\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrr}
 \toprule
 \midrule
 \centering
		& & \textbf{L} & \textbf{I} \\
		\midrule
 \midrule
		\textbf{Gender} & Man & 71 & 19 \\
 & Woman & 6 & 1 \\
 & Non-binary & 3 & 2 \\
 \midrule
		\textbf{Age} & 18-29 & 12 & 3 \\
 & 30-39 & 28 & 8 \\
 & 40-49 & 32 & 6 \\
 & 50+ & 8 & 5 \\
		\midrule
		\textbf{Residence} & Australia & 0 & 0 \\
 & Africa & 1 & 0 \\
 & Europe & 39 & 12 \\
 & Middle East & 3 & 1 \\
 & North America & 27 & 7 \\
 & South America & 2 & 1 \\
 & South Asia & 1 & 0 \\
 & Southeast Asia & 1 & 0 \\
 & Other & 6 & 1 \\
		\midrule
		\textbf{Education} & $\le$ Completed high school & 3 & 1 \\
 & Trade/technical/vocational & 1 & 1 \\
 & College, no degree & 8 & 2 \\
 & Associate’s degree & 1 & 1 \\
 & Professional degree & 5 & 0 \\
 & Bachelor's degree & 29 & 8 \\
 & Graduate degree & 33 & 9 \\
 \midrule
		\textbf{Years working} & < 1 year & 4 & 1 \\
 \textbf{in industry} & 1-5 years & 10 & 2 \\
 & 5-10 years & 4 & 1 \\
 & 10+ years & 62 & 18 \\
 \midrule
		\textbf{Years in OSS} & < 1 year & 1 & 0 \\
 \textbf{maintainence} & 1-5 years & 16 & 4 \\
 & 5-10 years & 18 & 5 \\
 & 10+ years & 45 & 13 \\
 \midrule
		\textbf{Has a security} & Yes & 29 & 9 \\
 \textbf{background} & No & 51 & 13 \\
 \midrule
 \textbf{Vulnerabilty} & Every 1-3 months & 10 & 1 \\
		\textbf{management} & Every 6 months & 11 & 3 \\
 \textbf{process review} & Every year & 19 & 4 \\
 \textbf{or update} & Every 2+ years & 8 & 1 \\
 \textbf{frequency} & Never & 32 & 13 \\
 \midrule
		\textbf{OSS project has} & Yes & 31 & 7 \\
 \textbf{funding} & No & 49 & 15 \\
 \midrule
 \midrule
 \textbf{\# of participants} & & \textbf{80} & \textbf{22} \\
 \midrule
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{The number of participants across the two studies along with their respective demographics, various backgrounds, and project details. Acronyms listed represent, respectively: listing (\textbf{L}) survey study and interview (\textbf{I}) study.}
\end{table}
\end{document}

cs

"Investigating Vulnerability Disclosures in Open-Source Software Using Bug Bounty Reports and Security Advisories" by Jessy Ayala, Yu-Jye Tung, and Joshua Garcia, arXiv:2501.17748v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{makecell}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|ccccccc}
\hline
 & \thead{Parametric\\ $\pi_{\beta}$} & \thead{Max. \\ value} & \thead{Relative \\sparsity} & \thead{Absolute\\ sparsity} & \thead{Close to\\ behavior} & \thead{Specify\\ behavior} & \thead{Specify value \\or transitions} \\
\hline
Proposed (relative sparsity):\\ $V_n(\beta,b)-\lambda L_1(\beta,b)$ & yes & no & yes & no & yes & yes & no \\
\hline
TRPO: \\$V_n(\pi_{\beta},\pi_b)-\lambda KL(\pi_{\beta},\pi_b)$ & no & no & no & no & yes & yes & no \\
\hline
Absolute relative sparsity: \\$V_n(\beta,b)-\lambda L_0(\beta,b)$ & yes & no & yes & no & no & yes & no \\
\hline
Absolute sparsity:\\ $V_n(\beta,b) - \lambda L_0(\beta)$ & yes & no & no & yes & no & yes & no \\
\hline
Absolute sparsity : \\$V_n(\beta,b) - \lambda L_1(\beta)$ & yes & no & no & yes & no & yes & no \\
\hline
Unconstrained: \\$V_n$ & no & yes & no & no & no & yes & no \\
\hline
Q-learning\\ & no & yes & no & no & no & no & yes \\
\hline
Sparse Q-learning\\ & no & no & no & no & no & no & yes \\
\hline
Model-based: \\(standard) & no & yes & no & no & no & no & yes \\
\hline
Model-based: \\($V_n-\lambda Likelihood$)
& no & yes & no & no & yes & no & yes \\
\hline
\end{tabular}
\caption{ {{\bf The properties of relative sparsity as it relates to other reinforcement learning approaches.} ``Parametric $\pi_{\beta}$'' is whether $\beta$ is parametric. ``Max. value'' indicates whether the policy is optimal with respect to $V_0,$ which will never be the case when there is a penalty. ``Relative sparsity'' indicates whether there will be only a sparse set of coefficients that differ between the behavioral and suggested policy. ``Absolute sparsity'' indicates whether the policy will have a sparse set of nonzero coefficients, which is equivalent to a relative sparsity to a randomized behavioral policy, which has $b_0=(0,\dots,0)$. ``Close to behavior'' indicates whether the suggested policy probabilities of treatment will be similar to the behavioral policy probabilities of treatment. ``Specify behavior'' indicates whether we must have a model for the behavioral policy; we consider only the policy search ($\arg\max V_n$) objectives for all but the Q-learning and model-based approaches, and hence we consider specification of behavior necessary, because it is necessary for policy search. ``Specify value or transitions'' indicates whether we must propose models for these aspects of the environment and reward structure, which can be non-trivial, and is required in Q-learning and model-based approaches. For the unconstrained approach, we consider the nonparametric optimizer that achieves maximal value. Note that TRPO can be formulated with a parametric policy, $\pi_{\beta}$, but this is not required, and it is often not the case given that the application area is robotics (the policy might be parametric, but it would be over-parameterized as in Deep Learning).}}
\end{table}
\end{document}

stat

Samuel J. Weisenthal
Sally W. Thurston
Ashkan Ertefaie

"Relative Sparsity for Medical Decision Problems" by Samuel J. Weisenthal, Sally W. Thurston, and Ashkan Ertefaie, arXiv:2211.16566v3, 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
\caption{CPU time overhead for different methods, measured in seconds.}
\begin{tabular}{l|ccc}
 \hline
 Methods & Graph-SST5 & Graph-Twitter & CRCG \\
 \hline
 \hline
 ERM & 101.22 & 22.54 & 25.00\\
 ERM+R-CAM &	103.32 & 23.98 & 27.20\\
 DIR & 194.95 & 93.83 & 91.32\\
 DIR+R-CAM & 204.23 & 95.77 & 94.49\\
 CIGA & 25.60 & 4.44 & 4.85 \\
 CIGA+R-CAM & 26.39 & 4.61 & 4.98 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Hang Gao
Chengyu Yao
Jiangmeng Li
Lingyu Si
Yifan Jin
Fengge Wu
Changwen Zheng
Huaping Liu

"Rethinking Causal Relationships Learning in Graph Neural Networks" by Hang Gao, Chengyu Yao, Jiangmeng Li, Lingyu Si, Yifan Jin, Fengge Wu, Changwen Zheng, and Huaping Liu, arXiv:2312.09613v1, 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{DoE table with actual values}
\begin{tabular}{|c|c|c|}
		\hline
		$L$ & $f_S$ & $f_D$ \\
		\hline
		0.025 & 0.084 & 0.0352 \\
		0.025 & 8.400 & 3.524 \\
		2.500 & 0.084 & 0.0352 \\
		2.500 & 8.400 & 3.524 \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

"Reinforcement Learning for Control of Valves" by Rajesh Siraskar, arXiv:2012.14668v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|}
 \hline
 \textbf{Model} & \textbf{Parameters} & \textbf{CosSim} & \textbf{BERTScore} & \textbf{STS} & \textbf{Average} \\
 \hline
 Gemma-2 & 9B & 0.487 & 0.478 & 0.489 & 0.485 \\
 Phi-3.5 & 7B & 0.502 & 0.489 & 0.503 & 0.498 \\
 Llama-3 & 7B & 0.477 & 0.477 & 0.479 & 0.478 \\
 Mistral v0.3 & 7B & \textbf{0.605}& \textbf{0.542} & \textbf{0.607} & \textbf{0.585} \\
 Qwen-2.5 & 7B & 0.578 & 0.528 & 0.572 & 0.559 \\
 Orca-2 & 7B & 0.544 & 0.508 & 0.546 & 0.533 \\
 Deepseek & 7B & 0.509 & 0.501 & 0.513 & 0.507 \\
 Olmo-2 & 7B & \textit{0.600} &\textit{0.537} & \textit{0.602} & \textit{0.580} \\
 Eurus2 & 7B & 0.528 & 0.523 & 0.530 & 0.527 \\
 Qwen-2.5 & 14B & 0.579 & 0.522 & 0.575 & 0.559 \\
 Orca-2 & 13B & 0.476 & 0.475 & 0.477 & 0.476 \\
 Mistral-Nemo & 12B & 0.577 & 0.503 & 0.578 & 0.553 \\
 Phi-3 & 14B & 0.571 & 0.522 & 0.572 & 0.555 \\
 Olmo-2 & 13B & 0.577 & 0.528 & 0.578 & 0.561 \\
 Solar Preview & 22B & 0.55 & 0.502 & 0.558 & 0.537 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Cosine Similarity Scores, BERTScores and STS Scores between generated response and the original abstract of the paper for various language models. Best performing model represented by \textbf{Bold} and second best by \textit{Italics}.}
\end{table}
\end{document}

cs

Aarush Sinha
Viraj Virk
Dipshikha Chakraborty
P. S. Sreeja

"ArxEval: Evaluating Retrieval and Generation in Language Models for Scientific Literature" by Aarush Sinha, Viraj Virk, Dipshikha Chakraborty, and P. S. Sreeja, arXiv:2501.10483v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
& {\small DAX} & {\small SMI} & {\small CAC} & {\small FTSE} \\ \hline\hline
${\small \alpha }$ & ${\small 1.6420}$ &
${\small 1.8480}$ & ${\small 1.6930}$
& ${\small 1.8740}$ \\
${\small \beta }$ & ${\small 0.1470}$ &
${\small 0.1100}$ & ${\small 0.0380}$
& ${\small 0.9500}$ \\
${\small \sigma }$ & ${\small 0.0046}$ &
${\small 0.0045}$ & ${\small 0.0006}$
& ${\small 0.0053}$ \\
${\small \mu }$ & ${\small -0.0001}$ &
${\small 0.0006}$ & ${\small -0.0001}$%
 & ${\small -0.0005}$ \\ \hline\hline
& & & &
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Involving copula functions in Conditional Tail Expectation" by Brahim Brahimi, arXiv:1205.4345v4, licensed under CC BY 3.0. 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{CIFAR10: The comparison of accuracy between standard models (Std., Std.+L1 and Std.+L2) and Min-Max model.}
\begin{tabular}{lllll}
		\toprule
		& Std. & Std.+L1 & Std.+L2 & Min-Max \\ \midrule
		No attack & 0.6352 & 0.5589 & \textbf{0.6573} & 0.5668 \\
		FGSM & 0.2382 & 0.3144 & 0.3716 & \textbf{0.3927} \\
		PGD & 0.1669 & 0.2937 & 0.3416 & \textbf{0.3712} \\
		CW & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\
		MIA & 0.1583 & 0.2918 & 0.3382 & \textbf{0.3633} \\
		L2BIA & 0.6249 & 0.5550 & 0.5561 & \textbf{0.6512} \\
		LinfBIA & 0.2128 & 0.3135 & 0.3594 & \textbf{0.3860} \\ \bottomrule
	\end{tabular}
\end{table}
\end{document}

stat

"A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks" by Haojing Shen, Sihong Chen, and Ran Wang, arXiv:2011.13719v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|}
 \hline
 Permutation & Elements in the right cell \\
 \hline
 $(n, \dots, 3, 1, 2)$
 & $(n, \dots, 3, 1, 2),(n, \dots, 4, 1, 3, 2), \dots, (1, n, n-1, \dots, 3, 2)$ \\
 \hline
 $(n-1, \dots, 2, 1, n)$
 & $(n-1, \dots, 2, 1, n), (n-1, \dots, 2, n, 1),\dots, (n-1, n, n-2, \dots, 2, 1)$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{List of elements in the right cell for the first case.}
\end{table}
\end{document}

