\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}{ccc}
				\hline
				Attention & $\phi$ &Top-1\\
				\hline
				SA.~ & - & 72.2 \\
				SA.$^{\textbf{*}}$ & - & 72.5\\
				LA. & L1 norm & 68.6 \\
				LA. & ReLU & 69.3 \\
				LA. &LeakyReLU & 67.6 \\
				Enhanced LA. & ReLU & \textbf{73.3} \\
				\hline
			\end{tabular}
\end{adjustbox}
\caption{\textbf{ImageNet-1k accuracy of DeiT-Tiny with different attention variants.} SA. is softmax attention, LA. is linear attention with feature map $\phi$. $^{*}$ indicates the results are reproduced by ours.}
\end{table}
\end{document}

cs

"The Linear Attention Resurrection in Vision Transformer" by Chuanyang Zheng, arXiv:2501.16182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llll}
\toprule
\multicolumn{2}{c}{Simulation} & {Estimates} & {RMSE} \\ \midrule
\multirow{4}{*}{Constant Current} & & {$\hat{V}_t$} & {$7\,mV$} \\
& & {$\Delta\hat{t}_b$} & {$7.3\,\mu m$} \\
& & {$\hat{c}_{s,avg}^-$} & {$89\,mol/m^3$} \\
& & {$\hat{c}_{ss}^+$} & {$94\,mol/m^3$} \\ \midrule
\multirow{4}{*}{Drive Cycle} & & {$\hat{V}_t$} & {$7.2\,mV$} \\
& & {$\Delta\hat{t}_b$} & {$6.1\,\mu m$} \\
& & {$\hat{c}_{s,avg}^-$} & {$109\,mol/m^3$} \\
& & {$\hat{c}_{ss}^+$} & {$70\,mol/m^3$} \\ \midrule
\multirow{4}{*}{Stoich Mismatch} & \multirow{2}{*}{V+EXP}
& {$\hat{c}_{s,avg}^-$} & {$90\,mol/m^3$} \\
& & {$\hat{c}_{ss}^+$} & {$83\,mol/m^3$} \\ \cmidrule(lr){2-4}
& \multirow{2}{*}{V Only}
& {$\hat{c}_{s,avg}^-$} & {$419\,mol/m^3$} \\
& & {$\hat{c}_{ss}^+$} & {$156\,mol/m^3$} \\ \midrule
\multirow{4}{*}{$\varepsilon_s^-$ Mismatch } & \multirow{2}{*}{V+EXP}
& {$\hat{c}_{s,avg}^-$} & {$314\,mol/m^3$} \\
& & {$\hat{c}_{ss}^+$} & {$116\,mol/m^3$} \\ \cmidrule(lr){2-4}
& \multirow{2}{*}{V Only}
& {$\hat{c}_{s,avg}^-$} & {$234\,mol/m^3$} \\
& & {$\hat{c}_{ss}^+$} & {$107\,mol/m^3$} \\
\bottomrule
\end{tabular}
\caption{Simulation Error Results}
\end{table}
\end{document}

eess

Sravan Pannala
Puneet Valecha
Peyman Mohtat
Jason B. Siegel
Anna G. Stefanopoulou

"Improved Battery State Estimation Under Parameter Uncertainty Caused by Aging Using Expansion Measurements" by Sravan Pannala, Puneet Valecha, Peyman Mohtat, Jason B. Siegel, and Anna G. Stefanopoulou, arXiv:2009.14270v1, 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{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 \hline
 Model & Weight ($w$) & Variance ($\sigma^2$) \\
 \hline
 1 & -6.7805 & 564.285 \\
 2 & -5.8464 & 625.412 \\
 3 & -2.7811 & 699.653 \\
 4 & -1.3851 & 710.190 \\
 5 & -0.0144 & 783.458 \\
 6 & 4.3483 & 846.372 \\
 7 & 6.3163 & 915.492 \\
 8 & 7.1061 & 922.537 \\
 9 & 8.5174 & 932.399 \\
 10 & 8.9350 & 978.001 \\
 \hline
 \end{tabular}
\caption{Summary of the 10 models used for linear regression. For each model, \(x \sim \mathcal{U}(-10,10)\) and \(y = wx + e\) with \(e \sim \mathcal{N}(0,\sigma^2)\).}
\end{table}
\end{document}

eess

Jonas Ohnemus
Marta Fochesato
Riccardo Zuliani
John Lygeros

"Loss-aware distributionally robust optimization via trainable optimal transport ambiguity sets" by Jonas Ohnemus, Marta Fochesato, Riccardo Zuliani, and John Lygeros, arXiv:2509.12689v1, 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}{lr}
 \toprule
 Parameter & Setting \\
 \midrule
 Hidden Size & 2560 \\
 Heads & 20 \\
 Layers & 32 \\
 Vocab Size & 28896 \\
 Sequence Length & 1024 \\
 \bottomrule
 \end{tabular}
\caption{Model Architecture Settings}
\end{table}
\end{document}

cs

Elliot Bolton
Abhinav Venigalla
Michihiro Yasunaga
David Hall
Betty Xiong
Tony Lee
Roxana Daneshjou
Jonathan Frankle
Percy Liang
Michael Carbin
Christopher D. Manning

"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text" by Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall, Betty Xiong, Tony Lee, Roxana Daneshjou, Jonathan Frankle, Percy Liang, Michael Carbin, and Christopher D. Manning, arXiv:2403.18421v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the CONV, Lewis-FRFT, COS and SFP methods in terms of error convergence and CPU time for pricing European at/around-the-money call options under the VG model in the range of $S_0$ from 0.5 to 2. Spectral convergence is observed in both the COS and SFP methods. The SFP method has the highest accuracy. The parameters are taken from \textbf{VG--Para2}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|c||c|cc|c|} % centered columns (4 columns)
\hline
\multicolumn{4}{|c||}{\textbf{CONV}}&\multicolumn{4}{c|}{\textbf{Lewis-FRFT}}\\
\hline
N&$R_\infty$&$R_2$&Time&N&$R_\infty$&$R_2$&Time\\
8&1.384&13.31&0.00816& 8&3.147e-01&3.913&0.02111\\
16&5.933e-01&4.344&0.00941&16&1.506e-01&1.645&0.02625\\
32&1.161e-01&6.922e-01&0.01012&32&7.126e-02&6.727e-01&0.04571\\
64&7.092e-03&4.533e-02&0.01641&64&3.986e-02&3.245e-01&0.09151\\
\hline
\multicolumn{6}{c}{\textbf{}}\\
\hline
\multicolumn{4}{|c||}{\textbf{COS}}&\multicolumn{4}{c|}{\textbf{SFP}}\\
\hline
N&$R_\infty$&$R_2$&Time&N&$R_\infty$&$R_2$&Time\\
8&1.477e-02&1.292e-0&0.00716&8&2.174e-03&1.476e-02&0.00921\\
16&1.472e-03&1.131e-02&0.00841&16&5.234e-05&2.300e-04&0.00961\\
32&2.217e-05&1.596e-04&0.01223&32&2.409e-09&7.566e-09&0.01372\\
64&4.694e-08&2.857e-07&0.01413&64&1.541e-11&2.485e-11&0.01741\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The used ranges and distributions for tuning the hyperparameters with random search for sine wave regression. }
\begin{tabular}{ll}
 \toprule
 Hyperparameter & Range \\
 \midrule
 Number of layers & Uniform(\{1,2,3,4\}) \\
 Hidden dimensions & Uniform(\{1,3,8,20,40\}) \\
 Meta-batch size & Uniform(\{1,2,3,4\}) \\
 Learning rate & LogUniform(1e-5, 4e-2) \\
 Unroll steps & Uniform(\{1,2,\ldots,14\}) \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Are LSTMs Good Few-Shot Learners?

Mike Huisman
Thomas M. Moerland
Aske Plaat
Jan N. van Rijn

"Are LSTMs Good Few-Shot Learners?" by Mike Huisman, Thomas M. Moerland, Aske Plaat, and Jan N. van Rijn, arXiv:2310.14139v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Haar-Laplacian for directed graphs

"Haar-Laplacian for directed graphs" by Theodor-Adrian Badea and Bogdan Dumitrescu, arXiv:2411.15527v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary statistics of demographic variables selected for the SWOLCA probit model, grouped by hypertension outcome, among sampled individuals with complete data ($n = 2003$). \textmd{Column-wise percentages are shown in parentheses.}}
\begin{tabular}{lllll}
 \toprule
 Variable & Level & No Hypertension & Hypertension & Total \\ \midrule
 Sample Size & & 930 & 1073 & 2003 \\ \addlinespace
 Age Group & {[}20, 40) & 544 (58.5\%) & 120 (11.2\%) & 664 (33.2\%) \\
 & {[}40, 60) & 277 (29.8\%) & 360 (33.6\%) & 637 (31.8\%) \\
 & \textgreater{}=60 & 109 (11.7\%) & 593 (55.3\%) & 702 (35.0\%) \\ \addlinespace
 Race/Ethnicity & NH White & 270 (29.0\%) & 323 (30.1\%) & 593 (29.6\%) \\
 & NH Black & 189 (20.3\%) & 307 (28.6\%) & 496 (24.8\%) \\
 & NH Asian & 84 (9.0\%) & 65 (6.1\%) & 149 (7.4\%) \\
 & Hispanic/Latino & 352 (37.8\%) & 331 (30.8\%) & 683 (34.1\%) \\
 & Other/Mixed & 35 (3.8\%) & 47 (4.4\%) & 82 (4.1\%) \\ \addlinespace
 Current Smoker & No & 731 (78.6\%) & 846 (78.8\%) & 1577 (78.7\%) \\
 & Yes & 199 (21.4\%) & 227 (21.2\%) & 426 (21.3\%) \\ \addlinespace
 Physical Activity & Inactive & 398 (42.8\%) & 624 (58.2\%) & 1022 (51.0\%) \\
 & Active & 532 (57.2\%) & 449 (41.8\%) & 981 (49.0\%) \\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Stephanie M. Wu
Matthew R. Williams
Terrance D. Savitsky
Briana J. K. Stephenson

"Derivation of outcome-dependent dietary patterns for low-income women obtained from survey data using a Supervised Weighted Overfitted Latent Class Analysis" by Stephanie M. Wu, Matthew R. Williams, Terrance D. Savitsky, and Briana J. K. Stephenson, arXiv:2310.01575v2, 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|cccc|}
 \hline
 Engineers & $T_1$ & $T_2$ & $T_3$ &$T_4$ \\ \hline
 $E_1$ & M & L & L &ML \\ \hline
 $E_2$ & ML & M & ML &M \\ \hline
 $E_3$ & L & EL & L &VL \\ \hline

 \end{tabular}
\end{adjustbox}
\caption{The evaluation of $P'_3$ based on the linguistic terms}
\end{table}
\end{document}

cs

"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{CASTOR ablation study (Non-linear case) with fixed minimum regime duration $\zeta = 90$, fixed number of nodes $d = 10$ and regime durations randomly sampled from \{400, 500, 600\}. We report running time, iterations, F1 score for instantaneous and time lagged links and regime accuracy per regime and per minimum regime duration $\zeta$.}
\begin{tabular}{c|ccccc|ccccc|}
 & \multicolumn{5}{c|}{$K=2$} & \multicolumn{5}{c|}{$K=3$} \\ \midrule
$w$& R.T & Iter. & F1 Inst. & F1 Lag & Reg Acc. & R.T & Iter. & F1 Inst. & F1 Lag & Reg Acc. \\ \midrule
100 & 11'30s & 10 & 89.7 & 100. & 97.4 & 11'50s & 10 & 94.1 & 81.1 & 93.1 \\
200 & 8'50s & 10 & 85.1 & 90.1 & 96.7 & 5'58s & 6 & 92.6 & 79.1 & 93.2 \\
250 & 4'46s & 7 & 89.7 & 100. & 97.2 & 3'24s & 4 & 92.1 & 78.0 & 92.1 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Causal Temporal Regime Structure Learning" by Abdellah Rahmani and Pascal Frossard, arXiv:2311.01412v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Toy example 4}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
mean & Variance & $x^* (\phi=0.01)$ & $L_i$ & OK to drop & RSC \\
$ \begin{bmatrix}
0 \\ 0 \\ 0 \\ 0\\
\end{bmatrix} $
&
$ \begin{bmatrix}
8 & 7 & 6 & 6 \\ 7 & 26 & 6 & 0 \\ 6 & 6 & 96 & -68 \\ 6 & 0 & -68 & 73 \\
\end{bmatrix} $
&
$\begin{bmatrix}
0.2913 \\ 0.1166 \\ 0.2714 \\ 0.3207
\end{bmatrix}$
&
$\begin{bmatrix}
1.81 \\ 13.81 \\ 83.31 \\ 74.81
\end{bmatrix}$
&
$\begin{bmatrix}
Yes \\ No \\ No \\ No
\end{bmatrix}$
&
$\begin{bmatrix}
0.392 \\ 0.433 \\ 2.477 \\ 2.773
\end{bmatrix}$
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Caihua Chen
Xindan Li
Caleb Tolman
Suyang Wang
Yinyu Ye

