\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Experiments on three datasets compare DGCF with six baseline models based on Mean Reciprocal Rank (MRR) and Recall@10 (R@10). The bold and underlined numbers mean the best and second-best results on each dataset and metric, respectively. "Improvement" means the minimum improvement among all baselines.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{clccccccccc}
 \toprule
 {Models}
 &\multicolumn{2}{c}{LastFM} &\multicolumn{2}{c}{Wikipedia} &\multicolumn{2}{c}{Reddit} \\
 \cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7}
 & MRR & R@10 & MRR & R@10 & MRR & R@10 \\
 \midrule
 LSTM & 0.081 & 0.127 & 0.332 & 0.459 & 0.367 & 0.573 \\
 Time-LSTM & 0.088 & 0.146 & 0.251 & 0.353 & 0.398 & 0.601 \\
 RRN & 0.093 & 0.199 & 0.530 & 0.628 & 0.605 & 0.751 \\
 CTDNE & 0.010 &0.010 &0.035 &0.056 &0.165 &0.257 \\
 DeepCoevolve & 0.021 & 0.042 & 0.515 & 0.563 & 0.243 & 0.305 \\
 Jodie & \underline{0.239} & \underline{0.387} & \underline{0.746} & \underline{0.821} & \underline{0.724} & \underline{0.851} \\
 DGCF & \textbf{0.321} & \textbf{0.456} & \textbf{0.786} & \textbf{0.852} & \textbf{0.726} & \textbf{0.856} \\
 \midrule
 Improvement & 34.3\% & 27.7\% & 5.4\% & 3.6\% & 0.2\% & 0.5\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Dynamic Graph Collaborative Filtering

Xiaohan Li
Mengqi Zhang
Shu Wu
Zheng Liu
Liang Wang
Philip S. Yu

"Dynamic Graph Collaborative Filtering" by Xiaohan Li, Mengqi Zhang, Shu Wu, Zheng Liu, Liang Wang, and Philip S. Yu, arXiv:2101.02844v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Alina Landowska
Robert A. Kłopotek
Dariusz Filip
Konrad Raczkowski

"GDP-GFCF Dynamics Across Global Economies: A Comparative Study of Panel Regressions and Random Forest" by Alina Landowska, Robert A. Kłopotek, Dariusz Filip, and Konrad Raczkowski, arXiv:2504.20993v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\large Regression Results: Impact of changes in active mines on unemployment rate accounting for county-level renewable energy investment}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \tabularnewline\midrule\midrule
 Dependent Variable: & \multicolumn{2}{c}{$\Delta$ Unemployment Rate}\\
 Model: & (1) & (2)\\
 \midrule \emph{Variables} & & \\
 $\Delta$ Active Mines\textsubscript{t} & -0.0640$^{**}$ & -0.0470$^{**}$\\
 & (0.0163) & (0.0144)\\
 $\Delta$ Active Mines\textsubscript{t-1} & -0.0015 & -0.0001\\
 & (0.0160) & (0.0113)\\
 $\Delta$ Active Mines\textsubscript{t-2} & 0.0434$^{**}$ & 0.0369$^{**}$\\
 & (0.0135) & (0.0111)\\

 $\Delta$ Active Mines\textsubscript{t} $\times$ REE\textsubscript{t} & 0.0606$^{.}$ & 0.0562$^{*}$\\
 & (0.0286) & (0.0262)\\
 $\Delta$ Active Mines\textsubscript{t-1} $\times$ REE\textsubscript{t-1} & 0.0150 & 0.0182\\
 & (0.0180) & (0.0149)\\
 $\Delta$ Active Mines\textsubscript{t-2} $\times$ REE\textsubscript{t-2} & -0.0427$^{*}$ & -0.0335\\
 & (0.0176) & (0.0272)\\
 REE $\geq 0.1$\% of GDP\textsubscript{t} & -0.0614$^{*}$ & -0.0015\\
 & (0.0232) & (0.0408)\\
 REE $\geq 0.1$\% of GDP\textsubscript{t-1} & -0.0227 & 0.0192\\
 & (0.0236) & (0.0494)\\
 REE $\geq 0.1$\% of GDP\textsubscript{t-2} & -0.0034 & 0.0765\\
 & (0.0233) & (0.0661)\\
 $\Delta$ (log) Real GDPPC\textsubscript{t} & -1.009$^{**}$ & -1.617$^{***}$\\
 & (0.2519) & (0.3656)\\
 \midrule \emph{Fixed-effects} & & \\
 County FIPS Code & Yes & Yes\\
 Year & Yes & Yes\\
 \midrule \emph{Fit statistics} & & \\
 Observations & 49,152 & 4,016\\
 R$^2$ & 0.62396 & 0.66401\\
 Within R$^2$ & 0.01613 & 0.04872\\
 \midrule\midrule\multicolumn{3}{l}{\emph{Clustered (County FIPS Code \& Year) standard-errors in parentheses}}\\
 \multicolumn{3}{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[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Traditional Text Mining (TM) vs. Software Development Process Mining (SDPM)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
	\hline\noalign{\smallskip}
			\textbf{} & \textbf{TM} & \textbf{SDPM}\\
	\noalign{\smallskip}\hline\noalign{\smallskip}

	\multicolumn{3}{l}{\cellcolor{gray!10}\textbf{Inputs}}\\
	\textbf{Corpus} & Documents/Articles & Development Work Sessions\\
	\textbf{Document} & Mixture of Topics & Mixture of Behaviours\\
	\textbf{Topic} & Frequent Words/Terms & Frequent Actions/Commands\\
 \multicolumn{3}{l}{\cellcolor{gray!10}\textbf{Outputs}}\\
	\textbf{Discovers} & Distinct Subjects/Topics & Development Patterns\\
	\textbf{Usefulness} & Identify Social Trends & Optimize Resource Allocation\\
	\textbf{} & Frame Research Interests & Detect Practices Deviations\\
	\textbf{} & Sentiment Analysis & Forensic Project Analysis\\
 \noalign{\smallskip}\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

João Caldeira
Fernando Brito e Abreu
Jorge Cardoso
Ricardo Ribeiro
Claudia Werner

"Profiling Software Developers with Process Mining and N-Gram Language Models" by João Caldeira, Fernando Brito e Abreu, Jorge Cardoso, Ricardo Ribeiro, and Claudia Werner, arXiv:2101.06733v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Julia Herbinger
Susanne Dandl
Fiona K. Ewald
Sofia Loibl
Giuseppe Casalicchio

"Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation" by Julia Herbinger, Susanne Dandl, Fiona K. Ewald, Sofia Loibl, and Giuseppe Casalicchio, arXiv:2310.03112v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Reference} & \textbf{Year} & \textbf{Authors} & \textbf{Total
Citations} & \textbf{Taxonomy} \\ \hline\hline
1 & 1948 & Shannon & 43150 & G0 \\ \hline
31 & 2000 & Richman, Moorman & 16606 & G2 \\ \hline
18 & 1988 & Tsallis & 14855 & G0 \\ \hline
22 & 1991 & Pincus & 13763 & G2 \\ \hline
148 & 2002 & Bandt, Pompe & 10260 & G1 \\ \hline
248 & 2000 & Schreiber & 9743 & G2 \\ \hline
226 & 1992 & Coifman, Wickerhause & 8003 & G1 \\ \hline
21 & 2005 & Costa, Goldberger, Pen & 5624 & G1 \\ \hline
101 & 1972 & De Luca, Termini & 5217 & G2 \\ \hline
178 & 1982 & Rao & 4027 & G1 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Most referenced publications by DOI as of this writting (December 2024). The total citations are based on Semantic Scholar, CrossRef and OpenCitations.}
\end{table}
\end{document}

math

Salomé A. Sepúveda Fontaine
José M. Amigó

"Applications of Entropy in Data Analysis and Machine Learning: A Review" by Salomé A. Sepúveda Fontaine and José M. Amigó, arXiv:2503.02921v1, 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{\textsc{Final results upon cross-validation.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \toprule[1.5pt]
 State & $P_+$ (\%) & Se (\%) & $F_1$ (\%) \\
 \midrule[1.5pt]
 S1 & 85.7 & 86.5 & 86.0 \\
 Sys & 90.0 & 90.0 & 90.0 \\
 S2 & 87.1 & 86.7 & 86.9 \\
 Dias & 94.9 & 94.6 & 94.8 \\
 \midrule[1pt]
 Average & 89.3 & 89.5 & 89.4 \\
 \midrule[1pt]
 ACC (\%) & 91.34 & & \\
 \bottomrule[1.5pt]
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Deep Recurrent Learning for Heart Sounds Segmentation based on Instantaneous Frequency Features" by Alvaro Joaquín Gaona and Pedro David Arini, arXiv:2201.11320v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary Statistics of Selected US Stocks (April 2018 -- March 2025)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllccl}
\toprule
\textbf{Ticker} & \textbf{Company Name} & \textbf{Sector} & \textbf{Mean Return (\%)} & \textbf{Mean SD (\%)} & \textbf{Skewness} \\
\midrule
MSFT & Microsoft Corporation & Information Technology & 0.09 & 1.81 & -0.29 \\
GOOGL & Alphabet Inc. (Class A) & Communication Services & 0.06 & 1.95 & -0.24 \\
NVDA & NVIDIA Corporation & Information Technology & 0.17 & 3.29 & -0.24 \\
AMZN & Amazon.com, Inc. & Consumer Discretionary & 0.06 & 2.16 & -0.14 \\
META & Meta Platforms, Inc. & Communication Services & 0.07 & 2.67 & -1.34 \\
TSLA & Tesla, Inc. & Consumer Discretionary & 0.16 & 4.06 & -0.07 \\
AAPL & Apple Inc. & Information Technology & 0.10 & 1.93 & -0.23 \\
HD & The Home Depot, Inc. & Consumer Discretionary & 0.05 & 1.70 & -1.43 \\
ADBE & Adobe Inc. & Information Technology & 0.03 & 2.33 & -0.83 \\
NFLX & Netflix, Inc. & Communication Services & 0.07 & 2.83 & -2.18 \\
BRK-B & Berkshire Hathaway Inc. & Financials & 0.06 & 1.30 & -0.21 \\
V & Visa Inc. & Financials & 0.06 & 1.64 & -0.07 \\
MA & Mastercard Incorporated & Financials & 0.07 & 1.84 & 0.03 \\
JNJ & Johnson \& Johnson & Health Care & 0.03 & 1.22 & -0.33 \\
UNH & UnitedHealth Group & Health Care & 0.06 & 1.80 & -0.53 \\
JPM & JPMorgan Chase \& Co. & Financials & 0.06 & 1.86 & -0.02 \\
PG & Procter \& Gamble Company & Consumer Staples & 0.05 & 1.27 & -0.03 \\
PEP & PepsiCo, Inc. & Consumer Staples & 0.03 & 1.31 & -0.57 \\
XOM & Exxon Mobil Corporation & Energy & 0.05 & 1.95 & -0.16 \\
COST & Costco Wholesale Corporation & Consumer Staples & 0.10 & 1.45 & -0.53 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Semiparametric Dynamic Copula Models for Portfolio Optimization" by Savita Pareek and Sujit K. Ghosh, arXiv:2504.12266v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model results for the sleep deprivation study.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
	\hline
	& \textit{lme4} & PLS & PRLS \\ \hline
	Intercept & 251.405 & 250.389 & 250.356 \\ \hline
	Days & 10.467 & 10.789 & 10.795 \\ \hline
	S.D. of random intercept & 25.051 & 25.555 & 26.407 \\ \hline
	S.D. of random slope & 5.988 & 6.228 & 6.232 \\ \hline
	S.D. of residuals & 25.565 & 20.140 & 19.509 \\ \hline \hline
	Marginal $R^2$ & 0.415 & 0.467 & 0.463 \\ \hline
	Conditional $R^2$ & 0.702 & 0.803 & 0.817 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects" by Hao Chen, Lanshan Han, and Alvin Lim, arXiv:2011.04538v9, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Mathieu Besançon
Sébastien Designolle
Jannis Halbey
Deborah Hendrych
Dominik Kuzinowicz
Sebastian Pokutta
Hannah Troppens
Daniel Viladrich Herrmannsdoerfer
Elias Wirth

