\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{CNN-3D Layers of 3DCNN+LSTM/GRU architecture.}
\begin{tabular}{|c|c|c|}
		\hline
			\textbf{Layer (type)} & \textbf{Output Shape} & \textbf{Param} \\
			\hline
			InputLayer & [(None, 128, 128, 64, 1)] & 0 \\
			\hline
			Conv3D & (None, 126, 126, 62, 64) & 1792\\
			\hline
 		MaxPooling3D & (None, 63, 63, 31, 64) & 0\\
			\hline
			BatchNormalization & (None, 63, 63, 31, 64) & 256\\
			\hline
			Conv3D & (None, 61, 61, 29, 64) & 110656\\
			\hline
 		MaxPooling3D & (None, 30, 30, 14, 64) & 0\\
			\hline
			BatchNormalization & (None, 30, 30, 14, 64) & 256\\
			\hline
			Conv3D & (None, 28, 28, 12, 128) & 221312\\
			\hline
			MaxPooling3D & (None, 14, 14, 6, 128) & 0\\
			\hline
			BatchNormalization & (None, 14, 14, 6, 128) & 512\\
			\hline
			Conv3D & (None, 12, 12, 4, 256) & 884992\\
			\hline
			MaxPooling3D & (None, 6, 6, 2, 256) & 0\\
			\hline
			BatchNormalization & (None, 6, 6, 2, 256) & 1024\\
			\hline
			GlobalMaxPooling3D & (None, 256) & 0\\
		\hline
		\end{tabular}
\end{table}
\end{document}

eess

Maria Frasca
Davide La Torre
Gabriella Pravettoni
Ilaria Cutica

"Predicting Parkinson's disease evolution using deep learning" by Maria Frasca, Davide La Torre, Gabriella Pravettoni, and Ilaria Cutica, arXiv:2312.17290v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Test game results with the memetic operators and random patterns.}
\begin{tabular}{c|c|c|c|c|c}
\toprule
\multirow{2}{*}{\textbf{Year}} & \multirow{2}{*}{\textbf{Human Survivor}} & \multicolumn{2}{c|}{\textbf{w/o memetic operators}} & \multicolumn{2}{c}{\textbf{w/ memetic operators}} \\\cline{3-6}
&&\textbf{Avg. Score} & \textbf{\#Victories} & \textbf{Avg. Score} & \textbf{\#Victories} \\
\midrule
2010 & FSM & 0.481 & 3/10 & 0.8 & 8/10 \\
2014 & IamAA & 0.478 & 6/10 & 0.9 & 9/10 \\
2016 & LoudBugFix & 0.402 & 2/10 & 0.9 & 9/10 \\
2022 & TheHeapMen & 0.494 & 4/10 & 1 & 10/10 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Irina Maliukov
Gera Weiss
Oded Margalit
Achiya Elyasaf

"Evolving Assembly Code in an Adversarial Environment" by Irina Maliukov, Gera Weiss, Oded Margalit, and Achiya Elyasaf, arXiv:2403.19489v2, 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}{cc}
\hline
\textbf{Key} & \textbf{Value} \\
\hline
Task ID & 08aced46-45a2-48d7-993b-ed3fb5b32302\\
Instruction & Give the slide 2 a right aligned title, "Note". \\
Visual & True\\
Max Steps & 30\\
Max Time & 60.0\\
Evaluation Procedure & Compare between \texttt{ref.pptx} and \texttt{target.pptx} \\
Reset Procedure & 1. Create folder structure, 2. Copy file, 3. Open PPTX file \\
Cleanup Procedure & 1. Delete folder structure, 2.Kill LibreOffice process\\
\hline
\end{tabular}
\caption{Task configuration with simplified evaluation/reset/cleanup procedures.}
\end{table}
\end{document}

cs

Longtao Zheng
Zhiyuan Huang
Zhenghai Xue
Xinrun Wang
Bo An
Shuicheng Yan

"AgentStudio: A Toolkit for Building General Virtual Agents" by Longtao Zheng, Zhiyuan Huang, Zhenghai Xue, Xinrun Wang, Bo An, and Shuicheng Yan, arXiv:2403.17918v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary Statistics of Investor Characteristics}
\begin{tabular}{ccccc}
 \midrule
 Characteristics & Description & \# of Investors & Value & Percentage \\
 \midrule
 \multirow{3}[1]{*}{Income} & Not Stable & 9,328 & 0 & 74.98\% \\
 & Stable & 2,125 & 1 & 17.08\% \\
 & Stable and High & 988 & 2 & 7.94\% \\
 \midrule
 \multirow{3}[0]{*}{Liability} & High & 246 & 0 & 1.98\% \\
 & Medium & 10,531 & 1 & 84.65\% \\
 & Low & 1,664 & 2 & 13.38\% \\
 \midrule
 \multirow{3}[0]{*}{Education} & High School or Below & 9,696 & 0 & 77.94\% \\
 & Bachelor & 2,223 & 1 & 17.87\% \\
 & Graduate & 522 & 2 & 4.20\% \\
 \midrule
 \multicolumn{5}{p{36em}}{\scriptsize Notes: Investor characteristics are encoded into ordinal values for investor segmentation by socioeconomic status. Higher values represent higher income, education, and fewer liabilities.} \\
 \end{tabular}
\end{table}
\end{document}

q-fin

"The Effect of Product Recommendations on Online Investor Behaviors" by Ruiqi Rich Zhu, Cheng He, and Yu Jeffrey Hu, arXiv:2303.14263v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c}
				\hline\hline
				TMHs & Haar-Schauder multiwavelet localized segments \\
				\hline
				1 & 120--134 \\
				2 & 233--253 \\
				3 & 359--373 \\
				4 & 505--523 \\
				5 & 678--699 \\
				6 & 824--842 \\
				7 & 1056--1069 \\
				8 & 1199--1212 \\
				\hline\hline
			\end{tabular}
\caption{The TMHs Segments for Haar-Schauder filtering of the coronavirus signal.}
\end{table}
\end{document}

eess

Malika Jallouli
Makerem Zemni
Anouar Ben Mabrouk
Mohamed Ali Mahjoub

"Towards New Multiwavelets: Associated Filters and Algorithms. Part I: Theoretical Framework and Investigation of Biomedical Signals, ECG and Coronavirus Cases" by Malika Jallouli, Makerem Zemni, Anouar Ben Mabrouk, and Mohamed Ali Mahjoub, arXiv:2103.08657v1, licensed under CC0 1.0. 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{Local maxima occurrences (\%) with high error}
\begin{tabular}{c|c|c|c|c|c|c|c|c|c|}
 \toprule
 Random Start & 1 & 5 & 10 & 20 & 30 & 40 & 50 & 70 & 100\\
 $\%$ of local maxima & 71 & 23 & 9 & 0 & 0 & 0 & 0 & 0 & 0 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Ilaria Bombelli
Domenica Fioredistella Iezzi
Emiliano Seri
Maurizio Vichi

"Spherical Double K-Means: a co-clustering approach for text data analysis" by Ilaria Bombelli, Domenica Fioredistella Iezzi, Emiliano Seri, and Maurizio Vichi, arXiv:2501.04562v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccc}
\hline
\multicolumn{6}{c}{\cellcolor[HTML]{C0C0C0}\textbf{Style loss}} \\
\textbf{Param.} & 1000 & 5000 & 15000 & 20000 & 30000 \\
\textbf{Acc.} & 38.6 & \textbf{44.5} & 44.0 & 44.2 & 40.1 \\ \hline
\multicolumn{6}{c}{\cellcolor[HTML]{C0C0C0}\textbf{Content loss}} \\
\textbf{Param.} & 100 & 500 & 700 & 1000 & 1500 \\
\textbf{Acc.} & 38.8 & 39.4 & 42.6 & \textbf{44.5} & 39.8 \\ \hline
\multicolumn{6}{c}{\cellcolor[HTML]{C0C0C0}\textbf{Marginal loss}} \\
\textbf{Param.} & 50 & 100 & 150 & 200 & 250 \\
\textbf{Acc.} & 38.7 & 38.9 & 41.6 & \textbf{44.5} & 42.4 \\ \hline
\end{tabular}
\caption{Hyperparameter analysis for ImageNet-R}
\end{table}
\end{document}

cs

Yun-Yun Tsai
Fu-Chen Chen
Albert Y. C. Chen
Junfeng Yang
Che-Chun Su
Min Sun
Cheng-Hao Kuo

"GDA: Generalized Diffusion for Robust Test-time Adaptation" by Yun-Yun Tsai, Fu-Chen Chen, Albert Y. C. Chen, Junfeng Yang, Che-Chun Su, Min Sun, and Cheng-Hao Kuo, arXiv:2404.00095v2, 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{Frequency of Special Characters}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

math

Dinesh Parthasarathy
Tommaso Bevilacqua
Martin Lanser
Axel Klawonn
Harald Köstler

"Towards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations" by Dinesh Parthasarathy, Tommaso Bevilacqua, Martin Lanser, Axel Klawonn, and Harald Köstler, arXiv:2412.08186v1, 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 of separation methods on LibriCSS eval set in terms of resulting downstream diarization (using spectral clustering) and ASR (using TDNN-F model) results. For comparison, we also show results obtained on a ``no separation'' baseline. Separation performance is reported on a simulated eval set. $^{\dag}$MVDR beamformer does not attempt to reconstruct the target signal at the reference microphone directly, so the separation metric reflects this mismatch.}
\begin{tabular}{lccc}
\toprule
\textbf{Method} & \textbf{SDR (dB)} & \textbf{DER (\%)} & \textbf{cpWER (\%)} \\ \midrule
No separation & - & 18.3 & 31.0 \\
Mask-based MVDR & 5.8$^{\dag}$ & \textbf{13.9} & 22.8 \\
Sequential multi-frame & \textbf{14.1} & 14.1 & \textbf{19.3} \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Desh Raj
Pavel Denisov
Zhuo Chen
Hakan Erdogan
Zili Huang
Maokui He
Shinji Watanabe
Jun Du
Takuya Yoshioka
Yi Luo
Naoyuki Kanda
Jinyu Li
Scott Wisdom
John R. Hershey

"Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis" by Desh Raj, Pavel Denisov, Zhuo Chen, Hakan Erdogan, Zili Huang, Maokui He, Shinji Watanabe, Jun Du, Takuya Yoshioka, Yi Luo, Naoyuki Kanda, Jinyu Li, Scott Wisdom, and John R. Hershey, arXiv:2011.02014v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\cline{2-3} \cline{3-3}
 & Relaxation Time & $\epsilon$-Mixing Time\tabularnewline
\hline
 & & $\tilde{O}\left(\kappa d^{\frac{1}{2}}\right)^{\bigtriangledown}$ \tabularnewline
\cline{3-3}
Upper Bound & $O(\kappa d)^{\bigtriangleup}$ & $\tilde{O}\left(\kappa d\log\frac{1}{\epsilon}\right)^{\bigtriangleup}$ \tabularnewline
\cline{3-3}
 & & $\tilde{O}\left(\kappa d^{\frac{2}{3}}\log\frac{1}{\epsilon}\right)^{\bigcirc}$ \tabularnewline
\hline
Lower Bound & $\Omega\left(\frac{\kappa d}{\log d}\right)^{\bigtriangledown}$ & $\Omega\left(\frac{\kappa d}{\log^{2}d}\right)^{\bigtriangledown}$ \tabularnewline
\hline
\end{tabular}
\caption{Recently published bounds on the relaxation time and $\epsilon$-mixing time of various MCMC samplers. The $\tilde{O}$ denotes that the bound excludes poly-logarithmic terms. Superscript $\bigtriangleup$ denotes a bound for RWM, superscript $\bigtriangledown$ for MALA, and superscript $\bigcirc$ for HMC. The bound in holds under the additional assumptions that $\nabla^2U$ is $L_H$-Lipschitz with $L_H^{2/3} = O(M)$, that $\kappa = O(d^{2/3})$, and that the chain is started from a warm start with constant $W = O(\exp(d^{2/3}))$.}
\end{table}
\end{document}

math

"Quantifying the effectiveness of linear preconditioning in Markov chain Monte Carlo" by Max Hird and Samuel Livingstone, arXiv:2312.04898v2, 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{Dataset-1: NASA Bearing dataset description}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Set \#} & \textbf{Batches}& \textbf{Batch Size}& \textbf{Anomaly} \\
\hline
 Set1 & 2156 & 4 x 20480 & B3 and B4 \\
 \hline
 Set2 & 984 & 4 x 20480 & B1 \\
 \hline
 Set3 & 6324 & 4 x 20480 & B3\\
 \hline
\end{tabular}
\end{table}
\end{document}

