\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overall generalization performance of the student model in $\%$ for ATop-$k$ accuracy and ADBA score.}
\begin{tabular}{l|l|ccc}
\hline
Metric& Methods & $L=8$ & $L=5$ & $L=3$ \\
\hline
\multirow{2}{*}{Test loss} & W/o KD & $1.342$ & $1.458$ & $1.582$ \\
& With KD & $1.045$ & $1.178$ & $1.283$ \\
\hline
\multirow{2}{*}{ATop-$1$} & W/o KD & $38.34$ & $33.89$ & $33.19$ \\
& With KD & $44.36$ & $42.88$ & $39.52$ \\
\hline
\multirow{2}{*}{ATop-$3$} & W/o KD & $\bf 71.98$ & $67.15$ & $\bf 64.86$ \\
& With KD & $\bf 80.23$ & $78.13$ & $\bf 73.13$ \\
\hline
\multirow{2}{*}{ATop-$5$} & W/o KD & $\bf 88.32$ & $85.68$ & $81.45$ \\
& With KD & $93.62$ & $91.90$ & $\bf 90.57$ \\
\hline
\multirow{2}{*}{ADBA} & W/o KD & $\bf 90.31$ & $87.66$ & $85.12$ \\
& With KD & $94.86$ & $93.28$ & $\bf 91.56$ \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Mengyuan Ma
Nhan Thanh Nguyen
Nir Shlezinger
Yonina C. Eldar
A. Lee Swindlehurst
Markku Juntti

"Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications" by Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger, Yonina C. Eldar, A. Lee Swindlehurst, and Markku Juntti, arXiv:2509.11419v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Pick/Place Accuracy. \textcolor{red}{s} means the object has been trained to be placed in the sink. \textcolor{blue}{d} means the object has been trained to be placed in the drawer.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccc}
 \toprule
 location / object & pineapple (\textcolor{red}{s},\textcolor{blue}{d}) & starfruit (\textcolor{red}{s}) & milk & duck (\textcolor{blue}{d}) & pan \\
 \midrule
 sink & 90\% & 90\% & 80\% & 80\% & 50\%\\
 \midrule
 drawer & 90\% & 80 \% & 70\% & 90\% & 40\%\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yuelei Li
Ge Yan
Annabella Macaluso
Mazeyu Ji
Xueyan Zou
Xiaolong Wang

"Integrating LMM Planners and 3D Skill Policies for Generalizable Manipulation" by Yuelei Li, Ge Yan, Annabella Macaluso, Mazeyu Ji, Xueyan Zou, and Xiaolong Wang, arXiv:2501.18733v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Memory requirements (expressed in MB) of direct and iterative solvers on the regular grid \emph{Cube~1} and the irregular grid \emph{Sphere~1} - average per process. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|ll|ll|}
\hline
\textbf{Processes} &\multicolumn{2}{c}{\textbf{Regular grid}} & \multicolumn{2}{|c|}{\textbf{Irregular grid}}\\
\hline
{} & Direct [MB] & Iterative [MB] & Direct [MB] & Iterative[MB] \\ \hline
1 & - & 588 & - & 1020 \\
2 & 17976 & 340 & 37603 & 529 \\
4 & 9284 & 196 & 17512 & 298 \\
8 & 4899 & 123 & 8471 & 192 \\
18 & 2405 & 82 & 3972 & 134 \\
36 & 1393 & 66 & 1850 & 112 \\
72 & 831 & 58 & 1198 & 76 \\
144 & 580 & 53 & 694 & 61 \\
288 & 417 & 50 & 428 & 54 \\
576 & 369 & 49 & 314 & 53 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Antonella Galizia
Simone Cammarasana
Andrea Clematis
Giuseppe Patane'

"Evaluating Accuracy and Efficiency of HPC Solvers for Sparse Linear Systems with Applications to PDEs" by Antonella Galizia, Simone Cammarasana, Andrea Clematis, and Giuseppe Patane', arXiv:2201.05413v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the MIONet's architecture. The total number of nodes at the center plane is $N = 1733$.}
\begin{tabular}{cccc}
\toprule
Component & Layer size & Activation & Dropout rate \\ \midrule
Branch network 1 & $[100, 512, 512, 512, N]$ & ReLU & $0.2$ \\
Branch network 2 & $[2, 512, 512, 512, N]$ & ReLU & $0.2$ \\
Trunk network & $[2, 300, 300, 300, 1]$ & ReLU & $0.2$ \\
Linear layer 1 & $[N, N]$ & None & None \\
Linear layer 2 & $[N, N]$ & None & None \\
Linear layer 3 & $[N, N]$ & None & None \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Kazuma Kobayashi
Farid Ahmed
Syed Bahauddin Alam

"Virtual Sensing to Enable Real-Time Monitoring of Inaccessible Locations \& Unmeasurable Parameters" by Kazuma Kobayashi, Farid Ahmed, and Syed Bahauddin Alam, arXiv:2412.00107v1, 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}{ll}
 \toprule
 Dataset & Mean Norm [$m$] \\ \midrule
 Single ShapeNet & 0.4004 \\
 Multi ShapeNet & 0.8827 \\
 FlyingThings3D & 0.7595 \\
 KITTI & 1.2514 \\
 KITTI No Ground & 0.9170 \\ \bottomrule
 \end{tabular}
\caption{Mean norm of the flow vector of each dataset in the benchmark. A hypothetical model with zEPE = 1 on all datasets performs as well on Single ShapeNet as on Kitti, even though the EPE on those datasets is rather different. In a scene in KITTI most points belong to the ground, the mean norm is skwed to the average ego motion of the car. When the ground is removed, the mean norm is closer to the average motion of the dynamic objects in the scene.}
\end{table}
\end{document}

eess

"Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation" by Victor Zuanazzi, arXiv:2011.07945v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Calculated and simulated performance indices of EMPC in Case 1}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 \hline
 & & 3$\boldsymbol\sigma$ zone & 4$\boldsymbol\sigma$ zone & 5$\boldsymbol\sigma$ zone \\
 \hline
 \multirow{2}{*}{$\bar J_{\rm ep}$} & Cal & 451.4201 & 453.9029 & 454.0342 \\
 & Sim & & 453.5738 & \\
 \multirow{2}{*}{$\bar J_{\rm tp}$} & Cal & 2.0345e-03 & 2.0831e-03 & 2.0475e-03 \\
 & Sim & & 2.0905e-03 & \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Jiangbang Liu
Song Bo
Benjamin Decardi-Nelson
Jinfeng Liu
Jingtao Hu
Tao Zou

"Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise" by Jiangbang Liu, Song Bo, Benjamin Decardi-Nelson, Jinfeng Liu, Jingtao Hu, and Tao Zou, arXiv:2201.01828v1, 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{Computation of the XPER value $\hat{\phi}_1$ in a three-feature model}
\begin{tabular}{ccc}
				\hline
				$S$ & $w_S$ & $\frac{1}{n^2}\sum_{u=1}^{n}\sum_{v=1}^{n}
				G\left(y_v;x_{v,j},\mathbf{x}_v^{S},\mathbf{x}_u^{\overline{S}};\hat{\delta}_n\right)- \frac{1}{n^2}\sum_{u=1}^{n}\sum_{v=1}^{n}
				G\left(y_v;x_{u,j},\mathbf{x}_v^{S},\mathbf{x}_u^{\overline{S}};\hat{\delta}_n\right)$ \\
				\hline
				$\left\{\emptyset\right\}$ & $1/3$ &
				$\frac{1}{n^2}\sum_{u=1}^{n}\sum_{v=1}^{n}
				G\left(y_v;x_{v,1},x_{u,2},x_{u,3};\hat{\delta}_n\right)-\frac{1}{n^2}\sum_{u=1}^{n}\sum_{v=1}^{n}
				G\left(y_v;x_{u,1},x_{u,2},x_{u,3};\hat{\delta}_n\right)$ \\
				$\left\{x_{2}\right\}$ & $1/6$ & $\frac{1}{n^2}\sum_{u=1}^{n}\sum_{v=1}^{n}
				G\left(y_v;x_{v,1},x_{v,2},x_{u,3};\hat{\delta}_n\right)-\frac{1}{n^2}\sum_{u=1}^{n}\sum_{v=1}^{n}
				G\left(y_v;x_{u,1},x_{v,2},x_{u,3};\hat{\delta}_n\right)$ \\
				$\left\{x_{3}\right\}$ & $1/6$ &
				$\frac{1}{n^2}\sum_{u=1}^{n}\sum_{v=1}^{n}
				G\left(y_v;x_{v,1},x_{u,2},x_{v,3};\hat{\delta}_n\right)-\frac{1}{n^2}\sum_{u=1}^{n}\sum_{v=1}^{n}
				G\left(y_v;x_{u,1},x_{u,2},x_{v,3};\hat{\delta}_n\right)$ \\
				$\left\{x_{2},x_{3}\right\}$ & $1/3$ & $\frac{1}{n}\sum_{v=1}^{n}
				G\left(y_v;x_{v,1},x_{v,2},x_{v,3};\hat{\delta}_n\right)-\frac{1}{n^2}\sum_{u=1}^{n}\sum_{v=1}^{n}
				G\left(y_v;x_{u,1},x_{v,2},x_{v,3};\hat{\delta}_n\right)$ \\
				\hline
			\end{tabular}
\end{table}
\end{document}

stat

Hué Sullivan
Hurlin Christophe
Pérignon Christophe
Saurin Sébastien

"Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring" by Hué Sullivan, Hurlin Christophe, Pérignon Christophe, and Saurin Sébastien, arXiv:2212.05866v4, 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{Average test accuracies on the concentric circles point cloud separation task, averaged across 10 runs.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
Model & Mean Acc & Std Dev \\
\midrule
RFRBoost & 0.997 & 0.002 \\
\midrule
RFNN & 0.887 & 0.037 \\
E2E MLP ResNet & 0.732 & 0.144 \\
Logistic Regression & 0.334 & 0.023 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Random Feature Representation Boosting

Nikita Zozoulenko
Thomas Cass
Lukas Gonon

"Random Feature Representation Boosting" by Nikita Zozoulenko, Thomas Cass, and Lukas Gonon, arXiv:2501.18283v4, 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{\textbf{FO and FP DEA models}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
			\hline
			FO DEA model {(Model 3)}& FP DEA model {(Model 4)} \\\hline
			For $k=1,2,3,...,n,$ & For $k=1,2,3,...,n,$\\
			$\max \tilde{E}_{k}^O = \dfrac{\sum\limits_{r=1}^{s}\tilde{y}_{rk}{v_{rk}}}{\sum\limits_{i=1}^{m}\tilde{x}_{ik}{u_{ik}}}$ & $\min \tilde{E}_{k}^P = \dfrac{\sum\limits_{r=1}^{s}\tilde{y}_{rk}{v_{rk}}}{\sum\limits_{i=1}^{m}\tilde{x}_{ik}{u_{ik}}}$ \\
			{\hbox {subject to }}$ \dfrac{\sum\limits_{r=1}^{s}\tilde{y}_{rj}{v_{rk}}}{\sum\limits_{i=1}^{m}\tilde{x}_{ij}{u_{ik}}}\leq \tilde{1}\,\, \forall j$, & {\hbox {subject to }} $ \dfrac{\sum\limits_{r=1}^{s}{\tilde{y}_{rj}v_{rk}}}{\sum\limits_{i=1}^{m}{\tilde{x}_{ij}u_{ik}}}\geq \tilde{1}\,\, \forall j,$\\
			$u_{ik}\geq \varepsilon\,\,\forall i,\,\, v_{rk}\geq \varepsilon\,\,\forall r,\,\,\,\varepsilon > 0.$ & $u_{ik}\geq \varepsilon\,\,\forall i, \,\, v_{rk}\geq \varepsilon\,\,\forall r,\,\,\forall k,\,\,\varepsilon > 0.$ \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{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|}
 \hline
 Grid & Timestep & $L_{1}$-error & order & $L_{\infty}$-error & order\\\hline
 32$^2$ & $1.6\times 10^{-3}$ & 1.59e-4 & -- & 5.31e-4 & -- \\
 64$^2$ & $4 \times 10^{-4}$ & 6.52e-05 & 1.28 & 0.000252 & 1.07 \\
 128$^2$ & $1 \times 10^{-4}$ & 1.55e-05 & 2.07 & 6.46e-05 & 1.96 \\
 256$^2$ & $2.5\times 10^{-5}$ & 4.06e-06 & 1.93 & 1.63e-05 & 1.99 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Convergence of the temperature field when refining grid size and time step}
\end{table}
\end{document}

math

A. Limare
S. Popinet
C. Josserand
Z. Xue
A. Ghigo

"A hybrid level-set / embedded boundary method applied to solidification-melt problems" by A. Limare, S. Popinet, C. Josserand, Z. Xue, and A. Ghigo, arXiv:2202.08300v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|lllll}
 & Total Dur (h)& Ul & Co & Mu & Nn \\ \hline
MG & 10.2 & 2.5 & 3.4 & 3.0 & 1.3 \\
SS & 28.2 & 7.5 & 10.4 & 10.3 & -
\end{tabular}
\caption{Duration breakdown by speaker variety of MíleGlór and Spontaneous Speech test sets.}
\end{table}
\end{document}

eess

Liam Lonergan
Ibon Saratxaga
John Sloan
Oscar Maharog
Mengjie Qian
Neasa Ní Chiaráin
Christer Gobl
Ailbhe Ní Chasaide

