\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
 & Num. cases & Num. sentences & Num. sent. with outcome & Prevalence \\
 \midrule
Training & 388 & 99565 & 536 & 0.54\% \\
Validation & 100 & 29208 & 126 & 0.43\% \\
Testing & 150 & 44881 & 208 & 0.46\% \\
\bottomrule \\
\end{tabular}
\end{adjustbox}
\caption{Data statistics for the annotations used in the case outcome extraction task.}
\end{table}
\end{document}

stat

Andreas Östling
Holli Sargeant
Huiyuan Xie
Ludwig Bull
Alexander Terenin
Leif Jonsson
Måns Magnusson
Felix Steffek

"The Cambridge Law Corpus: A Dataset for Legal AI Research" by Andreas Östling, Holli Sargeant, Huiyuan Xie, Ludwig Bull, Alexander Terenin, Leif Jonsson, Måns Magnusson, and Felix Steffek, arXiv:2309.12269v4, 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{Importance of top 15 individual features}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
\hline
\textbf{Feature Name} & \textbf{Information Value (IV)} \\ \hline
is\_collection & 0.010 \\
genome\_0 & 0.005 \\
keywords\_count & 0.004 \\
genome\_2 & 0.004 \\
genome\_13 & 0.004 \\
movies\_per\_month & 0.003 \\
male\_count & 0.002 \\
genome\_3 & 0.002 \\
genome\_5 & 0.002 \\
genome\_12 & 0.002 \\
is\_homepage & 0.001 \\
time\_discounted\_budget & 0.001 \\
female\_count & 0.001 \\
genome\_1 & 0.001 \\
genome\_10 & 0.001 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Devendra Swami
Yash Phogat
Aadiraj Batlaw
Ashwin Goyal

"Analyzing movies to predict their commercial viability for producers" by Devendra Swami, Yash Phogat, Aadiraj Batlaw, and Ashwin Goyal, arXiv:2101.01697v1, 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.


q-fin

Arega Getaneh Abate
Xiaobing Zhang
Xiufeng Liu
Dogan Keles

"Cost-benefit analysis of an AI-driven operational digital platform for integrated electric mobility, renewable energy, and grid management" by Arega Getaneh Abate, Xiaobing Zhang, Xiufeng Liu, and Dogan Keles, arXiv:2506.20631v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccc}
\hline
 & & \multicolumn{2}{c}{FAQs} & Total \\ \cline{3-5}
 & & Yes & No & \\ \cline{2-5}
\multirow{2}{*}{Agenda} & Yes & 103 & 68 & 171 \\
 & No & 45 & 66 & 111 \\
Total & & 148 & 134 & 282 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Agenda use case * FAQs use case crosstabulation }
\end{table}
\end{document}

cs

J. J. Merelo
P. A. Castillo
Antonio M. Mora
Francisco Barranco
Noorhan Abbas
Alberto Guillen
Olia Tsivitanidou

"Chatbots and messaging platforms in the classroom: an analysis from the teacher's perspective" by J. J. Merelo, P. A. Castillo, Antonio M. Mora, Francisco Barranco, Noorhan Abbas, Alberto Guillen, and Olia Tsivitanidou, arXiv:2201.10289v1, 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}{|c|c|c|c|}
\hline
{\bf year} & {\bf No trend} & {\bf Increasing} & {\bf Decreasing} \\
\hline
2019 & 17 (34.7\%) & 25 (51.0\%) & 7 (14.3\%)
\\
\hline
2020 & 21 (46.7\%) & 6 (13.3\%) & 18 (40.0\%)
\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Trend in daily update time series.}
\end{table}
\end{document}

cs

Ahmed Elmokashfi
Alfred Arouna
Ioana Livadariu
Mah-Rukh Fida
Amund Kvalbein
Anas Al-Selwi
Thomas Dreibholz
Haakon Bryhni

"A Multi-Perspective Study of Internet Performance during the COVID-19 Outbreak" by Ahmed Elmokashfi, Alfred Arouna, Ioana Livadariu, Mah-Rukh Fida, Amund Kvalbein, Anas Al-Selwi, Thomas Dreibholz, and Haakon Bryhni, arXiv:2101.05030v1, 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{Performance of the VI-LSPM across different network sizes $n$, assessed through Procrustes correlation (PC), the area under the receiver operating characteristic curve (AUROC), and the area under the precision recall curve (AUPR) through comparison with the truth and with the MCMC-LSPM solutions. Standard deviations are given in brackets. *The results for $n=1000$ are based on only five sets of initial positions for VI-LSPM, and only on two networks with one set of initial positions for MCMC-LSPM. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ccc|ccc|}
\hline
 & \multicolumn{3}{c|}{Comparison with truth} & \multicolumn{3}{c|}{{ Comparison with MCMC}} \\ \cline{2-7}
\multirow{-2}{*}{$n$} & PC & AUROC & AUPR & { PC} & { AUROC} & { AUPR} \\ \hline
20 & 0.78 (0.16) & { 0.920 (0.017)} & { 0.804 (0.059)} & { 0.77 (0.16)} & { 0.836 (0.060)} & { 0.850 (0.073)} \\ \hline
50 & 0.93 (0.04) & { 0.904 (0.009)} & { 0.783 (0.030)} & { 0.93 (0.04)} & { 0.885 (0.012)} & { 0.787 (0.041)} \\ \hline
100 & 0.95 (0.01) & { 0.904 (0.006)} & { 0.789 (0.018)} & { 0.95 (0.01)} & { 0.896 (0.010)} & { 0.800 (0.025)} \\ \hline
200 & 0.96 (0.01) & { 0.904 (0.004)} & { 0.796 (0.012)} & { 0.96 (0.01)} & { 0.904 (0.005)} & { 0.828 (0.025)} \\ \hline
{ 500} & { 0.97 (0.01)} & { 0.904 (0.002)} & { 0.805 (0.005)} & { 0.97 (0.01)} & { 0.910 (0.005)} & { 0.865 (0.020)} \\ \hline
{ 1000*} & { 0.97 (0.01)} & { 0.904 (0.001)} & { 0.806 (0.007)} & { 0.97 (0.01)} & { 0.912 (0.004)} & { 0.856 (0.003)} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Xian Yao Gwee
Isobel Claire Gormley
Michael Fop

"Variational Inference for the Latent Shrinkage Position Model" by Xian Yao Gwee, Isobel Claire Gormley, and Michael Fop, arXiv:2311.16451v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Horizon} \& \textbf{Cumulative VaR (1\%)} \& \textbf{Cumulative ES (1\%)} \\
\midrule
1 Day (t+1) \& 6.9\%\& 7.89\%\\
2 Days (t+2) \& 10.60\%\& 11.76\%\\
3 Days (t+3) \& 10.99\%\& 12.92\%\\
4 Days (t+4) \& 13.82\%\& 15.07\%\\
5 Days (t+5) \& 15.33\%\& 17.06\%\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{5-day cumulative VaR and ES using GARCH + Normal + MCS at 1\% confidence}
\end{table}
\end{document}

q-fin

"Comparative Evaluation of VaR Models: Historical Simulation, GARCH-Based Monte Carlo, and Filtered Historical Simulation" by Xin Tian, arXiv:2505.05646v1, 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{1D GP Periodic Kernel Results. In a paired t-test, TNP-KR: SA had a lower NLL than TNP-D and ConvCNP with p-value $<0.001$ and TNP-KR: DKA had a lower NLL than TNP-KR: PERF with p-value $0.015$. ConvCNP, TNP-D, TNP-KR: DKA, and TNP-KR: SA are indistinguishable on regret.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
\toprule
Model & NLL & Regret \\
\midrule
NP & $1.340\pm0.001$ & $0.192\pm0.015$ \\
CNP & $1.119\pm0.001$ & $0.247\pm0.018$ \\
BNP & $1.107\pm0.001$ & $0.227\pm0.016$ \\
ANP & $0.824\pm0.013$ & $0.105\pm0.011$\\
CANP & $0.786\pm0.007$ & $0.110\pm0.011$\\
BANP & $0.738\pm0.008$ & $0.081\pm0.009$\\
ConvCNP & $0.551\pm0.002$ & $\mathbf{0.028\pm0.004}$ \\
TNP-D & $0.536\pm0.003$ & $\mathbf{0.045\pm0.006}$\\
TNP-KR: PERF & $0.514\pm0.001$ & $0.049\pm0.006$ \\
TNP-KR: DKA & $0.510\pm0.001$ & $\mathbf{0.038\pm0.005}$\\
TNP-KR: SA & $\mathbf{0.491\pm0.001}$ & $\mathbf{0.036\pm0.006}$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Daniel Jenson
Jhonathan Navott
Mengyan Zhang
Makkunda Sharma
Elizaveta Semenova
Seth Flaxman

"Transformer Neural Processes - Kernel Regression" by Daniel Jenson, Jhonathan Navott, Mengyan Zhang, Makkunda Sharma, Elizaveta Semenova, and Seth Flaxman, arXiv:2411.12502v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Robustness and accuracy of mean opinion scores with hard and soft outlier detection" by Dietmar Saupe and Tim Bleile, arXiv:2509.06554v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Modified Dai-Liao Spectral Conjugate Gradient Method with Application to Signal Processing" by D. R. Sahu, Shikher Sharma, and Pankaj Gautam, arXiv:2501.15300v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Anisotropic Delaunay hypervolume meshing for space-time applications: point insertion, quality heuristics, and bistellar flips" by Jude T. Anderson and David M. Williams, arXiv:2312.17414v2, 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}{lrr}
 \toprule
 \textbf{Model} & \textbf{0-Shot} & \textbf{5-Shot} \\
 \midrule
 LeoLM 7B Chat & 0.305 & 0.463 \\
 LeoLM 13B Chat & 0.341 & 0.655 \\
 LeoLM 70B Chat & 0.591 & 0.762 \\
 \midrule
 GPT-3.5 & 0.790 & 0.783 \\
 GPT-4 & \textbf{0.809} & \textbf{0.833} \\
 \midrule
 \multicolumn{3}{c}{\textbf{Content Moderation APIs}} \\
 \midrule
 Perspective & \multicolumn{2}{r}{0.610} \\
 OpenAI & \multicolumn{2}{r}{\textbf{0.695}} \\
 \midrule
 \multicolumn{3}{c}{\textbf{Target Model}} \\
 \midrule
 gelectra-large R0 & \multicolumn{2}{r}{0.623} \\
 gelectra-large R4 & \multicolumn{2}{r}{\textbf{0.822}} \\
 \bottomrule
 \end{tabular}
\caption{Macro $F_1$ of LLMs and content moderation APIs on the GAHD test set. We include the results of gelectra-large, our target model, for comparison.}
\end{table}
\end{document}

cs

Janis Goldzycher
Paul Röttger
Gerold Schneider

"Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset" by Janis Goldzycher, Paul Röttger, and Gerold Schneider, arXiv:2403.19559v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Estimates of the Productivity Effects with Asymmetric Spillovers}
\begin{tabular}{lccc|c}
			\toprule
			& \multicolumn{3}{c}{\textit{Point Estimates}} & {\textit{Statistically $>0$}} \\
			Estimand & 1st Qu. & Median & 3rd Qu. & (\% Obs.) \\
			\midrule
			$SP$ 	& 0.301 & 0.331 & 0.362 & 99.54 \\
			& (0.277, 0.328) & (0.306, 0.361) & (0.335, 0.400) & \\
			$DL$ 	& 0.083 & 0.133 & 0.169 & 87.01 \\
			& (0.058, 0.106) & (0.104, 0.156) & (0.135, 0.192) & \\
			\midrule
			\multicolumn{5}{p{11.1cm}}{\footnotesize {\sc Notes:} Reported are the results based on the productivity process formulation with asymmetric productivity spillovers in using our baseline specification of $\mathcal{L}(i,t)$. The left panel summarizes point estimates of $SP_{it}$ and $DL_{it}$ with the corresponding two-sided 95\% bootstrap percentile confidence intervals in parentheses. The last column reports the share of observations for which the point estimates are statistically positive at the 5\% significance level using one-sided bootstrap percentile confidence intervals.} \\
			\bottomrule[1pt]
		\end{tabular}
\end{table}
\end{document}

