\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
Method & Metric & 25D & 50D & 100D & 150D & 200D \\ \hline\hline
\multirow{3}{*}{PINN } & Speed & 40.24it/s & 19.82it/s & 6.56it/s & 3.50it/s & N.A.\\ \cline{2-7}
 & Memory & 4006MB & 6199MB & 21453MB & 44631MB & $>$80GB \\ \cline{2-7}
 & Error & 2.38E-2$\pm$1.12E-2 & 2.34E-2$\pm$1.91E-2 & 6.01E-3$\pm$2.32E-3 & 3.11E-3$\pm$1.79E-3 & N.A. \\ \hline\hline
\multirow{3}{*}{HTE (Ours)} & Speed & 125.67it/s & 123.98it/s & 54.07it/s& 42.44it/s & 23.50it/s\\ \cline{2-7}
& Memory & 1859MB & 1861MB & 2089MB & 3593MB & 7299MB\\ \cline{2-7}
 & Error & 2.85E-2$\pm$1.15E-2 & 2.34E-2$\pm$1.96E-2 & 6.08E-3$\pm$2.28E-3 & 3.39E-3$\pm$1.99E-3 & 4.03E-3$\pm$3.82E-3\\ \hline
\end{tabular}
\caption{Computational results for the Biharmonic equation.}
\end{table}
\end{document}

math

Zheyuan Hu
Zekun Shi
George Em Karniadakis
Kenji Kawaguchi

"Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks" by Zheyuan Hu, Zekun Shi, George Em Karniadakis, and Kenji Kawaguchi, arXiv:2312.14499v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
Old Scheme&New Scheme&Ratio\\
\hline
68.82&65.94&1.044\\
\hline
\end{tabular}
\end{adjustbox}
\caption{\sf Example 1: The CPU times (in seconds) consumed by the studied schemes.}
\end{table}
\end{document}

math

New More Efficient A-WENO Schemes

Shaoshuai Chu
Alexander Kurganov
Ruixiao Xin

"New More Efficient A-WENO Schemes" by Shaoshuai Chu, Alexander Kurganov, and Ruixiao Xin, arXiv:2503.10221v2, 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.


eess

Maximilian Tschuchnig
Michael Gadermayr
Khalifa Djemal

"Hybrid Deep Learning and Handcrafted Feature Fusion for Mammographic Breast Cancer Classification" by Maximilian Tschuchnig, Michael Gadermayr, and Khalifa Djemal, arXiv:2507.19843v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \hline
Methodology & FAR & MDR \\
 \hline
 DRE-CUSUM (DNN, KLIEP) & \cellcolor[HTML]{D3D3D3}$0\%$ & \cellcolor[HTML]{D3D3D3}$0\%$ \\
DRE-CUSUM (DNN, LSIF) & $0\%$ & $14.3\%$ \\
DRE-CUSUM (Kernel, LSIF) & $0.0005\%$ & $14.3\%$\\
 Online BCD & $\sim 30\%$ & $\sim 0\%$\\
 Robust Online BCD & $0.04\%$ & $42\%$ \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Unsupervised Change Detection using DRE-CUSUM" by Sudarshan Adiga and Ravi Tandon, arXiv:2201.11678v1, 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}{lcccccccc}
 \hline
 \textbf{Phoneme} & \textbf{H} & \textbf{AO} & \textbf{H} & \textbf{AO} & \textbf{X} & \textbf{UE} & \textbf{X} & \textbf{I} \\
 \hline
 Origin & 4 & 8 & 3 & 5 & 4 & 7 & 5 & 11 \\
 Latent & 2 & 10 & 2 & 9 & 1 & 11 & 2 & 22 \\
 MFCC & 5 & 6 & 5 & 6 & 2 & 8 & 3 & 20 \\
 MelSpec & 8 & 5 & 4 & 6 & 2 & 10 & 6 & 10 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{TTS Predicted Phoneme duration}
\end{table}
\end{document}

eess

"Aligner-Guided Training Paradigm: Advancing Text-to-Speech Models with Aligner Guided Duration" by Haowei Lou, Helen Paik, Wen Hu, and Lina Yao, arXiv:2412.08112v1, 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|r|r|r|r|r|}
\hline
Methods &\multicolumn{5}{c|}{Tanzania dataset} \\
\cline{2-6}
 &Precision&Recall&F1-score&Time(sec)&Detection recall \\
\hline
Proposed & 0.47 & 0.84 & 0.60 & 90.0 & 0.99 \\
U-Net & 0.43 & 0.72 & 0.54 & 157.0 & - \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

John E. Vargas-Muñoz
Devis Tuia
Alexandre X. Falcão

"Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient" by John E. Vargas-Muñoz, Devis Tuia, and Alexandre X. Falcão, arXiv:2009.08188v1, 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}{lcrr|rr}
		variable & stat. & current & lag 1 & lag 2 & lag 3 \\
		\hline
		Markit NA credit index
			& correl. & 0.028 & \color{blue}{\textbf{0.282}} & \textbf{0.288} & \textbf{0.275} \\
			& p-value & 0.551 & 0.000 & 0.000 & 0.000 \\
		Bloomberg FRN index OAS
			& correl. & \textbf{0.441} & \color{blue}{\textbf{0.451}} & \textbf{0.327} & \textbf{0.272}\\
			& p-value & 0.000 & 0.000 & 0.000 & 0.006\\
		Bloomberg FRN index
			& correl. & \textbf{-0.369} & \color{blue}{\textbf{-0.488}} & \textbf{-0.432} & \textbf{-0.299}\\
			& p-value & 0.000 & 0.000 & 0.000 & 0.000 \\
		Bloomberg US corporate index OAS
			& correl. & 0.399 & \color{blue}{\textbf{0.464}} & \textbf{0.430} & \textbf{0.336}\\
			& p-value & 0.000 & 0.000 & 0.000 & 0.000 \\
		Bloomberg US corporate index
			& correl. & -0.027 & \color{blue}{\textbf{-0.268}} & \textbf{-0.238} & \textbf{-0.233}\\
			& p-value & 0.562 & 0.000 & 0.000 & 0.000 \\
		National financial conditions index
		 & correl. & \textbf{0.458} & \color{blue}{\textbf{0.562}} & \textbf{0.562} & \textbf{0.453}\\
		 & p-value & 0.000 & 0.000 & 0.000 & 0.000 \\
		Credit market distress index
		 & correl. & \textbf{0.197} & \color{blue}{\textbf{0.245}} & \textbf{0.206} & \textbf{0.087} \\
		 & p-value & 0.000 & 0.000 & 0.000 & 0.064 \\
 LIBOR-OIS spread
		 & correl. & \textbf{0.461} & \color{blue}{\textbf{0.497}} & \textbf{0.359} & \textbf{0.179} \\
		 & p-value & 0.000 & 0.000 & 0.000 & 0.000 \\
 TED spread
		 & correl. & \textbf{0.397} & \color{blue}{\textbf{0.406}} & \textbf{0.345} & \textbf{0.269} \\
		 & p-value & 0.000 & 0.000 & 0.000 & 0.000 \\
		\hline
		SPDR financial sector ETF
			& correl. & \textbf{-0.107} & \color{blue}{\textbf{-0.292}} & \textbf{-0.256} & \textbf{-0.217}\\
			& p-value & 0.022 & 0.000 & 0.000 & 0.000 \\
		\hline
		Economic policy uncertainty
		 & correl. & 0.016 & \color{blue}{\textbf{0.121}} & \textbf{0.079} & 0.024 \\
		 & p-val. & 0.731 & 0.009 & 0.091 & 0.614 \\
		Volatility index
			& correl. & 0.005 & \color{blue}{\textbf{0.130}} & \textbf{0.131} & \textbf{0.197}\\
			& p-value & 0.909 & 0.005 & 0.005 & 0.000 \\
	\end{tabular}
\end{adjustbox}
\caption{Correlations of AXI with selected financial indicators}
\end{table}
\end{document}

q-fin

A Case for AXI

"A Case for AXI" by Viktor Tsyrennikov, arXiv:2509.03035v1, 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|ccccc}
 $j \setminus i$ & 0 & 1 & 2 & 3 & 4\\
 \hline
 0 & 1 & . & . & . & .\\
 1 & . & . & . & . & .\\
 2 & . & 20 & 45 & 36 & 10
 \end{tabular}
\end{adjustbox}
\caption{Betti table of $\sigma_2\Delta$}
\end{table}
\end{document}

math

"A Fröberg type theorem for higher secant complexes" by Junho Choe and Jaewoo Jung, arXiv:2502.20987v2, 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{Hyper-parameter domain of the parameters for the LSTM model}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
			\hline
			\textbf{Parameters} & \textbf{Search Space}\\
			\hline
			Batch size & [8, 16, 32, 64]\\
			Hidden size & [16, 32, 64, 128] \\
			Number of Layers & [1, 2, 3, 4] \\
			Learning rate & [0.0001, 0.001, 0.01] \\
			Dropout rate & [0.1, 0.2, 0.3, 0.4] \\
			Activation function & [`relu', `silu', `sigmoid', `tanh'] \\
			Optimizer & [`Adam', `RMSprop', `SGD', `Adagrad', `Adamax'] \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small True parameter values for the 3 simulation study scenarios.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccccc}
\toprule
Scenario & $\mu_0$ & $\mu_1$ & $\sigma$ & $\xi$ & $\rho$ & $\beta_{10}$ & $\beta_{11}$ & $\beta_{20}$ & $\beta_{21}$ \\\midrule
1 & 12 & 3 & 2 & 0.2 & 0.4 & -1 & 1.8 & 0.2 & 2 \\
2 & 13 & 5 & 2 & 0.1 & 0.1 & 1 & -1.2 & -1 & 0.8\\
3 & 12 & 3 & 3 & -0.1 & 0.2 & -1.5 & 2 & -1.5 & 0.8 \\\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"A Deep Learning Synthetic Likelihood Approximation of a Non-stationary Spatial Model for Extreme Streamflow Forecasting" by Reetam Majumder and Brian Reich, arXiv:2212.07267v2, 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{High sampling density SVBRDF methods, their scanning area and the number of needed individual scans}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 \toprule
 Method & Authors & No. captures &Scan area\\
 &&
 & size (est.)\\
 \midrule
 Linear Lightsource Reflectometry & Gardner et al. & 400 & 20x30cm\\
 Generalized Linear Lightsource Reflectometry & Chen et al. & 240 & 20x30cm\\
 Pocket Reflectometry & Ren et al. & 900 & 16x17cm\\
 SVBRDF capturing in the Frequency Domain & Aittala et al. & 131 & 15x15cm\\
 Second Order Spherical Gradient Illumination & Ghosh et al. & 9 x "large set" & 5x15cm\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Willemijn Elkhuizen
Tessa Essers
Yu Song
Jo Geraedts
Clemens Weijkamp
Joris Dik
Sylvia Pont

"Gloss, Color and Topography Scanning for Reproducing a Painting's Appearance using 3D printing" by Willemijn Elkhuizen, Tessa Essers, Yu Song, Jo Geraedts, Clemens Weijkamp, Joris Dik, and Sylvia Pont, arXiv:1910.10836v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Anthony Christiana
Ben Clingenpeel
Huizheng Guo
Jinseok Oh
Jozef H. Przytycki
Xiao Wang
Hongdae Yun