math

"A characterization of Kazhdan-Lusztig right cells containing smooth elements" by Zhanqiang Bai and Zheng-an Chen, arXiv:2201.07439v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{CLIP zero-shot prediction accuracies} are reported for the binomial dimension in EraseBench: We present the non-target visually similar concepts (reflecting the sensitivity of erasure). The results reveal a significant degradation in sensitivity, particularly in scenarios where concept entanglement occurs, highlighting challenges in effectively disentangling related concepts during erasure. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 & Similar $\uparrow$ \\
 \cmidrule(lr){2-2}
 Techniques & "Moon" (Erase "Sun") \\
 \midrule
 Original SD & 0.73 \\
 \midrule
 ESD & 0.62 \\
 UCE & 0.70 \\
 Receler & 0.36 \\
 MACE & 0.51 \\
 AdvUnlearn & 0.56 \\
 \bottomrule
 & Similar $\uparrow$ \\
 \cmidrule(lr){2-2}
 Techniques & "Key" (Erase "Lock") \\
 \midrule
 Original SD & 0.98 \\
 \midrule
 ESD & 0.59 \\
 UCE & 0.83 \\
 Receler & 0.30 \\
 MACE & 0.50 \\
 AdvUnlearn & 0.72 \\
 \bottomrule
 & Similar $\uparrow$ \\
 \cmidrule(lr){2-2}
 Techniques & "Saucer" (Erase "Cup") \\
 \midrule
 Original SD & 0.87 \\
 \midrule
 ESD & 0.79 \\
 UCE & 0.80 \\
 Receler & 0.80 \\
 MACE & 0.74 \\
 AdvUnlearn & 0.68 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ibtihel Amara
Ahmed Imtiaz Humayun
Ivana Kajic
Zarana Parekh
Natalie Harris
Sarah Young
Chirag Nagpal
Najoung Kim
Junfeng He
Cristina Nader Vasconcelos
Deepak Ramachandran
Golnoosh Farnadi
Katherine Heller
Mohammad Havaei
Negar Rostamzadeh

"Erasing More Than Intended? How Concept Erasure Degrades the Generation of Non-Target Concepts" by Ibtihel Amara, Ahmed Imtiaz Humayun, Ivana Kajic, Zarana Parekh, Natalie Harris, Sarah Young, Chirag Nagpal, Najoung Kim, Junfeng He, Cristina Nader Vasconcelos, Deepak Ramachandran, Golnoosh Farnadi, Katherine Heller, Mohammad Havaei, and Negar Rostamzadeh, arXiv:2501.09833v2, 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{Additional model calibration benchmark with post-hoc calibration methods under unknown distribution shifts. Due to space limitations, we present the first six domain pairs in the main body and the rest of them in Table . }
\begin{tabular}{l|rrrrrr|r}
\hline
Method & Ar-Cl & Ar-Pr & Ar-Rw & Cl-Ar & Cl-Pr & Cl-Rw & Avg* \\ \hline
Vanilla & 40.61 & 25.62 & 15.56 & 33.83 & 25.34 & 24.75 & 27.37 \\
PTS & 31.91 & 24.36 & 10.65 & 22.81 & 20.42 & 15.92 & 21.91 \\
AvUTS & 29.59 & 25.55 & 10.40 & 31.81 & 26.06 & 26.15 & 28.17 \\
TransCal & 33.57 & 20.27 & 8.88 & 26.36 & 18.81 & 18.42 & 20.84 \\
IW-GAE & {12.78} & {4.70} & 12.93 & {7.52} & {4.42} & {4.11} & {8.93} \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

"IW-GAE: Importance Weighted Group Accuracy Estimation for Improved Calibration and Model Selection in Unsupervised Domain Adaptation" by Taejong Joo and Diego Klabjan, arXiv:2310.10611v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Xianyi Zeng
Giovanni Stabile
Efthymios N. Karatzas
Guglielmo Scovazzi
Gianluigi Rozza

"Embedded domain Reduced Basis Models for the shallow water hyperbolic equations with the Shifted Boundary Method" by Xianyi Zeng, Giovanni Stabile, Efthymios N. Karatzas, Guglielmo Scovazzi, and Gianluigi Rozza, arXiv:2201.09546v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance Measures of Machine Learning Models on CTU-13 Dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\textbf{Model} & \textbf{Accuracy} & \textbf{Precision} & \textbf{Recall} & \textbf{F1-Score} \\
\midrule
FFS-HTTP (Letteri et al., 2019) & 0.9879 & 0.9726 & 0.9803 & 0.9793 \\
k-NN (Ibrahim et al., 2021) & 0.9300 & 0.9000 & 0.9220 & 0.9151 \\
SVM-RBF (Ibrahim et al., 2021) & 0.8503 & 0.8487 & 0.8695 & 0.8495 \\
MLP (Ibrahim et al., 2021) & 0.8195 & 0.8691 & 0.8458 & 0.8388 \\
bot-DL (Pekta\c{s} \& Acarman, 2018) & 0.9660 & 0.9630 & 0.9680 & 0.9640 \\
SVM (Taheri et al., 2018) & 0.8073 & 0.9807 & 0.8315 & 0.8856 \\
NB & 0.78 & 0.79 & 0.76 & 0.77 \\
LR & 0.85 & 0.89 & 0.86 & 0.87 \\
AdaBoost & 0.90 & 0.95 & 0.94 & 0.94 \\
XGBoost & 0.96 & 0.98 & 0.98 & 0.98 \\
\midrule
\textit{LENSXAI Teacher} & \textit{0.9767} & \textit{0.9768 } & \textit{0.9767} & \textit{0.9767} \\
\textit{LENSXAI Student} & \textit{0.9695} & \textit{ 0.9696} & \textit{0.9695} & \textit{0.9695} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity" by Muhammet Anil Yagiz and Polat Goktas, arXiv:2501.00790v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Aulia Adila
Candy Olivia Mawalim
Masashi Unoki

"Detecting Spoof Voices in Asian Non-Native Speech: An Indonesian and Thai Case Study" by Aulia Adila, Candy Olivia Mawalim, and Masashi Unoki, arXiv:2412.01040v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparisons of training time and real time factor~(RTF) for two experiments from on dev-other.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c||c||c|c|}
		\hline
		\multirow{2}{*}{\#} & \multicolumn{2}{c||}{ \multirow{1}{*}{Outputs}} & \multirow{2}{*}{Train[h]} & \multirow{2}{*}{RTF} & WER [\%] \\ \cline{2-3} \cline{6-6}
		& Loss& Decode & && dev-other \\ \hline
		7 & $(\ell|h)(c|h)(r|h)$ & $(c|h)$ & \textbf{300} & \textbf{0.099} & 6.1\\ \hline
		8 & $(\ell,c|h)$ & $(\ell,c|h)$ & 330 & 0.118& \textbf{6.0}\\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Right Label Context in End-to-End Training of Time-Synchronous ASR Models" by Tina Raissi, Ralf Schlüter, and Hermann Ney, arXiv:2501.04521v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{hhline}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|c|c|c}
			& $T_{comp}$, s & \# of successful (failed) steps & \# of Newton iterations \\ \hhline{=|=|=|=}
			TPFA, 0 & 231.0 & 15 (31) & 391 \\
			1 & 169.2 & 10 (21) & 273 \\ \hline
			MPFA-O, 0 & & 1 ( & \\
			1 & & 1 ( ) & \\ \hline
			NTPFA-B, 0 & 91.8 & 16 (33) & 208 \\
			1 & 62.7 & 10 (21) & 141 \\\hline
			NMPFA-B, 0 & 63.46 & 13 (17) & 155 \\
			1 & 80.1 & 11 (23) & 168 \\\hline
			MFD, 0 & 335.5 & 18 (39) & 510 \\
			1 & 185.5 & 19 (41) & 312
		\end{tabular}
\end{adjustbox}
\caption{Comparison of zero- and first-order predictor for the real-life object test}
\end{table}
\end{document}

math

"First-order continuation method for steady-state variably saturated groundwater flow modeling" by Denis Anuprienko, arXiv:2201.05202v1, 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}{ll}
 \toprule
 \bfseries Dataset & \bfseries Result\\
 \midrule
 Data1 & 0.12345\\
 Data2 & 0.67890\\
 Data3 & 0.54321\\
 Data4 & 0.09876\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Rishab Khincha
Soundarya Krishnan
Tirtharaj Dash
Lovekesh Vig
Ashwin Srinivasan

"Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays" by Rishab Khincha, Soundarya Krishnan, Tirtharaj Dash, Lovekesh Vig, and Ashwin Srinivasan, arXiv:2012.10787v2, 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|}
 \hline
 Variable & Controller coefficient & $\sigma$ & $P_0$
 \\ \hline
 $u_r$ & $\theta_r$ & $I_3$ & $0.01$
 \\ \hline
 $u_v$ & $\theta_v$ & $I_9$ & $0.001$
 \\ \hline
 $u_q$ & $\theta_q$ & $I_3$ & $0.01$
 \\ \hline
 $u_\omega$ & $\theta_\omega$ & $I_{12}$ & $0.0001$
 \\ \hline
 \end{tabular}
\caption{\footnotesize Hyperparameters used by RCAC in the adaptive PX4 autopilot.}
\end{table}
\end{document}

eess

Ankit Goel
Juan Augusto Paredes
Harshil Dadhaniya
Syed Aseem Ul Islam
Abdulazeez Mohammed Salim
Sai Ravela
Dennis Bernstein

"Experimental Implementation of an Adaptive Digital Autopilot" by Ankit Goel, Juan Augusto Paredes, Harshil Dadhaniya, Syed Aseem Ul Islam, Abdulazeez Mohammed Salim, Sai Ravela, and Dennis Bernstein, arXiv:2012.02896v1, 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}{lrrr}
\toprule
 & True $\varphi$ & $\varphi$ coverage & $T$ coverage \\
 \midrule
 Expectation & 0.20 & 0.21 & 0.83 \\
Geometric mean & 0.20 & 0.20 & 0.82 \\
No scaling & 0.20 & 0.21 & 0.83 \\
Expectation & 0.50 & 0.48 & 0.74 \\
Geometric mean & 0.50 & 0.47 & 0.74 \\
No scaling & 0.50 & 0.45 & 0.74 \\
Expectation & 0.80 & 0.47 & 0.52 \\
Geometric mean & 0.80 & 0.46 & 0.52 \\
No scaling & 0.80 & 0.46 & 0.53 \\
 \bottomrule
 \end{tabular}
\caption{90\% coverage level for $\varphi$ and $T$ for the IG priors specification}
\end{table}
\end{document}

stat

"A Standardization Procedure to Incorporate Variance Partitioning Based Priors in Latent Gaussian Models" by Luisa Ferrari and Massimo Ventrucci, arXiv:2501.16057v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Actual counts in selecting one, two, three to five, or more than six times a specific item (e.g., date) in our experiments. For instance, the highlighted value indicates that \textbf{13} different days are repeated each more than $6$ times among the $200$ generated events. This table echoes the expected counts of Tab.~.} %
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \hline
 & & \multicolumn{4}{c}{Bin of counts} \\
 $N_{\text{events}}$ & feature & 1 & 2 & 3-5 & 6+ \\
 \hline
20 & time & 8 & 4 & 1 & 0 \\
20 & space & 6 & 5 & 1 & 0 \\
20 & entity & 8 & 4 & 1 & 0 \\
20 & content & 8 & 1 & 3 & 0 \\
200 & time & 6 & 4 & 14 & \textbf{13} \\
200 & space & 7 & 7 & 8 & 13 \\
200 & entity & 9 & 3 & 7 & 15 \\
200 & content & 9 & 4 & 7 & 14 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Episodic Memories Generation and Evaluation Benchmark for Large Language Models" by Alexis Huet, Zied Ben Houidi, and Dario Rossi, arXiv:2501.13121v1, 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{Main statistics for candidate validation set}
\begin{tabular}{cccc}
\toprule
\textbf{clips} & \textbf{duration} & \textbf{uploaders} \\
\midrule
4170 & 9.9h & 2224 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Eduardo Fonseca
Xavier Favory
Jordi Pons
Frederic Font
Xavier Serra

