\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results for Foundation Metrics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \toprule
 Treatment & Sensibleness & Specificity & Interestingness & Safety & Groundedness & Informativeness \\
 \midrule
 PT (2B) & 76.6 & 46.5 & 10.8 & 84.8 & 45 & 29.2 \\
 PT (8B) & 79.1 & 46.5 & 11.3 & 87.5 & 47.1 & 29.5 \\
 PT (137B) & 80.2 & 49.8 & 15.8 & 88 & 57.9 & 41.3 \\
 FT quality-safety (137B) & 92.8 & 77.1 & 23.2 & 94.6 & 67.9 & 50.5 \\
 LaMDA (2B) & 81.8 & 74.8 & 23.4 & 93.8 & 53 & 41.8 \\
 LaMDA (8B) & 88 & 77.4 & 22.2 & 93.5 & 64.6 & 50.2 \\
 LaMDA (137B) & 92.3 & 79 & 25.7 & 95.2 & 73.2 & 62.3 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Romal Thoppilan
Daniel De Freitas
Jamie Hall
Noam Shazeer
Apoorv Kulshreshtha
Heng-Tze Cheng
Alicia Jin
Taylor Bos
Leslie Baker
Yu Du
YaGuang Li
Hongrae Lee
Huaixiu Steven Zheng
Amin Ghafouri
Marcelo Menegali
Yanping Huang
Maxim Krikun
Dmitry Lepikhin
James Qin
Dehao Chen
Yuanzhong Xu
Zhifeng Chen
Adam Roberts
Maarten Bosma
Vincent Zhao
Yanqi Zhou
Chung-Ching Chang
Igor Krivokon
Will Rusch
Marc Pickett
Pranesh Srinivasan
Laichee Man
Kathleen Meier-Hellstern
Meredith Ringel Morris
Tulsee Doshi
Renelito Delos Santos
Toju Duke
Johnny Soraker
Ben Zevenbergen
Vinodkumar Prabhakaran
Mark Diaz
Ben Hutchinson
Kristen Olson
Alejandra Molina
Erin Hoffman-John
Josh Lee
Lora Aroyo
Ravi Rajakumar
Alena Butryna
Matthew Lamm
Viktoriya Kuzmina
Joe Fenton
Aaron Cohen
Rachel Bernstein
Ray Kurzweil
Blaise Aguera-Arcas
Claire Cui
Marian Croak
Ed Chi
Quoc Le

"LaMDA: Language Models for Dialog Applications" by Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Vincent Zhao, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Pranesh Srinivasan, Laichee Man, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, and Quoc Le, arXiv:2201.08239v3, 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{Ablation study on candidate generation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
\textbf{Variant} & \textbf{QoE} & \textbf{Rebuf (s)} & \textbf{BW Wastage (\%)} \\
\midrule
SC & 0.76 & 3.42 & 15.8 \\
MC & \textbf{0.88} & \textbf{2.97} & \textbf{13.9} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yili Jin
Ling Pan
Rui-Xiao Zhang
Jiangchuan Liu
Xue Liu

"Generative Flow Networks for Personalized Multimedia Systems: A Case Study on Short Video Feeds" by Yili Jin, Ling Pan, Rui-Xiao Zhang, Jiangchuan Liu, and Xue Liu, arXiv:2508.17166v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
{\bf year} & {\bf January} & {\bf February} & {\bf March} & {\bf April} \\
\hline
2019 & 8 & 5 & 7 & 4
\\
\hline
2020 & 6 & 4 & 16 & 11
\\
\hline
\end{tabular}
\end{adjustbox}
\caption{The number of outage reports}
\end{table}
\end{document}

cs

Ahmed Elmokashfi
Alfred Arouna
Ioana Livadariu
Mah-Rukh Fida
Amund Kvalbein
Anas Al-Selwi
Thomas Dreibholz
Haakon Bryhni

"A Multi-Perspective Study of Internet Performance during the COVID-19 Outbreak" by Ahmed Elmokashfi, Alfred Arouna, Ioana Livadariu, Mah-Rukh Fida, Amund Kvalbein, Anas Al-Selwi, Thomas Dreibholz, and Haakon Bryhni, arXiv:2101.05030v1, 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{Results from cryptocurrency market model regressions: \(r_i - r_f = \alpha_i + \beta_{CM,i} CM + \beta_{SMB,i} SMB + \beta_{Mom,i} Mom + \epsilon_i\) where \(i\) denotes the crypto portfolio formed on \(\Delta\frac{TVL}{Market\ Cap}\), \(CM\) is cryptocurrency excess market returns, \(SMB\) is the cryptocurrency small-minus-big factor, \(Mom\) is the cryptocurrency momentum factor, and \(r_f\) is the risk-free rate. TVL excludes all optional DefiLlama categories. P-values are below the coefficients in parentheses. \(^{***}\), \(^{**}\), and \(^{*}\), denote statistical significance at the 1\%, 5\%, and 10\% levels respectively. Test statistics and p-values for the F-test of whether alpha coefficients are jointly 0 are below the Adjusted-\(R^2\) values.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
\hline
 & Quartile 1 & Quartile 4 & Quartile 1 & Quartile 4\\
\hline
\(\alpha\) & -0.13 & -0.09 & 0.05 & -0.09\\
 & (0.79) & (0.90) & (0.93) & (0.90)\\
\(\beta_{CM}\) & 1.37\(^{***}\) & 1.43\(^{***}\) & 1.38\(^{***}\) & 1.42\(^{***}\)\\
 & (0.00) & (0.00) & (0.00) & (0.00)\\
\(\beta_{SMB}\) & & & 0.01 & 0.002\\
 & & & (0.42) & (0.92)\\
\(\beta_{Mom}\) & & & -0.13\(^{**}\) & 0.04\\
 & & & (0.04) & (0.68)\\
\hline
Adj-\(R^2\) & 0.76 & 0.63 & 0.77 & 0.62\\
\hline
 & GRS Stat. & 0.04 & GRS Stat. & 0.01\\
 & p-value & (0.96) & p-value & (0.99)\\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns" by Matt Brigida, arXiv:2506.03287v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
\textbf{Principles} & \textbf{BO (Best-off)} & \textbf{AVE (Average)} & \textbf{ATBO (All Better-off)} \\ \midrule
Maximin (Maximin) & Maximin+BO & Maximin+AVE & Maximin+ATBO \\
Additive (AP) & AP+BO & AP+AVE & AP+ATBO \\
Weighted Additive (WAP) & WAP+BO & WAP+AVE & WAP+ATBO \\
\textbf{Independent Criteria} & \multicolumn{3}{c}{Deviation, Gratuitousness, Social Responsibility} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{ Representation of Temkin's Principles and Complaint Bases}
\end{table}
\end{document}

q-fin

"Measure of Morality: A Mathematical Theory of Egalitarian Ethics" by Shuang Wei, arXiv:2503.00039v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Data-driven discovery of multiscale chemical reactions governed by the law of mass action" by Juntao Huang, Yizhou Zhou, and Wen-An Yong, arXiv:2101.06589v3, 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}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccc}
		\toprule
		& Clipart & Infograph & Painting & Quickdraw & Real & Sketch \\\hline
		DA Algorithm & tar\_acc & tar\_acc & tar\_acc & tar\_acc & tar\_acc & tar\_acc \\\hline
		ERM-Pool & \textbf{89.9$\pm$0.7} & 64.7$\pm$1.8 & \textbf{81.1$\pm$1.3} & 69.6$\pm$4.2 & 90.4$\pm$2.8 & \textbf{87.3$\pm$0.5} \\
		DIP-Pool & 89.7$\pm$0.7 & \textbf{\textcolor{red}{75.9$\pm$9.6}} & \textbf{80.4$\pm$2.4} & \textbf{71.1$\pm$4.5} & 90.9$\pm$1.2 & \textbf{\textcolor{red}{87.4$\pm$0.6}} \\
		CIP & 88.9$\pm$1.7 & \textbf{64.9$\pm$1.0} & 79.5$\pm$1.3 & \textbf{70.5$\pm$2.7} & 91.0$\pm$1.0 & 86.7$\pm$1.2 \\
		JointDIP-Pool & \textbf{90.0$\pm$0.9} & \textbf{66.0$\pm$1.6} & \textbf{\textcolor{red}{86.5$\pm$5.2}} & \textbf{\textcolor{red}{74.1$\pm$2.5}} & \textbf{\textcolor{red}{91.8$\pm$0.9}} & \textbf{87.2$\pm$0.9} \\
		IRM & \textbf{90.0$\pm$0.8} & 64.1$\pm$2.4 & \textbf{81.0$\pm$1.7} & 69.0$\pm$4.9 & \textbf{91.8$\pm$1.0} & \textbf{87.2$\pm$0.7} \\
		V-REx & \textbf{\textcolor{red}{90.0$\pm$0.5}} & \textbf{65.7$\pm$1.3} & 79.8$\pm$2.3 & \textbf{69.9$\pm$4.0} & \textbf{91.5$\pm$0.5} & 86.8$\pm$0.9 \\
		groupDRO & 86.8$\pm$0.4 & 64.2$\pm$2.2 & 77.9$\pm$0.8 & 68.0$\pm$6.1 & \textbf{91.6$\pm$0.9} & 84.1$\pm$0.8 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{Target accuracies in DomainNet for predicting the labels ``Furniture'' and ``Mammal.'' Each column corresponds to a specific target domain: Clipart, Infograph, Painting, Quickdraw, Real, and Sketch, where the remaining domains are used as source domains. The algorithms are run over 10 trials with different random seeds. The top four methods are highlighted in bold. The best method is colored in red.}
\end{table}
\end{document}

