\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Table Type Styles}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Table}&\multicolumn{3}{|c|}{\textbf{Table Column Head}} \\
\cline{2-4}
\textbf{Head} & \textbf{\textit{Table column subhead}}& \textbf{\textit{Subhead}}& \textbf{\textit{Subhead}} \\
\hline
copy& More table copy$^{\mathrm{a}}$& & \\
\hline
\multicolumn{4}{l}{$^{\mathrm{a}}$Sample of a Table footnote.}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jonghyun Lee
Yongqin Wang
Rachit Rajat
Murali Annavaram

"Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training" by Jonghyun Lee, Yongqin Wang, Rachit Rajat, and Murali Annavaram, arXiv:2501.11771v3, 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{Accuracy of Llama2-7B for four arithmetic reasoning finetuned on mixed dataset. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|ccccc}
 \toprule
 & & \multicolumn{5}{c}{\textbf{Arithmetic reasoning}} \\
 \textbf{Method} & \bf Bit & \textbf{GSM8K} & \textbf{SVAMP} & \textbf{MAWPS} & \textbf{AQuA} & \textbf{Avg.} $\uparrow$ \\
 \midrule
 LoftQ & 4 & 36.8 & \bf 55.3 & 83.2 & 24.8 & 50.0 \\
 CLoQ & 4 & \bf 38.8 & 54.9 & \bf 85.7 & \bf 25.2 & \bf 51.2 \\
 \midrule
LoftQ & 2 & 21.8 & 40.7 & 74.4 & 24.0 & 40.2 \\
CLoQ & 2 & \bf 27.2 & \bf 47.0 & \bf 80.3 & \bf 24.4 & \bf 44.7 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yanxia Deng
Aozhong Zhang
Selcuk Gurses
Naigang Wang
Zi Yang
Penghang Yin

"CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization" by Yanxia Deng, Aozhong Zhang, Selcuk Gurses, Naigang Wang, Zi Yang, and Penghang Yin, arXiv:2501.18475v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cllclllclll}
\hline
\multicolumn{3}{c}{\small{Control Info.}} & \multicolumn{4}{c}{\small{Rhythm}} & \multicolumn{4}{c}{\small{Pitch}} \\ \hline
\multicolumn{3}{c}{\small{Pitch Acc.}} & \multicolumn{4}{c}{\small{0.189}} & \multicolumn{4}{c}{\small{\textbf{0.881}}} \\
\multicolumn{3}{c}{\small{Rhythm Acc.}} & \multicolumn{4}{c}{\small{\textbf{0.973}}} & \multicolumn{4}{c}{\small{0.848}} \\ \hline
\end{tabular}
\caption{The accuracy of the virtual control experiment.}
\end{table}
\end{document}

eess

Ke Chen
Cheng-i Wang
Taylor Berg-Kirkpatrick
Shlomo Dubnov

"Music SketchNet: Controllable Music Generation via Factorized Representations of Pitch and Rhythm" by Ke Chen, Cheng-i Wang, Taylor Berg-Kirkpatrick, and Shlomo Dubnov, arXiv:2008.01291v1, 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}{cccc}
\hline
 & Estimate & Bootstrap Mean&Quantile CI (95\%)\\ \hline
Indirect Effect & 0.2600
 & 0.2531
&(0.1279, 0.3752)
\\ \hline
Direct Effect & -0.0223
 & -0.0178
&(-0.2496, 0.1688)
\\ \hline
Total Effect & 0.2378 & 0.2353&(-0.0079, 0.4346) \\ \hline
\end{tabular}
\caption{Summary of the effects in OLS (gene sites from other papers) with mean and confidence interval (CI) under 300-sample bootstrap.}
\end{table}
\end{document}

stat

Shi Bo
AmirEmad Ghassami
Debarghya Mukherjee

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation results of six estimands for different convergence rates of nuisance parameters }
\begin{tabular}{cccccccccc}
\toprule
 & \multicolumn{3}{c}{$\alpha = 0.3$} & \multicolumn{3}{c}{$\alpha = 0.1$} & \multicolumn{3}{c}{SuperLearner} \\
\cmidrule(l{3pt}r{3pt}){2-4} \cmidrule(l{3pt}r{3pt}){5-7} \cmidrule(l{3pt}r{3pt}){8-10}
Estimand & Bias & RMSE & Coverage & Bias & RMSE & Coverage & Bias & RMSE & Coverage\\
\midrule
$\tilde{\Psi}$ & 0.00 & 0.07 & 0.96 & 0.08 & 0.15 & 0.91 & 0.01 & 0.06 & 0.94\\
$\Omega_1$ & 0.01 & 0.02 & 0.95 & 0.03 & 0.03 & 0.63 & 0.00 & 0.02 & 0.96\\
$\Omega_2$ & 0.00 & 0.04 & 0.95 & 0.22 & 0.24 & 0.38 & 0.00 & 0.02 & 0.95\\
$\Omega_3$ & 0.00 & 0.02 & 0.94 & 0.05 & 0.05 & 0.65 & 0.00 & 0.01 & 0.95\\
$\Omega_4$ & 0.00 & 0.04 & 0.95 & 0.09 & 0.13 & 0.70 & -0.01 & 0.05 & 0.93\\
$\Omega_5$ & 0.00 & 0.04 & 0.94 & 0.00 & 0.07 & 0.96 & 0.00 & 0.04 & 0.94\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Max Rubinstein
Denis Agniel
Larry Han
Marcela Horvitz-Lennon
Sharon-Lise Normand

"Bounding causal effects with an unknown mixture of informative and non-informative censoring" by Max Rubinstein, Denis Agniel, Larry Han, Marcela Horvitz-Lennon, and Sharon-Lise Normand, arXiv:2411.16902v2, 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}{ccccc}
		\hline
		Scenario & $(\beta_{0},\beta_{1},\beta_{2},\beta_{3},\beta_{4},\beta_{5},\beta_{6})$ & $\pi_{(0)}$ & $\pi_{(1)}$ & RD \\ \hline
		4 &$( - 4, 2, 4.2, -3, 1, -0.2, 0.2)$ & $0.17$ & $0.28$ & $0.11$ \\
		5 &$(-2.2, 0.7, 0, 0, 0, 0, 0 )$ & $0.10$ & $0.18$ & $0.08$ \\
		\hline
	\end{tabular}
\caption{True parameters for simulation under model misspecification.}
\end{table}
\end{document}

stat

"Covariate adjustment and estimation of difference in proportions in randomized clinical trials" by Jialuo Liu and Dong Xi, arXiv:2308.15688v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A Deep Learning Synthetic Likelihood Approximation of a Non-stationary Spatial Model for Extreme Streamflow Forecasting" by Reetam Majumder and Brian Reich, arXiv:2212.07267v2, 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
 Label/Colour in Figure~ & Initial parameter assignment & Initial objective value \\
 \hline
 1/Green & $h = 0.015$, $p = 0.381$ & 0.37905 \\
 \hline
 2/Blue & $h= 0.015$, $p = 0.385$ & 0.37905 \\
 \hline
 3/Red & $h= 0.02$, $p = 0.375$ & 0.37905 \\
 \hline
 4/Purple & $h = 0.004$, $p = 0.3875$ & 0.37905 \\
 \hline
 5/Light green & $h = 0$, $p = 0.4$ & 0.3791 \\
 \hline
 6/Orange & $h = 0$, $p = 0.381$ & 0.3791\\
 \hline
 7/Light blue & $h = 0.03$, $p = 0.375$ & 0.3794 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Dual program output II \\ $N,T ,R= 25000,7000,10$, and $(q_1,q_2) = (-0.02,0.02)$}
\end{table}
\end{document}

math

Erdős' minimum overlap problem

"Erdős' minimum overlap problem" by Ethan Patrick White, arXiv:2201.05704v1, 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{Clustering Performance for Simulation Studies}
\begin{tabular}{cccc}
		\toprule
	 Scenario&n& $\hat{K}$& S.D. of $\hat{K}$ \\
		\midrule
		 Scenario 1 & $n=20$ &1.046& 0.210 \\
		&$n=30$&1.036&0.186 \\
		 Scenario 2 &$n=20$ &2.168& 0.417 \\
		&$n=30$&2.131&0.346 \\
 Scenario 3 &$n=20$ &1.945& 0.516 \\
		&$n=30$&2.105&0.376 \\
		 Scenario 4 &$n=20$ &2.661& 0.612 \\
		&$n=30$&2.929&0.559 \\
		 Scenario 5 &$n=20$ &1.026& 0.159 \\
		&$n=30$&1.020&0.140 \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

stat

"Mixture of Finite Mixtures Model for Basket Trial" by Junxian Geng and Guanyu Hu, arXiv:2011.04135v1, 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{ Inference time performance: Mean execution time and standard deviation (in seconds) over the test set for ALIIOS, TSMDL, and CHaRNet. \textbf{Bold} denotes the best result for each device.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
\toprule
Model & CPU (s) & GPU (s) \\
\midrule
ALIIOS & 612.13 $\pm$ 24.27 & 24.70 $\pm$ 3.54 \\
TSMDL & 3.89 $\pm$ 0.85 & 3.55 $\pm$ 0.81 \\
CHaRNet & \textbf{0.86 $\pm$ 0.06} & \textbf{0.24 $\pm$ 0.03} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

José Rodríguez-Ortega
Francisco Pérez-Hernández
Siham Tabik

"CHaRM: Conditioned Heatmap Regression Methodology for Accurate and Fast Dental Landmark Localization" by José Rodríguez-Ortega, Francisco Pérez-Hernández, and Siham Tabik, arXiv:2501.13073v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Kewin Pączek
Damian Jelito
Marcin Pitera
Agnieszka Wyłomańska

"Goodness-of-fit tests for the one-sided Lévy distribution based on quantile conditional moments" by Kewin Pączek, Damian Jelito, Marcin Pitera, and Agnieszka Wyłomańska, arXiv:2311.15359v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccrrrrrr}
 \toprule
 \multirow{2}{*}{Input} & \multirow{2}{*}{Class} & \multicolumn{3}{c}{Multi} & \multicolumn{3}{c}{Binary } \\
 \cmidrule(lr){3-5} \cmidrule(lr){6-8}
 & & P & R & $F_1$ & P & R & $F_1$ \\
 \midrule
 \multirow{3}{*}{Survey} & 0 & 0.62 & 0.69 & 0.65 & 0.74 & 0.82 & 0.78 \\
 & 1 & 0.27 & 0.21 & 0.24 & 0.61 & 0.49 & 0.54 \\
 & 2 & 0.55 & 0.59 & 0.57 & \\
 \cmidrule(lr){2-8}
 & Macro Avg. & 0.48 & 0.50 & 0.48 & 0.67 & 0.66 & 0.66 \\

 \midrule

 Survey & 0 & 0.57 & 0.57 & 0.57 & 0.81 & 0.85 & 0.83 \\
 + & 1 & 0.31 & 0.24 & 0.27 & 0.71 & 0.64 & 0.68 \\
 Data & 2 & 0.54 & 0.64 & 0.59 & \\
 \cmidrule(lr){2-8}
 & Macro Avg. & 0.47 & 0.48 & 0.48 & 0.76 & 0.75 & 0.75 \\

