math

"Revealing the Shape of Genome Space via K-mer Topology" by Yuta Hozumi and Guo-Wei Wei, arXiv:2412.20202v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Distribution of $\hat{N} - N$ over 100 simulated data sequences from the Gaussian or Cauchy models.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|}
\cline{1-6}
 & & & \multicolumn{3}{|c|}{} \\
 & & & \multicolumn{3}{|c|}{$\hat{N} - N$} \\
Model & $T$ & Method & 0 & 1 & $\geq 2$\\
\hline
Gaussian & 30 & $L_{\infty}$ & 97 & 3 & 0\\
 & & $L_{2}$ & 98 & 2 & 0\\
\cline{2-6}
& 75 & $L_{\infty}$ & 98 & 2 & 0\\
 & & $L_{2}$ & 100 & 0 & 0\\
\cline{2-6}
 & 200 & $L_{\infty}$ & 99 & 1 & 0\\
 & & $L_{2}$ & 100 & 0 & 0\\
\cline{2-6}
& 500 & $L_{\infty}$ & 95 & 5 & 0\\
 & & $L_{2}$ & 100 & 0 & 0\\
\hline
Cauchy & 30 & $L_{\infty}$ & 93 & 7 & 0\\
 & & $L_{2}$ & 99 & 1 & 0\\
\cline{2-6}
 & 75 & $L_{\infty}$ & 97 & 3 & 0\\
 & & $L_{2}$ & 100 & 0 & 0\\
\cline{2-6}
 & 200 & $L_{\infty}$ & 92 & 5 & 3\\
 & & $L_{2}$ & 99 & 0 & 1\\
\cline{2-6}
 & 500 & $L_{\infty}$ & 97 & 3 & 0\\
 & & $L_{2}$ & 100 & 0 & 0\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Non-parametric multiple change-point detection" by Andreas Anastasiou and Piotr Fryzlewicz, arXiv:2504.21379v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Economic Loss Function Comparison}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
Model & ALL (95\%) & ALL (99\%) & Quadratic Loss \\
\midrule
Historical Simulation & 0.0847 & 0.0234 & 0.0156 \\
Parametric Normal & 0.0623 & 0.0198 & 0.0134 \\
GARCH-t & 0.0445 & 0.0167 & 0.0098 \\
Filtered Historical & 0.0398 & 0.0152 & 0.0089 \\
EVaR & 0.0312 & 0.0128 & 0.0067 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Quantitative Risk Management in Volatile Markets with an Expectile-Based Framework for the FTSE Index" by Abiodun Finbarrs Oketunji, arXiv:2507.13391v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|} \hline
$z$ & $a$ & supersingular primes \\ \hline
$\left[17, 17, 17, 1\right]$ & $\left(1, 15, 1\right), \left(1, 14, 2\right), \left(1, 13, 3\right)$ & $p \not \equiv 1 \bmod{17}$ \\ \hline
$\left[18, 18, 9, 1\right]$ & $\left(1, 13, 4\right),\left(1, 1, 16\right)$ & $p \equiv 2 \bmod{3}$ \\ \hline
$\left[20, 20, 5, 1\right]$ & $\left(1, 11, 8\right)$ & $p \equiv 2,3,4 \bmod{5}$ \\
 & $\left(1, 7, 12\right)$ & $p \equiv 13, 17, 19 \bmod{20}$ \\ \hline
$\left[24, 24, 3, 1\right]$ & $\left(1, 7, 16\right)$ & $p \equiv 17, 23 \bmod{24}$ \\ \hline
$\left[32, 32, 2, 1\right]$ & $\left(1, 15, 16\right)$ & $p \not \equiv 1,15 \bmod{32}$ \\ \hline
$\left[34, 17, 2, 1\right]$ & $\left(1, 16, 17\right)$ & $p \not \equiv 1 \bmod{34}$ \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Cyclic genus $8$}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}\hline
Source & Acquisition Function & $u$ & $v$ & $w$ & $\beta$ \\
\hline
 & EI & 0 & -- & 1 & 0 \\
 & PEI &0 & -- & 2 & 0 \\
 & SEI & 1/2 & -- & 1 & 0 \\
 & VEI & 0 & 1 & 1 & $<0$ \\
-- & UEI & 0 & 1/2 & 1 & $>0$ \\
\hline
\end{tabular}
\end{adjustbox}
\caption{The parameter settings of $u,v,w$ and $\beta$ for recovering the different acquisition functions from the general form for the acquisition function family.}
\end{table}
\end{document}

stat

Jiajie Kong
Tony Pourmohamad
Herbert K. H. Lee

"Understanding an Acquisition Function Family for Bayesian Optimization" by Jiajie Kong, Tony Pourmohamad, and Herbert K. H. Lee, arXiv:2310.10614v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Citation network graph statistics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
		\toprule[\heavyrulewidth]\toprule[\heavyrulewidth]
		Dataset& Nodes & Edges & Features & Classes \\
		\toprule[\heavyrulewidth]\toprule[\heavyrulewidth]
		\bf{Cora} & 2,708 & 5,429 & 1,433 & 7\\
		\bf{Citseer} & 3,327 & 4,732 & 3,703 & 6\\
		\bf{Pubmed} & 19,717 & 44,338 & 500 & 3\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Ali Lotfi Rezaabad
Rahi Kalantari
Sriram Vishwanath
Mingyuan Zhou
Jonathan Tamir

"Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference" by Ali Lotfi Rezaabad, Rahi Kalantari, Sriram Vishwanath, Mingyuan Zhou, and Jonathan Tamir, arXiv:2011.00194v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \hline
 $n=30067$ & Predicted: Default & Predicted: Not default \\ \hline
Actual: Default & 66 & 2072 \\ \hline
Actual: Not default & 50 & 27879 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Confusion matrix for the logistic regression of the Kaggle dataset.}
\end{table}
\end{document}

q-fin

"Interpretable Selective Learning in Credit Risk" by Dangxing Chen, Weicheng Ye, and Jiahui Ye, arXiv:2209.10127v1, 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{Characteristics of Air Pollution Measuring Stations}
\begin{tabular}{lccccccccccccc}
				\toprule \toprule
				& \multicolumn{2}{c}{Air Pollution Measured} \\
				\cline{2-3}
				& $PM_{2.5}$ & $PM_{10}$ \\
				\cline{2-3}
				& (1) & (2) \\
				\midrule

	\textit{Panel A: Station type} \\

	\hspace{2mm} Background & 0.826& 0.881\\

	\hspace{2mm} Industry & 0.023& 0.030\\

	\hspace{2mm} Traffic & 0.151& 0.089\\

	\textit{Panel B: Station location} \\

	\hspace{2mm} Urban area & 0.884& 0.875\\

	\hspace{2mm} Sub-urban area & 0.093& 0.071\\

	\hspace{2mm} Outside urban area & 0.023& 0.054\\

	Number of observations & 86& 168\\

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

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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overall Mean Log-likelihood For Different Models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\hline
\textit{Model} & \textit{Mean log-likelihood}\\
\hline
GMM-1 & -26078.15\\
GMM-2 & -38514.32\\
GMM-3 & -52431.62\\
GMM-4 & -63794.70\\
GMM-5 & -73508.10\\
IGMM & \textbf{-24871.78} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Weizhu Qian
Fabrice Lauri
Franck Gechter

"A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data" by Weizhu Qian, Fabrice Lauri, and Franck Gechter, arXiv:1911.09355v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
		\hline
		Drug & Sign & Count \\
		\hline
		Lapatinib & (--) & 48 \\
		Lapatinib & (+) & 40 \\
		Lapatinib & zero & 4088 \\
		PLX-4720 & (--) & 58 \\
		PLX-4720 & (+) & 29 \\
		PLX-4720 & zero & 4089 \\
		\hline
	\end{tabular}
\end{adjustbox}
\caption{Signs of the estimated coefficients from the 1-se rule.}
\end{table}
\end{document}

stat

Seongoh Park
Seongjin Lee
Nguyen Thi Hai Yen
Nguyen Phuoc Long
Johan Lim

"Linear Shrinkage Convexification of Penalized Linear Regression With Missing Data" by Seongoh Park, Seongjin Lee, Nguyen Thi Hai Yen, Nguyen Phuoc Long, and Johan Lim, arXiv:2412.19963v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The impact of word reduction ratio $\alpha$ and the execution time of these models.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
		\toprule
		Words Reduc- & \multicolumn{2}{c}{CNSE} & \multicolumn{2}{c}{CNSS} & \multicolumn{2}{c}{Time per batch}\\
		tion Ratio $\alpha$ & Acc & F1 & Acc & F1 & Train & Eval\\
		\midrule
		0\% & 84.11 & 82.17 & 91.04 & 91.07 & 1.73s & 0.42s \\
		5\% & 85.68 & 83.65 & 90.70 & 90.73 & 1.58s & 0.37s\\
		10\% & \textbf{86.32} & \textbf{84.55} & \textbf{91.28} & \textbf{91.39} & 1.33s & 0.31s\\
		20\% & 82.55 & 79.66 & 90.25 & 90.21 & 1.07s & 0.21s\\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching" by Liang Pang, Yanyan Lan, and Xueqi Cheng, arXiv:2101.06423v2, 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{Dataset distribution by a corresponding disease.}
\begin{tabular}{llll}
\toprule
Disease & Label & Number of Scans & Number of Patients \\
\midrule
Age-related Macular Degeneration & AMD & 1231 & 421 \\
Diabetic Macular Edema & DME & 147 & 107 \\
Epiretinal Membrane & ERM & 155 & 71 \\
Normal & NO & 332 & 110 \\
Retinal Artery Occlusion & RAO & 22 & 11 \\
Retinal Vein Occlusion & RVO & 101 & 50 \\
Vitreomacular Interface Disease & VID & 76 & 51 \\
\midrule
Total & & 2064 & 821 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Mikhail Kulyabin
Aleksei Zhdanov
Anastasia Nikiforova
Andrey Stepichev
Anna Kuznetsova
Mikhail Ronkin
Vasilii Borisov
Alexander Bogachev
Sergey Korotkich
Paul A Constable
Andreas Maier

"OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods" by Mikhail Kulyabin, Aleksei Zhdanov, Anastasia Nikiforova, Andrey Stepichev, Anna Kuznetsova, Mikhail Ronkin, Vasilii Borisov, Alexander Bogachev, Sergey Korotkich, Paul A Constable, and Andreas Maier, arXiv:2312.08255v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Heterogeneous Response of Investment to $\epsilon^{X}_t$ \\ \footnotesize Bank Leverage }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 & \multicolumn{4}{c}{Firm investment - $\Delta \log k_{i,t}$ } \\
 & (1) & (2) & (3) & (4) \\ \hline \hline
 & & & & \\
$\left(\tilde{l}_{i,t-1} - \mathbb{E}_{i} \left[ \tilde{l}_{t}\right] \right) \epsilon^{X}_t$ & -0.126 & -0.133 & -0.157* & -0.159* \\
 & (0.0826) & (0.0843) & (0.0805) & (0.0854) \\
 & & & & \\
Firm FE & yes & yes & yes & yes \\
Sector-Time FE & yes & yes & yes & yes \\
Control for Lagged Inv. & no & yes & yes & yes \\
Control for Lagged Currency Mismatch & no & no & yes & yes \\
Control for $l_{i,t-1}$ & no & no & no & yes \\
 & & & & \\
Observations & 11,022 & 10,917 & 10,405 & 10,329 \\ \hline
$R^{2}$ & 0.148 & 0.153 & 0.155 & 0.165 \\ \hline \hline
\multicolumn{5}{c}{ Clustered standard errors in parentheses} \\
\multicolumn{5}{c}{ *** p$<$0.01, ** p$<$0.05, * p$<$0.1} \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Santiago Camara
Sebastian Ramirez Venegas

"The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity" by Santiago Camara and Sebastian Ramirez Venegas, arXiv:2209.11150v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The historical background of the liner shipping industry.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cll}
 Timing / Period & Event & Related Study\\\hline
 1875 & the first liner shipping conference (The U.K.-Calcutta) established & \\
 & & \\
 1916 & \textbf{The Shipping Act of 1916} prohibited deferred rebate &\\
 1918 & The end of WW1 & \\
 1936 & U.S. Maritime Commission established &\\
 1945 & The end of WW2 & \\
 1956 & The world's first container ship sailed & \\
 1961 & \textbf{The 1961 Amendment} enacted & \\
 & \textbf{Federal Maritime Commission (FMC)} established & \\
 & Technical Committee on Cargo Containers established (ISO/TC104) &\\
 1964 & ISO adopted seven sizes of containers as ISO standards &\\
 \hline
 1966 & The first Full-container (FC) ship sailed the Transatlantic route &\\
 1967 & The first FC ship sailed the Transpacific route &\\
 1971 & The first FC ship sailed the Asia-Europe route& \\
 1973 & Oil shock &\\
 1974 & The UNCTAD Code of Conduct for Liner Conferences adopted &\\
 1980 & \textbf{Withdrawal of Sea-Land from shipping conferences}&\\
 1983. April&The UNCTAD Code of Conduct for Liner Conferences accomplished &\\
 1984. June & \textbf{The Shipping Act of 1984} in the U.S. enacted, conference breakdown &\\
 & &\\
 & &\\
 1985.Sep & Yen appreciation due to the Plaza Accord&\\\hline
 1991 & Strategic alliance boom started &\\
 1998. Oct & \textbf{The Ocean Shipping Reform Act (OSRA) of 1998}, effective in 1999 &\\
 &&\\\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Unified Container Shipping Industry Data From 1966: Freight Rate, Shipping Quantity, Newbuilding, Secondhand, and Scrap Price" by Takuma Matsuda and Suguru Otani, arXiv:2211.16292v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccccc}
\toprule
Metric & \multicolumn{4}{c}{Top-1 Accuracy (\%)} & & \multicolumn{4}{c}{Seen-Unseen AUC (\%)} \\ \cmidrule(lr){2-5} \cmidrule(l){7-10}
Dataset & \multicolumn{2}{c}{CUB} & \multicolumn{2}{c}{NAB} & & \multicolumn{2}{c}{CUB} & \multicolumn{2}{c}{NAB} \\
Split-Mode & Easy & Hard & Easy & Hard & & Easy & Hard & Easy & Hard \\ \midrule
CIZSL-v2+SeGC \\ \cmidrule{1-1}
Standard (SD) & 42.4 & \textbf{16.4} & 35.2 & \textbf{10.6} & & \textbf{39.3} & \textbf{14.9} & \textbf{23.8} & \textbf{7.5}\\
SD+Norm($\eta=1$) & 42.4 & 15.5 & \textbf{35.4} & 10.0 & & 39.0 & 13.1 & 22.8 & 7.0\\
SD+Norm($\eta=3$) & 42.4 & 15.0 & 34.5 & 9.2 & & 39.1 & 13.2 & 23.3 & 6.7 \\
SD+Norm($\eta=5$) & \textbf{42.5} & 15.1 & 33.7 & 10.3 & & 39.1 & 13.6 & 22.3 & 7.4\\
SD+Norm($\eta=10$) & 42.1 & 15.4 & 33.9 & 9.9 & & 39.2 & 13.3 & 22.3 & 6.8\\
SD+Norm($\eta=20$) & 42.0 & 15.3 & 34.0 & 10.4 & & 38.9 & 13.2 & 22.9 & 7.3\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Semantic guided categorizer with normalize-scale.} %The chosen hyperparameters for CUB\&NAB easy\&hard are 1, 0.0001, 0.1, 0.1, respectively. Then we test the performance with best hyperparameters.}
\end{table}
\end{document}

cs

"CIZSL++: Creativity Inspired Generative Zero-Shot Learning" by Mohamed Elhoseiny, Kai Yi, and Mohamed Elfeki, arXiv:2101.00173v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Segmentation accuracy of the SWTR-Unet depending on the usage of a pre- or untrained ResNet within the encoder path, as well as the presence of a transformer-based bottleneck. Stated are the average DSCs for liver and hepatic lesions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
$ $ & $DSC_{liver}$ & $DSC_{lesion}$ \\
\hline
pretrained ResNet & $0.98~\pm~0.02$ & $0.81~\pm~0.28$ \\
untrained-ResNet & $0.97~\pm~0.03$ & $0.80~\pm~0.29$ \\
\hline
with transformer bottleneck & $0.98~\pm~0.02$ & $0.81~\pm~0.28$ \\
without transformer bottleneck & $0.93~\pm~0.06$ & $0.58~\pm~0.32$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

"Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers" by Georg Hille, Shubham Agrawal, Pavan Tummala, Christian Wybranski, Maciej Pech, Alexey Surov, and Sylvia Saalfeld, arXiv:2201.10981v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{WER impact of cleaning speech signals between certain SNR ranges, using a music source separator. End-to-end ConvNet GLU model is used without LM, and percentage of cleaned samples are reported.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \multirow{1}{*}{SNR} & \multicolumn{2}{c}{Cleaned Samples [\%] } &
 \multicolumn{2}{c}{Test WER [\%]} \\
 & 2018 & 2020 & 2018 & 2020 \\

 \midrule
 \multirow{1}{*}{$(-\infty, \infty)$} & \multirow{1}{*}{100} & 100 & 37.50 & 53.53\\
 \multirow{1}{*}{$(-\infty, 10)$} & 25.97 & 34.22 & -0.05 & -0.87\\
 \multirow{1}{*}{($-5$, $10$)} & 25.84 & 31.33 & -0.05 & -0.88\\
 \multirow{1}{*}{($-5$, $5$)} & 5.14 & 11.88 & -0.07 & \textbf{-1.03}\\
 \multirow{1}{*}{($-5$, $8$)} & 14.95 & 22.11 & \textbf{-0.08} & -0.97\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Martin Kocour
Guillermo Cámbara
Jordi Luque
David Bonet
Mireia Farrús
Martin Karafiát
Karel Veselý
Jan ''Honza'' Ĉernocký

"BCN2BRNO: ASR System Fusion for Albayzin 2020 Speech to Text Challenge" by Martin Kocour, Guillermo Cámbara, Jordi Luque, David Bonet, Mireia Farrús, Martin Karafiát, Karel Veselý, and Jan ''Honza'' Ĉernocký, arXiv:2101.12729v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Seasonal average of daily Temperature $[C^{\circ}]$, NH$_3$, NO$_x$ and SO$_2$ emissions $[mg/m^2]$ and PM$_{2.5}$ concentrations $[\mu g / m^3]$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
 \toprule
 Season & Temperature & NH$_3$ & NO$_x$ & SO$_2$ & PM$_{2.5}$ \\
 \midrule
 Winter & 3.88 & 5.21 & 19.97 & 4.11 & 35.87 \\
 Spring & 12.67 & 17.45 & 11.33 & 2.98 & 16.54 \\
 Summer & 23.23 & 14.71 & 10.45 & 2.80 & 12.19 \\
 Autumn & 13.83 & 8.70 & 14.59 & 3.46 & 21.62 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jacopo Rodeschini
Alessandro Fassò
Francesco Finazzi
Alessandro Fusta Moro

"Scenario analysis of livestock-related PM2.5 pollution based on a new heteroskedastic spatiotemporal model" by Jacopo Rodeschini, Alessandro Fassò, Francesco Finazzi, and Alessandro Fusta Moro, arXiv:2310.15837v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l}
Quartile[\#W/Tweet] & Overall & Train & Test \\
\hline
0.25 & 12.0 & 13.0 & 12.0\\
0.50 & 21.0 & 21.0 & 19.0\\
0.75 & 34.0 & 34.0 & 31.0\\
\hline
 Quartile[\#Ch/Tweet] & Overall & Train & Test \\
\hline
0.25 & 70.0 & 72.0 & 67.0\\
0.50 & 116.0 & 120.0 & 105.0\\
0.75 & 194.0 & 198.0 & 174.0\\
\end{tabular}
\end{adjustbox}
\caption{Quartile-wise Word and Character Count}
\end{table}
\end{document}

cs

Shreya Gupta
Parantak Singh
Megha Sundriyal
Md Shad Akhtar
Tanmoy Chakraborty

"LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content" by Shreya Gupta, Parantak Singh, Megha Sundriyal, Md Shad Akhtar, and Tanmoy Chakraborty, arXiv:2101.11891v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Implementation details of MetaVIM}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 Items & Details \\
 \midrule
 Number of policy steps & 3600 \\
	Discount factor $\gamma$ & 0.95 \\
	Policy minibatch & 16 \\
	mVAE minibatch & 25 \\
	Value loss coefficient & 0.5 \\
	Entropy coefficient & 0.01 \\
	ELBO loss coefficient & 1.0 \\
	Latent space dimensionality & 5 \\
	Aggregator hidden size & 64 \\
	\midrule
	Policy network & 2 hidden layers, \\
	architecture & 32 nodes each, \\
	 & Tanh activations \\
	\midrule
	Policy network & Adam with learning rate 0.0007 \\
	optimizer & and epsilon 1e-5 \\
	\midrule
	 & FC layer with 40 nodes, \\
	 & GRU with hidden size 64, \\
	Encoder architecture & output layer with \\
	 & 10 outputs ($\mu$ and $\sigma$), \\
	 & ReLU activations \\
	\midrule
	 & 2 hidden layers, \\
	Transition Decoder & 32 nodes each, \\
	architecture & 25 outputs heads, \\
	 & ReLU activations \\
	\midrule
	 & 2 hidden layers, \\
	Reward Decoder & 32 nodes each, \\
	architecture & 25 outputs heads, \\
	 & ReLU activations \\
	\midrule
	mVAE optimizer & Adam with learning rate 0.001 \\
	 & and epsilon 1e-5 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Liwen Zhu
Peixi Peng
Zongqing Lu
Xiangqian Wang
Yonghong Tian

"MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control" by Liwen Zhu, Peixi Peng, Zongqing Lu, Xiangqian Wang, and Yonghong Tian, arXiv:2101.00746v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cccc|}
\hline
Young's modulus $E$ & Poisson ratio $\nu$ & Density $\rho$& Fracture energy $G_0$\\
\hline
70 $GPa$&0.22&2500 $kg/m^3$& 8 $J/m^2$\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Mechanical properties for V-notched soda-lime glass specimens taken from .}
\end{table}
\end{document}

cs

"An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture" by Yue Yu, Huaiqian You, and Nathaniel Trask, arXiv:2101.01434v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc}
\hline\hline
& 0.0 & 1.0 & Accuracy & Macro Average & Weighted Average \\
\hline
Precision & 0.98 & 0.65 & 0.89 & 0.82 & 0.92 \\
Recall & 0.87 & 0.95 & 0.89 & 0.91 & 0.89 \\
f1-score & 0.93 & 0.77 & 0.89 & 0.85 & 0.90 \\
\hline\hline
\end{tabular}
\end{adjustbox}
\caption{Test Evaluation Metrics for Logistic Regression}
\end{table}
\end{document}

q-fin

Aadi Gupta
Priya Gulati
Siddhartha P. Chakrabarty

"Classification based credit risk analysis: The case of Lending Club" by Aadi Gupta, Priya Gulati, and Siddhartha P. Chakrabarty, arXiv:2210.05136v1, 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{An overview of the datasets used in this work.}
\begin{tabular}{lllll}
\toprule
\multirow{2}{*}{\textbf{Dataset}} &\textbf{Resolution} & \textbf{Train-set} & \textbf{Val-set} & \textbf{Test-set} \\
 &\textbf{\# Images} & \textbf{(\%)} & \textbf{(\%)} & \textbf{(\%)} \\
\midrule
\multirow{2}{*}{\textbf{TCIA }}& 512 x 512 & 407 & 58& 117 \\
& 582 & (70\%) & (10\%) & (20\%) \\
\midrule
\multirow{2}{*}{\textbf{VHBD }}& 512 x 512 & \multirow{2}{*}{--} & \multirow{2}{*}{--} & 167 \\
& 167 & & & (100\%) \\
\midrule
\multirow{2}{*}{\textbf{VHBD-2 }}& 512 x 512 & 116 & 17 & 34 \\
& 167 & (70\%) & (10\%) & (20\%) \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Prabhakara Subramanya Jois
Aniketh Manjunath
Thomas Fevens

"Boosting Segmentation Performance across datasets using histogram specification with application to pelvic bone segmentation" by Prabhakara Subramanya Jois, Aniketh Manjunath, and Thomas Fevens, arXiv:2101.11135v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{List of companies selected by sector and corresponding ticker symbols}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Sector} & \textbf{Company} & \textbf{Ticker Symbol} \\
\hline
\multirow{5}{*}{Financial Services}
& HDFC Bank & HDFCBANK \\
& ICICI Bank & ICICIBANK \\
& State Bank of India & SBIN \\
& Axis Bank & AXISBANK \\
& Kotak Mahindra Bank & KOTAKBANK \\
\hline
\multirow{5}{*}{Information Technology}
& Tata Consultancy Services & TCS \\
& Infosys & INFY \\
& Wipro & WIPRO \\
& HCL Technologies & HCLTECH \\
& Tech Mahindra & TECHM \\
\hline
\multirow{5}{*}{Consumer Goods}
& Hindustan Unilever & HINDUNILVR \\
& Nestlé India & NESTLEIND \\
& ITC Limited & ITC \\
& Dabur India & DABUR \\
& Britannia Industries & BRITANNIA \\
\hline
\multirow{5}{*}{Energy}
& Reliance Industries & RELIANCE \\
& NTPC Limited & NTPC \\
& Indian Oil Corporation & IOC \\
& Bharat Petroleum Corporation & BPCL \\
& Tata Power & TATAPOWER \\
\hline
\multirow{5}{*}{Pharmaceuticals}
& Sun Pharmaceutical Industries & SUNPHARMA \\
& Dr. Reddy’s Laboratories & DRREDDY \\
& Cipla & CIPLA \\
& Lupin Limited & LUPIN \\
& Aurobindo Pharma & AUROPHARMA \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Vidya Sagar G
Shifat Ali
Siddhartha P. Chakrabarty

"Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios" by Vidya Sagar G, Shifat Ali, and Siddhartha P. Chakrabarty, arXiv:2507.02011v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
		& MAFDS \\
		computer model 1 &$ 0.00084 $ \\
		computer model 2 &$ 0.00152 $ \\
	\end{tabular}
\end{adjustbox}
\caption{ Value of the area validation metric for both computer models of the MAFDS.}
\end{table}
\end{document}

eess

"Uncertainty Quantification in Case of Imperfect Models: A Review" by Sebastian Kersting and Michael Kohler, arXiv:2012.09449v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
		\hline
		Model & SSIM & PSNR \\
		\hline
		\hline
		\multicolumn{3}{|c|}{\textbf{Viseme Corruption for Viseme}} \\
		\hline

		\textbf{FCN3D} & 0.8385 & 18.5201 \\
		\hline
		\textbf{BDLSTM} & 0.7269 & 17.2827 \\
		\hline
		\textbf{FCN3D + ROI} & \textbf{0.9106} & \textbf{26.5401} \\
		\hline
		\textbf{Super SloMo} & \textbf{0.9129} & \textbf{25.2605} \\
		\hline
		\hline
		\multicolumn{3}{|c|}{\textbf{Word Level Corruption}} \\
		\hline
		\textbf{FCN3D} & 0.8109 & 18.4554 \\
		\hline
		\textbf{BDLSTM} & 0.7411 & 18.066 \\
		\hline
		\textbf{FCN3D + ROI} & \textbf{0.8804} & \textbf{25.3279}\\
		\hline
		\textbf{Super SloMo} & \textbf{0.8800} & \textbf{24.5971} \\
		\hline
		\hline
		\multicolumn{3}{|c|}{\textbf{Multi Word Corruption}} \\
		\hline
		\textbf{FCN3D} & 0.8064 & 19.6111 \\
		\hline
		\textbf{BDLSTM} & 0.7411 & 18.0665 \\
		\hline
		\textbf{FCN3D + ROI} & \textbf{0.8308} & \textbf{23.1354}\\
		\hline

		\textbf{Super SloMo} & \textbf{0.8481} & \textbf{23.8295}\\
		\hline

	\end{tabular}
\end{adjustbox}
\caption{Metrics obtained on the newly introduced challenge datasets. It can be seen that even the simpler models trained with ROI unit embedding give results close to the baseline (Super SloMo).More details can be found in Table in appendix.}
\end{table}
\end{document}

eess

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

"LIFI: Towards Linguistically Informed Frame Interpolation" by Aradhya Neeraj Mathur, Devansh Batra, Yaman Kumar, Rajiv Ratn Shah, and Roger Zimmermann, arXiv:2010.16078v5, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results for Dialectal Speech Generation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 \toprule
 \multirow{2}{*}{\textbf{Method}} &
 \multicolumn{5}{c}{\textbf{ASR Word Error Rate (\%) $\downarrow$}} \\
 \cmidrule(r){2-6}
 & \textbf{Cantonese} & \textbf{Northeastern}
 & \textbf{Henan} & \textbf{Shanghainese} & \textbf{Sichuanese} \\
 \midrule
 \textbf{Zipformer} & 89.54 & 13.63 & 61.91 & 76.81 & 41.53 \\
 \midrule
 \textbf{+ GOAT-TTS} & 26.48 & 6.52 & 24.19 & 17.34 & 14.36 \\
 \textbf{+ GT} & 23.83 & 5.55 & 21.16 & 15.60 & 11.50 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

BoSS: Beyond-Semantic Speech

Qing Wang
Zehan Li
Hang Lv
Hongjie Chen
Yaodong Song
Jian Kang
Jie Lian
Jie Li
Yongxiang Li
Zhongjiang He
Xuelong Li

"BoSS: Beyond-Semantic Speech" by Qing Wang, Zehan Li, Hang Lv, Hongjie Chen, Yaodong Song, Jian Kang, Jie Lian, Jie Li, Yongxiang Li, Zhongjiang He, and Xuelong Li, arXiv:2507.17563v1, 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
\textbf{Test Pair} & \(\Delta\mathbf{Rho}\) & \(\mathbf{p}\) \\
\midrule
Baseline vs.\ +Personalized & 0.200 & 0.233 \\
\textbf{Baseline vs.\ +Personalized (Successful)}$^\ddagger$ & 0.371 & \textbf{0.035} \\
\textbf{+Personalized (Successful) vs.\ +Personalized (Unsuccessful)}$^\ddagger$ & 0.709 & \textbf{0.008} \\
\midrule
+Extroverted vs.\ +Conscientious & 0.138 & 0.386 \\
\textbf{+Extroverted (Successful) vs.\ +Extroverted (Unsuccessful)}$^\ddagger$ & 0.529 & \textbf{0.046} \\
+Conscientious (Successful) vs.\ +Conscientious (Unsuccessful) & 0.390 & 0.142 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Takehiro Takayanagi
Kiyoshi Izumi
Javier Sanz-Cruzado
Richard McCreadie
Iadh Ounis

"Are Generative AI Agents Effective Personalized Financial Advisors?" by Takehiro Takayanagi, Kiyoshi Izumi, Javier Sanz-Cruzado, Richard McCreadie, and Iadh Ounis, arXiv:2504.05862v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Result of experiment - dataset Depresjon with epoch=10, n\_layers=2}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Missing rate} & \textbf{MICE + RF} & \textbf{BRITS} & \textbf{Transformer} & \textbf{SAITS} \\ \hline
10.0 & 0.286 & 0.587 & 0.866 & 0.499 \\ \hline
20.0 & 0.301 & 0.583 & 0.811 & 0.509 \\ \hline
30.0 & 0.313 & 0.605 & 0.762 & 0.507 \\ \hline
40.0 & 0.324 & 0.623 & 0.833 & 0.522 \\ \hline
50.0 & 0.342 & 0.603 & 0.792 & 0.514 \\ \hline
60.0 & 0.363 & 0.629 & 0.838 & 0.527 \\ \hline
70.0 & 0.395 & 0.624 & 0.821 & 0.538 \\ \hline
80.0 & 0.446 & 0.610 & 0.824 & 0.545 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Lien P. Le
Xuan-Hien Nguyen Thi
Thu Nguyen
Michael A. Riegler
Pål Halvorsen
Binh T. Nguyen