"Sparse Portfolio Selection via Quasi-Norm Regularization" by Caihua Chen, Xindan Li, Caleb Tolman, Suyang Wang, and Yinyu Ye, arXiv:1312.6350v1, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean $L_{2}$ prediction error for each CNN sensor relative to each image test set. The shaded and the non-shaded values correspond to normal and novel test data, respectively (relative to the training data of each sensor).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c|c|c|c|c|c|c|}
 \hline
 \multirow{2}{*}{Sensor} & \multicolumn{7}{c|}{Test Data} \\
 \cline{2-8}
 & Original & Blockages & Blurred & Fogged & Noised & Shifted & Splattered \\
 \hline \cline{2-8}
 A & \cellcolor{black!15}0.024 & 1.092 & 0.165 & 1.141 & 0.598 & 0.750 & 0.890 \\
 B & \cellcolor{black!15}0.060 & \cellcolor{black!15}0.293 & 0.197 & 0.620 & 0.148 & 0.958 & 0.903 \\
 C & \cellcolor{black!15}0.023 & 1.507 & \cellcolor{black!15}0.021 & 1.493 & 0.540 & 0.760 & 1.047 \\
 D & \cellcolor{black!15}0.029 & 1.094 & 0.143 & \cellcolor{black!15}0.066 & 0.382 & 0.972 & 0.576 \\
 E & \cellcolor{black!15}0.028 & 1.383 & 0.161 & 1.022 & \cellcolor{black!15}0.031 & 0.774 & 0.860 \\
 F & \cellcolor{black!15}0.046 & 1.209 & 0.138 & 1.483 & 0.707 & \cellcolor{black!15}0.199 & 0.703 \\
 G & \cellcolor{black!15}0.019 & 1.296 & 0.069 & 0.975 & 0.155 & 0.660 & \cellcolor{black!15}0.055 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Joshua L. Pulsipher
Luke D. J. Coutinho
Tyler A. Soderstrom
Victor M. Zavala

"SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control" by Joshua L. Pulsipher, Luke D. J. Coutinho, Tyler A. Soderstrom, and Victor M. Zavala, arXiv:2202.01816v1, 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}{llccc}
\hline
 & & \textbf{MULE-Large} & \textbf{MULE-Small} & \textbf{Ours} \\ \hline
Architecture & \#Params & 63M & 63M & 53M \\
 & Proj. head & \multicolumn{2}{c}{[4096,4096,4096,1728]} & [1728,1728,512] \\ \hline
Data & Hours & 117.5k & 5k & 2.9k \\
 & Samples & - & 1.8M & 55k \\ \hline
Hyperparams & batch size & 3840 & 512 & 256 \\
 & GPUs & 16 A100 80GB & 16 A100 80GB & 2 A5000 24GB \\
 & Precision & - & - & Mixed 16bit \\ \hline
\end{tabular}
\caption{Training and architecture differences between official MULE implementation and ours.}
\end{table}
\end{document}

eess

Julien Guinot
Elio Quinton
György Fazekas

"Leave-One-EquiVariant: Alleviating invariance-related information loss in contrastive music representations" by Julien Guinot, Elio Quinton, and György Fazekas, arXiv:2412.18955v1, 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|r|c|c|}
\hline
 & Dimension & Classification \\
Method & before PCA & Accuracy \\
\hline\hline
 MWSN & 90,000 &{\bf 97.34\%} \\
 \hline
Kymatio-STN (L = 2) & 11,875 & 94.50\% \\
 \hline
Kymatio-STN (L = 4) & 38,125 & 96.18\% \\
 \hline
Kymatio-STN (L = 6) & 79,375 & 96.56\% \\
 \hline
Kymatio-STN (L = 8) & 135,625 & 96.61\% \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Table of average test accuracy over 10 experiments} % for each method}
\end{table}
\end{document}

math

"Monogenic Wavelet Scattering Network for Texture Image Classification" by Wai Ho Chak and Naoki Saito, arXiv:2202.12491v1, 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{Selected features using Boruta algorithm}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|cc|cc}
			\hline
			Feature & meanImp & Feature & MeanImp & Feature & meanImp \\
			\hline
			Low & 8.6621 & EMA12 & 5.5459 & BBands5Down & 4.5395 \\
			stochOSC & 8.5909 & SMABollBands13 & 5.5384 & MACDline & 4.4343 \\
			High & 8.5353 & SMA13 & 5.5015 & BBands13Down & 4.2226 \\
			cci & 8.0096 & SMA30 & 5.3326 & ad & 3.9548 \\
			WMA5 & 7.3011 & EMA26 & 5.3188 & Volatility & 35332 \\
			EMA5 & 7.3638 & SMABollBands20 & 5.2633 & roc & 3.4091 \\
			Open & 6.8225 & SMA20 & 5.2340 & MACDSignalLine & 3.3965\\
			SMABollBands5 & 6.0390 & BBands5Up & 5.1564 & mom & 2.7701 \\
			meanMW & 6.0287 & BBands20up & 5.0809 & & \\
			SMA5 & 5.9783 & BBands13Up & 5.0423 & & \\
			WMA50 & 5.9061 & BBands20Down & 5.0072 & & \\
			medianMW & 5.8782 & EMA50 & 4.9579 & & \\
			SMA50 & 5.7759 & MASCDHistogram & 4.5757 & & \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Are Bitcoins price predictable? Evidence from machine learning techniques using technical indicators" by Samuel Asante Gyamerah, arXiv:1909.01268v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average differences of performance measures for ISIG}
\begin{tabular}{lllllll}
\hline\noalign{\smallskip}
& \multicolumn{3}{c}{with sensor} & \multicolumn{3}{c}{without sensor} \\
 Type& Delay(spv)& Queue(v) & MaxQueue(v) & Delay(spv) & Queue(v) & MaxQueue(v) \\
\hline\noalign{\smallskip}
isolated & 9.82 & 2.52 & 4.21 & 82.28 & 21.03 & 28.78 \\
 & 15\% & 15\% & 10\% & 125\% & 125\% & 66\% \\
 network & 5.00 & 0.81 & 1.76 & 23.14 & 3.62 & 7.14 \\
 & 4\% & 3\% & 3\% & 17\% & 15\% & 10\% \\
\noalign{\smallskip}\hline\noalign{\smallskip}
\end{tabular}
\end{table}
\end{document}

eess

Gurcan Comert
Mashrur Chowdhury
David M. Nicol

"Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks" by Gurcan Comert, Mashrur Chowdhury, and David M. Nicol, arXiv:2011.09436v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

DOFEN: Deep Oblivious Forest ENsemble

Kuan-Yu Chen
Ping-Han Chiang
Hsin-Rung Chou
Chih-Sheng Chen
Tien-Hao Chang

"DOFEN: Deep Oblivious Forest ENsemble" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

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

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


stat

Xi Lin
Daniel de Vassimon Manela
Chase Mathis
Jens Magelund Tarp
Robin J. Evans

"Exact Simulation of Longitudinal Data from Marginal Structural Models" by Xi Lin, Daniel de Vassimon Manela, Chase Mathis, Jens Magelund Tarp, and Robin J. Evans, arXiv:2502.07991v2, 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{Mean and Standard Deviation Comparison for FTG and Pure Pursuit.}
\begin{tabular}{lcccccc}
		\toprule
		\textbf{Method} & \multicolumn{3}{c}{\textbf{FTGAgent}} & \multicolumn{3}{c}{\textbf{Pure Pursuit}} \\
		\cmidrule(lr){2-4} \cmidrule(lr){5-7}
		& \textbf{Mean} & \textbf{Std} & \textbf{FP} & \textbf{Mean} & \textbf{Std} & \textbf{FP} \\
		\midrule
	Static & 98.09 & 5.92 & 106489 & 96.67 & 9.47 & 138326 \\
	TTC & 90.93 & 21.17 & 48336 & 84.38 & 23.29 & 297460 \\

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

eess

Thomas A. Henzinger
Fabian Kresse
Kaushik Mallik
Emily Yu
Đorđe Žikelić

"Predictive Monitoring of Black-Box Dynamical Systems" by Thomas A. Henzinger, Fabian Kresse, Kaushik Mallik, Emily Yu, and Đorđe Žikelić, arXiv:2412.16564v1, 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|}
 \hline
 \textbf{Configuration Parameter} & \textbf{Setting} \\
 \hline
 Start Date & January 1, 2012 \\
 End Date & December 31, 2017 \\
 SETF Restructure Trigger & Third Friday of each month \\
 Portfolio Rebalance Trigger & First trading day of each month \\
 Starting Capital & \$10,000,000,000 \\
 Backtest Frequency & Daily \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Learned sector universe backtest configuration parameters.}
\end{table}
\end{document}

q-fin

"Learned Sectors: A fundamentals-driven sector reclassification project" by Rukmal Weerawarana, Yiyi Zhu, and Yuzhen He, arXiv:1906.03935v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}\hline
 Metric & Value\\\hline
 IAD & $4.46$\\
 IOD & $28.68$\\
 DR & $0.1555$\\
 MSC & $0.2618$\\\hline
 \end{tabular}
\caption{Evaluation results with parameters \textit{t=2000}, \textit{h=25}, \textit{w=100} and \textit{m=2}. }
\end{table}
\end{document}

eess

Emmanuel Maqueda
Javier Alvarez-Jimenez
Carlos Mena
Ivan Meza

"Triplet loss based embeddings for forensic speaker identification in Spanish" by Emmanuel Maqueda, Javier Alvarez-Jimenez, Carlos Mena, and Ivan Meza, arXiv:2102.12564v2, 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|}
 \hline
 & \\
 RG-3 & Evaluate our risk-adjusted return optimal sector universe against the benchmark. \\
 & \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|} \toprule
 \multicolumn{2}{|c|}{\textbf{CPU Configuration}} \\ \hline
 \textbf{Model} & Intel(R) Core(TM) i9-14900KF \\ \hline
 \textbf{CPUs} & 32 \\ \hline
 \textbf{CPU GHz} & 3.2 \\ \hline
 \textbf{CPU Max GHz} & 6 \\ \hline
 \textbf{L1d cache} & 576 KiB \\ \hline
 \textbf{L1i cache} & 384 KiB \\ \hline
 \textbf{L2 cache} & 24 MiB \\ \hline
 \textbf{Memory} & 32 GiB \\ \hline
 \textbf{Swap Area} & 62.5 GiB \\ \hline
 \textbf{Operating System} & Ubuntu 20.04.6 LTS (Focal) \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{CPU Configuration}
\end{table}
\end{document}

cs

"ML-SceGen: A Multi-level Scenario Generation Framework" by Yicheng Xiao, Yangyang Sun, and Yicheng Lin, arXiv:2501.10782v1, 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}{lcccccc}
 \hline
 Dataset & \# Samples & \# Features & \# Categorical & \# Classes \\
 \hline
 statlog & 6,435 & 36 & 0 & 6 \\
 spambase & 4,601 & 57 & 0 & 2 \\
 electrical & 10,000 & 13 & 0 & 2 \\
 occupancy~ & 20,560 & 5 & 0 & 2 \\
 gas~ & 13,910 & 128 & 0 & 6 \\
 drybean~ & 13,611 & 16 & 0 & 7 \\
 \hline
 energy~ & 19,735 & 27 & 0 & - \\
 bike~ & 17,379 & 10 & 5 & - \\
 metro & 48,204 & 6 & 1 & - \\
 sgemm~ & 241,600 & 14 & 0 & - \\
 ovctt & 68,784 & 20 & 2 & - \\
 californiahousing & 20,640 & 8 & 0 & - \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Main characteristics of the datasets used in experiments, including number of samples, number of features, number of categorical features and number of classes.}
\end{table}
\end{document}