cs

Sabtain Ahmad
Kevin Styp-Rekowski
Sasho Nedelkoski
Odej Kao

"Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines" by Sabtain Ahmad, Kevin Styp-Rekowski, Sasho Nedelkoski, and Odej Kao, arXiv:2101.11539v1, 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{PSNR scores for TnT dataset.}
\begin{tabular}{l|cccccc|c}
 & Barn & Caterpillar & Courthouse & Ignatius & Meetingroom & Truck & Mean\\
\hline
SuGaR & 28.63 &	23.27 &	23.33 &	20.72 &	25.47 &	24.40 &	24.16 \\
3DGS & 27.99 &	24.82 &	23.33 &	23.95 &	26.89 &	25.01 &	25.33 \\
Ours & 28.79 &	24.23 &	23.51 &	23.82 &	26.15 &	26.85 & 25.56 \\
\end{tabular}
\end{table}
\end{document}

cs

Binbin Huang
Zehao Yu
Anpei Chen
Andreas Geiger
Shenghua Gao

"2D Gaussian Splatting for Geometrically Accurate Radiance Fields" by Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao, arXiv:2403.17888v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Technical indicators definitions and formulas. }
\begin{tabular}{c|c}
 \toprule
 \textbf{Indicator} & \textbf{Calculation Formula} \\
 \midrule
 max\_{oc} & $z_{max\_oc} = \max(p_{t}^{o}, p_{t}^{c})$ \\
 min\_{oc} & $z_{min\_oc} = \min(p_{t}^{o}, p_{t}^{c})$ \\
 kmid2 & $z_{kmid2} = (p_{t}^{c} - p_{t}^{o}) / (p_{t}^{h} - p_{t}^{l})$ \\
 kup2 & $z_{kup2} = (p_{t}^{h} - z_{max\_oc}) / (p_{t}^{h} - p_{t}^{l})$ \\
 klow & $z_{klow} = (z_{min\_oc} - p_{t}^{l}) / p_{t}^{o}$ \\
 klow2 & $z_{klow} = (z_{min\_oc} - p_{t}^{l}) / (p_{t}^{h} - p_{t}^{l} )$ \\
 ksft2 & $z_{sft2} = (2 \times p_{t}^{c} - p_{t}^{h} - p_{t}^{l}) / (p_{t}^{h} - p_{t}^{l})$ \\
 \midrule
 \multirow{3}[2]{*}{\textbf{Definitions}} & $w \in W = [5, 10, 20, 30, 60]$ \\
 & $ret1 = (p_{t}^{c} - p_{t-1}^{c}) / p_{t-1}^{c}$ \\
 & $abs\_ret1 = Abs(ret1)$ \\
 & $pos\_ret1 = ret1 < 0?0:ret1$ \\
 \midrule
 roc & $z_{roc\_w} = Shift(p_{t}^{c}, w) / p_{t}^{c}$ \\
 ma & $z_{ma\_w} = RollingMean(p_{t}^{c}, w) / p_{t}^{c}$ \\
 std & $z_{std\_w} = RollingStd(p_{t}^{c}, w) / p_{t}^{c}$ \\
 beta & $z_{roc\_w} = (Shift(p_{t}^{c}, w) - p_{t}^{c}) / (w \times p_{t}^{c})$ \\
 max & $z_{max\_w} = RollingMax(p_{t}^{c}, w) / p_{t}^{c}$ \\
 min & $z_{min\_w} = RollingMin(p_{t}^{c}, w) / p_{t}^{c}$ \\
 qtlu & $z_{qtlu\_w} = RollingQuantile[q = 0.8](p_{t}^{c}, w) / p_{t}^{c}$ \\
 qtld & $z_{qtlu\_w} = RollingQuantile[q = 0.2](p_{t}^{c}, w) / p_{t}^{c}$ \\
 rank & $z_{rank\_w} = RollingRank(p_{t}^{c}, w) / w$ \\
 imax & $z_{imax\_w} = RollingArgmax(p_{t}^{c}, w) / w$ \\
 imin & $z_{imin\_w} = RollingArgmin(p_{t}^{c}, w) / w$ \\
 imxd & $z_{imxd\_w} = (RollingArgmax(p_{t}^{h}, w) - RollingArgmin(p_{t}^{l}, w)) / w$ \\
 cntp & $z_{cntp\_w} = RollingSum(ret1 > 0, w) / w$ \\
 cntn & $z_{cntn\_w} = RollingSum(ret1 < 0, w) / w$ \\
 cntd & $z_{cntd\_w} = z_{cntp\_w} - z_{cntn\_w}$ \\
 sump & $z_{sump\_w} = RollingSum(pos\_ret1, w) / RollingSum(abs\_ret1, w)$ \\
 sumn & $z_{sumn\_w} = 1 - z_{sump\_w}$ \\
 sumd & $z_{sumd\_w} = 2 \times z_{sump\_w} - 1$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

Wentao Zhang
Yilei Zhao
Shuo Sun
Jie Ying
Yonggang Xie
Zitao Song
Xinrun Wang
Bo An

"Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock Pools" by Wentao Zhang, Yilei Zhao, Shuo Sun, Jie Ying, Yonggang Xie, Zitao Song, Xinrun Wang, and Bo An, arXiv:2311.10801v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Taeyoung Yun
Kiyoung Om
Jaewoo Lee
Sujin Yun
Jinkyoo Park

"Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization" by Taeyoung Yun, Kiyoung Om, Jaewoo Lee, Sujin Yun, and Jinkyoo Park, arXiv:2502.16824v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|l|l|l|}
\hline
Regime & Strategy & Mean Corr & Sharpe & Max Drawdown \\ \hline
\multirow{5}{*}{Test} & Example Model & 0.0264 & 0.9626 & 0.2608 \\ \cline{2-5}
 & Baseline Model & 0.0265 & 1.1943 & 0.1562 \\ \cline{2-5}
 & Tail Risk Model & 0.0015 & 0.1044 & 0.1754 \\ \cline{2-5}
 & Static Hedged Model & 0.0199 & 0.9978 & 0.1460 \\ \cline{2-5}
 & Dynamic Hedged Model & 0.0207 & 1.0760 & 0.0871 \\ \hline
\multirow{5}{*}{Bull} & Example Model & 0.0307 & 1.2512 & 0.0693 	 \\ \cline{2-5}
 & Baseline Model & 0.0300 & 1.5073 & 0.0343 \\ \cline{2-5}
 & Tail Risk Model & 0.0011 & 0.0743 & 0.1754 \\ \cline{2-5}
 & Static Hedged Model & 0.0227 & 1.2135 & 0.0377 \\ \cline{2-5}
 & Dynamic Hedged Model & 0.0233 & 1.2755 & 0.0434 \\ \hline
\multirow{5}{*}{Bear} & Example Model & -0.0060 & -0.2306 & 0.2608 \\ \cline{2-5}
 & Baseline Model & -0.0001 & -0.0053 & 0.1562 \\ \cline{2-5}
 & Tail Risk Model & 0.0051 & 0.2925 & 0.0743 \\ \cline{2-5}
 & Static Hedged Model & -0.0008 & -0.0483 & 0.1460 \\ \cline{2-5}
 & Dynamic Hedged Model & 0.0015 & 0.0998 & 0.0871 \\ \hline
\end{tabular}
\caption{Performances of Dynamic Hedged deep IL XGBoost ensemble model based on different learning rates and V4.2 Example Model from Era 901 to Era 1070 under different market regimes. }
\end{table}
\end{document}

q-fin

"Deep incremental learning models for financial temporal tabular datasets with distribution shifts" by Thomas Wong and Mauricio Barahona, arXiv:2303.07925v10, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Xenia Haslinger
Elisabeth Gaar
Sophie N. Parragh

"An exact approach for the multi-depot electric vehicle scheduling problem" by Xenia Haslinger, Elisabeth Gaar, and Sophie N. Parragh, arXiv:2504.13063v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{SCC comparison in IEEE 118-bus system}
\begin{tabular}{c|c||c|c|c}
 \toprule
 \multirow{2}{4.2em}{\textbf{\,\,\,\; SCC\\Constraint} } & \multirow{2}{4.2em}{\textbf{IBGs' PE\\Capacity} }& \multirow{2}{4em}{\textbf{ \, SCC\\Violation} } & \multirow{2}{2.8em}{\textbf{ Cost}\\$[\mathrm{k\pounds/h}]$} & \multirow{2}{3em}{\textbf{\,\,\,Time}\\ $[\mathrm{s/step}]$} \\
 & & & &\\
 \cline{1-5}
 \multirow{2}{2.8em}{w.o.} & $P^C$ & $67.93$\% & $56.71$ & $19.55$ \\
 \cline{2-5}
 & $1.5 P^C$ & $18.97\%$ & $-$ & $-$\\
 \cline{1-5}
 \multirow{2}{2.8em}{with} & $P^C$ & $0$ & $63.31$ & $32.32$ \\
 \cline{2-5}
 & $1.5P^C$ & $0$ & $58.78$ & $23.04$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"Short Circuit Current Constrained UC in High IBG-Penetrated Power Systems" by Zhongda Chu and Fei Teng, arXiv:2101.04918v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Madhab Barman
Madhurima Panja
Nachiketa Mishra
Tanujit Chakraborty

"Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak" by Madhab Barman, Madhurima Panja, Nachiketa Mishra, and Tanujit Chakraborty, arXiv:2502.10786v1, 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}{lcc}
 \toprule
 \textbf{Model} & \textbf{Accuracy (\%)} & \textbf{Std Dev (\%)} \\
 \midrule
 ReLU\_Bias & 96.62 & 0.17 \\
 ReLU2\_Bias & 56.31 & 19.31 \\
 ReLU2\_Neg & 96.46 & 0.17 \\
 Abs\_Bias & 95.87 & 0.22 \\
 Abs2\_Bias & 95.95 & 0.17 \\
 Abs2\_Neg\_Bias & 92.25 & 2.07 \\
 \midrule
 ReLU-L2 & 97.33 & 0.13 \\
 ReLU-L2-Neg & 97.36 & 0.14 \\
 Abs-L2 & 97.61 & 0.07 \\
 Abs-L2-Neg & 97.56 & 0.09 \\
 \bottomrule
 \end{tabular}
\caption{Performance metrics across all models with extended training (50,000 epochs), averaged over 20 runs.}
\end{table}
\end{document}

stat

Neural Networks Learn Distance Metrics

"Neural Networks Learn Distance Metrics" by Alan Oursland, arXiv:2502.02103v1, 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}{l|cc}
 \toprule
 ~ & ObjSDF++ & Ours
 \\
 \midrule
 Room0& 34 & 41\\
 Room1& 22 & 24\\
 Room2& 24 & 30\\
 Office0& 21 & 23\\
 Office1& 13 & 20\\
 Office2 & 28 & 38\\
 Office3 & 29 & 38\\
 Office4 & 21 & 28\\
 \bottomrule
 \end{tabular}
\caption{The number of decomposed reconstructed foreground objects on different scenes from Replica following the ObjSDF++ splits (100 images).}
\end{table}
\end{document}

cs

Xiaoyang Lyu
Chirui Chang
Peng Dai
Yang-Tian Sun
Xiaojuan Qi

"Total-Decom: Decomposed 3D Scene Reconstruction with Minimal Interaction" by Xiaoyang Lyu, Chirui Chang, Peng Dai, Yang-Tian Sun, and Xiaojuan Qi, arXiv:2403.19314v2, 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}{lllll}
 \toprule
 \textbf{Label} & \textbf{Task} & \textbf{Attribute} & \textbf{Condition} & \textbf{Granularity}\\
 \midrule
 t\textsubscript1 & Identify client	& Name & Age \textgreater 18 \\
 t\textsubscript2 & Process order list & Order list & \\
 t\textsubscript3 & Charge fees & CC Info & \\
 t\textsubscript4 & Ship parcel & Address & \\
 t\textsubscript5 & Inform client & Email & \\
 t\textsubscript6 & Send advertisements & Email & \\
 t\textsubscript7 & Check DOB & DOB	& \\
 t\textsubscript8 & Analyze based on Age & DOB & & Date2Age \\
 t\textsubscript9 & Analyze shopping habit & Order list & \\
 t\textsubscript{10} & Determine interest & Interest & \\
 \bottomrule
 \end{tabular}
\caption{Purpose-attributes permissions}
\end{table}
\end{document}

cs

"The Privacy Policy Permission Model: A Unified View of Privacy Policies" by Maryam Majedi and Ken Barker, arXiv:2403.17414v1, 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{Decoding method comparison}
\begin{tabular}{lllll}
 \toprule
 \textbf{decoding method} &
 \textbf{CTC weight} &
 \textbf{RTF} &
 \textbf{CER} \\ \midrule
 attention decoder & / & 0.197 & 4.92 \\
 ctc prefix beam search & / & / & 4.93 \\
 attention rescoring & 0.0 & / & 4.72 \\
 attention rescoring & 0.5 & \textbf{0.082} & 4.64 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Binbin Zhang
Di Wu
Zhuoyuan Yao
Xiong Wang
Fan Yu
Chao Yang
Liyong Guo
Yaguang Hu
Lei Xie
Xin Lei

