\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 Dataset & Nodes & Spectral Recovery Rate & QSD Recovery Rate\\
 \hline
 SBM-connected & 2000 & \textbf{1} & 0.992 \\
 SBM-bounded-degree & 2000 & .502 & \textbf{0.81}\\
 IONOSPHERE & 351 & \textbf{.69} & 0.67\\
 DIABETES & 768 & .522 & \textbf{0.67} \\
 WDBC & 683 & \textbf{.70} & 0.55\\
 POLBLOGS & 1224 & .915 & \textbf{0.943}\\
 SPAM & 4601 & .673 & \textbf{0.70 }\\
 GISETTE & 7000 & .906 & \textbf{0.959}\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Comparison of Recovery Rates for the Standard Spectral and QSD methods with a $\delta = 0.1$ fraction of revealed nodes.}
\end{table}
\end{document}

stat

"Semi-Supervised Community Detection via Quasi-Stationary Distributions" by Nicolas Fraiman and Michael Nisenzon, arXiv:2412.09793v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccccc}
			\toprule
			Method $\&$ Number of robots &	5	&	10	&	20	&	40 & 80 \\
			\midrule
			Star-<5>	& 97.30 & 97.56 & 96.90 & 97.20 & 96.72 \\
			Complete-<5>	& 95.60 & 93.21 & 86.55 & 85.46 & 63.36 \\
			Complete-<80>& 73.54 & 76.81 & 78.55 & 90.59 & 98.24 \\
			\toprule
	\end{tabular}
\end{adjustbox}
\caption{The classification accuracy (\%) tested with the various number of robots. All the data is evaluated without additional training.}
\end{table}
\end{document}

eess

Guangyi Liu
Arash Amini
Martin Takáč
Héctor Muñoz-Avila
Nader Motee

"Distributed Map Classification using Local Observations" by Guangyi Liu, Arash Amini, Martin Takáč, Héctor Muñoz-Avila, and Nader Motee, arXiv:2012.10480v2, 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|}
 \hline
 \textbf{Variables} & \textbf{Runtime (seconds)} & $n$\textbf{-cells} \\ \hline
 1 & 0.0308762 & 5 \\ \hline
 2 & 0.335376 & 29 \\ \hline
 3 & 6.06833 & 467 \\ \hline
 4 & 1883.13 & 7370 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Cylindrical Algebraic Decomposition in Macaulay2" by Corin Lee, Tereso del Río, and Hamid Rahkooy, arXiv:2503.21731v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing" by Cong William Lin and Wu Zhu, arXiv:2504.13629v1, 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}{ccc}
split & num. examples & label distribution\\
\midrule
labeled train & 1000 & [0.2 0.2 0.2 0.2 0.2]
\\
unlabeled train & 21300 & [0.2 0.2 0.2 0.2 0.2]
\\
labeled valid & 2000 & [0.2 0.2 0.2 0.2 0.2]
\\
labeled test & 24300 & [0.2 0.2 0.2 0.2 0.2]
\end{tabular}
\end{adjustbox}
\caption{NORB dataset.}
\end{table}
\end{document}

stat

Gabriel Hope
Madina Abdrakhmanova
Xiaoyin Chen
Michael C. Hughes
Michael C. Hughes
Erik B. Sudderth

"Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints" by Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C. Hughes, Michael C. Hughes, and Erik B. Sudderth, arXiv:2012.06718v1, 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}{lcccccc}
\toprule
& \multicolumn{3}{c}{p=1} & \multicolumn{3}{c}{p=2} \\
\cmidrule(r){2-4}\cmidrule(l){5-7}
\textbf{k} & \textbf{Bank} & \textbf{Adult} & \textbf{Diabetes} & \textbf{Bank} & \textbf{Adult} & \textbf{Diabetes} \\
5 & 19 & 18 & 30 & 27 & 17 & 10 \\
10 & 18 & 28 & 27 & 23 & 21 & 22 \\
15 & 25 & 26 & 11 & 23 & 17 & 10 \\
20 & 15 & 15 & 15 & 17 & 10 & 10 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Number of outliers detected after solving and thresholding with $\tau=0,$ for $k$-median ($p=1$) and $k$-means ($p=2$) clustering. For this experiment, we set $m=10.$}
\end{table}
\end{document}

stat

Binita Maity
Shrutimoy Das
Anirban Dasgupta

"Linear Programming based Approximation to Individually Fair k-Clustering with Outliers" by Binita Maity, Shrutimoy Das, and Anirban Dasgupta, arXiv:2412.10923v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{amsfonts}
\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
 Definition & $z \to 0$ & $z \to \infty$ \\ \hline
 $h(z)$ & $\sim z^{\beta}$, $\beta \in [1, 2)$ & $\sim z^{\alpha}$, $\alpha \in [0, \frac{4}{n + 2}) \cap [0, 1]$ \\ \hline
 $H(z) = \int_1^z h^{-1} (s) \mathrm d s$ &
 $\begin{cases}
 \sim \ln z, \ \beta = 1, \\
 \sim z^{1 - \beta}, \ \beta > 1.
 \end{cases}$ &
 $\begin{cases}
 \sim z^{1 - \alpha}, \ \alpha < 1, \\
 \sim \ln z, \ \alpha = 1
 \end{cases}$ \\ \hline
 $\mathcal{H} (z) = \int_1^z H(s) \mathrm d s + 1$ & $\sim 1$ &
 $\begin{cases}
 \sim z^{2 - \alpha}, \ \alpha < 1, \\
 \sim z \ln z, \ \alpha = 1
 \end{cases}$ \\ \hline
 $\widetilde{H} (z) = \int_0^z h^{-1/2} (s) \mathrm d s$ & $\sim z^{1 - \beta / 2}$ & $\sim z^{1 - \alpha / 2}$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Properties of the function $h$ and some related functions}
\end{table}
\end{document}

math

"Existence and asymptotic stability of a generic Lotka-Volterra system with nonlinear spatially heterogeneous cross-diffusion" by Tianxu Wang, Jiwoon Sim, and Hao Wang, arXiv:2501.12569v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 Methods & Acc.(mm) & Comp. (mm) & OA (mm) \\
 \midrule
 Camp & 0.835 & 0.554 & 0.695 \\
 Gipuma & \textbf{0.283} & 0.873 & 0.578 \\
 Furu & 0.613 & 0.941 & 0.777 \\
 \midrule
 SurfaceNet & 0.450 & 1.040 & 0.745 \\
 $\text{MVSNet}^*$ & 0.396 & 0.527 & 0.462 \\
 $\text{R-MVSNet}^*$ & 0.383 & 0.452 & 0.418 \\
 PruMVSNet & 0.495 & 0.433 & 0.464 \\
 PointMVSNet & 0.361 & 0.421 & 0.391 \\
 CasMVSNet & 0.325 & 0.385 & 0.355 \\
 CVP-MVSNet & 0.296 & 0.406 & 0.351 \\
 Ours($G = 8$) & 0.363 & 0.332 & 0.347 \\
 Ours($G = 2$) & 0.360 & 0.341 & 0.351 \\
 \midrule
 Ours($G = 4$) & 0.357 & \textbf{0.326} & \textbf{0.341} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Anzhu Yu
Wenyue Guo
Bing Liu
Xin Chen
Xin Wang
Xuefeng Cao
Bingchuan Jiang

"Attention Aware Cost Volume Pyramid Based Multi-view Stereo Network for 3D Reconstruction" by Anzhu Yu, Wenyue Guo, Bing Liu, Xin Chen, Xin Wang, Xuefeng Cao, and Bingchuan Jiang, arXiv:2011.12722v1, 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{Frequency of Bases for Judging Comprehensibility}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c}
Basis for Judgment & Frequency \\ \hline
Consistency of features within layers or subtrees & 100\% \\
Number of layers (depth) in the tree & 100\% \\
Overall structure and organization of the tree & 93.75\% \\
Intuitively reasonable feature selection & 93.75\% \\
Reasonableness of threshold selection & 20\% \\
Complexity of subtrees & 20\% \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Lianchen Jia
Chaoyang Li
Ziqi Yuan
Jiahui Chen
Tianchi Huang
Jiangchuan Liu
Lifeng Sun

"Beyond Interpretability: Exploring the Comprehensibility of Adaptive Video Streaming through Large Language Models" by Lianchen Jia, Chaoyang Li, Ziqi Yuan, Jiahui Chen, Tianchi Huang, Jiangchuan Liu, and Lifeng Sun, arXiv:2508.16448v1, 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{Primal and dual expressions for different divergence measures}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
 \textbf{Divergence} & $f(t)$ & $f^*(t)$ \\
 \hline
 $\chi^2$ & $\frac{1}{2} (t - 1)^2$ & $-1 + \frac{1}{4} (t + 2)^2_+$ \\
 \hline
 \textit{KL} & $t \log t - t + 1$ & $\exp(t) - 1$\\
 \hline
 \textit{smoothed CVaR} & $f_{\alpha}^{\text{smo}}(t) = \begin{cases}
 t \log t + \frac{1 - \alpha t}{\alpha} \log \frac{1 - \alpha t}{1 - \alpha}, & t \in [0, 1/\alpha)\\+\infty, & \textit{otherwise}
 \end{cases}$ & $\frac{1}{\alpha}\log(1-\alpha+\alpha\exp(t))$
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Nested Stochastic Algorithm for Generalized Sinkhorn distance-Regularized Distributionally Robust Optimization" by Yufeng Yang, Yi Zhou, and Zhaosong Lu, arXiv:2503.22923v2, 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{Comparison of total computation time and average computation time for Algorithm 1, fmincon, and Casadi in the MPC implementation for the trajectory tracking problem ()-().}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|}
		\hline
		\multicolumn{2}{|c|}{\multirow{1}{*}{Algorithm}} &\multirow{1}{*}{Total time [s]}&\multirow{1}{*}{Average time [s]} \\
		\hline
		\multirow{2}{*}{fmincon} & Interior Point & 3.1836 & 0.0199 \\
		\cline{2-4}
		\multirow{2}{*}{} & SQP & 1.2997 & 0.0081 \\
		\hline

		\multirow{1}{*}{Casadi} & IPOPT & 1.3768 & 0.0086 \\
		\hline

		\multicolumn{2}{|c|}{\multirow{1}{*}{Algorithm 1}} &\multirow{1}{*}{0.2809}&\multirow{1}{*}{0.0018} \\

