\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c}
 \hline
 \textbf{Metric} & \textbf{Fd-SDR} & \textbf{FOPG} & \textbf{GWIRE} \\
 \hline\hline
 Adjusted R-squared & 0.519 & 0.361 & 0.504 \\
 Fréchet R-squared & 0.367 & 0.344 & 0.365 \\
 Polynomial R-squared & 0.783 & 0.704 & 0.769 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Performance metrics for the bike rental data}
\end{table}
\end{document}

stat

Hsin-Hsiung Huang
Feng Yu
Kang Li
Teng Zhang

"Fréchet Sufficient Dimension Reduction for Metric Space-Valued Data via Distance Covariance" by Hsin-Hsiung Huang, Feng Yu, Kang Li, and Teng Zhang, arXiv:2412.13122v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The choice of $\lambda$ for SL and FB Phantoms.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c|c|c}
			\hline
			\multirow{7}{*}{SL} & Projection 					& Noise level & $\lambda$ &
			\multirow{7}{*}{FB} 		 & Projection 					& Noise level & $\lambda$ \\ \cline{2-4} \cline{6-8}
			& \multirow{3}{*}{90$^{\circ}$} & noiseless & 0.25 &
			& \multirow{3}{*}{90$^{\circ}$} & noiseless & 0.25 \\ \cline{3-4} \cline{7-8}
			& & 0.1\% & 0.25 & & & 0.1\% & 0.25 \\ \cline{3-4} \cline{7-8}
			& & 0.5\% & 1.00 & & & 0.5\% & 1.00 \\ \cline{2-4} \cline{6-8}
			& \multirow{3}{*}{150$^{\circ}$} & noiseless & 0.25 &
			& \multirow{3}{*}{150$^{\circ}$} & noiseless & 0.25 \\ \cline{3-4} \cline{7-8}
			& & 0.1\% & 0.25 & & & 0.1\% & 0.80 \\ \cline{3-4} \cline{7-8}
			& & 0.5\% & 1.00 & & & 0.5\% & 1.00 \\ \hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A Single-loop Proximal Subgradient Algorithm for A Class Structured Fractional Programs" by Deren Han, Min Tao, and Zihao Xia, arXiv:2503.12176v1, 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
\caption{Details On Battery Cell \& Experiment Parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
Cell Parameter & Values & Experiment Parameter & Values\\
\hline
Q & 5 $Ah$ & Parameter Uncertainty & 5\% \\
C & 4.3 $kF$ & Sampling Rate & 100 Hz \\
R & 3.8 $m \Omega$ & Measurement Noise & $\pm$ 2 $m\,V$ \\
$R_c$ & 4 $m \Omega$ & SC Resistance & 15 $\Omega$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Sanchita Ghosh
Soumyoraj Mallick
Tanushree Roy

"Koopman Mode-Based Detection of Internal Short Circuits in Lithium-ion Battery Pack" by Sanchita Ghosh, Soumyoraj Mallick, and Tanushree Roy, arXiv:2412.13115v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Leandro Aparecido Passos
João Paulo Papa

"A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines" by Leandro Aparecido Passos and João Paulo Papa, arXiv:2101.05795v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

The Narayana Morphism and Related Words

"The Narayana Morphism and Related Words" by Jeffrey Shallit, arXiv:2503.01026v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Dehua Bi
Tianjian Zhou
Wei Zhong
Yuan Ji

"PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data" by Dehua Bi, Tianjian Zhou, Wei Zhong, and Yuan Ji, arXiv:2310.20087v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Tight Bounds for a Class of Data-Driven Distributionally Robust Risk Measures" by Derek Singh and Shuzhong Zhang, arXiv:2010.05398v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Sepideh Mosaferi
Abolfazl Safikhani
Peiliang Bai

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


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc}
\toprule
Class & \multicolumn{2}{c}{$\mathbb{E}(N(\omega_{ix}) N(\omega_{iy}))$} \\
 & i.i.d. & transient repelling \\
\midrule
Same walk, $\omega_{ix} = \omega_{iy}$ & $m c^{\omega_{ix}} + m(m - 1) c^{2{\omega_{ix}}}$ & $mc^{\omega_{ix}}$ \\
Subwalk, $\omega_{ix} \in \omega_{iy}$ & $m c^{\omega_{iy}} + m(m - 1) c^{\omega_{ix} + \omega_{iy}}$ &$mc^{\omega_{iy}}$\\
Different walks, diverge at $i$ & $m(m-1)c^{\omega_{ix}+\omega_{iy}}$ & $\frac{d}{d-1} m(m-1)c^{\omega_{ix}+\omega_{iy}}$ \\
Different walks, diverge at $d \neq i$ & $m(m-1)c^{\omega_{ix}+\omega_{iy}}$ & $0$ \\
$\textrm{len}(\omega_{ix}) = 0$ & $m^2 c^{\omega_{iy}}$ & $m^2 c^{\omega_{iy}}$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Repelling Random Walks

Isaac Reid
Eli Berger
Krzysztof Choromanski
Adrian Weller

"Repelling Random Walks" by Isaac Reid, Eli Berger, Krzysztof Choromanski, and Adrian Weller, arXiv:2310.04854v2, 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{Transformer Model Parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrrlr}
 & \multicolumn{2}{c}{\textbf{CNN Part}} & & \textbf{Transformer Part} \\ \cline{2-3} \cline{5-5}
\textbf{Parameter} & \textbf{Block 1} & \textbf{Block 2} & & \textbf{All Blocks} \\ \hline
Convolutional 1D layer & & & & \\
Filter Size & 64 & 128 & & \\
Kernel Size & 3 & 1 & & \\
Padding & same & same & & \\ \hline
Multi-Head Attention Layer & & & & \\
Number of Heads & & & & 8 \\
Head Size & & & & 200 \\
Dropout & & & & 0.2 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, 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{\textbf{Results with different numbers of context views on RealEstate10K}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
 \toprule
 & 2-view PSNR$\uparrow$ & 3-view PSNR$\uparrow$ \\
 \midrule
 & 21.65 & 21.88 \\
 + GTA (Ours)& \textbf{22.85} & \textbf{23.22} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Takeru Miyato
Bernhard Jaeger
Max Welling
Andreas Geiger

"GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers" by Takeru Miyato, Bernhard Jaeger, Max Welling, and Andreas Geiger, arXiv:2310.10375v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Localization and channel charting performance varying the supervision level for the static and dynamic DNTs.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
\textbf{Scenario} & \textbf{Sup.} (\%) & \textbf{CT} $\uparrow$ & \textbf{KS} $\downarrow$ & \textbf{TW} $\uparrow$ & \textbf{Loc. error}\\
\noalign{\smallskip}
\hline
\noalign{\smallskip}
\multirow{5}{*}{Static DNT} & 5 \% & $0.92$ & $0.22$ & $0.92$ & $50.88$ m\\\noalign{\smallskip}
 & 10 \% & $0.96$ & $0.15$ & $0.96$ & $31.39$ m\\\noalign{\smallskip}
 & 25 \% & $0.98$ & $0.09$ & $0.98$ & $12.52$ m\\\noalign{\smallskip}
 & 35 \% & $0.99$ & $0.07$ & $0.99$ & $8.21$ m\\\noalign{\smallskip}
 & 50 \% & $1.00$ & $0.04$ & $1.00$ & $4.50$ m\\
\midrule
\multirow{5}{*}{Dynamic DNT} & 5 \% & $0.91$ & $0.24$ & $0.91$ & $57.18$ m\\\noalign{\smallskip}
 & 10 \% & $0.94$ & $0.18$ & $0.94$ & $36.54$ m\\\noalign{\smallskip}
 & 25 \% & $0.97$ & $0.12$ & $0.97$ & $17.36$ m\\\noalign{\smallskip}
 & 35 \% & $0.98$ & $0.10$ & $0.98$ & $12.28$ m\\\noalign{\smallskip}
 & 50 \% & $0.99$ & $0.06$ & $0.99$ & $5.99$ m\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Lorenzo Cazzella
Francesco Linsalata
Mahdi Maleki
Damiano Badini
Matteo Matteucci
Umberto Spagnolini

"Chartwin: a Case Study on Channel Charting-aided Localization in Dynamic Digital Network Twins" by Lorenzo Cazzella, Francesco Linsalata, Mahdi Maleki, Damiano Badini, Matteo Matteucci, and Umberto Spagnolini, arXiv:2508.09055v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Chengyuan Deng
Jie Gao
Kevin Lu
Feng Luo
Hongbin Sun
Cheng Xin

"Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms" by Chengyuan Deng, Jie Gao, Kevin Lu, Feng Luo, Hongbin Sun, and Cheng Xin, arXiv:2411.10889v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty" by Marius Protte and Behnud Mir Djawadi, arXiv:2507.15439v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Stephen D. Boyles
Nicholas E. Lownes
Avinash Unnikrishnan

"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccccc}
 \hline\hline
 Parameters & Formulation & Hessian & Platform & Build time & Solve time & Iterations & Time/iter. \\
 \hline
 -- & No surrogate & Exact & CPU & 45 ms & 0.4 s & 41 & 9 ms \\
 \hline
 7k & Full-space & Exact & CPU & 0.1 s & 2 s & 468 & 4 ms \\
 25k & Full-space & Exact & CPU & 0.3 s & 5 s & 642 & 8 ms \\
 578k & Full-space & Exact & CPU & 0.3 s & 699 s & 755 & 0.9 s \\
 7M & Full-space$^*$& Exact & CPU & -- & -- & -- & -- \\
 \hline
 7k & Reduced-space & Exact & CPU & 0.1 s & 7 s & 49 & 0.1 s \\
 25k & Reduced-space & Exact & CPU & 1 s & 1125 s & 41 & 27 s \\
 578k & Reduced-space$^\dag$ & Exact & CPU & -- & -- & -- & -- \\
 7M & Reduced-space$^\ddag$ & Exact & CPU & -- & -- & -- & -- \\
 \hline
 7k & Gray-box & Exact & CPU & 0.1 s & 8 s & 41 & 0.2 s \\
 25k & Gray-box & Exact & CPU & 0.1 s & 9 s & 42 & 0.2 s \\
 578k & Gray-box & Exact & CPU & 0.1 s & 11 s & 42 & 0.3 s \\
 7M & Gray-box & Exact & CPU & 0.1 s & 22 s & 42 & 0.5 s \\
 68M & Gray-box & Exact & CPU & 0.1 s & 140 s & 42 & 3 s \\
 592M & Gray-box & Exact & CPU & 0.6 s & 748 s & 42 & 18 s \\
 \hline
 7k & Gray-box & Exact & CPU+GPU & 0.1 s & 7 s & 41 & 0.2 s \\
 25k & Gray-box & Exact & CPU+GPU & 0.1 s & 7 s & 42 & 0.2 s \\
 578k & Gray-box & Exact & CPU+GPU & 0.1 s & 8 s & 42 & 0.2 s \\
 7M & Gray-box & Exact & CPU+GPU & 0.1 s & 8 s & 42 & 0.2 s \\
 68M & Gray-box & Exact & CPU+GPU & 0.2 s & 9 s & 42 & 0.2 s \\
 592M & Gray-box & Exact & CPU+GPU & 0.7 s & 15 s & 42 & 0.3 s \\
 \hline
 7k & Gray-box & Approx. & CPU & 0.1 s & 0.3 s & 61 & 6 ms \\
 25k & Gray-box & Approx. & CPU & 48 ms & 0.3 s & 57 & 6 ms \\
 578k & Gray-box & Approx. & CPU & 0.1 s & 1 s & 66 & 15 ms \\
 7M & Gray-box & Approx. & CPU & 0.1 s & 6 s & 57 & 0.1 s \\
 68M & Gray-box & Approx. & CPU & 0.1 s & 7 s & 56 & 0.1 s \\
 592M & Gray-box & Approx. & CPU & 0.9 s & 17 s & 56 & 0.3 s \\
 \hline
 7k & Gray-box & Approx. & CPU+GPU & 50 ms & 0.5 s & 63 & 7 ms \\
 25k & Gray-box & Approx. & CPU+GPU & 48 ms & 0.4 s & 58 & 7 ms \\
 578k & Gray-box & Approx. & CPU+GPU & 0.1 s & 0.5 s & 62 & 8 ms \\
 7M & Gray-box & Approx. & CPU+GPU & 0.1 s & 0.5 s & 57 & 9 ms \\
 68M & Gray-box & Approx. & CPU+GPU & 0.2 s & 1 s & 56 & 21 ms \\
 592M & Gray-box & Approx. & CPU+GPU & 0.7 s & 1 s & 56 & 23 ms \\
 \hline\hline
 \rowcolor{white}\multicolumn{8}{l}{\scriptsize $^*$ Fails with a segfault, possibly due to memory requirements of MA27}\\
 \rowcolor{white}\multicolumn{8}{l}{\scriptsize $^\dag$ Exceeds resource manager's memory limits}\\
 \rowcolor{white}\multicolumn{8}{l}{\scriptsize $^\ddag$ Exceeds ten-hour time limit}\\
 \end{tabular}
\end{table}
\end{document}

