\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}{|c|c|c|c|c|}
				\hline
				\small\textbf{Case}& \small\textbf{Conditions}& \small\textbf{$\boldsymbol{O_1}$}& \small\textbf{$\boldsymbol{O_2}$} & \small\textbf{Global} \\
				\hline
				\hline

					\multirow{3}{1cm}{\centering{2.3}}
				&
				$\mu=0$
				&
				L14
				&
				\multirow{2}{3cm}{\centering{Unstable node}}
				&
				\multirow{2}{1cm}{\centering{G43}} \\
				\cline{2-3}

				\multirow{3}{*}
				&
				$c_1=0$, $\mu>-1$
				&
				L36
				&
				\multirow{2}{*}
				&
				\multirow{2}{*} {\phantom{h}} \\
				\cline{2-5}

				\multirow{3}{*}
				&
				$c_1=0$, $\mu<-1$
				&
				L43
				&
				Saddle
				&
				G44 \\
				\hline
			\multirow{3}{1cm}{\centering{2.4}}
			&
			$\mu=0$
			&
			L5
			&
			Stable node
			&
			G45\\
			\cline{2-5}

			\multirow{3}{*}
			&
			$c_1=0$, $\mu>-1$
			&
			L35
			&
			Stable node
			&
			G46\\
			\cline{2-5}

			\multirow{3}{*}
			&
			$c_1=0$, $\mu<-1$
			&
			L39
			&
			Saddle
			&
			G47 \\
			\hline

			\multirow{3}{1cm}{\centering{2.5}}
			&
			$\mu=0$
			&
			L14
			&
			\multirow{2}{3cm}{\centering{Unstable node}}
			&
			\multirow{2}{1cm}{\centering{G48}}
			\\
			\cline{2-3}

			\multirow{3}{*}
			&
			$c_1=0$, $\mu>-1$
			&
			L45
			&
			\multirow{2}{*}
			&
			\multirow{2}{*} {\phantom{h}}
			\\
			\cline{2-5}

			\multirow{3}{*}
			&
			$c_1=0$, $\mu<-1$
			&
			L44
			&
			Saddle
			&
			G49 \\
			\hline

				\multirow{3}{1cm}{\centering{2.6}}
			&
			$\mu=0$
			&
			L5
			&
			Stable node
			&
			G50\\
			\cline{2-5}

			\multirow{3}{*}
			&
			$c_1=0$, $\mu>-1$
			&
			L38
			&
			Stable node
			&
			G51 \\
			\cline{2-5}

			\multirow{3}{*}
			&
			$c_1=0$, $\mu<-1$
			&
			L41
			&
			Saddle
			&
			G52 \\
			\hline
		\multirow{2}{1cm}{\centering{2.7}}
		&
		$\mu>-1$
		&
		L37
		&
		Stable node
		&
		G53\\
		\cline{2-5}

		\multirow{2}{*}
		&
		$\mu<-1$
		&
		L40
		&
		Saddle
		&
		G54 \\
		\hline

		\multirow{2}{1cm}{\centering{2.8}}
		&
		$\mu>-1$
		&
		L34
		&
		Unstable node
		&
		G55\\
		\cline{2-5}

		\multirow{2}{*}
		&
		$\mu<-1$
		&
		L42
		&
		Saddle
		&
		G56 \\
		\hline
		\multirow{3}{1cm}{\centering{2.9}}
		&
		$\mu=0$
		&
		L14
		&
		\multirow{2}{3cm}{\centering{Unstable node}}
		&
		\multirow{2}{1cm}{\centering{G57}}
		\\
		\cline{2-3}

		\multirow{3}{*}
		&
		$c_1=0$, $\mu>-1$
		&
		L24
		&
		\multirow{2}{*}
		&
		\multirow{2}{*} {\phantom{h}}
		\\
		\cline{2-5}

		\multirow{3}{*}
		&
		$c_1=0$, $\mu<-1$
		&
		L26
		&
		Saddle
		&
		G58 \\
		\hline

		\multirow{3}{1cm}{\centering{2.10}}
		&
		$\mu=0$
		&
		L5
		&
		Stable node
		&
		G59\\
		\cline{2-5}

		\multirow{3}{*}
		&
		$c_1=0$, $\mu>-1$
		&
		L23
		&
		Stable node
		&
		G60 \\
		\cline{2-5}

		\multirow{3}{*}
		&
		$c_1=0$, $\mu<-1$
		&
		L25
		&
		Saddle
		&
		G61 \\
		\hline
		3.1
		&

		&
		L12
		&
		Unstable node
		&
		G62 \\
		\hline
		\multirow{3}{1cm}{\centering{3.2}}
		&
		$\mu>0$
		&
		L6
		&
		Unstable node
		&
		G63\\
		\cline{2-5}

		\multirow{3}{*}
		&
		$\mu<-1$
		&
		L8
		&
		Stable node
		&
		G64 \\
		\cline{2-5}

		\multirow{3}{*}
		&
		$\mu\in(-1,0)$
		&
		L13
		&
		Unstable node
		&
		G65 \\
		\hline

		\multirow{3}{1cm}{\centering{3.3}}
		&
		$\mu>0$
		&
		L3
		&
		Stable node
		&
		G66\\
		\cline{2-5}

		\multirow{3}{*}
		&
		$\mu<-1$
		&
		L11
		&
		Stable node
		&
		G68 \\
		\cline{2-5}

		\multirow{3}{*}
		&
		$\mu\in(-1,0)$
		&
		L10
		&
		Stable node
		&
		G67 \\
		\hline
		\multirow{4}{1cm}{\centering{3.4}}
		&
		$\mu<-2$
		&
		L1
		&
		Saddle
		&
		G69 \\
		\cline{2-5}

		\multirow{4}{*}
		&
		$\mu\in(-1,0)$
		&
		L4
		&
		Stable node
		&
		G70 \\
		\cline{2-5}

		\multirow{4}{*}
		&
		$\mu\in(-2,-1)$
		&
		L15
		&
		Saddle
		&
		G71 \\
		\cline{2-5}

		\multirow{4}{*}
		&
		$\mu=-2$
		&
		L17
		&
		Saddle
		&
		G72 \\
		\hline

		\multirow{3}{1cm}{\centering{3.5}}
		&
		$\mu>0$
		&
		L3
		&
		Stable node
		&
		G7 \\
		\cline{2-5}

		\multirow{3}{*}
		&
		$\mu\in(-1,0)$
		&
		L10
		&
		Stable node
		&
		G74 \\
		\cline{2-5}

		\multirow{3}{*}
		&
		$\mu<-1$
		&
		L11
		&
		Saddle
		&
		G75 \\
		\hline

	 3.6
		&

		&
		L12
		&
		Unstable node
		&
		G76 \\
		\hline

			\multirow{2}{1cm}{\centering{3.7}}
		&
		$\mu<-1$
		&
		L8
		&
		Saddle
		&
		G77 \\
		\cline{2-5}

		\multirow{2}{*}
		&
		$\mu\in(-1,0)$
		&
		L13
		&
		Unstable node
		&
		G78 \\
		\hline

		3.8
		&

		&
		L3
		&
		Stable node
		&
		G79 \\
		\hline

			\multirow{4}{1cm}{\centering{3.9}}
		&
		$\mu<-2$
		&
		L1
		&
		Saddle
		&
		G80 \\
		\cline{2-5}

		\multirow{4}{*}
		&
		$\mu\in(-1,0)$
		&
		L4
		&
		Stable node
		&
		G81\\
		\cline{2-5}

		\multirow{4}{*}
		&
		$\mu\in(-2,-1)$
		&
		L15
		&
		Saddle
		&
		G82\\
		\cline{2-5}

		\multirow{4}{*}
		&
		$\mu=-2$
		&
		L17
		&
		Saddle
		&
		G83 \\
		\hline

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

math

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

"Phase portraits of a family of Kolmogorov systems depending on six parameters" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.13714v1, 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{ Location of estimated change points in the EEG data set with their CIs for the segment $(1,110)$ based on our method. True change point is $\lfloor T \tau \rfloor = 85$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\hline
Channel & value ($\lfloor T \tilde{\tau} \rfloor$) & $70\%$ CI & $80\%$ CI & $90\%$ CI & $95\%$ CI & $99\%$ CI \\ \hline\hline
P3 & 90 & (64,96) & (63,100) & (60,107) & (58,115) & (52,136) \\
T3 & 86 & (85,87) & (85,87) & (85,91) & (85,98) & (85,118) \\
T5 & 89 & (37,94) & (34,98) & (29,106) & (24,115) & (12,139) \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Sepideh Mosaferi
Abolfazl Safikhani
Peiliang Bai

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

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{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Libri2Vox statistics. "Total" column shows the total number of utterances combined with corresponding LibriTTS and VoxCeleb2 sets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{Set}} & \multicolumn{2}{c|}{\textbf{\#. Speakers}} & \textbf{\#. Utterances} & \multirow{2}{*}{\textbf{Duration (h)}} \\ \cline{2-4}
& \textbf{LibriTTS} & \textbf{VoxCeleb2} & \textbf{Total} & \\ \hline
\textbf{Training} & 1,151 & 5,900 & 149,691 & 250 \\ \hline
\textbf{Validation} & 40 & 94 & 7,200 & 8.97 \\ \hline
\textbf{Testing} & 39 & 118 & 6,000 & 8.56 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yun Liu
Xuechen Liu
Xiaoxiao Miao
Junichi Yamagishi

"Libri2Vox Dataset: Target Speaker Extraction with Diverse Speaker Conditions and Synthetic Data" by Yun Liu, Xuechen Liu, Xiaoxiao Miao, and Junichi Yamagishi, arXiv:2412.12512v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ll}
\toprule
 Model & Method & Time (Mean $\pm$ Std) \\
 \midrule
 Neural network & Early stopping & $2.98 \pm 0.24$ hours \\
 & Retrain & $5.44 \pm 0.37$ hours \\
 GBDT & Early stopping & $56.94 \pm 1.59$ seconds \\
 & Retrain & $154.12 \pm 4.27$ seconds \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Shapley value etimation running time comparison.}
\end{table}
\end{document}

stat

"Reliable and scalable variable importance estimation via warm-start and early stopping" by Zexuan Sun and Garvesh Raskutti, arXiv:2412.01120v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
\textnormal{$e< \beta^{-}(c)$} & \textnormal{$\beta^{+}(c)< e$} \\
\hline
\textnormal{$p(t)=\left(\int_{0}^{t}\Phi(t,s)c(s)\,ds+o(1)\right)^{-1}$} & \textnormal{$p(t)\to 0$ (exponentially)} \\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Behavior of the fraction of occupied patches $p(t)$ when $t\to +\infty$ and the colonization rate is constant.}
\end{table}
\end{document}

math

"Generalizing the Levins metapopulation model to time varying colonization and extinction rates" by Gonzalo Robledo and Ramiro Bustamante, arXiv:2504.20396v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|ll}
 \toprule
 \textbf{Metric} & \textbf{Formula} & \textbf{Metric} & \textbf{Formula} \\
 \midrule
 Recall & $\displaystyle \frac{TP}{TP + FN}$ &
 Accuracy & $\displaystyle \frac{TP + TN}{TP + TN + FP + FN}$ \\

