\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}{lcccc} % Adjust the width as needed
 \toprule
 \textbf{Difference} & \textbf{1} & \textbf{2} & \textbf{3} & \textbf{Overall} \\
 \midrule
 Accuracy & 7/10 & 9/10 & 9/10 & 25/30 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Wenqian Cui
Lei Zhu
Xiaohui Li
Zhihan Guo
Haoli Bai
Lu Hou
Irwin King

"Think Before You Talk: Enhancing Meaningful Dialogue Generation in Full-Duplex Speech Language Models with Planning-Inspired Text Guidance" by Wenqian Cui, Lei Zhu, Xiaohui Li, Zhihan Guo, Haoli Bai, Lu Hou, and Irwin King, arXiv:2508.07375v1, 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{Descriptive statistics for income-consumption data.}
\begin{tabular}{cccccccccc}
			\hline
			n & Min. & 1st Qu. & Median & Mean & 3rd Qu. & Max. & Std. dv. & CS & CK\\
			\hline
			37& 0.02 & 0.28 & 0.46 & 0.45 & 0.60 & 0.91 & 0.22 & 0.16 & -0.92 \\
			\hline
	\end{tabular}
\end{table}
\end{document}

stat

"A novel unit-asymmetric distribution based on correlated Fréchet random variables" by Roberto Vila and Felipe Quintino, arXiv:2501.00970v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 & ML Estimates & $95\%$ C.I. \\ \hline
 $\hat K_V$ & $-2.2085$ & $(-2.2850 -2.1321)$ \\
 $\hat \rho_V$ & $0.2530$ & $(0.2149 0.2910)$ \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{\small Maximum likelihood estimates for the Vasicek model.}
\end{table}
\end{document}

q-fin

"Credit risk for large portfolios of green and brown loans: extending the ASRF model" by Alessandro Ramponi and Sergio Scarlatti, arXiv:2506.12510v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Ruwani Herath
Alex Trindade
Fangyuan Zhang

"Monte Carlo Expectation-Maximization algorithm to detect imprinting and maternal effects for discordant sib-pair data" by Ruwani Herath, Alex Trindade, and Fangyuan Zhang, arXiv:2401.00520v1, 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
$\alpha_k= 0.5$ & $m_k= 5$ (MW)& $\sigma_k = 0.2 \ (hr^{-1})$ \\
\hline
$\text{SoC}_{\min}= 0$ & $\text{SoC}_{\max}= 1$ & $\text{I}_{\text{max}}= 3
 \ (\text{MWh})$
\\
\hline
$B_{\text{min}} = -1 (\text{MW})$ & $B_{\text{max}} = 1 \ (\text{MW})$ & $\eta = 1$ \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Parameters for the stationary example in Sect. }
\end{table}
\end{document}

math

"Intraday Battery Dispatch for Hybrid Renewable Energy Assets" by Thiha Aung and Mike Ludkovski, arXiv:2503.12305v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\large Coefficient Estimates of Spatial Error Model}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
~ & $\Delta$Unemployment Rate\\
\cmidrule{1-2}
\emph{Variables} & ~\\
Spatial error parameter ($\rho$) & 0.742***\\
~ & (0.004)\\
$\Delta$ Active Mines\textsubscript{t} & -0.026***\\
~ & (0.004)\\
$\Delta$ Active Mines\textsubscript{t-1} & -0.009*\\
~ & (0.004)\\
$\Delta$ Active Mines\textsubscript{t-2} & 0.021***\\
~ & (0.004)\\
$\Delta$ (log) Real GDPPC\textsubscript{t} & -0.543***\\
~ & (0.03)\\
\bottomrule
\multicolumn{2}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{multirow}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of Batch Sizes Across Stacked and Phased Training Strategies on MMLU and MTBench Benchmarks. Green cells indicate better scores, while blue cells highlight higher sample efficiency (fewer samples required).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccccccc}
\toprule
\multirow{2}{*}{\textbf{Benchmark}} & \multirow{2}{*}{\textbf{Strategy}} & \multicolumn{4}{c}{\textbf{Score}} & \multicolumn{3}{c}{\textbf{Samples}} \\
\cmidrule(lr){3-6} \cmidrule(lr){7-9}
 & & \textbf{Granite Base} & \textbf{128} & \textbf{4K} & \textbf{8K} & \textbf{128} & \textbf{4K} & \textbf{8K} \\
\midrule
\multirow{2}{*}{\textbf{MMLU}}
 & \textbf{Stacked} & 0.48 & 0.516 & 0.526 & \cellcolor{green!30}0.529 & \cellcolor{blue!20}2,099,328 & 3,694,080 & 8,885,760 \\
 & \textbf{Phased} & 0.48 & 0.513 & \cellcolor{green!30}0.524 & - & \cellcolor{blue!20}2,915,233 & 7,859,902 & - \\
\midrule
\multirow{2}{*}{\textbf{MTBench}}
 & \textbf{Stacked} & - & 6.406 & 6.768 & \cellcolor{green!30}6.831 & \cellcolor{blue!20}1,799,424 & 4,392,960 & 8,586,240 \\
 & \textbf{Phased} & - & 6.325 & \cellcolor{green!30}6.756 & - & \cellcolor{blue!20}2,815,265 & 8,057,918 & - \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

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

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


\documentclass{article}
\usepackage{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}{lcccc}
\toprule
Model & \textit{Layer} & SEAT-6 & SEAT-7 & SEAT-8 \\
\midrule
\multirow{3}{*}{BERT} & all & \textbf{0.44} & 0.25 & \textbf{0.46} \\
 & last & 0.56 & \textbf{0.12} & 0.47 \\
 & first & 0.52 & 0.22 & 0.49 \\
\midrule
\multirow{3}{*}{RoBERTa} & all & \textbf{0.59} & \textbf{0.23} & 0.61 \\
 & last & 0.73 & 0.24 & 0.65 \\
 & first & 0.69 & 0.28 & \textbf{0.59} \\
\midrule
\multirow{3}{*}{ALBERT} & all & \textbf{0.46} & 0.48 & \textbf{0.24} \\
 & last & 1.15 & \textbf{0.26} & 0.60 \\
 & first & 0.54 & 0.89 & 0.95 \\
\midrule
\multirow{3}{*}{DistilBERT} & all & \textbf{0.66} & \textbf{-0.16} & 0.37 \\
 & last & 0.88 & 0.19 & \textbf{0.35} \\
 & first & 0.90 & 0.40 & 0.52 \\
\midrule
\multirow{3}{*}{ELECTRA} & all & \textbf{0.21} & \textbf{0.02} & \textbf{0.18} \\
 & last & 0.34 & 0.20 & 0.21 \\
 & first & 0.28 & 0.13 & 0.34 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Averaged scores over all layers in an embedding debiased at token-level, measured on SEAT tests.}
\end{table}
\end{document}

cs

"Debiasing Pre-trained Contextualised Embeddings" by Masahiro Kaneko and Danushka Bollegala, arXiv:2101.09523v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
				\toprule
				 & \multicolumn{2}{c}{Change (Control - Treatment)} \\
				\cmidrule{2-3}
				Quantile & Mean & 95\% CI \\
				\midrule
				P10 & -0.17\% & [-3.06\%, 5.49\%] \\
				P30 & 0.45\% & [-4.25\%, 5.29\%] \\
				P50 & 0.47\% & [-4.53\%, 5.86\%] \\
				P70 & 0.30\% & [-4.80\%, 6.88\%] \\
				P90 & -0.56\% & [-6.21\%, 7.00\%] \\
				P98 & -6.26\% & [-7.68\%, 9.61\%] \\
				\bottomrule
			\end{tabular}
\end{adjustbox}
\caption{Comparison of quantile loss of VLT forecasts produced by Gen-QOT metrics on-policy (control arm) versus off-policy (treatment arm) on data from real world A/B test.}
\end{table}
\end{document}

stat

Sohrab Andaz
Carson Eisenach
Dhruv Madeka
Kari Torkkola
Randy Jia
Dean Foster
Sham Kakade

"Learning an Inventory Control Policy with General Inventory Arrival Dynamics" by Sohrab Andaz, Carson Eisenach, Dhruv Madeka, Kari Torkkola, Randy Jia, Dean Foster, and Sham Kakade, arXiv:2310.17168v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Steady-state time for different data lengths $\rho$ under data-driven STC and identification-based STC}
\begin{tabular}{ccccc}% 其中，tabular是表格内容的环境；c表示centering，即文本格式居中；c的个数代表列的个数
			\toprule %[2pt]设置线宽
			\multicolumn{2}{c}{The pre-collected state-input data length $\rho$} & 10 & 80 & 800 \\ \midrule %[2pt]
			\multirow{2}{*}{Steady-state time} & Data-driven STC & 7 & 3 & 2 \\
			& Identification-based STC & 6 & 5 & 3 \\
			\bottomrule %[2pt]
	\end{tabular}
\end{table}
\end{document}