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

math

"Finite-Horizon Discrete-Time Optimal Control for Nonlinear Systems under State and Control Constraints" by Chuanzhi Lv, Hongdan Li, and Huanshui Zhang, arXiv:2503.15794v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Architecture and parameters of the proposed model is presented.}
\begin{tabular}{l|l|l}
 \hline
 \textbf{Module} & \textbf{Layer Details} & \textbf{Feature Shape} \\ \hline \hline
 Input & $-$ & $120 \times 3$ \\ \hline
 \multirow{7}{*}{Conv. Blocks} & $[\textit{conv}, 1 \times 3, 128] \times 2$ & $116 \times 128$ \\
 & $[\textit{maxpool}, 1 \times 2, \textit{stride} = 2]$ & $58 \times 128$ \\
 & $[\textit{conv}, 1 \times 3, 256] \times 2$ & $54 \times 256$ \\
 & $[\textit{maxpool}, 1 \times 2, \textit{stride} = 2]$ & $27 \times 256$ \\
 & $[\textit{conv}, 1 \times 2, 384] \times 2$ & $23 \times 384$ \\
 & $[\textit{maxpool}, 1 \times 2, \textit{stride} = 2]$ & $11 \times 384$ \\
 & \textit{flatten} & $4224
$ \\ \hline
 \multirow{4}{*}{$H_{predict}$ FC} & $[\textit{dense}, 384]$ & $384$ \\
 & $[\textit{dense}, 120]$ & $120$ \\
 & $[\textit{dense}, 24]$ & $45$ \\
 & \textit{output} & $24
$ \\ \hline
 \multirow{7}{*}{$H_{HAR}$ FC} & $[\textit{dense}, 512 ] $ & $512$ \\
 & $[\textit{dense}, 250]$ & $250$ \\
 & $[\textit{dense}, 100]$ & $100$ \\
 & $[\textit{dense}, num\_classes]$ & $num\_classes$ \\
 & \textit{output} & 1
 \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

Setareh Rahimi Taghanaki
Michael Rainbow
Ali Etemad

"Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion" by Setareh Rahimi Taghanaki, Michael Rainbow, and Ali Etemad, arXiv:2010.13713v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc|ccc}
 & IMEX(3,3;1) & & & IMEX(2,2;1) & \\
 \#Dofs & \(\text{err}_{L\infty}(0.5)\) & rate & \#Dofs & \(\text{err}_{L\infty}(0.5)\) & rate \\ \hline
 257 & 6.15E-01 & -- & 257 & 4.58E-01 & -- \\
 513 & 1.08E-01 & 2.51 & 513 & 1.13E-01 & 2.03 \\
 1025 & 8.28E-03 & 3.72 & 1025 & 2.56E-02 & 2.14 \\
 2049 & 4.95E-04 & 4.07 & 2049 & 6.21E-03 & 2.05 \\
 4097 & 1.52E-05 & 5.02 & 4097 & 1.54E-03 & 2.01 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Convergence tables for the KdV equation on \(D = (-10,20)\). Left: IMEX(3,3;1) time stepping with CFL = 0.25 and \(\mathbb{P}_2\) elements. Right: IMEX(2,2;1) time stepping with CFL = 0.05 and \(\mathbb{P}_1\) elements.}
\end{table}
\end{document}

math

"A High Order IMEX Method for Generalized Korteweg de-Vries Equations" by Seth Gerberding, arXiv:2503.15397v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"The Largest Circle Enclosing $n$ Lattice Points" by Jianqiang Zhao, arXiv:2505.06234v2, 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{Hyper‑parameter settings for each task.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 \toprule
 {Hyper‑parameter} & {Synthetic} & {Mortgage} & {Equity} \\
 \midrule
 Set‑Seq layers & 5 & 6 & 6 \\
 Sequence layers & 1 & 0 & 0 \\
 $d_{\text{model}}$ & 800 & 300 & 64 \\
 Learning rate & 0.003 & 0.003 & 0.003 \\
 Dropout & 0 & 0.10 & 0.10 \\
 Epochs & 40 & 40 & 30 \\
 \# Samples & 250 & — & — \\
 Time steps & 100 & 50 & 246 \\
 Number of units & 1–1000 & 2500 & 500 \\
 Sequence model & LongConv& LongConv& LongConv\\
 Chunk size $L$ & 3 & 3 & 3 \\
 Set summary dim & 2 & 2 & 2 \\
 $\phi$ output dim & 5 & 5 & 5 \\
 Conv.\ weight decay & 0 & 0.05 & 0.05 \\

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

q-fin

A Set-Sequence Model for Time Series

Elliot L. Epstein
Apaar Sadhwani
Kay Giesecke

"A Set-Sequence Model for Time Series" by Elliot L. Epstein, Apaar Sadhwani, and Kay Giesecke, arXiv:2505.11243v1, 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{Threshold estimations of Cylindrical and M\"{o}bius codes.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 & $A = 1$ & $A = 10$ & $A = \infty$ \\
 \midrule
 Cylindrical Codes & $0.14$ & $0.12$ & $0.10$ \\
 M\"{o}bius Codes & $0.14$ & $0.12$ & $0.10$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Lorenzo Valentini
Diego Forlivesi
Marco Chiani

"Cylindrical and Möbius Quantum Codes for Asymmetric Pauli Errors" by Lorenzo Valentini, Diego Forlivesi, and Marco Chiani, arXiv:2502.19089v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|} \hline \textbf{Augmentation} & \textbf{Parameters} \\ \hline ColorJitter & brightness = 0.4, contrast = 0.4, saturation = 0.2, hue = 0.1, prob = 0.8 \\ \hline Grayscale & prob = 0.2 \\ \hline HorizontalFlip & prob = 0.5 \\ \hline CropSize & 32 \\ \hline \end{tabular}
\end{adjustbox}
\caption{Shared augmentations for VICReg and Barlow Twins}
\end{table}
\end{document}

stat

James Chapman
Lennie Wells
Ana Lawry Aguila

"Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning" by James Chapman, Lennie Wells, and Ana Lawry Aguila, arXiv:2310.01012v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Should Bank Stress Tests Be Fair?

"Should Bank Stress Tests Be Fair?" by Paul Glasserman and Mike Li, arXiv:2207.13319v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Taveena Lotey
Aman Verma
Partha Pratim Roy

"EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters" by Taveena Lotey, Aman Verma, and Partha Pratim Roy, arXiv:2412.17818v1, 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{Proposed CSS Compared with Existing Pre-Participation Evaluation (PPE)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
		\toprule
		\textbf{Criterion} & \textbf{14-point PPE} & \textbf{12-lead ECG} & \textbf{This work} \\
		\midrule
		Sensitivity & 18.8\% & 94\% & 97.3\% \\
 Specificity & 68.0\% & 93\% & 99.1\%\\
 FPR & 32.0\% & 7\% & 2\% \\
 Price & N/A & \$>1,000 & \$399 \\
 Require physician? & Yes & Yes & No \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"High-Throughput Detection of Risk Factors to Sudden Cardiac Arrest in Youth Athletes: A Smartwatch-Based Screening Platform" by Evan Xiang, Thomas Wang, and Vivan Poddar, arXiv:2412.12118v1, 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{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|r|r|r|r|r|r|r|r}
 \multicolumn{2}{c}{}&\multicolumn{4}{c}{supervised}&\multicolumn{4}{c}{unsupervised}\\
 \multicolumn{2}{c}{}&\multicolumn{2}{c}{TV}&\multicolumn{2}{c|}{WTV}&\multicolumn{2}{c|}{TV}&\multicolumn{2}{c}{WTV} \\
 Image&$\sigma$&IPSNR&ISSIM&IPSNR&ISSIM&IPSNR&ISSIM&IPSNR&ISSIM\\
 \hline
 \multirow{3}{*}{\#3}&$0.03$&3.419&0.199&\textbf{7.377}&\textbf{0.243}&3.354&0.209&\textbf{3.815}&\textbf{0.225}\\
 &$0.06$&4.712&0.342&\textbf{8.900}&\textbf{0.436}&4.629&0.365&\textbf{5.007}&\textbf{0.392}\\
 &$0.09$&5.697&0.387&\textbf{9.887}&\textbf{0.524}&5.585&0.423&\textbf{5.831} &\textbf{0.427} \end{tabular}
\end{adjustbox}
\caption{IPSNR and ISSIM values achieved by the considered models in case of additive white uniform noise corruption.}% for the three test images corrupted by different levels of AWGN.}
\end{table}
\end{document}

math

Monica Pragliola
Luca Calatroni
Alessandro Lanza

"Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising" by Monica Pragliola, Luca Calatroni, and Alessandro Lanza, arXiv:2503.07814v1, 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 \# & Quarter & Invested Stocks \\
\hline 1 & Q4 2020 & BRKM5 \\ % February 28, 2021
\hline 2 & Q1 2021 & ABEV3,ENEV3,TAEE11,USIM5 \\ % May 31, 2021
\hline 3 & Q2 2021 & USIM5, GGBR4, GOAU4 \\ % August 31, 2021
\hline 4 & Q3 2021 & GGBR4,GOAU4, CSNA3, PETR3 \\ % November 30, 2021
\hline 5 & Q4 2021 & GOAU4, KLBN11, WEGE3, CSAN3
 \\
\hline 6 & Q1 2022 & GGBR4, GOAU4, PRIO3, SUZB3 \\
\hline 7 & Q2 2022 & GOAU4, PETR3, CSNA3, SUZB3
\\
\hline 8 & Q3 2022 & PRIO3, PETR3, GOAU4
 \\
\hline 9 & Q4 2022 & No stocks \\
\hline 10 & Q1 2023 & CMIG4, SUZB3
 \\
\hline 11 & Q2 2023 & SLCE3, GGBR4, SUZB3, GOAU4
\\
\hline 12 & Q3 2023 & CMIG4, GGBR4
 \\
\hline 13 & Q4 2023 & CMIG4, UGPA3
 \\
\hline 14 & Q1 2024 & SUZB3, PRIO3, WEGE3, TAEE11
 \\
\hline 15 & Q2 2024 & GOAU4, GGBR4, ABEV3
\\
\hline 16 & Q3 2024 & PRIO3
 \\
\hline 17 & Q4 2024 & ELET3
\\
\hline 18 & Q1 2025 & ABEV3
\\
\hline
\end{tabular}
\end{adjustbox}
\caption{AlphaX´s Invested Stocks per Quarter}
\end{table}
\end{document}

