\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of a Softmax network under adversarial (FGSM-based) training for the CIFAR-10 dataset. Block size is $U=2$, wherever applicable.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
				\toprule
				\addlinespace
				Method & Benign & PGD & CW & BSA & RAND \tabularnewline
				\midrule
				\addlinespace
				Baseline (ReLU) & .864 & .070 & .080 & .040 & .404 \tabularnewline
				LWTA & .867 & .804 & .820 & \textbf{.870} & \textbf{.701} \tabularnewline
				LWTA \& IBP & \textbf{.869} & \textbf{.814} & \textbf{.860} & \textbf{.870} & .652
				\tabularnewline
				\midrule
				Baseline (ReLU) + FGSM & .780 & .200 & .380 & .340 & .185 \tabularnewline
				LWTA + FGSM & .820 & .812 & .810 & \textbf{.870} & .440 \tabularnewline
				LWTA \& IBP + FGSM & \textbf{.830} & \textbf{.825} & \textbf{.870} & \textbf{.870} & \textbf{.790} \tabularnewline
				\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Konstantinos P. Panousis
Sotirios Chatzis
Antonios Alexos
Sergios Theodoridis

"Local Competition and Stochasticity for Adversarial Robustness in Deep Learning" by Konstantinos P. Panousis, Sotirios Chatzis, Antonios Alexos, and Sergios Theodoridis, arXiv:2101.01121v2, 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}{lrrr}
\hline
\hline
\textbf{Sector} & \textbf{Unidirectional Cause} & \textbf{Unidirectional Effect} & \textbf{Bidirectional Causality} \\
\hline
\textbf{Pre-crash period} & & & \\
\hline
Consumer Discretionary & 1 & 0 & 0 \\
Consumer Staples & 1 & 1 & 1 \\
Energy & 0 & 3 & 0 \\
Financials & 2 & 0 & 0 \\
Healthcare & 0 & 3 & 1 \\
Industrials & 2 & 1 & 0 \\
IT & 1 & 1 & 0 \\
Materials & 0 & 2 & 1 \\
Real Estate & 3 & 0 & 0 \\
Utilities & 1 & 0 & 0 \\
Communications Services & 1 & 1 & 1 \\
\hline
\textbf{Crash period} & & & \\
\hline
Consumer Discretionary & 6 & 1 & 3 \\
Consumer Staples & 2 & 0 & 8 \\
Energy & 0 & 4 & 5 \\
Financials & 1 & 4 & 5 \\
Healthcare & 5 & 2 & 1 \\
Industrials & 2 & 2 & 5 \\
IT & 2 & 2 & 5 \\
Materials & 1 & 2 & 7 \\
Real Estate & 3 & 1 & 5 \\
Utilities & 0 & 3 & 6 \\
Communications Services & 2 & 3 & 2 \\
\hline
\textbf{Post-crash period} & & & \\
\hline
Consumer Discretionary & 1 & 2 & 0 \\
Consumer Staples & 0 & 2 & 1 \\
Energy & 3 & 0 & 1 \\
Financials & 1 & 4 & 1 \\
Healthcare & 0 & 3 & 4 \\
Industrials & 1 & 3 & 3 \\
IT & 2 & 2 & 1 \\
Materials & 4 & 0 & 2 \\
Real Estate & 2 & 2 & 1 \\
Utilities & 4 & 0 & 1 \\
Communications Services & 1 & 1 & 1 \\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Buddha Nath Sharma
Anish Rai
SR Luwang
Md. Nurujjaman
Sushovan Majhi

"Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective" by Buddha Nath Sharma, Anish Rai, SR Luwang, Md. Nurujjaman, and Sushovan Majhi, arXiv:2502.14431v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results on the COVID-19 prediction}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
Model & Accuracy & F1 \\ \midrule
End-to-End & $\textbf{0.985} \pm \textbf{0.007}$ & \textbf{0.95} \\
NS-RF & $0.973 \pm 0.009$ & 0.915 \\
NS-XGB & $0.973 \pm 0.009$ & 0.930 \\
NS-DTree & $0.973 \pm 0.009$ & 0.915 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Rishab Khincha
Soundarya Krishnan
Tirtharaj Dash
Lovekesh Vig
Ashwin Srinivasan

"Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays" by Rishab Khincha, Soundarya Krishnan, Tirtharaj Dash, Lovekesh Vig, and Ashwin Srinivasan, arXiv:2012.10787v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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

stat

Jiawen Zhang
Shun Zheng
Xumeng Wen
Xiaofang Zhou
Jiang Bian
Jia Li

"ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer" by Jiawen Zhang, Shun Zheng, Xumeng Wen, Xiaofang Zhou, Jiang Bian, and Jia Li, arXiv:2411.01842v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Garrett Honke
Irina Higgins
Nina Thigpen
Vladimir Miskovic
Katie Link
Sunny Duan
Pramod Gupta
Julia Klawohn
Greg Hajcak

"Representation learning for improved interpretability and classification accuracy of clinical factors from EEG" by Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Sunny Duan, Pramod Gupta, Julia Klawohn, and Greg Hajcak, arXiv:2010.15274v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The mean duration with the standard error (1.96 $\times$ standard deviation) taken for a function execution of a single event of a single learner in the PEEKC dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \hline
 Function & TrueLearn Model & Execution Time (s) \\
 \hline
 & Interest & 0.002016 $\pm$ 0.00478
 \\
 \texttt{fit()} & Novelty & 0.002232 $\pm$ 0.00468 \\
 & INK & 0.005000 $\pm$ 0.00712 \\
 \hline
 & Interest & 0.000084 $\pm$ 0.000020 \\
 \texttt{predict\_proba()} & Novelty & 0.000189 $\pm$ 0.000018 \\
 & INK & 0.000330 $\pm$ 0.000025 \\
 \hline
 & Interest & 0.000093 $\pm$ 0.000014 \\
 \texttt{predict()} & Novelty & 0.000203 $\pm$ 0.000027 \\
 & INK & 0.000335 $\pm$ 0.000029 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Yuxiang Qiu
Karim Djemili
Denis Elezi
Aaneel Shalman
María Pérez-Ortiz
Emine Yilmaz
John Shawe-Taylor
Sahan Bulathwela

"A Toolbox for Modelling Engagement with Educational Videos" by Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor, and Sahan Bulathwela, arXiv:2401.05424v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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

cs

Fabio Amadio
Alberto Dalla Libera
Ruggero Carli
Daniel Nikovski
Diego Romeres

"Model-based Policy Search for Partially Measurable Systems" by Fabio Amadio, Alberto Dalla Libera, Ruggero Carli, Daniel Nikovski, and Diego Romeres, arXiv:2101.08740v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\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[1pt]
		Methods&OS& MF &Aux& mIOU (\%) & FLOPs\\
		\midrule[0.5pt]
		\midrule[0.5pt]
		ResNet& 16 & & - & - & 59.85G \\
		-101& 8 & & - & - & 190.70G \\
		\midrule
		DANet& 8 & & & & +101.25G\\
		 & 8 & \checkmark & \checkmark &52.60 & - \\
		\midrule
		Axial & 16 & & & 50.27($\pm$0.2)& +8.85G\\
		Attention & 16 & \checkmark & & 52.01($\pm$0.2) & -\\
		 & 8 & & & 51.24($\pm$0.2) & +34.33G\\
		 & 8 & \checkmark & &52.51($\pm$0.2) & - \\
		\midrule
		Our & 16 & & & 51.06($\pm$0.2)& +8.85G\\
		CAA & 16 & \checkmark & &53.09($\pm$0.3) & - \\
		 & 8 & & & 52.73($\pm$0.1) & +34.33G\\
		 & 8 & \checkmark & & 54.05($\pm$0.1) & - \\
		\midrule
		Our & 16 & & \checkmark & 51.80($\pm$0.2)& +8.85G\\
		 CAA & 16 &\checkmark& \checkmark & 53.52($\pm$0.2)& - \\
		 + & 8 & & \checkmark& 53.48($\pm$0.3) & +34.33G\\
		 Aux loss & 8 & \checkmark &\checkmark &54.65($\pm$0.4) & - \\
		\bottomrule[1pt]
	\end{tabular}
\end{adjustbox}
\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{Three groups of operators by stage of expertise}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr} \toprule
 Group & Years of Experience & Number of Operators & Number of Entries\\ \midrule
 (1) Novice operators & $\leq 1$ & 54 & 390 \\
 (2) Intermediate operators & $1-4$ & 54 & 876 \\
 (3) Expert operators & $\geq 4$ & 26 & 822 \\ \midrule
 All operators & $0 - 14$ & 67 & 2088 \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Roussel Rahman
Jane Shtalenkova
Aashwin Ananda Mishra
Wan-Lin Hu

"Network Models of Expertise in the Complex Task of Operating Particle Accelerators" by Roussel Rahman, Jane Shtalenkova, Aashwin Ananda Mishra, and Wan-Lin Hu, arXiv:2412.17988v1, 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|}
			\hline
			\multicolumn{6}{|c|}{Numerical values for $\log_{10}\det(\Psi_{n})$ for several values of $n$}\\
			\hline
			$n$ &\hspace*{0.3cm}2\hspace*{0.3cm} &\hspace*{0.3cm}3\hspace*{0.3cm} &\hspace*{0.3cm}4\hspace*{0.3cm} &\hspace*{0.3cm}5\hspace*{0.3cm} &\hspace*{0.3cm}6\hspace*{0.3cm} \\
			\hline $\log_{10}\det(\Psi_{n})$&\hspace*{0.3cm}-4.97\hspace*{0.3cm} &\hspace*{0.3cm}-13.4\hspace*{0.3cm} &\hspace*{0.3cm}-33.9\hspace*{0.3cm} &\hspace*{0.3cm}-82.03\hspace*{0.3cm} &\hspace*{0.3cm}-192.81\hspace*{0.3cm} \\ \hline
		\end{tabular}
\end{adjustbox}
\caption{Numerical values for $\log_{10}\det(\Psi_{n})$ for several values of $n$. Already for small values of $n$, the determinant of $\Psi_{n}$ is extremely small, which makes the problem ill-posed from a numerical point of view.}
\end{table}
\end{document}

q-fin

"Robust Faber--Schauder approximation based on discrete observations of an antiderivative" by Xiyue Han and Alexander Schied, arXiv:2211.11907v5, 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{An Example of a Table}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c|}
\hline
One & Two\\
\hline
Three & Four\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Wenjie Mei
Xiaorui Wang
Yanrong Lu
Ke Yu
Shihua Li

"Learning and Current Prediction of PMSM Drive via Differential Neural Networks" by Wenjie Mei, Xiaorui Wang, Yanrong Lu, Ke Yu, and Shihua Li, arXiv:2412.09028v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The MSD, MAD, and ABWD for DJI stock index dataset. The best bootstrapping technique with the least MSD, MAD, and ABWD is marked in bold. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
			\hline
			& Bootstrap Methods & MSD & MAD & ABWD\\
			\hline

			\multirow{3}{*}{Open Price}&
			$\quad$NOBB$\quad$ & $423577.78$ & $521.95$ & $102842.9$ \\
			& $\quad$MBB$\quad$ & $122350.40$ & $269.28$ & $70829.4$ \\
			& $\quad$LBB$\quad$ & $\textbf{69056.29}$ & $\textbf{199.93}$ & $\textbf{62479.8}$ \\

