\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Election and Contribution Descriptive Statistics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
		\toprule[1.5pt]
		& All & Union Loss & Union Win \\
		\midrule
		& \multicolumn{3}{c}{[A] Election characteristics} \\
		Number of elections & 6,063 & 3,397 & 2,666 \\
		Union vote share (average) & .4950 & .3204 & .7175 \\
		Number of votes (average) & 119.37 & 135.31 & 99.06 \\
		Number of votes (total) & 723,752 & 459,661 & 264,091 \\
		& \multicolumn{3}{c}{[B] Contribution characteristics} \\
		Amount (total, in million 2010 USD) & 105.82 & 65.38 & 40.43 \\
		Number of contributions (total) & 357,436 & 204,797 & 152,639 \\
		Number of donors (total) & 46,719 & 26,661 & 20,243 \\
		Number of recipients (total) & 9,942 & 7,208 & 5,681 \\
		\hline\hline
		\multicolumn{4}{l}{
		\begin{minipage}{0.8\linewidth} \smallskip \scriptsize
		\textbf{Notes:} Data from NLRB union certification elections, which have at least one employee contribution matched in any of seven election cycles around the union election (three before, cycle of union election, three after). Contribution characteristics refer to the total numbers over all these seven election cycles.
		\end{minipage}} \\
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Do Unions Shape Political Ideologies at Work?" by Johannes Matzat and Aiko Schmeißer, arXiv:2209.02637v4, 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.


eess

Madhurananda Pahar
Igor Miranda
Andreas Diacon
Thomas Niesler

"Deep Neural Network based Cough Detection using Bed-mounted Accelerometer Measurements" by Madhurananda Pahar, Igor Miranda, Andreas Diacon, and Thomas Niesler, arXiv:2102.04997v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|c|}
\hline
Period & 1 & 2 & 3 & 4 & 5 & 6 \\
\hline
 S\&P~500 30 day return & -4.9\% & -5.1\% & -5.2\% & -5.2\% & -5.3\% & -4.8\% \\
\hline
Strategy decision & & Buy & Hold & Rollover & Hold & Sell \\
Days to expiration & & 21 & 20 & 29 ($F_t=6379$) & 28 & \\
Option price (sold) & & & & 80 & & 110 \\
Option price (bought/held) & & 90 & 95 & 75 & 77 & \\
\hline
Exercise price & & & & 6190 & & \\
Number of options held & & $\frac{1000u}{90}$ & $\frac{1000u}{90}$&
$ \frac{80 \times1000u}{90\times 75}$
&
$ \frac{80 \times1000u}{90\times 75}$
& \\
Cash & 1000 & & & & & $ \frac{110\times 80 \times1000u}{90\times 75}$ \\
Valuation & 1000 & $1000u$ & $\frac{95(1000u)}{90}$ &
$ \frac{80 \times1000u}{90}$ &
$ \frac{77 \times 80 \times1000u}{90\times 75}$
 & $ \frac{110\times 80 \times1000u}{90\times 75}$ \\
\hline
Valuation (1dp, $u=1$) & 1000 & 1000 & 1055.6 & 888.9 & 912.6 & 1303.7 \\
Return (\%, 1dp) & - & 0 & 5.6 & -15.8 & 2.7 & 42.9 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Option strategy evaluation - example}
\end{table}
\end{document}

q-fin

"Enhanced indexation using both equity assets and index options" by Cristiano Arbex Valle and John E Beasley, arXiv:2508.21192v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison between our ASFDA, DKD+ASD, and other SOTA methods when adapting Med-VFMs to the Abdominal Atlas domain. The performance was evaluated using Dice scores, and the results were reported as Mean$\pm$SD. \textbf{Bold} and \underline{underline} represent the best and the second best results.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
\toprule
\multirow{2}{*}{Methods} & \multicolumn{2}{c}{Query Budgets} \\
\cline{2-3}
 & $5\%$ & $10\%$ \\
\midrule
RAND & 73.74$\pm$11.98 & 76.72$\pm$11.48 \\
ENPY & 77.39$\pm$10.34 & 80.46$\pm$9.54 \\
LCON & 74.07$\pm$10.66 & 77.88$\pm$10.90 \\
MMAR & 75.74$\pm$11.17 & 78.33$\pm$9.81 \\
Core-set & 79.11$\pm$10.20 & 81.44$\pm$9.61 \\
BADGE & 79.23$\pm$10.01 & 81.48$\pm$9.52 \\
SANN & 79.56$\pm$9.42 & 81.57$\pm$9.16 \\
UGTST & 79.43$\pm$10.15 & 81.56$\pm$9.75 \\
CUP & 77.36$\pm$10.46 & 79.35$\pm$9.92 \\
\midrule
DKD+ASD & \underline{81.92}$\pm$9.31 & \underline{83.80}$\pm$8.70 \\
ASFDA & \textbf{83.12}$\pm$8.16 & \textbf{84.86}$\pm$7.52 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jin Yang
Daniel S. Marcus
Aristeidis Sotiras

"Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning" by Jin Yang, Daniel S. Marcus, and Aristeidis Sotiras, arXiv:2509.10784v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of Base matrices with $m=3, n=4$ for different circulant sizes}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Circulant size ($L$)} & \textbf{Exhaustive} & \textbf{RCPC} & $d_{min}^{\mathcal{C}} > 7$ & $d_{min}^{\mathcal{Q}} > 7$ \\ \hline
4 & $4^{6} = 4096$ & 700 & 0 & 0 \\ \hline
5 & 15625 & 1993 & 0& 0 \\ \hline
6 & 46656 & 3876 & 1008 & 720 \\ \hline
7 & 117649 & 7725 & 55440 & 53856 \\ \hline
8 & 262144 & 12628 & 128592 & 124704 \\ \hline
9 & 531441 & 20961 & 189360 & 183744\\ \hline
10 & 1000000 & 31084 & 656784 & 644544 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Nithin Raveendran
David Declercq
Bane Vasić

"On the Minimum Distances of Finite-Length Lifted Product Quantum LDPC Codes" by Nithin Raveendran, David Declercq, and Bane Vasić, arXiv:2503.07567v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc}

 & \multicolumn{2}{c}{Our Annotation}\\ \cline{2-3}
CLEF-2020 & Non-claim & Claim \\ \hline
\hline
Non-claim & 301 & 47 \\
Claim & 64 & 550 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Confusion matrix highlighting the differences and similarities between and our annotation guidelines for CLEF-2020 claim dataset.}
\end{table}
\end{document}

cs

Shreya Gupta
Parantak Singh
Megha Sundriyal
Md Shad Akhtar
Tanmoy Chakraborty

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|}
 \hline
 $h$ & 1/8 & 1/16 & 1/32 & 1/64 & 1/128 & 1/256 \\
 \hline
 $\varepsilon_h$ & 2.20E-04 & 6.99E-05 & 1.91E-05 & 5.04E-06 & 1.30E-06 & 3.30E-07 \\
 \hline
 $r$ & --- & 1.65 & 1.88 & 1.92 & 1.96 & 1.98 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{the magnitude of the error and order of convergence.}
\end{table}
\end{document}

math

Vladislav Byankin
Aleksandr Tynda
Denis Sidorov
Aliona Dreglea

"Numerical solution of locally loaded Volterra integral equations" by Vladislav Byankin, Aleksandr Tynda, Denis Sidorov, and Aliona Dreglea, arXiv:2503.21452v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\multirow{2}{*}{Ablation Option} & \multicolumn{2}{c}{FMD} & \multicolumn{2}{c}{SRDTrans} \\
\cmidrule(lr){2-3} \cmidrule(lr){4-5}
 & PSNR & SSIM & PSNR & SSIM \\
\midrule
(a) w/o Region & 32.41 & 0.795 & 28.62 & 0.895 \\
(b) w/o Overall & 33.89 & 0.876 & 28.65 & 0.910 \\
(c) w/o Channel+ & 34.02 & 0.882 & 28.68 & 0.900 \\
(d) w/ Stage 1 Only & 23.75 & 0.681 & 25.59 & 0.780 \\
(e) w/ Stage 2 Only & 34.08 & 0.884 & 28.82 & 0.916 \\ \midrule \rowcolor{gray!20}
(f) Full Conduction & \textbf{34.15} & \textbf{0.886} & \textbf{28.93} & \textbf{0.919} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Ablation study of Noise Injection components and training stages in FM2S. The `w/o' stands for `without,' and `/w' stands for `with.'}
\end{table}
\end{document}

eess

Jizhihui Liu
Qixun Teng
Qing Ma
Junjun Jiang

"FM2S: Towards Spatially-Correlated Noise Modeling in Zero-Shot Fluorescence Microscopy Image Denoising" by Jizhihui Liu, Qixun Teng, Qing Ma, and Junjun Jiang, arXiv:2412.10031v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Estimating Covariance for Global Minimum Variance Portfolio: A Decision-Focused Learning Approach" by Juchan Kim, Inwoo Tae, and Yongjae Lee, arXiv:2508.10776v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \toprule
 \multirow{2}{*}{Coin color} & Reward & Reward for \\
 & for self & co-player \\
 \midrule
 matching & $+3$ & $+2$ \\
 mismatching & $+3$ & $+0$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Alternative incentive structure for Coins.}
\end{table}
\end{document}

cs

Kevin R. McKee
Xuechunzi Bai
Susan T. Fiske

"Warmth and competence in human-agent cooperation" by Kevin R. McKee, Xuechunzi Bai, and Susan T. Fiske, arXiv:2201.13448v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing" by Po-Yi Liu, Chi-Hua Wang, and Henghsiu Tsai, arXiv:2208.09372v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Shi Bo
AmirEmad Ghassami
Debarghya Mukherjee

"A Debiased Estimator for the Mediation Functional in Ultra-High-Dimensional Setting in the Presence of Interaction Effects" by Shi Bo, AmirEmad Ghassami, and Debarghya Mukherjee, arXiv:2412.08827v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Goodness of fit comparison: our model (left), the model without covariates (middle), and the percentage decrease in error (right).}
\begin{tabular}{lccc}
 \toprule
 & \textbf{With Covariates} & \textbf{Without Covariates} & \textbf{\% Decrease} \\
 \midrule
 \textbf{ERROR} & $55,441.40$ & $59,643.38$ & $7.05\%$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

