\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|cc|cc}
		\hline \hline
		\multicolumn{2}{c|}{BL and LM} & 	\multicolumn{2}{c|}{BL and MPQA} & 	\multicolumn{2}{c}{LM and MPQA} \\
		{Positive (131)} & {Negative (322)} & {Positive (971)} & {Negative (1164)} & {Positive (32)} & {Negative (30)}\\
		\hline
{Gains} & {Losses} & {Free} & {Gross} & {Despite} & {Against} \\
(7,604) & (5,938) & (133,395) & (8,228) & (7,413) & (8,877) \\
{Gained} & {Missed} & {Well} & {Risk} & {Able} & {Cut} \\
(7,493) & (3,165) & (3,0270) & (7,471) & (5,246) & (3,401) \\
{Improved} & {Declining} & {Like} & {Limited} & {Opportunity} & {Challenge} \\
(7,407) & (3,053) & (24,617) & (5,884) & (4,398) & (1,042) \\
{Improve} & {Failed} & {Top} & {Motley} & {Profitable} & {Serious} \\
(5,726) & (2,421) & (14,899) & (5,165) & (3,580) & (1,022) \\
{Restructuring} & {Concerned} & {Guidance} & {Crude} & {Efficiency} & {Contrary} \\
(3,210) & (1,991) & (11,715) & (5,109) & (2,615) & (401) \\
{Gaining} & {Declines} & {Significant} & {Cloud} & {Popularity} & {Severely} \\
(3,150) & (1,654) & (10,576) & (4,906) & (1,588) & (348) \\
{Enhance} & {Suffered} & {Worth} & {Fall} & {Exclusive} & {Despite} \\
(2,753) & (1,435) & (10,503) & (4,732) & (1,225) & (342) \\
{Outperform} & {Weaker} & {Gold} & {Mar} & {Tremendous} & {Argument} \\
(2,518) & (1,288) & (9,303) & (3,190) & (611) & (324) \\
{Stronger} & {Critical} & {Support} & {Hard} & {Dream} & {Seriously} \\
(1,657) & (1,131) & (9,120) & (2,957) & (581) & (240) \\
{Win} & {Drag} & {Recommendation} & {Cancer} & {Satisfaction} & {Staggering} \\
(1,491) & (1,095) & (8,993) & (2,521) & (410) & (209) \\
		\hline \hline
	\end{tabular}
\end{adjustbox}
\caption{Lists of ten most frequent positive words and ten most frequent negative words that are shared only by BL and LM lexica, only by BL and MPQA lexica, or only by LM and MPQA lexica, along with their frequencies given in parentheses.}
\end{table}
\end{document}

