\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textsc{95th percentile positioning errors of different models}}
\begin{tabular}{lcccc}
\toprule
& \multicolumn{3}{c}{\textbf{Transmit Power [dBm]}} \\
\cmidrule(lr){2-4}
\textbf{Approach} & --20 & --10 & 0 \\
\midrule
Proposed approach 9 APs, $p_f=0$ & 1.35cm & 0.42cm & 0.14cm \\
Proposed approach 15 APs, $p_f=0$ & 0.50cm & 0.16cm & 0.05cm \\
Proposed approach 20 APs, $p_f=0$ & 0.80cm & 0.21cm & 0.07cm \\
\midrule
Proposed approach 9 APs, $p_f=10^{-3}$ & 1.43cm & 0.45cm & 0.15cm \\
Proposed approach 15 APs, $p_f=10^{-3}$ & 0.59cm & 0.19cm & 0.06cm \\
Proposed approach 20 APs, $p_f=10^{-3}$ & 0.85cm & 0.26cm & 0.08cm \\
\midrule
Proposed approach 9 APs, $p_f=10^{-2}$ & 1.88cm & 0.65cm & 0.21cm\\
Proposed approach 15 APs, $p_f=10^{-2}$ & 1.03cm & 0.50cm & 0.47cm\\
Proposed approach 20 APs, $p_f=10^{-2}$ & 0.98cm & 0.27cm & 0.11cm\\
\midrule
MLP-based approach in , $p_f=0$ & 4.54cm & 4.05cm & 4.01cm \\
CNN-based approach in , $p_f=0$ & 3.16cm & 2.83cm & 2.08cm \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Fatih Ayten
Mehmet C. Ilter
Akshay Jain
Ossi Kaltiokallio
Jukka Talvitie
Elena Simona Lohan
Henk Wymeersch
Mikko Valkama

"Failure Tolerant Phase-Only Indoor Positioning via Deep Learning" by Fatih Ayten, Mehmet C. Ilter, Akshay Jain, Ossi Kaltiokallio, Jukka Talvitie, Elena Simona Lohan, Henk Wymeersch, and Mikko Valkama, arXiv:2508.14739v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison between NDTM and existing methods for Linear IPs on distortion metrics like PSNR and SSIM. Missing entries indicate that the method was unstable for that specific task. \textbf{Bold}: best.}
\begin{tabular}{c|cc|cc|cc|cc}
\toprule
 & \multicolumn{4}{c|}{\textbf{Super-Resolution (4x)}} & \multicolumn{4}{c}{\textbf{Random Inpainting (90\%)}} \\ \midrule
 & \multicolumn{2}{c|}{FFHQ (256 × 256)} & \multicolumn{2}{c|}{Imagenet (256 × 256)} & \multicolumn{2}{c|}{FFHQ (256 × 256)} & \multicolumn{2}{c}{Imagenet (256 × 256)} \\ \midrule
Method & PSNR↑ & SSIM↑ & PSNR↑ & SSIM↑ & PSNR↑ & SSIM↑ & PSNR↑ & SSIM↑ \\ \midrule
DPS & 29.06 & 0.832 & 23.61 & 0.676 & 27.76 & 0.832 & 20.96 & 0.657 \\
DDRM & \textbf{30.12} & \textbf{0.864} & \textbf{24.15} & \textbf{0.701} & 17.34 & 0.371 & 15.91 & 0.257 \\
RED-diff & 27.67 & 0.720 & 24.06 & 0.685 & 20.84 & 0.581 & 18.63 & 0.466 \\
C-$\Pi$GDM & 27.93 & 0.773 & 23.20 & 0.631 & - & - & - & - \\ \midrule
NDTM (ours) & 29.06 & 0.833 & 23.12 & 0.674 & \textbf{28.03} & \textbf{0.834} & \textbf{21.34} & \textbf{0.665} \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Kushagra Pandey
Farrin Marouf Sofian
Felix Draxler
Theofanis Karaletsos
Stephan Mandt

"Variational Control for Guidance in Diffusion Models" by Kushagra Pandey, Farrin Marouf Sofian, Felix Draxler, Theofanis Karaletsos, and Stephan Mandt, arXiv:2502.03686v1, 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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean and SD of Model RMSE on Blind Canadian Test Data}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\toprule
 & \multicolumn{2}{c}{\textbf{4 Features}} & \multicolumn{2}{c}{\textbf{6 Features}} & \multicolumn{2}{c}{\textbf{8 Features}} \\
\textbf{Holdout}
 & Mean & SD & Mean & SD & Mean & SD \\
\midrule
London & 7.4 & 0.2 & 7.2 & 0.1 & \textbf{7.0} & 0.1 \\
Merthyr Tydfil & 7.1 & 0.1 & 6.8 & 0.1 & \textbf{6.8} & 0.1 \\
Nottingham & 7.1 & 0.1 & 7.0 & 0.2 & \textbf{6.9} & 0.2 \\
Southampton & 7.0 & 0.1 & 6.8 & 0.1 & \textbf{6.7} & 0.1 \\
Stevenage & 6.9 & 0.1 & 6.8 & 0.1 & \textbf{6.8} & 0.2 \\
Boston & 7.4 & 0.1 & 7.2 & 0.2 & \textbf{7.1} & 0.1 \\
\midrule
Mean & 7.1 & 0.1 & 7.0 & 0.1 & \textbf{6.9} & 0.2 \\
\midrule \midrule
No Holdout & 7.1 & 0.1 & 6.9 & 0.1 & \textbf{6.7} & 0.2 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jonathan Ethier
Mathieu Chateauvert
Ryan G. Dempsey
Alexis Bose

"Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Prediction" by Jonathan Ethier, Mathieu Chateauvert, Ryan G. Dempsey, and Alexis Bose, arXiv:2501.08306v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance Metrics for Referable DR (Grades DR3 and DR4)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|} %% Using |l|l|l| for left justification
\hline
\rule[-1ex]{0pt}{3.5ex} \textbf{Metric} & \textbf{Value (\%)} & \textbf{95\% CI} \\
\hline\hline
\rule[-1ex]{0pt}{3.5ex} Sensitivity & 100.0 & -- \\
\hline
\rule[-1ex]{0pt}{3.5ex} Specificity & 99.9 & 99.8--100.0 \\
\hline
\rule[-1ex]{0pt}{3.5ex} PPV & 85.7 & 72.9--98.5 \\
\hline
\rule[-1ex]{0pt}{3.5ex} NPV & 100.0 & -- \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Amit Kr Dey
Pradeep Walia
Girish Somvanshi
Abrar Ali
Sagarnil Das
Pallabi Paul
Minakhi Ghosh

"AI-Driven Diabetic Retinopathy Screening: Multicentric Validation of AIDRSS in India" by Amit Kr Dey, Pradeep Walia, Girish Somvanshi, Abrar Ali, Sagarnil Das, Pallabi Paul, and Minakhi Ghosh, arXiv:2501.05826v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Hsin-Hsiung Huang
Feng Yu
Kang Li
Teng Zhang

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
			& \multicolumn{2}{c}{full data} & \multicolumn{2}{c}{infl. obs. deleted data}
			\\ \hline
			classic exact t-test & value & $p$-value & value & $p$-value \\ \hline
			one-sided positive correlation with $T_{MP}$ &
			2.313 & 0.020 &
				1.601 & 0.072 \\ \hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

María Jaenada
Elena Castilla
Nirian Martín
Leandro Pardo

"Robust approach for comparing two dependent normal populations through Wald-type tests based on Rényi's pseudodistance estimators" by María Jaenada, Elena Castilla, Nirian Martín, and Leandro Pardo, arXiv:2202.00982v2, 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{Results for ablation study in section . The entries show cross attention removed from a particular block, this is represented by "-" in the table. For example, "CM3T - Block 1" represents CM3T without cross-attention in Block 1. Also, block 1 is closest to input and block 4 is the last block before classification head.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
\hline
Method & Performance \\
\hline
\multicolumn{2}{c}{Audio(MSE)}\\
\hline
UDIVA(CM3T) & 0.69\\
UDIVA(CM3T - Block 1) & 0.72\\
UDIVA(CM3T - Block 2) & 0.73\\
UDIVA(CM3T - Block 3) & 0.81\\
UDIVA(CM3T - Block 4) & 0.78\\
\hline
\multicolumn{2}{c}{Audio(Top-1 Accuracy)}\\
\hline
EK100(CM3T) & 48.2\%\\
EK100(CM3T - Block 1) & 47.8\%\\
EK100(CM3T - Block 2) & 47.5\%\\
EK100(CM3T - Block 3) & 46.4\%\\
EK100(CM3T - Block 4) & 47.1\%\\
\hline
\multicolumn{2}{c}{Transcript(MSE)}\\
\hline
UDIVA(CM3T) & 0.69\\
UDIVA(CM3T - Block 1) & 0.70\\
UDIVA(CM3T - Block 2) & 0.73\\
UDIVA(CM3T - Block 3) & 0.82\\
UDIVA(CM3T - Block 4) & 0.79\\
\hline
\multicolumn{2}{c}{Optical Flow(Top-1 Accuracy)}\\
\hline
EK100(CM3T) & 48.2\%\\
EK100(CM3T - Block 1) & 45.3\%\\
EK100(CM3T - Block 2) & 45.8\%\\
EK100(CM3T - Block 3) & 44.2\%\\
EK100(CM3T - Block 4) & 46.6\%\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Tanay Agrawal
Mohammed Guermal
Michal Balazia
Francois Bremond

"CM3T: Framework for Efficient Multimodal Learning for Inhomogeneous Interaction Datasets" by Tanay Agrawal, Mohammed Guermal, Michal Balazia, and Francois Bremond, arXiv:2501.03332v1, 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{Other process variables of four-CSTR model}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}\hline
 Variables & Defination \\\hline
 $V_i$ & Liquid holdup volume in the $i$-th vessel \\
 $F_i$ & Flow rate of the effluent of the $i$-th vessel \\
 $E_1$, $E_2$, $E_3$ & Activation energies\\
 $\Delta H_1$, $\Delta H_2$, $\Delta H_3$ & Enthalpies of reactions\\
 $k_1$, $k_2$, $k_3$ & Pre-exponential constants for reactions\\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Siyu Liu
Xunyuan Yin
Jinfeng Liu
Feng Ding

"Distributed simultaneous state and parameter estimation of nonlinear systems" by Siyu Liu, Xunyuan Yin, Jinfeng Liu, and Feng Ding, arXiv:2201.03474v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean and variance of each variable of tracking MPC in Case 1}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllllll}
 \hline
 & & $x_1$ & $x_2$ & $y_1$ & $y_2$ & $u_1$ & $u_2$ \\
 \hline
 \multirow{2}{*}{$\mu$} & Sim & 1.1601 & 615.7488 & 1.1601 & 615.7480 & 2.7890e+02 & 1.2773e+07 \\
 & Cal & 1.1601 & 615.7373 & 1.1601 & 615.7373 & 2.7885e+02 & 1.2769e+07 \\
 \multirow{2}{*}{$\sigma^2$} & Sim & 5.0130e-04 & 1.5292e+00 & 5.9141e-04 & 1.6164e+00 & 1.9444e+01 & 5.4403e+10 \\
 & Cal & 5.0824e-04 & 1.5499e+00 & 5.9824e-04 & 1.6399e+00 & 1.9513e+01 & 5.4636e+10 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Jiangbang Liu
Song Bo
Benjamin Decardi-Nelson
Jinfeng Liu
Jingtao Hu
Tao Zou

"Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise" by Jiangbang Liu, Song Bo, Benjamin Decardi-Nelson, Jinfeng Liu, Jingtao Hu, and Tao Zou, arXiv:2201.01828v1, 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{Radiomic feature selection percentage (\%) for ATE using SIS + GOAL, SIS + OAL and CBS for $10\,000$ bootstrap resamples. The exposure is tumor stage (stage III versus non-stage III) and the outcome is effectiveness of treatment neoadjuvant chemotherapy (NAC).}
\begin{tabular}{lrrrr}
\\
\hline
\hline
\textbf{Radiomic feature} & \textbf{SIS + OAL} & \textbf{CBS} & \textbf{SIS + GOAL}\\
\hline
\hline
Small-Dependence Low-Gray-Level Emphasis.7 & 80.7 & 68.5 & 83.7 \\
Sum Average.8 & 81.2 & 67.4 & 85.6 \\
Zone Percentage.9 & 84.9 & 55.7 & 87.5 \\
Cluster Tendency.10 & 71.4 & 66.7 & 76.6 \\
Dependence Entropy.10 & 83.9 & 51.5 & 87.2 \\
Large-Dependence Low-Gray-Level Emphasis.10 & 98.5 & 61.0 & 98.7 \\
Small-Dependence Low-Gray-Level Emphasis.10 & 95.0 & 50.5 & 96.3 \\
Short Run Low-Gray-Level Emphasis.10 & 93.4 & 62.2 & 94.6 \\
Long Run Low-Gray-Level Emphasis.10 & 96.4 & 68.6 & 97.3 \\
Variance.12 & 61.8 & 75.4 & 68.8 \\
Kurtosis.12 & 90.2 & 67.1 & 92.3 \\
Zone Entropy.12 & 50.9 & 67.1 & 58.1 \\
\hline
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Ultra-high dimensional confounder selection algorithms comparison with application to radiomics data" by Ismaïla Baldé and Debashis Ghosh, arXiv:2310.06315v1, 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{Min/max shape description for $k$ recursions.}
\begin{tabular}{c|c}
 recursion & formula of decision manifold $R_k$ \\ \hline
 $1$ & $\{x,y:\min(h_1,h_2,h_3)=0\}$\\
 $2$ & $\{x,y:\min(\underbrace{h_1,h_2,h_7,}_{} \underbrace{\max(h_3,h_4,h_5)}_{})=0\}$\\
 $\ldots$ & $\ldots$ \\
 $k$ & $\{x,y:\min(\underbrace{h_1,h_2,h_{r(k)},}_{\text{external half-spaces}} \underbrace{D_1, \ldots, D_{\lfloor r(k)/4\rfloor+1}}_{\text{``dents''~}})=0\}$
 \end{tabular}
\end{table}
\end{document}

stat

Michal Lewandowski
Hamid Eghbalzadeh
Bernhard A. Moser

"CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures" by Michal Lewandowski, Hamid Eghbalzadeh, and Bernhard A. Moser, arXiv:2411.19713v3, 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}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c}
 \hline
$J$ & Strata-preserving & Non-strata-preserving \\
 \hline
$J\mathfrak{z} =\mathfrak{z}$ & $J$ nilpotent of step $2$ & $J$ nilpotent of step $2$ \\
 \hline
 $J\mathfrak{z} \neq \mathfrak{z}$ & $J$ nilpotent of step $2$ & $J$ nilpotent of step $3$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{nilpotency of $J$}
\end{table}
\end{document}

math

"Complex structures on stratified Lie algebras" by Junze Zhang, arXiv:2202.02111v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Sound Classification of Four Insect Classes" by Yinxuan Wang and Sudip Vhaduri, arXiv:2412.12395v1, 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{Sample table title}
\begin{tabular}{ll}
\bf PART &\bf enumerate
\\ \hline \\
Dendrite &Input terminal \\
Axon &Output terminal \\
Soma &Cell body (contains cell nucleus) \\
\end{tabular}
\end{table}
\end{document}

stat

Prateek Jaiswal
Debdeep Pati
Anirban Bhattacharya
Bani K. Mallick

"Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors" by Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya, and Bani K. Mallick, arXiv:2309.06349v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Fine-tuning Data Scale.} The experiment involves 10 speakers from the VCTK~, each with more than 100 samples, and the steps are kept consistent.}
\begin{tabular}{lcccc}
 \toprule

 & \multicolumn{2}{c}{\textbf{Grad-StyleSpeech}~} & \multicolumn{2}{c}{\textbf{Stable-TTS}} \\

 \cmidrule(lr){2-3} \cmidrule(lr){4-5}

 \bf \# Samples & WER ($\downarrow$) & SECS ($\uparrow$) & WER ($\downarrow$) & SECS ($\uparrow$) \\

 \midrule[0.4pt]

 1 & 48.92$_{\pm \text{9.11}}$ & 84.23$_{\pm \text{0.23}}$ & 0.97$_{\pm \text{0.67}}$ & 83.88$_{\pm \text{0.88}}$ \\

 5 & 18.66$_{\pm \text{3.40}}$ & 86.87$_{\pm \text{0.87}}$ & 0.52$_{\pm \text{0.53}}$ & \bf {84.97$_{\pm \text{0.03}}$} \\

 20 & 18.58$_{\pm \text{3.33}}$ & \bf 86.93$_{\pm \text{0.07}}$ & 0.35$_{\pm \text{0.42}}$ & 84.74$_{\pm \text{0.74}}$ \\

 100 & \bf 14.16$_{\pm \text{3.02}}$ & 86.34$_{\pm \text{0.34}}$ & \bf 0.25$_{\pm \text{0.29}}$ & 84.61$_{\pm \text{0.61}}$ \\

 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Wooseok Han
Minki Kang
Changhun Kim
Eunho Yang

"Stable-TTS: Stable Speaker-Adaptive Text-to-Speech Synthesis via Prosody Prompting" by Wooseok Han, Minki Kang, Changhun Kim, and Eunho Yang, arXiv:2412.20155v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
 \hline
 Superpositional & Spectral Group & Sensor & mAP \\
 \hline
 - & - & - & 65.08\\
 - & - & \checkmark & 65.77 \\
 - & \checkmark & - & 66.88 \\
 - & \checkmark & \checkmark & 65.81\\
 \checkmark & - & - & 66.33\\\
 \checkmark & - & \checkmark & 66.00 \\
 \checkmark & \checkmark & - & \textbf{66.99}\\
 \checkmark & \checkmark & \checkmark & 66.46 \\
 \hline
 \end{tabular}
\caption{USatlas validation set performance when using different combinations of superpositional, spectral group, and sensor encodings. We pre-train with both sensors and fine-tune with Sentinel-2 for these experiments.}
\end{table}
\end{document}

stat

Jeremy Irvin
Lucas Tao
Joanne Zhou
Yuntao Ma
Langston Nashold
Benjamin Liu
Andrew Y. Ng

"USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery" by Jeremy Irvin, Lucas Tao, Joanne Zhou, Yuntao Ma, Langston Nashold, Benjamin Liu, and Andrew Y. Ng, arXiv:2312.02199v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
$(n,p)$ & $f=(f_{1},\dots, f_{t})$ & Unfolding monomials $< m$ & $r$ & $\sigma_{m}$\\
\hline\hline
 $(8,6)$ & $(x^{2} +y^{2} +z^{2}, y^{2}+\lambda z^{2}+w^{2}),\,
 \lambda\neq 1$ & $\{y,w\}e_{1},\{x,z\}e_{2}$ & $4$ &
 $z^{2}e_{2}$\\
\hline
\multirow{2}{*}{$(10+k,7),\,k\geq 0$} & $x^{3} +y^{3} +z^{3}+\lambda xyz +\sum_{i=1}^{k}\delta_{i}w_{i}^{2},$
 &$\{x,y,z, yz,xz,xy\}e_{1}$ & $6$& $xyze_{1}$\\
&$\delta_{i}=\pm 1,\, \lambda^{3}\neq -1$ &&&\\
 \hline
$(9,8)$&$x^{4}+y^{4}+\lambda x^{2}y^{2},\,\lambda \neq \pm 2$
 &$\{x, y, x^{2},xy, y^{2},
 x^{2}y, xy^{2}\}e_{1}$ &$7$&$x^{2}y^{2}e_{1}$ \\
\hline
 \end{tabular}
\end{adjustbox}
\caption{$n>p$}
\end{table}
\end{document}

math

"Old and new results on density of stable mappings" by Maria Aparecida Soares Ruas, arXiv:2201.03888v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance and efficiency across different sampling steps for generative objectives. Metrics: SE (DNSMOS OVRL). }
\begin{tabular}{lcccc}
\toprule
\textbf{Objective} & \textbf{Steps} & \textbf{OVRL} & \textbf{RTF} & \textbf{Mem (GB)} \\
\midrule
DDPM & 200 & 3.59 & 3.49 & 18.2 \\
DDPM & 32 & 3.35 & 0.52 & 17.9 \\
DDPM & 1 & 2.98 & 0.03 & 17.7 \\
\midrule
FM & 32 & 3.54 & 0.31 & 17.7 \\
FM & 1 & 3.20 & 0.03 & 17.6 \\
\midrule
MF & 1 & 3.50 & 0.02 & 17.6 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Ziqian Wang
Zikai Liu
Yike Zhu
Xingchen Li
Boyi Kang
Jixun Yao
Xianjun Xia
Chuanzeng Huang
Lei Xie

"UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling" by Ziqian Wang, Zikai Liu, Yike Zhu, Xingchen Li, Boyi Kang, Jixun Yao, Xianjun Xia, Chuanzeng Huang, and Lei Xie, arXiv:2508.07558v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Global Bias-Corrected Divide-and-Conquer by Quantile-Matched Composite for General Nonparametric Regressions" by Yan Chen and Lu Lin, arXiv:2201.12597v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

H. C. Donker
D. Neijzen
J. de Jong
G. A. Lunter

"Multinomial belief networks for healthcare data" by H. C. Donker, D. Neijzen, J. de Jong, and G. A. Lunter, arXiv:2311.16909v3, 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{Under- and over-identification rates for $\widehat{q}_{\widehat{c}}^{(n)}$, with $q = 0, 1, 2, 3$.}
\begin{tabular}{ccccc} \hline \hline
 & $q = 0$ & $q = 1$ & $q = 2$ & $q = 3$ \\ \hline
 Model (a) in~ & & & & \\
 Under-identification & 0 & 0 & 0 & 0 \\
 Over-identification & 0 & 0.10 & 0.08 &0.04 \\ \hline
Model (b) in~ & & & & \\
 Under-identification & 0 & 0 & 0 & 0 \\
 Over-identification & 0& 0 & 0 & 0 \\ \hline \hline
\end{tabular}
\end{table}
\end{document}

stat

Matteo Barigozzi
Davide La Vecchia
Hang Liu

"General Spatio-Temporal Factor Models for High-Dimensional Random Fields on a Lattice" by Matteo Barigozzi, Davide La Vecchia, and Hang Liu, arXiv:2312.02591v1, 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}
\usepackage{amssymb}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Ablation} showing the impact of the two filters used in our anti-aliasing efforts. Combining both filters reduces the reconstruction error.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|ccc|ccc}
 \multicolumn{2}{c|}{Filters} & \multicolumn{3}{c|}{Bonsai} & \multicolumn{3}{c}{dc263dfbf0} \\
 2D & 3D & PSNR & SSIM & LPIPS & PSNR & SSIM & LPIPS \\
 \midrule
 & & 30.84 & 0.929 & \cellcolor{yellow!25}0.196 & 22.46 & \cellcolor{yellow!25}0.870 & 0.267 \\
 \checkmark & & \cellcolor{yellow!25}30.96 & \cellcolor{orange!25}0.931 & \cellcolor{yellow!25}0.196 & \cellcolor{yellow!25}22.59 & \cellcolor{yellow!25}0.870 & \cellcolor{red!25}0.266 \\
 & \checkmark& \cellcolor{orange!25}30.97 & \cellcolor{yellow!25}0.930 & \cellcolor{red!25}0.195 & \cellcolor{orange!25}22.66 & \cellcolor{red!25}0.871 & \cellcolor{red!25}0.266 \\
 \checkmark & \checkmark & \cellcolor{red!25}31.21 & \cellcolor{red!25}0.936 & \cellcolor{red!25}0.195 & \cellcolor{red!25}22.67 & \cellcolor{red!25}0.871 & \cellcolor{red!25}0.266 \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"LinPrim: Linear Primitives for Differentiable Volumetric Rendering" by Nicolas von Lützow and Matthias Nießner, arXiv:2501.16312v4, 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{Optimal solutions for Problem and its deterministic variant.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 Formulation &$z_b^*$ & $M_{t, \xi(t)}f^*(t, \xi(t))$ \\
 \hline \hline
		Stochastic & 45.0 & 49.5 \\
		Deterministic & 78.3 & 55.2\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Random Field Optimization

