\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average ARI scores between 30 GEMINI-trained models and the best Deep LPM and LPM clustering according to ELBO criterion. $H$ represents the number of hidden layers in the trained model.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 GEMINI&$H$&LPM&Deep LPM\\
 \midrule
		\multirow{3}{*}{MI}&1&0.47 (0.08)&{\bf 0.31 (0.10)}\\
		&2&0.31 (0.07)&0.17 (0.09)\\
		&3&0.29 (0.04)&0.16 (0.05)\\
 \cmidrule(lr){3-4}
		\multirow{3}{*}{OvO Wasserstein}&1&{\bf 0.58 (0.04)}&0.25 (0.03)\\
		&2&0.54 (0.06)&0.22 (0.03)\\
		&3&0.49 (0.06)&0.21 (0.04)\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Louis Ohl
Pierre-Alexandre Mattei
Charles Bouveyron
Warith Harchaoui
Mickaël Leclercq
Arnaud Droit
Frédéric Precioso

"Generalised Mutual Information: a Framework for Discriminative Clustering" by Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron, Warith Harchaoui, Mickaël Leclercq, Arnaud Droit, and Frédéric Precioso, arXiv:2309.02858v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lll}
\hline
Scenario & $\delta$ & Runtime \\
\hline
Paris & 0.1s & 13.65ms \\
Paris & 0.2s & 0.01ms \\
New York & 0.1s & 92.50ms \\
Singapore & 0.1s & 33.33ms \\
Singapore & 0.2s & 23.01ms \\
\hline
\end{tabular}
\caption{Latex default table}
\end{table}
\end{document}

cs

Enze Xie
Wenjia Wang
Wenhai Wang
Peize Sun
Hang Xu
Ding Liang
Ping Luo

"Segmenting Transparent Object in the Wild with Transformer" by Enze Xie, Wenjia Wang, Wenhai Wang, Peize Sun, Hang Xu, Ding Liang, and Ping Luo, arXiv:2101.08461v3, 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.


stat

"Exact Designs for OofA Experiments Under a Transition-Effect Model" by Jiayi Zheng and Nicholas Rios, arXiv:2411.03504v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Sourav Medya
Mohammad Rasoolinejad
Yang Yang
Brian Uzzi

"An Exploratory Study of Stock Price Movements from Earnings Calls" by Sourav Medya, Mohammad Rasoolinejad, Yang Yang, and Brian Uzzi, arXiv:2203.12460v1, 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}{lc} %
 \toprule
 \textbf{Step} & \textbf{Predicted Class} \\
 \midrule
 Window 1 & 8 \\
 Window 2 & 0 \\
 Window 3 & 7 \\
 \cmidrule(lr){1-2} %
 \textbf{Final Prediction} & 8 \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Majority method}
\end{table}
\end{document}

eess

Taketo Akama
Zhuohao Zhang
Pengcheng Li
Kotaro Hongo
Hiroaki Kitano
Shun Minamikawa
Natalia Polouliakh

"Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings" by Taketo Akama, Zhuohao Zhang, Pengcheng Li, Kotaro Hongo, Hiroaki Kitano, Shun Minamikawa, and Natalia Polouliakh, arXiv:2412.15560v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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.


math

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

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


\documentclass{article}
\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}
 \hline
 software & OpenFOAM v10 \\
 \hline\hline
 Solver setting \\
 \hline
 solver, $p$ & PCG \\
 preconditioner, $p$ & DIC\\
 solver, $U$, $\tilde{\nu}$ & PBiCG \\
 preconditioner, $U$, $\tilde{\nu}$ & DILU\\
 tolerance & $1e^{-10}$\\
 residual & $1e^{-7}$\\
 relaxation factor & 0.3 \\
 \hline
 Numerical scheme setting \\
 \hline
 time scheme & steadyState\\
 gradient scheme & Gauss linear \\
 divergence scheme & linear \\
 div(phi,U) & bounded Gauss linearUpwind grad(U)\\
 div(phi,nuTilda) & bounded Gauss upwind\\
 div((nuEff*dev2(T(grad(U))))) & Gauss linear\\
 Laplacian scheme & Gauss linear corrected\\
 interpolation scheme & linear \\
 surface normal gradient scheme & corrected\\
 wall distance scheme & meshWave \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Solver settings used in NACA0012 demonstration.}
\end{table}
\end{document}

math

Kazuko W. Fuchi
Eric M. Wolf
Christopher R. Schrock
Philip S. Beran

"Acceleration of RANS Solver Convergence via Initialization with Wake Extension Models" by Kazuko W. Fuchi, Eric M. Wolf, Christopher R. Schrock, and Philip S. Beran, arXiv:2501.14699v1, licensed under CC0 1.0. 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{AdaRound on DeepLabv3 Semantic Segmentation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
 \toprule
 Configuration & mIOU (Mean Intersection over Union)\\
 \midrule
 FP32 & 72.94\% \\
 Nearest Rounding (W4A8) & 6.09\% \\
 AdaRound (W4A8)& 70.86\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Sangeetha Siddegowda
Marios Fournarakis
Markus Nagel
Tijmen Blankevoort
Chirag Patel
Abhijit Khobare

"Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)" by Sangeetha Siddegowda, Marios Fournarakis, Markus Nagel, Tijmen Blankevoort, Chirag Patel, and Abhijit Khobare, arXiv:2201.08442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 prompt & S/S & B/S & S/B & B/B \\
 \hline
 \textcolor{red}{"a red word label over a picture of a X"} & 87.69 \% & 44.16 \% & 29.64 \% & 66.23 \% \\
 \hline
 \textcolor{red}{"a word is printed in a red font over a picture of a X"} & 91.34 \% & 44.86 \% & 35.29 \% & 69.72 \% \\
 \hline
 \textcolor{red}{"a photo of a word written in a red font over a picture of a X"} & 86.53 \% & 43.07 \% & 30.78 \% & 62.69 \% \\
 \hline
 \textcolor{blue}{"a text that says X"} & 96.78 \% & 52.50 \% & 54.58 \% & 88.22 \% \\
 \hline
 \textcolor{blue}{"a word of a X is printed in a red font over a picture"} & 95.48 \% & 51.65 \% & 46.28 \% & 78.02 \% \\
 \hline
 \textcolor{blue}{"a photo of the word X written in a red font over a picture"} & 96.22 \% & 53.58 \% & 42.91 \% & 85.05 \% \\
 \hline
 \textcolor{orange}{"a photo of the word Y written in a red font over a picture of a X"} & 34.34 \% & 35.33 \% & 31.04 \% & 34.92 \% \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{CLIP's label switching rate for word-superimposed images on different prompts. The red-colored texts are the prompts that specify that the image content is what should be predicted. The blue-colored texts are the prompts that specify that the superimposed word is what should be predicted. The X means the label to be answered by the model. The orange-colored text is the condition in which a prompt varies according to the superimposed word on each image.}
\end{table}
\end{document}

cs

Yoann Lemesle
Masataka Sawayama
Guillermo Valle-Perez
Maxime Adolphe
Hélène Sauzéon
Pierre-Yves Oudeyer

"Language-biased image classification: evaluation based on semantic representations" by Yoann Lemesle, Masataka Sawayama, Guillermo Valle-Perez, Maxime Adolphe, Hélène Sauzéon, and Pierre-Yves Oudeyer, arXiv:2201.11014v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of parameters used to represent the bicycle frame design space.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|l}
\cline{1-3}
\textbf{Parameter Type} & \textbf{Data Type} & \textbf{Count} & \\ \cline{1-3}
Frame Geometry Relations & Continuous & 18 & \\ \cline{1-3}
Tube Outer Diameters & Continuous & 9 & \\ \cline{1-3}
Tube Thicknesses & Continuous & 7 & \\ \cline{1-3}
Frame Material & Categorical & 1 & \\ \cline{1-3}
Seat/Chain Stay Bridge Flags & Boolean & 2 & \\ \cline{1-3}
\textbf{Total} & & \textbf{37} & \\ \cline{1-3}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Lyle Regenwetter
Colin Weaver
Faez Ahmed

"FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames" by Lyle Regenwetter, Colin Weaver, and Faez Ahmed, arXiv:2201.10459v3, 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{Parameter values assumed for the true kinetic model}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
 \hline
 $Parameter$ & Value \\
 \hline
 $k_1^0 (\text{min}^{-1})$ & 8 \\
 $E_{\text{a}1} (\text{kJ/mol})$ & 29 \\
 $k_2^0 (\text{min}^{-1})$ & 5 \\
 $E_{\text{a}2} (\text{kJ/mol})$ & 35 \\
 $k_3^0 (\text{min}^{-1})$ & 3 \\
 $E_{\text{a}3} (\text{kJ/mol})$ & 32\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Panagiotis Petsagkourakis
Federico Galvanin

"Safe model-based design of experiments using Gaussian processes" by Panagiotis Petsagkourakis and Federico Galvanin, arXiv:2011.10009v2, 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{Comparison with benchmark ($f$ known) and other baseline methods dealing with unknown $f$, $\alpha=1\%$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
 \toprule
 Method & Average Detection Delay & False Alarm Rate(\%) \\
 \midrule
 \midrule
 $f$ known & \multirow{2}{*}{$3.67\pm 0.65$} & \multirow{2}{*}{$0.05\pm 0.03$}\\
 (benchmark) & & \\
 \midrule
 PGD & ${\bf 4.12}\pm 0.83$ & ${\bf 1.06}\pm 0.37$\\
 MLE & $4.56\pm 0.77$ & $38.2\pm 7.39$ \\
 GLRT & $5.03\pm 1.17$ & $7.52\pm 1.01$ \\
 Shewhart & $4.78\pm 0.84$ & $3.84\pm 0.95$ \\
 \midrule
 BGS & $4.85\pm 0.81$ & $4.95\pm 0.81$ \\
 ULR & $4.69\pm 1.01$ & $3.16\pm 0.74$ \\
 DIS & $5.03\pm 0.95$ & $4.28\pm 0.91$ \\
 DCQ & $4.77\pm 1.22$ & $5.16\pm 1.08$ \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee" by Chenhan Xiao, Yizheng Liao, and Yang Weng, arXiv:2309.07157v1, 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{Overview of model architectures, parameters, and pre-training datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcl}
\toprule
\textbf{Model Name} & \textbf{Backbone} & \textbf{Param} & \textbf{Pre-training Dataset} \\
\midrule
MetaFormer & CAFormer & 27.5M & ImageNet \\
MetaFormer\_in21k & CAFormer & 27.5M & ImageNet21k \\
SurgeNet\_Cholec
 & CAFormer & 27.5M & Cholecystectomy collection \\
SurgeNet\_Public & CAFormer & 27.5M & Public surgical videos \\
SurgeNet\_XL & CAFormer & 27.5M & Public \& inhouse surgical videos \\
TimeSformer & ViT Base & 121.4M & Kinetics-400 dataset \\
ConvNeXtV2 & ConvNeXtV2 & 31.9M & ImageNet \\
SurgeNet\_ConvNeXtV2 & ConvNeXtV2 & 31.9M & Public \& inhouse surgical videos \\
PVTv2 & PVTv2 & 28.0M & ImageNet \\
SurgeNet\_PVTv2 & PVTv2 & 28.0M & Public \& inhouse surgical videos \\
GastroNet\_RN50 & ResNet-50 & 28.8M & Inhouse endoscopic videos \\
GastroNet\_ViTS & ViT Small & 23.5M & Inhouse endoscopic videos \\
EndoFM & ViT Base & 123.9M & Large-scale surgical dataset \\
GSViT & ViT Base & 89.2M & Large-scale surgical dataset \\
EndoViT & ViT variant & 13.9M & Large-scale surgical dataset \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Prabhant Singh
Yiping Li
Yasmina Al Khalil

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


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Gender-dependent EER (\%) comparison between the speaker embedding vectors extracted from the x-vector-based embedding systems and the state-of-the-art i-vector-based systems.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
	\hline
	\multirow{2}{*}{Methods} & \multicolumn{2}{c|}{EER [\%]} \\ \cline{2-3}
	& Male & Female \\ \hline
	\textit{x-vector (Softmax)} & 2.09 & 2.48 \\ \hline
	\textit{DNN i-vectors} & 1.70 & 2.69 \\
	\textit{Uncertainty normalized HMM/i-vector} & 1.52 & 1.77 \\ \hline
	\textit{x-vector (GRL)} & 3.75 & 4.17 \\
	\textit{x-vector (Anti-loss)} & 1.25 & 1.66 \\ \hline
	\textit{\textbf{x-vector (JFE)}} & \textbf{0.82} & \textbf{1.29} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Woo Hyun Kang
Sung Hwan Mun
Min Hyun Han
Nam Soo Kim

