\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}{lccc}
 \hline
 \textbf{Model} & \textbf{SNLI} & \textbf{MultiNLI(m)} & \textbf{MultiNLI(mm)} \\ \hline
 ESIM & 88.0 & 72.3 & 72.1 \\
 KIM & 88.6 & 77.2 & 76.4 \\
 ADIN & 88.8 & 78.8 & 77.9 \\ \hline
 BERT & 89.8 & 83.3 & 82.7 \\
 PairSCL-BERT$_\texttt{base}$ & \textbf{91.9} & \textbf{85.5} & \textbf{84.6} \\ \hline
 RoBERTa$_\texttt{base}$ & $91.2$ & $90.8$ & $90.2$ \\
 PairSCL-RoBERTa$_\texttt{base}$ & \textbf{93.2} & \textbf{92.7} & \textbf{92.3} \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Performance on the test dataset. MultiNLI(m) and MultiNLI(mm) represent the accuracy on matched and mismatched datasets. The best performance is in \textbf{bold} among models with the same pre-trained encoder.}
\end{table}
\end{document}

cs

"Pair-Level Supervised Contrastive Learning for Natural Language Inference" by Shu'ang Li, Xuming Hu, Li Lin, and Lijie Wen, arXiv:2201.10927v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|ccc|c}
			\toprule
			Dataset	& Observation	&	1 robot	&	5 robots	&	10 robots & VGG-19 \\
				& size (\%)	&	 	&	 	&	 & w/ full image \\
			\midrule
			MNIST & 2.04 & 91.27 & 94.98 & 98.31 & 99.33 \\
			\midrule
			Map & 0.52 & 85.19 & 98.94 & 99.71 & 99.84 \\
			w/o clouds & 2.08 & 88.59 & 99.27 & 99.84 & 99.84 \\
			\midrule
			Map & 0.52 & 72.42 & 97.30 & 97.56 & 99.43 \\
			w/ clouds & 2.08 & 77.62 & 98.21 & 98.98 & 99.43 \\
			\toprule
	\end{tabular}
\end{adjustbox}
\caption{The classification accuracy (\%) over the MNIST and the satellite map dataset.}
\end{table}
\end{document}

eess

Guangyi Liu
Arash Amini
Martin Takáč
Héctor Muñoz-Avila
Nader Motee

"Distributed Map Classification using Local Observations" by Guangyi Liu, Arash Amini, Martin Takáč, Héctor Muñoz-Avila, and Nader Motee, arXiv:2012.10480v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Diarization results on AMI mixed-headset eval set, in terms of missed speech (MS), false alarm (FA), speaker confusion (Conf.), and diarization error rate (DER). For all the recordings in the test set, the NME-based speaker counting approach estimated between 3 and 6 speakers, which is close to the oracle count of 4 speakers.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\textbf{Method} & \textbf{MS} & \textbf{FA} & \textbf{Conf.} & \textbf{DER} \\ \midrule
AHC/PLDA & 19.9 & 0.0 & 8.4 & 28.3 \\
Spectral/cosine & 19.9 & 0.0 & 7.0 & 26.9 \\
VBx~ & 19.9 & 0.0 & 6.3 & 26.2 \\ \midrule
VB resegmentation~ & 13.0 & 3.6 & 7.2 & 23.8 \\
RPN~ & 9.5 & 7.7 & 8.3 & 25.5 \\ \midrule
Our method & 11.3 & 2.2 & 10.5 & 24.0 \\
Our method + oracle OD & 7.4 & 1.8 & 12.3 & \underline{21.5} \\
Our method + noise aug. & 11.3 & 2.2 & 10.1 & \textbf{23.6} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Multi-class Spectral Clustering with Overlaps for Speaker Diarization" by Desh Raj, Zili Huang, and Sanjeev Khudanpur, arXiv:2011.02900v1, 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}

q-fin

"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the expressive power of PCA on the original space of curves and on the space of TSRVFs. The lower the values are, the better.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
 & \multicolumn{3}{c|}{\textbf{PCA on curves}} & \multicolumn{3}{c|}{\textbf{PCA on TSRVFs}} \\
 \cline{2-7}
 & \textbf{Mean} & \textbf{Std} & \textbf{Median} & \textbf{Mean} & \textbf{Std} & \textbf{Median} \\
 \hline
 DFAUST & $0.926$ & $1.110$ & $0.693$ & $\textbf{0.756}$ & $0.104$ & $\textbf{0.715}$ \\
 \hline
 VOCA & $0.676$ & $0.182$ & $0.64$ & $\textbf{0.486}$ & $0.318$ & $\textbf{0.603}$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hamid Laga
Marcel Padilla
Ian H. Jermyn
Sebastian Kurtek
Mohammed Bennamoun
Anuj Srivastava

"4D Atlas: Statistical Analysis of the Spatiotemporal Variability in Longitudinal 3D Shape Data" by Hamid Laga, Marcel Padilla, Ian H. Jermyn, Sebastian Kurtek, Mohammed Bennamoun, and Anuj Srivastava, arXiv:2101.09403v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
 \toprule
 $ n $ & $ a_n = \frac{1}{n!} $ \\
 \midrule
 0 & 1.00000000 \\
 1 & 1.00000000 \\
 2 & 0.50000000 \\
 3 & 0.16666667 \\
 4 & 0.04166667 \\
 5 & 0.00833333 \\
 6 & 0.00138889 \\
 7 & 0.00019841 \\
 8 & 0.00002480 \\
 9 & 0.00000276 \\
 10 & 0.00000028 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Taylor coefficients $ a_n = \frac{1}{n!} $ for the expansion of $ e^z $.}
\end{table}
\end{document}

math

"Numerical Derivatives, Projection Coefficients, and Truncation Errors in Analytic Hilbert Space With Gaussian Measure" by M. W. AlMasri, arXiv:2504.16246v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c||}
 \hline
 Iteration & $L_2$ error \\
 \hline\hline
 1 & 9.6448e-02 \\
 2 & 2.7334-02 \\
 3 & 2.7342e-02 \\
 4 & 2.9523e-02 \\
 5 & 8.8202e-02 \\
 6 & 5.7279e-02 \\
 7 & 5.6871e-02 \\
 8 & 8.7658e-02 \\
 9 & 2.7334e-02 \\
 10 & 8.7835e-02 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Runge $\text{deg}(p)=\text{deg}(q)=4$, $n=10$ random start $L_2$ error}
\end{table}
\end{document}

math

"Deep Univariate Polynomial and Conformal Approximation" by Kingsley Yeon, arXiv:2503.00698v2, 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{Summary of setups of experiments in \S .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
Experiment & Architecture & Dataset & Loss Function & Batch Size \\ \midrule
1 & Deep Linear Network & Synthetic & MSE & 1024 \\
2 & Multi-Layer Perceptron & Synthetic & MSE & 1024 \\
3 & ResNet & CIFAR-10 & Cross-Entropy & 768 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Laura Balzano
Tianjiao Ding
Benjamin D. Haeffele
Soo Min Kwon
Qing Qu
Peng Wang
Zhangyang Wang
Can Yaras

"An Overview of Low-Rank Structures in the Training and Adaptation of Large Models" by Laura Balzano, Tianjiao Ding, Benjamin D. Haeffele, Soo Min Kwon, Qing Qu, Peng Wang, Zhangyang Wang, and Can Yaras, arXiv:2503.19859v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates" by Abhishek Ojha and Naveen N. Narisetty, arXiv:2411.17618v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{ Average effects of the model chosen in Figure , in which all effects and their interactions, but the interaction of the $"$common colonizer$"$ and the $"$same region$"$ effect are included. All effects but the regional effect were rounded to the third decimal place.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c|c|c}
 &comcol&sameRegion&intercept&contig&comcol and contig&sameRegion and contig&\\ \hline &0.008&2e-06&0.061&0.045&0.218&0.146 &\\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Martin Metodiev
Marie Perrot-Dockès
Sarah Ouadah
Bailey K. Fosdick
Stéphane Robin
Pierre Latouche
Adrian E. Raftery

