\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}{rrrr|rrrr}
\multicolumn{4}{c}{problem sizes} & \multicolumn{3}{c}{timing (s)} &
\multirow{2}{*}{iterations} \\
$n$ & $q$ & $m$ & $nm$ & MOSEK & PDMCF (CPU) & PDMCF (GPU) \\
\hline
$3000$ & $10$ & $34424$ & $1\times 10^8$ & OOM & $106274$ & $139$ & $4140$ \\
$5000$ & $10$ & $57338$ & $3\times 10^8$ & OOM & $382400$ & $421$ & $3970$ \\
$10000$ & $10$ & $114054$ & $1\times 10^9$ & OOM & $1809517$ & $2078$ & $4380$
\end{tabular}
\end{adjustbox}
\caption{Runtime table for large size problems (JAX).}
\end{table}
\end{document}

math

"Solving Large Multicommodity Network Flow Problems on GPUs" by Fangzhao Zhang and Stephen Boyd, arXiv:2501.17996v2, 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{Percentage of helpful and persona-consistent messages from each agent.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 & Helpful \% & Role Consistent \% \\
 \midrule
 LaMDA Everest & 65 & 91 \\
 PT Everest & 18 & 85 \\
 LaMDA Music & 57 & 89 \\
 PT Music & 31 & 84 \\
 \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.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c|c||}
 \hline
 Name & sorted? & epochs & batch size and hyper-parameters\\
 \hline
 \hline
 baseline & No & 100 & $\cdot$\\
 \hline
 sorted & Yes & 100 & $\cdot$\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Experimental details of baseline model vs sorted. $\cdot$ indicates that the \textit{batch size} = 50 and it uses the baseline hyper-parameters.}
\end{table}
\end{document}

cs

"Representations learnt by SGD and Adaptive learning rules: Conditions that vary sparsity and selectivity in neural networks" by Jin Hyun Park, arXiv:2201.11653v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Microscopic Traffic Models, Accidents, and Insurance Losses" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 \textsf{ Poisson brackets} & $\begin{matrix}
 \textsf{ No. of polynomials without} \\
 \textsf{ using the grading method }
 \end{matrix}$ & $\begin{matrix}
 \textsf{Maximum No. of polynomials after applying} \\
 \textsf{ the grading method}
 \end{matrix}$ \\
 \hline
 $\{\textbf{q}_2,\textbf{q}_2\}$ & $ 12 $ & $2 $ \\
 \hline
 $\{\textbf{q}_2,\textbf{q}_3\}$ & $24 $ & $6 $ \\
 \hline
 $\{\textbf{q}_3,\textbf{q}_3\}$ & $42 $ & $12 $ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Comparison of the number of polynomials }
\end{table}
\end{document}

math

Rutwig Campoamor-Stursberg
Danilo Latini
Ian Marquette
Junze Zhang
Yao-Zhong Zhang

"On the construction of polynomial Poisson algebras: a novel grading approach" by Rutwig Campoamor-Stursberg, Danilo Latini, Ian Marquette, Junze Zhang, and Yao-Zhong Zhang, arXiv:2503.03490v1, 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{FD Filter results for the experimental data.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|ll|}
\cline{3-4}
 & & \multicolumn{2}{c|}{FD filter} \\ \cline{3-4}
 & & Healthy & Faulty \\ \hline
\multirow{2}{*}{Ground truth} & Healthy & 48 & 0 \\ \cline{2-4}
 & Faulty & 0 & 99 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Casper van Peijpe
Farhad Ghanipoor
Youri de Loore
Pim Hacking
Nathan van de Wouw
Peyman Mohajerin Esfahani

"Fault Isolation for the Ink Deposition Process in High-End Industrial Printers" by Casper van Peijpe, Farhad Ghanipoor, Youri de Loore, Pim Hacking, Nathan van de Wouw, and Peyman Mohajerin Esfahani, arXiv:2412.07545v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
Dimension & Method & $N$ & Epochs & Test error & Training Time (Seconds) \\\hline
\multirow{2}{*}{$d=2$} & RF & 500 & 1000 & $2.22 \times 10^{-3}$ & 42.29 \\\cline{2-6}
 & PINN & - & 2000 & $8.98 \times 10^{-3}$ & 51.46 \\\hline
\multirow{2}{*}{$d=4$} & RF & 500 & 1000 & $1.12 \times 10^{-3}$ & 51.24 \\\cline{2-6}
 & PINN & - & 2000 & $9.58 \times 10^{-4}$ & 55.49 \\\hline
 \multirow{2}{*}{$d=8$} & RF & 100 & 1500 & $1.81\times 10^{-3}$ & 37.84 \\\cline{2-6}
 & PINN & - & 2000 & $3.21 \times 10^{-3}$ & 54.14 \\\hline
\end{tabular}
\caption{Comparison between random feature model and PINN for the nonlinear Poisson PDEs. We report the number of random features, number of epochs, test error, and training time for each model.}
\end{table}
\end{document}

math

"Solving Partial Differential Equations with Random Feature Models" by Chunyang Liao, arXiv:2501.00288v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments" by James Enouen, Tianshu Sun, and Yan Liu, arXiv:2309.01780v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A PINN-enriched finite element method for linear elliptic problems" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, 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{Parameter} & \textbf{Meaning} & \textbf{Value} \\
 \midrule
 $\beta$ & Discount factor & 0.97\\
 $\gamma$ & Risk aversion & 2.00\\
 $\nu$ & Inverse Frisch & 1.00\\

 $\psi$ & Labor scaling parameter & 1.00\\
 $\xi$ & Capital production parameter & 0.80\\
 $A$ & Capital production parameter & 1.25\\
 $\alpha$ & Capital share & 0.36\\
 $\delta$ & Depreciation rate & 0.025\\
 $\rho_Z$ & Persistence of log productivity & 0.90\\
 $\sigma_\varepsilon$ & Std dev of innovation & 0.08\\

 $\underline{Z}$ & Disaster log productivity & -0.15\\
 $\underline{\zeta}$ & Capital quality in bad state & 0.95\\
 $\pi_{\underline{\zeta}}$ & Prob. of switching to $\underline{\zeta}$ & 0.5\%\\
 $\pi_{\overline{\zeta}}$ & Prob. of switching to $\underline{\zeta}$ & 34\%\\
 $\lambda$ & Planner's Pareto weight & 0.01\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{ Parameters of the model.}
\end{table}
\end{document}

q-fin

Bailouts and Redistribution

"Bailouts and Redistribution" by Mikayel Sukiasyan, arXiv:2509.10933v1, 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{Proportion of vulnerable crypto projects.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|r|}
 \hline
 CVE ID & Type & CVSS & Number (\%) \\\hline
 CVE-2012-1909 & DoS & 5.0 & 0 (0.0\%) \\\hline
 CVE-2012-3789 & DoS & 5.0 & 0 (0.0\%) \\\hline
 CVE-2012-1910 & DoS, Execute code & 7.5 & 0 (0.0\%) \\\hline
 CVE-2012-2459 & DoS & 5.0 & 0 (0.0\%) \\\hline
 CVE-2014-0160 & Overflow, Gain information & 5.0 & 149 (29.2\%) \\\hline
 CVE-2013-4627 & DoS & 5.0 & 294 (57.7\%) \\\hline
 CVE-2013-4165 & Gain information & 4.3 & 43 (8.4\%) \\\hline
 CVE-2014-0224 & Gain information & 5.8 & 176 (34.5\%) \\\hline
 CVE-2018-12356 & Execute code & 7.5 & 0 (0.0\%) \\\hline
 CVE-2018-17144 & DoS & 5.0 & 27 (5.3\%) \\\hline
 CVE-2019-6250 & Execute code, Overflow & 9.0 & 113 (22.2\%) \\\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jusop Choi
Wonseok Choi
William Aiken
Hyoungshick Kim
Jun Ho Huh
Taesoo Kim
Yongdae Kim
Ross Anderson

"Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild" by Jusop Choi, Wonseok Choi, William Aiken, Hyoungshick Kim, Jun Ho Huh, Taesoo Kim, Yongdae Kim, and Ross Anderson, arXiv:2201.08678v1, 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{Segmentation accuracy of the SWTR-Unet depending on the number of used patient cases ($\#_{pc}$) for training purposes. Stated are the average DSCs for liver and hepatic lesions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
$\#_{pc}$ & $DSC_{liver}$ & $DSC_{lesion}$ \\
\hline
25 & $0.78~\pm~0.10$ & $0.60~\pm~0.18$ \\
30 & $0.86~\pm~0.07$ & $0.72~\pm~0.21$ \\
35 & $0.92~\pm~0.04$ & $0.76~\pm~0.19$ \\
40 & $0.98~\pm~0.02$ & $0.80~\pm~0.21$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Georg Hille
Shubham Agrawal
Pavan Tummala
Christian Wybranski
Maciej Pech
Alexey Surov
Sylvia Saalfeld

"Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers" by Georg Hille, Shubham Agrawal, Pavan Tummala, Christian Wybranski, Maciej Pech, Alexey Surov, and Sylvia Saalfeld, arXiv:2201.10981v3, 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{UAR values for predicting Arousal (A) and Valence (V) levels on the elderly sub-challenge. E2E systems combined ten DNNs to produce the predictions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}\hline
 {\bf System} & {\bf Dev (A/V)} & {\bf Test (A/V)} \\
 \hline
 Baseline (linguistic) & 40.6/\textbf{49.2} & 44.0/\textbf{49.0} \\
 \hline
 Baseline(acoustic) & 35.0/31.6 & \textbf{50.4}/40.3\\
 E2E (single task) & 35.0/39.7 & ?\\
 E2E (multitask) & 39.5/39.7 & ?\\
 E2E (single task + upsampl.) & 39.8/41.5 & ?\\
 E2E (multitask+ upsampl.) & \textbf{42.9}/42.4 & 38.0/39.5\\
 E2E (single task + prob. sampl.) & 35.6/39.6 & ?\\
 E2E (multitask+ prob. sampl.) & 40.0/\textbf{45.5} & 45.8/34.8\\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Tamás Grósz
Mittul Singh
Sudarsana Reddy Kadiri
Hemant Kathania
Mikko Kurimo

"Aalto's End-to-End DNN systems for the INTERSPEECH 2020 Computational Paralinguistics Challenge" by Tamás Grósz, Mittul Singh, Sudarsana Reddy Kadiri, Hemant Kathania, and Mikko Kurimo, arXiv:2008.02689v1, 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
 & \multicolumn{3}{c}{Clean} & \multicolumn{3}{c}{Perturbed} \\
 \cmidrule{2-4} \cmidrule{5-7}
 Agents & 2 & 4 & 6 & 2 & 4 & 6 \\
 \midrule
 Mean Pool & 82.09 & 89.25 & 92.43 & 0.90 & 12.93 & 41.77 \\
 GNN(Mean) & \textbf{82.19} & \textbf{89.93} & \textbf{92.94} & 7.55 & 52.31 & 76.18 \\
 GNN(Median) & 82.11 & 87.12 & 90.75 & 12.8 & \textbf{67.70} & \textbf{86.30} \\
 GNN(Soft Med) & 82.19 & 89.67 & 92.49 & \textbf{21.53} & 61.37 & 84.99 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Choice of fusion in V2VNet affects performance and robustness. We investigate using mean pooling and using a GNN with various aggregation methods.}
\end{table}
\end{document}

cs

James Tu
Tsunhsuan Wang
Jingkang Wang
Sivabalan Manivasagam
Mengye Ren
Raquel Urtasun