			\hline \multirow{3}{*}{High Price} & $\quad$NOBB$\quad$ & $400258.80$ & $511.40$ & $102246.5$ \\
			& $\quad$MBB$\quad$ & $142436.70$ & $294.90$ & $77182.3$ \\
			& $\quad$LBB$\quad$ & $\textbf{99061.31}$ & $\textbf{244.62}$ & $\textbf{65184.8}$ \\

			\hline\multirow{3}{*}{Low Price}&
			$\quad$NOBB$\quad$ & $520594.69$ & $580.24$ & $108897.8 $ \\
			& $\quad$MBB$\quad$ & $199595.09$ & $341.32$ & $87625.0$ \\
			& $\quad$LBB$\quad$ & $\textbf{136009.59}$ & $\textbf{301.05}$ & $\textbf{67656.0}$ \\

			\hline \multirow{3}{*}{Closing Price} & $\quad$NOBB$\quad$ & $562463.66$ & $579.19$ & $104487.9$ \\
			& $\quad$MBB$\quad$ & $283476.60$ & $431.77$ & $70167.3$ \\
			& $\quad$LBB$\quad$ & $\textbf{222248.98}$ & $\textbf{381.74}$ & $\textbf{64935.7}$ \\

			\hline \multirow{3}{*}{Volume}
			& $\quad$NOBB$\quad$ & $8.56e+15\quad$ & $6.63e+07\quad$ & $7.99e+09\quad$ \\
			& $\quad$MBB$\quad$ & $6.62e+15\quad$ & $5.58e+07\quad$ & $7.47e+09\quad$ \\
			& $\quad$LBB$\quad$ & $\textbf{6.27e+15}\quad$ & $\textbf{5.52e+07}\quad$ & $\textbf{6.95e+09}\quad$ \\

			\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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative results of the baselines, the optimized version of the supervised CNN, and the proposed algorithm}
\begin{tabular}{cccc}
\hline
Method & MAE (mm) & MAE (mm) \\
 & All Samples & NLOS \\ \hline
Uncorrected UWB & 162 & 194 \\
Supervised CNN & 124 & 153 \\
Optimized Supervised CNN & 112 & 137 \\
\textbf{Proposed RL Algorithm} & 112 & 128 \\ \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{booktabs}
\usepackage{amsfonts}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 \multirow{2}{*}{Parameter} & Agent & Tutorial & Co-play \\
 & training & episodes & episodes \\
 \midrule
 Number of & \multirow{2}{*}{$2$} & \multirow{2}{*}{$1$} & \multirow{2}{*}{$2$*} \\
 players ($n$) & & & \\
 Room width ($w$) & $\sim\mathcal{U}\{10, 15\}$ & $5$ & $11$ \\
 Room depth ($d$) & $\sim\mathcal{U}\{10, 15\}$ & $7$ & $11$ \\
 Coin spawn & \multirow{2}{*}{$0.0005$} & \multirow{2}{*}{$0.0015$} & \multirow{2}{*}{$0.0005$} \\
 probability ($P$) & & & \\
 Episode length ($T$) & $500$ & $1500$** & $300$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Parameters for the Coins task in agent training and human-agent cooperation studies. *In Study 3, co-play episodes involve either ${n = 1}$ or ${n = 2}$ players, depending on whether the participant chose to play alone or with the co-player from the prior episode, respectively. **Tutorial episodes terminated after participants collected five coins or after ${T = 1500}$ steps (5 minutes).}
\end{table}
\end{document}

cs

Kevin R. McKee
Xuechunzi Bai
Susan T. Fiske

"Warmth and competence in human-agent cooperation" by Kevin R. McKee, Xuechunzi Bai, and Susan T. Fiske, arXiv:2201.13448v4, 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{The results of MAP comparison on NUS-WIDE and MIRFlickr.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|}
\hline
Datasets & Methods & I2T & T2I & Average \\
\hline
\multirow{6}{*}{NUS-WIDE} & CCA & 0.3099 & 0.3103 & 0.3101 \\
 & KCCA & 0.3088 & 0.3174 & 0.3096 \\
 & ml-CCA & 0.2787 & 0.2801 & 0.2794 \\
 & KDM & 0.3247 & 0.3118 & 0.3183 \\
 & CKD & 0.4149 & 0.4211 & 0.4180 \\
 & DS²L & \textbf{0.4488} & \textbf{0.4514} & \textbf{0.4501} \\
\hline
\multirow{6}{*}{MIRFlickr} & CCA & 0.5466 & 0.5477 & 0.5472 \\
 & KCCA & 0.5521 & 0.5529 & 0.5525 \\
 & ml-CCA & 0.5309 & 0.5302 & 0.5306 \\
 & KDM & 0.5951 & 0.5823 & 0.5887 \\
 & CKD & 0.6103 & 0.5933 & 0.6018 \\
 & DS²L & \textbf{0.6288} & \textbf{0.6093} & \textbf{0.6191} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Haoming Zhang
Xiao-Jun Wu
Tianyang Xu
Donglin Zhang

"Discriminative Supervised Subspace Learning for Cross-modal Retrieval" by Haoming Zhang, Xiao-Jun Wu, Tianyang Xu, and Donglin Zhang, arXiv:2201.11843v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance and time comparisons of all methods}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|}
\hline
Algorithm & Training & Test & Test \\
& time (min) & sum-rate & time (msec) \\
\hline \hline
WMMSE~ & - & 82.94 & 16 \\
Tr-WMMSE & - & 76.49 & 1.0 \\
MLP~ & 0.5 & 78.17 & 3.2 \\
REGNN~ & 15 & 57.92 & 2.5 \\
IGCNet~ & 5 & 55.30 & 3.0 \\
UWMMSE & 15 & \textbf{83.21} & \textbf{2.0} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Arindam Chowdhury
Gunjan Verma
Chirag Rao
Ananthram Swami
Santiago Segarra

"Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation" by Arindam Chowdhury, Gunjan Verma, Chirag Rao, Ananthram Swami, and Santiago Segarra, arXiv:2009.10812v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface" by Oktay Karakuş, Igor Rizaev, and Alin Achim, arXiv:2012.06657v1, 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}{r|c|c|c|c}
 \hline
 & \multicolumn{2}{c|}{Kinetics 400~}
 & \multicolumn{2}{c}{Something V1~}\\
 Models & Top-1 & Top-5 & Top-1 & Top-5 \\
 \hline
 TSN (R-50)~ & 70.6\% & 89.2\% & 20.5\% & 47.5\% \\
 2-Stream I3D~ & 71.6\% & 90.0\% & 41.6\% & 72.2\% \\
 TSM (R-50)~ & 74.1\% & 91.2\% & 47.3\% & 76.2\% \\
 TPN (TSM)~ & 78.9\% & 93.9\% & 50.2\% & 75.8\% \\
 \hline
 \hline Skeleton-based
 & \multicolumn{2}{c|}{Kinetics 400~}
 & \multicolumn{2}{c}{NTU-RGB+D~}\\
 Models & Top-1 & Top-5 & X-Sub & X-View \\
 \hline
 Deep LSTM~ & 16.4\% & 35.3\% & 62.9\% & 70.3\% \\
 ST-GCN~ & 30.7\% & 52.8\% & 81.5\% & 88.3\% \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jihoon Chung
Cheng-hsin Wuu
Hsuan-ru Yang
Yu-Wing Tai
Chi-Keung Tang

"HAA500: Human-Centric Atomic Action Dataset with Curated Videos" by Jihoon Chung, Cheng-hsin Wuu, Hsuan-ru Yang, Yu-Wing Tai, and Chi-Keung Tang, arXiv:2009.05224v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Values of Simulation hyperparameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 \cmidrule(r){1-2}
 parameter name & value \\
 \midrule
 point mass & $1.0$\\
 viscous drag & $0.1$ \\
 gravity & $110$\\
 \cmidrule(r){1-2}
 contact stiffness coefficient & $2.1 \cdot 10^7$\\
 collision penetration depth additive & $5 \cdot 10^{-3}$\\
 coefficient of friction & $0.1$\\
 frictional penalty factor & $0.5$\\
 friction multiplier & $2.4 \cdot 10^3$\\
 \cmidrule(r){1-2}
 rigid main spring const & $\frac{3 \cdot 10^8}{3.5}$\\
 rigid structural spring const & $\frac{3 \cdot 10^8}{7}$\\
 soft main spring const & $\frac{3 \cdot 10^8}{5}$\\
 soft structural spring const & $\frac{3 \cdot 10^8}{10}$\\
 actuator main spring const & $\frac{3 \cdot 10^8}{6}$\\
 actuator structural spring const & $\frac{3 \cdot 10^8}{24}$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jagdeep Singh Bhatia
Holly Jackson
Yunsheng Tian
Jie Xu
Wojciech Matusik

"Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots" by Jagdeep Singh Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, and Wojciech Matusik, arXiv:2201.09863v1, 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}{lrr}
\toprule
\textbf{Model} & \textbf{\#Param.} & \textbf{FLD+} \\ \midrule
Stable Diffusion v1.5~ & 0.9 B & $3.3277 \pm 0.0005$\\
Stable Diffusion XL~ & 3.5 B & $3.2167 \pm 0.0002$ \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{When comparing the performance of text-to-image diffusion models, FLD+ effectively distinguishes the images generated by the larger Stable Diffusion XL model as higher quality than those produced by the smaller Stable Diffusion V1.5 model (mean $\pm$ std error).}
\end{table}
\end{document}

eess

"FLD+: Data-efficient Evaluation Metric for Generative Models" by Pranav Jeevan, Neeraj Nixon, and Amit Sethi, arXiv:2411.15584v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"An Auto-Stabilized Weak Galerkin Method for Elasticity Interface Problems on Nonconvex Meshes" by Chunmei Wang and Shangyou Zhang, arXiv:2501.13822v1, licensed under CC0 1.0. 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{Quantitative comparison between our model and the model by Ozcelik et al. in terms of reconstruction (after the full pipeline) performance on the test set, in various configurations, according to the type of architecture used for the fMRI-VDVAE latent variables regression and the number of the VDVAE latent variables used. These statistics were computed over subject \#1 only.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|cccc|cccc}
 \toprule
 \multicolumn{2}{c}{\textbf{Architecture}} & \multicolumn{4}{c}{\textbf{Low-level metrics}} & \multicolumn{4}{c}{\textbf{High-level metrics (eq. )}}\\
 \midrule
 & Latents & SSIM$\uparrow$ & PixCorr$\uparrow$ & AlexNet(2)$\uparrow$ & AlexNet(5)$\uparrow$ & InceptionV3$\uparrow$ & CLIP$\uparrow$ & EfficientNet-B$\downarrow$ & SwAV-ResNet50$\downarrow$ \\
 \midrule
 Ridge & 15 & 0.358 & 0.286 & 0.935 & 0.962 & 0.881 & 0.925 & 0.772 & \textbf{0.413} \\
 Ridge & 31 & 0.367 & \textbf{0.305} & \textbf{0.967} & \textbf{0.974} & 0.878 & 0.925 & \textbf{0.768} & 0.415 \\
 Ours (CLIP) & 15 & 0.357 & 0.230 & 0.870 & 0.922 & 0.860 & 0.916 & 0.778 & 0.437 \\
 Ours & 15 & 0.370 & 0.242 & 0.904 & 0.942 & 0.868 & 0.924 & 0.779 & 0.421 \\
 Ours & 31 & \textbf{0.374} & 0.207 & 0.880 & 0.929 & \textbf{0.890} & \textbf{0.958} & \textbf{0.768} & 0.416 \\
 \midrule
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Lorenzo Veronese
Andrea Moglia
Luca Mainardi
Pietro Cerveri