q-fin

"On the Estimation of Cross-Firm Productivity Spillovers with an Application to FDI" by Emir Malikov and Shunan Zhao, arXiv:2302.14602v1, 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{Implemented TE algorithms }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l||l|}
\hline
TE System & Description\\
\hline
KSP+LB & \textit{k-Shortest} Paths (KSP) for paths, LB for weights\\
KSP+AD & \textit{k-Shortest} Paths (KSP) for paths, AD for weights\\
RACKE+LB & Räcke’s oblivious routing for paths, LB for weights\\
RACKE+AD & Räcke’s oblivious routing for paths, AD for weights\\
OPTIMAL(LB) & All paths, LB for weights \\
OPTIMAL(AD) & All paths, AD for weights \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Boosting performance for software defined networks from traffic engineering perspective" by Mohammed I. Salman and Bin Wang, arXiv:2101.06311v1, 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}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 Model & RMSE & MdAE & PER10 ($\%$) & PER20 ($\%$) \\
 \midrule
 Scenario 1 & 1.11 & 0.75 & 30.0 & 55.0 \\
 Scenario 2 & 1.27 & 0.81 & 28.0 & 51.5 \\
 Scenario 3 & 0.99 & 0.43 & 49.0 & 72.5 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Accuracy of the point predictions in the scenario with synthetically generated sale prices from the gradient boosted tree model. The table displays Root Mean Squared Error (RMSE), Median Absolute Error (MdAE), and Percentage Error Range (PER$10$ and PER$20$). \textcolor{red}{Consider just writing this in the text, focusing on RMSE.}}
\end{table}
\end{document}

stat

Anders Hjort
Gudmund Horn Hermansen
Johan Pensar
Jonathan P. Williams

"Uncertainty quantification in automated valuation models with spatially weighted conformal prediction" by Anders Hjort, Gudmund Horn Hermansen, Johan Pensar, and Jonathan P. Williams, arXiv:2312.06531v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|l|l}
\hline
 & Acc. & F1 \\
\hline
 & 62.13 $\pm$ 0.34 & 46.96$\pm$0.43 \\
\hline
Our method & 63.13 $\pm$ 1.12 & 47.95$\pm$ 1.07 \\
\hline
-PDTB aug. & 63.82$\pm$ 1.07 & 48.72$\pm$ 0.11
\\ \hline
\end{tabular}
\caption{ Results of extrinsic evaluation.}
\end{table}
\end{document}

cs

"Automatic Alignment of Discourse Relations of Different Discourse Annotation Frameworks" by Yingxue Fu, arXiv:2403.20196v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
 & 1 month & 3 months & 6 months & 1 year \\
 \hline
SPX & -0.31 & -0.77 & -1.10 & -1.34 \\
EUROSTOXX & -1.01 & -0.13 & -1.03 & - \\
EURUSD & -0.09 & -0.48 & -0.44 & -0.87 \\
GBPUSD & -0.48 & -0.51 & -0.57 & -0.31 \\
USDJPY & -0.26 & -0.31 & -0.34 & -0.68
\end{tabular}
\end{adjustbox}
\caption{Sharpe Ratio of the only long strategies on different assets, using Variance Swaps with different maturities.}
\end{table}
\end{document}

q-fin

"Estimation of Historical volatility and Allocation strategies using Variance Swaps" by Lucio Fiorin, arXiv:2208.03164v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Out-of-Sample Performance Comparison on the Hold-Out Test Set}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Model} & \textbf{ROC AUC} & \textbf{Brier Score} \\
\midrule
\textbf{Primary SVM (RF Select)} & \textbf{0.8905} & \textbf{0.0170} \\
\midrule
\textit{Benchmark Models} \\
Vanilla SVM (All Features) & 0.9061 & 0.0176 \\
LassoCV & 0.9495 & 0.2528 \\
Heuristic (VIX > 40) & 0.6656 & 0.0140 \\
Gaussian Naive Bayes & 0.4878 & 0.0180 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation" by Peilin Rao and Randall R. Rojas, arXiv:2509.05922v1, 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{Comparisons with RFPose}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 Model & Single-Person & Multi-Person & Action \\
 \midrule
 RFPose(4) & 0.664 & 0.626 & 0.616 \\
 RFPose(12) & 0.675 & 0.631 & 0.614 \\
 RFPose(32) & 0.661 & 0.617 & 0.598 \\
 RFPose(64) & 0.641 & 0.589 & 0.604 \\
 RFMask(4) & 0.681 & 0.682 & 0.681 \\
 RFMask(12) & \textbf{0.706} & \textbf{0.711} & \textbf{0.705} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

"RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signals" by Zhi Wu, Dongheng Zhang, Chunyang Xie, Cong Yu, Jinbo Chen, Yang Hu, and Yan Chen, arXiv:2201.10175v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Demand and vehicle allocation costs per district for the Nepal instance}
\begin{tabular}{lllll}
\hline
District & Name & Demand per period & UAV costs & Truck costs \\ \hline
1 & Dolakha & 217 & 202 & 1256 \\
2 & Gorkha & 305 & 178 & 1266 \\
3 & Okhaldhunga & 55 & 266 & 1223 \\
4 & Sindhupalchok & 278 & 108 & 667 \\
5 & Bhaktapur & 117 & 26 & 169 \\
6 & Rasuwa & 49 & 108 & 1928 \\
7 & Ramechhap & 167 & 214 & 871 \\
8 & Makwanpur & 156 & 197 & 1085 \\
9 & Dhading & 352 & 113 & 731 \\
10 & Sindhuli & 156 & 82 & 683 \\
11 & Nuwakot & 333 & 67 & 437 \\
12 & Kavrepalanchok & 308 & 62 & 365 \\
13 & Lalitpur & 107 & 12 & 251 \\
\hline
\end{tabular}
\end{table}
\end{document}

math

Robert van Steenbergen
Wouter van Heeswijk
Martijn Mes

"The Stochastic Dynamic Post-Disaster Inventory Allocation Problem with Trucks and UAVs" by Robert van Steenbergen, Wouter van Heeswijk, and Martijn Mes, arXiv:2312.00140v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Top 10 Applications by Token Usage}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
Application & Usage (Millions of Tokens)\\
\midrule
\cellcolor{gray!10}{Cline} & \cellcolor{gray!10}{314 583 M}\\
Roo Code & 231 702 M\\
\cellcolor{gray!10}{shapes inc} & \cellcolor{gray!10}{37 620 M}\\
SillyTavern & 36 883 M\\
\cellcolor{gray!10}{Chub AI} & \cellcolor{gray!10}{17 435 M}\\
\addlinespace
DocsLoop & 14 264 M\\
\cellcolor{gray!10}{OpenRouter: Chatroom} & \cellcolor{gray!10}{13 405 M}\\
liteLLM & 11 461 M\\
\cellcolor{gray!10}{Fraction AI} & \cellcolor{gray!10}{8 085 M}\\
Fish Audio & 6 445 M\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Demand for LLMs: Descriptive Evidence on Substitution, Market Expansion, and Multihoming" by Andrey Fradkin, arXiv:2504.15440v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 \textbf{Hyperparameter} & \textbf{Value} \\ \midrule
 Optimiser & Adam \\
 Initial learning rate & \(10^{-4}\) \\
 Minibatch size & 64 \\
 Hidden layers & 2 \\
 Neurons per hidden layer & 512 \\
 Activation function & GELU \\
 Path length ($N_{\texttt{path length}}$ ) & 70 \\
 Parallel simulations & 512 \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Hyper-parameters of the DEQN training routine.}
\end{table}
\end{document}

q-fin

Felix Kübler
Simon Scheidegger
Oliver Surbek

"Using Machine Learning to Compute Constrained Optimal Carbon Tax Rules" by Felix Kübler, Simon Scheidegger, and Oliver Surbek, arXiv:2507.01704v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
\hline
 \emph{HGM} & \emph{RRWGM} & \emph{TM} & \textbf{ARSRG}$_{1^{st}}$ & \textbf{ARSRG}$_{2^{nd}}$\\ \hline
 0.2600 & 0.1322 & 0.1348 & 0.6115 & 1.0\\ \hline % old value 0.8000
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Structure of the Capital Control Measures Dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
\textbf{Field} & \textbf{Description} \\
\midrule
Year & The implemented year of the capital control policies \\
IFS code & Country code \\
Country & Name of the reporting country \\
Region & Region of the reporting country \\
Income group & Income group of the reporting country based on World Bank \\
Income subgroup & Income subgroup of the reporting country based on World Bank \\
Index Code & Index code provided by IMF \\
Category Index & Hierarchical index consistent with IMF AREAER classification \\
Category & Name of the capital control category (e.g., XI.A.2.a.1.i) \\
Date & Effective date of the intervention \\
Description & Full text description of the capital control measure \\
Date of Retroactive Changes & The date if there is a date for canceling this policy (if applicable) \\
Action & Verbs describing the measure (e.g., prohibit, permit, require approval) \\
Action Intensity & Restrictive / Conditional / Liberalizing / Neutral \\
Action Direction & Inward / Outward / Both / Undefined \\
Action Level & Supranational / National / Subnational / Undefined \\
Instrument & Policy tool involved (e.g., foreign exchange, credit, reserve requirement) \\
Actor & Entity implementing the policy (e.g., central bank, government) \\
Condition & With (e.g., subject to approval) / Without (e.g., no approval required) \\
Beneficiary & Group intended to benefit or be affected (e.g., exporters, travelers) \\
Target Country & Country explicitly targeted by the measure (if applicable) \\
Target Industry & Sector targeted by the policy (if applicable) \\
Target Industry & Sector targeted by the policy (if applicable) \\
Limit/Threshold & Limits number or threshold of the policy (if applicable) \\
Is Trade Policy & True if measure relates to trade in goods or tariffs \\
Is Sanction & True if measure involves bans, freezes, or restricted parties \\
Is National Security & True if measure references security or national interest \\
LLM Reasoning & Cited phrase and rationale reasoning extracted by LLMs \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Geyue Sun
Xiao Liu
Tomas Williams
Roberto Samaniego