q-fin

"AlphaX: An AI-Based Value Investing Strategy for the Brazilian Stock Market" by Paulo André Lima de Castro, arXiv:2508.13429v1, 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{Historical Statistics of Log($v$).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
Year&mean&standard deviation&skew&excess kurtorsis\\
\hline	% horizontal line to separate headings from data
2020&15.218&1.024&-0.047&-0.514\\
2010&15.156&1.019&-0.054&-0.537\\
2000&15.062&1.020&-0.052&-0.572\\
1990&14.939&1.018&-0.015&-0.630\\
1980&14.850&1.021&-0.073&-0.719\\
1970&14.714&1.063&-0.091&-0.719\\
1960&14.583&1.071&-0.172&-0.606\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"A New Perspective on Impartial and Unbiased Apportionment" by Ross Hyman and Nicolaus Tideman, arXiv:2201.11787v2, 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|c|}
\hline
 & \multicolumn{4}{c|}{{\small{}${\nu_{1}}=0.1$, $\nu_{2}=1$}} & \multicolumn{4}{c|}{{\small{}${\nu_{1}}=0.01$, $\nu_{2}=1$}}\tabularnewline
\hline
{\small{}$W$} & {\small{}$\rVert\textit{E}_{\textbf{u}}\rVert_{1}$} & {\small{}$\rVert\textit{E}_{\textit{p}}\rVert_{0}$} & {\small{}$\rVert\textit{E}_{\textit{c}}\rVert_{0}$} & \textbf{\small{}iters} & {\small{}$\rVert\textit{E}_{\textbf{u}}\rVert_{1}$} & {\small{}$\rVert\textit{E}_{\textit{p}}\rVert_{0}$} & {\small{}$\rVert\textit{E}_{\textit{c}}\rVert_{0}$} & \textbf{\small{}iters}\tabularnewline
\hline
{\small{}2} & {\small{}4.36E-02} & {\small{}5.73E-01} & {\small{}1.57E-01} & {\small{}32} & {\small{}1.88E-01} & {\small{}7.57E-01} & {\small{}7.16E+00} & 9\tabularnewline
\hline
{\small{}3} & {\small{}7.84E-03} & {\small{}1.55E-01} & {\small{}2.15E-02} & {\small{}93} & {\small{}5.97E-03} & {\small{}4.46E-01} & {\small{}1.19E-01} & 139\tabularnewline
\hline
{\small{}4} & {\small{}5.39E-04} & {\small{}2.07E-02} & {\small{}1.49E-03} & {\small{}250} & {\small{}1.74E-03} & {\small{}1.57E-01} & {\small{}4.94E-02} & {\small{}220}\tabularnewline
\hline
{\small{}5} & {\small{}3.80E-05} & {\small{}1.11E-03} & {\small{}1.38E-04} & {\small{}444} & {\small{}4.86E-04} & {\small{}3.13E-03} & {\small{}3.64E-03} & {\small{}452}\tabularnewline
\hline
{\small{}6} & {\small{}2.92E-06} & {\small{}2.87E-04} & {\small{}9.60E-06} & {\small{}888} & {\small{}4.90E-05} & {\small{}5.72E-04} & {\small{}5.59E-04} & {\small{}784}\tabularnewline
\hline
{\small{}7} & {\small{}1.47E-07} & {\small{}1.83E-05} & {\small{}5.12E-07} & {\small{}1528} & {\small{}6.77E-07} & {\small{}8.26E-05} & {\small{}8.72E-06} & {\small{}1429}\tabularnewline
\hline
\end{tabular}
\end{adjustbox}
\caption{Errors against $W$ for $\nu_{1}=0.1,0.01$}
\end{table}
\end{document}

math

Kishore Kumar Naraparaju
Shivangi Joshi
Subhashree Mohapatra

"Higher-order spectral element method for the stationary Stokes interface problem in two dimensions" by Kishore Kumar Naraparaju, Shivangi Joshi, and Subhashree Mohapatra, arXiv:2502.20039v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{RMSE of Conventional and SMC due to variation in buoyancy}
\begin{tabular}{|c|cccc|}
\hline
\multirow{2}{*}{Controller} & \multicolumn{4}{c|}{(B-W)/g (grams)} \\ \cline{2-5}
 & 100 & 340 & 650 & 1000 \\ \hline
Conventional & 0.513 & 0.505 & 0.495 & 0.483 \\ \hline
SMC & 1.613 & 1.614 & 1.612 & 1.613 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Rajini Makam
Pruthviraj Mane
Suresh Sundaram
P. B. Sujit

"A Comprehensive Study on Modelling and Control of Autonomous Underwater Vehicle" by Rajini Makam, Pruthviraj Mane, Suresh Sundaram, and P. B. Sujit, arXiv:2312.02690v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"FEET: A Framework for Evaluating Embedding Techniques" by Simon A. Lee, John Lee, and Jeffrey N. Chiang, arXiv:2411.01322v1, 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}{r|cc}
\toprule
Time (seconds) & 1970 model & 2005 model \\
\midrule
\texttt{checkConsistency}, $n = 100$ & $2.511 \pm 0.035$ & $7.367 \pm 0.136$ \\
\texttt{checkConsistency}, $n = 1000$ & $25.115 \pm 0.152$ & $73.584 \pm 0.489$ \\
\bottomrule
Diagnostic & $0.009 \pm 0.007$ & $0.012 \pm 0.0003$
\end{tabular}
\end{adjustbox}
\caption{Table showing median computation times (along with median absolute deviations) of each method, applied to each model.}
\end{table}
\end{document}

stat

"A probabilistic diagnostic for Laplace approximations: Introduction and experimentation" by Shaun McDonald and David Campbell, arXiv:2411.01697v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\\Sample size}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 & Black & Non-Black & Total \\
 \midrule
 \multicolumn{4}{c}{A. FairFace} \\
 No.\ of players & 335 & 170 & 505 \\
 \quad Mean L* value & 45.00 & 64.06 & 51.42 \\
 \quad \quad (SD) & (10.39) & (7.14) & (13.03) \\
 \quad \quad Min–max & 20.52–71.26 & 41.85–79.48 & 20.52–79.48 \\
 \addlinespace
 No.\ of referees & 23 & 46 & 69 \\
 \quad Mean L* value & 42.49 & 59.77 & 54.01 \\
 \quad \quad (SD) & (10.62) & (9.09) & (12.59) \\
 \quad \quad Min–max & 20.56–60.75 & 41.77–76.32 & 20.56–76.32 \\
 \addlinespace
 No.\ of games & 2,359 & 2,356 & 2,359 \\
 No.\ of player–games & 33,606 & 12,272 & 45,878 \\
 No.\ of player–minutes & 707,374 & 245,560 & 952,934 \\
 \multicolumn{4}{c}{B. Human raters} \\
 No.\ of players & 335 & 170 & 505 \\
 \quad Mean L* value & 45.04 & 64.00 & 51.42 \\
 \quad \quad (SD) & (10.43) & (7.21) & (13.03) \\
 \quad \quad Min–max & 20.52–71.26 & 41.85–79.48 & 20.52–79.48 \\
 \addlinespace
 No.\ of referees & 27 & 42 & 69 \\
 \quad Mean L* value & 43.94 & 60.48 & 54.01 \\
 \quad \quad (SD) & (10.83) & (8.89) & (12.59) \\
 \quad \quad Min–max & 20.56–63.32 & 43.21–76.32 & 20.56–76.32 \\
 \addlinespace
 No.\ of games & 2,359 & 2,354 & 2,359 \\
 No.\ of player–games & 33,527 & 12,351 & 45,878 \\
 No.\ of player–minutes & 704,010 & 248,924 & 952,934 \\
 \multicolumn{4}{c}{C. Referee crew compositions} \\
 No.\ of unique crews & & & 1,876 \\
 \quad Mean abs.\ L* diff. & & & 8.39 \\
 \quad \quad (SD) & & & (4.19) \\
 \quad \quad Min–max & & & 0.414–33.46 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Kenneth Colombe
Alex Krumer
Rosa Lavelle-Hill
Tim Pawlowski

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


stat

"On the computation of the cumulative distribution function of the Normal Inverse Gaussian distribution" by Guillermo Navas-Palencia, arXiv:2502.16015v1, 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 of the sentiment polarity mark-ups derived from two methods, rule-based and trained BERT, before and after the keyword update. The number of divergences represents the number of announcements labeled differently by these methods. Whereas, for example, the number of coinciding positives reflects the number of announcements that are positive from the perspective of both methods.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccccc}
\toprule
 & \# divergences & \# coinciding positives & \# coinciding negatives & \# coinciding neutrals \\
\midrule
With the initial keywords & 207 & 1447 & 445 & 3337 \\
With the updated keywords & 66 & 1562 & 765 & 3043 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Semen Budennyy
Alexey Kazakov
Elizaveta Kovtun
Leonid Zhukov

"New drugs and stock market: how to predict pharma market reaction to clinical trial announcements" by Semen Budennyy, Alexey Kazakov, Elizaveta Kovtun, and Leonid Zhukov, arXiv:2208.07248v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Tables and figures for each task.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
\textbf{Task} & \textbf{Feature} & \textbf{Figure} & \textbf{Table} \\
\midrule
\multirow{2}{*}{medium-sized classification} & numerical & & \\
& heterogeneous & & \\
\midrule
\multirow{2}{*}{medium-sized regression} & numerical & & \\
& heterogeneous & & \\
\midrule
\multirow{2}{*}{large-sized classification} & numerical & & \\
& heterogeneous & & \\
\midrule
\multirow{2}{*}{large-sized regression} & numerical & & \\
& heterogeneous & & \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

DOFEN: Deep Oblivious Forest ENsemble

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

"DOFEN: Deep Oblivious Forest ENsemble" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study on the Tangaroa dataset. PSNR ($\uparrow$), LPIPS ($\downarrow$), and CD ($\downarrow$) are reported.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
		\toprule
		\textbf{Model Variant} & \textbf{PSNR} $\uparrow$ & \textbf{LPIPS} $\downarrow$ & \textbf{CD} $\downarrow$ \\
		\midrule
		Full CD-TVD & 45.1457 & 0.0425 & 1.3609 \\
		w/o Contrastive Modeling & 42.5058 & 0.0889 & 2.2052 \\
		w/o Local Attention & 42.9507 & 0.0901 & 2.1871 \\
		w/o Pre-training & 41.9359 & 0.0991 & 2.5453 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Chongke Bi
Xin Gao
Jiangkang Deng
Guan Li
Jun Han

"CD-TVD: Contrastive Diffusion for 3D Super-Resolution with Scarce High-Resolution Time-Varying Data" by Chongke Bi, Xin Gao, Jiangkang Deng, Guan Li, and Jun Han, arXiv:2508.08173v2, 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{Observer locations considered in the numerical accuracy studies.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \toprule
index & $x_1$ & $x_2$ & $x_3$ & type \\ \hline
1 & $0$ & $\frac{l}{2}$ & $\frac{l}{2}$ & source \\
2 & $\frac{l}{2}$ & $\frac{l}{2}$ & $\frac{l}{2}$ & center \\
3 & $l$ & $\frac{l}{2}$ & $\frac{l}{2}$ & face \\
4,5,6,7 & $\frac{l}{2}$ & $\frac{l}{2} \pm \frac{l}{2}$ & $\frac{l}{2} \pm \frac{l}{2}$ & edge \\
8,9,19,11 & $l$ & $\frac{l}{2} \pm \frac{l}{2}$ & $\frac{l}{2} \pm \frac{l}{2}$ & corner \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Tim Bürchner
Lars Radtke
Philipp Kopp
Stefan Kollmannsberger
Ernst Rank
Alexander Düster

