rate & error & rate & error & rate \cr
 \midrule
 4 & \quad 2.7636e$+$00 $\quad$ & \quad - $\quad$ & \quad 2.2987e$+$00 $\quad$ & \quad - $\quad$ & \quad 1.4286e$+$00 $\quad$ & \quad - $\quad$ \cr
 8 & 1.1144e$+$00 & 1.3103 & 8.8892e$-$01 & 1.3707 & 4.6814e$-$01 & 1.6096 \cr
 16 & 3.6664e$-$01 & 1.6038 & 2.7275e$-$01 & 1.7045 & 1.3827e$-$01 & 1.7595 \cr
 32 & 1.2463e$-$01 & 1.5567 & 8.1260e$-$02 & 1.7470 & 4.5158e$-$01 & 1.6144 \cr
 64 & 4.6335e$-$02 & 1.4275 & 2.5152e$-$02 & 1.6919 & 1.5553e$-$01 & 1.5378 \cr
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"An efficient Chorin-Temam projection proper orthogonal decomposition based reduced-order model for nonstationary Stokes equations" by Xi Li, Yan Luo, and Minfu Feng, arXiv:2201.07398v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of DSC statistics (mean and standard deviation) between the baseline method and ICS for each anatomical region in the HVSMR dataset. Bold values indicate regions where ICS achieved significantly higher performance than the baseline method (p < 0.05).}
\begin{tabular}{lccccc}
\toprule
\multirow{2}{*}{Region} & \multicolumn{2}{c}{Baseline} & \multicolumn{2}{c}{ICS} & \multirow{2}{*}{p-value} \\
\cmidrule(lr){2-3}\cmidrule(lr){4-5}
 & Mean & Std & Mean & Std & \\
\midrule
LV & 0.5489 & 0.0893 & 0.5475 & 0.0770 & 0.8903 \\ % no sig diff
RV & 0.4663 & 0.1047 & 0.4741 & 0.0933 & 0.3820 \\ % no sig diff
LA & 0.3879 & 0.1210 & \textbf{0.4272} & 0.0925 & 0.0007 \\ % ICS better
RA & 0.5500 & 0.1191 & \textbf{0.5734} & 0.0791 & 0.0214 \\ % ICS better
AO & 0.4945 & 0.1269 & \textbf{0.5210} & 0.0679 & 0.0206 \\ % ICS better
PA & 0.3807 & 0.1336 & \textbf{0.4745} & 0.0974 & <0.0001 \\ % ICS better
SVC & 0.4079 & 0.1805 & \textbf{0.4938} & 0.1226 & <0.0001 \\ % ICS better
IVC & 0.4607 & 0.1258 & 0.4629 & 0.0871 & 0.8561 \\ % no sig diff
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"In-context learning for medical image segmentation" by Eichi Takaya and Shinnosuke Yamamoto, arXiv:2412.13299v2, 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}{cccc}
 \hline
 Models& EMNIST & CIFAR-10 & SVHM\\
 \hline
 FedAvg& 0.8910 & \textbf{0.7896} & 0.7975\\
 FedLaplace& 0.8297 &0.7513 &0.8222 \\
 FedIvon& \textbf{0.9032} & 0.7662 & \textbf{0.8233} \\
 \hline
 \end{tabular}
\caption{AUROC ($\uparrow$) score for OOD/in-domain data detection}
\end{table}
\end{document}

stat

Shivam Pal
Aishwarya Gupta
Saqib Sarwar
Piyush Rai

"Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization" by Shivam Pal, Aishwarya Gupta, Saqib Sarwar, and Piyush Rai, arXiv:2411.18385v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of Dynamic Time Warping similarity and distance between Narrative Maps and random samples using the expert-generated baselines of different lengths as ground truth, where $L$ is the length of the baseline timelines.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc|ccc||ccc|ccc}
\hline
\multirow{2}{*}{$L$}
 & \multicolumn{3}{c|}{\textbf{High-Dim Similarity}}
 & \multicolumn{3}{c||}{\textbf{High-Dim Distance}}
 & \multicolumn{3}{c|}{\textbf{Low-Dim Similarity}}
 & \multicolumn{3}{c}{\textbf{Low-Dim Distance}}
\\
\cline{2-13}
 & NM & RS & p-value
 & NM & RS & p-value
 & NM & RS & p-value
 & NM & RS & p-value
\\
\hline
5 & 0.823 & 0.841 & 0.3125 & 2.208 & 2.791 & 0.0838 & 0.126 & 0.094 & 0.7666 & 8.348 & 8.331 & 0.9821 \\
10 & 0.915 & 0.895 & 0.0399 & 2.313 & 2.615 & 0.0681 & 0.299 & 0.116 & $2.5\mathrm{e}{-4}$ & 8.691 & 10.495 & $8.2\mathrm{e}{-4}$ \\
15 & 0.950 & 0.936 & 0.0257 & 2.215 & 2.424 & 0.0516 & 0.331 & 0.189 & $6.4\mathrm{e}{-4}$ & 8.800 & 10.625 & $6.1\mathrm{e}{-5}$ \\
20 & 0.963 & 0.945 & 0.0033 & 2.108 & 2.401 & 0.0079 & 0.398 & 0.252 & $5\mathrm{e}{-5}$ & 8.339 & 10.086 & $3.4\mathrm{e}{-5}$ \\
25 & 0.960 & 0.954 & 0.3514 & 1.867 & 2.408 & $1.2\mathrm{e}{-4}$ & 0.480 & 0.261 & $1.6\mathrm{e}{-11}$ & 7.258 & 10.031 & $1.9\mathrm{e}{-11}$ \\
30 & 0.969 & 0.956 & 0.0087 & 2.026 & 2.460 & $2.1\mathrm{e}{-5}$ & 0.481 & 0.279 & $1.1\mathrm{e}{-9}$ & 7.327 & 10.029 & $9.4\mathrm{e}{-11}$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Fausto German
Brian Keith
Mauricio Matus
Diego Urrutia
Claudio Meneses