"Learning to Regulate: A New Event-Level Dataset of Capital Control Measures" by Geyue Sun, Xiao Liu, Tomas Williams, and Roberto Samaniego, arXiv:2505.23025v1, 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{Model Estimates October 1, 2024}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
\hline
Variable & Value & Standard Error \\
\hline \hline
mempool quartile 1 & $-3.7229 \times 10 ^{-3}$ & $1.069 \times 10^{-2}$\\
mempool quartile 2 & $3.5696 \times 10 ^{-3}$ & $1.072 \times 10^{-2}$ \\
mempool quartile 3 & $-4.9224 \times 10 ^{-3}$ & $1.073 \times 10^{-2}$ \\
max fee per gas$^{***}$ & $-8.5780 \times 10^{-4}$ & $3.117 \times 10^{-5}$ \\
to DEX$^{***}$ & $-7.7121 \times 10^{-1}$ & $1.386 \times 10^{-2}$ \\
to MEV$^{***}$ & $-1.7074 \times 10^0$ & $2.972 \times 10^{-2}$ \\
from DEX$^{***}$ & $-1.4161 \times 10^0$ & $8.489 \times 10^{-2}$ \\
from MEV$^{***}$ & $1.9906 \times 10^0$ & $3.238 \times 10^{-2}$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering" by Bruce Mizrach and Nathaniel Yoshida, arXiv:2508.04003v1, 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|r|r|r|r|r|r|r}
 \hline
 \multicolumn{3}{c}{Problem size} & \multicolumn{4}{|c|}{Solving subproblems} & Impl. sol. & Total 10 iter\\
 \hline
 $T$ & $|\Omega|$ & $|\Phi|$ & sec/$\phi$ $(k=0)$ & sec/iter $(k=0)$ & sec/$\phi$ & sec/iter & sec/iter & sec\\
 \hline
 7 & 2 & 128 & 0.03 & 3.84 & 0.01 & 1.28 & 0.2 & 16.26\\
 7 & 3 & 2,187 & 0.03 & 65.61 & 0.01 & 21.87 & 1.5 & 275,94 \\
 7 & 4 & 16,384 & 0.03 & 491.52 & 0.01 & 163.84 & 10.5 & 2,071.08\\
 7 & 5 & 78,125 & 0.03 & 2,343.75 & 0.01 & 781.25 & 73.5 & 10,110.00\\
 7 & 6 & 279,936 & 0.03 & 8,398.08 & 0.01 & 2799,36 & 514.5 & 38,737.32\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Runtime analysis for the progressive hedging algorithm}
\end{table}
\end{document}

math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

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


\documentclass{article}
\usepackage{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}{lllll}
 \toprule
 \textbf{Benchmark Task} & \textbf{Graph Learning Task} & \textbf{Node Features} & \textbf{Edge Features} & \textbf{Global Features} \\
 \midrule
 \multirow{5}{*} & & \ Player positions & Teammate or opponent & None \\
 & & \ Player velocities & & \\
 Receiver Prediction & Node classification & \ Player weights & & \\
 & & \ Player heights & & \\
 & &\ Ball possession & & \\
 \midrule
 \multirow{1}{*}{Shot Prediction} & Graph classification & \ Same as above & Same as above & Receiver ID\\
 \midrule
 Guided Generation & Node regression & \ Same as above & Same as above & Shot indicator \\
 & & & & Receiver ID \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{{\bf Summary of the features used in the corresponding tasks.}}
\end{table}
\end{document}

stat

Zhe Wang
Petar Veličković
Daniel Hennes
Nenad Tomašev
Laurel Prince
Michael Kaisers
Yoram Bachrach
Romuald Elie
Li Kevin Wenliang
Federico Piccinini
William Spearman
Ian Graham
Jerome Connor
Yi Yang
Adrià Recasens
Mina Khan
Nathalie Beauguerlange
Pablo Sprechmann
Pol Moreno
Nicolas Heess
Michael Bowling
Demis Hassabis
Karl Tuyls

"TacticAI: an AI assistant for football tactics" by Zhe Wang, Petar Veličković, Daniel Hennes, Nenad Tomašev, Laurel Prince, Michael Kaisers, Yoram Bachrach, Romuald Elie, Li Kevin Wenliang, Federico Piccinini, William Spearman, Ian Graham, Jerome Connor, Yi Yang, Adrià Recasens, Mina Khan, Nathalie Beauguerlange, Pablo Sprechmann, Pol Moreno, Nicolas Heess, Michael Bowling, Demis Hassabis, and Karl Tuyls, arXiv:2310.10553v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
\textit{Characteristic} & \textit{Sensing} & \textit{Data Processing} & \textit{System Reaction} \\ \hline
Effect & None & Major & Major \\
Time & Major & Minor & None \\
Focus & Minor & Minor & None \\
Space & Major & Minor & None
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Gesture-based Human-Machine Interaction: Taxonomy, Problem Definition, and Analysis" by Alessandro Carfì and Fulvio Mastrogiovanni, arXiv:2201.10186v1, 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
 & PINNs & IR-PINNs1 & IR-PINNs2 \\
 \midrule
 Relative $L_2$ error & 6.3784e-02 & 5.3724e-02 & 5.6185e-02 \\
 \midrule
 Relative KL divergence & 2.0582e-03 & 2.0117e-03 & 1.8202e-03 \\
 \midrule
 Running time (hours) & 9.612 & 21.06 & 24.47 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\textit{Time-dependent Fokker-Planck equation:} Relative $L_2$ errors and running time of different methods.}
\end{table}
\end{document}

math

Xiaodong Feng
Haojiong Shangguan
Tao Tang
Xiaoliang Wan

"Integral regularization PINNs for evolution equations" by Xiaodong Feng, Haojiong Shangguan, Tao Tang, and Xiaoliang Wan, arXiv:2503.23729v1, 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{Performance metrics for the different eigenfunctions ($32 \times 32$+dp+ma).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|} \hline
				Index of Eigenvalue & MaxAE & PSNR & RelL1 \\ \hline
				1 & 0.11 & 53.04 & 1.22\% \\
				2 & 0.37 & 42.13 & 7.04\%\\
				3 & 0.68 & 35.88 & 13.01\% \\ \hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Operator Inference for Elliptic Eigenvalue Problems" by Haoqian Li, Jiguang Sun, and Zhiwen Zhang, arXiv:2504.15733v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
			\hline
 Method & avg-min-CDR $\downarrow$ & JDR $\downarrow$ & Recall $\uparrow$ & FPS $\uparrow$\\
 \hline
 w/o SBD& 14.18\%& 2.261& 53.65\%&\textbf{33.4}\\
 w/o ST& \textbf{4.76\%}& 12.468& \textbf{82.33\%}&9.6\\
 \hline
 Our Framework & 10.94\%& \textbf{0.865}& 69.53\%&28.6\\
 \hline
		\end{tabular}
\end{adjustbox}
\caption{Ablation study of our {\it H2V} framework to investigate the contributions of shot boundary detection and subject tracking modules.}
\end{table}
\end{document}

cs

Horizontal-to-Vertical Video Conversion

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

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of parameters for each model }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
\hline
Dataset & MeshGraphNet & GRU & LSTM & Transformer & GMR-GMU\\
\hline
Cylinder flow& 2.2M & 9.4M & 11.5M &14.1M &1.2M\\
\hline
Sonic flow& 2.2M & 9.4M & 11.5M &14.1M & 1.2M\\
\hline
Vascular flow& 2.2M & 5.8M & 7.0M & 8.5M & 1.2M \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xu Han
Han Gao
Tobias Pfaff
Jian-Xun Wang
Li-Ping Liu

"Predicting Physics in Mesh-reduced Space with Temporal Attention" by Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, and Li-Ping Liu, arXiv:2201.09113v4, 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 metrics for ADHD classification.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \hline
 & \textbf{Precision} & \textbf{Recall} & \textbf{F1-Score} \\
 \hline
 \textbf{0 (No ADHD)} & 0.98 & 0.79 & 0.88 \\
 \textbf{1 (ADHD)} & 0.86 & 0.99 & 0.92 \\
 \hline
 \textbf{Accuracy} & - & - & 0.90 \\
 \textbf{Macro avg} & 0.92 & 0.89 & 0.90 \\
 \textbf{Weighted avg} & 0.91 & 0.90 & 0.90 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"An ADHD Diagnostic Interface Based on EEG Spectrograms and Deep Learning Techniques" by Medha Pappula and Syed Muhammad Anwar, arXiv:2412.02695v1, 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{Hyperparameters of the models built in our experiments.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
\hline
\textbf{Component} & \textbf{Parameter} & \textbf{Value} \\ \hline
\multirow{2}{*}{Embedding} & Word embedding dimension $d_w$ & 50 \\ \cline{2-3}
 & Position embedding dimension $d_p$ & 5 \\ \hline
\multirow{3}{*}{Encoder} & Hidden layer dimension $d_h$ & 230 \\ \cline{2-3}
 & Convolutional Window Size $u$ & 3 \\ \cline{2-3}
 & Max length $T$ & 40 \\ \hline
\multirow{2}{*}{Joint loss} & Margin $m$ & 0.5 \\ \cline{2-3}
 & Alpha $\alpha$ & 1 \\ \hline
\multirow{3}{*}{Optimization} & Initial Learning Rate & 0.1 \\ \cline{2-3}
 & Weight Decay & 10-5 \\ \cline{2-3}
 & Dropout rate & 0.2 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification" by Yan Xiao, Yaochu Jin, and Kuangrong Hao, arXiv:2101.03526v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Neural Networks Use Distance Metrics

"Neural Networks Use Distance Metrics" by Alan Oursland, arXiv:2411.17932v1, 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{ESG values and stock closing prices $S_0$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcrcrr}
	\toprule
	Equity & \multicolumn{2}{c}{11/19/2020} & \multicolumn{2}{c}{11/19/2021} & $S_0\ \ \ $\\
	\cline{2-5}

	\strut & ESG$^{(\text{R})}$ & $e_{n,k}\ \ \ \ $ & ESG$^{(\text{R})}$ & $e_{n,k}\ \ \ \ $ & (USD) \\
	\midrule
	MSFT & 96 & $\ \ 3.65 \cdot 10^{-3}$ & 98 & $\ \ 3.81 \cdot 10^{-3}$ & 336.32\\
	AMZN & 60 & $\ \ 0.79 \cdot 10^{-3}$ & 71 & $\ \ 1.67 \cdot 10^{-3}$ & 3,334.34\\
	AAPL & 25 & $ -1.98 \cdot 10^{-3}$ & 34 & $ -1.27 \cdot 10^{-3}$ & 177.57\\
	\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Yuan Hu
W. Brent Lindquist
Svetlozar T. Rachev

"ESG-valued discrete option pricing in complete markets" by Yuan Hu, W. Brent Lindquist, and Svetlozar T. Rachev, arXiv:2209.06276v1, 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{Quantitative comparison of axial strain estimation on simulated phantom.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
			\hline
			&LF&Inter.&	HF\\
			&OVERWIND&OVERWIND&	OVERWIND\\
			\hline
			ME	&$-3.13\times 10^{-5}$ & $-3.93\times 10^{-5}$	 & $-4.38\times 10^{-5}$\\
			VE	&$5.21\times 10^{-7}$ & $4.97\times 10^{-7}$ & $4.96\times 10^{-7}$ \\
			RMSE& $7.99\%$ & $7.82\%$ & $7.81\%$ \\
			CNR	& $50.76$ & $52.56$ & $54.09$\\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Virtual Source Synthetic Aperture for Accurate Lateral Displacement Estimation in Ultrasound Elastography" by Morteza Mirzaei, Amir Asif, and Hassan Rivaz, arXiv:2012.10562v2, 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{Perplexity of held-out mutations for inferred mutational signature attributions. \rm Signatures were based on COSMIC v3.3 signature weights. Super/subscripts indicate ninety-five per cent confidence intervals, computed by bootstrapping.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 \textbf{Method} & & \textbf{Hold out perplexity} \\
 \midrule
 SigProfilerExtractor$^{}$ & & 64.5$^{+0.7}_{-0.7}$ \\
 Zhou--Cong--Chen & (1 layer) & 62.0$^{+0.7}_{-0.7}$ \\
 & (2 layers) & 61.9$^{+0.7}_{-0.7}$ \\
 This work & (1 layer) & 62.0$^{+0.7}_{-0.7}$ \\
 & (2 layers) & 61.9$^{+0.7}_{-0.7}$ \\
 \bottomrule \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