eess

Yifei Li
Xin Wang
Jian Sun
Gang Wang
Jie Chen

"Self-triggered Consensus Control of Multi-agent Systems from Data" by Yifei Li, Xin Wang, Jian Sun, Gang Wang, and Jie Chen, arXiv:2310.12795v1, 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{Comparison between BLDNet and the Multiresolution Autoencoder (AE) with bold italics indicating better performance.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccc|cccccc}
\textbf{Set} & \multicolumn{6}{c|}{\textbf{AE}} & \multicolumn{6}{c}{\textbf{BLDNet}} \\ \hline
\multirow{5}{*}{\textbf{Pinery Bushfire}} & & \textbf{Acc} & \textbf{Precision} & \textbf{Recall} & \textbf{Specificity} & \textbf{F1} & \textbf{} & \textbf{Acc} & \textbf{Precision} & \textbf{Recall} & \textbf{Specificity} & \textbf{F1} \\
 & \textbf{Train} & 0.6044 & 0.4935 & 0.4811 & 0.7386 & 0.4495 & \textbf{Train} & \textit{\textbf{0.9524}} & \textit{\textbf{0.8444}} & \textit{\textbf{0.9813}} & \textit{\textbf{0.9822}} & \textit{\textbf{0.9023}} \\
 & \textbf{Test} & 0.5000 & 0.3338 & 0.3288 & 0.6357 & 0.3013 & \textbf{Test} & \textit{\textbf{0.7146}} & \textit{\textbf{0.4515}} & \textit{\textbf{0.5057}} & \textit{\textbf{0.7791}} & \textit{\textbf{0.4660}} \\
 & \textbf{Hold} & 0.4773 & 0.3439 & 0.3090 & 0.6651 & 0.3044 & \textbf{Hold} & \textit{\textbf{0.7145}} & \textit{\textbf{0.4517}} & \textit{\textbf{0.4941}} & \textit{\textbf{0.7743}} & \textit{\textbf{0.4646}} \\
 & \textbf{Full} & 0.3909 & 0.2993 & 0.3226 & 0.6028 & 0.2588 & \textbf{Full} & \textit{\textbf{0.7247}} & \textit{\textbf{0.4671}} & \textit{\textbf{0.5182}} & \textit{\textbf{0.7918}} & \textit{\textbf{0.4826}} \\ \hline
\multirow{5}{*}{\textbf{Joplin Tornado}} & & \textbf{Acc} & \textbf{Precision} & \textbf{Recall} & \textbf{Specificity} & \textbf{F1} & \textbf{} & \textbf{Acc} & \textbf{Precision} & \textbf{Recall} & \textbf{Specificity} & \textbf{F1} \\
 & \textbf{Train} & 0.6337 & 0.5450 & 0.4925 & 0.8033 & 0.5067 & \textbf{Train} & \textit{\textbf{0.9634}} & \textit{\textbf{0.9462}} & \textit{\textbf{0.9696}} & \textit{\textbf{0.9839}} & \textit{\textbf{0.6562}} \\
 & \textbf{Test} & 0.5171 & 0.5188 & 0.4274 & 0.7484 & 0.4457 & \textbf{Test} & \textit{\textbf{0.7610}} & \textit{\textbf{0.6968}} & \textit{\textbf{0.6968}} & \textit{\textbf{0.8802}} & \textit{\textbf{0.6900}} \\
 & \textbf{Hold} & 0.6179 & 0.5382 & 0.4967 & 0.7837 & 0.4967 & \textbf{Hold} & \textit{\textbf{0.7540}} & \textit{\textbf{0.6987}} & \textit{\textbf{0.6992}} & \textit{\textbf{0.8778}} & \textit{\textbf{0.6914}} \\
 & \textbf{Full} & 0.5988 & 0.5400 & 0.4893 & 0.7809 & 0.4961 & \textbf{Full} & \textit{\textbf{0.7606}} & \textit{\textbf{0.7049}} & \textit{\textbf{0.7027}} & \textit{\textbf{0.8810}} & \textit{\textbf{0.6954}} \\ \hline
\multirow{5}{*}{\textbf{Nepal Flooding}} & & \textbf{Acc} & \textbf{Precision} & \textbf{Recall} & \textbf{Specificity} & \textbf{F1} & \textbf{} & \textbf{Acc} & \textbf{Precision} & \textbf{Recall} & \textbf{Specificity} & \textbf{F1} \\
 & \textbf{Train} & 0.6484 & 0.3976 & 0.6864 & 0.6836 & 0.3905 & \textbf{Train} & \textit{\textbf{0.9707}} & \textit{\textbf{0.9310}} & \textit{\textbf{0.9871}} & \textit{\textbf{0.9889}} & \textit{\textbf{0.9566}} \\
 & \textbf{Test} & 0.5463 & 0.3664 & 0.3701 & 0.6958 & 0.3643 & \textbf{Test} & \textit{\textbf{0.6780}} & \textit{\textbf{0.4907}} & \textit{\textbf{0.4535}} & \textit{\textbf{0.7344}} & \textit{\textbf{0.4623}} \\
 & \textbf{Hold} & 0.5930 & 0.4056 & 0.4121 & 0.7059 & 0.4082 & \textbf{Hold} & \textit{\textbf{0.7086}} & \textit{\textbf{0.5589}} & \textit{\textbf{0.5199}} & \textit{\textbf{0.7613}} & \textit{\textbf{0.5328}} \\
 & \textbf{Full} & 0.5754 & 0.3712 & 0.3733 & 0.6907 & 0.3720 & \textbf{Full} & \textit{\textbf{0.7152}} & \textit{\textbf{0.5585}} & \textit{\textbf{0.5244}} & \textit{\textbf{0.7710}} & \textit{\textbf{0.5363}}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge" by Ali Ismail and Mariette Awad, arXiv:2201.10389v1, 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|crc|c}
	\toprule
		\textbf{BAIR} ($64\times64$)& $p$ & $k$ & $\# \text{pred}$ & FVD$\downarrow$ \\
		\cmidrule(){1-5}
 LVT & 1 & 15 & 15 & 125.8 \\
 DVD-GAN-FP & 1 & 15 & 15 & 109.8\\
 TrIVD-GAN-FP~ & 1 & 15 & 15 & 103.3\\
 VideoGPT~ & 1 & 15 & 15 & 103.3\\
		CCVS & 1 & 15 & 15 & 99.0\\
 FitVid & 1 & 15 & 15 & 93.6 \\
		MCVD~& 1 & 5 & 15 & 89.5\\
 NÜWA~& 1& 15& 15 & 86.9 \\
 RaMViD~& 1& 15& 15 & 84.2 \\
 VDM~& 1& 15& 15	& 66.9 \\
 RIVER~ & 1& 15& 15 &73.5 \\
 \textbf{CVP (Ours)}&1&\textbf{1}&15&\textbf{70.1}\\
		\cmidrule(){1-5}
 DVG~& 2& 14& 14 & 120.0\\
 SAVP~ & 2 & 14 & 14 & 116.4\\
		MCVD~ & 2 & 5 & 14 & 87.9 \\
 \textbf{CVP (Ours)}&2&2&14&68.2\\
 \textbf{CVP (Ours)}&2&\textbf{1}&14&\textbf{65.1}\\
		\cmidrule(){1-5}
 SAVP~ & 2 & 10 & 28 & 143.4 \\
 Hier-vRNN~& 2 & 10 & 28 & 143.4 \\
		MCVD~& 2 & 5 & 28 & 118.4 \\
 \textbf{CVP (Ours)}&2&\textbf{2}&28&95.1\\
		\textbf{CVP (Ours)}&2&\textbf{1}&28&\textbf{85.1}\\
 \bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Prediction" by Gaurav Shrivastava and Abhinav Shrivastava, arXiv:2412.04929v2, 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{Null rejection rates for testing methods at different perturbation levels in the RBM example.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}

 \textbf{PERTURBATION:} & \textbf{0} & \textbf{0.02} & \textbf{0.04} & \textbf{0.06} \\
 \hline
 \textbf{RFSD} & 0.00 & 0.48 & 0.93 & 0.98 \\
 \textbf{FSSD-opt} & 0.05 & 0.70 & 0.96 & 0.99 \\
 \textbf{IMQ KSD} & 0.08 & 0.99 & 1.00 & 1.00 \\
 \textbf{Gauss KSD} & 0.08 & 0.95 & 1.00 & 1.00 \\
 \textbf{PSD r1} & 0.08 & 0.51 & 0.96 & 0.99 \\
 \textbf{PSD r2} & 0.06 & 1.00 & 1.00 & 1.00 \\
 \textbf{PSD r3} & 0.09 & 0.97 & 1.00 & 1.00
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Narayan Srinivasan
Matthew Sutton
Christopher Drovandi
Leah F South

"The Polynomial Stein Discrepancy for Assessing Moment Convergence" by Narayan Srinivasan, Matthew Sutton, Christopher Drovandi, and Leah F South, arXiv:2412.05135v1, 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{Comparison of VI-LSPM and MCMC-LSPM average model run time in seconds across different network sizes $n$. Standard deviations are given in brackets. *The results for $n=1000$ are based on only five sets of initial positions for VI-LSPM, and only on two networks with one set of initial positions for MCMC-LSPM.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ll|}
\hline
 & \multicolumn{2}{c|}{Time (sec)} \\ \cline{2-3}
\multirow{-2}{*}{$n$} & VI & MCMC \\ \hline
20 & { 0.6 (0.6)} & 304 (15) \\ \hline
50 & { 24 (11)} & 481 (4) \\ \hline
100 & { 85 (29)} & 1,169 (8) \\ \hline
200 & { 389 (201)} & 3,920 (132) \\ \hline
{ 500} & { 1,669 (262)} & { 23,264 (2,541)} \\ \hline
{ 1000*} & { 8,419 (1,649)} & { 105,428 (10,112)} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Xian Yao Gwee
Isobel Claire Gormley
Michael Fop

"Variational Inference for the Latent Shrinkage Position Model" by Xian Yao Gwee, Isobel Claire Gormley, and Michael Fop, arXiv:2311.16451v2, 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|}{Power-law kernel}
 & \multicolumn{2}{c}{Exponential kernel}
 \\
\hline
Sample size $N$ &
$\textrm{err}^{(1)}$ & $\textrm{err}_{\textrm{proj}}^{(1)}$ & $\textrm{err}^{(2)}$
& $\textrm{err}_{\textrm{proj}}^{(2)}$ \\
\hline
63 & $ 8.4 \times 10^{-3} $ & $ 1.9 \times 10^{-3}$ & $ 9.8 \times 10^{-3} $ & $ 4.4 \times 10^{-3}$ \\
126 & $ 6.9 \times 10^{-3}$ & $1.7 \times 10^{-3}$ & $ 5.3 \times 10^{-3}$ & $ 2.9 \times 10^{-3}$ \\
252 & $ 4.2 \times 10^{-3}$ & $ 1.2 \times 10^{-3}$ & $ 4.2 \times 10^{-3}$ & $ 2. 2\times 10^{-3}$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Eyal Neuman
Wolfgang Stockinger
Yufei Zhang

"An Offline Learning Approach to Propagator Models" by Eyal Neuman, Wolfgang Stockinger, and Yufei Zhang, arXiv:2309.02994v1, 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 comparison of different multi-modal LLMs with retrieval.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrr}
\toprule
\textbf{Model} & \textbf{Open/Closed} & \textbf{Params (B)} & \textbf{Accuracy (\%)} \\
\midrule
GPT-4o-mini & Closed-source & -- & 21.53 \\
Gemini-2.0-Flash & Closed-source & -- & 18.42 \\
Kimi-VL-A3B-Instruct & Open-source & 16 & 17.63 \\
Qwen2.5-7B-Instruct & Open-source & 7 & 15.15 \\
DeepSeek R1 8B & Open-source & 8 & 13.41 \\
DeepSeek R1 14B & Open-source & 14 & 13.15 \\
Llama 3.2 11B & Open-source & 11 & 11.57 \\
Llama 3.2 3B & Open-source & 3 & 11.27 \\
Qwen-VL-Chat & Open-source & 7 & 8.89 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Fengbin Zhu
Junfeng Li
Liangming Pan
Wenjie Wang
Fuli Feng
Chao Wang
Huanbo Luan
Tat-Seng Chua

"Towards Temporal-Aware Multi-Modal Retrieval Augmented Generation in Finance" by Fengbin Zhu, Junfeng Li, Liangming Pan, Wenjie Wang, Fuli Feng, Chao Wang, Huanbo Luan, and Tat-Seng Chua, arXiv:2503.05185v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{WERs and relative benefits of adding modalities on SlideAVSR at different noise levels. The OCR words are ranked by FQ Ranker , and K indicates the maximum OCR word count.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rccccc}
 \noalign{\hrule height 1.2pt}
 \textbf{Inputs} & +$\infty$ & 10 & 0 & -10& \textbf{Average} \\
 \multicolumn{6}{c}{\cellcolor[HTML]{DEDCDC}WER $\downarrow$ (RB $\uparrow$)} \\
 \hline
 $A$ & 33.8\%& 42.5\%& 44.8\%& 70.6\%& 47.9\% \\
 \hline
 $O_{All}+A$ & 31.4\% (+7.0\%)& 37.2\% (+12.4\%)& 47.7\% (-6.4\%)& 75.9\% (-7.6\%)& 48.0\% (+1.4\%)\\
 $O_{K=30}+A$ & 30.5\% (+9.7\%)& 35.7\% (+15.8\%)& 46.9\% (-4.7\%)& 75.5\% (-6.9\%)& 47.2\% (+3.5\%)\\
 $O_{K=10}+A$ & 30.6\% (+9.5\%)& 34.6\% (+18.5\%)& 46.2\% (-3.1\%)& 77.9\% (-10.3\%)& 47.3\% (+3.6\%)\\
 $I+A$ & 28.5\% (+15.5\%)& 34.3\% (+19.3\%)& 46.1\% (-13.6\%)& 70.8\% (-0.2\%)& 46.1\% (+5.3\%)\\
 \noalign{\hrule height 1.2pt}
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yiwen Guan
Viet Anh Trinh
Vivek Voleti
Jacob Whitehill

