\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Left}: Results of mAP and calibration errors of probabilistic extensions of DETR, RetinaNet, and FasterRCNN. \textbf{Right}: The number of output detection instances classified as true positives (TP), duplicates, localization errors, and false positives (FP).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccccccccc}

 & & \multicolumn{2}{c}{\bf mAP(\%) $\uparrow$ } & \multicolumn{2}{c}{\bf MCE (Cls) $\downarrow$ } & \multicolumn{2}{c}{\bf CE (Reg) $\downarrow$ } & &\multicolumn{4}{c}{\bf Counts Per Partition (in-dist)}\\
 \cmidrule(lr){3-4} \cmidrule(lr){5-6} \cmidrule(lr){7-8} \cmidrule(lr){9-13}
 Detector& Loss & COCO & OpenIm& COCO& OpenIm& COCO& OpenIm & & TP & Dup. & Loc. Errors & FP\\
 \midrule
 \multirow{4}{*}{DETR} &NLL& 37.10 &35.16 &0.0735&\textbf{0.0973}& 0.0512 &0.0331& & 24452&1103&11228&12604\\
 \cmidrule(lr){2-8} \cmidrule(lr){9-13}
 &DMM& 39.82 &38.88 &0.0737& 0.0996&0.0091&0.0171& &24929&1197&10374&10406\\
 \cmidrule(lr){2-8} \cmidrule(lr){9-13}
 &ES& \textbf{39.96}& \textbf{39.31} & \textbf{0.0728} & 0.0993 & \textbf{0.0068} & \textbf{0.0167}& &24914&1216&10236&9761\\
 \midrule
 \multirow{4}{*}{RetinaNet}&NLL& 35.11&34.60 &0.0260&0.0385& 0.0242 &0.0176 & & 22733&2835&8992&6654\\
 \cmidrule(lr){2-8} \cmidrule(lr){9-13}
 &DMM& \textbf{36.28} &34.84& \textbf{0.0244}& 0.0372&0.0102& 0.0189& & 23096&2783&8902&6411\\
 \cmidrule(lr){2-8} \cmidrule(lr){9-13}
 &ES& 36.02& \textbf{34.97}& 0.0245 & 0.0370 & \textbf{0.0059} & \textbf{0.0138} & &23096&2783&8902&6411\\
 \midrule
 \multirow{4}{*}{FasterRCNN}&NLL& 37.14&34.30 &\textbf{0.0490}& \textbf{0.0713}&\textbf{0.0049}& 0.0133 & &23145&4841&7323&6989\\
 \cmidrule(lr){2-8} \cmidrule(lr){9-13}
 &DMM& 37.24 &34.42&0.0496&0.0854&0.0112&0.0229 & &23187&4628&7165&6905\\
 \cmidrule(lr){2-8} \cmidrule(lr){9-13}
 &ES& \textbf{37.32}& \textbf{34.75}&0.0495& 0.0849 & 0.0051 &\textbf{0.0121}& &23123&4612&7208&6760\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors" by Ali Harakeh and Steven L. Waslander, arXiv:2101.05036v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"An Inexact Projected Regularized Newton Method for Fused Zero-norms Regularization Problems" by Yuqia Wu, Shaohua Pan, and Xiaoqi Yang, arXiv:2312.15718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Randomized variations used to identify agents’ choice sensitivities.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 Ind. Variables & Exogenous Variation \\
 \midrule
 \midrule
 Position & Randomly permute the position of the eight products \\
 Sponsored Tag & Randomly assign a {\tt Sponsored} tag to $X$ listings, $X \sim \text{Unif}(\{1,\dots,4\})$ \\
 Overall Pick Tag & Randomly assign a {\tt Overall Pick} tag to a listing without {\tt Sponspored} tag \\
 Scarcity Tag & Randomly assign a {\tt Only X Remaining} tag to a listing without \\
 & a {\tt Sponsored} or {\tt Overall Pick} tag, $X \sim \text{Unif}(\{1,\dots,5\})$ \\
 Price & Randomly perturb the original price $p_j$ of each product $j$, \\
 & $p_{j}^\prime \gets p_j \cdot f_j$, where $f_j \sim \text{logNormal}(\mu= 0, \sigma = 0.3)$ \\
 Rating & Randomly perturb the original rating $r_j$ of each product $j$, \\
 & $r_j^{\prime} \gets r_j + \alpha_j \cdot (5 - r_j)$, where $\alpha_j \sim \text{Unif}([-0.8, 0.8])$ \\
 Number of reviews & Randomly perturb the original number of reviews $N_j$ of each product $j$, \\
 & $N_j^\prime \gets N_j \cdot f_j$, where $f_j \sim \text{logNormal}(\mu = 0, \sigma = 1)$
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Amine Allouah
Omar Besbes
Josué D Figueroa
Yash Kanoria
Akshit Kumar

"What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce" by Amine Allouah, Omar Besbes, Josué D Figueroa, Yash Kanoria, and Akshit Kumar, arXiv:2508.02630v1, 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}{lll}
\toprule
\textbf{Service Description} & \multicolumn{2}{c}{\textbf{Code}} \\
\midrule
\textbf{Panel A:} Infection Diagnoses & (ICD-9-CM) & (ICD-10-CM) \\
Acute Respiratory Infections & 460-466 & J00-J06 \\
Pneumonia and Influenza	& 480-488 & J09-J18\\
Otitis media	&381--382 & H65--H66\\
Streptococcal sore throat and scarlet fever	& 034 & A38, J02\\
Whooping cough & 033 & A37 \\
Infectious mononucleosis & 075 & B27 \\
Chickenpox & 052 & B01 \\
Urinary Tract Infections &	590, 595, 599 & N10--N12,N15,N16,N28,N30,N36,N39,R31\\
Food poisoning (bacterial) & 005 &	A05 \\
Other Intestinal Infections	& 008--009 & 	A04,A08,A09 \\
Nausea and Vomiting	& 536.2, 787	& R11,R12,R13,R14,R15,R19 \\
Dyspepsia & 	536.8,536.9	& K30--K31 \\
\midrule
\textbf{Panel B:} Injuries & (ICD-9-CM) & (ICD-10-CM) \\
Fracture of Upper Limb	& 810-819	& S12,S22,S32,S42,S52,S62 \\
Fracture of Lower Limb	& 820-829	& S72,S82,S92 \\
Dislocation & 	830-839	& S13,S23,S33,S43,S53,S63,S73,S83,S93 \\
Sprains and Strains & 	840-848 &	Combined with above \\
\midrule
\textbf{Panel C:} Place of Service Codes & (POS) & -- \\
Physician Office & 11, 72, 95 \\
Urgent Care Center & 17,20 \\
Emergency Department & 23 \\
Hospital (including on-campus outpatient) & 21, 22, 28 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{ Identifying Injuries, Infections, and Places of Service}
\end{table}
\end{document}

q-fin

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


\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
{\textbf{Metric}} & \textbf{$\boldsymbol{\ell_{cl}}$} & \textbf{$\boldsymbol{\ell_{bce}}$} & \textbf{$\boldsymbol{\ell_{cl+bce}}$} & \textbf{$\boldsymbol{\ell}$} \\
\hline
\textbf{SS} &0.39 &0.59 &0.62 &\textbf{0.85} \\
\hline
\textbf{ARI} &0.52 &0.55 &0.59 &\textbf{0.64} \\
\hline
\textbf{MI} &0.76 &0.84 &0.87 &\textbf{0.94} \\
\hline
\textbf{ACC (\%)} &70.16 &75.43 &76.04 &\textbf{79.34} \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Comparison of clustering metrics for training $g(\cdot)$ using $\ell_{cl}$, $\ell_{bce}$, $\ell_{cl+bce}$, and $l$, after clustering $z_i^u$ using K-means clustering}
\end{table}
\end{document}

eess

Parampreet Singh
Adwik Gupta
Aakarsh Mishra
Vipul Arora

"Identification and Clustering of Unseen Ragas in Indian Art Music" by Parampreet Singh, Adwik Gupta, Aakarsh Mishra, and Vipul Arora, arXiv:2411.18611v2, 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
 \textit{Parameter} & $t_0$ & $t_f$ & $\kappa$ & $\varrho$ & $\sigma$ & $\gamma$ & $\delta $ & $\varepsilon$ \\
 \hline
 \textit{Value} & 0 & 90 & 0.32 & 1 & 0.5 & 0.12 & 0.0294 & 0.0094 \\
 \hline
 \textit{Reference} & - & - & & & & & & \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Parameter values used for the numerical simulation and control of the CPDS .}
\end{table}
\end{document}

math

Simone Cacace
Alessio Oliviero
Mario Pezzella

"Modified Patankar Semi-Lagrangian Scheme for the Optimal Control of Production-Destruction systems" by Simone Cacace, Alessio Oliviero, and Mario Pezzella, arXiv:2501.13085v1, 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{Characteristics of battery electric vehicle and hybrid electric vehicle}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c||}
 \hline
{\textbf{EV Subsystem Type}} & {\textbf{Battery EV}} & {\textbf{Hybrid EV}} \\
 \hline\hline
 Propulsion & Electric Motor & Electric Motor, ICE \\
 \hline
 Energy Storage & Battery, Ultra Capacitor & Battery, Ultra Capacitor \\
 \hline
 Infrastructure & Grid Charging & Gasoline Stations\\
 \hline
 Characteristics & Zero Emissions, High Efficiency & Low Emissions, High Economy\\
 \hline
 Major Issues & Charging Facilities & Battery Sizing \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Degradation-based Energy Management for Microgrids in the Presence of Energy Storage Elements" by Satish Vedula, arXiv:2503.09028v2, 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|c|c|c|c|c}
Condition & Width [m] & Discount $\gamma$ & Action cost $\nu$ \\
\hline
Values &$[0.005, ... 0.04]$ & $[0.97,... 1.0]$ & $1e-5$ \\
\end{tabular}
\end{adjustbox}
\caption{Model and cost parameters, where $M=2I$, $D=0.3I$, $\sigma_\tau=1e2$, and $h=0.02$ unless otherwise noted.}
\end{table}
\end{document}

eess

"Planning Human-Robot Co-manipulation with Human Motor Control Objectives and Multi-component Reaching Strategies" by Kevin Haninger and Luka Peternel, arXiv:2412.13474v1, 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
 Description & Symbol & \multicolumn{2}{c}{Value}\\
 & & $s=1$ & $s=2$\\
 \midrule
 Investment horizon, quarters & $T$ & \multicolumn{2}{c}{40}\\
 Macro transition probability & $p_{ss^\prime}$ & 0.25 & 0.05 \\
 PE call rate, new commitments & $\lambda_N(s)$ & 0.18 & 0.047 \\
 PE call rate, existing commitments & $\lambda_K(s)$ & 0.050 & 0.078 \\
 PE distribution rate & $\lambda_D(s)$ & 0.028 & 0.071 \\
 PE liquidation discount & $\alpha(s)$ & 0.66 & 0.90 \\
 Risk-free rate & $\log R_f(s)$ & 0.0028 & 0.0051 \\
 Stock expected return & $\mu_S(s)$ & 0.0079 & 0.0238 \\
 Stock return volatility & $\sigma_S(s)$ & 0.1493 & 0.0829 \\
 Stock-PE return correlation & $\rho(s)$ & 0.9527 & 0.4575 \\
 PE return volatility & $\sigma_P(s)$ & 0.0768 & 0.0424 \\
 PE expected return autocorrelation & $\varrho_{P,1}+\varrho_{P,2}$ & \multicolumn{2}{c}{0.201} \\
 PE expected return state-dependance & $\nu_P(s)$ & 0.0024 & 0.0317 \\
 Risk aversion & $\gamma$ & \multicolumn{2}{c}{2}\\
 PE adjustment costs & $\gamma_N$ & \multicolumn{2}{c}{0.1}\\
 PE adjustment costs & $\overline{n}$ & \multicolumn{2}{c}{0}\\
 Stock adjustment costs & $\gamma_S$ & \multicolumn{2}{c}{0.01}\\
 Risk budget threshold & $\overline{\theta}$ & \multicolumn{2}{c}{1}\\
 Risk budget cost & $\kappa$ & \multicolumn{2}{c}{1}\\
 Risk weight, bonds & $\theta_B$ & \multicolumn{2}{c}{0}\\
 Risk weight, stocks & $\theta_S$ & \multicolumn{2}{c}{1.5}\\
 Risk weight, PE & $\theta_P$ & \multicolumn{2}{c}{1.5}\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\textbf{Parameters.} \small We simulate the model at a quarterly frequency using the parameters in this table.}
\end{table}
\end{document}

q-fin

Hui Chen
Giovanni Gambarotta
Simon Scheidegger
Yu Xu

"A Dynamic Model of Private Asset Allocation" by Hui Chen, Giovanni Gambarotta, Simon Scheidegger, and Yu Xu, arXiv:2503.01099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Luca De Benedictis
Vania Licio
Anna Pinna