"Low Dimensional Homology of the Yang-Baxter Operators Yielding the HOMFLYPT Polynomial" by Anthony Christiana, Ben Clingenpeel, Huizheng Guo, Jinseok Oh, Jozef H. Przytycki, Xiao Wang, and Hongdae Yun, arXiv:2502.20659v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results showing the speaker diarization performance using baseline and proposed methods on \textbf{development set}.}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 \multirow{2}{*}{Method} & \multicolumn{2}{|c|}{Full} & \multicolumn{2}{|c|}{Core}\\
 \cline{2-5}
 & DER & JER & DER & JER \\
 \hline
 Baseline	& 19.59	&43.01	&20.17&	47.28\\
 Proposed	& 17.40&	38.08&	17.95&	42.12\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

A Kishore Kumar
Shefali Waldekar
Goutam Saha
Md Sahidullah

"ABSP System for The Third DIHARD Challenge" by A Kishore Kumar, Shefali Waldekar, Goutam Saha, and Md Sahidullah, arXiv:2102.09939v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Mulugeta Weldezgina Asres
Christian Walter Omlin
The CMS-HCAL Collaboration

"Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data" by Mulugeta Weldezgina Asres, Christian Walter Omlin, and The CMS-HCAL Collaboration, arXiv:2412.11800v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Mark J. Meyer
Amia Graye
Kimberly F. Sellers

"On Non- and Weakly-Informative Priors for the Conway-Maxwell-Poisson (COM-Poisson) Distribution" by Mark J. Meyer, Amia Graye, and Kimberly F. Sellers, arXiv:2311.18053v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of the experimentation with Case 1 (without hidden sensors) and Case 2 (with hidden sensors) by varying the delay ($\tau$) for Mario Bros. example.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
 \hline
\textbf{Setup ($\tau$)}
& \textbf{Deceptive Winning Initial States}& \textbf{VoD} \\
\hline
 Case 1 (-)
&$\mathsf{[4, 10, 11, 16, 17, 5]}$ &$0$ \\ \hline Case 2 (0)
&$\mathsf{[4, 10, 11, 16, 17, 5]}$ &$0$ \\ \hline
 Case 2 (1)
&$\mathsf{15, 20, [4, 10, 11, 16, 17, 5]}$ &$0.105$ \\ \hline
 Case 2 (2)
&$\mathsf{21, 27, 32, 34, 15, 20, [4, 10, 11, 16, 17, 5]}$ &$0.315$ \\ \hline
 Case 2 (3)
&$\mathsf{26, 31, 21, 27, 32, 34, 15, 20, [4, 10, 11, 16, 17, 5]}$ &$0.421$ \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Sumukha Udupa
Ahmed Hemida
Charles A. Kamhoua
Jie Fu

"Reactive Synthesis of Sensor Revealing Strategies in Hypergames on Graphs" by Sumukha Udupa, Ahmed Hemida, Charles A. Kamhoua, and Jie Fu, arXiv:2412.01975v1, 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{Canadian Lynx time series optimal gamma.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
 \hline
Ahead & $\gamma$\_mape & $\gamma$\_smape\\
 \hline
1.00 & 0.01 & 0.06 \\
2.00 & 0.00 & 0.02 \\
3.00 & 0.02 & 0.04 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Short-term prediction of Time Series based on bounding techniques" by Pedro Cadahía and Jose Manuel Bravo Caro, arXiv:2101.10719v1, 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{CHARACTERISTICS OF THE DATASETS}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
		\hline\hline
		\multirow{2}{*}{\textbf{Dataset}} & \multicolumn{3}{|c}{\textbf{Characteristics}}\\
		\cline{2-4} & \textbf{Samples}	& \textbf{Dimensionality} &	\textbf{Classes} \\
		\hline\hline
		Synth1 &	2000 &	30 &	5\\
		Synth2 &	1000 &	2 &	2\\
		Breast &	106 &	 9 &	6\\
		Iris &	150 &	 4 &	3\\
		Wine &	178 & 	13 &	3\\
		Heart &	267 & 	22 &	2\\
		Boston &	506 & 	13 &	2\\
		Waveform & 	5000 &	21 &	3\\
		\hline\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jian-wei Liu
Zheng-ping Ren
Run-kun Lu
Xiong-lin Luo

"GMM Discriminant Analysis with Noisy Label for Each Class" by Jian-wei Liu, Zheng-ping Ren, Run-kun Lu, and Xiong-lin Luo, arXiv:2201.10242v1, 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{Bias.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrr}
Study & Units & Mean & 95\% CI \\
\midrule
Exercise & Weekly gym visits & 2.32 & (2.15, 2.50) \\
Flu & People per 100 & 2.41 & (0.33, 5.90) \\
RCT & Percentage points & 3.37 & (1.75, 5.31) \\
Effort & Points & 66.90 & (23.35, 126.63) \\
Reproducibility & Pr. replication & 0.22 & (0.08, 0.33) \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Simple models predict behavior at least as well as behavioral scientists" by Dillon Bowen, arXiv:2208.01167v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results with relationships between transparency modes. The relationship are based on mean ranks obtained through Friedman's Test. The symbol $^*$ denotes significant difference ($p<0.05$) and the symbol $^{**}$ denotes marginally significant difference ($p<0.10$). The symbol $^-$ denotes negative scales and lower ranking is a good ranking.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
\hline
\textbf{Attributes} & \textbf{Relationship} & \textbf{$\chi^2(3)$} & \textbf{$p$-value} \\ \hline
\multicolumn{4}{c}{\textbf{SART SUBJECTIVE SCALE}} \\ \hline\hline
Instability of Situation$^-$ & not significant & $4.192$ & $0.241$ \\
Complexity of Situation$^-$ & NT$>$MT$>$PT$>$CT$^{**}$ & $6.435$ & $0.092$ \\
Variability of Situation$^-$ & not significant & $4.192$ & $0.241$ \\
Arousal & NT$>$MT$>$PT$>$CT$^{**}$ & $7.093$ & $0.069$ \\
Concentration of Attention & not significant & $4.664$ & $0.198$ \\
Spare Mental Capacity & not significant & $3.526$ & $0.317$ \\
Information Quantity & MT$>$CT$=$PT$>$NT$^{*}$ & $16.160$ & $0.001$ \\
Information Quality & MT$>$CT$>$PT$>$NT$^{*}$ & $11.351$ & $0.010$ \\
Familiarity with Situation & not significant & $1.911$ & $0.591$ \\ \hline\hline
\multicolumn{4}{c}{\textbf{NASA TLX SUBJECTIVE SCALE}} \\ \hline\hline
Mental Demand$^-$ & not significant & $6.169$ & $0.104$ \\
Physical Demand$^-$ & not significant & $3.526$ & $0.317$ \\
Temporal Demand$^-$ & not significant & $0.564$ & $0.903$ \\
Performance$^-$ & not significant & $4.573$ & $0.206$ \\
Effort$^-$ & NT$>$PT$>$CT$>$MT$^{* }$ & $9.203$ & $0.027$ \\
Frustration$^-$ & NT$>$CT$>$MT$>$PT$^{* }$ & $9.205$ & $0.027$ \\ \hline\hline
\multicolumn{4}{c}{\textbf{TRUST SUBJECTIVE SCALE}} \\ \hline\hline
Competence & not significant & $3.703$ & $0.295$ \\
Predictability & PT$>$CT$>$MT$>$NT$^{**}$ & $6.359$ & $0.095$ \\
Reliability & not significant & $4.338$ & $0.227$ \\
Faith & not significant & $1.891$ & $0.595$ \\
Overall Trust & PT$>$MT$>$CT$=$NT$^{* }$ & $12.607$ & $0.005$ \\
Accuracy & PT$>$MT$=$CT$>$NT$^{* }$ & $12.214$ & $0.007$ \\ \hline\hline
\multicolumn{4}{c}{\textbf{INTERACTION SUBJECTIVE SCALE}} \\ \hline\hline
Teammate's Intent & MT$>$PT$>$CT$>$NT$^{*}$ & $23.976$ & $0.000$ \\
Teammate's Action & MT$>$PT$=$CT$>$NT$^{*}$ & $22.511$ & $0.000$ \\
Task Progress & MT$>$CT$>$PT$>$NT$^{*}$ & $25.619$ & $0.000$ \\
Robot Status & CT$>$PT$>$MT$>$NT$^{*}$ & $13.608$ & $0.003$ \\
Information Clarity & CT$>$PT$>$MT$>$NT$^{*}$ & $12.078$ & $0.007$ \\ \hline\hline
\multicolumn{4}{c}{\textbf{PERFORMANCE OBJECTIVE SCALE}} \\ \hline\hline
Points Scored & not significant & $5.554$ & $0.135$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jayam Patel
Tyagaraja Ramaswamy
Zhi Li
Carlo Pinciroli

"Transparency in Multi-Human Multi-Robot Interaction" by Jayam Patel, Tyagaraja Ramaswamy, Zhi Li, and Carlo Pinciroli, arXiv:2101.10495v2, 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 of different audio pruning methods across various metrics. PR: Pruning Rate, TF: TFLOPS, PT: Prefill time (ms), TM: Total memory (GB), SA: Storing activation (GB), RAP: Random Audio Pruning, RAC: Random Audio Cropping.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
\toprule[2pt]
Method & PR & TF $\downarrow$ & PT $\downarrow$ & TM $\downarrow$ & SA $\downarrow$ & SPIRAL $\uparrow$ & SPIRAL-H $\uparrow$ \\
\midrule[1pt]
Original & -- & 12.2 & 779 & 13.40 & 0.19 & 60.38\% & 0\% \\
\midrule[0.5pt]
RAP & \multirow{3}{*}{0.2} & \multirow{3}{*}{10.06} & \multirow{3}{*}{662} & \multirow{3}{*}{13.32} & \multirow{3}{*}{0.15} & 42.49\% & 21.45\% \\
RAC & & & & & & 65.71\% & 48.13\% \\
\textbf{Ours} & & & & & & \textbf{89.23\%} & \textbf{81.64\%} \\
\midrule[0.5pt]
RAP & \multirow{3}{*}{0.4} & \multirow{3}{*}{7.93} & \multirow{3}{*}{511} & \multirow{3}{*}{13.24} & \multirow{3}{*}{0.11} & 42.89\% & 22.19\% \\
RAC & & & & & & 62.45\% & 41.90\% \\
\textbf{Ours} & & & & & & \textbf{85.97\%} & \textbf{76.43\%} \\
\midrule[0.5pt]
RAP & \multirow{3}{*}{0.6} & \multirow{3}{*}{5.79} & \multirow{3}{*}{419} & \multirow{3}{*}{13.17} & \multirow{3}{*}{0.07} & 42.39\% & 21.45\% \\
RAC & & & & & & 58.20\% & 35.41\% \\
\textbf{Ours} & & & & & & \textbf{75.89\%} & \textbf{63.77\%} \\
\midrule[0.5pt]
RAP & \multirow{3}{*}{0.8} & \multirow{3}{*}{3.66} & \multirow{3}{*}{278} & \multirow{3}{*}{13.09} & \multirow{3}{*}{0.04} & 45.26\% & 23.19\% \\
RAC & & & & & & 55.83\% & 33.67\% \\
\textbf{Ours} & & & & & & \textbf{62.45\%} & \textbf{46.15\%} \\
\bottomrule[2pt]
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yueqian Lin
Yuzhe Fu
Jingyang Zhang
Yudong Liu
Jianyi Zhang
Jingwei Sun
Hai "Helen" Li
Yiran Chen