"Unified Streaming and Non-streaming Two-pass End-to-end Model for Speech Recognition" by Binbin Zhang, Di Wu, Zhuoyuan Yao, Xiong Wang, Fan Yu, Chao Yang, Liyong Guo, Yaguang Hu, Lei Xie, and Xin Lei, arXiv:2012.05481v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Introducing the $σ$-Cell: Unifying GARCH, Stochastic Fluctuations and Evolving Mechanisms in RNN-based Volatility Forecasting" by German Rodikov and Nino Antulov-Fantulin, arXiv:2309.01565v1, 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}{lrrrrr|lrrrrr}
 \hline&&&&&&&&&&&\\
 CP& MSE & Cov d.& Cov b. & se d. & se b.&Cor & MSE & Cov d.& Cov b. & se d. & se b. \\
 \hline&&&&&&&&&&&\\
0 & 0.0014 & 0.932 & 0.921 & 1.117 & 0.969&0.2&0.0007 & 0.937 & 0.944 & 1.083 & 1.017 \\
 0.5 & 0.0010 & 0.925 & 0.919 & 1.015 & 0.938&0.5&0.0009 & 0.920 & 0.928 & 0.998 & 0.967 \\
 1 & 0.0008 & 0.902 & 0.912 & 0.931 & 0.922 &0.75&0.0011 & 0.888 & 0.912 & 0.890 & 0.904 \\
 1.5 & 0.0006 & 0.846 & 0.877 & 0.895 & 0.894 &0.95&0.0011 & 0.862 & 0.847 & 0.983 & 0.838 \\
 \hline
\end{tabular}
\caption{Reliability measures by increasing cut-points values and correlation. CP: cut-point, MSE: mean squared error, Cov d.: coverage of the 95\% confidence interval using the distributional standard error, Cov b.: coverage of the 95\% confidence interval using the bootstrap standard error}
\end{table}
\end{document}

stat

"Estimation of survival functions for events based on a continuous outcome: a distributional approach" by Odile Sauzet, arXiv:2501.08228v1, 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{Johansen Cointegration Test using Trace Test Statistic (10\% Significance Level)}
\begin{tabular}{cccc}
\hline
\textbf{Rank (r)} & \textbf{Cointegrating Relations} & \textbf{Test Statistic} & \textbf{Critical Value} \\
\hline
0 & 2 & 32.10 & 13.43 \\
1 & 2 & 0.1839 & 2.705 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Juan Plazuelo Pascual
Carlos Tardon Rubio
Juan Toro Cebada
Angel Hernando Veciana

"Price Discovery in Cryptocurrency Markets" by Juan Plazuelo Pascual, Carlos Tardon Rubio, Juan Toro Cebada, and Angel Hernando Veciana, arXiv:2506.08718v1, 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{Productivity Process Coefficient Estimates}
\begin{tabular}{lccccc|c}
			\toprule[1pt]
			 & \multicolumn{5}{c}{\it Locationally Varying} & \it Location-Invariant \\
			Variables& Mean & 1st Qu. & Median & 3rd Qu.& $>0$ & Point Estimate\\
			\midrule
			Lagged Productivity & 0.576 & 0.518 & 0.597 & 0.641 & 99.9\% & 0.497 \\
			& (0.540, 0.591) & (0.469, 0.541) & (0.553, 0.614) & (0.580, 0.665) & & (0.455, 0.530) \\
			Skilled Labor Share & 0.387 & 0.287 & 0.419 & 0.500 & 85.7\% & 0.387 \\
			& (0.346, 0.395) & (0.241, 0.309) & (0.345, 0.459) & (0.471, 0.493) & & (0.345, 0.425) \\
			Foreign Equity Share & 0.054 & --0.001 & 0.062 & 0.103 & 47.7\% & 0.056		 \\
			& (0.006, 0.074) & (--0.034, 0.066) & (0.033, 0.069) & (0.099, 0.099) & & (0.036, 0.075) 	 \\
			Exporter & --0.001 & --0.032 & --0.005 & 0.038 & 24.0\% & 0.006 	 \\
			& (--0.011, 0.018) & (--0.041, --0.016) & (--0.012, 0.013) & 	(0.025, 0.067) & & (--0.008, 0.018) 	 \\
			State-Owned & 0.005 & --0.052 & 0.007 & 	0.073 & 	29.6\% & 	--0.043 \\
			& (--0.021, 0.010) & 	(--0.101, --0.014) & (--0.028, 0.025) & 	(0.062, 0.076) & & (--0.072, --0.009) 		 \\
			\midrule
			\multicolumn{7}{p{16.5cm}}{\scriptsize The left panel summarizes point estimates of $\rho_j(S_i)\ \forall\ j=1,\dots,\dim(G)$ with the corresponding two-sided 95\% bias-corrected confidence intervals in parentheses. Reported is also a share of locations in which location-specific point estimates are statistically positive as inferred via a one-sided test. The right panel reports the counterparts from a fixed-coefficient location-invariant model.} \\
			\bottomrule[1pt]
		\end{tabular}
\end{table}
\end{document}

q-fin

Emir Malikov
Jingfang Zhang
Shunan Zhao
Subal C. Kumbhakar

"Accounting for Cross-Location Technological Heterogeneity in the Measurement of Operations Efficiency and Productivity" by Emir Malikov, Jingfang Zhang, Shunan Zhao, and Subal C. Kumbhakar, arXiv:2302.13430v1, 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{Hyper-parameter tuning ranges and optimal values for SWAM model}
\begin{tabular}{ccc}
 \hline
 & Range & Optimal Value \\ \hline
 $\eta$ & 0.0001,0.0003,& 0.001 \\
 (learning rate) & 0.001,0.003 & \\
 $k$ & 1-10 & 4 \\
 (filter size) \\
 $d_{c}$ & 50-500 & 500 \\
 (number of filters) \\
 $q$ & 0.2-0.8 & 0.2 \\
 (dropout probability) \\
 \hline

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

cs

"An Explainable CNN Approach for Medical Codes Prediction from Clinical Text" by Shu Yuan Hu and Fei Teng, arXiv:2101.11430v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{PICP evaluation for the diffusion equation}
\begin{tabular}{|c|c|}
		\hline
		Kernel function & PICP \\ \hline
		Gaussian+Laplacian & 80.0 \% \\ \hline
		Gaussian+Exponential & 80.0 \% \\ \hline
		Gaussian & 60.0 \% \\ \hline
		Rational Quadratic+Laplacian & 80.0 \% \\ \hline
		Matérn+Laplacian & 80.0 \% \\ \hline
		Rational Quadratic+Gaussian & 80.0 \% \\ \hline
		Matérn+Gaussian+Laplacian & 80.0 \% \\ \hline
	\end{tabular}
\end{table}
\end{document}

math

Juan Zhang
Junyue Luo
Fangfang Zhang
Xiaoqiang Yue

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccc}
\toprule
 {Data} & {MSE}& {MAE} & {SSIM}\\\midrule
Vancouver93 & 5.9e-1 & 6.1e-1 & NA \\
CloudCast & 2.0e-7 & 6.0e-4 & 0.99 \\
\bottomrule
\end{tabular}
\caption{Similarity metrics for ensemble average.}
\end{table}
\end{document}

math

Siddharth Rout
Eldad Haber
Stéphane Gaudreault

"Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data" by Siddharth Rout, Eldad Haber, and Stéphane Gaudreault, arXiv:2503.12273v1, 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}{ccccc}
\toprule
 & Model & Expert M$_1$ & Expert M$_2$ & Expert M$_3$ \\
\midrule
RMSE & $0.27\pm .01$ & $1.23\pm .02$ & $1.85\pm .02$ & $0.91\pm .01$ \\
\bottomrule
\end{tabular}
\caption{Agent RMSE on the California Housing validation set ($20$\% of the dataset).}
\end{table}
\end{document}

stat

Yannis Montreuil
Axel Carlier
Lai Xing Ng
Wei Tsang Ooi

"Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees" by Yannis Montreuil, Axel Carlier, Lai Xing Ng, and Wei Tsang Ooi, arXiv:2502.01027v1, 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{Software and hardware specification for IBM Qiskit quantum simulation}
\begin{tabular}{|l|l|}
			\hline
			Qiskit & v0.20.2 \\ \hline
			Qiskit Element & Qiskit Aer \\ \hline
			Simulator & QASM \\ \hline
			Python & v3.8.5 \\ \hline
			Local OS & Linux Lite v5.2 \\ \hline
			Local Hardware & Intel Core i5, RAM 8GB \\ \hline

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

cs

Tapaswini Mohanty
Vikas Srivastava
Sumit Kumar Debnath
Debasish Roy
Kouichi Sakurai
Sourav Mukhopadhyay

"An Experimentally Validated Feasible Quantum Protocol for Identity-Based Signature with Application to Secure Email Communication" by Tapaswini Mohanty, Vikas Srivastava, Sumit Kumar Debnath, Debasish Roy, Kouichi Sakurai, and Sourav Mukhopadhyay, arXiv:2403.18247v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"ADPBA: Efficiently generating Lagrangian cuts for two-stage stochastic integer programs" by Xiaoyu Luo, Mingming Xu, and Chuanhou Gao, arXiv:2312.16445v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jiaru Zhang
Rui Ding
Qiang Fu
Bojun Huang
Zizhen Deng
Yang Hua
Haibing Guan
Shi Han
Dongmei Zhang

"Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning" by Jiaru Zhang, Rui Ding, Qiang Fu, Bojun Huang, Zizhen Deng, Yang Hua, Haibing Guan, Shi Han, and Dongmei Zhang, arXiv:2502.10883v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Ding Ning
Varvara Vetrova
Sébastien Delaux
Rachael Tappenden
Karin R. Bryan
Yun Sing Koh

"A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models" by Ding Ning, Varvara Vetrova, Sébastien Delaux, Rachael Tappenden, Karin R. Bryan, and Yun Sing Koh, arXiv:2502.13495v1, 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{Summary of the benchmarks.}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Benchmark} & \textbf{Exact Solution} & \textbf{Additional Invariants} & \textbf{Separable} & \textbf{Section} \\
 \midrule
 One-dimensional mass-spring problem & Yes & No & Yes & \\
 Two springs, two masses system & Yes & No & Yes & \\
 One-dimensional pendulum problem & Yes & No & Yes & \\
 Two-dimensional Kepler problem & No & Yes & Yes & \\
 Two-dimensional three-body problem & No & Yes & Yes & \\
 Outer Solar system & No & Yes & Yes & \\
 Particle in a 3D electromagnetic field & No & No & No & \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Stéphane Clain
Emmanuel Franck
Victor Michel-Dansac

"Structural schemes for hamiltonian systems" by Stéphane Clain, Emmanuel Franck, and Victor Michel-Dansac, arXiv:2501.13515v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zero-Leverage Puzzle