"Improved algorithms and novel applications of the FrankWolfe.jl library" by Mathieu Besançon, Sébastien Designolle, Jannis Halbey, Deborah Hendrych, Dominik Kuzinowicz, Sebastian Pokutta, Hannah Troppens, Daniel Viladrich Herrmannsdoerfer, and Elias Wirth, arXiv:2501.14613v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Event-Based Limit Order Book Simulation under a Neural Hawkes Process: Application in Market-Making" by Luca Lalor and Anatoliy Swishchuk, arXiv:2502.17417v1, 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}{|lllll|}
\hline
 Layer type & Kernel & Stride & Outputs & Activation \\ \hline
Waveform & - & - & $1\times 8192$& - \\
 Conv 1D + GN& 10 & 5 & $C\times 1637$& gelu \\
Conv 1D ($\times 4$)& 3 & 2 & $C\times 101$& gelu \\
 Conv 1D ($\times 2$)& 2 & 2 & $C\times 25$& gelu \\
 LN + Linear& - & - & $C\times 25$& - \\
 Conv 1D + residual & 64 & 1 & $C\times 25$& gelu \\
 & & & &\\%
 LN + Conv 1D ($\times 3$)& 3 & 2 & $C\times 2$& gelu \\
 Conv 1D & 2 & 2 & $C$& gelu \\
 & & & &\\
 FC ($\times 2$)& - & - & 150 & gelu \\
 FC & - & - & 78 &- \\
 Non-linearity & - & - & 78 &tanh \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Architecture details of our proposed real-time student networks. Here, GN and LN refer to group normalization and layer normalization, respectively. Our models differ in the number of channels $C$. The input size stays the same irrespective of the length of future context. }
\end{table}
\end{document}

eess

Zhen Han
Mattias Teye
Derek Yadgaroff
Judith Bütepage

"Tiny is not small enough: High-quality, low-resource facial animation models through hybrid knowledge distillation" by Zhen Han, Mattias Teye, Derek Yadgaroff, and Judith Bütepage, arXiv:2507.18352v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|lll}
\# & $\tau_{large}$ &$\tau_{mix}$ & $\tau_{small}$ \\\hline
1 & 19 & $\ddagger$ & 2 \\
2 & $\ddagger$ & $\ddagger$ & $\S$ \\
3 & $\ddagger$ & $\ddagger$ & 2 \\
4 & 12 & 20 & 2 \\
5 & 570 & $\ddagger$ & 4 \\
6 & $\ddagger$ & 58 & 12 \\
7 & 3 & 6 & 2 \\
8 & $\ddagger$ & 1 & 1
 \end{tabular}
\end{adjustbox}
\caption{Number of outer fGMRES iterations for the new scheme (symmetric case) with $M$-orthogonalization}
\end{table}
\end{document}

math

"Robust iterative methods for linear systems with saddle point structure" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Prediction Error for THz Channel Factors computed across 4 Channel Paths}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 Average Absolute Error & Mean & Standard Deviation\\
 \hline
 AoD$~\phi$~(deg) & 5.67 & 1.23\\
 AoD~$\theta$~(deg) & 2.36 & 0.47\\
 AoA~$\phi$~(deg) & 2.22 & 0.63\\
 AoA~$\theta$~(deg) & 4.43 & 0.91\\
 Phase (deg) & 5.93 & 1.08\\
 ToA ($10^{-18} s$) & 3.80 & 0.67\\
 Pathloss ($10^{-19}$) & 5.70 & 0.35\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Deep Learning for THz Channel Estimation and Beamforming Prediction via Sub-6GHz Channel" by Sagnik Bhattacharya and Abhishek K. Gupta, arXiv:2411.15589v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters settings of GPOPS Version 4.1}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 Parameter & Setting \\
 \midrule
 setup.autoscale & `on' \\
 limits(p).nodesPerInterval & 25 (p=1,...,7) \\
 limits(p).meshPoints & [-1,1](p=1,...,7) \\
 setup.mesh.iteration & 5 \\
 setup.derivatives & `finite-difference' \\
 setup.tolerances & 1e-1 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"A fast-solved model for energy-efficient train control based on convex optimization" by Minling Feng, Kunpeng Wu, and Shaofeng Lu, arXiv:2201.10731v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yangxinyu Xie
Xiang Li
Tanwi Mallick
Weijie J. Su
Ruixun Zhang

"Debiasing Watermarks for Large Language Models via Maximal Coupling" by Yangxinyu Xie, Xiang Li, Tanwi Mallick, Weijie J. Su, and Ruixun Zhang, arXiv:2411.11203v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Serafino Cicerone
Gabriele Di Stefano
Sandi Klavžar
Ismael G. Yero

"Burning some myths on privacy properties of social networks against active attacks" by Serafino Cicerone, Gabriele Di Stefano, Sandi Klavžar, and Ismael G. Yero, arXiv:2504.16944v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems" by Grzegorz Filcek and Janusz Miroforidis, arXiv:2401.00292v1, 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 zeroth-order sharpness result at converged minima}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
\toprule
 & \multicolumn{5}{c}{Individual domains} & \multirow{2}{*}{Mean (Std)} & \multirow{2}{*}{Total} & \multirow{2}{*}{Unseen} \\
 & Clipart & Painting & Quickdraw & Real & Sketch & & & \\ \midrule
SAM & 1.63 & 6.22 & 7.86 & 4.89 & 3.38 & 4.79 (2.17) & 19.68 & 70.59 \\
DGSAM & \textbf{1.17} & \textbf{2.78} & \textbf{4.74} & \textbf{4.39} & \textbf{1.80} & \textbf{2.98 (1.40)} & \textbf{6.41} & \textbf{42.46} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Youngjun Song
Youngsik Hwang
Jonghun Lee
Heechang Lee
Dong-Young Lim

"DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization" by Youngjun Song, Youngsik Hwang, Jonghun Lee, Heechang Lee, and Dong-Young Lim, arXiv:2503.23430v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Mohanad Sarhan
Siamak Layeghy
Marius Portmann

"Towards a Standard Feature Set for Network Intrusion Detection System Datasets" by Mohanad Sarhan, Siamak Layeghy, and Marius Portmann, arXiv:2101.11315v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{siunitx}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{DERs (\%) of each number of speakers on the CALLHOME dataset with \SI{10}{\second} chunk size. \textit{Both estimated the number of speakers and included the overlapping regions without using oracle SAD.}}
\begin{tabular}{lccc}
 \toprule
 &\multicolumn{3}{c}{Number of speakers} \\ \cmidrule(l){2-4}
 Method & 2 & 3 & 4 \\ \midrule
 BW-EDA-EEND & 11.8 & 18.3 & 26.0 \\
 EEND-EDA w/ FLEX-STB & \bf 10.0 & \bf14.0 & \bf 21.1 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Yawen Xue
Shota Horiguchi
Yusuke Fujita
Yuki Takashima
Shinji Watanabe
Paola Garcia
Kenji Nagamatsu

"Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers" by Yawen Xue, Shota Horiguchi, Yusuke Fujita, Yuki Takashima, Shinji Watanabe, Paola Garcia, and Kenji Nagamatsu, arXiv:2101.08473v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|}
\hline
\bf Method & \bf Accuracy & \bf F1-score\\
\hline
SVM Model(tf-idf) & 0.939 & 0.938 \\ \hline
Bagging Model(word2vec) & 0.910 & 0.910 \\
Boosting Model(word2vec) & 0.927 & 0.926 \\ \hline
roberta-base & 0.9864 & 0.9864 \\
electra-base & 0.9827 & 0.9827 \\
\hline
\hline
\end{tabular}
\caption{Results on the official test set for COVID-19 Fake news identification task for English language. The first section denotes the baseline models on tf-idf. Te second section denotes the baseline models on word2vec. The third section refers to the transformers models.}
\end{table}
\end{document}

cs

Tathagata Raha
Vijayasaradhi Indurthi
Aayush Upadhyaya
Jeevesh Kataria
Pramud Bommakanti
Vikram Keswani
Vasudeva Varma

"Identifying COVID-19 Fake News in Social Media" by Tathagata Raha, Vijayasaradhi Indurthi, Aayush Upadhyaya, Jeevesh Kataria, Pramud Bommakanti, Vikram Keswani, and Vasudeva Varma, arXiv:2101.11954v2, 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{ Evaluation results for the fine-tuned LongFormer model on the manually annotated Wikipedia dataset. A generic neural NER model was also used for comparison.} %
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
	\toprule %
	 & $R_{di+qi}$ & $\textit{ER}_{di}$ & $\textit{ER}_{qi}$ & $P_{di+qi}$ & $\textit{WP}_{di+qi} $ \\
\midrule
 RoBERTa (fined-tuned on Ontonotes) &	0.845 & 0.810 & 0.801 & 0.770 & 0.836\\
 \midrule
Longformer (fine-tuned on TAB) & 0.952 & 0.999 & 0.923 & 0.708 & 0.765\\
	\midrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ildikó Pilán
Pierre Lison
Lilja Øvrelid
Anthi Papadopoulou
David Sánchez
Montserrat Batet

"The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization" by Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi Papadopoulou, David Sánchez, and Montserrat Batet, arXiv:2202.00443v2, 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}{ccc}
 \toprule
 $h=1$ & $h=2$ & $h=3$\\
 \midrule
 $(\,2\,)$ & $(\,2\,\,2\,)$ & $(\,2\,\,2\,\,2\,)$\\
 $(\,4\,)$ & $(\,2\,\,4\,)$ & $(\,2\,\,2\,\,4\,)$\\
 $(\,6\,)$ & $(\,2\,\,6\,)$ & \\
 $(\,8\,)$ & $(\,4\,\,4\,)$ & \\
 $(\,10\,)$ & & \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Possible sub-signatures with $d=11$.}
\end{table}
\end{document}

math

"Relative period realization of holomorphic differentials with prescribed invariants" by Dawei Chen and Gianluca Faraco, arXiv:2503.21316v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc|ccc|c}
 \hline
 Problem &
 \multicolumn{3}{c|}{Max Poly. Degrees}&
 \multicolumn{3}{c|}{Time (seconds)}&
 Successful\\
 \cline{2-7}
 & Max & Avg & Median & Avg & Median & Total & runs \\
 \hline
 \multirow{2}{*}{\texttt{left\_strict}}
 & 8 & 5.8 & 6 & 0.041 & 0.036 & 3.557 & 87/100 \\
 & 14 & 7.16 & 6 & 0.092 & 0.057 & 9.185 & 100/100 \\
 \hline
 \multirow{2}{*}{\texttt{right\_strict}}
 & 8 & 5.775 & 6 & 0.041 & 0.036 & 3.287 & 81/100 \\
 & 8 & 6.88 & 8 & 0.064 & 0.058 & 6.411 & 100/100 \\
 \hline
 \multirow{2}{*}{\texttt{lifted\_prods}}
 & 8 & 5.775 & 6 & 0.007 & 0.007 & 0.548 & 74/100 \\
 & 8 & 6.88 & 6 & 0.064 & 0.057 & 6.387 & 100/100 \\
 \hline
 \multirow{2}{*}{\texttt{arch\_poly}}
 & 8 & 5.932 & 6 & 0.039 & 0.037 & 1.667 & 43/100 \\
 & 8 & 7.06 & 6 & 0.085 & 0.07 & 8.493 & 100/100 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Computing certificates of four kinds of strictly positive univariate polynomials}
\end{table}
\end{document}

math

Weifeng Shang
Chenqi Mou
Jose Abel Castellanos Joo
Deepak Kapur

"Computing Certificates of Strictly Positive Polynomials in Archimedean Quadratic Modules" by Weifeng Shang, Chenqi Mou, Jose Abel Castellanos Joo, and Deepak Kapur, arXiv:2503.11119v1, 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{RMSE, MAPE, and Mean NQL by the seasonality modes for Prophet models.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 \toprule
 Model & RMSE & MAPE & Mean NQL[0.1, 0.9] \\
 \midrule
 Prophet w/ additive seasonality & \bf 83.89$\pm$1.98 & 0.461$\pm$0.038 & \bf 0.202$\pm$0.009 \\
 Prophet w/ multiplicative seasonality & 97.02$\pm$16.85 & \bf 0.460$\pm$0.044 & 0.222$\pm$0.010 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Seungjae Jung
Kyung-Min Kim
Hanock Kwak
Young-Jin Park