"On the efficiency of explicit and semi-explicit immersed boundary finite element methods for wave propagation problems" by Tim Bürchner, Lars Radtke, Philipp Kopp, Stefan Kollmannsberger, Ernst Rank, and Alexander Düster, arXiv:2501.19013v1, 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{Character Table for $\mathrm{SL}_{2}(q)$ with $q$ even}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
Class & $1$ & $c$ & $a^t$ & $b^m$\\
Size & $1$ & $q^2-1$ & $q(q+1)$ & $q(q-1)$\\
\hline
 Tr & $1$ & $1$ & $1$ & $1$\\
$\psi$ & $q$ & $0$ & $1$ & $-1$\\
$\chi_s$ & $q+1$ & $1$ & $\rho^{st}+\rho^{-st}$ & $0$\\
$\theta_j$ & $q-1$ & $-1$ & $0$ & $-(\sigma^{jm}+\sigma^{-jm})$\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Strong Gelfand Pairs of the Symplectic Group $\boldsymbol{\rm Sp}_4(q)$ where $q$ is even" by Stephen P. Humphries and Joseph E. Marrow, arXiv:2504.20279v2, 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{MHE configurations. Values of $s$ with prefix $a$ use angular distance. Each model can be used with each $s$ .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 Parameter & Values \\
 \hline
 model & [MHE, half\_MHE] \\
 \hline
 $s$ value & [0, 1, 2, a0 ,a1, a2] \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Joaquin Perez-Lapillo
Oleksandr Galkin
Tillman Weyde

"Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical Energy" by Joaquin Perez-Lapillo, Oleksandr Galkin, and Tillman Weyde, arXiv:1910.10071v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Philipp Otto
Osman Doğan
Süleyman Taşpınar

"Dynamic Spatiotemporal ARCH Models: Small and Large Sample Results" by Philipp Otto, Osman Doğan, and Süleyman Taşpınar, arXiv:2312.05898v1, 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}{c}
 $G_{i, j} =
 \begin{cases}
 G^{red}_{i, j}, &\hspace{-0.2cm}\text{if }G^{red}_{i, j} \ge G^{green}_{i, j}\\
 G^{green}_{i, j}, &\hspace{-0.2cm}\text{otherwise.}
 \end{cases}$ where\\ \\
 $
 \begin{aligned}
 & G^{red}_{i, j} = \sum_{k \in \{0, 1, 2\}}{ s_k G_{i+1,j + k} } \text{~if $i \in [0, T)$, and $0$ if $i = T$}\\
 & G^{green}_{i, j} = S \cdot u^{j-i}-K
 \end{aligned}$
 \end{tabular}
\end{table}
\end{document}

q-fin

Zafar Ahmad
Reilly Browne
Rezaul Chowdhury
Rathish Das
Yushen Huang
Yimin Zhu

"Fast American Option Pricing using Nonlinear Stencils" by Zafar Ahmad, Reilly Browne, Rezaul Chowdhury, Rathish Das, Yushen Huang, and Yimin Zhu, arXiv:2303.02317v2, 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{graphicx}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccccccc}
\midrule
\multicolumn{8}{c}{Number of iterations until stabilization $(T=1/12)$ }\\\toprule
& 1 & 2 & 3 & 4 &5 & 6 & $\geq 7$\\
\midrule
 $j_n$ (\scalebox{0.9}{$\widehat C_n$}) &1.13\% & 27.21\% & 54.13\% & 15.69\% & 1.65\% & 0.17\% & 0.02\% \\
 $j_n^*$ (\scalebox{0.9}{$\widehat C^*_n$}) & 1.06\% & 27.15\% & 55.78\% & 14.45\% & 1.45\% & 0.10\% & 0.01\%\\\bottomrule
\end{tabular}
\end{adjustbox}
\caption{ \footnotesize Empirical distribution of $j_n$ and $j_n^*$ at the $T=1/12$ (1 month) time horizon. Reported values reflect the empirical percentages of the aggregated counts of iterations until stabilization across all computed values of each estimator (for $\widehat C_n$, across both settings $(3)$ and $(4)$; for $\widehat C_n^*$, across both (5ab) and (6ab)) and across Models 1-4. }
\end{table}
\end{document}

stat

B. Cooper Boniece
José E. Figueroa-López
Yuchen Han

"Data-driven fixed-point tuning for truncated realized variations" by B. Cooper Boniece, José E. Figueroa-López, and Yuchen Han, arXiv:2311.00905v3, 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|c|}
 \hline
 ranks & matrix dim & time (ms) & speedup over fp16 \\
 \hline
 0 & 11008x4096 & 13.89 +- 0.23 & 1.97 \\
 \hline
 128 & 11008x4096 & 18.04 +- 0.16 & 1.52 \\
 \hline
 256 & 11008x4096 & 19.019 +- 0.21 & 1.45 \\
 \hline
 \textbf{512} & 11008x4096 & 21.284 +- 0.2 & \textbf{1.29} \\
 \hline
 1024 & 11008x4096 & 25.87 +- 0.26 & 1.06 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Performance metrics for matrix dimension 11008x4096}
\end{table}
\end{document}

stat

Low-Rank Correction for Quantized LLMs

"Low-Rank Correction for Quantized LLMs" by Meyer Scetbon and James Hensman, arXiv:2412.07902v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Syeda Tasnim Fabiha
Rubaiyat Jahan Mumu
Farzana Aktar
B M Mainul Hossain

"A Regression-Based Share Market Prediction Model for Bangladesh" by Syeda Tasnim Fabiha, Rubaiyat Jahan Mumu, Farzana Aktar, and B M Mainul Hossain, arXiv:2507.18643v1, 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{Changes in steady-state values from the decrease in $c_1$ by 20\%.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccc}
 \hline
 \hline
 && {Low-income} & {Middle-income} & {High-income} \\
 && {countries} & {countries} & {countries} \\
 \hline
 {Aggregate}& {Intensive margin} & 7.8\% & 7.7\% & 8.8\%\\
 {output} & {Extensive margin} & 4.3\% & 3.3\% & 1.9\%\\
 &{Net change} & 12.1\% & 11.0\% & 10.7\% \\
 \hline
 {Aggregate} & {Intensive margin} & 8.5\% & 8.4\% & 9.6\%\\
 {consumption} & {Extensive margin} & 1.8\% & 1.4\% & 0.8\%\\
 & {Net change} & 10.3\% & 9.8\% & 10.4\%\\
 \hline
 {Aggregate}& {Intensive margin} & 8.1\% & 7.9\% & 8.9\%\\
 {capital} & {Extensive margin} & 10.1\% & 7.5\% & 4.0\%\\
 & {Net change} & 18.2\% & 15.4\% & 12.9\% \\
 \hline
 {Average}&{Intensive margin} & 7.6\% & 7.5\% & 8.7\%\\
 {wage} & {Extensive margin} & 0.8\% & 0.6\% & 0.4\%\\
 & {Net change} & 8.5\% & 8.1\% & 9.1\%\\
 \hline
 {Modern firms'}&{Intensive margin} & 0.4\% & 0.3\% & 0.2\%\\
 {share of output} & {Extensive margin} & 6.6\% & 5.1\% & 2.7\%\\
 &{Net change} & 7.0\% & 5.4\% & 2.9\%\\
 \hline
 {Firms' entry}& & & &\\
 {(Number of firms)} & {Net change} & 7.7\% & 10.6\% & 14.1\%\\
 \hline
 {Fraction of}& & & &\\
 {modern firms} & {Net change} & 10.5\% & 10.2\% & 8.0\% \\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Jafar M. Olimov
Yi-Chan Tsai
Hao-Yu Yang

"Heterogeneous Bribery, Technology Choice, and Capital Accumulation" by Jafar M. Olimov, Yi-Chan Tsai, and Hao-Yu Yang, arXiv:2507.13124v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc}
Regime & $\mathbb{M}$ & $\hat{p}_{\frac{\alpha}{2}}$ & $\hat{p}$ & $\hat{p}_{1-\frac{\alpha}{2}}$\\
\hline
\hline
$x^+$ & $8$ & $0.168246$ & $0.134076$ & $0.109883$\\
\hline
$x^-$ & $66$ & $0.195254$ & $0.24788$ & $0.322176$\\
\end{tabular}
\end{adjustbox}
\caption{Confidence interval estimation at level $99\%$ for $p$.}
\end{table}
\end{document}

q-fin

"Keynesian Beauty Contest in Morocco's Public Procurement Reform" by Nizar Riane, arXiv:2503.00883v1, 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}{cccccccccc}
\toprule
Metric & \multicolumn{4}{c}{Top-1 Accuracy (\%)} & & \multicolumn{4}{c}{Seen-Unseen AUC (\%)} \\ \cmidrule(lr){2-5} \cmidrule(l){7-10}
Dataset & \multicolumn{2}{c}{CUB} & \multicolumn{2}{c}{NAB} & & \multicolumn{2}{c}{CUB} & \multicolumn{2}{c}{NAB} \\
Split-Mode & Easy & Hard & Easy & Hard & & Easy & Hard & Easy & Hard \\ \midrule
GAZSL~ + CIZSL-v2 on $G$ & 41.9 & 15.5 & 35.0 & 9.6 & & 39.2 & 12.6 & 23.6 & 6.9\\
GAZSL~ + CIZSL-v2 on $G$ and $D$ & 39.6 & 10.3 & 28.0 & 6.8 & & 35.2 & 9.8 & 12.4 & 4.8\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Comparative study of adding identification and entropy loss to the generator. }
\end{table}
\end{document}

cs

"CIZSL++: Creativity Inspired Generative Zero-Shot Learning" by Mohamed Elhoseiny, Kai Yi, and Mohamed Elfeki, arXiv:2101.00173v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{\color{black} Summary for Simulated Systems}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
Test System & Area &Node &Total Variables & Constraints &DERs\\
\hline
TS-1 & 4 & 128 & 512& 898 &3\\
\hline
TS-2 & 4 & 128 & 518 & 910&9\\
\hline
TS-3 & 17 & 2522 & 10198 &17876 &113\\
\hline
TS-4 & 17 & 2522 & 10692 &18738 &544\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Distributed Optimization using Reduced Network Equivalents for Radial Power Distribution Systems" by Rabayet Sadnan and Anamika Dubey, arXiv:2012.02473v1, 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}{lcc}
\toprule
\textbf{Metric} & \textbf{Whisper} & \textbf{Qwen2-Audio} \\
\midrule
Error Cases & 153/700 (21.8\%)& 251/700 (35.8\%)\\
Restored Acc. & 136/153 (88.9\%)& 176/251 (70.1\%)\\
Via Encoder & 130/153 (85.0\%)& 171/251 (68.1\%)\\
Via Decoder & 126/153 (82.4\%)& 147/251 (58.6\%)\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Acoustic-contextual patching results using white noise disruption.}
\end{table}
\end{document}

eess

Neta Glazer
Yael Segal-Feldman
Hilit Segev
Aviv Shamsian
Asaf Buchnick
Gill Hetz
Ethan Fetaya
Joseph Keshet
Aviv Navon