H. C. Donker
D. Neijzen
J. de Jong
G. A. Lunter

"Multinomial belief networks for healthcare data" by H. C. Donker, D. Neijzen, J. de Jong, and G. A. Lunter, arXiv:2311.16909v3, 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{Benchmark results: It details the performance of our finetuned model compared to other closed embedding models. The table shows HR@5, improvement (our model's improvement compared to benchmark models), and embedding size, illustrating our model's efficiency and effectiveness.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
\toprule
 & HR@5 [\%] & Improvement [\%] & Embedding Size \\
\midrule
Google Text-Embedding-004 & 84 & 5 & 768 \\
Cohere Embed-English-v3.0 & 85 & 4 & 1024 \\
OpenAI Text-Embedding-3-Large & 86 & 2 & 3072 \\
MistralAI Mistral-Embed & 87 & 1 & 1024 \\
VoyageAI Voyage-Finance-2 & 88 & 0 & 1024 \\
Ours & 88 & - & 768 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Amin Haeri
Jonathan Vitrano
Mahdi Ghelichi

"Generative AI Enhanced Financial Risk Management Information Retrieval" by Amin Haeri, Jonathan Vitrano, and Mahdi Ghelichi, arXiv:2504.06293v2, 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}
Model & $R^2_{pool}$ (\%) & $R^2_{avg} (\%)$\\
\hline
E-GPR ($\gamma$-exp)&0.96& 0.58\\
E-GPR (affine)&0.74& 0.42\\
E-LR& 0.61 & 0.18\\
LR & 0.37 & 0.003\\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Comparison of predictive performance across all stocks, i.e., $R^2 (\%)$, among various models}
\end{table}
\end{document}

q-fin

"Empirical Asset Pricing via Ensemble Gaussian Process Regression" by Damir Filipović and Puneet Pasricha, arXiv:2212.01048v2, 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
$f_\nu(X^*)$&$f_\nu(Y^*)$&$f_\nu(Z^*)$&$\frac{f_\nu(X^*)-f_\nu(Y^*)}{f_\nu(X^*)}\leq \frac{\widehat{F}_\nu(Y^*)-\widehat{f}_\nu(Y^*)}{\widehat{f}_\nu(Y^*)}$&$d_g(X^*,Y^*)\leq N^{-\frac{1}{\nu}}2\widehat{F}_\nu(Y^*)$\\
\hline
\multicolumn{5}{||c||}{Clustered dataset, $(N,n, p, \beta,\nu) = (5,50,20,1,1)$}\\
\hline
4.384 & 4.384 & 4.384 &
 $1.114$e$-6 \leq 0.03344$ &
 $0.001613\leq 1.811$ \\
\hline
\multicolumn{5}{||c||}{Spread dataset, $(N,n, p,\beta,\nu) = (5,50,20,1,2)$}\\
\hline
11.59 & 11.66 & 11.66 &
 $0.005723 \leq 0.4886$ &
 $0.7046\leq 15.03$\\
\hline
\multicolumn{5}{||c||}{Clustered dataset, $(N,n, p,\beta,\nu) = (10,100,40,0.5,1)$}\\
\hline
8.426 & 8.427 & 8.427 &
 $9.751$e$-5 \leq 0.4707$ &
 $0.01176\leq 1.953$\\
\hline
\multicolumn{5}{||c||}{Spread dataset, $(N,n, p, \beta,\nu) = (10,100,40,0.5,2)$}\\
\hline
23.50 & 24.23 & 24.23 &
 $0.03116 \leq 1.076$ &
 $3.274\leq 23.52$\\
\hline
	\end{tabular}
\end{adjustbox}
\caption{Average results over 10 runs of the performances of algorithms (A1), (A2) and (A3) for the computation of an approximate Riemannian $l^\nu$-barycenter.}
\end{table}
\end{document}

math

Simon Mataigne
P. -A. Absil
Nina Miolane

"On the approximation of the Riemannian barycenter" by Simon Mataigne, P. -A. Absil, and Nina Miolane, arXiv:2504.15671v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

William N. Caballero
Matthew LaRosa
Alexander Fisher
Vahid Tarokh

"Indiscriminate Disruption of Conditional Inference on Multivariate Gaussians" by William N. Caballero, Matthew LaRosa, Alexander Fisher, and Vahid Tarokh, arXiv:2411.14351v1, 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{Modeled Unobservables}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\\\hline
\hline \\
 \\ Parameter & Estimate & Std. Error \\

 \hline \\

 Standard deviation of $q$, $\sigma_{q}$ & $0.042^{***}$ & 0.004 \\

 \\

 Parameter on $\sigma_q$, $\delta$ & $0.512^{***}$ & 0.024 \\

 \\
 $\gamma_{\text{Signing Bonus}}$ & $0.217^{***}$ & 0.053 \\

 \\
 $\gamma_{\text{Performance Bonus}}$ & $0.526^{***}$ & 0.049 \\

 \\
 $\gamma_{\text{Medical Insurance}}$ & $0.017\phantom{^{***}}$ & 0.079 \\
 \\
 $\gamma_{\text{Relocation Allowance}}$ & $0.286^{***}$ & 0.051 \\
 \\
 $\gamma_{\text{Restricted Stock Units}}$ & $0.487^{***}$ & 0.104 \\
\hline
\hline \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Making the Elite: Top Jobs, Disparities, and Solutions" by Soumitra Shukla, arXiv:2208.14972v3, 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{Experimental results of the proposed SWTR-Unet produced on in-house MRI data in comparison with state-of-the-art works. Stated are the Dice similarity coefficients (DSC) of the liver and liver lesion segmentations.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
& $DSC_{liver}$ & $DSC_{lesion}$\\
\hline
 & $0.87$ & $0.70$ \\
 & $0.91 \pm 0.01$ & $0.68 \pm 0.03$ \\
 & & $0.81 \pm 0.03$ \\
 & & $0.74 \pm 0.19$ \\
\textbf{SWTR-Unet} & $\mathbf{0.98 \pm 0.02}$ & $\mathbf{0.81 \pm 0.28}$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Georg Hille
Shubham Agrawal
Pavan Tummala
Christian Wybranski
Maciej Pech
Alexey Surov
Sylvia Saalfeld

"Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers" by Georg Hille, Shubham Agrawal, Pavan Tummala, Christian Wybranski, Maciej Pech, Alexey Surov, and Sylvia Saalfeld, arXiv:2201.10981v3, 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{DID Analysis of Offline Metrics (Natural Log Scale) -- \textit{Black Friday Adopters} vs. \textit{Organic Adopters}}
\begin{tabular}{lccccccc}
 \midrule \midrule
 DVs: & Spend & Quantity & Orders & Unique Items & Unique Brands & Subcategories & Categories\\
 Model: & (1) & (2) & (3) & (4) & (5) & (6) & (7)\\
 \midrule
 \emph{Variables}\\
 BlackFriday\_Adopter * Post & 0.082 (0.059) & 0.003 (0.020) & 0.005 (0.010) & 0.009 (0.017) & 0.013 (0.015) & 0.014 (0.014) & 0.013 (0.013)\\
 & [0.170] & [0.882] & [0.639] & [0.582] & [0.383] & [0.308] & [0.325]\\
 \midrule
 \emph{Fixed-effects}\\
 Customer & Yes & Yes & Yes & Yes & Yes & Yes & Yes\\
 YearMonth & Yes & Yes & Yes & Yes & Yes & Yes & Yes\\
 \midrule
 \emph{Fit statistics}\\
 Observations & 76,842 & 76,842 & 76,842 & 76,842 & 76,842 & 76,842 & 76,842\\
 R$^2$ & 0.35766 & 0.42251 & 0.41348 & 0.39672 & 0.38879 & 0.38097 & 0.37182\\
 Within R$^2$ & $5.17\times 10^{-5}$ & $5.66\times 10^{-7}$ & $6.82\times 10^{-6}$ & $9.35\times 10^{-6}$ & $2.27\times 10^{-5}$ & $2.87\times 10^{-5}$ & $3.05\times 10^{-5}$\\
 \midrule \midrule
 \multicolumn{8}{l}{\emph{Clustered (Customer) standard‐errors are presented in standard brackets, while p‑values are shown in square brackets.}}\\
 \end{tabular}
\end{table}
\end{document}

q-fin

Channel Choice and Customer Value

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

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


eess

"MusicTM-Dataset for Joint Representation Learning among Sheet Music, Lyrics, and Musical Audio" by Donghuo Zeng, Yi Yu, and Keizo Oyama, arXiv:2012.00290v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Brady Neal
Chin-Wei Huang
Sunand Raghupathi

"RealCause: Realistic Causal Inference Benchmarking" by Brady Neal, Chin-Wei Huang, and Sunand Raghupathi, arXiv:2011.15007v2, 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{Proportion of inactive crypto projects in each cluster in May 2020. Single feature (6 month).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 ID & \# & N/A in CoinMarketCap & N/A in GitHub & All \\\hline
 1 & 628 & 225 (35.8\%) & 14 (2.2\%) & 237 (37.7\%) \\\hline
 2 & 15 & 3 (20.0\%) & 0 (1.7\%) & 3 (20.0\%) \\\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

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


stat

Maciej Beręsewicz
Marek Wydmuch
Herman Cherniaiev
Robert Pater

"Multilingual hierarchical classification of job advertisements for job vacancy statistics" by Maciej Beręsewicz, Marek Wydmuch, Herman Cherniaiev, and Robert Pater, arXiv:2411.03779v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{This tables shows the different feature scenarios considered in our ablation study together with their total number of features. Note that the third column indicates the usage of both original and derived features from the given feature source. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{clc}
 \toprule
 Scenario & Feature Source & Features\\
 \midrule
 1 & Stock Price & 2 \\
 2 & Stock Price, Tweets & 8 \\
 3 & Implied Volatility & 3 \\
 4 & Implied Volatility, Tweets & 9 \\
 5 & Tweets & 6\\
 6 & Stock Price, Implied Volatility & 5 \\
 7 & Stock Price, Implied Volatility, Tweets & 11 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Thomas Dierckx
Jesse Davis
Wim Schoutens

"Nowcasting Stock Implied Volatility with Twitter" by Thomas Dierckx, Jesse Davis, and Wim Schoutens, arXiv:2301.00248v1, 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{DCI Format 1C for M-RNTI in LTE}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c||}
		\hline
		Field Names& Occupied Bits \\
		\hline\hline
		MCCH Change Notification & 8 bits \\
		\multirow{6}{*}{Reserved}&N/A~ (1.4MHz)\\&2bits~(3MHz)\\&4bits~(5MHz)\\&5bits~(10MHz)\\&6bits~(15MHz)\\&7bits~(20MHz)\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Hongzhi Chen
De Mi
Manuel Fuentes
Eduardo Garro
Jose Luis Carcel
Belkacem Mouhouche
Pei Xiao
Rahim Tafazolli