"Fotheidil: an Automatic Transcription System for the Irish Language" by Liam Lonergan, Ibon Saratxaga, John Sloan, Oscar Maharog, Mengjie Qian, Neasa Ní Chiaráin, Christer Gobl, and Ailbhe Ní Chasaide, arXiv:2501.00509v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Atanu Bhattacharjee
Gajendra K. Vishwakarma
Souvik Banerjee

"A modified risk detection approach of biomarkers by frailty effect on multiple time to event data" by Atanu Bhattacharjee, Gajendra K. Vishwakarma, and Souvik Banerjee, arXiv:2012.02102v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Multiplicity and frequency parameters.} Here, $d$ is the dimensions of each attention head. Since the baseline model on RealEsate10k and ACID uses different feature sizes for query-key vectors and value vectors, we also use different sizes of representation matrices for each feature.}
\begin{tabular}{ccccc}
 \toprule
 &$d$ & $\{s, t, u, v\}$ & $\{l_1, ..., l_{N_{\rm rot}}\}$ & $\{f_1, ..., l_{N_{\{\rm h, w\}}}\}$ \\
 \midrule
 CLEVR-TR & \multirow{2}{*}{64} & $\{8, 3, 1, 1\}$ & $\{1, 2\}$ & $\{1,... ,1/2^3\}$\\
 CLEVR-TR wo/ $SO(3)$ & &$\{8, 0, 1, 1\}$ & $-$ & $\{1,1/2,1/4,...,1/2^{7}\}$ \\
 MSH-Hard &\multirow{2}{*}{96}& $\{12, 3, 1, 1\}$ & $\{1, 2\}$ & $\{1,1/2,1/4,...,1/2^5\}$ \\
 MSH-Hard wo/ $SO(3)$ & &$\{12, 0, 1, 1\}$ & $-$ & $\{1,1/2,1/4,...,1/2^{11}\}$ \\
 Realestate10k and ACID (Encoder) & 64 & $\{8, 3, 1, 1\}$ & $\{1, 2\}$ & $\{1,...,1/2^3\}$ \\
 Realestate10k and ACID (Decoder, key) & 128 & $\{16, 6, 1, 1\}$ & $\{1, 2\}$ & $\{1,...,1/2^7\}$ \\
 Realestate10k and ACID (Decoder, value) & 256 & $\{32, 12, 1, 1\}$ & $\{1, 2\}$ & $\{1,...,1/2^{15}\}$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Takeru Miyato
Bernhard Jaeger
Max Welling
Andreas Geiger

"GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers" by Takeru Miyato, Bernhard Jaeger, Max Welling, and Andreas Geiger, arXiv:2310.10375v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{TPR@1\%FPR across different datasets with the varying number of generated data.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\toprule
 \multirow{2}*{AUC} & \multicolumn{4}{c}{Number of Generated Samples}\\\cline{2-5}
 & $\textcolor{green}{50}/150/\textcolor{orange}{200}$ & $\textcolor{green}{100}/200/\textcolor{orange}{300}$ & $\textcolor{green}{150}/250/\textcolor{orange}{400}$ & $\textcolor{green}{200}/300/\textcolor{orange}{500}$ \\
 \midrule
 MNIST & $0.9\%$ & $1.2\%$ & $0.9\%$ & $1.0\%$ \\
 CIFAR10 & $2.8\%$ & $2.7\%$ & $3.2\%$ & $3.0\%$ \\
 \textcolor{orange}{SkinCancer} & $2.2\%$ & $3.5\%$ & $4.7\%$ & $4.8\%$ \\%\hline
BBCNews & $1.7\%$ & $2.4\%$ & $2.9\%$ & $3.6\%$ \\%\hline
\textcolor{green}{IMDB} & $2.2\%$ & $2.8\%$ & $4.3\%$ & $4.5\%$ \\%\hline
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Dayong Ye
Tianqing Zhu
Shang Wang
Bo Liu
Leo Yu Zhang
Wanlei Zhou
Yang Zhang

"Data-Free Model-Related Attacks: Unleashing the Potential of Generative AI" by Dayong Ye, Tianqing Zhu, Shang Wang, Bo Liu, Leo Yu Zhang, Wanlei Zhou, and Yang Zhang, arXiv:2501.16671v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of performance on the Levy function for dimensions $d=40, 100, 1000$: Each optimization method undergoes ten independent runs. Accuracy is quantified using the average function log regret ($r_f$) and the average minima log regret ($r_m$) across these ten runs. Computational efficiency is represented by the average runtime ($t$) across all ten runs, measured in seconds. }
\begin{tabular}{crrrcrrrcrrr} \toprule
 & \multicolumn{3}{c}{$d=40$} & & \multicolumn{3}{c}{$d=100$} & & \multicolumn{3}{c}{$d=1000$} \\ \cmidrule{2-4} \cmidrule{6-8} \cmidrule {10-12}
 Method &\bf ${r_f}$ &\bf ${r_m}$
 &\bf ${t~ (\mathrm{s})}$ & &\bf ${r_f}$ &\bf ${r_m}$
 &\bf ${t~ (\mathrm{s})}$ & &\bf ${r_f}$ &${r_m}$
 &\bf ${t~ (\mathrm{s})}$ \\ \midrule
 ZoRD & 4.08 & 1.56 & 585.69 & & 5.26 & 1.56 & 1514.97 & & 8.05 & 1.58 & 272.29 \\
 GD & 4.10 & 1.58 & 0.16 & & 5.15 & 1.61 & 0.15 & & 7.59 & 1.59 & 0.14\\
 GLD & 4.89 & 1.59 & 0.40 & & 6.15 & 1.60 & 0.39 & & 8.88 & 1.59 & 0.39\\
 PRGF & 4.68 & 1.59 & 0.08 & & 5.96 & 1.61 & 0.07 & & 8.75 & 1.60 & 0.07 \\
 RGF & 4.59 & 1.58 & 0.08 & & 5.84 & 1.62 & 0.08 & & 8.74 & 1.59 & 0.09\\
 GP-UCB & 3.80 & 1.58 & 216.83 & & 5.28 & 1.58 & 651.38 & & 8.49 & 1.58 & 1400.41\\
 TuRBO & 3.58 & 1.56 & 77.66 & & 5.18 & 1.55 & 197.78 & & 8.70 & 1.59 & 265.71 \\
 \textbf{ProGO} & \textbf{-0.05} & \textbf{-0.96} & 11.85 & & \textbf{1.55} & \textbf{-0.50} & 27.33 & & \textbf{4.62} & \textbf{-0.01} & 311.99\\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

ProGO: Probabilistic Global Optimizer

"ProGO: Probabilistic Global Optimizer" by Xinyu Zhang and Sujit Ghosh, arXiv:2310.04457v2, 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|ccccccc}
 \hline
 \multirow{3}{*}{Methods} & \multicolumn{7}{c}{CC152K} \\ \cline{2-8}
 & \multicolumn{3}{c|}{Image-Text} & \multicolumn{3}{c|}{Text-Image} & \multirow{2}{*}{Rsum} \\ \cline{2-7}
 & R@1 & R@5 & R@10 & R@1 & R@5 & R@10 & \\ \hline
 SCAN & 30.5 & 55.3 & 65.3 & 26.9 & 53.0 & 64.7 & 295.7 \\
 IMRAM & 33.1 & 57.6 & 68.1 & 29.0 & 56.8 & 67.4 & 312.0 \\
 SAF & 31.7 & 59.3 & 68.2 & 31.9 & 59.0 & 67.9 & 318.0 \\
 SGR & 11.3 & 29.7 & 39.6 & 13.1 & 30.1 & 41.6 & 165.4 \\
 NCR* & 39.5 & 64.5 & 73.5 & 40.3 & 64.6 & 73.2 & 355.6 \\
 MSCN* & 40.1 & 65.7 & \underline{76.6} & 40.6 & 67.4 & 76.3 & 366.7 \\
 BiCro* & 40.8 & 67.2 & 76.1 & 42.1 & 67.6 & \underline{76.4} & 370.2 \\
 ESC* & \underline{42.8} & \underline{67.3} & \textbf{76.9} & \underline{44.8} & \textbf{68.2} & 75.9 & \underline{375.9} \\
 \textbf{TSVC(Ours)} & \textbf{44.7} & \textbf{69.4} & 76.5 & \textbf{45.1} & \underline{67.8} & \textbf{76.6} & \textbf{380.1} \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Image-Text Retrieval on CC152K. * indicates the noise robust method. The best and sub-optimal indicators are represented in \textbf{bold} and \underline{underline} respectively.}
\end{table}
\end{document}

cs

Shuai Lyu
Zijing Tian
Zhonghong Ou
Yifan Zhu
Xiao Zhang
Qiankun Ha
Haoran Luo
Meina Song

"TSVC:Tripartite Learning with Semantic Variation Consistency for Robust Image-Text Retrieval" by Shuai Lyu, Zijing Tian, Zhonghong Ou, Yifan Zhu, Xiao Zhang, Qiankun Ha, Haoran Luo, and Meina Song, arXiv:2501.10935v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Rank aggregation over different working correlation structures for the separate severity models at different ages, and overall across all ages. }
\begin{tabular}{rccccccl}
Age & Rank 1 & Rank 2 & Rank 3 & Rank 4 \\
 \hline
Age 9 & B.4 & B.2 & B.1 & B.3 \\
 Age 13 & B.3 & B.4 & B.1 & B.2 \\
 Age 17 & B.4 & B.2 & B.1 & B.3 \\
 Age 23 & B.4 & B.1 & B.2 & B.3 \\
 Overall & B.4 & B.2 & B.1 & B.3 \\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

Shoumi Sarkar
Anish Mukherjee
Jeremy T. Gaskins
Steven Levy
Peihua Qiu
Somnath Datta

"Analyzing zero-inflated clustered longitudinal ordinal outcomes using GEE-type models with an application to dental fluorosis studies" by Shoumi Sarkar, Anish Mukherjee, Jeremy T. Gaskins, Steven Levy, Peihua Qiu, and Somnath Datta, arXiv:2412.11348v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Baseline characteristics of the Dutch CRC EHR screening data (time is measured in years).}
\begin{tabular}{lrrr}
		\multicolumn{2}{c}{} & \multicolumn{2}{c}{Event in follow up} \\
		\cline{3-4}
		Statistic & Total & Adenoma & Right censored \\ \hline
		Sample size n & 810 & 330 & 480 \\
		Confirmed prevalent individuals (\%)& 20.4 & 50.0 & 0 \\
		Baseline test unsuccessful (\%)& 6.5 & 6.7 & 6.5 \\
		Censoring time (min/median/max) & 1.2/6.0/30.1 & 1.2/6.3/22.1 & 1.4/6.0/30.1 \\
		No. of screenings (min/median/max) & 1/1.5/7 & 1/1.5/7 & 1/2/7 \\
		Interval length in years (min/median/max) & 1.0/3.1/20.3 & 1/3.3/13.7 & 1/3.1/20.3 \\
		Age at baseline in years (min/median/max) & 24.3/53.2/86.3 & 27.6/54.7/86.3 & 24.26/52.3/73.5 \\
		Gender is female (in \%) & 55.8 & 53.0 & 57.8 \\
		\hline
	\end{tabular}
\end{table}
\end{document}

stat

Thomas Klausch
Birgit I. Lissenberg-Witte
Veerle M. Coupé

"A Bayesian prevalence-incidence mixture model for screening outcomes with misclassification" by Thomas Klausch, Birgit I. Lissenberg-Witte, and Veerle M. Coupé, arXiv:2412.16065v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study of the number of the frames for training. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccc}
\toprule[1.2pt]
\multirow{2}{*}{Num. of Frames} & \multicolumn{3}{c}{referring} & \multicolumn{3}{c}{reasoning} \\
\cmidrule(lr){2-4} \cmidrule(lr){5-7}
& $\mathcal{J}$ & $\mathcal{F}$ & $\mathcal{J}\&\mathcal{F}$ & $\mathcal{J}$ & $\mathcal{F}$ & $\mathcal{J}\&\mathcal{F}$ \\
\midrule
 1 & & & & & & \\
 2 & & & & & &\\ % 之后也可以去掉不放
 3 & & & & & &\\
 5 & & & & & &\\
\bottomrule[1.2pt]
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Sitong Gong
Yunzhi Zhuge
Lu Zhang
Zongxin Yang
Pingping Zhang
Huchuan Lu