Joshua L. Pulsipher
Benjamin R. Davidson
Victor M. Zavala

"Random Field Optimization" by Joshua L. Pulsipher, Benjamin R. Davidson, and Victor M. Zavala, arXiv:2201.09951v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrrrr}
\toprule
 & Coefficient & Standard error & t & p\\
\midrule
Intercept & 1.298 & 0.088 & 14.8 & < 0.001\\
\addlinespace
ln(AGB) & 0.584 & 0.006 & 105.4 & < 0.001\\
\addlinespace
ln(Perimeter) & -0.167 & 0.014 & -12.0 & < 0.001\\
\addlinespace
ln(Area) & -0.146 & 0.008 & -17.9 & < 0.001\\
\addlinespace
ln(\% Forest) & 0.123 & 0.005 & 26.7 & < 0.001\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Lucas K. Johnson
Grant M Domke
Stephen V Stehman
Michael J Mahoney
Colin M Beier

"From pixels to parcels: flexible, practical small-area uncertainty estimation for spatial averages obtained from aboveground biomass maps" by Lucas K. Johnson, Grant M Domke, Stephen V Stehman, Michael J Mahoney, and Colin M Beier, arXiv:2412.16403v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"datadriftR: An R Package for Concept Drift Detection in Predictive Models" by Ugur Dar and Mustafa Cavus, arXiv:2412.11308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
& Action selection method & Accuracy & \# of inferences \\
\midrule
\multirow{3}{*}{GSM8K} & UCT & 0.828 & 83.53 \\
 & PUCT & 0.840 & 85.39 \\
& \cellcolor[HTML]{EFEFEF}Semantic PUCT (Ours) & \cellcolor[HTML]{EFEFEF}\textbf{0.850} & \cellcolor[HTML]{EFEFEF}82.63 \\
\midrule
\multirow{3}{*}{ARC} & UCT & 0.815 & 95.16 \\
 & PUCT & 0.818 & 97.88 \\
& \cellcolor[HTML]{EFEFEF}Semantic PUCT (Ours) & \cellcolor[HTML]{EFEFEF}\textbf{0.830} & \cellcolor[HTML]{EFEFEF}96.32 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Ablation study of action selection algorithms, including UCT, PUCT, and Semantic PUCT (Ours), presenting average accuracy and the number of inferences for both GSM8K and ARC datasets.}
\end{table}
\end{document}

cs

Sungjae Lee
Hyejin Park
Jaechang Kim
Jungseul Ok

"Semantic Exploration with Adaptive Gating for Efficient Problem Solving with Language Models" by Sungjae Lee, Hyejin Park, Jaechang Kim, and Jungseul Ok, arXiv:2501.05752v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|cc|cc||}
 \hline
 \multirow{2}{3cm}{\textbf{Reference choice}} & \multicolumn{2}{c|}{\textbf{Range of mean test errors}} & \multicolumn{2}{c||}{\textbf{Range of std.deviation in test errors}} \\
 & Mild shearing & Severe shearing & Mild shearing & Severe shearing \\
 \hline
 1 Gaussian reference & $[0.0083,0.1298]$ & $[0.0198,0.2132]$ & $[0.0064,0.0291]$ & $[0.0108,0.0382]$\\
 2 unsheared MNIST references & $[0.0078,0.0880]$ & $[0.0220,0.1585]$ & $[0.0056,0.0244]$ & $[0.0111,0.0328]$\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{\scriptsize{Range of mean value and standard deviations of test errors for pairwise classification of sheared MNIST images across all pairs of digits for various reference choices. The reported values are across 20 experiments involving different choices of 50 randomly drawn training images per class and 500 randomly drawn test images per class for each experiment.}}
\end{table}
\end{document}

math

Varun Khurana
Harish Kannan
Alexander Cloninger
Caroline Moosmüller

"Supervised learning of sheared distributions using linearized optimal transport" by Varun Khurana, Harish Kannan, Alexander Cloninger, and Caroline Moosmüller, arXiv:2201.10590v1, 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}
\usepackage{amssymb}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of Lab-MAE and XGBoost models with and without follow-up values for imputation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 Model & Follow-Up & R² \\
 \midrule
 XGBoost & \checkmark & 0.7391 \\
 & & 0.6089 \\
 Lab-MAE & \checkmark & 0.7661 \\
 & & 0.6544 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

David Restrepo
Chenwei Wu
Yueran Jia
Jaden K. Sun
Jack Gallifant
Catherine G. Bielick
Yugang Jia
Leo A. Celi

"Representation Learning of Lab Values via Masked AutoEncoders" by David Restrepo, Chenwei Wu, Yueran Jia, Jaden K. Sun, Jack Gallifant, Catherine G. Bielick, Yugang Jia, and Leo A. Celi, arXiv:2501.02648v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lllllll}
 \toprule
 \textbf{Model} & \textbf{Acc} & \textbf{F1} & \textbf{WER} & \textbf{BERTScore} & \textbf{BLEU} & \textbf{ROUGE$_{L}$}\\
 \midrule
 End-to-end RoBERTa & 0.997 & 0.185 & 0.536 & 0.207 & 0.010 & 0.185 \\
 RoBERTa pipeline & 0.739 & 0.012 & 0.776 & 0.030 & 0.015 & 0.028\\
 GPT-3.5 & - & - &4.281 & 0.788 & 0.217 & 0.300 \\
 GPT-4 & - & - & 3.396 & 0.840 & 0.282 & 0.517 \\
 \bottomrule \\
 \end{tabular}
\caption{Evaluation results for the case outcome extraction task. The RoBERTa-based models are fine-tuned on the CLC annotations, while the GPT-based models are zero-shot---that is, they are evaluated without fine-tuning. We calculate accuracy and F1 scores for the RoBERTa-based models, and word error rate (WER), BERTScore, BLEU and ROUGE$_{L}$ for all baseline models.}
\end{table}
\end{document}

stat

Andreas Östling
Holli Sargeant
Huiyuan Xie
Ludwig Bull
Alexander Terenin
Leif Jonsson
Måns Magnusson
Felix Steffek

"The Cambridge Law Corpus: A Dataset for Legal AI Research" by Andreas Östling, Holli Sargeant, Huiyuan Xie, Ludwig Bull, Alexander Terenin, Leif Jonsson, Måns Magnusson, and Felix Steffek, arXiv:2309.12269v4, 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}{crrrrrrr}\hline
 & $c_{1,j}$ & $c_{2,j}$ & $c_{3,j}$ & $c_{4,j}$& $c_{5,j}$ \\ \hline
 $ j=0$ & $1$ & $5/4 $ & $4/3$ & $319/192$ & $73/40$ \\
 $ j=1$ & & $-1/8 $ & $-1/6$ & $-107/288$ & $-7/15$ \\
 $ j=2$ & & & & $47/1152$ & $13/240$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{$c_{p,j}$ values for $p=1,\hdots,5$. They are symmetric, $c_{p,j}=c_{p,-j}$, $j=0,\hdots,\left\lfloor\frac{p}{2}\right\rfloor$.}
\end{table}
\end{document}

math

Sergio Amat
David Levin
Juan Ruiz-Alvarez
Dionisio F. Yáñez

"Global and explicit approximation of piecewise smooth 2D functions from cell-average data" by Sergio Amat, David Levin, Juan Ruiz-Alvarez, and Dionisio F. Yáñez, arXiv:2201.10823v1, 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{Comparison with different models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
					\toprule
					Model & Accuracy \\
					\midrule
					VGG16 & 96.10\% \\
					VGG19 & 97.60\% \\
					Ensamble VGG16 and VGG19 & 99\% \\
					ECA-VGG19 & 98.80\% \\

					MTJNet & 99.71\% \\
					Proposed & 99\% \\
					\bottomrule
				\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Deepjyoti Chetia
Sanjib Kr Kalita
Prof Partha Pratim Baruah
Debasish Dutta
Tanaz Akhter

"Identification of Traditional Medicinal Plant Leaves Using an effective Deep Learning model and Self-Curated Dataset" by Deepjyoti Chetia, Sanjib Kr Kalita, Prof Partha Pratim Baruah, Debasish Dutta, and Tanaz Akhter, arXiv:2501.09363v1, 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{Performances on VGGSound dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 Method & ACC\,(\%) & MAP\,(\%) \\
 \midrule
 OGM & 48.29 & 49.78 \\
 AGM & 47.11 & 51.98 \\
 ReconBoost & 50.97 & 53.87 \\
 MMPareto & 51.25 & 54.73 \\
 SMV & 50.31 & 53.62 \\
 MLA & \underline{51.65} & 54.73 \\
 \midrule
 BSS-H & 51.61 & \underline{55.68} \\
 BSS-L & \textbf{52.80} & \textbf{56.61} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Balance-aware Sequence Sampling Makes Multi-modal Learning Better" by Zhi-Hao Guan, arXiv:2501.01470v1, 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}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation of the proposed components. Video-level AUC (\%) is reported for comparison.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
 \toprule
 Model & CDF & DFDCP & DFDC & DFD \\
 \midrule
 Baseline & 88.41 & 82.46 & 76.07 & 90.04 \\
 \midrule
 +$\Delta W$ & 90.70 & 85.97 & 78.25 & 93.08\\
 w/o S-Branch & 91.25 & 87.30 & 81.02 & 94.95\\
 w/o M-Branch & 90.51 & 87.68 & 80.33 & 92.25\\
 w/o $\mathcal{L}_{con}$ & 91.20 & 86.35 & 79.74 & 94.03\\
 w/o $\mathcal{L}_{align}$ & 90.52 & 87.01 & 80.18 & 94.62 \\
 \midrule
 Ours & \textbf{93.13} & \textbf{88.11} & \textbf{81.21} & \textbf{95.51}\\
 \bottomrule

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

cs

Xinghe Fu
Zhiyuan Yan
Taiping Yao
Shen Chen
Xi Li

"Exploring Unbiased Deepfake Detection via Token-Level Shuffling and Mixing" by Xinghe Fu, Zhiyuan Yan, Taiping Yao, Shen Chen, and Xi Li, arXiv:2501.04376v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|l}
 \hline
 & Top 10 most representative words per topic \\
 \hline
Computer Science & ``data" ``network" ``learning" ``networks" ``can" ``model"
 \\ &``using" ``new" ``paper" ``based" \\

 Physics & ``show" ``data" ``analysis" ``two" ``can"
 ``problem" \\& ``results" ``field" ``system" ``performance"
 \\
 Mathematics & ``can" ``used" ``models" ``using" ``model" ``paper"
 \\& ``number" ``method" ``proposed" ``approach" \\
 Statistics & ``model" ``results" ``show" ``can" ``learning" ``method"
\\& ``using" ``based" ``data" ``also" \\
 \hline
\end{tabular}
\caption{Most common words found by LDA}
\end{table}
\end{document}

stat

"Sparse topic modeling via spectral decomposition and thresholding" by Huy Tran, Yating Liu, and Claire Donnat, arXiv:2310.06730v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"A combined full-reference image quality assessment approach based on convolutional activation maps" by Domonkos Varga, arXiv:2010.09361v3, 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{List of main notations and typical values.}
\begin{tabular}{c|l|l}
\hline
 Notation & Physical meaning & Value \\ \hline
 $W$ & UAV weight in Newton & 20 \\ \hline
 $\rho$ & Air density in kg/m$^3$ & 1.225 \\ \hline
 $S_{\text{FP}}$ & Fuselage equivalent flat plate area in m$^2$ & 0.0151 \\ \hline
 $R$ & Rotor radius in meter (m) & 0.4 \\ \hline
 $A$ & Rotor disc area in m$^2$ & 0.503 \\ \hline
 $\Omega$ & Blade angular velocity in radians/second & 300 \\\hline
 $d_0$ & Fuselage drag ratio & 0.6
 \\ \hline
 $s$ & Rotor solidity & 0.05 \\\hline
 $\delta$ & Profile drag coefficient & 0.012 \\ \hline
 $k$ & Incremental correction factor to induced power & 0.1
 \\ \hline
 $m$ & UAV mass in kg & 2.04 \\ \hline
 $g$ & Gravitational acceleration in m/s$^2$ & 9.8 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Zhaohui Yang
Wei Xu
Mohammad Shikh-Bahaei

