\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Measuring the CONLeg method in error convergence and CPU time for pricing European at/around-the-money put prices, Delta $\Delta_t$ and Gamma $\Gamma_t$ under the BSM model with parameters taken from \textbf{GBM1}. 1000 put prices are computed in a range of $K$ from 80 to 120.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cc|cc|cc|c|} % centered columns (4 columns)
\hline
\multicolumn{2}{|c}{Price}& \multicolumn{2}{|c}{Delta\,$\Delta_t$}& \multicolumn{2}{|c|}{Gamma\,$\Gamma_t$}&\\
$R_\infty$&$R_2$&$R_\infty$&$R_2$&$R_\infty$&$R_2$&Time\\
\hline
5.329e-14 & 3.921e-13 & 5.645e-14 & 1.846e-13 & 8.538e-13 & 1.396e-12&9.11e-02\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Hedging and Pricing European-type, Early-Exercise and Discrete Barrier Options using Algorithm for the Convolution of Legendre Series" by Tat Lung Chan and Nicholas Hale, arXiv:1811.09257v3, 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{FVA Swaps Portfolio}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
Issued & Notional & Maturity & Rec / Pay Fixed & Coupon & Freq \\
 \hline
4/20/20 & 100 & 4/20/21 & Pay & 0.51\% & quarterly \\
\hline
4/20/20 & 100 & 4/20/22 & Rec & 0.41\% & quarterly \\
\hline
4/20/20 & 100 & 4/20/23 & Rec & 0.40\% & quarterly \\
\hline
4/20/20 & 100 & 4/20/25 & Pay & 0.47\% & quarterly \\
\hline
4/20/20 & 100 & 4/20/27 & Rec & 0.57\% & quarterly \\
 \hline
4/20/20 & 100 & 4/20/30 & Pay & 0.69\% & quarterly \\
\hline
4/20/20 & 100 & 4/20/30 & Pay & 0.74\% & quarterly \\
\hline
4/20/20 & 100 & 4/20/30 & Pay & 0.83\% & quarterly \\
\hline
4/20/20 & 100 & 4/20/30 & Rec & 0.83\% & quarterly \\
\hline
4/20/20 & 100 & 4/20/30 & Rec & 0.85\% & quarterly \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


math

"Moment-based multi-resolution HWENO scheme for hyperbolic conservation laws" by Jiayin Li, Chi-Wang Shu, and Jianxian Qiu, arXiv:2201.12193v1, 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{Example table}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|} \hline
 Species & \bf Mean & \bf Std.~Dev. \\ \hline
 1 & 3.4 & 1.2 \\
 2 & 5.4 & 0.6 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Tingwei Meng
Zhen Zhang
Jérôme Darbon
George Em Karniadakis

"SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problems" by Tingwei Meng, Zhen Zhang, Jérôme Darbon, and George Em Karniadakis, arXiv:2201.05475v1, 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{$t$-test statistics for hypotheses testing: Observed returns}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
		\hline \hline
		\textit{Hypothesis} & $t$-stat. & \textit{df} & $p$-value & \textit{Mean Difference} & Lower CI (95\%) & Upper CI (95\%)\\ \hline
		$H_{1,1}$: Firm Size & -3.1518 & 158 & 0.001 & 0.1277 & 0.197 & 0.723 \\
		$H_{1,2}$: P/E Ratio & -2.4303 & 45 & 0.019 & 0.2039 & 0.392 & 0.835 \\
		$H_{1,3}$: P/B Ratio & -2.2264 & 124 & 0.027 & 0.1100 & 0.194 & 0.503 \\
		$H_{1,4}$: Shareholding & 1.1963 & 155 & 0.2334 & 0.0419 & 0.327 & 0.784 \\
		$H_{1,5}$: Beta & 1.2401 & 156 & 0.2168 & 0.0124 & 0.269 & 0.486 \\
		$H_{1,6}$: Liquidity & -0.7202 & 150 & 0.472 & -0.0211 & -0.072 & 0.416 \\ \hline \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The Cross-section of Expected Returns on Penny Stocks: Are Low-hanging Fruits Not-so Sweet?" by Ananjan Bhattacharyya and Abhijeet Chandra, arXiv:1610.01338v1, 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{Error metrics for various models ordered by SMAPE -- Spring Season for Residence~2.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
 \toprule
 \textbf{Model} & \textbf{MAE} & \textbf{RMSE} & \textbf{SMAPE} \\
 \midrule
 AttentionLSTM & 15.48 & 21.73 & 6.80 \\
 MiniAutoEncXGBoost & 16.22 & 21.47 & 7.01 \\
 ARFFNN & 16.63 & 22.35 & 7.18 \\
 MiniWXGBoost & 17.09 & 22.96 & 7.37 \\
 Nbeats & 16.88 & 24.07 & 7.39 \\
 HyperNetLSTM & 17.48 & 23.79 & 7.50 \\
 GRU & 17.48 & 23.76 & 7.60 \\
 LSTM & 21.67 & 27.78 & 9.77 \\
 Transformer & 23.15 & 29.92 & 9.98 \\
 MLP & 23.29 & 30.10 & 10.04 \\
 RNN & 26.20 & 34.83 & 11.70 \\
 TempConv & 32.75 & 42.19 & 14.27 \\
 MiniWSGD & 34.13 & 47.84 & 15.22 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Muhammad Umair Danish
Mathumitha Sureshkumar
Tehara Fonseka
Umeshika Uthayakumar
Vinura Galwaduge

"An Investigation into Seasonal Variations in Energy Forecasting for Student Residences" by Muhammad Umair Danish, Mathumitha Sureshkumar, Tehara Fonseka, Umeshika Uthayakumar, and Vinura Galwaduge, arXiv:2501.07423v2, 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{Mean and standard deviation of the accuracy of the different approaches on AWGN-MNIST}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
& AWGN-MNIST \\
\hline
RSLDA + k-NN & 52.85 $\pm$ 2.01\% \\
K-NN & 84.15 $\pm$ 1.34 \% \\
Decision Tree & 39.70 $\pm$ 1.88 \% \\
SVM & 82.75 $\pm$ 1.29\% \\
fastSDA + k-NN & 84.80 $\pm$ 1.18\% \\
TRPCAA + k-NN& 88.05 $\pm$ 0.39 \% \\
\hline
\hline
KMFA-GE+k-NN & 65.55 $\pm$ 5.10 \% \\
MFA-GEU +k-NN& 48.40 $\pm$ 7.40 \% \\
KMFA-NGEU+k-NN & 75.20 $\pm$ 1.52 \% \\
KMFA-NGEU-N+k-NN & 79.40 $\pm$ 0.70 \% \\
\hline
KDA-GE +k-NN & 64.95 $\pm$ 1.04 \% \\
LDA-GEU +k-NN& 81.75 $\pm$ 3.17 \% \\
KDA-NGEU +k-NN& 81.45 $\pm$ 1.22 \% \\
KDA-NGEU-N +k-NN & 82.55 $\pm$ 1.66 \% \\
\hline
KPCA-GER +k-NN& 88.25 $\pm$ 1.01 \% \\
PCA-GEU +k-NN& 88.25 $\pm$ 0.89 \% \\
KPCA-NGEU +k-NN& 87.45 $\pm$ 0.88 \% \\
KPCA-NGEU-N +k-NN & 87.75 $\pm$ 0.43 \% \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Firas Laakom
Jenni Raitoharju
Nikolaos Passalis
Alexandros Iosifidis
Moncef Gabbouj

"Non-Linear Spectral Dimensionality Reduction Under Uncertainty" by Firas Laakom, Jenni Raitoharju, Nikolaos Passalis, Alexandros Iosifidis, and Moncef Gabbouj, arXiv:2202.04678v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparisons of the errors and the CPU times of the WG method and the two-grid WG method for Example , $\tau=h^{1/2}$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc|cc}
\toprule[1.5pt]
\multirow{2}*{$h$}&\multicolumn{2}{c|}{WG method}&\multicolumn{2}{c}{Two-grid WG method}\\ \cline{2-5}
 & $\|u-u_h\|_{1,h}$&CPU time (sec.)&$\|u-u^h\|_{1,h}$&CPU time (sec.) \\
\hline
 $1/4$&1.63E+00 &0.7031&1.66E+00&0.4375\\
 $1/16$&4.39E-01 &7.2031&4.76E-01&2.0469\\
 $1/36$&1.96E-01&33.4375&2.24E-01&6.8125\\
 $1/64$&1.10E-01&108.0156&1.28E-01&19.2500\\
 $1/100$&7.06E-02&271.5781&8.29E-02&53.8750\\
 \hline
 Rate&0.98&&0.93&\\
 \bottomrule[1.5pt]
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A weak Galerkin method and its two-grid algorithm for the quasi-linear elliptic problems of non-monotone type" by Peng Zhu and Shenglan Xie, arXiv:2202.11164v2, 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 3}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
 \hline
 & & \\
 Nr & Name & Description \\
 & & \\
 \hline
81 & agr3\_Max\_CMaxA\_Days & Max calc. on last 3 mths on unmissing max Cust. days for all product \\
82 & agr3\_Max\_CMaxC\_Due & Max calc. on last 3 mths on unmissing max Cust. due for Css product \\
83 & agr3\_Max\_CMaxI\_Due & Max calc. on last 3 mths on unmissing max Cust. due for Ins product \\
84 & agr3\_Max\_CMaxA\_Due & Max calc. on last 3 mths on unmissing max Cust. due for all product \\
85 & ags6\_Max\_CMaxC\_Days & Max calc. on last 6 mths on max Cust. days for Css product \\
86 & ags6\_Max\_CMaxI\_Days & Max calc. on last 6 mths on max Cust. days for Ins product \\
87 & ags6\_Max\_CMaxA\_Days & Max calc. on last 6 mths on max Cust. days for all product \\
88 & ags6\_Max\_CMaxC\_Due & Max calc. on last 6 mths on max Cust. due for Css product \\
89 & ags6\_Max\_CMaxI\_Due & Max calc. on last 6 mths on max Cust. due for Ins product \\
90 & ags6\_Max\_CMaxA\_Due & Max calc. on last 6 mths on max Cust. due for all product \\
91 & agr6\_Max\_CMaxC\_Days & Max calc. on last 6 mths on unmissing max Cust. days for Css product \\
92 & agr6\_Max\_CMaxI\_Days & Max calc. on last 6 mths on unmissing max Cust. days for Ins product \\
93 & agr6\_Max\_CMaxA\_Days & Max calc. on last 6 mths on unmissing max Cust. days for all product \\
94 & agr6\_Max\_CMaxC\_Due & Max calc. on last 6 mths on unmissing max Cust. due for Css product \\
95 & agr6\_Max\_CMaxI\_Due & Max calc. on last 6 mths on unmissing max Cust. due for Ins product \\
96 & agr6\_Max\_CMaxA\_Due & Max calc. on last 6 mths on unmissing max Cust. due for all product \\
97 & ags9\_Max\_CMaxC\_Days & Max calc. on last 9 mths on max Cust. days for Css product \\
98 & ags9\_Max\_CMaxI\_Days & Max calc. on last 9 mths on max Cust. days for Ins product \\
99 & ags9\_Max\_CMaxA\_Days & Max calc. on last 9 mths on max Cust. days for all product \\
100 & ags9\_Max\_CMaxC\_Due & Max calc. on last 9 mths on max Cust. due for Css product \\
101 & ags9\_Max\_CMaxI\_Due & Max calc. on last 9 mths on max Cust. due for Ins product \\
102 & ags9\_Max\_CMaxA\_Due & Max calc. on last 9 mths on max Cust. due for all product \\
103 & agr9\_Max\_CMaxC\_Days & Max calc. on last 9 mths on unmissing max Cust. days for Css product \\
104 & agr9\_Max\_CMaxI\_Days & Max calc. on last 9 mths on unmissing max Cust. days for Ins product \\
105 & agr9\_Max\_CMaxA\_Days & Max calc. on last 9 mths on unmissing max Cust. days for all product \\
106 & agr9\_Max\_CMaxC\_Due & Max calc. on last 9 mths on unmissing max Cust. due for Css product \\
107 & agr9\_Max\_CMaxI\_Due & Max calc. on last 9 mths on unmissing max Cust. due for Ins product \\
108 & agr9\_Max\_CMaxA\_Due & Max calc. on last 9 mths on unmissing max Cust. due for all product \\
109 & ags12\_Mean\_CMaxC\_Days & Mean calc. on last 12 mths on max Cust. days for Css product \\
110 & ags12\_Mean\_CMaxI\_Days & Mean calc. on last 12 mths on max Cust. days for Ins product \\
111 & ags12\_Mean\_CMaxA\_Days & Mean calc. on last 12 mths on max Cust. days for all product \\
112 & ags12\_Mean\_CMaxC\_Due & Mean calc. on last 12 mths on max Cust. due for Css product \\
113 & ags12\_Mean\_CMaxI\_Due & Mean calc. on last 12 mths on max Cust. due for Ins product \\
114 & ags12\_Mean\_CMaxA\_Due & Mean calc. on last 12 mths on max Cust. due for all product \\
115 & agr12\_Mean\_CMaxC\_Days & Mean calc. on last 12 mths on unmissing max Cust. days for Css product \\
116 & agr12\_Mean\_CMaxI\_Days & Mean calc. on last 12 mths on unmissing max Cust. days for Ins product \\
117 & agr12\_Mean\_CMaxA\_Days & Mean calc. on last 12 mths on unmissing max Cust. days for all product \\
118 & agr12\_Mean\_CMaxC\_Due & Mean calc. on last 12 mths on unmissing max Cust. due for Css product \\
119 & agr12\_Mean\_CMaxI\_Due & Mean calc. on last 12 mths on unmissing max Cust. due for Ins product \\
120 & agr12\_Mean\_CMaxA\_Due & Mean calc. on last 12 mths on unmissing
max Cust. due for all 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}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Cross-corpus SER performance (UAR) for baseline and proposed models: C→I (CREMA to IMPROV) and I→C (IMPROV to CREMA).}
\begin{tabular}{c|c|c|cccc}
\toprule\specialrule{\cmidrulewidth}{0pt}{0pt}
 & & 4-CAT & Neu & Ang & Hap & Sad \\ \hline \hline
\multirow{2}{*}{Upper Bound} & C→C & 66.36 & 89.44 & 88.27 &85.35 & 79.51\\
 & I→I & 62.10 & 87.84 & 85.33 &83.68 & 75.05 \\\hline
\multirow{2}{*}{\footnotesize{PA-CC }} & C→I & 55.33 & 75.35 & 73.33 &74.82 & 66.98 \\
 & I→C &53.18 & 75.46 & 72.14 & 73.64 & 63.35 \\\hline
\multirow{2}{*}{\footnotesize{LA-CC }} & C→I & 56.04 & 78.24 &75.49 &73.50 &66.73 \\
 & I→C & 52.95 &\textbf{ 77.67} &76.52 & 74.52 &\textbf{67.73} \\
 \toprule\specialrule{\cmidrulewidth}{0pt}{0pt}
\multirow{2}{*}{\footnotesize{\textbf{AG-CC}}} & C→I & \textbf{57.37} & \textbf{78.51 }& \textbf{75.03} &\textbf{76.74} &\textbf{68.10} \\
 & I→C &\textbf{53.83} & 77.46 & \textbf{77.72} & \textbf{75.30} & 65.85\\
\toprule\specialrule{\cmidrulewidth}{0pt}{0pt}
\multirow{2}{*}{\footnotesize{Hard-AG}} & C→I & 54.87 &72.35 &74.46 &71.90 & 69.41 \\
 & I→C &52.90 &70.68 & 71.53 & 70.24 &63.24 \\\hline
\specialrule{\cmidrulewidth}{0pt}{0pt}\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Shreya G. Upadhyay
Ali N. Salman
Carlos Busso
Chi-Chun Lee

