\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Group Testing as a PAC Learning Problem}
\begin{tabular}{|c|c|c|c|}
		\hline
		& \textbf{Target} & \textbf{Training Examples} & \textbf{Hypothesis} \\
		\hline
		\textcolor{black}{\textbf{Group testing case}} & $x(\cdot)$ & $(\textbf{a}_{i} \overset{\text{i.i.d.}}{\sim} \mathcal{D}, y_i = x(\mathbf{a}_i))$ & $\hat{x}(\cdot)$ \\
		\hline
		\textcolor{black}{\textbf{PAC learning case}} & $f(\cdot)$ & $(\textbf{a}_i \overset{\text{i.i.d.}}{\sim} \mathcal{D}, f(\mathbf{a}_i))$ & $h(\cdot)$ \\
		\hline
	\end{tabular}
\end{table}
\end{document}

stat

"A Probably Approximately Correct Analysis of Group Testing Algorithms" by Sameera Bharadwaja H. and Chandra R. Murthy, arXiv:2412.00466v1, 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{Quantitative comparison of different denoising methods on DSC (\%) for downstream multi-organ segmentation tasks.}
\begin{tabular}{l|cccccccc}
\hline
Noise level & LDCT & RED-CNN & DU-GAN & SeD & CTformer & CoreDiff & ASCON & ALDEN-DINOv2 \\ \hline
Low & 87.54 & 88.22 & 88.74 & 88.68 & 88.53 & 89.06 & 88.94 & 89.20 \\
High & 75.74 & 78.14 & 78.01 & 79.37 & 78.96 & 79.99 & 79.60 & 81.06 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Runze Wang
Zeli Chen
Zhiyun Song
Wei Fang
Jiajin Zhang
Danyang Tu
Yuxing Tang
Minfeng Xu
Xianghua Ye
Le Lu
Dakai Jin

"Anatomy-Aware Low-Dose CT Denoising via Pretrained Vision Models and Semantic-Guided Contrastive Learning" by Runze Wang, Zeli Chen, Zhiyun Song, Wei Fang, Jiajin Zhang, Danyang Tu, Yuxing Tang, Minfeng Xu, Xianghua Ye, Le Lu, and Dakai Jin, arXiv:2508.07788v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|r}
 Model & \#Params \\ \hline
 BENDR & 6,459,257 \\
 EGA-GCN & 998,432 \\
 EGA-GraphSAGE\ & 1,981,472 \\
 EGA-GAT & 1,029,216 \\ \hline
 \end{tabular}
\caption{Number of Trainable Parameters}
\end{table}
\end{document}

eess

Toyotaro Suzumura
Hiroki Kanezashi
Shotaro Akahori

"Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning" by Toyotaro Suzumura, Hiroki Kanezashi, and Shotaro Akahori, arXiv:2411.16155v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{CVA Exposure Calculations}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|l|}
 \hline
\textit{Term} & \text{CVA}\textsuperscript{U} & \text{DVA}\textsuperscript{U} \\
 \hline
$\text{EE}(t)$ & $\mathbb{E} [ V^+(t) ]$ & $\mathbb{E} [ V^-(t) ]$ \\
\hline
$\text{PFE}_\alpha(t)$ & $\inf \; \{ x \in \mathbb{R} : \alpha \leq F_{V^+(t)}(x) \}$ & $\inf \; \{ x \in \mathbb{R} : \alpha \leq F_{V^-(t)}(x) \}$ \\
 \hline
$\text{EPE}(t)$ & $\frac{1}{T} \int_0^T \text{EE}(t) dt$ & $\frac{1}{T} \int_0^T \text{EE}(t) dt$ \\
\hline
$\text{EffEE}(t)$ & $\max \; \{ \text{EE}(\tau) : \tau \in [0,t] \}$ & $\max \; \{ \text{EE}(\tau) : \tau \in [0,t] \}$ \\
\hline
$\text{EffEPE}(t)$ & $\frac{1}{T} \int_0^T \text{EffEE}(t) dt$ & $\frac{1}{T} \int_0^T \text{EffEE}(t) dt$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk" by Derek Singh and Shuzhong Zhang, arXiv:1910.01781v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Maximum $\log_q$ Likelihood Estimation for Parameters of Weibull Distribution and Properties: Monte Carlo Simulation" by Mehmet Niyazi Çankaya and Roberto Vila, arXiv:2012.08294v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Training data statistics. For CT and MRI the number of slices per image vary in the given interval.}
\begin{tabular}{l|c|cccccc}
\hline
 & Parameter & Resolution (x/y/z) [mm] & Windowing & Volumes & Volume size & Arms & Age\\
\hline
CT Patient & 100-130\,kVp & 1/1/2 & [-1024, 1500]\,HU & 22 & $512\times512\times[52, 151]$ & no & 66 $\pm$ 9 \\
CT Phantom & 90-120\,kVp & 1/1/2 & [-1024, 1500]\,HU & 56 & $512\times512\times[80, 124]$ & no & 51 $\pm$ 14 \\
CBCT Patient & 93-124.7\,kVp & 0.486/0.486/0.486 & [-1024, 2000]\,HU & 24 & $512\times512\times386$ & no & 67 $\pm$ 10 \\
CBCT Phantom & 90-120\,kVp & 0.486/0.486/0.486 & [-1024, 2000]\,HU & 56 & $512\times512\times386$ & no & 51 $\pm$ 14 \\
MRI Patient & 3\,T & 1/1/3 & [10th , 90th]\,percentile & 24 & $330\times450\times[48, 93]$ & yes & 67 $\pm$ 10 \\
MRI Phantom & 3\,T & 1/1/3 & [10th , 90th]\,percentile & 56 & $330\times450\times[59, 88]$ & yes & 51 $\pm$ 14 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Dominik F. Bauer
Tom Russ
Barbara I. Waldkirch
Christian Tönnes
William P. Segars
Lothar R. Schad
Frank G. Zöllner
Alena-Kathrin Golla

"Generation of annotated multimodal ground truth datasets for abdominal medical image registration" by Dominik F. Bauer, Tom Russ, Barbara I. Waldkirch, Christian Tönnes, William P. Segars, Lothar R. Schad, Frank G. Zöllner, and Alena-Kathrin Golla, arXiv:2012.01582v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Siyu Liu
Xunyuan Yin
Jinfeng Liu
Feng Ding

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


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l||r|r|r|}
		\hline
		\multicolumn{4}{|l|}{\cellcolor[gray]{0.95} \vphantom{\Big|} Comparison using different settings for density estimation.}\\ \hline\hline
		\textbf{Method} & Value & \textbf{Bias} & \textbf{Std.~Dev.} \\ \hline
\hline
Finite Difference (0.05) & 0.7359 & -0.0002 & 0.0110 \\ \hline
Stoch.~AD (0.05) & 0.7359 & -0.0001 & 0.0112 \\ \hline
Regression on density (0.05, 0.75) & 0.7352 & -0.0008 & 0.0036 \\ \hline
Regression of density (0.05,0.5) & 0.7357 & -0.0004 & 0.0046 \\ \hline
Regression on distribution (0.05, 0.75) & 0.7346 & -0.0014 & 0.0028 \\ \hline
Regression of distribution (0.05, 0.5) & 0.7355 & -0.0006 & 0.0036 \\ \hline
Likelihood Ratio & 0.7361 & 0.0000 & 0.0025 \\ \hline
Analytic & 0.7361 & 0.0000 & 0.0000 \\ \hline
	\end{tabular}
\end{adjustbox}
\caption{Results from 10000 Monte-Carlo experiments, each using 200000 paths. The width parameter for the conditional expectation was $w=0.05$ in all tests. We compare different settings for the estimation of the density. The first value in the bracket gives the value of $w$, the second given the value for the width of the regression interval $w_{\phi}$, where applicable.}
\end{table}
\end{document}

q-fin

"Stochastic Algorithmic Differentiation of (Expectations of) Discontinuous Functions (Indicator Functions)" by Christian P. Fries, arXiv:1811.05741v5, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets" by Shuo Sun, Molei Qin, Xinrun Wang, and Bo An, arXiv:2302.00586v2, 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}{cccccccc}
\hline
minute & $1$ & $2$ & $\ldots$ & $T_2$ & $T_2+1$ & $\ldots$ & $T_1$\\
\hline
fight & $p$ & $p\left(1-\frac{m}{N}\right)$ & $\ldots$ & $p\left[1-\frac{(T_2-1)m}{N}\right]$ & $1-\frac{T_2m}{N}$ & $\ldots$ & $1-\frac{T_2m}{N}$\\
hide & $1-\frac{m}{N}$ & $1-\frac{2m}{N}$ & $\ldots$ & $1-\frac{T_2m}{N}$ & $1-\frac{T_2m}{N}$ & $\ldots$ & $1-\frac{T_2m}{N}$ \\
\hline
\end{tabular}
\caption{Survival probabilities of the civilian of focus in a closed arena when $T_1>T_2$, where the $i$-th cell of the ``fight'' row is the survival probability of the civilian when he hides for the first $(i-1)$ minutes and starts to fight back at the beginning of the $i$-th minute.}
\end{table}
\end{document}

stat

"Analytic models for active shooter incidents with civilian resistance" by Liang Hong, arXiv:2310.17111v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Hong Zhang
Qing Li
Devan V. Mehrotra
Judong Shen

"CauchyCP: a powerful test under non-proportional hazards using Cauchy combination of change-point Cox regressions" by Hong Zhang, Qing Li, Devan V. Mehrotra, and Judong Shen, arXiv:2101.00059v1, 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
\begin{tabular}{ccccccccc}
			\toprule
			 & 0 & 1 & 5 & 15 & 50 & 100 & 200 \\
			\midrule
			\midrule
			\multirow{1}{*}{SIDD} & 39.37 & 39.33 & 39.17 & 39.08 & 39.11 & 39.12 & 39.05 \\
			\midrule
			\multirow{1}{*}{OOD avg} & 38.66 & 39.24 & 39.66 & 39.77 & 39.80 & 39.83 & 39.70 \\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

eess

Inju Ha
Donghun Ryou
Seonguk Seo
Bohyung Han

"Learning to Translate Noise for Robust Image Denoising" by Inju Ha, Donghun Ryou, Seonguk Seo, and Bohyung Han, arXiv:2412.04727v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Validation Results for ResNet50 (ArSL2018 Dataset)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 \textbf{Fold} & \textbf{Precision} & \textbf{Recall} & \textbf{F1-Score} & \textbf{Accuracy} \\
 \hline
 Fold 1 & 0.994310 & 0.993700 & 0.993871 & 0.993906 \\
 Fold 2 & 0.997829 & 0.997748 & 0.997749 & 0.997813 \\
 Fold 3 & 0.997330 & 0.997307 & 0.997289 & 0.997344 \\
 Fold 4 & 0.997528 & 0.997517 & 0.997473 & 0.997500 \\
 Fold 5 & 0.998274 & 0.998274 & 0.998242 & 0.998281 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mazen Balat
Rewaa Awaad
Ahmed B. Zaky
Salah A. Aly

"Revolutionizing Communication with Deep Learning and XAI for Enhanced Arabic Sign Language Recognition" by Mazen Balat, Rewaa Awaad, Ahmed B. Zaky, and Salah A. Aly, arXiv:2501.08169v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sample Table Title}
\begin{tabular}{ll}
\textbf{PART} &\textbf{DESCRIPTION} \\
\hline \\
Dendrite &Input terminal \\
Axon &Output terminal \\
Soma &Cell body (contains cell nucleus) \\
\end{tabular}
\end{table}
\end{document}

stat

Subhojyoti Mukherjee
Ardhendu Tripathy
Robert Nowak

"Chernoff Sampling for Active Testing and Extension to Active Regression" by Subhojyoti Mukherjee, Ardhendu Tripathy, and Robert Nowak, arXiv:2012.08073v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Parameters for feature maps in Fig.~ (butterfly) and Fig.~ (leopard). Gabor filters in the first column are summed to obtain a feature image. The expression $R\geq 230$ denotes a thresholding operation of the red channel in the RGB image.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
 \toprule
 Region & $\phi_1$ & $\phi_2$ \\
 \midrule
Butterfly & $n=3,4,5,6 \colon (0, 2^{n+1/2}/256)$ & $R \geq 230$\\
& $n=4,6 \colon (\pi/4, 2^{n+1/2}/256)$ &
\\
 \midrule
 Leopard & $n=4,5,6 \colon (0, 2^{n+1/2}/256)$ & \\
 & $n=4,6 \colon (\pi/4, 2^{n+1/2}/256 )$ & \\
 & $n=5,7 \colon (\pi/2, 2^{n+1/2}/256 ) $ & \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Nadja Gruber
Johannes Schwab
Sebastien Court
Elke Gizewski
Markus Haltmeier

"Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging" by Nadja Gruber, Johannes Schwab, Sebastien Court, Elke Gizewski, and Markus Haltmeier, arXiv:2202.04680v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Experiments on training phases and training data, where v1 represents continual SFT, v2 represents SFT from PLM, v3 represents SFT incorporating evaluator LM format data, and v4 represents data filtered using the Human-AI Consistency strategy.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 \hline
 Type & GSB & ACC & ACC(t) & pearson & general \\
 \hline
baseline & 0.784 & 0.339 & 0.584 & 0.263 & 730 \\
XD-v1 & 0.910 & 0.514 & 0.755 & 0.686 & 458 \\
XD-v2 & 0.895 & 0.551 & 0.802 & 0.761 & 684 \\
XD-v3 & 0.911 & 0.593 & 0.811 & 0.765 & 653 \\
XD-v4 & \textbf{0.941} & \textbf{0.664} & \textbf{0.854} & \textbf{0.829} & 685 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zhiyuan Fan
Weinong Wang
Xing Wu
Debing Zhang

"SedarEval: Automated Evaluation using Self-Adaptive Rubrics" by Zhiyuan Fan, Weinong Wang, Xing Wu, and Debing Zhang, arXiv:2501.15595v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jacopo Rodeschini
Alessandro Fassò
Francesco Finazzi
Alessandro Fusta Moro

"Scenario analysis of livestock-related PM2.5 pollution based on a new heteroskedastic spatiotemporal model" by Jacopo Rodeschini, Alessandro Fassò, Francesco Finazzi, and Alessandro Fusta Moro, arXiv:2310.15837v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c||c}
Stratum & $\dim(U_I(\omega))$ & $\dim(W_I(\omega))$ & Fibre dimension & $\dim(p^{-1}(W_I(\omega))$ \\ \hline
$U_{12}(1,1)$ & $2$ & $2$ & $4$ & $6$ \\
$U_{12}(1,-1)$ & $2$ & $-1$ & $-1$ & $-1$ \\
$U_{2}(-1,1)$ & $2$ & $-1$ & $-1$ & $-1$ \\
$U_{2}(-1,-1)$ & $2$ & $2$ & $3$ & $5$ \\
$U_{\emptyset}$ & $4$ & $4$ & $2$ & $6$
\end{tabular}
\end{adjustbox}
\caption{Summary of dimension counts for various strata of $S$.}
\end{table}
\end{document}

math

Sparse trace tests

"Sparse trace tests" by Taylor Brysiewicz and Michael Burr, arXiv:2201.04268v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Numeric example of equations - .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|r|rrrr|rrrr|}
\hline
\textbf{Step} & \textbf{Bias} & \multicolumn{4}{c|}{\textbf{Decision Tree 1}} & \multicolumn{4}{c|}{\textbf{Decision Tree 2}} \\ \hline \hline
\rowcolor[HTML]{EFEFEF}
Original & 0.00 & 2.00 & -0.10 & 0.50 & -0.70 & -0.40 & 0.80 & -1.40 & 0.00 \\
After Eq. & -2.10 & 2.70 & 0.60 & 1.20 & 0.00 & 1.00 & 2.20 & 0.00 & 1.40 \\
\rowcolor[HTML]{EFEFEF}
After Eq. & -5.44 & 7.00 & 1.56 & 3.11 & 0.00 & 2.59 & 5.70 & 0.00 & 3.63 \\
After Eq. & -5 & 7 & 2 & 3 & 0 & 3 & 6 & 0 & 4 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"TreeLUT: An Efficient Alternative to Deep Neural Networks for Inference Acceleration Using Gradient Boosted Decision Trees" by Alireza Khataei and Kia Bazargan, arXiv:2501.01511v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Major parts of the GIS algorithm and their computational requirements }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \hline
 Part & Time complexity & Significance \\ %& $\ell_{avg}$ (without disturbance) \\
 \hline
 Cell subdivision & $O(n_c)$ & Not significant \\ %& $0.465$ \\
 Graph construction & $O(n_c \text{log }n_c + |E|)$ & Significant \\ %& $0.483$ \\
 Graph analysis & $O(|V|+|E|)$ & Not significant \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"An efficient implementation of graph-based invariant set algorithm for constrained nonlinear dynamical systems" by Benjamin Decardi-Nelsona and Jinfeng Liu, arXiv:2202.06111v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Garyfallos Konstantinoudis
Anthony Hauser
Julien Riou

"Bayesian ensemble modelling to monitor excess deaths during summer 2022 in Switzerland" by Garyfallos Konstantinoudis, Anthony Hauser, and Julien Riou, arXiv:2308.15251v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Class-Wise performance of those classes that were originally difficult to predict in Totalsegmentator Dataset. Auto Window has rendered the model’s predictions for these classes essentially viable. Gain is determined based on the superior configuration among the two Window options. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
 \hline
 Class & Metric & Instance Norm & 4 Auto Windows & 8 Auto Windows & Gain\\
 \hline
 \multirow{3}{*}{Adrenal Gland}
 & Dice & 38.33 & 87.25 & 85.67 & \textbf{+127\%}\\
 & Recall & 67.51 & 89.45 & 87.60 & \textbf{+32\%}\\
 & Precision & 27.00 & 85.15 & 83.84 & \textbf{+215\%}\\
 \hline
 \multirow{3}{*}{Iliac Artery}
 & Dice & 53.89 & 86.66 & 88.20 & \textbf{+64\%}\\
 & Recall & 75.41 & 91.79 & 92.80 & \textbf{+23\%}\\
 & Precision & 42.04 & 82.10 & 84.05 & \textbf{+100\%}\\
 \hline
 \multirow{3}{*}{Gallbladder}
 & Dice & 51.86 & 89.37 & 89.47 & \textbf{+73\%}\\
 & Recall & 73.43 & 95.53 & 94.98 & \textbf{+30\%}\\
 & Precision & 40.09 & 83.95 & 84.56 & \textbf{+111\%}\\
 \hline
 \multirow{3}{*}{Duodenum}
 & Dice & 58.20 & 89.32 & 89.63 & \textbf{+54\%}\\
 & Recall & 79.19 & 89.49 & 90.48 & \textbf{+14\%}\\
 & Precision & 46.00 & 89.15 & 88.79 & \textbf{+94\%}\\
 \hline
 \multirow{3}{*}{Pancreas}
 & Dice & 57.47 & 92.44 & 92.78 & \textbf{+61\%}\\
 & Recall & 73.18 & 93.11 & 94.42 & \textbf{+29\%}\\
 & Precision & 47.32 & 91.79 & 91.20 & \textbf{+94\%}\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Interpretable Auto Window Setting for Deep-Learning-Based CT Analysis" by Yiqin Zhang, Meiling Chen, and Zhengjie Zhang, arXiv:2501.06223v1, 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|}
\hline
\textbf{Dataset} & \textbf{Example Signals} \\
\hline
UMD & COVID-like illness symptoms, influenza-like illness symptoms, mask usage \\
\hline
CMU & sore throat, loss of smell/taste, chronic lung disease \\
\hline
OxCGRT & containment and closure policies, economic policies, health system policies \\
\hline
\end{tabular}
\caption{Example Signal Information for the Datasets}
\end{table}
\end{document}

stat

Rohan Sukumaran
Parth Patwa
T V Sethuraman
Sheshank Shankar
Rishank Kanaparti
Joseph Bae
Yash Mathur
Abhishek Singh
Ayush Chopra
Myungsun Kang
Priya Ramaswamy
Ramesh Raskar

"COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms" by Rohan Sukumaran, Parth Patwa, T V Sethuraman, Sheshank Shankar, Rishank Kanaparti, Joseph Bae, Yash Mathur, Abhishek Singh, Ayush Chopra, Myungsun Kang, Priya Ramaswamy, and Ramesh Raskar, arXiv:2101.10266v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sample table title}
\begin{tabular}{ll}
\bf PART &\bf DESCRIPTION
\\ \hline \\
Dendrite &Input terminal \\
Axon &Output terminal \\
Soma &Cell body (contains cell nucleus) \\
\end{tabular}
\end{table}
\end{document}

stat

Daniel Paleka
Abhimanyu Pallavi Sudhir
Alejandro Alvarez
Vineeth Bhat
Adam Shen
Evan Wang
Florian Tramèr

"Consistency Checks for Language Model Forecasters" by Daniel Paleka, Abhimanyu Pallavi Sudhir, Alejandro Alvarez, Vineeth Bhat, Adam Shen, Evan Wang, and Florian Tramèr, arXiv:2412.18544v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Archchana Sindhujan
Diptesh Kanojia
Constantin Orasan
Shenbin Qian

"When LLMs Struggle: Reference-less Translation Evaluation for Low-resource Languages" by Archchana Sindhujan, Diptesh Kanojia, Constantin Orasan, and Shenbin Qian, arXiv:2501.04473v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

AmirEmad Ghassami
Chang Liu
Alan Yang
David Richardson
Ilya Shpitser
Eric Tchetgen Tchetgen

"Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects" by AmirEmad Ghassami, Chang Liu, Alan Yang, David Richardson, Ilya Shpitser, and Eric Tchetgen Tchetgen, arXiv:2201.10743v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
\cline{2-5}
 & \multicolumn{2}{c|}{FOA} & \multicolumn{2}{c|}{Mic-array} \\
\hline
Model & ER & F1 & ER & F1 \\
\hline
SED-M & 0.302 & 80.0 & 0.302 & 79.2 \\
\hline
SED-T & \bf{0.263} & \bf{82.1} & \bf{0.266} & \bf{82.1} \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Thi Ngoc Tho Nguyen
Ngoc Khanh Nguyen
Huy Phan
Lam Pham
Kenneth Ooi
Douglas L. Jones
Woon-Seng Gan

"A General Network Architecture for Sound Event Localization and Detection Using Transfer Learning and Recurrent Neural Network" by Thi Ngoc Tho Nguyen, Ngoc Khanh Nguyen, Huy Phan, Lam Pham, Kenneth Ooi, Douglas L. Jones, and Woon-Seng Gan, arXiv:2011.07859v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
\textbf{Year} & \textbf{Date} & \textbf{\#Pages} \\ \hline
2006 & 30/11/2006 & $\sim$ 1.4 M	\\ \hline
2008 & 03/01/2008 & $\sim$ 1.9 M	\\ \hline
2010 & 15/03/2010	& $\sim$ 2.8 M	\\ \hline
2012 & 02/09/2012 & $\sim$ 3.5 M	\\ \hline
2014 & 06/11/2014 & $\sim$ 4.1 M	\\ \hline
2016 & 01/07/2016 & $\sim$ 5.1 M	\\ \hline
\end{tabular}
\end{table}
\end{document}

cs

"On the Temporality of Priors in Entity Linking" by Renato Stoffalette Joao, arXiv:2101.05593v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Attack success rates of NAEs against white-box, black-box models, and various defense strategies. The best result is bolded.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|c}
 \toprule
 Method & Res50 & Inc-v3 & Vit & Mix-B\\
 \midrule
 DiffAttack & 94.3 & 70.8 & \textbf{50.4} & \textbf{61.1} \\
 AdvDiff & \textbf{99.8} & 36.52 & 12.83 & 29.24 \\
 VENOM & 98.7 & 45.8 & 17.20 & 36.00 \\
 VENOM-\textit{u} & 98.7 & \textbf{ 71.0} & 40.6 & 59.4 \\
 \midrule
 Method & Res50-adv & NRP &RS& DiffPure\\
 \midrule
 DiffAttack & 47.0 & 85.91 & 65.60 & 56.30 \\
 AdvDiff & 33.13 & 77.34 & 31.39 & 25.44 \\
 VENOM & 37.9 & 78.40 & 42.20 & 34.60 \\
 VENOM-\textit{u} & \textbf{53.3} & \textbf{90.10} & \textbf{67.90} & \textbf{60.10} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hui Kuurila-Zhang
Haoyu Chen
Guoying Zhao