"Disentangled speaker and nuisance attribute embedding for robust speaker verification" by Woo Hyun Kang, Sung Hwan Mun, Min Hyun Han, and Nam Soo Kim, arXiv:2008.03024v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets" by Shuo Sun, Molei Qin, Xinrun Wang, and Bo An, arXiv:2302.00586v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The numerical error for different $\delta$ by three methods}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|cccc||}
	 \hline
	 & $\delta=1e-4$ & $\delta=1e-3$ &$\delta=1e-2$&$\delta=1e-1$\\
	 \hline
	 Iterated Tikhonov & 0.2692 & 0.1035&0.0648&0.0051 \\

	 Landweber & 0.8654 & 0.1598 &0.1336&0.1370\\

	 New iterated Tikhonov & 0.2418 & 0.0734 &0.0222&0.0046\\
	 \hline
	 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A new iterated Tikhonov regularization method for Fredholm integral equation of first kind" by Xiaowei Pang and Jun Wang, arXiv:2504.00209v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 & & \textbf{MAR 1} & \textbf{MAR 2} & \textbf{MNAR 1} & \textbf{MNAR 2} \\
 \hline
 \multirow{4}{*}{\textbf{Data Model}} & $n$ & \multicolumn{4}{c|}{$2000 \quad (n_{train} = 1500, n_{test} = 500)$} \\
 \cline{2-6}
 & $p$ & \multicolumn{4}{c|}{$2$} \\
 \cline{2-6}
 & $q$ & \multicolumn{4}{c|}{$5$} \\
 \cline{2-6}
 & \#Data trees & \multicolumn{4}{c|}{$8$} \\
 \hline
 \multirow{4}{*}{\textbf{Missingness Model}} & Model & Probit Regression & Probit BART & Probit Regression & Probit BART \\
 \cline{2-6}
 & Model covariates & X only & X only & Y only & Y only \\
 \cline{2-6}
 & \#Missing trees & - & 3 & - & 5 \\
 \cline{2-6}
 & Observed proportion & (57.10\%, 78.05\%) & (86.50\% 56.10\%) & (66.00\%, 50.45\%) & (58.05\%, 75.15\%) \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Simulation recipes for the bivariate simulation studies. The complete dataset is consistent across all $4$ scenarios, while the missingness model follows a multivariate probit regression or probit BART model.}
\end{table}
\end{document}

stat

Yong Chen Goh
Wuu Kuang Soh
Andrew C. Parnell
Keefe Murphy

"Joint Models for Handling Non-Ignorable Missing Data using Bayesian Additive Regression Trees: Application to Leaf Photosynthetic Traits Data" by Yong Chen Goh, Wuu Kuang Soh, Andrew C. Parnell, and Keefe Murphy, arXiv:2412.14946v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jean Ogier du Terrail
Quentin Klopfenstein
Honghao Li
Imke Mayer
Nicolas Loiseau
Mohammad Hallal
Michael Debouver
Thibault Camalon
Thibault Fouqueray
Jorge Arellano Castro
Zahia Yanes
Laëtitia Dahan
Julien Taïeb
Pierre Laurent-Puig
Jean-Baptiste Bachet
Shulin Zhao
Remy Nicolle
Jérome Cros
Daniel Gonzalez
Robert Carreras-Torres
Adelaida Garcia Velasco
Kawther Abdilleh
Sudheer Doss
Félix Balazard
Mathieu Andreux

"FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings" by Jean Ogier du Terrail, Quentin Klopfenstein, Honghao Li, Imke Mayer, Nicolas Loiseau, Mohammad Hallal, Michael Debouver, Thibault Camalon, Thibault Fouqueray, Jorge Arellano Castro, Zahia Yanes, Laëtitia Dahan, Julien Taïeb, Pierre Laurent-Puig, Jean-Baptiste Bachet, Shulin Zhao, Remy Nicolle, Jérome Cros, Daniel Gonzalez, Robert Carreras-Torres, Adelaida Garcia Velasco, Kawther Abdilleh, Sudheer Doss, Félix Balazard, and Mathieu Andreux, arXiv:2311.16984v10, 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}{ccccccccccc}
 Parameter & $\gamma_0$ &
 $\gamma_1$ &
 $\gamma_{12}^0$ &
 $\gamma_{13}^0$ & \
 $\gamma_{23}^0$ &
 $\gamma_{12}^1$ &
 $\gamma_{13}^1$ &
 $\gamma_{23}^1$ &
 $\theta_{23}^0$ &
 $\theta_{23}^1$ \\ \hline
 Posterior Mean & -0.036 & 0.076 & 0.018 & 0.018 & 0.172 & 0.013 & 0.015 & 0.266 & 0.097 & 0.035 \\

 SE & 0.059 & 0.030 & 0.002 & 0.002 & 0.180 & 0.002 & 0.002 & 0.371 & 0.248 & 0.243 \\
 \\
 \end{tabular}
\end{adjustbox}
\caption{Parameter estimates for the prostate cancer data example. The posterior mean and estimated standard error are shown for each parameter. All $\alpha_{jk}$ and $\kappa_{jk}$ are set to 1.}
\end{table}
\end{document}

stat

Emily K. Roberts
Michael R. Elliott
Jeremy M. G. Taylor

"Surrogacy Validation for Time-to-Event Outcomes with Illness-Death Frailty Models" by Emily K. Roberts, Michael R. Elliott, and Jeremy M. G. Taylor, arXiv:2211.15826v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Bonus-Malus scale premiums for tweedie's compound poisson models" by Jean-Philippe Boucher and Raïssa Coulibaly, arXiv:2311.03222v1, licensed under CC0 1.0. 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{Test accuracy (\%) of DGI in standard contrastive correspondence (Std) and disordered correspondence (Dis).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccccc}
 \toprule
 Method & Contrast & Cora & CiteSeer & PubMed \\
 \midrule
 \multirow{2}*{DGI} & Std. & 83.38 $\pm$ 2.68 & 72.07 $\pm$ 2.37 & 84.77 $\pm$ 0.71 \\
 \cmidrule(r){2-5}
 ~ & Dis. & 83.35 $\pm$ 2.68 & 72.04 $\pm$ 2.17 & 84.70 $\pm$ 0.68 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Xiaojun Guo
Yifei Wang
Zeming Wei
Yisen Wang

"Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning" by Xiaojun Guo, Yifei Wang, Zeming Wei, and Yisen Wang, arXiv:2311.02687v1, 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}{rlrrrrrr}
 \hline
 & & fields & mean & mean (control) & sd & max & min \\
 \hline
 & Max Accuracy & 2840 & 0.84 & 0.87 & 0.36 & 1.00 & 0.00 \\
 & Balanced Accuracy & 2840 & 0.63 & 0.67 & 0.48 & 1.00 & 0.00 \\
 & Continuous & 2840 & 0.71 & 0.73 & 0.16 & 0.99 & 0.02 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the standard error and its estimators. The column labeled emp.se shows the empirical standard deviation of the ACE estimators over 2000 simulated replicates, eif.se shows the average of the standard error estimates based on only the efficient influence function, and infl.se shows the average of the standard error estimates that account for estimation of the propensity score and outcome models. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrrr}
 \hline
setting & type & emp.se & eif.se & infl.se \\
 \hline
linear & wrong outcome right weights & 0.067 & 0.110 & 0.064 \\
 linear & right outcome wrong weights & 0.047 & 0.045 & 0.045 \\
 linear & wrong both & 0.084 & 0.085 & 0.084 \\
 linear & right both & 0.049 & 0.049 & 0.049 \\
 log\_poisson & wrong outcome right weights & 0.163 & 0.209 & 0.164 \\
 log\_poisson & right outcome wrong weights & 0.157 & 0.161 & 0.158 \\
 log\_poisson & wrong both & 0.211 & 0.212 & 0.211 \\
 log\_poisson & right both & 0.159 & 0.161 & 0.161 \\
 logit\_binomial & wrong outcome right weights & 0.013 & 0.019 & 0.014 \\
 logit\_binomial & right outcome wrong weights & 0.012 & 0.012 & 0.013 \\
 logit\_binomial & wrong both & 0.018 & 0.018 & 0.018 \\
 logit\_binomial & right both & 0.012 & 0.012 & 0.012 \\
 inverse\_gaussian & wrong outcome right weights & 0.001 & 0.001 & 0.001 \\
 inverse\_gaussian & right outcome wrong weights & 0.001 & 0.001 & 0.001 \\
 inverse\_gaussian & wrong both & 0.001 & 0.001 & 0.001 \\
 inverse\_gaussian & right both & 0.001 & 0.001 & 0.001 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Erin E Gabriel
Michael C Sachs
Torben Martinussen
Ingeborg Waernbaum
Els Goetghebeur
Stijn Vansteelandt
Arvid Sjölander

"Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation" by Erin E Gabriel, Michael C Sachs, Torben Martinussen, Ingeborg Waernbaum, Els Goetghebeur, Stijn Vansteelandt, and Arvid Sjölander, arXiv:2309.05531v1, 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{\\Pulse width limit $w_{\text{lim}}$ with respect to pulse length $L$ for binary SSB-FSK.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
 \toprule
 $L$ & 2 & 4 & 6 & 8 & 10 & 12 \\ \midrule
 $w_{\text{lim}}$ & 1.6 & 3.2 & 4.8 & 6.4 & 7.9 & 9.5 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Karim Kassan
Haïfa Farès
D. Christian Glattli
Yves Louët

"Performance vs. Spectral Properties For Single-Sideband Continuous Phase Modulation" by Karim Kassan, Haïfa Farès, D. Christian Glattli, and Yves Louët, arXiv:2011.10541v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ccccccccccc|}
\hline
$r=$& $n=$& 1&2&3&4&5&6&7&8&9&10 \\
\hline
1&&1&1&1&1&1&1&1&1&1&1\\
2&&&1 & 3&8&46&790&37829&4134939&?&?\\
3&&&&1& 5& 27& 1063& 1434219&?&?&?\\
\hline
$r\le 3$&& 1& 2 & 5 &14&74&1854&1472049&?&?&?\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{ Numbers of isomorphism equivalence classes of loop-free affine oriented matroids with $n$ elements and rank $r\le 3$, also called abstract dissection types, as found in . The values in the last row are also the numbers of face-combinatorial equivalence classes of plane arrangements in $\mathbb{R}^3$, for $n\le 7$. }
\end{table}
\end{document}

math

"Counting plane arrangements via oriented matroids" by Stefan Forcey, arXiv:2504.11461v1, 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}{lccccccccccccc}
\toprule
product $i$ & $N^i$ & $N^i_\mathrm{SCC}$ & $N^i_\mathrm{WCC}$ & $L^i$ & $\langle k^i\rangle$ & $\langle (k^{i,\mathrm{out}})^2\rangle$ & {$k^{i,\mathrm{out}}_\mathrm{UKR}$} & {$r^{i,k_{out}}_\mathrm{UKR}$} \\
\midrule
maize & 192 & 108 & 182 & 8,025 & 41.8 & 5,285.0 & 178 & 1 \\
wheat & 192 & 111 & 181 & 12,487 & 65.0 & 10,331.4 & 180 & 1 \\
barley & 192 & 67 & 174 & 5,759 & 30.0 & 4,119.7 & 105 & 34 \\
sorghum & 192 & 33 & 110 & 1,064 & 5.5 & 283.3 & 70	 & 5 \\
{pork}& 192 & 96 & 172 & 5,940 & 30.9 & 3,993.5 & 77	 & 40 \\
{poultry} & 192 & 105 & 182 & 7,864 & 41.0 & 5,889.3 & 113 & 42 \\
eggs & 192 & 89 & 165 & 3,962 & 20.6 & 1,915.2 & 123 & 11 \\
sunflower seed & 192 & 51 & 136 & 1,922 & 10.0 & 729.0 & 67	 & 12 \\
r. \& m. seed & 192 & 50 & 151 & 2,815 & 14.7 & 1,386.6 & 146 & 1 \\
sunflower oil & 192 & 59 & 156 & 4,105 & 21.4 & 2,610.6 & 153 & 1 \\
r. \& m. oil & 192 & 50 & 140 & 2,868 & 14.9 & 1,339.5 & 77	 & 21 \\
maize germ oil & 192 & 30 & 149 & 1,459 & 7.6 & 660.5 & 60	 & 12 \\
beer & 192 & 132 & 178 & 8,446 & 44.0 & 5,902.3 & 112 & 45 \\
alc. beverages & 192 & 110 & 169 & 8,264 & 43.0 & 5,854.1 & 154 & 15 \\
sweeteners & 192 & 69 & 172 & 7,145 & 37.2 & 5,625.4 & 149 & 29 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Basic properties of the trade network layers for different products $i$. We report the total number of nodes $N^i$, the number of nodes in the largest strongly (weakly) connected component $N^i_\mathrm{SCC}$ ($N^i_\mathrm{WCC}$), the number of links $L^i$, the average degree $\langle k^i \rangle$, the second moment of the out-degree distribution $\langle (k^{i,\mathrm{out}})^2 \rangle$, the out-degree of Ukraine $k^{i,\mathrm{out}}_\mathrm{UKR}$ and its rank based on out-degree $r^{i,k_{out}}_\mathrm{UKR}$. We abbreviate {rapeseed and mustardseed} as {r.\& m.}.}
\end{table}
\end{document}

q-fin

Moritz Laber
Peter Klimek
Martin Bruckner
Liuhuaying Yang
Stefan Thurner

"Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability" by Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang, and Stefan Thurner, arXiv:2210.01846v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Jean-Philippe Boussemart
Walter Briec
Raluca Parvulescu
Paola Ravelojaona