 Precision & $\displaystyle \frac{TP}{TP + FP}$ &
 Specificity & $\displaystyle \frac{TN}{TN + FP}$ \\

 F1 Score & $\displaystyle 2 \cdot \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}$ &
 False Positive Rate & $\displaystyle \frac{FP}{FP + TN}$ \\

 False Negative Rate & $\displaystyle \frac{FN}{FN + TP}$ & & \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Evaluation Metrics}
\end{table}
\end{document}

q-fin

"BlockingPy: approximate nearest neighbours for blocking of records for entity resolution" by Tymoteusz Strojny and Maciej Beręsewicz, arXiv:2504.04266v2, 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{Standardized mean square errors on regression benchmark datasets, averaged across 20 training-testing splits, for obliqueBART and rotated versions of axis-aligned methods with 200 random rotations. Best performing method is bolded and errors that are statistically significantly larger than obliqueBART's are marked with an asterisk. NA indicates that the method could not be run with 200 random rotations.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 \hline
 data & obliqueBART & rotERT & rotRF & rotBART & rotXGB \\
 \hline
 \texttt{abalone} & \textbf{0.44} & 0.45* & 1.08* & 0.448* & 0.47*\\
 \texttt{ais} & \textbf{0.122} & 0.151* & 1.33* & 0.129 & 0.167*\\
 \texttt{amenity} & 0.283 & 0.349* & 1.07* & \textbf{0.281} & 0.357*\\
 \texttt{attend} & \textbf{0.246} & 0.304* & 1.06* & 0.297* & 0.305*\\
 \texttt{baseball} & \textbf{0.39} & 0.446* & 1.07* & 0.406* & 0.544*\\
 \texttt{basketball} & 0.762 & \textbf{0.68} & 1.06* & 0.691 & 0.767*\\
 \texttt{boston} & 0.778 & 0.833 & 22.6* & \textbf{0.577} & 0.853\\
 \texttt{budget} & \textbf{0.003} & 0.010* & 1.200* & 0.003 & 0.019*\\
 \texttt{cane} & \textbf{0.183} & 0.315* & 1.580* & 0.191 & 0.294*\\
 \texttt{cpu} & \textbf{0.139} & 0.201* & 1.480* & 0.313* & 0.249*\\
 \texttt{diabetes} & \textbf{0.495} & 0.520* & 0.967* & 0.499 & 0.606*\\
 \texttt{diamonds} & 0.020 & NA & NA & \textbf{0.020} & NA\\
 \texttt{edu} & \textbf{0.020} & 0.022* & 1.150* & 0.020 & 0.024*\\
 \texttt{labor} & \textbf{0.221} & NA & NA & NA & NA\\
 \texttt{mpg} & \textbf{0.135} & 0.14 & 1.09* & 0.138 & 0.16*\\
 \texttt{rice}& \textbf{0.015} & 0.204* & 1.670* & 0.026* & 0.234*\\
 \texttt{servo} & \textbf{0.167} & 0.25* & 2.24* & 0.196* & 0.225*\\
 \texttt{strikes} & 0.926 & 0.915 & 36.2* & \textbf{0.805} & 1.31\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Paul-Hieu V. Nguyen
Ryan Yee
Sameer K. Deshpande

"Oblique Bayesian additive regression trees" by Paul-Hieu V. Nguyen, Ryan Yee, and Sameer K. Deshpande, arXiv:2411.08849v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Step time (ms) for OLG and Ramsey models at varying population sizes ($N$), Per-agent time indicates average computational time per individual at each step.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\multirow{2}{*}{\textbf{Population Size}} & \multicolumn{2}{c}{\textbf{OLG Model}} & \multicolumn{2}{c}{\textbf{Ramsey Model}} \\
& \textbf{Step Time (ms)} & \textbf{Step Time / Agent (ms)} & \textbf{Step Time (ms)} & \textbf{Step Time / Agent (ms)} \\
\midrule
$\mathbf{N = 10}$ & 0.420 & 0.077 & 0.292 & 0.029 \\
$\mathbf{N = 100}$ & 0.723 & 0.031 & 0.665 & 0.007 \\
$\mathbf{N = 1{,}000}$ & 2.729 & 0.018 & 8.131 & 0.008 \\
$\mathbf{N = 10{,}000}$ & 62.276 & 0.016 & 451.798 & 0.045 \\
$\mathbf{N = 100{,}000}$ & 5173.004 & 0.096 & 39460.2 & 0.394 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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

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


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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Values of PPO hyperparameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 \cmidrule(r){1-2}
 parameter name & value \\
 \midrule
 use gae & True \\
 learning rate & $2.5 \cdot 10^{-4}$ \\
 use linear learning rate decay & True\\
 clip parameter & 0.1 \\
 value loss coefficient & 0.5 \\
 entropy coefficient & 0.01\\
 num steps & 128\\
 num processes & 4\\
 evaluation interval & 50\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jagdeep Singh Bhatia
Holly Jackson
Yunsheng Tian
Jie Xu
Wojciech Matusik

"Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots" by Jagdeep Singh Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, and Wojciech Matusik, arXiv:2201.09863v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll} % cccc
 \toprule
 \rowcolor[HTML]{FFF2CC}
 \textbf{Year} & \textbf{Method} & \textbf{Backbone} & \textbf{Financial Datasets Used} \\
 \midrule
 \rowcolor[HTML]{DDEBFF}
 2016 & C-RNN-GAN & GAN & / \\
 \rowcolor[HTML]{DDEBFF}
 2017 & RCGAN & GAN & / \\
 \rowcolor[HTML]{DDEBFF}
 2018 & T-CGAN & GAN & / \\
 \rowcolor[HTML]{DDEBFF}
 2019 & TimeGAN & GAN & Stocks \\
 \rowcolor[HTML]{DDEBFF}
 2019 & WaveGAN & GAN & / \\
 \rowcolor[HTML]{DDEBFF}
 2020 & COT-GAN & GAN & / \\
 \rowcolor[HTML]{DDEBFF}
 2020 & DoppelGANger & GAN & / \\
 \rowcolor[HTML]{DDEBFF}
 2020 & Quant-GAN & GAN & SPX \\
 \rowcolor[HTML]{DDEBFF}
 2020 & SigCWGAN & GAN & SPX \& DJI \\
 \rowcolor[HTML]{DDEBFF}
 2020 & TSGAN & GAN & / \\
 \rowcolor[HTML]{DDEBFF}
 2021 & RTSGAN & GAN & Stocks \\
 \rowcolor[HTML]{DDEBFF}
 2021 & Sig-WGAN & GAN & SPX \& DJI \\
 \rowcolor[HTML]{DDEBFF}
 2021 & TimeGCI & GAN & / \\
 \rowcolor[HTML]{DDEBFF}
 2022 & CEGEN & GAN & Stocks \& Electric Price \\
 \rowcolor[HTML]{DDEBFF}
 2022 & COSCI-GAN & GAN & / \\
 \rowcolor[HTML]{DDEBFF}
 2022 & PSA-GAN & GAN & / \\
 \rowcolor[HTML]{DDEBFF}
 2022 & TsT-GAN & GAN & Stocks \\
 \rowcolor[HTML]{DDEBFF}
 2022 & TTS-GAN & GAN & / \\
 \rowcolor[HTML]{DDEBFF}
 2023 & AEC-GAN & GAN & / \\
 \rowcolor[HTML]{DDEBFF}
 2023 & TT-AAE & GAN & Stocks \\
 \midrule
 2021 & TimeVAE & VAE & Stocks \\
 2023 & CRVAE & VAE & / \\
 2023 & TimeVQVAE & VAE & / \\
 2024 & KoVAE & VAE & Stocks \\
 \midrule

 \rowcolor[HTML]{DDEBFF}
 2023 & DiffTime & Diffusion & Stocks \\
 \rowcolor[HTML]{DDEBFF}
 2023 & TSGM & Diffusion & Stocks \\
 \rowcolor[HTML]{DDEBFF}
 2024 & Diffusion-TS & Diffusion & Stocks \\
 \rowcolor[HTML]{DDEBFF}
 2024 & FIDE & Diffusion & Stocks \\
 \rowcolor[HTML]{DDEBFF}
 2024 & ImagenTime & Diffusion & Stocks \\
 \rowcolor[HTML]{DDEBFF}
 2024 & SDformer & Diffusion & Stocks \\
 \rowcolor[HTML]{DDEBFF}
 2025 & PaD-TS & Diffusion & Stocks \\
 \midrule

 2020 & CTFP & Flow & / \\
 2021 & Fourier-Flow & Flow & Stocks \\
 2024 & FlowTS & Flow & Stocks \\
 \midrule

 \rowcolor[HTML]{DDEBFF}
 2018 & Neural ODE & ODE + RNN & / \\
 \rowcolor[HTML]{DDEBFF}
 2019 & ODE-RNN & ODE + RNN & / \\
 \rowcolor[HTML]{DDEBFF}
 2021 & Neural SDE & ODE + GAN & Stocks \\
 \rowcolor[HTML]{DDEBFF}
 2022 & GT-GAN & ODE + GAN & Stocks \\
 \rowcolor[HTML]{DDEBFF}
 2023 & LS4 & ODE + VAE & / \\
 \rowcolor[HTML]{DDEBFF}
 2024 & TimeLDM & Diffusion + VAE & Stocks \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Yihao Ang
Qiang Wang
Qiang Huang
Yifan Bao
Xinyu Xi
Anthony K. H. Tung
Chen Jin
Zhiyong Huang

"CTBench: Cryptocurrency Time Series Generation Benchmark" by Yihao Ang, Qiang Wang, Qiang Huang, Yifan Bao, Xinyu Xi, Anthony K. H. Tung, Chen Jin, and Zhiyong Huang, arXiv:2508.02758v1, 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}{l}
 \toprule
 \textbf{\footnotesize{Input Audio}} \\
 \footnotesize{...} \\
 \footnotesize{Ich zeig der, wo de Bartli de Moscht holt.} \\
 \footnotesize{...} \\
 \midrule
 \textbf{\footnotesize{Whisper Large-v2}} \\
 \footnotesize{...} \\
 \footnotesize{\texttt{[00:00:08]} Ich zeige dir, wo Bartli den Most holt. \texttt{[00:00:11]}} \\
 \footnotesize{...} \\
 \midrule
 \textbf{\footnotesize{Whisper Large-v2 (fine-tuned on sentences)}} \\
 \footnotesize{...} \\
 \footnotesize{Ich zeige dir, wo es die Bartli in den Most holt.} \\
 \footnotesize{...} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Vincenzo Timmel
Claudio Paonessa
Reza Kakooee
Manfred Vogel
Daniel Perruchoud