q-fin

Junni L. Zhang
Wolfgang Karl Härdle
Cathy Y. Chen
Elisabeth Bommes

"Distillation of News Flow into Analysis of Stock Reactions" by Junni L. Zhang, Wolfgang Karl Härdle, Cathy Y. Chen, and Elisabeth Bommes, arXiv:2009.10392v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The architecture of CNN network used for metric testing. (*) This activation function is changed in some experiments.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
layer & size & activation function \\
\midrule
(input) & - & -\\
Conv 2D & 3,3 X 32 & -\\
Maxpool & 2,2 & -\\
flatten & - & - \\
Dense & 128 & ReLu*\\
Dense & 64 & ReLu\\
Dense & 10 & Softmax\\
\midrule
\multicolumn{3}{l}{learning-rate: 0.01 }\\
\multicolumn{3}{l}{Loss function: categorical crossentropy }\\
\multicolumn{3}{l}{Optimizer: SGD with batch size 50}\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Fidel: Reconstructing Private Training Samples from Weight Updates in Federated Learning" by David Enthoven and Zaid Al-Ars, arXiv:2101.00159v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\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
Parameter & meaning in BLV model & meaning in synthetic neural data\\
\hline \hline
$|\mathbf{z}| \sim \lbrack \mathcal{\text{Bin}}(M,Q)\rbrack_{K_{min}}^{K_{max}}$ & distribution of \# active latent variables & distribution \# coactive assemblies \\
\hline
\hspace{0.4cm} $Q \sim \lbrack \mathcal{N}(K/M,\sigma_{Q})\rbrack_0^1$ & prob. that latent variable is active & prob. that cell assembly is active \\
\hline
\hspace{0.4cm} $M$ & dimension of latent vector $\mathbf{z}$ & \# different cell assemblies \\
\hline
\hspace{0.4cm} $K$ & most probable \# active latent variables & most probable \# coactive assemblies \\
\hline
\hspace{0.4cm} $K_{min}$ & minimum allowed |$\mathbf{z}$| & minimum \# coactive assemblies \\
\hline
\hspace{0.4cm} $K_{max}$ & maximum allowed |$\mathbf{z}$| & maximum \# coactive assemblies \\
\hline \hline
$P_{ia} \sim [\mathcal{N}(S_{ia}(1 - \mu_{P}),\sigma_{P})]_0^1$ & membership prob. of visible in group & membership prob. of neuron in CA\\
\hline
\hspace{0.4cm} $\mathbf{S}$ binary membership & between visible and groups & between neurons and cell assemblies\\
\hline
\hspace{0.4cm} $|\mathbf{S}_{\cdot a}| \sim \lbrack \mathcal{\text{Bin}}(N,C/N)\rbrack_{C_{min}}^{C_{max}}$ & distribution of group size & distribution of cell assembly size\\
\hline
\hspace{0.4cm} $N$ & dimension of observation vector $\mathbf{y}$ & \# neurons \\
\hline
\hspace{0.4cm} $C$ & \# most probable group size & most probable cell assembly size \\
\hline
\hspace{0.4cm} $C_{min}$ & minimal group size $|\mathbf{S}_{\cdot a}|$ & size of smallest cell assembly\\
\hline
\hspace{0.4cm} $C_{max}$ & maximal group size $|\mathbf{S}_{\cdot a}|$ & size of largest cell assembly\\
\hline \hline $R_i \sim \lbrack \mathcal{N}(1-\mu_{R},\sigma_{R})\rbrack_0^1$ & prob. of spurious activity & spurious firing, chattery-ness \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Generation of parameters for creating synthetic datasets. The three vertical sections of the table describe how we generate latent vectors, probabilistic cell assembly structure and spontaneous firing in synthetic datasets, respectively. Expressions use definitions (), (), further $[p(x)]_{l}^{u}$ means the distribution $p(x)$ truncated at the lower and upper bounds $l$ and $u$.}
\end{table}
\end{document}

cs

Christopher Warner
Kiersten Ruda
Friedrich T. Sommer

"A probabilistic latent variable model for detecting structure in binary data" by Christopher Warner, Kiersten Ruda, and Friedrich T. Sommer, arXiv:2201.11108v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ll|l}
\textbf{Beam horizontal component} & \textbf{Beam vertical component} & \textbf{Hit probability $g_L$} \\ \hline
1.000000 & 0.000000 & 0.424413 \\
0.999683 & 0.025180 & 0.424682 \\
0.998732 & 0.050345 & 0.425489 \\
$\vdots$ & $\vdots$ & $\vdots$ \\
0.009444 & 0.999955 & 0.848789 \\
0.000000 & 1.000000 & 0.848822
\end{tabular}
\caption{Values from a look-up table (LUT) for the hit probability $g_L$ at a given beam direction $L$, represented by horizontal and vertical component. The numbers here correspond to an erectophile distribution and are obtained using a numerical integration method on the formulae from . The probability $g_L$ is normalised over $2\pi$.}
\end{table}
\end{document}

eess

Lukas Winiwarter
Alberto Manuel Esmorís Pena
Hannah Weiser
Katharina Anders
Jorge Martínez Sanchez
Mark Searle
Bernhard Höfle

"Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic 3D laser scanning" by Lukas Winiwarter, Alberto Manuel Esmorís Pena, Hannah Weiser, Katharina Anders, Jorge Martínez Sanchez, Mark Searle, and Bernhard Höfle, arXiv:2101.09154v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of computation time for MIQP and MILP formulations, $n=15$.}
\begin{tabular}{l|c|c|c|c|c|c}
\hline
Formulation & F0Q & F1Q & F2Q & F0L & F1L & F2L \\
\hline
Median computation time, sec. & 24.86 & 21.59 & 8.95 & 34.80 & 1.16 & 3.14 \\
\hline
Median time rank & V & IV & III & VI & I & II \\
\hline
Average computation time, sec. & 79.40 & 683.23 & 25.08 & 99.15 & 7.06 & 8.35 \\
\hline
Average time rank & IV & VI & III & V & I & II \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

"Mixed-Integer Approaches to Constrained Optimum Communication Spanning Tree Problem" by Alexander Veremyev and Mikhail Goubko, arXiv:2101.03872v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Microscopic Traffic Models, Accidents, and Insurance Losses" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, 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}
 & \textbf{Voisard et al.} & \textbf{PerCDL} \\ \hline
\textbf{Sensitivity} & 0.959 & 0.988 \\
\textbf{FPR} & 0.004 & 0.033
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Axel Roques
Samuel Gruffaz
Kyurae Kim
Alain Oliviero-Durmus
Laurent Oudre

"Personalized Convolutional Dictionary Learning of Physiological Time Series" by Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero-Durmus, and Laurent Oudre, arXiv:2503.07687v1, 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}{lrrr}
		\hline
		Corpus & \#Sentence &\#SrcToken &\#TgtToken \\
		\hline
		Lang-8 & 1.09M & 14M & 15M \\
		HSK & 88K & 1.78M & 1.76M \\
		\hline
	\end{tabular}
\end{adjustbox}
\caption{Data statistics for the Lang-8 and HSK datasets}
\end{table}
\end{document}

cs

Shengsheng Zhang
Yaping Huang
Yun Chen
Liner Yang
Chencheng Wang
Erhong Yang

"Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning" by Shengsheng Zhang, Yaping Huang, Yun Chen, Liner Yang, Chencheng Wang, and Erhong Yang, arXiv:2101.12409v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A Simple Weak Galerkin Finite Element Method for a Class of Fourth-Order Problems in Fluorescence Tomography" by Chunmei Wang and Shangyou Zhang, arXiv:2503.18200v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc} \toprule
Retiree & 65-year old Canadian male\\
Tontine Gain $\mathbb{T}^g$ & equation () \\
Group Gain $G$ ( see equation ( ) ) & 1.0\\
Mortality table & CPM 2014\\
Investment horizon $T$ (years) & 30.0 \\
Equity market index & CRSP Cap-weighted index (real) \\
Bond index & 30-day T-bill (US) (real) \\
Initial portfolio value $W_0$ & 1000 \\
Cash withdrawal$/$rebalancing times & $t=0,1.0, 2.0,\ldots, 29.0$\\
Maximum withdrawal (per year) & $ q_{\max} = 80$\\
Minimum withdrawal (per year) & $ q_{\min} = 40$\\
Equity fraction range & $[0,1]$\\
Borrowing spread $\mu_c^{b}$ & 0.02 \\
Rebalancing interval (years) & 1.0 \\
$\alpha$ (EW-ES) & .05 \\
Fees $\mathbb{T}^f$ ( see equation () ) & 50 bps per year\\
Stabilization $\epsilon$ ( see equation () ) & $ -10^{-4} $ \\
Market parameters & See Table~ \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Peter A. Forsyth
Kenneth R. Vetzal
G. Westmacott

"Optimal performance of a tontine overlay subject to withdrawal constraints" by Peter A. Forsyth, Kenneth R. Vetzal, and G. Westmacott, arXiv:2211.10509v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Data set v3 vs v5}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc} \toprule
\textbf{Dataset} & \textbf{Val acc [\%]} & \textbf{Test acc [\%]} \\ \midrule
roeiboat-v3 & 90.09 & 87.40 \\
roeiboat-v5 & 95.46 & 90.10 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