"Beyond Transcription: Mechanistic Interpretability in ASR" by Neta Glazer, Yael Segal-Feldman, Hilit Segev, Aviv Shamsian, Asaf Buchnick, Gill Hetz, Ethan Fetaya, Joseph Keshet, and Aviv Navon, arXiv:2508.15882v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc|cc|cc|cc|ccc}
\toprule
\multirow{2}{*}{Dataset} & \multirow{2}{*}{Noise Rate} & \multicolumn{2}{c|}{Full-training} & \multicolumn{2}{c|}{Linear probing} & \multicolumn{2}{c|}{CoDis} & \multicolumn{3}{c}{CUFIT} \\ \cmidrule(l){3-11}
 & & ResNet & DINOv2 & ResNet & DINOv2 & ResNet & DINOv2 & ResNet & ResNet+rein & DINOv2 \\ \midrule
\multirow{2}{*}{HAM10000} & 0.2 & 73.1 & 66.5 & 71.1 & 75.6 & 74.9 & 80.1 & 77.7 & 79.9 & 82.6 \\
 & 0.4 & 59.6 & 62.6 & 67.8 & 75.3 & 72.4 & 74.1 & 73.8 & 75.4 & 81.5 \\ \midrule
\multirow{2}{*}{APTOS-2019} & 0.2 & 80.4 & 66.8 & 80.3 & 79.2 & 80.3 & 82.0 & 82.4 & 82.2 & 84.2 \\
 & 0.4 & 64.7 & 65.9 & 73.4 & 79.4 & 78.1 & 79.5 & 80.5 & 82.2 & 84.2 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Average test accuracy on simulated noisy datasets (HAM10000 and APTOS-2019) using the ResNet50 architecture. Test accuracy is averaged over the last ten epochs.}
\end{table}
\end{document}

eess

Yeonguk Yu
Minhwan Ko
Sungho Shin
Kangmin Kim
Kyoobin Lee

"Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise" by Yeonguk Yu, Minhwan Ko, Sungho Shin, Kangmin Kim, and Kyoobin Lee, arXiv:2412.00150v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 \hline
 & \multicolumn{2}{c}{$C_{\text{g}}$ [\texteuro]} & \multicolumn{2}{c}{$Q_{\text{loss}}$ [mAh]}\\
 Planner& summer & winter & summer & winter \\
 \hline
 SoH 100 \%&&&&\\
 \hline
 CPBDeg
& 24.7& 79.0& 202.0& 232.8\\
 BNoDeg & 14.9& 76.9& 212.0& 243.6\\
 \hline
 SoH 90 \%&&&&\\
 \hline
 CPBDeg
& 16.3& 78.7& 130.0& 149.0\\
 BNoDeg & 15.2& 77.6& 132.3& 158.3\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Cost and degradation summary.}
\end{table}
\end{document}

math

Darío Slaifstein
Gautham Ram Chandra Mouli
Laura Ramirez-Elizondo
Pavol Bauer

"Aging-aware Energy Management for Residential Multi-Carrier Energy Systems" by Darío Slaifstein, Gautham Ram Chandra Mouli, Laura Ramirez-Elizondo, and Pavol Bauer, arXiv:2503.16139v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ll}
 NPV (real growth) & $\epsilon=0$ & $\epsilon=1$ \\ \hline
 $\xi=0$ & 0.05 (0.12\%) & 1.5 (2.34\%) \\
 $\xi=1$ & 0.05 (0.12\%) & 1.8 (2.62\%)
 \end{tabular}
\end{adjustbox}
\caption{NPV generated by baseline fusion plants operating in each of the four modes described - toggling heat vs electricity output (columns) and grid vs recirculating electricity input (rows). NPV is expressed in units of overnight capital cost, whilst the number in brackets denotes the annual NPV growth during operation. Switching to heat output increases revenues by 30x whilst switching input sources may add a further 20\%.}
\end{table}
\end{document}

q-fin

Fusion for high-value heat production

S. H. Ward
M. Majeed
N. J. Lopes Cardozo

"Fusion for high-value heat production" by S. H. Ward, M. Majeed, and N. J. Lopes Cardozo, arXiv:2504.11072v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Asymptotic bias reduction of maximum likelihood estimates via penalized likelihoods with differential geometry" by Masayo Y. Hirose and Shuhei Mano, arXiv:2011.14747v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{CIFAR10 accuracy for different refinement parameters}
\begin{tabular}{c|c|c}
\toprule
Accuracy & Iterations & Step size \\
\toprule
67.26\% & 1 & 0.03 \\
66.89\% & 2 & 0.015 \\
66.86\% & 4 & 0.01 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data" by Koby Bibas, arXiv:2412.07520v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Mohanad Sarhan
Siamak Layeghy
Marius Portmann

"Towards a Standard Feature Set for Network Intrusion Detection System Datasets" by Mohanad Sarhan, Siamak Layeghy, and Marius Portmann, arXiv:2101.11315v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l}
 & \textbf{Healthy - Orthopedic} & \textbf{Healthy - Neurological} & \textbf{Orthopedic - Neurological} \\ \hline
\textbf{DTW distance} & 0.0531 & 0.0542 & 0.0367
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Axel Roques
Samuel Gruffaz
Kyurae Kim
Alain Oliviero-Durmus
Laurent Oudre

"Personalized Convolutional Dictionary Learning of Physiological Time Series" by Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero-Durmus, and Laurent Oudre, arXiv:2503.07687v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l|l|l}
 Char. & average-1 & std-1 & average-2 & std-2 \\
 \hline
 $Z^I$ & 0.16 & 0.08 & 0.78 & 0.38 \\
 $\bar{Q}$ & 0.17 & 0.11 & 0.02 & 0.01 \\
 $Y_{tot}^\alpha$ & 1.08 & 0.54 & 0.35 & 0.18 \\
 $Y_{tot}^I$ & $1.42\times10^{-5}$ & $7.14\times 10^{-6}$ & $1.35\times10^{-5}$ & $6.75\times 10^{-6}$
 \end{tabular}
\end{adjustbox}
\caption{Average and standard deviation for characteristics in Scenario 1 ($\phi_1=2$) and Scenario 2 ($\phi_1=20$). The other parameters are the same as in Figure .}
\end{table}
\end{document}

q-fin

"Liquidity Competition Between Brokers and an Informed Trader" by Ryan Donnelly and Zi Li, arXiv:2503.08287v1, 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}{ccccccc}
			\toprule
			 & Real & Adapted & Spatial Gaussian (15) & Adapted & Poisson (0.5) & Adapted \\
			\midrule
			Spatial domain & 0.080953 & 0.004417 & 0.004294 & 0.008760 & 0.423069 & 0.011621 \\
			\midrule
			Frequency domain & 0.995228 & 0.006732 & 0.579235 & 0.004813 & 0.001193 & 0.001737 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Inju Ha
Donghun Ryou
Seonguk Seo
Bohyung Han

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model comparisons of forecast combination models.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|} 	\hline
				Models & {\bf Strengths} & {\bf Weaknesses} \\ \hline
				AQR 	 & 1. Estimates are more robust to & 1. Parameters are harder to estimate \\
				& non-normal errors. & than other regression techniques. \\
				& 2. Estimates are robust against outliers. & \\
				& 3. Characterisation of the data is richer. & \\
				& 4. It is invariant to monotonic transformations. & \\ 		\hline
				PLAQR & 1. It is flexible to model data that is complex. &1. Challenge of the existence \\
				& 2. It is more parsimonious than & of non-smoothness of the loss function. \\
				& 	 other regression models. 				 & \\
				& 3. Very stable and flexible for & \\
				& large-scale interpolation. & \\
				& & \\ \hline
				QRNN & 1. It is flexible to model data on & 1. Not very well developed \\
				& 	modelling dependencies. 		 & for tasks that are not classified. \\
				& 2. Scalability is relatively good & 2. Sometimes gives overconfidence in \\
				& 	with large datasets. 				 & predictions when applying training sample. \\ 			\hline
				OPERA & 1. Combines multiple individual techniques & 1. Finding feature importance \\
				& to build a more powerful technique. & is a challenge. \\
				& 2. Better prediction. & \\
				& 3. Improved results as compared & \\
				& to individual models. & \\
				& & \\ 			\hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Edina Chandiwana
Caston Sigauke
Alphonce Bere

"Robust modelling framework for short-term forecasting of global horizontal irradiance" by Edina Chandiwana, Caston Sigauke, and Alphonce Bere, arXiv:2212.05978v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
 \hline
 \multirow{2}{*}{Dataset} & \multicolumn{2}{c}{Spectral Group Encoding Index} & \multirow{2}{*}{Diff}\\
 & Pre-Training & Fine-Tuning & \\
 \hline
 EuroSAT & 98.02 & 98.37 & +0.35 \\
 BigEarthNet & 85.70 & 85.82 & +0.12 \\
 METER-ML Sentinel-2 & 71.39 & 73.95 & +2.56 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Downstream performance of multi-sensor pre-trained USatMAE comparing the effect of using spectral group encodings corresponding to the indices used during pre-training and fine-tuning. As in the other downstream experiments, we use superpositional encodings and no sensor encoding for these experiments.}
\end{table}
\end{document}

stat

Jeremy Irvin
Lucas Tao
Joanne Zhou
Yuntao Ma
Langston Nashold
Benjamin Liu
Andrew Y. Ng

"USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery" by Jeremy Irvin, Lucas Tao, Joanne Zhou, Yuntao Ma, Langston Nashold, Benjamin Liu, and Andrew Y. Ng, arXiv:2312.02199v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
\hline
Dataset & Train & Validation & Test & Total \\ \hline
NIH & 1152 & 144 & 144 & 1440 \\ \hline
D1 + D3 & 800 & 100 & 100 & 1000 \\ \hline
D2 & - & - & 183 & 183 \\ \hline
Total & 1952 & 244 & 244 + 183 & 2623 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Distribution of Cardiomegaly CXR's}
\end{table}
\end{document}

cs

Tanveer Gupte
Mrunmai Niljikar
Manish Gawali
Viraj Kulkarni
Amit Kharat
Aniruddha Pant

"Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly" by Tanveer Gupte, Mrunmai Niljikar, Manish Gawali, Viraj Kulkarni, Amit Kharat, and Aniruddha Pant, arXiv:2101.07606v1, 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{Datasets details}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
\textbf{\textit{Name}} & \textbf{\textit{Images}} & \textbf{\textit{Lesions}} \\
\hline
PH2 & 200 & Nevus and Melanoma \\
ISBI 2017 & 2750 & Nevus, Seborrheic Keratosis and Melanoma \\
PAD-UFES & 220 & Nevus, Seborrheic Keratosis and Melanoma \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data" by José G. M. Esgario and Renato A. Krohling, arXiv:2201.10650v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
 \toprule
 Parameter Name & Value \\
 Replay buffer capacity & $100000$ \\
 Batch size & $512$ \\
 Discount $\gamma$ & $0.99$\\
 Optimizer & Adam \\
 Learning rate & $10^{-3}$ \\
 Critic target update frequency & $2$ \\
 Critic Q-function soft update rate $\tau$ & $0.01$\\
 Actor update frequency & $2$ \\
 Actor log stddev bounds & $[-10, 2]$ \\
 Temperature learning rate & $10^{-4}$ \\
 Initial Temperature & $0.1$ \\
 Entropy Target & $-\text{num actions}$\\
 Image augmentation pad (DrQ)& $4$\\