"Fine-tuning Whisper on Low-Resource Languages for Real-World Applications" by Vincenzo Timmel, Claudio Paonessa, Reza Kakooee, Manfred Vogel, and Daniel Perruchoud, arXiv:2412.15726v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small Support vector machine}
\begin{tabular}{|c|c|c|c|}
 \hline
 \multirow{2}{*}{Id}&
 \multicolumn{3}{c|}{Size}
 \\ \cline{2-4}
&$\overline n$ & $n$ & $l$\\
\hline
13&2 & 403 & 400 \\ \hline
14&3&604 & 600\\
\hline
15&5&1006 & 1000\\
\hline
\end{tabular}
\end{table}
\end{document}

cs

"Comparing different subgradient methods for solving convex optimization problems with functional constraints" by Thi Lan Dinh and Ngoc Hoang Anh Mai, arXiv:2101.01045v2, 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}{rccccc}
 \toprule
 & $\sigma$ & HP & NORM & TA & TVA \\
 \midrule
 Baseline & 800 & \textbf{0.92} & 0.66 & 0.66 & 0.67 \\
 Baseline & 1500 & 0.85 & 0.72 & 0.65 & 0.68 \\
 Baseline & 7000 & 0.60 & 0.78 & 0.76 & 0.84 \\
 Multi-resolution & - & 0.89 & \textbf{0.83} & \textbf{0.81} & \textbf{0.87} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Comparison of the class BA between the baseline and the proposed multi-resolution approach.}
\end{table}
\end{document}

cs

Carlo Alberto Barbano
Daniele Perlo
Enzo Tartaglione
Attilio Fiandrotti
Luca Bertero
Paola Cassoni
Marco Grangetto

"UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading" by Carlo Alberto Barbano, Daniele Perlo, Enzo Tartaglione, Attilio Fiandrotti, Luca Bertero, Paola Cassoni, and Marco Grangetto, arXiv:2101.09991v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of OFA-595 student with various teachers}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\toprule
 & \multicolumn{3}{c}{\textbf{LCS25000 Dataset}} & \multicolumn{3}{c}{\textbf{COVID-QU-Ex Dataset}} \\
\cmidrule(lr){2-4} \cmidrule(lr){5-7}
\textbf{Teacher} & \textbf{AUC} & \textbf{Acc.} & \textbf{F1} & \textbf{AUC} & \textbf{Acc.} & \textbf{F1} \\
\midrule
VGG-19 & 82.9 & 92.4 & 93.1 & 86.1 & 94.3 & 94.1 \\
DenseNet-201 & 85.1 & 93.8 & 95.6 & 88.9 & 96.8 & 96.2 \\
OFA-595 & 85.9 & 95.2 & 96.0 & 91.1 & 96.7 & 97.3 \\
Visformer-S & 83.6 & 97.2 & 95.09 & 92.0 & 97.0 & 97.1 \\
EEENA-Net C2 & 83.2 & 96.1 & 96.9 & 93.7 & 96.9 & 98.7 \\
AutoFormer & 83.7 & 95.6 & 95.4 & 89.9 & 97.0 & 97.5 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Explainable Knowledge Distillation for Efficient Medical Image Classification" by Aqib Nazir Mir and Danish Raza Rizvi, arXiv:2508.15251v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 & \multicolumn{2}{l}{Betting Odds} & \multicolumn{2}{l}{Transfermarkt regression}
\\ \midrule
 & Before & After & Before & After \\ \midrule
Premier League & -0.0269 & -0.0078 & -0.0226 & -0.0028 \\
Championship & -0.0095 & & -0.0050 & \\
League One & -0.0103 & & -0.0037 & \\
League Two & -0.0067 & & -0.0012 & \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Differences in Brier scores for Betting Odds and Transfermarkt regression model compared to the Null model before and after removing games involving the Big Six. All differences are significant at the $p<0.001$ level.}
\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.


\documentclass{article}
\usepackage{booktabs}
\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}{lcccc}
		\toprule
		& \textbf{Low} & \textbf{Medium}&\textbf{High}&\textbf{Extreme}\\
		\midrule
		\rowcolor{gray!15}
		Low & 490 & 1934 & 262 & 587 \\
		\rowcolor{gray!15}
		& (802) & (2602) & (404) & (810) \\
		Medium & 344 & 1240 & 209 & 285 \\
		& (494) & (2608) & (302) & (401) \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Michele Azzone
Matteo Ghesini
Davide Stocco
Lorenzo Viola

"Climate Physical Risk Assessment in Asset Management" by Michele Azzone, Matteo Ghesini, Davide Stocco, and Lorenzo Viola, arXiv:2504.19307v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Improved Upper Bound on Brun's Constant Under GRH" by Lachlan Dunn, arXiv:2504.15658v2, 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
& \multicolumn{2}{c}{Regional level} \\
Region & lower & higher \\
\hline
US & 3142 counties & 51 states \\
EU (NUTS3) & 1165 NUTS-3 & 244 EU NUTS-2\\
Germany & 401 districts & 38 gov.\ districts\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Regional levels of super-resolution datasets}
\end{table}
\end{document}

q-fin

Marcus Buckmann
Quynh Anh Nguyen
Edward Hill

"Revealing economic facts: LLMs know more than they say" by Marcus Buckmann, Quynh Anh Nguyen, and Edward Hill, arXiv:2505.08662v1, 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{Hyperparameter search space of GRANDE.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\toprule
\textbf{Hyperparameter} & \textbf{Distribution} \\ \midrule
depth & $[4, 6]$\\
n\_estimators & $[512, 1024, 2048]$\\
learning\_rate\_weights & $\text{log\_uniform}[1\mathrm{e}{-4},1\mathrm{e}{-1}]$\\
learning\_rate\_index & $\text{log\_uniform}[5\mathrm{e}{-3},2\mathrm{e}{-1}]$\\
learning\_rate\_values & $\text{log\_uniform}[5\mathrm{e}{-3},2\mathrm{e}{-1}]$\\
learning\_rate\_leaf & $\text{log\_uniform}[5\mathrm{e}{-3},2\mathrm{e}{-1}]$\\
cosine\_decay\_steps & $[0, 100, 1000]$\\
loss & $[\text{crossentropy},\text{focal\_crossentropy}](classification),[\text{mse}](regression)$\\
dropout & $[0.0, 0.25, 0.5]$ \\
selected\_variables & $\text{uniform}[0.5, 1.0]$\\
data\_subset\_fraction & $\text{uniform}[0.8, 1.0]$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

DOFEN: Deep Oblivious Forest ENsemble

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Estimator Performance for Case 2 ($\alpha_1=0.8$) across Different Sample Sizes ($T$)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 \textbf{Metric / Parameter} & \textbf{T = 200} & \textbf{T = 500} & \textbf{T = 1000} \\
 \midrule
 \multicolumn{4}{l}{\textit{Mean of Parameter Estimates (Bias Assessment)}} \\
 \quad Mean($\hat{\nu}$) & 101.49 & 101.03 & 100.83 \\
 \quad Mean($\hat{\alpha}_1$) & 0.7967 & 0.7971 & 0.7990 \\
 \quad Mean($\hat{\alpha}_2$) & -0.0059 & -0.0017 & -0.0021 \\
 \midrule
 \multicolumn{4}{l}{\textit{Error Metrics (Variance and Accuracy Assessment)}} \\
 \quad Mean Squared Error (MSE) & 86.39 & 65.48 & 50.11 \\
 \quad Relative $\ell^2$-Error ($\boldsymbol{\theta}$) & 1.486\% & 1.031\% & 0.832\% \\
 \quad Relative $\ell^2$-Error ($\boldsymbol{\alpha}$) & 1.291\% & 0.789\% & 0.674\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Statistical Inference for Cumulative INAR($\infty$) Processes via Least-Squares" by Yingli Wang, Xiaohong Duan, and Ping He, arXiv:2412.01569v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{marvosym}
\usepackage{pifont}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
\toprule
\multirow{2}[2]{*}{AEG Sampling} & \multicolumn{3}{c}{$\mathrm{TRA}$ Score} \\
\cmidrule(lr){2-4}
 & U373 & GOWT1 & Huh7\\
\midrule
\ding{55} & 0.556 & 0.941 & 0.942 \\
\ding{51} & \textbf{0.985} & \textbf{0.960} & \textbf{0.966}\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{ \textbf{Effectiveness of AEG Sampling.} AEG sampling is built to balance the cell event (division) in a training sequence.}
\end{table}
\end{document}

eess

Yaxuan Song
Jianan Fan
Heng Huang
Mei Chen
Weidong Cai

"Cell as Point: One-Stage Framework for Efficient Cell Tracking" by Yaxuan Song, Jianan Fan, Heng Huang, Mei Chen, and Weidong Cai, arXiv:2411.14833v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

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


stat

Alexander Decruyenaere
Heidelinde Dehaene
Paloma Rabaey
Christiaan Polet
Johan Decruyenaere
Stijn Vansteelandt
Thomas Demeester

"The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data" by Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet, Johan Decruyenaere, Stijn Vansteelandt, and Thomas Demeester, arXiv:2312.07837v2, 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 \hline
				& \multicolumn{2}{|c||}{Non-Seizure} & \multicolumn{2}{|c||}{Seizure} \\ \hline \hline
				Coefficients	& Value & CB95\% 			& Value & CB95\% 			\\ \hline \hline
				$a$ 	& -0.6855 & (-4.095, 2.725) & 1.368 & (-6.717, 9.453) \\
				$b$ 	& 0.9989 & (0.9989, 0.9989) & 0.9998 & (0.9998, 0.9998) \\
				$c$ 	& -36.05 & (-38.53, -33.57) 	 & -35.45 & (-41.47, -29.43) \\
				\hline \hline
			\end{tabular}
\end{adjustbox}
\caption{The quadratic linear-parabolic model coefficients and the associated 95\% confidence bounds (CB).}
\end{table}
\end{document}

eess

Antonio Quintero-Rincon
Carlos D'Giano
Hadj Batatia

"A quadratic linear-parabolic model-based classification to detect epileptic EEG seizures" by Antonio Quintero-Rincon, Carlos D'Giano, and Hadj Batatia, arXiv:1912.08359v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 $\lambda_c$ & $k_1$ & $k_2/\sigma_c$ \\
 $day^{-1}$ & & $mg/kg$ \\
 \hline $5.55$& $115.12$ & $65.67$\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Parameters for the cytotoxic drug.}
\end{table}
\end{document}

math

Luis A. Fernández
Isabel Lasheras
Cecilia Pola

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Testing prediction results for the S\&P 500 data}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
\hline \hline
{Method} & {MEI} & {MHI} & DD \\
\hline
LDA & 0.64 & 0.43 & 0.64 \\
QDA & 0.50 & 0.43 & 0.43\\
kNN & 0.35 & 0.40 & 0.57 \\
SVM & 0.45 & 0.47 & - \\
RF & 0.71 & 0.69 & - \\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Catalina Lesmes
Francisco Zuluaga
Henry Laniado
Andres Gomez
Andrea Carvajal