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


\documentclass{article}
\usepackage{adjustbox}
\usepackage{tikz}
\usepackage{amsmath}
\usepackage{hhline}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cccccc|}
 \hline
 $H$ & & $\Bar{H}$ & & $\mathcal{I}(H)$ & Extremal Construction \\
 \hhline{|= = = = = =|}
 $K_{3}=C_{3}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (2,0) {};
 \node[scale=0.6] (n3) at (1,1) {};
 \draw (n1) -- (n2);
 \draw (n2) -- (n3);
 \draw (n1) -- (n3);
 \end{tikzpicture}
 & $\overline{K_{3}}/A_{3}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (2,0) {};
 \node[scale=0.6] (n3) at (1,1) {};
 \end{tikzpicture} & 1 & A Complete Graph \\
 $P_{3}=K_{1,2}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (2,0) {};
 \node[scale=0.6] (n3) at (1,1) {};
 \draw (n1) -- (n2);
 \draw (n1) -- (n3);
 \end{tikzpicture}
 & $\overline{P_{3}}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (2,0) {};
 \node[scale=0.6] (n3) at (1,1) {};
 \draw (n2) -- (n3);
 \end{tikzpicture} & $\frac{3}{4}$ & A Complete Bipartite Graph \\
 $K_{4}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (1.5,0) {};
 \node[scale=0.6] (n3) at (1.5,1.5) {};
 \node[scale=0.6] (n4) at (0,1.5) {};
 \draw (n1) -- (n2);
 \draw (n2) -- (n3);
 \draw (n1) -- (n3);
 \draw (n1) -- (n4);
 \draw (n2) -- (n4);
 \draw (n3) -- (n4);
 \end{tikzpicture}
 & $\overline{K_{4}}/A_{4}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (1.5,0) {};
 \node[scale=0.6] (n3) at (1.5,1.5) {};
 \node[scale=0.6] (n4) at (0,1.5) {};
 \end{tikzpicture}
 & 1 & A Complete Graph \\
 $S_{4}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (1.5,0) {};
 \node[scale=0.6] (n3) at (1.5,1.5) {};
 \node[scale=0.6] (n4) at (0,1.5) {};
 \draw (n1) -- (n2);
 \draw (n1) -- (n3);
 \draw (n1) -- (n4);
 \end{tikzpicture}
 & $\overline{S_{4}}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (1.5,0) {};
 \node[scale=0.6] (n3) at (1.5,1.5) {};
 \node[scale=0.6] (n4) at (0,1.5) {};
 \draw (n2) -- (n3);
 \draw (n2) -- (n4);
 \draw (n3) -- (n4);
 \end{tikzpicture}& $\frac{1}{2}$ & A Complete Bipartite Graph \\
 $C_{4}=K_{2,2}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (1.5,0) {};
 \node[scale=0.6] (n3) at (1.5,1.5) {};
 \node[scale=0.6] (n4) at (0,1.5) {};
 \draw (n1) -- (n2);
 \draw (n2) -- (n3);
 \draw (n1) -- (n4);
 \draw (n3) -- (n4);
 \end{tikzpicture} & $\overline{C_{4}}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (1.5,0) {};
 \node[scale=0.6] (n3) at (1.5,1.5) {};
 \node[scale=0.6] (n4) at (0,1.5) {};
 \draw (n1) -- (n3);
 \draw (n2) -- (n4);
 \end{tikzpicture}& $\frac{3}{8}$ & A Complete Bipartite Graph \\
 $K_{paw}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (1.5,0) {};
 \node[scale=0.6] (n3) at (1.5,1.5) {};
 \node[scale=0.6] (n4) at (0,1.5) {};
 \draw (n1) -- (n2);
 \draw (n2) -- (n3);
 \draw (n1) -- (n3);
 \draw (n2) -- (n4);
 \end{tikzpicture}& $\overline{K_{paw}}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (1.5,0) {};
 \node[scale=0.6] (n3) at (1.5,1.5) {};
 \node[scale=0.6] (n4) at (0,1.5) {};
 \draw (n1) -- (n4);
 \draw (n3) -- (n4);
 \end{tikzpicture} & $\frac{3}{8}$ & Two Disjoint Complete Bipartite Graphs \\
 $K_{1,1,2}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (1.5,0) {};
 \node[scale=0.6] (n3) at (1.5,1.5) {};
 \node[scale=0.6] (n4) at (0,1.5) {};
 \draw (n2) -- (n3);
 \draw (n1) -- (n3);
 \draw (n1) -- (n4);
 \draw (n2) -- (n4);
 \draw (n3) -- (n4);
 \end{tikzpicture}& $\overline{K_{1,1,2}}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (1.5,0) {};
 \node[scale=0.6] (n3) at (1.5,1.5) {};
 \node[scale=0.6] (n4) at (0,1.5) {};
 \draw (n1) -- (n2);
 \end{tikzpicture}& $\frac{72}{125}$ & A Complete 5-equipartite Graph \\
 $P_{4}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (1.5,0) {};
 \node[scale=0.6] (n3) at (1.5,1.5) {};
 \node[scale=0.6] (n4) at (0,1.5) {};
 \draw (n1) -- (n2);
 \draw (n2) -- (n3);
 \draw (n1) -- (n4);
 \end{tikzpicture} & $\overline{P_{4}}$ &
 \begin{tikzpicture}
 [scale=.4,auto=left,every node/.style={circle,fill=black, minimum size=1em}]
 \node[scale=0.6] (n1) at (0,0) {};
 \node[scale=0.6] (n2) at (1.5,0) {};
 \node[scale=0.6] (n3) at (1.5,1.5) {};
 \node[scale=0.6] (n4) at (0,1.5) {};
 \draw (n1) -- (n3);
 \draw (n2) -- (n4);
 \draw (n3) -- (n4);
 \end{tikzpicture}& $?$ & Undetermined \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{The known inducibility of graphs of order 3 and 4 .}
\end{table}
\end{document}

math

"Bounds On The Inducibility Of Double Loop Graphs" by Su Yuan Chan, Kerri Morgan, and Julien Ugon, arXiv:2202.00411v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Causally Sound Priors for Binary Experiments" by Nicholas J. Irons and Carlos Cinelli, arXiv:2308.13713v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Latency ($ms$) on SENECA}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
 & \multicolumn{2}{c|}{Time-Mix} & \multicolumn{2}{c|}{Channel-Mix} & \multicolumn{2}{c|}{Overall} \\ \cline{2-7}
 & Sparse & Dense & Sparse & Dense & Sparse & Dense \\ \hline
Computation & 0.9 & 2.1 & 1.7 & 2.8 & 2.6 & 4.9 \\ \hline
Memory & 1.3 & 3.1 & 2.5 & 4.1 & 3.8 & 7.2 \\ \hline
Total & \textbf{2.2} & 5.2 & \textbf{4.2} & 6.9 & \textbf{6.4} & 12.1 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ivan Knunyants
Maryam Tavakol
Manolis Sifalakis
Yingfu Xu
Amirreza Yousefzadeh
Guangzhi Tang

"Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing" by Ivan Knunyants, Maryam Tavakol, Manolis Sifalakis, Yingfu Xu, Amirreza Yousefzadeh, and Guangzhi Tang, arXiv:2501.16337v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Konstantin Burlachenko
Samuel Horváth
Peter Richtárik

"FL_PyTorch: optimization research simulator for federated learning" by Konstantin Burlachenko, Samuel Horváth, and Peter Richtárik, arXiv:2202.03099v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Inferring Interpretable Models of Fragmentation Functions using Symbolic Regression" by Nour Makke and Sanjay Chawla, arXiv:2501.07123v1, 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{Accuracy (in \%) of logistic regression using SGD with a constant learning rate. }
\begin{tabular}{lllccc}
\hline
 Method & Dataset & Model & \!\!\!\!Epochs\!\!\!\! & $\epsilon$ & Acc. \\
\hline
DP-SGLD\!\! & CIFAR10 & Resnet18 & 30 & 1.0 & 70.3 \\
DP-SGD & CIFAR10 & Resnet18 & 30 & 1.0 & 68.0 \\
No DP & CIFAR10 & Resnet18 & 30 & - & 70.7 \\
\hline
DP-SGLD\!\! & CIFAR10 & Alexnet & 30 & 1.0 & 57.5 \\
DP-SGD & CIFAR10 & Alexnet & 30 & 1.0 & 56.4 \\
No DP & CIFAR10 & Alexnet & 30 & - & 57.7 \\
\hline
DP-SGLD\!\! & Pneumonia\!\!\!\! & Resnet18 & 50 & 1.0 & 58.8 \\
DP-SGD & Pneumonia\!\!\!\! & Resnet18 & 50 & 1.0 & 58.8 \\
No DP & Pneumonia\!\!\!\! & Resnet18 & 50 & - & 59.3 \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Théo Ryffel
Francis Bach
David Pointcheval

"Differential Privacy Guarantees for Stochastic Gradient Langevin Dynamics" by Théo Ryffel, Francis Bach, and David Pointcheval, arXiv:2201.11980v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Runzhou Mao
Juraj Fulir
Christoph Garth
Petra Gospodnetić

"Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities" by Runzhou Mao, Juraj Fulir, Christoph Garth, and Petra Gospodnetić, arXiv:2501.09579v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Md Umar Hashmi
Jonathan Cavaleiro
Lucas Pereira
Ana Bušić

"Sizing and Profitability of Energy Storage for Prosumers in Madeira, Portugal" by Md Umar Hashmi, Jonathan Cavaleiro, Lucas Pereira, and Ana Bušić, arXiv:1911.10458v1, 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{Optimal number of hidden layer neurons and relative test error (the minimum of the test error curve) for synthetic datasets with sample size $T=1200$ and input dimension $n_0=20$. For the traditional ELM the minimum is evaluated on the mean test error curve across 20 realizations and its standard deviation is reported.}
\begin{tabular}{l|cc|cc|cc}
\toprule
\multirow{2}{*}{\textbf{dataset}} & \multicolumn{2}{c|}{\textbf{A-ENR-ELM}} & \multicolumn{2}{c|}{\textbf{I-ENR-ELM}} & \multicolumn{2}{c}{\textbf{ELM}} \\
 & \textbf{n} & \textbf{value} & \textbf{n} & \textbf{value} & \textbf{n} & \textbf{value} \\
\midrule
Dataset 25 & 20 & 0.6003 & 17 & 0.5977 & 27 & 0.6083 $\pm$ 0.0032 \\
Dataset 26 & 247 & 0.3276 & 20 & 0.3304 & 30 & 0.3307 $\pm$ 0.0031 \\
Dataset 27 & 269 & 0.5687 & 16 & 0.5729 & 25 & 0.5727 $\pm$ 0.0032 \\
Dataset 28 & 397 & 0.3171 & 19 & 0.3233 & 31 & 0.3207 $\pm$ 0.0025 \\
Dataset 29 & 78 & 0.5731 & 17 & 0.5776 & 30 & 0.5839 $\pm$ 0.0048 \\
Dataset 30 & 21 & 0.3038 & 15 & 0.3001 & 34 & 0.3108 $\pm$ 0.0026 \\
Dataset 31 & 439 & 0.6399 & 15 & 0.6415 & 26 & 0.6422 $\pm$ 0.0042 \\
Dataset 32 & 40 & 0.4796 & 17 & 0.4842 & 26 & 0.4829 $\pm$ 0.0035 \\
Dataset 33 & 233 & 0.6389 & 18 & 0.6412 & 25 & 0.6412 $\pm$ 0.0030 \\
Dataset 34 & 157 & 0.3478 & 19 & 0.3502 & 33 & 0.3510 $\pm$ 0.0029 \\
Dataset 35 & 138 & 0.6113 & 18 & 0.6150 & 26 & 0.6128 $\pm$ 0.0043 \\
Dataset 36 & 362 & 0.3579 & 15 & 0.3630 & 49 & 0.3498 $\pm$ 0.0047 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Effective Non-Random Extreme Learning Machine" by Daniela De Canditiis and Fabiano Veglianti, arXiv:2411.16229v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Data statistics after the preprocessing.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \toprule
 Dataset & \#Users & \#Items & \#Interactions & Sparsity \\
 \midrule
 MovieLens 1M & 6,040 & 3,883 & 1,000,207 & 95.74\% \\
 Amazon Grocery & 21,027 & 18,857 & 358,602 & 99.91\% \\
 Steam & 38,503 & 6,267 & 1,722,038 & 99.29\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Wonbin Kweon
Sanghwan Jang
SeongKu Kang
Hwanjo Yu