M. J. A. van Wezel
L. J. Hamburger
Y. Napolean

"Fine-grained Classification of Rowing teams" by M. J. A. van Wezel, L. J. Hamburger, and Y. Napolean, arXiv:1912.05393v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Totally Disjoint 3-Digit Decimal Check Digit Codes" by Larry A. Dunning, arXiv:2504.05326v2, 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{Average Accuracy Rate by Model Type}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|lc|}
			\hline
			Model & Average accuracy rate \\
			\hline
			\hline
			COMPAS (baseline) & 65.4\% \\
			LASSO & 67.2\% \\
			Ridge & 67.1\% \\
			Elastic net & 67.4\%\\
			\hline
			\multicolumn{2}{r}{\textit{Note: Average accuracy rate from 1,000 iterations of model fit and evaluation.}}
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Pursuing Open-Source Development of Predictive Algorithms: The Case of Criminal Sentencing Algorithms" by Philip D. Waggoner and Alec Macmillen, arXiv:2011.06422v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Fitting Dynamically Misspecified Models: An Optimal Transportation Approach" by Jean-Jacques Forneron and Zhongjun Qu, arXiv:2412.20204v2, 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{Correlation coefficients and p-value with RUL under constant charging/discharging current}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\toprule
 & Correlation coefficient & p-value$^\mathrm{a}$ \\ \midrule
$\textbf{V}_{max}$ & -0.5097 & 1.9e-34 \\
$\textbf{V}_{min}$ & 0.2856 & 7.7e-11 \\
$\Delta\textbf{V}$ & -0.4108 & 8.8e-22 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"A Framework for Health-informed RUL-constrained Optimal Power Flow with Li-ion Batteries" by Jiahang Xie, Yu Weng, and Hung D. Nguyen, arXiv:2011.14318v1, 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{Video prediction results on KTH ($64\times64$), predicting 30 and 40 frames using models trained to predict $k$ frames at a time. All models condition on 10 past frames on 256 test videos.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|rc|lcl}
\toprule
\textbf{KTH} [10 $\rightarrow$ $\# \text{pred}$; trained on $k$] & $k$ & $\# \text{pred}$ & FVD$\downarrow$ & PSNR$\uparrow$ & SSIM$\uparrow$ \\ \hline
SVG-LP~ & 10 & 30 & 377 & 28.1 & 0.844 \\
SAVP~ & 10 & 30 & 374 & 26.5 & 0.756 \\
MCVD~ & 5 & 30 & 323 & 27.5 & 0.835 \\
SLAMP~ & 10 & 30 & 228 & 29.4 & 0.865 \\
SRVP~ & 10 & 30 & 222 & 29.7 & 0.870 \\
RIVER~& 10&30& 180& \textbf{30.4} & 0.86\\
\textbf{CVP (Ours) } & \textbf{1} & 30 & \textbf{140.6}& 29.8 & \textbf{0.872} \\
\midrule
Struct-vRNN~ & 10 & 40 & 395.0 & 24.29 & 0.766 \\
SVG-LP~ & 10 & 40 & 157.9 & 23.91 & 0.800 \\
MCVD~& 5 & 40 & 276.7 & 26.40 & 0.812 \\
SAVP-VAE~ & 10 & 40 & 145.7 & 26.00 & 0.806 \\
Grid-keypoints~& 10 & 40 & 144.2 & 27.11 & 0.837 \\
RIVER~& 10&40& 170.5& 29.0 & 0.82\\
\textbf{CVP (Ours)} & \textbf{1} & 40 & \textbf{120.1} & \textbf{29.2} & \textbf{0.841} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Prediction" by Gaurav Shrivastava and Abhinav Shrivastava, arXiv:2412.04929v2, 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{Percentage of claims with different activation delays for the four coverages}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
\toprule
& \multicolumn{5}{c}{Activation delays} \\
\midrule
 Coverage & No delay & 1 period & 2 periods & 3 periods & $\geq 4$ periods \\
 \midrule
 Accident Benefits & 93.84 & 5.73 & 0.29 & 0.08 & 0.06\\
 Bodily Injury & 85.86 & 9.86 & 1.46 & 1.13 & 1.69\\
 Vehicle Damage & 94.14 & 5.65 & 0.13 & 0.04 & 0.04 \\
 Loss of Use & 92.10 & 7.73 & 0.13 & 0.03 & 0.01\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Marie Michaelides
Mathieu Pigeon
Hélène Cossette