"A Structured Estimator for large Covariance Matrices in the Presence of Pairwise and Spatial Covariates" by Martin Metodiev, Marie Perrot-Dockès, Sarah Ouadah, Bailey K. Fosdick, Stéphane Robin, Pierre Latouche, and Adrian E. Raftery, arXiv:2411.04520v1, 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{Comparison results with other word-level noise filtering strategies.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
		\toprule
		 & \multicolumn{2}{c}{CNSE} & \multicolumn{2}{c}{CNSS} \\
		Word-level Filter & Acc & F1 & Acc & F1 \\
		\midrule
		Random & 80.38 & 79.19 & 87.68 & 88.20 \\
		Embedding Norm & 80.54 & 78.15 & 84.92 & 85.03 \\
		Attention Weight & 85.52 & 83.34 & 89.91 & 89.88 \\
		PageRank & 86.32 & 84.55 & 91.28 & 91.39 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching" by Liang Pang, Yanyan Lan, and Xueqi Cheng, arXiv:2101.06423v2, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The ranges of markups and quantities satisfying $q \ge 0$, $u' > 0$, $u'' < 0$, $0<\mu \le 1$, and the second-order condition .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|cc}
\multicolumn{5}{c}{Case 1: $\alpha >0$.} \\
\hline
 & \multicolumn{2}{c|}{Markups} & \multicolumn{2}{c}{Quantities} \\ \cline{2-5}
 & $\sigma \le 1$ & $\sigma > 1 $ & $\sigma \le 1$ & $\sigma > 1 $ \\ \hline
\multirow{2}{*}{Case 1a. $1-\beta \sigma>0$ and $\beta \neq 1$} &
\multirow{2}{*}{$\frac{1}{1-\beta} \le \frac{1}{\mu} < \infty$} &
\multirow{2}{*}{$\frac{1}{1-\beta} \le \frac{1}{\mu} < \frac{\sigma}{\sigma-1}$} &
\multirow{2}{*}{$0 \le q < -\frac{1-\beta}{\alpha (\sigma-1)}$$^\dagger$} &
\multirow{2}{*}{$0 \le q < \infty$} \\
&
&
&
&
\\ \hline
&
&
&
&
\\
\multirow{2}{*}{Case 1b.} \hskip .05cm $1-\beta \sigma<0$ and $\beta\in(0,1)$ &
\multirow{2}{*}{n.a.} &
$\frac{\sigma}{\sigma-1} < \frac{1}{\mu } \le \frac{1}{1-\beta}$ &
\multirow{2}{*}{n.a.} & $0 \le q < \infty$ \\
\hskip 1.73cm
$1-\beta \sigma<0$ and $\beta=1$
&
& $\frac{\sigma}{\sigma-1} < \frac{1}{\mu} < \infty$
&
& $0 < q < \infty$
\\ \hline
\multicolumn{5}{c}{} \\
\multicolumn{5}{c}{Case 2: $\alpha <0$.} \\ \hline
 & \multicolumn{2}{c|}{Markups} & \multicolumn{2}{c}{Quantities} \\ \cline{2-5}
 & $\sigma \le 1$ & $\sigma > 1 $ & $\sigma \le 1$ & $\sigma > 1 $ \\ \hline
\multirow{2}{*}{Case 2a. $1-\beta \sigma>0$ and $\beta \in ( 0, 1)$} &
\multirow{2}{*}{$\frac{(1-\beta)\sigma+1}{(1-\beta)\sigma+\Delta} < \frac{1}{\mu} \le \frac{1}{1-\beta}$$^\ddagger$} &
\multirow{2}{*}{$\frac{(1-\beta)\sigma+1}{(1-\beta)\sigma+\Delta} < \frac{1}{\mu} \le \frac{1}{1-\beta}$} &
\multirow{2}{*}{$ 0 \le q < -\frac{1 - \beta - \Delta}{\alpha (\sigma-1)}$$^\S$} &
\multirow{2}{*}{$ 0 \le q < -\frac{1 - \beta - \Delta}{\alpha (\sigma-1)} $} \\
&
&
&
&
\\ \hline
\multirow{2}{*}{Case 2b. $1-\beta \sigma<0$ and $\beta \in ( 0, 1)$}
&
\multirow{2}{*}{n.a.} &
\multirow{2}{*}{$\frac{1}{1-\beta} \le \frac{1}{\mu } < \infty$} &
\multirow{2}{*}{n.a.} &
\multirow{2}{*}{$0 \le q < -\frac{1-\beta}{\alpha (\sigma-1)}$} \\
&
&
&
&
\\ \hline
\multicolumn{5}{p{21.4cm}}{\scriptsize {\it Notes}: We let $\Delta \equiv \sqrt{(1-\beta)(1-\beta \sigma)}$.
$^{\dagger}$ means that when $\sigma=1$, $0 \le q < -\frac{1-\beta}{\alpha (\sigma-1)}$ must be replaced with $0 \le q < \infty$.
$^{\ddagger}$ means that when $\sigma=1$, $ \frac{(1-\beta)\sigma+1}{(1-\beta)\sigma+\Delta}$ must be replaced with $ \frac{2-\beta}{2(1-\beta)}$.
$^\S$ means that when $\sigma=1$, $ -\frac{1 - \beta - \Delta}{\alpha (\sigma-1)} $ must be replaced with $-\frac{\beta}{2\alpha}$.
Marginal cost pricing $\frac{1}{\mu}=1$ is admissible only when $\beta=0$ in Case 1a because $\frac{\sigma}{\sigma-1} > 1$ holds in Case 1b, $\frac{(1-\beta)\sigma+1}{(1-\beta)\sigma+\Delta} >1$ holds in Case 2a, and $\beta=0$ is not admissible in Case 2b.
The case with $\alpha=0$ corresponds to the CES and requires that $\beta \in (0,1)$.
The case with $1-\beta \sigma=0$ also corresponds to the CES and requires that $\sigma > 1 $.
If $\alpha=0$ or $\beta =\frac{1}{\sigma}$, then the markup is given by $\frac{1}{\mu} =\frac{1}{1-\beta} \in (1, \infty)$ or $\frac{1}{\mu} =\frac{\sigma}{\sigma-1} \in (1, \infty)$, respectively, and the range of quantities is given by $0\le q<\infty$.
Since these two cases are well known, we omit them from the table.}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Linear fractional relative risk aversion" by Kristian Behrens and Yasusada Murata, arXiv:2509.09865v1, licensed under CC BY-SA 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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Discriminating Power of Invariants for Higher Orders}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
		\toprule
		& & & \multicolumn{2}{c}{Non-isomorphic Models} \\ \cmidrule(lr){4-5}
		& Order & \multicolumn{1}{p{1.7cm}}{\centering \#Blocks} & \multicolumn{1}{p{1.4cm}}{\centering Total} & \multicolumn{1}{p{1.9cm}}{\centering Avg per Block} \\ \cmidrule{1-5}
		\#88 Quasi-MV-algebra & 7 & 567 & 477 & 1.19 \\
		& 8 & 153,163 & 55,544 & 2.76 \\
		& 9 & 264,972 & 141,750 & 1.87 \\
		\#15 Brouwerian semilattices & 6 & 745 & 745 & 1.00 \\
		& 7 & 8,272 & 8,272 & 1.00 \\
		& 8 & 115,801 & 114,943 & 1.01 \\
		\#4 BCK-join-semilattice & 9 & 26 & 26 & 1.00 \\
		& 10 & 47 & 47 & 1.00 \\
		& 11 & 82 & 82 & 1.00 \\ \bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Boosting Isomorphic Model Filtering with Invariants" by João Araújo, Choiwah Chow, and Mikoláš Janota, arXiv:2201.10516v1, 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{Classical orbital elements and orbital period.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\hline \hline
$a$ (km) & $e$ (-)& $i$ (deg)& $\Omega$ (deg)& $\omega$ (deg)& $\nu$ (deg) & (hrs) \\
\hline
26646.6808 & 0.74& 62.8& 0.0& 280 & 0 & 12.0247 \\
\hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Taehyeun Kim
Anouck Girard
Ilya Kolmanovsky

"CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor" by Taehyeun Kim, Anouck Girard, and Ilya Kolmanovsky, arXiv:2412.03710v2, 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
\begin{tabular}{lc}
 \toprule
 \textbf{Step} & \textbf{Setup} \\
 \midrule
 \multirow{2}{*}{Step 1} & 2000 starting points within \( [-8,8]^{20} \) \\
 & 10000 SGD iterations with step size \( \lambda = 0.02 \) \\
 \midrule
 \multirow{2}{*}{Step 2} & 5000 samples per component \\
 & 40000 Langevin iterations with step size \( \eta = 0.002 \) \\
 \midrule
 \multirow{2}{*}{Step 3} & explicit Euler scheme for reverse ODE with 100 time steps \\
 & 5000 labeled samples generated per component \\
 \midrule
 \multirow{4}{*}{Step 4} & Feedforward model with three hidden layers \\
 & Each layer has 1000 nodes with \texttt{tanh} activation \\
 & Adam with initial learning rate 0.001, halved every 500 epochs \\
 & Training over $2000$ epochs with batch size $1500$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Hoang Tran
Zezhong Zhang
Feng Bao
Dan Lu
Guannan Zhang

"Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions" by Hoang Tran, Zezhong Zhang, Feng Bao, Dan Lu, and Guannan Zhang, arXiv:2505.07825v1, 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{Classification accuracy of the GRAND-l and KuramotoGNN trained with different depth on the OGBN-arXiv graph node classification task.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \hline
 \hline
 Model & $T=1$ & $T=8$ & $T=32$ & $T=64$\\
 \hline
 KuramotoGNN& \textbf{66.00$\pm$0.8} & \textbf{69.87$\pm$0.2} & \textbf{69.31$\pm$0.3} & \textbf{68.32$\pm$0.2}\\
 \hline
 GRAND-l & 64.43$\pm$0.5 & 69 .02$\pm$0.4 & 67.81$\pm$0.4&66.58$\pm$1.2\\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Tuan Nguyen
Hirotada Honda
Takashi Sano
Vinh Nguyen
Shugo Nakamura
Tan M. Nguyen

"From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach" by Tuan Nguyen, Hirotada Honda, Takashi Sano, Vinh Nguyen, Shugo Nakamura, and Tan M. Nguyen, arXiv:2311.03260v2, 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{Numerical comparison of four methods using BraTS 2018 testing set. Note that smaller mean L1 error and larger SSIM mean error indicate higher similarity.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|cccc|ccc}
\hline
Methods& ~mean L1 error~$\downarrow$ ~~&~~~SSIM~$\uparrow$~~&	~~~PSNR~$\uparrow$~~	& Inception Score $\uparrow$\\\hline\hline
Patch-match 	&445.8&	0.9460&	29.55&	9.13\\
GLC 	&432.6&	0.9506&	30.34&	9.68\\
Partial Conv 	&373.2&	0.9512&	33.57&	9.77\\\hline
Proposed	&292.5&	0.9667&	34.26&	10.26\\
Proposed+symmetry&	\textbf{254.8}&	\textbf{0.9682}	&\textbf{34.52}&	\textbf{10.58}\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xiaofeng Liu
Fangxu Xing
Chao Yang
C. -C. Jay Kuo
Georges ElFakhri
Jonghye Woo

"Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology" by Xiaofeng Liu, Fangxu Xing, Chao Yang, C. -C. Jay Kuo, Georges ElFakhri, and Jonghye Woo, arXiv:2101.06775v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Automating the Design of Multigrid Methods with Evolutionary Program Synthesis" by Jonas Schmitt, arXiv:2312.14875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
		\toprule[1pt]
		Methods&OS& MF &Aux& mIOU (\%) & FLOPs\\
		\midrule[0.5pt]
		\midrule[0.5pt]
		ResNet& 16 & & - & - & 59.85G \\
		-101& 8 & & - & - & 190.70G \\
		\midrule
		DANet& 8 & & & & +101.25G\\
		 & 8 & \checkmark & \checkmark &52.60 & - \\
		\midrule
		Axial & 16 & & & 50.27($\pm$0.2)& +8.85G\\
		Attention & 16 & \checkmark & & 52.01($\pm$0.2) & -\\
		 & 8 & & & 51.24($\pm$0.2) & +34.33G\\
		 & 8 & \checkmark & &52.51($\pm$0.2) & - \\
		\midrule
		Our & 16 & & & 51.06($\pm$0.2)& +8.85G\\
		CAA & 16 & \checkmark & &53.09($\pm$0.3) & - \\
		 & 8 & & & 52.73($\pm$0.1) & +34.33G\\
		 & 8 & \checkmark & & 54.05($\pm$0.1) & - \\
		\midrule
		Our & 16 & & \checkmark & 51.80($\pm$0.2)& +8.85G\\
		 CAA & 16 &\checkmark& \checkmark & 53.52($\pm$0.2)& - \\
		 + & 8 & & \checkmark& 53.48($\pm$0.3) & +34.33G\\
		 Aux loss & 8 & \checkmark &\checkmark &54.65($\pm$0.4) & - \\
		\bottomrule[1pt]
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ye Huang
Di Kang
Wenjing Jia
Xiangjian He
Liu Liu

