\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The comparison of computational efficiency, with the fastest and second-fastest models highlighted in \textcolor{red}{red} and \textcolor{blue}{blue}, respectively. And the best and second-best performances are highlighted in \textbf{bold} and \underline{underlined}, respectively. FLOPs are calculated using an input tensor of shape $3 \times 512 \times 512$, and inference speed is averaged over 100 runs. All evaluations are conducted on the QADS database.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc|cc}
\toprule
Model & Params/M & Flops/G & Speed/ms & SRCC $\uparrow$& PLCC $\uparrow$ \\
\midrule
WaDIQaM~ & 6.287 & 30.479 & \textcolor{blue}{8.170} & 0.871 & 0.887 \\
HyperIQA~ & 27.375 & 107.831 & 29.980 & 0.954 & 0.957 \\
CLIP-IQA~ &\makebox[1em][c]{\textemdash} & 61.065 & 21.100 & 0.931 & 0.833 \\
TOPIQ~ & 36.039 & 50.138 & 22.140 & 0.969 & 0.970 \\
DeepSRQ~ & \textcolor{red}{1.317} & \textcolor{red}{1.589} & \textcolor{red}{2.291} & 0.953 & 0.956 \\
TADSRNet~ & 2.119 & 303.076 & 87.568 & 0.972 & 0.974 \\
PFIQA~ & 38.772 & 127.109 & 18.14 & \underline{0.982} & \underline{0.983} \\
PBAN & \textcolor{blue}{2.222} & \textcolor{blue}{26.278} & 16.995 & \textbf{0.986} & \textbf{0.987}\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Discrete Actions}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{\#} & \textbf{Action} & \textbf{\#} & \textbf{Action} \\
\hline
0 & 0.0 & 4 & -1.0 \\
\hline
1 & 1.0 & 5 & -0.5 \\
\hline
2 & 0.5 & 6 & -0.25 \\
\hline
3 & 0.25 & & \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Neurosymbolic Meta-Reinforcement Lookahead Learning Achieves Safe Self-Driving in Non-Stationary Environments" by Haozhe Lei and Quanyan Zhu, arXiv:2309.02328v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
 \toprule
 12 months ending & 2018-09-01 & 2019-09-01 & 2020-09-01 \\
 \midrule
 Commits & 4394 & 4702 & 5538 \\
 Commits per day & 12.0 & 12.9 & 15.2 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Creating a Virtuous Cycle in Performance Testing at MongoDB" by David Daly, arXiv:2101.10231v2, 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{Non-fluent In-context Learning: Zero-Shot ASR tasks Transfer}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc}
 \toprule

 & \textit{Libri-Dys} & \textit{Libri-Co-Dys (Multi-types)} \\
 \hline
 \hline
 WER (Whisper) ($\% \downarrow$) & 4.167 & 8.89 \\
WER-Zero-Shot (SSDM) ($\% \downarrow$) &10.08 & 17.45 \\
 WER-Zero-Shot (SSDM 2.0) ($\% \downarrow$) &\textbf{3.92} & \textbf{7.10} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jiachen Lian
Xuanru Zhou
Zoe Ezzes
Jet Vonk
Brittany Morin
David Baquirin
Zachary Mille
Maria Luisa Gorno Tempini
Gopala Krishna Anumanchipalli

"SSDM 2.0: Time-Accurate Speech Rich Transcription with Non-Fluencies" by Jiachen Lian, Xuanru Zhou, Zoe Ezzes, Jet Vonk, Brittany Morin, David Baquirin, Zachary Mille, Maria Luisa Gorno Tempini, and Gopala Krishna Anumanchipalli, arXiv:2412.00265v1, 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{$C_5$ Part 2 Summary}
\begin{tabular}{|l|l|l|} \hline
Term & Symmetric Terms & Table Name \\ \hline
 $- f (X)_{i_0,i_0} \cdot f(X)_{i_0,i_2} \cdot w(X)_{i_0,j_2} \cdot v_{j_1,j_0}$ & No & na \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Yichuan Deng
Zhao Song
Shenghao Xie
Chiwun Yang

"Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights" by Yichuan Deng, Zhao Song, Shenghao Xie, and Chiwun Yang, arXiv:2310.12462v1, licensed under CC BY-NC-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The performance of different models is evaluated on cross-domain dataset. M→D means that we train the model on Market1501 and evaluate it on DukeMTMC.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cclcl}
		\hline
		\multirow{3}{*}{Methods} & \multicolumn{4}{c}{Cross-Domain} \\ \cline{2-5}
		& \multicolumn{2}{c}{M→D} & \multicolumn{2}{c}{D→M} \\ \cline{2-5}
		& \multicolumn{2}{c}{Rank-1/mAP(\%)} & \multicolumn{2}{c}{Rank-1/mAP(\%)} \\ \hline
		SB+REA+RK & \multicolumn{2}{c}{33.6/24.3} & \multicolumn{2}{c}{51.6/32.3} \\
		SB+REA+GGT+RK(ours) & \multicolumn{2}{c}{\textbf{37.8/27.8}} & \multicolumn{2}{c}{\textbf{55.4/35.7}} \\
		SB-REA+RK & \multicolumn{2}{c}{45.5/37.0} & \multicolumn{2}{c}{58.2/37.8} \\
		SB-REA+GGT+RK(ours) & \multicolumn{2}{c}{\textbf{48.2/37.9}} & \multicolumn{2}{c}{\textbf{65.0/43.7}} \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method" by Yunpeng Gong, Liqing Huang, and Lifei Chen, arXiv:2101.08533v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Spearman's rho for bivariate zero-inflated data" by Jasper Arends and Elisa Perrone, arXiv:2503.13148v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 &\textbf{Group} $X$ &\textbf{Group} $Y$ \\
 \hline
\textbf{False Positive Rate}& 0 & $1-\phi$ \\
\textbf{False Negative Rate}& $F_X(0)$ & $1-\phi$ \\
 \hline
\textbf{Positive Predictive Value}& Undefined & $
\frac{F_Y\left(2\phi-1\right)\phi}{F_Y\left(2\phi-1\right)\phi+(1-F_Y\left(2\phi-1\right))\left(1-\phi\right)}
$\\
\textbf{Negative Predictive Value}&$1-F_X(0)$ & $
\frac{\left(1-F_Y\left(2\phi-1\right)\right)\phi}{\left(1-F_Y\left(2\phi-1\right)\right)\phi+F_Y\left(2\phi-1\right)\left(1-\phi\right)}
$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Error Rates \& Predictive Values With Discrimination}
\end{table}
\end{document}

stat

Algorithmic Fairness with Feedback

"Algorithmic Fairness with Feedback" by John W. Patty and Elizabeth Maggie Penn, arXiv:2312.03155v1, 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}{cccc}
\toprule
 & {[}1, 3) & {[}3, 5) & {[}5, 7) \\
 \hline
our best & 0.0553 & \textbf{0.4492} & 0.1956 \\
\hline
baseline & 0.0672 & 0.3947 & 0.1975 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{valence CCC among certain ranges}
\end{table}
\end{document}

eess

Jing-Tong Tzeng
Bo-Hao Su
Ya-Tse Wu
Hsing-Hang Chou
Chi-Chun Lee

"Lessons Learnt: Revisit Key Training Strategies for Effective Speech Emotion Recognition in the Wild" by Jing-Tong Tzeng, Bo-Hao Su, Ya-Tse Wu, Hsing-Hang Chou, and Chi-Chun Lee, arXiv:2508.07282v1, 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}{lllllllll}
meta-estimator & outcome\_model & mean\_pehe & ate\_rmse & ate\_abs\_bias & ate\_std\_error \\
standardization & Standardized\_SVM\_rbf & 3058.74 & 2052.88 & 1882.01 & 819.98 \\
stratified\_standardization & ElasticNet & 4889.81 & 3489.11 & 2159.56 & 2740.47 \\
stratified\_standardization & Ridge & 4927.52 & 3936.74 & 2904.55 & 2657.35 \\
stratified\_standardization & Lasso & 5064.64 & 3360.80 & 1826.03 & 2821.45 \\
stratified\_standardization & LinearRegression & 5124.72 & 3383.14 & 1813.37 & 2856.10 \\
stratified\_standardization & LinearSVM & 5209.82 & 4902.21 & 2173.07 & 4394.25 \\
stratified\_standardization & Standardized\_SVM\_rbf & 5326.08 & 4639.97 & 4406.92 & 1452.04 \\
standardization & LinearRegression\_degree2 & 5488.03 & 3593.31 & 1201.96 & 3386.32 \\
standardization & Standardized\_SVM\_sigmoid & 5633.50 & 1678.65 & 1478.32 & 795.26 \\
standardization & LinearRegression\_interact & 5637.26 & 3789.72 & 1560.70 & 3453.43 \\
stratified\_standardization & Standardized\_LinearSVM & 5995.41 & 3980.05 & 2405.59 & 3170.80 \\
stratified\_standardization & SVM\_rbf & 6240.25 & 5281.01 & 5212.29 & 849.16 \\
stratified\_standardization & SVM\_sigmoid & 6883.21 & 5510.08 & 5451.39 & 802.03 \\
stratified\_standardization & DecisionTree & 6953.37 & 4726.51 & 372.14 & 4711.83 \\
stratified\_standardization & Standardized\_SVM\_sigmoid & 7895.10 & 4708.39 & 4613.74 & 939.35 \\
standardization & Standardized\_LinearSVM & 9157.57 & 6530.70 & 6488.87 & 737.98 \\
standardization & LinearRegression & 9467.81 & 6971.02 & 6910.01 & 920.23 \\
standardization & Ridge & 9508.04 & 6998.69 & 6987.78 & 390.59 \\
standardization & ElasticNet & 9550.25 & 7050.64 & 7049.30 & 137.34 \\
standardization & Lasso & 9552.70 & 7053.98 & 7052.56 & 141.86 \\
standardization & KernelRidge & 9577.83 & 7092.93 & 7082.44 & 385.52 \\
standardization & kNN & 9613.85 & 7135.82 & 7135.82 & 0.41 \\
standardization & DecisionTree & 9613.87 & 7135.67 & 7135.67 & 0.00 \\
standardization & LinearSVM & 9613.87 & 7135.67 & 7135.67 & 0.00 \\
standardization & SVM\_rbf & 9613.87 & 7135.67 & 7135.67 & 0.00 \\
standardization & SVM\_sigmoid & 9613.87 & 7135.67 & 7135.67 & 0.00 \\
stratified\_standardization & LinearRegression\_degree2 & 100705.35 & 57156.93 & 15734.67 & 54948.48 \\
stratified\_standardization & LinearRegression\_interact & 105675.94 & 44091.19 & 11020.68 & 42691.66 \\
stratified\_standardization & kNN & & & & \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Brady Neal
Chin-Wei Huang
Sunand Raghupathi