"Missing data imputation for noisy time-series data and applications in healthcare" by Lien P. Le, Xuan-Hien Nguyen Thi, Thu Nguyen, Michael A. Riegler, Pål Halvorsen, and Binh T. Nguyen, arXiv:2412.11164v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Sepideh Mosaferi
Abolfazl Safikhani
Peiliang Bai

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


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

cs

"Hyperparameter Tuning for Deep Reinforcement Learning Applications" by Mariam Kiran and Melis Ozyildirim, arXiv:2201.11182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The power of the hypothesis testing with different $\tau$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
 $\tau$&$H(\tau)$ &Power (\%) \\ \hline
 0.28&5.5405 &56.28 \\ \hline
 0.29&5.53692 &94.556 \\ \hline
 0.3&5.53237 &99.915 \\ \hline
 0.31&5.52688 &100 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Variance of entropy for testing time-varying regimes with an application to meme stocks" by Andrey Shternshis and Piero Mazzarisi, arXiv:2211.05415v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\hline \hline
\textbf{Sector} & \textbf{Mean $WD_2$} & \textbf{Max $WD_2$} \\
\hline
Industrials & 0.07 & 0.17 \\
Energy & 0.07 & 0.26 \\
Financial & 0.05 & 0.16 \\
Healthcare & 0.06 & 0.15 \\
Consumer Discretionary & 0.08 & 0.21 \\
Consumer Staples & 0.05 & 0.13 \\
Real Estate & 0.07 & 0.24 \\
Utilities & 0.05 & 0.16 \\
Materials & 0.06 & 0.19 \\
Information Technology & 0.07 & 0.20 \\
Communications Services & 0.08 & 0.15 \\
\hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrl}
\hline \textbf{Type of Text} & \textbf{Font Size} & \textbf{Style} \\ \hline
paper title & 15 pt & bold \\
author names & 12 pt & bold \\
author affiliation & 12 pt & \\
the word ``Abstract'' & 12 pt & bold \\
section titles & 12 pt & bold \\
subsection titles & 11 pt & bold \\
document text & 11 pt &\\
captions & 10 pt & \\
abstract text & 10 pt & \\
bibliography & 10 pt & \\
footnotes & 9 pt & \\
\hline
\end{tabular}
\end{adjustbox}
\caption{ Font guide. }
\end{table}
\end{document}

cs

Zhuang Li
Lizhen Qu
Shuo Huang
Gholamreza Haffari

"Few-Shot Semantic Parsing for New Predicates" by Zhuang Li, Lizhen Qu, Shuo Huang, and Gholamreza Haffari, arXiv:2101.10708v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The size of the EV charging station transformer}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
			\hline
			 	 & \multicolumn{3}{|c|}{EV transformer size (kVA)}\\
			 \hline
			Year 	& Uncoordinated charging & Approach 1	& Approach 2 \\
			\hline
			\hline
 2027 & 452.7	& 66.9 & 410.1\\
 2032	& 898.7	& 199.2 & 923.3\\
 2037	& 1,404.5	& 380.2 & 1562.8 \\
 2042 & 1,705.1	& 528.9 & 2041.5\\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Quan Nguyen
Christine Holland
Siddharth Sridhar

"Design and Optimization of EV Charging Infrastructure with Battery in Commercial Buildings" by Quan Nguyen, Christine Holland, and Siddharth Sridhar, arXiv:2509.12160v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccc}
 \toprule
 Model TP & FN & TP & FN & TP & FN & TP \\
 & \multicolumn{2}{c}{T $\ge$ 1.3} & \multicolumn{2}{c}{T $\ge$ 1.2} & \multicolumn{2}{c}{T $\ge$ 1.1} \\

 \midrule
 GARCH &0 &1 &0 &7 &0 &29 \\

 LSTM &1 &0 &2 &5 &4 &25 \\

 Transformer (V) &1 &0 &4 &3 &6 &23 \\

 Synthesizer (R) &1 &0 &5 &2 &18 &11 \\

 Synthesizer (FR) &1 &0 &6 &1 &21 &8 \\

 Synthesizer (D) &1 &0 &6 &1 &17 &12 \\

 Synthesizer (FD) &1 &0 &6 &1 &21 &8 \\

 Synthesizer (MD) &1 &0 &4 &3 &15 & 14 \\

 Synthesizer (MR) &1 &0 &4 &3 &16 &13 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{True positive (TP) and false negative (FN) when predicting extreme volatility spikes with different thresholds. We use R for random, F for factorised, M for mixed, D for dense, and V for Vanilla models.}
\end{table}
\end{document}

q-fin

"Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models" by Dorien Herremans and Kah Wee Low, arXiv:2211.08281v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Robustness checks: Coefficients for the impact of average variability of ancestral climatic conditions on individual's self-reported attention to the environment}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 \hline
 \multicolumn{6}{c}{Dependent variable: Self reported measure of attention to environment}\\
 \hline
 \hline
 Avg. variability & -9.877***& -9.243***& -8.903***& -9.897***& -6.649***\\
 & (2.003) & (1.988) & (2.206) & (2.011) & (2.023)\\
 Avg. variability sq.& 111.767***& 107.503***& 103.956***& 111.862***& 71.929***\\
 & (21.346) & (21.557) & (25.184) & (21.505) & (22.402)\\
 Income & -0.001* & -0.001+ & -0.002+ & -0.001+ & -0.001+ \\
 & (0.001) & (0.001) & (0.001) & (0.001) & (0.001)\\
 Male & -0.013***& -0.012***& -0.014***& -0.013***& -0.014***\\
 & (0.003) & (0.004) & (0.004) & (0.003) & (0.003)\\
 Age & 0.002***& 0.002***& 0.002***& 0.001***& 0.002***\\
 & (0.000) & (0.000) & (0.000) & (0.000) & (0.000)\\
 Education level & 0.006***& 0.006***& 0.007***& 0.006***& 0.007***\\
 & (0.001) & (0.001) & (0.001) & (0.001) & (0.001)\\
 Occupation category Controls & Y & Y & Y & Y & Y \\
 Historical ethnic group characteristics & Y & Y & Y & Y & Y \\
 Historical topographic characteristics & Y & Y & Y & Y & Y \\
 Country-year Fixed effects & Y & Y & Y & Y & N \\
 Region-year Fixed effects & N & N & N & N & Y \\
 \multicolumn{6}{l}{\textit{Sample restrictions}}\\
 English speakers excluded & N & Y & Y & N & N \\
 Spanish speakers excluded & N & Y & Y & N & N\\
 Arabic speakers excluded & N & N & Y & N & N\\
 Lang. at home only & N & N & N & Y & N \\
 \hline
 Mean of dep var & 0.704 & 0.700 & 0.689 & 0.703 & 0.705 \\
 St. Dev. of dep var & 0.250 & 0.251 & 0.249 & 0.251 & 0.250 \\
 Min value Avg. Variability& 0.015 & 0.015 & 0.015 & 0.015 & 0.015 \\
 Max value Avg. Variability& 0.093 & 0.093 & 0.093 & 0.093 & 0.093 \\
 R-sq & 0.113 & 0.122 & 0.118 & 0.114 & 0.160 \\
 Adj. R-sq & 0.113 & 0.121 & 0.117 & 0.113 & 0.151 \\
 N & 138067 & 110484 & 93184 & 128532 & 135228 \\
 \hline
 \multicolumn{6}{c}{ {*}{*}{*} p$<$0.01, {*}{*}p$<$0.05, {*} p$<$0.1, {+} p$<$0.15}\\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


q-fin

"Forecasting realized volatility in the stock market: a path-dependent perspective" by Xiangdong Liu, Sicheng Fu, and Shaopeng Hong, arXiv:2503.00851v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 & value & \# nnz MacLaurin \\ \hline
 $\pi_3$ & $3.533277500570900$ & 22 \\
 $\pi_4$ & $3.708149354602744$ & 34 \\
 $\pi_5$ & $3.800600555953747$ & 46 \\
 $\pi_6$ & $3.855242593319996$ & 58 \\
 $\pi_7$ & $3.890174737625689$ & 69 \\
 $\pi_8$ & $3.913843287813181$ & 81 \\
 $\pi_9$ & $3.930614378886605$ & 92 \\
 $\pi_{10}$ & $3.942927897810032$ & 103
 \end{tabular}
\end{adjustbox}
\caption{Several values of $\pi_p$ and the number of nonzero MacLaurin coefficients needed to reach machine precision.}
\end{table}
\end{document}

math

"Derivative polynomials and infinite series for squigonometric functions" by Bart S. van Lith, arXiv:2503.19624v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{crr}
 Model & MSPE(sd) & Mean No. Parameters \\
 \hline
 GNAR(1, [1]) & 7.4(2.98) & 2 \\
 Sparse VAR(1) & 11.4(2.80) & 3089 \\
 Res. VAR(1) & 10.6(2.48) & 3773 \\
 VAR(1) & 12.3(3.18) & 19600 \\
 AR(1) & 87.4(5.15) & 140
 \end{tabular}
\end{adjustbox}
\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.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Speaker clustering results using purity (\%)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} \hline
\multirow{2}{*}{\textbf{Method}} & \multicolumn{2}{c}{\textbf{ASD}} & \multicolumn{2}{c}{\textbf{ASD-Infants}} \\
 & \textbf{K-Means} & \textbf{SC} & \textbf{K-Means} & \textbf{SC} \\ \hline
x-vectors & 77.05 & 75.22 & 77.98 & 75.97 \\
siamese & 78.22 & 79.18 & 78.30 & 76.86 \\
P20 & \textbf{81.39} & \textbf{80.70} & \textbf{85.51} & \textbf{85.55} \\
P30 & 79.80 & 80.24 & 83.57 & 83.26 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Nithin Rao Koluguri
Manoj Kumar
So Hyun Kim
Catherine Lord
Shrikanth Narayanan

"Meta-learning for robust child-adult classification from speech" by Nithin Rao Koluguri, Manoj Kumar, So Hyun Kim, Catherine Lord, and Shrikanth Narayanan, arXiv:1910.11400v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{makecell}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
 \Xhline{2\arrayrulewidth}
 & \multicolumn{4}{c}{\textbf{GF2} Dataset (Reduced-Resolution)} \\
 \hline
 Condition & SAM$\downarrow$ & ERGAS$\downarrow$ & SCC$\uparrow$ & Q4$\uparrow$\\
 \hline
 DWT & 0.646 $\pm$ 0.117 & 0.567 $\pm$ 0.095 & 0.993 $\pm$ 0.002 & 0.987 $\pm$ 0.007 \\
 SWT & {\color{red}{\textbf{0.603 $\pm$ 0.102}}} & {\color{red}{\textbf{0.537 $\pm$ 0.077}}} & {\color{red}{\textbf{0.994 $\pm$ 0.001}}} & {\color{red}{\textbf{0.988 $\pm$ 0.006}}} \\
 \Xhline{2\arrayrulewidth}
 \end{tabular}
\end{adjustbox}
\caption{Comparison of results between DWT and SWT conditioning at the SWTCA block in FSA-T $\Psi$, with the best values highlighted in red.}
\end{table}
\end{document}

eess

Sungpyo Kim
Jeonghyeok Do
Jaehyup Lee
Munchurl Kim

"U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening" by Sungpyo Kim, Jeonghyeok Do, Jaehyup Lee, and Munchurl Kim, arXiv:2412.06243v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Yixin Zhang
Nicholas Konz
Kevin Kramer
Maciej A. Mazurowski

"Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects" by Yixin Zhang, Nicholas Konz, Kevin Kramer, and Maciej A. Mazurowski, arXiv:2412.04243v2, 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{Adjusted Field Contribution received by papers 14 and 54.}
\begin{tabular}{cc|c}%
 {\bf } & {\bf } & {\bf Adjusted} \\
 {\bf Paper} & {\bf Judge} & {\bf Field Contribution} \\\hline
 14 & 35 & 1.33 \\
 14 & 23 & 1.41 \\
 14 & 48 & 1.33 \\
 14 & 57 & 0.70 \\
 14 & 44 & 0.71 \\\hline
 54 & 30 & 1.39 \\
 54 & 32 & 0.76 \\
 54 & 25 & 1.50 \\
 54 & 22 & 1.47 \\
 \end{tabular}
\end{table}
\end{document}

cs

"Joint aggregation of cardinal and ordinal evaluations with an application to a student paper competition" by Dorit S. Hochbaum and Erick Moreno-Centeno, arXiv:2101.04765v1, 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&Educational Services&$32.6\%$\tabularnewline
2&Apparel \& Accessory Stores&$31.8\%$\tabularnewline
3&Non-Classifiable Establishments&$31.6\%$\tabularnewline
4&Business Services&$27.7\%$\tabularnewline
5&Instruments \& Related Products&$24.9\%$\tabularnewline
6&Leather&$23.9\%$\tabularnewline
7&Pipelines, Except Natural Gas&$23.7\%$\tabularnewline
8&Transportation Services&$23.1\%$\tabularnewline
9&Chemical \& Allied products&$22.5\%$\tabularnewline
10&Engineering \& Management Services&$22.5\%$\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}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c||}
\hline
Forecasting Method & MSE$_{\text{pred}+\text{phys}}$\\
\hline\hline
PIDDRN & 0.133 (0.046) \\
\hline
DDRN & 0.262 (0.167) \\
\hline
PIDESN & 0.241 (0.130) \\
\hline
DESN & 0.267 (0.168) \\
\hline
ARFIMA & 0.296 (0.121) \\
\hline
Long Short-Term Memory & 0.275 (0.127) \\
\hline
Persistence (Na\"ive) & 0.267 (0.119) \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Forecasting performance for each model in terms of the median MSE$_{\text{pred}+\text{phys}}$ (IQR in parenthesis) across the 100 subsampled locations described in Section for the water field application data.}
\end{table}
\end{document}

stat

Matthew Bonas
David H. Richter
Stefano Castruccio

"A Physics-Informed, Deep Double Reservoir Network for Forecasting Boundary Layer Velocity" by Matthew Bonas, David H. Richter, and Stefano Castruccio, arXiv:2311.05728v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Combined forecasts.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|} 		\hline
				Model & CRPS & LogS & DSS & PL \\ 			 \hline
				GPR & 131.76 & 6.87 & 11.89 & 34.28 \\
				bstslong & 205.93 & Inf & 12.85 & 294.27 \\
				QRA & 143.79 & Inf & 12.50 & 43.45 \\
				QRNN & 150.14 & Inf & 12.53 & 38.23\\
				PLAQR & 146.80 & Inf & 12.53 & 43.15\\
				OPERA & 142.15 & Inf & 12.48 & 37.40\\ 		\hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Edina Chandiwana
Caston Sigauke
Alphonce Bere

"Robust modelling framework for short-term forecasting of global horizontal irradiance" by Edina Chandiwana, Caston Sigauke, and Alphonce Bere, arXiv:2212.05978v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Dataset distribution across four medical sources.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\toprule
\multirow{2}{*}{\textbf{Source}} & \multicolumn{2}{c}{\textbf{Scan Counts}} & \multicolumn{2}{c}{\textbf{Slice Counts}} \\
\cmidrule(lr){2-3} \cmidrule(lr){4-5}
& \textbf{COVID} & \textbf{Non-COVID} & \textbf{COVID} & \textbf{Non-COVID} \\
\midrule
0 & 218 & 209 & 59,697 & 57,898 \\
1 & 218 & 210 & 10,213 & 13,802 \\
2 & 39 & 210 & 10,771 & 61,129 \\
3 & 217 & 210 & 38,573 & 35,916 \\
\midrule
Testing & \multicolumn{2}{c}{--} & \multicolumn{2}{c}{--} \\
\midrule
\textbf{Total} & \textbf{692} & \textbf{839} & \textbf{119,254} & \textbf{168,745}\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Dataset-dependent hyperparameters for VQ-VAE.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 \toprule
 & MNIST & \textsc{Omniglot} & \textsc{CelebA} \\ \midrule
 Batch Size & 32 & 32 & 64 \\
 \# of Embeddings & 256 & 256 & 512 \\
 Hidden Units & 32 & 32 & 128 \\
 Training Steps & 60000 & 60000 & 100000 \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Posterior Matching for Arbitrary Conditioning" by Ryan R. Strauss and Junier B. Oliva, arXiv:2201.12414v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Log-evidence function estimation bias (mean and standard deviation over 10 runs). The first rows are from our method, while the rest are from . Note that the last three methods are using importance-weighted bound $B_{\rm RW}$. In bold font, we show the estimates that are unbiased at the one standard deviation level.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 Method&Gaussian mixture&Funnel\\\midrule
 HMC & $-1.876\pm0.527$ & $-0.835\pm0.257$ \\
 SMC & $-0.362\pm0.293$ & $-0.216\pm0.157$ \\
 On-policy PIS-NN & $-1.192\pm0.482$ & ${\bf -0.018\pm0.020}$ \\
 Off-policy GFlowNet TB & ${\bf -0.003\pm0.011}$ & $-0.026\pm0.020$ \\
 On-policy GFlowNet TB & $-1.301\pm0.434$ & ${\bf -0.012\pm0.108}$ \\\midrule
 Ours & ${\bf 0.029\pm0.132}$ & ${\bf -0.051 \pm 0.353}$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Pablo Lemos
Nikolay Malkin
Will Handley
Yoshua Bengio
Yashar Hezaveh
Laurence Perreault-Levasseur

"Improving Gradient-guided Nested Sampling for Posterior Inference" by Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, Yashar Hezaveh, and Laurence Perreault-Levasseur, arXiv:2312.03911v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
\hline
\textbf{Model} & \textbf{Size} \\
\hline
ACD & 5.5M \\
ACR-cloud & - \\
Pyannote & 31M \\
VoiceFilter & 6.8M \\
AQA-LLM & 7B \\
Whisper & 39M \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Model Sizes}
\end{table}
\end{document}

eess

Vakada Naveen
Arvind Krishna Sridhar
Yinyi Guo
Erik Visser

"Comprehensive Audio Query Handling System with Integrated Expert Models and Contextual Understanding" by Vakada Naveen, Arvind Krishna Sridhar, Yinyi Guo, and Erik Visser, arXiv:2412.03980v1, 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 the selected datasets (we preprocess the datasets to remove the sequences containing one word and the punctuations at the end of the sequences), where Explained set is randomly sampled from the Test set to avoid high computational complexity and Average sequence length denotes the average sequence length of the instances in Explained set (the length of Yelp-2 explained set is controlled within 50 and we will provide these Explained sets in the public version).}
\begin{tabular}{lcccccc}
\toprule
Datasets & Category & Train set & Test set & Explained set & Label set & Average sequence length \\
\hline
SST-2 & sentimental analysis & 6899 & 1819 & 1000 & 2 & 19.24 \\
SNIPS & intent detection & 13082 & 700 & 700 & 7 & 9.08 \\
SNLI & natural language inference & 54936 & 7824 & 1000 & 3 & 21.22 \\
Yelp-2 & sentimental analysis & 559694
 & 37981 & 200 & 2 & 32.12 \\
20Newsgroup & topic labeling & 10663 & 7019 & 300 & 20 & 143.28 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Suboptimal Shapley Value Explanations

"Suboptimal Shapley Value Explanations" by Xiaolei Lu, arXiv:2502.12209v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
 \toprule
 $\nu$ & $32\times 32$ & $64\times 64$ & $128\times 128$ & $256\times 256$\\
 \midrule
 $1\cdot 10^{-2}$ & 10 & 15 & 20 & 23 \\
 $5\cdot 10^{-3}$ & 11 & 15 & 22 & 30\\
 $2.5\cdot 10^{-3}$ & 17 & 16 & 21 & 32\\
 $1.25\cdot 10^{-3}$ & 26 & 24 & 23 & 30\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Lorenzo Lazzarino
Yuji Nakatsukasa
Umberto Zerbinati

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
 \hline
 \textbf{Controller} & \textbf{X $\pm$ SD (cm)} & \textbf{Y $\pm$ SD (cm)} & \textbf{Z $\pm$ SD (cm)} \\ \hline
 HL & 2.79 $\pm$ 0.30 & 2.91 $\pm$ 0.22 & 0.46 $\pm$ 0.10 \\ \hline
 No Dynamics & 4.18 $\pm$ 1.00 & 4.32 $\pm$ 1.36 & 0.54 $\pm$ 0.26 \\ \hline
 Dynamics & 3.95 $\pm$ 0.66 & 4.43 $\pm$ 1.15 & 0.51 $\pm$ 0.19 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Average controller error across trajectories. HL Commander outperforms the LL implementations. However, incorporating magnet dynamics appears to improve LL performance.}
\end{table}
\end{document}

eess

Magnisketch Drone Control

Ashley Kline
Abirami Elangovan
Dominique Escandon
Scott Wade
Aatish Gupta

"Magnisketch Drone Control" by Ashley Kline, Abirami Elangovan, Dominique Escandon, Scott Wade, and Aatish Gupta, arXiv:2412.10670v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison results of experiments}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
Methods &SSIM($ \times {\rm{1}}{{\rm{0}}^{{\rm{ - 2}}}}$) & PSNR \\
\hline
Unet${\rm{\#}}$1 &90.9861 &19.4012 \\
Unet${\rm{\#}}$2 &91.4635 &19.7707\\
Ynet &87.2459 &17.8173\\
DEUnet &90.6457 &19.3046\\
\hline
DuDoUnet &\bf{92.6300} &\bf{20.4012}\\
DuDoUnet${\rm{ + }}$MI &\bf{93.5622} &\bf{20.8859}\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jiadong Zhang
Hengrong Lan
Changchun Yang
Shanshan Guo
Feng Gao
Fei Gao