"Zero-Leverage Puzzle" by Mykola Pinchuk, arXiv:2302.00761v1, 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{Results for original vs proposed model for recovery rate. proposed version does not only has both lower MSE and NLL}
\begin{tabular}{ccccc}
\toprule
 & \multicolumn{2}{c}{MSE} & \multicolumn{2}{c}{NLL} \\ \midrule
 & benchmark & proposed & benchmark & proposed \\
\textit{test} & 84.333 $\pm$ 0.352 & \textbf{40.498 $\pm$ 4.745} & 2.757 $\pm$ 0.012 & \textbf{2.311 $\pm$ 0.024} \\
\textit{train} & 84.320 $\pm$ 0.216 & \textbf{39.909 $\pm$ 4.911} & 2.766 $\pm$ 0.009 & \textbf{2.314 $\pm$ 0.0228} \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"UQ for Credit Risk Management: A deep evidence regression approach" by Ashish Dhiman, arXiv:2305.04967v2, 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{Linear Trend -- \textit{Adopters (Organic and Black Friday)} vs. \textit{Offline-only}}
\begin{tabular}{lcc}
\midrule \midrule
 DVs & Spend & Profit\\
 Model: & (1) & (2)\\
 \midrule
 \emph{Variables}\\
 Adopter * t & 4.244 (1.571) & 1.081 (0.6015)\\
 & [0.007] & [0.072]\\
 \midrule
 \emph{Fixed-effects}\\
 Customer & Yes & Yes\\
 YearMonth & Yes & Yes\\
 \midrule
 \emph{Fit statistics}\\
 Observations & 3,855,908 & 3,855,908\\
 R$^2$ & 0.48754 & 0.40597\\
 Within R$^2$ & $5.24\times 10^{-6}$ & $2.25\times 10^{-6}$\\
 \midrule \midrule
 \multicolumn{3}{l}{\emph{Clustered (Customer) standard-errors are presented in standard brackets,}}\\ \multicolumn{3}{l}{\emph{while p‑values are shown in square brackets.}}\\
 \end{tabular}
\end{table}
\end{document}

q-fin

Channel Choice and Customer Value

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"Channel Choice and Customer Value" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2508.00208v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance on Out-of-domain Test Dataset.}
\begin{tabular}{cccccccc}
\hline
Algorithms & DSC & PPV & TPR & LFPR & LTPR & VC\\
\hline
w/o rotations and flips & 0.614 & \underline{0.809} & 0.523 & 0.154 & \bf{0.375} & 0.978 \\
w/o connected union & 0.605 & \bf{0.823} & 0.507 & \underline{0.124} & 0.361 & \underline{0.979} \\
w/ BatchNorm & \underline{0.618} & 0.803 & \underline{0.529} & 0.127 & 0.321 & \bf{0.980} \\
w/ InstanceNorm & \bf{0.633} & 0.759 & \bf{0.576} & \bf{0.104} & \underline{0.368} & 0.974 \\

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

eess

Jinwei Zhang
Lianrui Zuo
Blake E. Dewey
Samuel W. Remedios
Dzung L. Pham
Aaron Carass
Jerry L. Prince

"Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion" by Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Dzung L. Pham, Aaron Carass, and Jerry L. Prince, arXiv:2312.01460v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Accurate Linear Cutting-Plane Relaxations for ACOPF" by Daniel Bienstock and Matias Villagra, arXiv:2312.04251v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Merton model and Poisson process with Log Normal intensity function" by Masato Hisakado and Shintaro Mori, arXiv:2505.13822v1, 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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Averages of falling depth in cases with the price limit.}
\begin{tabular}{cr|rrrrrrr}
&& \multicolumn{5}{c}{$tr$} \\
&& 1000 & 2000 & 5000 & 10000 & 20000 \\ \hline
 & 10 & 298 & 157 & 73 & 45 & 33 \\
 & 20 & 577 & 302 & 134 & 79 & 52 \\
 & 50 & \cellcolor[gray]{0.90} 1227 & 721 & 313 & 167 & 110 \\
$Pr$ & 100 & \cellcolor[gray]{0.90} 2000 & \cellcolor[gray]{0.90} 1296 & 612 & 315 & 209 \\
 & 200 & \cellcolor[gray]{0.90} 2194 & \cellcolor[gray]{0.90} 2111 & 1140 & 615 & 408 \\
 & 500 & \cellcolor[gray]{0.90} 2054 & \cellcolor[gray]{0.90} 2054 & \cellcolor[gray]{0.90} 2053 & \cellcolor[gray]{0.90} 1419 & 906 \\
 & 1000 & \cellcolor[gray]{0.90} 2054 & \cellcolor[gray]{0.90} 2054 & \cellcolor[gray]{0.90} 2054 & \cellcolor[gray]{0.90} 2054 & \cellcolor[gray]{0.90} 1433
\end{tabular}
\end{table}
\end{document}

q-fin

"Comparing effects of price limit and circuit breaker in stock exchanges by an agent-based model" by Takanobu Mizuta and Isao Yagi, arXiv:2309.10220v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sample count in training, validation, and test sets.}
\begin{tabular}{ccc}
 \hline
 Training Set & Validation Set & Test Set \\
 \hline
 7008 & 876 & 875 \\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

"Estimating Solar and Wind Power Production using Computer Vision Deep Learning Techniques on Weather Maps" by Sebastian Bosma and Negar Nazari, arXiv:2103.08727v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{makecell}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{The performance of OV-Uni3DETR on the SUN RGB-D and ScanNet dataset for open-vocabulary 3D object detection.} P denotes the point cloud inputs and I denotes the image inputs. The experimental setting is totally the same as CoDA, and the utilized data are downloaded from CoDA officially released code.}
\begin{tabular}{c|c|ccc|ccc}
\Xhline{1.1pt}
\multirow{2}*{Method}& \multirow{2}*{Inputs}& \multicolumn{3}{c|}{SUN RGB-D} & \multicolumn{3}{c}{ScanNet} \\
 & & AP$_{novel}$ & AP$_{base}$ & AP$_{all}$ & AP$_{novel}$ & AP$_{base}$ & AP$_{all}$ \\
\hline
 Det-PointCLIP & P &0.09 & 5.04 & 1.17 & 0.13 & 2.38 & 0.50\\
 Det-PointCLIPv2 & P & 0.12 & 4.82 & 1.14 & 0.13 & 1.75 & 0.40 \\
 Det-CLIP$^2$ & P & 0.88 & 22.74 & 5.63 & 0.14 & 1.76 & 0.40 \\
 3D-CLIP & P+I & 3.61 & 30.56 & 9.47 & 3.74 & 14.14 & 5.47\\
 CoDA & P & 6.71 & 38.72 & 13.66 & 6.54 & 21.57 & 9.04\\
\hline
\multirow[c]{3}{*}{OV-Uni3DETR (ours)} & P & 9.66 & 48.29 & 18.06 & 12.09 & 30.47 & 15.15 \\
 & I & 5.41 & 29.51 & 10.65 & 8.10 & 20.87 & 10.23\\
 & P+I & \textbf{12.96} & \textbf{49.25} & \textbf{20.85} & \textbf{15.21} & \textbf{31.86} & \textbf{17.99}\\
\Xhline{1.1pt}
\end{tabular}
\end{table}
\end{document}

cs

Zhenyu Wang
Yali Li
Taichi Liu
Hengshuang Zhao
Shengjin Wang

"OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality Propagation" by Zhenyu Wang, Yali Li, Taichi Liu, Hengshuang Zhao, and Shengjin Wang, arXiv:2403.19580v2, 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{MNIST Classifier Accuracy}
\begin{tabular}{|l|l|}
		\hline
		& Accuracy \\
		\hline
		CNN & 98.81\% \\
		\hline
		Classifier Bias & 98.83\% \\
		\hline
		Generator Bias & 11.92\% \\
		\hline
		25\% Class Bias & 97.88\% \\
		\hline
		50\% Class Bias & 98.43\% \\
		\hline
		75\% Class Bias & 98.52\% \\

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

eess

"Symbiotic Hybrid Neural Network Watchdog For Outlier Detection" by Justin Bui and Robert J. Marks, arXiv:2103.00582v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Automating the Design of Multigrid Methods with Evolutionary Program Synthesis" by Jonas Schmitt, arXiv:2312.14875v1, 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{EV chargers power (MW) in Manhattan, NY, based on the NYC Department of Transportation electrification goal .}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
 \hline
 \multirow{ 2}{*}{Year} &\multicolumn{2}{c|}{Bus\#4} & \multicolumn{2}{c|}{Bus\#5} & \multicolumn{2}{c|}{Bus\#8} & \multicolumn{2}{c|}{Bus\#12}\\ \cline{2-9}
 &All&Tesla&All&Tesla&All&Tesla&All&Tesla \\ \hline
 2022&1.94&1.85&1.52&1.40&0.85&0.79&0.46&0.46 \\ \hline
 2030 & 101.6& 96.8& 79.6& 73.1& 44.7& 41.4& 24.01& 24.01 \\ \hline
 2050 & 617.1& 588.2& 483.8 & 444.1& 271.9 & 251.9& 145.8 & 145.8 \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

Samrat Acharya
Hafiz Anwar Ullah Khan
Ramesh Karri
Yury Dvorkin

"MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid Operation" by Samrat Acharya, Hafiz Anwar Ullah Khan, Ramesh Karri, and Yury Dvorkin, arXiv:2311.06226v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Channel Choice and Customer Value


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Residence permits issued to Albanian citizens in Italy, ISTAT 2022}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Residence permits} & \textbf{Male} & \textbf{Female} & \textbf{Total} \\
\hline
\textbf{Family} & 10035 & 11039 & 21074 \\
 & 54.7\% & 67.9\% & 60.9\%\\
\hline
\textbf{Work} & 4342 & 1796 & 6138 \\
 & 23.7\% & 11.0\% & 17.7\% \\
\hline
\textbf{Study} & 159 & 264 & 423\\
 & 0.9\% & 1.6\% & 1.2\%\\
\hline
\textbf{Asylum} & 388 & 218 & 606 \\
 & 2.1\% & 1.3\% & 1.8\% \\
\hline
\textbf{Other} & 3412 & 2941 & 6353 \\
 & 18.6\% & 18.1\% & 18.4\% \\
\hline
\textbf{\textit{Total}} & \textit{18336} & \textit{16258} & \textit{34594} \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Text Data Analysis of Maternal Narratives: Albanian Women in Italy" by Eleonora Miaci and Emiliano Seri, arXiv:2501.05181v1, 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}{lrr}
 & in Wikidata & not in Wikidata\\ \hline
 biographical & 4,300 & 2,590\\
 themed & 498 & 475\\ \hline
 \end{tabular}
\caption{The number of DHBB entries, by type, that using Wikimapper we could find in Wikidata or not.}
\end{table}
\end{document}

cs

"Towards a Brazilian History Knowledge Graph" by Valeria de Paiva and Alexandre Rademaker, arXiv:2403.19856v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\color{black}Performance comparison with varying amounts of graph sizes.}
\begin{tabular}{l|ccc|ccc|ccc}
\toprule
Metric & \multicolumn{3}{c|}{s-F1$\uparrow$} & \multicolumn{3}{c|}{v-F1$\uparrow$} & \multicolumn{3}{c}{o-F1$\uparrow$} \\
Size & 50 & 70 & 100 & 50 & 70 & 100 & 50 & 70 & 100 \\
\midrule
PC & $17.7$ & $14.8$ & $10.6$ & $6.4$ & $5.0$ & $3.7$ & $7.0$ & $5.6$ & $4.0$ \\
SiCL & $\mathbf{41.6}$ & $\mathbf{37.4}$ & $\mathbf{28.3}$ & $\mathbf{34.9}$ & $\mathbf{30.7}$ & $\mathbf{22.6}$ & $\mathbf{37.9}$ & $\mathbf{33.7}$ & $\mathbf{24.8}$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning" by Jiaru Zhang, Rui Ding, Qiang Fu, Bojun Huang, Zizhen Deng, Yang Hua, Haibing Guan, Shi Han, and Dongmei Zhang, arXiv:2502.10883v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
algebra & exponents & $g^\vee$ & $g$ \\
\hline
$\mathfrak{a}_k$ & $1, 2, \dotsc, k$ & $k + 1$ & $k + 1$ \\
$\mathfrak{b}_k$ & $1, 3, \dotsc, 2 k - 1$ & $2 k - 1$ & $2 k$ \\
$\mathfrak{c}_k$ & $1, 3, \dotsc, 2 k - 1$ & $k + 1$ & $2 k$ \\
$\mathfrak{d}_k$ & $1, 3, \dotsc, 2 k - 3, k - 1$ & $2 k - 2$ & $2 k - 2$ \\
$\mathfrak{e}_6$ & $1, 4, 5, 7, 8, 11$ & $12$ & $12$ \\
$\mathfrak{e}_7$ & $1, 5, 7, 9, 11, 13, 17$ & $18$ & $18$ \\
$\mathfrak{e}_8$ & $1, 7, 11, 13, 17, 19, 23, 29$ & $30$ & $30$ \\
$\mathfrak{f}_4$ & $1, 5, 7, 11$ & $9$ & $12$ \\
$\mathfrak{g}_2$ & $1, 5$ & $4$ & $6$
\end{tabular}
\caption{Indices, Coxeter number $g$ and dual Coxeter number $g^\vee$ for simple Lie algebras.}%
\end{table}
\end{document}

math

The Many Colours of Amplitudes

Jacob L. Bourjaily
Michael Plesser
Cristian Vergu

"The Many Colours of Amplitudes" by Jacob L. Bourjaily, Michael Plesser, and Cristian Vergu, arXiv:2412.21189v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Marcel Ausloos
Giulia Rotundo
Roy Cerqueti