math

Robert B. Parker
Oscar Dowson
Nicole LoGiudice
Manuel Garcia
Russell Bent

"Formulations and scalability of neural network surrogates in nonlinear optimization problems" by Robert B. Parker, Oscar Dowson, Nicole LoGiudice, Manuel Garcia, and Russell Bent, arXiv:2412.11403v1, 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{Quantitative Results on Best Parameter}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
		\hline
		Model & F1 & Precision & Recall \\ \hline
		SR & 0.4719 & 0.5137 & 0.4364 \\
		HBOS & 0.4969 & 0.3910 & 0.6817 \\
		S-H-ESD & 0.4711 & 0.3651 & 0.6638 \\
		Auto-Selector & \textbf{0.5714} & 0.4909 & 0.6837 \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yuanxiang Ying
Juanyong Duan
Chunlei Wang
Yujing Wang
Congrui Huang
Bixiong Xu

"Automated Model Selection for Time-Series Anomaly Detection" by Yuanxiang Ying, Juanyong Duan, Chunlei Wang, Yujing Wang, Congrui Huang, and Bixiong Xu, arXiv:2009.04395v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of UrbanSound8K by classes and salience.}
\begin{tabular}{llll}
Class & Foreground & Background & Total\\
\hline
Air Conditioner & 569 & 431 & 1000\\
Car Horn & 153 & 276 & 429\\
Children Playing & 588 & 412 & 1000\\
Dog Bark & 645 & 355 & 1000\\
Drilling & 902 & 98 & 1000\\
Engine Idling & 916 & 84 & 1000\\
Gun Shot & 304 & 70 & 374\\
Jackhammer & 731 & 269 & 1000\\
Siren & 269 & 660 & 929\\
Street Music & 625 & 375 & 1000\\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Chunheng Jiang
Jae-wook Ahn
Nirmit Desai

"Environment Transfer for Distributed Systems" by Chunheng Jiang, Jae-wook Ahn, and Nirmit Desai, arXiv:2101.01863v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|}
\hline
Abbreviation & Meaning \\ \hline
NTA & Not the Asshole \\ %\hline
YTA & You're the Asshole \\ %\hline
NAH & No assholes here \\ %\hline
ESH & Everyone sucks here \\ \hline
\end{tabular}
\caption{The four judgements that users can pass on the subreddit r/AmItheAsshole.}
\end{table}
\end{document}

cs

Analysis of Moral Judgement on Reddit

"Analysis of Moral Judgement on Reddit" by Nicholas Botzer, Shawn Gu, and Tim Weninger, arXiv:2101.07664v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcr}
Left&Centered&Right\\
\hline
1 & 2 & 3\\
10 & 20 & 30\\
100 & 200 & 300\\
\end{tabular}
\end{table}
\end{document}

q-fin

"Scaling and shape of financial returns distributions modeled as conditionally independent random variables" by Hernán Larralde and Roberto Mota Navarro, arXiv:2504.20488v1, 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{Table Type Styles}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Table}&\multicolumn{3}{|c|}{\textbf{Table Column Head}} \\
\cline{2-4}
\textbf{Head} & \textbf{\textit{Table column subhead}}& \textbf{\textit{Subhead}}& \textbf{\textit{Subhead}} \\
\hline
copy& More table copy$^{\mathrm{a}}$& & \\
\hline
\multicolumn{4}{l}{$^{\mathrm{a}}$Sample of a Table footnote.}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"M3-CVC: Controllable Video Compression with Multimodal Generative Models" by Rui Wan, Qi Zheng, and Yibo Fan, arXiv:2411.15798v2, 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}{llllll}
\hline
Ticker & Type & Sector & Company & Average Amount \\ \hline
SH601988 & Share & Bank & Bank of China Limited & 427,647,786 \\
SH601601 & Share & Public Utility & China Pacific Insurance (Group) & 819,382,926 \\
SH600028 & Share & Public Utility & China Petroleum \& Chemical Corporation & 748,927,952\\
SH600016 & Share & Bank & China Minsheng Banking Corporation &285,852,414 \\
SH601186 & Share & Public Utility & China Railway Construction Corporation & 594,970,588\\
SH601328 & Share & Bank & Bank of Communications Corporation & 484,445,915 \\
SH601628 & Share & Public Utility & China Life Insurance Company Limited&368,179,861 \\
SH601939 & Share & Bank & China Construction Bank Corporation &527,876,669 \\
\hline
SH510300 & ETF & CSI 300 & Huatai-PineBridge CSI 300 ETF &1,960,687,059 \\
SH510050 & ETF & CSI 50 & ChinaAMC China CSI 50 ETF &2,020,385,879 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Summary of the underlying portfolios in the China market, ten assets in total. The average amount (in the currency of RMB) is calculated in the period of the test set.}
\end{table}
\end{document}

q-fin

"Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies" by Jun Lu and Joerg Osterrieder, arXiv:2209.14532v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c|c||}
 \hline
 Optimizer name & Experimented hyper-parameters\\
 \hline
 \hline
 Common & weight decay = np.logspace(-5, 1, 11)\\
 \hline
 Common & learning rate = np.logspace(-5, 3, 15)\\
 \hline
 SGD & momentum = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.999]\\
 \hline
 Adagrad & $\cdot$\\
 \hline
 Adadelta & rho = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.999]\\
 \hline
 Adam & betas = [0.0001, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.999]\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Representations learnt by SGD and Adaptive learning rules: Conditions that vary sparsity and selectivity in neural networks" by Jin Hyun Park, arXiv:2201.11653v3, 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{Experimental Results Using Multiple IMU Data from PAMAP2 \\ (Estimator: LR, Metric: Mean F1)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \hline
 Input Pattern & Target & Baseline & AAE~ & C-AAE (Proposed) \\
 \hline \hline
 Input 1 & Activity & 0.881 & \textbf{0.898} & 0.811 \\
 & User & 0.449 & 0.226 & \textbf{0.153} \\
 \hline
 Input 2 & Activity & 0.832 & \textbf{0.855} & 0.790 \\
 & User & 0.457 & \textbf{0.152} & 0.168 \\
 \hline
 Input 3 & Activity & 0.863 & \textbf{0.880} & 0.779 \\
 & User & 0.502 & \textbf{0.138} & 0.142 \\
 \hline
 Input 4 & Activity & 0.889 & \textbf{0.906} & 0.872 \\
 & User & 0.558 & 0.240 & \textbf{0.193} \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ryusei Fujimoto
Yugo Nakamura
Yutaka Arakawa

"C-AAE: Compressively Anonymizing Autoencoders for Privacy-Preserving Activity Recognition in Healthcare Sensor Streams" by Ryusei Fujimoto, Yugo Nakamura, and Yutaka Arakawa, arXiv:2507.18072v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr} \\
 & \multicolumn{2}{c}{\textbf{Standardised differences}} & & \multicolumn{2}{c}{\textbf{Variance ratio}} \\ \cline{2-3} \cline{5-6}
\textbf{Confounder} & \textbf{Raw} & \textbf{Weighted} & & \textbf{Raw} & \textbf{Weighted} \\
\hline \\
 Gender & 0.0931272 & 0.0004124 & & 0.9771947 & 0.9999057 \\
 Age & -0.0613524 & -0.0038196 & & 0.8174922 & 0.7899075 \\
 Education & 0.0913642 & -0.0025822 & & 1.0147230 & 1.0250380 \\
 Race & -0.0022396 & 0.0023428 & & 1.0295870 & 1.0254230 \\
 Carcinoma & -0.1051837 & 0.0012232 & & 0.8386081 & 1.0226510 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Matthew J. Smith
Camille Maringe
Bernard Rachet
Mohammad A. Mansournia
Paul N. Zivich
Stephen R. Cole
Miguel Angel Luque-Fernandez

"Tutorial: Introduction to computational causal inference using reproducible Stata, R and Python code" by Matthew J. Smith, Camille Maringe, Bernard Rachet, Mohammad A. Mansournia, Paul N. Zivich, Stephen R. Cole, and Miguel Angel Luque-Fernandez, arXiv:2012.09920v2, 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{Some typical parameter values}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llc}
\hline
Parameter&
\multicolumn{2}{c}{$\quad\quad$ Value} \\
\hline
\\
$\text{Electron Charge}~(q)
$ &&\quad\quad$1.6\times10^{-19}$~\text{C} \\
$\text{Dark Current}~(I_\text{DC})
$ &&\quad\quad$5$~\text{pA} \\
$\text{Channel Noise Factor}~(\Gamma)
$ &&\quad\quad$1.5$\\
$\text{Photodiode Responsively}~(R_\text{p})
$ &&\quad\quad$0.4$~\text{mA/mW} \\
$\text{Equivalent Noise Bandwidth}~(B)
$ &&\quad\quad$400$~\text{MHz}\\
$\text{Open-Loop Voltage Gain}~(G)
$ &&\quad\quad$10$\\
$\text{Boltzmann's Constant}~(\kappa)
$ &&\quad\quad$1.38\times10^{-23}$\\
$\text{Absolute Temperature}~(T_\text{e})
$&&\quad\quad$300$~\text{K}\\
$\text{Active Area}~(S)
$&&\quad\quad$.2~\text{cm}^2$\\
$\text{Transconductance}~(g_\text{m})
$ &&\quad\quad$30$~\text{ms}\\
$\text{Optical Filter Bandwidth}~(\breve{\lambda})
$&&\quad\quad$400\;(380-780)$~\text{nm} \\
$\text{Spectral Irradiance }~(p_\text{BS})
$ &&\quad\quad$5.8\times10^{-6}$~\text{W/cm$^2\cdot$nm} \\
$\text{Photodiode Fixed Capacitance}~(\eta)
$ &&\quad\quad$112$~\text{pF/cm$^2$} \\
$\text{Interference}~(P_{\text{diff}})
$&&\quad\quad0~\text{W} \\
\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Changeez Amini
Paeiz Azmi
Seyed Sadra Kashef

