\documentclass{article}
\usepackage{amssymb}
\usepackage{amsfonts}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Cost Subadditivity Estimates: Robustness to Alternative Variable Specifications}
\begin{tabular}{lccc|ccc|ccc|ccc}
\toprule
& \multicolumn{3}{c}{{(I): Main Specification}} & \multicolumn{3}{c}{{(II)}} & \multicolumn{3}{c}{{(III)}} & \multicolumn{3}{c}{(IV)} \\
\midrule
Cost & Median & \multicolumn{2}{c}{\textit{Categories}} & Median & \multicolumn{2}{c}{\textit{Categories}} & Median & \multicolumn{2}{c}{\textit{Categories}} & Median & \multicolumn{2}{c}{\textit{Categories}} \\
Quantiles ($\tau$) & Est. & $\mathbf{=0}$ & $\mathbf{>0}$ & Est. & $\mathbf{=0}$ & $\mathbf{>0}$ & Est. & $\mathbf{=0}$ & $\mathbf{>0}$ & Est. & $\mathbf{=0}$ & $\mathbf{>0}$ \\
\midrule
$\mathcal{Q}(0.10)$ & 0.125 & 9.8\% & 92.0\% & 0.182 & 2.1\% & 98.3\% & 0.068 & 58.9\% & 40.8\% & 0.239 & 38.0\% & 65.0\% \\
$\mathcal{Q}(0.25)$ & 0.163 & 5.5\% & 95.7\% & 0.256 & 0.6\% & 99.4\% & 0.120 & 41.6\% & 59.1\% & 0.282 & 20.3\% & 82.3\% \\
$\mathcal{Q}(0.50)$ & 0.258 & 1.4\% & 98.9\% & 0.429 & 0.1\% & 99.7\% & 0.226 & 11.4\% & 89.0\% & 0.346 & 3.3\% & 97.5\% \\
$\mathcal{Q}(0.75)$ & 0.394 & 0.5\% & 99.5\% & 0.543 & 0.1\% & 99.7\% & 0.356 & 2.6\% & 97.6\% & 0.409 & 1.0\% & 99.2\% \\
$\mathcal{Q}(0.90)$ & 0.476 & 0.3\% & 99.6\% & 0.589 & 0.0\% & 99.7\% & 0.427 & 1.7\% & 98.5\% & 0.447 & 0.8\% & 99.4\% \\
\midrule
\scriptsize Nontraditional Output Measure: &\multicolumn{3}{c}{} &\multicolumn{3}{c}{} &\multicolumn{3}{c}{} &\multicolumn{3}{c}{} \\
$\quad$ \scriptsize Credit Equivalents &\multicolumn{3}{c}{$\checkmark$} &\multicolumn{3}{c}{$\checkmark$} &\multicolumn{3}{c}{} &\multicolumn{3}{c}{} \\
$\quad$ \scriptsize Net Non-Interest Income &\multicolumn{3}{c}{} &\multicolumn{3}{c}{} &\multicolumn{3}{c}{$\checkmark$} &\multicolumn{3}{c}{$\checkmark$}\\
\scriptsize Credit Risk Proxies: &\multicolumn{3}{c}{} &\multicolumn{3}{c}{} &\multicolumn{3}{c}{} &\multicolumn{3}{c}{} \\
$\quad$ \scriptsize Nonperforming Assets &\multicolumn{3}{c}{$\checkmark$} &\multicolumn{3}{c}{$\checkmark$} &\multicolumn{3}{c}{$\checkmark$} &\multicolumn{3}{c}{$\checkmark$} \\
$\quad$ \scriptsize Loan Loss Provision &\multicolumn{3}{c}{$\checkmark$} &\multicolumn{3}{c}{} &\multicolumn{3}{c}{$\checkmark$} &\multicolumn{3}{c}{} \\
\midrule
\multicolumn{13}{p{18.3cm}}{\scriptsize Reported are the median point estimates of $\mathcal{S}_t^*(\tau)$ and shares of the sample for which the estimates are statistically $>0$ (i.e., a bank-year is classified as exhibiting scope economies) and statistically not different from $0$ (i.e., a bank-year is classified as exhibiting scope invariance) at the 95\% level. Because the two hypotheses are tested separately, percentage points need not sum up to a hundred. Specification (I) is our main specification, the complete results for which are reported in Table .} \\
\bottomrule[1pt]
\end{tabular}
\end{table}
\end{document}

q-fin

"Off-Balance Sheet Activities and Scope Economies in U.S. Banking" by Jingfang Zhang and Emir Malikov, arXiv:2302.14603v1, 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{booktabs}
\usepackage{graphicx}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lclcc}
 \toprule
 & ID & Name & \#steps & \#objects \\
 \midrule
\multirow{7}{*}{\rotatebox[origin=c]{90}{easy}}
& T1 & Serve wine & 8 & 2\\
& T2 & Make coffee & 9 & 2\\
& T3 & Boil water in a pot & 10 & 2\\
& T4 & Fry egg in a pan & 11 & 2\\
& T5 & Toast bread & 12 & 3\\
& T6 & Warm water (in microwave) & 16 & 3\\
& T7 & Cook potato slice (in microwave) & 20 & 3\\
\midrule
\multirow{5}{*}{\rotatebox[origin=c]{90}{complex}}
& T8 & Simple salad & 26 & 4\\
& T9 & Clean/order kitchen & 28 & 7\\
& T10 & Vegetarian sandwich & 30 & 5\\
& T11 & Cook egg and potato slice & 32 & 7\\
& T12 & Complex salad & 33 & 7\\
\bottomrule
 \end{tabular}
\caption{Tasks implemented in the experiments}
\end{table}
\end{document}

cs

"Recover: A Neuro-Symbolic Framework for Failure Detection and Recovery" by Cristina Cornelio and Mohammed Diab, arXiv:2404.00756v1, licensed under CC0 1.0. 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
 & Mean & SD \\ \hline
$y$-axis & $-0.47$ cm & 0.2 cm \\ \hline
$z$-axis & $-1.74$ cm & 0.21 cm \\ \hline
pitch angle & $88.83^{\circ}$ & $0.62^{\circ}$ \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Chen-Huan Pi
Kai-Chun Hu
Yu-Ting Huang
Stone Cheng

"Reinforcement Learning Trajectory Generation and Control for Aggressive Perching on Vertical Walls with Quadrotors" by Chen-Huan Pi, Kai-Chun Hu, Yu-Ting Huang, and Stone Cheng, arXiv:2103.03011v1, 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{Number of images and devices used in each database.}
\begin{tabular}{|l|c|c|}
		\hline
		\textbf{Database} & \textbf{number of } & \textbf{number of}\\
		\textbf{Name} & \textbf{images} & \textbf{devices}\\
		\hline
		\hline
		ALASKA2 \textsuperscript{} & 80,005 & 40\\
		\hline
		BOSS \textsuperscript{} & 10,000 & 7\\
		\hline
		Stego App DB \textsuperscript{} & 24.120 & 26\\
		\hline
		Wesaturate \textsuperscript{} & 3.648 & /\\
		\hline
		RAISE \textsuperscript{} & 8,156 & 3\\
		\hline
		Dresden \textsuperscript{} & 1,491 & 73 (25 different models)\\
		\hline
		\hline
		\textbf{Total} & \textbf{127,420} & \textbf{101}\\
		\hline
	\end{tabular}
\end{table}
\end{document}

cs

Hugo Ruiz
Mehdi Yedroudj
Marc Chaumont
Frédéric Comby
Gérard Subsol

"LSSD: a Controlled Large JPEG Image Database for Deep-Learning-based Steganalysis "into the Wild"" by Hugo Ruiz, Mehdi Yedroudj, Marc Chaumont, Frédéric Comby, and Gérard Subsol, arXiv:2101.01495v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of FLOPs, trainable parameters, and Big-O analysis for different fine-tuning schemes M: million, K: thousand.}
\begin{tabular}{|c|c|c|c|}
 \hline
 & \textbf{Parameters} & \textbf{FLOPs} & \textbf{Big-O}\\
 \hline
 \textbf{EO} & 109 K & 475 M & $\mathcal{O}(N_tN_c)$\\
 \hline
 \textbf{FM} & 218 K & 475 M & $\mathcal{O}(N_tN_c)$ \\
 \hline
 \textbf{TM} & 8 K & 521 M & $\mathcal{O}(N_tN_c)$\\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Mehdi Sattari
Deniz Gündüz
Tommy Svensson

"Online Neural Model Fine-Tuning in Massive MIMO CSI Feedback: Taming The Communication Cost of Model Updates" by Mehdi Sattari, Deniz Gündüz, and Tommy Svensson, arXiv:2501.18250v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"One Parameter Families of Conformal Mappings of Bounded Doubly Connected Polygonal Domains" by A. Dyutin and S. Nasyrov, arXiv:2312.01112v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Stage-2 Time Masking. (\% WER)}
\begin{tabular}{lcccc}
 	 \toprule
 	 \toprule
 Model & \multicolumn{4}{c}{Test Data}\\
 	 \midrule
 {\bf DIRHA}& {\it BLA-L2L}& {\it BLA-NoMic}&{\it BLA-KA6}& {\it L3L-L4L}\\
 	 \midrule
 Stage2 BLA-L2L & 16.9 & 27.1 & 20.7 &20 \\
 - Input Dropout 0.2 & 17.7 &38.1 &22.1& 20.6 \\
 - Input Dropout 0.5 &19.2 &21 &23.6 &22.6 \\
 - Time Masking (\#mask=1) & 17 &	18&	19.3&	20.1 \\
 - Time Masking (\#mask=2) &16.9&	18.2&	19.4&	20\\
 - Time Masking (\#mask=3) &\bf{16.6}&	\bf{17.8}&\bf{	19.2}&	\bf{20}\\
 	 \midrule
 {\bf AMI}& {\it MDM-SMDM}& {\it MDM-NoMic}&{\it MDM-IHM0}& --\\
 	 \midrule
 Stage2 MDM-SMDM & 41.6&	43.1&	41.9&--\\
 - Input Dropout 0.2 & 42.3&	44.5&	42.6&--\\
 - Input Dropout 0.5 & 45.2&	49.5&	46.3&--\\
 - Time Masking (\#mask=1) & \bf{41.6}&	\bf{43.1}&	\bf{41.6}&--\\
 - Time Masking (\#mask=2) &41.6&	43&	41.9&--\\
 - Time Masking (\#mask=3) & 41.7&	43&	41.8&--\\
 \bottomrule
 \bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

"Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR" by Ruizhi Li, Gregory Sell, and Hynek Hermansky, arXiv:2102.03055v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Fanruiqi Zeng
Zaiwei Chen
John-Paul Clarke
David Goldsman

"Nested Vehicle Routing Problem: Optimizing Drone-Truck Surveillance Operations" by Fanruiqi Zeng, Zaiwei Chen, John-Paul Clarke, and David Goldsman, arXiv:2103.01528v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Algorithm for constructing SCB for errors-in-variables curves —— using the Lorenz curve as an example}
\begin{tabular}{lrrrrrr}
\toprule
1: Select a sample according to a sampling design \\
2: Based on the selected sample, build point estimators $\widehat{L}_{k(1)}$ and $\widehat{L}_{k(2)}$, $\forall k \in S$\\
3: Interpolate the point estimators to obtain an estimator of the Lorenz curve \\
4: Linearize the point estimators to estimate the variance matrix $\Sigma (t)$, $\forall t \in [1,(n-1)]$\\
5: Simulate C to approximate the empirical $(1-\alpha)$ quantile $u_{\alpha}$ of $\sup_{t\in [1,(n-1)]}g(t)$ \\
6. Plot the unions of the confidence ellipses of $\widehat{\mathcal{L}}(t), \forall t \in [1,(n-1)]$ based on the adjusted\\ \;\;\;critical value $u_{\alpha}$ as SCB of $\widehat{\mathcal{L}}(t)$ at the confidence level $(1-\alpha)$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Ziqing Dong
Francesco Bartolucci
Satoshi Kuriki
Antonietta Mira

"Constructing Simultaneous Confidence Bands for Errors-in-variables Curves with Application to the Lorenz Curve" by Ziqing Dong, Francesco Bartolucci, Satoshi Kuriki, and Antonietta Mira, arXiv:2501.17264v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{PI and PB at $1,000$ seconds time limit for \texttt{CL-TLNS}, \texttt{CL-LNS(np)} and \texttt{CL-LNS(np\&sn)}. Lower PI/PB values imply better performance}
\begin{tabular}{cccccc}
 \toprule
 \multicolumn{2}{c}{DATASET} & \texttt{CL-TLNS} &\texttt{CL-LNS(np)} &\texttt{CL-LNS(np\&sn)}\\
 \midrule