"$Λ$-Returns to Scale and Individual Minimum Extrapolation Principle" by Jean-Philippe Boussemart, Walter Briec, Raluca Parvulescu, and Paola Ravelojaona, arXiv:2212.04724v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

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


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

 \toprule
 \textbf{Model} & \textbf{FADscore} $\downarrow$ & \textbf{CLAPscore} $\uparrow$ \\
 \midrule
 MusicGen-Small (Baseline) & 6.49 & 0.303 \\
 TinyMusician & \textbf{6.44} & 0.301 \\
 MusicGen-Small + Quantization & 7.11 & \textbf{0.352} \\
 TinyMusician + Quantization & 7.05 & 0.343 \\
 \bottomrule

\end{tabular}
\end{adjustbox}
\caption{The Scores of Ablation Study. $\boldsymbol{\text{FADscore} \downarrow}$ represents lower is better, $\boldsymbol{\text{CLAPscore} \uparrow}$ represents higher is better}
\end{table}
\end{document}

eess

Hainan Wang
Mehdi Hosseinzadeh
Reza Rawassizadeh

"TinyMusician: On-Device Music Generation with Knowledge Distillation and Mixed Precision Quantization" by Hainan Wang, Mehdi Hosseinzadeh, and Reza Rawassizadeh, arXiv:2509.00914v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The mAP@20 results on image-to-text ($I \to T$) and text-to-image ($T \to I$) retrieval tasks for different values of $B$ for the UCMerced datasets. ``S'' and ``U'' represent the learning type of the considered methods as supervised and unsupervised, respectively.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|lc|cccc}
\hline
Task & Method && $B$=16 & $B$=32 & $B$=64 & $B$=128 \\
\hline\hline
\multirow{4}{*}{$I\to T$}
 & CPAH &S & 0.706 & \textbf{0.802} & \textbf{0.891} & \textbf{0.914} \\
 & DJSRH &U & 0.686 & 0.711 & 0.735 & 0.754 \\
 & JDSH &U & 0.462 & 0.751 & 0.820 & 0.829 \\
 & DUCH \textit{(proposed)}&U & \textbf{0.760} & 0.794 & 0.844 & 0.870 \\
\hline\hline
\multirow{4}{*}{$T\to I$}
 & CPAH &S & 0.782 & \textbf{0.891} & \textbf{0.987} & \textbf{0.982} \\
 & DJSRH &U & 0.738 & 0.755 & 0.776 & 0.800 \\
 & JDSH &U & 0.509 & 0.794 & 0.884 & 0.904 \\
 & DUCH \textit{(proposed)}&U & \textbf{0.799} & 0.851 & 0.916 & 0.927 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Georgii Mikriukov
Mahdyar Ravanbakhsh
Begüm Demir

"Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing" by Georgii Mikriukov, Mahdyar Ravanbakhsh, and Begüm Demir, arXiv:2201.08125v1, 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|}
 \hline
 n & $L^{2}$error & order & $H^{1}$error & order & $H^{2}$error & order\\
 \hline
 5 & 5.526e-08& - &1.119e-06& - &3.947e-05 & - \\
 \hline
 10 & 7.729e-09&2.838&2.755e-07&2.022&1.805e-05&1.129 \\
 \hline
 20 & 5.654e-10&3.773&3.956e-08&2.800&5.126e-06&1.816 \\
 \hline
 40 & 3.543e-11&3.996&4.929e-09&3.005&1.283e-06&1.999 \\
 \hline
 80 & 2.352e-12&3.913&6.526e-10&2.917&3.387e-07&1.921 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{error and order of basis $V_{h}^{+}$, biharmonic equation in 1d.}
\end{table}
\end{document}

math

"A PINN-enriched finite element method for linear elliptic problems" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cc|ccc} \hline
 \textbf{Methods} & \textbf{Acc} & \textbf{Comp} & \textbf{Prec} & \textbf{Recall} & \textbf{F1} \\ \hline \hline
 GeoTr~ & 0.1213 & 0.0917 & 95.84 & 87.17 & 90.11 \\ \hline
 SGA~ & 0.0094 & 0.0935 & 90.87 & 97.44 & 93.58 \\ \hline
 SG-PGM \small{(ours)} & \underline{0.0033} & \underline{0.0040} & \underline{99.81} & \underline{99.79} & \underline{99.80}\\ \hline
 SGPGM+R \small {(ours)} & \textbf{0.0024} & \textbf{0.0026} & \textbf{99.86} & \textbf{99.85} & \textbf{99.86} \\ \hline
 \end{tabular}
\caption{\textbf{Point cloud mosaicking from multiple fragments.} Our method outperforms others even without using RANSAC.}
\end{table}
\end{document}

cs

"SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks" by Yaxu Xie, Alain Pagani, and Didier Stricker, arXiv:2403.19474v1, 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{Comparison of truth inference methods}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc|ccc}
 \toprule
 Methods & MAE$\,\downarrow$ & RMSE$\,\downarrow$ & P$\,\uparrow$ & R$\,\uparrow$ & F1$\,\uparrow$ \\
 \midrule
 Majority voting & 0.134 & 0.173 & 0.321 & 0.419 & 0.364 \\
 \midrule
 TruthFinder & 0.129 & 0.153 & 0.279 & 0.374 & 0.320 \\
 PooledInvestment & 0.091 & 0.108 & 0.397 & 0.380 & 0.388 \\
 \midrule
 CATD & 0.127 & 0.145 & 0.432 & 0.423 & 0.427 \\
 \midrule
 LTM & 0.071 & 0.093 & 0.262 & 0.394 & 0.315 \\
 LCA & 0.106 & 0.130 & 0.364 & 0.404 & 0.383 \\
 MBM & 0.104 & 0.125 & 0.340 & \textbf{0.539} & 0.417 \\
 BWA & 0.088 & 0.102 & 0.414 & 0.408 & 0.411 \\
 \midrule
 TKGC & \textbf{0.054} & \textbf{0.062} & \textbf{0.524} & 0.491 & \textbf{0.507} \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jiacheng Huang
Yao Zhao
Wei Hu
Zhen Ning
Qijin Chen
Xiaoxia Qiu
Chengfu Huo
Weijun Ren

"Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data" by Jiacheng Huang, Yao Zhao, Wei Hu, Zhen Ning, Qijin Chen, Xiaoxia Qiu, Chengfu Huo, and Weijun Ren, arXiv:2201.08580v1, 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{\textbf{$\text{\LaTeX }$ formula patterns.} Each pattern was paired with both ``$\backslash pi =$'' and ``$= \backslash pi$,'' and the \texttt{\textbackslash cfrac}-based variants of the \texttt{\textbackslash frac}-containing regular expressions were also included, resulting in a total of 10 patterns.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
 \toprule
 Pattern for - & Python \textbf{re} pattern \\
 \midrule
 Series & \textbackslash{} sum \textbackslash{} s*\_\{(?s:.)*\}\textbackslash{} s*\textbackslash{} char`\^ \textbackslash{} s*
 \\ \\

 Sum of \textbackslash{} frac
 & (\textbackslash{} s*\textbackslash{} frac\textbackslash{} s*\{\textbackslash{} s*[\char`\^ \{\}]*\textbackslash{} s*\}\textbackslash{}s*\{\textbackslash{} s*[\char`\^ \{\}]*\textbackslash{} s*\}) \\ & ((?:\textbackslash{} s*\textbackslash{}+\textbackslash{} s*\textbackslash{} frac\textbackslash{} s*\{\textbackslash{} s*[\char`\^ \{\}]*\textbackslash{} s*\}\textbackslash{} s*\{\textbackslash{} s*[\char`\^ \{\}]*\textbackslash{} s*\})+) \\ \\

 Nested \textbackslash{} frac &
 (\textbackslash{} frac\textbackslash{} s*\{\textbackslash{} s*[\char`\^ \{\} ]*\textbackslash{} s*\}\textbackslash{} s*\{\textbackslash{} s*[\char`\^ \{\} ]*) \\ & ((?:\textbackslash{} frac\textbackslash{} s*\{\textbackslash{} s*[\char`\^ \{\} ]*\textbackslash{} s*\}\textbackslash{} s*\{\textbackslash{} s*[\char`\^ \{\} ]*)+\textbackslash{} s*\}\textbackslash{} s*\}) \\

 \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{multirow}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Forecasting performance of the proposed E-STGCN model in comparison to the temporal-only and spatiotemporal forecasting techniques for 30 30-day ahead forecast horizon of PM$_{10}$ pollutant (best results are \underline{\textbf{highlighted}}).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|cccccc|ccccccc|c|}
\hline Forecast & \multirow{3}{*}{Metric} & \multicolumn{6}{c|}{Temporal-only Model} & \multicolumn{7}{c|}{Spatiotemporal Model} & Proposed \\ \cline{3-15}
 \multirow{2}{*}{Period} & & \multirow{2}{*}{ARIMA} & \multirow{2}{*}{LSTM} & \multirow{2}{*}{TCN} & \multirow{2}{*}{DeepAR} & \multirow{2}{*}{Transformers} & \multirow{2}{*}{NBeats} & \multirow{2}{*}{STARMA} & \multirow{2}{*}{GSTAR} & \multirow{2}{*}{GpGp} & \multirow{2}{*}{{\color{black}STGCN}} & \multirow{2}{*}{STNN} & Modified & \multirow{2}{*}{DeepKrigging} & \multirow{2}{*}{E-STGCN} \\
 & & & & & & & & & & & & & STGCN & & \\ \hline