"Theoretical Accuracy Analysis of RSS-Based Range Estimation for Visible Light Communication" by Changeez Amini, Paeiz Azmi, and Seyed Sadra Kashef, arXiv:2011.14080v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model Evaluation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
Metric & Value\\\hline
Root Mean Squared Error & $3.06\%$ \\
Mean Squared Error & $0.09\%$ \\
Mean Absolute Error & $ 0.0214^\circ$ ($ 3.7{ e^{-4}}$ radians) \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Thermostatic control for demand response using distributed averaging and deep neural networks" by Kshitij Singh and Pratik K. Bajaria, arXiv:1912.11692v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Introduction to probability and statistics: a computational framework of randomness" by Lakshman Mahto, arXiv:2401.08622v2, 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{Unique speaker IDs used in training and development sets compared to the full S\&I corpus (TRAIN + DEV).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 Dataset version & Unique speaker IDs & \% of corpus \\
 \midrule
 Standard & 1684 & 77.2\% \\
 4x & 1871 & 85.8\% \\
 OE & 1134 & 52.0\% \\
 OE+OE$_{d}$ & 976 & 44.8\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

One Whisper to Grade Them All

Nhan Phan
Anusha Porwal
Yaroslav Getman
Ekaterina Voskoboinik
Tamás Grósz
Mikko Kurimo

"One Whisper to Grade Them All" by Nhan Phan, Anusha Porwal, Yaroslav Getman, Ekaterina Voskoboinik, Tamás Grósz, and Mikko Kurimo, arXiv:2507.17918v1, 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{Average Marginal Effects for October 1, 2024}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
 \toprule
 & & Marginal Effects & \\
 Variable & Prob. In First Quartile & Gas Equivalent Cost & Cost in USD \\
 \midrule
 \midrule
 max fee per gas & 0.02\% & 53 & \$0.0031 \\
 to DEX & 16.87\% & 899 & \$0.0524 \\
 to MEV & 37.34\% & 1,991 & \$0.1161 \\
 from DEX & 30.97\% & 1,651 & \$0.0963 \\
 from MEV & -43.54\% & 2,321 & \$0.1353 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering" by Bruce Mizrach and Nathaniel Yoshida, arXiv:2508.04003v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Causal Discovery on Dependent Binary Data" by Alex Chen and Qing Zhou, arXiv:2412.20289v1, 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{Ranges of quantities satisfying $q\ge 0$, $u'>0$, and $u'' < 0$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc}
\hline
&& \\
& \quad \quad $\alpha \ge 0$ & \quad $\alpha<0$ \\ \hline
&& \\
$\alpha \sigma \ge 0$ & \quad \quad $q \in [0,\infty)$ & \quad $q \in [0, - \frac{\beta}{\alpha} )$ \\
$\alpha \sigma<0$ & \quad \quad $q \in [0, -\frac{1}{\alpha \sigma} ) $ & \quad $q \in [0, -\frac{1}{\alpha \sigma}) \cap [0, -\frac{\beta}{\alpha} )$$^\dagger$ \\ \hline
\multicolumn{3}{p{10.6cm}}{\scriptsize{{\it Notes}: $^\dagger$ The CRRA case with $\beta =\frac{1}{\sigma}$ requires $\sigma>1$ for $\beta \in (0,1)$, which implies that either the top-left cell or the bottom-right cell is applicable. In the latter case, $q \in [0, -\frac{1}{\alpha \sigma}) \cap [0, -\frac{\beta}{\alpha} )$ must be replaced with $q \in [0,\infty)$ (see footnote~).}}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Linear fractional relative risk aversion" by Kristian Behrens and Yasusada Murata, arXiv:2509.09865v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"On the improved estimation of ordered parameters based on doubly type-II censored sample" by Shrajal Bajpai and Lakshmi Kanta Patra, arXiv:2411.06888v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|c|c|c|c|}
\hline
\textbf{Problem} & \textbf{Equation Type} & \textbf{PINN} & \textbf{A-PINN} & \textbf{SA-PINN} & \textbf{RISN} \\
\hline
1 & 1D Second-kind Linear Volterra Integral Equation & $9.53 \times 10^{-3}$ & $2.59 \times 10^{-4}$ & $2.23 \times 10^{-3}$ & $8.46 \times 10^{-6}$ \\
2 & 1D Second-kind Nonlinear Volterra Integral Equation & $2.80 \times 10^{-4}$ & $8.28 \times 10^{-3}$ & - & $1.05 \times 10^{-5}$ \\
3 & 1D Second-kind Volterra-Fredholm Integral Equation & $1.95 \times 10^{-4}$ & $1.91 \times 10^{-3}$ & - & $7.05 \times 10^{-6}$ \\
4 & 1D Second-kind Volterra-Fredholm Integral Equation & $3.40 \times 10^{-4}$ & $1.51 \times 10^{-3}$ & - & $1.48 \times 10^{-5}$ \\
5 & 1D Linear Abel Integral Equation & $3.27 \times 10^{-3}$ & - & - & $3.27 \times 10^{-3}$ \\
6 & 1D Nonlinear Abel Integral Equation & - & - & - & $1.70 \times 10^{-3}$ \\
7 & 2D Second-kind Fredholm Integral Equation & $2.85 \times 10^{-3}$ & $3.20 \times 10^{-2}$ & $1.30 \times 10^{-2}$ & $1.31 \times 10^{-4}$ \\
8 & 2D Second-kind Volterra Integral Equation & $4.34 \times 10^{-2}$ & - & - & $5.64 \times 10^{-3}$ \\
9 & System of Second-kind Fredholm Integral Equations & $\{4.57, 5.93\} \times 10^{-3}$ & $\{1.35, 1.07\} \times 10^{-3}$ & - & $\{1.33, 1.24\} \times 10^{-3}$ \\
10 & System of Second-kind Volterra Integral Equations & $\{9.01, 1.98\} \times 10^{-5}$ & $8.67 \times 10^{-4}, 1.03 \times 10^{-3}$ & $\{1.75, 4.19\} \times 10^{-3}$ & $3.49 \times 10^{-5}, 1.18 \times 10^{-4}$ \\
11 & System of First-kind Volterra Integral Equations & $\{4.70, 4.18\} \times 10^{-3}$ & - & - & $\{2.56, 2.88\} \times 10^{-3}$ \\
12 & Second-kind Fredholm Ordinary Integro-Differential Equation & $2.89 \times 10^{-6}$ & $6.47 \times 10^{-6}$ & $1.01 \times 10^{-4}$ & $5.76 \times 10^{-7}$ \\
13 & Second-kind Fredholm Ordinary Integro-Differential Equation & - & $2.95 \times 10^{-5}$ & $2.54 \times 10^{-5}$ & $1.86 \times 10^{-6}$ \\
14 & First-kind Volterra Ordinary Integro-Differential Equation & $5.56 \times 10^{-5}$ & $1.26 \times 10^{-5}$ & - & $9.48 \times 10^{-6}$ \\
15 & First-kind Volterra Ordinary Integro-Differential Equation & - & $1.70 \times 10^{-5}$ & - & $5.38 \times 10^{-4}$ \\
16 & Fredholm Partial Integro-Differential Equation & $3.00 \times 10^{-3}$ & $3.27 \times 10^{-1}$ & - & $2.51 \times 10^{-4}$ \\
17 & Fredholm Partial Integro-Differential Equation & $1.24 \times 10^{-3}$ & $2.86 \times 10^{-1}$ & - & $1.95 \times 10^{-4}$ \\
18 & Nonlinear Fredholm Partial Integro-Differential Equation & $1.89 \times 10^{-3}$ & $5.26 \times 10^{-1}$ & - & $1.05 \times 10^{-4}$ \\
19 & System of Volterra Integro-Differential Equations & $\{1.94, 8.53\} \times 10^{-5}$ & $6.87 \times 10^{-2}, 1.35 \times 10^{-1}$ & $\{2.09, 2.52\} \times 10^{-4}$ & $\{4.89, 7.81\} \times 10^{-6}$ \\
20 & Fractional Integro-Differential Equation & $5.50 \times 10^{-3}$ & - & - & $3.64 \times 10^{-3}$ \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Comparison of the MAE achieved by PINN, A-PINN, SA-PINN, and RISN across 20 different integral and integro-differential problems. The types of equations vary from one-dimensional Volterra and Fredholm equations to multidimensional systems and fractional integro-differential problems.}
\end{table}
\end{document}

math

Mahdi Movahedian Moghaddam
Kourosh Parand
Saeed Reza Kheradpisheh

"Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations" by Mahdi Movahedian Moghaddam, Kourosh Parand, and Saeed Reza Kheradpisheh, arXiv:2501.16370v2, 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{Routine Correlations}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccccccccccccc}
 \toprule
 & \textbf{Practice} & \textbf{Sleep} & \textbf{Exercise} & \textbf{MHC-SF} & \textbf{PSS} & \textbf{Health} & \textbf{Days Training} \\
 \midrule
\textbf{Practice} & 1\\
 \textbf{Sleep} & -.1 & 1\\
 \textbf{Exercise} & -.05 & -.08 & 1\\
 \textbf{MHC-SF} & .19 & .01 & -.05 & 1\\
 \textbf{PSS} & -.08 & .08 & -.01 & -.63*** & 1\\
 \textbf{Health} & -.01 & .07 & .01 & .27 & -.24 & 1\\
 \textbf{Days Training} & .55*** & -.06 & -.13 & .27 & -.07 & .22 & 1\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jichen Zhu
Jennifer Villareale
Nithesh Javvaji
Sebastian Risi
Mathias Löwe
Rush Weigelt
Casper Harteveld

"Player-AI Interaction: What Neural Network Games Reveal About AI as Play" by Jichen Zhu, Jennifer Villareale, Nithesh Javvaji, Sebastian Risi, Mathias Löwe, Rush Weigelt, and Casper Harteveld, arXiv:2101.06220v2, 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{Cash flow statement is a financial report that shows a company's cash inflows and outflows over a period of time, reflecting how the company generates and uses its cash through operating, investing, and financing activities}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lr}
\multicolumn{2}{c}{\textbf{CASH FLOW STATEMENT}} \\
\hline
\hline
\textbf{Cash Flows from Operating Activities} & \\ \cline{1-2}
Net profit & \underline{-1645141.43} \\
Depreciation & \underline{45751.41} \\
(Increase) Decrease in Current Assets & \\
\quad Accounts Receivable & \underline{(2429482.13)} \\
\quad Interest Receivable & \underline{(2672.87)} \\
\quad Inventory & \underline{(5090000))} \\
Total (Increase) Decrease in Current Assets & \underline{(7522155)} \\ \cline{1-2}
Increase (Decrease) in Current Liabilities &\\
\quad Accounts Payable & \underline{1590000} \\
\quad Tax Payable & \underline{271550.92} \\
Total Increase (Decrease) in Current Liabilities & \underline{1861550.92} \\
\hline
\hline
\textbf{\textit{Net Cash Flow From Operations}} & \underline{-7259994.1} \\ \cline{2-2}
\hline
\hline
\textbf{Cash Flows from Investing Activities} & \\ \cline{1-2}
Purchase of Fixed Assets & \underline{305354.43} \\ \cline{2-2}
\hline
\hline
\textbf{\textit{Net Cash Flows from Investing Activities}} & \underline{(305354.43)} \\ \cline{1-2}
\hline
\hline
\cline{1-2}
\textbf{Beginning Cash and Cash Equivalents Balance} & \underline{8000000} \\ \cline{2-2}
\textbf{Ending Cash and Cash Equivalents Balance} & \underline{434651.47} \\ \cline{2-2}
\textbf{Net Increase} & \underline{(7565348.53)} \\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Junzhe Jiang
Chang Yang
Aixin Cui
Sihan Jin
Ruiyu Wang
Bo Li
Xiao Huang
Dongning Sun
Xinrun Wang

"FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs" by Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Ruiyu Wang, Bo Li, Xiao Huang, Dongning Sun, and Xinrun Wang, arXiv:2505.13533v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Notation}
\begin{tabular}{ll}
\hline
\multicolumn{2}{l}{\textbf{Sets}} \\ \hline
$\mathcal{N}$ & Set of nodes \\
$\mathcal{N}_f$ & Set of nodes to be sprayed \\
$\mathcal{K}$ & Set of tanker's trips \\
\hline
\multicolumn{2}{l}{\textbf{Indices}} \\ \hline
$i,j$ & Node indices \\
$k,r$ & Trip indeces \\ \hline
\multicolumn{2}{l}{\textbf{Parameters}} \\ \hline
$numSp$ & Number of sprayers \\
$q_i$ & Quantity of fertilizer to be applied at node $i\in\mathcal{N}_f$ \\
$s_i$ & Service time of node $i\in\mathcal{N}_f$ \\
$tMax$ & Working hours/length of the planning horizon \\
$t_{ij}$ & Travelling time between nodes $i$ and $j$ \\
$Q_s$ & Capacity of the sprayer \\
$Q_t$ & Capacity of the tanker \\
$\xi$ & Time needed to refill a sprayer \\
$\gamma$ & Time needed to refill the tanker at the depot \\
$M$ & Big positive number \\ \hline
\multicolumn{2}{l}{\textbf{Decision variables}} \\ \hline
$x_{ij} $ & Binary variable that is equal to 1 if and only if sprayer travels along arc $(i,j)$ \\
$g_{ij}^k$ & Binary variable that is equal to 1 if and only if the tanker travels arc $(i,j)$ in trip $k$ \\
$\delta_i$ & Binary variable that is equal to 1 if and only if there is a refill at node $i\in\mathcal{N}_f$ \\
$z_{kr}$ & Binary variable that is equal to 1 if and only if trip $k\in\mathcal{K}$ proceeds trip $r\in\mathcal{K}$ \\
$\theta_i^k$ & Arrival time of the tender tanker at node $i\in\mathcal{N}_f$ on the $k^{th}$ trip\\
$y_i$ & Time of arrival of a sprayer at node $i\in\mathcal{N}_f$ \\
$a_i$ & Time of spraying service start time by a sprayer at node $i\in\mathcal{N}_f$ \\
$w_i^k$ & Refilling start time of the tender tanker at node $i\in\mathcal{N}_f$ on the $k^{th}$ trip\\
$h_i^k$ & Level of fertilizer of the tender tanker at arrival to node $i\in\mathcal{N}_f$ on the $k^{th}$ trip\\
$l_i$ & Level of fertilizer of sprayer when arriving to node $i\in\mathcal{N}_f$ \\
$m_i$ & Waiting time of a sprayer at node $i\in\mathcal{N}_f$ \\
$v_i$ & Quantity of fertilizer supplied to a sprayer at node $i\in\mathcal{N}_f$ \\
 \hline
\end{tabular}
\end{table}
\end{document}

math

"Adaptive Large Neighborhood Search Metaheuristic for Vehicle Routing Problem with Multiple Synchronization Constraints and Multiple Trips" by Faisal Alkaabneh, arXiv:2312.09414v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Consensus-based adaptive sampling and approximation for high-dimensional energy landscapes" by Liyao Lyu and Huan Lei, arXiv:2311.05009v4, 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{Firms' Summary Statistics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
	&	Mean	&	p10	&	p25 	&	Median	&	p75 	&	p90	&	SD	\\ \hline \hline
Investment Growth $\log \Delta k_{i,t} $	&	0.029	&	-0.051	&	-0.010	&	0.017	&	0.054	&	0.115	&	0.196	\\
Investment Growth $\log \Delta k_{i,t+4} $	&	0.136	&	-0.093	&	0.004	&	0.090	&	0.221	&	0.412	&	0.406	\\
\\
Leverage: Liab. - Assets	&	0.481	&	0.077	&	0.242	&	0.434	&	0.594	&	0.770	&	0.862	\\
Leverage: Short-Term Liab. - Assets	&	0.219	&	0.027	&	0.070	&	0.157	&	0.279	&	0.454	&	0.313	\\
\\
Currency-Mismatch & -0.025	&	-0.311	&	-0.020	&	0.000	&	0.015	&	0.181	&	0.242 \\ \hline \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Santiago Camara
Sebastian Ramirez Venegas

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Productivity Comparison: Traditional vs Weather-Aware AI Operations}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
\textbf{Metric} & \textbf{Traditional} & \textbf{Weather-AI} & \textbf{Improvement} \\
\midrule
Revenue per minute (¥) & 50.1 & 103.9 & +107.3\% \\
Wait time (minutes) & 9.1 & 5.1 & -43.8\% \\
Utilization rate (\%) & 48.1 & 78.4 & +63.0\% \\
Daily earnings (¥) & 15,493 & 53,326 & +244.2\% \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity" by Tatsuru Kikuchi, arXiv:2507.17099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cllrc}
 Symbol & Interpretation & True & Estimate & std.dev
 \\
 \hline
 $\lambda$ & Decay rate & 1 & 1.02 & 0.12 \\
 $\mu$ & Stationary mean & 2 & 2.04 & 0.03 \\
 $\sigma$ & Process noise intensity & 1 & 0.95 & 0.06 \\
 $s$ & Measurement noise standard deviation & 0.5 & - & - \\
 \end{tabular}
\caption{Parameters for the linear example: True values, estimates, standard errors.}
\end{table}
\end{document}

math

"Inference in stochastic differential equations using the Laplace approximation: Demonstration and examples" by Uffe Høgsbro Thygesen and Kasper Kristensen, arXiv:2503.21358v2, 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}{lrrrrrr}
\hline \hline
\textbf{Metric} & \textbf{Sector} & \textbf{ADF Stat} & \textbf{ADF p-val} & \textbf{PP Stat} & \textbf{PP p-val} \\
\hline
\textbf{Pre-crash period} & & & \\
\hline
$WD_1$ & Commodity & -1.51 & 0.53 & -1.57 & 0.50 \\
$WD_1$ & Stock & -1.57 & 0.50 & -1.75 & 0.40 \\
$WD_2$ & Commodity & -1.28 & 0.64 & -1.59 & 0.49 \\
$WD_2$ & Stock & -1.66 & 0.45 & -1.87 & 0.35 \\
\hline
\textbf{Crash period} & & & \\
\hline
$WD_1$ & Commodity & -2.09 & 0.25 & -0.61 & 0.87 \\
$WD_1$ & Stock & -1.89 & 0.34 & -1.35 & 0.60 \\
$WD_2$ & Commodity & -1.54 & 0.51 & -0.53 & 0.89 \\
$WD_2$ & Stock & -1.70 & 0.43 & -1.38 & 0.59 \\
\hline
\textbf{Post-crash period} & & & \\
\hline
$WD_1$ & Commodity & -1.31 & 0.62 & -1.62 & 0.47 \\
$WD_1$ & Stock & -2.91 & 0.04** & -1.85 & 0.36 \\
$WD_2$ & Commodity & -2.73 & 0.07 & -1.61 & 0.48 \\
$WD_2$ & Stock & -3.11 & 0.03** & -2.03 & 0.27 \\
\hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Best Test Accuracies of models using MovieQA and Youtube2TextQA datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
\textbf{Datasets} & \multicolumn{4}{c|}{\textit{\textbf{YouTube2TextQA}}} & \multicolumn{4}{c|}{\textit{\textbf{MovieQA}}} \\ \hline
\textit{\textbf{Model}} & r-ANL {[}5{]} & DLAN {[}9{]} & Fan et al. {[}13{]} & L-GCN {[}21{]} & DEMN {[}8{]} & MDAM {[}12{]} & PAMN {[}20{]} & AMN {[}23{]} \\ \hline
\textit{\textbf{Accuracy}} & 61.4 & 36.33 & 82.5 & 83.9 & 30 & 41.41 & 42.53 & 45.31 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Devshree Patel
Ratnam Parikh
Yesha Shastri

"Recent Advances in Video Question Answering: A Review of Datasets and Methods" by Devshree Patel, Ratnam Parikh, and Yesha Shastri, arXiv:2101.05954v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Ze-yu Liu
Jian-wei Liu
Xin Zuo
Ming-fei Hu

"Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection" by Ze-yu Liu, Jian-wei Liu, Xin Zuo, and Ming-fei Hu, arXiv:2201.09574v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{arydshln}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Analysis results given by ISPW lasso and ISPW likelihood function using MG data}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|}\hline
Parameter & lasso & likelihood (LN) & likelihood (W) & likelihood (LL)\\ \hline
Intercept & 3.78 & 4.47 & 2.40 & 2.64\\
Treatment group & - & 0.17 & 0.11 & 0.19\\
Age & - & -0.01 & 0.02 & 0.00\\
Years of diagnosis & 0.01 & 0.03 & - & -\\
Karnofsky performance score & 0.19 & 0.30 & - & 0.70\\
Race & -0.12 & -0.39 & - & -\\
Radiation therapy & - & 0.48 & - & -\\
Sex & 0.23 & - & - & -\\
Nitro & -0.18 & -0.38 & - & -\\
Path & 0.02 & 0.06 & 0.43 &0.28 \\
Grade & -0.01 & -0.58 & - & -\\
Error & - & 0.42 & 0.83 & 0.38\\\hdashline
AIC & - & \textcolor{red}{\bf 5387.56} & 5804.40 &5582.18\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Variable Selection using Inverse Survival Probability Weighting" by Masahiro Kojima, arXiv:2212.02109v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Fabio Baione
Davide Biancalana
Paolo De Angelis

"An application of Zero-One Inflated Beta regression models for predicting health insurance reimbursement" by Fabio Baione, Davide Biancalana, and Paolo De Angelis, arXiv:2011.09248v1, licensed under CC0 1.0. 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ccc|ccc|ccc|ccc|}
\hline
\multirow{2}{*}{Model Architecture} & \multicolumn{3}{c|}{Dice [\%]} & \multicolumn{3}{c|}{HD95 [mm]} & \multicolumn{3}{c|}{LW Dice [\%]} & \multicolumn{3}{c|}{LW HD95 [mm]} \\
\cline{2-13}
& WT & ET & TC & WT & ET & TC & WT & ET & TC & WT & ET & TC \\
\hline
Optimized U-Net & 81.44 & 72.25& 75.81& 22.30& 48.47& 29.94& 68.50& 67.85& 71.27& 84.84 & 77.05& 51.79\\
nnU-Net & 88.11 & 79.98 & 82.51 & 17.49 & 37.00 & 23.79 & 82.56 & 78.07 & 79.81 & 39.62 & 48.26 & 34.54 \\
Swin-UNetR & 78.59 & 72.51 & 74.75 & 22.85 & 48.88 & 31.28 & 60.96 & 65.75 & 68.03 & 116.09 & 90.51 & 67.75 \\
UMamba & 85.69 & 77.41 & 82.07 & 14.51 & 37.48 & 18.90 & 78.60 & 73.95 & 78.55 & 50.24 & 62.11 & 38.17 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Test performance metrics for the different training pipelines considered. Metrics include Dice, HD95, lesion-wise Dice (LW Dice), and lesion-wise HD95 for whole tumor (WT), tumor core (TC) and ET.}
\end{table}
\end{document}

eess

Juampablo E. Heras Rivera
Agamdeep S. Chopra
Tianyi Ren
Hitender Oswal
Yutong Pan
Zineb Sordo
Sophie Walters
William Henry
Hooman Mohammadi
Riley Olson
Fargol Rezayaraghi
Tyson Lam
Akshay Jaikanth
Pavan Kancharla
Jacob Ruzevick
Daniela Ushizima
Mehmet Kurt