"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
\textbf{Genre} & \textbf{Examples} \\
\hline
Bass Music & Dubstep, Drum and Bass, Jungle \\
Live Sounds & Rock, Jazz, Disco \\
Cinematic & Sound FX, Filmscore, Sci-Fi \\
Global & Reggae, Dancehall, Indian Music \\
Hip Hop & Trap, Boom Bap, Lofi Hip Hop \\
Electronic & Ambient, IDM, Chill Out \\
House / Techno & Deep House, Electro, Tech House \\
Other Dance Music & EDM, Psy Trance, Hardstyle \\
\end{tabular}
\end{adjustbox}
\caption{Taxonomy of genres used for the annotation and examples for each category.}
\end{table}
\end{document}

eess

Antonio Ramires
Frederic Font
Dmitry Bogdanov
Jordan B. L. Smith
Yi-Hsuan Yang
Joann Ching
Bo-Yu Chen
Yueh-Kao Wu
Hsu Wei-Han
Xavier Serra

"The Freesound Loop Dataset and Annotation Tool" by Antonio Ramires, Frederic Font, Dmitry Bogdanov, Jordan B. L. Smith, Yi-Hsuan Yang, Joann Ching, Bo-Yu Chen, Yueh-Kao Wu, Hsu Wei-Han, and Xavier Serra, arXiv:2008.11507v2, 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{Performance metrics for the first eigenvalue prediction with different settings. $d\times d$+dp+ma stands for $d\times d$ image with detailed pixelization on the boundaries and main axis alignment. $d\times d$+ma stands for $d\times d$ image with main axis alignment.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|} \hline
				Setting & RMSE & $R^2$ & MAPE \\ \hline
				$32 \times 32$+dp+ma & 2.2121 & 0.9988 & 1.15\% \\
				$32 \times 32$+ma & 5.9933 & 0.9917 & 1.75\%\\
				$32 \times 32$ & 7.3343 & 0.9864 & 1.67\% \\
				$64 \times 64$+dp+ma & 2.2113 & 0.9988 & 0.73\% \\
				$64 \times 64$+ma & 3.0124 & 0.9979 & 1.57\% \\
				$64 \times 64$ & 3.6447 & 0.9966 & 1.78\% \\ \hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Operator Inference for Elliptic Eigenvalue Problems" by Haoqian Li, Jiguang Sun, and Zhiwen Zhang, arXiv:2504.15733v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Makoto Araya
Masaaki Harada
Hadi Kharaghani
Sho Suda
Wei-Hsuan Yu

"Unbiased weighing matrices of weight $9$" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Dynamic Asset Pricing with α-MEU Model

"Dynamic Asset Pricing with α-MEU Model" by Jiacheng Fan, Xue Dong He, and Ruocheng Wu, arXiv:2507.04093v1, 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{Correct Frequency and running time (second) of different optimization methods (Matlab) in finding the global maximum of different functions in Subsection . SMCOtree has highest correct frequency with low running time.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccccccccccc}
\cline{1-15}
\multicolumn{3}{c}{Case} && \multicolumn{3}{c}{Method (\texttt{Matlab})} && \multicolumn{3}{c}{Correct Frequency} && \multicolumn{3}{c}{Time$\times10^{2}~$(second)} \\
\cline{1-15}
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCO} && \multicolumn{3}{c}{\textbf{0.95}} && \multicolumn{3}{c}{\textbf{0.18}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{0.97}} && \multicolumn{3}{c}{1.20}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{GD} && \multicolumn{3}{c}{0.26} && \multicolumn{3}{c}{2.51}\\
\multicolumn{3}{c}{Case 1, $d=1$} && \multicolumn{3}{c}{SA($T$= 80)} && \multicolumn{3}{c}{0.87} && \multicolumn{3}{c}{6.29}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA($T$= 100)} && \multicolumn{3}{c}{0.83} && \multicolumn{3}{c}{6.79}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 100)} && \multicolumn{3}{c}{0.88} && \multicolumn{3}{c}{2.07}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 150)} && \multicolumn{3}{c}{0.92} && \multicolumn{3}{c}{2.76}\\
\cline{1-15}
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCO} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{\textbf{0.21}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{3.38}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.88} && \multicolumn{3}{c}{10.55}\\
\multicolumn{3}{c}{Case 2, $d=2$} && \multicolumn{3}{c}{SA($T$= 80)} && \multicolumn{3}{c}{\textbf{0.97}} && \multicolumn{3}{c}{20.35}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA($T$= 100)} && \multicolumn{3}{c}{\textbf{0.98}} && \multicolumn{3}{c}{20.48}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 100)} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{3.97}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 150)} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{4.62}\\
\cline{1-15}
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCO} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{\textbf{0.11}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{1.32}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.78} && \multicolumn{3}{c}{11.46}\\
\multicolumn{3}{c}{Case 3, Griewank, $d=2$} && \multicolumn{3}{c}{SA($T$= 80)} && \multicolumn{3}{c}{0.43} && \multicolumn{3}{c}{13.13}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA($T$= 100)} && \multicolumn{3}{c}{0.40} && \multicolumn{3}{c}{13.21}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 100)} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{1.99}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 150)} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{2.37}\\
\cline{1-15}
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCO} && \multicolumn{3}{c}{0.00} && \multicolumn{3}{c}{0.06}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{0.93}} && \multicolumn{3}{c}{\textbf{2.85}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.75} && \multicolumn{3}{c}{11.30}\\
\multicolumn{3}{c}{Case 4, Rastrigin, $d=2$} && \multicolumn{3}{c}{SA($T$= 80)} && \multicolumn{3}{c}{0.65} && \multicolumn{3}{c}{14.07}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA($T$= 100)} && \multicolumn{3}{c}{0.58} && \multicolumn{3}{c}{14.48}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 100)} && \multicolumn{3}{c}{0.62} && \multicolumn{3}{c}{4.33}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 150)} && \multicolumn{3}{c}{0.68} && \multicolumn{3}{c}{5.94}\\
\cline{1-15}
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{0.99}} && \multicolumn{3}{c}{\textbf{80.57}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.01} && \multicolumn{3}{c}{62.14}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA($T$= 1000)} && \multicolumn{3}{c}{0.00} && \multicolumn{3}{c}{71.20}\\
\multicolumn{3}{c}{Case 5, Rastrigin, $d=10$} && \multicolumn{3}{c}{SA($T$= 1500)} && \multicolumn{3}{c}{0.00} && \multicolumn{3}{c}{76.06}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 1000)} && \multicolumn{3}{c}{0.47} && \multicolumn{3}{c}{98.84}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO($S$= 1500)} && \multicolumn{3}{c}{0.60} && \multicolumn{3}{c}{207.17}\\
\cline{1-15}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Xiaohong Chen
Zengjing Chen
Wayne Yuan Gao
Xiaodong Yan
Guodong Zhang

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\toprule
\textbf{Method} & \textbf{Bond Type} & \textbf{MAE} $\downarrow$ \\
\midrule
GAN & US\_10Y\_Yield & 0.437 \\
GAN & AAA\_Bond\_Yield & 0.343 \\
GAN & BAA\_Bond\_Yield & 0.372 \\
GAN & Junk\_Bond\_Yield & 0.594 \\
RL & US\_10Y\_Yield & \textbf{0.103} \\
RL & AAA\_Bond\_Yield & 0.124 \\
RL & BAA\_Bond\_Yield & 0.174 \\
RL & Junk\_Bond\_Yield & 0.458 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Mean Absolute Error for Different Bond Yields. Best score denoted by \textbf{bold}.}
\end{table}
\end{document}

q-fin

Jaskaran Singh Walia
Aarush Sinha
Srinitish Srinivasan
Srihari Unnikrishnan

"Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation" by Jaskaran Singh Walia, Aarush Sinha, Srinitish Srinivasan, and Srihari Unnikrishnan, arXiv:2502.17011v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Charles Clum
Dustin G. Mixon
Soledad Villar
Kaiying Xie

"Sketch-and-solve approaches to k-means clustering by semidefinite programming" by Charles Clum, Dustin G. Mixon, Soledad Villar, and Kaiying Xie, arXiv:2211.15744v1, 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{Dataset of 3178 images: Segmentation performance metrics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrrr}
\hline
 & \textbf{Dice} & \textbf{IoU} & \textbf{Accuracy} & \textbf{Precision} & \textbf{Recall} & \textbf{F1 Score} & \textbf{SSIM} & \textbf{Hausdorff} \\
\hline
\textbf{Mean} & 0.814431 & 0.694019 & 0.872015 & 0.803413 & 0.851245 & 0.814431 & 0.502930 & 57.287395 \\
\textbf{Std} & 0.078670 & 0.106993 & 0.096104 & 0.112562 & 0.124639 & 0.078670 & 0.245851 & 34.920233 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Introduction to a Low-Cost AI-Powered GUI for Unstained Cell Culture Analysis" by Surajit Das and Pavel Zun, arXiv:2509.11354v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters used for the simulation of the analytical benchmark.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \hline
 \textbf{Parameter} & \textbf{Value}\\
 \hline
 Thermal conductivity, $k$ 	& $3.0~ W/(m K)$ \\
 Heat transfer coefficient, $h$ 	& $5.0~W/(m^2 K)$\\
 $a$	 & $5 ~ K/m^2$ \\
 $b$	 & $10~ K/m^2$ \\
 $c$ 	 & $15~ K/m^2$ \\
 $L$ 	 & $1~m$\\
 $W$	 & $1~m$\\
 $H$	 & $1~m$\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Umberto Emil Morelli
Patricia Barral
Peregrina Quintela
Gianluigi Rozza
Giovanni Stabile

"A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation" by Umberto Emil Morelli, Patricia Barral, Peregrina Quintela, Gianluigi Rozza, and Giovanni Stabile, arXiv:2101.11985v1, 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{Robustness specifications on the effect of school closures on test scores in Sicily}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}

					\hline

					& Math (ln) & Math (IHS) & Math (Rasch) & Math (std. - PSM) \\

					& (1) & (2) & (3) & (4) \\
				\hline

				\textbf{Days of school closure} (ln) & -0.001*** & & & \\
				& (0.000) & & & \\
				\textbf{Days of school closure} (IHS) & & -0.001*** & & \\
			& & (0.000) & & \\

			\textbf{Days of school closure} (level) & & & -0.006*** & \\
		 & & & (0.002) & \\

		\textbf{Days of school closure} (std.) & & & & -0.006*** \\
	& & & & (0.002) \\
	& & & & \\ \hline
	\textbf{Other controls} & Yes & Yes & Yes & Yes \\
	\textbf{Student FEs} & Yes & Yes & Yes & Yes \\
	\textbf{Relative time dummies} & Yes & Yes & Yes & Yes \\
	\textbf{Grade dummies}& Yes & Yes & Yes & Yes \\ \hline

	\textbf{Observations} & 334,366 & 334,366 & 334,366 & 334,366 \\
	R-squared & 0.553 & 0.554 & 0.563 & 0.534 \\
	\textbf{Number of students} & 167,183 & 167,183 & 167,183 & 167,183 \\ \hline

