\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Cases when $P_I=1$ is the equilibrium.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
 Scenario & Range of $R$ & Condition on $C_I$\\
 \hline & & \\
 1 \& 2 & $R > c_w\left(\dfrac{2}{\mu}-\dfrac{1}{\mu+\lambda}\right) $ & $C_I\leq C_{I,1}^{1,2}(R)\equiv \dfrac{(1-\rho)\rho^{n_e}}{1-\rho^{n_e+1}}\left[\dfrac{c_wn_e}{\mu}+\dfrac{c_w}{\mu}-R\right]$ \\
 3 & $R \in \left(\dfrac{c_w}{\mu},c_w\left(\dfrac{2}{\mu}-\dfrac{1}{\mu+\lambda}\right)\right]$ & $C_I\leq C_{I,1}^{3}(R) \equiv \dfrac{R\mu-c_w}{\mu+\lambda}$ \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Jake Clarkson
Konstantin Avrachenkov
Eitan Altman

"Queues with inspection cost: To see or not to see?" by Jake Clarkson, Konstantin Avrachenkov, and Eitan Altman, arXiv:2503.13232v1, 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{TP, TN, FP and FN at the pixel level}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
& \textbf{\textit{Predicted Pixel}} & \textbf{\textit{Actual Pixel}} \\
\hline
TP & 1 & 1 \\
TN & 0 & 0 \\
FP & 1 & 0 \\
FN & 0 & 1 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data" by José G. M. Esgario and Renato A. Krohling, arXiv:2201.10650v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Test performance of single-view representation learning models with different encoder architectures, and trained on selected datasets using VIB , Category-dependent VIB (CDVIB) , and our proposed Gaussian Mixture MDL (GM-MDL).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\# & Encoder & Dataset & no reg. & VIB & CDVIB & GM-MDL \\ \hline
1 & CNN4 & CIFAR10 & 0.612 & 0.626 & 0.649 & \textbf{0.681} \\ \hline
2 & CNN4 & USPS & 0.948 & 0.952 & 0.955 & \textbf{0.963} \\ \hline
3 & CNN4 & INTEL & 0.756 & 0.759 & 0.763 & \textbf{0.776} \\ \hline
4 & ResNet18 & CIFAR10 & 0.824 & 0.829 & 0.835 & \textbf{0.848} \\ \hline
5 & ResNet18 & CIFAR100 & 0.454 & 0.458 & 0.463 & \textbf{0.497} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Milad Sefidgaran
Abdellatif Zaidi
Piotr Krasnowski

"Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior" by Milad Sefidgaran, Abdellatif Zaidi, and Piotr Krasnowski, arXiv:2504.18455v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A Simple Weak Galerkin Finite Element Method for a Class of Fourth-Order Problems in Fluorescence Tomography" by Chunmei Wang and Shangyou Zhang, arXiv:2503.18200v1, 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}{||c|c|c|c|c|c|c||}
\hline
Name & mass ratio & $\chi_{1,2} $ & precession & {\tt dim} & $\ell $ & GW cyles \\
\hline \hline
{\tt SpEC\_q1\_10\_NoSpin} & $\leq 10$ & 0 & no & 1 & $\leq 8$ & $\sim 25-31$ \\
\hline
{\tt NRSur4d2s\_TDROM\_grid12} & $\leq 2$ & $\leq 0.8$ & yes & 4 & $\leq 3$ & $\sim 25-35$ \\
\hline
{\tt NRSur7dq2} & $\leq 2$ & $\leq 0.8$ & yes & 7 & $\leq 4$ & $\sim 40$ \\
\hline
{\tt NRSur7dq4} & $\leq 4$ & $\leq 0.8$ & yes & 7 & $\leq 4$ & $\sim 40$ \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Characterization of the waveform surrogates described so far corresponding to NR binary black holes.}
\end{table}
\end{document}

cs

"Reduced Order and Surrogate Models for Gravitational Waves" by Manuel Tiglio and Aarón Villanueva, arXiv:2101.11608v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary statistics of cases by gender and race}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 & \multicolumn{2}{c}{\textbf{Child}} & \multicolumn{2}{c}{\textbf{Defendant}} \\
 \cmidrule(lr){2-3} \cmidrule(lr){4-5}
 & \textbf{Count} & \textbf{Percentage} & \textbf{Count} & \textbf{Percentage} \\
 \midrule
 \textbf{Gender} \\
 \hspace{5mm} Female & 2359 & 40\% & 1477 & 25\% \\
 \hspace{5mm} Male & 3277 & 55\% & 4332 & 73\% \\
 \hspace{5mm} Unknown & 292 & 5\% & 119 & 2\% \\
 \midrule
 \textbf{Race} \\
 \hspace{5mm} White & 1954 & 33\% & 2381 & 40\% \\
 \hspace{5mm} Black & 669 & 11\% & 890 & 15\% \\
 \hspace{5mm} Hispanic & 491 & 8\% & 666 & 11\% \\
 \hspace{5mm} Asian & 58 & 1\% & 67 & 1\% \\
 \hspace{5mm} Unknown & 2756 & 46\% & 1924 & 32\% \\
 \midrule
 \textbf{Total cases} & 5928 & 100\% & 5928 & 100\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Statistical Issues in the Diagnosis of Shaken Baby Syndrome/Abusive Head Trauma" by Maria Cuellar, arXiv:2412.10648v2, 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{Objective evaluation of the ECS model}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 & \multicolumn{3}{c}{} \\
 Input Speech & STOI${\tiny \uparrow}$ & ESTOI${\tiny \uparrow}$ & NCM${\tiny \uparrow}$ \\
 \midrule

 Clean & 0.7604 & 0.5866 & 0.6809 \\
 Noisy & 0.4870 & 0.2073 & 0.3258 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Meng-Ping Lin
Enoch Hsin-Ho Huang
Shao-Yi Chien
Yu Tsao

"End-to-End Audio-Visual Learning for Cochlear Implant Sound Coding in Noisy Environments" by Meng-Ping Lin, Enoch Hsin-Ho Huang, Shao-Yi Chien, and Yu Tsao, arXiv:2508.13576v1, 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[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{pDNSMOS scores on the ICASSP 2023 DNS Challenge blind test set.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\toprule
\textbf{Model} & \multicolumn{3}{c}{\textbf{Track 1}} & \multicolumn{3}{c}{\textbf{Track 2}} \\
\cmidrule(lr){2-4} \cmidrule(lr){5-7}
& SIG & BAK & OVRL & SIG & BAK & OVRL \\
\midrule
Noisy & 4.15 & 2.37 & 2.71 & 4.05 & 2.16 & 2.50 \\
TEA-PSE 3.0 & 4.12 & \textbf{4.05} & 3.65 & 3.99 & \textbf{3.95} & 3.49 \\
NAPSE & 3.81 & 3.99 & 3.38 & 3.92 & 4.17 & \textbf{3.56} \\
\rowcolor{gray!20} LLaSE-G1 & 4.21 & 3.99 & 3.72 & 4.08 & 3.84 & 3.55 \\
\midrule
\rowcolor{gray!20} UniFlow$_\text{DDPM}$ & \textbf{4.24} & 3.99 & \textbf{3.73} & \textbf{4.09} & 3.88 & \textbf{3.56} \\
\rowcolor{gray!20} UniFlow$_\text{FM}$ & 4.20 & 4.01 & 3.70 & 4.06 & 3.89 & 3.54 \\
\rowcolor{gray!20} UniFlow$_\text{MF}$ & 4.18 & 3.99 & 3.67 & 4.04 & 3.87 & 3.51 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ziqian Wang
Zikai Liu
Yike Zhu
Xingchen Li
Boyi Kang
Jixun Yao
Xianjun Xia
Chuanzeng Huang
Lei Xie

"UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling" by Ziqian Wang, Zikai Liu, Yike Zhu, Xingchen Li, Boyi Kang, Jixun Yao, Xianjun Xia, Chuanzeng Huang, and Lei Xie, arXiv:2508.07558v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
 \textbf{Level of Distortion} & \textbf{PLCC} & \textbf{SROCC} & \textbf{KROCC}\\
\midrule
 Level $1$ & 0.889 & 0.843 & 0.659 \\
 Level $2$ & 0.924 & 0.918 & 0.748 \\
 Level $3$ & 0.935 & 0.933 & 0.777 \\
 Level $4$ & 0.937 & 0.922 & 0.765 \\
 Level $5$ & 0.931 & 0.897 & 0.725 \\
\midrule
 \textbf{All}& 0.959 & 0.957 & 0.819 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"A combined full-reference image quality assessment approach based on convolutional activation maps" by Domonkos Varga, arXiv:2010.09361v3, 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}{lrrrr}
 \hline
	\textbf{Parameter}&\textbf{binary16} &
		\textbf{binary32} &
	\textbf{binary64} &\textbf{binary128} \\
 \hline
	p & 11 & 24 & 53 & 113 \\
	emax & 31 & 127 & 1023 & 16383 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Type-Based Approaches to Rounding Error Analysis" by Ariel Eileen Kellison, arXiv:2501.14598v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"On the Reasoning Capacity of AI Models and How to Quantify It" by Santosh Kumar Radha and Oktay Goktas, arXiv:2501.13833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Universal Inference for Incomplete Discrete Choice Models" by Hiroaki Kaido and Yi Zhang, arXiv:2501.17973v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
 \toprule
 variable & mean & std & q25 & median & q75 \\ \midrule
 home goals & 1.493 & 1.267 & 1 & 1 & 2 \\
 away goals & 1.222 & 1.149 & 0 & 1 & 2 \\
 home win odds & 2.534 & 1.368 & 1.84 & 2.24 & 2.75 \\
 draw odds & 3.658 & 0.807 & 3.29 & 3.42 & 3.68 \\
 away win odds & 3.756 & 2.523 & 2.49 & 3.12 & 4.13 \\
 lineup value & 4.9e+07 & 1.2e+08 & 2.6e+06 & 7.2e+06 & 3.4e+07 \\
 wins & 14.731 & 5.555 & 11 & 14 & 18 \\
 draws & 10.189 & 3.357 & 8 & 10 & 12 \\
 losses & 14.731 & 5.426 & 11 & 15 & 18 \\
 goals for & 53.828 & 15.032 & 43 & 52 & 63 \\
 goals against & 53.828 & 13.580 & 45 & 54 & 63 \\
 goal diff & 0 & 22.614 & -15 & -3 & 14 \\
 points & 54.264 & 16.815 & 42 & 52 & 65 \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Summary statistics of all leagues in dataset}
\end{table}
\end{document}

stat

Josh Brown
Yutong Bu
Zachary Cheesman
Benjamin Orman
Iris Horng
Samuel Thomas
Amanda Harsy
Adam Schultze