"Adversarial Attacks On Multi-Agent Communication" by James Tu, Tsunhsuan Wang, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren, and Raquel Urtasun, arXiv:2101.06560v2, 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{Comparison Between NVIDIA CUDA and Ascend CANN.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 \textbf{Component} & \textbf{CUDA} & \textbf{CANN} \\
 \midrule
 GE Graph Engine & TensorRT plugins \& parser & Graph Engine \\
 Collective Communication Library & NV NCCL & HCCL \\
 Library/Template & NV CUTLASS & Ascend C High-Level API \\
 General Programming & NV CUDA-C & Ascend C Low-Level API \\
 Operator Acceleration Library & NV cuDNN & Ascend aclNN \\
 Runtime & NV Runtime & Ascend Runtime \\
 Driver & NV Driver & Ascend Driver \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

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


math

"High Order in Space and Time Schemes Through an Approximate Lax-Wendroff Procedure" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2502.07798v1, 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{Quantitative evaluation of EO-1 data experiments for different HSI reconstruction methods from CASSI and RGB measurements.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc}
 \toprule
 Method & TV & DeSCI & TV-RGB & DeSCI-RGB & DLTR & Fusion & PFusion \\
 \midrule
 M-PSNR & 26.64 & 28.96 & 29.91 & 37.01 & 35.56 & 41.8 & \textbf{43.34} \\
 M-SSIM & 0.839 & 0.948 & 0.931 & 0.977 & 0.963 & 0.983 & \textbf{0.984} \\
 MSA & 4.929 & 4.253 & 3.864 & 1.6 & 1.78 & 1.03 & \textbf{0.85} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Fast Hyperspectral Image Recovery via Non-iterative Fusion of Dual-Camera Compressive Hyperspectral Imaging" by Wei He, Naoto Yokoya, and Xin Yuan, arXiv:2012.15104v1, 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{Quantiles for sum of three consecutive terms}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 \hline
 Quantile & Actual & Trans-Lin Reg-Var & Gaussian & Lin Reg-Var & Lin Reg-Var \\
 & & in $\mathbb{R}^+$ & & in $\mathbb{R}$ & in $\mathbb{R}^+$ \\\hline
 0.95 & 8.28 & 8.45 & 7.93 & 7.45 & 8.92 \\
 0.98 & 13.09 & 12.91 & 11.62 & 11.01 & 13.79 \\
 0.99 & 16.79 & 16.72 & 14.34 & 14.08 & 17.74 \\
 0.995 & 20.29 & 20.46 & 17.02 & 18.00 & 21.47 \\
 0.999 & 27.17 & 28.09 & 23.00 & 27.22 & 29.24 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Transformed-Linear Models for Time Series Extremes" by Nehali Mhatre and Daniel Cooley, arXiv:2012.06705v3, 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{Sample table title}
\begin{tabular}{lll}
 \toprule
 \multicolumn{2}{c}{Part} \\
 \cmidrule(r){1-2}
 Name & Description & Size ($\mu$m) \\
 \midrule
 Dendrite & Input terminal & $\sim$100 \\
 Axon & Output terminal & $\sim$10 \\
 Soma & Cell body & up to $10^6$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

"Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models" by Gang Hu, arXiv:2311.05743v1, 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{Mechanism Tests: OLS Regressions on `Open` Skill Classification}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
& \textbf{(1)} & \textbf{(2)} \\
& \textit{Dep. Var.:} & \textit{Dep. Var.:} \\
& AI Teaching Impact & AI Skill Disruption \\
\midrule
Open Skill & -0.837*** & -3.377*** \\
& (0.232) & (0.436) \\
\addlinespace
Intercept & 0.527*** & 1.623*** \\
& (0.165) & (0.260) \\
\midrule
Observations & 152 & 152 \\
Adj. $R^2$ & 0.113 & 0.441 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Training for Obsolescence? The AI-Driven Education Trap" by Andrew J. Peterson, arXiv:2508.19625v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameter estimates for Independent Frailty Model for Simulated Data}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
					\hline
					parameter & Progression & & & Relapse & & & Death & & \\
					\hline
					& true & estimate & std. & true & estimate & std.& true & estimate & std \\
					& value & estimate & error & value & estimate & error & value & estimate & error\\
					\hline
					Age & -0.06 & -0.063 & 0.030 & -0.05 & -0.076 & 0.033 & -0.03 & -0.047 & 0.023\\
					t-stage &0.1 & 0.859 & 0.901 & 0.2 & -0.01 & 0.841 & 0.3 & 0.150 & 0.724 \\
					n-stage &0.5 & 0.192 & 0.916 & 0.2 & 0.632 & 0.837 & 0.3 & 0.282 & 0.723\\
					\hline
					Frailty & 0.25 & 7.7e-09 & p-value & 0.243 & 7.7e-09 & p-value & 0.238 & 7.5e-09 & p-value\\
					Variances & & &= 0.95 & & & = 0.99& & & = 0.96\\
					\hline
				\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Atanu Bhattacharjee
Gajendra K. Vishwakarma
Souvik Banerjee

"A modified risk detection approach of biomarkers by frailty effect on multiple time to event data" by Atanu Bhattacharjee, Gajendra K. Vishwakarma, and Souvik Banerjee, arXiv:2012.02102v2, 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{Deep Neural Network Models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cll}
\toprule
\textbf{Domain/Model Class}&\textbf{Model}&\textbf{Reference} \\
\midrule
 & VGG16&\\
Computer Vision (CV) & Inceptionv3&\\
 & ResNet50&\\
\midrule
Language (NLP) & BERT&\\
\midrule
Geometric & DimeNet&\\
&SchNet&\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Nathan C. Frey
Baolin Li
Joseph McDonald
Dan Zhao
Michael Jones
David Bestor
Devesh Tiwari
Vijay Gadepally
Siddharth Samsi

"Benchmarking Resource Usage for Efficient Distributed Deep Learning" by Nathan C. Frey, Baolin Li, Joseph McDonald, Dan Zhao, Michael Jones, David Bestor, Devesh Tiwari, Vijay Gadepally, and Siddharth Samsi, arXiv:2201.12423v1, 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|l}
			\toprule
			\textbf{Feature} &
			\textbf{Calculation} \\
			\midrule
			$x_t^{o}$ &
			$x_t^{o}=S_t^o/S_t^c-1$ \\
			$x_t^{h}$ &
			$x_t^{h}=S_t^h/S_t^c-1$ \\
			$x_t^{l}$ &
			$x_t^{l}=S_t^l/S_t^c-1$ \\
			$x_t^{c}$ &
			$x_t^{c}=S_t^c/S_{t-1}^c-1$ \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Hyunwoo Lee
Jihyeong Jeon
Jaemin Hong
U Kang

"Mitigating Distribution Shift in Stock Price Data via Return-Volatility Normalization for Accurate Prediction" by Hyunwoo Lee, Jihyeong Jeon, Jaemin Hong, and U Kang, arXiv:2508.20108v2, 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{Root Mean Squared Error from 200 Experiments}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \hline
 \textbf{Method} & $n=1000$ & $n=3000$ & $n=5000$ & $n=7000$ & $n=9000$ & $n=11000$ \\
 \hline
 Monte Carlo (MC) & 0.0096 & 0.0062 & 0.0048 & 0.0039 & 0.0035 & 0.0029 \\
 PEMC & 0.0055 & 0.0028 & 0.0024 & 0.0019 & 0.0018 & 0.0015 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Fengpei Li
Haoxian Chen
Jiahe Lin
Arkin Gupta
Xiaowei Tan
Honglei Zhao
Gang Xu
Yuriy Nevmyvaka
Agostino Capponi
Henry Lam

"Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate" by Fengpei Li, Haoxian Chen, Jiahe Lin, Arkin Gupta, Xiaowei Tan, Honglei Zhao, Gang Xu, Yuriy Nevmyvaka, Agostino Capponi, and Henry Lam, arXiv:2412.11257v3, 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}{lllllllllllll}
Total & White & Black & Asian & Native & Other & Two+ & Male & Female & Young & Middle & Old \\
\hline \\
0.0004 & 0.016 & 0.065& 0.023 & 0.0367 & 0.020 & 0.033 & 0.016 & 0.008 & 0.040 & 0.045 & 0.030 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ira Globus-Harris
Michael Kearns
Aaron Roth

"An Algorithmic Framework for Bias Bounties" by Ira Globus-Harris, Michael Kearns, and Aaron Roth, arXiv:2201.10408v4, 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{Neural Network Confusion Matrix}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
 & Predicted NO & Predicted YES\tabularnewline
\hline
\hline
True NO & 2087 & 18\tabularnewline
\hline
True YES & 39 & 8\tabularnewline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Amir Mukeri
Habibullah Shaikh
D. P. Gaikwad

"Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction" by Amir Mukeri, Habibullah Shaikh, and D. P. Gaikwad, arXiv:2010.13892v2, 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}{lrrrr}
 \hline
 \multicolumn{5}{l}{Data 1: Player-level match performance (\textit{N} = 357)}\\
 \hline
 & Q1 & Median & Mean & Q3 \\
 Winning rate (\%) & 31 &46 &43 &81 \\
 \hline
 \multicolumn{5}{l}{Data 2: Move decisions (\textit{N} = 1,357,523)}\\
 \hline
 & Q1 &Median &Mean&Q3 \\
 Move counts within a match &176&212&216&254\\
 The Human-AI Gap (percentage points) &0.39&1.68&3.49&4.51 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Summary Statistics}
\end{table}
\end{document}

q-fin

"Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo" by Minkyu Shin, Jin Kim, and Minkyung Kim, arXiv:2012.15035v3, 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{System name} & \textbf{Acc} & \textbf{Alana Dataset use} \\ \hline
Random Forest & { \textbf{ 69.1\%}} & Training on 70\% \\
 & & Testing on 30\% \\
 & & with 25 trees \\ \hline
AFINN & 55.4\% & Testing on all \\ \hline
NLTK Vader & 55.1\% & Testing on all \\ \hline
Stanford CoreNLP & 53.6\% & Training on 70\% \\
 & & Testing on 30\% \\ \hline
Naive Bayes & 49.8\% & Testing on all \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Systems Tested and Accuracy}
\end{table}
\end{document}

cs

Mohamad Alissa
Issa Haddad
Jonathan Meyer
Jade Obeid
Nicolas Wiecek
Sukrit Wongariyakavee