"Semi-Supervised Image-Based Narrative Extraction: A Case Study with Historical Photographic Records" by Fausto German, Brian Keith, Mauricio Matus, Diego Urrutia, and Claudio Meneses, arXiv:2501.09884v1, 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
			Data set & sample number & feature dimension \\
			\hline
			rcv1 & 20242 & 47236\\
			\hline
			MNIST & 60000 & 784\\
			\hline
		\end{tabular}
\end{adjustbox}
\caption{data sets}
\end{table}
\end{document}

math

Xuyang Wu
Sindri Magnusson
Hamid Reza Feyzmahdavian
Mikael Johansson

"Delay-adaptive step-sizes for asynchronous learning" by Xuyang Wu, Sindri Magnusson, Hamid Reza Feyzmahdavian, and Mikael Johansson, arXiv:2202.08550v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{10-day 95\% VaR of Rainbow Option}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\hline
& Mean & Median & Standard Deviation & Back Testing & Time (in seconds) \\ \hline
LSM & 1.78698 & 1.78664 & 0.11482 & 0.0290 & 18.62 \\
LLSM& 1.61693 & 1.61108 & 0.10932 & 0.0442 & 20.25 \\
Delta-normal$^\dagger$ & 1.74591 & 1.74591 & - & 0.0329 & 7.62 \\
\textcolor{black}{Delta-gamma$^\dagger$} & 5.00858 & 5.00858 & - & 0 & 66.36 	\\
Oracle & 1.58089 & 1.58089 & - & 0.0483 & 163,850 \\
Oracle$^\dagger$ & 1.56778 & 1.56778 & - & 0.0500 & 3,634 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Simulation-based Value-at-Risk for Nonlinear Portfolios" by Junyao Chen, Tony Sit, and Hoi Ying Wong, arXiv:1904.09088v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{Classification accuracy (\%) on Office-Caltech dataset, for ResNet updated using FedAvg, ResNet + RF-TCA Module but updated using FedAvg, and FedRF-TCA.}}
\begin{tabular}{lccccc}
 \toprule
 Methods & C,D,W$\to$A & A,D,W$\to$C & A,C,W$\to$D & A,C,D$\to$W & Avg \\
 \midrule
 ResNet & \underline{91.8} & 84.4 & 95.7 & 94.5 & 91.6 \\
 ResNet + RF-TCA Module & 90.4 & \underline{84.9} & \underline{95.9} & \underline{95.3} & \underline{91.6} \\
 \midrule
 FedRF-TCA & \textbf{94.1} &\textbf{98.1} &\textbf{98.9} &\textbf{88.9} & \textbf{95.0} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Zhanbo Feng
Yuanjie Wang
Jie Li
Fan Yang
Jiong Lou
Tiebin Mi
Robert. C. Qiu
Zhenyu Liao

"Robust and Communication-Efficient Federated Domain Adaptation via Random Features" by Zhanbo Feng, Yuanjie Wang, Jie Li, Fan Yang, Jiong Lou, Tiebin Mi, Robert. C. Qiu, and Zhenyu Liao, arXiv:2311.04686v2, 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}{ccccclcccc}
 & \multicolumn{4}{c}{$K=2$} & & \multicolumn{4}{c}{$d=4$} \\ \cline{2-5} \cline{7-10}
 & $d=4$ & $d=8$ & $d=15$ & $d=20$ & & $K=5$ & $K=8$ & $K=10$ & $K=15$ \\ \cline{2-5} \cline{7-10}
Tr-LinUCB & 16.1 & 25.5 & 42.1 & 55.6 & & 76.3 & 138.2 & 180.4 & 282.8 \\ \cline{1-5} \cline{7-10}
LinUCB & 25.7 & 31.7 & 46.3 & 57.9 & & 113.6 & 198.0 & 250.8 & 366.9 \\ \cline{1-5} \cline{7-10}
Greedy-First & 34.0 & 38.8 & 120.3 & 246.0 & & 221.4 & 390.4 & 512.7 & 823.8 \\ \cline{1-5} \cline{7-10}
OLS & 43.0 & 62.6 & 111.9 & 219.0 & & 197.8 & 351.0 & 481.4 & 749.1 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Cumulative regret $R_T$ for algorithms with $T = 10^5$ and varying pairs of $K,d$.}
\end{table}
\end{document}

math

"Truncated LinUCB for Stochastic Linear Bandits" by Yanglei Song and Meng zhou, arXiv:2202.11735v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{arydshln}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{CO\textsubscript{2} equivalent operation intensity per technology averaged across countries. The dashed line indicates the split between non-fossil and fossil technologies. For details, see Table~1--3 in the supplementary material.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\hline
Technology & Intensity [kgCO\textsubscript{2}eq/MWh]\\
\hline
solar & \hspace{14.5mm}0.00410\\
geothermal & \hspace{14.5mm}0.00664\\
wind & \hspace{14.5mm}0.141\\
nuclear & \hspace{12.5mm}10.3\\
hydro			 & \hspace{12.5mm}16.2\\
biomass & \hspace{12.5mm}50.9\\
\hdashline
gas & \hspace{11mm}583\\
unknown & \hspace{11mm}927\\
oil & \hspace{9mm}1033\\
coal & \hspace{9mm}1167\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Bo Tranberg
Olivier Corradi
Bruno Lajoie
Thomas Gibon
Iain Staffell
Gorm Bruun Andresen

"Real-Time Carbon Accounting Method for the European Electricity Markets" by Bo Tranberg, Olivier Corradi, Bruno Lajoie, Thomas Gibon, Iain Staffell, and Gorm Bruun Andresen, arXiv:1812.06679v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the CONV, COS and CONLeg methods for pricing an American put option under the CGMY model with parameters taken from \textbf{CGMY1}. 58 and 88 option prices are computed for the CONV method and the COS method respectively in a range of $S$ from 0.5 to 1.5 and $K$ equal to 1. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|ccc|ccc|} % centered columns (4 columns)
\hline
\multirow{2}{*}{$L$ in Eq. ()}&\multirow{2}{*}{$$}&\multicolumn{3}{c|}{\textbf{CONV}}&\multicolumn{3}{c|}{\textbf{CONLeg}}\\
&$N$&$R_\infty$&$R_2$&Time (sec.)&$R_\infty$&$R_2$&Time (sec.)\\
\cline{2-8}
2&32768& 1.052e-04&8.051e-03&19.291&7.123e-05&6.113e-04&19.311\\
\hline
\multicolumn{8}{c}{}\\
\hline
\multirow{2}{*}{$L$ in Eq. ()}&\multirow{2}{*}{$$}&\multicolumn{3}{c|}{\textbf{COS}}&\multicolumn{3}{c|}{\textbf{CONLeg}}\\
&$N$&$R_\infty$&$R_2$&Time (sec.)&$R_\infty$&$R_2$&Time (sec.)\\
\cline{2-8}
2&32768& 9.234e-05&4.012e-04&19.298&7.123e-05&6.113e-04&19.311\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Hedging and Pricing European-type, Early-Exercise and Discrete Barrier Options using Algorithm for the Convolution of Legendre Series" by Tat Lung Chan and Nicholas Hale, arXiv:1811.09257v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 \textbf{Metrics} & \textbf{Model} & \textbf{Sines} & \textbf{Stocks} & \textbf{Energy}\\
 \toprule
 \multirow{4}{*}{FDDS}
 & PaD-TS & \textbf{0.0003} & \textbf{0.0588} & \textbf{0.0442}\\
 & w/o Temporal & 0.0005 & 	1.8838 & 0.5254 \\
 & w/o Dimension & 0.0087 & 0.0868 & 0.0533\\
 & w/o PAT & 0.0007 & 0.2459 & 0.0816\\
 & w/o SSS & 0.0286 & 0.0965 & 0.3626 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Ablation study for the effectiveness of PaD-TS components. \textbf{Bold} font indicates the best performance.}
\end{table}
\end{document}