"Optimized two-stage AI-based Neural Decoding for Enhanced Visual Stimulus Reconstruction from fMRI Data" by Lorenzo Veronese, Andrea Moglia, Luca Mainardi, and Pietro Cerveri, arXiv:2412.13237v1, 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{CES Share Parameters and Hicks-Neutral Productivity Innovation Standard Deviation Estimates}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccccc}
 \toprule
 & $\alpha_L$ & $\alpha_M$ & $\alpha_K$ & $\alpha_B$ & $\alpha_H$ & $\alpha_D$ & $\alpha_C$ & $\sigma_H$\\
 \midrule
 Estimate & 0.064 & 0.680 & 0.256 & 0.551 & 0.449 & 0.693 & 0.307 & 0.725\\
 SE & (0.026) & (0.281) & (0.307) & (-) & (-) & (-) & (-) & (0.121)\\
 \bottomrule

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

q-fin

"Do Temporary Workers Face Higher Wage Markdowns? Evidence from India's Automotive Sector" by Davide Luparello, arXiv:2507.08222v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing" by Cong William Lin and Wu Zhu, arXiv:2504.13629v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{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
 No. of elements & Proposed Method & Nurkanovic\\
 \hline
 10 & 26 & 470 \\
 20 & 56 & 759 \\
 50 & 65 & 750 \\
 100 & 303 & 1162\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Number of iterations in IPOPT}
\end{table}
\end{document}

math

Saif R. Kazi
Kexin Wang
Lorenz T. Biegler

"On Optimal Control of Hybrid Dynamical Systems using Complementarity Constraints" by Saif R. Kazi, Kexin Wang, and Lorenz T. Biegler, arXiv:2503.03879v2, 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{No ground truth vs 30\% ground truths vs approximate ground truths}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|}
\hline
 & no gt &30\% gt & appro gt \\ \hline
MovieLens & 0.701&\textbf{0.757} & 0.681 \\ \hline
Amazon & 0.820 & \textbf{0.850} & 0.823 \\ \hline
Echonest & 0.934&\textbf{0.951} & 0.868 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Chao Feng
Defu Lian
Xiting Wang
Zheng liu
Xing Xie
Enhong Chen

"Reinforcement Routing on Proximity Graph for Efficient Recommendation" by Chao Feng, Defu Lian, Xiting Wang, Zheng liu, Xing Xie, and Enhong Chen, arXiv:2201.09290v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of simulation parameters.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
		\hline
		\multicolumn{5}{|c|}{\textbf{Parameters for Fig.~~,~, ~, ~, ~\&~}} \\
		\hline
		$\eta_t= \frac{1}{\sqrt{T}}$ & $\mu_t = \frac{1}{\sqrt{T}}$ & $\nu_t = \frac{1}{\sqrt{T}}$ &
		$\iota = \frac{1}{\sqrt{T}}$ & $ \theta^t = T$ \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Krishnendu S. Tharakan
Hayssam Dahrouj
Nour Kouzayha
Hesham ElSawy
Tareq Y. Al-Naffouri

"Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching" by Krishnendu S. Tharakan, Hayssam Dahrouj, Nour Kouzayha, Hesham ElSawy, and Tareq Y. Al-Naffouri, arXiv:2501.11745v2, 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}{lccccccc}
 \toprule
 Model & T & Accuracy & F$_1$(0) & F$_1$(1) & Support 0 & Support 1 & Run time \\
 \midrule
 XGBoost & 1 & 0.5732 & 0.5757 & 0.5707 & 73\,953 & 74\,387 & 1 m 36 s \\
 XGBoost & 10 & 0.5949 & 0.5989 & 0.5909 & 73\,951 & 74\,387 & 7 m 04 s \\
 Logistic Reg. & 1 & 0.5551 & 0.5442 & 0.5656 & 73\,953 & 74\,387 & 1 m 11 s \\
 Logistic Reg. & 10 & 0.5716 & 0.5654 & 0.5777 & 73\,951 & 74\,387 & 9 m 13 s \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Comparison of model performance and runtimes (SG filter) across sequence lengths (Run time: time to train the model on 1 million data points on M2 chip MacBook}
\end{table}
\end{document}

q-fin

"Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layer" by Haochuan Wang, arXiv:2506.05764v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \hline
 \textbf{Method} & \textbf{Training time [s]} & \textbf{Execution time [s]} \\
 \hline
 \textbf{NB} & \phantom{00}0.004 & 0.008 \\
 \textbf{KNN} & \phantom{00}0.004 & 0.156 \\
 \textbf{Lexicon} & \phantom{00}0.246 & 0.102 \\
 \textbf{LR} & \phantom{00}0.255 & 0.004 \\
 \textbf{SVM} & \phantom{00}0.485 & 0.136 \\
 \textbf{RF} & \phantom{00}0.897 & 0.160 \\
 \textbf{CNN} & 119\phantom{.000} & 0.200 \\
 \textbf{FCNN} & 133\phantom{.000} & 0.319 \\
 \textbf{LSTM} & 185\phantom{.000} & 1.050 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Karina Shyrokykh
Maksym Girnyk
Lisa Dellmuth

"Short text classification with machine learning in the social sciences: The case of climate change on Twitter" by Karina Shyrokykh, Maksym Girnyk, and Lisa Dellmuth, arXiv:2310.04452v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Fei Wu
Danning Sui
Thomas Thiery
Mallesh Pai

"Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest" by Fei Wu, Danning Sui, Thomas Thiery, and Mallesh Pai, arXiv:2507.13023v3, 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}{llll}
			\hline

			\multicolumn{4}{c}{$\Delta\ln(\text{New Businesses Registered}_{it})$} \\

			\hline
			& (1) & (2) & (3) \\

			$\Delta\ln(y_{it}) $ & 1.994*** & 2.417*** & 2.605*** \\

			& (0.319)&(0.391) & (0.394)\\

			$\Delta\ln(y_{it})*y_i$ & & -0.024* & -0.035** \\

			& & (0.013)& (0.014)\\

			$\Delta\ln(\lambda_{it})$ & & & 0.226** \\

			& & &(0.104)\\

			$\Delta\ln(\lambda_{it})*y_i$ & & & -0.012** \\

			& & &(0.005)\\

			Country FE & Yes & Yes & Yes \\

			Year FE & Yes & Yes & Yes \\

			Observations & 810 & 810 & 810 \\

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

q-fin

Markus Brueckner
Tomoo Kikuchi
George Vachadze

"Transitional Dynamics of the Saving Rate and Economic Growth" by Markus Brueckner, Tomoo Kikuchi, and George Vachadze, arXiv:2012.15435v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 & Success \% & Distance \% \\
 \hline
 Without Setup Policies & 1.9 & 36.3 \\
 With Setup Policies & \textbf{71.2} & \textbf{80.2} \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Success rate and percentage of the total terrain length travelled from 1000 episodes of all 5 terrain types, randomly shuffled each episode.}
\end{table}
\end{document}

cs

Brendan Tidd
Nicolas Hudson
Akansel Cosgun
Jurgen Leitner

"Learning Setup Policies: Reliable Transition Between Locomotion Behaviours" by Brendan Tidd, Nicolas Hudson, Akansel Cosgun, and Jurgen Leitner, arXiv:2101.09391v2, 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{Average perceived noise (on a scale of 0-10) in speech samples with and without acoustic nonce collected in the three environments.}
\begin{tabular}{lcccc}
 \toprule[1.5pt]
 Location & \parbox[t]{1.7cm}{w/o Nonce} & \parbox[t]{1.7cm}{w/ Nonce} & t-test, $\alpha=0.05$ & Statistically Significant \\
 \midrule
 Conference Room & 3.8 & 4.8 & $t =4.2022, p =0.0001489$ & Yes\\
 Dining Hall & 5.7 & 6.1 & $t=1.4595, p =0.1524$ & No\\
 Gas Station (Car) & 5 & 5.6 & $t=3.0697, p=0.00389$ & Yes\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Yangyong Zhang
Maliheh Shirvanian
Sunpreet S. Arora
Jianwei Huang
Guofei Gu

"Practical Speech Re-use Prevention in Voice-driven Services" by Yangyong Zhang, Maliheh Shirvanian, Sunpreet S. Arora, Jianwei Huang, and Guofei Gu, arXiv:2101.04773v1, 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{System comparison on DNS-2020 synthetic test set.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
			\toprule
			Model & PESQ & STOI(\%) & SI-SDR(dB) \\
			\midrule
			Noisy & 1.582 & 91.5 & 9.071 \\
			DCRN & 2.615 & 95.7 & 17.275 \\
			DCCRN & 2.711 & 96.0 & 17.967 \\
			\midrule
			CEM & 2.753 & 96.1 & 17.539 \\
			\ +GHCM & 2.812 & 96.3 & 17.841 \\
			\ \ \ \ \ +HM & \textbf{2.883} & \textbf{96.5} & \textbf{18.144} \\
			\bottomrule

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

cs

Tianrui Wang
Weibin Zhu
Yingying Gao
Junlan Feng
Shilei Zhang

"HGCN: Harmonic gated compensation network for speech enhancement" by Tianrui Wang, Weibin Zhu, Yingying Gao, Junlan Feng, and Shilei Zhang, arXiv:2201.12755v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}\hline
 & $\mathcal{L}^2$-like norm & $\mathcal{L^{\infty}}$-like norm \\\hline
 asymptotic expansion up to 1st-order & 1.9521 & 0.0154 \\\hline
 asymptotic expansion up to 2nd-order & 0.4636 & 0.0037 \\\hline
 asymptotic expansion up to 3rd-order & 0.2471 & 0.0019 \\\hline
 asymptotic expansion up to 4th-order & 0.2113 & 0.0017 \\\hline
 \end{tabular}
\end{table}
\end{document}

cs

Haruki Tadano
Yoshihiko Susuki
Atsushi Ishigame

"Asymptotic Assessment of Distribution Voltage Profile Using a Nonlinear ODE Model" by Haruki Tadano, Yoshihiko Susuki, and Atsushi Ishigame, arXiv:2101.09945v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Sourav Medya
Mohammad Rasoolinejad
Yang Yang
Brian Uzzi

"An Exploratory Study of Stock Price Movements from Earnings Calls" by Sourav Medya, Mohammad Rasoolinejad, Yang Yang, and Brian Uzzi, arXiv:2203.12460v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
\textbf{Period} & \textbf{2.50\%} & \textbf{5.00\%} & \textbf{10.00\%} \\ \hline
1397-1420 & 72.66\% & 63.90\% & 51.11\% \\
1420-1435 & 91.13\% & 64.85\% & 40.69\% \\
1435-1450 & 99.82\% & 63.09\% & 36.11\%
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Cancellation of principal in banking: Four radical ideas emerge from deep examination of double entry bookkeeping in banking" by Brian P. Hanley, arXiv:2010.10703v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrrrrrrrrr}
 \hline
 $N$ & shape & $t_{\text{mean}}$ & $t_{\text{median}}$ & \# of knots& $t_{\text{mean}}^{lcd}$ & $t_{\text{median}}^{lcd}$ & $L_1(\hat{F}_{lc}$) & $L_1(\hat{F}_{un}$) & $L_1(\hat{F}_{lcd})$\\
 \hline
 1000& 0.3 & 0.17 & 0.11 & 8.38 (1.38) & 0.08 & 0.08 & 1.25 & 1.88 & 6.71\\
 & 1& 0.18 & 0.13 & 9.01 (1.62) & 13.8 & 13.4 & 1.56 & 2.49 & 1.20 \\
 &2 & 0.14 & 0.12 & 8.77 (1.36) & 12.6 & 12.5 & 1.44 & 2.38 & 1.24\\
 10000 & 0.3 & 2.81 & 1.97 & 13.95 (1.76) &3.12& 3.09 & 0.45 & 0.80 & 6.69 \\
& 1 & 3.12 & 2.10 & 14.90 (2.01)& 83.6 & 40.3 & 0.57 & 1.08 & 0.90\\
 &2 & 2.54 & 2.14 & 15.29 (1.87) & 1044 & 941 & 0.56 & 1.05 & 0.49\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Computational Performance of the Proposed Algorithm for the Log-concave MLE. The empirical absolute bias $L_1(\hat{F}_{lc})$, $L_1(\hat{F}_{un})$ and $L_1(\hat{F}_{lcd})$ are reported in the units of $10^2$. The underlying distribution of the event time is Weibull, with the shape parameter specified in the table and scale parameter equals $1$.}
\end{table}
\end{document}

stat

"Nonparametric Estimation for a Log-concave Distribution Function with Interval-censored Data" by Chi Wing Chu, Hok Kan Ling, and Chaoyu Yuan, arXiv:2411.19878v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Juan Zhang
Junyue Luo
Fangfang Zhang
Xiaoqiang Yue

"Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs" by Juan Zhang, Junyue Luo, Fangfang Zhang, and Xiaoqiang Yue, arXiv:2504.14930v1, 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}{cc} % centered columns (4 columns)
			\hline %inserts double horizontal lines
		 Measures & ~~~~~~~~~~~~~~~~~Values \\
			\hline

		$CCRI(\textbf{Y},\textbf{X})$& ~~~~~~~~~~~~~~~~~2.717615\\
	$CCRI(\textbf{Z},\textbf{X})$& ~~~~~~~~~~~~~~~~~4.328715\\
	$CCRI(\textbf{W},\textbf{X})$& ~~~~~~~~~~~~~~~~~2.767670\\
			\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Multivariate Rényi inaccuracy measures based on copulas: properties and application" by Shital Saha and Suchandan Kayal, arXiv:2502.17215v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
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\usepackage{adjustbox}
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\begin{document}
\begin{table}
\centering
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\cellcolor[HTML]{EFEFEF}{} &
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\cellcolor[HTML]{EFEFEF}{\textbf{QABC}} &
\cellcolor[HTML]{EFEFEF}{\textbf{OABC}} &
\cellcolor[HTML]{EFEFEF}{\textbf{AIWPSO}} &
\cellcolor[HTML]{EFEFEF}{\textbf{QAIWPSO}} &
\cellcolor[HTML]{EFEFEF}{\textbf{OAIWPSO}} &
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\cellcolor[HTML]{EFEFEF}{\textbf{CS}} &
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\cellcolor[HTML]{EFEFEF}{\textbf{PSO}} &
\cellcolor[HTML]{EFEFEF}{\textbf{QPSO}} &
\cellcolor[HTML]{EFEFEF}{\textbf{OPSO}}
\\ \hline
Arcene & 43.48s & 42.11s & 43.06s & 21.59s & 23.08s & 23.45s & 21.92s & 23.97s & 25.78s & \textbf{7.81s} & 10.06s & 11.68s & 22.90s & 27.62s & 34.57s & 22.59s & 27.62s & 31.83s & 22.21s & 22.70s & 23.42s
\\ \hline
BASEHOCK & 1114.33s & 1105.60s & 1108.30s & 563.38s & 563.89s & 566.88s & 568.81s & 571.82s & 571.79s & \textbf{189.89s} & 203.89s & 203.53s & 545.73s & 543.84s & 547.47s & 564.81s & 565.72s & 567.54s & 556.54s & 558.63s & 561.71s
\\ \hline
COIL20 & 179.37s & 181.11s & 181.33s & 90.92s & 93.17s & 93.01s & 90.47s & 92.77s & 94.90s & \textbf{30.19s} & 33.10s & 33.57s & 89.41s & 88.91s & 90.34s & 92.62s & 92.79s & 94.25s & 90.70s & 91.86s & 91.87s
\\ \hline
DNA & 240.43s & 241.94s & 242.54s & 171.38s & 171.86s & 171.85s & 124.60s & 123.88s & 123.78s & \textbf{45.01s} & 46.74s & 46.34s & 121.66s & 122.14s & 122.58s & 123.84s & 125.02s & 124.48s & 171.20s & 171.06s & 169.50s
\\ \hline
Isolet & 135.52s & 138.87s & 138.42s & 69.96s & 70.40s & 70.60s & 69.60s & 71.11s & 71.01s & \textbf{21.85s} & 25.73s & 26.05s & 68.44s & 68.02s & 69.73s & 70.96s & 71.09s & 70.74s & 68.78s & 69.86s & 69.86s
\\ \hline
Lung & 15.19s & 15.07s & 15.50s & 7.84s & 8.12s & 8.42s & 7.73s & 8.60s & 9.01s & \textbf{2.76s} & 3.61s & 4.17s & 7.98s & 9.61s & 11.74s & 7.98s & 9.87s & 11.24s & 7.71s & 7.78s & 8.05s
\\ \hline
Madelon & 704.37s & 698.42s & 698.56s & 354.86s & 356.56s & 357.16s & 358.11s & 359.54s & 361.50s & \textbf{120.80s} & 127.19s & 128.23s & 343.79s & 341.46s & 343.17s & 355.31s & 353.68s & 354.26s & 351.40s & 352.89s & 352.85s
\\ \hline
MPEG7-BAS & 38.05s & 35.59s & 37.25s & 25.01s & 25.71s & 25.25s & 18.78s & 18.46s & 18.67s & \textbf{6.61s} & 7.29s & 7.39s & 18.96s & 19.36s & 19.17s & 19.51s & 18.89s & 19.64s & 25.40s & 25.16s & 25.06s
\\ \hline
MPEG7-Fourier & 27.92s & 25.70s & 25.78s & 19.29s & 19.54s & 19.46s & 13.74s & 13.87s & 13.97s & \textbf{4.68s} & 5.00s & 5.02s & 13.31s & 13.33s & 13.25s & 13.37s & 13.17s & 13.26s & 18.91s & 18.69s & 18.87s
\\ \hline
Mushrooms & 791.66s & 785.72s & 785.15s & 600.75s & 600.03s & 601.13s & 405.80s & 408.04s & 409.10s & \textbf{143.20s} & 151.30s & 152.22s & 398.83s & 397.73s & 398.85s & 404.05s & 403.06s & 404.90s & 597.34s & 596.48s & 595.58s
\\ \hline
NTL-Commercial & 272.72s & 271.35s & 270.80s & 139.44s & 138.38s & 138.36s & 139.15s & 138.73s & 138.41s & 50.04s & 49.76s & \textbf{49.56s} & 132.70s & 133.49s & 133.30s & 138.46s & 137.85s & 138.23s & 138.58s & 137.60s & 137.83s
\\ \hline
NTL-Industrial & 113.70s & 111.91s & 111.74s & 57.60s & 56.71s & 56.55s & 57.79s & 57.30s & 57.27s & 20.58s & 20.62s & \textbf{20.55s} & 54.88s & 54.97s & 54.58s & 57.11s & 57.30s & 57.02s & 57.68s & 56.60s & 56.46s
\\ \hline
ORL & 13.07s & 13.05s & 13.08s & 6.73s & 6.77s & 6.93s & 6.38s & 6.96s & 7.20s & \textbf{2.07s} & 2.62s & 2.82s & 6.67s & 7.56s & 8.63s & 6.87s & 7.19s & 7.67s & 6.64s & 6.79s & 6.80s
\\ \hline
PCMAC & 751.25s & 742.09s & 744.23s & 377.87s & 381.31s & 380.74s & 382.81s & 382.50s & 386.86s & \textbf{128.28s} & 136.87s & 137.31s & 365.66s & 366.69s & 370.00s & 380.84s & 382.02s & 381.16s & 374.06s & 376.45s & 377.26s
\\ \hline
Phishing & 1119.79s & 1110.41s & 1110.58s & 943.66s & 941.99s & 939.85s & 576.86s & 578.64s & 578.91s & \textbf{206.02s} & 218.22s & 218.06s & 569.57s & 567.55s & 567.86s & 574.09s & 572.32s & 573.75s & 936.62s & 933.18s & 932.34s
\\ \hline
Segment & 72.41s & 72.66s & 72.15s & 36.87s & 36.99s & 36.63s & 37.23s & 37.26s & 37.11s & \textbf{13.02s} & 13.43s & 13.53s & 35.18s & 35.53s & 35.46s & 36.98s & 37.02s & 36.75s & 37.25s & 36.33s & 36.58s
\\ \hline
Sonar & 0.51s & 0.53s & 0.53s & 0.40s & 0.41s & 0.41s & 0.27s & 0.28s & 0.29s & \textbf{0.10s} & 0.11s & 0.13s & 0.28s & 0.34s & 0.41s & 0.28s & 0.31s & 0.33s & 0.40s & 0.40s & 0.41s
\\ \hline
Splice & 37.29s & 34.84s & 34.96s & 30.49s & 30.29s & 29.83s & 18.24s & 18.19s & 18.50s & 6.94s & \textbf{6.91s} & 6.99s & 17.79s & 17.92s & 17.83s & 18.33s & 18.41s & 18.75s & 31.50s & 30.83s & 29.60s
\\ \hline
Vehicle & 10.01s & 9.82s & 9.71s & 4.90s & 4.93s & 4.96s & 4.96s & 5.00s & 5.04s & \textbf{1.77s} & 1.79s & 1.82s & 4.86s & 4.86s & 4.89s & 5.00s & 5.04s & 5.07s & 5.02s & 4.98s & 4.96s
\\ \hline
Wine & 0.44s & 0.44s & 0.44s & 0.22s & 0.23s & 0.23s & 0.22s & 0.23s & 0.23s & \textbf{0.08s} & \textbf{0.08s} & 0.09s & 0.22s & 0.23s & 0.25s & 0.23s & 0.23s & 0.24s & 0.22s & 0.22s & 0.22s
\\
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\end{tabular}
\end{adjustbox}
\caption{Average computation time required by the optimization process considering all datasets.}
\end{table}
\end{document}

cs

Gustavo H. de Rosa
João Paulo Papa
Xin-She Yang

"A Nature-Inspired Feature Selection Approach based on Hypercomplex Information" by Gustavo H. de Rosa, João Paulo Papa, and Xin-She Yang, arXiv:2101.05652v1, 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{Environment Resources Configuration.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
 \hline
 \textbf{Component} & \textbf{Details} \\
 \hline
 CPU & Intel® Core™ i5-8400 \\
 \hline
 GPU & NVIDIA RTX GeForce 2080Ti \\
 \hline
 RAM & 32Gb \\
 \hline
 Python Version & Python 3.11.9 \\
 \hline
 Host Environment & Window 11 Pro 64 bits \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Quang-Anh N. D.
Manh-Hung Ha
Thai Kim Dinh
Minh-Duc Pham
Ninh Nguyen Van

"Emotional Vietnamese Speech-Based Depression Diagnosis Using Dynamic Attention Mechanism" by Quang-Anh N. D., Manh-Hung Ha, Thai Kim Dinh, Minh-Duc Pham, and Ninh Nguyen Van, arXiv:2412.08683v1, 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{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|c}
 \toprule
 \multirow{2}{*}{Model} & \multicolumn{2}{|c}{Valid}& \multicolumn{2}{|c}{Test} \\\cline{2-5}
 & CER & WER & CER & WER \\
 \midrule
 HMM-DNN & 8.5 & 21.6 & 7.9 & 19.8 \\
 Transformer & 4.5 & 13.7 & 3.9 & 12.4 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{The CER (\%) and WER (\%) performances of different ASR models built using KSC.}
\end{table}
\end{document}

eess

Yerbolat Khassanov
Saida Mussakhojayeva
Almas Mirzakhmetov
Alen Adiyev
Mukhamet Nurpeiissov
Huseyin Atakan Varol