"Limited-view Photoacoustic Imaging Reconstruction With Dual Domain Inputs Under Mutual Information Constraint" by Jiadong Zhang, Hengrong Lan, Changchun Yang, Shanshan Guo, Feng Gao, and Fei Gao, arXiv:2011.06147v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Inju Ha
Donghun Ryou
Seonguk Seo
Bohyung Han

"Learning to Translate Noise for Robust Image Denoising" by Inju Ha, Donghun Ryou, Seonguk Seo, and Bohyung Han, arXiv:2412.04727v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Training and Validation Dataset Statistics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 Statistics & Train & Val\\
 \midrule
 \emph{Data Distribution}\\
 total & 1,099,217 & 77,326 \\
 real & 297,389 (27.05\%) & 20,220 (26.15\%) \\
 audio\_modified & 266,116 (24.21\%) & 18,938 (24.49\%) \\
 visual\_modified & 269,901 (24,55\%) & 19,099 (24.70\%) \\
 both\_modified & 265,812 (24.18\%) & 19,069 (24.66\%) \\
 \midrule
 \emph{Duration of Videos} \\
 minimum & 1.32s & 2.52s \\
 maximum & 668.48s & 152.40s \\
 mean & 9.60s & 9.56s \\
 \midrule
 \emph{Number of Attacks per Video} \\
 Range & 0 to 5 & 0 to 5 \\
 \midrule
 \emph{Duration of Fake Segments} \\
 minimum & 0.02s & 0.02s \\
 maximum & 11.58s & 8.10s \\
 mean & 0.33s & 0.33s \\
 \midrule
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"KLASSify to Verify: Audio-Visual Deepfake Detection Using SSL-based Audio and Handcrafted Visual Features" by Ivan Kukanov and Jun Wah Ng, arXiv:2508.07337v1, 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
 Horizon $k$ & Upward (\%) & Stationary (\%) & Downward (\%) \\
 \hline
 1 & 18 & 63 & 19\\ \hline
 2 & 25 & 50 & 25 \\\hline
 3 & 28 & 43 & 29 \\\hline
 5 & 32 & 35 & 33 \\\hline
 10 & 37 & 25 & 38 \\
 \hline
 \end{tabular}
\caption{Class balancing on FI-2010.}
\end{table}
\end{document}

q-fin

Matteo Prata
Giuseppe Masi
Leonardo Berti
Viviana Arrigoni
Andrea Coletta
Irene Cannistraci
Svitlana Vyetrenko
Paola Velardi
Novella Bartolini

"LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study" by Matteo Prata, Giuseppe Masi, Leonardo Berti, Viviana Arrigoni, Andrea Coletta, Irene Cannistraci, Svitlana Vyetrenko, Paola Velardi, and Novella Bartolini, arXiv:2308.01915v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study results on MOT20 test set. \textbf{MM:} Motion Model, \textbf{$\Delta$:} Value Increment.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc|c}
\toprule
{\bf Method}&{\bf MOTA(\%){$\uparrow$}}&{\bf IDF1(\%){$\uparrow$}}&{$\Delta$}\\
\midrule
OIM-MM&49.2&41.4&\\
\midrule
iHOIM-MM&51.2&43.0&(+2.0, +1.6)\\
iHOIM+MM&51.5&44.5&(+0.3, +1.5)\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Fan Wang
Lei Luo
En Zhu
Siwei Wang
Jun Long

"Multi-object Tracking with a Hierarchical Single-branch Network" by Fan Wang, Lei Luo, En Zhu, Siwei Wang, and Jun Long, arXiv:2101.01984v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccl}\toprule
& \multicolumn{2}{c}{RTR} & \multicolumn{2}{c}{GFW}
\\ \cmidrule(lr){2-3} \cmidrule(lr){4-5}
 & $f$ & Time (s) & $f$ & Time (s) \\ \midrule
Reading 1 & 530.6896 & 12.2 & 532.3243 & 10.3 \\
Reading 2 & 532.3255 & 42.1 & 532.3379 & 20.6 \\
Reading 3 & 532.3384 & 78.2 & 532.3399 & 41.3 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Empirical comparison RTR and GFW for solving the Max-Cut SDP Relaxation on a GPU}
\end{table}
\end{document}

math

"Strongly Convex Maximization via the Frank-Wolfe Algorithm with the Kurdyka-Łojasiewicz Inequality" by Fatih Selim Aktas and Christian Kroer, arXiv:2505.00221v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The method compared in the experiment.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
Algorithm & $\lambda$ & $\lambda$ setting \\ \hline
MU & 0 &constant
\\
Penalized MU & 0.1 &constant for each column
\\
Penalized MU & 0.5 &constant for each column
\\
SHINBO & not required & automatically optimized,\\
 & & different penalization for each column
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Laura Selicato
Flavia Esposito
Andersen Ang
Nicoletta Del Buono
Rafal Zdunek

"Sparse Hyperparametric Itakura-Saito NMF via Bi-Level Optimization" by Laura Selicato, Flavia Esposito, Andersen Ang, Nicoletta Del Buono, and Rafal Zdunek, arXiv:2502.17123v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\toprule
{\bf Amplification} & \multirow{2}{*}{\bf Accuracy} & \multirow{2}{*}{\bf Loss} \\
{\bf Factor} &&\\
\midrule
10 & 70.24\% & 0.6036\\
20 & 68.36\% & 0.6043\\
30 & 65.77\% & 0.6051\\
40 & 63.54\% & 0.6243\\
50 & 60.49\% & 0.6189\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Rashmiranjan Das
Gaurav Negi
Alan F. Smeaton

"Detecting Deepfake Videos Using Euler Video Magnification" by Rashmiranjan Das, Gaurav Negi, and Alan F. Smeaton, arXiv:2101.11563v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Government Variables}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
\textbf{Symbol} & \textbf{Definition} \\
\midrule
$B_t$ & Government bond holdings at time $t$ \\
$T(i_t), T^a(a_t)$ & Income and asset tax functions \\
$\tau, \tau_a$ & Average tax rate for income and assets \\
$\xi, \xi_a$ & Curvature parameters for nonlinear tax function \\
$G_t$ & Government spending on goods and services \\
$r_t$ & Risk-free interest rate on government debt \\
$Y_t$ & Nominal Gross Domestic Product (GDP) at time $t$ \\
$P_t$ & Aggregate price level (CPI) at time $t$ \\
$\pi_t$ & Inflation rate: $\pi_t = \frac{P_t - P_{t-1}}{P_{t-1}}$ \\
$g_t$ & GDP growth rate: $g_t = \frac{Y_t - Y_{t-1}}{Y_{t-1}}$ \\
$\pi^*$ & Target inflation rate set by monetary authority \\
$g^*$ & Target GDP growth rate \\
$\phi_t$ & Reserve requirement ratio set by central bank \\
$\iota_t$ & Nominal interest rate set by central bank \\
$\lambda_\pi$ & Trade-off weight between inflation and growth objectives \\
$F_t$ & Total value of the national pension fund \\
$r_t^f$ & Investment return rate of the pension fund \\
$k$ & Minimum required pension fund growth rate \\
$\tau_p$ & Pension contribution rate paid by working individuals \\
$P_t^y(i)$ & Pension contribution from individual $i$ at time $t$ \\
$P_t^o(i)$ & Pension benefit received by retired individual $i$ \\
$A_t^{\text{personal}}(i)$ & Accumulated balance of personal pension account \\
$M$ & Annuity divisor based on retirement age \\
$\text{age}^r$ & Statutory retirement age \\
$T_i^p$ & Total years of contribution for individual $i$ \\
$\mathcal{I}_t$ & Income distribution across the population at time $t$ \\
$u(c_t^i, h_t^i)$ & Instantaneous utility from consumption and labor of individual $i$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Qirui Mi
Qipeng Yang
Zijun Fan
Wentian Fan
Heyang Ma
Chengdong Ma
Siyu Xia
Bo An
Jun Wang
Haifeng Zhang

"EconGym: A Scalable AI Testbed with Diverse Economic Tasks" by Qirui Mi, Qipeng Yang, Zijun Fan, Wentian Fan, Heyang Ma, Chengdong Ma, Siyu Xia, Bo An, Jun Wang, and Haifeng Zhang, arXiv:2506.12110v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Values of the calculated errors for pricing results for BTC, rounded to three significant figures.} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} % centered columns (4 columns)

		\hline %inserts double horizontal lines
		& RMSE& MAE &MAPE&MSLE \\ % inserts table
		\hline
		BS & 1180 &854 & 0.0923 & 0.0516\\
 Heston & 874 & 484 & 0.0581 & 0.0138 \\
 Kou &649 & 258 & 0.0264 & 0.00523\\
 MJD & 785 & 392 & 0.042 & 0.00778 \\
 SVJ &994 & 511 & 0.0448 & 0.00849 \\

		VG & 823 & 447 & 0.0466 & 0.00918 \\

 \hline

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

q-fin

"Pricing options on the cryptocurrency futures contracts" by Julia Kończal, arXiv:2506.14614v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Proportion of presumably global optima when carrying out 250 iterations for each model configuration when $n=500$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}\toprule
 &&\multicolumn{4}{c}{\textbf{fitted model}}\\
 && $p=0$ & $p=1$ & $p=2$ & $p=3$\\\midrule
 \multirow{4}{*}{\textbf{simulated model}}
 &$p=0$& $1.000$ & $0.976$ & $0.988$ & $0.992$ \\
 &$p=1$& $1.000$ & $1.000$ & $0.984$ & $0.988$ \\
 &$p=2$& $0.996$ & $0.996$ & $0.972$ & $0.988$ \\
 &$p=3$& $0.996$ & $0.992$ & $0.992$ & $0.992$ \\\bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Ferdinand V. Stoye
Annika Hoyer
Roland Langrock