"MLLM-based Speech Recognition: When and How is Multimodality Beneficial?" by Yiwen Guan, Viet Anh Trinh, Vivek Voleti, and Jacob Whitehill, arXiv:2507.19037v1, 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{Patch-level Anomaly Detection Results}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccccccccc}
\hline
 & & NLL & Regret & LLR & complexity & f-AnoGAN & AE & MemAE & AnoDDPM & AnoPILaD
\\ \hline
\multirow{2}{*}{LH} & AUC & 0.4982 & 0.6720 & 0.6078 & 0.7931 & 0.2289 & 0.9254 & 0.9290 & 0.8555 & $\mathbf{0.9587}$
\\
 & AUPR & 0.5552 & 0.6718 & 0.6260 & 0.7139 & 0.3377 & 0.8906 & 0.8886 & 0.7841 & $\mathbf{0.9499}$
\\ \hline
\multirow{2}{*}{C16} & AUC & 0.4983 & 0.6720 & 0.7065 & 0.7752 & 0.1735 & 0.6584 & 0.6611 & 0.6857 & $\mathbf{0.8884}$
\\
 & AUPR & 0.5552 & 0.6718 & 0.4765 & 0.5140 & 0.1104 & 0.4759 & 0.4880 & 0.5741 & $\mathbf{0.6987}$
\\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jiamu Wang
Keunho Byeon
Jinsol Song
Anh Nguyen
Sangjeong Ahn
Sung Hak Lee
Jin Tae Kwak

"Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis" by Jiamu Wang, Keunho Byeon, Jinsol Song, Anh Nguyen, Sangjeong Ahn, Sung Hak Lee, and Jin Tae Kwak, arXiv:2508.15236v1, 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{Comparison of TLT references}
\begin{tabular}{llrrrrr}
\hline
\hline
Method & N & ICC & R2 & MAE & MAPE & r\\
\hline
Proposed & 4,323 & \textbf{0.976} & \textbf{0.953} & \textbf{0.684} & \textbf{3.0} & \textbf{0.978} \\
Field 23280 & 4,323 & 0.969 & 0.941 & 0.856 & 3.7 & 0.970 \\
\hline
\hline
\multicolumn{7}{l}{*Comparison to the target values, listing both the proposed predictions}\\ \multicolumn{7}{l}{ and alternative UK Biobank reference values on the same subjects}
\end{tabular}
\end{table}
\end{document}

eess

Taro Langner
Fredrik K. Gustafsson
Benny Avelin
Robin Strand
Håkan Ahlström
Joel Kullberg

"Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI" by Taro Langner, Fredrik K. Gustafsson, Benny Avelin, Robin Strand, Håkan Ahlström, and Joel Kullberg, arXiv:2101.06963v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The impact of different CBAM.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
	CBAM & NSS & CC & AUC \\\hline
 no CBAM & 0.9653 & 0.5587 & 0.6069 \\ \hline
 with 2d-CBAM & 1.0271 & 0.5765 & 0.6278 \\ \hline
 with SP-CBAM & 1.1736 & 0.6234 & 0.6619 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jie Li
Ling Han
Cong Zhang
Qiyue Li
Zhi Liu

"Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback" by Jie Li, Ling Han, Cong Zhang, Qiyue Li, and Zhi Liu, arXiv:2201.12525v1, 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{Overview of elements in the MEN.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Element} & \textbf{Electricity} & \textbf{Gas} & \textbf{Heat} \\
 \midrule
 Nodes & 295 & 236 & 132 \\
 Edges & 291 & 235 & 106 \\
 Loads & 187 & 123 & 25 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Marwan Mostafa
Daniel Wenser
Payam Teimourzadeh Baboli
Christian Becker

"Unified Graph-Theoretic Modeling of Multi-Energy Flows in Distribution Systems" by Marwan Mostafa, Daniel Wenser, Payam Teimourzadeh Baboli, and Christian Becker, arXiv:2509.06447v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
\textbf{Method} & \textbf{Accuracy with Return Rate} & \textbf{Accuracy with Price} \\ \hline
MLP & 91.51\% & 93.13\% \\
CNN & 95.43\% & 94.15\% \\
ResNet & 95.64\% & 95.27\% \\
RNN & 95.77\% & 95.12\% \\
GRU & 96.56\% & 96.07\% \\
LSTM & 97.05\% & 94.61\% \\
Autoencoder & 97.83\% & 97.50\% \\
t-CNN & 95.19\% & 94.21\% \\
m-CNN & 96.58\% & 96.27\%\\ \hline
\end{tabular}
\end{adjustbox}
\caption{Accuracy of machine learning models on balanced return rate data (using daily data, return rate is calculated using the “close price"). The experiment for calculating the accuracy rate with price is the same as that in Subsection . And the experiment in calculating the accuracy with the return rate use the similar settings in data splitting and model training.}
\end{table}
\end{document}

q-fin

"Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks" by Yu Zhang, Zelin Wu, and Claudio Tessone, arXiv:2504.12771v1, 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}
 Attack & $\epsilon$ & LPIPS & NAD & $n_B$ & $t_B$ & Time $\sigma$ & Time $\sigma^{-1}$ & Time $\mathsf{eval}_h$\\
 \hline\hline
 FGSM &	 0.100 & 0.5596 & 1.1287 & 150 & 869 & 0.0303 &	0.1993 &	0.0309 \\
 PGD & 0.300 & 0.4485 & 0.8163 & 150	& 629	&	0.0280 & 0.1506 &	0.0220 \\
 Brightness	& 0.250 & 0.3183 &	6.8516 & 150	& 5280 &	0.0847 & 1.3360 &	0.2651 \\
 & 2.000 & 0.1955 & 5.8659	& 150 & 4520 & 0.0763 &	1.0865 &	0.2143 \\
 Contrast & 0.250 & 0.3204 & 1.7338	 & 150	& 1336	& 0.0386 & 0.2999 &0.0489 \\
 & 2.000 & 0.1208 & 1.4895 & 150 &	1147 &	0.0358 & 0.2592 &	0.0422
 \end{tabular}
\end{adjustbox}
\caption{Total time in seconds taken by our scheme to produce the $\sigma$-polynomial of an image, the $\sigma$-polynomial and its inverse of an image, and the solution using the $\mathsf{eval}_h$ function, where $t$ is chosen such that LPIPS is high for each attack.}
\end{table}
\end{document}

math

"Property-Preserving Hashing for $\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks" by Hassan Asghar, Chenhan Zhang, and Dali Kaafar, arXiv:2504.16355v1, 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|c|}
\hline
\textbf{Return Period} & \textbf{Return Value} & \textbf{Lower CI} & \textbf{Upper CI} \\
\hline
1.00 & 0.35 & 1.66 & 0.11 \\
2.00 & 0.15 & 1.31 & 0.04 \\
5.00 & 0.05 & 0.75 & 0.00 \\
10.00 & 0.02 & 0.58 & -0.20 \\
25.00 & 0.01 & 0.52 & -0.36 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Return Periods and Confidence Intervals - Zarco Use Case 1}
\end{table}
\end{document}

stat

Dimitrios P. Panagoulias
Elissaios Sarmas
Vangelis Marinakis
Maria Virvou
George A. Tsihrintzis

"Integrating Dynamic Correlation Shifts and Weighted Benchmarking in Extreme Value Analysis" by Dimitrios P. Panagoulias, Elissaios Sarmas, Vangelis Marinakis, Maria Virvou, and George A. Tsihrintzis, arXiv:2411.13608v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yunshi Wen
Tengfei Ma
Tsui-Wei Weng
Lam M. Nguyen
Anak Agung Julius

"Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification" by Yunshi Wen, Tengfei Ma, Tsui-Wei Weng, Lam M. Nguyen, and Anak Agung Julius, arXiv:2411.01006v3, 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
Config & Setting \\
\hline
\hline
Batch size & 1024 \\
Optimizer & Adam \\
Learning rate & $10^{-2}$ \\
Scheduler & AdaptiveLR \\
Training iterations & $2.5 \times 10^6$ \\
Step rate & $3 \times 10^3$ \\
Decay rate & 0.9 \\
EMA decay & 0.98 \\
EMA lambda & 2 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{High-precision training recipe settings for linear attention.}
\end{table}
\end{document}

math

Jerry Liu
Jessica Grogan
Owen Dugan
Ashish Rao
Simran Arora
Atri Rudra
Christopher Ré

"Towards Learning High-Precision Least Squares Algorithms with Sequence Models" by Jerry Liu, Jessica Grogan, Owen Dugan, Ashish Rao, Simran Arora, Atri Rudra, and Christopher Ré, arXiv:2503.12295v1, 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}{lr}
\toprule
\textbf{Incident Categories} & \textbf{Number of Observations} \\ \midrule
(PV) Privacy Violation & 51315 \\
(DB) Data Breach & 26492 \\
(FE) Fraud and Extortion & 4464 \\
(ITE) IT Error & 2102 \\
Other & 565 \\ \bottomrule
\end{tabular}
\caption{Number of historical cyber incidents in each category.}
\end{table}
\end{document}

q-fin

Incident-Specific Cyber Insurance

Wing Fung Chong
Daniel Linders
Zhiyu Quan
Linfeng Zhang

"Incident-Specific Cyber Insurance" by Wing Fung Chong, Daniel Linders, Zhiyu Quan, and Linfeng Zhang, arXiv:2308.00921v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|ll|ll|ll}
\# &\multicolumn{2}{c}{$\tau_{large}$} &\multicolumn{2}{c}{$\tau_{mix}$} & \multicolumn{2}{c}{$\tau_{small}$} \\
 & cg & lsqr & cg & lsqr & cg & lsqr \\ \hline
1 & 2.0 & 235.9 &2 & 641.8 & 1.0 & 866.5 \\
2 & 2.0 & 157.3 & 1.9 & 980.6 & $\S$ & $\S$ \\
3 & 2.4 & 153.0 & 1.9 & 958.3 & 2.5 & 766.3 \\
4 & 2.0 & 87.1 & 2.0 & 354.0 &1.0 & 599.5 \\
5 & 2.0 & 285.5 &3.0 & 848.9 & 1.8 & $\ddagger$ \\
6 & 1.0 & 68.5 & 1.0 & 511.8 &1.0 & $\ddagger$ \\
7 & 2.0 & 136.0 & 2.0 &141.2 & 1.0 & 144.8 \\
8 & 8.0 & 128.6 & 4.0 & 721.0 & 6.0 & 763.0 \\
 \end{tabular}
\end{adjustbox}
\caption{Average number of inner CG and LSQR iterations for the new scheme (symmetric case) with $M$-orthogonalization}
\end{table}
\end{document}

math

"Robust iterative methods for linear systems with saddle point structure" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, 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}{ccc}
 \hline
 \multicolumn{3}{c}{{\bf Robustness under Variation of Topological Noise}}\\\hline
 Percentage & Pre-specified Loc. ($m = 4$ vs $n = 4$) & Random Loc. ($m = 5$ vs $n = 5$) \\\hline
 100\% & 0.4567$\pm$0.2874 & 0.4133$\pm$0.2482\\
 95\% & 0.4777$\pm$0.2843 & 0.4498$\pm$0.2844\\
 90\% & 0.4455$\pm$0.2791 & 0.5214$\pm$0.2983\\\hline
 Percentage & Random Loc. ($m = 20$ vs $n = 20$) & Random Loc. ($m = 100$ vs $n = 100$) \\\hline
 100\% & 0.5060$\pm$0.3163 & 0.4328$\pm$0.2764\\
 95\% & 0.5016$\pm$0.2998 & 0.4193$\pm$0.2863\\
 90\% & 0.4827$\pm$0.2919 & 0.5260$\pm$0.2812\\\hline
 \multicolumn{3}{c}{{\bf Robustness under Variation of Hole Location}} \\\hline
 Percentage & Pre-specified Loc. ($m = 4$ vs $n = 4$) & Random Loc. ($m = 5$ vs $n = 5$) \\\hline
 100\% & 0.2917$\pm$0.2624 & 0.5005$\pm$0.2883\\
 95\% & 0.2973$\pm$0.2407 & 0.5342$\pm$0.2775\\
 90\% & 0.3065$\pm$0.2505 & 0.4434$\pm$0.3050\\\hline
 Percentage & Radom Loc. ($m = 20$ vs $n = 20$) & Random Loc. ($m = 100$ vs $n = 100$) \\\hline
 100\% & 0.4998$\pm$0.2901 & 0.3845$\pm$0.2620\\
 95\% & 0.4608$\pm$0.2999 & 0.3924$\pm$0.2777\\
 90\% & 0.4810$\pm$0.2568 & 0.4550$\pm$0.2835\\\hline
 \end{tabular}
\end{adjustbox}
\caption{Summary of mean$\pm$standard deviation of $p$-values from the spectral transposition test in 100 simulations. Top half: In each simulation, the test is used to compare a group of $m$ random samples with a varied percentage (90\%, 95\%, 100\%) of 100 points from the original key shape with a group of $n$ random samples with the same percentage of 100 points from the key shape with topological noise in the form of a much smaller hole next to the keyhole. The location of the smaller hole in each random sample of the second group can be pre-specified or randomly chosen from the pre-specified options. Bottom half: In each simulation, the test is used to compare a group of $m$ random samples with a varied percentage (90\%, 95\%, 100\%) of 100 points from the original key shape with only the top left quarter of the keyhole left, with a group of $n$ random samples with the same percentage of 100 points from the original key shape with a random quarter of the keyhole left.}
\end{table}
\end{document}

