\documentclass{article}
\usepackage{multirow}
\usepackage{amssymb}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation results of the interior space planning application to validate the effect of density map. The original DeepLabv3+ network is designed to output the pixel-wise segmentation while our URE-Net produces region-wise segmentation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc|c|cc}
 \toprule
 \multirow{3}*{Network} & \multirow{3}*{Density} &
 \multirow{3}*{mIoU$(\uparrow)$} & \multicolumn{2}{c}{Boundary F score$(\uparrow)$} \\
 \cline{4-5}
 {} & {}& {} & All& Internal \\
 \midrule
 DeepLabv3+ & - &69.54 &59.77& 19.63 \\
 DeepLabv3+ & \checkmark &\textbf{70.06} &\textbf{60.58}& \textbf{23.69} \\
 \midrule
 URE-Net & - &71.60 &71.97& 62.72 \\
 URE-Net & \checkmark &\textbf{72.32} &\textbf{72.71}& \textbf{63.19} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Huichao Zhang
Pengyu Wang
Manyi Li
Zuojun Li
Yaguang Wu

"Unit Region Encoding: A Unified and Compact Geometry-aware Representation for Floorplan Applications" by Huichao Zhang, Pengyu Wang, Manyi Li, Zuojun Li, and Yaguang Wu, arXiv:2501.11097v1, 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|c|}
 \hline
 $m$ & 0 & 1 & 2 & 3 & 4 & 5 \\
 \hline
 $\rho(m,0)$ & 1 & 1 & 0 & $-1$ & $-1$ & 0\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Initial values that determine $\rho(m,n)$}
\end{table}
\end{document}

math

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

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison with SDT (4-class and 6-class). SDT* uses our feature extractors.}
\begin{tabular}{lccccccc}
\toprule
 & Happy & Sad & Neutral & Angry & Excited & Frustrated & Overall \\
\midrule
\textbf{LPGNet (4-class)} & \textbf{92.06} & 85.38 & \textbf{83.99} & 86.75 & -- & -- & \textbf{87.83} \\
\textbf{SDT* (4-class)} & 89.72 & \textbf{86.35} & 82.32 & \textbf{88.15} & -- & -- & 86.62 \\
\midrule
\textbf{LPGNet (6-class)} & \textbf{51.3} & \textbf{79.1} & \textbf{73.99} & 72.19 & \textbf{77.83} & \textbf{68.59} & \textbf{71.78} \\
\textbf{SDT* (6-class)} & 38.59 & 78.09 & 69.52 & \textbf{73.53} & 70.63 & 65.13 & 67.47 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"LPGNet: A Lightweight Network with Parallel Attention and Gated Fusion for Multimodal Emotion Recognition" by Zhining He and Yang Xiao, arXiv:2508.08925v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"A Method of Sequential Log-Convex Programming for Engineering Design" by Cody Karcher and Robert Haimes, arXiv:2201.08436v1, 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}{ccccc}
\hline
\textbf{Group} & \textbf{Data Type} & \textbf{Number of Images} & \textbf{Number of Devices} \\ \hline
Group 1 & Capital Letters + Digits & 800 & 60 \\ \hline
Group 2 & Lowercase Letters + Digits & 1,000 & 100 \\ \hline
Group 3 & Capital/Lowercase Letters + Digits & 600 & 40 \\ \hline
\end{tabular}
\caption{Data Structure of FEMNIST-3-groups}
\end{table}
\end{document}

stat

"SALR: Sharpness-aware Learning Rate Scheduler for Improved Generalization" by Xubo Yue, Maher Nouiehed, and Raed Al Kontar, arXiv:2011.05348v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Benchmark timing results on GPU.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
			\toprule
			\multirow{3}{*}{Library} & \multirow{3}{*}{Method} & \multirow{3}{*}{\shortstack{Permutation / \\Preconditioner}} & \multirow{3}{*}{\shortstack{Small\\problems\\time (ms)}} &
			\multirow{3}{*}{\shortstack{Medium\\problems\\time (ms)}} &
			\multirow{3}{*}{\shortstack{Large\\problems\\time (ms)}}\\
			&&&&&\\
			&&&&&\\
			\midrule
			\multirow{8}{*}{\shortstack{cuSOLVER\\(GPU)}} & \multirow{4}{*}{Cholesky} & - & 40.4 & 310 & 1339\\
			& & METIS & 30.7 & 136 & 393 \\
			& & SymAMD & 35.1 & 212 & 817 \\
			& & SymRCM & 34.5 & 226 & 866 \\
			\cmidrule{2-6}
			& \multirow{4}{*}{QR} & - & 103 & 806 & 4151 \\
			& & METIS & 154 & 1521 & 7466 \\
			& & SymAMD & 212 & 2974 & 20784\\
			& & SymRCM & 89.1 & 594 & 2571 \\
			\midrule
			\multirow{5}{*}{\shortstack{SuperLU\\(GPU)}} & \multirow{5}{*}{LU} & - & 1767 & 21399 & -\\
			& & ColAMD & 418 & 775 & 1631\\
			& & METIS & 374 & 531 & 835\\
			& & \multirow{2}{*}{\shortstack{Minimum\\Degree}} & \multirow{2}{*}{425} & \multirow{2}{*}{626} & \multirow{2}{*}{906}\\
			&&&&&\\
			\midrule
			\multirow{14}{*}{\shortstack{ViennaCL\\(GPU)}} & \multirow{7}{*}{\shortstack{Conjugate\\Gradient}} & - & 10.3 & \textbf{15.0} & \textbf{23.8} \\
			& & Row Scaling & 19.3 & 30.3 & 41.7 \\
			& & Jacobi & 19.1 & 29.9 & 41.0\\
			& & Chow-Patel & 26.0 & 43.9 & 70.2 \\
			& & \textbf{ICC} & \textbf{6.26} & 19.0 & 43.5\\
			& & ILU(0) & 6.58 & 19.4 & 43.6 \\
			& & Block ILU(0) & - & - & -\\
			\cmidrule{2-6}
			& \multirow{7}{*}{GMRES} & - & - & - & - \\
			& & Row Scaling & - & - & - \\
			& & Jacobi & - & - & - \\
			& & Chow-Patel & - & - & -\\
			& & ICC & - & - & -\\
			& & ILU(0) & - & - & -\\
			& & Block ILU(0) & - & - & -\\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"StAnD: A Dataset of Linear Static Analysis Problems" by Luca Grementieri and Francesco Finelli, arXiv:2201.05356v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
\hline \hline
Random Time & Mean & Variance \\ \hline
Jittered Sampling & -0.001518635 & 3.016777 \\
Renewal Sampling & -0.01345249 & 2.982566 \\
\hline \hline
\end{tabular}
\caption{Mean and variance of the sequence $N \hat{a}_{N}$}
\end{table}
\end{document}

stat

"Limit distribution of the least square estimator with observations sampled at random times driven by standard Brownian motion" by Tania Roa, Soledad Torres, and Ciprian tudor, arXiv:2012.08966v1, 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{Results of grasping moving objects over 20 trials.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 & \textbf{Ours} & \textbf{RRTConnect}~\\
 \hline
 success rate (\%) & 95 & 75 \\
 comp. violation (num. of trials) & 0 & 2 \\
 missed object (num. of trials) & 1 & 3 \\
 \hline

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

cs

"Online Trajectory Replanner for Dynamically Grasping Irregular Objects" by Minh Nhat Vu, Florian Grander, and Anh Nguyen, arXiv:2501.17968v1, 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{Hyperparameter} & \textbf{Value} \\
 \hline
 Bootstrap & True\\
 Max depth & 5\\
 Max features & 5\\
 Min samples per leaf & 50\\
 Min samples per split & 100\\
 Number of trees & 500\\
 Class weights& Balanced\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Hyperparameters values used in the training phase.}
\end{table}
\end{document}

math

Mouad Morabit
Guy Desaulniers
Andrea Lodi

"Machine-learning-based arc selection for constrained shortest path problems in column generation" by Mouad Morabit, Guy Desaulniers, and Andrea Lodi, arXiv:2201.02535v1, 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}{ll|rrrr|rr}
 \hline \hline
&& \multicolumn{4}{c|}{Test with $(\gamma,\eta)$}&AIC&BIC\\
& & $(\frac14,\frac14)$& $(\frac12,\frac12)$ & $(\frac12,1)$ & $(1,\frac12)$ &&\\
 \hline
$M_1$ & $l=10$ & 0.107 & 0.229 & 0.298 & 0.244 &2379.546&2402.511 \\
 & $l=30$ & 0.101 & 0.260 & 0.259 & 0.236 & &\\
 \hline
$M_2$ & $l=10$ & 0.102 & 0.211 & 0.219 & 0.193 &2376.500&2404.058\\
 & $l=30$ & 0.124 & 0.212 & 0.184 & 0.185 & &\\
 \hline \hline
\end{tabular}
\caption{p-values for the bootstrap goodness-of-fit test for Poisson models $M_1$ and $M_2$ for daily series of pedestrian collisions. }
\end{table}
\end{document}

stat

Šárka Hudecová
Marie Hušková
Simos G. Meintanis

"Specifications tests for count time series models with covariates" by Šárka Hudecová, Marie Hušková, and Simos G. Meintanis, arXiv:2310.10331v1, 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{Mapping GES model parameters to physical resources}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 GES model & Physical & Physical & Physical \\
 parameters & BES & TCL (IVA/FFA) & EV \\
 \midrule
 \specialrule{0em}{0.5em}{0em}
 $SoC_{t}$ & $SoC_{t}$ & $\dfrac{\overline{T}^{\text{in}}-T_{t}^{\text{in}}}{\overline{T}^{\text{in}}-\underline{T}^{\text{in}}}$ & $SoC_{t}$ \\\specialrule{0em}{0.5em}{0em}
 $\overline{P}_{\text{c},t}$ & $\overline{P}_{\text{c}}$ & $\overline{P}-P_{t}^{\text{B}}$ & $\overline{P}_{\text{c}}-P_{\text{c},t}^{\text{B}}$\\ \specialrule{0em}{0.5em}{0em}
 $\overline{P}_{\text{d},t}$ & $\overline{P}_{\text{d}}$ & $P_{t}^{\text{B}}-\underline{P}$ & $\overline{P}_{\text{d}}-P_{\text{d},t}^{\text{B}}$\\\specialrule{0em}{0.5em}{0em}
 $\underline{SoC}_{t}$ & $\underline{SoC}$ & $\dfrac{\overline{T}^{\text{in}}-\overline{T}_{t}^{\text{in}}}{\overline{T}^{\text{in}}-\underline{T}^{\text{in}}}$ & $\underline{SoC}_{t}$\\\specialrule{0em}{0.5em}{0em}
 $\overline{SoC}_{t}$ & $\overline{SoC}$ & $\dfrac{\overline{T}^{\text{in}}-\underline{T}_{t}^{\text{in}}}{\overline{T}^{\text{in}}-\underline{T}^{\text{in}}}$ & $\overline{SoC}_{t}$\\\specialrule{0em}{0.5em}{0em}
 ${{\varepsilon }}$ & ${{\varepsilon}}$ & $1-{{e}^{-\Delta t/{{R}}{{C}}}}$ & $\varepsilon _{{}}$\\\specialrule{0em}{0.5em}{0em}
 ${{S}}$ & ${{S}}$ & $\dfrac{\Delta t({\overline{T}^{\text{in}}-\underline{T}^{\text{in}}})}{K R (1-{{e}^{-\Delta t/{{R}}{{C}}}})}$ & ${{S}}$\\\specialrule{0em}{0.5em}{0em}
 $\eta _{\text{c/d}}$ & $\eta _{\text{c/d}}$ & 1 & $\eta _{\text{c/d}}$\\\specialrule{0em}{0.5em}{0em}
 $\alpha _{t}$ & 0 & $(1-{{e}^{-\Delta t/{{R}}{{C}}}})SoC_{t}^{\text{B}}$ & $\Delta SoC_{t}^{\text{B}}$\\\specialrule{0em}{0.5em}{0em}
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Ning Qi
Pierre Pinson
Mads R. Almassalkhi
Lin Cheng
Yingrui Zhuang

"Chance-Constrained Generic Energy Storage Operations under Decision-Dependent Uncertainty" by Ning Qi, Pierre Pinson, Mads R. Almassalkhi, Lin Cheng, and Yingrui Zhuang, arXiv:2201.06407v4, 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}{cccc}\hline \hline

