\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Linear probe accuracy on different datasets. For each semantic group of datasets, we show the average recognition accuracy.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
\toprule
Dataset & Full visual field & \underline{Central vision} \\
\midrule
\multicolumn{3}{c}{Hard category recognition} \\
\midrule
ImageNet-1k 100\% & $48.982$ & $\mathbf{50.572}$ \\
ImageNet-1k 10\% & $35.265$ & $\mathbf{36.277}$ \\
ImageNet-1k 1\% & $20.114$ & $\mathbf{20.656}$ \\
ImageNet-100 & $70.900$ & $\mathbf{71.463}$ \\
CIFAR100 & $55.989$ & $\mathbf{56.759}$ \\
Average & $46.250$ & $\mathbf{47.145}$\\
\midrule
\multicolumn{3}{c}{Easy category recognition} \\
\midrule
STL10 & $\mathbf{71.689}$ & $71.514$ \\
CIFAR10 & $\mathbf{79.574}$ & $78.654$ \\
Average &$\mathbf{75.632}$ & $75.084$ \\
\midrule
\multicolumn{3}{c}{Fine-grained recognition} \\
\midrule
DTD & $52.176$ & $\mathbf{54.565}$ \\
FGVCAircraft & $14.418$ & $\mathbf{14.808}$ \\
Flowers102 & $46.163$ & $\mathbf{48.211}$ \\
OxfordIIITPet & $34.858$ & $\mathbf{47.576}$ \\
StanfordCars & $21.191$ & $\mathbf{24.111}$ \\
Average & $33.761$ & $\mathbf{37.854}$ \\
\midrule
\multicolumn{3}{c}{Instance recognition} \\
\midrule
ToyBox & $92.593$ & $\mathbf{92.739}$ \\
COIL100 & $75.563$ & $\mathbf{79.490}$ \\
Core50 & $25.919$ & $\mathbf{30.437}$ \\
Average & $64.691$ & $\mathbf{67.556}$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Timothy Schaumlöffel
Arthur Aubret
Gemma Roig
Jochen Triesch

"Human Gaze Boosts Object-Centered Representation Learning" by Timothy Schaumlöffel, Arthur Aubret, Gemma Roig, and Jochen Triesch, arXiv:2501.02966v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c}
 \hline
 \text{$(p,K)$} & \text{mode $H^*$} & \text{IQR} \\
 \hline
 $(6, 2)$ & 2.00 & 0.00 \\
 $(10, 3)$ & 3.00 & 0.00 \\
 $(30, 5)$ & 5.00 & 0.00 \\
 $(50, 8)$ & 8.00 & 0.00 \\
 $(100, 15)$ & 15.00 & 0.00 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

"A Review of Bayesian Methods for Infinite Factorisations" by Margarita Grushanina, arXiv:2309.12990v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 & FBA & DVA \\ \hline
 buy/call & positive & nil\\ \hline
 sell/call & nil & positive\\ \hline
 buy/put & nil& nil \\ \hline
 sell/put & positive & positive \\ \hline
\end{tabular}
\end{adjustbox}
\caption{DVA and FBA with respect to option contracts}
\end{table}
\end{document}

q-fin

"Binary Funding Impacts in Derivative Valuation" by Junbeom Lee and Chao Zhou, arXiv:1703.00259v4, 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{Validation Results for MobileNetV3 (AASL Dataset)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 \textbf{Fold} & \textbf{Precision} & \textbf{Recall} & \textbf{F1-Score} & \textbf{Accuracy} \\
 \hline
 Fold 1 & 0.994597 & 0.994206 & 0.994310 & 0.994628 \\
 Fold 2 & 1.000000 & 1.000000 & 1.000000 & 1.000000 \\
 Fold 3 & 0.999151 & 0.999104 & 0.999116 & 0.999105 \\
 Fold 4 & 1.000000 & 1.000000 & 1.000000 & 1.000000 \\
 Fold 5 & 1.000000 & 1.000000 & 1.000000 & 1.000000 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mazen Balat
Rewaa Awaad
Ahmed B. Zaky
Salah A. Aly

"Revolutionizing Communication with Deep Learning and XAI for Enhanced Arabic Sign Language Recognition" by Mazen Balat, Rewaa Awaad, Ahmed B. Zaky, and Salah A. Aly, arXiv:2501.08169v1, licensed under CC0 1.0. 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{Radiomic feature selection percentage (\%) for ATE using SIS + GOAL, SIS + OAL and CBS for $1\, 000$ bootstrap resamples. The exposure is Edema and the outcome is Gliosarcoma.}
\begin{tabular}{lrrrr}
\\
\hline
\hline
\textbf{Radiomic feature} & \textbf{SIS + OAL} & \textbf{CBS} & \textbf{SIS + GOAL}\\
\hline
\hline
Original shape-MajorAxis & 58.2 & 57.9 & 65.4 \\
Original shape-Sphericity & 99.8 & 60.9 & 99.9 \\
Original glcm-JointEntropy & 98.2 & 56.8 & 98.7 \\
Original glcm-MaximumProbability & 87.8 & 54.0 & 91.9 \\
LoG-sigma-3-0-mm-3D glszm-SizeZoneNonUniformity & 93.1 & 57.3 & 94.5 \\
LoG-sigma-5-0-mm-3D firstorder-RootMeanSquared & 88.4 & 64.0 & 91.7 \\
Wavelet-LLL firstorder-Kurtosis & 54.5 & 62.0 & 65.7 \\
Wavelet-LHH firstorder-Variance & 77.1 & 61.9 & 82.0 \\
Wavelet-HHL firstorder-Skewness & 68.1 & 58.8 & 77.1 \\
Wavelet-HHL gldm-LowGrayLevelEmphasis & 71.2 & 69.7 & 78.1 \\
Squareroot glrlm-HighGrayLevelRunEmphasis & 71.1 & 62.2 & 77.1 \\

Squareroot glrlm-RunVariance & 94.0 & 62.3 & 95.5 \\
\hline
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Ultra-high dimensional confounder selection algorithms comparison with application to radiomics data" by Ismaïla Baldé and Debashis Ghosh, arXiv:2310.06315v1, 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}{ccc}
\hline
$n$ & Galerkin & Riesz \\
\hline
6 & $3.12\cdot 10^{-1\strut}$ & $7.44\cdot 10^{-9}$ \\
10 & $8.78\cdot 10^{-1}$ & $5.16\cdot 10^{-9}$ \\
20 & $4.38\cdot 10^{4\phantom{-}}$ & $1.94\cdot 10^{-8}$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Patricia Díaz de Alba
Luisa Fermo
Federica Pes
Giuseppe Rodriguez

"Regularized minimal-norm solution of an overdetermined system of first kind integral equations" by Patricia Díaz de Alba, Luisa Fermo, Federica Pes, and Giuseppe Rodriguez, arXiv:2201.12054v2, 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{RMSPE averaged over the three coins of interest (Bitcoin, Ethereum, and Monero) when predicting price up to 3 days in advance for ARIMA baseline and top performing neural network (LSTM) models. Lowest values highlighted in bold.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|rrr|r}
		\hline
		&&\multicolumn{4}{c}{Forecasting Window (days)}\\
		Model&Signal & 1 & 2 & 3 & mean \\
		\hline
		LSTM & \tiny $\$+R_{Lang}$ &
		6.70 & \bf 9.88 & \bf 12.06 & \bf 9.55 \\
		LSTM & \tiny $\$+GH_{Pop}+R_{Lang}$ &
		6.64 & 9.99 & 12.40 & 9.68 \\
		LSTM & \tiny $\$+R_{Vol}$ &
		6.78 & 9.98 & 12.48 & 9.75 \\
		LSTM & \tiny $\$$ &
		\bf 6.60 & 10.46 & 12.32 & 9.79 \\
		ARIMA & \tiny $\$$ &
		7.30 & 10.56 & 13.10 & 10.32 \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Maria Glenski
Tim Weninger
Svitlana Volkova

"Improved Forecasting of Cryptocurrency Price using Social Signals" by Maria Glenski, Tim Weninger, and Svitlana Volkova, arXiv:1907.00558v1, 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{CR Example Dataset}
\begin{tabular}{ccccccccl}
\hline
ID & $X$ & $\delta$ & $\delta_0$ & $\delta_1$ & $\delta_2$ & $Z$ & $A$ & $\text{Label}_\delta$\\
\hline \hline
1 & 0.041 & 2 & 1 & 0 & 1 & -0.860 & 0 & Cause 2\\
2 & 1.887 & 0 & 0 & 0 & 0 & -0.171 & 1 & Censor\\
3 & 0.065 & 1 & 1 & 1 & 0 & -0.512 & 0 & Cause 1\\
4 & 1.100 & 2 & 1 & 0 & 1 & -1.845 & 0 & Cause 2\\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Christina W. Zhou
Nikki L. B. Freeman
Katharine L. McGinigle
Michael R. Kosorok

"Optimal individualized treatment regimes for survival data with competing risks" by Christina W. Zhou, Nikki L. B. Freeman, Katharine L. McGinigle, and Michael R. Kosorok, arXiv:2411.08315v1, 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}{lllccc}
Layer & Flat & Sequential & Heuristic \\
\toprule
Embeddings & yes & yes & $3 \cdot (d_{\text{in}})^{\mathstrut 0.25} $ \\
Regressors & yes & yes & $16 \cdot \text{max}(1, \text{ln}(d_{\text{in}})) $ \\
Context Compressor & no & yes & $64 \cdot \text{max}(1, \text{ln}(d_{\text{ctx}})) $ \\
History Compressor & no & yes & $32 \cdot \text{max}(1, \text{ln}(d_{\text{tgt}} \cdot s_{\text{q50}} )) $ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Heuristic for calculating the size of the model layers. $d_{\text{in}}$ is the cardinality of the input sub-column. $d_{\text{ctx}}$ is the full dimension of the concatenated flat context column embeddings. $d_{\text{tgt}}$ is the full dimension of the concatenated sequential target column embeddings. $s_{\text{q50}}$ is the median sequence length for the sequential table.}
\end{table}
\end{document}

cs

Paul Tiwald
Ivona Krchova
Andrey Sidorenko
Mariana Vargas Vieyra
Mario Scriminaci
Michael Platzer

"TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data" by Paul Tiwald, Ivona Krchova, Andrey Sidorenko, Mariana Vargas Vieyra, Mario Scriminaci, and Michael Platzer, arXiv:2501.12012v2, 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{Initial States of the Target}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 Name & Minimum & Maximum \\
 \midrule
 Dist. to chaser & 25 m & 35 m \\
 Orientation & $[0, 0, 0]$ rad & $[\frac{\pi}{2}, 0, 0]$ rad \\
 Angular Vel. & $[0, -\frac{\pi}{18}, -\frac{\pi}{18}]$ rad/s & $[0, \frac{\pi}{18}, \frac{\pi}{18}]$ rad/s \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Chen Zeng
Grant Hecht
Prajit KrisshnaKumar
Raj K. Shah
Souma Chowdhury
Eleonora M. Botta