math

"Robust supervised learning with coordinate gradient descent" by Stéphane Gaïffas and Ibrahim Merad, arXiv:2201.13372v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Advances in the training, pruning and enforcement of shape constraints of Morphological Neural Networks using Tropical Algebra" by Nikolaos Dimitriadis and Petros Maragos, arXiv:2011.07643v1, 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{\bf Confidence Interval for Queue Variables of System One}
\begin{tabular}{|c|c|c|c|}
\hline
\multicolumn{2}{|c|}{} & \multicolumn{2}{c|}{\textbf{Confidence Interval}} \\
\hline
 \textbf{Variables} & \textbf{Values} & \textbf{ Lower Bound }& \textbf{Upper Bound} \\
\hline
 \(\bar{N}\) & 2.936 & 2.466 & 3.406 \\
\(V_N\)& 3.867& & \\
\(\bar{Q}\)& 0.208& 0.0126& 0.4034 \\
\(V_{N_q}\)& 0.666 & & \\
\(\bar{T}\)& 2.629& 2.027& 3.231 \\
\(V_T\)& 6.331 & & \\
\(\bar{W}\)& 0.187& 0.042& 0.332\\
\(V_{T_q}\)& 0.367& &\\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Optimization of COVID-19 Vaccination Process Using Queueing Theory" by Sarkis Der Wartanian and Baydaa Al Ayoubi, arXiv:2312.13522v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Taxonomy mapping from GTA to Mapillary}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
\toprule[1.5pt]
Source Label & Target Label Set \\
\midrule
road & road, sidewalk, snow, sand, water, catch basin, manhole, \\ & rail track, lane marking-crosswalk, lane marking-general \\
sidewalk & sidewalk, curb, snow, sand, water \\
building & building, bridge, tunnel, phone booth, billboard, \\
wall & wall, bridge, tunnel, trash can, banner, billboard \\
fence & fence, guard rail \\
pole & pole, utility pole, trash can, banner, street light, traffic sign frame \\
traffic light & traffic light, street light \\
traffic sign & traffic sign (front), traffic sign (back), billboard, banner \\
vegetation & vegetation, snow \\
terrain & terrain, mountain, snow, sand, water \\
{sky} & {sky} \\
{person} & {person} \\
rider & bicyclist, motorcyclist \\
car & car, trailer, boat \\
truck & truck, caravan \\
{bus} & {bus} \\
{train} & {on rails} \\
{motorcycle} & {motorcycle} \\
{bicycle} & {bicycle} \\
unlabeled & bench, billboard, bridge, tunnel \\
\bottomrule[1.5pt]
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Han Sun
Rui Gong
Ismail Nejjar
Olga Fink

"DynAlign: Unsupervised Dynamic Taxonomy Alignment for Cross-Domain Segmentation" by Han Sun, Rui Gong, Ismail Nejjar, and Olga Fink, arXiv:2501.16410v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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

cs

"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf{GARCH statistics for the DISCOVER stock exchange}} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
Parameter & Estimate & Error & t-value & p-value \\ % inserts table
		\hline
	$a_0$ & 2.86E-08 & 3.89E-09 & 7.349 & 2.0E-13 \\
	$a_1$ & 1.79E-01 & 2.42E-02 & 7.401 & 1.4E-13 \\
	$b_1$ & 7.09E-01 & 3.14E-02 & 22.58 & $<$2E-16 \\
\hline \hline%inserts single line
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Maria C Mariani
Md Al Masum Bhuiyan
Osei K Tweneboah
Hector Gonzalez-Huizar
Ionut Florescu

"Volatility Models Applied to Geophysics and High Frequency Financial Market Data" by Maria C Mariani, Md Al Masum Bhuiyan, Osei K Tweneboah, Hector Gonzalez-Huizar, and Ionut Florescu, arXiv:1901.09145v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
 & \multicolumn{3}{|c|}{Rosenzweig-MacArthur} & \multicolumn{3}{|c|}{Leslie-Gower-May} \\
 \hline
 & Holling & Ivlev & Trigonometric & Holling & Ivlev & Trigonometric \\
 \hline

 $a_H$ & \textbf{3.05} & 2.0026 & 1.4695 & \textbf{1683.333} & 1228.2780 & 1114.6253\\
 $b_H$ & \textbf{2.68} & 1.0453 & 0.0978 & \textbf{3.333} & 1.9855 & 1.5905\\
 \hline

 $a_I$ & 0.6282 & \textbf{1} & 7.5417 & 399.8009 & \textbf{451.447} & 492.0405\\
 $b_I$ & 4.8204 & \textbf{2} & 0.1948 & 3.1656 & \textbf{2.313} & 2.0060\\
 \hline

 $a_T$ & 0.3707 & 0.4069 & \textbf{0.99} & 384.1465 & 418.5697 & \textbf{446.182}\\
 $b_T$ & 7.6281 & 4.7698 & \textbf{1.48} & 2.4028 & 1.9188 & \textbf{1.743}\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Values of the parameters obtained from the fitted functional response where a specified functional response is fixed and the other functional responses are fit to the fixed response via the parameters $a_i$ and $b_i$. The entries in the second row give the fixed functional response for each corresponding column. The parameters, $a_i$ and $b_i$, used in the fixed sub-models are given in bold.}
\end{table}
\end{document}

math

Sarah K. Wyse
Maria M. Martignoni
May Anne Mata
Eric Foxall
Rebecca C. Tyson

"Structural sensitivity in the functional responses of predator-prey models" by Sarah K. Wyse, Maria M. Martignoni, May Anne Mata, Eric Foxall, and Rebecca C. Tyson, arXiv:2201.11256v2, 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{Representative Feeder (RF) features .}}
\begin{tabular}{c|c|c|c|c}
 \hline
 id & RF1 & RF2 & RF3 & RF4 \\
 \hline
 feeder length ($km$) & 0.164 & 5.206 & 1.013 & 0.735 \\
 feeder resistance ($\Omega$) & 0.219 & 4.617 & 0.907 & 0.376 \\
 feeder reactance ($\Omega$) & 0.014 & 0.416 & 0.081 & 0.055 \\
 R/X & 14.100 & 10.479 & 12.971 & 12.286\\
 impedance ($\Omega$) & 0.22 & 4.64 & 0.91 & 0.38 \\
 number of loads & 4 & 175 & 30 & 23 \\
 number of buses & 12 & 359 & 65 & 52\\
 area ($m^2$) & 1280.1 & 67695.6 & 10291.7 & 14434.7 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Olivia Rubbers
Sari Kerckhove
Md Umar Hashmi
Dirk Van Hertem

"Fairness for distribution network hosting capacity" by Olivia Rubbers, Sari Kerckhove, Md Umar Hashmi, and Dirk Van Hertem, arXiv:2508.16834v1, 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{Models Evaluation Summary: Domain-level distances, such that lower is better }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccc|ccc|l}
 \hline
 & \multicolumn{3}{ c |}{Discrete} & \multicolumn{3}{c|}{Continuous} & Unit\\
 & FF & CNN & RNN & FF & CNN & RNN & \\
 \hline
 \hline
 \multicolumn{8}{l}{}\\
 \multicolumn{8}{l}{\textbf{Density Estimation}} \\
 \hline
 Participations & 0.152 & 0.162 & 0.817 & 0.062 & \textbf{0.046} & 0.064 & rate EMD \\
 & $\pm$ 0.018 & $\pm$ 0.026 & $\pm$ 0.130 & $\pm$ 0.015 & $\pm$ 0.014 & $\pm$ 0.020 & \\
 \hline
 Transitions & 0.008 & 0.006 & 0.165 & \textbf{0.004} & 0.005 & \textbf{0.004} & rate EMD \\
 & $\pm$ 0.001 & $\pm$ 0.001 & $\pm$ 0.141 & $\pm$ 0.001 & $\pm$ 0.001 & $\pm$ 0.001 & \\
 \hline
 Timing & 0.051 & 0.044 & 0.210 & 0.056 & 0.055 & \textbf{0.025} & days EMD \\
 & $\pm$ 0.008 & $\pm$ 0.005 & $\pm$ 0.054 & $\pm$ 0.001 & $\pm$ 0.001 & $\pm$ 0.009 \\
 \hline
 \hline
 \multicolumn{8}{l}{}\\
 \textbf{Validity} & \textbf{0.000} & \textbf{0.000} & 0.050 & 0.057 & 0.030 & 0.018 & prob. \\
 & $\pm$ 0.001 & $\pm$ 0.000 & $\pm$ 0.079 & $\pm$ 0.014 & $\pm$ 0.006 & $\pm$ 0.007 \\
 \hline
 \hline
 \multicolumn{8}{l}{}\\
 \textbf{Creativity} & 0.284 & 0.313 & 0.641 & 0.082 & 0.179 & \textbf{0.017} & prob. \\
 & $\pm$ 0.026 & $\pm$ 0.011 & $\pm$ 0.076 & $\pm$ 0.012 & $\pm$ 0.016 & $\pm$ 0.003 \\
 \hline
 \multicolumn{8}{l}{}\\
 \multicolumn{8}{l}{\footnotesize(All results are means and standard deviations calculated from 5 model runs)} \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Synthesising Activity Participations and Scheduling with Deep Generative Machine Learning" by Fred Shone and Tim Hillel, arXiv:2501.10221v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{arydshln}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
\textbf{Prompt Type} & \multicolumn{2}{c}{\textbf{Accuracy}}\\
 & GPT-3.5 & GPT-4o\\
\hline
Variation 1 & 0.74 & 0.95\\
Variation 2 & 0.79 & 0.92\\
Variation 3 & 0.76 & 0.94\\
Variation 4 & 0.74 & 0.93\\
\hdashline
Best Variation Rephrase 1 & 0.79 & 0.95\\
Best Variation Rephrase 2 & 0.67 & 0.95\\
Best Variation Rephrase 3 & 0.69 & 0.96\\
\end{tabular}
\end{adjustbox}
\caption{Prompt Variation Results for Exp 1 - CxNLI in the three-shot setting.}
\end{table}
\end{document}

cs

Wesley Scivetti
Melissa Torgbi
Austin Blodgett
Mollie Shichman
Taylor Hudson
Claire Bonial
Harish Tayyar Madabushi

"Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructions" by Wesley Scivetti, Melissa Torgbi, Austin Blodgett, Mollie Shichman, Taylor Hudson, Claire Bonial, and Harish Tayyar Madabushi, arXiv:2501.04661v2, 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{Cross-Proxy Amplification in Low Breadth and High Volatility (bps per one s.d.)}
\begin{tabular}{lccccc}
\toprule
Proxy & Flipped & $\hat\kappa$ [95\% CI] & $\hat\rho$ [95\% CI] & Half-life (m) [95\% CI] \\
\midrule
UMCSENT & 0 & 1.06 [0.80, 1.30] & 0.940 [0.920, 0.960] & 11.2 [7.9, 18.8] \\
IBES Revisions & 1 & 0.0031 [0.0000, 0.2938] & 0.950 [0.063, 1.000]\textsuperscript{\textdagger} & 13.5 [0.3, $\infty$) & \\
MarketPsych & 1 & 0.0000 [0.0000, 0.1461] & 0.950 [0.089, 1.000]\textsuperscript{\textdagger} & 13.5 [0.3, $\infty$) \\
PCA Common Factor & 1 & 0.0000 [0.0000, 0.0791] & 0.950 [0.141, 1.000]\textsuperscript{\textdagger} & 13.5 [0.4, $\infty$) \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Sentiment Feedback in Equity Markets: Asymmetries, Retail Heterogeneity, and Structural Calibration" by Lucas Marques Sneller, arXiv:2509.11970v1, 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|c|c|c|c|c|}
\hline
\textbf{Policy} & \textbf{Mean Correct Rate} & \textbf{SD Correct Rate} & \textbf{Mean outcome} & \textbf{SD outcome} & \textbf{WAPTS Superior Count} \\
\hline
TS\_multi & 0.654 & 0.241 & 0.564 & 0.075 & 131 \\
\hline
UR\_multi & 0.496 & 0.068 & 0.546 & 0.075 & 133 \\
\hline
WAPTS\_multi & 0.879 & 0.149 & 0.590 & 0.072 & -- \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Comparison of policies: Mean and standard deviation (SD) of correct rates and outcomes for experiments with \(N = 50\) participants and \(K = 2\) treatments under \textbf{Flexible Gap Scenario}. The final column indicates the number of simulations (out of 200) in which WAPTS outperformed the respective policy.}
\end{table}
\end{document}

cs

Haochen Song
Ilya Musabirov
Ananya Bhattacharjee
Audrey Durand
Meredith Franklin
Anna Rafferty
Joseph Jay Williams