		Models & $\beta_0$ & $\beta_1$ & $\beta_2$ \\\hline
		 & 0.455 & 0.322 & 0.284 \\\hline
		SE & (0.022) & (0.013) & (0.008) \\
		\hline
		\hline
		Models & Chi-sq. & DV & Bootstrap \\
		 & 8.999 & - & - \\
		 & - & 0.038 & 0.515 \\
		 & - & - & 0.498 \\
		\hline
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Nonlinear Network Autoregression

"Nonlinear Network Autoregression" by Mirko Armillotta and Konstantinos Fokianos, arXiv:2202.03852v2, 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|rrr}
						observational cluster & 1 & 2 & 3 \\
						\hline
						Pop 1 & 53 & 47 & 0 \\
						Pop 2 & 56 & 44 & 0 \\
						Pop 3 & 48 & 0 & 52 \\
						Pop 4 & 0 & 52 & 48 \\
						\hline
					\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Antonio Lijoi
Igor Prünster
Giovanni Rebaudo

"Flexible clustering via hidden hierarchical Dirichlet priors" by Antonio Lijoi, Igor Prünster, and Giovanni Rebaudo, arXiv:2201.06994v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A fully adaptive explicit stabilized integrator for advection-diffusion-reaction problems" by Ibrahim Almuslimani, arXiv:2201.10206v2, 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{Historical probabilities of selected point differentials.}
\begin{tabular}{r|c||r|c}
Point & & Point & \\
Differential & Percentage & Differential & Percentage \\
\hline\hline
0 & 0\% & 8 & 2.4\% \\
1 & 3.4\% & 9 & 1.2\% \\
2 & 2.7\% & 10 & 4.3\% \\
3 & 9.6\% & 11 & 2.3\% \\
4 & 3.9\% & 12 & 1.8\% \\
5 & 2.6\% & 13 & 1.8\% \\
6 & 2.9\% & 14 & 4.3\% \\
7 & 7.3\% & 15 & 1.1\% \\
\end{tabular}
\end{table}
\end{document}

stat

"Converting College Football Point Spread Differentials to Probabilities" by Ryan Sides and Jane L. Harvill, arXiv:2212.08116v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"The Pearson Bayes factor: An analytic formula for computing evidential value from minimal summary statistics" by Thomas J. Faulkenberry, arXiv:2011.09549v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Modbus Data Exchange with RTAC}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Modbus Entity} & \textbf{Type} & \textbf{Description} \\
\hline
Holding Register 1 & Voltage Data & Node 1 Voltage \\
Holding Register 2 & Voltage Data & Node 2 Voltage \\
\(\vdots\) & \(\vdots\) & \(\vdots\) \\
Holding Register 206 & Voltage Data & Node 206 Voltage \\
\hline
Coil 1 & Topology Control & Switch Control S1 \\
Coil 2 & Topology Control & Switch Control S2 \\
\(\vdots\) & \(\vdots\) & \(\vdots\) \\
Coil 6 & Topology Control & Switch Control S6 \\
\hline
Holding Register 207 & Load Control & Setpoint 1 \\
\(\vdots\) & \(\vdots\) & \(\vdots\) \\
Holding Register 215 & Load Control & Setpoint 9 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"Cyber-Physical Testbed Integrating RTAC with RTDS for Game-Theoretic Topology Control Under Load Altering Attacks" by Alaa Selim and Junbo Zhao, arXiv:2312.05727v1, 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{Comparison of the different clustering solutions based on the VI and ARI to measure distance between the true and estimated clustering for Simulations 1 and 2.}
\begin{tabular}{llllllll}
 & \multicolumn{2}{l}{General} & \multicolumn{2}{l}{Empirical} & \multirow{2}{*}{Seurat} & \multirow{2}{*}{CIDR} & \multirow{2}{*}{TSCAN} \\ \cline{2-5}
 & Linear & Quadratic & Linear & Quadratic & & & \\ \cline{1-8}
Simulation 1 - VI & 0.00 & 0.00 & 0.00 & 0.00 & 0.31 & 1.30 & 0.58 \\ \cline{1-8}
Simulation 2 - VI & 0.00 & 0.00 & 0.00 & 0.00 & 0.31 & 1.38 & 0.75 \\ \cline{1-8}
Simulation 1 - ARI & 1.00 & 1.00 & 1.00 & 1.00 & 0.87 & 0.28 & 0.72 \\ \cline{1-8}
Simulation 2 - ARI & 1.00 & 1.00 & 1.00 & 1.00 & 0.87 & 0.26 & 0.63
\end{tabular}
\end{table}
\end{document}

stat

"Shared Differential Clustering across Single-cell RNA Sequencing Datasets with the Hierarchical Dirichlet Process" by Jinlu Liu, Sara Wade, and Natalia Bochkina, arXiv:2212.02505v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Anass Aghbalou
François Portier
Anne Sabourin

"On the bias of K-fold cross validation with stable learners" by Anass Aghbalou, François Portier, and Anne Sabourin, arXiv:2202.10211v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{DNSMOS Prediction Model}
\begin{tabular}{l|r}
 \textbf{Layer} & \textbf{Output dimension} \\
 \hline
 Input & 900 x 120 x 1\\
 \hline
 Conv: 32, (3 x 3), `ReLU' & 900 x 120 x 32 \\
 MaxPool: (2 x 2), Dropout(0.3) & 450 x 60 x 32 \\
 \hline
 Conv: 32, (3 x 3), `ReLU' & 450 x 60 x 32 \\
 MaxPool: (2 x 2), Dropout(0.3) & 225 x 30 x 32 \\
 \hline
 Conv: 32, (3 x 3), `ReLU' & 225 x 30 x 32 \\
 MaxPool: (2 x 2), Dropout(0.3) & 112 x 15 x 32 \\
 \hline
 Conv: 64, (3 x 3), `ReLU' & 112 x 15 x 64 \\
 GlobalMaxPool & 1 x 64 \\
 \hline
 Dense: 64, `ReLU' & 1 x 64 \\
 Dense: 64, `ReLU' & 1 x 64 \\
 Dense: 1 & 1 x 1 \\
 \hline

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

eess

Chandan K A Reddy
Vishak Gopal
Ross Cutler

"DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors" by Chandan K A Reddy, Vishak Gopal, and Ross Cutler, arXiv:2010.15258v2, 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}{lll}
\hline
Event Type & \# of User Utter. & Avg. length\\\hline
SuspiciousActivity & 1227 & 6.98 \\
AccidentTrafficParking & 116 & 6.24 \\
DrugsAlcohol & 618 & 6.23 \\
EmergencyMessage & 1032 & 7.06 \\
FacilitiesMaintenance & 157 & 5.26 \\
HarassmentAbuse & 431 & 7.19 \\
MentalHealth & 242 & 8.00 \\
NoiseDisturbance & 1359 & 6.12 \\
TheftLostItem & 245 & 7.86 \\ \hline
TOTAL & 5427 & 6.48 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Statistics on the number of user utterances across 9 different incident types in this study}
\end{table}
\end{document}

cs

"VicSim: Enhancing Victim Simulation with Emotional and Linguistic Fidelity" by Yerong Li, Yiren Liu, and Yun Huang, arXiv:2501.03139v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Perfect sampling of stochastic matching models with reneging" by Thomas Masanet and Pascal Moyal, arXiv:2202.09341v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Emmanuel Jordy Menvouta
Jolien Ponnet
Robin Van Oirbeek
Tim Verdonck

"mCube: Multinomial Micro-level reserving Model" by Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek, and Tim Verdonck, arXiv:2212.00101v1, 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}{cl|ccc}
\toprule
\#&\textbf{Method} & \textbf{Task Loss $\downarrow$} & \textbf{Prediction Loss $\downarrow$} & \textbf{Total Loss$ \downarrow$} \\
\midrule
1&\textbf{ETO} & 682.91 & \textbf{332.62} & 1015.53 \\
2&\textbf{Ours}$^*$ & \textbf{623.34} & 341.27 & \textbf{964.61} \\
\bottomrule
\end{tabular}
\caption{Comparison of model performances.}
\end{table}
\end{document}

eess

"Uncertainty-Aware Capacity Expansion for Real-World DER Deployment via End-to-End Network Integration" by Yiyuan Pan, Yiheng Xie, and Steven Low, arXiv:2412.05956v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Elena M. De-Diego
Adrián Perez-Suay
Paula Gordaliza
Jean-Michel Loubes

"PLS-based approach for fair representation learning" by Elena M. De-Diego, Adrián Perez-Suay, Paula Gordaliza, and Jean-Michel Loubes, arXiv:2502.16263v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison on different video generation pipelines.}
\begin{tabular}{lccccc}
\toprule
 Model & Resolution & FPS & FVD $\downarrow$ & IB Gen-V \& GT-T $\uparrow$ & IB Gen-V \& GT-A $\uparrow$ \\
\midrule
 GroundTruth& $256\times 256$ & $16$& $0.6$& $0.332$&$0.276$\\
\midrule
CoDi & $256\times 256$ & $4$& $718.8$& $0.338$& $0.181$\\
 OpenSora & $128\times 128$ & $16$& $506.9$& $0.308$& $0.164$\\
 OpenSora & $256\times 256$ & $16$& $397.7$& $0.356$& $0.203$\\
OpenSora & $512\times 512$ & $16$ & $331.8$ & $\mathbf{0.374}$ & $0.209$ \\
SyncFlow-VGG-AV-FT & $256\times 256$ & $16$ & $\mathbf{298.4}$& $0.350$& $\mathbf{0.220}$\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Haohe Liu
Gael Le Lan
Xinhao Mei
Zhaoheng Ni
Anurag Kumar
Varun Nagaraja
Wenwu Wang
Mark D. Plumbley
Yangyang Shi
Vikas Chandra

"SyncFlow: Toward Temporally Aligned Joint Audio-Video Generation from Text" by Haohe Liu, Gael Le Lan, Xinhao Mei, Zhaoheng Ni, Anurag Kumar, Varun Nagaraja, Wenwu Wang, Mark D. Plumbley, Yangyang Shi, and Vikas Chandra, arXiv:2412.15220v1, 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{\textbf{Different ratio of ViT Block.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
			\toprule
			Ratio Setting & Params (M) & GMACs & Latency (ms) & Top-1 Acc. (\%)\\
			\midrule
			Baseline & 9.4M &1760M & 1.0ms & 79.4 \\
			Replacing 22\% Conv Blocks in Stage 3 as SHA & 9.1M &1724M & 1.02ms & 79.5 \\
			Replacing 22\% Conv Blocks in Stage 3 as SHMA & 9.2M &1739M & 1.04ms & 79.6 \\
			Replacing 50\% Conv Blocks in Stage 3 as SHA & 8.8M &1689M & 1.04ms & 79.5 \\
			Replacing 50\% Conv Blocks in Stage 3 as SHMA & 8.9M &1712M & 1.07ms & 79.8 \\
			Replacing 78\% Conv Blocks in Stage 3 as SHA & 8.3M &1635M & 1.12ms & 79.3 \\
			Replacing 78\% Conv Blocks in Stage 3 as SHMA & 8.5M &1685M & 1.17ms & 79.6 \\
			Replacing 100\% Conv Blocks in Stage 3 as SHA & 7.9M &1599M & 1.17ms & 78.1 \\
			Replacing 100\% Conv Blocks in Stage 3 as SHMA & 8.3M &1665M & 1.25ms & 79.0 \\
			\midrule
			Replacing 100\% Conv Blocks in Stage 3 as SHMA and 100\% in Stage 4 &10.0M &1792M & 1.15ms & 80.4 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"iFormer: Integrating ConvNet and Transformer for Mobile Application" by Chuanyang Zheng, arXiv:2501.15369v2, 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|c|}
 \hline
 & $\rho$ &$\rho(1)$ & $\rho(2)$ & $\rho(3)$ & $\rho(4)$& $\rho(5)$ \\
 \hline
 {\sc fcfm} &0.0293&0.00026& 0.00032 & 0.0132 & 0.0152 & 0.00032 \\
 \hline
 {\sc ml} & 0.03122 &0.00028 & 0.0003 & 0.01536 & 0.01486 & 0.00042 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math