"Mouth Articulation-Based Anchoring for Improved Cross-Corpus Speech Emotion Recognition" by Shreya G. Upadhyay, Ali N. Salman, Carlos Busso, and Chi-Chun Lee, arXiv:2412.19909v1, 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{Critical Parameter Settings}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
\textbf{System Parameter} & \textbf{Value} \\ \hline \hline
V2I transmit power & 23 dBm \\ \hline
V2V transmit power & [-100, 5, 15, 23] dBm \\ \hline
V2I deviation of shadowing & 3 dB \\ \hline
V2V deviation of shadowing & 8 dB \\ \hline
Number of NN layers & 3 \\ \hline
Clipping parameter $\epsilon$ & 0.5 \\ \hline
Initial learning rate $\alpha$ & $1 \times 10^{-4}$ \\ \hline
Final learning rate $\alpha$ & $1 \times 10^{-8}$ \\ \hline
Discount factor $\gamma$ & 0.99 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ruichen Zhang
Changyuan Zhao
Hongyang Du
Dusit Niyato
Jiacheng Wang
Suttinee Sawadsitang
Xuemin Shen
Dong In Kim

"Embodied AI-Enhanced Vehicular Networks: An Integrated Large Language Models and Reinforcement Learning Method" by Ruichen Zhang, Changyuan Zhao, Hongyang Du, Dusit Niyato, Jiacheng Wang, Suttinee Sawadsitang, Xuemin Shen, and Dong In Kim, arXiv:2501.01141v1, 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}{lccccc}
 \hline
 Estimator & Training time (mins) & Estimation time (s) & RMSE$_{\text{MCAR}}$ & RMSE$_{\text{MICB}}$ \\
 \hline
 EM NBE & \textbf{37.3} & 0.09 & \textbf{0.032} & \textbf{0.031} \\
 Masking NBE & 72.1 & \textbf{0.01} & 0.035 & 0.097 \\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

Matthew Sainsbury-Dale
Andrew Zammit-Mangion
Noel Cressie
Raphaël Huser

"Neural Parameter Estimation with Incomplete Data" by Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Noel Cressie, and Raphaël Huser, arXiv:2501.04330v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|rrrrrr}
						number of observational clusters & 0 & 1 & 2 & 3 & 4 & 5 \\
						\hline
						only in the second cluster of hospitals & 0.3530 & 0.3670 & 0.2040 & 0.0640 & 0.0100 & 0.0020 \\
						only in the first cluster of hospitals & 0.7750 & 0.1850 & 0.0340 & 0.0060 & 0 & 0 \\
						shared across clusters of hospitals & 0 & 0.1680 & 0.4800 & 0.2660 & 0.0780 & 0.0080 \\
						\hline
					\end{tabular}
\end{adjustbox}
\caption{ Posterior distributions of the number of clusters shared and not shared across the two clusters of hospitals.}
\end{table}
\end{document}

math

Antonio Lijoi
Igor Prünster
Giovanni Rebaudo

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Upper bounds on the minimum $\lambda$ for which inequality~ holds. The analytical minimum $\lambda_d^*$ is reported for comparison.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
		\toprule
		$\omega$ & $d=1$ & $d=2$ & $d=3$ & $d=4$\\
		\midrule
		1 & 4.000000 & 16.00000 & 64.00000 & 256.0000\\
		2 & 1.333333 & 1.777775 & 2.370369 & 3.160493\\
		3 & 0.854470 & 0.730117 & 0.623862 & 0.533069\\
		4 & 0.677588 & 0.459121 & 0.311073 & 0.210776\\
		5 & 0.590550 & 0.348746 & 0.205849 & 0.121443\\
		\midrule
		$\lambda_d^*$ & 0.405285 & 0.164256 & 0.066570 & 0.026980\\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Verification of some functional inequalities via polynomial optimization" by Giovanni Fantuzzi, arXiv:2201.00362v1, 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|}
 \hline
 \textbf{UDM} & \textbf{UDM (w prompt)} & \textbf{CDM} & \textbf{CDM (w prompt)} \\
 \hline
 95.2\% & \textbf{96.58\%} & 65.23\% & \textbf{81.84\%} \\
 \hline
 \end{tabular}
\caption{The evaluation SSIM(Structual Metric,higher is better) results of Inversion with StainStructPrompt.}
\end{table}
\end{document}

eess

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

"Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion" by Bing Xiong, Yue Peng, RanRan Zhang, Fuqiang Chen, JiaYe He, and Wenjian Qin, arXiv:2412.11106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the COS and CONLeg methods for pricing daily-monitored ($L = 252$) DO Call and DO Put under the NIG model with parameters taken from \textbf{NIG1}. 38 option prices are computed in the range of $S$ from 90 to 110 and $K$ equal to 100. The barrier level $H$ is equal to 80.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccc|ccc|} % centered columns (4 columns)
\hline
&\multicolumn{4}{c|}{\textbf{COS}}&\multicolumn{3}{c|}{\textbf{CONLeg}}\\
&$N$&$R_\infty$&$R_2$&Time (sec.)&$R_\infty$&$R_2$&Time (sec.) \\
\hline
DO Call&8192&6.701e-08&4.641e-07&94.231&4.167e-09&8.651e-08&93.812\\
\hline
DO Put&8192&7.232e-09&3.231e-08&92.532&1.619e-09&6.886e-08&92.634\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Runtime comparisons for different baselines vs NDTM for super-resolution task on both datasets. The runtime numbers are in wall-clock time (seconds) and tested on a single RTX A6000 GPU.}
\begin{tabular}{cccccc|ccccc}
\toprule
 & \multicolumn{5}{c|}{FFHQ ($256 \times 256$)} & \multicolumn{5}{c}{Imagenet ($256 \times 256$)} \\ \midrule
 & DPS & RED-diff & C-$\Pi$GDM & DDRM & NDTM (Ours) & DPS & RED-diff & C-$\Pi$GDM & DDRM & NDTM (Ours) \\ \midrule
Runtime (secs / Img) & 199.1 & 5.8 & 3.68 & 1.3 & 13.6 & 399.3 & 7.1 & 16.4 & 2.4 & 38.3 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Kushagra Pandey
Farrin Marouf Sofian
Felix Draxler
Theofanis Karaletsos
Stephan Mandt

"Variational Control for Guidance in Diffusion Models" by Kushagra Pandey, Farrin Marouf Sofian, Felix Draxler, Theofanis Karaletsos, and Stephan Mandt, arXiv:2502.03686v1, 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 Number of Samples at ROI-Level Used for Training and Evaluation.}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Dataset} &\textbf{Anatomical Region} & \textbf{\# Training Samples} & \textbf{\# Evaluation Samples} & \textbf{\# Samples} \\
 \midrule
 PathCap &Unspecified&207,000 &-&207,000\\
 PathInstruct &Unspecified &180,000 &-&180,000\\
 Quilt-1M &Unspecified&1,017,712 &-&1,017,712\\
 CCRCC &Kidney & 22,530 &5,635 &28,165\\
 Chaoyang &Colon &4,021 &2,139&6,160\\
 CRC-MSI &Colon &19,557 &32,361&51,918\\
 ESCA &Esophagus&178,187&189,142&367,329\\
 Pancancer-TIL &Unspecified&209,221&56,275&265,496\\
 UniToPatho &Colon &6,270&2,399&8,669\\
 PathMMU-PubMed &Unspecified&2,787 &514 &3,301\\
 PathMMU-Atlas &Unspecified&799 &288 &1,087\\
 PathMMU-EduContent &Unspecified&1,683 &401 &2,084\\
 PathMMU-PathCLS &Unspecified &1,632 &257 &1,889\\
 PathVQA &Unspecified &19,654 & 6,719&26,373\\
 BCSS & Breast &7,322 &1,870&9,192\\
 CoCaHis &Liver &58 &24&82\\
 CoNIC2022 &Colon &15,387 &3,945&19,332\\
 CRAG &Colon &1,429 &321&1,750\\
 DigestPath2019 Cell &Colon &352 &85&437\\
 DigestPath2019 Tissue &Colon &10,725 &2,666&13,391\\
 MIDOG Breast &Breast &3,032 &762 &3,794\\
 MIDOG Neuroendocrine & Pancreas&1,448 &354 &1,802\\
 Rings &Prostate &29,451 & 14,288 &43,739\\
 SICAPv2 &Prostate &19,140 &4,784 &23,924\\
 TNBC &Breast &134 &33&167\\
 WSS4LUAD &Lung &4,590 &1,145 &5,735\\
 BreakHis &Breast &- &1,582 &1,582\\
 PanCancer-TCGA &Unspecified &- &2,000 &2,000\\
 GlaS &Colon&- &80&80\\
 NuCLS &Breast &- &905&905\\
 \midrule
 Total & & 1,964,121 & 330,974 &2,295,095 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Zhe Xu
Ziyi Liu
Junlin Hou
Jiabo Ma
Cheng Jin
Yihui Wang
Zhixuan Chen
Zhengyu Zhang
Fuxiang Huang
Zhengrui Guo
Fengtao Zhou
Yingxue Xu
Xi Wang
Ronald Cheong Kin Chan
Li Liang
Hao Chen