"Learning Robust Policies for Generalized Debris Capture with an Automated Tether-Net System" by Chen Zeng, Grant Hecht, Prajit KrisshnaKumar, Raj K. Shah, Souma Chowdhury, and Eleonora M. Botta, arXiv:2201.04180v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}		\toprule
		& \textbf{Bole} & \textbf{Tamale} & \textbf{Yendi} \\
		\midrule
		\textbf{Bole} & 1 & 0.8733 & 0.8547
		\\
		\textbf{Tamale} & 0.8733 & 1 & 0.8998
		\\
		\textbf{Yendi} & 0.8547 & 0.8998 & 1
		\\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{Correlation matrix of the de-seasonalized daily average temperature.}
\end{table}
\end{document}

q-fin

Samuel Asante Gyamerah
Philip Ngare
Dennis Ikpe

"Hedging crop yields against weather uncertainties -- a weather derivative perspective" by Samuel Asante Gyamerah, Philip Ngare, and Dennis Ikpe, arXiv:1905.07546v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Benchmark results. In the table, $K$ denotes the size of the partition of $X_s$, and $t_\text{synth}$ is the optimization (PWC s-CBF synthesis) time. For all experiments, a total of $N=50$ time-steps is used.}
\begin{tabular}{llllll}
\toprule
System & $K$ & $\eta$ & $\beta$ & $P_s$ & $t_\text{synth}$ (s) \\ \midrule
2D Linear & 81 & 0.090 & 0.00420 & 0.70 & 0.91 \\
Convex & 100 & 0.002 & 0.00036 & 0.98 & 2.42 \\
 & 400 & 0.002 & 0.00034 & 0.98 & 19.85 \\ \midrule
2D Linear & 81 & 0.11 & 0.00610 & 0.59 & 1.47 \\
Non-Convex & 100 & 0.03 & 0.00022 & 0.96 & 3.19 \\
 & 400 & 0.03 & 0.00019 & 0.96 & 27.89 \\ \midrule
3D Temp. Model & 500 & 0.0475 & 0.00025 & 0.94 & 301.47 \\
 & 900 & 0.0429 & 0.00023 & 0.95 & 497.03 \\ \midrule
4D Unicycle & 1800 & 0.07 & 0.0220 & 0.82 & 2789.67 \\
 & 2400 & 0.02 & 0.0006 & 0.95 & 3945.77 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Rayan Mazouz
Luca Laurenti
Morteza Lahijanian

"Piecewise Control Barrier Functions for Stochastic Systems" by Rayan Mazouz, Luca Laurenti, and Morteza Lahijanian, arXiv:2507.17703v1, 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{Stanford Drone Dataset Quantitative Evaluation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 Method & Average KL-Div & Average rKL-Div & Average EMD \\
 \midrule
 {\tt semapp} & $0.58\pm0.14$ & $2.43\pm1.24$ & $41.16\pm26.98$ \\
 {\tt semapp2} & $0.46\pm0.16$ & $2.19\pm1.50$ & $27.65\pm19.89$ \\
 {\tt MAE-semapp2} & $0.34\pm0.21$ & $2.19\pm1.84$ & $45.77\pm30.74$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Placido Falqueto
Alberto Sanfeliu
Luigi Palopoli
Daniele Fontanelli

"Learning Priors of Human Motion With Vision Transformers" by Placido Falqueto, Alberto Sanfeliu, Luigi Palopoli, and Daniele Fontanelli, arXiv:2501.18543v1, 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{Comparison of our method to existing atomic-distance , atomic-centred and molecular surface based descriptors. In all cases the similarity scores given are bound by 0 (no similarity) and 1 (identical).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
 & {\bf RGMolSA (this work)} & {\bf USRCAT} & {\bf Shape-It} & {\bf MolSG} & {\bf Morgan Fingerprint}\\
\hline
Sildenafil-Vardenafil & 0.903 & 0.384 & 0.388 & 0.704 & 0.667 \\
\hline
Sildenafil-Tadalafil & 0.809 & 0.269 & 0.278 & 0.746 & 0.201 \\
\hline
Vardenafil-Tadalafil & 0.725 & 0.291 & 0.353 & 0.887 & 0.209 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Daniel J. Cole
Stuart J. Hall
Rachael Pirie

"Riemannian Geometry and Molecular Surfaces I: Spectrum of the Laplacian" by Daniel J. Cole, Stuart J. Hall, and Rachael Pirie, arXiv:2201.04230v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"FEET: A Framework for Evaluating Embedding Techniques" by Simon A. Lee, John Lee, and Jeffrey N. Chiang, arXiv:2411.01322v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jacob Englert
Stefanie Ebelt
Howard Chang

"Estimating Heterogeneous Exposure Effects in the Case-Crossover Design using BART" by Jacob Englert, Stefanie Ebelt, and Howard Chang, arXiv:2311.12016v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccccccccc}
	\multirow{2}{*}{Run} & \multicolumn{10}{c}{Number of Gaussians that minimises BIC} \\
	& 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\
		\hline
		$A$ high & & & 49 & 89 & 12 & & & & & \\
		$A$ low & & 1 & 7 & 31 & 73 & 29 & 5 & 4 & & \\
		$B$ high & 1 & & 6 & 35 & 66 & 30 & 10 & & 1 & 1 \\
		$B$ low & & & & 6 & 85 & 55 & 4 & & & \\
		$\sigma_{\rm int}$ high & 30 & 66 & 32 & 12 & 5 & 2 & 1 & & 1 & 1 \\
		$\sigma_{\rm int}$ low & & & & 13 & 105 & 32 & & & & \\
		Fiducial & & & 49 & 83 & 16 & 1 & & 1 & & \\
	\end{tabular}
\end{table}
\end{document}

stat

"Marginalised Normal Regression: Unbiased curve fitting in the presence of x-errors" by Deaglan Bartlett and Harry Desmond, arXiv:2309.00948v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{hhline}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Respiratory Signal: Performance of the Extension Methods and the Associated Boundary-Free TF Representations}
\begin{tabular}{|c||c||c|c|c|c|}
 \hline
 \multirow{2}{38pt}{\centering Extension method} & \multirow{2}{29pt}{\centering MSE} & \multicolumn{4}{c|}{Performance index $D$ (mean $\pm$ SD)} \\
 \cline{3-6}
 & & STFT & SST & RS & ConceFT \\
 \hhline{|=#=#=|=|=|=|}
 \multirow{2}{38pt}{\centering {\sf SigExt}} & $0.292$ & $0.370$ & $0.408$ & $0.866$ & $0.423$ \\
 & $\pm 4.438$ & $\pm 0.623$ & $\pm 0.436$ & $\pm 0.879$ & $\pm 0.344$ \\
 \hline
 \multirow{2}{38pt}{\centering Symmetric} & $0.044$ & $1.162$ & $1.173$ & $1.022$ & $1.144$ \\
 & $\pm 0.111$ & $\pm 0.893$ & $\pm 0.886$ & $\pm 0.281$ & $\pm 0.579$ \\
 \hline
 \multirow{2}{38pt}{\centering EDMD} & $0.026$ & $0.359$ & $0.422$ & $0.828$ & $0.449$ \\
 & $\pm 0.112$ & $\pm 0.266$ & $\pm 0.282$ & $\pm 0.248$ & $\pm 0.296$ \\
 \hline
 \multirow{2}{38pt}{\centering GPR} & $0.331$ & $0.391$ & $0.411$ & $0.897$ & $0.430$ \\
 & $\pm 4.858$ & $\pm 0.853$ & $\pm 0.406$ & $\pm 1.140$ & $\pm 0.364$ \\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

"An Efficient Forecasting Approach to Reduce Boundary Effects in Real-Time Time-Frequency Analysis" by Adrien Meynard and Hau-Tieng Wu, arXiv:2102.07226v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average Integrated Mean Squared Error (MISE) and its standard error (in parentheses) over 100 repetitions for the estimation of three regression functions (details in the text). TF-4: Trend filtering with penalization on fourth derivative only; TF-1: Trend filtering with penalization on the first derivative only; MTF: Trend filtering with penalization on both fourth and first derivative; SPL: Penalized splines as in .}
\begin{tabular}{lccc}
\hline
\textit{Function} & $f_1(\omega)$ & $f_2(\omega)$ & $f_3(\omega)$ \\
\hline \\
\text{Scenario a)} & & & \\
\textit{TF-4} & 0.335 \footnotesize{(0.401)} & 0.123 \footnotesize{(0.173)} & 0.265 \footnotesize{(0.073)}\\
\textit{TF-1} & 1.969 \footnotesize{(1.821)} & 2.120 \footnotesize{(0.680)} & 0.595 \footnotesize{(0.339)}\\
\textit{MTF} & 0.588 \footnotesize{(0.514)} & 0.297 \footnotesize{(0.296)} & 0.184 \footnotesize{(0.081)}\\
\textit{SPL} & 0.669 \footnotesize{(0.666)} & 0.207 \footnotesize{(0.860)} & 0.357 \footnotesize{(0.405)}\\
\text{Scenario b)} & & & \\
\textit{TF-4} & 0.382 \footnotesize{(0.434)} & 0.139 \footnotesize{(0.189)} & 0.269 \footnotesize{(0.083)}\\
\textit{TF-1} & 1.584 \footnotesize{(0.335)} & 2.035 \footnotesize{(0.189)} & 0.561 \footnotesize{(0.069)}\\
\textit{MTF} & 0.513 \footnotesize{(0.395)} & 0.302 \footnotesize{(0.299)} & 0.194 \footnotesize{(0.083)}\\
\textit{SPL} & 1.101 \footnotesize{(1.014)} & 0.221 \footnotesize{(0.941)} & 0.368 \footnotesize{(0.477)}\\
\text{Scenario c)} & & & \\
\textit{TF-4} & 2.051 \footnotesize{(1.861} & 0.822 \footnotesize{(0.586)} & 0.680 \footnotesize{(0.472)} \\
\textit{TF-1} & 4.334 \footnotesize{(2.180)} & 2.705 \footnotesize{(1.091)} & 0.907 \footnotesize{(0.363)} \\
\textit{MTF} & 3.713 \footnotesize{(2.386)} & 1.165 \footnotesize{(0.974)} & 0.579 \footnotesize{(0.298)} \\
\textit{SPL} & 2.698 \footnotesize{(1.122)} & 0.908 \footnotesize{(1.676)} & 0.630 \footnotesize{(0.366)} \\
\hline \\
\end{tabular}
\end{table}
\end{document}

stat

Federico Ferraccioli
Alessandro Casa
Marco Stefanucci

"An adaptive functional regression framework for spatially heterogeneous signals in spectroscopy" by Federico Ferraccioli, Alessandro Casa, and Marco Stefanucci, arXiv:2309.06999v1, 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{Signs of roots in regions of $(x,z)$-plane.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
\hline
 & $\left(\dfrac{(a-\mu)(a-\rho{)}}{a(a-1)}\right)^{1/2}$ &$\left(\dfrac{(1-\mu)(1-\rho)}{a-1}\right)^{1/2} $ &$\left(\dfrac{-\mu\rho}{a}\right)^{1/2}_{\vphantom{q_q}}$
 \\
 \hline
 $x>0,\ z>0, \ x^2+z^2<1$ & $+$& $+$&$+$\\
 $x>0, \ z>0, \ x^2+z^2>1$ & $+$&$-$&$+$\\
 $x>0,\ z<0, \ x^2+z^2>1$ & $+$& $-$& $-$\\
 $x>0, \ z<0, \ x^2+z^2<1$ & $+$& $+$&$-$\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Expansion for a Fundamental Solution of Laplace's Equation in Flat-Ring Cyclide Coordinates" by Lijuan Bi, Howard S. Cohl, and Hans Volkmer, arXiv:2202.08918v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"On "A General Framework for Pricing Asian Options Under Markov Processes"" by Zhenyu Cui, Chihoon Lee, and Yanchu Liu, arXiv:1601.05306v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison in mIoU (\%) for evaluating activation maps on the PASCAL VOC training and validation sets.}
\begin{tabular}{lcc|cc}
		 \toprule
	 & \multicolumn{2}{c}{Training Set} & \multicolumn{2}{c}{Validation Set} \\
		 \midrule
		 Method & CAM & CAM+RW & CAM & CAM+RW \\
		 \midrule
		 AffinityNet & 48.0 & 58.1 & 46.8 & 57.0 \\
		 Ours & 50.9 & 63.4 & 49.6 & 61.2 \\
		\bottomrule

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

eess

Yu-Ting Chang
Qiaosong Wang
Wei-Chih Hung
Robinson Piramuthu
Yi-Hsuan Tsai
Ming-Hsuan Yang