"Sentiment Analysis for Open Domain Conversational Agent" by Mohamad Alissa, Issa Haddad, Jonathan Meyer, Jade Obeid, Nicolas Wiecek, and Sukrit Wongariyakavee, arXiv:2101.00675v2, 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{Performance of each metric with different summary functions on RAMIE and AutoLaparo, whereby $r$ denotes Pearson and $\tau$ denotes Kendall correlation. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
 Metric & AutoLaparo ($r$) & AutoLaparo ($\tau)$ & RAMIE ($r$) & RAMIE ($\tau$) \\
\midrule
LogME (mean) & 0.627 & 0.833 & 0.653 & 0.835 \\
LogME (min) & 0.655 & 0.825 & 0.674 & 0.835 \\
LogME (max) & 0.594 & 0.696 & 0.608 & 0.747 \\
\midrule
Hscore (mean) & 0.056 & -0.203 & -0.024 & 0.365 \\
Hscore (min) & 0.501 & 0.158 & 0.056 & 0.352 \\
Hscore (max) & -0.031 & -0.190 & 0.074 & -0.138 \\
\midrule
TransRate (mean) & -0.252 & -0.195 & -0.095 & -0.181 \\
TransRate (min) & -0.194 & -0.188 & -0.052 & -0.201 \\
TransRate (max) & -0.208 & -0.203 & -0.095 & -0.187 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Prabhant Singh
Yiping Li
Yasmina Al Khalil

"Analysis of Transferability Estimation Metrics for Surgical Phase Recognition" by Prabhant Singh, Yiping Li, and Yasmina Al Khalil, arXiv:2508.16730v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Predicted plane error $e$ vs evaluation score}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccccc}
\hline
$e = |p - t|$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\
\hline
\textbf{Score} & 1.00 & 0.74 & 0.50 & 0.32 & 0.18 & 0.10 & 0.05 & 0.02 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of equilibria concerning $P_I=0$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
 Scenario & Range of $R$ & Condition on $C_I$ & Equilibrium \\
 \hline & & & \\
 1 & $R > \dfrac{c_w}{\mu-\lambda} $ & $C_I\geq C_{I,0}^{1}(R)\equiv \rho^{n_e} \left[ \dfrac{c_w n_e}{\mu}+\dfrac{c_w}{\mu-\lambda}-R\right]$ & $(0,1)$ \\
 2 \& 3 & $R \in \left(\dfrac{c_w}{\mu},\dfrac{c_w}{\mu-\lambda}\right]$ & $C_I\geq C_{I,0}^{2,3}(R) \equiv \dfrac{n_ec_w}{\mu}\left(1-\dfrac{c_w}{R\mu} \right)^{n_e}$ & $(0,\tilde{P}_J)$\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Jake Clarkson
Konstantin Avrachenkov
Eitan Altman

"Queues with inspection cost: To see or not to see?" by Jake Clarkson, Konstantin Avrachenkov, and Eitan Altman, arXiv:2503.13232v1, 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{Sources for definitions of network classes in Figure .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l||}
\hline
Network class & Source for definition \\ \hline
Time-consistent galled trees & , p.~3\\
Galled trees & , p.~16 \\
Galled tree-child networks & , Definition 5 \\
Normal networks & , p.~10\\
Tree-child networks & , p.~9\\
Galled networks & , p.~16 \\
Reticulation-visible networks & , p.~11\\
Phylogenetic networks & , p.~5 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Lily Agranat-Tamir
Michael Fuchs
Bernhard Gittenberger
Noah A. Rosenberg

"Enumerative combinatorics of unlabeled and labeled time-consistent galled trees" by Lily Agranat-Tamir, Michael Fuchs, Bernhard Gittenberger, and Noah A. Rosenberg, arXiv:2504.16302v1, 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
\textbf{ Lang/Model} &\textbf{MUSE} & \textbf{XLM} & \textbf{XLM-R} \\
CS & 87.0 & 86.6 & 94.4 \\
CS + EN & 88.1& 93.0 & 95.4 \\
\hline
EN & 39.2 & 54.8 & 66.6 \\
ES & 69.9 & 78.3 & 88.1\\
EN+ES & 88.2 & 87.8 & 91.2\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Full-training (top) and zero-shot (bottom) accuracy of XL models when using different monolingual corpora. ES is an internal dataset to showcase the effect of having a big Spanish corpus.}
\end{table}
\end{document}

cs

Arash Einolghozati
Abhinav Arora
Lorena Sainz-Maza Lecanda
Anuj Kumar
Sonal Gupta

"El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing" by Arash Einolghozati, Abhinav Arora, Lorena Sainz-Maza Lecanda, Anuj Kumar, and Sonal Gupta, arXiv:2101.10524v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

J. P. Brooks
D. J. Edwards
C. E. Larson
N. Van Cleemput

"Conjecturing-Based Discovery of Patterns in Data" by J. P. Brooks, D. J. Edwards, C. E. Larson, and N. Van Cleemput, arXiv:2011.11576v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison of Sphere-GAN: estimating saliency based on previous predictions and using previous ground-truth saliency at intervals of N Frames.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\# of Frames & CC ↑ & NSS ↑ & KL ↓ & AUC\_JUDD ↑ \\ \hline
1 & \textbf{0.9082} & \textbf{6.6322} & \textbf{0.3896} & \textbf{0.9717} \\ \hline
2 & 0.8869 & 6.4312 & 6.4312 & 0.9685 \\ \hline
3 & 0.8632 & 6.2071 & 0.5447 & 0.9647 \\ \hline
4 & 0.8377 & 5.9764 & 0.6317 & 0.9605 \\ \hline
5 & 0.8121 & 5.7479 & 0.7191 & 0.9563 \\ \hline
6 & 0.7879 & 5.5397 & 0.8038 & 0.9519 \\ \hline
7 & 0.7631 & 5.3249 & 0.8864 & 0.9477 \\ \hline
8 & 0.7371 & 5.1114 & 0.9741 & 0.9430 \\ \hline
9 & 0.7143 & 4.9208 & 1.0840 & 0.9373 \\ \hline
10 & 0.6914 & 4.7191 & 1.1862 & 0.9321 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mahmoud Z. A. Wahba
Sara Baldoni
Federica Battisti

"Sphere-GAN: a GAN-based Approach for Saliency Estimation in 360° Videos" by Mahmoud Z. A. Wahba, Sara Baldoni, and Federica Battisti, arXiv:2509.11948v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
{\bf Test} & {\bf Time Stamp} & {\bf Mean} & {\bf STD} & {\bf Max
Distance} & {\bf Bot Answer}\\
\hline\hline
Baseline $T=0$ & 6:13PM & 14.02 & 0 & 0 & 15 \\
\hline
Pronoun {\bf He} & 6:20PM & {\bf 14.64} & 2.03 & 5.47E-02 & {\bf {\color{blue} 15}} \\
\hline
Pronoun {\bf She} & 6:21PM & {\bf 13.74} & 1.47 & 7.37E-02 & {\bf {\color{red} 12}} \\
\hline
Pronoun {\bf She} & 6:42PM & 13.74 & 1.48 & 7.62E-02 & 12\\
\hline
Pronoun {\bf He} & 6:45PM & {\bf 13.39} & 1.43 & 5.46E-02 & {\bf {\color{blue} 12}} \\
\hline
\end{tabular}
\end{adjustbox}
\caption{ {\bf Effect of Sensitive Attributes: Gender.} I test GPT-3 by changing the original pronoun ``They'' in the prompt, to ``He'' and ``She.'' Gendered pronouns changed the response, dropping from a consistent answer of \$15 to \$12 when using the pronoun ``she.'' However, in a later identical test (see the time stamps), the response to the pronoun ``he'' returned by GPT-3 changed to match ``she'' response despite identical prompt sequences. This suggests that additional post-processing steps are implemented in the DaVinci interface beyond sampling from the softmax probabilities. }
\end{table}
\end{document}

q-fin

"The Minimum Wage as an Anchor: Effects on Determinations of Fairness by Humans and AI" by Dario G. Soatto, arXiv:2210.10585v3, 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{ Evaluation results for the two baselines (generic neural NER model and Microsoft Presidio) on the development and test sections of the TAB corpus. We report both the standard, token-level recall $R_{di+qi}$ and precision $P_{di+qi}$ on all identifiers (micro-averaged over all annotators) as well as the three proposed evaluation metrics $\textit{ER}_{di}$, $\textit{ER}_{qi}$ and $\textit{WP}_{di+qi}$ from Section .} %
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccccc}
	\toprule %
	 System & Set & $R_{di+qi}$ & $\textit{ER}_{di}$ & $\textit{ER}_{qi}$ & $P_{di+qi}$ & $\textit{WP}_{di+qi} $ \\
	 \midrule
Neural NER (RoBERTa &	Dev & 0.910 & 0.970 & 0.874 & 0.447 & 0.531 \\
fine-tuned on Ontonotes v5) & Test & 0.906 & 0.940 & 0.874 & 0.441 & 0.515\\
	\midrule
 Presidio (default) & Dev & 0.696 & 0.452 & 0.739 & 0.771 & 0.795 \\
 & Test & 0.707 & 0.460 & 0.758 & 0.761 & 0.790 \\
 \midrule
Presidio (+ORG) & Dev & 0.767 & 0.465 & 0.779 & 0.549 & 0.622 \\
 & Test & 0.782 & 0.463 & 0.802 & 0.542 & 0.609 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ildikó Pilán
Pierre Lison
Lilja Øvrelid
Anthi Papadopoulou
David Sánchez
Montserrat Batet