"RealCause: Realistic Causal Inference Benchmarking" by Brady Neal, Chin-Wei Huang, and Sunand Raghupathi, arXiv:2011.15007v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 $\lambda_1$ &$\lambda_2$ & $b$ &$d$ \\
 $day^{-1}$$ $& $day^{-1}$$ $ & $day^{-1}$ & $day^{-1}mm^{-2}$ \\
 \hline $0.0741$&$0.0021$&$1.3383$&$0.002$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Parameter values for the ODE system ().}
\end{table}
\end{document}

math

Luis A. Fernández
Isabel Lasheras
Cecilia Pola

"A mathematical perspective on the paradox that chemotherapy sometimes works backwards" by Luis A. Fernández, Isabel Lasheras, and Cecilia Pola, arXiv:2503.10670v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of Weight Priorities, Quadratic Forms, and Offset Terms in GFL and GFM Behavior Realization}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\textbf{Mode} & \textbf{$\alpha$‐Weights} & {\boldmath$Q_{P\omega}$} & {\boldmath$Q_{QV}$} & {\boldmath$\boldsymbol{\phi}_N$} \\ \midrule
GFL &
$\alpha_3 > \alpha_1 = \alpha_2$ &
$\begin{bmatrix} 0 & 0 \\ 0 & 1 \end{bmatrix}\!\otimes\! I_N$ &
$\begin{bmatrix} 0 & 0 \\ 0 & 1 \end{bmatrix}\!\otimes\! I_N$ &
$\boldsymbol{\phi}_N\!\in\!\mathbb{R}^N$ \\
GFM &
$\alpha_2 = \alpha_3 > \alpha_1$ &
$\begin{bmatrix} M^{\!\top}M & M^{\!\top} \\ M & I_N \end{bmatrix}$ &
$\begin{bmatrix} 1 & 0 \\ 0 & 0 \end{bmatrix}\!\otimes\! I_N$ &
$\dfrac{JM^{-1} d_0}{T_{\rm S}}$ \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ruohan Leng
Linbin Huang
Huanhai Xin
Ping Ju
Xiongfei Wang
Eduardo Prieto-Araujo
Florian Dörfler

"DeePConverter: A Data-Driven Optimal Control Architecture for Grid-Connected Power Converters" by Ruohan Leng, Linbin Huang, Huanhai Xin, Ping Ju, Xiongfei Wang, Eduardo Prieto-Araujo, and Florian Dörfler, arXiv:2508.08578v1, 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|}
\hline
Algorithm & $K=10$ & $K=20$ & $K=40$ & $K=80$ \\
\hline
UCB & 0.038 & 0.039 & 0.042 & 0.042 \\
\hline
B-UCB & 0.963 & 0.980 & 1.052 & 1.185 \\
\hline
TS & 0.992 & 0.967 & 1.207 & 1.864 \\
\hline
O-TS & 0.968 & 0.939 & 1.034 & 1.609 \\
\hline
G-AI & 0.041 & 0.041 & 0.054 & 0.072 \\
\hline
A-AI & 0.034 & 0.036 & 0.039 & 0.040 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Compute times per decision presented in milliseconds. The compute times were estimated as an average over ten repetitions of a $T=10000$ long loop, consisting of only action selection and learning algorithm. Outcomes were kept fixed on all trials. Each algorithm was executed with $N=1000$ parallel simulations. For comparison, we show run times of classical variants of UCB and TS .}
\end{table}
\end{document}

cs

Dimitrije Markovic
Hrvoje Stojic
Sarah Schwoebel
Stefan J. Kiebel

"An empirical evaluation of active inference in multi-armed bandits" by Dimitrije Markovic, Hrvoje Stojic, Sarah Schwoebel, and Stefan J. Kiebel, arXiv:2101.08699v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Evaluation for the classification accuracy with different training strategies and ResNet as backbones. For the strategy names, suffix \textit{CR} indicates training for both classifiers and regressors, \textit{GAN} for semi-supervised training strategy using unlabeled data, \textit{WP} for using semi-supervised windows dataset as pre-training weights, e.g., results of ResNetCR+GAN, and \textit{NP} for using no pre-trained weights.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
			\hline
			\multirow{2}{*}{Name} & \multicolumn{2}{c}{Labeled Data} & Unlabeled & \multicolumn{2}{c}{Pre-trained Weights} & \multirow{2}{*}{Top-1 Accurary} \\
			& Classes & Parameters & Data & ImageNet & Window & \\ \hline
			ResNetCR+GAN & \checkmark & \checkmark & \checkmark & \checkmark & & 75.54 \\
			ResNet+GAN+WP & \checkmark & & \checkmark & & \checkmark & 76.14 \\
			ResNet+WP & \checkmark & & & & \checkmark & 78.91 \\
			ResNet & \checkmark & & & \checkmark & & 67.74 \\ \hline
			ResNet+NP & \checkmark & & & & & 38.75 \\
			ResNet+GAN+NP & \checkmark & & \checkmark & & & 61.25 \\ \hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Han Hu
Xinrong Liang
Yulin Ding
Qisen Shang
Bo Xu
Xuming Ge
Min Chen
Ruofei Zhong
Qing Zhu

"Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Façade Modeling" by Han Hu, Xinrong Liang, Yulin Ding, Qisen Shang, Bo Xu, Xuming Ge, Min Chen, Ruofei Zhong, and Qing Zhu, arXiv:2201.08977v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of 2-branch and 3-branch MBRB designs on two datasets. While the 3-branch structure slightly improves compression ratio, it incurs higher memory and computational cost(batchsize = 8192).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\textbf{Dataset} & \textbf{Branch} & \textbf{Ratio ↑} & \textbf{Speed (kB/min) ↑} & \textbf{Mem (MB) ↓} \\
\midrule
\multirow{2}{*}{\textit{Backup}}
& 2-branch & 1.94 & 10214.8 & 2592 \\
& 3-branch & 1.96 & 7826.4 & 2820 \\
\midrule
\multirow{2}{*}{\textit{Silesia}}
& 2-branch & 5.31 & 10113.5 & 2592 \\
& 3-branch & 5.34 & 7817.8 & 2820 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Zeyi Lu
Xiaoxiao Ma
Yujun Huang
Minxiao Chen
Bin Chen
Baoyi An
Shu-Tao Xia

"EDPC: Accelerating Lossless Compression via Lightweight Probability Models and Decoupled Parallel Dataflow" by Zeyi Lu, Xiaoxiao Ma, Yujun Huang, Minxiao Chen, Bin Chen, Baoyi An, and Shu-Tao Xia, arXiv:2507.18969v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Experiment results for capital control binary and hierarchical classification}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|ccccc}
\toprule
\textbf{Model} & \multirow{2}{*}{\textbf{Binary Acc}} & \multicolumn{5}{c}{\textbf{Hierarchical Acc}} \\
\cmidrule(lr){3-7}
 & & \textbf{L3} & \textbf{L4} & \textbf{L5} & \textbf{L6} & \textbf{Avg} \\
\midrule
Llama 3.1-8B & 90.67 & 84.00 & 59.05 & 44.68 & 26.42 & 53.54 \\
GPT-4o & 76.07 & 70.09 & 59.63 & 42.07 & 40.74 & 53.13 \\
GPT-4.1 & 94.44 & 82.91 & 68.81 & 56.55 & 48.15 & 64.10 \\
\midrule
CCM-Llama & \textbf{99.55} & \textbf{90.09} & \textbf{75.85} & \textbf{65.19} & \textbf{60.78} & \textbf{72.98} \\
\rowcolor{gray!10}
$\Delta$ over Best Baseline& \textbf{+5.11} & \textbf{+7.18} & \textbf{+7.04} & \textbf{+8.64} & \textbf{+12.63} & \textbf{+8.88} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Geyue Sun
Xiao Liu
Tomas Williams
Roberto Samaniego

"Learning to Regulate: A New Event-Level Dataset of Capital Control Measures" by Geyue Sun, Xiao Liu, Tomas Williams, and Roberto Samaniego, arXiv:2505.23025v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Gaurab Aryal
Anirban Chattopadhyaya
Federico Ciliberto

"Bridging Retrospective and Prospective Merger Analyses: The Case of US Airline Mergers" by Gaurab Aryal, Anirban Chattopadhyaya, and Federico Ciliberto, arXiv:2503.15785v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 \textbf{Poisson Brackets} & \textbf{Result} & \textbf{Description} \\
 \hline
 $\{ L_{12}, L_{13} \}$ & $L_{23}$ & $SO(4)$ angular momentum algebra \\
 $\{ L_{12}, L_{14} \}$ & $L_{24}$ & $SO(4)$ angular momentum algebra \\
 $\{ L_{13}, L_{14} \}$ & $L_{34}$ & $SO(4)$ angular momentum algebra \\
 $\{ I_{ii}, I_{jj} \}$ & $0$ & Commuting quadratic integrals \\
 $\{ I_{ii}, L_{ij} \}$ & $2 \,\omega^2 \,I_{ij}$ & Fradkin tensor coupling to $SO(4)$ \\
 $\{ H_{4D}, L_{ij} \}$ & $0$ & Energy is a Casimir element \\
 $\{ H_{4D}, I_{ii} \}$ & $0$ & Energy commutes with quadratic integrals \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Poisson algebra of the 7 algebraically independent integrals of motion ($H_{4D},L_{12}, L_{13},L_{14}, I_{11}, I_{22}, I_{33}$) in the 4D isotropic harmonic oscillator.}
\end{table}
\end{document}

math

Adrian M Escobar-Ruiz
Manuel Fernandez-Guasti

"On the four-body limaçon choreography: maximal superintegrability and choreographic fragmentation" by Adrian M Escobar-Ruiz and Manuel Fernandez-Guasti, arXiv:2504.16403v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Klemens Flöge
Mohammed Abdul Moeed
Vincent Fortuin