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

q-fin

"Will the last be the first? School closures and educational outcomes" by Michele Battisti and Giuseppe Maggio, arXiv:2208.11606v1, 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{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Convergence rate comparison between different methods. The rows of $\mathbf{ A}$ are normalized, $\|\mathbf{ e}_k\|^2_2=\|\mathbf{A}\mathbf{x}^k-\mathbf{b}\|^2_2$, $|\hat{\mathbf{x}}|_{\min}$ means the smallest nonzero absolute element, $\tilde{\sigma}_{\min}(\mathbf{A})$ is the non-zero smallest singular value, and $\beta_k/\gamma_k\geq 1$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
		\hline & \text { Selection Rule } & \text { Convergence Rate } \\
		\hline $\mathrm{RK}$ &$ \mathbb{P}\left(t_{j}=i\right)=\frac{\left\|\mathbf{a}_{i}\right\|^{2}_2}{\|\mathbf{A}\|_{\text{F}}^{2}}$ &$ \mathbb{E}\left\|\mathbf{e}_{k}\right\|^{2} \leq\left(1-\frac{\sigma_{\min }^{2}(\mathbf{A})}{\|\mathbf{A}\|_{\text{F}}^{2}}\right)^{k}\left\|\mathbf{e}_{0}\right\|^{2}_2$ \\
		\hline
		$\mathrm{SRK}$
		&$\mathbb{P}\left(t_{j}=i\right)=\frac{\left\|\mathbf{a}_{i}\right\|^{2}_2}{\|\mathbf{A}\|_{\text{F}}^{2}}$&
		$\mathbb{E}\left\|\mathbf{e}_{k}\right\|^{2} \leq\left(1-\frac{\tilde{\sigma}_{\min }^{2}(A)}{2m}\cdot\frac{|\hat{\mathbf{ x}}|_{\min}}{|\hat{\mathbf{ x}}|_{\min}+2\lambda}\right)^{k}\left\|\mathbf{e}_{0}\right\|^{2}_2$
		\\
		\hline
			\multirow{2}{*}{$\mathrm{SSKM}$}
	 & $\tau_j\sim\binom{[m]}{\beta}$&	 \multirow{2}{*}{$\mathbb{E}\left\|\mathbf{e}_{k}\right\|^{2} \leq\prod_{i=0}^{k}\left(1-\frac{\beta_k\tilde{\sigma}_{\min }^{2}(A)}{2\gamma_km}\cdot\frac{|\hat{\mathbf{ x}}|_{\min}}{|\hat{\mathbf{ x}}|_{\min}+2\lambda}\right)\left\|\mathbf{e}_{0}\right\|^{2}_2$ }\\
	 & $t_{j}=\arg \max _{i\in\tau_j}(\mathbf{a}_{i}^{\top} \mathbf{x}_{j-1}-b_{i})^2$ &\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Sparse Sampling Kaczmarz-Motzkin Method with Linear Convergence" by Ziyang Yuan, Hui Zhang, and Hongxia Wang, arXiv:2101.04807v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
& Auditory task & Visual task & Rest&P-value \\ \hline
 \multirow{2}{*}{Auditory task} & 0.000 & 0.104 & 0.029& \\
&-- & (0.98) & (0.98) & \multirow{2}{*}{0.451}\\
\cline{1-4}
 \multirow{2}{*}{Visual task} & 0.104 & 0.000 & 0.080 & \\
&(0.98) & -- & (0.98) & \\ \hline
\end{tabular}
\caption{Statistical analysis of the intrinsic discrepancy between the tasks.}
\end{table}
\end{document}

eess

"Analysis of EEG data using complex geometric structurization" by Eddy Kwessi and Lloyd Edwards, arXiv:2102.09061v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|}
		\hline
		& Power Consumption & Total \\ \hline
		$2\times$ AP7312 & 45 mW & \multirow{4}{*}{1.5695 W} \\ \cline{1-2}
		$2\times$ TVP5150 & 230 mW & \\ \cline{1-2}
		MCP1700 & 44.5 mW & \\ \cline{1-2}
		ADV7171 & 1.25 W & \\ \hline
	\end{tabular}
\end{adjustbox}
\caption{Power consumption values for the main devices of the implemented system according to their corresponding datasheets.}
\end{table}
\end{document}

eess

Mohamed Awad
Islam T. Abougindia
Ahmed Elliethy
Hussein A. Aly

"Flexible Architecture for Real-time Processing of Multiple Video Signals" by Mohamed Awad, Islam T. Abougindia, Ahmed Elliethy, and Hussein A. Aly, arXiv:2001.02048v1, 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{Bias models with corresponding functions and parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
 Model & Equation & Parameters \\
 \hline
 Static & $H_b(\theta,t_s,t_k) = A$ & $\theta = [A]$ \\
 \hline
 Linear & $H_b(\theta,t_s,t_k) = A + B(t_k-t_s)$ & $\theta = [A,B]$ \\
 \hline
 Quadratic & $\!\begin{aligned}[t]
 H_b(\theta,t_s,t_k) &=A + B(t_k-t_s) \\
 &+ C(t_k-t_s)^2
 \end{aligned}$ & $\theta = [A,B,C]$\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Artem Mustaev
Nicholas Galioto
Matt Boler
John D. Jakeman
Cosmin Safta
Alex Gorodetsky

"A switching Kalman filter approach to online mitigation and correction of sensor corruption for inertial navigation" by Artem Mustaev, Nicholas Galioto, Matt Boler, John D. Jakeman, Cosmin Safta, and Alex Gorodetsky, arXiv:2412.06601v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Full Exceptional Sequence for a Fine Quiver Moduli Space" by Svetlana Makarova and Junyu Meng, arXiv:2412.15390v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
 \hline
 & \multicolumn{2}{|c|}{top-1} & \multicolumn{2}{|c|}{top-5}\\
 \hline
 Model & CIFAR10 & Flowers102 & CIFAR10 & Flowers102\\
 \hline
 AdaBoost & 31.08 & 2.1 & 77.2 & 4.8 \\
 \hline
 LigthGBM & 53.1 & 12.2 & 96.3 & 20.0\\
 \hline
 logistic regression & 37.5 & 18.2 & 77.5 & 71.8 \\
 \hline
 random forest & 47.2 & 15.6 & 98.2 & 83.2\\
 \hline
 XGBoost & 53.8 & 12.9 & 94.3& 94.6\\
 \hline
 DenseNet121 & 86.9 & 41.7 & 97.5 & 96.4\\
 \hline
 resNet18 & 84.6 & 34.01 & 95.5 & 95.1\\
 \hline
 resNet34 & 84.9 & 35.98 & 95.8 & 95.6\\
 \hline
 resNet50 & 87.8 & 31.07 & 98.6 & 97.7\\
 \hline
 VGG11 & 85.3 & 42.3 & 96.2& 96.1\\
 \hline
 VGG16 & 86.1 & 43.6 & 97.3 & 96.5\\
 \hline
\end{tabular}
\caption{Accuracy of different models for different datasets.}
\end{table}
\end{document}

math

Disha Ghandwani
Neeraj Sarna
Yuanyuan Li
Yang Lin

"An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms" by Disha Ghandwani, Neeraj Sarna, Yuanyuan Li, and Yang Lin, arXiv:2412.04166v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
Source & Accuracy & 95\%CI & Data\\
\hline
 ALL-IDB2 & 1.00 & (0.9824, 1.0000) & Training \\ \hline
 C-NMC & 1.00 & (0.9996, 1.0000) & Training \\ \hline
 \hline
 ALL-IDB2 & 1.00 & (0.9315, 1.000) & Validation \\ \hline
 C-NMC & 0.8645 & (0.8493, 0.8788) & Validation \\ \hline
\end{tabular}
\end{adjustbox}
\caption{The accuracy and 95\% confidence interval (CI) for the various datasets from our RF model.}
\end{table}
\end{document}

cs

"Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features" by William Franz Lamberti, arXiv:2201.11864v1, 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|l|}
		\hline
		\hline
		\textbf{Step 1} & Traffic event matrix (TEM)\\
		\textbf{Step 2} & Event trigger conditions\\
		\textbf{Step 3} & Event counter\\
		\textbf{Step 4} & Scenario time frame\\
		\textbf{Step 5} & Storage of vehicle states\\
		\hline
		\hline
	\end{tabular}
\end{adjustbox}
\caption{Definitions of required implementation steps for STM.}
\end{table}
\end{document}

eess

Demin Nalic
Hexuan Li
Arno Eichberger
Christoph Wellershaus
Aleksa Pandurevic
Branko Rogic

"Stress Testing Method for Scenario Based Testing of Automated Driving Systems" by Demin Nalic, Hexuan Li, Arno Eichberger, Christoph Wellershaus, Aleksa Pandurevic, and Branko Rogic, arXiv:2011.06553v2, 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{Model Rankings Based on Average Metrics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{clccccc}
\toprule
\textbf{Rank} & \textbf{Model} & \textbf{Avg R-1} & \textbf{Avg R-2} & \textbf{Avg R-L} & \textbf{Avg Cosine} & \textbf{Avg Jaccard} \\
\midrule
1 & Gemini & 0.56 & 0.22 & 0.16 & 0.63 & 0.22 \\
2 & GPT & 0.31 & 0.08 & 0.10 & 0.68 & 0.13 \\
3 & Perplexity & 0.29 & 0.08 & 0.10 & 0.71 & 0.13 \\
4 & Claude & 0.27 & 0.08 & 0.09 & 0.67 & 0.12 \\
5 & DeepSeek & 0.16 & 0.03 & 0.06 & 0.59 & 0.09 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis" by Md Talha Mohsin, arXiv:2507.22936v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Deviation in installed capacity across approaches relative to perfect information for different profiles and cases.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccccc}
\hline
Model & Tech & Profile & PI & \textbf{$ARSO_{\beta=0}$} & \textbf{$ARO_{\beta=0}$} & \textbf{$ARSO_{\beta=10}$} & \textbf{$ARO_{\beta=10}$} & \textbf{$ARSO_{\beta=20}$} & \textbf{$ARO_{\beta=20}$} \\ \hline
\multirow{2}{*}{Only PV} & PV & A & 0.053 & 0.001 & -0.003 & 0.005 & 0.014 & 0.048 & 0.084 \\
 & PV & B & 0.050 & -0.050 & 0.000 & 0.005 & 0.020 & 0.027 & 0.087 \\ \hline
\multirow{4}{*}{PV and BESS} & PV & A & 0.053 & 0.004 & -0.002 & 0.012 & 0.034 & 0.050 & 0.089 \\
 & BESS & A & 0.000 & 0.038 & 0.069 & 0.060 & 0.053 & 0.087 & 0.090 \\
 & PV & B & 0.050 & 0.000 & 0.000 & 0.007 & 0.055 & 0.031 & 0.085 \\
 & BESS & B & 0.000 & 0.000 & 0.000 & 0.040 & 0.058 & 0.051 & 0.036 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Fernando García-Muñoz
Cristian Duran-Mateluna