stat

"Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms" by Keru Wu, Yuansi Chen, Wooseok Ha, and Bin Yu, arXiv:2309.10301v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|rrrrrrrr}
 \hline
$\gamma$ & 0.10 & 0.20 & 0.30 & 0.40 & 0.50 & 0.60 & 0.70 & 0.80 \\
\hline
 Unscaled eigenvectors & 0.00 & 0.00 & 0.12 & 0.20 & 0.23 & 0.27 & 0.39 & 0.49 \\
 Scaled eigenvectors & 0.00 & 0.00 & 0.02 & 0.13 & 0.22 & 0.25 & 0.34 & 0.49 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{{Misclustering error rate of spherical spectral clustering using the unscaled vs. scaled eigenvectors as a function of $\gamma$, the connectivity between communities. Graphs are generated from a DCSBM with $K=4$ unbalanced communities and $n=800$.}}
\end{table}
\end{document}

stat

Joshua Agterberg
Zachary Lubberts
Jesús Arroyo

"Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels" by Joshua Agterberg, Zachary Lubberts, and Jesús Arroyo, arXiv:2212.05053v3, 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}{cccccc}
\hline
Model & DeepONet & Shift & Flex & NOMAD & Hyper(ours) \\ \hline
Identity & 0.578$\pm$0.003 & 0.777$\pm$0.018 & 0.678$\pm$0.062 & 0.578$\pm$0.020 & \textbf{0.036$\pm$0.005} \\
Differentiation & 0.559$\pm$0.001 & 0.624$\pm$0.015 & 0.562$\pm$0.016 & 0.558$\pm$0.003 & \textbf{0.127$\pm$0.043} \\ \hline
\end{tabular}
\caption{The mean relative $L^2$ test error with standard deviation for the identity operator and the differentiation operator. The DeepONet, its variants, and the HyperDeepONet use the target network $d_y$-20-20-10-1 with $\textit{tanh}$ activation function. Five training trials are performed independently.}
\end{table}
\end{document}

math

Jae Yong Lee
Sung Woong Cho
Hyung Ju Hwang

"HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork" by Jae Yong Lee, Sung Woong Cho, and Hyung Ju Hwang, arXiv:2312.15949v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Weighted approximation By Max-product Kantrovich type Exponential Sampling Series" by Satyaranjan Pradhan and Madan Mohan Soren, arXiv:2504.19668v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 \toprule
 Name & Start node $i$ & End node $j$ & $\Tilde{c}^s_{ij}$\\
 \midrule
 Pull-out & $i = n_o^d$ & $ j \in \mathcal{V}$ & $c^s_{ij} - \pi_d$\\
 Connection & $ i \in \mathcal{V}$ & $j \in \mathcal{V} \cup \mathcal{H}^W$ & $c^s_{ij} - u_i$\\
 Charging (from charg.)& $i = h_r^c \in \mathcal{H}^C$ & $j \in \mathcal{H}^C \cup \mathcal{H}^W$ & $c^s_{ij} - \alpha^{hr}$\\
 Charging (from wait.) & $i \in \mathcal{H}^W$ & $j \in V \cup \mathcal{H}^C \cup \mathcal{H}^W $& $c^s_{ij}$ \\
 To charge & $ i \in \mathcal{V}$ & $ j \in \mathcal{H}^W$ & $c^s_{ij}$ \\
 Pull-in (from terminal) & $i \in \mathcal{V}$ & $j = n_1^d$ & $c^s_{ij} - u_i - \theta \> \ln P_s$\\
 Pull-in (from wait.) & $i \in \mathcal{H}^W$ & $j = n_1^d$ & $c^s_{ij} - \theta \> \ln P_s$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Modified arc costs}
\end{table}
\end{document}

math

Léa Ricard
Guy Desaulniers
Andrea Lodi
Louis-Martin Rousseau

"Chance-constrained battery management strategies for the electric bus scheduling problem" by Léa Ricard, Guy Desaulniers, Andrea Lodi, and Louis-Martin Rousseau, arXiv:2503.19853v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Tanmay Tirpankar
Arnab Das
Ganesh Gopalakrishnan

"Satire: Computing Rigorous Bounds for Floating-Point Rounding Error in Mixed-Precision Loop-Free Programs" by Tanmay Tirpankar, Arnab Das, and Ganesh Gopalakrishnan, arXiv:2503.05924v1, 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{\textsc{95th percentile positioning errors of different models}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
& \multicolumn{3}{c}{\textbf{Transmit Power [dBm]}} \\
\cmidrule(lr){2-4}
\textbf{Approach} & --20 & --10 & 0 \\
\midrule
Proposed approach, $p_f=0$ & 0.80cm & 0.21cm & 0.07cm \\
Proposed approach, $p_f=10^{-3}$ & 0.85cm & 0.26cm & 0.08cm \\
Proposed approach, $p_f=10^{-2}$ & 0.98cm & 0.27cm & 0.11cm\\
MLP-based approach in , $p_f=0$ & 4.54cm & 4.05cm & 4.01cm \\
CNN-based approach in , $p_f=0$ & 3.16cm & 2.83cm & 2.08cm \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Fatih Ayten
Mehmet C. Ilter
Akshay Jain
Ossi Kaltiokallio
Jukka Talvitie
Elena Simona Lohan
Henk Wymeersch
Mikko Valkama

"Failure Tolerant Phase-Only Indoor Positioning via Deep Learning" by Fatih Ayten, Mehmet C. Ilter, Akshay Jain, Ossi Kaltiokallio, Jukka Talvitie, Elena Simona Lohan, Henk Wymeersch, and Mikko Valkama, arXiv:2508.14739v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Dataset / LF} & \textbf{SR-I} & \textbf{L1} & \textbf{Combo} & \textbf{Hybrid} \\
 \midrule
 FUSAR & 38.020 & 38.363 & \underline{38.705} & \textbf{38.736} \\
 MSTAR & 28.890 & \textbf{30.130} & 30.127 & \underline{30.129} \\
 HRSID & 26.750 & \textbf{38.750} & \underline{38.667} & 38.633 \\
 \midrule
 \textbf{Average} & 31.220 & 35.750 & \underline{35.833} & \textbf{35.833} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ch Muhammad Awais
Marco Reggiannini
Davide Moroni
Oktay Karakus

"A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery" by Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, and Oktay Karakus, arXiv:2508.06407v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, 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}{lccc}
\hline
NIST test & Proportion & P-value & Pass \\
\hline
Frequency & 10/10 & 0.911413 & Y \\
BlockFrequency & 10/10 & 0.911413 & Y \\
CumulativeSums & 10/10 & 0.534146 & Y \\
Runs & 10/10 & 0.213309 & Y \\
LongestRun & 10/10 & 0.534146 & Y \\
Rank & 10/10 & 0.534146 & Y \\
FFT & 10/10 & 0.534146 & Y \\
ApproximateEntropy & 10/10 & 0.122325 & Y \\
Serial & 10/10 & 0.017912 & Y \\
LinearComplexity & 10/10 & 0.004301 & Y \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Statistical Test Results for Pulsar RNG on PSR J0030+0451 (EPTA).}
\end{table}
\end{document}

math

Random Number Generation from Pulsars

"Random Number Generation from Pulsars" by Hayder Tirmazi, arXiv:2502.18430v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The results of varying the measure function of NSW.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
\hline
 & & ip-NSW & $l_2$-NSW & cos-NSW \\ \hline
\multirow{2}{*}{MovieLens} & Recall1@1 & $\textbf{0.681}$ & 0.000 & 0.580 \\ \cline{2-5}
 & Recall10@10 & $\textbf{0.425}$ & 0.000 & 0.371 \\ \hline
\multirow{2}{*}{Amazon} & Recall1@1 & $\textbf{0.823}$ & 0.000 & 0.457 \\ \cline{2-5}
 & Recall10@10 & $\textbf{0.543}$ & 0.000 & 0.329 \\ \hline
\multirow{2}{*}{Echonest} & Recall1@1 & $\textbf{0.868}$ & 0.126 & 0.573 \\ \cline{2-5}
 & Recall10@10 & $\textbf{0.547}$ & 0.089 & 0.344 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Chao Feng
Defu Lian
Xiting Wang
Zheng liu
Xing Xie
Enhong Chen

"Reinforcement Routing on Proximity Graph for Efficient Recommendation" by Chao Feng, Defu Lian, Xiting Wang, Zheng liu, Xing Xie, and Enhong Chen, arXiv:2201.09290v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overall evaluation of the approximation methods: mean values of the quality criteria for all the centerlines distorted by radius noise addition. The cells in gray correspond to the lowest error for each metric.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|ccccc|}
 \hline
 & GNP & GNP-AIC & GP-AIC & SRP-AIC\\
 \hline
$\mathrm{RMSE_{spatial}}$ & 8.462 & 0.034 & 0.053 & \cellcolor{gray!25}0.029 \\
$\mathrm{RMSE_{radius}}$ & 17.523 & 0.095 & \cellcolor{gray!25}0.042 & 0.043 \\
$\mathrm{RMSEder_{spatial}}$ & 0.218 & 0.118 & 0.042 & \cellcolor{gray!25}0.009 \\
$\mathrm{RMSEder_{radius}}$ & 0.391 & 0.214 & \cellcolor{gray!25}0.032 & \cellcolor{gray!25}0.032 \\
$\mathrm{RMSEcurv}$ & 1919.428 & 190.531 & 0.060 & \cellcolor{gray!25}0.035 \\
$\mathrm{L_{diff}}$ & 718.906 & 0.057 & 0.207 & \cellcolor{gray!25}0.004 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Méghane Decroocq
Carole Frindel
Pierre Rougé
Makoto Ohta
Guillaume Lavoué