"Energy Efficient UAV Communication with Energy Harvesting" by Zhaohui Yang, Wei Xu, and Mohammad Shikh-Bahaei, arXiv:1911.00351v2, 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}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Glossary of Notation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
\textbf{Symbol} & \textbf{Description} \\
\midrule
\(\mathcal{V}, \mathcal{E}\) & Set of nodes (vertices) \(\mathcal{V}\) and edges \(\mathcal{E}\) in a molecular graph. \\
\(\vert \mathcal{V} \vert, \vert \mathcal{E} \vert\) & Number of nodes and edges in the graph, respectively. \\
\(N\) & Often used to denote \(\vert \mathcal{V} \vert\), the number of atoms (nodes). \\
\(d\) & Hidden feature dimension (number of channels) at each spherical degree. \\
\(s\) & Spherical dimension: the number of irreps (from \(\ell=0\) to \(\ell=L\)). \\
\(L, L_{\mathrm{max}}\) & Maximum angular momentum (highest spherical degree). \\
\(\ell \ge 0\), \(m \in \{-\ell,\dots,\ell\}\) & Angular momentum quantum numbers for spherical harmonics. \\
\(Y_m^{(\ell)}(\vec{r})\) & Real spherical harmonic of degree \(\ell\) and order \(m\), evaluated at position \(\vec{r}\). \\
\(U^{(\ell)}\) & An irreducible representation (irrep) of \(\mathrm{SO}(3)\) at angular momentum \(\ell\). \\
\(\mathbf{h}_i \in \mathbb{R}^{s \times d}\)
 & Feature representation of node \(i\), containing scalar/vector components up to spherical degree \(L\). \\
\(\vec{r}_{ij} \in \mathbb{R}^3\) & Relative position vector from node \(i\) to node \(j\). \\
\(\otimes\) & Clebsch–Gordan (CG) tensor product of two irreps. \\
\(\otimes^{6j}\) & Wigner \(6j\)-based tensor product (recoupling) of irreps, reorganizing the CG couplings. \\
\(\alpha_{ij}\) & Attention coefficient between node \(i\) and \(j\) in an equivariant attention layer. \\
\(C^{(\ell_3 m_3)}_{\ell_1 m_1, \,\ell_2 m_2}\)
 & Clebsch–Gordan coefficient coupling two irreps \(\ell_1, \ell_2\) to an output irreps \(\ell_3\). \\
\(\displaystyle \begin{pmatrix}
\ell_1 & \ell_2 & \ell_3 \\
m_1 & m_2 & m_3
\end{pmatrix}\)
 & Wigner \(3j\) symbol (equivalently related to Clebsch–Gordan coefficients). \\
\(\displaystyle \begin{Bmatrix}
j_1 & j_2 & j_3 \\
j_4 & j_5 & j_6
\end{Bmatrix}\)
 & Wigner \(6j\) symbol, governing recoupling of three angular momenta in different orders. \\
\(D^{(\ell)}(R)\) & Wigner \(D\)-matrix describing how spherical harmonics of degree \(\ell\) transform under rotation \(R\). \\
\(\mathrm{SO}(3)\) & 3D rotation group; E2Former is equivariant to transformations in \(\mathrm{SO}(3)\). \\
E2Former & The proposed \textbf{E}fficient and \textbf{E}quivariant Trans\textbf{former} architecture. \\
Wigner \(6j\) Conv & The core convolution module leveraging Wigner \(6j\) recoupling to shift edge-based operations to nodes. \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yunyang Li
Lin Huang
Zhihao Ding
Chu Wang
Xinran Wei
Han Yang
Zun Wang
Chang Liu
Yu Shi
Peiran Jin
Jia Zhang
Mark Gerstein
Tao Qin

"E2Former: A Linear-time Efficient and Equivariant Transformer for Scalable Molecular Modeling" by Yunyang Li, Lin Huang, Zhihao Ding, Chu Wang, Xinran Wei, Han Yang, Zun Wang, Chang Liu, Yu Shi, Peiran Jin, Jia Zhang, Mark Gerstein, and Tao Qin, arXiv:2501.19216v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of classification benchmarks. We report average numbers of nodes, edges, and features across graphs in graph classification datasets. For datasets lacking feature attributes, we use all-one vectors as pseudo attributes in practice.}
\begin{tabular}{lllccccc}
 \toprule
 Task &Category &Dataset &\#Graphs &\# Nodes &\# Edges &\# Features &\# Classes\\
 \midrule
 \multirow{8}*{Node} &\multirow{3}*{Citation} &Cora &1 &2,708 &5,278 &1,433 &7\\
 ~ &~ &CiteSeer &1 &3,327 &4,552 &3,703 &6\\
 ~ &~ &PubMed &1 &19,717 &44,338 &500 &3\\
 \cmidrule(r){2-8}
 ~ &\multirow{2}*{Co-purchase} &Photo &1 &7,650 &119,081 &745 &8\\
 ~ &~ &Computers &1 &13,752 &245,861 &767 &10\\
 \cmidrule(r){2-8}
 ~ &\multirow{2}*{Wikipedia} &Chameleon &1 &2,277 &36,101 &500 &6\\
 ~ &~ &Squirrel &1 &5,201 &217,073 &2,089 &4\\
 \midrule
 \multirow{7}*{Graph} &\multirow{2}*{Protein} &MUTAG &188 &17.9 &39.6 &7 &2 \\
 ~ &~ &PTC-MR &344 &14.3 &29.4 &18 &2\\
 \cmidrule(r){2-8}
 ~ & Bioinformatics & PROTEINS & 1113 & 39.1 & 145.6 & 0 & 2 \\
 \cmidrule(r){2-8}
 ~ &\multirow{3}*{Social Networks} &IMDB-BINARY &1000 &19.8 &193.1 &0 &2 \\
 ~ &~ &IMDB-MULTI &1500 &13.0 &131.9 &0 &3\\
 ~ &~ &REDDIT-BINARY &2000 &429.6 &995.5 &0 &2 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Xiaojun Guo
Yifei Wang
Zeming Wei
Yisen Wang

"Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning" by Xiaojun Guo, Yifei Wang, Zeming Wei, and Yisen Wang, arXiv:2311.02687v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c|ccc||}
 \hline
 $n$ & Precision & Recall & Structural Hamming Distance \\
 \hline\hline
 500 & 0.705 (0.114) & 0.717 (0.146) & 0.118 (0.043) \\
 \hline
 1000 & 0.684 (0.097) & 0.797 (0.137) & 0.116 (0.042) \\
 \hline

 5000 & 0.599 (0.097) & 0.877 (0.105) & 0.146 (0.51) \\
 \hline
\end{tabular}
\caption{Mean and standard deviation of precision, recall, and Hamming distance for predicted edges obtained via partial correlation conditional independence testing versus true edges across 100 replicates of a linear quantile dependent dataset with underlying graph depicted in Figure . The linear coefficients $\alpha_i \sim Uniform(-0.4,0.4)$. }
\end{table}
\end{document}

stat

Zain Khan
Daniel Malinsky
Martin Picard
Alan A. Cohen
Columbia SOH Group
Ying Wei

"Quantile Graph Discovery through QuACC: Quantile Association via Conditional Concordance" by Zain Khan, Daniel Malinsky, Martin Picard, Alan A. Cohen, Columbia SOH Group, and Ying Wei, arXiv:2411.17033v2, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The results of ablation study on ASD metric when adapting Med-VFMs to AMOS2022-CT and AMOS2022-MRI domains. The performance was evaluated using Dice scores, and the results were reported as Mean$\pm$SD.}
\begin{tabular}{c|c|c|c|c|c}
\toprule
\multirow{2}{*}{Methods} & Temperature & \multicolumn{2}{c|}{AMOS2022-CT} & \multicolumn{2}{c}{AMOS2022-MR} \\
\cline{3-6}
 & function & $5\%$ & $10\%$ & $5\%$ & $10\%$ \\
\midrule
ENPY & - & 74.24$\pm$14.53 & 78.17$\pm$13.73 & 40.43$\pm$23.29 & 58.74$\pm$19.88 \\
\midrule
High ASD & \multirow{2}{*}{$\tau=1$} & 75.30$\pm$14.98 & 79.13$\pm$13.69 & 42.39$\pm$21.50 & 61.45$\pm$19.14 \\
Low ASD & & 62.96$\pm$15.36 & 63.57$\pm$14.35 & 29.05$\pm$22.38 & 39.08$\pm$20.13 \\
\midrule
High ASD & \multirow{2}{*}{$\tau(r)$} & 75.92$\pm$14.39 & 79.41$\pm$13.24 & 44.10$\pm$20.41 & 63.77$\pm$18.48 \\
Low ASD & & 64.73$\pm$15.44 & 66.43$\pm$13.88 & 29.64$\pm$21.35 & 40.15$\pm$19.49 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Jin Yang
Daniel S. Marcus
Aristeidis Sotiras

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


stat

Michael Hellstern
Byol Kim
Zaid Harchaoui
Ali Shojaie

"Spectral Differential Network Analysis for High-Dimensional Time Series" by Michael Hellstern, Byol Kim, Zaid Harchaoui, and Ali Shojaie, arXiv:2412.07905v2, 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 human evaluation results for the SciCap challenge and MLBCAP is compared with the second-best solutions for each track. Lower average ranks indicate higher preference by the judges.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc} \hline
\textbf{Team} & \textbf{Long Caption} & \textbf{Short Caption} \\ \hline
Author-written & 2.84 & \textbf{1.52} \\ \hline
LM-Ensemble~ & 2.82 & 3.56 \\
Length-Adaptive LLM~ & 3.08 & 3.18 \\ \hline
MLBCAP & \textbf{1.27} & 1.74 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jaeyoung Kim
Jongho Lee
Hong-Jun Choi
Ting-Yao Hsu
Chieh-Yang Huang
Sungchul Kim
Ryan Rossi
Tong Yu
Clyde Lee Giles
Ting-Hao 'Kenneth' Huang
Sungchul Choi

"Multi-LLM Collaborative Caption Generation in Scientific Documents" by Jaeyoung Kim, Jongho Lee, Hong-Jun Choi, Ting-Yao Hsu, Chieh-Yang Huang, Sungchul Kim, Ryan Rossi, Tong Yu, Clyde Lee Giles, Ting-Hao 'Kenneth' Huang, and Sungchul Choi, arXiv:2501.02552v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Lihu Xu
Fang Yao
Qiuran Yao
Huiming Zhang

"Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption" by Lihu Xu, Fang Yao, Qiuran Yao, and Huiming Zhang, arXiv:2201.03182v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\textbf{Thread Blocks / Kernel H100} & \textbf{33} & \textbf{66} & \textbf{99} & \textbf{132} \\
\midrule
\textbf{Memory Bandwidth Utilization} [\%] & 21.33 & 40.12 & 55.06 & 69.18 \\
\textbf{Slowdown} [col/alone] & 1.06 & 1.14 & 1.23 & 1.36 \\
\midrule
\textbf{Thread Blocks / Kernel RTX3090} & \textbf{10} & \textbf{20} & \textbf{40} & \textbf{80} \\
\midrule
\textbf{Memory Bandwidth Utilization} [\%] & 24.48 & 45.34 & 71.96 & 90.69 \\
\textbf{Slowdown} [col/alone] & 1.06 & 1.27 & 1.61 & 1.91 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Memory Bandwidth Interference. Slowdown of \texttt{copy} kernel under colocation over running alone on H100/RTX3090 on 50\% of the SMs using MPS. Reported Memory Bandwidth Utilization is relative to the highest actual achieved memory bandwidth.}
\end{table}
\end{document}

cs

Paul Elvinger
Foteini Strati
Natalie Enright Jerger
Ana Klimovic

"Measuring GPU utilization one level deeper" by Paul Elvinger, Foteini Strati, Natalie Enright Jerger, and Ana Klimovic, arXiv:2501.16909v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccc}
 \hline
 Method & CIFAR-10 Top-1 & CIFAR-10 Top-5 & CIFAR-100 Top-1 & CIFAR-100 Top-5 \\
 \hline
 Barlow Twins & \textbf{92.1} & 99.73 & \textbf{71.38} & \textbf{92.32} \\
 VICReg & 91.68 & 99.66 & 68.56 & 90.76 \\
 \textbf{SSL-EY} & 91.43 & \textbf{99.75} & 67.52 & 90.17 \\
 \hline
 SSL-EY No Proj. & 90.98 & 99.69 & 65.21 & 88.09\\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