"A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting" by Seungjae Jung, Kyung-Min Kim, Hanock Kwak, and Young-Jin Park, arXiv:2011.10715v1, 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{Fees in USD on Ethereum, Polygon and Solana}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 \hline
 & Pre- & Post- \\ \hline \hline
 Ether Transactions & \$1.9453 & \$1.7031 \\
 Ethereum Tokens & \$3.2387 & \$2.6106\\
 Polygon Matic Transactions & \$0.0232 & \$0.0242 \\
 Polygon Tokens & \$0.1333 & \$0.1244 \\
 Solana Tokens & \$0.0011 & \$0.0008\\
\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"An Event Study of the Ethereum Transition to Proof-of-Stake" by Elie Kapengut and Bruce Mizrach, arXiv:2210.13655v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|rr}
 \toprule
 \multirow{2}{*}{\textbf{Language}} & \multicolumn{2}{c}{\textbf{Error rate level}} \\
 & \textbf{Character} & \textbf{Word} \\
 \midrule
 Dutch & 0.286 & 0.536 \\
 English & 0.075 & 0.146 \\
 French & 0.064 & 0.193 \\
 German & 0.240 & 0.813 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{OCR error rates per language, averaged over documents.}
\end{table}
\end{document}

cs

"An Assessment of the Impact of OCR Noise on Language Models" by Konstantin Todorov and Giovanni Colavizza, arXiv:2202.00470v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Conversion of Neural Radiology Model output to Symbolic Tree input}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
Max Prob. & ASO & GGO \\
\midrule
ASO & 1 & 0 \\
GGO & 0 & 1 \\
ASO\_GGO & 1 & 1 \\
No\_ASO\_GGO & 0 & 0 \\
Missing\_ASO\_GGO & ? & ? \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Rishab Khincha
Soundarya Krishnan
Tirtharaj Dash
Lovekesh Vig
Ashwin Srinivasan

"Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays" by Rishab Khincha, Soundarya Krishnan, Tirtharaj Dash, Lovekesh Vig, and Ashwin Srinivasan, arXiv:2012.10787v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Monetary policy rules}
\begin{tabular}{lll}
\hline \hline
 Nr.\ & Name & Equation\\\hline\\
(1) & Taylor rule with smoothing & $\tilde{i}_t = \rho_i \tilde{i}_{t-1} + (1-\rho_i)\left(\phi_{\pi}\pi_t + \phi_x \widehat{x}_t
\right)$\\\\
(2) & Taylor rule without smoothing & $\tilde{i}_t = \phi_{\pi}\pi_t $\\\\
(3) & Optimal RE commitment policy & $\pi_t + \tfrac{\Lambda}{\kappa}\left(\widehat{x}_t - \widehat{x}_{t-1}\right) = 0$ \\\\
(4) & Optimal RE discretionary policy & $\pi_t + \tfrac{\Lambda}{\kappa} \widehat{x}_t = 0$ \\\\
(5) & Strict inflation targeting & $\pi_t = 0$ \\\\
\hline \hline
\end{tabular}
\end{table}
\end{document}

q-fin

"The Inflation Attention Threshold and Inflation Surges" by Oliver Pfäuti, arXiv:2308.09480v3, 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{Performance of sentences identified randomly. Total sentences in training data: 1331, out of which 207 (15.6\%) contain discrepancies with adjudicated ground truth. Highest score per column in bold.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|lllll|}
 \hline
 \textbf{Threshold} & \textbf{Sentences to check} & \textbf{Precision}& \textbf{Recall}&\textbf{F-score}\\
&&&&\textbf{(Std Dev)}\\
\hline
100&7.5\% & \textbf{16.0} & 8.6 & 11.2 (1.6)\\
200& 15.0\% & 14.0 & 15.5 & 14.7 (2.2)\\
500& 37.6\% & 15.9 & \textbf{41.0} & \textbf{22.8} (1.6)\\
\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

David Martinez Iraola
Antonio Jimeno Yepes

"Single versus Multiple Annotation for Named Entity Recognition of Mutations" by David Martinez Iraola and Antonio Jimeno Yepes, arXiv:2101.07450v1, 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{Mean annotation time across all datasets with different prompt numbers.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Time (secs)} & \textbf{MRI Ovarian} & \textbf{MRI Renal} & \textbf{CT Lung} & \textbf{US Breast} \\
\hline
\textbf{Radiologist} & 237.0 ± 72.0 & 150.6 ± 68.4 & 286.8 ± 109.2 & 12.6 ± 5.4 \\
\hline
\textbf{RL Agent} & 21.8 ± 1.8 & 10.8 ± 9.2 & 29.0 ± 4.8 & 1.4 ± 1.2 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yuli Wang
Victoria Shi
Wen-Chi Hsu
Yuwei Dai
Sophie Yao
Zhusi Zhong
Zishu Zhang
Jing Wu
Aaron Maxwell
Scott Collins
Zhicheng Jiao
Harrison X. Bai

"Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities" by Yuli Wang, Victoria Shi, Wen-Chi Hsu, Yuwei Dai, Sophie Yao, Zhusi Zhong, Zishu Zhang, Jing Wu, Aaron Maxwell, Scott Collins, Zhicheng Jiao, and Harrison X. Bai, arXiv:2412.17943v2, 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
		Parameter & Definition & Dimension \\
		\hline
		T & Time & time\\
		N & Prey density & biomass \\
		P & Predator density & biomass \\
		A & Additional food & biomass \\
		r & Prey intrinsic growth rate & time$^{-1}$ \\
		K & Prey carrying capacity & biomass \\
		c & Rate of predation & time$^{-1}$ \\
		a & Half Saturation value of the predators & biomass \\
		$\delta$ & Conversion efficiency & time$^{-1}$ \\
		m & death rate of predators in absence of prey & time$^{-1}$ \\
		d & Predator Intra-specific competition & biomass$^{-1}$ time$^{-1}$ \\
		$\alpha$ & quality of additional food & Dimensionless \\
		$\xi$ & quantity of additional food & biomass$^{2}$ \\
		\hline
	\end{tabular}
\end{adjustbox}
\caption{Description of variables and parameters present in the systems () and ().}
\end{table}
\end{document}

math

"Dynamics and Control of Additional Food Provided Prey-Predator Systems exhibiting Holling Type-III Functional Response and Intra-specific Competition among Predators" by D Bhanu Prakash and D K K Vamsi, arXiv:2504.18035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Shape of Data through the TAES Model}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{clccc}
\toprule
\textbf{Layer} & \textbf{Type} & \textbf{Input size} & \textbf{Output Size} & \textbf{Feature Num} \\ \midrule
1 & Transformer & batch size*360*1 & batch size*360 & 360 \\
2 & Conv1 & batch size*360 & batch size*252 & 252 \\
3 & Conv2 & batch size*252 & batch size*128 & 128 \\
4 & Conv3 & batch size*128 & batch size*72 & 72 \\
5 & Conv4 & batch size*72 & batch size*56 & 56 \\
6 & Conv5 & batch size*56 & batch size*32 & 32 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"High-Throughput Detection of Risk Factors to Sudden Cardiac Arrest in Youth Athletes: A Smartwatch-Based Screening Platform" by Evan Xiang, Thomas Wang, and Vivan Poddar, arXiv:2412.12118v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Detailed information of the apps that were successfully attacked. App names are omitted for security.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \toprule
 Category & Downloads & App description \\
 \midrule
Finance & 100,000,000+ & payment app \\
Finance & 10,000,000+ & personal finance app \\
Finance & 10,000,000+ & financial service app \\
Photography & 5,000,000+ & camera with blur effects \\
Photography & 5,000,000+ & photo filter for sky \\
Entertainment & 5,000,000+ & palm reader, fun photo editor \\
Finance &1,000,000+ & credit card service \\
Entertainment & 1,000,000+ & piloting app for drones \\
Photography & 1,000,000+ & photo \& art word editor \\
Photography & 1,000,000+ & photo editing app \\
Finance & 1,000,000+ & financial mobile service app \\
Photography & 1,000,000+ & photo beauty camera \\
Tools & 500,000+ & parental control app for child phone \\
Lifestyle & 500,000+ & photo editor \\
Business & 100,000+ & document scanner \\
Productivity & 100,000+ & document scanner and translator \\
Education & 100,000+ & helper for career fairs \\
Entertainment & 100,000+ & AI guess drawing game \\
Arcade & 100,000+ & AI guess drawing game \\
Finance & 100,000+ & bank app \\
Business & 100,000+ & internal app access control tool \\
Libraries\&Demo & 50,000+ & face recognition demo \\
Education & 50,000+ & insect study app \\
Entertainment & 10,000+ & camera frame classifier \\
Libraries\&Demo & 10,000+ & object detection demo \\
Education & 10,000+ & drawing teaching app \\
Music\&Audio & 5,000+ & record scanner and detector \\
Health\&Fitness & 5,000+ & skin cancer detection \\
Libraries\&Demo & 5,000+ & object detector demo \\
Libraries\&Demo & 5,000+ & camera frame classifier \\
Libraries\&Demo &1,000+ & demo app for a mobile AI SDK \\
Medical & 1,000+ & confirm medication ingestion \\
Tools & 1,000+ & attendance checker \\
Libraries\&Demo & 1,000+ & machine learning kit demo \\
Libraries\&Demo & 1,000+ & image classifier demo \\
Libraries\&Demo & 1,000+ & flower image classifier \\
Auto\&Vehicles & 1,000+ & traffic sign detector \\
Tools & 1,000+ & sneaker classifier \\
Education & 500+ & object detection demo \\
Tools & 500+ & machine learning benchmark tool \\
Education & 500+ & hand-written digit recognition demo \\
Auto\&Vehicles & 500+ & car image classifier \\
Medical & 500+ & screening app for diabetic retinopathy \\
Medical & 100+ & histology classifier \\
Libraries\&Demo & 100+ & image classifier demo \\
Education & 100+ & accessibility tool for visually impaired \\
Education & 100+ & FTC game robot detection demo \\
Health\&Fitness & 100+ & feces image classifier \\
Productivity & 100+ & image classifier demo \\
Tools & 100+ & mobile image classification \\
Finance & 100+ & tax rate retriever for goods \\
Tools & 100+ & cash recognizer for visually impaired \\
Tools & 50+ & camera frame classifier \\
Medical & 10+ & diagnostics of dermatoscopic images \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yuanchun Li
Jiayi Hua
Haoyu Wang
Chunyang Chen
Yunxin Liu

"DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection" by Yuanchun Li, Jiayi Hua, Haoyu Wang, Chunyang Chen, and Yunxin Liu, arXiv:2101.06896v1, 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}{llll}
		\toprule
		& True value & Estimation $T=5000$ & Quadratic error \\ \midrule
		$b(1)$& 6& 6.0027876 & 0.000008 \\ \midrule
		$b(2)$& 3& 2.9989096 & 0.000001 \\ \midrule
		$\lambda$& 2& 2.4268411 & 0.18219 \\ \midrule
		$\delta$& 1& 0.6528876 & 0.12049 \\ \bottomrule
	\end{tabular}
\end{adjustbox}
\caption{Estimation of $\theta$ for $T = 5000$, $h=0.1$, $\epsilon=0.03$.}
\end{table}
\end{document}

stat

"Quasi-likelihood-based EM algorithm for regime-switching SDE" by Yuzhong Cheng and Hiroki Masuda, arXiv:2412.06305v1, 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}{lcccc}
\hline
 & \textbf{Axial} & \textbf{Coronal} & \textbf{Sagittal} & \textbf{Average} \\ \hline
\textbf{PSNR (dB)} & 32.4796 & 31.8621 & 33.0792 & 32.4736 \\
\textbf{SSIM} & 0.9664 & 0.9618 & 0.9746 & 0.9676 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{SSIM and PSNR metrics for each axis and overall average. The model converged after approximately \textbf{25.9} minutes of training.}
\end{table}
\end{document}

eess

"InnerGS: Internal Scenes Rendering via Factorized 3D Gaussian Splatting" by Shuxin Liang, Yihan Xiao, and Wenlu Tang, arXiv:2508.13287v1, 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{Summary of the HSI datasets used for experimental evaluation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l||c||c||c||c|c} \hline
 --- & \textbf{IP} & \textbf{PU} & \textbf{KSC} & \textbf{UH} & \textbf{UT}\\ \hline
 \textbf{Year} & 1992 & 2001 & 1996 & 2013 & \\
 \textbf{Source} & AVIRIS & ROSIS-03 & AVIRIS & CASI & AISA \\
 \textbf{Spatial} & $145\times 145$ & $610 \times 610$ & $512\times 614$ & $340\times 1905$ & $600 \times 166$ \\
 \textbf{Spectral} & 220 & 115 & 176 & 144 & 63 \\
 \textbf{Wavelength} & $400-2500$ & $430-860$ & $400-2500$ & $0.35-1.05$ & $0.42-0.99$ \\
 \textbf{Samples} & 21025 & 207400 & 314368 & 1329690 & 199200 \\
 \textbf{Classes} & 16 & 9 & 13 & 15 & 6 \\
 \textbf{Sensor} & Aerial & Aerial & Aerial & Aerial & Aerial \\
 \textbf{Resolution} & $20~m$ & $1.3~m$ & $10~nm$ & $2.5~mpp$ & $1~mpp$\\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Muhammad Ahmad
Sidrah Shabbir
Swalpa Kumar Roy
Danfeng Hong
Xin Wu
Jing Yao
Adil Mehmood Khan
Manuel Mazzara
Salvatore Distefano
Jocelyn Chanussot

"Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects" by Muhammad Ahmad, Sidrah Shabbir, Swalpa Kumar Roy, Danfeng Hong, Xin Wu, Jing Yao, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, and Jocelyn Chanussot, arXiv:2101.06116v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|llll}
\cline{1-3}
\multirow{2}{*}{Method} & \multicolumn{2}{c}{Model} & & \\ \cline{2-3}
 & EfficientNet & Xception & & \\ \cline{1-3}
Clean image & 97\% & 93\% & & \\
PGD $L_{inf} (\epsilon=0.3)$ & \textbf{0\%} & 5\% & & \\
PGD $L_2 (\epsilon=0.3)$ & 60\% & 63\% & & \\
FGSM $L_{inf}$ $(\epsilon=0.3)$ & 13\% & 5\% & & \\
Noise(ours) & \textbf{0\%} & \textbf{0\%} & & \\
Latent(ours) & \textbf{0\%} & \textbf{0\%} & & \\
Noise and latent(ours) & \textbf{0\%} & \textbf{0\%} & & \\\cline{1-3}
\end{tabular}
\end{adjustbox}
\caption{Accuracy different models perform on our method and other adversarial attack method, PGD $L_2$, PGD $L_{inf}$ and FGSM attack. our method has the same ability to bypass the forensic detectors as norm-based adversarial attack, PGD $L_{inf}$ and has better adversarial strength than FGSM and PGD $L_2$ attack. PGD $L_2$ attack shows poor performance on both models because of the limited perturbation scale.}
\end{table}
\end{document}

cs

Dongze Li
Wei Wang
Hongxing Fan
Jing Dong

"Exploring Adversarial Fake Images on Face Manifold" by Dongze Li, Wei Wang, Hongxing Fan, and Jing Dong, arXiv:2101.03272v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistic and comparisons of the baselines and VQShape. The best case is marked with bold, the second best is marked with italic, and the third best is marked with underline. Some baselines fail are incompatible with some datasets which result in ``N/A''. For fair comparison, we report the statistics with and without ``N/A''. Complete results are presented in Table .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccccccccc}
\toprule
 & \multicolumn{2}{c}{Classical} & \multicolumn{5}{c}{Supervised} & \multicolumn{4}{c}{Unsupervised Representation} & \multicolumn{3}{c}{Pre-trained}\\
 \cmidrule(lr){2-3} \cmidrule(lr){4-8} \cmidrule(lr){9-12} \cmidrule(lr){13-15}
 & DTW & STRF & DLinear & Autoformer & FEDformer & PatchTST & TimesNet & TS-TCC & TST & T-Rep & TS2Vec & MOMENT & UniTS & VQShape \\
\midrule
\textbf{Statistics with N/A} \\
Mean Accuracy & 0.648 & 0.660 & 0.635 & 0.570 & 0.612 & 0.669 & 0.710 & 0.682 & 0.630 & \textit{0.719} & \underline{0.712} & 0.686 & 0.629 & \textbf{0.723} \\
Median Accuracy & 0.711 & 0.679 & 0.673 & 0.553 & 0.586 & 0.756 & 0.797 & 0.753 & 0.620 & \underline{0.804} & \textbf{0.812} & 0.759 & 0.684 & \textit{0.810} \\
Mean Rank & 7.138 & 7.828 & 8.690 & 9.448 & 7.750 & 8.296 & \textit{5.143} & 7.172 & 8.448 & \underline{5.207} & \textbf{4.897} & 5.929 & 9.828 & 5.621 \\
Median Rank & 7.0 & 8.0 & 9.0 & 10.0 & 8.0 & 8.0 & \underline{5.0} & 8.0 & 9.0 & \textit{4.0} & \textbf{3.0} & 5.5 & 10.0 & \underline{5.0} \\
Num. Top-1 & 3 & 2 & 1 & 0 & 1 & 0 & 1 & 3 & 1 & 4 & \textbf{6} & \underline{5} & 0 & \textbf{6} \\
Num. Top-3 & 8 & 5 & 5 & 0 & 8 & 2 & 8 & 5 & 4 & \textit{12} & \textbf{16} & \underline{11} & 0 & 9 \\
Num. Win/Tie & 14 & 20 & 22 & 25 & 19 & 20 & 14 & 18 & 20 & 15 & 13 & 13 & 25 & - \\
Num. Lose & 15 & 9 & 7 & 4 & 9 & 7 & 14 & 11 & 9 & 14 & 16 & 15 & 4 & - \\
Wilcoxon p-value & 0.206 & 0.023 & 0.002 & 0.000 & 0.051 & 0.000 & 0.898 & 0.156 & 0.022 & 0.536 & 0.576 & 0.733 & 0.000 & - \\
\midrule
\textbf{Statistics without N/A} \\
Mean Accuracy & 0.642 & 0.658 & 0.635 & 0.561 & 0.601 & 0.657 & 0.703 & 0.669 & 0.623 & \textit{0.710} & \underline{0.704} & 0.688 & 0.618 & \textbf{0.720} \\
Median Accuracy & 0.714 & 0.712 & 0.690 & 0.552 & 0.585 & 0.739 & \underline{0.797} & 0.752 & 0.638 & \textit{0.802} & 0.748 & 0.759 & 0.679 & \textbf{0.812} \\
Mean Rank & 7.231 & 7.692 & 8.923 & 9.462 & 7.731 & 8.346 & \underline{5.308} & 7.538 & 8.462 & \textit{5.192} & \textbf{5.038} & 5.885 & 10.115 & 5.538 \\
Median Rank & 6.5 & 7.5 & 9.5 & 9.5 & 8.0 & 8.5 & 5.000 & 8.0 & 9.5 & \textit{4.5} & \textbf{3.0} & 5.5 & 10.5 & \textit{4.5} \\
Num. Top-1 & 3 & 2 & 1 & 0 & 1 & 0 & 1 & 3 & 1 & 3 & \textbf{5} & \textbf{5} & 0 & \textbf{5} \\
Num. Top-3 & 7 & 5 & 5 & 0 & 8 & 2 & 7 & 4 & 4 & \textit{10} & \textbf{14} & \textit{10} & 0 & 8 \\
Num. Win/Tie & 13 & 18 & 20 & 22 & 17 & 20 & 13 & 17 & 19 & 14 & 12 & 12 & 23 & - \\
Num. Lose & 13 & 8 & 6 & 4 & 9 & 6 & 13 & 9 & 7 & 12 & 14 & 14 & 3 & - \\
Wilcoxon p-value & 0.187 & 0.036 & 0.001 & 0.001 & 0.111 & 0.001 & 0.803 & 0.094 & 0.036 & 0.653 & 0.696 & 1.000 & 0.000 & - \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

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

"Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification" by Yunshi Wen, Tengfei Ma, Tsui-Wei Weng, Lam M. Nguyen, and Anak Agung Julius, arXiv:2411.01006v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Silhouette scores for the clustering based on XPER values and features }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 Number of Clusters & XPER clustering & Feature clustering \\
 \midrule
 2 & 0.2037 & 0.2129 \\
 3 & -0.0244 & 0.1846 \\
 4 & -0.0608 & 0.1718 \\
 5 & -0.1858 & 0.1609 \\
 6 & -0.0624 & 0.1589 \\
 7 & -0.0734 & 0.1595 \\
 8 & -0.0596 & 0.1501 \\
 9 & -0.1027 & 0.1514 \\
 10 & -0.0665 & 0.1471 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Hué Sullivan
Hurlin Christophe
Pérignon Christophe
Saurin Sébastien

"Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring" by Hué Sullivan, Hurlin Christophe, Pérignon Christophe, and Saurin Sébastien, arXiv:2212.05866v4, 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|ccc|c|}
		\hline
		& Poisson & Gamma & CPG & Tweedie \\
		\hline
		Surcharge per Claim & 0.324 & 0.054 & 0.395 & 0.401 \\
		Claims Free Discount & -0.089 & -0.026 & -0.113 & -0.106 \\
		Min. \& Max. Relativities & [0.626, 1.753] & [0.855, 1.000] & [0.535, 1.753] & [0.570, 1.568] \\
		\hline
	\end{tabular}
\end{adjustbox}
\caption{Impacts of past claims for all BMS Models}
\end{table}
\end{document}

stat

"Bonus-Malus scale premiums for tweedie's compound poisson models" by Jean-Philippe Boucher and Raïssa Coulibaly, arXiv:2311.03222v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{Simulation parameters}}
\begin{tabular}{l|l}
\hline
\textbf{Parameters} & \textbf{Values} \\
\hline
\hline
Carrier frequency, $f_c$ & 2 GHz \\
\hline
Bandwidth, $B$ & 10 MHz \\
\hline
Length of coherence block, $\tau_c$ & 100\\
\hline
Coherence bandwidth, $B_c$ & 100 KHz\\
\hline
Coherence time, $T_c$ & 1 ms\\
\hline
Power of pilots, $p$ & 0.2 W\\
\hline
Maximum transmitted power of symbols, $\rho$ & 1 W\\
\hline
Symbol duration, $T_s$ & 10 $\mu s$\\
\hline
Oscillator constants, $c_i,\forall i$ & $1\times 10^{-18}$\\
\hline
Spacings of RIS elements, $d_{\text{RIS}}$ & $\lambda/4$\\
\hline
Spacings of AP elements, $d_{\text{AP}}$ & $\lambda/2$\\
\hline
Height of UEs & $1.5$ m\\
\hline
Height of RIS & $30$ m\\
\hline
Height of APs & $12.5$ m\\
\hline
Max inertia weight, $\omega_\text{max}$ & $0.9$\\
\hline
Min inertia weight, $\omega_\text{min}$ & $0.4$\\
\hline
Number of UEs in the reflection space, $K_R$ & $3$\\
\hline
Number of UEs in the reflection space, $K_T$ & $3$\\
\hline
Number of antennas with each AP, $L$ & $4$\\
\hline
Horizontal width of each RIS element, $d_H$ & $\lambda/4$\\
\hline
Vertical height of each RIS element, $d_V$ & $\lambda/4$\\
\hline
Number of pilots, $\tau_p$ & $3$\\
\hline
Tolerance parameter of bisection algorithm, $\epsilon_\text{bi}$ & $0.01$\\
\hline
Max number of APSO iterations, $T_P$ & $100$\\
\hline
Number of APSO particles, $L_P$ & $10$\\
\hline
RIS phase error parameter, $\vartheta$ & $3$\\
\hline
Number of APs, $M$, & $16$\\
\hline
Number of RIS elements, $N$, & $16$\\
\hline
\end{tabular}
\end{table}
\end{document}

math

Zeping Sui
Hien Quoc Ngo
Michail Matthaiou
Lajos Hanzo

"Performance Analysis and Optimization of STAR-RIS-Aided Cell-Free Massive MIMO Systems Relying on Imperfect Hardware" by Zeping Sui, Hien Quoc Ngo, Michail Matthaiou, and Lajos Hanzo, arXiv:2501.00546v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance Metrics for Community}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|}
 \hline
 \textbf{Simulation Type} & \textbf{$TRM_{h}$} & \textbf{$LRM_{cri}$} & \textbf{$LRM_o$} & \textbf{$LGR$} \\ \hline
 Off-Grid BL (WACSC) & 0.31 & 0.19 & 0.1 & 1 \\ \hline
 Off-Grid BL (WOACSC) & 0.54 & 0.27 & 0.14 & 1 \\ \hline
 Off-Grid RB (WACSC) & 0.77 & 0.56 & 0.27 & 1 \\ \hline
 Off-Grid RB (WOACSC) & 0.78 & 0.52 & 0.25 & 1 \\ \hline
 On-Grid BL (WOACSC) & 1 & 1 & 1 & 0.76 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Smart Residential Community Simulator for Developing and Benchmarking Energy Management Systems" by Ninad Gaikwad and Anamika Dubey, arXiv:2508.09106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The two domains we experiment with LaMDA for domain grounding}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 Name & Domain & Role \\
 \midrule
 Everest & Education & It teaches facts about Mount Everest, while pretending to be Mount Everest itself. \\
 Music & Recommendation & It is a music recommendation agent. \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Romal Thoppilan
Daniel De Freitas
Jamie Hall
Noam Shazeer
Apoorv Kulshreshtha
Heng-Tze Cheng
Alicia Jin
Taylor Bos
Leslie Baker
Yu Du
YaGuang Li
Hongrae Lee
Huaixiu Steven Zheng
Amin Ghafouri
Marcelo Menegali
Yanping Huang
Maxim Krikun
Dmitry Lepikhin
James Qin
Dehao Chen
Yuanzhong Xu
Zhifeng Chen
Adam Roberts
Maarten Bosma
Vincent Zhao
Yanqi Zhou
Chung-Ching Chang
Igor Krivokon
Will Rusch
Marc Pickett
Pranesh Srinivasan
Laichee Man
Kathleen Meier-Hellstern
Meredith Ringel Morris
Tulsee Doshi
Renelito Delos Santos
Toju Duke
Johnny Soraker
Ben Zevenbergen
Vinodkumar Prabhakaran
Mark Diaz
Ben Hutchinson
Kristen Olson
Alejandra Molina
Erin Hoffman-John
Josh Lee
Lora Aroyo
Ravi Rajakumar
Alena Butryna
Matthew Lamm
Viktoriya Kuzmina
Joe Fenton
Aaron Cohen
Rachel Bernstein
Ray Kurzweil
Blaise Aguera-Arcas
Claire Cui
Marian Croak
Ed Chi
Quoc Le

"LaMDA: Language Models for Dialog Applications" by Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Vincent Zhao, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Pranesh Srinivasan, Laichee Man, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, and Quoc Le, arXiv:2201.08239v3, 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{An Example of a Table}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c|}
\hline
One & Two\\
\hline
Three & Four\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Yuhang Mei
Amirhossein Taghvaei
Ali Pakniyat

"A Time-Reversal Control Synthesis for Steering the State of Stochastic Systems" by Yuhang Mei, Amirhossein Taghvaei, and Ali Pakniyat, arXiv:2504.00238v2, 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 Models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Model} & \textbf{PICP} & \textbf{CWC} \\ \hline
quantile Gradient Boosting Regression & 0.144 & 24.923 \\ \hline
quantile linear regression & 0.541 & 14.034 \\ \hline
quantile LSTM & 0.549 & 14.085 \\ \hline
quantile TCN & 1.0 & 20.609 \\ \hline
quantile-attention-LSTM & 0.998 & 7.246 \\ \hline
\textbf{FuturQuant Transformer} & \textbf{0.962} & \textbf{3.175} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Wenhao Guo
Yuda Wang
Zeqiao Huang
Changjiang Zhang
Shumin ma

"Trading Under Uncertainty: A Distribution-Based Strategy for Futures Markets Using FutureQuant Transformer" by Wenhao Guo, Yuda Wang, Zeqiao Huang, Changjiang Zhang, and Shumin ma, arXiv:2505.05595v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Rongye Shi
Zhaobin Mo
Kuang Huang
Xuan Di
Qiang Du

"Physics-Informed Deep Learning for Traffic State Estimation" by Rongye Shi, Zhaobin Mo, Kuang Huang, Xuan Di, and Qiang Du, arXiv:2101.06580v1, 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{Estimated coefficients of boosted linear models (GB-LM) and boosted negative binomial regression (GB-NB) on respective train data sets. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\\ \hline
\hline \\
\textbf{Variable} & \textbf{GB-LM} & \textbf{GB-NB} \\
\hline \\
Clinical & 0.128 & 0.154 \\
Research & 0.113 & 0.180 \\
H Score & -0.303 & \\
A Score & -0.230 & -0.078 \\
C Score & 0.321 & 0.128 \\
Title & -0.036 & -0.057 \\
Triangle ACH & 0.192 & 0.103 \\
Triangle C & -0.056 & -0.039 \\
Triangle H & 0.039 & -0.032 \\
Triangle Outside & 0.159 & 0.066 \\
References & 0.707 & 0.566\\
Age & -0.504 & -0.243 \\
MeSH & 0.128 & 0.112 \\
Access & 0.065 & 0.256 \\
Language 2 & -0.807 & -0.568 \\
Language 3 & -1.369 & -1.885 \\
Length & 0.063 & 0.143 \\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Mitigating Consequences of Prestige in Citations of Publications" by Michael Balzer and Adhen Benlahlou, arXiv:2411.05584v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Counts of images in each dataset with a positive label for the pathology listed.}
\begin{tabular}{lrrrrrr}
\toprule
Dataset & Atelectasis & Cardiomegaly & Effusion & Lung Opacity & Mass & Pneumothorax \\
\midrule
NIH & 5728 & 1563 & 6589 & 0 & 3567 & 3407 \\
PC & 3981 & 8420 & 3342 & 0 & 806 & 223 \\
RSNA & 0 & 0 & 0 & 1348 & 0 & 0 \\
SIIM & 0 & 0 & 0 & 0 & 0 & 3576 \\
MIMIC\_CH & 10076 & 9831 & 12064 & 13825 & 0 & 2350 \\
CheX & 3195 & 2909 & 8078 & 9736 & 0 & 1802 \\
Google & 0 & 0 & 0 & 221 & 0 & 46 \\
OpenI & 271 & 185 & 120 & 327 & 6 & 14 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Joseph Paul Cohen
Rupert Brooks
Sovann En
Evan Zucker
Anuj Pareek
Matthew P. Lungren
Akshay Chaudhari

"Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays" by Joseph Paul Cohen, Rupert Brooks, Sovann En, Evan Zucker, Anuj Pareek, Matthew P. Lungren, and Akshay Chaudhari, arXiv:2102.09475v2, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ccccc|}
\hline
\multirow{2}{*}{\textbf{Political position}} & \multicolumn{5}{c|}{\textbf{Share in...}}\tabularnewline
 & \textbf{2004} & \textbf{2008} & \textbf{2012} & \textbf{2016} & \textbf{2020}\tabularnewline
\hline
\textbf{0 (Left)} & 1.169\% & 1.641\% & 1.790\% & 2.734\% & 4.469\%\tabularnewline
\textbf{1} & 2.682\% & 2.478\% & 1.769\% & 2.623\% & 2.784\%\tabularnewline
\textbf{2} & 4.478\% & 3.880\% & 4.359\% & 4.845\% & 5.825\%\tabularnewline
\textbf{3} & 6.211\% & 5.126\% & 5.405\% & 6.007\% & 7.018\%\tabularnewline
\textbf{4} & 7.316\% & 6.516\% & 6.802\% & 6.717\% & 7.728\%\tabularnewline
\textbf{5} & 28.280\% & 30.580\% & 33.277\% & 29.168\% & 25.292\%\tabularnewline
\textbf{6} & 9.868\% & 11.313\% & 10.281\% & 10.594\% & 7.998\%\tabularnewline
\textbf{7} & 14.086\% & 12.148\% & 11.607\% & 9.796\% & 10.074\%\tabularnewline
\textbf{8} & 12.395\% & 10.739\% & 9.689\% & 11.612\% & 10.935\%\tabularnewline
\textbf{9} & 5.558\% & 6.731\% & 5.207\% & 6.815\% & 5.366\%\tabularnewline
\textbf{10 (Right)} & 7.956\% & 8.849\% & 9.815\% & 9.090\% & 12.511\%\tabularnewline
\hline
\textbf{Average position} & 5.875 & 5.925 & 5.858 & 5.803 & 5.723\tabularnewline
\textbf{Variance of positions} & 5.336 & 5.426 & 5.397 & 6.108 & 7.376\tabularnewline
\hline
\end{tabular}
\end{adjustbox}
\caption{Shares of respondents by self-reported position on a 0-10 left-right axis, by year. Source: ANES dataset, electionstudies.org/data-tools/anes-continuity-guide/\#left-right. Answers are weighted using the weights provided by ANES. Respondents who did not know how to answer or refused to answer are omitted.}
\end{table}
\end{document}