"FSD50K: An Open Dataset of Human-Labeled Sound Events" by Eduardo Fonseca, Xavier Favory, Jordi Pons, Frederic Font, and Xavier Serra, arXiv:2010.00475v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ll}
\toprule
\textbf{Prompt Style} & \textbf{GSM8K} & \textbf{CSQA}\\
\hline
\multicolumn{3}{l}{\textbf{SOLAR 10.7B Instruct}}\\
\hline
TAB COT + SG & 70.13 & 67.90\\
TAB COT + SG + SC & 75.51 & 77.72\\
COT STEP + SG & 76.50 & 68.71\\
COT STEP + SG + SC & \textbf{83.78} & \textbf{79.28}\\
\hline
\multicolumn{3}{l}{\textbf{Phi3 Mini 128K Instruct}}\\
\hline
TAB COT + SG & 79.30 & 70.19\\
TAB COT + SG + SC & 79.30 & 72.81\\
COT STEP + SG & 85.37 & 73.22\\
COT STEP + SG + SC & \textbf{90.30} & \textbf{77.56}\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Comparison of accuracy from Step-wise Greedy (SG) with and without Self-Consistency (SC) on GSM8K and CSQA (CommonsenseQA). SC represents Self-consistency with majority voting of 20 sample generations. We bold the best result per block per dataset.}
\end{table}
\end{document}

cs

"Zero-Shot Verification-guided Chain of Thoughts" by Jishnu Ray Chowdhury and Cornelia Caragea, arXiv:2501.13122v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Qunxi Zhu
Yifei Shen
Dongsheng Li
Wei Lin

"Neural Piecewise-Constant Delay Differential Equations" by Qunxi Zhu, Yifei Shen, Dongsheng Li, and Wei Lin, arXiv:2201.00960v1, 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}{lcc}
\hline
\textbf{ EN} & \bf{EN+$\textrm{ES}^*$ Attn} & \bf{EN+$\textrm{ES}^*$ aligned} \\
 66.6 & 65.8 & 69.2 \\
\hline
\end{tabular}
\caption{Zero-shot accuracy when fine-tuning XLM-R on EN monolignual data as well as the auto-translated and aligned ES data (called ES*).}
\end{table}
\end{document}

cs

Arash Einolghozati
Abhinav Arora
Lorena Sainz-Maza Lecanda
Anuj Kumar
Sonal Gupta

"El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing" by Arash Einolghozati, Abhinav Arora, Lorena Sainz-Maza Lecanda, Anuj Kumar, and Sonal Gupta, arXiv:2101.10524v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Tariq M Khan
Dawn Lin
Shahzaib Iqbal
Erik Meijering

"TAFM-Net: A Novel Approach to Skin Lesion Segmentation Using Transformer Attention and Focal Modulation" by Tariq M Khan, Dawn Lin, Shahzaib Iqbal, and Erik Meijering, arXiv:2411.17556v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
		\textbf{Hyperparameter}	& \textbf{Value} \\
		Optimiser & Adam \\
	 Batch size & $25$ \\
	 Initial learning rate & $0.001$ \\
	 Epochs & $500$ \\
	 Dropout value & $0.3$\\
	 Inflation factor & $2$\\
	 Dimension of latent space & $20$ \\
 Learning rate schedule & $0.5$ [decaying*]
	\end{tabular}
\end{adjustbox}
\caption{Hyperparameters for the rotating MNIST experiment. (*) The learning rate decays exponentially at steps of 30 epochs.}
\end{table}
\end{document}

math

Imbedding Deep Neural Networks

"Imbedding Deep Neural Networks" by Andrew Corbett and Dmitry Kangin, arXiv:2202.00113v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrrr}
 \toprule
 & $i=1$ & $i=2$ & $i=3$ & $i=4$ & $i=5$ & $i=6$ & $i=7$ & $i=8$ \\
 \midrule
 $CP^1_i$ & 0.075 & 0.105 & 0.09 & 0.053 & 0.083 & 0.098 & 0.12 & 0.128 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"On Carbon Taxes Effectiveness to Induce a Clean Technology Transition: An Evaluation Framework Based on Optimal Strategic Capacity Planning" by N. Wolf, P. Escalona, A. Angulo, and J. Weston, arXiv:2202.11507v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{diagbox}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Example~: Relative errors for different $L$, $h=2^{-7}$, $H=2^{-3}$, $\gamma_{0}$=10.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|} \hline
\multirow{2}{*}{\diagbox{\qquad $L$}{Error}}&
\multicolumn{2}{c|}{Error in $\Omega$}&\multicolumn{2}{c|}{Error in $\Omega_{1}$}\cr\cline{2-5}
&Energy&$L^2$&Energy&$L^2$\cr
\hline
1 & 0.1360e-00 & 0.2929e-01 & 0.2580e-01 & 0.1429e-01 \\ \hline
2 & 0.7361e-01 & 0.1127e-01 & 0.5881e-02 & 0.1371e-02 \\ \hline
3 & 0.5712e-01 & 0.8685e-02 & 0.1453e-02 & 0.1712e-03 \\ \hline
6 & 0.5534e-01 & 0.8625e-02 & 0.2586e-04 & 0.6106e-05 \\ \hline
10 & 0.5509e-01 & 0.8570e-02 & 0.5213e-06 & 0.1604e-06 \\ \hline
ideal solution & 0.5509e-01 & 0.8569e-02 & 0.5984e-13 & 0.2713e-13 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A combined multiscale finite element method based on the LOD technique for the multiscale elliptic problems with singularities" by Kuokuo Zhang, Weibing Deng, and Haijun Wu, arXiv:2202.13044v1, 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}{cccccc}
\hline
 \textbf{Rank} & \textbf{Feature} & $\boldsymbol{D_{\mathrm s}}$ & \textbf{Rank} & \textbf{Feature} & $\boldsymbol{D_{\mathrm s}}$ \\ \hline
1&I CCM mean & 2.9403 & 12 & I std & 0.7968 \\
2 & H CCM sosvh & 2.5413 & 13 & H std & 0.7956 \\
3 & H CCM autoc & 2.1417 & 14 & H mean & 0.7632 \\
4 & S CCM mean & 1.4099 & 15 & I mean & 0.7541 \\
5 & H CCM mean & 1.1237 & 16 & S mean & 0.7268 \\
6 & SR & 0.9424 & 17 & I CCM covariance & 0.7228 \\
7 & S CCM 2nd moment & 0.8354 & 18 & NIR mean & 0.6997\\
8 & I CCM 2nd moment & 0.8354 & 19 & ARVI & 0.6622 \\
9 & I 2nd moment & 0.8345 & 20 & NDVI & 0.6594\\
10 & I variance & 0.8345 & 21 & DCT & 0.5792 \\
11 & NIR std & 0.7980 & 22 & EVI & 0.3207 \\
\hline
\end{tabular}
\caption{Ranking of features based on Distribution Separability Criterion for SAT-6. Here CCM refers to Color Cooccurrence Matrix , DCT to Discrete Cosine Transform, sosvh to sum of sqaures for variance, autoc to autocorrelation, std to standard deviation.}
\end{table}
\end{document}

eess

Qun Liu
Saikat Basu
Sangram Ganguly
Supratik Mukhopadhyay
Robert DiBiano
Manohar Karki
Ramakrishna Nemani

"DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image Classification" by Qun Liu, Saikat Basu, Sangram Ganguly, Supratik Mukhopadhyay, Robert DiBiano, Manohar Karki, and Ramakrishna Nemani, arXiv:1911.07747v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average RMSEs of four cases}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}\hline
 & Case 1 & Case 2 & Case 3 & Case 4 \\\hline
 ${\rm RMSE}_x(\%)$ & 3.23 & 3.26 & 3.45 & 12.65 \\
 ${\rm RMSE}_{\theta}(\%)$ & 4.80 & 5.19 & 5.42 & 41.06 \\
 ${\rm RMSE}_{x_{\theta}}(\%)$& 4.44 & 4.76 & 4.96 & 35.64 \\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Siyu Liu
Xunyuan Yin
Jinfeng Liu
Feng Ding

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{FBA Investment Grade Wasserstein Radii}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
Confidence Level & 0.80 & 0.85 & 0.90 & 0.95 & 0.99 & 0.999 \\
 \hline
W Radius delta & 1.0 & 1.1 & 1.2 & 1.3 & 1.7 & 2.1 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Distributionally Robust XVA via Wasserstein Distance Part 2: Wrong Way Funding Risk" by Derek Singh and Shuzhong Zhang, arXiv:1910.03993v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccccccccccccc|}
 \hline
 $d$ & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15\\\hline
 $C_d \ge $ & 26 & 27 & 40 & 60 & 78 & 84 & 120 & 162 & 190 & 198 & 228 & 260 & 294 & 330\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{}
\end{table}
\end{document}

math

"Polynomials with many rational preperiodic points" by John R. Doyle and Trevor Hyde, arXiv:2201.11707v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|c|c}
 & Podcast& CEFC& DIHARD\\ \hline
 eGeMAPSv02& 0.87 $\pm$ 0.02 & 0.53 $\pm$ 0.04 & 0.46 $\pm$ 0.06\\
 Handcrafted& 0.91 $\pm$ 0.02& 0.74 $\pm$ 0.03& 0.59 $\pm$ 0.02\\
 YAMNet& 0.90 $\pm$ 0.01 & 0.43 $\pm$ 0.01 & 0.55 $\pm$ 0.03\\
 Whisper& \textbf{0.95 $\pm$ 0.01}& \textbf{0.92 $\pm$ 0.02}&\textbf{0.95 $\pm$ 0.03}\\
 FT Whisper& \textbf{0.95 $\pm$ 0.01}& 0.91 $\pm$ 0.02 &0.92 $\pm$ 0.01\\
 \end{tabular}
\caption{AUC computed on median file-level scores for each feature model across all datasets, evaluated using the labels assigned in SM:Table 1. Measures are an average and standard deviation across the 5-fold CV. Bold figures indicate the best performance per dataset.}
\end{table}
\end{document}

eess

Shahar Elisha
Andrew McDowell
Mariano Beguerisse-Díaz
Emmanouil Benetos

"Classification of Spontaneous and Scripted Speech for Multilingual Audio" by Shahar Elisha, Andrew McDowell, Mariano Beguerisse-Díaz, and Emmanouil Benetos, arXiv:2412.11896v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\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{adjustbox}
\end{table}
\end{document}

cs

Xiuqi Tommy Zhu
Ziyue Qiu
Ye Wei
Jianhao Wang
Yang Jiao

"Understanding the Practice, Perception, and Challenge of Blind or Low Vision Students Learning through Accessible Technologies in Non-Inclusive 'Blind Colleges'" by Xiuqi Tommy Zhu, Ziyue Qiu, Ye Wei, Jianhao Wang, and Yang Jiao, arXiv:2501.07736v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rccccc}
		\toprule
		& $\alpha = 0.1$ & $\alpha = 0.25$ & $\alpha = 0.5$ & $\alpha = 0.75$ & $\alpha = 0.9$ \\
		\midrule
		ex. () & 1.99 & 1.99 & 1.99 & 1.99 & 1.99 \\
		ex. () & 2.00 & 2.00 & 2.00 & 2.00 & 2.00 \\
		ex. () & 1.99 & 1.99 & 1.98 & 1.98 & 1.97 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{Estimated orders of convergence for examples (-). Here, the temporal grid was taken to be $\Delta t = 2\times 10^{-3}$ while the spatial mesh $h = 10^{-2}$.}
\end{table}
\end{document}

math

"Error of the Galerkin scheme for a semilinear subdiffusion equation with time-dependent coefficients and nonsmooth data" by Łukasz Płociniczak, arXiv:2202.13728v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rlll}
\toprule
 &Empirical&\multicolumn{2}{c}{95\% CI}\\\cline{3-4}
$q$ & coverage & Lower & Upper \\
\midrule
0.01 & 0.953 & 0.949 & 0.957 \\
 0.10 & 0.949 & 0.944 & 0.953 \\
 0.25 & 0.950 & 0.946 & 0.955 \\
 0.50 & 0.949 & 0.945 & 0.954 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Empirical coverage rate for the confidence intervals produced by Algorithm for the difference-in-quantiles for quantiles 0.01, 0.1, 0.25, and, 0.25, for sample size $10^5$ with $10^5$ bootstrap samples over 10000 replications.}
\end{table}
\end{document}

math

"Resampling-free bootstrap inference for quantiles" by Mårten Schultzberg and Sebastian Ankargren, arXiv:2202.10992v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{5y CDS Spreads}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
CDX Index & IG & HY \\
 \hline
CDS Spread & 0.933\% & 6.432\% \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk" by Derek Singh and Shuzhong Zhang, arXiv:1910.01781v2, 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{BagSep statistics for each dataset formed with Criteo SSCL Dataset}
\begin{tabular}{llcccc}
\toprule
Col1 & Col2 & ${\sf MeanInterBagSep}$ & ${\sf MeanIntraBagSep}$ & ${\sf InterIntraRatio}$ & \textbf{Bag Separation cluster}\\
\midrule
$\tilde{C}$2 & $\tilde{C}$5 & 39174.79 & 33071.37 & 1.18 &Less-separated\\
$\tilde{C}$3 & $\tilde{C}$5 & 38570.65 & 31055.51 & 1.24 &Medium-separated\\
$\tilde{C}$3 & $\tilde{C}$8 & 40055.06 & 32919.02 & 1.22 &Medium-separated\\
$\tilde{C}$3 & $\tilde{C}$16 & 39716.97 & 32398.83 & 1.23 &Medium-separated\\
$\tilde{C}$5 & $\tilde{C}$6 & 39344.84 & 32880.52 & 1.2 & Less-separated\\
$\tilde{C}$5 & $\tilde{C}$7 & 39543.59 & 32951.49 & 1.2 &Less-separated\\
$\tilde{C}$5 & $\tilde{C}$8 & 39540.86 & 32970.05 & 1.2 &Less-separated\\
$\tilde{C}$5 & $\tilde{C}$9 & 39384.92 & 33032.5 & 1.19 &Less-separated\\
$\tilde{C}$5 & $\tilde{C}$16 & 39132.17 & 31713.31 & 1.23 &Medium-separated\\
$\tilde{C}$8 & $\tilde{C}$16 & 38848.47 & 30922.17 & 1.26 &Well-separated\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Anand Brahmbhatt
Mohith Pokala
Rishi Saket
Aravindan Raghuveer

"LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions" by Anand Brahmbhatt, Mohith Pokala, Rishi Saket, and Aravindan Raghuveer, arXiv:2310.10096v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||l|rr|rr||}
 	\hline
 	& \multicolumn{2}{|c|}{Yelp} & \multicolumn{2}{|c||}{Psych} \\
	Variant & Downstream & Concept & Downstream & Concept \\
	\hline\hline
	TM-2 & 0.71$\pm$0.01 & 0.61$\pm$0.01 & 0.61$\pm$0.00 & 0.64$\pm$0.00	\\ \hline
 \hline
 \end{tabular}
\caption{Downstream accuracy and concept accuracy $\pm$ standard errors for the TM-2 variation of the anchor topic modeling baseline. These results are worse than the TM variant reported in the main text.}
\end{table}
\end{document}

stat

"Learning Interpretable Concept-Based Models with Human Feedback" by Isaac Lage and Finale Doshi-Velez, arXiv:2012.02898v1, 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}{lccc}
\hline
\textbf{Method} & \textbf{SSNR (dB)} & \textbf{PESQ} & \textbf{STOI(\%)} \\ \hline
Conv-TasNet & 13.26 & 2.92 & 83.14 \\
DCCRN & 13.94 & 2.89 & 85.83 \\
PHASEN & 15.89 & 3.04 & 87.94 \\
U-Former & 15.34 & 3.06 & 88.87 \\
CASE-Net & 16.47 & 3.11 & 89.33 \\ \hline
SE-TerrNet & \textbf{17.24} & \textbf{3.19} & \textbf{89.69} \\ \hline
\end{tabular}
\caption{Model comparison on AVSpeech + AudioSet.}
\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}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf{Summary statistics for DISCOVER stock exchange}} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
Parameter & Estimate & Standard Error \\ % inserts table
		\hline
$\alpha_0$ & 0.0053 & 0.0008
\\
$\alpha_1$ & 0.9799 & 0.0036
\\
$\sigma_{\omega}$ & 0.1841 & 0.0004
\\
$\lambda$ &-16.801 & 0.1687
\\
$\sigma_0$ & 1.0697 & 0.0008
\\
$\phi_1$ &-2.2973 & 0.0011
\\
$\sigma_1$ & 2.8771 & 0.0008
\\
\hline \hline %inserts single line
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Maria C Mariani
Md Al Masum Bhuiyan
Osei K Tweneboah
Hector Gonzalez-Huizar
Ionut Florescu

"Volatility Models Applied to Geophysics and High Frequency Financial Market Data" by Maria C Mariani, Md Al Masum Bhuiyan, Osei K Tweneboah, Hector Gonzalez-Huizar, and Ionut Florescu, arXiv:1901.09145v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Caihua Chen
Xindan Li
Caleb Tolman
Suyang Wang
Yinyu Ye

"Sparse Portfolio Selection via Quasi-Norm Regularization" by Caihua Chen, Xindan Li, Caleb Tolman, Suyang Wang, and Yinyu Ye, arXiv:1312.6350v1, licensed under CC BY 3.0. 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{Posterior estimates of model parameters in the water level dataset.}
\begin{tabular}{cccc}
 \hline
 Parameter & Mean & Median & 95\% CI \\ \hline
 $\beta_0$ & -0.02 & -0.02 & (-0.06, 0.02) \\
 $\beta_1$ & 0.024 & 0.024 & (0.021, 0.027) \\
 $\phi$ & 0.878 & 0.878 & (0.874, 0.884) \\
 $\sigma^2$ & 0.014 & 0.014 & (0.013, 0.014) \\
 $\tau^2$ & 0.0043 & 0.0043 & (0.0041, 0.0044) \\
 $l$ & 34.14 & 34.14 & (32.14, 36.35) \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Victor Hugo Nagahama
James Sweeney
Niamh Cahill

"A Scalable Bayesian Spatiotemporal Model for Water Level Predictions using a Nearest Neighbor Gaussian Process Approach" by Victor Hugo Nagahama, James Sweeney, and Niamh Cahill, arXiv:2412.06934v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A PINN-enriched finite element method for linear elliptic problems" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, 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{Information of the 30 benchmark datasets from the UEA archive.}
\begin{tabular}{lcccccc}
 \toprule
 Dataset & Train Size & Test Size & Variables & Length & Categories & Type \\
 & $N_{\text{train}}$ & $N_{\text{test}}$ & $M$ & $T$ & $C$ & \\
 \midrule
 ArticularyWordRecognition & 275 & 300 & 9 & 144 & 25 & MOTION \\
 AtrialFibrillation & 15 & 15 & 2 & 640 & 3 & ECG \\
 BasicMotions & 40 & 40 & 6 & 100 & 4 & HAR\\
 CharacterTrajectories & 1422 & 1436 & 3 & 119 & 20 & MOTION\\
 Cricket & 108 & 72 & 6 & 1197 & 12 & HAR \\
 DuckDuckGeese & 50 & 50 & 1345 & 270 & 5 & AUDIO\\
 ERing & 30 & 270 & 4 & 65 & 6 & HAR\\
 EigenWorms & 128 & 131 & 6 & 17948 & 5 & MOTION\\
 Epilepsy & 137 & 138 & 3 & 206 & 4 & HAR\\
 EthanolConcentration & 261 & 263 & 3 & 1751 & 4 & OTHER\\
 FaceDetection & 5890 & 3524 & 144 & 62 & 2 & EEG\\
 FingerMovements & 316 & 100 & 28 & 50 & 2 & EEG\\
 HandMovementDirection & 160 & 74 & 10 & 400 & 4 & EEG\\
 Handwriting & 150 & 580 & 3 & 52 & 26 & HAR\\
 Heartbeat & 204 & 205 & 61 & 405 & 2 & AUDIO\\
 InsectWingbeat & 25000 & 25000 & 200 & 22 & 10 & AUDIO\\
 JapaneseVowels & 270 & 370 & 12 & 29 & 9 & AUDIO\\
 LSST & 2459 & 2466 & 6 & 36 & 14 & OTHER\\
 Libras & 180 & 180 & 2 & 45 & 15 & HAR\\
 MotorImagery & 278 & 100 & 64 & 3000 & 2 & EEG\\
 NATOPS & 180 & 180 & 24 & 51 & 6 & HAR\\
 PEMS-SF & 267 & 173 & 263 & 144 & 7 & MISC\\
 PenDigits & 7494 & 3498 & 2 & 8 & 10 & MOTION\\
 PhonemeSpectra & 3315 & 3353 & 11 & 217 & 39 & SOUND\\
 RacketSports & 151 & 152 & 6 & 30 & 4 & HAR\\
 SelfRegulationSCP1 & 268 & 293 & 6 & 896 & 2 & EEG\\
 SelfRegulationSCP2 & 200 & 180 & 7 & 1152 & 2 & EEG\\
 SpokenArabicDigits & 6599 & 2199 & 13 & 93 & 10 & SPEECH\\
 StandWalkJump & 12 & 15 & 4 & 2500 & 3 & ECG\\
 UWaveGestureLibrary & 120 & 320 & 3 & 315 & 8 & HAR\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Yunshi Wen
Tengfei Ma
Tsui-Wei Weng
Lam M. Nguyen
Anak Agung Julius

"Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification" by Yunshi Wen, Tengfei Ma, Tsui-Wei Weng, Lam M. Nguyen, and Anak Agung Julius, arXiv:2411.01006v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|}
\hline
Heading level & Example & Font size and style\\
\hline
Title (centered) & {\Large\bfseries Lecture Notes} & 14 point, bold\\
1st-level heading & {\large\bfseries 1 Introduction} & 12 point, bold\\
2nd-level heading & {\bfseries 2.1 Printing Area} & 10 point, bold\\
3rd-level heading & {\bfseries Run-in Heading in Bold.} Text follows & 10 point, bold\\
4th-level heading & {\itshape Lowest Level Heading.} Text follows & 10 point, italic\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Kirsten W. H. Maas
Danny Ruijters
Nicola Pezzotti
Anna Vilanova

"NerT-CA: Efficient Dynamic Reconstruction from Sparse-view X-ray Coronary Angiography" by Kirsten W. H. Maas, Danny Ruijters, Nicola Pezzotti, and Anna Vilanova, arXiv:2507.19328v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|c}
\hline
\bf Methods & \bf Many & \bf Medium & \bf Few & \bf{All} \\ \hline
RSG-LDAM & 94.56 & 88.43 & 75.42 & 92.75 \\ \hline
RSG-SCL-CE & 96.01 & 90.59 & 77.08 & 94.45 \\ \hline\hline
SCL-LDAM & 95.80 & 94.21 & 93.17 & 95.22 \\ \hline
RSG-SCL-LDAM & \textbf{96.03} & \textbf{94.80} & \textbf{95.42} & \textbf{95.61} \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Top-1 accuracy of pre-trained ResNeXt-50-32x4d on mini-ImageNet-LT with an imbalance factor of 50 running for 20 epochs. }
\end{table}
\end{document}

cs

"Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques" by Ziheng Wang, Toni Lassila, and Sharib Ali, arXiv:2501.13756v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Xiaohong Chen
Zengjing Chen
Wayne Yuan Gao
Xiaodong Yan
Guodong Zhang

"Optimization via Strategic Law of Large Numbers" by Xiaohong Chen, Zengjing Chen, Wayne Yuan Gao, Xiaodong Yan, and Guodong Zhang, arXiv:2412.05604v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
 \hline
 \textbf{Metric/Dataset} & \textbf{Saraga} & \textbf{PIM} \\
 \hline
 OOD Accuracy & 85.6\% & 80.87\% \\
 \hline
 \end{tabular}
\caption{Comparison of OOD detection Accuracy for Saraga and PIM datasets}
\end{table}
\end{document}

eess

Parampreet Singh
Adwik Gupta
Aakarsh Mishra
Vipul Arora

"Identification and Clustering of Unseen Ragas in Indian Art Music" by Parampreet Singh, Adwik Gupta, Aakarsh Mishra, and Vipul Arora, arXiv:2411.18611v2, 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|}
\hline
	Setup & RC-LCB & CVaR-TS & \textsc{MaRaB}\\\hline
	$\sigma_>^2$ & $1.00$ & $0.02$ & $0.99$\\\hline
	$\sigma_\approx^2$ & $1.00$ & $0.03$ & $1.00$\\
	\hline
\end{tabular}
\caption{ Comparison of $r/N$ of flagging an infeasible instance as feasible. When $\tau=2$, to have $r/N\approx0$, we need $n\ge 312,678$ for RC-LCB. We omit this due to computational limitations. }
\end{table}
\end{document}

stat

Joel Q. L. Chang
Qiuyu Zhu
Vincent Y. F. Tan

"Risk-Constrained Thompson Sampling for CVaR Bandits" by Joel Q. L. Chang, Qiuyu Zhu, and Vincent Y. F. Tan, arXiv:2011.08046v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