"The EE-Classifier: A classification method for functional data based on extremality indexes" by Catalina Lesmes, Francisco Zuluaga, Henry Laniado, Andres Gomez, and Andrea Carvajal, arXiv:2411.14999v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l|l}
& SSIM & LPIPS & MSE & PSNR(dB) \\ \hline
w supervision & \textbf{0.8823} & \textbf{1.81e-05} & \textbf{0.0033} & \textbf{34.6733} \\ \hline
w\textbackslash{}o supervision & 0.7570 & 2.54e-04 & 0.0059 & 27.2784
\end{tabular}
\end{adjustbox}
\caption{Quantitative comparison between model trained with supervision and model trained without supervision.}
\end{table}
\end{document}

eess

Peyman Tahghighi
Reza A. Zoroofi
Sare Safi
Alireza Ramezani

"Analysis of Macula on Color Fundus Images Using Heightmap Reconstruction Through Deep Learning" by Peyman Tahghighi, Reza A. Zoroofi, Sare Safi, and Alireza Ramezani, arXiv:2012.14140v1, 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}
 Scenario & Lower 2.5\% quantile & Lower median & Upper median & Upper 97.5\% quantile\\
 \hline
 P0 & 42.42 & 43.07 & 46.22 & 46.88 \\
 P1 & 44.60 & 45.24 & 48.49 & 49.14 \\
 P2 & 40.30 & 40.89 & 43.92 & 44.59 \\
 P3 & 42.37 & 43.05 & 46.22 & 46.90 \\
 P4 & 42.00 & 42.96 & 46.21 & 47.20
 \end{tabular}
\end{adjustbox}
\caption{ Statistics for the 20-year return level. Estimated 2.5\% and 50\% quantiles from the conservative lower bounds and the 50\% and 97.5\% quantiles from the conservative upper bounds in million £s to 4s.f. for different portfolio scenarios. For scenarios P3 and P4 the estimates are from a single pooled sample of all of the $R\times M$ simulations of the return level.}
\end{table}
\end{document}

stat

"Fast return-level estimates for flood insurance via an improved Bennett inequality for random variables with differing upper bounds" by Anna Maria Barlow and Chris Sherlock, arXiv:2311.10001v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Léa Ricard
Guy Desaulniers
Andrea Lodi
Louis-Martin Rousseau

"Chance-constrained battery management strategies for the electric bus scheduling problem" by Léa Ricard, Guy Desaulniers, Andrea Lodi, and Louis-Martin Rousseau, arXiv:2503.19853v1, 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}{c|c|r|r|r}
 \hline
 \multicolumn{5}{c}{Including component demand} \\
 \hline
 Utilization & $\lambda$ & Runtime (s) & Total costs PH & $\Delta$ (\%) \\
 \hline
 \multirow{4}{*}{50\%}
 & 0.1 & 1931.3 & 28425.5 & 3.72 \\
 & 1 & 779.5 & 27377.4 & \textbf{0.50} \\
 & 10 & 390.5 & 28130.6 & 3.06 \\
 & 100 & 191.3 & 28504.8 & 4.35 \\
 \hline
 \multirow{4}{*}{90\%}
 & 0.1 & 2891.1 & 62734.9 & 0.86 \\
 & 1 & 1743.9 & 62577.5 & \textbf{0.77} \\
 & 10 & 912.6 & 63336.3 & 1.94 \\
 & 100 & 802.7 & 64298.0 & 3.45 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Averaging consensus calculation: Evaluation of the $\lambda$-multiple for the penalty parameter $\rho$ in terms of total costs and runtime for the case including component demands on a scenario tree with $|\Omega| = 2$ and therefore $|\Phi| = 128$ on 96 test instances}
\end{table}
\end{document}

math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

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


q-fin

Luca De Benedictis
Vania Licio
Anna Pinna

"From the historical Roman road network to modern infrastructure in Italy" by Luca De Benedictis, Vania Licio, and Anna Pinna, arXiv:2208.06675v1, 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{Estimation of initial population sizes for model .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\hline
Initial population & Initial value & Source\\
\hline
S(0)&1,97,112 & Estimated \\
I(0)&5,646&Estimated\\
B(0)&21,259&Estimated\\
R(0)&118&Estimated\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Adrita Ghosh
Parthasakha Das
Tanujit Chakraborty
Pritha Das
Dibakar Ghosh

"Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study" by Adrita Ghosh, Parthasakha Das, Tanujit Chakraborty, Pritha Das, and Dibakar Ghosh, arXiv:2503.14009v1, 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
 scenario & 1 & 2 & 3 & 4 \\ \hline
 $U^c(1)$ & 198 (224) & 159 (138)& 172 (198) & 159 (138) \\
 $U^c(2)$ & 253 (219) & 308 (330)& 253 (275) & 264 (286) \\
 $U^c(3)$ & 261 (261) & 289 (275)& 261 (193) & 289 (275) \\
 $U^f(1)$ & 30 & -12.5 & 27 & -12.5 \\
 $U^f(2)$ & -30 & 25 & 27 & 25 \\
 $U^f(3)$ & 0 & -12.5 & -54 & -12.5 \\
 $RCP$ & 15 & 12.5 & 13.5 & 12.5 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Resulting consumption and financial utilities and $RCP$ values in the example. In the rows of $U^c$ the original $U^c$ values without redistribution is indicated in parentheses.}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of the Airoboros dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc}
 \toprule
 & \# Train \\
 \midrule
 Airobors & 29,400 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Abdessalam Ed-dib
Zhanibek Datbayev
Amine Mohamed Aboussalah

"GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning" by Abdessalam Ed-dib, Zhanibek Datbayev, and Amine Mohamed Aboussalah, arXiv:2412.09250v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c||c|c|c|c|c|c|}
\hline
 \textbackslash & $z_1=3$ & $z_2=6$ & $z_3=1$ &
$z_3=2$ & $z_3=3$ & $ z_3=4 $ & $z_3=5 $ & $ z_3=6 $\\
\hline \hline
1, OT & 13.08 & 8.24 & 15.51 & 13.28 & 12.33 & 11.29 & 10.22 & 8.48 \\ \hline
2, OT & 0 & 8.99 & 16.71 & 14.19 & 13.13 & 11.97 & 10.76 & 8.60 \\ \hline
1, CM & 5.20 & 6.72 & 4.06 & 4.46 & 4.65 & 4.90 & 5.20 & 6.98\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Optimal abatement, delayed implementation}
\end{table}
\end{document}

q-fin

"Climate uncertainty, financial frictions and constrained efficient carbon taxation" by Felix Kübler, arXiv:2210.09066v1, 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{Analysis results given by Tian's methods using MG data}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}\hline
Parameter & Mean & p-value [$95\%$CI]\\ \hline
Intercept & 3.74 & 0.000 [2.95, 4.52]\\
Treatment group & 0.37 & 0.001 [0.15, 0.58]\\
Age & -0.02 & 0.001 [-0.02, -0.01]\\
Years of diagnosis & 0.02 & 0.224 [-0.01, 0.05]\\
Karnofsky performance score & 0.37 & 0.003 [0.12, 0.62]\\
Race & -0.21 & 0.240 [-0.57, 0.14]\\
Radiation therapy & 0.35 & 0.010 [0.08, 0.61]\\
Sex & 0.12 & 0.348 [-0.13, 0.36]\\
Nitro & -0.27 & 0.031 [-0.51, -0.02]\\
Path & 0.27 & 0.000 [0.14, 0.40]\\
Grade & -0.32 & 0.115 [-0.73, 0.08]\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Variable Selection using Inverse Survival Probability Weighting" by Masahiro Kojima, arXiv:2212.02109v1, 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|l|l|}
				\hline
				\hline
				\multicolumn{3}{|c|}{\textbf{Parabolic Equation}}\\
				\hline
				\multicolumn{3}{|c|}{$y(t) = At^{2} + Bt +C$}\\

				\multicolumn{3}{|c|}{$A = (a_{0} - a_{1}) / \delta^{2}$}\\

				\multicolumn{3}{|c|}{$B = -2A\delta$}\\

				\multicolumn{3}{|c|}{$C = a_{0}$}\\
				\hline
				\multicolumn{3}{|c|}{\textbf{Parameters Description}}\\
				\hline
				\textbf{Variable} & \textbf{Comments} & \textbf{Unit}\\
				\hline
				t & Time variable & s\\
				\hline
				a$_{0}$ & Initial deceleration rate& m/s$^{2}$\\
				\hline
				a$_{1}$ & Final deceleration rate& m/s$^{2}$\\
				\hline
				y(t) & Deceleration rate& m/s$^{2}$\\
				\hline
				$\delta$ & Allowed maximum deceleration gradient rate & m/$s^{3}$\\
				\hline
				\hline
			\end{tabular}
\end{adjustbox}
\caption{ACC braking deceleration model. \hspace{\textwidth}}
\end{table}
\end{document}

eess

Demin Nalic
Hexuan Li
Arno Eichberger
Christoph Wellershaus
Aleksa Pandurevic
Branko Rogic

"Stress Testing Method for Scenario Based Testing of Automated Driving Systems" by Demin Nalic, Hexuan Li, Arno Eichberger, Christoph Wellershaus, Aleksa Pandurevic, and Branko Rogic, arXiv:2011.06553v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
Section & Description & Results \\
\hline
Section~ & Comparison of Bayesian and PCA projection approach & Figure~ \\
 & on transition probabilities &\\
\hline
Section~ & Convergence of Monte Carlo simulation for expected PD & Figure~\\
\hline
Section~ & Comparison of Bayesian and PCA projection & Figures~, \\
 & approach on multi-year PD estimation & \\
\hline
Section~ & Black-Scholes formula for expected LGD & Figure~ \\
\hline
Section~ & Comparison of Bayesian and PCA projection approach & Figures~- \\
 & on loss distribution of the target unite bullet loan & \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Waterfalls}
\end{table}
\end{document}

stat

"A dimension reduction approach for loss valuation in credit risk modelling" by Jian He, Asma Khedher, and Peter Spreij, arXiv:2401.00085v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc}
 \toprule
 Dataset & Chapman & Chapman \\
 \# of Leads & 12 & 4 \\
 \hline
 AUC & 0.891 $\pm$ 0.003 & 0.886 $\pm$ 0.002 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{AUC on test set of Chapman dataset in the presence of a different number of leads. The inference strategy involves using the nearest PCP ($E=128$) as input to the hypernetwork. Results are shown for five seeds.}
\end{table}
\end{document}

eess

PCPs: Patient Cardiac Prototypes

Dani Kiyasseh
Tingting Zhu
David A. Clifton

"PCPs: Patient Cardiac Prototypes" by Dani Kiyasseh, Tingting Zhu, and David A. Clifton, arXiv:2011.14227v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||cc||cc||} \hline
SU-CD & 2 & SGS-CD & $N_i +1$ \\ \hline
SL-CD & 2 & SGSL-CD & $N_i +1$ \\ \hline
SeL-CD & $2 + 2I$ & SGSeL-CD & $N_i + 1 + 2I$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Marina Costantini
Nikolaos Liakopoulos
Panayotis Mertikopoulos
Thrasyvoulos Spyropoulos

"Setwise Coordinate Descent for Dual Asynchronous Decentralized Optimization" by Marina Costantini, Nikolaos Liakopoulos, Panayotis Mertikopoulos, and Thrasyvoulos Spyropoulos, arXiv:2504.19004v1, 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{Descriptive statistics for the severity of payments of the four insurance coverages}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrr}
\toprule
\multirow{2}{*}{Coverage}& \multirow{2}{*}{Mean} & \multirow{2}{*}{Std. dev.} & \multicolumn{4}{c}{Quantiles} & \multirow{2}{*}{Max.} \\\cline{4-7}
& & & 0.5 & 0.75 & 0.95 & 0.99 & \\
\midrule
Accident Benefits & 12,386 & 53,561 & 3,215 & 6,909 & 47,757 & 127,896 & 2,435,334 \\
Bodily Injury & 23,271 & 76,027 & 4,000 & 15,150 & 98,449 & 322,612 & 2,039,570\\
Vehicle Damage & 5,040 & 8,121 & 2,605 & 5,830 & 17,984 & 40,611 & 149,399 \\
Loss of Use & 545 & 620 & 419 & 714 & 1,000 & 2,336 & 52,777 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Marie Michaelides
Mathieu Pigeon
Hélène Cossette

