\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\textbf{Discretization} & 41 & 81 & 161 & 321 & 641 & 1281 \\ \hline
\textbf{E} & 21 & 81 & 81 & 81 & 81 & 81 \\ \hline
\textbf{F} & 11 & 21 & 21 & 41 & 41 & 41 \\ \hline
\end{tabular}
\caption{Hes1 System}
\end{table}
\end{document}

stat

"EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems" by Jianhong Chen and Shihao Yang, arXiv:2501.14107v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Edson Pindza
Jules Clement Mba
Sutene Mwambi
Nneka Umeorah

"Neural Network for valuing Bitcoin options under jump-diffusion and market sentiment model" by Edson Pindza, Jules Clement Mba, Sutene Mwambi, and Nneka Umeorah, arXiv:2310.09622v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Detailed statistics for each dataset.}
\begin{tabular}{c|ccccl}
 \hline
 \rowcolor{gray!40} Dataset & \#Users & \#Items & \#Interactions & Density \\
 \hline
 Food & 7,809 & 6,309 & 216,407 & $4.4\times10^{-3}$\\
 KuaiRec & 7,175 & 10,611 & 1,153,797 & $1.5\times10^{-3}$ \\
 Yelp2018 & 8,090 & 13,878 & 398,216 & $3.5\times 10^{-3}$ \\
 Douban & 8,735 & 13,143& 354,933 & $3.1\times 10^{-3}$ \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Chu Zhao
Enneng Yang
Yuliang Liang
Jianzhe Zhao
Guibing Guo
Xingwei Wang

"Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model" by Chu Zhao, Enneng Yang, Yuliang Liang, Jianzhe Zhao, Guibing Guo, and Xingwei Wang, arXiv:2501.15555v1, 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}{|r|rrrr|}
	\hline
	Resolution & Accuracy & Precision & Recall & F1 \\
	\hline\hline
	8$\times$8 & 0.57 & 0.59 & 0.57 & 0.54\\
	\hline
	16$\times$16 & {\bf 0.61} & {\bf 0.62} & 0.64 & {\bf 0.61} \\
	\hline
	32$\times$32 & {\bf 0.61} & {\bf 0.62} & {\bf 0.65} & {\bf 0.61} \\
	\hline
\end{tabular}
\caption{Virtual patient's ability to identify people and cars in outdoor scenes as a function of electrode grid size (bold: best overall).}
\end{table}
\end{document}

cs

Nicole Han
Sudhanshu Srivastava
Aiwen Xu
Devi Klein
Michael Beyeler

"Deep Learning--Based Scene Simplification for Bionic Vision" by Nicole Han, Sudhanshu Srivastava, Aiwen Xu, Devi Klein, and Michael Beyeler, arXiv:2102.00297v1, 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}{lll}
 Operation & Latency & Time complexity\\
\hline
(g,p) calculation & $2GN$ & $O(N)$ \\
Carry calculation & $3GN$ & $O(N)$ \\
Sum calculation & $GN$ & $O(N)$ \\
\hline
Total latency & $6GN$ & $O(N)$
\end{tabular}
\caption{Adder scalability, single core}
\end{table}
\end{document}

cs

"CryptoEmu: An Instruction Set Emulator for Computation Over Ciphers" by Xiaoyang Gong and Dan Negrut, arXiv:2101.03403v1, 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{\textbf{CLIP Score results.} Note that the ablations were evaluated with a subset of the prompts to save computational costs, hence the ablation CLIP score of our method differs from the main CLIP score.}
\begin{tabular}{lc}
 \toprule
 \textit{Method} & CLIP \\\midrule
 Ours & 32.01 \\
 4D-fy & 31.90 \\
 DreamGaussian4D & 28.70 \\
 \midrule
 \textit{Ablation Study} & \\\midrule
 Ours & 32.69\\
 w/o local deformation & 32.11 \\
 w/o trajectory & 32.16 \\
 w/o traj. training & 32.16 \\
 w/o traj.-aware VSDS & 32.62 \\
 w/o smooth. penalty & 32.68\\
 w/o timestep annealing & 32.62 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Sherwin Bahmani
Xian Liu
Wang Yifan
Ivan Skorokhodov
Victor Rong
Ziwei Liu
Xihui Liu
Jeong Joon Park
Sergey Tulyakov
Gordon Wetzstein
Andrea Tagliasacchi
David B. Lindell

"TC4D: Trajectory-Conditioned Text-to-4D Generation" by Sherwin Bahmani, Xian Liu, Wang Yifan, Ivan Skorokhodov, Victor Rong, Ziwei Liu, Xihui Liu, Jeong Joon Park, Sergey Tulyakov, Gordon Wetzstein, Andrea Tagliasacchi, and David B. Lindell, arXiv:2403.17920v3, 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
 & Min & 25th & Median & 75th & Max\tabularnewline
\hline
\hline
$\widetilde{B_{t+1}}^{(1)}$ & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000\tabularnewline
$\widetilde{B_{t+1}}^{(2)}$ & -0.0006 & -0.0002 & 0.0000 & 0.0002 & 0.0006\tabularnewline
$p_{jt}$ & -0.0003 & -0.0001 & 0.0000 & 0.0000 & 0.0084\tabularnewline
$V_{it}^{C}(x_{it}=\emptyset)$ & -0.0004 & -0.0003 & -0.0001 & 0.0000 & 0.0000\tabularnewline
\hline
\end{tabular}
\caption{Distribution of the ratios of relative approximation errors}
\end{table}
\end{document}

q-fin

"When do firms sell high durability products? The case of light bulb industry" by Takeshi Fukasawa, arXiv:2503.23792v2, 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{Augmentation} &\textbf{Top-1 Accuracy} &\textbf{Top-5 Accuracy}\\ \hline
RandAug &0.696 &0.9 \\ \hline
AutoAug-Imagenet &0.546 &0.776\\ \hline
SimCLR-like Augmentation &0.499 &0.77\\ \hline
\end{tabular}
\caption{Accuracy of different augmentation method on Tiny ImageNet. It is shown that the Random Augmentation yield the best result for both Top-1 and Top-5. The results are trained on batch size 1024.}
\end{table}
\end{document}

cs

Huanran Li
Manh Nguyen
Daniel Pimentel-Alarcón

"Preventing Collapse in Contrastive Learning with Orthonormal Prototypes (CLOP)" by Huanran Li, Manh Nguyen, and Daniel Pimentel-Alarcón, arXiv:2403.18699v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation evaluation results of baseline: $I_s$ and $GT$ denote that baseline only uses a limited region of image $I$ pointed by their bounding boxes.}
\begin{tabular}{lrr}
 \toprule
 Models & Acc & Bs \\
 \cmidrule(lr){0-2}
 ClipCap & 36.80 & 81.75 \\
 \quad w/o image series & 16.10 & 78.48 \\
 \quad w/o question tokens & 3.66 & 65.93 \\
 ClipCap $(I_s)$ & 34.53 & 81.28 \\
 ClipCap $(GT)$ & 34.27 & 81.26 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Shun Inadumi
Seiya Kawano
Akishige Yuguchi
Yasutomo Kawanishi
Koichiro Yoshino

"A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions" by Shun Inadumi, Seiya Kawano, Akishige Yuguchi, Yasutomo Kawanishi, and Koichiro Yoshino, arXiv:2403.17545v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Javad Mirzapour Kaleybar
Hooman Saadat
Hooman Khaloo

"Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer" by Javad Mirzapour Kaleybar, Hooman Saadat, and Hooman Khaloo, arXiv:2311.01731v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Liquidity Dynamics in RFQ Markets and Impact on Pricing" by Philippe Bergault and Olivier Guéant, arXiv:2309.04216v3, 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{Clinical validation of the proposed framework through the Pearson correlation coefficient ($r_c$) and its statistical significance ($p$-value).}
\begin{tabular}{ccccc}
 \toprule
 Metric & Clinician-1 & Clinician-2 & Clinician-3 &Clinician-4 \\ \hline
 $r_c$ & 0.7260 &	0.8416 &	0.6863 &	0.9236 \\
 $p$-value & 1.04 $\times$ 10\textsuperscript{-23} & 6.10 $\times$ 10\textsuperscript{-38} & 2.10 $\times$ 10\textsuperscript{-20} & 4.40 $\times$ 10\textsuperscript{-58}\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Hina Raja
Taimur Hassan
Muhammad Usman Akram
Naoufel Werghi

"Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression" by Hina Raja, Taimur Hassan, Muhammad Usman Akram, and Naoufel Werghi, arXiv:2010.03872v1, 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{Evaluation in Matched and Mismatched Conditions. (\% WER)}
\begin{tabular}{lcc}
 	 \toprule
 	 \toprule
 	 & \multicolumn{2}{c}{Test Data}\\
 Model & {\it BLA-L2L} & {\it BLA-NoMic}\\
 	 \midrule
 	 Stage-2 {\it BLA-L2L} & 16.9 & \bf{27.1} \\
 	 Stage-2 {\it BLA-NoMic} & 17.6 & 17.2 \\
 \bottomrule
 \bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llll}
\toprule
 & $\Gamma\Gamma$ & $\vdash\Gamma$ & $\Gamma\vdash$ \\ \midrule
a & $\omega^{-2}$ (I) & $\omega^{-2}$ (I) & $\omega^{-2}$ (I) \\
b & $(\omega\delta)^{-1}$ (II) & $\delta^{-1}$ (III) & $(\omega\delta)^{-1}$ (II) \\
c & $\omega^{2}\delta$ (I) & $\omega^{2}\delta$ (I) & $\omega\delta$ (II) \\
d & $\omega^{2}$ (I) & $\omega^{2}$ (I) & $\omega^{2}$ (I) \\ \midrule
M & 1 & 1 & 2 \\
v.N. & 0 & 1 & 0 \\ \bottomrule \\
\end{tabular}
\caption{Table showing the minimum generators in the von Neumann (v.N.) and Moore neighbourhoods of a false fundamental given its chroma configuration. }
\end{table}
\end{document}

eess

Tom Goodman
Karoline van Gemst
Peter Tino

"A Geometric Framework for Pitch Estimation on Acoustic Musical Signals" by Tom Goodman, Karoline van Gemst, and Peter Tino, arXiv:2012.04517v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"aura-net : robust segmentation of phase-contrast microscopy images with few annotations" by Ethan Cohen and Virginie Uhlmann, arXiv:2102.01389v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccc}
 \toprule
 \textbf{Dataset} & \textbf{Data type} & \textbf{Number of envs.} & \textbf{Number of classes} & \textbf{Dataset size} \\
 \midrule
 Waterbirds & Image & 2 & 2 & 11788 \\
 CelebA & Image & 2 & 2 & 202599 \\
 CivilComments & Text & 8 & 2 & 242436 \\
 MultiNLI & Text & 2 & 3 & 412349 \\
 MetaShift & Image & 2 & 2 & 3499 \\
 ImageNetBG & Image & 2 & 9 & 192255 \\
 VLCS & Image & 4 & 5 & 10729 \\
 PACS & Image & 4 & 7 & 9991 \\
 OfficeHome & Image & 4 & 65 & 15588 \\
 Terra Incognita & Image & 4 & 10 & 24788 \\
 DomainNet & Image & 6 & 345 & 586575 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Discovering environments with XRM

Mohammad Pezeshki
Diane Bouchacourt
Mark Ibrahim
Nicolas Ballas
Pascal Vincent
David Lopez-Paz