"A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model" by Zhe Xu, Ziyi Liu, Junlin Hou, Jiabo Ma, Cheng Jin, Yihui Wang, Zhixuan Chen, Zhengyu Zhang, Fuxiang Huang, Zhengrui Guo, Fengtao Zhou, Yingxue Xu, Xi Wang, Ronald Cheong Kin Chan, Li Liang, and Hao Chen, arXiv:2507.17303v2, 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{Ablation of Task ID global conditioning. "w/ Task ID" refers to results from main experiments; "w/o Task ID" denotes performance after removing Task ID embedding. Confusion rate is the percentage of outputs misclassified into other tasks.}
\begin{tabular}{lcccc}
\toprule
\textbf{Task} & \multicolumn{2}{c}{\textbf{Key Metric}} & \textbf{Confusion Rate (\%)} \\
& w/ Task ID & w/o Task ID & \\
\midrule
SE (OVRL) & 3.48 & 2.89 & 37.2 (mostly AEC) \\
TSE (OVRL) & 3.68 & 3.02 & 41.5 (mostly SE) \\
AEC (EMOS) & 4.54 & 3.91 & 34.8 (mostly SE) \\
LASS (CLAPScore) & 22.7 & 16.3 & 39.7 (mostly TSE) \\
\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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small{Statistics of the results obtained for transient subsurface pressure data in a binary channelized domain. The naming convention is the same and the letters (a-x) refer to the models in Figure . RMSE$_{\rm data}$ denotes the RMSE in data space, ${\rm l}_1$ refers to the ${\rm l}_1$-norm and SSIM to the structural similarity index. The ${\rm l}_1$-norm is calculated in terms of $\log_{10}K_s$ (-) while the SSIM is computed in the rescaled $\left[0,1\right]$ domain. Closest TR means the closest training model in data space and Predicted \#1 and Predicted \#2 signify predicted models from two different noise realizations.}}
\begin{tabular}{cccc}%
			\hline
			True model & RMSE$_{\rm data}$ (m) & ${\rm l}_1$ (m) & SSIM (-)\\
			\hline
			(a) True & 0.010 & 0 & 1 \\
			(b) Closest TR & 0.385 & 9908 & 0.21 \\
			(c) Predicted \#1 & 0.589 & 2504 & 0.67 \\
			(d) Predicted \#2 & 0.590 & 2594 & 0.67 \\

			(e) True & 0.010 & 0 & 1 \\
			(f) Closest TR & 0.281 & 8180 & 0.28 \\
			(g) Predicted \#1 & 0.363 & 2536 & 0.64 \\
			(h) Predicted \#2 & 0.215 & 2246 & 0.65 \\

			(i) True & 0.010 & 0 & 1 \\
			(j) Closest TR & 0.052 & 8028 & 0.32 \\
			(k) Predicted \#1 & 0.229 & 3068 & 0.64 \\
			(l) Predicted \#2 & 0.067 & 2950 & 0.65 \\

			(m) True & 0.010 & 0 & 1 \\
			(n) Closest TR & 0.037 & 2754 & 0.71 \\
			(o) Predicted \#1 & 0.021 & 1338 & 0.81 \\
			(p) Predicted \#2 & 0.033 & 1420 & 0.80 \\

			(q) True & 0.010 & 0 & 1 \\
			(r) Closest TR & 0.027 & 3398 & 0.68 \\
			(s) Predicted \#1 & 0.014 & 1638 & 0.80 \\
			(t) Predicted \#2 & 0.014 & 1624 & 0.80 \\

			(u) True & 0.010 & 0 & 1 \\
			(v) Closest TR & 0.067 & 9774 & 0.20 \\
			(w) Predicted \#1 & 0.379 & 3418 & 0.60 \\
			(x) Predicted \#2 & 0.416 & 3334 & 0.60 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

eess

Eric Laloy
Niklas Linde
Diederik Jacques

"Approaching geoscientific inverse problems with vector-to-image domain transfer networks" by Eric Laloy, Niklas Linde, and Diederik Jacques, arXiv:1912.09954v3, 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{Dice Coefficients and the 95th Percentile Hausdorff Distance (HD95) across all datasets for various point location trategies.}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Point Location} & \multicolumn{4}{|c|}{\textbf{Dice Coefficient (Mean ± Standard Deviations)}} \\
\cline{2-5}
& \textbf{Ovarian Tumor} & \textbf{Renal Tumor} & \textbf{Lung Tumor} & \textbf{Breast Tumor} \\
\hline
Center & 0.491 ± 0.246 & 0.563 ± 0.249 & 0.628 ± 0.156 & 0.650 ± 0.176 \\
\hline
Surface & 0.580 ± 0.272 & 0.548 ± 0.222 & 0.625 ± 0.228 & 0.708 ± 0.185 \\
\hline
Union & 0.604 ± 0.284 & 0.583 ± 0.245 & 0.633 ± 0.245 & 0.724 ± 0.189 \\
\hline
\multicolumn{5}{|c|}{\textbf{HD95 (mm, Mean ± Standard Deviations)}} \\
\hline
Center & 22.559 ± 12.613 & 11.470 ± 11.107 & 9.266 ± 5.019 & 19.359 ± 9.860 \\
\hline
Surface & 18.654 ± 12.713 & 11.038 ± 10.986 & 8.363 ± 4.545 & 15.714 ± 10.016 \\
\hline
Union & 17.402 ± 12.400 & 10.552 ± 11.007 & 9.713 ± 5.640 & 15.439 ± 9.658 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Yuli Wang
Victoria Shi
Wen-Chi Hsu
Yuwei Dai
Sophie Yao
Zhusi Zhong
Zishu Zhang
Jing Wu
Aaron Maxwell
Scott Collins
Zhicheng Jiao
Harrison X. Bai

"Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities" by Yuli Wang, Victoria Shi, Wen-Chi Hsu, Yuwei Dai, Sophie Yao, Zhusi Zhong, Zishu Zhang, Jing Wu, Aaron Maxwell, Scott Collins, Zhicheng Jiao, and Harrison X. Bai, arXiv:2412.17943v2, 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}{llll}
Bank & Country & Bank & Country\tabularnewline
\hline
ABN Amro & NL & Fionia (Nova Bank) & DK \tabularnewline
ATE Bank & GR & First Business Bank & GR \tabularnewline
Aareal Bank & DE & Fjordbank Mors & DK \tabularnewline
Aegon & NL & Fortis Bank & LU, NL, BE \tabularnewline
Agricultural Bank of Greece & GR & HBOS & UK \tabularnewline
Allied Irish Banks & IE & HSH Nordbank & DE \tabularnewline
Alpha Bank & GR & Hellenic & GR \tabularnewline
Amagerbanken & DK & Hypo Alpe Adria Group & AT \tabularnewline
Anglo Irish Bank & IE & Hypo Real Estate & DE \tabularnewline
Attica Bank & GR & Hypo Tirol Bank & AT \tabularnewline
BBK & ES & IKB & DE \tabularnewline
BNP Paribas & FR & ING & NL \tabularnewline
BPCE & FR & Irish Life \& Permanent & IE \tabularnewline
BPP & PT & Irish Nationwide Building Society & IE \tabularnewline
Banca Civica & ES & KBC & BE \tabularnewline
Banca Popolare & IT & Kommunalkredit & AT \tabularnewline
Banca Popolare di Milano & IT & LBBW & DE \tabularnewline
Banco Mare Nostrum & ES & Lloyds TSB & UK \tabularnewline
Banco Popolare & IT & Lokken & DK \tabularnewline
Banco de Valencia & ES & Magyar Fejlesztesi Bank Zrt & HU \tabularnewline
Bank of Cyprus & CY & Marfin Popular Bank & CY \tabularnewline
Bank of Ireland & IE & Max Bank & DK \tabularnewline
Bankia & ES & Monte dei Paschi di Siena & IT \tabularnewline
Banque Populaire & FR & Mortgage and Bank of Latvia & LV \tabularnewline
Bawag & AT & National Bank of Greece & GR \tabularnewline
BayernLB & DE & NordLB & DE \tabularnewline
CAM & ES & Nordea & SE \tabularnewline
Caisse d'Epargne & FR & Northern Rock & UK \tabularnewline
Caixa General de Depositos & PT & Nova Ljubljanska banka & SI \tabularnewline
Caja Castilla-La Mancha & ES & Novacaixagalicia & ES \tabularnewline
Caja Espana & ES & OTP Bank Nyrt & HU \tabularnewline
Carnegie Investment Bank & SE & Panellinia Bank & GR \tabularnewline
Catalunyacaica & ES & Pantebrevsselskabet & DK \tabularnewline
Commerzbank & DE & Parex & LV \tabularnewline
Cooperative Central Bank & CY & Piraeus Bank & GR, CY \tabularnewline
Credit Agricole & FR & Proton Bank & GR \tabularnewline
Credit Mutuel & FR & RBS & UK \tabularnewline
Credito Valtellinese & IT & RZB Group & AT \tabularnewline
Cyprus Development Bank & CY & Roskilde Bank & DK \tabularnewline
Cyprus Popular & CY & SNS Reaal & NL \tabularnewline
Danske Bank & DK & SachsenLB & DE \tabularnewline
Dexia & BE, FR, LU & Societe Generale & FR \tabularnewline
Dunfermline & UK & Swedbank & SE \tabularnewline
EBH & DK & T-Bank & GR \tabularnewline
EBS Building Society & IE & UBS & CH \tabularnewline
EFG Eurobank & GR & UNNIM & ES \tabularnewline
Eik Bank & DK & USB Bank & CY \tabularnewline
Erste Bank & AT & VBAG & AT \tabularnewline
Ethias & BE & Vestjysk & DK \tabularnewline
FHB Jelzalogbank Nyrt & HU & WestLB & DE \tabularnewline
Finansieringsselskabet & DK \tabularnewline
\tabularnewline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Bank distress in the news: Describing events through deep learning" by Samuel Rönnqvist and Peter Sarlin, arXiv:1603.05670v2, 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{Demographic and session distribution of the dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
Metric & Value \\
\midrule
Unique subjects & 345 \\
Unique sessions & 6007 \\
Average sessions per subject (± SD) & 17.41 $\pm$ 10.48 \\
Female & 161 (46.7\%) \\
Male & 136 (39.4\%) \\
Unspecified sex & 48 (13.9\%) \\
Age, years (mean ± SD) & 27.0 $\pm$ 15.9 \\
Age, years (range) & 17.0--85.0 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Matin Fallahi
Patricia Arias-Cabarcos
Thorsten Strufe