"Adaptive Robust Optimization Models for DER Planning in Distribution Networks under Long- and Short-Term Uncertainties" by Fernando García-Muñoz and Cristian Duran-Mateluna, arXiv:2503.17839v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 Resolution & Error & Order \\
 \hline
 $h_0$ & 6.38E$-$4 & $-$ \\
 \hline
 $h_1$ & 7.99E$-$5 & $3.00$ \\
 \hline
 $h_2$ & 1.00E$-$5 & $3.00$ \\
 \hline
 $h_3$ & 1.25E$-$6 & $3.00$ \\
 \hline
 $h_4$ & 1.56E$-$7 & $3.00$ \\
 \hline
 $h_5$ & 1.95E$-$8 & $3.00$ \\
 \hline
 $h_6$ & 2.44E$-$9 & $3.00$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Improved weights (), $\beta=h^2$, example 2.}
\end{table}
\end{document}

math

"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, 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{PAD-UFES dataset classification results}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\hline
\textbf{Experiment} & $\textbf{\textit{n}}_f$ & \textbf{SE} & \textbf{SP} & \textbf{AC} & \textbf{BAC} \\ \hline
Image features & 59 & 0.664 & 0.816 & 0.640 & 0.740 \\
Context information & 7 & 0.684 & 0.836 & 0.692 & 0.760 \\
Combined & 59+7 & \textbf{0.744} & \textbf{0.872} & \textbf{0.753} & \textbf{0.808} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data" by José G. M. Esgario and Renato A. Krohling, arXiv:2201.10650v1, 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{Lid-driven cavity flow: steady-state location $(x,y)$ of the primary vortex.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 \textbf{} & \textbf{Present (Taylor--Hood)} & \textbf{Present (equal-order)}\\
 \hline
 $(0.5547,0.6055)$ & $(0.555,0.605)$ & $(0.555,0.605)$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Consistent splitting SAV schemes for finite element approximations of incompressible flows" by Douglas R. Q. Pacheco, arXiv:2503.20474v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the temporal model performances with and without explicit multi-scale modeling}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|rrrrrr}
 \toprule
 \multirow{2}[2]{*}{Model} & \multirow{2}[2]{*}{Multiscale?} & \multicolumn{3}{c}{CSI300} & \multicolumn{3}{c}{SP500} \\
 & & IC & AR & Sharpe & IC & AR & Sharpe \\
 \midrule
 \multirow{2}[1]{*}{RNN} & Single-scale & & & & & & \\
 & Multi-scale & & & & & & \\
 \multirow{2}[0]{*}{CNN} & Single-scale & & & & & & \\
 & Multi-scale & & & & & & \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Saizhuo Wang
Hao Kong
Jiadong Guo
Fengrui Hua
Yiyan Qi
Wanyun Zhou
Jiahao Zheng
Xinyu Wang
Lionel M. Ni
Jian Guo

"QuantBench: Benchmarking AI Methods for Quantitative Investment" by Saizhuo Wang, Hao Kong, Jiadong Guo, Fengrui Hua, Yiyan Qi, Wanyun Zhou, Jiahao Zheng, Xinyu Wang, Lionel M. Ni, and Jian Guo, arXiv:2504.18600v1, 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{The caption to the table with multiple columns.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|rr|rr|}\hline
 Row $1$ & c1 & c2 & c3 & c4 \\
 Row $2$ & d1 & d2 & d3 & d4\\
 Row $3$ & e1 & e2 & e3 & e4 \\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"The emergence of visual semantics through communication games" by Daniela Mihai and Jonathon Hare, arXiv:2101.10253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Expectile hidden Markov regression models for analyzing cryptocurrency returns" by Beatrice Foroni, Luca Merlo, and Lea Petrella, arXiv:2301.09722v2, 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{The result of ablation study.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c||c|}
\hline
Condition & NIQE($\downarrow$) & ENTROPY($\uparrow$)\\
\hline
with $D_C$, w/o $D_{T}$, w/o $D_{M}$ & 5.416 & 7.083 \\
\hline
with $D_T$, w/o $D_{C}$, w/o $D_{M}$ & 5.656 & 6.910 \\
\hline
with $D_M$, w/o $D_{T}$, w/o $D_{C}$ & 4.840 & 6.743\\
\hline
with $D_{T}$, with $D_{M}$, w/o $D_C$ & 4.589 & 7.132 \\
\hline
with $D_{C}$, with $D_{M}$, w/o $D_T$ & 3.789 & 7.291 \\
\hline
with $D_{T}$, with $D_{C}$, w/o $D_M$ & 3.929 & 6.992 \\
\hline
with $D_M$, $D_{T}$, $D_{C}$, w/o $CPAF$ & 6.625 & 7.067 \\
\hline
default configuration & 3.485 & 7.506 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yangyang Qu
Kai Chen
Chao Liu
Yongsheng Ou

"UMLE: Unsupervised Multi-discriminator Network for Low Light Enhancement" by Yangyang Qu, Kai Chen, Chao Liu, and Yongsheng Ou, arXiv:2012.13177v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Sound Classification of Four Insect Classes" by Yinxuan Wang and Sudip Vhaduri, arXiv:2412.12395v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
\textbf{Experiment} & \textbf{SSIM$\uparrow$} & \textbf{FID$\downarrow$} \\
\midrule
baseline & 0.8015 & 268.491 \\
baseline+\textbf{StainStylePrompt} & \textbf{0.8121} & \textbf{255.261} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Evaluation of effectiveness of StainStylePrompt using FID, SSIM.}
\end{table}
\end{document}

eess

Bing Xiong
Yue Peng
RanRan Zhang
Fuqiang Chen
JiaYe He
Wenjian Qin

"Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion" by Bing Xiong, Yue Peng, RanRan Zhang, Fuqiang Chen, JiaYe He, and Wenjian Qin, arXiv:2412.11106v1, 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{Heterogeneous Effects of XBRL Implementation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
& \multicolumn{4}{c}{Incorporation Speed (Log Days)} \\
& (1) & (2) & (3) & (4) \\
\midrule
XBRL Implementation & -0.089* & -0.067 & -0.045 & -0.078 \\
& (0.0456) & (0.0423) & (0.0398) & (0.0434) \\
\\
XBRL × Small Firm & -0.145*** & & & \\
& (0.0378) & & & \\
XBRL × High Retail & & -0.156*** & & \\
& & (0.0392) & & \\
XBRL × Low Institutional & & & -0.134*** & \\
& & & (0.0367) & \\
XBRL × Low Analyst Coverage & & & & -0.142*** \\
& & & & (0.0383) \\
\\
Controls & Yes & Yes & Yes & Yes \\
Fixed Effects & Yes & Yes & Yes & Yes \\
\\
Observations & 847,523 & 847,523 & 847,523 & 847,523 \\
R-squared & 0.371 & 0.369 & 0.368 & 0.370 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Cognitive Load and Information Processing in Financial Markets: Theory and Evidence from Disclosure Complexity" by Yimin Du and Guolin Tang, arXiv:2507.07037v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Voltage regulation and var sharing index comparison }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\hline
\multirow{2}{*}{Case Type} & \multicolumn{2}{c}{Event 1} & \multicolumn{2}{c}{Event 2}\\
 & $V_{error}$ & $MQSI$ & $V_{error}$ & $MQSI$ \\
\hline
no-control & 0.015 & 0.22 & 0.006 & 0.30\\
uncoordinated & 0.001 & 2.03 & 0.000 & 1.66\\
GFM-coordinated & 0.002 & 1.77 & 0.002 & 1.07\\
fully-coordinated & 0.002 & 0.04 & 0.001 & 0.05\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Coordinated Frequency and Voltage Regulation of Grid-Following and Grid-Forming Inverters" by Ankit Singhal, Thanh Long Vu, and Wei Du, arXiv:2012.06685v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Yuan Hu
W. Brent Lindquist
Svetlozar T. Rachev

"ESG-valued discrete option pricing in complete markets" by Yuan Hu, W. Brent Lindquist, and Svetlozar T. Rachev, arXiv:2209.06276v1, 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}{lccc}
 \toprule
 MLE parameters & $b$ & $g$ & $q$\\
 \midrule
 Standard MLE density (Green) & $5.66 \cdot 10^{-3}$& $50.5$ & - \\
 Flexible MLE density (Red) & $2.29 \cdot 10^{-3}$ & $94.9$ & $0.0339$ \\
 \bottomrule
 \end{tabular}
\caption{MLE parameters of the MBBEFD example rounded to three significant digits (Section~).}
\end{table}
\end{document}

stat

"Modeling lower-truncated and right-censored insurance claims with an extension of the MBBEFD class" by Selim Gatti and Mario V. Wüthrich, arXiv:2310.11471v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Demetrio Lacava
Giampiero M. Gallo
Edoardo Otranto

"Unconventional Policies Effects on Stock Market Volatility: A MAP Approach" by Demetrio Lacava, Giampiero M. Gallo, and Edoardo Otranto, arXiv:2010.08259v2, 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{Different Pooling Options}
\begin{tabular}{|c|c|c|}
			\hline
			\textbf{Pooling [$p_{ant}$, $p_{sc}$]}&\textbf{Antenna Configuration} & \textbf{ME (m)}\\
			\hline

			[1,4] & ULA & 0.00612 \\
			\hline
			[2,2] & ULA & 0.01010 \\
			\hline
			[4,1] & ULA & 0.01633 \\
			\hline
			[1,4] & distributed & 0.00521 \\
			\hline
			[2,2] & distributed &0.00732 \\
			\hline
			[4,1] & distributed & 0.00982 \\
			\hline
			[1,4] & URA & 0.01096 \\
			\hline
			[2,2] & URA & 0.01183 \\
			\hline
			[4,1] & URA & 0.01908 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

eess

Anastasios Foliadis
Mario H. Castañeda Garcia
Richard A. Stirling-Gallacher
Reiner S. Thomä

"CSI-Based Localization with CNNs Exploiting Phase Information" by Anastasios Foliadis, Mario H. Castañeda Garcia, Richard A. Stirling-Gallacher, and Reiner S. Thomä, arXiv:2101.08983v1, 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{different choices of phase features $x(t)$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
			\hline
			feature name & phase features $x(t)$ \\
			\hline
			\hline
			phase & $\phi_p(t)$ \\
			\hline
			phase delta & $\Delta \phi_p(t)$ \\
			\hline
			phase delta + double-delta & $[\Delta \phi_p(t), \Delta \Delta\phi_p(t))]$ \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jian Luo
Jianzong Wang
Ning Cheng
Guilin Jiang
Jing Xiao