"Stein Variational Newton Neural Network Ensembles" by Klemens Flöge, Mohammed Abdul Moeed, and Vincent Fortuin, arXiv:2411.01887v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The execution time and speedup of different algorithms across 13 graphs. The R0-R10 graphs are the real-world network from SNAP, while the S0-S1 are the synthesis network generated from 1st DIMACS Challenge . The edge capacity of graphs in SNAP is set to 1. The bold font time represents the best execution time among these four algorithms.}
\begin{tabular}{|l|r|r|rrrr|rr|}
\hline
\rowcolor[HTML]{EFEFEF}
\cellcolor[HTML]{EFEFEF} & \cellcolor[HTML]{EFEFEF} & \cellcolor[HTML]{EFEFEF} & \multicolumn{4}{c|}{\cellcolor[HTML]{EFEFEF}Execution time (ms)} & \multicolumn{2}{c|}{\cellcolor[HTML]{EFEFEF}Speedup (TC/VC)} \\ \cline{4-9}
\rowcolor[HTML]{EFEFEF}
\multirow{-2}{*}{\cellcolor[HTML]{EFEFEF}Graph} & \multirow{-2}{*}{\cellcolor[HTML]{EFEFEF}$|V|$} & \multirow{-2}{*}{\cellcolor[HTML]{EFEFEF}$|E|$} & \cellcolor[HTML]{EFEFEF}TC+RCSR & \cellcolor[HTML]{EFEFEF}TC+BCSR & \cellcolor[HTML]{EFEFEF}VC+RCSR & \cellcolor[HTML]{EFEFEF}VC+BCSR & \cellcolor[HTML]{EFEFEF}RCSR & \cellcolor[HTML]{EFEFEF}BCSR \\ \hline
{\color[HTML]{000000} Amazon0302 (R0)} & 262,111 & 1,234,877 & 5,728 & \textbf{2,307} & 8,477 & 5,191 & 0.67x & 0.44x \\ \hline
{\color[HTML]{000000} roadNet-CA (R1)} & 1,965,206 & 2,766,607 & 57,966 & 70,468 & 74,707 & \textbf{32,842} & 0.78x & 2.15x \\ \hline
{\color[HTML]{000000} roadNet-PA (R2)} & 1,088,092 & 1,541,898 & 27,667 & \textbf{14,822} & 43,283 & 18,078 & 0.64x & 0.82x \\ \hline
{\color[HTML]{000000} web-BerkStan (R3)} & 685,230 & 7,600,595 & 82,984 & 23,959 & 35,129 & \textbf{23,596} & 2.36x & 1.02x \\ \hline
{\color[HTML]{000000} web-Google (R4)} & 875,713 & 5,105,039 & 28,053 & 12,165 & 17,664 & \textbf{7,927} & 1.58x & 1.53x \\ \hline
{\color[HTML]{000000} cit-Patents (R5)} & 3,774,768 & 16,518,948 & 80,223 & 237,968 & 4,879 & \textbf{2,992} & 16.44x & 79.53x \\ \hline
cit-HepPh (R6) & 34,546 & 421,578 & 651 & 214 & 312 & \textbf{141} & 2.09x & 1.52x \\ \hline
{\color[HTML]{000000} soc-LiveJounal1 (R7)} & 4,847,571 & 68,993,773 & 833,189 & 703,077 & 572,705 & \textbf{385,912} & 1.45x & 1.82x \\ \hline
{\color[HTML]{000000} soc-Pokec (R8)} & 81,306 & 1,768,149 & 82,525 & 66,319 & 73,060 & \textbf{34,214} & 1.13x & 1.94x \\ \hline
{\color[HTML]{000000} com-YouTube (R9)} & 1,134,890 & 2,987,624 & 398,794 & \textbf{91,859} & 177,555 & 114,003 & 2.25x & 0.81x \\ \hline
{\color[HTML]{000000} com-Orkut (R10)} & 3,072,441 & 117,185,083 & 5,001,263 & 326,534 & 3,141,124 & \textbf{325,351} & 1.59x & 1.00x \\ \hline
Washington-RLG (S0) & 262,146 & 785,920 & 162,792 & 132,482 & 287,390 & \textbf{99,410} & 0.56x & 1.33x \\ \hline
Genrmf (S1) & 2,097,152 & 10,403,840 & \textbf{2,138} & 2,900 & 2,685 & 2,503 & 0.79x & 1.15x \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

"Engineering A Workload-balanced Push-Relabel Algorithm for Massive Graphs on GPUs" by Chou-Ying Hsieh, Po-Chieh Lin, and Sy-Yen Kuo, arXiv:2404.00270v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Table showing correlation analyses determining the relationship between Utterance Frequency, Average Pointing Time, Stationary Time Ratio and Task Completion Time (\textit{* = $p < .05$; ** = $p < .01$; *** = $p < .001$})}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|l|l|l|}
\hline
\textbf{\textit{Modality}} & \textbf{\textit{Name}} & \textbf{\textit{r}} & \textbf{\textit{p}} & \textbf{\textit{Sig}} & \textbf{\textit{$M_1$}} & \textbf{\textit{$SD_1$}} & \textbf{\textit{$M_2$}} & \textbf{\textit{$SD_2$}} \\ \hline
Voice & Utterance Frequency (Task 1) & 0.39 & $< .05$ & * & 11.94 & 5.07 & 382.84 & 148.7 \\ \hline
Voice & Utterance Frequency (Task 2) & 0.85 & $< .05$ & * & 17.32 & 5.67 & 451.4 & 133.77 \\ \hline
Hand & Avg. Pointing Time (Task 1) & 0.18 & 0.32 & No & 1.93 & 1.00 & 382.84 & 148.7 \\ \hline
Hand & Avg. Pointing Time (Task 2) & 0.15 & 0.41 & No & 4.24 & 2.42 & 451.4 & 133.77 \\ \hline
Eye Gaze & Stationary Time Ratio (Task 1) & 0.37 & $< .05$ & * & 0.29 & 0.12 & 382.84 & 148.7 \\ \hline
Eye Gaze & Stationary Time Ratio (Task 2) & -0.025 & 0.88 & No & 0.39 & 0.14 & 451.4 & 133.77 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ryan Ghamandi
Yahya Hmaiti
Mykola Maslych
Ravi Kiran Kattoju
Joseph J. LaViola

"Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks" by Ryan Ghamandi, Yahya Hmaiti, Mykola Maslych, Ravi Kiran Kattoju, and Joseph J. LaViola, arXiv:2508.17124v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average Number of Data Samples per Class for Training and Testing.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
 \hline
 Class & Training Samples & Testing Samples \\ \hline
 Imagined Speech & 53,293 & 13,325 \\ \hline
 Idle State & 25,580 & 10,492 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Byung-Kwan Ko
Jun-Young Kim
Seo-Hyun Lee

"Imagined Speech State Classification for Robust Brain-Computer Interface" by Byung-Kwan Ko, Jun-Young Kim, and Seo-Hyun Lee, arXiv:2412.12215v1, 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{Relative errors of the final regularized solutions and corresponding early stopping iterations (in parentheses), where $\varepsilon=10^{-3}$}
\begin{tabular}{lllll}
		\toprule
		Problem & Example 1 & Example 2 & Example 3 & Example 4 \\
		\midrule
		Tikh-opt & 0.0361 & 0.0060 & 0.0062 & 0.0038 \\
		WLSQR-opt & 0.031 (9) & 0.0057 (11) & 0.0037 (3) & 0.0029 (7) \\
 WLSQR-DP & 0.0474 (7) & 0.0089 (8) & 0.0538 (2) & 0.0066 (5) \\
 WLSQR-LC & 0.0451 (8) & 0.0186 (14) & 0.0037 (3) & 0.0233 (12) \\
 LSQR-opt & 0.3178 (9) & 0.3163 (11) & 0.3166 (3) & 0.3162 (7) \\
 LSQR-DP & 0.3194 (7) & 0.3163 (8) & 0.3206 (2) & 0.3163 (5) \\
 LSQR-LC & 0.3191 (8) & 0.3164 (14) & 0.3166 (3) & 0.3170 (12) \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

math

"Generalizing the SVD of a matrix under non-standard inner product and its applications to linear ill-posed problems" by Haibo Li, arXiv:2312.10403v1, 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{Physical validation study demonstrates modest error in FEA simulation results.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|ccc|ccc|ccc|}
\hline
 & \multicolumn{3}{c|}{DeRosa SLX} & \multicolumn{3}{c|}{Casati Gold Line} & \multicolumn{3}{c|}{Holland SL/SP} \\ \hline
 & Front & Rear & Model & Front & Rear & Model & Front & Rear & Model \\
 & Defl. & Defl. & Mass & Defl. & Defl. & Mass & Defl. & Defl. & Mass \\ \hline
Actual & 0.40 & 0.15 & 1.966 & 0.44 & 0.15 & 1.966 & 0.38 & 0.13 & 1.962 \\
Simulated & 0.297 & 0.116 & 1.69 & 0.3028 & 0.124 & 1.80 & 0.26 & 0.107 & 1.77 \\ \hline
Error & 26\% & 23\% & 14\% & 31\% & 17\% & 8\% & 32\% & 18\% & 10\% \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Lyle Regenwetter
Colin Weaver
Faez Ahmed

"FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames" by Lyle Regenwetter, Colin Weaver, and Faez Ahmed, arXiv:2201.10459v3, 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{\textbf{Top 10 institutions' impact on acceptance, separate}, ICLR 2020. Logistic regression summary for predicting paper acceptance. The top 10 institution ranks each have their own indicator variable. Statistically significant effects are in bold. The accuracy of the classifier was 92\% on a hold-out set containing 30\% of 2020 papers}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\bf Variable &\bf Coefficient &\bf Std. Error &\bf Z-score &\bf p-value
\\ \hline \\
\texttt{mean reviewer score} & 2.4600 & 0.099 & 24.949 & 0.000\\ %2.267 2.653
\bf \texttt{Carnegie Mellon} & $\mathbf{0.7510}$ & 0.363 & 2.071 & $\mathbf{0.038}$\\ %0.040 1.462
\bf \texttt{MIT} & $\mathbf{0.7498}$ & 0.357 & 2.100 & $\mathbf{0.036}$\\ %0.050 1.450
\texttt{U. Illinois, Urbana-Champaign} & 0.6698 & 0.535 & 1.252 & 0.211\\ %-0.379 1.718
\texttt{Stanford} & -0.0312 & 0.371 & -0.084 & 0.933\\ %-0.759 0.696
\texttt{U.C. Berkeley} & 0.2299 & 0.337 & 0.681 & 0.496\\ %-0.432 0.891
\texttt{U. Washington} & -0.8189 & 0.684 & -1.198 & 0.231\\ %-2.159 0.521
\bf \texttt{Cornell} & $\mathbf{1.4267}$ & 0.607 & 2.350 & $\mathbf{0.019}$\\ %0.237 2.617
\texttt{Tsinghua U. \& U. Michigan (tied)} & -0.0118 & 0.369 & -0.032 & 0.974\\ %-0.735 0.711
\texttt{ETH Zurich} & 0.0608 & 0.640 & 0.095 & 0.924\\ %-1.193 1.314
\texttt{constant} & -13.4048 & 0.535 & -25.038 & 0.000\\ %-14.454 -12.356
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

David Tran
Alex Valtchanov
Keshav Ganapathy
Raymond Feng
Eric Slud
Micah Goldblum
Tom Goldstein

"Analyzing the Machine Learning Conference Review Process" by David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, and Tom Goldstein, arXiv:2011.12919v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Wei-Ning Chen
Graham Cormode
Akash Bharadwaj
Peter Romov
Ayfer Özgür

"Federated Experiment Design under Distributed Differential Privacy" by Wei-Ning Chen, Graham Cormode, Akash Bharadwaj, Peter Romov, and Ayfer Özgür, arXiv:2311.04375v1, 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{Main notations in this paper.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
 \hline
 Variable & Denotation \\ \hline
 $P$ & High-resolution PAN (HRPAN) image\\
 $Q$ & Low-resolution PAN (LRPAN) image\\
 $H$ & High-resolution HS (HRHS) image\\
 $L$ & Low-resolution HS (LRHS) image\\
 $N$ & Noisy LRHS (NLRHS) image\\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Shuang Xu
Zixiang Zhao
Haowen Bai
Chang Yu
Jiangjun Peng
Xiangyong Cao
Deyu Meng

