q-fin

"Workplace Breastfeeding Legislation and Female Labor Force Participation in the United States" by Julia Hatamyar, arXiv:2209.05916v2, 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{ Reconstruction quality on DTU dataset by AACVP-MVSNet with different parameter $H$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 \toprule
 \quad & Singe-headed & $H = 2$ & $H = 4$ & $H=8$ \\
 \midrule
 Acc. (mm) & $\textbf{0.357}$ & $0.362$ & $0.359$ & $0.375$ \\
 Comp. (mm) & $0.326$ & $\textbf{0.325}$ & $0.332$ & $0.339$ \\
 OA (mm) & $\textbf{0.341}$ & $0.343$ & $0.345$ & $0.357$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Anzhu Yu
Wenyue Guo
Bing Liu
Xin Chen
Xin Wang
Xuefeng Cao
Bingchuan Jiang

"Attention Aware Cost Volume Pyramid Based Multi-view Stereo Network for 3D Reconstruction" by Anzhu Yu, Wenyue Guo, Bing Liu, Xin Chen, Xin Wang, Xuefeng Cao, and Bingchuan Jiang, arXiv:2011.12722v1, 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{Entropy Measures of Nifty 50 P/E Ratio}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 \textbf{Library} & \textbf{Entropy Type} & \textbf{Normalized Value} \\
 \midrule
 SciPy & Shannon Entropy & 0.86 \\
 Manual & Tsallis Entropy ($q=0.1$) & 0.92 \\
 Manual & Tsallis Entropy ($q=2$) & 0.98 \\
 nolds & Sample Entropy & 0.10 \\
 antropy & Permutation Entropy & 0.94 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Multi Scale Analysis of Nifty 50 Return Characteristics Valuation Dynamics and Market Complexity 1990 to 2024" by Chandradew Sharma, arXiv:2509.00697v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
\textbf{Hyperbolic Tangent} & \textbf{Logistic} & \textbf{Rectified Linear Unit (ReLU)} \\
\hline
$tanh(x)=\frac{e^x - e^{-x}}{e^x + e^{-x}}$ & $\sigma(x)=\frac{1}{1+e^{-x}}$ & $ReLU(x)= max(0,x)$ \\
\hline
 \textbf{Softplus} & \textbf{Sigmoid} & \textbf{Softmax} (vector output) \\
\hline
$ln(1+e^x)$ & $\frac{x}{1+e^{-x}}$ & $ \forall i \quad soft_i=\frac{e^{x_i}}{\sum_{j=1}^J e^{x_j}}$ \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Example of common activation functions: The $x_i$ are the inputs, and we have $x=\sum_i w_i x_i$, with $w_i$ the neural weights.}
\end{table}
\end{document}

stat

"The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?" by Jérémie Sublime, arXiv:2411.18656v1, 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 Statistics: Median and interquartile range presented for continuous features. Counts and proportions presented for discrete features. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
Features & $N=292$ \\
\hline
Follow-up (years) 		& 2.6 (1.3-3.7 ) \\
Death 		& 114 (40\%) 		\\
Num. Treatment Courses, ($\kappa$) 		& 		\\
1 		& 22 (8\%) 		\\
2 		& 36 (12\%) 		 \\
3 		& 46 (16\%) 		 \\
4 		& 188 (63\%) 		 \\
AML Risk Classification (high) 		& 64 (23\%) 		 \\
WBC (cells/uL) 			& 20 (6.8-65) \\
Male 			& 152 (52\%) 		 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Arman Oganisian
Kelly D. Getz
Todd A. Alonzo
Richard Aplenc
Jason A. Roy

"Bayesian Semiparametric Model for Sequential Treatment Decisions with Informative Timing" by Arman Oganisian, Kelly D. Getz, Todd A. Alonzo, Richard Aplenc, and Jason A. Roy, arXiv:2211.16393v1, 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{Architecture of the $f_l$ Network}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
Layer
&Filter size, stride
&Output size$(C,F)$
\\
\midrule
Input
& - & 1$\times$128,1$\times$3
\\
Concatenation
& - & 1$\times$131
\\
\midrule
Dense
& - & 1$\times$131
\\
ReLU
& - & 1$\times$131
\\
\midrule
Dense
& - & 1$\times$131
\\
ReLU
& - & 1$\times$131
\\
\midrule
Dense
& - & 1$\times$128
\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Context Matters: Graph-based Self-supervised Representation Learning for Medical Images" by Li Sun, Ke Yu, and Kayhan Batmanghelich, arXiv:2012.06457v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Machine learning performances from predictive models for learning patterns under the integration framework}
\begin{tabular}{llccccc}
\hline
 Pattern& Method & Accuracy(\%) & Precision(\%) & Recall(\%) & F1 Score(\%) & AUC \\ \hline
 &logistic regression & 95.64& 99.92& 95.64& 97.73& 0.9872
\\
 &decision tree & 93.44& 99.96& 93.36& 96.55& 0.9735
\\
low &random forest & 91.64& 99.86& 91.63& 95.57& 0.9193
\\
 autonomy &K-Nearest neighbor & 97.20& 99.79& 97.36& 98.56& 0.9472
\\
 &multilayer perceptron &96.45&98.79& 96.45&97.32&0.9915
\\
 &support vector classifier & 96.59&98.77&96.59& 97.40&0.9854 \\
&extreme gradient boosting & 98.68& 99.55& 99.10& 99.33& 0.9929\\
\hline
 & logistic regression
& 71.17& 68.15& 71.88& 69.96&0.7880\\
 & decision tree
& 70.80& 80.00& 50.00& 61.54&0.6565\\
 & random forest
& 70.80& 68.18& 70.31& 69.23&0.7582\\
motivated& K-Nearest neighbor
& 72.26& 75.00 & 60.94& 67.24&0.7892\\
& multilayer perceptron
& 70.44& 68.50& 67.97& 68.24&0.7900\\
& support vector classifier & 73.72& 72.95& 69.53& 71.20&0.7694\\
& extreme gradient boosting
& 73.72& 73.33& 68.75& 70.97&0.7960\\
 \hline
\end{tabular}
\end{table}
\end{document}

cs

Jin Yuan
Xuelan Qiu
Jinran Wu
Jiesi Guo
Weide Li
You-Gan Wang

"Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study" by Jin Yuan, Xuelan Qiu, Jinran Wu, Jiesi Guo, Weide Li, and You-Gan Wang, arXiv:2406.11847v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Variable} & \textbf{Suggested Weight $w_i$} \\
\hline
Humidity (v1)& $w_1 = 0.05$ \\
Temperature (v2) & $w_2 = 0.1$ \\
Cloud Cover (v3) & $w_3 = 0.2$ \\
Windspeed (v4)& $w_4 = 0.05$ \\
Solar (Radiation) (v5) & $w_5 = 0.4$ \\
Diffuse Solar (v6) & $w_6 = 0.2$ \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Suggested Weights for Each Variable}
\end{table}
\end{document}

stat

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

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of computation cost (wall-clock time in seconds)}
\begin{tabular}{cc}
 \hline
 Method \quad \quad \quad \quad \quad \quad & Cost \\
 \hline
 FEM (Fluid domain) \quad \quad \quad \quad \quad \quad & $46$ (CPU) \\
 FEM (Extended domain*) \quad \quad \quad \quad \quad \quad & $558$ (CPU) \\
 FEM (Extended domain) \quad \quad \quad \quad \quad \quad & $10360$ (CPU) \\
 FFT \quad \quad \quad \quad \quad \quad & $52$ (CPU) \\
 FFT (Parallel) \quad \quad \quad \quad \quad \quad & $2.7$ (GPU) \\
 \hline
 *without considering the auxiliary velocity \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

"Accelerated Computational Micromechanics for Reactive Flow in Porous Media" by Mina Karimi and Kaushik Bhattacharya, arXiv:2312.15554v1, 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{Model Size Comparison Used in the Experiment}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
\hline
\textbf{Model} & \textbf{Size (KB)} \\
\hline
CMG & 2194 \\
BiLSTM & 3557 \\
GRU & 1354 \\
LSTM & 1786 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data" by Arif Pathan, arXiv:2506.17244v1, 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{Regression results for different mathematical abilities}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\hline
 & (1) & (2) \\
 & Low & High \\
VARIABLES & Cont. PGG & Cont. PGG \\
\hline
 & & \\
$AR$ & 0.014 & -0.168 \\
 & (0.146) & (0.124) \\
$RA$ & 0.011 & 0.033 \\
 & (0.146) & (0.132) \\
$AA$ & 0.070 & -0.001 \\
 & (0.146) & (0.131) \\
\emph{risk aversion} & -0.629** & -0.466** \\
 & (0.211) & (0.184) \\
Constant & 2.516*** & 2.310*** \\
 & (0.393) & (0.381) \\
 & & \\
Observations & 808 & 619 \\
R-squared & 0.025 & 0.058 \\
Controls & Yes & Yes \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Pablo Brañas-Garza
Antonio Cabrales
María Paz Espinosa
Diego Jorrat

"The effect of ambiguity in strategic environments: an experiment" by Pablo Brañas-Garza, Antonio Cabrales, María Paz Espinosa, and Diego Jorrat, arXiv:2209.11079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Solving Large Multicommodity Network Flow Problems on GPUs" by Fangzhao Zhang and Stephen Boyd, arXiv:2501.17996v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \hline
 \textbf{Parameter} & \textbf{Value} &\textbf{Ref.}\\
 \hline
 $d_{max}$& 4 meters&\\
 $\eta$&1&\\
 $\Psi$&1&\\
 $\xi$&$20\times20$&$^*$\\
 $E_{\text{max}}$&100 units&$^\dag$\\
 $E_{\text{idle}}$&1 units&$^\dag$\\
 $E_{\text{WuR}}$&1/14 units&$^\dag$\\
 $E_{\text{Tx}}$&10 units&$^\dag$\\
 $p(d_{i,j})$&$e^{-\eta d_{i,j}}$&\\
 $N$&[10, 250]&$^*$\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

David Ernesto Ruiz-Guirola
Samuel Montejo-Sanchez
Israel Leyva-Mayorga
Zhu Han
Petar Popovski
Onel L. A. Lopez