"Advancing Brainwave-Based Biometrics: A Large-Scale, Multi-Session Evaluation" by Matin Fallahi, Patricia Arias-Cabarcos, and Thorsten Strufe, arXiv:2501.17866v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l||r|r|r|l|}
		\hline
		\multicolumn{5}{|l|}{\cellcolor[gray]{0.95} \vphantom{\Big|} Comparison using width parameter $w=0.5$.}\\ %
\textbf{Method} & Value & \textbf{Bias} & \textbf{Std.~Dev.} & \textbf{Improve} \\ \hline
\hline
Finite Difference & 0.7281 & -0.0079 & 0.0032 & \\ \hline
Stoch.~AD & 0.7288 & -0.0072 & 0.0033 & 0.97 \\ \hline
Stoch.~AD with Regression & 0.7291 & -0.0069 & 0.0036 & 0.90 \\ \hline
Likelihood Ratio & 0.7361 & 0.0000 & 0.0025 & 1.42 \\ \hline
Analytic & 0.7361 & 0.0000 & 0.0000 & $\infty$ \\ \hline
	\end{tabular}
\end{adjustbox}
\caption{Results from 10000 Monte-Carlo experiments, each using 200000 paths, with width parameter $w=0.5$: While both AAD methods achieve similar variance reductions, the classical approach (corresponding to a pay-off smoothing) has a biased mean.}
\end{table}
\end{document}

q-fin

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


math

Siyu Liu
Xunyuan Yin
Jinfeng Liu
Feng Ding

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


stat

Tianyu Pan
Weining Shen
Clintin P. Davis-Stober
Guanyu Hu

"Precision education: A Bayesian nonparametric approach for handling item and examinee heterogeneity in assessment data" by Tianyu Pan, Weining Shen, Clintin P. Davis-Stober, and Guanyu Hu, arXiv:2211.11888v1, 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}{ll}
\toprule
\textbf{Parameter} & \textbf{Description} \\
\midrule
Model & Forward regression model with priors for emotional response \\
Setup Parameters & Prize values, probabilities, outcome, LLM \\
Observations & Prediction in natural language of how a player feels and why\\
Goals & Predicting what a participant thinks a player feels on a likert scale of 8 emotions.\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Emotions From Outcomes Environment}
\end{table}
\end{document}

cs

Kanishk Gandhi
Michael Y. Li
Lyle Goodyear
Agam Bhatia
Louise Li
Aditi Bhaskar
Mohammed Zaman
Noah D. Goodman

"BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery" by Kanishk Gandhi, Michael Y. Li, Lyle Goodyear, Agam Bhatia, Louise Li, Aditi Bhaskar, Mohammed Zaman, and Noah D. Goodman, arXiv:2501.01540v2, 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{TTS training data of text-to-token LLM with 12.5Hz speech tokenizer. Tokens measured in billions.}
\begin{tabular}{ccccc}
\toprule
Language & Speech Hours & Speech Tokens & Text Tokens & Total Tokens \\ \midrule
Chinese & 94,980 & 4.27B & 1.18B & 5.45B \\
English & 42,726 & 1.92B & 0.56B & 2.49B \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Aohan Zeng
Zhengxiao Du
Mingdao Liu
Lei Zhang
Shengmin Jiang
Yuxiao Dong
Jie Tang

"Scaling Speech-Text Pre-training with Synthetic Interleaved Data" by Aohan Zeng, Zhengxiao Du, Mingdao Liu, Lei Zhang, Shengmin Jiang, Yuxiao Dong, and Jie Tang, arXiv:2411.17607v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of LLM-based multi-agent frameworks on Website Design with GPT-3.5-Turbo}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
\toprule
\multirow{2}{*}{\textbf{Model}} & \multicolumn{4}{c|}{\textbf{Success Rate (\%) }}\\
\cline{2-5}
 & \textbf{Compilable} & \textbf{Basic Information} & \textbf{Sections} & \textbf{Overall Score}\\
\midrule
AutoGen & 20 & 0 & 0 & 7\\
MetaGPT & 80 & 60 & \textbf{60} & 67\\
CAMEL & 40 & 40 & 20 & 33\\
\textbf{Flow (Ours)} & \textbf{100} & \textbf{100} & 40 & \textbf{80}\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Boye Niu
Yiliao Song
Kai Lian
Yifan Shen
Yu Yao
Kun Zhang
Tongliang Liu

"Flow: Modularized Agentic Workflow Automation" by Boye Niu, Yiliao Song, Kai Lian, Yifan Shen, Yu Yao, Kun Zhang, and Tongliang Liu, arXiv:2501.07834v2, 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{Optimization Results with Different Models and Uncertainties}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 Metric & M1 & M2 & M3 \\
 \midrule
 $\text{Cost}^{\text{DA}}$ (CNY) & 2034.6 & 2727.6 & 2799.7 \\ \specialrule{0em}{0.5em}{0em}
 $\sum{P_{\text{d,}i,t}\Delta t}$ (kWh) & 750.6 & 337.9 & 164.9 \\ \specialrule{0em}{0.5em}{0em}
 $\sum{P_{\text{c,}i,t}\Delta t}$ (kWh) & 35.7 & 60.5 & 40.3 \\ \specialrule{0em}{0.5em}{0em}
 $\sum{P_{t}^{\text{G}}\Delta t}$ (kWh) & 1495.1 & 2288.8 & 2443.2 \\ \specialrule{0em}{0.5em}{0em}
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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

"Chance-Constrained Generic Energy Storage Operations under Decision-Dependent Uncertainty" by Ning Qi, Pierre Pinson, Mads R. Almassalkhi, Lin Cheng, and Yingrui Zhuang, arXiv:2201.06407v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance with different filter length of sinc filterbank}
\begin{tabular}{|c|c|c|}
		\hline
		 filter length & pooled min t-DCF& min t-DCF (A17) 	\\
	 \hline
	 128&0.1294&0.1811\\
	 \hline
	 251&0.1287&0.2465\\
	 \hline
	 301&0.1067&0.2773\\
	 \hline
	 512&0.1507&0.3186\\

		 \hline

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

eess

End-to-end anti-spoofing with RawNet2

Hemlata Tak
Jose Patino
Massimiliano Todisco
Andreas Nautsch
Nicholas Evans
Anthony Larcher

"End-to-end anti-spoofing with RawNet2" by Hemlata Tak, Jose Patino, Massimiliano Todisco, Andreas Nautsch, Nicholas Evans, and Anthony Larcher, arXiv:2011.01108v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"A Physics-Informed Deep Learning Paradigm for Car-Following Models" by Zhaobin Mo, Xuan Di, and Rongye Shi, arXiv:2012.13376v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|}
\hline
 & B-DeepONet & Prob-DeepONet \\
\hline
\hline
$\epsilon_\text{ratio}$ & 92.87 \% & 93.27 \% \\
\hline
\end{tabular}
\end{adjustbox}
\caption{The average $\epsilon_\text{ratio}$ for 100 post-fault trajectories selected from~$\mathcal{D}_\text{test}$ whose inputs are contaminated with noise.}
\end{table}
\end{document}

math

Christian Moya
Shiqi Zhang
Meng Yue
Guang Lin

"DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories" by Christian Moya, Shiqi Zhang, Meng Yue, and Guang Lin, arXiv:2202.07176v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|rrr|r}
\toprule
Model & V (r) & A (r) & D (r) & Avg. \\ \midrule
\textbf{Zero-Shot} & & & \\
~~1. RoBERTa \scriptsize{(CE, SE)} & .685 & .315 & .278 & .426 \\
~~2. RoBERTa \scriptsize{(Ours, SE)} & .715 & .319 & .308 & .448 \\
\midrule
\textbf{Supervised} & & & \\
~~3. RoBERTa \scriptsize{(Random, EB)} & .381 & .386 & .253 & .340 \\
~~4. BERT \scriptsize{(Pretrained, EB)} & .794 & .537 & .514 & .615 \\
~~5. RoBERTa \scriptsize{(Pretrained, EB)} & .829 &	.569 & .513 & .637 \\
~~6. Ours, SE-EB \scriptsize{(RoBERTa)} & .838 & .570 & .518 & .642 \\
\bottomrule
\end{tabular}
\caption{Ablation study results. Given that the model architecture is the same (RoBERTa-Large), the architecture and its pre-trained knowledge are effective for VAD regression. Overall, initialization with our model trained on categorical emotions (SE, SemEval) and then fine-tuning on VAD (EB, EmoBank) helps improve performance. Validation set results are in Appendix.}
\end{table}
\end{document}

eess

Sungjoon Park
Jiseon Kim
Seonghyeon Ye
Jaeyeol Jeon
Hee Young Park
Alice Oh

"Dimensional Emotion Detection from Categorical Emotion" by Sungjoon Park, Jiseon Kim, Seonghyeon Ye, Jaeyeol Jeon, Hee Young Park, and Alice Oh, arXiv:1911.02499v2, 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|ccc}
\hline
 & music & weather & sports \\ \hline
Synthetic dataset& 44K & 31K & 46K \\
Evaluation dataset & 2.1K & 2.8K & 5.1K \\ \hline
\end{tabular}
\end{adjustbox}
\caption{\# of samples for each dataset used in this study.}
\end{table}
\end{document}

cs

Minh Tran
Yutong Pang
Debjyoti Paul
Laxmi Pandey
Kevin Jiang
Jinxi Guo
Ke Li
Shun Zhang
Xuedong Zhang
Xin Lei

"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data" by Minh Tran, Yutong Pang, Debjyoti Paul, Laxmi Pandey, Kevin Jiang, Jinxi Guo, Ke Li, Shun Zhang, Xuedong Zhang, and Xin Lei, arXiv:2501.12501v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Mostafa Shabani
Dat Thanh Tran
Juho Kanniainen
Alexandros Iosifidis

"Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification" by Mostafa Shabani, Dat Thanh Tran, Juho Kanniainen, and Alexandros Iosifidis, arXiv:2207.11577v1, 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 used in QKP and MKP experiments.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
Experiment & Penalty & MCS/run & Number of runs & $\beta_{max}$ & $\eta$ \\ \midrule
QKP & $2dN$ & 1000 & 2000 & 10 & 20 \\ \midrule
MKP & $5dN$ & 1000 & 5000 & 50 & 0.05 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

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{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\multirow{2}{*}{\textbf{Entity Type}} & \multicolumn{2}{c}{\textbf{\# Fine-tuning Samples} (train/val/test)} \\ \cmidrule(l){2-3}
 & \textit{Arabic} & \textit{English} \\ \midrule
Food \& Beverage & 6,908/768/853 & 27,635/3,071/1,617 \\ \midrule
Religious & 2,655/295/328 & 12,623/1,403/1,559 \\ \midrule
Sports Clubs & 2,484/277/307 & 18,585/2,066/2,295 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Number of NER fine-tuning examples (as train/val/test splits) constructed automatically from Wikipedia articles for \textit{food}, \textit{beverage}, \textit{sports clubs}, and \textit{religious place} entities. }
\end{table}
\end{document}

cs

"On The Origin of Cultural Biases in Language Models: From Pre-training Data to Linguistic Phenomena" by Tarek Naous and Wei Xu, arXiv:2501.04662v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Random Subspace Cubic-Regularization Methods, with Applications to Low-Rank Functions" by Coralia Cartis, Zhen Shao, and Edward Tansley, arXiv:2501.09734v1, 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{Comparative analysis of 3DCNN models for smoke detection.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Model} & \textbf{AUC} & \textbf{Recall} & \textbf{F1-score} & \textbf{Accuracy} \\
\hline
S1 & 0.93 & 0.87 & 0.87 & 0.83 \\
S2 & 0.95 & 0.89 & 0.89 & 0.89 \\
S3 & 0.95 & 0.90 & 0.89 & 0.89 \\
S4 & 0.96 & 0.89 & 0.89 & 0.89 \\
S5 & 0.98 & 0.93 & 0.93 & 0.925 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Kenneth Bonilla-Ormachea
Horacio Cuizaga
Edwin Salcedo
Sebastian Castro
Sergio Fernandez-Testa
Misael Mamani