James Chapman
Lennie Wells
Ana Lawry Aguila

"Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning" by James Chapman, Lennie Wells, and Ana Lawry Aguila, arXiv:2310.01012v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{GAS Hyperparameters for Policy Distillation}
\begin{tabular}{lrrrrr}
 \toprule
 Environment & Student LR & Generator LR & Student Steps & Generator Steps & Generator Reset\\
 \midrule
 Humanoid & 0.001 & 0.001 & 5 & 2 & 10\\
 Standup & 0.001 & 0.001 & 5 & 2 & 10\\
MountainCar & 0.001 & 0.001 & 5 & 2 & 10\\
 Cartpole & 0.001 & 0.001 & 5 & 2 & 10\\
 ALE & 0.0003 & 0.1 & 7 & 5 & 256\\
 \end{tabular}
\end{table}
\end{document}

stat

Generative Adversarial Simulator

"Generative Adversarial Simulator" by Jonathan Raiman, arXiv:2011.11472v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
Parameter & Value \\ \hline
$\eta$ & 0.0003 \\ \hline
MaxIters & 500 \\ \hline
FPIters & 10 \\ \hline
SQIters & 10 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Parameters used when performing the maximum likelihood estimation.}
\end{table}
\end{document}

math

Matthew Werenski
Ruijie Jiang
Abiy Tasissa
Shuchin Aeron
James M. Murphy

"Measure Estimation in the Barycentric Coding Model" by Matthew Werenski, Ruijie Jiang, Abiy Tasissa, Shuchin Aeron, and James M. Murphy, arXiv:2201.12195v2, 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|l}
 Prefix & IRI \\
 \hline
 \texttt{dash} & \texttt{http://datashapes.org/dash\#} \\
 \texttt{foaf} & \texttt{http://xmlns.com/foaf/0.1/} \\
 \texttt{hash} & \texttt{http://purls.helmholtz-metadaten.de/herbie/hash/\#} \\
 \texttt{mbs} & \texttt{http://purls.helmholtz-metadaten.de/herbie/mb/mbs/\#} \\
 \texttt{nfdi} & \texttt{http://nfdi.fiz-karlsruhe.de/ontology/} \\
 \texttt{pmd} & \texttt{https://w3id.org/pmd/co/} \\
 \texttt{prima} & \texttt{https://purls.helmholtz-metadaten.de/prima/core\#} \\
 \texttt{prima\_experiment} & \texttt{https://purls.helmholtz-metadaten.de/prima/experiment\#} \\
 \texttt{prov} & \texttt{http://www.w3.org/ns/prov\#} \\
 \texttt{qudt} & \texttt{http://qudt.org/schema/qudt/} \\
 \texttt{rdfs} & \texttt{http://www.w3.org/2000/01/rdf-schema\#} \\
 \texttt{sh} & \texttt{http://www.w3.org/ns/shacl\#} \\
 \end{tabular}
\end{adjustbox}
\caption{Namespace prefix bindings used in the text}
\end{table}
\end{document}

cs

Fabian Kirchner
D. C. Florian Wieland
Sarah Irvine
Sven Schimek
Jan Reimers
Rossella Aversa
Alexey Boubnov
Christian Lucas
Silja Flenner
Imke Greving
André Lopes Marinho
Tak Ming Wong
Regine Willumeit-Römer
Catriona Eschke
Berit Zeller-Plumhoff

"An ontology-based description of nano computed tomography measurements in electronic laboratory notebooks: from metadata schema to first user experience" by Fabian Kirchner, D. C. Florian Wieland, Sarah Irvine, Sven Schimek, Jan Reimers, Rossella Aversa, Alexey Boubnov, Christian Lucas, Silja Flenner, Imke Greving, André Lopes Marinho, Tak Ming Wong, Regine Willumeit-Römer, Catriona Eschke, and Berit Zeller-Plumhoff, arXiv:2501.07398v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Confusion matrix for voting experts for the 3 class recognition task.}
\begin{tabular}{l|l|c|c|c}
\multicolumn{2}{c}{} & \multicolumn{3}{c}{True class}\\
\cline{3-5}
\multicolumn{2}{c|}{} & 0 & 0.1 & 1\\
\cline{2-5}
\multirow{3}{*}{Expert guess} & 0 & 84 & 6 & 3\\
\cline{2-5}
& 0.1 & 16 & 58 & 53\\
\cline{2-5}
& 1 & 0 & 36 & 44\\
\cline{2-5}
\end{tabular}
\end{table}
\end{document}

eess

Aleksei Shpilman
Dmitry Boikiy
Marina Polyakova
Daniel Kudenko
Anton Burakov
Elena Nadezhdina

"Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks" by Aleksei Shpilman, Dmitry Boikiy, Marina Polyakova, Daniel Kudenko, Anton Burakov, and Elena Nadezhdina, arXiv:2012.12125v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Pricing European Options under Stochastic Volatility Models: Case of five-Parameter Variance-Gamma Process" by A. H. Nzokem, arXiv:2201.03378v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the result of the simulation study showing the bias, RMSE, and Coverage Probability for the prediction of the outcome $Y$}
\begin{tabular}{lccc}
\toprule
 & {Bias} & {RMSE} & {Coverage Probability} \\
\midrule
\multicolumn{4}{c}{{Scenario 1}} \\
\midrule
{Model 1} & -3.05e-10 & 5.48e-09 & 95.32\% \\
{Model 2} & 1.86e-09 & 1.37e-08 & 96.76\% \\
{Model 3} & 9.82e-08 & 2.64e-07 & 97.78\% \\
\midrule
\multicolumn{4}{c}{{Scenario 2}} \\
\midrule
{Model 1} & -3.87e-11 & 2.91e-09 & 95.74\% \\
{Model 2} & -1.16e-11 & 2.38e-09 & 95.37\% \\
{Model 3} & -1.60e-10 & 1.10e-08 & 96.75\% \\
\midrule
\multicolumn{4}{c}{{Scenario 3}} \\
{Model 1} & 2.56e-05 & 1.65e-04 & 97.93\% \\
{Model 2} & 9.82e-08 & 2.64e-07 & 96.78\% \\
{Model 3} & -1.00e-08 & 5.83e-08 & 95.22\% \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Olatunji Johnson
Bedilu A Ejigu
Ezra Gayawan

"Accounting for Multiple Covariates in Non-Stationary Geostatistical Modelling" by Olatunji Johnson, Bedilu A Ejigu, and Ezra Gayawan, arXiv:2412.09225v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative clinical evaluation results of various methods for in vivo liver data with real motion artifact.}
\begin{tabular}{c|c|c|c}
			\toprule
			\ & Artifact & Blurring & Lesion conspicuity \\ \midrule\midrule
			\ Input (original) & 3.20 $\pm$ 1.28 & 1.15 $\pm$ 0.36 & 2.05 $\pm$ 1.05 \\
			\ MARC & 1.75 $\pm$ 0.96 & 2.95 $\pm$ 0.22 & 2.40 $\pm$ 1.14 \\
			\ Cycle-MedGAN V2.0 & 2.35 $\pm$ 1.08 & 1.15 $\pm$ 0.36 & 2.05 $\pm$ 0.99 \\
			\ Proposed & 1.95 $\pm$ 0.94 & 1.20 $\pm$ 0.41 & 1.85 $\pm$ 0.98 \\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

eess

"Unsupervised MR Motion Artifact Deep Learning using Outlier-Rejecting Bootstrap Aggregation" by Gyutaek Oh, Jeong Eun Lee, and Jong Chul Ye, arXiv:2011.06337v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c}
 Jet type & Approx. Number \\
 \hline
 QCD & $1.8\times 10^{8}$\\
 $W$ & $6.3\times 10^{5}$ \\
 $Z$ & $1.8\times 10^{5}$ \\
 top & $1.3\times 10^{5}$ \\
 $H \to b\bar{b}$ & 500 \\
 \end{tabular}
\end{table}
\end{document}

stat

Oz Amram
Luca Anzalone
Joschka Birk
Darius A. Faroughy
Anna Hallin
Gregor Kasieczka
Michael Krämer
Ian Pang
Humberto Reyes-Gonzalez
David Shih

"Aspen Open Jets: Unlocking LHC Data for Foundation Models in Particle Physics" by Oz Amram, Luca Anzalone, Joschka Birk, Darius A. Faroughy, Anna Hallin, Gregor Kasieczka, Michael Krämer, Ian Pang, Humberto Reyes-Gonzalez, and David Shih, arXiv:2412.10504v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jiyan He
Xuechen Li
Da Yu
Huishuai Zhang
Janardhan Kulkarni
Yin Tat Lee
Arturs Backurs
Nenghai Yu
Jiang Bian

"Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping" by Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, and Jiang Bian, arXiv:2212.01539v1, 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|c|}
 \hline
 & \multicolumn{3}{|c|}{Rosenzweig-MacArthur} & \multicolumn{3}{|c|}{Leslie-Gower-May} \\
 \hline

 \textbf{\large Original Model (, )} & \textbf{Holling} & \textbf{Ivlev} & \textbf{Trig} & \textbf{Holling} & \textbf{Ivlev} & \textbf{Trig} \\
 \hline
 Hopf & 0.4452 & 1.071 & 10.12 & 5.554 & 12.09 & 26.36 \\
 Limit Cycle Saddle & - & - & 2.644 & - & 10.51 & 12.94\\
 \hline

 \textbf{\large Holling Fixed (, )} & \textbf{Holling} & \textbf{Ivlev} & \textbf{Trig} & \textbf{Holling} & \textbf{Ivlev} & \textbf{Trig} \\
 \hline
 Hopf & 0.4452 & 0.4632 & 0.7729 & 5.554 & 5.857 & 6.052 \\
 Limit Cycle Saddle & - & - & 0.5057 & - & - & 5.964\\
 \hline
 \textbf{\large Pw-I Holling (, )} & \textbf{HTT} & \textbf{THT} & \textbf{HHT} & \textbf{HTT} & \textbf{THT} & \textbf{HHT} \\
 \hline
 Hopf & 0.4451 & 0.4451 & 0.4451 & 26.36 & 26.36 & 5.554 \\
 Limit Cycle Saddle & - & - & - & 7.95 & 7.95 & - \\
 \hline

 \textbf{\large Pw-I Trigonometric (, S8)} & \textbf{THH} & \textbf{HTH} & \textbf{TTH} & \textbf{THH} & \textbf{HTH} & \textbf{TTH} \\
 \hline
 Hopf & 10.12 & 10.12 & 10.12 & 5.554 & 5.554 & 26.36 \\
 Limit Cycle Saddle & 2.431 & 2.431 & 2.644 & - & - & 13.13 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Values of the bifurcation parameter, the carrying capacity, for each bifurcation shown in the respective figures. The values in parentheses give the figure number of the corresponding bifurcation diagrams. Pw-I stands for piecewise identical, H stands for Holling, and T stands for trigonometric.}
\end{table}
\end{document}

math

Sarah K. Wyse
Maria M. Martignoni
May Anne Mata
Eric Foxall
Rebecca C. Tyson

"Structural sensitivity in the functional responses of predator-prey models" by Sarah K. Wyse, Maria M. Martignoni, May Anne Mata, Eric Foxall, and Rebecca C. Tyson, arXiv:2201.11256v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Information about bin size and bin numbers used to model branch lengths for DS1 to DS8.}
\begin{tabular}{lrr}
\toprule
Dataset & \# Bin Size & \# Bins Number \\
\midrule
DS1 & 0.001 & 50 \\
DS2 & 0.004 & 50 \\
DS3 & 0.004 & 50 \\
DS4 & 0.002 & 100 \\
DS5 & 0.002 & 100 \\
DS6 & 0.001 & 100 \\
DS7 & 0.001 & 200 \\
DS8 & 0.001 & 100 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Mingyang Zhou
Zichao Yan
Elliot Layne
Nikolay Malkin
Dinghuai Zhang
Moksh Jain
Mathieu Blanchette
Yoshua Bengio

"PhyloGFN: Phylogenetic inference with generative flow networks" by Mingyang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, and Yoshua Bengio, arXiv:2310.08774v2, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation settings.}
\begin{tabular}{ccc}
 \toprule
 Hypothesis & Setting & Explanation \\\midrule
 \multirow{5}{*}{Null}
 & (a) & Normal distributions\\
 & (b) & Gaussian mixture distributions \\
 & (c) & Generalized normal distributions \\
 & (d) & $t$-distributions \\
 & (e) & Gamma distributions \\
 \midrule
 \multirow{10}{*}{Alternative} & (I) & Normal versus $t$ distributions \\
 & (II) & Normal versus Gaussian mixture distributions \\
 & (III) & Normal versus generalized normal distributions \\
 & (IV) & Log-normal versus gamma distributions \\
 & (V) & $t$-distributions with different parameters \\
 & (i) & Full difference: Distributional differences in all dimensions \\
 & (ii) & Partial difference: Distributional differences in one third of the dimensions \\
 & (iii) & Correlated difference: Dimensions are correlated \\
 & (iv) & Directional difference: Strength of the difference varies across dimensions \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