"Covariates-Adjusted Mixed-Membership Estimation: A Novel Network Model with Optimal Guarantees" by Jianqing Fan, Jiawei Ge, and Jikai Hou, arXiv:2502.06671v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\large Asymmetric treatment on subset of US coal counties}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \tabularnewline\midrule\midrule
 Dependent Variable: & \multicolumn{3}{c}{Change Unemployment Rate}\\
 Model: & (1) & (2) & (3)\\
 \midrule \emph{Variables} & & & \\
 $\Delta$ Active Mines\textsubscript{t} & -0.0193 & -0.0752$^{**}$ & \\
 & (0.0201) & (0.0235) & \\
 $\Delta$Active Mines\textsubscript{t-1} & 0.0221 & -0.0301 & \\
 & (0.0285) & (0.0219) & \\
 $\Delta$Active Mines\textsubscript{t-2} & 0.0090 & 0.0546$^{***}$ & \\
 & (0.0236) & (0.0116) & \\
 $\Delta$ (log) Real GDPPC\textsubscript{t} & -0.9724$^{***}$ & -0.9724$^{***}$ & -0.9724$^{***}$\\
 & (0.2138) & (0.2138) & (0.2138)\\
 $\Delta$ Active Mines\textsubscript{t} $\times$ $\Delta_{negative_t}$ & -0.0559 & & -0.0752$^{**}$\\
 & (0.0370) & & (0.0235)\\
 $\Delta$ Active Mines\textsubscript{t-1} $\times$ $\Delta_{negative_{t-1}}$ & -0.0521 & & -0.0301\\
 & (0.0422) & & (0.0219)\\
 $\Delta$ Active Mines\textsubscript{t-2} $\times$ $\Delta_{negative_{t-2}}$ & 0.0457 & & 0.0546$^{***}$\\
 & (0.0277) & & (0.0116)\\
 $\Delta$ Active Mines\textsubscript{t} $\times$ $\Delta_{positive_{t}} $ & & 0.0559 & -0.0193\\
 & & (0.0370) & (0.0201)\\
 $\Delta$ Active Mines\textsubscript{t-1} $\times$ $\Delta_{positive_{t-1}}$ & & 0.0521 & 0.0221\\
 & & (0.0422) & (0.0285)\\
 $\Delta$ Active Mines\textsubscript{t-2} $\times$ $\Delta_{positive_{t-2}}$ & & -0.0457 & 0.0090\\
 & & (0.0277) & (0.0236)\\
 \midrule \emph{Fixed-effects} & & & \\
 County FIPS Code & Yes & Yes & Yes\\
 Year & Yes & Yes & Yes\\
 \midrule \emph{Fit statistics} & & & \\
 Observations & 55,295 & 55,295 & 55,295\\
 R$^2$ & 0.61355 & 0.61355 & 0.61355\\
 Within R$^2$ & 0.01513 & 0.01513 & 0.01513\\
 \midrule\midrule\multicolumn{4}{l}{\emph{Clustered (County FIPS Code \& Year) standard-errors in parentheses}}\\
 \multicolumn{4}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Same setup as Table~, but using \textbf{dilation} annealing.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
& \multicolumn{5}{c}{sample size $N$}\\
& 100 & 300 & 1000 & 3000 & 10000 \\
\midrule
SBTM (ours) & 0.34 & 0.25 & 0.19 & 0.15 & \textbf{0.13} \\
SDE & \textbf{0.25} & \textbf{0.19} & \textbf{0.14} & \textbf{0.13} & 0.13 \\
SVGD & 0.96 & 1.4 & 1.1 & 1.3 & 1.2 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Score-Based Deterministic Density Sampling" by Vasily Ilin, Peter Sushko, and Jingwei Hu, arXiv:2504.18130v2, 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{Data of test system}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
 \hline
 Ambient temperature ($T_a$) & K & 308\\
 Battery voltage rating ($V_{b}$) & V & 259 \\
 Buying/selling rate at grid ($P_{grid}$) & \$/MW & 30/26 \\
 Buying/selling rate for ESS ($P_{ess}$) & \$/MW & 26/32 \\
 Charging efficiency ($\eta_c$) & \% & 0.95\\
 Coefficient of conduction $(k_b)$ & W/mk & 205\\
 Coefficient of convection loss ($h_{0}$) & $W/m^2K$ & 5\\
 Initial SOC ($E_t$) & kWh& 40 \\
 Discharging efficiency ($\eta_d$) & \% & 0.95\\
 ESS rated voltage & V & 259\\
 Fan speed coefficient of h $(\lambda)$ & min/rotations & 0.01814 \\
 Initial fan speed $(u_f)$ & rpm & 2000\\
 Initial current through each battery ($u_i$) & A & 50\\
 Maximum SOC ($\overline{E}$) & kWh & 66.304\\
 Maximum charge power of ESS $(\overline{P_{bc}})$ & kW & 60\\
 Maximum discharge power of ESS $(\overline{P_{bd}})$ & kW & 60\\
 Maximum bus voltage ($\overline{V}$) & p.u. & 1.1 \\
 Minimum SOC ($\underline{E}$) & kWh & 5\\
 Minimum bus voltage ($\underline{V}$) & p.u. & 0.9 \\
 Resistance of each module ($R_{ref}$) & ohm & 0.1\\
 Temperature coefficient of resistance $(\alpha_T)$ & $K^{-1}$ & 0.004\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"A Two-Layer Framework with Battery Temperature Optimal Control and Network Optimal Power Flow" by Anshuman Singh, Wang Peng, and Hung D. Nguyen, arXiv:2011.11496v1, 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{Simulation parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
		\hline
		$\bf{Parameters}$ & $\bf{Assumptions}$ \\ \hline
		Macrocell Radius & $100$m \\ \hline
		GS Coordinate & [50,50] \\ \hline
		Bandwidth $B$ & 1MHz \\ \hline
		Transmitting Power $P_{ij}^T$ & 1W \\ \hline
		Input Data Size ${D_i}$ & 100kB \\ \hline
		Required Number of CPU Cycles $f_i^{L}$ & $10^9$ cycles/s \\ \hline
		Local Computational Capability $F_{max}^{L}$ & $10^9$ cycles/s \\ \hline
		Latency Request $T^{req}$ & 2s \\ \hline
		UAV Altitude ${H^{UAV}}$ & 20m \\ \hline
		Remote Computational Capability (UAV) & $10^{10}$ cycles/s \\ \hline
		Remote Computational Capability (GV) & $10^{11}$ cycles/s \\ \hline
		Remote Computational Capability (GS) & $10^{12}$ cycles/s \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Feibo Jiang
Kezhi Wang
Li Dong
Cunhua Pan
Wei Xu
Kun Yang

"Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks" by Feibo Jiang, Kezhi Wang, Li Dong, Cunhua Pan, Wei Xu, and Kun Yang, arXiv:1911.09598v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c|l}
Symbol & PRMS symbol & Quantity & Type & Range & Units & Description \\
\hline
Tms & tmax\_allsnow & 1 & constant & -10, 40 & $^{o}$F & temperature below which P is all snow \\
Tmr & tmax\_allrain & 1 & monthly & -8, 60 & $^{o}$F & temperature above which P is all rain \\
$\alpha$1 & smidx\_coef & per HRU & constant & 0.001, 0.06 & - & linear surface runoff (Fsr) coeficient \\
Asr & carea\_max & per HRU & constant & 0.0, 1.0 & - & max areal fraction contributing to surface runoff Fsr \\
$\beta$ & dday\_intcp & 1 & monthly & -60.0, 10.0 & $^{o}$day & intercept of degree-day equation \\
jc & jh\_coef & 1 & monthly & 0.005, 0.06 & /$^{o}$F & coef. used in ETp \\
Sszmax & soilmoist\_max & per HRU & constant & 0.001, 60.0 & inches & maximum soilzone water holding capacity \\
$\alpha$4 & gwflow\_coef & per GWR & constant & 0.001, 0.5 & -/day & linear coefficient routing Sgw to streamflow \\
Fzgwmax & soil2gw\_max & per HRU & constant & 0.0, 5.0 & inches & max soil excess water routed to gwStr \\
$\alpha$5 & gwsink\_coef & per GWR & constant & 0.0, 1.0 & -/day & linear coefficient for groundwater sink \\
$\alpha$2 & ssr2gw\_rate & per SSR & constant & 0.05, 0.8 & -/day & linear coefficient routing Sss to Sgw \\
$\alpha$3 & ssrcoef\_sq & per SSR & constant & 0.0, 1.0 & - & coeficient routing Sss to streamflow \\
$\beta$3 & ssrcoef\_lin & per SSR & constant & 0.0, 1.0 & -/day & linear coeficient routing Sss to streamflow\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Perturbed and updated parameters.}
\end{table}
\end{document}

eess

Sami A. Malek
Alexandre M. Bayen
Steven D. Glaser

"Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter" by Sami A. Malek, Alexandre M. Bayen, and Steven D. Glaser, arXiv:1912.03861v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The True Positive Rate (TPR), True Negative Rate (TNR), and Balanced Accuracy (BA) from \texttt{TULIK-VI} and the baselines on 13-node SADs testing data. The standard deviations of TPR, TNR, and BA over 10 training replicas are in parentheses. The largest metrics are bolded. The training data and testing data consist of 800 and 110 trajectories (patients), respectively. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}\toprule
 & {\texttt{TULIK-VI}} & {\texttt{TULIK-VI} (Stationary)} & {\texttt{GLM-L}} & {\texttt{GLM-S}} & {\texttt{HP-E}}\\
 \cmidrule(lr){2-6}
			\multirow{2}{*} {TPR} &{\bf 0.7068} &0.6929 &0.5408 &0.6678 &0.5044\\
 & (0.0251) & (0.0242) & (0.0249) & (0.0192) & (0.0202)\\
\multirow{2}{*} {TNR} &{\bf 0.7070} &0.6919 &0.6989 &0.6320 &0.5061\\
 & (0.0204) & (0.0177) & (0.0175) & (0.0168) & (0.0112)\\
\multirow{2}{*} {BA} &{\bf 0.7069} &0.6924 &0.6199 &0.6499 &0.5053\\
 & (0.0045) & (0.0054) & (0.0097) & (0.0088) & (0.0074)
 \\
 \bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Point processes with event time uncertainty" by Xiuyuan Cheng, Tingnan Gong, and Yao Xie, arXiv:2411.02694v1, 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{HSD-test for pairwise comparison of features extraction (Imbalanced dataset)}
\begin{tabular}{rllrrrr}
		\hline
		& term & comparison & estimate & conf.low & conf.high & adj.p.value \\
		\hline
		1 & feat\_extr & mfcc-wfe & 5.31 & 4.29 & 6.34 & 0.00 \\
		2 & feat\_extr & coif1-wfe & 7.33 & 6.30 & 8.36 & 0.00 \\
		3 & feat\_extr & db2-wfe & 7.89 & 6.86 & 8.91 & 0.00 \\
		4 & feat\_extr & db4-wfe & 9.49 & 8.46 & 10.51 & 0.00 \\
		5 & feat\_extr & coif1-mfcc & 2.01 & 0.99 & 3.04 & 0.00 \\
		6 & feat\_extr & db2-mfcc & 2.57 & 1.54 & 3.60 & 0.00 \\
		7 & feat\_extr & db4-mfcc & 4.17 & 3.14 & 5.20 & 0.00 \\
		8 & feat\_extr & db2-coif1 & 0.56 & -0.47 & 1.59 & 0.58 \\
		9 & feat\_extr & db4-coif1 & 2.16 & 1.13 & 3.19 & 0.00 \\
		10 & feat\_extr & db4-db2 & 1.60 & 0.57 & 2.63 & 0.00 \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Cyrille Feudjio
Victoire Djimna Noyum
Younous Perieukeu Mofendjou
Rockefeller
Ernest Fokoué