"Weakly-Supervised Semantic Segmentation via Sub-category Exploration" by Yu-Ting Chang, Qiaosong Wang, Wei-Chih Hung, Robinson Piramuthu, Yi-Hsuan Tsai, and Ming-Hsuan Yang, arXiv:2008.01183v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{marvosym}
\usepackage{pifont}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of TKGQA datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
\toprule
\textbf{} & \textbf{TempQuestions} & \textbf{Time-Sensitive QA} & \textbf{TimeQuestions} & \textbf{CronQuestions} & \textbf{TIQ} \\
\midrule
Author & & & & & \\
Year & 2018 & 2021 & 2021 & 2021 & 2024 \\
Size & 1,364 & 20,000 & 16,181 & 410k & 10,000 \\
Source & 3 KGQA datasets & WikiData & 8 KGQA datasets & WikiData & WikiData \\
\midrule
Explicit/Implicit & \ding{51} & \ding{51} & \ding{51} & \ding{55} & \ding{51} \\
Simple Time & \ding{51} & \ding{51} & \ding{51} & \ding{51} & \ding{51} \\
Simple Entity & \ding{55} & \ding{55} & \ding{55} & \ding{51} & \ding{55} \\
Before/After & \ding{55} & \ding{55} & \ding{55} & \ding{51} & \ding{55} \\
Time Join & \ding{55} & \ding{55} & \ding{55} & \ding{51} & \ding{55} \\
First/Last & \ding{55} & \ding{55} & \ding{55} & \ding{51} & \ding{55} \\
Ordinal & \ding{51} & \ding{51} & \ding{51} & \ding{55} & \ding{55} \\
Aggregation & \ding{55} & \ding{51} & \ding{55} & \ding{55} & \ding{55} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Qiang Sun
Sirui Li
Du Huynh
Mark Reynolds
Wei Liu

"TimelineKGQA: A Comprehensive Question-Answer Pair Generator for Temporal Knowledge Graphs" by Qiang Sun, Sirui Li, Du Huynh, Mark Reynolds, and Wei Liu, arXiv:2501.04343v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of LIDAR-based methods ranked on KITTI data~set.}
\begin{tabular}{ccccc}
\toprule
\textbf{Method} & \textbf{KITTI Ranking Position} & \textbf{Translational Error [\%]} & \textbf{Angular Error [deg/m]} & \textbf{Fail-Aware}\\
\midrule
LOAM~ & 2 & 0.55 & 0.0013 & \\
IMLS-SLAM~ & 4 & 0.69 & 0.0018 & \\
MC2SLAM~ & 5 & 0.69 & 0.0016 & \\
LIMO2-GP~ & 11 & 0.84 & 0.0022 & \\
CAE-LO~ & 13 & 0.86 & 0.0025 & \\
LIMO2 & 15 & 0.86 & 0.0022 & \\
ICP-LO & 16 & 0.87 & 0.0036 & \\
CPFG~ & 17 & 0.87 & 0.0025 & \\
PNDT LO~ & 22 & 0.89 & 0.0030 & \\
LIMO & 24 & 0.93 & 0.0026 & \\
S4-SLAM & 28 & 0.98 & 0.0044 & \\
RIS & 29 & 0.98 & 0.0026 & \\
\textbf{Ours} & - & 1.00 & 0.0039 & x \\
KF-SLAM & 30 & 1.00 & 0.0041 & \\
S4OM & 34 & 1.03 & 0.0053 & \\
NDT-LO & 35 & 1.05 & 0.0043 & \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Iván García Daza
Monica Rentero
Carlota Salinas Maldonado
Rubén Izquierdo Gonzalo
Noelia Hernández Parra
Augusto Luis Ballardini
David Fernández Llorca

"Fail-Aware LIDAR-Based Odometry for Autonomous Vehicles" by Iván García Daza, Monica Rentero, Carlota Salinas Maldonado, Rubén Izquierdo Gonzalo, Noelia Hernández Parra, Augusto Luis Ballardini, and David Fernández Llorca, arXiv:2103.03626v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Hybrid deep additive neural networks

"Hybrid deep additive neural networks" by Gyu Min Kim and Jeong Min Jeon, arXiv:2411.09175v2, 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{Hyperparameters of CCV for different datasets}
\begin{tabular}{lllllllccl}
\toprule
Dataset & Point & Dimension & Q & Batchsize \\ \midrule
MNIST-test & 10000 & 784 & 15 & 5000 \\
MNIST-full & 70000 & 784 & 20 & 4000 \\
USPS & 9296 & 256 & 5 & 1000 \\
Fashion-MNIST & 70000 & 784 & 20 & 4000 \\
Coil-100 & 7200 & 49152 & 3 & 2400 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Consistent Representation Learning for High Dimensional Data Analysis" by Stan Z. Li, Lirong Wu, and Zelin Zang, arXiv:2012.00481v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
\multicolumn{5}{|c|}{Square Loss, Simulated Parameters}\tabularnewline
\hline
\hline
Min/Max & \multicolumn{2}{c|}{Min} & \multicolumn{2}{c|}{Max}\tabularnewline
\hline
True/Best{*} & \multicolumn{2}{c|}{0} & \multicolumn{2}{c|}{12,666,974{*}}\tabularnewline
\hline
Performance & RMSE & Time & RMSE & Time\tabularnewline
\hline
\hline
\textbf{SMCOms-R} & \textbf{11.94} & {8,120.73} & \textbf{0} & {8,908.95}\tabularnewline
\hline
\textbf{SMCOms-BR} & \textbf{12.47} & {8,333.22} & \textbf{0} & {13,322.10}\tabularnewline
\hline
GenSA & 0.110 & 19,963.20 & 557,175.7 & 5709.79\tabularnewline
\hline
DEoptim & 287.03 & 890.06 & 8,360,738.1 & 810.37\tabularnewline
\hline
CMAES & 381.81 & 2,343.98 & 1,401,342.4 & 30.15\tabularnewline
\hline
STOGO & ERROR & 7,850.71 & ERROR & 8,850.60\tabularnewline
\hline
GA & 203.22 & 28.46 & 11,749,582.4 & 32.25\tabularnewline
\hline
PSO & 11.40 & 217.88 & 557,175.7 & 233.98\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

stat

Xiaohong Chen
Zengjing Chen
Wayne Yuan Gao
Xiaodong Yan
Guodong Zhang

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
Heading level & Example & Font size and style\\
\hline
Title (centered) & {\Large\bfseries Lecture Notes} & 14 point, bold\\
1st-level heading & {\large\bfseries 1 Introduction} & 12 point, bold\\
2nd-level heading & {\bfseries 2.1 Printing Area} & 10 point, bold\\
3rd-level heading & {\bfseries Run-in Heading in Bold.} Text follows & 10 point, bold\\
4th-level heading & {\itshape Lowest Level Heading.} Text follows & 10 point, italic\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Ontological analysis of proactive life event services" by Kuldar Taveter, arXiv:2501.19308v1, 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}{l|ll|lll}
\toprule
& \multicolumn{2}{c|}{Coarse mesh, $\gamma_d = 10^6$} & \multicolumn{3}{c}{Fine mesh, $\gamma_d = 10^5$} \\
\midrule
Branch & BM & OK & BM & OK & IK \\ \midrule
0& 	828 & 829 (1.00) & 49& 110 (2.24) &	10,405 (15.77) \\
1& 444 & 445 (1.00) & 52 & 121 (2.33) & 12,176	(16.77) \\
2& 409& 422 (1.03) & 53 & 135 (2.55) &13,085 (16.15)\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Cumulative number of BM iterations, outer FGMRES iterations (OK), and for the fine mesh, inner FGMRES iterations preconditioned with the multigrid scheme of (IK) for the 3D cross-channel problem. The bracketed numbers in the OK and IK columns are the average number of outer FGMRES iterations per BM iteration and average number of inner FGMRES iterations per augmented momentum block solve, respectively. The barrier parameter is initialized at $\mu_0 =100$ on the coarse mesh and $\mu_0=10^{-6}$ on the fine mesh.}
\end{table}
\end{document}

math

Ioannis P. A. Papadopoulos
Patrick E. Farrell

"Preconditioners for computing multiple solutions in three-dimensional fluid topology optimization" by Ioannis P. A. Papadopoulos and Patrick E. Farrell, arXiv:2202.08248v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{pifont}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{marvosym}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 Method & Input & Output & Num. & Semantic Analysis? \\
 \midrule
 Verbalization & $L_1+L_2$ & 1 & 1 & \ding{55}\\
 P(True) & $L_1+L_2$ & 1 & 1 & \ding{55}\\
 Sem. Entropy & $L_1$ & $L_2$ & $N$ & \ding{51}\\
 Pred. Entropy & $L_1$ & $L_2$ & $N$ & \ding{55}\\
 \textbf{BCE (AFICE)} & $L_1$ & $K$ & $N$ & \ding{55}\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Comparative analysis of scales in five methods.}
\end{table}
\end{document}

cs

Yong Zhao
Yang Deng
See-Kiong Ng
Tat-Seng Chua

"Aligning Large Language Models for Faithful Integrity Against Opposing Argument" by Yong Zhao, Yang Deng, See-Kiong Ng, and Tat-Seng Chua, arXiv:2501.01336v1, 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{Architecture of a Small CNN Model with MNIST Dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
 \hline
 \textbf{Layer} & \textbf{\shortstack{Input \\ Channel}} & \textbf{\shortstack{Output \\ Channel}} & \textbf{\shortstack{Kernel \\ Size}} & \textbf{\shortstack{Input \\ Size}} & \textbf{\shortstack{Output \\ Size}} \\
 \hline
 Convolution &1 & 2 & 3 & 28 $\times$ 28 & 28 $\times$ 28 \\
 Max Pooling &2 & 2 & 2 & 28 $\times$ 28 & 14 $\times$ 14 \\
 ReLU &2 & 2 &N/A & 14 $\times$ 14 & 14 $\times$ 14 \\
 Convolution &2 & 3 & 3 & 14 $\times$ 14 & 14 $\times$ 14 \\
 Max Pooling &3 & 3 & 2 & 14 $\times$ 14 & 7 $\times$ 7 \\
 ReLU &3 & 3 &N/A & 7 $\times$ 7 & 7 $\times$ 7 \\
 Linear &147 & 10 & N/A & 1 & 1 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Learning Provably Improves the Convergence of Gradient Descent" by Qingyu Song, Wei Lin, and Hong Xu, arXiv:2501.18092v5, 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)} & .682 &	.310 & .249	& .414 \\
~~2. RoBERTa \scriptsize{(Ours, SE)} & \textbf{.710}& \textbf{.327} & \textbf{.282} & \textbf{.440} \\
\midrule
\textbf{Supervised} & & & & \\
~~3. RoBERTa \scriptsize{(Random, EB)} & .400 & .411 &	.184 & .332\\
~~4. BERT \scriptsize{(Pretrained, EB)} & .806 & .596 &	.473 & .625\\
~~5. RoBERTa \scriptsize{(Pretrained, EB)} & \textbf{.839} & \textbf{.605} & .512 & \textbf{.652}\\
~~6. RoBERTa \scriptsize{(Ours, EB-SE)} & .834& .594 &	\textbf{.517} & .648\\
\bottomrule
\end{tabular}
\caption{Validation Set performance of models in Ablation study.}
\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.


q-fin

"Power-law correlations in finance-related Google searches, and their cross-correlations with volatility and traded volume: Evidence from the Dow Jones Industrial components" by Ladislav Kristoufek, arXiv:1502.00225v1, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Jiahua Xu
Dawei Zhou
Lei Hu
Jianfeng Guo
Feng Yang
Zaiyi Liu
Nannan Wang
Xinbo Gao

"Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model" by Jiahua Xu, Dawei Zhou, Lei Hu, Jianfeng Guo, Feng Yang, Zaiyi Liu, Nannan Wang, and Xinbo Gao, arXiv:2412.07590v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation results of $\hat{\theta}_0$, $\hat{\theta}_1$, $\hat{\eta}_1$, $\hat{\theta}_2$ and $\hat{\sigma}^2$ }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc} \hline
		&$\hat{\theta}_{0}$&$\hat{\theta}_{1}$&$\hat{\eta}_{1}$&$\hat{\theta}_{2}$&$\hat{\sigma}^2$
\\ \hline
{\color{black} true value} &0 & 0.2 & 0.2 &0.2 &1
\\ \hline
mean &-0.862& 0.190& 0.191 & 0.191& 0.904
 \\
s.d. & (3.393)& (0.080)& (0.081) &(0.081) & (0.416)
 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Yozo Tonaki
Yusuke Kaino
Masayuki Uchida