"SpeechPrune: Context-aware Token Pruning for Speech Information Retrieval" by Yueqian Lin, Yuzhe Fu, Jingyang Zhang, Yudong Liu, Jianyi Zhang, Jingwei Sun, Hai "Helen" Li, and Yiran Chen, arXiv:2412.12009v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\infty$) processes and applications" by Yingli Wang and Zhenyu Cui, arXiv:2503.18259v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\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}{lcccc}
		\toprule
		& \textbf{Low} & \textbf{Medium}&\textbf{High}&\textbf{Extreme}\\
		\midrule
		\rowcolor{gray!15}
		Low & 0.006 & 0.018 & 0.042 & 0.073 \\
		Medium-High & 0.015 & 0.046 & 0.107 & 0.183 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Michele Azzone
Matteo Ghesini
Davide Stocco
Lorenzo Viola

"Climate Physical Risk Assessment in Asset Management" by Michele Azzone, Matteo Ghesini, Davide Stocco, and Lorenzo Viola, arXiv:2504.19307v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Edge isoperimetry of lattices

"Edge isoperimetry of lattices" by Cameron Strachan and Konrad Swanepoel, arXiv:2503.09591v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Maxime Ryckewaert
Diego Marcos
Christophe Botella
Maximilien Servajean
Pierre Bonnet
Alexis Joly

"Applying the maximum entropy principle to neural networks enhances multi-species distribution models" by Maxime Ryckewaert, Diego Marcos, Christophe Botella, Maximilien Servajean, Pierre Bonnet, and Alexis Joly, arXiv:2412.19217v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
 \toprule
 $\nu$ & $32\times 32$ & $64\times 64$ & $128\times 128$ & $256\times 256$&$512\times 512$\\
 \midrule
 $1\cdot 10^{-2}$ & 9 & 14 & 21 & 24 & 26\\
 $5\cdot 10^{-3}$ & 13 & 13 & 19 & 28 & 33\\
 $2.5\cdot 10^{-3}$ & 19 & 17 & 17 & 25 & 37\\
 $1.25\cdot 10^{-3}$ & 27 & 24 & 21 & 22 & 33\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Lorenzo Lazzarino
Yuji Nakatsukasa
Umberto Zerbinati

"Preconditioned normal equations for solving discretised partial differential equations" by Lorenzo Lazzarino, Yuji Nakatsukasa, and Umberto Zerbinati, arXiv:2502.17626v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Reduced Order and Surrogate Models for Gravitational Waves" by Manuel Tiglio and Aarón Villanueva, arXiv:2101.11608v1, 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}
\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
Team & Dice (\%) $\uparrow$ & AVD $\downarrow$ & F1 (\%) $\uparrow$ & ALCD $\downarrow$ \\
\hline
\textbf{Kurtlab (Ours)} & \textbf{28.50 (21.27)} & \textbf{21.23 (37.22)} & \textcolor{blue}{14.39 (21.19)} & \textcolor{blue}{7.18 (7.67)} \\
AMC-Axolotls & \textcolor{blue}{26.27 (24.73)} & \textcolor{blue}{21.31 (35.23)} & \textbf{14.94 (25.12)} & 7.66 (7.94) \\
Ninjas & 25.46 (19.08) & 26.29 (39.73) & 9.92 (13.46) & \textbf{5.98 (6.46)} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

"Clinically-Informed Preprocessing Improves Stroke Segmentation in Low-Resource Settings" by Juampablo E. Heras Rivera, Hitender Oswal, Tianyi Ren, Yutong Pan, William Henry, Caitlin M. Neher, and Mehmet Kurt, arXiv:2508.16004v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\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}{ll} \toprule
\rowcolor{lightgray}
 {\textit{Parameter(s)}} & {\textit{Values}} \\ \midrule
 Convergence threshold of value iteration & $0.1$.\\
 Number of particles $M$ & $50$ [cf. ()] \\
 Discount factor $\gamma$ & $0.99$ [cf. ()]\\
 \bottomrule\\
 \end{tabular}
\end{adjustbox}
\caption{Hyperparameters.}
\end{table}
\end{document}

eess

"Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization" by Kim Hammar and Tao Li, arXiv:2508.14385v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach" by Marc Hallin, Davide La Vecchia, and Hang Liu, arXiv:2011.06062v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Customers' Spend Before/After Adopting the Online Channel}
\begin{tabular}{lcc}
 \midrule \midrule
 DV: & \multicolumn{2}{c}{Spend}\\
 Model: & (1) & (2)\\
 \midrule
 \emph{Variables}\\
 Constant & 318.4 (0.621) & \\
 & [0.000] & \\
 Post\_online\_adoption & 147.4 (0.764) & 124.2 (0.782)\\
 & [0.000] & [0.000]\\
 \midrule
 \emph{Fixed-effects}\\
 Customer & & Yes\\
 YearMonth & & Yes\\
 \midrule
 \emph{Fit statistics}\\
 Observations & 34,226,864 & 34,226,864\\
 R$^2$ & 0.00754 & 0.34036\\
 Within R$^2$ & & 0.00275\\
 \midrule \midrule
\multicolumn{3}{l}{\emph{Clustered (Customer) standard‐errors are presented in standard brackets,}}\\
\multicolumn{3}{l}{\emph{while p‑values are shown in square brackets.}}
 \end{tabular}
\end{table}
\end{document}

q-fin

Channel Choice and Customer Value

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"Channel Choice and Customer Value" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2508.00208v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Reinforcement Learning with Latent Flow

Wenling Shang
Xiaofei Wang
Aravind Srinivas
Aravind Rajeswaran
Yang Gao
Pieter Abbeel
Michael Laskin

"Reinforcement Learning with Latent Flow" by Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, and Michael Laskin, arXiv:2101.01857v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Tanmay Tirpankar
Arnab Das
Ganesh Gopalakrishnan

"Satire: Computing Rigorous Bounds for Floating-Point Rounding Error in Mixed-Precision Loop-Free Programs" by Tanmay Tirpankar, Arnab Das, and Ganesh Gopalakrishnan, arXiv:2503.05924v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Selected Training Parameters for the Classification Model of ESG-related News Articles}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llr}
\hline
\textbf{Parameter} %
& \textbf{Value} \\ \hline
Learning Rate - Step 1
& 10-3 \\
Learning Rate - Step 2
& 10-4 \\
Early Stopping Criteria
& 1 \\
Learning Rate Reduction Criteria %
& 5 \\ \hline
Maximum Sequence Length %
& 512 \\
Batch Size %
& 32 \\
Loss Funcion & %
Sparse categorical cross-entropy loss \\
Evaluation Metric & %
Accuracy \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Linear fractional relative risk aversion" by Kristian Behrens and Yasusada Murata, arXiv:2509.09865v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean and standard deviations of Absolute Error for MAGI and EFiGP for each parameter on the LV system with a tuned learning rate}
\begin{tabular}{lccccccc}
\toprule
 & & \textbf{41}& \textbf{81} & \textbf{161} & \textbf{321} & \textbf{641} & \textbf{1281} \\
\midrule
\multirow{6}{*}{\textbf{EFiGP}}
 & \textnormal{a} & \hfill .026$\pm$.019 & \hfill .026$\pm$.019 & \hfill .036$\pm$.025 & \hfill .054$\pm$.028 & \hfill .067$\pm$.028 & \hfill .078$\pm$.024 \\
 & \textnormal{b} & \hfill .027$\pm$.019 & \hfill .027$\pm$.020 & \hfill .033$\pm$.025 & \hfill .049$\pm$.029 & \hfill .056$\pm$.028 & \hfill .040$\pm$.026 \\
 & \textnormal{c} & \hfill .028$\pm$.019 & \hfill .029$\pm$.019 & \hfill .036$\pm$.025 & \hfill .057$\pm$.028 & \hfill .067$\pm$.027 & \hfill .073$\pm$.019 \\
 & \textnormal{d} & \hfill .051$\pm$.032 & \hfill .049$\pm$.034 & \hfill .066$\pm$.044 & \hfill .102$\pm$.053 & \hfill .121$\pm$.055 & \hfill .159$\pm$.034 \\
\midrule
\multirow{4}{*}{\textbf{MAGI}}
 & \textnormal{a} & \hfill .028$\pm$.023 & \hfill .026$\pm$.021 & \hfill .035$\pm$.024 & \hfill .056$\pm$.028 & \hfill .079$\pm$.029 & \hfill .119$\pm$.035 \\
 & \textnormal{b} & \hfill .028$\pm$.022 & \hfill .027$\pm$.021 & \hfill .031$\pm$.022 & \hfill .049$\pm$.027 & \hfill .058$\pm$.029 & \hfill .042$\pm$.029 \\
 & \textnormal{c} & \hfill .022$\pm$.019 & \hfill .022$\pm$.018 & \hfill .032$\pm$.021 & \hfill .058$\pm$.025 & \hfill .069$\pm$.025 & \hfill .072$\pm$.022 \\
 & \textnormal{d} & \hfill .045$\pm$.036 & \hfill .044$\pm$.035 & \hfill .059$\pm$.042 & \hfill .105$\pm$.052 & \hfill .139$\pm$.053 & \hfill .170$\pm$.050 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems" by Jianhong Chen and Shihao Yang, arXiv:2501.14107v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Detailed information of the designed low-complexity explicit controllers.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
\hline & EMPC1 & EMPC2 & EMPC3 \\
\hline Prediction horizon & 15 & 15 & 15 \\
 Constraint horizon & 1 & 2 & 3 \\
 \# of parameters & 5 & 8 & 11 \\
 \# of critical regions & 39 & 1,211 & 28,300 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Sang Hwan Son
Se-Kyu Oh
Byung Jun Park
Min Jun Song
Jong Min Lee

"Idle speed control with low-complexity offset-free explicit model predictive control in presence of system delay" by Sang Hwan Son, Se-Kyu Oh, Byung Jun Park, Min Jun Song, and Jong Min Lee, arXiv:2012.02859v2, 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{Statistical Analysis Results for Human Effort Comparison}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|}
 \hline
 \textbf{Test} & \textbf{Statistic} & \textbf{p-value} \\
 \hline
 Two-sample t-test & t = -5.8450 & $< 0.0001$ \\
 Mann-Whitney U test & U = 145784.0000 & $< 0.0001$ \\
 \hline
 \multicolumn{3}{|l|}{\textbf{Significance:}} \\
 \multicolumn{3}{|l|}{- Significant difference based on t-test ($p < 0.05$)} \\
 \multicolumn{3}{|l|}{- Significant difference based on Mann-Whitney U test ($p < 0.05$)} \\
 \multicolumn{3}{|l|}{- N for each category = 1176} \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Negin Amirshirzad
Mehmet Arda Eren
Erhan Oztop