"From the historical Roman road network to modern infrastructure in Italy" by Luca De Benedictis, Vania Licio, and Anna Pinna, arXiv:2208.06675v1, 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{Welch’s $t$-test results comparing agent profits under extreme $\lambda$ settings.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
Comparison & Mean Difference & $t$-Statistic & $p$-Value \\
\midrule
$\lambda = 0.1$ vs.\ $\lambda = 1.0$ & 5.1 & 7.34 & $<$ 0.01 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
			\hline
			\textbf{Method} & \textbf{Model Size} & \textbf{Pre-training Time} & \textbf{Fine-tuning Time} \\
			\hline
			CD-TVD & 19.5& 36 & 5 \\
			SSR-VFD & 51.4 & 0 & 4 \\
			SRGAN & 20.9 & 0 & 3 \\
			PSRFlow & 16.6 & 0 & 4 \\
			\hline
		\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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{For the $L^2$ loss function, values of the objective function (and efficiencies), under each Gibbs expected utility, and for each design, including the maximum projection Latin hypercube design (LHD).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll} \hline
 & \multicolumn{3}{c}{Design} \\ \cline{2-4}
 & NSE & SH & LHD \\
 & utility & utility & \\ \hline
NSE utility & -0.6950 & -0.7882 & -0.9191 \\
& (100\%) & (88\%) & (75\%) \\ \hline
SH utility & -12.21 & -4.299 & -7.932 \\
& (45\%) & (100\%) & (70\%) \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Gibbs optimal design of experiments

Antony M. Overstall
Jacinta Holloway-Brown
James M. McGree

"Gibbs optimal design of experiments" by Antony M. Overstall, Jacinta Holloway-Brown, and James M. McGree, arXiv:2310.17440v2, 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
\textbf{Model} & \textbf{SSIM} & \textbf{PSNR} & \textbf{MSE} \\ \hline
AUNet(1500eps) & 0.9995 & 20.1592 & 0.0002 \\ \hline
AUNet+GANs(1500eps) & 0.9997 & 20.4588 & 0.0002 \\ \hline
MA3T-Fv1+GANs(20eps) & 0.9996 & 16.2408 & 0.0015 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Validation performance metrics for various frameworks tested for the inpainting task. Metrics include SSIM(closer to 1 is better), PSNR(higher is better), and MSE(smaller is better).}
\end{table}
\end{document}

eess

Juampablo E. Heras Rivera
Agamdeep S. Chopra
Tianyi Ren
Hitender Oswal
Yutong Pan
Zineb Sordo
Sophie Walters
William Henry
Hooman Mohammadi
Riley Olson
Fargol Rezayaraghi
Tyson Lam
Akshay Jaikanth
Pavan Kancharla
Jacob Ruzevick
Daniela Ushizima
Mehmet Kurt

"An Ensemble Approach for Brain Tumor Segmentation and Synthesis" by Juampablo E. Heras Rivera, Agamdeep S. Chopra, Tianyi Ren, Hitender Oswal, Yutong Pan, Zineb Sordo, Sophie Walters, William Henry, Hooman Mohammadi, Riley Olson, Fargol Rezayaraghi, Tyson Lam, Akshay Jaikanth, Pavan Kancharla, Jacob Ruzevick, Daniela Ushizima, and Mehmet Kurt, arXiv:2411.17617v1, 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{Method} & {DiNO ViT-S/14} & {DiNO ViT-B/14} & {DiNO ViT-L/14} \\
\midrule
Baseline & 55.51 & 63.03 & 65.72 \\
\hspace{12pt}+ KNNs & 56.43 & {64.26} & 65.70 \\
\hspace{12pt}+ 1-walks on KNN & \textbf{58.09} & \textbf{64.29} &
{65.97} \\
\hspace{12pt}+ 2-walks on KNN & 57.84 & 64.27 & \textbf{67.26} \\
\hspace{12pt}+ 3-walks on KNN & 57.82 & 64.15 & 67.02 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Ablation Study on Neighbor Propagation. Adding both KNNs and walks of length 1 or 2 on the KNN graph achieves the best performance. }
\end{table}
\end{document}

math

Shaden Alshammari
John Hershey
Axel Feldmann
William T. Freeman
Mark Hamilton

"I-Con: A Unifying Framework for Representation Learning" by Shaden Alshammari, John Hershey, Axel Feldmann, William T. Freeman, and Mark Hamilton, arXiv:2504.16929v1, 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{All Crypto Value-Weighted Portfolio Descriptive Statistics. Values are portfolio returns in percentage points and are at the weekly frequency. Crypto portfolios are formed on total TVL to market cap. There are 105 weekly observations. \(^{***}\), \(^{**}\), and \(^{*}\), denote statistical significance at the 1\%, 5\%, and 10\% levels respectively.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
\hline
 & HML & Q1 & Q2 & Q3 & Q4\\
\hline
mean & -0.14 & 1.78\(^{**}\) & 1.84 & 1.64\(^{*}\) & 1.64\(^{*}\)\\
std & 5.67 & 7.08 & 11.22 & 9.02 & 8.67\\
min & -14.18 & -14.86 & -22.82 & -24.21 & -18.29\\
25\% & -3.32 & -2.13 & -4.31 & -3.22 & -3.57\\
50\% & -0.71 & 0.04 & -0.49 & 0.51 & 0.49\\
75\% & 1.87 & 5.92 & 6.34 & 7.63 & 6.06\\
max & 19.75 & 26.98 & 44.31 & 31.87 & 28.22\\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


q-fin

"Deformation of semi-circle law for the correlated time series and Phase transition" by Masato Hisakado and Takuya Kaneko, arXiv:2508.07192v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
 \toprule
 Instance&$|\mathcal{V}|$&$|A|$&Char. station\\
 &&&capacity\\
 \midrule
 I1&60&2,190&1\\
 I2&155&11,900&2\\
 I3&248&29,499&3\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Léa Ricard
Guy Desaulniers
Andrea Lodi
Louis-Martin Rousseau

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


\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|l}
Symbol & PRMS symbol & Quantity & Units & Description \\ \hline
Rsw & swrad & per HRU & Langleys & shortwave radiation \\
r & solf & per HRU & - & ratio of actual to potential daily solar radiation \\
Pn & ppt\_net & per HRU & inches & total net precipitation \\
Pt & throughfall & per HRU & inches & precipitation throughfall after interception \\
Sc & AvailCanSt & per HRU & acre-inch & available storage in canopy \\
ETp & potet & per HRU & inches & potential evapotranspiration \\
ETpa & apet & per HRU & inches & available potential evapotranspiration \\
$\Delta$E & tcal & per HRU & Langleys & net snowpack energy balance \\
I & cal & per HRU & Langleys & total incoming longwave \\
Qv & cecsub & per HRU & Langleys & convection and latent heat from condensation \\
Rn & swn & per HRU & Langleys & net shortwave radiation \\
Ip & lwp & per HRU & Langleys & perfect black-body emission \\
Tavg & temp & per HRU & $^{o}$F & average temperatue \\
Qc & qcond & per HRU & Langleys & conducted heat \\
B & sub & per HRU & inches & sublimation \\
Pns & net\_snow & per HRU & inches & snow portion of Pn \\
D & pk\_depth & per HRU & inches & snowdepth \\
M & snowmelt & per HRU & inches & snowmelt \\
fsa & snowcov\_area & perHRU & . & fractional snow covered area \\
Pnr & net\_rain & per HRU & inches & rain portion of Pn \\
Psri & hru\_sroffi & per HRU & inches & impervious surface runoff \\
ei & hru\_impervevap & per HRU & inches & imprevious region evaporation \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Model variables}
\end{table}
\end{document}

eess

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

"Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter" by Sami A. Malek, Alexandre M. Bayen, and Steven D. Glaser, arXiv:1912.03861v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Settings for server performance benchmark.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
\toprule
\textbf{Parameter} & \textbf{Minimum} & \textbf{Maximum} & \textbf{Steps} \\
\midrule
Supply air temperature & 25\textdegree{}C & 37\textdegree{}C & 13\\
Room air flow rate ($\text{m}^3/\text{h}$) & 2,500 & 12,500 & 5 \\
CPU utilization & 10\% & 90\% & 7 \\
HDD throughput (MB/s) & 10 & 100 & 6 \\
Memory block size (KB) & 8 & 256 & 6 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Duc Van Le
Rongrong Wang
Yingbo Liu
Rui Tan
Yew-Wah Wong
Yonggang Wen

"Deep Reinforcement Learning for Tropical Air Free-Cooled Data Center Control" by Duc Van Le, Rongrong Wang, Yingbo Liu, Rui Tan, Yew-Wah Wong, and Yonggang Wen, arXiv:2012.06834v1, 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{The amount of users, items, interactions and action repetition rate in each dataset.}
\begin{tabular}{ccccc}
 \toprule
 Data & Users & Items & Interactions &Action Repetition \\
 \midrule
 Reddit & 10000 & 1000 & 672447 &79\% \\
 Wikipedia & 8227 & 1000 & 157474 & 61\% \\
 LastFM & 1000 & 1000 & 1293103 & 8.6\% \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Dynamic Graph Collaborative Filtering

Xiaohan Li
Mengqi Zhang
Shu Wu
Zheng Liu
Liang Wang
Philip S. Yu

"Dynamic Graph Collaborative Filtering" by Xiaohan Li, Mengqi Zhang, Shu Wu, Zheng Liu, Liang Wang, and Philip S. Yu, arXiv:2101.02844v1, 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{Performance consistency across different architectures.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Architecture} & \textbf{F1-score (\%)} & \textbf{AUC-ROC}\\
\midrule
EfficientNet-B3~ & 94.68 & 0.9813 \\
Swin Transformer~ & 93.34 & 0.9797 \\
ResNet-101~ & 91.07 & 0.9421 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Chia-Ming Lee
Bo-Cheng Qiu
Ting-Yao Chen
Ming-Han Sun
Fang-Ying Lin
Jung-Tse Tsai
I-An Tsai
Yu-Fan Lin
Chih-Chung Hsu

"Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization" by Chia-Ming Lee, Bo-Cheng Qiu, Ting-Yao Chen, Ming-Han Sun, Fang-Ying Lin, Jung-Tse Tsai, I-An Tsai, Yu-Fan Lin, and Chih-Chung Hsu, arXiv:2507.19858v1, 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{Feature importance of DOFEN on red wine dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 ~ & \textbf{1st} & \textbf{2nd} & \textbf{3rd} \\
 \midrule
 Random Forest & alcohol (27.17\%) & sulphates (15.44\%) & volatile acidity (10.92\%) \\
 XGBoost & alcohol (35.42\%) & sulphates (15.44\%) & volatile acidity (7.56\%) \\
 LightGBM & alcohol (26.08\%) & sulphates (15.75\%) & volatile acidity (10.63\%) \\
 CatBoost & sulphates (16.29\%) & alcohol (15.67\%) & volatile acidity (10.40\%) \\
 GradientBoostingTree & alcohol (26.27\%) & sulphates (16.24\%) & volatile acidity (11.12\%) \\
 Trompt & alcohol (11.83\%) & sulphates (10.94\%) & total sulfur dioxide (9.78\%) \\
 DOFEN (ours) & alcohol (11.16\%) & volatile acidity (10.77\%) & sulphates (10.17\%) \\
 \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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The properties of the considered multi-class classification data sets. The last column denotes the proportion of non-zero elements in the data table.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\toprule
 Data set & Instances & Features & Classes & Proportion of non-zero entries \\
\midrule
 chess & 3196 & 38 & 2 & 0.726558 \\
 biodeg-p2-discrete & 328 & 61 & 4 & 0.107357 \\
 optdigits & 5620 & 62 & 10 & 0.528117 \\
 madelon & 2000 & 500 & 2 & 0.999999 \\
 php88ZB4Q & 10299 & 561 & 6 & 0.999860 \\
pd\_speech\_features & 756 & 753 & 2 & 0.995294 \\
 dlbcl & 77 & 7070 & 2 & 0.997388 \\
 tumors C & 60 & 7129 & 2 & 0.995722 \\
 AP\_Ovary\_Kidney & 458 & 10935 & 2 & 1.000000 \\
 ohscal.wc & 11162 & 11465 & 10 & 0.005270 \\
 genes & 801 & 20531 & 5 & 0.857824 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Blaž Škrlj
Sašo Džeroski
Nada Lavrač
Matej Petković

"ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings" by Blaž Škrlj, Sašo Džeroski, Nada Lavrač, and Matej Petković, arXiv:2101.09577v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overall evaluation of the approximation methods: mean values of the quality criteria for all the centerlines distorted by spatial noise addition. The cells in gray correspond to the lowest error for each metric.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|ccccc|}
 \hline
 & GNP & GNP-AIC & GP-AIC & SRP-AIC\\
 \hline
$\mathrm{RMSE_{spatial}}$ & 0.511 & 0.152 & 0.099 & \cellcolor{gray!25}0.096 \\
$\mathrm{RMSE_{radius}}$ & 0.008 & 0.009 & 0.018 & \cellcolor{gray!25}0.007 \\
$\mathrm{RMSEder_{spatial}}$ & 0.314 & 0.343 & \cellcolor{gray!25}0.075 & 0.076 \\
$\mathrm{RMSEder_{radius}}$ & 0.015 & 0.019 & 0.021 & \cellcolor{gray!25}0.013 \\
$\mathrm{RMSEcurv}$ & 1.524 & 2.362 & \cellcolor{gray!25}0.085 & 0.091 \\
$\mathrm{L_{diff}}$ & 50.180 & 15.071 & 0.252 & \cellcolor{gray!25}0.207 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Méghane Decroocq
Carole Frindel
Pierre Rougé
Makoto Ohta
Guillaume Lavoué

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Proportion of crypto projects with the number of vulnerabilities in each group categorized according to code maintenance activities. Column `$\geq x$' refers to the number (proportion) of projects having at least $x$ vulnerabilities.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 ID & 0& $\geq 1$& $\geq 2$& $\geq 3$& $\geq 4$ \\\hline
 1 & 43 (16.4\%) & 219 (83.6\%) & 135 (51.5\%) & 93 (35.5\%) & 17 (6.5\%) \\\hline
 2 & 60 (23.4\%) & 196 (76.6\%) & 96 (37.5\%) & 45 (17.6\%) & 8 (3.1\%) \\\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

"Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild" by Jusop Choi, Wonseok Choi, William Aiken, Hyoungshick Kim, Jun Ho Huh, Taesoo Kim, Yongdae Kim, and Ross Anderson, arXiv:2201.08678v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{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{Method} & \multicolumn{2}{c}{\textbf{CIFAR10 (in distribution)}} & \multicolumn{2}{c}{\textbf{CIFAR100 (out of distribution)}} \\ \cline{2-5}
 & Linear Probing & KNN & Linear Probing & KNN \\ \midrule
\multicolumn{5}{c}{$q_\phi$ is a Gaussian Distribution} \\ \midrule
SimCLR & 77.79 & 80.02 & 31.82 & 40.27 \\
DCL & 78.32 & 83.11 & 32.44 & 42.10 \\
\textbf{Our Debiasing $\alpha = 0.2$} & 79.50 & 84.07 & 32.53 & 43.19 \\
\textbf{Our Debiasing $\alpha = 0.4$} & 79.07 & 85.06 & 32.53 & \textbf{43.29} \\
\textbf{Our Debiasing $\alpha = 0.6$} & 79.32 & 85.90 & 30.67 & 29.79 \\ \midrule
\multicolumn{5}{c}{$q_\phi$ is a Student's t-distribution} \\ \midrule
t-SimCLR & 90.97 & 88.14 & 38.96 & 30.75 \\
DCL & Diverges & Diverges & Diverges & Diverges \\
\textbf{Our Debiasing $\alpha = 0.2$} & 91.31 & 88.34 & 41.62 & 32.88 \\
\textbf{Our Debiasing $\alpha = 0.4$} & 92.70 & 88.50 & \textbf{41.98} & 34.26 \\
\textbf{Our Debiasing $\alpha = 0.6$} & \textbf{92.86} & \textbf{88.92} & 38.92 & 32.51 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{{ Contrastive feature learning evaluation results for CIFAR10 and CIFAR100 datasets with various debasing $\alpha$ factors. Adding some amount of debasing helps raising accuracy in both linear probing and KNN classification.}}
\end{table}
\end{document}

math


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Data availability by SDG region}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
\toprule
SDG region & Observations & Countries & Country-years\\
\midrule
Central and Southern Asia & 163 & 7 & 65\\
Eastern and South-Eastern Asia & 57 & 8 & 53\\
Latin America and the Caribbean & 272 & 13 & 171\\
North America, Europe, Australia and New Zealand & 460 & 30 & 368\\
Northern Africa and Western Asia & 43 & 8 & 42\\
Oceania (exc. Australia and New Zealand) & 1 & 1 & 1\\
Sub-Saharan Africa & 280 & 25 & 158\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Estimating the timing of stillbirths in countries worldwide using a Bayesian hierarchical penalized splines regression model" by Michael Y. C. Chong and Monica Alexander, arXiv:2212.06219v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yuchen Fang
Sen Na
Michael W. Mahoney
Mladen Kolar

"Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems" by Yuchen Fang, Sen Na, Michael W. Mahoney, and Mladen Kolar, arXiv:2211.15943v2, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \textbf{Nomenclature:}\\
 Sets:\\
 $h$ & Time periods (usually hours)\\
 $i$ & Bus of distribution grid\\
 $g$ & Generation units\\
 $pv(g)$ & Subset of photovoltaic renewable generation units\\
 $gi(g,i)$ & Generator g connected to node i\\
 \\
 Parameters:\\
 $FL$ & Feed-in power limitation; maximum feed-in depending on installed power (\%)\\
 $D_{h,i}^P$ & Active power demand (MW)\\
 $\overline{P}_g$ & Technical maximum power of unit (MW)\\
 $CF_{h,i,g}$ & Renewable profile per technology and node; Capacity factor (p.u.)\\
 $C^{Imp}$ & Cost of imports (€/MWh)\\
 $C^{Exp}$ & Cost of exports (€/MWh)\\
 $C^{PNS/QNS}$ & Cost of power non-served (€/MWh)/(€/MVArh)\\
 $C^{EPS/EQS}$ & Cost of excess power served (€/MWh)/(€/MVArh)\\
 $M$ & Big positive constant for activating or deactivating constraints\\
 \\
 Variables:\\
 $p_{h,g}$ & Active power generation of the unit (MW)\\
 $sp_{h,g}$ & Curtailment of renewable generation units (MW)\\
 $p_{h}^{Imp/Exp}$ & Active power import/export (MW)\\
 $q_{h}^{Imp/Exp}$ & Reactive power import/export (MVAr)\\
 $\alpha_{h,i}$ & Allows curtailment \{0,1\}\\
 $scal$ & Scaling variable for all scalable PV systems (positive continuous)\\
 $pns_{h,i} / qns_{h,i}$ & Active / reactive power non-served (MW / MVAr)\\
 $eps_{h,i} / eqs_{h,i}$ & Excess active / reactive power served (MW / MVAr)\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Alexander Konrad
Robert Gaugl
Christoph Maier
Sonja Wogrin

"Implementing Dynamic Power Feed-In Limitations of Photovoltaic Systems in Distribution Grids for Generation Expansion Planning" by Alexander Konrad, Robert Gaugl, Christoph Maier, and Sonja Wogrin, arXiv:2503.20619v1, 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{Our new Whisper Large-v2 based SOTA model, fine-tuned on long-form audio created with data generation method 'All' and supplemented with SRG (PL) and Common Voice 13 de, compared to the original Whisper Large-v2}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrrr}
 \toprule
 \textbf{Test Dataset} & \textbf{Model} & \textbf{WER} & \textbf{SubER} & \textbf{BLEU} \\
 \midrule
 \multirow{2}{*}{SPC} & Our SOTA & \textbf{20.98} & - & \textbf{68.34} \\
 & Large-v2 & 28.21 & - & 58.08 \\
 \midrule
 \multirow{2}{*}{SDS-200}& Our SOTA & \textbf{16.70} & - & \textbf{72.69} \\
 & Large-v2 & 27.92 & - & 57.00 \\
 \midrule
 \multirow{2}{*}{STT4SG-350} & Our SOTA & \textbf{12.11} & - & \textbf{78.08} \\
 & Large-v2 & 22.41 & - & 64.13 \\
 \midrule
 \multirow{2}{*}{SRG (SW-TXT)} & Our SOTA & \textbf{26.31} & \textbf{29.76} & \textbf{64.67} \\
 & Large-v2 & 28.42 & 30.63 & 63.61 \\
 \midrule
 \multirow{2}{*}{Dataset-A} & Our SOTA & \textbf{34.50} & \textbf{35.31} & \textbf{51.40} \\
 & Large-v2 & 38.00 & 39.12 & 48.89 \\
 \midrule
 \multirow{2}{*}{CV13 de}& Our SOTA & \textbf{6.42} & - & - \\
 & Large-v2 & 6.53 & - & - \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Vincenzo Timmel
Claudio Paonessa
Reza Kakooee
Manfred Vogel
Daniel Perruchoud