"Parameter estimation for linear parabolic SPDEs in two space dimensions based on high frequency data" by Yozo Tonaki, Yusuke Kaino, and Masayuki Uchida, arXiv:2201.09036v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the CONV, COS, filter-COS and SFP methods in terms of the absolute error for pricing a call option under the BSM model. The reference values are $0$ ($S_0= 95$) and $0.007491657716010$ ($S_0=99.999$). The SFP method is more accurate than the others with fewer summation terms required. The parameters are taken from \textbf{BSM--Para4}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cccc||cccc|} % centered columns (4 columns)
\hline
$S_0= 95$&\textbf{CONV}&\textbf{filter-COS}&\textbf{SFP}&$S_0=99.999$&\textbf{COS}&\textbf{filter-COS}&\textbf{SFP}\\
\hline
$N/U$& Error &Error &Error &$N/U$& Error &Error &Error \\
\hline
8& 2.004e+01& 5.099& 2.813e-04& 8& 2.475e-01& 4.482e-01& 3.849e-02\\
16& 1.940e+01& 4.987& 7.268e-08&16& 1.307e-01& 1.944e-01& 4.704e-03\\
32& 1.311e+01& 5.001& 0.000& 32& 7.008e-02& 1.030e-01& 3.473e-03\\
64& 6.696 & 5.000& 0.000& 64& 3.960e-2& 5.587e-02& 6.268e-05\\
\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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average test RMSE and single-core CPU fit times on the OpenML regression datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
Model & Mean RMSE & Fit Time (s)\\
\midrule
Gradient RFRBoost & 0.408 & 1.688 \\
Greedy RFRBoost $ A_{dense}$\hspace{-16pt} & 0.408 & 2.734 \\
Greedy RFRBoost $ A_{diag}$\hspace{-16pt} & 0.415 & 1.631 \\
Greedy RFRBoost $ A_{scalar}$\hspace{-16pt} & 0.434 & 1.024 \\
\midrule
XGBoost & 0.394 & 1.958 \\
E2E MLP ResNet & 0.412 & 19.309 \\
RFNN & 0.434 & 0.053 \\
Ridge Regression & 0.540 & 0.001 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Random Feature Representation Boosting

Nikita Zozoulenko
Thomas Cass
Lukas Gonon

"Random Feature Representation Boosting" by Nikita Zozoulenko, Thomas Cass, and Lukas Gonon, arXiv:2501.18283v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Predicted noise rate on different datasets (mean of three runs)}
\begin{tabular}{|c|c|c|c|c|c|}
		\hline
		Actual noise rate	&0 			&0.1 		&0.2 		&0.3 		&0.4		\\ \hline
		Path (Symmetric)	&0.005 		&0.079 		&0.173 		&0.267 		&0.362		\\ \hline
		Path (Asymmetric)	&/ 			&0.078 		&0.147 		&0.230 		&0.234		\\ \hline
		OCT (Symmetric)		&0 			&0.133 		&0.226 		&0.340 		&0.449		\\ \hline
		Pneumonia 			&0 			&0.068 		&0.162 		&0.257 		&0.382		\\ \hline
		Organ (Symmetric)	&0.165 		&0.176 		&0.256 		&0.316 		&0.364		\\ \hline
		Organ (Asymmetric)	&/ 			&0.199 		&0.246 		&0.367 		&0.415		\\ \hline
		Vessel 				&0.072 		&0.142 		&0.236 		&0.265 		&0.392		\\ \hline
		NoisyCXR 			&0.114 		&/ 			&/ 			&/ 			&0.379 		\\ \hline
	\end{tabular}
\end{table}
\end{document}

eess

"Sample selection with noise rate estimation in noise learning of medical image analysis" by Maolin Li and Giacomo Tarroni, arXiv:2312.15233v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jana Gauss
Fabian Scheipl
Moritz Herrmann

"DCSI -- An improved measure of cluster separability based on separation and connectedness" by Jana Gauss, Fabian Scheipl, and Moritz Herrmann, arXiv:2310.12806v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
\textbf{Algorithm/Circuit} & \textbf{\# qubits} & \textbf{\# 2-qubit gates} \\ \hline
QAOA & 16 & 168 \\ \hline
QAOA & 20 & 246 \\ \hline
QFT & 16 & 252 \\ \hline
QFT & 20 & 353 \\ \hline
Quantum Volume & 16 & 72 \\ \hline
Quantum Volume & 20 & 90 \\ \hline
TFIM & 16 & 199 \\ \hline
TFXY & 16 & 236 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Benchmarking circuits for the experiments with SHAPER, SHAW, QCCDSim and 2-stage QCCDSim.}
\end{table}
\end{document}

cs

"Efficient Compilation for Shuttling Trapped-Ion Machines via the Position Graph Architectural Abstraction" by Bao Bach, Ilya Safro, and Ed Younis, arXiv:2501.12470v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The first, second, and fourth cumulants of various models.} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|} % centered columns (4 columns)
\hline
L\'evy models&cumulants\\
\hline %inserts double horizontal lines
BS&$c_1=(r-q+\omega)t$\quad$c_2=\sigma^2 t,$\quad $c_4=0,$ $\omega=-0.5\sigma^2$\\
\hline
NIG&$c_1=(r-q+\omega)t+\delta t\beta/\sqrt{\alpha^2-\beta^2}$\\
\,&$c_2=\delta t \alpha^2 (\alpha^2-\beta^2)^{-3/2}$\\
\,&$c_4=\delta t \alpha^2 (\alpha^2+4\beta^2)^{-3/2}(\alpha^2-\beta^2)^{-7/2}$\\
\,&$\omega=-0.5\sigma^2-\delta(\sqrt{\alpha^2-\beta^2}-\sqrt{\alpha^2-(\beta+1)^2}) $\\
\hline %inserts single line
VG&$c_1=(r-q+\theta+\omega)t$\\
\,&$c_2=(\sigma^2+\upsilon\theta^2)t$\\
\,&$c_4=3(\sigma^4\upsilon+2\theta^4\upsilon^3+4\sigma^2\theta^2\upsilon^2)t$\\
\,&$\omega={1 / \upsilon}\log(1-\theta\upsilon-\sigma^2\upsilon/2)$\\
\hline %inserts single line
$\rm CGMY$&$c_1=(r-q+\omega)t$\\
\,&$c_2=(C\Gamma(2-Y)(M^{Y-2} + G^{Y-2})t$\\
\,&$c_4=(C\Gamma(4-Y)(M^{Y-4} + G^{Y-4})t$\\
\,&$\omega=\left(C\Gamma(-Y)G^Y\left(\left(1+\frac{1}{G}\right)^Y-1-\frac{Y}{G}\right)+C\Gamma(-Y)M^Y\left(\left(1-\frac{1}{M}\right)^Y-1+\frac{Y}{M}\right)\right)$\\
\hline %inserts single line
\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.


q-fin

"On "A General Framework for Pricing Asian Options Under Markov Processes"" by Zhenyu Cui, Chihoon Lee, and Yanchu Liu, arXiv:1601.05306v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
 \hline
		Embedding method & Prompt template & Avg. Dice$\uparrow$ (CT)& Avg. Dice$\uparrow$ (MR)\\ \hline
 Vision-Only & - & 82.50 & 81.91\\
 One Hot~ & - & 86.64 & 84.50 \\
 BioBERT~ & A \{CT/MR\} imaging of a [CLS]. & 86.62 & 84.59\\
 MedCLIP~ & A \{CT/MR\} imaging of a [CLS]. & 86.30 & 84.10 \\
 V1-CLIP & A photo of [CLS]. & 85.60 & 84.30\\
 V2-CLIP & There is [CLS] in this \{CT/MR\}. & 86.20 & 84.70\\
 V3-CLIP (\textbf{Ours}) & A \{CT/MR\} imaging of a [CLS].& \textbf{87.14} & \textbf{85.33}\\
 \hline
	\end{tabular}
\caption{Performance comparison of different text embeddings of MulModSeg on AMOS dataset with UNet backbone. V3-CLIP achieves the highest mean Dice scores for both CT and MR modalities. The \textbf{bolded} text represents the best performance. CT: computerized tomography, MR: magnetic resonance. The default Vison-Only model does not use text information and is trained with ALT. }
\end{table}
\end{document}

eess

Chengyin Li
Hui Zhu
Rafi Ibn Sultan
Hassan Bagher Ebadian
Prashant Khanduri
Chetty Indrin
Kundan Thind
Dongxiao Zhu

"MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training" by Chengyin Li, Hui Zhu, Rafi Ibn Sultan, Hassan Bagher Ebadian, Prashant Khanduri, Chetty Indrin, Kundan Thind, and Dongxiao Zhu, arXiv:2411.15576v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{hhline}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{FNAF attack evaluations, when the loss was maximized using random search. Metrics computed on (A) 4$\times$ and (B) 8$\times$ retrospectively undersampled MRIs.}
\begin{tabular}{c|c|ccc|c|c}
\hhline{===~===}
AF=$4\times$ & Attack Rate (\%) & NMSE & & AF=$8\times$ & Attack Rate (\%) & NMSE \\
\hhline{---~---}
 U-Net & 88.89 & 0.5798 & &U-Net & 92.96 & 0.7928 \\
 FNAF U-Net & 3.406 & 0.0422 & &FNAF U-Net & 13.38 & 0.0781 \\
 B-Box U-Net & 88.09 & 0.5793 & & B-Box U-Net & 92.98 & 1.072 \\
 I-RIM & 10.62 & 0.1948 & & I-RIM & 91.00 & 0.3305 \\
 FNAF I-RIM & 0.014 & 0.0072 & & FNAF I-RIM & 2.859 & 0.0245 \\
 B-Box I-RIM & 17.39 & 0.2056 & & B-Box I-RIM & 92.81 & 0.3489 \\
\hhline{===~===}
 \\
\multicolumn{3}{c}{(A)} & \multicolumn{3}{c}{(B)} \\
\end{tabular}
\end{table}
\end{document}

eess

Francesco Calivá
Kaiyang Cheng
Rutwik Shah
Valentina Pedoia

"Adversarial Robust Training of Deep Learning MRI Reconstruction Models" by Francesco Calivá, Kaiyang Cheng, Rutwik Shah, and Valentina Pedoia, arXiv:2011.00070v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Optimizing for Rotisserie Fantasy Basketball" by Zach Rosenof, arXiv:2501.00933v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A high-order velocity-based discontinuous Galerkin scheme for the shallow water equations: local conservation, entropy stability, well-balanced property, and positivity preservation" by Guosheng Fu, arXiv:2201.13040v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 & dimension of $V_n^{Markov}$ &$v(Y_n)$& $v(V_n^{Markov})$\\
 \hline
 $n=4$&6&$\frac{1}{72}\approx 1.39\times 10^{-2}$&$\frac{1}{36}\approx 2.78\times 10^{-2}$\\
 \hline
 $n=5$&10&$\frac{653}{4838400}\approx 1.35\times 10^{-4}$&$\frac{1}{4608}\approx 2.17\times 10^{-4}$\\
 \hline
 $n=6$&15&$\frac{433}{653837184000}\approx 6.22 \times 10^{-10}$&$\frac{1}{1728000}\approx 5.79\times 10^{-7}$\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{The exact volume $v(Y_n), n\in\{4,5,6\}$ computed using \texttt{Polymake}.}
\end{table}
\end{document}

math

Muhammad Ardiyansyah
Dimitra Kosta
Jordi Roca-Lacostena

"Embeddability of centrosymmetric matrices capturing the double-helix structure in natural and synthetic DNA" by Muhammad Ardiyansyah, Dimitra Kosta, and Jordi Roca-Lacostena, arXiv:2202.05889v2, licensed under CC BY 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.