"Discovering environments with XRM" by Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, and David Lopez-Paz, arXiv:2309.16748v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin


q-fin

Zero-Leverage Puzzle

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


q-fin

Guilherme V. Moura
André P. Santos
Hudson S. Torrent

"Variable selection for minimum-variance portfolios" by Guilherme V. Moura, André P. Santos, and Hudson S. Torrent, arXiv:2508.14986v1, 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|ll|lll|lll}
\hline\multicolumn{9}{c}{Dependent Variable: Within-Occupation Skill Change compared to 2010 by Detailed Occupation} \\ \hline
 & \multicolumn{2}{c|}{Wave 1} & \multicolumn{3}{c|}{Wave 2} & \multicolumn{3}{c}{Wave 3} \\
 Variable & Model 1 & Model 2 & Model 3 & Model 4 & Model 5 & Model 6 & Model 7 & Model 8 \\ \hline
 Acemoglu \& Autor$^{[14]}$ Computer Usage & -0.275$^{***}$ & & & & & & & \\
 Acemoglu \& Autor$^{[14]}$ Routine Cognitive & 0.062 & & & & & & & \\
 Acemoglu \& Autor$^{[14]}$ Routine Manual & 0.069 & & & & & & & \\
 ONET Education \% Workers w/ Bachelors & & -0.216$^{***}$ & & & & & & \\
 Frey \& Osborne$^{[17]}$ Prob. of Computerisation & & & 0.345$^{***}$ & & & & & \\
 Arntz et al$^{[18]}$ Probability & & & & 0.245$^{***}$ & & & & \\
 ONET Automation Degree of Automation & & & & & 0.050 & & & \\
 Brynjolfsson et al$^{[23]}$ Suitability for ML & & & & & & 0.363$^{***}$ & & \\
 Felten et al$^{[24]}$ AI Exposure & & & & & & & -0.256$^{***}$ & \\
 Webb$^{[25]}$ \% AI & & & & & & & & -0.203$^{***}$ \\
 Webb$^{[25]}$ \% Robot & & & & & & & & 0.261$^{***}$ \\
 Webb$^{[25]}$ \% Software & & & & & & & & 0.016 \\
 \hline Year F.E. & No & No & No & No & No & No & No & No \\
 Major SOC F.E. & No & No & No & No & No & No & No & No \\
 \hline
$R^2$ & 0.108 & 0.047 & 0.119 & 0.060 & 0.002 & 0.132 & 0.065 & 0.081 \\
 adj. $R^2$ & 0.103 & 0.045 & 0.117 & 0.058 & 0.001 & 0.130 & 0.064 & 0.076 \\
\hline \multicolumn{9}{c}{$p_{val}<0.1^*$, $p_{val}<0.01^{**}$, $p_{val}<0.001^{***}$} \\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

AI exposure predicts unemployment risk

"AI exposure predicts unemployment risk" by Morgan Frank, Yong-Yeol Ahn, and Esteban Moro, arXiv:2308.02624v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Main track eval set in DER, JER and DA-WER(\%).}
\begin{tabular}{rc|ccc|c}
\toprule
& & \textbf{CHiME-6} & \textbf{DiPCo} & \textbf{Mixer6} & \textbf{Macro} \\
\midrule
\multirow{3}{*}{\textbf{System-1}}
& DER & 56.1 & 28.7 & 18.0 & 34.3 \\
& JER & 56.7 & 35.7 & 17.8 & 36.7 \\
& WER & 53.1 & 34.5 & 28.0 & 38.6 \\ \midrule
\multirow{3}{*}{\textbf{System-2}}
& DER & 56.0 & 30.1 & 20.2 & 35.4 \\
& JER & 55.7 & 35.6 & 17.1 & 36.1 \\
& WER & 52.8 & 36.6 & 25.9 & 38.4 \\ \midrule
\multirow{3}{*}{\textbf{System-3}}
& DER & 53.5 & 26.8 & 15.5 & 31.9 \\
& JER & 57.3 & 35.4 & 22.1 & 38.3 \\
& WER & 54.1 & 35.5 & 30.3 & 40.0 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Tae Jin Park
He Huang
Ante Jukic
Kunal Dhawan
Krishna C. Puvvada
Nithin Koluguri
Nikolay Karpov
Aleksandr Laptev
Jagadeesh Balam
Boris Ginsburg

"The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System" by Tae Jin Park, He Huang, Ante Jukic, Kunal Dhawan, Krishna C. Puvvada, Nithin Koluguri, Nikolay Karpov, Aleksandr Laptev, Jagadeesh Balam, and Boris Ginsburg, arXiv:2310.12378v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{diagbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|ccccccccc}
 	\hline
 	\diagbox{$\alpha$}{$g_{\alpha\beta}$}{$\beta$} & $t$ & $D_\theta$ & $s$ & $y$ & $z$ & $\overline{y}$ & $\overline{z}$ \\ % <-- added & and content for each column
 	\hline
 	$t$ & & $\surd_2$ & & $\surd_2$ &
 $\surd_4$ & $\surd_2$ & $\surd_4$ \\
 $D_\theta$ & $\surd_2$ & & & $\surd_2$ & $\surd_2$ & & \\% <--
 	$s$ & & & & & \\
 	$y$ & $\surd_2$ & $\surd_2$ & & $\surd_2$ & $\surd_4$ & $\surd_2$ & $\surd_4$ \\
 	$z$ & $\surd_4$ & $\surd_2$ & & $\surd_4$ & $\surd_4$ & $\surd_2$ & $\surd_4$ \\
 	$\overline{y}$ & $\surd_2$ & & & $\surd_2$ & $\surd_2$ & & \\
 $\overline{z}$ & $\surd_4$ & & & $\surd_4$ & $\surd_4$ & & \\
 	\hline
 \end{tabular}
\caption{Second-order derivatives of $g$ required to be $p$-integrable}
\end{table}
\end{document}

math

"A Malliavin Calculus Approach to Backward Stochastic Volterra Integral Equations" by Qian Lei and Chi Seng Pun, arXiv:2412.19236v2, 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{Overall and class-specific prediction rates with 95\% prediction intervals (car versus other modes, excluding distance)}
\begin{tabular}{lccc}
		\toprule
		Mode & Mean Accuracy & 95\% PI Lower & 95\% PI Upper \\
		\midrule
		\textbf{Overall} & \textbf{0.7080} & \textbf{0.6994} & \textbf{0.7167} \\
		Car & 0.714 & 0.702 & 0.727 \\
		Other modes & 0.702 & 0.689 & 0.716 \\
		\hline
	\end{tabular}
\end{table}
\end{document}

q-fin

"Predicting Children's Travel Modes for School Journeys in Switzerland: A Machine Learning Approach Using National Census Data" by Hannes Wallimann and Noah Balthasar, arXiv:2504.09947v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Peng Ding
Jiading Fang
Peng Li
Kangrui Wang
Xiaochen Zhou
Mo Yu
Jing Li
Matthew R. Walter
Hongyuan Mei

"MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models" by Peng Ding, Jiading Fang, Peng Li, Kangrui Wang, Xiaochen Zhou, Mo Yu, Jing Li, Matthew R. Walter, and Hongyuan Mei, arXiv:2403.19913v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of trips and travelers tolled by entry time}
\begin{tabular}{lllll}
\hline
 & Entry time & Distance & Cordon & Area \\
 \hline
\multirow{3}{*}{Passenger trips (million)} & AM & 1.11 & 0.255 & \\
 & PM & 1.42 & 0.181 & \\
 & All & 2.57 & 0.436 & 3.78 \\
 \hline
\multirow{3}{*}{Passengers (million)} & AM & 1.01 & 0.247 & \\
 & PM & 1.17 & 0.202 & \\
 & All & 1.82 & 0.447 & 2.42 \\
 \hline
\multirow{3}{*}{Freight trips (thousand)} & AM & 75.3 & 13.1 & \\
 & PM & 312 & 48.9 & \\
 & All & 387 & 62.0 & 655 \\
 \hline
\multirow{3}{*}{Freight vehicles (thousand)} & AM & 30.1 & 8.73 & \\
 & PM & 104 & 27.2 & \\
 & All & 124 & 32.5 & 182 \\
 \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Peiyu Jing
Ravi Seshadri
Takanori Sakai
Ali Shamshiripour
Andre Romano Alho
Antonios Lentzakis
Moshe E. Ben-Akiva

"Evaluating congestion pricing schemes using agent-based passenger and freight microsimulation" by Peiyu Jing, Ravi Seshadri, Takanori Sakai, Ali Shamshiripour, Andre Romano Alho, Antonios Lentzakis, and Moshe E. Ben-Akiva, arXiv:2305.07318v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary statistics for HMDA data}
\begin{tabular}{lrrr}
 \hline
 & LA & WI & NY \\
 \hline
Number of Observations & 73790 & 165004 & 14708 \\
 Sex Ratio (Female) & 0.4033 & 0.3776 & 0.3799 \\
 Race Ratio (Non-White) & 0.3287 & 0.1205 & 0.3704 \\
 Loan Purpose (Home Purchase) & 0.5963 & 0.3854 & 0.6322 \\
 Age Ratio (Above 62) & 0.1525 & 0.1731 & 0.1355 \\
 Debt Ratio (Low) & 0.4414 & 0.5164 & 0.5623 \\
 Mean Loan Amount & 193174 & 176538 & 1066191 \\
 Mean Action Taken (Non-White) & 0.4669 & 0.7337 & 0.7950 \\
 Mean Action Taken (White) & 0.7071 & 0.8367 & 0.8320 \\
 Mean Action Taken (Female) & 0.5618 & 0.8112 & 0.8171 \\
 Mean Action Taken (Male) & 0.6729 & 0.8323 & 0.8190 \\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

Privilege Scores

Ludwig Bothmann
Philip A. Boustani
Jose M. Alvarez
Giuseppe Casalicchio
Bernd Bischl
Susanne Dandl

"Privilege Scores" by Ludwig Bothmann, Philip A. Boustani, Jose M. Alvarez, Giuseppe Casalicchio, Bernd Bischl, and Susanne Dandl, arXiv:2502.01211v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||cl||c|}
 \hline
 Notation & Definition & Space\\
 \hline\hline
 $\boldsymbol{\cdot}^N$ & Posterior distribution & \multirow{2}{*}{Any probability measure}\\
 $\boldsymbol{\cdot}^*$ & Related to a subsample & \\
 \hline \hline
 $\pi$ & Prior distribution & \multirow{2}{*}{Parameters $\Theta$}\\
 $\pi_w$ & Weighted &\\
 \hline\hline
 $p$& Likelihood &\multirow{7}{*}{Data $\mathbb{X}$} \\
 $p_\theta$ & Parametrized likelihood & \\
 $p_\theta^w$ & Likelihood based on weighted data &\\
 $p_N = p_{y_{1:N}}$ & Posterior predictive under a DP & \\
 $p^0$ & True distribution & \\
 $\tilde{y}$ & Samples of $p_N$ & \\
 $F_\theta$ & Parametric family& \\
 \hline\hline
 $\hat{p}$ & Empirical measure & \multirow{3}{*}{ Probabilities $\mathcal{P}(\mathbb{X})$}\\
 \multirow{2}{*}{$\mathbb{P}_\theta$} & \multirow{2}{*}{Law of $p_\theta$ or
 mixing measure }& \\
 & &\\
 \hline
 \end{tabular}
\caption{Table with notation for the probability measures used in throughout this work.}
\end{table}
\end{document}

stat

Predictive Coresets

"Predictive Coresets" by Bernardo Flores, arXiv:2502.05725v1, 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{Dataset statistics. The ``Class" row lists the distribution of positive/negative/neutral classes. }
\begin{tabular}{c|ccc}
\toprule
\textbf{} & \textbf{Train} & \textbf{Dev} & \textbf{Test} \\ \midrule
\# samples & 43600 & 1158 & 1319 \\
\# dialogues & 1124 & 56 & 56 \\
\# speakers & 385 & 86 & 72 \\
Class & 13,074/7,317/23,209 & 349/222/587 & 316/190/813 \\\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Guan-Ting Lin
Prashanth Gurunath Shivakumar
Ankur Gandhe
Chao-Han Huck Yang
Yile Gu
Shalini Ghosh
Andreas Stolcke
Hung-yi Lee
Ivan Bulyko

"Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue" by Guan-Ting Lin, Prashanth Gurunath Shivakumar, Ankur Gandhe, Chao-Han Huck Yang, Yile Gu, Shalini Ghosh, Andreas Stolcke, Hung-yi Lee, and Ivan Bulyko, arXiv:2312.15316v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Margin settings}
\begin{tabular}{cccc}
Page & Top & Bottom & Left/Right \\\hline
First & 3.5 & 2.5 & 1.5 \\
Rest & 2.5 & 2.5 & 1.5 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Mohamed Abdalmoaty
Jared Miller
Mingzhou Yin
Roy S. Smith