"An Ensemble Approach for Brain Tumor Segmentation and Synthesis" by Juampablo E. Heras Rivera, Agamdeep S. Chopra, Tianyi Ren, Hitender Oswal, Yutong Pan, Zineb Sordo, Sophie Walters, William Henry, Hooman Mohammadi, Riley Olson, Fargol Rezayaraghi, Tyson Lam, Akshay Jaikanth, Pavan Kancharla, Jacob Ruzevick, Daniela Ushizima, and Mehmet Kurt, arXiv:2411.17617v1, 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}{|cccc|}
\hline
Cell 1 & Cell 2 & Cell 3 & Cell 4 \\
\hline
8 & 10 & 30 & 5 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Stephen D. Boyles
Nicholas E. Lownes
Avinash Unnikrishnan

"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, 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}{lccccccc} \toprule
Task & \shortstack{Epoch \ ber} & \shortstack{Batch \\ size} & \shortstack{Learning \\ rate} \\ \midrule
LoDoPaB-CT &70 &32 & 0.001 \\
fastMRI &70 &8 & 0.001 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{\textbf{Hyperparameters used for the U-Net.} }
\end{table}
\end{document}

eess

Tianlin Liu
Anadi Chaman
David Belius
Ivan Dokmanić

"Learning Multiscale Convolutional Dictionaries for Image Reconstruction" by Tianlin Liu, Anadi Chaman, David Belius, and Ivan Dokmanić, arXiv:2011.12815v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Hybrid Censored Quantile Regression Forest to Assess the Heterogeneous Effects" by Huichen Zhu, Yifei Sun, and Ying Wei, arXiv:2212.05672v1, 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{Fixed model size $d=100$ and the number of sampling $n=100$, SHD results of causal discovery using Algorithm 1 for different $C_m$ values ($10$ runs).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
 \toprule[1pt]
 $C_m$ & $1$ & $2$ & $4$ & $8$ & $16$ \\
 \midrule
 SHD & $2941.0\pm29.5$ & $2905.7 \pm 50.8$ & $2900.6 \pm 80.8$ & $2637.1 \pm 200.4$ & $1512.4 \pm 283.6$\\
 \midrule[1pt]
 $C_m$ & $32$ & $64$ & $128$ & $256$ & $512$\\
 \midrule
 SHD & $413.9 \pm 93.4$ & $55.0 \pm 16.0$ & $ 23.9 \pm 4.6$ & $21.2 \pm 5.0$ & $13.8 \pm 1.8$ \\
\bottomrule[1pt]
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Zhenyu Zhu
Francesco Locatello
Volkan Cevher

"Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling" by Zhenyu Zhu, Francesco Locatello, and Volkan Cevher, arXiv:2310.18123v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Application of machine learning regression models to inverse eigenvalue problems" by Nikolaos Pallikarakis and Andreas Ntargaras, arXiv:2212.04279v1, 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{Distribution of patients on the basis of the discharge tag and according to the actual list of the outcomes.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|c|c|c}
 & {\sc White} & {\sc Green} & {\sc Yellow} & {\sc Red} & \\ \hline
 \emph{O1} & 121 & 1025 & 23 & & 1169 \\
 \emph{O2} & 39 & 496 & 21 & & 556 \\
 \emph{O3} & & 29 & 182 & 14 & 225 \\
 \emph{O4} & & & 4 & 4 & 8 \\
 \emph{O5} & & 19 & 10 & & 29 \\
 \emph{O6} & 3 & 17 & 2 & & 22 \\
 \emph{O7} & 8 & 26 & 1 & & 35 \\
 \emph{O8} & & & & 2 & 2 \\
 \emph{O9} & & & & & - \\ \hline
 & 171 & 1612 & 243 & 20 & 2046 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

G. Fava
T. Giovannelli
M. Messedaglia
M. Roma

"Effect of different patient peak arrivals on an Emergency Department via discrete event simulation" by G. Fava, T. Giovannelli, M. Messedaglia, and M. Roma, arXiv:2101.12432v1, 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}
 $\gamma$& white & lightgray & darkgray & black \\ \hline
 0.01 &[0,\, 0.0175)&[0.0175,\, 0.05)&[0.05,\, 150)&$[150,\, \infty)$ \\ \hline
 0.1 &[0,\, 0.12)&[0.12,\, 0.5)&[0.5,\, 150)&$[150,\, \infty)$ \\ \hline
 1 	 &[0,\, 1.25)&[1.25,\, 3.0)&[3.0,\, 150)&$[150,\, \infty)$ \\ \hline
 10 &[0,\, 12)&[12,\, 20)&[20,\, 150)&$[150,\, \infty)$
 \end{tabular}
\end{adjustbox}
\caption{Time intervals used to determine tipping for each of the different gray shadings in Figure~; if a randomly chosen initial condition for $y$ results in tipping in the corresponding time interval then the shading is used, otherwise we used black to indicate the tipping has not occurred by time $150$.}
\end{table}
\end{document}

math

"The effect of timescale separation on the tipping window for chaotically forced systems" by Raphael Römer and Peter Ashwin, arXiv:2504.12530v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Raphael Flepp
Tim Pawlowski
Travis Richardson

"Suspense and Surprise in European Football" by Raphael Flepp, Tim Pawlowski, and Travis Richardson, arXiv:2506.21253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Numerical Derivatives, Projection Coefficients, and Truncation Errors in Analytic Hilbert Space With Gaussian Measure" by M. W. AlMasri, arXiv:2504.16246v2, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Percentage of observed values for the variables in the model, categorized according to their role in the domain and stratified by cohort type (PBC, CBC).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Variable}
& \textbf{Label}
& \textbf{Category}
& \textbf{PBC}
& \textbf{CBC} \\
\hline\hline
Age>35 years & \texttt{age35} & Prognostic factor & 100\% & 100\%\\
Tumor Hystology & \texttt{histology} & Prognostic factor & 100\% & 100\%\\
Tumor grading & \texttt{grade} & Prognostic factor & 0\% & 89\%\\
Vascular invasion & \texttt{vascular} & Prognostic factor & 0\% & 57\%\\
Ki67 positivity & \texttt{ki67} & Prognostic factor & 0\% & 92\%\\
Tumor receptor status & \texttt{receptors} & Prognostic factor & 0\% & 94\%\\
Tumor dimension & \texttt{pT} & Prognostic factor & 0\% & 65\%\\
Lymph nodes involvement & \texttt{pN} & Prognostic factor & 0\% & 68\%\\
Death in 5 years & \texttt{death\_in\_5y} & Survival & 90\% & 45\%\\
Neo-adjuvant chemotherapy & \texttt{chemo\_neo} & Treatment & 100\% & 99\%\\
Neo-adjuvant radiotherapy & \texttt{radio\_neo} & Treatment & 100\%& 99\%\\
Neo-adjuvant target therapy & \texttt{target\_neo} & Treatment & 100\% & 99\%\\
Neo-adjuvant hormon therapy & \texttt{hormons\_neo} & Treatment & 100\% & 99\%\\
Surgery type & \texttt{surgery} & Treatment & 100\% & 100\%\\
adjuvant chemotherapy & \texttt{chemo\_adju} & Treatment & 100\% & 96\%\\
adjuvant radiotherapy & \texttt{radio\_adju} & Treatment & 100\% & 96\%\\
adjuvant target therapy & \texttt{target\_adju} & Treatment & 100\% & 96\%\\
adjuvant hormon therapy & \texttt{hormons\_adju} & Treatment & 100\% & 96\%\\
Dyslipidemia & \texttt{dyslipidemia} & CVDs risk factor & 100\% & 5\%\\
Hypertension & \texttt{hypertension} & CVDs risk factor & 100\% & 3\%\\
Type 2 Diabete Mellitus & \texttt{t2db} & CVDs risk factor & 100\% & 1\%\\
Cardiotoxicity & \texttt{cardiotoxicity} & CVDs risk factor & 100\% & 0\%\\
Ischemic heart disease & \texttt{ischemic\_heart\_disease} & CVDs risk factor & 100\% & 0\%\\
Cardiovascular disease in 5 years & \texttt{cvds} & Target variable & 100\% & 0\%\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Alice Bernasconi
Alessio Zanga
Peter J. F. Lucas
Marco Scutari
Fabio Stella

"Towards a Transportable Causal Network Model Based on Observational Healthcare Data" by Alice Bernasconi, Alessio Zanga, Peter J. F. Lucas, Marco Scutari, and Fabio Stella, arXiv:2311.08427v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccccc}
\toprule
Metric & \multicolumn{4}{c}{Top-1 Accuracy (\%)} & & \multicolumn{4}{c}{Seen-Unseen AUC (\%)} \\ \cmidrule(lr){2-5} \cmidrule(l){7-10}
Dataset & \multicolumn{2}{c}{CUB} & \multicolumn{2}{c}{NAB} & & \multicolumn{2}{c}{CUB} & \multicolumn{2}{c}{NAB} \\
Split-Mode & Easy & Hard & Easy & Hard & & Easy & Hard & Easy & Hard \\ \midrule
$K^u=100 (w/o)$& \textbf{42.3} & 14.8 & 33.5 & \textbf{9.8} & & \textbf{38.6} & 12.4 & \textbf{21.7} & 7.2\\
$K^u=100 (w/)$ & 41.8 & \textbf{15.2} & \textbf{33.7} & 9.5 & & 38.5 & \textbf{12.9} & 21.3 & \textbf{7.3}\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Comparative results of adding categorization loss on hallucinated visual features of hallucinated text in a semantically guided way.}
\end{table}
\end{document}

cs

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


q-fin

"Estimation of Gender Wage Gap in the University of North Carolina System" by Zihan Zhang and Jan Hannig, arXiv:2505.24078v1, 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{Basket Constituents}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 Ticker & Name & Industry & Market Cap (bn) \\
 \hline
 APA & Apache Corporation & Energy: Oil and Gas & 4.11 \\
 \hline
 AXP & American Express Company & Credit Services & 76.80 \\
 \hline
 CAT & Caterpillar Inc. & Farm Machinery & 78.60 \\
 \hline
 COF & Capital One Financial Corp. & Credit Services & 31.10 \\
 \hline
 FCX & Freeport-McMoRan Inc. & Copper & 22.35 \\
 \hline
 IBM & 1nternational Business Machines Corp. & Technology & 105.17 \\
 \hline
 MMM & 3M Company & Industrial Machinery & 92.41 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Tight Bounds for a Class of Data-Driven Distributionally Robust Risk Measures" by Derek Singh and Shuzhong Zhang, arXiv:2010.05398v2, 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{NLL($\downarrow$) for zero-shot and fine-tuning with varying sample sizes. Pretraining on a context length of 32 yields the best zero-shot performance, while pretraining on a longer context length of 128 improves fine-tuning, especially with 10-100 samples.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rccccc}
\toprule
Model & Zero-Shot & 1 Sample & 10 Samples & 100 Samples & 1000 Samples\\ \midrule
Small-16 &
-0.0449 &
-0.1189 &
-0.1523 &
-0.1824 &
-0.1870 \\
Small-32 & \textbf{-0.0978} & -0.0842 & -0.1681 & -0.1873 & -0.1898 \\
Small-64 &
-0.0808 &
\textbf{-0.1418} &
-0.1684 &
-0.1861 &
-0.1853
\\
Small-128 & -0.0842 & -0.1287 & \textbf{-0.1825} & \textbf{-0.1907} & \textbf{-0.1918} \\
\midrule
Medium-16 &
-0.0483 &
-0.0893 &
-0.1542 &
-0.1767 &
-0.1897\\
Medium-32 & \textbf{-0.1330} & \textbf{-0.1486} & -0.1673 & -0.1792 & -0.1847\\
Medium-64 &
-0.0793 &
-0.1146 &
\textbf{-0.1612} &
\textbf{-0.1873} &
-0.1875 \\
Medium-128 & -0.1020 & -0.1113& -0.1711 & -0.1843 & \textbf{-0.1899} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Xueying Ding
Aakriti Mittal
Achintya Gopal