"End-to-end Silent Speech Recognition with Acoustic Sensing" by Jian Luo, Jianzong Wang, Ning Cheng, Guilin Jiang, and Jing Xiao, arXiv:2011.11315v1, 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{ \small In-sample validation of age-specific net migration projection on the scale of counts and rates (migrants per 100 people). The three methods were validated over seven time periods (between 1950-1960 and 2010-2020), 39 counties and 20 5-year age groups, i.e., 5460 values. MAE is mean absolute error. RMSE is root mean squared error. The cov80 and cov95 columns refer to the percentage of the observations that fell within their probability interval.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|rrrrr}\hline
\bf Scale & \bf Method & \bf MAE & \bf RMSE & \bf Bias & \bf cov80 & \bf cov95\\\hline
Counts & Basic Rogers-Castro & 585.4 & 2054.0 & \bf 0.00 & --- & --- \\
& Deterministic FDM & 349.2 & 1207.3 & -0.04 & --- & --- \\
& Bayesian FDM & \bf 319.7 & \bf 1118.3 & 1.00 & \bf 86.7 & \bf 95.6 \\\hline
Rates & Basic Rogers-Castro & 12.14 & 24.91 & 1.61 & --- & --- \\
& Deterministic FDM & 8.39 & 16.32 & \bf -0.18 & --- & --- \\
& Bayesian FDM & \bf 7.76 & \bf 13.83 & -1.03 & \bf 86.7 & \bf 95.6 \\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Hana Ševčíková
James Raymer
Adrian E. Raftery

"Forecasting Net Migration By Age: The Flow-Difference Approach" by Hana Ševčíková, James Raymer, and Adrian E. Raftery, arXiv:2411.09878v1, 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{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overview of the analytical test functions from BoTorch that were used for benchmarking the described optimization strategies. For each function, the first row describes the analytical function with only output-dependent objectives, and the second row includes an additional input-dependent objective. Visualizations of the objective ranges and inter-objective correlations are provided in the following figures.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
Function & Number of Inputs & Number of Outputs & Objective \#1 & Objective \#2 & Objective \#3 \\ \midrule \midrule
BNH & \multirow{2}{*}{2} & \multirow{2}{*}{2} & $y_0 > -60.0$ & $y_1 > -11.0$ & \\
BNH* & & & $y_0 > -60.0$ & $x_0 - x_1 > 2.0$ & $y_1 > -15.0$ \\ \midrule
DH4 & \multirow{2}{*}{6} & \multirow{2}{*}{2} & $y_0 > -0.15$ & $y_1 > -15.0$ & \\
DH4* & & & $y_0 > -0.15$ & $x_1 > 0.6$ & $y_1 > -15.0$ \\ \midrule
DTLZ5 & \multirow{2}{*}{4} & \multirow{2}{*}{2} & $y_0 > -0.5$ & $y_1 > -0.95$ & \\
DTLZ5* & & & $y_0 > -0.5$ & $x_2 + x_3 < 1.0$ & $y_1 > -0.95$ \\ \midrule
ZDT1 & \multirow{2}{*}{10} & \multirow{2}{*}{2} & $y_0 > -0.18$ & $y_1 > -2.5$ & \\
ZDT1* & & & $y_0 > -0.18$ & $x_1 + x_5 < 0.5$ & $y_1 > -2.5$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mohammad Haddadnia
Leonie Grashoff
Felix Strieth-Kalthoff

"BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives" by Mohammad Haddadnia, Leonie Grashoff, and Felix Strieth-Kalthoff, arXiv:2501.15554v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|l|}
			\hline
			\textbf{Class}& \textbf{Nº finite singular points} & \textbf{$\boldsymbol{ind_F}$} & \textbf{Global phase portraits} \\
			\hline
			\hline
			1
			&
			3
			&
			1
			&
			G1, G2, G3, G4, G5, G6, G7, G8, G9, G10.
			\\
			\hline

			2
			&
			\multirow{3}{1cm}{\centering{2}}
			&
			1
			&
			G11, G12, G13, G14.
			\\
			\cline{1-1}\cline{3-4}

			3
			&
			\multirow{3}{*}
			&
			0
			&
			G15, G16, G17, G18, G23, G24, G25, G26.
			\\
			\cline{1-1}\cline{3-4}

			4
			&
			\multirow{3}{*}
			&
			2
			&
			G19, G20, G27, G28.
			\\
			\hline

			5
			&
			\multirow{3}{1cm}{\centering{1}}
			&
			0
			&
			G21, G22, G29, G30.
			\\
			\cline{1-1}\cline{3-4}

			6
			&
			\multirow{3}{*}
			&
			-1
			&
			G31, G32.
			\\
			\cline{1-1}\cline{3-4}

			7
			&
			\multirow{3}{*}
			&
			1
			&
			G33, G34, G35, G36.
			\\
			\hline

		\end{tabular}
\end{adjustbox}
\caption{Classes of equivalence according to the number of finite singular points and to the $ind_F$. }
\end{table}
\end{document}

math

Érika Diz-Pita
Jaume Llibre
M. Victoria Otero-Espinar

"Planar Kolmogorov systems with infinitely many singular points at infinity" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.14416v1, 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{Optimal Learning Rates and Convergence Times from Figure~}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
 & \textbf{optimal learning rate} & \textbf{optimal convergence time} \\
\hline
$\eta$ coordinates & $\alpha_{\eta}=0.0036$ & \hspace{0.2cm}$k=29$ \\
natural gradient & $\alpha_{ng}\in [0.7141,1.16]$ & $k=2$\\
$\theta$ coordinates & $\alpha_{\theta}\in [11.96,14.24]$ & \hspace{0.2cm}$k=12$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Convergence Properties of Natural Gradient Descent for Minimizing KL Divergence" by Adwait Datar and Nihat Ay, arXiv:2504.19259v2, 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
 Method & Precision & Recall & $F_1$ Score \\ \hline
 No Augmentation & \textbf{0.8901} & 0.8265 & 0.8571 \\ \hline
 Diffusion & 0.8737 & \textbf{0.8469} & \textbf{0.8601} \\ \hline
 \end{tabular}
\caption{Precision, recall, and $F_1$ score for XGBoost on a test set after being trained with and without data augmentation with diffusion models. The maximum value in each column is bolded.}
\end{table}
\end{document}

math

Generative Modeling with Diffusion

"Generative Modeling with Diffusion" by Justin Le, arXiv:2412.10948v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{soul}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average true positive rate per class for differing IR sensor activation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l}
 & \multicolumn{2}{l}{Per Class True Positive Rate} \\
IR Sensor Activation & Average & STD Error \\
\hline
\textbf{Required}, N=13 & \hl{38.92}\% & \hl{0.01} \\
Not Required, N=8 & \hl{13.71}\% & \hl{0.02} \\
Majority & 5.34\% & N/A \\
Chance & 4.76\% & N/A \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Nibhrat Lohia
Raunak Mundada
Arya D. McCarthy
Eric C. Larson

"AirWare: Utilizing Embedded Audio and Infrared Signals for In-Air Hand-Gesture Recognition" by Nibhrat Lohia, Raunak Mundada, Arya D. McCarthy, and Eric C. Larson, arXiv:2101.10245v1, 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{Detection accuracy of ET with RE-AP on the 10 five-class domains with 1 attack, 2 attacks, and no attack, compared with RED-AP (original) and RED-AP (MAD).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
				&1 attack &2 attacks &clean
				\\ \hline
				ET (RE-AP) &10/10 &10/10 &10/10\\
				RED-AP (original) &10/10 &0/10 &9/10\\
				RED-AP (MAD) &6/10 &2/10 &7/10
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zhen Xiang
David J. Miller
George Kesidis

"Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios" by Zhen Xiang, David J. Miller, and George Kesidis, arXiv:2201.08474v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{graphicx}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Datasets vs. Security services}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|}
\hline
Dataset & \rotatebox{90}{~\parbox{2cm}{Integrity~}} & \rotatebox{90}{~\parbox{2cm}{Authentication~}} & \rotatebox{90}{~\parbox{2cm}{Availability~}}& \rotatebox{90}{~\parbox{2cm}{Confidentiality~}} \\ \hline
VeReMi & X & & & \\ \hline
VeReMi extension & X & & X & \\ \hline
DARPA & & X & X & \\ \hline
CAIDA DDos 2007 & & & X & \\ \hline
AWID2 & & X & X & X \\ \hline
KDD CUP 99 & & X & X & \\ \hline
NSL-KDD & & X & X & \\ \hline
Kyoto & & X & X & \\ \hline
UNSW-NB15 & & X & X & \\ \hline
CICIDS2017 & X & X & X & \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks" by Abdelwahab Boualouache and Thomas Engel, arXiv:2201.10500v1, 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{The results of the Chi-square for one day vs year with sideswipe crashes.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrr}
 & Sun & M & T & W & T & F & Sat \\
statistic & 8.58E-01 & 1.50E+00 & 2.73E+00 & 2.81E+00 & 3.40E+00 & 5.18E+00 & 3.72E+00 \\
P-value & 9.31E-01 & 8.27E-01 & 6.05E-01 & 5.90E-01 & 4.93E-01 & 2.69E-01 & 4.46E-01
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xiwen Chen
Hao Wang
Abolfazl Razi
Brendan Russo
Jason Pacheco
John Roberts
Jeffrey Wishart
Larry Head
Alonso Granados Baca

"Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study" by Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head, and Alonso Granados Baca, arXiv:2201.13229v2, 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{Summary of out-of-sample validation with simulations ($a_{1|0}=3, \psi=1$, $\Delta=0.5$).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\toprule
 & \multicolumn{3}{c}{RMSE} & \multicolumn{3}{c}{GDEV} \\
\cmidrule(l{3pt}r{3pt}){2-4} \cmidrule(l{3pt}r{3pt}){5-7}
 & Independent & B{\"u}hlmann & SSM & Independent & B{\"u}hlmann & SSM\\
\midrule
Homogeneous & 6067.64 & 6133.54 & 5981.14 & 6680.86 & 6764.54 & 6499.17\\
 & (724.26) & (690.46) & (701.65) & (180.72) & (189.24) & (175.27)\\
 \hline
Heterogeneous & 5974.26 & 6025.61 & 5878.33 & 6417.36 & 6473.82 & 6229.60\\
 & (727.88) & (695.29) & (704.12) & (176.98) & (190.43) & (175.06)\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jae Youn Ahn
Himchan Jeong
Mario V. Wüthrich