q-fin

"A Flexible Measure of Voter Polarization" by Boris Ginzburg, arXiv:2507.07770v1, 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|rrrll}
\#& Matrix Name &n & m & nnz & $\lambda_{min}(A)$ & $\lambda_{max}(A)$\\\hline
1 & reorientation\_1 & 396 & 281 &7,326 & $-1.7\times10^6$ & $1.0\times 10^9$\\
2 & reorientation\_4 &1,596 & 1,121 &33,630 & -43.2 & $3.8\times10^8$ \\
3 & reorientation\_8 &1,826 & 1,282 &37,894 & -43.3 & $5.9\times10^8$ \\
4 & dynamicSoaringProblem\_1 &363 & 284 &5,367 & $7.1\times10^{-7}$ & 237.1 \\
5 & dynamicSoaringProblem\_4 &1,794 & 1,397 & 36,516 & $1.1\times10^{-8}$ & $4.2\times 10^5$ \\
6 & goddardRocketProblem\_2 &434 & 433 & 9,058 & $-1.1\times 10^3$ & $1.1\times 10^3$ \\
7 & tumorAntiAngiogenesis\_1 &123 & 82 &2,748 & -25.3 & 65.2\\
8 & tumorAntiAngiogenesis\_8 & 294 & 196 &4,776 & -99.6 & $7.3\times 10^{11}$
\end{tabular}
\end{adjustbox}
\caption{Symmetric case: test problems}
\end{table}
\end{document}

math

"Robust iterative methods for linear systems with saddle point structure" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Ilerioluwakiiye Abolade
Emmanuel Idoko
Solomon Odelola
Promise Omoigui
Adetola Adebanwo
Aondana Iorumbur
Udunna Anazodo
Alessandro Crimi
Raymond Confidence

"SharpXR: Structure-Aware Denoising for Pediatric Chest X-Rays" by Ilerioluwakiiye Abolade, Emmanuel Idoko, Solomon Odelola, Promise Omoigui, Adetola Adebanwo, Aondana Iorumbur, Udunna Anazodo, Alessandro Crimi, and Raymond Confidence, arXiv:2508.08518v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
		\toprule[1pt]
		 Methods & Backbone & mIOU (\%) \\
		\midrule[0.5pt]
		\midrule[0.5pt]
		ENCNet~ & ResNet-101 & 51.7 \\
		ANNet~ & ResNet-101 & 52.8 \\
		EMANet~ & ResNet-101 & 53.1 \\
		SPYGR~ & ResNet-101 & 52.8 \\
		CPN~ & ResNet-101 & 53.9 \\
		CFNet~ & ResNet-101 & 54.0 \\
		\midrule[0.5pt]
		DANet~ & ResNet-101 & 52.6\\
		\midrule[0.5pt]
		Our CAA (OS = 16) & ResNet-101 & 53.7 \\
		\textbf{Our CAA (OS =\enspace8)} & ResNet-101 & \textbf{55.0} \\
		\bottomrule[1pt]
	\end{tabular}
\end{adjustbox}
\caption{Comparisons with other state-of-the-art approaches on the PASCAL Context test set. For a fair comparison, all compared methods used ResNet-101 and naive upsampling.}
\end{table}
\end{document}

cs

Ye Huang
Di Kang
Wenjing Jia
Xiangjian He
Liu Liu

"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{siunitx}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Reflection coefficients $R_s$ and $R_p$ (see Equation ), and percentage of reflection that remains unpolarized, for minimum, average and maximum refractive index found in oil paintings, when the scanner is configured at the average Brewster's angle ($\theta$=\SI{56.3}{\degree}), at the left image boundary ($A$), center ($B$) and right image boundary ($C$).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllllllll}
 \cmidrule{2-10}
 & \multicolumn{3}{c}{$n_2$=1.47} & \multicolumn{3}{c}{$n_2$=1.495 (average)} & \multicolumn{3}{c}{$n_2$=1.52}\\
 \cmidrule(r){2-4}\cmidrule(r){5-7}\cmidrule(r){8-10}
 & $A$ & $B$ & $C$ & $A$ & $B$ & $C$ & $A$ & $B$ & $C$\\
\midrule
$R_\mathrm{p}$ coefficient &0.0037 &\num{2.08e-5} &0.0047 &0.0044 &0 	 &0.0042 &0.0052 &\num{2.18e-5} &0.0037 \\
$R_\mathrm{s}$ coefficient &0.098 &0.138 		 &0.181	 &0.1045 &0.1456 &0.1899 &0.111 &0.153 		&0.199 \\
$R_\mathrm{p}$/($R_\mathrm{p}$+$R_\mathrm{s}$) error (\%)	&3.66 &0.015 &2.18 &4.06 &0 &2.18 &4.47 &0.014 &1.84\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Willemijn Elkhuizen
Tessa Essers
Yu Song
Jo Geraedts
Clemens Weijkamp
Joris Dik
Sylvia Pont

"Gloss, Color and Topography Scanning for Reproducing a Painting's Appearance using 3D printing" by Willemijn Elkhuizen, Tessa Essers, Yu Song, Jo Geraedts, Clemens Weijkamp, Joris Dik, and Sylvia Pont, arXiv:1910.10836v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Charging service features}
\begin{tabular}{|l|c|}
 \hline
 \hline
 \textbf{Feature Name} & \textbf{Range of Values}\\
 \hline
 \hline
 Charging requests weighted rolling average & Float\\
 \hline
 Province-wide number of available public EVCSs & Integer\\
 \hline
 Province-wide number of registered EVs & Integer\\
 \hline
 Region-specific number of available public EVCSs & Integer\\
 \hline
 Region-specific number of registered EVs & Integer\\
 \hline
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Nassr Al-Dahabreh
Mohammad Ali Sayed
Khaled Sarieddine
Mohamed Elhattab
Maurice Khabbaz
Ribal Atallah
Chadi Assi

"A Data-Driven Framework for Improving Public EV Charging Infrastructure: Modeling and Forecasting" by Nassr Al-Dahabreh, Mohammad Ali Sayed, Khaled Sarieddine, Mohamed Elhattab, Maurice Khabbaz, Ribal Atallah, and Chadi Assi, arXiv:2312.05333v1, 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{Computational complexity of different methods.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
Method & FLOPs & Params \\ \hline
D-NTSCC & 69.07 G & 70.19 M \\
NTSCC & 28.35 G & 44.04 M \\
D-DJSCC & 60.79 G & 13.35 M \\
DHF-JSCC & 163.68 G & 25.37 M \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Distributed Nonlinear Transform Source-Channel Coding for Wireless Correlated Image Transmission" by Yufei Bo and Meixia Tao, arXiv:2503.21249v1, 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||ccc||}
 \hline
 Parameter & Value & Pearson & Parameter & Value & Pearson \\
 Changed & & Correlation & Changed & & Correlation\\
 \hline\hline
 Head Count ($H$) & 2 & 0.684 & Depth ($L$) & 4 & 0.700\\
 \hline
 & 4 & 0.696 & & 4 & 0.702\\
 \hline
 & 4 & 0.731 & & 4 & 0.723\\
 \hline
 & 4 & 0.707 & & 8 & 0.746\\
 \hline
 & 16 & 0.751 & & 16 & 0.771\\
 \hline
 & 32 & 0.765 & & 16 & 0.792\\
 \hline
 & 32 & 0.757 & MLP Dim ($d_{f f}$) & 32 & 0.706\\
 \hline
 & 32 & 0.767 & & 64 & 0.696\\
 \hline
 & 64 & 0.742 & & 64 & 0.715\\
 \hline
 Embedding Dim ($d_t$) & 32 & 0.641 & & 128 & 0.713\\
 \hline
 & 64 & 0.684 & & 256 & 0.725\\
 \hline
 & 256 & 0.706 & & 256 & 0.731\\
 \hline
 & 256 & 0.703 & Residual Block Size & 0 & 0.713 \\
 \hline
 Patch Size ($d_1 = d_2 = d_3$)& 8 & 0.696 & & 0 & 0.717 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Selected results of hyperparameter tuning trials on the dipeptide dataset. }
\end{table}
\end{document}

math

Tom Pan
Evan Dramko
Mitchell D. Miller
George N. Phillips
Anastasios Kyrillidis

"RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runs" by Tom Pan, Evan Dramko, Mitchell D. Miller, George N. Phillips, and Anastasios Kyrillidis, arXiv:2503.00143v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c|c|c||c|c|c|}
 \hline
 & \multicolumn{3}{c||}{\multirow{2}{*}{\shortstack{In-memory \\ Compute Model}}} & \multicolumn{2}{c|}{\multirow{2}{*}{\shortstack{Analog Core\\ Precision}}} & \multirow{2}{*}{\shortstack{ADC \\ Precision}} \tabularnewline
 & \multicolumn{3}{c||}{} & \multicolumn{2}{c|}{} & \tabularnewline
 \cline{2-7}
 & QS & IS & QR & $B_x$ & $B_w$ & $B_\text{ADC}$ \tabularnewline
 \hline
 \hline
 Kang \emph{et al.} & \checkmark & & \checkmark & 8 & 8 & 8\tabularnewline

 \hline
 Biswas \emph{et al.} & & & \checkmark & 8 & 1& 7 \tabularnewline

 \hline
 Zhang \emph{et al.} & \checkmark & & & 5 & 1 & 1\tabularnewline

 \hline
 Valavi \emph{et al.} & & & \checkmark & 1 & 1& 1 \tabularnewline

 \hline
 Khwa \emph{et al.} & &\checkmark & & 1 & 1 & 1\tabularnewline

 \hline
 Jiang \emph{et al.} & & \checkmark & & 1 & 1& 3.46 \tabularnewline

 \hline
 Si \emph{et al.} & \checkmark & & \checkmark & 2 & 5 & 5\tabularnewline

 \hline
 Jia \emph{et al.} & & & \checkmark & 1 & 1& 8 \tabularnewline

 \hline
 Okumura \emph{et al.} & & \checkmark& & 1 & T & 8\tabularnewline

 \hline
 Kim \emph{et al.} & &\checkmark & &1 & 1& 1\tabularnewline

 \hline
 Guo \emph{et al.} & \checkmark & & & 1 & 1 & 3\tabularnewline

 \hline
 Yue \emph{et al.} & \checkmark & & \checkmark & 2 & 5 & 5\tabularnewline

 \hline
 Su \emph{et al.} & \checkmark & & & 2 & 1 & 5\tabularnewline

 \hline
 Dong \emph{et al.} & \checkmark & & \checkmark & 4 & 4 & 4\tabularnewline

 \hline
 Si \emph{et al.} & \checkmark & & & 2 & 2 & 5\tabularnewline

 \hline
 Jiang \emph{et al.} & & & \checkmark & 1 & 1 & 5\tabularnewline

 \hline
 Jaiswal \emph{et al.} & & \checkmark & & 4 & 4 & 4\tabularnewline

 \hline
 Ali \emph{et al.} &\checkmark & & \checkmark & 4 & 4 & 4 \tabularnewline

 \hline
 Si \emph{et al.} & \checkmark & & & 1 & 1 & 1 \tabularnewline

 \hline
 Liu \emph{et al.} & & \checkmark & & A & 1 & 1 \tabularnewline

 \hline
 Zhang \emph{et al.} & & \checkmark & & 8 & 8 & 8 \tabularnewline

 \hline
 Gong \emph{et al.} & \checkmark & & & 2 & 3 & 8 \tabularnewline

 \hline
 Agrawal \emph{et al.} & & & \checkmark & 1 & 1 & 5 \tabularnewline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Sujan Kumar Gonugondla
Charbel Sakr
Hassan Dbouk
Naresh R. Shanbhag