"Hipandas: Hyperspectral Image Joint Denoising and Super-Resolution by Image Fusion with the Panchromatic Image" by Shuang Xu, Zixiang Zhao, Haowen Bai, Chang Yu, Jiangjun Peng, Xiangyong Cao, and Deyu Meng, arXiv:2412.04201v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"On Pricing of Discrete Asian and Lookback Options under the Heston Model" by Leonardo Perotti and Lech A. Grzelak, arXiv:2211.03638v2, 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{Cointegrating Vector Coefficients for Crypto}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|rrrrr}
 \toprule
 & \textbf{ADA} & \textbf{BTC} & \textbf{ETH} & \textbf{LTC} & \textbf{XRP} \\
 \midrule
 CEV$_1$ & $+2.62$ & $+3.69$ & $-4.60$ & $-0.99$ & $+2.23$ \\
 CEV$_2$ & $+5.15$ & $-0.19$ & $-5.64$ & $+1.82$ & $-3.51$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Time-Varying Factor-Augmented Models for Volatility Forecasting" by Duo Zhang, Jiayu Li, Junyi Mo, and Elynn Chen, arXiv:2508.01880v1, 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{Results of average part-worth utilities from Quantification Theory Type 1 and average ranges for each components.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccccc}
\toprule
Attribute & Level & \multicolumn{3}{c}{Dummy} & Part-Worth Utility & Range \\
\cmidrule{3-5}
 & & D1 & D2 & D3 & & \\
\midrule\midrule
title & short & 1 & 0 & - & 0.44 & 0.44 \\
 & long & 0 & 1 & - & 0.82 & \\ \midrule
price & cheap & 1 & 0 & - & 0.67 & 0.38\\
 & expensive & 0 & 1 & - & 0.59 & \\ \midrule
product description & short & 1 & 0 & 0 & -0.42 & 1.85 \\
 & middle & 0 & 1 & 0 & 0.35 & \\
 & long & 0 & 0 & 1 & 1.34 & \\ \midrule
image & 1 image & 1 & 0 & - & 0.27 & 0.73 \\
 & 3 images & 0 & 1 & - & 0.99 & \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Analysis of Information Digestion Differences among Players in Online C2C Markets" by Jun Sashihara and Teruaki Hayashi, arXiv:2503.20834v1, 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{Our model’s accuracy (\%) on the \textbf{NABirds} dataset with different SotA base CNN architectures. Previous best accuracy is 86.4\% (Luo et al. 2019) for primary only and 87.9\% (Cui et al. 2018) for combined primary and secondary datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c}
 \toprule

 {Base CNN} & {Accuracy(\%)} \\
 \midrule
 ResNet-50 &	88.8 \\
 Inception V3 & 89.1 \\
 Xception & \textbf{91.0} \\
 DenseNet-121 &88.3 \\
NASNet-Mobile &88.7 \\
MobileNet V2 &89.1 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ardhendu Behera
Zachary Wharton
Pradeep Hewage
Asish Bera

"Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification" by Ardhendu Behera, Zachary Wharton, Pradeep Hewage, and Asish Bera, arXiv:2101.06635v1, 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{Diarization result break-down on LibriCSS evaluation set, in terms of \% missed speech (MS), false alarm (FA), and speaker confusion (Conf.).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\textbf{Method} & \textbf{MS} & \textbf{FA} & \textbf{Conf.} & \textbf{DER} \\ \midrule
VB & \textbf{1.7} & \textbf{0.5} & 6.4 & 8.6 \\
SC & 2.5 & 1.1 & 5.7 & 9.3 \\
RPN & 2.9 & 3.3 & 3.3 & 9.5 \\
TS-VAD & 3.2 & 1.3 & 2.9 & 7.4 \\
\midrule
DL & 2.7 & 0.7 & \textbf{2.0} & \textbf{5.4} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

"DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs" by Desh Raj, Leibny Paola Garcia-Perera, Zili Huang, Shinji Watanabe, Daniel Povey, Andreas Stolcke, and Sanjeev Khudanpur, arXiv:2011.01997v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Time comparison between constraint in the STL formula and actual path duration. Time in [s].}
\begin{tabular}{c|c|c|c|c|c}
 	& $\phi'$ & $\phi''$ & $\phi'''$ & $\phi''''$ & $t_{total}$\\
	\hline
	\textbf{STL constraint} & 10 & 30 & 10 & 10 & 60\\
 \hline
 \textbf{Actual path duration} & 6.51 & 25.06 & 9.01 & 6.50 & 47.08
 \end{tabular}
\end{table}
\end{document}

cs

Andrea Ruo
Lorenzo Sabattini
Valeria Villani

"CBF-Based Motion Planning for Socially Responsible Robot Navigation Guaranteeing STL Specification" by Andrea Ruo, Lorenzo Sabattini, and Valeria Villani, arXiv:2404.00356v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Diffusion models used for numerical experiments}. \\$\dag$: The MNIST diffusion model uses the upsampled $3\times 32\times 32$ RGB pixel space as sample space, while the original MNIST data set consists of $28\times 28$ single channel black and white images. Thus the effective dimensionality of these images is around $784$. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
Data set & Hugging Face model\_id & Dimensionality\\
\midrule
MNIST & \text{dimpo/ddpm-mnist} & 3x32x32=3072 $\dag$ \\
CIFAR-10 & \text{google/ddpm-cifar10-32} & 3x32x32=3072 \\
Lsun-Church & \text{google/ddpm-church-256} & 3x256x256 = 196,608\\
CelebA-HQ & \text{google/ddpm-celebahq-256} & 3x256x256 = 196,608 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"The Hidden Linear Structure in Score-Based Models and its Application" by Binxu Wang and John J. Vastola, arXiv:2311.10892v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c}
\hline
 & UCF101~ & ActNet 100~ & HMDB51~ \\
\textbf{Pre-trained} & Top-1 & Top-1 & Top-1 \\
\hline
None & 58.87\% & 43.54\% & 28.56\% \\
AVA~ & 48.54\% & 30.51\% & 25.28\% \\
Gym288~ & \textbf{69.94\%} & 43.79\% & 36.24\% \\
UCF101~ & - & 42.94\% & 32.37\% \\
ActNet 100~ & 57.52\% & - & 28.63\% \\
HMDB51~ & 53.36\% & 39.33\% & - \\
\hline
HAA500 & 68.70\% & \textbf{47.75\%} & \textbf{40.45\%}\\
\:\:\:Relaxed & 62.24\% & 38.30\% & 33.29\% \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Fine-tuning performance on I3D.}
\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{amssymb}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison with existing Bayesian methods: AP, CG, and PK are acronyms for Analytic form of Posterior, Consistency Guarantee, and Prior Knowledge incorporation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 Method & AP & CG & PK \\
 \midrule
 & $\checkmark$ & $\checkmark$ & \\
 & & & $\checkmark$ \\
 \textbf{Proposed method} & $\checkmark$ & $\checkmark$ & $\checkmark$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model" by Shunsuke Horii and Yoichi Chikahara, arXiv:2312.10435v1, 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{The summary of performance test with Neumann condition. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
		$\theta$ & $\max|y''-f|$ & $\max(|y_{opt}-y_{b}|)$ & $\max(|y_{rk4}-y_{b}|)$ \\ \hline\hline
		$\pi/2$ & 1.1E-07 & 8.8E-10 & 2.4E-07 \\
			$3\pi/2$ & 1.1E-06 & 1.8E-08 & 1.1E-04 \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions" by Xiaorong Zou, arXiv:2504.19280v1, 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{Recognition accuracy on the ETH-$80$ database.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
\textbf{Method} & \textbf{Accuracy}\\
\hline
\textbf{LLP}+\textbf{ARSRG}emb & \textbf{89.26}\%\\
\hline
\textbf{LLP}+BoW & 58.83\%\\
\hline
gdFil& 47.59\%\\
\hline
APGM & 84.39\%\\
\hline
VEAM & 82.68\%\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jacob Englert
Stefanie Ebelt
Howard Chang

"Estimating Heterogeneous Exposure Effects in the Case-Crossover Design using BART" by Jacob Englert, Stefanie Ebelt, and Howard Chang, arXiv:2311.12016v2, 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{Training set, test set, and retraining set for using GPR to predict the optimal parameters for AMG solving the variable coefficient Poisson equation}
\begin{tabular}{|c|c|c|c|c|c|}
			\hline
			Training set & $n:128\sim512,\Delta n=16$
			&Test set & $n:150\sim600, \Delta n=32$
			&Retraining set & $n:150\sim600, \Delta n=32$ \\
			\hline
		\end{tabular}
\end{table}
\end{document}

math

Juan Zhang
Junyue Luo
Fangfang Zhang
Xiaoqiang Yue

"Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs" by Juan Zhang, Junyue Luo, Fangfang Zhang, and Xiaoqiang Yue, arXiv:2504.14930v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Anisotropic local constant smoothing for change-point regression function estimation" by John R. J. Thompson and W. John Braun, arXiv:2012.00180v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|cccc|}
\hline
Fingerprint & logistic regression & Random Forest & LightGBM \\
\hline
ECFP & 58.00 & 67.48 ± 0.97 & 60.26\\
\hline
Atom Pair & 61.02 & \textbf{71.76 ± 0.82} & 68.55\\
\hline
Topological Torsion & 57.20 & 66.23 ± 0.91 & 63.41\\
\hline
MACCS Keys & 71.02 & 69.56 ± 0.78 & 68.17\\
\hline
ErG & 53.41 & 71.42 ± 0.82 & 71.52\\
\hline
MAP4 & 61.00 & 59.54 ± 1.54 & 59.23\\
\hline
MHFP & 53.16 & 56.19 ± 3.50 & 57.38\\
\hline
\end{tabular}
\caption{Test AUROC scores for BBBP dataset.}
\end{table}
\end{document}

cs

Michał Szafarczyk
Piotr Ludynia
Przemysław Kukla

"A Python library for efficient computation of molecular fingerprints" by Michał Szafarczyk, Piotr Ludynia, and Przemysław Kukla, arXiv:2403.19718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Augmented dataset single channel model hyperparameters.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
\toprule
\textbf{Hyperparameter} & \textbf{CinC PCG Model} \\
\hline
\midrule
Learning rate & 0.001 \\
Weight decay & 4.11e-5 \\
Momentum & 0.57562 \\
Gamma & 0.167 \\
Step size & 2 \\
Batch size & 32 \\
Number of hidden layers & 3 \\
Hidden layer size & 512 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Milan Marocchi
Matthew Fynn
Kayapanda Mandana
Yue Rong