"Modeling and hexahedral meshing of cerebral arterial networks from centerlines" by Méghane Decroocq, Carole Frindel, Pierre Rougé, Makoto Ohta, and Guillaume Lavoué, arXiv:2201.08279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|r|r|r}
 \hline
 \multicolumn{5}{c}{End item demand only} \\
 \hline
 Utilization & $\lambda$ & Runtime (s) & Total costs PH & $\Delta$ (\%)\\
 \hline
 \multirow{4}{*}{50\%}
 & 0.1 & 801.8 & 31689.2 & 5.11 \\
 & 1 & 307.9 & 30360.0 & \textbf{1.26} \\
 & 10 & 246.9 & 30626.4 & 2.08 \\
 & 100 & 274.1 & 30670.6 & 2.23 \\
 \hline
 \multirow{4}{*}{90\%}
 & 0.1 & 841.8 & 79067.8 & 8.82 \\
 & 1 & 379.5 & 74022.6 & \textbf{4.31}\\
 & 10 & 281.7 & 74096.6 & 4.32\\
 & 100 & 329.3 & 74820.8 & 5.88 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Majority voting consensus calculation: Evaluation of the $\lambda$-multiple for the penalty parameter $\rho$ in terms of total costs and runtime and end item demands only on a scenario tree with $|\Omega| = 2$ and therefore $|\Phi| = 128$}
\end{table}
\end{document}

math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation Parameters and Model Configuration}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
\textbf{Parameter} & \textbf{Value / Description} \\
\midrule
Number of agents & 10 \\
Episodes per training run & 10,000 \\
Treaties per episode & 1 \\
State representation & Treaty features + market indicators \\
Action space & Continuous: premium rate, quota share, commission \\
Reward function & Profit minus $\lambda \cdot$CVaR$_{95\%}$ penalty \\
Risk aversion coefficients $\lambda$ & \{0.01, 0.1, 1.0, 10.0\} \\
Loss modeling & Mixed attritional/catastrophe distribution \\
Policy network & 3-layer MLP (128 units/layer, ReLU) \\
Critic network & 3-layer MLP (256 units/layer, ReLU) \\
Replay buffer capacity & 1 million transitions \\
Mini-batch size & 1,024 \\
Optimizer & Adam \\
Learning rates & $10^{-4}$ (actor), $10^{-3}$ (critic) \\
Discount factor $\gamma$ & 0.99 \\
CVaR confidence level $\alpha$ & 95\% \\
Evaluation interval & Every 500 episodes \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning" by Stella C. Dong and James R. Finlay, arXiv:2506.13113v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|c|}
			\hline
			& \multicolumn{2}{c|}{$m=500$} & \multicolumn{2}{c|}{$m=1000$} & \multicolumn{2}{c|}{$m=3000$} \\ \hline
			& PLS
			& PRLS
			& PLS
			& PRLS
			& PLS
			& PRLS \\ \hline
			$n=3$ & 97.10\% & 97.55\% & 97.75\% & 97.70\% & 96.75\% & 97.00\% \\ \hline
			$n=5$ & 97.05\% & 96.75\% & 97.25\% & 97.05\% & 97.00\% & 96.90\% \\ \hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects" by Hao Chen, Lanshan Han, and Alvin Lim, arXiv:2011.04538v9, 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}{lll}
			\hline\hline
			Cause & Effect & p-values\\
			\hline
			FRM &GT& $ 1.1 \times 10^{-10}$ \\
			\hline
			GT &FRM & $2.1\times 10^{-12}$ \\
			\hline
			DFRM &GT & $6.8 \times 10^{-11}$ \\
			\hline
			GT &DFRM & $4.1 \times 10^{-10}$\\
			\hline\hline
		\end{tabular}
\end{adjustbox}
\caption{$p$ values of Granger causality test}
\end{table}
\end{document}

q-fin

Lining Yu
Wolfgang Karl Härdle
Lukas Borke
Thijs Benschop

"An AI approach to measuring financial risk" by Lining Yu, Wolfgang Karl Härdle, Lukas Borke, and Thijs Benschop, arXiv:2009.13222v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cccccc|}
\hline
\multicolumn{6}{|c|}{\multirow{2}{*}{\textbf{FID - LDM}}} \\
\multicolumn{6}{|c|}{} \\ \hline
epoch/dataset & \textbf{2-cell} & \textbf{4-cell} & \textbf{8-cell} & \textbf{blastocyst} & \textbf{morula} \\ \hline
\textbf{10} & 87 & 98 & 93 & 76 & 69 \\ \hline
\textbf{50} & 34 & 37 & 38 & 31 & 37 \\ \hline
\textbf{100} & 26 & \textbf{28} & 33 & 34 & 35 \\ \hline
\textbf{150} & \textbf{24} & 29 & \textbf{31} & 54 & 47 \\ \hline
\textbf{200} & 35 & 36 & 33 & 69 & 65 \\ \hline
\textbf{250} & 55 & 54 & 38 & 61 & 73 \\ \hline
\textbf{300} & 84 & 83 & 51 & 47 & 73 \\ \hline
\textbf{350} & 104 & 105 & 69 & 35 & 65 \\ \hline
\textbf{400} & 114 & 118 & 81 & 27 & 59 \\ \hline
\textbf{450} & 119 & 124 & 85 & 23 & 49 \\ \hline
\textbf{500} & 118 & 126 & 89 & 19 & 45 \\ \hline
\textbf{550} & 119 & 123 & 89 & 16 & 37 \\ \hline
\textbf{600} & 117 & 117 & 85 & 15 & 33 \\ \hline
\textbf{650} & 116 & 114 & 85 & 13 & 27 \\ \hline
\textbf{700} & 110 & 103 & 80 & 12 & 24 \\ \hline
\textbf{750} & 110 & 96 & 74 & 11 & 21 \\ \hline
\textbf{800} & 106 & 92 & 72 & 11 & 19 \\ \hline
\textbf{850} & 99 & 82 & 60 & 11 & 18 \\ \hline
\textbf{900} & 94 & 74 & 60 & 11 & 17 \\ \hline
\textbf{950} & 91 & 68 & 53 & 11 & 14 \\ \hline
\textbf{1000} & 86 & 61 & 50 & \textbf{10} & \textbf{14} \\ \hline
\end{tabular}
\end{adjustbox}
\caption{FID scores for the LDM across different training epochs for the five class categories: 2-cell, 4-cell, 8-cell, morula, and blastocyst.}
\end{table}
\end{document}

eess

Oriana Presacan
Alexandru Dorobantiu
Vajira Thambawita
Michael A. Riegler
Mette H. Stensen
Mario Iliceto
Alexandru C. Aldea
Akriti Sharma

"Merging synthetic and real embryo data for advanced AI predictions" by Oriana Presacan, Alexandru Dorobantiu, Vajira Thambawita, Michael A. Riegler, Mette H. Stensen, Mario Iliceto, Alexandru C. Aldea, and Akriti Sharma, arXiv:2412.01255v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}\hline
 Measure & Healthy& Schizophrenic \\
 \hline
 $\alpha_{1}^{(\rm birth)}$ & $4.85 \pm 0.04$ & $3.52 \pm 0.05$ \\
 \hline
 $H_{(\rm birth)}$ & $0.84 \pm 0.02$ & $0.66 \pm 0.03$ \\
 \hline
 $\alpha_{1}^{(\rm death)}$ & $3.96 \pm 0.04$ & $2.83 \pm 0.04$ \\
 \hline
 $H_{(\rm death)}$ & $0.85 \pm 0.02$ & $0.68 \pm 0.03$ \\
 \hline
 $\alpha_{1}^{(\rm lifetime)}$ & $7.03 \pm 0.03$ & $5.00 \pm 0.03$ \\
 \hline
 $H_{(\rm lifetime)}$ & $0.89 \pm 0.02$ & $0.73 \pm 0.03$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{The value of topological coefficients and corresponding Hurst exponents for healthy and schizophrenic cases at $1\sigma$ level of confidence.}
\end{table}
\end{document}

cs

H. Masoomy
B. Askari
M. N. Najafi
S. M. S. Movahed

"Persistent Homology of Fractional Gaussian Noise" by H. Masoomy, B. Askari, M. N. Najafi, and S. M. S. Movahed, arXiv:2101.03328v2, 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{Back Depression Inventory BDI-II Scoring Standard for depression detection ().}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|l}
 \hline
 Depression Score & Depression Levels\\
 \hline
 0–13 & minimal depression \\
14–19 & mild depression\\
20-28 & moderate depression\\
29-63 &Severe Depression\\
\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Sana Yasin
Syed Asad Hussain
Sinem Aslan
Imran Raza
Muhammad Muzammel
Alice Othmani

"EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review" by Sana Yasin, Syed Asad Hussain, Sinem Aslan, Imran Raza, Muhammad Muzammel, and Alice Othmani, arXiv:2009.13402v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A Single-loop Proximal Subgradient Algorithm for A Class Structured Fractional Programs" by Deren Han, Min Tao, and Zihao Xia, arXiv:2503.12176v1, 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}{lcccc}
\hline
Benchmark & Task & Metric & Size \\ \hline
OAB-2023 & Multiple-choice (4) & Accuracy & 160 \\
ENADE-2022-Law & Multiple-choice (5) & Accuracy & 35 \\ \hline
\end{tabular}
\caption{Configuration of law knowledge benchmarks.}
\end{table}
\end{document}

cs

Roseval Malaquias Junior
Ramon Pires
Roseli Romero
Rodrigo Nogueira

"Juru: Legal Brazilian Large Language Model from Reputable Sources" by Roseval Malaquias Junior, Ramon Pires, Roseli Romero, and Rodrigo Nogueira, arXiv:2403.18140v1, 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{Results on Washinghton DC AVIRIS hyperspectral image}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\bf Methods &\multicolumn{2}{c}{\bf F1-scores}
\\ \cline{2-3}
 & r = 80\% & r = 90\% \\ \hline \\
 DPCP-PSGM (unknown $c$) & 0.994 & 0.993\\
RSGM (unknown $c$) &0 & 0 \\
DPCP-IRLS (unknown $c$) & 0 & 0 \\
RSGM ($c=5$) & 0.999 &0.993 \\
DPCP-IRLS $c=5$ & 1 & 0.995 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Paris V. Giampouras
Benjamin D. Haeffele
René Vidal

"Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension" by Paris V. Giampouras, Benjamin D. Haeffele, and René Vidal, arXiv:2201.09079v1, 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{\large Spatial Model Comparison by Criterion}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|r|r|r}
\hline
 & Log Likelihood & AIC & BIC\\
\hline
Spatial Lag-Error Model & -127,721.6 & 261,635.3 & 289,253.0\\
\hline
Spatial Lag Model & -272,367.1 & 550,924.2 & 578,533.0\\
\hline
Spatial Error Model & -272,426.2 & 551,042.3 & 578,651.1\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance on TIMIT test data with model trained on 1000 training instances of TIMIT. Performance with voicing applied shown in parentheses.}
\begin{tabular}{|c|c|c|c|}
 \hline
 \textbf{Method} & \textbf{Corr Coef} & \textbf{SR error} & \textbf{F-score} \\
 \textbf{} & \textbf{of \#syl} & \textbf{rate (\%)} & \textbf{}\\ \hline \hline
 Jiao et al. & 0.842 & 13.7 & - \\ \hline
 Yarra et al. & - & - & 84.16 \\ \hline
 Proposed (1/F-score) & 0.906 (0.917) & 11.4 (11.89) & 87.73 (88.29) \\ \hline
 Proposed (MAE) & 0.890 (0.913) & 10.8 (9.94) & 86.15 (87.88) \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

Kamini Sabu
Syomantak Chaudhuri
Preeti Rao
Mahesh Patil

"An Optimized Signal Processing Pipeline for Syllable Detection and Speech Rate Estimation" by Kamini Sabu, Syomantak Chaudhuri, Preeti Rao, and Mahesh Patil, arXiv:2103.04346v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\toprule
\textbf{Variable} & \textbf{Included for which authors} & \textbf{Lags} \\ \midrule
\textit{h}-index & Up to 20 authors & 4 years \\
Number of publications & Up to 20 authors & 4 years \\
Number of citations received (excluding self-citations) & Up to 20 authors & 4 years \\
Total grant funding received by authors’ institution & All & 5 years \\
Ranking of institution & Up to 20 authors & — \\
Number of authors by institution type (academic, govm, company) & All & — \\
Number of authors on publication & — & — \\
Publication date & — & — \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Economic impacts of AI-augmented R&D

Tamay Besiroglu
Nicholas Emery-Xu
Neil Thompson

"Economic impacts of AI-augmented R&D" by Tamay Besiroglu, Nicholas Emery-Xu, and Neil Thompson, arXiv:2212.08198v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{CIM core performance comparison between MBRAI, RPN\&BLM and the proposed}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 & MBRAI &RPN\&BLM &\multicolumn{2}{c|}{Proposed} \\ \hline
 \multirow{2}{*}{Supply} & \multirow{2}{*}{1.1 V} & \multirow{2}{*}{1 V} & M-RD4 Recoder & 0.6 V \\
 \cline{4-5}
 &&&Neuron Circuit& 1 V \\
 \hline
 Computing speed & 1.85 M/s&1.85 M/s & \multicolumn{2}{c|}{1.85 M/s}\\ \hline
 SFDR & 67.42 dB &59.13 dB& \multicolumn{2}{c|}{63.41 dB} \\ \hline
 SNDR & 45.48 dB &46.13 dB& \multicolumn{2}{c|}{46.48 dB} \\ \hline
 ENOB & 7.26 bit &7.37 bit& \multicolumn{2}{c|}{7.42 bit} \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Rui Xiao
Kejie Huang
Yewei Zhang
Haibin Shen

"A Low Power In-Memory Multiplication andAccumulation Array with Modified Radix-4 Inputand Canonical Signed Digit Weights" by Rui Xiao, Kejie Huang, Yewei Zhang, and Haibin Shen, arXiv:2101.02419v1, 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{SensEval/SemEval evaluation datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccccc}%p{.3\columnwidth}}
			\hline
			Dataset 		& Task 		& \multicolumn{5}{c}{\# of Senses} 	\\
			\cline{3-7}
			Name			& Method	& NN 	& V 	& Adj 	& Adv 	& Total \\%(Domains) 	\\
			\hline
			SensEval2 (SE2) 	& LS, AW	& 1066	& 517	& 445	& 254	& 2282	\\
			SensEval3 (SE3) 	& LS, AW	& 900	& 588	& 350	& 12	& 1850 	\\ %(editorial, news and fiction)\\
			SemEval-07 (SE07) 	& LS		& 159	& 296	&	-	&	-	& 455 	\\
			SemEval-13 (SE13) 	& LS, AW	& 1644	& -	&	-	&	-	& 1644 	\\% (various domains)	\\
			SemEval-15 (SE15) 	& LS, AW	& 531	& 251	& 160	& 80	& 1022 	\\%(biomedical, social issues and mathematics/computing)	\\
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mohannad AlMousa
Rachid Benlamri
Richard Khoury

"A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph" by Mohannad AlMousa, Rachid Benlamri, and Richard Khoury, arXiv:2101.02875v1, 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{Balance sheet is a financial status report for a company at a specific time, reflecting all assets, debts and shareholder rights owned by the company}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
\multicolumn{3}{c}{\textbf{BALANCE SHEET}} \\
\hline
\hline
\textbf{Assets} & \textbf{Initial Amount} & \textbf{End Amount} \\ \cline{1-3}
Current Assets & & \\
\quad Cash on Hand & \underline{3000000} & \underline{270005.9} \\
\quad Bank Deposits & \underline{5000000} & \underline{164645.57} \\
\quad Interest Receivable & \underline{0} & \underline{2672.87} \\
\quad Accounts Receivable & \underline{0} & \underline{2429482.13} \\
\quad Inventory & \underline{0} & \underline{5090000} \\
\textbf{Total Current Assets} & \underline{8000000} & \underline{7956806.47} \\ \cline{1-3}
Non-Current Assets & & \\
\quad Fixed Assets & \underline{5000000} & \underline{5305354.43} \\
\quad Accumulated Depreciation & \underline{0} & \underline{(45751.41)} \\
\quad Net Fixed Assets & \underline{5000000} & \underline{5259603.02} \\
\textbf{Total Non-Current Assets} & \underline{5000000} & \underline{5259603.02} \\ \cline{1-3}
\textbf{\textit{Total Assets}} & \underline{13000000} & \underline{13216409.49} \\
\hline
\hline
\textbf{Liabilities} & & \\
\hline
Current Liabilities & & \\
\quad Accounts Payable & \underline{0} & \underline{1590000} \\
\quad Taxes Payable & \underline{0} & \underline{271550.92} \\
\textbf{Total Current Liabilities} & \underline{0} & \underline{1861550.92} \\ \cline{1-3}
\textbf{\textit{Total Liabilities}} & \underline{0} & \underline{1861550.92} \\
\hline
\hline
\textbf{Owner’s Equity} & & \\
\quad Paid-in Capital & \underline{13000000} & \underline{13000000} \\
\quad Retained Earnings & \underline{0} & \underline{-1645141.46} \\
\textbf{\textit{Total Owner's Equity}} & \underline{13000000} & \underline{11354859} \\
\hline
\hline
\textbf{Total Liabilities and Equity} & \underline{13000000} &\underline{13216409} \\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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

"FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs" by Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Ruiyu Wang, Bo Li, Xiao Huang, Dongning Sun, and Xinrun Wang, arXiv:2505.13533v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The MSD, MAD, and ABWD for S\&P $500$ stock index dataset. The best bootstrapping technique with the least MSD, MAD, and ABWD is marked in bold.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
			\hline
			& Bootstrap Methods & MSD & MAD & ABWD\\
			\hline

			\multirow{3}{*}{Open Price}&
			$\quad$NOBB$\quad$ & $6946.36$ & $65.72$ & $18181.5$ \\
			& $\quad$MBB$\quad$ & $4118.35$ & $48.47$ & $12993.9$ \\
			& $\quad$LBB$\quad$ & $\textbf{2209.72}$ & $\textbf{35.88}$ & $\textbf{11545.4}$ \\

			\hline \multirow{3}{*}{High Price} & $\quad$NOBB$\quad$ & $6752.75$ & $65.76$ & $17270.2$ \\
			& $\quad$MBB$\quad$ & $4073.18$ & $50.24$ & $12771.3$ \\
			& $\quad$LBB$\quad$ & $\textbf{2788.50}$ & $\textbf{42.34}$ & $\textbf{11857.1}$ \\

			\hline \multirow{3}{*}{Low Price}&
			$\quad$NOBB$\quad$ & $8383.30$ & $73.70$ & $16886.9$ \\
			& $\quad$MBB$\quad$ & $5736.40$ & $58.16$ & $15095.6$ \\
			& $\quad$LBB$\quad$ & $\textbf{4027.22}$ & $\textbf{50.35}$ & $\textbf{11601.9}$ \\

			\hline \multirow{3}{*}{Closing Price} & $\quad$NOBB$\quad$ & $10198.64$ & $80.64$ & $15953.9$ \\
			& $\quad$MBB$\quad$ & $8053.63$ & $72.07$ & $12183.8$ \\
			& $\quad$LBB$\quad$ & $\textbf{6342.93}$ & $\textbf{65.40}$ & $\textbf{10855.8}$ \\

			\hline \multirow{3}{*}{Volume} &
			$\quad$NOBB$\quad$ & $4.33e+17\quad$ & $\textbf{4.36e+08}\quad$ & $6.82e+10$ \\
			& $\quad$MBB$\quad$ & $4.41e+17\quad$ & $4.47e+08\quad$ & $6.75e+10$ \\
			& $\quad$LBB$\quad$ & $\textbf{6.50e+10}\quad$ & $3.83e+17\quad$ & $\textbf{4.25e+08}$ \\