"Adaptive Experiments Under Data Sparse Settings: Applications for Educational Platforms" by Haochen Song, Ilya Musabirov, Ananya Bhattacharjee, Audrey Durand, Meredith Franklin, Anna Rafferty, and Joseph Jay Williams, arXiv:2501.03999v4, 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{\bf{Summary statistics for IAG stock exchange}} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
Parameter & Estimate & Standard Error \\ % inserts table
		\hline
$\alpha_0$ & -0.1994 & 0.4848\\
$\alpha_1$ & 0.8440 &0.0738\\
$\sigma_{\omega}$ & 0.6462 &0.1976\\
$\lambda$ &-14.164 & 3.0113\\
$\sigma_0$ & 0.9536 & 0.1434\\
$\phi_1$ &-3.3967 & 0.2038\\
$\sigma_1$ & 3.6077 & 0.1261\\
\hline \hline %inserts single line
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}\toprule
 {Parameter} & {Lower Bound} & {Upper Bound} & {Distribution} \\ \midrule
 $\beta_{11}=\beta_{12}$ &0 & $10^{-5}$ & T\\
 $\beta_{13}=\beta_{14}$ &0 & $10^{-5}$ & T\\
 $\beta^v_{11}=\beta^v_{12}$ &0 & $10^{-5}$ & T\\
 $\beta^v_{13}=\beta^v_{14}$ &0 & $10^{-5}$ & T\\
 $\epsilon$ &0 & 1 & T\\
 $\beta_{21}=\beta_{22}$ &0 & $10^{-5}$ & T\\
 $\beta_{23}=\beta_{24}$ &0 & $10^{-5}$ & T\\
 $\omega$ &0 & $0.3$ & T\\
 $\tau_1$ &0 & 0.3 & T\\
 $\tau_2$ &0 & 0.3 & T\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Xiaoying Wang
Qing Han
Jude Dzevela Kong

"Studying the mixed transmission in a community with age heterogeneity: COVID-19 as a case study" by Xiaoying Wang, Qing Han, and Jude Dzevela Kong, arXiv:2202.03478v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Neural Networks for Delta Hedging

"Neural Networks for Delta Hedging" by Guijin Son and Joocheol Kim, arXiv:2112.10084v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

María Jaenada
Elena Castilla
Nirian Martín
Leandro Pardo

"Robust approach for comparing two dependent normal populations through Wald-type tests based on Rényi's pseudodistance estimators" by María Jaenada, Elena Castilla, Nirian Martín, and Leandro Pardo, arXiv:2202.00982v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{graphicx}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{C-OPF vs. ENApp D-OPF for Unbalanced System (TS-5)}
\begin{tabular}{|c|c|c|c|}
\hline
{}&{Bus}&{C-OPF$^{\dag}$} &{D-OPF$^{\dag}$}\\
\hline
\parbox[t]{3mm}{\multirow{6}{*}{\rotatebox[origin=c]{90}{Q Injection}}}&{3 (A)}&{66.3 kVAr}&{66.3 kVAr}\\\cline{2-4}
{}&{3 (B)}&{66.3 kVAr}&{66.3 kVAr}\\\cline{2-4}
{}&{3 (C)}&{66.3 kVAr}&{66.3 kVAr}\\\cline{2-4}
{}&{7 (A)}&{132.7 kVAr}&{132.7 kVAr}\\\cline{2-4}
{}&{7 (B)}&{132.7 kVAr}&{132.7 kVAr}\\\cline{2-4}
{}&{7 (C)}&{132.7 kVAr}&{132.7 kVAr}\\\cline{1-4}
\multicolumn{2}{|c|}{Total Loss}&{44.5 kW}&{44.5 kW}\\\cline{1-4}
\multicolumn{3}{|c|}{Macro-Iteration for D-OPF$^{\dag}$}&{2}\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"Distributed Optimization using Reduced Network Equivalents for Radial Power Distribution Systems" by Rabayet Sadnan and Anamika Dubey, arXiv:2012.02473v1, 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{Comparisons of various LTSF models across multiple LTSF benchmark datasets, evaluated based on MSE under input-96-output-96 setting (input-36-output-24 for ILI).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \toprule
 & ETTm2 & ILI & Weather & Electricity & Traffic \\ \hline
 PSLD & \bf 0.164 & \bf 2.872 & 0.177 & \bf 0.180 & \bf 0.400 \\
 PatchTST & 0.166 & 2.896 &\bf 0.152 & 0.195 & 0.544 \\
 DLinear & 0.167 & 2.915 & 0.196 & 0.197 & 0.65 \\
 FEDformer & 0.204 & 3.228 & 0.217 & 0.193 & 0.587 \\
 Autoformer & 0.255 & 3.228 & 0.266 & 0.201 & 0.613 \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Progressive Supervision via Label Decomposition: An Long-Term and Large-Scale Wireless Traffic Forecasting Method" by Daojun Liang, Haixia Zhang, and Dongfeng Yuan, arXiv:2501.06255v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 Abbreviation & Explanation \\
 \midrule
 \midrule
 MIN\_BAL & monthly min account balance\\
 MAX\_BAL & monthly max account balance\\
 MEAN\_BAL & monthly average account balance\\
 MEAN\_CRBAL & monthly average credit balance\\
 MEAN\_DBBAL & monthly average debit balance\\
 TCREDIT & monthly total credits \\
 TDEBIT & monthly total debits \\
 INT\_CNVIOL & cumulative number of intended violations from the beginning of the year \\
 REJ\_CNVIOL & cumulative number of rejected violations from the beginning of the year \\
 INT\_CAVIOL & cumulative amount of intended violations from the beginning of the year \\
 REJ\_CAVIOL & cumulative amount of rejected violations from the beginning of the year \\
 $MEAN\_TCREDIT_t$ & mean TCREDIT during the period [t-23, t], used for nomalisation\\
 \end{tabular}
\end{adjustbox}
\caption{Variable Abbreviations}
\end{table}
\end{document}

q-fin

Jinglun Yao
Maxime Levy-Chapira
Mamikon Margaryan

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


\documentclass{article}
\usepackage{adjustbox}
\usepackage{makecell}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \Xhline{2pt}
 \textbf{Model} & \textbf{\#Failure} & \textbf{LLM-Score} & \textbf{Rouge-L} & \textbf{FII} \\
 \hline
 Qwen-7B & 208 & 81.4 & 0.263 & 41.19 \\
 Qwen1.5-7B & 75 & 84.0 & 0.235 & 31.60 \\
 Yi1.5-9B & 1726 & 83.6 & 0.244 & 35.30 \\
 glm4-9b & 22 & 84.4 & 0.230 & 41.81 \\
 \Xhline{2pt}
 \end{tabular}
\end{adjustbox}
\caption{Average Results for a single simulation in each MIRAGE scenario w/ E, with E indicating cases of forced self-exposure.}
\end{table}
\end{document}

cs

Yin Cai
Zhouhong Gu
Zhaohan Du
Zheyu Ye
Shaosheng Cao
Yiqian Xu
Hongwei Feng
Ping Chen

"MIRAGE: Exploring How Large Language Models Perform in Complex Social Interactive Environments" by Yin Cai, Zhouhong Gu, Zhaohan Du, Zheyu Ye, Shaosheng Cao, Yiqian Xu, Hongwei Feng, and Ping Chen, arXiv:2501.01652v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{makecell}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c|cccc||}
 \hline
 Model \textbackslash SNR (dB) & 17.5 & 12.5 & 7.5 & 2.5 \\
 \hline\hline
 \thead{Codec Only}& 2.89& 2.79& 2.61& 2.34\\
 \hline
 \thead{Enhancing Codec LN}& \textbf{3.27}& \textbf{3.28}& \textbf{3.22}& \textbf{3.11}\\
 \hline
 \thead{Enhancement, then Codec}& 2.77& 2.64& 2.53& 2.46\\
 \hline
 \end{tabular}
\caption{MOS comparison across SNRs on VCTK at $1.35\,\text{kb/s}$. The largest performance difference is in low SNR scenes.}
\end{table}
\end{document}

eess

Jonah Casebeer
Vinjai Vale
Umut Isik
Jean-Marc Valin
Ritwik Giri
Arvindh Krishnaswamy

"Enhancing into the codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders" by Jonah Casebeer, Vinjai Vale, Umut Isik, Jean-Marc Valin, Ritwik Giri, and Arvindh Krishnaswamy, arXiv:2102.06610v1, 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|}{ACAT2}&\multicolumn{2}{c|}{WBACAT2}& \multicolumn{2}{c|}{ACAT4} & \multicolumn{2}{c|}{WBACAT4} \\
 Points & Order & Error & Order & Error & Order & Error & Order& Error \\
 \hline
 50 & - & 8.21E-3 & - & 5.54E-5 & - & 7.85E-4 & - & 6.15E-5 \\
 \hline
 100 & 1.00 & 4.11E-3 & 1.63 & 1.79E-5 & 1.22 & 3.35E-4 & 2.86 & 1.69E-5 \\
 \hline
 200 & 1.25 & 1.73E-3 & 1.94 & 4.68E-6 & 1.67 & 1.05E-4 & 4.78 & 6.17E-7 \\
 \hline
 400 & 1.51 & 6.06E-4 & 1.98 & 1.19E-6 & 1.87 & 2.86E-5 & 4.31 & 4.65E-8 \\
 \hline
 800 & 1.54 & 2.08E-4 & 1.98 & 3.01E-7 & 1.98 & 7.31E-6 & 4.05 & 1-41E-9\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Test : Errors in $L^1$ norm and convergence rates related to $q$ for ACAT2, ACAT4, WBACAT2 and WBACAT4 at time $t=0.15$ and CFL$=0.8.$}
\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{multirow}
\usepackage{adjustbox}
\usepackage{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Accuracy of reconstructed samples across different datasets with the varying number of generated data.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\toprule
 \multirow{2}*{Accuracy} & \multicolumn{4}{c}{Number of Generated Samples}\\\cline{2-5}
 & $\textcolor{green}{50}/150/\textcolor{orange}{200}$ & $\textcolor{green}{100}/200/\textcolor{orange}{300}$ & $\textcolor{green}{150}/250/\textcolor{orange}{400}$ & $\textcolor{green}{200}/300/\textcolor{orange}{500}$ \\
 \midrule
 MNIST & $91.2$ & $91.7$ & $93.4$ & $94.7$ \\
 CIFAR10 & $55.5$ & $58.2$ & $60.8$ & $62.4$ \\
 \textcolor{orange}{SkinCancer} & $62.9$ & $68.5$ & $72.2$ & $78.1$ \\%\hline
BBCNews & $60.7$ & $61.6$ & $60.3$ & $61.2$ \\%\hline
\textcolor{green}{IMDB} & $58.4$ & $61.7$ & $60.8$ & $62.3$ \\%\hline
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\usepackage[T1]{fontenc}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c|c|c||}
\hline \# & Asset ID & Company \\
\hline 1 & ALPA4 & ALPARGATAS \\
\hline 2 & ABEV3 & AMBEV SA \\
\hline 3 & B3SA3 & B3 SA \\
\hline 4 & BRKM5 & BRASKEM \\
\hline 5 & BRFS3 & BRF S.A. \\
\hline 6 & CMIG4 & CEMIG \\
\hline 7 & COGN3 & KROTON EDUCACIONAL SA \\
\hline 8 & CSAN3 & COSAN SA INDUSTRIA E COMERCIO \\
\hline 9 & CPFE3 & CPFL ENERGIA SA \\
\hline 10 & DXCO3 & DURATEX SA \\
\hline 11 & ELET3 & ELETROBRAS \\
\hline 12 & EMBR3 & EMBRAER \\
\hline 13 & ENGI11 & ENERGISA \\
\hline 14 & ENEV3 & ENEVA SA \\
\hline 15 & EQTL3 & EQUATORIAL ENERGIA SA \\
\hline 16 & EZTC3 & EZ TEC EMPREEND. PAR SA \\
\hline 17 & FLRY3 & FLEURY SA \\
\hline 18 & GGBR4 & GERDAU S.A. \\
\hline 19 & GOAU4 & METALURGICA GERDAU HOLDING SA \\
\hline 20 & KLBN11 & KLABIN \\
\hline 21 & RENT3 & LOCALIZA RENT A CAR \\
\hline 22 & BEEF3 & MINERVA FOODS S/A \\
\hline 23 & PETR3 & PETROBRAS \\
\hline 24 & PRIO3 & PETRO RIO S.A. \\
\hline 25 & RAIL3 & ALL - AMÉRICA LATINA LOGÍSTICA SA \\
\hline 26 & CSNA3 & CSN \\
\hline 27 & SLCE3 & SLC AGRICOLA SA \\
\hline 28 & SUZB3 & SUZANO PAPEL E CELULOSE SA \\
\hline 29 & TAEE11 & TRANSMISSORA ALIANÇA E. ELÉT. \\
\hline 30 & VIVT3 & TELEFÔNICA BRASIL \\
\hline 31 & TOTS3 & TOTVS SA \\
\hline 32 & UGPA3 & ULTRAPAR PARTICIPAÇÕES SA \\
\hline 33 & USIM5 & USIMINAS \\
\hline 34 & WEGE3 & WEG SA \\
\hline 35 & YDUQ3 & ESTACIO PARTICIPAÇÕES SA \\
\hline 36 & MOTV3 & MOTIVA \\
\hline
\end{tabular}
\caption{List of B3 Assets used in Simulations}
\end{table}
\end{document}