"On the Performance of PDCCH in LTE and 5G New Radio" by Hongzhi Chen, De Mi, Manuel Fuentes, Eduardo Garro, Jose Luis Carcel, Belkacem Mouhouche, Pei Xiao, and Rahim Tafazolli, arXiv:2001.02757v1, licensed under CC0 1.0. 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{Summary of datasets used in experiments.}
\begin{tabular}{lrrrr}
\toprule
Dataset & Training Samples & Test Samples & Classes & Input Features \\
\midrule
MNIST & 60,000 & 10,000 & 10 & 784 (28$\times$28$\times$1) \\
F-MNIST & 60,000 & 10,000 & 10 & 784 (28$\times$28$\times$1) \\
K-MNIST & 60,000 & 10,000 & 10 & 784 (28$\times$28$\times$1) \\
CIFAR-10 & 50,000 & 10,000 & 10 & 3,072 (32$\times$32$\times$3) \\
CIFAR-100 & 50,000 & 10,000 & 100 & 3,072 (32$\times$32$\times$3) \\
Tiny ImageNet & 100,000 & 10,000 & 200 & 12,288 (64$\times$64$\times$3) \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Chris Kolb
Tobias Weber
Bernd Bischl
David Rügamer

"Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries" by Chris Kolb, Tobias Weber, Bernd Bischl, and David Rügamer, arXiv:2502.02496v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Efficient normal scoring mechanism.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \toprule
 \toprule
 \multirow{2}{*}{Normal scoring function}&
 \multicolumn{2}{c}{CIFAR10}&\multicolumn{2}{c}{CIFAR100}\cr
 \cmidrule(lr){2-3}\cmidrule(lr){4-5}
 &AVG&STD&AVG&STD\cr
 \cmidrule(lr){1-5}
 $NormalScore_{rand}$ & 86.4 & 1.04 & 75.6 & 1.26\cr
 $NormalScore_{mc}$ & 86.9 & 0.05 & 78.5 & 0.12\cr
 $NormalScore_{ori}$ & 86.7 & $-$ & 77.4 & $-$\cr
 \bottomrule
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Fei Ye
Huangjie Zheng
Chaoqin Huang
Ya Zhang

"Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework" by Fei Ye, Huangjie Zheng, Chaoqin Huang, and Ya Zhang, arXiv:2012.04837v1, 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{Compression factor (cf) and Top-1 accuracies with two different binary approximation procedures as function of $M$. For each network the single-precision floating-point accuracy is indicated for comparison.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{crcccc}
		\hline
		& & \multicolumn{2}{c}{acc. w/ Algorithm~} & \multicolumn{2}{c}{acc. w/ Algorithm~} \\
		$M$ & cf & no retrain & w/ retrain & no retrain & w/ retrain\\
		\hline
		\multicolumn{6}{c}{CNN-A (baseline acc.\ 97.86\%)} \\
		\hline
		2	& 15.8 & 84.68\%	&97.09\%	& 87.43\%	& 97.13\%\\
		3	& 10.6 & 93.40\%	&97.51\% 	& 95.92\%	& 97.29\%\\
		4	& 7.9 & 95.64\%	&96.60\% 	& 97.51\%	& 98.01\%\\
		\hline
		\multicolumn{6}{c}{CNN-B1 (baseline acc. 56.3\%)} \\
		\hline
		4	& 7.6 & 0.10\% & 43.17\% & 0.18\% & 51.55\% \\
		5	& 6.1 & 0.08\% & 46.29\% & 0.64\% & 54.46\% \\
		6	& 5.1 & 0.10\% & 50.96\% & 5.03\% & 55.03\% \\
		\hline
		\multicolumn{6}{c}{CNN-B2 (baseline acc.\ 70.9\%)} \\
	 \hline
		4	& 7.9 & 0.11\% & 46.90\% & 0.2\% & 47.82\% \\
		5	& 6.2 & 0.12\% & 46.84\% & 6.8\%& 53.59\% \\
		6	& 5.2 & 0.08\% & 51.23\% & 25.2\% & 69.10\% \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"BinArray: A Scalable Hardware Accelerator for Binary Approximated CNNs" by Mario Fischer and Juergen Wassner, arXiv:2012.03481v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Harder settings.} Mean HRP (\%, $\uparrow$) for ALOCC, NHP, and runner-ups, across all VS experiments. Best method in \textbf{bold}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccc}
\toprule
& \multicolumn{2}{c}{Test set} & \multicolumn{2}{c}{OOD} & \multicolumn{2}{c}{Adv. Ex.} \\
Method & \small{HRP $\uparrow$} & \small{Val. Gap} & \small{HRP $\uparrow$} & \small{Val. Gap} & \small{HRP $\uparrow$} & \small{Val. Gap} \\
\cmidrule(lr){1-1} \cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7}
ALOCC & -0.7\textsubscript{±29.48} & - & -3.18\textsubscript{±27.34} & - &-6.7\textsubscript{±26.11} & - \\
NHP (GMM) & 48.93\textsubscript{±15.07} & 2.43\textsubscript{±11.99} & 47.94\textsubscript{±13.61} & 5.65\textsubscript{±12.68} & 42.87\textsubscript{±19.34} & 10.66\textsubscript{±17.32} \\
NHP (linear) & 51.58\textsubscript{±14.75} & 1.23\textsubscript{±10.08} & 49.57\textsubscript{±12.52} & 4.25\textsubscript{±10.58} & 43.52\textsubscript{±17.15} & 10.3\textsubscript{±16.66} \\
NHP (Ours) & \textbf{52.4\textsubscript{±14.3}} & 1.42\textsubscript{±9.61} &
\textbf{49.62\textsubscript{±13.34}} & 3.99\textsubscript{±11.34} & \textbf{44.87\textsubscript{±16.22}} & 8.75\textsubscript{±16.92} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ji-Hun Oh
Kianoush Falahkheirkhah
Rohit Bhargava

"Detecting Hallucinations in Virtual Histology with Neural Precursors" by Ji-Hun Oh, Kianoush Falahkheirkhah, and Rohit Bhargava, arXiv:2411.15060v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
\hline
\textbf{Real} & \textbf{StyleGAN} & \textbf{LDM} & \textbf{Accuracy} & \textbf{F1 Score} & \textbf{Precision} & \textbf{Recall} & \textbf{MCC} \\
\hline
0 & 0 & 250 & 47\% & 0.52 & 0.60 & 0.47 & 0.40 \\\hline
0 & 0 & 500 & 67\% & 0.69 & 0.74 & 0.66 & 0.61 \\\hline
0 & 0 & 1000 & 87\% & 0.87 & 0.88 & 0.87 & 0.84 \\\hline
0 & 0 & 2000 & 90\% & 0.90 & 0.90 & 0.90 & 0.88 \\\hline
0 & 0 & 3000 & 87\% & 0.87 & 0.89 & 0.87 & 0.84 \\\hline
0 & 0 & 4000 & 89\% & 0.89 & 0.89 & 0.90 & 0.85 \\\hline
0 & 0 & 5000 & 89\% & 0.89 & 0.89 & 0.90 & 0.86 \\\hline
\end{tabular}
\end{adjustbox}
\caption{Classification results of the VGG16 model on the test dataset (consisting of real images), trained using various amounts of LDM-generated images.}
\end{table}
\end{document}

eess

Oriana Presacan
Alexandru Dorobantiu
Vajira Thambawita
Michael A. Riegler
Mette H. Stensen
Mario Iliceto
Alexandru C. Aldea
Akriti Sharma

"Merging synthetic and real embryo data for advanced AI predictions" by Oriana Presacan, Alexandru Dorobantiu, Vajira Thambawita, Michael A. Riegler, Mette H. Stensen, Mario Iliceto, Alexandru C. Aldea, and Akriti Sharma, arXiv:2412.01255v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of Trainable Parameters}
\begin{tabular}{lc}
\toprule
\textbf{Model} & \textbf{Trainable Parameters (Millions)} \\
\midrule
Wav2vec2-base & 94.57 \\
Whisper-base & 20.72 \\
\textbf{Whisper-base (Strategy 1)} & \textbf{11.27} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Huma Ameer
Seemab Latif
Rabia Latif
Sana Mukhtar

"Whisper in Focus: Enhancing Stuttered Speech Classification with Encoder Layer Optimization" by Huma Ameer, Seemab Latif, Rabia Latif, and Sana Mukhtar, arXiv:2311.05203v1, 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{PESQ comparisons of different deep models for multi-channel speech enhancement on the WSJ0 corpus. The average PESQ score for unprocessed ch-1 noisy speech is 2.02.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l||c|c|c}
 \hline
 \hline
 Model 		& Channel \# & Parameter \# & PESQ	 \\
 \hline
 \hline
 DNN		&	1		 & 	27M	 & 	2.86	\\
 DNN		&	2		 & 	33M	 & 	3.00	\\
 \hline
 CNN 	& 1		 &	9.4M 	& 	3.03\\
 CNN 	& 2		 & 	9.4M 	& 	3.11\\
 \hline
 CNN-TT 		&		1	 &	1.6M 	& 	2.99	\\
 CNN-TT 		& 	1		& 	2.8M		& 	3.04	\\
 CNN-TT 		&		2	 &	1.6M 	& 	3.08	\\
 CNN-TT 		& 	2		& 	2.8M		& 	3.13	\\
 	\hline
 CNN-Tucker-3 & 1 & 9.2M & 2.63 \\
 CNN-Tucker-3 & 2 & 9.2M & 2.56 \\
 	\hline
 	\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jun Qi
Hu Hu
Yannan Wang
Chao-Han Huck Yang
Sabato Marco Siniscalchi
Chin-Hui Lee

"Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement" by Jun Qi, Hu Hu, Yannan Wang, Chao-Han Huck Yang, Sabato Marco Siniscalchi, and Chin-Hui Lee, arXiv:2007.13024v2, 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{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 SDE 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{SDE} method \\ \hline\hline
1 & 700 & 24 \\
& 702 & 88 \\
 & 731 & 91 \\
& 762 & 96 \\
& 764 & 104 \\
\hline
2 & 48 & 259 \\
& 78 & 246 \\
& 110 & 231 \\
& 174 & 237 \\
& 209 & 236 \\
& 241 & 201 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Sepideh Mosaferi
Abolfazl Safikhani
Peiliang Bai