\documentclass{article}
\usepackage{multirow}
\usepackage{soul}
\usepackage{adjustbox}
\usepackage{graphicx}
\usepackage{rotating}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllllll}
 \toprule
 \multicolumn{2}{c}{op-AndOr} & {\textbf{BL}}
 & {\textbf{LE}}
 & {\textbf{RO}}
 & {\textbf{ME}}
 &{\textbf{BS}}
 & {\textbf{SP}} \\
 \hline
 \multirow{6}{*}{\rotatebox{90}{FOLIO}}
 &\textbf{BLEU} & \hl{0.88} & 0.81 & 0.92 & 0.92 & 0.94 & 0.91 \\
 &\textbf{LE} & - & \hl{0.74} & 0.85 & 0.85 & 0.87 & 0.83 \\
 &\textbf{Rouge} & - & - & \hl{0.96} & 0.96 & 0.98 & 0.95 \\
 &\textbf{Meteor} & - & - & - & \hl{0.96} & 0.98 & 0.94 \\
 &\textbf{BertScore} & - & - & - & - & \hl{1.00} & 0.96 \\
 &\textbf{Smatch++} & - & - & - & - & - & \hl{0.93} \\
 \hline
 \multirow{6}{*}{\rotatebox{90}{MALLS}}
 &\textbf{BLEU} & \hl{0.79} & 0.73 & 0.86 & 0.86 & 0.89 & 0.86 \\
 &\textbf{LE} & - & \hl{0.66} & 0.79 & 0.79 & 0.83 & 0.79 \\
 &\textbf{Rouge} & - & - & \hl{0.92} & 0.93 & 0.96 & 0.92 \\
 &\textbf{Meteor} & - & - & - & \hl{0.93} & 0.96 & 0.92 \\
 &\textbf{BertScore} & - & - & - & - & \hl{0.99} & 0.96 \\
 &\textbf{Smatch++} & - & - & - & - & - & \hl{0.92} \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{And-Or perturbation pairwise comparison}
\end{table}
\end{document}

cs

Ramya Keerthy Thatikonda
Wray Buntine
Ehsan Shareghi

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


\documentclass{article}
\usepackage{multirow}
\usepackage{makecell}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Time-steps used for validation and test.}
\begin{tabular}{cccccc}
 \Xhline{3\arrayrulewidth}
 \multirow{2}{*}{}&\multirow{2}{*}{} & \multirow{2}{*}{\makebox{\textbf{Spring}}} & \multirow{2}{*}{\makebox{\textbf{Summer}}} & \multirow{2}{*}{\makebox{\textbf{Autumn}}}& \multirow{2}{*}{\makebox{\textbf{Winter}}}\\
 & & & & \\\Xhline{3\arrayrulewidth}
 \multirow{2}{*}{\textbf{Validation}} & \textbf{From} & 01/04/2018 & 01/07/2018 & 01/10/2018 & 01/01/2019 \\
 & \textbf{To} & 22/04/2018 & 22/07/2018 & 22/10/2018 & 22/01/2019 \\ \hline
 \multirow{2}{*}{\textbf{Test}} & \textbf{From} & 23/04/2018 & 23/07/2018 & 23/10/2018 & 23/01/2019 \\
 & \textbf{To} & 13/05/2018 & 12/08/2018 & 13/11/2018 & 13/02/2019\\
 \Xhline{3\arrayrulewidth}
 \end{tabular}
\end{table}
\end{document}

eess

Jesús García Fernández
Ismail Alaoui Abdellaoui
Siamak Mehrkanoon

"Deep coastal sea elements forecasting using U-Net based models" by Jesús García Fernández, Ismail Alaoui Abdellaoui, and Siamak Mehrkanoon, arXiv:2011.03303v2, 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}{lc}
		\toprule
		\textbf{Property} & \textbf{Value} \\
		\midrule
		Blood density, $\rho$ & 1.050 g/cm$^3$ \\
		Blood viscosity, $\mu$ & 0.025 dyne$\cdot$s/cm$^2$ \\
		Velocity profile order, $\zeta$ & 9 \\
		Young's modulus, $E$ & 1.2$\cdot$10$^7$ dyne/cm$^2$ \\
		Diastolic pressure, $P_d$ & 0 \\
		External pressure, $p_{ext}$ & 0 \\
		Outflow pressure, $P_{out}$ & 4.2663$\cdot$10$^3$ dyne/cm$^2$ (3.2 mmHg)\\
		Initial velocity, $u(x, 0)/U(0)$ & 0\\
		Initial pressure, $p(x, 0)/P(0)$ & 0\\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{General model parameters of the 37-artery network.}
\end{table}
\end{document}

math

Beatrice Ghitti
Eleuterio F. Toro
Lucas O. Müller

"Nonlinear lumped-parameter models for blood flow simulations in networks of vessels" by Beatrice Ghitti, Eleuterio F. Toro, and Lucas O. Müller, arXiv:2201.01617v1, 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{Zero-shot vs. Fine-tuning.} Analysis in zero-shot and fine-tuning utilizing 24 speakers from the VCTK~.}
\begin{tabular}{lcc}
 \toprule

 & \multicolumn{2}{c}{\textbf{Stable-TTS}} \\
 \cmidrule(lr){2-3}

 \bf Setting & WER ($\downarrow$) & SECS ($\uparrow$) \\

 \midrule[0.4pt]

 Zero-shot & 0.91$_{\pm \text{0.33}}$ & 84.18$_{\pm \text{0.18}}$ \\
 Fine-tuning & \bf 0.49$_{\pm \text{0.34}}$ & \bf {85.26$_{\pm \text{0.26}}$} \\

 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Wooseok Han
Minki Kang
Changhun Kim
Eunho Yang

"Stable-TTS: Stable Speaker-Adaptive Text-to-Speech Synthesis via Prosody Prompting" by Wooseok Han, Minki Kang, Changhun Kim, and Eunho Yang, arXiv:2412.20155v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
 \hline
 & RMSE & Bias & Variance & RMSE & Bias & Variance & RMSE & Bias & Variance \\
 \hline
 & \multicolumn{9}{c|}{Case 1}\\
 \hline
 & \multicolumn{3}{c|}{EQD} & \multicolumn{3}{c|}{Wadsworth} & \multicolumn{3}{c|}{Northrop}\\
 \hline
 Original & 0.048 & 0.034 & 0.001 & 0.349 & 0.111 & 0.110 & 0.536 & 0.276 & 0.212 \\
 Above mode & 0.038 & 0.018 & 0.001 & 0.577 & 0.236 & 0.277 & 0.597 & 0.348 & 0.235\\
 \hline
 & \multicolumn{9}{c|}{Case 4}\\
 \hline
 Original & 0.526 & $-0.515$ & 0.012 & 0.627 & $-0.407$ & 0.230 & 0.543 & $-0.222$ & 0.246 \\
 Above mode & 0.514 & $-0.505$ & 0.009 & NA & NA & NA & 0.589 & $-0.127$ & 0.331 \\
 \hline
 \end{tabular}
\caption{RMSE, bias and variance of threshold choice for Case 1 and Case 4 samples for EQD, Wadsworth and Northrop methods. The methods are evaluated for two sets of candidate threshold sets: Original, using candidate threshold across the whole sample; Above mode, using candidate thresholds which only lie above the mode.}
\end{table}
\end{document}

stat

"Automated threshold selection and associated inference uncertainty for univariate extremes" by Conor Murphy, Jonathan A. Tawn, and Zak Varty, arXiv:2310.17999v5, 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{Specific Configuration of Generator in the proposed CR-GAN.}
\begin{tabular}{ccc}
\hline
\toprule
\textbf {Layer} & \textbf{Network} & \textbf{Parameters} \\
\hline
\multirow{2}*{1} & Convolution layer& Filter=18, Kernel=2, Strides=1, Activation=Relu\\
\cline{2-3}
 & Max-pooling layer& Pool size=2, Strides=2\\
\hline
\multirow{2}*{2} & Convolution layer & Filter=18, Kernel=2, Strides=1, Activation=Relu \\
\cline{2-3}
 & Max-pooling layer & Pool size=2, Strides=2\\
\hline
\multirow{2}*{3} & Convolution layer & Filter=18, Kernel=2, Strides=1, Activation=Relu \\
\cline{2-3}
 & Max-pooling layer & Pool size=2, Strides = 2\\
\hline
4 & Fully connected layer & Unit =100, Activation =Relu, Dropout=0.2 \\
\hline
5 & LSTM layer & Cell=100, Dropout=0.2\\
\hline
6 & Fully connected layer & Unit=1, Activation=Sigmoid\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Xuewen Zhang
Yan Qin
Chau Yuen
Lahiru Jayasinghe
Xiang Liu

"Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation" by Xuewen Zhang, Yan Qin, Chau Yuen, Lahiru Jayasinghe, and Xiang Liu, arXiv:2101.03678v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \toprule
 Type & Hyperparameter & Value \\
 \midrule
 \multirow{4}{*}{Patch and tokenize} & Patch size & $8\times8$ \\
 & Token dim $D$ & 256 \\
 & Tokenizer hidden dim & 256 \\
 & Detokenizer hidden dim & 256 \\
 \midrule
 \multirow{3}{*}{Energy Transformer} & Heads of energy attention layer & 8 \\
 & Hopfield type & softmax \\
 & Hopfield hidden dim & 2048 \\
 \midrule
 \multirow{2}{*}{Update rule} & Iteration steps $T$ & 12 \\
 & $\alpha$ & 0.1 \\
 \midrule
 \multirow{2}{*}{Training} & Optimizer & Adam \\
 & Batch size & 16 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Model hyperparameters (the same for all three examples).}
\end{table}
\end{document}

cs

Qian Zhang
Dmitry Krotov
George Em Karniadakis

"Operator Learning for Reconstructing Flow Fields from Sparse Measurements: an Energy Transformer Approach" by Qian Zhang, Dmitry Krotov, and George Em Karniadakis, arXiv:2501.08339v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of links from different link types containing snippets relevant to the ones generated by the LLM-based assistants}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrr}
 \multicolumn{6}{c}{\sc Bing CoPilot} \\
 \midrule
 \multirow{2}{*}{\textbf{Languages}} & \multirow{2}{*}{\textbf{Q\&A}} & \multirow{2}{*}{\textbf{Repository}} & \multirow{2}{*}{\textbf{Tutorial}} & \textbf{Official} & \textbf{Gist/Code} \\
 & & & & \textbf{Doc.} & \textbf{Example}\\
 \midrule
 \textbf{Go} & 3 & 2 & 3 & 0 & 2 \\
 \textbf{Java} & 9 & 9 & 14 & 0 & 0 \\
 \textbf{JavaScript} & 15 & 19 & 11 & 1 & 0 \\
 \textbf{PHP} & 15 & 19 & 8 & 2 & 1 \\
 \textbf{Python} & 26 & 3 & 36 & 2 & 2 \\
 \textbf{Ruby} & 8 & 1 & 0 & 2 & 0 \\
 \midrule
 \multicolumn{6}{c}{\sc Gemini} \\
 \midrule
 \multirow{2}{*}{\textbf{Languages}} & \multirow{2}{*}{\textbf{Q\&A}} & \multirow{2}{*}{\textbf{Repository}} & \multirow{2}{*}{\textbf{Tutorial}} & \textbf{Official} & \textbf{Gist/Code} \\
 & & & & \textbf{Doc.} & \textbf{Example} \\
 \midrule
 \textbf{Go} & 1 & 0 & 0 & 0 & 0 \\
 \textbf{Java} & 2 & 0 & 1 & 0 & 0 \\
 \textbf{JavaScript} & 1 & 0 & 2 & 0 & 0 \\
 \textbf{PHP} & 1 & 0 & 1 & 0 & 0 \\
 \textbf{Python} & 0 & 0 & 2 & 3 & 0 \\
 \textbf{Ruby} & 0 & 0 & 0 & 0 & 0 \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Daniele Bifolco
Pietro Cassieri
Giuseppe Scanniello
Massimiliano Di Penta
Fiorella Zampetti

"Do LLMs Provide Links to Code Similar to what they Generate? A Study with Gemini and Bing CoPilot" by Daniele Bifolco, Pietro Cassieri, Giuseppe Scanniello, Massimiliano Di Penta, and Fiorella Zampetti, arXiv:2501.12134v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
 \toprule
 Catalog & Number of Events \\
 \midrule
 GWTC-1-confident & 11 \\
 GWTC-2 & 31 \\
 GWTC-2.1-auxiliary & 1 \\
 GWTC-2.1-confident & 7 \\
 GWTC-3-confident & 32 \\
 \bottomrule
 \textbf{Total} & \textbf{82} \\