"Fine-tuning Whisper on Low-Resource Languages for Real-World Applications" by Vincenzo Timmel, Claudio Paonessa, Reza Kakooee, Manfred Vogel, and Daniel Perruchoud, arXiv:2412.15726v3, 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|c|c|c|}
\hline
\multicolumn{3}{|c|}{Setting} & A &B & C & D \\ \hline
\multicolumn{2}{|c|}{Algorithm} & Feat. Sel. & \multicolumn{4}{c|}{Mean F1 score in 10 DS1 instances} \\ \hline
\multicolumn{2}{|c|}{PLS} & ReliefF & \textbf{0.653} & 0.349 & \textbf{0.654} & \textbf{0.400} \\ \hline
\multicolumn{3}{|c|}{Undersampling} & 0.629 & \textbf{0.378} & 0.653 & 0.339 \\ \hline
\multicolumn{3}{|c|}{Oversampling} & 0.625 & 0.364 & 0.606 & 0.380 \\ \hline \hline
\multicolumn{3}{|c|}{} & \multicolumn{4}{c|}{Mean F1 score in 10 DS2 instances} \\ \hline
\multicolumn{2}{|c|}{KNN} & ReliefF & 0.634 & 0.363 & 0.638 & 0.352 \\ \hline
\multicolumn{3}{|c|}{Undersampling} & \textbf{0.658} & \textbf{0.393} & 0.628 & 0.352 \\ \hline
\multicolumn{3}{|c|}{Oversampling} & 0.640 & 0.381 & \textbf{0.650} & \textbf{0.357} \\ \hline \hline
\multicolumn{3}{|c|}{} & \multicolumn{4}{c|}{Mean F1 score in 10 DS3 instances} \\ \hline
\multicolumn{2}{|c|}{XGB} & ReliefF & \textbf{0.653} & 0.349 & \textbf{0.654} & \textbf{0.400} \\ \hline
\multicolumn{3}{|c|}{Undersampling} & 0.629 & \textbf{0.378} & 0.635 & 0.339 \\ \hline
\multicolumn{3}{|c|}{Oversampling} & 0.625 & 0.364 & 0.606 & 0.380 \\ \hline \hline
\multicolumn{3}{|c|}{} & \multicolumn{4}{c|}{Mean F1 score in 10 DS4 instances} \\ \hline
\multicolumn{2}{|c|}{KNN} & PCA & \textbf{0.628} & \textbf{0.368} & \textbf{0.660} & 0.343 \\ \hline
\multicolumn{3}{|c|}{Undersampling} & 0.589 & 0.362 & 0.588 & 0.346 \\ \hline
\multicolumn{3}{|c|}{Oversampling} & 0.617 & 0.355 & 0.649 & \textbf{0.364} \\ \hline \hline
\multicolumn{3}{|c|}{} & \multicolumn{4}{c|}{Mean F1 score in 10 DS5 instances} \\ \hline
\multicolumn{2}{|c|}{KNN} & ReliefF & 0.621 & 0.351 & \textbf{0.617} & 0.340 \\ \hline
\multicolumn{3}{|c|}{Undersampling} & \textbf{0.639} & \textbf{0.386} & 0.611 & 0.363 \\ \hline
\multicolumn{3}{|c|}{Oversampling} & 0.625 & 0.383 & 0.611 & \textbf{0.369} \\ \hline \hline
\multicolumn{3}{|c|}{} & \multicolumn{4}{c|}{Mean F1 score in 10 DS6 instances} \\ \hline
\multicolumn{2}{|c|}{PLS} & ReliefF & \textbf{0.500} & 0.344 & \textbf{0.501} & 0.233 \\ \hline
\multicolumn{3}{|c|}{Undersampling} & 0.482 & \textbf{0.367} & 0.435 & \textbf{0.318} \\ \hline
\multicolumn{3}{|c|}{Oversampling} & 0.489 & 0.163 & 0.497 & 0.211 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Results for regression predictors after balancing the training dataset.}
\end{table}
\end{document}

cs

Petros Petsinis
Athanasios Naskos
Anastasios Gounaris

"Analysis of key flavors of event-driven predictive maintenance using logs of phenomena described by Weibull distributions" by Petros Petsinis, Athanasios Naskos, and Anastasios Gounaris, arXiv:2101.07033v1, 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{Comparative results of image restoration tasks on ImageNet for lookup-table noise level correction. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
\hline
ImageNet & {4 x SR} & {Deblurring} & {Colorization} & {CS 25\%} & {Inpainting} \\
Method & PSNR$\uparrow$/SSIM$\uparrow$/FID$\downarrow$ & PSNR$\uparrow$/SSIM$\uparrow$/FID$\downarrow$ & Cons$\downarrow$/FID$\downarrow$ & PSNR$\uparrow$/SSIM$\uparrow$/FID$\downarrow$ & PSNR$\uparrow$/SSIM$\uparrow$/FID$\downarrow$ \\ \hline
DDNM & 27.45 / 0.870/ 39.56 & 44.93 / 0.993 / 1.17 & 42.32 / 36.32 & 21.62 / 0.748 / 64.68 & 31.60 / 0.946 / 9.79 \\
DDNM-LT-NLC & 27.47 / 0.870/ 39.03 & 45.14 / 0.993 / 1.02 & 42.12 / 36.07 & 21.48 / 0.751 / 62.64 & 31.84 / 0.951 / 9.19 \\ \hline
\textbf{DDNM-NLC} & 27.50 / 0.872 / 37.82 & 46.20 / 0.995 / 0.79 & 41.60 / 35.89 & 21.27 / 0.769 / 58.96 & 32.51 / 0.957 / 7.20 \\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Abulikemu Abuduweili
Chenyang Yuan
Changliu Liu
Frank Permenter

"Enhancing Sample Generation of Diffusion Models using Noise Level Correction" by Abulikemu Abuduweili, Chenyang Yuan, Changliu Liu, and Frank Permenter, arXiv:2412.05488v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \textbf{Name} & \textbf{Average} & \textbf{\# Connected} & \textbf{Density} & \textbf{Average} \\
 & \textbf{Degree} & \textbf{Components} & & \textbf{Clustering} \\
 & & & & \textbf{Coefficient} \\
 \hline
 {Cora} & 3.8980 & 78 & $1.4\times10^{-3}$ & 0.2406 \\
 {CiteSeer} & 2.7363 & 438 & $8\times10^{-4}$ & 0.1414 \\
 {PubMed} & 4.4960 & 1 & $2\times10^{-4}$ & 0.0601 \\
 {DBLP} & 5.9682 & 589 & $3\times10^{-4}$ & 0.1344 \\
 {ogbn-arxiv} & 13.6740 & 1 & $8\times10^{-5}$ & 0.2261 \\
 {ogbn-products}& 50.5171 & 52,658 & $2\times10^{-5}$ & x \\
 \end{tabular}
\end{adjustbox}
\caption{Dataset statistics}
\end{table}
\end{document}

cs

Reza Namazi
Elahe Ghalebi
Sinead Williamson
Hamidreza Mahyar

"SMGRL: Scalable Multi-resolution Graph Representation Learning" by Reza Namazi, Elahe Ghalebi, Sinead Williamson, and Hamidreza Mahyar, arXiv:2201.12670v3, 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}{ccccccc}
		\toprule
		Variant && Name && Spike distribution && Slab distribution \\
		\midrule
		1 && CSS && Independent Student's-$t$ && Independent Student's-$t$\\
		2 && DSS-i && Dirac-delta && Independent Student's-$t$ \\
		3 && DSS-g && Dirac-delta && Correlated Student's-$t$ \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{Variants of spike-and-slab priors considered in this study.}
\end{table}
\end{document}

stat

Rajdip Nayek
Ramon Fuentes
Keith Worden
Elizabeth J. Cross

"On spike-and-slab priors for Bayesian equation discovery of nonlinear dynamical systems via sparse linear regression" by Rajdip Nayek, Ramon Fuentes, Keith Worden, and Elizabeth J. Cross, arXiv:2012.01937v2, 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{Example 1B. A comparison of $L^2$-error by SAV/BDF2, R-SAV/BDF2, GSAV/BDF2 and R-GSAV/BDF2 schemes for Allen-Cahn equation at $T=200$ with different time step.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllllll}
		\hline
		 & SAV & R-SAV & GSAV & R-GSAV \\
		\hline
	1e-1 & 4.30E-04 & 2.72E-04 & 1.27E-03 & 2.65E-04 \\
	1e-2 & 4.53E-05 & 2.93E-06 & 1.47E-04 & 2.90E-06 \\
	1e-3 & 6.26E-07 & 2.96E-08 & 2.38E-06 & 2.94E-08 \\
	\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"A generalized SAV approach with relaxation for dissipative systems" by Yanrong Zhang and Jie Shen, arXiv:2201.12587v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Impact of the COVID-19 pandemic on the financial market efficiency of price returns, absolute returns, and volatility increment: Evidence from stock and cryptocurrency markets" by Tetsuya Takaishi, arXiv:2504.18960v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Joint calibration of the volatility surface and variance term structure" by Jiwook Yoo, arXiv:2509.08096v1, 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{Detailed class mapping from ImageNet classes to CIFAR-10 classes. This mapping is used for the second transfer experiment in Section .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc} \hline
\textbf{CIFAR-10 Class} & \textbf{ImageNet Class} \\ \hline
Airplane & Airliner \\
Car & Wagon \\
Bird & Humming Bird \\
Cat & Siamese Cat \\
Deer & Ox \\
Dog & Golden Retriever \\
Frog & Tailed Frog \\
Horse & Zebra \\
Ship & Container Ship \\
Truck & Trailer Truck \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hanxun Huang
Xingjun Ma
Sarah Monazam Erfani
James Bailey
Yisen Wang

"Unlearnable Examples: Making Personal Data Unexploitable" by Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, and Yisen Wang, arXiv:2101.04898v2, licensed under CC BY-SA 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}{lrlr}
 \toprule
 & & & PubMedQA \\
 Model & Params & Method & Test Accuracy \\
 \midrule
 BioGPT (w/extra data) & 1.5B & fine-tune & 81.0 \\
 Flan-PaLM & 540B & few-shot & 79.0 \\
 Galactica & 120B & zero-shot & 77.6 \\
 GPT-4 & - & zero-shot & 75.20 \\
 BioMedLM & 2.7B & fine-tune & \textbf{74.4} \\
 DRAGON & 360M & fine-tune & 73.4 \\
 BioLinkBERT & 340M & fine-tune & 72.4 \\
 GPT-Neo 2.7B & 2.7B & fine-tune & 66.1 \\
 \bottomrule
 \end{tabular}
\caption{PubMedQA Performance of Various Systems}
\end{table}
\end{document}

cs

Elliot Bolton
Abhinav Venigalla
Michihiro Yasunaga
David Hall
Betty Xiong
Tony Lee
Roxana Daneshjou
Jonathan Frankle
Percy Liang
Michael Carbin
Christopher D. Manning

"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text" by Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall, Betty Xiong, Tony Lee, Roxana Daneshjou, Jonathan Frankle, Percy Liang, Michael Carbin, and Christopher D. Manning, arXiv:2403.18421v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative Evaluation of the lip sync model. Directly using the pre-trained Wav2Lip model or fine-tuning it on noisy audios leads to poor results on noisy speech. Our student network leads to more clear audio-visual correspondence as denoted by LSE-C.}
\begin{tabular}{lccc}
 \hline
 \textbf{Model} & LSE-C$\uparrow$ & LSE-D$\downarrow$\\
 \hline
 Wav2Lip trained on noisy data & 1.181 & 10.35\\
 Pre-trained Wav2Lip & 1.330 & \textbf{8.933} \\
 \textbf{Lip-sync student (ours)} & \textbf{4.572} & 9.743\\