"Scaling to Multimodal and Multichannel Heart Sound Classification: Fine-Tuning Wav2Vec 2.0 with Synthetic and Augmented Biosignals" by Milan Marocchi, Matthew Fynn, Kayapanda Mandana, and Yue Rong, arXiv:2509.11606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\Xhline{1.2pt}
 & \multicolumn{3}{c}{Pure Endowment} \\
\hline
 & No Hedging & Static Hedging & Continuos Hedging \\
\hline
Mean & $5.842$ & $2.186$ & $-0.0016$ \\
St. Dev. & $2.84$ & $1.807$ & $0.1184$ \\
\Xhline{1.2pt}
 & \multicolumn{3}{c}{Term Insurance} \\
\hline
 & No Hedging & Static Hedging & Continuos Hedging \\
\hline
Mean & $1.863$ & $0.503$ & $-0.0004$\\
St. Dev. & $0.184$ & $0.474$ & $0.121$ \\
\Xhline{1.2pt}
 & \multicolumn{3}{c}{Endowment Insurance} \\
\hline
 & No Hedging & Static Hedging & Continuos Hedging \\
\hline
Mean & $7.721$ & $2.708$ & $0.0006$ \\
St. Dev. & $2.846$ & $1.727$ & $0.012$ \\
\Xhline{1.2pt}
\end{tabular}
\end{adjustbox}
\caption{Mean and standard deviation of the distribution of the hedging cost for a portfolio of 1000 pure endowment policies (top panel), term insurance policies (middle panel), and endowment insurance policies (bottom panel) under no hedging, static hedging, and continuous hedging.}
\end{table}
\end{document}

q-fin

Katia Colaneri
Alessandra Cretarola
Edoardo Lombardo
Daniele Mancinelli

"Design and hedging of unit linked life insurance with environmental factors" by Katia Colaneri, Alessandra Cretarola, Edoardo Lombardo, and Daniele Mancinelli, arXiv:2509.05676v1, 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{Decision Tree Regression Hyperparameter Selection (Reduction=5)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|}
\hline
Hyperparameter & Datatype & Values/{[}Min, Max{]} & Log Scaling & Gridpoint Count & Best Value \\ \hline
Maximum Tree Depth & Integer & {[}2, 10{]} & FALSE & 5 & 7 \\ \hline
Splitting Strategy & Categorical & {[}'best', 'random'{]} & FALSE & N/A & best \\ \hline
Split Quality Criterion & Categorical & {[}'squared\_error', 'friedman\_mse', 'absolute\_error'{]} & FALSE & N/A & absolute\_error \\ \hline
Min. Samples to Split Internal Node & Integer & {[}2, 20{]} & TRUE & 3 & 5 \\ \hline
Min. Samples to Split Leaf Node & Integer & {[}1, 20{]} & TRUE & 3 & 11 \\ \hline
Features Considered During Split & Integer & {[}1, 39{]} & TRUE & 3 & 32 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames" by Lyle Regenwetter, Colin Weaver, and Faez Ahmed, arXiv:2201.10459v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Information-theoretic subset selection of multivariate Markov chains via submodular optimization" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\hline
\textbf{Method} & \textbf{Architecture} \\ \hline
MLP & FC(32)-FC(64)-FC(64)-FC(128)-FC(1) \\
CNN & CONV(32)-CONV(64)-CONV(64)-CONV(128)-FC(1) \\
ResNet & CONV(64)- Resblock(CONV(64)-CONV(64)*6-FC(1) \\
RNN & RNN(32)-RNN(32)-FC(1) \\
GRU & GRU(32)-GRU(32)-FC(1) \\
LSTM & LSTM(32)-LSTM(32)-FC(1) \\
Autoencoder & CONV(64)-CONV(128)-CONV(256)-FC(256)-CONV(256)-CONV(128)-CONV(64)-FC(1)\\
Time-CNN & CONV(6)-CONV(12)-FC(1) \\
Multi-Channel-CNN & (concatenate CNN(32), CNN(64), CNN(128)-FC(64)-FC(1) \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks" by Yu Zhang, Zelin Wu, and Claudio Tessone, arXiv:2504.12771v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc}
& \textbf{Pred: False} & \textbf{Pred: True} \\
\hline
\textbf{Label: False} & 502 & 197 \\
\textbf{Label: True} & 180 & 500 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Hallucination Removal (our) dialogue refinement feedback detection confusion matrix.}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Standardized RMSPE of the Borehole Example. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
Type of mean & EVIGP & mlegp \\\hline
Constant & 0.01151 & 0.3354 \\
Linear & 0.04191 & 0.1810 \\
Quadratic & 0.01212 & 0.2019\\
Quadratic, after selection & 0.01019 & N/A\\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Lulu Kang
Yuanxing Cheng
Yiwei Wang
Chun Liu

"Energetic Variational Gaussian Process Regression for Computer Experiments" by Lulu Kang, Yuanxing Cheng, Yiwei Wang, and Chun Liu, arXiv:2401.00395v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|lll}
\# & $\tau_{large}$ &$\tau_{mix}$ & $\tau_{small}$ \\\hline
1 & 36,413 & 1,642 & 73,858 \\
2 & 142,321 &1,590 & $\S$ \\
3 & 162,901 &2,061 & 1,272,471 \\
4 & 45,250 & 27,759& 50,769 \\
5 & 881,668 & 94,735 & 1,218,169 \\
6 & 183 & 87 & 185 \\
7 &4,681 & 2,276 & 8,145 \\
8 &6,045 & 1,952& 24,301 \\
 \end{tabular}
\end{adjustbox}
\caption{Number of nonzeros of the new scheme (symmetric case) with $M$-orthogonalization}
\end{table}
\end{document}

math

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


stat

Simulation Based Composite Likelihood

Lorenzo Rimella
Chris Jewell
Paul Fearnhead

"Simulation Based Composite Likelihood" by Lorenzo Rimella, Chris Jewell, and Paul Fearnhead, arXiv:2310.10761v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Risk in Network Economies

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of WSD gold standard dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
			\hline
			Criteria		&SE2	&SE3	&SE07	&SE13	&SE15\\
			\hline
			\#Doc			&	3	&	3	&	3	&	13	&	4	\\
			\#Sent*			&	242	&	297	&	120	&	301	&	133	\\
			\#Terms			&2282	&1850	&	455	&	1644&	1022\\
			AvgSentSize		&	9	&	6	&	3	&	5	&	7	\\
			Single sense	&	442	&311	&	26	&	348	&189\\
			Ambiguity rate	&81\%	&83\%	&94\%	&79\%	&82\% \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mohannad AlMousa
Rachid Benlamri
Richard Khoury

"A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph" by Mohannad AlMousa, Rachid Benlamri, and Richard Khoury, arXiv:2101.02875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Lorenzo Lucchese
Mikko Pakkanen
Almut Veraart

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{F1-scores for different models explained in Sec.~. We train each model 3 times. Note that U-Net* stands for the modified U-Net.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccc|c}
 \hline
 Model Name & Run1 & Run2 & Run3 & Average \\ \hline
 U-Net & 0.683 & 0.672 & 0.669 & 0.675 \\
 U-Net + BN & 0.906 & 0.903 & 0.904 & 0.904 \\
 U-Net* & 0.860 & 0.847 & 0.852 & 0.853 \\
 U-Net* + BN & 0.908 & 0.905 & 0.902 & 0.905 \\
 U-Net* + Res1 & 0.910 & 0.906 & 0.901 & 0.906 \\
 U-Net* + Res2 & 0.908 & 0.911 & 0.909 & 0.910 \\
 U-Net* + SE-Res1 & 0.911 & 0.908 & 0.909 & 0.909 \\
 U-Net* + SE-Res2 & 0.912 & 0.911 & 0.911 & \textbf{0.911} \\
 SegNet & 0.907 & 0.910 & 0.909 & 0.909 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Liang Xu
Taro Hatsutani
Xing Liu
Engkarat Techapanurak
Han Zou
Takayuki Okatani

"Pushing the Envelope of Thin Crack Detection" by Liang Xu, Taro Hatsutani, Xing Liu, Engkarat Techapanurak, Han Zou, and Takayuki Okatani, arXiv:2101.03326v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Fernando García-Muñoz
Cristian Duran-Mateluna

"Adaptive Robust Optimization Models for DER Planning in Distribution Networks under Long- and Short-Term Uncertainties" by Fernando García-Muñoz and Cristian Duran-Mateluna, arXiv:2503.17839v1, 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{Number of optimal, sub-optimal, and incorrect plans expressed as a fraction of the total number of plans. Reasoning with the learned axioms improves performance.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 \textbf{Plans} & \textbf{Without} & \textbf{With}\\
 \hline
 Optimal & 0.15 & 0.49 \\
 \hline
 Sub-optimal & 0.31 & 0.51 \\
 \hline
 Incorrect & 0.54 & 0 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Combining Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning in Robotics" by Mohan Sridharan and Tiago Mota, arXiv:2201.10266v1, 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}{lrrrrrrrr}
\toprule
{} & maize & pork & poultry & eggs & maize g. oil & beer & alc. beverages & sweeteners \\
\midrule
Africa N & 17.1 & 0.0 & 0.2 & 0.1 & 0.1 & 0.0 & 0.1 & 0.1 \\
Africa S & 1.9 & 0.0 & 0.1 & 0.2 & 0.0 & 0.0 & 0.1 & 0.1 \\
America S & 1.7 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.1 \\
America N & 0.1 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.1 & 0.1 \\
Asia C & 2.2 & 0.0 & 7.2 & 0.0 & 1.4 & 0.4 & 1.5 & 28.3 \\
Asia E & 1.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\
Asia S & 5.9 & 0.0 & 0.0 & 0.0 & 1.2 & 0.0 & 0.0 & 0.0 \\
Asia SE & 2.6 & 0.0 & 0.0 & 0.0 & 0.1 & 0.0 & 0.0 & 0.0 \\
Asia W & 22.2 & 0.0 & 0.5 & 1.6 & 1.1 & 0.1 & 0.5 & 1.2 \\
Australia & 9.3 & 0.0 & 0.0 & 0.0 & 0.1 & 0.0 & 0.2 & 0.1 \\
Europe E & 5.4 & 0.1 & 0.6 & 0.1 & 4.6 & 1.3 & 2.8 & 1.7 \\
Europe N & 39.1 & 0.0 & 0.0 & 0.0 & 0.0 & 0.1 & 0.1 & 0.3 \\
Eurpe S & 30.1 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.1 & 0.0 \\
Europe W & 15.7 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.1 & 0.1 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Relative loss in \% of different food products in different world regions after a shock to the respective product. These numbers correspond to the right half of each cell in the upper part of fig. 5 in the main text.}
\end{table}
\end{document}

q-fin

Moritz Laber
Peter Klimek
Martin Bruckner
Liuhuaying Yang
Stefan Thurner

"Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability" by Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang, and Stefan Thurner, arXiv:2210.01846v3, 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}{|ccccc|}
 \hline
 Dataset & \# Graphs & Avg. \# Nodes & Avg. \# Edges & \# Classes \\
 \hline\hline
 HIV & 41127 & 25.5 & 27.5 & 2 \\
 \hline
\end{tabular}
\caption{The properties of HIV dastaset from MoleculeNet benchmark.}
\end{table}
\end{document}

cs

"A Python library for efficient computation of molecular fingerprints" by Michał Szafarczyk, Piotr Ludynia, and Przemysław Kukla, arXiv:2403.19718v1, 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}{c|c||c|c||c|c}\toprule
			$\Delta t$ & $10^{-6}$ &
			$T$ & $3\cdot10^4$ &
			$T_{\mathrm{trans}}$ & $2\cdot10^3$
			\\
			\midrule
			$\beta$ & $0.1$ &
			$\nu_0$ & $10^{-1}$ &
			$ $ & \\\bottomrule
		\end{tabular}
\caption{Simulation parameters and hyperparameters. }
\end{table}
\end{document}

math

Jan Bartsch
Ahmed A. Barakat
Simon Buchwald
Gabriele Ciaramella
Stefan Volkwein
Eva M. Weig