"Energy Management and Wake-up for IoT Networks Powered by Energy Harvesting" by David Ernesto Ruiz-Guirola, Samuel Montejo-Sanchez, Israel Leyva-Mayorga, Zhu Han, Petar Popovski, and Onel L. A. Lopez, arXiv:2508.13825v1, 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{DDPG hyperparameter settings}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
		\hline
		Hyperparameter & Setting \\
		\hline
		Critic learning rate	& 1$e^{-03}$ \\
		Actor learning rate		& 1$e^{-04}$ \\
		Critic hidden layer-1	& 50 fully-connected \\
		Critic hidden layer-2	& 25 fully-connected \\
		Action-path neurons		& 25 fully-connected \\
		Action-path bound		& tanh layer\\
		Gamma					& 0.9 \\
		Batch size				& 64 \\
		OUP Variance 			& 1.5 \\
		OUP Variance Decay Rate	& 1$e^{-05}$ \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Reinforcement Learning for Control of Valves" by Rajesh Siraskar, arXiv:2012.14668v2, 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{Resilience by Region: Average Failed Assets per Simulation (\(n = 1000\)).}
\begin{tabular}{lc}\hline
 \textbf{Region} & \textbf{Average Failed Assets} \\\hline
 Brazil & 2.000 \\
 US & 0.000 \\
 Europe & 0.000 \\
 Asia & 0.000 \\\hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"Systemic Risk and Default Cascades in Global Equity Markets: Extending the Gai-Kapadia Framework with Stochastic Simulations and Network Analysis" by Ana I. C. Pereda, arXiv:2504.01969v2, 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{Model Performance Comparison (Averages)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
Model & IoU & Accuracy & Precision & Recall & F1 Score \\
\midrule
Our Model & 0.431 & 0.871 & 0.531 & 0.726 & 0.601 \\
StarDist & 0.087 & 0.672 & 0.130 & 0.267 & 0.172 \\
Cellpose & 0.130 & 0.865 & 0.358 & 0.164 & 0.205 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"GUI Based Fuzzy Logic and Spatial Statistics for Unsupervised Microscopy Segmentation" by Surajit Das and Pavel Zun, arXiv:2508.15979v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Secondary terms in the counting functions of quartic fields" by Arul Shankar and Jacob Tsimerman, arXiv:2503.02381v2, 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{Execution time data for the double integrator example.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|} \hline
 & FG($N = 10$) & MPC($N =10$) & MPC ($N = 236$) & CG \\\hline
 TAVE [ms] & $0.0126$ & $0.0884$ & $255$ & $0.00833$ \\ \hline
 TMAX [ms] & $0.063$ & $0.345$ & $2170$ & $0.0369$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Dominic Liao-McPherson
Terrence Skibik
Torbjørn Cunis
Ilya Kolmanovsky
Marco M. Nicotra

"A Feasibility Governor for Enlarging the Region of Attraction of Linear Model Predictive Controllers" by Dominic Liao-McPherson, Terrence Skibik, Torbjørn Cunis, Ilya Kolmanovsky, and Marco M. Nicotra, arXiv:2011.01924v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|r|r|r|r|r}
\hline
black box & model & $R^2_{train}$ & sd $R^2_{train}$ & $R^2_{test}$ & sd $R^2_{test}$ & time in sec\\
\hline
standalone & basic lm & 0.9633 & 0.0035 & 0.9469 & 0.0048 & 102.2842\\
standalone & penalized poly & 0.9824 & 0.0015 & 0.9781 & 0.0020 & 195.4385\\
standalone & B-Splines & 0.9929 & 0.0013 & 0.9714 & 0.0029 & 113.2447\\
standalone & gam & 0.9880 & 0.0016 & 0.9835 & 0.0017 & 1544.3393\\
\hline
XGBoost & basic lm & 0.9198 & 0.0516 & 0.9030 & 0.0417 & 72.3790\\
XGBoost & penalized poly & 0.9750 & 0.0033 & 0.9634 & 0.0051 & 206.5870\\
XGBoost & B-Splines & 0.9865 & 0.0141 & 0.9591 & 0.0092 & 97.9321\\
XGBoost & gam & 0.9786 & 0.0127 & 0.9672 & 0.0115 & 1728.6021\\
\hline
XGBoost & XGBoost & 0.9392 & 0.0342 & 0.9216 & 0.0405 & 3.0131\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Mean performance simulation results nonlinear effects variant 2}
\end{table}
\end{document}

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.


stat

Yingfan Wang
Haiyang Huang
Cynthia Rudin
Yaron Shaposhnik

"Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization" by Yingfan Wang, Haiyang Huang, Cynthia Rudin, and Yaron Shaposhnik, arXiv:2012.04456v2, 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{Parameters of the water tank system.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 Parameter & Short form & Value & Unit \\ \hline
 Cross-sectional area &
 $A$ &
 $0.015$ &
 $\text{m}^2$ \\
 Maximum flow rate of pump one &
 $u_{1,{\text{max}}}$ &
 $2\cdot 10^{-4}$&
 ${\text{m}^3}/{\text{s}}$ \\
 Valve parameter $V_{12}$ &
 $c_{12}$ &
 $2.5\cdot 10^{-5}$&
 $\text{m}^2$ \\
 Valve parameter $V_{2R}$ &
 $c_{2\text{R}}$&
 $2.5\cdot 10^{-5}$&
 $\text{m}^2$ \\
 Gravitational force &
 $g$ &
 $9.81$ &
 ${\text{m}}/{\text{s}^2}$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Physics-informed Gaussian Processes for Model Predictive Control of Nonlinear Systems" by Adrian Lepp, Jörn Tebbe, and Andreas Besginow, arXiv:2504.21377v1, 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{First-order condition whose solution yields the markup function $\frac{1}{\mu}=\frac{1}{{\cal W} (x, \alpha, \beta,\sigma)}$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|lll}
\hline
 & \multicolumn{2}{c}{ } \\
& $\beta=0$ & & $\beta \in (0,1]$ \\ \hline
\\
$\sigma = 0$ & $\textstyle
1-\mu = \ln \left( \frac{\mu}{x} \right)$ & & $\textstyle 1-\mu = \ln \left( \frac{\mu}{x} \right) + \beta \left[ 1 - \ln \left( \frac{1-\beta-\mu}{\alpha} \right) \right]^\dagger$
\\
$\sigma \neq 0$ & $\textstyle 1-\mu = \frac{1- \left( \frac{\mu}{x} \right)^{-\sigma}}{\sigma}$ & & $\textstyle 1-\mu= \frac{1- \left( \frac{\mu}{x} \right)^{-\sigma} (1 -\beta \sigma) \left[ \frac{1-\beta -\mu}{\alpha ( 1 -\beta \sigma) } \right]^{\beta \sigma}}{\sigma}$ \\ \hline
\multicolumn{4}{p{12cm}}{
\scriptsize {\it Note}: $^\dagger$ means that $\beta=1$ must be excluded since $\beta=1$ is possible only when $\sigma>1$ (see Case~1b in Table~). If $\alpha=0$ or $\beta=\frac{1}{\sigma}$, then we cannot use
 as it relies on $\alpha\neq0$ or $\beta \neq \frac{1}{\sigma}$.
However,
we can combine and to obtain the first-order condition $1-\mu = \beta$
or $1-\mu = \frac{1}{\sigma}$.}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


math

Aida Abiad
Ángeles Carmona
Andrés M. Encinas
Maria José Jiménez
Álvaro Samperio

"A Spectral Approach to Kemeny's Constant" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Tensor Topic Modeling Via HOSVD

"Tensor Topic Modeling Via HOSVD" by Yating Liu and Claire Donnat, arXiv:2501.00535v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ccc|c|c}
 \hline
 $\rho$& & & \\
\hline
0.2& -10.44&-10.27&-10.19\\
\hline
0.5& -11.23&-10.79&-10.57\\
\hline
1.0&-12.23&-11.55&-11.13\\
\hline
2.0&-13.84&-12.85&-12.12\\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Objective values and 95\% bounds with different sizes of uncertainty for different models}
\end{table}
\end{document}

math

"A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications" by Jiaqi Lei, Akhil Singla, and Sanjay Mehrotra, arXiv:2501.18051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

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


q-fin

"Quantile Predictions for Equity Premium using Penalized Quantile Regression with Consistent Variable Selection across Multiple Quantiles" by Shaobo Li and Ben Sherwood, arXiv:2505.16019v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|} \hline
 Brushed DC motor & HPI Racing Saturn & Provides thrust to all wheels\\ \hline
 Servo motor & & Drives the steering\\ \hline
 Microcontroller unit & STM32F765VIT6 (ST Microcontrollers) & Onboard Matek f765-Wing autopilot\\ \hline
 Inertial Measurement Unit & MPU-6000 and ICM20602 (InvenSense) & Onboard Matek f765-Wing autopilot\\ \hline
 GPS & ublox GPS (SAM-M8Q) & Provides speed and positioning \\ \hline
 RF transceiver & Digi Xbee S2C RF & Provides communication to ground station\\ \hline
 Radio receiver controller & RC Futaba with Sbus protocol & Allows to remote control the rover \\ \hline
 Battery & 7.4 V Li-Po 3000 mAh & Rover power\\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Rover's hardware components}
\end{table}
\end{document}

eess

Alfredo González-Calvin
Lía García-Pérez
Juan Jiménez

"Singularity-Free Guiding Vector Field over Bézier's Curves Applied to Rovers Path Planning and Path Following" by Alfredo González-Calvin, Lía García-Pérez, and Juan Jiménez, arXiv:2412.13033v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparative statics.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
\multicolumn{4}{c}{Case 1: $\alpha >0$.} \\
\hline
&&& \\
 & markups & prices & quantities \\ \hline
 &&& \\
Case 1a. $1-\beta \sigma>0$ & $\frac{\partial}{\partial m} \bigl( \frac{1}{\mu} \bigr)<0 $ & $\frac{\partial}{\partial m} \bigl( \frac{m}{\mu} \bigr)>0$ & $ \frac{\partial}{\partial m} \left[\frac{1}{\alpha} \frac{1-\beta -\mu}{1-\sigma(1-\mu)} \right] <0$\\
Case 1b. $1-\beta \sigma<0$ & $\frac{\partial}{\partial m} \bigl( \frac{1}{\mu} \bigr)>0 $ & $\frac{\partial}{\partial m} \bigl( \frac{m}{\mu} \bigr) >0$ & $ \frac{\partial}{\partial m} \left[\frac{1}{\alpha} \frac{1-\beta -\mu}{1-\sigma(1-\mu)} \right] <0$ \\
\hline
\multicolumn{4}{c}{} \\
\multicolumn{4}{c}{Case 2: $\alpha <0$.} \\
\hline
&&& \\
 & markups & prices & quantities \\ \hline
 &&& \\
Case 2a. $1-\beta \sigma>0$ & $\frac{\partial}{\partial m} \bigl( \frac{1}{\mu} \bigr)>0 $ &$\frac{\partial}{\partial m} \bigl( \frac{m}{\mu} \bigr)>0$ & $ \frac{\partial}{\partial m} \left[\frac{1}{\alpha} \frac{1-\beta -\mu}{1-\sigma(1-\mu)} \right] <0$ \\
Case 2b. $1-\beta \sigma<0$ & $\frac{\partial}{\partial m} \bigl( \frac{1}{\mu} \bigr)<0 $ & $\frac{\partial}{\partial m} \bigl( \frac{m}{\mu} \bigr) > 0$ & $ \frac{\partial}{\partial m} \left[\frac{1}{\alpha} \frac{1-\beta -\mu}{1-\sigma(1-\mu)} \right] <0$ \\
\hline
\multicolumn{4}{p{12.5cm}}{\scriptsize {\it Notes}:
The case with $\alpha=0$ corresponds to the CES and requires that $\beta \in (0,1)$.
The case with $1-\beta \sigma=0$ also corresponds to the CES and requires that $\sigma > 1$.
If $\alpha=0$ or $\beta =\frac{1}{\sigma}$, then the price is given by $p(m) =\frac{m}{1-\beta}$ or $p (m) =\frac{\sigma m}{\sigma-1}$, respectively, and thus
$\frac{\partial}{\partial m} \bigl( \frac{1}{\mu} \bigr)=0$, $\frac{\partial}{\partial m} \bigl( \frac{m}{\mu} \bigr)>0$, and $\frac{\partial}{\partial m} \bigl( q(m) \bigr)=
\frac{\partial}{\partial m} \bigl( (u')^{-1} (\nu p(m)) \bigr)<0$.
Since these two cases are well known, we omit them from the table.}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Changes in steady-state values from the elimination of bribery only in the traditional sector.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccc}
 \hline
 \hline
 && {Low-income} & {Middle-income} & {High-income} \\
 && {countries} & {countries} & {countries} \\
 \hline
 {Aggregate}& {Intensive margin} & 0.7\% & 0.3\% & 0.2\% \\
 {output} & {Extensive margin} & -0.9\% & -0.5\% & -0.4\% \\
 &{Net change} & -0.2\% & -0.2\% & -0.2\% \\
 \hline
 {Aggregate} & {Intensive margin} & 0.3\% & 0.1\% & 0.1\% \\
 {consumption} & {Extensive margin} & -0.3\% & -0.2\% & -0.1\% \\
 & {Net change} & 0.0\% & -0.1\% & 0.0\% \\
 \hline
 {Aggregate}& {Intensive margin} & 0.7\% & 0.3\% & 0.1\% \\
 {capital} & {Extensive margin} & -2.4\% & -1.4\% & -0.9\% \\
 & {Net change} & -1.7\% & -1.1\% & -0.8\% \\
 \hline
 {Average}&{Intensive margin} & 1.7\% & 0.7\% & 0.4\% \\
 {wage} & {Extensive margin} & 0.1\% & 0.1\% & 0.1\% \\
 & {Net change} & 1.8\% & 0.8\% & 0.5\% \\
 \hline
 {Modern firms'}&{Intensive margin} & -0.4\% & -0.2\% & -0.2\% \\
 {share of output} & {Extensive margin} & -2.0\% & -1.2\% & -0.8\% \\
 &{Net change} & -2.4\% & -1.4\% & -1.0\%\\
 \hline
 {Firms' entry}& & & & \\
 {(Number of firms)} &{Net change} & 3.0\% & 1.1\% & 0.8\% \\
 \hline
 {Fraction of}& & & & \\
 {modern firms} & {Net change} & -2.1\% & -1.7\% & -1.4\% \\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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