q-fin

"AlphaX: An AI-Based Value Investing Strategy for the Brazilian Stock Market" by Paulo André Lima de Castro, arXiv:2508.13429v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rl}
 Operation & Meaning \\
 \midrule
 \midrule
 $X_t$ & Value of X at month t\\
 $\Delta X_t$ & $X_t-X_{t-11}$ \\
 $\Delta\Delta X_t$ & $X_t-X_{t-23}$ \\
 $mean_t(X)$ & Mean of X during the period [t-11, t] \\
 $sd_t(X)$ & Standard Deviation of X during the period [t-11, t] \\
 \end{tabular}
\end{adjustbox}
\caption{Operations for creating variables}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Variable ranking for the flood model.}
\begin{tabular}{ccccccc}
		\toprule
		 & Variable & $S_{T_i}$	& $\nu_i$-variance 	& $\kappa_{T_i}$	 & $l_i$-entropy & 	$\nu_i$-entropy \\
		\cmidrule(lr){1-7}
$x_1$ & $Q$ & 1 & 1 & 1 & 1 & 1 \\
$x_2$ & $K_s$ & 4 & 4 & 4 & 4 & 4 \\
$x_3$ & $Z_{\nu}$ & 3 & 3 & 3 & 3 & 3 \\
$x_3$ & $Z_m$ & 6 & 6 & - & 6 & 6 \\
$x_5$ & $D_d$ & 2 & 2 & 2 & 2 & 2 \\
$x_6$ & $C_b$ & 5 & 5 & - & 5 & 5 \\
$x_7$ & $L$ & 7 & 7 & - & 8 & 8 \\
$x_8$ & $B$ & 8 & 8 & - & 7 & 7\\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

stat

"Derivative based global sensitivity analysis and its entropic link" by Jiannan Yang, arXiv:2310.00551v3, 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}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of cost-efficient portfolios for different information sets and reliability requirements.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
\textbf{Fortified switches} & 1 & 5 & 10 & 15 & 20 \\
\midrule
\textbf{Feasible portfolios} & 23 & 35.4k & 1.7M & 4M & 4.2M \\
\textbf{Non-informative $S^0$} & 5 & 163 & 972 & 1.7k & 1.9k \\
\textbf{Incomplete information $S^1$} & 2 & 51 & 434 & 673 & 738 \\
\textbf{$\mathcal{S}^{1} \wedge \alpha = 0.95$} & 0 & 27 & 337 & 575 & 640 \\
\textbf{$\mathcal{S}^{1} \wedge \alpha = 0.96$} & 0 & 4 & 116 & 266 & 325 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Joaquín de la Barra
Ahti Salo
Leevi Olander
Kash Barker
Jussi Kangaspunta

"Fortifying Critical Infrastructure Networks with Multicriteria Portfolio Decision Analysis: An Application to Railway Stations in Finland" by Joaquín de la Barra, Ahti Salo, Leevi Olander, Kash Barker, and Jussi Kangaspunta, arXiv:2501.06279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation Parameters}
\begin{tabular}{|c|c||c|c|} \hline % 其中，|c|表示文本居中，文本两边有竖直表线。
			\bf{Parameter} & \bf{Value} & 	\bf{Parameter} & \bf{Value} \\ \hline\hline
			$K$ & $4$ & $J$ & $4$ \\ \hline
			$M$ & $32$ & $M_s$ & 8 \\ \hline
			$N$ & $8$ & $P^{\rm{max}}_{\rm{total}}$ &	$45$ $\mathrm{dBm}$ \\ \hline
			$P^{\rm{max}}_r$ &	$15$ $\mathrm{dBm}$& $P^{\rm{max}}_b$ &	$30$ $\mathrm{dBm}$ \\ \hline
			$R_{\rm{th}}$ & $1$ $\mathrm{bit/s/Hz}$ & $\epsilon_1$ & $10^{-3} $ \\ \hline
			$\epsilon_2$ & $10^{-3} $ & $\epsilon_3$ & $10^{-3} $ \\ \hline
			$\sigma_k^2$ & $-80$ $\mathrm{dBm}$ & $\sigma_r^2$ & $-80$ $\mathrm{dBm}$ \\ \hline
			$\sigma_s^2$ & $-80$ $\mathrm{dBm}$ & $\kappa$ & $10$ $\mathrm{dB}$\\ \hline
			$h_0$ & $-30$ $\mathrm{dBm}$ & $\alpha$ & $-2.8$ \\ \hline
	\end{tabular}
\end{table}
\end{document}

eess

Dongsheng Han
Peng Wang
Wanli Ni
Wen Wang
Ailing Zheng
Dusit Niyato
Naofal Al-Dhahir

"Multi-Functional RIS Integrated Sensing and Communications for 6G Networks" by Dongsheng Han, Peng Wang, Wanli Ni, Wen Wang, Ailing Zheng, Dusit Niyato, and Naofal Al-Dhahir, arXiv:2412.01251v3, 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{Inference efficiency of UniFlow variants.}
\begin{tabular}{lccc}
\toprule
\textbf{Objective} & \textbf{Steps} & \textbf{RTF} & \textbf{Memory (GB)} \\
\midrule
DDPM & 200 & 3.49 & 18.2 \\
FM & 32 & 0.31 & 17.7 \\
MF & 1 & 0.02 & 17.6 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Ziqian Wang
Zikai Liu
Yike Zhu
Xingchen Li
Boyi Kang
Jixun Yao
Xianjun Xia
Chuanzeng Huang
Lei Xie

"UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling" by Ziqian Wang, Zikai Liu, Yike Zhu, Xingchen Li, Boyi Kang, Jixun Yao, Xianjun Xia, Chuanzeng Huang, and Lei Xie, arXiv:2508.07558v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of Spider-Route and Bird-Route train set}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 Parameter &Spider-Route&Bird-Route\\
 \midrule
 Databases in train set &140&69\\
 Databases in in-domain test set &140&69\\
 Databases in cross-domain test set &20&11\\
 Questions in train set &5959&7948\\
 Questions in in-domain test set &1041&1480\\
 Questions in cross-domain test set &1034&1533\\
 Metadata available &Yes &No\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Priyangshu Mandal
Manasi Patwardhan
Mayur Patidar
Lovekesh Vig

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Prediction accuracy and training time on synthetic graphs generated by TSBM. "Fixed $\gamma_{y_i y_j}$" represents scenarios satisfying Assumption 4, while "Random $\gamma_{y_i y_j}$" represents scenarios that do not. Time is reported for the entire training process over 200 epochs.}
\begin{tabular}{l|lllll|lllll}
\hline
 & \multicolumn{5}{l|}{SGC} & \multicolumn{5}{l}{GCN} \\ \hline
 & Baseline & MMP & PMP & PNY & JJnorm & Baseline & MMP & PMP & PNY & JJnorm \\ \hline
Fixed $\gamma_{y_i y_j}$ & 0.2243 & 0.15 & 0.2653 & 0.2758 & \textbf{0.2777} & 0.2035 & 0.1439 & 0.2245 & 0.2178 & \textbf{0.2311} \\ \hline
Random $\gamma_{y_i y_j}$ & 0.1331 & 0.1063 & 0.1854 & 0.1832 & \textbf{0.1862} & 0.1298 & 0.1022 & 0.1565 & \textbf{0.1613} & 0.1609 \\ \hline
Time (sec) & 0.325 & 0.315 & 0.303 & 1.178 & 0.538 & 0.771 & 0.728 & 0.773 & 268.5 & 448.81 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs" by Sejun Park, Joo Young Park, and Hyunwoo Park, arXiv:2411.10957v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|}
\cline{2-5}
 & \multicolumn{2}{c|}{Experiment 1} & \multicolumn{2}{c|}{Experiment 2} \\ \hline
\# of Simulations & \multicolumn{2}{c|}{500} & \multicolumn{2}{c|}{500} \\ \hline
Maximum \# of blocks & \multicolumn{2}{c|}{14} & \multicolumn{2}{c|}{14} \\ \hline
Maximum block height & \multicolumn{2}{c|}{45 mm} & \multicolumn{2}{c|}{34 mm} \\ \hline
\% of leg length & \multicolumn{2}{c|}{14 \%} & \multicolumn{2}{c|}{11 \%} \\ \hline
Method & DDP & \textbf{Risk} & DDP & \textbf{Risk} \\ \hline
\# of Successful Sim. & 237 & \textbf{325} & 358 & \textbf{458} \\ \hline
\% of Successful Sim. & 47.4\% & \textbf{65\%} & 72.6\% & \textbf{91.6\%} \\ \hline
\end{tabular}
\caption{Parameters and results of the trotting experiments}
\end{table}
\end{document}

eess

Bilal Hammoud
Majid Khadiv
Ludovic Righetti

"Impedance Optimization for Uncertain Contact Interactions Through Risk Sensitive Optimal Control" by Bilal Hammoud, Majid Khadiv, and Ludovic Righetti, arXiv:2011.04684v2, 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}{ccc}
 \toprule
 & Stage1 & Stage2 \\ \midrule
 Batch size & 1 & 1 \\
 Learning rate & 1e-3 & 1e-3 \\
 Adam $\beta$ & (0.9, 0.999) & (0.9, 0.999) \\
 Steps & 5 & $Epoch\times SampleSize$ \\
 \bottomrule
 \end{tabular}
\caption{Training details.}
\end{table}
\end{document}

eess

Jizhihui Liu
Qixun Teng
Qing Ma
Junjun Jiang

"FM2S: Towards Spatially-Correlated Noise Modeling in Zero-Shot Fluorescence Microscopy Image Denoising" by Jizhihui Liu, Qixun Teng, Qing Ma, and Junjun Jiang, arXiv:2412.10031v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Self-Adaptive Ising Machines for Constrained Optimization" by Corentin Delacour, arXiv:2501.04971v1, 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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{CLIP zero-shot prediction accuracies} are reported for the visual similarity (objects) dimension in EraseBench: the erased concept (evaluating the efficacy of erasure), the paraphrased concepts (demonstrating the generality of erasure), the non-target visually similar concepts (reflecting the sensitivity of erasure), and the non-target unrelated concepts (indicating the specificity of erasure). The results reveal a significant degradation in sensitivity, particularly in scenarios where concept entanglement occurs, highlighting challenges in effectively disentangling related concepts during erasure.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 \toprule
 & Erased$\downarrow$ & \multicolumn{2}{c}{Similar $\uparrow$} \\
 \cmidrule(lr){2-2}
 \cmidrule(lr){3-4}
 Techniques & "Ukelele" & "Acoustic Guitar" & "Violin" \\
 \midrule
 Original SD & 0.71 & 0.96 & 1.0 \\
 \midrule
 ESD & 0.15 & 0.43 & 0.76 \\
 UCE & 0.13 & 0.78 & 0.97 \\
 Receler & 0.07 & 0.21 & 0.52 \\
 MACE & 0.05 & 0.47 & 0.74 \\
 AdvUnlearn & 0.00 & 0.33 & 0.43 \\
 \bottomrule
 & Erased$\downarrow$ & \multicolumn{2}{c}{Similar $\uparrow$} \\
 \cmidrule(lr){2-2}
 \cmidrule(lr){3-4}
 Techniques & "Goldfish" & "Guppy" & "Clownfish" \\
 \midrule
 Original SD & 0.99 & 0.65 & 1.0\\
 \midrule
 ESD & 0.08 & 0.32 & 0.97 \\
 UCE & 0.54 & 0.39 & 1.0\\
 Receler & 0.01 & 0.15 & 0.19 \\
 MACE & 0.09 & 0.24 & 0.96 \\
 AdvUnlearn & 0.06 & 0.26 & 0.95 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ibtihel Amara
Ahmed Imtiaz Humayun
Ivana Kajic
Zarana Parekh
Natalie Harris
Sarah Young
Chirag Nagpal
Najoung Kim
Junfeng He
Cristina Nader Vasconcelos
Deepak Ramachandran
Golnoosh Farnadi
Katherine Heller
Mohammad Havaei
Negar Rostamzadeh