 \end{tabular}
\end{adjustbox}
\caption{Architecture and training hyperparameters}
\end{table}
\end{document}

cs

Martin Bertran
Walter Talbott
Nitish Srivastava
Joshua Susskind

"Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation" by Martin Bertran, Walter Talbott, Nitish Srivastava, and Joshua Susskind, arXiv:2201.12300v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|l|l|}
 \hline
 Method & CV1 Error & Non-zeros & Condition No. & Delta WS \\ \hline
 Glasso1 & 0.73 & 5.7E+4 & 41.62 & 6.15E-15 \\ \hline
 Clime1 & 0.87 & 2.3E+4 & 5.37 & 8.49E-15 \\ \hline
 Greedy Prune1 & 0.97 & 1.1E+4 & 16.02 & 1.38E-15 \\ \hline
 MB1 & 0.78 & 3.4E+4 & 8.52 & 6.18E-15 \\ \hline
 HybridMB1 & 0.94 & 3.1E+4 & 37.82 & 1.72E-14 \\ \hline
 \end{tabular}
\caption{ Results for precision matrix selected via 5-fold CV1 on Nifty500 constituents}
\end{table}
\end{document}

q-fin

"Precision versus Shrinkage: A Comparative Analysis of Covariance Estimation Methods for Portfolio Allocation" by Sumanjay Dutta and Shashi Jain, arXiv:2305.11298v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|cc|}
 \hline
 Solar Panels & $SD^q$ & $\mathcal{S}(SD^q)$ & $SD^p$ & $\mathcal{S}(SD^p)$ \\ \hline
 $P_1$ & $\langle (0.60,0.39);0.73\rangle$ & $0.596$ & $\langle (0.60,0.39);0.75\rangle$ & $0.598$ \\
 $P_2$ & $\langle (0.62,0.38);0.61\rangle$ & $0.601$ & $\langle (0.62,0.38);0.62\rangle$ & $0.602$ \\
 $P_3$ & $\langle (0.56,0.43);0.42\rangle$ & $0.538$ & $\langle (0.56,0.43);0.42\rangle$ & $0.538$ \\
 $P_4$ & $\langle (0.60,0.40);0.66\rangle$ & $0.587$ & $\langle (0.60,0.40);0.68\rangle$ & $0.588$ \\
 $P_5$ & $\langle (0.56,0.44);0.70\rangle$ & $0.587$ & $\langle (0.56,0.44);0.71\rangle$ & $0.554$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{The results of significance degrees $SD$ based on $WSM$ and $WPM$}
\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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{r|rrr}
 Model & MicroLlama 300M & TinyLlama 1.1B & OpenLlama 3B \\
 \midrule
 $n_{\mathrm{params}}$ & 304.6M & 1.1B & 3.4B \\
 $d_{\mathrm{model}}$ & 2048 & 2048 & 2048 \\
 $n_{\mathrm{layers}}$ & 12 & 22 & 26 \\
 $n_{\mathrm{heads}}$ & 12 & 32 & 32 \\
 $d_{\mathrm{head}}$ & 64 & 64 & 100 \\
 \bottomrule
 \end{tabular}
\caption{Specifications of models}
\end{table}
\end{document}

math

Tim Tsz-Kit Lau
Weijian Li
Chenwei Xu
Han Liu
Mladen Kolar

"Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism" by Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu, Han Liu, and Mladen Kolar, arXiv:2412.21124v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|lll}
\# & $\tau_{large}$ &$\tau_{mix}$ & $\tau_{small}$ \\\hline
1 & 25,850 & 25,640 & 194,360 \\
2 & 1,510 & 1,458 & 2,522 \\
3 &12,408 & 12,452 & 27,714 \\
4 & 62,499 & 61,007 & 94,424 \\
5 & 62,508 & 60,989 & 94,444 \\
6 & 64,497 & 63,109 & 95,064 \\
7 & 65,518 & 66,266 & 95,916 \\
8 & 70,609 & 77,180 & 98,515
 \end{tabular}
\end{adjustbox}
\caption{Number of nonzeros of the new scheme for the new scheme (generalized case) with $M$-orthogonalization}
\end{table}
\end{document}

math

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


stat

"Conditional-mean Multiplicative Operator Models for Count Time Series" by Christian H. Weiß and Fukang Zhu, arXiv:2212.05831v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Complexity of trust-region methods with unbounded Hessian approximations for smooth and nonsmooth optimization" by Geoffroy Leconte and Dominique Orban, arXiv:2312.15151v3, 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}{crrr}
 &&\multicolumn{2}{c}{Mean-squared Prediction Error(MSPE)}\\
 Model & No.\ Parameters & One-Step & Two-Step\\
 \hline
 GNAR(1, [1]) & 2 & 5.09 & 8.81 \\
 GNAR(1, [6]) & 7 & 5.06 & 8.8 \\
 GNAR(6, [6, \ldots , 6]) & 42& 5.24 & 9.88
 \end{tabular}
\end{adjustbox}
\caption{One- and two-step predictive performance for different model order choices.}
\end{table}
\end{document}

stat

Guy Nason
Daniel Salnikov
Mario Cortina-Borja

"New tools for network time series with an application to COVID-19 hospitalisations" by Guy Nason, Daniel Salnikov, and Mario Cortina-Borja, arXiv:2312.00530v1, 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{Baseline comparison of the AD classification. Our test results below are corresponding to the hard ensemble model.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccccc}
 \toprule
 & Model & Accuracy & Precision & Recall & F1-Score\\
 \hline
 LOSO & Luz et al. & 0.77 & 0.77 & 0.76 & 0.77\\
 & Ensemble (\textit{ours}) & \textbf{0.99} & \textbf{0.99} & \textbf{1.00} & \textbf{0.99}\\
 \hline
 TEST & Luz et al. & 0.75 & 0.83 & 0.62 & 0.71\\
 & Ensemble (\textit{ours}) & \textbf{0.83} & \textbf{0.83} & \textbf{0.83} & \textbf{0.83}\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Utkarsh Sarawgi
Wazeer Zulfikar
Nouran Soliman
Pattie Maes

"Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity" by Utkarsh Sarawgi, Wazeer Zulfikar, Nouran Soliman, and Pattie Maes, arXiv:2009.00700v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of Best Models and Their Scores by Metric}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcc}
\toprule
\textbf{Metric} & \textbf{Best Model} & \textbf{Avg Score} & \textbf{Range} \\
\midrule
ROUGE-1 & Gemini & 0.56 & 0.20--0.62 \\
ROUGE-2 & Gemini & 0.22 & 0.05--0.28 \\
ROUGE-L & Gemini & 0.16 & 0.06--0.17 \\
Cosine Similarity & Claude & 0.68 & 0.44--0.80 \\
Jaccard & Gemini & 0.21 & 0.07--0.26 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis" by Md Talha Mohsin, arXiv:2507.22936v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Discordance Minimization-based Imputation Algorithms for Missing Values in Rating Data" by Young Woong Park, Jinhak Kim, and Dan Zhu, arXiv:2311.04035v1, 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
\caption{Overview of literature on optimal power and gas flow.}
\begin{tabular}{lrll}
\hline
\multirow{2}{*}{Model} & \multirow{2}{*}{Exact} & \multicolumn{2}{c}{Relaxation} \\ \cline{3-4}
 & & Linear & Conic \\ \hline
Dynamic ($\mathrm{DY}$) & & - & \\
Quasi-dynamic ($\mathrm{QD}$) & & & \\
Steady-state ($\mathrm{ST}$) & & & \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Enrica Raheli
Yannick Werner
Jalal Kazempour

"Flexibility of Integrated Power and Gas Systems: Gas Flow Modeling and Solution Choices Matter" by Enrica Raheli, Yannick Werner, and Jalal Kazempour, arXiv:2311.05744v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"An Information Theory Approach to the Stock and Cryptocurrency Market: A Statistical Equilibrium Perspective" by Emanuele Citera and Francesco De Pretis, arXiv:2310.04907v1, 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{Truth Table for CNOT-Gate}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
 \multicolumn{2}{|c|}{Input}&\multicolumn{2}{c|}{Output}\\
 \hline
 Control Qubit& Target Qubit& Control Qubit& Target Qubit \\
 \hline
 0 & 0 &0&0\\
 \hline
 0 & 1&0& 1\\
 \hline
 1&1&1&0\\
 \hline
 1&0&1&1\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Amoldeep Singh
Kapal Dev
Harun Siljak
Hem Dutt Joshi
Maurizio Magarini

"Quantum Internet- Applications, Functionalities, Enabling Technologies, Challenges, and Research Directions" by Amoldeep Singh, Kapal Dev, Harun Siljak, Hem Dutt Joshi, and Maurizio Magarini, arXiv:2101.04427v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistical comparison of the learning strategies (shuffled vs. curriculum) used for training different networks on each motion category with $\alpha = 0.05$.}
\begin{tabular}{c|c|c|c|c|c} % @ property can be modified
\toprule
 & UNet Attention & UNet 2.5D & LSTM UNet & LSTM ResNet34 & LSTM CorNet \\
\midrule
Minimal & 0.0005 & 4.91e-5 & 0.0002 & 71e-5 & 0.0029 \\
Mild & 8.81e-6 & 0.257 & 0.232 & 0.473 & 0.0012 \\
Moderate & 8.41e-8 & 0.007 & 0.003 & 0.056 & 0.0045 \\
Severe & 6.81e-5 & 0.012 & 6.81e-5 & 0.038 & 0.0003 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Tejas Sudharshan Mathai
Yi Wang
Nathan Cross

"Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI" by Tejas Sudharshan Mathai, Yi Wang, and Nathan Cross, arXiv:2010.06027v1, 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
 Dataset/Arch & Linear & Indi Nonlinear & Nonlinear Groups \\ \hline
 TCS & $x_2-x_{5}$ & $x_{1},x_6$ & \\
 & $x_7-x_{23}$ & & \\ \hline
 PCB & $x_1-x_{16}$ & $x_{19}$ & $(x_{17},x_{22})$ \\
 & $x_{18}, x_{20}, x_{21}$ & & \\
 & $x_{23} - x_{34}$ & & \\ \hline
 GE & $x_1-x_8$ & $x_9$ & & \\
 & $x_{10} - x_{13}$ & & \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance" by Dangxing Chen and Weicheng Ye, arXiv:2209.10082v2, 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{The distribution of MC integers $n<1100$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
$\lfloor n/100 \rfloor$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 &10 \\
\hline
$\sharp\{n|$ $n$ is MC$\}$ & 79& 84& 80& 81& 87& 83& 83& 86& 77& 87 & 84 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"The Largest Circle Enclosing $n$ Lattice Points" by Jianqiang Zhao, arXiv:2505.06234v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Type}& \textbf{\# Entities} & \textbf{\% Entities} & \textbf{\# Mentions} & \textbf{\% Mentions} \\
 \midrule
 \textsc{char} & 282 & 25.0\% & 290 & 19.7\% \\
 \textsc{fac} & 71 & 6.4\% & 106 & 7.2\% \\
 \textsc{loc} & 186 & 16.7\% & 221 & 15.0\% \\
 \textsc{org} & 73 & 6.6\% & 137 & 9.3\% \\
 \textsc{per} & 490 & 44.0\% & 708 & 48.0\% \\
 \textsc{misc} & 11 & 1.0\% & 12 & 0.8\% \\
 \midrule
 \textbf{Total} & \textbf{1113} & \textbf{100.0\%} & \textbf{1474} & \textbf{100.0\%} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Dataset entities statistics}
\end{table}
\end{document}

cs

Timofey Atnashev
Veronika Ganeeva
Roman Kazakov
Daria Matyash
Michael Sonkin
Ekaterina Voloshina
Oleg Serikov
Ekaterina Artemova