 \multirow{2}{*}{SC} & PI & \textbf{633.5$\pm$43.6} &3469.4$\pm$627.8 &734.8$\pm$79.7 \\
 & PB & 113.0$\pm$2.9& \textbf{112.6$\pm$2.2} & 113.0$\pm$2.9 \\ \midrule

 \multirow{2}{*}{CA} & PI &\textbf{25.5$\pm$1.7} & 39.3$\pm$5.3 & 108.2$\pm$4.8 \\
 & PB & \textbf{-9391997.4$\pm$50556.4} & -9265724.3$\pm$43317.0 & -8925042.3$\pm$65739.2 \\ \midrule

 \multirow{2}{*}{MIS} & PI &\textbf{50.6$\pm$4.9} & 78.1$\pm$10.8 & 201.3$\pm$14.6 \\
 & PB & \textbf{-6435.8$\pm$16.8} & -6222.8$\pm$39.7 & -5453.0$\pm$90.0 \\ \midrule

 \multirow{2}{*}{MVC} & PI & \textbf{3.7$\pm$0.3} & 6.0$\pm$0.6 & 5.2$\pm$0.6 \\
 & PB & \textbf{9394.8$\pm$41.5} & 9400.0$\pm$40.7 & 9411.9$\pm$38.6 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Wenbo Liu
Akang Wang
Wenguo Yang
Qingjiang Shi

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Input parameters of the algorithm}
\begin{tabular}{ll}
 \toprule
 $K_0$ & finite input data \\
 $r,s$ & rank parameters of the Minkowski tensors \\
 $n$ & number of equations considered ($n\geq d+1$) \\
 $R_n$ & maximal radius considered (we recommend $R_n\geq (d+1)R_1$) \\
 $W$ & observation window containing $K_0$ (optional instead of $R_n$) \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Daniel Hug
Michael A. Klatt
Dominik Pabst

"Minkowski tensors for voxelized data: robust, asymptotically unbiased estimators" by Daniel Hug, Michael A. Klatt, and Dominik Pabst, arXiv:2502.00092v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Debashrita Mohapatra
Debi Prasad Mohapatra
Ram Sewak Dubey

"Price dispersion across online platforms: Evidence from hotel room prices in London (UK)" by Debashrita Mohapatra, Debi Prasad Mohapatra, and Ram Sewak Dubey, arXiv:2310.12341v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of token address validation process}
\begin{tabular}{lcccc}
\toprule
\textbf{Blockchain} & \textbf{Invalid} & \textbf{Not Deployed} & \textbf{Removed} & \textbf{Remaining} \\
\midrule
BSC & 739 & 211 & 950 & 15,455 \\
Ethereum & 51 & 43 & 94 & 3,720 \\
Solana & - & 20 & 20 & 11,809 \\
Base & 1 & 0 & 1 & 827 \\
\midrule
\textbf{Total} & \textbf{791} & \textbf{254} & \textbf{1,065} & \textbf{31,811} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"A Midsummer Meme's Dream: Investigating Market Manipulations in the Meme Coin Ecosystem" by Alberto Maria Mongardini and Alessandro Mei, arXiv:2507.01963v1, 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{Disperive estimates for the DW}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 & $\Sigma_1'$ & $\Sigma_1''$ & $\Sigma_2$ & $\Sigma_3$ \\
 \hline
 dim 2 & $|t|^{-5/6}$ & $|t|^{-3/4}$ & & \\
 \hline
 dim 3 & $|t|^{-4/3}$ & $|t|^{-5/4}$ & $|t|^{-7/6}$ & \\
 \hline
 dim 4 & $|t|^{-3/2}$ & $|t|^{-3/2}$ & $|t|^{-5/3}$ & $|t|^{-3/2}\log(|t|)$\\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

"The Wave Equation on Lattices and Oscillatory Integrals" by Cheng Bi, Jiawei Cheng, and Bobo Hua, arXiv:2312.04130v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Christopher Hemmens
Stephan Robert-Nicoud

"Can linear algebra create perfect knockoffs?" by Christopher Hemmens and Stephan Robert-Nicoud, arXiv:2502.02148v1, 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{Statistics of datasets after preprocessing.}
\begin{tabular}{|l|c|c|c|c|}
 \hline
 \textbf{Datasets} & \textbf{\#Docs} & \textbf{\#Labels} & \textbf{\#Vocab.} & \textbf{Avg. Length} \\
 \hline
 \textit{Tweet} & 2,133 & 89 & 1,127 & 5.550 \\
 \textit{AGNews} & 14,845 & 4 & 3,290 & 4.268 \\
 \textit{TagMyNews} & 27,369 & 7 & 4,325 & 4.483 \\
 \textit{YahooAnswer} & 12,258 & 10 & 3,423 & 4.151 \\
 \hline
\end{tabular}
\end{table}
\end{document}

cs

Shuyu Chang
Rui Wang
Peng Ren
Qi Wang
Haiping Huang

"A Large Language Model Guided Topic Refinement Mechanism for Short Text Modeling" by Shuyu Chang, Rui Wang, Peng Ren, Qi Wang, and Haiping Huang, arXiv:2403.17706v2, 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{LineRWKV architecture configurations.}
\begin{tabular}{cccccccc}
\textbf{Model Size} & \textbf{Params} & $N_\text{enc}$ & $N_\text{lp}$ & $N_\text{sp}$ & $N_\text{dec}$ & $F$ & \textbf{FLOPS/sample} \\
\hline
\hline
XS & 30k & 1 & 2 & 2 & 1 & 32 & 120k \\
S & 135k & 2 & 2 & 2 & 2 & 64 & 508k \\
M & 286k & 4 & 4 & 4 & 4 & 64 & 1M \\
L & 900k & 4 & 6 & 6 & 4 & 96 & 3.2M \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Diego Valsesia
Tiziano Bianchi
Enrico Magli

"Onboard deep lossless and near-lossless predictive coding of hyperspectral images with line-based attention" by Diego Valsesia, Tiziano Bianchi, and Enrico Magli, arXiv:2403.17677v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Computation time of different algorithms under the last tested finite distribution. }
\begin{tabular}{cccc|cccc|cccc}
	\toprule
	$T$ & Algorithm & Avg. Regret & Avg. Time(s) & $T$ & Algorithm & Avg. Regret & Avg. Time(s) & $T$ & Algorithm & Avg. Regret & Avg. Time(s) \\
	\midrule
	\multirow{3}{*}{$10^3$} & & $15.26$ & $<0.001$ & \multirow{3}{*}{$10^4$} & & $24.39$ & $<0.01$ & \multirow{3}{*}{$10^5$} & & $71.38$ & $0.080$ \\
 & & $ 3.61 $ & $<0.001$ & & & $3.00$ & $<0.01$ & & & $3.23$ & $0.084$ \\
 & & $3.04$ & $0.69$ & & & $4.03$ & $6.91$ & & & $3.62$ & $69.23$ \\
 \bottomrule
	\end{tabular}
\end{table}
\end{document}

stat

Wenzhi Gao
Dongdong Ge
Chenyu Xue
Chunlin Sun
Yinyu Ye

"Beyond $\mathcal{O}(\sqrt{T})$ Regret: Decoupling Learning and Decision-making in Online Linear Programming" by Wenzhi Gao, Dongdong Ge, Chenyu Xue, Chunlin Sun, and Yinyu Ye, arXiv:2501.02761v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"On the computation of the cumulative distribution function of the Normal Inverse Gaussian distribution" by Guillermo Navas-Palencia, arXiv:2502.16015v1, 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}{lc}
 \hline
 \bfseries UKB Dataset & \bfseries $\lceil DICE \rceil$\\
 \hline
 AE (Dense) (Baur et al., 2020a) & 0.079 \\
 AE (Spatial) (Baur et al., 2020a) & 0.054 \\
 VAE (Dense) (Baur et al., 2020a) & 0.071 \\
 VQ-VAE (van den Oord et al., 2017) & 0.056 \\
 VQ-VAE + Transformer + Masked Residuals + Different Orderings (Ours) & \bfseries 0.297 \\
 \hline
 \bfseries MSLUB Dataset & \\
 \hline
 AE (Dense) (Baur et al., 2020a) & 0.106 \\
 AE (Spatial) (Baur et al., 2020a) & 0.067 \\
 VAE (Dense) (Baur et al., 2020a) & 0.106 \\
 VQ-VAE (van den Oord et al., 2017) & 0.077 \\
 VQ-VAE + Transformer + Masked Residuals + Different Orderings (Ours) & \bfseries 0.465 \\
 \hline
 \bfseries WMH Dataset & \\
 \hline
 AE (Dense) (Baur et al., 2020a) & 0.166 \\
 AE (Spatial) (Baur et al., 2020a) & 0.151 \\
 VAE (Dense) (Baur et al., 2020a) & 0.161 \\
 VQ-VAE (van den Oord et al., 2017) & 0.143 \\
 VQ-VAE + Transformer + Masked Residuals + Different Orderings (Ours) & \bfseries 0.441 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Walter Hugo Lopez Pinaya
Petru-Daniel Tudosiu
Robert Gray
Geraint Rees
Parashkev Nachev
Sebastien Ourselin
M. Jorge Cardoso

"Unsupervised Brain Anomaly Detection and Segmentation with Transformers" by Walter Hugo Lopez Pinaya, Petru-Daniel Tudosiu, Robert Gray, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, and M. Jorge Cardoso, arXiv:2102.11650v1, 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{Preference test: \textbf{w/} or \textbf{w/o} CMSD}
\begin{tabular}{c|ccc|c}
& w/o & neutral & w/ & p-value\\ \hline
Quality & 20\% & 30\% & 50\% & 0.01 \\ \hline
Similarity & 20\% & 30\% & 50\% & 0.01 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Haohan Guo
Heng Lu
Na Hu
Chunlei Zhang
Shan Yang
Lei Xie
Dan Su
Dong Yu

"Phonetic Posteriorgrams based Many-to-Many Singing Voice Conversion via Adversarial Training" by Haohan Guo, Heng Lu, Na Hu, Chunlei Zhang, Shan Yang, Lei Xie, Dan Su, and Dong Yu, arXiv:2012.01837v1, 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|cccc}
 \textbf{AMC} & $\text{MOON}_{1,t}$& $\text{MOON}_{2,t}$& $\text{YOLO}_{1,t}$& $\text{YOLO}_{2,t}$\\
 \hline
$V_t$& 0.459 &-0.363 &0.618 &0.417 \\
$\text{MOON}_{1,t}$& &0.057 &0.593 &0.688 \\
$\text{MOON}_{2,t}$& & &-0.257 &0.068 \\
$\text{YOLO}_{1,t}$& & & &0.574
 \end{tabular}
\caption{AMC Correlation}
\end{table}
\end{document}

q-fin

Tim Matthies
Thomas Löhden
Stephan Leible
Jun-Patrick Raabe

"To the Moon: Analyzing Collective Trading Events on the Wings of Sentiment Analysis" by Tim Matthies, Thomas Löhden, Stephan Leible, and Jun-Patrick Raabe, arXiv:2308.09968v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistical details of the task's dataset~. \textbf{w:} post with links, \textbf{w/o:} posts without links}
\begin{tabular}{|l||ll||ll||l|l|l}
 \hline
 & \multicolumn{2}{l}{\textbf{Real}} & \textbf{Fake} && \multirow{2}{*}{\textbf{Total}} \\
 \cline{2-5}
 & \textbf{w} & \textbf{w/o} & \textbf{w} & \textbf{w/o} &\\
 \hline
 \textbf{Train} & 2321 & 1039 & 1002 & 2058 & 6420\\
 \textbf{Dev} & 780 & 340 & 327 &693 & 2140\\
 \textbf{Test} & 779 & 341 &319 & 701 & 2140\\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

"ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information" by Ipek Baris and Zeyd Boukhers, arXiv:2101.05499v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Prognostic Power of Texture Based Morphological Operations in a Radiomics Study for Lung Cancer" by Paul Desbordes, Diksha, and Benoit Macq, arXiv:2012.12652v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Probabilistic Overview of Probabilities of Default for Low Default Portfolios by K. Pluto and D. Tasche" by Andrius Grigutis, arXiv:2303.06148v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|cc|c} %{p{0.25\linewidth} | p{.25\linewidth} p{0.25\linewidth} | p{0.15\linewidth}}
\toprule
	& $H_p$ & $H_d$ & Total \\
	\midrule
	 $E$ & 1 & 5,000,000 & 5,000,001 \\
	$\neg E$ & 0 & ??? & ??? \\
	\midrule
	Total & 1 & ??? & ???\\
\bottomrule
\end{tabular}
\caption{Evidence from the case in table form. The four cells with question marks show the values that cannot be known from the facts presented in the case.}
\end{table}
\end{document}

stat

"The Prosecutor's Fallacy and Expert Testimony: A Modern Take Using Likelihood Ratios" by Maria Cuellar, arXiv:2502.03217v1, 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{Summary of the red core from Fig. .}
\begin{tabular}{ll}
\hline
\textbf{Category} & \textbf{Details} \\ \hline
\textbf{Source Set} & C1a formaldehyde \\ \hline
\textbf{Autocatalytic Set} & C2a, C2b, C3a, C3b, C4a, C4b, C5a, C5b, C6a, C6b, C7b \\ \hline
\textbf{Amplification Factor} & 1.1056824557955995 \\ \hline
\textbf{Reactions} & \\ \hline
R1 & C2a $\to$ C2b \\
R2 & C1a formaldehyde + C2b $\to$ C3a \\
R3 & C3a $\to$ C3b \\
R5 & C1a formaldehyde + C3b $\to$ C4b \\
R6 & C4b $\to$ C4a \\
R8 & C1a formaldehyde + C4a $\to$ C5b \\
R11 & C5b $\to$ C5a \\
R13 & C1a formaldehyde + C5a $\to$ C6a \\
R17 & C6a $\to$ C6b \\
R20 & C1a formaldehyde + C6b $\to$ C7b \\
R34 & C7b $\to$ C2a + C5a \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Víctor Blanco
Gabriel González
Praful Gagrani

"Identifying Self-Amplifying Hypergraph Structures through Mathematical Optimization" by Víctor Blanco, Gabriel González, and Praful Gagrani, arXiv:2412.15776v2, 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{Theoretical Expections}
\begin{tabular}{ll}
\toprule
Category & Sign \\
\midrule
Freedom & $-$ \\
Democracy & $-$ \\
Election & $-$ \\
Collective Action & $-$ \\
Negative Figures & $-$ \\
Social Control & $+$ \\
Surveillance & $+$ \\
CCP & $+$ \\
Historical Events & $+$ \\
Positive Figures & $+$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{A summary of two scenarios for experiments}
\begin{tabular}{c|c|c|c}
\hline
No. & Description & Training data size & Model \\ \hline\hline
1 & Limited training data & Small (6 hours) & Transformer \\ \hline
2 & Knowledge distillation & Large (18 hours) & FastSpeech \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Yeunju Choi
Youngmoon Jung
Youngjoo Suh
Hoirin Kim

"Learning to Maximize Speech Quality Directly Using MOS Prediction for Neural Text-to-Speech" by Yeunju Choi, Youngmoon Jung, Youngjoo Suh, and Hoirin Kim, arXiv:2011.01174v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Quantile and expectile copula-based hidden Markov regression models for the analysis of the cryptocurrency market" by Beatrice Foroni, Luca Merlo, and Lea Petrella, arXiv:2307.06400v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Block and swap counts according to the whether arbitrage opportunity exists in the block}
\begin{tabular}{ccccc}
 \toprule
 & \textbf{Non-arbitrage block} & \textbf{Arbitrage block} \\
 \midrule
 \textbf{Block counts} & 241,265 & 8,377 \\
 \textbf{Swap counts in the block} & 91,604 & 8,396 \\
 \textbf{Arbitrage swap counts in the block} & 0 & 		5,614 \\
 \midrule
 \textbf{Average non-arbitrage swap per block} & 0.3797	 & 0.3321	 \\

 \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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|cccc}
 \hline
 & GFLOPs & Params & FPS & Params-size \\
 \hline
 FCN & 15.45 & 15.12M & 155 & 57.67MB \\
 SegNet & 30.07 & 29.44M & 108 &112.32MB \\
 U-Net & 41.90 & 31.04M & 47 &118.40MB \\
 AttU-Net & 51.01 & 34.88M & 38 &135.27MB \\
 EDA-Net & 0.85 & 0.68M & 90 & 2.60MB \\
 PSPNet(pretrained) & \textbf{0.58} & 2.41M & 115 & 9.20MB \\
 Deeplabv3+(pretrained) & 5.05 & 5.81M & 101 &22.18MB \\
 FAT-Net(pretrained) & 30.51 & 28.76M & 36 & 123.30MB \\
 SLP-Net & 2.30 & \textbf{0.20M} & \textbf{190} & \textbf{0.75MB} \\
 \hline
 \end{tabular}
\caption{Comparison of the number of parameters and computational complexity of each model.}
\end{table}
\end{document}

eess

Bo Yang
Hong Peng
Chenggang Guo
Xiaohui Luo
Jun Wang
Xianzhong Long

"SLP-Net:An efficient lightweight network for segmentation of skin lesions" by Bo Yang, Hong Peng, Chenggang Guo, Xiaohui Luo, Jun Wang, and Xianzhong Long, arXiv:2312.12789v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis" by Paul Glasserman and Caden Lin, arXiv:2309.17322v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"High Resolution Optimized High-Order Schemes for Discretization of Non-Linear Straight and Mixed Second Derivative Terms" by Hemanth Chandravamsi and Steven H. Frankel, arXiv:2312.12069v2, 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{Johansen Cointegration Test using Trace Test Statistic (10\% Significance Level)}
\begin{tabular}{cccc}
\hline
\textbf{Rank (r)} & \textbf{Cointegrating Relations} & \textbf{Test Statistic} & \textbf{Critical Value} \\
\hline
0 & 2 & 16.80 & 13.43 \\
1 & 2 & 6.755 & 2.705 \\
\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.


q-fin

Distributionally Robust Deep Q-Learning

"Distributionally Robust Deep Q-Learning" by Chung I Lu, Julian Sester, and Aijia Zhang, arXiv:2505.19058v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

A. Felipe
M. Jaenada
P. Miranda
L. Pardo

"Robust parameter estimation of the log-logistic distribution based on density power divergence estimators" by A. Felipe, M. Jaenada, P. Miranda, and L. Pardo, arXiv:2312.02662v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Niall Taylor
Dan Schofield
Andrey Kormilitzin
Dan W Joyce
Alejo Nevado-Holgado

"Developing Healthcare Language Model Embedding Spaces" by Niall Taylor, Dan Schofield, Andrey Kormilitzin, Dan W Joyce, and Alejo Nevado-Holgado, arXiv:2403.19802v1, 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}{ccccc|cc}
\hline
Library & Region & Success & Median & Mean & Time (s) & Time ($\mu s$)\\
\hline
SciPy & Small & 3983 / 5000 (79.66\%) & $1.78\cdot 10^{-15}$ & $1.89 \cdot 10^{-5}$ & 2.53 & 506\\
Paper & Small & 4939 / 5000 (98.78\%) & $3.89\cdot 10^{-15}$ & $1.01\cdot 10^{-13}$ & 0.12 & 24\\
	\hline\
SciPy & Large & 2949 / 4296 (68.65\%) & $4.49\cdot 10^{-14}$ & $7.91\cdot 10^{-3}$ & 2.79 & 559\\
Paper & Large & 4296 / 4296 (100\%) & $6.66\cdot 10^{-16}$ & $1.09\cdot 10^{-14}$ & 0.44 &88\\
	\hline\
SciPy & Large (hard) & 70 / 704 (9.94\%) & $0.16\cdot 10^{-1}$ & $*$ & 0.16 & 223\\
Paper & Large (hard) & 409 / 704 (58.10\%) & $8.07\cdot 10^{-14}$ & $*$ & 0.07 & 95\\
	\hline
	\end{tabular}
\caption{Summary of the accuracy and performance comparison for the general case. Symbol $*$ indicates numerical issues were encountered.}
\end{table}
\end{document}

stat

"On the computation of the cumulative distribution function of the Normal Inverse Gaussian distribution" by Guillermo Navas-Palencia, arXiv:2502.16015v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{r|cccc}
 \hline
 \multirow{2}{*}{model} & \multirow{2}{*}{acc}
 & \multirow{2}{*}{time} & \multicolumn{2}{c}{speedup} \\
 \cline{4-5} & & & raw & adj \\
 \hline
 \textbf{HAM10000$_S$} & 67.85\% & 1m35s & 6720.0 & 2520.0 \\
 \textbf{HAM10000$_M$} & 68.60\% & 13m35s & 746.7 & 280.0 \\
 \textbf{HAM10000$_L$} & 69.85\% & 1h03m & 160.0 & 60.0 \\
 Glyph~ & 69.20\% & 7d & 1.0 & 1.0 \\
 \hline
 \end{tabular}
\caption{Training time comparison for HAM10000.}
\end{table}
\end{document}

cs

Leonardo Neumann
Antonio Guimarães
Diego F. Aranha
Edson Borin

"Homomorphic WiSARDs: Efficient Weightless Neural Network training over encrypted data" by Leonardo Neumann, Antonio Guimarães, Diego F. Aranha, and Edson Borin, arXiv:2403.20190v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

A. Ramirez-Hassan
C. Gomez
S. Velasquez
K. Tangarife

"Marijuana on Main Streets? The Story Continues in Colombia: An Endogenous Three-part Model" by A. Ramirez-Hassan, C. Gomez, S. Velasquez, and K. Tangarife, arXiv:2306.10031v1, 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{Mean difference tests between men and women in time use}
\begin{tabular}{lcccccccc}
 \hline
 & Women & Mean & Men & Mean & Diff & St Err & P-value \\
 \hline
Daily hours spent in HH work & 415 & 2.549 & 415 & .808 & 1.741 & .101 & .000 \\
Daily hours spent in childcare & 415 & 2.488 & 415 & 1.59 & .898 & .112 & .000 \\
Daily hours spent in paid work & 415 & 3.163 & 415 & 5.619 & -2.455 & .219 & .000 \\
\hline
 \end{tabular}
\end{table}
\end{document}

q-fin

Francesca Barigozzi
Pietro Biroli
Chiara Monfardini
Natalia Montinari
Elena Pisanelli
Sveva Vitellozzi

"Beyond Time: Unveiling the Invisible Burden of Mental Load" by Francesca Barigozzi, Pietro Biroli, Chiara Monfardini, Natalia Montinari, Elena Pisanelli, and Sveva Vitellozzi, arXiv:2505.11426v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Empirical sparsity evaluation for different distributions.}
\begin{tabular}{lllll}
 \toprule
 Method & Sparsity & Maximum Absolute Value & Contrast Ratio & SNR \\
 \midrule
 Bernoulli \& Uniform & $5.0\%$ & $ 0.51 $ & $2.50$& $3.40$ \\
 Gauss $\mathcal{N}(0,0.051^2)$& $5.0\%$ & $ 0.25 $ & $1.19$ & $0.38$ \\
 Laplace$\left(0,\frac{1}{3}\right)$ & $5.0\%$ & $ 0.53 $ & $1.33$ & $0.73$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

"SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input" by Panagiotis Misiakos and Markus Püschel, arXiv:2501.03130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jianbin Chen
Dennis K. J. Lin
Nicholas Rios
Xueru Zhang

"Design and analysis for constrained order-of-addition experiments" by Jianbin Chen, Dennis K. J. Lin, Nicholas Rios, and Xueru Zhang, arXiv:2501.06559v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Financing Costs, Per-Shipment Costs and Shipping Frequency: Firm-Level Evidence from Bangladesh" by Md Deluair Hossen, arXiv:2303.04223v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{rotating}
\usepackage{graphicx}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average registration performance (structural integrity, spatial alignment and intensity-based metrics). \textbf{Bold} values are best performance across methods while \underline{underline} is best among deep learning methods. $\simeq X$ indicates a value closest to X is best, $\downarrow$ lowest value is best, $\uparrow$ highest value is best. $\Delta PV_{vent}\times10{^-3}$, $\Delta PV_{wml} \times 10^{-3}$, $MAID \times 10^{-3}$, $MAID-zp \times 10^{-3}$.}
\begin{tabular}{llcccccc}
\toprule
& & ANTs & Demons & SE & VM & FlowReg-A & FlowReg-A+O \\ \midrule
\multirow{6}{*}{\rotatebox{-90}{Structural}}&$\Delta V_{brain}\simeq1$ & 1.28 & 1.47 & 1.25 & \underline{\textbf{1.01}} & 1.14 & 1.11 \\
& $\Delta V_{vent}\simeq1$ & 1.27 & 1.31 & 1.60 & 0.64 & \underline{\textbf{0.96}} & 0.86 \\
& $\Delta V_{wml}\simeq1$ & 0.84 & \textbf{1.01} & 0.81 & 0.35 & \underline{0.67} & 0.53 \\
& $\Delta PV_{vent}\simeq0$ &\textbf{ 0.52} & -2.80 & 7.19 &-23.11 & \underline{-7.31} &-11.52 \\
& $\Delta PV_{wml}\simeq0$ & -5.17 &\textbf{-4.74} &-5.67 &-20.17 & \underline{-7.14} &-11.20 \\
& $\Delta SSD\simeq0$ &\textbf{-4.70} & -8.66 & -21.81 & 29.63 & 14.58 & \underline{13.82}\\ \midrule
\multirow{3}{*}{\rotatebox{-90}{Spatial}}& HA-$\varsigma\downarrow$ & 6.21 & 5.16 & \textbf{2.981} & 10.83 & 6.89 & \underline{3.91} \\
& PWA-$\Sigma\downarrow$ & 1.24 & 2.40 & 1.92 & 1.47 & 1.15 & \underline{\textbf{0.65}} \\
& Brain-DSC$\uparrow$ & \textbf{0.88} & 0.77 & 0.87 & 0.84 & \underline{0.86} & 0.85 \\ \midrule
\multirow{4}{*}{\rotatebox{-90}{Intensity}}& MI$\uparrow$ & 0.24 & 0.13 & 0.16 & 0.20 & 0.25 & \underline{\textbf{0.29}} \\
& R$\uparrow$ & 0.64 & 0.41 & 0.39 & 0.60 & 0.65 & \underline{\textbf{0.80}} \\
& MAID$\simeq0$ & 5.24 & 6.26 & 5.99 & 5.54 & \underline{\textbf{5.08}} & 5.33 \\
& MAID-zp$\simeq0$ & \textbf{0.53} & 1.99 & 1.35 & 1.16 & 0.86 & \underline{0.84} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Sergiu Mocanu
Alan R. Moody
April Khademi

"FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow" by Sergiu Mocanu, Alan R. Moody, and April Khademi, arXiv:2101.09639v2, 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.}
\begin{tabular}{lcccccccc} \hline \hline
				&\textbf{Mean} &\textbf{Std. dev.} & \textbf{Min} & \textbf{Max} \\ \hline
\textit{\textbf{A. Economic variables:}} \\ \hline
\hspace{2mm}Male wage rate & 13.63 & 3.71 & 6.88 & 29.90 \\
\hspace{2mm}Female wage rate & 12.05 & 3.16 & 4.03 & 21.80 \\
\hspace{2mm}Male weekly hours worked & 37.43 & 4.74 &12 & 60 \\
\hspace{2mm}Female weekly hours worked & 25.98 & 7.99 & 10 & 48 \\
\hspace{2mm}Full income & 2820.69 & 576.79 & 1357.20 & 4770.11 \\
\hspace{2mm}Household private consumption & 2241.59 & 472.04 &1142.50 & 4089.12 \\
\hspace{2mm}Assig. male private consumption & 89.97 &51.78 & 15 &453.72 \\
\hspace{2mm}Assig. female private consumption & 95.25 & 54.11 & 19.38 &507.66 \\
\hspace{2mm}Public consumption & 579.10 & 229.75 & 102.96 & 1898.35 \\
\hspace{2mm}Total household consumption & 764.32 & 256.07 & 173.21 & 2284.98 \\
\hspace{2mm}Male weekly leisure 	 & 74.56 & 4.74 & 52 & 100 \\
\hspace{2mm}Female weekly leisure 	 & 86.01 & 7.99 & 64 & 102 \\\hline
\textit{\textbf{B. Demographic variables:}} \\\hline
\hspace{2mm}Male age 	 & 47.39& 9.76 & 25 & 65 \\
\hspace{2mm}Female age 	 & 45.46& 9.90 & 25 & 65 \\
\hspace{2mm}Number of children 	 			 & 1.16 & 1.11 & 0 & 5 \\
\hspace{2mm}Male dummy low education 		 & .20 & .40 & 0 & 1 \\
\hspace{2mm}Female dummy low education 	 & .43 & .49 & 0 & 1 \\
\hspace{2mm}Male dummy middle education 	 & .36 &.48 & 0 & 1 \\
\hspace{2mm}Female dummy middle education 	 & .23 & .42 & 0 & 1 \\
\hspace{2mm}Male dummy high education 		 & .43 & .49 & 0 & 1 \\
\hspace{2mm}Female dummy high education 	 & .32 & .47 & 0 & 1 \\ \hline
\textit{\textbf{C. Personality traits:}} \\ \hline
\hspace{2mm}Male Openness & 3.07& .26 & 1.37& 3.87\\
\hspace{2mm}Female Openness& 3.07& .28 & 1.87& 3.87\\
\hspace{2mm}Male Extraversion& 3.18 & .51 & 1.33 & 4.50 \\
\hspace{2mm}Female Extraversion& 3.12 & .51 & 1.33 & 4.50\\
\hspace{2mm}Male Agreeableness & 3.07 & .25 & 2.00 & 3.75 \\
\hspace{2mm}Female Agreeableness & 3.16 & .20 & 2.37 & 3.75\\
\hspace{2mm}Male Neuroticism & 2.29& .57 & 1.00 & 4.22 \\
\hspace{2mm}Female Neuroticism & 2.59 & .59 & 1.00 & 4.33 \\
\hspace{2mm}Male Conscientiousness & 2.78 & .27 & 1.88 & 3.66 \\
\hspace{2mm}Female Conscientiousness &2.85 & .24 & 1.77 & 3.55\\
 \hspace{2mm}Male Self-esteem &5.98 & .65 & 3.80 & 7.00 \\
 \hspace{2mm}Female Self-esteem & 5.85 & .72 & 3.70 & 7.00\\
 \hspace{2mm}Male Cognitive engagement & 4.78 & .86& 2.66 & 7.00 \\
 \hspace{2mm}Female Cognitive engagement & 4.39 & .84 & 2.25 & 6.75\\

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

q-fin

"Does personality affect the allocation of resources within households?" by Gastón P. Fernández, arXiv:2307.02918v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Multi-kernel property in high-frequency price dynamics under Hawkes model" by Kyungsub Lee, arXiv:2302.11822v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Umaseh Sivanesan
Luis H. Braga
Ranil R. Sonnadara
Kiret Dhindsa

"TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors" by Umaseh Sivanesan, Luis H. Braga, Ranil R. Sonnadara, and Kiret Dhindsa, arXiv:2102.02690v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Worst-group accuracy of three methods on two Dominoes datasets. Dominoes MF and MC are concatenation of MNIST digits with FashionMNIST and Cifar-10, respectively. The MNIST digit is spurious with 99\% correlation in the training set and random correlation in the validation and test sets. The core feature (either FashionMNIST or Cifar) is however always fully correlated with the labels. GT denotes ground-truth group annotations.}
\begin{tabular}{lccc}
\toprule
 & ERM& XRM+GroupDRO& GT+GroupDRO \\
\midrule
Dominoes MF & 50.74 & 86.68 & 85.28 \\
Dominoes MC & 48.30 & 68.78 & 69.43\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Discovering environments with XRM

Mohammad Pezeshki
Diane Bouchacourt
Mark Ibrahim
Nicolas Ballas
Pascal Vincent
David Lopez-Paz

"Discovering environments with XRM" by Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, and David Lopez-Paz, arXiv:2309.16748v2, 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{A comparison of simulation methods for studying complex systems.}
\begin{tabular}{c|c|c|c}
 \hline
 \textbf{Simulation Method} & \textbf{Research Scale} & \textbf{Individual Behavior} & \textbf{System State} \\
 \hline
 Microsimulation & Micro & Simple (statistics) & Static \\
 System dynamics & Macro & None (system-level behavior) & Dynamic \\
 ABM & Micro & Complex & Dynamic \\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

Zengqing Wu
Run Peng
Xu Han
Shuyuan Zheng
Yixin Zhang
Chuan Xiao

"Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations" by Zengqing Wu, Run Peng, Xu Han, Shuyuan Zheng, Yixin Zhang, and Chuan Xiao, arXiv:2311.06330v4, 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{Performance comparison on the multi-species Lotka--Volterra system. Next to accuracy and balanced accuracy scores, we evaluate BA scores for detecting positive and negative interactions.}
\begin{tabular}{l||c|c|c|c}
 \textbf{Model} & \textbf{ACC($\pm$SD)} & \textbf{BA($\pm$SD)} & \textbf{BA\textsubscript{pos}($\pm$SD)} & \textbf{BA\textsubscript{neg}($\pm$SD)} \\
 \hline
 VAR & 0.383($\pm$0.095) & 0.635($\pm$0.060) & 0.845($\pm$0.024) & 0.781($\pm$0.042) \\
 cMLP & 0.825($\pm$0.035) & 0.834($\pm$0.043) & 0.889($\pm$0.031) & 0.846($\pm$0.084) \\
 cLSTM & NA & NA & 0.491($\pm$0.026) & 0.604($\pm$0.042) \\
 TCDF & 0.832($\pm$0.013) & 0.500($\pm$0.012) & 0.538($\pm$0.045) & 0.504($\pm$0.090) \\
 eSRU & 0.703($\pm$0.048) & 0.755($\pm$0.010) & 0.501($\pm$0.025) & 0.650($\pm$0.078) \\
 GVAR (ours) & \textbf{0.977($\pm$0.005)} & \textbf{0.961($\pm$0.014)} & \textbf{0.932($\pm$0.027)} & \textbf{0.999($\pm$0.001)} \\
 \hline
\end{tabular}
\end{table}
\end{document}

cs

"Interpretable Models for Granger Causality Using Self-explaining Neural Networks" by Ričards Marcinkevičs and Julia E. Vogt, arXiv:2101.07600v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Tinggang Zhao
Zhenyu Zhao
Changpin Li
Dongxia Li

"Spectral approximation of $ψ$-fractional differential equation based on mapped Jacobi functions" by Tinggang Zhao, Zhenyu Zhao, Changpin Li, and Dongxia Li, arXiv:2312.16426v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lllll}
\toprule
& MIN & MIN $\rightarrow$ CUB & CUB & CUB $\rightarrow$ MIN \\
\midrule
Finetuning & \textbf{r=0.82, p=2e-4} & \textbf{r=0.71, p=3e-3} & \textbf{r=0.96, p=7e-9} & r=0.28, p=0.31 \\
MAML & \textbf{r=-0.77, p=8e-4} & \textbf{r=-0.85, p=6e-5} & r=0.36, p=0.18 & \textbf{r=0.90, p=4e-6} \\
Reptile & r=0.27, p=0.3 & r=0.50, p=0.06 & r=0.3, p=0.28 & r=0.31, p=0.27 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Understanding Transfer Learning and Gradient-Based Meta-Learning Techniques" by Mike Huisman, Aske Plaat, and Jan N. van Rijn, arXiv:2310.06148v1, 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
 $[[n,k,d]]$ & $c_0$ & $c_1$ & $c_2$ \\
 \hline
 $[[30,4,5]]$ & 12.869 & -340.43 & 15878 \\
 \hline
 $[[48,4,7]]$ & 18.256 & -260.44 & 680.65 \\
 \hline
 \end{tabular}
\caption{Constants in the fitting formula $p_L=p^{(d+1)/2}e^{c_0+c_1p+c_2p^2}$ for BB5 codes.}
\end{table}
\end{document}

math

"Quantum error correction for long chains of trapped ions" by Min Ye and Nicolas Delfosse, arXiv:2503.22071v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Simone Cacace
Roberto Ferretti
Giulia Tatafiore