"Quantile Graph Discovery through QuACC: Quantile Association via Conditional Concordance" by Zain Khan, Daniel Malinsky, Martin Picard, Alan A. Cohen, Columbia SOH Group, and Ying Wei, arXiv:2411.17033v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{OLS Regression Results - Metropolitan Areas (MA) with year, MA, region, and country as fixed effects}
\begin{tabular}{lcccccc}
\hline
\multirow{2}{*}{Coefficient} & \multicolumn{3}{c}{With Fitness (F) \& Coherence ($\Gamma$)} & \multicolumn{3}{c}{Without Fitness (F) \& Coherence ($\Gamma$)} \\
\cline{2-7}
 & Estimate & Std. Error & & Estimate & Std. Error & \\
\hline
$F$ & -0.0337*** & 0.000 & & -- & -- & \\
$\Gamma$ & 0.0234*** & 0.002 & & -- & -- & \\
div & 0.0401*** & 0.001 & & -0.0110*** & 0.000 & \\
GDPpc & -0.0138*** & 0.000 & & 0.0061*** & 0.000 & \\
\hline
\multicolumn{4}{l}{Adjusted \( R^2 \): 0.708} & \multicolumn{3}{l}{0.464} \\
\hline
\multicolumn{7}{l}{\footnotesize *** $p<0.001$, ** $p<0.01$, * $p<0.05$} \\
\end{tabular}
\end{table}
\end{document}

q-fin

Matteo Straccamore
Matteo Bruno
Andrea Tacchella

"Comparative Analysis of Technological Fitness and Coherence at different geographical scales" by Matteo Straccamore, Matteo Bruno, and Andrea Tacchella, arXiv:2503.22666v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of MSEs between the traditional CV and the proposed CVDCART estimator in different scenarios}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc|cc|cc}
\hline
&\multicolumn{2}{c|}{Scenario 1} &\multicolumn{2}{c|}{Scenario 2} & \multicolumn{2}{c}{Scenario 3}\\
\hline
$n$ &Traditional CV & CVDCART &Traditional CV & CVDCART &Traditional CV &CVDCART\\
\hline
128 & 0.011 (0.0013) & 0.019 (0.0032) & 0.018 (0.0014) & 0.021 (0.0018) & 0.0007 ($10^{-4}$) & 0.0005 ($10^{-4}$) \\
256 & 0.003 (0.0004) & 0.005 (0.0009) & 0.009 (0.0011) & 0.014 (0.0008) & 0.0004 ($10^{-5}$) & 0.0004 ($10^{-5}$) \\
512 & 0.001 (0.0001) & 0.001 (0.0003) & 0.005 (0.0001) & 0.006 (0.0003) & 0.0003 ($10^{-5}$) & 0.0003 ($10^{-5}$) \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Anamitra Chaudhuri
Sabyasachi Chatterjee

"A Cross Validation Framework for Signal Denoising with Applications to Trend Filtering, Dyadic CART and Beyond" by Anamitra Chaudhuri and Sabyasachi Chatterjee, arXiv:2201.02654v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Composition of Wage Differentials between Migrants and Natives" by Panagiotis Nanos and Christian Schluter, arXiv:1306.1781v3, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Coloring distance graphs on the plane

Joanna Chybowska-Sokół
Konstanty Junosza-Szaniawski
Krzysztof Węsek

"Coloring distance graphs on the plane" by Joanna Chybowska-Sokół, Konstanty Junosza-Szaniawski, and Krzysztof Węsek, arXiv:2201.04499v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|lll|lll|}
\hline
\multirow{2}{*}{} & \multicolumn{3}{l|}{Hospital} & \multicolumn{3}{l|}{GP} \\ \cline{2-7}
 & 30th May & 30th Aug & 29th Nov & 30th May & 30th Aug & 29th Nov \\ \hline
30th May & 1.00 & 0.94 & 0.87 & 1.00 & 0.95 & 0.90 \\ \hline
30th Aug & 0.94 & 1.00 & 0.89 & 0.95 & 1.00 & 0.93 \\ \hline
29th Aug & 0.87 & 0.89 & 1.00 & 0.90 & 0.93 & 1.00 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{\textbf{Correlation matrix} showing pairwise correlations between TTVs of the three selected dates in May (30th), August (30th), and November (29th). The matrix shows overwhelmingly high correlations between TTV distributions throughout the year, indicating that seasonal variations of theoretical TTV is low.}
\end{table}
\end{document}

cs

"The geography of inequalities in access to healthcare across England: the role of bus travel time variability" by Zihao Chen and Federico Botta, arXiv:2501.19231v2, 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{Obtained antenna for $R1=0,12~[m]$}
\begin{tabular}{|c||c|}
\hline
Separation $D1$ & $5~[\text{cm}]$\\
\hline
First dipole length $\{L_{3,0}\}$ & $6~[\text{cm}]$\\
\hline
Second dipole length $\{L_{3,1}\}$ & $5,5~[\text{cm}]$\\
\hline
Third dipole length $\{L_{3,2}\}$ & $5~[\text{cm}]$\\
\hline
Angular separation $\theta_1$ & $24,99^{\text{o}}$\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"A Reusable Framework Based on Reinforcement Learning to Design Antennas for Curved Surfaces" by Enrique Lizarraga and Walter Herrera, arXiv:2011.12131v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\hline
\small Variable & \small DAX &
\small SMI & \small CAC &
\small FTSE \\ \hline\hline
\small DAX & ${\small \infty }$ &
${\small 1.6815}$ & ${\small 1.7777}
$ & ${\small 1.5888}$ \\ \hline
\small SMI & ${\small 1.6815}$ &
${\small \infty }$ & ${\small 1.6106%
}$ & ${\small 1.6459}$ \\ \hline
\small CAC & ${\small 1.7777}$ &
${\small 1.6106}$ & ${\small \infty
}$ & ${\small 1.6880}$ \\ \hline
\small FTSE & ${\small 1.5888}$ &
${\small 1.6459}$ & ${\small 1.6880}
$ & ${\small \infty }$ \\ \hline\hline
& & & &
\end{tabular}
\end{adjustbox}
\caption{Fitted copula parameter correspoding to Kendall's tau, Gumbel copula.}%
\end{table}
\end{document}

q-fin

"Involving copula functions in Conditional Tail Expectation" by Brahim Brahimi, arXiv:1205.4345v4, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|l|}
\hline
CRN & Chemical reaction network \\
LMA & Law of mass action \\
(r)MJP & (Reversible) Markov jump process\\
(r)LDG & (Reversible) linear dynamics on graph\\
PDS & Polynomial dynamical systems\\
FPE & Fokker Planck Equation\\
CB & complex-balanced\\
DB & detail-balanced\\
LDB & local detailed balance\\
EPR & entropy production rate\\
HHK & Helmholtz-Hodge-Kodaira\\
MCMC & Markov chain Monte Carlo\\
\hline
 \end{tabular}
\caption{List of abbreviations.}
\end{table}
\end{document}

stat

Tetsuya J. Kobayashi
Dimitri Loutchko
Atsushi Kamimura
Shuhei A. Horiguchi
Yuki Sughiyama

"Information Geometry of Dynamics on Graphs and Hypergraphs" by Tetsuya J. Kobayashi, Dimitri Loutchko, Atsushi Kamimura, Shuhei A. Horiguchi, and Yuki Sughiyama, arXiv:2211.14455v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Information Criterion for various models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
Model & AIC & SIC & HQC\\
 \hline\hline
GARCH(1,1) & -11.56876 & -11.56366 & -11.57469 \\
 \hline
TARCH & -11.57676 & -11.56826 & -11.57373\\
 \hline
EGARCH(1,1) & -11.60566 & -11.59716 & -11.60263\\
 \hline
PARCH(1,1,1) & -11.56799 & -11.55949 & -11.57273\\
\hline
IGARCH(1,1) & -11.45785 & -11.45615 & -11.45725\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Sai Srikar Nimmagadda
Pawan Sasanka Ammanamanchi

"BitMEX Funding Correlation with Bitcoin Exchange Rate" by Sai Srikar Nimmagadda and Pawan Sasanka Ammanamanchi, arXiv:1912.03270v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
		\hline
		& Scale 7 & Scale 6 & Scale 5 \\
		\hline
		Frequency & 0.0078hz-0.0156hz & 0.0156hz - 0.0312hz & 0.0312hz - 0.0625hz \\
		\hline
		& Scale 4 & Scale 3 & Scale 2 \\
		\hline
		Frequency & 0.0625hz - 0.125hz & 0.125hz - 0.25hz& 0.25hz - 0.50h \\
		\hline
		& Scale 1 & & \\
		\hline
		Frequency & 0.50hz - 1hz & & \\
		\hline
	\end{tabular}
\caption{The frequency domain from scale 1 to scale 7 by using DWT. }
\end{table}
\end{document}

eess

Xi Chen
Yu Guan
Jian Qing Shi
Xiu-Li Du
Janet Eyre

"Designing Compact Features for Remote Stroke Rehabilitation Monitoring using Wearable Accelerometers" by Xi Chen, Yu Guan, Jian Qing Shi, Xiu-Li Du, and Janet Eyre, arXiv:2009.08798v3, 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 Metrics for the three methods on Rectified01}
\begin{tabular}{lccc}
\toprule
\textbf{Metric} & \textbf{DAC} & \textbf{DA:v1} & \textbf{DA:v2} \\ \midrule
Mean RMSE & 51.8964 & 26.5211 & 22.1255 \\
Mean MAE & 35.5692 & 15.3696 & 11.6590 \\
Mean Sq Rel & 33.0954 & 6.4422 & 3.8836 \\
Mean $\delta$ Accuracy & 0.3821 & 0.6839 & 0.7815 \\
Mean SSIM & 0.5848 & 0.6489 & 0.6611 \\
Mean Log RMSE & 0.6520 & 0.3826 & 0.2978 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"Tech Report: Divide and Conquer 3D Real-Time Reconstruction for Improved IGS" by Yicheng Zhu, arXiv:2501.01465v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Xianyi Zeng
Giovanni Stabile
Efthymios N. Karatzas
Guglielmo Scovazzi
Gianluigi Rozza

"Embedded domain Reduced Basis Models for the shallow water hyperbolic equations with the Shifted Boundary Method" by Xianyi Zeng, Giovanni Stabile, Efthymios N. Karatzas, Guglielmo Scovazzi, and Gianluigi Rozza, arXiv:2201.09546v1, 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{Noise levels for feature vector.}
\begin{tabular}{lcccccccc}
 \toprule \toprule
 Feature & $r$ (m) & $\theta$ (deg) & $\phi$ (deg) & $v$ (m/s) & $\gamma$ (deg) & $\psi$ (deg) & $a_i$ (g) & $P_{0,2}$ (\%) \\ \midrule
 $3\sigma$ & 5.0 & $8.4\cdot10^{-5}$ & $8.4\cdot10^{-5}$ & 1.0 & 0.01 & 0.01 & $10^{-7}$ & 1.0 \\ \bottomrule\bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Jens A. Rataczak
Davide Amato
Jay W. McMahon

"Density Estimation for Entry Guidance Problems using Deep Learning" by Jens A. Rataczak, Davide Amato, and Jay W. McMahon, arXiv:2310.19684v1, 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|}
			\hline
			Fidelity & Training Samples & Epochs & Model Error & Filter Error \\ \hline
		 Low & $10^3$ & 20 & $\delta\approx 2.16$ & 2.59 \\ \hline
 Medium & $10^4$ & 50 & $\delta\approx 1.02 $ & 1.16 \\ \hline
 High & $10^6$ & 200 & $\delta\approx 0.35$ & 0.99 \\ \hline
		\end{tabular}
\caption{Summary of the training details, estimated model error, and average filter error for each model. }
\end{table}
\end{document}

stat

"Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems" by Daniel Sanz-Alonso and Nathan Waniorek, arXiv:2412.14318v1, 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{Results of paired $t$-test on the difference between the testing MSE of Heckman-FA and other baselines.}
\begin{tabular}{|c||c|c|}
 \hline
 Comparison & Test MSE Difference & P-value \\
 \hline
 \hline
 \multicolumn{3}{|c|}{CRIME} \\
 \hline
 \hline
 Heckman-FA vs. Naive & -0.0008 $\pm$ 0.0004 & 0.0001 \\
 Heckman-FA vs. RU & -0.0007 $\pm$ 0.0007 & 0.0053 \\
 \hline
 \hline
 \multicolumn{3}{|c|}{COMPAS} \\
 \hline
 \hline
 Heckman-FA vs. Naive & -0.0068 $\pm$ 0.0006 & 7.723e-11 \\
 Heckman-FA vs. RU & -0.0059 $\pm$ 0.0051 & 0.0055 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