"Context-Based Echo State Networks with Prediction Confidence for Human-Robot Shared Control" by Negin Amirshirzad, Mehmet Arda Eren, and Erhan Oztop, arXiv:2412.00541v1, 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{Model Version of Considered PTMs}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
 \toprule
 \textbf{Model Architecture} &\textbf{Model Version}\\
 \midrule
 BERT & bert-base-uncased\\
 ALBERT & albert-base-v2\\
 RoBERTa & roberta-base\\
 XLNet & xlnet-base-cased\\
 BERTOverflow & jeniya/BERTOverﬂow\\
 CostSensBERT & bert-base-uncased\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

chengran Yang
Bowen Xu
Junaed younus Khan
Gias Uddin
Donggyun Han
Zhou Yang
David Lo

"Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?" by chengran Yang, Bowen Xu, Junaed younus Khan, Gias Uddin, Donggyun Han, Zhou Yang, and David Lo, arXiv:2201.11327v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Settings of Multitask Autoencoder}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
	& & & \\
	Training Set&$[-60^{\circ}\!:\!0.1^{\circ}\!:\!60^{\circ}]$&Validation set&$[-60^{\circ}\!:\!0.1^{\circ}\!:\!60^{\circ}]$\\
	\hline
	& & & \\
	Size&$1201 \times 40$&Size&$1201 \times 10$\\
	\hline
	& & & \\
	Layer 1&$16\times16\times8$& Layer 2& $16\times8\times16$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Shengheng Liu
Zihuan Mao
Xingkang Li
Mengguan Pan
Peng Liu
Yongming Huang
Xiaohu You

"Model-driven deep neural network for enhanced direction finding with commodity 5G gNodeB" by Shengheng Liu, Zihuan Mao, Xingkang Li, Mengguan Pan, Peng Liu, Yongming Huang, and Xiaohu You, arXiv:2412.10644v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Multiscale detection of practically significant changes in a gradually varying time series" by Patrick Bastian and Holger Dette, arXiv:2504.15872v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Selected Quantitative Predictors and Elementary Statistics}
\begin{tabular}{lcc}
\toprule
Statistic & years of enrollement at 2021/22 & earned ECTS at 2021-22 \\
\midrule
Mean & 2.838 & 31.483 \\
Std Deviation & 2.577 & 21.082 \\
Median & 2 & 0 \\
Mode & 1 & 32 \\
Min & 1 & 0 \\
Max & 24 & 113 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Lorenzo Giammei
Laura Terzera
Fulvia Mecatti

"Statistical Challenges in Analyzing Migrant Backgrounds Among University Students: a Case Study from Italy" by Lorenzo Giammei, Laura Terzera, and Fulvia Mecatti, arXiv:2501.06166v1, 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{Statistics of benchmark datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 \hline
 Datesets & Class & Training & Testing \\
 \hline
 \hline
 CUB-$200$-$2011$ & $200$ & $5997$ & $5794$ \\
 Stanford Cars & $196$ & $8144$ & $8041$ \\
 FGVC Aircraft & $100$ & $6667$ & $3333$ \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Tian Zhang
Dongliang Chang
Zhanyu Ma
Jun Guo

"Progressive Co-Attention Network for Fine-grained Visual Classification" by Tian Zhang, Dongliang Chang, Zhanyu Ma, and Jun Guo, arXiv:2101.08527v2, 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{Issue close time prediction data. From~ study. Note that because of the manner of data collection, i.e., using bin-sequences for each day for each report, there are many more data samples generated from the number of reports mined.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrrr}
 \toprule \\
 \textbf{Project} & \textbf{Observation Period} & \textbf{\# Reports} & \textbf{\# Train} & \textbf{\# Test} \\ \midrule
 Eclipse & Jan 2010--Mar 2016 & 16,575 & 44,545 & 25,459 \\
 Chromium & Mar 2014--Aug 2015 & 15,170 & 44,801 & 25,200 \\
 Firefox & Apr 2014--May 2016 & 13,619 & 44,800 & 25,201 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Old but Gold: Reconsidering the value of feedforward learners for software analytics" by Rahul Yedida, Xueqi Yang, and Tim Menzies, arXiv:2101.06319v2, 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{ASR finetuning hyperparameters.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 \toprule
 Dataset	& Encoder LR & Decoder LR &	Warmup steps	&Freezing steps \\
 \midrule
Librispeech 100hrs	& 2e-4 & 2e-3	& 1000	&1500\\
Librispeech 960hrs	&1e-4	& 1e-3 &6000	&12000\\
TEDLIUMv3	& 1e-4	& 1e-3 &3000	&6000\\
NSC subset	& 1e-4	& 1e-3& 3000	&6000\\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

"MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond" by Muhammad Huzaifah, Geyu Lin, Tianchi Liu, Hardik B. Sailor, Kye Min Tan, Tarun K. Vangani, Qiongqiong Wang, Jeremy H. M. Wong, Jinyang Wu, Nancy F. Chen, and Ai Ti Aw, arXiv:2412.11538v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Solving tiling enumeration problems by tensor network contractions" by Kai Liang, arXiv:2503.17698v4, 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}{lr}
\hline
\textbf{Metric} & \textbf{Value} \\
\hline
Samples & 417 \\
Original class correct & 283 (67.87\%) \\
Original class audible & 39 (9.35\%) \\
Original class visible & 22 (5.88\%) \\
\hline
Original class absent & 102 (24.46\%) \\
\hline
Original class is only class & 71 (17.03\%) \\
Classes total & 309 \\
\hline
Classes only visible & 6 \\
Classes only audible & 25 \\
\hline
Average labels added per sample & 1.39 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Relabelling statistics for the gold-standard subset.}
\end{table}
\end{document}

eess

Daniil Zverev
Thaddäus Wiedemer
Ameya Prabhu
Matthias Bethge
Wieland Brendel
A. Sophia Koepke

"VGGSounder: Audio-Visual Evaluations for Foundation Models" by Daniil Zverev, Thaddäus Wiedemer, Ameya Prabhu, Matthias Bethge, Wieland Brendel, and A. Sophia Koepke, arXiv:2508.08237v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of GMRES-IR and RGMRES-IR refinement steps with the number of GMRES iterations for each refinement step for random dense matrices having geometrically distributed singular values (mode 3) with various condition numbers, using precisions ($u_f,u,u_r$) = (half, double, quad) and ($m,k$) = (90,40).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cc|cc|}
		\hline
		$\kappa_\infty(A)$ & $\kappa_2(A)$ & GMRES-IR (90) & RGMRES-IR (90,40) \\ \hline
		$9 \cdot 10^4$ & $10^4$ & 33 (16,17) & 33 (16,17) \\
		$8 \cdot 10^5$ & $10^5$ & 85 (41,44) & 50 (41,9) \\
		$7 \cdot 10^6$ & $10^6$ & 134 (66,68) & 81 (66,15) \\
		$7 \cdot 10^7$ & $10^7$ & 167 (83,84) & 100 (83,17) \\
		$7 \cdot 10^8$ & $10^8$ & - & 149 (119,30) \\
		$6 \cdot 10^9$ & $10^9$ & - & 179 (134,45) \\
		$6 \cdot 10^{10}$ & $10^{10}$ & - & 470 (388,41,41) \\
		$6 \cdot 10^{11}$ & $10^{11}$ & - & - \\
		$6 \cdot 10^{12}$ & $10^{12}$ & - & - \\
		$5 \cdot 10^{13}$ & $10^{13}$ & - & - \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Kolmogorov--Smirnov test for equality of distributions}
\begin{tabular}{lcccc}
\hline\hline
\\
 &	\shortstack{Men vs Women \\in female-dominated sectors}	&	\shortstack{Men vs Women	\\in male-dominated sectors}	&	\shortstack{Workers in female-dominated sectors \\ vs male-dominated sectors if women	}&	\shortstack{Workers in female-dominated sectors \\ vs male-dominated sectors if men	} \\
& (1)&(2)&(3)&(4)\\
\hline
\\
Predicted wage&	0.1700 	&	0.1541 	&	0.2438 	&	0.1890 	\\
Residual wage	&	0.0366 	&	0.0243 	&	0.0442 	&	0.0231 	\\
Counterfactual predicted wage	&	0.1055	&	0.0871&	0.0867	&	0.0471	\\
Counterfactual residual wage	&	0.0821 	&	0.1136 	&	0.1518 	&	0.1791 	\\
 \hline\hline
	\end{tabular}
\end{table}
\end{document}

q-fin

Riccardo Leoncini
Mariele Macaluso
Annalivia Polselli

"Gender Segregation: Analysis across Sectoral-Dominance in the UK Labour Market" by Riccardo Leoncini, Mariele Macaluso, and Annalivia Polselli, arXiv:2303.04539v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Homomorphism densities for the $P_1, P_2, P_3, C_3$ and $S_3$ subgraphs for the $C^{-}, \Pi, C^{+}$ copula graphons}
\begin{tabular}{c|c|c|c|c|c}
 \hline
 $W$ & $t(P_1,W)$ & $t(P_2,W)$ & $t(P_3,W)$ & $t(C_3,W)$ & $t(S_3,W)$\\ \hline \hline
 $C^{-}$& 1/6 & 1/20 & 1/280 & 1/120 & 1/56 \\
 $\Pi$ & 1/4 & 1/12 & 1/36 & 1/27 & 1/32 \\
 $C^{+}$ & 1/3 & 2/15 & 1/14 & 2/15 & 2/35\\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