"The Devil is in Temporal Token: High Quality Video Reasoning Segmentation" by Sitong Gong, Yunzhi Zhuge, Lu Zhang, Zongxin Yang, Pingping Zhang, and Huchuan Lu, arXiv:2501.08549v1, 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}{lcc}
 \toprule
 & Flow cytometry & Gene expression \\
 & $d = 11, N = 4,200$ & $d = 61, N = 2,628$ \\
 \midrule
 MH-MC\textsuperscript{3} & $\mathbf{2.578 \times 10^{4}}$ & $1.073 \times 10^{6}$\\
 Gibbs-MC\textsuperscript{3} & $3.490 \times 10^{5}$ & $4.882 \times 10^{6}$\\
 \midrule
 JSP-GFN & $2.679 \times 10^{4}$ & $\mathbf{2.651 \times 10^{5}}$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Generative Flow Networks: Theory and Applications to Structure Learning" by Tristan Deleu, arXiv:2501.05498v1, 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 computational time (in seconds) for set-up and solution of continuous elements formulation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cc|c|c|cc|}
\hline
& $K$ & Set-up time	& Solve time & Total time & \\
\hline
$N=1$ & & & & & \\
\hline
& 4 		&		$<0.1$		& 	$<0.1$		& $<0.2$ 		& \\
& 8 		&		$<0.1$		& 	$<0.1$		& $<0.2$ 		& \\
& 16 		& 		0.3			& 6.5 			& 6.8 		& \\
& 32 	&		3.3			& 1327.1		& 1330.4 	& \\
& 64 	& Out of memory & & & \\
\hline
$N=2$ & & & & & \\
\hline
& 4 		& 	$<0.1$	& $<0.1$ 		& $<0.2$ 		& \\
& 8 		& 	$<0.1$	& 4.9			& 5.0 		& \\
& 16 		& 	1.3	& 1117.3 	& 1118.6	& \\
& 32 	& Out of memory & & & \\
\hline
$N=3$ & & & & & \\
\hline
& 4 		& $<0.1$		& 0.8		& 0.9 	& \\
& 8 		& 	0.5		& 96.1		& 96.6 	& \\
& 16 	& Out of memory & & & \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Use of algebraic dual representations in domain decomposition methods for Darcy flow in 3D domain" by V. Jain, A. Palha, and M. Gerritsma, arXiv:2202.10073v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Patient recruitment forecasting in clinical trials using time-dependent Poisson-gamma model and homogeneity testing criteria" by Volodymyr Anisimov and Lucas Oliver, arXiv:2411.17393v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yassine Abbahaddou
Fragkiskos D. Malliaros
Johannes F. Lutzeyer
Michalis Vazirgiannis

"Centrality Graph Shift Operators for Graph Neural Networks" by Yassine Abbahaddou, Fragkiskos D. Malliaros, Johannes F. Lutzeyer, and Michalis Vazirgiannis, arXiv:2411.04655v1, 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{\small{Dimensionless poro-elasto-hydrodynamics parameters corresponding to tumor tissue with their value range}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
				\hline\hline
				{Dimensionless parameter} & {Range of values}
				& {Supporting References}\\
				\hline
				$Da$ & $10^{-4}-10^{-1}$ & \\\hline
				$\alpha_{t}$ & $0<\alpha_{t}\leq 10$ & \\\hline
				$\varrho_{t}$ & $10^2\leq \varrho_{t}\leq 10^5$ & \\\hline
				$\nu_{p}$ & $0.45\leq \nu_{p} \leq 0.49$ & \\\hline
				$\phi_f$ & $0.6\leq \phi_f \leq0.8$ &
				\\\hline %\hline
				$K$
				& $0.00006\leq K \leq1.4$ &
				 \\ %\hline
				\hline\hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Nonlinear biphasic mixture model: existence and uniqueness results" by M. Alam, A. Muntean, and G. P. Raja Sekhar, arXiv:2202.06059v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Mohammadmehdi Hakimifar
Vera Hemmelmayr
Fabien Tricoire

"A lexicographic maximin approach to the selective assessment routing problem" by Mohammadmehdi Hakimifar, Vera Hemmelmayr, and Fabien Tricoire, arXiv:2201.09599v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
$\#$ & algorithm & Fig. -left & Fig. -center
& Fig. -right \\
\hline
& {\sc inpoly2} & 2.1$e-$01$s$ & 5.2$e-$02$s$ & 6.5$e-$02$s$\\
$10^3$ & $inRS2$ & 2.8$e-$03$s$ & 1.3$e-$03$s$ & 1.3$e-$03$s$\\
& speed-up & 75 & 40 & 50\\
\hline
\hline
& {\sc inpoly2} & 2.5$e-$01$s$ & 6.4$e-$02$s$ & 8.0$e-$02$s$\\
$10^4$ & $inRS2$ & 4.4$e-$03$s$ & 3.2$e-$03$s$ & 3.2$e-$03$s$\\
& speed-up & 58 & 20 & 25\\
\hline
\hline
& {\sc inpoly2} & 3.1$e-$01$s$ & 8.7$e-$02$s$ & 1.1$e-$01$s$\\
$10^ 5$ & $inRS2$ & 1.7$e-$02$s$ & 2.1$e-$02$s$ & 2.0$e-$02$s$\\
& speed-up & 18 & 4.1 & 5.5\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"InRS: implementing the indicator function of NURBS-shaped planar domains" by Alvise Sommarivaa and Marco Vianello, arXiv:2201.02393v1, 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{Number of GMRES-IR and RGMRES-IR refinement steps with the number of GMRES iterations for each refinement step for prolate matrices with various $\alpha$ values, using precisions ($u_f,u,u_r$) = (half,single,double) and ($m,k$) = (16,5).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|}
		\hline
		$\alpha$ & GMRES-IR (16) & RGMRES-IR (16,5) \\ \hline
		0.475 & 12 (6,6) & 8 (6,2) \\
		0.47 & 16 (8,8) & 10 (8,2) \\
		0.467 & 19 (9,10) & 11 (9,2) \\
		0.455 & 50 (15,25,10) & 19 (15,4) \\%44 (15,16,13) & 19 (15,4) \\
		0.45 & 89 (14,43,32) & - \\%61 (14,16,16,15) & - \\
		0.4468 & - & - \\
		0.44 & - & - \\
		0.434 & - & - \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Mixed Precision GMRES-based Iterative Refinement with Recycling" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, 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{TABLE I. 	PRECISION (P), RECALL (R), AND F1 MEASURE VALUES FOR TOPICS AND SUB-TOPICS OF THE EVALUATION MODULES.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|lll|lll|l|}
\hline
\multirow{2}{*}{} & \multicolumn{3}{l|}{\textbf{Topic}} & \multicolumn{3}{l|}{\textbf{Sub-Topic}} & \textbf{Description} \\ \cline{2-8}
 & \textbf{P} & \textbf{R} & \textbf{F1} & \textbf{P} & \textbf{R} & \textbf{F1} & \textbf{P} \\ \hline
ESa & 0,99 & 0,94 & 0,96 & 1 & 0,97 & \textbf{0,98} & \textbf{1} \\ \hline
FPGA & 0,97 & 1 & \textbf{0,98} & 0,89 & 0,99 & 0,94 & 0,89 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hasan Abu-Rasheed
Constance Jumbo
Rashed Al Amin
Christian Weber
Veit Wiese
Roman Obermaisser
Madjid Fathi

"LLM-Assisted Knowledge Graph Completion for Curriculum and Domain Modelling in Personalized Higher Education Recommendations" by Hasan Abu-Rasheed, Constance Jumbo, Rashed Al Amin, Christian Weber, Veit Wiese, Roman Obermaisser, and Madjid Fathi, arXiv:2501.12300v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Chiu-Yen Kao
Braxton Osting
Jackson C. Turner

"Flat tori with large Laplacian eigenvalues in dimensions up to eight" by Chiu-Yen Kao, Braxton Osting, and Jackson C. Turner, arXiv:2202.08351v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
\toprule
\textbf{Route} & \textbf{Flow} & \textbf{Time} \\
\midrule
Route 1. & 4 agents & 15 min \\
Route 2. & 6 agents & 14 min \\
Route 3. & 8 agents & 9 min \\
\bottomrule
\end{tabular}
\caption{Example system-optimal assignment for OD pair with $18$ drivers and average travel time $\hat{t} = 12 \text{min}$.}
\end{table}
\end{document}

eess

Michał Hoffmann
Michał Bujak
Grzegorz Jamróz
Rafał Kucharski

"Wardropian Cycles make traffic assignment both optimal and fair by eliminating price-of-anarchy with Cyclical User Equilibrium for compliant connected autonomous vehicles" by Michał Hoffmann, Michał Bujak, Grzegorz Jamróz, and Rafał Kucharski, arXiv:2507.19675v1, 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{Parameter values.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 \toprule
 Parameter & Value~(range) & Dimension & Reference\\
 \midrule
 $N(x)$ & 110 & dimensionless & \\
 $M^*(t,x)$ & 220 & dimensionless & \\
 $d$ & $1/(72\times 12)$ & month$^{-1}$ & \\
 $\eta(t,x)$ & 0.8 & month$^{-1}$ & \\
 $D_h$ & 0.4 & km$^2\cdot$ month$^{-1}$ & \\
 $D_v$ & 0.02 & km$^2\cdot$ month$^{-1}$ & \\
 $p$ & 0.8~(0,1) & dimensionless & \\
 $l$ & 0.2~(0,1) & dimensionless & \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A two-strain reaction-diffusion malaria model with seasonality and vector-bias" by Huijie Chu and Zhenguo Bai, arXiv:2201.05559v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Correlation matrix of the physiological variables}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\textbf{} & \textbf{SDNN} & \textbf{SDSD} & \textbf{Mean\_HR} & \textbf{LF\_HF\_ratio} & \textbf{Resp\_rate} \\
\hline
\textbf{SDNN} & 1.000 & 0.401 & 0.412 & 0.320 & -0.305 \\
\textbf{SDSD} & 0.401 & 1.000 & -0.217 & -0.292 & -0.064 \\
\textbf{Mean\_HR} & 0.412 & -0.217 & 1.000 & 0.175 & -0.177 \\
\textbf{LF\_HF\_ratio} & 0.320 & -0.292 & 0.175 & 1.000 & -0.136 \\
\textbf{Resp\_rate} & -0.305 & -0.064 & -0.177 & -0.136 & 1.000 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Dorota Młynarczyk
Gabriel Calvo
Francisco Palmi-Perales
Carmen Armero
Virgilio Gómez-Rubio
Ursula Martinez-Iranzo

"Bayesian network approach to building an affective module for a driver behavioural model" by Dorota Młynarczyk, Gabriel Calvo, Francisco Palmi-Perales, Carmen Armero, Virgilio Gómez-Rubio, and Ursula Martinez-Iranzo, arXiv:2502.03254v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Calculated and simulated performance indices of tracking MPC in group 2 of Case 1}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 \hline
 & & 3$\boldsymbol\sigma$ zone & 4$\boldsymbol\sigma$ zone & 5$\boldsymbol\sigma$ zone \\
 \hline
 \multirow{2}{*}{$\bar J_{\rm ep}$} & Cal & 200.6039 & 201.6109 & 201.6389 \\
 & Sim & & 202.5862 & \\
 \multirow{2}{*}{$\bar J_{\rm tp}$} & Cal & 6.5879e-03 & 6.7543e-03 & 6.7537e-03 \\
 & Sim & & 6.7702e-03 & \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Jiangbang Liu
Song Bo
Benjamin Decardi-Nelson
Jinfeng Liu
Jingtao Hu
Tao Zou

"Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise" by Jiangbang Liu, Song Bo, Benjamin Decardi-Nelson, Jinfeng Liu, Jingtao Hu, and Tao Zou, arXiv:2201.01828v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of TN and NTN}
\begin{tabular}{|l|l|l|}
 \hline
 \textbf{Characteristic} & \textbf{TN} & \textbf{NTN} \\ \hline
 Delay/Latency & Lower & Higher \\ \hline
 Cost & Lower & Higher \\ \hline
 Trustworthiness & Robust & Vulnerable\\ \hline
 Data Rates & Higher & Lower \\ \hline
 Availability & Unreachable in Remote Areas & Reachable in Remote Areas\\ \hline
 Coverage & Regional & Global \\ \hline
 Mobility Management & Frequent Hand Overs & Seamless Mobility \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

Muhammad Ali Jamshed
Aryan Kaushik
Sanaullah Manzoor
Muhammad Zeeshan Shakir
Jaehyup Seong
Mesut Toka
Wonjae Shin
Malte Schellmann

"A Tutorial on Non-Terrestrial Networks: Towards Global and Ubiquitous 6G Connectivity" by Muhammad Ali Jamshed, Aryan Kaushik, Sanaullah Manzoor, Muhammad Zeeshan Shakir, Jaehyup Seong, Mesut Toka, Wonjae Shin, and Malte Schellmann, arXiv:2412.16611v1, 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{Wilcoxon Signed-Rank Test Results}
\begin{tabular}{lcc}
\toprule
\\textbf{Comparison} & \textbf{Metric} & \textbf{p-value} \\
\midrule
Our Model vs. Cellpose & IoU & 0.004 \\
Our Model vs. StarDist & IoU & 0.002 \\
Our Model vs. Cellpose & F1-score & 0.002 \\
Our Model vs. StarDist & F1-score & 0.005 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"GUI Based Fuzzy Logic and Spatial Statistics for Unsupervised Microscopy Segmentation" by Surajit Das and Pavel Zun, arXiv:2508.15979v1, 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{Scalability of MLPs trained with DLL.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
\hline \hline
\textbf{DLL-MLP Architecture} & \textbf{Accuracy on MNIST}\\
\hline
784-1024-10 & $71.15\%$ \\
784-1024-512-10 & $89.61\%$ \\
784-1024-512-256-10 & $97.57\%$ \\
\hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Changze Lv
Jingwen Xu
Yiyang Lu
Xiaohua Wang
Zhenghua Wang
Zhibo Xu
Di Yu
Xin Du
Xiaoqing Zheng
Xuanjing Huang

"Dendritic Localized Learning: Toward Biologically Plausible Algorithm" by Changze Lv, Jingwen Xu, Yiyang Lu, Xiaohua Wang, Zhenghua Wang, Zhibo Xu, Di Yu, Xin Du, Xiaoqing Zheng, and Xuanjing Huang, arXiv:2501.09976v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Filipe Costa de Souza
Wilton Bernardino
Silvio Cabral Patricio

"How life-table right-censoring affected the Brazilian Social Security Factor: an application of the gamma-Gompertz-Makeham model" by Filipe Costa de Souza, Wilton Bernardino, and Silvio Cabral Patricio, arXiv:2309.03498v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Miguel Manuel de Villena
Samy Aittahar
Sebastien Mathieu
Ioannis Boukas
Eric Vermeulen
Damien Ernst

"Allocation of locally generated electricity in renewable energy communities" by Miguel Manuel de Villena, Samy Aittahar, Sebastien Mathieu, Ioannis Boukas, Eric Vermeulen, and Damien Ernst, arXiv:2009.05411v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lr}
 \hline Release
 & Forecast inclusion\\
 \hline
 Dec 2021 & 0.97\\
 Mar 2022 & 1 \\
 Jun 2022 & 0.81 \\
 Sep 2022 & 1 \\
 Dec 2022 & 1\\
 Mar 2023 & 1 \\
 Jun 2023 & 1 \\
 Sep 2023 & 0.78 \\
 Dec 2023 & 1\\
 \hline
\end{tabular}
\caption{Proportion of nodes (industries) for which the true value lies within the union forecast interval from MA over different lags, for GNAR-ex with 1-stage neighbours, across releases.}
\end{table}
\end{document}

stat

Anastasia Mantziou
Kerstin Hotte
Mihai Cucuringu
Gesine Reinert

"GDP nowcasting with large-scale inter-industry payment data in real time -- A network approach" by Anastasia Mantziou, Kerstin Hotte, Mihai Cucuringu, and Gesine Reinert, arXiv:2411.02029v1, 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{ CPU time (secs) for simulating from the small, standard and large portfolios. Timings are for the standard method and when using the concentration inequalities that give $B_1$ and $B_2$, and, finally, with $B_2$ using sampling-importance-resampling (SIR). Timings are reported for obtaining $M=100$ and $M=1000$ samples from the loss distribution for all $1000$ years. Each complete simulation was repeated 10 times; the table reports the mean and standard deviation of the timings. }
\begin{tabular}{c|r|r|rr|r}
 Portfolio&M&Std. Method&$B_1$ (direct)&$B_2$ (direct)&$B_2$ (SIR)\\
 \hline
 small&100&49.2 (0.97)&196 (6.30)&98.2 (3.82)&1.35 (0.056)\\
 standard&100&298 (6.3)&194 (13.2)&108 (9.18)&1.47 (0.239)\\
 large&100&2719 (100)&200 (25.7)&116 (17.2)&1.40 (0.035)\\
 \hline
 small&1000&511 (25.3)&1845 (235)&965 (142)&2.82 (0.131)\\
 standard&1000&2870 (96.4)&1740 (253)&1070 (152)&2.79 (0.093)\\
 large&1000&26221 (352)&2006 (399)&1119 (214)&2.90 (0.068)
 \end{tabular}
\end{table}
\end{document}

stat

"Fast return-level estimates for flood insurance via an improved Bennett inequality for random variables with differing upper bounds" by Anna Maria Barlow and Chris Sherlock, arXiv:2311.10001v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Comparison with state-of-the-art methods on speech-language benchmarks.} In each case, the best result among Omnimodal LLMs is indicated in \textbf{bold}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
 \toprule
 \multirow{3}{*}{\textbf{Model}} &\multicolumn{4}{c}{\textbf{\small{AIShell-2~(ZH-CER)}}} & \multicolumn{4}{c}{\textbf{\small{Librispeech~(EN-WER)}}}
 \\
 \cmidrule(l){2-5}
 \cmidrule(l){6-9}
 & \multicolumn{2}{c}{\textbf{\small{Dev}}} & \multicolumn{2}{c}{\textbf{\small{Test}}} & \multicolumn{2}{c}{\textbf{\small{Test\_clean}}} &
 \multicolumn{2}{c}{\textbf{\small{Test\_other}}} \\
 \cmidrule(l){2-3}
 \cmidrule(l){4-5}
 \cmidrule(l){6-7}
 \cmidrule(l){8-9}
 & S2T & T2S & S2T & T2S & S2T & T2S & S2T & T2S \\
 \midrule[0.6pt]
 \multicolumn{8}{l}{\textbf{Speech LLM}}\\
 SpeechT5~ & - & - & - & - & 2.4 & - & 5.8 & - \\
 SALMONN~ & - & - & - & - & 2.1 & - & 4.9 & - \\
 Mini-Omni (7B) ~ & - & - & - & - & 4.7 & - & 9.4 & - \\
 Freeze-Omni (7B) ~ & - & - & - & - & 3.2 & - & 7.7 & - \\
 Qwen2-Audio (7B) ~ & 3.1 & - & 3.3 & - & 2.0 & - & 4.5 & - \\
 \midrule[0.6pt]
 \multicolumn{8}{l}{\textbf{Omnimodal LLM}}\\
 AnyGPT (13B) ~ & - & - & - & - & 8.5 & - & - & -\\
 VITA (7$\times$8B)~ & - & - & - & - & 8.1 & - & 18.4 & - \\
 EMOVA (7B)~ & 10.3 & 7.9 & - & - & 4.0 & 3.4 & - & - \\
 VITA 1.5 (7B) ~ & - & - & - & - & 3.4 & - & 7.5 & - \\\midrule
 OpenOmni (7B) & \textbf{6.8} & \textbf{7.3} & \textbf{6.9} & \textbf{13.1} & \textbf{3.1} & \textbf{2.6} & \textbf{4.1} & \textbf{5.6} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Run Luo
Ting-En Lin
Haonan Zhang
Yuchuan Wu
Xiong Liu
Min Yang
Yongbin Li
Longze Chen
Jiaming Li
Lei Zhang
Xiaobo Xia
Hamid Alinejad-Rokny
Fei Huang

"OpenOmni: Advancing Open-Source Omnimodal Large Language Models with Progressive Multimodal Alignment and Real-Time Self-Aware Emotional Speech Synthesis" by Run Luo, Ting-En Lin, Haonan Zhang, Yuchuan Wu, Xiong Liu, Min Yang, Yongbin Li, Longze Chen, Jiaming Li, Lei Zhang, Xiaobo Xia, Hamid Alinejad-Rokny, and Fei Huang, arXiv:2501.04561v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Available elevation angle in different environment for frequencies between 1.5 and 3~GHz}
\begin{tabular}{|c|l|}
\hline
\textbf{Environment} & \textbf{Available Elevation Angles (degrees)} \\
\hline
Urban & 20, 30, 45, 60, 70 \\
\hline
Suburban & 20, 30, 45, 60, 70 \\
\hline
Village & 20, 30, 45, 60, 70 \\
\hline
Rural wooded & 20, 30, 45, 60, 70 \\
\hline
Residential & 20, 30, 60, 70 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"Evaluating the Influence of Satellite Systems on Terrestrial Networks: Analyzing S-Band Interference" by Lingrui Zhang, Zheng Li, and Sheng Yang, arXiv:2501.05462v1, 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{Composition of text-to-text pretraining dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|r|l|}
\hline
\textbf{Category} & \textbf{Size (GB)} & \textbf{Description} \\
 \hline \hline
Web Crawl & 421.3 & CommonCrawl (CC100 ZHTW , \\
 & & C4 ZHTW , FineWeb-zhtw ) \\
 & & and other miscellaneous web data \\ \hline
Academic & 94.0 & Theses, research plans, and reports \\ \hline
Code & 110.0 & Programming-related content (The Stack ) \\ \hline
News & 131.0 & News articles \\ \hline
Dialogue & 37.0 & Transcribed conversation, legislative records \\ \hline
Blog & 20.7 & Blog content \\ \hline
Forum & 60.0 & Q\&A content, BBS, forum discussions \\ \hline
Judicial & 21.4 & Legal judgments and documents \\ \hline
Books & 1.6 & Book excerpts and summaries \\ \hline
Wiki & 1.1 & Wikipedia content in Traditional Chinese \\ \hline
\hline
\textbf{Total} & \textbf{898.1} & \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

MediaTek Research
:
Chan-Jan Hsu
Chia-Sheng Liu
Meng-Hsi Chen
Muxi Chen
Po-Chun Hsu
Yi-Chang Chen
Da-Shan Shiu

"The Breeze 2 Herd of Models: Traditional Chinese LLMs Based on Llama with Vision-Aware and Function-Calling Capabilities" by MediaTek Research, :, Chan-Jan Hsu, Chia-Sheng Liu, Meng-Hsi Chen, Muxi Chen, Po-Chun Hsu, Yi-Chang Chen, and Da-Shan Shiu, arXiv:2501.13921v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Mengxi Yang
Dai Shi
Xuebin Zheng
Jie Yin
Junbin Gao

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


stat

"Bayesian analysis of seasonally cointegrated VAR model" by Justyna Wróblewska, arXiv:2012.14820v2, licensed under CC0 1.0. 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}{lcc}
\toprule
\multirow{2}{*}{Strategy} & \multicolumn{2}{c}{MESA} \\
\cmidrule(r){2-3}
& $\kappa$ & Accuracy \\
\midrule
PPG & 0.675 & 0.783 \\
Augmented PPG& 0.630 & 0.754 \\
\midrule
PPG + Augmented PPG & \textbf{0.745} & \textbf{0.833} \\
PPG + Synthetic ECG & 0.723 & 0.820 \\
\midrule
PPG + Real ECG & 0.739 & 0.829 \\
\bottomrule
\end{tabular}
\caption{Performance (in accuracy and Cohen's kappa $\kappa$) of single-stream and dual-stream sleep staging models.}
\end{table}
\end{document}

eess

Jiawei Wang
Yu Guan
Chen Chen
Ligang Zhou
Laurence T. Yang
Sai Gu

"On Improving PPG-Based Sleep Staging: A Pilot Study" by Jiawei Wang, Yu Guan, Chen Chen, Ligang Zhou, Laurence T. Yang, and Sai Gu, arXiv:2508.02689v1, 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{CWE Frequency Distribution}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|}
\hline
\textbf{Dataset} & [1-500] & [501-1,000] & [1,001-1,336] & UNK. \\
\hline
\textbf{Total} & 3852 & 1,271 & 170 & 118 \\
\textbf{Evaluation} & 1,883 & 644 & 84 & 67 \\
\textbf{Fine-tuning} & 1,969 & 627 & 86 & 51 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mohammad Jalili Torkamani
Joey NG
Nikita Mehrotra
Mahinthan Chandramohan
Padmanabhan Krishnan
Rahul Purandare