cs

Yang Li
Han Meng
Zhenyu Bi
Ingolv T. Urnes
Haipeng Chen

"Population Aware Diffusion for Time Series Generation" by Yang Li, Han Meng, Zhenyu Bi, Ingolv T. Urnes, and Haipeng Chen, arXiv:2501.00910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
$(n,p)$ & $f=(f_{1},\dots, f_{t})$ & Unfolding monomials $< m$ & $r$ & $\sigma_{m}$\\
\hline\hline
\multirow{4}{*}{$(6s+2, 7s+1)$} &$f_{\lambda}:=( x^{2}+y^{2}+z^{2},
 y^{2}+\lambda z^{2}+w^{2}, $ &$\{x,y\}e_1,\,\{z,x\}e_{2}$ & $6s-2$ & $z^{2}e_{2}$\\
 &$xy,xz,xw,yz, yw, zw, \underbrace{0, \dots, 0})$ &$\{x,y,z,w\}e_{3+i}$ &$s \geq 5 $ &\\
 &\phantom{$xy,xz,xw,yz, yw, zw,$ }$ s- 5$ & & &\\
 $s\geq 5$ &$t=s+3,\, s\geq 5$ &$0\leq i\leq s,\, s\geq 5$ & &\\
\hline
 \end{tabular}
\end{adjustbox}
\caption{$6(p-n)+8,\, p-n\geq 4,\, n\geq 4 $ }
\end{table}
\end{document}

math

"Old and new results on density of stable mappings" by Maria Aparecida Soares Ruas, arXiv:2201.03888v1, 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 $(x_c,r,\theta)$ & $(2.5,40,\pi/80)$ & $(4,10,\pi/12)$ & $(2,5,\pi/6)$ \\ \hline relative error on $x_c$ & $1.8\%$ & $0\%$ &$7.6\%$ \\ \hline relative error on $r$ & $3.0\%$ & $7.5\%$ & $23.8\%$ \\ \hline relative error on $\theta$ & $1.6\%$ & $10.7\%$ & $16.9\%$\\ \hline \end{tabular}
\end{adjustbox}
\caption{ Relative errors on the reconstruction of $(x_c,r,\theta)$ for different bends. In each case, $K$ is the discretization of $[0.01,40]$ with $100$ points, and the reconstruction is obtain by .}
\end{table}
\end{document}

math

Eric Bonnetier
Angèle Niclas
Laurent Seppecher
Grégory Vial

"Small defects reconstruction in waveguides from multifrequency one-side scattering data" by Eric Bonnetier, Angèle Niclas, Laurent Seppecher, and Grégory Vial, arXiv:2202.08168v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Summary of the calculated statistics and their CI for people who identified themselves as female}}
\begin{tabular}{ccccc}
 \toprule [1pt]
 \hline
 \multirow{2}{*}{\centering \textbf{$\chi^{2}$}} &
 \multirow{2}{*}{\centering \textbf{p-value}} &
 \multirow{2}{*}{\centering \textbf{Cramer's V}} &
 \multirow{2}{*}{\centering \textbf{Risk Ratio}} &
 \multirow{2}{*}{\centering \textbf{Odds Ratio}} \cr \\
 \hline
 \multirow{2}{*}{\centering $0.10$} &
 \multirow{2}{*}{\centering $0.75$} &
 \multirow{2}{*}{\centering $0.04$} &
 \multirow{2}{*}{\centering $0.68$} &
 \multirow{2}{*}{\centering $0.52$} \cr \\
 \hline
 \multirow{2}{*}{\centering []} &
 \multirow{2}{*}{\centering []} &
 \multirow{2}{*}{\centering []} &
 \multirow{2}{*}{\centering [0.28, 1.65]} &
 \multirow{2}{*}{\centering [0.09, 2.83]} \cr \\
 \hline
 \bottomrule[1pt]
 \end{tabular}
\end{table}
\end{document}

stat

"Skateboarding stance and handedness: A brief analysis of relationship, proportions and influences" by Alexandre Mascarenhas, arXiv:2310.11460v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Ibon Gracia
Luca Laurenti
Manuel Mazo
Alessandro Abate
Morteza Lahijanian

"Temporal Logic Control for Nonlinear Stochastic Systems Under Unknown Disturbances" by Ibon Gracia, Luca Laurenti, Manuel Mazo, Alessandro Abate, and Morteza Lahijanian, arXiv:2412.11343v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Global Bias-Corrected Divide-and-Conquer by Quantile-Matched Composite for General Nonparametric Regressions" by Yan Chen and Lu Lin, arXiv:2201.12597v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Shraddha M. Naik
Tanujit Chakraborty
Madhurima Panja
Abdenour Hadid
Bibhas Chakraborty

"Skew-Probabilistic Neural Networks for Learning from Imbalanced Data" by Shraddha M. Naik, Tanujit Chakraborty, Madhurima Panja, Abdenour Hadid, and Bibhas Chakraborty, arXiv:2312.05878v2, 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}{lccccccc}
 \toprule
 & \textbf{Households} & \textbf{C-Firms} & \textbf{K-Firms} & \textbf{Banks} & \textbf{Central Bank} & $\sum$ \\
 \midrule
 Capital & & $K$ & & & & $K$ \\
 Deposits & $M_H$ & $M_C$ & $M_K$ & $ -M$ & & 0 \\
 Debt & & $-D_C$ & $-D_K$ & $D$ & & 0 \\
 Reserves & & & & $R$ & $-R$ & 0 \\
 Advances & & & & $-A$ & $A$ & 0 \\
 Equity & $-E_H$ & $-E_C$ & $-E_K$ & $-E_B$ & $-E_{CB}$ & $-K$ \\
 \midrule
 $\sum$ & 0 & 0 & 0 & 0 & 0 & 0 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Macro financial balance sheet matrix.}
\end{table}
\end{document}

cs

"Implications of zero-growth economics analysed with an agent-based model" by Dylan C. Terry-Doyle and Adam B. Barrett, arXiv:2501.19168v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Cross correlation between the volatilities for various $\gamma$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccccc}
\hline
$\gamma$ & 0.0 & 0.05 & 0.07 & 0.10 & 0.15 \\
\hline
cross correlation & $8.2\times 10^{-3}$ & $5.8\times 10^{-2}$ & $8.4\times 10^{-2}$ & 0.15 & 0.31\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Multiple Time Series Ising Model for Financial Market Simulations" by Tetsuya Takaishi, arXiv:1611.08088v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|}
		\hline \textbf{Metrics} & \textbf{Pretrain} & \textbf{Trainval} & \textbf{Test} \\ \hline