 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Addiction prediction performance of MLP models using different input sources. In the multi-label setting, class 0/1/2 represents LLA/MLA/HLA respectively. In the binary setting, class 0 represents LLA+MLA while class 1 represents HLA.}
\end{table}
\end{document}

cs

Cai Yang
Sepehr Mousavi
Abhisek Dash
Krishna P. Gummadi
Ingmar Weber

"Studying Behavioral Addiction by Combining Surveys and Digital Traces: A Case Study of TikTok" by Cai Yang, Sepehr Mousavi, Abhisek Dash, Krishna P. Gummadi, and Ingmar Weber, arXiv:2501.15539v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccccc}
 \toprule
 Test set & \multicolumn{3}{c}{Offline} & \multicolumn{3}{c}{Streaming} \\
 & w/o CS & w/o RL & w/ RL & w/o CS & w/o RL & w/ RL \\
 \midrule
 A & 4.53 & 1.70 & 1.59 & 6.19 & 5.85 & 2.28\\
 \midrule
 B & 4.76 & 4.56 & 4.50 & 6.32 & 5.68 & 5.07\\
 \bottomrule
 \end{tabular}
\caption{Evaluation results in terms of WER (\%) on \textbf{code-switched test sets}.}
\end{table}
\end{document}

eess

FunAudio-ASR Technical Report

Keyu An
Yanni Chen
Chong Deng
Changfeng Gao
Zhifu Gao
Bo Gong
Xiangang Li
Yabin Li
Xiang Lv
Yunjie Ji
Yiheng Jiang
Bin Ma
Haoneng Luo
Chongjia Ni
Zexu Pan
Yiping Peng
Zhendong Peng
Peiyao Wang
Hao Wang
Wen Wang
Wupeng Wang
Biao Tian
Zhentao Tan
Nan Yang
Bin Yuan
Jieping Ye
Jixing Yu
Qinglin Zhang
Kun Zou
Han Zhao
Shengkui Zhao
Jingren Zhou

"FunAudio-ASR Technical Report" by Keyu An, Yanni Chen, Chong Deng, Changfeng Gao, Zhifu Gao, Bo Gong, Xiangang Li, Yabin Li, Xiang Lv, Yunjie Ji, Yiheng Jiang, Bin Ma, Haoneng Luo, Chongjia Ni, Zexu Pan, Yiping Peng, Zhendong Peng, Peiyao Wang, Hao Wang, Wen Wang, Wupeng Wang, Biao Tian, Zhentao Tan, Nan Yang, Bin Yuan, Jieping Ye, Jixing Yu, Qinglin Zhang, Kun Zou, Han Zhao, Shengkui Zhao, and Jingren Zhou, arXiv:2509.12508v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The results of different methods on the IEMOCAP database.}
\begin{tabular}{c|ccc}
		\toprule
		Method & ACC(\%) & WF1(\%) & Year \\
		\midrule
		DialogueTRM & 68.92 & 69.23 & 2020 \\
		HiTrans & - & 64.5 & 2020 \\
		DialogXL & - & 65.94 & 2021 \\
		MMGCN & - & 66.22 & 2021 \\
		COGMEN & 68.2 & 67.63 & 2022 \\
		MM-DFN & 68.21 & 68.18 & 2022 \\
		M2FNet & 69.69 & 69.86 & 2022 \\
		HAAN-ERC & 69.48 & 69.47 & 2023 \\
		\textbf{Ours} & \textbf{70.53} & \textbf{70.6} & \textbf{2024} \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

"WavFusion: Towards wav2vec 2.0 Multimodal Speech Emotion Recognition" by Feng Li, Jiusong Luo, and Wanjun Xia, arXiv:2412.05558v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}\hline
 \textbf{Time} & \textbf{$\alpha$ of Krippendorf} & \textbf{$\tau$ of Kendall}\\ \hline
 Phase 1 & 0.62 & \\ \hline
 Phase 2 & 0.60 & \\ \hline
 Phases 1 and 2 & \textbf{0.64} & \textbf{0.63} \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Agreement score of the evaluation in phases 1 and 2, from all the taggers.}
\end{table}
\end{document}

cs

Guillermo Ruiz
Tania Ramírez
Daniela Moctezuma

"VCRScore: Image captioning metric based on V\&L Transformers, CLIP, and precision-recall" by Guillermo Ruiz, Tania Ramírez, and Daniela Moctezuma, arXiv:2501.09155v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of out-of-sample validation with simulations ($a_{1|0}=3, \psi=1$, $\Delta=1.0$).}
\begin{tabular}{ccccccc}
\toprule
 & \multicolumn{3}{c}{RMSE} & \multicolumn{3}{c}{GDEV} \\
\cmidrule(l{3pt}r{3pt}){2-4} \cmidrule(l{3pt}r{3pt}){5-7}
 & Independent & B{\"u}hlmann & SSM & Independent & B{\"u}hlmann & SSM\\
\midrule
Homogeneous & 5962.36 & 5116.05 & 5118.61 & 6639.29 & 5206.91 & 5212.95\\
 & (469.85) & (355.58) & (359.82) & (192.44) & (119.02) & (119.25)\\
 \hline
Heterogeneous & 5868.95 & 4939.25 & 4939.39 & 6371.98 & 4901.10 & 4901.98\\
 & (471.67) & (351.47) & (351.82) & (186.97) & (116.44) & (116.38)\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Jae Youn Ahn
Himchan Jeong
Mario V. Wüthrich

"An Observation-Driven State-Space Model for Claims Size Modeling" by Jae Youn Ahn, Himchan Jeong, and Mario V. Wüthrich, arXiv:2412.21099v1, 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}{llccc}
			\toprule
			\textbf{Name} & \textbf{Type} & \textbf{P (\%)} & \textbf{\textit{k}} & \textbf{Ref.}\\
			\midrule
			{LPROS} & Random labelset clonning & 25 & - & \\
			{MLROS} & Random label clonning & 25 & - & \\
			{MLSMOTE} & Synthetic (nearest neighbors) & - & 3 & \\
			{MLSOL} & Synthetic (nearest neighbors) & 25 & 3 & \\
			{REMEDIAL} & Labelset splitting & - & - & \\
			{\textbf{MLDM}} & Synthetic (diffusion model) & 25 & - & - \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\caption{Resampling algorithms compared in the experiments. \textit{P} denotes the percentage of synthetic new samples to create with respect to the original size of the MLD, while \textit{k} is the number of instances for algorithms that that use NN.}
\end{table}
\end{document}

cs

Francisco Charte
Miguel Ángel Dávila
María Dolores Pérez-Godoy
María José del Jesus

"Addressing Multilabel Imbalance with an Efficiency-Focused Approach Using Diffusion Model-Generated Synthetic Samples" by Francisco Charte, Miguel Ángel Dávila, María Dolores Pérez-Godoy, and María José del Jesus, arXiv:2501.10822v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|l|l|l}
 \toprule \midrule
 \textbf{Split} & \textbf{\# Speakers} & \textbf{\# Utterances} & \textbf{\# hrs} \\
 \midrule
 Test & 199 & 5,699 & 9 \\
 Train & 1,246 & 956,645 & 1158\\
 Val. & 24 & 358 & 0.64 \\
 \bottomrule
 \end{tabular}
\caption{Final size of our prompted speech dataset.}
\end{table}
\end{document}

eess

Jimmy Tobin
Katrin Tomanek
Subhashini Venugopalan

"Towards a Single ASR Model That Generalizes to Disordered Speech" by Jimmy Tobin, Katrin Tomanek, and Subhashini Venugopalan, arXiv:2412.19315v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|}
\hline
City & \textit{Max Lag} & \textit{Correlation Value} & \textit{P Values} \\ \hline
New York City & 1 & 0.07 & .50 \\ \hline
Boston & 1 & 0.42 & \textless{}.001 \\ \hline
Minneapolis & 6 & 0.60 & \textless{}.001 \\ \hline
Seattle & 1 & 0.65 & \textless{}.001 \\ \hline
Dallas & 1 & 0.40 & \textless{}.001 \\ \hline
\end{tabular}
\caption{Pearson’s correlation coefficient and P-values, between Lagged cumulative COVID-19 cases and daily percentages of people wearing masks. }
\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.


stat

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

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


\documentclass{article}
\usepackage{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{Parameter} & \textbf{Description} \\
\midrule
Model & Logistic regression model with priors for moral decision-making \\
Setup Parameters & Character attributes, intervention type, LLM \\
Observations & Prediction in natural language of which group to save and why \\
Goals & Predicting which group participants choose to save \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Moral Machines Environment}
\end{table}
\end{document}

cs

Kanishk Gandhi
Michael Y. Li
Lyle Goodyear
Agam Bhatia
Louise Li
Aditi Bhaskar
Mohammed Zaman
Noah D. Goodman

"BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery" by Kanishk Gandhi, Michael Y. Li, Lyle Goodyear, Agam Bhatia, Louise Li, Aditi Bhaskar, Mohammed Zaman, and Noah D. Goodman, arXiv:2501.01540v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Abulikemu Abuduweili
Chenyang Yuan
Changliu Liu
Frank Permenter

"Enhancing Sample Generation of Diffusion Models using Noise Level Correction" by Abulikemu Abuduweili, Chenyang Yuan, Changliu Liu, and Frank Permenter, arXiv:2412.05488v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Structure of the classification of occupations and specializations (as of 01.01.2023)}
\begin{tabular}{lrrrrr}
 \hline
(1 digit) & 2 digits & 3 digits & 4 digits & \multicolumn{2}{c}{6 digits} \\
Major Group & Sub-major & Minor & Unit & with & without \\
 & Groups & Groups & Groups & the rest & the rest \\
 \hline
 0 -- Armed Forces Occupations & 3 & 3 & 3 & 3 & 3 \\
 1 -- Public Authorities, Senior Officials and Managers & 4 & 11 & 31 & 202 & 172 \\
 2 -- Professionals & 6 & 31 & 99 & 789 & 705 \\
 3 -- Technicians and Associate Professionals & 5 & 20 & 87 & 610 & 535 \\
 4 -- Clerical Support Workers & 4 & 8 & 27 & 89 & 71 \\
 5 -- Services and Sales Workers & 4 & 13 & 39 & 166 & 137 \\
 6 -- Skilled Agricultural, Forestry and Fishery Workers & 3 & 9 & 17 & 63 & 54 \\
 7 -- Craft and Related Trades Workers & 5 & 14 & 69 & 476 & 412 \\
 8 -- Plant and Machine Operators and Assemblers & 3 & 14 & 41 & 387 & 349 \\
 9 -- Elementary Occupations & 6 & 11 & 32 & 126 & 111 \\
 \hline
 All categories & 43 & 134 & 445 & 2,911 & 2,549 \\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

Maciej Beręsewicz
Marek Wydmuch
Herman Cherniaiev
Robert Pater

"Multilingual hierarchical classification of job advertisements for job vacancy statistics" by Maciej Beręsewicz, Marek Wydmuch, Herman Cherniaiev, and Robert Pater, arXiv:2411.03779v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Computation times in [s] for scenario .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccccc|} \hline
		$n$ & 5 & 6 & 7 & 8 & 9 & 10 \\
		\hline
		DFS & 0.01 & 0.22 & 31 & $>$600 & - & - \\
		Alg. & 0.04 & 0.09 & 0.39 & 2.2 & 14 & 126\\
		$N$ & 24 & 120 & 720 & 5040 & 40320 & 362880\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"How to find all connections in the Pantelides algorithm for delay differential-algebraic equations" by Daniel Collin, arXiv:2202.01772v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
 \textbf{WFCRL environment} & \textbf{Real wind farm} \\
 Ablaincourt & Ablaincourt Energies onshore wind farm, Somme, France \\
 Ormonde & Ormonde Offshore Wind Farm, Irish Sea, UK\\
 WMR & Westermost Rough Wind Farm is an offshore wind farm, North Sea, UK \\
 HornsRev1 & Horns Rev 1 Offshore Wind Farm, North Sea, Denmark\\
 HornsRev2 & Horns Rev 2 Offshore Wind Farm, North Sea, Denmark\\
 &\\
 \end{tabular}
\end{adjustbox}
\caption{Correspondences between WFCRL environments and real wind farms.}
\end{table}
\end{document}

cs

Claire Bizon Monroc
Ana Bušić
Donatien Dubuc
Jiamin Zhu

"WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control" by Claire Bizon Monroc, Ana Bušić, Donatien Dubuc, and Jiamin Zhu, arXiv:2501.13592v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ccc|}
 \hline
 Set & Sample points & Embeddable sample points & Rel. vol. of embeddable matrices \\
 \hline
 $V^{Markov}_6$& $10^8$& $1370$ &$0.0000137$\\
 $V_{\rm{DLC}}$& $1034607$ & $1362$ & $0.0013164$\\
 $V_{\rm{DD}}$&$3048$&$84$&$0.0275590$\\
 \hline