"Autoregressive hidden Markov models for high-resolution animal movement data" by Ferdinand V. Stoye, Annika Hoyer, and Roland Langrock, arXiv:2412.11612v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overview of the dataset per gallery. All sale values are in USD converted at the time of the sale.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrr}
 &
 \textbf{Sales} &
 \textbf{Volume} &
 \textbf{Sale (avg)} &
 \textbf{Sale (med)} &
 \textbf{Sellers} &
 \textbf{Buyers} \\
SuperRare & 21,717 & 145,187,662 & 6685 & 457 & 2856 & 4461 \\
Async Art & 196 & 3,505,909 & 17,887 & 537 & 87 & 143 \\
KnownOrigin & 4306 & 16,344,121 & 3796 & 131 & 1265 & 2412 \\
Foundation & 1091 & 43,304,375 & 39,692 & 436 & 676 & 782 \\
Makersplace & 11,344 & 28,656,875 & 2526 & 227 & 1220 & 3439 \\
Zora & 286 & 106,289 & 372 & 104 & 189 & 244 \\
\textbf{Overall} &
 \textbf{38,940} &
 \textbf{237,105,230} &
 \textbf{6089} &
 \textbf{316} &
 \textbf{5636} &
 \textbf{10,011}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Seller-buyer networks in NFT art are driven by preferential ties" by Giovanni Colavizza, arXiv:2210.04339v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\toprule
observed\_sales & observed\_transactions & observed\_unique\_customers \\
average\_transaction\_value & transactions\_per\_customer & sales\_per\_customer \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{List of consumer transactions fields}
\end{table}
\end{document}

q-fin

Xueying Ding
Aakriti Mittal
Achintya Gopal

"DELPHYNE: A Pre-Trained Model for General and Financial Time Series" by Xueying Ding, Aakriti Mittal, and Achintya Gopal, arXiv:2506.06288v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Gravity Equation Coefficients and Variance Decomposition}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
\toprule
& \multicolumn{4}{c}{Regression Coefficients (S.E.)} & \multicolumn{4}{c}{$R^2$ Loss (\%)} \\
\cmidrule(lr){2-5} \cmidrule(lr){6-9}
Sample & Geo Score & Distance (log) & Border & Linguistic Dist. & Geo Score & Distance & Border & Ling. Dist. \\
\midrule
\multicolumn{9}{l}{\textit{Panel A: All Countries}} \\
Full Period (1962--2024) & 1.10 (0.027) & -1.45 (0.016) & 0.84 (0.079) & -1.70 (0.075) & 3.4 & 42.5 & 1.1 & 2.6 \\
Pre-1990 & 1.08 (0.043) & -1.29 (0.023) & 0.54 (0.104) & -0.88 (0.112) & 4.8 & 48.1 & 0.4 & 0.9 \\
Post-1990 & 1.08 (0.032) & -1.50 (0.017) & 1.02 (0.082) & -2.10 (0.076) & 2.5 & 40.0 & 1.1 & 3.2 \\
\addlinespace
\multicolumn{9}{l}{\textit{Panel B: Major Economies}} \\
Full Period & 0.46 (0.106) & -0.81 (0.052) & 0.23 (0.171) & -1.10 (0.330) & 2.2 & 49.7 & 0.6 & 2.8 \\
Pre-1990 & 0.78 (0.141) & -0.68 (0.069) & 0.10 (0.213) & -0.91 (0.422) & 8.6 & 44.8 & 0.0 & 2.6 \\
Post-1990 & 0.20 (0.139) & -0.92 (0.052) & 0.33 (0.172) & -1.24 (0.332) & 0.4 & 50.6 & 0.8 & 3.1 \\
\midrule
Full $R^2$ (Full Period) & \multicolumn{4}{c}{All: 0.268 \quad Major: 0.179} & \multicolumn{4}{c}{Observations: 1,087,543 / 58,948} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Geopolitical Barriers to Globalization

"Geopolitical Barriers to Globalization" by Tianyu Fan, Mai Wo, and Wei Xiang, arXiv:2509.12084v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 $\lambda_a$ & $\sigma_a e_a$\\
 $day^{-1}$ &$kg/mg$ \\
 \hline $0.0799$&$0.4755$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Parameters for the antiangiogenic drug Bevacizumab ().}
\end{table}
\end{document}

math

Luis A. Fernández
Isabel Lasheras
Cecilia Pola

"A mathematical perspective on the paradox that chemotherapy sometimes works backwards" by Luis A. Fernández, Isabel Lasheras, and Cecilia Pola, arXiv:2503.10670v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameter ranges for XGBoost and Gradient Boosting models used for N prediction in grapevine leaves.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|l|}
\hline
\textbf{Model} & \textbf{Hyperparameter} & \textbf{Values Explored} \\
\hline
\multirow{4}{*}{XGBoost}
& Number of Estimators & 100, 500, 1000 \\
& Maximum Depth & 3, 5, 7, 10 \\
& Learning Rate & 0.01, 0.05, 0.1 \\
& Subsample & 0.5, 0.7, 1.0 \\
\hline
\multirow{4}{*}{Gradient Boosting}
& Number of Estimators & 100, 200 \\
& Maximum Depth & 3, 5 \\
& Learning Rate & 0.01, 0.1 \\
& Subsample & 0.5, 0.7 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Atif Bilal Asad
Achyut Paudel
Safal Kshetri
Chenchen Kang
Salik Ram Khanal
Nataliya Shcherbatyuk
Pierre Davadant
R. Paul Schreiner
Santosh Kalauni
Manoj Karkee
Markus Keller

"Integrating Feature Selection and Machine Learning for Nitrogen Assessment in Grapevine Leaves using In-Field Hyperspectral Imaging" by Atif Bilal Asad, Achyut Paudel, Safal Kshetri, Chenchen Kang, Salik Ram Khanal, Nataliya Shcherbatyuk, Pierre Davadant, R. Paul Schreiner, Santosh Kalauni, Manoj Karkee, and Markus Keller, arXiv:2507.17869v1, 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|c|}
 \hline
 Base CNN & Train correlation & Test correlation \\
 \hline \hline
 EfficientNet-b7 & 0.95 & 0.92 \\
 EfficientNet-b0 & 0.90 & 0.88 \\
 DenseNet121 & 0.85 & 0.85 \\
 ResNet50-v2 & 0.87 & 0.82 \\
 VGG16 &0.88 & 0.87 \\
 \hline
 \end{tabular}
\caption{Pearson correlation coefficient results from various base CNN models}
\end{table}
\end{document}

eess

Juan Carlos Mier
Eddie Huang
Hossein Talebi
Feng Yang
Peyman Milanfar

"Deep Perceptual Image Quality Assessment for Compression" by Juan Carlos Mier, Eddie Huang, Hossein Talebi, Feng Yang, and Peyman Milanfar, arXiv:2103.01114v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overview of the datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrrr}
\toprule
 & Dataset & \#Samples & \#Benign & \#DDoS \\
\midrule
\multirow{2}{*}{Training} & Custom-SYN & 52698 & 26349 & 26349 \\
 & UNB201X & 265902 & 132746 & 133156 \\
\midrule
\multirow{2}{*}{Evaluation} & Scapy-SYN & 18830 & – & 18830 \\
 & CICIDS2017 & 13193 & – & 13193 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Maged Abdelaty
Sandra Scott-Hayward
Roberto Doriguzzi-Corin
Domenico Siracusa

"GADoT: GAN-based Adversarial Training for Robust DDoS Attack Detection" by Maged Abdelaty, Sandra Scott-Hayward, Roberto Doriguzzi-Corin, and Domenico Siracusa, arXiv:2201.13102v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Maciej Beręsewicz
Marek Wydmuch
Herman Cherniaiev
Robert Pater

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


q-fin

Pablo Brañas-Garza
Antonio Cabrales
María Paz Espinosa
Diego Jorrat

"The effect of ambiguity in strategic environments: an experiment" by Pablo Brañas-Garza, Antonio Cabrales, María Paz Espinosa, and Diego Jorrat, arXiv:2209.11079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|}
 \cline{2-5}
 & \multicolumn{4}{c|}{$k=-1$} \\
 \cline{2-5}
 & $t_\mu^{-1}$ & $f_{1,\mu}^{-1}$ & $f_{2,\mu}^{-1}$ & $f_{3,\mu}^{-1}$ \\
 \hline
 $r$ & $-3$ & $-2$ & $-2$ & $-3$ \\
 $u$ & $-2$ & $-1$ & $0$ & $0$ \\
 $z$ & $-1$ & $0$ & $0$ & $-1$ \\
 $\bar z$ & $-2$ & $0$ & $-1$ & 0 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{The three refined slices $\mathfrak{S}_{-1,f_i}$ that strictify $\mathfrak{Kin}^{(-1)}$.}
\end{table}
\end{document}

math

Felipe Díaz-Jaramillo
Silvia Nagy
Giorgio Pizzolo

"Homotopy kinematic algebras at null infinity" by Felipe Díaz-Jaramillo, Silvia Nagy, and Giorgio Pizzolo, arXiv:2503.21035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A Random Effects Model-based Method of Moments Estimation of Causal Effect in Mendelian Randomization Studies" by Wenhao Cao and Saonli Basu, arXiv:2312.10926v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The comparison of MSE among different $\beta$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
		\hline \multicolumn{2}{|c|}{Name}&$\beta=1$&$\beta=50$&$\beta=100$&$\beta=200$\\
		\hline
		\multirow{2}{*}{Inexact}&Mean MSE &0.01&$\mathbf{6.28\times10^{-4}}$&$6.34\times10^{-4}$&$1.73\times10^{-3}$\\
		\cline{2-6} % 跨过2-4列的横线
		&Variation&$1.71\times10^{-3}$&
		$\mathbf{5.40\times10^{-5}}$&$8.16\times10^{-5}$&$8.71\times10^{-15}$\\
		\hline
		\multirow{2}{*}{Exact}&Mean MSE
		&$2.37\times10^{-5}$&$1.77\times10^{-13}$&$\mathbf{1.22\times10^{-17}}$&$6.43\times10^{-7}$\\
		\cline{2-6} % 跨过2-4列的横线
		&Variation&$1.18\times10^{-4}$&$1.07\times10
		^{-12}$&$\mathbf{1.07\times10^{-17}}$&$8.51\times10^{-22}$\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Sparse Sampling Kaczmarz-Motzkin Method with Linear Convergence" by Ziyang Yuan, Hui Zhang, and Hongxia Wang, arXiv:2101.04807v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


math

"Non-parametric multiple change-point detection" by Andreas Anastasiou and Piotr Fryzlewicz, arXiv:2504.21379v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Effects of brain-focused patch discriminator, the utilization of perceptual loss with a pre-trained VGG network, and a detailed inspection of the effect of Octave Convolutions and its low-frequency ratio ($\alpha$).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{clccccc}
\hline
\multicolumn{2}{c}{Methods} & SSIM ($\uparrow$) & PSNR ($\uparrow$) & MAE ($\downarrow$)& MSE ($\downarrow$) & Time ($\downarrow$) \\ \hline \hline
\multicolumn{2}{c}{Macro2Micro (w/o brainPD)} & \textbf{0.8663} & \textbf{26.2560} & 0.1440 & 0.1288 & 0.0110 \\
\multicolumn{2}{c}{Macro2Micro (w/o perct.loss)} & 0.8565 & 25.7207 & 0.1407 & 0.1251 & 0.0110 \\
\multicolumn{2}{c}{Macro2Micro} & 0.8631 & 26.0478 & \textbf{0.1374} & \textbf{0.1221} & 0.0110 \\ \hline
\multicolumn{2}{c}{No Octave} & 0.8598 & 25.8797 & \textbf{0.1280} & \textbf{0.1122} & \textbf{0.0056} \\
\multicolumn{2}{c}{Octave $\alpha=0.25$} & \textbf{0.8640} & \textbf{26.1565} & 0.1417 & 0.1264 & 0.0126 \\
\multicolumn{2}{c}{Octave $\alpha=0.50$ (Default)} & 0.8631 & 26.0478 & 0.1374 & 0.1221 & 0.0110 \\
\multicolumn{2}{c}{Octave $\alpha=0.75$} & 0.8597 & 25.9887 & 0.1414 & 0.1258 & 0.0129 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Sooyoung Kim
Joonwoo Kwon
Junbeom Kwon
Sangyoon Bae
Yuewei Lin
Shinjae Yoo
Jiook Cha