cs

Anh Bui
Trang Vu
Long Vuong
Trung Le
Paul Montague
Tamas Abraham
Junae Kim
Dinh Phung

"Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them" by Anh Bui, Trang Vu, Long Vuong, Trung Le, Paul Montague, Tamas Abraham, Junae Kim, and Dinh Phung, arXiv:2501.18950v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Logistics Regression Model for Differentially-Private Matrix Masked Data" by Linh H Nghiem, Aidong A. Ding, and Samuel Wu, arXiv:2412.15520v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

YH. Hur
J. G. Hoskins
M. Lindsey
E. M. Stoudenmire
Y. Khoo

"Generative modeling via tensor train sketching" by YH. Hur, J. G. Hoskins, M. Lindsey, E. M. Stoudenmire, and Y. Khoo, arXiv:2202.11788v6, 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{Modeling parameters of the three methods.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \toprule
 Method & Solver & $D (\rm m)$ & $T (\rm s)$ & $N$\\
 \midrule
 Convex optimization & GUROBI 9.5.0 & \multirow{3}{*}{2707} & \multirow{3}{*}{140} & \multirow{3}{*}{238}\\
 MILP & GUROBI 9.5.0 & & & \\
 RPM & GPOPS 4.1 & & & \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A fast-solved model for energy-efficient train control based on convex optimization" by Minling Feng, Kunpeng Wu, and Shaofeng Lu, arXiv:2201.10731v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Correlations between $\{\xi_i\}_i$ for Karhunen-Loève decomposition of moneyness-indexed smile log-return. expiry=10Y, tenor=10Y, currency=USD}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|r|r|r|}
 \toprule
 & \multicolumn{3}{c|}{combinations of projections $\{\xi_i\}_i$} \\
 \midrule
 & 1st and 2nd & 1st and 3rd & 2nd and 3rd \\
 \midrule
 Pearson's correlation & 0.00001\% & 0.00016\% & -0.00007\% \\
 \midrule
 p-value & 99.99975\% & 99.99346\% & 99.99715\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Jinglun Yao
Sabine Laurent
Brice Bénaben

"Managing Volatility Risk: An Application of Karhunen-Loève Decomposition and Filtered Historical Simulation" by Jinglun Yao, Sabine Laurent, and Brice Bénaben, arXiv:1710.00859v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc}
 \toprule
 & \multicolumn{2}{c}{Training Set} \\
 Batch Size & Cross Entropy & Accuracy \\\hline
 128 & 3.586 & 43.59\% \\
 512 & 3.971 & 37.27\% \\
 2048 & 4.01 & 37.55\% \\
 8192 & 4.057 & 36.73\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Loss over the entire training set is also better for lower physical batch sizes.}
\end{table}
\end{document}

cs

Ryan McKenna
Yangsibo Huang
Amer Sinha
Borja Balle
Zachary Charles
Christopher A. Choquette-Choo
Badih Ghazi
George Kaissis
Ravi Kumar
Ruibo Liu
Da Yu
Chiyuan Zhang

"Scaling Laws for Differentially Private Language Models" by Ryan McKenna, Yangsibo Huang, Amer Sinha, Borja Balle, Zachary Charles, Christopher A. Choquette-Choo, Badih Ghazi, George Kaissis, Ravi Kumar, Ruibo Liu, Da Yu, and Chiyuan Zhang, arXiv:2501.18914v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
\hline
\textbf{Hyperparameter} & \textbf{Value} \\ \hline
Learning Rate & 0.0003 \\ \hline
Optimizer & Adam \\ \hline
Betta 1 & 0.9 \\ \hline
Betta 2 & 0.99 \\ \hline
Batch Size & 64 \\ \hline
Gradient Accumulation Steps & 2 \\ \hline
Transformer Layers & 20 \\ \hline
Transformer Heads & 16 \\ \hline
Context Length & 8192 \\ \hline
Transformer Hidden Dim & 1024 \\ \hline
FF Hidden Size & 4096 \\ \hline
MLP Type & GptNeoxMLP \\ \hline
Normalization Type & LayerNorm \\ \hline
Training Precision & bf16 \\ \hline
Parameters & 332100768 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Andrey Polubarov
Nikita Lyubaykin
Alexander Derevyagin
Ilya Zisman
Denis Tarasov
Alexander Nikulin
Vladislav Kurenkov

"Vintix: Action Model via In-Context Reinforcement Learning" by Andrey Polubarov, Nikita Lyubaykin, Alexander Derevyagin, Ilya Zisman, Denis Tarasov, Alexander Nikulin, and Vladislav Kurenkov, arXiv:2501.19400v1, 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{Augmented dataset multimodal model hyperparameters.}
\begin{tabular}{llllll}
\toprule
\textbf{Hyperparameter} & \textbf{EPCG Model} \\
\hline
\midrule
Learning rate & 0.001 \\
Weight decay & 3.11e-5 \\
Momentum & 0.17562 \\
Gamma & 0.002444 \\
Step size & 7 \\
Batch size & 64 \\
Number of hidden layers & 3 \\
Hidden layer size & 1024 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Milan Marocchi
Matthew Fynn
Kayapanda Mandana
Yue Rong

"Scaling to Multimodal and Multichannel Heart Sound Classification: Fine-Tuning Wav2Vec 2.0 with Synthetic and Augmented Biosignals" by Milan Marocchi, Matthew Fynn, Kayapanda Mandana, and Yue Rong, arXiv:2509.11606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Modou Ngom
Moumouni Diallo
Adja Mbarka Fall
Gane Samb Lo

"The Pseudo-Lindley Alpha Power transformed distribution, mathematical characterizations and asymptotic properties" by Modou Ngom, Moumouni Diallo, Adja Mbarka Fall, and Gane Samb Lo, arXiv:2201.07292v1, 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{adjustbox}
\usepackage{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccc}
 \toprule
 \multirow{2}{*}{\textbf{Model}} & \multicolumn{6}{c}{\textbf{E.T. Bench-Grounding} }\\
 & TVG$_{F1}$ & EPM$_{F1}$ & TAL$_{F1}$ & EVS$_{F1}$ & VHD$_{F1}$ & \textbf{Mean$_{F1}$}\\
 \midrule
 \multicolumn{7}{l}{\textit{Proprietary models}} \\
 GPT-4V & \color{lightgray}27.0 & \color{lightgray}1.8 & \color{lightgray}18.0 & \color{lightgray}\underline{28.6} & \color{lightgray}55.1 & \color{lightgray}26.1\\
 GPT-4o & \color{lightgray}40.4 & \color{lightgray}4.5 & \color{lightgray}20.0 & \color{lightgray}17.6 & \color{lightgray}56.9 & \color{lightgray}27.9\\
 Gemini-1.5-Flash & \color{lightgray}\underline{43.9} & \color{lightgray}5.4 & \color{lightgray}27.0 & \color{lightgray}5.4 & \color{lightgray}60.8 & \color{lightgray}28.5\\
 Gemini-1.5-Pro & \color{lightgray}43.1 & \color{lightgray}6.2 & \color{lightgray}33.8 & \color{lightgray}7.9 & \color{lightgray}47.0 & \color{lightgray}27.6\\
 \midrule
 \multicolumn{7}{l}{\textit{Open-source models ($<$10B)}} \\
 LITA & 22.2 & 4.6 & 18.0 & 29.7 & 23.9 & 19.7 \\
 VTG-LLM & 15.9 & 3.7 & 14.4 & 26.8 & 48.2 & 21.8 \\
 TimeChat $^\dag$ & - & - & - & - & - & 24.3 \\
 E.T. Chat $^\dag$ & 38.6 & 10.2 & 30.8 & \textbf{25.4} & 62.5 & 33.5 \\
 Tarsier-7B $^\dag$ & 39.6 & 9.0 & 25.0 & \textbf{25.4} & 47.6 & 30.9 \\
 Qwen2-VL-7B $^\dag$ & \textbf{39.7} & 7.0 & 26.9 & 17.1 & \underline{\textbf{66.9}} & 33.5 \\
 \midrule
 Tarsier2-7B $^\dag$ & 38.4 & \underline{\textbf{11.0}} & \underline{\textbf{31.8}} & 19.4 & 66.8 & \underline{\textbf{35.5}} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Evaluation results on E.T. Bench-Grounding. Results marked in gray are tested on a subset. $\dag$ denotes the model is fine-tuned on E.T. Instruct 164K.}
\end{table}
\end{document}

cs

Liping Yuan
Jiawei Wang
Haomiao Sun
Yuchen Zhang
Yuan Lin

"Tarsier2: Advancing Large Vision-Language Models from Detailed Video Description to Comprehensive Video Understanding" by Liping Yuan, Jiawei Wang, Haomiao Sun, Yuchen Zhang, and Yuan Lin, arXiv:2501.07888v3, 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{Various parameterizations of the wrapped Cauchy distribution}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 Number & Parameter & $A$ & $B$ & $C$ & Setting \\ \hline
 1& $0 \leq \lambda<1$ & $1-\lambda^2$ & $1+\lambda^2$ & $2 \lambda $
 & wrapped Cauchy, AR(1)\\
 2& $0 <b \leq 1$ & $ 2b$ & $1+b^2$ & $1-b^2$ & doubled ACG, \\
 &&&&& stereographic projection\\
 3& $0<\mu \leq \pi/2 $ & $\sin \mu$ & $1$ & $\cos \mu$ & angular rep\\
 4& $0 \leq \alpha <1/2$ & $\sqrt{1-4\alpha^2}$ & $1$ & $2\alpha$ & CAR(1) \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Directional distributions and the half-angle principle" by John T. Kent, arXiv:2202.06611v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llr} \toprule
			Variable & Value & Sensitivity\\ \midrule
			$CAC_{mean}$ & -\$35 & 1\\
			$CC$ & $\approx$ \$0 & 1\\
			$R_{P_t}$ & \$6 & 1.58\\
			$P_{Tr}$ & 0.36 & -14.78\\
			$CR_{nat}$ & 0.05 & 167.17 \\
			$CR_{init}$ & 0.30 & -111.81\\
			$k$ & 0.6 & -2.01\\ \bottomrule
		\end{tabular}
\end{adjustbox}
\caption{Sensitivities computed across different model parameters}
\end{table}
\end{document}

q-fin

"A generalized Bayesian framework for the analysis of subscription based businesses" by Rahul Madhavan and Ankit Baraskar, arXiv:1704.05729v1, 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{Hyperparameter setting for the Cartpole benchmark.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccr}
\toprule
\textbf{Method} & \textbf{$\eta$} & \textbf{$\alpha$} & \textbf{$p_t$} \\
\midrule
GPOMDP & $10^{-4}$ & -- & --\\
SVRPG & $10^{-4}$ & -- & --\\
SRVRPG & $10^{-6}$ & -- & --\\
STORM-PG & $4\cdot 10^{-5}$ & 0.99 & -- \\
PAGE-PG & $5\cdot 10^{-5}$ & -- & $0.8$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Matilde Gargiani
Andrea Zanelli
Andrea Martinelli
Tyler Summers
John Lygeros

"PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation" by Matilde Gargiani, Andrea Zanelli, Andrea Martinelli, Tyler Summers, and John Lygeros, arXiv:2202.00308v1, 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{Safety evaluation of STM and Baseline Fine-tuning trained on the MBPP Ground Truth data using Llama 3 8B Instruct and Gemma 2 IT 2B. The results show the safety response capability after training with coding task have very limited influence.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
\toprule
\bf Gemma 2 IT 2B & \bf refuse rate (\%) & \bf harmful rate (\%) & \bf BWT (\%) \\
\midrule
original & 98.46 & 0.96 & -\\
Baseline Fine-tuning & 98.84 & 0.58 & -1.7 \\
STM$_{\tau=2.5}$ & 98.65 & 0.76 & 0.7 \\
\midrule
\bf Llama 3 8B Instruct & \bf refuse rate (\%) & \bf harmful rate (\%) & \bf BWT (\%) \\
\midrule
original & 97.69 & 1.92 & -\\
Baseline Fine-tuning & 98.46 & 1.15 & -1.6 \\
STM$_{\tau=2.5}$ & 98.46 & 1.15 & 1.4 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Chao-Chung Wu
Zhi Rui Tam
Chieh-Yen Lin
Yun-Nung Chen
Shao-Hua Sun
Hung-yi Lee