"Frequency-Domain Identification of Discrete-Time Systems using Sum-of-Rational Optimization" by Mohamed Abdalmoaty, Jared Miller, Mingzhou Yin, and Roy S. Smith, arXiv:2312.15722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
\hline
signal & condition \\ \hline
-1 & low\textgreater{}close \\
1 & low\textless{}close \\
0 & low==close \\ \hline
\end{tabular}
\caption{Computation of long and short signal}
\end{table}
\end{document}

q-fin

"Sizing Strategies for Algorithmic Trading in Volatile Markets: A Study of Backtesting and Risk Mitigation Analysis" by S. M. Masrur Ahmed, arXiv:2309.09094v2, 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}{rrrr}
\toprule
Accuracy & Precision & Recall & AUC \\
\midrule
0.73 & 0.86 & 0.56 & 0.86 \\
\bottomrule
\end{tabular}
\caption{Accuracy, precision, recall (thresholded on 0.5), and AUC for predicting probability a person is Black in the COMPAS dataset.}
\end{table}
\end{document}

stat

Hadi Elzayn
Emily Black
Patrick Vossler
Nathanael Jo
Jacob Goldin
Daniel E. Ho

"Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features" by Hadi Elzayn, Emily Black, Patrick Vossler, Nathanael Jo, Jacob Goldin, and Daniel E. Ho, arXiv:2310.01679v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccccc}
\toprule
Sample size of external arms & Strategy & $|\text{Bias}|$ & Variance & 95\% CI widths & 95\% CI coverage & Power \\
\midrule
\multirow{1}{*}{0} & RCT data only & 0.002 & 0.081 & 1.169 & 0.97 & 0.36 \\
\midrule
\multirow{2}{*}{500}
& Random & 0.119 & 0.103 & 0.959 & 0.86 & 0.40 \\
& TES + PS matching & \textbf{0.007} & \textbf{0.041} & \textbf{0.725} & \textbf{0.95} & \textbf{0.85} \\
\midrule
\multirow{2}{*}{600}
& Random & 0.170 & 0.084 & 0.923 & 0.85 & 0.35 \\
& TES + PS matching & \textbf{0.028} & \textbf{0.045} & \textbf{0.698} & \textbf{0.94} & \textbf{0.87} \\
\midrule
\multirow{2}{*}{700}
& Random & 0.156 & 0.093 & 0.905 & 0.79 & 0.39 \\
& TES + PS matching & \textbf{0.016} & \textbf{0.045} & \textbf{0.680} & \textbf{0.94} & \textbf{0.89} \\
\midrule
\multirow{2}{*}{800}
& Random & 0.157 & 0.103 & 0.880 & 0.78 & 0.39 \\
& TES + PS matching & \textbf{0.012} & \textbf{0.046} & \textbf{0.679} & \textbf{0.95} & \textbf{0.89} \\
\midrule
\multirow{2}{*}{900}
& Random & 0.134 & 0.080 & 0.881 & 0.84 & 0.42 \\
& TES + PS matching & \textbf{0.011} & \textbf{0.040} & \textbf{0.618} & \textbf{0.96} & \textbf{0.90} \\
\midrule
\multirow{2}{*}{1000}
& Random & 0.167 & 0.085 & 0.859 & 0.80 & 0.38 \\
& TES + PS matching & \textbf{0.019} & \textbf{0.040} & \textbf{0.605} & \textbf{0.95} & \textbf{0.93} \\
\bottomrule
\end{tabular}
\caption{Absolute bias, variance, average 95\% confidence interval widths, coverage, and power by external data sample size and sampling strategy. ``RCT data only" denotes the RCT-only design, analyzed with the TMLE estimator. ``Random" denotes a randomly sampled external cohort of the specified size, analyzed with the A-TMLE estimator. ``TES + PS matching" denotes our proposed trial enrollment score and propensity score matching strategy for sampling the external cohort of the specified size, also analyzed with the A-TMLE estimator.}
\end{table}
\end{document}

stat

Sky Qiu
Jens Tarp
Andrew Mertens
Mark van der Laan

"An Estimator-Robust Design for Augmenting Randomized Controlled Trial with External Real-World Data" by Sky Qiu, Jens Tarp, Andrew Mertens, and Mark van der Laan, arXiv:2501.17835v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Edgar C. Merkle
Sonja D. Winter
Ellen Fitzsimmons

"Identification and Scaling of Latent Variables in Ordinal Factor Analysis" by Edgar C. Merkle, Sonja D. Winter, and Ellen Fitzsimmons, arXiv:2501.06094v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overall evaluation analysis.}
\begin{tabular}{lll}
 \toprule
 & 4D-Lung & CHRU \\ \midrule
 Initial VV (\%) & 6.86 $\pm$ 5.32 & 11.66 $\pm$ 5.13 \\
 After synthesis VV (\%) & 1.50 $\pm$ 1.19 & 3.05 $\pm$ 2.26 \\
 Initial MAE & 35.71 $\pm$ 15.95 & 56.47 $\pm$ 10.45 \\
 After synthesis MAE & 29.87 $\pm$ 10.74 & 42.26 $\pm$ 6.35 \\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Yi-Heng Cao
Vincent Bourbonne
François Lucia
Ulrike Schick
Julien Bert
Vincent Jaouen
Dimitris Visvikis

"CT respiratory motion synthesis using joint supervised and adversarial learning" by Yi-Heng Cao, Vincent Bourbonne, François Lucia, Ulrike Schick, Julien Bert, Vincent Jaouen, and Dimitris Visvikis, arXiv:2404.00163v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of retracted papers (in \%) from top 10 publishing houses and journal impact (in Quartile). }
\begin{tabular}{|l|cc|ccccc|}
\hline
\multirow{2}{*}{\textbf{Publisher}} &
 \multicolumn{2}{l|}{\textbf{Total Retractions}} &
 \multicolumn{5}{c|}{\textbf{Journal Quartile}} \\ \cline{2-8}
 &
 \textbf{Count} &
 \textbf{\%age} &
 \textbf{Q1} &
 \textbf{Q2} &
 \textbf{Q3} &
 \textbf{Q4} &
 \textbf{Others} \\ \hline
Elsevier &
 124 &
 31.00 &
 41.94 &
 35.48 &
 12.10 &
 0.81 &
 9.68 \\ \hline
Springer &
 46 &
 11.50 &
 36.96 &
 54.35 &
 2.17 &
 2.17 &
 4.35 \\ \hline
Cold Spring Harbor Laboratory Press &
 31 &
 7.75 &
 0 &
 0 &
 0 &
 0 &
 100 \\ \hline
Wiley &
 24 &
 6.00 &
 20.83 &
 75.00 &
 0 &
 0 &
 4.17 \\ \hline
Cureus &
 17 &
 4.25 &
 0 &
 0 &
 0 &
 0 &
 100 \\ \hline
Frontiers &
 16 &
 4.00 &
 75.00 &
 6.25 &
 0 &
 0 &
 18.75 \\ \hline
Taylor and Francis &
 15 &
 3.75 &
 80.00 &
 20.00 &
 0 &
 0 &
 0 \\ \hline
Hindawi &
 13 &
 3.25 &
 15.38 &
 76.92 &
 7.69 &
 0 &
 0 \\ \hline
SAGE Publications &
 12 &
 3.00 &
 50.00 &
 8.33 &
 16.67 &
 25.00 &
 0 \\ \hline
Oxford Academic &
 8 &
 2.00 &
 87.50 &
 12.50 &
 0 &
 0 &
 0 \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

"Unraveling Retraction Dynamics in COVID-19 Research: Patterns, Reasons, and Implications" by Parul Khurana, Ziya Uddin, and Kiran Sharma, arXiv:2404.15298v1, 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{Values of the first five eigenvalues of the considered quantum star graph.}
\begin{tabular}{|c|l|}
\hline
\ $n$ \ & $k_n$ \\ \hline
1 & 0.34519971576497 \\ \hline
2 & 0.61599855952741 \\ \hline
3 & 0.83908748094051 \\ \hline
4 & 1.04930134453149 \\ \hline
5 & 1.35717565248649 \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Driven transparent quantum graphs

J. R. Yusupov
M. Ehrhardt
Kh. Sh. Matyokubov
D. U. Matrasulov

"Driven transparent quantum graphs" by J. R. Yusupov, M. Ehrhardt, Kh. Sh. Matyokubov, and D. U. Matrasulov, arXiv:2312.01448v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Privilege Scores


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the OPs (floating point + binary operations) count and ImageNet-1K classification of the two binary backbones used along with floating point (FP) ResNet-18 for reference. $\cdot^{*}$ indicates the performance including advanced training settings from ~.}
\begin{tabular}{lcc}\\\toprule
Backbone & OPs ($\times10^8$) & ImageNet-1K Acc. (\%) \\
\midrule
ResNet-18 (FP) & 18.00 & 69.60 \\ %\cmidrule(lr){1-1}\cmidrule(lr){2-2}\cmidrule(lr){3-3}
ReActNet-A (Binary) & 0.87 & 69.40\\ %\cmidrule(lr){1-1}\cmidrule(lr){2-2}\cmidrule(lr){3-3}
BNext-Tiny$^{*}$ (Binary)& 0.88 & 72.40\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Jihun Kim
Dahyun Kim
Hyungrok Jung
Taeil Oh
Jonghyun Choi

"Long-Tailed Recognition on Binary Networks by Calibrating A Pre-trained Model" by Jihun Kim, Dahyun Kim, Hyungrok Jung, Taeil Oh, and Jonghyun Choi, arXiv:2404.00285v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"An Approximate Lax-Wendroff-Type Procedure for High Order Accurate Schemes for Hyperbolic Conservation Laws" by David Zorío, Antonio Baeza, and Pep Mulet, arXiv:2501.17108v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
		\hline
		\textbf{Method} &
		Adam &
		SGD &
		Adagrad &
		RMSProp &
		L-BFGS \\
		\hline
		\textbf{Learning rate} &
		$1.0 \times 10^{-3}$ &
		$1.0 \times 10^{-1}$ &
		$1.0 \times 10^{-2}$ &
		$1.0 \times 10^{-2}$ &
		$1.0 \times 10^0$ \\
		\hline
	\end{tabular}
\caption{Learning rates for each update rule.}
\end{table}
\end{document}

stat

Aditya Ranganath
Mukesh Singhal
Roummel Marcia

"Symmetric Rank-One Quasi-Newton Methods for Deep Learning Using Cubic Regularization" by Aditya Ranganath, Mukesh Singhal, and Roummel Marcia, arXiv:2502.12298v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|lllc}
\toprule
& \multicolumn{4}{c}{In-domain Topics} \\
 & Accuracy & Precision & Recall & F1 \\
\hline
Response & 0.971 & 0.977 & 0.993 & 0.985 \\
Suggestion & 0.977 & 0.985 & 0.991 & 0.988 \\
\midrule
& \multicolumn{4}{c}{Out-of-domain Topics} \\
 & Accuracy & Precision & Recall & F1 \\
\hline
Response & 0.982 & 0.985 & 0.996 & 0.990 \\
Suggestion & 0.970 & 0.972 & 0.996 & 0.984 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Shuyao Xu
Long Qin
Tianyang Chen
Zhenzhou Zha
Bingxue Qiu
Weizhi Wang

"Large Language Model based Situational Dialogues for Second Language Learning" by Shuyao Xu, Long Qin, Tianyang Chen, Zhenzhou Zha, Bingxue Qiu, and Weizhi Wang, arXiv:2403.20005v1, 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}{l|cccc}
 \toprule
 & & & \\
 \textbf{Parameter} & \textbf{Classif.} & \textbf{Matching} & \textbf{ListOps} & \textbf{CIFAR} \\
 & & & \\
 \midrule
 Seq. Length & 4000 & 4000 & 2000 & 1024\\
 Batch Size & 32 & 32 & 32 & 64\\
 Training Steps & 25 000 & 30 000 & 50 000 & 30 000\\
 Optimizer & \multicolumn{4}{c}{AdamW ($\beta_1 = 0.9$, $\beta_2 = 0.999$)}\\
 Base LR & 0.005 & 0.015 & 0.005 & 0.01\\
 Weight Decay & 0.01 & 0.04 & 0.01 & 0.01\\
 Warmup Steps & 8000 & 8000 & 1000 & 700\\
 Schedule & \multicolumn{4}{c}{Base LR * Warmup * Sqrt Decay}\\
 Warmup Mul. & \multicolumn{4}{c}{$min(1, \text{Current Step} / \text{Warmup Steps})$}\\
 Sqrt Decay Mul. & \multicolumn{4}{c}{$1 / \sqrt{max(\text{CurrentStep}, \text{WarmupSteps})}$}\\
 Loss & \multicolumn{4}{c}{CCE}\\
 Blocks & 4 & 4 & 6 & 8\\
 Heads & \multicolumn{4}{c}{4}\\
 Hidden dim. & \multicolumn{4}{c}{128}\\
 QKV dim. & \multicolumn{4}{c}{128}\\
 MLP dim. & \multicolumn{4}{c}{512}\\
 Filter Op. & MaxPool & MaxPool & Conv & MaxPool\\
 Kernel Size & 4 & 4 & 32 & 4\\
 Stride & \multicolumn{4}{c}{1}\\
 Dropout & 0.1 & 0.1 & 0.1 & 0.0\\
 Activation & \multicolumn{4}{c}{GELU}\\
 Pooling & \multicolumn{4}{c}{Memory Cells Average}\\
 Pos. encoding & \multicolumn{4}{c}{Learnable Absolute}\\
 \bottomrule
 \end{tabular}
\caption{Hyperparameters used for the LRA experiments.}
\end{table}
\end{document}

cs

Uladzislau Yorsh
Martin Holeňa
Ondřej Bojar
David Herel

"On Difficulties of Attention Factorization through Shared Memory" by Uladzislau Yorsh, Martin Holeňa, Ondřej Bojar, and David Herel, arXiv:2404.00798v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Channel Choice and Customer Value

