\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|} \hline
 Parameters& Skinner box& Navigation\\ \hline
 $N_\text{agents}$& 100&30\\ \hline
 $N_\text{clones}$& 2&3\\ \hline
 $N_\text{episodes}$& 4 000& 40 000\\ \hline
 Length episodes& \multicolumn{2}{|c|}{80}\\ \hline
 Forgetting rate $\gamma$& \multicolumn{2}{|c|}{0.001}\\ \hline
 Reward scale R& \multicolumn{2}{|c|}{3}\\ \hline
 scaling parameter $\zeta_\text{wandering}$& \{0\} & \{-3, -1, 0, 1, 3\}\\ \hline
 scaling parameter $\zeta_\text{task}$& -1& -3\\ \hline
 p*& \multicolumn{2}{|c|}{0.99}\\ \hline
 $N_\text{pref}$& \multicolumn{2}{|c|}{1}\\\hline
 Prediction horizon $T_h$& 2 & 3\\\hline
 \end{tabular}
\end{adjustbox}
\caption{Parameters used during the training and the testing of the agents}
\end{table}
\end{document}

stat

Joséphine Pazem
Marius Krumm
Alexander Q. Vining
Lukas J. Fiderer
Hans J. Briegel

"Free Energy Projective Simulation (FEPS): Active inference with interpretability" by Joséphine Pazem, Marius Krumm, Alexander Q. Vining, Lukas J. Fiderer, and Hans J. Briegel, arXiv:2411.14991v1, 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{PESQ comparisons of single-channel deep speech enhancement models on the Edinburgh noisy speech database. The average PESQ score for unprocessed noisy speech is $1.97$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
 \hline
 \hline
 Model 		& Parameters \# 	& PESQ	 \\
 \hline
 \hline
 DNN		 & 5.5M		 & 2.82		\\ %2.84	\\
 \hline
 CNN 		 & 9.1M 	 & 3.04 	\\%3.11	 \\
 \hline
 DNN-TT	 &	0.55M		 & 2.81		\\%2.83	\\
 \hline
 CNN-TT 	 & 0.73M	 	& 3.02		\\%3.08	\\
 CNN-TT 	 &	2.9M	 	& 3.09		\\	%3.12
 CNN-TT 	 & 5.1M		 & 3.13		\\	%3.15
 \hline
 CNN-Tucker-3 & 8.9M & 2.89		 \\
 \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.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Inter-comparison results for different combinations A to G of PTP-WR devices. Combinations A-D were measured in a laboratory at room temperature: 22.5\textcelsius{} ($\pm$2.5\textcelsius{}) while combinations E-G were measured in a climate chamber at 23.5\textcelsius{} ($\pm$0.1\textcelsius{}).}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccc}
		\cline{2-6}
		 & {} & PPS residual & n & $\sigma$ & figure \\
		 & {grandmaster $\rightarrow$ follower} & (ps) & & (ps) & \\
		\hline
		 & {SPEC7\textsubscript{Sn01\_Nikhef}$\rightarrow$SPEC7\textsubscript{Sn07\_Nikhef}} & 43 & 80 & 3.6 & \\
		A & {SPEC7\textsubscript{Sn07\_Nikhef}$\rightarrow$SPEC7\textsubscript{Sn01\_Nikhef}} & -64 & 77 & 3.4 & \\
		\cline{2-6}
		 & {$\mu$, $\Delta$ (ps)} & 54, 21 & & & \\
		\hline
		\hline
		 & {SPEC7\textsubscript{Sn07\_Nikhef}$\rightarrow$SPEC7\textsubscript{Sn10\_NPL}} & 536 & 6 & 19.1 & \\
		B & {SPEC7\textsubscript{Sn10\_NPL}$\rightarrow$SPEC7\textsubscript{Sn07\_Nikhef}} & -524 & 8 & 16.4 &\\
		\cline{2-6}
		 & {$\mu$, $\Delta$ (ps)} & 530, 12 & & &\\
		\hline
		\hline
		 & SPEC7\textsubscript{Sn07\_Nikhef}$\rightarrow$SPEC7\textsubscript{Sn11\_NPL} & 491 & 6 & 23.5 & \\
		C & {SPEC7\textsubscript{Sn11\_NPL}$\rightarrow$SPEC7\textsubscript{Sn07\_Nikhef}} & -464 & 6 & 29.0 & \\
		\cline{2-6}
		 & {$\mu$, $\Delta$ (ps)} & 478, 27 & & & \\
		\hline
		\hline
		 & SPEC7\textsubscript{Sn10\_NPL}$\rightarrow$SPEC7\textsubscript{Sn11\_NPL} & -37 & 6 & 15.4 & \\
		D & {SPEC7\textsubscript{Sn11\_NPL}$\rightarrow$SPEC7\textsubscript{Sn10\_NPL}} & 13 & 6 & 21.4 & \\
		\cline{2-6}
		 & {$\mu$, $\Delta$ (ps)} & 25, 24 & & & \\
		\hline
		\hline
		 & {SPEC\textsubscript{Nikhef}$\rightarrow$SPEC\textsubscript{VTT}} & 96 & 80 & 37.6 & \\
		E & {SPEC\textsubscript{VTT}$\rightarrow$SPEC\textsubscript{Nikhef}} & -119 & 80 & 38.4 & \\
		\cline{2-6}
		 & {$\mu$, $\Delta$ (ps)} & 107, 23 & & & \\
		\hline
		\hline
		 & {SPEC7\textsubscript{Sn01\_Nikhef}$\rightarrow$SPEC\textsubscript{Nikhef}} & 162 & 80 & 32.2 & \\
		F & {SPEC\textsubscript{Nikhef}$\rightarrow$SPEC7\textsubscript{Sn01\_Nikhef}} & -112 & 80 & 29.4 & \\
		\cline{2-6}
		 & {$\mu$, $\Delta$ (ps)} & 137, 50 & & & \\
		\hline
		\hline
		 & SPEC7\textsubscript{Sn01\_Nikhef}$\rightarrow$SPEC\textsubscript{VTT} & 241 & 77 & 24.0 & \\
		G & {SPEC\textsubscript{VTT}$\rightarrow$SPEC7\textsubscript{Sn01\_Nikhef}} & -208 & 78 & 19.1 & \\
		\cline{2-6}
		 & {$\mu$, $\Delta$ (ps)} & 224, 33 & & & \\
		\hline

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

cs

P. P. M. Jansweijer
N. A. D. Boukadida
K. Hanhijärvi
A. Wallin
B. Eglin
E. Laier English

"First electrical White Rabbit absolute calibration inter-comparison" by P. P. M. Jansweijer, N. A. D. Boukadida, K. Hanhijärvi, A. Wallin, B. Eglin, and E. Laier English, arXiv:2201.08640v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The initial values and observation noise of the concentrations (states $x_i$)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccc} \hline
		& $x_1$&$x_2$&$x_3$&$x_4$&$x_5$&$x_6$\\ \hline %head
 initial\ values& 66& 0.054& 0.019& 59& 0.09& 0.012 \\
 noise STD &0.005& $5\times 10^{-5}$& $2\times 10^{-5}$& 0.035& 0.0005& $5\times 10^{-6}$ \\ \midrule
 &$x_7$&$x_8$&$x_9$&$x_{10}$&$x_{11}$& \\ \hline
 initial\ values& 65& 26& 175& 161& 2.18& \\
 noise STD & 0.05& 0.02& 0.03& 0.003& 0.002& \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jiangqi Wu
Linjie Wen
Peter L Green
Jinglai Li
Simon Maskell

"Ensemble Kalman filter based Sequential Monte Carlo Sampler for sequential Bayesian inference" by Jiangqi Wu, Linjie Wen, Peter L Green, Jinglai Li, and Simon Maskell, arXiv:2012.08848v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c|c||}
 \hline
 Model & $R^2$ with $\mathbf{AUROC}_{t+1}$ & $R^2$ without $\mathbf{AUROC}_{t+1}$ & $p$-value of difference \\
 \hline\hline
 Linear & $\mathbf{0.360^*}$ & 0.107 & 0.00 \\
 \hline
 Tree & $\mathbf{0.357^*}$ &	0.310 & 0.043 \\
 \hline
 MLP & $\mathbf{0.380^*}$ &	0.317 & 0.003 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Results of $\text{log}$-$\text{RV}$ forecasting at the 1-hour frequency}
\end{table}
\end{document}

q-fin

Dragos Gorduza
Xiaowen Dong
Stefan Zohren

"Understanding stock market instability via graph auto-encoders" by Dragos Gorduza, Xiaowen Dong, and Stefan Zohren, arXiv:2212.04974v1, 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}{lll}
\toprule
Ratio & Formula & Global Range \\ \midrule
Market Share & $\frac{\text{Premiums}}{\text{Total Market Premiums}} \times 100$ & 5\% - 30\% \\
Claims Paid Ratio & $\frac{\text{Claims Paid}}{\text{Gross Premium Income}} \times 100$ & 50\% - 80\% \\
Loss Ratio & $\frac{\text{Claims Incurred}}{\text{Gross Premium Income}} \times 100$ & 60\% - 75\% \\
Underwriting Profit Ratio & $\frac{\text{Underwriting Profit}}{\text{Gross Premium Income}} \times 100$ & 5\% - 15\% \\
Expense Ratio & $\frac{\text{Expenses}}{\text{Gross Premium Income}} \times 100$ & 10\% - 25\% \\
Combined Ratio & $\text{Loss Ratio} + \text{Expense Ratio}$ & 90\% - 105\% \\
Claims Payout Ratio & $\frac{\text{Claims Paid}}{\text{Claims Incurred}} \times 100$ & 60\% - 90\% \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{\footnotesize Key Financial Ratios used to evaluate insurance providers. The global range represents typical industry standards for each ratio, indicating the desired financial performance for insurance companies.}
\end{table}
\end{document}

q-fin

"Decoding Financial Health in Kenyas' Medical Insurance Sector: A Data-Driven Cluster Analysis" by Evans Kiptoo Korir and Zsolt Vizi, arXiv:2502.17072v2, 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|r|r|r|r|r|r|} \hline
& \multicolumn{3}{c|}{FR} & \multicolumn{3}{c|}{ES} \\\cline{2-7}
& B1 & B2 & B3 & B1 & B2 & B3 \\\hline
ORIG & \bf{79.80} & 6.59 & \underline{13.61} & \bf{77.80} & 6.83 & \underline{15.36} \\\hline
PB-SMT & 81.78 & 6.48 & 11.74 & 79.77 & 6.86 & 13.36 \\\hline
LSTM & 82.95 & 6.18 & 10.88 & 80.34 & 6.84 & 12.81 \\\hline
TRANS & 82.01 & 6.24 & 11.75 & 82.35 & 6.99 & 10.67 \\\hline\hline
& \multicolumn{3}{c|}{EN$_{FR}$} & \multicolumn{3}{c|}{EN$_{ES}$}\\\cline{2-7}
& B1 & B2 & B3 & B1 & B2 & B3 \\\hline
ORIG & \textbf{80.83} & 7.10 & \underline{12.07} & \bf{80.81} & 7.11 & \underline{12.08}\\\hline
PB-SMT & 82.06 & 7.04 & 10.90 & 82.25 & 7.01 & 10.74\\\hline
LSTM & 83.23 & 6.93 & 9.81 & 83.29 & 6.93 & 9.78 \\\hline
TRANS & 82.25 & 7.05 & 10.70 & 82.35 & 6.99 & 10.67\\\hline
\end{tabular}
\end{adjustbox}
\caption{Lexical Frequency Profile (French, Spanish, English (EN$_{FR}$ and EN$_{ES}$) with 3 bands (B1: 0-1000, B2: 1001-2000, B3: 2001-end) for the original data and the output of the MT systems.}
\end{table}
\end{document}

cs

Eva Vanmassenhove
Dimitar Shterionov
Matthew Gwilliam

"Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation" by Eva Vanmassenhove, Dimitar Shterionov, and Matthew Gwilliam, arXiv:2102.00287v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Censorship of Online Encyclopedias: Implications for NLP Models" by Eddie Yang and Margaret E. Roberts, arXiv:2101.09294v1, 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{Class distribution in training and test dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
Dataset & \#Non-bankrupt & \#Bankrupt\tabularnewline
\hline
\hline
Training & 5487 & 113\tabularnewline
\hline
Test & 2105 & 47\tabularnewline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Amir Mukeri
Habibullah Shaikh
D. P. Gaikwad

"Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction" by Amir Mukeri, Habibullah Shaikh, and D. P. Gaikwad, arXiv:2010.13892v2, 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}{l|ccc}
\toprule
Preprocessing method & BCE loss & Binary accuracy (\%) \\
\midrule
No preprocessing & $0.1900 \pm 0.0036$ & $0.9168 \pm 0.0017$ \\
$z$-score & $0.1871 \pm 0.0011$ & $0.9176 \pm 0.0007$ \\
$z$-score + YJ & $0.1789 \pm 0.0009$ & $0.9211 \pm 0.0006$ \\
Winsorize + $z$-score & $0.1876 \pm 0.0010$ & $0.9172 \pm 0.0006$ \\
Winsorize + $z$-score + YJ & $0.1788 \pm 0.0009$ & $0.9211 \pm 0.0006$ \\
\underline{CDF inversion} & $\underline{0.1627 \pm 0.0009}$ & $\underline{0.9289 \pm 0.0006}$ \\
BIN & $0.2191 \pm 0.0010$ & $0.9036 \pm 0.0006$ \\
DAIN & $0.2153 \pm 0.0015$ & $0.9048 \pm 0.0008$ \\
EDAIN (local-ware) & $0.2099 \pm 0.0010$ & $0.9071 \pm 0.0006$ \\
\textbf{EDAIN (global-aware)} & $\mathbf{0.1636 \pm 0.0009}$ & $\mathbf{0.9283 \pm 0.0005}$ \\
EDAIN-KL & $0.1760 \pm 0.0009$ & $0.9224 \pm 0.0006$ \\
\underline{KDIT} ($\alpha=0.1$) & $\underline{0.1532 \pm 0.0011}$ & $\underline{0.9329 \pm 0.0006}$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Marcus A. K. September
Francesco Sanna Passino
Leonie Goldmann
Anton Hinel

"Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks" by Marcus A. K. September, Francesco Sanna Passino, Leonie Goldmann, and Anton Hinel, arXiv:2310.14720v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc|ccc}
\toprule
& \multicolumn{2}{c}{Spending/Person} & & \multicolumn{2}{c}{Conditional Differences}\\\cmidrule{2-3}\cmidrule{5-6}
& Average & Median & \% Diff $> 0$ & Average & Median \\
\midrule
\multicolumn{2}{l}{\textbf{Panel A: Non-Learning Model}} \\
Observed Data & \$2,001 & \$1,123 & -- & -- & -- \\
Full Information ($\beta$ = 1) & \$1,899 & \$1,066 & 11.9\% & \$856 & \$486 \\
Deductible Resets Quarterly & \$3,499 & \$2,040 & 12.7\% & \$751 & \$424 \\
Deductible Resets Monthly & \$3,745 & \$2,272 & 8.5\% & \$657 & \$361 \\
\midrule
\multicolumn{2}{l}{\textbf{Panel B: Learning Model}} \\
Observed Data & \$2,119 & \$1,181 & -- & -- & -- \\
Full Information ($\beta$ = 1) & \$1,899 & \$1,068 & 21.0\% & \$1,051 & \$575 \\
Re-centered Priors ($\mu_{\beta,0}=1$) & \$1,896 & \$1,065 & 21.7\% & \$1,031 &\$562 \\
Deductible Resets Quarterly & \$3,662 & \$2,135 & 29.9\% & \$863 & \$477 \\
Deductible Resets Monthly & \$3,851 & \$2,335 & 22.7\% & \$714 & \$404 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{ Comparison of Predicted Spending Across Counterfactual States of the World}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{clccc}
\hline
Type & Variable Name & Abbrev. & ECMWF ID & Levels \\
\hline
 & Land-sea mask & LSM & 172 \\
Static & Orography & OROG & 228002\\
 & Soil Type & SLT & 43\\
\hline
 & 2 metre temperature & T2m & 167 \\
Single & 10 metre U wind component & U10 & 165 \\
 & 10 metre V wind component & V10 & 166 \\
\hline
 & Geopotential & Z & 129 & 50, 250, 500, 600, 700, 850, 925 \\
 & U wind component & U & 131 & 50, 250, 500, 600, 700, 850, 925 \\
Atmospheric & V wind component & V & 132 & 50, 250, 500, 600, 700, 850, 925 \\
 & Temperature & T & 130 & 50, 250, 500, 600, 700, 850, 925 \\
 & Specific humidity & Q & 133 & 50, 250, 500, 600, 700, 850, 925 \\
 & Relative humidity & R & 157 & 50, 250, 500, 600, 700, 850, 925 \\
\hline
\end{tabular}
\caption{Variables considered for global weather prediction model.}
\end{table}
\end{document}

math

Siddharth Rout
Eldad Haber
Stéphane Gaudreault

"Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data" by Siddharth Rout, Eldad Haber, and Stéphane Gaudreault, arXiv:2503.12273v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Rui Tian
Yunzhou Zhang
Delong Zhu
Shiwen Liang
Sonya Coleman
Dermot Kerr

"Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry" by Rui Tian, Yunzhou Zhang, Delong Zhu, Shiwen Liang, Sonya Coleman, and Dermot Kerr, arXiv:2101.05995v2, 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}{||cccc||}
 \hline
 Method & Total Return(\%) & Vol & Sharpe \\
 \hline
 Market capitalization of holding & 108.47 & 25.53 & 0.326 \\
 \hline
 Numbers of holding & 110.37 & 24.56 & 0.343 \\
 \hline
 Weighted sum of holding & 112.01 & 25.10 & 0.338 \\
\hline
\end{tabular}
\caption{Performance of different weight method from 2013/12/31 to 2023/03/01, data from uqer.datayes.com}
\end{table}
\end{document}

q-fin

"Construct sparse portfolio with mutual fund's favourite stocks in China A share market" by Ke Zhang, arXiv:2305.01642v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
 & \textbf{Passage WER} & \textbf{Recall@5} & \textbf{Recall@10} & \textbf{Recall@100} \\
\midrule
\multicolumn{5}{c}{\textbf{SpokenSQuAD}} \\ \midrule
GT Text Baseline & 0\% & 0.9707 & 0.9871 & 0.9985 \\
Low WER cascaded & $\sim$20\% & 0.9525 & 0.9745 & 0.9974 \\
High WER cascaded & $\sim$35\% & 0.8768 & 0.9271 & 0.9926 \\
Speech Retriever & N/A & \textbf{0.9702} & \textbf{0.9869} & \textbf{ 0.9986} \\ \midrule
\multicolumn{5}{c}{\textbf{VoxPopuli}} \\ \midrule
GT Text Baseline & 0\% & 0.9942 & 0.9971 & 1.0 \\
Low WER cascaded & $\sim$17\% & 0.9826 & 0.9855 & 0.9961 \\
High WER cascaded & $\sim$45\% & 0.7106 & 0.7493 & 0.8858 \\
Speech Retriever & N/A & \textbf{0.9952} & \textbf{0.9981} & \textbf{0.9990} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Do June Min
Karel Mundnich
Andy Lapastora
Erfan Soltanmohammadi
Srikanth Ronanki
Kyu Han

"Speech Retrieval-Augmented Generation without Automatic Speech Recognition" by Do June Min, Karel Mundnich, Andy Lapastora, Erfan Soltanmohammadi, Srikanth Ronanki, and Kyu Han, arXiv:2412.16500v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"dYdX: Liquidity Providers' Incentive Programme Review" by Colin Chan, arXiv:2307.03935v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study showing individual component contributions, evaluated with EfficientNet-B3 and Swin Transformer.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\toprule
\textbf{SSFL++} & \textbf{KDS} & \textbf{F1-score (\%)} & \textbf{AUC-ROC} & \textbf{Improvement} \\
\midrule
& & 70.73 & 0.7804 & -- \\
\checkmark & & 80.49 & 0.8957 & +9.76\% \\
\checkmark & \checkmark & 94.68 & 0.9813 & +23.95\% \\
\midrule
& & 68.53 & 0.7654 & -- \\
\checkmark & & 78.82 & 0.8672 & +10.82\% \\
\checkmark & \checkmark & 93.34 & 0.9797 & +24.81\% \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

"Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization" by Chia-Ming Lee, Bo-Cheng Qiu, Ting-Yao Chen, Ming-Han Sun, Fang-Ying Lin, Jung-Tse Tsai, I-An Tsai, Yu-Fan Lin, and Chih-Chung Hsu, arXiv:2507.19858v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 \hline
 \textbf{Parameter} & \textbf{Description} & \textbf{Units} &\textbf{NMC (SANYO cell)} &\textbf{LFP (A123 cell)}\\ \hline
 $n_{\textrm{SEI}}$ & Number of e$^-$ transferred in SEI side reaction & - &\multicolumn{2}{c}{2.0}\\
 $\lambda$ & Constant $\lambda = \frac{c_s \sqrt{D_s}}{c_p \sqrt{D_p}}$ & - &\multicolumn{2}{c}{5.51$\times 10^{-5}$}\\
 $OCV_s$& OCV of the side reaction & V &\multicolumn{2}{c}{0.4}\\
 $\varepsilon_{\textrm{AM}}$ & Active material volume fraction & - &\multicolumn{2}{c}{0.552}\\
 $R_s$ & Particle radius & m &7.5$\times 10^{-6}$&5$\times 10^{-6}$\\
 $a_s$ & Specific surface area of the anode & m$^{-1}$ &\multicolumn{2}{c}{\( \frac{3 \cdot \varepsilon_{\textrm{AM}}}{R_s} \)}\\
 $A_n$ & Active surface area of the anode & m$^2$ &0.105
 &0.18\\
 $L_n$ & Thickness of anode & m &50$\times 10^{-6}$&34$\times 10^{-6}$\\
 $i_0$ & Exchange current of the intercalation current & A/m$^2$ &\multicolumn{2}{c}{1.5}\\
 $k_{\textrm{SEI}}$ & Kinetic rate & 1/$\sqrt{\text{s}}$ &\multicolumn{2}{c}{66.85}\\
 $E_{\textrm{SEI}}$ & Activation energy & J/mol &\multicolumn{2}{c}{39146.0}\\
 $\delta_{SEI,0}$& Initial value of the SEI layer thickness & m &\multicolumn{2}{c}{2.0 $\times 10^{-9}$}\\
 $M_{\textrm{SEI}}$ & Molecular weight of the SEI layer & kg/mol &\multicolumn{2}{c}{0.162}\\
 $\rho_{\textrm{SEI}}$ & Density of the SEI layer & kg/m$^3$ &\multicolumn{2}{c}{1690.0}\\
 $z_{100\%}$& Full electrode stoichiometry & - &0.9
 &0.81\\
 $z_{0\%}$& Empty electrode stoichiometry & - &0
 &0.0176\\
 $k_{\textrm{AM}}$ & Kinetic rate & 1/Ah &\multicolumn{2}{c}{0.0137}\\
 $E_{\textrm{AM}}$ & Activation energy & J/mol &\multicolumn{2}{c}{39500.0}\\
 $\beta$ & Tuning parameter & - &\multicolumn{2}{c}{1.7}\\
 $t^{+}_0$ & Transport/transference number & - &0.363
 &0.36\\
 $E_{\kappa}$ & Activation energy for $\kappa$ & J/mol &\multicolumn{2}{c}{34700.0}\\
 $E_{D_e}$& Activation energy for $D_e$ & J/mol &\multicolumn{2}{c}{34700.0}\\
 $\kappa_{\textrm{ref}}$& Reference ionic conductivity for $\kappa$ at reference temperature & S/m &\multicolumn{2}{c}{0.174}\\
 $D_{e,\textrm{ref}}$& Reference value for $D_e$ at reference temperature & m$^2$/s &\multicolumn{2}{c}{7.5$\times 10^{-11}$}\\
 $brug$ & Bruggeman coefficient & - &\multicolumn{2}{c}{3/2}\\
 $c_{e,\textrm{avg}}$& Average volume concentration of Li in the electrolyte & mol/m$^3$ &1000&1200\\
 $c_{e,\textrm{max}}$& Maximum volume concentration of Li in the electrolyte & mol/m$^3$ &1000&1200\\
 $\sigma_n$ & Electronic conductivity & S/m &100&215\\
 $\varepsilon_{s}$ & Volume fraction of solid electrolyte & - &0.59 &0.58\\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Parameter values, descriptions, and units for PBROMs.}
\end{table}
\end{document}

math

Darío Slaifstein
Gautham Ram Chandra Mouli
Laura Ramirez-Elizondo
Pavol Bauer

"Aging-aware Energy Management for Residential Multi-Carrier Energy Systems" by Darío Slaifstein, Gautham Ram Chandra Mouli, Laura Ramirez-Elizondo, and Pavol Bauer, arXiv:2503.16139v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Alina Landowska
Robert A. Kłopotek
Dariusz Filip
Konrad Raczkowski