"An Observation-Driven State-Space Model for Claims Size Modeling" by Jae Youn Ahn, Himchan Jeong, and Mario V. Wüthrich, arXiv:2412.21099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the clean simulated data set}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|c|c}
 \toprule
 & Train & \multicolumn{2}{c|}{Dev} & \multicolumn{2}{c}{Test} \\
 & & short & long & short & long \\
 \midrule
 Source & train\_960 & dev\_clean & dev\_clean & test\_clean & test\_clean \\
 \# of samples & 50,083 & 513 & 319 & 496 & 310 \\
 Avg. words. & 193 & 106 & 171 & 106 & 170 \\
 Avg. dur. (sec) & 61.9 & 34.1 & 54.1 & 35.4 & 56.1 \\ \midrule
 Total dur. (hr) & 861.6 & 4.9 & 4.8 & 4.9 & 4.8 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{kNN val accuracy for several ways of sampling TF patches.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc|lc}
\toprule
\textbf{Sampling method} & \textbf{kNN} & \textbf{Sampling method} & \textbf{kNN} \\
\midrule
Sampling at random & \textbf{70.1} & $d = 125$ & 67.9 \\
$d = 0$ (same patch) & 51.1 & $d = 200$ & 69.9 \\
$d = 25$ & 61.5 & $d = 300$ & 68.5 \\
$d = 75$ & 65.1 & $d = 400$ & 69.7 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Eduardo Fonseca
Diego Ortego
Kevin McGuinness
Noel E. O'Connor
Xavier Serra

"Unsupervised Contrastive Learning of Sound Event Representations" by Eduardo Fonseca, Diego Ortego, Kevin McGuinness, Noel E. O'Connor, and Xavier Serra, arXiv:2011.07616v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{OLS regression - Cycalility of occupational mobility}
\begin{tabular}{rlllll}
 \toprule
 \multicolumn{6}{c}{Level and Responsiveness of Mobility to the Unemployment Rate} \\
 \midrule
 & Level & \multicolumn{4}{c}{Cyclical responsiveness} \\ \cmidrule(lr){2-2} \cmidrule(lr){3-6}
 & Ave. & \multicolumn{2}{c}{HP Filtered (1600)} & \multicolumn{2}{c}{Band-Pass Filtered} \\
 \cmidrule(lr){3-4} \cmidrule(lr){5-6}
& Mob. & up to '21 & up to `19 & up to '21 & up to '19 \\
 \midrule
 \multicolumn{6}{c}{Quarterly Occupational Mobility Rate, Hires from U (2010 MOG)} \\
 \midrule
 All unemployed, 1976- & 0.475 & -0.223$^{\star\star\star}$ & -0.105$^{\star\star}$ & -0.105 $^{\star\star\star}$ & -0.104$^{\star\star\star}$ \\
 & & (0.030) & (0.039) & (0.016) & (0.017) \\
 All unemployed, 1994- & 0.464 & -0.254$^{\star\star\star}$ & -0.103$^{\star\star}$ & -0.096$^{\star\star\star}$ & -0.104$^{\star\star\star}$ \\
 & & (0.036) & (0.050) & (0.018) & (0.019) \\
 Unemployed, not on temporary layoffs, 1994- & 0.569 & -0.044$^{\diamond}$ & -0.091$^{\star}$ & -0.063$^{\star\star}$ & -0.085$^{\star\star\star}$ \\
 & & (0.024) & (0.037) & (0.019) & (0.017) \\
 \bottomrule
 \bottomrule
 \multicolumn{5}{l}{\footnotesize $^{\star\star\star}$ p-value<0.001; $^{\star\star}$ p-value<0.01; $^{\star}$ p-value<0.05; $^{\diamond}$ p-value<0.1.}
 \end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
		\toprule
		&	Risk & Estimation function class& Error bound\\
		\midrule
	\tiny{	}&	natural & FNNs& $n^{-2\alpha/(d+2\alpha)}$\\
		&	natural & FNNs with norm constraints&$n^{-\alpha/(d+2\alpha+1)}$\\
		This paper &		adversarial & FNNs with norm constraints& $n^{-2\alpha/(2d+5\alpha)}$\\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Changyu Liu
Yuling Jiao
Junhui Wang
Jian Huang

"Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models" by Changyu Liu, Yuling Jiao, Junhui Wang, and Jian Huang, arXiv:2309.00771v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Configurations with Different Scales}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \hline
 \textbf{Index} & \textbf{$d$} & \textbf{$b$} & \textbf{Dimension of $L-1$ Layer's Output} & \textbf{Training Samples} \\
 \hline
 1 & 32 & 25 & 1024 & 32 \\
 2 &512 & 400 & 5120 & 10 \\
 3 &512 & 400 & 20 & 2048 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Learning Provably Improves the Convergence of Gradient Descent" by Qingyu Song, Wei Lin, and Hong Xu, arXiv:2501.18092v4, 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{Results of the Diebold-Mariano-like test for different method pairs in the North zone. Each cell contains the interval of values of \(\mu_0\) leading to not reject the null hypothesis that $E(d_{t,ij})=\mu_0$ at the \(5\%\) significance level.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\hline & Method 1 & Method 2 & Method 3 & Method 4 & Method 5 & Method 6 \\ \toprule Method 1 & - & & & & & \\ Method 2 & \([-9, -5]\) & - & & & & \\ Method 3 & \( [17, 26] \) & \( [23, 34] \) & - & & & \\ Method 4 & \( [-3917, -2870] \) & \( [-3909, -2865] \) & \( [-3942, -2889] \) & - & & \\ Method 5 & \( [-4037, -2959] \) & \( [-4028, -2954] \) & \( [-4061, -2978] \) & \( [-123, -85] \) & - & \\ Method 6 & \( [-3187, -2338] \) & \( [-3179, -2333] \) & \( [-3212, -2357] \) & \( [531, 731] \) & \( [621, 849] \) & - \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Marina Bertolini
Pierdomenico Duttilo
Francesco Lisi

"Accounting carbon emissions from electricity generation: a review and comparison of emission factor-based methods" by Marina Bertolini, Pierdomenico Duttilo, and Francesco Lisi, arXiv:2411.13663v1, 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{XGBoost Classification Hyperparameter Selection (Reduction=5)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|}
\hline
Hyperparameter & Datatype & Values/{[}Min, Max{]} & Log Scaling & Gridpoint Count & Best Value \\ \hline
Maximum Tree Depth & Integer & {[}2, 8{]} & FALSE & 3 & 2 \\ \hline
L2 Weight Regularization (lambda) & Continuous & {[}0.1, 100{]} & TRUE & 3 & 0.37363 \\ \hline
Learning Rate (eta) & Continuous & {[}0.0, 1.0{]} & FALSE & 3 & 0.688255 \\ \hline
Minimum Split Loss (gamma) & Continuous & {[}0, 1{]} & FALSE & 3 & 0.695424 \\ \hline
Minimum Child Weight & Continuous & {[}0.5, 10{]} & TRUE & 3 & 0.694833 \\ \hline
Early Stopping Patience & Integer & {[}1, 100{]} & TRUE & 3 & 33 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Lyle Regenwetter
Colin Weaver
Faez Ahmed

"FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames" by Lyle Regenwetter, Colin Weaver, and Faez Ahmed, arXiv:2201.10459v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Preprocessing pipelines considered in this work.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \Xhline{2\arrayrulewidth} \\
 preprocessing step & Raw & Filt & ICA & ICA+ASR\\
 \\ \Xhline{2\arrayrulewidth}
 non-EEG channels removal & \checkmark & \checkmark & \checkmark & \checkmark \\
 \hline
 time segments removal & & & \checkmark & \checkmark \\
 \hline
 baseline removal & & \checkmark & \checkmark & \checkmark \\
 \hline
 resampling & \checkmark & \checkmark & \checkmark & \checkmark \\
 \hline
 filtering & & \checkmark & \checkmark & \checkmark \\
 \hline
 independent component analysis & & & \checkmark & \checkmark \\
 \hline
 automatic component rejection & & & \checkmark & \checkmark \\
 \hline
 bad-channel removal & & & & \checkmark \\
 \hline
 bad-time windows correction (ASR) & & & & \checkmark \\
 \hline
 spherical interpolation & & & & \checkmark \\
 \hline
 re-reference & & \checkmark & \checkmark & \checkmark \\
 \hline
 template alignment & \checkmark & \checkmark & \checkmark & \checkmark \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Federico Del Pup
Andrea Zanola
Louis Fabrice Tshimanga
Alessandra Bertoldo
Manfredo Atzori

"The more, the better? Evaluating the role of EEG preprocessing for deep learning applications" by Federico Del Pup, Andrea Zanola, Louis Fabrice Tshimanga, Alessandra Bertoldo, and Manfredo Atzori, arXiv:2411.18392v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Lily Agranat-Tamir
Michael Fuchs
Bernhard Gittenberger
Noah A. Rosenberg

"Enumerative combinatorics of unlabeled and labeled time-consistent galled trees" by Lily Agranat-Tamir, Michael Fuchs, Bernhard Gittenberger, and Noah A. Rosenberg, arXiv:2504.16302v1, 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{Transducer characteristics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c}
\hline
Parameter & Value\\
\hline\hline
Resistance ($R_t$) & 10.6 $\Omega$ \\
Inductance ($L$) & 0.0219 H \\
Permanent-magnet flux linkage ($\Lambda_{PM}$) & 0.1603 V-s \\
No. of poles ($N_p$) & 6 \\
Rotational inertia ($J_t$) & 3.54 $\times 10^{-5}$ kg-m$^2$\\
Rotational viscous damping ($B_t$) & 3.25 $\times 10^{-4}$ N-m-s\\
Coulomb friction ($f_c$) & 35 N\\
Lead length ($\ell$) & 1.27 $\times 10^{-3}$ m-rad$^{-1}$\\
Efficiency ($\eta$) & 0.91\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Connor Ligeikis
Heath Hofmann
Jeff Scruggs

"Large-Scale Self-Powered Vibration Control: Theory and Experiment" by Connor Ligeikis, Heath Hofmann, and Jeff Scruggs, arXiv:2509.11346v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameter $\alpha$ for different lens designs.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
			\hline
			\hline
			& Design 1 & Design 2 \\
			\hline
			$\alpha$ & $\dfrac{{\pi {{\sin }^{\rm{2}}}\theta }}{{\lambda F}} - \dfrac{{\pi {{\cos }^{\rm{2}}}\phi }}{{\lambda d}}$ & $\dfrac{{\pi {{\sin }^{\rm{2}}}\theta }}{{\lambda F}} - \dfrac{{\pi {{\cos }^{\rm{2}}}\phi }}{{\lambda d}} + \dfrac{\pi }{{\lambda {F_0}}}$ \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jie Yang
Yong Zeng
Shi Jin
Chao-Kai Wen
Pingping Xu