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

stat

"Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network" by Aryan Bhambu and Arabin Kumar Dey, arXiv:2211.13915v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Forecasting realized volatility in the stock market: a path-dependent perspective" by Xiangdong Liu, Sicheng Fu, and Shaopeng Hong, arXiv:2503.00851v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Parameter} & \textbf{Symbol} & \textbf{Value}
\\ \hline
GVD &$\beta_2$ & 21.67ps$^2$/km
\\ \hline
Nonlinearity & $\gamma$ & 1.27W$^{-1}$km$^{-1}$
\\ \hline
Bandwidth & $B$ & 100GHz
\\ \hline
Amplifier Distance & $L$ & 100km
\\ \hline
Maximum Distance & $K_{max}L$ & 2000km
\\ \hline
Photon Energy & $E_{ph}$ & $13.2\cdot10^{-20}$J
\\ \hline
Emission Factor& $n_{sp}$ & 1
\\ \hline
Attenuation & $\alpha_{loss}$ & 0.2dB/km
\\ \hline
Noise & $\sigma^2$ & $n_{sp}E_{ph}(e^{\alpha_{loss}L}-1)$
\\ \hline
\end{tabular}
\end{adjustbox}
\caption{Parameter values () for the simulations in Section .}
\end{table}
\end{document}

math

"Spectral Efficiency Expression for the Non-Linear Schrödinger Channel in the Low Noise Limit Using Scattering Data" by Pavlos Kazakopoulos and Aris L. Moustakas, arXiv:2502.17702v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Zardad Khan
Naz Gul
Nosheen Faiz
Asma Gul
Werner Adler
Berthold Lausen

"Optimal trees selection for classification via out-of-bag assessment and sub-bagging" by Zardad Khan, Naz Gul, Nosheen Faiz, Asma Gul, Werner Adler, and Berthold Lausen, arXiv:2012.15301v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Deep Learning Models Meet Financial Data Modalities" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2504.13521v2, licensed under CC BY-SA 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{Details of evaluation benchmarks, we show the number of classes, dataset statistics, selected backbone, and batch size for training.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \toprule
 \textbf{Dataset} & \textbf{Num. Class} & \textbf{Train Samples} & \textbf{Test Samples} & \textbf{Backbone} & \textbf{Batch size}\\
 \midrule
 CIFAR-10 & 10 & 50,000 & 10,000 & ResNet-18 & 512 \\
 CIFAR-100 & 100 & 50,000 & 10,000 & ResNet-18 & 512 \\
 ImageNet-100 & 100 & 128,545 & 5,000 & ResNet-50 & 512 \\
 Tiny-ImageNet & 200 & 100,000 & 10,000 & ResNet-18 & 256 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Shengjie Niu
Lifan Lin
Jian Huang
Chao Wang

"OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning" by Shengjie Niu, Lifan Lin, Jian Huang, and Chao Wang, arXiv:2411.01833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Alexander Decruyenaere
Heidelinde Dehaene
Paloma Rabaey
Christiaan Polet
Johan Decruyenaere
Thomas Demeester
Stijn Vansteelandt

"Debiasing Synthetic Data Generated by Deep Generative Models" by Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet, Johan Decruyenaere, Thomas Demeester, and Stijn Vansteelandt, arXiv:2411.04216v2, 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}{lll}
\hline
\textbf{Phase} & & \textbf{Scene names} \\
\hline
Training & \hspace{1cm} & Beechwood\_0\_int, Beechwood\_1\_int, \\
 & \hspace{1cm} & Benevolence\_0\_int, Benevolence\_1\_int, Benevolence\_2\_int\\
 & \hspace{1cm} & Merom\_0\_int, Merom\_1\_int, \\
 & \hspace{1cm} & Pomaria\_0\_int, Pomaria\_1\_int, Pomaria\_2\_int,\\
 & \hspace{1cm} & Wainscott\_0\_int, Wainscott\_1\_int \\
Testing & \hspace{1cm} & Ihlen\_0\_int, Ihlen\_1\_int, Rs\_int\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Train-test scenes splits for iGibsion 1.0 dataset. }
\end{table}
\end{document}

stat

Rudra P. K. Poudel
Harit Pandya
Stephan Liwicki
Roberto Cipolla

"ReCoRe: Regularized Contrastive Representation Learning of World Model" by Rudra P. K. Poudel, Harit Pandya, Stephan Liwicki, and Roberto Cipolla, arXiv:2312.09056v2, 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}

cs

Etienne Brangbour
Pierrick Bruneau
Thomas Tamisier
Stéphane Marchand-Maillet

"Cold Start Active Learning Strategies in the Context of Imbalanced Classification" by Etienne Brangbour, Pierrick Bruneau, Thomas Tamisier, and Stéphane Marchand-Maillet, arXiv:2201.10227v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \hline
 Model/Metrics & Classification error & AUC \\ \hline
 LR & $6.5\%$ & $77.9\%$ \\ \hline
 FCNN & $6.5\%$ & $80.0\%$ \\ \hline
 NAM & $6.6\%$ & $80.0\%$ \\ \hline
 MNAM & $6.6\%$ & $80.0\%$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Model performance of the GMSC dataset.}
\end{table}
\end{document}

q-fin

"Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring" by Dangxing Chen and Weicheng Ye, arXiv:2209.10070v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Richard Gerlach
Antonio Naimoli
Giuseppe Storti

"Using quantile time series and historical simulation to forecast financial risk multiple steps ahead" by Richard Gerlach, Antonio Naimoli, and Giuseppe Storti, arXiv:2502.20978v2, 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}{ccccccc}
\#Annotations & 0 & 1 & 2 & 3 & 4 & 5 \\ \hline
\#videos & 101 & 141 & 203 & 342 & 805 & 18166 \\
Per. (\%) & 0.51 & 0.71 & 1.03 & 1.73 & 4.07 & 91.94
\end{tabular}
\end{adjustbox}
\caption{For our Kinetics-GEBD Val set, \#annotations received per video vs. \#videos and its percentage .}
\end{table}
\end{document}

cs

Mike Zheng Shou
Stan Weixian Lei
Weiyao Wang
Deepti Ghadiyaram
Matt Feiszli

"Generic Event Boundary Detection: A Benchmark for Event Segmentation" by Mike Zheng Shou, Stan Weixian Lei, Weiyao Wang, Deepti Ghadiyaram, and Matt Feiszli, arXiv:2101.10511v5, 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{Interest Rate Discrepancy by Model and Prompt Type}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
 \hline
 & Mean Interest Difference &\hspace{1em} Bias &\hspace{1em} Bias \\
 Model &(Basis Points) &\hspace{1em} Frequency &\hspace{1em} Proportion\\
\hline
 \multicolumn{4}{c}{Simple Prompt} \\
\hline
 \texttt{Llama 3.1 (8b)} & 59 & 70 & 0.36\\
 \texttt{Llama 3.1 (70b)} & 13 & 31 & 0.16\\
 \texttt{Mistral v0.3} & 20 & 100& 0.51\\
 \texttt{Command R} & -97 & 176& 0.89\\
 \texttt{Command R+} & 10 & 107& 0.54\\
 \hline
 \multicolumn{4}{c}{Expanded Prompt}\\
 \hline
 \texttt{Llama 3.1 (8b)} & 5 & 43 & 0.22\\
 \texttt{Llama 3.1 (70b)} & 32 & 34 & 0.17\\
 \texttt{Mistral v0.3} & 14 & 46 & 0.23\\
 \texttt{Command R} & 23 & 70 & 0.35\\
 \texttt{Command R+} & 16 & 82 & 0.42\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Social Group Bias in AI Finance