"Individual Claims Reserving using Activation Patterns" by Marie Michaelides, Mathieu Pigeon, and Hélène Cossette, arXiv:2208.08430v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{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
 \textbf{Model} & \textbf{Acc.} & \textbf{Prec.} & \textbf{Rec.} & \textbf{F1$_{macro}$} \\
 \hline
 Majority Class & 0.59 & 0.35 & 0.59 & 0.37 \\ \hline
 {\scriptsize } & 0.80 & 0.75 & 0.78 & 0.80 \\ \hline
 FakeNewsDetector & 0.86 & 0.86 & 0.86 & 0.86 \\ \hline
 LSTM & 0.91 & 0.86 & 0.91 & 0.90 \\ \hline
 CNN & 0.91 & 0.89 & 0.89 & 0.91 \\ \hline
 {\scriptsize }& 0.92 & 0.92 & 0.92 & 0.92 \\ \hline
 BERT & 0.93 & 0.93 & 0.94 & 0.93‡ \\ \hline
 EIN & 0.93 & 0.94 & 0.93 & 0.93‡ \\ \hline
 HAN & 0.94 & 0.94 & 0.94 & 0.93‡ \\ \hline
 Longformer & 0.97 & 0.97 & 0.97 & \textbf{0.97}† \\ \hline %longformer1500_4_97.npy

 \hline
 FakeFlow & 0.96 & 0.93 & 0.97 & 0.96 \\
 \hline
 \scriptsize{FakeFlow -- Topic only} & 0.91 & 0.89 & 0.90 & 0.90 \\ \hline
 \scriptsize{FakeFlow -- Affective only} & 0.61 & 0.38 & 0.60 & 0.40 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Results on the MultiSourceFake dataset. (‡) indicates a statistically significant improvement of \textit{FakeFlow} over the referred model using McNemar test; (†) indicates no statistically significant improvement over \textit{FakeFlow}.}
\end{table}
\end{document}

cs

Bilal Ghanem
Simone Paolo Ponzetto
Paolo Rosso
Francisco Rangel

"FakeFlow: Fake News Detection by Modeling the Flow of Affective Information" by Bilal Ghanem, Simone Paolo Ponzetto, Paolo Rosso, and Francisco Rangel, arXiv:2101.09810v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Analysis of Clusters formed by K-Means and GMM algorithms with Silhouette Score using input as extracted embeddings of mentioned classification approaches.}
\begin{tabular}{|l|l|l|}
\hline
\rule[-1ex]{0pt}{3.5ex} Model Name / Clustering Approach & K-Means & Gaussian Mixture Models \\
\hline
\rule[-1ex]{0pt}{3.5ex} Logistic Regression & 0.156 & 0.158 \\
\hline
\rule[-1ex]{0pt}{3.5ex} Convolutional Neural Network & 0.165 & 0.171 \\
\hline
\rule[-1ex]{0pt}{3.5ex} Transfer Learning & 0.189 & 0.185\\
\hline
\rule[-1ex]{0pt}{3.5ex} PCA+TSNE & \textit{0.578} & \textit{0.575} \\
\hline
\rule[-1ex]{0pt}{3.5ex} Siamese Networks & 0.490 & 0.487 \\
\hline
\rule[-1ex]{0pt}{3.5ex} Siamese Networks (Transfer Learning) & \textbf{0.592} & \textbf{0.583} \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach" by Shruti Jadon, arXiv:2102.06285v2, 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
 $n$ & Error $\|\cdot\|_1$ & Order $\|\cdot\|_1$ & Error
 $\|\cdot\|_{\infty}$ & Order $\|\cdot\|_{\infty}$ \\
 \hline
 20 & 6.14E$-3$ & $-$ & 9.07E$-2$ & $-$ \\
 \hline
 40 & 4.22E$-5$ & 7.19 & 2.98E$-4$ & 8.25 \\
 \hline
 80 & 6.90E$-7$ & 5.93 & 3.47E$-6$ & 6.42 \\
 \hline
 160 & 1.95E$-8$ & 5.15 & 9.13E$-8$ & 5.25 \\
 \hline
 320 & 5.94E$-10$ & 5.04 & 2.81E$-9$ & 5.02 \\
 \hline
 640 & 1.84E$-11$ & 5.01 & 8.90E$-11$ & 4.98 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Error table for problem - , Square domain, WLS-UW.}
\end{table}
\end{document}

math

"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, 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{Heterogeneous Boundary Effects by Region Type}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
& \multicolumn{2}{c}{Treatment Effect} & \multicolumn{3}{c}{Boundary Dynamics} \\
\cmidrule(lr){2-3} \cmidrule(lr){4-6}
Region Type & PE Only & With GE & First Passage & Jump Prob. & Boundary (km) \\
\midrule
Tokyo Metro Area & 0.287 & 0.481 & 1.3 years & 0.89 & 28.3 \\
Osaka-Kyoto-Kobe & 0.251 & 0.392 & 1.8 years & 0.81 & 31.2 \\
Other Major Cities & 0.223 & 0.318 & 2.6 years & 0.71 & 36.7 \\
Regional Centers & 0.194 & 0.259 & 3.8 years & 0.52 & 42.1 \\
Rural Areas & 0.168 & 0.198 & 5.9 years & 0.31 & 48.6 \\
\midrule
Overall & 0.234 & 0.342 & 2.8 years & 0.73 & 35.2 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Stochastic Boundaries in Spatial General Equilibrium: A Diffusion-Based Approach to Causal Inference with Spillover Effects" by Tatsuru Kikuchi, arXiv:2508.06594v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\textbf{Method} & {DiNO ViT-S/14} & {DiNO ViT-B/14} & {DiNO ViT-L/14} \\
\midrule
Baseline & 55.51 & 63.03 & 65.70 \\
\hspace{2pt} + Debiasing & 57.27$\pm$ 0.07 & 63.72 $\pm$ 0.09 & 66.87 $\pm$ 0.07 \\
\hspace{2pt} + KNN Propagation & \textbf{58.45} $\pm$ 0.23 & 64.87 $\pm$ 0.19 & 67.25 $\pm$ 0.21 \\
\hspace{2pt} + EMA & {57.8} $\pm$ 0.26 & \textbf{64.75} $\pm$ 0.18 & \textbf{67.52} $\pm$ 0.28 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Ablation study of new techniques discovered through the I-Con framework. We compare ImageNet-1K clustering accuracy across different sized backbones. }
\end{table}
\end{document}

math

Shaden Alshammari
John Hershey
Axel Feldmann
William T. Freeman
Mark Hamilton

"I-Con: A Unifying Framework for Representation Learning" by Shaden Alshammari, John Hershey, Axel Feldmann, William T. Freeman, and Mark Hamilton, arXiv:2504.16929v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A comprehensive numerical investigation of a coupled mathematical model of neuronal excitability" by Burcu Gürbüz, Aytül Gökçe, and Mahmut Modanlı, arXiv:2501.18013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing" by Cong William Lin and Wu Zhu, arXiv:2504.13629v1, 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 from cryptocurrency market model regressions on L1 tokens: \(r_i - r_f = \alpha_i + \beta_i CM + \epsilon_i\) where \(i\) denotes the crypto portfolio, \(CM\) is cryptocurrency excess market returns, and \(r_f\) is the risk-free rate. TVL values include all DefiLlama categories. P-values are below the coefficients in parentheses. \(^{***}\), \(^{**}\), and \(^{*}\), denote statistical significance at the 1\%, 5\%, and 10\% levels respectively. Test statistics and p-values for the F-test of whether alpha coefficients are jointly 0 are below the Adjusted-\(R^2\) values.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllllr}
\hline
 & Quartile 2 & Quartile 3 & Quartile 4 & Quartile 2 & Quartile 3 & Quartile 4\\
\hline
\(\alpha\) & -0.00 & 0.10 & -0.29 & 0.09 & 0.20 & -0.29\\
 & (0.99) & (0.86) & (0.40) & (0.90) & (0.73) & (0.41)\\
\(\beta_{CM}\) & 1.645\(^{***}\) & 1.11\(^{***}\) & 1.12\(^{***}\) & 1.65\(^{***}\) & 1.12\(^{***}\) & 1.12\(^{***}\)\\
 & (0.00) & (0.00) & (0.00) & (0.00) & (0.00) & (0.00)\\
\(\beta_{SMB}\) & & & & 0.01 & 0.00 & -0.00\\
 & & & & (0.59) & (0.97) & (0.81)\\
\(\beta_{Mom}\) & & & & 0.03 & -0.16\(^{**}\) & -0.03\\
 & & & & (0.77) & (0.02) & (0.48)\\
\hline
Adj-\(R^2\) & 0.71 & 0.62 & 0.82 & 0.70 & 0.63 & 0.81\\
\hline
 & GRS Stat. & 0.25 & & GRS Stat. & 0.22 & \\
 & p-value & (0.86) & & p-value & (0.88) & \\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns" by Matt Brigida, arXiv:2506.03287v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance on NASDAQ-100 index}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 \textbf{Models} & \textbf{MAE} & \textbf{MAPE} & \textbf{Accuracy} \\ \hline
 \textbf{MLP} & 218.32973474 & 0.01767204122 & 0.98232795877 \\ \hline
 \textbf{LSTM} & 180.58083886 & 0.01456811176 & 0.98543188823 \\ \hline
 \textbf{FinBERT-LSTM} & 174.94284259 & 0.01409574846 & 0.98590425153 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis" by Shayan Halder, arXiv:2211.07392v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Outage probabilities for ground level deployment and high ground windy weather deployment scenarios}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
			\hline
			Scenario & $\zeta$ & $N$ & $L_W$ & Outage probability \\
			\hline \hline
			\multirow{4}{*}{Case 1} & $40^{\circ}$ & $0.5$ & $3.128\times10^{-4}$ & $0.8166$ \\
			& $30^{\circ}$ & $0.5$ & $3.128\times10^{-4}$ & $0.1619$ \\
			& $15^{\circ}$ & $0.5$ & $3.128\times10^{-4}$ & $5.583\times10^{-4}$ \\
			& $0^{\circ}$ & $0.5$ & $3.128\times10^{-4}$ & $2.165\times10^{-5}$ \\ \hline
			\multirow{4}{*}{Case 2}
			& $40^{\circ}$ & $0.5$ & $3.128\times10^{-4}$ & 0.0986\\
			& $30^{\circ}$ & $0.5$ & $3.128\times10^{-4}$ & $2.314\times10^{-4}$ \\
			& $15^{\circ}$ & $0.5$ & $3.128\times10^{-4}$ & $2.27\times10^{-9}$\\
			& $0^{\circ}$ & $0.5$ & $3.128\times10^{-4}$ & $1.28\times10^{-11}$ \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Eylem Erdogan
Ibrahim Altunbas
Gunes Kurt
Michel Bellemare
Guillaume Lamontagne
Halim Yanikomeroglu