"Streamlining Security Vulnerability Triage with Large Language Models" by Mohammad Jalili Torkamani, Joey NG, Nikita Mehrotra, Mahinthan Chandramohan, Padmanabhan Krishnan, and Rahul Purandare, arXiv:2501.18908v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average F1 Scores of PCA+KNN with various $N_{PC}$ and $K$}
\begin{tabular}{ccccccc}
			\toprule
			 $N_{PC}$ & 1000 & 800 & 500 & 300 & 200 \\
			\midrule
			K=1 & 0.513 & 0.547 & 0.594 & 0.584 & 0.536 \\
			\midrule
			K=5 & 0.463 & 0.506 & 0.588 & 0.575 & 0.464 \\
			\midrule
			K=10 & 0.435 & 0.467 & 0.540 & 0.525 & 0.446 \\
			\midrule
			K=50 & 0.372 & 0.382 & 0.398 & 0.433 & 0.369 \\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

eess

"Power System Event Identification based on Deep Neural Network with Information Loading" by Jie Shi, Brandon Foggo, and Nanpeng Yu, arXiv:2011.06718v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of accuracy, ECE and MCE in percent of classic model ensemble with ResNet18 on CIFAR-100 .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
 \toprule
\multirow{2}{*}{Model} & \multicolumn{4}{c}{ResNet18} \\
 & Acc. & ECE & MCE & Params \\
 \midrule
Model 1 & 74.89 & 5.96 & 27.61 & 11.22 M\\
Model 2 & 75.03 & 6.26 & 17.23 & 11.22 M \\
Model 3 & 74.20 & 9.72 & 22.02 & 11.22 M \\
Model 4 & 73.08 & 11.30 & 25.82 & 11.22 M \\
Model 5 & 71.66 & 7.98 & 18.26 & 11.22 M \\
 \hline
Ensemble & 75.85 & 6.91 & 18.32 & 56.10 M \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Michael Schulze
Nikolas Ebert
Laurenz Reichardt
Oliver Wasenmüller

"Classifier Ensemble for Efficient Uncertainty Calibration of Deep Neural Networks for Image Classification" by Michael Schulze, Nikolas Ebert, Laurenz Reichardt, and Oliver Wasenmüller, arXiv:2501.10089v1, 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{\small \textbf{Semi-supervised evaluation}: Top-1 accuracy (ACC), ECE, and NLL for semi-supervised CIFAR-10/100 classification using 1\% and 10\% training examples.}
\begin{tabular}{l|ccc|ccc|ccc|ccc}
 \toprule
 Method & \multicolumn{3}{c}{ CIFAR-10 (1\%)} & \multicolumn{3}{c}{CIFAR-10 (10\%)} & \multicolumn{3}{|c}{ CIFAR-100 (1\%)} & \multicolumn{3}{c}{CIFAR-100 (10\%)}\\
 \midrule
 & ACC & ECE & NLL & ACC & ECE & NLL & ACC & ECE & NLL & ACC & ECE & NLL \\
 \midrule
 Baseline & 89.1 & 0.075 & 0.364 & 91.1 & 0.039 & 0.274 & 56.2 & 0.097 & 2.01 & 59.5 & 0.086 & 1.79 \\
 SSL-Ensemble & 90.1 & 0.056 & 0.334 & 92.2 & 0.050 & 0.257 & 59.7 & 0.081 & 1.86 & 62.6 & 0.053 & 1.48 \\
 Our method & 90.4 & 0.018 & 0.296 & 92.6 & 0.016 & 0.249 & 59.3 & 0.060 & 1.71 & 62.4 & 0.042 & 1.56\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Amirhossein Vahidi
Lisa Wimmer
Hüseyin Anil Gündüz
Bernd Bischl
Eyke Hüllermeier
Mina Rezaei

"Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning" by Amirhossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz, Bernd Bischl, Eyke Hüllermeier, and Mina Rezaei, arXiv:2308.14705v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Dataset Utilization. }We outline the dataset utilization for each experiment part here, a checkmark means the corresponding dataset is use for pretraining.}
\begin{tabular}{lccc}
\toprule
Dataset & Offline RL & Goal Reaching RL & Online Finetuning \\
\midrule
\texttt{hopper-medium-v2} & \checkmark & \checkmark & \checkmark\\
\texttt{hopper-medium-replay-v2} & \checkmark & & \checkmark\\
\texttt{walker2d-medium-v2} & \checkmark & \checkmark & \checkmark\\
\texttt{walker2d-medium-replay-v2} & \checkmark & & \checkmark\\
\texttt{halfcheetah-medium-v2} & \checkmark & & \checkmark\\
\texttt{halfcheetah-medium-replay-v2} & \checkmark & \checkmark & \checkmark\\
\midrule
\texttt{Can-Pair} & \checkmark & & \\
\texttt{Square-MH} & \checkmark & & \\
\texttt{Lift-MG} & \checkmark & & \\
\texttt{Can-Lim} & \checkmark & \checkmark & \\
\texttt{Can-Real} & \checkmark & \checkmark & \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"M$^3$PC: Test-time Model Predictive Control for Pretrained Masked Trajectory Model" by Kehan Wen, Yutong Hu, Yao Mu, and Lei Ke, arXiv:2412.05675v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Cumulative Number of Evs and chargers in 4 studied years}
\begin{tabular}{|c|c|c|c|c|}
			\hline
			Equipment type 	 & 2027	 & 2032 & 2037 & 2042 \\
			\hline
			\hline
	 eLionD buses & 14 & 31	& 49	& 61 \\
	 BlueBirdD buses	& 4	 & 4	 & 4	 & 4 \\
	 Ford eTransit vans & 12 	& 24	& 36	& 48 \\
	 \hline
	 19.5 kW chargers & 17	& 29	& 37	& 44 \\
	 62.5 kW chargers & 1	 & 4	 & 5	 & 6 \\
	 100 kW chargers & 0	 & 0	 & 3	 & 3 \\
	 11.3 kW chargers & 12	& 24	& 36	& 48 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

eess

Quan Nguyen
Christine Holland
Siddharth Sridhar

"Design and Optimization of EV Charging Infrastructure with Battery in Commercial Buildings" by Quan Nguyen, Christine Holland, and Siddharth Sridhar, arXiv:2509.12160v1, 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{Performance analysis with varying coefficient $\lambda$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
 \toprule
 $\lambda$ & ASR$\downarrow$ & FID$\downarrow$ & CLIP Score$\uparrow$ \\
 \midrule
 DES ($\lambda=0.0$) & 0.18 & 113.38 & 10.00 \\
 DES ($\lambda=0.1$) & 0.44 & 58.53 & 17.41 \\
 DES ($\lambda=0.2$) & 0.35 & 18.77 & 24.21 \\
 DES ($\lambda=0.3$) & 0.64 & 15.44 & 25.52 \\
 DES ($\lambda=0.4$) & 2.04 & 14.96 & 26.11 \\
 DES ($\lambda=0.5$) & 2.50 & 14.97 & 26.13 \\
 DES ($\lambda=0.6$) & 8.54 & 15.23 & 26.41 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Skill Comparison.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
 \toprule
 \multicolumn{2}{c|}{Ours} & \multicolumn{2}{c|}{OWL-v2} & \multicolumn{2}{c}{Voxposer}\\
 \midrule
 Grasp & Place & Grasp & Place & Grasp & Place \\
 90\% & 65\% & 60\% & 45\% & 60\% & 50\%\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yuelei Li
Ge Yan
Annabella Macaluso
Mazeyu Ji
Xueyan Zou
Xiaolong Wang

"Integrating LMM Planners and 3D Skill Policies for Generalizable Manipulation" by Yuelei Li, Ge Yan, Annabella Macaluso, Mazeyu Ji, Xueyan Zou, and Xiaolong Wang, arXiv:2501.18733v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Tuned hyperparameter configurations for the XGBoost algorithm with correctly specified interaction effects for the scenario non-linear.}
\begin{tabular}{l|r}
 \hline
 & non-linear \\
 \hline
 max\_depth & 4 \\
 eta & 0.825 \\
 alpha & 0.75 \\
 gamma & 1 \\
 nrounds & 700 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Julia Herbinger
Susanne Dandl
Fiona K. Ewald
Sofia Loibl
Giuseppe Casalicchio

"Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation" by Julia Herbinger, Susanne Dandl, Fiona K. Ewald, Sofia Loibl, and Giuseppe Casalicchio, arXiv:2310.03112v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{Performance Comparison of Single-template Prompt (ST Prompt), Multi-template Prompt (MT Prompt), and CLSPrompt for ILR.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc}
\hline
\multirow{2}{*}{\textbf{Project}} &\multirow{2}{*}{\textbf{Template}} && &\textbf{Measures}&& \\
& & \textbf{F1} & \textbf{Precision} & \textbf{Recall} & \textbf{MCC} & \textbf{AUC} & \textbf{ACC} \\
 \hline
\multirow{5}{*}{\textbf{log4net}} & \textbf{ST Prompt 1}
& 84.62 &	95.65 &	75.86 &	\textcolor{red}{89.64} &	87.27& 	92.45
 \\
 &\textbf{ST Prompt 2}&85.19 &	92.00& 	\textcolor{red}{79.31} 	&80.56 &	88.36& 	92.45
\\
& \textbf{ST Prompt 3}&81.48 &	88.00& 	75.86 &	75.57 &	85.98 &	90.57
 \\
 & \textbf{MT Prompt}&

 \textcolor{red}{86.79} &	\textcolor{red}{96.83} &	\textcolor{red}{79.31} &	86.79& 	86.79 &	\textcolor{red}{93.40}
 \\
 &\textbf{CLSPrompt} &86.45& 	95.00& 	\textcolor{red}{79.31} 	&85.78 	&\textcolor{red}{89.66}& 	88.46 \\
\hline
\multirow{5}{*}{\textbf{OODT}} & \textbf{ST Prompt 1}
& 82.70 &	85.96 	&79.67 	&80.43& 	88.92 	&95.86
 \\
 &\textbf{ST Prompt 2}&83.56 &	\textcolor{red}{92.16} &	76.42 &	81.92 	&87.75 	&96.27
\\
& \textbf{ST Prompt 3}&84.26 &	88.39 &	80.49 &	82.26 	&89.50& 	96.27
 \\
 & \textbf{MT Prompt}&\textcolor{red}{86.32} &	90.99 &	\textcolor{red}{82.11} &	\textcolor{red}{84.64}& 	\textcolor{red}{90.48} &	\textcolor{red}{96.77}
 \\
 &\textbf{CLSPrompt} &81.82 &	83.19& 	80.49 &	79.30 &	89.09& 	95.56
 \\
 \hline
\multirow{5}{*}{\textbf{Giraph}} & \textbf{ST Prompt 1}
& 87.74 &	95.77 &	80.95 &	86.70 &	90.25 &	86.74
 \\
 &\textbf{ST Prompt 2}&82.08& 	91.30 	&74.56& 	80.21 &	86.07 &	95.73
\\
& \textbf{ST Prompt 3}&88.31 &97.14 &	\textcolor{red}{80.95} 	&87.42 &	90.32 &	97.57
 \\
 & \textbf{MT Prompt}&\textcolor{red}{88.89} &	\textcolor{red}{98.55} &	\textcolor{red}{80.95} &	\textcolor{red}{88.15} &	\textcolor{red}{90.40} &	\textcolor{red}{97.71}
\\
 &\textbf{CLSPrompt} &86.62 	&93.15& 	\textcolor{red}{80.95}& 	85.31 &	90.10 &	97.17