"Social Group Bias in AI Finance" by Thomas R. Cook and Sophia Kazinnik, arXiv:2506.17490v1, 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{Vaccine Performance Information}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccccc}
\hline\hline\\
Vaccine &
\begin{minipage}[c]{3cm}
\centering
Platform
\end{minipage} &
\begin{minipage}[c]{4cm}
\centering
Development \\ Countries
\end{minipage} &
\begin{minipage}[c]{4cm}
\centering
Recommended \\ Doses
\end{minipage}&
\begin{minipage}[c]{4cm}
\centering
Current \\ Phase
\end{minipage}\\ \hline
AstraZeneca
	& virus vector 	&UK/Sweden	& 2; Day 0 $+$ 28 & 4\\
Johnson \& Johnson
	& virus vector	&Netherland/Belgium/US & 1 & 4\\
Moderna
	& mRNA			&US 		&2; Day 0 $+$ 28 & 4\\
Pfizer
	& mRNA			&US/Germany &2; Day 0 $+$ 21 &4\\
Sinovac
	& inactive virus&China 		&2; Day 0 $+$ 14 & 4\\
\hline\hline\\
Vaccine &
\begin{minipage}[c]{3cm}
\centering
Efficacy
\end{minipage} &
\begin{minipage}[c]{4cm}
\centering
Hospitalization \\ Prevention Rate
\end{minipage} &
\begin{minipage}[c]{4cm}
\centering
Adverse \\ Event Rates
\end{minipage}&
\begin{minipage}[c]{4cm}
\centering
Severe Adverse\\ Event Rates
\end{minipage}\\ \hline
AstraZeneca
	& 70.4\%	& 100\%
	& \begin{minipage}[c]{4cm}\centering Vaccinated: 27.03\%\\ Placebo: 16.33\% \end{minipage}
	& \begin{minipage}[c]{4cm}\centering Vaccinated: 0.7\%\\ Placebo: 0.8\% \end{minipage}\\
Johnson \& Johnson
	& 66.9\%	& 93.1\%
	& \begin{minipage}[c]{4cm}\centering Vaccinated: 68.1\%\\ Placebo: 29.4\% \end{minipage}
	& \begin{minipage}[c]{4cm}\centering Vaccinated: 0.1\%\\ Placebo: 0.1\% \end{minipage}\\
Moderna
	& 94.1\%	& 100\%
	& \begin{minipage}[c]{4cm}\centering Vaccinated: 79.4\%\\ Placebo: 36.5\% \end{minipage}
	& \begin{minipage}[c]{4cm}\centering Vaccinated: 1.5\%\\ Placebo: 1.3\% \end{minipage}\\
Pfizer
	& 95.0\%	& 88.9\%
	& \begin{minipage}[c]{4cm}\centering Vaccinated: 26.7\%\\ Placebo: 12.2\% \end{minipage}
	& \begin{minipage}[c]{4cm}\centering Vaccinated: 1.1\%\\ Placebo: 0.6\% \end{minipage}\\
Sinovac
	& 83.5\%	& 100\%
	& \begin{minipage}[c]{4cm}\centering Vaccinated: 18.9\%\\ Placebo: 16.9\% \end{minipage}
	& \begin{minipage}[c]{4cm}\centering Vaccinated: 0.3\%\\ Placebo: 0.2\% \end{minipage}\\
\hline\hline \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Self-selection of Information and Belief Update: An Experiment on COVID-19 Vaccine Information Acquisition" by ChienHsun Lin and Hans H. Tung, arXiv:2506.19056v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of MusicTM-Dataset applied in our experiments}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccl}
 Modality &Feature Extractor &Dimension &Number\\
 \hline
 Audio & Vggish &(20, 128) &14,454\\
 Lyrics & Skip-gram &(20, 20) &14,454\\
 Sheet music & Lilypond\&ASMCMR & (20, 100, 32) &14,454\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"MusicTM-Dataset for Joint Representation Learning among Sheet Music, Lyrics, and Musical Audio" by Donghuo Zeng, Yi Yu, and Keizo Oyama, arXiv:2012.00290v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{PASTA hyperparameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
Learning rate & 1e-5 \\ \hline
Training steps & 1e4 \\ \hline
Batch size & 128 \\ \hline
Optimizer & Adafactor (weight decay = 1e-2) \\ \hline
Gradient norm clipping & 1 \\ \hline
Update target network phase & 256 \\ \hline
Expectile parameter $\alpha$ & 0.7 \\ \hline
$\ell_q$ & 651 \\ \hline
$\ell_v$ & 651 \\ \hline
$L$ & 4 \\ \hline
$M$ & 4 \\ \hline
$L_C$ & 25 \\ \hline
Number of categories & 5 \\ \hline
$H$ & 5 \\ \hline
$N_w^{max}$ & 62 \\ \hline
\end{tabular}
\end{adjustbox}
\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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 $n$ & Ideal & AI \\
 & Length n(n-1)/2 & Length \\
 \hline
 22 & 231 & 231 \\
 \hline
 23 & 253 & 253 \\
 \hline
 24 & 276 & 276 \\
 \hline
 25 & 300 & 300 \\
 \hline
 26 & 325 & 325 \\
 \hline
 27 & 351 & 351 \\
 \hline
 28 & 378 & 378 \\
 \hline
 29 & 406 & 408 \\
 \hline
 30 & 435 & 437 \\
 \hline
 31 & 465 & 478 \\
 \hline
 32 & 496 & 728 \\
 \hline
 33 & 528 & 642 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{n - permutation length. Comparison of GAP vs AI methods. }
\end{table}
\end{document}

math

A. Chervov
A. Soibelman
S. Lytkin
I. Kiselev
S. Fironov
A. Lukyanenko
A. Dolgorukova
A. Ogurtsov
F. Petrov
S. Krymskii
M. Evseev
L. Grunvald
D. Gorodkov
G. Antiufeev
G. Verbii
V. Zamkovoy
L. Cheldieva
I. Koltsov
A. Sychev
M. Obozov
A. Eliseev
S. Nikolenko
N. Narynbaev
R. Turtayev
N. Rokotyan
S. Kovalev
A. Rozanov
V. Nelin
S. Ermilov
L. Shishina
D. Mamayeva
A. Korolkova
K. Khoruzhii
A. Romanov

"CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs" by A. Chervov, A. Soibelman, S. Lytkin, I. Kiselev, S. Fironov, A. Lukyanenko, A. Dolgorukova, A. Ogurtsov, F. Petrov, S. Krymskii, M. Evseev, L. Grunvald, D. Gorodkov, G. Antiufeev, G. Verbii, V. Zamkovoy, L. Cheldieva, I. Koltsov, A. Sychev, M. Obozov, A. Eliseev, S. Nikolenko, N. Narynbaev, R. Turtayev, N. Rokotyan, S. Kovalev, A. Rozanov, V. Nelin, S. Ermilov, L. Shishina, D. Mamayeva, A. Korolkova, K. Khoruzhii, and A. Romanov, arXiv:2502.18663v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation on VAE design (DNS Challenge blind test set “No Reverb” subset, 48 kHz input audio).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
\textbf{Configuration} & \textbf{SIG} & \textbf{BAK} & \textbf{OVRL} \\
\midrule
Full (dim 256, 50 Hz, $\mathcal{L}_{\text{VAE}}$, frozen) & 3.72 & 4.20 & 3.48 \\
– joint fine-tuning (SE only) & \textbf{3.80} & \textbf{4.25} & \textbf{3.55} \\
– dim 128 & 3.59 & 4.05 & 3.32 \\
– w/o $\mathcal{L}_{\text{adv}}$ & 3.64 & 3.97 & 3.37 \\
– w/o $\mathcal{L}_{\text{spec}}$ & 3.58 & 3.73 & 3.28 \\
– 100 Hz (downsampling ×480) & 3.68 & 4.08 & 3.38 \\
– 25 Hz (downsampling ×1920) & 3.51 & 3.83 & 3.19 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ziqian Wang
Zikai Liu
Yike Zhu
Xingchen Li
Boyi Kang
Jixun Yao
Xianjun Xia
Chuanzeng Huang
Lei Xie

"UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling" by Ziqian Wang, Zikai Liu, Yike Zhu, Xingchen Li, Boyi Kang, Jixun Yao, Xianjun Xia, Chuanzeng Huang, and Lei Xie, arXiv:2508.07558v1, 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}{ccc}
\toprule
\textbf{Set} & \textbf{Correlation} & \textbf{Feature} \\ \midrule
A & 0.1696 & line spectral pair coefficient \\
E & -0.219 & sadness score from $s_{18}$ \\
L & 0.105 & log($\frac{\text{speaker-\textit{negative}-proportion}}{\text{speaker-\textit{positive}-proportion}}$) \\
T & 0.173 & std-dev($\Delta$ no. of words per turn) \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Feature most correlated with suicidal risk, for every feature set. All correlations are statistically significant $(p<0.05)$}
\end{table}
\end{document}

eess

Sandeep Nallan Chakravarthula
Md Nasir
Shao-Yen Tseng
Haoqi Li
Tae Jin Park
Brian Baucom
Craig J. Bryan
Shrikanth Narayanan
Panayiotis Georgiou

"Automatic prediction of suicidal risk in military couples using multimodal interaction cues from couples conversations" by Sandeep Nallan Chakravarthula, Md Nasir, Shao-Yen Tseng, Haoqi Li, Tae Jin Park, Brian Baucom, Craig J. Bryan, Shrikanth Narayanan, and Panayiotis Georgiou, arXiv:1911.11927v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Accuracy ($\%$) for the 3-class problem}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l|l|l}
\hline
Model & Telegram & Alpha & OTC & Alpha+ & OTC+ \\
\hline
Haar & 83.17$\pm$0.32 & 84.65$\pm$0.79 & 84.77$\pm$0.35 & 85.05$\pm$0.50 & 85.28$\pm$0.44 \\
MagNet & \textbf{83.28}$\pm$0.43 & \textbf{84.76}$\pm$0.62 & \textbf{85.11}$\pm$0.54 & \textbf{85.40}$\pm$0.77 & \textbf{85.53}$\pm$0.63 \\
SigMaNet & 82.72$\pm$0.47 & 84.44$\pm$0.46 & 83.90$\pm$0.53 & 85.12$\pm$0.41 & 85.08$\pm$0.66 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Haar-Laplacian for directed graphs

"Haar-Laplacian for directed graphs" by Theodor-Adrian Badea and Bogdan Dumitrescu, arXiv:2411.15527v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c} \hline
		Mistaken Prompts & Avg. Dice$\uparrow$ (CT)& Avg. Dice$\uparrow$ (MR)\\ \hline
 No & \textbf{87.14} & \textbf{85.33} \\
 Yes & 86.37 & 85.10\\
 \hline
	\end{tabular}
\end{adjustbox}
\caption{During testing, CT prompts were mistakenly applied to MRI images, and MR prompts to CT images. The \textbf{bolded} font indicates the best performance on AMOS test sets with the UNet backbone.}
\end{table}
\end{document}

eess

Chengyin Li
Hui Zhu
Rafi Ibn Sultan
Hassan Bagher Ebadian
Prashant Khanduri
Chetty Indrin
Kundan Thind
Dongxiao Zhu

"MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training" by Chengyin Li, Hui Zhu, Rafi Ibn Sultan, Hassan Bagher Ebadian, Prashant Khanduri, Chetty Indrin, Kundan Thind, and Dongxiao Zhu, arXiv:2411.15576v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
 \toprule
 & Min & Max & \#
 \\
 \bottomrule
 \toprule
 Value Dates & $23^{\mathrm{rd}}$ of April 2020 & $31^{\mathrm{st}}$ of August 2020 & 91 \\
 Strikes & 2.5 \$ & 245 \$ & 314 \\
 Expiries & JUN20 & DEC22 & 9 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\small{Value dates, grid of strikes and option expiries in the dataset. We report the number of value dates, strikes and expiries in the dataset.}}
\end{table}
\end{document}

q-fin

Roberto Baviera
Michele Domenico Massaria

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\toprule
Ratio & \textbf{0.0(default)} & \textbf{0.02} & \textbf{0.05} & \textbf{0.10} & \textbf{0.2} \\ \midrule
Classification & \underline{0.7725} & \underline{0.7725} & 0.7715 & 0.7667 & 0.763 \\
Regression & \underline{0.6605} & 0.6571 & 0.6484 & 0.6383 & 0.601 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