"A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG Data" by Cyrille Feudjio, Victoire Djimna Noyum, Younous Perieukeu Mofendjou, Rockefeller, and Ernest Fokoué, arXiv:2102.01647v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Inverse single facility location problem in the plane with variable coordinates" by Nazanin Tour-Savadkoohi and Jafar Fathali, arXiv:2503.07016v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccc}
 \toprule
 Dataset & CIFARBinary & CIFAR10/CIFAR10-N & CIFAR100 & SVHN & Places365 & IMDB\\
 \midrule
 Class Number & 2&10&100&10&10&2\\
 \hline
 Size of the probe set & 2,000 & 10,000& 20,000& 10,000&10,000&5,000\\
 Start learning rate 1 & 0.1 & 0.1 & 0.1 & 0.1 &0.1 &0.1\\
 Learning rate schedule 1 &schedule 1 & schedule 1 & schedule 1 & schedule 1 &
 schedule 1& no schedule\\
 Optimizer 1 & SGD& SGD& SGD& SGD& SGD& AdamW\\
 Epoch 1&100 & 100 & 100 & 100 & 100& 20 \\

 \hline
 Size of the sampling set &8,000 & 40,000& 30,000 &63,257&40,000&20,000 \\
 Start learning rate 2 & 0.1 & 0.1 & 0.1 & 0.1 &0.1 &0.1\\
 Learning rate schedule 2 &schedule 2 & schedule 2 & schedule 2 & schedule 2 &
 schedule 1& no schedule\\
 Optimizer 2 & AdamW& AdamW& AdamW& AdamW& SGD& AdamW\\
 Epoch 2&150&150&150&150&100&20 \\
 \hline
 Size of the testing set &2,000 &10,000& 10,000 & 26,032& 1,000 & 25,000\\

 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{This table illustrates the training details. Here we set weight decay as 5e-4 for all the experiments. Here no schedule means using the start learning rate without modification during training. Schedule 1 stands for the decaying of the learning rate by 0.1 every 30 epochs. Schedule 2 means using the cosine learning schedule with $T_{max}=50$ and $eta_{min}=0$}
\end{table}
\end{document}

stat

Yong Lin
Chen Liu
Chenlu Ye
Qing Lian
Yuan Yao
Tong Zhang

"Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning" by Yong Lin, Chen Liu, Chenlu Ye, Qing Lian, Yuan Yao, and Tong Zhang, arXiv:2309.02476v1, 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{MDP2 Transition Function}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c||c}\hline
 State & Action & Next State \\\hline
 0 & 0 & 0 \\
 0 & 1 & 1 \\
 1 & 0 & 1\\
 1 & 1 & 1\\\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Karine Karine
Susan A. Murphy
Benjamin M. Marlin

"BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings" by Karine Karine, Susan A. Murphy, and Benjamin M. Marlin, arXiv:2412.00308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Identified physiological parameters of the specific subject (knee case)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
Parameter Indices & Muscle Indices & Estimations & Physiological Ranges \\ \midrule
\multirow{2}{*}{$l^{m}_{o}$(m)} & BFS & 0.1819 & 0.1630 - 0.1830 \\
 & RF & 0.1143 & 0.1040 - 0.1240 \\ \hline
\multirow{2}{*}{$F^{m}_{o}$(N)} & BFS & 805.7 & 402 - 1206 \\
 & RF & 1199.6 & 584.5 - 1753.5 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Shuhao Ma
Jie Zhang
Chaoyang Shi
Pei Di
Ian D. Robertson
Zhi-Qiang Zhang

"Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals" by Shuhao Ma, Jie Zhang, Chaoyang Shi, Pei Di, Ian D. Robertson, and Zhi-Qiang Zhang, arXiv:2412.04213v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
\textbf{Binary dihedral groups} & $\mathsf{BD}_{2t}=\langle 2,2,t\rangle$, & order $4t$, & $t\geq2$,\\
\textbf{Binary tetrahedral group} & $\mathsf{BT}=\langle 2,3,3\rangle$, & order $24$, &\\
\textbf{Binary octahedral group} & $\mathsf{BO}=\langle 2,3,4\rangle$, & order $48$, &\\
\textbf{Binary icosahedral group} & $\mathsf{BI}=\langle 2,3,5\rangle$, & order $120$. &\\
\end{tabular}
\end{adjustbox}
\caption{Binary polyhedral groups.}
\end{table}
\end{document}

math

José Antonio Arciniega Nevárez
José Luis Cisneros-Molina
Agustín Romano Velázquez

"Classification of indecomposable reflexive modules on quotient singularities through Atiyah--Patodi--Singer theory" by José Antonio Arciniega Nevárez, José Luis Cisneros-Molina, and Agustín Romano Velázquez, arXiv:2504.21204v1, 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{Fonts sizes to be used for various parts of the manuscript. Table captions should be centered above the table. When the caption is too long to fit on one line, it should be justified to the right and left margins of the body of the text.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|} %% this creates two columns
\hline
\rule[-1ex]{0pt}{3.5ex} Article title & 16 pt., bold, centered \\
\hline
\rule[-1ex]{0pt}{3.5ex} Author names and affiliations & 12 pt., normal, centered \\
\hline
\rule[-1ex]{0pt}{3.5ex} Keywords & 10 pt., normal, left justified \\
\hline
\rule[-1ex]{0pt}{3.5ex} Abstract Title & 11 pt., bold, centered \\
\hline
\rule[-1ex]{0pt}{3.5ex} Abstract body text & 10 pt., normal, justified \\
\hline
\rule[-1ex]{0pt}{3.5ex} Section heading & 11 pt., bold, centered (all caps) \\
\hline
\rule[-1ex]{0pt}{3.5ex} Subsection heading & 11 pt., bold, left justified \\
\hline
\rule[-1ex]{0pt}{3.5ex} Sub-subsection heading & 10 pt., bold, left justified \\
\hline
\rule[-1ex]{0pt}{3.5ex} Normal text & 10 pt., normal, justified \\
\hline
\rule[-1ex]{0pt}{3.5ex} Figure and table captions & \, 9 pt., normal \\
\hline
\rule[-1ex]{0pt}{3.5ex} Footnote & \, 9 pt., normal \\
\hline
\rule[-1ex]{0pt}{3.5ex} Reference Heading & 11 pt., bold, centered \\
\hline
\rule[-1ex]{0pt}{3.5ex} Reference Listing & 10 pt., normal, justified \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Han Liu
Kathryn L. Holloway
Dario J. Englot
Benoit M. Dawant

"A Multi-rater Comparative Study of Automatic Target Localization Methods for Epilepsy Deep Brain Stimulation Procedures" by Han Liu, Kathryn L. Holloway, Dario J. Englot, and Benoit M. Dawant, arXiv:2201.11002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Risk in Network Economies

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyper-parameters of F2DDPG.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
 \toprule
 F2DDPG Hyper-Parameter & \\
 \midrule
 \# Policy network MLP units &(64, 64) \\
 \# $Q$-network MLP units &(64, 64) \\
 Network parameter initialization &Xavier uniform \\
 Nonlinear activation &ReLU \\
 Policy network learning rate &$10^{-2}$ \\
 $Q$-network learning rate &$10^{-2}$ \\
 $\tau$ for updating target networks &$10^{-2}$ \\
 $\gamma$ &0.95 \\
 Replay buffer size &$10^{6}$ \\
 Mini-batch size &1024 \\
 Optimizer &Adam \\
 $\delta^A$ &$10^{-5}$ \\
 $\delta^E$ &$10^{-3}$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning" by Heechang Ryu, Hayong Shin, and Jinkyoo Park, arXiv:2101.06890v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|l|c|c|c|}
 \hline
 \# & K & \hspace{3.8 cm} Scenario & no reg. & VIB & \textbf{GPM-MDL} \\ \hline
 1 & 2&CIFAR10, Enc.=CNN4, Dist.=(Light,Light) & 0.632 & 0.639 & \textbf{0.675}\\
 \hline
 2 &2& CIFAR10, Enc.=CNN4, Dist.=(Light,Heavy) & 0.596 & 0.597 & \textbf{0.621}\\\hline
 3 &2& CIFAR100, Enc.=ResNet18, Dist.=(Light,Light) & 0.426 & 0.441 & \textbf{0.468}\\
 \hline
 4 &2& USPS, Enc.=CNN4, Dist.=(Light,Light) & 0.952 & 0.953 & \textbf{0.957}\\
 \hline
 5 & 2&CIFAR10, Enc.=CNN4, Dist.=Occ.(L,R) & 0.607 & 0.610 & \textbf{0.652}\\
 \hline
 6 & 2&CIFAR10, Enc.=CNN4, Dist.= Occ.(L,R)
 + (Light, Light) & 0.560 & 0.567 & \textbf{0.606}\\
 \hline
 7 & 2& CIFAR10, Enc.=CNN4, Dist.= Occ.(L,R) + (Medium, Medium) & 0.548 & 0.553 & \textbf{0.577}\\
 \hline
 8 & 2& USPS, Enc.=CNN4, Dist.=Occ.(L,R) + (Heavy, Heavy) & 0.507 & 0.515 & \textbf{0.627}\\
 \hline
 9 & 3 & CIFAR100, Enc.=ResNet18, Dist.=(Medium, Medium, Medium) & 0.373 & 0.381 & \textbf{0.412}\\
 \hline
 10 & 3 & CIFAR100, Enc.=ResNet18, Dist.=(Light, Heavy, Heavy) & 0.375 & 0.380 & \textbf{0.427}\\
 \hline
 11 & 3 & CIFAR100, Enc.=ResNet18, Dist.=(Medium, Heavy, Heavy) & 0.324 & 0.325 & \textbf{0.366}\\
 \hline
 12 & 4 & CIFAR10, Enc.=CNN4, Dist.=(Medium, Medium, Medium, Medium) & 0.602 & 0.605 & \textbf{0.639}\\
 \hline
 13 & 4 & CIFAR10, Enc.=CNN4, Dist.=(Medium, Heavy, Heavy, Heavy) & 0.574 & 0.576 & \textbf{0.600}\\\hline
 14 & 4 & USPS, Enc.=CNN4, Dist.= (Heavy, Heavy, Heavy, Heavy) & 0.587 & 0.588 & \textbf{0.696}\\
 \hline
 15 & 4 & CIFAR10, Enc.=CNN4, Dist.= Occ.(L,R,U,B) & 0.646 & 0.647 & \textbf{0.674}\\
 \hline
 16 & 4 & CIFAR10, Enc.=CNN4, Dist.=Occ.(L,R,U,B)+(Light,\ldots,Light) & 0.599 & 0.601 & \textbf{0.634}\\
 \hline
 17 & 4 & CIFAR10, CNN4, D =Occ.(LU,RU,LB,RB)& 0.620 & 0.621 & \textbf{0.646}\\
 \hline
 18 & 4 & CIFAR10, Enc.=CNN4, Dist.=Occ.(LU,RU,LB,RB)+(Light,\ldots,Light) & 0.585 & 0.590 & \textbf{0.620}\\
 \hline 19 & 8 & CIFAR10, Enc.=CNN4, Dist.=(Heavy,Heavy,\ldots,Heavy) & 0.396 & 0.447 & \textbf{0.529}\\
 \hline 20 & 8 & CIFAR10, Enc.=CNN4, Dist.=(Heavy,Heavy,Ultimate,\ldots,Ultimate) & 0.256 & 0.302 & \textbf{0.335}\\\hline
 \end{tabular}
\end{adjustbox}
\caption{Test performance of Multi-view representation learning models with different encoder architectures, and trained on selected datasets using no regularizer, per-view VIB , and our proposed Gaussians-product Mixture MDL (GPM-MDL). Encoder and distortion choices are abbreviated as ``Enc.'' and ``Dist.'', respectively, and Occlusion as ``Occ.''. }
\end{table}
\end{document}