 \\
 \hline
 \multirow{5}{*}{\textbf{Keras}} & \textbf{ST Prompt 1}
& 88.24 &	88.24 &	88.24& 	72.33 &	86.11 &	86.11
 \\
 &\textbf{ST Prompt 2}&91.03& 	90.97 &	\textcolor{red}{91.67}& 	90.97& 	90.97 &	90.97
\\
& \textbf{ST Prompt 3}&90.65 &	94.03 &	87.50 &	82.14 	&90.97& 	90.97
 \\
 & \textbf{MT Prompt}&\textcolor{red}{91.55} &	\textcolor{red}{92.86} &	90.28 &	\textcolor{red}{91.67} &	\textcolor{red}{91.67} 	&\textcolor{red}{91.67}
 \\
 &\textbf{CLSPrompt} &81.82 &	83.19& 	80.49 &	79.30& 	89.09& 	95.56
 \\
 \hline
 \multirow{5}{*}{\textbf{Nutch}} & \textbf{ST Prompt 1}
& 80.00 	&89.71 	&72.19 &	77.93& 	85.47 	&95.26
 \\
 &\textbf{ST Prompt 2}&81.70 &	91.24 &	73.96 &	79.82 &	86.45 	&95.65
\\
& \textbf{ST Prompt 3}&81.79 &	88.89 &	\textbf{75.74} 	&79.62 &	87.15 &	95.57
 \\
 & \textbf{MT Prompt}&\textcolor{red}{82.08} &	\textcolor{red}{91.30} 	&74.56&	80.21 &	\textcolor{red}{86.74} 	&\textcolor{red}{95.73}
 \\
 &\textbf{CLSPrompt} &73.37 &	91.85& 	73.37& 	\textcolor{red}{86.19} &	79.79 &	95.65
 \\
 \hline
 \multirow{5}{*}{\textbf{Isis}} & \textbf{ST Prompt 1}
& 86.67 &	92.51 	&81.53 &	85.57 &	90.42 &	97.61
 \\
 &\textbf{ST Prompt 2}&86.92 &	\textcolor{red}{95.42} &	79.81 	&86.09 &	89.70 &	97.71
\\
& \textbf{ST Prompt 3}&88.58 	&94.28 &	83.54 &	87.65& 	\textcolor{red}{91.50} &	97.94
 \\
 & \textbf{MT Prompt}&\textcolor{red}{88.90 }&	93.09 &	\textcolor{red}{85.07} 	&\textcolor{red}{92.20}& 	87.89& 	\textcolor{red}{97.97}
 \\
 &\textbf{CLSPrompt} &88.55& 	94.08 &	83.64 &	87.60 &	91.54 &	97.94
 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Bangchao Wang
Yang Deng
Ruiqi Luo
Peng Liang
Tingting Bi

"MPLinker: Multi-template Prompt-tuning with Adversarial Training for Issue-commit Link Recovery" by Bangchao Wang, Yang Deng, Ruiqi Luo, Peng Liang, and Tingting Bi, arXiv:2501.19026v1, 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{Number of GMRES-IR and RGMRES-IR refinement steps with the number of GMRES iterations for each refinement step for real matrices, using precisions ($u_f,u,u_r$) = (half, double, quad) and ($m,k$) = (40,10).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|}
		\hline
		Matrix & GMRES-IR (40) & RGMRES-IR(40,10) \\ \hline
		orsirr\_1 & 22 (11,11) & 20 (11,9) \\
		comsol & 52 (25,27) & 34 (25,9) \\
		circuit204 & 59 (18,20,21) & 47 (18,14,15) \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Mixed Precision GMRES-based Iterative Refinement with Recycling" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|l}
Name & $N_{c}$ & Description \\
\hline
INPUT & $1$ & Input $I^{T}_{\textrm{in}}$\\
CONV0 & $32$ & K3-C1-S2-P1 \\
RELU & & \\
CONV1 & $64$ & K3-C64-S2-P1 \\
RELU & & \\
CONV2 & $128$ & K3-C64-S2-P1 \\
RELU & & \\
CONV3 & $256$ & K3-C128-S2-P1 \\
RELU & & \\
CONV4 & $512$ & K3-C256-S2-P1 \\
RELU & & \\
CONV5 & $1024$ & K3-C512-S2-P1 \\
RELU & & \\
CONV6 & $2048$ & K3-C1024-S2-P1 \\
RELU & & \\
CONV4 & $4096$ & K3-C2048-S2-P1 \\
\midrule
\end{tabular}
\caption{Network architecture of $\mathcal{M}$ in 2DRN.}
\end{table}
\end{document}

eess

Cheng Peng
Haofu Liao
Gina Wong
Jiebo Luo
Shaohua Kevin Zhou
Rama Chellappa

"XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors" by Cheng Peng, Haofu Liao, Gina Wong, Jiebo Luo, Shaohua Kevin Zhou, and Rama Chellappa, arXiv:2012.02407v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Diarization performance on LibriCSS evaluation set (sessions 2-10), evaluated condition-wise, in terms of \% DER. 0S and 0L refer to 0\% overlap with short and long inter-utterance silences, respectively. Our overlap detector obtained 96.3\% precision and 83.8\% recall on this data. RPN does not use ground truth VAD.}
\begin{tabular}{lccccccc}
\toprule
\multirow{2}{*}{\textbf{Method}} & \multicolumn{6}{c}{\textbf{Overlap ratio in \%}} & \multirow{2}{*}{\textbf{Average}} \\
\cmidrule(r{4pt}){2-7}
 & \textbf{0L} & \textbf{0S} & \textbf{10} & \textbf{20} & \textbf{30} & \textbf{40} & \\
\midrule
AHC/PLDA & 5.1 & 2.7 & 11.5 & 19.3 & 22.7 & 28.8 & 16.3 \\
Spectral/cosine & 2.2 & 1.9 & 8.5 & 13.5 & 19.0 & 23.4 & 12.6 \\
RPN & 4.5 & 9.1 & 8.3 & 6.7 & 11.6 & 14.2 & 9.5 \\
\midrule
Our method & 2.6 & 3.4 & 6.8 & 10.0 & 13.9 & 15.2 & 9.3 \\
 + oracle OD & 2.2 & 3.3 & 6.7 & 9.6 & 12.9 & 14.4 & 8.8 \\
\bottomrule
\end{tabular}
\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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Historical Statistics of Log($v$).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
Year&mean&standard deviation&skew&excess kurtorsis\\
\hline	% horizontal line to separate headings from data
2020&15.218&1.024&-0.047&-0.514\\
2010&15.156&1.019&-0.054&-0.537\\
2000&15.062&1.020&-0.052&-0.572\\
1990&14.939&1.018&-0.015&-0.630\\
1980&14.850&1.021&-0.073&-0.719\\
1970&14.714&1.063&-0.091&-0.719\\
1960&14.583&1.071&-0.172&-0.606\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A New Perspective on Impartial and Unbiased Apportionment" by Ross Hyman and Nicolaus Tideman, arXiv:2201.11787v2, 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}{lccccc}
\toprule
Nr. of samples & $10^2$ & $10^3$ & $10^4$ & $10^5$ \\
\midrule
VDE det. (ours) & \textbf{3.4} {\small$\pm$ 2.1} \% & \textbf{1.7} {\small $\pm$ 0.6} \% & \textbf{1.6} {\small $\pm$ 1.3} \% & \textbf{0.3} {\small $\pm$ 0.3} \% \\
MC det. & 533 {\small $\pm$ 660 } \% & 348 \small{$\pm$ 262} \% & 104 \small{$\pm$ 30} \% & 59 \small{$\pm$ 43} \% \\
\bottomrule
\end{tabular}
\caption{Comparison of determinant estimates. Results are in mean absolute relative difference for $5$ unit Gaussian sampled $10 \times 10$ matrices. Deviations are given in one standard deviation.}
\end{table}
\end{document}

stat

"Variational Determinant Estimation with Spherical Normalizing Flows" by Simon Passenheim and Emiel Hoogeboom, arXiv:2012.13311v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of Responsiveness of Models to Non-verbal Vocal Signals}
\begin{tabular}{cc}
\hline
\textbf{Model} & \textbf{Score (\%) $\uparrow$} \\
\hline
GLM-4-Voice & 1.89 \\
MiniCPM-o 2.6 & 2.08 \\
Baichuan-Omni-1.5 & 1.80 \\
SpeechGPT-2.0-preview & 1.52 \\
Freeze-Omni & 1.85 \\
Qwen2.5-Omni & 2.19 \\
Kimi-Audio & 9.19 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

BoSS: Beyond-Semantic Speech

Qing Wang
Zehan Li
Hang Lv
Hongjie Chen
Yaodong Song
Jian Kang
Jie Lian
Jie Li
Yongxiang Li
Zhongjiang He
Xuelong Li

"BoSS: Beyond-Semantic Speech" by Qing Wang, Zehan Li, Hang Lv, Hongjie Chen, Yaodong Song, Jian Kang, Jie Lian, Jie Li, Yongxiang Li, Zhongjiang He, and Xuelong Li, arXiv:2507.17563v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Automated Analysis of Experiments using Hierarchical Garrote" by Wei-Yang Yu and V. Roshan Joseph, arXiv:2411.01383v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 \toprule
 \textbf{Instrument} & \texttt{VGGish (\%)} & \texttt{PANN (\%)} \\
 \midrule
 \texttt{Guitar} & \textbf{27.39} & \textbf{43.97} \\
 \texttt{Drums} & 19.14 & 13.75 \\
 \texttt{Piano} & 5.80 & 9.82 \\
 \midrule
 \texttt{Accordion} & 1.77 & 2.5 \\
 \texttt{Clarinet} & 0.24 & 1.81 \\
 \texttt{Bagpipes} & 0.20 & 1.52 \\
 \texttt{Sitar} & 0.09 & 1.35 \\
 \texttt{Tabla} & 0.08 & 1.47 \\
 \texttt{Bassoon} & 0.08 & -- \\
 \bottomrule
\end{tabular}
\caption{Percentage Composition of Instruments in the Collection for VGGish and PANN Models}
\end{table}
\end{document}

eess

Atharva Mehta
Shivam Chauhan
Monojit Choudhury

"Missing Melodies: AI Music Generation and its "Nearly" Complete Omission of the Global South" by Atharva Mehta, Shivam Chauhan, and Monojit Choudhury, arXiv:2412.04100v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Play Like the Pros? Solving the Game of Darts as a Dynamic Zero-Sum Game" by Martin B. Haugh and Chun Wang, arXiv:2011.11031v2, 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{Model} & \textbf{LPIPS$\downarrow$} & \textbf{Tracked Point Texture Variance$\downarrow$} \\ \hline
 InTex & 0.467 & 0.439 \\ \hline
 SDS+Lipschitz & 0.261 & 0.015 \\ \hline
 ProcTex & \textbf{0.160} & \textbf{0.006} \\ \hline
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Quantitative evaluation of texture transfer performance using perceptual (LPIPS) and correspondence-based (tracked point texture variance) metrics. Lower values indicate better visual similarity and texture consistency. ProcTex achieves the best performance across both measures, demonstrating its ability to preserve both global perceptual similarity and local texture consistency across procedural shape variations.}
\end{table}
\end{document}

cs

Ruiqi Xu
Zihan Zhu
Ben Ahlbrand
Srinath Sridhar
Daniel Ritchie

"ProcTex: Consistent and Interactive Text-to-texture Synthesis for Part-based Procedural Models" by Ruiqi Xu, Zihan Zhu, Ben Ahlbrand, Srinath Sridhar, and Daniel Ritchie, arXiv:2501.17895v2, 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|}
 Model & Log-likelihood & $\#$ Parameters & AIC \\ \hline
 IM & $22.28663$ &8 & $-28.57326$ \\ %\hline
 SCF & $34.53614$ & 7 & $ -55.07228$ \\ %\hline
 MMG & $36.64354$ &11& $-51.28708$\\ %\hline
 SMM-0 & $38.34603$ & 9 & $ -58.69206$ \\ %\hline
 SMM-R & $37.66356$ & 10 & $-55.32712$ \\ %\hline
 SMM-C & $37.16868$ & 12 & $-50.33736$ \\ \hline
 \end{tabular}
\caption{Multivariate Mat\'ern maximum likelihoods, number of parameters, and Akaike information criterion (AIC) values for the Argo temperature data.}
\end{table}
\end{document}

stat

"Multivariate Matérn Models -- A Spectral Approach" by Drew Yarger, Stilian Stoev, and Tailen Hsing, arXiv:2309.02584v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A modified Susceptible-Infected-Recovered model for observed under-reported incidence data" by Imelda Trejo and Nicolas Hengartner, arXiv:2012.05294v2, 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{Planar surface perception -- comparison with factory accuracy.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|}
\cline{2-4}
 & \textbf{Factory accuracy} & \textbf{Standard deviation} & \textbf{Bias} \\ \hline
\textbf{D435} & $<$2\% at 2\;m & $0.75$\% at 2\;m & $1.87$\% at 2\;m \\ \hline
\textbf{D455} & $<$2\% at 4\;m & $3.05$\% at 4\;m & $0.13$\% at 4\;m \\ \hline
\textbf{ZED} & $<$0.8\% at 2\;m & $0.17$\% at 2\;m & $0.22$\% at 2\;m \\ \hline
\textbf{OAK-D Pro} & $<$2\% at 4\;m & $1.47$\% at 4\;m & $1.65$\% at 4\;m \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Lukas Rustler
Vojtech Volprecht
Matej Hoffmann