"Site Diversity in Downlink Optical Satellite Networks Through Ground Station Selection" by Eylem Erdogan, Ibrahim Altunbas, Gunes Kurt, Michel Bellemare, Guillaume Lamontagne, and Halim Yanikomeroglu, arXiv:2010.02176v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Krzysztof J. Szajowski
Małgorzata Średnicka

"Operation comfort vs. the importance of system components" by Krzysztof J. Szajowski and Małgorzata Średnicka, arXiv:2101.08205v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Exact Post-selection Inference For Tracking S&P500" by Farshad Noravesh and Hamid Boustanifar, arXiv:2112.15448v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|ccccc}
 \toprule
 Methods & Metrics & Temperature & Cloud cover & Humidity & Wind \\
 \midrule
 CLCRN\_log & MAE & 1.2638±0.1554 & 1.5599±0.0019 & 0.4663±0.0082 & 1.3958±0.0120 \\
 & RMSE & 2.0848±0.1719 & 2.5171±0.0255 & 0.7341±0.0151 & 2.2659±0.0211 \\
 \midrule
 CLCRN\_hor & MAE & 1.1688±0.0457 & 1.4906±0.0037 & 0.4531±0.0065 & 1.3260±0.0483 \\
 & RMSE & 1.8825±0.1509 & 2.4559±0.0027 & 0.7078±0.0146 & 2.1292±0.0733\\
 \bottomrule
 \end{tabular}
\caption{MAE and RMSE comparison in forecasting length of 12h for logarithmic and horizon maps.}
\end{table}
\end{document}

cs

Haitao Lin
Zhangyang Gao
Yongjie Xu
Lirong Wu
Ling Li
Stan. Z. Li

"Conditional Local Convolution for Spatio-temporal Meteorological Forecasting" by Haitao Lin, Zhangyang Gao, Yongjie Xu, Lirong Wu, Ling Li, and Stan. Z. Li, arXiv:2101.01000v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c}
\hline\hline
Hyperparameter & - \\
\hline\hline
Batch Size & 256 \\
Epoch & 15 \\
Optimizer & AdamW \\
betas & (0.9, 0.999) \\
eps & 1e-08 \\
Initial Learning Rate & 0.001 \\
Weight Decay & 0.01 \\
Activation Function & GeLU \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Default Training Settings}
\end{table}
\end{document}

q-fin

Neural Networks for Delta Hedging

"Neural Networks for Delta Hedging" by Guijin Son and Joocheol Kim, arXiv:2112.10084v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"EFS: Evolutionary Factor Searching for Sparse Portfolio Optimization Using Large Language Models" by Haochen Luo, Yuan Zhang, and Chen Liu, arXiv:2507.17211v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Markus Kreer
Ayse Kizilersu
Anthony W. Thomas

"Censored EM algorithm for Weibull mixtures: application to arrival times of market orders" by Markus Kreer, Ayse Kizilersu, and Anthony W. Thomas, arXiv:2012.10601v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment Approach" by Federico D'Amario and Milos Ciganovic, arXiv:2210.00883v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Andrey Sarantsev
Angel Piotrowski
Ian Anderson

"A Time Series Model for Three Asset Classes used in Financial Simulator" by Andrey Sarantsev, Angel Piotrowski, and Ian Anderson, arXiv:2508.06010v1, 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{Point Prediction performance for CIFAR-10 dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|cccccc}
\hline
Prediction Method & Residual Type & MSE\\
\hline
MB-LC & P & {1.98e-01}\\
\hline
& F & {2.20e-01}\\
\hline
MB-LL & P & 1.79e-01\\
\hline
& F & 1.95e-01\\
\hline
MF-LC & P & {1.79e-01}\\
\hline
& F & {2.12e-01}\\
\hline
MF-LLH & P & {1.60e-01}\\
\hline
& F & {1.89e-01}\\
\hline
MF-LLM & P & {1.64e-01}\\
\hline
& F & {1.70e-01}\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Srinjoy Das
Yiwen Zhang
Dimitris N. Politis

"Model-Based and Model-Free point prediction algorithms for locally stationary random fields" by Srinjoy Das, Yiwen Zhang, and Dimitris N. Politis, arXiv:2212.03079v1, 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{System level simulation setup}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
			\hline
			Parameter & Default Value \\ \hline
			Bandwidth & 400 MHz \\ \hline
			Carrier frequency & 28 GHz \\ \hline
			FFT size & 4096 \\ \hline
			Sub-carrier spacing & 120 kHz \\ \hline
			UE transmit power & 23 dBm \\ \hline
			UE beam gain & 0 dB \\ \hline
			BS peak beam gain & 28 dB \\ \hline
			number of analog beams in PAA & 16 \\ \hline
 BS noise figure & 5 dB \\ \hline
 Path-loss exponent & 3 \\ \hline
 TTD delay stepsize & 2.5 ns \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Jianhua Mo
Ahmad AlAmmouri
Shenggang Dong
Younghan Nam
Won-Suk Choi
Gary Xu
Jianzhong
Zhan

"Beamforming with Joint Phase and Time Array: System Design, Prototyping and Performance" by Jianhua Mo, Ahmad AlAmmouri, Shenggang Dong, Younghan Nam, Won-Suk Choi, Gary Xu, Jianzhong, and Zhan, arXiv:2502.00139v1, 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{t-DCF cost function parameters assumed in ASVspoof 2019.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
		\toprule
		\multicolumn{3}{c}{\textbf{Priors}}&
		\multicolumn{2}{c}{\textbf{SD costs}}&
		\multicolumn{2}{c}{\textbf{ASV costs}}\\
		\cmidrule(lr){1-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7}
		${\pi _{{\rm{tar}}}}$&${\pi _{{\rm{non}}}}$&${\pi _{{\rm{spoof}}}}$&$C_{{\rm{miss}}}^{{\rm{SD}}}$&$C_{{\rm{fa}}}^{{\rm{SD}}}$&$C_{{\rm{miss}}}^{{\rm{ASV}}}$&$C_{{\rm{fa}}}^{{\rm{ASV}}}$\\
		\midrule
		0.9405&0.0095&0.05&1&10&1&10 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yuanjun Zhao
Roberto Togneri
Victor Sreeram

"Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System" by Yuanjun Zhao, Roberto Togneri, and Victor Sreeram, arXiv:2012.03154v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of GMRES-IR and RGMRES-IR refinement steps with the number of GMRES iterations for each refinement step for real matrices, using precisions ($u_f,u,u_r$) = (half,single,double) and ($m,k$) = (40,6).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|}
		\hline
		Matrix & GMRES-IR (40) & RGMRES-IR(40,6) \\ \hline
		orsirr\_1 & 12 (6,6) & 12 (6,6) \\
		comsol & 46 (22,24) & 30 (22,8) \\
		circuit204 & 40 (12,14,14) & 29 (12,9,8) \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Mixed Precision GMRES-based Iterative Refinement with Recycling" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, 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{Summary of on-treatment (non-bold) and observed off-treatment means (bold) by pattern of deviation and missingness in SWEGMD \emph{Perforated} data set}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|c|c||c|c|c|c|c|c|c|}\hline
& \multicolumn{7}{c||}{\emph{\scriptsize{Placebo}}}
 & \multicolumn{7}{c|}{\emph{\scriptsize{Active}}}
 \\ \hline
& \multicolumn{7}{c||}{\scriptsize{Last Visit on treatment}}
 & \multicolumn{7}{c|}{\scriptsize{Last Visit on treatment}}
 \\
& \multicolumn{2}{c|}{ 1} & \multicolumn{2}{c|}{ 2} & \multicolumn{2}{c|}{ 3} & 4
& \multicolumn{2}{c|}{ 1} & \multicolumn{2}{c|}{ 2}& \multicolumn{2}{c|}{ 3} & 4
 \\ \hline
 \scriptsize{Visit} & \scriptsize{Stop} & \scriptsize{Cont.} & \scriptsize{Stop}
& \scriptsize{Cont.} & \scriptsize{Stop} & \scriptsize{Cont.}
& \scriptsize{Cont.}
& \scriptsize{Stop} & \scriptsize{Cont.} & \scriptsize{Stop}
& \scriptsize{Cont.} & \scriptsize{Stop} & \scriptsize{Cont.}
& \scriptsize{Cont.}
\\ \hline
 1 & -1.00 & 0.40 & -6.00 & -0.50 & 0.13 & -2.67 & -1.82 &
 0.17 & & -5.00 & -3.25 & -0.67 & -3.67 & -1.75 \\
\cline{2-3} \cline{9-10} 2 & & \textbf{-0.67} & -4.00 & 2.00 & -0.75 & -5.00 & -3.11 &
 & & -7.00 & -3.25 & -1.33 & -3.67 & -4.87 \\
\cline{4-5} \cline{11-12} 3 & & \textbf{-2.93} & & \textbf{-0.97} & 0.00 & -6.67 & -4.45 &
 & & & \textbf{-4.18} & 0.33 & -5.50 & -7.25 \\
\cline{6-7} \cline{13-14} 4 & & \textbf{-2.97} & & \textbf{-1.32} & & \textbf{-7.25} & -5.14 &
 & & & \textbf{-5.29} & & \textbf{-6.20} & -8.34 \\ \hline
\scriptsize{Count} & 2 & 5 & 1 & 4 & 8 & 3 & 65 & 6 & 0 & 1 & 4 & 3 & 6 & 64 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Suzie Cro
James H Roger
James R Carpenter

"Multiple imputation of partially observed data after treatment-withdrawal" by Suzie Cro, James H Roger, and James R Carpenter, arXiv:2308.13256v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Shengjie Niu
Lifan Lin
Jian Huang
Chao Wang

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model performance for the temperature-dependent chemical reaction network. The first three rows show the error of the N-CTMC model as we increase the number of trajectories in the training set. For the largest training set, we also show GLM model errors. Training is halted when $\Delta\text{loss}$ reaches a minimum. W denotes that the error metric is weighted. For the GLM model, training is halted when $\| \nabla \theta\| < 0.01$.} % This table has been updated
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l|l|l}
Trajectories & MAE & W-MAE & MSE & W-MSE & Epochs \\ \hline
100 & 0.024 & $ 3.13 \cdot 10^{-3}$ & 0.004 & $ 7.65 \cdot 10^{-5}$ & 7,700 \\
300 & 0.020 & $ 1.88 \cdot 10^{-3}$ & 0.003 & $ 2.61 \cdot 10^{-5}$ & 6,900 \\
500 & 0.015 & $ 1.45 \cdot 10^{-3}$ & 0.002 & $ 1.62 \cdot 10^{-5}$ & 4,000 \\ \hline
500 (GLM) & 0.250 & $ 2.88 \cdot 10^{-2}$ & 1.345 & $ 5.80 \cdot 10^{-3}$ & 12,000 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Neural Continuous-Time Markov Models

"Neural Continuous-Time Markov Models" by Majerle Reeves and Harish S. Bhat, arXiv:2212.05378v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Achieving Fair PCA Using Joint Eigenvalue Decomposition" by Vidhi Rathore and Naresh Manwani, arXiv:2502.16933v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Dataset details. Size indicates the number of data points, Cont.feat - number of continous features and Cat.feat - number of categorical features.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}

			\toprule
			Dataset & Classes & Size & Cont.feat. & Cat.feat.\\ %& Description\\
			\midrule