"Erasing More Than Intended? How Concept Erasure Degrades the Generation of Non-Target Concepts" by Ibtihel Amara, Ahmed Imtiaz Humayun, Ivana Kajic, Zarana Parekh, Natalie Harris, Sarah Young, Chirag Nagpal, Najoung Kim, Junfeng He, Cristina Nader Vasconcelos, Deepak Ramachandran, Golnoosh Farnadi, Katherine Heller, Mohammad Havaei, and Negar Rostamzadeh, arXiv:2501.09833v2, 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}{cc}
\toprule[1.5pt]
\ \ \ \ \ \ \ \ \qquad {\bf Hyper-parameter} \ \ \ \ \ \ \ \ \qquad & \ \ \ \ \ \ \ \ \qquad Value \ \ \ \ \ \ \ \ \qquad \cr
\midrule[1.0pt]
 Layers & 12 \cr
 Hidden layers & 64 \cr
 Sequence length & 9 \cr
 Learning rate & .001 \cr
 Epochs & 500 \cr
\bottomrule[1.5pt]
\end{tabular}
\end{adjustbox}
\caption{Hyperparameters of NODE and HBNODE for learning ROMs from transient to steady-state VKS dynamics.}
\end{table}
\end{document}

math

Justin Baker
Elena Cherkaev
Akil Narayan
Bao Wang

"Learning POD of Complex Dynamics Using Heavy-ball Neural ODEs" by Justin Baker, Elena Cherkaev, Akil Narayan, and Bao Wang, arXiv:2202.12373v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lllll}
\toprule
TrakEM2 - Min & TrakEM2 - Max & RunTime & Error Rate & Accumulated Error \\ \midrule
400 & 2000 & 100min & 35\% & 35\% \\
400 & 3000 & 260min & 15\% & 10\% \\
400 & 3500 & 450min & 9\% & 6\% \\
1000 & 3500 & 520min & 6\% & 1\% \\ \bottomrule
\end{tabular}
\caption{Execution times of TrakEM2 headless montage macro on a test dataset (6x2 tiles of 15000x15000 pixels and 1128 slices divided into 8 different folders that represent the acquisition sessions). Each line shows the results of 8 Balsam jobs with 32 nodes, 4 ranks per node and the values for the TrakEM2 parameters for the minimum and maximum octaves used by the montage macro script. }
\end{table}
\end{document}

eess

Rafael Vescovi
Hanyu Li
Jeffery Kinnison
Murat Keceli
Misha Salim
Narayanan Kasthuri
Thomas D. Uram
Nicola Ferrier

"Toward an Automated HPC Pipeline for Processing Large Scale Electron Microscopy Data" by Rafael Vescovi, Hanyu Li, Jeffery Kinnison, Murat Keceli, Misha Salim, Narayanan Kasthuri, Thomas D. Uram, and Nicola Ferrier, arXiv:2011.03204v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Quantile Predictions for Equity Premium using Penalized Quantile Regression with Consistent Variable Selection across Multiple Quantiles" by Shaobo Li and Ben Sherwood, arXiv:2505.16019v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{csquotes}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc}
 & \multicolumn{7}{c}{Estimator $f_\theta$} \\ \cline{2-8}
Detector $g_\phi$ & \textbf{XGB} & \textbf{Log.Reg} & \textbf{Lasso} & \textbf{Ridge} & \textbf{Rand.Forest} & \textbf{Dec.Tree} & \textbf{MLP} \\ \hline
\textbf{XGB} & 0.583 & 0.619 & 0.596 & 0.586 & 0.558 & 0.522 & 0.597 \\
\textbf{LogisticReg.} & 0.605 & 0.609 & 0.583 & 0.625 & 0.578 & 0.551 & 0.605 \\
\textbf{Lasso} & 0.599 & 0.572 & 0.551 & 0.595 & 0.557 & 0.541 & 0.596 \\
\textbf{Ridge} & 0.606 & 0.61 & 0.588 & 0.624 & 0.564 & 0.549 & 0.616 \\
\textbf{RandomForest} & 0.586 & 0.607 & 0.574 & 0.612 & 0.566 & 0.537 & 0.611 \\
\textbf{DecisionTree} & 0.546 & 0.56 & 0.559 & 0.569 & 0.543 & 0.52 & 0.569
\end{tabular}
\end{adjustbox}
\caption{Comparison of explanation shift detection performance, measured by AUC, for different combinations of explanation shift detectors and estimators on the UCI Adult Income dataset using the Novel Covariate Group Shift for the \enquote{Asian} group with a fraction ratio of $0.5$ (cf. Section ). The table shows that the choice of detector and estimator can impact the OOD explanation performance. We can see how, for the same detector, different estimators flag different OOD explanations performance. On the other side, for the same estimators, different detectors achieve different results.}
\end{table}
\end{document}

cs

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison between different distributed estimation (and detection) methods for SLS and LDS systems in terms of network-connectivity $\times$ communication-rate. }
\begin{tabular}{|c|c|c|c|}
			\hline
			Ref. & time-scale & system dynamics & links $\times$ rate \\
			\hline
			 & STS & SLS & $N(N-1)\times 1$ \\
			\hline
			 & STS & LDS & $3N\times 1$ \\\hline
			This work	 & STS & LDS & $N\times 1$ \\\hline
			 &	DTS & SLS/LDS & $N\times L$ with $L\geq d_n$\\
			\hline
			\hline
		\end{tabular}
\end{table}
\end{document}

eess

Mohammadreza Doostmohammadian
Themistoklis Charalambous

"Distributed Target Tracking based on Localization with Linear Time-Difference-of-Arrival Measurements: A Delay-Tolerant Networked Estimation Approach" by Mohammadreza Doostmohammadian and Themistoklis Charalambous, arXiv:2412.16988v1, 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{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf{Summary statistics for Explosion data sets}} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|cc|cl}
\cline{1-5}
\multirow{2}{*}{Parameter} &
 \multicolumn{2}{|c|}{TUC station} & \multicolumn{2}{|c|}{ANMO station} \\ \cline{2-5}
 &
 Estimates & Standard Error & Estimates & Standard Error & \\ \cline{1-5}%
		$\alpha_0$ & 0.1778 & 0.1025 & 0.1889 & 0.1121 \\ % inserting body of the table
		$\alpha_1$ & 0.9874 & 0.0028 & 0.9814 & 0.0032 \\
		$\sigma_\omega$ & 0.7492 & 0.0181 & 0.7003 & 0.0171 \\
		$\lambda$ & -10.151 & 6.7569 & -10.076 & 5.2518 \\
		$\sigma_0$ & 2.13E-05 & 0.0517 & 7.09E-05 & 0.0501 \\
		$\phi_1$ & -2.3475 & 0.0798 & -2.3063 & 0.0740 \\
		$\sigma_1$ & 2.1657 & 0.0491 & 2.0917& 0.0462 \\ % [1ex] adds vertical space
		\cline{1-5} %inserts single line
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Maria C Mariani
Md Al Masum Bhuiyan
Osei K Tweneboah
Hector Gonzalez-Huizar
Ionut Florescu

"Volatility Models Applied to Geophysics and High Frequency Financial Market Data" by Maria C Mariani, Md Al Masum Bhuiyan, Osei K Tweneboah, Hector Gonzalez-Huizar, and Ionut Florescu, arXiv:1901.09145v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
		\hline
		Color & Mode Sl. no. & MTT & MTT (all modes) & Std. deviation \\
		\hline \hline
		Green & 1 & 413.08 & & 2.07 \\
		& 2 & 440.57& 440.51 & 3.08 \\
		& 3 & 467.88& & 1.39 \\
		\hline
		Magenta & 1 & 390.75 & 401.15 & 1.31\\
		& 2 & 411.54 & & 0.72\\
		\hline
		Black & 1 & 370.58 & 379.34 & 0.52\\
		& 2 & 388.10 & & 0.67\\
		\hline
		Red & 1 & 366.47& 366.47 & 0.97\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Debarghya Pattanayak
Arindam Mishra
Syamal K. Dana
Nandadulal Bairagi

"Multimodal distribution of transient time of predator extinction in a three-species food chain" by Debarghya Pattanayak, Arindam Mishra, Syamal K. Dana, and Nandadulal Bairagi, arXiv:2202.13294v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 $n$ & Error $\|\cdot\|_1$ & Order $\|\cdot\|_1$ & Error
 $\|\cdot\|_{\infty}$ & Order $\|\cdot\|_{\infty}$ \\
 \hline
 20 & 5.36E$-4$ & $-$ & 1.56E$-3$ & $-$ \\
 \hline
 40 & 1.66E$-5$ & 5.01 & 5.40E$-5$ & 4.85 \\
 \hline
 80 & 5.24E$-7$ & 4.99 & 1.71E$-6$ & 4.98 \\
 \hline
 160 & 1.65E$-8$ & 4.99 & 5.33E$-8$ & 5.00 \\
 \hline
 320 & 5.15E$-10$ & 5.00 & 1.62E$-9$ & 5.04 \\
 \hline
 640 & 1.63E$-11$ & 4.98 & 5.04E$-11$ & 5.01 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Error table for problem - , Square domain, IW.}
\end{table}
\end{document}

cs

"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Marjeta Knez
Francesca Pelosi
Maria Lucia Sampoli

"Construction of $G^2$ planar Hermite interpolants with prescribed arc lengths" by Marjeta Knez, Francesca Pelosi, and Maria Lucia Sampoli, arXiv:2202.11371v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{array}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Project Statistics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr|rrrr}\toprule
 & \multicolumn{2}{|c|}{Production} & \multicolumn{4}{c}{Tests} \\\cmidrule{2-7}
 & LOC & \#files & LOC &\#tests &\multicolumn{1}{>{\raggedleft}p{1cm}}{\#describe blocks}& \multicolumn{1}{>{\raggedleft}p{1cm}}{\#test suites} %\#tests
 \\\midrule
Mean & 1791 & 32 & 690 &79 &18 &11 \\
Total & 145092 & 2583 & 55911 &6398 &1420 &873 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Negar Hashemi
Amjed Tahir
Shawn Rasheed
August Shi
Rachel Blagojevic

"Detecting and Evaluating Order-Dependent Flaky Tests in JavaScript" by Negar Hashemi, Amjed Tahir, Shawn Rasheed, August Shi, and Rachel Blagojevic, arXiv:2501.12680v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Juno Kim
Jaehyuk Kwon
Mincheol Cho
Hyunjong Lee
Joong-Ho Won