"Uncertainty Quantification and Decomposition for LLM-based Recommendation" by Wonbin Kweon, Sanghwan Jang, SeongKu Kang, and Hwanjo Yu, arXiv:2501.17630v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The size of the dataset used in the experiments.}
\begin{tabular}{c|c|c|c}
	\hline
	 Image name & HSI & CASSI & RGB \\ \hline
	 CAVE & 512$\times$512$\times$31 & 512$\times$512 & 512$\times$512$\times$3 \\ \hline
	 ICVL & 512$\times$512$\times$31 & 512$\times$512 & 512$\times$512$\times$3 \\ \hline
	 RS & 400$\times$200$\times$128 & 400$\times$200 & 400$\times$200$\times$3 \\ \hline
 Bird & 1021$\times$703$\times$24 & 1021$\times$703 & 1021$\times$703$\times$3 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"Fast Hyperspectral Image Recovery via Non-iterative Fusion of Dual-Camera Compressive Hyperspectral Imaging" by Wei He, Naoto Yokoya, and Xin Yuan, arXiv:2012.15104v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 & \# parameters & Relative error (noiseless) & Relative error ($1\%$ noise)) \\ \hline
 Feed forward network & $17,712$ & $0.0157$ & $\mathbf{0.0180}$ \\ \hline
 SANN & $17,640$ & $0.0234$ & $0.0273$ \\ \hline
 C\&M & --- & $\mathbf{2.49e-07}$ & $0.0469$
 \end{tabular}
\end{adjustbox}
\caption{Results of training neural networks from data to solve the network EIT problem on $2 \times 2$ rectangular electrical networks. The last row (C\&M) shows the reconstruction algorithm from .}
\end{table}
\end{document}

cs

S. David Mis
Matti Lassas
Maarten V. de Hoop

"Semialgebraic Neural Networks: From roots to representations" by S. David Mis, Matti Lassas, and Maarten V. de Hoop, arXiv:2501.01564v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison ofEqual Treatment and Demographic Parity measured in different ways. The cases of equal Treatment violation but no demographic parity violation are highlighted in red.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|ccc}
Pair & Data & Equal treatment & \multicolumn{3}{c}{Demographic Parity} \\ \hline
 & & C2ST(AUC) & C2ST(AUC) & KS(pvalue) & Wasserstein \\ \hline
Asian-Other & Income & $0.794 \pm 0.004$ & $0.709 \pm 0.004$ & $0.338 \pm 0.007$ & $0.256 \pm 0.004$ \\
White-Other & Income & $0.734 \pm 0.002$ & $0.675 \pm 0.003$ & $0.282 \pm 0.003$ & $0.209 \pm 0.002$ \\
Other-Black & Income & $0.724 \pm 0.004$ & $0.628 \pm 0.006$ & $0.216 \pm 0.007$ & $0.143 \pm 0.004$ \\
Other-Mixed & Income & $0.707 \pm 0.005$ & $0.593 \pm 0.005$ & $0.169 \pm 0.006$ & $0.117 \pm 0.004$ \\
Asian-Black & Income & $0.664 \pm 0.008$ & $0.587 \pm 0.004$ & $0.142 \pm 0.005$ & $0.111 \pm 0.004$ \\
Asian-Mixed & Income & $0.644 \pm 0.005$ & $0.607 \pm 0.006$ & $0.159 \pm 0.008$ & $0.128 \pm 0.006$ \\
White-Mixed & Income & $0.613 \pm 0.005$ & $0.546 \pm 0.005$ & $0.082 \pm 0.004$ & \textcolor{red}{$0.058 \pm 0.002$} \\
White-Black & Income & $0.613 \pm 0.005$ & $0.57 \pm 0.007$ & $0.113 \pm 0.008$ & $0.08 \pm 0.006$ \\
Black-Mixed & Income & $0.603 \pm 0.006$ & \textcolor{red}{$0.523 \pm 0.007$} & \textcolor{red}{$0.055 \pm 0.007$} & \textcolor{red}{$0.023 \pm 0.004$} \\ \hline
Asian-Black & TravelTime & $0.677 \pm 0.052$ & \textcolor{red}{$0.502 \pm 0.011$} & \textcolor{red}{$0.021 \pm 0.009$} & \textcolor{red}{$0.01 \pm 0.003$} \\
Asian-Other & TravelTime & $0.653 \pm 0.024$ & \textcolor{red}{$0.528 \pm 0.006$} & \textcolor{red}{$0.053 \pm 0.011$} & \textcolor{red}{$0.027 \pm 0.004$} \\
Asian-Mixed & TravelTime & $0.647 \pm 0.013$ & $0.557 \pm 0.003$ & $0.096 \pm 0.004$ & \textcolor{red}{$0.045 \pm 0.002$} \\
White-Other & TravelTime & $0.636 \pm 0.02$ & $0.568 \pm 0.007$ & $0.107 \pm 0.01$ & $0.06 \pm 0.005$ \\
Other-Mixed & TravelTime & $0.618 \pm 0.017$ & \textcolor{red}{$0.546 \pm 0.011$} & $0.079 \pm 0.012$ & \textcolor{red}{$0.043 \pm 0.006$} \\
Other-Black & TravelTime & $0.615 \pm 0.021$ & \textcolor{red}{$0.526 \pm 0.011$} & \textcolor{red}{$0.049 \pm 0.014$} & \textcolor{red}{$0.026 \pm 0.006$} \\
White-Black & TravelTime & $0.599 \pm 0.006$ & $0.569 \pm 0.004$ & $0.12 \pm 0.006$ & \textcolor{red}{$0.057 \pm 0.003$} \\
Black-Mixed & TravelTime & $0.588 \pm 0.012$ & $0.557 \pm 0.012$ & $0.098 \pm 0.015$ & \textcolor{red}{$0.0557 \pm 0.001$} \\
White-Mixed & TravelTime & $0.557 \pm 0.008$ & \textcolor{red}{$0.497 \pm 0.006$} & \textcolor{red}{$0.016 \pm 0.004$} & \textcolor{red}{$0.006 \pm 0.002$} \\ \hline
Other-Black & Employment & $0.744 \pm 0.008$ & \textcolor{red}{$0.524 \pm 0.005$} & \textcolor{red}{$0.036 \pm 0.005$} & \textcolor{red}{$0.036 \pm 0.004$} \\
Asian-Other & Employment & $0.711 \pm 0.011$ & $0.557 \pm 0.003$ & $0.066 \pm 0.004$ & $0.066 \pm 0.003$ \\
White-Other & Employment & $0.695 \pm 0.007$ & \textcolor{red}{$0.524 \pm 0.003$} & $0.019 \pm 0.005$ & $0.019\pm 0.002$ \\
Other-Mixed & Employment & $0.683 \pm 0.022$ & $0.557 \pm 0.008$ & $0.083 \pm 0.005$ & $0.083 \pm 0.003$ \\
Black-Mixed & Employment & $0.678 \pm 0.028$ & \textcolor{red}{$0.534 \pm 0.007$} & \textcolor{red}{$0.049 \pm 0.007$} & \textcolor{red}{$0.048 \pm 0.004$} \\
Asian-Mixed & Employment & $0.671 \pm 0.019$ & $0.61 \pm 0.006$ & $0.0144 \pm 0.006$ & $0.145 \pm 0.004$ \\
Asian-Black & Employment & $0.655 \pm 0.021$ & $0.587 \pm 0.004$ & $0.106 \pm 0.006$ & $0.106 \pm 0.004$ \\
White-Mixed & Employment & $0.651 \pm 0.009$ & $0.581 \pm 0.006$ & $0.095 \pm 0.004$ & $0.095 \pm 0.003$ \\
White-Black & Employment & $0.619 \pm 0.011$ & \textcolor{red}{$0.544 \pm 0.004$} & \textcolor{red}{$0.049 \pm 0.003$} & \textcolor{red}{$0.049 \pm 0.002$} \\\hline
Asian-Mixed & Mobility & $0.753 \pm 0.02$ & \textcolor{red}{$0.511 \pm 0.014$} & \textcolor{red}{$0.04 \pm 0.012$} & \textcolor{red}{$0.014\pm 0.006$} \\
Other-Mixed & Mobility & $0.748 \pm 0.02$ & $0.573 \pm 0.015$ & $0.113 \pm 0.017$ & \textcolor{red}{$0.062 \pm 0.009$} \\
Asian-Other & Mobility & $0.714 \pm 0.011$ & $0.565 \pm 0.01$ & $0.114 \pm 0.011$ & \textcolor{red}{$0.054 \pm 0.005$} \\
Asian-Black & Mobility & $0.672 \pm 0.012$ & \textcolor{red}{$0.503 \pm 0.014$} & \textcolor{red}{$0.032 \pm 0.011$} & \textcolor{red}{$0.012 \pm 0.004$} \\
Other-Black & Mobility & $0.66 \pm 0.012$ & \textcolor{red}{$0.526 \pm 0.009$} & \textcolor{red}{$0.044 \pm 0.009$} & \textcolor{red}{$0.02 \pm 0.004$} \\
White-Mixed & Mobility & $0.655 \pm 0.007$ & $0.568 \pm 0.005$ & $0.105 \pm 0.007$ & \textcolor{red}{$0.044 \pm 0.003$} \\
White-Other & Mobility & $0.626 \pm 0.017$ & $0.555 \pm 0.009$ & $0.091 \pm 0.01$ & \textcolor{red}{$0.046\pm 0.005$} \\
White-Black & Mobility & $0.611 \pm 0.009$ & \textcolor{red}{$0.518 \pm 0.008$} & \textcolor{red}{$0.043 \pm 0.008$} & \textcolor{red}{$0.017 \pm 0.004$} \\
Black-Mixed & Mobility & $0.602 \pm 0.035$ & \textcolor{red}{$0.503 \pm 0.016$} & \textcolor{red}{$0.031 \pm 0.013$} & \textcolor{red}{$0.012 \pm 0.006$} \\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Model Monitoring in the Absence of Labeled Data via Feature Attributions Distributions" by Carlos Mougan, arXiv:2501.10774v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc}
 \hline
 Sample Size & Methods & CI (Coverage) & Bias & RMSE & SD \\
 \hline
n=1000 & BSIV & [-3.30, 4.34] (0.99) & 0.16 & 3.03 & 3.02 \\
 & proximal & [0.64, 0.99] (0.71) & -0.13 & 0.16 & 0.09 \\
\hline
n=2000 & BSIV & [-0.98, 2.36] (0.98) & -0.01 & 0.97 & 0.97 \\
 & proximal & [0.62, 0.88] (0.76) & -0.07 & 0.10 & 0.07 \\
 \hline
 n=4000 & BSIV & [-0.25, 1.62] (0.967) & 0.00 & 0.49 & 0.49 \\
& proximal & [0.62, 0.81] (0.87) & -0.03 & 0.06 & 0.05 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Inference results for influence-function-based estimators.}
\end{table}
\end{document}

math

"Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects" by AmirEmad Ghassami, Chang Liu, Alan Yang, David Richardson, Ilya Shpitser, and Eric Tchetgen Tchetgen, arXiv:2201.10743v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Alexander Decruyenaere
Heidelinde Dehaene
Paloma Rabaey
Christiaan Polet
Johan Decruyenaere
Thomas Demeester
Stijn Vansteelandt