"Predictive Modeling of Lower-Level English Club Soccer Using Crowd-Sourced Player Valuations" by Josh Brown, Yutong Bu, Zachary Cheesman, Benjamin Orman, Iris Horng, Samuel Thomas, Amanda Harsy, and Adam Schultze, arXiv:2411.09085v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc} % centered columns (4 columns)
			\hline\hline %inserts double horizontal lines
			Copula & Copula function $(C(u,v))$ & Dual copula extropy ($J_C^*(X,Y)$) \\
			\hline
				Product copula& $uv$ &$\frac{11}{72}$	\\
	Marshall and Olkin copula &
 $uv\text{min}(u^{-\alpha},v^{-\beta})$=	$\begin{cases}
				u^{1-\alpha}v,~u^{-\alpha}\le v^{-\beta}
				\\
				uv^{1-\beta},~ u^{-\alpha}\ge v^{-\beta}
			\end{cases}$
				&
			$\begin{cases}
		\frac{1}{4}\left[\frac{7}{6}-\frac{12-5\alpha}{3(2-\alpha)(3-\alpha)}-\frac{1}{3(3-2\alpha)}\right]\\
	\frac{1}{4}\left[\frac{7}{6}-\frac{12-5\beta}{3(2-\beta)(3-\beta)}-\frac{1}{3(3-2\beta)}\right]
	\end{cases}$\\

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

stat

"Copula-based extropy measures, properties and dependence in bivariate distributions" by Shital Saha and Suchandan Kayal, arXiv:2311.08061v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Érika Diz-Pita
Jaume Llibre
M. Victoria Otero-Espinar

"Planar Kolmogorov systems with infinitely many singular points at infinity" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.14416v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Automating the Design of Multigrid Methods with Evolutionary Program Synthesis" by Jonas Schmitt, arXiv:2312.14875v1, 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}{r|cc}
		\hline \hline
		& market cap weighting & liquidity weighting \\
		\hline
		$\beta_{i,t^-_l}$ & 1 & $\frac{Vol_{i, t^-_{l}} }{ P_{i, t^-_{l}}Q_{i, t^-_{l}} }$ \\
		\hline \hline
	\end{tabular}
\end{adjustbox}
\caption{Weighting schemes for derivation of CRIX}
\end{table}
\end{document}

q-fin

CRIX an index for cryptocurrencies

"CRIX an index for cryptocurrencies" by Simon Trimborn and Wolfgang Karl Härdle, arXiv:2009.09782v1, 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{Comparison of GFLOPs and Params based on Audio-Visual fusion model.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\hline
Network & GFLOPs & Params(M) \\
\hline
TalkNet &\textbf{1.53} & \underline{15.80} \\
AV-ped & 5.67 & 32.36 \\
AV-FDTI & 11.37 & 92.28 \\
\textbf{AV-DTEC}(Ours)& \underline{1.98} &\textbf{10.16} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Zhenyuan Xiao
Yizhuo Yang
Guili Xu
Xianglong Zeng
Shenghai Yuan

"AV-DTEC: Self-Supervised Audio-Visual Fusion for Drone Trajectory Estimation and Classification" by Zhenyuan Xiao, Yizhuo Yang, Guili Xu, Xianglong Zeng, and Shenghai Yuan, arXiv:2412.16928v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"F&O Expiry vs. First-Day SIPs: A 22-Year Analysis of Timing Advantages in India's Nifty 50" by Siddharth Gavhale, arXiv:2507.04859v2, 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}{ccccccccc}
\toprule
 & \textbf{Beijing} & \textbf{Chengdu} & \textbf{Harbin} & \textbf{Shenyang} & \textbf{Shijiazhuang} & \textbf{Xian} & \textbf{Yuncheng} & \textbf{Zhengzhou} \\
\midrule
mean $T_o$ & 6.92 & 12.36 & -3.37 & 1.87 & 8.27 & 8.40 & 2.53 & 8. 81 \\
(std $T_o$) & (7.58) & (4.78) & (10.01) & (1.87) & (7.20) & (6.67) & (5.81) & (6.69) \\
max $\phi$ & 634 & 374 & 533 & 582 & 555 & 510 & 631 & 580 \\
(std max $\phi$) & (186) & (261) & (167) & (198) & (211) & (226) & (315) & (230) \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Adrien Le-Coz
Tahar Nabil
Francois Courtot

"Towards Optimal District Heating Temperature Control in China with Deep Reinforcement Learning" by Adrien Le-Coz, Tahar Nabil, and Francois Courtot, arXiv:2012.09508v2, 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{GCN hyperparameters used in the experiments.}
\begin{tabular}{cccc}
\toprule
Dataset & LR & Dropout & HiddenDimension \\ \midrule
Cora & 0.01 & 0.41 & 128 \\
Citeseer & 0.01 & 0.31 & 32 \\
Pubmed & {0.01} & {0.41} & {32} \\
Cornell & 0.001 & 0.51 & {128} \\
Texas & 0.001 & {0.51} & 128 \\
Wisconsin & 0.001 & 0.51 & 128 \\
Chameleon & 0.001 & 0.21 & 128 \\
Squirrel & 0.001 & 0.51 & 128 \\
Actor & 0.001 & 0.51 & 128 \\
CS & 0.001 & 0.51 & 512 \\
Photo & 0.01 & 0.51 & 512 \\
Physics & 0.01 & 0.51 & 512 \\
Roman-empire & 0.003 & 0.31 & 512 \\
Amazon-ratings & 0.003 & 0.31 & 512 \\
Minesweeper & 0.003 & 0.31 & 512 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Celia Rubio-Madrigal
Adarsh Jamadandi
Rebekka Burkholz

"GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring" by Celia Rubio-Madrigal, Adarsh Jamadandi, and Rebekka Burkholz, arXiv:2502.04891v1, 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{Character degrees for $\mathrm{Sp}_{2}\left(q^2\right)$ with $q$ even}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc}
Character & Degree&Multiplicity\\
\hline
 Tr & $1$& $1$\\
$\psi$ & $q^2$& $1$\\
$\chi_s$ & $q^2+1$ & $(q^2-2)/2$\\
$\theta_j$ & $q^2-1$&$q^2/2$
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Strong Gelfand Pairs of the Symplectic Group $\boldsymbol{\rm Sp}_4(q)$ where $q$ is even" by Stephen P. Humphries and Joseph E. Marrow, arXiv:2504.20279v2, 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{MM estimates of regression parameters under Model S1.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\toprule
 Variable & Estimate & s.e. & $z$-value & $p$-value \\
\midrule
 $Intercept$ & -1.8803 & 0.1515 &-12.4086 & $<$ 0.001 \\
 $lab2$ & 2.9848 & 1.2097 & 2.4689 & 0.0136 \\
 $edu3$ & -1.3809 & 0.5033 & -2.7445 & 0.0061 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

M. Boubeta
M. J. Lombardía
F. Marey-Pérez
D. Morales

"Area-level spatio-temporal Poisson mixed models for predicting domain counts and proportions" by M. Boubeta, M. J. Lombardía, F. Marey-Pérez, and D. Morales, arXiv:2012.00069v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Will the last be the first? School closures and educational outcomes" by Michele Battisti and Giuseppe Maggio, arXiv:2208.11606v1, 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}{c|ccc}
\toprule
Method & top1 & top5 & mAP \\
\midrule
STE-NVAN~ & 42.2 & - & 41.3 \\
TKP~ & 77.9 & - & 75.9 \\
NVAN~ & 78.4 & - & 76.7 \\
VKD~ & 85.6 & 93.9 & 83.8 \\
READ~ & 86.3 & 94.4 & 83.4 \\
\midrule
MDKT & \textbf{86.8} & \textbf{94.9} & \textbf{84.8} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Comparison with SOTA methods on Duke-video dataset.}
\end{table}
\end{document}

cs

"Image-to-Video Re-Identification via Mutual Discriminative Knowledge Transfer" by Pichao Wang, Fan Wang, and Hao Li, arXiv:2201.08887v1, 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{Summarization of compared methods.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\hline
Methods & Unsupervised & Supervised & Kernel-based & Correlation-based \\
\hline
\textbf{CCA} & Y & N & N & Y \\
\textbf{KCCA} & Y & N & Y & Y \\
\textbf{ml-CCA} & N & Y & N & Y \\
\textbf{KDM} & N & Y & Y & Y \\
\textbf{CKD} & N & Y & Y & Y \\
\textbf{DS²L} & N & Y & Y & Y \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Haoming Zhang
Xiao-Jun Wu
Tianyang Xu
Donglin Zhang

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
			\hline
			Fidelity & Training Samples & Epochs & Model Error & Filter Error \\ \hline
		 Low & $10^3$ & 20 & $\delta\approx 2.16$ & 2.59 \\ \hline
 Medium & $10^4$ & 50 & $\delta\approx 1.02 $ & 1.16 \\ \hline
 High & $10^6$ & 200 & $\delta\approx 0.35$ & 0.99 \\ \hline
		\end{tabular}
\caption{Summary of the training details, estimated model error, and average filter error for each model. }
\end{table}
\end{document}

math

"Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems" by Daniel Sanz-Alonso and Nathan Waniorek, arXiv:2412.14318v1, 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{HisarMod2019.1 includes 26 different modulation types from 5 different modulation families.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cl}
\toprule
Modulation Family & Modulation Types \\ \midrule
\multirow{6}{*}{Analog} & AM--DSB \\
 & AM--SC \\
 & AM--USB \\
 & AM--LSB \\
 & FM \\
 & PM \\ \midrule
\multirow{4}{*}{FSK} & 2--FSK \\
 & 4--FSK \\
 & 8--FSK \\
 & 16--FSK \\ \midrule
\multirow{3}{*}{PAM} & 4--PAM \\
 & 8--PAM \\
 & 16--PAM \\ \midrule
\multirow{6}{*}{PSK} & BPSK \\
 & QPSK \\
 & 8--PSK \\
 & 16--PSK \\
 & 32--PSK \\
 & 64--PSK \\ \midrule
\multirow{7}{*}{QAM} & 4--QAM \\
 & 8--QAM \\
 & 16--QAM \\
 & 32--QAM \\
 & 64--QAM \\
 & 128--QAM \\
 & 256--QAM \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Kürşat Tekbıyık
Ali Rıza Ekti
Ali Görçin
Güneş Karabulut Kurt
Cihat Keçeci

"Robust and Fast Automatic Modulation Classification with CNN under Multipath Fading Channels" by Kürşat Tekbıyık, Ali Rıza Ekti, Ali Görçin, Güneş Karabulut Kurt, and Cihat Keçeci, arXiv:1911.04970v2, 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
\caption{Test accuracy (\%) of on various neural networks trained on CIFAR10.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc}
 \textbf{Model} & $\l=0.0$ & $\l=0.5$ & $\l=1.0$ & $\l=1/t$ & $\l=1-1/t$ \\
 \hline
 ResNet-18 & $96.12_{\pm 0.02}$ & $96.10_{\pm 0.05}$ & $96.22_{\pm 0.11}$ & $96.03_{\pm 0.04}$ & $\boldsymbol{96.34}_{\pm 0.01}$ \\
 WideResNet-28-10 & $96.77_{\pm 0.07}$ & $96.93_{\pm 0.03}$ & $97.03_{\pm 0.06}$ & $96.71_{\pm 0.06}$ & $\boldsymbol{97.06}_{\pm 0.09}$ \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Sharpness-Aware Minimization: General Analysis and Improved Rates" by Dimitris Oikonomou and Nicolas Loizou, arXiv:2503.02225v1, 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{Subjects Performance Predictions and Ratio Before the Test}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \hline
 \hline
 & & \multicolumn{2}{c}{Pooled sample} \\\cline{3-4}
 Variable & & Mean & Std. dev. \\
 \hline
 Predicted own performance & & 13.94 & 3.612 \\
 Predicted group performance & & 12.99 & 2.594 \\
 Better & & 1.088 & 0.269 \\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)" by Marius Protte, arXiv:2507.15568v1, 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{Neural Network Classification Hyperparameter Selection (Reduction=5)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|}
\hline
Hyperparameter & Datatype & Values/{[}Min, Max{]} & Log Scaling & Gridpoint Count & Best Value \\ \hline
Batch Size & Integer & {[}32, 1024{]} & TRUE & 2 & 775 \\
Early Stopping Patience & Integer & {[}1, 100{]} & TRUE & 2 & 100 \\
Dropout Rate & Continuous & {[}0.0, 1.0{]} & FALSE & 2 & 0.70054 \\
Learning Rate & Continuous & {[}1e-05, 1.0{]} & TRUE & 2 & 0.015856 \\
Number of Layers & Integer & {[}1, 6{]} & FALSE & 2 & 3 \\
Include Batchnormalization & Categorical & {[}False, True{]} & FALSE & N/A & TRUE \\
Activation Function & Categorical & {[}'ReLU', 'Leaky ReLU'{]} & FALSE & N/A & ReLU \\
Layer Size & Integer & {[}4, 100{]} & TRUE & 2 & 18 \\
Feasible Sample Weight & Continuous & {[}0.01, 100.0{]} & TRUE & 2 & 0.447665 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Lyle Regenwetter
Colin Weaver
Faez Ahmed

"FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames" by Lyle Regenwetter, Colin Weaver, and Faez Ahmed, arXiv:2201.10459v3, 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{Hyperparameters for Baleen's condensers on HoVer and HotPotQA.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Hyperparameter} & \textbf{HoVer} & \textbf{HotPotQA} \\ \midrule
Learning rate & $1 \times 10^{-5}$ & $1 \times 10^{-5}$ \\
Batch size & 64 & 64 \\
Maximum Sequence Length & 512 & 512 \\
Warmup Steps & 1000 & 1000 \\
Training steps (stage \#1) & 5k & 10k \\
Training steps (stage \#2) & 5k & 10k \\
Negative Sampling Depth (stage \#1; per hop) & 20, 20, 20, 20 & 10, 30 \\
Negative Sampling Depth (stage \#2; for each hop) & 10 & 10 \\
Context Sampling Depth (from each hop) & 5 & 5 \\
Positive Sampling Depth (stage \#1; per hop) & 10, 10, 10, all & 10, all \\
Positive Sampling Depth (stage \#2; for each hop) & 10 & 10 \\
Facts fed to stage \# 2: training (inference) & 7--9 (9) & 7--9 (9) \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Omar Khattab
Christopher Potts
Matei Zaharia

"Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval" by Omar Khattab, Christopher Potts, and Matei Zaharia, arXiv:2101.00436v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{hhline}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\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|}
\hhline{-|-|-|-|}
\rowcolor{lightgray!10} Solver &Outer loop & Linear system solves & Run time (s) \\
\hline
Proximal Galerkin & 13 & 20 & 61.70 \\
Moreau--Yosida Penalty & 14 & 51 & 78.01 \\
Semismooth Active Set & 7 & 236 & 112.60 \\
Fixed Point & 164 & 8493 & 3633.72\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Example 5. The performance of four solvers, terminating when $\|u^{k} - u^{k-1}\|_{H^1(\Omega)} \leq 10^{-5}$. The Moreau--Yosida penalty solver and fixed point approach deliver an infeasible solution, whereas proximal Galerkin and the semismooth active set solver provide a feasible one.}
\end{table}
\end{document}

math

Jørgen S. Dokken
Patrick E. Farrell
Brendan Keith
Ioannis P. A. Papadopoulos
Thomas M. Surowiec

"The latent variable proximal point algorithm for variational problems with inequality constraints" by Jørgen S. Dokken, Patrick E. Farrell, Brendan Keith, Ioannis P. A. Papadopoulos, and Thomas M. Surowiec, arXiv:2503.05672v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccc|c|}
 \hline
 unknowns & cell & face & grad & edge & $k$\\
 \hline
 & $k$ & $k$ & $k^n$ & -- & $\geq 1$\\
 \hline
 (2D) & $k+2$ & $k+1$ & $k$ & -- & $\geq 0$\\
 (3D) & $k+2$ & $k+2$ & $k$ & -- & $\geq 0$\\
 \hline
 \shortstack{present \\ = ($k \geq 1$)}) & $\geq \min\{k-2,0\}$ & $\min\{k-1,0\}$ & $k$ & $k$ & $\geq 0$\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Degrees of freedom of HHO methods in the literature and the present method. The degrees associated with grad are faced-based.}
\end{table}
\end{document}

math

"A hybrid high-order method for the biharmonic problem" by Yizhou Liang and Ngoc Tien Tran, arXiv:2504.16608v2, 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{Results of comparative statics after telework firm appears around $ b $}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c||c|c|c|c|c|c|}
			\hline
			& $ \theta^* $
			& $ w^* $
			& $ z^* $
			& $ b_1(\theta )$
			& $ b_2(\theta ) $
			& $f(\theta )$
			& $\phi_t(x) $
			& $\phi_o(x) $
			& $ \psi(x) $\\
			\hline
			$ MC_t $ & + & $ - $ & $ - $ & + & + & + & + & + & + \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Shifting to Telework and Firms' Location: Does Telework Make Our Society Efficient?" by Kazufumi Tsuboi, arXiv:2212.00934v1, 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}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison of PEP-Net and baselines for 30-day mortality prediction. {\color{blue}Input: Cardiac-ROI.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\hline
\textbf{Method} & \textbf{Accuracy} & \textbf{AUC} & \textbf{Sensitivity} & \textbf{Specificity} \\ \hline
ResNet18 & \(0.693 \pm 0.159\) & \(0.733 \pm 0.100\) & \(0.889 \pm 0.106\) & \(0.582 \pm 0.174\) \\
ResNet50 & \(0.709 \pm 0.176\) & \(0.563 \pm 0.148\) & \(0.607 \pm 0.334\) & \(0.654 \pm 0.294\) \\
EfficientNetB0 & \(0.765 \pm 0.141\) & \(0.660 \pm 0.131\) & \(0.746 \pm 0.174\) & \(0.721 \pm 0.207\) \\ \hline
\textbf{PEP-Net} & \textbf{0.940 \(\pm\) 0.007} & \textbf{0.901 \(\pm\) 0.008} & \textbf{0.962 \(\pm\) 0.004}& \textbf{0.852 \(\pm\) 0.013} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yalcin Tur
Vedat Cicek
Tufan Cinar
Elif Keles
Bradlay D. Allen
Hatice Savas
Gorkem Durak
Alpay Medetalibeyoglu
Ulas Bagci

"Mortality Prediction of Pulmonary Embolism Patients with Deep Learning and XGBoost" by Yalcin Tur, Vedat Cicek, Tufan Cinar, Elif Keles, Bradlay D. Allen, Hatice Savas, Gorkem Durak, Alpay Medetalibeyoglu, and Ulas Bagci, arXiv:2411.18063v1, 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{Interaction Data With the Vaccine Information}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
	\hline\hline
	& Top-Ranked & Requested & All \\ \hline
	\textit{Bottom Clicked (\%)} \\
	Vaccine Platform &	83.4	&	82.5	&	79.7\\
	Countries		&	82.3	&	82.2	&	79.3 \\
	Doses			&	82.3	&	82.1	&	79.1\\
	Research Phase	&	82.7	&	82.3	&	79.1\\
	Efficacy		&	82.7	&	82.9	&	80.0\\
	Hospital		&	82.1	&	81.9	&	79.1\\
	Adverse Event	&	82.3	&	81.9	&	78.7\\
	Severe AE		&	80.7	&	79.8	&	76.7\\
	\textit{Time on page (sec)}\\
	Mean			& 	52.90	&	50.00 	& 	47.06 \\
	Median			& 	45.44	&	39.55	&	37.44 \\ \hline
	$N$ 			&	481		&	1,150	&	1,762 \\ \hline\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Self-selection of Information and Belief Update: An Experiment on COVID-19 Vaccine Information Acquisition" by ChienHsun Lin and Hans H. Tung, arXiv:2506.19056v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Composition of the population dataset ($N=2,342$) and the expert-labeled validation cohort ($N=105$).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|ccc|cccc}
\hline\hline
\multirow{2}{*}{\textbf{Dataset}} & \multirow{2}{*}{\textbf{Labels}} & \multicolumn{3}{c}{\textbf{Gender}} & \multicolumn{4}{c}{\textbf{Age}} \\
\cline{3-9}
& & M & F & N/A & 18-30 & 30-50 & 50-70 & $>$70 \\
\hline
Population & - & 1064 & 1243 & 35 & 425 & 839 & 795 & 283\\
\hline
\hline
\multirow{3}{*}{Validation} & CN & 10 & 13 & 1 & - & - & 13 & 11\\
 & MCI & 12 & 18 & 1 & - & 2 & 21 & 8\\
 & AD & 6 & 42 & 2 & - & 1 & 25 & 24\\
\hline\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Teja Gupta
Neeraj Wagh
Samarth Rawal
Brent Berry
Gregory Worrell
Yogatheesan Varatharajah

"Tensor Decomposition of Large-scale Clinical EEGs Reveals Interpretable Patterns of Brain Physiology" by Teja Gupta, Neeraj Wagh, Samarth Rawal, Brent Berry, Gregory Worrell, and Yogatheesan Varatharajah, arXiv:2211.13793v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
 \toprule & CLEVR-TR & MSN-Hard\\
 \midrule
 GTA-Kronecker & 38.32 & 24.52\\
 GTA-Euclid & 38.59 & 24.75 \\
 GTA & \textbf{38.99} & \textbf{24.80}\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Takeru Miyato
Bernhard Jaeger
Max Welling
Andreas Geiger

"GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers" by Takeru Miyato, Bernhard Jaeger, Max Welling, and Andreas Geiger, arXiv:2310.10375v3, 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}{|c|c|c|c|}
\hline
$ \ $ & $q$ & $\eta$ & $\tau_{comp}$ \\ \hline
Model 1 & 5 & 1.0 & $10^{-3}$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Constructing Gaussian Processes via Samplets" by Marcel Neugebauer, arXiv:2411.07277v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Alessandro Brusaferri
Matteo Matteucci
Stefano Spinelli
Andrea Vitali