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

eess

Sindhu B Hegde
K R Prajwal
Rudrabha Mukhopadhyay
Vinay Namboodiri
C. V. Jawahar

"Visual Speech Enhancement Without A Real Visual Stream" by Sindhu B Hegde, K R Prajwal, Rudrabha Mukhopadhyay, Vinay Namboodiri, and C. V. Jawahar, arXiv:2012.10852v1, 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{\textbf{Summary of unification results}, among all validated formulas (left columns) and among the canonical forms (right columns). Formulas are harvested from 154 arXiv papers (), of which 151/154 (98\%) had at least one formula proved connected by UMAPS and 84/154 (55\%) have a formula residing in the same CMF.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 Found relation & Same CMF & Found relation (canonical) & Same CMF (canonical) \\
 381/407 (94\%) & 188/407 (46\%) & 136/153 (89\%) & 81/153 (53\%) \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Tomer Raz
Michael Shalyt
Elyasheev Leibtag
Rotem Kalisch
Shachar Weinbaum
Yaron Hadad
Ido Kaminer

"From Euler to AI: Unifying Formulas for Mathematical Constants" by Tomer Raz, Michael Shalyt, Elyasheev Leibtag, Rotem Kalisch, Shachar Weinbaum, Yaron Hadad, and Ido Kaminer, arXiv:2502.17533v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Predicted vs. Empirical Sup-norms and KS Test $p$-values (DR vs. RS)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rcccccccc}
\hline
 & \multicolumn{2}{c}{Linear} & & \multicolumn{2}{c}{Exponential} & & \multicolumn{2}{c}{Constant} \\ \cline{2-3} \cline{5-6} \cline{8-9}
 & $c=0.5$ & $c=0.8$ & & $c=2.5$ & $c=4$ & & $c=0.4$ & $c=0.8$ \\
 & (small) & (large) & & (small) & (large) & & (small) & (large) \\ \hline
\multicolumn{9}{l}{\textit{Sup-norm} (\emph{Direct Response vs. Reduced Strategy Methods})} \\
Predicted & 0.128 & 0.251 & & 0.188 & 0.251 & & 0.072 & 0.460 \\
Empirical & 0.094 & 0.104 & & 0.052 & 0.052 & & 0.073 & 0.156 \\ \hline
\multicolumn{9}{l}{\textit{Kolmogorov-Smirnov Test $p$-value}} \\
\;\;\;\;Predicted & 0.413 & \phantom{$^\dagger$}0.005$^\dagger$ & & 0.068 & \phantom{$^\dagger$}0.005$^\dagger$ & & 0.966 & \phantom{$^\dagger$}0.000$^\dagger$\\
Empirical & 0.796 & 0.678 & & 1.000 & 1.000 & & 0.962 & 0.192 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Shiang-Hung Hu
Po-Hsuan Lin
Thomas R. Palfrey
Joseph Tao-yi Wang
Yu-Hsiang Wang

"Strategy Method Effects in Centipede Games: An Optimal Design Approach" by Shiang-Hung Hu, Po-Hsuan Lin, Thomas R. Palfrey, Joseph Tao-yi Wang, and Yu-Hsiang Wang, arXiv:2508.06425v1, 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{Average EH Efficiency for MU ($K=3$) Scenario}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
 Algorithm & TS & HERA \\ \hline
Exhaustive search & 63.7\% & 84.8\% \\ \hline
Proposed & 58.4\% & 73.2\% \\ \hline
TD3 & 47.2\% & 68.4\% \\ \hline
DDPG & 60.0\% & 37.2\% \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mahmoud M. Salim
Khaled M. Rabie
Ali H. Muqaibel

"Energy-Efficient Irregular RIS-aided UAV-Assisted Optimization: A Deep Reinforcement Learning Approach" by Mahmoud M. Salim, Khaled M. Rabie, and Ali H. Muqaibel, arXiv:2504.15031v1, 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}{ll}
	 \toprule
 Indicator & Formula \\
 \midrule
 A/D & $\frac{H_t-C_{t-1}}{H_t-L_t}$\\
 CCI & $\frac{M_t-SM_t}{0.015D_t}$\\
 LWR & $\frac{H_n-C_t}{H_n-L_n}\times 100$\\
 MACD & $MACD(n)_{t-1}+2/n+1\times(DF_t-MACD(n)_{t-1})$\\
 MOM & $C_t-C_{t-n}$\\
 RSI & $100-\frac{100}{1+(\sum^{n-1}_{i=0}Up_{t-i}/n)/(\sum^{n-1}_{i=0}Dw_{t-i}/n)}$ \\
 SMA & $\frac{C_t+C_{t-1}+...+C_{t-n+1}}{n}$\\
 SD & $\frac{\sum^{n-1}_{i=0}K_{t-i}\%}{n}$\\
 SK & $\frac{C_t-LL_{t-n}}{HH_{t-n}-LL_{t-n}}\times 100$\\
 WMA & $\frac{n \times C_t +(n-1) \times C_{t-1}+...+C_{t-n+1}}{n+(n-1)+...+1}$\\
 \bottomrule
 \multicolumn{2}{l}{\begin{minipage}{5.2in}\baselineskip=12pt \scriptsize $C_t$: closing price; $L_t$: lowest price; $H_t$: highest price at time $t$; $DF$: $EMA(12)_t-EMA(26)_t$; EMA: Exponential moving average; $EMA(k)_t$: $EMA(k)_{t-1}+\alpha \times (c_t-EMA(k)_{t-1})$; $\alpha$: smoothing factor: $2/1+k$; $k$: time period of $k$ minute exponential moving average; $LL_t$ and $HH_t$: mean lowest low and highest high in the last $t$ minutes; $M_t:H_t+L_t+C_t/3$; $SM_t: \sum^n_{i=1}M_{t-i+1})/n$; $D_t:(\sum^n_{i=1}|M_{t-i+1}-SM_t|)/n; Up_t$: upward price change; $Dw_t$: downward price change at time $t$. \\
 \end{minipage}
 } \\
 \end{tabular}
\end{adjustbox}
\caption{Technical indicators used and formulas as reported in .}
\end{table}
\end{document}

q-fin

Andrés L. Suárez-Cetrulo
Alejandro Cervantes
David Quintana

"ProteuS: A Generative Approach for Simulating Concept Drift in Financial Markets" by Andrés L. Suárez-Cetrulo, Alejandro Cervantes, and David Quintana, arXiv:2509.11844v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Youngjun Song
Youngsik Hwang
Jonghun Lee
Heechang Lee
Dong-Young Lim

"DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization" by Youngjun Song, Youngsik Hwang, Jonghun Lee, Heechang Lee, and Dong-Young Lim, arXiv:2503.23430v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Eugene Larsen-Hallock
Adam Rej
David Thesmar

"Expectations Formation with Fat-tailed Processes: Evidence from Sales Forecasts" by Eugene Larsen-Hallock, Adam Rej, and David Thesmar, arXiv:2210.10169v1, 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|lllll}
 & Full & Ul & Co & Mu & Nn \\ \hline
Syn & 41.4 & 22.2 & 8.8 & 10.4 & - \\
MG & 17.3 & 3.1 & 5.6 & 3.4 & 5.2 \\
SS & 259.7 & 55.5 & 104.9 & 95.4 & 3.9 \\
AB & 36.6 & 10.3 & 2.5 & 14.6 & 9.2 \\
CCh & 25 & - & 25.0 & - & - \\
BBhÁ & 17.9 & - & 17.9 & - & - \\ \hline
\textbf{Total} & \textbf{397.9} & \textbf{91.1} & \textbf{164.7} & \textbf{123.8} & \textbf{18.3}
\end{tabular}
\end{adjustbox}
\caption{Duration breakdown in hours of ASR training set by speaker variety.}
\end{table}
\end{document}

eess

Liam Lonergan
Ibon Saratxaga
John Sloan
Oscar Maharog
Mengjie Qian
Neasa Ní Chiaráin
Christer Gobl
Ailbhe Ní Chasaide

"Fotheidil: an Automatic Transcription System for the Irish Language" by Liam Lonergan, Ibon Saratxaga, John Sloan, Oscar Maharog, Mengjie Qian, Neasa Ní Chiaráin, Christer Gobl, and Ailbhe Ní Chasaide, arXiv:2501.00509v1, 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{Number of codebooks and vocabulary size per codebook, with the corresponding results on SUPERB ASR and IC tasks.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
\mbox{No.} codebooks&\mbox{Vocab.} size&ASR WER(\%)&IC Acc(\%)\\
\midrule
\multirow{3}{*}{16}&1024&14.83&91.83\\
&2048&14.51&91.09\\
&8192&14.62&92.25\\
\midrule
1&2048&17.89&90.43\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Muhammad Huzaifah
Geyu Lin
Tianchi Liu
Hardik B. Sailor
Kye Min Tan
Tarun K. Vangani
Qiongqiong Wang
Jeremy H. M. Wong
Jinyang Wu
Nancy F. Chen
Ai Ti Aw

"MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond" by Muhammad Huzaifah, Geyu Lin, Tianchi Liu, Hardik B. Sailor, Kye Min Tan, Tarun K. Vangani, Qiongqiong Wang, Jeremy H. M. Wong, Jinyang Wu, Nancy F. Chen, and Ai Ti Aw, arXiv:2412.11538v3, 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 & 2 & 2 & 1 \\
2 & 2 & 2 & 2 \\
3 & 2 & 1 & 1 \\
4 &3 & 4 & 2 \\
5 & 3 & 4 & 2 \\
6 & 3 & 3 & 2 \\
7 & 3 & 5 & 2 \\
8 & 4 & 5 & 2
 \end{tabular}
\end{adjustbox}
\caption{Number of outer fGMRES iterations 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.


\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|} \hline
$z$ & $a$ & supersingular primes \\ \hline
$\left[19, 19, 19, 1\right]$ & $\left(1, 17, 1\right),\left(1, 16, 2\right)$, & $p \equiv QNR \bmod{19}$ \\
& $\left(1, 15, 3\right), \left(1, 11, 7\right)$ & $p \equiv QNR \bmod{19}$ \\ \hline
$\left[20, 20, 10, 1\right]$ & $\left(1, 17, 2\right)$ & $p \equiv 3, 7, 19 \bmod{20}$ \\ \hline
 & $\left(1, 1, 18\right)$ & $p \equiv 19 \bmod{20}$ \\ \hline
$\left[21, 21, 7, 1\right]$ & $\left(1, 17, 3\right), \left(1, 11, 9\right)$ & $p \equiv 5, 10, 17, 19, 20 \bmod{21}$ \\
 & $\left(1, 8, 12\right)$ & $p \equiv QNR \bmod{7}$ \\ \hline
$\left[24, 8, 6, 1\right]$ & $\left(1, 3, 20\right)$ & $p \equiv 23 \bmod{24}$ \\ \hline
$\left[24, 24, 4, 1\right]$ & $\left(1, 17, 6\right)$ & $p \equiv 7, 23 \bmod{24}$ \\ \hline
 & $\left(1, 5, 18\right)$ & $p \equiv 19, 23 \bmod{24}$ \\ \hline
$\left[28, 7, 4, 1\right]$ & $\left(1, 20, 7\right)$ & $p \equiv 3, 19, 27 \bmod{28}$ \\ \hline
$\left[27, 27, 3, 1\right]$ & $\left(1, 17, 9\right)$ & $p \equiv 2 \bmod{3}$ \\ \hline
$\left[30, 10, 3, 1\right]$ & $\left(1, 9, 20\right)$ & $p \equiv 29 \bmod{30}$ \\ \hline
$\left[36, 36, 2, 1\right]$ & $\left(1, 17, 18\right)$ & $p \equiv 3 \bmod{4}$ \\ \hline
$\left[38, 19, 2, 1\right]$ & $\left(1, 18, 19\right)$ & $p \equiv QNR \bmod{38}$ \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Cyclic genus $9$}
\end{table}
\end{document}