"The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization" by Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi Papadopoulou, David Sánchez, and Montserrat Batet, arXiv:2202.00443v2, 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{Intensities of limit order and cancellation arrivals for noise traders.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
					\toprule
					$k$ (ticks) & $\displaystyle \lambda^{L,k}$ & $10 \lambda^{C,k}$ \\
					\hline
					1 & 0.2842 & 0.8636 \\
					2 & 0.5255 & 0.4635 \\
					3 & 0.2971 &0.1487 \\
					4 & 0.2307 & 0.1096 \\
					5 & 0.0826 & 0.0402 \\
					6 & 0.0682 & 0.0341 \\
					7 & 0.0631 & 0.0311 \\
					8 & 0.0481 & 0.0237 \\
					9 & 0.0462 & 0.0233 \\
					10 & 0.0321 & 0.0178 \\
					11 & 0.0178 & 0.0127 \\
					12 & 0.0015 & 0.0012 \\
					13 & 0.0001 & 0.0001 \\
					14 & 0.0000 & 0.0000 \\
					\vdots & \vdots & \vdots \\
					30 & 0.0000 & 0.0000 \\
					\hline
				\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Reinforcement Learning for Trade Execution with Market Impact" by Patrick Cheridito and Moritz Weiss, arXiv:2507.06345v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\hline
 & \textbf{less\_5} & \textbf{5\_above} \\ \hline \hline %& \textbf{15\_above} \\ \hline \hline
\textbf{NPTEL-tr} & 23.0\% & 77.0\% \\%& 4.0\% \\ %\hline
\textbf{NPTEL-dt} & 25.0\% & 75.0\% \\%& 3.0\% \\ %hline
\textbf{NPTEL-et} & 24.0\% & 76.0\% \\%& 3.0\% \\ %hline
\textbf{NPTEL-Unseen\_1} & 21.0\% & 79.0\% \\%& 4.0\% \\ %\hline
\textbf{NPTEL-Unseen\_2} & 23.0\% & 77.0\% \\\hline %& 4.0\% \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Percentage wise data split based on utterance duration in NPTEL data set}
\end{table}
\end{document}

eess

Vishwas M. Shetty
Metilda Sagaya Mary N J
S. Umesh

"Investigation of Speaker-adaptation methods in Transformer based ASR" by Vishwas M. Shetty, Metilda Sagaya Mary N J, and S. Umesh, arXiv:2008.03247v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{siunitx}
\usepackage{amsmath}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Technical data and characteristics of the selected EMLAs for implementation in the manipulator's joints}
\begin{tabular}{c||c||c||c}
 \hline
 \hline
 \textbf{Term} & \textbf{Lift EMLA} & \textbf{\textcolor{black}{Tilt EMLA}} & \textbf{Tel. EMLA}\\
 \hline
 \hline
 \multicolumn{4}{c}{\textit{Linear Unit and Gearbox}}\\
 \hline
 \hline
 $F_{c0}$& \SI{145.7}{\kilo\newton} & \SI{120.6}{\kilo\newton}&\SI{6.4}{\kilo\newton}\\
 \hline
 $F_{c}$&\SI{89.8}{\kilo\newton}&\SI{68.6}{\kilo\newton}&\SI{6.1}{\kilo\newton}\\
 \hline
 $F_{p0}$&\SI{261.1}{\kilo\newton}&\SI{199.7}{\kilo\newton}&\SI{17.1}{\kilo\newton}\\
 \hline
 $F_{p}$&\SI{147.4}{\kilo\newton}&\SI{145.5}{\kilo\newton}&\SI{17.1}{\kilo\newton}\\
 \hline
 $a_{max}$&\SI{3.4}{\meter\per\second^2}&\SI{2}{\meter\per\second^2}&\SI{6}{\meter\per\second^2}\\
 \hline
 $v_{max}$&\SI{136}{\milli\meter\per\second}&\SI{100}{\milli\meter\per\second}&\SI{210}{\milli\meter\per\second}\\
 \hline
 $M_{BS}$&\SI{156.5}{\kilo\gram}&\SI{83.6}{\kilo\gram}&\SI{30.4}{\kilo\gram}\\
 \hline
 $l$&\SI{0.02}{\meter}&\SI{0.01}{\meter}&\SI{0.01}{\meter}\\
 \hline
 $d$&\SI{0.075}{\meter}&\SI{0.06}{\meter}&\SI{0.04}{\meter}\\
 \hline
 $1/{\rho}$&\SI{7}{}&\SI{5}{}&\SI{1}{}\\
 \hline
 \hline
 \multicolumn{4}{c}{\textit{Electric Motor (PMSM)}}\\
 \hline
 \hline
 $J_m$&\SI{0.016}{\kilogram\meter^2}&\SI{0.0105}{\kilogram\meter^2}&\SI{0.00085} {\kilogram\meter^2}\\
 \hline
 $n_N$&\SI{2850}{rpm}&\SI{3000}{rpm}&\SI{3000}{rpm}\\
 \hline
 $\tau_N$&\SI{53}{\newton\cdot\meter}&\SI{29}{\newton\cdot\meter}&\SI{8}{\newton\cdot\meter}\\
 \hline
 $\tau_{max}$&\SI{190}{\newton\cdot\meter}&\SI{129}{\newton\cdot\meter}&\SI{32}{\newton\cdot\meter}\\
 \hline
 $i_N$&\SI{29.5}{\ampere} & \SI{18.5}{\ampere} & \SI{7.6}{\ampere}\\
 \hline
 $u_N$&\SI{315}{\volt} & \SI{300}{\volt} & \SI{600}{\volt}\\
 \hline
 $P_N$&\SI{15.8}{\kilo\watt} & \SI{9.1}{\kilo\watt} & \SI{2.5}{\kilo\watt}\\
 \hline
 $R_s$&\SI{0.14}{\ohm}&\SI{0.16}{\ohm}&\SI{0.35}{\ohm}\\
 \hline
 $L_N$&\SI{2.4}{\milli\henry}&\SI{3.2}{\milli\henry}&\SI{12}{\milli\henry}\\
 \hline
 $\Phi_{P\!M}$&\SI{0.15}{\weber}&\SI{0.13}{\weber}&\SI{0.12}{\weber}\\
 \hline
 $N_p$&\SI{8}{}&\SI{8}{}&\SI{6}{}\\
 \hline
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Mehdi Heydari Shahna
Mohammad Bahari
Jouni Mattila

"Robustness-Guaranteed Observer-Based Control Strategy with Modularity for Cleantech EMLA-Driven Heavy-Duty Robotic Manipulator" by Mehdi Heydari Shahna, Mohammad Bahari, and Jouni Mattila, arXiv:2311.15843v4, 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{MOSFET device parameters and traditional scaling factors}
\begin{tabular}{l|l}
					\hline
					 \textbf{Parameter} & \textbf{Scaling factor}\\
\hline
Device dimension ~{\ensuremath{(W, L, tox)}} & ~{\ensuremath{1{/}K}}\\
\hline
Doping concentration ~{\ensuremath{Na}} & ~{\ensuremath{K}}\\
\hline
Voltage ~{\ensuremath{V}} & ~{\ensuremath{1{/}K}}\\
\hline
Current ~{\ensuremath{I}} & ~{\ensuremath{1{/}K}}\\
\hline
Capacitance~{\ensuremath{\epsilon{A{/}t}}} & ~{\ensuremath{1{/}K}}\\
\hline
Delay time {\ensuremath{VC{/}I}} & ~{\ensuremath{1{/}K}}\\
\hline
Power dissipation ~{\ensuremath{VI}} & ~{\ensuremath{1{/}{K^2}}}\\
\hline
Power density ~{\ensuremath{VI{/}A}} & ~{\ensuremath{1}}\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"DeepScaleTool : A Tool for the Accurate Estimation of Technology Scaling in the Deep-Submicron Era" by Satyabrata Sarangi and Bevan Baas, arXiv:2102.10195v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\emph{Supplemental results on GQA-SGL.} Mean and standard deviation of accuracy (\%) for four question types in GQA-SGL across at least three repetitions. We use a pre-trained object detector as a feature extractor.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|c}
\toprule
\multirow{2}{*}{Methods} & \multicolumn{4}{c}{Four question types in GQA-SGL} \\ \cmidrule{2-5}
 & verify & query & choose & logical \\ \midrule %\hline
Transformer & 50.0 $\pm$ 1.6 & 35.3 $\pm$ 5.0 & 50.0 $\pm$ 7.5 & 55.3 $\pm$ 4.1 \\ %\hline
Transformer w/ PR & 51.6 $\pm$ 7.7 & 45.6 $\pm$ 5.6 & \textbf{56.0} $\pm$ 6.6 & 55.6 $\pm$ 5.4\\ %\hline
TMN-Stack (ours) & 51.6 $\pm$ 4.1 & \textbf{49.6} $\pm$ 5.0 & 44.8 $\pm$ 3.7 & 56.4 $\pm$ 7.1 \\ %\hline
TMN-Tree (ours) & \textbf{56.0} $\pm$ 7.3 & 48.0 $\pm$ 5.5 & 47.2 $\pm$ 3.2 & \textbf{64.0} $\pm$ 4.6 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Retrieval performances of our model on the MUSDB18 dataset. All metrics are in percentages.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
 \toprule
 & \multicolumn{3}{c}{Recall $\uparrow$} & & \multicolumn{2}{c}{Normalized Rank $\downarrow$} \\
 Model &
 R@1 & R@5 & R@10 & & mean & median \\
 \midrule
 CLAP conditioning (6a7099c4) &
 33.5 & 63.0 & 74.5 & & 2.5 & 0.5 \\
 +contrastive pretraining (6484864f) &
 32.2 & \textbf{91.0} & \textbf{96.2} & & \textbf{0.6} & \textbf{0.3} \\
 +FiLM conditioning (dfe9828d) &
 \textbf{38.8} & 89.7 & 95.0 & & 0.7 & \textbf{0.3} \\
 \midrule
 Contrastive (3ff57432) &
 2.2 & 72.5 & 86.0 & & 1.5 & 0.7 \\
 Stem-JEPA &
 33.0 & 63.2 & 76.2 & & 2.0 & 0.5 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Alain Riou
Antonin Gagneré
Gaëtan Hadjeres
Stefan Lattner
Geoffroy Peeters

"Zero-shot Musical Stem Retrieval with Joint-Embedding Predictive Architectures" by Alain Riou, Antonin Gagneré, Gaëtan Hadjeres, Stefan Lattner, and Geoffroy Peeters, arXiv:2411.19806v2, 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}{rr}
 \hline
 & value \\
 \hline
Accuracy & 0.75 \\
 Kappa & 0.45 \\
 Sensitivity (No burn accuracy) & 0.74 \\
 Specificity (Burn accuracy) & 0.76 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Kendra Walker
Ben Moscona
Kelsey Jack
Seema Jayachandran
Namrata Kala
Rohini Pande
Jiani Xue
Marshall Burke