"Probabilistic electric load forecasting through Bayesian Mixture Density Networks" by Alessandro Brusaferri, Matteo Matteucci, Stefano Spinelli, and Andrea Vitali, arXiv:2012.14389v2, 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{FI-2010 evaluation of the RDAIN method : evaluating the performance of different normalization methods for the first prediction horizon (next 10 timesteps).}
\begin{tabular}{llll}
 \toprule
 \textbf{Model} & \textbf{Method} & \textbf{F1-score} & $\boldsymbol{\kappa}$ \textbf{score} \\ \midrule
 \textbf{MLP} & Standardization & $56.79 \pm 0.47$ & $0.3536 \pm 0.0071$ \\
 \textbf{MLP} & Sample Average & $56.21 \pm 1.80$ & $0.3426 \pm 0.0235$ \\
 \textbf{MLP} & Sample Standardization & $63.86 \pm 1.31$ & $0.4468 \pm 0.0188$ \\
 \textbf{MLP} & Batch Normalization & $56.26 \pm 0.51$ & $0.3455 \pm 0.0085$ \\
 \textbf{MLP} & Instance Normalization & $60.77 \pm 0.92$ & $0.4006 \pm 0.0114$ \\
 \textbf{MLP} & DAIN & $69.50 \pm 0.35$ & $0.5346 \pm 0.0051$ \\
 \textbf{MLP} & Proposed & $\underline{69.94 \pm 0.55}$ & $\underline{0.5413 \pm 0.0086}$ \\
 \textbf{CNN} & Standardization & $57.27 \pm 1.12$ & $0.3563 \pm 0.0183$ \\
 \textbf{CNN} & Sample Average & $58.41 \pm 0.63$ & $0.3663 \pm 0.0101$ \\
 \textbf{CNN} & Sample Standardization & $60.44 \pm 1.02$ & $0.3972 \pm 0.0167$ \\
 \textbf{CNN} & Batch Normalization & $55.93 \pm 1.08$ & $0.3372 \pm 0.0182$ \\
 \textbf{CNN} & Instance Normalization & $60.17 \pm 0.69$ & $0.3917 \pm 0.0120$ \\
 \textbf{CNN} & DAIN & $48.87 \pm 3.07$ & $0.2488 \pm 0.0372$ \\
 \textbf{CNN} & Proposed & $\underline{66.77 \pm 0.48}$ & $\underline{0.4926 \pm 0.0086}$ \\
 \textbf{RNN} & Standardization & $55.48 \pm 1.29$ & $0.3285 \pm 0.0207$ \\
 \textbf{RNN} & Sample Average & $54.24 \pm 1.38$ & $0.3030 \pm 0.0221$ \\
 \textbf{RNN} & Sample Standardization & $59.62 \pm 0.91$ & $0.3850 \pm 0.0151$ \\
 \textbf{RNN} & Batch Normalization & $54.85 \pm 1.32$ & $0.3649 \pm 0.0135$ \\
 \textbf{RNN} & Instance Normalization & $58.33 \pm 0.84$ & $0.3649 \pm 0.0135$ \\
 \textbf{RNN} & DAIN & $67.86 \pm 0.52$ & $0.5102 \pm 0.0085$ \\
 \textbf{RNN} & Proposed & $\underline{67.90 \pm 0.80}$ & $\underline{0.5106 \pm 0.0122}$ \\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

Paraskevi Nousi
Loukia Avramelou
Georgios Rodinos
Maria Tzelepi
Theodoros Manousis
Konstantinos Tsampazis
Kyriakos Stefanidis
Dimitris Spanos
Manos Kirtas
Pavlos Tosidis
Avraam Tsantekidis
Nikolaos Passalis
Anastasios Tefas

"Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management" by Paraskevi Nousi, Loukia Avramelou, Georgios Rodinos, Maria Tzelepi, Theodoros Manousis, Konstantinos Tsampazis, Kyriakos Stefanidis, Dimitris Spanos, Manos Kirtas, Pavlos Tosidis, Avraam Tsantekidis, Nikolaos Passalis, and Anastasios Tefas, arXiv:2309.16679v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The averaged performance on the MNIST dataset. The standard deviation of the metric between clients is reported in parentheses. w. is the abbreviation of 'weighted', and the $\uparrow$ denotes that the higher the metric is, the better performance a model achieved, and the best performance is highlighted.}
\begin{tabular}{l|c|c}
 ~ & w. AUC ($10^{-2}$) $\uparrow$ & w. F1 ($10^{-2}$) $\uparrow$ \\
 \hline
 Local Only & 85.18(4.62) &47.80(10.12)\\
 FedAvg+FT & 96.97(2.54) &80.81(10.14) \\
 FedAvg+BN & 99.69(0.14) &93.38(1.76) \\
 FedBN & 99.32(0.75) &83.85(18.82)\\
 FedRep & 75.72(15.56) &38.53(20.52)\\
 PerFL & 99.48(0.20) &91.40(1.95)\\
 \hline
 FedCS-FC1(ours) & \bf{99.72(0.15)} &\bf{93.72}(1.71)\\
 FedCS-FC2(ours) & 99.71(0.16) &93.43(1.78)\\
 FedCS-FC3(ours) & 99.72(0.26) &93.64(1.56)\\
\end{tabular}
\end{table}
\end{document}

cs

"Client-supervised Federated Learning: Towards One-model-for-all Personalization" by Peng Yan and Guodong Long, arXiv:2403.19499v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\usepackage{arydshln}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small{Results across multiple sampling-distribution variances and state noise levels. Mean $\pm$ std. dev. over 10 trials. Only successful runs are considered for total time and traveled distance statistics. Dashes indicate missing data due to incomplete task.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cl|cccccc}
 \toprule
 & & \multicolumn{2}{c}{Success Rate $\uparrow$} & \multicolumn{2}{c}{Total Time (s) $\downarrow$} & \multicolumn{2}{c}{Trav. Distance (m) $\downarrow$} \\
 Noise $\sigma^2$ & Model & Mean & Std. Dev. & Mean & Std. Dev. & Mean & Std. Dev. \\
 \bottomrule
 \rowcolor{gray!20} \multicolumn{8}{c}{\textit{Mission 1}} \\
 \multirow{2}{*}{\textbf{0.0}} & \textbf{Ours} & \textbf{1.00} & \textbf{0.00} & 145.87 & 33.98 & 120.65 & 15.65 \\
 \textbf{} & \textbf{PETS} & 0.45 & 0.50 & \textbf{143.00} & \textbf{41.05} & \textbf{116.27} & \textbf{10.94} \\
 \hdashline
 \multirow{2}{*}{\textbf{0.01}} & \textbf{Ours} & \textbf{1.00} & \textbf{0.00} & \textbf{131.68} & \textbf{9.26} & \textbf{114.13} & \textbf{4.49} \\
 \textbf{} & \textbf{PETS} & 0.68 & 0.47 & 154.75 & 42.66 & 119.14 & 11.23 \\
 \hdashline
 \multirow{2}{*}{\textbf{0.05}} & \textbf{Ours} & \textbf{1.00} & \textbf{0.00} & \textbf{143.44} & \textbf{34.60} & 117.68 & 12.68 \\
 \textbf{} & \textbf{PETS} & 0.75 & 0.44 & 154.99 & 29.38 & \textbf{116.79} & \textbf{5.88} \\
 \hdashline
 \multirow{2}{*}{\textbf{0.10}} & \textbf{Ours} & \textbf{0.97} & \textbf{0.16} & \textbf{152.40} & \textbf{37.19} & \textbf{122.06} & \textbf{15.73} \\
 \textbf{} & \textbf{PETS} & 0.88 & 0.33 & 181.71 & 46.55 & 123.72 & 12.15 \\
 \hdashline
 \multirow{2}{*}{\textbf{0.20}} & \textbf{Ours} & \textbf{1.00} & \textbf{0.00} & 183.11 & 65.68 & 133.34 & 26.13 \\
 \textbf{} & \textbf{PETS} & 0.93 & 0.27 & \textbf{170.18} & \textbf{48.90} & \textbf{119.32} & \textbf{10.94} \\
 \hdashline
 \multirow{2}{*}{\textbf{0.50}} & \textbf{Ours} & \textbf{0.72} & \textbf{0.45} & \textbf{237.05} & \textbf{69.13} & \textbf{149.97} & \textbf{24.68} \\
 \textbf{} & \textbf{PETS} & 0.40 & 0.50 & 289.11 & 56.09 & 163.90 & 22.30 \\
 \hdashline
 \multirow{2}{*}{\textbf{1.0}} & \textbf{Ours} & \textbf{0.15} & \textbf{0.36} & \textbf{283.11} & \textbf{22.82} & \textbf{160.15} & \textbf{9.77} \\
 \textbf{} & \textbf{PETS} & 0.00 & 0.00 & - & - & - & - \\
 \bottomrule
 \rowcolor{gray!20} \multicolumn{8}{c}{\textit{Mission 2}} \\
 \multirow{2}{*}{\textbf{0.0}} & \textbf{Ours} & \textbf{0.68} & \textbf{0.47} & \textbf{218.30} & \textbf{128.10} & \textbf{146.31} & \textbf{55.92} \\
 \textbf{} & \textbf{PETS} & 0.00 & 0.00 & - & - & - & - \\
 \hdashline
 \multirow{2}{*}{\textbf{0.01}} & \textbf{Ours} & \textbf{0.65} & \textbf{0.48} & \textbf{228.47} & \textbf{139.21} & \textbf{145.85} & \textbf{55.98} \\
 \textbf{} & \textbf{PETS} & 0.00 & 0.00 & - & - & - & - \\
 \hdashline
 \multirow{2}{*}{\textbf{0.05}} & \textbf{Ours} & \textbf{0.28} & \textbf{0.45} & \textbf{189.16} & \textbf{66.60} & \textbf{128.77} & \textbf{22.50} \\
 \textbf{} & \textbf{PETS} & 0.00 & 0.00 & - & - & - & - \\
 \hdashline
 \multirow{2}{*}{\textbf{0.10}} & \textbf{Ours} & \textbf{0.17} & \textbf{0.38} & \textbf{275.12} & \textbf{139.38} & \textbf{173.95} & \textbf{67.78} \\
 \textbf{} & \textbf{PETS} & 0.00 & 0.00 & - & - & - & - \\
 \hdashline
 \multirow{2}{*}{\textbf{0.20}} & \textbf{Ours} & 0.05 & 0.22 & 471.54 & 37.85 & 238.77 & 9.25 \\
 \textbf{} & \textbf{PETS} & \textbf{0.07} & \textbf{0.27} & \textbf{176.64} & \textbf{19.07} & \textbf{129.45} & \textbf{12.54} \\
 \hdashline
 \multirow{2}{*}{\textbf{0.50}} & \textbf{Ours} & 0.03 & 0.16 & 387.82 & - & 193.95 & - \\
 \textbf{} & \textbf{PETS} & \textbf{0.15} & \textbf{0.36} & \textbf{277.98} & \textbf{133.41} & \textbf{162.35} & \textbf{45.66} \\
 \hdashline
 \multirow{2}{*}{\textbf{1.0}} & \textbf{Ours} & 0.00 & 0.00 & - & - & - & - \\
 \textbf{} & \textbf{PETS} & 0.00 & 0.00 & - & - & - & - \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Alejandro Murillo-Gonzalez
Junhong Xu
Lantao Liu