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

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


stat

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"No-Regret Caching via Online Mirror Descent" by T. Si Salem, G. Neglia, and S. Ioannidis, arXiv:2101.12588v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|l|l|l|l|}
 \hline
 Model & p-value \\ \hline
 Glasso1 & 0.953 \\ \hline
 Greedy1 & 0.974\\ \hline
 \end{tabular}
\caption{Out-of-Sample SPA test for Daily Estimation Horizon - Normal period }
\end{table}
\end{document}

q-fin

"Precision versus Shrinkage: A Comparative Analysis of Covariance Estimation Methods for Portfolio Allocation" by Sumanjay Dutta and Shashi Jain, arXiv:2305.11298v1, 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{Average results for the three evaluated methods on the full Corsican Fire Database as specified on section ~ on the first three columns, and for the \emph{FIRe-GAN} method before and after transfer learning on the validation set of the Corsican Fire Database (section ~) on the last two columns.}
\begin{tabular}{llllll}
\hline\noalign{\smallskip}
Metric & VGG19 & FusionGAN & FIRe-GAN & Before & After \\
\noalign{\smallskip}\hline\noalign{\smallskip}
EN & 6.3415 & 6.0072 & 10 & 10 & 10 \\
CC IR-fused & 0.7976 & 0.9657 & 0.5650 & 0.5774 & 0.7270 \\
CC RGB-fused & 0.7637 & 0.4104 & 0.6651 & 0.6799 & 0.6499 \\
PSNR IR-fused & 19.2014 & 22.5090 & 14.2065 & 14.3196 & 16.4212 \\
PSNR RGB-fused & 19.2255 & 15.3900 & 16.7435 & 16.7687 & 15.6484 \\
SSIM IR-fused & 0.8337 & 0.8389 & 0.6129 & 0.6187 & 0.7064 \\
SSIM RGB-fused & 0.9007 & 0.7783 & 0.8000 & 0.8020 & 0.7935 \\
\noalign{\smallskip}\hline
\end{tabular}
\end{table}
\end{document}

eess

J. F. Ciprián-Sánchez
G. Ochoa-Ruiz
M. Gonzalez-Mendoza
L. Rossi

"FIRe-GAN: A novel Deep Learning-based infrared-visible fusion method for wildfire imagery" by J. F. Ciprián-Sánchez, G. Ochoa-Ruiz, M. Gonzalez-Mendoza, and L. Rossi, arXiv:2101.11745v2, 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{Summary of the results obtained for $k=6$}
\begin{tabular}{|c|c|c|c|}
 			\hline
 			Girth $g$ & $n(6,g)$ & Orders to be investigated & $N(6,g)$ \\
 			\hline
 			3 & 7 & -- & 7 \\
 			\hline
 			4 & 12 & -- & 14 \\
 			\hline
 		\end{tabular}
\end{table}
\end{document}

math

L. C. Eze
R. Jajcay
T. Jajcayová
D. Závacká

"Theoretical and Computational Approaches to Determining Sets of Orders for $(k,g)$-Graphs" by L. C. Eze, R. Jajcay, T. Jajcayová, and D. Závacká, arXiv:2503.06466v1, 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{Comparative values from the base case}
\begin{tabular}{lcc}
 \hline
 \textbf{Systems} & \textbf{Highest profit(\%)} & \textbf{Lowest profit (\%)} \\
 \hline
 Risk-taking &96.06 & 65.85 \\
 No risk adjustment & 91.51 & 23.08 \\
 Auto-ARIMA & 92.50& 21.60\\
 Pure reserved & 89.85 & -4.42 \\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

"A Risk-taking Broker Model to Optimise User Requests placement on On-demand and Contract VMs" by Chalee Boonprasop, Yuhui Lin, and Adam Barker, arXiv:2103.07133v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|}
\hline
 & \multicolumn{2}{|c|}{Mean Absolute Distance} \\ \hline
Model & SQUAD & MNLI \\ \hline
Transparency & 4.1 & 0.9 \\ \hline
Baseline & 5.9 & 2.1 \\ \hline
 & \multicolumn{2}{|c|}{R2 Score} \\ \hline
Model & SQUAD & MNLI \\ \hline
Transparency & 0.49 & 0.92 \\ \hline
Baseline & 0.21 & 0.59 \\ \hline
\end{tabular}
\caption{MAD (lower is better) and R2 (higher is better) of using \textsc{benchmark transparency} to predict OOD performance compared to a uniform sampling baseline}
\end{table}
\end{document}

cs

"Benchmark Transparency: Measuring the Impact of Data on Evaluation" by Venelin Kovatchev and Matthew Lease, arXiv:2404.00748v1, 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{DiD State Capacity Analysis: Intensive Margin, Top 20\% Most Violent}
\begin{tabular}{lccccccc}
\toprule
& Tax & Operational & Ratio Gov. Trans. & Fiscal & Administrative & Rule & \textbf{Anderson} \tabularnewline & Revenue (pc) & Costs (pc) & to Revenue & Performance & Performance & Compliance & \textbf{Index} \tabularnewline
{}&{(1)}&{(2)}&{(3)}&{(4)}&{(5)}&{(6)}&{(7)} \tabularnewline
\midrule
\midrule Ceasefire \( \times \) FARC&--0.008&0.008*&1.347*&--0.016&--0.089&--0.031&--0.020 \tabularnewline
&(0.009)&(0.005)&(0.786)&(0.056)&(0.074)&(0.070)&(0.038) \tabularnewline
&&&&&&& \tabularnewline
Treated Municip.&153&153&153&153&153&153&153 \tabularnewline
Control Municip.&153&153&153&153&153&153&153 \tabularnewline
Mean Dependent Var.&0.106&0.134&7.904&--0.187&--0.061&--0.087&--0.023 \tabularnewline
Observations&3,362&3,362&3,353&3,358&3,366&3,055&3,366 \tabularnewline
\(R^2\)&0.817&0.843&0.318&0.547&0.412&0.406&0.453 \tabularnewline
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Peace Dividends: The Economic Effects of Colombia's Peace Agreement" by Miguel Fajardo-Steinhäuser, arXiv:2301.01843v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"New technique for parameter estimation and improved fits to experimental data for a set of compound Poisson distributions" by S. R. Mane, arXiv:2502.03237v2, 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}{cccc}
 \toprule
 Method & F.C. Soln.& Retrain & Task \\
 \cmidrule(lr){1-1}\cmidrule(lr){2-2}\cmidrule(lr){3-3}\cmidrule(lr){4-4}
 SNGP , DDU & SN & Yes & Cl. \\
 DUQ & Jacob. & Yes & Cl. \\
 DVE & Skip & No & Reg. \\
 Ours & Skip & No & Cl. \\
 \bottomrule
 \end{tabular}
\caption{\textbf{Our method enables deterministic UQ for classification without the need for retraining.} In this table, ``F.C. Soln.'' refers to the method used to address feature collapse (e.g., spectral normalization, SN), and ``Task'' indicates whether the model is applied to classification (Cl.) or regression (Reg.).}
\end{table}
\end{document}

stat

"Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks" by Felix Jimenez and Matthias Katzfuss, arXiv:2501.04816v1, 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}{crrrr}
\hline
& $\Delta t = 1$ minute & $\Delta t = 5$ minutes & $\Delta t = 1$ hour & $\Delta t = 1$ day \\
\hline
Correlation & $-2.1 \%\ \ $ & $-2.4 \%$ & $-2.6 \%$ & $-10.9 \%$ \\
{Two-tailed p-value} & {$2.0\%\ \ $} & {$8.9\%$} & {$8.2\%$} & {$16.7\%$} \\
{$R^2$ regression} & {$0.01\%$} & {$1.1\%$} & {$2.9\%$} & {$5.0\%$} \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Álvaro Cartea
Fayçal Drissi
Marcello Monga

"Decentralised Finance and Automated Market Making: Predictable Loss and Optimal Liquidity Provision" by Álvaro Cartea, Fayçal Drissi, and Marcello Monga, arXiv:2309.08431v3, licensed under CC0 1.0. 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
\begin{tabular}{ccccccc}
\hline
 & & Min & 25th & Median & 75th & Max\tabularnewline
\hline
\hline
\multirow{2}{*}{Own} & Inc. & 2.67 & 4.11 & 7.38 & 9.56 & 13.51\tabularnewline
 & CFL & 2.67 & 5.22 & 6.63 & 8.15 & 13.62\tabularnewline
\hline
\multirow{4}{*}{Cross} & Inc.$\rightarrow$ Inc. & 0.0048 & 0.0348 & 0.1315 & 0.3645 & 1.6503\tabularnewline
 & CFL $\rightarrow$ CFL & 0.001 & 0.0085 & 0.0433 & 0.1497 & 0.5703\tabularnewline
 & Inc. $\rightarrow$ CFL & 0.0002 & 0.0014 & 0.005 & 0.0176 & 0.1256\tabularnewline
 & CFL$\rightarrow$Inc. & -0.0929 & -0.0192 & -0.0035 & -0.0002 & 0.3393\tabularnewline
\hline
\end{tabular}
\caption{Long-run price elasticities of demand}
\end{table}
\end{document}

q-fin

"When do firms sell high durability products? The case of light bulb industry" by Takeshi Fukasawa, arXiv:2503.23792v2, 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{Proportion of the type of entities of NER dataset.}
\begin{tabular}{lcc}
\toprule
\textbf{Category} & \textbf{Tag} & \textbf{Amount} \\
\midrule
NUMBER & NUM & 64,876 \\
CIVILIZATION & CVL & 60,918 \\
PERSON & PER & 48,321 \\
ORGANIZATION & ORG & 45,550 \\
DATE & DAT & 33,944 \\
TERM & TRM & 22,070 \\
LOCATION & LOC & 21,095 \\
EVENT & EVT & 17,430 \\
ANIMAL & ANM & 6,544 \\
ARTIFACTS\_WORKS & AFW & 6,069 \\
TIME & TIM & 4,337 \\
FIELD & FLD & 2,386 \\
PLANT & PLT & 267 \\
MATERIAL & MAT & 252 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Hyunjae Lee
Jaewoong Yoon
Bonggyu Hwang
Seongho Joe
Seungjai Min
Youngjune Gwon

"KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding" by Hyunjae Lee, Jaewoong Yoon, Bonggyu Hwang, Seongho Joe, Seungjai Min, and Youngjune Gwon, arXiv:2101.11363v1, 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}{llllll}
\hline
\textbf{Sub Sample} & \textbf{a} & \textbf{s} & \textbf{h} & \textbf{$h_{dummy}$} & \textbf{hs} \\ \hline
Big & 11.37 & -0.74 & 0.10 & -0.94 & 3.58 \\
 & (8.08) & (-8.13) & (0.71) & (-0.60) & (2.42) \\
 & & & & & \\
Small & 23.40 & -2.06 & 0.69 & -4.23 & 2.08 \\
 & (13.06) & (-13.45) & (4.96) & (-4.21) & (1.46) \\
 & & & & & \\ \hline
\end{tabular}
\caption{Big and Small subsample of firms are determined by yearly median of size(log value of market cap) value. If the firm's size at year $t$ is bigger than the size median at time $t$, is classified as big firm, and vise versa. On each month, we conducted the following regression. \\ $R_i-RF=a + s \cdot log(ME_i) + h \cdot log(BE/ME)_i^{+} + h_{dummy} \cdot BE_{Dummy, i} + hs \cdot HSE_i + \epsilon_i $.\\ $log(BE/ME)^{+}_i$ is BE/ME value that fills BE/ME with BE less than 0. $BE_{Dummy, i}$ is 1 when BE is less than 0, else 1. Then we calculated time-series average and its t-value of coefficients and intercept. Returns are observed at July, year $t$ to June, year $t+1$, and BE/ME are observed at December, year $t-1$ and HSE, ME is observed at June, year $t$.}
\end{table}
\end{document}

q-fin

"Do We Price Happiness? Evidence from Korean Stock Market" by HyeonJun Kim, arXiv:2308.10039v1, 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}{l|ccc|c}
 \toprule
 \textbf{Model} &
 \textbf{MCD $\downarrow$} & \textbf{FFE $\downarrow$} & $\log f_{\mathrm{o}} \downarrow$ & \textbf{Parameters} \\
 \midrule
 Tacotron 2 & 4.218 & 47.31\% & 0.292& 28.19M\\
 EBM$^{(1)}$ (small) & 4.163 & 47.06\% & 0.289& 2.30M\\
 EBM$^{(1)}$ (large) & 4.178& 47.05\% & 0.291& 7.64M\\
 \bottomrule
 \end{tabular}
\caption{Comparison between Tacotron 2 and two EBMs utilising Tacotron 2 hypotheses as negative samples}
\end{table}
\end{document}

eess

"Energy-Based Models For Speech Synthesis" by Wanli Sun, Zehai Tu, and Anton Ragni, arXiv:2310.12765v1, 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{Number of graphs for a given number of junctions}
\begin{tabular}{cc}
\toprule
Junctions & Number of Graphs \\ \hline
J = 1 & $F_1(N) = G(N)$ \\
J = 2 & $F_2(N) = \sum_{M=1}^{N} {N\choose M} G(M) F_1(N-M)$ \\
J = J & $F_J(N) = \sum_{M=1}^{N} {N\choose M} G(M) F_{J-1}(N-M)$ \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Saeid Bayat
Nastaran Shahmansouri
Satya RT Peddada
Alexander Tessier
Adrian Butscher
James T Allison