 \end{tabular}
\end{adjustbox}
\caption{The catalogs we used to devise our dataset.}
\end{table}
\end{document}

math

"Application of Common Spatial Patterns in Gravitational Waves Detection" by Damodar Dahal, arXiv:2201.04086v1, 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{Share of the Bitcoin volume and nodes considered when filtering out transactions with an annual cumulative value of less than 0.0001 BTC.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccccccccccc}
 \hline
 Year &2009 & 2010 & 2011 & 2012 & 2013 & 2014 &2015 & 2016 & 2017 & 2018& 2019& 2020 & 2021& 2022& 2023 \\ \hline \hline
 BTC Considered & 1 & 1 & 1 & 1 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 & NA \\
 Nodes Considered & 1 & 0.99 & 0.89 & 0,99 & 0.97 & 0.98 & 0.96 & 0.98 & 0.96 & 0.92 & 0.95 & 0.96 & NA & 0.94 & NA \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Marco Venturini
Daniel García-Costa
Elena Álvarez-García
Francisco Grimaldo
Flaminio Squazzoni

"Mapping network structures and dynamics of decentralised cryptocurrencies: The evolution of Bitcoin (2009-2023)" by Marco Venturini, Daniel García-Costa, Elena Álvarez-García, Francisco Grimaldo, and Flaminio Squazzoni, arXiv:2501.11416v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects" by Hao Chen, Lanshan Han, and Alvin Lim, arXiv:2011.04538v9, 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 across baseline model configurations when greedy decoding was applied. For AISHELL-2, we measured CER (\%) instead of WER (\%).}
\begin{tabular}{cccccc}
\toprule
Dataset & Model Type & Unit Set & WER (\%) & Params. (M)\\
\midrule
& Jasper DR & Character & 3.61 & 332.63\\
& DeepSpeech2 & Character & 7.64 & 13.19\\
& Jasper Mini & Character & 8.66 & 8.19\\
LibriSpeech & QuartzNet & Character & 5.66 & 13.09\\
& AED & BPE & 5.80 & 174.10\\
& Transformer & BPE & 3.21 & 78.36\\
& DeepSpeech2 & BPE & 8.82 & 21.71\\
\midrule
& Jasper DR & Character & 9.69 & 337.94\\
AISHELL-2& Jasper Mini & Character & 11.77 & 13.50\\
& DeepSpeech2 & Character & 13.40 & 15.77\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Ji Won Yoon
Hyeonseung Lee
Hyung Yong Kim
Won Ik Cho
Nam Soo Kim

"TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition" by Ji Won Yoon, Hyeonseung Lee, Hyung Yong Kim, Won Ik Cho, and Nam Soo Kim, arXiv:2008.00671v2, 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{Annotations for the sub-models in the scene understanding and its status.}
\begin{tabular}{lclclclclcl}

			\hline

			Scene condition & Category &One-hot vector & Condition \\

			\hline

			\multirow{5}{11em}{Glasses and illumination conditions} &1 &10000 & Day bare face \\

			&2& 01000 & Day glasses \\

			&3& 00100 & Night glasses \\

			&4& 00010 & Night bare face \\

			&5& 00001 & Day sunglasses \\

			\hline

			\multirow{3}{11em}{Head condition} &1& 100 & Normal status \\

			&2& 010 & Looking at both sides \\

			&3& 001 & Nodding \\

			\hline

			\multirow{3}{11em}{Mouth condition} &1& 100 & Normal status \\

			&2& 010 & Talking and laughing \\

			&3& 001 & Yawning \\

			\hline

			\multirow{2}{11em}{Eye condition} &1& 10 & Sleepiness eye \\

			&2& 01 & Normal status \\

			\hline

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

eess

Jongmin Yu
Sangwoo Park
Sangwook Lee
Moongu Jeon

"Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework" by Jongmin Yu, Sangwoo Park, Sangwook Lee, and Moongu Jeon, arXiv:1910.09722v1, 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{KNN classification accuracy with different number of neighbors. }
\begin{tabular}{lcrrrr}
 \toprule
 Similarity Function & Nearest neighbors ($K$) & STL10 & CIFAR10 & CIFAR100 & SVHN \\ \midrule
 \multirow{2}{*}{TWD (TV)} & $K=10$ & 76.24 {\small $\pm$ 0.22} & 68.62 {\small $\pm$ 0.40} & 25.70 {\small $\pm$ 0.14} & 79.28 {\small $\pm$ 0.22}\\
 & $K=50$ & {76.28 {\small $\pm$ 0.07}} & {68.60 {\small $\pm$ 0.36}} & 26.49 {\small $\pm$ 0.24} & { 79.70 {\small $\pm$ 0.23}} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Makoto Yamada
Yuki Takezawa
Guillaume Houry
Kira Michaela Dusterwald
Deborah Sulem
Han Zhao
Yao-Hung Hubert Tsai

"An Empirical Study of Self-supervised Learning with Wasserstein Distance" by Makoto Yamada, Yuki Takezawa, Guillaume Houry, Kira Michaela Dusterwald, Deborah Sulem, Han Zhao, and Yao-Hung Hubert Tsai, arXiv:2310.10143v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|l}
 \toprule[1.5pt]
 MPC & Model Predictive Control \\
 CMPC & Centralized Model Predictive Control \\
 NCen-MPC & Non-Centralized Model Predictive Control \\
 Dec-MPC & Decentralized Model Predictive Control \\
 DMPC & Distributed Model Predictive Control \\
 HMPC & Hierarchical Model Predictive Control \\
 Coal-MPC & Coalitional Model Predictive Control \\
 NLin-MPC & Nonlinear Model Predictive Control \\
 LS-MAS & Large-Scale Multi-Agent System \\
 MIMO & Multiple-Input Multiple-Output \\
 \bottomrule[1.5pt]
		\end{tabular}
\caption{List of abbreviations}
\end{table}
\end{document}

eess

Alessandro Riccardi
Luca Laurenti
Bart De Schutter

"Partitioning techniques for non-centralized predictive control: A systematic review and novel theoretical insights" by Alessandro Riccardi, Luca Laurenti, and Bart De Schutter, arXiv:2509.11470v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Zhaoheng Ni
Felix Grezes
Viet Anh Trinh
Michael I. Mandel

"Improved MVDR Beamforming Using LSTM Speech Models to Clean Spatial Clustering Masks" by Zhaoheng Ni, Felix Grezes, Viet Anh Trinh, and Michael I. Mandel, arXiv:2012.02191v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Banana-shaped distribution and X-shaped mixture of Gaussians.}
\begin{tabular}{llr}
 \hline
 \textbf{Name} & $\pi(\textbf{z})$ & \textbf{Parameters}\\
 \hline
 Banana-shaped & $\textbf{z}=\left( v_{1}, v_{1}^{2}+v_{2}+1\right), \textbf{v}\sim \mathcal{N}(0, \Sigma)$ & $\Sigma=\left[[1,0.9],[0.9, 1]\right]/0.19$\\
 X-shaped & $0.5 \mathcal{N}(\textbf{z}|0, \Sigma_{1})+0.5\mathcal{N}(\textbf{z}|0, \Sigma_{2})$ & $\Sigma_{1}=\left[[2,1.8],[1.8, 2]\right]/0.76$,$\Sigma_{2}=\left[[2, 1.8],[1.8, 2]\right]/0.76$\\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Dai Hai Nguyen
Tetsuya Sakurai
Hiroshi Mamitsuka

"Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference" by Dai Hai Nguyen, Tetsuya Sakurai, and Hiroshi Mamitsuka, arXiv:2310.16705v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
\textbf{Plane} & \textbf{Isometry group}\tabularnewline
\hline
\hline
$\mathbb{O}P^{2}\left(\mathbb{C}\right)$ & $F_{4}\left(\mathbb{C}\right)$\tabularnewline
\hline
$\mathbb{O}P^{2}$ & $F_{4\left(-52\right)}$\tabularnewline
\hline
$\mathbb{O}_{s}P^{2}$ & $F_{4\left(4\right)}$\tabularnewline
\hline
$\mathbb{O}H^{2}$ & $F_{4\left(-20\right)}$\tabularnewline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Daniele Corradetti
Alessio Marrani
David Chester
Ray Aschheim

"Conjugation Matters. Bioctonionic Veronese Vectors and Cayley-Rosenfeld Planes" by Daniele Corradetti, Alessio Marrani, David Chester, and Ray Aschheim, arXiv:2202.02050v1, 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|c|}
 \hline &\multicolumn{6}{c|}{2D density} \\
 & \multicolumn{2}{c|}{2D ACAT2} & \multicolumn{2}{c|}{2D ACAT4} & 2D WBACAT2 & 2D WBACAT4 \\
 Points & Error & Order & Error & Order & Error & Error\\
 \hline
 20$\times$20 & 3.85E-5 & - & 3.87E-5 & - & 2.50E-17 & 3.77E-17 \\
 \hline
 40$\times$40 & 1.58E-5 & 1.28 & 5.16E-6 & 2.91 & 3.23E-17 & 3.59E-17 \\
 \hline
 80$\times$80 & 4.78E-6 & 1.72 & 4.45E-7 & 3.53 & 3.33E-17 & 3.33E-17 \\
 \hline
 160$\times$160& 1.23E-6 & 1.96 & 2.89E-8 & 3.95 & 3.15E-17 & 3.23E-17 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Test : 2D Euler equation with gravity and gravitational potential $H_2$. Errors in $L^1$ norm for density at time $t=0.3.$ }
\end{table}
\end{document}

math

"Well-balanced adaptive compact approximate Taylor methods for systems of balance laws" by H. Carrillo, E. Macca, C. Pares, and G. Russo, arXiv:2202.02068v1, 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{An approximation of the principal eigenvector of $O_R, O_B, \partial^* O_R$ and $\partial^* O_B$. Approximations were computed via Sage .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 Vertex & $y(O_R)$ & $y(O_B)$ & $x(\partial^*(O_R))=x(\partial^*(O_B))$ \\ \hline
 $p$ & 0.5938 & 0.5938 & 0.4871 \\
 $q$ & 0.5938 & 0.5938 & 0.4871 \\
 $t$ & 0.5159 & 0.5121 & 0.3579 \\
 $b$ & 0.5121 & 0.5159 &0.3579 \\
 $r$ & 0.4986 & 0.4986 &0.3348\\
 $s$ & 0.4986 & 0.4986 &0.3348\\
 $u$ & 0.3951 &0.3951 & 0.2121 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Gregory J. Clark
Felipe Thomaz
Andrew Stephen