"Reconstructing the system coefficients for coupled harmonic oscillators" by Jan Bartsch, Ahmed A. Barakat, Simon Buchwald, Gabriele Ciaramella, Stefan Volkwein, and Eva M. Weig, arXiv:2412.07301v1, licensed under CC BY-SA 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 lowest average out-degree when there is no critical nodes or edges found in the network.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
		\hline
		&ER&SW&SF&QS&QR&RT&RR&HO&OL \\ \hline
		Node&9&3& 23&6&4&8&6&3&22 \\ \hline
		Edge&10&3&24&5&6&8&6&3&22 \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"A Framework of Hierarchical Attacks to Network Controllability" by Yang Lou, Lin Wang, and Guanrong Chen, arXiv:2008.04414v1, 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}{lrr}
 \toprule
 \textbf{Release Cycle} & \textbf{4.2.0} & \textbf{4.4.0} \\
 \midrule
 Performance improvements & 21 & 40 \\
 Percentage of tickets that are improvements & 7.69\% & 10.18\% \\
 Days per performance improvement & 19.62 & 8.80 \\
 Performance regressions & 15 & 13 \\
 Percentage of tickets that are regressions & 5.49\% & 3.31\% \\
 Days per performance regression & 27.47 & 27.08 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Creating a Virtuous Cycle in Performance Testing at MongoDB" by David Daly, arXiv:2101.10231v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Jian-wei Liu
Zheng-ping Ren
Run-kun Lu
Xiong-lin Luo

"GMM Discriminant Analysis with Noisy Label for Each Class" by Jian-wei Liu, Zheng-ping Ren, Run-kun Lu, and Xiong-lin Luo, arXiv:2201.10242v1, 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{Optimal AMMs for Trading Pairs with Various LST Types Based on Capital Efficiency. Concentrated Liquidity Market Maker (CLMM)~ Requires Periodic Rebalancing~.}
\begin{tabular}{|l|l|l|}
\hline
\textbf{Token 1} & \textbf{Token 2} & \textbf{AMMs} \\ \hline
rebase-LST & ETH & Stableswap~ \\
rebase-LST & rebase-LST & Stableswap \\
reward-LST & ETH & Cryptoswap~, CLMM(*) \\
reward-LST & reward-LST & Cryptoswap, CLMM(*) \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

Krzysztof Gogol
Yaron Velner
Benjamin Kraner
Claudio Tessone

"SoK: Liquid Staking Tokens (LSTs) and Emerging Trends in Restaking" by Krzysztof Gogol, Yaron Velner, Benjamin Kraner, and Claudio Tessone, arXiv:2404.00644v3, 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|l|l|l|}
\hline
Image Size & Encoder & \textit{Recall} & Accuracy & IOU \\ \hline
100 x 100 & Resnet 50 & 0.96 & 0.98 & 0.95 \\ \hline
150 x 150 & Resnet 50 & 0.96 & 0.98 & 0.95 \\ \hline
224 x 224 & Resnet 32 & 0.96 & 0.98 & 0.95 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Face Mask Fit Analyser Model Results. IOU refers to the Intersection Over Union (IOU) score.}
\end{table}
\end{document}

eess

Asmit Kumar Singh
Paras Mehan
Divyanshu Sharma
Rohan Pandey
Tavpritesh Sethi
Ponnurangam Kumaraguru

"(Un)Masked COVID-19 Trends from Social Media" by Asmit Kumar Singh, Paras Mehan, Divyanshu Sharma, Rohan Pandey, Tavpritesh Sethi, and Ponnurangam Kumaraguru, arXiv:2011.00052v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|r|}
\hline
\textbf{Negative Pairs Strategy} & \textbf{Linear} \\ \hline \hline
Default & $\mathbf{0.876 \pm 0.013}$ \\ \hline
Same Laterality only & $0.872 \pm 0.011$ \\ \hline
Same Laterality (reweighted) & $0.864 \pm 0.006$ \\ \hline
Same Laterality (appended) & $0.875 \pm 0.006$ \\ \hline
Same Laterality (synthetic) & $0.870 \pm 0.004$ \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Yen Nhi Truong Vu
Richard Wang
Niranjan Balachandar
Can Liu
Andrew Y. Ng
Pranav Rajpurkar

"MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation" by Yen Nhi Truong Vu, Richard Wang, Niranjan Balachandar, Can Liu, Andrew Y. Ng, and Pranav Rajpurkar, arXiv:2102.10663v2, 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}{lccc}
 \hline
 \textbf{Variable} & \textbf{Estimate} & \textbf{Std. Error} & \textbf{p-value} \\
 \hline
 Gender (Female) & -0.0321 & 0.0039 & 2.32e-16 *** \\
 University Fixed Effects & Yes & - & - \\
 Department Fixed Effects & Yes & - & - \\
 log$_{10}$(i10-index) & 0.0094 & 0.0031 & 0.00256 ** \\
 Titles (Associate Professor) & 0.0526 & 0.0049 & $<$2e-16 *** \\
 Titles (Professor) & 0.1760 & 0.0057 & $<$2e-16 *** \\
 Working Years & -0.0015 & 0.0002 & 6.31e-10 *** \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Fixed Effects Regression Controlling for Institutional and Department-Level Factors}
\end{table}
\end{document}

q-fin

"Estimation of Gender Wage Gap in the University of North Carolina System" by Zihan Zhang and Jan Hannig, arXiv:2505.24078v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of the Hard test sets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Dataset} & \textbf{Positive} & \textbf{Negative} & \textbf{Neutral} & \textbf{Total (\% of Test Set)} \\
\hline
\textbf{Laptop} & 31 & 24 & 46 & 101 (15.8 \%) \\
\hline
\textbf{Restaurants} & 81 & 60 & 83 & 224 (20.0 \%) \\
\hline
\textbf{Men's T-shirt} & 23 & 24 & 1 & 48 (10.2 \%) \\
\hline
\textbf{Television} & 43 & 40 & 19 & 102 (10.8 \%) \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Rajdeep Mukherjee
Shreyas Shetty
Subrata Chattopadhyay
Subhadeep Maji
Samik Datta
Pawan Goyal

"Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild" by Rajdeep Mukherjee, Shreyas Shetty, Subrata Chattopadhyay, Subhadeep Maji, Samik Datta, and Pawan Goyal, arXiv:2101.09449v1, 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{Clustering algorithms used for the \textit{C. elegans} homeobox gene network}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l}
\textbf{Method} & \textbf{Source} & \textbf{Parameters} \\ \hline
Geometric modularity & this paper & $-0.5<log_{10}\tau<0$, $\kappa_0$=0 \\
Modularity & & Resolution parameter = = 1, 0.1, 0.01 \\
Wavelet & & $2<s<5$ \\
Markov stability & & $-0.5<log_{10}\tau<0$ \\
Markov clustering & & expansion=2, inflation=2, loop=1, iterations=100, threshold=0.001\\
PercoMVC & & \\
Label propagation & & \\
walktrap & & \\
Spinglass & & \\
EdMot & & \\
Non-negative matrix factorisation & & \\
Greedy modularity & & \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature" by Adam Gosztolai and Alexis Arnaudon, arXiv:2106.05847v3, 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{Location of estimated change points from the video data set for the ``\textit{first person walks out of the lobby} (so-called action 1)" and ``\textit{second person walks into the lobby} (so-called action 2)" using LOCAL method. Note that the true value is 116 for action 1 and 174 for action 2.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
Action & Pixel & Estimated change points based on \textbf{LOCAL} method \\ \hline\hline
1 & 700 & 84 \\
& 702 & 79 \\
 & 731 & 91 \\
& 762 & 96 \\
& 764 & 98 \\
\hline
2 & 48 & 260 \\
& 78 & 278 \\
& 110 & 276 \\
& 174 & 170 \\
& 209 & 203 \\
& 241 & 142 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Sepideh Mosaferi
Abolfazl Safikhani
Peiliang Bai

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l}
 authblk & babel & cjk & dvips \\
 epsf & epsfig & euler & float \\
 fullpage & geometry & graphics & hyperref \\
 layout & linespread & lmodern & maltepaper \\
 navigator & pdfcomment & pgfplots & psfig \\
 pstricks & t1enc & titlesec & tocbind \\
 ulem
\end{tabular}
\end{adjustbox}
\caption{LaTeX style packages that must not be used.}
\end{table}
\end{document}

stat

Dong Li
Ruoming Jin
Zhenming Liu
Bin Ren
Jing Gao
Zhi Liu

"Towards Reliable Item Sampling for Recommendation Evaluation" by Dong Li, Ruoming Jin, Zhenming Liu, Bin Ren, Jing Gao, and Zhi Liu, arXiv:2211.15743v3, 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
\begin{tabular}{l|ccc|ccc}
\hline
\multirow{2}{*}{Run (Retriever)} & \multicolumn{3}{c|}{Cutoff = 10} & \multicolumn{3}{c}{Cutoff = 20} \\ \cline{2-7}
 & DEJA-VU & nDCG & Recall & DEJA-VU & nDCG & Recall \\ \hline
ANCE & 0.947 & 0.645 & 0.145 & 0.939 & 0.616 & 0.240 \\
BM25 & 0.885 & 0.506 & 0.129 & 0.926 & 0.491 & 0.201 \\
SBERT & 0.940 & 0.693 & 0.148 & 0.956 & 0.656 & 0.232 \\
SPLADE++ & \underline{0.948} & \textbf{0.731} & \textbf{0.172} & \underline{0.974} & \textbf{0.717} & \textbf{0.289} \\
TCT-ColBERT &\textbf{0.974} & \underline{0.720} & \underline{0.167} & \textbf{0.981} & \underline{0.680} & \underline{0.261} \\
uniCOIL & 0.914 & 0.643 & 0.162 & 0.966 & 0.614 & 0.247 \\ \hline
\end{tabular}
\caption{Scores of DEJA-VU, nDCG, and Recall for the six runs on TRDL 19 at cutoffs of 10 and 20 respectively. Bold indicates the highest score for a run, while underscore denotes the second highest score.}
\end{table}
\end{document}

cs

Nuo Chen
Jiqun Liu
Hanpei Fang
Yuankai Luo
Tetsuya Sakai
Xiao-Ming Wu

"Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability" by Nuo Chen, Jiqun Liu, Hanpei Fang, Yuankai Luo, Tetsuya Sakai, and Xiao-Ming Wu, arXiv:2403.18462v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Andrey Sarantsev
Angel Piotrowski
Ian Anderson