"Numerical Hopf-Lax formulae for Hamilton-Jacobi equations on unstructured geometries" by Simone Cacace, Roberto Ferretti, and Giulia Tatafiore, arXiv:2503.13311v1, 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}{ccc}
 Model & Active Parameters & One-Step SPE \\
\hline
GNAR(1, [1]) & 2 & 7.525 \\
 VAR(1) & $140^2$ & 13.82 \\
 Res. VAR(1) & 3761 & 10.815 \\
 Sparse VAR(1) & 3315 & 11.995 \\
 AR(1) & $140$ & 89.097 \\
 GNAR(1, [1])* & 141 & 7.431
\end{tabular}
\caption{One-step prediction error, $\hat{\boldsymbol{X}}_{447}$ is predicted using the previous 446 observations.}
\end{table}
\end{document}

stat

Guy Nason
Daniel Salnikov
Mario Cortina-Borja

"New tools for network time series with an application to COVID-19 hospitalisations" by Guy Nason, Daniel Salnikov, and Mario Cortina-Borja, arXiv:2312.00530v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Effects of Air Pollution on Teenagers' Cognitive Performance: Evidence from School Leaving Examination in Poland" by Agata Galkiewicz, arXiv:2506.19801v1, 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
					Method & MSE scenario A & MSE scenario B & MSE scenario C \\ \hline
					LNA (without restart) & $8.8\cdot 10^{-6}$ & $2.3\cdot 10^{-3}$ & $1.3 \cdot 10^{-3}$\\
					Diffusion guiding term & $9.0\cdot 10^{-6}$ & $1.8 \cdot 10^{-3}$ & $1.6 \cdot 10^{-3}$ \\ \hline
				\end{tabular}
\end{table}
\end{document}

math

"Guided simulation of conditioned chemical reaction networks" by Marc Corstanje and Frank van der Meulen, arXiv:2312.04457v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Lead Times in Flux: Analyzing Airbnb Booking Dynamics During Global Upheavals (2018-2022)" by Harrison Katz, Erica Savage, and Peter Coles, arXiv:2501.10535v1, licensed under CC BY-SA 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|c}
 \textbf{Method} & \textbf{MCD$\downarrow$} & \textbf{GPE$\downarrow$} & \textbf{VDE$\downarrow$} & \textbf{FFE$\downarrow$} \\
 \hline
 Conv-1 & \textbf{09.78}& \textbf{24.45} & \textbf{14.47} & \textbf{28.90}\\
 Attention-1 & 10.56 & 26.45 & 16.12 & 30.36\\
 Conv-2 & \textbf{11.05} & \textbf{26.76} & \textbf{16.67} & \textbf{30.58} \\
 Attention-2 & 12.71 & 27.52 & 17.23 & 31.62 \\
 \end{tabular}
\caption{Quantitative metrics on FSM-SS on few shot approach (5 samples). Conv: Convolution based normalization, Attention : Multi head attention based normalization, 1- VCTK dataset, 2- LibriTTS dataset}
\end{table}
\end{document}

eess

Neeraj Kumar
Srishti Goel
Ankur Narang
Brejesh Lall

"Few Shot Adaptive Normalization Driven Multi-Speaker Speech Synthesis" by Neeraj Kumar, Srishti Goel, Ankur Narang, and Brejesh Lall, arXiv:2012.07252v1, licensed under CC BY-SA 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{\footnotesize\textbf{Results of runtimes of NA$^{1}$M, NB$^{1}$M, ANOVA-T$^{1}$PNN, and NBM-T$^{1}$PNN.}}
\begin{tabular}{c|c|c|c|c|c|c}
\hline
 Dataset & Size of dataset & \# of features & NA$^{1}$M & NB$^{1}$M & ANOVA-T$^{1}$PNN & NBM-
 T$^{1}$PNN \\
 \hline \hline
\textsc{Abalone} & 4K & 10 & 6.6 sec & 3.0 sec & 1.6 sec & 1.5 sec\\
\textsc{Calhousing} & 21K & 8 & 14.1 sec & 4.1 sec & 3.8 sec & 3.5 sec\\
\textsc{Online} & 40K & 58 & 68 sec & 15.6 sec & 65 sec & 9.8 sec\\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Seokhun Park
Insung Kong
Yongchan Choi
Chanmoo Park
Yongdai Kim

"Tensor Product Neural Networks for Functional ANOVA Model" by Seokhun Park, Insung Kong, Yongchan Choi, Chanmoo Park, and Yongdai Kim, arXiv:2502.15215v3, 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{Statistics on Overall Accuracy and the Inference Time expressed as a real-time percentage.}
\begin{tabular}{lccc}
\toprule
{Model x Embeddings} & Accuracy (\%) & \multicolumn{2}{c}{Inference Time (\%)} \\ \cmidrule(r){2-2} \cmidrule(lr){3-4}
& Mean & Averaged percentage \\ \midrule
VGGish & 88.11 $\pm$ 0.73 & 0.423\\
MS-Clap & \textbf{98.02} $\pm$ 0.18 & 1.82 \\
PANN-Wavegram & 93.15 $\pm$ 0.34 & 0.318 \\
PANNcnn14\_32k & 93.04 $\pm$ 0.32 & 0.234 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Hafsa Ouajdi
Oussama Hadder
Modan Tailleur
Mathieu Lagrange
Laurie M. Heller

"Detection of Deepfake Environmental Audio" by Hafsa Ouajdi, Oussama Hadder, Modan Tailleur, Mathieu Lagrange, and Laurie M. Heller, arXiv:2403.17529v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jane She
Matthew Egberg
Michael R. Kosorok

"An optimal dynamic treatment regime estimator for indefinite-horizon survival outcomes" by Jane She, Matthew Egberg, and Michael R. Kosorok, arXiv:2501.18070v1, 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}{llr}
\hline\hline
&3-digit SIC Industry&Fraction\tabularnewline
\hline
1&Local Passenger Transit&$0.0\%$\tabularnewline
2&Automative Dealers \& Service Stations&$1.8\%$\tabularnewline
3&Tobacco Products&$1.8\%$\tabularnewline
4&Petroleum \& Coal&$2.0\%$\tabularnewline
5&Auto Services&$2.2\%$\tabularnewline
6&General Building Contractors&$2.5\%$\tabularnewline
7&Water Transportation&$2.7\%$\tabularnewline
8&Paper \& Allied Products&$2.9\%$\tabularnewline
9&Transportation by Air&$3.8\%$\tabularnewline
10&Social Services&$3.9\%$\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Zero-Leverage Puzzle

"Zero-Leverage Puzzle" by Mykola Pinchuk, arXiv:2302.00761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc}
\hline
Variable & Description \\
\hline\hline
$X$ & Realization of the loan portfolio \\
$\delta$ & Cost of equity \\
$\rho(x)$ & Risk of the loan portfolio $x$ \\
$L$ & The total amount of loss from liquidating the whole position \\
$\theta_{1}$ & Upper bound on risk ($> 0$) \\
$\theta_{2}$ & Lower bound on risk ($> 0$) \\
$\beta_{i}$ & Liquidation strategy for the $i$-th asset at time $t=1$ \\
$\gamma$ & Amount of loss due to liquidation \\
$k_{lev}$ & Leverage Ratio. \\
$K(x)$ & Internal Ratings Based (IRB) capital requirement for portfolio $x$ \\
$e$ & Equity component of the bank's portfolio. \\
$\eta_{i}$ & Loss Given Default (LGD) for the $i$-th loan \\
$EL_{i}$ & Expected Loss (EL) for the $i$-th loan \\
\hline
\end{tabular}
\caption{Description of the model variables}
\end{table}
\end{document}

q-fin

Deb Narayan Barik
Siddhartha P. Chakrabarty

"Loan portfolio management and Liquidity Risk: The impact of limited liability and haircut" by Deb Narayan Barik and Siddhartha P. Chakrabarty, arXiv:2308.06525v1, 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{Relation Between Current and Future Annual Return}
\begin{tabular}{llll}
 \midrule
 Variable & CurrentAnnualReturn & ThreeMonthAnnualReturn & Gain\\
 \midrule
 Recommended Funds & 0.569 & 0.339 & -0.230*** \\
 Non-Recommended Funds & 0.455 & 0.310 & -0.144*** \\
 Difference & 0.114*** & 0.029** & \\
 \midrule
 \multicolumn{4}{p{40em}}{\scriptsize Notes: {***} $p < 0.01$, {**} $p < 0.05$, {*} $p < 0.1$. CurrentAnnualReturn is the current annual return of the fund. ThreeMonthAnnualReturn is the annual return of the fund after three months. Gain is the difference between ThreeMonthAnnualReturn and CurrentAnnualReturn. The significance level is based on Paired t-test.}
 \\
 \end{tabular}
\end{table}
\end{document}

q-fin

"The Effect of Product Recommendations on Online Investor Behaviors" by Ruiqi Rich Zhu, Cheng He, and Yu Jeffrey Hu, arXiv:2303.14263v2, 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{Modified Vicsek model drift estimation summary}
\begin{tabular}{|c|c|} \hline
 Relative $L^2(\rho)$ Error& 0.044\\ \hline
 Relative Trajectory Error& $4.63e-3$ $\pm$ $4.65e-3$\\ \hline
 Wasserstein Distance at $t = 0.25$& 0.001075\\ \hline
 Wasserstein Distance at $t = 0.5$&0.002169\\\hline
 Wasserstein Distance at $t = 1$&0.004433\\\hline
 \end{tabular}
\end{table}
\end{document}

math

"Learning Stochastic Dynamical Systems with Structured Noise" by Ziheng Guo, James Greene, and Ming Zhong, arXiv:2503.01077v1, 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{Average results of the models with the weighted MSE loss with $w_{90\%}=1.5$ and $w_{80\%}=1.25$ for two, three, and six lead months SSTA and MHW forecasts.}
\begin{tabular}{llllll}
\textbf{Across All Locations} & \textbf{MSE↓} & \textbf{CSI↑} & \textbf{CSI 80↑} & \textbf{Training Time↓} & \textbf{PUR↓} \\ \hline
Average (two lead months) & \textbf{0.5257} & 0.0 & 0.2222 & 30.7924 & 0\% \\
Average (three lead months) & \textbf{0.6323} & 0.0 & 0.0536 & 31.113 & 26.7\% \\
Average (six lead months) & \textbf{0.7036} & 0.0 & 0.0 & 30.1553 & 66.7\%
\end{tabular}
\end{table}
\end{document}

stat

Ding Ning
Varvara Vetrova
Sébastien Delaux
Rachael Tappenden
Karin R. Bryan
Yun Sing Koh

"A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models" by Ding Ning, Varvara Vetrova, Sébastien Delaux, Rachael Tappenden, Karin R. Bryan, and Yun Sing Koh, arXiv:2502.13495v1, 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{Average dice score performance (\%) of the GCN refinement at different uncertainty thresholds $\tau$ for the \textbf{pancreas} segmentation problem. The table compares the results for the 6-surrounding + 16 random connectivity (ours) vs. a 26 surrounding connectivity (n26). cnn10 indicates results obtained with a CNN trained on ten samples.}
\begin{tabular}{l|c|c|c|c|c}
			\hline
			Connectivity & GCN & GCN & GCN & GCN & GCN \\
			 & $\tau = 1e-3$ & $\tau = 0.3$ & $\tau = 0.5$ & $\tau = 0.8 $ & $\tau = 0.999$ \\
			\hline
			Ours & $77.71 \pm 6.3$ & $77.79 \pm 6.4$ & $77.77 \pm 6.3$ & $77.81 \pm 6.3$ & $77.79 \pm 6.3$ \\
			\hline
			n26& $ 76.93 \pm 6.1$ & $77.16 \pm 5.8$ & $77.18 \pm 5.8$ & $77.18 \pm 5.8$ & $77.4 \pm 5.9$ \\
			\hline
			\hline
			Ours-cnn10& $54.55 \pm 22.1$ & $54.32 \pm 22.1$ & $54.15 \pm 22.2$ & $53.91 \pm 22.4$ & $53.14 \pm 22.9$ \\
			\hline
			n26-cnn10& $52.50 \pm 22.1$ & $52.66 \pm 22.4$ & $52.56 \pm 22.4$ & $52.53 \pm 22.5$ & $52.43 \pm 22.9$\\
			\hline
			\end{tabular}
\end{table}
\end{document}

eess

Roger D. Soberanis-Mukul
Nassir Navab
Shadi Albarqouni

"An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation" by Roger D. Soberanis-Mukul, Nassir Navab, and Shadi Albarqouni, arXiv:2012.03352v1, 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{Relative Performance Gain of SYCL compared to CUDA for Function Approximation}
\begin{tabular}{c|ccc}
 & Mean & Min & Max \\
 \hline
 Training throughput & 1.01 & 0.74 & 1.37\\
 Inference throughput & 1.34 & 1.03 & 1.68
 \end{tabular}
\end{table}
\end{document}

cs

Kai Yuan
Christoph Bauinger
Xiangyi Zhang
Pascal Baehr
Matthias Kirchhart
Darius Dabert
Adrien Tousnakhoff
Pierre Boudier
Michael Paulitsch

"Fully-fused Multi-Layer Perceptrons on Intel Data Center GPUs" by Kai Yuan, Christoph Bauinger, Xiangyi Zhang, Pascal Baehr, Matthias Kirchhart, Darius Dabert, Adrien Tousnakhoff, Pierre Boudier, and Michael Paulitsch, arXiv:2403.17607v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{A case study of the generated margin of sampled training triplets. The movie genre label is from the dataset.}
\begin{tabular}{|c|l|l|c|}
 \hline
 User & Positive & Sampled Movie & Margin \\
 \hline
 \multirow{4}{*}{405} & \multirow{2}{*}{\textit{Scream} (Thriller)} & \textit{Four Rooms} (Thriller) & \textbf{1.2752} \\ \cline{3-4}
 & & \textit{Toy Story} (Animation) & 12.8004 \\ \cline{2-4}
 & \multirow{2}{*}{\textit{French Kiss} (Comedy)} & \textit{Addicted to Love} (Comedy) & \textbf{2.6448} \\ \cline{3-4}
 & & \textit{Batman} (Action) & 12.4607 \\ \hline
 \multirow{4}{*}{66} & \multirow{2}{*}{\textit{Air Force One} (Action)} & \textit{GoldenEye} (Action) & \textbf{0.3216} \\ \cline{3-4}
 & & \textit{Crumb} (Documentary) & 5.0010 \\ \cline{2-4}
 & \multirow{2}{*}{\textit{The Godfather} (Crime)} & \textit{The Godfather II} (Crime) & \textbf{0.0067} \\ \cline{3-4}
 & & \textit{Terminator} (Sci-Fi) & 3.6335 \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