"On the Effect of Data Dimensionality on Eigenvector Centrality" by Gregory J. Clark, Felipe Thomaz, and Andrew Stephen, arXiv:2201.12034v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the data set of weed species frequency counts at Soft Bridge county in the North of Taiwan, with $T=12$.}
\begin{tabular}{lccccccccccccc}
\hline
Frequency & $Q_1$ & $Q_2$ & $Q_3$ & $Q_4$ & $Q_5$ & $Q_6$ & $Q_7$ & $Q_8$ & $Q_9$ & $Q_{10}$ & $Q_{11}$ & $Q_{12}$\\
\hline
Counts & 18 & 9 & 12 & 8 & 6 & 4 & 1 & 4 & 3 & 3 & 2 & 3 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Richness estimation with species identity error" by Jai-Hua Yen and Chun-Huo Chiu, arXiv:2012.07485v2, 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{List of Acronyms.}
\begin{tabular}{|c|c|}
\hline
\textbf{Acronym} &	\textbf{Definition}	\\ \hline
Amplified Spontaneous Emission &ASE\\ \hline
Artificial Intelligence &AI\\ \hline
Artificial Neural Network &ANN\\ \hline
Asynchronous Amplitude Histograms &AAH\\ \hline
Bit Error Rate &BER\\ \hline
Bit Rate-Modulation Format Identification &BR-MFI\\ \hline
Carrier Less Amplitude Phase &CAP\\ \hline
Channel State Information &CSI\\ \hline
Chromatic Dispersion &CD\\ \hline
Orthogonal Frequency Division Multiplexing &OFDM\\ \hline
Constant Module Algorithm &CMA\\ \hline
Convolutional Neural Network &CNN\\ \hline
Decision Tree &DT\\ \hline
Deep Learning &DL\\ \hline
Deep Neural Network &DNN\\ \hline
Deep Reinforcement Learning &DRL\\ \hline
Differential Group Delay &DGD\\ \hline
Digital Back-propagation &DBP\\ \hline
Elastic Optical Network &EON\\ \hline
Expectation Maximization &EM\\ \hline
Extended Gradient Boosting &XGB\\ \hline
Forward Error Correction &FEC\\ \hline
Free Space Optical &FSO\\ \hline
Gated Recurrent Units &GRU\\ \hline
Independent Component Analysis &ICA\\ \hline
Intensity Modulation/Direct Detection &IM/DD\\ \hline
Least Mean Square &LMS\\ \hline
Light Emitting Diode &LED\\ \hline
Long Short-Term Memory &LSTM\\ \hline
Machine Learning &ML\\ \hline
Maximum A Posteriori &MAP\\ \hline
Mean Absolute Error &MAE\\ \hline
Minimum Mean Square Error &MMSE\\ \hline
Mobile Ad-hoc Network &MANET\\ \hline
Multi-input Multi-output &MIMO\\ \hline
Multi-layer Perceptron &MLP\\ \hline
Non-return to Zero-on-off Keying &NRZ-OOK\\ \hline
Nonlinear Equalizer &NLE\\ \hline
Nonlinear Phase Noise &NLPN\\ \hline
Optical Burst Switching &OBS\\ \hline
Optical Communication Networking &OCN\\ \hline
Optical Fiber Communication &OFC\\ \hline
Optical Performance Monitoring &OPM\\ \hline
Optical Signal to Noise Ratio &OSNR\\ \hline
Optical Wireless Communication &OWC\\ \hline
Orbital Angular Momentum &OAM\\ \hline
Passive Optical Network &PON\\ \hline
Polarization Mode Dispersion &PMD\\ \hline
Principal Component &PC\\ \hline
Principal Component Analysis &PCA\\ \hline
Pulse Amplitude Modulation &PAM\\ \hline
Q-learning &QL\\ \hline
Quadrature Amplitude Modulation &QAM\\ \hline
Quadrature Phase Shift Keying &QPSK\\ \hline
Quality of Service &QoS\\ \hline
Quality of Transmission &QoT\\ \hline
Radial Basis Function Neural Network &RBFNN\\ \hline
Random Forest &RF\\ \hline
Received Signal Strength Indicator &RSSI\\ \hline
Recurrent Neural Network &RNN\\ \hline
Reinforcement Learning &RL\\ \hline
Routing and Resource Allocation &RRA\\ \hline
Routing and Spectrum Allocation &RSA\\ \hline
Routing and Wavelength Allocation &RWA\\ \hline
Routing, Core, and Resource Allocation &RCSA\\ \hline
Software Defined Network &SDN\\ \hline
Spatial Division Multiplexing &SDM\\ \hline
Supervised Learning &SL\\ \hline
\end{tabular}
\end{table}
\end{document}

eess

M. A. Amirabadi
S. A. Nezamalhosseini
M. H. Kahaei
Lawrence R. Chen

"A Survey on Machine and Deep Learning for Optical Communications" by M. A. Amirabadi, S. A. Nezamalhosseini, M. H. Kahaei, and Lawrence R. Chen, arXiv:2412.17826v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
\hline
N &Grid Size & Wave-speed $v_N$ & ReD$_N$ & GRR$_N$\\ \hline
9 &$512$ & 0.019450 & 0.308242 &-- \\
10 &$1024$ & 0.017100 & 0.150125 & 2.05\\
11 &$2048$ & 0.015909 & 0.070031 & 2.14\\
12 &$4096$ & 0.015335 & 0.031434 & 2.23\\
13 &$8192$ & 0.015064 & 0.013201 & 2.38\\
14 &$16384$ & 0.014944 & 0.005165 & 2.56\\ \hline
\end{tabular}
\end{adjustbox}
\caption{Wave-speeds calculated from the various grid resolutions along with the relative difference calculations ReD$_N$ and GRR$_N$ from . The wave speed for the $2^{16}$ grid simulation is $v_{16}=0.014868$.}
\end{table}
\end{document}

math

Koondanibha Mitra
Jack M. Hughes
Stefanie Sonner
Hermann J. Eberl
Jack D. Dockery

"Travelling waves in a PDE-ODE coupled system with nonlinear diffusion" by Koondanibha Mitra, Jack M. Hughes, Stefanie Sonner, Hermann J. Eberl, and Jack D. Dockery, arXiv:2202.07748v1, 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{A preliminary experiment testing the impact of individual clinicians, conducted for the optic cup segmentation on REFUGE2 test set under U-Net's structure with Dice Score (\%). The results indicate that the segmentation performance is consistent for individual clinician but varies significantly across different clinicians.}
\begin{tabular}{c|cccc}
\hline
\hline
 & Clinician 1 & Clinician 2 & Clinician 3 & Clinician 4 \\ \hline
Model 1 & \textbf{71.28} & 60.28 & 47.67 & 52.25 \\
Model 2 & 61.06 & \textbf{66.46} & 63.30 & 63.84 \\
Model 3 & 52.30 & 62.29 & \textbf{69.30} & 64.06 \\
Model 4 & 53.05 & 62.72 & 65.45 & \textbf{67.19} \\ \hline\hline
\end{tabular}
\end{table}
\end{document}

eess

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

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


cs

"Temporal Analysis of Adversarial Attacks in Federated Learning" by Rohit Mapakshi, Sayma Akther, and Mark Stamp, arXiv:2501.11054v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Unlocking Retrospective Prevalent Information in EHRs -- a Pairwise Pseudolikelihood Approach" by Nir Keret and Malka Gorfine, arXiv:2309.01128v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative evaluation of different methods for lung and liver segmentation. Bold font highlights the best results obtained by our final segmentation model. $^\#$ denotes learning from pseudo labels obtained by our $G_A$. $^\square$ denotes learning from manual annotations. $^\triangle$ and $^\perp$ denote existing unsupervised and weakly supervised methods, respectively. $^\lozenge$ denotes unsupervised domain adaptation methods. }
\begin{tabular}{lcccc}
	\toprule
	\multirow{2}{*}{Methods} &\multicolumn{2}{c}{Lung} &\multicolumn{2}{c}{Liver} \\ \cline{2-5}
	&Dice & ASSD (pixel) &Dice & ASSD (pixel)\\ \hline
 $^\#$U-Net (baseline) &0.895$\pm$0.040 & 5.462$\pm$2.322 & 0.896$\pm$0.064 & 4.896$\pm$1.869\\
	$^\#$U-Net (LQSS) &0.907$\pm$0.031 & 4.687$\pm$1.607 &0.908$\pm$0.052 & 4.491$\pm$1.858\\
	$^\#$U-Net (LQSS+IT) & 0.922$\pm$0.029 & 3.997$\pm$1.396 &0.923$\pm$0.040 & 3.708$\pm$1.628\\
	$^\#$U-Net (LQSS+IT+wDice) &\textbf{0.926$\pm$0.025} & \textbf{3.693$\pm$1.178} & \textbf{0.933$\pm$0.031} &\textbf{3.151$\pm$1.071}\\ \hline
	U-Net (no adapt.)&0.797$\pm$0.162 & 5.186$\pm$2.482 & 0.458$\pm$0.189 & 27.357$\pm$7.709\\
	$^\square$U-Net (manual)&0.947$\pm$0.024 &3.179$\pm$2.002 & 0.954$\pm$0.018 &2.414$\pm$0.869\\
 $^\lozenge$Wu et al.~ &0.835$\pm$0.054 &6.826$\pm$2.169 &0.898$\pm$0.052 &4.607$\pm$1.985\\
	$^\perp$Kervadec et al.~ & 0.820$\pm$0.064 & 8.620$\pm$5.056 &0.920$\pm$0.044 &4.013$\pm$1.726\\
	$^\triangle$Moriya et al.~ & 0.655$\pm$0.094 & 16.553$\pm$4.684 &0.516$\pm$0.121 &26.389$\pm$6.035\\
	\bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

Lu Wang
Dong Guo
Guotai Wang
Shaoting Zhang

"Annotation-Efficient Learning for Medical Image Segmentation based on Noisy Pseudo Labels and Adversarial Learning" by Lu Wang, Dong Guo, Guotai Wang, and Shaoting Zhang, arXiv:2012.14584v1, 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{\small\emph{Summary of the datasets used in the experiments. Train units and test units refer to the number of simulations, videos, or images in the training and test sets, respectively. The batch size refers to the number of simulations or images in a single batch. The batch shape refers to the original shape of a single batch before performing any reshaping/resizing operations. The total size refers to the total size of the dataset on disk. Sims, vids, and ims refer to simulations, videos, and images, respectively.}}
\begin{tabular}{l|ccccc}
 Name & Train units& Test units & Batch size& Batch shape & Total size \\
 \hline
 PDEBench & 480 sims& 120 sims& 1 sim & $64\times 64\times 64\times 5 \times 21\times 1$ & 66 GB \\
 Self-oscillating gels & 8,000 sims& 15,000 sims & 1 sim & $3367\times 3\times 10\times 1$ & 18 GB \\
 Basalt MineRL & 5449 vids & 17 vids & 20 frames & $360\times 640 \times 3\times20$ & 183 GB \\
 BigEarthNet & 566,712 ims & 23,612 ims & 100 ims & $120\times120\times12\times100$ & 104 GB \\
 \end{tabular}
\end{table}
\end{document}

eess

"Incremental Hierarchical Tucker Decomposition" by Doruk Aksoy and Alex A. Gorodetsky, arXiv:2412.16544v1, 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}{ll|l}
Mean & Covariance & Model\\\hline
LR & Independent & $Y_i = \beta_0 + \sum_{p=1}^PX_{ip}\beta_p + \varepsilon_i$ \\
LR & Spatial & $Y_i = \beta_0 + \sum_{p=1}^PX_{ip}\beta_p + T_i + \varepsilon_i$ \\
SL & Independent & $Y_i = \beta_0 + \sum_{l=1}^LZ_{il}\beta_l + \varepsilon_i$ \\
SL & Spatial & $Y_i = \beta_0 + \sum_{l=1}^LZ_{il}\beta_l + T_i + \varepsilon_i$\\
&\\
\end{tabular}
\caption{Linear Regression (LR) versus Super Learner (SL) for the mean with spatial versus independent errors for $p\in\{1,...,P\}$ covariates $l\in\{1,...,L\}$ base learners. The models differ by whether the original covariates ($X_{ip}$) or base learners ($Z_{il}$) are included in the mean and whether the spatial random effects ($T_i$) are included in the covariance. }
\end{table}
\end{document}

stat

Nicholas Simafranca
Bryant Willoughby
Erin O'Neil
Sophie Farr
Brian J Reich
Naomi Giertych
Margaret Johnson
Madeleine Pascolini-Campbell

"Modelling wildland fire burn severity in California using a spatial Super Learner approach" by Nicholas Simafranca, Bryant Willoughby, Erin O'Neil, Sophie Farr, Brian J Reich, Naomi Giertych, Margaret Johnson, and Madeleine Pascolini-Campbell, arXiv:2311.16187v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c||}
\hline\hline
\textbf{Control strategy} & \textbf{IAE-Optimal Control} & \textbf{Energy consumption (Wh)} \\ \hline
Optimal control & 1458.9 & 21.18 \\ \hline
Optimal PI control & 1683.13 & 26.07 \\ \hline\hline
\end{tabular}
\end{adjustbox}
\caption{Comparative table of numerical values for the IAE and energy consumption for the Optimal Control and Optimal PI control}
\end{table}
\end{document}

math

Jorge Ortega
Omar Santos
Liliam Rodríguez
Sabine Mondié

"Technical comments on the optimal control for linear distributed time-delay" by Jorge Ortega, Omar Santos, Liliam Rodríguez, and Sabine Mondié, arXiv:2201.05676v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the main properties of the existing algorithms}
\begin{tabular}{|c|c|c|c|c|c|}
				\hline
				Algorithm & Feasibility & RRLs & Connectivity & Delay & Nonlinearity\\
				\hline
				This work & All-Time & $\checkmark$ & Uniform & Varying & $\checkmark$\\
				\hline
				Gradient-Laplacian & All-Time & $\times$ & All-Time & $\times$ & $\times$\\
				\hline
				ADMM & Asymptotic & $\times$ & All-Time & $\times$ & $\times$\\
				\hline
				ADMM & Asymptotic & $\times$ & All-Time & Invariant & $\times$\\
				\hline
				\hline
		\end{tabular}
\end{table}
\end{document}

eess

Mohammadreza Doostmohammadian
Hamid R. Rabiee

"Distributed Automatic Generation Control subject to Ramp-Rate-Limits: Anytime Feasibility and Uniform Network-Connectivity" by Mohammadreza Doostmohammadian and Hamid R. Rabiee, arXiv:2509.06588v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Xiaokang Chen
Zhiyu Wu
Xingchao Liu
Zizheng Pan
Wen Liu
Zhenda Xie
Xingkai Yu
Chong Ruan

"Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling" by Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, and Chong Ruan, arXiv:2501.17811v1, 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}{ccccc}
\toprule %\hline
Method & Return Period & Region 1 & Region 2 & Region 3 \\
\toprule %\hline
1. PARE Model
&25-Year&11.634(0.6874)&10.442(0.5857)&11.399(0.5461)\\
&100-Year&16.48(1.2435)&14.291(1.0239)&15.856(0.9666)\\
&500-Year&24.131(2.3097)&20.002(1.8186)&22.7(1.7504)\\
 \hline
2. Block Kriging
& 25-Year & 12.01 (2.27) & 11.21 (2.06) & 9.14 (1.44) \\
& 100-Year & 17.28 (4.48) & 15.46 (3.89) & 11.87 (2.54) \\
& 500-Year & 25.80 (8.97) & 21.91 (7.36) & 15.61 (4.44) \\
 \hline
3. Regional Max
& 25-Year & 12.96 (1.33) & 14.68 (1.44) & 13.75 (1.35) \\
& 100-Year & 17.54 (2.39) & 19.68 (2.53) & 18.37 (2.39) \\
& 500-Year & 24.23 (4.32) & 26.83 (4.48) & 24.94 (4.22) \\
 \hline \hline
ACEC Estimates
& 25-Year & 10.90 & 11.50* & 12.30* \\
& 100-Year & 16.30 & 16.90* & 18.00* \\
& 500-Year & 24.20 & 25.00* & 27.20* \\
\bottomrule
\end{tabular}
\caption{Comparison of return level estimates (in inches) across the proposed models for the last 40 years of data, 1981-2020. Standard error estimates are displayed in parentheses. Additionally, the model estimates are compared to region estimates produced by the American Council of Engineering Companies (ACEC) in Houston . The ACEC estimates for regions 2 and 3 are marked with asterisks because the hydrologic regions are slightly different than the HCFCD designation. Our proposed PARE Model produces very similar estimates to the ACEC study for region 1, which is the same as our region 1.}
\end{table}
\end{document}

stat

Carlynn Fagnant
Julia C. Schedler
Katherine B. Ensor

"Spatial-Temporal Extreme Modeling for Point-to-Area Random Effects (PARE)" by Carlynn Fagnant, Julia C. Schedler, and Katherine B. Ensor, arXiv:2311.17271v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|cc|cc|cc|}
\hline
 & \multicolumn{2}{c|}{\textbf{Variance Explained (\%)}} & \multicolumn{2}{c|}{\textbf{$\Delta$ NLL ($\downarrow$, \%)}} & \multicolumn{2}{c|}{\textbf{Sparse probing ($\uparrow$)}} & \multicolumn{2}{c|}{\textbf{Absorption score ($\downarrow$)}} \\
\textbf{Layer} & SAE & SST & SAE & SST & SAE & SST & SAE & SST \\
\hline
L10 & 16.5 & \textbf{67.1} & 1.1 & \textbf{0.5} & \textbf{71.5} & 70.6 & 36.3 $\pm$ 20.2 & \textbf{28.6 $\pm$ 13.3} \\
L14 & 17.0 & \textbf{72.4} & 1.1 & \textbf{0.5} & \textbf{80.0} & 76.0 & 33.1 $\pm$ 19.4 & \textbf{25.0 $\pm$ 18.4} \\
L18 & 20.9 & \textbf{81.7} & 2.1 & \textbf{0.5} & \textbf{78.9} & 75.2 & \textbf{26.3 $\pm$ 23.7} & 53.3 $\pm$ 19.6 \\
L22 & 21.6 & \textbf{73.2} & 1.6 & \textbf{0.5} & 76.1 & & 24.4 $\pm$ 22.0 & \textbf{18.8 $\pm$ 29.5} \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Results for gemma-2-2B. $\Delta$ NLL represents the increase in cross-entropy loss.}
\end{table}
\end{document}

cs

Gonçalo Paulo
Stepan Shabalin
Nora Belrose

"Transcoders Beat Sparse Autoencoders for Interpretability" by Gonçalo Paulo, Stepan Shabalin, and Nora Belrose, arXiv:2501.18823v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Shuzhou Sun
Li Liu
Yongxiang Liu
Zhen Liu
Shuanghui Zhang
Janne Heikkilä
Xiang Li

"Uncovering Bias in Foundation Models: Impact, Testing, Harm, and Mitigation" by Shuzhou Sun, Li Liu, Yongxiang Liu, Zhen Liu, Shuanghui Zhang, Janne Heikkilä, and Xiang Li, arXiv:2501.10453v1, 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{Perplexities on PTB and WikiText-2 (plain LSTMs).}
\begin{tabular}{lrrr|rrr}
 \toprule
 \multirow{2}{*}{Model} & \multicolumn{3}{c}{PTB} & \multicolumn{3}{c}{WikiText-2}\\
 \cmidrule(lr) {2-4} \cmidrule(lr) {5-7}
 & \#Params & Dev & Test & \#Params & Dev & Test \\
 \midrule
 5gram KN & 2M & - & 141.2 & - & - & -\\
 LSTM (medium) & 20M & 86.2 & 82.7 & - & - & -\\
 PSMM & 21M & 72.4 & 70.9 & 47M & 84.8 & 80.8\\
 \midrule
 LSTM & 13.3M & 73.6 & 71.9 & 28.5M & 89.1 & 84.8\\
 PSMM (Ours) & 13.7M & 73.5 & 71.6 & 28.9M & 86.8 & 82.8\\
 LSTM + Neural Cache (L=50) & 13.7M & 69.3 & 68.5 & 28.9M & 81.3 & 77.0\\
 Proposed w/o Memory Aug& 13.3M & 70.1 & 69.8& 28.5M & 80.6 & 76.7\\
 Proposed w/ Memory Aug & 13.3M & \textbf{68.1} & \textbf{67.8} & 28.5M & \textbf{78.2} & \textbf{74.3}\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"Neural Language Modeling With Implicit Cache Pointers" by Ke Li, Daniel Povey, and Sanjeev Khudanpur, arXiv:2009.13774v1, 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|c|c|c|}
 \hline &\multicolumn{2}{c|}{CAT2}&\multicolumn{2}{c|}{WBCAT2}& \multicolumn{2}{c|}{CAT4} & \multicolumn{2}{c|}{WBCAT4} \\
 Points & Order & Error & Order & Error & Order & Error & Order& Error \\ \hline
 6 & - & 1.79E-1 & - & 1.80E-1 & - & 6.03E-2 & - & 6.03E-2 \\
 \hline
 11 & 1.57 & 6.00E-2 & 1.56 & 6.05E-2 & 2.86 & 8.31E-3 & 2.86 & 8.31E-3\\
 \hline
 21 & 1.91 & 1.60E-2 & 1.92 & 1.60E-2 & 3.58 & 6.94E-4 & 3.58 & 6.94E-4 \\
 \hline
 41 & 2.02 & 3.93E-3 & 2.02 & 3.94E-3 & 3.88 & 4.69E-5 & 3.89 & 4.69E-5 \\
 \hline
 81 & 2.03 & 9.63E-4 & 2.02 & 9.66E-4 & 4.01 & 2.90E-6 & 4.00 & 2.90E-7 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Test : Errors in $L^1$ norm and convergence rates for CAT2, CAT4, WBCAT2 and WBCAT4 at time $t=1.$}
\end{table}
\end{document}

math

"Well-balanced adaptive compact approximate Taylor methods for systems of balance laws" by H. Carrillo, E. Macca, C. Pares, and G. Russo, arXiv:2202.02068v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Global Bias-Corrected Divide-and-Conquer by Quantile-Matched Composite for General Nonparametric Regressions" by Yan Chen and Lu Lin, arXiv:2201.12597v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
\hline
\textbf{procedure} $\mathsf{VeHG2}(i,W_a^0,W_a^1,W_b^0,W_b^1,F_i)$:			& \textbf{procedure} $\mathsf{VeHG0\&1}(i,W_a^0,W_a^1,W_b^0,W_b^1)$:		 \\
\quad \textbf{if} $W_a^0\oplus W_a^1\neq W_b^0\oplus W_b^1$					&\quad \textbf{if} $W_a^0\oplus W_a^1\neq W_b^0\oplus W_b^1$ 				\\
 \quad \quad \textbf{return} $0$												& \quad \quad \textbf{return} $0$										 \\
 \quad $\Delta\gets W_a^0\oplus W_a^1$										& \quad $\Delta\gets W_a^0\oplus W_a^1$								\\
 \quad $K_{1}\gets H(i,W_a^0)$ 				& \quad $K_{1}\gets H(i,W_a^0)$ 									 \\
 \quad $K_{2}\gets W_b^0\oplus H(i,W_a^0\oplus\Delta) \oplus F_i$ & \quad $K_{2}\gets W_b^0\oplus H(i,W_a^0\oplus\Delta) $ 				\\
 \quad \textbf{if} $K_{1}\neq K_{2}$ 			& \quad \textbf{if} $K_{1}\neq K_{2}$ 									 \\
 \quad \quad \textbf{return} $0$ 					 & \quad \quad \textbf{return} $0$ 		 			\\
 \quad \textbf{return} $(K_{1},K_{1}\oplus\Delta)$ & \quad \textbf{return} $(K_{1},K_{1}\oplus\Delta)$ 													\\
 																		 & \\
\textbf{procedure} $\mathsf{VeXOR}(W_a^0,W_a^1,W_b^0,W_b^1)$:		 & \textbf{procedure} $\mathsf{VeNOT}(W_a^0,W_a^1)$: \\
\quad \textbf{if} $W_a^0\oplus W_a^1\neq W_b^0\oplus W_b^1$ 									 & \quad \textbf{return} $(W_a^1,W_a^0)$ \\
 \quad \quad \textbf{return} $0$ 																	& \\
\quad \textbf{return} $(W_a^0\oplus W_b^0,W_a^0\oplus W_b^1) $ 																 & \textbf{procedure} $\mathsf{VeITAND}(W_a^0,W_a^1,W_b^0,W_b^1):	$ 		\\
						 											& \quad \textbf{if} $W_a^0\neq W_b^0$ \\
						 											& \quad \quad \textbf{return} $0$ \\
						 											& \quad \textbf{return} $(W_a^0,W_a^1\oplus W_b^1)$ \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Gate verification algorithms that are used in AuthOr.}
\end{table}
\end{document}

cs

"AuthOr: Lower Cost Authenticity-Oriented Garbling for Arbitrary Boolean Circuits" by Osman Biçer and Ali Ajorian, arXiv:2501.18387v6, 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{Failure rate of the devices deployed in the evacuation zone}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccccccccc}
\hline
 Device & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 \\ \hline
Rural & 2 & 0 & 0 & 0 & 0 & 3 & 0 & 0 & 6 & 0 & 8 & 11 \\
Urban & 0 & 0 & 0 & 3 & 0 & 1 & 6 & 10 & 5 & 3 & 2 & 0 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devices" by Talha Azfar, Kaicong Huang, and Ruimin Ke, arXiv:2501.15305v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|c|c|}
 \hline
 \textbf{Model} & \textbf{Accuracy} & \textbf{Precision} & \textbf{Recall} & \textbf{F1 Score} \\
 \hline
 \textbf{InceptionV3} & \textbf{0.9052} & \textbf{0.9001} & \textbf{0.9716} & \textbf{0.9345} \\
 EfficientNet B7 & 0.7146 & 0.7648 & 0.8518 & 0.8059 \\
 RadImagenet - DenseNet101 & 0.7746 & 0.8424 & 0.8892 & 0.8650 \\
 ResNet101 & 0.8146 & 0.9024 & 0.9492 & 0.9252 \\
 \hline
 \end{tabular}
\caption{Performance Metrics of Various Models on the PCOS Dataset}
\end{table}
\end{document}