math

Milad Sefidgaran
Abdellatif Zaidi
Piotr Krasnowski

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


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sub-groups by maternal location in the year of childbirth }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc|c|c|}
 & & \multicolumn{2}{c}{mothers delivered in another municipality} \\
 & & yes & no \\ \cline{3-4}
 & \multirow{2}{*}{yes} & Group (1) move after delivery & Group (2) move before delivery \\
mothers lived in another & & 4.2\% & 10.3\% \\ \cline{3-4}
\textit{grunnkrets} last year & \multirow{2}{*}{no} & Group (3) no move, far hospital & Group (4) no move, local hospital \\
 & & 24.9\% & 60.6\% \\ \cline{3-4}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The effect of ambient air pollution on birth outcomes in Norway" by Xiaoguang Ling, arXiv:2208.06271v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the performance among different models combined with transformer based models on the validation dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
\hline
Model & Accuracy & \multicolumn{2}{c|}{f1-score} & \multicolumn{2}{c|}{Precision} & \multicolumn{2}{c|}{Recall} \\ \hline
 & & Fake & Real & Fake & Real & Fake & Real \\ \hline
BERT + RCNN & 0.967 & 0.965 & 0.969 & 0.980 & 0.956 & 0.950 & 0.982 \\ \hline
RoBERTa + RCNN & 0.968 & 0.966 & 0.970 & \textbf{0.988} & 0.956 & 0.950 & \textbf{0.989} \\ \hline
RoBERTa + SVM & 0.978 & 0.977 & 0.979 & 0.987 & 0.970 & 0.967 & 0.988 \\ \hline
RoBERTa + MLP & \textbf{0.979} & \textbf{0.977} & \textbf{0.980} & 0.981 & \textbf{0.976} & \textbf{0.974} & 0.983 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

S. M. Sadiq-Ur-Rahman Shifath
Mohammad Faiyaz Khan
Md. Saiful Islam

"A transformer based approach for fighting COVID-19 fake news" by S. M. Sadiq-Ur-Rahman Shifath, Mohammad Faiyaz Khan, and Md. Saiful Islam, arXiv:2101.12027v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 $d$ & $1500$ & $500$ & $100$ & $10$\\
 \midrule
 Time (s) & $124.62$ & $58.98$ & $12.62$ & $6.54$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Time (seconds) required to construct the calibration bands on the mean of independent normal responses $(Y_i)_{i=1}^{n}$ using the set $\mathcal{J}^{dist}_{d}$ for various distances $d$.}
\end{table}
\end{document}

math

Łukasz Delong
Selim Gatti
Mario V. Wüthrich

"Calibration Bands for Mean Estimates within the Exponential Dispersion Family" by Łukasz Delong, Selim Gatti, and Mario V. Wüthrich, arXiv:2503.18896v1, 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}{cc|ccc}
	\toprule[1.1pt]
	ID & Ticker & DPS & Closing Prices & Initial Quantity \\
	\midrule
	1 & A & 22 & 454.17, \dots, 445.60 & 1,200 \\
	2 & B & 23 & 354.17, \dots, 465.80 & 1,000 \\
	3 & C & 25 & 500.47, \dots, 440.60 & 1,600 \\
	\dots & \dots & \dots & \dots & \dots \\
	\bottomrule[1.1pt]
	\end{tabular}
\end{adjustbox}
\caption{\textbf{Stock Details.} \texttt{DPS} denotes dividend per share; \texttt{Closing Prices} are historical end-of-day closing prices; and \texttt{Initial Quantity} specifies the starting number of shares.}
\end{table}
\end{document}

q-fin

Tianmi Ma
Jiawei Du
Wenxin Huang
Wenjie Wang
Liang Xie
Xian Zhong
Joey Tianyi Zhou

"Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents" by Tianmi Ma, Jiawei Du, Wenxin Huang, Wenjie Wang, Liang Xie, Xian Zhong, and Joey Tianyi Zhou, arXiv:2502.17967v2, 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|cc|cc|cc|}
 \hline
 n & $L^{2}$error & order & $H^{1}$error & order & $H^{2}$error & order\\
 \hline
 5 & 7.608e-04& - &1.320e-02& - &4.284e-01 & - \\
 \hline
 10 & 4.861e-05&3.968&1.685e-03&2.971&1.092e-01&1.972 \\
 \hline
 20 & 3.054e-06&3.992&2.116e-04&2.993&2.743e-02&1.993 \\
 \hline
 40 & 1.911e-07&3.998&2.648e-05&2.998&6.865e-03&1.998 \\
 \hline
 80 & 1.193e-08&4.001&3.311e-06&3.000&1.717e-03&2.000 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{error and order of basis $V_{h}$, biharmonic equation in 1d.}
\end{table}
\end{document}

math

"A PINN-enriched finite element method for linear elliptic problems" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average duration of a BO loop for each AF. We measure the runtime on the \texttt{Branin}, \texttt{Levy}, and \texttt{Hartmann} benchmarks and average over benchmarks, BO iterations, and 10 random restarts. For $N=5$ outer repetitions, VES has a higher runtime than the other acquisition functions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lr}
 \toprule
 AF & average time per BO iteration\\
 \midrule
 EI & $1.627s\, (\pm 0.916s)$ \\
 MES & $1.120s\, (\pm 0.472s)$ \\
 VES & $10.910s\, (\pm 12.323)$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Nuojin Cheng
Leonard Papenmeier
Stephen Becker
Luigi Nardi

"A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization" by Nuojin Cheng, Leonard Papenmeier, Stephen Becker, and Luigi Nardi, arXiv:2501.18756v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
 \hline
 Optimizer setting & Name/value\\
 \hline
 optimizer & AdamW \\\
 $\beta_{1}$ & 0.9 (default)\\
 $\beta_{2}$ & 0.999 (default)\\
 weight decay & 0.01 (default)\\
 batch size & 8\\
 learning rate scheduling & cosine annealing \\
 $T_{0}$ &10\\
 $T_{mult}$ & 2\\
 $\eta_{min}$ & $1e^{-6}$\\
 initial learning rate & $1e^{-3}$\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{CNN model training optimizer setting.}
\end{table}
\end{document}

math

Kazuko W. Fuchi
Eric M. Wolf
Christopher R. Schrock
Philip S. Beran

"Acceleration of RANS Solver Convergence via Initialization with Wake Extension Models" by Kazuko W. Fuchi, Eric M. Wolf, Christopher R. Schrock, and Philip S. Beran, arXiv:2501.14699v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|c|c|}\hline
		\textbf{Number of orders} & $1$ & $2$ & $3$ & $4$ & $5$ & $6$ & $7$ & $8$ & $9$ & $10+$ \\\hline
		\textbf{Number of buyers} & $430,743$ & $21,688$ & $1,832$ & $284$ & $99$ & $42$ & $37$ & $22$ & $19$ & $131$ \\\hline
		\textbf{Percentage $\%$} & $94.69$ & $4.77$ & $0.40$ & $0.06$ & $0.02$ & $0.01$ & $0.01$ & $< 0.01$ & $< 0.01$ & $0.03$ \\\hline
	\end{tabular}
\end{adjustbox}
\caption{Number of orders made by buyer users}
\end{table}
\end{document}

stat

Saed Alizamir
Kasun Bandara
Ali Eshragh
Foaad Iravani

"A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study" by Saed Alizamir, Kasun Bandara, Ali Eshragh, and Foaad Iravani, arXiv:2212.02255v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
$\hat{\mu}$ & $\hat{a}_1$ & $\hat{b}_0$ & $T$ \\ \hline
0.2440 & 0.7540 & 0.4877 & 5000\\
0.2121 & 0.7127 & 0.4954 & 15000\\
0.2044 & 0.7016 & 0.4951 & 25000\\
0.1992 & 0.7042 & 0.4990 & 50000\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Parameter estimates from MME for a Hawkes process. True parameters are $\mu=0.2$, $a_1=0.7$ and $b_0=0.5$.}
\end{table}
\end{document}

q-fin

Lorenzo Mercuri
Andrea Perchiazzo
Edit Rroji

"A Hawkes model with CARMA(p,q) intensity" by Lorenzo Mercuri, Andrea Perchiazzo, and Edit Rroji, arXiv:2208.02659v3, 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}