"On Prediction Feature Assignment in the Heckman Selection Model" by Huy Mai and Xintao Wu, arXiv:2309.08043v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Descriptive statistics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
\toprule \toprule
&price&change in price\\
\midrule \midrule
mean&43.7700&0.0001\\
SD&16.2551&6.6093\\
skewness&0.1040&0.4136\\
excess kurtosis&1.6686&7.4503\\
\midrule
Shapiro-Wilk test&14.9120&19.6280\\
$p$-value&$<0.01$&$<0.01$\\
Jarque-Bera test&4041&80315\\
$p$-value&$<0.01$&$<0.01$\\
\midrule
ADF - test (50)&-13.9594&-36.1585\\
$p$-value&$<0.01$&$<0.01$\\
KPSS (50)&8.7456&0.0012\\
$p$-value&$<0.01$&$>0.1$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Long-term memory in electricity prices: Czech market evidence" by Ladislav Kristoufek and Petra Lunackova, arXiv:1309.0582v1, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Attack success rates of UAEs against white-box (1st row), black-box models (2-4 rows), and various defense strategies (5-8 rows). The best result is bolded.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|c}
 \toprule
 Method & DiffAttack & AdvDiff & VENOM & VENOM-\textit{u}\\
 \midrule
 Res50 & 94.30 & \textbf{99.80} & 98.70 & 98.70 \\
 \midrule
 Inc-V3 & 70.80 & 36.52 & 45.80 & \textbf{71.00} \\
 Vit & \textbf{50.40} & 12.83 & 17.20 & 40.60 \\
 Mix-B & \textbf{61.10} & 29.24 & 36.00 & 59.40 \\
 \midrule
 Res50-adv & 47.00 & 33.13 & 37.90 & \textbf{53.30} \\
 NRP & 85.91 & 77.34 & 78.40 & \textbf{90.10} \\
 RS & 65.60 & 31.39 & 42.20 & \textbf{67.90} \\
 DiffPure & 56.30 & 25.44 & 34.60 & \textbf{60.10}\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hui Kuurila-Zhang
Haoyu Chen
Guoying Zhao

"VENOM: Text-driven Unrestricted Adversarial Example Generation with Diffusion Models" by Hui Kuurila-Zhang, Haoyu Chen, and Guoying Zhao, arXiv:2501.07922v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{graphicx}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|l|c|c|c|c|}
\hline
 & \textbf{Synthon Split} & Matrix NLL $\downarrow$ & Target NLL $\downarrow$ & Sum NLL $\downarrow$ & PRC-AUC $\uparrow$ \\
 \hline
 \multirow{7}{*}{\rotatebox[origin=c]{90}{\textbf{CA-IX}}} & Diff Enrichment & - & - & - & 0.22 $\pm$ 0.02 \\
 \cline{2-6}
 & Ratio Enrichment & - & - & - & 0.25 $\pm$ 0.02 \\
 \cline{2-6}
 & Poisson Enrichment & - & - & - & 0.24 $\pm$ 0.02 \\
 \cline{2-6}
 & DEL-Compose & 3.58 $\pm$ 0.34 & 2.83 $\pm$ 0.17 & 6.41 $\pm$ 0.46 & 0.13 $\pm$ 0.01 \\
 \cline{2-6}
 & DEL-Compose (Pre) & 3.13 $\pm$ 0.13 & 2.81 $\pm$ 0.18 & 5.94 $\pm$ 0.31 & 0.63 $\pm$ 0.42 \\
 \cline{2-6}
 & DEL-Compose (Rep) & 3.54 $\pm$ 0.29 & 2.64 $\pm$ 0.14 & 6.19 $\pm$ 0.39 & 0.73 $\pm$ 0.33 \\
 \cline{2-6}
 & DEL-Compose (Pre+Rep) & \textbf{3.11 $\pm$ 0.11} & \textbf{2.61 $\pm$ 0.14} & \textbf{5.72 $\pm$ 0.25} & \textbf{0.75 $\pm$ 0.33} \\
 \hline
 \multirow{7}{*}{\rotatebox[origin=c]{90}{\textbf{HRP}}} & Diff Enrichment & - & - & - & 0.59 $\pm$ 0.01 \\
 \cline{2-6}
 & Ratio Enrichment & - & - & - & 0.48 $\pm$ 0.01 \\
 \cline{2-6}
 & Poisson Enrichment & - & - & - & 0.56 $\pm$ 0.01 \\
 \cline{2-6}
 & DEL-Compose & 8.09 $\pm$ 2.04 & 7.36 $\pm$ 1.28 & 15.45 $\pm$ 3.32 & 0.85 $\pm$ 0.16 \\
 \cline{2-6}
 & DEL-Compose (Pre) & 7.44 $\pm$ 2.09 & 6.46 $\pm$ 1.35 & 13.90 $\pm$ 3.44 & 0.85 $\pm$ 0.13 \\
 \cline{2-6}
 & DEL-Compose (Rep) & 8.75 $\pm$ 3.73 & 7.36 $\pm$ 1.69 & 16.11 $\pm$ 5.41 & 0.88 $\pm$ 0.05 \\
 \cline{2-6}
 & DEL-Compose (Pre+Rep) & \textbf{7.29 $\pm$ 2.11} & \textbf{6.26 $\pm$ 1.26} & \textbf{13.55 $\pm$ 3.36} & \textbf{0.90 $\pm$ 0.06} \\
 \hline
\end{tabular}
\caption{Metrics for different variants of DEL-Compose compared to baseline enrichment metrics on \textbf{synthon splits}. Compared to random splits, the average NLL loss is higher, which confirms our belief that these are more challenging splits of the data for a model to learn over. Our model still outperforms baselines and ablations even on these more challenging splits. }
\end{table}
\end{document}

stat

Benson Chen
Mohammad M. Sultan
Theofanis Karaletsos

"Compositional Deep Probabilistic Models of DNA Encoded Libraries" by Benson Chen, Mohammad M. Sultan, and Theofanis Karaletsos, arXiv:2310.13769v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Size statistics for YAGO 4 Full, Wikipedia (W), and English Wikipedia (E)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
 \toprule
 Triples & Yago Full & Yago W & Yago E\\
 \midrule
 Facts & 343M & 48M & 20M\\
 rdf:type & 70M & 16M & 5M\\
 Labels & 303M & 137M & 66M\\
 Descriptions & 1399M & 139M & 50M\\
 Avg. facts per entity & 5.1 & 3.2 & 4\\
 Classes & 10124 & 10124 & 10124\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"A Semantic Partitioning Method for Large-Scale Training of Knowledge Graph Embeddings" by Yuhe Bai, arXiv:2501.04613v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Boundary alignment analysis at different compression rates. CR: Compression Rate; ASD: Avg Span Duration; WBA: Word Boundary Alignment; PBA: Phoneme Boundary Alignment; MTD: Mean Time Deviation.}
\begin{tabular}{lccccc}
\hline
\textbf{CR (Hz)} & \textbf{ASD (ms)} & \textbf{WBA (\%)} & \textbf{PBA (\%)} & \textbf{MTD (ms)} \\ \hline
Original (50Hz) & 20 & - & - & - \\
Deduplicated (26Hz) & 38 & 11.4 & 32.7 & 42.3 \\
24Hz & 42 & 18.9 & 35.3 & 38.7 \\
15Hz & 67 & 22.6 & 83.2 & 15.8 \\
7Hz & 143 & 89.7 & 28.1 & 28.6 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Jialong Zuo
Guangyan Zhang
Minghui Fang
Shengpeng Ji
Xiaoqi Jiao
Jingyu Li
Yiwen Guo
Zhou Zhao

"Entropy-based Coarse and Compressed Semantic Speech Representation Learning" by Jialong Zuo, Guangyan Zhang, Minghui Fang, Shengpeng Ji, Xiaoqi Jiao, Jingyu Li, Yiwen Guo, and Zhou Zhao, arXiv:2509.00503v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Coordinates of cluster centers on the flat map of cortex}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
		\toprule
		Cluster & Center coordinate $(x,y)$ \\
		\midrule
		MFA\_L & $(137.783, 149.590)$ \\
		pSTS\_L & $(100.472, 10.090)$ \\
		FFA\_L & $(164.458, -18.507)$ \\
		MFA\_R & $(-125.207, 125.119)$ \\
		pSTS\_R & $(-107.803, -19.216)$ \\
		FFA\_R & $(-164.030, -44.811)$ \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"On The Causal Network Of Face-selective Regions In Human Brain During Movie Watching" by Ali Bavafa and Gholam-Ali Hossein-Zadeh, arXiv:2501.02333v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf{Standardised Residuals Tests for INTEL stock exchange}} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} % centered columns (4 columns)
		\hline\hline %inserts double horizontal lines
		 & Residuals & Tests & Statistics & p-value \\ % inserts table
		\hline
		Jarque-Bera Test & $R$ & $\chi^2$ & 1769.854 & 0 \\ %inserting body of the table
		Shapiro-Wilk Test & $R$ & W & 0.971157 & 0 \\
	 Ljung-Box Test & $R$ & Q(10) & 296.0897 & 0\\
		Ljung-Box Test & $R$ & Q(15) & 301.6321 & 0\\
		Ljung-Box Test & $R$ & Q(20) & 304.7640 & 0 \\
		Ljung-Box Test & $R^2$ & Q(10) & 7.968585 & 0.6319\\
		Ljung-Box Test & $R^2$ & Q(15) & 16.48274 & 0.3507\\
		Ljung-Box Test & $R^2$ & Q(20) & 59.49665 & 8.5E-06 \\
		LM-Arch Test & R & T$R^2$ & 14.76281 & 0.2547 \\
		\hline \hline%inserts single line
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Maria C Mariani
Md Al Masum Bhuiyan
Osei K Tweneboah
Hector Gonzalez-Huizar
Ionut Florescu

"Volatility Models Applied to Geophysics and High Frequency Financial Market Data" by Maria C Mariani, Md Al Masum Bhuiyan, Osei K Tweneboah, Hector Gonzalez-Huizar, and Ionut Florescu, arXiv:1901.09145v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrr}
\toprule
 Requested inference-time & Avg attempts needed & Percent successful \\
compute level & for successful attack ($\uparrow$) & transfer attacks (n=80) ($\downarrow$) \\
\midrule
 1 & 11.0 & 36 \\
 2 & 16.8 & 30 \\
 3 & 15.6 & 21 \\
 4 & 15.9 & 20 \\
 5 & {\bf 22.6} & {\bf 19} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Results of human red-teaming. The lowest test-time compute level is clearly weakest. The highest is most resistant to direct attack but all top three levels are similar on transfer attacks.}
\end{table}
\end{document}

cs

Wojciech Zaremba
Evgenia Nitishinskaya
Boaz Barak
Stephanie Lin
Sam Toyer
Yaodong Yu
Rachel Dias
Eric Wallace
Kai Xiao
Johannes Heidecke
Amelia Glaese

"Trading Inference-Time Compute for Adversarial Robustness" by Wojciech Zaremba, Evgenia Nitishinskaya, Boaz Barak, Stephanie Lin, Sam Toyer, Yaodong Yu, Rachel Dias, Eric Wallace, Kai Xiao, Johannes Heidecke, and Amelia Glaese, arXiv:2501.18841v1, 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 3: values for the parameters for the potential post-treatment variables distribution.}
\begin{tabular}{l|ccccccccccccc}
 & Intercept & $X_3$ & $X_4$ & $X_5$ & $X_6$ & $X_7$ & $X_8$ & $X_9$ & $X_{10}$ & $X_{11}$ & $X_{12}$ & $X_{13}$ & $X_{14}$ \\ \hline
 \( \beta^{(0)} \) & -1 & 0.5 & 1.5 & 0.2 & 0.5 & 0.7 & 1 & -0.5 & -1.2 & 0.1 & 0.1 & 0.1 & 0.1 \\
 \( \beta^{(1)} \) & -0.5 & 1 & 1.8 & 0.2 & 0.5 & 0.7 & 1.2 & -0.3 & -1 & 0.1 & 0.1 & 0.1 & 0.1
 \end{tabular}
\end{table}
\end{document}

stat

Dafne Zorzetto
Paolo Dalla Torre
Sonia Petrone
Francesca Dominici
Falco J. Bargagli-Stoffi