"Mitigating Forgetting in LLM Fine-Tuning via Low-Perplexity Token Learning" by Chao-Chung Wu, Zhi Rui Tam, Chieh-Yen Lin, Yun-Nung Chen, Shao-Hua Sun, and Hung-yi Lee, arXiv:2501.14315v6, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of number of Newton iterations with number of elements used for discretization}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
 \hline
 Authors & \vtop{\hbox{\strut \# Newton iteration}\hbox{\strut \;\;\;\;\;\;\;(8 elements)}} & \vtop{\hbox{\strut \# Newton iteration}\hbox{\strut \;\;\;\;\;\;\;(16 elements)}}\\
 \hline
 Present & 3 & 3 \\
 Schulz and B\"ol & 15 & 15 \\
 Meier \textit{et al.} & 438 & 625 \\
 Meier \textit{et al.} &875 & 1220\\
 Simo & 892 & 1011 \\
 Jeleni\'c and Crisfield &1497 & 1540\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Jamun Kumar N
Bensingh Dhas
Arun R Srinivasa
J N Reddy
Debasish Roy

"A novel four-field mixed variational approach to Kirchhoff rods implemented with finite element exterior calculus" by Jamun Kumar N, Bensingh Dhas, Arun R Srinivasa, J N Reddy, and Debasish Roy, arXiv:2202.12376v1, 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 study results of multitask data.}
\begin{tabular}{cccccccccc}
 \toprule
 \textbf{Model} & \textbf{MME} & \textbf{MMBench} & \textbf{OCRBench} & \textbf{DocVQA} & \textbf{ChartQA} & \textbf{InfoVQA} & \textbf{Overall} \\
 \midrule
 MindVL-V1Data & 79.8 & 81.5 & 83.3 & 92.1 & 83.7 & 73.2 & 82.3 \\
 MindVL-V2Data & 82.4 & 83.4 & 88.2 & 94.7 & 87.2 & 79.9 & 86.0 \\
 MindVL-V3Data & 84.1 & 84.3 & 87.6 & 94.7 & 87.2 & 81.1 & 86.5 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Feilong Chen
Yijiang Liu
Yi Huang
Hao Wang
Miren Tian
Ya-Qi Yu
Minghui Liao
Jihao Wu

"MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs" by Feilong Chen, Yijiang Liu, Yi Huang, Hao Wang, Miren Tian, Ya-Qi Yu, Minghui Liao, and Jihao Wu, arXiv:2509.11662v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overall time (seconds) with different sampling ratio of $T$ when $k=20$}
\begin{tabular}{rcccc}
\toprule
\textbf{Dataset} & \textbf{100\%} & \textbf{50\%} & \textbf{20\%} & \textbf{10\%} \\ \midrule
Bank & 42.03 & 21.20 & 9.16 & 5.44 \\ \hline
Adult & 69.19 & 31.24 & 12.31 & 7.34 \\ \hline
Creditcard & 83.42 & 35.62 & 14.20 & 8.57 \\ \hline
Census & 80.23 & 41.62 & 17.86 & 10.48 \\ \hline
Moons & 4.07 & 2.15 & 0.97 & 0.60 \\ \hline
Breastcancer & 11.78 & 6.05 & 2.68 & 1.67 \\ \hline
Cluto & 201.54 & 100.64 & 45.82 & 27.74 \\ \hline
Complex & 52.23 & 27.25 & 11.66 & 7.05 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Shihong Song
Guanlin Mo
Qingyuan Yang
Hu Ding

"Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems" by Shihong Song, Guanlin Mo, Qingyuan Yang, and Hu Ding, arXiv:2411.01115v3, 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{Model performance against a noisy channel. Here, we model the noise as packet error rate (PER) with different percentages. We have used the random policy with $p=0.25$ on the cityscape dataset. }
\begin{tabular}{|c||c|c|c|c|}
\hline
\textbf{PER} & \textbf{FID}$\downarrow$ & \textbf{LPIPS}$\downarrow$\\
\hline
1\%& 23.18 & 0.1148 \\
\hline
2\%& 23.59 & 0.1179 \\
\hline
3\%& 24.49& 0.1150\\
\hline
5\%& 25.49 & 0.1270 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Achintha Wijesinghe
Suchinthaka Wanninayaka
Weiwei Wang
Yu-Chieh Chao
Songyang Zhang
Zhi Ding

"LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency" by Achintha Wijesinghe, Suchinthaka Wanninayaka, Weiwei Wang, Yu-Chieh Chao, Songyang Zhang, and Zhi Ding, arXiv:2412.17839v1, 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{ERC-20 Token Transaction Fees Pre- and Post-Merge}
\begin{tabular}{lrrrr}
 \hline
 Measure & Pre- & Post- & t-stat & p-val \\
\hline \hline
Gas Price (Gwei) & 18.8273 & 18.3042 & -0.35 & 0.7246 \\
 Gas Used \(\cdot10^{5}\) & 1.1104 & 1.1443 & 1.86 & 0.0670 \\
 Fees in Ether \(\cdot10^{-3}\) & 1.9458 & 1.9706 & 0.14 & 0.8868 \\
 Fees in USD & \$3.2387 & \$2.6106 & -2.56 & 0.0131 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"An Event Study of the Ethereum Transition to Proof-of-Stake" by Elie Kapengut and Bruce Mizrach, arXiv:2210.13655v2, 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{Comparing the view and feature dimension resulting from applying MVS with different meta-learners to the breast cancer and colitis data. ANSV denotes the average number of selected views, ANSF denotes the average number of selected features. We show the mean and standard deviation across the $10 \times 10$ different fitted models.}
\begin{tabular}{l|rr|rr}
		\hline
		& \multicolumn{2}{c|}{Colitis} & \multicolumn{2}{c}{Breast cancer} \\
		\hline
		Metalearner & ANSV & ANSF & ANSV & ANSF \\
		\hline
		lasso & $10.7 \pm 1.9$ & $678.4 \pm 119.6$ & $3.6 \pm 1.8$ & $ 99.3 \pm 65.0$ \\
		elastic net & $40.0 \pm 4.6$ & $1978.2 \pm 282.8$ & $13.1 \pm 2.8$ & $388.6 \pm 115.5$ \\
		adaptive lasso & $7.4 \pm 1.6$ & $482.6 \pm 90.9$ & $2.6 \pm 1.2$ & $71.4 \pm 42.0$ \\
		ridge & $132.7 \pm 18.1$ & $5349.6 \pm 749.6$ & $52.1 \pm 15.9$ & $1643.8 \pm 489.3$ \\
		interpolating predictor & $242.0 \pm 1.6$ & $9418.2 \pm 114.6$ & $298.6 \pm 1.6$ & $9778.9 \pm 172.1$ \\
		NNFS & $2.6 \pm 0.5$ & $180.1 \pm 49.7$ & $3.1 \pm 1.0$ & $99.3 \pm 56.6$ \\
		stability selection & $2.0 \pm 0.9$ & $161.7 \pm 76.9$ & $1.5 \pm 1.1$ & $46.7 \pm 49.6$ \\
		\hline
	\end{tabular}
\end{table}
\end{document}

stat

Wouter van Loon
Marjolein Fokkema
Botond Szabo
Mark de Rooij

"View selection in multi-view stacking: Choosing the meta-learner" by Wouter van Loon, Marjolein Fokkema, Botond Szabo, and Mark de Rooij, arXiv:2010.16271v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|c|}
\hline
$\mu$& \textit{r} & Strategy &$\mathcal{Q}_{0.25}$($X_0$) & $\mathcal{Q}_{0.50}$($X_0$) & $\mathcal{Q}_{0.75}$($X_0$) & $\mathcal{Q}_{0.95}$($X_0$) \\ \hline
1$\%$&0$\%$&$\hat{\pi}$& -2$\%$&309$\%$&602$\%$&1'024$\%$ \\

1$\%$&0$\%$&$\hat{\pi}_m$&82$\%$&112$\%$&149$\%$&228$\%$ \\\hline
0$\%$&-1$\%$&$\hat{\pi}$&-2$\%$&253$\%$&493$\%$&838$\%$ \\

0 $\%$&-1$\%$&$\hat{\pi}_m$&67$\%$&91$\%$&122$\%$&187$\%$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Quantiles of $X^{\hat{\pi}}_T$ and $X^{\hat{\pi}_m}_T$ as total return (varying \textit{r}) }
\end{table}
\end{document}

q-fin

"Retirement Wealth under Fixed Limits: The Optimal Strategy for Exponential Utility" by Lena Schutte, arXiv:1712.00463v1, 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{Summary of other results for 2D}
\begin{tabular}{llllllll}
 \toprule
 Model & Generator Depth & Discriminator Depth & Fusion Type & Fusion Pos & MS-SSIM & SSIM & PSNR \\
 \midrule
 Deterministic & 4 & 3 & Dot & Mid & 0.7157 & 0.565 & 21.02 \\
 Probabilistic & 32 & 7 & Concat & Mid & 0.727 & 0.565 & 21.13 \\
 Deterministic & 8 & 3 & Dot & Late & 0.7603 & 0.6 & 22.13 \\
 Probabilistic & 32 & 7 & Concat & Mid & 0.727 & 0.565 & 21.13 \\
 Probabilistic & 8 & 3 & Concat & Early & 0.741 & 0.575 & 20.87 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Praneet Dutta
Bruce Power
Adam Halpert
Carlos Ezequiel
Aravind Subramanian
Chanchal Chatterjee
Sindhu Hari
Kenton Prindle
Vishal Vaddina
Andrew Leach
Raj Domala
Laura Bandura
Massimo Mascaro

"3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement" by Praneet Dutta, Bruce Power, Adam Halpert, Carlos Ezequiel, Aravind Subramanian, Chanchal Chatterjee, Sindhu Hari, Kenton Prindle, Vishal Vaddina, Andrew Leach, Raj Domala, Laura Bandura, and Massimo Mascaro, arXiv:1911.06932v1, 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|llll|llll|llll|llll}
 \hline
 \textbf{} & \multicolumn{4}{l|}{\textbf{Syllable Complexity}} & \multicolumn{4}{l|}{\textbf{Semantic Conflict Count}} & \multicolumn{4}{l|}{\textbf{Homonym Count}} & \multicolumn{4}{l}{\textbf{Exaggeration Count}} \\ \hline
 \textbf{Predicted Class} & \textbf{Mean} & \textbf{Std} & \textbf{Min} & \textbf{Max} & \textbf{Mean} & \textbf{Std} & \textbf{Min} & \textbf{Max} & \textbf{Mean} & \textbf{Std} & \textbf{Min} & \textbf{Max} & \textbf{Mean} & \textbf{Std} & \textbf{Min} & \textbf{Max} \\ \hline
 Affiliative & 1.183 & 0.118 & 1.0 & 1.48 & 29.755 & 114.755 & 0 & 794 & 6.796 & 4.743 & 1 & 33 & 1.388 & 1.304 & 0 & 5 \\ \hline
 Aggressive & 1.148 & 0.118 & 1.0 & 1.50 & 13.439 & 37.424 & 0 & 231 & 6.667 & 3.772 & 2 & 22 & 1.456 & 1.593 & 0 & 9 \\ \hline
 Neutral & 1.355 & 0.245 & 1.0 & 2.00 & 7.025 & 7.347 & 0 & 48 & 2.662 & 1.987 & 0 & 11 & 0.738 & 0.853 & 0 & 3 \\ \hline
 Self-deprecating & 1.192 & 0.171 & 1.0 & 2.00 & 18.717 & 47.830 & 0 & 294 & 6.087 & 3.817 & 0 & 22 & 1.848 & 1.738 & 0 & 8 \\ \hline
 Self-enhancing & 1.206 & 0.143 & 1.0 & 1.64 & 7.557 & 7.274 & 0 & 30 & 4.902 & 2.350 & 0 & 12 & 0.885 & 0.950 & 0 & 4 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Complexity Statistics}
\end{table}
\end{document}