"Detecting Crop Burning in India using Satellite Data" by Kendra Walker, Ben Moscona, Kelsey Jack, Seema Jayachandran, Namrata Kala, Rohini Pande, Jiani Xue, and Marshall Burke, arXiv:2209.10148v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Medical Bill Shock and Imperfect Moral Hazard" by Alex Hoagland, David M. Anderson, and Ed Zhu, arXiv:2211.01116v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Empirical Asset Pricing via Ensemble Gaussian Process Regression" by Damir Filipović and Puneet Pasricha, arXiv:2212.01048v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
\textbf{Method} & \textbf{Scale factor} & \textbf{Acc (\%)} & \textbf{Rel. Cont. (\%)} \\\midrule
\multirow{3}{*}{Dense} & 1$\times$ & 66.24 & - \\
 & 2$\times$ & 72.67 & - \\
 & 4$\times$ & 76.6 & - \\\midrule
\multirow{2}{*}{$f$-RepeatNet} & 2$\times$ & 69.96 & 57.85 \\
 & 4$\times$ & 72.41 & 59.56 \\\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Performance scores obtained on the task of classification of Imagenet samples obtained using ResNet-18, its RepeatNet variants as well as the corresponding dense scaled-up models. Here, `Rel. Cont.' denotes the relative contribution of RepeatNet and can be interpreted as the gain obtained by using RepeatNet normalized by the total gain observed by the corresponding dense architecture.}
\end{table}
\end{document}

cs

Arnav Chavan
Udbhav Bamba
Rishabh Tiwari
Deepak Gupta

"Rescaling CNN through Learnable Repetition of Network Parameters" by Arnav Chavan, Udbhav Bamba, Rishabh Tiwari, and Deepak Gupta, arXiv:2101.05650v2, 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
 Index & Species & Name & $\phi_i^0$ & Sc \\
 \hline\hline
 1 & IXa & Enzym IXa & 0.09 & 15949 \\
 2 & IX & Zymogen IX & 90 & 17762 \\
 3 & VIIIa & Enzym VIIIa & 0.0007 & 25510 \\
 4 & VIII & Zymogen VIII & 0.7 & 32051 \\
 5 & Va & Enzym Va &0.02 & 26178 \\
 6 & V & Zymogen V & 20 & 17762 \\
 7 & Xa & Enzym Xa & 0.17 & 13568 \\
 8 & X & Zymogen X & 170 & 17762 \\
 9 & IIa & Thrombin & 1.4 & 15456 \\
 10 & II & Prothrombin & 1400 & 19194 \\
 11 & Ia & Fibrin & 7.0 & 40486 \\
 12 & I & Fibrinogen & 7000 & 32256 \\
 13 & XIa & Enzym XIa & 0.03 & 20000 \\
 14 & XI & Zymogen XI & 30 & 25189 \\
 15 & ATIII & AntiThrombin-III & 2410 & 17953 \\
 16 & TFPI & Tissue factor pasthway inhibitor & 2.5 & 15873 \\
 17 & APC & Active Protein C & 0.06 & 18182 \\
 18 & PC & Protein C & 60 & 18382 \\
 19 & $\alpha_1$ AT& $\alpha_1$-AntiTrypsin & 45000 & 17182 \\
 20 & tPA & Tissue plasminogen activator & 0.08 & 18939 \\
 21 & PLA & Plasmin & 2.18 & 20284 \\
 22 & PLS & Plasminogen & 2180 & 20790 \\
 23 & $\alpha_2$AP & $\alpha_2$-AntiPlasmin & 105 & 19048 \\
 24 & Z & Tenase & 1.125$\times10^{-4}$ & ... \\
 25 & W & Prothrombinase & 0.034 & ... \\

 \hline
\end{tabular}
\end{adjustbox}
\caption{Name of species used in this problem with their initial concentrations and Schmidt numbers.}
\end{table}
\end{document}

cs

Donya Ramezanian
Arash G. Nouri
Hessam Babaee

"On-the-fly Reduced Order Modeling of Passive and Reactive Species via Time-Dependent Manifolds" by Donya Ramezanian, Arash G. Nouri, and Hessam Babaee, arXiv:2101.03847v1, 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{Ablation of guidance scale (MAISI Rflow on MS).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
 \hline
 \textbf{Guidance} & \textbf{Text Alignment (\%) $\uparrow$} & \textbf{Subject MS-SSIM $\uparrow$} & \textbf{PSNR (dB) $\uparrow$} \\
 \hline
 No CFG & 89.17 & 0.964 & 27.92 \\
 \hline
 0.5 & 70.08 & 0.985 & 32.30 \\
 1 & 85.96 & 0.964 & 27.89 \\
 2 & 99.39 & 0.926 & 24.39 \\
 3 & 100 & 0.898 & 22.75 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mohamed Mohamed
Brennan Nichyporuk
Douglas L. Arnold
Tal Arbel

"Imagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidance" by Mohamed Mohamed, Brennan Nichyporuk, Douglas L. Arnold, and Tal Arbel, arXiv:2509.05978v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sample table title}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 \multicolumn{2}{c}{Part} \\
 \cmidrule(r){1-2}
 Name & Description & Size ($\mu$m) \\
 \midrule
 Dendrite & Input terminal & $\sim$100 \\
 Axon & Output terminal & $\sim$10 \\
 Soma & Cell body & up to $10^6$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Sami A. Malek
Alexandre M. Bayen
Steven D. Glaser

"Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter" by Sami A. Malek, Alexandre M. Bayen, and Steven D. Glaser, arXiv:1912.03861v1, 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{Top 20 Sponsors}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lclc}
\toprule
\multicolumn{2}{c}{Initialized} &\multicolumn{2}{c}{Completed} \\
\cmidrule(lr){1-2} \cmidrule(lr){3-4}
 Sponsor & \#SPAC & Sponsor & \#SPAC \\
\midrule
 The Gores Group & 13 & The Gores Group & 9 \\
 Cohen \& Company & 11 & Chardan & 7 \\
 Social Capital & 10 & Social Capital & 6 \\
 Hennessy Capital Group & 8 & Cohen \& Company & 6 \\
 Cantor Fitzgerald & 8 & TPG & 5 \\
 TPG & 8 & Cantor Fitzgerald & 5 \\
 Michael Klein & 8 & Riverstone Investment Group & 5 \\
 Jonathan J. Ledecky & 7 & Hennessy Capital Group & 5 \\
 Chardan & 7 & Harry L. You & 5 \\
 Hedosophia & 6 & Eagle Equity Partners & 5 \\
 Harry L. You & 6 & Michael Klein & 4 \\
Riverstone Investment Group & 6 & Jonathan S. Huberman & 4 \\
 Fortress Investment Group & 6 & Hedosophia & 4 \\
 GigCapital Global & 6 & GigCapital Global & 4 \\
 Apollo Global Management & 6 & Chinh Chu & 4 \\
 Bill Foley & 6 & Niccolo de Masi & 4 \\
 Chinh Chu & 5 & Bill Foley & 4 \\
 Jaws Estates Capital & 5 & Apollo Global Management & 4 \\
 Craig-Hallum Capital Group & 5 & Union Acquisition Group & 3 \\
 Niccolo de Masi & 5 & Casdin Capital & 3 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Gaurab Aryal
Zhaohui Chen
Yuchi Yao
Chris Yung

"Security Issuance, Institutional Investors and Quid Pro Quo" by Gaurab Aryal, Zhaohui Chen, Yuchi Yao, and Chris Yung, arXiv:2211.16643v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
 \hline
 Linguistic term & D-IFV \\ \hline
 Extremely High (EH) & $\langle ( 0.9,0.1);0.9\rangle$ \\
 Very High (VH) & $\langle ( 0.8,0.2);0.8\rangle$ \\
 High (H) & $\langle ( 0.7,0.3);0.7\rangle$ \\
 Moderately High (MH) & $\langle ( 0.6,0.4),0.6\rangle$ \\
 Medium (M) & $\langle ( 0.5,0.5);0.5\rangle$ \\
 Moderately Low (ML) & $\langle ( 0.4,0.6);0.4\rangle$ \\
 Low (L) & $\langle ( 0.3,0.7);0.3\rangle$ \\
 Very Low (VL) & $\langle ( 0.2,0.8);0.2\rangle$ \\
 Extremely Low (EL) & $\langle ( 0.1,0.9);0.1\rangle$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Linguistic terms corresponding D-IFV}
\end{table}
\end{document}

math

"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Speech naturalness and Speaker similarity}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \toprule[1.5pt]
 Method & Speech naturalness$\uparrow$ & Speaker similarity$\uparrow$ \\
 \midrule
 ground truth & 4.62$\pm$0.14 & 4.75$\pm$0.16 \\
 ppg-fast & 3.46$\pm$0.19 & 3.69$\pm$0.20 \\
 ppg-taco & 3.87$\pm$0.21 & 3.91$\pm$0.17 \\
 proposed & \textbf{4.01$\pm$0.20} & \textbf{3.98$\pm$0.19} \\
 \bottomrule[1.5pt]
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ziyi Chen
Hua Hua
Yuxiang Zhang
Ming Li
Pengyuan Zhang

"The HCCL-DKU system for fake audio generation task of the 2022 ICASSP ADD Challenge" by Ziyi Chen, Hua Hua, Yuxiang Zhang, Ming Li, and Pengyuan Zhang, arXiv:2201.12567v1, 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{Variable parameter values.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
		\hline
		\textbf{Publisher frequency}			& 1, 10, \dots, 90, 100 \\ \hline
		\textbf{Payload} & 128\,B, 1\,KB, 10\,KB, 100\,KB, 500\,KB \\ \hline
		\textbf{Number of Nodes} & 3, 5, \dots, 21, 23 \\ \hline
		\textbf{DDS Backend} & Connext, FastRTPS, CycloneDDS \\ \hline
		\textbf{Reliability} & reliable, best effort \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Tobias Kronauer
Joshwa Pohlmann
Maximilian Matthe
Till Smejkal
Gerhard Fettweis

"Latency Analysis of ROS2 Multi-Node Systems" by Tobias Kronauer, Joshwa Pohlmann, Maximilian Matthe, Till Smejkal, and Gerhard Fettweis, arXiv:2101.02074v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
Method & Coverage probability & Mean length \\
\hline
Bayes with Jeffreys prior & 0.861 & 2.31 \\
Bayes with right Haar prior & 0.929 & 3.26 \\
{\tt t.test} with Welch df & 0.881 & 2.79 \\
Marginal IM's inner approximation & 0.904 & 2.71 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Coverage probabilities and mean lengths for the 90\% confidence intervals returned by the various methods. Standard errors for all of the coverage probabilities and mean lengths are roughly 0.003 and 0.01, respectively.}
\end{table}
\end{document}

math

"No-prior Bayesian inference reIMagined: probabilistic approximations of inferential models" by Ryan Martin, arXiv:2503.19748v2, 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{Voltages for points $A$ and $B$ for the 3-bus test system in~.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
 & Point A & Point B\tabularnewline
\hline
\hline
1 & 0.8962 - 0.0919i & 0.8846 - 0.1660i\tabularnewline
\hline
2 & 0.9040 + 0.0000i & 0.9996 + 0.0000i\tabularnewline
\hline
3 & 1.0282 + 0.0888i & 0.9278 - 0.0740i\tabularnewline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mohammad Rasoul Narimani
Katherine R. Davis
Daniel K. Molzahn