"Multi-split configuration design for fluid-based thermal management systems" by Saeid Bayat, Nastaran Shahmansouri, Satya RT Peddada, Alexander Tessier, Adrian Butscher, and James T Allison, arXiv:2310.15500v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|c|}
\hline
& depth-13 & depth-17 & depth-19 \\
\hline
N-1 Accuracy & 63.08\% & 63.47\% & 63.81\% \\
\hline
N-3 Accuracy & 82.90\% & 82.93\% & 85.59\% \\
\hline
\end{tabular}
\caption{Evaluation on input with different valid hypotheses. The model is trained on depth-17, and is evaluated on depth-13, 17 and 19.}
\end{table}
\end{document}

eess

"Learning to Estimate Kernel Scale and Orientation of Defocus Blur with Asymmetric Coded Aperture" by Jisheng Li, Qi Dai, and Jiangtao Wen, arXiv:2103.05843v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Determinants of Net Interest Margin in the Turkish Banking Sector: Does Bank Ownership Matter?" by Fatih Kansoy, arXiv:2506.04384v2, 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{Modified Vicsek model diffusion estimation}
\begin{tabular}{|c|c|} \hline
 True $\sigma$& Estimated $\hat{\sigma}$\\ \hline
 0.0800& 0.0800\\ \hline
 \end{tabular}
\end{table}
\end{document}

math

"Learning Stochastic Dynamical Systems with Structured Noise" by Ziheng Guo, James Greene, and Ming Zhong, arXiv:2503.01077v1, 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{Overhead from byte-based AC termination.}
\begin{tabular}{|c|c|c|c|c|} \hline
\bf Bidirectional &\bf Reversed &\bf Share &\bf Avrg. extra bits \\
\bf byte packing &\bf bits &\bf ratio &\bf per bitstream $\bar{T}$ \\ \hline \hline
 No & --- & --- & 4.56 \\ \hline
 Yes & No & 45\% & 2.77 \\ \hline
 Yes & Yes & 69\% & 1.78 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"Bitstream Organization for Parallel Entropy Coding on Neural Network-based Video Codecs" by Amir Said, Hoang Le, and Farzad Farhadzadeh, arXiv:2312.00921v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Modelling superspreading dynamics and circadian rhythms in online discussion boards using Hawkes processes" by Joe Meagher and Nial Friel, arXiv:2501.13823v1, 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{Experimental evaluation results for the selected method combinations (pretraining on SIXray10-D, downstream finetuning on CFRay).}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 \multicolumn{2}{|c|}{Configuration}
 &
 \multicolumn{3}{|c|}{Metrics} \\
 \hline
 Backbone & Detector & mAP@50 & mAP@50-95 & Inf. Time \\
 \hline
 \fontsize{9}{8}\selectfont SWIN-b & DINO & \textbf{0.993} & 0.816 & 175.4 \\
 \fontsize{9}{8}\selectfont NextViT-s & RT-DETR-l & 0.943 & 0.765 & 16 \\
 \fontsize{9}{8}\selectfont NextViT-s & YOLOv8-l & 0.986 & \textbf{0.82} & \textbf{11.6} \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Jorgen Cani
Ioannis Mademlis
Adamantia Anna Rebolledo Chrysochoou
Georgios Th. Papadopoulos

"Illicit object detection in X-ray images using Vision Transformers" by Jorgen Cani, Ioannis Mademlis, Adamantia Anna Rebolledo Chrysochoou, and Georgios Th. Papadopoulos, arXiv:2403.19043v2, 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}{lll}
 \toprule
 Bundle & Columns Name & Description \\
 \midrule
 Investments & uuid & Investment unique identifier\\
 & name & Investment's name (e.g., Accel investment in Series A - Facebook)\\
 & permalink & \\
 & cb\_url & Crunchbase's investment url\\
 & created\_at & Record creation date\\
 & updated\_at & Record last update\\
 & funding\_round\_uuid & \\
 & funding\_round\_name & \\
 & investor\_uuid & \\
 & investor\_name & \\
 & investor\_type & Either "organization" or "person"\\
 & is\_lead\_investor & \\
 \bottomrule
 \end{tabular}
\caption{Data entries in the Crunchbase investment bundle.}
\end{table}
\end{document}

q-fin

Luca Mungo
Silvia Bartolucci
Laura Alessandretti

"Cryptocurrency co-investment network: token returns reflect investment patterns" by Luca Mungo, Silvia Bartolucci, and Laura Alessandretti, arXiv:2301.02027v3, 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}{l|ccccc}
 & Spambase & Toxicity & QSAR & Breast Cancer & Religious\\ \toprule
 $n$ & 4601 & 171 & 8992 & 569 & 8265 \\
 $p$ & 57 & 1203 & 1024 & 30 & 590 \\
 \# classes & $2$ & $2$ & $2$ & $2$ & $8$ \\ \bottomrule
\end{tabular}
\caption{Feature Selection Dataset details}
\end{table}
\end{document}

stat

Krunoslav Lehman Pavasovic
David Lopez-Paz
Giulio Biroli
Levent Sagun

"A Differentiable Rank-Based Objective For Better Feature Learning" by Krunoslav Lehman Pavasovic, David Lopez-Paz, Giulio Biroli, and Levent Sagun, arXiv:2502.09445v1, 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}{lcc}
\toprule
\textbf{Method} & \textbf{CHAIR\_I $\downarrow$} & \textbf{CHAIR\_S $\downarrow$} \\
\midrule
InstructBLIP & 10.7 & 20.0 \\
VCD & 9.3 & 18.2 \\
\textbf{ICD} & \textbf{8.0} & \textbf{15.2} \\
\bottomrule
\end{tabular}
\caption{\textbf{Evaluation on MSCOCO validation set} using metrics CHAIR\_I and CHAIR\_S, instance-level and sentence-level hallucinations, followed by .}
\end{table}
\end{document}

cs

Xintong Wang
Jingheng Pan
Liang Ding
Chris Biemann

"Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding" by Xintong Wang, Jingheng Pan, Liang Ding, and Chris Biemann, arXiv:2403.18715v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameters used in this paper.}
\begin{tabular}{c|c}
 \hline \hline
 Models & Parameters \\ \hline
 \multirow{3}{*} & Kernel = rbf \\
 SVR & $C$ = 10 \\
 & $\gamma$ = 0.001 \\ \hline
 \multirow{3}{*} & Kernel = rbf \\
 KR & $\alpha$ = 1 \\
 & $\gamma$ = 0.01 \\ \hline
 DT & Maximum depth = 10 \\ \hline
 RF & Maximum depth = 30 \\ \hline
 \multirow{5}{*} & Learning rate = 0.0009 \\
 LSTM & Hidden size = 100 \\
 & Epochs = 300 \\
 & Dropout rate = 0.2 \\
 & Window = 5 \\ \hline \hline
 \end{tabular}
\end{table}
\end{document}

eess

Forough Yaghoubi
Armin Catovic
Arthur Gusmao
Jan Pieczkowski
Peter Boros

"Traffic Flow Estimation using LTE Radio Frequency Counters and Machine Learning" by Forough Yaghoubi, Armin Catovic, Arthur Gusmao, Jan Pieczkowski, and Peter Boros, arXiv:2101.09143v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Matthew West
Colin Magdamo
Lily Cheng
Yingnan He
Sudeshna Das

"Leveraging Pre-trained and Transformer-derived Embeddings from EHRs to Characterize Heterogeneity Across Alzheimer's Disease and Related Dementias" by Matthew West, Colin Magdamo, Lily Cheng, Yingnan He, and Sudeshna Das, arXiv:2404.00464v1, 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
Tickers & ks\_values\_5 & p\_values\_5 \\ \hline
A & 0.04916050 & 0.84794566 \\ \hline
AAL & 0.09811723 & 0.09824362 \\ \hline
ABC & 0.06077138 & 0.61167223 \\ \hline
ADS & 0.09359102 & 0.12200421 \\ \hline
CERN & 0.04982252 & 0.83633810 \\
\hline
\end{tabular}
\caption{KS Test Output}
\end{table}
\end{document}

q-fin

"Sizing Strategies for Algorithmic Trading in Volatile Markets: A Study of Backtesting and Risk Mitigation Analysis" by S. M. Masrur Ahmed, arXiv:2309.09094v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins" by Apostolos Ampountolas, arXiv:2307.08853v1, 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{Training accuracy(\%) of the Mirrored OCTs for the unstructured datasets. We report the average accuracy and standard deviation across the five splits. }
\begin{tabular}{ccc}
 \textbf{Dataset} & \textbf{Tabular model} & \textbf{Multimodal model}\\ \midrule
 TAVR & $79.03\pm 3.09 $& $75.73\pm 0.69$\\
 Liver trauma & $87.66\pm 3.50$ & $84.07\pm 3.11$ \\
 \end{tabular}
\end{table}
\end{document}

stat

Multimodal Prescriptive Deep Learning

Dimitris Bertsimas
Lisa Everest
Vasiliki Stoumpou

"Multimodal Prescriptive Deep Learning" by Dimitris Bertsimas, Lisa Everest, and Vasiliki Stoumpou, arXiv:2501.14152v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Lourens Touwen
Doina Bucur
Remco van der Hofstad
Alessandro Garavaglia
Nelly Litvak

"Learning the mechanisms of network growth" by Lourens Touwen, Doina Bucur, Remco van der Hofstad, Alessandro Garavaglia, and Nelly Litvak, arXiv:2404.00793v3, 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{Operating Points}
\begin{tabular}{cc}
 \hline
 Reference & Values\\
 \hline\hline
 Grid Voltage Reference ($V_{g}^*$) & $\begin{bmatrix}0.92 & 1.0 & 1.08
 \end{bmatrix}$\\
 Turbine Voltage Reference ($V_{turb}^*$) & $\begin{bmatrix}0.92 & 1.0 & 1.08
 \end{bmatrix}$\\
 Turbine Power Reference ($P_{turb}^*$) & $\begin{bmatrix}0.1 & 0.5 & 1.0
 \end{bmatrix}$\\\hline
 \end{tabular}
\end{table}
\end{document}

eess

Sulav Ghimire
Kanakesh V. Kkuni
Emerson D. Guest
Kim H. Jensen
Guangya Yang

"Small-Signal Stability and SCR Enhancement of Offshore WPPs with Synchronous Condensers" by Sulav Ghimire, Kanakesh V. Kkuni, Emerson D. Guest, Kim H. Jensen, and Guangya Yang, arXiv:2310.06457v2, 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{arydshln}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
 \toprule
 Action &
 $E^{\text{batt}}_{t,i} \in [-1, 1]$
 \\ \hdashline[.4pt/1pt]

 State &
 $\{T_{t}^{\text{out}}, H_{t}^{\text{out}}, B_{t}^{\text{soc}}, E_{t}^{\text{net}}, C_{t}, t\}$
 \\ \hdashline[.4pt/1pt]

 Reward &
 $-E_{t,i}^{\text{grid}} = -\max\{ E_{t,i}^{\text{load}} + E^{\text{batt}}_{t,i} - E^{\text{solar}}_{t,i}, 0\}$
 \\ \bottomrule