eess

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}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Inference Time Comparison Between the Baseline and the Proposed NLC Method. For the CIFAR-10 experiment, the baseline is DDPM and the proposed method is DDPM+NLC. For the ImageNet experiment, the baseline is DDNM and the proposed method is DDNM+NLC (inpainting experiments). }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc}
\hline
$\epsilon$ model & CIFAR10-DDPM & ImageNet - DDNM \\ \hline
baseline & 0.32 & 0.93 \\
baseline + NLC & 0.34 & 0.95 \\ \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{Scale of the Experiment}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
			\hline
			Number of Nodes & 10 & 20 & 30 & 40 & 50 & 60 \\ \hline
			Average Number of Edges & 30 & 125 & 280 & 420 & 700 & 1000 \\ \hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Multi-Objective Routing Optimization Using Coherent Ising Machine in Wireless Multihop Networks" by Yu-Xuan Lin, Chu-Yao Xu, and Chuan Wang, arXiv:2503.07924v1, 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{predictors and their corresponding coefficients for lasso regression}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
 \hline
 \textbf{predictors} & \textbf{~~~~~~Coefficients~~~~~~} & \textbf{~~~~~~$95\%$ Confidence Interval~~~~~~} \\ \hline
 abnormal\_ALP & -0.02 & (-0.08 , 0.04) \\ \hline
 abnormal\_MPV	& 0.01	& (-0.06 , 0.11) \\ \hline
 abnormal\_hematocrit	& 0.00 & (-0.11 , 0.14) \\ \hline
 abnormal\_PO2 & -0.11 & (-0.19 , 0.00) \\ \hline
 abnormal\_creatinine	& 0.03 & (-0.03 , 0.11) \\ \hline
 abnormal\_INR & 0.06 & (-0.02 , 0.22) \\ \hline
 abnormal\_MCHb & 	-0.03 & (-0.10 , 0.04) \\ \hline
 abnormal\_MCHb\_conc & -0.03 & (-0.10 , 0.04) \\ \hline
 abnormal\_hb	& 0.05	& (-0.04 , 0.19) \\ \hline
 abnormal\_mcv	& -0.03	& (-0.11 , 0.04) \\ \hline
 abnormal\_plt	& 0.23	& (0.02 , 0.36) \\ \hline
 abnormal\_redcellwidth & 0.07	& (0.00 , 0.15) \\ \hline
 abnormal\_wbc	& -0.02	& (-0.09 , 0.03) \\ \hline
 abnormal\_ALC	& 0.01	& (-0.05 , 0.08) \\ \hline
 location\_GeneralMedicine & -0.11 & (-0.21 , 0.00) \\ \hline
 location\_Hematology & 0.04 & (-0.02 , 0.16) \\ \hline
 location\_IntensiveCare & 0.05 & (-0.01 , 0.15) \\ \hline
 location\_CardiovascularSurgery & 0.04 & (-0.03 , 0.11) \\ \hline
 location\_Pediatric	& 0.04	& (-0.02 , 0.10) \\ \hline
 Monday & 0.07 & (0.00 , 0.16) \\ \hline
 Tuesday & 0.07 & (0.00 , 0.14) \\ \hline
 Wednesday & 0.00 & (-0.04 , 0.07) \\ \hline
 Thursday & 0.01 & (-0.03 , 0.09) \\ \hline
 Friday & -0.39 & (-0.46 , -0.31) \\ \hline
 Saturday & -0.31 & (-0.39 , -0.23) \\ \hline
 Sunday & 0.10 & (0.03 , 0.18) \\ \hline
 lastWeek\_Usage & 0.12 & (0.05 , 0.19) \\ \hline
 yesterday\_Usage & 0.10 & (0.02 , 0.17) \\ \hline
 yesterday\_ReceivedUnits & 0.06 & (0.00 , 0.14) \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Maryam Motamedi
Jessica Dawson
Na Li
Douglas G. Down
Nancy M. Heddle

"Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models" by Maryam Motamedi, Jessica Dawson, Na Li, Douglas G. Down, and Nancy M. Heddle, arXiv:2101.02305v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The size of communicated messages (in million) in each update of FedAvg and FedRF-TCA (with $N = 1\,000$).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 & Data-dependent features & Model parameters & Sum \\
 \midrule
 FedAvg & 0 & 25.637 & 25.637 \\
 FedRF-TCA & 0.001 & 1.691 & 1.692 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Zhanbo Feng
Yuanjie Wang
Jie Li
Fan Yang
Jiong Lou
Tiebin Mi
Robert. C. Qiu
Zhenyu Liao

"Robust and Communication-Efficient Federated Domain Adaptation via Random Features" by Zhanbo Feng, Yuanjie Wang, Jie Li, Fan Yang, Jiong Lou, Tiebin Mi, Robert. C. Qiu, and Zhenyu Liao, arXiv:2311.04686v2, 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{Nodes on the designed ISIG BNs with assumed states}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \hline
node & description & states \\
 \hline
 SC & signal controller & N,L,M,H,C \\
 RSE & roadside equipment & L,M,H \\
 TC & traffic composition & UP,EP,UV,CR,EM,FR,TR \\
 UP & unequipped pedestrians & N,L,M,H,C \\
 EP & equipped pedestrians & N,L,M,H,C \\
 UV & unequipped vehicles & N,L,M,H,C \\
 CR & regular equipped vehicles & N,L,M,H,C \\
 EM & emergency vehicles & N,L,M,H,C \\
 FR & freight trucks & N,L,M,H,C \\
 TR & transit vehicles & N,L,M,H,C \\
 ITS & other ITS devices & L,M,H \\
 TMC & traffic management center & L,M,H \\
 S & sensor & True,False \\
 DD & detection by driver & L,M,H \\
 DP & detection by pedestrian & L,M,H \\
 DS & detection by system (vehs) & L,M,H \\
 PDS & detection by system (pedes) & L,M,H \\
 RDS & detection by system (RSE) & L,M,H \\
 SDS & detection by system (SA) & L,M,H \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Gurcan Comert
Mashrur Chowdhury
David M. Nicol

"Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks" by Gurcan Comert, Mashrur Chowdhury, and David M. Nicol, arXiv:2011.09436v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Diarization performance on LibriCSS evaluation set (sessions 2-10), evaluated condition-wise, in terms of \% DER. 0S and 0L refer to 0\% overlap with short and long inter-utterance silences, respectively. The DL results are using rank-based weighting.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
\toprule
\multirow{2}{*}{\textbf{Method}} & \multicolumn{6}{c}{\textbf{Overlap ratio in \%}} & \multirow{2}{*}{\textbf{Average}} \\
\cmidrule(r{4pt}){2-7}
 & \textbf{0L} & \textbf{0S} & \textbf{10} & \textbf{20} & \textbf{30} & \textbf{40} & \\
\midrule
VB & 3.9 & 3.8 & 6.5 & 8.2 & 12.6 & 13.4 & 8.6 \\
SC & 2.6 & 3.4 & 6.8 & 10.0 & 13.9 & 15.2 & 9.3 \\
RPN & 4.5 & 9.1 & 8.3 & 6.7 & 11.6 & 14.2 & 9.5 \\
TS-VAD & 6.0 & 4.6 & 6.6 & 7.3 & 10.3 & 9.5 & 7.4 \\
\midrule
DL & \textbf{2.3} & \textbf{2.2} & \textbf{4.0} & \textbf{5.3} & \textbf{7.9} & \textbf{9.0} & \textbf{5.4} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Desh Raj
Leibny Paola Garcia-Perera
Zili Huang
Shinji Watanabe
Daniel Povey
Andreas Stolcke
Sanjeev Khudanpur

"DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs" by Desh Raj, Leibny Paola Garcia-Perera, Zili Huang, Shinji Watanabe, Daniel Povey, Andreas Stolcke, and Sanjeev Khudanpur, arXiv:2011.01997v1, 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{Level 1 Crypto TVL Change Portfolio Descriptive Statistics. Values are portfolio returns in percentage points and are at the weekly frequency. TVL is adjusted for staking, pool2, governance tokens, borrows, double count, liquid staking, and vesting. There are 104 weekly observations. \(^{***}\), \(^{**}\), and \(^{*}\), denote statistical significance at the 1\%, 5\%, and 10\% levels respectively.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
\hline
 & HML & Q1 & Q2 & Q3 & Q4\\
\hline
mean & -0.97 & 2.34\(^{*}\) & 2.83\(^{**}\) & 1.59 & 1.37\(^{*}\)\\
std & 8.92 & 13.27 & 13.72 & 12.58 & 8.36\\
min & -36.89 & -26.24 & -28.50 & -47.72 & -17.73\\
25\% & -3.38 & -4.87 & -5.59 & -5.43 & -3.93\\
50\% & 0.25 & -0.38 & 0.21 & 0.84 & 0.29\\
75\% & 3.45 & 8.05 & 10.71 & 8.11 & 6.13\\
max & 22.76 & 65.89 & 48.46 & 56.44 & 28.99\\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \toprule
 \textbf{Metric} & \textbf{JSD} & \textbf{MMD} \\
 \midrule
 \textbf{Score } & 0.015 & 0.0001 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ainaz Jamshidi
Muhammad Arif
Sabir Ali Kalhoro
Alexander Gelbukh

"Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study" by Ainaz Jamshidi, Muhammad Arif, Sabir Ali Kalhoro, and Alexander Gelbukh, arXiv:2412.16207v1, 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{ASR (\%) of Untargeted Soft-Label Attacks. $\sigma$ = 0.1.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|ccc}