"A theory of best choice selection through objective arguments grounded in Linear Response Theory concepts" by Marcel Ausloos, Giulia Rotundo, and Roy Cerqueti, arXiv:2405.00041v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
Regret at Period & 50 & 60 & 70 & 80 & 90 & 100 & 110 & 120\\ \hline
One-Time MILP & 36.25 & 55.31 & 60.56 & 60.61 & 60.79 & 60.97 & 61.03 & 61.12 \\ \hline
One-Time LP & 36.25 & 55.31 & 61.95 & 64.99 & 67.78 & 71.07 & 74.31 & 77.16 \\ \hline
\end{tabular}
\caption{Comparison of Regrets Without Cost Structure Assumption (Exploration Period is of Length $60$).}
\end{table}
\end{document}

stat

Hansheng Jiang
Chunlin Sun
Zuo-Jun Max Shen
Shunan Jiang

"Learning While Repositioning in On-Demand Vehicle Sharing Networks" by Hansheng Jiang, Chunlin Sun, Zuo-Jun Max Shen, and Shunan Jiang, arXiv:2501.19208v1, 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{Absolute error on the longitudinal outcomes for the Joint cause-specific Spatiotemporal and reference models on PRO-ACT data (Benchmark dataset)}
\begin{tabular}{|l|r|rr|rr|}
\hline
{} & Joint & Longitudinal & p-value & JMbayes2 & p-value \\
\hline
Bulbar & 1.187 (1.312) & 1.179 (1.301) &2.8e-18 & \textbf{1.166 (1.233)} & 3.4e-02 \\
Fine motor & 1.510 (1.425) & \textbf{ 1.499 (1.417)} &5.1e-24 & 1.502 (1.397) & 9.8e-01 \\
Gross motor & 1.424 (1.331) & 1.414 (1.335) &6.5e-30 & \textbf{1.365 (1.288)} & 6.1e-08 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Juliette Ortholand
Stanley Durrleman
Sophie Tezenas du Montcel

"A joint spatiotemporal model for multiple longitudinal markers and competing events" by Juliette Ortholand, Stanley Durrleman, and Sophie Tezenas du Montcel, arXiv:2501.08960v1, 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}{lccccc}
\hline
\textbf{Method} & \textbf{PESQ} & \textbf{CSIG} & \textbf{CBAK} & \textbf{COVL} & \textbf{Param.} \\ \hline
Noise & 1.97 & 3.35 & 2.44 & 2.63 & - \\
SEGAN & 2.16 & 3.48 & 2.94 & 2.80 & - \\
DEMUCS & 3.07 & 4.31 & 3.40 & 3.63 & 60.80 M \\
DCCRN & 2.68 & 3.88 & 3.18 & 3.27 & 3.67 M \\
TFT-Net & 2.75 & 3.93 & 3.44 & 3.34 & 5.81 M \\
PHASEN & 2.99 & 4.21 & 3.55 & 3.62 & 6.83 M \\
SN-Net & 3.12 & 4.39 & 3.60 & 3.77 & 8.14 M \\
FAF-Net & 3.19 & 4.13 & 3.38 & 3.66 & - \\ \hline
SE-TerrNet & \textbf{3.25} & \textbf{4.58} & \textbf{3.71} & \textbf{3.98} & 6.31 M \\ \hline
\end{tabular}
\caption{Model Comparison on VoiceBank + DEMAND.}
\end{table}
\end{document}

eess

"SE Territory: Monaural Speech Enhancement Meets the Fixed Virtual Perceptual Space Mapping" by Xinmeng Xu, Yuhong Yang, and Weiping Tu, arXiv:2311.01679v2, 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}{llr}
\hline\hline
&3-digit SIC Industry&Fraction\tabularnewline
\hline
1&Local Passenger Transit&$0.0\%$\tabularnewline
2&Automative Dealers \& Service Stations&$1.8\%$\tabularnewline
3&Tobacco Products&$1.8\%$\tabularnewline
4&Petroleum \& Coal&$2.0\%$\tabularnewline
5&Auto Services&$2.2\%$\tabularnewline
6&General Building Contractors&$2.5\%$\tabularnewline
7&Water Transportation&$2.7\%$\tabularnewline
8&Paper \& Allied Products&$2.9\%$\tabularnewline
9&Transportation by Air&$3.8\%$\tabularnewline
10&Social Services&$3.9\%$\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Zero-Leverage Puzzle

"Zero-Leverage Puzzle" by Mykola Pinchuk, arXiv:2302.00761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The Structural Parameters of an RIS Element}
\begin{tabular}{|c|c|}
		\hline\hline
		 \textbf{Parameter} & \textbf{Value} \\ \hline
		 Period & $P_x=14.3 \, \mathrm{mm}, P_y = 10.27 \, \mathrm{mm}$ \\ \hline
		Rectangular patch size & $P_1=9.23 \, \mathrm{mm}, P_2=1.82 \, \mathrm{mm}, P_3=3.64 \, \mathrm{mm}$ \\ \hline
		 Patch slot width & $d_1=0.26 \, \mathrm{mm}, d_2=1.43 \, \mathrm{mm}$ \\ \hline
		 Via-holes clearance & $ d_3=1.95 \, \mathrm{mm} $ \\ \hline
		 Ground hole opening & $ d_v=0.6 \, \mathrm{mm} $ \\ \hline
		 Via diameter & $ d_0=0.5 \, \mathrm{mm} $ \\ \hline
		 Substrate 1 & FR4($\varepsilon _r=4.4, \tan \delta =0.02, t_0 = 0.254 \, \mathrm{mm}$) \\ \hline
		 Substrate 2 & F4B ($\varepsilon _r=2.65, \tan \delta =0.005, t_1 = 2 \, \mathrm{mm}$) \\ \hline\hline
	\end{tabular}
\end{table}
\end{document}

eess

Xilong Pei
Haifan Yin
Li Tan
Lin Cao
Zhanpeng Li
Kai Wang
Kun Zhang
Emil Björnson

"RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials" by Xilong Pei, Haifan Yin, Li Tan, Lin Cao, Zhanpeng Li, Kai Wang, Kun Zhang, and Emil Björnson, arXiv:2103.00534v2, 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{\textit{A-set}, baseline classification accuracies for fsix CNN architectures and three class definitions.}
\begin{tabular}{|l|ccc|}
\hline
\textbf{CNN Architect.} & \textbf{Detection} & \textbf{Classification}&\textbf{Parameterization}\\\hline
MISLnet &99.12\% & 99.08\% & 87.44\%\\
TransferNet& 99.54\%& 98.66\%& 67.81\% \\
PHNet & 99.58\%& 98.76\%&84.79\%\\
SRNet & 99.47\%& 98.50\%& 83.78\%\\
DenseNet\textunderscore BC &98.89\%& 95.91\%&68.60\%\\
VGG-19& 99.90\% &99.41\% &82.11\%\\
\textbf{Avg.}&\textbf{99.42\%} & \textbf{98.39\%} & \textbf{79.08\%}\\\hline
\end{tabular}
\end{table}
\end{document}

eess

"A Transferable Anti-Forensic Attack on Forensic CNNs Using A Generative Adversarial Network" by Xinwei Zhao, Chen Chen, and Matthew C. Stamm, arXiv:2101.09568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Lukas Pin
Sofía S. Villar
William F. Rosenberger

"Revisiting Optimal Proportions for Binary Responses: Insights from Incorporating the Absent Perspective of Type-I Error Rate Control" by Lukas Pin, Sofía S. Villar, and William F. Rosenberger, arXiv:2502.06381v2, 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{Scenario overview.}
\begin{tabular}{|c|ccccc|}
		\hline
		 & Seller's mar- & Seller's sur- & Discount & Standard & \\
		Scenario & ginal cost & plus sharing & factor & deviation & Exploration policy \\
		 & $\lambda_S$ & $\Gamma$ & $\gamma$ & $\sigma[\theta_S]$ & \\
		\hline
		$1$ & $0.5$ & $0.5$ & $(0,0.9)$ & $0$ & Boltzmann \\
		$2$ & $0.83$ & $0.1$ & $(0,0.9)$ & $0$ & Boltzmann \\
		$3$ & $(0.5,0.83)$ & $(0.1,...,0.9)$ & $(0,...,0.9)$ & $0$ & Boltzmann \\
		$4$ & $(0.5,...,0.83)$ & $(0.1,...,0.9)$ & $(0,...,0.9)$ & $(0,5,10)$ & (BM, Greedy, UCB) \\
		\hline
	\end{tabular}
\end{table}
\end{document}

q-fin

"The impact of surplus sharing on the outcomes of specific investments under negotiated transfer pricing: An agent-based simulation with fuzzy Q-learning agents" by Christian Mitsch, arXiv:2301.12255v1, 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{Mean and Standard deviation of test loss of the optimizers for the Allen-Cahn equation.}
\begin{tabular}{lrr}
\toprule
 & mean & std \\
\midrule
ANaGRAM & \textbf{2.19e-04} & 4.16e-04 \\
Adam & 7.81e-04 & \textbf{1.01e-04} \\
E-NGD & 2.92e+00 & 4.51e-01 \\
GD & 3.95e-03 & 5.41e-03 \\
L-BFGS & 1.77e-03 & 1.19e-03 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning" by Nilo Schwencke and Cyril Furtlehner, arXiv:2412.10782v2, 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{Approximate number of computations in each iteration of KMAP-IIC}
\begin{tabular}{ll}
\hline\noalign{\smallskip}
 Steps involved in KMAP-IIC & Computational complexity \\
\noalign{\smallskip}\hline\noalign{\smallskip}
Finding low-complexity matrix inverse (required only once) & $(2N_r-1)MU$ \\
Finding favorable MAPs & $2MN_rU$ \\
Performing low-complexity search & $(2N_r+4)KU$ \\
Finding the solution using greedy search & $(6N_r-1)U^2 $ \\
\noalign{\smallskip}\hline
\end{tabular}
\end{table}
\end{document}

cs

"Low-Complexity Interference Cancellation Algorithms for Detection in Media-based Modulated Uplink Massive-MIMO Systems" by Manish Mandloi and Devendra Singh Gurjar, arXiv:2101.01905v1, 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}{lrrr}
 \toprule
 \textbf{Round} & \textbf{Hate} & \textbf{No Hate} & \textbf{Total} \\
 \midrule
 R1 & 1,000 (46.0\%) & 1,175 (54.9\%) & 2,175 \\
 R2 & 3,043 (73.6\%) & 1,091 (26.4\%) & 4,134 \\
 R3 & 48 (01.5\%) & 3,179 (98.5\%) & 3,227 \\
 R4 & 575 (39.4\%) & 885 (60.6\%) & 1,460 \\
 \midrule
 Total & 4,666 (42.4\%) & 6,330 (57.6\%) & 10,996 \\
 \bottomrule
 \end{tabular}
\caption{Number of examples in GAHD across rounds.}
\end{table}
\end{document}

cs

Janis Goldzycher
Paul Röttger
Gerold Schneider

"Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset" by Janis Goldzycher, Paul Röttger, and Gerold Schneider, arXiv:2403.19559v1, 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{Demographics of VOTE400 Dialog Speech}
\begin{tabular}{lcr}
\hline
Region(\texttt{R}) & No. Participants & Age ($\mu/\sigma$) \\ \hline \hline
Seoul-si(SE) & 251(F:210,M:41) & 78.98/5.13 \\ \hline
Daegu-si(DG) & 108(F:95,M:13) & 80.33/6.08 \\ \hline
Gyoungki-do(GG) & 110(F:83,M:27) & 80.17/5.41 \\ \hline
Chungcheongnam-do(CN) & 6(F:6,M:0) & 77.00/3.69 \\ \hline
Jeollanam-do(JN) & 70(F:56,M:14) & 80.76/4.90 \\ \hline
Busan-si(PS) & 160(F:137,M:23) & 78.70/5.51 \\ \hline
Daejeon-si(DJ) & 96(F:72,M:24) & 78.81/5.24 \\ \hline
Gangwon-do(GW) & 109(F:94,M:15) & 80.07/5.50 \\ \hline
Gyeongsangbuk-do(GB) & 98(F:95,M:3) & 80.87/4.48 \\ \hline
Gwangju-si(GJ) & 87(F:70,M:17) & 79.39/5.77 \\ \hline
Chungcheongbuk-do(CB) & 17(F:17,M:0) & 80.47/5.51 \\ \hline
Ulsan-si(WS) & 58(F:49,M:9) & 76.97/4.48 \\ \hline \hline
Total & 1,170(F:984,M:186) & 79.47/5.37 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Minsu Jang
Sangwon Seo
Dohyung Kim
Jaeyeon Lee
Jaehong Kim
Jun-Hwan Ahn