"Individual Claims Reserving using Activation Patterns" by Marie Michaelides, Mathieu Pigeon, and Hélène Cossette, arXiv:2208.08430v3, 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{Filter design equations ...}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
Order & Arbitrary coefficients &
coefficients\\
of filter & $e_m$ & $b_{ij}$ \\
\hline
1& $b_{ij}=\hat{e}.\hat{\beta_{ij}}$,
& $b_{00}=0$\\
\hline
2&$\beta_{22}=(~1,-1,-1,~~1,~~1,~~1)$ &\\
\hline
3& $b_{ij}=\hat{e}.\hat{\beta_{ij}}$,
& $b_{00}=0$,\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Kevin Hu
Retsef Levi
Raphael Yahalom
El Ghali Zerhouni

"Supply Chain Characteristics as Predictors of Cyber Risk: A Machine-Learning Assessment" by Kevin Hu, Retsef Levi, Raphael Yahalom, and El Ghali Zerhouni, arXiv:2210.15785v5, 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{An Example of a Table}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c||c||c|}
\hline
Policy & Steps When No Noise & Steps when encounter random noise\\
\hline
Non-smooth & & Steps when encounter random noise\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Shili Wu
Yancheng Zhu
Aniruddha Datta
Sean B. Andersson

"Multi-agent Robust and Optimal Policy Learning for Data Harvesting" by Shili Wu, Yancheng Zhu, Aniruddha Datta, and Sean B. Andersson, arXiv:2508.16490v1, 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}{ccccccc}
\hline
Method & $< 0.5\%$ & $< 1.25\%$ & $< 2.5\%$ & $>97.5\%$ & $> 98.75\%$ & $>99.5\%$ \\
\hline
1st order & 5.00 & 5.73 & 8.53 & 1.52 & 0.74 & 0.35 \\
 & 0.28 & 0.70 & 1.19 & 1.20 & 0.67 & 0.30 \\
 & 0.29 & 0.69 & 2.18 & 2.05 & 0.95 & 0.41 \\
3rd order (a) & 0.37 & 0.90 & 2.30 & 2.41 & 1.13 & 0.47 \\
3rd order (b) & 0.37 & 0.91 & 2.30 & 2.41 & 1.13 & 0.47 \\
{\em Profile marginal IM} & 0.22 & 0.65 & 1.57 & 2.71 & 1.31 & 0.59 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Results for the gamma mean simulation in Example~. The table shows the percentage of $p$-values in the stated bins (across 10000 simulations) for the various methods which relates to the coverage probability of the corresponding confidence intervals for the mean $\Phi$. All the values except for the last row are taken from Table~2 in . The standard errors across the board are all less than 0.16\%.}
\end{table}
\end{document}