Chen Ma
Liheng Ma
Yingxue Zhang
Ruiming Tang
Xue Liu
Mark Coates

"Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation" by Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu, and Mark Coates, arXiv:2101.04849v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccc}
 \toprule
 & Asian & Smile & Gender & Beard\\ \hline
 Asian & \textbf{53.6} & 15.8 & -12.1 & -3.5 \\
 Smile & -20.4 & \textbf{41.2} & 11.8 & -7.2 \\
 Gender & 2.0 & -4.3 & \textbf{94.7} & -19.8 \\
 Beard & -2.6 & -8.1 & -0.05 & \textbf{28.3} \\\hline
 \end{tabular}
\caption{\textbf{Re-scoring Analysis.} \texttt{GANTASTIC} can perform edits efficiently on several attributes. The attributes edited are shown as the rows whereas the measured attributes are shown as the columns.}
\end{table}
\end{document}

cs

Yusuf Dalva
Hidir Yesiltepe
Pinar Yanardag

"GANTASTIC: GAN-based Transfer of Interpretable Directions for Disentangled Image Editing in Text-to-Image Diffusion Models" by Yusuf Dalva, Hidir Yesiltepe, and Pinar Yanardag, arXiv:2403.19645v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
\toprule
& \multicolumn{4}{c}{Subset Percent}\\
 \cmidrule{2-5}
& 10\% & 30\% & 50\% & 70\%\\
\midrule[0.7pt]
ConvNorm + BN & 77.96 $\pm$ 0.11 & 87.66 $\pm$ 0.23 & 90.49 $\pm$ 0.17 & 90.71 $\pm$ 0.15 \\
ConvNorm & 69.23 $\pm$ 0.94 & 83.93 $\pm$ 0.34 & 87.83 $\pm$ 0.21 & 89.85 $\pm$ 0.10\\
BN & 67.10 $\pm$ 2.59 & 84.24 $\pm$ 0.51 & 88.74 $\pm$ 0.73 & 90.41 $\pm$ 0.33\\
Vanilla & 67.56 $\pm$ 0.50 & 81.98 $\pm$ 0.78 & 86.57 $\pm$ 0.35 & 87.61 $\pm$ 0.86\\
\bottomrule
\end{tabular}
\caption{\textbf{Adding ConvNorm and BatchNorm together helps improve data efficiency} The influence of BatchNorm and ConvNorm for data scarcity on the CIFAR-10 dataset is evaluated. The mean test accuracy and its standard deviation are computed over three random seeds.}
\end{table}
\end{document}

eess

Sheng Liu
Xiao Li
Yuexiang Zhai
Chong You
Zhihui Zhu
Carlos Fernandez-Granda
Qing Qu

"Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training" by Sheng Liu, Xiao Li, Yuexiang Zhai, Chong You, Zhihui Zhu, Carlos Fernandez-Granda, and Qing Qu, arXiv:2103.00673v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Lingjing Kong
Guangyi Chen
Petar Stojanov
Haoxuan Li
Eric P. Xing
Kun Zhang

"Towards Understanding Extrapolation: a Causal Lens" by Lingjing Kong, Guangyi Chen, Petar Stojanov, Haoxuan Li, Eric P. Xing, and Kun Zhang, arXiv:2501.09163v1, 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{Results of the Wilcoxon rank-sum test: non-privacy vs. privacy issues}
\begin{tabular}{lllll}
			\toprule
			\textbf{Project} & \textbf{Attribute} & \textbf{One-sided tail} & \textbf{p-value} & \textbf{Effect size}\\
			\midrule
			Google Chrome & Resolution time & Less & $<$0.001 & 0.578 \\
			Google Chrome & \#Comments & Less & $<$0.001 & 0.691 \\
			Moodle & Resolution time & Greater & $<$0.001 & 0.609 \\
			Moodle & \#Comments & Greater & $<$0.001 & 0.604\\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

cs

Pattaraporn Sangaroonsilp
Hoa Khanh Dam
Morakot Choetkiertikul
Chaiyong Ragkhitwetsagul
Aditya Ghose

"A Taxonomy for Mining and Classifying Privacy Requirements in Issue Reports" by Pattaraporn Sangaroonsilp, Hoa Khanh Dam, Morakot Choetkiertikul, Chaiyong Ragkhitwetsagul, and Aditya Ghose, arXiv:2101.01298v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Edgar C. Merkle
Sonja D. Winter
Ellen Fitzsimmons

"Identification and Scaling of Latent Variables in Ordinal Factor Analysis" by Edgar C. Merkle, Sonja D. Winter, and Ellen Fitzsimmons, arXiv:2501.06094v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|r|r|r|}
\hline
Outlier Percentile & 0.1\% & 2.5\% & 5.0\% \\ \hline
Paired t-test (marked to day) & \textbf{2.53E-04} & \textbf{2.00E-06} & \textbf{1.00E-06} \\ \hline
Paired t-test (marked to week) & \textbf{6.00E-06} & \textbf{3.00E-06} & \textbf{2.72E-07} \\ \hline
Unpaired t-test & \textbf{3.46E-07} & 1.20E-01 & 1.17E-01 \\ \hline
Log Paired t-test (marked to day) & \textbf{2.50E-07} & \textbf{2.81E-04} & \textbf{3.57E-04} \\ \hline
Log Paired t-test (marked to week) & \textbf{1.28E-07} & \textbf{4.50E-05} & \textbf{3.70E-05} \\ \hline
Log Unpaired t-test & \textbf{1.01E-02} & {3.63E-01} & 2.39E-01 \\ \hline
\end{tabular}
\caption{P-values varying outlier detection methods and types of one-sided t-tests for hypothesis that price of Dark CryptoPunks $>$ Light CryptoPunks. \textbf{Bold} indicates p-value$<0.05$}
\end{table}
\end{document}

q-fin

"Exploring Gender and Race Biases in the NFT Market" by Howard Zhong and Mark Hamilton, arXiv:2304.06484v1, 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{Example table}
\begin{tabular}{|c|c|c|} \hline
 Species & \bf Mean & \bf Std.~Dev. \\ \hline
 1 & 3.4 & 1.2 \\
 2 & 5.4 & 0.6 \\ \hline
 \end{tabular}
\end{table}
\end{document}

math

George Pantazis
Barbara Franci
Sergio Grammatico

"On data-driven Wasserstein distributionally robust Nash equilibrium problems with heterogeneous uncertainty" by George Pantazis, Barbara Franci, and Sergio Grammatico, arXiv:2312.03573v2, 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}{lccccc} \hline
							 		& $\{X, Y\}$	& $\{X, Z\}$	& $\{Y, Z\}$		\\ \hline
						Egalitarian Welfare 	& (2, 2, 3)	& (1, 1, 3)	& (0, 3, 3)		\\ \hline
							\end{tabular}
\caption{Egalitarian welfares of certain project sets to be funded.}
\end{table}
\end{document}

cs

"Participatory Funding Coordination: Model, Axioms and Rules" by Haris Aziz and Aditya Ganguly, arXiv:2101.11722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Tracking error (in terms of RMSE values) comparison of five different RL algorithms for batch transesterification process}
\begin{tabular}{llllll}
\hline
\multirow{2}{*}{Reward} & \multicolumn{2}{c}{Continuous Action} && \multicolumn{2}{c}{Discrete Action} \\
\cline{2-4}
\cline{5-6}
& TATD3 &
TD3 & DDPG& DQN & GP \\
\hline
PI & 1.1626 & 1.1785 & 1.2365 & 1.3088 & 1.3866\\
PID & 1.1502 & 1.1666 & 1.2051 & 1.2763 & 1.2875 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Tanuja Joshi
Shikhar Makker
Hariprasad Kodamana
Harikumar Kandath

"Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control" by Tanuja Joshi, Shikhar Makker, Hariprasad Kodamana, and Harikumar Kandath, arXiv:2102.13012v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 \toprule
 $K$ & Breakout & Qbert \\
 \midrule
 10 & 231.6\scriptsize{$\pm$16.2} & 56.4\scriptsize{$\pm$16.6} \\
 30 & 239.2\scriptsize{$\pm$26.4} & 42.3\scriptsize{$\pm$8.5} \\
 40 & 295.9\scriptsize{$\pm$34.7} & 28.1\scriptsize{$\pm$4.5} \\
 60 & 271.1\scriptsize{$\pm$70.8} & 15.7\scriptsize{$\pm$5.5} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

"Decision Mamba: Reinforcement Learning via Sequence Modeling with Selective State Spaces" by Toshihiro Ota, arXiv:2403.19925v1, 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}{ccccc} \hline
 {\textbf{}} & \multicolumn{4}{c}{\textbf{Brisbane Zones}} \\
 & \textbf{Business} & \textbf{Residential} & \textbf{Education} & \textbf{Recreation} \\ \hline

 Melb. Cluster 1 & \textbf{0.045} & 0.156 & 0.419 & 0.387 \\ %\hline

 Melb. Cluster 2 & 0.206 & \textbf{0.204} & 0.611 & 0.607 \\%\hline

 Melb. Cluster 3 & \textbf{0.098} & 0.613 & 0.184 & 0.90 \\%\hline

 Melb. Cluster 4 & 0.239 & 0.190 & 0.834 & \textbf{0.117} \\\hline
 \end{tabular}
\caption{MSE between Melbourne \& Brisbane reference clusters}
\end{table}
\end{document}

cs

"Land Use Detection & Identification using Geo-tagged Tweets" by Saeed Khan and Md Shahzamal, arXiv:2101.03337v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Variable operating costs of process units.}
\begin{tabular}{lrlll}
		\toprule[1.5pt]
		\textbf{Item} & \textbf{Operating Cost} & \textbf{Units} & \textbf{Utilities} & \textbf{Notes} \\
		Anaerobic Digester & $0.096c_{AD}$ & USD/yr & electricity & $c_{AD}$ denotes cost of AD (USD) \\
		Solid-Liquid Separator & $0.488m_{in}+0.1c_{SLS}$ & USD/yr & electricity & $m_{in}$ and $c_{SLS}$ denote capacity and cost of SLS (USD) \\
		H$_2$S Scrubber & 66.7 & USD/tonne biogas & activated carbon & gas removal via carbon bed adsorption \\
		CO$_2$ Scrubber & 40.0 & USD/tonne CO$_2$ & amine solution, steam & gas removal via amine scrubbing \\
		Photobioreactors & 12100 & USD/acre/yr & electricity, bags, urea, water & $-$ \\
		Flocculation Tank & 100 & USD/tonne CB & electricity & source includes cost of chemical flocculant \\
		Lamella Clarifier & 0.43 & USD/tonne CB & electricity & $-$ \\
		Pressure Filter & 2.06 & USD/tonne CB & electricity & $-$ \\
		Dryer & 19.3 & USD/tonne water & natural gas & basis is in terms of water removed \\
		\bottomrule[1.5pt]
	\end{tabular}
\end{table}
\end{document}

stat

"On the Implementation of a Bayesian Optimization Framework for Interconnected Systems" by Leonardo D. González and Victor M. Zavala, arXiv:2501.00967v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
\toprule
Method & Kinetics & SSv2$^\dagger$ \\ \midrule
$\Omega{=}\{2,3\}$ order reversed & {\bf 85.9} & 51.3 \\
$\Omega{=}\{2,3\}$ & {\bf 85.9} & {\bf 59.1} \\
\bottomrule
\end{tabular}
\caption{Results assess the importance of temporal ordering. When the tuple orders are reversed for the query video, a large drop is observed for SSv2$^\dagger$, but not for Kinetics.}
\end{table}
\end{document}