DOFEN: Deep Oblivious Forest ENsemble

Kuan-Yu Chen
Ping-Han Chiang
Hsin-Rung Chou
Chih-Sheng Chen
Tien-Hao Chang

"DOFEN: Deep Oblivious Forest ENsemble" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, 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{Material properties ($[\lambda, G]=\rm{GPa}$, $[e]=\rm{C/m^2}$, $[\kappa]=\rm{nC/Vm}$, $[f]=\rm{\mu C/m}$)}
\begin{tabular}{ccccccccccc}
 \toprule
 $\lambda$ & $G$& $e_{31}$& $e_{33}$ & $e_{15}$& $k_{11}$ & $k_{33}$ & $f_{1}$ & $f_{2}$\\
 \midrule
 $179$ & $54$ & $-2.7$ & $3.65$& $21.3$ & $12.5$ & $14.4$ & $1$& $1$\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Xiaoying Zhuang
Bin Li
S. S. Nanthakumar
Thomas Bohlke

"Second-order computational homogenization of flexoelectric composites" by Xiaoying Zhuang, Bin Li, S. S. Nanthakumar, and Thomas Bohlke, arXiv:2312.05556v1, 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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Country Name Mappings}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 \textbf{Original Name} & \textbf{Standardized Name} \\
 \midrule
 Türkiye & Turkey \\
 Hong Kong SAR & Hong Kong \\
 Hong Kong Special Administrative Region & Hong Kong \\
 Democratic Republic of the Congo (DRC) & Democratic Republic of the Congo \\
 Democratic Republic of the Congo & Democratic Republic of the Congo \\
 Republic of Korea & South Korea \\
 Korea & South Korea \\
 Islamic Republic of Iran & Iran \\
 Republic of Yemen & Yemen \\
 Syrian Arab Republic & Syria \\
 Lao P.D.R. & Laos \\
 Lao People’s Democratic Republic & Laos \\
 People’s Republic of China & China \\
 People’s Republic of China—Hong Kong SAR & Hong Kong \\
 Swaziland & Eswatini \\
 Kingdom of Eswatini & Eswatini \\
 Cape Verde & Cabo Verde \\
 República Bolivariana de Venezuela & Venezuela \\
 República Bolivariana De Venezuela & Venezuela \\
 Russian Federation & Russia \\
 Papua New-Guinea & Papua New Guinea \\
 Federated States of Micronesia & Micronesia \\
 Serbia and Montenegro & Serbia \\
 Republic of Serbia & Serbia \\
 Republic of Montenegro & Montenegro \\
 Islamic Republic of Afghanistan & Afghanistan \\
 Islamic State of Afghanistan & Afghanistan \\
 Czech Republic & Czechia \\
 Former Yugoslav Republic of Macedonia & North Macedonia \\
 Republic of North Macedonia & North Macedonia \\
 Republic of Congo (Congo) & Republic of Congo \\
 Democratic Republic of Timor-Leste & Timor-Leste \\
 Republic of Azerbaijan & Azerbaijan \\
 Republic of Fiji & Fiji \\
 Socialist People’s Libyan Arab Jamahiriya & Libya \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Geyue Sun
Xiao Liu
Tomas Williams
Roberto Samaniego

"Learning to Regulate: A New Event-Level Dataset of Capital Control Measures" by Geyue Sun, Xiao Liu, Tomas Williams, and Roberto Samaniego, arXiv:2505.23025v1, 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{\\Kappa scores for interrater reliability}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc} % Remove padding on the left side of the first column
\toprule
\toprule
Comparison & Kappa Score \\
\midrule
Human Rater 1 $\&$ Human Rater 2 & 0.892 \\
Human Rater 1 $\&$ FairFace & 0.948 \\
Human Rater 2 $\&$ FairFace & 0.888 \\
Human Raters $\&$ FairFace & 0.948 \\
\bottomrule
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Kenneth Colombe
Alex Krumer
Rosa Lavelle-Hill
Tim Pawlowski

"Racial bias, colorism, and overcorrection" by Kenneth Colombe, Alex Krumer, Rosa Lavelle-Hill, and Tim Pawlowski, arXiv:2508.10585v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Errors in the reconstructed distribution of kinetic parameters in the ocular pharmacokinetic model. Here, ReLU refers to using the ReLU activation function for forward propagation and ResNet refers to using the ResNet technique (described in Fig.~).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\toprule width & \# of layers & forward propagation & initialization for weights \& biases & error \\
\midrule
 10 & 1 & ReLU & $\mathcal{N}(0, 0.03^2)$& $8.73\times10^{-3}$
 \\
 10 & 2 & ReLU & $\mathcal{N}(0, 0.03^2)$& $1.46\times10^{-3}$
 \\
10 & 3 & ReLU & $\mathcal{N}(0, 0.03^2)$& $1.13\times10^{-3}$ \\
10 & 4 & ReLU & $\mathcal{N}(0, 0.03^2)$& $1.27\times10^{-3}$ \\
5 & 3 & ReLU & $\mathcal{N}(0, 0.03^2)$& $1.05\times10^{-3}$ \\
15 & 3 & ReLU & $\mathcal{N}(0, 0.03^2)$& $8.50\times10^{-4}$ \\
 20 & 3 & ReLU & $\mathcal{N}(0, 0.03^2)$ & $1.15\times10^{-3}$ \\
 10 & 2 & ResNet & $\mathcal{N}(0, 0.03^2)$& $7.68\times10^{-3}$ \\
10 & 3 & ResNet & $\mathcal{N}(0, 0.03^2)$& $9.23\times10^{-4}$ \\
10 & 4 & ResNet & $\mathcal{N}(0, 0.03^2)$& $9.49\times10^{-4}$ \\
 10 & 3 & ReLU & $\mathcal{N}(0, 0)$& 0.609
 \\
 10 & 3 & ReLU & $\mathcal{N}(0, 0.01^2)$& $7.89\times10^{-4}$
 \\
 10 & 3 & ReLU & $\mathcal{N}(0, 0.02^2)$& $7.54\times10^{-4}$
 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mingtao Xia
Qijing Shen
Philip Maini
Eamonn Gaffney
Alex Mogilner

"A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems" by Mingtao Xia, Qijing Shen, Philip Maini, Eamonn Gaffney, and Alex Mogilner, arXiv:2503.05068v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}

			\hline\hline
			\textbf{Previous Methods} & \textbf{Accuracy\%} \\\hline\hline
			F. Ofli et al.~ & 97.81 \\\hline
			Alireza Shafaei et al.~ & 98.1 \\\hline
			Earnest Paul Ijjina et al.~ & 98.38 \\\hline
			Chen Chen et al.~ & 99.54 \\\hline
			Z.ahmad et al.~ & 99.8 \\\hline
			 \textbf{Proposed MGAF (SFI + SI)} & \textbf{99.85} \\
			\hline\hline
		\end{tabular}
\end{adjustbox}
\caption{Comparison of Accuracies of proposed MGAF network with previous methods on Berkeley MHAD dataset using depth and inertial modalities.}
\end{table}
\end{document}

eess

"CNN based Multistage Gated Average Fusion (MGAF) for Human Action Recognition Using Depth and Inertial Sensors" by Zeeshan Ahmad and Naimul khan, arXiv:2010.16073v1, 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{Estimates of the NB model for IUD, after excluding non-significant covariates}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
\hline
IUD-NB & Estimate({ $\hat{\boldsymbol\beta}_2$}) & StdDev & $p$-value \\
\hline
Intercept & -2.7306 & 0.4330 & 0.0000\\
AGE=2 & 2.4043 & 0.3859 & 0.0000 \\
AGE=3 & 2.7176 & 0.6282 & 0.0000 \\
AFS=2 & -0.7880 & 0.4222 & 0.0620 \\
AFS=3 & -1.2700 & 0.4450 & 0.0043 \\
Dispersion & 0.3055 & & \\
\hline
\multicolumn{2}{ l }{AIC = 588.18} & \multicolumn{2}{ l }{-2log-Lik.= 576.18} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Modeling Count Data via Copulas