"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{SW Model: parameters, bounds, and prior distribution}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|c|ccc}
 \hline \hline
 \multirow{2}{*}{${\theta }$} & \multirow{2}{*}{Parameter Interpretation} & \multirow{2}{*}{Bounds} & \multicolumn{3}{c}{Prior Distribution} \\
 & & & { \textsc{density}} & { \textsc{mean}} & { \textsc{sd}} \\ \hline
 ${ \rho }_{ga}$ & { Corr.: tech. and exog. spending shocks} &
 { [0.01, 2]} & { Normal} & { 0.50} & { 0.25} \\
 ${ \mu }_{w}$ & { Wage mark-up shock MA} & { [0.01, 0.99]}
 & { Beta} & { 0.50} & { 0.20} \\
 ${ \mu }_{p}$ & { Price mark-up shock MA} & { [0.01, 0.99]}
 & { Beta} & { 0.50} & { 0.20} \\
 ${ \alpha }$ & { Share of capital in production} & { %
 [0.01, 1]} & { Normal} & { 0.30} & { 0.05} \\
 ${ \psi }$ & { Elast. of capital utilization adjustment cost} &
 { [0.01, 1]} & { Beta} & { 0.50} & { 0.15} \\
 ${ \varphi }$ & { Investment adjustment cost} & { [3,15]}
 & { Normal} & { 4.00} & { 1.50} \\
 ${ \sigma }_{c}$ & { Elast. of inertemporal substitution} &
 { [1, 3]} & { Normal} & { 1.50} & { 0.38} \\
 ${ \lambda }$ & { Habit persistence} & { [0.001, 0.99]} &
 { Beta} & { 0.70} & { 0.10} \\
 ${ \phi }_{p}$ & { Fixed costs in production} & { [1, 3]}
 & { Normal} & { 1.25} & { 0.13} \\
 ${ \iota }_{w}$ & { Wage indexation} & { [0.01, 0.99]} &
 { Beta} & { 0.50} & { 0.15} \\
 ${ \xi }_{w}$ & { Wage stickiness} & { [0.5, 0.95]} &
 { Beta} & { 0.50} & { 0.10} \\
 ${ \iota }_{p}$ & { Price indexation} & { [0.01, 0.99]} &
 { Beta} & { 0.50} & { 0.15} \\
 ${ \xi }_{p}$ & { Price stickiness} & { [0.1, 0.95]} &
 { Beta} & { 0.50} & { 0.10} \\
 ${ \sigma }_{l}$ & { Labor supply elasticity} & { [1, 10]}
 & { Normal} & { 2.00} & { 0.75} \\
 ${ r}_{\pi }$ & { Taylor rule: inflation weight} & { [1, 3]%
 } & { Normal} & { 1.50} & { 0.25} \\
 ${ r}_{\Delta y}$ & { Taylor rule: output gap change weight} &
 { [0.001, 0.5]} & { Normal} & { 0.13} & { 0.05} \\
 ${ r}_{y}$ & { Taylor rule: output gap weight} & { [0.001,
 0.5]} & { Normal} & { 0.13} & { 0.05} \\
 ${ \rho }$ & { Taylor rule: interest rate smoothing} & { %
 [0.5, 0.975]} & { Beta} & { 0.75} & { 0.10} \\
 ${ \rho }_{a}$ & { Productivity shock AR} & { [0.01, 0.99]}
 & { Beta} & { 0.50} & { 0.20} \\
 ${ \rho }_{b}$ & { Risk premium shock AR} & { [0.01, 0.99]}
 & { Beta} & { 0.50} & { 0.20} \\
 ${ \rho }_{g}$ & { Exogenous spending shock AR } & { %
 [0.01, 0.99]} & { Beta} & { 0.50} & { 0.20} \\
 ${ \rho }_{i}$ & { Investment shock AR} & { [0.01, 0.99]}
 & { Beta} & { 0.50} & { 0.20} \\
 ${ \rho }_{r}$ & { Monetary policy shock AR} & { [0.01,
 0.99]} & { Beta} & { 0.50} & { 0.20} \\
 ${ \rho }_{p}$ & { Price mark-up shock AR} & { [0.01, 0.99]%
 } & { Beta} & { 0.50} & { 0.20} \\
 ${ \rho }_{w}$ & { Wage mark-up shock AR} & { [0.001, 0.99]%
 } & { Beta} & { 0.50} & { 0.20} \\
 ${ \sigma }_{a}$ & { Productivity shock std. dev.} & { %
 [0.01, 3]} & { IGamma} & { 0.10} & { 2.00} \\
 ${ \sigma }_{b}$ & { Risk premium shock std. dev.} & { %
 [0.025, 5]} & { IGamma} & { 0.10} & { 2.00} \\
 ${ \sigma }_{g}$ & { Exogenous spending shock std. dev.} &
 { [0.01, 3]} & { IGamma} & { 0.10} & { 2.00} \\
 ${ \sigma }_{i}$ & { Investment shock std. dev.} & { %
 [0.01, 3]} & { IGamma} & { 0.10} & { 2.00} \\
 ${ \sigma }_{r}$ & { Monetary policy shock std. dev.} & { %
 [0.01, 3]} & { IGamma} & { 0.10} & { 2.00} \\
 ${ \sigma }_{p}$ & { Price mark-up shock std. dev.} & { %
 [0.01, 3]} & { IGamma} & { 0.10} & { 2.00} \\
 ${ \sigma }_{w}$ & { Wage mark-up shock std. dev.} & { %
 [0.01, 3]} & { IGamma} & { 0.10} & { 2.00} \\
 $\overline{\gamma }$ & { Trend growth: real GDP, Infl., Wages} &
 { [0.1, 0.8]} & { Normal} & { 0.40} & { 0.10} \\
 ${ r}$ & { Discount rate} & { [0.01, 2]} & { Gamma}
 & { 0.25} & { 0.10} \\
 $\overline{\pi }$ & { Steady state inflation rate} & { [0.1, 2]}
 & { Gamma} & { 0.62} & { 0.10} \\
 $\overline{l}$ & { Steady state hours worked} & { [-10,10]} &
 { Normal} & { 0.00} & { 2.00} \\ \hline \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Fitting Dynamically Misspecified Models: An Optimal Transportation Approach" by Jean-Jacques Forneron and Zhongjun Qu, arXiv:2412.20204v2, 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{Parameters used in testing the inverse problem solvers for the analytical benchmark case.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \hline
 \textbf{Parameter} & \textbf{Value}\\
 \hline
 N. of thermocouples	& 16 \\
 Thermocouples plane	& $y = 0.2~m$ \\
 $g^0$			& $0~W/m^2$\\
 RBF kernel		& Gaussian\\
 N. of RBF		& 16 \\
 Shape parameter, $\eta$	& 0.7 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Umberto Emil Morelli
Patricia Barral
Peregrina Quintela
Gianluigi Rozza
Giovanni Stabile

"A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation" by Umberto Emil Morelli, Patricia Barral, Peregrina Quintela, Gianluigi Rozza, and Giovanni Stabile, arXiv:2101.11985v1, 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{Simulation Parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
Symbol & Description & Value\\
\hline
$A_{sim}$& Simulation area &$20$m × $20$m\\
\hline
$\mathit{L}$& Number of RIS elements & $16$ \\
\hline
$C_X$ and $C_Y$ &LoS environmental constants & $9.61$ and $0.16$\\
\hline
$\upsilon$ &Reference path loss at {\footnotesize$d_{ref} = 1$} m & $-30$ dB\\
\hline
$\sigma_k^2$& User $k$ noise power & $-102$ dBm \\
\hline
$\varphi$& Non-LoS attenuation & $20$ dBm \\
\hline
$P_{BS}^{\max}$& BS max transmit power & $500$ W \\
\hline
$\alpha$& Path loss exponent for BS-RIS links & $3$ \\
\hline
$\nu$& Path loss exponent for RIS-user links & $2.5$ \\
\hline
$R_{\text{min}} $ & Minimum user QoS requirements& $70$ Mbps\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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

"Energy-Efficient Irregular RIS-aided UAV-Assisted Optimization: A Deep Reinforcement Learning Approach" by Mahmoud M. Salim, Khaled M. Rabie, and Ali H. Muqaibel, arXiv:2504.15031v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|}
\hline
Heading level & Example & Font size and style\\
\hline
Title (centered) & {\Large\bfseries Lecture Notes} & 14 point, bold\\
1st-level heading & {\large\bfseries 1 Introduction} & 12 point, bold\\
2nd-level heading & {\bfseries 2.1 Printing Area} & 10 point, bold\\
3rd-level heading & {\bfseries Run-in Heading in Bold.} Text follows & 10 point, bold\\
4th-level heading & {\itshape Lowest Level Heading.} Text follows & 10 point, italic\\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Safa Ayadi
Ahmed ben said
Rateb Jabbar
Chafik Aloulou
Achraf Chabbouh
Ahmed Ben Achballah

"Dairy Cow rumination detection: A deep learning approach" by Safa Ayadi, Ahmed ben said, Rateb Jabbar, Chafik Aloulou, Achraf Chabbouh, and Ahmed Ben Achballah, arXiv:2101.10445v1, 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}{lllll}
 \toprule
 Symbol & Value & Symbol & Value \\
 \midrule
 $n_c$ & 5776 & $\overline{T}$ & 1273 K \\
 $C_h$ & $173.28\cdot 10^6$ J/K & $\underline{T}$ & 1073 K \\
 $R_t$ & $1.3067\cdot 10^{-3}$ K/W & $\overline{j}$ & 10000 A/m$^2$ \\
 $U_{tn}$ & 1.2995 V & $\underline{j}$ & 2000 A/m$^2$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Parameters used in SOE operational model}
\end{table}
\end{document}

q-fin

Matthias Derez
Alexander Hoogsteyn
Erik Delarue

"Optimisation of Electrolyser Operation: Integrating External Heat" by Matthias Derez, Alexander Hoogsteyn, and Erik Delarue, arXiv:2507.06796v1, 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{Cohen's Kappa Agreement Scores}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
\toprule
\textbf{Comparison} & \textbf{Kappa ($\kappa$)} \\
\midrule
Expert 1 vs. Expert 2 & 0.81 \\
Expert 1 vs. Our Model & 0.78 \\
Expert 2 vs. Our Model & 0.75 \\
Expert vs. Cellpose & 0.42 \\
Expert vs. StarDist & 0.35 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"GUI Based Fuzzy Logic and Spatial Statistics for Unsupervised Microscopy Segmentation" by Surajit Das and Pavel Zun, arXiv:2508.15979v1, 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{OpenML Task ID mappings for \textbf{classification} datasets with \textbf{numerical features only}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\toprule
\textbf{OpenML ID} & \textbf{Dataset} \\ \midrule
361055 & credit \\
361060 & electricity \\
361061 & covertype \\
361062 & pol \\
361063 & house\_16H \\
361065 & MagicTelescope \\
361066 & bank-marketing \\
361068 & MiniBooNE \\
361069 & Higgs \\
361070 & eye\_movements \\
361273 & Diabetes130US \\
361274 & jannis \\
361275 & default-of-credit-card-clients \\
361276 & Bioresponse \\
361277 & california \\
361278 & heloc \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

DOFEN: Deep Oblivious Forest ENsemble

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative assessment on DEER-Lite ($\mathrm{MEAN}\pm \mathrm{STD}$) for directly testing on 30-view data using a trained 75-view network. The measurements were obtained by averaging the values on the testing dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\toprule
 & FDK & DEER-Lite \\
\midrule
PSNR & $22.1137\pm6.3728$ & $28.0983\pm8.4834$ \\
SSIM & { }{ }$0.7795\pm 0.1485$ & { }{ }$0.9018\pm0.0985$ \\
MAE & { }{ }$0.0289\pm0.0270$ & { }{ }$0.0148\pm0.0167$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Huidong Xie
Hongming Shan
Wenxiang Cong
Chi Liu
Xiaohua Zhang
Shaohua Liu
Ruola Ning
Ge Wang

"Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction" by Huidong Xie, Hongming Shan, Wenxiang Cong, Chi Liu, Xiaohua Zhang, Shaohua Liu, Ruola Ning, and Ge Wang, arXiv:1912.04278v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Pariyakorn Maneekul
Zelda B. Zabinsky
Giulia Pedrielli