 \end{tabular}
\caption{RL case definition}
\end{table}
\end{document}

cs

Yunxiang Li
Nicolas Mauricio Cuadrado
Samuel Horváth
Martin Takáč

"Generalized Policy Learning for Smart Grids: FL TRPO Approach" by Yunxiang Li, Nicolas Mauricio Cuadrado, Samuel Horváth, and Martin Takáč, arXiv:2403.18439v1, 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{Statistics of the TCS with and without profit threshold}
\begin{tabular}{lll}
\hline\noalign{\smallskip}
Factors & Without & With \\
\noalign{\smallskip}\hline\noalign{\smallskip}
Profit threshold & 0 & \$1 \\
Sell No. Trans. &22780 &8992 \\
Buy No. Trans. &16852 &12173 \\
Traded credits &1.24$\times 10^6$ & 1.04$\times 10^6$\\
Travelers with buyback &4881 &1651 \\
Welfare gain &\$0.38($\pm 0.08$) &\$0.36($\pm 0.1$) \\
Credit price &\$0.041 &\$0.035 \\
Tariff profile &(8:59, 72, 0.0194) &(8:54, 66, 0.0196) \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Renming Liu
Dimitrios Argyros
Yu Jiang
Moshe E. Ben-Akiva
Ravi Seshadri
Carlos Lima Azevedo

"Assessing the impacts of tradable credit schemes through agent-based simulation" by Renming Liu, Dimitrios Argyros, Yu Jiang, Moshe E. Ben-Akiva, Ravi Seshadri, and Carlos Lima Azevedo, arXiv:2502.11822v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Class-wise and overall accuracies achieved on the test set by a linear classifier used with the different methods considering 1000 SAR-optical training samples.}
\begin{tabular}{ccccccc}
		\hline
		\multirow{2}{*}{Class} & \multicolumn{6}{c}{Average Accuracies \%} \\ \cline{2-7}
		 & WSL & DCCA & MCL & PixIF& PixLF& PixEF \\ \hline
		Forest & 90.2 & 92.7 & 90.9 & 92.6 & 92.3 & 91.3\\
		Shrub-land & 70.4 & 26.3 & 40.4 & 50.8 & 46.3 & 53.2\\
		Grassland & 57.6 & 63.0 & 68.7 & 73.0 & 64.5 & 74.5\\
		Wetlands & 70.4 & 56.9 & 64.9 & 62.7 & 57.2 & 59.9\\
		Croplands & 81.2 & 72.8 & 82.8 & 84.7 & 85.8 & 80.0\\
		Urban & 86.6 & 86.9 & 87.7 & 87.8 & 84.4 & 87.1\\
		Barren & 34.5 & 6.3 & 46.3 & 30.9 & 29.5 & 37.0\\
		Water & 99.2 & 99.0 & 99.3 & 99.3 & 99.4 & 99.2\\ \hline
		AA & 73.8 & 63.0 & 72.7 & 72.7 & 69.9 & 72.8\\
		mIoU & 0.490& 0.411& 0.487& 0.498& 0.476&0.490\\\hline
	\end{tabular}
\end{table}
\end{document}

eess

"Self-supervised SAR-optical Data Fusion and Land-cover Mapping using Sentinel-1/-2 Images" by Yuxing Chen and Lorenzo Bruzzone, arXiv:2103.05543v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of the datasets for the transductive node classification task}
\begin{tabular}{lrrrrr}
 \toprule
 \multirow{2}{*}{\textbf{Dataset}} &
 \textbf{Total} & \textbf{Num} & \textbf{Num} & \textbf{Dim} & \textbf{Num} \\
 & \textbf{Graphs} & \textbf{Nodes} & \textbf{Edges} & \textbf{Features} & \textbf{Classes} \\
 \midrule
 $\mathtt{Cora}$ &1& 2,708 & 10,556 & 1,433 & 7 \\
 $\mathtt{CiteSeer}$ &1& 3,327 & 9,104 & 3,703 & 6 \\
 $\mathtt{PubMed}$ &1& 19,717 & 88,648 & 500 & 3 \\
 $\mathtt{Texas}$ &1& 183 & 309 & 1,703& 5\\
 $\mathtt{Wisconsin}$ &1& 251 & 499 & 1,703 & 5\\
 $\mathtt{Cornell}$ &1& 183 & 295 & 1,703 & 5\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Non-backtracking Graph Neural Networks

Seonghyun Park
Narae Ryu
Gahee Kim
Dongyeop Woo
Se-Young Yun
Sungsoo Ahn

"Non-backtracking Graph Neural Networks" by Seonghyun Park, Narae Ryu, Gahee Kim, Dongyeop Woo, Se-Young Yun, and Sungsoo Ahn, arXiv:2310.07430v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
\hline
\textbf{Route Position} & \textbf{Visited City} \\
\hline
1 & City 1 \\
2 & City 2 \\
3 & City 5 \\
4 & City 4 \\
5 & City 3 \\
\hline
\end{tabular}
\caption{Best city sequence for the execution with the lowest cost}
\end{table}
\end{document}

math

Kael Silva Araújo
Francisco Márcio Barboza

"PSO and the Traveling Salesman Problem: An Intelligent Optimization Approach" by Kael Silva Araújo and Francisco Márcio Barboza, arXiv:2501.15319v1, 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}{ccc}
 \hline
 \hline
 {\bf Method} & {\bf Complexity} & {\bf Iterations} \\
 \hline
 \hline
 Newton & $O((mr)^6)$ & a few \\
 FPI & $O((mr)^3)$ & thousands \\
 \hline
 \hline
\end{tabular}
\end{table}
\end{document}

math

"Bauer's Spectral Factorization Method for Low Order Multiwavelet Filter Design" by Vasil Kolev, Todor Cooklev, and Fritz Keinert, arXiv:2312.05418v1, 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{Some examples of PDMP and their main features.}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline
 &mode & Eucl. var. & & & &\\
 \cline{2-3}
 name &cardinal& dimension & boundaries & flow &time& Graphical & reference\\
 &of $\mathsf{M}$ & of $\mathsf{E_m}$ & & & augmentation & representation&\\
 \hline
 Telegraph model &2& - & no & constant&no &Fig.~& \\
 Stochastic SIR&&&&&&\\
 model &N& - & no & constant&no &Fig.~&\\
 \hline
 Transmission Control &&&&&&\\
 Protocol (TCP)& - & 1 & no & linear &no &Fig.~&\\
 Individual cell growth &&&&&&\\
 and fragmentation & - & 1& no & exponential &no & Fig.~ &\\
 \hline
 Zig-zag & $2$ & $1$ & no & linear &no &Fig.~& \\
 Optronic equipment& $5$ & $1$ & no & linear &yes &Fig.~ & \\
 Gene expression & $2$ & $2$ & no & analytical expr. &no & Fig.~ &\\
 Corrosion&$6$&$3$&no&analytical expr.&yes &Fig.~ &\\
 Morris Lecarr& $N$ & $1$ & no & sol. of an EDO &no &Fig.~ & \\
 \hline
 Crack propagation & $3$ & $1$ & yes & exponential & no & Fig.~&\\
 Repair workshop & $3$ & $1$ & yes & linear &yes & - &\\
 Heated hold-up tank & $64$ & $2$ & yes & analytical expr. & no & - &\\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Alice Cleynen
Benoîte de Saporta
Orlane Rossini
Régis Sabbadin
Amélie Vernay

"Bridging Impulse Control of Piecewise Deterministic Markov Processes and Markov Decision Processes: Frameworks, Extensions, and Open Challenges" by Alice Cleynen, Benoîte de Saporta, Orlane Rossini, Régis Sabbadin, and Amélie Vernay, arXiv:2501.04120v1, 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{Average and minimum losses 100 repetitions of removing 100 randomly selected SOC constraints.}
\begin{tabular}{lrrrrrrrr}
 \toprule
 & & \multicolumn{4}{c}{Weak Jabr} & \multicolumn{2}{c}{Jabr} & AC \\
 \cmidrule(l{0.5em}r{0.40em}){3-6} \cmidrule(l{0.5em}r{0.40em}){7-8} \cmidrule(l{0.5em}r{0.40em}){9-9}
 & & \multicolumn{2}{c}{Avg Losses} & Min Losses & & & \\
 \cmidrule(l{0.5em}r{0.40em}){3-4} \cmidrule(l{0.5em}r{0.40em}){5-5}
 {Case} & Load & Total & Sample & Sample & Objective & Objective & Losses & Losses \\
 \midrule
 1354pegase & 730.60 & -150.38 & -379.60 & -764.52 & 58021.55 & 74008.58 & 9.49 & 10.09 \\
 2869pegase & 1324.37 & -197.83 & -402.59 & -1119.85 & 112663.19 & 133872.69 & 14.24 & 15.51 \\
 3375wp & 483.63 & -65.50 & -117.11 & -343.72 & 6467352.82 & 7385372.70 & 6.90 & 8.30 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

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


q-fin

A. Ramirez-Hassan
C. Gomez
S. Velasquez
K. Tangarife

"Marijuana on Main Streets? The Story Continues in Colombia: An Endogenous Three-part Model" by A. Ramirez-Hassan, C. Gomez, S. Velasquez, and K. Tangarife, arXiv:2306.10031v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccc}
		$j(E)$ & Minimal $X_{0}(n)$ & $g(X_{0}(n))$ & $\deg(x)$ \\ \hline
		$-121$ & \multirow{2}*{$X_{0}(11)$} & \multirow{2}*{1} & 1 \\
		$-24729001$ & & & 1 \\ \hline
		$-\frac{25}{2}$ & \multirow{4}*{$X_{0}(15)$} & \multirow{4}*{1} & 1 \\
		$-\frac{121945}{32}$ & & & 1 \\
		$\frac{46969655}{32768}$ & & & 1 \\
		$-\frac{349938025}{8}$ & & & 1 \\ \hline
		$-\frac{297756989}{2}$ & \multirow{2}*{$X_{0}(17)$} & \multirow{2}*{1} & 1 \\
		$-\frac{882216989}{131072}$ & & & 1 \\ \hline
		$\frac{3375}{2}$ & \multirow{4}*{$X_{0}(21)$} & \multirow{4}*{1} & 1 \\
		$-\frac{140625}{8}$ & & & 1 \\
		$-\frac{1159088625}{2097152}$ & & & 1 \\
		$-\frac{189613868625}{128}$ & & & 1 \\ \hline
		$-9317$ & \multirow{2}*{$X_{0}(37)$} & \multirow{2}*{2} & 1 \\
		$-162677523113838677$ & & & 1 \\ \hline
	\end{tabular}
\caption{The $j$-invariants of the non-cuspidal, non-CM isolated closed points with rational $j$-invariant on the modular curves $X_{0}(n)$. For each $j$-invariant, the smallest modular curve $X_{0}(n)$ for which there exists an isolated closed point $x \in X_{0}(n)$ with the given $j$-invariant is listed. The genus of the modular curve $X_{0}(n)$ and the degree of the closed point $x$ are also given.}
\end{table}
\end{document}

math

Isolated points on modular curves

"Isolated points on modular curves" by Kenji Terao, arXiv:2412.13108v2, 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}
\textbf{BiGRU} & \textbf{F1-micro \%} & \textbf{F1-macro \%} & \textbf{LRAP \%} & \textbf{\#Params} \\ \hline
1 (128)
 & 43.76 $\pm$ 1.95
 & 37.37 $\pm$ 1.90
 & 57.26 $\pm$ 3.28
 & \textbf{103.4K} \\
1 (256)
 & 43.51 $\pm$ 2.46
 & 39.19 $\pm$ 2.23
 & 58.47 $\pm$ 2.73
 & 403.4K\\

2 & \textbf{49.28 $\pm$ 2.45}
 & \textbf{43.18 $\pm$ 3.11}
 & \textbf{67.07 $\pm$ 1.81}
 & 225.6K\\

\end{tabular}
\caption{Results for the Recurrent Networks discussed in Sec.~3.1, subject to the number of GRU layers and the number of units.}
\end{table}
\end{document}

eess

Kleanthis Avramidis
Agelos Kratimenos
Christos Garoufis
Athanasia Zlatintsi
Petros Maragos

"Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio Waveforms" by Kleanthis Avramidis, Agelos Kratimenos, Christos Garoufis, Athanasia Zlatintsi, and Petros Maragos, arXiv:2102.06930v1, 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{DiD Violence Analysis: Intensive Margin, Top 20\% Most Violent}
\begin{tabular}{lccccccc}
\toprule
{}&{(1)}&{(2)}&{(3)}&{(4)}&{(5)}&{(6)}&{(7)} \tabularnewline
\midrule
\midrule \textbf{Panel A. UARIV}&Terror. Act&Threats&Disapp.&Sex Crimes&Child Recruit.&Torture&Prop. Loss \tabularnewline
\midrule Ceasefire \( \times \) FARC&--0.361*&--0.940***&--0.053***&--0.012&--0.034***&--0.006**&--0.338 \tabularnewline
&(0.200)&(0.192)&(0.016)&(0.008)&(0.006)&(0.002)&(0.231) \tabularnewline
&&&&&&& \tabularnewline
Treated Munic.&153&153&153&153&153&153&153 \tabularnewline
Control Munic.&153&153&153&153&153&153&153 \tabularnewline
Mean Dep. Var.&0.555&2.124&0.087&0.059&0.031&0.012&0.669 \tabularnewline
Observations&3,366&3,366&3,366&3,366&3,366&3,366&3,366 \tabularnewline
\(R^2\)&0.273&0.549&0.277&0.547&0.453&0.196&0.362 \tabularnewline
&&&&&&& \tabularnewline
\midrule \textbf{Panel B. Other}&Kidnap.&Homicides&Theft&Mines&Forced Mig.&Clash/Att.&\textbf{\textbf{And. Index}} \tabularnewline
\midrule Ceasefire \( \times \) FARC&--0.002&--0.133***&--0.295**&--0.383***&--12.996***&--0.021***&--0.454*** \tabularnewline
&(0.003)&(0.030)&(0.137)&(0.063)&(2.117)&(0.006)&(0.095) \tabularnewline
&&&&&&& \tabularnewline
Treated Munic.&153&153&153&153&153&153&153 \tabularnewline
Control Munic.&153&153&153&153&153&153&153 \tabularnewline
Mean Dep. Var.&0.013&0.376&0.817&0.370&17.196&0.031&0.161 \tabularnewline
Observations&3,366&3,366&3,366&3,366&3,366&3,060&3,366 \tabularnewline
\(R^2\)&0.156&0.537&0.722&0.625&0.482&0.329&0.483 \tabularnewline
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Peace Dividends: The Economic Effects of Colombia's Peace Agreement" by Miguel Fajardo-Steinhäuser, arXiv:2301.01843v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Monotone Curve Estimation via Convex Duality" by Tongseok Lim, Kyeongsik Nam, and Jinwon Sohn, arXiv:2501.06975v2, 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|ccc}
 \thead{SR quality\\(\# of slices)} & Baseline U-Net & TopoCP & Equal \\
 \hline
 Acceptable (50) & 12\% & \textbf{84\%} & 4\% \\
 Excellent (80) & 17\% & \textbf{79\%} & 4\% \\
 All (130) & 15\% & \textbf{81\%} & 4\%
\end{tabular}
\caption{Experts' qualitative evaluation results in the comparison of Baseline U-Net and TopoCP automatic segmentations.}
\end{table}
\end{document}

eess

Priscille de Dumast
Hamza Kebiri
Chirine Atat
Vincent Dunet
Mériam Koob
Meritxell Bach Cuadra

"Segmentation of the cortical plate in fetal brain MRI with a topological loss" by Priscille de Dumast, Hamza Kebiri, Chirine Atat, Vincent Dunet, Mériam Koob, and Meritxell Bach Cuadra, arXiv:2010.12391v2, 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{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{SCC comparison in IEEE 118-bus system}
\begin{tabular}{c|c||c|c|c}
 \toprule
 \multirow{2}{4.2em}{\textbf{\,\,\,\; SCC\\Constraint} } & \multirow{2}{4em}{\textbf{\quad PE\\Capacity} }& \multirow{2}{4em}{\textbf{ \, SCC\\Violation} } & \multirow{2}{2.8em}{\textbf{ Cost}\\$[\mathrm{k\pounds/h}]$} & \multirow{2}{2.8em}{\textbf{Time}$\,[\mathrm{s}]\,$} \\
 & & & &\\
 \cline{1-5}
 \multirow{2}{2.8em}{w.o.} & $P^C$ & 32.21\% & $104.04$ & $2762.17$ \\
 \cline{2-5}
 & $1.5 P^C$ & $10.22\%$ & $104.07$ & $2859.72$\\
 \cline{1-5}
 \multirow{2}{2.8em}{with} & $P^C$ & $0$ & $107.44$ & $4766.57$ \\
 \cline{2-5}
 & $1.5P^C$ & $0$ & $105.20$ & $3562.02$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Accuracy comparison across different methods on MRPC under three ranks, i.e. $r=8\,,32\,,128$. LoRA-One (-) stands for training with AdamW without preconditioning under initialization by line 1-8 in .}
\begin{tabular}{cccccc}
\toprule
\textbf{Rank} & LoRA-One & LoRA-One (-) & Spectral (-) & LoRA-GA (-) & LoRA-GA (+) \\
\midrule
8 & 86.77$_{\pm 0.53}$ & \textbf{87.50}$_{\pm 0.60}$ &86.19$_{\pm 0.42}$& 85.29$_{\pm 0.24}$& 85.87$_{\pm 0.31}$\\
32 & \textbf{87.34}$_{\pm 0.31}$ & 87.34$_{\pm 0.42}$ &86.02$_{\pm 0.20}$& 86.36$_{\pm 0.42}$ &85.78$_{\pm 0.20}$\\
128 & \textbf{88.40}$_{\pm 0.70}$ & 87.26$_{\pm 0.20}$ &86.03$_{\pm 0.20}$& 85.46$_{\pm 0.23}$ &87.01$_{\pm 0.35}$\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"One-step full gradient suffices for low-rank fine-tuning, provably and efficiently" by Yuanhe Zhang, Fanghui Liu, and Yudong Chen, arXiv:2502.01235v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{rotating}
\usepackage{graphicx}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{r|cccccc}
 & \rotatebox{90}{Dominant Pres.} & \rotatebox{90}{Clone Inv.} & \rotatebox{90}{Mixture Inv.} & \rotatebox{90}{Offset Inv.} & \rotatebox{90}{N-Player} & \rotatebox{90}{General-Sum} \\ \hline
 Elo & \checkmark & & & & & \\
 Uniform Averaging & \checkmark & & & & \checkmark & \checkmark \\
 Payoff Rating & \checkmark & & & & \checkmark & \checkmark \\
 $\sigma$-Rank & \checkmark & & & & \checkmark & \checkmark \\
 Nash Averaging & \checkmark & \checkmark & \checkmark & & \\ \hline
 Deviation Rating & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark
 \end{tabular}
\caption{Comparison of rating methods.}
\end{table}
\end{document}

stat

Luke Marris
Siqi Liu
Ian Gemp
Georgios Piliouras
Marc Lanctot

"Deviation Ratings: A General, Clone-Invariant Rating Method" by Luke Marris, Siqi Liu, Ian Gemp, Georgios Piliouras, and Marc Lanctot, arXiv:2502.11645v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Variations in the similarity of the last smelly words in the automatically created dictionary}
\begin{tabular}{lll}
 \hline
 Domains & The similarity of the last smelly word & Absolute change in similarity value \\
 \hline
 \rowcolor[rgb]{0.886,0.937,0.851} All domains & 0.5943 & 0 \\
 All domains except SS & 0.5943 & 0 \\
 All domains except Lw & 0.5943 & 0 \\
 All domains except Ec & 0.6032 & 0.0089 \\
 All domains except Ci & 0.5984 & 0.0041 \\
 All domains except At & 0.6050 & 0.0107 \\
 All domains except Li & 0.5983 & 0.0040 \\
 All domains except EE & 0.5867 & 0.0076 \\
 All domains except ME & 0.5810 & 0.0133 \\
 All domains except Sp & 0.5994 & 0.0051 \\
 All domains except Me & 0.5913 & 0.0030 \\
 Average & -- & 0.0057 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

"Natural Language Requirements Testability Measurement Based on Requirement Smells" by Morteza Zakeri-Nasrabadi and Saeed Parsa, arXiv:2403.17479v1, 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{Characteristics of the study population in SHHS 1.}
\begin{tabular}{ll}
\hline
 & SHHS 1 (min, max) \\
\hline
Total subjects & $5080$ \\
Age & $63.1 \pm 11.2 \quad (39.0,\, 90.0)$ \\
Percentage of woman & $52.3\%$ \\
Epworth sleepiness scale & $7.8 \pm 4.4 \quad (0.0,\, 24.0)$ \\
Arousal index(/hr) & $19.2 \pm 10.7 \quad (0.0,\, 110.4)$ \\
Apnea/hypopnea Index (/hr) & $9.6 \pm 12.7 \quad (0.0,\, 115.8)$ \\
Total sleep time (min) & $587.7 \pm 107.6 \quad (35.0,\, 858.0)$ \\
Time awake / Time sleeping & $0.742$ \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Ramiro Casal
Leandro E. Di Persia
Gastón Schlotthauer

"Temporal convolutional networks and transformers for classifying the sleep stage in awake or asleep using pulse oximetry signals" by Ramiro Casal, Leandro E. Di Persia, and Gastón Schlotthauer, arXiv:2102.03352v1, 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{Result of factors under different scales}
\begin{tabular}{lccccccc}
\toprule
Strategy & AR & AER & $\sigma_p$ & WR & SR & Alpha & VaR\\
QF\_20\% & 17.35\% & 19.43\% & 0.162 & 57.8\% & 0.915 & 0.162 & 2.81\%\\
QF\_10\% & 13.12\% & 17.73 & 0.159 & \textbf{61.8\%} & 0.574 & 0.128 & 2.023\% \\
QF\_5\% & 12.59\% & 16.02\% & \textbf{0.136} & 61\% & 0.63 & 0.117 & \textbf{2.015\%} \\
QF\_1\% & \textbf{24.71\%} & \textbf{35.74\%} & 0.214 & 53.3\% & \textbf{0.967} & \textbf{0.249} & 3.048\% \\
\hline \\
CSI300 & 1.77\% & \textbackslash & 0.197 & \textbackslash & -0.015 & \textbackslash & 3.19\% \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Zhaofeng Zhang
Banghao Chen
Shengxin Zhu
Nicolas Langrené

"Quantformer: from attention to profit with a quantitative transformer trading strategy" by Zhaofeng Zhang, Banghao Chen, Shengxin Zhu, and Nicolas Langrené, arXiv:2404.00424v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Convex optimization over a probability simplex" by James Chok and Geoffrey M. Vasil, arXiv:2305.09046v2, 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}{lcc}
		\toprule
		Framework & Aggregation(s) & ITR (bit/min)\\
		\midrule
		MFF & Choquet Integral & 901.40\\
		EMF & Choquet, OWA$_3$ & 1710.74 \\
		\bottomrule
	\end{tabular}
\caption{ITR comparison table.}
\end{table}
\end{document}

eess

Javier Fumanal-Idocin
Yu-Kai Wang
Chin-Teng Lin
Javier Fernández
Jose Antonio Sanz
Humberto Bustince

"Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions" by Javier Fumanal-Idocin, Yu-Kai Wang, Chin-Teng Lin, Javier Fernández, Jose Antonio Sanz, and Humberto Bustince, arXiv:2101.06968v2, 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{Cost to compute the solution to Burgers' equation with exponential initial data at the final time using the ENN method and the WENO scheme}
\begin{tabular}{|c|c|c|}
\hline
Method & \# Mesh points & \# Time steps \\ \hline
ENN & $<83$ & $418$ \\ \hline
WENO & $2000$ & $5000$ \\ \hline
\end{tabular}
\end{table}
\end{document}

math

"Evolving Neural Network (ENN) Method for One-Dimensional Scalar Hyperbolic Conservation Laws: I Linear and Quadratic Fluxes" by Zhiqiang Cai and Brooke Hejnal, arXiv:2312.06919v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yaopei Zeng
Lei Liu
Shaoguo Liu
Hongjian Dou
Baoyuan Wu
Li Liu

"Reliable Imputed-Sample Assisted Vertical Federated Learning" by Yaopei Zeng, Lei Liu, Shaoguo Liu, Hongjian Dou, Baoyuan Wu, and Li Liu, arXiv:2501.06429v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Policing and crime within time of day}
\begin{tabular}{lcccccc}
					\\\hline
					\hline \\
					& 	\multicolumn{2}{c}{Murder} & 	\multicolumn{2}{c}{Robbery} & 	\multicolumn{2}{c}{Blitz (Hours)} \\
					\hline \\
					& Total & Mean & Total & Mean & Total & Mean \\

					Dawn (12:00 am to 5:59 am) & 645 & 0.00043 & 5,983 & 0.00324 & 3,332 & 0.0018 \\ %&

					Morning (6:00 am to 11:59 am) & 478 & 0.00035 & 15,927 & 0.00862 & 1,962 & 0.0011 \\ %&