"ForestProtector: An IoT Architecture Integrating Machine Vision and Deep Reinforcement Learning for Efficient Wildfire Monitoring" by Kenneth Bonilla-Ormachea, Horacio Cuizaga, Edwin Salcedo, Sebastian Castro, Sergio Fernandez-Testa, and Misael Mamani, arXiv:2501.09926v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average Correlations Across Different Scenarios and Layers Compared to the Sampled Inference}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 \toprule
 \multirow{2}{*}{\textbf{Scenario}} & \multirow{2}{*}{\textbf{\shortstack{Sampled \\ Inference}}} & \multicolumn{4}{c}{\textbf{Layer \#}} \\
 \cmidrule(lr){3-6}
 & & \textbf{1} & \textbf{9} & \textbf{16} & \textbf{25} \\
 \midrule
 Exception & 0.743 & 0.972 & 0.633 & 0.978 & 0.790 \\
 Exception Fewshot & 0.744 & 0.956 & 0.952 & 0.959 & 0.954 \\
 Comment & 0.743 & 0.687 & 0.887 & 0.751 & 0.860 \\
 Comment Fewshot & 0.740 & 0.941 & 0.937 & 0.908 & 0.926 \\
 Print & 0.740 & 0.960 & 0.971 & \textbf{0.994} & 0.913 \\
 Print Fewshot & 0.725 & 0.953 & 0.957 & 0.923 & 0.957 \\
 \hline
 \emph{Average} & 0.740 & 0.911 & 0.890 & \textbf{0.918} & 0.900 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Predictive Prompt Analysis

"Predictive Prompt Analysis" by Jae Yong Lee, Sungmin Kang, and Shin Yoo, arXiv:2501.18883v2, 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{Training times (mins) for the first 1000 iterations on A30 GPU for separation in \textbf{Adult}, including/excluding the pre-training time. Note that for Scenario II, CON, NEU, and SBP need a pre-training (additional NN models) course but KDE and HGR do not. Refer to Supplementary~ for more details.}
\begin{tabular}{c|cc|c}
\toprule
 & \multicolumn{2}{c|}{Without}& With \\
 & \multicolumn{2}{c|}{pre-training}& pre-training \\
 \hline
Model & Scenario I & Scenario II & Scenario II \\
 \hline
 \hline
SBP & 0.22 (0.02) & 0.59 (0.05) & 0.80 (0.07) \\
CON & 0.21 (0.03) & 0.40 (0.03) & 1.45 (0.09) \\
HGR & 0.47 (0.08) & 0.77 (0.10) & - \\
KDE & 1.31 (0.09) & - & - \\
NEU & 1.01 (0.09) & 1.46 (0.03) & 3.61 (0.09) \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes" by Jinwon Sohn, Qifan Song, and Guang Lin, arXiv:2311.05866v2, 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.


math

"Markov decision processes with observation costs: framework and computation with a penalty scheme" by Christoph Reisinger and Jonathan Tam, arXiv:2201.07908v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Stationarity in Funding Rate}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\toprule
Test for Unit root in & Exogenous & t-Statistic & Probability & 1\% level & 5\% level & 10\% level\\
 \hline\hline
 Level & Constant & -9.176579 & 0.0000 & -3.431965 & -2.862139 & -2.567132\\
 \hline
Level & Constant and Linear Trend & -9.345478 & 0.0000 & -3.960568 & -3.411044 & -3127399\\
 \hline
 Level & None & -9.010413 & 0.0000 & -2.56599 & -1.94011 & -1.616641\\
 \hline
 First difference & Constant & -16.35916 & 0.0000 & -3.431971 & -2.86141 & -2.567134\\
\hline
First difference & Constant and Linear Trend & -16.35687 & 0.0000 & -3.960576 & -3.411048 & -3.127342\\
\hline
First difference & None & -16.6138 & 0.0000 & -2.565601 & -1.940911 & -1.616641\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Sai Srikar Nimmagadda
Pawan Sasanka Ammanamanchi

"BitMEX Funding Correlation with Bitcoin Exchange Rate" by Sai Srikar Nimmagadda and Pawan Sasanka Ammanamanchi, arXiv:1912.03270v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
\hline
\textbf{Model} & \textbf{Perplexity} \\ \hline
Standard GPT-2 & 78.7413 \\ \hline
AF Fine Tuned & 41.8261 \\ \hline
Proofnet Fine Tuned & 67.8906 \\ \hline
LeanDojo4 Fine Tuned & 71.8377 \\ \hline
C4 Fine Tuned & 87.4636 \\ \hline
Wikitext Fine Tuned & 94.9470 \\ \hline
Docstring Fine Tuned & 75.4504 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Perplexity Loss for Models Fine-Tuned on Proof Datasets}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of Datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 \hline\hline
 Datasets & Nodes & Samples & Sample Rate \\ \hline
 PEMS03 & 358 & 26208 & 5min \\ \hline
 PEMS04 & 307 & 16992 & 5min \\ \hline
 PEMS07 & 883 & 28224 & 5min \\ \hline
 PEMS08 & 170 & 17856 & 5min \\ \hline\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jing Chen
Haocheng Ye
Zhian Ying
Yuntao Sun
Wenqiang Xu

"Dynamic Trend Fusion Module for Traffic Flow Prediction" by Jing Chen, Haocheng Ye, Zhian Ying, Yuntao Sun, and Wenqiang Xu, arXiv:2501.10796v1, 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}
\hline
\textbf{} & \textbf{Arousal} & \textbf{Valence} & \textbf{Dominance} \\ \hline
CCC & 0.72 & 0.75 & 0.57 \\ \hline
\end{tabular}
\caption{Concordance Correlation Coefficient (CCC) between dimensional labels from annotators 1 and 2.}
\end{table}
\end{document}

eess

Maja J. Hjuler
Harald V. Skat-Rørdam
Line H. Clemmensen
Sneha Das

"EmoTale: An Enacted Speech-emotion Dataset in Danish" by Maja J. Hjuler, Harald V. Skat-Rørdam, Line H. Clemmensen, and Sneha Das, arXiv:2508.14548v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|}
\hline
\textbf{Metric} & \textbf{Weight} \\ \hline
PSNR & 1/4 \\ \hline
SSIM & 1/4 \\ \hline
Reconstruction speed & 1/2 \\ \hline
\end{tabular}
\caption{Weights of metrics}
\end{table}
\end{document}

eess

Jonathan Zhao
Matthew Westerham
Mark Lakatos-Toth
Zhikang Zhang
Avi Moskoff
Fengbo Ren

"OpenICS: Open Image Compressive Sensing Toolbox and Benchmark" by Jonathan Zhao, Matthew Westerham, Mark Lakatos-Toth, Zhikang Zhang, Avi Moskoff, and Fengbo Ren, arXiv:2103.00652v4, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lllllllll}
meta-estimator & prop\_score\_model & spearman & kendall & pearson & prob\_better\_better & prob\_better\_or\_equal & class\_score \\
ipw & DecisionTree & -0.63 & -0.57 & -0.55 & 0.03 & 0.58 & mean\_test\_f1 \\
ipw & DecisionTree & -0.27 & -0.26 & -0.28 & 0.06 & 0.81 & mean\_test\_average\_precision \\
ipw & DecisionTree & 0.56 & 0.54 & 0.58 & 0.25 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw & LogisticRegression\_l1\_liblinear & 0.57 & 0.56 & 0.59 & 0.18 & 1.00 & mean\_test\_f1 \\
ipw & LogisticRegression\_l1\_liblinear & 0.87 & 0.83 & 0.97 & 0.51 & 1.00 & mean\_test\_average\_precision \\
ipw & LogisticRegression\_l1\_liblinear & & & & 0.00 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw & LogisticRegression\_l1\_saga & & & & 0.00 & 1.00 & mean\_test\_f1 \\
ipw & LogisticRegression\_l1\_saga & 0.83 & 0.76 & 0.94 & 0.47 & 1.00 & mean\_test\_average\_precision \\
ipw & LogisticRegression\_l1\_saga & 0.90 & 0.85 & 0.73 & 0.53 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw & LogisticRegression\_l2 & & & & 0.00 & 1.00 & mean\_test\_f1 \\
ipw & LogisticRegression\_l2 & 1.00 & 1.00 & 0.99 & 0.38 & 1.00 & mean\_test\_average\_precision \\
ipw & LogisticRegression\_l2 & 0.25 & 0.24 & 0.24 & 0.09 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw & LogisticRegression\_l2\_liblinear & & & & 0.00 & 1.00 & mean\_test\_f1 \\
ipw & LogisticRegression\_l2\_liblinear & 0.31 & 0.22 & 0.67 & 0.36 & 0.80 & mean\_test\_average\_precision \\
ipw & LogisticRegression\_l2\_liblinear & & & & 0.00 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw & SVM\_sigmoid & & & & 0.00 & 1.00 & mean\_test\_f1 \\
ipw & SVM\_sigmoid & & & & 0.00 & 1.00 & mean\_test\_average\_precision \\
ipw & SVM\_sigmoid & & & & 0.00 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw & Standardized\_SVM\_rbf & 0.96 & 0.93 & 0.90 & 0.56 & 1.00 & mean\_test\_f1 \\
ipw & Standardized\_SVM\_rbf & 0.38 & 0.37 & 0.30 & 0.22 & 0.96 & mean\_test\_average\_precision \\
ipw & Standardized\_SVM\_rbf & -0.93 & -0.86 & -0.86 & 0.00 & 0.44 & mean\_test\_balanced\_accuracy \\
ipw & Standardized\_SVM\_sigmoid & 0.37 & 0.35 & 0.34 & 0.13 & 1.00 & mean\_test\_f1 \\
ipw & Standardized\_SVM\_sigmoid & 0.60 & 0.58 & 0.87 & 0.20 & 1.00 & mean\_test\_average\_precision \\
ipw & Standardized\_SVM\_sigmoid & -0.31 & -0.30 & -0.30 & 0.00 & 0.89 & mean\_test\_balanced\_accuracy \\
ipw & kNN & 0.45 & 0.41 & 0.61 & 0.31 & 0.93 & mean\_test\_f1 \\
ipw & kNN & 0.00 & 0.00 & -0.05 & 0.13 & 0.87 & mean\_test\_average\_precision \\
ipw & kNN & -0.76 & -0.76 & -0.76 & 0.00 & 0.69 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & DecisionTree & -0.63 & -0.57 & -0.55 & 0.03 & 0.58 & mean\_test\_f1 \\
ipw\_trimeps.01 & DecisionTree & -0.27 & -0.26 & -0.28 & 0.06 & 0.81 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & DecisionTree & 0.56 & 0.54 & 0.58 & 0.25 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & LogisticRegression\_l1\_liblinear & 0.57 & 0.56 & 0.59 & 0.18 & 1.00 & mean\_test\_f1 \\
ipw\_trimeps.01 & LogisticRegression\_l1\_liblinear & 0.87 & 0.83 & 0.97 & 0.51 & 1.00 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & LogisticRegression\_l1\_liblinear & & & & 0.00 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & LogisticRegression\_l1\_saga & & & & 0.00 & 1.00 & mean\_test\_f1 \\
ipw\_trimeps.01 & LogisticRegression\_l1\_saga & 0.83 & 0.76 & 0.94 & 0.47 & 1.00 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & LogisticRegression\_l1\_saga & 0.90 & 0.85 & 0.73 & 0.53 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & LogisticRegression\_l2 & & & & 0.00 & 1.00 & mean\_test\_f1 \\
ipw\_trimeps.01 & LogisticRegression\_l2 & 1.00 & 1.00 & 0.99 & 0.38 & 1.00 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & LogisticRegression\_l2 & 0.25 & 0.24 & 0.24 & 0.09 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & LogisticRegression\_l2\_liblinear & & & & 0.00 & 1.00 & mean\_test\_f1 \\
ipw\_trimeps.01 & LogisticRegression\_l2\_liblinear & 0.31 & 0.22 & 0.67 & 0.36 & 0.80 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & LogisticRegression\_l2\_liblinear & & & & 0.00 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & SVM\_sigmoid & & & & 0.00 & 1.00 & mean\_test\_f1 \\
ipw\_trimeps.01 & SVM\_sigmoid & & & & 0.00 & 1.00 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & SVM\_sigmoid & & & & 0.00 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & Standardized\_SVM\_rbf & 0.96 & 0.93 & 0.90 & 0.56 & 1.00 & mean\_test\_f1 \\
ipw\_trimeps.01 & Standardized\_SVM\_rbf & 0.38 & 0.37 & 0.30 & 0.22 & 0.96 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & Standardized\_SVM\_rbf & -0.93 & -0.86 & -0.86 & 0.00 & 0.44 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & Standardized\_SVM\_sigmoid & 0.37 & 0.35 & 0.34 & 0.13 & 1.00 & mean\_test\_f1 \\
ipw\_trimeps.01 & Standardized\_SVM\_sigmoid & 0.60 & 0.58 & 0.87 & 0.20 & 1.00 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & Standardized\_SVM\_sigmoid & -0.31 & -0.30 & -0.30 & 0.00 & 0.89 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & kNN & 0.45 & 0.41 & 0.61 & 0.31 & 0.93 & mean\_test\_f1 \\
ipw\_trimeps.01 & kNN & 0.00 & 0.00 & -0.05 & 0.13 & 0.87 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & kNN & -0.76 & -0.76 & -0.76 & 0.00 & 0.69 & mean\_test\_balanced\_accuracy
\end{tabular}
\end{table}
\end{document}