stat

"Valid and efficient imprecise-probabilistic inference with partial priors, III. Marginalization" by Ryan Martin, arXiv:2309.13454v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{\small States of different facilities in microgrids}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
 \hline
 \toprule
 State & 1 & 2 & 3 & 4 & 5 & 6\\ \hline
 Wind power/MW & 0 & 1 & 2 & 3 & 4 & 5 \\ \hline
 Demand load/MW & 0.6 & 1.2 & 1.8 & 2.4 & 3.0 & 3.6 \\ \hline
 Storage energy level/MWh & 0 & 1 & 2 & 3 & 4 & 5 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games" by Junkai Hu and Li Xia, arXiv:2503.22779v2, 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{Quality of data manifold global structure preservation at projection Spheres dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 & \multicolumn{4}{c}{Quality measure} \\
 \cmidrule(r){2-5}
 Method & L. C. & W. D. $H_{0}$ & T. A. & RTD \\
 \midrule
 \multicolumn{5}{c}{\textbf{into 2D space}} \\
 UMAP & 0.021 & {44.90 $\pm$ 1.8} & 0.54 $\pm$ 0.01 & 42.60 $\pm$ 1.8 \\
 AE & 0.311 & {46.07 $\pm$ 1.5} & 0.41 $\pm$ 0.01 & 41.02 $\pm$ 1.4 \\
 TopoAE & {0.495} & \underline{43.92 $\pm$ 2.5} & {0.54 $\pm$ 0.02} & \underline{39.69 $\pm$ 1.4} \\
 RTD & \underline{0.626} & {45.29 $\pm$ 2.2} &\underline{0.68 $\pm$ 0.02} & \underline{39.60 $\pm$ 1.9}\\
 RTD-L& {0.570} & {45.72 $\pm$ 2.0} & {0.64 $\pm$ 0.02} & \underline{40.00 $\pm$ 1.7} \\
 \midrule
 \multicolumn{5}{c}{\textbf{into 3D space} } \\
 UMAP & 0.041 & 45.56 $\pm$ 2.2 & 0.54 $\pm$ 0.01 & 41.79 $\pm$ 2.2 \\
 AE & 0.376 & {46.06 $\pm$ 2.3} & 0.41 $\pm$ 0.02 & 41.05 $\pm$ 2.2 \\
 TopoAE & {0.622} & \underline{40.41 $\pm$ 2.7} & 0.71 $\pm$ 0.01 & 34.74 $\pm$ 1.9 \\
 RTD & \underline{0.687} & {41.39 $\pm$ 2.3} & \underline{0.74 $\pm$ 0.02} & \underline{33.74 $\pm$ 1.6} \\
 RTD-L & 0.615 & {41.76 $\pm$ 1.8} & 0.66 $\pm$ 0.02 & 35.80 $\pm$ 1.5 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Eduard Tulchinskii
Daria Voronkova
Ilya Trofimov
Evgeny Burnaev
Serguei Barannikov

"RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks" by Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, and Serguei Barannikov, arXiv:2503.11910v1, 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{Quantitative comparison with state-of-the-art: we compare the Frechet Inception Distance (FID) obtained with the different methods. The lower the better.}
\begin{tabular}{cccc}
 \toprule
	\textbf{Artist} & \textbf{ComboGAN} & \textbf{StarGAN} & \textbf{\textit{Ada$^2$Net}}\\
 \midrule
	Beksinski & 179.05 & 146.05 & \bf 111.5\\
	Boudin & 160.3 & 148.54 & \bf 107.07\\
	Burliuk & 142.84 & 136.57 & \bf 105.16\\
	Cezanne & 146.44 & 129.98 & \bf 92.28\\
	Chagall & 142.66 & 109.96 & \bf 102.43\\
	Corot & 173.73 & 165.66 & \bf 106.37\\
	Earle & 178.68 & 172.73 & \bf 147.95\\
	Gauguin & 149.87 & 141.56 & \bf 98.51\\
	Hassam & 140.98 & 141.24 & \bf 98.49\\
	Levitan & 151.83 & 186.92 & \bf 112.44\\
	Monet & 144.96 & 131.48 & \bf 78.81\\
	Picasso & 145.1 & 134.1 & \bf123.69\\
	Ukiyo-e & 151.81 & 110.22 & \bf 99.97\\
	Van Gogh & 151.91 & 135.45 & \bf 97.25\\
\midrule
	\textbf{Average$\pm$Std} & 154.3$\pm$12.91 & 142.18$\pm$20.72 & \textbf{105.85$\pm$15.38}\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