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


math

"An Auto-Stabilized Weak Galerkin Method for Elasticity Interface Problems on Nonconvex Meshes" by Chunmei Wang and Shangyou Zhang, arXiv:2501.13822v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average South American league ratings of top 5 teams}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
 \hline
 \# & league & rating \\ \hline
 1 & Brazil. First & 1868.4 \\ \hline
 2 & Argentina. First & 1779.9 \\ \hline
 3 & Paraguay. First & 1743.8 \\ \hline
 4 & Ecuador. First & 1720.2 \\ \hline
 5 & Brazil. Second & 1676.0 \\ \hline
 6 & Uruguay. First & 1673.1 \\ \hline
 7 & Colombia. First & 1665.6 \\ \hline
 8 & Chile. First & 1605.5 \\ \hline
 9 & Argentina. Second & 1604.4 \\ \hline
 10 & Peru. First & 1592.5 \\ \hline
 11 & Venezuela. First & 1557.8 \\ \hline
 12 & Bolivia. First & 1556.2 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Ratings of European and South American Football Leagues Based on Glicko-2 with Modifications" by Andrei Shelopugin and Alexander Sirotkin, arXiv:2310.11459v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\toprule
 & \multicolumn{5}{c}{Period} & \tabularnewline
\cmidrule{2-7}
Treatment & 1 & 1-5 & 6-10 & 11-15 & 15 & Total\tabularnewline
\midrule
IT & 0.71 & 0.61 & 0.66 & 0.67 & 0.60 & 0.65\tabularnewline
TT & 0.69 & 0.75 & 0.77 & 0.76 & 0.73 & 0.76\tabularnewline
ITex & 0.71 & 0.55 & 0.51 & 0.42 & 0.35 & 0.49\tabularnewline
TTex & 0.71 & 0.74 & 0.76 & 0.67 & 0.53 & 0.73\tabularnewline
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Michalis Drouvelis
Johannes Jarke-Neuert
Johannes Lohse

"Should transparency be (in-)transparent? On monitoring aversion and cooperation in teams" by Michalis Drouvelis, Johannes Jarke-Neuert, and Johannes Lohse, arXiv:2112.12621v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


\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}{|ccccccccc|}
\hline
Reg & Size & Stdev & Skew & Kurt & SW & JB & L1O & L1A \\
\hline
$Z_V$ & 96 & 0.3644 & 0.590 & 0.057 & 0.009 & 0.061 & 0.401 & 0.237 \\
$Z_R$ & 97 & 0.1357 & 0.814 & 1.409 & 0.007 & 0.000 & 0.177 & 0.360 \\
$Z_Q$ & 97 & 0.0149 & 0.226 & 0.068 & 0.420 & 0.655 & 0.480 & 0.435 \\
$Z_I$ & 55 & 0.0183 & -0.059 & -0.501 & 0.754 & 0.738 & 0.401 & 0.519 \\
$Z_B$ & 52 & 0.0263 & 0.193 & 0.238 & 0.857 & 0.800 & 0.878 & 0.706 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Analysis of regression residuals. Skew = skewness, Kurt = kurtosis, SW and JB = Shapiro-Wilk and Jarque-Bera $p$-values, L1O and L1A = L1 norm for the first 5 lags of the ACF for $Z$ and $|Z|$, Length = the size of $Z$}
\end{table}
\end{document}

q-fin

Andrey Sarantsev
Angel Piotrowski
Ian Anderson

"A Time Series Model for Three Asset Classes used in Financial Simulator" by Andrey Sarantsev, Angel Piotrowski, and Ian Anderson, arXiv:2508.06010v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Workforce pDEI: Productivity Coupled with DEI" by Lanqing Du and Jinwook Lee, arXiv:2311.11231v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Multilevel Richardson-Romberg and Importance Sampling in Derivative Pricing" by Devang Sinha and Siddhartha P. Chakrabarty, arXiv:2209.00821v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 \textbf{Divergence} & $\pmb{\varphi(t)}$ & $\pmb{D_\varphi(q\vert\vert p)}$ & $\pmb{\varphi^*(s)}$ \\
 \hline
 Chi-Square & $(t-1)^2$& $\sum_{i=1}^n \frac{(q_i-p_i)^2}{p_i}$ & $\left\{\begin{aligned}
 &s + s^2/4 \quad &s\geq -2\\
 &-1 \quad &s\leq -2
 \end{aligned}
 \right.$\\
 \hline
 Kullback-Leibler & $t\log t -t +1$ & $\sum_{i=1}^n q_i\log(q_i/p_i)$& $e^s-1$ \\
 \hline
 Burg entropy & $-\log t + t -1$& $\sum_{i=1}^n p_i\log(p_i/q_i)$ & $-\log(1-s)$, $s<1$ \\
 \hline
 Hellinger distance & $(\sqrt{t}-1)^2$& $\sum_{i=1}^n \left(\sqrt{p_i}-\sqrt{q_i}\right)^2$ & $\frac{s}{1-s}$, $s\leq 1$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Some coherent $\varphi$-divergences and their characterizations.}
\end{table}
\end{document}

stat

"Improving Offline Contextual Bandits with Distributional Robustness" by Otmane Sakhi, Louis Faury, and Flavian Vasile, arXiv:2011.06835v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|cc|c|}
 \hline
 & \#Nodes & & \#Fans & & \#Fans per page \\
 \hline
 Pro-vaccines & 124 & 9.3\% & 6.9M & 8.1\% & 55.1k\\
 Neutral & 885 & 66.7\% & 74.1M & 87\% & 83.9k\\
 Anti-vaccines & 317 & 23.9\% & 4.2M & 4.9\% & 13.1k\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Summary table of the proportion of nodes and fans per faction.}
\end{table}
\end{document}

cs

"Ensemble of Opinion Dynamics Models to Understand the Role of the Undecided in the Vaccination Debate" by Jacopo Lenti and Giancarlo Ruffo, arXiv:2201.08822v1, 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[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{One-Way ANOVA Results}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccccc}
	\hline\noalign{\smallskip}
	\textbf{} & \textbf{Factor} & \textbf{Df.} & \textbf{Sum Sq.} & \textbf{Mean Sq.} & \textbf{F-value} & \textbf{\textit{p-value}}\\
	\noalign{\smallskip}\hline\noalign{\smallskip}
	\multicolumn{7}{l}{\cellcolor{gray!10}\textbf{Analysis of Variance Test - Top5/Bottom 5/Others}}\\
 \textbf{\multirow{1}{*}{\shortstack[c]{Simplicity}}} & \textbf{Performance} & 2 & 0.01150 & 0.005750 & 5.984 & \textbf{0.00594*}\\
 & \textbf{Residuals} & 34 & 0.03267 & 0.000961 & & \\

 \multicolumn{7}{l}{\cellcolor{gray!10}\textbf{Post Hoc Test}}\\

 & \textbf{Treatments} & \textbf{Diff} & \textbf{Lower} & \textbf{Upper} & \textbf{\textit{p-adj}} & \\
 & Others-Top5 & 0.01418519 & -0.02279834 & 0.05116871 & 0.6192293 & \\
 & Bottom5-Top5 & 0.06160000 & 0.01355700 & 0.10964300 & \textbf{0.0094695*} & \\
 & Bottom5-Others & 0.04741481 & 0.01043129 & 0.08439834 & \textbf{0.0094774*} & \\

 \textbf{\multirow{1}{*}{\shortstack[c]{Interactions}}} & \textbf{Performance} & 2 & 1431864 & 715932 & 0.555 & 0.579\\
 & \textbf{Residuals} & 34 & 43821404 & 1288865 & & \\

 \multicolumn{7}{l}{\cellcolor{gray!10}\textbf{Analysis of Variance Test - $>$ Q3(Top 8)/Q1(Bottom 15)/Others}}\\
 \textbf{\multirow{1}{*}{\shortstack[c]{Simplicity}}} & \textbf{Performance} & 2 & 0.00335 & 0.001676 & 1.396 & 0.262\\
 & \textbf{Residuals} & 34 & 0.04082 & 0.001201 & & \\

 \textbf{\multirow{1}{*}{\shortstack[c]{Interactions}}} & \textbf{Course} & 2 & 165352 & 82676 & 0.062 & 0.94\\
 & \textbf{Residuals} & 34 & 45087916 & 1326115 & & \\

 \multicolumn{7}{l}{\cellcolor{gray!10}\textbf{Analysis of Variance Test - Top8/Bottom 8/Others}}\\
 \textbf{\multirow{1}{*}{\shortstack[c]{Simplicity}}} & \textbf{Performance} & 2 & 0.00656 & 0.003279 & 2.963 & 0.0651\\
 & \textbf{Residuals} & 34 & 0.03762 & 0.001106 & & \\

 \textbf{\multirow{1}{*}{\shortstack[c]{Interactions}}} & \textbf{Performance} & 2 & 34612 & 17306 & 0.013 & 0.987\\
 & \textbf{Residuals} & 34 & 45218656 & 1329960 & & \\

 \multicolumn{7}{l}{\cellcolor{gray!10}\textbf{Analysis of Variance Test - LEI/ETI/LCD/IGE}}\\
 \textbf{\multirow{1}{*}{\shortstack[c]{Simplicity}}} & \textbf{Course} & 3 & 0.00371 & 0.001237 & 1.009 & 0.401\\
 & \textbf{Residuals} & 33 & 0.04046 & 0.001226 & & \\

 \textbf{\multirow{1}{*}{\shortstack[c]{Interactions}}} & \textbf{Course} & 3 & 3919333 & 1306444 & 1.043 & 0.386\\
 & \textbf{Residuals} & 33 & 41333934 & 1252543 & & \\
 \noalign{\smallskip}\hline
 \multicolumn{7}{l}{\textit{*Statistically significant if p-value $<$ 0.05}}\\
 \multicolumn{7}{l}{\shortstack[l]{\textbf{Df.} - Degrees of freedom, \textbf{Sum Sq.} - Sum of Square, \textbf{Mean Sq.} - Mean of Square}}
\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.