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of Key Metrics}
\begin{tabular}{lrr}
\toprule
 Metric & Initial (Iter 0) & Iter 225 \\
\midrule
 Pri Res & 0.488 & 1.550000e-07 \\
 Dua Res & 15.300 & 9.030000e-08 \\
 Gap & 274.000 & 5.540000e-07 \\
Objective & 153.000 & 1.240000e+01 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"ConvMesh: Reimagining Mesh Quality Through Convex Optimization" by Alexander Valverde, arXiv:2412.08484v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Taban Baghfalaki
Reza Hashemi
Catherine Helmer
Helene Jacqmin-Gadda

"A Two-stage Joint Modeling Approach for Multiple Longitudinal Markers and Time-to-event Data" by Taban Baghfalaki, Reza Hashemi, Catherine Helmer, and Helene Jacqmin-Gadda, arXiv:2412.05765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|l|l|l|l|}
 \hline
 & $\ell$ & $C\circ \Phi^*$ & $S\circ \Gamma$ & $B$ &$\Xi$ \\
 \hline
 PSRF & 1.032 &(1.000, 1.012)&(1.000, 1.042)&(1.001, 1.028)&(1.000, 1.021) \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Potential scale reduction factors (PSRF) for log-likelihood ($\ell$) and 2.5 and 97.5 elementwise PSRF quantile for model parameters. *Approximately 15\% of factor switches cil are equal to zero with probability $< 1/10000$ \textit{a posteriori}, so PSRF is undefined; the reported range reflects convergence diagnostic statistics for those $(i, l)$ which have nonzero posterior expected value.}
\end{table}
\end{document}

stat

Braden Scherting
Otso Ovaskainen
David B. Dunson

"Joint species distribution modeling of abundance data through latent variable barcodes" by Braden Scherting, Otso Ovaskainen, and David B. Dunson, arXiv:2412.08793v2, 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 average ranks of the imputation scores across the various UCI datasets and various percentages of missing values. Each imputation experiment was repeated 100 times with different random initialization. RF-GAP generally produces the best imputations across all percentages of missing values. See Table~ for the full imputation results.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
& 5\% & 10\% & 25\% & 50\% & 75\% \\
 \hline
RF-GAP	&	1.10	&	1.20	&	1.15	&	1.35	&	1.65	\\
Original	&	2.55	&	2.70	&	2.65	&	2.60	&	2.35	\\
OOB	&	2.50	&	2.55	&	2.45	&	2.40	&	2.30	\\
RFProxIH	&	3.20	&	3.33	&	3.33	&	3.40	&	3.20	\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jake S. Rhodes
Adele Cutler
Kevin R. Moon

"Geometry- and Accuracy-Preserving Random Forest Proximities" by Jake S. Rhodes, Adele Cutler, and Kevin R. Moon, arXiv:2201.12682v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Lining Yu
Wolfgang Karl Härdle
Lukas Borke
Thijs Benschop

"An AI approach to measuring financial risk" by Lining Yu, Wolfgang Karl Härdle, Lukas Borke, and Thijs Benschop, arXiv:2009.13222v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of Booster-related electronic log (Elog)~ entries corresponding to spikes in Figures~ and~. Original Central Time times are given with values in parenthesis designating UTC. Here, ``RF'' denotes radiofrequency.}
\begin{tabular}{|c|l|}
\hline
Date & Elog Entry Summary \\ \hline
6/8/2019 & GMPS in AC mode until 8:00 AM (13:00) on 6/08, then switched off until 6/17 \\ \hline
\multirow{2}{*}{6/10/2019} & GMPS was locked/tagged out for outage, West Booster gallery RF off from \\
 & 8:30-10:00 (13:30-3:00) for work \\ \hline
6/17/2019 & GMPS turned back on and put in AC mode \\ \hline
6/22/2019 & High energy physics beam turned off at 8:00 PM (1:00 +1) (GMPS remained in AC mode) \\ \hline
6/24/2019 & DC studies from 8:00 AM - 6:00 PM (13:00 - 23:00), back to AC mode \\ \hline
6/26/2019 & Alternated between AC and DC mode, GMPS off for 30 min around 5:30 PM (22:30) \\ \hline
\multirow{2}{*}{6/27/2019} & GMPS in AC mode all day, but removing certain study events caused bias to creep up, \\
 & eventually tripping the RF \\ \hline
6/28/2019 & Alternated between AC and DC mode \\ \hline
12/8/2019 & \texttt{B\_VIMIN} adjusted, GMPS in AC mode \\ \hline
12/12/2019 & AC mode, operators reset virtual machine environment locally \\ \hline
12/28 - 12/30/2019 & No beam from injector, GMPS in AC mode \\ \hline
12/31/2019 & Booster injection back, GMPS in AC mode \\ \hline
1/1/2020 & GMPS off for 15 min around 9:30 AM (14:30), in AC mode for rest of day \\ \hline
2/4/2020 & RF sparking in gallery due to reverting of RF capture settings, GMPS in AC mode \\ \hline
2/5/2020 & GMPS off from 6:00 AM - 3:30 PM (11:00 - 20:30), then in AC mode for rest of day \\ \hline
2/6/2020 & Lowered beam intensity to users, but GMPS was in AC mode all day \\ \hline
3/5/2020 & Beam tails were large, so turned \texttt{B\_VIMIN} down \\ \hline
\multirow{2}{*}{3/10/2020} & GMPS in AC mode all day, \texttt{B:ACMNPG} changed from 6.5 to 13.5, \texttt{B\_VIMIN} decreased \\
 & from 103.440 to 103.420 \\ \hline
\multirow{2}{*}{3/11/2020} & GMPS in AC mode from 12:00 AM - 6:00 AM (5:00 - 11:00), off from 6:00 - 10:00 AM \\
 & (11:00 - 15:00), then alternated between AC and DC mode, \texttt{B\_VIMIN} adjusted from \\
 & 103.418 to 103.386 \\ \hline
3/13/2020 & GMPS off from 9:30 AM - 11:00 AM (14:30-16:00), back on and put in AC mode \\ \hline
3/20/2020 & Booster turned off on account of Covid-19 pandemic at 12:00 PM (17:00) \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"BOOSTR: A Dataset for Accelerator Control Systems" by Diana Kafkes and Jason St. John, arXiv:2101.08359v3, 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{Summary of the train, validation and internal test splits.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|c|ccccc|cc}
& & Cam16 & \multicolumn{5}{c}{ Cam17 (5 Centers)}\vline & \multicolumn{2}{c}{ PanNuke (2 Folds) } \\
& Label & & C. 0 & C. 1 & C. 2 & C. 3 & C. 4 & F. 1 & F. 2 \\\hline
\multirow{2}{*}{ Train } & Neg. & 12954 & 31108 & 25137 & 38962 & 25698 & 0 & 1425 & 1490 \\
& Pos. & 6036 & 8036 & 5998 & 2982 & 1496 & 0 & 2710 & 2255 \\\hline
\multirow{2}{*}{ Val. } & Neg. & 0 & 325 & 0 & 495 & 0 & 0 & 0 & 0 \\
& Pos. & 0 & 500 & 0 & 500 & 0 & 0 & 0 & 0 \\\hline
\multirow{2}{*}{ Int. Test } & Neg. & 0 & 0 & 274 & 483 & 458 & 0 & 0 & 0 \\
& Pos. & 0 & 500 & 999 & 0 & 0 & 0 & 0 & 0 \\%\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mara Graziani
Sebastian Otalora
Stephane Marchand-Maillet
Henning Muller
Vincent Andrearczyk

"Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization" by Mara Graziani, Sebastian Otalora, Stephane Marchand-Maillet, Henning Muller, and Vincent Andrearczyk, arXiv:2008.01478v3, 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}{cc}
 \hline
 \textbf{Mode} & \textbf{Multiturn\_memory-zh} \\ \hline
 GLM-4-Voice & 80.00 \\
 MiniCPM-o-2.6 & 86.67 \\
 Baichuan-Omni-1.5 & 78.67 \\
 SpeechGPT-2.0-preview & 20.00 \\
 Freeze-Omni & 62.67 \\
 Qwen2.5-Omni & 88.67 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Model scores (\%) on the Context Memory task.}
\end{table}
\end{document}

eess

Zehan Li
Hongjie Chen
Yuxin Zhang
Jing Zhou
Xuening Wang
Hang Lv
Mengjie Du
Yaodong Song
Jie Lian
Jian Kang
Jie Li
Yongxiang Li
Zhongjiang He
Xuelong Li

"TELEVAL: A Dynamic Benchmark Designed for Spoken Language Models in Chinese Interactive Scenarios" by Zehan Li, Hongjie Chen, Yuxin Zhang, Jing Zhou, Xuening Wang, Hang Lv, Mengjie Du, Yaodong Song, Jie Lian, Jian Kang, Jie Li, Yongxiang Li, Zhongjiang He, and Xuelong Li, arXiv:2507.18061v1, 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|ll|}
			\hline
			\textbf{Case} & \textbf{Conditions} & \textbf{Finite singular points} \\
			\hline
			\hline
			1& $ b_3 \neq 0$, $b_2\neq 0$. & $P_0$, $P_1$, $P_2$. \\	\hline
			2 & $ b_3 \neq 0$, $b_2=0$, $b_0\neq0$. & $P_0$, $P_1$.\\
			\hline
			3 & $ b_3=0$, $c_0\neq 0$, $b_2\neq 0$. & $P_0$, $P_2$. \\
			\hline
			4 & $ b_3=0$, $c_0\neq 0$, $b_2=0$, $b_0\neq 0$. & $P_0$. \\
			\hline
		\end{tabular}
\end{adjustbox}
\caption{The different cases for the finite singular points.}
\end{table}
\end{document}

math

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

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Measured Outputs of ACC system and Inputs to Neural Network Controller}
\begin{tabular}{|l||l|}
\hline
Driver-set velocity & $v_{\mathrm{set}}$ \\
 \hline
Time gap & $t_{\mathrm{gap}}$ \\
 \hline
Velocity of the ego car & $v_e$ \\
 \hline
Relative distance to the lead car & $d_{\mathrm{rel}} = x_l - x_e$\\
 \hline
 Relative velocity to the lead car & $v_{\mathrm{rel}} = v_l - v_e$\\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

"Run-Time Safety Monitoring of Neural-Network-Enabled Dynamical Systems" by Weiming Xiang, arXiv:2101.08297v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{makecell}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the Statistical Test Results for level of significance $0.01$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccccccccc}
\toprule
\multirow[b]{2}{*}{\thead{\textbf{Clustering Algorithm}}}
 & \multicolumn{2}{c}{\thead{\textbf{Sign Test}}}
 & \multicolumn{2}{c}{\thead{\textbf{WSR Test}}}
 \\
\cmidrule(lr){2-3}\cmidrule(lr){4-5} %\cmidrule(lr){11-13}
& \thead{Statistic} & \thead{\textit{\textbf{p}}-value} & \thead{Statistic} & \thead{\textit{\textbf{p}}-value}
\\
\midrule
\addlinespace
$k$-means ++ & 15 & 0.0002594 & 135 & 0.0000305 \\
Sparse $k$-means & 15 & 0.0002594 & 135 & 0.0000305 \\
$k$-medians & 15 & 0.0002594 & 135 & 0.0000305 \\
Partition Around Medoids & 15 & 0.0002594 & 134 & 0.0000458 \\
Robust Continuous Clustering & 15 & 0.0002594 & 135 & 0.0000305 \\
DP means & 15 & 0.0002594 & 133 & 0.0000763 \\
Kb MoM & 15 & 0.0002594 & 135 & 0.0000305 \\
MoM Power $k$-means & 15 & 0.0002594 & 135 & 0.0000305 \\
OWL $k$-means & 14 & 0.0020900 & 125 & 0.0008392\\
\addlinespace
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Supratik Basu
Jyotishka Ray Choudhury
Debolina Paul
Swagatam Das