		\textbf{\# Speakers} & 5091 & 4005 & 413 \\ \hline

		\textbf{\# Utterances} & 118,516 & 31,982 & 1321 \\ \hline

		\textbf{\# Word instances} & 3,894,800 & 807,375 & 9890 \\ \hline

		\textbf{Vocab} & 51,211 & 17,546 & 2002 \\ \hline

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

eess

Aradhya Neeraj Mathur
Devansh Batra
Yaman Kumar
Rajiv Ratn Shah
Roger Zimmermann

"LIFI: Towards Linguistically Informed Frame Interpolation" by Aradhya Neeraj Mathur, Devansh Batra, Yaman Kumar, Rajiv Ratn Shah, and Roger Zimmermann, arXiv:2010.16078v5, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lr}
		\hline
		Variable & $p-value$ \\
		\hline
		Age & $0.32$ \\
		Sex & $0.16$ \\
		FPG & $0.50$ \\
		HOMA-IR & $0.52$ \\
		BMI & $0.42$ \\
		A1C & $0.03$ \\
		CONGA & $0.24$ \\
		MAGE & $0.68$ \\
		MODD & $0.16$ \\
		Glucodensity & $<0.001$\\
		\hline
	\end{tabular}
\caption{Estimated raw p-values of A1C total variation vs each biomarker using the procedure defined in Section with normoglycemic patients. The figure shows the marginal dependence relation between examined variables}
\end{table}
\end{document}

stat

Marcos Matabuena
Paulo Félix
Carlos Meijide-Garcia
Francisco Gude

"Glucose values prediction five years ahead with a new framework of missing responses in reproducing kernel Hilbert spaces, and the use of continuous glucose monitoring technology" by Marcos Matabuena, Paulo Félix, Carlos Meijide-Garcia, and Francisco Gude, arXiv:2012.06564v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average results on ten translation directions for NLLB. $\eta$ is set to be 0.82 for RS-DPO.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
 \hline
 \multirow{2}{*}{\textbf{Method}} & \multicolumn{4}{c}{\textbf{Average}} \\\cline{2-5}
 & \textbf{KIWI22} & \textbf{COMET22} & \textbf{XCOMET} & \textbf{KIWI-XL} \\\hline
 NLLB-1.3B & 0.8009 & 0.8362 & 0.8947 & 0.7001 \\
 QE Ft. & 0.7890 & 0.8201 & 0.8670 & 0.6770 \\
 Goal Ref. & 0.8098 & - & \textbf{0.9118} & \textbf{0.7268} \\\hline
 RSO & 0.8142 & 0.8466 & 0.9066 & 0.7183 \\
 RS-DPO & 0.8078 & 0.8406 & 0.8972 & 0.7084 \\
 Triplet & 0.8138 & 0.8465 & 0.9025 & 0.7163 \\
 MBR-BW & 0.8104 & 0.8447 & 0.9026 & 0.7134\\
 MBR-BMW & 0.8073 & 0.8421 & 0.9005 & 0.7080 \\\hline
 CRPO$+$ & \textbf{0.8149} & \textbf{0.8469} & \textcolor{gray}{\textbf{0.9074}} & \textcolor{gray}{\textbf{0.7207}} \\
 CRPO$\times$ & \textcolor{gray}{\textbf{0.8148}} & \textcolor{gray}{\textbf{0.8467}} & 0.9063 & 0.7202 \\\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Guofeng Cui
Pichao Wang
Yang Liu
Zemian Ke
Zhu Liu
Vimal Bhat

"CRPO: Confidence-Reward Driven Preference Optimization for Machine Translation" by Guofeng Cui, Pichao Wang, Yang Liu, Zemian Ke, Zhu Liu, and Vimal Bhat, arXiv:2501.13927v1, 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{BCVA Wasserstein Radii}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
Percentage of $\delta^u$ & 50\% & 60\% & 70\% & 80\% & 90\% & 100\% \\
 \hline
W Radius delta & 14.41 & 17.30 & 20.18 & 23.06 & 25.95 & 28.83 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk" by Derek Singh and Shuzhong Zhang, arXiv:1910.01781v2, 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{Pair-wise Pearson correlations among variables}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
		\hline \hline
		 & Avg. Ret. & Firm Size & P/E Ratio & P/B Ratio & Shareholding & Beta & Liquidity\\ \hline
		 Avg. Ret. & 1.000 & & & & & & \\
		 Firm Size & -0.140* & 1.000 & & & & & \\
		 P/E Ratio & -0.358*** & 0.332** & 1.000 & & & & \\
		 P/B Ratio & -0.297*** & 0.259*** & 0.461*** & 1.000 & & & \\
		 Shareholding & 0.116 & 0.071 & 0.056 & 0.116 & 1.000 & & \\
		 Beta & 0.172** & 0.309*** & -0.257* & -0.042 & -0.51 & 1.000 & \\
		 Liquidity & 0.071 & -0.047 & 0.088 & -0.033 & 0.059 & -0.054 & 1.000\\ \hline \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average over 1000 trials of the number of iterations required to obtain an optimal solution starting from a guess within +/- 50\% of the known optimum}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \cline{2-4}
 & SQP & LSQP & SLCP \\ \hline
 Floudas & 30.17 & 14.30 (-52.60\%) & 12.92 (-57.18\%) \\
 Kirschen-Ozturk & 72.55 & 18.89 (-73.96\%) & 16.38 (-77.42\%) \\
 Hoburg-0 & - & 17.79 & 16.29 (-8.44\%) \\
 Hoburg-1 & - & 17.82 & 16.66 (-6.47\%) \\
 Hoburg-3 & - & 17.66 & 16.87 (-4.49\%) \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A Method of Sequential Log-Convex Programming for Engineering Design" by Cody Karcher and Robert Haimes, arXiv:2201.08436v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Maik Steinhauser
Laurenz Reichardt
Nikolas Ebert
Oliver Wasenmüller