"Characterizing the Effects of Environmental Exposures on Social Mobility: Bayesian Semi-parametrics for Principal Stratification" by Dafne Zorzetto, Paolo Dalla Torre, Sonia Petrone, Francesca Dominici, and Falco J. Bargagli-Stoffi, arXiv:2412.00311v2, 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{Failure rate for REFUGE2 reaching Dice 70\% and 80\%, for LIDC reaching Dice 60\% and 70\%.}
\begin{tabular}{c|cc|cc|cc|cc|cc|cc}
 & \multicolumn{2}{c|}{SAM} & \multicolumn{2}{c|}{MedSAM} & \multicolumn{2}{c|}{MSA} & \multicolumn{2}{c|}{SAM-U V1} & \multicolumn{2}{c|}{SAM-U V2} & \multicolumn{2}{c}{SPA} \\ \hline
REFUGE2 & 10.2\% & 28.2\% & 11.7\% & 29.2\% & 9.0\% & 27.2\% & 10.5\% & 33.0\% & 15.0\% & 38.7\% & \textbf{4.7\%} & \textbf{17.0\%} \\
LIDC & 29.4\% & 32.5\% & 27.2\% & 29.8\% & 29.1\% & 31.9\% & 31.9\% & 34.9\% & 27.9\% & 31.3\% & \textbf{7.0\%} & \textbf{13.0\%}
\end{tabular}
\end{table}
\end{document}

eess

Jiayuan Zhu
Junde Wu
Cheng Ouyang
Konstantinos Kamnitsas
J. Alison Noble

"SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation" by Jiayuan Zhu, Junde Wu, Cheng Ouyang, Konstantinos Kamnitsas, and J. Alison Noble, arXiv:2411.15513v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 \textbf{Model} & \textbf{SR Task} & \textbf{AR Task}\\
 & (0..1 tools) & (2+ tools) \\
 \hline
 Claude 3.5 Sonnet & 95.23\% & 95\% \\
 \hline
 Claude 3 Haiku & 89.52\% & 30\% \\
 \hline
 Claude 3 Opus & 90\% & 95\% \\
 \hline
 Mistral Largest & 94.76\% & 45\% \\
 \hline
 Mixtral 8x22B & 2.38\% & 0\% \\
 \hline
 Mistral Small 7B & 76.19\% & 0\% \\
 \hline
 GPT 3.5 Turbo & 85.71\% & 0\% \\
 \hline
 GPT 4-o& 87.14\% & 100\% \\
 \hline
 GPT 4-o Mini & 85.71\% & 77.5\% \\
 \hline
 GPT 4 Turbo & 85.71\% & 90\% \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Empowering AIOps: Leveraging Large Language Models for IT Operations Management" by Arthur Vitui and Tse-Hsun Chen, arXiv:2501.12461v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation analysis of the proposed methodology in the $\times 8$ UR task, and the progressive self-cascade strategy is used as an example.}
\begin{tabular}{cccccccc}
\toprule
Model & $\tau_{\theta}$ & High-frequency & Self-cascade & Param.(M) & FID & SRE & NIQE \\\midrule
Baseline & & & & 36.84 & 81.28 & 27.74 & 18.89 \\
Model-1 & $\checkmark$ & & & 24.82 & 63.62 & 26.49 & 16.82 \\
Model-2 & & $\checkmark$ & & 14.14 & 87.82 & 19.15 & 15.52 \\
Proposed-1 & $\checkmark$ & $\checkmark$ & & 29.12 & 59.15 & 16.24 & 14.33 \\
Proposed-2 & $\checkmark$ & $\checkmark$ & $\checkmark$ & 29.12 & 54.82 & 9.81 & 14.03 \\\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Yue Shi
Liangxiu Han
Darren Dancy
Lianghao Han

"WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images" by Yue Shi, Liangxiu Han, Darren Dancy, and Lianghao Han, arXiv:2412.18996v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
 \toprule
 & Scraped & & Thomson Reuters \\
 \midrule
 \rowcolor[rgb]{ .875, .89, .898} abs. diff. in coefficients & 0.1557** & & 0.973*** \\
 std. error & 0.0678 & & 0.0382 \\
 \rowcolor[rgb]{ .875, .89, .898} t-stat & 2.30 & & 2.55 \\
 p-value & 0.011 & & 0.005 \\
 \bottomrule
 \end{tabular}
\caption{\textbf{Independent samples t-test comparing the coefficient of the excess market return in-sample and the coefficient of the excess market return out-of-sample.}}
\end{table}
\end{document}

q-fin

"Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis" by Paul Glasserman and Caden Lin, arXiv:2309.17322v1, 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{Model performance using blockV evaluation method. The best metric within the blackbox/interpretable model is bold. All models show a reasonably well performance even in this extremely imbalanced dataset. Compared to TabNet, \textbf{\textsc{RELand}} has a significant advantage on PR and Height and is close to TabNet on ROC and rHeight.}
\begin{tabular}{l|cc|cc}
\toprule
Model & ROC (↑) & PR (↑) & Height (↓) & rHeight (↓) \\ \midrule
MLP-single & 89.98 (5.72) & \textbf{13.13 (19.46)} & 0.62 (0.92) & 322.20 (225.03) \\
MLP-full & 87.78 (9.25) & 3.87 (6.79) & 0.74 (0.84) & 721.71 (1121.65) \\
MLP-IRM & \textbf{91.79 (2.35)} & 4.74 (7.69) & \textbf{0.54 (0.78)} & \textbf{292.29 (232.03)} \\ \midrule
TabNet & \textbf{93.90 (7.14)} & 5.89 (3.01) & 1.00 (1.80) & \textbf{236.85 (333.91)} \\
\textbf{\textsc{RELand}} (ours) & 93.71 (1.15) & \textbf{10.42 (12.42)} & \textbf{0.47 (0.69)} & 246.74 (242.89) \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Mateo Dulce Rubio
Siqi Zeng
Qi Wang
Didier Alvarado
Francisco Moreno
Hoda Heidari
Fei Fang

"RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization" by Mateo Dulce Rubio, Siqi Zeng, Qi Wang, Didier Alvarado, Francisco Moreno, Hoda Heidari, and Fei Fang, arXiv:2311.03115v1, 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{Group tables among different methods}
\begin{tabular}{lrrrrrrr}
 FMEC method & & & & & & & \\
 Group number & 1 & 2 & 3 & 4 & 5 & & \\
 Sample size & 54 & 19 & 42 & 25 & 55 & & \\
 Average method & & & & & & & \\
 Group number & 1 & 2 & 3 & 4 & 5 & & \\
 Sample size & 37 & 29 & 64 & 25 & 40 & & \\
 Naive method (Day 2) & & & & & & & \\
 Group number & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\
 Sample size & 2 & 35 & 22 & 9 & 52 & 23 & 52 \\
 Naive method (Day 4) & & & & & & & \\
 Group number & 1 & 2 & 3 & & & & \\
 Sample size & 64 & 31 & 100 & & & & \\
 \end{tabular}
\end{table}
\end{document}

stat

"Clustering of functional data prone to complex heteroscedastic measurement error" by Andi Mai, Lan Xue, Roger Zoh, and Carmen Tekwe, arXiv:2501.14919v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l|l}
\cline{2-4}
 & Estimated $\mu^{-1}$ & Estimated $\alpha$ & Estimated $\beta^{-1}$ & \\ \cline{1-4}
ITXEB & 22 min & 0.62 & 20 min & \\ \cline{1-4}
ITXEX & 24 min & 0.60 & 23 min & \\ \cline{1-4}
ITXES & 58 min & 0.65 & 36 min & \\ \cline{1-4}
\end{tabular}
\end{adjustbox}
\caption{Estimated parameters for \textsc{ITXEB}, \textsc{ITXES} and \textsc{ITXEX} over the period (2017-01-15 to 2017-12-15)}
\end{table}
\end{document}

q-fin

Achraf Bahamou
Maud Doumergue
Philippe Donnat

"Hawkes processes for credit indices time series analysis: How random are trades arrival times?" by Achraf Bahamou, Maud Doumergue, and Philippe Donnat, arXiv:1902.03714v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
\hline
\textbf{Model} & \textbf{Perplexity} \\ \hline
AF Fine Tuned & 41.8261 \\ \hline
AF Split Fine Tuned & 57.8004 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Perplexity Loss Scores for AF and AF Split}
\end{table}
\end{document}

cs

Krrish Chawla
Aryan Sahai
Mario DePavia
Sudharsan Sundar
Brando Miranda
Elyas Obbad
Sanmi Koyejo