"Generating Networks to Target Assortativity via Archimedean Copula Graphons" by Victory Idowu, arXiv:2503.03061v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|cc}
			\hline
			Type & Amount& Avg \#Subject& Avg \#Person \\
			\hline
			\hline
			Images & 9,500 & 1.1417 & 4.2820 \\
			\hline
			Videos & 125(132k frames) & 1.0049 & 1.8272 \\
			\hline
		\end{tabular}
\end{adjustbox}
\caption{Detailed statistics of {\it H2V-142K} Dataset.}
\end{table}
\end{document}

cs

Horizontal-to-Vertical Video Conversion

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

"Horizontal-to-Vertical Video Conversion" by Tun Zhu, Daoxin Zhang, Yao Hu, Tianran Wang, Xiaolong Jiang, Jianke Zhu, and Jiawei Li, arXiv:2101.04051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllllll}
 \toprule
 \textbf{Order} &1&2 &3&4&5&6 &7 &8 \\
 \midrule
 \textbf{Task type} &$Q_0$ &$P$ &$B_R$&$B_{DR}$&$L_{DR}$&$ Q_1$&$L_{R}$&$Q_2$ \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"A proposal and evaluation of new timbre visualisation methods for audio sample browsers" by Etienne Richan and Jean Rouat, arXiv:2011.15096v1, 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{Classifiers accuracy obtained using different feature selection (FS) algorithms. The resulting number of features is given in parentheses.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|}
\hline \hline
FS algorithm & \begin{minipage}[c]{30mm}\centering Accuracy with \\ initial subset \end{minipage} & \begin{minipage}[c]{30mm}\centering Accuracy after backward- \\ stepwise selection \end{minipage} \\
\hline
QoV 	& \begin{minipage}{30mm}\centering $79.5\pm3.5$\% \\ $(4)$ \end{minipage} & \begin{minipage}{30mm}\centering $79.5\pm3.5$\% \\ $(4)$ \end{minipage} \\
Relief &\begin{minipage}{30mm}\centering $72.5\pm3.4$\% \\ $(16)$ \end{minipage} & \begin{minipage}{30mm}\centering $80.3\pm2.3$\% \\ $(5)$ \end{minipage} \\
LASSO & \begin{minipage}{30mm}\centering \bf 97.0$\pm$2.4\% \\ $(43)$ \end{minipage} & \begin{minipage}{30mm}\centering \bf 99.7$\pm$0.6\% \\ $(32)$ \end{minipage} \\
RelieFF & \begin{minipage}{30mm}\centering $75.9\pm4.2$\% \\ $(20)$ \end{minipage} & \begin{minipage}{30mm}\centering $82.9\pm2.8$\% \\ $(11)$ \end{minipage} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Classification of ALS patients based on acoustic analysis of sustained vowel phonations" by Maxim Vashkevich and Yulia Rushkevich, arXiv:2012.07347v2, 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}{lccccccccc}\toprule
Method & \multicolumn{4}{c}{PSNR} & \multicolumn{4}{c}{SSIM} & SAM (10\textsuperscript{-2}) \\\midrule
Band & NIR & R & G & B & NIR & R & G & B & \\\midrule
Bilinear Upsampling & 20.0 & 19.0 & 21.0 & 21.1 & 0.568 & 0.550 & 0.633 & 0.639 & 3.87 \\
ESTARFM~ & 19.6 & 20.2& 21.8& 22.3& 0.555 & 0.640 & 0.688 & 0.696 & 4.88 \\
cGAN Fusion + $L_1$ & 22.1 & 21.8 & 23.7 & 23.8 & 0.675 & 0.697 & 0.747 & 0.747 & 2.75 \\
cGAN Fusion + $L_1$ + SSIM & {\bf 22.3} & {\bf 22.0} & {\bf 23.9} & {\bf 24.0} & {\bf 0.694} & {\bf 0.714} & {\bf 0.761} & {\bf 0.760} & {\bf 2.70} \\\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Image quality scores on testing set; cGAN models scores are averaged over 3 independently trained models}
\end{table}
\end{document}

eess

Shahine Bouabid
Maxim Chernetskiy
Maxime Rischard
Jevgenij Gamper

"Predicting Landsat Reflectance with Deep Generative Fusion" by Shahine Bouabid, Maxim Chernetskiy, Maxime Rischard, and Jevgenij Gamper, arXiv:2011.04762v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation" by Alexandre Rame and Matthieu Cord, arXiv:2101.05544v1, 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{$\log_{10}(p\text{-values})$ Statistics under Synthetic Setting 1 with 100 Covariates.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrrrrrr} \toprule
\textbf{Model} & \textbf{Min} & \textbf{25\%} & \textbf{Median} & \textbf{Mean} & \textbf{75\%} & \textbf{Max} \\
\midrule
 TARNet & $-34.3$ & $-31.3$ & $-30.9$ & $-29.9$ & $-30.3$ & $-29.0$ \\
 CausalForest & $-2.39$ & $-1.35$ & $-0.821$ & $-0.670$ & $-0.479$ & $-0.122$ \\
 S-BART & $-9.62$ & $-7.32$ & $-6.68$ & $-5.22$ & $-5.99$ & $-3.96$ \\
 T-BART & $-1.04$ & $-0.76$ & $-0.36$ & $-0.31$ & $-0.12$ & $0.00$ \\
 S-engression & $-28.6$ & $-26.1$ & $-25.3$ & $-23.6$ & $-24.0$ & $-22.6$ \\
 T-engression & $-2.46$ & $-0.663$ & $-0.393$ & $-0.366$ & $-0.137$ & $-0.107$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Daniel de Vassimon Manela
Linying Yang
Robin J. Evans

"Testing Generalizability in Causal Inference" by Daniel de Vassimon Manela, Linying Yang, and Robin J. Evans, arXiv:2411.03021v2, 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{CORDIC Linear Vectoring Mode (Division)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccr}
 \hline
 \textbf{i (clk)} & \textbf{Ei=2\textsuperscript{-i}} & \textbf{Xi+1 $\rightarrow$ Xi} & \textbf{Yi+1 $\rightarrow$ 0} & \textbf{Zi+1 $\rightarrow$ Y/X} & \textbf{di} \\ \hline
 \textbf{1} & 0.5 & 2.51 & -0.734 & 0.5 &-1\\
 \textbf{2} & 0.25 & 2.51 & -0.1065 & 0.25 &1\\
 \textbf{3} & 0.125 & 2.51 & 0.20725 & 0.125 &1\\
 \textbf{4} & 0.0625 & 2.51 & 0.050375 & 0.1875 &-1\\
 \textbf{5} & \textbf{0.03125} & \textbf{2.51} & \textbf{-0.02806} & \textbf{0.21875} &\textbf{-1}\\
 \textbf{6} & 0.015625 & 2.51 & 0.011156 & 0.203125 &1\\
 \textbf{7} & 0.007812 & 2.51 & -0.00845 & 0.210937 &-1\\
 \textbf{8} & 0.003906 & 2.51 & 0.001351 & 0.207031 &1\\
 \textbf{9} & 0.001953 & 2.51 & -0.00355 & 0.208984 &-1\\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mukul Lokhande
Gopal Raut
Santosh Kumar Vishvakarma

"Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads" by Mukul Lokhande, Gopal Raut, and Santosh Kumar Vishvakarma, arXiv:2412.11702v1, 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{Averages of the 10 smallest SP scores whose AUCs are greater than the thresholds ($\geq 0.85$ for Adult and $\geq 0.65$ for Law School) are reported. Table~ in SM~ presents consistent results with different thresholds. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|ccc}
\hline
Data & Model & RF ($\downarrow$)& SVM ($\downarrow$)& LR ($\downarrow$) \\
\hline
\hline
\multirow{3}{*}{Adult}
& \multirow{1}{*}{FairPTGAN} & \bf{0.064 (0.006)} & \bf{0.077 (0.014)} & \bf{0.084 (0.006)} \\
& \multirow{1}{*}{FairWGANGP}& 0.083 (0.010) & 0.088 (0.010) & 0.095 (0.005) \\
&\multirow{1}{*}{GeoRepair} & 0.082 (0.012) & 0.089 (0.009) & 0.106 (0.009) \\
\hline
\multirow{3}{*}{Law School.}
& \multirow{1}{*}{FairPTGAN} & \bf{0.054 (0.014)} & \bf{0.056 (0.006)} & \bf{0.079 (0.020)} \\
& \multirow{1}{*}{FairWGANGP} & 0.105 (0.006) & 0.115 (0.007) & 0.175 (0.003) \\
&\multirow{1}{*}{GeoRepair} & 0.102 (0.011) & 0.129 (0.013) & 0.187 (0.003) \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Parallelly Tempered Generative Adversarial Nets: Toward Stabilized Gradients" by Jinwon Sohn and Qifan Song, arXiv:2411.11786v2, 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|c|c}
		\toprule[1pt]
		 Methods & Backbone & mIOU (\%) \\
		\midrule[0.5pt]
		\midrule[0.5pt]
		DSSPN~ & ResNet-101 & 38.9 \\
		SVCNet~ & ResNet-101 & 39.6 \\
		EMANet~ & ResNet-101 & 39.9 \\
		SPYGR~ & ResNet-101 & 39.9 \\
		OCR~ & ResNet-101 & 39.5 \\
		\midrule[0.5pt]
		DANet~ & ResNet-101 & 39.7\\
		\midrule[0.5pt]
		\textbf{Our CAA} & ResNet-101 & \textbf{41.2} \\
		\bottomrule[0.5pt]
	\end{tabular}
\end{adjustbox}
\caption{Comparisons with other state-of-the-art approaches on the COCO-Stuff 10K test set. For a fair comparison, all compared methods use ResNet-101 and naive upsampling.}
\end{table}
\end{document}

cs

Ye Huang
Di Kang
Wenjing Jia
Xiangjian He
Liu Liu

"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, 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{Accuracy and computational cost of the three considered algorithms}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
\textbf{PDE} & \textbf{RandNet-Parareal} & \textbf{Parareal} & \textbf{nnGParareal} \\ \midrule
Burgers' $d = 128$ & $1.06e^{-8}$ (1h 2m) & $1.85e^{-8}$ (8h 54m) & $1.32e^{-7}$ (1h 39m) \\
Diffusion-Reaction $d = 7.2e^{2}$ & $3.56e^{-8}$ (23m) & $1.83e^{-8}$ (1h 40m) & $5.71e^{-7}$ (1h 11m) \\
Diffusion-Reaction $d = 3.3e^{3}$ & $8.56e^{-10}$ (33m) & $2.45e^{-8}$ (7h 52m) & not converged \\
Diffusion-Reaction $d = 2.5e^{4}$ & $8.09e^{-11}$ (1h 57m) & $7.43e^{-9}$ (9h 50m) & not converged \\
SWE $d = 3.1e^{4}$ & $6.75e^{-8}$ (4h 9m) & $5.15e^{-8}$ (15h 43m) & not converged \\
SWE $d = 6.1e^{4}$ & $8.54e^{-9}$ (12h 34m) & $2.84e^{-8}$ (19h 30m) & not converged \\
Brusselator 2D $d=2e^3$ & $2.09e^{-8} $ (2m) & $3.16e^{-8}$ (7m) & $3.38e^{-7}$ (7h 31m)\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Guglielmo Gattiglio
Lyudmila Grigoryeva
Massimiliano Tamborrino

"RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks" by Guglielmo Gattiglio, Lyudmila Grigoryeva, and Massimiliano Tamborrino, arXiv:2411.06225v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|r|r|r}
 \hline
 \multicolumn{5}{c}{Including component demand} \\
 \hline
 Utilization & $\lambda$ & Runtime (s) & Total costs PH & $\Delta$ (\%)\\
 \hline
 \multirow{4}{*}{50\%}
 & 0.1 & 1651.4 & 29543.8 & 7.83 \\
 & 1 & 533.4 & 27957.7 & \textbf{2.69} \\
 & 10 & 354.4 & 28052.1 & 3.03 \\
 & 100 & 377.3 & 28077.2 & 3.06 \\
 \hline
 \multirow{4}{*}{90\%}
 & 0.1 & 2023.4 & 65682.4 & 5.12 \\
 & 1 & 1070.7 & 63633.3 & 2.46\\
 & 10 & 644.6 & 63548.1 & 2.46\\
 & 100 & 851.0 & 63103.2 & \textbf{2.39} \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Majority voting consensus calculation: Evaluation of the $\lambda$-multiple for the penalty parameter $\rho$ in terms of total costs and runtime for the case including component demands on a scenario tree with $|\Omega| = 2$ and therefore $|\Phi| = 128$}
\end{table}
\end{document}

math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Relative camera pose evaluation of initial guess and our optimized results, where $R$ and $t$ represent the rotation and translation, respectively.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\hline
\toprule
& \multicolumn{2}{c}{$R\downarrow$} && \multicolumn{2}{c}{${t\downarrow}$} \\
\cmidrule{2-3} \cmidrule{5-6}
& mean ($^\circ$) & med ($^\circ$) && mean ($^\circ$) & med ($^\circ$) \\
\midrule
GeoFill (optim) & {\bf 1.588} & {\bf 1.062} && {\bf 3.688} & {\bf 3.457} \\\hline
GeoFill (init) & 7.378 & 7.807 && 11.861 & 11.096
\\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yunhan Zhao
Connelly Barnes
Yuqian Zhou
Eli Shechtman
Sohrab Amirghodsi
Charless Fowlkes

"GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding" by Yunhan Zhao, Connelly Barnes, Yuqian Zhou, Eli Shechtman, Sohrab Amirghodsi, and Charless Fowlkes, arXiv:2201.08131v2, 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{Mathematical symbols used throughout this article}
\begin{tabular}{rl}
 \hline
 \textbf{Symbol} & \textbf{Description} \\ \hline
 $ref$ & Localization system reference point \\
 $t_l$& UWB tag \\
 $a_k$ & UWB anchor \\
 L & Total number of tags\\
 K & Total number of anchors \\
 $\Delta a_k t_l$ & Euclidean distance between $a_k$ and $t_l$, shortened to $\Delta$ \\
 $ToF_{a_k t_l}$ & Time of flight between $a_k$ and $t_l$, shortened to $ToF$\\
 $CIR_{a_k t_l}$ & Channel impulse response, shortened to $CIR$\\
 $\widehat{Tof}_{a_k t_l}$ & Estimated ToF between $a_k$ and $t_l$, shortened to $\widehat{Tof}$ \\
 $e_{a_k t_l}$ & Ranging error between $a_k$ and $t_l$, shortened to $e$ \\
 $\widehat{\Delta a_k t_l}$ & Estimated range between $a_k$ and $t_l$, shortened to $\widehat{\Delta}$ \\
 $\widehat{e}$ & Estimated range error by RL agent \\
 $\Delta'$ & Corrected estimated range by RL agent \\
 $S_t$ & The state of the environment at time $t$ ($CIR$)\\
 $A_t$ & The action of the agent at time $t$ ($\widehat{e}$)\\
 $\pi$ & The agent's policy, $\pi: CIR \rightarrow \widehat{e}$ \\
 $\mu$ & The actor network of the RL agent \\
 $\theta$ & Weights of the actor network $\mu$ \\
 $Q$ & The critic network of the RL agent \\
 $\phi$ & Weights of the critic network $Q$ \\
 $y$ & Target Q-value \\
 $R_t$ & Reward received at time $t$ \\
 $\gamma$ & Discount factor, determining the weight of target critic\\
 $\dot{Q}$ & The target critic network \\
 $\dot{\phi}$ & Weights of the target critic network \\
 $\tau_{critic}$ & Soft copy factor of the critic \\
 $J$ & The sampled policy gradient \\
 $B$ & Number of samples in a batch \\
 $\epsilon$ & Exploration rate of the RL agent \\
 $\lambda$ & Decay factor of the exploration rate \\
 $\dot{\mu}$ & The target actor \\
 $\dot{\theta}$ & Weights of the target actor \\
 $\tau_{actor}$ & Soft copy factor of the actor \\
 $p_{EKF}$ & Extended Kalman Filter position \\
 $m$ & Middle position in smoothing buffer \\
 $N$ & Length of circular smoothing buffer\\
 $p_{avg,m}$ & Averaged position related to middle data $m$ in buffer \\
 $\Delta_{avg,m}$ & Resulting range from filtering and smoothing \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Dieter Coppens
Ben Van Herbruggen
Adnan Shahid
Eli De Poorter

"Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning" by Dieter Coppens, Ben Van Herbruggen, Adnan Shahid, and Eli De Poorter, arXiv:2403.19262v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 $n$ & Error $\|\cdot\|_1$ & Order $\|\cdot\|_1$ & Error
 $\|\cdot\|_{\infty}$ & Order $\|\cdot\|_{\infty}$ \\
 \hline
 20 & 8.22E$-4$ & $-$ & 2.07E$-3$ & $-$ \\
 \hline
 40 & 2.12E$-5$ & 5.28 & 8.10E$-5$ & 4.68 \\
 \hline
 80 & 6.39E$-7$ & 5.05 & 2.89E$-6$ & 4.81 \\
 \hline
 160 & 1.94E$-8$ & 5.04 & 9.03E$-8$ & 5.00 \\
 \hline
 320 & 5.94E$-10$ & 5.03 & 2.80E$-9$ & 5.01 \\
 \hline
 640 & 1.84E$-11$ & 5.01 & 8.91E$-11$ & 4.98 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Error table for problem - , Square domain, WLS-GAW.}
\end{table}
\end{document}

math

"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, 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{Error analysis for activator-inhibitor example using both proposed algorithms}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
variable&bias&MAPE&MAE&RMSE&R$^2$\\ \hline
$x_1$&0.0083&0.0603&0.0257&0.0307&0.9903\\
$x_2$&-0.0199&0.0443&0.0285&0.0357&0.9957\\
$f_1(\cdot)$&1.6785e-04&3.0247&0.0089&0.0110&0.6228\\
$f_2(\cdot)$&4.1262e-04&0.5862&0.0038&0.0047&0.9949\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

S. Syafiie
Aries Subiantoro
Vivi Andasari
Fernando Tadeo

"Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods" by S. Syafiie, Aries Subiantoro, Vivi Andasari, and Fernando Tadeo, arXiv:2501.12856v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \cline{2-5}
 & daily & six-hourly & three-hourly & 10min \\
 \hline
 daily&1&$4.20\cdot10^{-108}$&$2.04\cdot10^{-163}$& $3.60\cdot10^{-265}$ \\ \hline
 six-hourly&&1&$1.47\cdot10^{-17}$&$1.64\cdot10^{-187}$ \\ \hline
 three-hourly&&&1& $6.61\cdot10^{-184}$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{$p$-values of Kolmogorov-Smirnov test for averaged Fichtelberg wind speed distributions. All values where $p \leq 0.05$ are marked \textbf{bold} and the corresponding distributions are not considered to be significantly different.}
\end{table}
\end{document}

stat

Nina Effenberger
Nicole Ludwig
Rachel H. White

"Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts" by Nina Effenberger, Nicole Ludwig, and Rachel H. White, arXiv:2309.09540v1, 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{Location of estimated change points from the video data set with their CIs for the ``\textit{first person walks out of the lobby}". Note that the true value is $116$. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\hline
pixel & value ($\lfloor T \tilde{\tau} \rfloor$) & $70\%$ CI & $80\%$ CI & $90\%$ CI & $95\%$ CI & $99\%$ CI \\ \hline\hline
700 & 114 & (113,120) & (113,121) & (113,125) & (113,129) & (113,139) \\
702 & 115 & (114,121) & (114,123) & (114,127) & (114,131) & (114,142) \\
731 & 109 & (108,123) & (108,127) & (108,136) & (108,147) & (108,173) \\
762 & 106 & (105,138) & (105,150) & (105,172) & (105,181) & (105,181) \\
764 & 118 & (117,169) & (117,169) & (117,169) & (117,169) & (117,169) \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Sepideh Mosaferi
Abolfazl Safikhani
Peiliang Bai

"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, 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{Absolute error in price $\times 10^5$ for a continuously monitored double knock-out barrier. The reference price of 3.460714 is obtained on a grid of $I=4000$ space-steps and the same number of time-steps. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}\toprule
			$I$ & Ghost (Linear) & Ghost (3-points) & On Grid 		\\\midrule
			20 & 3801.7 & 825.9 & 652.2 \\
			40 & 899.1 & 178.9 & 125.7\\
			80 & 230.0 & 28.9 & 27.1 \\
			160 & 52.3 & 11.1 & 9.6 \\\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Inserting or Stretching Points in Finite Difference Discretizations" by Jherek Healy, arXiv:2210.02541v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|} \hline
$z$ & $a$ & supersingular primes \\ \hline
$\left[21, 21, 21, 1\right]$ & $\left(1, 19, 1\right), \left(1, 16, 4\right)$ & $p \equiv 5, 17, 20 \bmod{21}$ \\ \hline
$\left[22, 22, 11, 1\right]$ & $\left(1, 19, 2\right), \left(1, 17, 4\right),\left(1, 15, 6\right) $ & $p \equiv QNR \bmod{22}$ \\
 & $\left(1, 9, 12\right),\left(1, 1, 20\right)$ & $p \equiv QNR \bmod{22}$ \\ \hline
$\left[24, 12, 8, 1\right]$ & $\left(1, 14, 9\right), \left(1, 2, 21\right)$ & $p \equiv 23 \bmod{24}$ \\ \hline
$\left[24, 24, 6, 1\right]$ & $\left(1, 19, 4\right)$ & $p \equiv 5, 23 \bmod{24}$ \\ \hline
$\left[25, 25, 5, 1\right]$ & $\left(1, 19, 5\right),\left(1, 14, 10\right)$ & $p \neq 1 \bmod{5}$ \\ \hline
$\left[30, 6, 5, 1\right]$ & $\left(1, 5, 24\right)$ & $p \equiv 29 \bmod{30}$ \\ \hline
$\left[28, 14, 4, 1\right]$ & $\left(1, 6, 21\right)$ & $p \equiv 3, 19, 27 \bmod{28}$ \\ \hline
$\left[30, 30, 3, 1\right]$ & $\left(1, 19, 10\right)$ & $p \equiv 11, 29 \bmod{30}$ \\ \hline
$\left[33, 11, 3, 1\right]$ & $\left(1, 21, 11\right)$ & $p \equiv 2, 8, 17, 29, 32 \bmod{33}$ \\ \hline
$\left[40, 40, 2, 1\right]$ & $\left(1, 19, 20\right)$ & $p \equiv 21, 39 \bmod{40}$ \\ \hline
$\left[42, 21, 2, 1\right]$ & $\left(1, 20, 21\right)$ & $p \equiv 5, 17, 41 \bmod{42}$ \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Cyclic genus $10$}
\end{table}
\end{document}

math

"Supersingular curves via the Shimura--Taniyama method" by Jeremy Booher and Rachel Pries, arXiv:2503.04878v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|r|r|l|r|r|r}
\hline
Predictors & Coefficients & SE & pvalue & Lower CI & Upper CI & Odds Ratio\\
\hline
Intercept & 0.56 & 0.31 & 0.070 & -0.04 & 1.17 & 1.75\\
\hline
negative & 1.41 & 0.17 & <0.001 & 1.08 & 1.76 & 4.10\\
\hline
positive & -2.88 & 0.40 & <0.001 & -3.68 & -2.12 & 0.06\\
\hline
uncertainty & -0.64 & 0.20 & 0.002 & -1.06 & -0.26 & 0.53\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Bankruptcy prediction using disclosure text features" by Sridhar Ravula, arXiv:2101.00719v1, 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}{ccccc}
		\hline
		& & & & \\
		&MUSIC&DeepMUSIC&CNN&\textbf{Ours}\\
		\hline
		& & & & \\
		training [h]&-& 30.8 & 1.6 & \textbf{15.6} \\