"GDP-GFCF Dynamics Across Global Economies: A Comparative Study of Panel Regressions and Random Forest" by Alina Landowska, Robert A. Kłopotek, Dariusz Filip, and Konrad Raczkowski, arXiv:2504.20993v1, 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|}
 \hline
 $j$ & Original & No bootstraps \\
 \hline
 0 & 0.214 & 0.217 \\
 1 & 0.430 & 0.435 \\
 2 & 0.703 & 0.715 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{RMSEs of estimated $(1-p_{j,n})$-quantiles where $p_{j,n}=1/(10^jn)$, for $j=0,1,2$, for the Gaussian case.}
\end{table}
\end{document}

stat

"Automated threshold selection and associated inference uncertainty for univariate extremes" by Conor Murphy, Jonathan A. Tawn, and Zak Varty, arXiv:2310.17999v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
$n=m$ & lrAA iterations &iterations reported in & final rank\\
\hline
21 &109 (7) &1083 & 13 (4) \\
61 &287 (8) &8967 & 18 (7) \\
101 &443 (13) &23849 & 20 (7)\\
221 & 675 (11) &107388 & 26 (8) \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Iterations needed and final rank for the lrAA method for solving the elliptic Monge-Amp\`ere equation for an exact solution $u(x,y) = \frac{2 \sqrt{2}}{3}(x^2+y^2)^{\frac{3}{4}}$. The numbers corresponds to a fixed tolerance $10^{-10}$ or a local truncation error guided tolerance $0.01h_x$ (in parenthesis).}
\end{table}
\end{document}

math

lrAA: Low-Rank Anderson Acceleration

"lrAA: Low-Rank Anderson Acceleration" by Daniel Appelo and Yingda Cheng, arXiv:2503.03909v2, 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{WER (\%) results on CHiME-8 MMCSG eval dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|cccc}
 \hline
 Latency range & \ System & All & Wearer & Partner & Latency(s) \\ \hline
 \multirow{3}{*}{0s$\ \sim\ $0.15s} & baseline~ & 22.10 & 17.80 & 26.30 & 0.138 \\
 & NPU-TEA~ & 16.80 & 12.20 & 21.30 & 0.142 \\
 & \textbf{Ours} & \textbf{15.20} & \textbf{10.50} & \textbf{19.90} & \textbf{0.127} \\ \hline
 \multirow{3}{*}{0.15s$\ \sim\ $0.35s} & baseline~ & 18.90 & 15.00 & 22.90 & \textbf{0.333} \\
 & NPU-TEA~ & 15.00 & 10.80 & 19.20 & 0.337 \\
 & \textbf{Ours} & \textbf{13.65} & \textbf{9.30} & \textbf{18.00} & 0.343 \\ \hline
 \multirow{3}{*}{0.35s$\ \sim\ $1s} & baseline~ & 17.90 & 14.10 & 21.70 & 0.616 \\
 & FOSAFER~ & 14.00 & 9.60 & 18.40 & 0.686 \\
 & \textbf{Ours} & \textbf{13.05} & \textbf{8.90} & \textbf{17.20} & \textbf{0.587} \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yiheng Jiang
Haoxu Wang
Yafeng Chen
Gang Qiao
Biao Tian

"Exploring Efficient Directional and Distance Cues for Regional Speech Separation" by Yiheng Jiang, Haoxu Wang, Yafeng Chen, Gang Qiao, and Biao Tian, arXiv:2508.07563v1, 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|l|l|}
		\hline
		\textbf{Corruption Types} & \textbf{\# Sp} & \textbf{\# Ut} & \textbf{\# WI} & \textbf{Vo}\\ \hline

		\textbf{\textsc{RandomFrame and}} & 4,004 & 31,982 & 356,940 & 17,545\\
		\textbf{\textsc{Extreme Sparsity}} & & & &\\
		\hline
		\textbf{\textsc{Visemic}} & 2,883 & 6,152 & 338,207 & 16,663\\ \hline

	\textbf{\textsc{InterWord}} & 3,008 & 10,421 & 141,850 & 12,621 \\ \hline
	\textbf{\textsc{IntraWord}} & 2,756 & 10,360 & 141,296 & 11,824\\ \hline
	\textbf{\textsc{Prefix and Suffix}} & 4,004 & 31,982 & 356,940 & 17,545\\ \hline
	\textbf{\textsc{POS Tagged Corruptions}} & 4,004 & 31,982 & 356,940 & 17,545\\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Aradhya Neeraj Mathur
Devansh Batra
Yaman Kumar
Rajiv Ratn Shah
Roger Zimmermann

"LIFI: Towards Linguistically Informed Frame Interpolation" by Aradhya Neeraj Mathur, Devansh Batra, Yaman Kumar, Rajiv Ratn Shah, and Roger Zimmermann, arXiv:2010.16078v5, 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{Leaderboard for the validation phase and the final test phase at deadline.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|cc}
 \toprule
 Track 1 & Validation phase & Track 1 & Test phase\\%\multicolumn{4}{c}{Track 1}\\
 \midrule
 ZLI & 8.404499999999793 & ZLI & 11.320000000002786 \\
 Convexers & 7.8099999999988885 & Margaridinhas & 11.320000000002786\\
 Margaridinhas & 7.533834999999982 & Convexers & 11.320000000002786
 \\
 Topline & 3.540000000000663 & Topline & 4.300000000000301\\
 VK & 0.5300000000000102\\
 \midrule
 Track 2 & Validation phase & Track 2 & Test phase\\
 \midrule
 Ratel & 10.69104 & RISEup & 10.77341\\
 ML for TSP & 9.82955 & Ratel & 10.58859\\
 RISEup & 9.16567 & ML for TSP & 10.39341\\
 VK & -6.69772 & & \\
 UniBw & -13.14861 & & \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Laurens Bliek
Paulo da Costa
Reza Refaei Afshar
Yingqian Zhang
Tom Catshoek
Daniël Vos
Sicco Verwer
Fynn Schmitt-Ulms
André Hottung
Tapan Shah
Meinolf Sellmann
Kevin Tierney
Carl Perreault-Lafleur
Caroline Leboeuf
Federico Bobbio
Justine Pepin
Warley Almeida Silva
Ricardo Gama
Hugo L. Fernandes
Martin Zaefferer
Manuel López-Ibáñez
Ekhine Irurozki

"The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems" by Laurens Bliek, Paulo da Costa, Reza Refaei Afshar, Yingqian Zhang, Tom Catshoek, Daniël Vos, Sicco Verwer, Fynn Schmitt-Ulms, André Hottung, Tapan Shah, Meinolf Sellmann, Kevin Tierney, Carl Perreault-Lafleur, Caroline Leboeuf, Federico Bobbio, Justine Pepin, Warley Almeida Silva, Ricardo Gama, Hugo L. Fernandes, Martin Zaefferer, Manuel López-Ibáñez, and Ekhine Irurozki, arXiv:2201.10453v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Christoph Adam
Jorge Castelo Mourelle
Alberto García Martín-Caro
Andrzej Wereszczynski

"Spinning boson stars in nonlinear sigma models and Universal Relations" by Christoph Adam, Jorge Castelo Mourelle, Alberto García Martín-Caro, and Andrzej Wereszczynski, arXiv:2502.20923v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c}
		\toprule
		Attention Base & Eval OS & Channelized & mIOU (\%) \\
		\midrule[0.5pt]
		\midrule[0.5pt]
		\multirow{2}{*}{Axial Attention }& 16 & & 50.27 \\
		& 16 	 & \checkmark & 51.06 \\
		\midrule
		\multirow{2}{*}{ Self Attention} & 16 	 & & 50.42 \\
		& 16 	 & \checkmark & 51.09 \\
		\bottomrule[0.5pt]
	\end{tabular}
\end{adjustbox}
\caption{Ablation study of applying our Channelized Attention on self-attention with ResNet-101. \textbf{Eval OS}: Output strides~ during evaluation.}
\end{table}
\end{document}

cs

Ye Huang
Di Kang
Wenjing Jia
Xiangjian He
Liu Liu

"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}%
$e$ & Eigenvalues of $f_{e}(G)$ & \# positive eigenvalues & \# negative
eigenvalues\\\hline
$00$ & $(1,1,1)$ & 3 & 0\\
$01$ & $(-4,-4,8)$ & 1 & 2\\
$10$ & $(4,4,16)$ & 3 & 0\\
$11$ & $(-16,-16,128)$ & 1 & 2
\end{tabular}
\end{table}
\end{document}

math

Hoon Hong
Daniel Profili
J. Rafael Sendra

"Conditions for eigenvalue configurations of two real symmetric matrices" by Hoon Hong, Daniel Profili, and J. Rafael Sendra, arXiv:2401.00866v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
 \hline
 $N$ & $M$ & Upper bound & Upper bound & Upper bound for & $\kappa(I+W)$ \\
 & & of $\|X^{-1}\|_{2}$ & of $\|Y\|_2$ & $\kappa(I+W)$& \\
 & & & & from Theorem &\\
 \hline \hline
 25 & 800 & $1935.87\times10^{-6}$ & 9215.83 & 18.84 & 15.93\\
 50 & 3200 & $4840.86\times 10^{-7}$ & 38415.83 & 19.60 & 17.42\\
 100 & 12800 & $1210.29\times 10^{-7}$ & 156815.83 & 19.98 & 18.30\\
 200 & 51200 & $3025.75\times 10^{-8}$ & 633615.83 & 20.17 &18.84\\
 400 & 204800 & $7564.41\times 10^{-9}$ & 2547215.83 & 20.27 & 19.17\\
 800 & 819200 & $1891.10\times10^{-9}$ & 10214415.83 & 20.32 & 19.39\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Upper bound for $\kappa(I+W)$, i.e. condition number of $(I+W)$, with parameters $a(z)=z+1$, $b(z)=(z+1)^2$, $T=1,$ $z_l=0$, and $z_r=1$. The condition numbers given in the last column are computed using MATLAB.}
\end{table}
\end{document}

q-fin

Anindya Goswami
Kuldip Singh Patel
Pradeep Kumar Sahu

"A novel difference equation approach for the stability and robustness of compact schemes for variable coefficient PDEs" by Anindya Goswami, Kuldip Singh Patel, and Pradeep Kumar Sahu, arXiv:2209.02873v3, 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}
 & Size & Modality \\
 \hline
 LAION-Aesthetics-3M & 2M* & T,I \\
 CC12M & 12M & T,I\\
 COYO-700M(Subset) & 5M & T,I \\
 LAION-COCO & 7M & T,I \\
 SoundNet & 2M & T,A,I$\dag$ \\
 VGGSound & 0.2M & T,A,I$\dag$ \\
 T2I-2M & 2M & T,I \\
 AudioSet & 2M & T,A \\
 AudioCaps & 46K & T,A \\
 WavCaps & 0.4M & T,A \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{\textbf{List of all datasets used in training. }*Some image URLs are no longer accessible. $\dag$ We generate synthetic captions using BLIP. }
\end{table}
\end{document}

eess

Shufan Li
Konstantinos Kallidromitis
Akash Gokul
Zichun Liao
Yusuke Kato
Kazuki Kozuka
Aditya Grover

"OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows" by Shufan Li, Konstantinos Kallidromitis, Akash Gokul, Zichun Liao, Yusuke Kato, Kazuki Kozuka, and Aditya Grover, arXiv:2412.01169v2, 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{Classification accuracy ($\%$) of different kernels of RF-TCA (with $N = 500$) on Office-31 dataset with DECAF6 features.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccccc}
 \toprule
 Kernel & A$\rightarrow$D & A$\rightarrow$W & D$\rightarrow$A & D$\rightarrow$W & W$\rightarrow$A & W$\rightarrow$D & Avg \\ \midrule
 Laplace & \textbf{59.64} & 50.44 & \textbf{35.78} & 87.80 & \textbf{34.26} & \textbf{93.98} & \textbf{60.32} \\
 Gauss & 55.62 & \textbf{51.06} & 29.42 & \textbf{87.92} & 32.58 & 93.57 & 58.36 \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Zhanbo Feng
Yuanjie Wang
Jie Li
Fan Yang
Jiong Lou
Tiebin Mi
Robert. C. Qiu
Zhenyu Liao

"Robust and Communication-Efficient Federated Domain Adaptation via Random Features" by Zhanbo Feng, Yuanjie Wang, Jie Li, Fan Yang, Jiong Lou, Tiebin Mi, Robert. C. Qiu, and Zhenyu Liao, arXiv:2311.04686v2, 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}{lccc}
 \toprule
 \textbf{Model} & \textbf{UA (\%)} & \textbf{WA (\%)} & \textbf{F1 (\%)} \\
 \midrule
 \textit{Emotion Recognition} \\
 \quad \textsc{Speech-only} & 62.8 & 61.3 & 61.3 \\
 \quad \textsc{AVLM} & \textbf{67.2} & \textbf{67.1} & \textbf{66.2} \\
 \midrule
 \textit{Speech Generation} \\
 \quad \textsc{Speech-only} & 43.79 & 38.85 & 38.39 \\
 \quad \textsc{AVLM} & \textbf{46.03} & \textbf{42.99} & \textbf{42.49} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Performance comparison of AVLM and Speech-only model in terms of unweighted accuracy (UA), weighted accuracy (WA), and macro F1-score (F1).}
\end{table}
\end{document}

eess

Weiting Tan
Jiachen Lian
Hirofumi Inaguma
Paden Tomasello
Philipp Koehn
Xutai Ma

"Seeing is Believing: Emotion-Aware Audio-Visual Language Modeling for Expressive Speech Generation" by Weiting Tan, Jiachen Lian, Hirofumi Inaguma, Paden Tomasello, Philipp Koehn, and Xutai Ma, arXiv:2508.16188v2, 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{List of semantic attributes used in method}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl}
 \toprule
 Category&Attribute: Class\\
 \midrule
 Top & high neck: ruffle semi-high, turtle,...\\
&collar: rib collar, puritan collar,...\\
&lapel: notched, shawl, collarless,...\\
&neckline: V, square, round,...\\
&sleeves length: sleeveless, cap, short,...\\
&body length: high waist, long, regular,...\\
 \midrule
 Bottom & skirt length: short, knee, midi, ankle,...\\
&trousers length: short, mid, 3/4, cropped,...\\
 \midrule
 Shoes & heel height: flat, 1 in-7/4 in, under 1 in,...\\
&boots height: ankle, knee-high, mid-calf,...\\
&closure: lace-up, slip-on, zipper,...\\
&toe style: round, pointed, peep, open,...\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Aesthetics, Personalization and Recommendation: A survey on Deep Learning in Fashion" by Wei Gong and Laila Khalid, arXiv:2101.08301v1, licensed under CC0 1.0. 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{Designee Tracts} & \textbf{Finalist Tracts} & \textbf{Nearby Tracts} \\
 \hline
 Neighborhood Status Index & 0.16 (0.83) & -0.20 (0.94) & 1.01 (1.04) \\
 Med HH Income (\$1000) & 28.8 (7.65) & 27.0 (9.73) & 41.0 (17.3) \\
 Poverty Rate (\%) & 0.34 (0.12) & 0.37 (0.13) & 0.24 (0.14) \\
 Emp-Pop Ratio (\%) & 0.40 (0.09) & 0.35 (0.08) & 0.46 (0.11) \\
 HS Dropout Rate (\%) & 0.11 (0.13) & 0.13 (0.14) & 0.09 (0.13) \\
 \% Single Parent HH & 0.35 (0.13) & 0.36 (0.11) & 0.25 (0.13) \\
 \% Nonwhite & 0.85 (0.20) & 0.85 (0.20) & 0.67 (0.31) \\
 \% College Degree & 0.13 (0.11) & 0.10 (0.08) & 0.25 (0.21) \\
 Unique Tracts & 326 & 409 & 514 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Summary Statistics by Tract Type}
\end{table}
\end{document}

q-fin

"Place-Based Policies for Neighborhood Improvement: Evidence from Promise Zones" by Adamson Bryant, arXiv:2503.05946v1, 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|} \hline
\textbf{Operation}		&	\textbf{Current Draw} 	\\ \hline
CPU and Idle Radio		&	8 mA 				\\
Packet Reception			&	7.03 mA 			\\
Packet Transmission		&	10.4 mA 			\\
EEPROM Read				&	6.2 mA 			\\
EEPROM Write			&	18.4 mA 			\\ \hline
\end{tabular}
\end{adjustbox}
\caption{Energy required by common operations}
\end{table}
\end{document}

cs

"Harvest: A Reliable and Energy Efficient Bulk Data Collection Service for Large Scale Wireless Sensor Networks" by Vinayak Naik and Anish Arora, arXiv:2101.11211v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c||c|c|c|c|c|c|}
 \hline
 $p^c_{1,1}$ & 25 & $p^c_{2,1}$ & 20 & $p^c_{3,1}$ & 25 & $q^c_{1,1}$ & 3 & $q^c_{2,1}$ & 5 & $q^c_{3,1}$ & 7 \\ \hline
 $p^c_{1,2}$ & 21 & $p^c_{2,2}$ & 17 & $p^c_{3,2}$ & 18 & $q^c_{1,2}$ & 4 & $q^c_{2,2}$ & 9 & $q^c_{3,2}$ & 4 \\ \hline
 $p^c_{1,3}$ & 13 & $p^c_{2,3}$ & 11 & $p^c_{3,3}$ & 14 & $q^c_{1,3}$ & 5 & $q^c_{2,3}$ & 10 & $q^c_{3,3}$ & 4 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Parameters of the inverse demand functions considered in the example.}
\end{table}
\end{document}

q-fin

"Solidarity in natural gas storage: A potential allocation mechanism of stored quantities among several players during times of crisis" by Dávid Csercsik and Anne Neumann, arXiv:2209.05089v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Baseline Spatial Concentration Patterns by Industry (2019)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
\toprule
\multirow{2}{*}{Industry} & Location & Gini & Herfindahl- & Primary & \multicolumn{2}{c}{Employment Share} & AI Adoption \\
& Quotient & Coefficient & Hirschman Index & Ward & Central & Peripheral & Rate (\%) \\
\midrule
Information \& Communications & 3.42 & 0.68 & 0.31 & Shibuya & 67.4 & 8.2 & 34.7 \\
Finance \& Insurance & 2.87 & 0.72 & 0.28 & Chiyoda & 71.8 & 6.1 & 28.9 \\
Professional Services & 2.34 & 0.61 & 0.22 & Minato & 58.3 & 12.4 & 22.1 \\
Manufacturing & 0.78 & 0.45 & 0.15 & Ota & 23.7 & 31.2 & 8.3 \\
Retail Trade & 1.12 & 0.32 & 0.08 & Shinjuku & 28.1 & 25.6 & 5.7 \\
Healthcare & 0.95 & 0.28 & 0.06 & Setagaya & 22.4 & 28.9 & 7.2 \\
\midrule
\textbf{Average} & \textbf{1.91} & \textbf{0.51} & \textbf{0.18} & -- & \textbf{45.3} & \textbf{18.7} & \textbf{17.8} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"AI-Driven Spatial Distribution Dynamics: A Comprehensive Theoretical and Empirical Framework for Analyzing Productivity Agglomeration Effects in Japan's Aging Society" by Tatsuru Kikuchi, arXiv:2507.19911v2, 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}{lcc}
\hline
 & \textbf{Duartion(hrs)} & \textbf{\#Speakers} \\ \hline \hline
\textbf{NPTEL-tr} & 358.0 & 273 \\ %\hline
\textbf{NPTEL-dt} & 9.9 & 273 \\ %\hline
\textbf{NPTEL-et} & 9.8 & 273 \\ %\hline
\textbf{NPTEL-Unseen\_1} & 2.0 & 50 \\ %\hline
\textbf{NPTEL-Unseen\_2} & 2.0 & 50 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{NPTEL data set statistics}
\end{table}
\end{document}

eess

Vishwas M. Shetty
Metilda Sagaya Mary N J
S. Umesh

"Investigation of Speaker-adaptation methods in Transformer based ASR" by Vishwas M. Shetty, Metilda Sagaya Mary N J, and S. Umesh, arXiv:2008.03247v2, 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}{|c|c|c|c|}
\hline
 & \multicolumn{3}{c|}{Execution time (seconds)} \\ \hline
Stack name & Median filter (once) & Median filter (twice) & Median filter (three-times) \\ \hline
G\_stack & 0.4111 & 0.6983 & 0.9364 \\ \hline
S\_stack & 0.4107 & 0.6931 & 0.9160 \\ \hline
\end{tabular}
\caption{Execution time for the median filtering on the $G$ and $S$ 3D-volume stack using the MATLAB tool . }
\end{table}
\end{document}

eess

Varun Mannam
Yide Zhang
Xiaotong Yuan
Takashi Hato
Pierre C. Dagher
Evan L. Nichols
Cody J. Smith
Kenneth W. Dunn
Scott Howard

"Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)" by Varun Mannam, Yide Zhang, Xiaotong Yuan, Takashi Hato, Pierre C. Dagher, Evan L. Nichols, Cody J. Smith, Kenneth W. Dunn, and Scott Howard, arXiv:2103.05448v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of Stacked vs.\ Phased Training Strategies on the MRC Dataset Using the LLaMA 3B Model. Cells highlighted in green indicate better scores, and blue indicates higher sample efficiency (fewer samples used).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
\multirow{2}{*}{\textbf{Benchmark}} & \multicolumn{3}{c}{\textbf{Score}} & \multicolumn{2}{c}{\textbf{Samples}} \\ \cmidrule(lr){2-4} \cmidrule(lr){5-6}
 & \textbf{LLaMA Base} & \textbf{Stacked} & \textbf{Phased} & \textbf{Stacked} & \textbf{Phased} \\ \midrule
\textbf{Leaderboard (MATH Lvl 5)} & 0.02 & \cellcolor{green!30}0.04 & 0.03 & \cellcolor{blue!20}9,980,259 & 18,455,850 \\
\textbf{Leaderboard (MuSR)} & 0.05 & \cellcolor{green!30}0.08 & \cellcolor{green!30}0.08 & \cellcolor{blue!20}16,966,128 & 18,206,922 \\
\textbf{ARC} & \cellcolor{green!30}0.78 & 0.75 & 0.71 & \cellcolor{blue!20}2,745,290 & 14,964,367 \\
\textbf{GSM8K} & 0.27 & \cellcolor{green!30}0.69 & 0.67 & \cellcolor{blue!20}12,225,143 & 14,964,367 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Aldo Pareja
Nikhil Shivakumar Nayak
Hao Wang
Krishnateja Killamsetty
Shivchander Sudalairaj
Wenlong Zhao
Seungwook Han
Abhishek Bhandwaldar
Guangxuan Xu
Kai Xu
Ligong Han
Luke Inglis
Akash Srivastava

"Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs" by Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang, Krishnateja Killamsetty, Shivchander Sudalairaj, Wenlong Zhao, Seungwook Han, Abhishek Bhandwaldar, Guangxuan Xu, Kai Xu, Ligong Han, Luke Inglis, and Akash Srivastava, arXiv:2412.13337v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overhead from byte-based AC termination.}
\begin{tabular}{|c|c|c|c|c|} \hline
\bf Bidirectional &\bf Reversed &\bf Share &\bf Avrg. extra bits \\
\bf byte packing &\bf bits &\bf ratio &\bf per bitstream $\bar{T}$ \\ \hline \hline
 No & --- & --- & 4.56 \\ \hline
 Yes & No & 45\% & 2.77 \\ \hline
 Yes & Yes & 69\% & 1.78 \\ \hline
\end{tabular}
\end{table}
\end{document}

math

"Bitstream Organization for Parallel Entropy Coding on Neural Network-based Video Codecs" by Amir Said, Hoang Le, and Farzad Farhadzadeh, arXiv:2312.00921v1, 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{Summary Statistics of Pooled Distributions (\%/day).}
\begin{tabular}{cccccccc}
 \hline \\
 & Min & Mean & Median & StDev & Skew & Kurt & Max\\
 \hline \hline \\
 Cryptocurrencies & -86.33 & 0.04 & -0.09 & 8.56 & 0.58 & 13.27 & 88.87\\
 S\&P 500 Companies & -17.99 & 0.05 & 0.09 & 2.11 & -0.14 & 8.41 & 17.98\\ \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"An Information Theory Approach to the Stock and Cryptocurrency Market: A Statistical Equilibrium Perspective" by Emanuele Citera and Francesco De Pretis, arXiv:2310.04907v1, 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{Effect of Top-$k$ Size (in Eqn. () on MSCOCO Image Captioning Dataset. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{crrrrrr}
			\toprule
			top-$k$ & B1 & B4 & M & R & S & C \\
			\midrule
			\midrule
			1 & 80.1 &	\bf37.4 &	28.0 &	57.9 &	21.7 &	123.7 \\
			2 & \bf80.3 &	 37.3 &	\bf28.1 	& \bf58.0 	&\bf21.8 &	\bf124.0 \\
			5 & 80.1 &	37.3 &	28.0 &	58.0 &	21.7 &	123.7 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Longteng Guo
Jing Liu
Xinxin Zhu
Hanqing Lu

"Fast Sequence Generation with Multi-Agent Reinforcement Learning" by Longteng Guo, Jing Liu, Xinxin Zhu, and Hanqing Lu, arXiv:2101.09698v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of literal-entity alignment methods}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccc|ccc}
 \toprule
 \multirow{2}{*}{Methods} & \multicolumn{3}{c|}{Inside KG} & \multicolumn{3}{c}{Outside KG} \\
 \cmidrule{2-4} \cmidrule{5-7}
 & P$\,\uparrow$ & R$\,\uparrow$ & F1$\,\uparrow$ & P$\,\uparrow$ & R$\,\uparrow$ & F1$\,\uparrow$ \\
 \midrule
 OWE & 0.358 & 0.428 & 0.390 & 0.314 & 0.384 & 0.346 \\
 ConMask & 0.376 & 0.457 & 0.413 & 0.373 & 0.367 & 0.370 \\
 \midrule
 Knowledge Vault & 0.386 & 0.471 & 0.424 & 0.381 & 0.369 & 0.375 \\
 OKELE & 0.436 & 0.491 & 0.462 & 0.434 & 0.452 & 0.443 \\
 \midrule
 TKGC & \textbf{0.537} & \textbf{0.492} & \textbf{0.514} & \textbf{0.461} & \textbf{0.483} & \textbf{0.471} \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

"Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data" by Jiacheng Huang, Yao Zhao, Wei Hu, Zhen Ning, Qijin Chen, Xiaoxia Qiu, Chengfu Huo, and Weijun Ren, arXiv:2201.08580v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{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
& AUROC & AUPR & DSC\\
 \hline
 VAE & 0.668 & 0.704 & 0.110 $\pm$ 0.021\\
 \hline
 Context-encoding VAE & 0.766 & 0.640 & \textbf{0.112 $\pm$ 0.021 } \\
 \hline
 \textbf{VAE with Pretraining (ours) }& 0.673 & 0.772 & \textbf{0.112 $\pm$ 0.021 }\\
 \hline
 \textbf{Multi-task Framework (ours)} & \textbf{0.822} & \textbf{0.868} & 0.086 $\pm$ 0.024\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Comparison with state-of-the-art anomaly detection methods.}
\end{table}
\end{document}

eess

Abinav Ravi Venkatakrishnan
Seong Tae Kim
Rami Eisawy
Franz Pfister
Nassir Navab

"Self-Supervised Out-of-Distribution Detection in Brain CT Scans" by Abinav Ravi Venkatakrishnan, Seong Tae Kim, Rami Eisawy, Franz Pfister, and Nassir Navab, arXiv:2011.05428v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Bayesian Despeckling of Structured Sources" by Ali Zafari and Shirin Jalali, arXiv:2501.11860v2, 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}{lllllllll}
meta-estimator & outcome\_model & mean\_pehe & ate\_rmse & ate\_abs\_bias & ate\_std\_error \\
stratified\_standardization & kNN & 3065.36 & 1683.85 & 406.78 & 1633.97 \\
stratified\_standardization & Standardized\_SVM\_rbf & 4312.85 & 2703.05 & 2438.89 & 1165.47 \\
stratified\_standardization & SVM\_rbf & 4416.33 & 2674.25 & 2320.72 & 1328.86 \\
stratified\_standardization & KernelRidge & 4584.82 & 3965.28 & 258.97 & 3956.81 \\
stratified\_standardization & Lasso & 4614.18 & 4123.09 & 2797.95 & 3028.43 \\
stratified\_standardization & ElasticNet & 4767.89 & 4240.31 & 2716.97 & 3255.51 \\
stratified\_standardization & Ridge & 4825.75 & 4287.98 & 2706.11 & 3326.22 \\
stratified\_standardization & Standardized\_SVM\_sigmoid & 5333.67 & 2787.82 & 2398.72 & 1420.60 \\
stratified\_standardization & LinearRegression & 5379.93 & 3694.49 & 2855.89 & 2343.74 \\
standardization & LinearRegression\_degree2 & 5961.98 & 3814.48 & 3122.71 & 2190.65 \\
standardization & LinearRegression\_interact & 6094.42 & 3877.79 & 3157.29 & 2251.40 \\
standardization & Standardized\_SVM\_rbf & 6375.94 & 2298.04 & 2098.06 & 937.60 \\
stratified\_standardization & Standardized\_LinearSVM & 8109.11 & 6060.79 & 5676.84 & 2122.89 \\
stratified\_standardization & DecisionTree & 8570.48 & 2894.01 & 1429.13 & 2516.52 \\
stratified\_standardization & LinearSVM & 8962.51 & 6703.79 & 2077.53 & 6373.75 \\
stratified\_standardization & SVM\_sigmoid & 10744.12 & 1374.50 & 435.80 & 1303.58 \\
standardization & Standardized\_SVM\_sigmoid & 15968.21 & 10574.70 & 10567.02 & 402.89 \\
standardization & kNN & 18065.93 & 13125.92 & 13125.92 & 1.29 \\
standardization & DecisionTree & 18066.16 & 13124.53 & 13124.53 & 0.00 \\
standardization & ElasticNet & 18066.16 & 13124.53 & 13124.53 & 0.00 \\
standardization & Lasso & 18066.16 & 13124.53 & 13124.53 & 0.00 \\
standardization & LinearSVM & 18066.16 & 13124.53 & 13124.53 & 0.00 \\
standardization & SVM\_rbf & 18066.16 & 13124.53 & 13124.53 & 0.00 \\
standardization & SVM\_sigmoid & 18066.16 & 13124.53 & 13124.53 & 0.00 \\
standardization & Ridge & 18066.73 & 13125.30 & 13125.30 & 1.18 \\
standardization & KernelRidge & 18120.34 & 13198.56 & 13198.06 & 113.96 \\
standardization & LinearRegression & 18818.59 & 14189.19 & 14097.20 & 1613.08 \\
standardization & Standardized\_LinearSVM & 19110.22 & 14556.98 & 14486.66 & 1429.18 \\
stratified\_standardization & LinearRegression\_interact & 105866.18 & 53972.80 & 37749.80 & 38574.81 \\
stratified\_standardization & LinearRegression\_degree2 & 201053.48 & 103618.34 & 93842.31 & 43936.11
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

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{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study investigating the impact of the velocity loss and fine-tuning on studio-quality audio during heterogeneous KD.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
\toprule
& PBM accuracy $\uparrow$& Jitter\\
\midrule
$S_0$& 92.4& 0.0412\\
\midrule
 w/o velocity loss& 93.8&0.0536\\
 w/o fine-tuning& 86.2&0.0446\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Zhen Han
Mattias Teye
Derek Yadgaroff
Judith Bütepage

"Tiny is not small enough: High-quality, low-resource facial animation models through hybrid knowledge distillation" by Zhen Han, Mattias Teye, Derek Yadgaroff, and Judith Bütepage, arXiv:2507.18352v1, 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{Annotator agreement.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
 & Raw & Five Bins \\
\hline
Spearman's $\rho$ & 0.556 & 0.547\\
\hline
Weighted Cohen's $\kappa$ & 0.553 & 0.542 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Pardon the Interruption: An Analysis of Gender and Turn-Taking in U.S. Supreme Court Oral Arguments" by Haley Lepp and Gina-Anne Levow, arXiv:2009.07391v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Model} & \textbf{N. Params} & \textbf{Optimizer}& \textbf{Time (ms/it)} & \textbf{Rel. $L^2$ Error} \\
 \midrule
 Baseline& 2011&SSBroyden & 26.1 & $1.67 \times 10^{-7}$ \\
 $v RBA:\Phi(r)=r^2$ &2011&SSBroyden & 28.2&$1.74 \times 10^{-8}$ \\
 $v RBA:\Phi(r)=e^r$ & 2011&SSBroyden & 27.6 & $\mathbf{1.51 \times 10^{-8}}$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Performance of the vRBA method for the Burgers equation. The vRBA strategies, using either a quadratic ($\Phi(r)=r^2$) or exponential ($\Phi(r)=e^r$) potential, are benchmarked against a baseline model with uniform sampling. All models are trained with the second-order SSBroyden optimizer. The results demonstrate that the vRBA methods improve the final relative $L^2$ error by an order of magnitude over the baseline, underscoring that an advanced adaptive sampling strategy is critical for achieving high accuracy.}
\end{table}
\end{document}

math

Juan Diego Toscano
Daniel T. Chen
Vivek Oommen
George Em Karniadakis

"A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning" by Juan Diego Toscano, Daniel T. Chen, Vivek Oommen, and George Em Karniadakis, arXiv:2509.14198v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Neural Network Description}
\begin{tabular}{|l|l|l|l|}
\hline
\textbf{Layers} & \textbf{Layer type} & \textbf{Size} & \textbf{Activation function} \\ \hline
Input layer & - & (22) & - \\ \hline
Hidden layer 1 & Fully connected & (24) & Leaky ReLU \\ \hline
Hidden layer 2 & Fully connected & (16) & Leaky ReLU \\ \hline
Output layer & - & (5) & - \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Shreyas Devaraju
Alexander Ihler
Sunil Kumar