"A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline" by Yerbolat Khassanov, Saida Mussakhojayeva, Almas Mirzakhmetov, Alen Adiyev, Mukhamet Nurpeiissov, and Huseyin Atakan Varol, arXiv:2009.10334v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Hao Sun
Ashkan Ertefaie
Luke Duttweiler
Brent A. Johnson

"Constructing optimal treatment length strategies to maximize quality-adjusted lifetimes" by Hao Sun, Ashkan Ertefaie, Luke Duttweiler, and Brent A. Johnson, arXiv:2412.05108v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccc|}
\hline
Entry time & Node 1 & Node 2 & Node 3 & Node 4 \\
\hline
6 & $\infty$ & $\infty$ & 6 & 6 \\
5 & $\infty$ & $\infty$ & $\infty$ & 5 \\
4 & $\infty$ & $\infty$ & 4 & $\infty$ \\
3 & $\infty$ & $\infty$ & $\infty$ & $\infty$ \\
2 & $\infty$ & 1 & $\infty$ & $\infty$ \\
1 & 0 & $\infty$ & $\infty$ & $\infty$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Stephen D. Boyles
Nicholas E. Lownes
Avinash Unnikrishnan

"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
		status & number & $\max(|y''-f|)$ & $\max(|y_{opt}-y_{b}|)$ \\ \hline\hline
		$\to y_b$ & 23 & 1.0E-07 & 4.1E-10 \\
		$\to y_s$ & 0 & NA & NA \\
		$diverge$ & 2 & diverge & diverge \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions" by Xiaorong Zou, arXiv:2504.19280v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average training time of different methods using default and optimal hyperparameter settings on 50 medium-sized datasets. Numbers are in Seconds, with lower values indicating faster training speed.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 \textbf{Model Name} & \textbf{Training Time (Default)} & \textbf{Training Time (Optimal)} \\
 \midrule
 DOFEN & 332.6998 +/- 125.1965 & 1143.7674 +/- 804.3809 \\
 Trompt & 552.3495 +/- 213.3278 & 535.1781 +/- 291.9933 \\
 FT-Transformer & 80.3425 +/- 57.2647 & 99.1068 +/- 79.2272 \\
 NODE & 95.0274 +/- 54.7463 & 427.8625 +/- 394.1191 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

DOFEN: Deep Oblivious Forest ENsemble

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

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


math

"The dimension and Bose distance of certain primitive BCH codes" by Run Zheng, Nung-Sing Sze, and Zejun Huang, arXiv:2503.01118v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lll}
 \toprule
 \textbf{Parameters} & \textbf{Value} & \textbf{Explanation} \\
 \midrule
 $C$ & $2$ & Clipping bound \\
 $\delta^{\ell}$ & $10^{-5}$ & Indistinguishability parameter \\
 $\epsilon^{\ell}$ & $[0.01,2]$ & Privacy budget \\
 $\eta$ & $0.05$ & Step size of the gradient \\
 $m$ & $100$ & The number of clients \\
 $n$ & $60,000$ & Total number of training samples \\
 $T$ & $50$ & The number of epochs \\
 \bottomrule
 \end{tabular}
\caption{Experiment Setting for the Shuffled Personalized-SGD on the MNIST dataset.}
\end{table}
\end{document}

math

"A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility" by E Chen, Yang Cao, and Yifei Ge, arXiv:2312.14388v3, 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 possible dose-escalation paths in the standard 3+3 design, as a function of the number $D$ of prespecified doses.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrrc}
 \hline
$D$ & $J$ & $\log(J)$ & $\Delta \log(J)$ \\
 \hline
1 & 10 & 2.30 & -- \\
 2 & 46 & 3.83 & 1.53 \\
 3 & 154 & 5.04 & 1.21 \\
 4 & 442 & 6.09 & 1.05 \\
 5 & 1162 & 7.06 & 0.97 \\
 6 & 2890 & 7.97 & 0.91 \\
 7 & 6922 & 8.84 & 0.87 \\
 8 & 16138 & 9.69 & 0.85 \\
 9 & 36874 & 10.52 & 0.83 \\
 10 & 82954 & 11.33 & 0.81 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"What Were They Thinking? Pharmacologic priors implicit in a choice of 3+3 dose-escalation design" by David C. Norris, arXiv:2012.05301v2, 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|}
 \hline
 Model & Accuracy, \% & MAPE, \% \\\hline
 LSTM-Qwen-Mean & 48.552 & \textbf{0.256} \\\hline
 LSTM-Qwen-Sum & 46.970 & 0.367 \\\hline
 LSTM & \textbf{52.020 }& 0.397 \\\hline
 LSTM-RuBert-Mean & 49.798 & 0.437 \\\hline
 LSTM-RuBert-Sum & 48.148 & 0.445 \\\hline
 \end{tabular}
\end{adjustbox}
\caption{The Dual-Modality Approach forecast. Accuracy, MAPE in percentage.}
\end{table}
\end{document}

q-fin

"Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2503.08696v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{SSIM and CW-SSIM scores on the test set.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l||c|c|c}
 & \multicolumn{3}{c}{{SSIM / CW-SSIM}} \\
\cline{2-4}
speaker & ~~FC-DNN~~ & ~~~CNN~~~ & ~~LSTM~~ \\
\hline\hline
f1 & 0.77 / 0.94 & 0.75 / 0.94 & 0.80 / 0.95 \\
f2 & 0.70 / 0.92 & 0.68 / 0.92 & 0.73 / 0.94 \\
m1 & 0.80 / 0.92 & 0.77 / 0.91 & 0.81 / 0.92 \\
m2 & 0.75 / 0.92 & 0.73 / 0.92 & 0.77 / 0.94 \\
\hline
average & 0.76 / 0.93 & 0.73 / 0.92 & 0.78 / 0.94 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Speaker dependent acoustic-to-articulatory inversion using real-time MRI of the vocal tract" by Tamás Gábor Csapó, arXiv:2008.02098v1, 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{\centering The comparison of precision, recall and F1-score for our model and Double-net in percentage from three different views for VerSe 2020 dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\hline
\textbf{Plane} & \textbf{Model}& \textbf{Phase }& \textbf{Precision(\%)} &\textbf{Recall(\%)}&\textbf{F1-score(\%)} \\\hline
\multirow{4}{*}{Sagittal } & \multirow{2}{*}{DoubleU-Net } &Valid& 92.19 & 91.49 & 91.83 \\ & & Test & 90.23 & 90.14 & 90.18 \\\cline{2-6}
& \multirow{2}{*}{\textbf{DoubleU-Net ++} } &Valid& \textbf{96.34} & \textbf{95.94} & \textbf{96.13} \\ & & Test & \textbf{95.18} & \textbf{94.08} & \textbf{94.62} \\
\hline
\multirow{4}{*}{Coronal } & \multirow{2}{*}{DoubleU-Net } &Valid& 90.09 & 90.13 & 90.10 \\ & & Test & 89.51 & 89.04 & 89.27 \\\cline{2-6}
& \multirow{2}{*}{\textbf{DoubleU-Net ++ }} &Valid&\textbf{95.12} &\textbf{95.43} & \textbf{95.27} \\ & & Test & \textbf{94.02} & \textbf{94.24} &\textbf{94.12 } \\ \hline
\multirow{4}{*}{Axial } & \multirow{2}{*}{DoubleU-Net } &Valid& 92.37 & 92.51 & 92.43 \\ & & Test & 91.45 & 91.73 & 91.58 \\\cline{2-6}
& \multirow{2}{*}{\textbf{DoubleU-Net ++}} &Valid& \textbf{94.69} & \textbf{94.93} & \textbf{94.80} \\ & & Test & \textbf{94.01} & \textbf{93.86}& \textbf{93.93} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Simindokht Jahangard
Mahdi Bonyani
Abbas Khosravi

"DoubleU-Net++: Architecture with Exploit Multiscale Features for Vertebrae Segmentation" by Simindokht Jahangard, Mahdi Bonyani, and Abbas Khosravi, arXiv:2201.12389v1, 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{\large Regression Results: Impact of renewable energy investments on detected unemployment rate recovery following a change in active mines}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \tabularnewline\midrule\midrule
 Dependent Variable: & \multicolumn{4}{c}{$\Delta$Unemployment Rate}\\
 Model: & (1) & (2) & (3) & (4)\\
 \midrule \emph{Variables} & & & & \\
 $\Delta$Active Mines\textsubscript{t-2} & 0.0402$^{*}$ & 0.0400$^{*}$ & 0.0433$^{*}$ & 0.0427$^{*}$\\
 & (0.0151) & (0.0161) & (0.0175) & (0.0178)\\
 $\Delta$ Active Mines\textsubscript{t-2} $\times$ REE\textsubscript{t-1} & & 0.0049 & & 0.0229\\
 & & (0.0249) & & (0.0300)\\
 $\Delta$ Active Mines\textsubscript{t-2} $\times$ REE\textsubscript{t-2} & & & -0.0440$^{.}$ & -0.0518$^{*}$\\
 & & & (0.0209) & (0.0204)\\
 $\Delta$ (log) Real GDPPC\textsubscript{t} & -0.9846$^{***}$ & -0.9986$^{***}$ & -1.021$^{**}$ & -1.021$^{**}$\\
 & (0.2138) & (0.2246) & (0.2519) & (0.2519)\\
 \midrule \emph{Fixed-effects} & & & & \\
 County FIPS Code & Yes & Yes & Yes & Yes\\
 Year & Yes & Yes & Yes & Yes\\
 \midrule \emph{Fit statistics} & & & & \\
 Observations & 55,295 & 52,224 & 49,152 & 49,152\\
 R$^2$ & 0.61267 & 0.61459 & 0.62292 & 0.62292\\
 Within R$^2$ & 0.01288 & 0.01308 & 0.01340 & 0.01341\\
 \midrule\midrule\multicolumn{5}{l}{\emph{Clustered (County FIPS Code \& Year) standard-errors in parentheses}}\\
 \multicolumn{5}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .:0.1}}\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Case description for example 5.1.2.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllllll}
	\hline
 Case No. & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8\\
 \hline
 number of mixture components, $K$ & 1 & 2 & 3 & 5 & 10 & 20 & 100 & BIC\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jianpeng Chan
Iason Papaioannou
Daniel Straub