"Debiasing Synthetic Data Generated by Deep Generative Models" by Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet, Johan Decruyenaere, Thomas Demeester, and Stijn Vansteelandt, arXiv:2411.04216v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Assessing Estimation Uncertainty under Model Misspecification" by Rong Li, Yichen Qin, and Yang Li, arXiv:2312.11393v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the Experimental Settings}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
		\hline
		\textbf{Model} & Llama3 8B - Instruct \\ \hline
		\textbf{Snippets} & 1,000 \\ \hline
		\textbf{PETs} & 3 \\ \hline
		\textbf{Custom Prompts} & 5 \\ \hline
		\textbf{Repetitions} & 5 \\ \hline
		\textbf{Pause} & 10 seconds \\ \hline
		\textbf{Metrics (Performance)} & Energy Consumption, Execution Time \\ \hline
		\textbf{Metrics (Accuracy)} & Exact Match, Edit Distance \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Riccardo Rubei
Aicha Moussaid
Claudio di Sipio
Davide di Ruscio

"Prompt engineering and its implications on the energy consumption of Large Language Models" by Riccardo Rubei, Aicha Moussaid, Claudio di Sipio, and Davide di Ruscio, arXiv:2501.05899v1, 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}{cccccccc}\toprule
Dependent & Care Intensity & Sex & Ethnicity & Age & $ATE_{t+3}$ & Lower CI & Upper CI \\ \midrule
HH Income & High & All & All & All & -£235 & -£375 & -£96 \\
HH Income & Medium High & All & All & All & -£78 & -£256 & £88 \\
HH Income & Medium Low & All & All & All & -£78 & -£172 & £12 \\
HH Income & Low & All & All & All & -£21 & £84 & £38 \\
HH Income & High and Medium High & Male & All & All & -£306 & -£536 & -£79 \\
HH Income & Low and Medium Low & Male & All & All & -£61 & -£144 & £22 \\
HH Income & High and Medium High & Female & All & All & -£95 & -£220 & £24 \\
HH Income & Low and Medium Low & Female & All & All & -£41 & -£99 & £26 \\
HH Income & High and Medium High & All & White & All & -£262 & -£412 & -£120 \\
HH Income & Low and Medium Low & All & White & All & -£37 & -£93 & £18 \\
HH Income & High and Medium High & All & non-White & All & -£53 & -£239 & £134 \\
HH Income & Low and Medium Low & All & non-White & All & -£62 & -£171 & £45 \\
HH Income & High and Medium High & All & All & Below 25 & -£484 & -£1295 & £101 \\
HH Income & Low and Medium Low & All & All & Below 25 & -£250 & -£720 & £165 \\
HH Income & High and Medium High & All & All & 25-65 & -£107 & -£249 & £29 \\
HH Income & Low and Medium Low & All & All & 25-65 & -£89 & -£155 & -£24 \\
HH Income & High and Medium High & All & All & 65 up & -£145 & -£241 & -£43 \\
HH Income & Low and Medium Low & All & All & 65 up & £12 & -£44 & £68 \\
Ind. Income & High & All & All & All & -£154 & -£251 & -£62 \\
Ind. Income & Medium High & All & All & All & -£112 & -£186 & -£30 \\
Ind. Income & Medium Low & All & All & All & -£87 & -£128 & -£39 \\
Ind. Income & Low & All & All & All & -£20 & -£57 & £14 \\
Ind. Income & High and Medium High & Male & All & All & -£146 & -£284 & -£8 \\
Ind. Income & Low and Medium Low & Male & All & All & -£48 & -£103 & £12 \\
Ind. Income & High and Medium High & Female & All & All & -£105 & -£160 & -£54 \\
Ind. Income & Low and Medium Low & Female & All & All & -£31 & -£60 & -£3 \\
Ind. Income & High and Medium High & All & White & All & -£132 & -£199 & -£71 \\
Ind. Income & Low and Medium Low & All & White & All & -£31 & -£62 & £3 \\
Ind. Income & High and Medium High & All & non-White & All & -£77 & -£208 & £14 \\
Ind. Income & Low and Medium Low & All & non-White & All & -£38 & -£88 & £6 \\
Ind. Income & High and Medium High & All & All & Below 25 & -£355 & -£813 & £51 \\
Ind. Income & Low and Medium Low & All & All & Below 25 & -£54 & -£205 & £101 \\
Ind. Income & High and Medium High & All & All & 25-65 & -£171 & -£254 & -£80 \\
Ind. Income & Low and Medium Low & All & All & 25-65 & -£77 & -£115 & -£37 \\
Ind. Income & High and Medium High & All & All & 65 up & -£8 & -£30 & £24 \\
Ind. Income & Low and Medium Low & All & All & 65 up & -£7 & -£20 & £7 \\
\bottomrule
\end{tabular}
\caption{\textbf{Aggregated Results for Inflation Adjusted Individual and Household Income.} This table shows the Average Treatment effect on treated at time t+3 for all the different specifications considered in the analysis. Source: UKHLS data (years 2009-2020), authors' calculations.}
\end{table}
\end{document}

stat

Maria Petrillo
Daniel Valdenegro
Charles Rahal
Yanan Zhang
Gwilym Pryce
Matthew R. Bennett

"Estimating the Cost of Informal Care with a Novel Two-Stage Approach to Individual Synthetic Control" by Maria Petrillo, Daniel Valdenegro, Charles Rahal, Yanan Zhang, Gwilym Pryce, and Matthew R. Bennett, arXiv:2411.10314v2, 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{Parameters and Values}
\begin{tabular}{|l|l|l|}
\hline\
\textbf{Parameters} & \textbf{Values}\\
\hline
Air flow rate $\dot{v}$ & [2000, 10000] $\text{m}^3/\text{h}$\\
\hline
Temperature reduction $\Delta t$ & [0\textdegree{}C, 15\textdegree{}C]\\
\hline
Mixing damper system setpoint $\alpha$ & [0, 1]\\
\hline
Cooling coil CoP $\xi$ & 2\\
\hline
Temperature threshold $t_{th}$ & [32\textdegree{}C, 35\textdegree{}C, 40\textdegree{}C] \\
\hline
RH threshold $\phi_{th}$ & [65\%, 80\%] \\
\hline
Decision period $\rho$ & 1 min\\
\hline
Exploration $\epsilon$ & [1, 0.1]\\
\hline
Replay memory and minibatch sizes ($\Omega, M$) & (50000, 64)\\
\hline
Learning step sizes ($\eta_1, \eta_2$)& (0.01, 0.001)\\
\hline
Unconstrained DRL's penalty coefficients $(\zeta_1, \zeta_2)$ & (2, 2)\\
\hline
Maximum penalty coefficients ($\lambda_1^{\max}$ and $\lambda_2^{\max}$) & (100, 100)\\
\hline
Number of training episodes and steps ($N, T$) & (3000, 1000)\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Duc Van Le
Rongrong Wang
Yingbo Liu
Rui Tan
Yew-Wah Wong
Yonggang Wen

"Deep Reinforcement Learning for Tropical Air Free-Cooled Data Center Control" by Duc Van Le, Rongrong Wang, Yingbo Liu, Rui Tan, Yew-Wah Wong, and Yonggang Wen, arXiv:2012.06834v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{soul}
\usepackage{adjustbox}
\usepackage{graphicx}
\usepackage{rotating}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllllll}
 \toprule
 \multicolumn{2}{c}{op-Negation} & {\textbf{BL}}
 & {\textbf{LE}}
 & {\textbf{RO}}
 & {\textbf{ME}}
 &{\textbf{BS}}
 & {\textbf{SP}} \\
 \hline
 \multirow{6}{*}{\rotatebox{90}{FOLIO}}
 &\textbf{BLEU} & \hl{0.68} & 0.72 & 0.80 & 0.75 & 0.82 & 0.53 \\
 &\textbf{LE} & - & \hl{0.77} & 0.85 & 0.80 & 0.86 & 0.57 \\
 &\textbf{Rouge} & - & - & \hl{0.93} & 0.88 & 0.94 & 0.65 \\
 &\textbf{Meteor} & - & - & - & \hl{0.83} & 0.90 & 0.61 \\
 &\textbf{BertScore} & - & - & - & - & \hl{0.96} & 0.67 \\
 &\textbf{Smatch++} & - & - & - & - & - & \hl{0.38} \\
 \hline
 \multirow{6}{*}{\rotatebox{90}{MALLS}}
 &\textbf{BLEU} & \hl{0.62} & 0.71 & 0.77 & 0.72 & 0.79 & 0.35 \\
 &\textbf{LE} & - & \hl{0.80} & 0.87 & 0.82 & 0.88 & 0.45 \\
 &\textbf{Rouge} & - & - & \hl{0.93} & 0.88 & 0.94 & 0.51 \\
 &\textbf{Meteor} & - & - & - & \hl{0.83} & 0.89 & 0.46 \\
 &\textbf{BertScore} & - & - & - & - & \hl{0.96} & 0.52 \\
 &\textbf{Smatch++} & - & - & - & - & - & \hl{0.09} \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Negation perturbation pairwise comparison}
\end{table}
\end{document}

cs

Ramya Keerthy Thatikonda
Wray Buntine
Ehsan Shareghi

"Assessing the Sensitivity and Alignment of FOL Closeness Metrics" by Ramya Keerthy Thatikonda, Wray Buntine, and Ehsan Shareghi, arXiv:2501.08613v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{acc@10 values for the test suites augmented by different variants of RLFDC}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
\toprule
\multirow{2}{*}{FDC Metric} & \multicolumn{4}{c}{\# Tests} \\ \cmidrule{2-5}
 & 0 & 1 & 5 & 10 \\ \midrule
$\text{RLFDC}_{split}$ & 65 & 92 & 133 & 163 \\
$\text{RLFDC}_{cover}$ & 65 & \textbf{130} & 173 & 195 \\
$\text{RLFDC}_{simpleNet}$ & 65 & 102 & 161 & 198 \\
$\text{RLFDC}_{regularQ}$ & 65 & 91 & 163 & 183 \\
$\text{RLFDC}_{weight\_0.1}$ & 65 & 106 & 164 & 195 \\
$\text{RLFDC}_{weight\_0.3}$ & 65 & 97 & 156 & 202 \\
$\text{RLFDC}_{weight\_0.5}$ & 65 & 95 & 160 & 196 \\
$\text{RLFDC}_{weight\_0.7}$ & 65 & 95 & 159 & 196 \\
$\text{RLFDC}_{weight\_0.9}$ & 65 & 93 & 158 & 191 \\ \midrule
RLFDC & 65 & 103 & \textbf{181} & \textbf{203} \\
TfD & 65 & 84 & 130 & 149 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yifan Zhao
Zeyu Sun
Guoqing Wang
Qingyuan Liang
Yakun Zhang
Yiling Lou
Dan Hao
Lu Zhang

"Automatically Learning a Precise Measurement for Fault Diagnosis Capability of Test Cases" by Yifan Zhao, Zeyu Sun, Guoqing Wang, Qingyuan Liang, Yakun Zhang, Yiling Lou, Dan Hao, and Lu Zhang, arXiv:2501.02216v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
		\textbf{Hyperparameter}	& \textbf{Value} \\
		Optimiser & Adam \\
		Batch size & $25$ \\
		Initial learning rate & $0.001$ \\
		Epochs & $100$ \\
		Dropout value & $0.3$\\
		Inflation factor & $2$\\
		Dimension of latent space & $50$ \\
		Learning rate schedule & $0.5$ [decaying*]
	\end{tabular}
\end{adjustbox}
\caption{Hyperparameters for the bouncing balls experiment. (*) The learning rate decays exponentially at steps of 30 epochs.}
\end{table}
\end{document}

math

Imbedding Deep Neural Networks

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Regression of Actual Performance over Predicted Own Performance}
\begin{tabular}{lcccccc}
 \hline
 \hline
 & & $R^{2}$ & & $F$ & & Prob $ > F$ \\\cline{3-7}
 & & 0.0501 & & 13.66 & & 0.0003 \\
 \midrule
 Actual own performance & & Coefficient & & $t$ & & $P>t$ \\
 \hline
 Predicted own performance & & 0.2201 (0.0595) & & 3.70 & & 0.000 \\
 Constant & & 9.7702 (0.8420) & & 11.60 & & 0.000 \\
 \hline
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)" by Marius Protte, arXiv:2507.15568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small \textbf{Evaluation of Computational Efficiency} We conduct a thorough analysis of the computational efficiency of our novel probabilistic approach in comparison to alternative self-supervised methods. This evaluation encompasses memory utilization and computational expenditure.}
\begin{tabular}{lccccc}
 \toprule
 Method (ViT-s) & parameters (M) & im/s & time/ 300-epochs (hr) & number of GPUs & memory (G)\\
 \midrule
DINO~ & 21 & 1007 & 72.6 & 16 & 15.4 \\
i-BOT~ & 21 & 1007 & 73.8 & 16 & 19.5 \\
\rowcolor{cyan!10}
ProSMin (ours) & 21 & 1007 & 98 & 8 & 21.1 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Amirhossein Vahidi
Simon Schoßer
Lisa Wimmer
Yawei Li
Bernd Bischl
Eyke Hüllermeier
Mina Rezaei