"A Deep Q-Learning based, Base-Station Connectivity-Aware, Decentralized Pheromone Mobility Model for Autonomous UAV Networks" by Shreyas Devaraju, Alexander Ihler, and Sunil Kumar, arXiv:2311.16409v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Ancestral origins of attention to environmental issues" by César Barilla and Palaash Bhargava, arXiv:2509.09598v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{multirow}
\usepackage{graphicx}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Confusion matrix, without normalization}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\cline{3-6}
\multirow{4}{*}{\rotatebox{90}{True label}} & Trees & 108 & 37 & 55 & 0 \\
 & Grass & 42 & 131 & 27 & 0 \\
 & Impervious & 35 & 17 & 147 & 1 \\
 & Water & 1 & 0 & 0 & 199 \\ \cline{3-6}
\multicolumn{2}{c}{\multirow{2}{*}{}} & Trees\hspace{1mm} & Grass\hspace{1mm} & Impervious\hspace{0mm} & Water\hspace{1mm} \\
\multicolumn{2}{c}{} & \multicolumn{4}{c}{Predicted label}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Juan Carrillo
Daniel Garijo
Mark Crowley
Rober Carrillo
Yolanda Gil
Katherine Borda

"Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees" by Juan Carrillo, Daniel Garijo, Mark Crowley, Rober Carrillo, Yolanda Gil, and Katherine Borda, arXiv:2009.10263v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Information-theoretic subset selection of multivariate Markov chains via submodular optimization" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccc|}
 \hline
 $\Delta$& obj & LB & UB & gap \\
 \hline
0.05&-240.11&-250.27&-234.51&6.56\%\\
\hline
0.1&-254.90&-267.46&-252.10&6.02\%\\
\hline
0.2&-269.14&-279.31&-267.25&4.48\%\\
\hline
0.5&-307.21&-308.34&-304.10&1.38\%\\
\hline
0.8&-335.96&-337.72&-334.56&0.94\%\\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Objective value for the proposed model and 95\% bounds with different sizes of the ambiguity set.}
\end{table}
\end{document}

math

"A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications" by Jiaqi Lei, Akhil Singla, and Sanjay Mehrotra, arXiv:2501.18051v2, 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{Top 5 Arbitrage Potential Cases (Gas Fees Considered)}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Timestamp} & \textbf{Price (Centralized)} & \textbf{Price (Defi)} & \textbf{Gas Fee (USD)} & \textbf{Arbitrage Potential (USD)} \\
\hline
19:36:08 & 3503.95 & 3538.99 & 34.92 & 0.11 \\
19:36:05 & 3505.42 & 3538.99 & 34.92 & -1.36 \\
19:36:10 & 3506.40 & 3538.99 & 34.92 & -2.34 \\
19:36:06 & 3507.64 & 3538.99 & 34.92 & -3.58 \\
19:36:07 & 3508.80 & 3538.99 & 34.92 & -4.74 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Juan Plazuelo Pascual
Carlos Tardon Rubio
Juan Toro Cebada
Angel Hernando Veciana

"Price Discovery in Cryptocurrency Markets" by Juan Plazuelo Pascual, Carlos Tardon Rubio, Juan Toro Cebada, and Angel Hernando Veciana, arXiv:2506.08718v1, 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}{cc|ccc}
 \midrule
 \multirow{2}{*}{HuBERT CNN Encoder}
 & strides & \multicolumn{3}{c}{5, 2, 2, 2, 2, 2, 2} \\
 & kernel width & \multicolumn{3}{c}{10, 3, 3, 3, 3, 2, 2} \\
 ~ & channel & \multicolumn{3}{c}{512} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of the tower detection component using the $DmAP$ metric (higher better) of \textit{GridTracer} with the "conventional" data handling scheme in Fig. (a)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l||c|c|c|c}
 \hline
 Metric & Arizona & Kansas & New Zealand & Average \\
 \hline \hline
 $DmAP$ & 0.73 & 0.54 & 0.55 & 0.63 \\
 \hline
 $mAP_{0.75}$ & 0.09 & 0.02 & 0.08 & 0.06 \\
 $mAP_{0.5}$ & 0.60 & 0.44 & 0.51 & 0.52 \\
 \hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Bohao Huang
Jichen Yang
Artem Streltsov
Kyle Bradbury
Leslie M. Collins
Jordan Malof

"GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery" by Bohao Huang, Jichen Yang, Artem Streltsov, Kyle Bradbury, Leslie M. Collins, and Jordan Malof, arXiv:2101.06390v1, 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 results for the discounted sales example.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|} \hline
		& \textit{lme4} & PLS & RPLS \\ \hline
		$ \beta_0 $ & $0.106$ & $0.164$ & $0.164$ \\ \hline
	 $ \beta_1 $ & $-0.319$ & $0.259$ & $0.270$ \\ \hline
	 S.D. of random intercept & $0.671$ & $0.095$ & $0.094$ \\ \hline
	 S.D. of random slope & $0.361$ & $0.075$ & $0.156$ \\ \hline
	 S.D. of residuals & $1.388$ & $1.445$ & $1.447$ \\ \hline \hline
	Marginal $R^2$ & $0.000$ & $0.000$ & $0.000$ \\ \hline
	Conditional $ R^2 $ & $0.190$ & $0.488$ & $0.606$ \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects" by Hao Chen, Lanshan Han, and Alvin Lim, arXiv:2011.04538v9, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Algorithms vs. Peers: Shaping Engagement with Novel Content" by Shan Huang, Yi Ji, and Leyu Lin, arXiv:2503.11561v2, 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{Classification Accuracy} (smaller indicates better results) of different models on targeted adversarial samples. Here, we pick $\epsilon_{\infty}=0.18$, $m=100$, $\gamma=0.25$ and select DGCNN as victim model. Other settings are the same as Sec 4.1. In terms of misclassification rate, our AOF outperforms the other baselines when transferred to different networks.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccr}
\toprule
Model & PointNet & PointNet++ & DGCNN & PointConv \\
\midrule
3D-Adv & 84.12 & 44.37 & \bf0.04 & 52.84 \\
kNN & 71.72 & 49.07 & 8.06 & 47.08 \\
AdvPC & 60.04 & 20.81 & 3.77 & 20.00 \\
AOF & \bf53.60 & \bf13.56 & 3.85 & \bf13.16 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Binbin Liu
Jinlai Zhang
Lyujie Chen
Jihong Zhu

"Boosting 3D Adversarial Attacks with Attacking On Frequency" by Binbin Liu, Jinlai Zhang, Lyujie Chen, and Jihong Zhu, arXiv:2201.10937v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance on generic recognition benchmarks with varying imbalance factors (IF), with and without prior class distribution. These benchmarks come with both novel class ratio and label ratio of 50\%.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccccccc}
 \toprule
 \textbf{Dataset} & \textbf{Prior} & \multicolumn{3}{c}{\textbf{Uniform (IF=1)}} & \multicolumn{3}{c}{\textbf{IF=10}} & \multicolumn{3}{c}{\textbf{IF=20}}\\
 \cmidrule(lr){3-5} \cmidrule(l){6-8} \cmidrule(lr){9-11}
 & & Seen & Novel & All & Seen & Novel & All & Seen & Novel & All \\
 \midrule
 \multirow{2}{7em}{\textbf{CIFAR-10}} & w/ & 96.5 & 97.1 & 96.8 & 93.7 & 72.1 & 82.5 & 92.9 & 70.1 & 80.9 \\
 & w/o & 96.9 & 90.9 & 93.9 & 95.8 & 66.5 & 80.3 & 95.3 & 64.2 & 78.8 \\
 \multirow{2}{7em}{\textbf{CIFAR-100}} & w/ & 80.1 & 63.9 & 71.9 & 76.8 & 42.0 & 57.3 & 76.1 & 35.2 & 51.9 \\
 & w/o & 82.5 & 57.9 & 69.2 & 74.6 & 39.7 & 54.1 & 73.9 & 33.9 & 49.2 \\
 \multirow{2}{7em}{\textbf{Tiny-ImageNet}} & w/ & 68.8 & 42.4 & 55.0 & 61.7 & 25.1 & 41.6 & 62.4 & 21.7 & 38.3 \\
 & w/o & 69.6 & 40.6 & 54.8 & 61.0 & 24.9 & 40.1 & 61.3 & 20.3 & 36.9 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Shengjie Niu
Lifan Lin
Jian Huang
Chao Wang

"OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning" by Shengjie Niu, Lifan Lin, Jian Huang, and Chao Wang, arXiv:2411.01833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Mining Unstructured Medical Texts With Conformal Active Learning" by Juliano Genari and Guilherme Tegoni Goedert, arXiv:2502.04372v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}
\hline
\multirow{4}{*}{Algorithm} & \multicolumn{2}{|c|}{Reference-Sim} & \multicolumn{8}{|c|}{Catfish} \\ \cline{2-11}
 & \multicolumn{2}{|c|}{Human} & \multicolumn{2}{|c|}{Zebrafish} & \multicolumn{2}{|c|}{Mouse} & \multicolumn{2}{|c|}{Human} & \multicolumn{2}{|c|}{Human (salmon)} \\ %& \multicolumn{2}{|c|}{All} \\
& \multicolumn{2}{|c|}{25.6MB} & \multicolumn{2}{|c|}{122MB} & \multicolumn{2}{|c|}{137MB} & \multicolumn{2}{|c|}{157MB} & \multicolumn{2}{|c|}{2.5GB} \\ %& \multicolumn{2}{|c|}{2.9GB} \\
\cline{2-11}
 & Time & Mem & Time & Mem & Time & Mem & Time & Mem & Time & Mem \\ % & Time & Mem \\
 \hline
Unitigs & 0.68s & 3.58MB & 13.82s & 3.51MB & 15.62s & 3.53MB & 18.22s & 3.54MB & 303.72s & 3.66MB \\
ExtUnitigs & 0.99s & 3.63MB & 18.31s & 3.52MB & 20.87s & 3.57MB & 23.64s & 3.56MB & 404.50s & 3.68MB \\
Safe\&Comp & 2.56s & 4.47MB & 20.17s & 3.56MB & 25.76s & 3.66MB & 28.59s & 3.54MB & 667.27s & 3.84MB \\
Greedy & 7.71s & 4.88MB & 108.30s & 6.00MB & 127.38s & 6.29MB & 148.46s & 6.34MB & 2684.30s & 8.47MB \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Time and Memory requirements of the different algorithms for the evaluated datasets.}
\end{table}
\end{document}

cs

Shahbaz Khan
Milla Kortelainen
Manuel Cáceres
Lucia Williams
Alexandru I. Tomescu

"Safety and Completeness in Flow Decompositions for RNA Assembly" by Shahbaz Khan, Milla Kortelainen, Manuel Cáceres, Lucia Williams, and Alexandru I. Tomescu, arXiv:2201.10372v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Rutwig Campoamor-Stursberg
Danilo Latini
Ian Marquette
Junze Zhang
Yao-Zhong Zhang