"Certifying the Nonexistence of Feasible Path Between Power System Operating Points" by Mohammad Rasoul Narimani, Katherine R. Davis, and Daniel K. Molzahn, arXiv:2509.05935v1, 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{Coefficients for the impact of average variability of ancestral climatic conditions on individual's self-reported attention to the environment }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \hline
 \multicolumn{5}{c}{Dependent variable: Self reported measure of attention to environment}\\
 \hline
 \hline
 Avg. variability & -9.877***& -17.402** & -9.055***& -9.677***\\
 & (2.003) & (7.035) & (2.108) & (2.135) \\
 Avg. variability sq.& 111.767***& 276.862* & 105.537***& 109.681***\\
 & (21.346) & (145.560) & (21.700) & (21.829) \\
 Avg. variability cub.& & -1130.897 & & \\
 & & (951.849) & & \\
 Variability across generations& & & -0.166 & \\
 & & & (0.123) & \\
 Occupation category Controls & Y & Y & Y & Y \\
 Historical ethnic group characteristics & Y & Y & Y & Y \\
 Historical topographic characteristics & Y & Y & Y & Y \\
 Moments of full temp. distribution & N & N & N & Y \\
 Country-year Fixed effects & Y & Y & Y & Y \\
 \hline
 Mean of dep var & 0.704 & 0.704 & 0.704 & 0.704 \\
 St. Dev. of dep var & 0.250 & 0.250 & 0.250 & 0.250 \\
 Min value Avg. Variability& 0.015 & 0.015 & 0.015 & 0.015 \\
 Max value Avg. Variability& 0.093 & 0.093 & 0.093 & 0.093 \\
 R-sq & 0.113 & 0.114 & 0.114 & 0.114 \\
 Adj. R-sq & 0.113 & 0.113 & 0.113 & 0.113 \\
 N & 138067 & 138067 & 138067 & 138067 \\
 \hline
 \multicolumn{5}{c}{ {*}{*}{*} p$<$0.01, {*}{*}p$<$0.05, {*} p$<$0.1, {+} p$<$0.15}\\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Ancestral origins of attention to environmental issues" by César Barilla and Palaash Bhargava, arXiv:2509.09598v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A symbolic computational approach to the generalized gambler's ruin problem in one and two dimensions" by Lucy Martinez, arXiv:2412.07667v1, 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{Parameters : Scenario 1 }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
				\toprule
				Parameter & Value\\
				\midrule
				$\Delta T$ & 2 seconds.\\
				$\sigma^1_r$, $\sigma^1_\theta$, $\sigma^1_\phi$ & $100$ m, $2^\circ$, $2^\circ$ \\
				$\sigma^2_r$, $\sigma^2_\theta$, $\sigma^2_\phi$ & $100$ m, $2^\circ$, $2^\circ$ \\
				$\sigma^3_r$, $\sigma^3_\theta$, $\sigma^3_\phi$ & $100$ m, $1.5^\circ$, $1.5^\circ$ \\
				$\tilde{q}_x$ & 0.5 $\text{m}^2/\text{sec}^3$\\
				$\tilde{q}_y$ & 0.5 $\text{m}^2/\text{sec}^3$\\
				$\tilde{q}_z$ & 0.001 $\text{m}^2/\text{sec}^3$\\
				Simulation time & 120 seconds.\\
				\bottomrule
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Nikhil Sharma
Shovan Bhaumik
Ratnasingham Tharmarasa
Thiagalingam Kirubarajan

"On Pooling-Based Track Fusion Strategies : Harmonic Mean Density" by Nikhil Sharma, Shovan Bhaumik, Ratnasingham Tharmarasa, and Thiagalingam Kirubarajan, arXiv:2412.06716v1, 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{Transistor sizing for the proposed PFD circuit}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 \textbf{Transistors} & \textbf{W/L Ratio} \\
 \hline
 P1, P6 & 4 \\ \hline
 P2, P7 & 4 \\ \hline
 P3, P8 & 2 \\ \hline
 P4, P9 & 4 \\ \hline
 P5, P10 & 4 \\ \hline
 N1, N6 & 2 \\ \hline
 N2, N7 & 4 \\ \hline
 N3, N8 & 4 \\ \hline
 N4, N9 & 2 \\ \hline
 N5, N10 & 2 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Dhandeep Challagundla
Venkata Krishna Vamsi Sundarapu
Ignatius Bezzam
Riadul Islam

"TSPC-PFD: TSPC-Based Low-Power High-Resolution CMOS Phase Frequency Detector" by Dhandeep Challagundla, Venkata Krishna Vamsi Sundarapu, Ignatius Bezzam, and Riadul Islam, arXiv:2508.16933v1, 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{Performance Objectives}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllccc}
 \hline
 \textbf{Requirement} & \textbf{Achieved Value} & \\
 \hline
 $|e_z|_{\max,r}$ & 0.5 & [g] \\
Overshoot & 20 & [\%] \\
$|\eta|_{\max}$ & 15 & [º] \\
$\eta_{noise,r}$ & 1 & [rad] \\
$\eta_{noise,t}$ & 0.2 & [rad] \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Reinforcement Learning for Robust Missile Autopilot Design" by Bernardo Cortez, arXiv:2011.12956v2, 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{Number of plans and planning time with the learned axioms expressed as a fraction of the values without the learned axioms. Reasoning with the learned axioms improves performance.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 \textbf{Measures} & \textbf{Ratio (with/without)}\\
 \hline
 Number of plans & 0.33\\
 \hline
 Planning time & 0.89\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Combining Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning in Robotics" by Mohan Sridharan and Tiago Mota, arXiv:2201.10266v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|}
 \hline
 \bfseries Classifier & \bfseries Accuracy & \bfseries Precision &	\bfseries Recall & \bfseries F-Measure & \bfseries AUC \\ \hline
 Random Forrest & 0.877 & 0.879 & 0.877 & 0.877 & 0.942 \\ \hline
 Multilayer Perceptron&	0.830&	0.830&	0.830&	0.830&	0.895 \\ \hline
 Decision Tree&	0.825&	0.827&	0.826&	0.826&	0.870 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{\fontsize{10pt}{11pt}\selectfont{\itshape{The classification results of the prediction model}}}
\end{table}
\end{document}

cs

"Investigating Software Testability and Test cases Effectiveness" by Mamdouh Alenezi, arXiv:2201.10090v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Pariyakorn Maneekul
Zelda B. Zabinsky
Giulia Pedrielli

"Branching Adaptive Surrogate Search Optimization (BASSO)" by Pariyakorn Maneekul, Zelda B. Zabinsky, and Giulia Pedrielli, arXiv:2504.18002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Quasi-rectifiable Lie algebras for partial differential equations" by A. M. Grundland and J. de Lucas, arXiv:2312.05238v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
\toprule
\textbf{Parameter} & \textbf{Value} \\
\hline
Clipping Range & 0.08 \\
Discount Factor & 0.993 \\
Entropy Coefficient & 0.003 \\
GAE & 0.95 \\
Gradient Norm Clipping & 0.64 \\
Learning Rate & .000343 \\
Number of Actors & 16\\
Training Epochs per Update & 2 \\
Value Function Coefficient & 0.5 \\
\bottomrule
\end{tabular}
\caption{Hyperparameters used for each global step of the Agent training. We reused these for selfplay too.}
\end{table}
\end{document}

cs

"Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning" by Paul Garnier and Théophane Gregoir, arXiv:2101.05388v1, 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|cc|} \hline
\multicolumn{3}{|c|}{Poverty data ($n=89,755$)} \\ \hline
 & Main effects ($p=60$) & Interactions ($p=1,469$)\\
gZellner ALA & 19.5 sec. & 5.2 min. \\
gZellner LA & 10.1 days & 17.3 days \\
\hline
\multicolumn{3}{|c|}{Simulation under true AFT model ($J=100,p=300$)} \\ \hline
 & $n=100$ & $n=500$\\
gMOM ALA & 4.0 sec. & 7.5 sec. \\
gZellner ALA & 9.6 sec. & 13.2 sec. \\
gZellner LA & 188.9 sec.& 251.1 sec.\\
\hline
\multicolumn{3}{|c|}{Simulation under true PH model ($J=100,p=300$)} \\ \hline
 & $n=100$ & $n=500$\\
gMOM ALA & 2.0 sec. & 4.1 sec.\\
gZellner ALA & 2.3 sec. & 10.7 sec.\\
gZellner LA & 152.4 sec.& 81.1 sec.\\
\hline
\multicolumn{3}{|c|}{Colon cancer data ($n=260$, $p=175$)} \\ \hline
 & Uniform $p(\gamma)$ & BetaBin $p(\gamma)$ \\
gMOM ALA & 3.3min & 39.3 sec.\\
gZellner ALA & 11.1min & 48.8 sec. \\
gZellner LA & 55.0h & 15.1min \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

David Rossell
Oriol Abril
Anirban Bhattacharya

"Approximate Laplace approximations for scalable model selection" by David Rossell, Oriol Abril, and Anirban Bhattacharya, arXiv:2012.07429v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Aida Abiad
Ángeles Carmona
Andrés M. Encinas
Maria José Jiménez
Álvaro Samperio

"A Spectral Approach to Kemeny's Constant" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
\hline
Label & Sample Size \tabularnewline
\hline
A10 & 44 \tabularnewline
\hline
A11 & 92 \tabularnewline
\hline
A12 & 75 \tabularnewline
\hline
A20 & 135 \tabularnewline
\hline
A21 & 54 \tabularnewline
\hline
A30 & 127 \tabularnewline
\hline
A31 & 115 \tabularnewline
\hline
A40 & 18 \tabularnewline
\hline
A41 & 105 \tabularnewline
\hline
A50 & 89 \tabularnewline
\hline
A51 & 78 \tabularnewline
\hline
A60 & 72 \tabularnewline
\hline
A61 & 75 \tabularnewline
\hline
A70 & 96 \tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

cs

Nurettin Sergin
Jiayu Huang
Tzyy-Shuh Chang
Hao Yan

"Image-based Novel Fault Detection with Deep Learning Classifiers using Hierarchical Labels" by Nurettin Sergin, Jiayu Huang, Tzyy-Shuh Chang, and Hao Yan, arXiv:2403.17891v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
 $h$ & 1/512 & 1/1024 & 1/2048 & 1/4096 & 1/8192 & 1/16384\\
 \hline
 $\varepsilon_h$ & 8.31E-08 & 2.09E-08 & 5.23E-09 & 1.31E-09 & 3.27E-10 & 8.18E-11\\
 \hline
 $r$ & 1.99 & 1.99 & 1.93 & 2.07 & 2.00 & 2.00\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{the magnitude of the error and order of convergence.}
\end{table}
\end{document}