"Learning Causal Structure Distributions for Robust Planning" by Alejandro Murillo-Gonzalez, Junhong Xu, and Lantao Liu, arXiv:2508.06742v1, 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 of topic modeling results (Google Data)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \hline
 n-gram & S$^1$ & $p^2$ & $c^3$ & $|T|$ & score\\
 \hline
 uni & neg & $-6.884$ & $0.474$ & 5 & --- \\
 bi & neg & $-25.687$ & $0.610$ & 15 & 13.65 \\
 tri & neg & $-18.014$ & $0.644$ & 5 & 9.83 \\
 \hline
 uni & neu & $-8.119$ & $0.363$ & 14 & --- \\
 bi & neu & $-24.891$ & $0.679$ & 15 & $^*$ \\
 tri & neu & $-19.002$ & $0.630$ & 7 & --- \\
 \hline
 uni & pos & $-6.856$ & $0.500$ & 5 & --- \\
 bi & pos & $-24.796$ & $0.612$ & 15 & 13.20 \\
 tri & pos & $-34.593$ & $0.464$ & 14 & 18.03\\
 \hline
 \emph{Notes:} & \multicolumn{5}{l}{1: Sentiment \ \ \ 2: Perplexity \ \ \ 3: Coherence}\\
 \multicolumn{6}{l}{*: Both $p$ and $c$ are best.}\\
 \multicolumn{6}{l}{Score is not calculated for the worst performing topic model.}\\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Banking on Feedback: Text Analysis of Mobile Banking iOS and Google App Reviews" by Yekta Amirkhalili and Ho Yi Wong, arXiv:2503.11861v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
Loan Type & Return & PD & LGD \\
\hline
Safe Loan & $r_{rf}=3\%$ & $0$ & $0$ \\
\hline
Less Risky Loan & $r_{s}=9\%$ & $p_{s}=6.1\%$ & $lgd_{s}=10\%$ \\
\hline
More Risky Loan & $r_{r}=13.2\%$ & $p_{r}=12.2\%$ & $lgd_r=9\%$ \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Risk parameters for the three loans}
\end{table}
\end{document}

q-fin

Deb Narayan Barik
Siddhartha P. Chakrabarty

"Does limited liability reduce leveraged risk?: The case of loan portfolio management" by Deb Narayan Barik and Siddhartha P. Chakrabarty, arXiv:2209.12636v1, 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}{llll}
\hline
\textbf{AI lab} & \textbf{Year} & \textbf{Labor Share} & \textbf{Compute Share} \\ \hline
C3.ai & 2020 & 0.275 & 0.725 \\
OpenAI & 2018 & 0.335 & 0.665 \\
Splunk & 2021 & 0.808 & 0.192 \\
Alteryx & 2021 & 0.900 & 0.100 \\
DocuSign & 2021 & 0.911 & 0.089 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Economic impacts of AI-augmented R&D

Tamay Besiroglu
Nicholas Emery-Xu
Neil Thompson

"Economic impacts of AI-augmented R&D" by Tamay Besiroglu, Nicholas Emery-Xu, and Neil Thompson, arXiv:2212.08198v2, 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 average relative rollout error for the cylinder flow, with unit of $\times 10^{-3}$. We compare MeshGraphNet with noise injection (NI) to our model with transformer. We train the model on the training trajectories with 400 steps, and test it on the unseen trajectories with 800 steps.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccccc}
 \hline
rollout step &\multicolumn{3}{c}{800} \\
{Variable} &$u$ & $v$ & $p$ \\
 \hline
 MeshGraphNet-NI &43 &1274 &258 \\
\hline
 Ours-Transformer & \textbf{6} & \textbf{158} & \textbf{48} \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

"Predicting Physics in Mesh-reduced Space with Temporal Attention" by Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, and Li-Ping Liu, arXiv:2201.09113v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
\textbf{Method} & \textbf{Type} & $\lambda$ \textbf{Sch.} & \textbf{Obj. Func.} \\
\midrule
Forward KL & Forward & -- & $\mathbb{E}_P[\log(P/Q)]$ \\
Backward KL & Backward & -- & $\mathbb{E}_Q[\log(Q/P)]$ \\
Fixed-Param BiKL & Bidirectional & Constant & $\lambda_{\text{fix}}$(KL\textsubscript{F} + KL\textsubscript{R}) \\
BiKL & Bidirectional & $\lambda$=1 & KL\textsubscript{F} + KL\textsubscript{R} \\
Stepped BiKL & Bidirectional & Adaptive & $\lambda(t)$KL\textsubscript{F} + [1-$\lambda(t)$]KL\textsubscript{R} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{KL Divergence Method. a) Forward KL: Forward KL Divergence, b) Backward KL: Backward KL Divergence, Fixed-Param BiKL: Fixed-Parameter Bi-directional KL Divergence, BiKL: Bi-directional KL Divergence, and Stepped BiKL: Stepped Bi-directional KL Divergence. Specifically, Forward and Backward KL Divergence only have one direction. Bi-directional KL has both forward and backward, yet it realizes this through three distinct methods, as presented in the table (where the formulas in the “Obj. Func.” column correspond to these different realization logics)}
\end{table}
\end{document}

eess

Hainan Wang
Mehdi Hosseinzadeh
Reza Rawassizadeh

"TinyMusician: On-Device Music Generation with Knowledge Distillation and Mixed Precision Quantization" by Hainan Wang, Mehdi Hosseinzadeh, and Reza Rawassizadeh, arXiv:2509.00914v1, 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{Test results comparison amongst different KD methods in supervised learning.}
\begin{tabular}{lll}
 \toprule
 & \textbf{PnL} & \textbf{Accuracy} \\ \midrule
 \textbf{Baseline} & $0.128\%$ & $40.091\%$ \\
 \textbf{KD} & $0.757\%$ & $42.453\%$ \\
 \textbf{OKD} & $0.687\%$ & $42.328$ \\
 \textbf{*Teachers SD} & $\underline{2.322\%}$ & $43.092\%$ \\
 \textbf{*Teachers-Student} SD & $2.268\%$ & $\underline{43.109\%}$ \\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin


\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}{ll}
		\textbf{Hyperparameter}	& \textbf{Value} \\
		Optimiser & Adam \\
		Batch size & $2$ \\
		Learning rate & $0.001$ \\
		Epochs & $20$ \\
		Learning rate schedule & Constant
	\end{tabular}
\end{adjustbox}
\caption{Hyperparameters for the convolutional Bouncing balls experiment.}
\end{table}
\end{document}

cs

Imbedding Deep Neural Networks

"Imbedding Deep Neural Networks" by Andrew Corbett and Dmitry Kangin, arXiv:2202.00113v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c||c|c|}
			\hline
			$m$ & $k$ & Talbot-Gaver-Stehfest & Euler-Gaver-Stehfest \\ \hline \hline
			30 & 15 & $0.0047812$ & $0.0047684$ \\ \hline
			30 & 18& $0.0047812$ & $0.0047684$\\ \hline
		\end{tabular}
\end{adjustbox}
\caption{Joint density $p(1,6,4)$.}
\end{table}
\end{document}

q-fin

Runhuan Feng
Pingping Jiang
Hans Volkmer

"Geometric Brownian motion with affine drift and its time-integral" by Runhuan Feng, Pingping Jiang, and Hans Volkmer, arXiv:2012.09661v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Noisy Low-Rank Matrix Completion via Transformed $L_1$ Regularization and its Theoretical Properties" by Kun Zhao, Jiayi Wang, and Yifei Lou, arXiv:2503.02289v1, 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 summary of performance test under $Mix$ type with $\theta=3\pi/2$. Eight scenarios (1,3,5,6,8,9,11,14) approach to $y_b$, three scenarios (2,4,7 ) go to the other solution $y_s$, fourteen scenarios (10,12-13,15-25) diverges. Note that $\max|y_{rk4}-y_{b}|$ values are not presented and they are all larger than $10^{-2}$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
	status & number & $\max|y''-f|$ & $\max|y_{opt}-y_{b}|$ \\ \hline\hline
		 $\to y_b$ & 8	 & 1.1E-06 & 6.8E-08 \\
		$\to y_s$ & 3 & 1.1E-06 & 2.0E-01 \\
		$diverge$ & 14 & diverge & diverge \\ \hline
	 \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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


math

"Performance evaluation of accelerated real and complex multiple-precision sparse matrix-vector multiplication" by Tomonori Kouya, arXiv:2412.17510v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Demographic characteristics of the sample group (N)}
\begin{tabular}{llr} \toprule
\multirow{2}{*}{Gender} & Male & 9 \\
 & Female & 7 \\ \hline
\multirow{3}{*}{Age} & <40 & 2 \\
 & 40-50 & 6 \\
 & >50 & 8 \\ \hline
\multirow{6}{*}{Dominant background} & Healthcare entities - managerial position & 5 \\
 & Healthcare sector- administrative position & 4 \\
 & Healthcare unit- medical staff & 2 \\
 & Insurance scheme & 1 \\
 & Government – department of healthcare; ministry & 3 \\
 & Third party payer & 2 \\ \hline
\multirow{4}{*}{Education} & MBA & 5 \\
 & Ph.D. & 8 \\
 & Professor & 1 \\
 & M.Sc. & 2 \\ \hline
\multirow{3}{*}{Workplace} & Public sector employee & 13 \\
 & Private sector employee & 2 \\
 & Entrepreneur, self-employed & 1 \\ \hline
\multirow{3}{*}{Years of experience} & 10-15 & 2 \\
 & 16-20 & 5 \\
 & > 20 & 9 \\ \hline
\multirow{11}{*}{Country of origin} & Poland & 2 \\
 & Hungary & 2 \\
 & Latvia & 2 \\
 & France & 1 \\
 & Belgium & 1 \\
 & Finland & 1 \\
 & Sweden & 1 \\
 & Norway & 1 \\
 & Slovenia & 2 \\
 & United Kingdom & 2 \\
 & Germany & 1 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Jarosław Wątróbski
Aleksandra Bączkiewicz
Iga Rudawska

"A Strong Sustainability Paradigm Based Analytical Hierarchy Process (SSP-AHP) Method to Evaluate Sustainable Healthcare Systems" by Jarosław Wątróbski, Aleksandra Bączkiewicz, and Iga Rudawska, arXiv:2306.00718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrrrrrrrrr}
 \hline
$J-k$ & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\
 ARI & 0.779 & 1.656 & 2.662 & 3.847 & 5.278 & 7.048 & 9.285 & 12.149 & 15.745 \\
 rate ($\rho_{J-k}$) & & 1.124 & 0.608 & 0.445 & 0.372 & 0.335 & 0.317 & 0.308 & 0.296 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{\textbf{Interval expansion}. We report the average range of intervals (ARI, from Equation , along with its growth with the number of free mappings ($J-k$). }
\end{table}
\end{document}

stat

Forking paths in financial economics

"Forking paths in financial economics" by Guillaume Coqueret, arXiv:2401.08606v1, 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{Classification accuracy on CIFAR-10. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
				\toprule
				\addlinespace
				Model & Params & Benign & PGD & CW & BSA & Rand \tabularnewline
				\addlinespace
				\midrule
				Tanh16(U=4) & 773,600 & .510 & .460 & .550 & .600 & .368 \tabularnewline
				Softmax(U=2) & \textbf{772,628} & .869 & .814 & \textbf{.860} & \textbf{.870} & .652 \tabularnewline
				Tanh16(U=2) & 773,600 & .872 & \textbf{.826} & .830 & \textbf{.830} & .765 \tabularnewline
				LogEns10(U=2) & 1,197,998 & .882 & .806 & .830 & .800 & \textbf{1.0} \tabularnewline
				\midrule
				Madry & 45,901,914 & .871 & .470 & .080 & 0 & .981 \tabularnewline
				TanhEns64 & 3,259,456 & \textbf{.896} & .601 & .760 & .760 & \textbf{1.0} \tabularnewline
				\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Konstantinos P. Panousis
Sotirios Chatzis
Antonios Alexos
Sergios Theodoridis

"Local Competition and Stochasticity for Adversarial Robustness in Deep Learning" by Konstantinos P. Panousis, Sotirios Chatzis, Antonios Alexos, and Sergios Theodoridis, arXiv:2101.01121v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{CES Substitution Parameters Estimates}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
& $\sigma^O$ & $\sigma^M$ & $\sigma^I$ \\
\midrule
 Estimate & 0.501 & 0.773 & 0.222 \\
 SE & (0.066) & (0.232) & (0.268) \\
 \bottomrule