stat

Brady Neal
Chin-Wei Huang
Sunand Raghupathi

"RealCause: Realistic Causal Inference Benchmarking" by Brady Neal, Chin-Wei Huang, and Sunand Raghupathi, arXiv:2011.15007v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
 \toprule
 \textbf{ID} & \textbf{Classes} & \textbf{Super-class}\\
 \midrule
 0 & boxer, pug, Rottweiler & dogs\\
 1 & orangutan, weasel, panda & other mammals\\
 2 & toucan, flamingo, ostrich & birds\\
 3 & eel, scorpion, hammerhead & other animals\\
 4 & minivan, ambulance, taxi & land vehicles\\
 5 & submarine, canoe, pirate & sea vehicles\\
 6 & guacamole, hotdog, banana & food\\
 7 & backpack, pyjama, kimono & clothes\\
 8 & monitor, iPod, photocopier & tech devices\\
 9 & xylophone, harp, trumpet & instruments\\
 \bottomrule
 \end{tabular}
\caption{\textbf{Super-class division for $E=10$}. For each super-class, we list three randomly chosen class names (which turn out to be semantically related) together with a possible super-class name.}
\end{table}
\end{document}

stat

Erik Daxberger
Floris Weers
Bowen Zhang
Tom Gunter
Ruoming Pang
Marcin Eichner
Michael Emmersberger
Yinfei Yang
Alexander Toshev
Xianzhi Du

"Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts" by Erik Daxberger, Floris Weers, Bowen Zhang, Tom Gunter, Ruoming Pang, Marcin Eichner, Michael Emmersberger, Yinfei Yang, Alexander Toshev, and Xianzhi Du, arXiv:2309.04354v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

On Lie algebras associated with a spray

"On Lie algebras associated with a spray" by Manelo Anona and Hasina Ratovoarimanana, arXiv:2201.10867v2, 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
\caption{Spread of tranches (in 100 bps) with maturity time $T$ in the Base Case (Affine jump diffusion without self-exciting jumps)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
				\hline
				& $T=3$ & $T=4$ & $T=5$ & $T=6$ \\
				\hline
				Tranche 1 & 1.9368 & 1.9368&1.9368&1.9368 \\
				\hline
				Tranche 2 & 0.9758 & 0.9758&0.9758&0.9758 \\
				\hline
				Tranche 3 & 0.0357& 0.0357 & 0.0334 & 0.0306 \\
				\hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Puneet Pasricha
Dharmaraja Selvamuthu
Selvaraju Natarajan

"A contagion process with self-exciting jumps in credit risk applications" by Puneet Pasricha, Dharmaraja Selvamuthu, and Selvaraju Natarajan, arXiv:2202.12946v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

"Centrality Graph Shift Operators for Graph Neural Networks" by Yassine Abbahaddou, Fragkiskos D. Malliaros, Johannes F. Lutzeyer, and Michalis Vazirgiannis, arXiv:2411.04655v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

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


cs

Preston M. Smith
Jill Gemmill
David Y. Hancock
Brian W. O'Shea
Winona Snapp-Childs
James Wilgenbusch

"Application of the Cyberinfrastructure Production Function Model to R1 Institutions" by Preston M. Smith, Jill Gemmill, David Y. Hancock, Brian W. O'Shea, Winona Snapp-Childs, and James Wilgenbusch, arXiv:2501.10264v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{A comparison between TLM and other age estimation models using TIMIT dataset for training and testing}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\hline \\
\textbf{Method} & \textbf{Age RMSE} & \textbf{Age MAE} \\
\hline \\
Common sense test &7.52 & 5.55 \\
TLM with oracle &1.18 & 0.49 \\
\hline
Linear Regression& 5.79 & 4.49 \\
K-means and Linear Regression & 7.69 & 5.88 \\
Random Forest & 6.58 & 4.85 \\
MLP with ReLU & 6.79 & 4.73 \\
\hline
Singh et al. & 8.16 & 5.83 \\
NRT &9.13 & 7.19 \\
Manav et al. (single-task) & 7.16 & 5.03 \\
Manav et al. (multi-task) & 8.07 & 5.63 \\
Gupta et al. & 5.86 & 4.13 \\
\hline
TitaNet + TLM (Hard routing) & 5.62 & 4.09 \\
TitaNet + TLM (Soft routing) & 5.49 & 4.02 \\
TitaNet + TLM (Features Optimization) & \textbf{5.36} & \textbf{3.97} \\
\hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Dareen Alharthi
Mahsa Zamani
Bhiksha Raj
Rita Singh

"Tessellated Linear Model for Age Prediction from Voice" by Dareen Alharthi, Mahsa Zamani, Bhiksha Raj, and Rita Singh, arXiv:2501.09229v2, 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{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c} \hline
 Dataset & Method & PSNR$\uparrow$ & MUSIQ$\uparrow$ & CLIPIQA $\uparrow$ & TOPIQ$\uparrow$ \\ \hline
 \multirow{4}[0]{*}{RealSR} & Baseline & 24.39 & 70.22 & 0.6751 & 0.6391 \\
 & w/o TAE & \textcolor{blue}{\underline{24.89}} & 70.08 & 0.6857 & 0.6466 \\
 & w/o TAVSD & 24.84 & \textcolor{blue}{\underline{70.96}} & \textcolor{blue}{\underline{0.6930}} & \textcolor{blue}{\underline{0.6553}} \\
 & Full & \textcolor{red}{\textbf{25.16}} & \textcolor{red}{\textbf{71.18}} & \textcolor{red}{\textbf{0.7283}} & \textcolor{red}{\textbf{0.7082}} \\ \hline
 \multirow{4}[0]{*}{DrealSR} & Baseline & 27.45 & 65.90 & 0.6887 & 0.6275 \\
 & w/o TAE & 27.95 & 65.95 & \textcolor{blue}{\underline{0.7030}} & \textcolor{blue}{\underline{0.6396}} \\
 & w/o TAVSD & \textcolor{blue}{\underline{28.03}} & \textcolor{blue}{\underline{66.95}} & 0.7015 & 0.6373 \\
 & Full & \textcolor{red}{\textbf{28.39}} & \textcolor{red}{\textbf{67.02}} & \textcolor{red}{\textbf{0.7398}} & \textcolor{red}{\textbf{0.6758}} \\ \hline
 \end{tabular}
\caption{Quantitative Comparison of ablation study on TAVSD and TAE. Baseline uses the original VAE encoder in SD and VSD loss.}
\end{table}
\end{document}

eess

Tainyi Zhang
Zheng-Peng Duan
Peng-Tao Jiang
Bo Li
Ming-Ming Cheng
Chun-Le Guo
Chongyi Li

"Time-Aware One Step Diffusion Network for Real-World Image Super-Resolution" by Tainyi Zhang, Zheng-Peng Duan, Peng-Tao Jiang, Bo Li, Ming-Ming Cheng, Chun-Le Guo, and Chongyi Li, arXiv:2508.16557v2, 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{Comparison of the $R_\infty$ and $R_2$ errors of the SFP--FCC method for pricing an American put option under the CGMY model with parameters taken from \textbf{CGMY1} when $\tilde{N}$ increases and $L$ and $U$ are kept the same. 14 option prices are computed for the CONV method and the COS method, respectively, in a range of $S$ from 0.5 to 1.5, and $K$ is equal to 1.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ccccc|} % centered columns (4 columns)
\hline
\multirow{2}{*}{$L$ in Eq.~()}&\multicolumn{5}{c|}{\textbf{SFP--FCC}}\\
&$U$&$\tilde{N}$&$R_\infty$&$R_2$&Time (sec.)\\
\hline
2& 256& 64& 3.180e-03 & 1.114e-02 & 1.530\\
2& 256& 128& 1.659e-05 & 1.011e-04 & 3.021\\
2& 256& 256 & 1.670e-05 & 1.021e-04& 5.282\\
2& 256& 512& 1.670e-05 & 1.021e-04& 10.082\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"An SFP--FCC Method for Pricing and Hedging Early-exercise Options under Lévy Processes" by Tat Lung and Chan, arXiv:1909.07319v1, licensed under CC BY-SA 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{MOS test for different domains}
\begin{tabular}{cccc}
		\toprule
		Domain & Recording & Baseline & s-Transformer \\
		\midrule
		Short & 4.32 & 4.29 & 4.29 \\
		Long & 4.46 & 4.22 & 4.2 \\
		Extra-long & 4.63 & 3.79 & 3.99 \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

Xi Wang
Huaiping Ming
Lei He
Frank K. Soong

"s-Transformer: Segment-Transformer for Robust Neural Speech Synthesis" by Xi Wang, Huaiping Ming, Lei He, and Frank K. Soong, arXiv:2011.08480v1, 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}
 \hline
 \hline
 \textbf{Parameter} & \textbf{Value}\\
 \hline
 \hline
 Rician factor~$\kappa$ & 4\\
 \hline
 Number of BSs $N$ & 4\\
 \hline
 Number of antennas of each BS $N_a$ & 8\\
 \hline
 Number of users $K$ & 8\\
 \hline
 Noise power $\sigma^2$ & $-90$~dBm\\
 \hline
 Carrier frequency & 5.9GHz\\
 \hline
 Size of each RIS element & 0.02m\\
 \hline
 Hardware static power consumption of each BS & 10dBw\\
 \hline
 Hardware static power consumption of each RIS element with 1-bit phase shifts & 5dBm\\
 \hline
 Hardware static power consumption of each RIS element with 2-bit phase shifts & 10dBm\\
 \hline
 Hardware static power consumption of each RIS element with 3-bit phase shifts & 15dBm\\
 \hline
 Hardware static power consumption of each RIS element with continuous phase shifts & 25dBm\\
 \hline
 Hardware static power consumption of each distributed antenna in DAS & 20dBm\\
 \hline
 Hardware static power consumption of each user & 10dBm\\
 \hline
 Thresholds $\varrho$ and $\varepsilon$ & 0.001\\
 \hline
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Yutong Zhang
Boya Di
Hongliang Zhang
Jinlong Lin
Yonghui Li
Lingyang Song