"Bayesian improved cross entropy method with categorical mixture models" by Jianpeng Chan, Iason Papaioannou, and Daniel Straub, arXiv:2309.12490v1, 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}
		\toprule[1pt]
		 Methods & mIOU (\%) \\
		\midrule[0.5pt]
		\midrule[0.5pt]
		ResNet-50 + DeepLabV3+ ~ & 38.4\\
		HRNetV2 + OCR & 42.3 \\
		SETR~ & 45.8\\
	 Segformer-B5~ & 46.7 \\
		\midrule[0.5pt]
		\textbf{Our CAA (EfficientNet-B5)} & \textbf{47.30}\\
		\bottomrule[1pt]
	\end{tabular}
\end{adjustbox}
\caption{Result comparison with the state-of-the-art approaches on the COCO-Stuff-164K test set for multi-scale prediction. Note that, the listed methods were not trained under same settings, or using same backbone. \textit{Methods} other than CAA and Segformer are reproduced in Segformer paper. }
\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[table]{xcolor}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrrrrrrrr}
 \hline
{\footnotesize Dataset} & {\footnotesize } & {\footnotesize HIVAE} & {\footnotesize Mean} & {\footnotesize MICE} & {\footnotesize MissForest} & {\footnotesize MIWAE} & {\footnotesize VAEAC} & {\footnotesize NIMIWAE} & {\footnotesize IMIWAE} \\
 \hline
banknote & \textit{ MCAR } & 1.62 & 2.00 & 1.63 & \textcolor{red}{0.95} & 1.37 & 1.32 & 1.49 & 1.19 \\
{\footnotesize $n=1372$} & \textit{ MAR } & 2.00 & 2.27 & 2.43 & 1.88 & 2.90 & \textcolor{red}{1.76} & 1.89 & 1.96 \\
 & \textit{ MNAR } & 3.39 & 3.92 & 3.78 & 3.18 & 3.10 & 3.46 & \textcolor{red}{1.46} & 3.30 \\
 \rowcolor[gray]{0.95}concrete & \textit{ MCAR } & 47.54 & 51.28 & 29.97 & \textcolor{red}{25.57} & 40.53 & 33.03 & 42.12 & 33.69 \\
 \rowcolor[gray]{0.95}{\footnotesize $n=1030$} & \textit{ MAR } & 67.37 & 60.00 & \textcolor{red}{44.77} & 53.19 & 66.35 & 61.09 & 55.82 & 57.56 \\
 \rowcolor[gray]{0.95} & \textit{ MNAR } & 59.48 & 95.85 & 68.24 & 79.87 & 76.79 & 70.12 & \textcolor{red}{47.46} & 74.44 \\
 hepmass & \textit{ MCAR } & 0.75 & 0.82 & 0.74 & NA & 0.80 & \textcolor{red}{0.68} & 0.78 & 0.69 \\
 {\footnotesize $n=525,123$} & \textit{ MAR } & 0.76 & 0.84 & 0.75 & NA & 0.84 & 0.72 & 0.72 & \textcolor{red}{0.71} \\
 & \textit{ MNAR } & 1.41 & 1.54 & 1.40 & NA & 1.36 & 1.30 & \textcolor{red}{0.99} & 1.36 \\
 \rowcolor[gray]{0.95}power & \textit{ MCAR } & 0.54 & 0.66 & 0.50 & NA & 0.59 & \textcolor{red}{0.48} & 0.56 & 0.49 \\
 \rowcolor[gray]{0.95}{\footnotesize $n=1,000,000$} & \textit{ MAR } & 0.61 & 0.75 & 0.56 & NA & 0.67 & \textcolor{red}{0.54} & 0.78 & 0.57 \\
 \rowcolor[gray]{0.95} & \textit{ MNAR } & 0.75 & 1.14 & 0.84 & NA & 0.86 & 0.79 & \textcolor{red}{0.73} & 0.79 \\
 red & \textit{ MCAR } & 1.15 & 1.64 & 1.04 & \textcolor{red}{0.86} & 1.10 & 1.10 & 1.08 & 0.98 \\
 {\footnotesize $n=1599$} & \textit{ MAR } & 1.23 & 1.67 & 1.16 & \textcolor{red}{0.98} & 1.30 & 1.29 & 1.09 & 1.06 \\
 & \textit{ MNAR } & 2.12 & 3.24 & 2.46 & 2.06 & 1.87 & 2.73 & \textcolor{red}{0.90} & 1.74 \\
 \rowcolor[gray]{0.95}white & \textit{ MCAR } & 2.14 & 2.61 & 1.97 & \textcolor{red}{1.60} & 2.18 & 1.93 & 2.16 & 1.88 \\
 \rowcolor[gray]{0.95}{\footnotesize $n=4898$} & \textit{ MAR } & 2.28 & 2.63 & 1.99 & \textcolor{red}{1.69} & 2.15 & 2.11 & 1.89 & 1.89 \\
 \rowcolor[gray]{0.95} & \textit{ MNAR } & 4.42 & 5.36 & 4.21 & 4.27 & 4.46 & 4.10 & \textcolor{red}{3.47} & 4.70 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Average L1 distance between true and imputed missing values in various datasets, under different mechanisms of simulated missingness. Best imputation performance (lowest average L1) in each row is highlighted in red. Proportion of missing entries was fixed at 50\% per feature, with 50\% of the features containing missingness. We see that NIMIWAE consistently performs best in imputing MNAR missingness, while performance of the ``Ignorable'' IMIWAE model is comparable to other methods under MCAR and MAR. Although MissForest claims superiority in MCAR and some MAR cases in the smaller datasets, it was not scalable to larger datasets like hepmass and power.}
\end{table}
\end{document}

cs

David K. Lim
Naim U. Rashid
Junier B. Oliva
Joseph G. Ibrahim

"Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders" by David K. Lim, Naim U. Rashid, Junier B. Oliva, and Joseph G. Ibrahim, arXiv:2101.07357v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Maryam Mohammadi
Alvise Sommariva
Marco Vianello

"Unisolvence of randomized MultiQuadric Kansa collocation for convection-diffusion with mixed boundary conditions" by Maryam Mohammadi, Alvise Sommariva, and Marco Vianello, arXiv:2503.00963v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Taban Baghfalaki
Reza Hashemi
Christophe Tzourio
Catherine Helmer
Helene Jacqmin-Gadda

"A Two-stage Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers and Competing Risk Outcomes" by Taban Baghfalaki, Reza Hashemi, Christophe Tzourio, Catherine Helmer, and Helene Jacqmin-Gadda, arXiv:2412.03797v1, 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}
Macro Factor & PC1 Loading \\
\hline
Real disposable income growth & -0.229 \\
Real GDP growth & -0.525 \\
Change House Price Index & -0.467 \\
CPI inflation rate & -0.079 \\
Change unemployment & 0.529 \\
Change Dow & -0.293 \\
Change Treasury Spread & 0.287 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Loadings of first principal component on macro variables.}
\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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Paired t-Test Results (CMG vs. Other Models)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
\hline
\textbf{Comparison} & \textbf{p-value} \\
\hline
CMG vs. BiLSTM & 0.0063 \\
CMG vs. Gaussian Naive Bayes & 0.0001 \\
CMG vs. GRU & 0.0153 \\
CMG vs. Logistic Regression & 0.0006 \\
CMG vs. LSTM & 0.0073 \\
CMG vs. Multi Logistic Reg. & 0.0000 \\
CMG vs. Random Forest & 0.0001 \\
CMG vs. XGBoost & 0.0008 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data" by Arif Pathan, arXiv:2506.17244v1, 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}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{PLCC Performance Comparison with or without LR image as Reference}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
Methods 	& SISAR	& Waterloo-15 & CVIU-17 &QADS\\ \midrule
 w/o 		&0.8610	 	&0.6207	 	&0.5024 	&{\color[HTML]{CB0000} \textbf{0.8403}}	\\
 w/		&{\color[HTML]{CB0000} \textbf{0.9143}} 	&{\color[HTML]{CB0000} \textbf{0.8232}} 	&{\color[HTML]{CB0000} \textbf{0.6440}}		&0.8173	\\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Tiesong Zhao
Yuting Lin
Yiwen Xu
Weiling Chen
Zhou Wang

"Learning-Based Quality Assessment for Image Super-Resolution" by Tiesong Zhao, Yuting Lin, Yiwen Xu, Weiling Chen, and Zhou Wang, arXiv:2012.08732v1, 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
 $k$ & 1 & 2 & 3 & 4 \\ \hline
 MSE & $5.0 \times 10^{-13}$ & $2.5 \times 10^{-11}$ & $2.5 \times 10^{-11}$ & $3.1 \times 10^{-10}$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{We can learn up to $4$ iterations of GD at once with our current training techniques. Model stability becomes a bottleneck with harder tasks.}
\end{table}
\end{document}

math

Jerry Liu
Jessica Grogan
Owen Dugan
Ashish Rao
Simran Arora
Atri Rudra
Christopher Ré

"Towards Learning High-Precision Least Squares Algorithms with Sequence Models" by Jerry Liu, Jessica Grogan, Owen Dugan, Ashish Rao, Simran Arora, Atri Rudra, and Christopher Ré, arXiv:2503.12295v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Reconfigurable Intelligent Surface Based Hybrid Precoding for THz Communications" by Yu Lu, Mo Hao, and Richard MAcKenzie, arXiv:2012.06261v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Experimental results of our experimental setup using spoken English data}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrrcc}
\toprule
model & type & mean & max & sd & entropy \\
\midrule
\multirow{2}{*}{wav2vec} & Large & \textbf{-69.1} & \textbf{68.2} & 64.0 & \textbf{-69.9} \\
& VQ & -60.1 & 58.4 & \textbf{68.3} & -69.0 \\
\midrule[\heavyrulewidth]
\multirow{5}{*}{\parbox{2cm}{wav2vec2 \\ ASR}} & base 10m & 6.5 & 23.4 & 26.8 & -23.0 \\
& base 100h & -23.8 & 45.5 & 46.3 & \textbf{-39.2} \\
& base 960h & \textbf{-45.7} & \textbf{50.6} & \textbf{52.4} & -37.3 \\
& large 960h & -9.9 & 39.9 & 40.3 & -25.7 \\
& lv60k 960h & 32.4 & -1.6 & 45.8 & -37.4 \\
& voxpop.\ en & -39.0 & 32.1 & 42.7 & -43.6 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Aditya Ravuri
Erica Cooper
Junichi Yamagishi

"Uncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Prediction" by Aditya Ravuri, Erica Cooper, and Junichi Yamagishi, arXiv:2312.15616v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccc|}
 \hline
 \textbf{beta} & \textbf{0.5} & \textbf{1} & \textbf{2} & \textbf{5} \\ \hline
 \textbf{N=2} & 1.46\% & 1.26\% & 1.01\% & 0.60\% \\
 \textbf{N=3} & 0.10\% & 0.05\% & 0.02\% & 0.02\% \\
 \textbf{N=5} & 0.01\% & 0.01\% & 0.01\% & 0.01\% \\
 \textbf{N=8} & 0.01\% & 0.01\% & 0.01\% & 0.00\% \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Relative absolute difference for different $n$ and $\beta$ on the option price compared to $n=10$, with $Y_0=D, h_0=1, \alpha=0.1, T_0=2$, strike $K=2$ and the time to maturity $\vartheta=1$. Pure jump case.}
\end{table}
\end{document}