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

q-fin

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Original Dataset 5-class Output Distribution}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
\toprule
Bin \# & 0 & 1 & 2 & 3 & 4 \\ \midrule
Ceiling (W) & 90 & 900 & 9000 & 90000 & 200000 \\
\# of samples & 73 & 94 & 100 & 126 & 149 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

James Daniell
Kazuma Kobayashi
Ayodeji Alajo
Syed Bahauddin Alam

"Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction" by James Daniell, Kazuma Kobayashi, Ayodeji Alajo, and Syed Bahauddin Alam, arXiv:2211.13157v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Makoto Araya
Masaaki Harada
Hadi Kharaghani
Sho Suda
Wei-Hsuan Yu

"Unbiased weighing matrices of weight $9$" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, 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{MCD scores on the test set.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l||c|c|c}
 & \multicolumn{3}{c}{{Mel-Cepstral Distortion (dB)}} \\
\cline{2-4}
 & ~~Continuous~~ & WaveGlow- & WaveGlow- \\
Speaker & ~~Vocoder~~ & EN & HU \\
\hline\hline
Speaker \#048 & 5.54 & 5.27 & 5.34 \\
Speaker \#049 & 5.67 & 5.66 & 5.65 \\
Speaker \#102 & 5.26 & 5.20 & 5.18 \\
Speaker \#103 & 5.41 & 5.34 & 5.37 \\
\hline
Mean & 5.47 & 5.37 & 5.38 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Tamás Gábor Csapó
Csaba Zainkó
László Tóth
Gábor Gosztolya
Alexandra Markó

"Ultrasound-based Articulatory-to-Acoustic Mapping with WaveGlow Speech Synthesis" by Tamás Gábor Csapó, Csaba Zainkó, László Tóth, Gábor Gosztolya, and Alexandra Markó, arXiv:2008.03152v1, 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}{rcc}\toprule
 & Robust

 & Bilevel
 \\
 \midrule
 Continuous variables & $n+1$ & $2n+1$
 \\
 Binary variables & $2n$ & $2n$
 \\
 Continuous auxiliary variables & $n$ & $2n$
 \\
 \midrule
 Constraints & $2n+2$ & $4n+4$
 \\
 McCormick constraints & $4n$ & $8n$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Henri Lefebvre
Martin Schmidt
Simon Stevens
Johannes Thürauf

"Solving Decision-Dependent Robust Problems as Bilevel Optimization Problems" by Henri Lefebvre, Martin Schmidt, Simon Stevens, and Johannes Thürauf, arXiv:2503.01559v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Monica Pragliola
Luca Calatroni
Alessandro Lanza

"Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising" by Monica Pragliola, Luca Calatroni, and Alessandro Lanza, arXiv:2503.07814v1, 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{Architecture of the autoencoder used in our method}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 \multicolumn{3}{|c|}{\footnotesize Encoder}\\
 \cline{1-3}
 \footnotesize Layer & \footnotesize Filter & \footnotesize Output Size\\
 \cline{2-3}
 & \footnotesize $k\times k, s$ &\footnotesize $H\times W\times C$\\
 \hline
 \footnotesize Input & -- &\footnotesize $1024\times1024\times1$\\
 \hline
 \footnotesize Conv-BN-ReLU & \footnotesize $5\times5,2$ & \footnotesize $512\times512\times32$\\
 \hline
 \footnotesize Conv-BN-ReLU & \footnotesize $5\times5,2$ & \footnotesize $256\times256\times64$\\
 \hline
 \footnotesize Conv-BN-ReLU & \footnotesize $5\times5,2$ & \footnotesize $128\times128\times128$\\
 \hline
 \footnotesize Conv-BN-ReLU & \footnotesize $5\times5,2$ & \footnotesize $64\times64\times128$\\
 \hline
 \hline
 \multicolumn{3}{|c|}{\footnotesize Decoder}\\
 \cline{1-3}
 \footnotesize Upsample & \footnotesize $2\times2,1$ & \footnotesize $128\times128\times128$\\
 \hline
 \footnotesize Conv-BN-LReLU & \footnotesize $5\times5,1$ & \footnotesize $128\times128\times128$\\
 \hline
 \footnotesize Upsample & \footnotesize $2\times2,1$ & \footnotesize $256\times256\times64$\\
 \hline
 \footnotesize Conv-BN-LReLU & \footnotesize $5\times5,1$ & \footnotesize $256\times256\times64$\\
 \hline
 \footnotesize Upsample & \footnotesize $2\times2,1$ & \footnotesize $512\times512\times32$\\
 \hline
 \footnotesize Conv-BN-LReLU & \footnotesize $5\times5,1$ & \footnotesize $512\times512\times32$\\
 \hline
 \footnotesize Upsample & \footnotesize $2\times2,1$ & \footnotesize $1024\times1024\times32$\\
 \hline
 \footnotesize Conv-BN-Sigmoid & \footnotesize $5\times5,1$ & \footnotesize $1024\times1024\times1$\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hao-Chiang Shao
Hsing-Lei Ping
Kuo-shiuan Chen
Weng-Tai Su
Chia-Wen Lin
Shao-Yun Fang
Pin-Yian Tsai
Yan-Hsiu Liu

"Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning" by Hao-Chiang Shao, Hsing-Lei Ping, Kuo-shiuan Chen, Weng-Tai Su, Chia-Wen Lin, Shao-Yun Fang, Pin-Yian Tsai, and Yan-Hsiu Liu, arXiv:2201.09717v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A PINN-enriched finite element method for linear elliptic problems" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, 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|cccc|}
 \hline
 number of scenarios & objective value & 95\% upper bound & 95\% lower bound & relative difference \\
 \hline
25 &351.85&356.46&331.94&6.97\%\\
50&365.61&369.94&353.50&4.50\%\\
100&369.72&374.05&358.54&4.19\%\\
250&371.87&376.14&359.84&4.38\%\\
500&390.50&394.49&381.79&3.25\%%\\
\\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Objective values and 95\% bounds with different number of scenarios with the sampled stochastic model}
\end{table}
\end{document}

math

"A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications" by Jiaqi Lei, Akhil Singla, and Sanjay Mehrotra, arXiv:2501.18051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|} \hline
 & $n$ & Running time (seconds) & Memory cost (GiB) & Accuracy \\
 \hline
 Split-weight & 4 & 1.906 & 0.850 & 0.942 \\ \hline
 Robinson-Foulds & 4 & 11.193 & 5.295 & 0.927 \\ \hline
 Split-weight & 8 & 5.929 & 4.572 & 0.996 \\ \hline
 Robinson-Foulds & 8 & 37.146 & 25.557 & 0.999 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Running time in seconds, memory cost in gibibytes (GiB), and accuracy for hierarchical clustering under two types of distances: distance from the split-weight embeddings and Robinson-Foulds distance on trees directly.}
\end{table}
\end{document}

stat

Yibo Kong
George P. Tiley
Claudia Solis-Lemus

"Unsupervised Learning of Phylogenetic Trees via Split-Weight Embedding" by Yibo Kong, George P. Tiley, and Claudia Solis-Lemus, arXiv:2312.16074v2, 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|c|}\hline
 Location & 10min & 3h avrg. & 3h inst. & 6h inst. \\ \hline
 Aachen& 0 & -40.28 & -3.47 &1.99 \\\hline
 Zugspitze& 0 & -39.16& 4.76 & 6.06 \\\hline
 Boltenhagen &0 & -111.23& 9.86 & 3.69 \\\hline
 Fichtelberg & 0 & -62.71 & 1.83& -9.80 \\\hline

 \end{tabular}
\end{adjustbox}
\caption{Errors (in $MW$) of wind power generation prediction when using 30 years of data of lower resolution (three-hourly average and three-hourly and six-hourly instantaneous) wind speed observations compared to 10min wind speed observations. }
\end{table}
\end{document}

stat

Nina Effenberger
Nicole Ludwig
Rachel H. White

"Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts" by Nina Effenberger, Nicole Ludwig, and Rachel H. White, arXiv:2309.09540v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
\hline
Parameter & Value & Type & Derivation \\
\hline
$\alpha$ & 0.62 & Direct & - \\
$\gamma$ & 0.1 & Direct & - \\
$\rho$ & -0.681 & Direct & - \\
$\nu$ & 0.331 & Direct & - \\
$\theta$ & 0.3156 & Direct & - \\
$V_0$ & 0.0392 & Direct & - \\
$S_0$ & 100 & Direct & - \\
\hline
$\beta$ & 27.5583 & Derived & Solved from $\rho=\frac{1-\beta}{\sqrt{2(1+\beta^2)}}$ \\
$\mu$ & 26.8592 & Derived & Solved from $\nu=\sqrt{\frac{\theta(1+\beta^2)}{\gamma\mu(1+\beta)^2}}$ \\
$\xi$ & 0.124208 & Derived & Derived from $V_0=\xi\theta$ \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Model parameters used in numerical experiments. The table shows both direct input parameters and derived parameters with their corresponding calculation methods.}
\end{table}
\end{document}

math

"Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\infty$) processes and applications" by Yingli Wang and Zhenyu Cui, arXiv:2503.18259v3, 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{Commanded velocity components in $c_{\text{cmd}}$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
 \textbf{Description} & \textbf{Command Range} & \textbf{Units} \\
\midrule
Desired forward velocity & $[-0.3, 0.9]$ & m/s \\
Desired lateral velocity & $[-0.3, 0.3]$ & m/s \\
Desired yaw rate & $[-0.3, 0.3]$ & rad/s \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Arturo Flores Alvarez
Fatemeh Zargarbashi
Havel Liu
Shiqi Wang
Liam Edwards
Jessica Anz
Alex Xu
Fan Shi
Stelian Coros
Dennis W. Hong

"Learning to Walk in Costume: Adversarial Motion Priors for Aesthetically Constrained Humanoids" by Arturo Flores Alvarez, Fatemeh Zargarbashi, Havel Liu, Shiqi Wang, Liam Edwards, Jessica Anz, Alex Xu, Fan Shi, Stelian Coros, and Dennis W. Hong, arXiv:2509.05581v1, 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{5way1shot classification accuracy (\%) on \textit{mini}ImageNet with different backbones.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc}
\hline
 {Backbones}
 & without DC & with DC \\
 \hline
conv4~ & $42.11 \pm 0.71$ & $\textbf{54.62} \pm \textbf{0.64}$ ($\uparrow \textbf{12.51}$)\\
conv6~ & $46.07 \pm 0.26$ & $\textbf{57.14} \pm \textbf{0.45}$ ($\uparrow \textbf{11.07}$)\\
resnet18~ & $52.32 \pm 0.82$&$\textbf{61.50} \pm \textbf{0.47}$ ($\uparrow \textbf{9.180}$)\\
WRN28~ & $54.53 \pm 0.56$ & $\textbf{64.38} \pm \textbf{0.63}$ ($\uparrow \textbf{9.850}$)\\
WRN28 + Rotation Loss~ & $56.37\pm 0.68$ & $\textbf{68.57} \pm \textbf{0.55}$ ($\uparrow \textbf{12.20}$)\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Free Lunch for Few-shot Learning: Distribution Calibration" by Shuo Yang, Lu Liu, and Min Xu, arXiv:2101.06395v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation of fusion design and continual learning strategy.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \textbf{Depth} & \textbf{Adapter} & \textbf{Encoder Freezing} & \textbf{PSNR} & \textbf{$\Delta$PSNR} \\
 \hline\hline
 None & $\times$ & $\times$ & 34.52 dB & - \\
 \hline
 NN $\times 8$ & $\checkmark$ & $\times$ & 36.17 dB & +1.65 dB \\
 NN $\times 8$ & concat. & $\checkmark$ & 35.24 dB & +0.72 dB \\
 \hline
 NN $\times 8$ & $\checkmark$ & $\checkmark$ & \textbf{36.62 dB} & \textbf{+2.10 dB} \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Deep Lidar-guided Image Deblurring