					Afternoon (12:00 pm to 5:59 pm) & 788 & 0.00026 & 21,803 & 0.0118 & 2,489 & 0.0014 \\ %&

					Night (6:00 pm to 11:59 pm) & 1,472 & 0.00080 & 22,147 & 0.0120 & 13,014 & 0.0071 \\ %&
					\\\hline
					\hline \\
					\multicolumn{7}{l}{\footnotesize{Source: Ceará Secretary of Public Security (SSPDS-CE).}}\\
				\end{tabular}
\end{table}
\end{document}

q-fin

"Is Crime Displacement Inevitable? Evidence from Police Crackdowns in Fortaleza, Brazil" by José Raimundo Carvalho and Marcelino Guerra, arXiv:2503.13571v1, 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{Total Fertility Rate, 2020 (ISTAT, UN)}
\begin{tabular}{|c|c|c|}
\hline
\textbf{TFR 2020 } & \textbf{Italy} & \textbf{Albania} \\
\hline
Italian & 1.2 & - \\
\hline
Albanian & 2.1 & 1.4 \\
\hline
overall migrant population & 1.8 & - \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Text Data Analysis of Maternal Narratives: Albanian Women in Italy" by Eleonora Miaci and Emiliano Seri, arXiv:2501.05181v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"MGCNN: a learnable multigrid solver for sparse linear systems from PDEs on structured grids" by Yan Xie, Minrui Lv, and Chensong Zhang, arXiv:2312.11093v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Dataset statistics of all the LT datasets used in our experiments. The dataset source denotes the original non-LT dataset from which the LT datasets were derived. The imbalance ratio for the large scale datasets are not explicitly calculated.}
\begin{tabular}{clccc}
 \toprule
 \multicolumn{2}{c}{Dataset Source} & Imbalance Ratio & \# Train Samples & \# Test Samples \\ \cmidrule(lr){1-2} \cmidrule(lr){3-5}
 \multirow{12}{*}{Small Scale} & Caltech101 & 10 & 1151 & 6084\\
 & CIFAR-10 & 10 & 20431 & 10000\\
 & CIFAR-10 & 100 & 12406 & 10000\\
 & CIFAR-100 & 10 & 19573 & 10000\\
 & CIFAR-100 & 100 & 10847 & 10000\\
 & CUB-200-2011 & 10 & 2253 & 5794\\
 & Stanford Dogs & 10 & 4646 & 8580\\
 & Stanford Cars & 10 & 3666 & 8041\\
 & DTD & 10 & 1457 & 1880\\
 & FGVC-Aircraft & 10 & 2564 & 3333\\
 & Flowers-102 & 10 & 753 & 1020\\
 & Fruits-360 & 100 & 1770 & 3110\\ \cmidrule(lr){1-2} \cmidrule(lr){3-5}
 \multirow{3}{*}{Large Scale} & Places-LT & N/A & 62500 & 7300\\
 & ImageNet-LT & N/A & 115846 & 50000\\
 & iNat-LT & N/A & 437513 & 24426\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Jihun Kim
Dahyun Kim
Hyungrok Jung
Taeil Oh
Jonghyun Choi

"Long-Tailed Recognition on Binary Networks by Calibrating A Pre-trained Model" by Jihun Kim, Dahyun Kim, Hyungrok Jung, Taeil Oh, and Jonghyun Choi, arXiv:2404.00285v1, 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{Quantitative comparison on UCCS dataset using UIQM, UCIQE, and NIQE metrics.}
\begin{tabular}{|l|l|l|l|}
\hline
 & UIQM$\uparrow$ & UCIQE$\uparrow$ & NIQE$\downarrow$ \\ \hline
Raw\_Images & 2.29 & 0.410 & 4.57 \\ \hline
Fusion & 2.99 & 0.476 & 4.38 \\ \hline
IBLA & 2.36 & 0.480 & 4.29 \\ \hline
U-Transformer& 3.02 & 0.539 & \textbf{3.96}\\ \hline
Funie-GAN & 3.05 & \underline{0.558} & 4.37 \\ \hline
Water-Net & \underline{3.13} & 0.550 & 4.07 \\ \hline
UWCNN & 2.78 & 0.455 & 4.12 \\ \hline
Ours & \textbf{3.17} & \textbf{0.568} & \underline{4.03} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Ahsan Baidar Bakht
Zikai Jia
Muhayy ud Din
Waseem Akram
Lyes Saad Soud
Lakmal Seneviratne
Defu Lin
Shaoming He
Irfan Hussain

"MuLA-GAN: Multi-Level Attention GAN for Enhanced Underwater Visibility" by Ahsan Baidar Bakht, Zikai Jia, Muhayy ud Din, Waseem Akram, Lyes Saad Soud, Lakmal Seneviratne, Defu Lin, Shaoming He, and Irfan Hussain, arXiv:2312.15633v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Counterfactual Fairness through Transforming Data Orthogonal to Bias" by Shuyi Chen and Shixiang Zhu, arXiv:2403.17852v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c}
\toprule
 & \textbf{Independence} & \textbf{Tilde Relation} \\
\midrule
$P(A)=1$ & $P(B\mid A)=P(B)$ & $P(B\mid A)=P(B)$ \\
$P(A)=\tfrac12$ & $P(B\mid A)=P(B)$ & $P(B\mid A)=P(B)$ \\
$P(A)=0,\;0<P(B)<1$ & $P(B\mid A)=P(B)$ & $P(B\mid A)=P(\neg B)$ \\
$P(A)=0,\;P(B)\in\{0,1\}$ & $P(B\mid A)=P(B)$ & $P(B\mid A)$ \text{ unconstrained} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

The Mathematics of Questions

"The Mathematics of Questions" by R. O'Flanagan, arXiv:2503.18992v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Pengfei Guo
Puyang Wang
Jinyuan Zhou
Shanshan Jiang
Vishal M. Patel

"Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning" by Pengfei Guo, Puyang Wang, Jinyuan Zhou, Shanshan Jiang, and Vishal M. Patel, arXiv:2103.02148v4, 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{SA-WER (\%) on clean simulated evaluation set.}
\begin{tabular}{l|c|cc|cc}
\toprule
Decoding method & Segment &\multicolumn{2}{c|}{Dev} & \multicolumn{2}{c}{Test} \\
& overlap & short & long & short & long \\
\midrule
 No segmentation &- & 14.2 & 17.2 & 13.1 & 21.1 \\
 Block-wise inference &0\% & 11.8 & 12.4 & 12.5 & 15.2 \\
 Overlapping inference&50\% & 12.0 & 12.8 & 12.7 & 15.2 \\
\midrule
 Stitcher (alignment-based)&50\% & 11.4 & 12.0 & 12.3 & 14.5 \\
Stitcher (serialized, WC) &50\% & 10.6 & 11.9 & 10.8 & 13.7 \\
Stitcher (serialized, WCO/E) &50\% & \textbf{10.5} & \textbf{11.5} & \textbf{10.6} & \textbf{13.4} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Xuankai Chang
Naoyuki Kanda
Yashesh Gaur
Xiaofei Wang
Zhong Meng
Takuya Yoshioka

"Hypothesis Stitcher for End-to-End Speaker-attributed ASR on Long-form Multi-talker Recordings" by Xuankai Chang, Naoyuki Kanda, Yashesh Gaur, Xiaofei Wang, Zhong Meng, and Takuya Yoshioka, arXiv:2101.01853v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Simulation Parameters}
\begin{tabular}{ccccccc}
	{Simulation length} & {$4$ [Hr]} \\\hline
 {Gyro sampling rate} & {10 [Hz]} \\
 {Gyro ARW} & {$Q_{\eta} = (3.1623 \times 10^{-7})^2 I_3$ [rad/s$^{1/2}$]} \\
 {Gyro RRW} & {$Q_{\zeta} = (3.1623 \times 10^{-10})^2 I_3$ [rad/s$^{3/2}$]} \\
 {True Initial Gyro Bias} & {$(20 , ~ 20 , ~ 20)$ [deg/hr]} \\\hline
 {Magnetometer sampling rate} & {1 [Hz]} \\
 {Magnetometer measurement noise $1 \sigma$} & {50 [nT]} \\\hline
 {Initial Attitude Covariance} & {$(10\pi/180)^2 I_3$} \\
 {Initial Bias Covariance} & {$(20\pi/(180\times 3600))^2 I_3$} \\
 {Initial Attitude Estimate} & {Randomly Sampled} \\
 {Initial Gyro Bias Estimate} & {$(0 , ~ 0 , ~ 0)$ [deg/hr]} \\\hline
 {Whitening Tolerance} & {$10^{-15}$} \\\hline
 {UT $\lambda$} & 0 \\
\end{tabular}
\end{table}
\end{document}

math

"Dynamics on Lie groups with applications to attitude estimation" by T. Forrest Kieffer and Michael Wall, arXiv:2502.19714v2, 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{Externalizing (R-scores) mean (SD) 4.03 (5.44), sex, baseline age, American Indian or Native American, Asian, Black, Hispanic/Latinx, Native Hawaiian/Pacific Islander, White, or Other Race N (\%), family income in the past 12 months, parental highest education 7.44 (2.31) and marital status 17.37 (2.47) ordinal categorical treated as continuous.}
\begin{tabular}{lll}
\hline
\textbf{Variable} & \textbf{Level} & \textbf{Value} \\
\hline
n & & 7370 \\
\hline
Externalizing Problems (Raw) (mean (SD)) & & 4.03 (5.44) \\
Sex (At Birth) (\%) & Male & 3861 (52.4) \\
 & Female & 3509 (47.6) \\
Age (Months) (mean (SD)) & & 119.07 (7.49) \\ \hline
American Indian or Native American N (\%) % & No & 1510 (20.5) \\
 & & 5860 (79.5) \\
Asian N (\%) % & No & 6123 (83.1) \\
 & & 1247 (16.9) \\
Black N (\%) % & No & 7145 (96.9) \\
 & & 225 (3.1) \\
Hispanic/Latinx N (\%) % & No & 7323 (99.4) \\
 & & 47 (0.6) \\
Native Hawaiian or Pacific Islander N (\%) % & No & 6895 (93.6) \\
 & & 475 (6.4) \\
Other Race N (\%) % & No & 6931 (94.0) \\
 & & 439 (6.0) \\
White N (\%) % & No & 6006 (81.5) \\
 & & 1364 (18.5) \\ \hline
Total Family Income (Past 12 Months) (mean (SD)) & & 7.44 (2.31) \\
Highest Parent Education Completed (mean (SD)) & & 17.37 (2.47) \\ \hline
Parent Marital Status (\%) & Married & 5446 (73.9) \\
 & Widowed & 43 (0.6) \\
 & Divorced & 576 (7.8) \\
 & Separated & 217 (2.9) \\
 & Never married & 708 (9.6) \\
 & Living with partner & 380 (5.2) \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Aidan Neher
Apostolos Stamenos
Mark Fiecas
Sandra Safo
Thierry Chekouo

"A Bayesian Integrative Mixed Modeling Framework for Analysis of the Adolescent Brain and Cognitive Development Study" by Aidan Neher, Apostolos Stamenos, Mark Fiecas, Sandra Safo, and Thierry Chekouo, arXiv:2501.17705v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
 \hline
 \bfseries Method & \bfseries $\lceil DICE \rceil$\\
 \hline
 8 different raster orderings & 0.895 \\
 8 different S-curve orderings & 0.883 \\
 8 different Hilbert curve orderings & 0.890 \\
 8 different random orderings & 0.843 \\
 \hline
 32 different orderings & \bfseries 0.899 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Walter Hugo Lopez Pinaya
Petru-Daniel Tudosiu
Robert Gray
Geraint Rees
Parashkev Nachev
Sebastien Ourselin
M. Jorge Cardoso

"Unsupervised Brain Anomaly Detection and Segmentation with Transformers" by Walter Hugo Lopez Pinaya, Petru-Daniel Tudosiu, Robert Gray, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, and M. Jorge Cardoso, arXiv:2102.11650v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of control effort for continuous action space algorithms{batch exothermic process} }
\begin{tabular}{lllll}
\hline
Algorithm & Control Effort
\\
\hline
TATD3 & 92532.72 \\
TD3 & 92720.78 \\
DDPG & 92614.60 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Tanuja Joshi
Shikhar Makker
Hariprasad Kodamana
Harikumar Kandath

"Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control" by Tanuja Joshi, Shikhar Makker, Hariprasad Kodamana, and Harikumar Kandath, arXiv:2102.13012v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Machine Proofs for Adams Differentials and Extension Problems among CW Spectra" by Weinan Lin, Guozhen Wang, and Zhouli Xu, arXiv:2412.10876v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.