\multirow{6}{*}{JAN} & MAE & 99.31 & 276.07 & 262.61 & 275.41 & 165.16 & 87.24 & 121.40 & 82.99 & 86.54 & {\color{black} 94.94} & 115.31 & 89.89 & 233.97 & \underline{\textbf{82.67}} \\
 & MASE & 1.23 & 3.46 & 3.31 & 3.45 & 2.05 & 1.10 & 1.49 & \underline{\textbf{1.02}} & 1.09 & {\color{black} 1.18} & 1.47 & 1.12 & 2.94 & 1.03 \\
 & RMSE & 126.85 & 294.14 & 307.81 & 293.53 & 192.87 & 112.07 & 147.81 & 110.33 & 109.86 & {\color{black} 123.03} & 145.59 & 119.16 & 273.87 & \underline{\textbf{108.41}} \\
 & SMAPE & 35.90 & 175.50 & 124.70 & 174.60 & 70.50 & 30.80 & 47.10 & 29.70 & 30.30 & {\color{black} 34.22} & 37.00 & 32.00 & 120.60 & \underline{\textbf{29.10}} \\
 & {\color{black}Pinball Loss	} & {\color{black}73.38} & {\color{black}220.85} & {\color{black}	197.76	} & {\color{black}	220.33	} & {\color{black}	131.76	} & {\color{black}	49.21	} & {\color{black}	95.66	} & {\color{black}	58.43	} & {\color{black}	44.33	} & {\color{black} 66.80} & {\color{black}	\underline{\textbf{38.69}}	} & {\color{black}	67.15	} & {\color{black}	174.11	} & {\color{black}	55.12	} \\
 & {\color{black}	CRPS	} & {\color{black}	148.08	} & {\color{black}	181.85	} & {\color{black}	191.48	} & {\color{black}	181.85	} & {\color{black}	181.60	} & {\color{black}	126.42	} & {\color{black}	166.91	} & {\color{black}	133.98	} & {\color{black}	125.78	} & {\color{black} 140.99 } & {\color{black}	149.49	} & {\color{black}	142.56	} & {\color{black}	193.51	} & {\color{black}	\underline{\textbf{123.22}}	} \\ \hline
\multirow{6}{*}{FEB} & MAE & 59.32 & 218.02 & 212.75 & 216.10 & 102.77 & 68.89 & 157.65 & 153.76 & 106.83 & {\color{black} 57.98} & 77.69 & 70.35 & 201.06 & \underline{\textbf{56.49}} \\
 & MASE & 1.25 & 4.72 & 4.58 & 4.68 & 2.14 & 1.50 & 3.38 & 3.32 & 2.22 & {\color{black} 1.23} & 1.75 & 1.48 & 4.41 & \underline{\textbf{1.21}} \\
 & RMSE & 77.99 & 228.33 & 247.69 & 226.50 & 122.29 & 83.55 & 175.50 & 171.40 & 125.72 & {\color{black} 74.10} & 101.26 & 88.41 & 223.28 & \underline{\textbf{71.36}} \\
 & SMAPE & 25.40 & 172.10 & 116.00 & 169.00 & 49.80 & 29.50 & 96.70 & 93.30 & 52.60 & {\color{black} 24.96} & 32.20 & 32.20 & 140.50 & \underline{\textbf{24.30}} \\
 & {\color{black}	Pinball Loss	} & {\color{black}	42.62	} & {\color{black}	174.42	} & {\color{black}	151.88	} & {\color{black}	172.88	} & {\color{black}	81.67	} & {\color{black}	34.85	} & {\color{black}	126.07	} & {\color{black}	122.95	} & {\color{black}	85.05	} & {\color{black} 38.28} & {\color{black}	36.88	} & {\color{black}	51.30	} & {\color{black}	158.45	} & {\color{black}	\underline{\textbf{31.21}}	} \\

 & {\color{black}	CRPS	} & {\color{black}	96.23	} & {\color{black}	143.51	} & {\color{black}	157.80	} & {\color{black}	143.52	} & {\color{black}	135.78	} & {\color{black}	101.07	} & {\color{black}	144.43	} & {\color{black}	144.32	} & {\color{black}	137.02	} & {\color{black} 92.88} & {\color{black}	109.52	} & {\color{black}	111.50	} & {\color{black}	149.42	} & {\color{black}	\underline{\textbf{90.31}}	} \\ \hline
\multirow{6}{*}{MAR} & MAE & 51.41 & 159.77 & 168.58 & 154.64 & 52.33 & 52.00 & 54.18 & 51.87 & 60.80 & {\color{black} 49.95} & 252.19 & 56.17 & 167.16 & \underline{\textbf{42.54}} \\
 & MASE & 1.29 & 3.93 & 4.20 & 3.80 & 1.23 & 1.31 & 1.30 & 1.26 & 1.61 & {\color{black} 1.26} & 6.54 & 1.39 & 4.12 & \underline{\textbf{1.05}} \\
 & RMSE & 62.16 & 166.91 & 203.62 & 162.02 & 63.41 & 64.42 & 64.10 & 61.11 & 71.00 & {\color{black} 60.05} & 309.41 & 70.32 & 174.16 & \underline{\textbf{51.96}} \\
 & SMAPE & 29.60 & 169.10 & 115.00 & 158.10 & 31.30 & 29.50 & 33.40 & 31.60 & 34.40 & {\color{black} 29.01} & 78.20 & 36.20 & 185.20 & \underline{\textbf{25.30}} \\
 & {\color{black}	Pinball Loss	} & {\color{black}	14.42	} & {\color{black}	127.82	} & {\color{black}	113.45	} & {\color{black}	123.71	} & {\color{black}	37.08	} & {\color{black}	\underline{\textbf{14.23}}	} & {\color{black}	34.13	} & {\color{black}	30.48	} & {\color{black}	18.40	} & {\color{black} 16.54} & {\color{black}	52.64	} & {\color{black}	40.34	} & {\color{black}	133.73	} & {\color{black}	16.90	} \\

 & {\color{black}	CRPS	} & {\color{black}	73.61	} & {\color{black}	105.90	} & {\color{black}	117.70	} & {\color{black}	105.90	} & {\color{black}	82.38	} & {\color{black}	75.46	} & {\color{black}	80.74	} & {\color{black}	77.35	} & {\color{black}	78.00	} & {\color{black} 72.26} & {\color{black}	212.69	} & {\color{black}	89.06	} & {\color{black}	105.94	} & {\color{black}	\underline{\textbf{66.12}}	} \\ \hline
\multirow{6}{*}{APR} & MAE & 59.82 & 181.01 & 171.35 & 179.36 & 71.49 & 67.72 & 151.14 & 106.86 & 61.34 & {\color{black} 55.68} & 62.35 & \underline{\textbf{53.04}} & 229.48 & \underline{\textbf{53.04}} \\
 & MASE & 1.37 & 4.18 & 3.89 & 4.14 & 1.61 & 1.54 & 3.46 & 2.45 & 1.42 & {\color{black} 1.31} & 1.45 & \underline{\textbf{1.24}} & 5.38 & \underline{\textbf{1.24}} \\
 & RMSE & 75.86 & 191.41 & 191.06 & 189.86 & 88.30 & 85.42 & 165.07 & 124.18 & 74.55 & {\color{black} 66.76} & 77.04 & \underline{\textbf{62.40}} & 311.57 & \underline{\textbf{62.40}} \\
 & SMAPE & 30.90 & 164.50 & 130.50 & 161.40 & 37.90 & 36.10 & 117.10 & 68.50 & 31.90 & {\color{black} 29.03} & 32.70 & \underline{\textbf{27.70}} & 143.40 & \underline{\textbf{27.70}} \\
 & {\color{black}	Pinball Loss	} & {\color{black}	43.18	} & {\color{black}	144.81	} & {\color{black}	132.12	} & {\color{black}	143.49	} & {\color{black}	53.27	} & {\color{black}	51.59	} & {\color{black}	120.90	} & {\color{black}	83.76	} & {\color{black}	38.60	} & {\color{black} 27.75} & {\color{black}	39.20	} & {\color{black}	\underline{\textbf{25.20}}	} & {\color{black}	156.05	} & {\color{black}	\underline{\textbf{25.20}}	} \\
 & {\color{black}	CRPS	} & {\color{black}	96.36	} & {\color{black}	122.26	} & {\color{black}	126.72	} & {\color{black}	122.26	} & {\color{black}	108.53	} & {\color{black}	108.55	} & {\color{black}	122.69	} & {\color{black}	122.69	} & {\color{black}	92.03	} & {\color{black} 82.19} & {\color{black}	93.58	} & {\color{black}	\underline{\textbf{78.66}}	} & {\color{black}	145.23	} & {\color{black}	\underline{\textbf{78.66}}	} \\
 \hline
\multirow{6}{*}{MAY} & MAE & \underline{\textbf{71.01}} & 176.32 & 167.71 & 170.78 & 79.39 & 71.75 & 117.99 & 88.82 & 78.77 & {\color{black} 78.42} & 94.51 & 77.46 & 188.01 & 77.46 \\
 & MASE & \underline{\textbf{1.12}} & 2.81 & 2.67 & 2.72 & 1.24 & 1.15 & 1.86 & 1.41 & 1.23 & {\color{black} 1.25} & 1.53 & 1.24 & 3.01 & 1.24 \\
 & RMSE & \underline{\textbf{91.48}} & 199.93 & 205.51 & 195.07 & 107.15 & 92.81 & 147.03 & 118.57 & 105.91 & {\color{black} 97.83} & 117.57 & 96.88 & 211.09 & 96.88 \\
 & SMAPE & \underline{\textbf{38.00}} & 167.30 & 119.70 & 155.40 & 42.90 & 38.60 & 77.90 & 49.30 & 42.50 & {\color{black} 41.91} & 47.90 & 41.40 & 194.50 & 41.40 \\
 & {\color{black}	Pinball Loss	} & {\color{black}	35.98	} & {\color{black}	141.06	} & {\color{black}	122.77	} & {\color{black}	136.63	} & {\color{black}	53.76	} & {\color{black}	33.77	} & {\color{black}	91.62	} & {\color{black}	61.73	} & {\color{black}	52.29	} & {\color{black} 36.63} & {\color{black}	38.56	} & {\color{black}	\underline{\textbf{31.28}}	} & {\color{black}	150.38	} & {\color{black}	\underline{\textbf{31.28}}	} \\
 & {\color{black}	CRPS	} & {\color{black}	\underline{\textbf{90.80}}	} & {\color{black}	122.60	} & {\color{black}	127.58	} & {\color{black}	122.60	} & {\color{black}	108.91	} & {\color{black}	90.80	} & {\color{black}	122.96	} & {\color{black}	119.12	} & {\color{black}	107.33	} & {\color{black} 96.06} & {\color{black}	111.76	} & {\color{black}	96.87	} & {\color{black}	122.88	} & {\color{black}	96.87	} \\ \hline
\multirow{6}{*}{JUN} & MAE & 67.66 & 117.99 & 123.54 & 114.96 & 47.59 & \underline{\textbf{43.32}} & 87.20 & 74.67 & 69.23 & {\color{black} 72.83} & 43.44 & 53.60 & 130.25 & 51.65 \\
 & MASE & 2.00 & 3.36 & 3.53 & 3.27 & 1.42 & 1.27 & 2.46 & 2.11 & 2.09 & {\color{black} 2.17} & \underline{\textbf{1.25}} & 1.58 & 3.78 & 1.51 \\
 & RMSE & 81.88 & 129.68 & 148.78 & 126.93 & 58.79 & 56.31 & 100.34 & 89.19 & 82.67 & {\color{black} 82.26} & \underline{\textbf{55.60}} & 65.15 & 154.60 & 65.44 \\
 & SMAPE & 45.10 & 150.10 & 124.00 & 142.20 & 36.00 & 33.40 & 90.00 & 70.90 & 47.00 & {\color{black} 48.73} & \underline{\textbf{33.00}} & 39.00 & 137.00 & 37.90 \\
 & {\color{black}	Pinball Loss	} & {\color{black}	20.16	} & {\color{black}	94.39	} & {\color{black}	90.49	} & {\color{black}	91.96	} & {\color{black}	20.32	} & {\color{black}	23.77	} & {\color{black}	69.76	} & {\color{black}	59.68	} & {\color{black}	23.20	} & {\color{black} 19.06} & {\color{black}	23.70	} & {\color{black}	23.90	} & {\color{black}	98.71	} & {\color{black}	\underline{\textbf{16.17}}	} \\