"Dirichlet Process-based Robust Clustering using the Median-of-Means Estimator" by Supratik Basu, Jyotishka Ray Choudhury, Debolina Paul, and Swagatam Das, arXiv:2311.15384v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of Algorithm on Example }
\begin{tabular}{cccc}
\hline
Number of& Algorithm &Iterations &CPU time\\
initial points& &(Min, Max, Mean, Median, Mode, SD) &(Min, Max, Mean, Median, $\lceil\text{Mode}\rceil$, SD) \\
\hline
$100$ & QNM&($1,~6,~5.7400,~6,~6,~0.4845$) & ($1.2551,~42.8398,~23.8484,~29.5973,~11,~11.9814$) \\
 & SD &($1,~14,~13.9900,~14,14,0.1000$) & ($0.112,~4.2931,~3.2962,~3.2843,~3,~0.1097$) \\
\hline
\end{tabular}
\end{table}
\end{document}

math

Debdas Ghosh
Anshika
Jen-Chih Yao
Xiaopeng Zhao

"Quasi-Newton Method for Set Optimization Problems with Set-Valued Mapping Given by Finitely Many Vector-Valued Functions" by Debdas Ghosh, Anshika, Jen-Chih Yao, and Xiaopeng Zhao, arXiv:2501.04711v1, 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{GARCH(1,1) Model Coefficients for Selected Companies}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
\hline
\textbf{Company} & $\omega$ & $\alpha$ & $\beta$ \\
\hline
Britannia & 0.2291 & 0.0824 & 0.8340 \\
Dabur & 0.1756 & 0.1535 & 0.8149 \\
BPCL & 55.9961 & 0.0082 & 0.0000 \\
Axis Bank & 0.0964 & 0.0768 & 0.9077 \\
Infosys & 0.5283 & 0.1633 & 0.6948 \\
Cipla & 0.3283 & 0.0958 & 0.8022 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Vidya Sagar G
Shifat Ali
Siddhartha P. Chakrabarty

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} % centered columns (4 columns)
		\hline\hline %inserts double horizontal lines
		$h$ & $L$ & $\texttt{err}$ & \texttt{MCerr} \\ % inserts table
		\hline % inserts single horizontal line
		0.2 & $10^{5}$ & 0.00537 & 0.0003 \\
		0.1 & $10^{5}$ & 0.00162 & 0.0003 \\
		0.05 & $10^{7}$ & 0.000605 & 0.00003 \\
		0.025 & $10^7$ & 0.000258 & 0.00003 \\%[1ex] % [1ex] adds vertical space
		\hline %inserts single line
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Karthik Bharath
Alexander Lewis
Akash Sharma
Michael V Tretyakov

"Sampling and estimation on manifolds using the Langevin diffusion" by Karthik Bharath, Alexander Lewis, Akash Sharma, and Michael V Tretyakov, arXiv:2312.14882v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jie Li
Jian Zhang
Samantha L. Winter
Mark Burnley

"Modelling Loss of Complexity in Intermittent Time Series and its Application" by Jie Li, Jian Zhang, Samantha L. Winter, and Mark Burnley, arXiv:2411.14635v2, 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|l|c|c}
\toprule
& \textbf{Layer Description} & \textbf{K} & \textbf{Output Tensor Dim.} \\
\toprule
\#0 & Input RGB image & & 3$\times$H$\times$W \\
\midrule
\midrule
\multicolumn{4}{c}{\textbf{ResidualBlock}} \\ \hline
& Conv2d + BatchNorm + ReLU & 3 & \\
& Conv2d + BatchNorm & 3 & \\
\midrule
\multicolumn{4}{c}{\textbf{Depth Encoder}} \\ \hline
\#1 & Conv2d (S2) + BatchNorm + ReLU & 7 & 64$\times$H/2$\times$W/2 \\
\#2 & Conv2d + BatchNorm + ReLU & 3 & 64$\times$H/2$\times$W/2 \\
\#3 & ResidualBlock (\#2) x2 & - & 64$\times$H/2$\times$W/2 \\
\#4 & Max. Pooling ($\times$1/2) & 3 & 64$\times$H/4$\times$W/4 \\
\#5 & ResidualBlock (\#3 + \$2) x2 & - & 128$\times$H/4$\times$W/4 \\
\#6 & Max. Pooling ($\times$1/2) & 3 & 128$\times$H/8$\times$W/8 \\
\#7 & ResidualBlock (\#4 + \#3) x2 & - & 256$\times$H/8$\times$W/8 \\
\#8 & Max. Pooling ($\times$1/2) & 3 & 256$\times$H/16$\times$W/16 \\
\#9 & ResidualBlock (\#5 + \#4) x2 & - & 512$\times$H/16$\times$W/16 \\
\midrule
\multicolumn{4}{c}{\textbf{Depth Decoder}} \\
\midrule
\#10 & Conv2D + ELU (\#9) & 3 & 128$\times$H/16$\times$W/16 \\
\#11 & Conv2D + Upsample (\#10) & 3 & 128$\times$H/8$\times$W/8 \\
\textbf{\#12} & Conv2D + Sigmoid & 3 & 1$\times$H/8$\times$W/8 \\
\#13 & Conv2D + ELU & 3 & 64$\times$H/8$\times$W/8 \\
\#14 & Conv2D + Upsample(\#7 $\oplus$ \#13) & 3 & 64$\times$H/4$\times$W/4 \\
\textbf{\#15} & Conv2D + Sigmoid & 3 & 1$\times$H/8$\times$W/8 \\
\#16 & Conv2D + ELU & 3 & 32$\times$H/4$\times$W/4 \\
\#17 & Conv2D + Upsample (\#5 $\oplus$ \#16) & 3 & 32$\times$H/2$\times$W/2 \\
\textbf{\#18} & Conv2D + Sigmoid & 3 & 1$\times$H/8$\times$W/8 \\
\#19 & Conv2D + ELU & 3 & 16$\times$H/2$\times$W/2 \\
\#20 & Conv2D + Upsample (\#3 $\oplus$ \#19) & 3 & 16$\times$H$\times$W \\
\textbf{\#21} & Conv2D + Sigmoid & 3 & 1$\times$H$\times$W \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{\textbf{ResNet architecture.} Line numbers in bold indicate output inverse depth layer scales. \emph{Upsample} is a nearest-neighbor interpolation operation that doubles the spatial dimensions of the input tensor. $\oplus$ denotes feature concatenation for skip connections.}
\end{table}
\end{document}

cs

Rares Ambrus
Vitor Guizilini
Naveen Kuppuswamy
Andrew Beaulieu
Adrien Gaidon
Alex Alspach

"Monocular Depth Estimation for Soft Visuotactile Sensors" by Rares Ambrus, Vitor Guizilini, Naveen Kuppuswamy, Andrew Beaulieu, Adrien Gaidon, and Alex Alspach, arXiv:2101.01677v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ll|ll|ll}
 & lag = 10 & p value & lag = 15 & p value & lag = 20 & p value \\
 \hline
Constvol & 3505.63 & \cellcolor[HTML]{FD6864}0.00 & 4913.57 & \cellcolor[HTML]{FD6864}0.00 & 6086.93 & \cellcolor[HTML]{FD6864}0.00 \\
P & 103.92 & \cellcolor[HTML]{FD6864}0.00 & 113.66 & \cellcolor[HTML]{FD6864}0.00 & 115.56 & \cellcolor[HTML]{FD6864}0.00 \\
GK & 31.32 & \cellcolor[HTML]{FD6864}0.00 & 35.96 & \cellcolor[HTML]{FD6864}0.00 & 37.12 & \cellcolor[HTML]{FD6864}0.01 \\
RS & 2.05 & \cellcolor[HTML]{67FD9A}1.00 & 2.42 & \cellcolor[HTML]{67FD9A}1.00 & 3.53 & \cellcolor[HTML]{67FD9A}1.00 \\
GKYZ & 32.26 & \cellcolor[HTML]{FD6864}0.00 & 38.20 & \cellcolor[HTML]{FD6864}0.00 & 39.21 & \cellcolor[HTML]{FD6864}0.01 \\
YZ & 21.71 & \cellcolor[HTML]{FD6864}0.02 & 22.38 & \cellcolor[HTML]{67FD9A}0.10 & 23.27 & \cellcolor[HTML]{67FD9A}0.28 \\
MA21days & 44.66 & \cellcolor[HTML]{FD6864}0.00 & 60.92 & \cellcolor[HTML]{FD6864}0.00 & 97.31 & \cellcolor[HTML]{FD6864}0.00 \\
MA63days & 415.37 & \cellcolor[HTML]{FD6864}0.00 & 438.82 & \cellcolor[HTML]{FD6864}0.00 & 446.16 & \cellcolor[HTML]{FD6864}0.00 \\
Garch & 21.50 & \cellcolor[HTML]{FD6864}0.02 & 24.04 & \cellcolor[HTML]{67FD9A}0.06 & 24.64 & \cellcolor[HTML]{67FD9A}0.22 \\
GJRGarch & 22.43 & \cellcolor[HTML]{FD6864}0.01 & 25.78 & \cellcolor[HTML]{FD6864}0.04 & 26.72 & \cellcolor[HTML]{67FD9A}0.14 \\
eGarch & 25.97 & \cellcolor[HTML]{FD6864}0.00 & 30.74 & \cellcolor[HTML]{FD6864}0.01 & 33.39 & \cellcolor[HTML]{FD6864}0.03 \\
EWMA & 26.54 & \cellcolor[HTML]{FD6864}0.00 & 30.04 & \cellcolor[HTML]{FD6864}0.01 & 31.29 & \cellcolor[HTML]{67FD9A}0.05
\end{tabular}
\end{adjustbox}
\caption{Ljung - Box test with three different lags in the SPX asset for all the estimators. }
\end{table}
\end{document}

q-fin

"Estimation of Historical volatility and Allocation strategies using Variance Swaps" by Lucio Fiorin, arXiv:2208.03164v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Abnormal component analysis

Romain Valla
Pavlo Mozharovskyi
Florence d'Alché-Buc

"Abnormal component analysis" by Romain Valla, Pavlo Mozharovskyi, and Florence d'Alché-Buc, arXiv:2312.16139v2, 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{Performance metrics for the first three eigenvalues ($32 \times 32$+dp+ma).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|} \hline
				Eigenvalue index & RMSE & $R^2$ & MAPE \\ \hline
				1 & 2.2121 & 0.9988 & 1.15\% \\
				2 & 6.7920 & 0.9963 & 1.22\%\\
				3 & 9.2938 & 0.9850 & 1.78\% \\ \hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Operator Inference for Elliptic Eigenvalue Problems" by Haoqian Li, Jiguang Sun, and Zhiwen Zhang, arXiv:2504.15733v1, 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{Optimality gap summary. Sample size $s$ takes values of 1$\%$, 5$\%$, and 10$\%$ of $n$. Each value represents mean and sample standard deviation of 5 runs.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
& & \multicolumn{3}{c}{Optimality Gap} \\
\cmidrule(r){3-5}
Dataset & $s$ & det & prob & unif \\
\midrule
Ethylene CO & 1$\%$ & 4.66 & 1.01 $\pm$ 0.11 & 5.47 $\pm$ 0.65 \\
& 5$\%$ & 0.02 & 0.43 $\pm$ 0.03 & 4.40 $\pm$ 1.03 \\
& 10$\%$ & 5.67E-06 & 0.25 $\pm$ 0.04 & 3.24 $\pm$ 0.48 \\
Ethylene CH4 & 1$\%$ & 1.59 & 1.48 $\pm$ 0.09 & 3.53 $\pm$ 0.42 \\
& 5$\%$ & 0.05 & 0.66 $\pm$ 0.05 & 1.69 $\pm$ 0.15 \\
& 10$\%$ & 0.01 & 0.37 $\pm$ 0.08 & 1.35 $\pm$ 0.11 \\
Skin & 1$\%$ & 0.75 & 0.07 $\pm$ 0.02 & 0.25 $\pm$ 0.13 \\
& 5$\%$ & 0.56 & 0.02 $\pm$ 0.01 & 0.05 $\pm$ 0.03 \\
& 10$\%$ & -3.55E-15 & 0.03 $\pm$ 0.01 & 0.04 $\pm$ 0.02
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Elizabeth Harris
Ali Eshragh
Bishnu Lamichhane
Jordan Shaw-Carmody
Elizabeth Stojanovski