cs

Mary Ogbuka Kenneth
Foaad Khosmood
Abbas Edalat

"Explaining Humour Style Classifications: An XAI Approach to Understanding Computational Humour Analysis" by Mary Ogbuka Kenneth, Foaad Khosmood, and Abbas Edalat, arXiv:2501.02891v2, 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}{ccccc}
 \toprule
 n & raw dataset & $C_K(P)=4.3$ & $C_K(P)=6.74$ & $C_K(P)=9.20$ \\
 \midrule
 $1000$ & $0.989 \pm 0.0011 $ & $0.963 \pm 0.0223$ $(\Delta_{\nu,\lambda,n} \leq 0.07)$ & $0.968 \pm 0.0137$ $(\Delta_{\nu,\lambda,n} \leq 0.11)$ & $0.986 \pm 0.0039$ $(\Delta_{\nu,\lambda,n} \leq 0.15)$ \\
 $5000$ & $0.993 \pm 0.0012 $ & $0.967 \pm 0.0184$ $(\Delta_{\nu,\lambda,n} \leq 0.03)$ & $0.971 \pm 0.0282$ $(\Delta_{\nu,\lambda,n} \leq 0.05)$ & $0.984 \pm 0.0055$ $(\Delta_{\nu,\lambda,n} \leq 0.07)$ \\
 $10000$ & $0.994 \pm 0.0006 $ & $0.971 \pm 0.0141$ $(\Delta_{\nu,\lambda,n} \leq 0.02)$ & $0.977 \pm 0.0082$ $(\Delta_{\nu,\lambda,n} \leq 0.03)$ & $0.984 \pm 0.0055$ $(\Delta_{\nu,\lambda,n} \leq 0.05)$ \\
 \bottomrule
 \\
 \end{tabular}
\caption{Shows the accuracy of classifiers on MNIST dataset. The column \textbf{n} displays the dataset size. The column \textbf{raw dataset} displays the accuracy of the neural network on the original dataset. Each column of the column \textbf{discretized datasets} displays the accuracy of a neural network on the discretized dataset associated to the constant $C_K(P)$.}
\end{table}
\end{document}

stat

Eric Aubinais
Elisabeth Gassiat
Pablo Piantanida

"Fundamental Limits of Membership Inference Attacks on Machine Learning Models" by Eric Aubinais, Elisabeth Gassiat, and Pablo Piantanida, arXiv:2310.13786v5, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overall measures of entropy $S_{J}$ for the 4 iterations.}
\begin{tabular}{c|c}
\toprule
Iteration&$S_{J}$ \\
\midrule
\textbf{1}&\textbf{115}\\
2&1813 \\
3&1904 \\
4&1345 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Advances in Kth nearest-neighbour clutter removal" by Nicoletta D'Angelo, arXiv:2310.06467v1, 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|l|l|}
\hline
 & Full Truncation Euler & QE-M & Direct-Inversion \\ \hline
Relative Error & \(6.14\times 10^{-3}\) & \(3.85\times 10^{-3}\) & \(3.12\times 10^{-3}\) \\ \hline
Length of timestep
\(h\)& \(1/10\) & \(1/4\) & \(10\) \\ \hline
CPU time & \(28.38\) & \(6.14\) & \(0.42\) \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

"Direct Inversion for the Squared Bessel Process and Applications" by Simon J. A. Malham, Anke Wiese, and Yifan Xu, arXiv:2412.16655v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l||c|c||c|c|}
 \hline
 \multirow{2}{*}{\textbf{Augmentation}} & \multicolumn{2}{c||}{\textbf{R0}} & \multicolumn{2}{c|}{\textbf{R1}} \\
 & \textbf{OMP [\%]} & \textbf{OM [\%]} & \textbf{OMP [\%]} & \textbf{OM [\%]} \\ \hline
 without all Augs. & 3.14 & 8.23 & 3.03 & 7.35 \\
 with all Augs. & 84.05 & 92.64 & 79.74 & 89.88 \\ \hline
 -Amplify & 78.90 & 87.59 & 58.14 & 64.70 \\
 -FreqShift & 83.35 & 92.05 & 42.45 & 57.74 \\
 -\(\text{SimTone}_1\) & 83.90 & 92.61 & 81.19 & 90.02 \\
 -\(\text{SimTone}_{1/2}\) & 80.67 & 90.57 & 74.41 & 84.75 \\
 -Noise & 4.73 & 11.90 & 5.23 & 17.21 \\ \hline

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

cs

Maximilian Bundscherer
Thomas H. Schmitt
Ilja Baumann
Tobias Bocklet

"Digital Operating Mode Classification of Real-World Amateur Radio Transmissions" by Maximilian Bundscherer, Thomas H. Schmitt, Ilja Baumann, and Tobias Bocklet, arXiv:2501.07337v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Bijay Adhikari
Pratibha Kulung
Jakesh Bohaju
Laxmi Kanta Poudel
Confidence Raymond
Dong Zhang
Udunna C Anazodo
Bishesh Khanal
Mahesh Shakya

"Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset" by Bijay Adhikari, Pratibha Kulung, Jakesh Bohaju, Laxmi Kanta Poudel, Confidence Raymond, Dong Zhang, Udunna C Anazodo, Bishesh Khanal, and Mahesh Shakya, arXiv:2412.14100v1, 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{Average marginal effects of policy variables on $\mu_t$ -- Sample period: June 1, 2009 - December 31, 2019 (parameter estimates by country as in Table ).}
\begin{tabular}{lrrr|rrr}
			& \multicolumn{3}{c|}{Marginal effect of $x_{t-1}$ on $\mu_t$ } & \multicolumn{3}{c}{Marginal effect of $\Delta_{t}$ on $\mu_t$} \\ %\cline{2-7}
			& MAP ~ & L-MAP ~ & P-MAP ~ & MAP ~ & L-MAP ~ & P-MAP \\
			\hline
			France & -1.836 ~ & -1.571 ~ & -2.172 ~ & 2.817 ~ & 2.236 ~ & 3.177 \\
			Germany & -1.328 ~ & -1.158 ~ & -1.577 ~ & 2.317 ~ & 1.821 ~ & 2.603 \\
			Italy & -2.354 ~ & -2.033 ~ & -2.693 ~ & 3.448 ~ & 2.785 ~ & 3.953 \\
			Spain & -2.823 ~ & -2.489 ~ & -3.382 ~ & 3.428 ~ & 2.912 ~ & 4.146
		\end{tabular}
\end{table}
\end{document}

q-fin

Demetrio Lacava
Giampiero M. Gallo
Edoardo Otranto

"Unconventional Policies Effects on Stock Market Volatility: A MAP Approach" by Demetrio Lacava, Giampiero M. Gallo, and Edoardo Otranto, arXiv:2010.08259v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Ruwani Herath
Alex Trindade
Fangyuan Zhang

"Monte Carlo Expectation-Maximization algorithm to detect imprinting and maternal effects for discordant sib-pair data" by Ruwani Herath, Alex Trindade, and Fangyuan Zhang, arXiv:2401.00520v1, 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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Countries in clusters.} Clusters from Fig. are listed here and the average values with standard errors are reported as well. The countries are listed alphabetically within the clusters.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\toprule \toprule
\multicolumn{5}{c}{Cluster \#1 -- average $82.92 \pm 1.82$}\\
\midrule
Australia&Austria&Belgium&Canada&Chile\\
Denmark&Finland&France&Germany&Hong Kong\\
Ireland&Japan&Luxembourg&Netherlands&New Zealand\\
Norway&Singapore&Sweden&Switzerland&United Arab Emirates\\
United Kingdom&USA&&&\\
\midrule
\multicolumn{5}{c}{Cluster \#2 -- average $59.21 \pm 1.47$}\\
\midrule
Bahamas&Bahrain&Barbados&Botswana&Cyprus\\
Estonia&Israel&Kuwait&Malta&Oman\\
Portugal&Slovenia&Spain&Taiwan&Uruguay\\
\midrule
\multicolumn{5}{c}{Cluster \#3 -- average $24.15 \pm 0.78$}\\
\midrule
Albania&Azerbaijan&Bangladesh&Belarus&Bolivia\\
Brazil&Bulgaria&Burkina Faso&Burma&Cameroon\\
China&Colombia&Cuba&Dominican Republic&Ecuador\\
Ethiopia&Fiji&Georgia&Guinea&Guyana\\
Haiti&Honduras&India&Indonesia&Iran\\
Ivory Coast&Kenya&Laos&Lebanon&Lesotho\\
Libya&Madagascar&Malawi&Mali&Mauritania\\
Moldova&Mozambique&Nepal&Nicaragua&Niger\\
Nigeria&North Korea&Pakistan&Paraguay&Peru\\
Philippines&Republic of Kongo&Romania&Russia&Suriname\\
Swaziland&Syria&Tanzania&Turkey&Uganda\\
Ukraine&Venezuela&Vietnam&Yemen&Zimbabwe\\
\midrule
\multicolumn{5}{c}{Cluster \#4 -- average $41.28 \pm 0.98$}\\
\midrule
Algeria&Argentina&Armenia&Belize&Benin\\
Cape Verde&Costa Rica&Croatia&Czech Republic&Egypt\\
El Salvador&Gabon&Ghana&Greece&Guatemala\\
Hungary&Italy&Jamaica&Jordan&Latvia\\
Lithuania&Malaysia&Mexico&Mongolia&Morocco\\
Panama&Poland&Saudi Arabia&Senegal&Slovakia\\
South Africa&South Korea&Sri Lanka&Thailand&Trinidad and Tobago\\
Tunisia&Zambia&&&\\
\bottomrule \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Worldwide clustering of the corruption perception" by Michal Paulus and Ladislav Kristoufek, arXiv:1502.00104v1, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Elizabeth Harris
Ali Eshragh
Bishnu Lamichhane
Jordan Shaw-Carmody
Elizabeth Stojanovski

"Efficient Data-Driven Leverage Score Sampling Algorithm for the Minimum Volume Covering Ellipsoid Problem in Big Data" by Elizabeth Harris, Ali Eshragh, Bishnu Lamichhane, Jordan Shaw-Carmody, and Elizabeth Stojanovski, arXiv:2411.03617v1, 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}{lllllll}
 \toprule
 Training Method & Flow Extractor & Dataset & EPE & zEPE & Acc 01 & Acc 005 \\ \midrule
 Supervised & FlowNet3D & KITTI & \underline{0.1729} & \underline{0.1381} & \underline{57.68}\% & \underline{22.73}\% \\
 Self-Supervised & FlowNet3D & KITTI & 1.0903 & 0.8712 & \textbf{9.81}\% & \textbf{3.08}\% \\
 Self-Supervised & PointPWC-net & KITTI & 2.5717 & 2.0551 & 0.00\% & 0.00\% \\
 Adversarial Metric & FlowNet3D & KITTI & \textbf{0.9673} & \textbf{0.7729} & 3.01\% & 0.76\% \\
 Adversarial Metric & PointPWC-net & KITTI & 1.0497 & 0.8388 & 3.41\% & 1.02\% \\ \midrule
 Supervised & FlowNet3D & KITTI No Ground & \underline{0.1880} & \underline{0.2050} & \underline{52.12}\% & \underline{22.81}\% \\
 Self-Supervised & FlowNet3D & KITTI No Ground & 0.7002 & 0.7635 & 5.05\% & 1.43\% \\
 Self-Supervised & PointPWC-net & KITTI No Ground & 1.4671 & 1.5998 & 0.03\% & 0.00\% \\
 Adversarial Metric & FlowNet3D & KITTI No Ground & 0.6733 & 0.7342 & \textbf{5.82}\% & 1.03\% \\
 Adversarial Metric & PointPWC-net & KITTI No Ground & \textbf{0.5542} & \textbf{0.6043} & 5.58\% & \textbf{1.45}\% \\ \bottomrule
 \end{tabular}
\caption{Comparison of different methods performing flow estimation on KITTI. The best metrics of self-supervised methods are reported in bold. The underlined metrics indicate the best overall performance, regardless of the training method.}
\end{table}
\end{document}