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Correlation coefficients for the logarithmic returns of media attention and absolute values of logarithmic returns of the Bitcoin price, computed only for the time moments classified as jumps.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
& Pearson & Kendall's \(\tau\) & Spearman's \(\rho\) \\ \hline
Coefficient & 0.44357 & 0.3026 & 0.43869 \\
p-value & 1.1204\(\cdot 10^{-17}\) & 1.2176\(\cdot 10^{-16}\) & 2.77314\(\cdot 10^{-17}\) \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Modelling the Bitcoin prices and the media attention to Bitcoin via the jump-type processes" by Ekaterina Morozova and Vladimir Panov, arXiv:2210.13824v1, 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{Running Time Summary in Seconds (Numerical Features: Standardized; Categorical Features: One-hot encoding).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\toprule
{Models} & Total Fits & Tuning Time& Average Tuning & Fitting Time (Best Model) \\
 \midrule
LightGBM & 2000 & 213.44 & 0.11 & 0.46\\
Linear Regression & 150 & 44.39 & 0.30 & 0.82\\
MLP & 40 & 2147.05 & 53.68 & 65.81\\
Random Forest & 1200 & 3633.05 & 3.03 & 115.67 \\
XGBoost & 5760 & 13320.13 & 2.31 & 4.16 \\
\bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Yinpu Li
Siqi Mao
Yaping Yuan
Ziren Wang
Yixin Kang
Yuanxin Yao

"Beyond Tides and Time: Machine Learning Triumph in Water Quality" by Yinpu Li, Siqi Mao, Yaping Yuan, Ziren Wang, Yixin Kang, and Yuanxin Yao, arXiv:2309.16951v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 \textbf{Aspect} & \textbf{\# (\%) of Instances}\\
 \midrule
 Performance& 348 (7.7) \\
 Usability &1,437 (31.8)\\
 Security &163 (3.6)\\
 Bug&189 (4.2)\\
 Community&93 (2.1)\\
 Compatibility&93 (2.1)\\
 Documentation&256 (5.6)\\
 Legal&50 (1.1)\\
 Portability&70 (1.5)\\
 OnlySentiment&348 (7.7)\\
 Others&1,699 (37.6)\\\hline
 Total &4,522\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

chengran Yang
Bowen Xu
Junaed younus Khan
Gias Uddin
Donggyun Han
Zhou Yang
David Lo

"Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?" by chengran Yang, Bowen Xu, Junaed younus Khan, Gias Uddin, Donggyun Han, Zhou Yang, and David Lo, arXiv:2201.11327v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Properties of audio samples from dataset Part 1.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 Label & \thead{No.\\ of samples} & \thead{Total\\ duration (s)} & \thead{Mean\\ duration (s)} & \thead{Std.\\ deviation of\\ duration (s)} \\
 \hline
 0 & 1000 & 8255.93 & 8.26 & 2.75 \\
 \hline
 1 & 1000 & 6426.55 & 6.43 & 2.08	 \\
 \hline
 2 & 1000 & 6359.18	& 6.36	 & 	2.12 \\
 \hline
 3 & 1000 & 8136.84 & 8.14 & 	2.56 \\
 \hline
 4 & 1000 & 5615.91 & 	5.62 & 1.91 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mahieyin Rahmun
Rafat Hasan Khan
Tanjim Taharat Aurpa
Sadia Khan
Zulker Nayeen Nahiyan
Mir Sayad Bin Almas
Rakibul Hasan Rajib
Syeda Sakira Hassan

"Synthetic Speech Classification: IEEE Signal Processing Cup 2022 challenge" by Mahieyin Rahmun, Rafat Hasan Khan, Tanjim Taharat Aurpa, Sadia Khan, Zulker Nayeen Nahiyan, Mir Sayad Bin Almas, Rakibul Hasan Rajib, and Syeda Sakira Hassan, arXiv:2412.13279v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l}
Model & $r_ \kappa(SBM)$* & $r_ \kappa(IM)$* \\
\hline
${\rm LS}(d=1)$ & 81 & 222\\
${\rm LS}(d=2)$ & 77 & 212\\
${\rm LS}(d=3)$ & 87 & 297\\
${\rm DS}(q=0.3)$ & 239 & 1637\\
${\rm DS}(q=0.5)$ & 98 & 622\\
${\rm DS}(q=0.7)$ & 55 & 707\\
${\rm WS}(K=\{1\}, p)$ & $-$ & $-$\\
${\rm WS}(K=\{1,2\}, p=0.1)$ & 115 & 184\\
${\rm WS}(K=\{1,2\}, p=0.2)$ & 271 & 574\\
${\rm WS}(K=\{1,2\}, p=0.3)$ & 2377 & 2510\\
${\rm WS}(K=\{1,2,3\}, p=0.1)$ & 59 & 75\\
${\rm WS}(K=\{1,2,3\}, p=0.2)$ & 81 & 151\\
${\rm WS}(K=\{1,2,3\}, p=0.3)$ & 168 & 418\\
${\rm WS}(K=\{1,2,3\}, p=0.4)$ & 723 & 1783\\
${\rm BA}(m=2)$ & $-$ & $-$
\end{tabular}
\end{adjustbox}
\caption{Ramsey community numbers for different network models and $\epsilon=0.05$, when using the stochastic block model correcting for the degree sequence (SBM) or regularized Infomap (IM). $-$Does not have the emergent communities property. *An estimate, not an exact number, with variations in the last digit due to the stochastic nature of the network generative models and the communities detection method.}
\end{table}
\end{document}

math

"Emergence of network communities driven by local rules" by Alexei Vazquez, arXiv:2501.17042v4, 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}{cccccc}
\toprule
Method & CRPS & MAE & RMSE & Coverage & Width \\ \hline
Raw ensemble & 1.017 & 1.255 & 1.730 & 63.071 & 2.922 \\ \hline
EMOS & 0.718 & \textbf{0.985} & 1.387 & 95.573 & \textbf{5.441} \\
DVQR & 0.717 & 0.985 & 1.366 & \textbf{96.339} & 5.990 \\ \hline
GAM-DVQR-C & 0.719 & 0.989 & 1.358 & 96.499 & 5.994 \\
GAM-DVQR-T1 & \textbf{0.713} & 0.985 & \textbf{1.350} & 96.594 & 5.889\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Verification scores aggregated over all stations and time points in the validation period. Bold values represent the best value for each score.}
\end{table}
\end{document}

stat

"D-Vine GAM Copula based Quantile Regression with Application to Ensemble Postprocessing" by David Jobst, Annette Möller, and Jürgen Groß, arXiv:2309.05603v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

C. Biliotti
F. J. Bargagli-Stoffi
N. Fraccaroli
M. Puliga
M. Riccaboni

"Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic" by C. Biliotti, F. J. Bargagli-Stoffi, N. Fraccaroli, M. Puliga, and M. Riccaboni, arXiv:2212.01705v3, 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{True negative and positive rates (TNR/TPR) and negative and positive predicted values (NPV/PPV) estimated using 70/30\% cross-validation and 1,000 Monte Carlo splits. The full model which yields larger estimated propensities for many units yields a marked improvement in the true positive rate with a negligble decline in the true negative rate. Furthermore, the full model yields marked improvements in both the negative and positive predicted values. This implies a betterment in the proportion of correctly classified treatments and the proportion of classified treatments (controls) which are actually treatments (controls).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\toprule
 & TNR & TPR & NPV & PPV\\
\midrule
Restricted Model & 0.987 & 0.089 & 0.834 & 0.599\\
Full Model & 0.974 & 0.185 & 0.847 & 0.612\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"The causal effect of a timeout at stopping an opposing run in the NBA" by Connor Gibbs, Ryan Elmore, and Bailey Fosdick, arXiv:2011.11691v3, 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{Descriptive Analysis - Online vs. Offline Prices at the SKU-Date Level}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \tabularnewline \midrule \midrule
 Dependent Variable: & \multicolumn{3}{c}{ln(SKU\_Price)}\\
 & Dry Dog Food & Dog Hygiene & Cat Hygiene \\
 Model: & (1) & (2) & (3) \\
 \midrule
 \emph{Variables}\\
 isOnline & -0.0322$^{***}$ & -0.0361$^{*}$ & -0.0607$^{***}$\\
 & (0.0022) & (0.0135) & (0.0087)\\
 \midrule
 \emph{Fixed-effects}\\
 SKU & Yes & Yes & Yes\\
 DATE & Yes & Yes & Yes\\
 \midrule
 \emph{Fit statistics}\\
 Observations & 554,191 & 328,323 & 244,723\\
 R$^2$ & 0.95202 & 0.98493 & 0.91763\\
 Within R$^2$ & 0.01819 & 0.00912 & 0.02441\\
 \midrule \midrule
 \multicolumn{4}{l}{\emph{Clustered (SKU \& DATE) standard-errors in parentheses}}\\
 \multicolumn{4}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05}}\\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"How does online shopping affect offline price sensitivity?" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Davide Liessi
Enrico Santi
Rossana Vermiglio
Mayank Thakur
Hil G. E. Meijer
Francesca Scarabel

"New developments in MatCont: delay equation importer and Lyapunov exponents" by Davide Liessi, Enrico Santi, Rossana Vermiglio, Mayank Thakur, Hil G. E. Meijer, and Francesca Scarabel, arXiv:2504.12785v2, 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{Summary statistics for the ARBAE of VIX calls across all calibration dates. The table reports key metrics of ARBAE, including the mean, standard deviation (SD), minimum, maximum, and the 95th and 99th percentiles. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
 \toprule
 Model & mean ARBAE & SD & min & max & $95\%$ & $99\%$ \\ \midrule
 One-factor & $9.08\times 10^{-5}$ & $0.00051$ & $0$ & $0.00987$ & $5.0\times 10^{-4}$ & $0.00225$\\
 Two-factor & $7.56\times 10^{-5}$ & $ 0.00051$ & $0$ & $0.00992$ & $2.33\times 10^{-4}$ & $0.00174$\\
 \bottomrule