stat

"Topological inference on brain networks across subtypes of post-stroke aphasia" by Yuan Wang, Jian Yin, and Rutvik H. Desai, arXiv:2311.01625v1, 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{Risk for behavioral scientists and models.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrr}
Study & Predictor & Mean & 95\% CI \\
\midrule
Exercise & Scientist & 6.02 & (5.19, 6.93) \\
 & Null & 0.03 & (0.01, 0.05) \\
 & Oracle & 0.00 & (0.00, 0.01) \\
Flu & Scientist & 64.24 & (14.80, 184.52) \\
 & Null & 4.80 & (2.76, 7.28) \\
 & Oracle & 0.11 & (0.03, 0.38) \\
RCT & Scientist & 54.59 & (27.97, 101.94) \\
 & Null & 7.48 & (2.24, 17.04) \\
 & Oracle & 1.13 & (0.18, 4.70) \\
Effort & Scientist & 49884 & (38858, 64352) \\
 & Selfish & 49865 & (26356, 80066) \\
 & Altruistic & 34639 & (18785, 55836) \\
 & Oracle & 727 & (232, 1636) \\
Reproducibility & Scientist & 0.31 & (0.28, 0.35) \\
 & Null & 0.31 & (0.20, 0.43) \\
 & Random & 0.25 & (0.25, 0.25) \\
 & Linear regression & 0.23 & (0.18, 0.28) \\
 & Oracle & 0.16 & (0.12, 0.21) \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Simple models predict behavior at least as well as behavioral scientists" by Dillon Bowen, arXiv:2208.01167v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc}
\toprule
& \multicolumn{5}{c}{\% tuples retained} \\
\cmidrule(){2-6}
Cardinalities \hspace{3mm} & 20 & 40 & 60 & 80 & 100 \\ \midrule
$\Omega{=}\{2\}$ & 128 & 204 & 256 & 346 & 462\\
$\Omega{=}\{3\}$ & 228 & 390 & 540 & 702 & 844\\
$\Omega{=}\{4\}$ & 294 & 546 & 754 & 922 & 1152\\
$\Omega{=}\{2,3\}$ & 356 & 594 & 796 & 1048 & 1306\\
\bottomrule
\end{tabular}
\caption{GPU usage in MiB when randomly dropping tuples, corresponding to experiments in Fig.~.}
\end{table}
\end{document}

cs

Toby Perrett
Alessandro Masullo
Tilo Burghardt
Majid Mirmehdi
Dima Damen

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


stat

"Addressing Positivity Violations in Extending Inference to a Target Population" by Jun Lu and Sanjib Basu, arXiv:2412.09845v2, 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
 Intervention &Mean &CI width \\ \hline
 1 & 1030.89 & 2.86 \\ \hline
 2 & 1031.89 & 2.83 \\ \hline
 3 & 1030.44 & 2.86 \\ \hline
 4 & 681.02 & 2.3 \\ \hline
 5 & 680.07 & 2.28 \\ \hline
 6 & 680.15 & 2.2 \\ \hline
 7 & 150.93 & 1.08 \\ \hline
 8 & 150.66 & 1.06 \\ \hline
 9 & 150.59 & 1.1 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Brute-force results for different interventions given that each intervention got 2000 samples or 18000 samples in total.}
\end{table}
\end{document}

stat

Abdulrahman A. Ahmed
M. Amin Rahimian
Mark S. Roberts

"Estimating Treatment Effects Using Costly Simulation Samples from a Population-Scale Model of Opioid Use Disorder" by Abdulrahman A. Ahmed, M. Amin Rahimian, and Mark S. Roberts, arXiv:2308.13040v1, 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{Quantitative comparison between our and state-of-the-art models in terms of reconstruction (after the full pipeline) performance on the test set, in various configurations, according to the type of architecture used for the fMRI-VDVAE latent variables regression and the number of the VDVAE latent variables used. In this case, 4 subjects (\#1, \#2, \#5, \#7) were considered and metrics values were averaged.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|cccc|cccc}
 \toprule
 \multicolumn{2}{c}{\textbf{Architecture}} & \multicolumn{4}{c}{\textbf{Low-level metrics}} & \multicolumn{4}{c}{\textbf{High-level metrics (eq. )}}\\
 \midrule
 & Latents & SSIM$\uparrow$ & PixCorr$\uparrow$ & Alexnet(2)$\uparrow$ & AlexNet(5)$\uparrow$ & InceptionV3$\uparrow$ & CLIP$\uparrow$ & EfficientNet-B$\downarrow$ & SwAV-ResNet50$\downarrow$ \\
 \midrule
 Lin et al. & - & - & - & - & - & 0.782 & - & - & - \\
Takagi et al. & - & - & - & 0.830 & 0.830 & 0.760 & 0.770 & - & - \\
Gu et al. & - & 0.150 & 0.325 & - & - & - & - & 0.862 & 0.465 \\
 Ridge & 31 & 0.356 & \textbf{0.254} & \textbf{0.942} & \textbf{0.962} & \textbf{0.872} & 0.915 & \textbf{0.775} & \textbf{0.423} \\
 Ours & 15 & \textbf{0.361} & 0.202 & 0.878 & 0.931 & 0.862 & 0.914 & 0.786 & 0.434 \\
 Ours & 31 & 0.360 & 0.177 & 0.864 & 0.923 & 0.869 & \textbf{0.922} & 0.785 & 0.434 \\
 \midrule
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Lorenzo Veronese
Andrea Moglia
Luca Mainardi
Pietro Cerveri

"Optimized two-stage AI-based Neural Decoding for Enhanced Visual Stimulus Reconstruction from fMRI Data" by Lorenzo Veronese, Andrea Moglia, Luca Mainardi, and Pietro Cerveri, arXiv:2412.13237v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Experimental settings}
\begin{tabular}{lll}
\toprule
\textbf{Parameter} & \textbf{Description} & \textbf{Values}\\
\midrule
$f_1$ & Initial mean estimate & 0.2, 0.5 \\
$\sqrt{MSE_1}$ & Initial coefficient of variation & 4.0 \\
$\alpha$ & Smoothing parameter for DGP & 0.05, 0.1, 0.15 \\
$l$ & Lead time & 1, 5, 10, 20 \\
$P_1^*$ & In stock probability target & 0.9, 0.91, $\cdots$, 0.98 \\
$h$ & Holding cost & 1 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

Lotte van Hezewijk
Nico Dellaert
Willem van Jaarsveld

"On non-negative auto-correlated integer demand processes" by Lotte van Hezewijk, Nico Dellaert, and Willem van Jaarsveld, arXiv:2312.11273v2, 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}{rrrrrrrr}
\toprule
\multicolumn{3}{c}{ } & \multicolumn{3}{c}{Efficiency (\%)} & \multicolumn{2}{c}{DF} \\
\cmidrule(l{3pt}r{3pt}){4-6} \cmidrule(l{3pt}r{3pt}){7-8}
$\kappa_{DP}$ & $\kappa_{LoF-DP}$ & $\kappa_{MSE(D)}$ & $(DP)_S$ & LoF-$DP$ & $MSE(D_S)$ & PE & LoF\\
\midrule
0.33 & 0.33 & 0.33 & 89.11 & 100.54 & 95.84 & 16 & 10\\
0.40 & 0.20 & 0.40 & 94.20 & 89.30 & 99.50 & 17 & 9\\
0.25 & 0.25 & 0.50 & 90.71 & 94.26 & 99.84 & 15 & 11\\
1.00 & 0.00 & 0.00 & 100.00 & 67.05 & 98.73 & 22 & 4\\
0.00 & 1.00 & 0.00 & 77.47 & 100.00 & 90.35 & 14 & 12\\
0.00 & 0.00 & 1.00 & 73.08 & 80.31 & 100.00 & 9 & 17\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Efficiencies under individual criteria and degrees of freedom for the Egorova and Gilmour case study designs defined by six different sets of criteria weights.}
\end{table}
\end{document}

stat

Vasiliki Koutra
Olga Egorova
Steven G. Gilmour
Luzia A. Trinca

"MOODE: An R Package for Multi-Objective Optimal Design of Experiments" by Vasiliki Koutra, Olga Egorova, Steven G. Gilmour, and Luzia A. Trinca, arXiv:2412.17158v1, 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}{cll}
\toprule
& Hyperparameter & Value \\
\midrule
\multirow{5}{*}{\shortstack{Network\\Hyperparameters}} & Optimizer & Adam~ \\
& Learning rate & 3e-4 \\
& Mini-batch size & 256 \\
& Target update rate (FQE) & 5e-3 \\
& Discount factor & 0.99 \\
& Terminal Discount factor & 0.0 \\
\midrule
\multirow{3}{*}{Architecture} & Network Hidden dim & 256 \\
& Network Hidden layers & 2 \\
& Activation function & ReLU \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Hyperparameters and architecture.}
\end{table}
\end{document}

cs

Scott Fujimoto
David Meger
Doina Precup
Ofir Nachum
Shixiang Shane Gu

"Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error" by Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, and Shixiang Shane Gu, arXiv:2201.12417v2, 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{Simulated results for solving our $10$-unit, $24$-hour, UC problem. The approximation error in the rightmost column is computed by comparing the approximation produced by our quantum-classical heuristic against the global optimum computed by CPLEX~. The number of iterations and the approximation error are averaged across the $24$ time periods with power load profile described in Table~, and across $7$ independent trials for each period. The results summarized in this table are illustrated in Figure~.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
Ansatz layers & TQG & Params & Depth & Iterations & Error (\%) \\
\midrule
1 & 15 & 5 & 16 & 59.14 & 1.78 \\
2 & 30 & 10 & 31 & 103.63 & 1.18 \\
3 & 45 & 15 & 46 & 148.23 & 0.95 \\
4 & 60 & 20 & 61 & 194.07 & 1.03 \\
5 & 75 & 25 & 76 & 238.61 & 0.75 \\
6 & 90 & 30 & 91 & 277.00 & 0.74 \\
7 & 105 & 35 & 106 & 324.45 & 0.73 \\
8 & 120 & 40 & 121 & 366.80 & 0.72 \\
9 & 135 & 45 & 136 & 407.51 & 0.55 \\
10 & 150 & 50 & 151 & 449.38 & 0.72 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Willie Aboumrad
Phani R V Marthi
Suman Debnath
Martin Roetteler
Evgeny Epifanovsky