q-fin

"Pricing Options on Forwards in Function-Valued Affine Stochastic Volatility Models" by Jian He, Sven Karbach, and Asma Khedher, arXiv:2508.14813v1, 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{The Dual-Modality returns vector forecasting metrics. Accuracy (the upper row), MAPE (the lower row) in percentage.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|rr|rr|rr|rr|rr|rr|rr|rr|rr|}
 \hline
 \multirow{2}{*}{Model} &
 \multicolumn{2}{c|}{Metals and Mining} &
 \multicolumn{2}{c|}{Oil and Gas} &
 \multicolumn{2}{c|}{Consumer Sector} &
 \multicolumn{2}{c|}{Construction} &
 \multicolumn{2}{c|}{Telecommunications} &
 \multicolumn{2}{c|}{Transport} &
 \multicolumn{2}{c|}{Finance} &
 \multicolumn{2}{c|}{Chemical Industry} &
 \multicolumn{2}{c|}{Power Engineering} \\
 \cline{2-19}
 & MTLR & TRMK & SNGS & SIBN & MGNT & LENT & PIKK & SMLT & MTSS & RTKMP & AFLT & FLOT & BSPB & SFIN & PHOR & KZOSP & HYDR & MRKC\\
 \hline
 \multirow{2}{*}{vanilla LSTM}
 & \textbf{56.364\%} & \textbf{56.364\%} % MTLR | TRMK
 & 50.303\% & \textbf{58.182\%} % SNGS | SIBN
 & 46.667\% & \textbf{56.364\%} % MGNT | LENT
 & 49.091\% & \textbf{53.939\%} % PIKK | SMLT
 & \textbf{56.970\%} & \textbf{55.152\%} % MTSS | RTKMP
 & \textbf{55.152\%} & 47.273\% % AFLT | FLOT
 & 46.061\% & 49.697\% % BSPB | SFIN
 & 41.818\% & \textbf{57.576\%} % PHOR | KZOSP
 & 59.394\% & 40.000\% % HYDR | MRKC
 \\
 & 0.410\% & 0.362\% % MTLR | TRMK
 & 0.352\% & 0.341\% % SNGS | SIBN
 & 0.331\% & 0.371\% % MGNT | LENT
 & 0.484\% & 0.328\% % PIKK | SMLT
 & 0.541\% & 0.246\% % MTSS | RTKMP
 & 0.419\% & \textbf{0.258\%} % AFLT | FLOT
 & 0.410\% & 0.447\% % BSPB | SFIN
 & 0.231\% & 0.458\% % PHOR | KZOSP
 & 0.380\% & 0.768\% % HYDR | MRKC
 \\\hline
 \multirow{2}{*}{LSTM\_RuBert\_SUM}
 & 39.394\% & 35.152\% % MTLR | TRMK
 & 53.939\% & \textbf{58.182\%} % SNGS | SIBN
 & \textbf{53.333\%} & 49.091\% % MGNT | LENT
 & 50.303\% & 38.788\% % PIKK | SMLT
 & 53.939\% & 49.697\% % MTSS | RTKMP
 & 51.515\% & 43.636\% % AFLT | FLOT
 & 47.879\% & 44.848\% % BSPB | SFIN
 & 53.333\% & 42.424\% % PHOR | KZOSP
 & 58.788\% & 42.424\% % HYDR | MRKC
 \\
 & 0.409\% & 0.392\% % MTLR | TRMK
 & 0.865\% & 0.265\% % SNGS | SIBN
 & 0.417\% & 0.400\% % MGNT | LENT
 & 0.462\% & \textbf{0.200\%} % PIKK | SMLT
 & 0.473\% & 0.274\% % MTSS | RTKMP
 & 0.641\% & 0.532\% % AFLT | FLOT
 & 0.406\% & 0.445\% % BSPB | SFIN
 & 0.264\% & 0.492\% % PHOR | KZOSP
 & 0.326\% & 0.742\% % HYDR | MRKC
 \\\hline
 \multirow{2}{*}{LSTM\_RuBert\_MEAN}
 & 38.788\% & 42.424\% % MTLR | TRMK
 & \textbf{58.182\%} & \textbf{58.182\%} % SNGS | SIBN
 & 47.879\% & 50.909\% % MGNT | LENT
 & \textbf{57.576\%} & 46.061\% % PIKK | SMLT
 & 55.152\% & 45.455\% % MTSS | RTKMP
 & 50.303\% & \textbf{52.121\%} % AFLT | FLOT
 & 50.909\% & 47.273\% % BSPB | SFIN
 & 55.152\% & 41.212\% % PHOR | KZOSP
 & 55.758\% & \textbf{43.030\%} % HYDR | MRKC
 \\
 & 0.410\% & \textbf{0.192\%} % MTLR | TRMK
 & 1.824\% & 0.216\% % SNGS | SIBN
 & 0.299\% & 0.359\% % MGNT | LENT
 & 0.436\% & 0.270\% % PIKK | SMLT
 & 0.368\% & 0.271\% % MTSS | RTKMP
 & 0.348\% & 0.262\% % AFLT | FLOT
 & 0.326\% & 0.390\% % BSPB | SFIN
 & 0.238\% & 0.491\% % PHOR | KZOSP
 & 0.321\% & 0.839\% % HYDR | MRKC
 \\\hline
 \multirow{2}{*}{LSTM\_QWEN\_SUM}
 & 45.455\% & 36.364\% % MTLR | TRMK
 & 44.848\% & 39.394\% % SNGS | SIBN
 & 46.061\% & 53.333\% % MGNT | LENT
 & 47.273\% & 36.364\% % PIKK | SMLT
 & 47.879\% & 44.848\% % MTSS | RTKMP
 & 45.455\% & 43.636\% % AFLT | FLOT
 & 47.879\% & \textbf{56.970\%} % BSPB | SFIN
 & \textbf{60.000\%} & 48.485\% % PHOR | KZOSP
 & 47.879\% & 42.424\% % HYDR | MRKC
 \\
 & 0.522\% & 0.504\% % MTLR | TRMK
 & 0.307\% & 0.368\% % SNGS | SIBN
 & 0.307\% & 0.346\% % MGNT | LENT
 & 0.529\% & 0.311\% % PIKK | SMLT
 & 0.316\% & \textbf{0.171\%} % MTSS | RTKMP
 & 0.259\% & 0.392\% % AFLT | FLOT
 & 0.369\% & \textbf{0.195\%} % BSPB | SFIN
 & 0.354\% & 0.369\% % PHOR | KZOSP
 & 0.292\% & 0.660\% % HYDR | MRKC
 \\\hline
 \multirow{2}{*}{LSTM\_QWEN\_MEAN}
 & 52.121\% & 35.758\% % MTLR | TRMK
 & 49.697\% & 47.879\% % SNGS | SIBN
 & 48.485\% & 52.121\% % MGNT | LENT
 & 53.333\% & 43.030\% % PIKK | SMLT
 & 45.455\% & 44.242\% % MTSS | RTKMP
 & 52.121\% & 43.636\% % AFLT | FLOT
 & \textbf{52.121\%} & \textbf{56.970\%} % BSPB | SFIN
 & 44.848\% & 49.697\% % PHOR | KZOSP
 & \textbf{61.212\%} & 41.818\% % HYDR | MRKC
 \\
 & \textbf{0.246}\% & 0.419\% % MTLR | TRMK
 & \textbf{0.106\%} & \textbf{0.165\%} % SNGS | SIBN
 & \textbf{0.235\%} & \textbf{0.331\%} % MGNT | LENT
 & \textbf{0.322\%} & 0.241\% % PIKK | SMLT
 & \textbf{0.193\%} & 0.178\% % MTSS | RTKMP
 & \textbf{0.182\%} & 0.345\% % AFLT | FLOT
 & \textbf{0.227\%} & 0.272\% % BSPB | SFIN
 & \textbf{0.219\%} & \textbf{0.352\%} % PHOR | KZOSP
 & \textbf{0.178\%} & \textbf{0.543\%} % HYDR | MRKC
 \\\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Error analysis for Lorenz's system example using NR method}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
variable&bias&MAPE&MAE&RMSE&R$^2$\\ \hline
$x_1$&0.0392&0.2008&1.1157&1.3487&0.9498\\
$x_2$&0.1503&0.2391&1.3254&1.6295&0.9420\\
$x_3$&0.5704&0.0727&1.4757&1.8549&0.9380\\
$f_1(\cdot)$&1.0479&5.1133&14.3894&17.8915&0.7762\\
$f_2(\cdot)$&0.7459&1.1711&13.4830&17.2733&0.9036\\
$f_3(\cdot)$&-0.3850&2.4390&16.7952&22.1846&0.8856\\
\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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Detailed information of the restricted output network architecture}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
			\hline
			Layer number & Layer & output shape & number of parameter & activation function \\ \hline
			1 & dense\_1 (Dense) & (None, 24) & 600 & relu \\ \hline
			2 & dropout\_1 (Dropout) & (None, 24) & 0 & - \\ \hline
			3 & dense\_2 (Dense) & (None, 500) & 12500 & relu \\ \hline
			4 & dropout\_2 (Dropout) & (None, 500) & 0 & - \\ \hline
			5 & dense\_3 (Dense) & (None, 500) & 250500 & relu \\ \hline
			6 & dropout\_3 (Dropout) & (None, 500) & 0 & - \\ \hline
			7 & dense\_4 (Dense) & (None, 500) & 250500 & relu \\ \hline
			8 & dropout\_4 (Dropout) & (None, 500) & 0 & - \\ \hline
			9 & dense\_5 (Dense) & (None, 500) & 250500 & relu \\ \hline
			10 & dense\_6 (Dense) & (None, 48) & 24048 & sigmoid \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Fardin Ghorbani
Sina Beyraghi
Javad Shabanpour
Homayoon Oraizi
Hossein Soleimani
Mohammad Soleimani

"Deep neural network-based automatic metasurface design with a wide frequency range" by Fardin Ghorbani, Sina Beyraghi, Javad Shabanpour, Homayoon Oraizi, Hossein Soleimani, and Mohammad Soleimani, arXiv:2101.10866v1, 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{Fitting Parameters for the Function in Equation~()}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrr}
 \toprule
 City & \multicolumn{2}{c}{$\alpha$} & \multicolumn{2}{c}{$\beta$} & \multicolumn{2}{c}{$\gamma$} \\
 & Weekday & Weekend & Weekday & Weekend & Weekday & Weekend \\
 \midrule
 New York & $0.129$ & $0.153$ & $5.6 \times 10^{-4}$ & $2.5 \times 10^{-4}$ & $1.16$ & $1.26$ \\
 London & $0.267$ & $0.289$ & $3.6 \times 10^{-8}$ & $4.4 \times 10^{-8}$ & $2.57$ & $2.54$ \\
 Tokyo & $0.314$ & $0.324$ & $2.2 \times 10^{-6}$ & $2.1 \times 10^{-5}$ & $2.06$ & $1.70$ \\
 Boston & $0.305$ & $0.379$ & $2.0 \times 10^{-5} $ & $1.1 \times 10^{-5}$ & $1.99$ & $2.08$ \\
 Chicago & $0.221$ & $0.224$ & $1.0 \times 10^{-4}$ & $1.3 \times 10^{-4}$ & $1.62$ & $1.57$ \\
 Washington D.C. & $0.230$ & $0.157$ & $3.7 \times 10^{-5}$ & $2.0 \times 10^{-3}$ & $1.82$ & $1.18$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Shu-ichi Kinoshita
Yuya Bando
Hiroki Sayama

"Spatio-Temporal Differences in Bike Sharing Usage: A Tale of Six Cities" by Shu-ichi Kinoshita, Yuya Bando, and Hiroki Sayama, arXiv:2412.19294v1, 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}{cccc}
\toprule
Combination & First & Second & Metric (PSNR/SSIM) \\ \midrule
1. & App. Flow & - & 32.55 / 0.891 \\
2. & Intensity & - & 32.31 / 0.891 \\
3. & App. Flow & App. Flow & 32.35 / 0.903 \\
4. & Intensity & Intensity & 32.32 / 0.903 \\
5. & Intensity & App. Flow & 33.01 / 0.904 \\
6. & App. Flow & Intensity & \textbf{33.04 / 0.904} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Quantitative evaluation of multi-stage (first and second) residual estimation on \textit{Flower} dataset. Appearance flow and intensity denotes ${L}_{rf}(\textbf{x},\textbf{u})$ and ${L}_{ri}(\textbf{x},\textbf{u})$, respectively.}
\end{table}
\end{document}

eess

"Joint Spatial and Angular Super-Resolution from a Single Image" by Andre Ivan, Williem, and In Kyu Park, arXiv:1911.11619v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of physicians on duty in the weekdays (WD) and in the public holidays (PH).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
 & WD & PH \\ \hline
 Morning (8.00 a.m. -- 2.00 p.m.) & 2 & 1 \\ %\hline
 Afternoon (2.00 p.m. -- 9.00 p.m.) & 2 & 1 \\ %\hline
 Night (9.00 p.m. -- 8.00 a.m.) & 1 & 1 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