cs

Toby Perrett
Alessandro Masullo
Tilo Burghardt
Majid Mirmehdi
Dima Damen

"Temporal-Relational CrossTransformers for Few-Shot Action Recognition" by Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmehdi, and Dima Damen, arXiv:2101.06184v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|}
\hline
Players & Team count \\
\hline
Dwight Howard & 56 \\
Giannis Antetokounmpo & 50 \\
Dwight Howard / Shaquille O'Neal & 33 \\
Andre Drummond / Dwight Howard & 28 \\
Giannis Antetokounmpo / Andre Drummond & 13 \\
Andre Drummond & 13 \\
Shaquille O'Neal & 11 \\
Giannis Antetokounmpo / Dwight Howard & 3 \\
\hline
Total & 207 \\
\hline
\end{tabular}
\caption{Table of players on free-throw punting teams, defined by those with average scores below 1.5 points in the Free Throw \% category. From the $\chi=0.25$ simulations. All of the 207 free throw punting teams had at least one of Dwight Howard (49 to 67\% free throw shooter), Shaquille O'Neal (42-62\%), Giannis Antetokounmpo (61-77\%), or Andre Drummond (35-61\%). The NBA average free throw percent is currently 78\%}
\end{table}
\end{document}

stat

"Optimizing for Rotisserie Fantasy Basketball" by Zach Rosenof, arXiv:2501.00933v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
\hline
 & & Margins (Dynamic Est.) & Margins (Static est.) & Price\tabularnewline
\hline
\hline
\multirow{3}{*}{1000h Inc.} & Panasonic & 30 & 29.2 & 106.1\tabularnewline
 & Toshiba & 21.8 & 25.9 & 82.6\tabularnewline
 & Others & 14.6 & 14.7 & 44.8\tabularnewline
\hline
\multirow{2}{*}{2000h Inc.} & Panasonic & 41.6 & 28.9 & 171.7\tabularnewline
 & Toshiba & 36.7 & 25.9 & 172.2\tabularnewline
\hline
\multirow{3}{*}{CFL} & Panasonic & 179.1 & 162.4 & 880.5\tabularnewline
 & Toshiba & 214.5 & 189.5 & 657.4\tabularnewline
 & Others & 115.8 & 115.7 & 569\tabularnewline
\hline
\end{tabular}
\caption{Estimated margins and Prices}
\end{table}
\end{document}

q-fin

"When do firms sell high durability products? The case of light bulb industry" by Takeshi Fukasawa, arXiv:2503.23792v2, 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{Supply levels of the limit order book in Table~ (right).}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
 points & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 \\
\hline
$x$ & 0 & 500 & 1700 & 3300 & 4240 & 5440 & 6760 & 9560 & 14660 & 27160 & 52160 \\
\hline
$y$ & 284 & 234 & 204 & 184 & 174 & 162 & 150 & 130 & 100 & 50 & 0 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Aggregation of financial markets

"Aggregation of financial markets" by Georg Menz and Moritz Voß, arXiv:2309.04116v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Effect of adding the proposed TLRM to different layer of EGNN. The results on $5$-way $1$-shot are reported.}
\begin{tabular}{c|c|c}
 \cline{1-3}
 \multicolumn{2}{c|}{Model} & mini-ImageNet \\ \cline{1-3}
 \multicolumn{2}{c|}{EGNN} & $52.86\pm0.42$ \\ \cline{1-3}
 \multirow{4}{*}{EGNN+TLRM} & $L=1$ & $53.11\pm0.43$ \\
 & $L=2$ & $53.29\pm0.44$ \\
 & $L=3$ & $53.42\pm0.42$ \\
 &$ \bf{L=1 ~\&~2 ~\&~3}$ & $\bf{53.65\pm0.43}$ \\ \cline{1-3}
 \end{tabular}
\end{table}
\end{document}

cs

Yurong Guo
Zhanyu Ma
Xiaoxu Li
Yuan Dong

"TLRM: Task-level Relation Module for GNN-based Few-Shot Learning" by Yurong Guo, Zhanyu Ma, Xiaoxu Li, and Yuan Dong, arXiv:2101.09840v3, 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{Datasets Statistics. The percentage in parenthesis represents the proportion of positive samples.}
\begin{tabular}{lllll}
 Dataset & Train& Validation & Test \\
 \hline
 moltox21(t0) & 5834 (4.25\%) & 722 (4.01\%) & 709 (4.51\%)\\
 molmuv(t1) & 11466 (0.18\%) & 1559 (0.13\%) & 1709 (0.35\%) \\
 molpcba(t0) & 120762 (9.32\%) & 19865 (11.74\%) & 20397 (11.61\%) \\
 \end{tabular}
\end{table}
\end{document}

stat

"Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization" by Quanqi Hu, Dixian Zhu, and Tianbao Yang, arXiv:2310.03234v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Lourens Touwen
Doina Bucur
Remco van der Hofstad
Alessandro Garavaglia
Nelly Litvak

"Learning the mechanisms of network growth" by Lourens Touwen, Doina Bucur, Remco van der Hofstad, Alessandro Garavaglia, and Nelly Litvak, arXiv:2404.00793v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Fleurianne Bertrand
Maximilian Brodbeck
Tim Ricken
Henrik Schneider

"Least-Squares Finite Element Methods for nonlinear problems: A unified framework" by Fleurianne Bertrand, Maximilian Brodbeck, Tim Ricken, and Henrik Schneider, arXiv:2503.18739v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Bayes estimates of UW$(\alpha,\beta)$ for $c=0.5$, $t=0.5$ and Prior I: Order Statistics}
\begin{tabular}{l|rrr|rrr|rrr}
		\toprule
		\multirow{2}[3]{*}{Method} & & SELF & & & LINEX & & & GELF & \\
		\cmidrule{2-10} & $\alpha$ & $\beta$ & $R(t)$ & $\alpha$ & $\beta$ & $R(t)$ & $\alpha$ & $\beta$ & $R(t)$ \\
		\midrule
		Lindley & 0.4876 & 1.2564 & 0.2134 & 0.4981 & 1.6754 & 0.2806 & 0.4129 & 1.6987 & 0.1659 \\
		T-K & 0.5969 & 1.8678 & 0.2615 & 0.5354 & 1.8043 & 0.2513 & 0.4293 & 1.7724 & 0.1840 \\
		MCMC & 0.2508 & 2.9533 & 0.1057 & 0.2183 & 2.7360 & 0.0989 & 0.0563 & 2.6828 & 0.0156 \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

stat

Qazi J. Azhad
Abdul Nasir Khan
Bhagwati Devi
Jahangir Sabbir Khan
Ayush Tripathi

"Bayesian estimation of Unit-Weibull distribution based on dual generalized order statistics with application to the Cotton Production Data" by Qazi J. Azhad, Abdul Nasir Khan, Bhagwati Devi, Jahangir Sabbir Khan, and Ayush Tripathi, arXiv:2502.02927v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Testing and training MSE for various methods when tested on the synthetic datasets of and . }
\begin{tabular}{lrcccccc}
 \toprule
 & & CGM & WU & HMC-DF & HMC-DFI & DCC-Tree\\
 \midrule
 \multirow{2}{*}{CGM} & Train MSE & 0.043(1.9e-4) & \textbf{0.042(5.8e-5)} & 0.043(2.0e-4) & 0.043(1.9e-4) & 0.043(5.2e-5) \\
 & Test MSE & 0.064(0.014) & 0.062(0.001) & 0.041(4.6e-4) & 0.041(4.5e-4) & \textbf{0.040(1.4e-4)} \\
 \midrule
 \multirow{2}{*}{WU} & Train MSE & 0.059(2.3e-4) & \textbf{0.054(1.4e-3)} & 0.060(7.4e-4) & 0.060(4.4e-4) & 0.058(3.3e-5) \\
 & Test MSE & 0.112(0.034) & 0.073(0.038) & 0.059(2.1e-3) & 0.060(2.5e-3) & \textbf{0.059(5.3e-4)} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Jodie A. Cochrane
Adrian Wills
Sarah J. Johnson

"Divide, Conquer, Combine Bayesian Decision Tree Sampling" by Jodie A. Cochrane, Adrian Wills, and Sarah J. Johnson, arXiv:2403.18147v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

On the Last Kervaire Invariant Problem

"On the Last Kervaire Invariant Problem" by Weinan Lin, Guozhen Wang, and Zhouli Xu, arXiv:2412.10879v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zhi Su
Danni Wu
Zhenkun Zhou
Junran Wu
Libo Yin

"The Impact of Customer Online Satisfaction on Stock Returns: Evidence from the E-commerce Reviews in China" by Zhi Su, Danni Wu, Zhenkun Zhou, Junran Wu, and Libo Yin, arXiv:2306.12119v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c|c|c|c||}
\hline \# & Stock Ex.&Asset ID & Company \\
\hline 1 & Nasdaq&AAPL& Apple Inc. \\
\hline 2 & Nasdaq&AMD& Advanced Micro Devices, Inc. \\
\hline 3 & Nasdaq&MSFT& Microsoft \\
\hline 4 & Nasdaq& FB& Facebook \\
\hline 5 & Nasdaq&AAL& American Airlines Group, Inc. \\
\hline 6 & Nasdaq&TSL& Tesla, Inc. \\
\hline 7 & Nasdaq&TLRY& Tilray, Inc. \\
\hline 8 & Nasdaq&V& Visa Inc. \\
\hline 9 & Nasdaq&JNJ& Johnson \& Johnson \\
\hline 10 & Nasdaq&MU& Micron Technology, Inc. \\
\hline 11 & NYSE& BABA & Alibaba Group \\
\hline 12 & NYSE&SQ& Square Inc. Cl A \\
\hline 13 & NYSE&BA& Boeing Co. \\
\hline 14 & NYSE&NIO& NIO Inc. \\
\hline 15 & NYSE&AMC& AMC Entertainment Inc. \\
\hline 16 & NYSE&JPM& JPMorgan Chase \\
\hline 17 & NYSE&ABEV& Ambev \\
\hline 18 & NYSE&SHOP& Shopify Inc. Cl A \\
\hline 19 & NYSE& AA & Alcoa corp. \\
\hline 20 & NYSE & DI & Didi Inc. \\
\hline 21 & B3&VALE3& Vale \\
\hline 22 & B3&ITUB4& Itaú \\
\hline 23 & B3&PETR4& Petrobras \\
\hline 24 & B3&BBDC4& Bradesco \\
\hline 25 & B3&B3SA3& B3\\
\hline 26 & B3&PETR3& Petrobras\\
\hline 27 & B3&ABEV3& Ambev\\
\hline 28 & B3& OIBR3& Oi \\
\hline 29 & B3&ITSA4& Itausa\\
\hline 30 & B3&BBAS3& Banco do Brasil\\
\hline
\end{tabular}
\caption{List of Assets used in SX scenarios}
\end{table}
\end{document}

cs

Murilo Sibrao Bernardini
Paulo Andre Lima de Castro

"Is it a great Autonomous FX Trading Strategy or you are just fooling yourself" by Murilo Sibrao Bernardini and Paulo Andre Lima de Castro, arXiv:2101.07217v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Peace Dividends: The Economic Effects of Colombia's Peace Agreement" by Miguel Fajardo-Steinhäuser, arXiv:2301.01843v2, 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}
 Parameter & Value \\
 \hline
 Slot time ($\mu s$) & 50 \\
 SIFS ($\mu s$) & 28 \\
 DIFS ($\mu s$) & 128 \\
 PHY Header (bits) & 128\\
 MAC Header (bits) & 272\\
 ACK ($\mu s$) & PHY Header + 14*8/base rate \\
 Base rate (Mbit/s) & 1 \\
 $CW_{min}$ exponent & 4 \\
 $CW_{min}$ & 15 \\
 $CW_{max}$ & 1023 \\
 $max retries$ & 6 \\
 packet size & 1023 \\
 Back-off window size & $2^{CW_{min}\text{ exponent} +\text{Number of retries}}$
 \end{tabular}
\caption{Parameters used for comparison to the model of Bianchi}
\end{table}
\end{document}

cs

Bjørn Ivar Teigen
Neil Davies
Kai Olav Ellefsen
Tor Skeie
Jim Torresen

"A Model of WiFi Performance With Bounded Latency" by Bjørn Ivar Teigen, Neil Davies, Kai Olav Ellefsen, Tor Skeie, and Jim Torresen, arXiv:2101.12512v1, 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}{ccccccc}
		\hline
		$h^{-1}$ & $\mathrm{H}^2$ & EOC & $\mathrm{H}^2$ & EOC & $\mathrm{H}^2$ & EOC\\
		& $p=1$ & & $p=2$ & & $p=3$ & \\
		\hline
		32 & 1.67e-04 & & 9.79e-07 & & 3.60e-09 & \\
		64 & 4.19e-05 & 1.99 & 1.24e-07 & 2.98 & 2.26e-10 & 3.99 \\
		128 & 1.05e-05 & 2.00 & 1.56e-08 & 2.99 & 1.42e-11 & 3.99 \\
		256 & 2.63e-06 & 2.00 & 1.95e-09 & 3.00 & & \\
		512 & 6.57e-07 & 2.00 & & & & \\
		\hline
	\end{tabular}
\caption{Quasi-1D test with quartic solution, $L^2$ convergence history of the $\mathrm{H}^2$ method on uniform meshes for different polynomial approximations.}
\end{table}
\end{document}

math

Maxim Olshanskii
Jan-Phillip Bäcker
Dmitri Kuzmin

"Projected gradient stabilization of sharp and diffuse interface formulations in unfitted Nitsche finite element methods" by Maxim Olshanskii, Jan-Phillip Bäcker, and Dmitri Kuzmin, arXiv:2501.16594v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\hspace{0.5em}Non-linearized permutation binomials $x^i+ax$ over $\mathbb{F}_{2^{10}}$}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 $i$&34&67&94&187\\
 \hline
 Index&31&31&11&11\\
 \hline
 Refs&&&&\textbf{this paper}\\
 \hline
 $i$&280&331&342&397\\
 \hline
 Index&11&31&3&31\\
 \hline
 Refs&\multicolumn{4}{|c|}{Implicit characterization }\\
 \hline
 $i$&466&559&652&683\\
 \hline
 Index&11&11&11&3\\
 \hline
 Refs&\multicolumn{4}{|c|}{Implicit characterization }\\
 \hline
 $i$&745&838&931&\\
 \hline
 Index&11&11&11&\\
 \hline
 Refs&\multicolumn{3}{|c|}{Implicit characterization }&\\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