"D-PLS: Decoupled Semantic Segmentation for 4D-Panoptic-LiDAR-Segmentation" by Maik Steinhauser, Laurenz Reichardt, Nikolas Ebert, and Oliver Wasenmüller, arXiv:2501.15870v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}\hline
		Nb& Dim&$T_\text{basis}$&$T_\text{ass}$&$T_\text{solve}$& $e_{L^2}$& $e_{H^1}$ \tabularnewline\hline
		/&417,911&/&258.7& 3515.4&/&/ \tabularnewline\hline
		4+0 & 2576 & 19.3 & 195.2 & 90.6 & 1.94e-04 & 5.95e-01 \tabularnewline\hline
		6+0 & 3864 & 23.0 & 199.9 & 175.2 & 1.72e-04 & 5.30e-01 \tabularnewline\hline
		8+0 & 5152 & 27.9 & 194.8 & 278.9 & 1.65e-04 & 5.03e-01 \tabularnewline\hline
		3+1 & 2576 & 25.0 & 242.1 & 115.0 & 4.67e-05 & 1.22e-01 \tabularnewline\hline
		3+2 & 3220 & 33.4 & 254.3 & 177.5 & 4.00e-05 & 7.72e-02 \tabularnewline\hline
		4+1 & 3220 & 28.3 & 243.0 & 169.0 & 3.18e-05 & 1.02e-01 \tabularnewline\hline
		4+2 & 3864 & 38.0 & 246.2 & 229.4 & 2.80e-05 & 6.27e-02 \tabularnewline\hline
		5+1 & 3864 & 32.0 & 248.7 & 225.2 & 2.34e-05 & 8.27e-02 \tabularnewline\hline
		4+1(1 update) & 3220 & 38.6 & 209.5 & 148.8 & 2.13e-05 & 6.97e-02 \tabularnewline\hline
		4+1(3 updates)& 3220 & 54.3 & 210.2 & 146.5 & 1.38e-05 & 4.30e-02 \tabularnewline\hline
		5+1(1 update) & 3864 & 42.0 & 218.8 & 197.9 & 1.53e-05 & 5.59e-02 \tabularnewline\hline
		5+1(3 updates) & 3864 & 64.5 & 214.5 & 194.1 & 9.58e-06 & 3.43e-02 \tabularnewline\hline
	\end{tabular}
\end{adjustbox}
\caption{Computational performance comparison between the reference solution and GMsFEM solution, $K_3$, full zero Neumann boundary condition.}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccr}
\toprule
Data set & Naive & Flexible & Better? \\
\midrule
Breast & 95.9$\pm$ 0.2& 96.7$\pm$ 0.2& $\surd$ \\
Cleveland & 83.3$\pm$ 0.6& 80.0$\pm$ 0.6& $\times$\\
Glass2 & 61.9$\pm$ 1.4& 83.8$\pm$ 0.7& $\surd$ \\
Credit & 74.8$\pm$ 0.5& 78.3$\pm$ 0.6& \\
Horse & 73.3$\pm$ 0.9& 69.7$\pm$ 1.0& $\times$\\
Meta & 67.1$\pm$ 0.6& 76.5$\pm$ 0.5& $\surd$ \\
Pima & 75.1$\pm$ 0.6& 73.9$\pm$ 0.5& \\
Vehicle & 44.9$\pm$ 0.6& 61.5$\pm$ 0.4& $\surd$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Ofir Nabati
Guy Tennenholtz
ChihWei Hsu
Moonkyung Ryu
Deepak Ramachandran
Yinlam Chow
Xiang Li
Craig Boutilier

"Preference Adaptive and Sequential Text-to-Image Generation" by Ofir Nabati, Guy Tennenholtz, ChihWei Hsu, Moonkyung Ryu, Deepak Ramachandran, Yinlam Chow, Xiang Li, and Craig Boutilier, arXiv:2412.10419v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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

math

Anamitra Chaudhuri
Sabyasachi Chatterjee

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Jacque-Bera and Anderson-Darling test of normality for the residuals of the returns series}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}		\hline
		& Jacque-Bera test & Anderson-Darling test \\
		\hline
		P-value & $< 2.2e-16$ & $< 2.2e-16$
		\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Estimating the volatility of Bitcoin using GARCH models" by Samuel Asante Gyamerah, arXiv:1909.04903v2, 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|r|r|}
				\hline
				Graph & $\alpha_{d-1}$ & Bound\\
				\hline\hline
				$O_2$ ($K_3$) & $\alpha_0=3$ & $m_0+m_1=3$\\
				\hline
				$O_3$ (Petersen) & $\alpha_1=4$ & $m_1=4$\\
				\hline
				$O_4$ & $\alpha_2=7$ & $ m_0+m_1 =7$\\
				\hline
				$O_5$ & $\alpha_3=7$ & $m_1=8$ \\
				\hline
				$O_6$ & $\alpha_4=11$ & $m_0+m_1=11$\\
				\hline
				$O_7$ & $\alpha_5=12$ & $m_1= 12$\\
				\hline
				$O_8$ & $\alpha_6=15$ & $m_0+m_1=15$\\
				\hline
				$O_9$ & $\alpha_7=15$ & $m_1=16$\\
				\hline
				$O_{10}$ & $\alpha_8=19$ & $m_0+m_1=19$\\
				\hline
				$O_{11}$ & $\alpha_9=19$ & $m_1=20$\\
				\hline
				$O_{12}$ & $\alpha_{10}=23$ & $m_0+m_1=23$\\
				\hline
				$O_{14}$ & $\alpha_{12}=27$ & $m_0+m_1=27$\\
				\hline
				$O_{16}$ & $\alpha_{14}=31$ & $m_0+m_1=31$\\
				\hline
			\end{tabular}
\end{adjustbox}
\caption{The known exact values of $\alpha_{d-1}$ and the upper bounds for the Odd graphs $O_{d+1}$.}
\end{table}
\end{document}