 \end{tabular}
\end{adjustbox}
\caption{ Relative volume of embeddable matrices within relevant subsets of $6\times6$ centrosymmetric Markov matrices. The results were obtained {\color{black}using} the hit-and-miss Monte Carlo integration with $10^7$ sample points.}
\end{table}
\end{document}

math

Muhammad Ardiyansyah
Dimitra Kosta
Jordi Roca-Lacostena

"Embeddability of centrosymmetric matrices capturing the double-helix structure in natural and synthetic DNA" by Muhammad Ardiyansyah, Dimitra Kosta, and Jordi Roca-Lacostena, arXiv:2202.05889v2, 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{Computation time for VRG in the practical example.}
\begin{tabular}{||c||c||c||}
\hline
& \bfseries Explicit QP & \bfseries Implicit QP\\
\hline\hline
average & $8.71\times 10^{-5}$s & $0.45 \times 10^{-2}$s\\
\hline
\hline
maximum & $7.66 \times 10^{-4}$s & $2.3 \times 10^{-2}$s\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Yudan Liu
Joycer Osorio
Hamid R. Ossareh

"Decoupled Reference Governors: A Constraint Management Technique for MIMO Systems" by Yudan Liu, Joycer Osorio, and Hamid R. Ossareh, arXiv:2012.02020v1, 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{Performance evaluation of the proposed framework with different backbone networks and loss functions in terms of mean DC scores.}
\begin{tabular}{ccccc}
 \toprule
 Backbone & $L_c$ & $L_d$ & $L_{ft}$ & $L_h$ \\\hline
 MobileNet & 0.4918 & 0.5219 & 0.5059 & 0.5532 \\
 VGG-16 & 0.5282 & 0.5384 & 0.5554 & 0.5665 \\
 ResNet-50 & 0.5414 & 0.5623 & 0.5691 & 0.5776 \\
 DRN (Proposed) & \textbf{0.5583} & \textbf{0.5694} & \textbf{0.5821} & \textbf{0.5908} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Taimur Hassan
Bilal Hassan
Ayman El-Baz
Naoufel Werghi

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


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Federated unlearning results on CIFAR10 using the first countermeasure under the adopted de-duplication.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\toprule\rowcolor{gray!40}
 & Mode. fide. & Test accu. & Unle. effi. & Acc. on $\mathcal{D}_u$\\
 \midrule
Before unlearn & $65.75$ & $62.77$ & $47.44$ & $52.56$ \\\hline \rowcolor{gray!40}
Retrain & $59.12$ & $60.56$ & $53.45$ & $46.55$ \\
GA & $26.03$ & $34.89$ & $93.85$ & $6.15$ \\%\rowcolor{gray!40}
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Dayong Ye
Tianqing Zhu
Jiayang Li
Kun Gao
Bo Liu
Leo Yu Zhang
Wanlei Zhou
Yang Zhang

"Data Duplication: A Novel Multi-Purpose Attack Paradigm in Machine Unlearning" by Dayong Ye, Tianqing Zhu, Jiayang Li, Kun Gao, Bo Liu, Leo Yu Zhang, Wanlei Zhou, and Yang Zhang, arXiv:2501.16663v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Preston M. Smith
Jill Gemmill
David Y. Hancock
Brian W. O'Shea
Winona Snapp-Childs
James Wilgenbusch

"Application of the Cyberinfrastructure Production Function Model to R1 Institutions" by Preston M. Smith, Jill Gemmill, David Y. Hancock, Brian W. O'Shea, Winona Snapp-Childs, and James Wilgenbusch, arXiv:2501.10264v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of GMRES-IR and RGMRES-IR refinement steps with the number of GMRES iterations for each refinement step for random dense matrices having geometrically distributed singular values (mode 3) with various condition numbers, using precisions ($u_f,u,u_r$) = (half, double, quad) and ($m,k$) = (90,40).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cc|cc|}
		\hline
		$\kappa_\infty(A)$ & $\kappa_2(A)$ & GMRES-IR (90) & RGMRES-IR (90,40) \\ \hline
		$9 \cdot 10^4$ & $10^4$ & 33 (16,17) & 33 (16,17) \\
		$8 \cdot 10^5$ & $10^5$ & 85 (41,44) & 50 (41,9) \\
		$7 \cdot 10^6$ & $10^6$ & 134 (66,68) & 81 (66,15) \\
		$7 \cdot 10^7$ & $10^7$ & 167 (83,84) & 100 (83,17) \\
		$7 \cdot 10^8$ & $10^8$ & - & 149 (119,30) \\
		$6 \cdot 10^9$ & $10^9$ & - & 179 (134,45) \\
		$6 \cdot 10^{10}$ & $10^{10}$ & - & 470 (388,41,41) \\
		$6 \cdot 10^{11}$ & $10^{11}$ & - & - \\
		$6 \cdot 10^{12}$ & $10^{12}$ & - & - \\
		$5 \cdot 10^{13}$ & $10^{13}$ & - & - \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative Evaluation of 13 Semantic Classes}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 & KL-div & rKL-div & EMD \\
 \midrule
 {\tt semapp} & $0.58\pm0.14$ & $2.43\pm1.24$ & $41.16\pm26.98$ \\
 {\tt semapp2} & $0.46\pm0.16$ & $2.19\pm1.50$ & $27.65\pm19.89$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Placido Falqueto
Alberto Sanfeliu
Luigi Palopoli
Daniele Fontanelli

"Learning Priors of Human Motion With Vision Transformers" by Placido Falqueto, Alberto Sanfeliu, Luigi Palopoli, and Daniele Fontanelli, arXiv:2501.18543v1, 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{MSEs of CV Dyadic CART estimator in different scenarios}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
\hline
 $n$ &Scenario 1 &Scenario 2 &Scenario 3\\
\hline
128 & 0.019 & 0.021 & 0.0005 \\
256 & 0.005 & 0.014 & 0.0004 \\
512 & 0.001 & 0.006 & 0.0003 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Anamitra Chaudhuri
Sabyasachi Chatterjee

"A Cross Validation Framework for Signal Denoising with Applications to Trend Filtering, Dyadic CART and Beyond" by Anamitra Chaudhuri and Sabyasachi Chatterjee, arXiv:2201.02654v3, 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 for the various databases. Chunk size is given in number of characters. Min., max., and avg. represent the minimum, maximum, and average number of words in each database.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
\hline
\textbf{Chunk Size} & \textbf{Documents} & \textbf{Min. Size} & \textbf{Max. Size} & \textbf{Avg. Size} \\
\hline
2K & 4780 & 4 & 372 & 235.67 \\
4K & 2661 & 5 & 735 & 428.78 \\
8K & 1635 & 7 & 1401 & 697.25 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

A RAG-Based Institutional Assistant

Gustavo Kuratomi
Paulo Pirozelli
Fabio G. Cozman
Sarajane M. Peres

"A RAG-Based Institutional Assistant" by Gustavo Kuratomi, Paulo Pirozelli, Fabio G. Cozman, and Sarajane M. Peres, arXiv:2501.13880v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Huyen Giang Thi Thu
Thang Viet Doan
Tai Le Quy

"An experimental study on fairness-aware machine learning for credit scoring problem" by Huyen Giang Thi Thu, Thang Viet Doan, and Tai Le Quy, arXiv:2412.20298v1, 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{Network formation and coherent beamforming protocols.}
\begin{tabular}{c|c}
RRB & Random network formation and random beamforming protocol \\ \hline
RRT & Random network formation and random target protocol \\ \hline
RBT & Random network formation and best target protocol \\ \hline
DRB & Distance-based network formation and random beamforming protocol \\ \hline
DRT & Distance-based network formation and random target protocol \\ \hline
DBT & Distance-based network formation and best target protocol
\end{tabular}
\end{table}
\end{document}

eess

"Coherent Communications in Self-Organizing Networks with Distributed Beamforming" by Yi Shi and Yalin E. Sagduyu, arXiv:1912.10112v1, licensed under CC0 1.0. 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
 & \textbf{less\_5} & \textbf{5\_15} & \textbf{15\_above} \\ \hline \hline
\textbf{train\_other\_500} & 9\% & 65\% & 26\% \\ %\hline
\textbf{dev\_clean} & 41.0\% &52.0\% &7.0\% \\
\textbf{test\_clean} & 42.0\% & 49.0\% & 9.0\% \\ \hline %\hline
\textbf{dev\_other} & 47.0\% &48.0\% & 5.0\% \\
 \textbf{test\_other} & 48.0\% & 46.0\% & 6.0\% \\ \hline
\end{tabular}
\caption{Percentage wise data split based on utterance duration in Librispeech train set}
\end{table}
\end{document}

eess

Vishwas M. Shetty
Metilda Sagaya Mary N J
S. Umesh

"Investigation of Speaker-adaptation methods in Transformer based ASR" by Vishwas M. Shetty, Metilda Sagaya Mary N J, and S. Umesh, arXiv:2008.03247v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amssymb}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc|ccc}
\toprule
 Fine-tuning & Category & Region & Q $\rightarrow$ A & QA $\rightarrow$ R & Q $\rightarrow$ AR \\
\midrule
 \checkmark & \checkmark & mask & \textbf{88.5} & \textbf{90.1} & \textbf{79.9}\\\midrule
\checkmark & & mask & 88.2 & \textbf{90.1} & 79.8 \\
\checkmark & & bbox & 87.7 & 90.0 & 79.2\\
 & & bbox & 85.8 & 87.5 & 75.3\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{\textbf{Accuracy (\%) on VCR with different settings.} }
\end{table}
\end{document}

cs

Miran Heo
Min-Hung Chen
De-An Huang
Sifei Liu
Subhashree Radhakrishnan
Seon Joo Kim
Yu-Chiang Frank Wang
Ryo Hachiuma

"Omni-RGPT: Unifying Image and Video Region-level Understanding via Token Marks" by Miran Heo, Min-Hung Chen, De-An Huang, Sifei Liu, Subhashree Radhakrishnan, Seon Joo Kim, Yu-Chiang Frank Wang, and Ryo Hachiuma, arXiv:2501.08326v2, 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{Prposed model parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|l|}
\hline
\textbf{SN} & \textbf{Model Parameters} & \textbf{Value Count and Function} \\ \hline
1 & Total Parameter & 8,138,104 \\ \hline
2 & Trainable Parameter & 8,095,640 \\ \hline
3 & Non-trainable Parameter & 42,464 \\ \hline
4 & Batch Size & 16 \\ \hline
5 & Epoch & 100 \\ \hline
6 & Loss function & Sparse\_categorical\_crossentropy \\ \hline
7 & Optimizer & Adam \\ \hline
8 & Activation Function & SoftMax \\ \hline
9 & Learning Rate & 0.001 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Subrata Kumer Paul
Abu Saleh Musa Miah
Rakhi Rani Paul
Md. Ekramul Hamid
Jungpil Shin
Md Abdur Rahim

"IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for Healthcare" by Subrata Kumer Paul, Abu Saleh Musa Miah, Rakhi Rani Paul, Md. Ekramul Hamid, Jungpil Shin, and Md Abdur Rahim, arXiv:2501.07039v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 Collection & map & Rprec & ndcg & P.100 & P.10 \\
 \hline
 \hline
 chatgpt23 & 0.71 & 0.81 & 0.8 & 0.87 & 0.2 \\
 \hline
 trec5 & 0.53 & 0.76 & 0.78 & 0.83 & 0.65 \\
 \hline
 trec6 & 0.64 & 0.8 & 0.87 & 0.84 & 0.78 \\
 \hline
 trec7 & 0.73 & 0.83 & 0.87 & 0.87 & 0.78 \\
 \hline
 trec8 & 0.7 & 0.84 & 0.88 & 0.85 & 0.82 \\
 \hline
 robust2004 & 0.74 & 0.87 & 0.9 & 0.87 & 0.82 \\
 \hline
 combined & 0.8 & 0.9 & 0.92 & 0.9 & 0.86 \\
 \hline
 chatgpt23 (w/o TNR) & 0.71 & 0.84 & 0.85 & 0.87 & 0.26 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Impact of Tricky Non-relevant Documents}
\end{table}
\end{document}

cs

Mehmet Deniz Türkmen
Mucahid Kutlu
Bahadir Altun
Gokalp Cosgun

"GenTREC: The First Test Collection Generated by Large Language Models for Evaluating Information Retrieval Systems" by Mehmet Deniz Türkmen, Mucahid Kutlu, Bahadir Altun, and Gokalp Cosgun, arXiv:2501.02408v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llll}
\toprule
\textbf{Community 1} & \textbf{Community 2} & \textbf{statistic} & \textbf{p-value} \\ \midrule
White Matter (R) & White Matter (R) & 0.885 & 6.43e-06 \\
White Matter (L) & White Matter (L) & 0.857 & 1.02e-05 \\
Hindbrain (L) & White Matter (L) & 0.849 & 1.14e-05 \\
Midbrain (R) & White Matter (R) & 0.845 & 1.21e-05 \\
Isocortex (L) & Isocortex (L) & 0.844 & 1.22e-05 \\
Pallium (R) & White Matter (R) & 0.831 & 1.5e-05 \\
Isocortex (R) & White Matter (R) & 0.823 & 1.68e-05 \\
Isocortex (R) & Isocortex (R) & 0.819 & 1.8e-05 \\
Isocortex (L) & White Matter (L) & 0.811 & 2.02e-05 \\
Hindbrain (R) & White Matter (R) & 0.810 & 2.02e-05 \\ \bottomrule
\end{tabular}
\caption{The top 10 signal communities (out of 105 total communities) ranked by the order of their Holm--Bonferroni corrected p-values as calculated by the Multivariate Weighted method.}
\end{table}
\end{document}

stat

Multiscale Comparative Connectomics

Vivek Gopalakrishnan
Jaewon Chung
Eric Bridgeford
Benjamin D. Pedigo
Jesús Arroyo
Lucy Upchurch
G. Allan Johnson
Nian Wang
Youngser Park
Carey E. Priebe
Joshua T. Vogelstein

"Multiscale Comparative Connectomics" by Vivek Gopalakrishnan, Jaewon Chung, Eric Bridgeford, Benjamin D. Pedigo, Jesús Arroyo, Lucy Upchurch, G. Allan Johnson, Nian Wang, Youngser Park, Carey E. Priebe, and Joshua T. Vogelstein, arXiv:2011.14990v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Description of the covariates in the working model. }
\begin{tabular}{l|lrrr}
\toprule
Variables & Description & Response & Proportions
 \\
\hline
\texttt{Doctor\_Type} & Type of medical professional for the visit & GP & 7.00 \% \\
 & & Specialist & 39.27 \% \\
 & & Non-doctor & 53.73 \% \\ \hline
\texttt{Care\_Category} & Main reason of the outpatient visit & Diagnosis or Treatment & 51.67 \% \\
& & Others & 48.33 \% \\ \hline
\texttt{Special\_Cond} & Indicator for an existing special condition for the visit & Yes & 83.62 \% \\
& & No & 16.38 \% \\ \hline
\texttt{Surgery} & Whether the visit involved a surgery & Yes & 8.68 \% \\
& & No & 91.32 \% \\ \hline
\texttt{Prescription} & Whether any medicine prescribed for the visit & Yes & 8.33 \% \\
& & No & 91.67 \% \\ \hline
\texttt{Telehealth} & Whether the visit was a telehealth event & Yes & 7.61 \% \\
& & No & 40.35 \% \\
& & Unknown & 52.04 \% \\
\hline \hline
\end{tabular}
\end{table}
\end{document}

stat

Jae Youn Ahn
Himchan Jeong
Mario V. Wüthrich

"An Observation-Driven State-Space Model for Claims Size Modeling" by Jae Youn Ahn, Himchan Jeong, and Mario V. Wüthrich, arXiv:2412.21099v1, 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{Empirical size at of the test statistics for testing $H_0:\alpha_0=\alpha_1=\alpha_2=0$ in $S=200$ simulations of model , for $N=8$ and $T=1000$. Data are continuous-valued and generated from the linear model . The empirical power is also reported for data generated from model with $\alpha=(\alpha_0, \alpha_1, \alpha_2)^\prime=(0.5, 0.2, 0.1)^\prime$ and $\gamma=1$. The network is derived from Ex.~, with $K=2$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|ccc|ccc}\hline\hline
				\multicolumn{2}{c|}{} &\multicolumn{3}{c|}{Size} & \multicolumn{3}{c}{Power} \\\hline
				\multicolumn{2}{c|}{} &\multicolumn{3}{c|}{} & \multicolumn{3}{c}{$\alpha=(0.5, 0.2, 0.1)^\prime$, $\gamma=1$} \\\hline
				$N$ & $T$ & 10\% & 5\% & 1\% & 10\% & 5\% & 1\% \\\hline
				8 & 1000 & 0.045 & 0.002 & 0.000 & 1.000 & 1.000 & 1.000 \\%
				\hline
				\hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Nonlinear Network Autoregression

"Nonlinear Network Autoregression" by Mirko Armillotta and Konstantinos Fokianos, arXiv:2202.03852v2, 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}{lccc}
\hline
\hline
\textbf{\#$L$ - Layers} & \textbf{Precision} & \textbf{Recall} & \textbf{F1}\\
\hline
\hline
0 (GRU only) & 75.6 & 75.6 & 75.6 \\
1 Layer & 76.2 & 76.1 & 76.1 \\
3 Layers & 77.4 & 77.2 & 77.2 \\
5 Layers & 77.6 & 77.6 & 77.6 \\
\hline
\hline
\end{tabular}
\caption{Ablation study with varying number of attention layers \#$L$ and fixed Heads \textbf{\#$H$ = 8} on the Sarcasm Corpus V2 Dialogues dataset .}
\end{table}
\end{document}

cs

"Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media" by Ramya Akula and Ivan Garibay, arXiv:2101.05875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Emmanuel Jordy Menvouta
Jolien Ponnet
Robin Van Oirbeek
Tim Verdonck

"mCube: Multinomial Micro-level reserving Model" by Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek, and Tim Verdonck, arXiv:2212.00101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Structure of Classifier Boundaries: Case Study for a Naive Bayes Classifier" by Alan F. Karr, Zac Bowen, and Adam A. Porter, arXiv:2212.04382v2, 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{\texttt{Markdown Awareness} Scores and Rankings}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cclc}
 \toprule
 Ranking & LLM & Score & Average Ranking \\
 \midrule
 \#1 & \textbf{Deepseek-v2-chat} & \textbf{0.946} & \#1 \\
 \#2 & GPT-4o & 0.865 & \#2 \\
 \#3 & GPT-4o-mini & 0.830 & \#3 \\
 \#4 & Gemini-1.5-pro & 0.803 & \#5 \\
 \#5 & GPT-4-turbo & 0.787 & \#4 \\
 \#6 & Llama-3.1-8b & 0.710 & \#6 \\
 \#7 & Claude-3.5-sonnet & 0.569 & \#7 \\
 \#8 & GPT-3.5-turbo & 0.482 & \#8 \\
 \#9 & Baichuan2-13b-chat-v1 & 0.171 & \#9 \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zhongpu Chen
Yinfeng Liu
Long Shi
Xingyan Chen
Yu Zhao
Fuji Ren

"MDEval: Evaluating and Enhancing Markdown Awareness in Large Language Models" by Zhongpu Chen, Yinfeng Liu, Long Shi, Xingyan Chen, Yu Zhao, and Fuji Ren, arXiv:2501.15000v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Silhouette Scores for unsupervised K-means (k=2).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
 \toprule
 \textbf{Model} & \textbf{Silhouette Score} \\
 \midrule
 UNSW-NB15\_3 + UNSW-NB15\_4 & 0.5061 \\ \hline
 \textbf{HERA's CSV} & \textbf{0.7258} \\ \hline
 Researcher's CSV & 0.6879 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Daniela Pinto
Ivone Amorim
Eva Maia
Isabel Praça

"A Novel Approach to Network Traffic Analysis: the HERA tool" by Daniela Pinto, Ivone Amorim, Eva Maia, and Isabel Praça, arXiv:2501.07475v1, 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{Optimal hyperparameter setting}
\begin{tabular}{c|c|}
\hline
\bfseries SVD & $T_c = 100$, $T_b = 150$ \\ \hline
\bfseries GoDec & $r_G=41, \tau_{G} = 5\times 10^3$ \\ \hline
\bfseries BD-RPCA & $\lambda_{B} = 0.0051, \mu_{B} = 0.0403, \rho_{B} = 1$ \\ \hline
\bfseries fast BD-RPCA & $\lambda_{f} = 8\times 10^{-5}, r_f = 41$ \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Duong-Hung Pham
Adrian Basarab
Jean-Pierre Remenieras
Paul Rodríguez
Denis Kouamé

"Fast High Resolution Blood Flow Estimation and Clutter Rejection via an Alternating Optimization Problem" by Duong-Hung Pham, Adrian Basarab, Jean-Pierre Remenieras, Paul Rodríguez, and Denis Kouamé, arXiv:2011.01811v1, 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{AUC for the networks on the validation set}%
\begin{tabular}{lc} \hline
 model &AUC \\
 \hline
 DUnet &0.49\\
 DDUnet&0.53\\
 DDAUnet-noChA2&0.73\\
 DDAUnet-plusChA1-noChA2&0.63\\
 DDAUnet-noSpA-plusChA1-noChA2&0.71\\
 DDAUnet&0.76\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Sahar Yousefi
Hessam Sokooti
Mohamed S. Elmahdy
Irene M. Lips
Mohammad T. Manzuri Shalmani
Roel T. Zinkstok
Frank J. W. M. Dankers
Marius Staring

"Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)" by Sahar Yousefi, Hessam Sokooti, Mohamed S. Elmahdy, Irene M. Lips, Mohammad T. Manzuri Shalmani, Roel T. Zinkstok, Frank J. W. M. Dankers, and Marius Staring, arXiv:2012.03242v3, 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{A contingency table showing an observed association (RR $=11.4$, $T=0.58$) between marijuana use and hard drug use.}
\begin{tabular}{rcc}
\toprule
 & \multicolumn{2}{c}{Marijuana use}\\
\cmidrule{2-3}
Hard drug use&No&Yes\\
\hline
Yes&114&978\\
No&3,649&1,864\\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Observational Causality Testing

Brian Knaeble
Braxton Osting
Placede Tshiaba

"Observational Causality Testing" by Brian Knaeble, Braxton Osting, and Placede Tshiaba, arXiv:2309.02621v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
 \toprule
 Fa Datasets & E5-Mistral & LUSIFER \\
 \midrule
 MassiveScenarioClassification & 76.37 & 77.94 \\
 MassiveIntentClassification & 71.98 & 73.32 \\
 MultilingualSentimentClassification & 80.07 & 80.54 \\
 FarsTail & 63.49 & 67.98 \\
 WikipediaRerankingMultilingual & 75.60 & 78.75 \\
 WikipediaRetrievalMultilingual & 67.77 & 78.49 \\
 \midrule
 Avg. & 72.55 & 76.17 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Detailed results of E5-Mistral and LUSIFER on the Farsi benchmark datasets.}
\end{table}
\end{document}

cs

Hieu Man
Nghia Trung Ngo
Viet Dac Lai
Ryan A. Rossi
Franck Dernoncourt
Thien Huu Nguyen

"LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models" by Hieu Man, Nghia Trung Ngo, Viet Dac Lai, Ryan A. Rossi, Franck Dernoncourt, and Thien Huu Nguyen, arXiv:2501.00874v3, 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{ Values of unknowns for $ m = 4 $ and for different values of $ \alpha $ }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{{|c|ccccc|}}
\hline
 Unknowns & $c_{0}$ & $c_{1}$ & $c_{2}$ & $c_{3}$ & $c_{4}$ \\ \hline
$\alpha =0.7$ & $-2.\,\allowbreak 270\,6$ & $0.575\,55$ & $1.\,\allowbreak
616\,8$ & $-7.\,\allowbreak 593\,7\times 10^{-2}$ & $1.\,\allowbreak
856\,8\times 10^{-2}$ \\ \hline
$\alpha =0.8$ & $-2.\,\allowbreak 289\,5$ & $7.\,\allowbreak 948\,1\times
10^{-2}$ & $1.\,\allowbreak 635\,2$ & $0.145\,92$ & $9.\,\allowbreak
545\,0\times 10^{-3}$ \\ \hline
$\alpha =1$ & $-1.\,\allowbreak 000\,4$ & $-0.999\,65$ & $1.\,\allowbreak
000\,2$ & $0.999\,65$ & $2.\,\allowbreak 221\,6\times 10^{-5}$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simple example - input data $X$ for a small choice of flights.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rccc}
 \toprule
 &price[Euro]&time-of-transfer[h]&stops\\
 \midrule
 flight A & $300$ & $10$ & $2$\\
 flight B & $600$ & $5$ & $2$\\
 flight C & $1500$ & $4$ & $1$\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization" by Philipp Wissgott, arXiv:2501.19113v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of error bounds (up to a logarithmic factor) in the paper, where $\varepsilon$ represents the adversarial attack level, $r_1=\alpha/(2d+3\alpha),$ $r_2=2\alpha/(2d+5\alpha),$ $r_3=(d+3\alpha-1)/(2d+3\alpha)$, $r_4=(d+1)/(2d+3\alpha),$ and $r_5=(d+1)/(2d+5\alpha).$}
\begin{tabular}{lll}
		\toprule
		Loss function & Measurement & Error bound \\
		\midrule
		\multirow{3}{*}{
			Lipschitz
		} &adversarial excess risk&$ n^{-r_1} +n^{-r_3}\varepsilon$ \\
		\cmidrule{2-3}
		& excess risk&$n^{-r_1} +\varepsilon$\\
		\cmidrule{2-3}
		&local worst-case excess risk&$n^{-r_1} + n^{r_4} \varepsilon$\\
		\midrule Classification
		& adversarial excess risk& $n^{-r_1} + \varepsilon$\\
		\midrule
		Quadratic & $L_2$-norm &$n^{-r_2} +n^{r_5}\varepsilon$\\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

stat

Changyu Liu
Yuling Jiao
Junhui Wang
Jian Huang

"Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models" by Changyu Liu, Yuling Jiao, Junhui Wang, and Jian Huang, arXiv:2309.00771v1, 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{Dice Coefficients, BVs of two FEA Methods for 4 Cases}
\begin{tabular}{c|c|c|c|c}
 \toprule
 \textbf{Cases} & \textbf{FEA} & \textbf{Fat} & \textbf{Gland} & \textbf{BV} \\
 \midrule
 Case 1 & NiftySim & 0.91 & 0.22 & 1.52\% \\
 Case 1 & FEBio & 0.69 & 0.20 & 3.22\% \\
 \midrule
 Case 2 & NiftySim & 0.78 & 0.31 & 1.63\% \\
 Case 2 & FEBio & 0.59 & 0.28 & 4.11\% \\
 \midrule
 Case 3 & NiftySim & 0.85 & 0.20 & 1.87\% \\
 Case 3 & FEBio & 0.72 & 0.19 & 4.30\% \\
 \midrule
 Case 4 & NiftySim & 0.89 & 0.19 & 1.94\% \\
 Case 4 & FEBio & 0.65 & 0.14 & 4.18\% \\
 \midrule
 \textbf{Mean $\pm$ SD} & \textbf{NiftySim} & 0.85 $\pm$ 0.05 & 0.23 $\pm$ 0.05 & - \\
 \textbf{Mean $\pm$ SD} & \textbf{FEBio} & 0.66 $\pm$ 0.05 & 0.20 $\pm$ 0.05 & - \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Melika Pooyan
Hadeel Awwad
Eloy García
Robert Martí

"MRI Breast tissue segmentation using nnU-Net for biomechanical modeling" by Melika Pooyan, Hadeel Awwad, Eloy García, and Robert Martí, arXiv:2411.18784v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs" by He Yu and Jing Liu, arXiv:2501.11880v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Human evaluation on VoxCeleb2.} Mean Opinion Scores (MOS) (1-5) with 95\% confidence intervals for Intelligibility, Naturalness, and Synchronization. VTTS (VT-ordered) achieves the best performance in Intelligibility (3.48) and Naturalness (3.20), while VTTS (TV-ordered) performs best in Synchronization (2.50). Both significantly outperform TTS baseline across all metrics. Ground truth (GT) serves as a reference, and GT (discrete) is an upper bound due to speech discretization.}
\begin{tabular}{lcccccc}\toprule
 \textbf{Method} & \textbf{Intelligibility} ($\uparrow$) & \textbf{Naturalness} ($\uparrow$) & \textbf{Synchronization} ($\uparrow$) \\\midrule
 GT & 4.55 \textpm 0.09 & 4.79 \textpm 0.05 & 4.57 \textpm 0.10 & \\
 GT (discrete) & 3.95 \textpm 0.13 & 3.77 \textpm 0.15 & 4.36 \textpm 0.12 & \\ \midrule
 TTS & 3.17 \textpm 0.19 & 2.92 \textpm 0.21 & 1.98 \textpm 0.15 & \\ \midrule
 VTTS (TV-streaming) & 3.19 \textpm 0.17 & 2.99 \textpm 0.16 & 2.28 \textpm 0.17 & \\
 VTTS (TV-ordered) & 3.35 \textpm 0.17 & 3.02 \textpm 0.19 & \textbf{2.50 \textpm 0.21} & \\
 VTTS (VT-ordered) & \textbf{3.48 \textpm 0.15} & \textbf{3.20 \textpm 0.19} &
 2.48 \textpm 0.19 & \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Akshita Gupta
Tatiana Likhomanenko
Karren Dai Yang
Richard He Bai
Zakaria Aldeneh
Navdeep Jaitly

"Visatronic: A Multimodal Decoder-Only Model for Speech Synthesis" by Akshita Gupta, Tatiana Likhomanenko, Karren Dai Yang, Richard He Bai, Zakaria Aldeneh, and Navdeep Jaitly, arXiv:2411.17690v2, licensed under CC BY-SA 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{{Results of testing the joint mediation effect after the first screening step.}}
\begin{tabular}{c|cccc} \hline
tests & AB Test & PT-N & PT-NP & SLR \\ \hline
$p$-values &0.0166 & 0.0856 & 0.1020 & 0.0726\\ \hline
\end{tabular}
\end{table}
\end{document}

stat

"Adaptive Bootstrap Tests for Composite Null Hypotheses in the Mediation Pathway Analysis" by He Yinqiu, Song Peter X. -K., and Xu Gongjun, arXiv:2310.11630v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
& Uniform Coloured Graph & Petersen Graph\\
\hline
$\deg(\mathcal L^{-1})$ &$r-1$& 2\\%[10pt]
$\operatorname{mld}(\mathcal L)$ &$r-1$& 2\\%[10pt]
$\operatorname{rmld}(\mathcal L)$ &$2r-3$ & 3\\%[10pt]
no. linear forms
 &$\binom{n+1}{2}-r$&52\\%[2.5pt]
no. quadratic forms
 & $\binom{r-1}{2}$&1 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Coloured Graphical Models and their Symmetries" by Isobel Davies and Orlando Marigliano, arXiv:2012.01905v5, 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{Performance across different datasets and Pre-training based demonstration selection methods with $k=3$ shots for Mistral-7b-v0.3.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \toprule

\textbf{Dataset} &
\textbf{\textsc{Pre}\textsubscript{[D,BSR]}} &
\textbf{\textsc{Pre}\textsubscript{[D,\textbf{\textsc{Cos}]}}} &
\textbf{\textsc{Pre}\textsubscript{[T,BSR]}} &
\textbf{\textsc{Pre}\textsubscript{[T,\textbf{\textsc{Cos}]}}}\\
 \midrule
 \textbf{medical-genetics} & 86.00 & 88.00 & 86.00 & 88.00 \\
 \textbf{prof-psychology} & 87.50 & 83.50 & 85.00 & 85.50 \\
 \textbf{formal-logic} & 64.29 & 65.87 & 65.08 & 65.87 \\
 \textbf{moral-disputes} & 86.00 & 85.00 & 85.00 & 85.50 \\
 \textbf{public-relations} & 85.45 & 80.00 & 86.36 & 84.55 \\
 \textbf{comp-security} & 82.00 & 88.00 & 85.00 & 89.00 \\
 \textbf{astronomy} & 88.16 & 90.13 & 87.50 & 89.47 \\
 \textbf{abstract-algebra} & 62.00 & 59.00 & 62.00 & 60.00 \\
 \textbf{nutrition} & 86.50 & 84.00 & 86.50 & 84.50 \\
 \textbf{high-school-biology} & 86.00 & 85.50 & 86.50 & 87.00 \\
 \textbf{formal-fallacies} & 54.50 & 46.00 & 51.00 & 50.00 \\
 \textbf{tracking-3} & 49.50 & 48.50 & 50.00 & 53.00 \\
 \midrule
 \textbf{Average} & 76.49 & 75.29 & 76.32 & 76.87 \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hadi Askari
Shivanshu Gupta
Terry Tong
Fei Wang
Anshuman Chhabra
Muhao Chen

"Unraveling Indirect In-Context Learning Using Influence Functions" by Hadi Askari, Shivanshu Gupta, Terry Tong, Fei Wang, Anshuman Chhabra, and Muhao Chen, arXiv:2501.01473v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Amartya Banerjee
Christopher J. Hazard
Jacob Beel
Cade Mack
Jack Xia
Michael Resnick
Will Goddin

"Surprisal Driven $k$-NN for Robust and Interpretable Nonparametric Learning" by Amartya Banerjee, Christopher J. Hazard, Jacob Beel, Cade Mack, Jack Xia, Michael Resnick, and Will Goddin, arXiv:2311.10246v2, 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{Error metrics for various models ordered by SMAPE -- Winter Season for Residence~2.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
 \toprule
 \textbf{Model} & \textbf{MAE} & \textbf{RMSE} & \textbf{SMAPE} \\
 \midrule
 ARFFNN & 12.36 & 15.58 & 5.60 \\
 AttentionLSTM & 12.10 & 15.47 & 5.62 \\
 MiniAutoEncXGBoost & 12.92 & 16.33 & 5.78 \\
 MiniWXGBoost & 13.51 & 17.56 & 6.04 \\
 HyperNetLSTM & 14.00 & 18.26 & 6.29 \\
 GRU & 15.58 & 21.66 & 7.05 \\
 Nbeats & 15.60 & 20.50 & 7.05 \\
 LSTM & 19.09 & 23.68 & 8.93 \\
 Transformer & 20.67 & 25.85 & 9.17 \\
 MLP & 20.77 & 26.01 & 9.22 \\
 RNN & 26.06 & 34.18 & 11.73 \\
 MiniWSGD & 27.78 & 37.22 & 12.51 \\
 TempConv & 30.68 & 39.45 & 13.99 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Muhammad Umair Danish
Mathumitha Sureshkumar
Tehara Fonseka
Umeshika Uthayakumar
Vinura Galwaduge