"Communication and Localization with Extremely Large Lens Antenna Array" by Jie Yang, Yong Zeng, Shi Jin, Chao-Kai Wen, and Pingping Xu, arXiv:2012.11161v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 \hline\hline
 Methods & $\|\nabla f\|$ & Time \\
 \hline
 ASK & 5.3550e-07 & 9.3056e-03 \\
 GD & 9.9561e-07 & 7.4152e-03 \\
 NAG & 2.9041e-03 & 6.5248e-03 \\
 HB & 9.8639e-07 & 5.1908e-03 \\
 \hline\hline
 \end{tabular}
\caption{Six-hump Camel Function, init=$6\times\text{rand}(2,1)-3$. }
\end{table}
\end{document}

math

Mengqi Hu
Bian Li
Yi-An Ma
Yifei Lou
Xiu Yang

"A Gradient-Based Optimization Method Using the Koopman Operator" by Mengqi Hu, Bian Li, Yi-An Ma, Yifei Lou, and Xiu Yang, arXiv:2312.14361v1, 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|c|c||}
 \hline
 $(\alpha,s)$ & $(0.3,0.2)$ & $(0.3,0.9)$ & $(0.8,0.2)$ & $(0.8,0.9)$ \\
 \hline
$E_k$ & $0.00654$ & $0.00811$ & $0.00783$ & $0.00827$ \\
 \hline
\end{tabular}
\caption{Choices of the fractional derivatives orders $\alpha$ and $s$ along with the errors $E_k$.}
\end{table}
\end{document}

math

"Identification of the initial value for a space-time fractional diffusion equation" by Mohamed BenSalah and Salih Tatar, arXiv:2412.05387v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Prediction Accuracy by Component Type}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
Component Type & MAE (mm) & RMSE (mm) & $R^2$ Score & 95\% CI (mm) \\
\midrule
Hydraulic Manifold & 0.0068 & 0.0083 & 0.912 & $\pm$0.011 \\
Turbine Blade & 0.0074 & 0.0092 & 0.893 & $\pm$0.014 \\
Mounting Bracket & 0.0052 & 0.0063 & 0.934 & $\pm$0.009 \\
Gear Housing & 0.0071 & 0.0087 & 0.905 & $\pm$0.012 \\
Medical Implant & 0.0085 & 0.0104 & 0.881 & $\pm$0.016 \\
Overall & 0.0070 & 0.0086 & 0.907 & $\pm$0.012 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Hamidreza Samadi
Md Manjurul Ahsan
Shivakumar Raman

"Hybrid Machine Learning Framework for Predicting Geometric Deviations from 3D Surface Metrology" by Hamidreza Samadi, Md Manjurul Ahsan, and Shivakumar Raman, arXiv:2508.06845v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Markus Brueckner
Tomoo Kikuchi
George Vachadze

"Transitional Dynamics of the Saving Rate and Economic Growth" by Markus Brueckner, Tomoo Kikuchi, and George Vachadze, arXiv:2012.15435v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Description of data}
\begin{tabular}{ccc}
\toprule
 Asset & Start date & End date \\
\midrule
 S\&P500 & 22/Sep/2003 & 31/Dec/2018 \\
 NASDAQ100 & 01/Jan/2003 & 31/Dec/2018 \\
 Gold & 03/Jun/2008 & 31/Dec/2018 \\
 Silver & 16/Mar/2011 & 31/Dec/2018 \\
 WTI Crude Oil & 10/May/2007 & 31/Dec/2018 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Wenbo Ge
Pooia Lalbakhsh
Leigh Isai
Artem Lensky
Hanna Suominen

"Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Data" by Wenbo Ge, Pooia Lalbakhsh, Leigh Isai, Artem Lensky, and Hanna Suominen, arXiv:2306.12446v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Donya Ramezanian
Arash G. Nouri
Hessam Babaee

"On-the-fly Reduced Order Modeling of Passive and Reactive Species via Time-Dependent Manifolds" by Donya Ramezanian, Arash G. Nouri, and Hessam Babaee, arXiv:2101.03847v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"datadriftR: An R Package for Concept Drift Detection in Predictive Models" by Ugur Dar and Mustafa Cavus, arXiv:2412.11308v1, 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{Performance measures of imputations using state space model and Random Forest in `mice'. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\hline
\multirow{2}{*}{\textbf{Variable}} & \multicolumn{3}{c}{\textbf{State space model}}& %
 \multicolumn{3}{c}{\textbf{Random Forest in mice}}\\
\cline{2-7}
 & SMAPE(60/40) & SMAPE(70/30) & SMAPE(80/20) & SMAPE(60/40) & SMAPE(70/30) & SMAPE(80/20) \\\hline
\% patients with septicemia &0.162 &0.140&0.093&0.466&0.476&0.443\\\hline
Number of staff working for the facility&0.167 &0.125&0.063&0.485&0.512&0.493 \\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Missing Data Imputation and Multilevel Conditional Autoregressive Modeling of Spatial End-Stage Renal Disease Incidence" by Supraja Malladi, Indranil Sahoo, and QiQi Lu, arXiv:2309.06270v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{Political position}} & \multicolumn{2}{c|}{\textbf{Share in 2004}} & \multicolumn{2}{c|}{\textbf{Share in 2016}}\tabularnewline
\cline{2-5}
 & \textbf{Before} & \textbf{After} & \textbf{Before} & \textbf{After}\tabularnewline
\hline
\textbf{1 (Extremely liberal)} & 3.04\% & 2.54\% & 4.27\% & 3.62\%\tabularnewline
\textbf{2} & 11.63\% & 10.84\% & 15.00\% & 14.52\%\tabularnewline
\textbf{3} & 10.39\% & 14.31\% & 12.29\% & 13.42\%\tabularnewline
\textbf{4} & 33.49\% & 32.44\% & 27.11\% & 29.11\%\tabularnewline
\textbf{5} & 15.82\% & 16.68\% & 15.44\% & 14.35\%\tabularnewline
\textbf{6} & 21.62\% & 19.88\% & 21.20\% & 20.70\%\tabularnewline
\textbf{7 (Extremely conservative)} & 4.01\% & 3.30\% & 4.69\% & 4.28\%\tabularnewline
\hline
\textbf{Average position} & 4.283 & 4.227 & 4.168 & 4.153\tabularnewline
\textbf{Variance of positions} & 2.147 & 2.013 & 2.503 & 2.375\tabularnewline
\hline
\end{tabular}
\end{adjustbox}
\caption{Shares of respondents by self-reported position on a 1-7 liberal-conservative scale, by year. Before and After refer to surveys taken before and after the election, respectively. Source: ANES dataset, https://electionstudies.org/data-tools/anes-continuity-guide/\#liberal-conservative. Answers are weighted using the weights provided by ANES. Respondents who did not know how to answer or refused to answer are omitted.}
\end{table}
\end{document}

q-fin

"A Flexible Measure of Voter Polarization" by Boris Ginzburg, arXiv:2507.07770v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

The Narayana Morphism and Related Words

"The Narayana Morphism and Related Words" by Jeffrey Shallit, arXiv:2503.01026v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A Single-loop Proximal Subgradient Algorithm for A Class Structured Fractional Programs" by Deren Han, Min Tao, and Zihao Xia, arXiv:2503.12176v1, 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{Scenario Parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 Parameter & Sub-6GHz & THz\\
 \hline
 No. of Users & 100,000 & 100,000\\
 BS Antenna Array Length & 4 & 128\\
 BS Antenna Height (m) & 8 & 8\\
 User Antenna Height (m) & 2 & 2\\
 Frequency (GHz) & 2.4 & 100\\
 Propagation Model & sbr+gas+cloud & sbr+gas+cloud\\
 Max No. of Paths & 8 & 4\\
 Bandwidth (MHz) & 20 & 500\\
 No. of OFDM Subcarriers & 64 & 64\\
 Codebook size (X,Y,Z) & $4\times 64\times 4$ & $4 \times 128\times 4$\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Deep Learning for THz Channel Estimation and Beamforming Prediction via Sub-6GHz Channel" by Sagnik Bhattacharya and Abhishek K. Gupta, arXiv:2411.15589v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Makoto Araya
Masaaki Harada
Hadi Kharaghani
Sho Suda
Wei-Hsuan Yu

"Unbiased weighing matrices of weight $9$" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Sohrab Andaz
Carson Eisenach
Dhruv Madeka
Kari Torkkola
Randy Jia
Dean Foster
Sham Kakade

"Learning an Inventory Control Policy with General Inventory Arrival Dynamics" by Sohrab Andaz, Carson Eisenach, Dhruv Madeka, Kari Torkkola, Randy Jia, Dean Foster, and Sham Kakade, arXiv:2310.17168v2, 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{Quantitative comparison of ensemble models on the validation dataset. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
\hline
Model & Accuracy & \multicolumn{2}{c|}{f1-score} & \multicolumn{2}{c|}{Precision} & \multicolumn{2}{c|}{Recall} \\ \hline
 & & Fake & Real & Fake & Real & Fake & Real \\ \hline
Ensemble-v1 & 0.981 & 0.980 & 0.982 & 0.980 & 0.983 & 0.981 & 0.981 \\ \hline
Ensemble-v2 & 0.983 & 0.982 & 0.984 & \textbf{0.986} & 0.980 & 0.978 & \textbf{0.988} \\ \hline
Ensemble-v3 & \textbf{0.984} & \textbf{0.983} & \textbf{0.984} & 0.983 & \textbf{0.984} & \textbf{0.982} & 0.985 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

S. M. Sadiq-Ur-Rahman Shifath
Mohammad Faiyaz Khan
Md. Saiful Islam

"A transformer based approach for fighting COVID-19 fake news" by S. M. Sadiq-Ur-Rahman Shifath, Mohammad Faiyaz Khan, and Md. Saiful Islam, arXiv:2101.12027v1, licensed under CC BY-SA 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{MARs (\%) based on autosuspension data}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\hline
\hline
Approach &K-means &FPCM &KPCA & I-MPPCA\\
\hline
Fault 1 &2.82 &1.41 &0 &\textbf{0}\\
Fault 2 &2.85 &1.90 &0 &\textbf{0}\\
Fault 3 &7.14 &6.52 &6.21 &\textbf{0.62}\\
Fault 4 &8.11 &7.66 &6.76 &\textbf{0.45}\\
Fault 5 &13.66 &13.04 &12.11 &\textbf{1.86}\\
Fault 6 &15.77 &13.96 &13.06 &\textbf{3.15}\\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jingxin Zhang
Hao Chen
Songhang Chen
Xia Hong

"An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring" by Jingxin Zhang, Hao Chen, Songhang Chen, and Xia Hong, arXiv:2012.06830v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.