eess

"Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging" by Atharva Divekar and Atharva Sonawane, arXiv:2501.01984v1, 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|c|c|c|}\hline
		Model & Fine grid &$h$ & $H$ &$\delta t$ & $T$\tabularnewline\hline
		$K_1$&$64^3$& 20 meters &$8h$ & 7 days& $20 \delta t$ \tabularnewline\hline
		$K_2$&$64^3$& 20 meters &$8h$ &7 days&$20 \delta t$ \tabularnewline\hline
		$K_3$&$220\times 30\times 80$& 20 meters & $10h$ & 1 day& $20 \delta t$ \tabularnewline\hline
	\end{tabular}
\end{adjustbox}
\caption{Parameter settings in the performance tests, ``Fine Grid'' is the resolution of the permeability field, $h$ is the fine grid size, $H$ is the coarse grid size $\delta t$ is the time step, $T$ the total simulation time. }
\end{table}
\end{document}

math

"Generalized multiscale finite element method for highly heterogeneous compressible flow" by Shubin Fu, Eric Chung, and Lina Zhao, arXiv:2201.07371v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c||}
 \hline
 \hline
 Example & $L_I$ & $L_{\circ}$ \\
 \hline
 \hline
 Example~ & $(a^2)^*$ & $(a^2)^*$ \\
 Example~ & $(a^2)^*$ & $a(a^2)^*$ \\
 Example~ & $(a^2)^*$ & $a^{\ast}$ \\
 Example~ & second from last letter is $b$ & -- \\
 Example~ & -- & has two consecutive $a$ \\
 Example~ & -- & has $\cdots a \cdots b \cdots a \cdots b\cdots $ \\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Languages for examples in Section~. }
\end{table}
\end{document}

math

Topological theories and automata

"Topological theories and automata" by Mee Seong Im and Mikhail Khovanov, arXiv:2202.13398v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
\toprule
Minimax optimal rule & mean regret & mean square regret\tabularnewline
\midrule
\multirow{1}{*}{point-identified welfare} & singleton & fractional \medskip \tabularnewline
\multirow{1}{*}{partially-identified welfare} & either singleton or fractional & \multirow{1}{*}{\textbf{aim of this paper}}\tabularnewline
\bottomrule
\end{tabular}
\caption{Treatment choice with partial identification: existing results and aim of this paper}
\end{table}
\end{document}

stat

"Treatment Choice, Mean Square Regret and Partial Identification" by Toru Kitagawa, Sokbae Lee, and Chen Qiu, arXiv:2310.06242v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l}
		\hline
		Algorithm 1 \quad\quad GMsFEM with Newton's method for solving \\
		\hline

		{Superscript $n$ denotes time index and $n$, $k$ Newton index $k$ at time n. }\\
		Given $p^0_{H}$,\quad \{Initial Condition\}\\
		\textbf{for} $n=1$,\ldots,$N_T$ \{Time index\}\quad {\bf do}\\
		\quad $p^{n,0}_{H}=p^{n-1}_{H}$\quad \{Initial guess for Newton iteration\}\\
		\quad {\bf for} $k=0,\ldots,$ {\text{MAX}\_\text{NEWT}\_\text{IT}} \{Newton index\} \quad {\bf do}\\
		\quad \quad $F^{n,k}=[F_j^{n,k}]$\quad {Form Newton residual}\\
		\quad\quad {\bf if}\quad ($||F^{n,k} ||<\text{NEWT}_\text{TOL}$)\quad
		{\bf then break} $k$-loop \quad \{Check nonlinear convergence\}\\
		\quad\quad $J^{n,k}=[J^{n,k}_{ji}]$\quad {Form matrix}\\
		\quad\quad $\delta p^{n,k}_H=(R^T J^{n,k} R)^{-1}(R^TF^{n,k})$\quad \{Solve linear system \}\\
		\quad\quad $p^{n,k+1}_{H}=p^{n,k}_{H}+R\delta p^{n,k}_H$\quad \{increments unknowns\}\\
		\quad {\bf end for}\\
		\quad $p^n_{H}=p^{n,k}_{H}$\\
		{\bf end for}\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math


math

"Sparse Markov Models for High-dimensional Inference" by Guilherme Ost and Daniel Takahashi, arXiv:2202.08007v3, 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{Numerical results of and the ADMM on the real dark raw video datasets. The computational time is in the format of ``hours:minutes:seconds". The ``time" and ``iter'' columns represent the computational time and the number of iterations required for both algorithms to achieve the same objective function value, which is selected as the maximum of the objective values (in the ``obj'' column) returned by and the ADMM.}
\begin{tabular}{ccccccc}
			\toprule
			& & & \multicolumn{2}{c}{AltMin} & \multicolumn{2}{c}{ADMM} \\
			\cmidrule(rl){4-5}
			\cmidrule(rl){6-7}
			dataset & $n_2$ & obj & time & iter & time & iter \\
			\midrule
			billiards & 113 & 1726.985 & {\bf 54:02} & {\bf 365} & 58:05 & 612 \\
			store & $112$ & 10007.030 & {\bf 6:02} & {\bf 42} & 31:32 & 326 \\
			tea set & $113$ & { 11729.987} & {\bf 8:01} & {\bf 63} & 13:35 & 204 \\
			table tennis & 114 & 1196.135 & {\bf 2:04} & {\bf 15} & 21:24 & 245\\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

stat

Shengxiang Deng
Xudong Li
Yangjing Zhang

"Alternating minimization for square root principal component pursuit" by Shengxiang Deng, Xudong Li, and Yangjing Zhang, arXiv:2501.00471v2, 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{RQ1 -- Comparison of unsafe results between the coverage-based test generator and the baseline test generator across different execution models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
\toprule
 & & \textbf{Baseline} & \textbf{Coverage-based} \\
 & & \textbf{Test Generator} & \textbf{Test Generator} \\
\cmidrule{2-4}
 & \textbf{\#testcases} & \textbf{\#unsafe} & \textbf{\#unsafe} \\ \cmidrule{1-4}
\textbf{GPT 4}& 1260 & 26 & 53\\
\textbf{GPT 4o} & 1260 & 34& 77\\
\textbf{GPT 3.5} & 1260 & 107 & 271\\
\textbf{Mistral} & 1260 & 128 & 368 \\
\textbf{Llama 2} & 1260 & 8 & 21\\
\textbf{Llama 3}& 1260 & 59 & 56\\
\textbf{Vicuna} & 1260 & 240 & 275 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Miriam Ugarte
Pablo Valle
José Antonio Parejo
Sergio Segura
Aitor Arrieta

"ASTRAL: Automated Safety Testing of Large Language Models" by Miriam Ugarte, Pablo Valle, José Antonio Parejo, Sergio Segura, and Aitor Arrieta, arXiv:2501.17132v1, 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{Risk Measure per flight hour}
\begin{tabular}{ccc}

 Groups & P(LoWC)/flight hr & Qualitative Likelihood \\
 \hline \hline
 B-1 & $2.22\times10^{-7}$ & Remote \\
 B-2 & $2.36\times10^{-7} $& Remote \\
 IC-1& $2.92\times10^{-7}$ & Remote \\
 ID-1 & $2.77\times10^{-7}$ & Remote\\
 ID-2 & $2.81\times10^{-7}$ & Remote\\

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

eess

"Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid" by Asma Tabassum and He Bai, arXiv:2103.07820v1, 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{Automated Detected Errors (WAVE + axe) and Their Resolution}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Error Description} & \textbf{Fully Resolved} & \textbf{Iterations} \\ \hline
Missing form labels (forms) & Yes & 3 \\ \hline
Missing form label (footer) & Yes & 1 \\ \hline
No main landmark & Yes & 1 \\ \hline
No level one heading & Yes & 2 \\ \hline
Low contrast text (play button) & Yes & 1 \\ \hline
Low contrast text (forms) & Yes & 1 \\ \hline
Low contrast text (form modals) & Yes & 2 \\ \hline
Low contrast text (section headings) & Yes & 2 \\ \hline
Low contrast text (buttons in footer) & Yes & 1 \\ \hline
Low contrast text (buttons in forms) & Yes & 1 \\ \hline
Low contrast text (headings in footer) & Yes & 1 \\ \hline
Low contrast text (links in footer) & Yes & 1 \\ \hline
Device dependent event handler & Yes & 1 \\ \hline
\multicolumn{2}{|l|}{\textbf{Total Iterations}} & \textbf{18} \\ \hline
\multicolumn{2}{|l|}{\textbf{WAI}} & \textbf{1.4} \\ \hline
\multicolumn{2}{|l|}{\textbf{Accuracy}} & \textbf{100\%} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ammar Ahmed
Margarida Fresco
Fredrik Forsberg
Hallvard Grotli

"From Code to Compliance: Assessing ChatGPT's Utility in Designing an Accessible Webpage -- A Case Study" by Ammar Ahmed, Margarida Fresco, Fredrik Forsberg, and Hallvard Grotli, arXiv:2501.03572v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|c|c|c|}
\hline \rowcolor{lightgray} \multicolumn{5}{|c|}{ \textbf{Recent Wave Parameters}} \\
\hline \rowcolor{lightgray} \textbf{Division} & \textbf{Code} & $a$ & $b$ & $\gamma$ \\
\hline Argentina & ARG & $9.47 \pm 0.06 $ & $9.92\pm 6.60$ & $0.94 \pm 0.15$ \\
\hline Bolivia & BOL & $8.53 \pm 0.05 $ & $1.10\pm 0.15$ & $0.44 \pm 0.04$ \\
\hline Brazil & BRA & $10.78 \pm 0.03 $ & $2.18\pm 0.33$ & $0.69 \pm 0.04$ \\
\hline Dominican Republic & DOM & $8.03 \pm 0.02 $ & $2.29\pm5.50$ & $0.67 \pm 0.05$ \\
\hline Ecuador & ECU & $8.01 \pm 0.08 $ & $0.85\pm0.50$ & $0.54 \pm 0.05$ \\
\hline Mexico & MEX & $7.87 \pm 0.02 $ & $6.86\pm 1.53$ & $0.52 \pm 0.03$ \\
\hline
Nepal & NPL & $7.48 \pm 0.01 $ & $21.39\pm 5.01$ & $1.02 \pm 0.05$ \\
\hline
Thailand & THA & $8.35 \pm 0.02 $ & $1.08\pm 0.11$ & $0.44 \pm 0.02$ \\
\hline
\end{tabular}
\caption{Parameters of the eRG model for the most recent Dengue wave in Latin American and Asian countries under study.}
\end{table}
\end{document}

stat

Alessandra D'Alise
Davide Iacobacci
Francesco Sannino

"A Temporal Playbook for Multiple Wave Dengue Pandemic from Latin America and Asia to Italy" by Alessandra D'Alise, Davide Iacobacci, and Francesco Sannino, arXiv:2411.03837v1, 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{Computation times in [s] for scenario .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccccc|} \hline
		$n$ & 5 & 6 & 7 & 8 & 9 & 10 \\
		\hline
		DFS & 0.01 & 0.11 & 8.3 & $>$600 & - & - \\
		Alg. & 0.02 & 0.04 & 0.06 & 0.16 & 0.43 & 1.32\\
		$N$ & 21 & 55 & 144 & 377 & 987 & 2584\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"How to find all connections in the Pantelides algorithm for delay differential-algebraic equations" by Daniel Collin, arXiv:2202.01772v1, 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}{||cccc||}
 \hline
 $\sigma_{apop}(0.01)$ & $\sigma_{IC_{50}}(0.9)$ & $w_1$ = 0.01 & $w_1$ = 0.1\\
 \hline\hline
 $\xi^{\ast}$ = Casp-act & $\xi^{\ast}$ = mRNA-Bax & $\xi^{\ast}$ = Casp-act
 & $\xi^{\ast}$ = mRNA-Bax \\
 \hline
\end{tabular}
\caption{Table of optimal experimental designs, $\xi^{\ast}$ for each objective considered in the present study.}
\end{table}
\end{document}

stat

Natalie M. Isenberg
Susan D. Mertins
Byung-Jun Yoon
Kristofer Reyes
Nathan M. Urban

"Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models" by Natalie M. Isenberg, Susan D. Mertins, Byung-Jun Yoon, Kristofer Reyes, and Nathan M. Urban, arXiv:2309.06540v2, 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{Surface Coverage and Data Quality Metrics by Component Type}
\begin{tabular}{lcccc}
\toprule
Component Type & Avg. Surface Coverage (\%) & Point Density (pts/mm²) & Avg. Alignment RMS (mm) & Scan Time (min) \\
\midrule
Hydraulic Manifold & 97.3 $\pm$ 1.2 & 68.4 & 0.007 & 18.5 \\
Turbine Blade & 95.8 $\pm$ 1.8 & 85.2 & 0.009 & 26.3 \\
Mounting Bracket & 98.7 $\pm$ 0.9 & 52.1 & 0.005 & 12.1 \\
Gear Housing & 96.9 $\pm$ 1.3 & 60.7 & 0.008 & 20.4 \\
Medical Implant & 95.2 $\pm$ 2.1 & 92.3 & 0.011 & 27.8 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Hamidreza Samadi
Md Manjurul Ahsan
Shivakumar Raman

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccc}
\hline
\textbf{} & \textbf{Baseline} & \textbf{x\_add} & \textbf{s\_add} & \textbf{x\_cat} & \textbf{s\_cat} \\ \hline \hline
\textbf{NPTEL-dt - less\_5} & 27.1 & 28.0 & 27.9 & 25.8 & 25.6 \\ %\hline
\textbf{NPTEL-et - less\_5} & 26.1 & 27.2 & 26.9 & 25.1 & 24.7 \\ %\hline
\textbf{NPTEL-Unseen\_1 - less\_5} & 26.3 & 27.2 & 26.6 & 25.3 & 24.9 \\ %\hline
\textbf{NPTEL-Unseen\_2 - less\_5} & 27.0 & 28.6 & 26.9 & 25.6 & 25.7 \\ \hline
\end{tabular}
\caption{NPTEL data set: Average error rate (\%WER) on utterances with duration less than five seconds}
\end{table}
\end{document}

eess

Vishwas M. Shetty
Metilda Sagaya Mary N J
S. Umesh

"Investigation of Speaker-adaptation methods in Transformer based ASR" by Vishwas M. Shetty, Metilda Sagaya Mary N J, and S. Umesh, arXiv:2008.03247v2, 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}{rrrrrr} \toprule
		& & \multicolumn{4}{c}{$C$} \\ \cmidrule(l){3-6}
		$N$ & $M$ &$a=10$ & $a=10^2$ & $a=10^4$ & $a=10^8$ \\ \midrule
		2 & 256 & 1.77e4 & 2.97e4 & 3.16e4 & 3.16e4 \\
		2 & 512 & 7.10e4 & 1.19e5 & 1.27e5 & 1.27e5 \\ \midrule
		4 & 128 & 8.65e3 & 2.21e4 & 2.51e4 & 2.51e4 \\
		4 & 256 & 3.48e4 & 8.91e4 & 1.01e5 & 1.01e5 \\ \midrule
		8 & 64 & 4.99e3 & 1.89e4 & 2.30e4 & 2.31e4 \\
		8 & 128 & 2.02e4 & 7.69e4 & 9.36e4 & 9.38e4 \\ \midrule
		16 & 32 & 2.93e3 & 1.68e4 & 2.21e4 & 2.22e4 \\
		16 & 64 & 1.20e4 & 6.89e4 & 9.10e4 & 9.13e4 \\ \midrule
		32 & 16 & 1.67e3 & 1.44e4 & 2.09e4 & 2.09e4 \\
		32 & 32 & 6.98e3 & 6.09e4 & 8.80e4 & 8.84e4 \\
		\bottomrule \end{tabular}
\end{adjustbox}
\caption{Approximation constant $C$ for multigrid on the discretized Poisson problem with coefficient jumps.}
\end{table}
\end{document}

math

"Optimal polynomial smoothers for multigrid V-cycles" by James Lottes, arXiv:2202.08830v3, 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{ Hyperparameters used for each numerical example: For each example, the learning rate is decayed when the training loss plateaus.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
\multicolumn{7}{|c|}{\bf{1D locally periodic coefficient (Section 4.1)} }\\
\hline
\multicolumn{2}{|c|}{NN depth} & \multicolumn{2}{c|}{NN width}& \multirow{2}{*}{Initial learning rate} & \multirow{2}{*}{$\#$ of epochs} & \multirow{2}{*}{Batch size} \\
\cline{1-4}$\hat{A}^*(x)$&$\hat{u}_{0}(x)$&$\hat{A}^*(x)$&$\hat{u}_{0}(x)$&&&\\
\hline
 3&3& 30&30 & 0.001 & $40000$ & $64$ \\
 \hline
 \multicolumn{7}{|c|}{\bf{1D heavily oscillatory coefficient (Section 4.2)}}\\
\hline
\multicolumn{2}{|c|}{NN depth} & \multicolumn{2}{c|}{NN width}& \multirow{2}{*}{Initial learning rate} & \multirow{2}{*}{$\#$ of epochs} & \multirow{2}{*}{Batch size} \\
\cline{1-4}$\hat{A}^*(x)$&$\hat{u}_{0}(x)$&$\hat{A}^*(x)$&$\hat{u}_{0}(x)$&&&\\
\hline
 3&3& 50&50 & 0.0001 & $80000$ & $64$ \\
 \hline
 \multicolumn{7}{|c|}{\bf{2D non-periodic coefficient (Section 4.3)}}\\
\hline
\multicolumn{2}{|c|}{NN depth} & \multicolumn{2}{c|}{NN width}& \multirow{2}{*}{Initial learning rate} & \multirow{2}{*}{$\#$ of epochs} & \multirow{2}{*}{Batch size} \\
\cline{1-4}$\hat{A}^*_{ii}(x)$&$\hat{u}_{0}(x)$&$\hat{A}^*_{ii}(x)$&$\hat{u}_{0}(x)$&&&\\
\hline
 2&4& 40&45 & 0.001 & $100000$ & $200$ \\
 \hline
 \multicolumn{7}{|c|}{\bf{1D ergodic random coefficient (Section 4.4)}}\\
\hline
\multicolumn{2}{|c|}{NN depth} & \multicolumn{2}{c|}{NN width}& \multirow{2}{*}{Initial learning rate} & \multirow{2}{*}{$\#$ of epochs} & \multirow{2}{*}{Batch size} \\
\cline{1-4}$\hat{A}^*(x)$&$\hat{u}_{0}(x)$&$\hat{A}^*(x)$&$\hat{u}_{0}(x)$&&&\\
\hline
 2&3& 10&30 & 0.001 & $60000$ & $64$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Physics-informed neural networks for learning the homogenized coefficients of multiscale elliptic equations" by Jun Sur Richard Park and Xueyu Zhu, arXiv:2202.09712v1, 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{The evolution of Average Accuracy \textit{Acc} and Forgetting \textit{FG} with the growing incremental step on the UPMC-Food101 dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc}
\toprule \toprule
Num. Step & \textit{Acc} $(\%) \uparrow$ & \textit{FG} (\%) $\downarrow$ \\ \hline
5 & 73.19 & 4.13 \\
10 & 72.59 & 4.92 \\
20 & 72.46 & 5.84 \\
50 & 72.16 & 5.36 \\
\bottomrule \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xianghu Yue
Yiming Chen
Xueyi Zhang
Xiaoxue Gao
Mengling Feng
Mingrui Lao
Huiping Zhuang
Haizhou Li

"PAL: Prompting Analytic Learning with Missing Modality for Multi-Modal Class-Incremental Learning" by Xianghu Yue, Yiming Chen, Xueyi Zhang, Xiaoxue Gao, Mengling Feng, Mingrui Lao, Huiping Zhuang, and Haizhou Li, arXiv:2501.09352v1, 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|r|r|r}
\hline
 & age & duration & sex\\
\hline
SLIM shallow & 0.38 & 0.62 & 0.00\\
GUIDE shallow & 0.30 & 0.70 & 0.00\\
MOB shallow & 0.08 & 0.84 & 0.08\\
CTree shallow & 0.23 & 0.71 & 0.07\\
\hline
SLIM deep & 0.35 & 0.60 & 0.04\\
GUIDE deep & 0.20 & 0.78 & 0.02\\
MOB deep & 0.08 & 0.76 & 0.16\\
CTree deep & 0.20 & 0.67 & 0.13\\
\hline
\end{tabular}
\caption{Share of observations split by the different features K2204 PPV B-spline MBTs}
\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.


stat

"(Decision and regression) tree ensemble based kernels for regression and classification" by Dai Feng and Richard Baumgartner, arXiv:2012.10737v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l|l|l|l|l|l|l|l|l|}
\cline{2-12}
 &
 R1 &
 R2 &
 R3 &
 R4 &
 R5 &
 R6 &
 R7 &
 R8 &
 R9 &
 R10 &
 Average \\ \hline
Mean Threshold &
 {\textbf{ 100\%}} &
 {\textbf{ 100\%}} &
 {\textbf{ 100\%}} &
 {\textbf{97.92\%}} &
 {\textbf{ 100\%}} &
 {\textbf{ 100\%}} &
 {\textbf{ 100\%}} &
 {\textbf{ 100\%}} &
 {\textbf{ 100\%}} &
 {\textbf{ 100\%}} &
 {\textbf{99.79\%}} \\ \hline
Majority Samples &
 {\textbf{ 100\%}} &
 {\textbf{ 100\%}} &
 {\textbf{ 100\%}} &
 {\textbf{97.92\%}} &
 {\textbf{ 100\%}} &
 {\textbf{ 100\%} }&
 {\textbf{ 100\%}} &
 {\textbf{ 100\%} }&
 {\textbf{ 100\%}} &
 {\textbf{ 100\%}} &
 {\textbf{99.79\%}} \\ \hline
All Samples &
 97.97\% &
 98.59\% &
 99.07\% &
 96.62\% &
 98.79\% &
 98.84\% &
 98.63\% &
 98.92\% &
 98.23\% &
 97.76\% &
 98.34\% \\ \hline
Hedges &
 89.58\% &
 93.75\% &
 95.83\% &
 91.67\% &
 87.50\% &
 \textbf{95.83\%} &
 91.67\% &
 93.75\% &
 89.58\% &
 87.50\% &
 91.67\% \\ \hline
\end{tabular}
\end{adjustbox}
\caption{TNN testing results across the 10 runs for our four different measures of accuracy when identifying stable versions. }
\end{table}
\end{document}

cs

Ashley Andrews
George Oikonomou
Simon Armour
Paul Thomas
Thomas Cattermole

"IoT Firmware Version Identification Using Transfer Learning with Twin Neural Networks" by Ashley Andrews, George Oikonomou, Simon Armour, Paul Thomas, and Thomas Cattermole, arXiv:2501.06033v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccc}
 \hline
 \toprule
 Pretraining Dataset & \multicolumn{2}{c}{CholecSeg8k} & \multicolumn{2}{c}{RAMIE} & \multicolumn{2}{c}{RARP} \\
 & Median & [\%] & Median & [\%] & Median & [\%] \\
 \midrule
 SurgeNet & 0.710 & - & 0.700 & - & 0.675 & -\\
 SurgeNet + SS fine-tuning & 0.709 & -0.1\% & 0.704 & 0.6\% & 0.677 & 0.3\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Median Dice scores and percentage-wise differences of pretraining on SurgeNet vs. adding self-supervised (SS) fine-tuning.}
\end{table}
\end{document}

cs

Tim J. M. Jaspers
Ronald L. P. D. de Jong
Yiping Li
Carolus H. J. Kusters
Franciscus H. A. Bakker
Romy C. van Jaarsveld
Gino M. Kuiper
Richard van Hillegersberg
Jelle P. Ruurda
Willem M. Brinkman
Josien P. W. Pluim
Peter H. N. de With
Marcel Breeuwer
Yasmina Al Khalil
Fons van der Sommen

"Scaling up self-supervised learning for improved surgical foundation models" by Tim J. M. Jaspers, Ronald L. P. D. de Jong, Yiping Li, Carolus H. J. Kusters, Franciscus H. A. Bakker, Romy C. van Jaarsveld, Gino M. Kuiper, Richard van Hillegersberg, Jelle P. Ruurda, Willem M. Brinkman, Josien P. W. Pluim, Peter H. N. de With, Marcel Breeuwer, Yasmina Al Khalil, and Fons van der Sommen, arXiv:2501.09436v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.