"VOTE400(Voide Of The Elderly 400 Hours): A Speech Dataset to Study Voice Interface for Elderly-Care" by Minsu Jang, Sangwon Seo, Dohyung Kim, Jaeyeon Lee, Jaehong Kim, and Jun-Hwan Ahn, arXiv:2101.11469v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary table to compare CV-HAL and undersmoothed HAL. The results are averaged over 500 Monte Carlo data sets. SD is the empirical standard error, SE is the averaged standard error estimation, CP is the averaged coverage probability, and $\lambda$ is the averaged tuning parameter in $l_1$ regression.}
\begin{tabular}{lcccccc}
\hline
Example & Method & Bias & SD & SE & CP (\%) & $\lambda$\\ \hline
\multirow{3}{*}{One-stage} & Logit & 0.171 & 0.065 & 0.066 & 11 & NA\\
 & CV-HAL & 0.085 & 0.048 & 0.055 & 69 & 0.025\\
 & Under-HAL & 0.006 & 0.054 & 0.054 & 94 & 0.001\\ \hline
\multirow{3}{*}{Multi-stage} & Logit & 0.388 & 0.365 & 0.361 & 84 & NA\\
 & CV-HAL & 0.245 & 0.365 & 0.350 & 85 & 0.024\\
 & Under-HAL & 0.021 & 0.387 & 0.364 & 94 & 0.007 \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Hao Sun
Ashkan Ertefaie
Luke Duttweiler
Brent A. Johnson

"Constructing optimal treatment length strategies to maximize quality-adjusted lifetimes" by Hao Sun, Ashkan Ertefaie, Luke Duttweiler, and Brent A. Johnson, arXiv:2412.05108v1, 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{Ablation study for the various components proposed and discussed in }
\begin{tabular}{lrrr}
 \toprule
 Method & RMSE & MAE & REL \\
 \midrule
 SAID-NeRF (Full) (ours) & $\bf{0.0843}$ & $\bf{0.0599}$ & $\bf{0.0698}$ \\
 SAID-NeRF w/o Position Encoding & 0.1890 & 0.1013 & 0.1202 \\
 SAID-NeRF w/o Semantic Info. & 0.1308 & 0.0832 & 0.0964 \\
 SAID-NeRF w/o Diffuse/Specular & 0.0921 & 0.0675 & 0.0788 \\
 SAID-NeRF w/o Depth Supervision & 0.6328 & 0.6100 & 0.7854 \\
 Instant-NGP & 0.2051 & 0.1754 & 0.2250 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Avinash Ummadisingu
Jongkeum Choi
Koki Yamane
Shimpei Masuda
Naoki Fukaya
Kuniyuki Takahashi

"SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects" by Avinash Ummadisingu, Jongkeum Choi, Koki Yamane, Shimpei Masuda, Naoki Fukaya, and Kuniyuki Takahashi, arXiv:2403.19607v1, 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{Baseline average weekly level per ZIP code in the pre-event period.}
\begin{tabular}{lcc}
\hline
\textbf{Variable} & \textbf{Control Group} & \textbf{Treated Group} \\
\hline
Transaction Volume & 228.833 & 221.300 \\
Default & 8.714 & 10.418 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

"Keeping in Place After the Storm-Emergency Assistance and Evictions" by Bilal Islah and Ahmed Zoulati, arXiv:2505.14548v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR" by Ruizhi Li, Gregory Sell, and Hynek Hermansky, arXiv:2102.03055v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Unemployment and Endogenous Reallocation over the Business Cycle" by Carlos Carrillo-Tudela and Ludo Visschers, arXiv:2304.00544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Jonas Schmidt
Thomas Schwentick
Till Tantau
Nils Vortmeier
Thomas Zeume

"Work-sensitive Dynamic Complexity of Formal Languages" by Jonas Schmidt, Thomas Schwentick, Till Tantau, Nils Vortmeier, and Thomas Zeume, arXiv:2101.08735v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Ding Ning
Varvara Vetrova
Sébastien Delaux
Rachael Tappenden
Karin R. Bryan
Yun Sing Koh

"A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models" by Ding Ning, Varvara Vetrova, Sébastien Delaux, Rachael Tappenden, Karin R. Bryan, and Yun Sing Koh, arXiv:2502.13495v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Algorithmic Collusion in Auctions: Evidence from Controlled Laboratory Experiments" by Pranjal Rawat, arXiv:2306.09437v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|} % Row label + 3 columns
\hline
\textbf{Models}& selected functions & \textbf{Overall score } \\ \hline
\textbf{M3} & 87\% & 52\% (104/200)
\\ \hline
\textbf{M135} & 100\% & 69.5\% (139/200)
\\ \hline
\textbf{M135AL} & 98\% & 73.5\% (147/200)
\\ \hline
\textbf{GPT4} & 7\% & 9 \% (18/200)
\\ \hline
\end{tabular}
\caption{Comparison between our models M3, M135 and M135AL trained from scratch on a sampling of $f \in {\cal P}^3$ and gpt4 for finding the zero of an unknown continuous function}
\end{table}
\end{document}

stat

"Two in context learning tasks with complex functions" by Omar Naim and Nicholas Asher, arXiv:2502.03503v1, 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{Comparisons of sample size, performance, and learning behaviors across clusters}
\begin{tabular}{llllllllll}
\hline
 & & & \multicolumn{7}{c}{Learning behaviors} \\ \cline{4-10}
cluster & size(\%) & certified(\%) & 1(\%) & 2(\%) & 3 & 4 & 5 & 6 & 7 \\ \hline
1 & 91811(99.02) & 1544(1.68) & 42796(46.61) & 5039(5.49) & 3.28 & 94.61 & 23.06 & 2.89 & 0.01 \\
2 & 911(0.98) & 485(53.24) & 911(100) & 846(92.86) & 41.72 & 5855.63 & 1022.94 & 14.25 & 0.10 \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c}
 & \\
 &
 \end{tabular}
\caption{Caption}
\end{table}
\end{document}

stat

Rajdeep Haldar
Ziyi Wang
Qifan Song
Guang Lin
Yue Xing

"LLM Safety Alignment is Divergence Estimation in Disguise" by Rajdeep Haldar, Ziyi Wang, Qifan Song, Guang Lin, and Yue Xing, arXiv:2502.00657v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The experimental results for various methods under different metrics are presented for the FOP defined as $ \min \{ (\theta \circ u)^{\top} x \mid x \in [-1, 1]^{p} \}= \min \left\{\sum_{k=1}^{p} (\theta_k * u_k) x_k \mid x \in [-1, 1]^{p} \right\}$ in the \textbf{Noisy Decision} setting. For the FY loss, we set $\lambda=0.1$ and $\Omega(x) = 1/2\|x\|_2^2$.}
\begin{tabular}{c|cccc|cccc|cccc}
\hline
\multirow{2}{*}{Sample size} & \multicolumn{4}{c|}{Parameter Error} & \multicolumn{4}{c|}{Decision Error} & \multicolumn{4}{c}{Regret} \\ \cline{2-13}
 & FY & SPA & KKA & VIA & FY & SPA & KKA & VIA & FY & SPA & KKA & VIA \\ \hline
50 & \textbf{2.76} & 5.50 & 5.50 & 5.50 & \textbf{0.00} & 19.99 & 19.99 & 0.00 & \textbf{0.00} & 2.75 & 2.75 & 0.00 \\
100 & \textbf{2.42} & 5.50 & 5.50 & 5.50 & \textbf{0.00} & 19.99 & 19.99 & 0.00 & \textbf{0.00} & 2.76 & 2.76 & 0.00 \\
300 & \textbf{2.25} & 5.50 & 5.50 & 5.50 & \textbf{0.00} & 20.00 & 20.00 & 0.00 & \textbf{0.00} & 2.75 & 2.75 & 0.00 \\
500 & \textbf{2.27} & 5.50 & 5.50 & 5.50 & \textbf{0.00} & 20.01 & 20.01 & 0.00 & \textbf{0.00} & 2.76 & 2.76 & 0.00 \\
1000 & \textbf{2.19} & 5.50 & 5.50 & 5.50 & \textbf{0.00} & 19.99 & 19.99 & 0.00 & \textbf{0.00} & 2.75 & 2.75 & 0.00 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

"A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization" by Zhehao Li, Yanchen Wu, and Xiaojie Mao, arXiv:2502.16120v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Marina T. Dietrich
Dennis Dobler
Mathisca C. M. de Gunst

"Wild Bootstrap for Counting Process-Based Statistics" by Marina T. Dietrich, Dennis Dobler, and Mathisca C. M. de Gunst, arXiv:2310.17308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Outcome Regression Methods for Analyzing Hybrid Control Studies: Balancing Bias and Variability" by Zhiwei Zhang, Jialuo Liu, and Wei Liu, arXiv:2501.17358v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\multicolumn{2}{|c|}{Angle $\varphi$}&$\varphi_1=1$&$\varphi_2=0.5$&$\varphi_3=0.1$&$\varphi_4=0.003$ \\
\hline
{\bf Case 1}&$\mathcal{M}(\varphi)$&$5.3118$ &$2.8583$&$0.6780$&$0.0229$ \\
\hline
{\bf Case 2}&$\mathcal{M}(\varphi)$&$179.99$ &$62.500$&$5.3610$&$0.0229$\\
\hline
\end{tabular}
\caption{Stress values corresponding to different angles of twist for the two cases}
\end{table}
\end{document}

math

"Simultaneous identification of the parameters in the plasticity function for power hardening materials : A Bayesian approach" by Salih Tatar and Mohamed BenSalah, arXiv:2412.05241v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Ibrahim Abdelrazeq
Hardy Smith
Dinmukhammed Zhanbyrshy

"How to verify that a given process is a Lévy-Driven Ornstein-Uhlenbeck Process" by Ibrahim Abdelrazeq, Hardy Smith, and Dinmukhammed Zhanbyrshy, arXiv:2501.03434v2, 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}{lllllllll}
\hline
\textbf{Category} & \textbf{(1) Y/N} & \textbf{(2) Fact.} & \textbf{(3)
 Num.} & \textbf{(4) Open.}\\
\hline
Context & 963.81 & 1036.63 & 1020.04 & 1017.25\\
Question & 67.75 & 61.26 & 60.36 & 65.44\\
Answer & 3.77 & 16.01 & 8.22 & 65.97\\
\hline
\end{tabular}
\caption{Average character length.}
\end{table}
\end{document}

cs

Eri Onami
Shuhei Kurita
Taiki Miyanishi
Taro Watanabe

"JDocQA: Japanese Document Question Answering Dataset for Generative Language Models" by Eri Onami, Shuhei Kurita, Taiki Miyanishi, and Taro Watanabe, arXiv:2403.19454v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Charly Robinson La Rocca
Jean-François Cordeau
Emma Frejinger

"Supervised Large Neighbourhood Search for MIPs" by Charly Robinson La Rocca, Jean-François Cordeau, and Emma Frejinger, arXiv:2501.10778v1, 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{Frequency of Special Characters}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

math

Dinesh Parthasarathy
Tommaso Bevilacqua
Martin Lanser
Axel Klawonn
Harald Köstler

"Towards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations" by Dinesh Parthasarathy, Tommaso Bevilacqua, Martin Lanser, Axel Klawonn, and Harald Köstler, arXiv:2412.08186v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{BD-rate (\%) on the CityScapes~, KITTI 2012~, and KITTI 2015~ datasets. MV-HEVC~ is set as the baseline. Lower is better, a negative number indicating bitrate \emph{savings}.}
\begin{tabular}{lccc}
 \toprule
 Method &CityScapes~ &KITTI 2012~ &KITTI 2015~ \\
 \midrule
 HEVC~ &33.3 &7.9 &12.7 \\ %\multirow{2}{*}{CityScape}
 FVC~ &-15.6 &-2.3 &1.0 \\
 DCVC~ &-15.2 &-13.7 &-12.3 \\
 LSVC~ &-32.7 &-17.1 &-13.4 \\
 Ours &\textbf{-50.6} &\textbf{-18.2} &\textbf{-15.8}\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Low-Latency Neural Stereo Streaming