"Fundamental Limits on Energy-Delay-Accuracy of In-memory Architectures in Inference Applications" by Sujan Kumar Gonugondla, Charbel Sakr, Hassan Dbouk, and Naresh R. Shanbhag, arXiv:2012.13645v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The Predicted Sup-norm of the Selected Centipede Games}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccccc}
\hline
 & \multicolumn{2}{c}{Linear} & & \multicolumn{2}{c}{Exponential} & & \multicolumn{2}{c}{Constant} \\ \cline{2-3} \cline{5-6} \cline{8-9}
 & $c=0.5$ & $c=0.8$ & & $c=2.5$ & $c=4$ & & $c=0.4$ & $c=0.8$ \\
 & (small) & (large) & & (small) & (large) & & (small) & (large) \\ \hline
\multirow{2}{*}{Sup-norm} & \multirow{2}{*}{0.128} & \multirow{2}{*}{\textbf{0.251$^\dagger$}} & \multirow{2}{*}{} & \multirow{2}{*}{0.188} & \multirow{2}{*}{\textbf{0.251$^\dagger$}} & \multirow{2}{*}{} & \multirow{2}{*}{0.072} & \multirow{2}{*}{\textbf{0.460$^\dagger$}} \\
 & & & & & & & & \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Shiang-Hung Hu
Po-Hsuan Lin
Thomas R. Palfrey
Joseph Tao-yi Wang
Yu-Hsiang Wang

"Strategy Method Effects in Centipede Games: An Optimal Design Approach" by Shiang-Hung Hu, Po-Hsuan Lin, Thomas R. Palfrey, Joseph Tao-yi Wang, and Yu-Hsiang Wang, arXiv:2508.06425v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Theoretical probabilities for unique recovery of $\mathbf{s}$ \\ in Scenario $2$.}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
 \hline
 N&5&6&7&8&10&11&14&16 \\ \hline
 Prob.&0.47&0.14&0.49&0.04&0.22&0.50&0.24&0.03\\ \hline
 \end{tabular}
\end{table}
\end{document}

math

Qi Zhang
Jiang Zhu
Fengzhong Qu
Zheng Zhu
De Wen Soh

"On the Identifiability from Modulo Measurements under DFT Sensing Matrix" by Qi Zhang, Jiang Zhu, Fengzhong Qu, Zheng Zhu, and De Wen Soh, arXiv:2401.00194v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc}
 \hline
 Geometry & $\eta=1$ & $\eta=2$ & $\eta=3$ & $\eta=4$ & $\eta=5$ & $\eta=6$ & Hard \\
 \hline
 $[0,1]$ & 2.994 & 2.945 & 2.888 & 2.849 & 2.826 & 2.812 & \textbf{2.771} \\
 $\mathbb{T}^1$ & 2.998 & 2.982 & 2.929 & 2.893 & 2.870 & 2.854 & \textbf{2.812} \\
 $[0,1]^2$ & 2.999 & 2.992 & 2.978 & 2.960 & 2.949 & 2.940 & 2.907 \\
 $\mathbb{T}^2$ & 2.999 & 2.998 & 2.994 & 2.988 & 2.983 & 2.978 & 2.962 \\
 $[0,1]^3$ & 2.999 & 2.998 & 2.991 & 2.983 & 2.976 & 2.969 & 2.942 \\
 $\mathbb{T}^3$ & 2.999 & 2.999 & 2.999 & 2.996 & 2.994 & 2.991 & 2.975 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Maximum entropy for $n=3$ in different geometries and connection functions with uniform node distributions. Values in bold are calculated analytically.}
\end{table}
\end{document}

math

"Entropy of Random Geometric Graphs in High and Low Dimensions" by Oliver Baker and Carl P. Dettmann, arXiv:2503.11418v3, 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{In this table, we evaluate the performance of targeted attacks under different defenses using \textbf{1 - accuracy} (higher indicates better attacks)). We pick $\epsilon_{\infty}=0.18$, $m=100$, $\gamma=0.25$ and select DGCNN as victim model. Other settings are the same as Sec 4.1. Our AOF consistently and significantly outperforms other SOTA attacks. The ConvNet-Opt, ONet-Remesh and ONet-Opt are three variants of IF-Defense.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccr}
\toprule
Defenses & 3D-Adv & kNN & AdvPC & AOF \\
\midrule
No defense & \bf99.76 & 91.94 & 96.23 & 96.15 \\
SRS & 54.42 & 71.11 & 88.02 & \bf91.26 \\
SOR & 27.92 & 54.78 & 75.47 & \bf85.63 \\
DUP-Net & 29.58 & 51.66 &71.90 & \bf83.16 \\
ConvNet-Opt & 21.76 & 22.85 & 45.59 & \bf61.38 \\
ONet-Remesh & 30.27 & 30.19 & 49.51 & \bf58.46 \\
ONet-Opt & 24.51 & 25.24 & 41.34 & \bf51.34 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Binbin Liu
Jinlai Zhang
Lyujie Chen
Jihong Zhu

"Boosting 3D Adversarial Attacks with Attacking On Frequency" by Binbin Liu, Jinlai Zhang, Lyujie Chen, and Jihong Zhu, arXiv:2201.10937v1, 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{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The $(n-1)$th order approximation $|\vec{H}_a|$ for angular transients $\theta$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|l|}
 \hline
\thead{n} & \thead{Associated mean curvature $f:=|\vec{H}_a|$}& \thead{Difference $\mathfrak{T}(\widetilde{f})-\mathfrak{T}(e^{\mathrm{i}\theta})$} \\
 \hline
$10$ & $\frac{1}{9}\left(\frac{1-\cos\theta}{\sin\theta}\right)^{10}\sin^{11}\left(\frac{\pi-\theta}{2}\right)$ & $\mathfrak{T}(\widetilde{f})-\mathfrak{T}(e^{\mathrm{i}\theta})=-\frac{1}{9216}\theta^{10}+O(\theta^{11})$ \\
 \hline
$50$ & $\frac{1}{49}\left(\frac{1-\cos\theta}{\sin\theta}\right)^{50}\sin^{51}\left(\frac{\pi-\theta}{2}\right)$ & $\mathfrak{T}(\widetilde{f})-\mathfrak{T}(e^{\mathrm{i}\theta})=c_{50}\theta^{50}+\mathcal{O}(\theta^{51})$\\
 \hline
$100$ & $\frac{1}{99}\left(\frac{1-\cos\theta}{\sin\theta}\right)^{100}\sin^{101}\left(\frac{\pi-\theta}{2}\right)$ & $\mathfrak{T}(\widetilde{f})-\mathfrak{T}(e^{\mathrm{i}\theta})=c_{100}\theta^{100}+\mathcal{O}(\theta^{101})$\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"The Associated Discrete Laplacian in $\mathbb{R}^3$ and Mean Curvature with Higher order Approximations" by Wei-Hung Liao, arXiv:2501.11871v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Abba M. Krieger
David Azriel
Adam Kapelner

"Better Experimental Design by Hybridizing Binary Matching with Imbalance Optimization" by Abba M. Krieger, David Azriel, and Adam Kapelner, arXiv:2012.03330v2, 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{Node Weights Assigned to Political Positions}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
 \hline
 \textbf{Position} & \textbf{Weight} \\
 \hline
 Councilor & 2 \\
 Provincial Board Member & 2 \\
 Vice Mayor & 3 \\
 Vice Governor & 3 \\
 Mayor & 5 \\
 Member, House of Representatives & 5 \\
 Governor & 5 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Rafael Acuna
Aldie Alejandro
Robert Leung

"The Families that Stay Together: A Network Analysis of Dynastic Power in Philippine Politics" by Rafael Acuna, Aldie Alejandro, and Robert Leung, arXiv:2505.21280v1, 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
\caption{Paired $t$-test Results on Test NLL Differences Across 500 Portfolios.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
\textbf{Comparison} & \textbf{Mean NLL Difference} & \textbf{$t$-statistic} & \textbf{$p$-value} \\
\midrule
\multicolumn{4}{l}{\textit{U.S. Market}} \\
LSTM-BEKK $-$ DCC & -1.518 & -15.326 & \makebox[1.5cm][r]{<0.000\textsuperscript{***}} \\
LSTM-BEKK $-$ Scalar BEKK & -0.247 & -2.587 & \makebox[1.5cm][r]{0.009\textsuperscript{***}} \\
\midrule
\multicolumn{4}{l}{\textit{U.K. Market}} \\
LSTM-BEKK $-$ DCC & -0.430 & -2.468 & \makebox[1.25cm][r]{0.014\textsuperscript{**}} \\
LSTM-BEKK $-$ Scalar BEKK & -0.259 & -1.676 & \makebox[0.95cm][r]{0.094\textsuperscript{*}} \\
\midrule
\multicolumn{4}{l}{\textit{Japan Market}} \\
LSTM-BEKK $-$ DCC & -0.422 & -3.856 & \makebox[1.55cm][r]{<0.001\textsuperscript{***}} \\
LSTM-BEKK $-$ Scalar BEKK & -0.383 & -3.498 & \makebox[1.55cm][r]{0.001\textsuperscript{***}} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Haoyuan Wang
Chen Liu
Minh-Ngoc Tran
Chao Wang

"Deep Learning Enhanced Multivariate GARCH" by Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, and Chao Wang, arXiv:2506.02796v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Adaptive Sequential SAA for Solving Two-stage Stochastic Linear Programs" by Raghu Pasupathy and Yongjia Song, arXiv:2012.03761v1, 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{Segmentation Performance}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 \toprule
 \cmidrule(r){1-1}
 & IoU & Dice & Accuracy \\
 \midrule
 Reduced-ResUnet & \(0.96\) & \(0.97\) & \(0.96\)\\
 Unet & \(0.86\) & \(0.88\) & \(0.87\) \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Shuteng Niu
Meryl Liu
Yongxin Liu
Jian Wang
Houbing Song

"Distant Domain Transfer Learning for Medical Imaging" by Shuteng Niu, Meryl Liu, Yongxin Liu, Jian Wang, and Houbing Song, arXiv:2012.06346v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average detection delays for SKF on \textbf{misspecified} configurations.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cc|cc|c|}
\hline
\multicolumn{2}{|c|}{Configuration} & \multicolumn{2}{c|}{SKF} & BOCPD \\ \hline
True & Missepcified & M1 & M2 & \\ \hline
Mat{\'e}rn kernel & Exponential kernel & 5.20 & \textbf{3.90} & 12.81 \\ \hline
$\sigma^2 = 1$ & $\sigma^2 = 4$ & 2.89 & \textbf{2.71} & 12.81 \\ \hline
$\gamma = 10$ & $\gamma = 5$ & 3.85 & \textbf{3.17} & 12.81 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Sequential Kalman filter for fast online changepoint detection in longitudinal health records" by Hanmo Li, Yuedong Wang, and Mengyang Gu, arXiv:2310.18611v3, 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 models.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}			\hline
				Model & RMSE & MAE\\ 		 \hline
				GPR & 75.92 & 45.32\\
				BSTS\_short & 3010.72 & 2533.07\\
				BSTS\_long & 257.482 & 196.14\\
				GBM & 78.37 & 36.26 \\
				AQR & 92.83 & 37.49 \\
				Two-layer DGPR & 360.74 & 256.55 \\		\hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Edina Chandiwana
Caston Sigauke
Alphonce Bere