math

"Supersingular curves via the Shimura--Taniyama method" by Jeremy Booher and Rachel Pries, arXiv:2503.04878v2, 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|c|c|c|c|c|}
			\hline
			\multirow{2}{1.5cm}{\centering Model}&
			\multirow{2}{1.5cm}{\centering Real Images}&
			\multirow{2}{1.5cm}{\centering PGAN}& \multicolumn{3}{c|}{ APGAN} \\
			\cline{4-6}& & & Stage 32 & Stage 64 & Stage 128 \\ \hline
			GAN-train & 78.2& 67.7 & 63.3 & \textbf{70.1} & 64.7 \\ \hline
			GAN-test & - &60.8 & 56.0 & \textbf{62.2} & 58.1 \\ \hline
		\end{tabular}
\end{adjustbox}
\caption{ Comparison of APGAN and PGAN. The SNL block is added to different stages of the generator and discriminator. All models have been presented with over 6M images, and the best GAN-train and GAN-test are reported.}
\end{table}
\end{document}

eess

Ibrahim Saad Ali
Mamdouh Farouk Mohamed
Yousef Bassyouni Mahdy

"Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network" by Ibrahim Saad Ali, Mamdouh Farouk Mohamed, and Yousef Bassyouni Mahdy, arXiv:1910.11960v1, 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
\textbf{Experiment} & \textbf{SSIM} & \textbf{FID} & \textbf{PSNR} \\
\midrule
baseline & 0.8015 & 268.491 & 19.746 \\
$\lambda=0$ & 0.699 & 302.929 & 14.086 \\
$\lambda=0.05$ & \textbf{0.909} & 278.253 & \textbf{20.867} \\
$\lambda=0.25$ & 0.9002 & 280.730 & 20.8184 \\
$\lambda=0.45$ & 0.8863 & 278.305 & 20.2810 \\
$\lambda=0.65$ & 0.8651 & 263.039 & 19.862 \\
$\lambda=0.85$ & 0.8474 & \textbf{246.999} & 19.833 \\
$\lambda=0.95$ & 0.8121 & 255.261 & 19.220 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Bing Xiong
Yue Peng
RanRan Zhang
Fuqiang Chen
JiaYe He
Wenjian Qin

"Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion" by Bing Xiong, Yue Peng, RanRan Zhang, Fuqiang Chen, JiaYe He, and Wenjian Qin, arXiv:2412.11106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Spearman's rho for bivariate zero-inflated data" by Jasper Arends and Elisa Perrone, arXiv:2503.13148v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Error analysis for population dynamic example using constrained NLS method}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
variable&bias&MAPE&MAE&RMSE&R$^2$\\ \hline
$x_1$&0.0769&0.0760&0.1139&0.1741&0.9830\\
$x_2$&0.0537&0.0525&0.0764&0.1003&0.9535\\
$f_1(\cdot)$&-0.0727&0.6252&0.5815&0.8566&0.9010\\
$f_2(\cdot)$&0.0064&1.7939&0.2709&0.3492&0.7928\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

S. Syafiie
Aries Subiantoro
Vivi Andasari
Fernando Tadeo

"Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods" by S. Syafiie, Aries Subiantoro, Vivi Andasari, and Fernando Tadeo, arXiv:2501.12856v1, 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}{ccccccc}
\hline
minute & $1$ & $2$ & $\ldots$ & $T_1-1$ & $T_1$ & $T_1+1$ \\
\hline
fight & $p_{21}$ & $p_{22}A_1$ & $\ldots$ & $p_{2(T_1-1)}\prod_{k=1}^{T_1-2}A_k$ & $p_{2T_1}\prod_{k=1}^{T_1-1}A_k$ & $0$ \\
hide & $1-\frac{m}{N-e}$ & $\prod_{k=1}^2A_k$ & $\ldots$ & $\prod_{k=1}^{T_1-1}A_k$ & $0$ & $0$ \\
run & $p_r$ & $p_rA_1$ & $\ldots$ & $p_r\prod_{k=1}^{T_1-2}A_k$ & $p_r\prod_{k=1}^{T_1-1}A_k$ & $0$ \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Survival probabilities of a civilian in an open arena when $T_1\leq T_2$, where $A_k=\left[1-\frac{m}{(N-e)-(k-1)(e+m)}\right]$ and the $i$-th cell of the ``fight'' (respectively ``run'') row is the survival probability of the civilian when he hides for the first $(i-1)$ minutes and starts to fight back (respectively ``run'') at the beginning of the $i$-th minute..}
\end{table}
\end{document}

stat

"Analytic models for active shooter incidents with civilian resistance" by Liang Hong, arXiv:2310.17111v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
\toprule
Algorithm & Median Abs. Error\tabularnewline
\midrule
\midrule
S-CFM & $\boldsymbol{0.5829_{5.0\text{e-}2}}$\tabularnewline
CausalNF-NSF & $0.6018_{2.0\text{e-}2}$\tabularnewline
CausalNF-MAF & $0.6025_{3.0\text{e-}2}$\tabularnewline
CAREFL & $\boldsymbol{0.5983_{3.0\text{e-}2}}$\tabularnewline
ANM & $0.6746_{4.8\text{e-}8}$\tabularnewline
Linear SCM & $0.6045_{0.0}$\tabularnewline
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Learning Structural Causal Models from Ordering: Identifiable Flow Models" by Minh Khoa Le, Kien Do, and Truyen Tran, arXiv:2412.09843v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Optimising the decision threshold in a weighted voting system: The case of the IMF's Board of Governors" by Dóra Gréta Petróczy, arXiv:2505.16654v1, 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{Generator specs for our $3$-unit grid UC instance. The data are taken from Ref.~.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
\toprule
 & $p_{j, \min}$ & $p_{j, \max}$ & $c_j$ & $b_j$ & $a_j$ \\
Unit & (MW) & (MW) & & & \\
\midrule
0 & 100 & 600 & 500 & 10 & 0.002 \\
1 & 100 & 400 & 300 & 8 & 0.0025 \\
2 & 50 & 200 & 100 & 6 & 0.005 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Willie Aboumrad
Phani R V Marthi
Suman Debnath
Martin Roetteler
Evgeny Epifanovsky

"A New Hybrid Quantum-Classical Algorithm for Solving the Unit Commitment Problem" by Willie Aboumrad, Phani R V Marthi, Suman Debnath, Martin Roetteler, and Evgeny Epifanovsky, arXiv:2505.00145v1, 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{The architecture of the multi-layer perceptron.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
 		\cline{1-5}
 		\textbf{Layer} & Input & Hidden 1 & Hidden 2 & Output & \\ \cline{1-5}
 		\textbf{Units} & 1 & 8 & 12 & 3 & \\ \cline{1-5}
 		\textbf{Activation} & \texttt{softplus} & \texttt{softplus} & \texttt{softplus} & \texttt{softplus} & \\ \cline{1-5}
 	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Aidan J. Hughes
Jack Poole
Nikolaos Dervilis
Paul Gardner
Keith Worden

"Quantifying the value of information transfer in population-based SHM" by Aidan J. Hughes, Jack Poole, Nikolaos Dervilis, Paul Gardner, and Keith Worden, arXiv:2311.03083v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Two-Way Fixed Effects Model Estimates -- ln(Price\_Paid)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \tabularnewline \midrule \midrule
 Dependent Variable: & \multicolumn{4}{c}{ln(Price\_Paid)}\\
 & Dry Dog Food & Dry Cat Food & Dog Hygiene & Cat Hygiene \\
 Model: & (1) & (2) & (3) & (4)\\
 \midrule
 \emph{Variables}\\
 $\mathcal{I}\{\textrm{Adopter}_{i}\} \times \mathcal{I}\{ t \geq T_{i}^{\textrm{adopt}}\}$ & -0.0102$^{*}$ & -0.0106$^{**}$ & -0.0046 & -0.0160$^{*}$\\
 & (0.0040) & (0.0038) & (0.0030) & (0.0063)\\
 \midrule
 \emph{Fixed-effects}\\
 Customer & Yes & Yes & Yes & Yes\\
 YearMonth & Yes & Yes & Yes & Yes\\
 \midrule
 \emph{Fit statistics}\\
 Observations & 99,233 & 27,623 & 40,183 & 42,596\\
 R$^2$ & 0.89566 & 0.70460 & 0.62699 & 0.66927\\
 Within R$^2$ & 0.00030 & 0.00065 & 0.00012 & 0.00074\\
 \midrule \midrule
 \multicolumn{5}{l}{\emph{Clustered (Customer \& YearMonth) standard-errors in parentheses}}\\
 \multicolumn{5}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05}}\\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"How does online shopping affect offline price sensitivity?" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll|ccc}
Fire & Mean & Covariance & RMSE & CA & CA-High \\\hline
Combined & LR & Independent & 223 & 29.0 & 8.26 \\
 & LR & Spatial & 100 & 64.5 & 76.54 \\
 & SL & Independent & 108 & 62.4 & 59.06\\
 & SL & Spatial & 97 & 65.7 & 62.98
\end{tabular}
\end{adjustbox}
\caption{Combined-fire cross-validation root mean squared prediction error (RMSE), classification accuracy (CA) percent for fire severity level (high enhanced growth (dNBR from -500 - -251), low enhanced regrowth (-250 - -101), unburned (-100 - 99), low (100 - 269), moderate-low (270 - 439), moderate-high (440 - 659), and high (660 - 1300)) and CA for the high categories for Linear Regression (LR) and Super Learner (SL) models with independent and spatial error structure. }
\end{table}
\end{document}

stat

Nicholas Simafranca
Bryant Willoughby
Erin O'Neil
Sophie Farr
Brian J Reich
Naomi Giertych
Margaret Johnson
Madeleine Pascolini-Campbell

"Modelling wildland fire burn severity in California using a spatial Super Learner approach" by Nicholas Simafranca, Bryant Willoughby, Erin O'Neil, Sophie Farr, Brian J Reich, Naomi Giertych, Margaret Johnson, and Madeleine Pascolini-Campbell, arXiv:2311.16187v1, 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{\texttt{nxcorr} values for the different reconstructions presented.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 & \texttt{nxcorr} intrinsic source & \texttt{nxcorr} blurred screen & \texttt{nxcorr} non-blurred screen\\
 \midrule
 Figure~ & 0.99 (top row); 0.99 (bottom row) & 0.55 (top row); 0.57 (bottom row) & 0.73 (top row); 0.75 (bottom row) \\
 Figure~ & 0.99 (top row); 0.99 (bottom row) & 0.57 (top row); 0.45 (bottom row) & 0.75 (top row); 0.55 (bottom row)\\
 Figure~ & 0.97 & 0.81 & 0.88 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Alejandro Mus
Teresa Toscano
Hendrik Müller
Guang-Yao Zhao
Andrei Lobanov
Ciriaco Goddi

"Multiobjective optimization for scattering mitigation and scattering screen reconstruction in VLBI observations of the Galactic Center" by Alejandro Mus, Teresa Toscano, Hendrik Müller, Guang-Yao Zhao, Andrei Lobanov, and Ciriaco Goddi, arXiv:2504.16257v2, 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{Robustness specifications on the effect of school closures on test scores at national level}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}

					\hline

					& Math (ln) & Italian (ln) & Math (Rasch) & Italian (Rasch) & Math (std. - PSM) & Italian (std. - PSM) \\