Hadi Safari-Katesari
S. Yaser Samadi
Samira Zaroudi

"Modeling Count Data via Copulas" by Hadi Safari-Katesari, S. Yaser Samadi, and Samira Zaroudi, arXiv:2012.02021v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\hline
\textbf{Material category} & \hspace{1.5cm} & \textbf{Held-out texture ids for test}\\
\hline
asphalt && 06, 15 \\
bricks && 08, 19 \\
concrete && 06, 15, 17 \\
fabric && 01, 02, 28 \\
fabric\_carpet && 02, 05, 13 \\
ground && 13, 19 \\
leather && 03, 12 \\
marble && 02, 03 \\
metal && 10, 19 \\
metal\_diamond\_plate && 04 \\
moss && 01, 03 \\
paint && 05 \\
paving\_stones && 24, 38 \\
planks && 07, 09, 16 \\
plaster && 03 \\
plastic && 04, 05 \\
porcelain && 02, 04 \\
rocks && 04 \\
terrazzo && 06, 08 \\
tiles && 43, 49 \\
wood && 02, 05, 16, 22, 32 \\
wood\_floor && 06, 10, 17, 28 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{iGibson 1.0 environment held out texture ids for test.}% All remaining textures ids are used during training phase.}
\end{table}
\end{document}

stat

"ReCoRe: Regularized Contrastive Representation Learning of World Model" by Rudra P. K. Poudel, Harit Pandya, Stephan Liwicki, and Roberto Cipolla, arXiv:2312.09056v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Water Quality Prediction Results (Numerical Features: Standardized; Categorical Features: One-Hot Encoding).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
 \toprule
 Models & RMSE & MAPE & WMAPE & WUPRED & WOPRED \\ %\tnote{1}
 \midrule
SADL-II (Original Paper) & 11.50 & N/A & N/A & N/A & N/A \\
Benchmarking & 29.52 & 32.21 & 32.35 & 14.55 & 17.80 \\
LightGBM & \textcolor{red}{11.04} & \textcolor{red}{9.22} & \textcolor{red}{9.36} & \textcolor{red}{5.15} & 4.21 \\
Linear Regression & 11.91 & 10.79 & 10.96 & 6.14 & 4.82 \\
MLP & 14.42 & 14.54 & 14.61 & 9.29 & 5.32 \\
Random Forest & 11.71 & 9.71 & 9.87 & 5.26 & 4.61 \\
XGBoost & 11.14 & 9.29 & 9.44 & 5.28 & \textcolor{red}{4.16} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Yinpu Li
Siqi Mao
Yaping Yuan
Ziren Wang
Yixin Kang
Yuanxin Yao

"Beyond Tides and Time: Machine Learning Triumph in Water Quality" by Yinpu Li, Siqi Mao, Yaping Yuan, Ziren Wang, Yixin Kang, and Yuanxin Yao, arXiv:2309.16951v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Makoto Araya
Masaaki Harada
Hadi Kharaghani
Sho Suda
Wei-Hsuan Yu

"Unbiased weighing matrices of weight $9$" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
			\hline\hline
			\textbf{Datasets} & \textbf{conv1} & \textbf{conv2} & \textbf{conv3} \\\hline\hline
			UTD-MHAD & 0.00032 & 0.0026 & 0.033 \\\hline
			Berkeley MHAD & 0.0037 & 0.012 & 0.015 \\\hline
			Kinect V2 & 0.0003 & 0.0016 & 0.0089\\\hline\hline
	\end{tabular}
\end{adjustbox}
\caption{Normalized cross correlation coefficients between features extracted from all three convolutional layers of CNN.}
\end{table}
\end{document}

eess


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
$n_{1}$ & $n_{2}$ & Subadditivity\tabularnewline
\hline
\hline
$0$ & $0$ & $log_{2}1\leq log_{2}1+log_{2}1$\tabularnewline
\hline
$0$ & $1$ & $log_{2}2\leq log_{2}1+log_{2}2$\tabularnewline
\hline
$1$ & $1$ & $log_{2}3\leq log_{2}2+log_{2}2$\tabularnewline
\hline
$1$ & $2$ & $log_{2}4\leq log_{2}2+log_{2}3$\tabularnewline
\hline
$2$ & $2$ & $log_{2}5\leq log_{2}3+log_{2}3$\tabularnewline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Information Content and Entropy of Finite Patterns from a Combinatorial Perspective" by Zsolt Pocze, arXiv:2501.10824v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{clccc} \hline
 & & LSTM & NAS & UGRNN \\ \hline
\multirow{ 3}{*}{CMM} &
CONTROL & 2.14 $\pm$ 0.60 & 1.90 $\pm$ 0.49 & 2.11 $\pm$ 0.50 \\
&INTERVAL & 2.99 $\pm$ 1.08 & 2.44 $\pm$ 1.00 & 2.42 $\pm$ 0.74 \\
&DB12 & 2.49 $\pm$ 0.59 & 4.28 $\pm$ 2.71 & \textbf{1.66 $\pm$ 0.40} \\
\hline
\multirow{ 3}{*}{LM} &
CONTROL & \textbf{1.47 $\pm$ 0.46} & 1.77 $\pm$ 0.65 & 1.51 $\pm$ 0.46 \\
&INTERVAL & 2.37 $\pm$ 1.56 & 1.59 $\pm$ 0.75 & 1.66 $\pm$ 0.84 \\
&DB12 & 1.57 $\pm$ 0.64 & 2.56 $\pm$ 1.11 & 2.40 $\pm$ 0.65 \\
\hline
\multirow{ 3}{*}{CENTR} &
CONTROL & 0.20 $\pm$ 0.08 & 0.22 $\pm$ 0.09 & 0.21 $\pm$ 0.09 \\
&INTERVAL & 0.16 $\pm$ 0.08 & 0.16 $\pm$ 0.09 & 0.17 $\pm$ 0.12 \\
&DB12 & 0.16 $\pm$ 0.08 & 0.25 $\pm$ 0.26 & \textbf{0.23 $\pm$ 0.09} \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Means and standard deviation for the 100 songs generated by the final models with the 3 metrics.}
\end{table}
\end{document}

eess

Sebastian Garcia-Valencia
Alejandro Betancourt
Juan G. Lalinde-Pulido

"Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music" by Sebastian Garcia-Valencia, Alejandro Betancourt, and Juan G. Lalinde-Pulido, arXiv:2012.01231v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Nodes and links in different kinds of transportation networks. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
	\hline
	Network type & Nodes & Links \\
	\hline
	Roadway & Intersections & Street segments \\
	Public transit & Bus or train stops & Route segments \\
	Freight & Factories, warehouses, retailers & Shipping options \\
	Air & Airports & Flights \\
	Maritime & Ports & Shipping channels \\
	\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Stephen D. Boyles
Nicholas E. Lownes
Avinash Unnikrishnan

"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
\toprule & $h_1$ & $h_2$ & $h_3$ \\
\midrule IIA & 1 & 1 & 1 \\
IIB & 1 & 1 & 1.5 \\
IIC & 1.5 & 1 & 1 \\
IID & 1.5 & 1 & 1.5 \\
\bottomrule% & & &
\end{tabular}
\caption{Holding cost parameters for different cases.}
\end{table}
\end{document}

math

"Singular Control of (Reflected) Brownian Motion: A Computational Method Suitable for Queueing Applications" by Baris Ata, J. Michael Harrison, and Nian Si, arXiv:2312.11823v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Metrics between subspaces $\mathcal{V}$ and $\mathcal{U}$, with ${\dim \mathcal{V} = k}$ and ${\dim \mathcal{U} = l}$, in terms of the principal angles $\theta_i(\mathcal{V},\mathcal{U})$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll} \hline
Name	& Metric 										 \\ \hline
Chordal
& $d_\kappa(\mathcal{V},\mathcal{U})	=\left(|k -l|+\displaystyle\sum_{i=1}^{\min(k,l)} (\sin \theta_i)^2 \right)^{1/2}$ \\
Grassmann
& $d_\gamma(\mathcal{V},\mathcal{U})	=\left(|k -l| \tfrac{\pi^2}{4} + \displaystyle\sum_{i=1}^{\min(k,l)} \theta_i^2 \right)^{1/2}$ \\
Procrustes
& $d_\pi(\mathcal{V},\mathcal{U})	=\left(|k -l|+\displaystyle2\sum_{i=1}^{\min(k,l)} \left(\sin \tfrac{\theta_i}{2}\right)^2 \right)^{1/2}$\\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Distances between finite-horizon linear behaviors" by Alberto Padoan and Jeremy Coulson, arXiv:2503.22849v2, 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{Sample sequence CGCATGCGCGACC and the positron of the $k$-mers.}
\begin{tabular}{c|ccccccccccccc}\hline
		Position & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 \\ \hline
		Sequence & C & G & C & A & T & G & C & G & C & G & A & C & C\\ \hline
		1-mer & C & G & C & A & T & G & C & G & C & G & A & C & C \\
		2-mer & CG & GC & CA & AT & TG & GC & CG & GC & CG & GA & AC & CC & - \\
		3-mer & CGC & GCA & CAT & ATG & TGC & GCG & CGC & GCG & CGA & GAC & ACC & - & - \\ \hline
	\end{tabular}
\end{table}
\end{document}

math

"Revealing the Shape of Genome Space via K-mer Topology" by Yuta Hozumi and Guo-Wei Wei, arXiv:2412.20202v1, 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{ Real datasets used for the experiments,sorted by size} % title name of the table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} % creating 10 columns
\hline
Dataset &Instances& Numerical attributes & Categorical attributes & Classes
\\
\hline
Iris & 150&4& 0&3 \\
 \\
Glass & 214&9& 0&6 \\
 \\
Ionosphere& 351&33 & 0&2 \\
 \\
 Diabetes&520&0& 17&2\\
 \\
 Breast-Cancer& 569&30 & 0&2 \\
 \\
 Balance-scale & 625&4 &0 &3\\
 \\
 Australian & 690&6 & 8&2 \\
 \\
Pima-indians &768&8 & 0&2 \\
 \\
 Vehicle & 846&18 & 0&4\\
 \\
 Raisin & 900 &7& 0&2\\
 \\
 Tic-tac-toe &958&0 & 9&2\\
 \\
 German &1000&6 & 14&2\\
 \\
 Biodegradation & 1051&38 & 3&2 \\
 \\
 BHP&1075&20& 1&4\\
 \\
 Diabetic & 1151 &16 & 3&2\\
 \\
 Banknote &1372&4 & 0&2\\
 \\
 Contraceptive&1473&2 & 7&3\\
 \\
Obesity&2111&3& 13&7\\
 \\
Segments&2310&19& 0&7\\
 \\
Waveform+noise&5000&40& 0&3\\
 \\
Pendigits&10000&16& 0&10\\
 \\
Letter&20000&16& 0&26\\
\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

On Subagging Boosted Probit Model Trees

"On Subagging Boosted Probit Model Trees" by Tian Qin and Wei-Min Huang, arXiv:2311.02827v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.