"Macro2Micro: Cross-modal Magnetic Resonance Imaging Synthesis Leveraging Multi-scale Brain Structures" by Sooyoung Kim, Joonwoo Kwon, Junbeom Kwon, Sangyoon Bae, Yuewei Lin, Shinjae Yoo, and Jiook Cha, arXiv:2412.11277v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\large\bf Costs Estimates}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc} \toprule
Stages& {\bf Middle 90\%}& {\bf Bottom 95\%} \\ \midrule
\emph{Panel (a):} {\bf Total Costs} & & \\
All Drugs&\$58.51 mm & \$60.72 mm \\
Drugs with Complete Path&\$69.24 mm & \$77.01 mm \\
&&\\
\cline{2-3}\\
\emph{Panel (b):} {\bf FDA Review}& \$638.75 mm & \$648.04 mm\\
\cline{2-3}\\
\emph{Panel (c):} {\bf Clinical Trials}& & \\
Phase I	&\$0.62 mm & \$0.22 mm\\ Phase II	&\$30.48 mm&\$34.46 mm\\
Phase III	&\$41.09 mm&\$39.71 mm\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Valuing Pharmaceutical Drug Innovations

Gaurab Aryal
Federico Ciliberto
Leland E. Farmer
Ekaterina Khmelnitskaya

"Valuing Pharmaceutical Drug Innovations" by Gaurab Aryal, Federico Ciliberto, Leland E. Farmer, and Ekaterina Khmelnitskaya, arXiv:2212.07384v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Dynamics of Liquidity Surfaces in Uniswap v3" by Jimmy Risk, Shen-Ning Tung, and Tai-Ho Wang, arXiv:2509.05013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results for each method for different scenarios. The NN-VFA method mostly outperforms all other methods. Re-optimization works best for the trucks-only instance and well for the low-uncertainty variant. The gap for re-optimization is the average gap over the 30 solutions in the horizon.}
\begin{tabular}{lrrrrrrrr}
\hline
\multirow{2}{*}{\textbf{Trucks only}} & \multirow{2}{*}{Runtime (s)} & \multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\
 & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\ \hline
PI-bound & 1113.18 & 0.00\% & 8788 (±333) & 3358 & 0 & 5430 & 35 & 80\% \\
Re-optimization & 2968.10 & 0.01\% & \textbf{9500 (±573)} & 3510 & 0 & 5990 & 35 & 80\% \\
Rule-based & 0.00 & & 12,191 (±735) & 3251 & 0 & 8940 & 24 & 97\% \\
PPO & 3647.49 & & 10,893 (±826) & 3243 & 0 & 7650 & 30 & 99\% \\
DL-VFA & 342.30 & & 10,045 (±614) & 3855 & 0 & 6190 & 37 & 85\% \\
NN-VFA & 7145.99 & & 9923 (±250) & 3223 & 0 & 6700 & 36 & 87\% \\
\hline
\multirow{2}{*}{\textbf{Low uncertainty}} & \multirow{2}{*}{Runtime (s)} & \multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\
 & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\ \hline
PI-bound & 1410.15 & 0.00\% & 7570 (±147) & 2820 & 2200 & 2550 & 20 & 78\% \\
Re-optimization & 3767.23 & 0.05\% & 8554 (±118) & 3459 & 2755 & 2340 & 25 & 77\% \\
Rule-based & 0.00 & & 11,357 (±986) & 2712 & 665 & 7980 & 16 & 96\% \\
PPO & 3697.48 & & 10,041 (±315) & 2881 & 3520 & 3640 & 25 & 96\% \\
DL-VFA & 417.39 & & 8506 (±564) & 2931 & 3145 & 2430 & 24 & 81\% \\
NN-VFA & 7596.05 & & \textbf{8016 (±126)} & 2706 & 3480 & 1830 & 21 & 81\% \\
\hline
\multirow{2}{*}{\textbf{High uncertainty}} & \multirow{2}{*}{Runtime (s)} & \multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\
 & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\ \hline
PI-bound & 2015.60 & 0.00\% & 7421 (±371) & 2536 & 1765 & 3120 & 19 & 80\% \\
Re-optimization & 5610.57 & 0.12\% & 8921 (±497) & 2951 & 3420 & 2550 & 23 & 82\% \\
Rule-based & 0.01 & & 10,824 (±1189) & 2624 & 640 & 7560 & 16 & 97\% \\
PPO & 3307.25 & & 10,298 (±808) & 2998 & 3570 & 3730 & 26 & 95\% \\
DL-VFA & 387.43 & & 8542 (±284) & 2382 & 3490 & 2670 & 26 & 87\% \\
NN-VFA & 6463.33 & & \textbf{8036 (±384)} & 2746 & 3190 & 2100 & 22 & 82\% \\
\hline
\multirow{2}{*}{\textbf{Decreasing demand}} & \multirow{2}{*}{Runtime (s)} & \multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\
 & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\ \hline
PI-bound & 2166.06 & 0.00\% & 8624 (±351) & 3854 & 2460 & 2310 & 19 & 67\% \\
Re-optimization & 4876.01 & 0.27\% & 10,048 (±407) & 3933 & 3715 & 2400 & 25 & 71\% \\
Rule-based & 0.00 & & 11,880 (±785) & 3475 & 1055 & 7350 & 13 & 91\% \\
PPO & 3314.15 & & 11,389 (±501) & 4114 & 4375 & 2900 & 28 & 92\% \\
DL-VFA & 515.10 & & 9811 (±330) & 3321 & 4990 & 1500 & 23 & 74\% \\
NN-VFA & 7049.12 & & \textbf{9547 (±422)} & 3572 & 4355 & 1620 & 21 & 71\% \\
\hline
\multirow{2}{*}{\textbf{Increasing demand}} & \multirow{2}{*}{Runtime (s)} & \multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\
 & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\ \hline
PI-bound & 2596.36 & 0.00\% & 6347 (±249) & 1602 & 845 & 3900 & 22 & 90\% \\
Re-optimization & 5160.64 & 0.12\% & 7505 (±331) & 1775 & 1980 & 3750 & 25 & 92\% \\
Rule-based & 0.00 & & 9373 (±856) & 2053 & 510 & 6810 & 12 & 96\% \\
PPO & 3695.31 & & 8894 (±671) & 2804 & 2860 & 3230 & 21 & 98\% \\
DL-VFA & 430.07 & & 7449 (±303) & 2774 & 2365 & 2310 & 30 & 86\% \\
NN-VFA & 6193.41 & & \textbf{6828 (±149)} & 1888 & 2660 & 2280 & 20 & 93\% \\
\hline
\end{tabular}
\end{table}
\end{document}

math

Robert van Steenbergen
Wouter van Heeswijk
Martijn Mes

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


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study of fact scoring functions}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc|ccc}
 \toprule & MAE$\,\downarrow$ & RMSE$\,\downarrow$ & P$\,\uparrow$ & R$\,\uparrow$ & F1$\,\uparrow$ \\
 \midrule TKGC w/o $\mathcal{F}_{lit}$ & N/A & N/A & 0.425 & 0.481 & 0.451 \\
 TKGC w/o $\mathcal{F}_{rel}$ & 0.071 & 0.083 & N/A & N/A & N/A \\
 TKGC (full) & 0.054 & 0.062 & 0.524 & 0.491 & 0.507 \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{EER (\%) comparison between the speaker embedding vectors extracted from the proposed joint factor embedding and the other embedding techniques.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
		\hline
		 & Objective & EER [\%] \\ \hline
		\multirow{4}{*}{d-vector} & \textit{Softmax} & 10.72 \\ %\cline{2-3}
		& \textit{Gradient reversal} & 10.37 \\ %\cline{2-3}
		& \textit{Anti-loss} & 10.47 \\ %\cline{2-3}
		& \textit{\textbf{JFE (proposed)}} & \textbf{8.43} \\ \hline
		\multirow{4}{*}{x-vector} & \textit{Softmax} & 2.26 \\ %\cline{2-3}
		& \textit{Gradient reversal} & 5.87 \\ %\cline{2-3}
		& \textit{Anti-loss} & 1.46 \\ %\cline{2-3}
		& \textit{\textbf{JFE (proposed)}} & \textbf{1.07} \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Woo Hyun Kang
Sung Hwan Mun
Min Hyun Han
Nam Soo Kim

"Disentangled speaker and nuisance attribute embedding for robust speaker verification" by Woo Hyun Kang, Sung Hwan Mun, Min Hyun Han, and Nam Soo Kim, arXiv:2008.03024v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study of $\mathcal{N}_{\textrm{old}}$ for Stock 1} % title of the Figure
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
\hline
Model & F1-score \\ \hline
layers\_{[}{[}60, 5{]}, {[}200, 10{]}{]} & 0.694022 \\ \hline
layers\_{[}{[}60, 10{]}, {[}120, 5{]}{]} & 0.687618 \\ \hline
layers\_{[}{[}60, 5{]}, {[}120, 10{]}, {[}50, 5{]}{]} & 0.686768 \\ \hline
layers\_{[}{[}60, 10{]}, {[}200, 10{]}{]} & 0.68425 \\ \hline
layers\_{[}{[}50, 10{]}, {[}250, 5{]}{]} & 0.68172 \\ \hline
layers\_{[}{[}60, 10{]}, {[}200, 5{]}{]} & 0.67418 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Mostafa Shabani
Dat Thanh Tran
Juho Kanniainen
Alexandros Iosifidis

"Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification" by Mostafa Shabani, Dat Thanh Tran, Juho Kanniainen, and Alexandros Iosifidis, arXiv:2207.11577v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Experimental scale factor values of buyer compared with theoretical scale factor values for case-3}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c|c||}\toprule
 \textit{} & \textit{} & \textit{$\alpha_{B1}$} & \textit{$\alpha_{B2}$} \\ \midrule
 Theoretical Equilibrium & & 0.882782 & 0.588521 \\
 \hline
 DDPG Empirical Equilibrium & mean & 0.855816 & 0.302352 \\
 \hline
 & std & 0.050594 & 0.221870 \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Sanjay Chandlekar
Easwar Subramanian
Sanjay Bhat
Praveen Paruchuri
Sujit Gujar

"Multi-unit Double Auctions: Equilibrium Analysis and Bidding Strategy using DDPG in Smart-grids" by Sanjay Chandlekar, Easwar Subramanian, Sanjay Bhat, Praveen Paruchuri, and Sujit Gujar, arXiv:2201.10127v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

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