"On the construction of polynomial Poisson algebras: a novel grading approach" by Rutwig Campoamor-Stursberg, Danilo Latini, Ian Marquette, Junze Zhang, and Yao-Zhong Zhang, arXiv:2503.03490v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c}
			\hline
			Method \rule[-1ex]{0pt}{3.5ex} & Relative ${\mathbb L}_2$ errors ($x, y, z$) \\
			\hline
			Causal training~ \rule[-1ex]{0pt}{3.5ex} & $1.14\times 10^{-2}$, $1.66\times 10^{-2}$, $7.04\times 10^{-3}$ \\
			{\bf PIDD initialization} & $\bf{2.0\times 10^{-4}}$, $\bf{6.7\times 10^{-4}}$, $\bf{1.3\times 10^{-4}}$\\
			{\bf NbN training} &
			$\bf{9.8\times 10^{-4}}$, $\bf{1.4\times 10^{-3}}$, $\bf{6.0\times 10^{-4}}$ \\
			\hline
		\end{tabular}
\caption{Lorentz system: Relative ${\mathbb L}_2$ errors obtained by different approaches}
\end{table}
\end{document}

math

Vasiliy A. Es'kin
Alexey O. Malkhanov
Mikhail E. Smorkalov

"Are Two Hidden Layers Still Enough for the Physics-Informed Neural Networks?" by Vasiliy A. Es'kin, Alexey O. Malkhanov, and Mikhail E. Smorkalov, arXiv:2412.19235v1, 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
\caption{Fail rate of different models on rating-based rationality tests (std. {errors} in parentheses).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 & Rating of one listing & Random ratings & Random ratings \\
 & increased by $0.1$ & (low variance) & (high variance) \\
 \midrule
 \midrule
 Claude Sonnet 3.5 & $57.3\%$ $(1.5\%)$ & $16.3\%$ $(0.8\%)$ & $2.7\%$ $(0.2\%)$ \\
 Claude Sonnet 3.7 & $6.7\%$ $(0.5\%)$ & \textcolor{gray}{$0.0\%$} & \textcolor{gray}{$0.0\%$} \\
 Claude Sonnet 4.0 & $28.7\%$ $(1.2\%)$ & $9.4\%$ $(0.6\%)$ & $4.7\%$ $(0.3\%)$ \\
 GPT\textendash4o & $71.7\%$ $(0.9\%)$ & $16.0\%$ $(0.5\%)$ & $7.3\%$ $(0.4\%)$ \\
 GPT\textendash4.1 & $15.1\%$ $(0.6\%)$ & $11.7\%$ $(0.5\%)$ & $6.0\%$ $(0.4\%)$ \\
 Gemini 2.0 Flash & \textcolor{gray}{$0.0\%$} & \textcolor{gray}{$0.0\%$} & $0.3\%$ $(0.1\%)$ \\
 Gemini 2.5 Flash & \textcolor{gray}{$0.0\%$} & \textcolor{gray}{$0.0\%$} & \textcolor{gray}{$0.0\%$} \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Amine Allouah
Omar Besbes
Josué D Figueroa
Yash Kanoria
Akshit Kumar

"What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce" by Amine Allouah, Omar Besbes, Josué D Figueroa, Yash Kanoria, and Akshit Kumar, arXiv:2508.02630v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\emph{Effect of pre-training.} Mean accuracy (\%) on CLEVR-CoGenT validation condition B and CLOSURE with two different feature extractors, which are an object detector pre-trained on Visual Genome (VG) and a ResNet-101 (without pre-training on VG).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|c|c|c}
\toprule
\multirow{2}{*}{Methods} & \multicolumn{3}{c|}{CLEVR-CoGenT} & \multicolumn{3}{c}{CLOSURE} \\ \cmidrule{2-7}
& w/o VG & w/ VG & $\Delta$ & w/o VG & w/ VG & $\Delta$ \\ \midrule %\hline
Transformer & 78.9 & 87.5 & 8.6 & 57.4 & 62.1 & 4.7 \\ %\hline
Transformer w/ PR & \textbf{81.7} & \textbf{88.7} & 6.9 & 64.5 & 64.5 & 0.005 \\ %\hline
TMN-Stack (ours) & 80.6 & 86.9 & 6.3 & 90.9 & 92.7 & 1.7 \\ %\hline
TMN-Tree (ours) & 80.1 & 86.4 & 6.4 & \textbf{95.4} & \textbf{96.4} & 1.0 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Moyuru Yamada
Vanessa D'Amario
Kentaro Takemoto
Xavier Boix
Tomotake Sasaki

"Transformer Module Networks for Systematic Generalization in Visual Question Answering" by Moyuru Yamada, Vanessa D'Amario, Kentaro Takemoto, Xavier Boix, and Tomotake Sasaki, arXiv:2201.11316v2, 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{Properties of the Equilibrium Strategy $(\alpha^*,\Phi^*,D_A^*)$}
\begin{tabular}{ccc}
 \toprule
& Case (i) & Case (ii)\\
\midrule
\textbf{Gas fee density $\phi^*(g)$} & \multicolumn{2}{c}{Strictly decreasing in $g\in[\hat g_L,g_H]$}\\ \midrule
\textbf{Trading amount given gas fee $D_A^*(g)$} & \multicolumn{2}{c}{Strictly increasing in $g\in[\hat g_L,g_H]$ and $D_A^*(g_H)=\hat D_A$}\\\midrule
\textbf{Effect of base gas fee $\hat g_L$} & $\hat g_L\le \hat g_H- L_B\hat z$ & $\hat g_L> \hat g_H- L_B\hat z$\\\midrule
Trading probability $\alpha^*$ & $=1$ & $<1$ and strictly decreasing in $\hat g_L$\\
Abitrageur's expected profit $\Pi^*$ & $>0$ and strictly decreasing in $\hat g_L$ & $=0$\\
Gas fee paid $\tilde{g}^*$ & Increasing in $\hat g_L$ & decreasing in $\hat g_L$\\
Relative trading amount $\tilde{D}^*/y_A$ & Independent of $\hat g_L$ & decreasing in $\hat g_L$\\ \midrule
\textbf{Effect of pool liquidity $L_B$} & $L_B\ge \hat g_L/((1-O^{-1/2})^2-\hat z)$ & $L_B< \hat g_L/((1-O^{-1/2})^2-\hat z)$\\\midrule
Trading probability $\alpha^*$ & $=1$ & $<1$ and strictly increasing in $L_B$\\
Abitrageur's expected profit $\Pi^*$ & $>0$ and strictly increasing in $L_B$ & $=0$\\
Gas fee paid $\tilde g^*$ & \multicolumn{2}{c}{Increasing in $L_B$}\\
Relative trading amount $\tilde D^*/y_A$ & Independent of $L_B$ & Increasing in $L_B$\\ \midrule
\textbf{Effect of arbitrage opportunity $O$} & $O\ge \bar{O}$ & $O<\bar{O}$\\\midrule
Trading probability $\alpha^*$ & $=1$ & $<1$ and strictly increasing in $O$\\
Abitrageur's expected profit $\Pi^*$ & $>0$ and strictly increasing in $O$ & $=0$\\
Gas fee paid $\tilde g^*$ & \multicolumn{2}{c}{Increasing in $O$}\\
Relative trading amount $\tilde D^*/y_A$ & \multicolumn{2}{c}{Increasing in $O$}\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Arbitrage on Decentralized Exchanges

"Arbitrage on Decentralized Exchanges" by Xue Dong He, Chen Yang, and Yutian Zhou, arXiv:2507.08302v1, 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}{ccc}
\toprule
& Insurance & Credit \\
\midrule
Firm A & 0.947 & 0.823\\
Firm B & 0.947 & 0.885 \\
Firm C & - & 0.793 \\
Firm D & - & 0.902 \\
Firm E & - & 0.763 \\
Global & 0.895 & 0.791 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Local and global fairness values under a fairness-maximizing SP tax policy.}
\end{table}
\end{document}

q-fin

Jesse Thibodeau
Hadi Nekoei
Afaf Taïk
Janarthanan Rajendran
Golnoosh Farnadi

"Balancing Profit and Fairness in Risk-Based Pricing Markets" by Jesse Thibodeau, Hadi Nekoei, Afaf Taïk, Janarthanan Rajendran, and Golnoosh Farnadi, arXiv:2506.00140v2, 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{MDP1 Reward Function}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c||c}\hline
 State & Action & Reward \\\hline
 0 & 0 & 0 \\
 0 & 1 & 10 \\
 1 & 0 & 10 \\
 1 & 1 & 0 \\\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Karine Karine
Susan A. Murphy
Benjamin M. Marlin

"BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings" by Karine Karine, Susan A. Murphy, and Benjamin M. Marlin, arXiv:2412.00308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Mitigating Consequences of Prestige in Citations of Publications" by Michael Balzer and Adhen Benlahlou, arXiv:2411.05584v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{F1 Scores for Different Event Classes}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
			\toprule
			 & \multirow{2}{*}{\textit{Non-event}} & \multirow{2}{*}{\textit{Line-event}} & \textit{Generator} & \textit{Oscillation} \\
			 & & &\textit{event} & \textit{event}\\
			\midrule
			\textit{Baseline} & 0.885 & 0.966 & 0.895 & 0.911 \\
			\midrule
			\textit{Baseline+info} & 0.884 & 0.965 & 0.908 & 0.894 \\
			\midrule
			\textit{Baseline+GSP} & 0.928 & 0.976 & 0.904 & 0.944 \\
			\midrule
			\textit{Baseline+GSP+info} & 0.950 & 0.970 & 0.931 & 0.972 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Power System Event Identification based on Deep Neural Network with Information Loading" by Jie Shi, Brandon Foggo, and Nanpeng Yu, arXiv:2011.06718v2, 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{Fraction of sales and volume (in USD) controlled by the top 10\% sellers (columns 1 and 2), and by the top 10\% buyers (columns 3 and 4).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
 & \multicolumn{2}{c}{\textbf{Top 10\% sellers}} & \multicolumn{2}{c}{\textbf{Top 10\% buyers}} \\
 & \textit{Sales} & \textit{Volume} & \textit{Sales} & \textit{Volume} \\
SuperRare & 67\% & 79\% & 67\% & 88\% \\
Async Art & 38\% & 84\% & 27\% & 93\% \\
KnownOrigin & 61\% & 86\% & 37\% & 93\% \\
Foundation & 37\% & 80\% & 30\% & 85\% \\
Makersplace & 78\% & 80\% & 55\% & 76\% \\
Zora & 33\% & 60\% & 22\% & 68\% \\
\textbf{Overall} & \textbf{71\%} & \textbf{86\%} & \textbf{64\%} & \textbf{92\%}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Seller-buyer networks in NFT art are driven by preferential ties" by Giovanni Colavizza, arXiv:2210.04339v2, 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{Complexity of PGD-VAE and comparison models ($n$ is the size of the basic unit, $m$ is the size of the global pattern)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline Model & Number of generation steps & Time complexity & Space complexity\\
 \hline
 VGAE & $O(1)$ & $O((mn)^2)$ & $O((nm)^2)$\\
 \hline
 GraphRNN & $O((nm)^2)$ & $O((mn)^2)$ & $O((nm)^2)$\\
 \hline
 GRAN & $O(nm)$ & $O((mn)^2)$ & $O((nm)^2)$\\
 \hline
 PGD-VAE & $O(1)$ & $O(m^2+n^2+mn)$ & $O(max\{n^2, m^2\})$\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Deep Generative Model for Periodic Graphs" by Shiyu Wang, Xiaojie Guo, and Liang Zhao, arXiv:2201.11932v4, 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{Outline of the Computational Study.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rccc}
 \toprule
 & \multicolumn{3}{c}{Uncertainty Set} \\\cmidrule{2-4}
 & Budgeted & Continuous Knapsack & Binary Knapsack \\\midrule
 Shortest Path & Sect.~ & --- & Sect.~ \\
 Knapsack & Sect.~ & Sect.~ & Sect.~ \\
 Portfolio Selection & Sect.~ & --- & --- \\\toprule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Henri Lefebvre
Martin Schmidt
Simon Stevens
Johannes Thürauf