"Efficient Data-Driven Leverage Score Sampling Algorithm for the Minimum Volume Covering Ellipsoid Problem in Big Data" by Elizabeth Harris, Ali Eshragh, Bishnu Lamichhane, Jordan Shaw-Carmody, and Elizabeth Stojanovski, arXiv:2411.03617v1, 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}{lcc}
\toprule
Agreement & Accuracy & \% Remaining \\
\cmidrule(lr){1-1}\cmidrule(lr){2-2}\cmidrule(lr){3-3}
None & 81.1 & 100 \\
8/10 & 89.7 & 73 \\
10/10 & 97.2 & 47 \\
\bottomrule
\end{tabular}
\caption{Sanity-check experiment on StrategyQA dev set. This compares the performance and remaining instances if we only evaluate instances where a certain number of predictions out of 10 samples agree. Experiments shown use Llama-70B-instruct and CoT.}
\end{table}
\end{document}

cs

Ben Zhou
Hongming Zhang
Sihao Chen
Dian Yu
Hongwei Wang
Baolin Peng
Dan Roth
Dong Yu

"Conceptual and Unbiased Reasoning in Language Models" by Ben Zhou, Hongming Zhang, Sihao Chen, Dian Yu, Hongwei Wang, Baolin Peng, Dan Roth, and Dong Yu, arXiv:2404.00205v1, 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{Hyperparameters used for the training}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|ccc}
\hline
\multicolumn{2}{c|}{Settings} & CIFAR-10 & ImageNet & Celeba-HQ \\ \hline
\multirow{3}{*}{Hyperparameters} & Batch size & 128 & 64 & 64 \\
 & Learning rate & 0.0003 & 0.0003 & 0.0003 \\
 & \# Iterations & 150 K & 300 K & 300 K \\ \hline
\multirow{2}{*}{\# Samples} & Training $r_\theta(\cdot)$ & 19.2 M & 19.2 M & 19.2 M \\
 & \textit{Training $\epsilon_\theta(\cdot)$} & $\approx$ 200 M & $\approx$ 2500 M & $\approx$ 1000 M \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Abulikemu Abuduweili
Chenyang Yuan
Changliu Liu
Frank Permenter

"Enhancing Sample Generation of Diffusion Models using Noise Level Correction" by Abulikemu Abuduweili, Chenyang Yuan, Changliu Liu, and Frank Permenter, arXiv:2412.05488v3, 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{14 types of lectures in the VLEngagement dataset and their abbriviation (Abbr.) and frequency (Freq).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrllr} \hline
 Abbr.&Description&Freq.&Abbr.&Description&Freq.\\ \hline
 \texttt{vbp}&Best Paper&16&\texttt{vdb}&Debate&30 \\
 \texttt{vdm}&Demonstration&124&\texttt{viv}&Interview&52 \\
 \texttt{vid}&Introduction&15&\texttt{vit}&Invited Talk&300 \\
 \texttt{vkn}&Keynote&115&\texttt{vl}&Lecture&2956 \\
 \texttt{vop}&Opening&31&\texttt{oth}&Other&15 \\
 \texttt{vpa}&Panel&44&\texttt{vps}&Poster&56 \\
 \texttt{vpr}&Promotional Video&23&\texttt{vtt}&Tutorial&269 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Sahan Bulathwela
Maria Perez-Ortiz
Emine Yilmaz
John Shawe-Taylor

"VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement" by Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, and John Shawe-Taylor, arXiv:2011.02273v1, 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{Hyperparameters of the baseline machine learning models employed to predict soybean yield.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \hline
 \textbf{Model} & \textbf{Parameters} & \textbf{Best Parameter} \\
 \hline
 Random Forest & Number of estimators & 550 \\
 & Max. feature numbers & Sqrt \\
 & Max. depth & 55 \\
 & Min. samples split & 5 \\
 & Min. samples leaf & 1 \\
 & Bootstrap & FALSE \\
 \hline
 XGBoost & Max. depth & 13 \\
 & Objective & [reg:squared error] \\
 & regularization alpha & 0.0001 \\
 & Min. child weight & 5 \\
 & Gamma & 0.05 \\
 & Learning rate & 0.09 \\
 & Booster & Gbtree \\
 & Subsample & 0.7 \\
 & Column sample by tree & 0.9 \\
 \hline
 Lasso regression & alpha & 0.0001\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Zahra Khalilzadeh
Motahareh Kashanian
Saeed Khaki
Lizhi Wang

"A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection" by Zahra Khalilzadeh, Motahareh Kashanian, Saeed Khaki, and Lizhi Wang, arXiv:2309.13021v1, 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{NICE reduces estimation bias in the presence of bad controls. The table reports the average MAE and bootstrap standard deviation of SATT. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|cccc}
\multicolumn{3}{l}{Bad Controls in Adjustment Set} & \multicolumn{3}{c}{$\epsilon_{att}$} \\
\hline
& & {\textsc \textbf{Mod1}} & {\textsc\textbf{Mod2} } & {\textsc \textbf{Mod3}} & {\textsc \textbf{Mod4}}\\
\hline
\textbf{low} &Adjust All &$.26\pm .09$ & $.42\pm .03$ & $.34 \pm .08$ & $.46 \pm .09$\\
& NICE &$.09 \pm .07$ & $.03\pm .01$ & $.11 \pm .04$ & $.08 \pm .04$\\
\hline
\textbf{med}& Adjust All &$.38 \pm .10$ & $.35\pm .06$ & $.40 \pm .17$ & $.3\pm .09$\\
& NICE &$.06\pm .03$ & $.06 \pm .03$ & $.06 \pm .02$ & $.03 \pm .03$\\
\hline
\textbf{high} & Adjust All&$.32 \pm .14$ & $.38\pm .09$ & $.42 \pm .05$ & $.28\pm .05$\\
& NICE &$.05\pm .03$ & $.11 \pm .03$ & $.16\pm .05$ & $.11 \pm .05$\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Invariant Representation Learning for Treatment Effect Estimation" by Claudia Shi, Victor Veitch, and David Blei, arXiv:2011.12379v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
				\hline
				\textbf{Statistic} & \textbf{Option price} & \textbf{Stock price} \\
				\hline
				Max & $0.106250$ & $498.5040$ \\
				Min & $9.50000$ & $496.4980$ \\
				Mean & $9.971304$ & $497.3208$ \\
				\hline
			\end{tabular}
\caption{Option and the stock index prices.}
\end{table}
\end{document}

q-fin

"The inverse Black-Scholes problem in Radon measures space revisited: towards a new measure of market uncertainty" by Nizar Riane, arXiv:2303.16773v1, 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{Overview of the datasets used for training, validation, and testing, including totals per split.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 \textbf{Name (Variant)} & \textbf{Split} & \textbf{Hours} & \textbf{\# Speakers} \\
 \midrule
 SDS-200 (Clean) & Train & 50 & 1,799 \\
 STT4SG-350 (All) & Train & 276 & 219 \\
 SPC (0.9 IOU) & Train & 176 & 194 \\
 SRG (PL) & Train & 406 & -- \\
 \midrule
 Total & & 908 & $>$ 2,212 \\
 \midrule
 SDS-200 (Clean) & Val & 5.2 & 288 \\
 STT4SG-350 (All) & Val & 21 & 219 \\
 SRG (PL) & Val & 20 & -- \\
 \midrule
 Total & & 46.2 & $>$ 507 \\
 \midrule
 SDS-200 (Clean) & Test & 5.2 & 281 \\
 STT4SG-350 (All) & Test & 34 & 56 \\
 SPC (0.9 IOU) & Test & 6 & 26 \\
 SRG (SWISS TXT) & Test & 20 & -- \\
 Dataset-A & Test & 0.22 & 2 \\
 \midrule
 Total & & 65.42 & $>$ 365 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Vincenzo Timmel
Claudio Paonessa
Reza Kakooee
Manfred Vogel
Daniel Perruchoud

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ll|cc|c}
			\multirow{2}{*}{Bottom pressure} & \multirow{2}{*}{$n_x$} & \multicolumn{2}{c|}{Numerical simulation} & {Linear stability} \\
			& & Top row & Eigenprofile & analysis \\
			\hline
			2464 Pa (base) & 8 & 73 050 s & 33 250 s & 22 693 s \\ \hline
			2958 Pa & 8 & 65 400 s & 26 150 s & 19 317 s \\
			3451 Pa & 18 & 38 500 s & 23 650 s & 20 899 s\\
			3944 Pa & 16 & 27 400 s & 6 750 s & 10 100 s \\ \hline
		\end{tabular}
\caption{Onset times for numerical simulations and linear stability analysis considering a specific number of waves. The numerical simulation considers two different types of perturbation; either perturbing only at the top grid cells, or using the eigenprofile from Figure .}
\end{table}
\end{document}

math

C. Bringedal
S. Kiemle
C. J. van Duijn
R. Helmig

"Impact of saturation on evaporation-driven density instabilities in porous media: mathematical and numerical analysis" by C. Bringedal, S. Kiemle, C. J. van Duijn, and R. Helmig, arXiv:2501.12784v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The mAP@20 results on image-to-text ($I \to T$) and text-to-image ($T \to I$) retrieval tasks performed for $B=64$ with different data augmentation setups.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 \hline
 Modality & Setup & $I \to T$ & $T \to I$ \\
 \hline\hline
 \multirow{4}{*}{Image} & NoAugmentation & 0.801 & 0.804 \\
 & AugmentedCenterCrop & \textbf{0.836} & \textbf{0.824} \\
 & AugmentedRandomCrop & {0.832} & {0.822} \\
 & RandomAugmentation & 0.827 & 0.815 \\
 \hline\hline
 \multirow{3}{*}{Text} & RuleBased & {0.836} & {0.824} \\
 & Back-translation & {0.836} & \textbf{0.829} \\
 & Back-translationChain & \textbf{0.839} & {0.824} \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Georgii Mikriukov
Mahdyar Ravanbakhsh
Begüm Demir

"Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing" by Georgii Mikriukov, Mahdyar Ravanbakhsh, and Begüm Demir, arXiv:2201.08125v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
 \hline
 & ODE & Sinkhorn \\ \hline
 Computed regularized primal value & 0.3807 & 0.3807\\ \hline
 Optimal unregularized primal value & 0.3767 & 0.3767\\ \hline
 Optimal unregularized primal value + Entropy & 0.4127 & 0.4127 \\ \hline
 Iterations & 25 & 60 \\ \hline
 CPU time (sec) & 8.71 & 7.40 \\ \hline
 \end{tabular}
\caption{Comparison of the performance between 4-th order Runge-Kutta ODE method and Sinkhorn algorithm for multi-period martingale transport problem.}
\end{table}
\end{document}

cs

Joshua Zoen-Git Hiew
Luca Nenna
Brendan Pass

"An ordinary differential equation for entropic optimal transport and its linearly constrained variants" by Joshua Zoen-Git Hiew, Luca Nenna, and Brendan Pass, arXiv:2403.20238v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.