"A New Hybrid Quantum-Classical Algorithm for Solving the Unit Commitment Problem" by Willie Aboumrad, Phani R V Marthi, Suman Debnath, Martin Roetteler, and Evgeny Epifanovsky, arXiv:2505.00145v1, 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|cc|cr|cc}
\hline
 & \multicolumn{2}{c|}{Hodge Scattering + SVM} & \multicolumn{2}{c|}{UGT} & \multicolumn{2}{c}{GFN} \\ \hline
Graph & Accuracy & \# Param & Accuracy & \# Param & Accuracy & \# Param \\ \hline
Collab & 80.38 & 256 & 77.84 & 866,746 & 81.50 & 68,754 \\
DD & 72.71 & 256 & 80.23 & 76,928 & 79.37 & 68,754 \\
IMDB-B & 73.10 & 256 & 77.04 & 55,508 & 73.40 & 68,754 \\
IMDB-M & 49.67 & 256 & 53.60 & 48,698 & 51.80 & 68,818 \\
MUTAG & 85.78 & 256 & 80.23 & 4,178 & 85.83 & 68,818 \\
PROTEINS & 75.35 & 256 & 78.53 & 1,878 & 76.46 & 68,818 \\
PTC & 68.28 & 256 & 69.63 & 12,038 & 66.60 & 68,818 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Naoki Saito
Stefan C. Schonsheck
Eugene Shvarts

"Multiscale Hodge Scattering Networks for Data Analysis" by Naoki Saito, Stefan C. Schonsheck, and Eugene Shvarts, arXiv:2311.10270v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Matriz A que representa $4$ opciones de pedido para un horizonte de planificación de tamaño $3$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|lll|}
\hline
Periodo $i$ & 1 & 2 & 3 \\ \hline
Estrategia $j$ & \multicolumn{3}{c|}{$\alpha_{i}$} \\ \hline
 & 10 & 20 & 15 \\ \hline
1 & 1 & 1 & 1 \\ \hline
2 & 1 & 2 & 0 \\ \hline
3 & 2 & 0 & 1 \\ \hline
4 & 3 & 0 & 0 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Margarita Miguelina Mieras
Tania Daiana Tobares
Ricardo Raúl Palma
Antonio José Ramirez-Pastor
Fabricio Orlando Sanchez Varretti

"Nuevo modelo para el dimensionamiento de lotes de pedidos en función del volumen de compra y deterioro temporal de los artículos" by Margarita Miguelina Mieras, Tania Daiana Tobares, Ricardo Raúl Palma, Antonio José Ramirez-Pastor, and Fabricio Orlando Sanchez Varretti, arXiv:2412.02714v1, 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{Functions involving Stirling numbers of the first and second kind , denoted $s$ and $S$, respectively, and their derivatives appearing in HAC densities and their associated Fisher information. See -- for notation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|ll}
\hline
$s_{nk}(x)$ & $\sum_{j=k}^{n}x^js(n,j)S(j,k)$ &
$(x)_n$ & $\sum_{j=1}^{n}x^js(n,j)$\\
$s_{nk}'(x)$ & $\sum_{j=k}jx^{j-1}s(n,j)S(j,k)$ &
$(x)'_n$ & $\sum_{j=1}^{n}jx^{j-1}s(n,j)$\\
$s_{nk}^{(2)}(x)$ & $\sum_{j=k}j(j-1)x^{j-2}s(n,j)S(j,k)$ &
$(x)^{(2)}_n$ & $\sum_{j=1}^{n}j(j-1)x^{j-2}s(n,j)$\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Samuel Perreault
Yanbo Tang
Ruyi Pan
Nancy Reid

"Inference for overparametrized hierarchical Archimedean copulas" by Samuel Perreault, Yanbo Tang, Ruyi Pan, and Nancy Reid, arXiv:2411.10615v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Juan Zhang
Junyue Luo
Fangfang Zhang
Xiaoqiang Yue

"Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs" by Juan Zhang, Junyue Luo, Fangfang Zhang, and Xiaoqiang Yue, arXiv:2504.14930v1, 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
 \multicolumn{3}{|c|}{Fit exponents} \\
 \hline
 Schedule & Random initial. & Minifold initial.\\
 \hline
 Fixed $\beta$ &$0.70\pm 0.08$ & $0.93\pm 0.06$\\
 Logarithmic & $0.29\pm 0.07$ & $0.88\pm 0.09$\\
 Linear &$0.34\pm0.07$ & $0.86\pm 0.11$\\
 Exponential &$0.37\pm 0.07$ & $1.00\pm 0.12$\\
 Geometric & $0.29\pm 0.07$ & $0.85\pm 0.18$\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Table of scaling exponents for different annealing schedules and initialization options. The peptides are the same, except that for fixed $\beta$ we have also included dipeptides with 5 bits of precision, what is costly for the rest of schedules. For fixed $\beta$, the value heuristically chosen was $\beta = 1000$, while the initial $\beta$ value in each of the schedules, defined in , is $\beta(1) = 50$. The uncertainty is expressed via the standard deviation in the expected exponent, calculated with the bootstrapping method .}
\end{table}
\end{document}

cs

P A M Casares
Roberto Campos
M A Martin-Delgado

"QFold: Quantum Walks and Deep Learning to Solve Protein Folding" by P A M Casares, Roberto Campos, and M A Martin-Delgado, arXiv:2101.10279v2, 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{Frequency of Special Characters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments" by James Enouen, Tianshu Sun, and Yan Liu, arXiv:2309.01780v1, 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}{lccccc}
\toprule
& \bf Irrelevant & \multicolumn{3}{c}{\bf Relevant} & \bf Very Relevant \\
\cmidrule(l@{0pt}r@{0pt}){2-2}\cmidrule(l@{5pt}r@{0pt}){3-5} \cmidrule(l@{5pt}r@{0pt}){6-6}
\bf Words & \bf c1 & \bf c2 & \bf c3 & \bf c4 & \bf c5\\
\midrule
Pro & 14\% & 10\% & 36\% & 34\% & 6\% \\
Con & 36\%& 2\% & 34\% & 26\% & 2\% \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Distribution of the pro/con bag-of-words, averaged across the top-10 big issues, over the five considered categories: c2 means wrong stance, c3 words that fit both stances, and c3 and c4 represent correct stance.}
\end{table}
\end{document}

cs

Milad Alshomary
Wei-Fan Chen
Timon Gurcke
Henning Wachsmuth

"Belief-based Generation of Argumentative Claims" by Milad Alshomary, Wei-Fan Chen, Timon Gurcke, and Henning Wachsmuth, arXiv:2101.09765v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Sourav Medya
Mohammad Rasoolinejad
Yang Yang
Brian Uzzi

"An Exploratory Study of Stock Price Movements from Earnings Calls" by Sourav Medya, Mohammad Rasoolinejad, Yang Yang, and Brian Uzzi, arXiv:2203.12460v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|l|}
 \hline
 &
 \multicolumn{3}{c|}{Concentration} &
 \multicolumn{3}{c|}{Emission}\\
 \hline
 & \small Mannix & \small LC & \small ML & \small Mannix & \small LC & \small ML \\
 \hline
 \small Ours$_{Forward}$ & 0.5425 & \textbf{0.3101} & 0.5814 & 0.0992 & 0.1615 & 0.0517 \\
 \small Ours$_{Reverse}$ & 0.6566 & 0.4301 & 0.5702 & 0.1085 & 0.1369 & 0.0543\\
 \small Ours$_{Poly}$ & \textbf{0.4624} & 0.3140 & \textbf{0.4225} & 0.5380 & 0.1977 & 0.0942\\
 \small RNN$_{mod}$ & 0.6239 & 0.4314 & 2.1859 & 0.0661 & 0.0737 & 0.7127\\
 \small NN & 0.5683 & 0.3132 & 0.4618 & \textbf{0.0721} & \textbf{0.0754} & \textbf{0.0187} \\
 \small LSTM & 0.6454 & 0.3914 & 0.5550 & 0.2426 & 0.2568 & 0.0711\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Average of relative training and validation errors for predicting concentrations and emissions from each station and its corresponding tailing pond. For each column (station), the lowest error is highlighted.}
\end{table}
\end{document}

stat

Esha Saha
Oscar Wang
Amit K. Chakraborty
Pablo Venegas Garcia
Russell Milne
Hao Wang

"Methane projections from Canada's oil sands tailings using scientific deep learning reveal significant underestimation" by Esha Saha, Oscar Wang, Amit K. Chakraborty, Pablo Venegas Garcia, Russell Milne, and Hao Wang, arXiv:2411.06741v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

"Unbiased weighing matrices of weight $9$" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Experiment parameters configuration.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\toprule
\textbf{Parameter} & \textbf{Description} & \textbf{Value} \\ \midrule \midrule
m & Meta input dimension & 120 \\ \hline
$K_{neigh}$ & KNN-graph in DEC & 15 \\ \hline
Opt & Optimizer & ADAM \\ \hline
$\eta$ & Outer learning rate & 0.001 \\ \hline
$\mu$ & inner learning rate & 0.0005 \\ \hline
s & Trajectory data points sampling step & 100 \\ \hline
L & Number of hidden layers & 2 \\ \hline
h & Hidden layer size & 128 \\ \hline
N & Number of local SGD steps & 5 \\ \hline
I & Number of global iterations & 1500 \\ \hline
b & batch size & 8 \\ \hline
K & Total number of training tasks & 2 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yaya Etiabi
Eslam Eldeeb
Mohammad Shehab
Wafa Njima
Hirley Alves
Mohamed-Slim Alouini
El Mehdi Amhoud

"MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion" by Yaya Etiabi, Eslam Eldeeb, Mohammad Shehab, Wafa Njima, Hirley Alves, Mohamed-Slim Alouini, and El Mehdi Amhoud, arXiv:2411.17781v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcr}%% Table column specifiers
 1 & 2 & 3 \\ %% A tabular row ends with \\
 4 & 5 & 6 \\
 7 & 8 & 9 \\
\end{tabular}
\end{adjustbox}
\caption{Table Caption}
\end{table}
\end{document}

q-fin

Gurkirat Wadhwa
Akansh Verma
Veeraruna Kavitha
Priyank Sinha

"Price equilibria with positive margins in loyal-strategic markets with discrete prices" by Gurkirat Wadhwa, Akansh Verma, Veeraruna Kavitha, and Priyank Sinha, arXiv:2506.17239v1, 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{PH2 dataset classification results}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\hline
\textbf{Method} & \textbf{AUC} & \textbf{SE} & \textbf{SP} & \textbf{AC} & \textbf{BAC} \\ \hline
Barata & - - - & 0.960 & 0.800 & 0.823 & 0.880 \\
Lei Bi & 0.938 & 0.875 & 0.931 & 0.920 & 0.903 \\
Omar & - - - & \textbf{1.000} & 0.915 & 0.932 & \textbf{0.958} \\
Pennisi & - - - & 0.935 & 0.871 & \textbf{0.936} & 0.903 \\
\textbf{Proposed} & \textbf{0.974} & 0.902 & \textbf{0.933} & 0.926 & 0.917 \\
\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.


q-fin

C. Biliotti
F. J. Bargagli-Stoffi
N. Fraccaroli
M. Puliga
M. Riccaboni

"Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic" by C. Biliotti, F. J. Bargagli-Stoffi, N. Fraccaroli, M. Puliga, and M. Riccaboni, arXiv:2212.01705v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Joint optimization for production operations considering reworking" by Yilan Shen, Boyang Li, and Xi Zhang, arXiv:2411.01772v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Parenthood Penalty in Russia: Evidence from Exogenous Variation in Family Size" by Vadim Ustyuzhanin, arXiv:2506.11858v1, 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|c|}
\hline
\multicolumn{1}{|p{1cm}|}{\centering Model}
& \multicolumn{1}{|p{1cm}|}{\centering All data}
& \multicolumn{1}{|p{1.5cm}|}{\centering Long delay advertisers}
& \multicolumn{1}{|p{1.5cm}|}{\centering New advertisers}
\\\hline
M3 & 0.0\% & 0.0\% & 0.0\% \\ % inserting body of the table
\hline
M1 & -6.6\% & -7.68\% & -0.32\% \\
\hline
M2\_delay\_7d & -6.8\% & -7.97\% & -0.6\% \\
\hline
M2\_delay\_15d & -5.9\% & -7.1\% & -1.13\% \\
\hline
M4 & -7.7\% & -9.13\% & -0.4\% \\
\hline
M5 & -7.92\% & -9.3\% & -1.7\% \\
\hline
\textbf{Proposed} & \textbf{-8.6\%} & \textbf{-10.16\%} &\textbf{-1.81\%} \\
\hline
Oracle & -9.1\% & -10.87\% & -2.0\% \\ % [1ex] adds vertical space
\hline %inserts single line
\end{tabular}
\end{adjustbox}
\caption{Poisson log loss improvements of various model variants on different training slices} % title of Table
\end{table}
\end{document}

cs

Ashwinkumar Badanidiyuru
Andrew Evdokimov
Vinodh Krishnan
Pan Li
Wynn Vonnegut
Jayden Wang