Ziyao Yi
Diego Valsesia
Tiziano Bianchi
Enrico Magli

"Deep Lidar-guided Image Deblurring" by Ziyao Yi, Diego Valsesia, Tiziano Bianchi, and Enrico Magli, arXiv:2412.07262v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{arydshln}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ASR finetuning results for the NSC subset measured in WER(\%). The ``finetuning'' column indicates the total duration of finetuning data used. The best results are displayed in bold while the second best results are underlined.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
 \toprule
 Model & Finetuning (hrs) & Part 1 & Part 2 &Part 3&Part 4&Part 5&Part 6 \\
 \midrule
 MERaLiON-SpeechEncoder & 420 & 7.2 & \underline{11.5} & 20.4 & \textbf{27.4} & \textbf{14.4} & \textbf{10.4} \\
 \noalign{\vskip 1mm}
 \hdashline
 \noalign{\vskip 1mm}
 WavLM large (finetuned in-house) & 420 & 8.5 & 18.1 & \underline{20.3} & \underline{29.1} & \underline{14.8} & \underline{10.7} \\
 Whisper large v3 (zero-shot) & - & \underline{6.9} & 31.9 & 30.0 & 47.5 & 22.0 & 17.5 \\
 Whisper large v3 (finetuned in-house) & 8169 & \textbf{4.4}&\textbf{3.8} & \textbf{18.8} & 29.8 & 20.6 & 24.0 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Muhammad Huzaifah
Geyu Lin
Tianchi Liu
Hardik B. Sailor
Kye Min Tan
Tarun K. Vangani
Qiongqiong Wang
Jeremy H. M. Wong
Jinyang Wu
Nancy F. Chen
Ai Ti Aw

"MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond" by Muhammad Huzaifah, Geyu Lin, Tianchi Liu, Hardik B. Sailor, Kye Min Tan, Tarun K. Vangani, Qiongqiong Wang, Jeremy H. M. Wong, Jinyang Wu, Nancy F. Chen, and Ai Ti Aw, arXiv:2412.11538v3, 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}{c|ccc}
			\toprule
			\ 				 & PSNR (dB) & SSIM	 & RMSE (\%) \\ \midrule\midrule
			\ CycleGAN & 40.1006	& 0.9753 & 4.2208	\\
		 \ CycleGAN with linear kernel & 41.9220	& 0.9825	& 3.4224	\\
		 \ Proposed		 & 43.4656	& 0.9890 & 2.8652	\\ \bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Gyutaek Oh
Hyokyoung Bae
Hyun-Seo Ahn
Sung-Hong Park
Jong Chul Ye

"CycleQSM: Unsupervised QSM Deep Learning using Physics-Informed CycleGAN" by Gyutaek Oh, Hyokyoung Bae, Hyun-Seo Ahn, Sung-Hong Park, and Jong Chul Ye, arXiv:2012.03842v1, 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{Frequency of Special Characters}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

math

Dinesh Parthasarathy
Tommaso Bevilacqua
Martin Lanser
Axel Klawonn
Harald Köstler

"Towards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations" by Dinesh Parthasarathy, Tommaso Bevilacqua, Martin Lanser, Axel Klawonn, and Harald Köstler, arXiv:2412.08186v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccc|ccc|ccc}
 \hline
 \multirow{2}{*}{Method} & \multicolumn{3}{c|}{COCO} & \multicolumn{3}{c|}{OCHuman} & \multicolumn{3}{c}{OCPose [6]}\\
 & $\text{AP}$ & $\text{AP}^{50}$ & $\text{AP}^{75}$ & $\text{AP}$ & $\text{AP}^{50}$ & $\text{AP}^{75}$ & $\text{AP}$ & $\text{AP}^{50}$ & $\text{AP}^{75}$ \\
 \hline
 HRNet & 75.5 & 92.5 & 83.3 & 37.2 & 46.7 & 40.0 & 30.2 & 48.3 & 23.1\\
 HRNet* & \textbf{75.8} & \textbf{92.8} & \textbf{83.6} & 37.8 & 46.9 & 41.2 & 31.1 & 49.1 & 24.7\\
 MHPNet & 75.3 & 92.1 & 83.0 & \textbf{42.5} & \textbf{51.8} & \textbf{46.3} & \textbf{39.2} & \textbf{61.2} & \textbf{32.6}\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Comparison of HRNet (single-scale, 1 inference), HRNet* (six-scale, 6 inferences) and MHPNet (single-scale, 2 inferences) on \texttt{test} sets using HRNet-48-384 backbone and Faster-RCNN.}
\end{table}
\end{document}

cs

Rawal Khirodkar
Visesh Chari
Amit Agrawal
Ambrish Tyagi

"Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation" by Rawal Khirodkar, Visesh Chari, Amit Agrawal, and Ambrish Tyagi, arXiv:2101.11223v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Geoff K. Nicholls
Jeong Eun Lee
Nicholas Karn
David Johnson
Rukuang Huang
Alexis Muir-Watt

"Bayesian inference for partial orders from random linear extensions: power relations from 12th Century Royal Acta" by Geoff K. Nicholls, Jeong Eun Lee, Nicholas Karn, David Johnson, Rukuang Huang, and Alexis Muir-Watt, arXiv:2212.05524v3, 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{Accuracy of the model trained on labeled datasets of sizes $10$, $20$, $40$, and $10,000$ with varying amounts of unlabeled data from the same distribution with $\alpha = 0$ (\textbf{left}), and different distribution with $\alpha = 0.5\|\boldsymbol{\mu}_0\|_2$ (\textbf{right}).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cclccclc}
 \toprule
 \multicolumn{4}{c}{Same distribution} & \multicolumn{4}{c}{Different distribution} \\
 \cmidrule(lr){1-4} \cmidrule(lr){5-8}
 Labeled size & Acc & Unlabeled size & Acc & Labeled size & Acc & Unlabeled size & Acc \\
 \midrule
 & & 10 & 0.63 & & & 10 & 0.61 \\
 10 & 0.59 & 100 & 0.66 & 10 & 0.59 & 100 & 0.65 \\
 & & 1,000 & 0.79 & & & 1,000 & 0.78 \\
 & & 10,000 & \textbf{0.82} & & & 10,000 & \textbf{0.81} \\
 \midrule
 & & 20 & 0.64 & & & 20 & 0.65 \\
 20 & 0.62 & 200 & 0.69 & 20 & 0.62 & 200 & 0.65 \\
 & & 2,000 & 0.80 & & & 2,000 & 0.79 \\
 & & 10,000 & \textbf{0.82} & & & 10,000 & \textbf{0.80} \\
 \midrule
 & & 40 & 0.65 & & & 40 & 0.65 \\
 40 & 0.65 & 400 & 0.71 & 40 & 0.65 & 400 & 0.73 \\
 & & 4,000 & 0.81 & & & 4,000 & 0.78 \\
 & & 10,000 & \textbf{0.82} & & & 10,000 & \textbf{0.80} \\
 \midrule
 10,000 & \textbf{0.83} & - & - & 10,000 & \textbf{0.83} & - & - \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Amir Hossein Saberi
Amir Najafi
Alireza Heidari
Mohammad Hosein Movasaghinia
Abolfazl Motahari
Babak H. Khalaj

"Out-Of-Domain Unlabeled Data Improves Generalization" by Amir Hossein Saberi, Amir Najafi, Alireza Heidari, Mohammad Hosein Movasaghinia, Abolfazl Motahari, and Babak H. Khalaj, arXiv:2310.00027v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Risk in Network Economies

"Risk in Network Economies" by Victor Sellemi, arXiv:2208.01467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
 \textbf{Entity Type}& \textbf{Precision} & \textbf{Recall} & \textbf{F1-score} \\\midrule
\textsc{org}-\textsc{loc}-\textsc{fac} & 0.94 & 0.92 & 0.94 \\
\textsc{per}-\textsc{char} & 0.89 & 0.81 & 0.82 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{\texttt{ruRoBERTa} scores in the binary classification task}
\end{table}
\end{document}

cs

Timofey Atnashev
Veronika Ganeeva
Roman Kazakov
Daria Matyash
Michael Sonkin
Ekaterina Voloshina
Oleg Serikov
Ekaterina Artemova

"Razmecheno: Named Entity Recognition from Digital Archive of Diaries "Prozhito"" by Timofey Atnashev, Veronika Ganeeva, Roman Kazakov, Daria Matyash, Michael Sonkin, Ekaterina Voloshina, Oleg Serikov, and Ekaterina Artemova, arXiv:2201.09997v1, 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}{lcr}
Left&Centered&Right\\
\hline
1 & 2 & 3\\
10 & 20 & 30\\
100 & 200 & 300\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"On the Reasoning Capacity of AI Models and How to Quantify It" by Santosh Kumar Radha and Oktay Goktas, arXiv:2501.13833v1, 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{{\bf Quantitative evaluation on private photos with 43 filters.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllllll}
\hline\noalign{\smallskip}
Methods & Ours (w. R, AU) & Ours (w. R, U) & WCT2~ & Color Transfer~\\
\noalign{\smallskip}
\hline
\noalign{\smallskip}
PSNR & {\bf 25.814 } & {\bf 25.850 } & 17.234 & 6.985 \\
$\Delta E_{00}^*$ & {\bf 5.881 } & {\bf 5.854 } & 15.723 & 35.123 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Filter Style Transfer between Photos

Jonghwa Yim
Jisung Yoo
Won-joon Do
Beomsu Kim
Jihwan Choe

"Filter Style Transfer between Photos" by Jonghwa Yim, Jisung Yoo, Won-joon Do, Beomsu Kim, and Jihwan Choe, arXiv:2007.07925v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Occupational Groups.}
\begin{tabular}{lll}
\toprule
Code & Label & Examples \\ \midrule
00 & missing & Undefined or missing occupation \\
01 & executives & Politicians, business executives, managers \\
02 & professionals & Professions requiring advanced college degrees \\
03 & nurses & Nurses, midwives, physical therapy \\
04 & teacher\_lower & Pre-school and elementary school teachers \\
05 & law & Law professionals \\
06 & lower\_professionals & Professions requiring shorter college education \\
07 & secretary/clerical & Secretary, bank clerks, administrators \\
08 & other\_administrative & Sales persons, customer service agents, postal workers, local politicians \\
09 & services & Waiters, beauticians, security personnel, public transport workers \\
10 & retail & Shop assistants, cashiers, phone marketing \\
11 & care & Assistant nurse, personal assistant, nursery staff \\
12 & agriculture & Farming, forestry, fishery \\
13 & construction/craft & Carpenters, welders, printers, food processing, tailors \\
14 & machine\_operators & Mining workers, steelworkers, fitters, industry machine operators \\
15 & transportation & Truck drivers, sailors, bus drivers, train drivers \\
16 & cleaning & Cleaning and domestic services \\
17 & military & Military personnel, officers \\
18 & medecine & Medical doctors, veterinaries, dentists, psychologists \\
19 & teaching\_professionals & University teachers, high school teachers, vocational teachers \\
20 & farm \_help & Planters, croppers \\
21 & manual & Dock workers, factory workers \\
22 & other\_services & Garbage collectors, market vendors, fast food workers, janitors \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Ulrika Ahrsjö
René Karadakic
Joachim Kahr Rasmussen