"MATES: Multi-view Aggregated Two-Sample Test" by Zexi Cai, Wenbo Fei, and Doudou Zhou, arXiv:2412.16684v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
\toprule
 & & \multicolumn{3}{c}{Signal Ratio} \\
 \cmidrule(lr){3-5}
 & Scenario & $\eta = 0$ & $\eta = 5$ & $\eta^* = 5$ \\
 \cmidrule(lr){2 - 2}
$k$ & $P(\tau \leq 10 \mid \eta = \infty)$
 & & & \\
\midrule
\multirow{2}{*}{0.90} & 0.01 ($c = 7.83$) & 100.0\% & 100.0\% & 44.7\% \\
 & 0.05 ($c = 6.50$) & 100.0\% & 100.0\% & 62.8\% \\
\cline{1-5}
\multirow{2}{*}{0.95} & 0.01 ($c = 7.29$) & 99.9\% & 99.7\% & 10.6\% \\
 & 0.05 ($c = 5.89$) & 99.9\% & 100.0\% & 25.4\% \\
\cline{1-5}
\multirow{2}{*}{1.05} & 0.01 ($c = 7.18$) & 99.9\% & 99.6\% & 14.3\% \\
 & 0.05 ($c = 5.97$)& 100.0\% & 99.9\% & 26.2\% \\
\cline{1-5}
\multirow{2}{*}{1.10} & 0.01 ($c = 8.49$) & 100.0\% & 100.0\% & 35.3\% \\
 & 0.05 ($c = 6.47$) & 100.0\% & 100.0\% & 64.2\% \\
\cline{1-5}
\bottomrule
\end{tabular}
\caption{The table reports proportions of cases when the CUSUM signals in a setting where individuals arrive over calendar time according to a Poisson process with rate $\lambda_a=3700$, and the out-of-control model is $h_{1i}(\cdot)=h_P(\cdot,Z_i)+k h_{E,0}(k \cdot,X_i)$ with the latter having a piecewise constant baseline excess hazard. Different shifts in terms of $k$ and different shift points $\eta$ are considered. For the shift after 5 years of monitoring, both the setting that the shift affects all individuals ($\eta = 5$) and the setting when the shift only affects new individuals ($\eta^* = 5$) are considered. 1000 simulations are performed for each combination of $k$ and scenario of shift, and for finding thresholds $c$.}
\end{table}
\end{document}

stat

Jimmy Huy Tran
Jan Terje Kvaløy
Hartwig Kørner

"Monitoring time to event in registry data using CUSUMs based on relative survival models" by Jimmy Huy Tran, Jan Terje Kvaløy, and Hartwig Kørner, arXiv:2411.09353v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Antonio Cauchi
Francesco Lemma
Joaquín Rodrigues Jacinto

"Algebraic cycles and functorial lifts from $G_2$ to $\mathrm{PGSp}_6$" by Antonio Cauchi, Francesco Lemma, and Joaquín Rodrigues Jacinto, arXiv:2202.09394v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
\hline
\textbf{Hyperparameter} & \textbf{Value} \\
\hline
Optimizer & Adam \\
Learning rate & 1e-4 \\
Replay Buffer Size (Pendulum) & 1e5 \\
Replay Buffer Size (BipedalWalker) & 1e6 \\
Batch Size & 256 \\
Decay Rate & 0.99 \\
Policy noise & 0.2 \\
Policy noise clipping & (-0.5, 0.5) \\
Initial $\lambda$ & 0.5 \\
\hline
Number of Layers for Actor Network & 2 \\
Number of Layers for Critic Network & 2 \\
Hidden dim & 128 \\
Activation function & ReLU \\
\hline
\end{tabular}
\caption{Hyper-parameters sheet.}
\end{table}
\end{document}

stat

Han Zhong
Xun Deng
Ethan X. Fang
Zhuoran Yang
Zhaoran Wang
Runze Li

"Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy" by Han Zhong, Xun Deng, Ethan X. Fang, Zhuoran Yang, Zhaoran Wang, and Runze Li, arXiv:2012.14098v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|c|}
\hline
Dataset	&TP	&TN	&FP	&FN	&ACC&REC&SPE\\
Type& & & & &(\%)&(\%)&(\%)\\
\hline
Unbalanced	&213	&496	&46	&35	&89.75	&85.89	&91.51\\
\hline
Balanced	&246	&218	&30	&2	&93.55	&99.19	&87.90\\
\hline
\end{tabular}
\caption{Comparison of balanced and unbalanced data set performance.}
\end{table}
\end{document}

eess

"Performance Comparison of Balanced and Unbalanced Cancer Datasets using Pre-Trained Convolutional Neural Network" by Ali Narin, arXiv:2012.05585v1, 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}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Newly Introduced Notations}
\begin{tabular}{|l|l|}
\hline
$\mathbf{A}_1 = \sum_{j \in \mathcal{K}} \mathbf{G} \mathbf{p}_j \mathbf{p}_j^H \mathbf{G}^H$ & $\mathbf{F}_1 = \mathbf{A}_1 \mathbf{I} \mathbf{B}_1$ \\ \hline
$\mathbf{A}_2 = \sum_{i \in \mathcal{I}} |\tau_{\mathsf{u}, i}|^2 \mathbf{G}^* \mathbf{w}_i \mathbf{w}_i^H \mathbf{G}^\top$ & $\mathbf{F}_2 = \mathbf{A}_2 \mathbf{I} \mathbf{B}_2$ \\ \hline
$\mathbf{B}_1 = \sum_{k \in \mathcal{K}} |\tau_{\mathsf{d}, k}|^2 \mathbf{h}_{\mathsf{d}, k}^* \mathbf{h}_{\mathsf{d}, k}^\top$ & $\mathbf{J}_1 = \mathbf{B}_2 \mathbf{I} \mathbf{B}_1$ \\ \hline
$\mathbf{B}_2 = \sum_{p \in \mathcal{I}} \mathbf{h}_{u,p} \mathbf{h}_{u,p}^H$ & $\mathbf{J}_2 = \mathbf{A}_1 \mathbf{I} \mathbf{A}_2$ \\ \hline
$\mathbf{C}_1 = \sum_{k \in \mathcal{K}} \sqrt{1+\iota_{\mathsf{d}, k}} \tau_{\mathsf{d}, k}^* \mathbf{G} \mathbf{p}_k \mathbf{h}_{\mathsf{d}, k}^\top$ & $\mathbf{C}_2 = \sum_{i \in \mathcal{I}} \sqrt{1+\iota_{\mathsf{u}, i}} \tau_{\mathsf{u}, i}^* \mathbf{h}_{u,i} \mathbf{w}_i^H \mathbf{G}^\top$ \\
\hline
\multicolumn{2}{|l|}{$\mathbf{D} = \mathbf{G} \sum_{j \in \mathcal{K}} \mathbf{p}_j \mathbf{p}_j^H \mathbf{H}_\mathsf{SI}^H \sum_{i \in \mathcal{I}} |\tau_{\mathsf{u}, i}|^2 \mathbf{w}_i \mathbf{w}_i^H \mathbf{G}^\top $}\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"Non-reciprocal Beyond Diagonal RIS: Sum-Rate Maximization in Full-Duplex Communications" by Ziang Liu, Hongyu Li, and Bruno Clerckx, arXiv:2411.18523v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Nandan Maiti
Manon Seppecher
Ludovic Leclercq

"Scaling Methods To Estimate Macroscopic Fundamental Diagrams in Urban Networks with Sparse Sensor Coverage" by Nandan Maiti, Manon Seppecher, and Ludovic Leclercq, arXiv:2411.19721v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{TTS results on LibriSpeech test-clean subset. Evaluated with Whisper-Large V3 and ERes2Net}
\begin{tabular}{lcc}
\toprule
\textbf{Model} & \textbf{WER (\%)} & \textbf{Spk Sim} \\
\midrule
ChatTTS & 6.84 & - \\
GPT-SoVITs & 5.13 & 0.405 \\
CosyVoice 2 & \textbf{2.47} & \textbf{0.745} \\
\midrule
UniFlow$_{\text{DDPM}}$-TTS & 3.50 & 0.697 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Ziqian Wang
Zikai Liu
Yike Zhu
Xingchen Li
Boyi Kang
Jixun Yao
Xianjun Xia
Chuanzeng Huang
Lei Xie

"UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling" by Ziqian Wang, Zikai Liu, Yike Zhu, Xingchen Li, Boyi Kang, Jixun Yao, Xianjun Xia, Chuanzeng Huang, and Lei Xie, arXiv:2508.07558v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \hline
 & $x_{n+1}'$ & $y_{n+1}'$ & $z_{n+1}'$ \\
 \hline%\hline
 1 & $h\sigma k_x(\bar{y}-\bar{x})$ & $hk_y(\rho\bar{x}-\bar{y}-\bar{x}\bar{z})$ & $hk_z(\bar{x}\bar{y}-\beta \bar{z})$ \\
 $x_n'$ & $1-h\sigma$ & $h(\rho-\bar{z})k_y/k_x$ & $h\bar{y}k_z/k_x$ \\
 $y_n'$ & $h\sigma k_x/k_y$ & $1-h$ & $h\bar{x}k_z/k_y$ \\
 $z_n'$ & 0 & $-h\bar{x}k_y/k_z$ & $1-h\beta$ \\
 $x_n'^2$ & 0 & 0 & 0 \\
 $y_n'^2$ & 0 & 0 & 0 \\
 $z_n'^2$ & 0 & 0 & 0 \\
 $x_n' z_n'$ & 0 & $-hk_y/k_xk_z$ & 0 \\
 $x_n' y_n'$ & 0 & 0 & $hk_z/k_yk_x$ \\
 $y_n' z_n'$ & 0 & 0 & 0 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{ $\mathbf{W}_{out}^{L63 norm}$ of the normalized L63 using the Euler scheme}
\end{table}
\end{document}

math

Tse-Chun Chen
Stephen G. Penny
Timothy A. Smith
Jason A. Platt

"`Next Generation' Reservoir Computing: an Empirical Data-Driven Expression of Dynamical Equations in Time-Stepping Form" by Tse-Chun Chen, Stephen G. Penny, Timothy A. Smith, and Jason A. Platt, arXiv:2201.05193v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Generalized multiscale finite element method for highly heterogeneous compressible flow" by Shubin Fu, Eric Chung, and Lina Zhao, arXiv:2201.07371v1, 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{Estimated coefficients, and 95\% confidence intervals, for the scalar covariates.}
\begin{tabular}{lrrr}
 \hline
Covariate & Lower ($0.025$) & Estimate & Upper ($0.975$)\\
 \hline
Intercept & 0.257 & $\boldsymbol{0.438}$ & 0.627 \\
 Spring & -0.222 & $\boldsymbol{-0.133}$ & -0.038 \\
 Summer & -0.075 & -0.019 & 0.026 \\
 Milk time (morning) & -0.186 & $\boldsymbol{-0.129}$ & -0.074 \\
 Parity (2) & -0.061 & -0.019 & 0.030 \\
 Parity (3) & -0.043 & 0.004 & 0.052 \\
 DIM & -0.001 & -0.000 & 0.000 \\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