"Robust modelling framework for short-term forecasting of global horizontal irradiance" by Edina Chandiwana, Caston Sigauke, and Alphonce Bere, arXiv:2212.05978v1, 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{Logit regression of Vote for Test Over Ratio of Performance Predictions}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \hline
 \hline
 & & pseudo-$R^{2}$ & & $\chi^{2}$ & & Prob $ > \chi^{2}$ \\\cline{3-7}
 & & 0.2209 & & 37.61 & & 0.0000\\
 \midrule
 Vote for Test & & Coefficient & & $z$ & & $P>z$ \\
 \hline
 Better & & 5.0265 & & 6.13 & & 0.000 \\
 Constant & & -4.3622 & & -5.23 & & 0.000 \\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)" by Marius Protte, arXiv:2507.15568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textit{\#Docs} is the number of documents. \textit{Docs Year} is the documents' publication time. \textit{\#Annotations} is the number of annotations (Number of \textit{non-NIL} annotations). \textit{Annot. Year} is the reference KB time period where the annotations were taken from.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|}
 \hline
 \textbf{Dataset} & \textbf{\#Docs} & \textbf{Docs Year} & \textbf{\#Annotations} & \textbf{Annot. Year} \\ \hline
		ACE04	~ 						 		 & 57 	 & 2000 	 & 257 		 & 2010 \\ \hline
		AIDA/CONLL	~						 & 231 	 & 1996 & 4.485 		 & 2010 \\ \hline
		AQUAINT50	~ 							 & 50 & 1998-2000 & 727 	 		 & 2007 \\ \hline
		IITB	~								 & 107 & 2008 	 & 12.099 	 & 2008 \\ \hline
		MSNBC	~ 						 		 & 20 & 2007 & 747		 & 2006 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"On the Temporality of Priors in Entity Linking" by Renato Stoffalette Joao, arXiv:2101.05593v1, 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{Details of BRATS $2018$, $2019$ and $2020$ datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
\hline
Dataset&{Type}&Patients&
HGG&
LGG\\
\hline
BRATS 2018 &Training &285&210&75\\
& Validation &66&&\\
BRATS 2019 &Training &335&259&76\\
& Validation &125&&\\
BRATS 2020 &Training &369&293&76\\
& Validation &125&&\\
& Testing &166&&\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Parvez Ahmad
Saqib Qamar
Linlin Shen
Adnan Saeed

"Context Aware 3D UNet for Brain Tumor Segmentation" by Parvez Ahmad, Saqib Qamar, Linlin Shen, and Adnan Saeed, arXiv:2010.13082v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Notations used in the TDTL MIP formulation. \strut }
\begin{tabular}{ll}
		\hline
		\multicolumn{2}{l}{\textbf{Sets}}\\
		\hline
 $ \mathcal{N}$ & Set of nodes of graph $G = (\mathcal{N}, \mathcal{A})$. \\
 $\mathcal{A}$ & Set of directed links in $G = (\mathcal{N}, \mathcal{A})$.\\
		$o/e$ & The origin/ending node of the mission, $o,e \in \mathcal{N}$.\\
		$\delta^+(i)$ & Nodes that can be reached from $i$, $i \in \mathcal{N}$.\\
		$\delta^-(i)$ & Nodes that can reach to node $i$, $i \in \mathcal{N}$.\\
		\hline
		\multicolumn{2}{l}{\textbf{Parameters}} \\
		\hline
		Q & Battery capacity expressed in time units. \\
		$t_{ij}^T/$ & Truck travel time at link $(i,j) \in \mathcal{A}$.\\
		$t_{ij}^D$ & Drone travel time at link $(i,j) \in \mathcal{A}$.\\
		M & A big enough constant.\\
		\hline
	 \multicolumn{2}{l}{\textbf{Variables}} \\
		\hline
	 $u_{ij}$ & If $u_{ij} = 1$, link $(i,j)$ is traversed by the truck. \\
	 $v_{ij}$ & If $v_{ij} = 1$, link $(i,j)$ is traversed by the drone.\\
 $s_i$ & The earliest departure time from node $i\in \mathcal{N}$.\\
 $b_i^-$ & Drone battery level when if drone is just coming to the node $i \in \mathcal{N}$.\\
 $b_i^+$ & Drone battery level when the drone is just departing from node $i \in \mathcal{N}$.\\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Fanruiqi Zeng
Zaiwei Chen
John-Paul Clarke
David Goldsman

"Nested Vehicle Routing Problem: Optimizing Drone-Truck Surveillance Operations" by Fanruiqi Zeng, Zaiwei Chen, John-Paul Clarke, and David Goldsman, arXiv:2103.01528v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Vulnerability Detection datasets, from the Devign paper. VFCs = Vulnerability-Fixing Commits.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 \toprule
 \textbf{Project} & \textbf{Total commits} & \textbf{VFCs} & \textbf{Non-VFCs} \\
 \midrule
 qemu & 11,910 & 4,932 & 6,978 \\
 ffmpeg & 13,962 & 5,962 & 8,000 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Old but Gold: Reconsidering the value of feedforward learners for software analytics" by Rahul Yedida, Xueqi Yang, and Tim Menzies, arXiv:2101.06319v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Benchmark Functions for Global Optimization}
\begin{tabular}{|c|c|}
			\hline
			\textbf{Name} & \textbf{Formula} \\
			\hline
			Rastrigin & $f(x) = An + \sum_{i=1}^n \left(x_i^2 - A \cos(2 \pi x_i)\right), \; A = 10$ \\
			\hline
			Ackley & $\begin{aligned}
				f(x, y) = &-20 \exp\left(-0.2 \sqrt{0.5(x^2 + y^2)}\right) \\
				& - \exp\left(0.5\left(\cos(2\pi x) + \cos(2\pi y)\right)\right) + e + 20
			\end{aligned}$ \\
			\hline
			Sphere & $f(x) = \sum_{i=1}^n x_i^2$ \\
			\hline
			Rosenbrock & $f(x) = \sum_{i=1}^{n-1} \left[100(x_{i+1} - x_i^2)^2 + (1 - x_i)^2\right]$ \\
			\hline
			Beale & $f(x, y) = (1.5 - x + xy)^2 + (2.25 - x + xy^2)^2 + (2.625 - x + xy^3)^2$ \\
			\hline
			Booth & $f(x, y) = (x + 2y - 7)^2 + (2x + y - 5)^2$ \\
			\hline
		\end{tabular}
\end{table}
\end{document}

math

"Uncomputability of Global Optima for Nonconvex Functions in the Oracle Model" by K Lakshmanan, arXiv:2401.09436v2, 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{Rel-Noise Ratios grouped by Market Capitalization}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{clll}
 & \textbf{2018} & \textbf{2019} & \textbf{2020} \\ \hline
\textit{\textbf{small cap}} & 0.44 & 0.39 & 0.27 \\
\textit{\textbf{mid cap}} & 0.44 & 0.40 & 0.29 \\
\textit{\textbf{large cap}} & 0.46 & 0.41 & 0.29 \\
\textit{\textbf{none}} & 0.34 & 0.24 & 0.19 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Iulian Emil Tampu
Neda Haj-Hosseini
Anders Eklund

"Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?" by Iulian Emil Tampu, Neda Haj-Hosseini, and Anders Eklund, arXiv:2010.13460v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Self-supervised SV results on the VoxCeleb1 test set using proposed contrastive methods.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccc}
 \toprule
 Model & CDS EER (\%) & DCF \\
 \hline
 Disent~ & 22.09 & N/A \\
 CDDL~ & 17.52 & N/A \\
 GCL~ & 15.26 & N/A \\
 i-vector~ & 15.28 & 0.63 (p=0.05) \\
 AP~ & 9.56 & 0.51 (p=0.05) \\
 AP+AAT~ & 8.65 & 0.45 (p=0.05) \\
 \hline
 SimCLR & 18.14 & 0.81 \\
 MoCo & 15.11 & 0.80 \\
 MoCo + SpecAug & 15.50 & 0.81 \\
 MoCo + WavAug & \bf{8.63} & \bf{0.64} \\
 MoCo + WavAug (ProtoNCE) & \bf{8.23} & \bf{0.59} \\
 \bottomrule

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

eess

Wei Xia
Chunlei Zhang
Chao Weng
Meng Yu
Dong Yu

"Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning" by Wei Xia, Chunlei Zhang, Chao Weng, Meng Yu, and Dong Yu, arXiv:2012.07178v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameter search space of NODE.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\toprule
\textbf{Hyperparameter} & \textbf{Distribution} \\ \midrule
num\_layers & $[2,4,8]$\\
total\_tree\_count & $[1024,2048]$\\
tree\_depth & $[6,8]$\\
tree\_output\_dimension & $[2,3](regression),[\text{num\_classes}](classification)$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

DOFEN: Deep Oblivious Forest ENsemble

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

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


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Training settings for each example.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
\toprule {Loss} & Example & Example &
Example &
Example \\
\midrule
Gradient descent method & AdamW & AdamW & AdamW & AdamW \\
Learning rate & 0.001 & 0.0005 & 0.0005 & 0.001\\
Weight decay & 0.01 & 0 & 0.02 & 0.02\\
No. of epochs & 500 & 400 & 2000 & 400\\
No. of training trajectories & 200 & 400 & 200 & 300 \\
Hidden layers & 1 & 3 & 3 & 2\\
Activation function & ReLU & ReLU & ReLU & ReLU\\
Neurons in each layer & 40 & 50 &
10 & 50 \\
time step $\Delta t$ & 0.1 & 0.1 & 0.05 & 0.1 \\
Number of timesteps $N_T$ &81 & 21 & 41 & 21 \\
Initialization for biases & 2 & 0.01
& $\mathcal{N}(0, 0.03^2)$ & 0.01 \\
Initialization for weights & $\mathcal{N}(0, 0.01^2)$ & $\mathcal{N}(0, 0.01^2)$ & $\mathcal{N}(0, 0.03^2)$ & $\mathcal{N}(0, 0.01^2)$
 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mingtao Xia
Qijing Shen
Philip Maini
Eamonn Gaffney
Alex Mogilner

"A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems" by Mingtao Xia, Qijing Shen, Philip Maini, Eamonn Gaffney, and Alex Mogilner, arXiv:2503.05068v1, 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{Notations of various achievable regions, where ``cr'' means ``common randomness''}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c|c|c|} % Define 5 columns
 \hline
 &DP joint coding & RDP & DP separate coding \\ \hline\hline
strong-sense, w/ cr & $\mathcal{J}$ & $\mathcal{G}$ & $\mathcal{J}'$ \\ \hline
weak-sense, w/ cr & $\mathcal{J}_w$ & $\mathcal{G}_w$ & $\mathcal{J}'_w$ \\ \hline
strong-sense, w/o cr & $\hat{\mathcal{J}}$ & $\hat{\mathcal{G}}$ & $\hat{\mathcal{J}}'$ \\ \hline
weak-sense, w/o cr & $\hat{\mathcal{J}}_w$ & $\hat{\mathcal{G}}_w$ & $\hat{\mathcal{J}}'_w$\\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Source-Channel Separation Theorems for Distortion Perception Coding" by Chao Tian, Jun Chen, and Krishna Narayanan, arXiv:2501.17706v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.