Qiqi Hou
Farzad Farhadzadeh
Amir Said
Guillaume Sautiere
Hoang Le

"Low-Latency Neural Stereo Streaming" by Qiqi Hou, Farzad Farhadzadeh, Amir Said, Guillaume Sautiere, and Hoang Le, arXiv:2403.17879v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|ccc}
 \toprule
 & \(\varepsilon=1\) & \(\varepsilon=5\) & \(\varepsilon=10\) \\
 \midrule
 $\Delta(\varepsilon)$ [\%]
 & 9.5 & 17.4 & 950 \\
 \bottomrule
 \end{tabular}
\caption{Impact of shape-function misalignment on final-time error (quadratic test with \(b=0.1\)).}
\end{table}
\end{document}

math

Dante Kalise
Lucas M. Moschen
Grigorios A. Pavliotis
Urbain Vaes

"A Spectral Approach to Optimal Control of the Fokker-Planck Equation" by Dante Kalise, Lucas M. Moschen, Grigorios A. Pavliotis, and Urbain Vaes, arXiv:2503.15125v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c||c|c|}
 \hline Method & SSIM & PSNR \\\hline\hline
 Orig. & $ 0.945 \pm 0.007 $& $ 30.189 \pm 0.690 $ \\\hline\hline
 L1 & $\textbf{ 0.984} \pm \textbf{0.002	}$ & $\textbf{ 	42.365} \pm \textbf{0.642 }$ \\\hline
 SPL & \textit{ 0.983} $\pm$ \textit{0.002 } & $ 40.888 \pm 0.216 $ \\\hline\hline
 Pix2Pix~ & $ 0.978 \pm 0.003 $ & $ 40.897 \pm 0.697 $ \\\hline
 Pix2PixHD~ & $ 0.971 \pm 0.004 $ & $ 38.739 \pm 0.624 $ \\\hline
 CRN~ & $ 0.371 \pm 0.551 $& $ 19.482 \pm 16.033 $\\\hline\hline
 Pix2PixHD* & $ 0.971 \pm 0.004 $ & $ 38.415 \pm 1.278 $ \\\hline
 CRN* & $ 0.976 \pm 0.0045 $& $ 37.582 \pm 1.574 $\\\hline\hline
 Ours & $\textbf{0.984}\pm \textbf{0.002}$&$ \textit{41.706} \pm \textit{0.547}$\\\hline

 \end{tabular}
\caption{Reconstruction results of various Image-Translation methods. Best and second-best result in $\textbf{bold}$ and $\textit{cursive}$.}
\end{table}
\end{document}

eess

Constantin Seibold
Matthias A. Fink
Charlotte Goos
Hans-Ulrich Kauczor
Heinz-Peter Schlemmer
Rainer Stiefelhagen
Jens Kleesiek

"Prediction of low-keV monochromatic images from polyenergetic CT scans for improved automatic detection of pulmonary embolism" by Constantin Seibold, Matthias A. Fink, Charlotte Goos, Hans-Ulrich Kauczor, Heinz-Peter Schlemmer, Rainer Stiefelhagen, and Jens Kleesiek, arXiv:2102.01445v2, 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{\textit{I-set}, baseline classification accuracies for six CNN architectures and three class definitions.}
\begin{tabular}{|l|ccc|}
\hline
\textbf{CNN Architect.} & \textbf{Detection} & \textbf{Classification}&\textbf{Parameterization}\\\hline
MISLnet& 99.84\% & 99.55\% & 86.24\% \\
TransferNet& 99.20\%& 98.04\%& 65.27\% \\
PHNet &99.58\% & 98.94\%&86.58\% \\
SRNet & 99.16\%& 99.36\% & 81.30\% \\
DenseNet\textunderscore BC &98.13\%&95.66\%&65.50\%\\
VGG-19& 99.87\% & 99.50\%&82.67\%\\
\textbf{Avg.}& \textbf{99.29\%}&\textbf{98.51\%}&\textbf{77.93\%} \\\hline
\end{tabular}
\end{table}
\end{document}

cs

"A Transferable Anti-Forensic Attack on Forensic CNNs Using A Generative Adversarial Network" by Xinwei Zhao, Chen Chen, and Matthew C. Stamm, arXiv:2101.09568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Rafał Bistroń
Jakub Czartowski
Karol Życzkowski

"Quantum convolutional channels and multiparameter families of 2-unitary matrices" by Rafał Bistroń, Jakub Czartowski, and Karol Życzkowski, arXiv:2312.17719v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Since the Scientific Literature Is Multilingual, Our Models Should Be Too" by Abteen Ebrahimi and Kenneth Church, arXiv:2403.18251v1, 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{Macro-averaged precision, recall, and F1-score of each fine-tuned model across all classes on FUNSD and IDL datasets.}
\begin{tabular}{c|ccc|ccc}
 \toprule
 & \multicolumn{3}{c}{\textbf{FUNSD}} & \multicolumn{3}{c}{\textbf{IDL}} \\
 \textbf{Model} & \textbf{Precision} & \textbf{Recall} & \textbf{F1-score} & \textbf{Precision} & \textbf{Recall} & \textbf{F1-score} \\
 \midrule
 \textbf{T+L+I} & 0.834 & 0.835 & 0.834 & 0.923 & 0.920 & 0.921 \\
 \textbf{T+L} & 0.820 & 0.821 & 0.821 & 0.908 & 0.907 & 0.907 \\
 \textbf{T+I} & 0.770 & 0.769 & 0.769 & 0.916 & 0.914 & 0.915 \\
 \textbf{T} & 0.762 & 0.764 & 0.762 & 0.879 & 0.874 & 0.875 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Hsiu-Wei Yang
Abhinav Agrawal
Pavlos Fragkogiannis
Shubham Nitin Mulay

"Can AI Models Appreciate Document Aesthetics? An Exploration of Legibility and Layout Quality in Relation to Prediction Confidence" by Hsiu-Wei Yang, Abhinav Agrawal, Pavlos Fragkogiannis, and Shubham Nitin Mulay, arXiv:2403.18183v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

MacKenzie Carr
Eun-Kyung Cho
Nicholas Crawford
Vesna Iršič
Leilani Pai
Rebecca Robinson

"On the interval coloring impropriety of graphs" by MacKenzie Carr, Eun-Kyung Cho, Nicholas Crawford, Vesna Iršič, Leilani Pai, and Rebecca Robinson, arXiv:2312.14881v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{CIFAR10 evaluation using sample quality (FID)}
\begin{tabular}{llc}
\hline
\textbf{Class} & \textbf{Method} & \textbf{FID} $\downarrow$ \\ \hline
\multirow{6}{*}{OT} & VSCLD (Ours) & 2.89 \\
 & VSDM () & 2.28 \\
 & SB-FBSDE () & 3.01 \\
 & DOT () & 15.78 \\
 & DGflow () & 9.63 \\ \hline
\multirow{4}{*}{SGMs} & SDE () & 2.92 \\
 & CLD () & \textbf{2.23} \\
 & VDM () & 4.00 \\
 & LSGM () & 2.10 \\
 & EDM () & \textbf{1.97} \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Kevin Rojas
Yixin Tan
Molei Tao
Yuriy Nevmyvaka
Wei Deng

"Variational Schrödinger Momentum Diffusion" by Kevin Rojas, Yixin Tan, Molei Tao, Yuriy Nevmyvaka, and Wei Deng, arXiv:2501.16675v1, 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{Questions selected to evoke Positive Sentiment}
\begin{tabular}{|c|l|}
 \hline
 \multicolumn{1}{|p{3.5em}|}{\centering\textbf{ID}} & \multicolumn{1}{|p{40em}|}{\centering\textbf{Questions}} \\
 \hline
 Q\_01 & Tell us about a fun adventure you had with your friends\\
 Q\_02 & What is your favourite time of day? Please, describe it \\
 Q\_03 & Are there any hobbies or activities you began during the lockdown? If so, please describe them \\
 Q\_04 & Where would you like to go on vacation? \\
 Q\_05 & What is your favorite season and why?\\
 Q\_06 & Tell us about the happiest moment of your life \\
 Q\_07 & Do you have grandchildren? Please, tell us about them\\
 Q\_08 & Describe a happy moment spent with your parents\\
 Q\_09 & Are you married? If so, how did you meet each other? \\
 Q\_10 & Have you ever traveled? If so, tell us about your most beautiful trip \\
 Q\_11 & Describe the happiest moment you experienced during the lockdown \\
 Q\_12 & \multicolumn{1}{p{39em}|}{Were you able to keep in touch with friends and relatives during the lockdown? If so, please describe how} \\
 Q\_13 & Has the lockdown period allowed you to rediscover activities or passions that you had set aside? \\
 Q\_14 & What did you enjoy most during the lockdown period? \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

"SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults" by Alessandra Grossi and Francesca Gasparini, arXiv:2311.14483v2, 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}{lcc}
\toprule
mAP@10 & Text Retrieval & Audio Retrieval \\ \midrule
CLAP & 12.24 & \textbf{21.21} \\
CLAP & \textbf{13.80} & 20.40 \\ \midrule
CLAP & \textbf{51.34} & \textbf{55.60} \\
CLAP & 45.70 & 51.30 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Mengjie Zhao
Junya Ono
Zhi Zhong
Chieh-Hsin Lai
Yuhta Takida
Naoki Murata
Wei-Hsiang Liao
Takashi Shibuya
Hiromi Wakaki
Yuki Mitsufuji

"On the Language Encoder of Contrastive Cross-modal Models" by Mengjie Zhao, Junya Ono, Zhi Zhong, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Wei-Hsiang Liao, Takashi Shibuya, Hiromi Wakaki, and Yuki Mitsufuji, arXiv:2310.13267v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
 \toprule
 & MSE ($\downarrow$) & NLL ($\downarrow$) \\
 \midrule
 Graph Matérn & ${\bf 0.030 \pm 0.000}$
 & $-684.54 \pm 4.20$ \\
 Edge Matérn (ours) & ${\bf 0.029 \pm 0.001}$ & ${\bf -703.42 \pm 5.10}$ \\
 \bottomrule
\end{tabular}
\caption{Mean square error (MSE) and negative log-likelihood (NLL) of ocean current magnitude predictions using (a) a graph Matérn baseline, and (b) the edge Matérn GP. Mean and standard error are shown.}
\end{table}
\end{document}

stat

Mathieu Alain
So Takao
Brooks Paige
Marc Peter Deisenroth

"Gaussian Processes on Cellular Complexes" by Mathieu Alain, So Takao, Brooks Paige, and Marc Peter Deisenroth, arXiv:2311.01198v2, 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{Number of FLOPs and trainable parameters for different fine-tuning schemes.}
\begin{tabular}{|c|c|c|}
 \hline
 & Parameters & FLOPs \\
 \hline
 No FT & 0 & 131072037 (131 M) \\
 \hline
 EO & 8034 (8 K) & 131072037 (131 M) \\
 \hline
 FM & 16096 (16 K) & 131072037 (131 M) \\
 \hline
 GA & 16096 (16 K) & 131072037 (131 M)\\
 \hline
 TM3 & 8368 & 460,062,720 (460 M) + 12,533,760 \\
 \hline
 TM12 & 8368 & 1,819,017,216 + 12,533,760 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Mehdi Sattari
Deniz Gündüz
Tommy Svensson