"An Investigation into Seasonal Variations in Energy Forecasting for Student Residences" by Muhammad Umair Danish, Mathumitha Sureshkumar, Tehara Fonseka, Umeshika Uthayakumar, and Vinura Galwaduge, arXiv:2501.07423v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Antonio Di Crescenzo
Barbara Martinucci
Serena Spina

"Continuous-time multi-type Ehrenfest model and related Ornstein-Uhlenbeck diffusion on a star graph" by Antonio Di Crescenzo, Barbara Martinucci, and Serena Spina, arXiv:2201.03977v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|c|cc|c|cc|}
		\hline
		\multicolumn{3}{|c|}{algorithm without adaptivity}&\multicolumn{6}{c|}{adaptive algorithm}\\
		\cline{4-9}
		\multicolumn{3}{|c|}{ }
		&\multicolumn{3}{c|}{one level $\mathcal{M}=1$}&\multicolumn{3}{c|}{two levels $\mathcal{M}=2$}\\
		\hline
		cells &$L^1$-error &rate&cells	&$L^1$-error &rate&cells	&$L^1$-error &rate\\
		\hline
		$2\times100\times100$&0.0045&&6,284 &0.0045&&5,928 &0.0045&	\\
		$2\times200\times200$&0.0021&1.10&22,546 &0.0021&1.10&11,260 & 0.0021&1.10\\
		$2\times400\times400$&7.9252e-04&1.41&85,132 &8.5079e-04&1.30&33,904& 8.9818e-04&1.23\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Adaptive Central-Upwind Scheme on Triangular Grids for the Shallow Water Model with variable density" by Thuong Nguyen, arXiv:2201.10073v1, 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}{lrrrrr}
\toprule
 & shaheen & nllb & S-T5 & No Best System & Total Samples \\ \midrule
Books & \textbf{53.36\%} & 50.59\% & 32.81\% & 11.86\% & 253 \\
STEM Papers & 28.05\% & \textbf{58.54\%} & 14.63\% & 18.29\% & 82 \\
Wiki Encyclopedia & 28.26\% & \textbf{68.48\%} & 31.52\% & 10.87\% & 92 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Human evaluation scores for English-to-MSA translation. Annotators were allowed to mark multiple best systems for each sample, or explicitly mark if none of the translations were good. The scores indicate the percentage of samples for which a system was in the best translations list.}
\end{table}
\end{document}

cs

Fanar Team
Ummar Abbas
Mohammad Shahmeer Ahmad
Firoj Alam
Enes Altinisik
Ehsannedin Asgari
Yazan Boshmaf
Sabri Boughorbel
Sanjay Chawla
Shammur Chowdhury
Fahim Dalvi
Kareem Darwish
Nadir Durrani
Mohamed Elfeky
Ahmed Elmagarmid
Mohamed Eltabakh
Masoomali Fatehkia
Anastasios Fragkopoulos
Maram Hasanain
Majd Hawasly
Mus'ab Husaini
Soon-Gyo Jung
Ji Kim Lucas
Walid Magdy
Safa Messaoud
Abubakr Mohamed
Tasnim Mohiuddin
Basel Mousi
Hamdy Mubarak
Ahmad Musleh
Zan Naeem
Mourad Ouzzani
Dorde Popovic
Amin Sadeghi
Husrev Taha Sencar
Mohammed Shinoy
Omar Sinan
Yifan Zhang
Ahmed Ali
Yassine El Kheir
Xiaosong Ma
Chaoyi Ruan

"Fanar: An Arabic-Centric Multimodal Generative AI Platform" by Fanar Team, Ummar Abbas, Mohammad Shahmeer Ahmad, Firoj Alam, Enes Altinisik, Ehsannedin Asgari, Yazan Boshmaf, Sabri Boughorbel, Sanjay Chawla, Shammur Chowdhury, Fahim Dalvi, Kareem Darwish, Nadir Durrani, Mohamed Elfeky, Ahmed Elmagarmid, Mohamed Eltabakh, Masoomali Fatehkia, Anastasios Fragkopoulos, Maram Hasanain, Majd Hawasly, Mus'ab Husaini, Soon-Gyo Jung, Ji Kim Lucas, Walid Magdy, Safa Messaoud, Abubakr Mohamed, Tasnim Mohiuddin, Basel Mousi, Hamdy Mubarak, Ahmad Musleh, Zan Naeem, Mourad Ouzzani, Dorde Popovic, Amin Sadeghi, Husrev Taha Sencar, Mohammed Shinoy, Omar Sinan, Yifan Zhang, Ahmed Ali, Yassine El Kheir, Xiaosong Ma, and Chaoyi Ruan, arXiv:2501.13944v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Description of Compression Datasets}
\begin{tabular}{lll}
 \toprule
 \textbf{Name} & \textbf{Size} & \textbf{Description} \\
 \midrule
 Book & 1000 MB & First 1000M bytes of BookCorpus~. \\
 Enwik9 & 1000 MB & First 1000M bytes of English Wikipedia~. \\
 Float & 1.1 GB & Spitzer Space Telescope data~. \\
 Sound & 842 MB & ESC~ dataset for environmental sound. \\
 Image & 1.2 GB & 100{,}000 images from ImageNet~. \\
 Backup & 1000 MB & Random 1000M byte extract from a disk backup. \\
 Silesia & 206 MB & A standard compression benchmark~. \\
 \bottomrule
 \end{tabular}
\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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of VoxelHop and 3D CNNs}
\begin{tabular}{c|c|c}\hline\hline
 & VoxelHop (SSL) & CNNs \\ \hline

Model interpretability & Easy & Difficult \\ \hline Weak supervision & Easy & Difficult \\ \hline
Training/testing complexity & Low & High \\ \hline
Model parameter search & Feedforward design & Backpropagation \\ \hline
Model expandability & Non-parametric model & Parametric model \\ \hline\hline

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

eess

Xiaofeng Liu
Fangxu Xing
Chao Yang
C. -C. Jay Kuo
Suma Babu
Georges El Fakhri
Thomas Jenkins
Jonghye Woo

"VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI" by Xiaofeng Liu, Fangxu Xing, Chao Yang, C. -C. Jay Kuo, Suma Babu, Georges El Fakhri, Thomas Jenkins, and Jonghye Woo, arXiv:2101.05131v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Economic performance loss resulted from boundary moving and target moving}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 \hline
 & & Ideal $\bar J_{\rm{ep}}$ & Actual $\bar J_{\rm{ep}}$ & Loss of $\bar J_{\rm{ep}}$ \\
 \hline
 \multirow{2}{*}{Economic MPC} & Cal & 501 & 503.5158 & 2.5158 (0.50\%) \\
 & Sim & 501 & 503.5214 & 2.5214 (0.50\%) \\
 \multirow{2}{*}{Tracking MPC} & Cal & 501 & 505.3345 & 4.3345 (0.87\%) \\
 & Sim & 501 & 505.2858 & 4.2858 (0.86\%) \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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

"Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise" by Jiangbang Liu, Song Bo, Benjamin Decardi-Nelson, Jinfeng Liu, Jingtao Hu, and Tao Zou, arXiv:2201.01828v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 Group & Order \\
 \hline
 \hline
 $\mathcal{A}_3^p$ & $24$ \\
 \hline
 $\mathcal{B}_3^p$ & $48$ \\
 \hline
 $\mathcal{H}_3^p$ & $120$ \\
 \hline
 $[3, 6]_{(s, 0)}, \; s \geq 2$ & $12s^2$ \\
 \hline
 $O(3, q, 0)$ & $2q(q^2 - 1)$ \\
 \hline
 $O_1(3, q, 0)$ & \multirow{2}{*}{$q(q^2 - 1)$} \\
 \cline{1-1}
 $O_2(3, q, 0)$ & \\
 \hline
 $O(4, q, 1)$ & $2q^2(q^2 - 1)^2$ \\
 \hline
 $O_1(4, q, 1)$ & \multirow{2}{*}{$q^2(q^2 - 1)^2$} \\
 \cline{1-1}
 $O_2(4, q, 1)$ & \\
 \hline
 $O(4, q, -1)$ & $2q^2(q^2 + 1)(q^2 - 1)$ \\
 \hline
 $O_1(4, q, -1)$ & \multirow{2}{*}{$q^2(q^2 + 1)(q^2 - 1)$} \\
 \cline{1-1}
 $O_2(4, q, -1)$ & \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Relevant groups in the classification of the reduction modulo an odd prime $p$ of the star Coxeter group $[5, 3; k]$ and its distinguished subgroups of rank 3.}
\end{table}
\end{document}

math

Mark L. Loyola
Nonie Elvin S. Leyrita
Ma. Louise Antonette N. De Las Penas

"Non-crystallographic tail-triangle C-groups of rank 4 and interlacing number 2" by Mark L. Loyola, Nonie Elvin S. Leyrita, and Ma. Louise Antonette N. De Las Penas, arXiv:2202.02598v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Shaheer Ahmad Khan
Muhammad Usamah Shahid
Ahmad Abdullah
Ibrahim Hashmat
Muddassar Farooq

"An Explainable Disease Surveillance System for Early Prediction of Multiple Chronic Diseases" by Shaheer Ahmad Khan, Muhammad Usamah Shahid, Ahmad Abdullah, Ibrahim Hashmat, and Muddassar Farooq, arXiv:2501.15969v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Daniel J. Cole
Stuart J. Hall
Rachael Pirie

"Riemannian Geometry and Molecular Surfaces I: Spectrum of the Laplacian" by Daniel J. Cole, Stuart J. Hall, and Rachael Pirie, arXiv:2201.04230v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
 \hline &\multicolumn{2}{c|}{ACAT2}&\multicolumn{2}{c|}{WBACAT2}& \multicolumn{2}{c|}{ACAT4} & \multicolumn{2}{c|}{WBACAT4} \\
 Points & Order & Error & Order & Error & Order & Error & Order& Error \\
 \hline
 80 & - & 1.74E-3 & - & 1.91E-3 & - & 2.41E-4 & - & 2.42E-4 \\
 \hline
 160 & 1.93 & 4.11E-4 & 1.92 & 5.05E-4 & 2.40 & 4.56E-5 & 2.41 & 4.57E-5 \\
 \hline
 320 & 1.93 & 1.08E-4 & 1.94 & 1.32E-4 & 3.17 & 5.05E-6 & 3.18 & 5.06E-6 \\
 \hline
 640 & 1.98 & 2.74E-5 & 1.98 & 3.34E-5 & 3.68 & 3.95E-7 & 3.68 & 3.94E-7 \\
 \hline
 1280 & 1.99 & 6.91E-6 & 1.99 & 8.41E-6 & 3.91 & 2.60E-8 & 3.92 & 2.61E-8 \\
 \hline
 2560 & 2.00 & 1.73E-6 & 2.00 & 2-11E-6 & 3.98 & 1.65E-9 & 3.98 & 1.65E-9 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Test : Errors in $L^1$ norm and convergence rates for ACAT2, ACAT4, WBACAT2 and WBACAT4 at time $t=0.5$ and CFL$=0.9.$}
\end{table}
\end{document}

math

"Well-balanced adaptive compact approximate Taylor methods for systems of balance laws" by H. Carrillo, E. Macca, C. Pares, and G. Russo, arXiv:2202.02068v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The influence of different input information on the performance of the LSTM on 5-shot sine wave regression. 95\% confidnce intervals are displayed as $\pm x$. The best performances are dislpayed in bold font.}
\begin{tabular}{ccccc}
 \toprule
 Input $\mathbf{x}_t$ & Prev target $y_{t-1}$ & Prev pred $\hat{y}_{t-1}$ & Prev error $e_{t-1}$ & 5-shot MSE \\
 \midrule
 \checkmark & \checkmark & & & 0.04 $\pm$ 0.002 \\
 \checkmark & \checkmark & \checkmark & & \textbf{0.03} $\pm$ 0.002 \\
 \checkmark & \checkmark & & \checkmark & 0.05 $\pm$ 0.004 \\
 \checkmark & \checkmark & \checkmark & \checkmark & 0.06 $\pm$ 0.011 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Are LSTMs Good Few-Shot Learners?