		& & & & \\
		testing [ms]&9.4 &31.6 &1.8 &\textbf{14.9}\\
		\hline
	\end{tabular}
\end{adjustbox}
\caption{Training and testing times.}
\end{table}
\end{document}

eess

Shengheng Liu
Xingkang Li
Zihuan Mao
Peng Liu
Yongming Huang

"Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB" by Shengheng Liu, Xingkang Li, Zihuan Mao, Peng Liu, and Yongming Huang, arXiv:2501.00009v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

"On the efficiency of explicit and semi-explicit immersed boundary finite element methods for wave propagation problems" by Tim Bürchner, Lars Radtke, Philipp Kopp, Stefan Kollmannsberger, Ernst Rank, and Alexander Düster, arXiv:2501.19013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of Counties Adopting Subsidies by Year}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrrrrrrrr}
\toprule
 & 2010 & 2011 & 2012 & 2013 & 2014 & 2015 & 2016 & 2017 & 2018 & 2019 & 2020 & 2021 & 2022\\
\midrule
DC Adopters & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 2 & 11 & 6 & 2 & 13 & 0\\
Level 2 Adopters & 0 & 1 & 0 & 1 & 1 & 1 & 1 & 3 & 9 & 6 & 3 & 12 & 0\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Powering Up a Slow Charging Market: How Do Government Subsidies Affect Charging Station Supply?" by Zunian Luo, arXiv:2210.14908v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistical analysis considering Semeion dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccccc}
\cline{2-9}
 & IHS & AIWPSO & CS & FA & BSA & JADE & CoBiDE & RS \\ \hline
 IHS & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} \\
 AIWPSO & $\neq$ & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} &\cellcolor[gray]{0.8} \\
CS & $\neq$ & $\neq$ & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} \\
FA & $\neq$ & $\neq$ & $=$ & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} &\cellcolor[gray]{0.8} \\
BSA & $\neq$ & $=$ & $\neq$ & $\neq$ & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} \\
JADE & $=$ & $=$ & $\neq$ & $\neq$ & $=$ & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} &\cellcolor[gray]{0.8} \\
CoBiDE & $\neq$ & $=$ & $\neq$ & $\neq$ & $=$ & $=$ & \cellcolor[gray]{0.8} & \cellcolor[gray]{0.8} \\
RS & $\neq$ & $=$ & $\neq$ & $\neq$ & $=$ & $\neq$ & $\neq$ & \cellcolor[gray]{0.8} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Leandro Aparecido Passos
João Paulo Papa

"A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines" by Leandro Aparecido Passos and João Paulo Papa, arXiv:2101.05795v1, 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
 & L2M Acc $\uparrow$ & M2L Acc $\uparrow$ \\
 \hline
 \textbf{SongMASS} & 62.6 & 45.4 \\
 - TC & 62.1 & 44.8 \\
 - SC & 56.2 & 44.0 \\ %To be comp
 - TC - SC & 55.3 & 43.8 \\
 - TC - SC - PT & 48.3 & 37.1 \\
 - DP & 15.7 & 11.3 \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Analyses of the designs in alignment constraints.}
\end{table}
\end{document}

eess

Zhonghao Sheng
Kaitao Song
Xu Tan
Yi Ren
Wei Ye
Shikun Zhang
Tao Qin

"SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint" by Zhonghao Sheng, Kaitao Song, Xu Tan, Yi Ren, Wei Ye, Shikun Zhang, and Tao Qin, arXiv:2012.05168v1, 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{White-box attack robustness accuracy for ResNet-18 on SVHN. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
\hline
 Defense & Natural & $PGD-20$ & $CW$ & AA \\
\hline
 Standard-AT & \textbf{92.57}\tiny{ $\pm$ 0.31}
 & 55.67\tiny{ $\pm$ 0.14} & 52.92\tiny{ $\pm$ 0.16} &45.95\tiny{ $\pm$ 0.12} \\
\textbf{AT-SDI} \tiny{(Ours)}& 92.10\tiny{ $\pm$ 0.20} & \textbf{58.10}\tiny{ $\pm$ 0.09} & \textbf{58.15}\tiny{ $\pm$ 0.06} & \textbf{47.15}\tiny{ $\pm$ 0.05} \\
\hline
 \textit{TRADES} & \textbf{90.83}\tiny{ $\pm$ 0.14} & 57.27\tiny{ $\pm$ 0.08} & 53.59\tiny{ $\pm$ 0.05} &46.45\tiny{ $\pm$ 0.07} \\
\textbf{TRADES-SDI} \tiny{(Ours)}
 & 90.54\tiny{ $\pm$ 0.07}
 & \textbf{59.21}\tiny{ $\pm$ 0.10}
 & \textbf{56.39}\tiny{ $\pm$ 0.10}
 & \textbf{49.21}\tiny{ $\pm$ 0.03} &\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Olukorede Fakorede
Modeste Atsague
Jin Tian

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


stat

Junghyun Lee
Hanseul Cho
Se-Young Yun
Chulhee Yun

"Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint" by Junghyun Lee, Hanseul Cho, Se-Young Yun, and Chulhee Yun, arXiv:2310.18593v1, 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{ROI Sensitivity Analysis}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
\textbf{Scenario} & \textbf{Weather-AI ROI} & \textbf{Route-AI ROI} & \textbf{Ratio} \\
\midrule
Conservative (-20\% benefits) & 7,285\% & 1,142\% & 6.4x \\
Base case & 9,106\% & 1,427\% & 6.4x \\
Optimistic (+20\% benefits) & 10,927\% & 1,712\% & 6.4x \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity" by Tatsuru Kikuchi, arXiv:2507.17099v1, 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{\small{Statistics of the ensemble-based uncertainty quantification results obtained for transient subsurface pressure data in a multi-Gaussian domain. The True and Mean models are those depicted in Figure . The mean model is obtained from the deep ensemble models \#1 to \#5. RMSE$_{\rm data}$ denotes the RMSE in data space, ${\rm l}_1$ refers to the ${\rm l}_1$-norm and SSIM to the structural similarity index. The ${\rm l}_1$-norm is calculated in terms of $\log_{10}K_s$ (-) while the SSIM is computed in the rescaled $\left[0,1\right]$ domain.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}%
			\hline
			True model & RMSE$_{\rm data}$ (m) & ${\rm l}_1$ (m) & SSIM (-)\\
			\hline
			(a) True & 0.010 & 0 & 1 \\
			(b) Mean & 0.032 & 1858 & 0.71\\
			(d) Model \#1 & 0.023 & 1837 & 0.73 \\
			(e) Model \#2 & 0.040 & 1965 & 0.68 \\
			(f) Model \#3 & 0.025 & 1949 & 0.67 \\
			(g) Model \#4 & 0.032 & 1876 & 0.73 \\
			(h) Model \#5 & 0.028 & 2073 & 0.67 \\

			(i) True & 0.010 & 0 & 1 \\
			(j) Mean & 0.068 & 1597 & 0.73\\
			(l) Model \#1 & 0.054 & 1559 & 0.76 \\
			(m) Model \#2 & 0.046 & 1779 & 0.68 \\
			(n) Model \#3 & 0.055 & 1513 & 0.72 \\
			(o) Model \#4 & 0.059 & 1660 & 0.71 \\
			(p) Model \#5 & 0.061 & 1922 & 0.64 \\

			(q) True & 0.010 & 0 & 1 \\
			(r) Mean & 0.016 & 1571 & 0.77\\
			(t) Model \#1 & 0.016 & 1525 & 0.75 \\
			(u) Model \#2 & 0.020 & 1572 & 0.75 \\
			(v) Model \#3 & 0.015 & 1795 & 0.72 \\
			(w) Model \#4 & 0.018 & 1492 & 0.77 \\
			(x) Model \#5 & 0.016 & 1752 & 0.74 \\

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

eess

Eric Laloy
Niklas Linde
Diederik Jacques

"Approaching geoscientific inverse problems with vector-to-image domain transfer networks" by Eric Laloy, Niklas Linde, and Diederik Jacques, arXiv:1912.09954v3, 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{Mean Terminal Nodes and Signed-Rank Tests in Figure~}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\hline
\multirow{2}{*}{} & \multicolumn{2}{c}{Linear} & \multicolumn{2}{c}{Exponential} & \multicolumn{2}{c}{Constant} \\
 & ($c=0.5$) & ($c=0.8$) & ($c=2.5$) & ($c=4$) & ($c=0.4$) & ($c=0.8$) \\ \hline
\multicolumn{7}{l}{\emph{Panel A. Mean Terminal Nodes Across Elicitation Methods}} \\
Direct Response Method & 4.604 & 5.396 & 5.365 & 4.646 & 1.229 & 1.917 \\
 & (1.765) & (1.544) & (1.284) & (1.315) & (0.467) & (0.898) \\
Reduced Strategy Method & 4.906 & 5.375 & 5.385 & 4.698 & 1.406 & 2.240 \\
 & (1.627) & (1.715) & (1.278) & (1.174) & (0.811) & (0.997) \\
Full Strategy Method & 4.312 & 5.490 & 5.344 & 4.510 & 1.510 & 2.000 \\
 & (1.710) & (1.541) & (1.171) & (1.422) & (0.890) & (1.109) \\ \hline
\multicolumn{7}{l}{\emph{Panel B. Pairwise Signed-Rank Tests $p$-Values}} \\
DR vs.~RS & 0.365 & 1.000 & 1.000 & 1.000 & 0.022 & 0.005 \\
DR vs.~FS & 0.486 & 1.000 & 1.000 & 1.000 & 0.001 & 1.000 \\
FS vs.~RS & 0.005 & 1.000 & 1.000 & 0.502 & 0.374 & 0.119 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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