"Razmecheno: Named Entity Recognition from Digital Archive of Diaries "Prozhito"" by Timofey Atnashev, Veronika Ganeeva, Roman Kazakov, Daria Matyash, Michael Sonkin, Ekaterina Voloshina, Oleg Serikov, and Ekaterina Artemova, arXiv:2201.09997v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l||c|c|}
\hline
\textbf{Metric} & \textbf{MultiWOZ} & \textbf{CrossWOZ} \\
\hline
\textbf{Language} & English & Chinese (Simplified) \\
\textbf{\# Dialogues} & ~~~8,438 & ~~5,012 \\
\textbf{Total \# turns} & 113,556 & 84,692 \\
\# \textbf{Domains} & 7 & 5 \\
\# \textbf{Slots} & 24 &72 \\
\# \textbf{Values} & 4,510 & 7,871 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Statistics of MultiWOZ and CrossWOZ. Note that the translated version of these two datasets have the same metrics}
\end{table}
\end{document}

cs

"An Empirical Study of Cross-Lingual Transferability in Generative Dialogue State Tracker" by Yen-Ting Lin and Yun-Nung Chen, arXiv:2101.11360v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Optimal-$k$ difference sequence in nonparametric regression" by Wenlin Dai, Xingwei Tong, and Tiejun Tong, arXiv:2211.15087v1, 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{Detection Results in Different Types of Attacks.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
		\hline
		& \textbf{TPR} & \textbf{FPR} & \textbf{Acc.} & \textbf{AUROC} \\ \hline
		\textbf{Playback} & 99.3\% & 1.0\% & 99.2\% & 0.999 \\ \hline
		\textbf{Tampering} & 99.1\% & 2.6\% & 98.3\% & 0.995 \\ \hline
		\textbf{Overall} & 99.2\% & 1.8\% & 98.7\% & 0.997 \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yong Huang
Xiang Li
Wei Wang
Tao Jiang
Qian Zhang

"Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information" by Yong Huang, Xiang Li, Wei Wang, Tao Jiang, and Qian Zhang, arXiv:2201.09487v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Attack success rate (ASR) and clean test accuracy (ACC) for the classifiers being attacked (with one, two, and three attacks/target classes) for CIAFR-10, CIFAR-100, and STL-10, respectively; and ACC for the clean classifiers trained for the three domains respectively.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
			&CIFAR-10 &CIFAR-100 &STL-10
			\\ \hline \\
			one attack &\thead{ASR: 97.0\\ACC: 93.7} &\thead{ASR: 97.7\\ACC: 71.5} &\thead{ASR: 95.1\\ACC: 79.7}\\
			two attacks &\thead{ASR=96.0, 93.7\\ACC: 92.5} &\thead{ASR=89.6, 95.3\\ACC: 71.9} &\thead{ASR=99.6, 96.0\\ACC: 80.8}\\
			three attacks &\thead{ASR: 94.5, 80.2, 97.9\\ACC: 92.8} &\thead{ASR: 95.7, 74.7, 97.4\\ACC: 70.5} &\thead{ASR: 78.1, 99.0, 95.1\\ACC: 79.1}\\
			\hline
			no attack &ACC: 92.5 & ACC: 70.4 &ACC: 78.8
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zhen Xiang
David J. Miller
George Kesidis

"Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios" by Zhen Xiang, David J. Miller, and George Kesidis, arXiv:2201.08474v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation Study}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 & H & H \& V & H \& V \\
 \midrule
 Dual-Branch & & \checkmark & \checkmark \\
 Multi-Head Fusion & & & \checkmark \\
 \midrule
 Single-Person(4) & 0.634 & 0.644 & \textbf{0.681} \\
 Multi-Person(4) & 0.587 & 0.604 & \textbf{0.682} \\
 Action(4) & 0.582 & 0.585 & \textbf{0.681} \\
 Single-Person(12) & 0.655 & 0.670 & \textbf{0.706} \\
 Multi-Person(12) & 0.638 & 0.642 & \textbf{0.711} \\
 Action(12) & 0.603 & 0.603 & \textbf{0.705} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zhi Wu
Dongheng Zhang
Chunyang Xie
Cong Yu
Jinbo Chen
Yang Hu
Yan Chen

"RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signals" by Zhi Wu, Dongheng Zhang, Chunyang Xie, Cong Yu, Jinbo Chen, Yang Hu, and Yan Chen, arXiv:2201.10175v1, 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{ Estimated exponent $a$ [$95$\% CI] for the convergence rate $n^{-a}$ for empirical SE.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Estimator} & \textbf{CTGAN} & \textbf{TVAE} & \textbf{CTGAN} & \textbf{TVAE} \\
 \midrule
 & \multicolumn{2}{c}{\textbf{Default synthetic datasets}} & \multicolumn{2}{c}{\textbf{Debiased synthetic datasets}} \\
 Mean age & 0.01 [-0.16; 0.18] & 0.21 [0.04; 0.39] & 0.50 [0.46; 0.54] & 0.47 [0.44; 0.50]\\
 Effect therapy & 0.31 [0.22; 0.40] & 0.09 [-0.13; 0.31] & 0.43 [0.31; 0.55] & 0.46 [0.38; 0.55]\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

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

"Debiasing Synthetic Data Generated by Deep Generative Models" by Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet, Johan Decruyenaere, Thomas Demeester, and Stijn Vansteelandt, arXiv:2411.04216v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\hline
& \textbf{Estimate} & \textbf{S.E.} & \textbf{t-stat} & \textbf{p-value} \\
\hline
$\hat\beta_0$ & $0.068831$ & $0.0030407$ & $22.637$ & $2.0416$E$-74$ \\
$\hat\beta_1$ & $-1.77$E$-05$ & $5.2761$E$-06$ & $-3.3628$ & $0.00084348$ \\
\hline
\end{tabular}
\end{adjustbox}
\caption{OLS estimation results for the regression defined by equation .}
\end{table}
\end{document}

q-fin

Katia Colaneri
Alessandra Cretarola
Edoardo Lombardo
Daniele Mancinelli

"Design and hedging of unit linked life insurance with environmental factors" by Katia Colaneri, Alessandra Cretarola, Edoardo Lombardo, and Daniele Mancinelli, arXiv:2509.05676v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Serafino Cicerone
Gabriele Di Stefano
Sandi Klavžar
Ismael G. Yero

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


\documentclass{article}
\usepackage{amsmath}
\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
 Model & RMSE & MdAE & PER10 ($\%$) & PER20 ($\%$) \\
 \midrule
 Gradient boosted trees & 0.62 & 0.24 & 71.0 & 93.8 \\
 Random forests & 0.64 & 0.24 & 70.4 & 93.6 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Accuracy of the point predictions on the real data with a random forests model in addition the gradient boosted trees model depicted in the main text. The table displays Root Mean Squared Error (RMSE), Median Absolute Error (MdAE), and Percentage Error Range (PER$10$ and PER$20$).}
\end{table}
\end{document}

stat

Anders Hjort
Gudmund Horn Hermansen
Johan Pensar
Jonathan P. Williams

"Uncertainty quantification in automated valuation models with spatially weighted conformal prediction" by Anders Hjort, Gudmund Horn Hermansen, Johan Pensar, and Jonathan P. Williams, arXiv:2312.06531v2, 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 of the Gaussians defining synthetic seafloor deformation for the numerical example.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
		\toprule
		Symbol & Description & & Value & & Unit \\
		\cmidrule{3-5}
		& & $i=1$ & $i=2$ & $i=3$ & \\
		\midrule
		$A^{(i)}$ & Amplitude & 4 & 1 & -0.5 & m \\
		$T_{\text{r}}^{(i)}$ & Rise time & 20 & 10 & 10 & s \\
		$X_{\text{r}}^{(i)}$ & Rise width in $x$ & 16 & 4 & 4 & km \\
		$Y_{\text{r}}^{(i)}$ & Rise width in $y$ & 32 & 4 & 8 & km \\
		$X_{\text{c}}^{(i)}$ & Center in $x$ & 64 & 64 & 70 & km \\
		$Y_{\text{c}}^{(i)}$ & Center in $y$ & 64 & 88 & 56 & km \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Stefan Henneking
Sreeram Venkat
Omar Ghattas

"Goal-Oriented Real-Time Bayesian Inference for Linear Autonomous Dynamical Systems With Application to Digital Twins for Tsunami Early Warning" by Stefan Henneking, Sreeram Venkat, and Omar Ghattas, arXiv:2501.14911v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{wind speed data: model selection criteria (DIC) and predictive performance measures (Logarithmic Score) with automatic groups construction for $m=3$, $m=5$, and $m=10$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{crrrr}
\hline
\multirow{2}{*}{Model for $u$} & \multirow{2}{*}{DIC} & \multicolumn{3}{c}{Logarithmic Score} \\
& & m=3 & m=5 & m=10 \\
\hline
$M_A$ & 3499.78 & 6202.79 & 6692.26 & 7461.16 \\
$M_B$ & \textbf{1622.16} & \textbf{6054.93} & \textbf{6563.31} & \textbf{7413.71} \\
$M_C$ & 3433.40 & 6214.94 & 6678.89 & 7435.29 \\
$M_D$ & 2078.38 & 7123.03 & 7587.39 & 8342.42 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