"Probabilistic Self-supervised Learning via Scoring Rules Minimization" by Amirhossein Vahidi, Simon Schoßer, Lisa Wimmer, Yawei Li, Bernd Bischl, Eyke Hüllermeier, and Mina Rezaei, arXiv:2309.02048v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
Criteria & \% MIPLIB & \% NN-Verification \\
\hline
Tags: \textit{feasibility}, \textit{numerics}, \textit{infeasible}, \textit{no solution} & 4.5\%, 17.5\%, 2.8\%, 0.9\% & - \\
Unbounded objective, MIPLIB solution unavailable & 0.9\%, 2.6\% & - \\
Presolve longer than 300s under default conditions & 3.6\% & 0\% \\
No feasible solution found in 600s under default conditions & 10.9\% & 0.2\% \\
Solved to optimality at root & 13.7\% & 0\% \\
Root solve longer than 20s & 22.5\% & 7.1\% \\
Too few cuts applied (< 250) & 7.5\% & 26.4\% \\
Primal-dual difference < 0.5 & 0.9\% & 40.5\% \\
LP errors & 0.5\% & 0.1\% \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Percentage of instances removed from MIPLIB and NN-Verification data sets.}
\end{table}
\end{document}

math

Mark Turner
Thorsten Koch
Felipe Serrano
Michael Winkler

"Adaptive Cut Selection in Mixed-Integer Linear Programming" by Mark Turner, Thorsten Koch, Felipe Serrano, and Michael Winkler, arXiv:2202.10962v3, 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{Initial conditions and constraints for CSTR.}
\begin{tabular}{ccccc} \hline
\textbf{Variable} & \textbf{Init. cond.} & \textbf{Min.} &\textbf{Max.} & \textbf{Unit} \\\hline
$c_\text{A}$ &1.0			&-- 	&-- &mol/L \\
$c_\text{B}$ &1.0 			&-- 	&-- &mol/L \\
$T_\text{R}$ &100.0	&100.0 	&150.0 &$^\circ\text{C}$ \\
$T_\text{K}$ &100.0 	&-- &-- &$^\circ\text{C}$ \\
$F$ &-- &5.0 &35.0 &h$^{-1}$ \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Farshud Sorourifar
Georgios Makrygirgos
Ali Mesbah
Joel A. Paulson

"A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization" by Farshud Sorourifar, Georgios Makrygirgos, Ali Mesbah, and Joel A. Paulson, arXiv:2011.11841v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Stylized network properties of interbank lending networks (ILNs) according to selected studies. For some ILN measures conflicting values are reported, in which case we separate them by a backslash. For example, low clustering coefficients are reported by~. The values for the ILN measures in bold apply to the collapsed Russian ILN (see~ and Appendix A). With the layer homogeneity we indicate whether the quoted ILN measures apply to all maturity layers of the Russian ILN.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
\toprule
ILN measure & Value & Layer homogeneity & Selected studies \\ \midrule
density & \textbf{sparse} & No & \\
degree distribution (in and out degrees) & \textbf{heavy-tailed} & Yes & \\
topological structure & scale-free / core-periphery & &/ \\
clustering coefficients & low / \textbf{high} & No & / \\
average shortest path length & \textbf{small} / "small world" & No & / \\
bank size mixing & \textbf{disassortative} & No & \\
distribution of transaction volumes & \textbf{heavy-tailed} & Yes & \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Marnix Van Soom
Milan van den Heuvel
Jan Ryckebusch
Koen Schoors

"Loan maturity aggregation in interbank lending networks obscures mesoscale structure and economic functions" by Marnix Van Soom, Milan van den Heuvel, Jan Ryckebusch, and Koen Schoors, arXiv:1906.08617v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Khandaker Tasnim Huq
Giovanni Luca Ciampaglia

"Survival of the Notable: Gender Asymmetry in Wikipedia Collective Deliberations" by Khandaker Tasnim Huq and Giovanni Luca Ciampaglia, arXiv:2411.04340v1, 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{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|r|r}
 \toprule
 \multirow{2}{*}{Instances} & \multirow{2}{*}{$e_3$} & \multicolumn{2}{c}{Oracle calls} \\ \cline{3-4}
 & & & This work \\
 \midrule
 SIKEp434 & 137 & 230 & 364 \\
 SIKEp503 & 159 & 266 & 422 \\
 SIKEp610 & 192 & 323 & 512 \\
 SIKEp751 & 239 & 398 & 630 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Faulty isogenies: a new kind of leakage

Gora Adj
Jesús-Javier Chi-Domínguez
Víctor Mateu
Francisco Rodríguez-Henríquez

"Faulty isogenies: a new kind of leakage" by Gora Adj, Jesús-Javier Chi-Domínguez, Víctor Mateu, and Francisco Rodríguez-Henríquez, arXiv:2202.04896v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Train-- and test--set accuracy attained by our NNs trained on Fashion-MNIST.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 Model & Train accuracy & Test accuracy \\
 \midrule
 Deterministic NN & 98.6\% & 88.6\%\\
 MC-Dropout & 98.7\% & 88.7\%\\
 MC-DropConnect & 98.7\% & 87.7\%\\
 Ensemble & 98.5\% & 88.3\%\\
 Flipout & 95.5\% & 85.9\%\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Can Bayesian Neural Networks Explicitly Model Input Uncertainty?" by Matias Valdenegro-Toro and Marco Zullich, arXiv:2501.08285v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the CONV, COS and SFP methods in terms of absolute error for pricing vanilla call options under the CGMY model with $K=100$. The reference values are $19.812948843118576$ ($Y= 0.5$) and $49.790905468523860$ ($Y=1.5$). The SFP method is more accurate than others with fewer summation terms required. The parameters are taken from \textbf{CGMY--Para1}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cccc||cccc|} % centered columns (4 columns)
\hline
$Y=0.5$&\textbf{CONV}&\textbf{COS}&\textbf{SFP}&$Y=1.5$&\textbf{CONV}&\textbf{COS}&\textbf{SFP}\\
\hline
$N/U$& Error &Error &Error &$N/U$& Error &Error &Error \\
\hline
8& 2.320& 4.443& 2.984e-02&8& 5.246& 9.304e-01& 3.020e-02\\
16& 1.079& 5.283e-01& 3.163e-04&16& 7.763e-01& 2.863e-02& 2.566e-05\\
32& 8.172e-01& 1.240e-02 & 2.608e-08&32& 7.607e-01& 1.240e-05& 5.060e-10\\
64& 2.089e-01& 2.801e-05 & 7.687e-11&48& 1.384& 5.286e-12& 8.527e-14\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Singular Fourier-Padé Series Expansion of European Option Prices" by Tat Lung Chan, arXiv:1706.06709v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
\hline\hline
ARIMA model selected & AIC & BIC \\
\hline
ARIMA(2,0,0) & -2468.83 & \textcolor{blue}{-2451.15}\\
ARIMA(2,0,2) & \textcolor{blue}{-2474.25} & \textcolor{blue}{-2447.73}\\
ARIMA(2,0,3) & -2472.72 & -2441.78\\
ARIMA(4,0,2) & \textcolor{blue}{-2476.35} & -2440.99\\
ARIMA(2,1,1) & -2459.15 & -2441.47\\
ARIMA(2,1,3) & -2464.14 & -2437.62\\
\hline\hline
\end{tabular}
\caption{The ARIMA model selection with AIC and BIC.}
\end{table}
\end{document}

q-fin

Shi Chen
Cathy Yi-Hsuan Chen
Wolfgang Karl Härdle

"A first econometric analysis of the CRIX family" by Shi Chen, Cathy Yi-Hsuan Chen, and Wolfgang Karl Härdle, arXiv:2009.12129v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The comparison results of existing SOTA plug-and-play methods on GPT-3.5.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|cccccc|c|}
			\hline
			\multirow{7}{*}{\shortstack{GPT-3.5 \\ turbo}} &Prompt & AddSub & SVAMP & GSM8K & AQuA & CSQA & SQA & Average \\
			\hline
			&CoT~ & 81.2 & 81.2 & 76.2 & 58.7 & 75.5 & 69.7 & 73.75 \\
			&PHP-CoT~ & \underline{86.1} & 83.1 &\textbf{84.6} &\textbf{65.4} & 76.2 & 69.2 & \underline{77.43} \\
			&RE2-CoT~ & 82.7 & \underline{84.9} & 81.2 & 63.3 & 77.9 & 67.1 & 76.60 \\
			&ERA-CoT~ & 83.8 &82.2 & 80.2 & 56.9 & \textbf{83.2} & \textbf{71.4} & 76.28 \\
			&$\text{ISP}^{2}$-CoT &\textbf{88.6} &\textbf{88.7} &\underline{83.4} &\underline{64.4} & \underline{78.7} & \textbf{71.4} & \textbf{79.43} \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Dong-Hai Zhu
Yu-Jie Xiong
Jia-Chen Zhang
Xi-Jiong Xie
Chun-Ming Xia

"Understanding Before Reasoning: Enhancing Chain-of-Thought with Iterative Summarization Pre-Prompting" by Dong-Hai Zhu, Yu-Jie Xiong, Jia-Chen Zhang, Xi-Jiong Xie, and Chun-Ming Xia, arXiv:2501.04341v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation" by Peilin Rao and Randall R. Rojas, arXiv:2509.05922v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
Methods&Train&Test& $\#$ params\\
\hline
\textbf{EqnDis Poly (2,1) }& \textbf{0.9994} & \textbf{0.9905} & 14\\
EqnDis Poly (3,1) & 0.9831 & 0.9585 & 23\\
EqnDis Poly (4,1) & 0.9800 & 0.9040 & 34\\
EqnDis Poly (2,2) & 0.9817 & 0.9700 & 20\\
\hline
EqnDis Fourier (2,1) & 0.9614 & 0.9670& 26\\
EqnDis Fourier (2,2) &0.9365	& 0.9345&58\\
\hline
 RNN (1,5) & 0.9741 & 0.9715 & 41\\
 LSTM (1,5) & 0.9791 & 0.9750 &146 \\
 CFN (7,5) & 0.9113 & 0.9180 & 891 \\
 NCDE (32,32-1) & 0.9919 & 0.9854 & 1221 \\
 \hline
 \end{tabular}
\caption{A comparison of the accuracy (training and test datasets) and number of parameters for various methods on the synthetic dataset based on the forced harmonic oscillator. In this and in subsequent tables, we use boldface to indicate methods with the highest accuracy. See Section .}
\end{table}
\end{document}