cs

"DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation" by Alexandre Rame and Matthieu Cord, arXiv:2101.05544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|l|}
 \hline
 & $L=10^{9}$ & $L=10^{10}$ & $L=10^{11}$ \\ \hline
 $D_{\text{E}}^{\text{(G)}}[s,d]$ & $2.7 \times 10^{-3}$ & $1.7 \times 10^{-3}$ & $1.5 \times 10^{-3}$\\ \hline
 $D_{\text{CLE}}^{\text{(G)}}[s,d]$ & $0.6 \times 10^{-3}$ & $0.2 \times 10^{-3}$ & $0.7 \times 10^{-4}$\\ \hline
 $D_{\text{CFE}}^{\text{(G)}}[s,d]$ & $2.1 \times 10^{-3}$ & $1.5 \times 10^{-5}$ & $1.4 \times 10^{-3}$\\
 \hline
 \end{tabular}
\caption{The evaulation of $D_{\text{E}}^{\text{(G)}}[s,d]$, $D_{\text{CLE}}^{\text{(G)}}[s,d]$ and $D_{\text{CFE}}^{\text{(G)}}[s,d]$ for various computational limit $L$ values considereing $(128,64)$-random tree codes under BSC with $p=0.03$ where $s$ is optimized using the SBP algorithm and $d$ is in the form where $\gamma=0.9992$. }
\end{table}
\end{document}

math

"The Optimization of Random Tree Codes for Limited Computational Resources" by B. Tan Bacinoglu, arXiv:2501.12834v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Monica Pragliola
Luca Calatroni
Alessandro Lanza

"Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising" by Monica Pragliola, Luca Calatroni, and Alessandro Lanza, arXiv:2503.07814v1, 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{Uniform coverage probability based on different methods}%$/100$ of %simultaneous coverage band and pointwise interval}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccccc}
\toprule
& & & \multicolumn{2}{c}{$15\%$\ Censoring\ rate\ \ } & & \multicolumn{2}{c}{ $35\%$ Censoring\ rate\ \ }\\
\cline{4-5} \cline{7-8}
$n$ & Bandwidth & & SCB & CI & & SCB & CI \\
\midrule
400 & $h_1=n^{-0.35}, h_2=n^{-0.35}$ & & 91.3 & 34.8 & & 90.4 & 34.3 \\
 & $h_1=n^{-0.35}, h_2=n^{-0.45}$ & & 92.0 & 32.5 & & 90.5 & 29.3\\
 & auto & & 92.4 & 31.9 & & 90.3 & 27.2\\
 900 & $h_1=n^{-0.35}, h_2=n^{-0.35}$ & & 93.1 & 28.0 & & 93.4 & 26.0\\
 & $h_1=n^{-0.35}, h_2=n^{-0.45}$ & & 92.9 & 23.2 & & 93.0 & 21.0\\
 & auto & & 93.8 & 17.6 & & 91.9 & 16.6\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Regression analysis of multiplicative hazards model with time-dependent coefficient for sparse longitudinal covariates" by Zhuowei Sun and Hongyuan Cao, arXiv:2310.15877v3, 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}{ccccc}
 \hline
 TS-RE&SM &WM &IVW & Egger\\
 \hline
 0.31 (0.41) & 2.42 (0.25)&2.42 (0.25)& 2.27 (0.17)& 2.63 (0.42)\\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Causal effect of BMI on SBP for independent black British: TS-RE used all SNPs and the other MR methods used the selected top 20 significant SNPs}
\end{table}
\end{document}

stat

"A Random Effects Model-based Method of Moments Estimation of Causal Effect in Mendelian Randomization Studies" by Wenhao Cao and Saonli Basu, arXiv:2312.10926v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|ccc}
 \toprule
 {\textbf{Data}} & \textbf{Race} & \textbf{Sex} & \textbf{Both} \\ \midrule
 Employment & OCCP & COW & OCCP, COW, \textcolor{red}{POB} \\ \midrule
 \multirow{2}{*}{Income} & \multirow{2}{*}{MAR, ANC} & \multirow{2}{*}{MAR, ANC, CIT, MIG} & MAR, ANC, CIT, \\
 & & & MIG, \textcolor{red}{SCHL}, \textcolor{red}{NAT} \\ \bottomrule
 \end{tabular}
\caption{ACS dataset features which were not selected when conditioned on race, sex or both, represented in first, second and last column, respectively.}
\end{table}
\end{document}

stat

Krunoslav Lehman Pavasovic
David Lopez-Paz
Giulio Biroli
Levent Sagun

"A Differentiable Rank-Based Objective For Better Feature Learning" by Krunoslav Lehman Pavasovic, David Lopez-Paz, Giulio Biroli, and Levent Sagun, arXiv:2502.09445v1, 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}{lrrr}
 Word & $I_{w, \text{social media}}$ & $I_{w, \text{newspaper}}$ & $\Delta \: I_{w}$ \\ \toprule
 zap & 0.179 & 1.000 & -0.821 \\
 block & 0.153 & 0.857 & -0.704 \\
 hype & 0.393 & 0.995 & -0.602 \\
 link & 0.335 & 0.872 & -0.536 \\
 like & 0.115 & 0.649 & -0.534 \\
 ... & ... & ... & ... \\
 pitch & 0.998 & 0.988 & 0.011 \\
 host & 0.990 & 0.972 & 0.018 \\
 google & 0.561 & 0.531 & 0.030 \\
 rock & 0.787 & 0.648 & 0.139 \\
 DM & 1.000 & 0.120 & 0.880 \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Loanwords with biggest differences in integration between newspaper and social media.}
\end{table}
\end{document}

cs

"Tuiteamos o pongamos un tuit? Investigating the Social Constraints of Loanword Integration in Spanish Social Media" by Ian Stewart, Diyi Yang, and Jacob Eisenstein, arXiv:2101.06368v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Jason A. Platt
Stephen G. Penny
Timothy A. Smith
Tse-Chun Chen
Henry D. I. Abarbanel

"A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics" by Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen, and Henry D. I. Abarbanel, arXiv:2201.08910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Information-theoretic subset selection of multivariate Markov chains via submodular optimization" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, 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{Aggregated Descriptive Statistics of Daily Log Returns (\%) for the Top 250 Japan Equities.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \hline
 Statistic & Minimum & Average & Maximum \\
 \hline
 Mean Return (\%) & -0.004 & 0.003 & 0.010 \\
 Standard Deviation (\%) & 1.333 & 1.939 & 3.010 \\
 Minimum Return (\%) & -30.544 & -13.010 & -7.085 \\
 Maximum Return (\%) & 6.971 & 13.008 & 23.159 \\
 Skewness (log returns) & -1.110 & -0.010 & 0.856 \\
 Kurtosis (log returns) & 1.640 & 5.693 & 56.401 \\
 \hline
 \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

"A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization" by Zhehao Li, Yanchen Wu, and Xiaojie Mao, arXiv:2502.16120v2, 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 $\mathbf{accuracy_{class}}$ and $\mathbf{accuracy_{steps}}$ for all experimental setups.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
\hline
{\bfseries Approach} & {\bfseries Training Data} & {\bfseries Testing Data} & { $\mathbf{accuracy_{class}}$} & {$\mathbf{accuracy_{steps}}$}\\
\hline
PAA + Merge (Baseline) & N/A & Pedometer & N/A & 89.73 \\
PAA + Merge (Baseline) & N/A & AZ & N/A & 86.05 \\
LSTM-General I & Pedometer & Pedometer & 85.98 $\pm\,2.46$ & 98.63 $\pm\,2.37$\\
LSTM-General II & Pedometer & AZ & 60.01 & 94.49\\
LSTM-Personalized & Pedometer + $\sim$AZ & AZ & 91.76 & 98.81\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Arvind Pillai
Halsey Lea
Faisal Khan
Glynn Dennis

"Personalized Step Counting Using Wearable Sensors: A Domain Adapted LSTM Network Approach" by Arvind Pillai, Halsey Lea, Faisal Khan, and Glynn Dennis, arXiv:2012.08975v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Horizon} \& \textbf{Cumulative VaR (1\%)} \& \textbf{Cumulative ES (1\%)} \\
\midrule
1 Day (t+1) \& 8.61\%\& 10.84\%\\
2 Days (t+2) \& 12.37\%\& 17.44\%\\
3 Days (t+3) \& 13.93\%\& 17.18\%\\
4 Days (t+4) \& 16.65\%\& 19.57\%\\
5 Days (t+5) \& 22.04\%\& 24.31\%\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{5-day cumulative VaR and ES using GARCH + FHS at 1\% confidence}
\end{table}
\end{document}

q-fin

"Comparative Evaluation of VaR Models: Historical Simulation, GARCH-Based Monte Carlo, and Filtered Historical Simulation" by Xin Tian, arXiv:2505.05646v1, 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{Comparison of performance on the Ackley function for dimensions $d=20, 40, 1000$: Each optimization method has ten independent runs. Accuracy is quantified using the average function log regret ($r_f$) and the average minima log regret ($r_m$) across these ten runs. Computational efficiency is represented by the average runtime ($t$) across all ten runs, measured in seconds.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{crrrcrrrcrrr} \toprule
 & \multicolumn{3}{c}{$d=20$} & & \multicolumn{3}{c}{$d=40$} & & \multicolumn{3}{c}{$d=1000$} \\ \cmidrule{2-4} \cmidrule{6-8} \cmidrule {10-12}
 Method &\bf ${r_f}$ &\bf ${r_m}$
 &\bf ${t~ (\mathrm{s})}$ & &\bf ${r_f}$ &\bf ${r_m}$
 &\bf ${t~ (\mathrm{s})}$ & &\bf ${r_f}$ &${r_m}$
 &\bf ${t~ (\mathrm{s})}$ \\ \midrule
 ZoRD & 2.83 & 1.68 & 1265.43 & & 2.85 & 1.72 & 1092.04 & & 2.73 & 1.58 & 406.16\\
 GD & 2.85 & 1.72 & 0.12 & & 2.87 & 1.75 & 0.13 & & 2.89 & 1.75 & 0.14\\
 GLD & 2.73 & 1.65 & 0.38 & & 2.89 & 1.70 & 0.39 & & 2.97 & 1.75 & 0.40 \\
 PRGF & 2.85 & 1.70 & 0.06 & & 2.89 & 1.74 & 0.07 & & 2.97 & 1.75 & 0.08\\
 RGF & 2.86 & 1.66 & 0.06 & & 2.89 & 1.72 & 0.07 & & 2.97 & 1.74 & 0.08 \\
GP-UCB & 2.01 & 1.62 & 132.13 & & 2.07 & 1.63 & 323.12 & & 2.93 & 1.74 & 1132.61 \\
 TuRBO & 1.73 & 1.60 & 30.83 & & 2.54 & 1.62 & 83.84 & & 2.95 & 1.73 & 272.56\\
 \textbf{ProGO} & \textbf{-35.35} & \textbf{-35.86} & 6.77 & & \textbf{-31.56} & \textbf{-9.50} & 16.25 & & \textbf{1.99} & \textbf{0.52} & 280.05\\\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