math

Vladislav Byankin
Aleksandr Tynda
Denis Sidorov
Aliona Dreglea

"Numerical solution of locally loaded Volterra integral equations" by Vladislav Byankin, Aleksandr Tynda, Denis Sidorov, and Aliona Dreglea, arXiv:2503.21452v1, 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{Notations and definitions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
		\hline
		$X$ & Training sample set\\
		$Y$ & Set of training samples in a low-dimensional space\\
		$n$ & Number of training samples\\
		$D$ & Dimensionality of the samples in the original space\\
		$d$ & Dimensionality of embedding features\\
		$c_i$ & Labels of samples $x_i$\\
		$C$ & Number of classes\\
		$P$ & Projection matrix\\
		$CLG$& Contrastive learning graph\\
		$G^{pos}$ & Positive graph\\
		$G^{neg}$ & Negative graph\\
		$S^{pos}$ & Similarity matrix for positive pairs\\
		$S^{neg}$ & Dissimilarity matrix of negative pairs\\
		$\sigma$ & Positive parameter\\
		$k$ & Number of neighbors\\
		$NK(x_j)$ &The $k$ nearest neighbors of $x_j$\\
		$NK^+_{(x_j)}$ &The $k$ nearest neighbors of $x_j$ in the $c_j$ th class\\
		$\nabla L(P)$ & Gradient of $L(P)$ with respect to $P$\\
		$T$ & Number of iterations\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Unified Framework for Feature Extraction based on Contrastive Learning" by Hongjie Zhang, arXiv:2101.11703v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of results for the Bayes classifier applied to alternative datasets. Columns for Decision and Boundary are percentages.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|r|rrr|rr}
 \hline
 & & \multicolumn{3}{c|}{Decision} & \multicolumn{2}{c}{Boundary} \\
Dataset & Count & Adeno & COVID & SARS & No & Yes \\
 \hline
 ReadsOriginal & 5869 & 32.94 & 34.09 & 32.97 & 69.48 & 30.52 \\
 ReadsNew & 6000 & 32.43 & 33.82 & 33.75 & 68.7 & 31.3 \\
 ReadsMixed2 & 5969 & 33.86 & 33.59 & 32.55 & 69.69 & 30.31 \\
 ReadsMixed1 & 7869 & 48.1 & 26.2 & 25.7 & 74.1 & 25.9 \\
 ReadsEcoli & 6000 & 92.767 & 2.933 & 4.300 & 87.35 & 12.65 \\
 Random6K & 6000 & 90.8333 & 0.3833 & 8.7833 & 78.75 & 21.25 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Structure of Classifier Boundaries: Case Study for a Naive Bayes Classifier" by Alan F. Karr, Zac Bowen, and Adam A. Porter, arXiv:2212.04382v2, 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}{lcccc}
 \toprule
 Method & LSE-D $\downarrow$ & PSNR $\uparrow$ & SSIM $\uparrow$ & LPIPS $\downarrow$ \\
 \toprule
 w/o alignment & 12.66 & 21.02 & 0.9104 & 0.1855 \\
 w/o audio finetune & 11.78 & 22.73 & 0.9355 & 0.1198 \\
 w/o wrinkle features & 11.28 & 23.14 & 0.9311 & 0.1162 \\
 w/o color MLP \& latent finetune & 11.32 & 23.96 & 0.9367 & 0.1133 \\
 \bottomrule
 Ours (Full) & \textbf{11.15} & \textbf{24.97} & \textbf{0.9470} & \textbf{0.1101} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Shivangi Aneja
Artem Sevastopolsky
Tobias Kirschstein
Justus Thies
Angela Dai
Matthias Nießner

"GaussianSpeech: Audio-Driven Gaussian Avatars" by Shivangi Aneja, Artem Sevastopolsky, Tobias Kirschstein, Justus Thies, Angela Dai, and Matthias Nießner, arXiv:2411.18675v1, 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{Previously proposed lung segmentation methods and respective dice similarity coefficient}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\hline
Authors, citation & Dice similarity coef.\\
\hline
Li et al., & 0.964 \\
Candemir et al., & 0.967 \\
Shao et al., & 0.972 \\
Novikov et al., & 0.974 \\
Yang et al., & 0.975 \\
Hwang et al., & 0.980 \\
Ours & \bfseries{0.988} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Douglas P. S. Gomes
Anwaar Ulhaq
Manoranjan Paul
Michael J. Horry
Subrata Chakraborty
Manas Saha
Tanmoy Debnath
D. M. Motiur Rahaman

"Potential Features of ICU Admission in X-ray Images of COVID-19 Patients" by Douglas P. S. Gomes, Anwaar Ulhaq, Manoranjan Paul, Michael J. Horry, Subrata Chakraborty, Manas Saha, Tanmoy Debnath, and D. M. Motiur Rahaman, arXiv:2009.12597v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"To Buy an Electric Vehicle or Not? A Bayesian Analysis of Consumer Intent in the United States" by Nafisa Lohawala and Mohammad Arshad Rahman, arXiv:2504.09854v1, 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}{llll}
\hline
 Description & Independent variable \\
\hline
 Fulfillment method category & \texttt{fm\_categories} \\
 Give away method category & \texttt{giveaways\_categories} \\
 Campaign flight total days & \texttt{cf\_total\_days} \\
 Allocated deliveries total & \texttt{ad\_total} \\
 Price type category & \texttt{pt\_categories} \\
 Social channel category & \texttt{sch\_categories} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Moisés Ramírez
Raziel Ruíz
Nathan Klarer

"The Effects of Just-in-time Delivery on Social Engagement: A Cluster Analysis" by Moisés Ramírez, Raziel Ruíz, and Nathan Klarer, arXiv:2212.12285v1, 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{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
 \hline
		\hline
		\multicolumn{3}{c}{Computational Approach} \\
		\hline
 Robust Gradient Descent & Algorithm~ & {\color{blue} slow} as parameter dimension goes large \\
 \hline
 \multirow{2}{*}{Empirical Risk-based Methods} & Algorithm~ & parameter dimension-free\\ \cline{2-3}
 & Algorithm~ & easy-implementable with DL framework \\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Advantages and disadvantages of three algorithms discussed in the appendix.}
\end{table}
\end{document}

stat

Guanhua Fang
Ping Li
Gennady Samorodnitsky

"Empirical Risk Minimization for Losses without Variance" by Guanhua Fang, Ping Li, and Gennady Samorodnitsky, arXiv:2309.03818v1, 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{Average accuracy on the CIFAR-10/100 and ImageNet-100 with both novel class ratio and label ratio of 50\%. We compare OwMatch with existing literature on OwSSL. Also compared with other related approaches of traditional SSL, OSSL, and NCD approaches following . Proper modifications are made to make these approaches compatible with OwSSL; the details are in Appendix~. The results are averaged over three independent runs. The baseline figures are sourced from the respective papers.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccccc}
 \toprule
 \textbf{Method} & \multicolumn{3}{c}{\textbf{CIFAR-10}} & \multicolumn{3}{c}{\textbf{CIFAR-100}} & \multicolumn{3}{c}{\textbf{ImageNet-100}}\\
 \cmidrule(lr){2-4} \cmidrule(l){5-7} \cmidrule(lr){8-10}
 & Seen & Novel & All & Seen & Novel & All & Seen & Novel & All \\
 \midrule
 FixMatch & 71.5 & 50.4 & 49.5 & 39.6 & 23.5 & 20.3 & 65.8 & 36.7 & 34.9 \\
 DS\textsuperscript{3}L & 77.6 & 45.3 & 40.2 & 55.1 & 23.7 & 24.0 & 71.2 & 32.5 & 30.8 \\
 CGDL & 72.3 & 44.6 & 39.7 & 49.3 & 22.5 & 23.5 & 67.3 & 33.8 & 31.9 \\
 DTC & 53.9 & 39.5 & 38.3 & 31.3 & 22.9 & 18.3 & 25.6 & 20.8 & 21.3 \\
 RankStats & 86.6 & 81.0 & 82.9 & 36.4 & 28.4 & 23.1 & 47.3 & 28.7 & 40.3 \\
 SimCLR & 58.3 & 63.4 & 51.7 & 28.6 & 21.1 & 22.3 & 39.5 & 35.7 & 36.9 \\
 UNO & 91.6 & 69.3 & 80.5 & 68.3 & 36.5 & 51.5 & - & - & - \\
 ORCA & 88.2 & 90.4 & 89.7 & 66.9 & 43.0 & 48.1 & 89.1 & 72.1 & 77.8 \\
 NACH & 89.5 & 92.2 & 91.3 & 68.7 & 47.0 & 52.1 & 91.0 & 75.5 & 79.6 \\
 OpenLDN & 95.7 & 95.1 & 95.4 & 73.5 & 46.8 & 60.1 & 89.6 & 68.6 & 79.1 \\
 TRSSL & \textbf{96.8} & 92.8 & 94.8 & 80.0 & 49.3 & 64.7 & - & - & - \\
 OpenCon & 89.3 & 91.1 & 90.4 & 69.1 & 47.8 & 52.7 & 90.6 & 80.8 & 83.8 \\
 \midrule
 OwMatch & 93.0 & 95.9 & 94.4 & 74.5 & 55.9 & 65.1 & \textbf{91.7} & 72.0 & 81.8 \\
 OwMatch+ & 96.5 & \textbf{97.1} & \textbf{96.8} & \textbf{80.1} & \textbf{63.9} & \textbf{71.9} & 91.5 & 79.6 & \textbf{85.5} \\
 \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.


math

Margarita A. Guerrero
Braghadeesh Lakshminarayanan
Cristian R. Rojas

"Data-Driven Estimation of Structured Singular Values" by Margarita A. Guerrero, Braghadeesh Lakshminarayanan, and Cristian R. Rojas, arXiv:2503.13410v1, 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.


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{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of users correctly re-identified after 1, 2, and 3 observations for several simulation runs.}
\begin{tabular}{cccc}
 \toprule
 Simulation& 1 observation & 2 observations & 3 observations \\
 \midrule
 Run 1 &29 users &28 users &42 users \\
 Run 2 &28 users &37 users &49 users \\
 Run 3 &29 users &47 users &63 users \\
 Run 4 &24 users &33 users &46 users \\
 Run 5 &30 users &39 users &52 users \\
 Run 6 &24 users &33 users &37 users \\
 Run 7 &31 users &38 users &49 users \\
 Run 8 &32 users &41 users &56 users \\
 Run 9 &29 users &27 users &48 users \\
 Run 10 &27 users &44 users &62 users \\ \hline
 \textbf{mean $\pm$ std} & \textbf{28.3 $\pm$ 2.7} & \textbf{36.7 $\pm$ 6.5} & \textbf{50.4 $\pm$ 8.2} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

"A Public and Reproducible Assessment of the Topics API on Real Data" by Yohan Beugin and Patrick McDaniel, arXiv:2403.19577v3, 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{\small{Empirical probability of Type-I error of the different tests. } }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
				\toprule
				Distribution
				& Multivariate Gaussian & Gaussian Copula & Cauchy \\
				\midrule
				Proposed & 0.038 & 0.042 & 0.042\\