stat

Ryeongkyung Yoon
Harish S. Bhat
Braxton Osting

"A non-autonomous equation discovery method for time signal classification" by Ryeongkyung Yoon, Harish S. Bhat, and Braxton Osting, arXiv:2011.11096v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}\hline
$x$ & $1$ & $2$ & $3$ & $4$\\\hline
$\theta=\theta_{1}$ & $1/6$ & $1/6$ & $2/6$ & $2/6$\\\hline
$\theta=\theta_{2}$ & $1/12$ & $3/12$ & $5/12$ & $3/12$\\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"On Resolving Problems with Conditionality and Its Implications for Characterizing Statistical Evidence" by Michael Evans and Constantine Frangakis, arXiv:2202.09911v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
 \hline
 \textbf{Timestep} & \textbf{\textit{p}-value} \\ % inserts table %heading
 \hline
 Daily Limit Buy	& 0.011 \\
 Daily Limit Sell & 0.033 \\
 Weekly Limit Buy & 0.003 \\
 Weekly Limit Sell &	0.020 \\
 Monthly Limit &	0.061 \\
 Hourly Limit Buy & 0.092\\
 Hourly Limit Sell &	0.039 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Welch's \textit{t}-test results between Discrete Weibull and Beta-Binomial on Different Timesteps}
\end{table}
\end{document}

q-fin

Can Yilmaz Altinigne
Harun Ozkan
Veli Can Kupeli
Zehra Cataltepe

"An Empirical Study on Arrival Rates of Limit Orders and Order Cancellation Rates in Borsa Istanbul" by Can Yilmaz Altinigne, Harun Ozkan, Veli Can Kupeli, and Zehra Cataltepe, arXiv:1909.08308v1, 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{The experimental results for objective evaluation.}
\begin{tabular}{lcccccc}
 \toprule
 \multirow{2}{*}{\textbf{Model}} & \multicolumn{2}{c}{\textbf{Clean}} & \multicolumn{2}{c}{\textbf{Noisy (0--5 dB)}} & \multicolumn{2}{c}{\textbf{Noisy (5--10 dB)}} \\
 \cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7}
 & \textbf{CER $\downarrow$} & \textbf{SECS $\uparrow$} & \textbf{CER $\downarrow$} & \textbf{SECS $\uparrow$} & \textbf{CER $\downarrow$} & \textbf{SECS $\uparrow$} \\
 \midrule
 Ground Truth & 1.37 & 90.4 & 1.37 & 90.4 & 1.37 & 90.4\\
 \midrule
 FaCodec-VC & 3.66 & 82.33 & 3.56 & 74.78 & 3.26 & 77.88 \\
 FreeVC & 3.31 & 80.07 & 3.49 & 73.83 & 3.13 & 76.14 \\
 DiffVC & 16.91 & 85.32 & 15.72 & 82.00 & 13.91 & 82.33 \\
 \midrule
 Our Baseline & 4.71 & 82.35 & 7.26 & 77.28 & 6.43 & 78.89 \\
 Noro & 4.74 & 82.38 & 4.66 & 80.09 & 5.06 & 80.71 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Haorui He
Yuchen Song
Yuancheng Wang
Haoyang Li
Xueyao Zhang
Li Wang
Gongping Huang
Eng Siong Chng
Zhizheng Wu

"Noro: Noise-Robust One-shot Voice Conversion with Hidden Speaker Representation Learning" by Haorui He, Yuchen Song, Yuancheng Wang, Haoyang Li, Xueyao Zhang, Li Wang, Gongping Huang, Eng Siong Chng, and Zhizheng Wu, arXiv:2411.19770v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Xiandong Zou
Xiangyu Zhao
Pietro Liò
Yiren Zhao

"Will More Expressive Graph Neural Networks do Better on Generative Tasks?" by Xiandong Zou, Xiangyu Zhao, Pietro Liò, and Yiren Zhao, arXiv:2308.11978v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean evaluation metrics of saliency map consistency for VIBA explanations of VGG \& Xception models. We show separate results for deepfakes and real videos.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 \textbf{Model} & \textbf{IoU} & \textbf{TCS} & \textbf{RPI} \\
 \midrule
 Xception Fake & 0.8226 & 0.4306 & 0.0255\\
 Xception Real & 0.8320 & 0.4086 & 0.0250 \\
 VGG Fake & 0.7633 & 0.8611 & 0.0341 \\
 VGG Real & 0.8001 & 0.8766 & 0.0267 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Veronika Solopova
Lucas Schmidt
Dorothea Kolossa

"Extending Information Bottleneck Attribution to Video Sequences" by Veronika Solopova, Lucas Schmidt, and Dorothea Kolossa, arXiv:2501.16889v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{FBA High Yield Wasserstein Radii}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
Confidence Level & 0.80 & 0.85 & 0.90 & 0.95 & 0.99 & 0.999 \\
 \hline
W Radius delta & 2.0 & 2.2 & 2.4 & 2.8 & 3.4 & 4.3 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c}
 \hline \\
 & R case 1 & R case 2 & $S^{(C)}_1$ & $S^{(C)}_2$\\
 \hline\\
 Accuracy & 0.966 & 0.961 & 0.912 & 0.909\\
 \hline\\
\end{tabular}
\caption{Accuracy scores}
\end{table}
\end{document}

eess

Armani Rodriguez
Silvija Kokalj-Filipovic

"VQalAttent: a Transparent Speech Generation Pipeline based on Transformer-learned VQ-VAE Latent Space" by Armani Rodriguez and Silvija Kokalj-Filipovic, arXiv:2411.14642v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|r|r|r|}
 \toprule
 \multicolumn{2}{|c|}{\multirow{2}[4]{*}{}} & \multicolumn{3}{c|}{Data} \\
 \cmidrule{3-5} \multicolumn{2}{|c|}{} & Definition 1 & Financial \& managerial & Merged \\
 \midrule
 \multicolumn{2}{|c|}{} & Group 1 & Group 5 & Group 6 \\
 \midrule
 \multicolumn{2}{|c|}{Number of variables used in the group} & 20 & 11 & 31 \\
 \midrule
 \multirow{4}[8]{*}{Test AUC} & logit & 70.87\% & 75.33\% & 79.55\% \\
 \cmidrule{2-5} & imbalanced random forest & 78.41\% & 72.66\% & 83.35\% \\
 \cmidrule{2-5} & balanced random forest & 80.02\% & 76.05\% & 83.18\% \\
 \cmidrule{2-5} & boosting & 79.66\% & 76.17\% & 84.24\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{The AUCs of checking account data, financial and managerial data, merged data in logit, random forest and boosting. Group 6 comes from the fusion of Group 1 and Group 5. This merged data has the best performance in balanced random forest and boosting.}
\end{table}
\end{document}

q-fin

Jinglun Yao
Maxime Levy-Chapira
Mamikon Margaryan

"Checking account activity and credit default risk of enterprises: An application of statistical learning methods" by Jinglun Yao, Maxime Levy-Chapira, and Mamikon Margaryan, arXiv:1707.00757v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Funding Spreads}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
Funding Tenor & 1y & 2y & 3y & 5y & 7y & 10y \\
 \hline
IG Spread & 0.13\% & 0.21\% & 0.31\% & 0.59\% & 0.84\% & 1.07\% \\
 \hline
HY Spread & N/A & N/A & 3.02\% & 3.62\% & 4.01\% & 4.09\% \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Backtesting results for $\epsilon_1$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|r|r|r|r|r|r|}
 \toprule
 & Kupiec test & \multicolumn{5}{c|}{Christofferson test} \\
 \midrule
 & p-value & $T_{00}$ & $T_{01}$ & $T_{10}$ & $T_{11}$ & p-value \\
 \midrule
 q1 & 29.36\% & 2226 & 28 & 28 & 0 & 40.42\% \\
 \midrule
 q99 & 65.30\% & 2233 & 24 & 24 & 1 & 27.71\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Jinglun Yao
Sabine Laurent
Brice Bénaben

"Managing Volatility Risk: An Application of Karhunen-Loève Decomposition and Filtered Historical Simulation" by Jinglun Yao, Sabine Laurent, and Brice Bénaben, arXiv:1710.00859v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of $T_{in}$ and $T_{out}$ of \emph{Spider-Route}. For $T_{in}$, averaged over 7 sets of 20 DBs sampled w/o replacement}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \toprule
 &\multicolumn{3}{c}{In-domain}% & Within vertical& \multicolumn{3}{c}{Across vertical}
 &\multicolumn{3}{c}{Cross-domain}\\
 Model &R1&R3&mAP %& R1 &R1&R3&mAP
 &R1&R3&mAP\\
 \midrule
 Llama3&79.81&93.78&86.21%&85.54& 94.26&97.84&95.90
 &95.45&99.12&97.15\\
 Pretrained Emb. &63.14&89.93&75.32%&74.12&89.02&96.65 &92.67
 &87.71&99.35&92.61\\
 Task-spec. Emb.&72.65&93.35&82.03%&78.60&94.03&98.34&96.09
 &91.78&97.97&94.70\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Priyangshu Mandal
Manasi Patwardhan
Mayur Patidar
Lovekesh Vig

"DBRouting: Routing End User Queries to Databases for Answerability" by Priyangshu Mandal, Manasi Patwardhan, Mayur Patidar, and Lovekesh Vig, arXiv:2501.16220v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of wall-clock time (Nvidia 4060, ms) between different NeuralOP models and the one iteration of the numerical approach for different delay lengths and step sizes. }
\begin{tabular}{l|ccc|ccc|ccc}
\hline
\textbf{Delay (s)} & \multicolumn{3}{c|}{0.1} & \multicolumn{3}{c|}{0.5} & \multicolumn{3}{c}{1} \\ \hline
\textbf{Step size $\delta t$} & 0.1 & 0.05 & 0.01 & 0.1 & 0.05 & 0.01 & 0.1 & 0.05 & 0.01 \\ \hline
Numerical (ms) & 1.78 & 3.68 & 18.09 & 8.89 & 18.4 & 90.74 & 18.05 & 35.77 & 185.11 \\ \hline
DeepONet & 1.69 & 1.34 & 1.27 & 1.32 & 1.26 & 1.36 & 1.29 & 1.32 & 1.36 \\
FNO & 0.57 & {\color[HTML]{009901} \textbf{0.32}} & {\color[HTML]{009901} \textbf{0.32}} & {\color[HTML]{009901} \textbf{0.31}} & {\color[HTML]{009901} \textbf{0.32}} & {\color[HTML]{009901} \textbf{0.34}} & {\color[HTML]{009901} \textbf{0.32}} & {\color[HTML]{009901} \textbf{0.32}} & {\color[HTML]{009901} \textbf{0.44}} \\
FNO+GRU & 1.36 & 1.48 & 1.43 & 1.42 & 1.42 & 1.57 & 1.43 & 1.49 & 1.68 \\
DeepONet+GRU & {\color[HTML]{009901} \textbf{0.45}} & 0.47 & 0.46 & 0.45 & 0.46 & 0.54 & 0.46 & 0.48 & 0.63 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Luke Bhan
Peijia Qin
Miroslav Krstic
Yuanyuan Shi

"Neural Operators for Predictor Feedback Control of Nonlinear Delay Systems" by Luke Bhan, Peijia Qin, Miroslav Krstic, and Yuanyuan Shi, arXiv:2411.18964v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cccc|}
 \hline
 Model & Input & DSC(\%) & IoU(\%)\\
 \hline
 Basic Unet &\multirow{7}{3.5em}{RGB} & 69.58±9.66 & 58.20±9.31\\
 FCN & & 79.12±3.39 & 67.60±3.92\\
 TransUnet & & 74.89 ± 4.04 & 63.73±4.28\\
 EfficientUnet-B0 & & 82.50±2.19 & 71.88±2.89\\
 EfficientUnet-B2 & & 82.37±8.35 & 73.87±7.08\\
 EfficientUnet-B6 & & 85.81±1.34 & 76.58±1.62\\
 EfficientUnet-V2-M & & 80.37±2.70 & 69.56±2.76\\
 \hline
 Basic Unet &\multirow{5}{3.5em}{RGB $+$ Guiding Map} & 79.96±6.10 $\uparrow$ & 69.14±6.31 $\uparrow$\\
 EfficientUnet-B0 & & 83.59±3.73 $\uparrow$ & 73.88±3.50 $\uparrow$\\
EfficientUnet-B2 & & \textbf{87.37±1.25} $\uparrow$ & \textbf{78.49±1.87} $\uparrow$\\
 EfficientUnet-B6 & & 85.61±1.25 & 75.47±1.74\\
 EfficientUnet-V2-M & & 83.91±1.91 $\uparrow$ & 73.30±2.83 $\uparrow$\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Segmentation performance of \textbf{tumor BVs} from the test set by different methods.}
\end{table}
\end{document}

cs

Jiaqi Lv
Stefan S Antonowicz
Shan E Ahmed Raza

"Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images" by Jiaqi Lv, Stefan S Antonowicz, and Shan E Ahmed Raza, arXiv:2501.12323v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"The multivariate local dependence function" by Ismihan Bayramoglu and Pelin Ersin, arXiv:2411.05512v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Optimization of COVID-19 Vaccination Process Using Queueing Theory" by Sarkis Der Wartanian and Baydaa Al Ayoubi, arXiv:2312.13522v1, licensed under CC0 1.0. 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{Human evaluation of generation quality of SpeechSSM-2B at and away from chunk boundaries. We report Mean Opinion Scores (MOS) with 99\% confidence intervals along with rater-flagged error categories.}
\begin{tabular}{lccccccc}
\toprule
\textbf{Location} & \textbf{MOS} & Artifacts & Pronunciation & Speed & Prosody & Sentiment & Other \\
\midrule
Chunk boundary & 4.05 ± 0.07 & 115 & 16 & 41 & 72 & 38 & 26 \\
Chunk center & 4.07 ± 0.07 & 122 & 16 & 53 & 78 & 32 & 20 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Se Jin Park
Julian Salazar
Aren Jansen
Keisuke Kinoshita
Yong Man Ro
RJ Skerry-Ryan