Federico Ferraccioli
Alessandro Casa
Marco Stefanucci

"An adaptive functional regression framework for spatially heterogeneous signals in spectroscopy" by Federico Ferraccioli, Alessandro Casa, and Marco Stefanucci, arXiv:2309.06999v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Comparison of FLOPs and MACs for VAPT and VPT.} The experiments were conducted on FGVC benchmark. We used ViT-B/16 Supervised Pre-trained on ImageNet-21K as the backbone.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|c|c|c|c|c}
\toprule
\textbf{Metric} & \textbf{Method} & \textbf{Stanford Cars} & \textbf{CUB-200-2011} & \textbf{Oxford Flowers} & \textbf{NABirds} & \textbf{Stanford Dogs} \\ \midrule
\multirow{2}{*}{FLOPs (GFLOPS)}
 & VAPT & 73.67 ({\color{blue} $\uparrow 0.18\%$}) & {37.04} ({\color{blue} $\uparrow 0.11\%$}) & {36.10} ({\color{blue} $\uparrow 0.08\%$}) & {44.61} ({\color{blue} $\uparrow 0.31\%$}) & {54.30} ({\color{blue} $\uparrow 0.59\%$}) \\
 & VPT & 73.54 & 37.00 & 36.07 & 44.47 & 53.98 \\ \cmidrule(lr){1-7}
\multirow{2}{*}{MACs (GMACs)}
 & VAPT & {36.80} ({\color{blue} $\uparrow 0.16\%$}) & {18.51} ({\color{blue} $\uparrow 0.11\%$}) & {18.03} ({\color{blue} $\uparrow 0.06\%$}) & {22.29} ({\color{blue} $\uparrow 0.32\%$}) & {27.13} ({\color{blue} $\uparrow 0.59\%$}) \\
 & VPT & 36.74 & 18.49 & 18.02 & 22.22 & 26.97 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Minh Le
Anh Nguyen
Huy Nguyen
Chau Nguyen
Anh Tran
Nhat Ho

"On the Expressiveness of Visual Prompt Experts" by Minh Le, Anh Nguyen, Huy Nguyen, Chau Nguyen, Anh Tran, and Nhat Ho, arXiv:2501.18936v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Polynomials with many rational preperiodic points" by John R. Doyle and Trevor Hyde, arXiv:2201.11707v2, 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{Performance on the complete BraTs’20 Online Validation Data for the merging strategy, unless otherwise specified.}
\begin{tabular}{|l|l|l|l|}
 \hline
 Metric (mean) & ET & WT & TC \\
 \hline
 Dice (Pipeline A alone) & 0.80585 & 0.89518 & 0.85415 \\
 Dice (Pipeline B alone) & 0.72738 & 0.91123 & 0.84921 \\
 \hline
 Dice & 0.80585 & 0.91148 & 0.85416 \\
 Sensitivity & 0.81488 & 0.91938 & 0.84485 \\
 Specificity & 0.99970 & 0.99915 & 0.99963 \\
 Hausdorff (95\%) & 20.55756 & 4.30103 & 5.69298 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Theophraste Henry
Alexandre Carre
Marvin Lerousseau
Theo Estienne
Charlotte Robert
Nikos Paragios
Eric Deutsch

"Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution" by Theophraste Henry, Alexandre Carre, Marvin Lerousseau, Theo Estienne, Charlotte Robert, Nikos Paragios, and Eric Deutsch, arXiv:2011.01045v2, 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}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Analysis} of the number of primitives in memory and in the tile-list, as well as the count of total primitives iterated over and intersected by pixel rays. Output images are of size $ 1752 \times 1168$ with patch size $16 \times 16$, and values are evaluated on ScanNet++ v2 scene 39f36da05b.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
 & Frustum & Tile-List & Iterated & Intersected \\
 \midrule
 3DGS & 327k & \cellcolor{yellow!25}5.0M & \cellcolor{yellow!25}937M & \cellcolor{yellow!25}937M\\
 GS-MCMC & \cellcolor{orange!25}113k & 5.5M & 1.3B & 1.3B \\
 LinPrim & \cellcolor{yellow!25}139k & \cellcolor{red!25}1.4M & \cellcolor{red!25}275M & \cellcolor{red!25}52M \\
 LinPrim + MCMC & \cellcolor{red!25}102k & \cellcolor{orange!25}1.7M & \cellcolor{orange!25}373M & \cellcolor{orange!25}95M\\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"LinPrim: Linear Primitives for Differentiable Volumetric Rendering" by Nicolas von Lützow and Matthias Nießner, arXiv:2501.16312v4, 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}{rll}
 \toprule
 Objective ID & Pyiqa Metric ID & Reference \\
 \midrule
 1 & \texttt{nima} & \\
 2 & \texttt{nima-vgg16-ava} & \\
 3 & \texttt{topiq-iaa-res50} & \\
 4 & \texttt{laion-aes} & \\
 5 & \texttt{hyperiqa} & \\
 6 & \texttt{tres} & \\
 7 & \texttt{liqe} & \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\texttt{pyiqa} metric ID strings used to identify the $T=7$ target image quality metrics used for image tone mapping. Each metric is a no-reference image aesthetic (IAA) or quality (IQA) assessment metric obtained from the \texttt{pyiqa} library~.}
\end{table}
\end{document}

cs

Natalie Maus
Kyurae Kim
Yimeng Zeng
Haydn Thomas Jones
Fangping Wan
Marcelo Der Torossian Torres
Cesar de la Fuente-Nunez
Jacob R. Gardner

"Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization" by Natalie Maus, Kyurae Kim, Yimeng Zeng, Haydn Thomas Jones, Fangping Wan, Marcelo Der Torossian Torres, Cesar de la Fuente-Nunez, and Jacob R. Gardner, arXiv:2501.19342v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Economic performance loss resulted from boundary moving and target moving in group 2 of Case 2}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 \hline
 & & Ideal $\bar J_{\rm{ep}}$ & Actual $\bar J_{\rm{ep}}$ & Loss of $\bar J_{\rm{ep}}$ \\
 \hline
 \multirow{2}{*}{Economic MPC} & Cal & 601 & 608.0289 & 7.0289 (1.17\%) \\
 & Sim & 601 & 608.0316 & 7.0316 (1.17\%) \\
 \multirow{2}{*}{Tracking MPC} & Cal & 601 & 605.5987 & 4.5987 (0.77\%) \\
 & Sim & 601 & 605.5677 & 4.5677 (0.76\%) \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Jiangbang Liu
Song Bo
Benjamin Decardi-Nelson
Jinfeng Liu
Jingtao Hu
Tao Zou

"Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise" by Jiangbang Liu, Song Bo, Benjamin Decardi-Nelson, Jinfeng Liu, Jingtao Hu, and Tao Zou, arXiv:2201.01828v1, 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{ Absolute error for different values of $\alpha$ for $ m=2 $ }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ccccc|}
\hline
$x$ & $0.1$ & $0.3$ & $0.5$ & $0.7$ & $0.9$ \\ \hline
$\alpha =1$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ \\ \hline
$\alpha =0.85$ & $\allowbreak 6.\,\allowbreak 760\,7\times 10^{-2}$ & $%
1.07\,59\times 10^{-2}$ & $\allowbreak 8.\,\allowbreak 371\,2\times 10^{-3}$
& $\allowbreak 6.\,\allowbreak 982\,7\times 10^{-3}$ & $1.18\,70\times
10^{-3}$ \\ \hline
$\alpha =0.75$ & $\allowbreak 9.\,\allowbreak 703\,2\times 10^{-2}$ & $%
1.02\,64\times 10^{-2}$ & $5.\,\allowbreak 164\,3\times 10^{-3}$ & $%
4.\,\allowbreak 989\,1\times 10^{-3}$ & $1.99\,24\times 10^{-3}$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A new operational matrix based on Boubaker polynomials for solving fractional Emden-Fowler problem" by Abdelkrim Bencheikh and Lakhdar Chiter, arXiv:2202.09787v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\multicolumn{3}{|c|}{\textbf{ISA}} & \multicolumn{3}{|c|}{\textbf{Collisional OT}}\\
\hline
No. Coll. & Time [s] & Rel. Error [-] & No. Coll. & Time [s] & Rel. Error [-] \\
\hline
$N_p^2/2$ &
\textbf{0.217±0.021}&
\textbf{9.796e-03 ±2.636e-02}&
300$(N_p/2)$ &
\textbf{0.031 ± 0.003}&
\textbf{1.028e-02 ± 2.860e-02}
\\ \hline
2$(N_p^2/2)$ &
0.384 ± 0.031&
8.879e-04 ± 1.596e-04&
500$(N_p/2)$ &
0.054 ± 0.011&
6.254e-03 ± 1.840e-03
\\ \hline
3$(N_p^2/2)$ &
0.561 ± 0.046&
5.775e-04 ± 6.024e-06&
1000$(N_p/2)$ &
0.103 ± 0.005&
4.561e-03 ± 1.359e-04
\\ \hline
4$(N_p^2/2)$ &
0.803 ± 0.195&
4.578e-04 ± 4.702e-04&
2000$(N_p/2)$&
0.232 ± 0.059&
2.923e-03 ± 5.096e-03
\\ \hline
5$(N_p^2/2)$ &
0.987 ± 0.080&
3.681e-04 ± 6.452e-04&
10000$(N_p/2)$&
1.158 ± 0.130&
9.886e-04 ± 1.745e-03
\\ \hline
\end{tabular}
\caption{Evolution of relative error and the execution time in finding the Wasserstein distance between 100 randomly selected pairs of images from the Butterfly dataset using $N_p=8000$ pixels repeated 20 times using ISA and collisional OT.}
\end{table}
\end{document}

stat

"Collision-based Dynamics for Multi-Marginal Optimal Transport" by Mohsen Sadr and Hossein Gorji, arXiv:2412.16385v2, 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{$\text{RMST}_{\text{diff}}$ for E1690 and E1684 datasets; All Scenarios}
\begin{tabular}{lrr}
 \hline
Model & $\text{RMST}_{\text{diff}}$ & WAIC \\
 \hline
 Royston Parmar (non-PH) & 0.23 & 2003.03 \\
 Royston Parmar (PH) & 0.78 & 2006.70 \\
 Change-point: Converging Hazards & 0.75 & 2009.18 \\
 Change-point: Equal Final Hazards & 0.67 & 2009.35 \\
 Change-point: HR (step) & 0.46 & 2011.09 \\
 Generalized-Gamma & 1.08 & 2012.50 \\
 Log-Normal & 0.93 & 2036.70 \\
 Log-Logistic & 0.79 & 2059.52 \\
 Gompertz & 0.75 & 2061.88 \\
 Weibull & 0.74 & 2098.73 \\
 Exponential & 0.74 & 2100.23 \\
 Gamma & 0.74 & 2101.65 \\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

"Extrapolation of Relative Treatment Effects using Change-point Survival Models" by Philip Cooney and Arthur White, arXiv:2401.00568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Unsupervised Variable Selection for Ultrahigh-Dimensional Clustering Analysis" by Tonglin Zhang and Huyunting Huang, arXiv:2411.19448v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A comparison of learning rate selection methods in generalized Bayesian inference" by Pei-Shien Wu and Ryan Martin, arXiv:2012.11349v1, 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{Results reported on the test set, in the order of submission. The first two entries use 2d projections, while all the others make predictions at slice levels. (CE suffix represents models with CrossEntropyLoss and BCE models with BinaryCrossEntropyLoss}
\begin{tabular}{c|c|c}
 Method & mean AUC & min AUC \\
 \hline
 ResNet50Proj & 0.825 &0.766\\
 \hline
 PreProcProj & 0.793 & 0.703\\
 \hline
 NaiveInception & 0.860 & 0.772\\
 \hline
 ThresholdInception & 0.887 & 0.821\\
 \hline
 AttentionInception &0.853 & 0.788\\
 \hline
 TwoPicInception & 0.892 & 0.830\\
 \hline
 NaiveSliceCE & \textbf{0.924} & \textbf{0.885}\\
 \hline
 MicroVolSliceCE & 0.922 & 0.860\\
 \hline
 NaiveSliceBCE & 0.899 & 0.862\\
 \end{tabular}
\end{table}
\end{document}

eess

Radu Miron
Cosmin Moisii
Mihaela Breaban

"Revealing Lung Affections from CTs. A Comparative Analysis of Various Deep Learning Approaches for Dealing with Volumetric Data" by Radu Miron, Cosmin Moisii, and Mihaela Breaban, arXiv:2009.04160v1, 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{Summary of the train and test splits for the six diseases under observation in CheXpert. Note: Individual images can reflect the presence of several concurrent diseases.}
\begin{tabular}{cccc}
\hline
\textbf{Class} & \textbf{Training} & \textbf{Validation} & \textbf{Test} \\ \hline
Cardiomegaly & 3173 & 1195 & 4515 \\
Lung Opacity & 10269 & 4075 & 14658 \\
Edema & 6447 & 2584 & 9210 \\
No Finding & 1801 & 722 & 2591 \\
Pneumothorax & 2196 & 827 & 3027 \\
Pleural Effusion & 9001 & 3505 & 12972 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"Pixels Under Pressure: Exploring Fine-Tuning Paradigms for Foundation Models in High-Resolution Medical Imaging" by Zahra TehraniNasab, Amar Kumar, and Tal Arbel, arXiv:2508.14931v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lll}
		\toprule
		Parameter & Value & Description \\
		\midrule
		$n_{sim}$ & $770,000$ & Population size \\
		$n_{repeats}$ & $2000$ & Number of simulation replications \\
		$n_{days}$ & $550$ & Number of unique time-steps (days) in the simulation \\
		$d_{risk}$ & 21 & Risk duration after vaccination in days \\
		$d_{immune}$ & 42 & Duration after an event where the event cannot occur again \\
		$RR_{vacc}$ & 3.24 & Relative risk of vaccination \\
		$P_{myocarditis}$ & 0.00016 & Probability of developing an acute myocarditis in a year \\
		$P_{vacc}(t)$ & RKI data & Time-dependent probability of receiving the first vaccination \\
		$P_{vacc\_type}$ & RKI data & Probability distribution determining the type of received \\
		& & vaccine \\
		$P_{False Match}$ & 0.00005 & Proportion of false matches \\
		$P_{Missing Match}$ & Varied & Proportion of missing matches \\
		\bottomrule
	\end{tabular}
\caption{An overview of the parameters used in the simulation study.}
\end{table}
\end{document}

stat

Robin Denz
Katharina Meiszl
Peter Ihle
Doris Oberle
Ursula Drechsel-Bäuerle
Katrin Scholz
Ingo Meyer
Nina Timmesfeld