G. Fava
T. Giovannelli
M. Messedaglia
M. Roma

"Effect of different patient peak arrivals on an Emergency Department via discrete event simulation" by G. Fava, T. Giovannelli, M. Messedaglia, and M. Roma, arXiv:2101.12432v1, 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 for the ``\textit{first person walks out of the lobby} (so-called action 1)" and ``\textit{second person walks into the lobby} (so-called action 2)" using DP-UNIVAR method. Note that the true value is 116 for action 1 and 174 for action 2.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
Action & Pixel & Estimated change points based on \textbf{DP-UNIVAR} method \\ \hline\hline
1 & 700 & $\{48, 91, 104, 114\}$ \\
& 702 & $\{83, 90, 101, 108 \}$ \\
 & 731 & $\{93, 109 \}$ \\
& 762 & $\{97, 106 \}$ \\
& 764 & $\{104, 118 \}$ \\
\hline
2 & 48 & $\{126, 144, 151, 182, 188, 207, 218, 224, 230, 260, 274 \}$ \\
& 78 & $\{157, 178, 248, 278, 285, 287 \}$ \\
& 110 & $\{133, 145, 152, 173, 176, 231, 234, 276, 283 \}$ \\
& 174 & $\{121, 128, 157, 170, 227, 246, 262, 272, 284 \}$ \\
& 209 & $\{138, 147, 158, 164, 203, 254, 259 \}$ \\
& 241 & $\{142, 219, 260 \}$ \\
\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.


eess

Md Umar Hashmi
Jonathan Cavaleiro
Lucas Pereira
Ana Bušić

"Sizing and Profitability of Energy Storage for Prosumers in Madeira, Portugal" by Md Umar Hashmi, Jonathan Cavaleiro, Lucas Pereira, and Ana Bušić, arXiv:1911.10458v1, 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{MILLI hyperparameters.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
			\toprule
			Dataset & Sample Size & $\alpha$ & $\beta$ & $\mathop{\mathbb{E}}[|z|]$ \\
			\midrule
			SIVAL & 200 & 0.05 & -0.01 & 13 \\
			4-MNIST-Bags & 150 & 0.05 & 0.01 & 16 \\
			CRC & 1000 & 0.008 & -5.0 & 2 \\
			MUSK & 150 & 0.3 & -1.0 & 2 \\
			TEF & 150 & 0.3 & 0.01 & 3 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Joseph Early
Christine Evers
Sarvapali Ramchurn

"Model Agnostic Interpretability for Multiple Instance Learning" by Joseph Early, Christine Evers, and Sarvapali Ramchurn, arXiv:2201.11701v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Accurate Linear Cutting-Plane Relaxations for ACOPF" by Daniel Bienstock and Matias Villagra, arXiv:2312.04251v3, 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{Average over 1000 trials of the number of iterations required to obtain an optimal solution starting from a guess within +/- 80\% of the known optimum}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \cline{2-4}
 & SQP & LSQP & SLCP \\ \hline
 Floudas & 32.79 & 16.29 (-50.32\%) & 14.92 (-54.50\%) \\
 Kirschen-Ozturk & 91.18 & 21.06 (-76.90\%) & 17.66 (-80.63\%) \\
 Hoburg-0 & - & 19.33 & 17.13 (-11.40\%) \\
 Hoburg-1 & - & 19.30 & 17.70 (-8.30\%) \\
 Hoburg-3 & - & 19.33 & 18.02 (-6.77\%) \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"A Method of Sequential Log-Convex Programming for Engineering Design" by Cody Karcher and Robert Haimes, arXiv:2201.08436v1, 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}{cc|c|c|c|}
\cline{2-4}
 & aa & aA & AA \\
\cline{1-5}
Case & $r_0$ & $r_1$ & $r_2$ & $r$ \\
\cline{1-5}
Control & $s_0$ & $s_1$ & $s_2$ & $s$ \\
\cline{1-5}
& $n_0$ & $n_1$ & $n_2$ & $n$ \\
\cline{2-5}
\end{tabular}
\end{adjustbox}
\caption{Nomenclature for Contingency Table with one SNP}
\end{table}
\end{document}

stat

"On the Use of the Kantorovich-Rubinstein Distance for Dimensionality Reduction" by Gaël Giordano, arXiv:2309.09442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Beyond the heat: The mental health toll of temperature and humidity in India" by Manuela Fritz, arXiv:2503.08761v1, 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}{cccc}
		\hline
		& $k$-proto. & KAMILA & CPF \\
		\hline
		Cov Type& 	52603.612& 	283.262 & 17782.344\\
		Poker &	74835.257	& 298.656	 & 	30229.664\\
		KDD '99 DOS vs. NORM& -	& 	416.293	& 	137283.899	 \\
		\hline
	\end{tabular}
\end{adjustbox}
\caption{	 Time (seconds) for each method on large datasets.}
\end{table}
\end{document}

stat

"Clustering of Big Data with Mixed Features" by Joshua Tobin and Mimi Zhang, arXiv:2011.06043v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc}
 $e_2 = \frac{1}{2}\cdot[2]$, \quad \quad & $e_1 = [1] -\frac{1}{2}\cdot[2]$. \\
 \end{tabular}
\end{table}
\end{document}

math

David Barnes
Magdalena Kędziorek
Niall Taggart

"An algebraic model for rational excisive functors" by David Barnes, Magdalena Kędziorek, and Niall Taggart, arXiv:2412.12281v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study on ConstraintMatch, results averaged over 5 folds with $n_c=10000$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 Model & \multicolumn{3}{c}{Test Performance} \\
 & ACC & NMI & ARI \\
 \midrule
 SCAN & 45.90 & 46.80 & 30.10 \\
 + Constrained & 52.45 & 46.57 & 33.79 \\
 + Pseudo-Labeling & 55.38 & 53.49 & 39.98 \\
 \textbf{+ Pseudo-Constraining} & \textbf{57.15} & \textbf{54.37} & \textbf{40.59}\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jann Goschenhofer
Bernd Bischl
Zsolt Kira

"ConstraintMatch for Semi-constrained Clustering" by Jann Goschenhofer, Bernd Bischl, and Zsolt Kira, arXiv:2311.15395v1, 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{Improvements by adding each of the major design choice of ETTA (evaluated on MusicCaps)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
\textbf{Ablation} &
\textbf{FD$_{P}\!\downarrow$} &
\textbf{KL$_{S}\!\downarrow$} &
\textbf{CL$_{\mathrm{M}}\!\uparrow$}
\\
\hline
Stable Audio Open & 39.96 & 1.81 & 0.41 \\
\hline
~+ AF-Synthetic & 26.22 & 1.57 & 0.43 \\
~~+ ETTA-DiT & 20.48 & 1.38 & 0.45 \\
~~~+ OT-CFM, $t\sim\mathcal{U}(0, 1)$ & 22.16 & 1.35 & 0.45 \\
~~~~+ $t\sim\sigma(\mathcal{N}(0, 1))$ & 21.59 & 1.41 & 0.45 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Sang-gil Lee
Zhifeng Kong
Arushi Goel
Sungwon Kim
Rafael Valle
Bryan Catanzaro

"ETTA: Elucidating the Design Space of Text-to-Audio Models" by Sang-gil Lee, Zhifeng Kong, Arushi Goel, Sungwon Kim, Rafael Valle, and Bryan Catanzaro, arXiv:2412.19351v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Features selection in bipartite representations}
\begin{tabular}{cccc}
\toprule
& name & description \\
\midrule
\multirow{7}{*}{Variable} & obj & normalized coefficient of variables in the objective function\\
& v\_coeff &average coefficient of the variable in all constraints\\
& Nv\_coeff & degree of variable node in the bipartite representation\\
& max\_coeff & maximum value among all coefficients of the variable\\
& min\_coeff &minimum value among all coefficients of the variable\\
&int & binary representation to show if the variable is an
integer variable\\
& cur\_sol & value in the current solution\\
\midrule
\multirow{6}{*}{Constraint} & c\_coeff & average of all coefficients in the constraint\\
& Nc\_coeff & degree of constraint nodes in the bipartite representation\\
& rhs &right-hand-side value of the constraint\\
& sense & the sense of the constraint\\
& norm & $\ell_2$-norm of the coefficient vector(e.g. the vector formed by all coefficients in the constraint.)\\
& cos\_sim & cosine similarity between the coefficient vectors of the constraint and objective.\\
\midrule
Edge& coeff& coefficient of variables in constraints\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

Wenbo Liu
Akang Wang
Wenguo Yang
Qingjiang Shi

"Mixed-Integer Linear Optimization via Learning-Based Two-Layer Large Neighborhood Search" by Wenbo Liu, Akang Wang, Wenguo Yang, and Qingjiang Shi, arXiv:2412.08206v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\protect MATLAB MCS: $\mathcal{FOR}$ and $\mathcal{SAIDI}$ Results}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|l|l|}
 \hline \thead{Run} & \thead{$\mathcal{FOR}_{PV}$} & \thead{$\mathcal{FOR}_{STEAM}$} & \thead{$\mathcal{SAIDI}$} \\
\hline \hline
 1 & 88.55e-2 & 36.18e-3 & 5.8023 \\
\hline
 2 & 107.19e-2 & 40.03e-3 & 6.5045 \\
\hline
 3 & 93.52e-2 & 36.35e-3 & 6.0222 \\
\hline
 5 & 110.17e-2 & 43.03e-3 & 7.0495 \\
 \hline
 4 & 95.24e-2 & 38.66e-3 & 6.3960 \\
\hline
Average & 98.93e-2 & 38.85e-3 & 6.3549 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Formal FT-based Cause-Consequence Reliability Analysis using Theorem Proving" by Mohamed Abdelghany and Sofiene Tahar, arXiv:2101.07174v1, 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{ Ranges of dimensionless numbers.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
\hline\hline
Froude number $Fr$ & $7$--$31$ \\
Ohnesorge number $Oh$ & $6\times10^{-3}$--$7\times10^{-3}$ \\
Reynolds number $Re$ & $7.7\times10^{2}$--$3.0\times10^{3}$ \\
Stokes number $St$ & $4.6\times10^{4}$--$1.8\times10^{5}$ \\
Weber number $We$ & 31--472\\
\hline\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jingzu Yee
Akinori Yamanaka
Yoshiyuki Tagawa

"Image features of a splashing drop on a solid surface extracted using a feedforward neural network" by Jingzu Yee, Akinori Yamanaka, and Yoshiyuki Tagawa, arXiv:2201.09541v1, 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{Scenarios for the Brown-Resnick model with parameters $ \lambda, \nu $.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
		\hline
		Scenario & 1 & 2 & 3 & 4 \\
		\hline
		$ \lambda	$ & $ 0.5 $ & $ 0.5 $ & $ 1.5 $ & $ 1.5 $ \\
		$ \nu		$ & $ 0.5 $ & $ 1.5 $ & $ 0.5 $ & $ 1.5 $ \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Variational inference for max-stable processes" by Patrik Andersson and Alexander Engberg, arXiv:2212.07210v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.