"Handling many conversions per click in modeling delayed feedback" by Ashwinkumar Badanidiyuru, Andrew Evdokimov, Vinodh Krishnan, Pan Li, Wynn Vonnegut, and Jayden Wang, arXiv:2101.02284v1, 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{Top Categories Ranked By Total Sales (Offline and Online) During the Observation Period.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
\toprule
Category & Share of sales(\%) & Cumulative share (\%) \\
\midrule
Dry Dog Food & 32.62 & 32.62 \\
Medical & 12.18 & 44.80 \\
Dry Cat Food & 9.41 & 54.21 \\
Dog Hygiene & 5.48 & 59.68 \\
Cat Hygiene & 3.62 & 63.30 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"How does online shopping affect offline price sensitivity?" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, 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{Integral Rod Worth for Reactor Core Configurations}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cllll}
\toprule
\multirow{2}{*}{Core Configuration} & \multicolumn{4}{c}{Worth (\$)} \\ \cmidrule(l){2-5}
 & Rod 1 & Rod 2 & Rod 3 & Reg Rod \\ \midrule
120 & 6.387 & 5.380 & 2.963 & 0.488 \\
121 & 6.387 & 5.380 & 2.963 & 0.488 \\
122 & 6.597 & 5.398 & 2.963 & 0.387 \\
123 & 6.583 & 5.267 & 3.017 & 0.433 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

James Daniell
Kazuma Kobayashi
Ayodeji Alajo
Syed Bahauddin Alam

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Transfer Learning to PRISMA - LineRWKV lossless rate difference vs. CCSDS-123.0-B-2.}
\begin{tabular}{ccc}
\textbf{Model size} & \textbf{Zero-shot transfer} & \textbf{After finetuning} \\
\hline
\hline
M & +0.053 & -0.160 \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Diego Valsesia
Tiziano Bianchi
Enrico Magli

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameter estimates for Correlated frailty model on Simulated Data}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
					\hline
					parameter & Progression & & & Relapse & & & Death & & \\
					\hline
					& true & estimate & CI & true & estimate & CI & true & estimate & CI\\
					& value & estimate & CI &value & estimate & CI & value & estimate & CI\\
					\hline
					Age & -0.06 & -0.058 & (-0.114,-0.002)& -0.05 & -0.024 & (-0.063,0.015) & -0.03 & -0.023 & (-0.063,0.017)\\
					t-stage &0.1 & 0.290 & (-1.570,2.152) & 0.2 & 0.108 & (-1.172,1.387)& 0.3 & 0.067 & (-1.23,1.371) \\
					n-stage &0.5 & 0.191 & (-1.717,2.098) & 0.2 & 0.026 & (-1.265,1.318) & 0.3 & 0.102 & (-1.304,1.302)\\
					\hline
					Frailty & 0.286 & 0.346 & (0.315-0.371)& 0.25 & 0.299 & (0.266-0.310) & 0.222 & 0.268 & (0.220,0.296)\\
					Variances & & & EmpSE=0.271& & & EmpSE=0.243 & & & EmpSE=0.239\\
					& & & RMSE=0.270& & & RMSE=0.241 & & &RMSE=0.237\\
					\hline
				\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Atanu Bhattacharjee
Gajendra K. Vishwakarma
Souvik Banerjee

"A modified risk detection approach of biomarkers by frailty effect on multiple time to event data" by Atanu Bhattacharjee, Gajendra K. Vishwakarma, and Souvik Banerjee, arXiv:2012.02102v2, 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{Simulation parameters -- Scenario 2}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
			\toprule
			Parameter & Value \\
			\midrule
			Process noise intensity ($\tilde{q}$) & 0.15 $\text{m}^2/\text{s}^3$ \\
			Number of targets & 20 \\
			Detection probability & 0.99 \\
			False alarm probability $(P_{fa})$ & $10^{-6}$ \\
			Clutter density ($\lambda$)& $10^{-6}$ \\
			Maximum target speed & 30 $\text{m}/\text{s}$ \\
			Number of sensors & 3 \\
			Sensor type & Monostatic 2-D Radar \\
			Sensor coverage & $300,000 \text{ m}\times 360^\circ $ \\
			Sensor std. deviation - range ($\sigma_r$) & 50 m \\
			Sensor std. deviation - azimuth ($\sigma_\theta$) & $2^\circ$ \\
			Gating threshold ($\mu$) & 0.95 \\
			Track deletion threshold (num. of misses) & 6 \\
			Track loss threshold & 500 m \\
			Fusion sample period ($T_f$) & 10 seconds \\
			Total simulation time & 4537 seconds \\
			Sensor sample time & 2 seconds \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Nikhil Sharma
Ratnasingham Tharmarasa
Thiagalingam Kirubarajan

"Harmonic Mean Density Fusion in Distributed Tracking: Performance and Comparison" by Nikhil Sharma, Ratnasingham Tharmarasa, and Thiagalingam Kirubarajan, arXiv:2412.06725v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|}
 \multicolumn{5}{c}{English as source}\\\hline
 & \multicolumn{2}{c|}{EN$\rightarrow$FR} & \multicolumn{2}{c|}{EN$\rightarrow$ES} \\\cline{2-5}
 System & BLEU$\uparrow$ & TER$\downarrow$ & BLEU$\uparrow$ & TER$\downarrow$ \\\hline
PB-SMT & 35.7 & 50.7 & 38.6 & 45.9 \\\hline
LSTM & 34.2 & 50.9 & 38.2 & 45.3 \\\hline
TRANS & \textbf{37.2} & \textbf{48.7} & \textbf{40.9} & \textbf{43.4} \\\hline
\multicolumn{5}{c}{English as target}\\\hline
System & \multicolumn{2}{c|}{FR$\rightarrow$EN} & \multicolumn{2}{c|}{ES$\rightarrow$EN}\\ \hline
PB-SMT & 36.2 & 47.1 & 39.3 & 44.0\\\hline
LSTM & 34.6 & 48.2 & 38.1 & 44.7\\\hline
TRANS & \textbf{37.0} & \textbf{46.4} & \textbf{41.3} & \textbf{41.4}\\\hline
 \end{tabular}
\end{adjustbox}
\caption{Quality evaluation scores for our MT systems. TRANS denotes Transformer systems.}
\end{table}
\end{document}

cs

Eva Vanmassenhove
Dimitar Shterionov
Matthew Gwilliam

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


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
& & DeepONet & FNO & RF(Cauchy) & RF(Gaussian) \\ \hline
\multirow{2}{*}{Helmholtz} & Relative error (\%) & 5.88 & 1.86 & 2.54 & 2.92 \\\cline{2-6}
& Training time (seconds) & - & - & 21.8 & 12.5 \\\hline
\multirow{2}{1.5cm}{Structural \\Mechanics} & Relative error (\%) & 5.20 & 4.76 & 6.08 & 6.25 \\\cline{2-6}
& Training time (seconds) & - & - & 4.2 & 5.8 \\\hline
\multirow{2}{1.5cm}{Navier- \\Stokes} & Relative error (\%) & 3.63 & 0.26 & 0.73 & 0.11 \\\cline{2-6}
& Training time (seconds) & - & - & 24.9 & 23.9 \\\hline
\end{tabular}
\caption{Summary of relative test errors of random feature methods and neural operator benchmarks DeepONet and FNO. We also report the training times of random feature methods.}
\end{table}
\end{document}

math

Chunyang Liao
Deanna Needell
Hayden Schaeffer

"Cauchy Random Features for Operator Learning in Sobolev Space" by Chunyang Liao, Deanna Needell, and Hayden Schaeffer, arXiv:2503.00300v2, 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}{llrrl}
 \textit{INP} Format & Abbrev. & E & M & \textit{ACT} \\
 \hline
 binary32 & FP32 & 8 & 23 & FP32 \\
 TensorFloat32 & TF32 & 8 & 10 & FP32 \\
 BrainFloat16 & bfloat16 & 8 & 7 & FP32 \\
 BlockFloat16 & BFP16 & 8 & 7 & FP32 \\
 binary16 & FP16.16 & 5 & 10 & FP16 \\
 binary16 & FP16.32 & 5 & 10 & FP32 \\
 Signed 8-bit int & INT8 & 0 & 8 & INT8 \\
 \end{tabular}
\end{adjustbox}
\caption{A summary of the numerical formats evaluated in this paper. The binary32 and binary16 formats are as defined in the IEEE 754-2008 standard. The E and M columns refer to the number of bits used for the exponent and mantissa respectively. The \textit{INP} and \textit{ACT} columns relate to the values used in \figurename~.}
\end{table}
\end{document}

eess

Sam Davis
Giuseppe Coccia
Sam Gooch
Julian Mack

"Empirical Evaluation of Deep Learning Model Compression Techniques on the WaveNet Vocoder" by Sam Davis, Giuseppe Coccia, Sam Gooch, and Julian Mack, arXiv:2011.10469v1, 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}{ccc}
 \toprule
 & Specificity & C.I. (95\%)\\
 \midrule
 Radiologist 1	& 87.1\% & [75.6\% -- 94.3\%]\\
 Radiologist 2 &	87.1\% & [75.6\% -- 94.3\%]\\
 Radiologist 3 &	90.3\% & [79.5\% -- 96.5\%]\\
 \midrule
 AI Model without lung segmentation &	87.1\% & [75.6\% -- 94.3\%]\\
 AI Model with lung segmentation	& \textbf{93.5}\% &\textbf{[83.5\% -- 98.5\%]}\\
 \bottomrule
 \hline
 \end{tabular}
\caption{Specificity (together with 95\% confidence interval) comparison between manual readings of expert radiologists and the AI model for COVID-19 detection without lung segmentation and AI model with segmentation.}
\end{table}
\end{document}

cs

Matteo Pennisi
Isaak Kavasidis
Concetto Spampinato
Vincenzo Schininà
Simone Palazzo
Francesco Rundo
Massimo Cristofaro
Paolo Campioni
Elisa Pianura
Federica Di Stefano
Ada Petrone
Fabrizio Albarello
Giuseppe Ippolito
Salvatore Cuzzocrea
Sabrina Conoci

"An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans" by Matteo Pennisi, Isaak Kavasidis, Concetto Spampinato, Vincenzo Schininà, Simone Palazzo, Francesco Rundo, Massimo Cristofaro, Paolo Campioni, Elisa Pianura, Federica Di Stefano, Ada Petrone, Fabrizio Albarello, Giuseppe Ippolito, Salvatore Cuzzocrea, and Sabrina Conoci, arXiv:2101.11943v1, 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}{rr}
 \hline
 & value \\
 \hline
Accuracy & 0.72 \\
 Kappa & 0.40 \\
 Sensitivity (No burn accuracy) & 0.72 \\
 Specificity (Burn accuracy) & 0.72 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Kendra Walker
Ben Moscona
Kelsey Jack
Seema Jayachandran
Namrata Kala
Rohini Pande
Jiani Xue
Marshall Burke