"Branching Adaptive Surrogate Search Optimization (BASSO)" by Pariyakorn Maneekul, Zelda B. Zabinsky, and Giulia Pedrielli, arXiv:2504.18002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
\textbf{Name} & \textbf{Cost (USD)} & \textbf{GHG emissions (kgCO\textsubscript{2}eq)} \\ \hline
Hardware & 650 & 54 \\ \hline
Electricity & 330 & 1225 \\ \hline % Original: 1074
Total & 980 & 1279 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Final breakdown of costs and emissions for one RX 590 GPU over 2 years.}
\end{table}
\end{document}

cs

"Green NFTs: A Study on the Environmental Impact of Cryptoart Technologies" by Samuele Marro and Luca Donno, arXiv:2202.00003v3, 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}{ccc} \hline
CMM & LM & CENTR \\ \hline
2.42 $\pm$ 0.98 & 1.67 $\pm$ 0.86 & 0.20 $\pm$ 0.12 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Dataset evaluation}
\end{table}
\end{document}

eess

Sebastian Garcia-Valencia
Alejandro Betancourt
Juan G. Lalinde-Pulido

"Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music" by Sebastian Garcia-Valencia, Alejandro Betancourt, and Juan G. Lalinde-Pulido, arXiv:2012.01231v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Asian Basket Spread Options: A New Approximation Based on Stochastic Taylor Expansions" by Fabien Le Floc'h, arXiv:2504.16011v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Example: Color-Singlet States in QCD}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
		\hline
		State & Color Composition \\ \hline
		Meson & $q\bar{q}$ with $\mathbf{3}\otimes\overline{\mathbf{3}} \rightarrow \mathbf{1}$ \\ \hline
		Baryon & $qqq$ with $\mathbf{3}\otimes\mathbf{3}\otimes\mathbf{3} \rightarrow \mathbf{1}$ \\ \hline
		Tetraquark & $qq\bar{q}\bar{q}$ with proper contraction to yield $\mathbf{1}$ \\ \hline
		Pentaquark & $qqqq\bar{q}$ with proper contraction to yield $\mathbf{1}$ \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Symmetry Packaging II: A Group-Theoretic Framework for Packaging Under Finite, Compact, Higher-Form, and Hybrid Symmetries" by Rongchao Ma, arXiv:2503.20295v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{~ Definition of Variables and Their Expected Effect on the Net Interest Margin}
\begin{tabular}{l|llc}
 Variables & Notation & Definition & Expected Sign \\
 \midrule
 \midrule
 Net Interest Margin \% & NIM & Net interest income divided by total assets & \\
 Risk Aversion \% & RA & Equity over total assets & \textbf{?} \\
 Credit Risk \% & RBD & Non-performing loan over total loan & \textbf{+} \\
 Operating Cost \% & OC & Operation cost over total assets & \textbf{?} \\
 Bank Size & LOGTA & Logarithm of total assets & \textbf{?} \\
 Liquidity Ratio \% & LQR & Ratio of liquid assets to total assets & \textbf{-} \\
 Management Quality \% & MNGMT & Total expenses over total generated revenues & \textbf{-} \\
 Implicit Interest Payment \% & IIP & Net non-interest income over total assets & \textbf{+} \\
 Deposits Growth \% & DPZTG & Quarterly growth of deposits & \textbf{-} \\
 Operation Diversity \% & DVRSTY & Non-interest income over operating income & \textbf{-} \\
 Herfindahl Index \% & HHI & Herfindahl index for assets & \textbf{+} \\
 Real GDP Growth \% & GDP & Quarterly real GDP growth & \textbf{?} \\
 Inflation \% & INF & CPI growth rate & \textbf{+} \\
 \bottomrule
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

"The Determinants of Net Interest Margin in the Turkish Banking Sector: Does Bank Ownership Matter?" by Fatih Kansoy, arXiv:2506.04384v2, 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{Table showing coefficient of Multimodal Correlation Analyses determining the effect of Utterance Frequency, Average Pointing Time, And Gaze Stationary Time Ratio on Task Completion Time (\textit{* = $p < .05$; ** = $p < .01$; *** = $p < .001$})}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|l|}
\hline
\textbf{\textit{Modality}} & \textbf{\textit{Name}} & \textbf{\textit{F}} & \textbf{\textit{p}} & \textbf{\textit{Sig}} & \textbf{\textit{R}} & \textbf{\textit{$R^2$}} \\ \hline
Multimodal & Multimodal Correlation (Task 1) & $F_{3, 30} = 5.438$ & $< .01$ & ** & 0.535 & 0.287 \\ \hline
Multimodal & Multimodal Correlation (Task 2) & $F_{3, 30}$ = 27.62 & $< .001$ & *** & 0.841 & 0.708 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ryan Ghamandi
Yahya Hmaiti
Mykola Maslych
Ravi Kiran Kattoju
Joseph J. LaViola

"Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks" by Ryan Ghamandi, Yahya Hmaiti, Mykola Maslych, Ravi Kiran Kattoju, and Joseph J. LaViola, arXiv:2508.17124v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Specifications of the forecasting~models.}
\begin{tabular}{lcrrrrr}
\toprule
 & & \textbf{Year} & & & & \\
\textbf{Asset} & \textbf{Model} & \textbf{2018--2021} & \textbf{2018} & \textbf{2019} & \textbf{2020} & \textbf{2021} \\ \midrule
BTC-USD & ARIMA & (4, 1, 1) & (2, 1, 2) & (1, 0, 5) & (4, 1, 4) & (3, 0, 3) \\
GDAXI & & (1, 1, 1) & (3, 1, 2) & (1, 1, 5) & (3, 0, 4) & (1, 1, 4) \\
FTSE & & (4, 1, 3) & (5, 0, 2) & (5, 1, 1) & (3, 0, 5) & (2, 1, 4) \\
N100 & & (5, 1, 0) & (3, 1, 2) & (2, 1, 1) & (1, 0, 0) & (1, 1, 5) \\
FCHI & & (5, 1, 4) & (3, 0, 4) & (2, 1, 2) & (3, 0, 2) & (1, 1, 4) \\
SSMI & & (1, 1, 5) & (2, 1, 5) & (5, 1, 0) & (4, 0, 2) & (4, 1, 5) \\
\multicolumn{6}{l}{{Note}%MDPI: We removed the empty row and added midrule. Please confirm.
: optimal parameters based on information criterion `AIC'.} & \\\midrule
BTC-USD & kNN & k = 26 & k = 27 & k = 26 & k = 9 & k = 27 \\
GDAXI & & k = 19 & k = 27 & k = 18 & k = 21 & k = 18 \\
FTSE & & k = 25 & k = 27 & k = 27 & k = 16 & k = 25 \\
N100 & & k = 27 & k = 20 & k = 23 & k = 27 & k = 21 \\
FCHI & & k = 14 & k = 24 & k = 20 & k = 6 & k = 18 \\
SSMI & & k = 27 & k = 22 & k = 15 & k = 14 & k = 27 \\
\multicolumn{6}{l}{{Note}%MDPI: We removed the empty row and added midrule. Please confirm.
: weights: uniform; algorithm: brute; p: 2; k: optimal k.} & \\\midrule
ETS-NN & ETS & NN & & & & \\
 & ETS(A, Ad, N) & \multicolumn{2}{l}{LSTM layers: 50} & & & \\
 & & \multicolumn{2}{l}{Dropout rate: 0.2} & & & \\
 & & \multicolumn{2}{l}{Optimizer: Adam} & & & \\
 & & \multicolumn{3}{l}{Loss Function: Mean Squared Error} & & \\
 & & \multicolumn{2}{l}{Number of Epochs: 100} & & & \\
 & & \multicolumn{2}{l}{Batch Size: 32} & & & \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins" by Apostolos Ampountolas, arXiv:2307.08853v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

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


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{3 Malaysian Water Quality Index~}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc}\toprule
 Parameters&	Unit&\multicolumn{6}{c}{	Classes} \\\midrule
 Ammoniacal Nitrogen&	mg/l&	0.1&	0.3&	0.3&	0.9&	2.7&	>2.7\\
 BOD&	mg/l&	1&	3&	3&	6&	12&	>12\\
COD&	mg/l&	10&	25&	25&	50&	100&	>100\\
DO&	mg/l&	7&	5-7&	5-7&	3-5&	<3&	<1\\
pH&	mg/l&	6.5-8.5&	6.5-9.0&	6.5-9.0&	5-9&	5-9&	-\\
Color&	TUC&	15&	150&	150&	-&	-\\
Elec. Conductivity&	$\mu$ S/cm&	1000&	1000&	-&	-&	6000&	-\\\bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

MM Rashid
Rupal Roy
Md Manjurul Ahsan
Zahed Siddique

"Design and Development of an Autonomous Surface Vehicle for Water Quality Monitoring" by MM Rashid, Rupal Roy, Md Manjurul Ahsan, and Zahed Siddique, arXiv:2201.10685v1, 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{Individual performance allowing hyperparameters to vary within environment/taskc; ``dog-trot', $n=3$. Figures are mean normalised scores, with 0 and 100 representing random and expert policies, respectively. Highest score highlighted in bold }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 Environment -dataset & & & & \\
 \midrule
 DogTrot (BCQ) & $\tau=0.025$ & $\tau=0.05$ & $\tau=0.1$ & $\tau=0.25$ \\
 -expert & 57.8 & 85 & 93.6 & \textbf{94.4} \\
 -medium-expert & 3.9 & 10.1 & 42.7 & \textbf{74.9} \\
 -medium & 39.8 & 38.8 & 34.2 & \textbf{49.1} \\
 -random-medium-expert & 5 & 5.6 & \textbf{9} & 0.1 \\
 \midrule
 DogTrot (CQL) & $\alpha=0.25$ & $\alpha=0.5$ & $\alpha=1$ & $\alpha=2$ \\
 -expert & 90.8 & 95.7 & 99.5 & \textbf{100.2} \\
 -medium-expert & 76.6 & 81.7 & \textbf{84.8} & 75.1 \\
 -medium & \textbf{50.6} & 48.3 & 46.5 & 45.2 \\
 -random-medium-expert & 41.2 & 40.8 & \textbf{43.4} & 38.6 \\
 \midrule
 DogTrot (IQL $\tau=0.5$) & $\lambda=1$ & $\lambda=2$ & $\lambda=5$ & $\lambda=10$ \\
 -expert & 37.9 & 82.5 & 98.9 & \textbf{99.5} \\
 -medium-expert & 33 & 64 & 89.3 & \textbf{98.6} \\
 -medium & \textbf{58.8} & 56.5 & 52 & 47.3 \\
 -random-medium-expert & 10.6 & 28.6 & 44.1 & \textbf{44.7} \\
 \midrule
 DogTrot (OneStep) & $\lambda=1$ & $\lambda=2$ & $\lambda=5$ & $\lambda=10$ \\
 -expert & 53.3 & 91.7 & 101.2 & \textbf{102} \\
 -medium-expert & 44.5 & 79.4 & 93.9 & \textbf{96.6} \\
 -medium & \textbf{59.3} & 57.5 & 50.2 & 48 \\
 -random-medium-expert & 23.8 & 43.9 & 44.9 & \textbf{45.1} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Alex Beeson
David Ireland
Giovanni Montana

"An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces" by Alex Beeson, David Ireland, and Giovanni Montana, arXiv:2411.11088v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Péter Biró
Flip Klijn
Xenia Klimentova
Ana Viana