"Strategy Method Effects in Centipede Games: An Optimal Design Approach" by Shiang-Hung Hu, Po-Hsuan Lin, Thomas R. Palfrey, Joseph Tao-yi Wang, and Yu-Hsiang Wang, arXiv:2508.06425v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
Parameter & Value \\ \hline
Horizontal Viscosity $A_{u,v}$& 2 $m^2/s$\\
\hline
Horizontal Diffusivity $A_{T,S}$& 2 $m^2/s$\\
\hline
Bottom Drag factor $C_d^b$ & 0.003 \\
\hline
Sponge factor & 1 \\
\hline
Wind drag factor $C_d^s$ & $6.0 \cdot 10^{-5}$ \\
\hline
Bathymetry & NAVIONICS \\
\hline
Boundary conditions & Clamped \\
\hline
\end{tabular}
\end{adjustbox}
\caption{This table presents the numerical values associated with each parameter utilized in the base solution}
\end{table}
\end{document}

math

G. Garcia-Sanchez
A. M. Mancho
A. G. Ramos
J. Coca
J. A. Jimenez-Madrid

"Dynamical systems for remote validation of very high-resolution ocean models" by G. Garcia-Sanchez, A. M. Mancho, A. G. Ramos, J. Coca, and J. A. Jimenez-Madrid, arXiv:2501.16026v1, 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{Projected Effects of Executive Demographic Changes}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
Scenario & AI Adoption Rate & Productivity Gain & GDP Impact \\
\midrule
Current Demographics & 30.1\% & 2.4\% & ¥388 billion \\
+5 Years Younger CEOs & 38.7\% & 2.6\% & ¥504 billion \\
Double Female CEO Share & 33.4\% & 2.5\% & ¥419 billion \\
+50\% Technical Education & 41.2\% & 2.7\% & ¥558 billion \\
Combined Scenario & 52.3\% & 2.8\% & ¥732 billion \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises" by Tatsuru Kikuchi, arXiv:2508.03757v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Settings} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc} % centered columns (4 columns)
\hline %inserts double horizontal lines
\rowcolor{lightgray}
Parameters & Description & Value \\ % inserts table
\hline % inserts single horizontal line
\textit{N} & \# Slices & 3 \\ % inserting body of the table
\textit{F} & \# Input features & 3 \\
\textit{K} & \# Total AEs & 10, (40, 50) \\
\textit{m} & \# Selected AEs & 5, 25 \\
\textit{$D_{k,n}$} & Local dataset size & 1000 samples \\
\textit{$I$} & Attribution samples & 150 samples \\
\textit{T} & \# Max FL rounds & 30 \\
\textit{L} & \# Local epochs & 150 \\
\textit{$\eta_{\lambda}$} & Learning rate & 0.0015 \\
\textit{$S_{d}$} & Seed & 42 \\ % [1ex] adds vertical space
\hline\hline %inserts double horizontal lines
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Martino Chiarani
Swastika Roy
Christos Verikoukis
Fabrizio Granelli

"XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing" by Martino Chiarani, Swastika Roy, Christos Verikoukis, and Fabrizio Granelli, arXiv:2503.12435v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
 \hline\hline
 \textbf{Labels used} & \textbf{\texttt{\small Precision}} & \textbf{\texttt{\small Recall}} & \textbf{\texttt{\small F1-score}} & \textbf{\texttt{\small Accuracy}}\\\hline
 \textbf{Supervised} & $0.66$ & $0.89$ & $0.75$ & $0.72$\\
 \textbf{Semi-Supervised} & $0.62$ & $0.82$ &	$0.70$ & $0.66$\\
 \hline\hline
 \end{tabular}
\end{adjustbox}
\caption{Backchannel opportunity prediction: comparison of model trained using supervised manually-annotated labels with the one using labels generated by the identification module.}
\end{table}
\end{document}

cs

Vidit Jain
Maitree Leekha
Rajiv Ratn Shah
Jainendra Shukla

"Exploring Semi-Supervised Learning for Predicting Listener Backchannels" by Vidit Jain, Maitree Leekha, Rajiv Ratn Shah, and Jainendra Shukla, arXiv:2101.01899v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Prasanna Vijayaraghavan
Jeffrey Frederic Queisser
Sergio Verduzco Flores
Jun Tani

"Development of Compositionality and Generalization through Interactive Learning of Language and Action of Robots" by Prasanna Vijayaraghavan, Jeffrey Frederic Queisser, Sergio Verduzco Flores, and Jun Tani, arXiv:2403.19995v2, 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}{lrrrr}
\toprule
 & CC & FL & CRE & CI \\
\midrule
mean & 25.5 & 559.0 & 106.6 & 88.5 \\
median & 10.4 & 120.8 & 14.2 & 9.8 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Mean and median of relative prediction differences between the pooled and the FEO estimates (in \%) for GAMs.}
\end{table}
\end{document}

q-fin

Should Bank Stress Tests Be Fair?

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mappings and Detailed Description for \texttt{schoolparents}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cl}
		\toprule
		\textbf{Final Level} & \textbf{Former Level / Description} \\
		\midrule
		\textit{HauptS} & Secondary school leaving certificate I $\rightarrow$ Haupt-, Volksschulabschluss \\
		\textit{HighSDip} & Secondary school leaving certificate III $\rightarrow$ (Technical) Abitur \\
		\textit{RealS} & Secondary school leaving certificate II $\rightarrow$ Mittlere Reife, Realschulabschluss \\
		\textit{NoSCert} & No school-leaving qualification \\
		\textit{Other} & Other school-leaving qualification \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jakob Schwerter
Andrés Romero
Florian Dumpert
Markus Pauly

"Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study" by Jakob Schwerter, Andrés Romero, Florian Dumpert, and Markus Pauly, arXiv:2412.13570v1, 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{Statistical Comparison of MaxDD between High and Low PFS Groups (Threshold = 0.9)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
\textbf{Test} & \textbf{Statistic} & \textbf{p-value} \\
\midrule
T-test & $t$ = 4.122 & 0.0001 \\
Mann-Whitney U test & $U$ = 11,381.5 & 0.0001 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Hongjun Ding
Binqi Chen
Jinsheng Huang
Taian Guo
Zhengyang Mao
Guoyi Shao
Lutong Zou
Luchen Liu
Ming Zhang

"AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining" by Hongjun Ding, Binqi Chen, Jinsheng Huang, Taian Guo, Zhengyang Mao, Guoyi Shao, Lutong Zou, Luchen Liu, and Ming Zhang, arXiv:2508.13174v1, 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}{llllll}
 \toprule
 \textbf{Measure}&\textbf{Label} & \multicolumn{4}{c}{\textbf{Placement}} \\
 \cmidrule(r){3-6}
 &&\multicolumn{2}{c}{\textbf{DR}}& \multicolumn{2}{c}{\textbf{Random}} \\
 && \textbf{$\overline{B} -\overline{L}$} &\textbf{\textit{p-val.}}&\textbf{$\overline{B} -\overline{L}$} &\textbf{\textit{p-val.}}\\
 \midrule
 Time (s) &&&&\\
 & Shape & 1.59 & 0.18 &-1.58 & 0.26\\
 & Color & 0.76 & 1.0 &-0.9 & 1.0\\
 & Texture & 0.73 & 0.96 &-2.91 & \textbf{0.03} \\
 Hovered&&&&\\
 samples& Shape & 6.1 & \textbf{4e-07} &5.5 & \textbf{0.002}\\
 & Color & 1.1 & 0.89 & 2.3 & 0.52 \\
 & Texture & 1.3 & 0.33 & 2.7 & 0.13 \\
 Distance &&&&\\
 (pixels) & Shape & 494 & \textbf{0.04} & -30 & 1.0 \\
 & Color & 41 & 1.0 & -493 & 0.20 \\
 & Texture & 120 & 0.94 & -307 & 0.61 \\

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

eess

"A proposal and evaluation of new timbre visualisation methods for audio sample browsers" by Etienne Richan and Jean Rouat, arXiv:2011.15096v1, 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{List of Parameters (at set point temperature = $27^{\circ} C$)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
Parameter & Meaning & Value & Unit\\\hline
$f$ & frequency of TCL & 0.0031 & Hz \\
$\delta$ & thermostat deadband & 3 & $^{\circ}C$\\
$d$ & duty cycle of TCL & 0.452 & - \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Thermostatic control for demand response using distributed averaging and deep neural networks" by Kshitij Singh and Pratik K. Bajaria, arXiv:1912.11692v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{List of basis functions for the meta function}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl}
		\toprule
1 & Linear & $f^1(x) = x $ \\
2 & Quadratic & $f^2(x) = x^2 $ \\
3 & Cubic & $f^3(x) = x^3 $ \\
4 & Exponential & $f^4(x) = (e^x - 1) / (e - 1)$ \\
5 & Periodic & $f^5(x) = 0.5 \sin(2 \pi x) + 0.5$ \\
6 & Discontinuous & $f^6(x) = 1$ if $x \geq 0.5$ and $0$ otherwise \\
7 & Dummy & $f^7(x) = 0 $ \\
8 & Non-monotonic & $f^8(x) = 4(x-0.5)^2 $ \\
9 & Inverse & $f^9(x) = (10 -1/1.1)^{-1} (x+0.1)^{-1} - 0.1 $ \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Derivative based global sensitivity analysis and its entropic link" by Jiannan Yang, arXiv:2310.00551v3, 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{2DU and 3DFF Estimator Structure}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c||c|c|c|c||c}
 & \multicolumn{2}{c|}{2DU} & \multicolumn{2}{c||}{3DFF} & \\\cline{2-5}
 Layer&Kernel&In chan.&Kernel&In chan.&Out chan.\\\hline\hline
 1&$7\times5$&2 & $7\times5\times5$ & 2& 10 \\\hline
 2&$7\times5$&10 & $7\times5\times5$ & 10 & 10 \\\hline
 3&$7\times5$&10 & $7\times5\times5$ & 10 & 10 \\\hline
 4&$7\times5$&10 & $7\times5\times5$ & 10 & 10 \\\hline
 5&$5\times5$&20 & $5\times5\times5$ & 10 & 20 \\\hline
 6&$5\times5$&30 & $5\times5\times5$ & 20 & 5 \\\hline
 7&$3\times3$&15 & $3\times3\times3$ & 5 & 2\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ben Marinberg
Ariel Cohen
Eilam Ben-Dror
Haim Permuter

"A Study on MIMO Channel Estimation by 2D and 3D Convolutional Neural Networks" by Ben Marinberg, Ariel Cohen, Eilam Ben-Dror, and Haim Permuter, arXiv:2011.08970v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of the datasets in our experiments.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
 &\textbf{Electronics} & \textbf{Beauty}\\
\midrule
\#Users &45,224 &5,122\\
\#Items &61,687 &11,616\\
\#Reviews &744,453 &90,247\\
\#features &434&518\\
$\mid\mathcal{V}\mid$ &20,568 &7,152\\
sparsity &99.999\% & 99.998\%\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hanxiong Chen
Xu Chen
Shaoyun Shi
Yongfeng Zhang

"Generate Natural Language Explanations for Recommendation" by Hanxiong Chen, Xu Chen, Shaoyun Shi, and Yongfeng Zhang, arXiv:2101.03392v1, 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|c|c|}
 \hline
 & \textnormal{\small{$\mathcal{E}(p)=p(1-p)$}} & \textnormal{\small{$\mathcal{E}_{L}(p)=p$}} \\
\hline
\textnormal{\small{$\mathcal{C}(p)=(1-p)$}} & \textnormal{Case I)} & \textnormal{Case III)} \\
\hline
\textnormal{\small{$\mathcal{C}_{L}(p)=p(1-p)$}} & \textnormal{Case II)} & \textnormal{Levins Model} \\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Extensions of the classical Levins model. The first co\-lumn considers the rescue effect whereas the first row considers the propagule rain.}
\end{table}
\end{document}

math

"Generalizing the Levins metapopulation model to time varying colonization and extinction rates" by Gonzalo Robledo and Ramiro Bustamante, arXiv:2504.20396v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.