Mike Huisman
Thomas M. Moerland
Aske Plaat
Jan N. van Rijn

"Are LSTMs Good Few-Shot Learners?" by Mike Huisman, Thomas M. Moerland, Aske Plaat, and Jan N. van Rijn, arXiv:2310.14139v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Fine-grained behavioral analysis of SFT-72b and MaKTO-72b models against GPT-4o}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccc|cc|cc|cc}
\toprule
\multirow{2}{*}{Model} & \multicolumn{3}{c|}{General Performance} & \multicolumn{2}{c|}{Seer Role} & \multicolumn{2}{c|}{Witch Role} & \multicolumn{2}{c}{Guard Role} \\
\cmidrule{2-10}
 & Vote Acc. & Abstention & WW Check & Save & Poison & Mispoison & Protect & Misprotect & Performance↓ \\
\midrule
\multicolumn{10}{l}{\textit{As Villagers (vs GPT-4o Werewolf)}} \\
SFT-72b & 66.3\% & 6.7\% & 60.0\% & 58.0\% & 48.0\% & 38.0\% & 34.1\% & 9.5\% & - \\
MaKTO-72b & \textbf{72.7\%} & \textbf{3.2\%} & \textbf{75.7\%} & \textbf{100.0\%} & \textbf{72.0\%} & \textbf{26.0\%} & \textbf{37.6\%} & \textbf{2.3\%} & - \\
\midrule
\multicolumn{10}{l}{\textit{As Werewolves (vs GPT-4o Villagers)}} \\
SFT-72b & - & 2.2\% & 60.4\% & - & - & 32.0\% & - & 33.6\% & 32.1\% \\
MaKTO-72b & - & \textbf{4.6\%} & \textbf{57.4\%} & - & - & \textbf{48.0\%} & - & \textbf{34.7\%} & \textbf{36.2\%} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Rong Ye
Yongxin Zhang
Yikai Zhang
Haoyu Kuang
Zhongyu Wei
Peng Sun

"Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game" by Rong Ye, Yongxin Zhang, Yikai Zhang, Haoyu Kuang, Zhongyu Wei, and Peng Sun, arXiv:2501.14225v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The classification accuracies (in \%) of SAFE-OCC Configuration 1 for each CNN sensor. The shaded and non-shaded values denote the normal and novelties accuracies, respectively, (with respect to the training data of each sensor).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c|c|c|c|c|c|c|}
 \hline
 \multirow{2}{*}{Sensor} & \multicolumn{7}{c|}{Test Data} \\
 \cline{2-8}
 & Original & Blockages & Blurred & Fogged & Noised & Shifted & Splattered \\
 \hline \cline{2-8}
 A & \cellcolor{black!15}98.57 & 74.61 & 100.00 & 100.00 & 100.00 & 74.87 & 100.00 \\
 B & \cellcolor{black!15}95.70 & \cellcolor{black!15}95.70 & 21.68 & 100.00 & 100.00 & 11.65 & 100.00 \\
 C & \cellcolor{black!15}98.76 & 82.49 & \cellcolor{black!15}98.76 & 100.00 & 100.00 & 44.92 & 100.00 \\
 D & \cellcolor{black!15}99.74 & 67.12 & 87.89 & \cellcolor{black!15}99.74 & 1.30 & 27.93 & 100.00 \\
 E & \cellcolor{black!15}99.35 & 63.15 & 100.00 & 100.00 & \cellcolor{black!15}99.35 & 28.58 & 100.00 \\
 F & \cellcolor{black!15}97.20 & 68.68 & 97.85 & 100.00 & 100.00 & \cellcolor{black!15}97.20 & 100.00 \\
 G & \cellcolor{black!15}98.44 & 48.50 & 100.00 & 100.00 & 100.00 & 26.17 & \cellcolor{black!15}98.44 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Joshua L. Pulsipher
Luke D. J. Coutinho
Tyler A. Soderstrom
Victor M. Zavala

"SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control" by Joshua L. Pulsipher, Luke D. J. Coutinho, Tyler A. Soderstrom, and Victor M. Zavala, arXiv:2202.01816v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|c|c|}\hline
 \textbf{Cluster} &0 &1 &2 & 3 \\\hline
 \textbf{SKUs} & 3754 & 3365 & 4685 & 1921
 \\\hline
 \end{tabular}
\caption{Number of SKUs assigned to each cluster}
\end{table}
\end{document}

stat

Saed Alizamir
Kasun Bandara
Ali Eshragh
Foaad Iravani

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ \textbf{The proposed and geometric average efficiency scores and ranks of DMUs} }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|ll|ll|}
				\hline
				DMUs & $E_{k}^{*O}$& $E_{k}^{*P}$ & $E_{k}^{geometric}$ & Proposed rank & Wang et al.& Rank\\ \hline

				$D_A$ &0.6579&	1.2611 & 0.9108&~~~~~~~4&1.090&3
				\\
				$D_B$ &0.7347&1.4335&1.0262&~~~~~~~1&1.154&1
				\\
				$D_C$ &0.6822&1.1158&0.8725&~~~~~~~5&1.092&2

				\\
				$D_D$ &0.8061&1.2151&0.9867&~~~~~~~3&1.154&1

				\\
				$D_E$ &0.8011&1.2675&1.0076 &~~~~~~~2&1.154&1
				\\
				\hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Performance evaluation of DMUs using hybrid fuzzy multi-objective data envelopment analysis" by Awadh Pratap Singh and Shiv Prasad Yadav, arXiv:2202.01585v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Main Results.} The table compares the experimental results of various methods, including Inference Only and Frozen LLM w/ PT. Our model (Ours) demonstrated the highest performance, surpassing both Inference Only and Frozen LLM w/ PT approaches. Compared to G-Retriever, our model shows performance improvements ranging from 2.38\% to 5.77\%, with an average improvement of 4.13\%. \newline}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccc}
\hline
 & & \# of Prompt Tokens & Expla Graphs & SceneGraphs & WebQSP & Average \\ \hline
\multirow{4}{*}{Inference Only} & Zero-Shot & - & 56.50 & 39.74 & 41.06 & 45.77 \\
 & Zero-CoT & - & 57.04 & 52.60 & 51.30 & 53.65 \\
 & CoT-BAG & - & 57.94 & 56.80 & 39.60 & 51.45 \\
 & KAPING & - & 62.27 & 43.75 & 52.64 & 52.89 \\ \hline
\multirow{5}{*}{Frozen LLM w/ PT} & Prompt Tuning & 10 & 57.63 & 63.41 & 48.34 & 56.46 \\
 & Graph Token & 1 & 85.08 & 49.03 & 57.05 & 63.72 \\
 & G-Retriever & 1 & 85.16 & 81.31 & 70.49 & 78.99 \\ \cline{2-7}
 & Ours & \multirow{2}{*}{8} & \textbf{90.07} & \textbf{84.50} & \textbf{72.17} & \textbf{82.25} \\
 & $\Delta$ G-Retriever & & +5.77\% & +3.92\% & +2.38\% & +4.13\% \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Query-Aware Learnable Graph Pooling Tokens as Prompt for Large Language Models" by Wooyoung Kim, Byungyoon Park, and Wooju Kim, arXiv:2501.17549v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Thomas A. Henzinger
Fabian Kresse
Kaushik Mallik
Emily Yu
Đorđe Žikelić

"Predictive Monitoring of Black-Box Dynamical Systems" by Thomas A. Henzinger, Fabian Kresse, Kaushik Mallik, Emily Yu, and Đorđe Žikelić, arXiv:2412.16564v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc|cc}
 \toprule
 \multirow{3}{*}{\shortstack{Training \\ Scenario}} & \multicolumn{2}{c|}{\shortstack{FF++}} & \multicolumn{2}{c}{\shortstack{Celeb DF}} \\
 \cmidrule(lr){2-3} \cmidrule(lr){4-5}
 & Acc (\%) & EER (\%) & Acc (\%) & EER (\%) \\
 \midrule
 FRM Finetuned & \textbf{99.43} & \textbf{1.23} & \textbf{95.23} & \textbf{4.00} \\
 UAD Finetuned & 95.03 & 5.46 & 86.50 & 15.3 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Face matching accuracy on FF++ and CelebDF datasets: (Row 1) Swin Transformer model fine-tuned for FRM using CASIA-WebFace dataset and (Row 2) the same model further fine-tuned for UAD using augmented UniAttack dataset, tested for FRAM (face recognition performance).}
\end{table}
\end{document}

cs

"Unified Face Matching and Physical-Digital Spoofing Attack Detection" by Arun Kunwar and Ajita Rattani, arXiv:2501.09635v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation results of outlier detection using the proposed and MS plot methods for different models. The values are the means shown as percentages in the 500 experiments for each case, and the values in parentheses are the corresponding standard deviations.}
\begin{tabular}{|c|cc|cc|cc|ll}
\cline{1-7}
\multirow{2}{*}{\textbf{Method}} & \multicolumn{2}{c|}{\textbf{Model 1}} & \multicolumn{2}{c|}{\textbf{Model 2}} & \multicolumn{2}{c|}{\textbf{Model 3}} & \multicolumn{2}{c}{\textbf{}} \\ \cline{2-7}
 & \textbf{TPR} & \textbf{FPR} & \textbf{TPR} & \textbf{FPR} & \textbf{TPR} & \textbf{FPR} & \textbf{} & \\ \cline{1-7}
\textbf{Proposed} & 99.90 (0.98) & 1.51 (0.81) & 100.00 (0.00) & 1.54 (0.78) & 100.00 (0.00) & 1.49 (0.72) & & \\ \cline{1-7}
\textbf{MS plot} & 0.56 (2.48) & 0.19 (0.50) & 100.00 (0.00) & 0.22 (0.54) & 1.33 (4.21) & 0.13 (0.44) & & \\ \cline{1-7}
\end{tabular}
\end{table}
\end{document}

stat

Cristian F. Jimenez-Varon
Fouzi Harrou
Ying Sun

"Pointwise Data Depth for Univariate and Multivariate Functional Outlier Detection" by Cristian F. Jimenez-Varon, Fouzi Harrou, and Ying Sun, arXiv:2311.02634v1, 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{Prototype self-powered system loss parameters}
\begin{tabular}{lc}
Parameter & Value\\
\hline\hline
Leakage time constant ($\tau_s$) & 344.9 s \\
Transmission time constant ($\tau_r$) & 0.0085 s \\
Resistance ($R$) & 10.96 $\Omega$ \\
Capacitance ($C_s$) & 0.0991 F \\
Leakage resistance ($R_s$) & 3480 $\Omega$ \\
Series resistance ($R_r$) & 0.086 $\Omega$ \\
Static power loss ($P_0$) & 0.155 W \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Connor Ligeikis
Heath Hofmann
Jeff Scruggs