A. Adin
E. Krainski
A. Lenzi
Z. Liu
J. Martínez-Minaya
H. Rue

"Automatic cross-validation in structured models: Is it time to leave out leave-one-out?" by A. Adin, E. Krainski, A. Lenzi, Z. Liu, J. Martínez-Minaya, and H. Rue, arXiv:2311.17100v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|lll}
\toprule
\textbf{Dataset} & \textbf{Prompt \#1} & \textbf{Prompt \#2} & \textbf{ Prompt \#3} \\
\midrule
\textbf{Yelp} & Check out trusted reviews & Discover popular attractions & Find honest business information \\
\textbf{Twitter} & Emotion Insights & Exploring Emotional Tweets & Understanding Emotions \\
\midrule
\textbf{SQuAD} & Addressing Unanswerable Questions & Improving Paragraph Understanding & Enhancing Answer Verification \\
\textbf{RACE} & Designing informative passages & Enhancing dataset diversity & Exploring contextual understanding \\
\midrule
\textbf{CNN/DM} & Sift through crucial content & Delve into valuable content & Abstract significant content \\
\textbf{XSum} & Exploring importance of context & Studying role of discourse structure & Analyzing role of sentence compression \\
\bottomrule
\end{tabular}
\caption{The top-3 prefix prompts for each dataset.}
\end{table}
\end{document}

cs

"GPTA: Generative Prompt Tuning Assistant for Synergistic Downstream Neural Network Enhancement with LLMs" by Xiao Liu and Jiawei Zhang, arXiv:2404.00189v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of successes of PGD attacks crafted using cross-entropy, KL-divergence, and \textit{SDI} measure on defences \textit{AT} and \textit{TRADES} on CIFAR-10 for Resnet-18.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
\hline
PGD Attack Measure & \textit{AT} & \textit{TRADES} & \\
\hline
 Cross-entropy & \textbf{52.78}\tiny{ $\pm$ 0.10}
 & \textbf{52.82}\tiny{ $\pm$ 0.12} & \\
KL-divergence & 68.03\tiny{ $\pm$ 0.15} & 68.87\tiny{ $\pm$ 0.14}& \\
\textbf{SDI} & 53.95\tiny{ $\pm$ 0.14}& 54.32\tiny{ $\pm$ 0.19}& \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Olukorede Fakorede
Modeste Atsague
Jin Tian

"Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness" by Olukorede Fakorede, Modeste Atsague, and Jin Tian, arXiv:2412.19947v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Water Quality Prediction Results (Numerical Features Only; Standardized).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
\toprule
 Models & RMSE & MAPE & WMAPE & WUPRED & WOPRED \\ %\tnote{1}
\midrule
SADL-II (Original Paper) & 11.50 & N/A & N/A & N/A & N/A \\
Benchmarking & 29.52 & 32.21 & 32.35 & 14.55 & 17.80 \\
LightGBM & \textcolor{red}{10.84} & 9.33 & 9.46 & \textcolor{red}{5.16} & 4.30 \\
Linear Regression & 12.04 & 10.74 & 10.92 & 5.66 & 5.25 \\
MLP & 14.20 & 12.92 & 13.16 & 3.83 & 9.33 \\
Random Forest & 11.47 & 9.71 & 9.87 & 5.35 & 4.51 \\
XGBoost & 10.96 & \textcolor{red}{9.22} & \textcolor{red}{9.36} & 5.17 & \textcolor{red}{4.19} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The approximate KL divergence between the targets and $q$ using 1000 updates of particles, averaged over five runs.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccr}
 \hline
 \textbf{Targets} & \textbf{WVI} & \textbf{NGVI} & \textbf{GFlowVI} & \textbf{NGFlowVI}\\
 \hline
 Banana-shaped & 0.15$\pm$0.02 & 0.32$\pm$0.01 & 0.21$\pm$0.02 & 0.12$\pm$0.02\\
 X-shaped & 0.03$\pm$0.02 & 0.05$\pm$0.03 & 0.04$\pm$0.05 & 0.02$\pm$0.02\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Dai Hai Nguyen
Tetsuya Sakurai
Hiroshi Mamitsuka

"Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference" by Dai Hai Nguyen, Tetsuya Sakurai, and Hiroshi Mamitsuka, arXiv:2310.16705v5, 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 of the BD method with other defense methods against untargeted black-box attacks in terms of ACC degradation and ASR} %The defense performing best while maintaining the lowest model ACC degradation is highlighted in bold.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|cc}
		\toprule
		Dataset & Defense & Degradation & SA & SimBA\\
		 & Method & of ACC (\%) & ASR (\%) & ASR (\%)\\
		\midrule
		 & RND & -1.9 & 51.9 & 38.8
		 \\
		 & GF & -0.2 & 95.8 & 88.8 \\
		 IMAGE- & FD & -20.8 & 51.8 & 38.7 \\
		 NET & AT & -13.4 & 47.2 & 35 \\
		 & RND+AT & -16.8 & 20
		 & 4.7 \\
		 & BD(0.4, 0.1) & -2.0 &\textbf{30} & \textbf{0}\\
		 & BD(0.6, 0.1) & -3.5 &\textbf{15} & \textbf{-}\\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Boundary Defense Against Black-box Adversarial Attacks" by Manjushree B. Aithal and Xiaohua Li, arXiv:2201.13444v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Modified Dai-Liao Spectral Conjugate Gradient Method with Application to Signal Processing" by D. R. Sahu, Shikher Sharma, and Pankaj Gautam, arXiv:2501.15300v2, 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}{lccc}
 \hline
 \textbf{Covariate} & \textbf{Type} & \textbf{Diff. Unadjusted} & \textbf{Diff. Adjusted} \\
 \hline
 Distance & Distance & 0.8043 & -0.0001 \\
 Google Scholar ID Yes & Binary & -0.1162 & -0.2857 \\
 Titles: Assistant Professor & Binary & 0.1258 & 0.0181 \\
 Titles: Associate Professor & Binary & 0.0178 & -0.0347 \\
 Titles: Professor & Binary & -0.1436 & 0.0166 \\
 Working Years & Continuous & -0.2927 & -0.0833 \\
 University Code: Bachelor/Master & Binary & 0.0052 & 0.0029 \\
 University Code: DRU(H) & Binary & -0.0037 & -0.0100 \\
 University Code: DU/VA & Binary & -0.0015 & -0.0130 \\
 Department Code: AH & Binary & 0.0119 & 0.0288 \\
 Department Code: B & Binary & -0.0273 & -0.0261 \\
 Department Code: MHS & Binary & 0.0967 & 0.0487 \\
 Department Code: NS & Binary & -0.0541 & -0.0217 \\
 Department Code: SS & Binary & 0.0543 & -0.0108 \\
 Department Code: TE & Binary & -0.0814 & -0.0190 \\
 $\log_{10}$(i10index) & Continuous & -0.6261 & 0.2676 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Covariate Balance Before and After Matching}
\end{table}
\end{document}

q-fin

"Estimation of Gender Wage Gap in the University of North Carolina System" by Zihan Zhang and Jan Hannig, arXiv:2505.24078v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A Bimodal Weibull Distribution: Properties and Inference" by Roberto Vila and Mehmet Niyazi Çankaya, arXiv:2012.01238v1, 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{Classification results on the ImageNet dataset with 0.5bpp using ResNet18 as downstream AI task.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllll}
		\toprule[1pt]
		Method & SI & ACC & F1-score & PSNR & MSE & SSIM \\
		\hline
		original & 128.50 & 89.42\% & 0.8937 & - & 0 & 1 \\
		TDIC & 57.02 & 78.35\% & 0.7752 & 29.37 & 0.0016 & 0.8953 \\
		APIC & 80.60 & 84.53\% & 0.8451 & 26.44 & 0.0027 & 0.8637 \\
		\hline
		SAIC & \textbf{86.18} & \textbf{85.25\%} & \textbf{0.8522} & 26.40 & 0.0028 & 0.8391 \\
		\toprule[1pt]
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Semantic-assisted image compression

Qizheng Sun
Caili Guo
Yang Yang
Jiujiu Chen
Xijun Xue

"Semantic-assisted image compression" by Qizheng Sun, Caili Guo, Yang Yang, Jiujiu Chen, and Xijun Xue, arXiv:2201.12599v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Jakob Albers
Mihai Cucuringu
Sam Howison
Alexander Y. Shestopaloff

"To Make, or to Take, That Is the Question: Impact of LOB Mechanics on Natural Trading Strategies" by Jakob Albers, Mihai Cucuringu, Sam Howison, and Alexander Y. Shestopaloff, arXiv:2502.18625v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{A2C hyperparameters}
\begin{tabular}{c|c}
 \hline
 Parameter & Value \\
 \hline
 Discounting factor $\gamma$ & $0.9$ \\
 Learning rate $\alpha$ & $1\cdot10^{-4}$ \\
 Rollout length & $10$ \\
 Entropy coefficient $\beta$ & $0.01$ \\
 GAE $\lambda$ & $1.0$ \\
\end{tabular}
\end{table}
\end{document}

eess

"GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platform" by David Cotton and Zenon Chaczko, arXiv:2101.11188v1, 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}{rr|rrr|rrr}
		\toprule
		\multirow{2}{*}{Ref} &
		\multirow{2}{*}{\# nodes} &
		\multicolumn{3}{c|}{fixed-point method} &
		\multicolumn{3}{c}{semismooth Newton method} \\
		&&
		iter & time [s] & avg time [s] & iter & time [s]& avg time [s] \\
		\midrule
 4 & 545 & 532 & 2.50 & 4.704e$-$03 & 11 & 0.07 & 6.244e$-$03 \\
 5 & 2113 & 536 & 8.29 & 1.547e$-$02 & 13 & 0.33 & 2.519e$-$02 \\
 6 & 8321 & 542 & 33.47 & 6.175e$-$02 & 13 & 1.65 & 1.270e$-$01 \\
 7 & 33025 & 517 & 138.21 & 2.673e$-$01 & 12 & 9.20 & 7.670e$-$01 \\
 8 & 131585 & 574 & 678.78 & 1.183e$+$00 & 12 & 58.74 & 4.895e$+$00 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{Number of iterations, total calculation time and average time (per iteration) for different refinements of a coarse mesh.}
\end{table}
\end{document}

math

"Numerical solution of optimal control problems using quadratic transport regularization" by Nicolas Borchard and Gerd Wachsmuth, arXiv:2503.07105v1, 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{Properties of the network data sets. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccccc}
Network & $|V|$ & average & average degree \\
&& degree & centrality \\
\midrule\midrule
\textbf{Boston} & 431 & 2.92 & 0.005 \\
\textbf{Daniels County} & 631 & 2.53 & 0.008 \\
\textbf{Los Angeles} & 561 & 2.85 & 0.001\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Han-Ching Ou
Christoph Siebenbrunner
Jackson Killian
Meredith B Brooks
David Kempe
Yevgeniy Vorobeychik
Milind Tambe

"Networked Restless Multi-Armed Bandits for Mobile Interventions" by Han-Ching Ou, Christoph Siebenbrunner, Jackson Killian, Meredith B Brooks, David Kempe, Yevgeniy Vorobeychik, and Milind Tambe, arXiv:2201.12408v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\hline
Scenario & $\delta$ & Runtime \\
\hline
Paris & 0.1s & 13.65ms \\
Paris & 0.2s & 0.01ms \\
New York & 0.1s & 92.50ms \\
Singapore & 0.1s & 33.33ms \\
Singapore & 0.2s & 23.01ms \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Latex default table}
\end{table}
\end{document}

cs

Debarati B. Chakraborty
Mukesh Sharma
Bhaskar Vijay

"Controlling by Showing: i-Mimic: A Video-based Method to Control Robotic Arms" by Debarati B. Chakraborty, Mukesh Sharma, and Bhaskar Vijay, arXiv:2101.11451v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.