"Beyond Cell-free MIMO: Energy Efficient Reconfigurable Intelligent Surface Aided Cell-free MIMO Communications" by Yutong Zhang, Boya Di, Hongliang Zhang, Jinlong Lin, Yonghui Li, and Lingyang Song, arXiv:2011.08473v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The limit of $\gamma^2-1$, $c_e$, and $c_d$ for various graph families including the complete graph with an edge removed; a particular complete triparite graph, a complete split graph (i.e., $S(n,m) = K_{n+m} - K_{m})$; the complete graph with a pendant edge, a kite whose head is an $r$-regular graph; the star; and the Cartesian product of a graph with itself.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 $G_n$ & $\lim \gamma^2-1$ & $\lim c_e$ & $\lim c_d$ \\ \hline \hline
 $K_{n}-K_2$ & $0$ & $0$ & $0$ \\ \hline
 $K_{1,n,n}$ & $3$ & $0$ & $0$ \\ \hline
 $S(n,kn)$ & $\left(\frac{\sqrt{4k+1}+1}{2}\right)^2-1$ & $ \frac{k\left(\frac{\sqrt{4k+1}-1}{k}-2\right)^2}{(\sqrt{4k+1}+1)^2}$ & $\frac{k^3}{(2k+1)^2}$ \\ \hline
 $P_2K_{n-1}$ & $\infty$ & $0$ &$0$\\\hline
 $P_nG_n^r$ & $\infty$ & $-$ & $\left(\frac{r-2}{r+2}\right)^2$ \\\hline
 $K_{1,n}$ & $\infty$ & 1 & $\infty$ \\\hline
 $G^{\square n}$ & $\infty$ & $\infty$ & 0 \\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Comparing Eigenvector and Degree Dispersion with the Principal Ratio of a Graph" by Gregory J. Clark, arXiv:2202.00325v1, 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}{llcccc}
		\toprule
		\textbf{Language} & \textbf{Context} & \textbf{F1} & \textbf{B-1} & \textbf{B-2} & \textbf{R-L} \\
		\midrule
		Chinese & Full & 18.37 & 11.60 & 4.68 & 5.44 \\
		Chinese & - 30\% & 18.52 & 12.19 & 4.77 & 5.85 \\
		English & Full & 19.92 & 12.80 & 5.73 & 5.54 \\
		English & - 30\% & 19.36 & 12.60 & 5.41 & 4.91 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{Performance comparison with and without 30\% context removal on the LLaMA 3 8B model. Context removal excludes the final two turns.}
\end{table}
\end{document}

cs

Zhongtian Hu
Yiwen Cui
Ronghan Li
Meng Zhao
Lifang Wang

"Can MLLMs Generalize to Multi-Party dialog? Exploring Multilingual Response Generation in Complex Scenarios" by Zhongtian Hu, Yiwen Cui, Ronghan Li, Meng Zhao, and Lifang Wang, arXiv:2501.11269v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Daniel Jenson
Jhonathan Navott
Mengyan Zhang
Makkunda Sharma
Elizaveta Semenova
Seth Flaxman

"Transformer Neural Processes - Kernel Regression" by Daniel Jenson, Jhonathan Navott, Mengyan Zhang, Makkunda Sharma, Elizaveta Semenova, and Seth Flaxman, arXiv:2411.12502v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{makecell}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cc|cc|}
		\Xhline{2\arrayrulewidth}
		\textbf{Preset Statistic} : & Primary Equiaxed & \textbf{Mu} : & 2.3 \\ \hline
		\textbf{Max cutoff} : & 4 & \textbf{Sigma} : & 0.4 \\ \hline
		\textbf{Bin step size} : & 1 & \textbf{Min/Max cutoff} : & 4 \\
		\Xhline{2\arrayrulewidth}
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Ankit Shrivastava
Jingxiao Liu
Kaushik Dayal
Hae Young Noh

"Predicting Peak Stresses In Microstructured Materials Using Convolutional Encoder-Decoder Learning" by Ankit Shrivastava, Jingxiao Liu, Kaushik Dayal, and Hae Young Noh, arXiv:2201.00722v1, 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}
\hline
$N$ & $h$ & $E_h$ & $r_h$ \\
\hline
 \hline
20736 & 0.0833 & 0.15988 & \\
65536 & 0.0625 & 0.09132 & 1.94678\\
160000 & 0.05 & 0.05886 & 1.968276\\
331776 & 0.0041677 & 0.04104 & 1.977904\\
614656 & 0.0035714 & 0.03022 & 1.98531\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Errors and convergence rates}
\end{table}
\end{document}

math

"A finite element method to a periodic steady-state problem for an electromagnetic field system using the space-time finite element exterior calculus" by Masaru Miyashita and Norikazu Saito, arXiv:2201.04481v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
\toprule
\textbf{Name} & \textbf{Symbol} & \textbf{Value} \\
\midrule
\multicolumn{3}{l}{\textbf{General}} \\
\midrule
Replay capacity (FIFO) & --- & $10^6\!\!$ \\
Batch size & $B$ & 16 \\
Batch length & $T$ & 64 \\
Activation & --- & $\operatorname{LayerNorm}+\operatorname{SiLU}$ \\
\midrule
\multicolumn{3}{l}{\textbf{World Model}} \\
\midrule
Number of latents & --- & 32 \\
Classes per latent & --- & 32 \\
Reconstruction loss scale & $\beta_{\mathrm{pred}}$ & 1.0 \\
Dynamics loss scale & $\beta_{\mathrm{dyn}}$ & 0.5 \\
Representation loss scale & $\beta_{\mathrm{rep}}$ & 0.1 \\
Learning rate & --- & $10^{-4}$ \\
Adam epsilon & $\epsilon$ & $10^{-8}$ \\
Gradient clipping & --- & 1000 \\
\midrule
\multicolumn{3}{l}{\textbf{Actor Critic}} \\
\midrule
Imagination horizon & $H$ & 15 \\
Discount horizon & $1/(1-\gamma)$ & 333 \\
Return lambda & $\lambda$ & 0.95 \\
Critic EMA decay & --- & 0.98 \\
Critic EMA regularizer & --- & 1 \\
Return normalization scale & $S$ & $\operatorname{Per}(R, 95) - \operatorname{Per}(R, 5)$ \\
Return normalization limit & $L$ & 1 \\
Return normalization decay & --- & 0.99 \\
Actor entropy scale & $\eta$ & { $\boldsymbol{3\cdot10^{-4}}$} \\
Learning rate & --- & $3\cdot10^{-5}$ \\
Adam epsilon & $\epsilon$ & $10^{-5}$ \\
Gradient clipping & --- & 100 \\
\bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Dreamer v3 hyper parameters .}
\end{table}
\end{document}

cs

Hang Wang
Xin Ye
Feng Tao
Chenbin Pan
Abhirup Mallik
Burhaneddin Yaman
Liu Ren
Junshan Zhang

"AdaWM: Adaptive World Model based Planning for Autonomous Driving" by Hang Wang, Xin Ye, Feng Tao, Chenbin Pan, Abhirup Mallik, Burhaneddin Yaman, Liu Ren, and Junshan Zhang, arXiv:2501.13072v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Filipe Costa de Souza
Wilton Bernardino
Silvio Cabral Patricio

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Complex Fourier transforms for a variety of financial contingency claims} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|ccc|} % centered columns (4 columns)
\hline
Financial Contingency Claim&Fourier Transform& Fourier Transform\\
&$\widehat{G}_k$&$\widehat{G}_0$\\
\hline

Call&$\frac{K(d-c)}{i 2\pi k+(d-c)}\left(e^{(i\frac{2\pi}{d-c}k+1)d}-1\right)-\frac{K(d-c)}{i 2\pi k}\left(e^{(i\frac{2\pi}{d-c}k)d}-1\right)$&$K(e^d-1)-K d$\\
Put&$\frac{K(d-c)}{i 2\pi k+(d-c)}\left(e^{(i\frac{2\pi}{d-c}k+1)c}-1\right)-\frac{K(d-c)}{i 2\pi k}\left(e^{(i\frac{2\pi}{d-c}k)c}-1\right)$&$K(e^c-1)-K c$\\
Covered Call&$-\frac{K(d-c)}{i 2\pi k+(d-c)}\left(e^{(i\frac{2\pi}{d-c}k+1)c}-1\right)+\frac{K(d-c)}{i 2\pi k}\left(e^{(i\frac{2\pi}{d-c}k)c}-1\right)$&$-K(e^c-1)+K c$\\
Cash-or-Nothing Call&$\frac{(d-c)}{i 2\pi k}\left(e^{(i\frac{2\pi}{d-c}k)d}-1\right)$&$d$\\
Cash-or-Nothing Put&$-\frac{(d-c)}{i 2\pi k}\left(e^{(i\frac{2\pi}{d-c}k)c}-1\right)$&$-c$\\
Asset-or-Nothing Call&$\frac{(d-c)}{i 2\pi k+(d-c)}\left(e^{(i\frac{2\pi}{b-a}k+1)d}-1\right)$&$(e^d-1)$\\
Asset-or-Nothing Put&$-\frac{(d-c)}{i 2\pi k+(d-c)}\left(e^{(i\frac{2\pi}{d-c}k+1)c}-1\right)$&$-(e^c-1)$\\
Asymmetric Call&$\frac{K^n(d-c)}{i 2\pi k+n(d-c)}\left(e^{(i\frac{2\pi}{d-c}k+n)d}-1\right)-\frac{K^n(d-c)}{i 2\pi k}\left(e^{(i\frac{2\pi}{d-c}k)d}-1\right)$&${K^n\over n}(e^{nd}-1)-K^n d$\\
Asymmetric Put&$\frac{K^n(d-c)}{i 2\pi k+n(d-c)}\left(e^{(i\frac{2\pi}{d-c}k+1)c}-1\right)-\frac{K^n(d-c)}{i 2\pi k}\left(e^{(i\frac{2\pi}{d-c}k)c}-1\right)$&${K^n\over n}(e^{nc}-1)-K^n c$\\
Symmetric Call&$\sum\limits_{j=0}^n \binom{n}{j} (-1)^{(n-j)}\frac{K^n(d-c)}{i 2\pi k+j(d-c)}\left(e^{(i\frac{2\pi}{d-c}k+j)d}-1\right)$&$\sum\limits_{j=1}^n \binom{n}{j} (-1)^{(n-j)}\times$\\
&&${K^n\over j}(e^{jd}-1)+(-1)^{n}K^nd$\\
Symmetric Put&$\sum\limits_{j=0}^n \binom{n}{j} (-1)^{(n-j+1)}\frac{K^n(d-c)}{i 2\pi k+(n-j)(d-c)}\left(e^{(i\frac{2\pi}{d-c}k+n-j)c}-1\right)$&$\sum\limits_{j=0}^{n-1} \binom{n}{j} (-1)^{(n-j+1)}\times$\\
&&${K^n\over n-j}(e^{(n-j)c}-1)-K^nc $\\
\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{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf{GARCH statistics for INTEL 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-statistic & p-value \\ % inserts table
		\hline
		$a_0$ & 0.02167 & 0.00982 & 2.206 & 0.0274 \\
		$a_1$ & 0.15914 & 0.03682 & 4.322 & 1.5E-05\\
		$b_1$ & 0.61185 & 0.13686 & 4.471 & $<$7.8E-06\\
\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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ASR performance under different token compression strategies.}
\begin{tabular}{lcc}
\toprule
\textbf{Method} & \textbf{WER (\%)} & \textbf{CER (\%)} \\
\midrule
HuBERT (original, 50Hz) & 6.2 & 3.3 \\
HuBERT (deduplicated, 26Hz) & 5.9 & 3.1 \\
Entropy-guided (medium, 15Hz) & \textbf{5.6} & \textbf{2.9} \\
Fixed-length Pooling (2, 25Hz) & 6.4 & 3.5 \\
Entropy-guided (high, 7Hz) & 10.5 & 6.2 \\
Fixed-length Pooling (4, 12.5Hz) & 6.8 & 3.6 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Jialong Zuo
Guangyan Zhang
Minghui Fang
Shengpeng Ji
Xiaoqi Jiao
Jingyu Li
Yiwen Guo
Zhou Zhao