math

Aida Abiad
Cristina Dalfó
Miquel Àngel Fiol
Sjanne Zeijlemaker

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\hline \hline
($n,N$)&(2000,5000) & (2000, 15,000)&(3000,5000) & (3000, 15,000)
\\\hline
Estimates&\multicolumn{4}{c}{$E(Q^*_{n})$=0.667} \\
$\mathbb{P}_N\widehat{Q}^*_{n}$ &0.665&0.663&0.664&0.667\\
$\widehat{\sigma}(\mathbb{P}_N\widehat{Q}^*_{n})$ &0.024&0.023&0.020&0.019 \\
\multicolumn{4}{l}{Coverage} \\
90\% CI &0.838&0.828&0.874&0.864 \\
95\% CI&0.898&0.896&0.932&0.928\\
99\% CI &0.958&0.952&0.988&0.990\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Statistical Learning for Individualized Asset Allocation" by Yi Ding, Yingying Li, and Rui Song, arXiv:2201.07998v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Table showing paired samples T-Tests determining the differences in specific Modality Usages and Behaviors for Voice and Eye Gaze modalities due To Task Nature as well as Multimodal Input Usages (\textit{* = $p < .05$; ** = $p < .01$; *** = $p < .001$})}
\begin{tabular}{|l|l|l|l|l|l|l|l|l|}
\hline
\textbf{\textit{Modality}} & \textbf{\textit{Name}} & \textbf{\textit{t}} & \textbf{\textit{p}} & \textbf{\textit{Sig}} & \textbf{\textit{$M_1$}} & \textbf{\textit{$SD_1$}} & \textbf{\textit{$M_2$}} & \textbf{\textit{$SD_2$}} \\ \hline
Voice & Implicit Utterances Ratio & -8.14 & $< .001$ & *** & 0.37 & 0.31 & 0.82 & 0.24 \\ \hline
Voice & Explicit Utterances Ratio & 8.14 & $< .001$ & *** & 0.63 & 0.31 & 0.18 & 0.24 \\ \hline
Eye Gaze & Average Time Looking Left & 3.2 & $< .01$ & ** & 2.60 & 0.59 & 2.20 & 0.47 \\ \hline
Multimodal & Unimodal Modality Usage & 2.78 & $< .01$ & ** & 0.24 & 0.17 & 0.15 & 0.13 \\ \hline
Multimodal & Bimodal Modality Usage & 1.91 & 0.064 & No & 0.53 & 0.18 & 0.43 & 0.19 \\ \hline
Multimodal & Trimodal Modality Usage & -5.07 & $< .001$ & *** & 0.15 & 0.21 & 0.42 & 0.24 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Ryan Ghamandi
Yahya Hmaiti
Mykola Maslych
Ravi Kiran Kattoju
Joseph J. LaViola

"Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks" by Ryan Ghamandi, Yahya Hmaiti, Mykola Maslych, Ravi Kiran Kattoju, and Joseph J. LaViola, arXiv:2508.17124v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Mara Sherlin D. Talento
Sarbojit Roy
Tania Reyes Vallejo
Leena A Ibrahim
Hernando C. Ombao

"KenCoh: A Ranked-Based Canonical Coherence" by Mara Sherlin D. Talento, Sarbojit Roy, Tania Reyes Vallejo, Leena A Ibrahim, and Hernando C. Ombao, arXiv:2412.10521v2, 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
\textbf{Dataset} & \textbf{Labels} & \textbf{Type} \\ \hline
ToolBench & 100 & Multi-class \\
RestGPT & 75 & Multi-class \\
AG\_news & 4 & Single-class \\
SST-5 & 5 & Single-class \\
TREC & 6 & Single-class
\end{tabular}
\end{adjustbox}
\caption{Number of labels for each dataset}
\end{table}
\end{document}

cs

Rahul Atul Bhope
Praveen Venkateswaran
K. R. Jayaram
Vatche Isahagian
Vinod Muthusamy
Nalini Venkatasubramanian

"OptiSeq: Ordering Examples On-The-Fly for In-Context Learning" by Rahul Atul Bhope, Praveen Venkateswaran, K. R. Jayaram, Vatche Isahagian, Vinod Muthusamy, and Nalini Venkatasubramanian, arXiv:2501.15030v2, 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
Player & \(t_Y\) & \(c=0\) & \(c=1\) & \(c=Cont\) \\ \hline
\(X\) & Non-Guilty & \(1\) & \(-1\) & 0 \\
\(X\) & Guilty & \(-1\) & \(1\) & 0 \\
\(Y\) (Non-Guilty) & Non-Guilty & \(1\) & \(-1\) & \(1\) \\
\(Y\) (Guilty) & Guilty & \(1\) & \(-1\) & \(1\) \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Utility outcomes for players \(X\) and \(Y\) under different types and actions in an interrogation game.}
\end{table}
\end{document}

cs

"Conversation Games and a Strategic View of the Turing Test" by Kaveh Aryan, arXiv:2501.18455v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\textbf{Model} & \textbf{Attack} & \textbf{Detection Method} & \textbf{1\% FP} & \textbf{2\% FP} & \textbf{5\% FP} \\ \hline
\multirow{6}{*}{Inception V3}
 & \multirow{3}{*}{IFGSM} & Vote & 98.2\% & 99.3\% & 99.7\% \\ \cline{3-6}
 & & Anomaly & 94.0\% & 97.6\% & 99.3\% \\ \cline{3-6}
 & & Likelihood Ratio & 98.7\% & 99.4\% & 99.7\% \\ \cline{2-6}
 & \multirow{3}{*}{PGD} & Vote & 97.9\% & 99.2\% & 99.5\% \\ \cline{3-6}
 & & Anomaly & 97.4\% & 98.8\% & 99.4\% \\ \cline{3-6}
 & & Likelihood Ratio & 98.6\% & 99.1\% & 99.5\% \\ \hline
\multirow{6}{*}{ViT}
 & \multirow{3}{*}{IFGSN} & Vote & 97.4\% & 98.8\% & 99.8\% \\ \cline{3-6}
 & & Anomaly & 93.6\% & 97.8\% & 99.5\% \\ \cline{3-6}
 & & Likelihood Ratio & 96.9\% & 99.0\% & 99.9\% \\ \cline{2-6}
 & \multirow{3}{*}{PGD} & Vote & 96.8\% & 98.5\% & 99.3\% \\ \cline{3-6}
 & & Anomaly & 95.0\% & 97.2\% & 98.4\% \\ \cline{3-6}
 & & Likelihood Ratio & 96.5\% & 97.6\% & 99.4\% \\ \hline
\end{tabular}
\caption{Adversarial attack detection rate for the ImageNet dataset using ViT and Inception V3 models, tested against IFGSM and PGD attacks across 3 detection methods: Vote, Anomaly, and Likelihood Ratio with $20$ fingerprints.}
\end{table}
\end{document}

stat

Haim Fisher
Moni Shahar
Yehezkel S. Resheff

"Neural Fingerprints for Adversarial Attack Detection" by Haim Fisher, Moni Shahar, and Yehezkel S. Resheff, arXiv:2411.04533v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study on block sizes in the block causal attention of speech tokenizers.}
\begin{tabular}{crrrrrr}
 \toprule
 Model & Attention & Block & \multicolumn{2}{c}{LibriSpeech} & AISHELL-1 \\
 & Type & Size & test-clean & test-other & test\\
 \midrule
 whisper-large-v3 & Bidirectional & - & 3.45 & 5.82 & 4.71 \\
 whisper-large-v3 & Causal & - & 3.13 & 7.10 & 6.27\\
 whisper-large-v3 & Block Causal & 0.5s & 3.85 & 7.30 & 5.70 \\
 whisper-large-v3 & Block Causal & 1s & 3.37 & 6.50 & 5.14 \\
 whisper-large-v3 & Block Causal & 2s & 3.39 & 6.16 & 4.76 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Aohan Zeng
Zhengxiao Du
Mingdao Liu
Lei Zhang
Shengmin Jiang
Yuxiao Dong
Jie Tang