"$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence" by Juno Kim, Jaehyuk Kwon, Mincheol Cho, Hyunjong Lee, and Joong-Ho Won, arXiv:2312.01133v2, 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{Experiment Configurations}
\begin{tabular}{ll}
 \toprule
 \multicolumn{2}{c}{Configuration} \\
 \midrule
 Transceiver & Huawei Router \\
 Receiver (R) & Raspberry Pi 4B \\
 Frequency Band & 5 GHz \\
 Packet Rate & 1000 Hz \\
 Number of sub-carriers & 256 (230 used) \\
 Training samples & 12000 packets \\
 Testing samples & 24000 packets \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"BiCSI: A Binary Encoding and Fingerprint-Based Matching Algorithm for Wi-Fi Indoor Positioning" by Pei Tang, Jingtao Guo, and Ivan Wang-Hei Ho, arXiv:2412.02260v1, 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{Mean and variance of each variable of EMPC in Case 1}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllllll}
 \hline
 & & $x_1$ & $x_2$ & $y_1$ & $y_2$ & $u_1$ & $u_2$ \\
 \hline
 \multirow{2}{*}{$\mu$} & Sim & 1.1603 & 615.7342 & 1.1603 & 615.7334 & 2.7894e+02 & 1.2774e+07 \\
 & Cal & 1.1601 & 615.7373 & 1.1601 & 615.7373 & 2.7885e+02 & 1.2769e+07 \\
 \multirow{2}{*}{$\sigma^2$} & Sim & 8.2736e-04 & 2.4467e+00 & 9.1822e-04 & 2.5323e+00 & 5.5399e+01 & 1.1233e+11 \\
 & Cal & 8.3498e-04 & 2.4718e+00 & 9.2498e-04 & 2.5618e+00 & 5.5615e+01 & 1.1276e+11 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
 \tabularnewline\midrule\midrule
 Dependent Variable: & \multicolumn{2}{c}{$\Delta$ Unemployment Rate}\\
 Model: & (1) & (2)\\
 \midrule \emph{Variables} & & \\
 $\Delta$ Active Mines\textsubscript{t} & -0.0308$^{*}$ & -0.0119\\
 & (0.0135) & (0.0134)\\
 $\Delta$ Active Mines\textsubscript{t-1} & -0.0371$^{*}$ & -0.0306.\\
 & (0.0163) & (0.0160)\\
 $\Delta$ Active Mines\textsubscript{t-2} & 0.0027 & -0.0039\\
 & (0.0144) & (0.0140)\\
 $\Delta$ (log) Real GDPPC\textsubscript{t} & -0.9736$^{***}$ & -1.583$^{***}$\\
 & (0.2137) & (0.3493)\\
 $\Delta$ Active Mines\textsubscript{} $\times$ Rural & -0.0288 & -0.0335\\
 & (0.0218) & (0.0221)\\
 $\Delta$ Active Mines\textsubscript{t-1} $\times$ Rural & 0.0346 & 0.0301\\
 & (0.0237) & (0.0238)\\
 $\Delta$ Active Mines\textsubscript{t-2} $\times$ Rural & 0.0420$^{*}$ & 0.0435$^{*}$\\
 & (0.0152) & (0.0172)\\
 \midrule \emph{Fixed-effects} & & \\
 County FIPS Code & Yes & Yes\\
 Year & Yes & Yes\\
 \midrule \emph{Fit statistics} & & \\
 Observations & 55,295 & 4,518\\
 R$^2$ & 0.61349 & 0.65598\\
 Within R$^2$ & 0.01496 & 0.04851\\
 \midrule\midrule\multicolumn{3}{l}{\emph{Clustered (County FIPS Code \& Year) standard-errors in parentheses}}\\
 \multicolumn{3}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\
\end{tabular}
\end{table}
\end{document}

q-fin

"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, 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|ccccccc}
$d$ & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\
\hline
$a_{k,d}$ &
$\frac{2 \kappa^2-4}{\kappa^2}$ &
$\frac{3 \kappa^2 -8}{ \kappa^2}$ &
$4 \frac{\kappa^2 - 4 }{\kappa^2 }$ &
$6 \frac{ \kappa^2 - 4}{\kappa^2}$ &
$8 \frac{ \kappa^2 - 5} {\kappa^2}$ &
$4 \frac{ 3 \kappa^2 - 16} {\kappa^2}$ &
$18 \frac{ \kappa^2 - 6} {\kappa^2}$
\\
$b_{k,d}$ &
$\cdot$ &
$\frac{2 \kappa^2-4} {\kappa^2}$ &
$2 \frac{\kappa^2 - 4}{\kappa^2 }$ &
$2 \frac{ \kappa^2 - 3} {\kappa^2}$ &
$2 \frac{ \kappa^2 - 4} {\kappa^2}$ &
$2 \frac{ \kappa^2 - 4} {\kappa^2}$ &
$\frac{ 2 \kappa^2 - 9} {\kappa^2}$
\end{tabular}
\end{adjustbox}
\caption{The values of $a_{k,d}$ and $b_{k,d}$ used in the definition of the vector $c^\circ$. See Section~.}
\end{table}
\end{document}

math

Chiu-Yen Kao
Braxton Osting
Jackson C. Turner

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


math

Erdős' minimum overlap problem

"Erdős' minimum overlap problem" by Ethan Patrick White, arXiv:2201.05704v1, 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}{llr}
\hline
 \textbf{Ref. set} & \textbf{MMT Generation Mode} & \textbf{\boldmath$\mathrm{FMD}_{clamp2}^{midi}$} \\
\hline
SOD & Unconditioned & 363.57 \\
SOD & Instrument Informed & 354.17 \\
SOD & 4-beat continuation & 335.77 \\
SOD & 16-beat continuation & 328.74 \\
\hline
\end{tabular}
\caption{FMD results for the MMT model, with the full SOD dataset serving as the the reference.}
\end{table}
\end{document}

eess

Jan Retkowski
Jakub Stępniak
Mateusz Modrzejewski

"Frechet Music Distance: A Metric For Generative Symbolic Music Evaluation" by Jan Retkowski, Jakub Stępniak, and Mateusz Modrzejewski, arXiv:2412.07948v2, 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|ccc|ccc}
 &\multicolumn{3}{c}{SK $\rightarrow$ NS} & \multicolumn{3}{c}{NS $\rightarrow$ SK} \\
 Method & SK & NS & \% drop & NS & SK & \% drop \\
 \hline
 C\&L~& 50,2 & 31,6 & \textbf{-37\%} & 54,4 & 33,7 & -38\%\\
 3DLabelProp & \textbf{69,0} & \textbf{42,7} & -38\% & \textbf{66,5} & \textbf{50.5} & \textbf{-24\%} \\
 \end{tabular}
\end{adjustbox}
\caption{Comparison of 3DLabelProp with C\&L~ on the C\&L label set (mIoU), trained on SemanticKITTI and evaluated on nuScenes, and vice versa. C\&L results are their unsupervised domain adaptation results.}
\end{table}
\end{document}

cs

Jules Sanchez
Jean-Emmanuel Deschaud
François Goulette

"3DLabelProp: Geometric-Driven Domain Generalization for LiDAR Semantic Segmentation in Autonomous Driving" by Jules Sanchez, Jean-Emmanuel Deschaud, and François Goulette, arXiv:2501.14605v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|lcccc|}
 \hline
 Algorithm & $M^\text{idis}$ & $M^\text{adis}$ & $M^\text{sdcr}$ & $M^\text{wdcr}$ \\
 \hline
 Random* & 11.75 & 9.0 & 11.0 & 9.83 \\
 FSS-Random & 11.08 & 10.17 & 9.83 & 11.33 \\
 FSS-LHD & 5.67 & 7.83 & 7.42 & \cellcolor[HTML]{50c878} 1.58 \\
 FSS-LHD-VP & 5.5 & \cellcolor[HTML]{77dd77} 2.5 & 5.08 & \cellcolor[HTML]{77dd77} 2.67 \\
 TT-LHD* & 8.83 & \cellcolor[HTML]{50c878} 1.67 & 7.08 & 5.0 \\
 P-LHD* & 9.83 & 4.33 & 7.17 & 5.17 \\
 Random-MP* & 7.75 & 9.0 & 8.25 & 9.67 \\
 FSS-Random-MP & \cellcolor[HTML]{d0f0c0} 3.17 & 10.17 & 7.25 & 8.58 \\
 FSS-LHD-MP & \cellcolor[HTML]{77dd77} 2.33 & 7.83 & 5.33 & 6.0 \\
 FSS-LHD-VP-MP & \cellcolor[HTML]{50c878} 1.0 & \cellcolor[HTML]{77dd77} 2.5 &\cellcolor[HTML]{77dd77} 2.5 & \cellcolor[HTML]{d0f0c0} 4.25 \\
 TT-LHD-MP* & 5.58 & \cellcolor[HTML]{50c878} 1.67 & \cellcolor[HTML]{50c878} 2.25 & 7.17 \\
 P-LHD-MP* & 5.5 & 4.33 & \cellcolor[HTML]{d0f0c0} 4.08 & 6.75 \\
 \hline
 \end{tabular}
\caption{Average algorithm ranking for each combination of input space and design size. Shading indicates the strongest algorithms, and an asterisk indicates common divergence from the target number of points for each full-sub-space causing measurement biases.}
\end{table}
\end{document}

stat

"Rapid Experimentation with Python Considering Optional and Hierarchical Inputs" by Neil Ranly and Torrey Wagner, arXiv:2501.03398v1, 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{Statistics of datasets}
\begin{tabular}{cccccccc}\toprule
 Dataset & Users & Items & Interactions & $\mu_0 $& $\mu_1$ & $\Delta_l^{prior} $ (prior) & $ {\hat{\Delta}_l} $ (ours)\\ \midrule
 Amazon-book & 52,643 & 91,599 & 2,984,108 & 0.33 & 0.99 & 1.12 $\times \sqrt{p} $ & 0.99 \\
 MovieLens & 71,567 & 10,677 & 7,972,582 & 0.39 & 1.00 & 1.19 $\times \sqrt{p} $ & 1.00 \\
 EachMovie & 74,425 & 1,649 & 2,216,887 & 0.49 & 1.00 & 1.33 $\times \sqrt{p} $ & 1.00 \\
 Jester & 54,906 & 151 & 1,450,010 & 0.73 & 1.00 & 1.64 $\times \sqrt{p} $ & 1.00 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Julien Nicolas
César Sabater
Mohamed Maouche
Sonia Ben Mokhtar
Mark Coates

"Differentially private and decentralized randomized power method" by Julien Nicolas, César Sabater, Mohamed Maouche, Sonia Ben Mokhtar, and Mark Coates, arXiv:2411.01931v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Pseudo Label Dice Score, Specificity and Sensitivity. TEM-M stands for the TEM-MACAQUE dataset. Applying the pre-trained TEM model directly on the SEM/BF datasets produced empty masks, so we omit these cases.}
\begin{tabular}{cclcll}
Class &
 $X_U$ &
 Method &
 Dice Score &
 Specificity &
 Sensitivity \\ \hline
\multirow{7}{*}{Axon} &
 \multirow{3}{*}{TEM-M} &
 TEM model &
 0.895 ± 0.004 &
 0.935 ± 0.007 &
 0.905 ± 0.015 \\
 &
 &
 Adaptation &
 0.878 ± 0.005 &
 0.936 ± 0.006 &
 0.872 ± 0.017 \\
 &
 &
 Tutorship &
 \textbf{0.902 ± 0.003} &
 0.932 ± 0.001 &
 0.922 ± 0.006 \\ \cline{2-6}
 &
 \multirow{2}{*}{SEM} &
 Adaptation &
 0.238 ± 0.088 &
 0.984 ± 0.010 &
 0.144 ± 0.061 \\
 &
 &
 Tutorship &
 \textbf{0.736 ± 0.005} &
 0.914 ± 0.002 &
 0.705 ± 0.009 \\ \cline{2-6}
 &
 \multirow{2}{*}{BF} &
 Adaptation &
 \textbf{0.586 ± 0.025} &
 0.949 ± 0.008 &
 0.889 ± 0.022 \\
 &
 &
 Tutorship &
 0.454 ± 0.010 &
 0.915 ± 0.003 &
 0.942 ± 0.003 \\ \hline
\multirow{7}{*}{Myelin} &
 \multirow{3}{*}{TEM-M} &
 TEM model &
 \textbf{0.820 ± 0.004} &
 0.945 ± 0.009 &
 0.811 ± 0.021 \\
 &
 &
 Adaptation &
 0.791 ± 0.007 &
 0.938 ± 0.005 &
 0.777 ± 0.022 \\
 &
 &
 Tutorship &
 0.813 ± 0.001 &
 0.934 ± 0.000 &
 0.822 ± 0.001 \\ \cline{2-6}
 &
 \multirow{2}{*}{SEM} &
 Adaptation &
 0.165 ± 0.060 &
 0.995 ± 0.003 &
 0.092 ± 0.036 \\
 &
 &
 Tutorship &
 \textbf{0.652 ± 0.005} &
 0.962 ± 0.002 &
 0.512 ± 0.008 \\ \cline{2-6}
 &
 \multirow{2}{*}{BF} &
 Adaptation &
 \textbf{0.510 ± 0.004} &
 0.949 ± 0.003 &
 0.635 ± 0.020 \\
 &
 &
 Tutorship &
 0.469 ± 0.005 &
 0.947 ± 0.002 &
 0.566 ± 0.012 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Arthur Boschet
Armand Collin
Nishka Katoch
Julien Cohen-Adad