"Detecting Crop Burning in India using Satellite Data" by Kendra Walker, Ben Moscona, Kelsey Jack, Seema Jayachandran, Namrata Kala, Rohini Pande, Jiani Xue, and Marshall Burke, arXiv:2209.10148v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"The Maximum Number of Sets for 12 Cards is 14" by Justin Stevens and Duncan Wilson, arXiv:2501.12565v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Sparse Fréchet Sufficient Dimension Reduction with Graphical Structure Among Predictors" by Jiaying Weng, Kai Tan, Cheng Wang, and Zhou Yu, arXiv:2310.19114v2, 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{Object detection on the receiver end. We have used DETRs~ as the object detection model. We perform object detection on the original image and the recovered image and present mAP. Here, random masking is used for image recovery and the evaluation is on a subset of each dataset: COCO-Stuff (5k images), COCO (2k images), and Flickr (2.5k images) }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c|c|}
\hline
\textbf{Dataset} & \textbf{Steps} & \textbf{mAP }(\%)\\
\hline
& 28/32 & 63.50\\
\cline{2-3}
COCO-Stuff & 94/100 & 63.77\\
\cline{2-3}
& 194/200 & 64.21\\
\hline
COCO& 194/200 & 64.99\\
\hline
& 4/8 & 44.54\\
\cline{2-3}
Flickr& 28/32 & 44.72\\
\cline{2-3}
 & 94/100 & 46.85\\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Achintha Wijesinghe
Suchinthaka Wanninayaka
Weiwei Wang
Yu-Chieh Chao
Songyang Zhang
Zhi Ding

"LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency" by Achintha Wijesinghe, Suchinthaka Wanninayaka, Weiwei Wang, Yu-Chieh Chao, Songyang Zhang, and Zhi Ding, arXiv:2412.17839v1, 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{Performance of Different Methods in Terms of FPR and TPR}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
\toprule
 & \textbf{Baseline} & \textbf{Overlapping} & \multicolumn{2}{c}{\textbf{Representative}} \\
 & \textbf{Approach} & $(\eta_o = 0.5)$ & $(\eta_r = 0.8)$ & $(\eta_r = 0.9)$ \\
\midrule
\textbf{False Positive Rate (FPR)} & 11.3\% & 9.4\% & 8.8\% & 8.0\% \\
\textbf{True Positive Rate (TPR)} & 35.6\% & 55.6\% & 48.9\% & 48.9\% \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Enhancing External Validity of Experiments with Ongoing Sampling" by Chen Wang, Shichao Han, and Shan Huang, arXiv:2502.18253v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
\textbf{Function to Approximate} & \textbf{Approximation Notation} & \textbf{Input} \\ \hline
\multicolumn{3}{|c|}{\textbf{Approximations at \( t = 0, \text{ i.e., } n=0\)}} \\ \hline
\( Y \) & \( \widetilde{Y}_0 \) & None (constant) \\
\( D_x u \) & \( \mathcal{D}_x \mathcal{U}_0 \) & None (constant) \\
\( \Delta u \) & \( \Delta \mathcal{U}_0^{\varphi_0} \) & \( \Delta \widetilde{X}_0 \) \\
\( C \) & \( \mathcal{C}_0 \) & None (constant) \\ \hline
\multicolumn{3}{|c|}{\textbf{Approximations for \( t > 0, \text{ i.e., } n\in\{1,2,\ldots,M-1\} \)}} \\ \hline
\( D_x u \) & \( \mathcal{D}_x \mathcal{U}_n^{\rho_n} \) & \( \widetilde{X}_n \) \\
\( \Delta u \) & \( \Delta \mathcal{U}_n^{\varphi_n} \) & \( \widetilde{X}_n,\ \Delta \widetilde{X}_n \) \\
\( C \) & \( \mathcal{C}_n^{\vartheta_n} \) & \( \widetilde{X}_n \) \\ \hline
\end{tabular}
\end{adjustbox}
\caption{{Summary of neural network approximations of the functions in the BSDE with jumps.}}
\end{table}
\end{document}

q-fin

A deep solver for BSDEs with jumps

Kristoffer Andersson
Alessandro Gnoatto
Marco Patacca
Athena Picarelli

"A deep solver for BSDEs with jumps" by Kristoffer Andersson, Alessandro Gnoatto, Marco Patacca, and Athena Picarelli, arXiv:2211.04349v3, 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{Peak signal-to-noise ratio (PSNR) and precision/recall scores for image inpaintings. We report mean and standard deviation across five evaluations with different random masks. VAEAC and ACFlow results are taken from . Higher is better for all metrics.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccccc}
\toprule
 & \multicolumn{3}{c}{MNIST} & \multicolumn{3}{c}{\textsc{Omniglot}} & \multicolumn{3}{c}{\textsc{CelebA}} \\ \cmidrule(l){2-10}
 &
 PSNR &
 Precision &
 Recall &
 PSNR &
 Precision &
 Recall &
 PSNR &
 Precision &
 Recall \\ \midrule
VDVAE + PM (ours) & \textbf{21.603 $\pm$ 0.022} & \textbf{0.996} & \textbf{0.996} & 18.256 $\pm$ 0.038 & \textbf{0.995} & \textbf{0.994} & \textbf{27.190 $\pm$ 0.049} & \textbf{0.995} & \textbf{0.995} \\
VQ-VAE + PM (ours) & 19.981 $\pm$ 0.021 & 0.989 & 0.993 & 17.954 $\pm$ 0.046 & 0.979 & 0.973 & 25.531 $\pm$ 0.036 & 0.982 & 0.984 \\ \midrule
VAEAC & 19.613 $\pm$ 0.042 & 0.877 & 0.975 & 17.693 $\pm$ 0.023 & 0.525 & 0.926 & 23.656 $\pm$ 0.027 & 0.966 & 0.967 \\ \midrule
ACFlow & 17.349 $\pm$ 0.018 & 0.945 & 0.984 & 15.572 $\pm$ 0.031 & 0.962 & 0.971 & 22.393 $\pm$ 0.040 & 0.970 & 0.988 \\
ACFlow+BG & 20.828 $\pm$ 0.031 & 0.947 & 0.983 & \textbf{18.838 $\pm$ 0.009} & 0.967 & 0.970 & 25.723 $\pm$ 0.020 & 0.964 & 0.987 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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


\documentclass{article}
\usepackage{arydshln}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation results comparing one-step and partial TML estimators for the nontransported interventional indirect and direct effects under two data-generating mechanisms: one with a single, binary $M$ and a single, binary $Z$; and another with multiple, binary $M$s and multiple, binary $Z$s.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rcccccc}
\toprule
 & \multicolumn{3}{c}{Direct effect} & \multicolumn{3}{c}{Indirect effect} \\
\cmidrule(l{3pt}r{3pt}){2-4} \cmidrule(l{3pt}r{3pt}){5-7}
$n$ & $|\text{Bias}|$ & $\sqrt{n} \times |\text{Bias}|$ & 95\% Cov. & $|\text{Bias}|$ & $\sqrt{n} \times |\text{Bias}|$ & 95\% Cov.\\
\midrule
\multicolumn{7}{l}{\textbf{Binary $M$ and $Z$}} \\
\hdashline\noalign{\vskip 1ex}
\multicolumn{7}{l}{One-step} \\
500 & 0.00 & 0.02 & 0.94 & 0.00 & 0.03 & 0.95\\
1000 & 0.00 & 0.03 & 0.94 & 0.00 & 0.00 & 0.93\\
5000 & 0.00 & 0.06 & 0.94 & 0.00 & 0.02 & 0.94\\
10000 & 0.00 & 0.01 & 0.95 & 0.00 & 0.02 & 0.94\\
\hdashline\noalign{\vskip 1ex}
\multicolumn{7}{l}{Partial TMLE} \\
500 & 0.00 & 0.09 & 0.95 & 0.00 & 0.02 & 0.95\\
1000 & 0.00 & 0.00 & 0.94 & 0.00 & 0.02 & 0.95\\
5000 & 0.00 & 0.02 & 0.93 & 0.00 & 0.04 & 0.95\\
10000 & 0.00 & 0.07 & 0.93 & 0.00 & 0.03 & 0.94\\
\midrule
\multicolumn{7}{l}{\textbf{Multivariate $M$ and $Z$}} \\
\hdashline\noalign{\vskip 1ex}
\multicolumn{7}{l}{One-step} \\
500 & 0.01 & 0.18 & 0.93 & 0.00 & 0.03 & 0.91\\
1000 & 0.01 & 0.27 & 0.95 & 0.00 & 0.03 & 0.90\\
5000 & 0.00 & 0.19 & 0.94 & 0.00 & 0.07 & 0.93\\
10000 & 0.00 & 0.05 & 0.94 & 0.00 & 0.10 & 0.92\\
\hdashline\noalign{\vskip 1ex}
\multicolumn{7}{l}{Partial TMLE} \\
500 & 0.01 & 0.18 & 0.93 & 0.00 & 0.01 & 0.92\\
1000 & 0.01 & 0.19 & 0.95 & 0.00 & 0.05 & 0.93\\
5000 & 0.00 & 0.19 & 0.94 & 0.00 & 0.07 & 0.91\\
10000 & 0.00 & 0.11 & 0.94 & 0.00 & 0.08 & 0.93\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Kara E Rudolph
Nicholas Williams
Ivan Diaz

"Nonparametric estimators of interventional (transported) direct and indirect effects that accommodate multiple mediators and multiple intermediate confounders" by Kara E Rudolph, Nicholas Williams, and Ivan Diaz, arXiv:2212.08164v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Usman Anwar
Shehryar Malik
Alireza Aghasi
Ali Ahmed

"Inverse Constrained Reinforcement Learning" by Usman Anwar, Shehryar Malik, Alireza Aghasi, and Ali Ahmed, arXiv:2011.09999v3, 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
Methods & Estimate for $\theta$& SE of the Estimate & LOWER & UPPER \\
 \hline
 CB & 0.693 & 0.392 & 0.191 & 1.195 \\
 BMA & 0.000 & 0.000 & 0.000 & 0.000 \\
 \hline
 LSW & -0.094 & 0.310 & -0.491 & 0.303 \\
 WLP & 0.451 & 0.221 & 0.168 & 0.734 \\
 NAIVE & 0.186 & 0.340 & -0.250 & 0.622 \\
 DS & 0.810 & 0.220 & 0.528 & 1.092 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Estimates for $\theta$, standard errors (SE), lower and upper ends of interval estimators (90\% coverage) for CKD data analysis.}
\end{table}
\end{document}

stat

"Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates" by Abhishek Ojha and Naveen N. Narisetty, arXiv:2411.17618v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Computational complexity of the proposed algorithms in terms of the number of arithmetic operations }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
			\hline
			Algorithm & Number of arithmetic operations \\
			\hline
			Algorithm 1 & $\mathcal{O} (M^2 K^3)$ \\
			\hline
			Algorithm 2 & $\log[2]{{[t_{max}-t_{min}]}/{2}} \times \mathcal{O} (M^2 K^3)$ \\
			\hline
			Algorithm 3 & $\log[2]{{[t_{max}-t_{min}]}/{2}} \times \mathcal{O} (M^2 K^3)^2$ \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Diluka Loku Galappaththige
Rajan Shrestha
Gayan Amarasuriya Aruma Baduge