 & {\color{black}	CRPS	} & {\color{black}	80.27	} & {\color{black}	84.39	} & {\color{black}	87.31	} & {\color{black}	84.39	} & {\color{black}	63.00	} & {\color{black}	62.06	} & {\color{black}	83.63	} & {\color{black}	83.14	} & {\color{black}	81.28	} & {\color{black} 77.44} & {\color{black}	\underline{\textbf{61.75}}	} & {\color{black}	67.01	} & {\color{black}	91.18	} & {\color{black}	69.55	} \\
 \hline
\multirow{6}{*}{JUL} & MAE & 101.08 & 60.00 & 72.71 & 57.24 & 72.83 & \underline{\textbf{30.07}} & 38.87 & 31.80 & 96.52 & {\color{black} 125.26} & 60.67 & 48.40 & 76.07 & 48.40 \\
 & MASE & 5.72 & 3.21 & 3.77 & 3.05 & 4.37 & \underline{\textbf{1.65}} & 2.01 & 1.66 & 5.83 & {\color{black} 7.09} & 3.52 & 2.74 & 4.11 & 2.74 \\
 & RMSE & 108.53 & 65.60 & 88.01 & 63.07 & 77.47 & \underline{\textbf{36.78}} & 46.32 & 38.07 & 103.92 & {\color{black} 128.13} & 75.32 & 55.25 & 80.74 & 55.25 \\
 & SMAPE & 81.10 & 120.10 & 117.10 & 110.20 & 69.50 & \underline{\textbf{36.90}} & 62.10 & 46.70 & 79.90 & {\color{black} 93.13} & 56.40 & 51.70 & 186.90 & 51.70 \\
 & {\color{black}	Pinball Loss	} & {\color{black}	20.77	} & {\color{black}	48.00	} & {\color{black}	51.51	} & {\color{black}	45.79	} & {\color{black}	14.95	} & {\color{black}	\underline{\textbf{10.36}}	} & {\color{black}	30.43	} & {\color{black}	22.67	} & {\color{black}	20.41	} & {\color{black} 26.11} & {\color{black}	16.01	} & {\color{black}	10.67	} & {\color{black}	60.86	} & {\color{black}	10.67	} \\
 & {\color{black}	CRPS	} & {\color{black}	74.62	} & {\color{black}	47.71	} & {\color{black}	52.75	} & {\color{black}	47.70	} & {\color{black}	58.41	} & {\color{black}	\underline{\textbf{38.46}}	} & {\color{black}	46.71	} & {\color{black}	43.11	} & {\color{black}	72.39	} & {\color{black} 83.19} & {\color{black}	61.67	} & {\color{black}	48.14	} & {\color{black}	47.75	} & {\color{black}	48.14	} \\ \hline
\multirow{6}{*}{AUG} & MAE & 60.84 & 108.73 & 112.74 & 105.64 & 48.29 & 47.00 & 71.27 & 84.91 & 60.04 & {\color{black} 42.14} & 87.94 & 47.44 & 94.30 & \underline{\textbf{39.70}} \\
 & MASE & 2.86 & 4.88 & 5.02 & 4.73 & 2.23 & 2.03 & 3.16 & 3.80 & 2.88 & {\color{black} 1.90} & 3.98 & 2.19 & 4.25 & \underline{\textbf{1.80}} \\
 & RMSE & 69.72 & 116.57 & 124.58 & 113.71 & 56.02 & 59.81 & 87.31 & 97.19 & 69.23 & {\color{black} 51.87} & 97.95 & 55.36 & 114.38 & \underline{\textbf{47.97}} \\
 & SMAPE & 42.60 & 146.70 & 141.80 & 138.40 & 37.20 & 39.20 & 71.80 & 94.80 & 43.10 & {\color{black} 32.29} & 104.10 & 36.00 & 93.00 & \underline{\textbf{31.00}} \\
 & {\color{black}	Pinball Loss	} & {\color{black}	\underline{\textbf{14.02}}	} & {\color{black}	86.98	} & {\color{black}	88.22	} & {\color{black}	84.51	} & {\color{black}	16.72	} & {\color{black}	35.18	} & {\color{black}	56.80	} & {\color{black}	67.91	} & {\color{black}	15.76	} & {\color{black} 18.73} & {\color{black}	69.87	} & {\color{black}	14.09	} & {\color{black}	59.35	} & {\color{black}	14.25	} \\
 & {\color{black}	CRPS	} & {\color{black}	67.48	} & {\color{black}	77.86	} & {\color{black}	79.92	} & {\color{black}	77.86	} & {\color{black}	60.84	} & {\color{black}	68.49	} & {\color{black}	79.02	} & {\color{black}	78.59	} & {\color{black}	67.32	} & {\color{black} 58.65} & {\color{black}	77.33	} & {\color{black}	60.26	} & {\color{black}	85.83	} & {\color{black}	\underline{\textbf{54.41}}	} \\ \hline
\multirow{6}{*}{SEP} & MAE & 82.97 & 92.66 & 104.00 & 91.22 & 52.34 & 68.16 & 48.30 & 47.63 & 66.26 & {\color{black} 89.70} & 79.02 & 44.58 & 119.67 & \underline{\textbf{35.90}} \\
 & MASE & 4.60 & 5.68 & 6.07 & 5.59 & 3.08 & 3.89 & 2.95 & 2.90 & 3.91 & {\color{black} 5.15} & 4.93 & 2.54 & 7.04 & \underline{\textbf{2.05}} \\
 & RMSE & 94.06 & 102.86 & 122.03 & 101.53 & 63.62 & 81.38 & 60.46 & 58.86 & 78.06 & {\color{black} 101.05} & 90.40 & 55.68 & 166.10 & \underline{\textbf{44.51}} \\
 & SMAPE & 60.70 & 130.70 & 122.30 & 126.70 & 46.50 & 53.40 & 48.50 & 46.60 & 53.70 & {\color{black} 63.63} & 96.90 & 41.50 & 98.90 & \underline{\textbf{35.70}} \\
 & {\color{black}	Pinball Loss	} & {\color{black}	16.86	} & {\color{black}	74.08	} & {\color{black}	74.96	} & {\color{black}	72.92	} & {\color{black}	13.99	} & {\color{black}	14.91	} & {\color{black}	26.92	} & {\color{black}	24.25	} & {\color{black}	14.97	} & {\color{black} 19.36} & {\color{black}	59.08	} & {\color{black}	14.96	} & {\color{black}	63.87	} & {\color{black}	\underline{\textbf{13.98}}	} \\

 & {\color{black}	CRPS	} & {\color{black}	78.86	} & {\color{black}	70.71	} & {\color{black}	76.44	} & {\color{black}	70.71	} & {\color{black}	62.09	} & {\color{black}	69.93	} & {\color{black}	65.91	} & {\color{black}	64.31	} & {\color{black}	69.96	} & {\color{black} 84.24} & {\color{black}	71.51	} & {\color{black}	59.82	} & {\color{black}	84.12	} & {\color{black}	\underline{\textbf{50.39}}	} \\ \hline
\multirow{6}{*}{OCT} & MAE & 56.62 & 214.64 & 202.97 & 206.32 & 81.46 & 94.69 & 120.44 & 118.95 & 67.79 & {\color{black} 60.10} & 178.89 & 62.18 & 218.93 & \underline{\textbf{56.42}} \\
 & MASE & \underline{\textbf{1.54}} & 6.00 & 5.57 & 5.75 & 2.17 & 2.61 & 3.27 & 3.29 & 1.80 & {\color{black} 1.64} & 5.03 & 1.70 & 6.12 & 1.55 \\
 & RMSE & 76.41 & 225.67 & 223.43 & 217.78 & 101.38 & 114.30 & 148.51 & 147.03 & 88.08 & {\color{black} 79.08} & 192.39 & 80.64 & 231.48 & \underline{\textbf{74.86}} \\
 & SMAPE & \underline{\textbf{25.00}} & 176.90 & 140.50 & 162.80 & 37.90 & 47.60 & 66.20 & 65.60 & 30.20 & {\color{black} 26.74} & 134.70 & 27.80 & 181.60 & \underline{\textbf{25.00}} \\
 & {\color{black}	Pinball Loss	} & {\color{black}	38.14	} & {\color{black}	171.71	} & {\color{black}	160.34	} & {\color{black}	165.06	} & {\color{black}	62.31	} & {\color{black}	74.59	} & {\color{black}	95.89	} & {\color{black}	94.70	} & {\color{black}	47.51	} & {\color{black} 40.09} & {\color{black}	142.75	} & {\color{black}	39.99	} & {\color{black}	175.13	} & {\color{black}	\underline{\textbf{33.52}}	} \\
 & {\color{black}	CRPS	} & {\color{black}	93.92	} & {\color{black}	139.86	} & {\color{black}	142.87	} & {\color{black}	139.86	} & {\color{black}	119.26	} & {\color{black}	133.38	} & {\color{black}	141.47	} & {\color{black}	141.89	} & {\color{black}	105.70	} & {\color{black} 95.93} & {\color{black}	138.79	} & {\color{black}	95.26	} & {\color{black}	140.35	} & {\color{black}	\underline{\textbf{90.50}}	} \\ \hline
\multirow{6}{*}{NOV} & MAE & 160.63 & 370.52 & 342.23 & 365.48 & 230.28 & 114.14 & 164.50 & 163.94 & \underline{\textbf{101.36}} & {\color{black} 139.78} & 248.39 & 105.00 & 378.61 & 105.00 \\
 & MASE & 2.00 & 4.64 & 4.27 & 4.57 & 2.86 & 1.43 & 2.04 & 2.04 & \underline{\textbf{1.28}} & {\color{black} 1.74} & 3.08 & 1.32 & 4.77 & 1.32 \\
 & RMSE & 181.25 & 387.20 & 370.87 & 382.37 & 255.63 & 137.63 & 183.15 & 182.67 & \underline{\textbf{123.56}} & {\color{black} 161.36} & 273.40 & 125.82 & 406.14 & 125.82 \\
 & SMAPE & 49.20 & 182.20 & 140.80 & 176.90 & 78.10 & 31.90 & 51.80 & 51.80 & \underline{\textbf{27.60}} & {\color{black} 40.51} & 88.70 & 29.40 & 169.70 & 29.40 \\
 & {\color{black}	Pinball Loss	} & {\color{black}	124.79	} & {\color{black}	296.41	} & {\color{black}	268.32	} & {\color{black}	292.38	} & {\color{black}	183.96	} & {\color{black}	73.64	} & {\color{black}	125.90	} & {\color{black}	125.97	} & {\color{black}	\underline{\textbf{49.25}}	} & {\color{black} 105.57} & {\color{black}	198.49	} & {\color{black}	71.86	} & {\color{black}	302.31	} & {\color{black}	71.86	} \\
 & {\color{black}	CRPS	} & {\color{black}	222.28	} & {\color{black}	237.70	} & {\color{black}	241.89	} & {\color{black}	237.70	} & {\color{black}	237.70	} & {\color{black}	178.26	} & {\color{black}	216.77	} & {\color{black}	219.46	} & {\color{black}	\underline{\textbf{159.67}}	} & {\color{black} 206.01} & {\color{black}	237.94	} & {\color{black}	165.00	} & {\color{black}	243.63	} & {\color{black}	165.00	} \\ \hline
\multirow{6}{*}{DEC} & MAE & 111.36 & 320.05 & 303.66 & 311.54 & 174.14 & \underline{\textbf{82.85}} & 131.11 & 148.68 & 112.51 & {\color{black} 134.13} & 100.10 & 117.10 & 326.28 & 134.80 \\
 & MASE & 2.02 & 5.87 & 5.58 & 5.71 & 3.12 & \underline{\textbf{1.54}} & 2.36 & 2.71 & 2.02 & {\color{black} 2.43} & 1.95 & 2.14 & 6.03 & 2.50 \\
 & RMSE & 139.68 & 329.61 & 342.88 & 321.36 & 191.44 & \underline{\textbf{100.84}} & 163.30 & 183.15 & 139.38 & {\color{black} 155.58} & 118.07 & 141.74 & 347.20 & 157.71 \\
 & SMAPE & 36.60 & 184.50 & 129.80 & 174.50 & 66.20 & \underline{\textbf{24.70}} & 45.00 & 55.20 & 36.10 & {\color{black} 46.92} & 28.90 & 39.50 & 168.30 & 48.20 \\
 & {\color{black}	Pinball Loss	} & {\color{black}	83.53	} & {\color{black}	256.04	} & {\color{black}	228.05	} & {\color{black}	249.23	} & {\color{black}	139.30	} & {\color{black}	\underline{\textbf{39.40}}	} & {\color{black}	95.89	} & {\color{black}	117.67	} & {\color{black}	80.35	} & {\color{black} 108.21} & {\color{black}	32.82	} & {\color{black}	93.29	} & {\color{black}	259.98	} & {\color{black}	107.55	} \\
 & {\color{black}	CRPS	} & {\color{black}	174.92	} & {\color{black}	202.04	} & {\color{black}	219.05	} & {\color{black}	202.04	} & {\color{black}	200.90	} & {\color{black}	\underline{\textbf{128.38}}	} & {\color{black}	189.84	} & {\color{black}	201.10	} & {\color{black}	171.97	} & {\color{black} 190.48} & {\color{black}	140.95	} & {\color{black}	177.56	} & {\color{black}	209.45	} & {\color{black}	189.76	} \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Madhurima Panja
Tanujit Chakraborty
Anubhab Biswas
Soudeep Deb