ProGO: Probabilistic Global Optimizer

"ProGO: Probabilistic Global Optimizer" by Xinyu Zhang and Sujit Ghosh, arXiv:2310.04457v2, 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{MobileNet statistic and pvalue (ISIC-2016)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
\textbf{Model} & \textbf{MobileNet} & \textbf{MobileNet+Wavelet} & \textbf{MobileNet+Wavelet+IGWO} & \textbf{MobileNet+Wavelet+Fox} & \textbf{MobileNet+Wavelet+MGTO} \\
\midrule
MobileNet & 0.0, 1.0 & -0.2828, 0.7844 & -3.3737, 0.0097 & -0.2502, 0.8087 & -0.6540, 0.5314 \\
MobileNet+Wavelet & 0.2828, 0.7844 & 0.0, 1.0 & -2.7292, 0.0258 & -0.0691, 0.9465 & -0.4482, 0.6658 \\
MobileNet+Wavelet+IGWO & 3.3737, 0.0097 & 2.7292, 0.0258 & 0.0, 1.0 & 1.3575, 0.2116 & 1.0879, 0.3082 \\
MobileNet+Wavelet+Fox & 0.2502, 0.8087 & 0.0691, 0.9465 & -1.3575, 0.2116 & 0.0, 1.0 & -0.2873, 0.7811 \\
MobileNet+Wavelet+MGTO & 0.6540, 0.5314 & 0.4482, 0.6658 & -1.0879, 0.3082 & 0.2873, 0.7811 & 0.0, 1.0 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ramin Mousa
Saeed Chamani
Mohammad Morsali
Mohammad Kazzazi
Parsa Hatami
Soroush Sarabi

"Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers" by Ramin Mousa, Saeed Chamani, Mohammad Morsali, Mohammad Kazzazi, Parsa Hatami, and Soroush Sarabi, arXiv:2412.00472v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Andrés L. Suárez-Cetrulo
Alejandro Cervantes
David Quintana

"ProteuS: A Generative Approach for Simulating Concept Drift in Financial Markets" by Andrés L. Suárez-Cetrulo, Alejandro Cervantes, and David Quintana, arXiv:2509.11844v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|}
			\hline
			& holds & does not hold \\
			\hline
			 does not hold & $S_\infty=\infty$ a.s., $\P{D=\infty}\in(0,1)$ & $S_\infty<\infty$ a.s., $\P{D=\infty}\in(0,1)$ \\
			\hline
			 holds & \multicolumn{2}{c|}{$D$ and $ S_{D+1}$ are finite a.s.} \\
			\hline
		\end{tabular}
\end{adjustbox}
\caption{An overview of the behaviour of the random variables $D$ and $S_{D+1}$, based on Assumptions~ and~.}
\end{table}
\end{document}

math

"Preferential Attachment Trees with Vertex Death: Lack of Persistence of the Maximum Degree" by Markus Heydenreich and Bas Lodewijks, arXiv:2503.08675v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc}
 \toprule
 & & Construct T & LP on T & k-means & LP on S & Rounding & Total \\ \hline
\multirow{2}{*}{Bank} & Algorithm & 0.01 & 2.4 & $<$0.01 & 1.23 & \textless{}0.01 & 3.78 \\
 & NIPS19 & / & / & 0.14 & 0.81 & \textless{}0.01 & 1.11 \\ \hline
\multirow{2}{*}{Creditcard} & Algorithm~ & 0.01 & 4.06 & $<$0.01 & 2.27 & \textless{}0.01 & 6.51 \\
 & NIPS19 & / & / & 0.18 & 2.05 & \textless{}0.01 & 2.39 \\ \hline
\multirow{2}{*}{Census1990} & Algorithm~ & 0.01 & 7.51 & 0.02 & 5.19 & \textless{}0.01 & 12.99 \\
 & NIPS19 & / & / & 0.30 & 3.94 & \textless{}0.01 & 4.42 \\ \hline
\multirow{2}{*}{Adult} & Algorithm~ & 0.01 & 4.14 & $<$0.01 & 1.80 & \textless{}0.01 & 6.12 \\
 & NIPS19 & / & / & 0.18 & 1.23 & \textless{}0.01 & 1.59 \\ \hline
\multirow{2}{*}{Breastcancer} & Algorithm~ & 0.01 & 0.19 & $<$0.01 & 0.82 & \textless{}0.01 & 1.33 \\
 & NIPS19 & / & / & 0.10 & 0.22 & \textless{}0.01 & 0.45 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Running time (s) on non-strictly fair datasets}
\end{table}
\end{document}

stat

Shihong Song
Guanlin Mo
Qingyuan Yang
Hu Ding

"Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems" by Shihong Song, Guanlin Mo, Qingyuan Yang, and Hu Ding, arXiv:2411.01115v3, 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{List of the proposed standard NetFlow features}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
\hline
\textbf{Feature} & \textbf{Description} \\ \hline
IPV4\_SRC\_ADDR & IPv4 source address \\ \hline
IPV4\_DST\_ADDR & IPv4 destination address \\ \hline
L4\_SRC\_PORT & IPv4 source port number \\ \hline
L4\_DST\_PORT & IPv4 destination port number \\ \hline
PROTOCOL & IP protocol identifier byte \\ \hline
L7\_PROTO & Layer 7 protocol (numeric) \\ \hline
IN\_BYTES & Incoming number of bytes \\ \hline
OUT\_BYTES & Outgoing number of bytes \\ \hline
IN\_PKTS & Incoming number of packets \\ \hline
OUT\_PKTS & Outgoing number of packets \\
\hline
FLOW\_DURATION\_MILLISECONDS & Flow duration in milliseconds \\ \hline
TCP\_FLAGS & Cumulative of all TCP flags \\ \hline
CLIENT\_TCP\_FLAGS & Cumulative of all client TCP flags \\ \hline
SERVER\_TCP\_FLAGS & Cumulative of all server TCP flags \\ \hline
DURATION\_IN & Client to Server stream duration (msec) \\ \hline
DURATION\_OUT & Client to Server stream duration (msec) \\ \hline
MIN\_TTL & Min flow TTL \\ \hline
MAX\_TTL & Max flow TTL \\ \hline
LONGEST\_FLOW\_PKT & Longest packet (bytes) of the flow \\ \hline
SHORTEST\_FLOW\_PKT & Shortest packet (bytes) of the flow \\ \hline
MIN\_IP\_PKT\_LEN & Len of the smallest flow IP packet observed \\ \hline
MAX\_IP\_PKT\_LEN & Len of the largest flow IP packet observed \\ \hline
SRC\_TO\_DST\_SECOND\_BYTES & Src to dst Bytes/sec \\ \hline
DST\_TO\_SRC\_SECOND\_BYTES & Dst to src Bytes/sec \\ \hline
RETRANSMITTED\_IN\_BYTES & Number of retransmitted TCP flow bytes (src-$>$dst) \\ \hline
RETRANSMITTED\_IN\_PKTS & Number of retransmitted TCP flow packets (src-$>$dst) \\ \hline
RETRANSMITTED\_OUT\_BYTES & Number of retransmitted TCP flow bytes (dst-$>$src) \\ \hline
RETRANSMITTED\_OUT\_PKTS & Number of retransmitted TCP flow packets (dst-$>$src) \\ \hline
SRC\_TO\_DST\_AVG\_THROUGHPUT & Src to dst average thpt (bps) \\ \hline
DST\_TO\_SRC\_AVG\_THROUGHPUT & Dst to src average thpt (bps) \\ \hline
NUM\_PKTS\_UP\_TO\_128\_BYTES & Packets whose IP size $<$= 128 \\ \hline
NUM\_PKTS\_128\_TO\_256\_BYTES & Packets whose IP size $>$ 128 and $<$= 256 \\ \hline
NUM\_PKTS\_256\_TO\_512\_BYTES & Packets whose IP size $>$ 256 and $<$= 512 \\ \hline
NUM\_PKTS\_512\_TO\_1024\_BYTES & Packets whose IP size $>$ 512 and $<$= 1024 \\ \hline
NUM\_PKTS\_1024\_TO\_1514\_BYTES & Packets whose IP size $>$ 1024 and $<$= 1514 \\ \hline
TCP\_WIN\_MAX\_IN & Max TCP Window (src-$>$dst) \\ \hline
TCP\_WIN\_MAX\_OUT & Max TCP Window (dst-$>$src) \\ \hline
ICMP\_TYPE & ICMP Type * 256 + ICMP code \\ \hline
ICMP\_IPV4\_TYPE & ICMP Type \\ \hline
DNS\_QUERY\_ID & DNS query transaction Id \\ \hline
DNS\_QUERY\_TYPE & DNS query type (e.g., 1=A, 2=NS..) \\ \hline
DNS\_TTL\_ANSWER & TTL of the first A record (if any) \\ \hline
FTP\_COMMAND\_RET\_CODE & FTP client command return code \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{THE DATA ATTRIBUTES }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
 \hline
 Serial & AttributeTitle & DataType \\
 \hline
 1 & Model & String \\
 \hline
 2 & Year & String \\
 \hline
 3 & Battery & String \\
 \hline
 4 & Price & Number \\
 \hline
 5 & Miles & Number \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Second Hand Price Prediction for Tesla Vehicles" by Sayed Erfan Arefin, arXiv:2101.03788v1, 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{amsfonts}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparative experiments to evaluate the effectiveness of replacing the video feature with prediction text in propagation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\rowcolor[HTML]{FAFAFA}
\cellcolor[HTML]{FAFAFA}
& \multicolumn{2}{c|}{\cellcolor[HTML]{FAFAFA}M3} & \multicolumn{2}{c|}{\cellcolor[HTML]{FAFAFA}S4} \\
\rowcolor[HTML]{FAFAFA}
\multirow{-2}{*}{\cellcolor[HTML]{FAFAFA}Method}
 & \cellcolor[HTML]{FAFAFA}$\mathcal {M_J}$ & \cellcolor[HTML]{FAFAFA}$\mathcal {M_F}$ &\cellcolor[HTML]{FAFAFA} $\mathcal {M_J}$ & \cellcolor[HTML]{FAFAFA}$\mathcal {M_F}$ \\ \hline\hline
Co-Prop (Direct-guided) & 63.6	 & 74 & 83.7 & 90.9 \\
Text-guided & 52.8(↓10.8) & 63.7(↓10.3) & 78.5(↓5.2) & 87.1(↓3.8) \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Collaborative Hybrid Propagator for Temporal Misalignment in Audio-Visual Segmentation" by Kexin Li, Zongxin Yang, Yi Yang, and Jun Xiao, arXiv:2412.08161v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\infty$) processes and applications" by Yingli Wang and Zhenyu Cui, arXiv:2503.18259v3, 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{Validity for the number of groups in group convolution of GMDC on the CVIU database. As multi-scale kernels are utilized, the minimum number of groups is 2. In experiments with different numbers of groups, half of the groups had kernel sizes set to 3, while the other half had kernel sizes set to 7.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
 \toprule
 GMDC (Groups) & SRCC $\uparrow$ & KRCC $\uparrow$ & PLCC $\uparrow$ & RMSE $\downarrow$\\
 \midrule
 Groups 2 & \textbf{0.978} & \textbf{0.872} & \textbf{0.981} & \textbf{0.397} \\
 Groups 4 & 0.974 & 0.866 & 0.977 & 0.551 \\
 Groups 8 & 0.972 & 0.864 & 0.976 & 0.541 \\
 Groups 16 & 0.967 & 0.849 & 0.971 & 0.681 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yixiao Li
Xiaoyuan Yang
Guanghui Yue
Jun Fu
Qiuping Jiang
Xu Jia
Paul L. Rosin
Hantao Liu
Wei Zhou

"Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment" by Yixiao Li, Xiaoyuan Yang, Guanghui Yue, Jun Fu, Qiuping Jiang, Xu Jia, Paul L. Rosin, Hantao Liu, and Wei Zhou, arXiv:2509.06442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
			\hline
		 Coeffs & $A_0= 0.0995$ & $B= 2.3671$ &$\sim$\\ \hline
 $A_1^0= -0.2865$	& $A_1^1= 0.1931$ & $A_1^2= 0.0015$ & $A_1^3= 1.6727$ \\ \hline
	$A_2^0= -0.1449$& $A_2^1=-0.1312$ & $A_2^2=0.8725 $& $A_2^3= 3.7680 $\\ \hline
	$A_3^0= -0.2036$ & $A_3^1= -0.2946 $& $A_3^2= 0.5836$ & $A_3^3= 2.0845$\\ \hline
 $A_4^0= -0.0445$ & $A_4^1= -0.0778$& $A_4^2= 0.1903$ & $A_4^3= 0.4011$\\ \hline
\end{tabular}
\end{adjustbox}
\caption{Coefficients that fit the universal $\sqrt{C}-\chi-Q$ surface for $n=1$ BSs, corresponding to the set of data and fitting surface shown in .}
\end{table}
\end{document}

math

Christoph Adam
Jorge Castelo Mourelle
Alberto García Martín-Caro
Andrzej Wereszczynski

"Spinning boson stars in nonlinear sigma models and Universal Relations" by Christoph Adam, Jorge Castelo Mourelle, Alberto García Martín-Caro, and Andrzej Wereszczynski, arXiv:2502.20923v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
 \toprule
 Model & Target & CPU & Real \\
 \midrule
 ResNet50 & $1.00\times$ & AMD & 0.478s -- $1.00\times$ \\
 ResNet50 & $2.00\times$ & AMD & 0.234s -- $2.04\times$ \\
 ResNet50 & $2.50\times$ & AMD & 0.178s -- $2.69\times$ \\
 ResNet50 & $3.00\times$ & AMD & 0.143s -- $3.34\times$ \\
 ResNet50 & $3.50\times$ & AMD & 0.121s -- $3.95\times$ \\
 \midrule
 MobileNetV1 & $1.00\times$ & Intel & 0.045s -- $1.00\times$ \\
 MobileNetV1 & $1.50\times$ & Intel & 0.031s -- $1.45\times$ \\
 \midrule
 YOLOv5s & $1.00\times$ & Intel & 0.641s -- $1.00\times$ \\
 YOLOv5s & $1.50\times$ & Intel & 0.449s -- $1.43\times$ \\
 YOLOv5s & $1.75\times$ & Intel & 0.380s -- $1.68\times$ \\
 YOLOv5m & $1.00\times$ & Intel & 1.459s -- $1.00\times$ \\
 YOLOv5m & $1.75\times$ & Intel & 0.848s -- $1.72\times$ \\
 YOLOv5m & $2.00\times$ & Intel & 0.725s -- $2.01\times$ \\
 \midrule
 BERT SQuAD & $1.00\times$ & Intel & 0.969s -- $1.00\times$ \\
 BERT SQuAD & $3.00\times$ & Intel & 0.320s -- $3.03\times$ \\
 BERT SQuAD & $3.50\times$ & Intel & 0.271s -- $3.58\times$ \\
 BERT SQuAD & $4.00\times$ & Intel & 0.223s -- $4.35\times$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"SPDY: Accurate Pruning with Speedup Guarantees" by Elias Frantar and Dan Alistarh, arXiv:2201.13096v2, 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{The ProxyFAUG Parameters Selected for this Setting}
\begin{tabular}{|c|c|}
 \hline
 \textbf{Parameter} & \textbf{Explanation} \\ \hline
 $r$ & 20 meters \\ \hline
 $S_{max}$ & 2\\ \hline
 $N$ & 8 \\ \hline
 $p_m$ & 0.3\\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

Grigorios G. Anagnostopoulos
Alexandros Kalousis

"ProxyFAUG: Proximity-based Fingerprint Augmentation" by Grigorios G. Anagnostopoulos and Alexandros Kalousis, arXiv:2102.02706v2, 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|c|}
 \hline
 \multicolumn{2}{|c|}{BNF} & \multicolumn{2}{|c|}{Filter Bank} & \multicolumn{2}{|c|}{MFCC} \\\hline
 iteration & SWER (\%) & iteration & SWER (\%) & iteration & SWER (\%) \\\hline
 iter 1 & 31.43 & iter 1 & 32.59 & iter 1 & 32.66 \\\hline
 iter 2 & 6.26 & iter 2 & 7.36 & iter 2 & 7.12 \\\hline
 iter 3 & 3.75 & iter 3 & 4.64 & iter 3 & 4.54 \\\hline
 iter 4 & 2.97 & iter 4 & 4.27 & iter 4 & 4.53 \\\hline
 iter 5 & 2.23 & iter 5 & 4.27 & iter 5 & 4.54 \\\hline
 \end{tabular}
\end{adjustbox}
\caption{Difference in input and output labels on bottleneck and filter bank feature}
\end{table}
\end{document}

eess

"Unsupervised Spoken Term Discovery on Untranscribed Speech" by Man-Ling Sung, arXiv:2011.14060v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison of proposed framework with CST and CHR on each SIXray subset in terms of mAP. Bold indicates the best performance while the second-best performance is underlined. Here, CST and CHR are driven through ResNet-50 .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 Subset & Proposed & CST & CHR \\\hline
 SIXray10 & \underline{0.9601} & \textbf{0.9634} & 0.7794\\
 SIXray100 & \underline{0.8749} & \textbf{0.9318} & 0.5787\\
 SIXray1000 & \underline{0.7814} & \textbf{0.8903} & 0.3700\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Taimur Hassan
Samet Akcay
Mohammed Bennamoun
Salman Khan
Naoufel Werghi

"Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion" by Taimur Hassan, Samet Akcay, Mohammed Bennamoun, Salman Khan, and Naoufel Werghi, arXiv:2009.13158v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Prediction of high-frequency futures return directions based on the mean uncertainty classification methods: An application in China's future market" by Ying Peng, Yifan Zhang, and Xin Wang, arXiv:2508.06914v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
\hline\noalign{\smallskip}
 Parameter &
DQN & DDPG & TRPO & A2C
 \\
\noalign{\smallskip}\hline\noalign{\smallskip}
Episodes\\
(50, 200, 500) & $\checkmark$ & $\checkmark$ & $\checkmark$ & $\checkmark$\\
\hline
Gamma $\gamma$\\
(0.01,0.1,0.5,0.99)& $\checkmark$ & $\checkmark$ & $\checkmark$ & $\checkmark$\\
\hline
Learning rate\\
(0.1, 0.01, 0.001)& $\checkmark$ & $\checkmark$ & $\checkmark$ & $\checkmark$\\
\hline
Batch Size& $\checkmark$ & $\checkmark$ & $\checkmark$ & $\checkmark$\\
\hline
Neurons No.& $\checkmark$ & $\checkmark$ & $\checkmark$ & $\checkmark$\\
\hline
Layers & $\checkmark$ & $\checkmark$ & $\checkmark$ & $\checkmark$\\
\hline
Optimization function \\(Adam, CG LBFGS, LM)& $\checkmark$ & $\checkmark$ & $\checkmark$ & $\checkmark$\\
\hline Activation function \\(tanh, relu)& $\checkmark$ & $\checkmark$ & $\checkmark$ & $\checkmark$\\
\hline
Trajectory Size \\(10, 20, 50, 100, 1000)& & & & $\checkmark$ \\
\hline
KL value \\(0.001, 0.01, 0.1)& & & & $\checkmark$\\
\hline
\noalign{\smallskip}\hline
\end{tabular}
\end{adjustbox}
\caption{Hyperparameters for deep RL algorithms.}
\end{table}
\end{document}

cs

"Hyperparameter Tuning for Deep Reinforcement Learning Applications" by Mariam Kiran and Melis Ozyildirim, arXiv:2201.11182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Description of the training modes and the loss functions used in this work.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|} \hline
\textbf{Training mode} & \multicolumn{2}{c|}{\textbf{Loss function}} \\ \hline \hline
Classical & \multicolumn{2}{c|}{Cross entropy} \\ \hline
\multirow{2}{*}{Siamese} & \multicolumn{2}{c|}{Single margin contrastive loss (SMCL)} \\
 & \multicolumn{2}{c|}{Double margin contrastive loss (DMCL)} \\ \hline
\multirow{6}{*}{Triplet} & \multicolumn{2}{c|}{Offline triplet mining} \\ \cline{2-3}
 & \multirow{3}{*}{Online triplet mining} & Random negative \\
 & & Semi-hard negative \\
 & & Hardest negative \\ \cline{2-3}
 & \multirow{2}{*}{Multi-class N-pair} & All positive pair \\
 & & Hard negative pair \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"One-shot Representational Learning for Joint Biometric and Device Authentication" by Sudipta Banerjee and Arun Ross, arXiv:2101.00524v1, 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{Compression ratios (sparsities) of sparsest model within $\varepsilon_{acc}$ percentage points of the dense model test accuracy. Random pruning is left out for clarity.}
\begin{tabular}{lcccccc}
\hline\hline
CR ($\uparrow$) & $\varepsilon_{acc}$ & \textbf{LeNet-300-100} & \textbf{LeNet-5} & \textbf{ResNet-18} & \textbf{VGG-19} & \textbf{ResNet-18} \\
 & & \tiny{F-MNIST} & \tiny{K-MNIST} & \tiny{CIFAR10} & \tiny{CIFAR100} & \tiny{Tiny ImageNet} \\
\cline{2-7}
Depth 2 & 5\% & 141 \scriptsize{(99.29\%)} & 52 \scriptsize{(98.08\%)} & 466 \scriptsize{(99.79\%)} & \textbf{484} \scriptsize{(99.79\%)} & 60 \scriptsize{(98.34\%)} \\
 & 10\% & 362 \scriptsize{(99.72\%)} & 78 \scriptsize{(98.71\%)} & 1169 \scriptsize{(99.91\%)} & 939 \scriptsize{(99.89\%)} & 99 \scriptsize{(98.99\%)} \\
Depth 3 & 5\% & \textbf{506} \scriptsize{(99.80\%)} & \textbf{75} \scriptsize{(98.67\%)} & \textbf{573} \scriptsize{(99.83\%)} & 440 \scriptsize{(99.77\%)} & \textbf{67} \scriptsize{(98.51\%)} \\
 & 10\% & 1422 \scriptsize{(99.93\%)} & 134 \scriptsize{(99.25\%)} & \textbf{1456} \scriptsize{(99.93\%)} & \textbf{1014} \scriptsize{(99.90\%)} & \textbf{161} \scriptsize{(99.38\%)} \\
Depth 4 & 5\% & 486 \scriptsize{(99.79\%)} & \textbf{75} \scriptsize{(98.67\%)} & 445 \scriptsize{(99.78\%)} & 215 \scriptsize{(99.53\%)} & 13 \scriptsize{(92.39\%)} \\
 & 10\% & \textbf{1442} \scriptsize{(99.93\%)} & \textbf{139} \scriptsize{(99.28\%)} & 1161 \scriptsize{(99.91\%)} & 675 \scriptsize{(99.85\%)} & 113 \scriptsize{(99.12\%)} \\
GMP & 5\% & 156 \scriptsize{(99.36\%)} & 22 \scriptsize{(95.37\%)} & 211 \scriptsize{(99.53\%)} & 37 \scriptsize{(97.27\%)} & 60 \scriptsize{(98.33\%)} \\
 & 10\% & 235 \scriptsize{(99.58\%)} & 32 \scriptsize{(96.84\%)} & 484 \scriptsize{(99.79\%)} & 68 \scriptsize{(98.52\%)} & 133 \scriptsize{(99.25\%)} \\
SNIP & 5\% & 76 \scriptsize{(98.69\%)} & 17 \scriptsize{(94.10\%)} & 140 \scriptsize{(99.29\%)} & 28 \scriptsize{(96.47\%)} & 18 \scriptsize{(94.34\%)} \\
 & 10\% & 146 \scriptsize{(99.32\%)} & 24 \scriptsize{(95.91\%)} & 339 \scriptsize{(99.70\%)} & 42 \scriptsize{(97.59\%)} & 41 \scriptsize{(97.56\%)} \\
Synflow & 5\% & 141 \scriptsize{(99.29\%)} & 21 \scriptsize{(95.23\%)} & 210 \scriptsize{(99.52\%)} & 46 \scriptsize{(97.81\%)} & 24 \scriptsize{(95.84\%)} \\
 & 10\% & 302 \scriptsize{(99.67\%)} & 37 \scriptsize{(97.30\%)} & 721 \scriptsize{(99.86\%)} & 218 \scriptsize{(99.54\%)} & 71 \scriptsize{(98.60\%)} \\
\hline\hline
\end{tabular}
\end{table}
\end{document}