"Scaling Speech-Text Pre-training with Synthetic Interleaved Data" by Aohan Zeng, Zhengxiao Du, Mingdao Liu, Lei Zhang, Shengmin Jiang, Yuxiao Dong, and Jie Tang, arXiv:2411.17607v2, 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{Treatment Effects Estimates}
\begin{tabular}{cccccc}
\hline \hline
 & & & & & \small{Implied} \\ %\midrule
Outcome & $\text{CDE}_0$ & $\text{CDE}_1$ & $\text{CTE}$ & \text{LATE} & \text{LATE} \\ \midrule

\small{	Household is Economically}&	0.024	&	0.048	&	0.059	&	0.035	&	0.039	\\
\small{Self-Sufficient	}&\small{(0.050)}&\small{(0.063)}&\small{(0.078)}&\small{(0.039)}&\small{ }\\	\\

\small{	Sampled Adult}&	$0.127^{*}$	&	-0.003	&	0.132	&	0.066	&	0.070	\\
\small{is Employed	}&\small{(0.066)}&\small{(0.079)}&\small{(0.122)}&\small{(0.051)}&\small{ }\\	\\

\small{	Sampled Adult not }&	-0.042	&	-0.074	&	-$0.412^{**}$	&	-$0.113^{**}$	&	-0.106	\\
\small{in Labor Force	}&\small{(0.071)}&\small{(0.072)}&\small{(0.144)}&\small{(0.049)}&\small{ }\\	\\

\small{	Household Total }&	0.498	&	2.928	&	2.479	&	1.811	&	1.840	\\
\small{Income	}&\small{(2.262)}&\small{(2.191)}&\small{(3.014)}&\small{(1.523)}&\small{ }\\

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

stat

Manu Navjeevan
Rodrigo Pinto
Andres Santos

"Identification and Estimation in a Class of Potential Outcomes Models" by Manu Navjeevan, Rodrigo Pinto, and Andres Santos, arXiv:2310.05311v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc|ccc}
\hline
Jockey & Model Rank & Saratoga Rank & Belmont Rank & Aqueduct Rank \\ \hline
Irad Ortiz Jr. & 1 & 1 & 1 & 4 \\
Jose L. Ortiz & 2 & 5 & 7 & 6 \\
Luis Saez & 3 & 3 & 6 & NR \\
John R. Velazquez & 4 & 8 & 14 & NR \\
Javier Castellano & 5 & 7 & 10 & 2 \\
Manuel Franco & 6 & 6 & 3 & 1 \\
Jose Lezcano & 7 & 9 & 8 & NR \\
Joe Bravo & 8 & DNC & DNC & DNC \\
Joel Rosario & 9 & 4 & 4 & NR \\
Junior Alvarado & 10 & 12 & 22 & NR \\ \hline
\end{tabular}
\caption{Top ten jockeys in the random effect from the Forward model, compared to jockey rankings provided by Saratoga, Belmont Park, and Aqueduct in 2022 based on total earnings. NR = not ranked; DNC = did not compete in 2022.}
\end{table}
\end{document}

stat

Tyrel Stokes
Gurashish Bagga
Kimberly Kroetch
Brendan Kumagai
Liam Welsh

"A generative approach to frame-level multi-competitor races" by Tyrel Stokes, Gurashish Bagga, Kimberly Kroetch, Brendan Kumagai, and Liam Welsh, arXiv:2310.01748v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|l|}
\hline
$N_{x}$ & $N_{y}$ & $Ra$ & $St$ & $\theta_{m}$ & $\beta$ & $h_{0}$ \\ \hline
$512$ & $64$ & $[10^3:10^6]$ & $10$ & $0.3$ & $8$ & $0.05$ \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Numerical set up for the study of the critical Rayleigh number $Ra_{c}$}
\end{table}
\end{document}

math

A. Limare
S. Popinet
C. Josserand
Z. Xue
A. Ghigo

"A hybrid level-set / embedded boundary method applied to solidification-melt problems" by A. Limare, S. Popinet, C. Josserand, Z. Xue, and A. Ghigo, arXiv:2202.08300v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Huyen Giang Thi Thu
Thang Viet Doan
Tai Le Quy

"An experimental study on fairness-aware machine learning for credit scoring problem" by Huyen Giang Thi Thu, Thang Viet Doan, and Tai Le Quy, arXiv:2412.20298v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf{GARCH statistics for BAC stock exchange}} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
Parameter & Estimate & Error & t-statistic & p-value \\ % inserts table
		\hline
		$a_0$ & 1.25E-07 & 1.42E-08 & 8.788 & $<$2E-06 \\
		$a_1$ & 2.47E-01 & 5.91E-02 & 4.171 & 3.0E-05\\
		$b_1$ & 7.21E-01 & 3.63E-02 & 19.84 & $<$2E-16\\
\hline \hline%inserts single line
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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

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


stat

"Automated threshold selection and associated inference uncertainty for univariate extremes" by Conor Murphy, Jonathan A. Tawn, and Zak Varty, arXiv:2310.17999v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model performance comparison on AIS dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\multirow{2}{*}{Model} & \multirow{2}{*}{Open-set} & \multirow{2}{*}{Closed-set} & \multicolumn{2}{c}{Chain-of-Thought} \\
 & & & Open-set & Closed-set \\ \hline
GPT-4o & 64.20 & 64.27 & 64.42 & 69.03 \\
Claude-3.5-Sonnet & 63.84 & 66.30 & 68.96 & 73.29 \\
Gemini-1.5-Pro & 60.62 & 63.10 & 66.67 & 66.24 \\
LLaVA-1.5-7B & 41.63 & 42.06 & 45.76 & 45.90 \\
Phi-3.5-Vision & 45.10 & 45.65 & 50.38 & 49.76 \\
MiniCPM-Llama3 & 39.90 & 41.77 & 45.14 & 44.09 \\
InternVL2-8B & 35.50 & 36.05 & 40.69 & 40.20 \\
LLaVA-Next & 49.30 & 52.05 & 54.75 & 55.42 \\
CogVLM & 55.34 & 57.69 & 60.72 & 64.34 \\
OpenFlamingo-v2 & 28.82 & 30.89 & 35.83 & 35.81 \\
InstructBLIP & 23.13 & 24.08 & 25.40 & 33.07 \\
BLIP2 & 16.94 & 17.17 & 19.83 & 25.70 \\
CLIP & 11.48 & 11.62 & 13.64 & 14.62 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yasiru Ranasinghe
Vibashan VS
James Uplinger
Celso De Melo
Vishal M. Patel

"Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models" by Yasiru Ranasinghe, Vibashan VS, James Uplinger, Celso De Melo, and Vishal M. Patel, arXiv:2501.07396v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of patients and healthy subjects and corresponding number of annotated speech segments.}
\begin{tabular}{lcc}
 \hline
 \textbf{Condition of subjects} & \textbf{No.} & \textbf{Segments}\\
 \hline
 Patient in severe respiratory distress &5 &65~~~ \\
 Asthma patient in mild respiratory distress &5 &216 ~~~ \\
 Patient in mild respiratory distress &26 &673~~~ \\
 Healthy subjects with no respiratory distress &52 &1003~~~ \\
 \hline
 Total &88 & 1957~~~\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Meemnur Rashid
Kaisar Ahmed Alman
Khaled Hasan
John H. L. Hansen
Taufiq Hasan

"Respiratory Distress Detection from Telephone Speech using Acoustic and Prosodic Features" by Meemnur Rashid, Kaisar Ahmed Alman, Khaled Hasan, John H. L. Hansen, and Taufiq Hasan, arXiv:2011.09270v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{pifont}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\usepackage{marvosym}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{An ablation study of our method computed over the Nerf-Synthethic dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc|ccccc|}
\cline{2-9}
\textbf{} & \multicolumn{3}{c|}{Adjustments} & \multicolumn{5}{c|}{\textbf{Dataset - Nerf Synthethic}} \\ \hline
\textbf{Method} &
 Matrix &
 Loss &
 Keypoints &
 LPIPS \textdownarrow &
 PSNR \textuparrow &
 SSIM \textuparrow &
 FPS &
 Memory \\ \midrule
3DGS & \ding{55} & \ding{55} & \ding{55} & 0.0298 & 33.476 & 0.9700 & 561 & 62MB \\ \hline
 & \ding{51} & \ding{55} & \ding{55} & 0.0303 & 33.513 & 0.9699 & 457 & 64MB \\
 &
 \ding{51} &
 \ding{51} &
 \ding{55} &
 \cellcolor[HTML]{F5999A}0.0295 &
 \cellcolor[HTML]{F8CD99}33.782 &
 \cellcolor[HTML]{F5999A}0.9711 &
 394 &
 66MB \\
\multirow{-3}{*}{\textbf{VoD-3DGS[S]}} &
 \ding{51} &
 \ding{51} &
 \ding{51} &
 \cellcolor[HTML]{F8CD99}0.0296 &
 \cellcolor[HTML]{F5999A}33.792 &
 \cellcolor[HTML]{F8CD99}0.9710 &
 396 &
 65MB \\ \hline
\textbf{VoD-3DGS[L]} &
 \ding{51} &
 \ding{51} &
 \ding{51} &
 \cellcolor[HTML]{FCF9AD}0.0297 &
 \cellcolor[HTML]{FCF9AD}33.690 &
 \cellcolor[HTML]{FCF9AD}0.9706 &
 374 &
 66MB \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mateusz Nowak
Wojciech Jarosz
Peter Chin

"VoD-3DGS: View-opacity-Dependent 3D Gaussian Splatting" by Mateusz Nowak, Wojciech Jarosz, and Peter Chin, arXiv:2501.17978v2, 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}{lccc}
 \toprule
 \bfseries \textbf{Model} & \textbf{HJ vs. A-VGG} & \textbf{HJ vs. VGG-Face} & \textbf{A-VGG vs. VGG-Face}\\
 \midrule
 WS-distance 2D & 0.04 $\pm$ 0.01 & 0.12 $\pm$ 0.01 & 0.12 $\pm$ 0.01\\
 WS-distance 3D & 0.07 $\pm$ 0.01 & 0.13 $\pm$ 0.01 & 0.07 $\pm$ 0.01\\
 Heat-distance 2D & 1.6 $\pm$ 0.04 & 2.05 $\pm$ 0.03 & 1.98 $\pm$ 0.03\\
 Heat-distance 3D & 1.69 $\pm$ 0.04 & 2.08 $\pm$ 0.04\ & 1.79 $\pm$ 0.03\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Nahid Torbati
Michael Gaebler
Simon M. Hofmann
Nico Scherf

"Geometry matters: insights from Ollivier Ricci Curvature and Ricci Flow into representational alignment through Ollivier-Ricci Curvature and Ricci Flow" by Nahid Torbati, Michael Gaebler, Simon M. Hofmann, and Nico Scherf, arXiv:2501.00919v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccc}
\midrule & \textbf{MSE} & \textbf{SSIM} & \textbf{AL} \\
\midrule DEAR-2D$_1$ & \checkmark & & \\
\midrule DEAR-2D$_2$ & \checkmark & \checkmark & \\
\midrule DEAR-2D-i & \checkmark & \checkmark & \\
\midrule DEAR-3D$_1$ & \checkmark & \checkmark & \\
\midrule DEAR-3D & \checkmark & \checkmark & \checkmark\\
\midrule
\end{tabular}
\end{table}
\end{document}

eess

"Deep Encoder-decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-view Data" by Huidong Xie, Hongming Shan, and Ge Wang, arXiv:1911.05880v2, 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{Number of GMRES-IR and RGMRES-IR refinement steps with the number of GMRES iterations for each refinement step for random dense matrices having geometrically distributed singular values (mode 3) with various condition numbers, using precisions ($u_f,u,u_r$) = (half, single, double) and ($m,k$) = (90,30).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cc|cc|}
		\hline
		$\kappa_\infty(A)$ & $\kappa_2(A)$ & GMRES-IR (90) & RGMRES-IR ($90,30$) \\ \hline
		$9 \cdot 10^4$ & $10^4$ & 20 (9,11) & 20 (9,11) \\
		$8 \cdot 10^5$ & $10^5$ & 70 (32,38) & 44 (32,12) \\
		$7 \cdot 10^6$ & $10^6$ & 129 (64,65) & 77 (64,13)
		\\
		$7 \cdot 10^7$ & $10^7$ & 164 (82,82) & 107 (82,25)
		\\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Mixed Precision GMRES-based Iterative Refinement with Recycling" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.