"Entropy-based Coarse and Compressed Semantic Speech Representation Learning" by Jialong Zuo, Guangyan Zhang, Minghui Fang, Shengpeng Ji, Xiaoqi Jiao, Jingyu Li, Yiwen Guo, and Zhou Zhao, arXiv:2509.00503v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 Geometry & Contextual & \# of Copies \\
 \hline
 $\mathcal{H}_C$ & No & $120$\\
 $\mathcal{H}_S$ & No &$7560$\\
 $\overline{\mathcal{H}}_C$ & No & $120$\\
 $\overline{\mathcal{H}}_S$ & Yes & $7560$\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Results on the contextuality properties of differently-embedded hexagons and their complements. The difference between the two embeddings reveals itself in terms of contextuality when one considers the complement of the configuration.}
\end{table}
\end{document}

math

Frédéric Holweck
Henri de Boutray
Metod Saniga

"Three-Qubit-Embedded Split Cayley Hexagon is Contextuality Sensitive" by Frédéric Holweck, Henri de Boutray, and Metod Saniga, arXiv:2202.00726v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Classification achievements of the proposed scheme on the CTU-13 dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\hline
\textbf{Model} & \textbf{Accuracy (\%)} & \textbf{Precision (\%)} & \textbf{Recall (\%)} & \textbf{F1-Score (\%)} \\
\midrule
\hline
FFS-HTTP & 98.79 & 97.26 & 98.03 & 97.93 \\
k-NN & 93.00 & 90.00 & 92.20 & 91.51 \\
SVM-RBF & 85.03 & 84.87 & 86.95 & 84.95 \\
MLP & 81.95 & 86.91 & 84.58 & 83.88 \\
bot-DL & 96.60 & 96.30 & 96.80 & 96.40 \\
SVM & 80.73 & 98.07 & 83.15 & 88.56 \\
NB & 78.00 & 79.00 & 76.00 & 77.00 \\
LR & 85.00 & 89.00 & 86.00 & 87.00 \\
AdaBoost & 90.00 & 95.00 & 94.00 & 94.00 \\
XGBoost & 96.00 & 98.00 & 98.00 & 98.00 \\
\rowcolor{green!20} \textbf{LENS-XAI Teacher} & \textbf{98.42} & \textbf{98.43} & \textbf{98.42} & \textbf{98.42} \\
\rowcolor{green!20} \textbf{LENS-XAI Student} & \textbf{98.12} & \textbf{98.14} & \textbf{98.12} & \textbf{98.12} \\
\hline
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity" by Muhammet Anil Yagiz and Polat Goktas, arXiv:2501.00790v2, 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}{ccc}
\hline
\textbf{POS Tag} & \textbf{EN Count} & \textbf{ZH Count} \\ \hline
VERB & 11169 & 19427 \\
PRON & 7089 & 5476 \\
DET & 4620 & 810 \\
NOUN & 18454 & 20217 \\
PART & 1451 & 8356 \\
ADP & 5998 & 2447 \\
PROPN & 2719 & 923 \\
AUX & 3039 & - \\
ADV & 1616 & 6317 \\
ADJ & 6686 & 2197 \\
CCONJ & 1437 & 1231 \\
SCONJ & 683 & 67 \\
INTJ & 43 & 8 \\
X & 9 & 137 \\
NUM & 46 & 2469 \\
PUNCT & - & 1 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{POS tag counts for English (EN) and Chinese (ZH)}
\end{table}
\end{document}

cs

Yupei Li
Zifan Wei
Heng Yu
Jiahao Xue
Huichi Zhou
Björn W. Schuller

"DOTA-ME-CS: Daily Oriented Text Audio-Mandarin English-Code Switching Dataset" by Yupei Li, Zifan Wei, Heng Yu, Jiahao Xue, Huichi Zhou, and Björn W. Schuller, arXiv:2501.12122v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Simulation-based Value-at-Risk for Nonlinear Portfolios" by Junyao Chen, Tony Sit, and Hoi Ying Wong, arXiv:1904.09088v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average computational time (in seconds) for i) continuous formulation, ii) DD formulation, and the speed-up in simulation time .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cc|c|c|cc|}
\hline
& $K$ & Continuous	& DD & Speed-up & \\
\hline
$N=1$ & & & & & \\
\hline
& 4 		&	$<0.2$					& 	$<0.3$		& 	-	 	&	\\
& 8 		&	$<0.2$					& 	$<0.3$		& 	-			& \\
& 16 		& 	6.8					& $<1.0$ 		&	6.8x 			&	\\
& 32 	&	1330.4				& 6.9			&	192.8x 	& \\
& 64 	&	Out of memory	& 71.9			& 	-				&	\\
\hline
$N=2$ & & & & & \\
\hline
& 4 		& 	$<0.2$					& $<0.4$ 	& - 		& \\
& 8 		& 	5.0					& $<0.4$ 	& 12.5x			& \\
& 16 		& 	1118.6				& 2.3 		& 486.4x 	& \\
& 32 	& 	Out of memory	& 27.9 		& 	- 				& \\
\hline
$N=3$ & & & & & \\
\hline
& 4 		& 0.9					& $<0.4$	& 	-	 	& \\
& 8 		& 96.6					& $<0.9$	&	107.3x	 	& \\
& 16		& Out of memory	& 8.2		&	-				& \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Benchmark timing results on CPU.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
			\toprule
			\multirow{3}{*}{Library} & \multirow{3}{*}{Method} & \multirow{3}{*}{\shortstack{Permutation / \\Preconditioner}} & \multirow{3}{*}{\shortstack{Small\\problems\\time (ms)}} &
			\multirow{3}{*}{\shortstack{Medium\\problems\\time (ms)}} &
			\multirow{3}{*}{\shortstack{Large\\problems\\time (ms)}}\\
			&&&&&\\
			&&&&&\\
			\midrule
			\multirow{4}{*}{\shortstack{SuiteSparse\\(CPU)}} & KLU & - & 44.8 & 600 & 3925 \\
			& LU & - & 26.2 & 127 & 420 \\
			\cmidrule{2-6}
			& \multirow{2}{*}{Cholesky} & Simplicial & 21.9 & 227 & 896 \\
			& & Supernodal & 13.4 & 64.6 & 239 \\
			\midrule
			\multirow{45}{*}{\shortstack{PETSc\\(CPU)}} & \multirow{6}{*}{\shortstack{Conjugate\\Gradient}} & - & 11.7 & 73.4 & 253 \\
			&& Jacobi & 9.35 & 63.1 & 229 \\
			&& SOR & 8.15 & 51.9 & 180 \\
			&& SSOR & 5.87 & 38.1 & 124 \\
			&& ICC & 2.28 & 17.0 & 53.1 \\
			&& \textbf{ILU(0)} & \textbf{2.13} & 15.8 & 48.6\\
			\cmidrule{2-6}
			&\multirow{6}{*}{\shortstack{Conjugate\\Residuals}} & - & 11.0 & 67.6 & 239 \\
			&& Jacobi & 8.30 & 53.9 & 194\\
			&& SOR & 7.55 & 46.7 & 159 \\
			&& SSOR & 5.70 & 35.6 & 113 \\
			&& ICC & 2.33 & 16.8 & 51.8 \\
			&& \textbf{ILU(0)} & 2.18 & \textbf{15.7} & \textbf{47.9}\\
			\cmidrule{2-6}
			&\multirow{6}{*}{GMRES} & - & 74.7 & 149 & 442\\
			&& Jacobi & 32.1 & 141 & 440 \\
			&& SOR & 16.7 & 105 & 407 \\
			&& SSOR & 13.9 & 85.1 & 305 \\
			&& ICC & 3.44 & 23.1 & 75.5 \\
			&& ILU(0) & 3.25 & 21.9 & 71.0 \\
			\cmidrule{2-6}
			&\multirow{6}{*}{\shortstack{Flexible\\GMRES}} & - & 74.2 & 149 & 443 \\
			&& Jacobi & 38.3 & 146 & 447 \\
			&& SOR & 16.8 & 105 & 407 \\
			&& SSOR & 12.2 & 76.7 & 280 \\
			&& ICC & 3.13 & 21.2 & 69.8 \\
			&& ILU(0) & 2.96 & 20.2 & 66.0 \\
			\cmidrule{2-6}
			&\multirow{6}{*}{\shortstack{Minimum\\Residual}} & - & 14.4 & 88.2 & 312 \\
			&& Jacobi & 11.7 & 74.9 & 271 \\
			&& SOR & 8.95 & 55.3 & 191 \\
			&& SSOR & 6.81 & 42.2 & 138 \\
			&& ICC & 2.53 & 18.1 & 57.1 \\
			&& ILU(0) & 2.38 & 17.1 & 53.3 \\
			\cmidrule{2-6}
			&\multirow{6}{*}{QMR} & - & 142 & 254 & 876\\
			&& Jacobi & - & - & - \\
			&& SOR & 63.6 & 201 & 740 \\
			&& SSOR & 54.7 & 155 & 529 \\
			&& ICC & 12.0 & 62.6 & 233 \\
			&& ILU(0) & 11.9 & 57.9 & 212\\
			\cmidrule{2-6}
			&\multirow{6}{*}{SymmLQ} & - & 16.0 & 99.4 & 338 \\
			&& Jacobi & - & - & -\\
			&& SOR & - & - & -\\
			&& SSOR & - & - & -\\
			&& ICC & - & - & -\\
			&& ILU(0) & - & - & - \\
			\cmidrule{2-6}
			& Cholesky & - & 63.7 & 721 & 3526 \\
			\cmidrule{2-6}
			& LU & - & 44.3 & 490 & 2811 \\
			\cmidrule{2-6}
			& QR & - & 65.5 & 331 & 1110 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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


eess

"Uncertainty Quantification in Case of Imperfect Models: A Review" by Sebastian Kersting and Michael Kohler, arXiv:2012.09449v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
 & var27 & var32 & var33 & var34 \\
 var27 & \cellcolor[rgb]{ 1, .753, 0} 100.00\% & \cellcolor[rgb]{ 1, .847, .392} -18.39\% & \cellcolor[rgb]{ 1, .808, .224} 77.80\% & \cellcolor[rgb]{ 1, .773, .075} 92.58\% \\
 var32 & \cellcolor[rgb]{ 1, .847, .392} -18.39\% & \cellcolor[rgb]{ 1, .753, 0} 100.00\% & \cellcolor[rgb]{ 1, .976, .902} 9.97\% & \cellcolor[rgb]{ 1, .753, 0} -30.37\% \\
 var33 & \cellcolor[rgb]{ 1, .808, .224} 77.80\% & \cellcolor[rgb]{ 1, .976, .902} 9.97\% & \cellcolor[rgb]{ 1, .753, 0} 100.00\% & \cellcolor[rgb]{ 1, .835, .325} 67.68\% \\
 var34 & \cellcolor[rgb]{ 1, .773, .075} 92.58\% & \cellcolor[rgb]{ 1, .753, 0} -30.37\% & \cellcolor[rgb]{ 1, .835, .325} 67.68\% & \cellcolor[rgb]{ 1, .753, 0} 100.00\% \\
 \end{tabular}
\end{adjustbox}
\caption{Spearman correlation between var27, var32, var33, var34}
\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{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrrr}
 \toprule
 & $\Delta \uparrow$ & $\%\geq0.25 \uparrow$ & $\%\geq0.5 \uparrow$ \\
 \midrule
 \multicolumn{4}{c}{Dummy speech} \\
 \midrule
 AVG & 0.120 & 16.7 & 1.0 \\
 TF & \textbf{0.308} & \textbf{63.0} & \textbf{10.3} \\
 \midrule
 \multicolumn{4}{c}{Part swapping} \\
 \midrule
 AVG & 0.000 & 0.0 & 0.0 \\
 TF & -0.012 & 0.0 & 0.0 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

One Whisper to Grade Them All

Nhan Phan
Anusha Porwal
Yaroslav Getman
Ekaterina Voskoboinik
Tamás Grósz
Mikko Kurimo

"One Whisper to Grade Them All" by Nhan Phan, Anusha Porwal, Yaroslav Getman, Ekaterina Voskoboinik, Tamás Grósz, and Mikko Kurimo, arXiv:2507.17918v1, 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}{|l|l|}
			\hline
			\hline
			$s_{ego}$ & Position of the ego vehicle\\
			$v_{ego}$ & Velocity of the ego vehicle\\
			$a_{ego}$ & Acceleration of the ego vehicle\\
			$s_{i,j}^{T}$ & Position of all traffic vehicles\\
			$v_{i,j}^{T}$ & Velocity of all traffic vehicles\\
			$a_{i,j}^{T}$ & Acceleration of all traffic vehicles\\
			$d_{i,j}$ & All entries of the $\textbf{D}^{l_{2}}$ and $\textbf{D}^{l_{3}}$ matrices\\
			distance & Driven distance of the ego and traffic vehicles\\
			time & Simulation time\\
			\hline
			\hline
		\end{tabular}
\caption{Vehicle states and parameters which are saved if a STE occur.}
\end{table}
\end{document}

eess

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

"Stress Testing Method for Scenario Based Testing of Automated Driving Systems" by Demin Nalic, Hexuan Li, Arno Eichberger, Christoph Wellershaus, Aleksa Pandurevic, and Branko Rogic, arXiv:2011.06553v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.