"Intergenerational Mobility Trends and the Changing Role of Female Labor" by Ulrika Ahrsjö, René Karadakic, and Joachim Kahr Rasmussen, arXiv:2302.14440v2, 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{Illustration on effect of the MD iterates on the average prediction losses of BNNs on three datasets: 'Australian scale' (negative log-likelihood), 'Boston' and 'Concrete' (mean square error). The results compare the the average prediction losses after 1000 iterations of GFlowVI and NGFlowVI (with MD iterates) against their counterparts without MD iterates (w/o-MD), demonstrating that incorporating MD iterates enhances prediction accuracy.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccr}
\hline
\textbf{Methods}&	Australian &	Boston &	Concrete\\
\hline
GFlowVI &	0.51$\pm$0.02 &	1.73$\pm$0.28	& 1.49$\pm$0.08\\
GFlowVI-w/o-MD &	0.52$\pm$0.05	& 1.81$\pm$0.15 &	1.74$\pm$0.03\\
NGFlowVI &	0.6$\pm$0.03	& 1.71$\pm$0.09 &	1.25$\pm$0.06\\
NGFlowVI-w/o-MD &	0.72$\pm$0.05	& 1.87$\pm$0.15 &	1.34$\pm$0.11\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Dai Hai Nguyen
Tetsuya Sakurai
Hiroshi Mamitsuka

"Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference" by Dai Hai Nguyen, Tetsuya Sakurai, and Hiroshi Mamitsuka, arXiv:2310.16705v5, 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{Compariosn of $\mu_4$ in fBm decy}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|c|}
\multicolumn{4}{c}{}\\ \hline
$r$ &0&0.1&0.2&0.3&0.4&0.5&0.6&0.7&0.8&0.9\\
 \hline \hline
$N=1024$ $\mu_6$ &
5.0111	&	5.092	&	5.350	&	5.832	&	6.644	&	8.001& 10.4469& 15.4136& 28.4742& 91.4928\\
\hline
Theory $\mu 6$ &
5	&	5.081	&	5.342	&	5.839	&	6.686	&	8.111 &10.58 & 15.19712 & 25.2904& 57.5221\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Deformation of semi-circle law for the correlated time series and Phase transition" by Masato Hisakado and Takuya Kaneko, arXiv:2508.07192v1, 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}{|l||c|cc|cc|}
\hline
$M$ &
$\langle \overline{K}_{\rm Bayes}\rangle$ &
$\langle \overline{K}_{\rm ML}\rangle_{b=b_{\rm opt}}$ &
$\langle \overline{K}_{\rm ML}\rangle_{b=1}$ &
$\langle\overline{K}_{1} \rangle$ &
$\langle\overline{K}_{1+2} \rangle$ \\ \hline
45 & 0.015 & 0.172 & 0.196 & 0.045 & 0.042 \\ %\hline
65 & 0.014 & 0.157 & 0.180 & 0.042 & 0.035 \\ %\hline
85 & 0.014 & 0.145 & 0.164 & 0.040 & 0.031 \\ %\hline
241 & 0.013 & 0.087 & 0.091 & 0.038 & 0.024 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Maximum Entropy competes with Maximum Likelihood" by A. E. Allahverdyan and N. H. Martirosyan, arXiv:2012.09430v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters of the approximation settings}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c||c|c|c|c|c|c}
\thead{Arrival \\ process} & \thead{Service \\ process} & \thead{approx. \\ formula \\ $v_A$} & \thead{approx. \\ formula \\ $v_S$} & \thead{service rate \\ per route \\ $\mu_r$} & \thead{total \\ train count \\ $n_{total}$} & \thead{quality \\ threshold \\$L_{limit}$} & \thead{share of \\ main traffic \\ $p_{main}$} \\ \hline \hline
M & M & 0.8 & 0.3 & 0.3 & $4, \dots, 40$ & $0.13$ & $0.5$ \\ \hline
Ph & M & 1 & 0.3 & 0.3 & $4, \dots, 40$ & $0.13$ & $0.5$ \\ \hline
M & Ph & 0.8 & 1 & 0.3 & $4, \dots, 40$ & $0.13$ & $0.5$ \\ \hline
Ph & Ph & 1 & 1 & 0.3 & $4, \dots, 40$ & $0.13$ & $0.5$
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Utilizing phase-type distributions for queueing-based railway junction performance determination" by Tamme Emunds and Nils Nießen, arXiv:2412.04188v1, 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{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccrrr}
			\hline
			\hline
			& & \multicolumn{3}{c}{ \text{ Prediction }} \\
			$\alpha$ & $w$ (hours) & 0 & 1 & 2 \\
			\hline

			\multirow{3}{*}{0.01} & 24 & 0.01 & \textcolor{red}{0.15} & \textcolor{blue}{-0.15} \\
			& 2880 & 0.01 & \textcolor{red}{0.14} & \textcolor{blue}{-0.14} \\
			& 4320 & 0.01 & \textcolor{red}{0.13} & \textcolor{blue}{-0.14} \\
			\\
			\multirow{3}{*}{0.025} & 24 & 0.01 & \textcolor{red}{0.15} & \textcolor{blue}{-0.15} \\
			& 2880 & 0.01 & \textcolor{red}{0.14} & \textcolor{blue}{-0.14} \\
			& 4320 & 0.01 & \textcolor{red}{0.13} & \textcolor{blue}{-0.14} \\
			\\
			\multirow{3}{*}{0.05} & 24 & 0.01 & \textcolor{red}{0.15} & \textcolor{blue}{-0.15}\\
			& 2880 & 0.01 & \textcolor{red}{0.14} & \textcolor{blue}{-0.14}\\
			& 4320 & 0.01 & \textcolor{red}{0.13} & \textcolor{blue}{-0.14} \\
			\\
			\multirow{3}{*}{0.1} & 24 & 0.01 & \textcolor{red}{0.15} & \textcolor{blue}{-0.15} \\
			& 2880 & 0.01 & \textcolor{red}{0.14} & \textcolor{blue}{-0.14}\\
			& 4320 & 0.01 & \textcolor{red}{0.13} & \textcolor{blue}{-0.14}\\

			\hline
			\hline
		\end{tabular}
\end{adjustbox}
\caption{Class 2 misclassification costs for different $\operatorname{TVaR}^{\alpha, w}$}
\end{table}
\end{document}

q-fin

"Tail-risk protection: Machine Learning meets modern Econometrics" by Bruno Spilak and Wolfgang Karl Härdle, arXiv:2010.03315v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{graphicx}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small Experiment 1: $5$-way, $1$-shot, $5$-shot, and $10$-shot classification accuracies on \textit{new repetitions with few-shot observation}. The classification on new repetitions with few-shot observation are performed by using Meta-supervised Learning approach. This table also shows a comparison between our methodology (Meta-supervised) learning and previous works where Supervised learning methodology is used.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
			\hline
			\hline
			\multirow{6}[2]{*}{\rotatebox[origin=c]{90}{\textbf{Meta-Supervised Learning}}} \newline \multirow{6}[2]{*}{\rotatebox[origin=c]{90}{\textbf{Proposed Method}}}
			& \multirow{1}[5]{*}{\textbf{The Embedding}}
			& \multicolumn{3}{c}{\textbf{5-way Accuracy}} \\
			\cline{3-5}
			&
			\multirow{1}[-7]{*}{\textbf{Module}}
			& \textbf{1-shot}
			& \textbf{5-shot} & \textbf{10-shot}
			\\
			\cline{2-5}
			&
			\textbf{FC Embedding} & 72.59$\%$ & 85.13$\%$ & 89.26$\%$
			\\
			&
			\textbf{LSTM Embedding} & \textbf{75.03}$\%$ & 84.06$\%$ & 88.45$\%$
			\\
			&
			\textbf{T-Block Embedding I} & 73.46$\%$ & \textbf{85.94}$\%$ & 89.40$\%$
			\\
			&
			\textbf{T-Block Embedding II} & 74.89$\%$ & 85.88$\%$ & \textbf{89.70}$\%$
			\\
			\hline
			\multirow{7}[4]{*}{\rotatebox[origin=c]{90}{\textbf{Supervised Learning}}} \newline \multirow{6}[10]{*}{\rotatebox[origin=c]{90}{\textbf{Previous Works}}}
			& \textbf{Previous Works}
			& \multicolumn{2}{|c}{\multirow{2}[-5]{-12cm}{\textbf{Accuracy}}}
			\\
			\cline{2-5}
			&
			Wei \textit{et al.}~ & \multicolumn{2}{|c}{\multirow{2}[-5]{-12cm}{\textbf{83.70$\%$}}}
			\\
			&
			Hu et al.~ & \multicolumn{2}{|c}{\multirow{2}[-5]{-12cm}{82.20$\%$}}
			\\
			&
			Ding \textit{et al.}~& \multicolumn{2}{|c}{\multirow{2}[-5]{-12cm}{78.86$\%$}}
			\\
			&
			Zhai \textit{et al.}~ & \multicolumn{2}{|c}{\multirow{2}[-5]{-12cm}{78.71$\%$}}
			\\
			&
			Geng \textit{et al.}~ & \multicolumn{2}{|c}{\multirow{2}[-5]{-12cm}{77.80$\%$}}
			\\
			&
			Atzori \textit{et al.}~ & \multicolumn{2}{|c}{\multirow{2}[-5]{-12cm}{75.27$\%$}}
			\\

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

eess

Elahe Rahimian
Soheil Zabihi
Amir Asif
Dario Farina
Seyed Farokh Atashzar
Arash Mohammadi

"FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography" by Elahe Rahimian, Soheil Zabihi, Amir Asif, Dario Farina, Seyed Farokh Atashzar, and Arash Mohammadi, arXiv:2011.06104v1, 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}{cccrrrrr} \hline\hline \\
Data / Model & True & Emp & AMH & CLA & FRA & GUM & JOE \\
\hline \\
AMH & 0.04 & 0.01 & 0.02 & 0.04 & 0.02 & 0.05 & -0.04 \\
CLA & 0.25 & 0.14 & 0.12 & 0.22 & 0.22 & 0.33 & 0.19\\
FRA & 0.00 & -0.07 & -0.05 & -0.01 & -0.07 & 0.05 & -0.08\\
GUM &0.85 & 0.84 & 0.33 & 0.78 & 0.83 & 0.85 & 0.81\\
JOE & 0.59 & 0.54 & 0.30 & 0.48 & 0.58 & 0.61 & 0.59\\
\hline\hline
\end{tabular}
\end{adjustbox}
\caption{Simulation study 1: Bivariate data from a mixture of Archimedean copulas. True, empirical and estimated Kendall's tau values with $n=500$.}
\end{table}
\end{document}

stat

Ruyi Pan
Luis E. Nieto-Barajas
Radu V. Craiu

"Bayesian nonparametric mixtures of Archimedean copulas" by Ruyi Pan, Luis E. Nieto-Barajas, and Radu V. Craiu, arXiv:2412.09539v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.