"Unpaired Modality Translation for Pseudo Labeling of Histology Images" by Arthur Boschet, Armand Collin, Nishka Katoch, and Julien Cohen-Adad, arXiv:2412.02858v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

CRIX an index for cryptocurrencies

"CRIX an index for cryptocurrencies" by Simon Trimborn and Wolfgang Karl Härdle, arXiv:2009.09782v1, 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{Reviewing} Query Types}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{clccc}
 \toprule
Code & What did they ask ChatGPT to do? & Count & \# of Participants (\%) \\
 \hline
 RE01 & Proofread & 27 & 16 (20.8\%) \\
 RE02 & Answer spelling/grammar questions & 13 & 10 (13.0\%) \\
 RE03 & Give feedback & 19 & 7 (9.1\%) \\
 RE04 & Shorten text/remove some content & 8 & 7 (9.1\%) \\
 RE05 & Rewrite existing text based on a user's prompt & 11 & 6 (7.8\%) \\
 RE06 & Improve the essay & 8 & 6 (7.8\%) \\
 RE07 & Check if the essay meets the prompt & 4 & 3 (3.9\%) \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Andrew Jelson
Daniel Manesh
Alice Jang
Daniel Dunlap
Young-Ho Kim
Sang Won Lee

"An Empirical Study to Understand How Students Use ChatGPT for Writing Essays" by Andrew Jelson, Daniel Manesh, Alice Jang, Daniel Dunlap, Young-Ho Kim, and Sang Won Lee, arXiv:2501.10551v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters for the contrastive training. Parameters that are only listed once are shared between datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 ~& \multicolumn{3}{c}{\textbf{Dataset}} \\
 \cmidrule(lr){2-4}
 \textbf{Parameter} & \textbf{US} & \textbf{JP} & \textbf{ES} \\
 \toprule
 Training epochs & \multicolumn{3}{c}{$200$} \\
 Learning rate & \multicolumn{3}{c}{$5.0$e$-6$} \\
 Batch size & \multicolumn{3}{c}{$1024$} \\
 $\tau$ (Eq. ) & $0.1$ & $1.0$ & $1.0$ \\
 $\lambda_{\text{Agg}}$ (Eq. ) & \multicolumn{3}{c}{1} \\
 $\lambda_{\text{Fields}}$ (Eq. ) & \multicolumn{3}{c}{1} \\
 $\lambda_{\text{Max}}$ (Eq. ) & \multicolumn{3}{c}{1} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Niklas Freymuth
Dong Liu
Thomas Ricatte
Saab Mansour

"Hierarchical Multi-field Representations for Two-Stage E-commerce Retrieval" by Niklas Freymuth, Dong Liu, Thomas Ricatte, and Saab Mansour, arXiv:2501.18707v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"On "A General Framework for Pricing Asian Options Under Markov Processes"" by Zhenyu Cui, Chihoon Lee, and Yanchu Liu, arXiv:1601.05306v1, 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{Fixed Effects Estimates and $p$-values of the tests}
\begin{tabular}{ccccc}
		\hline
		Effect & Estimate & Standard Error & $t$-value & $p$-value\tabularnewline
		\hline
		Intercept & 2.8975 & 0.09006 & 32.17 & $<$.0001\tabularnewline
		Intervention & 0.5070 & 0.09071 & 5.59 & $<$.0001 \tabularnewline
		Class Size & 0.1711 & 0.0918 & 1.86 & 0.1213 \tabularnewline
		Intervention{*}Class Size & -0.3405 & 0.1414 & -2.41 & 0.0162\tabularnewline
		\hline
	\end{tabular}
\end{table}
\end{document}

stat

Shubhadeep Chakraborty
Bo Wang
Ram Tiwari
Samiran Ghosh

"Subgroup analysis in multi level hierarchical cluster randomized trials" by Shubhadeep Chakraborty, Bo Wang, Ram Tiwari, and Samiran Ghosh, arXiv:2411.11301v1, 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{\small Percentage of the computational time needed by each ILS component}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrr}
 	 \toprule
 	 Set & LS1 & LS2 & LS3 & LS4 & LS5 & S1 & S2 \\
 	 \cmidrule(lr){1-1} \cmidrule(lr){2-8}
 	 Small-size & 3.63\% & 39.41\% & 28.65\% & 6.37\% & 20.50\% & 0.37\% & 1.07\% \\
 	 Medium-size & 59.50\% & 9.02\% & 1.30\% & 18.36\% & 10.69\% & 0.23\% & 0.89\% \\
 	 Large-size & 50.03\% & 3.48\% & 3.33\% & 32.35\% & 9.98\% & 0.13\% & 0.71\% \\
 	 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Reza Atefi
Manuel Iori
Majid Salari
Dario Vezzali

"Solution of a Practical Vehicle Routing Problem for Monitoring Water Distribution Networks" by Reza Atefi, Manuel Iori, Majid Salari, and Dario Vezzali, arXiv:2202.02549v2, 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|cccc}
\multicolumn{3}{l}{bad controls in adjustment} & \multicolumn{3}{c}{$\epsilon_{pehe}$} \\
\hline
&Dragonnet & {\textsc \textbf{Mod1}} & {\textsc\textbf{Mod2} } & {\textsc \textbf{Mod3}} & {\textsc \textbf{Mod4}}\\
\hline
\textbf{low} &Adjust All &$.47\pm .05$ & $.23\pm .05$ & $.37 \pm .05$ & $.46\pm .14$\\
& NICE &$.16\pm .08$ & $.05\pm .02$ & $.18 \pm .04$ & $.05 \pm .02$\\
\hline
\textbf{med}& Adjust All &$.32 \pm .05$ & $.37\pm .06$ & $.55 \pm .18$ & $.15\pm .02$\\
& NICE &$.07\pm .03$ & $.13\pm .04$ & $.11\pm .05$ & $.04 \pm .01$\\
\hline
\textbf{high} & Adjust All &$.38\pm .06$ & $.29\pm .05$ & $.39 \pm .05$ & $.14 \pm .02$\\
& NICE &$.09\pm .03$ & $.11\pm .04$ & $.13 \pm .05$ & $.04 \pm .01$\\
\hline
\end{tabular}
\caption{}
\end{table}
\end{document}

stat

"Invariant Representation Learning for Treatment Effect Estimation" by Claudia Shi, Victor Veitch, and David Blei, arXiv:2011.12379v2, 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{ABT variables, part 2}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
 \hline
 & & \\
 Nr & Name & Description \\
 & & \\
 \hline
41 & act\_ccss\_min\_lninst & Cust. minimal number of left installments on product css \\
42 & act\_cins\_min\_lninst & Cust. minimal number of left installments on product ins \\
43 & act\_ccss\_min\_pninst & Cust. minimal number of paid installments on product css \\
44 & act\_cins\_min\_pninst & Cust. minimal number of paid installments on product ins \\
45 & act\_ccss\_min\_seniority & Cust. minimal seniority on product css \\
46 & act\_cins\_min\_seniority & Cust. minimal seniority on product ins \\
47 & act3\_n\_arrears & Cust. number in arrears on all loans during the last 3 months \\
48 & act6\_n\_arrears & Cust. number in arrears on all loans during the last 6 months \\
49 & act9\_n\_arrears & Cust. number in arrears on all loans during the last 9 months \\
50 & act12\_n\_arrears & Cust. number in arrears on all loans during the last 12 months \\
51 & act3\_n\_arrears\_days & Cust. number of days greter than 15 on all loans during the last 3 months \\
52 & act6\_n\_arrears\_days & Cust. number of days greter than 15 on all loans during the last 6 months \\
53 & act9\_n\_arrears\_days & Cust. number of days greter than 15 on all loans during the last 9 months \\
54 & act12\_n\_arrears\_days & Cust. number of days greter than 15 on all loans during the last 12 months \\
55 & act3\_n\_good\_days & Cust. number of days lower than 15 on all loans during the last 3 months \\
56 & act6\_n\_good\_days & Cust. number of days lower than 15 on all loans during the last 6 months \\
57 & act9\_n\_good\_days & Cust. number of days lower than 15 on all loans during the last 9 months \\
58 & act12\_n\_good\_days & Cust. number of days lower than 15 on all loans during the last 12 months \\
59 & act\_ccss\_seniority & Cust. seniority on product css \\
60 & act\_cins\_seniority & Cust. seniority on product ins \\
61 & ags12\_Max\_CMaxC\_Days & Max calc. on last 12 mths on max Cust. days for Css product \\
62 & ags12\_Max\_CMaxI\_Days & Max calc. on last 12 mths on max Cust. days for Ins product \\
63 & ags12\_Max\_CMaxA\_Days & Max calc. on last 12 mths on max Cust. days for all product \\
64 & ags12\_Max\_CMaxC\_Due & Max calc. on last 12 mths on max Cust. due for Css product \\
65 & ags12\_Max\_CMaxI\_Due & Max calc. on last 12 mths on max Cust. due for Ins product \\
66 & ags12\_Max\_CMaxA\_Due & Max calc. on last 12 mths on max Cust. due for all product \\
67 & agr12\_Max\_CMaxC\_Days & Max calc. on last 12 mths on unmissing max Cust. days for Css product \\
68 & agr12\_Max\_CMaxI\_Days & Max calc. on last 12 mths on unmissing max Cust. days for Ins product \\
69 & agr12\_Max\_CMaxA\_Days & Max calc. on last 12 mths on unmissing max Cust. days for all product \\
70 & agr12\_Max\_CMaxC\_Due & Max calc. on last 12 mths on unmissing max Cust. due for Css product \\
71 & agr12\_Max\_CMaxI\_Due & Max calc. on last 12 mths on unmissing max Cust. due for Ins product \\
72 & agr12\_Max\_CMaxA\_Due & Max calc. on last 12 mths on unmissing max Cust. due for all product \\
73 & ags3\_Max\_CMaxC\_Days & Max calc. on last 3 mths on max Cust. days for Css product \\
74 & ags3\_Max\_CMaxI\_Days & Max calc. on last 3 mths on max Cust. days for Ins product \\
75 & ags3\_Max\_CMaxA\_Days & Max calc. on last 3 mths on max Cust. days for all product \\
76 & ags3\_Max\_CMaxC\_Due & Max calc. on last 3 mths on max Cust. due for Css product \\
77 & ags3\_Max\_CMaxI\_Due & Max calc. on last 3 mths on max Cust. due for Ins product \\
78 & ags3\_Max\_CMaxA\_Due & Max calc. on last 3 mths on max Cust. due for all product \\
79 & agr3\_Max\_CMaxC\_Days & Max calc. on last 3 mths on unmissing max Cust. days for Css product \\
80 & agr3\_Max\_CMaxI\_Days & Max calc. on last 3 mths on unmissing max Cust. days for Ins product \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Credit acceptance process strategy case studies - the power of Credit Scoring" by Karol Przanowski, arXiv:1403.6531v1, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Experimental results with context and contrastive learning}
\begin{tabular}{ll}
\hline
\textbf{Experimental Setting} & \textbf{Dev Set (WER)} \\ \hline
No context & 19.85\% \\
Context only (no contrastive learning) & 18.92\% \\
Context with contrastive learning (no LoRA) & 17.18\% \\
Context with contrastive learning (with LoRA) & 18.62\% \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Lorenzo Concina
Jordi Luque
Alessio Brutti
Marco Matassoni
Yuchen Zhang

"The Eloquence team submission for task 1 of MLC-SLM challenge" by Lorenzo Concina, Jordi Luque, Alessio Brutti, Marco Matassoni, and Yuchen Zhang, arXiv:2507.19308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.