"A classification of permutation binomials of the form $x^i+ax$ over $\mathbb{F}_{2^n}$ for dimensions up to 8" by Yi Li, Xiutao Feng, and Qiang Wang, arXiv:2312.16908v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Rakhymzhan Kazbek
Yogi Erlangga
Yerlan Amanbek
Dongming Wei

"Isogeometric Analysis for the Pricing of Financial Derivatives with Nonlinear Models: Convertible Bonds and Options" by Rakhymzhan Kazbek, Yogi Erlangga, Yerlan Amanbek, and Dongming Wei, arXiv:2412.08987v1, 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{LSTM classifier performance metrics.}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Precision}&\textbf{Recall} &\textbf{F1-Score}&\textbf{Accuracy}\\
\hline
1 & 0.35655 & 0.52568 & 0.35655 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Abdulrezzak Zekiye
Semih Utku
Fadi Amroush
Oznur Ozkasap

"AI-Assisted Investigation of On-Chain Parameters: Risky Cryptocurrencies and Price Factors" by Abdulrezzak Zekiye, Semih Utku, Fadi Amroush, and Oznur Ozkasap, arXiv:2308.08554v1, 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}{|c||c|}
 \hline
 \textbf{Test images} & \textbf{Bitrate (bpp)} \\ \hline \hline
 Friends\_1 & 2.05 - 2.24\\ \hline
 Bikes & 2.45 - 2.66 \\\hline
 Flowers & 2.71 - 2.93 \\\hline
 Ankylosaurur \& Diplodocus 1 & 1.45 - 1.61\\ \hline
 \end{tabular}
\caption{ The bitrates of SAIs encoded by JPEG with default setting from the iPhone}
\end{table}
\end{document}

eess

Yung-Hsuan Chao
Haoran Hong
Gene Cheung
Antonio Ortega

"Pre-demosaic Graph-based Light Field Image Compression" by Yung-Hsuan Chao, Haoran Hong, Gene Cheung, and Antonio Ortega, arXiv:2102.07883v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of Datasets for Evaluating LLMs}
\begin{tabular}{lrrrrrr}
\toprule
\textbf{Dataset} & \textbf{\#Lines} & \textbf{\#VLines} & \textbf{\#AvgT} & \textbf{\#Train} & \textbf{\#Valid} & \textbf{\#Test} \\ \midrule
BV-LOC &777,155 &56,215 & 1,515.4 & 8,648 & 1,082 & 1,082 \\
SC-LOC & 29,688 & 4,183 & 467.1& 1,095 & 137 & 137\\
\midrule
Total & 806,843 & 60,398 & - & 9,743 & 1,219 & 1,219 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Jian Zhang
Chong Wang
Anran Li
Weisong Sun
Cen Zhang
Wei Ma
Yang Liu

"An Empirical Study of Automated Vulnerability Localization with Large Language Models" by Jian Zhang, Chong Wang, Anran Li, Weisong Sun, Cen Zhang, Wei Ma, and Yang Liu, arXiv:2404.00287v1, 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{Author coreference dataset statistics.}
\begin{tabular}{lccccc}
\toprule
 & \# mentions & \# blocks & \# clusters & \textrm{nnz}($W$) & \# features \\
\midrule
\bf PubMed & 315 & 5 & 34 & 3,973 & 14,093\\
\bf QIAN & 410 & 38 & 77 & 5,158 & 10,366\\
 \bf SCAD-zbMATH\qquad & 1,196 & 120 & 166 & 18,608 & 8,203\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching" by Rico Angell and Andrew McCallum, arXiv:2312.11801v2, 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{List of Key Abbreviations.}
\begin{tabular}{|l|l|}
\hline
\textbf{Acronym} & \textbf{Description} \\
\hline \hline
3GPP & 3rd Generation Partnership Project \\ \hline
6G & Sixth generation \\ \hline
AI & Artificial intelligence \\ \hline
AWGN & Additive white Gaussian noise \\ \hline
BER & Bit error rate \\ \hline
BS & Base station \\ \hline
CRLB & Cram\'er-Rao lower bound \\ \hline
CSI & Channel state information \\ \hline
CW & Continuous wave \\ \hline
DBF &
Digital beamforming \\ \hline
DFRC & Dual-functional radar-communication \\ \hline
 DL & Deep learning \\ \hline
DNN & Deep neural network \\ \hline
EVM & Error vector magnitude \\ \hline
FMCW & Frequency modulated continuous wave \\ \hline
GNN & Graph neural network \\ \hline
HBF & Hybrid beamforming \\ \hline
IoT & Internet of things \\ \hline
ISAC & Integrated sensing and communications \\ \hline
LiDAR & Light detection and ranging \\ \hline
LSTM & Long short-term memory \\ \hline
MI & Mutual information \\ \hline
MIMO & Multiple-input multiple-output \\ \hline
ML & Machine learning \\ \hline
MMSE & Minimum mean squared error \\ \hline
MOL & Multi-objective learning \\ \hline
MSE & Mean squared error \\ \hline
MTL & Multi-task learning \\ \hline
MUI& Multi-user
interference \\ \hline
OFDM & Orthogonal frequency division multiplexing \\ \hline
OTFS & Orthogonal time frequency spacing \\ \hline
PMCW & Phase modulated continuous wave \\ \hline
QPSK & Quadrature phase-shift keying \\ \hline
RF & Radio frequency \\ \hline
ROC & Receiver operating characteristic \\ \hline
SDR & Semi-definite relaxation \\ \hline
SER & Symbol error rate \\ \hline
SINR & Signal-to-interference-plus-noise ratio \\ \hline
SNR & Signal-to-noise ratio \\ \hline
SVD & Singular value decomposition \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Mojtaba Vaezi
Gayan Aruma Baduge
Esa Ollila
Sergiy A. Vorobyov

"AI-Empowered Integrated Sensing and Communications" by Mojtaba Vaezi, Gayan Aruma Baduge, Esa Ollila, and Sergiy A. Vorobyov, arXiv:2504.13363v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Huawei Lin
Jun Woo Chung
Yingjie Lao
Weijie Zhao

"Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data" by Huawei Lin, Jun Woo Chung, Yingjie Lao, and Weijie Zhao, arXiv:2502.01634v1, 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
 Dataset & Gaussian family & $d$ & $n$ \\ \hline
 Pima & full-covariance & 9 & 768 \\ \hline
 Sonar & mean-field & 62 & 128 \\ \hline
 Sonar & full-covariance & 62 & 128 \\ \hline
 MNIST & mean-field & 784 & 11,774 \\ \hline
 \end{tabular}
\end{table}
\end{document}

stat

Least squares variational inference

Yvann Le Fay
Nicolas Chopin
Simon Barthelmé

"Least squares variational inference" by Yvann Le Fay, Nicolas Chopin, and Simon Barthelmé, arXiv:2502.18475v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{WER on the eval'2000 using 40- and 80 hours training data subsets from the SWB corpus of proposed MixRep method.}
\begin{tabular}{clcc|cc}
 \toprule
 \multirow{2}{*}{\centering Train Data} & \multirow{2}{*}{\centering Model} & \multicolumn{2}{c}{\textbf{With LM (\%)}} & \multicolumn{2}{c}{\textbf{No LM (\%)}}\\
 & & swb & chm & swb & chm \\
 \midrule
 \multirow{5}{*}{\centering SWB 40hr} & \textbf{Conformer} & & & & \\
 & SpecAug. baseline & 16.8 & 29.6 & 18.5 & 31.6 \\
 & + MixRep $S=\{0\}$ & 17.1 & 28.4 & 18.9 & 30.6 \\
 & + MixRep $S=\{5\}$ & \textbf{16.1} & 29.1 & \textbf{17.6} & 30.9 \\
 & + MixRep $S=\{0, 5\}$ & 16.3 & \textbf{27.7} & 17.7 & \textbf{29.5} \\
 \midrule
 \multirow{5}{*}{\centering SWB 80hr}
 & SpecAug. baseline & 12.0 & 21.8 & 13.2 & 23.3 \\
 & + MixRep $S=\{0\}$ & 12.1 & \textbf{21.1} & 13.0 & 22.6 \\
 & + MixRep $S=\{0, 5\}$ & 11.9 & 21.3 & \textbf{12.8} & 22.8 \\
 & + MixRep $S=\{0, 9\}$ & \textbf{11.8} & 21.2 & \textbf{12.8} & \textbf{22.5} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"MixRep: Hidden Representation Mixup for Low-Resource Speech Recognition" by Jiamin Xie and John H. L. Hansen, arXiv:2310.18450v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"$k$-Neighbor Based Curriculum Sampling for Sequence Prediction" by James O' Neill and Danushka Bollegala, arXiv:2101.09313v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c}
\toprule
\multirow{2}{*}{Method} & \multicolumn{3}{c}{Dice Similarity [\%]} \\ \cline{2-5}
 & LV & MYO & RV & Average \\
\midrule
Baseline &92.88 &86.48 &87.78 &89.05\\
LP &92.43 &86.73 &89.59 &89.58\\
LP+HM &92.46 &87.00 &90.94 &90.13\\
\bottomrule
\end{tabular}
\caption{Results on training set}
\end{table}
\end{document}

eess

Yao Zhang
Jiawei Yang
Feng Hou
Yang Liu
Yixin Wang
Jiang Tian
Cheng Zhong
Yang Zhang
Zhiqiang He

"Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer" by Yao Zhang, Jiawei Yang, Feng Hou, Yang Liu, Yixin Wang, Jiang Tian, Cheng Zhong, Yang Zhang, and Zhiqiang He, arXiv:2012.14785v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
 \hline
 {\textbf{Crack Level}} & {\textbf{Depth (mm)}} & {\textbf{Damaged Depth (\%)}} \\
 \hline
 D0 & 0 & 0 \\
 D1 & 5.7 & 0.03 \\
 D2 & 10.9 & 0.06 \\
 D3 & 15 & 0.08 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Darío C. Larese
Almudena Bravo Cerrada
Gabriel Dambrosio Tomei
Alejandro Guerrero-López
Pablo M. Olmos
María Jesús Gómez García

"Transformer Vibration Forecasting for Advancing Rail Safety and Maintenance 4.0" by Darío C. Larese, Almudena Bravo Cerrada, Gabriel Dambrosio Tomei, Alejandro Guerrero-López, Pablo M. Olmos, and María Jesús Gómez García, arXiv:2501.11730v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Green Shields: The Role of ESG in Uncertain Time" by Fatih Kansoy and Dominykas Stasiulaitis, arXiv:2506.02143v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Table I: Compared Loss and Optimizers}
\begin{tabular}{lcccc}
\toprule
Segmentation Method & Whole & Core & Enh. & Mean \\
\midrule
DL+A & 89.62 & 89.2 & 85.54 & 88.11 \\
CE+A & 86.22 & 84.9 & 79.11 & 83.41 \\
DL+CE+A & 89.62 & \textbf{89.8} & 84.89 & 88.10 \\
LC+A & \textbf{90.13} & 89.14 & 85.85 & 88.37 \\
LC+R & 89.9 & 89.37 & 85.89 & 88.39 \\
LC+RA & 90.12 & 89.45 & 85.62 & 88.4 \\
LC+A+LH & 89.13 & 88.97 & \textbf{87.47} & \textbf{88.52} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction" by Zachary Schwehr and Sriman Achanta, arXiv:2401.00587v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.