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

q-fin

Nelson Kyakutwika
Mesias Alfeus
Erik Schlögl

"Pricing and Calibration of VIX Derivatives in Mixed Bergomi Models via Quantisation" by Nelson Kyakutwika, Mesias Alfeus, and Erik Schlögl, arXiv:2506.23409v1, 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 list of the number of images used in the test, validation, and the training sets of the generic database}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
Gesture Class & Test Set & Validation Set & Training Set & Total\\ \hline
SingleOne & 337 & 151 & 2886 & 3374 \\ \hline
SingleTwo & 376 & 169 & 3218 & 3763 \\ \hline
SingleThree & 376 & 169 & 3223 & 3768 \\ \hline
SingleFour & 376 & 169 & 3222 & 3767 \\ \hline
SingleFive & 375 & 169 & 3211 & 3755 \\ \hline
SingleSix & 375 & 169 & 3213 & 3757 \\ \hline
SingleSeven & 377 & 169 & 3227 & 3773 \\ \hline
SingleEight & 338 & 152 & 2890 & 3380 \\ \hline
Total & 2930 & 1317 & 25090 & 29337 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mohammad Mahmudul Alam
Mohammad Tariqul Islam
S. M. Mahbubur Rahman

"Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection" by Mohammad Mahmudul Alam, Mohammad Tariqul Islam, and S. M. Mahbubur Rahman, arXiv:2101.02047v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|}
\cline{1-3}
Parameter & Symbol & Value \\
\hline
\multicolumn{3}{|l|}{\textit{Fiber parameters}} \\
\hline
Attenuation Coefficient @ 1550 nm & $\alpha$ & $\mathrm{0.165 dB/km}$\\
Attenuation Coefficient @ 1590 nm & $\alpha$ & $\mathrm{0.171 dB/km}$\\
Attenuation Coefficient @ 1495 nm & $\alpha$ & $\mathrm{0.177 dB/km}$\\
Attenuation Coefficient @ 1410 nm & $\alpha$ & $\mathrm{0.217 dB/km}$\\
Dispersion Coefficient & $D$ & $\mathrm{21.3~{ps}/{nm}/{km}}$\\
Dispersion Slope & $S$ & $\mathrm{0.067~{ps}/{nm}^{2}/{km}}$ \\
Raman gain coefficient @ 13 THz &$C_r$& $\mathrm{0.028}$ $\mathrm{(1/W/km)}$\\
Nonlinear coefficient & $\gamma$ & $\mathrm{1.2~ W^{-1}km^{-1}}$ \\
Optical Reference Bandwidth & ${\Delta}_{ref}$ & $\mathrm{12.5 GHz}$ \\
Span length & $L$ & $\mathrm{100 km}$\\
\hline
\multicolumn{3}{|l|}{\textit{Amplifier parameters}} \\
\hline
Noise figure & ${F}$ & $\mathrm{5 dB} $\\
\hline
\multicolumn{3}{|l|}{\textit{Signal parameters}} \\
\hline
Central frequency WDM & $v$ & $\mathrm{1480 nm (200.67 THz)}$\\
Symbol Rate & $R$ & $\mathrm{12.5 GBaud}$ \\
Channel spacing & $\Delta f$ & $\mathrm{12.5 GHz}$\\
Optical bandwidth & BW & $\mathrm{2720 FSUs (34 THz)}$\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Fiber parameters for maximum reach calculation}
\end{table}
\end{document}

eess

"Maximum transmission reach for optical signals in elastic optical networks employing band division multiplexing" by Esteban Paz and Gabriel Saavedra, arXiv:2011.03671v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The statistics comparison of several audio-caption datasets. Quan denotes the number of captions, Len represents the average caption length in words, and Voc indicates the vocabulary size. Caption generation methods include human annotation (H), audio model (A), visual model (V), and language model (L).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\toprule
\textbf{Dataset} & \textbf{Quan} & \textbf{Len} & \textbf{Voc} & \textbf{Source} \\ \midrule
Clotho & $30$~K & $11$ & $4$~K & H \\
AudioCaps & $57$~K & $9$ & $5$~K & H \\ \midrule
LAION-Audio-630K & $630$~K & $7$ & $311$~K & L\\
WavCaps & $400$~K & $8$ & $24$~K & L \\
Auto-ACD & $1.5$~M & $18$ & $20$~K & A+V+L \\
Sound-VECaps & $1.6$~M & $40$ & $50$~K & A+V+L \\
AudioSetCaps & $1.9$~M & $28$ & $21$~K & A+L \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jisheng Bai
Haohe Liu
Mou Wang
Dongyuan Shi
Wenwu Wang
Mark D. Plumbley
Woon-Seng Gan
Jianfeng Chen

"AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Large Audio and Language Models" by Jisheng Bai, Haohe Liu, Mou Wang, Dongyuan Shi, Wenwu Wang, Mark D. Plumbley, Woon-Seng Gan, and Jianfeng Chen, arXiv:2411.18953v1, 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{Marginal effects of policy variables $x_{t-1}$ and $\Delta_t$ on $\mu_{t+\tau}$, $\tau\geq0$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
			Model & Marginal effect on $\mu_{t+\tau}$\\
			\hline \\

			MAP& $\kappa \psi^\tau$ \\
			X--MAP& $\kappa \beta^\tau$ \\
			L-MAP & $2\varsigma_{t+\tau} \kappa \psi^\tau \frac{exp(\xi_{t+\tau})}{(1+exp(\xi_{t+\tau}))^2}$ \\
			P-MAP &$\kappa \psi^\tau \varsigma_{t+\tau}$ \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Demetrio Lacava
Giampiero M. Gallo
Edoardo Otranto

"Unconventional Policies Effects on Stock Market Volatility: A MAP Approach" by Demetrio Lacava, Giampiero M. Gallo, and Edoardo Otranto, arXiv:2010.08259v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The COVID-19 pandemic's impact on returns, absolute returns (AR), and volatility increment (VI) time series. The meanings of the symbols are as follows. $\bigcirc$: impact observed, $\times$: no impact observed, $\triangle$: impact unclear. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccc}
\toprule
 & \textbf{DAX} & \textbf{Nikkei 225} & \textbf{SSE} & \textbf{VIX} & \textbf{Bitcoin} & \textbf{Ethereum} \\
\midrule
Returns $h(2)$ &negative &$\bigcirc$ &$\times$ & positive & $\times$ & $\times$ \\
Returns $\Delta h(5)$ &$\bigcirc$ &$\bigcirc$ & negative & positive & negative & negative \\
AR $h(2)$ &negative &negative & negative & negative & $\times$ & $\times$ \\
AR $\Delta h(5)$ &$\bigcirc$ &negative & $\triangle$ & positive & $\triangle$ & negative \\
VI $h(2)$ &$\times$ &$\times$ &$\times$ & $\times$ & $\times$ & $\times$\\
VI $\Delta h(5)$ &$\times$ &$\times$ &$\times$ & $\times$ & $\times$ & $\times$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Impact of the COVID-19 pandemic on the financial market efficiency of price returns, absolute returns, and volatility increment: Evidence from stock and cryptocurrency markets" by Tetsuya Takaishi, arXiv:2504.18960v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A Randomization-Based Method for Evaluating Time-Varying Treatment Effects" by Sangjin Lee and Kwonsang Lee, arXiv:2412.09697v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
\toprule
 & Val. Acc. (\%) & Diff (pp.) & Parameters \\ \midrule
Resnet18 & 39.9 $\pm$ 0.3 & 0.0 & 11.2M \\
WideResnet18 & 42.3 $\pm$ 0.3 & 2.4 & 25.3M \\
DeepResnet18 & 43.1 $\pm$ 0.4 & 3.2 & 13.3M \\
DWResnet18 & 43.7 $\pm$ 0.1 & 3.8 & 19.0M \\ \midrule
GapCatNoSSAL & 40.2 $\pm$ 0.3 & 0.3 & 15.6M \\
CatFCNoSSAL & 35.3 $\pm$ 0.8 & -4.6 & 13.6M \\ \midrule
\textbf{LinearComb} & \textbf{44.1 $\pm$ 0.1} & \textbf{4.2} & 12.8M \\
\textbf{SSAL x1} & \textbf{45.8 $\pm$ 0.2} & \textbf{5.9} & 12.6M \\
\textbf{SSAL x3} & \textbf{50.0 $\pm$ 0.4} & \textbf{10.1} & 15.6M \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Baselines for SSAL models on TinyImagenet. Models that have a deeper architecture, wider layers, or lack the SSAL objective fail to reach the level of accuracy of SSAL models.}
\end{table}
\end{document}

cs

Sebastian Palacio
Philipp Engler
Jörn Hees
Andreas Dengel

"Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks" by Sebastian Palacio, Philipp Engler, Jörn Hees, and Andreas Dengel, arXiv:2101.03057v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
\hline
Network & Max Novelty Score \\
\hline\hline
GAN & 0.029\\
CAN & 0.013 \\
Unrolled CAN & \textbf{0.043}\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Results. Unrolled CAN is better.}
\end{table}
\end{document}

eess

"Unrolled Creative Adversarial Network For Generating Novel Musical Pieces" by Pratik Nag, arXiv:2501.00452v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The impact of channel shuffle, dot-product, and upsampling methods in SubEC on CVIU database, with the best performance values highlighted in bold. The Flops is calculated with input tensor shape $3\times\ 32\times 32$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc|cccc}
\toprule
Model & Params/M & Flops/G & Speed/ms & SRCC $\uparrow$ & KRCC $\uparrow$ & PLCC $\uparrow$ & RMSE $\downarrow$ \\
\midrule
w/o Channel Shuffle & 2.220 & 0.179 & 10.981 & 0.970 & 0.853 & 0.971 & 0.654 \\
Element-wise Add & 2.220 & 0.179 & 10.690 & 0.963 & 0.838 & 0.966 & 0.699 \\
Transposed Conv & 2.194 & 0.160 & 10.905 & 0.975 & 0.871 & 0.979 & 0.528\\
Bilinear & 2.193 & 0.156 & 9.695 & 0.974 & 0.864 & 0.978 & 0.602 \\
Bicubic & 2.193 & 0.156 & 10.336 & 0.971 & 0.857 & 0.976 & 0.655 \\
PBAN (Sub-pixel Conv) & 2.220 & 0.179 & 11.010 & \textbf{0.978} & \textbf{0.872} & \textbf{0.981} & \textbf{0.397} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study of different filter dimensions N in the GDFG. \textbf{Bold} represents the best result.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\hline
Dimensions $N$ & PSNR$\uparrow$ & SSIM$\uparrow$ & LPIPS$\downarrow$ & G-PSNR$\uparrow$ & S-PSNR$\uparrow$ \\ \hline
2 & 27.891 & 0.899 & 0.0426 & 24.257 & 23.138 \\
3 & 27.958 & 0.901 & 0.0426 & 24.435 & \textbf{23.355} \\
4 & \textbf{28.030} & \textbf{0.903} & \textbf{0.0422} & \textbf{24.509} & 23.300 \\
5 & 27.991 & 0.901 & 0.0423 & 24.415 & 23.275 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Minglong Xue
Aoxiang Ning
Shivakumara Palaiahnakote
Mingliang Zhou

"DFDNet: Dynamic Frequency-Guided De-Flare Network" by Minglong Xue, Aoxiang Ning, Shivakumara Palaiahnakote, and Mingliang Zhou, arXiv:2507.17489v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.