					& (1) & (2) & (3) & (4) & (5) & (6) \\
					\hline

					\textbf{School closures} & -0.011*** & -0.003*** & -1.569*** & -0.434*** & -0.017*** & -0.006*** \\
					(Treated=1 X t=1) 	& (0.000) & (0.000) & (0.041) & (0.042) & (0.001) & (0.001) \\ \hline

					\textbf{Other controls} & Yes & Yes & Yes & Yes & Yes & Yes \\
					\textbf{Student FEs} & Yes & Yes & Yes & Yes & Yes & Yes \\
					\textbf{Relative time dummies} & Yes & Yes & Yes & Yes & Yes & Yes \\
					\textbf{Grade dummies}& Yes & Yes & Yes & Yes & Yes & Yes \\ \hline

					\textbf{Observations} & 4,613,714 & 4,613,714 & 4,613,714 & 4,613,714 & 4,613,714 & 4,613,714 \\
					R-squared & 0.395 & 0.274 & 0.413 & 0.280 & 0.379 & 0.261 \\
					\textbf{Number of students} & 2,306,857 & 2,306,857 & 2,306,857 & 2,306,857 & 2,306,857 & 2,306,857 \\ \hline

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

q-fin

"Will the last be the first? School closures and educational outcomes" by Michele Battisti and Giuseppe Maggio, arXiv:2208.11606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"On Solving the Minimum Spanning Tree Problem with Conflicting Edge Pairs" by Roberto Montemanni and Derek H. Smith, arXiv:2504.14503v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccccccc}
 \toprule
 & $a_\gamma$ & $b_\gamma$ & $a_\lambda$ &$b_\lambda$ & $a_1$ & $b_1$ & $a_2$ & $b_2$ & $\mu$ & $\Sigma$\\
 \midrule
 vague & 0.2 & 0.1 & 2.001 & 1 & 2.001 & 1 & 2.001 & 1 & $(0,\ldots,0)^\top$ & $100 \mathrm{I}_{p}$\\
 regularized & 1 & 1 & 2.1 & 1.1 & 2.1 & 1.1 & 2.1 & 1.1 & $(0,\ldots,0)^\top$ & $10 \mathrm{I}_{p}$\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Parameters of the prior distribution.}
\end{table}
\end{document}

stat

Panagiotis Papastamoulis
Fotios Milienos

"Bayesian inference and cure rate modeling for event history data" by Panagiotis Papastamoulis and Fotios Milienos, arXiv:2310.06926v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{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|c|c|c|}
\hline
$\mathfrak{g}$ & $\mathbf{R}$ & $A_{\mathbf{R}}$ & $B_{\mathbf{R}}$ & $C_{\mathbf{R}}$\\ \hline
$\mathfrak{su}(N),$ & $\mathbf{Adj}$ &$2N$&$0$&$N+6$\\ \cline{2-5}
$N=2,3$ & $\mathbf{F}$ &$1$&$0$&$\frac{1}{2}$\\ \hline
$\mathfrak{su}(N),$ & $\mathbf{Adj}$ &$2N$&$2N$&$6$\\ \cline{2-5}
$N\geq 4$ & $\mathbf{F}$ &$1$&$1$&$0$\\ \hline
\multirow{2}{*}{$\mathfrak{g}_2$} & $\mathbf{Adj}$ &$8$&$0$&$10$\\ \cline{2-5}
 & $\mathbf{7}$ &$2$&$0$&$1$\\ \hline
\end{tabular}
\end{adjustbox}
\caption{Group theory coefficients.}
\end{table}
\end{document}

math

Min-xin Huang
Sheldon Katz
Albrecht Klemm
Xin Wang

"Refined BPS numbers on compact Calabi-Yau threefolds from Wilson loops" by Min-xin Huang, Sheldon Katz, Albrecht Klemm, and Xin Wang, arXiv:2503.16270v2, 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|c|c|}
		\cline{2-6}
		 &
		\textbf{Centralized} &
		\multicolumn{4}{c|}{\textbf{Independent
				planning}}
		\\ \cline{2-6}
		& \textbf{4 UAS} & \textbf{UAS 1} & \textbf{UAS 2} &
		\textbf{UAS 3} & \textbf{UAS 4} \\ \cline{2-6}
		\hline\hline
		\textbf{Mean (s)} &
		81.918 & 15.596 &
		17.444 &
		17.553 & 16.128 \\ \hline
		\textbf{SD (s)} &
		27.215 & 6.257 &
		7.404 &
		6.912 & 6.764 \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Alëna Rodionova
Yash Vardhan Pant
Connor Kurtz
Kuk Jang
Houssam Abbas
Rahul Mangharam

"Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme" by Alëna Rodionova, Yash Vardhan Pant, Connor Kurtz, Kuk Jang, Houssam Abbas, and Rahul Mangharam, arXiv:2101.10404v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Graph signal denoising using $t$-shrinkage priors" by Sayantan Banerjee and Weining Shen, arXiv:2012.13696v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Sandip K Pal
Arnab Koley
Pritam Ranjan
Debasis Kundu

"Modeling time to failure using a temporal sequence of events" by Sandip K Pal, Arnab Koley, Pritam Ranjan, and Debasis Kundu, arXiv:2412.05836v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance Evaluation of the SHAP-enabled Lighter Model using 5-fold Repeated Cross Validation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 \thead{Performance \\Metric} & \thead{Baseline ML \\Model using \\all features} & \thead{Lighter ML\\ Model using \\Top 5 features} & \thead{Net\\Gain}\\
 \hline
 RMSE & $3.542 \pm 0.036$ & $3.661 \pm 0.038$ & $-3.35\%$\\
 \hline
 $R^2$ & $0.854 \pm 0.003$ & $0.844 \pm 0.003$ & $-1.17\%$\\
 \hline
 Training Time & $1.896 \pm 0.090$ & $0.681 \pm 0.049$ & $\mathbf{+64.07\%}$\\
 \hline
 Prediction Time & $0.077 \pm 0.005$ & $0.023 \pm 0.002$ & $\mathbf{+70.13\%}$\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Usama Masood
Hasan Farooq
Ali Imran
Adnan Abu-Dayya

"Interpretable AI-based Large-scale 3D Pathloss Prediction Model for enabling Emerging Self-Driving Networks" by Usama Masood, Hasan Farooq, Ali Imran, and Adnan Abu-Dayya, arXiv:2201.12899v1, 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 detection methods on Folktables data.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
Method & Power & FDP & Mean detection time \\ \midrule
Quantile detector & 0.48 & 0.019 & 3727 \\
Mean detector & 0.01 & 0.19 & 4945 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Salim I. Amoukou
Tom Bewley
Saumitra Mishra
Freddy Lecue
Daniele Magazzeni
Manuela Veloso

"Sequential Harmful Shift Detection Without Labels" by Salim I. Amoukou, Tom Bewley, Saumitra Mishra, Freddy Lecue, Daniele Magazzeni, and Manuela Veloso, arXiv:2412.12910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance on the BraTs’20 Testing Data.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|}
 \hline
 Metric (mean) & ET & WT & TC \\
 \hline
 Dice & 0.78507 & 0.88595 & 0.84273 \\
 Sensitivity & 0.81308 & 0.91690 & 0.85934 \\
 Specificity & 0.99967 & 0.99905 & 0.99964 \\
 Hausdorff (95\%) & 20.36071 & 6.66665 & 19.54915 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Theophraste Henry
Alexandre Carre
Marvin Lerousseau
Theo Estienne
Charlotte Robert
Nikos Paragios
Eric Deutsch

"Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution" by Theophraste Henry, Alexandre Carre, Marvin Lerousseau, Theo Estienne, Charlotte Robert, Nikos Paragios, and Eric Deutsch, arXiv:2011.01045v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Mingxi Cheng
Shahin Nazarian
Paul Bogdan

"VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text" by Mingxi Cheng, Shahin Nazarian, and Paul Bogdan, arXiv:2102.00816v1, 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{Main parameters of real data. Values in parentheses represent the standard deviation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cl}
 \toprule
 \textbf{Parameter} & \textbf{Value(s)} \\
 \midrule
 fps & 10-20 \\
 Duration & 10 seconds \\
 Frames per sweep & 100-300 \\
 Plane & sagittal / transversal \\
 \midrule
 Age & 38.33 (8.48) \\
 Height [m] & 1.65 (0.04)\\
 Weight [kg] & 64.53 (5.32)\\
 Ethnicity & White/European\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Max Krähenmann
Sergio Tascon-Morales
Fabian Laumer
Julia E. Vogt
Ece Ozkan

"From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound" by Max Krähenmann, Sergio Tascon-Morales, Fabian Laumer, Julia E. Vogt, and Ece Ozkan, arXiv:2508.14552v1, 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{15Hz token rate for ASR experiment.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Method} & \textbf{WER} & \textbf{CER} \\
\midrule
boundary + max pooling (w/o CALE) & 6.4 & 3.4 \\
boundary + average pooling (w/o CALE) & 6.8 & 3.6 \\
boundary + attention pooling (w/o CALE) & 6.1 & 3.3 \\
\textbf{Ours (with CALE)} & \textbf{5.6} & \textbf{2.9} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jialong Zuo
Guangyan Zhang
Minghui Fang
Shengpeng Ji
Xiaoqi Jiao
Jingyu Li
Yiwen Guo
Zhou Zhao

"Entropy-based Coarse and Compressed Semantic Speech Representation Learning" by Jialong Zuo, Guangyan Zhang, Minghui Fang, Shengpeng Ji, Xiaoqi Jiao, Jingyu Li, Yiwen Guo, and Zhou Zhao, arXiv:2509.00503v1, 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
Attacker & Guesswork & ReId AUC\\
\hline
SAU & $8476 $ $(1971, 20225)$ & $0.50$ $(0.49, 0.52)$ \\
ViT & $8411 $ $(5219, 12033)$ & $0.50$ $(0.49, 0.51)$ \\
Resnet-18 & $10070$ $(9871, 10300)$ & $0.50$ $(0.47, 0.53)$ 	 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

"Syfer: Neural Obfuscation for Private Data Release" by Adam Yala, Victor Quach, Homa Esfahanizadeh, Rafael G. L. D'Oliveira, Ken R. Duffy, Muriel Médard, Tommi S. Jaakkola, and Regina Barzilay, arXiv:2201.12406v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\hline\hline
& \multicolumn{6}{c}{\hspace{3ex} \textbf{State}}\\
\cline{2-7}
& $S_{0}$ & $S_{1}$ & $S_{2}$ & $S_{3}$ & $S_{4}$ & $S_{5+}$\\
\hline
abs.freq & 64,253& 76,977& 58,342& 27,476& 15,462& 53,520\\
 \hline\hline
\end{tabular}
\end{adjustbox}
\caption{Number of observations in each state to model the time process.}
\end{table}
\end{document}

stat

Emmanuel Jordy Menvouta
Jolien Ponnet
Robin Van Oirbeek
Tim Verdonck

"mCube: Multinomial Micro-level reserving Model" by Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek, and Tim Verdonck, arXiv:2212.00101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small REP results over the year.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rllrrrrrrrrr} \toprule
 \multicolumn{3}{c}{\textbf{ }}
 &
 \textbf{Electrolyzer}&
 \textbf{REP} &
 \textbf{Average}
 \\
 &
 &
 &
 \textbf{average load} &
 \textbf{profit} &
 \textbf{LMP}$^{\rm a}$\\
 \multicolumn{3}{c}{\textbf{Case}} &
 [MW] &
 [M \$] &
 [\$/MWh]\\
 \toprule
\multirow{4}{*}{100 MW} & \multirow{2}{*}{$\$1.5$/kg} & bidder & 91.44 & 28.88 & 17.81 \\
 & & fixed & 91.44 & 28.14 & 17.75\\
 & \multirow{2}{*}{$\$6$/kg} & bidder & 100.00 & 98.12 & 17.83\\
 & & fixed & 100.00 & 98.12 & 17.83\\ \midrule
\multirow{4}{*}{500 MW} & \multirow{2}{*}{$\$1.5$/kg} & bidder & 421.15 & 59.73 & 21.55 \\
 & & fixed & 421.15 & 47.36 & 24.30\\
 & \multirow{2}{*}{$\$6$/kg} & bidder & 499.12 & 362.15 & 31.77\\
 & & fixed & 499.01 & 352.36 & 33.92\\ \midrule
\multirow{4}{*}{1000 MW} & \multirow{2}{*}{$\$1.5$/kg} & bidder & 629.77 & 67.76 & 24.45\\
 & & fixed & 617.38 & -730.61 & 179.60\\
 & \multirow{2}{*}{$\$6$/kg} & bidder & 729.34 & 320.83 & 81.54\\
 & & fixed & 672.3 & -563.48 & 241.06 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Andrea Gloppen Johnsen
Lesia Mitridati
Jalal Kazempour
Line Roald