			COMPAS & 2 & 5238 &	13 & 0 \\ %& all features categorical/high dimensional/small size\\

			Hill-Valley & 2 & 1212 & 100 & 0 \\ %& 	 multi class/moderately high dimensional/large size \\

			Arrhythmia & 2 & 452 &	177 &	85 \\ %& low dimensional/large size\\

			LSVT & 2 & 126 & 304 & 3\\ %& have numerical \& categorical features/ large size\\

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

stat

Shashank Shekhar
Asif Salim
Adesh Bansode
Vivaswan Jinturkar
Anirudha Nayak

"A PSO Based Method to Generate Actionable Counterfactuals for High Dimensional Data" by Shashank Shekhar, Asif Salim, Adesh Bansode, Vivaswan Jinturkar, and Anirudha Nayak, arXiv:2311.12825v2, 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{Impact of Destination College Fraction on Wage}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \hline
 & (a) & (b) & (c) &(d) \\
 Year & \(\delta_{P20}\) & \(\delta_{P80}\) & Impact & SD \\
 \hline
 \(1980\) & 0.16 & 0.26 & \hphantom{1}\$7900 & \hphantom{1}\$6300\\
 \(1990\)& 0.18 & 0.31 & \hphantom{1}\$9100 & \hphantom{1}\$7100\\
 \(2000\) & 0.21 & 0.35 & \$10300 & \$10900 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"What You See is What You Get: Local Labor Markets and Skill Acquisition" by Benjamin Niswonger, arXiv:2209.03892v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of error measures}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
		\hline & \textbf{Max. AL} &\textbf{Max. RL} & \textbf{Max. CV} & \textbf{Max. MIRNS}\\
 & \textbf{Error} & \textbf{Error} & \textbf{Error} & \textbf{Error}\\
		\hline DC & 3.6e-01 & 1.1e-04 & 24.1 & 1.4e-03\\
 \hline IRR-DC & 2.6e-03 & 2.9e-06 & 3.6e-02 & 6.5e-08\\
		\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Reliable Solution to Dynamic Optimization Problems using Integrated Residual Regularized Direct Collocation" by Yuanbo Nie and Eric C. Kerrigan, arXiv:2503.09123v1, 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}{|ccccc|}
\hline
& $n_0$ & max TTP & min TTP & mean TTP\\
\hline
\hline
`On-Off' AT &$0.25$ & $575$ days & $109$ days & $213$ days\\
\hline
`Off-On' AT & $0.25$ & $589$ days & $107$ days & $217$ days\\
\hline
\hline
`On-Off' AT &$0.50$ & $764$ days & $155$ days & $285$ days\\
\hline
`Off-On' AT & $0.50$ & $822$ days & $167$ days & $306$ days\\
\hline
\hline
`On-Off' AT &$0.75$ & $1196$ days & $283$ days & $462$ days\\
\hline
`Off-On' AT & $0.75$ & $1500$ days & $400$ days & $589$ days\\
\hline
\end{tabular}
\end{adjustbox}
\caption{\footnotesize Comparison in terms of Time-to-Progression (TTP) between `On-Off'~AT and `Off-On'~AT. The mean TTP is computed by taking the average over the elements of the set $\Theta_N$ (see ).}
\end{table}
\end{document}

math

Alessandro Scagliotti
Federico Scagliotti
Laura Deborah Locati
Federico Sottotetti

"Ensemble optimal control for managing drug resistance in cancer therapies" by Alessandro Scagliotti, Federico Scagliotti, Laura Deborah Locati, and Federico Sottotetti, arXiv:2503.08927v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c||}
\hline
Prediction Interval & Uncalibrated & Calibrated \\
\hline\hline
95\% & 100.0 (0.0) & 95.0 (1.0) \\
\hline
90\% & 100.0 (1.0) & 90.0 (2.0) \\
\hline
80\% & 90.0 (3.0) & 90.0 (2.0) \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Uncertainty quantification methods compared in terms of their nominal 95\% PI coverage on water field data. The ensemble-based (uncalibrated) approach is outlined in and the quantile regression (calibrated) approach is the method detailed in . The median across the 100 subsampled locations is reported with the IQR in parenthesis.}
\end{table}
\end{document}

stat

Matthew Bonas
David H. Richter
Stefano Castruccio

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|r|r|r|r|r|r|r|}
\hline
Source &
 Mean &
 Std &
 Min &
 Max &
 Q25 &
 Q50 &
 Q75 \\ \hline
RDV & 134 & 88 & 8 & 512 & 65 & 123 & 187 \\ \hline
Finam & 221 & 135 & 18 & 512 & 116 & 178 & 284 \\ \hline
BCS Express & 20 & 10 & 4 & 82 & 13 & 17 & 26 \\ \hline
BCS Technical Analysis & 502 & 37 & 29 & 512 & 512 & 512 & 512 \\ \hline
RBC & 43 & 7 & 16 & 75 & 39 & 44 & 48 \\ \hline
SmartLab & 21 & 8 & 5 & 82 & 15 & 19 & 25 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2503.08696v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lllll}
Operation & Category & Parallel? & 16-bit latency (ms) & 32-bit latency (ms) \\
\hline
AND(imm) & Bitwise operations & Yes & 26.9195 & 27.1288\\
AND(reg) & Bitwise operations & Yes & 47.7228 & 55.4597\\
OR(imm) & Bitwise operations & Yes & 28.1923 & 27.8774 \\
OR(reg) & Bitwise operations & Yes & 47.6171 & 50.089 \\
XOR(imm) & Bitwise operations & Yes & 29.2375 & 27.9389\\
XOR(reg) & Bitwise operations & Yes & 47.1986 & 50.3683 \\
ORN(imm) & Bitwise operations & Yes & 27.8467 & 27.776\\
ORN(reg) & Bitwise operations & Yes & 47.6422& 57.0639\\
NOT(imm) & Bitwise operations & Yes & 0.0072794 & 0.0116968\\
NOT(reg) & Bitwise operations & No & 0.006089 & 0.01232\\
\hline
BFC & Bitwise operations & No & 0.0028094 & 0.0026892\\
BFI & Bitwise operations & No & 0.003278 & 0.0033974\\
RBIT & Bitwise operations & No & 0.0104582 & 0.0216222\\
REV & Bitwise operations & No & 0.0097926 & 0.0187618\\
\end{tabular}
\caption{Bitwise operation and data movement latency}
\end{table}
\end{document}

cs

"CryptoEmu: An Instruction Set Emulator for Computation Over Ciphers" by Xiaoyang Gong and Dan Negrut, arXiv:2101.03403v1, 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{Propensity estimation algorithms.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
		\toprule
		~ & base algorithm & hyperparameters \\
		\midrule
		\textsc{ConvexPE} (Algorithm~) & proximal-proximal-gradient & $\tau$ and $\gamma$ \\
		\textsc{NonconvexPE} (Algorithm~) & gradient descent & step size $t$ and target rank\\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Chengrun Yang
Lijun Ding
Ziyang Wu
Madeleine Udell

"TenIPS: Inverse Propensity Sampling for Tensor Completion" by Chengrun Yang, Lijun Ding, Ziyang Wu, and Madeleine Udell, arXiv:2101.00323v3, 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
\caption{\textbf{Comparison of options against load-shaped forward contracts}. Change in surplus is calculate against the three-contract unrestricted case. SFPFC performs better than options when trading is limited to single instrument, particularly when sold collectively by a portfolio of technologies.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccccccccc}
\hline
\hline
& \multicolumn{3}{c}{$\beta=0.2$} & \multicolumn{3}{c}{$\beta=0.4$} \\
&Options & Sep & Col &Options & Sep & Col \\
\hline
Capacity (GW) \\
\quad Baseload &0.006 & 89.0 &40.7 & 0.02 &84.1 &34.6 \\
\quad Peaker &137.3 & 72.5 &99.9 & 135.2 &75.0 &104.4 \\
\quad Variable &163.4 & 26.0 &143.2 & 176.2 &36.9 &153.6 \\
\\
Average Price (\$/MWh) \\
\quad Spot & 62.22 & 61.69 & 57.49 & 61.31 & 61.30 & 57.39\\
\quad Hedged &62.86 & 63.01 & 59.59 & 61.94 & 62.32 & 59.43 \\
\\
Interannual Volatility(\$/MWh) \\
\quad Spot & 25.86 & 22.33 & 24.89 & 25.42 & 23.41 & 24.43 \\
\quad Hedged & 13.79 & 1.02 & 1.75 & 13.63 & 1.17 &1.69\\
\\
Expected unserved energy (GWh) &5.54 & 4.05 &5.83 &5.53 &4.58 &5.52 \\
Proximity to Equilibrium &0.066\% & 0.058\% &0.016\% & 0.029\% &0.090\% &0.004\% \\
Change in Surplus (\$M/yr) &-5,042 & -3,500 &-517 &-4,206 & -2,906 &-375\\
\hline
& \multicolumn{3}{c}{$\beta=0.6$} & \multicolumn{3}{c}{$\beta=0.8$} \\
&Option & Sep & Col &Option & Sep & Col \\
\hline
Capacity (GW) \\
\quad Baseload &0.2 & 75.6 &33.6 &12.4 & 52.9 &31.4 \\
\quad Peaker &133.6 & 79.6 &105.4 &122.6 & 92.8 &107.0 \\
\quad Variable &185.0 & 57.1 &154.3 &177.3 & 109.8 &157.7 \\
\\
Average Price (\$/MWh) \\
\quad Spot & 60.41 & 61.00 & 57.44 & 59.40 & 60.23 & 57.59\\
\quad Hedged & 61.03 & 61.44 & 59.26 & 59.98 & 60.36 & 59.06 \\
\\
Interannual Volatility(\$/MWh) \\
\quad Spot & 25.18 & 24.38 & 24.20 & 24.63 & 26.85 & 24.34 \\
\quad Hedged & 13.61 & 1.28 & 1.38 & 13.45 & 1.36 & 1.14\\
\\
Expected unserved energy (GWh) &5.52 & 5.35 &5.35 &5.53 & 6.83 &5.39 \\
Proximity to Equilibrium &0.048\% & 0.071\% &0.008\% &0.073\% & 0.070\% &0.012\%\\
Change in Surplus (\$M/yr) &-3,411 & -2,183 &-212 &-2,448 & -1,243 &-41 \\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Beyond capacity: contractual form in electricity reliability obligations" by Han Shu and Jacob Mays, arXiv:2210.10858v1, 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{Hyperparameters used to train the control policies }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c||}
 \hline
 Parameter & Value \\
 \hline
 Training iterations & $2 \times 10^{6}$ \\
 Learning rate & 5e-5 \\
 Neural network optimizer & Adam \\
 Adam parameters $(\beta_1, \beta_2)$ & $(0.9, 0.999)$ \\
 Discount factor $\gamma$ & $0.95$ \\
 Max. episode length & $1500$ \\
 Minibatch size & $32$ \\
 Clipping parameter for PPO & $0.2$ \\
 GAE parameter $\lambda$ & $0.95$ \\
 Epochs per PPO update & 10 \\
 State-value loss coefficient & 0.5 \\
 Variance of action distribution $\sigma^2$ & 1.0 \\
 Learning rate schedule & linear\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Aditya M. Deshpande
Rumit Kumar
Ali A. Minai
Manish Kumar

"Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors" by Aditya M. Deshpande, Rumit Kumar, Ali A. Minai, and Manish Kumar, arXiv:2007.07793v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Sabbir Ahmed
Nursadul Mamun
Md Azad Hossain

"Cochleagram-based Noise Adapted Speaker Identification System for Distorted Speech" by Sabbir Ahmed, Nursadul Mamun, and Md Azad Hossain, arXiv:2508.21347v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Performance of exactly normalized importance sampling (ENIS), self-normalized importance sampling (SNIS) and MoM-SNIS in the Exponential example.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|l|r|r|r|r|r}
\hline
p & method & cost & MSE & bias & q95 & q99\\
\hline
\cellcolor{gray!10}{2.01} & \cellcolor{gray!10}{ENIS} & \cellcolor{gray!10}{1000} & \cellcolor{gray!10}{0.0023} & \cellcolor{gray!10}{0.0000} & \cellcolor{gray!10}{0.0721} & \cellcolor{gray!10}{0.1124}\\
\hline
\cellcolor{gray!10}{2.01} & \cellcolor{gray!10}{SNIS} & \cellcolor{gray!10}{1000} & \cellcolor{gray!10}{0.0021} & \cellcolor{gray!10}{0.0014} & \cellcolor{gray!10}{0.0921} & \cellcolor{gray!10}{0.1450}\\
\hline
\cellcolor{gray!10}{2.01} & \cellcolor{gray!10}{MoM-SNIS} & \cellcolor{gray!10}{1152} & \cellcolor{gray!10}{0.0016} & \cellcolor{gray!10}{0.0370} & \cellcolor{gray!10}{0.0634} & \cellcolor{gray!10}{0.0735}\\
\hline
2.10 & ENIS & 1000 & 0.0018 & 0.0000 & 0.0637 & 0.0964\\
\hline
2.10 & SNIS & 1000 & 0.0016 & 0.0010 & 0.0768 & 0.1206\\
\hline
2.10 & MoM-SNIS & 1152 & 0.0015 & 0.0349 & 0.0609 & 0.0708\\
\hline
\cellcolor{gray!10}{3.00} & \cellcolor{gray!10}{ENIS} & \cellcolor{gray!10}{1000} & \cellcolor{gray!10}{0.0004} & \cellcolor{gray!10}{0.0000} & \cellcolor{gray!10}{0.0379} & \cellcolor{gray!10}{0.0509}\\
\hline
\cellcolor{gray!10}{3.00} & \cellcolor{gray!10}{SNIS} & \cellcolor{gray!10}{1000} & \cellcolor{gray!10}{0.0004} & \cellcolor{gray!10}{0.0001} & \cellcolor{gray!10}{0.0396} & \cellcolor{gray!10}{0.0528}\\
\hline
\cellcolor{gray!10}{3.00} & \cellcolor{gray!10}{MoM-SNIS} & \cellcolor{gray!10}{1152} & \cellcolor{gray!10}{0.0006} & \cellcolor{gray!10}{0.0173} & \cellcolor{gray!10}{0.0456} & \cellcolor{gray!10}{0.0564}\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

George Deligiannidis
Pierre E. Jacob
El Mahdi Khribch
Guanyang Wang

"On importance sampling and independent Metropolis-Hastings with an unbounded weight function" by George Deligiannidis, Pierre E. Jacob, El Mahdi Khribch, and Guanyang Wang, arXiv:2411.09514v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison between our LGT conversion and DGNet data augmentation on Market1501.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cclcl}
		\hline
		\multirow{2}{*}{Methods} & \multicolumn{4}{c}{Market1501} \\ \cline{2-5}
		& \multicolumn{2}{c}{Rank-1} & \multicolumn{2}{c}{mAP(\%)} \\ \hline
		Baseline & \multicolumn{2}{c}{88.8} & \multicolumn{2}{c}{71.6} \\
		Baseline + DGNet & \multicolumn{2}{c}{{88.9}} & \multicolumn{2}{c}{{72.1}} \\
		Baseline+ LGT(ours) & \multicolumn{2}{c}{\textbf{90.0}} & \multicolumn{2}{c}{\textbf{74.9}} \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method" by Yunpeng Gong, Liqing Huang, and Lifei Chen, arXiv:2101.08533v5, 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
 & BPE & GP-ThreDS & REDS \\ \midrule
 Branin & $29.84 \pm 6.13$ & $4.37 \pm 0.28$ & $\mathbf{0.32} \pm 0.08$ \\ \midrule
 Hartmann-4D & $38.45 \pm 3.93$ & $7.59 \pm 0.54$ & $\mathbf{0.47} \pm 0.11$\\ \midrule
 Hartmann-6D & $119.71 \pm 23.75$ & $19.33 \pm 0.54$ & $\mathbf{1.19} \pm 0.08$ \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Time taken (in seconds) by different algorithms across the different benchmark functions.}
\end{table}
\end{document}

stat

"Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency" by Sudeep Salgia, Sattar Vakili, and Qing Zhao, arXiv:2310.15351v2, 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{Second special class of circles with apparent symmetric lattice points on each boundary.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}
\hline
$k$ & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\
\hline
$g(k)$ & 5& 13& 29& 49& 81& 113& 149& 197& 253 & 317\\
\hline
$\pi R_{g(k)}^2\approx $ & 6& 16& 31& 53& 82& 116& 157& 204& 258 & 317 \\
\hline
$k$ & 11 & 12 & 13 & 14 & 15 & 16 & 17 & 18 &19 &20 \\
\hline
$g(k)$ & 377& 441& 529& 613& 709& 797& 901& 1009 &1129 &1257\\
\hline
$\pi R_{g(k)}^2\approx $ & 383& 456& 534& 619& 710& 807& 911& 1021 & 1137 & 1260 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"The Largest Circle Enclosing $n$ Lattice Points" by Jianqiang Zhao, arXiv:2505.06234v2, 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{PAD-UFES - Segmentation Results}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\hline
\textbf{Method} & \textbf{JI(\%)} & \textbf{SE(\%)} & \textbf{SP(\%)} & \textbf{AC(\%)} \\
\hline
ISNN$_{C=0.1}$ & \textbf{84.08} & \textbf{94.02} & 98.50 & \textbf{98.21} \\
ISNN$_{C=0.25}$ & 83.02 & 93.65 & 98.38 & 98.07 \\
ISNN$_{C=0.5}$ & 81.00 & 93.68 & 97.93 & 97.69 \\
S-FCM & 82.88 & 91.35 & \textbf{98.66} & 98.06 \\ \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.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c}
\toprule
& Full Specification & Event-Specific Time Trends & No Household FEs \\
\midrule
Post Service & 0.218***& 0.253*** & 0.726*** \\
& (0.0032) & (0.0051)& (0.0036) \\
Post Bill & -0.109*** & -0.071*** & -0.016***\\
& (0.0030) & (0.0043) & (0.0038) \\
\midrule
$\overline{\text{spend}_{it}}$ & \$120.49 & \$120.49 & \$120.49 \\
Observations & 61,860,735 & 61,860,735 & 61,860,735 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{ Robustness of Main Specification}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative studies for the regularization terms and mesh extraction methods on the DTU dataset. }
\begin{tabular}{l|ccc}
 & Accuracy~$\downarrow$ & Completion ~$\downarrow$ & Average~$\downarrow$ \\
\hline
A. w/o normal consistency & 1.35 & 1.13 & 1.24\\
B. w/o depth distortion & 0.89 & 0.87 & 0.88 \\
\hline
C. w / expected depth & 0.88 & 1.01 & 0.94 \\
D. w / SPSR & 1.25 & 0.89 & 1.07 \\
\hline
E. Full Model & 0.79 & 0.86 & 0.83\\
\end{tabular}
\end{table}
\end{document}

cs

Binbin Huang
Zehao Yu
Anpei Chen
Andreas Geiger
Shenghua Gao

"2D Gaussian Splatting for Geometrically Accurate Radiance Fields" by Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao, arXiv:2403.17888v3, 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|ccccccc}
 Layer & Dense & Dense & Dense & Dense & Dense & Dropout & Dense \\ \hline
 Units & 256 & 128 & 128 & 1024 & 128 & - & 8 \\
 Activation & selu & relu & relu & LeakyReLu & LeakyReLu & - & softmax \\
 Parameter & - & - & - & 0.05 & 0.01 & 0.3 & -
 \end{tabular}
\end{adjustbox}
\caption{Architecture of the neural network with 100 training epochs for the surrogate model in section from left to right. There are eight input units for the eight features of the surrogate model and eight output units for the eight clusters.}
\end{table}
\end{document}

q-fin

"Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data" by Tobias Wand, Martin Heßler, and Oliver Kamps, arXiv:2208.14106v3, 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{Relative error of approxiamtion with $k$-truncated SVD}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}\hline
$k$ & 5 & 15 & 25 & 35 & 45 \\\hline
$\frac{\|A_k - A\|_F}{\|A\|_F}$ & 0.2546-0.2304$\epsilon$ & 0.1446-0.1345$\epsilon$ & 0.1052-0.0932$\epsilon$ & 0.0838-0.0752$\epsilon$ & 0.0696-0.0619$\epsilon$ \\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Liqun Qi
David M. Alexander
Zhongming Chen
Chen Ling
Ziyan Luo

"Low Rank Approximation of Dual Complex Matrices" by Liqun Qi, David M. Alexander, Zhongming Chen, Chen Ling, and Ziyan Luo, arXiv:2201.12781v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparision of LoRAE against modern state-of-the-art deterministic autoencoders (FID Score is used for comparison).}
\begin{tabular}{ccccc}
 \hline
 & \multicolumn{2}{c}{\textbf{MNIST}} & \multicolumn{2}{c}{\textbf{CelebA}} \\
 \cline{2-5}
 & $\mathcal{N}$ & GMM & $\mathcal{N}$ & GMM \\
 \hline
 \hline
 WAE & 21.04 & 11.32 & 57.6 & 45.91 \\
 \hline
 RAE & 22.12 & 11.54 & \textbf{50.31} & 46.05 \\
 \hline
 \textbf{LoRAE} & \textbf{19.50} & \textbf{11.09} & 56.29 & \textbf{45.43}\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Alokendu Mazumder
Tirthajit Baruah
Bhartendu Kumar
Rishab Sharma
Vishwajeet Pattanaik
Punit Rathore

"Learning Low-Rank Latent Spaces with Simple Deterministic Autoencoder: Theoretical and Empirical Insights" by Alokendu Mazumder, Tirthajit Baruah, Bhartendu Kumar, Rishab Sharma, Vishwajeet Pattanaik, and Punit Rathore, arXiv:2310.16194v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
\toprule
Dataset & Style & Train & Dev & Test \\
\hline
\multirow{2}{*}{Yelp} & Positive & 266041 & 2000 & 500 \\ \cline{2-5}
 & Negative & 177218 & 2000 & 500 \\ \hline
\multirow{2}{*}{Amazon} & Positive & 277228 & 1015 & 500 \\ \cline{2-5}
 & Negative & 277769 & 985 & 500 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Data Statistics for Yelp and Amazon Dataset}
\end{table}
\end{document}

cs

"Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning" by Chenghao Fan, Ziao Li, and Wei wei, arXiv:2202.00469v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.