"E-STGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting" by Madhurima Panja, Tanujit Chakraborty, Anubhab Biswas, and Soudeep Deb, arXiv:2411.12258v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
 \toprule
 & Train & Validation & Test \\
 \midrule
 Topics & 25 & 8 & 10 \\
 Docs & 594 & 196 & 206 \\
 Mentions & 3808/4758 & 1245/1476 & 1780/2055 \\
 Clusters & 411/472 & 129/125 & 182/196 \\
 \bottomrule
 \end{tabular}
\caption{ECB+ dataset statistics. The slash numbers for Mentions and Clusters represent event/entity statistics.}
\end{table}
\end{document}

cs

CDLM: Cross-Document Language Modeling

Avi Caciularu
Arman Cohan
Iz Beltagy
Matthew E. Peters
Arie Cattan
Ido Dagan

"CDLM: Cross-Document Language Modeling" by Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew E. Peters, Arie Cattan, and Ido Dagan, arXiv:2101.00406v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l||ll||ll}
 \hline
 & \multicolumn{2}{c||}{\bf $P\!\!\rightarrow\!\!C$} & \multicolumn{2}{c}{\bf $C\!\!\rightarrow\!\!P$} \\
 \hline
 {\bf Datasets} & {\bf $A_T ( \uparrow )$} & {\bf $F_T ( \downarrow )$} & {\bf $A_T ( \uparrow )$} & {\bf $F_T ( \downarrow )$} \\
 \hline
 {\bf CIFAR-100} & $88.87 \pm 0.33$ & $4.75 \pm 0.15$ & $88.24 \pm 0.31$ & $3.86 \pm 0.34$ \\
 {\bf ImageNet-R} & $77.86 \pm 0.25$ & $4.33 \pm 0.24$ & $77.76 \pm 0.28$ & $3.65 \pm 0.27$ \\
 {\bf CUB-200} & $80.2 \pm 0.52$ & $5.6 \pm 0.38$ & $80.1 \pm 0.45$ & $5.7 \pm 0.26$ \\ \hline
 \end{tabular}
\caption{{Prefixes before and after projection on 10-task setup.}}
\end{table}
\end{document}

cs

Anurag Roy
Riddhiman Moulick
Vinay K. Verma
Saptarshi Ghosh
Abir Das

"Convolutional Prompting meets Language Models for Continual Learning" by Anurag Roy, Riddhiman Moulick, Vinay K. Verma, Saptarshi Ghosh, and Abir Das, arXiv:2403.20317v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The LFC controller performance in an event of multiple microgrid switching }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\hline
&$|f-60|$ & MPSI & $V_error$ & MQSI \\
\hline
microgrid 1 & 0.00 & 0.00 & 0.000 & 0.00\\
microgrid 2 & 0.00 & 0.00 & 0.000 & 0.00\\
microgrid 3 & 0.00 & 0.00 & 0.000 & 0.00\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Coordinated Frequency and Voltage Regulation of Grid-Following and Grid-Forming Inverters" by Ankit Singhal, Thanh Long Vu, and Wei Du, arXiv:2012.06685v2, 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}{l|l}
 \textbf{Individual} & \textbf{Position} \\
 \midrule
 Chris Odom & Founder of Open-Transactions \\
 David Hartley & CEO of Pacio \\
 G. Ken Holman & CTO at Crane Softwrights Ltd and former editor of ISO/IEC 15944-21 \\
 Ian Grigg & Co-founder at Solidus/Chamapesa \\
 Jason Meyers & Founder at Auditchain \\
 Robert Haugen & Developer at Mikorizal Software \\
 Todd Boyle & Founder at International Accounting Services \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Juan Ignacio Ibañez
Chris N. Bayer
Paolo Tasca
Jiahua Xu

"Triple-entry Accounting, Blockchain and Next of Kin: Towards a Standardisation of Ledger Terminology" by Juan Ignacio Ibañez, Chris N. Bayer, Paolo Tasca, and Jiahua Xu, arXiv:2101.02632v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"F&O Expiry vs. First-Day SIPs: A 22-Year Analysis of Timing Advantages in India's Nifty 50" by Siddharth Gavhale, arXiv:2507.04859v2, 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}{lr}
\toprule
Hyper-parameter & Value \\
\midrule
 Total steps & 2e7 \\
 Number of envs & 8 \\
 Episode length $T$ & 1000 \\
 Number of mini-batch & 40 \\
 Actor/critic network & MLP \\
 Network hidden sizes & 64 \\
 Hidden layer & 1 \\
 Activation function & Relu \\
 Optimizer & Adam \\
 Network learning rate & 5e-3 \\
 Optimization epochs & 5 \\
 Max grad norm & 0.5 \\
 GAE parameter $\lambda$ & 0.95 \\
 Learning rate of average performance $\alpha$ & 0.1 \\
 Average value constraint coefficient in AVC & 0.01 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Hyper-parameters sheet}
\end{table}
\end{document}

math

"Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games" by Junkai Hu and Li Xia, arXiv:2503.22779v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|l|l|l|}
 \hline
 $\rho$ & \emph{SIFT} & \emph{SURF} & \emph{ORB} & \emph{FREAK} & \emph{BRIEF} & \textbf{ARSRG}$_{1^{st}}$ & \textbf{ARSRG}$_{2^{nd}}$\\ \hline
 $0.6$ & 0.7485 & 0.8400 & 0.6500 & 0.3558 & 0.4300 & 0.6700 & 0.6750\\ \hline
 $0.7$ & 0.7051 & 0.6800 & 0.6116 & 0.3360 & 0.3995 & 0.7133 & 0.7500\\ \hline
 $0.8$ & 0.6963 & 0.5997 & 0.5651 & 0.2645 & 0.4227 & 0.6115 & 0.8000\\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications" by Mario Manzo, arXiv:1912.09972v1, 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{Values for the reliability measures of accuracy, precision and coverage for the capture-recapture population size estimators of Horvitz-Thompson, generalised Chao and generalised Zelterman, where $S=1000$, $N=1000$, $\Bar{t}=900$, $\lambda^C=0.0004$, $\lambda^L=0.007$, $\lambda^U=0.009$, $\gamma=1.5$, $\sigma=0.8$, $\alpha=36$, $\beta=8.5$ and $\rho=0.4$ for various proportions of outliers.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
 \toprule
 & & \multicolumn{5}{c}{Proportion of Outliers} \\ \cline{3-7}
 Measure & Estimator & 0.0\% & 0.1\% & 0.5\% & 1.0\% & 2.0\% \\ \hline
 & Horvitz-Thompson & 16 & 30 & 211 & 677 & 2.1e+06 \\
 Accuracy & Generalised Chao & 25 & 27 & 27 & 27 & 26 \\
 & Generalised Zelterman & 29 & 32 & 31 & 32 & 32 \\ \hline
 & Horvitz-Thompson & 95 & 100 & 136 & 290 & 6.7e+07 \\
 Precision & Generalised Chao & 162 & 162 & 163 & 162 & 162 \\
 & Generalised Zelterman & 181 & 181 & 185 & 184 & 187 \\ \hline
 & Horvitz-Thompson & 95.5\% & 69.6\% & 7.0\% & 7.4\% & 60.6\% \\
 Coverage & Generalised Chao & 96.4\% & 96.0\% & 96.4\% & 96.7\% & 95.6\% \\
 & Generalised Zelterman & 95.7\% & 94.7\% & 95.8\% & 96.7\% & 94.8\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Performance of capture-recapture population size estimators under covariate information" by Layna Charlie Dennett and Dankmar Böhning, arXiv:2312.08391v1, 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{List of input parameters and binary variables of ILP program.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 \textbf{Input} & \textbf{Meaning}\\
 \midrule
 \footnotesize
 $|B|=2$ & Number of BSs. \\
$|I|=8$ & Number of RISs.\\
 $E=200$ & Number of RIS elements .\\
 $|N|=50$ & Number of time slots.\\
 $\theta=10^o$ & Beamwidth .\\
 $V=20 \cdot 10^6$ Hz & Bandwidth . \\
 $T=290$ Kelvin & Absolute temperature .\\
 $P_b=1$ mW & Power of BS.\\
 $f=28 \cdot 10^9$ Hz & Frequency .\\

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

eess

Cao Vien Phung
Max Franke
Ehsan Tohidi
June Heinemann
Andre Drummond
Stefan Schmid
Slawomir Stanczak
Admela Jukan

"An Optimization Driven Link SINR Assurance in RIS-assisted Indoor Networks" by Cao Vien Phung, Max Franke, Ehsan Tohidi, June Heinemann, Andre Drummond, Stefan Schmid, Slawomir Stanczak, and Admela Jukan, arXiv:2412.20254v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||l|l|l||}
\hline
\textbf{Parameter} & \textbf{Value} & \textbf{Description} \tabularnewline \hline
Height & $h$ & 60~cm \tabularnewline
Depression angle & $\theta$ & $36^{\circ}$ \tabularnewline
Focal length & $f$ & 0.0367~cm \tabularnewline
Square tile sides & $s$ & 20~cm \tabularnewline \hline
Signal-to-intrinsic noise ratio & SINR & 3~dB (IP), 10~dB (MI) \tabularnewline
Signal-to-sensor noise ratio & $\sigma^2/N_0$ & 10--80, 45~dB (AR1) \tabularnewline \hline
Tile depth count & $N_d$ & 11 tiles \tabularnewline
Tile width count & $N_w$ & 6 tiles \tabularnewline \hline
\end{tabular}
\end{adjustbox}
\caption{These parameters are employed in the numerical simulations.}
\end{table}
\end{document}

eess

Vishnu Teja Kunde
Jean-Francois Chamberland
Siddharth Agarwal

"Camera-Based Localization and Enhanced Normalized Mutual Information" by Vishnu Teja Kunde, Jean-Francois Chamberland, and Siddharth Agarwal, arXiv:2412.16137v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Baoluo Sun
Wang Miao
Deshanee S. Wickramarachchi

"On Doubly Robust Estimation with Nonignorable Missing Data Using Instrumental Variables" by Baoluo Sun, Wang Miao, and Deshanee S. Wickramarachchi, arXiv:2311.08691v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Kirsten W. H. Maas
Danny Ruijters
Nicola Pezzotti
Anna Vilanova

"NerT-CA: Efficient Dynamic Reconstruction from Sparse-view X-ray Coronary Angiography" by Kirsten W. H. Maas, Danny Ruijters, Nicola Pezzotti, and Anna Vilanova, arXiv:2507.19328v1, 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}{lcccccccccccc}
 \toprule
 \(a_{0,1}\) & \(a_{0,2}\) & \(a_{0,3}\) & \(a_{0,4}\) & \(a_{0,5}\) & \(a_{0,6}\) &
 \(a_{0,7}\) & \(a_{0,8}\) & \(a_{0,9}\) & \(a_{0,10}\) & \(a_{0,11}\) & \(a_{0,12}\) \\
 \midrule
 0 & 0.002 & 0.009 & 0.021 & 0.037 & 0.056 & 0.076 & 0.095 & 0.111 & 0.093 & 0.070 & 0.039 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Initial asset distribution obtained from a deterministic simulation without climate change.}
\end{table}
\end{document}

q-fin

Felix Kübler
Simon Scheidegger
Oliver Surbek

"Using Machine Learning to Compute Constrained Optimal Carbon Tax Rules" by Felix Kübler, Simon Scheidegger, and Oliver Surbek, arXiv:2507.01704v1, 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|cc|cc||} % centered columns (4 columns)
			\hline\hline %inserts double horizontal lines
			Algorithms & bad data & bad data & data attack & data attack \\
& (EPFL) & (Texas) & (EPFL) & (Texas) \\			\hline\hline
			Anomaly-free Meas.	 & 6.6e-07	& 2.5 e-06 & 6.7e-07	& 4.3 e-06	\\
			Anomaly Meas.		 & 1.1e-02	& 1.4 e-02 & 2.3e-02	& 2.3 e-02\\
			Cleaned by ICA-GAN		& 1.7e-03 	& 3.2 e-03 & 3.7e-03	& 6.4 e-03 \\
			Cleaned by OC-SVM 	 & 3.2e-03	& 6.2 e-03 & 1.5e-02	& 1.2 e-02	\\
			Cleaned by F-AnoGAN	 & 6.1e-03	& 8.2 e-03 	& 1.3e-02 & 2.1 e-02	\\
			Cleaned by NRT	 & 8.7e-03 & 4.0 e-03	& 2.1e-02 & 2.2 e-02	\\
			\hline\hline %inserts single line
	\end{tabular}
\end{adjustbox}
\caption{Average squared error of sate estimation. }
\end{table}
\end{document}

eess

Kursat Rasim Mestav
Xinyi Wang
Lang Tong

"A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems" by Kursat Rasim Mestav, Xinyi Wang, and Lang Tong, arXiv:2012.05163v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The effect of $z$ on $\delta_{min}$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
 $z$ &$\delta_{min}$ \\
\hline
1 & 0,2 \\
4 & 0,2857143 \\
10& 0,3125\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Exploring the stability of solar geoengineering agreements" by Niklas V. Lehmann, arXiv:2210.09145v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
\hline
\textbf{} & \textbf{a1 vs.~a2} & \textbf{a1 vs.~GT} & \textbf{a2 vs.~GT} \\ \hline
$\kappa$ & 0.71 & 0.75 & 0.85 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Cohen's Kappa reliability between categorical labels from annotators 1 and 2 (a1, a2) and the predefined emotion (GT).}
\end{table}
\end{document}

eess

Maja J. Hjuler
Harald V. Skat-Rørdam
Line H. Clemmensen
Sneha Das

"EmoTale: An Enacted Speech-emotion Dataset in Danish" by Maja J. Hjuler, Harald V. Skat-Rørdam, Line H. Clemmensen, and Sneha Das, arXiv:2508.14548v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Lei Zhu
Zhihao Yan
Hongbo Duan
Yongyang Cai
Xiaobing Zhang