		\toprule
		Dataset & Attacks %& & ASR (\%) &\\ &
		& No defense & $\theta_1$ = 0.5 & $\theta_2$ = 0.7 \\
		\midrule
		 & AZ & 97 & 0 & 0 \\
		 MNIST & SA & 92 & 89 & 10\\
		 & SimBA & 82 & 31 & 0 \\
		 \midrule \midrule
		 Dataset & Attacks %& &ASR (\%) &\\
		 & No Defense & $\theta_1$ = 0.3 & $\theta_2$ = 0.4 \\
		 \midrule
		 & AZ & 100 & 0 & 0 \\
		 IMAGENET & SA & 100 & 38 & 30\\
		 & SimBA & 100 & 96 & 0 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Boundary Defense Against Black-box Adversarial Attacks" by Manjushree B. Aithal and Xiaohua Li, arXiv:2201.13444v1, 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{Ordinary least squares and instrumental variables estimates of the simple specification (left panel) and the extended specification including additional covariates (right panel) for the recent subsample (1992-2022). The 95\% confidence intervals for the OLS and IV regression are based on the Gaussian distribution. The Deming regression estimates are obtained using $\delta \in \{0.2, 0.5, 1, 2, 5\}$. Standard errors and confidence intervals for Deming regression are computed using 9999 bootstrap replications.\\}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|lll|lll}
 \toprule
 Recent subsample &\multicolumn{3}{l|}{Simple specification, eqn. ()}&\multicolumn{3}{l}{Extended specification, eqn. ()}\\
 Model			&	Est.	&	S. e.	&	Conf.	interval	&	Est.	&	S. e.	&	Conf.	interval \\
 \midrule
 OLS Regression		&	0.4497	&	0.0173	&	[0.4157, 0.4836]		&	0.4613	&	0.0112	&	[0.4392, 0.4833]		\\
 IV	reg.	(H\&N)	&	0.4496	&	0.0173	&	[0.4157, 0.4836]		&	0.4622	&	0.0112	&	[0.4402, 0.4842]		\\
 IV	reg.	(vMA)	&	0.4502	&	0.0245	&	[0.4022, 0.4982]		&	0.4623	&	0.0112	&	[0.4402, 0.4843]		\\
 IV	reg.	(H\&N-vMA)	&	0.4495	&	0.0173	&	[0.4156, 0.4834]		&	0.4622	&	0.0112	&	[0.4401, 0.4842]		\\
 Deming	reg.	(0.2)	&	0.4598	&	0.0176	&	[0.4509, 0.4675]		&	0.4662	&	0.0105	&	[0.4611, 0.4709]		\\
 Deming	reg.	(0.5)	&	0.4555	&	0.0175	&	[0.4468, 0.4630]		&	0.4645	&	0.0106	&	[0.4594, 0.4692]		\\
 Deming	reg.	(1)	&	0.4531	&	0.0172	&	[0.4446, 0.4603]		&	0.4636	&	0.0107	&	[0.4584, 0.4683]		\\
 Deming	reg.	(2)	&	0.4515	&	0.0172	&	[0.4431, 0.4587]		&	0.4630	&	0.0107	&	[0.4578, 0.4677]		\\
 Deming	reg.	(5)	&	0.4504	&	0.0171	&	[0.4422, 0.4576]		&	0.4625	&	0.0106	&	[0.4574, 0.4672]		\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Robust estimation of carbon dioxide airborne fraction under measurement errors" by J. Eduardo Vera-Valdés and Charisios Grivas, arXiv:2411.07836v1, 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}{ll}
 \toprule
 \textbf{Model}& \textbf{Error Rate}\\
 \midrule
 XD Gradient& 35 \%\\
 M/D/1 Queue& 30 \%\\
 Random Forest& 24 \%\\
 Random Forest M/D/1 Hybrid& 19 \% \\
 \bottomrule \\
 \end{tabular}
\end{adjustbox}
\caption{Distributed Edge Provisioning Techniques}
\end{table}
\end{document}

eess

Jenna Kline
Austin O'Quinn
Tanya Berger-Wolf
Christopher Stewart

"Characterizing and Modeling AI-Driven Animal Ecology Studies at the Edge" by Jenna Kline, Austin O'Quinn, Tanya Berger-Wolf, and Christopher Stewart, arXiv:2412.01000v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\hline
Model & Precision & Recall & F1 \\
\hline
{LR$_{SarcF}$} & 60.55 &	60.45 &	60.2 \\
{LR$_{SarcF+ArgF}$} & 60.75 &	60.75	& \textbf{60.75} \\
\hline
{LSTM$_{Attn}$} & 52.85 &	52.80&	 52.90 \\
{LSTM$_{MT}$} & 59.50 &	59.25&	\textbf{59.35} \\
\hline

{BERT$_{orig}$} & 57.39 & 57.39 & 57.39\\
{BERT$_{MT}$} & 61.79 & 61.74 & \textbf{61.76} \\
{BERT$_{{MT}_{uncert}}$} & 64.05 & 63.5 & \textbf{64.00} \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Evaluations of sarcasm detection on the test set of $IAC_{orig}$.}
\end{table}
\end{document}

cs

Debanjan Ghosh
Ritvik Shrivastava
Smaranda Muresan

""Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space" by Debanjan Ghosh, Ritvik Shrivastava, and Smaranda Muresan, arXiv:2101.10952v1, 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}{|l|r|r|r|}
\hline
Outlier Percentile & 0.1\% & 2.5\% & 5.0\% \\ \hline
Paired t-test (marked to day) & \textbf{0.027} & {0.719} & 0.803 \\ \hline
Paired t-test (marked to week) & 0.103 & 0.815 & 0.778 \\ \hline
Unpaired t-test & 0.647 & 0.978 & 0.886 \\ \hline
Log Paired t-test (marked to day) & \textbf{0.000} & {0.144} & 0.250 \\ \hline
Log Paired t-test (marked to week) & 0.114 & 0.240 & 0.129 \\ \hline
Log Unpaired t-test & \textbf{0.005} & {0.623} & 0.549 \\ \hline
\end{tabular}
\caption{P-values varying outlier detection methods and types of one-sided t-tests for hypothesis that price of Male CryptoPunks $>$ Female CryptoPunks. \textbf{Bold} indicates p-value$<0.05$. }
\end{table}
\end{document}

q-fin

"Exploring Gender and Race Biases in the NFT Market" by Howard Zhong and Mark Hamilton, arXiv:2304.06484v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|}
 \toprule
 & JUN20 & SEP20 & DEC20 & MAR21 & JUN21 & SEP21 & DEC21 & JUN22 & DEC22 \\
 \bottomrule
 \toprule
 Mean & 148 & 104 & 100 & 19 & 22 & 4 & 24 & 3 & 7 \\
 Median & 148 & 107 & 114 & 18 & 21 & 4 & 24 & 3 & 8 \\
 Std & 0 & 17 & 30 & 7 & 5 & 2 & 5 & 1 & 2 \\
 $q_{0.05}$ & 148 & 96 & 28 & 11 & 17 & 1 & 16 & 0 & 4 \\
 $q_{0.95}$ & 148 & 114 & 117 & 31 & 30 & 6 & 29 & 3 & 9 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\small{We report the number of call-put options' couples traded for each maturity and value date: mean, median, standard deviation (std), and quantiles (q) 5\%, 95\% considering each value date.}}
\end{table}
\end{document}

q-fin

Roberto Baviera
Michele Domenico Massaria

"The Additive Bachelier model with an application to the oil option market in the Covid period" by Roberto Baviera and Michele Domenico Massaria, arXiv:2506.09760v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
$n$ & Galerkin & Riesz \\
\hline
6 & $3.12\cdot 10^{-1\strut}$ & $7.44\cdot 10^{-9}$ \\
10 & $8.78\cdot 10^{-1}$ & $5.16\cdot 10^{-9}$ \\
20 & $4.38\cdot 10^{4\phantom{-}}$ & $1.94\cdot 10^{-8}$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Patricia Díaz de Alba
Luisa Fermo
Federica Pes
Giuseppe Rodriguez

"Regularized minimal-norm solution of an overdetermined system of first kind integral equations" by Patricia Díaz de Alba, Luisa Fermo, Federica Pes, and Giuseppe Rodriguez, arXiv:2201.12054v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


cs

"Higher Order Correlation Analysis for Multi-View Learning" by Jiawang Nie, Li Wang, and Zequn Zheng, arXiv:2201.11949v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Yuhang Li
Xin Dong
Chen Chen
Jingtao Li
Yuxin Wen
Michael Spranger
Lingjuan Lyu

"Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization" by Yuhang Li, Xin Dong, Chen Chen, Jingtao Li, Yuxin Wen, Michael Spranger, and Lingjuan Lyu, arXiv:2403.19866v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c|c}
			\hline
			\multirow{2}{*}{rule}
			& training & test	&number
			&\multicolumn{3}{c}{suboptimality}\\
			\cline{5-7}
			& data & data & feasible & min &avg & max\\
			\hline
			$B^\nu,b^\nu$ & beta & unif & 33 &0.010 &0.048 &0.076\\
			MDR & beta & unif & 100 &0.033 &0.052 &0.095\\
			AMDR & beta & unif & 100 &0.000 &0.000 &0.007\\
 \hline
		\end{tabular}
\caption{Performance of decision rules obtain from ($\beta$-SP1)$^\nu$ against dispersed target. Suboptimality statistics are computed over feasible decisions.}
\end{table}
\end{document}

eess

Risk-Adaptive Local Decision Rules

"Risk-Adaptive Local Decision Rules" by Johannes O. Royset and Miguel A. Lejeune, arXiv:2310.09844v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Empirical Asset Pricing via Ensemble Gaussian Process Regression" by Damir Filipović and Puneet Pasricha, arXiv:2212.01048v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Pariyakorn Maneekul
Zelda B. Zabinsky
Giulia Pedrielli

"Branching Adaptive Surrogate Search Optimization (BASSO)" by Pariyakorn Maneekul, Zelda B. Zabinsky, and Giulia Pedrielli, arXiv:2504.18002v1, 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{Example table.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|} \hline
 Species & \bf Mean & \bf Std.~Dev. \\ \hline
 1 & 3.4 & 1.2 \\
 2 & 5.4 & 0.6 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Lorenzo Lazzarino
Yuji Nakatsukasa
Umberto Zerbinati

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


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.


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{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Robust mean estimation for $n =200$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
Method & Estimation error & Run-time\\
\hline
ROBOT-Sinkhorn & $0.196 \pm 0.1$ & $35s \pm 0.6s$\\
 & $0.212 \pm 0.074$ & $7745s \pm 1670s$
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Outlier-Robust Optimal Transport

Debarghya Mukherjee
Aritra Guha
Justin Solomon
Yuekai Sun
Mikhail Yurochkin

"Outlier-Robust Optimal Transport" by Debarghya Mukherjee, Aritra Guha, Justin Solomon, Yuekai Sun, and Mikhail Yurochkin, arXiv:2012.07363v2, 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}{lrr}
		\cmidrule[\heavyrulewidth]{2-3} & $\alpha = 0.05$ & $\alpha = 0.01$ \\
		\toprule
		benchmark & 0\% & 0\% \\
		\midrule
		deepLOB(L1, universal) & 0\% & 0\% \\
		\midrule
		deepOF(L1, universal) & 0\% & 0\% \\
		\midrule
		deepLOB(L2, universal) & 8\% & 24\% \\
		\midrule
		deepOF(L2, universal) & 42\% & 62\% \\
		\midrule
		deepVOL(L2, universal) & 71\% & 81\% \\
		\midrule
		deepVOL(L3, universal) & 92\% & 100\% \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{\% of times each model is in the $\alpha$-MCS when predictability is identified at the corresponding level $\alpha$.}
\end{table}
\end{document}

q-fin

Lorenzo Lucchese
Mikko Pakkanen
Almut Veraart

"The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective" by Lorenzo Lucchese, Mikko Pakkanen, and Almut Veraart, arXiv:2211.13777v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
\hline
Method & Frobnability \\
\hline\hline
Theirs & Frumpy \\
Yours & Frobbly \\
Ours & Makes one's heart Frob\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Results. Ours is better.}
\end{table}
\end{document}

eess

"Pose2RGBD. Generating Depth and RGB images from absolute positions" by Mihai Cristian Pîrvu, arXiv:2007.07013v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Power Delay Profile for Modified Echo Channel}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c||}
		\hline
		Path & Amplitude(dB) & Delay($\mu s$) \\
		\hline\hline
		1 & 0 & 0\\
	 2 & 0 & $\alpha$*$T_{cp}$ \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Hongzhi Chen
De Mi
Belkacem Mouhouche
Pei Xiao
Rahim Tafazolli