stat

Chris Kolb
Tobias Weber
Bernd Bischl
David Rügamer

"Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries" by Chris Kolb, Tobias Weber, Bernd Bischl, and David Rügamer, arXiv:2502.02496v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|ccccc}
 Fire & Location & Ignited & Fully Contained & Acreage & \# of Pixels \\
 \hline
Kincade & Sonoma County, CA & Oct. 23, 2019 & Nov. 6, 2019 & 77,758 & 82,125 \\
CZU & Santa Cruz \& San Mateo, CA & Aug. 16, 2020 & Sept. 22, 2020 & 86,509 & 88,581 \\
Windy & Sierra Nevada, CA & Sept. 9, 2021 & Nov. 15, 2021 & 97,528 & 99,458 \\
KNP & Sierra Nevada, CA & Sept. 9, 2021 & Dec. 16, 2021 & 88,307 & 92,171
\end{tabular}
\end{adjustbox}
\caption{Summary of wildland fires used in the study.}
\end{table}
\end{document}

stat

Nicholas Simafranca
Bryant Willoughby
Erin O'Neil
Sophie Farr
Brian J Reich
Naomi Giertych
Margaret Johnson
Madeleine Pascolini-Campbell

"Modelling wildland fire burn severity in California using a spatial Super Learner approach" by Nicholas Simafranca, Bryant Willoughby, Erin O'Neil, Sophie Farr, Brian J Reich, Naomi Giertych, Margaret Johnson, and Madeleine Pascolini-Campbell, arXiv:2311.16187v1, 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{Selected datasets from the UEA Archive.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr} % Left for Dataset, right for numbers
\toprule
Dataset & Size & Dimension & Length \\
\midrule
AtrialFibrillation & 30 & 2 & 640 \\
BasicMotions & 80 & 6 & 100 \\
CharacterTrajectories & 2,858 & 3 & 182 \\
Epilepsy & 275 & 3 & 206 \\
JapaneseVowels & 640 & 12 & 29 \\
NATOPS & 360 & 24 & 51 \\
UWaveGestureLibrary & 440 & 3 & 315 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Quantized symbolic time series approximation" by Erin Carson, Xinye Chen, and Cheng Kang, arXiv:2411.15209v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{mpc.gmd\_bus}
\begin{tabular}{|c|c|c|c|}
\hline
\textit{parent} & \textit{status} & \textit{g\_gnd} & \textit{name} \\
\hline
\hline
1 & 1 & 5 & `dc\_sub1' \\
\hline
2 & 1 & 5 & `dc\_sub2' \\
\hline
1 & 1 & 0 & `dc\_bus1' \\
\hline
2 & 1 & 0 & `dc\_bus2' \\
\hline
3 & 1 & 0 & `dc\_bus3' \\
\hline
4 & 1 & 0 & `dc\_bus4' \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Adam Mate
Arthur K. Barnes
Russell W. Bent
Eduardo Cotilla-Sanchez

"Analyzing and Mitigating the Impacts of GMD and EMP Events on the Electrical Grid with PowerModelsGMD.jl" by Adam Mate, Arthur K. Barnes, Russell W. Bent, and Eduardo Cotilla-Sanchez, arXiv:2101.05042v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
 \textbf{Dataset} & \textbf{Total KIDs} & \textbf{Size} & \textbf{Manufacturers} \\ \hline
 DATASET-1 & 1240 & ~250MB & 36 \\ \hline
 DATASET-2 & 7736 & ~3GB & 52 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Dataset Info}
\end{table}
\end{document}

cs

Domenico Lembo
Alessandra Limosani
Francesca Medda
Alessandra Monaco
Federico Maria Scafoglieri

"Information Extraction through AI techniques: The KIDs use case at CONSOB" by Domenico Lembo, Alessandra Limosani, Francesca Medda, Alessandra Monaco, and Federico Maria Scafoglieri, arXiv:2202.01178v1, 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{Average detection and localization performance on PASCAL VOC 2012.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
		\hline\noalign{\smallskip}
		Methods & mAP & CorLoc\\
		\noalign{\smallskip}\hline\noalign{\smallskip}
		DSTL & 38.3 & 58.8 \\
		OICR &37.9 & 62.1 \\
		WCCN & 37.9 & - \\
		PCL & 40.6 & 63.2 \\
		TS2C & 40.0 & 64.4 \\
		C-WSL & 41.5 & 64.2 \\
		WSD+FSD1 & 42.4 & 65.5 \\
		WeakRPN & 40.8 & 64.9 \\
		MELM & 42.4 & - \\
		ZLDN & 42.9 & 61.5 \\
		WSCDN & 43.3 & 65.2 \\
		C-MIL & 46.6 & 67.4 \\
		SDCN & 43.5 & 67.9 \\
		BOICR & 46.7 & 66.3 \\
		MIL-OICR+GAM+REG & 46.8 & \textbf{69.5} \\
		Ours & \textbf{46.9} & 66.5 \\
		\noalign{\smallskip}\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Cascade Attentive Dropout for Weakly Supervised Object Detection" by Wenlong Gao, Ying Chen, and Yong Peng, arXiv:2011.10258v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ADF test stationarity with AIC optimization.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|c|ccc|}
		\cline{2-8}
		& \textbf{Test Statistic} & \textbf{p-value} & \textbf{Lags} & \textbf{Observations} & \multicolumn{3}{c|}{\textbf{Critical Value}} \\ \cline{6-8}
		& & & & & \textbf{1\%} & \textbf{5\%} & \textbf{10\%} \\ \hline
		\textit{ANF} & -2.302 & 0.171 & 5 & 2529 & -3.432 & -2.863 & -2.567 \\ \hline
		\textit{EOG} & -2.422 & 0.135 & 5 & 2529 & -3.433 & -2.862 & -2.567 \\ \hline

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

q-fin

Ivan Letteri
Giuseppe Della Penna
Giovanni De Gasperis
Abeer Dyoub

"DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networks" by Ivan Letteri, Giuseppe Della Penna, Giovanni De Gasperis, and Abeer Dyoub, arXiv:2210.11532v1, 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}{ccccccccc}
\multicolumn{2}{l}{} & \multicolumn{2}{c}{Baselines} & \multicolumn{5}{c}{Non-baselines} \\
Targets & Matrix & BC\textsubscript{w} & BF\textsubscript{w} & RS &
CS &
AS &
BOISE & total \\
 \hline
 PKnB & PKIS1 & 1 & 7 & 7 & 2& 3 & \textbf{7} & 8 \\
 BGLF4 & PKIS1 & 3 & 9 & 3 & 7 & 10 & \textbf{10} & 11 \\
 ROP18 & PKIS1 & 4 & 7 & 4 & 4 & 2 & \textbf{7} &16 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Peng Yu
Spencer S. Ericksen
Anthony Gitter
Michael A. Newton

"Bayes Optimal Informer Sets for Early-Stage Drug Discovery" by Peng Yu, Spencer S. Ericksen, Anthony Gitter, and Michael A. Newton, arXiv:2011.06122v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
Model & MAPE\\
\hline
*GAN-MC & 0.08\%\\
MC & 0.53\% \\
RBF Network(Gauss) & 0.90\%\\
RBF Network(Sqrt) & 2.33\%\\
MLP Regression & 1.11\%\\
PPR & 1.24\%\\
LR & 1.13\%\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"GAN-MC: a Variance Reduction Tool for Derivatives Pricing" by Weishi Wang, arXiv:2212.00197v1, 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{PCa grading comparison. For fairness, all models use proposed DRN network as a backbone. Bold indicates the best performance while the second-best scores are underlined. The abbreviations are: CC: Classification Category, PF: Proposed Framework, DL: Dual Super-Resolution Learning , PN: PSPNet , UN: UNet , and F8: FCN-8 .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
 \toprule
 CC & MC & PF & DL & PN & UN & F8 \\\hline
 GrG1 & TPR & \textbf{0.560} & 0.493 & \underline{0.524} & 0.494 & 0.461\\
 & PPV & \textbf{0.346} & 0.284 & 0.274 & \underline{0.286} & 0.217\\
 & F1 & \textbf{0.428} & 0.361 & 0.360 & \underline{0.362} & 0.295\\\hline

 GrG2 & TPR & \textbf{0.723} & 0.630 & 0.598 & \underline{0.702} & 0.462\\
 & PPV & \textbf{0.564} & \underline{0.511} & 0.484 & \underline{0.511} & 0.401\\
 & F1 & \textbf{0.634} & 0.564 & 0.535 & \underline{0.592} & 0.429\\\hline

 GrG3 & TPR & \textbf{0.450} & \underline{0.406} & 0.389 & 0.292 & 0.390\\
 & PPV & \textbf{0.107} & \underline{0.084} & 0.076 & 0.064 & 0.064\\
 & F1 & \textbf{0.174} & \underline{0.140} & 0.127 & 0.105 & 0.110\\\hline

 GrG4 & TPR & \underline{0.752} & \textbf{0.770} & 0.706 & 0.727 & 0.678\\
 & PPV & \textbf{0.335} & \underline{0.300} & 0.273 & 0.294 & 0.232\\
 & F1 & \textbf{0.463} & \underline{0.431} & 0.394 & 0.418 & 0.346\\\hline

 GrG5 & TPR & \textbf{0.578} & \underline{0.544} & 0.460 & 0.422 & 0.437\\
 & PPV & \textbf{0.138} & \underline{0.113} & 0.093 & 0.093 & 0.075\\
 & F1 & \textbf{0.223} & \underline{0.188} & 0.155 & 0.153 & 0.128\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Taimur Hassan
Bilal Hassan
Ayman El-Baz
Naoufel Werghi

"A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images" by Taimur Hassan, Bilal Hassan, Ayman El-Baz, and Naoufel Werghi, arXiv:2011.00527v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overview of the data sets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \toprule
 Name&\# samples&\# features&Skewness\ /\ Kurtosis&Source\\
 \midrule
 \texttt{concrete}&1030&8&0.42\ /\ 2.68&\\
 \texttt{turbine}&9568&4&0.31\ /\ 1.95&\\
 \texttt{puma32H}&8192&32&0.02\ /\ 3.04&\\
 \texttt{residential}&372&105&1.26\ /\ 5.15&\\
 \texttt{crime2}&1994&123&1.52\ /\ 4.83&\\
 \texttt{star}&2161&39&0.29\ /\ 2.63&\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Nicolas Dewolf
Bernard De Baets
Willem Waegeman