"Empirical Comparison of Four Stereoscopic Depth Sensing Cameras for Robotics Applications" by Lukas Rustler, Vojtech Volprecht, and Matej Hoffmann, arXiv:2501.07421v2, 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}{cccc}
 \toprule
 \multicolumn{4}{c}{Non-hallucination Samples} \\
 {[0.0,0.25)} & {[0.25,0.5)} & {[0.5,0.75)} & {[0.75,1.0]} \\
 0.987 & 0.013 & 0.0 & 0.0 \\
 \midrule
 \multicolumn{4}{c}{Hallucination Samples} \\
 {[0.0,0.25)} & {[0.25,0.5)} & {[0.5,0.75)} & {[0.75,1.0]} \\
 0.907 & 0.070 & 0.015 & 0.008 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Distribution of non-attentive consistency scores $\mathop r\nolimits^{non\_att}$ with Qwen2.5-14b on the Books dataset.}
\end{table}
\end{document}

cs

Qiang Liu
Xinlong Chen
Yue Ding
Bowen Song
Weiqiang Wang
Shu Wu
Liang Wang

"Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models" by Qiang Liu, Xinlong Chen, Yue Ding, Bowen Song, Weiqiang Wang, Shu Wu, and Liang Wang, arXiv:2501.09997v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Hao Wen
Ziqian Lu
Fengli Shen
Zhe-Ming Lu
Jialin Cui

"Improving Skeleton-based Action Recognition with Interactive Object Information" by Hao Wen, Ziqian Lu, Fengli Shen, Zhe-Ming Lu, and Jialin Cui, arXiv:2501.05066v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Generalized difference-in-differences

Denis Agniel
Max Rubinstein
Jessie Coe
Maria DeYoreo

"Generalized difference-in-differences" by Denis Agniel, Max Rubinstein, Jessie Coe, and Maria DeYoreo, arXiv:2312.05400v1, 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{Comparison of segmentation approaches based on Window IoU}
\begin{tabular}{lr}
\toprule
Approach & Window IoU \\
\midrule
UNet (SCM only) & 0.178 \\
MaskRCNN & 0.357 \\
Fusion & \textbf{0.467} \\
Fusion (with shape approx.) & 0.420 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Franz Hanke
Antonia Bieringer
Olaf Wysocki
Boris Jutzi

"CM2LoD3: Reconstructing LoD3 Building Models Using Semantic Conflict Maps" by Franz Hanke, Antonia Bieringer, Olaf Wysocki, and Boris Jutzi, arXiv:2508.15672v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\footnotesize{Hyper-parameter tuning: crossvalidated hyperparameters $\lambda_1$ and $\lambda_2$ obtained for MNIST and CIFAR10 datasets with the classifiers used in our experiments.}}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
 \hline
 \multirow{3}{3em}{\bf Dataset} & \multirow{3}{4em}{\bf Classifier} & \multicolumn{6}{|c|}{\bf Random missing entries} & \multicolumn{2}{|c|}{\bf Occlusion} \\ \cline{3-10}
 & & \multicolumn{2}{|c|}{\bf 75\%} & \multicolumn{2}{|c|}{\bf 50\%} &\multicolumn{2}{|c|}{\bf 25\%} &\multicolumn{2}{|c|}{\bf 50\%} \\ \cline{3-10}
 & & $\lambda_1$ & $\lambda_2$ & $\lambda_1$ & $\lambda_2$ & $\lambda_1$ & $\lambda_2$ & $\lambda_1$ & $\lambda_2$ \\
 \hline
 \multirow{2}{3em}{\bf MNIST} & \bf Log. Reg. & 0.32 & 1.28 & 0.64 & 1.28 & 0.64 & 1.28 & - & - \\ \cline{2-10}
 &\bf CNN4 & 1.28 & 1.28 & 2.56 & 1.28 & 5.12 & 1.28 & 10.24 & 10.24 \\
 \hline
 \bf CIFAR10 & \bf Resnet18 & 0.024 & 0.008 & 0.032 & 0.004 & 0.032 & 0.01 & - & - \\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

Cesar F. Caiafa
Ziyao Wang
Jordi Solé-Casals
Qibin Zhao

"Learning from Incomplete Features by Simultaneous Training of Neural Networks and Sparse Coding" by Cesar F. Caiafa, Ziyao Wang, Jordi Solé-Casals, and Qibin Zhao, arXiv:2011.14047v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison between \textit{PadAug(HT) } and \textit{PadAug(HMT)}, where the SV model is ResNet34.}
\begin{tabular}{lccccccc}
\toprule
\multirow{2}{*}{Method} & \multirow{2}{*}{Testset type} & \multicolumn{2}{c}{Voxceleb1-O} & \multicolumn{2}{c}{Voxceleb1-E} & \multicolumn{2}{c}{Voxceleb1-H} \\
\cmidrule(lr){3-4}\cmidrule(lr){5-6}\cmidrule(lr){7-8}
 & & EER & minDCF & EER & minDCF & EER & minDCF \\
\midrule
no \textit{PadAug} & Original & 0.962 & 0.091 & 1.007 & 0.110 & 1.859 & 0.178 \\
PadAug(HT) & Original & 0.914 & \textbf{0.077} & \textbf{0.978} & \textbf{0.104} & \textbf{1.798} & 0.174 \\
PadAug(HMT) & Original & \textbf{0.904} & \textbf{0.077} & 0.990 & 0.107 & 1.809 & \textbf{0.173} \\
\midrule
\midrule
no \textit{PadAug} & Chunk3s & 1.345 & 0.143 & \textbf{1.259} & 0.142 & 2.348 & \textbf{0.227} \\
PadAug(HT) & Chunk3s & \textbf{1.170} & \textbf{0.114} & 1.261 & \textbf{0.140} & \textbf{2.326} & 0.231 \\
PadAug(HMT) & Chunk3s & 1.233 & 0.125 & 1.278 & 0.144 & 2.345 & 0.234 \\
\midrule
\midrule
no \textit{PadAug} & Chunk3s+Head1s+Tail1s & 1.409 & 0.142 & 1.415 & 0.156 & 2.560 & 0.249 \\
PadAug(HT) & Chunk3s+Head1s+Tail1s & \textbf{1.170} & 0.122 & \textbf{1.261} & 0.144 & \textbf{2.311} & \textbf{0.229} \\
PadAug(HMT) & Chunk3s+Head1s+Tail1s & 1.207 & \textbf{0.120} & 1.269 & \textbf{0.142} & 2.328 & 0.230 \\
\midrule
\midrule
no \textit{PadAug} & Chunk3s+Head1s+Tail1s+Mid1s & 1.526 & 0.169 & 1.512 & 0.170 & 2.724 & 0.265 \\
PadAug(HT) & Chunk3s+Head1s+Tail1s+Mid1s & \textbf{1.213} & \textbf{0.133} & 1.309 & 0.147 & 2.365 & \textbf{0.227} \\
PadAug(HMT) & Chunk3s+Head1s+Tail1s+Mid1s & 1.223 & 0.146 & \textbf{1.302} & \textbf{0.144} & \textbf{2.363} & 0.234 \\
\toprule
\end{tabular}
\end{table}
\end{document}

eess

Zijun Huang
Chengdong Liang
Jiadi Yao
Xiao-Lei Zhang

"PadAug: Robust Speaker Verification with Simple Waveform-Level Silence Padding" by Zijun Huang, Chengdong Liang, Jiadi Yao, and Xiao-Lei Zhang, arXiv:2508.14732v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average Kendall rank correlation coefficient, Spearman's Rank Correlation Coefficient, and p-values on both BFS-ordered graphs and randomly ordered graphs}
\begin{tabular}{|l|ll|ll|}
\hline
\multirow{2}{*}{Graph} & \multicolumn{2}{l|}{Spearman's Rank Correlation} & \multicolumn{2}{l|}{Kendall rank correlation} \\ \cline{2-5}
 & Coefficient & p-value & Coefficient & p-value \\ \hline
BFS-ordered graphs (G’) & 0.6734 & 0.0114 & 0.5742 & 0.0106 \\ \hline
Randomly-ordered graphs (G’’) & 0.1243 & 0.5040 & 0.0852 & 0.5009 \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

"Deep Generative Model for Periodic Graphs" by Shiyu Wang, Xiaojie Guo, and Liang Zhao, arXiv:2201.11932v4, 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{Default experimental settings for CIFAR-10}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 Dataset & CIFAR-10 \\
 Architecture & VGG-11 \\
 Training objective & Cross entropy loss \\
 Evaluation objective & Top-1 accuracy \\
 \midrule
 Batch size per worker & $64$ \\
 Momentum & 0.9 \\
 Learning rate & 0.1 \\
 LR decay & 0.1 at epoch 80 and 120 \\
 LR warmup & No \\
 Weight decay & No \\
 \midrule
 Repetitions & 1 \\%, with varying seeds \\
 Reported metric & Mean test accuracy over the last 150 iterations \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Lie He
Sai Praneeth Karimireddy
Martin Jaggi

"Byzantine-Robust Decentralized Learning via ClippedGossip" by Lie He, Sai Praneeth Karimireddy, and Martin Jaggi, arXiv:2202.01545v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative comparison with generated whole brain FA (FA) images and white matter (WM) whose FA value is bigger than 0.2 from synthesis outputs. The best outcomes are shown in \textbf{bold}. ↑: Higher is better. ↓: Lower is better.}
\begin{tabular}{cccccc}
\hline
Methods & Input (GT) &SSIM ($\uparrow$) &PSNR ($\uparrow$) &MAE ($\downarrow$)&MSE($\downarrow$) \\ \hline
Pix2Pix & FA & 0.8310 & 24.7738 & 0.1469 & 0.1292 \\
CycleGAN & FA & 0.8332 & 24.6660 & 0.1477 & 0.1299 \\
Macro2Micro (Ours) & FA & \textbf{0.8600} & \textbf{25.7560} & \textbf{0.1383} & \textbf{0.1226} \\ \hline \hline
Pix2Pix & WM & 0.8354 & 24.8992 & 0.1374 & 0.1200 \\
CycleGAN & WM & 0.8369 & 24.7585 & 0.1383 & 0.1208 \\
Macro2Micro (Ours) & WM & \textbf{0.8627} & \textbf{25.8493} & \textbf{0.1300} & \textbf{0.1146} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Sooyoung Kim
Joonwoo Kwon
Junbeom Kwon
Sangyoon Bae
Yuewei Lin
Shinjae Yoo
Jiook Cha