"Large-Scale Self-Powered Vibration Control: Theory and Experiment" by Connor Ligeikis, Heath Hofmann, and Jeff Scruggs, arXiv:2509.11346v1, 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{Performance comparison with parameter adjustment algorithms and meta-learning algorithms}
\begin{tabular}{l|ccc}
\textbf{Dataset} & \textbf{MERINA} & \textbf{ComTree(M)} & \textbf{Oboe} \\
\hline
Norway & 0.97($\pm$ 0.61) & 0.94($\pm$ 0.65) & 0.88($\pm$ 0.68) \\
\hline
Oboe & 2.20($\pm$ 1.09) & 2.19($\pm$ 1.10) & 2.19($\pm$ 1.15) \\
\hline
Puffer-2110 & 0.77($\pm$ 2.01) & 0.76($\pm$ 1.98) & 0.77($\pm$ 1.89) \\
\hline
Puffer-2202 & 0.85($\pm$ 2.98) & 0.82($\pm$ 2.99) & 0.77($\pm$ 3.10) \\
\end{tabular}
\end{table}
\end{document}

eess

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

"Beyond Interpretability: Exploring the Comprehensibility of Adaptive Video Streaming through Large Language Models" by Lianchen Jia, Chaoyang Li, Ziqi Yuan, Jiahui Chen, Tianchi Huang, Jiangchuan Liu, and Lifeng Sun, arXiv:2508.16448v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Van Quang Nguyen
Quoc Chuong Nguyen
Thu Huong Dang
Truong-Son Hy

"Hybridising Reinforcement Learning and Heuristics for Hierarchical Directed Arc Routing Problems" by Van Quang Nguyen, Quoc Chuong Nguyen, Thu Huong Dang, and Truong-Son Hy, arXiv:2501.00852v1, 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{Simulation Parameters}
\begin{tabular}{ll}\hline
Sampling rate $F_s$ & 15.36~MHz / 200~MHz \\
FFT size $N_{\mathrm{FFT}}$ & 512 \\
CP length $N_{\mathrm{CP}}$ & $N_{\mathrm{FFT}}/8$\\
Carrier $f_c$ & 2.4~GHz or 77~GHz\\
Modulation index $m$ & $0.6/(2\pi)$, $0.9/(2\pi)$\\
Channel model & AWGN / multipath \\
\\\hline
\end{tabular}
\end{table}
\end{document}

eess

"FM OFDM Unifying High Mobility Communications and Sensing" by Amir Bouziane and Huseyin Arslan, arXiv:2508.16107v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
Layer & Number of neurons & Activation \\
& & function \\ \hline
input & 100 - 1000 & ReLU \\ \hline
hidden layer 1 & 100 - 1000 & ReLU \\ \hline
output 2 & equal to & none \\
 & the number of coefficients & \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Network architecture of the DNN used to approximate the coefficients of the heat transfer solution. The number of neurons in the hidden layer varied with inputs. For 169 output coefficients the hidden layer had 100 neurons and the number rose to as much as 1000 neurons for 2025 output coefficients. ReLU stands for Rectified Linear Unit.}
\end{table}
\end{document}

math

Kamil Doległo
Anna Paszyńska
Maciej Paszyński
Leszek Demkowicz

"Deep neural networks for smooth approximation of physics with higher order and continuity B-spline base functions" by Kamil Doległo, Anna Paszyńska, Maciej Paszyński, and Leszek Demkowicz, arXiv:2201.00904v1, 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{Parameters for IEEE floating point precisions. The range denotes the order of magnitude of the largest and smallest positive normalized floating point numbers. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l}
Type & Size & Range & Unit Roundoff $u$ \\ \hline
half & 16 bits & $10^{\pm 5}$ & $2^{-11} \approx 4.9\cdot 10^{-4}$ \\
single & 32 bits & $10^{\pm 38}$ & $2^{-24} \approx 6.0\cdot 10^{-8}$ \\
double & 64 bits & $10^{\pm 308}$ & $2^{-53} \approx 1.1\cdot 10^{-16}$ \\
quad & 128 bits & $10^{\pm 4932}$ & $2^{-113} \approx 9.6\cdot 10^{-35}$
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Mixed Precision Iterative Refinement with Sparse Approximate Inverse Preconditioning" by Erin Carson and Noaman Khan, arXiv:2202.10204v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Assessing Copula Models for Mixed Continuous-Ordinal Variables" by Shenyi Pan and Harry Joe, arXiv:2310.08020v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Ensemble methods for testing a global null" by Yaowu Liu, Zhonghua Liu, and Xihong Lin, arXiv:2310.10407v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Intersection-Aware Assessment of EMS Accessibility in NYC: A Data-Driven Approach" by Haoran Su and Joseph Y. J. Chow, arXiv:2412.04369v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{hhline}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The performance of DA in identifying the correct network topology: the impact of loads type and loading level.}
\begin{tabular}{c|c|c|c|c|c|c|c}
\hline
\multicolumn{2}{c||}{Scenario $\rightarrow$} &
\multicolumn{2}{c||}{Main} &
\multicolumn{2}{c||}{Loss of commu.} &
\multicolumn{2}{c}{DER3 anomalous} \\
\hhline{~~~~~~~~}
\multicolumn{2}{c||}{} &
\multicolumn{2}{c||}{} &
\multicolumn{2}{c||}{from DER3} &
\multicolumn{2}{c}{measurements} \\
\hline
\multicolumn{2}{c||}{Misidentification of: $\rightarrow$} &
SC$^{\mathrm{a}}$ &
PDS$^{\mathrm{b}}$ &
SC &
PDS &
SC &
PDS \\
\hhline{--~~~~~~}
Load type $\downarrow$ &
Loading $\downarrow$ &
 &
 &
 &
 &
 &
 \\
\hline
Original &
Original &
0.07\% &
1.44\% &
2.20\% &
2.06\% &
2.27\% &
2.04\% \\
\hhline{~~~~~~~~}
PQ$^{\mathrm{c}}$ &
Original &
0.10\% &
1.70\% &
1.70\% &
2.37\% &
1.72\% &
2.37\% \\
\hhline{~~~~~~~~}
Z$^{\mathrm{d}}$ &
Original &
0.02\% &
3.50\% &
1.61\% &
3.40\% &
1.61\% &
3.40\% \\
\hhline{~~~~~~~~}
I$^{\mathrm{e}}$ &
Original &
0.01\% &
3.30\% &
1.42\% &
3.01\% &
1.44\% &
3.01\% \\
\hhline{~~~~~~~~}
Original &
P 120\% &
0.02\% &
0.32\% &
1.18\% &
0.48\% &
1.21\% &
0.48\% \\
\hhline{~~~~~~~~}
Original &
Q 120\% &
0.02\% &
1.50\% &
1.23\% &
2.10\% &
1.25\% &
2.10\% \\
\hline
\multicolumn{8}{l}{$^{\mathrm{a}}$ Switching configuration; $^{\mathrm{b}}$ Protective devices status;} \\
\hhline{~~~~~~~~}
\multicolumn{8}{l}{$^{\mathrm{c}}$ Constant power; $^{\mathrm{d}}$ Constant impedance; $^{\mathrm{e}}$ Constant current.} \\
\hhline{~~~~~~~~}
\end{tabular}
\end{table}
\end{document}

eess

Mohammad Jafarian
Alireza Soroudi
Andrew Keane

"Resilient Identification of Distribution Network Topology" by Mohammad Jafarian, Alireza Soroudi, and Andrew Keane, arXiv:2011.07981v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of models for detecting billboards after fine-tuning on our dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|lcc|}
 \hline
 Model &	AP@0.5 &	AP@0.5:0.95 \\
 \hline
YOLOv5 &	96.6\% &	69.5\% \\
Faster R-CNN &	89.6\% &	58.4\% \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zuzana Berger Haladova
Michal Zrubec
Zuzana Cernekova

"A method for estimating roadway billboard salience" by Zuzana Berger Haladova, Michal Zrubec, and Zuzana Cernekova, arXiv:2501.07342v1, 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}{lcccc}
Test & Adherence To & Unison & Audio \\
 Case & Melody & Perception & Quality \\ \toprule
UTS & $3.6 \pm 0.93$& & $2.1 \pm 0.65$ \\
STU\_PS & $3.3 \pm 0.83$ & $2.6 \pm 0.85$ & $2.8 \pm 0.45$ \\
STU\_PTS & $2.9 \pm 1.14$& $3.2 \pm 0.96$ & $3.1 \pm 0.63$ \\
STU\_TS & & $2.3 \pm 1.11$& \\
STU\_PT & & $3.0 \pm 1.23$& \\
\end{tabular}
\caption{Mean Opinion Score (MOS) $\pm$ Standard Deviation for the perceptual listening tests across the test cases provided. The models shown corresdond to the Unison to Solo (UTS) model, the Solo to Unison with pitch, timing and singer variations, indicated by the addition of the letters P,T and S as suffixes to the abbreviation, respectively. The scores for each question were normalized by the responses to the upper and lower limits for the responses defined in section .}
\end{table}
\end{document}

eess

Pritish Chandna
Helena Cuesta
Emilia Gómez

"A Deep Learning Based Analysis-Synthesis Framework For Unison Singing" by Pritish Chandna, Helena Cuesta, and Emilia Gómez, arXiv:2009.09875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
 \toprule
 \textbf{Feature} & \textbf{Permutation} & \textbf{SHAP} & \textbf{LOCO} & \textbf{CMR} \\
 \midrule
 NO2 & 1 & 2 & 1 & 1 \\
 Skin reservoir content & 5 & 4 & 3 & 3 \\
 Thermal radiation & 2 & 3 & 5 & 6 \\
 Snow depth & 4 & 6 & 4 & 5 \\
 PM2.5 & 7 & 5 & 6 & 2 \\
 Net annual income & 9 & 8 & 2 & 4 \\
 White ethnicity & 3 & 1 & 21 & 14 \\
 Pct commute by metro rail & 10 & 11 & 8 & 11 \\
 Total precipitation & 8 & 14 & 9 & 13 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Anxiety variable importance and ranks for the top 10 variables after reordering by mean rank.}
\end{table}
\end{document}

cs

Ishaan Maitra
Raymond Lin
Eric Chen
Jon Donnelly
Sanja Šćepanović
Cynthia Rudin

"How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data" by Ishaan Maitra, Raymond Lin, Eric Chen, Jon Donnelly, Sanja Šćepanović, and Cynthia Rudin, arXiv:2501.02111v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|}
\hline
 & B-DeepONet & Prob-DeepONet \\
\hline
\hline
$\epsilon_\text{ratio}$ & 93.32 \% & 94.27 \% \\
\hline
\end{tabular}
\end{adjustbox}
\caption{The average $\epsilon_\text{ratio}$ of all the post-fault trajectories in~$\mathcal{D}_\text{test}$.}
\end{table}
\end{document}

math

Christian Moya
Shiqi Zhang
Meng Yue
Guang Lin

"DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories" by Christian Moya, Shiqi Zhang, Meng Yue, and Guang Lin, arXiv:2202.07176v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The settings for training surrogate models.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
 \toprule
 Config & Value \\
 \midrule
 Optimizer & SGD, AdamW (InceptionNeXt)\\
 Weight decay & $5\times 10^{-4}$ \\
 learning rate & 0.01 (CIFAR10, GTSRB), 0.001 (ImageNet200) \\
 epoch & 200 (GTSRB, CIFAR10), 100 (ImageNet200) \\
 \multirow{2}{*}{learning rate schedule} & MultiStepLR (100, 150) for CIFAR10 and GTSRB, \\ ~ & CosineAnnealingLR for ImageNet200\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xiaoyun Xu
Zhuoran Liu
Stefanos Koffas
Stjepan Picek

"Towards Backdoor Stealthiness in Model Parameter Space" by Xiaoyun Xu, Zhuoran Liu, Stefanos Koffas, and Stjepan Picek, arXiv:2501.05928v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.