"Conditional validity of heteroskedastic conformal regression" by Nicolas Dewolf, Bernard De Baets, and Willem Waegeman, arXiv:2309.08313v2, 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{Evaluation of mean square error (MSE) of different depths in the domain $[0,20] \times [0.01,10]$ with $N_x=2$ and $N_y=20$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c} \hline
 Number of hidden layers & MSE of \(u\) & MSE of \(u^{r_0} \) \\
 \hline
 5 & \(2.291\times10^{-4}\) & \(1.982\times10^{-4}\) \\
 1 & \(5.877\times10^{-3}\) & \(4.650\times10^{-4}\) \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Physics-Informed Neural Networks for Optimal Vaccination Plan in SIR Epidemic Models" by Minseok Kim, Yeongjong Kim, and Yeoneung Kim, arXiv:2502.19890v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|rcc|c}
\toprule
\textbf{Human3.6M} &p& $k$ & $\# \text{pred}$ & FVD$\downarrow$ \\
\midrule
 SVG-LP~ &5 & 10 & 30 &718 \\
 Struct-VRNN~ &5& 10 & 30 &523.4 \\
 DVG~&5&10&30& 479.5 \\
 SRVP~ &5& 10 & 30 &416.5 \\
 Grid keypoint~ &8& 8 & 30 & 166.1\\
 \textbf{CVP (Ours)} &5& 1 & 30 &\textbf{144.5} \\
\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Prediction" by Gaurav Shrivastava and Abhinav Shrivastava, arXiv:2412.04929v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc|cc}
 & \multicolumn{2}{c|}{\textbf{CPR}} & \multicolumn{2}{c}{\textbf{DoGD}} \\
\toprule
\textbf{Model} & $\tau$ & $\rho$ & $\tau$ & $\rho$ \\
\midrule
SAM, ViT-H & -0.77 & -0.93 & 0.61 & 0.80 \\
SAM, ViT-B & -0.75 & -0.93 & 0.66 & 0.84 \\
SAM 2, ViT-L & -0.73 & -0.91 & 0.67 & 0.85 \\
SAM 2, ViT-B+ & -0.71 & -0.90 & 0.69 & 0.86 \\
HQ-SAM, ViT-H & -0.74 & -0.92 & 0.70 & 0.87 \\
HQ-SAM, ViT-B & -0.81 & -0.95 & 0.66 & 0.84 \\ \midrule
\textbf{Average} & \textbf{-0.75} & \textbf{-0.92} & \textbf{0.67} & \textbf{0.84} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Rank correlation between SFM IoU and object tree-likeness (CPR and DoGD), on the synthetic dataset.}
\end{table}
\end{document}

eess

Yixin Zhang
Nicholas Konz
Kevin Kramer
Maciej A. Mazurowski

"Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects" by Yixin Zhang, Nicholas Konz, Kevin Kramer, and Maciej A. Mazurowski, arXiv:2412.04243v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc}
 \toprule
Setting & $\sigma=1$ & $\sigma=1.5$ & $\sigma=2$ & $\sigma=2.5$ & $\sigma=3$ \\
 \hline
 (i) Both linear
 & 99.62 & 144.28 & 215.62 & 318.04 & 459.01 \\
 \hline
 (ii) Linear outcome & 88.23 & 136.14 & 204.60 & 302.97 & 444.19 \\
 \hline
(iii) Log-linear density & 65.89 & 107.54 & 169.56 & 255.98 & 378.10 \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Sample mean of $n_2$ for the power-calculated approach. Results are evaluated over $500$ independent repeats in three sub-settings in Section~. $\sigma$ is the noise level in~.}
\end{table}
\end{document}

stat

Ying Jin
Kevin Guo
Dominik Rothenhäusler

"Diagnosing the role of observable distribution shift in scientific replications" by Ying Jin, Kevin Guo, and Dominik Rothenhäusler, arXiv:2309.01056v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc|cc}
 \hline
 & Extract$_{20k}$ & Rand$_{20k}$ & Extract$_{200k}$ & Rand$_{200k}$ \\ \hline
 PSNR$\uparrow$ & \textbf{30.09} & 25.44 & \textbf{33.00} & 32.01 \\
 SSIM$\uparrow$ & \textbf{0.963} & 0.932 & \textbf{0.978} & 0.972 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Qiangeng Xu
Zexiang Xu
Julien Philip
Sai Bi
Zhixin Shu
Kalyan Sunkavalli
Ulrich Neumann

"Point-NeRF: Point-based Neural Radiance Fields" by Qiangeng Xu, Zexiang Xu, Julien Philip, Sai Bi, Zhixin Shu, Kalyan Sunkavalli, and Ulrich Neumann, arXiv:2201.08845v7, licensed under CC0 1.0. 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{Comparison of model performances on the AudioCaps evaluation set. AC is short for AudioCaps dataset, CM is for Customized-Concatenation and CE is for Customized-Overlay. The dataset marked with $\ast$ indicates fine-tuning on AudioCaps. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
\toprule
Model & Dataset & FAD$\downarrow$ & KL$\downarrow$ & CLAP$\uparrow$ & CLAP$_{A} \uparrow$ \\
\midrule
AudioLDM & AC+AS+2 others & $5.25$ & $1.90$ & $42.1$ & $53.5$ \\
AudioGen & AC+AS+8 others & $2.87$ & $1.52$ & $46.4$ & $60.2$ \\
Make-an-Audio & AC+AS+13 others & $2.39$ & $1.64$ & $45.4$ & $59.8$ \\
Tango & AudioCaps & $2.24$ & $1.04$ & $51.7$ & $66.2$ \\
AudioLDM2 & AC+AS+6 others & $2.56$ & $1.75$ & $45.8$ & $55.5$ \\
Re-AudioLDM & AudioCaps & $1.85$ & $1.46$ & $49.9$ & $62.0$ \\
\midrule
DreamAudio & AC+CM+CE & $4.25$ & $2.48$ & $34.9$ & $43.6$ \\
DreamAudio & AudioCaps$^\ast$ & $1.92$ & $1.51$ & $47.5$ & $58.8$ \\
\midrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yi Yuan
Xubo Liu
Haohe Liu
Xiyuan Kang
Zhuo Chen
Yuxuan Wang
Mark D. Plumbley
Wenwu Wang

"DreamAudio: Customized Text-to-Audio Generation with Diffusion Models" by Yi Yuan, Xubo Liu, Haohe Liu, Xiyuan Kang, Zhuo Chen, Yuxuan Wang, Mark D. Plumbley, and Wenwu Wang, arXiv:2509.06027v1, 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{Overall measures of entropy $S_{J}$ for the 4 iterations.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
\toprule
Iteration&$S_{J}$ \\
\midrule
1&274\\
\textbf{2}&\textbf{38} \\
3&274 \\
4&242 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Advances in Kth nearest-neighbour clutter removal" by Nicoletta D'Angelo, arXiv:2310.06467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc}
& \textbf{Pred: False} & \textbf{Pred: True} \\
\hline
\textbf{Label: False} & 2144 & 358 \\
\textbf{Label: True} & 474 & 613 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Dialogue (off-the-shelf dialogue reconstruction) feedback detection confusion matrix.}
\end{table}
\end{document}

eess

Firdavs Nasriddinov
Rafal Kocielnik
Arushi Gupta
Cherine Yang
Elyssa Wong
Anima Anandkumar
Andrew Hung

"Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment" by Firdavs Nasriddinov, Rafal Kocielnik, Arushi Gupta, Cherine Yang, Elyssa Wong, Anima Anandkumar, and Andrew Hung, arXiv:2412.00760v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Robust Inception Score} against pixel perturbations on CIFAR10.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
\toprule
\midrule
$\epsilon$ & 0.0 & $5\times10^{-3}$ & $0.01$ & random noise \\
\midrule
R-IS & 9.94 & 5.49 & 3.91 & 1.01 \\
\midrule
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Motasem Alfarra
Juan C. Pérez
Anna Frühstück
Philip H. S. Torr
Peter Wonka
Bernard Ghanem

"On the Robustness of Quality Measures for GANs" by Motasem Alfarra, Juan C. Pérez, Anna Frühstück, Philip H. S. Torr, Peter Wonka, and Bernard Ghanem, arXiv:2201.13019v2, 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{\small{Sample statistics, JB and ADF test results of $GDP$ and $IR$ ($p$-values in brackets).}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
\toprule
Series & Mean & Sd & Skewness & Kurtosis & JB & ADF \\
\midrule
$GDP$ & 1.22 & 2.37 & 2.22 & 0.73 &14.85 & -4.27\\
& & & & & (0.00) & ($< 0.01$) \\
$IR$ & -3.32 & 32.51 & 3.87 & 9.84 & 382.83 & -4.33 \\
& & & & & (0.00) & ($< 0.01$) \\
($GDP,IR$) & & & 1.76 & 18.78 & 179.54 \\
& & & & &(0.00) \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models" by Ufuk Beyaztas and Han Lin Shang, arXiv:2011.07664v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Automated Analysis of Experiments using Hierarchical Garrote" by Wei-Yang Yu and V. Roshan Joseph, arXiv:2411.01383v1, 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{$|u_h - u_d|_{H^2(\Omega)}$ in , $\ell =6$, $k=1$, $p=2$, $\Delta U_h\subset F_h$.}
\begin{tabular}{|c||c|c|c|}
 \hline
 $\beta^2 \;\backslash\; \gamma^2$ & $10^0$ & $10^{2}$ & $10^{4}$ \\ \hline \hline
 $1$ & 9.76e-03 & 9.76e-03 & 9.76e-03 \\ \hline
 $10^{-2}$ & 9.76e-03 & 9.76e-03 & 9.76e-03 \\ \hline
 $10^{-4}$ & 9.76e-03 & 9.76e-03 & 9.88e-03 \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Kent-Andre Mardal
Jarle Sogn
Marius Zeinhofer

"Variational Formulations of the Strong Formulation -- Forward and Inverse Modeling using Isogeometric Analysis and Physics-Informed Networks" by Kent-Andre Mardal, Jarle Sogn, and Marius Zeinhofer, arXiv:2312.03496v1, 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{Validation accuracies of the scene understanding model using the evaluation dataset in NTHU-DDD dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lclclclclcl}

			\hline\noalign{\smallskip}

			Scenario & Glasses and illumination & Head & Mouth & Eye \\

			\noalign{\smallskip}

			\hline

			\noalign{\smallskip}

			{Day bare face} & 0.99 & 0.99 & 0.98 & 0.89\\

			{Day glasses} & 0.97 & 0.93 & 0.95 & 0.81\\

			{Day sunglasses} & 0.98 & 0.97 & 0.78 & 0.78\\

			{Night bare face} & 0.99 & 0.95 & 0.97 & 0.82\\

			{Night glasses} & 0.97 & 0.96 & 0.88 & 0.92\\

			\hline

			{Average} & 0.98 & 0.96 & 0.912 & 0.844\\

			\hline

			{Total average} & & & & 0.924\\

			\hline

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

eess

Jongmin Yu
Sangwoo Park
Sangwook Lee
Moongu Jeon

"Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework" by Jongmin Yu, Sangwoo Park, Sangwook Lee, and Moongu Jeon, arXiv:1910.09722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.