"Long Coalition Leads to Shrink? The Roles of Tipping and Technology-Sharing in Climate Clubs" by Lei Zhu, Zhihao Yan, Hongbo Duan, Yongyang Cai, and Xiaobing Zhang, arXiv:2506.16162v1, 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{Experiment results.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 \textbf{Error Measure}& \textbf{$X$ Offset (cm)}& \textbf{$Y$ Offset (cm)} & \textbf{$\theta$ Offset (deg)}\\
 \hline
 Max Abs. Error & 3.800 & 3.642 & 6.200\\
 \hline
 Mean Abs. Error & 0.822 & 0.934 & 1.533\\
 \hline
 RMSE & 0.896 & 1.182 & 1.661\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ali Taghibakhshi
Nathan Ogden
Matthew West

"Local Navigation and Docking of an Autonomous Robot Mower using Reinforcement Learning and Computer Vision" by Ali Taghibakhshi, Nathan Ogden, and Matthew West, arXiv:2101.06248v3, 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|ccc|ccc}
 \hline
 MIMBs & \multicolumn{3}{c|}{COCO} & \multicolumn{3}{c}{OCHuman}\\
 Placement & $\text{AP}$ & $\text{AP}^{50}$ & $\text{AP}^{75}$ & $\text{AP}$ & $\text{AP}^{50}$ & $\text{AP}^{75}$ \\
 \hline
 Stage 1 & 78.7 & 94.4 & 85.4 & 72.3 & 89.7 & 78.2 \\
 Stage 2 & 78.8 & 94.4 & 85.6 & 74.0 & 90.1 & 80.3\\
 Stage 3 & 78.8 & 94.4 & 85.8 & 74.4 & 90.7 & 80.9\\
 Stage 4 & 78.5 & 94.4 & 85.5 & 70.8 & 89.8 & 77.5\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{MIMBs placement at various stages in HRNet and its impact on COCO and OCHuman \texttt{val} sets using ground truth bounding boxes.}
\end{table}
\end{document}

cs

Rawal Khirodkar
Visesh Chari
Amit Agrawal
Ambrish Tyagi

"Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation" by Rawal Khirodkar, Visesh Chari, Amit Agrawal, and Ambrish Tyagi, arXiv:2101.11223v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Víctor Blanco
José-Fernando Camacho-Vallejo
Carlos Corpus

"A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology" by Víctor Blanco, José-Fernando Camacho-Vallejo, and Carlos Corpus, arXiv:2503.00561v1, 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{i-vector statistic in DAC 13 i-vector dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
 & \textbf{SWB} & \textbf{SRE} & \textbf{SRE-1phn} \\
\hline\hline
\textbf{\#spks} & 3114 & 3790 & 3787 \\
\textbf{\#calls} & 33039 & 36470 & 25640 \\
\textbf{\#calls/spkrs} & 10.6 & 9.6 & 6.77 \\
\textbf{\#phone\_num/spkrs} & 3.8 & 2.8 & 1.0 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Adversarial Training for Multi-domain Speaker Recognition" by Qing Wang, Wei Rao, Pengcheng Guo, and Lei Xie, arXiv:2011.08623v1, 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{Understanding the influence of vocal cord articulators on linguistic learning}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccl}
 \toprule
 Database & Model's input & CER $\downarrow$ & WER $\downarrow$ \\
 \midrule
 & Lip only & 19.48 & 25.50\\
 USC-TIMIT & Masked Lip & 14.38 & 19.04\\
 & Full rtMRI & \textbf{10.95} & \textbf{14.38}\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Neil Shah
Ayan Kashyap
Shirish Karande
Vineet Gandhi

"MRI2Speech: Speech Synthesis from Articulatory Movements Recorded by Real-time MRI" by Neil Shah, Ayan Kashyap, Shirish Karande, and Vineet Gandhi, arXiv:2412.18836v2, 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{Upper bounds for $f(r,s,t)$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|}
\hline
$(\min\{r,s\}, t)$ & Bounds &
$\min\{r,s,t\}$ & Bounds \\
\hline
$(3,3)$ & $24626$ & $4$ & $2283$ \\
$(3,4)$ & $14750$ & $5$& $907$ \\
$(3,n)$, $n\ge 5$ & $6648$ & $6$& $697$ \\
$(4,3)$ & $7254$ & $7$& $635$ \\
$(n,3)$, $n\ge 5$ & $3406$ & $\ge 8$& $573$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"The inter-universal Teichmüller theory and new Diophantine results over the rational numbers. I" by Zhong-Peng Zhou, arXiv:2503.14510v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|ccc|cccccc|cccccc|}
\hline
\rowcolor{gray!20}
\textbf{Model} & \textbf{Learning Rate} & \multicolumn{3}{c|}{\textbf{Min Size Th.}} & \multicolumn{6}{c|}{\textbf{Dice Scores}} & \multicolumn{6}{c|}{\textbf{HD 95}} \\
\cline{2-16}
\rowcolor{gray!20}
 & & \textbf{ET} & \textbf{TC} & \textbf{WT} & \textbf{ET} & \textbf{TC} & \textbf{WT} & \textbf{NETC} & \textbf{CC} & \textbf{ED} & \textbf{ET} & \textbf{TC} & \textbf{WT} & \textbf{NETC} & \textbf{CC} & \textbf{ED} \\
\hline
\multirow{6}{*}{\textbf{MedNeXt Base}}
& \multirow{3}{*}{0.0027}
& 100 & 150 & 500
& 0.555 & 0.869 & 0.869 & 0.834 & 0.71 & \cellcolor{green!30}0.967
& 122.219 & 20.595 & 20.595 & 23.842 & 91.287 & \cellcolor{green!30}12.33 \\

& & 50 & 75 & 250
& 0.543 & 0.838 & 0.838 & 0.798 & 0.655 & \cellcolor{green!30}0.967
& 124.183 & 33.228 & 33.226 & 34.421 & 98.004 & \cellcolor{green!30}12.33 \\

& & 25 & 37 & 125
& 0.507 & 0.832 & 0.832 & 0.791 & 0.634 & \cellcolor{green!30}0.967
& 122.545 & 37.176 & 37.173 & 38.199 & 104.089 & \cellcolor{green!30}12.33 \\
\cline{2-16}

& \multirow{3}{*}{0.0005}
& 100 & 150 & 500
& 0.654 & \cellcolor{green!30}0.89 & \cellcolor{green!30}0.89 & \cellcolor{green!30}0.853 & \cellcolor{green!30}0.723 & \cellcolor{green!30}0.967
& 88.651 & \cellcolor{green!30}17.269 & \cellcolor{green!30}17.269 & 18.571 & \cellcolor{green!30}83.234 & \cellcolor{green!30}12.33 \\

& & 50 & 75 & 250
& \cellcolor{green!30}0.657 & \cellcolor{green!30}0.89 & \cellcolor{green!30}0.89 & \cellcolor{green!30}0.853 & \cellcolor{green!30}0.723 & \cellcolor{green!30}0.967
& \cellcolor{green!30}76.553 & \cellcolor{green!30}17.269 & \cellcolor{green!30}17.269 & \cellcolor{green!30}18.391 & \cellcolor{green!30}83.234 & \cellcolor{green!30}12.33 \\

& & 25 & 37 & 125
& 0.619 & 0.885 & 0.885 & 0.848 & 0.659 & \cellcolor{green!30}0.967
& 86.829 & 19.312 & 19.312 & 20.398 & 104.002 & \cellcolor{green!30}12.33 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{LesionWise Dice Scores, HD 95 Results, Minimum Size Thresholds, and Learning Rates for Various Runs}
\end{table}
\end{document}

eess

Sarim Hashmi
Juan Lugo
Abdelrahman Elsayed
Dinesh Saggurthi
Mohammed Elseiagy
Alikhan Nurkamal
Jaskaran Walia
Fadillah Adamsyah Maani
Mohammad Yaqub

"Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics" by Sarim Hashmi, Juan Lugo, Abdelrahman Elsayed, Dinesh Saggurthi, Mohammed Elseiagy, Alikhan Nurkamal, Jaskaran Walia, Fadillah Adamsyah Maani, and Mohammad Yaqub, arXiv:2411.15872v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Risk in Network Economies

"Risk in Network Economies" by Victor Sellemi, arXiv:2208.01467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Function describing angular transients, the M\"{o}bius transformation on graph function, Taylor expansion of sum of coordinate functions, and the initial value}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|l|l|}
 \hline
\thead{Function} & \thead{M\"{o}bius transformation $e^{\mathrm{i}z}$}& \thead{The sum of Taylor expansions $\mathfrak{T}(\mathfrak{Re})$ and $\mathfrak{T}(\mathfrak{Im})$} & \thead{$\theta=0$}\\
 \hline
$f(\theta)$ & $\widetilde{f}:=e^{-\cos\frac{\theta}{2}}e^{\mathrm{i}\theta}$ & $\mathfrak{T}(\widetilde{f})=\frac{1}{e}+\frac{\theta}{e}-\frac{3\theta^2}{8e}-\frac{\theta^3}{24e}+\mathcal{O}(\theta^4)$ & $1$\\
 \hline
$g(\theta)$ & $\widetilde{g}:=e^{-\frac{1}{2}\sin\frac{\theta}{2}\sin\theta}e^{\mathrm{i}\theta}$ & $\mathfrak{T}(\widetilde{g})=1+\theta-\frac{3\theta^2}{4}-\frac{5\theta^3}{12}+O(\theta^4)$ & $0$\\
 \hline
$p(\theta)$ & $\widetilde{p}:=e^{-\cot\frac{\theta}{2}}e^{\mathrm{i}\theta}$ & $ \mathfrak{T}(\widetilde{p})=e^{-\frac{2}{\theta}+\mathcal{O}(\theta)}\left(1+\theta-\frac{\theta^2}{2}-\frac{\theta^3}{6}+\mathcal{O}(\theta^4)\right)$ & $\infty$\\
 \hline
$q(\theta)$ & $\widetilde{q}:=e^{-\frac{1}{2}\csc^4\frac{\theta}{2}\sin\theta}e^{\mathrm{i}\theta}$ & $\mathfrak{T}(\widetilde{q})=e^{-\frac{8}{\theta^3}+\mathcal{O}(\theta)}\left(1+\theta-\frac{\theta^2}{2}-\frac{\theta^3}{6}+\mathcal{O}(\theta^4)\right)$ & $\infty$\\
 \hline
$\theta$ & $\widetilde{\theta}:=e^{\mathrm{i}\theta}$ & $\mathfrak{T}(\widetilde{\theta})=1+\theta-\frac{\theta^2}{2}-\frac{\theta^3}{6}+\mathcal{O}(\theta^4)$ & $0$\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"The Associated Discrete Laplacian in $\mathbb{R}^3$ and Mean Curvature with Higher order Approximations" by Wei-Hung Liao, arXiv:2501.11871v1, 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{Disease\_tag} & Bronchiolitis & \\ \midrule
\textbf{Exp Sym} & Runny Nose: 1 & Cough: 1 \\
\textbf{Imp Sym} & Sore Throat: 1 & Emesis: 0 \\
\textbf{} & Harsh Breath: 1 & Fever: 0 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{An example of a user goal in the Muzhi corpus, containing explicit symptoms and implicit symptoms. 1 means a symptom is confirmed by the patient, while 0 means that a symptom is denied by the patient.}
\end{table}
\end{document}