"Exploiting Cell-Free Massive MIMO for Enabling Simultaneous Wireless Information and Power Transfer" by Diluka Loku Galappaththige, Rajan Shrestha, and Gayan Amarasuriya Aruma Baduge, arXiv:2010.13545v2, licensed under CC0 1.0. 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{Air quality standards in different countries }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccccc}
\toprule
Pollutant&\multicolumn{4}{c}{averaging period}& &EU &USA &China &WHO \\
\midrule
\multirow{3}{*}{$NO_2$} & && \multicolumn{2}{l}{annual} && 40 & 100 & 40 & 40 \\
 							 & && \multicolumn{2}{l}{daily} && - & - & 80 & - \\
 						 & && \multicolumn{2}{l}{hourly} && 200 & 188 & 200 & 200 \\
\hline
\multirow{2}{*}{$PM_{10}$}	 & && \multicolumn{2}{l}{annual} && 40 & - & 70 & 20 \\
 						 & && \multicolumn{2}{l}{daily} && 50 & 150 & 150 & 50 \\
\hline
\multirow{2}{*}{$PM_{2.5}$} & && \multicolumn{2}{l}{annual} && 25 & 15 & 35 & 20 \\
 						 & && \multicolumn{2}{l}{daily} && - & 35 & 75 & 25 \\
\hline
\multirow{2}{*}{$SO_2$} 	 & && \multicolumn{2}{l}{daily} && - & 125 & 150 & 20 \\
		 						 & && \multicolumn{2}{l}{hourly} && 350 & 196& 500 & - \\
\hline
\multirow{2}{*}{$CO$} 	 & && \multicolumn{2}{l}{daily} && - & - & 4 & - \\
		 						 & && \multicolumn{2}{l}{8-hour} && 10 & 10 & - & - \\
\hline
\multirow{1}{*}{$O_3$} 						 & && \multicolumn{2}{l}{8-hour} && 120 & 137& 160 & 100 \\
\bottomrule\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The effect of ambient air pollution on birth outcomes in Norway" by Xiaoguang Ling, arXiv:2208.06271v6, 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{Linear evaluation accuracy (\%) of contrastive learning methods with the original and discrete projectors on CIFAR-10, CIFAR-100 and ImageNet-100.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccc}
 \toprule
 Dataset & Framework & Baseline & Discretized Projector & Gains \\ \midrule
 \multirow{2}{*}{CIFAR-10} & SimCLR & 87.47 & 88.5 & +1.03 \\
 & Barlow Twins & 89.00 & 89.38 & +0.38 \\ \midrule
 \multirow{2}{*}{CIFAR-100} & SimCLR & 58.12 & 60.16 & +2.04 \\
 & Barlow Twins & 64.16 & 65.18 & +1.02 \\ \midrule
 \multirow{2}{*}{ImageNet-100} & SimCLR & 67.36 & 68.38 & +1.02 \\
 & Barlow Twins & 69.96 & 71.68 & +1.72 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Zhuo Ouyang
Kaiwen Hu
Qi Zhang
Yifei Wang
Yisen Wang

"Projection Head is Secretly an Information Bottleneck" by Zhuo Ouyang, Kaiwen Hu, Qi Zhang, Yifei Wang, and Yisen Wang, arXiv:2503.00507v2, 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{Number of FLOPs of Activation Functions}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|ll}
\toprule
\textbf{Activation Functions} & \textbf{FLOPs} & \textbf{Activation Functions} & \textbf{FLOPs} \\
\midrule
ReLU & $N$ & ELU & $8N$ \\
Leak ReLU & $2N$ & Tanh & $13N$ \\
SELU & $9N$ & Swish & $8N$ \\
Softmax & $7N - 1$ & Sigmoid & $7N$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Omar Alnaseri
Laith Alzubaidi
Yassine Himeur
Mohammed Alaa Ala'anzy
Jens Timmermann
Mohammed S. M. Gismalla

"A Review on Deep Learning Autoencoder in the Design of Next-Generation Communication Systems" by Omar Alnaseri, Laith Alzubaidi, Yassine Himeur, Mohammed Alaa Ala'anzy, Jens Timmermann, and Mohammed S. M. Gismalla, arXiv:2412.13843v2, 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{Changes in steady-state values from the decrease in $c_e$ by 20\%.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccc}
 \hline
 \hline
 && {Low-income} & {Middle-income} & {High-income} \\
 && {countries} & {countries} & {countries} \\
 \hline
 {Aggregate}& {Intensive margin} & 14.9\% & 15.7\% & 15.3\%\\
 {output} & {Extensive margin} & -1.9\% & -1.4\% & -0.8\%\\
 &{Net change} & 13.0\% & 14.3\% & 14.5\%\\
 \hline
 {Aggregate} & {Intensive margin} & 13.9\% & 14.6\% & 14.3\%\\
 {consumption} & {Extensive margin} & 0.0\% & 0.0\% & 0.1\%\\
 & {Net change} & 13.9\% & 14.6\% & 14.4\%\\
 \hline
 {Aggregate}& {Intensive margin} & 15.6\% & 16.3\% & 15.6\%\\
 {capital} & {Extensive margin} & -5.6\% & -3.9\% & -2.1\%\\
 & {Net change} & 10.0\% & 12.4\% & 13.5\%\\
 \hline
 {Average}&{Intensive margin} & 14.4\% & 15.3\% & 15.1\%\\
 {wage} & {Extensive margin} & 0.5\% & 0.3\% & 0.2\%\\
 & {Net change} & 14.9\% & 15.6\% & 15.3\%\\
 \hline
 {Modern firms'}&{Intensive margin} & 0.8\% & 0.7\% & 0.4\%\\
 {share of output} & {Extensive margin} & -4.4\% & -3.1\% & -1.8\%\\
 &{Net change} & -3.6\% & -2.4\% & -1.4\%\\
 \hline
 {Firms' entry}& & & &\\
 {(Number of firms)} & {Net change} & 43.6\% & 41.1\% & 38.1\%\\
 \hline
 {Fraction of}& & & &\\
 {modern firms} & {Net change} & -5.9\% & -6.2\% & -6.0\% \\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Jafar M. Olimov
Yi-Chan Tsai
Hao-Yu Yang

"Heterogeneous Bribery, Technology Choice, and Capital Accumulation" by Jafar M. Olimov, Yi-Chan Tsai, and Hao-Yu Yang, arXiv:2507.13124v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Relative caring penalty by age - Household Income.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \toprule
 & \multicolumn{2}{c}{Pre-Treatment Av. Income} & \multicolumn{2}{c}{Post-Treatment Av. Loss} & \multicolumn{2}{c}{Penalty}\\
 \cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7}
 & HI & LI & HI & LI & HI & LI \\
 \midrule
 Aged 25 and below & £1,843 & £2,893 & £351 & 35 & 19\% & 1\% \\
 Aged 26-64 & £2,208 & 3,143 & £213 & £154 & 7\% & 5\% \\
 Aged 65 and above & £1,793 & 1,902 & £39 & £(-14) & 2\% & (-1)\% \\
 \midrule
 \multicolumn{6}{p{0.9\linewidth}}{\footnotesize Note: HI = High-Intensity, LI = Low-Intensity. Source: UKHLS data (years 2009-2020), authors' calculations.}
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Maria Petrillo
Daniel Valdenegro
Charles Rahal
Yanan Zhang
Gwilym Pryce
Matthew R. Bennett

"Estimating the Cost of Informal Care with a Novel Two-Stage Approach to Individual Synthetic Control" by Maria Petrillo, Daniel Valdenegro, Charles Rahal, Yanan Zhang, Gwilym Pryce, and Matthew R. Bennett, arXiv:2411.10314v2, 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}{lccccc}
\hline
Joint Goal Accuracy &
 \textbf{Hotel} &
 \textbf{Restaurant} &
 \textbf{Taxi} &
 \textbf{Attraction} &
 \textbf{Train} \\ \hline
TRADE & 13.7 & 11.52 & 60.58 & 19.87 & 22.37 \\ \hline
STARC & 28.6 & 28.2 & 65 & 36.9 & 26.1 \\
STARC + D-REPTILE & \textbf{32.4} & \textbf{47.8} & \textbf{67.2} & \textbf{45.9} & \textbf{46.1} \\ \hline
\end{tabular}
\end{adjustbox}
\caption{ Zero-shot performance on MultiWoz 2.0 dataset. Domains like restaurant and train witness a significant boost in performance over baselines}
\end{table}
\end{document}

cs

Saket Dingliwal
Bill Gao
Sanchit Agarwal
Chien-Wei Lin
Tagyoung Chung
Dilek Hakkani-Tur

"Few Shot Dialogue State Tracking using Meta-learning" by Saket Dingliwal, Bill Gao, Sanchit Agarwal, Chien-Wei Lin, Tagyoung Chung, and Dilek Hakkani-Tur, arXiv:2101.06779v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study : AP performance(\%) of each category on the PASCAL VOC 2007 test set}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccccc}
		\hline\noalign{\smallskip}
		Methods & aero & bike & bird & boat & bottle & bus & car & cat & chair & cow \\
		\noalign{\smallskip}\hline\noalign{\smallskip}
		Baseline 		& 63.1 & \textbf{66.4} & 46.1 & 25.4 & 16.9 & 70.8 & 68.8 & 53.2 & 14.9 & 56.8 \\
		 + CADM & 64.6 & 64.7 & 53.1 & \textbf{33.1} & \textbf{23.2} & 70.4 & \textbf{70.6} & 22.2 & \textbf{22.1} & 64.1	 \\
		 + GCM 	&66.0 & 66.0 & \textbf{56.8} & 20.3 & 19.1 & \textbf{72.5} & \textbf{70.6} & 59.5 & 19.6 & 64.3 \\
		 + Both & \textbf{66.5} & 65.6 & 56.5 & 26.8 & 19.7 & 69.9 & 69.0 & \textbf{61.3} & 21.5 & \textbf{66.9} \\
		\hline\noalign{\smallskip}
		Methods								 & table & dog & horse & mbike & person & plant & sheep & sofa & train & tv \\
		\noalign{\smallskip}\hline\noalign{\smallskip}
		Baseline 	 & 41.5 & \textbf{53.7} & 42.7 & 70.0 & \textbf{2.9} & \textbf{20.6} & 42.8 & 44.8 & 50.8 & 68.3 \\
		 + CADM & \textbf{46.2} & 27.1 & \textbf{49.3} & \textbf{70.8} & 2.6 & 19.5 & \textbf{57.1} & \textbf{55.3} & 64.6 & 69.9 \\
		 + GCM & 42.8 & 47.8 & 42.8 & 68.5 & 2.5 & 20.0 & 47.6 & 48.7 & 62.9 & 64.2 \\
		 + Both & 43.1 & 50.4 & 49.0 & 70.1 & 2.3 & 20.1 & 53.9 & 47.4 & \textbf{65.7} & \textbf{70.7} \\
		\noalign{\smallskip}\hline

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

eess

"Cascade Attentive Dropout for Weakly Supervised Object Detection" by Wenlong Gao, Ying Chen, and Yong Peng, arXiv:2011.10258v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|r|rrrrrr}
\toprule
 & & $M=512$ & $M=1024$ & $M=2048$ & $M=4096$ & $M=8192$ \\
\midrule
\multirow{4}{*}{Gaussian Mixture}
 & ACF & 105 & 189 & 352 & 565 & 1056 \\
 & NSF & 118 & 203 & 359 & 646 & 1327 \\
\cmidrule{2-7}
 & FMPE & 715 & 1278 & 2008 & 3973 & 7508 \\
 & CMPE & 867 & 1471 & 2401 & 4390 & 10102 \\
\midrule
\multirow{4}{*}{Two Moons}
 & ACF & 42 & 66 & 118 & 225 & 453 \\
 & NSF & 60 & 86 & 146 & 257 & 515 \\
\cmidrule{2-7}
 & FMPE & 175 & 289 & 498 & 890 & 1805 \\
 & CMPE & 195 & 323 & 571 & 1064 & 2173 \\
\midrule
\multirow{4}{*}{Inverse Kinematics}
 & ACF & 43 & 70 & 124 & 234 & 439 \\
 & NSF & 63 & 99 & 169 & 297 & 552 \\
\cmidrule{2-7}
 & FMPE & 97 & 166 & 311 & 551 & 1095 \\
 & CMPE & 107 & 194 & 358 & 719 & 1397 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Wall-clock training times on the benchmark tasks. $M$ denotes the available simulation budget for the neural network training stage.}
\end{table}
\end{document}

stat

Marvin Schmitt
Valentin Pratz
Ullrich Köthe
Paul-Christian Bürkner
Stefan T Radev

"Consistency Models for Scalable and Fast Simulation-Based Inference" by Marvin Schmitt, Valentin Pratz, Ullrich Köthe, Paul-Christian Bürkner, and Stefan T Radev, arXiv:2312.05440v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.