"Mobility and Coverage Evaluation of New Radio PDCCH for Point-to-Multipoint Scenario" by Hongzhi Chen, De Mi, Belkacem Mouhouche, Pei Xiao, and Rahim Tafazolli, arXiv:1912.01502v1, 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}{|c|c|c|}
\hline
 & Predicted NO & Predicted YES\tabularnewline
\hline
\hline
True NO & 2101 & 4\tabularnewline
\hline
True YES & 47 & 0\tabularnewline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Amir Mukeri
Habibullah Shaikh
D. P. Gaikwad

"Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction" by Amir Mukeri, Habibullah Shaikh, and D. P. Gaikwad, arXiv:2010.13892v2, 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}{lccc}
 \toprule
 \textbf{asset} & \textbf{Our approach}& \\ \midrule
 1 & 0.0& 0.0\\
 2 & 0.0 & 0.0 \\
 3 & 0.2549& 0.2621 \\
 4 & 0.0022 & 0.0 \\
 5 & 0.0& 0.0\\
 6 & 0.3763 & 0.4081 \\
 7 & 0.2485 & 0.2403 \\
 8 &0.1180 & 0.0892 \\ \midrule
 \multicolumn{1}{c}\textbf{objective}&11.03 $\%$&11.02$\%$ \\ \midrule
 \multicolumn{1}{c}\textbf{number of TSD constraints}&\textbf{3}& 508\\ \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Third order stochastic dominance (case $p=2$): a comparison with }
\end{table}
\end{document}

math

Rajmadan Lakshmanan
Alois Pichler
Miloš Kopa

"Higher-Order Stochastic Dominance Constraints in Optimization" by Rajmadan Lakshmanan, Alois Pichler, and Miloš Kopa, arXiv:2501.14565v3, 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}{ll}
 \bfseries Dataset & \bfseries Result\\
 Data1 & 0.12345\\
 Data2 & 0.67890\\
 Data3 & 0.54321\\
 Data4 & 0.09876
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mehak Aggarwal
Nishanth Arun
Sharut Gupta
Ashwin Vaswani
Bryan Chen
Matthew Li
Ken Chang
Jay Patel
Katherine Hoebel
Mishka Gidwani
Jayashree Kalpathy-Cramer
Praveer Singh

"Towards Trainable Saliency Maps in Medical Imaging" by Mehak Aggarwal, Nishanth Arun, Sharut Gupta, Ashwin Vaswani, Bryan Chen, Matthew Li, Ken Chang, Jay Patel, Katherine Hoebel, Mishka Gidwani, Jayashree Kalpathy-Cramer, and Praveer Singh, arXiv:2011.07482v1, 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|}
Ribbon Graph Property & Combinatorial Map Property\\
\hline
Number of Vertices & $c(\sigma)$\\
\hline
Number of Edges & $c(\alpha)$\\
\hline
Number of Faces & $c(\varphi)$\end{tabular}
\end{table}
\end{document}

math

"Classification of Minimal Separating Sets of Low Genus Surfaces" by Christopher N. Aagaard and J. J. P. Veerman, arXiv:2312.02357v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 Contamination $\delta$ & $0$ & $0.5$ & $1$\\
 \midrule
 Global shift &$5/1000$& $291/1000$ & $1000/1000$ \\
 Local shift of level $l = 10$ & - & $13/1000$ & $612/1000$ \\
 Local shift of level $l = 13$ & - &$128/1000$& $994/1000$ \\
 Local shift of level $l = 15$ & - & $3/1000$ & $270/1000$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Power of the performed statistical tests with confidence level $1-\alpha = 0.95$.}
\end{table}
\end{document}

math

Łukasz Delong
Selim Gatti
Mario V. Wüthrich

"Calibration Bands for Mean Estimates within the Exponential Dispersion Family" by Łukasz Delong, Selim Gatti, and Mario V. Wüthrich, arXiv:2503.18896v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Bart van Oort
Luís Cruz
Babak Loni
Arie van Deursen

""Project smells" -- Experiences in Analysing the Software Quality of ML Projects with mllint" by Bart van Oort, Luís Cruz, Babak Loni, and Arie van Deursen, arXiv:2201.08246v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|l|l}
 $n$ & Max sets with $n$ cards & $C(81,n)$ configurations to check & Computational Time \\ \hline
 $3$ & $1$ & $85320$ & $<1$ second \\
 $4$ & $1$ & $1,663,740$ & ~ $6$ seconds \\
 $5$ & $2$ & $25,621,596$ & ~ $4$ minutes \\
 $6$ & $3$ & $324,540,216$ & ~ $1$ hour, $33$ minutes \\
 $7$ & $5$ & $3,477,216,600$ & $29$ hours, $12$ minutes
 \end{tabular}
\end{adjustbox}
\caption{The maximum number of sets for $3\le n\le 7$ by computer proof.}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 Attackers & 0 & 1 & 2 & 3 \\
 \midrule
 Train On 0 & 89.93 & 52.31 & 28.31 & 12.07 \\
 Train On 1 & \textbf{90.09} & \textbf{90.00} & 81.95 & 75.28 \\
 Train On 2 & 89.71 & 89.68 & 88.91 & 88.33 \\
 Train On 3 & 89.55 & 89.51 & \textbf{88.94} & \textbf{88.51} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Adversarial training with multiple attackers in the V2V setting. We train on settings with various number of attackers and evaluate the models across the settings.}
\end{table}
\end{document}

cs

James Tu
Tsunhsuan Wang
Jingkang Wang
Sivabalan Manivasagam
Mengye Ren
Raquel Urtasun

"Adversarial Attacks On Multi-Agent Communication" by James Tu, Tsunhsuan Wang, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren, and Raquel Urtasun, arXiv:2101.06560v2, 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{The first value in brackets denotes Player 1's payoff, and the second Player 2's. The bold values correspond to the payoffs of the typical NE strategies.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|cc|}
 \cline{3-4}
 & & \multicolumn{2}{c|} {Player 2} \\
 & & a & b \\ \cline{1-4}
 \multirow{2}{*}{Player 1} & a & (p, p) & (\textbf{q}, \textbf{r}) \\
 & b & (\textbf{r}, \textbf{q}) & (s, s) \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Victoria Sánchez Muñoz
Michael Mc Gettrick

"Nash Equilibria in certain two-choice multi-player games played on the ladder graph" by Victoria Sánchez Muñoz and Michael Mc Gettrick, arXiv:2101.09103v1, 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}{lcc}
\toprule
Method & Year & Test Error {[}\%{]} \\ \hline
Multi-Column Deep Neural Networks for Image Classification & 2012 & 0.23 \\
Regularization of Neural Networks using DropConnect & 2013 & 0.21 \\
RMDL:Random Multimodel Deep Learning for Classification & 2018 & 0.18 \\
Base-Branching \& Merging CNNw/HFCs & 2020 & 0.16 \\
Efficient-CapsNet & 2021 & 0.16 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Test error (\%) on the MNIST classification task of state-of-the-art methodologies based on ensemble over the years.}
\end{table}
\end{document}

cs

Vittorio Mazzia
Francesco Salvetti
Marcello Chiaberge

"Efficient-CapsNet: Capsule Network with Self-Attention Routing" by Vittorio Mazzia, Francesco Salvetti, and Marcello Chiaberge, arXiv:2101.12491v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
(n,m)&$ (1000,10)$& $(3000,20)$ & $(5000,20)$\\ \hline
Benchmark & 15.27s (3.48s) & 194.8s (24.4s) & 744.5s (107.5s) \\
 Random & 2.37s (1.39s) & 6.4s (2.0s) & 11.3s (2.5s)\\
Spatial & 2.36s (1.60s) & 6.0s (1.5s) & 10.7s (2.4s)\\
\end{tabular}
\end{adjustbox}
\caption{\scriptsize Data-based rescaled (MMLE) squared exponential Gaussian process prior. Average run time for the computation of the posterior. Benchmark: Non-distributed method. Method 1: Random partitioning, Method 2: Spatial partitioning.}
\end{table}
\end{document}

stat

Botond Szabo
Amine Hadji
Aad van der Vaart

"Adaptation using spatially distributed Gaussian Processes" by Botond Szabo, Amine Hadji, and Aad van der Vaart, arXiv:2312.14130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{} & \textbf{P-mse ($rad^2/s^2$)} & \textbf{Q-mse ($rad^2/s^2$)} & \textbf{R-mse ($rad^2/s^2$)} & \textbf{PQR-mse ($rad^2/s^2$)} \\ \hline
\textbf{Split-S Maneuver} & 0.0125 & 0.0030 & 7.5693e-5 & 0.0052 \\ \hline
\textbf{Chandelle Maneuver} & 0.0481 & 0.0174 & 0.0021 & 0.0226 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Gulay Goktas Sever
Umut Demir
Abdullah Sadik Satir
Mustafa Cagatay Sahin
Nazim Kemal Ure

"An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data" by Gulay Goktas Sever, Umut Demir, Abdullah Sadik Satir, Mustafa Cagatay Sahin, and Nazim Kemal Ure, arXiv:2401.08663v1, 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{Uncertainty in imputed values when the data was generated with an assumed rank of 15.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \hline
s2n & Missingness Type & Mean RSE & Mean Coverage & Mean CI Width \\
 \hline
9 & Entrywise & 0.2261 & 0.9350 & 5.5594 \\
 9 & Blockwise & 0.6384 & 0.6283 & 4.2770 \\
 9 & MNAR & 0.5729 & 0.3897 & 6.3075 \\
 \hline
 3 & Entrywise & 0.4602 & 0.9133 & 4.6483 \\
 3 & Blockwise & 0.7633 & 0.7656 & 4.0584 \\
 3 & MNAR & 0.8706 & 0.2846 & 4.5944 \\
 \hline
 1 & Entrywise & 0.7903 & 0.9039 & 4.1939 \\
 1 & Blockwise & 0.9166 & 0.8533 & 3.9010 \\
 1 & MNAR & 1.3614 & 0.2599 & 3.3007 \\
 \hline
 0.333 & Entrywise & 0.9773 & 0.9156 & 3.9481 \\
 0.333 & Blockwise & 0.9923 & 0.8997 & 3.7835 \\
 0.333 & MNAR & 1.2615 & 0.3140 & 3.0191 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Sarah Samorodnitsky
Chris H. Wendt
Eric F. Lock

"Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data" by Sarah Samorodnitsky, Chris H. Wendt, and Eric F. Lock, arXiv:2211.16403v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c||}
 \hline
 Name & Conditions\\
 \hline
 \hline
 SGD combined & SGD batch size = 1 and SGD momentum = 0.9\\
 \hline
 Adam combined & Adam single and Adam 768 neurons in the single hidden layer\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Experimental details for the combined model of SGD and Adam}
\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{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Matrices from used for numerical experiments and their properties.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
		\hline
		Name & Size & nnz & $\kappa_\infty(A)$ & Group & Kind \\ \hline
		orsirr\_1 & 1030 & 1030 & 9.96E+04 & HB & Computational Fluid Dynamics Problem \\
		comsol & 1500 & 1500 & 3.42E+06 & Langemyr & Structural Problem \\
		circuit204 & 1020 & 1020 & 9.03E+09 & Yzhou & Circuit Simulation Problem \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Mixed Precision GMRES-based Iterative Refinement with Recycling" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\hline\hline
 Model & Kolmogorov distance & P-value \\
\hline
ARIMA-GARCH & 0.495 & $2.861e-10$ \\
\hline\hline
\end{tabular}
\end{adjustbox}
\caption{Test of model residuals of ARIMA-GARCH process.}
\end{table}
\end{document}

q-fin

Shi Chen
Cathy Yi-Hsuan Chen
Wolfgang Karl Härdle

"A first econometric analysis of the CRIX family" by Shi Chen, Cathy Yi-Hsuan Chen, and Wolfgang Karl Härdle, arXiv:2009.12129v1, 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{\emph{Effect of regional features.} Mean and standard deviation of accuracy (\%) on CLEVR-CoGenT validation condition B with grid features and regional features. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
Methods & Grid features & Regional features \\ \hline
Transformer & 78.9 $\pm$ 0.80 & 77.4 $\pm$ 0.68 \\
Transformer w/ PR & \textbf{81.7} $\pm$ 1.1 & 78.2 $\pm$ 0.44 \\
TMN-Stack (ours) & 80.6 $\pm$ 0.21 & 79.4 $\pm$ 0.37 \\
TMN-Tree (ours) & 80.1 $\pm$ 0.72 & \textbf{80.9} $\pm$ 0.25 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Moyuru Yamada
Vanessa D'Amario
Kentaro Takemoto
Xavier Boix
Tomotake Sasaki

"Transformer Module Networks for Systematic Generalization in Visual Question Answering" by Moyuru Yamada, Vanessa D'Amario, Kentaro Takemoto, Xavier Boix, and Tomotake Sasaki, arXiv:2201.11316v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{C-OPF vs. ENApp D-OPF for IEEE 8500-node Test System}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{Test System}& \multicolumn{2}{c|}{Total Loss (kW)} & \multicolumn{2}{c|}{Time (sec)}& \#Iterations\\\cline{2-6}
 &{C-OPF{\color{black}$^*$}}&{D-OPF}&{C-OPF}&{D-OPF}&{D-OPF}\\
\hline
{TS-3} &{249}&{249.1} & {NA}& {180} & 17\\
\hline
{TS-4} &{152} & {152.8} & {NA} & {700} & 17\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Distributed Optimization using Reduced Network Equivalents for Radial Power Distribution Systems" by Rabayet Sadnan and Anamika Dubey, arXiv:2012.02473v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{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
{\small{}{}Rebalancing} & {\small{}E{[}$R_{T}${]}} & {\small{}Median{[}$R_{T}${]}} & {\small{}$R_{T}:5^{th}$} & {\small{}$R_{T}:95^{th}$} & {\small{}ES( 5\% )} & {\small{}$\Omega(1)$} & {\small{}$Prob[R_{T}>1]$}\tabularnewline
{\small{}interval} & & & {\small{}percentile} & {\small{}percentile} & & & \tabularnewline
\midrule
 & \multicolumn{7}{c}{{\small{}Using optimal $\alpha^{\ell\ast}$ for $CD\left(\delta=0.02\right)$}}\tabularnewline
\midrule
{\small{}Quarterly} & {\small{}1.1153 (<0.001)} & {\small{}1.1423} & {\small{}0.8857} & {\small{}1.2407} & {\small{}0.7134} & {\small{}7.8316} & {\small{}0.8956}\tabularnewline
\midrule
 & \multicolumn{7}{c}{{\small{}Using optimal $\alpha^{\ell\ast}$ for $CD\left(\delta=0.04\right)$}}\tabularnewline
\midrule
{\small{}Quarterly} & {\small{}1.2419 (<0.001)} & {\small{}1.3073} & {\small{}0.6995} & {\small{}1.4576} & {\small{}0.4919} & {\small{}8.0831} & {\small{}0.8814}\tabularnewline
\midrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Peter Forsyth
Pieter van Staden
Yuying Li

"Making Leveraged Exchange-Traded Funds Work for your Portfolio" by Peter Forsyth, Pieter van Staden, and Yuying Li, arXiv:2506.19200v1, 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}{ll}
		\textbf{Hyperparameter}	& \textbf{Value} \\
		Optimiser & Adam \\
		Batch size & $25$ \\
		Initial learning rate & $0.001$ \\
		Epochs & $100$ \\
		Dropout value & $0.3$\\
		Inflation factor & $2$\\
		Dimension of latent space & $50$ \\
		Learning rate schedule & $0.5$ [decaying*]
	\end{tabular}
\end{adjustbox}
\caption{Hyperparameters for the bouncing balls experiment. (*) The learning rate decays exponentially at steps of 30 epochs.}
\end{table}
\end{document}

cs

Imbedding Deep Neural Networks

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Details of \texttt{ComTree} in Evaluation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
\hline
\texttt{ComTree}(P)& The optimal tree generated by teacher Pensieve\\
\hline
\texttt{ComTree}\_C&The most comprehensible tree in Rashomon\\
\hline
'-L' suffix& Adjusted by LLM
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Lianchen Jia
Chaoyang Li
Ziqi Yuan
Jiahui Chen
Tianchi Huang
Jiangchuan Liu
Lifeng Sun

"Beyond Interpretability: Exploring the Comprehensibility of Adaptive Video Streaming through Large Language Models" by Lianchen Jia, Chaoyang Li, Ziqi Yuan, Jiahui Chen, Tianchi Huang, Jiangchuan Liu, and Lifeng Sun, arXiv:2508.16448v1, 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}{|lll|}
\hline
Approximating Exchangeable RGN & Parameter Specification & Features\\
\hline
$ER$ & $K$=1, $N=100$ (fig. 3) & Blocks and Density\\
\hline
$EG_1$ & Point of ER after 100 moves (fig. 5) & Blocks and Density \\
\hline
$EG_2$ & $\lambda=0.5$, $\epsilon=0.5$, $N=100$ & Blocks and Density \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Approximating Exchangeable Random graph models, parameter vectors and graph features considered for setting up simulation regimes.}
\end{table}
\end{document}

stat

Robustness on Networks

Marios Papamichalis
Simon Lunagomez
Patrick J. Wolfe

"Robustness on Networks" by Marios Papamichalis, Simon Lunagomez, and Patrick J. Wolfe, arXiv:2012.02914v1, 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}{lc|cccc}
		\hline\hline
		Level $\alpha$ & Method & False discoveries & True discoveries & FDP \\ \hline
		$\alpha = 0.01$ & Multiplication & 13 & 59 & 0.1806 \\
		& Minimum & 13 & 58 & 0.1831 \\
		& Addition & 13 & 60 & 0.1781 \\
 & SDA & \textbf{0} & {18} & \textbf{0}\\
 & BHq & 1 & 26 & 0.0370 \\
 \hline
 		$\alpha = 0.05$ & Multiplication & 20 & 84 & 0.1923 \\
		& Minimum & 20 & 83 & 0.1942 \\
		& Addition & 20 & 84 & 0.1923 \\
 & SDA & \textbf{2} & {25} & \textbf{0.0741}\\
 & BHq & 10 & 53 & 0.1587 \\
 \hline
 		$\alpha = 0.1$ & Multiplication & 24 & \textbf{95} & \textbf{0.2017} \\
		& Minimum & 24 & 94 & 0.2034 \\
		& Addition & 25 & 95 & 0.2083 \\
 & SDA & \textbf{8} & {49} & \textbf{0.1404} \\
 & BHq & 22 & 76 & 0.2245 \\
 \hline
 $\alpha = 0.2$ & Multiplication & 33 & 108 & 0.2340 \\
		& Minimum & 33 & 108 & 0.2340 \\
		& Addition & 33 & 108 & 0.2340 \\
 & SDA & \textbf{23} & 89 & \textbf{0.2054} \\
 & BHq & 36 & 115 & 0.2384 \\
 \hline \hline
	\end{tabular}
\caption{Numbers of the discovered entry pairs with FDP by different data aggregation methods under various levels on MovieLens data.}
\end{table}
\end{document}

math

"Multiple Testing of Linear Forms for Noisy Matrix Completion" by Wanteng Ma, Lilun Du, Dong Xia, and Ming Yuan, arXiv:2312.00305v2, 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{Confusion matrix for the random forest algorithm on the metrics dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|ccc|}
 \cline{3-5}
 \multicolumn{2}{c|}{\multirow{2}{*}{Diagnosis}} & \multicolumn{3}{|c|}{Actual} \\
 \cline{3-5}
 \multicolumn{2}{c|}{} & P & C & H \\
 \hline
 & P & 95.1\,\% & 55.0\,\% & 0\,\% \\
 & C & 4.9\,\% & 37.5\,\% & 0\,\% \\
 \multirow{-3}{*}{Predicted} & H & 0\,\% & 7.5\,\% & 100\,\% \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

C. V. Vletter
H. L. Burger
H. Alers
N. Sourlos
Z. Al-Ars

"Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features" by C. V. Vletter, H. L. Burger, H. Alers, N. Sourlos, and Z. Al-Ars, arXiv:2201.11852v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Microscopic Traffic Models, Accidents, and Insurance Losses" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, 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{Numerical summary of implementation details.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
		\hline
		\bfseries Implementation Detail & \bfseries Value\\
		\hline
		Batch size ($b$) & $10$\\
		\hline
		Kernel initialization mean(std.) & $0(0.001)$ \\
		\hline
		Learning rate& $10^{-4}$\\
		\hline
		L1, L2 coefficients & $10^{-8}, 10^{-6}$ \\
		\hline
		Dropout & $0.5$\\
		\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Modelling brain lesion volume in patches with CNN-based Poisson Regression" by Kevin Raina, arXiv:2011.13927v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.