"Shapley-Scarf Housing Markets: Respecting Improvement, Integer Programming, and Kidney Exchange" by Péter Biró, Flip Klijn, Xenia Klimentova, and Ana Viana, arXiv:2102.00167v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Spoken Question Answering results.} Results for baselines are taken from .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
\toprule
 & \textbf{Modality} & \textbf{\# Params} & \textbf{Web Questions} & \textbf{Llama Questions} & \textbf{TriviaQA} \\ \midrule
TWIST & S$\rightarrow$S & 7B & 1.5 & 4.0 & - \\
SpeechGPT & S$\rightarrow$T & 7B & 6.5 & 21.6 & 14.8 \\
Spectron & S$\rightarrow$T & 1B & 6.1 & 21.9 & - \\
Moshi & S$\rightarrow$T & 7B & 26.6 & 62.3 & 22.8 \\
Moshi & S$\rightarrow$S & 7B & 9.2 & 21.0 & 7.3 \\
\midrule
GLM-4-Voice & S$\rightarrow$T & 9B & \textbf{32.2} & \textbf{64.7} & \textbf{39.1} \\
GLM-4-Voice & S$\rightarrow$S & 9B & 15.9 & 50.7 & 26.5 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Aohan Zeng
Zhengxiao Du
Mingdao Liu
Kedong Wang
Shengmin Jiang
Lei Zhao
Yuxiao Dong
Jie Tang

"GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot" by Aohan Zeng, Zhengxiao Du, Mingdao Liu, Kedong Wang, Shengmin Jiang, Lei Zhao, Yuxiao Dong, and Jie Tang, arXiv:2412.02612v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
Link $(i,j)$ & $L_{ij}$ & $W_{ij}$ & $x_{ij}$ \\
\hline
(1,2) & 1 & 1 & 1000 \\
(1,3) & 1 & 1 & 1368 \\
(2,3) & 0 & 0 & 0 \\
(2,4) & 1 & 2 & 2000 \\
(3,2) & 1 & 1 & 1000 \\
(3,5) & $e^{-1}$ & $e^{-1}$ & 368 \\
(4,5) & 1 & 2 & 2000 \\
\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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance Metrics of Pathfinding Algorithms}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
\textbf{Algorithm} & \textbf{Accuracy} & \textbf{Precision} & \textbf{F1 Score} \\
\midrule
Autoencoder & 0.98 & 0.97 & 0.97 \\
Transformer & 0.98 & 0.97 & 0.98 \\
GRU & 0.98 & 0.97 & 0.97 \\
LSTM & 0.98 & 0.97 & 0.97 \\
MLP & 0.98 & 0.96 & 0.97 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Mohamed Hussein Abo El-Ela
Ali Hamdi Fergany

"Deep Heuristic Learning for Real-Time Urban Pathfinding" by Mohamed Hussein Abo El-Ela and Ali Hamdi Fergany, arXiv:2411.05044v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{p-values of out-of-sample performance for top strategies (2010--2020).} Each entry shows the p-value for the null hypothesis that the Sharpe ratio of the column strategy is greater than that of the row strategy.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
& CTRL & min\_v & drmv & pmv \\
\midrule
CTRL & -- & 0.13 & 0.44 & 0.17 \\
min\_v & 0.87 & -- & 0.94 & 0.63 \\
drmv & 0.56 & 0.06 & -- & 0.36 \\
pmv & 0.83 & 0.37 & 0.64 & -- \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study" by Yilie Huang, Yanwei Jia, and Xun Yu Zhou, arXiv:2412.16175v2, 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{\small Experiment Legend}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|}
\hline
\textbf{Expt.} & \textbf{Model} & \textbf{Opt.} & \textbf{Loss} \\ \hline
\textit{E1} & 1-Layer MLP & Adam & BCE \\ \hline
\textit{E2} & 1-Layer MLP & Adam & MSE \\ \hline
\textit{E3} & 2-Layer MLP & Adam & BCE \\ \hline
\textit{E4} & 2-Layer MLP & Adam & MSE \\ \hline
\textit{E5} & AutoInt & Adam & BCE \\ \hline
\textit{E6} & AutoInt & Adam & MSE \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Anand Brahmbhatt
Mohith Pokala
Rishi Saket
Aravindan Raghuveer

"LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions" by Anand Brahmbhatt, Mohith Pokala, Rishi Saket, and Aravindan Raghuveer, arXiv:2310.10096v2, 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{ Simulation results for the mean and standard deviation (in parentheses) of the classification errors obtained with 200 repetitions of the experiment for different sample sizes and zero error variance. PC$_1$, first principal component; PC$_m$, principal component with lowest kurtosis coefficient; IC$_q$, $q$th independent component (minimal kurtosis); SIC$_q$, $q$th smoothed independent component; PCA, principal component analysis whitening; PCA-cor, principal component analysis correlated whitening; ZCA, zero-phase component analysis or Mahalanobis whitening; ZCA-cor, zero-phase component analysis or Mahalanobis correlated whitening; Cholesky, Cholesky whitening.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
 & & \multicolumn{5}{l}{Results (\%) for the centroid classifiers:} \\
 \cmidrule{2-7}
Data & $n_k$ & PC$_1$ & PC$_m$ & Whitening & IC$_q$ & SIC$_q$ \\
 \midrule
 & & Scenario I (Gaussian) \\
Example 1 & 30 & 45.49 (3.295) & 6.167 (10.43) & PCA & 2.583 (3.698) & 2.520 (5.193) \\
 & & & & PCA-cor & 2.583 (3.698) & 2.540 (5.869) \\
 & & & & ZCA & 2.592 (3.709) & 1.590 (2.632) \\
 & & & & ZCA-cor & 2.592 (3.709) & 1.955 (3.234) \\
 & & & & Cholesky & 2.575 (3.702) & 1.219 (2.083) \\
 & 50 & 45.47 (3.382) & 3.985 (7.754) & PCA & 0.970 (1.662) & 0.588 (1.058) \\
 & & & & PCA-cor & 0.970 (1.662) & 0.885 (2.122) \\
 & & & & ZCA & 0.965 (1.661) & 0.531 (1.051) \\
 & & & & ZCA-cor & 0.970 (1.671) & 0.678 (1.066) \\
 & & & & Cholesky & 0.965 (1.661) & 0.490 (1.266) \\
 Example 2 & 30 & 44.83 (4.103) & 17.35 (15.10) & PCA & 4.500 (4.971) & 1.716 (2.318) \\
 & & & & PCA-cor & 4.492 (4.960) & 1.766 (2.411) \\
 & & & & ZCA & 4.475 (4.969) & 0.876 (1.431) \\
 & & & & ZCA-cor & 4.475 (4.969) & 0.903 (1.455) \\
 & & & & Chol & 4.492 (4.966) & 1.688 (2.342) \\
 & 50 & 45.98 (3.047) & 11.35 (11.32) & PCA & 1.750 (2.198) & 0.758 (1.308) \\
 & & & & PCA-cor & 1.755 (2.200) & 0.969 (1.451) \\
 & & & & ZCA & 1.755 (2.200) & 0.397 (0.790) \\
 & & & & ZCA-cor & 1.750 (2.203) & 0.420 (0.787) \\
 & & & & Cholesky & 1.740 (2.202) & 0.942 (1.517) \\
 Example 3 & 30 & 45.16 (3.810) & 2.975 (4.474) & PCA & 3.525 (4.031) & 1.667 (2.472) \\
 & & & & PCA-cor & 3.533 (4.026) & 1.988 (4.355) \\
 & & & & ZCA & 3.533 (4.026) & 2.264 (4.476) \\
 & & & & ZCA-cor& 3.525 (4.018) & 2.004 (4.214) \\
 & & & & Cholesky & 3.517 (4.023) & 1.286 (2.117) \\
 & 50 & 45.54 (3.230) & 1.910 (1.560) & PCA & 1.530 (1.974) & 0.941 (1.541) \\
 & & & & PCA-cor & 1.535 (1.974) & 1.129 (1.775) \\
 & & & & ZCA & 1.535 (1.974) & 1.043 (1.700) \\
 & & & & ZCA-cor & 1.535 (1.974) & 0.867 (1.505) \\
 & & & & Cholesky & 1.530 (1.974) & 0.663 (1.044) \\
 & & \\
 \midrule
 & & Scenario II (non-Gaussian) \\
Example 1 & 30 & 45.40 (3.592) & 6.742 (10.45) & PCA & 3.917 (3.967) & 3.909 (5.068) \\
 & & & & PCA-cor & 3.917 (3.967) & 4.167 (5.950) \\
 & & & & ZCA & 3.900 (3.970) & 3.438 (2.643) \\
 & & & & ZCA-cor & 3.900 (3.970) & 3.452 (2.602) \\
 & & & & Cholesky & 3.908 (3.967) & 3.563 (2.543) \\
 & 50 & 45.97 (3.271) & 4.875 (9.073) & PCA & 3.075 (3.547) & 2.920 (3.190) \\
 & & & & PCA-cor & 3.075 (3.547) & 2.819 (2.889) \\
 & & & & ZCA & 3.075 (3.547) & 3.010 (3.448) \\
 & & & & ZCA-cor & 3.075 (3.547) & 3.071 (3.936) \\
 & & & & Cholesky & 3.075 (3.547) & 2.678 (2.864) \\
 Example 2 & 30 & 45.208 (4.058) & 7.45 (10.58) & PCA & 8.250 (9.897) & 8.349 (10.48) \\
 & & & & PCA-cor & 8.250 (9.897) & 8.063 (10.77) \\
 & & & & ZCA & 8.250 (9.889) & 7.155 (9.419) \\
 & & & & ZCA-cor & 8.242 (9.892) & 6.800 (9.561) \\
 & & & & Cholesky & 8.233 (9.897) & 7.343 (9.434) \\
 & 50 & 46.29 (2.748) & 7.805 (11.97) & PCA & 7.145 (10.48) & 6.053 (8.575) \\
 & & & & PCA-cor & 7.150 (10.50) & 5.840 (8.591) \\
 & & & & ZCA & 7.155 (10.50) & 6.694 (10.56) \\
 & & & & ZCA-cor & 7.155 (10.50) & 6.640 (10.69) \\
 & & & & Cholesky & 7.150 (10.48) & 6.075 (8.519) \\
 Example 3 & 30 & 45.15 (4.049) & 10.92 (11.87) & PCA & 6.858 (7.936) & 5.951 (6.266) \\
 & & & & PCA-cor & 6.858 (7.936) & 6.516 (7.232) \\
 & & & & ZCA & 6.867 (7.934) & 7.060 (8.419) \\
 & & & & ZCA-cor & 6.867 (7.934) & 6.425 (7.626) \\
 & & & & Cholesky & 6.858 (7.936) & 5.654 (6.642) \\
 & 50 & 45.94 (3.092) & 10.92 (11.69) & PCA & 5.085 (6.098) & 4.832 (5.616) \\
 & & & & PCA-cor & 5.085 (6.098) & 5.119 (6.249) \\
 & & & & ZCA & 5.095 (6.120) & 4.935 (6.120) \\
 & & & & ZCA-cor & 5.100 (6.119) & 4.873 (5.696) \\
 & & & & Cholesky & 5.095 (6.095) & 4.173 (4.228) \\
\midrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Functional independent component analysis by choice of norm: a framework for near-perfect classification" by Marc Vidal, Marc Leman, and Ana M. Aguilera, arXiv:2412.17971v2, 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}{lccc}
\hline
\textbf{Domain} & \textbf{\# intents} & \textbf{\# slots} & \textbf{\# utterances}\\
Weather & 2 & 4 & 3692\\
Device & 17 & 6 & 2112\\
\hline
\end{tabular}
\end{adjustbox}
\caption{CSTOP Statistics}
\end{table}
\end{document}

cs

Arash Einolghozati
Abhinav Arora
Lorena Sainz-Maza Lecanda
Anuj Kumar
Sonal Gupta

"El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing" by Arash Einolghozati, Abhinav Arora, Lorena Sainz-Maza Lecanda, Anuj Kumar, and Sonal Gupta, arXiv:2101.10524v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.