eess

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\textbf{Solution Type} & \textbf{Count} & \textbf{\% of Total} \\ \hline
rewrite math formula & 63 & 25.00\% \\ \hline
increase precision/change variable type & 59 & 23.41\% \\ \hline
use a different algorithm & 43 & 17.06\% \\ \hline
limit input range & 21 & 8.33\% \\ \hline
relax accuracy test tolerance & 14 & 5.56\% \\ \hline
add overflow check & 14 & 5.56\% \\ \hline
add/fix assertion or unit test & 13 & 5.16\% \\ \hline
ignore unit test/exceptions & 12 & 4.76\% \\ \hline
mixed precision training & 6 & 2.38\% \\ \hline
other & 7 & 2.78\% \\ \hline
Total & 252 & 100.00\% \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"DeepStability: A Study of Unstable Numerical Methods and Their Solutions in Deep Learning" by E. Kloberdanz, K. G. Kloberdanz, and W. Le, arXiv:2202.03493v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 \hline
 \textbf{Model} & \#Num Sources & Retrieval Time \\
 \hline
 VLP (baseline) & 50 & 250 milliseconds\\
 \hline
 GNN(Ours) & 50 & 1 millisecond \\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Latency analysis for retrieval problem on WebQA dataset}
\end{table}
\end{document}

cs

"Multimodal Multihop Source Retrieval for Web Question Answering" by Navya Yarrabelly and Saloni Mittal, arXiv:2501.04173v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 \multicolumn{4}{c}{Non-hallucination Samples} \\
 {[0.0,0.25)} & {[0.25,0.5)} & {[0.5,0.75)} & {[0.75,1.0]} \\
 0.026 & 0.117 & 0.320 & 0.537 \\
 \midrule
 \multicolumn{4}{c}{Hallucination Samples} \\
 {[0.0,0.25)} & {[0.25,0.5)} & {[0.5,0.75)} & {[0.75,1.0]} \\
 0.330 & 0.434 & 0.219 & 0.017 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Distribution of attentive consistency scores $\mathop r\nolimits^{att}$ with Llama2-13b on the Movies dataset.}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Rolling Forecast Error Metrics for each variable: MSE stands for Mean Square Error and MAE stands for Mean Absolute Error. }
\begin{tabular}{|c|c|c|c|c|c|c|}
				\hline
				Method & \multicolumn{2}{c|}{\textbf{YIELD10Y}} & \multicolumn{2}{c|}{\textbf{UNR}} & \multicolumn{2}{c|}{\textbf{RRE}}\\\hline
				& MSE & MAE & MSE & MAE & MSE & MAE \\\hline
				Satellite Modeling (BMS) & 3.34 & 1.29 & 0.4 & 0.48 & 18.02 & 3.32\\\hline
				Deep Learning (MXNET) & 1.34 & 0.87 & 0.74 & 0.66 & 29.40 & 4.34 \\\hline
				Deep Learning (Bayesian ReLU) & \textbf{0.50} & \textbf{0.49} & \textbf{0.09} & \textbf{0.23} & \textbf{15.50} & \textbf{3.08} \\\hline
				Deep Learning (Bayesian LWTA) & 1.44 & 0.96 & 0.26 & 0.38 & 22.16 & 3.77 \\\hline
				& \multicolumn{2}{c|}{\textbf{INFLAT}} & \multicolumn{2}{c|}{\textbf{STOCKS}} & \multicolumn{2}{c|}{\textbf{GDP}}\\\hline
				& MSE & MAE & MSE & MAE & MSE & MAE \\\hline
				Satellite Modelling(BMA) & 3.07 & 1.30 & 203.02 & 11.43 & 2.59 & 1.26\\
				Deep Learning (MXNET) & 5.40 & 1.82 & \textbf{144.25} & 9.29 & 2.59 & 1.28\\
				Deep Learning (Bayesian ReLU) & \textbf{2.96} & \textbf{1.07} & 151.93 & \textbf{7.99} & \textbf{0.48} & \textbf{0.51}\\
				Deep Learning (Bayesian LWTA) & 4.62 & 1.49 & 157.06 & 8.49 & 3.25 & 1.38\\\hline
				& \multicolumn{2}{c|}{\textbf{EXPORT}} & \multicolumn{2}{c|}{\textbf{DEBT}} & \multicolumn{2}{c|}{\textbf{GOVEXP}}\\\hline
				& MSE & MAE & MSE & MAE & MSE & MAE \\\hline
				Satellite Modelling(BMA) & \textbf{72.56} & \textbf{6.96} & 6.98 & 2.05 & 14.40 & 1.12\\
				Deep Learning (MXNET) & 92.16 & 7.43 & 14.95 & 2.72 & 1.10& 0.82\\
				Deep Learning (Bayesian ReLU) & 87.93 & 7.26 & \textbf{2.26} & \textbf{1.13} & \textbf{0.40} & \textbf{0.50}\\
				Deep Learning (Bayesian LWTA) & 102.19 & 8.18 & 2.99 & 1.42 & 1.14 & 0.90
				\\\hline

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

q-fin

Anastasios Petropoulos
Vassilis Siakoulis
Konstantinos P. Panousis
Loukas Papadoulas
Sotirios Chatzis

"Macroeconomic forecasting and sovereign risk assessment using deep learning techniques" by Anastasios Petropoulos, Vassilis Siakoulis, Konstantinos P. Panousis, Loukas Papadoulas, and Sotirios Chatzis, arXiv:2301.09856v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Performance on the VoxCeleb1 test data when the VoxCeleb1 training data are split into two subsets.}
\begin{tabular}{ccccccccccc}
 \toprule
 \multirow{3}{*}{\textbf{ID}} & \multirow{3}{*}{\textbf{VoxCeleb1 Training data}}& \multicolumn{4}{c}{\textbf{VoxCeleb1 Testing}}\\
 \cmidrule(lr){3-6}
 &&\multicolumn{2}{c}{\textbf{\# filters}}&\multicolumn{2}{c}{\textbf{EER (\%)}}\\
 \cmidrule(lr){3-4} \cmidrule(lr){5-6}
 &&\textbf{16k}&\textbf{8k}&\textbf{16k}&\textbf{8k}\\
 \midrule
 1&Subset1 16k&64&48&\textbf{5.66}&11.08\\
 2&Subset2 8k&64$\rightarrow$48&48&6.11&\textbf{6.07}\\
 \midrule
 3& Subset1 8k + Subset2 8k &64$\rightarrow$48&48&4.95&4.92\\
 4&\textbf{Subset1 16k + Subset2 8k}&64&48&\textbf{4.53}&\textbf{4.51}\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"A Unified Deep Speaker Embedding Framework for Mixed-Bandwidth Speech Data" by Weicheng Cai and Ming Li, arXiv:2012.00486v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 & \\
 RG-1 & Utilize data-driven algorithms to derive a truly objective classification heuristic. \\
 & \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{List of the models considered to evaluate the performance of our method.}
\begin{tabular}{l|c}
Model & Description \\
\hline
mfVI & BNN with Automatic Differentiation VI with mean-field family \\
HMC & BNN with Hamiltonian Monte Carlo with No-U-Turn sampler \\
BBB & Bayes by Backprob \\
HSBNN & BNN with Horseshoe priors inferred with Black Box VI \\
VBNN & Our model inferred with CAVI \\
GVBNN & VBNN with Gaussian priors inferred with CAVI \\
SVBNN & Our model inferred with SVI
\end{tabular}
\end{table}
\end{document}

stat

"Variational Bayesian Bow tie Neural Networks with Shrinkage" by Alisa Sheinkman and Sara Wade, arXiv:2411.11132v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{diagbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc}
\toprule
\diagbox{Model (delay 200ms)}{Seed} & 0 & 1 & 2 & Maximum & Average \\
\midrule
2D CNN, PredANN loss weight 0.05 (our best model) & \textbf{0.662} & \textbf{0.622} & \textbf{0.588} & \textbf{0.662} & \textbf{0.624} \\
1D CNN, PredANN loss weight 0.05 & 0.487 & 0.465 & 0.494 & 0.494 & 0.482 \\
2D CNN, PredANN loss weight 0 & 0.537 & 0.589 & 0.516 & 0.589 & 0.547 \\
2D CNN, PredANN loss weight 0.05, stop-gradient-free & 0.648 & 0.529 & 0.315 & 0.648 & 0.497 \\
\bottomrule
\end{tabular}
\caption{Accuracy for different models (delay 200ms)}
\end{table}
\end{document}

eess

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

"Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings" by Taketo Akama, Zhuohao Zhang, Pengcheng Li, Kotaro Hongo, Hiroaki Kitano, Shun Minamikawa, and Natalia Polouliakh, arXiv:2412.15560v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
				$(p, \rho, n)$& (20, 0.6, 500) & (700, 0.6, 700) &(700, 0.6, 1000) \\ \hline &&&\\
				$\frac{1}{p(p-1)}\sum_{1\le k,l\le p; l\not = k}(\check{\Sigma}_{lk}^{(f)} - \check{\Sigma}_{lk})^2$&0.0010&0.0019&0.0006\\
				\hline

			\end{tabular}
\caption{Each entry is the average of results over $10$ repetitions, and is rounded to $4$ decimals. The values of $(p, \rho, n)$ are indicated on the top of each column for each case.}
\end{table}
\end{document}

stat

"Testing for Regression Heteroskedasticity with High-Dimensional Random Forests" by Chi Chien-Ming, arXiv:2212.02101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Lihu Xu
Fang Yao
Qiuran Yao
Huiming Zhang

"Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption" by Lihu Xu, Fang Yao, Qiuran Yao, and Huiming Zhang, arXiv:2201.03182v2, 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}{lrrr}
\toprule
\textbf{Class} & \textbf{Training Count} & \textbf{Test Count} & \textbf{Total Count} \\
\midrule
Audio/Text to Audio & 1909 & 478 & 2387 \\
LLM & 1893 & 473 & 2366 \\
Music recommendation & 917 & 230 & 1147 \\
ASR whisper & 778 & 194 & 972 \\
Music identification & 643 & 161 & 804 \\
Speaker ID, Diarization, counting & 602 & 150 & 752 \\
Source separation/removal & 243 & 61 & 304 \\
Unsupported & 2343 & 586 & 2929 \\
\midrule
\textbf{Total} & \textbf{10328} & \textbf{2333} & \textbf{12661} \\
\bottomrule
\end{tabular}
\caption{Proposed Dataset for audio intent classification}
\end{table}
\end{document}

eess

Vakada Naveen
Arvind Krishna Sridhar
Yinyi Guo
Erik Visser

"Comprehensive Audio Query Handling System with Integrated Expert Models and Contextual Understanding" by Vakada Naveen, Arvind Krishna Sridhar, Yinyi Guo, and Erik Visser, arXiv:2412.03980v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
 \hline
 Variable & Description & $U_Y$ & $\hat{\theta}_0 - \hat{\theta}$ \\
 \hline
 \texttt{inc3} & Cube-root-transformed family income & 150 & -.67 \\
 \hline
 \texttt{pov} & 1 if family income below poverty line, else 0 & 1 & .022 \\
 \hline
 \texttt{nf} & Number of family members & 20 & .27 \\
 \hline
 \texttt{bern} & iid Bernoulli(.5) random draws & 1 & .004 \\
 \hline
 \end{tabular}
\caption{Selected PSID variables and the simulated Bernoulli variable.}
\end{table}
\end{document}

stat

"Differentially Private Finite Population Estimation via Survey Weight Regularization" by Jeremy Seeman, Yajuan Si, and Jerome P Reiter, arXiv:2411.04236v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Dongyoung Lee
Seungkyu Choi
Ik Joon Chang

"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring" by Dongyoung Lee, Seungkyu Choi, and Ik Joon Chang, arXiv:2501.13331v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.