"Impact of Record-Linkage Errors in Covid-19 Vaccine-Safety Analyses using German Health-Care Data: A Simulation Study" by Robin Denz, Katharina Meiszl, Peter Ihle, Doris Oberle, Ursula Drechsel-Bäuerle, Katrin Scholz, Ingo Meyer, and Nina Timmesfeld, arXiv:2310.15016v1, 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{MR reconstruction performance comparison.}
\begin{tabular}{|l|c|c|c|}
 \hline
 \textbf{Method} & \textbf{NMSE} & \textbf{PSNR} & \textbf{SSIM}\\ \hline
	IFT & $5.61\times10^{-2}$ & $27.72$ & $0.796$ \\ \hline
	IFT + PP & $4.26\times10^{-2}$ & $28.65$ & $0.813$ \\ \hline
	DC + IFT & $4.32\times10^{-2}$ & $28.90$ & $0.812$ \\ \hline
	DC + IFT + PP & $3.03\times10^{-2}$ & $29.85$ & $0.823$ \\ \hline
	PnP-MBIR & $2.51\times10^{-2}$ & $30.10$ & $0.822$ \\ \hline
	DIPIIR (Explicit) & $2.47\times10^{-2}$ & $30.41$ & $0.827$ \\ \hline
	DIPIIR (Implicit) & $\mathbf{2.30\times10^{-2}}$ & $\mathbf{30.57}$ & $\mathbf{0.828}$ \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

"Data and Image Prior Integration for Image Reconstruction Using Consensus Equilibrium" by Muhammad Usman Ghani and W. Clem Karl, arXiv:2009.00092v1, 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{Assess study for the preliminary implementation of parallelization on a CPU and a GPU}
\begin{tabular}{|l|l|l|l|}
 \hline
 Processor & Language & Run time(sec) & Note \\ \hline
 Intel Xeon & NA & 158.08 & Sequential code \\ \hline
 Intel Xeon & OpenMP & 6.36 & Double checked locking \\ \hline
 Nvidia Tesla K20c & CUDA & 9.34 & Global memory only\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

"GPU acceleration of a patient-specific airway image segmentation and its assessment" by Yu-Wei Chang and Tony W. H. Sheu, arXiv:2012.10684v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|}
			\hline
		index (n)&... & -1 & 0 & 1 & 2 & 3 & 4 & 5 & 6 & ...&15 &16&17&18&19&20&... \\
			\hline
		Lucas Seq.($ l_{3,n}$) & ...& -1 & \textbf{3} & \textbf{1} & \textbf{3} & 7 & 11 & 21 & 39 & ...&9327 &17155 &31553 & 58035 & 106743 &196331&... \\
			\hline
	\end{tabular}
\end{adjustbox}
\caption{Lucas sequence of order 3}
\end{table}
\end{document}

math

Kalika Prasad
Hrishikesh Mahato
Munesh Kumari

"A novel public key cryptography based on generalized Lucas matrices" by Kalika Prasad, Hrishikesh Mahato, and Munesh Kumari, arXiv:2202.08156v1, 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{Fit measures for the proposed functional random coefficient and its competitors including a simple linear effect. The fit is measured in terms of the log-likelihood (logLik), the integrated Brier score (IBS) and the AIC.}
\begin{tabular}{lccc}
 \hline & logLik & IBS & AIC \\
 \hline
 Heterogeneous time-variation & -4099.36 & 0.1162 & 8238.87 \\
 Heterogeneity and time-variation & -4108.27 & 0.1174 & 8250.03 \\
 Heterogeneity only & -4111.04 & 0.1185 & 8250.50 \\
 Time-variation only & -4111.00 & 0.1184 & 8251.44 \\
 Linear effect & -4111.01 & 0.1184 & 8251.20 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Niklas Hagemann
Thomas Kneib
Kathrin Möllenhoff

"Capturing heterogeneous time-variation in covariate effects in non-proportional hazard regression models" by Niklas Hagemann, Thomas Kneib, and Kathrin Möllenhoff, arXiv:2501.13525v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
\hline
Dataset & Clip Length & Irr. Actions & Camera Cuts \\ \hline
UCF101~ & Varies & & \\
HMDB51~ & Varies & & \checkmark \\
AVA~ & 1 second & \checkmark & \checkmark \\
HACS~ & 2 second & \checkmark & \\
Kinetics~ & 10 second & \checkmark & \checkmark \\
M.i.T.~ & 3 second & & \\
\textbf{HAA500} & Just Right & & \\ \hline
\end{tabular}
\caption{Clip length and irrelevant frames of video action datasets.}
\end{table}
\end{document}

eess

Jihoon Chung
Cheng-hsin Wuu
Hsuan-ru Yang
Yu-Wing Tai
Chi-Keung Tang

"HAA500: Human-Centric Atomic Action Dataset with Curated Videos" by Jihoon Chung, Cheng-hsin Wuu, Hsuan-ru Yang, Yu-Wing Tai, and Chi-Keung Tang, arXiv:2009.05224v2, 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{ Natural Frequencies Resulted From Rayleigh-Ritz Method and The Analytical Full Formulation }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\textbf{n} & \textbf{Rayleigh-Ritz } & \textbf{Full Formulation } \\
\textbf{} & $rad/sec$ & $rad/sec$ \\ \hline
\textbf{0} & 51.434449987 & 51.434449987 \\ \hline
\textbf{1} & 62.153777189 & 62.994144454 \\ \hline
\textbf{2} & 86.525263883 & 86.823947954 \\ \hline
\textbf{3} &115.855327968 & 115.941895367 \\ \hline
\textbf{4} &146.580164630 & 146.610087987 \\ \hline
\textbf{5} &177.820039783 & 177.832640732 \\ \hline
\textbf{6} &209.298426663 & 209.304856685 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Dynamic Modeling Of Spherical Variable-Shape Wave Energy Converters" by Mohamed A. Shabara and Ossama Abdelkhalik, arXiv:2201.08942v2, 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 MAEs on Boston housing dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
			\toprule
			& \multicolumn{2}{c}{Standard LAD regression } & & \multicolumn{2}{c}{Deep LAD regression} \\
			\cmidrule{1-3}\cmidrule{4-6} 	$\beta$ & Truncation & Non-truncation & & Truncation & Non-truncation \\
			\cmidrule{1-3}\cmidrule{4-6} 1.1 & 6.2538 & 6.4843 & & 6.2822 & 6.3186 \\
			1.2 & 6.2551 & 6.4843 & & 6.2203 & 6.3186 \\
			1.3 & 6.2563 & 6.4843 & & 6.2150 & 6.3186 \\
			1.4 & 6.2578 & 6.4843 & & 6.1898 & 6.3186 \\
			1.5 & 6.2592 & 6.4843 & & 6.1810 & 6.3186 \\
			1.6 & 6.2611 & 6.4843 & & 6.2096 & 6.3186 \\
			1.7 & 6.2631 & 6.4843 & & 6.1620 & 6.3186 \\
			1.8 & \textbf{6.2448} & 6.4843 & & \textbf{6.0628} & 6.3186 \\
			1.9 & 6.2676 & 6.4843 & & 6.1598 & 6.3186 \\
			2.0 & 6.2705 & 6.4843 & & 6.1711 & 6.3186 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Lihu Xu
Fang Yao
Qiuran Yao
Huiming Zhang

"Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption" by Lihu Xu, Fang Yao, Qiuran Yao, and Huiming Zhang, arXiv:2201.03182v2, 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{Results obtained by using DMoN--DPR pooling using the best variance weight of 0.1, and best entropy weight of 0.01 on the CiteSeer dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
\textbf{Seed} & \textbf{Method} & \textbf{Conductance} & \textbf{Modularity} & \textbf{NMI} & \textbf{F1} \\ \midrule
993 & DMoN--DPR (VE) & 7.51 & 73.51 & 21.85 & 28.66 \\
550 & DMoN--DPR (VE) & 4.35 & 75.43 & 26.50 & 42.19 \\
243 & DMoN--DPR (VE) & 4.48 & 75.15 & 32.58 & 46.11 \\
16 & DMoN--DPR (VE) & 4.79 & 76.51 & 33.09 & 45.03 \\
716 & DMoN--DPR (VE) & 4.50 & 75.95 & 37.46 & 51.15 \\
383 & DMoN--DPR (VE) & 4.61 & 74.94 & 33.20 & 46.98 \\
277 & DMoN--DPR (VE) & 4.24 & 75.33 & 30.08 & 43.40 \\
274 & DMoN--DPR (VE) & 4.37 & 75.69 & 31.72 & 46.90 \\
188 & DMoN--DPR (VE) & 6.39 & 72.92 & 25.40 & 36.39 \\
796 & DMoN--DPR (VE) & 5.23 & 74.71 & 29.84 & 45.84 \\
\midrule
\multicolumn{2}{c}{\textbf{Mean $\pm$ Std}} & 5.05 $\pm$ 1.07 & 75.01 $\pm$ 1.09 & 30.17 $\pm$ 4.53 & 43.27 $\pm$ 6.40 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Deep Modularity Networks with Diversity-Preserving Regularization" by Yasmin Salehi and Dennis Giannacopoulos, arXiv:2501.13451v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Fairness in LLM-Generated Surveys

"Fairness in LLM-Generated Surveys" by Andrés Abeliuk, Vanessa Gaete, and Naim Bro, arXiv:2501.15351v1, 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}{|cc|}
 \hline
	State $w$ & Unnormalized probability $\Psi(w)$\\
 \hline
 1234 & $9t^3+7t^2+7t+1$\\
	1243 & $3(t^3+3t^2+3t+1)$\\
	1324 & $3t^3+11t^2+5t+5$\\
	1342 & $3(t^3+3t^2+3t+1)$\\
	1423 & $5t^3+5t^2+11t+3$\\
	1432 & $t^3+7t^2+7t+9$\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"The combinatorics of hopping particles and positivity in Markov chains" by Lauren K. Williams, arXiv:2202.00214v1, 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{Ablation study results comparing the baseline, baseline with energy predictor, and phoneme-level energy input models. Explicit energy input provides significantly better control over dynamics and improves synthesis quality compared to implicit energy prediction.}
\begin{tabular}{l|ccc} & Energy MAE $\downarrow$ & F0 MAE $\downarrow$ & MCD $\downarrow$ \\ \hline
Baseline & \textbf{0.33} & 10.67 & 12.89 \\
Baseline + Energy Predictor & \textbf{0.30} & 9.43 & 12.97 \\
Phoneme-Level & \textbf{0.14} & 9.73 & 12.07
\end{tabular}
\end{table}
\end{document}

eess

"Controllable Singing Voice Synthesis using Phoneme-Level Energy Sequence" by Yerin Ryu, Inseop Shin, and Chanwoo Kim, arXiv:2509.07038v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.