"Quantifying the Importance of Data Alignment in Downstream Model Performance" by Krrish Chawla, Aryan Sahai, Mario DePavia, Sudharsan Sundar, Brando Miranda, Elyas Obbad, and Sanmi Koyejo, arXiv:2501.08496v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Coloring Inside the Lines: The Jagged Legacy of the HOLC Neighborhood Risk Maps" by Arunav Gupta, arXiv:2212.04117v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Impact of different safe vector selections and scaling factors ($s_g$) on model performance.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
 \toprule
 Safe Vector Selections and $s_g$ & ASR$\downarrow$ & FID$\downarrow$ & CLIP Score$\uparrow$ \\
 \midrule
 Highest Similarity & 48.75 & 17.02 & 26.32 \\
 Random Safe Vector & 40.64 & 16.61 & 25.82 \\
 Lowest Similarity & 34.07 & 17.25 & 25.75 \\
 \midrule
 Target Vector w/ $s_g=50$ & 18.63 & 16.16 & 25.97 \\
 Target Vector w/ $s_g=100$ & 8.32 & 15.72 & 26.00 \\
 Target Vector w/ $s_g=150$ & 1.54 & 15.73 & 25.53 \\
 Target Vector w/ $s_g=200$ & 0.64 & 15.44 & 25.52 \\
 Target Vector w/ $s_g=250$ & 0.59 & 15.91 & 25.22 \\
 Target Vector w/ $s_g=300$ & 0.36 & 16.82 & 24.87 \\
 Target Vector w/ $s_g=350$ & 0.49 & 16.77 & 24.86 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Distorting Embedding Space for Safety: A Defense Mechanism for Adversarially Robust Diffusion Models" by Jaesin Ahn and Heechul Jung, arXiv:2501.18877v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{Node classification accuracy (\%) under binary noise settings. ART (ms): Average Running Time per epoch in ms. The best and second-best learning accuracy are in bold and underlined. We also note that for the p-Laplacian framelet model, we fixed $p =2.5$ so that the model is more suitable for citation networks . We also highlight that our results on pL-UFG is different compared to due to the difference in data split.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|cccccc|}
\hline
Datasets & Noise Level (\%) & Clean & 5 & 15 & 25 & 50 &ART (ms)\\
\hline
\multirow{6}{*}{\textbf{Cora}} & Spectral CNN
& 73.3 & 26.0$\pm$5.1 & 19.5$\pm$4.1 & 15.2$\pm$4.7 & 19.1$\pm$6.9 & 12.3\\
& ChebyNet
& 81.2 & 30.6$\pm$4.4 & 30.9$\pm$2.6 & 21.5$\pm$7.6 & 18.1$\pm$4.1 & 16.2\\
& GCN
& 81.5 & 76.7$\pm$0.8 & 67.8$\pm$1.0 & 61.0$\pm$0.9 & 54.5$\pm$2.4 & 9.25 \\
& GWNN
& 82.8 & 43.5$\pm$1.6 & 30.2$\pm$1.0 & 23.6$\pm$1.3 & 20.1$\pm$3.2 &15.2\\
& GPRGNN
& 81.6\(\pm\)0.7 &65.9\(\pm\)7.2 &24.1\(\pm\)5.3 &22.4\(\pm\)7.0 &21.5\(\pm\)6.1 &14.2 \\
& H2GCN
& 82.9\(\pm\)0.5 &70.3\(\pm\)1.3 &34.6\(\pm\)5.6 &26.1\(\pm\)7.5 &24.1\(\pm\)8.2 &26.5\\
& UFG
& 83.6$\pm$0.6 & 75.6$\pm$0.8 & \underline{70.6$\pm$0.7} & {66.9$\pm$1.7} & {60.6$\pm$2.7} & 29.4\\
& pL-UFG$^{2.5}$ &\underline{83.9$\pm$1.7} &\underline{77.8\(\pm\)2.4} & \underline{68.8\(\pm\)0.3} &\textbf{68.4\(\pm\)0.9} &\underline{66.5\(\pm\)1.6} &40.8 \\
& QUFG & \textbf{83.9$\pm$0.8} & \textbf{79.1$\pm$0.9} & \textbf{71.6$\pm$1.2} & \underline{68.2$\pm$2.1}& \textbf{62.7$\pm$2.1} & 32.7\\
\hline
\multirow{6}{*}{\textbf{Citeseer}} & Spectral CNN
& 58.9 & 19.0$\pm$1.4 & 17.6$\pm$3.9 & 18.0$\pm$2.6 & 20.3$\pm$4.5 &9.3\\
& ChebyNet
& 69.8 & 19.4$\pm$1.8 & 17.3$\pm$4.1 & 18.2$\pm$2.7 & 17.2$\pm$3.3 & 31.4\\
& GCN
& 70.3 & \underline{62.6$\pm$0.8} & {47.3$\pm$1.2} & {39.4$\pm$1.1} & {31.9$\pm$2.5} & 10.1\\
& GWNN
& 71.7 & 49.4$\pm$1.4 & 21.1$\pm$0.3 & 21.3$\pm$0.5 & 15.8$\pm$0.9 &16.5\\
& GPRGNN
 & 70.9\(\pm\)1.5 & 19.3\(\pm\)2.1 & 18.6\(\pm\)1.7 & 18.4\(\pm\)2.0 & 18.1\(\pm\)2.3 & 13.6\\
& H2GCN & \underline{ 73.1\(\pm\)0.4 } & 47.8\(\pm\)2.2 & 24.2\(\pm\)1.3 &19.5\(\pm\)1.5 & 19.2\(\pm\)0.9 & 26.3\\
& UFG
& 72.7$\pm$0.6 & {61.6$\pm$1.3} & 40.8$\pm$7.0 & 26.0$\pm$3.2 & 23.5$\pm$2.7 & 49.1 \\
& pL-UFG$^{2.5}$ &72.2\(\pm\)1.4 &\textbf{64.8\(\pm\)3.1} & \textbf{50.3\(\pm\)3.3} &\underline{42.8\(\pm\)2.8} &\underline{39.7\(\pm\)1.4} & 50.1 \\
& QUFG & \textbf{73.4$\pm$0.6} & 60.9$\pm$1.8 & \underline{47.4$\pm$1.8} & \textbf{44.9$\pm$2.3} & \textbf{42.4$\pm$1.6} & 45.6\\
\hline
\multirow{6}{*}{\textbf{Coauthor-CS}} & Spectral CNN
& 54.3$\pm$0.5 & 12.3$\pm$4.0 & 8.5$\pm$4.4 & 6.1$\pm$2.7 & 6.1$\pm$2.3 &33.6\\
& ChebyNet
& 61.4$\pm$0.9 & 14.5$\pm$4.5 & 10.1$\pm$2.3 & 8.3$\pm$1.3& 5.0$\pm$1.5 &190.6\\
& GCN
& 91.3$\pm$0.2 & 89.3$\pm$0.6 & \underline{84.5$\pm$1.1} & \underline{64.9$\pm$0.7} & 8.3$\pm$3.0 & 32.4\\
& GWNN
& 89.5$\pm$2.3 & 80.8$\pm$0.2 & 71.5$\pm$1.3 & 46.3 $\pm$1.1 & 9.2$\pm$5.4 & 49.1\\
& GPRGNN & 92.4\(\pm\)0.2 & 88.1\(\pm\)1.0 & 32.6\(\pm\)12.4 & 20.4\(\pm\)6.2 & 10.7\(\pm\)2.9 & 30.2\\
& H2GCN & \underline{ 93.0\(\pm\)0.1 } & 71.3\(\pm\)0.7 & 12.7\(\pm\)5.6 & 11.9\(\pm\)6.4 & 10.2\(\pm\)4.8 & 147.2\\
& UFG
& 91.9$\pm$0.3 & {90.2$\pm$0.5} & 80.8$\pm$1.5 & 60.8$\pm$0.4 & \underline{9.4$\pm$1.5} &411.7\\
& pL-UFG$^{2.5}$ &92.4\(\pm\)0.1 &\textbf{91.1\(\pm\)0.8} &81.4\(\pm\)2.3 &66.0\(\pm\)5.3 &\textbf{14.8\(\pm\)1.1} & 474 \\
& QUFG & \textbf{93.7$\pm$0.6} & \underline{90.2$\pm$0.3} & \textbf{85.5$\pm$0.6} & \textbf{66.0$\pm$1.1} & \underline{10.3$\pm$2.8} & 329.4 \\
\hline
\multirow{6}{*}{\textbf{Coauthor-Physics}} & Spectral CNN
& 51.2$\pm$2.0 & 16.5$\pm$0.3 & 11.2$\pm$1.8 & 7.9$\pm$0.7 & 8.1$\pm$4.5 & 84.7\\
& ChebyNet
& 65.8$\pm$3.5 & 16.8$\pm$2.3 & 12.1$\pm$2.9 & 10.9$\pm$2.4 & 9.0$\pm$3.6 & 791.4\\
& GCN
& 94.1$\pm$0.5 & 92.7$\pm$0.4 & 84.5$\pm$1.1 & \textbf{66.4$\pm$7.0} & 30.3$\pm$18.5 & 92.7\\
& GWNN
& 79.2$\pm$1.3 & 76.9$\pm$0.3 & 68.3$\pm$2.1 & 50.0$\pm$4.5 & 19.2$\pm$3.4 & 201.3\\
& GPRGNN & 93.5\(\pm\)1.8 & 81.4\(\pm\)2.6 & 39.1\(\pm\)4.9 & 28.8\(\pm\)6.4 & 20.1\(\pm\)2.5 & 129.4\\
& H2GCN & 94.2\(\pm\)1.5 & 60.5\(\pm\)2.7 & 38.4\(\pm\)6.5 & 22.1\(\pm\)3.8 & 18.2\(\pm\)2.5 & 511.2\\
& UFG
& \underline{94.3$\pm$0.4} & \textbf{93.0$\pm$0.6} & {84.7$\pm$1.7} & 43.6$\pm$12.5 & {40.4$\pm$0.5} & 1490.5 \\
& pL-UFG$^{2.5}$ &91.7\(\pm\)0.1 &88.9\(\pm\)1.5 &\underline{86.1\(\pm\)2.4} &{51.4\(\pm\)4.5} &\textbf{49.8\(\pm\)3.6} &1630 \\
& QUFG & \textbf{94.4$\pm$0.4} & \underline{92.8$\pm$0.4} & \textbf{87.0$\pm$0.4} & \underline{56.0$\pm$8.1} & \underline{43.8$\pm$2.3} & 1380.4\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mengxi Yang
Dai Shi
Xuebin Zheng
Jie Yin
Junbin Gao

"Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution" by Mengxi Yang, Dai Shi, Xuebin Zheng, Jie Yin, and Junbin Gao, arXiv:2201.04728v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Mitchell Black
Ehsan Arabi
Dimitra Panagou

"A Fixed-Time Stable Adaptation Law for Safety-Critical Control under Parametric Uncertainty" by Mitchell Black, Ehsan Arabi, and Dimitra Panagou, arXiv:2011.12362v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Method} & \textbf{GPU Time (hours)} & \textbf{Peak Memory (GB)} \\
\midrule
UCE & 0.00121 & 5.92 \\
RECELER & 0.991 & 15.62 \\
AdvUnlearn & 1.094 & 29.00 \\
ESD & 0.8874 & 9.40 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Computational cost of concept erasure methods. UCE demonstrates superior efficiency in both GPU time and peak memory consumption.}
\end{table}
\end{document}

cs

Ibtihel Amara
Ahmed Imtiaz Humayun
Ivana Kajic
Zarana Parekh
Natalie Harris
Sarah Young
Chirag Nagpal
Najoung Kim
Junfeng He
Cristina Nader Vasconcelos
Deepak Ramachandran
Golnoosh Farnadi
Katherine Heller
Mohammad Havaei
Negar Rostamzadeh

"Erasing More Than Intended? How Concept Erasure Degrades the Generation of Non-Target Concepts" by Ibtihel Amara, Ahmed Imtiaz Humayun, Ivana Kajic, Zarana Parekh, Natalie Harris, Sarah Young, Chirag Nagpal, Najoung Kim, Junfeng He, Cristina Nader Vasconcelos, Deepak Ramachandran, Golnoosh Farnadi, Katherine Heller, Mohammad Havaei, and Negar Rostamzadeh, arXiv:2501.09833v2, 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{Hyperparameters in Peg Maze}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 Parameter & TD3/C-TD3 & HIRO \\
 \hline
 Gaussian noise std & HL:1.0, LL:0.2 & HL:1.0, LL:1.0 \\
 \hline
 gradient clipping & 0.1 & 0.1 \\
 \hline
 target update period & 100& 100 \\
 \hline
 train steps & HL:100, LL:200 & HL:100, LL:200 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Bing Song
Jean-Jacques Slotine
Quang-Cuong Pham

"Stable Modular Control via Contraction Theory for Reinforcement Learning" by Bing Song, Jean-Jacques Slotine, and Quang-Cuong Pham, arXiv:2311.03669v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\textbf{Action} & \textbf{ID} & \textbf{Action} & \textbf{ID} & \textbf{Action} & \textbf{ID}\\
\hline
BG & 0 & Talking & 10 & Talking phone &20\\
Walking & 1 & Transport & 11 & Tunning & 21\\
Standing & 2 & Unloading & 12 & PickUp & 22\\
Carrying & 3 & Pull & 13 & Using Tool & 23\\
Gesturing & 4 & Loading & 14 & SetDown & 24\\
Closing & 5 & Open Trunk & 15 & Crouching & 25\\
Opening & 6 & Closing Trunk & 16 & sitting & 26\\
Interacts & 7 & Riding & 17 & Object Transfer & 27\\
Exiting & 8 & Texting Phone & 18 & Push & 28\\
Entering & 9 & PP Interaction & 19 & PickUp & 29\\
\hline
\end{tabular}
\caption{Correspondence between \textbf{Actions} and \textbf{IDs}.}
\end{table}
\end{document}

eess

Bo Chen
Decai Li
Yuqing He
Chunsheng Hua

"SCR-Graph: Spatial-Causal Relationships based Graph Reasoning Network for Human Action Prediction" by Bo Chen, Decai Li, Yuqing He, and Chunsheng Hua, arXiv:1912.05003v1, 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|}
\hline
\textbf{Alpha ($\alpha$)} & \textbf{FWER (\%)} \\ \hline
0.05 & 0.53\% \\ \hline
0.1 & 1.53\% \\ \hline
0.2 & 4.25\% \\ \hline
\end{tabular}
\caption{Family-Wise Error Rate (FWER) at Different Alpha Levels}
\end{table}
\end{document}

stat

Gaussian Rank Verification

Jeremy Goldwasser
Will Fithian
Giles Hooker

"Gaussian Rank Verification" by Jeremy Goldwasser, Will Fithian, and Giles Hooker, arXiv:2501.14142v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model configurations and training details.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc}
\toprule
\textbf{Component} & \textbf{SimKGC} & \textbf{CSProm-KG} \\
\midrule
\textbf{Entity Encoder} & BERT-base (uncased) & BERT-base (uncased)\\
\textbf{Input Sequence Length} & 50 tokens & 120 tokens \\
\textbf{Training Epochs (FB15k-237)} & 10 & 60 \\
\textbf{Training Epochs (MKG-W/Y)} & 10 & 200 \\
\textbf{Neighbors per Entity} & 5 & 8 \\
\midrule
\textbf{Images per Entity} & \multicolumn{2}{c}{10 (FB15k), 3 (MKG-W/Y)} \\
\textbf{Image Relevance Threshold $\tau_r$} & \multicolumn{2}{c}{0.85 (FICHAD-1)} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Haodi Ma
Dzmitry Kasinets
Daisy Zhe Wang

"Transformer-Based Multimodal Knowledge Graph Completion with Link-Aware Contexts" by Haodi Ma, Dzmitry Kasinets, and Daisy Zhe Wang, arXiv:2501.15688v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.