"Electricity Market Bidding for Renewable Electrolyzer Plants: An Opportunity Cost Approach" by Andrea Gloppen Johnsen, Lesia Mitridati, Jalal Kazempour, and Line Roald, arXiv:2501.16844v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc|cc|cc}
 N & $L^2$-err in $h_h$ & rate
 & $L^2$-err in $u_h$ & rate
& $L^2$-err in $m_h$ & rate\\
\hline
 25& 1.420e-03 & -- & 1.670e-03 & -- & 1.379e-02 &-- \\
 50& 1.567e-04 & 3.18 & 2.083e-04 & 3.00 & 1.683e-03 & 3.03 \\
100& 1.917e-05 & 3.03 & 2.820e-05 & 2.89 & 2.371e-04 & 2.83 \\
200& 2.363e-06 & 3.02 & 3.880e-06 & 2.86 & 3.403e-05 & 2.80 \\
\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"A high-order velocity-based discontinuous Galerkin scheme for the shallow water equations: local conservation, entropy stability, well-balanced property, and positivity preservation" by Guosheng Fu, arXiv:2201.13040v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Voltages for points $A$ and $B$ for the 9-bus test system in~.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
 & Point A & Point B\tabularnewline
\hline
\hline
1 & 0.9025 + 0.0000i & 0.9058 + 0.0000i\tabularnewline
\hline
2 & 0.9042 - 0.1541i & 0.9220 + 0.0789i\tabularnewline
\hline
3 & 0.9063 - 0.1819i & 0.9082 + 0.2334i\tabularnewline
\hline
4 & 0.9052 - 0.0927i & 0.9089 - 0.0063i\tabularnewline
\hline
5 & 0.8961 - 0.1606i & 0.9103 + 0.0105i\tabularnewline
\hline
6 & 0.9082 - 0.1888i & 0.9297 + 0.1444i\tabularnewline
\hline
7 & 0.8934 - 0.2080i & 0.9246 + 0.0647i\tabularnewline
\hline
8 & 0.9034 - 0.1787i & 0.9275 + 0.0545i\tabularnewline
\hline
9 & 0.8848 - 0.1644i & 0.8995 - 0.0302i\tabularnewline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mohammad Rasoul Narimani
Katherine R. Davis
Daniel K. Molzahn

"Certifying the Nonexistence of Feasible Path Between Power System Operating Points" by Mohammad Rasoul Narimani, Katherine R. Davis, and Daniel K. Molzahn, arXiv:2509.05935v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{avg performance of PATH-STAB}
\begin{tabular}{c|cccc}\toprule
time & $\frac{\text{best primal value}}{\text{best known dual bound}}$ & $\frac{\text{best primal value}}{\text{best dual bound of Gurobi}}$ \\
\midrule
60s & 1.6\% & 17.2\% \\
1200s & 1.6\% & 12.1\% \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

Diego Cifuentes
Santanu S. Dey
Jingye Xu

"Sensitivity analysis for mixed binary quadratic programming" by Diego Cifuentes, Santanu S. Dey, and Jingye Xu, arXiv:2312.06714v2, 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{Comparison of various PFD circuits}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|c|c|}
\hline
 & \textbf{This Work} & & & & & & \\
\hline
\textbf{Process Technology (nm)} & \textbf{28} & 180 & 28 & 28 & 28 & 28 & 65 \\
\hline
\textbf{Dead Zone (ps)} & \textbf{40} & 0 & 0 & 0 & 70 & 45 & -- \\
\hline
\textbf{Power Consumption (uW)} & \textbf{4.41} & 201.6* & 11.04** & 15.44** & 2.92** & 21.9** & 422.4* \\
\hline
\textbf{Transistor Count} & \textbf{20} & 8 & 22 & 16 & 54 & 22 & 14 \\
\hline
\textbf{Max Frequency (GHz)} & \textbf{3} & 4 & 2.9 & 3.3 & 0.8 & 1 & 2.2 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Dhandeep Challagundla
Venkata Krishna Vamsi Sundarapu
Ignatius Bezzam
Riadul Islam

"TSPC-PFD: TSPC-Based Low-Power High-Resolution CMOS Phase Frequency Detector" by Dhandeep Challagundla, Venkata Krishna Vamsi Sundarapu, Ignatius Bezzam, and Riadul Islam, arXiv:2508.16933v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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

&

&
\multicolumn{2}{c}{Off policy}
&
\multicolumn{2}{c}{On policy}
&
\multicolumn{2}{c}{Train time}
\\
\cmidrule(l){3-4}\cmidrule(l){5-6}\cmidrule{7-8}
Target Date & Metric &Cf& Ss & Cf & Ss & Cf & Ss \\
\midrule
& Demand Error& & & &\\
21-11-2022 & & \textbf{61.48} & 62.46 & 60.00 & \textbf{58.41} & 2.55 & 1.63\\
23-11-2020 & &\textbf{65.94} & 68.67 &\textbf{62.50} & 64.24 & 1.17 & 0.71\\
25-11-2019 & &\textbf{63.61} & 67.52 &61.85 &\textbf{61.60} & 0.83 & 0.52\\
\midrule
25-04-2022 & && & 46.55 & \textbf{46.28} & 1.96 & 1.27\\
06-06-2022 & && & \textbf{47.84} & 50.26 & 2.11 & 1.36\\
10-10-2022 & && &\textbf{51.97} & 52.66 & 2.44 & 1.61\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Causal Forecasting for Pricing

Douglas Schultz
Johannes Stephan
Julian Sieber
Trudie Yeh
Manuel Kunz
Patrick Doupe
Tim Januschowski

"Causal Forecasting for Pricing" by Douglas Schultz, Johannes Stephan, Julian Sieber, Trudie Yeh, Manuel Kunz, Patrick Doupe, and Tim Januschowski, arXiv:2312.15282v3, 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}
 Metric~ & FAQ & FAKE & GEN \\ \hline\hline
 Precision 	& 0.44 & 0.22 & 0.58 \\
 Recall & 0.61 & 0.08 & 0.64 \\
 F1 score & 0.51 & 0.12 & 0.61 \\
 Accuracy & 0.67 & 0.602 & 0.68 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Performance metrics of the DNN classification.}
\end{table}
\end{document}

cs

Ankur Gupta
Yash Varun
Prarthana Das
Nithya Muttineni
Parth Srivastava
Hamim Zafar
Tanmoy Chakraborty
Swaprava Nath

"TruthBot: An Automated Conversational Tool for Intent Learning, Curated Information Presenting, and Fake News Alerting" by Ankur Gupta, Yash Varun, Prarthana Das, Nithya Muttineni, Parth Srivastava, Hamim Zafar, Tanmoy Chakraborty, and Swaprava Nath, arXiv:2102.00509v1, 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
\caption{Contract trading results of unrestricted and mandatory contracting with different $\beta$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|ccccc}
\hline
\hline
& & $\beta=0.2$ & $\beta=0.4$ & $\beta=0.6$ & $\beta=0.8$ \\
\hline
\multirow{18}{*}{Unrestricted}&Trade Volume (GW) \\
&\quad Baseload \\
&\qquad Future & 27.9 & 28.8 & 30.0 & 31.2 \\
&\qquad Options & 2.5 & 2.6 & 3.4 & 4.1 \\
&\qquad Unit & 0 & 0 & 0 & 0 \\
&\quad Peaker \\
&\qquad Future & 0 & 0 & 0 & 0 \\
&\qquad Options & 120.0 & 119.4 & 119.5 & 121.4 \\
&\qquad Unit & 0 & 0 & 0 & 0 \\
&\quad Variable \\
&\qquad Future & 0 & 0 & 0 & 0 \\
&\qquad Options & 0 & 0 & 0 & 0 \\
&\qquad Unit & 159.0 & 157.3 & 155.4 & 154.8 \\
&&&&\\
&Contract risk premium (\$/MW-yr) \\
&\qquad Future & 16470 & 16113 & 16032 & 13481 \\
&\qquad Options & 6747 & 6738 & 6261 & 3961 \\
&\qquad Unit & 3455 & 3315 & 3451 & 3185 \\
\hline
\multirow{18}{*}{Mandatory}&Trade Volume (GW) \\
&\quad Baseload \\
&\qquad Future & 52.5 & 45.7 & 41.6 & 33.7 \\
&\qquad Options & 52.5 & 45.7 & 41.6 & 32.8 \\
&\qquad Unit & 0 & 0 & 0 & 0 \\
&\quad Peaker \\
&\qquad Future & 0 & 0 & 0.08 & 0.3 \\
&\qquad Options & 80.0 & 84.3 & 86.9 & 93.4 \\
&\qquad Unit & 0 & 0 & 0 & 0 \\
&\quad Variable \\
&\qquad Future & 0 & 0 & 0 & 59.6 \\
&\qquad Options & 16.0 & 18.5 & 20.1 & 22.4 \\
&\qquad Unit & 106.8 & 122.7 & 131.6 & 0.0002 \\
&&&&\\
&Contract risk premium (\$/MW-yr) \\
&\qquad Future & 5638 & 5839 & 6646 & 5524 \\
&\qquad Options & 17366 & 4597 & 10108 & 10870 \\
&\qquad Unit & 693 & 773 & 1171 & 1125 \\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Beyond capacity: contractual form in electricity reliability obligations" by Han Shu and Jacob Mays, arXiv:2210.10858v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of GMRES-IR and RGMRES-IR refinement steps with the number of GMRES iterations for each refinement step for prolate matrices with various $\alpha$ values, using precisions ($u_f,u,u_r$) = (half,single,double) and ($m,k$) = (16,5).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|}
		\hline
		$\alpha$ & GMRES-IR (16) & RGMRES-IR (16,5) \\ \hline
		0.475 & 12 (6,6) & 8 (6,2) \\
		0.47 & 16 (8,8) & 10 (8,2) \\
		0.467 & 19 (9,10) & 11 (9,2) \\
		0.455 & 50 (15,25,10) & 19 (15,4) \\%44 (15,16,13) & 19 (15,4) \\
		0.45 & 89 (14,43,32) & - \\%61 (14,16,16,15) & - \\
		0.4468 & - & - \\
		0.44 & - & - \\
		0.434 & - & - \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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