cs

"Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks" by Hongyin Luo, Shang-Wen Li, and James Glass, arXiv:2101.09773v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Iterative Greedy Matching for 3D Human Pose Tracking from Multiple Views" by Julian Tanke and Juergen Gall, arXiv:2101.09745v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation studies on CASAS datasets - Kyoto8, Milan, Kyoto7, Aruba and Cairo. The baseline is a 2 layer fully connected network comparable to the graph in terms of the network size (measured by the number of parameters). The graph approach involves the use of the neural message-passing framework (outlined in Section ). \textit{Embedding} refers to adding the embedding layer (outlined in Section ). \textit{Attn} refers to the inclusion of the attention mechanism (outlined in Section ). We report the mean and standard deviation of F1 scores across five runs.}
\begin{tabular}{l|l|l|l|l|l}
\toprule
\multirow{2}{*}{Methods \textbackslash Datasets} & Kyoto8 & Milan & Kyoto7 & Aruba & Cairo \\ \cmidrule{2-6}
 & F1 score & F1 score & F1 score & F1 score & F1 score \\ \midrule
 Baseline & 20.2 $\pm$ 2.47 & 41.3 $\pm$ 1.71 & 54.9 $\pm$ 1.84 & 66.7 $\pm$ 3.07 & 72.6 $\pm$ 0.88 \\
 Graph & 71.5 $\pm$ 0.62 & 70.9 $\pm$ 0.89 & 76.8 $\pm$ 0.83 & 83.7 $\pm$ 1.68 & 78.4 $\pm$ 0.84\\
 Graph + Embedding & 73.5 $\pm$ 0.24 & 72.2 $\pm$ 2.11 & 80.6 $\pm$ 0.09 & 87.3 $\pm$ 0.94 & 82.2 $\pm$ 1.11\\
Graph + Embedding + Attn & \textbf{78.3 $\pm$ 0.95} & \textbf{80.4 $\pm$ 0.52} & \textbf{88.7 $\pm$ 0.60} & \textbf{92.4 $\pm$ 0.34} & \textbf{88.7 $\pm$ 0.22} \\\midrule
\end{tabular}
\end{table}
\end{document}

eess

"Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes" by Srivatsa P and Thomas Plötz, arXiv:2311.09514v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Shuhua Gao
Yunyi Zhao
Cheng Xiang
Yu Ming
Tan Kuan Tak
Tong Heng Lee

"On Solar Photovoltaic Parameter Estimation: Global Optimality Analysis and a Simple Efficient Differential Evolution Method" by Shuhua Gao, Yunyi Zhao, Cheng Xiang, Yu Ming, Tan Kuan Tak, and Tong Heng Lee, arXiv:2011.12114v4, 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|cc|}
\hline
Acceleration factor & Gaussian & Spiral\\
\hline
R1/actual value & \multicolumn{2}{c|}{15.526}\\
R8 & 15.829 & 13.813 \\
R16 & 18.716 & 13.791 \\
R32 & 23.043 & 13.844\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Estimated standard deviation of the noise for each mask and acceleration factor, using the magnitude reconstructed from R2 for acceleration factors greater than 1}
\end{table}
\end{document}

math

Miriam Löcke
Pim van Ooij
Cristóbal Bertoglio

"Parameter estimation in fluid flow models from undersampled frequency space data" by Miriam Löcke, Pim van Ooij, and Cristóbal Bertoglio, arXiv:2503.04092v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameters values for the optimized computer vision models with different input sizes.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
\toprule
Hyperparameter & $32 \times 32$ & $64 \times 64$ & $128 \times 128$\\
\midrule
Number of base filters & 32 & 64 & 64\\
Dropout rate for convolutional blocks & 0.4 & 0.3 & 0.4\\
Batch normalization for convolutional blocks & true & true & true\\
Type of global pooling & max & average & average\\
Ignore additional inputs & false & false & false\\
\addlinespace
Number of units for the fully-connected layer & 256 & 256 & 256\\
Batch normalization for the fully-connected layer & false & true & true\\
Dropout rate for the fully-connected layer & 0.2 & 0.4 & 0.1\\
Learning rate & 0.0003 & 0.0006 & 0.0052\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Weihao Li
Dianne Cook
Emi Tanaka
Susan VanderPlas
Klaus Ackermann

"Automated Assessment of Residual Plots with Computer Vision Models" by Weihao Li, Dianne Cook, Emi Tanaka, Susan VanderPlas, and Klaus Ackermann, arXiv:2411.01001v1, 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{A Comparison of the average accuracy for each repetition over $19$ participants, the overall accuracy and the overall STD for the LDA model.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\hline
\hline
\textbf{Acc. F1 (\%)} \!\!\!\!&\!\!\!\! \textbf{Acc. F2} \!\!\!\!&\!\!\!\! \textbf{Acc. F3} \!\!\!\!&\!\!\!\! \textbf{Acc. F4} \!\!\!\!&\!\!\!\! \textbf{Acc. F5} \!\!\!\!&\!\!\!\! \textbf{Avg. Acc.} \!\!\!\!&\!\!\!\! \textbf{STD (\%)} \\
\hline
82.58 & 69.65 & 84.21 & 82.74 & 75.27 &\textbf{78.89} & 11.15 \\ \hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mansooreh Montazerin
Soheil Zabihi
Elahe Rahimian
Arash Mohammadi
Farnoosh Naderkhani

"ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals" by Mansooreh Montazerin, Soheil Zabihi, Elahe Rahimian, Arash Mohammadi, and Farnoosh Naderkhani, arXiv:2201.10060v1, 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{Case study 2, 95\% $\chi^2,$ $\chi^2_{ref} = 132.14$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \hline
 Method & $\chi^2$ \\
 \hline
 GP-MBDoE & $1805$\\
 MC-MBDoE & $3892$\\
 DE-MBDoE & $3079$\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Panagiotis Petsagkourakis
Federico Galvanin

"Safe model-based design of experiments using Gaussian processes" by Panagiotis Petsagkourakis and Federico Galvanin, arXiv:2011.10009v2, 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{Simulation parameters}
\begin{tabular}{|l|c|r|}
 \hline
 \textbf{Sl.} & \textbf{Parameters} & \textbf{Features} \\
 \hline
 1 & Satellite & GEO \\
 2 & Satellite Spot Beams No & 1 and 4 \\
 3 & Environment & Rural, clear sky, complete LOS\\
 4 & Operating Frequency & 2 GHz and 20 GHz\\
 5 & Satellite Altitude & 35786 km\\
 6 & Beam diameter & 2 GHz: 250 km; 20 GHz: 110 km\\
 7 & UE max height & 1.5 m\\
 8 & No of UE per SB & varies - all outdoor\\
 9 & EIRP & 2 GHz: 29 dBm/Hz; 20 GHz: 10 dBm/Hz\\
 10 & Bandwidth & 2 GHz: 30 MHz; 20 GHz: 400 MHz\\
 11 & Sub Carrier Spacing (SCS) & 15 KHz\\
 12 & Number of RBs in RBG & 1\\
 13 & Noise power density, $\sigma^2$ & -174 dBm\\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Mahfuzur Rahman
Zoheb Hassan
Jeffrey H. Reed
Lingjia Liu

"Non-Terrestrial Network (NTN): a Novel Alternate Fractional Programming for the Downlink Channels Power Allocation" by Mahfuzur Rahman, Zoheb Hassan, Jeffrey H. Reed, and Lingjia Liu, arXiv:2401.10251v1, 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{Main characteristics of each subset of SER\_AMPEL dataset.}
\begin{tabular}{|c|ccc|ccc|}
 \hline
 \multicolumn{1}{|p{4em}|}{\centering \textbf{Subset}} & \multicolumn{1}{p{4.665em}}{\centering \textbf{No. of \newline{} Speakers}} &
 \multicolumn{1}{p{4.5em}}{\centering \textbf{No. of \newline{} Female}} &
 \multicolumn{1}{p{6.5em}|}{\centering \textbf{Age (in years): \newline{}$\mu \pm \sigma$}} & \multicolumn{1}{p{6em}}{\centering \textbf{No of \newline{} Recordings }} &
 \multicolumn{1}{p{5em}}{\centering \textbf{Average duration}} & \multicolumn{1}{p{7.3em}|}{\centering \textbf{Transcriptions}} \\
 \hline
 NOLD & 81 & 31 & 73.03 ± 5.4 & 591 & 40 sec & manual \\
 NYNG & 10 & 6 & 31.09 ± 11,38 & 150 & 1 min & manual \\
 AOLD & 10 & 3 & 71.4 ± 5.3 & 120 & 15 sec & automatic \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

"SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults" by Alessandra Grossi and Francesca Gasparini, arXiv:2311.14483v2, 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|}
 \hline
 $\mathtt{client(ann)\cong t}$ & $1.00$ \\
 $\mathtt{loan(l\_1)\cong t}$ & $1.00$ \\
 $\mathtt{loan(l\_2)\cong t}$ & $1.00$ \\
 $\mathtt{has\_loan(ann,l\_1)\cong t}$ & $0.20$ \\
 $\mathtt{has\_loan(ann,l\_2)\cong t}$ & $0.20$ \\
 $\mathtt{status(l\_1)\cong a}$ & $0.30$ \\
 $\mathtt{status(l\_2)\cong d}$ & $0.70$ \\
 $\mathtt{credit\_score(ann)\cong 601.2}$ & $0.04$ \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Nitesh Kumar
Ondrej Kuzelka
Luc De Raedt

"First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs" by Nitesh Kumar, Ondrej Kuzelka, and Luc De Raedt, arXiv:2201.11165v2, 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}{lc}
\toprule
\textbf{Category Pair} & \textbf{Accuracy} \\
\midrule
Countries vs. Tools & 100.0\% \\
Countries vs. Clothing & 100.0\% \\
Musical Instr. vs. Countries & 96.7\% \\
Countries vs. Weather & 93.3\% \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Top Performing Category Pairs (Layer 31)}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
 \hline

 Solar Panels & $T_1$ &$T_2$&$T_3$&$T_4$ \\ \hline
 $P_1$ &$\langle (0.87,0.13);0.86\rangle$&$\langle (0.32,0.68);0.68\rangle$&$\langle (0.84,0.16);0.82\rangle$ &$\langle (0.49,0.51);0.51\rangle$\\
 $P_2$ &$\langle (0.71,0.29);0.69\rangle$&$\langle (0.52,0.48);0.48\rangle$&$\langle (0.73,0.27);0.72\rangle$&$\langle (0.46,0.54);0.54\rangle$\\
 $P_3$ &$\langle (0.53,0.47);0.51\rangle$&$\langle (0.66,0.34);0.34\rangle$&$\langle (0.44,0.56);0.44\rangle$&$\langle (0.65,0.35);0.35\rangle$\\
 $P_4$ &$\langle (0.78,0.22);0.78\rangle$&$\langle (0.38,0.62);0.62\rangle$&$\langle (0.73,0.27);0.72\rangle$&$\langle (0.52,0.48);0.48\rangle$\\
 $P_5$ &$\langle (0.75,0.25);0.74\rangle$&$\langle (0.32,0.68);0.68\rangle$&$\langle (0.78,0.22);0.78\rangle$&$\langle (0.42,0.58);0.58\rangle$\\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Aggregated disc intuitionistic fuzzy decision matrix}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{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{7}{|c|}{Temporal convergence (fixed $h=1/64$)}\\\hline
			&\multicolumn{2}{|c|}{$\epsilon=0.001$}
			& \multicolumn{2}{|c|}{$\epsilon=0.01$}& \multicolumn{2}{|c|}{$\epsilon=0.1$} \\ \hline
			$\Delta t$ & $\|<e_v>\|_{2,1}$ & rate &$\|<e_v>\|_{2,1}$ & rate & $\|<e_v>\|_{2,1}$ & rate \\ \hline
			$\frac{T}{4}$ & 2.8765e-1 & & 2.8767e-1 & & 2.9004e-1& \\ \hline
			$\frac{T}{8}$ & 8.4966e-2 & 1.76 & 8.4974e-2 &1.76 & 8.5986e-2 & 1.75 \\ \hline
			$\frac{T}{16}$& 2.3855e-2 & 1.83 & 2.3860e-2 & 1.83 & 2.4048e-2 & 1.84 \\ \hline
			$\frac{T}{32}$& 6.2895e-3 &1.92 & 6.2899e-3 &1.92 & 6.3445e-3 & 1.92 \\ \hline
			$\frac{T}{64}$&1.5801e-3 & 1.99 & 1.5801e-3 &1.99 & 1.5938e-3& 1.99\\ \hline
		\end{tabular}
\end{adjustbox}
\caption{\footnotesize Errors and convergence rates for $v$ with $\theta=1/9$, $\nu=0.01$, and $\nu_m=0.001$.}
\end{table}
\end{document}

cs

"High order efficient algorithm for computation of MHD flow ensembles" by Muhammad Mohebujjaman, arXiv:2101.06852v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.