"Macro2Micro: Cross-modal Magnetic Resonance Imaging Synthesis Leveraging Multi-scale Brain Structures" by Sooyoung Kim, Joonwoo Kwon, Junbeom Kwon, Sangyoon Bae, Yuewei Lin, Shinjae Yoo, and Jiook Cha, arXiv:2412.11277v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
\hline
\textbf{Subset} & \textbf{Number of Tokens} \\ \hline
C4 Subset Original & 4096 \\ \hline
C4 Subset 1 & 4032 \\ \hline
C4 Subset 2 & 4080 \\ \hline
C4 Subset 3 & 3990 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{subsets and their corresponding number of tokens}
\end{table}
\end{document}

cs

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

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


math

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{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
		Grid & $L_{1}$-error & order & $L_{\infty}$-error & order\\\hline
		32$^2$ & 1.51e-05 & -- & 1.67e-4 & -- \\
		64$^2$ & 5.52e-06 & 1.45 & 8.78e-05 & 0.92 \\
		128$^2$ & 1.4e-06 & 1.97 & 2.28e-05 & 1.94 \\
		256$^2$ & 3.32e-07 & 2.07 & 4.73e-06 & 2.26 \\ \hline
	\end{tabular}
\end{adjustbox}
\caption{Convergence of the temperature field with grid refinement, fixed timestep = $10^{-6}$}
\end{table}
\end{document}

math

A. Limare
S. Popinet
C. Josserand
Z. Xue
A. Ghigo

"A hybrid level-set / embedded boundary method applied to solidification-melt problems" by A. Limare, S. Popinet, C. Josserand, Z. Xue, and A. Ghigo, arXiv:2202.08300v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rcccc}
Result \# & $G$ & Order & $mr_+^{\mathbb{C}}(G)$ & $mr_+^{\mathbb{R}}(G)$ \\ \toprule
, & $P_n \boxtimes P_m$ & $nm$ & $(n-1)(m-1)$ & $(n-1)(m-1)$ \\
 & $T \Box K_n$ & $nm$ & $mn-n$ & $mn-n$ \\
 & $C_3 \Box P_n$ & $3n$ &$ 3n-3$ & $3n-3$ \\
 & $K_n \Box K_m$ &$nm$ & $\leq n+m-1$ & $n+m-2$ or $n+m-1$ \\
 & $T \circ T'$& $mm'+m$ &$mm'-1$ & $mm'-1$\\
 & $T \circ K_n, n \geq 2 $ & $mn+m$ & $2m-1$ & $2m-1$\\
 & $K_n \circ T$ & $mn+n$ & $nm-n+1$ & $nm-n+1$ \\
, & $K_n \circ K_m$ & $mn+n$ &$n+1$& $n+1$ \\
 & $C_n \circ T$ & $mn+n$ &$nm-2$& $nm-2$ \\
 & $T \circ C_n$ & $mn+m$ &$m(n-1)-1$& $m(n-1)-1$ \\
, & $C_n \circ K_m$ & $mn+n$ &$2n-2$& $2n-2$\\
 & $K_m \circ C_n$ & $mn+m$ &$m(n-2)+1$& $m(n-2)+1$ \\
 & $C_n \circ C_m$ & $mn+n$ &$n(m-1)-2$& $n(m-1)-2$ \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Summary of minimum positive semidefinite rank results established in this paper ($T$ and $T'$ are trees with $|T|=m$ and $|T'|=m'$).}
\end{table}
\end{document}

math

"Characterization of some classes of graphs and their products as frame graph" by H. Najafi and F. Abdollahi, arXiv:2201.01640v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


cs

Daniel Udekwe
Ruimin Ke
Jiaqing Lu
Qian-wen Guo

"Q-RESTORE: Quantum-Driven Framework for Resilient and Equitable Transportation Network Restoration" by Daniel Udekwe, Ruimin Ke, Jiaqing Lu, and Qian-wen Guo, arXiv:2501.11197v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Ramin Mousa
Saeed Chamani
Mohammad Morsali
Mohammad Kazzazi
Parsa Hatami
Soroush Sarabi

"Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers" by Ramin Mousa, Saeed Chamani, Mohammad Morsali, Mohammad Kazzazi, Parsa Hatami, and Soroush Sarabi, arXiv:2412.00472v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|l|l|l|l}

 Condition & Condition Type & Male & Female & \textbf{Total} \\
 \hline
 Vocal cord cancer & Malignant & 21 & 1 & \textbf{22} \\
 Hypopharyngeal tumor & Malignant & 6 & 0 & \textbf{6} \\
 Larynx tumor & Malignant & 4 & 1 & \textbf{5} \\
 Epiglottic cancer & Malignant & 0 & 1 & \textbf{1} \\
 Nesopharyngeal tumor & Malignant & 1 & 0 & \textbf{1} \\
 Carcinoma in situ & Malignant & 1 & 0 & \textbf{1} \\
 Dysplastic dysphonia & Pre-malignant & 1 & 0 & \textbf{1} \\
 Dysplastic larynx & Pre-malignant & 1 & 0 & \textbf{1} \\

 \end{tabular}
\caption{The number of patients in the SVD per condition. This is also split into the number of male and female patients per condition.}
\end{table}
\end{document}

eess

"A Classification Benchmark for Artificial Intelligence Detection of Laryngeal Cancer from Patient Voice" by Mary Paterson, James Moor, and Luisa Cutillo, arXiv:2412.16267v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|}
\hline
 Layer (type)& Output shape & Parameter number\\
\hline
Cov-1D-1& [50, 10, 5000] & 3160\\
\hline
BatchNorm & [50, 10, 5000] & 20\\
\hline
MaxPool1D & [50, 10, 100] & 0\\
\hline
Cov-1D-2& [50, 20, 100] & 1820 \\
\hline
BatchNorm& [50, 20, 100] & 40\\
\hline
MaxPool1D &[50, 20, 10] & 0\\
\hline
Cov-1D-3& [50, 30, 10] & 5430 \\
\hline
BatchNorm & [50, 30, 10] & 60\\
\hline
MaxPool1D &[50, 30, 1] & 0\\
\hline
Cov-1D-1& [50, 40, 1] & 10840\\
\hline
BatchNorm & [50, 40,1] & 80\\
\hline
Dropout & [50, 40, 1] &0\\
\hline
FullyConnected & [50, 5] & 205\\
\hline
\textbf{Total parameters} &- & \textbf{21655}\\
\hline
\textbf{Trainable parameters} & - & \textbf{21655}\\
\hline
\textbf{Non-trainable parameters} & - & \textbf{0}\\
\hline
\end{tabular}
\caption{Calculation of parameters in the CNN. The output shape is given as [batch size, number of channels, channel length].}
\end{table}
\end{document}

eess

Silas Ruhrberg Estévez
Josée Mallah
Dominika Kazieczko
Chenyu Tang
Luigi G. Occhipinti

"Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals" by Silas Ruhrberg Estévez, Josée Mallah, Dominika Kazieczko, Chenyu Tang, and Luigi G. Occhipinti, arXiv:2411.16273v1, 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
 Region & (1) & (2) &ARIMA \\
 \hline
 Matara& 11.38& \bf{11.36} & 11.92\\
 Anuradhapura&14.70&\bf{12.81}&14.70\\
 Polonnaruwa & 8.65&\bf{8.04}&8.65\\
 Kurunegala & 14.70 &\bf{10.08} & 10.35\\
 Hambantota & 32.76&\bf{32.12} &34.63 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{RMSE prediction errors of different models}
\end{table}
\end{document}

math

"An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics" by Zhijian Li, Jack Xin, and Guofa Zhou, arXiv:2201.09394v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Detailed data information of AHSD}
\begin{tabular}{ccc}
 \hline
 \textbf{Whether the airway is abnormal}& \textbf{normal} & \textbf{unusual} \\
 \hline
 \textbf{Total quantity}& 13 & 227 \\
 \textbf{Sex (male/female)}& 6 / 7 & 106 / 121 \\
 \textbf{Age (mean±SD)}& 5.78±0.82 / 5±2.68 & 6.53±2.15 / 5.95±2.26 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Rulin Zhou
Yingjie Feng
Guankun Wang
Xiaopin Zhong
Zongze Wu
Qiang Wu
Xi Zhang

"TSUBF-Net: Trans-Spatial UNet-like Network with Bi-direction Fusion for Segmentation of Adenoid Hypertrophy in CT" by Rulin Zhou, Yingjie Feng, Guankun Wang, Xiaopin Zhong, Zongze Wu, Qiang Wu, and Xi Zhang, arXiv:2412.00787v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Test metrics on CLEVR-TR.}}
\begin{tabular}{lrrr}
 \toprule
 & PSNR$\uparrow$ & LPIPS $\downarrow$ & SSIM$\uparrow$ \\
 \midrule
 APE & 33.66 & 0.161 & 0.960 \\
 RPE & 36.06 & 0.159 & 0.971\\
 SRT & 33.51 & 0.158 & 0.960 \\
 RePAST & 37.27 &0.119 &0.977 \\
 GTA (Ours)& \textbf{39.63} & \textbf{0.108} & \textbf{0.984} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Takeru Miyato
Bernhard Jaeger
Max Welling
Andreas Geiger

"GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers" by Takeru Miyato, Bernhard Jaeger, Max Welling, and Andreas Geiger, arXiv:2310.10375v3, licensed under CC0 1.0. 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{\textsc{Specifications of Machine }}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
 \textbf{System} & \textbf{Configuration} \\
 \hline
 Operating System & Windows 10 \\
 Processor & 3.2 GHz Intel core i7-8700\\
 Video adapter & Intel UHD Graphics 630 (350 - 1200 MHz) \\
 RAM & 16 GB \\
 Hardware memory & 1000 GB \\
 DirectX version & 12 \\
 OpenGL version & 4.5 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Oleg Perezyabov
Mikhail Gavrilenkov
Ilya Afanasyev

"Depth and Image Fusion for Road Obstacle Detection Using Stereo Camera" by Oleg Perezyabov, Mikhail Gavrilenkov, and Ilya Afanasyev, arXiv:2501.07245v1, 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{The table presents a comparison of F1-scores (averaged across the employed classification models), demonstrating the impact of SR-FT on both classification performance and image quality, LR is low resolution data, HR is high resolution data, SRHR is the data which is super-resolved using SR-I models without any training on current dataset. The best scores for each loss functions are highlighted in bold.}
\begin{tabular}{lcccccc}
\toprule
 & LR & HR & SRHR & L1 & Combo & Hybrid \\
\midrule
No Train & 61.66 & 64.35 & 55.16 & -- & -- & -- \\
SR-PT & -- & -- & -- & 60.35 & 60.30 & 60.53 \\
SR-FT & -- & -- & -- & \textbf{60.56} & \textbf{61.60} & \textbf{61.20} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Ch Muhammad Awais
Marco Reggiannini
Davide Moroni
Oktay Karakus

"A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery" by Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, and Oktay Karakus, arXiv:2508.06407v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
\hline
\multirow{2}{*}{Method} & \cellcolor{gray!20} 2048 & \cellcolor{gray!20} 4096 & \cellcolor{gray!20} 8192 \\
						& depth 11 & depth 12 & depth 13 \\
\hline
 \rowcolor{gray!20} 	Our approach	& 24.71 & 5.95 & 1.54 \\ \hline
 OCT-TR 		& 81.15 & 23.45 & 4.86 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{This table shows the precision guaranteed by both algorithms for the excavation model. The measurements are in square centimetres. The header of the columns shows the texture size (gray) and depth of octree (white) that are tested against each other.}
\end{table}
\end{document}

cs

Luis López
Juan Carlos Torres
Germán Arroyo
Pedro Cano
Domingo Martín

"An efficient GPU approach for designing 3D cultural heritage information systems" by Luis López, Juan Carlos Torres, Germán Arroyo, Pedro Cano, and Domingo Martín, arXiv:2501.14807v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Taketo Akama
Zhuohao Zhang
Pengcheng Li
Kotaro Hongo
Hiroaki Kitano
Shun Minamikawa
Natalia Polouliakh

"Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings" by Taketo Akama, Zhuohao Zhang, Pengcheng Li, Kotaro Hongo, Hiroaki Kitano, Shun Minamikawa, and Natalia Polouliakh, arXiv:2412.15560v1, 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{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of linear regressions considering $d_{1i}$ as the explanatory variable and $\hat{\mu}_i^p$, $\hat{\mu}_i^d$ from the 2-component Poisson mixture model as the response variables. Piccadilly Circus is considered to be the nucleus in London and City Hall in Seoul.}
\begin{tabular}{cc|cccc}
 \multicolumn{2}{c}{} & Intercept & Slope & $R$ & $p$-value\\
 \hline
 \multirow{2}{*}{\textbf{London}} & Proximal & $4.295 \pm 0.437$ & $0.180 \pm 0.041$ & $0.228$ & $<0.05$ \\
 & Distal & $10.348 \pm 0.435$ & $0.427 \pm 0.041$ & $0.488$ & $<0.05$ \\
 \hline
 \multirow{2}{*}{\textbf{Seoul}}& Proximal & $5.018 \pm 0.116$ & $0.013 \pm 0.007$ & $0.086$ & $0.06$ \\
 & Distal & $10.826 \pm 0.191$ & $0.403 \pm 0.012$ & $0.844$ & $<0.05$ \\
 \end{tabular}
\end{table}
\end{document}

stat

Carmen Cabrera-Arnau
Chen Zhong
Michael Batty
Ricardo Silva
Soong Moon Kang

"Inferring urban polycentricity from the variability in human mobility patterns" by Carmen Cabrera-Arnau, Chen Zhong, Michael Batty, Ricardo Silva, and Soong Moon Kang, arXiv:2212.03973v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.