"Long-Form Speech Generation with Spoken Language Models" by Se Jin Park, Julian Salazar, Aren Jansen, Keisuke Kinoshita, Yong Man Ro, and RJ Skerry-Ryan, arXiv:2412.18603v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Computational setup of the test cases}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
		\hline
		&1D M1 & 1D M2 & 2D M1 \\
		\hline
		$T$ & $(0,0.7]$ & $(0,0.7]$ & $(0,10]$ \\	\hline
		Time steps & $8750$ & $8750$ & $33333$\\ 	\hline
		$\mathbf X$ & $[0,1]$ & $[0,1]$& $[-1.5,1.5]\times[-1.5,1.5]$ \\	\hline
		Grid cells $n_x$&$5000$ & $5000$ & $2000^2$ \\	\hline
		Quadrature & Gauss-Legendre & Gauss-Legendre & Tensorized Gauss-Legendre\\	\hline
		$\mathbf{V}$ & $[-1,1]$ & $[-1,1]$ & $[-1,1]\times[0,2\pi)$ \\ 	\hline
		Quadrature points & $28$ & $28$ & $400$\\	\hline
		Basis & Monomial & Monomial & Monomial\\ 	\hline
		CFL number & $0.4$ & $0.4$ & $0.4$ \\ 	\hline
		$\sigma$ & $1.0$ & $1.0$ & $0.0$\\ 	\hline
		$\tau$ & $0.5$ & $0.5$ & $0.0$\\ 	\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Steffen Schotthöfer
Tianbai Xiao
Martin Frank
Cory D. Hauck

"Neural network-based, structure-preserving entropy closures for the Boltzmann moment system" by Steffen Schotthöfer, Tianbai Xiao, Martin Frank, and Cory D. Hauck, arXiv:2201.10364v1, 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{Comparative analysis of inputs in guided sampling with respect to $\text{ECE}_t$. The ResNet is the official Pytorch ResNet checkpoint; the diffusion model is from and the dataset is ImageNet 128x128.}
\begin{tabular}{lccccc}
\toprule
 & $t=0$ & $t=20$ & $t=40$ & $t=60$ & $t=80$ \\
\midrule
$\text{ECE}_t$ $\hat{x}_t$ & 0.25 & 0.41 & 0.46 & 0.39 & 0.31\\
$\text{ECE}_t$ $\hat{x}_0(t)$ &0.25& 0.25& 0.28&0.30& 0.33\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Jiajun Ma
Tianyang Hu
Wenjia Wang
Jiacheng Sun

"Elucidating The Design Space of Classifier-Guided Diffusion Generation" by Jiajun Ma, Tianyang Hu, Wenjia Wang, and Jiacheng Sun, arXiv:2310.11311v1, 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}{lccccccccc}
\toprule & \multicolumn{3}{c}{Proportion of short videos} &
\multicolumn{3}{c}{Stay durations} & \multicolumn{3}{c}{Finishing rates} \\
\midrule & Bias & STD & SE & Bias & STD & SE & Bias & STD & SE \\
Weighted & 0.002 & 0.004 & 0.001 & -0.008 & 0.014 & 0.011 & 0.000 & 0.002 &
0.001 \\
Data splitting & -0.019 & 0.020 & 0.001 & 0.021 & 0.052 & 0.011 & -0.007 &
0.006 & 0.001 \\
Data pooling & 0.017 & 0.002 & 0.001 & -0.056 & 0.012 & 0.011 & 0.006 & 0.001
& 0.001 \\
Snapshot & 0.013 & 0.001 & 0.001 & -0.046 & 0.011 & 0.011 & 0.005 & 0.001 &
0.001 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach" by Nian Si, arXiv:2310.17496v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"CPTuning: Contrastive Prompt Tuning for Generative Relation Extraction" by Jiaxin Duan, Fengyu Lu, and Junfei Liu, arXiv:2501.02196v1, licensed under CC0 1.0. 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{Data-Free Distillation Benchmark Hyperparameters}
\begin{tabular}{llrll}
 \toprule
 Task & Optimizer & Student/Generator LR & Teacher & Student \\
 \midrule
 MNIST & Adam & 0.0001 / 0.01 & LeNet & LeNetHalf\\
 Fashion-MNIST & Adam & 0.0001 / 0.01 & LeNet & LeNetHalf\\
 CIFAR-10 & Adam & schedule / 0.015 & Resnet34 & Resnet18\\
 \end{tabular}
\end{table}
\end{document}

stat

Generative Adversarial Simulator

"Generative Adversarial Simulator" by Jonathan Raiman, arXiv:2011.11472v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
$\lambda _{U}$ & $\theta _{i}$ & $\tau $ \\ \hline\hline
$0.013$ & $1.01$ & $0.009$ \\
$0.585$ & $2$ & $0.500$ \\
$0.928$ & $10$ & $0.900$ \\ \hline\hline
 & & %
\end{tabular}
\end{adjustbox}
\caption{Upper tail, Kendall's tau and Gumbel copula parameters used in calculate of risk measures.}%
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Node classification accuracy (\%) on Pubmed under Gaussian noise settings (Best results highlighted by bold).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccc||c|cccc|}
\hline
 Noise (\%) & Clean & 5 & 10 & 20 & & Clean & 5 & 10 & 20\\
\hline
Spectral
& 73.9 & 37.4$\pm$3.5 & 36.7$\pm$3.2 & 36.5$\pm$3.1 & GWNN & 79.1 & 20.9$\pm$0.4 & 19.6$\pm$0.3 & 19.6$\pm$0.2 \\
ChebyNet
& 74.4 & 37.6$\pm$3.3 & 35.7$\pm$2.8 & 36.8$\pm$3.0 &UFG
& 79.9 & 43.3$\pm$2.1 & 42.9$\pm$1.5 & 41.3$\pm$1.9 \\
H2GCN & 78.8 & 32.4\(\pm\)2.6 & 32.2\(\pm\)3.6 &31.3\(\pm\)4.4 &
GPRGNN & 79.7 & 37.2\(\pm\)3.1 & 36.8\(\pm\)2.9 & 38.1\(\pm\)4.5\\
GCN
& 79.0 & \textbf{44.1$\pm$1.4} & 42.6$\pm$1.4 & 41.1$\pm$2.7 & QUFG & \textbf{80.6}
& 43.6$\pm$2.9 & \textbf{43.8$\pm$1.7} & \textbf{41.5$\pm$2.2} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mengxi Yang
Dai Shi
Xuebin Zheng
Jie Yin
Junbin Gao

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c}
 {} & Avg. log-likelihood & RMSE mean ($10^{-2}$) & RMSE std ($10^{-2}$) \\ \hline
 CKDE LOO-CV & 3.8142 $\pm$ 0.0000 & 0.5344 $\pm$ 0.0000 & 0.3672 $\pm$ 0.0000\\
 NKDE LOO-CV & 3.3435 $\pm$ 0.0000 & 0.7943 $\pm$ 0.0000 & 0.4831 $\pm$ 0.0000 \\
 LSCDE 10-fold CV & 3.5250 $\pm$ 0.0040 & 0.6836 $\pm$ 0.0056 & 0.5538 $\pm$ 0.0059 \\ \hline
 MDN 10-fold CV & \textbf{3.8351 $\pm$ 0.0114} & \textbf{0.5271 $\pm$ 0.0060} & \textbf{ 0.3269 $\pm$ 0.0026}\\
 KMN 10-fold CV & \textbf{3.8299 $\pm$ 0.0141} & \textbf{0.5315 $\pm$ 0.0075} & \textbf{0.3247 $\pm$ 0.0047}
\end{tabular}
\end{adjustbox}
\caption{$~$ \textbf{Out-of-sample validation on EuroStoxx 1-day returns - Hyper-Parameter selection via cross-validation.}}
\end{table}
\end{document}

q-fin

Jonas Rothfuss
Fabio Ferreira
Simon Walther
Maxim Ulrich

"Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks" by Jonas Rothfuss, Fabio Ferreira, Simon Walther, and Maxim Ulrich, arXiv:1903.00954v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jingtan Wang
Xinyang Lu
Zitong Zhao
Zhongxiang Dai
Chuan-Sheng Foo
See-Kiong Ng
Bryan Kian Hsiang Low

"Source Attribution for Large Language Model-Generated Data" by Jingtan Wang, Xinyang Lu, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, and Bryan Kian Hsiang Low, arXiv:2310.00646v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 Layer name & scale & kernel size & stride\\
 \hline
 \hline
 stem & $1/4$ & $7\times7$ & 2\\
 \hline
 backbone 1 & $1/4$ & $\begin{bmatrix} 1 & \times & 1\\ 3 & \times & 3 \\ 1 & \times & 1\end{bmatrix}\times 3$ & 1\\
 \hline
 backbone 2 & $1/8$ & $\begin{bmatrix} 1 & \times & 1\\ 3 & \times & 3 \\ 1 & \times & 1\end{bmatrix}\times 4$ & 1\\
 \hline
 backbone 3 & $1/16$ & $\begin{bmatrix} 1 & \times & 1\\ 3 & \times & 3 \\ 1 & \times & 1\end{bmatrix}\times 23$ & 1\\
 \hline
 backbone 4 & $1/32$ & $\begin{bmatrix} 1 & \times & 1\\ 3 & \times & 3 \\ 1 & \times & 1\end{bmatrix}\times 3$ & 1\\
 \hline
 \hline
 max pooling layer & $1/64$ & $1 \times 1$ & 2\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Overview of the ResNeXt structure in the RCNN model, highlighting the number of bottleneck blocks and the scales of feature maps at each stage.}
\end{table}
\end{document}

cs

"Comparison of Feature Learning Methods for Metadata Extraction from PDF Scholarly Documents" by Zeyd Boukhers and Cong Yang, arXiv:2501.05082v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation results of $\hat{\theta}_0$, $\hat{\theta}_1$, $\hat{\eta}_1$, $\hat{\theta}_2$ and $\hat{\sigma}^2$ }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc} \hline
		&$\hat{\theta}_{0}$&$\hat{\theta}_{1}$&$\hat{\eta}_{1}$&$\hat{\theta}_{2}$&$\hat{\sigma}^2$
\\ \hline
{\color{black} true value} &0 & 0.2 & 0.2 &0.2 &1
\\ \hline
mean &-1.235& 0.176& 0.179 & 0.179& 0.85
 \\
s.d. & (3.238)& (0.077)& (0.081) &(0.082) & (0.381)
 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Yozo Tonaki
Yusuke Kaino
Masayuki Uchida

"Parameter estimation for linear parabolic SPDEs in two space dimensions based on high frequency data" by Yozo Tonaki, Yusuke Kaino, and Masayuki Uchida, arXiv:2201.09036v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.