"Solving Decision-Dependent Robust Problems as Bilevel Optimization Problems" by Henri Lefebvre, Martin Schmidt, Simon Stevens, and Johannes Thürauf, arXiv:2503.01559v2, 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|cccccc}
frequency & 3904 (97.6\%) & 34 (0.85\%) &1 (0.02\%) &1 (0.02\%) &1 (0.02\%) &59 (1.48\%) \\
\hline
 $I_\delta(x,\widehat x)$ & 0 & 1 & 2 & 3 & 4 & $\geq 5$
\end{tabular}
\end{adjustbox}
\caption{The frequencies of occurrency of dissimilarity index values. The total sample size is 4000, and $I_\delta(x,\widehat x)=0$ means perfect identification of the atoms, while $I_\delta(x,\widehat x) = k>0$ indicates that $k$ atoms were either not identified or an incorrect identification occurred.}
\end{table}
\end{document}

stat

Alberto Bocchinfuso
Daniela Calvetti
Erkki Somersalo

"Bayesian sparsity and class sparsity priors for dictionary learning and coding" by Alberto Bocchinfuso, Daniela Calvetti, and Erkki Somersalo, arXiv:2309.00999v1, 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{Coverage rates of the 95\% bootstrap confidence intervals when employing the WQ-EE method in Simulation 1}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \hline
 &$m$ & $\psi_{10}$ & $\psi_{11}$ & $\psi_{20}$ & $\psi_{2A}$ & $\psi_{22}$ \\
 \hline
 Sample size 500 & 473 & 0.947 & 0.937 & 0.951 & 0.941 & 0.946 \\
 Sample size 2000 & 1956 & 0.947 & 0.945 & 0.953 & 0.950 & 0.945 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Weighted Q-learning for optimal dynamic treatment regimes with nonignorable missing covariates" by Jian Sun, Bo Fu, and Li Su, arXiv:2312.01735v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
		\toprule
		{Problem name} & {box} 		& $\ell_2$	\\
		\midrule
		10teams & 870	 & 870	\\
		app2-1 & 870 & 870	\\
		app2-2 & 870 & 870	\\
		exp-1-500-5-5 & 870 & 870	\\
		flugpl & 870 & 870	\\
		g200x740 & 870 & 870	\\
		gen-ip054 & 870 & 870	\\
		gr4x6 & 870 & 870	\\
		iis-glass-cov & 870 & 870	\\
		k16x240b & 870 & 870	\\
		mod010 & 870 & 870	\\
		mtest4ma & 870 & 870	\\
		neos-860300 & 870 & 870	\\
		neos5 & 870 & 870	\\
		p0201 & 870 & 870	\\
		r50x360 & 870 & 870	\\
		ran12x21 & 870 & 870	\\
		ran13x13 & 870 & 870	\\
		seymour1 & 870 & 870	\\
		sp98ir & 870 & 870	\\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{This table shows the number of instances solved for each problem at round \(0\). Each problem needs to solve at most 10 seeds \(\times\) 3 s$\_$increase \(\times\) 29 scenario reductions = 870 instances.}
\end{table}
\end{document}

math

Kevin-Martin Aigner
Sebastian Denzler
Frauke Liers
Sebastian Pokutta
Kartikey Sharma

"Scenario Reduction for Distributionally Robust Optimization" by Kevin-Martin Aigner, Sebastian Denzler, Frauke Liers, Sebastian Pokutta, and Kartikey Sharma, arXiv:2503.11484v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Network architecture used for all experiments. \textit{K}, \textit{C}\textsubscript{in}, and \textit{C}\textsubscript{out} represent the kernel size, number of input channels, and number of output channels, respectively. A stride of 3 was used for all convolutional layers. $E$ represents the dimension of the final representation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\toprule
Layer Number &Layer Components&Kernel Dimension\\
\midrule
	\multirow{5}{*}{1}&Conv 1D & 7 x 1 x 4 (\textit{K} x \textit{C}\textsubscript{in} x \textit{C}\textsubscript{out})\\
										& BatchNorm &\\
										& ReLU& \\
										& MaxPool(2)& \\
										& Dropout(0.1) &\\
	\midrule
	\multirow{5}{*}{2}&Conv 1D & 7 x 4 x 16\\
										& BatchNorm& \\
										& ReLU &\\
										& MaxPool(2) &\\
										& Dropout(0.1)& \\
	\midrule
	\multirow{5}{*}{3}&Conv 1D & 7 x 16 x 32 \\
										& BatchNorm &\\
										& ReLU &\\
										& MaxPool(2) &\\
										& Dropout(0.1) &\\
	\midrule
	\multirow{2}{*}{4}&Linear&320 x $E$ \\
										& ReLU &\\
	\midrule
	\multirow{1}{*}{5}&Linear &$E$ x C (classes) \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

PCPs: Patient Cardiac Prototypes

Dani Kiyasseh
Tingting Zhu
David A. Clifton

"PCPs: Patient Cardiac Prototypes" by Dani Kiyasseh, Tingting Zhu, and David A. Clifton, arXiv:2011.14227v1, 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|}
 \hline
 & P & PI & PLI \\
 \hline
 Baseline $o_{base}$ & 3.425 $\pm$ 0.643 & 3.688 $\pm$ 0.664 & 3.653 $\pm$ 0.686 \\
 \hline
 Initial $d^{\rho}_{init}$ & 2.138 $\pm$ 0.39 & 1.08 $\pm$ 0.194 & 2.272 $\pm$ 0.41 \\
 \hline
 Tuned $d^{\rho}_{end}$ & 0.028 $\pm$ 0.006 & 0.067 $\pm$ 0.016 & 0.043 $\pm$ 0.009 \\
 \hline
 Resampled $d^{\rho}_{init}$ & 0.025 $\pm$ 0.009 & 0.068 $\pm$ 0.025 & 0.042 $\pm$ 0.01 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Behavioral distance $d^{\rho}$ between system and specification at the different steps in the tuning pipeline. The error is given by the standard deviation.}
\end{table}
\end{document}

eess

"Probabilistic Behavioral Aggregation: A Case Study on the Nordic Power Grid" by Anna Büttner and Frank Hellmann, arXiv:2412.11899v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc|cc}
			\hline
			{\multirow{2}*{Algorithms}} &
			\multicolumn{2}{c|}{Low} &
			\multicolumn{2}{c}{High} \\
			\cline{2-5}
			& PSNR & SSIM & PSNR & SSIM \\
			\hline
			direct &
			35.45 & 0.9518 & 32.71 & 0.9200 \\
			fixed averaging weights &
			35.50 & 0.9449 & 32.60 & 0.9058 \\
			rigid sampling grid $3\times3\times3$ &
			36.02 & 0.9555 & 33.33 & 0.9256 \\
			rigid sampling grid $5\times5\times5$ &
			36.37 & 0.9592 & 33.73 & 0.9320 \\
			w/o concatenating offset features~~ &36.10&0.9590&33.46&0.9348 \\
			w/o regularization term &
			36.16 & 0.9601 & 33.48 & 0.9341 \\
			our full model $3\times3\times3$ &
			\bf 36.91 & 0.9622 & 34.23 & 0.9366 \\
			our full model $5\times5\times5$ &
			36.88 & \bf 0.9631 & \bf 34.25 & \bf 0.9379\\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xiangyu Xu
Muchen Li
Wenxiu Sun
Ming-Hsuan Yang

"Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising" by Xiangyu Xu, Muchen Li, Wenxiu Sun, and Ming-Hsuan Yang, arXiv:2101.10760v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison between the activation patterns model and the independence model (in M~CAD)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
\toprule
\multirow{2}{*}{Coverage} & \multirow{2}{*}{True reserve} & \multicolumn{2}{c}{Activation patterns} & \multicolumn{2}{c}{Independence} \\
\cline{3-6}
 & & Mean & $\text{VaR}_{0.95}$ & Mean & $\text{VaR}_{0.95}$ \\
\midrule
Accident Benefits & $>$158.90 & 149.61 & 170.46 & 151.72 & 158.23 \\
Bodily Injury & $>$288.17 & 283.27 & 295.02 & 283.03 & 292.95 \\
Vehicle Damage & $>$73.63 & 70.77 & 76.43 & 68.99 & 78.59 \\
Loss of Use & $>$4.20 & 4.17 & 4.24 & 4.06 & 4.13 \\
\midrule
Global reserve & $>$524.91 & 504.82 & 546.15 & 507.8 & 533.90 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Marie Michaelides
Mathieu Pigeon
Hélène Cossette

"Individual Claims Reserving using Activation Patterns" by Marie Michaelides, Mathieu Pigeon, and Hélène Cossette, arXiv:2208.08430v3, 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}{ccccc}
 \toprule
 \textbf{Metric} & \textbf{MAE} & \textbf{MSE} & \textbf{SAMPE} & \textbf{ACD} \\
 \midrule
 \textbf{Score Value} & 0.02 & 0.002 & 3.51 & 0.006 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ainaz Jamshidi
Muhammad Arif
Sabir Ali Kalhoro
Alexander Gelbukh

"Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study" by Ainaz Jamshidi, Muhammad Arif, Sabir Ali Kalhoro, and Alexander Gelbukh, arXiv:2412.16207v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 Arm $k$ & $\pi_k$ \\
 $1$ & $0.2$\\
 $2$& $0.3$\\
 $3$& $0.87$\\
 $4$ & $0.15$ \\
 $5$ & $0.79$ \\
 $6$ & $0.12$\\
 $7$& $0.85$\\
 $8$ & $0.1$\\
 $9$ & $0.83$\\
 $10$ & $0.75$ \\
 $11$ & $0.14$\\
 $12$ & $0.9$\\
 $13$ & $0.2$\\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Environment $2$}
\end{table}
\end{document}

stat

Discrete Choice Multi-Armed Bandits

"Discrete Choice Multi-Armed Bandits" by Emerson Melo and David Müller, arXiv:2310.00562v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of the enrollment partition of the ASVspoof 2019 database.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|cc|cc}
		\toprule
		\multirow{3}{*}{Subset}&\multicolumn{2}{c}{\#Speaker}&\multicolumn{4}{c}{\#Utterances}\\
		\cmidrule(lr){2-3} \cmidrule(lr){4-7}
		&\multirow{2}{*}{M}&\multirow{2}{*}{F}&\multicolumn{2}{c}{LA}&\multicolumn{2}{c}{PA}\\
		\cmidrule(lr){4-5} \cmidrule(lr){6-7}
		&&&M&F&M&F\\
		\midrule
		Enrollment\_Dev. & 4 & 6 & 76 & 66 & 2052 & 1782 \\
		Enrollment\_Eval. & 21 & 27 & 399 & 297 & 10773 & 8019 \\
		\hline
		Total & 25 & 33 & 475 & 363 & 12825 & 9801 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yuanjun Zhao
Roberto Togneri
Victor Sreeram

"Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System" by Yuanjun Zhao, Roberto Togneri, and Victor Sreeram, arXiv:2012.03154v1, 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{Final submissions F1-score results.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
 submission name & model & F1-score\\ \hline
 btb\_e8\_4 & neural stacking & \textbf{0.9720} \\ \hline
 btb\_e8\_3 & LSA & 0.9416 \\ \hline
 btb\_e8\_1 & tax2vec & 0.9219 \\ \hline
 btb\_e8\_2 & linear stacking & 0.8464 \\ \hline
 btb\_e8\_5 & distilbert & 0.5059
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Boshko Koloski
Timen Stepišnik Perdih
Senja Pollak
Blaž Škrlj

"Identification of COVID-19 related Fake News via Neural Stacking" by Boshko Koloski, Timen Stepišnik Perdih, Senja Pollak, and Blaž Škrlj, arXiv:2101.03988v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing" by Po-Yi Liu, Chi-Hua Wang, and Henghsiu Tsai, arXiv:2208.09372v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.