				JdCov & 0.036 & 0.030& 0.046 \\

				Matteson-Tsay (MT) & 0.056 & 0.046& 0.056 \\

				dHSIC & 0.032 & 0.074& 0.050 \\
				\bottomrule
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Distribution-free joint independence testing and robust independent component analysis using optimal transport" by Ziang Niu and Bhaswar B. Bhattacharya, arXiv:2211.15639v2, 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
Candidate & voter-1 & voter-2 &P & N & P-N & N/P & $W_{1}^{1}$ \\
 \hline
\hline
A & 10 & -5 & 10 & 5 & 5 & 0.5 & 3.33 \\
\hline
B & 0 & 4 & 4 & 0 & 4 & 0 & 4 \\
\hline
C & 0 & 1 & 1 & 0 & 1 & 0 & 1 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Negative votes to depolarize politics

"Negative votes to depolarize politics" by Karthik H. Shankar, arXiv:2012.13657v1, 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}{l|cccc}
 \hline
 Method & max-IoU $\uparrow$ & min-CDR $\downarrow$ & min-BDE $\downarrow$ & mAP $\uparrow$\\
 \hline
 SOD & 53.3\% & 11.94\% & 12.31\% & 47.48\% \\
 FP & 23.29\% & 14.49\% & 19.04\% & 11.41\% \\
 Image Cropping & 12.19\% & 42.92\%&36.25\%& 10.42\%\\
 Image Cropping & 17.84\% & 32.82\%&28.62\%& 27.22\%\\
 \hline
 Rank-SS ({\it H2V-142K})& 54.54\% & 4.69\% & 4.74\%& 59.59\%\\
 Rank-SS (ASR)& 60.26\% & 4.52\% & 4.22\% & 67.16\% \\
 Rank-SS (ASR-ft) & \textbf{60.42\%} & \textbf{4.39\%} & \textbf{4.13\%} & \textbf{67.25\%} \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Comparison of the proposed {\it Sub-Select} modules with state-of-the-art Salient Object Detection, Fixation Prediction and Image Cropping methods in the ASR dataset . }
\end{table}
\end{document}

cs

Horizontal-to-Vertical Video Conversion

Tun Zhu
Daoxin Zhang
Yao Hu
Tianran Wang
Xiaolong Jiang
Jianke Zhu
Jiawei Li

"Horizontal-to-Vertical Video Conversion" by Tun Zhu, Daoxin Zhang, Yao Hu, Tianran Wang, Xiaolong Jiang, Jianke Zhu, and Jiawei Li, arXiv:2101.04051v2, 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
 \textbf{Dataset} & \textbf{Link Type} & \textbf{$n_\mathrm{r}$ (\%)} & $h$ & \textbf{$N$} \\
 \midrule
 Mobile Phone & Phone Calls & 55.1 & $0.1$ & 2,173,030 \\
 Pokec & Friendship & 46.6 & $-0.2$ & 383,943 \\
 Last.fm & Friendship & 68.3 & $0.1$ & 188,672 \\
 Oklahoma FB & Friendship & 49.3 & $0.2$ & 16,245 \\
 CPH & Proximity, Calls, etc. & 78.1 & $0.0$ & 779 \\
 SocioPatterns & Proximity & 57.4 & $0.0$ & 115 \\
 \bottomrule
 \end{tabular}
\caption{Summary of datasets used in this study. The variable $n_\mathrm{r}$ represents the relative size of the red (male) group. The Coleman index $h$ is for overall sex homophily.}
\end{table}
\end{document}

math

Homophily Within and Across Groups

Abbas K. Rizi
Riccardo Michielan
Clara Stegehuis
Mikko Kivelä

"Homophily Within and Across Groups" by Abbas K. Rizi, Riccardo Michielan, Clara Stegehuis, and Mikko Kivelä, arXiv:2412.07901v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
 \textbf{Acronym} & \textbf{Long-form} & \textbf{Rule} \\ \hline \hline
 AABM & Analyzing Avatar Boundary Matching & Character Match \\ \hline
 ABBREX & Abbreviation Expander & Bounded Schwartz's \\ \hline
 AD & acronym disambiguation & Character Match \\ \hline
 AI & Acronym identification & Character Match \\ \hline
 BADREX & Biomedical Abbreviations using & Bounded Schwartz's \\
 & Dynamic Regular Expressions & \\ \hline
 BiLSTM & Bi - directional Long ShortTerm Memory & Bounded Schwartz's \\ \hline
 DOG & Diverse acrOnym Glossary & Bounded Schwartz's \\ \hline
 MAD & Massive Acronym Disambiguation & Capital Initials \\ \hline
 MF & most frequent & Character Match \\ \hline
 USMC & User - guided Social Media Crawling & Capital Initials
 \end{tabular}
\end{adjustbox}
\caption{The acronym glossary extracted from the text of this paper using MadDog.}
\end{table}
\end{document}

cs

Amir Pouran Ben Veyseh
Franck Dernoncourt
Walter Chang
Thien Huu Nguyen

"MadDog: A Web-based System for Acronym Identification and Disambiguation" by Amir Pouran Ben Veyseh, Franck Dernoncourt, Walter Chang, and Thien Huu Nguyen, arXiv:2101.09893v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c||c|c|}
\hline
\textbf{Dice Score} & \textbf{Fold 1} & \textbf{Fold 2} & \textbf{Fold 3} & \textbf{Fold 4} & \textbf{Fold 5} & \textbf{Mean} & \textbf{STD} \\
\hline
Best Preproc. & 0.2167 & \textcolor{blue}{0.3184} & 0.2181 & 0.1521 & 0.1696 & 0.2150 & 0.0647 \\
+ Vessel Segm. & 0.2257 & \textcolor{blue}{0.3273} & 0.2129 & 0.2852 & 0.2495 & \textbf{0.2601} & \textbf{0.0466} \\
\hline
\(\Delta\) (\%) & +4.2 & +2.8 & -2.4 & +87.5 & +47.1 & \textbf{+21.0} & \textbf{-27.9} \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Validation Dice scores for the best preprocessing pipeline from Section versus the same model trained with the vessel segmentation replacing CTA, evaluated on the same 5-fold split. \(\Delta\) gives the relative percentage change with respect to the best preprocessing pipeline from Section . The best fold for both experiments is highlighted in \textcolor{blue}{blue}.}
\end{table}
\end{document}

eess

Juampablo E. Heras Rivera
Hitender Oswal
Tianyi Ren
Yutong Pan
William Henry
Caitlin M. Neher
Mehmet Kurt

"Clinically-Informed Preprocessing Improves Stroke Segmentation in Low-Resource Settings" by Juampablo E. Heras Rivera, Hitender Oswal, Tianyi Ren, Yutong Pan, William Henry, Caitlin M. Neher, and Mehmet Kurt, arXiv:2508.16004v1, 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}{|l|lccccccc|c|c|}
\hline
j \textbackslash r&0& 1&2&3&4&5&6&7&$\chi $& HZ\\
\hline
21& & &&& & & &\fbox{1} & -1& numerator\\
19& & &&& &\fbox{1}& & $\ast$ &-1 & \\
\hline
17& & && & &1 &$\underline{1}$ & &0 &\\
15& & &&1& 1& & & &0& \\
\hline
13& & && $\ast$ & $\textcircled{\underline{1}}$& & & & 1&\\
11& & &\textcircled{1}& & & && &1& \\
9& \textcircled{1} & & & & & & && 1 & denominator \\
7& \textcircled{1} & & & & & & && 1& \\
\hline
\end{tabular}
\end{table}
\end{document}

math

"The HOMFLY-PT polynomial and HZ factorisation" by Andreani Petrou and Shinobu Hikami, arXiv:2412.04933v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Xueqing Liu
Tianchen Qian
Lauren Bell
Bibhas Chakraborty

"Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes" by Xueqing Liu, Tianchen Qian, Lauren Bell, and Bibhas Chakraborty, arXiv:2310.18905v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\hline
 & \textbf{Baseline} & \textbf{x\_add} & \textbf{s\_add} & \textbf{x\_cat} & \textbf{s\_cat} \\ \hline \hline
\textbf{dev\_clean - less\_5} & 7.2 & 7.2 & 7.5 & 7.2 & 7.7 \\ %\hline
\textbf{test\_clean - less\_5} & 7.4 & 7.9 & 7.8 & 7.8 & 8.3 \\ \hline %\hline
\textbf{dev\_other - less\_5} & 14.2 & 16.2 & 15.5 & 16.1 & 16.4 \\ %\hline
\textbf{test\_other - less\_5} & 15.5 & 17.9 & 17.5 & 18.3 & 18.7 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Librispeech: Average error rate (\%WER) on utterances with duration lesser than five seconds }
\end{table}
\end{document}

eess

Vishwas M. Shetty
Metilda Sagaya Mary N J
S. Umesh

"Investigation of Speaker-adaptation methods in Transformer based ASR" by Vishwas M. Shetty, Metilda Sagaya Mary N J, and S. Umesh, arXiv:2008.03247v2, 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{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Time summary for the two algorithms. Tests run on Gaussian datasets of dimension $d = 100$, with varying $n$. Each value represents mean and sample standard deviation (when multiple runs are computed).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
& & & & \multicolumn{2}{c}{Time (s)} \\
\cmidrule(r){5-6}
$d$ & $n$ & $\delta$ & $\#$ runs & Our algorithm & Fixed Point algorithm\\
\midrule
100 & $10\,000$ & $10^{-2}$ & 100 & 0.0927 $\pm$ 0.0402 & 0.7270 $\pm$ 0.1886 \\
& & $10^{-3}$ & 100 & 0.2578 $\pm$ 0.1991 & 5.6216 $\pm$ 1.5382 \\
& & $10^{-4}$ & 100 & 0.3013 $\pm$ 0.0833 & 18.6236 $\pm$ 4.8307 \\
& & $10^{-5}$ & 50 & 0.5162 $\pm$ 0.1292 & 82.4097 $\pm$ 26.7802 \\
& $100\,000$ & $10^{-3}$ & 20 & 1.9359 $\pm$ 0.1371 & 160.1398 $\pm$ 34.8062 \\
& $1\,000\,000$ & $10^{-3}$ & 1 & 101.0625 & 4837.5313
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Elizabeth Harris
Ali Eshragh
Bishnu Lamichhane
Jordan Shaw-Carmody
Elizabeth Stojanovski

"Efficient Data-Driven Leverage Score Sampling Algorithm for the Minimum Volume Covering Ellipsoid Problem in Big Data" by Elizabeth Harris, Ali Eshragh, Bishnu Lamichhane, Jordan Shaw-Carmody, and Elizabeth Stojanovski, arXiv:2411.03617v1, 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
Technique & Inference time & Inference time\\
 & With GPU & Without GPU\\
\hline
2D & 70 & 1145\\
3D & 17 & 229\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Inference time for 2D and 3D techniques in seconds}
\end{table}
\end{document}

cs

Abhishek Shivdeo
Rohit Lokwani
Viraj Kulkarni
Amit Kharat
Aniruddha Pant

"Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans" by Abhishek Shivdeo, Rohit Lokwani, Viraj Kulkarni, Amit Kharat, and Aniruddha Pant, arXiv:2101.07612v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.