"Online Neural Model Fine-Tuning in Massive MIMO CSI Feedback: Taming The Communication Cost of Model Updates" by Mehdi Sattari, Deniz Gündüz, and Tommy Svensson, arXiv:2501.18250v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{Performance comparison between proposed approach and AGMM on the `An Outdoor Area', `12 Angry Men', and `Outdoor Canteen-2' dataset.}}
\begin{tabular}{c|cccc}
\toprule
Dataset&Method & No. of clusters & AUC(\%) & Replaced/Merged \\
 \hline
 &AGMM & 3 & \textbf{78.02} &2928 \\
 &AGMM & 4 & 75.87 &2152 \\
Outdoor Canteen-2 &AGMM & 5 &74.00 &1739 \\
 &AGMM & 6 & 72.94 &1363 \\
 &Ours & adaptive & \textbf{86.00} &235 \\
 \hline
 &AGMM & 3 & 57.91 &3567 \\
 &AGMM & 4 & \textbf{58.48 } &2534 \\
An Outdoor Area &AGMM & 5 &51.42 &2078 \\
 &AGMM & 6 &54.41 &1736 \\
 &Ours & adaptive & \textbf{61.12} &76 \\
 \hline
 &AGMM & 3 & 61.36 &3063 \\
 &AGMM & 4 & 58.30 &2551 \\
12 Angry Men &AGMM & 5 & \textbf{81.36 } &2290 \\
 &AGMM & 6 & 72.60 &1776 \\
 &Ours & adaptive & \textbf{83.61} &22 \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"Anomaly Detection in Audio with Concept Drift using Adaptive Huffman Coding" by Pratibha Kumari and Mukesh Saini, arXiv:2102.10515v2, 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{Atomic Densities (b$^{-1}$cm$^{-1}$) of the Simplified Big Ten Model}
\begin{tabular}{|l|c|c|} \hline
			& Homogenized Core			& Reflector					\\ \hline
$^{234}$U	& $4.8416 \times 10^{-5}$	& $2.8672 \times 10^{-7}$	\\
$^{235}$U	& $4.8151 \times 10^{-3}$	& $1.0058 \times 10^{-4}$	\\
$^{236}$U	& $1.7407 \times 10^{-5}$	& $1.1468 \times 10^{-6}$	\\
$^{238}$U	& $4.3181 \times 10^{-2}$	& $4.7677 \times 10^{-2}$	\\ \hline
\end{tabular}
\end{table}
\end{document}

math

"Transit-Length Distribution for Particle Transport in Binary Markovian Mixed Media" by Brian C. Kiedrowski and Emily H. Vu, arXiv:2412.19359v1, 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{Execution time (min) with Load flow and ST-PVSA}
\begin{tabular}{|c|c|c|c|c|}
	\hline
 Test Network & Scenarios\textsuperscript{*} & 1k & 10k & 30k \\
 \cline{1-5}
 \multirow{ 2}{*}{IEEE 37} & LF & 1.93 & 18.8 & 55.7 \\
 \cline{2-5}
 & ST-PVSA & \multicolumn{3}{c|}{1.13} \\
 \hline
 \multirow{ 2}{*}{IEEE 123} & LF & 43.93 & 400.2 & 1195.6 \\
 \cline{2-5}
 & ST-PVSA & \multicolumn{3}{c|}{3.91} \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Sai Munikoti
Mohammad Abujubbeh
Kumarsinh Jhala
Balasubramaniam Natarajan

"Spatio-Temporal Probabilistic Voltage Sensitivity Analysis - A Novel Framework for Hosting Capacity Analysis" by Sai Munikoti, Mohammad Abujubbeh, Kumarsinh Jhala, and Balasubramaniam Natarajan, arXiv:2009.08490v2, 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{Parameter settings for Experiment~3 (Affiliated Valuations + Bandits).}
\begin{tabular}{ll}
\toprule
\textbf{Parameter} & \textbf{Possible Values}\\
\midrule
Number of bidders ($n$) & $\{2,4,6\}$\\
Reserve price ($r$) & $\{0.0,0.1,0.2,0.3,0.4,0.5\}$\\
Affiliation ($\eta$) & e.g.\ $\{0.0,0.25,0.5,0.75,1.0\}$\\
Bandit type & UCB or LinUCB\\
Exploration parameter ($c$) & e.g.\ $\{0.01,\dots,2.0\}$\\
Regularization ($\lambda$) & e.g.\ $\{0.1,1.0,5.0\}$\\
State features & e.g.\ $s_i,\,\text{median-of-others},\,\text{winner-bid}$\\
Number of rounds & e.g.\ $10^5$\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Algorithmic Collusion in Auctions: Evidence from Controlled Laboratory Experiments" by Pranjal Rawat, arXiv:2306.09437v2, 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{This is an example table.}
\begin{tabular}{ccc}
 \hline
 Column 1 & Column 2 & Column 3 \\
 \hline
 Cell 1 & Cell 2 & Cell 3\\
 Cell 4 & Cell 5 & Cell 6 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

Yaojun Zhang
Lanpeng Ji
Georgios Aivaliotis
Charles Taylor

"Bayesian CART models for insurance claims frequency" by Yaojun Zhang, Lanpeng Ji, Georgios Aivaliotis, and Charles Taylor, arXiv:2303.01923v3, 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{Average test RMSE and single-core CPU fit times on the OpenML regression tasks.}
\begin{tabular}{lcc}
\toprule
Model & Mean RMSE & Fit Time (s)\\
\midrule
Gradient RFRBoost & 0.4086 & 1.749\\
Greedy RFRBoost $\Delta_{dense}$\hspace{-16pt} & 0.4094 & 2.888\\
Greedy RFRBoost $\Delta_{diag}$\hspace{-16pt} & 0.4152 & 0.918\\
Greedy RFRBoost $\Delta_{scalar}$\hspace{-16pt} & 0.4333 & 0.638\\
\midrule
XGBoost & 0.3954 & 1.873\\
E2E MLP ResNet & 0.4167 & 38.909\\
RFNN & 0.4340 & 0.044\\
Ridge Regression & 0.5395 & 0.001\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Random Feature Representation Boosting

Nikita Zozoulenko
Thomas Cass
Lukas Gonon

"Random Feature Representation Boosting" by Nikita Zozoulenko, Thomas Cass, and Lukas Gonon, arXiv:2501.18283v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Leo Zhang
Peter Potaptchik
Arnaud Doucet
Hai-Dang Dau
Saifuddin Syed

"Generalised Parallel Tempering: Flexible Replica Exchange via Flows and Diffusions" by Leo Zhang, Peter Potaptchik, Arnaud Doucet, Hai-Dang Dau, and Saifuddin Syed, arXiv:2502.10328v1, 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}{ccc}
 \toprule
 & \multicolumn{2}{c}{Transition intensity}\\
 \cmidrule(lr){2-3}Transition & Fully parametric & Semi-parametric\\
 \cmidrule(lr){1-1}\cmidrule(lr){2-2}\cmidrule(lr){3-3}$1\longrightarrow2$ & Weibull & B-spline, $\kappa = 1,\ \xi_1 = 5$ days \\
 $2\longrightarrow3$ & Exponential & B-spline, $\kappa = 1$, $\xi_1 = 7$ days \\
 $2\longrightarrow4$ & Weibull & B-spline, $\kappa = 1$, $\xi_1 = 7$ days \\
 $4\longrightarrow5$ & Exponential & B-spline, $\kappa = 1$, $\xi_1 = 7$ days \\
 \bottomrule
 \end{tabular}
\caption{Parameterization of transition intensities for the five-state inferential models used in Section that are depicted in Figure . Exponential log-intensities are parameterized as $\log(\lambda_i(t, Z_i)) = \log(\lambda_0) + \beta Z_i$, where $\lambda_0>0$ and $Z_i$ is the treatment assignment indicator for participant $i$ with value 0 if $i$ is assigned to placebo and 1 if assigned to mAb. Weibull log-intensities are parameterized as $\log(\lambda_i(t, Z_i)) = \log(\lambda_0) + \log(\alpha) + t(\kappa - 1) + \beta Z_i$, where $\lambda_0 > 0$ and $\kappa > 0$. B-spline transition intensities are parameterized as $\log(\lambda_i(t,Z_i)) = \log\left(\sum_{\ell = 1}^L\gamma_\ell B_\ell\left(t;\boldsymbol{\xi},\kappa\right)\right) + \beta Z_i$, where $\boldsymbol{\gamma}>0$ are the B-spline coefficients and $B_\ell(t;\boldsymbol{\xi},\kappa)$ is the $\ell-$th basis function for an B-spline with interior knots $\boldsymbol{\xi}$ and degree $\kappa$ evaluated at time $t$.}
\end{table}
\end{document}

stat

Raphael Morsomme
C. Jason Liang
Allyson Mateja
Dean A. Follmann
Meagan P. O'Brien
Chenguang Wang
Jonathan Fintzi

"Assessing treatment efficacy for interval-censored endpoints using multistate semi-Markov models fit to multiple data streams" by Raphael Morsomme, C. Jason Liang, Allyson Mateja, Dean A. Follmann, Meagan P. O'Brien, Chenguang Wang, and Jonathan Fintzi, arXiv:2501.14097v1, 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{Fiber parameters for C- and O-band transmission}
\begin{tabular}{|c||c|c|}
\hline
 Parameter & C-band & O-band \\
 \hline
 Wavelength $\lambda$ [nm] & 1550 & 1310 \\
$\alpha$ [dB/km] & $0.2$ &$0.4$ \\
$\beta_2$ [ps$^2$/km] & $-21.67$ & $-0.2$ \\
$\gamma$ [$1$/W/km] & $1.2$ &$1.4$ \\
$\beta_3$ [ps$^3$/km] & $-$ & $0.0765$\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Vinícius Oliari
Erik Agrell
Gabriele Liga
Alex Alvarado

"Frequency Logarithmic Perturbation on the Group-Velocity Dispersion Parameter with Applications to Passive Optical Networks" by Vinícius Oliari, Erik Agrell, Gabriele Liga, and Alex Alvarado, arXiv:2103.05972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Fanwei Kong
Nathan Wilson
Shawn C. Shadden

"A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction" by Fanwei Kong, Nathan Wilson, and Shawn C. Shadden, arXiv:2102.07899v2, 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|l|c}
\hline
\textbf{TLD} & \textbf{Count} & \textbf{TLD} & \textbf{Count} \\
\hline
1. com & 12942 & 7. de & 758 \\
2. org & 1069 & 8. edu & 414 \\
3. net & 996 & 9. ca & 398 \\
4. uk & 758 & 10. nl & 207\\
5. au & 414 & 11. jp & 183 \\
6. br & 398 & 12. co & 156\\
\end{tabular}
\caption{Top 12 Top Level Domains (from a total of 218) and their counts.}
\end{table}
\end{document}

cs

Jens Frieß
Tobias Gattermayer
Nethanel Gelernter
Haya Schulmann
Michael Waidner

"Cloudy with a Chance of Cyberattacks: Dangling Resources Abuse on Cloud Platforms" by Jens Frieß, Tobias Gattermayer, Nethanel Gelernter, Haya Schulmann, and Michael Waidner, arXiv:2403.19368v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Cyrille Feudjio
Victoire Djimna Noyum
Younous Perieukeu Mofendjou
Rockefeller
Ernest Fokoué

"A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG Data" by Cyrille Feudjio, Victoire Djimna Noyum, Younous Perieukeu Mofendjou, Rockefeller, and Ernest Fokoué, arXiv:2102.01647v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Edgar C. Merkle
Sonja D. Winter
Ellen Fitzsimmons

"Identification and Scaling of Latent Variables in Ordinal Factor Analysis" by Edgar C. Merkle, Sonja D. Winter, and Ellen Fitzsimmons, arXiv:2501.06094v1, 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{ WAIC, DIC, and effective sample size values from the proposed BGWR algorithm using different kernels for the case study.}
\begin{tabular}{llll}
\hline
 & Exponential kernel & Bi-square kernel & Gaussian kernel \\
\hline
WAIC & -801304.5 & -802014.9 & -801161.4 \\
DIC & -742933.6 & -743896.3 & -744056.8 \\
\(P_D\) & 3253.3 & 3176.5 & 3176.5 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Wala Draidi Areed
Aiden Price
Helen Thompson
Conor Hassan
Reid Malseed
Kerrie Mengersen

"Bayesian Cluster Geographically Weighted Regression for Spatial Heterogeneous Data" by Wala Draidi Areed, Aiden Price, Helen Thompson, Conor Hassan, Reid Malseed, and Kerrie Mengersen, arXiv:2311.12347v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|l|r|r|r|r}
 & Coef & Estimate & Std. Error & t value & Pr(>|t|)\\
 \hline
 \multirow{2}{*}{$<10$ $nmol$} & $a_0$ & 1.546 & 0.071 & 21.796 & 0.000\\
 &$a_1$ & 0.005 & 0.010 & 0.480 & 0.652\\
 \hline
 \multirow{2}{*}{$>10$ $nmol$} & $a_0$ & 1.095 & 0.029 & 37.817 & 0.000\\
 &$a_1$ & 0.063 & 0.003 & 19.201 & 0.000\\
 \hline
 \end{tabular}
\caption{The OLS results for fitting $\lambda$ via $\exp(-\mu)$ partitioned at $10$ $nmol$. Note the final column is the $p-$value associated with each coefficient. Assuming a significance level of 0.05, then only $a_1$ for $<10$ $nmol$ is not statistically significantly. This suggests that $\lambda$ is not a function of concentration when $<10$ $nmol$. }
\end{table}
\end{document}

stat

M. Ross Kunz
Debtanu Maiti
Gregory Yablonsky
Rebecca Fushimi

"Statistical Distributions for Transient Transport" by M. Ross Kunz, Debtanu Maiti, Gregory Yablonsky, and Rebecca Fushimi, arXiv:2501.03969v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.