The-Phuc Nguyen
Stéphane Lathuilière
Elisa Ricci

"Multi-Domain Image-to-Image Translation with Adaptive Inference Graph" by The-Phuc Nguyen, Stéphane Lathuilière, and Elisa Ricci, arXiv:2101.03806v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Sparse grid implementation of a fixed-point fast sweeping WENO scheme for Eikonal equations" by Zachary M. Miksis and Yong-Tao Zhang, arXiv:2201.08912v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Optimal-$k$ difference sequence in nonparametric regression" by Wenlin Dai, Xingwei Tong, and Tiejun Tong, arXiv:2211.15087v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{crrrrr}
\hline
\multirow{2}{*}{\textbf{Sample Size}} & \multicolumn{5}{c}{$\boldsymbol{D}$}\\
& $\mathbf{10}$ & $\mathbf{25}$ & $\mathbf{50}$ & $\mathbf{100}$ & $\mathbf{200}$ \\
\hline
10 & 0.13 $\pm$ 0.01 & 0.90 $\pm$ 0.25 & 3.5 $\pm$ 0.04 & 14 $\pm$ 0.07 & 61 $\pm$ 1.40 \\
25 & 0.21 $\pm$ 0.01 & 1.40 $\pm$ 0.04 & 5.7 $\pm$ 0.09 & 23 $\pm$ 0.07 & 94 $\pm$ 0.36 \\
50 & 0.37 $\pm$ 0.01 & 2.45 $\pm$ 0.05 & 10.0 $\pm$ 0.09 & 41 $\pm$ 0.25 & 169 $\pm$ 4.56 \\
100 & 0.71 $\pm$ 0.07 & 4.66 $\pm$ 0.09 & 19.1 $\pm$ 0.23 & 77 $\pm$ 0.46 & 314 $\pm$ 2.54 \\
200 & 1.50 $\pm$ 0.10 & 9.62 $\pm$ 0.16 & 39.4 $\pm$ 0.41 & 158 $\pm$ 3.19 & 625 $\pm$ 2.88 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Computation time (seconds) for vine copula fitting across dimensions $D$ and sample sizes, on a MacBook Pro M1 Pro (2023). The results were averaged over 10 different datasets, per dimension/datasize pair.}
\end{table}
\end{document}

stat

Daniel de Vassimon Manela
Linying Yang
Robin J. Evans

"Testing Generalizability in Causal Inference" by Daniel de Vassimon Manela, Linying Yang, and Robin J. Evans, arXiv:2411.03021v2, 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{Most Commonly Excluded and Included Expenditure Categories}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 \hline \hline
 & Median & Trimmed Mean & Middle 80\% \\
 & & & (10, 10) Trim \\
 \multicolumn{4}{c}{Most Commonly Excluded}\\
 \hline
 1 & & Eggs & Eggs \\
 2 & 66 series are & Food on farms & Vegetables \\
 3 & never median & Vegetables & Food on farms \\
 4 & & Fruit & Fuel Oil \\
 5 & & Gasoline & Gasoline \\
 \hline
 \multicolumn{4}{c}{Most Commonly Included}\\ \hline
 1 & Owner-occ homes & Owner-occ homes & Owner-occ homes \\
 2 & Other purchased meals & Other purchased meals & Other purch meals \\
 3 & Tenant-occ homes & Casino gambling & Tenant-occ homes \\
 4 & Nonprofit hospitals & Owner-occ mobile homes & Casino gambling \\
 5 & Physician services & Tenant-occ homes & Lotteries \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Sergio Ocampo
Raphael Schoenle
Dominic A. Smith

"Extending the Range of Robust PCE Inflation Measures" by Sergio Ocampo, Raphael Schoenle, and Dominic A. Smith, arXiv:2207.12494v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|}
 \hline
 \textbf{Model} & \textbf{Score} & \textbf{Change (\%)} \\
 \hline
 Ours & 0.344 & -- \\
 \hline
 - $\mathcal{R}_{PAC}$ & 0.338 & \textcolor{red!70!black}{-1.74\% $\downarrow$} \\
 \hline
 - $\mathcal{L}_{out}$ & 0.336 & \textcolor{red!70!black}{-0.59\% $\downarrow$} \\
 \hline
 - $\mathcal{L}_{sim}$ & 0.331 & \textcolor{red!70!black}{-1.48\% $\downarrow$} \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{ \textbf{Impact of Removing Individual Loss Components on Full Similarity Scores} in the ABC Dataset. This table presents the Full Sim. scores for the complete model and each ablated variant, along with the corresponding percentage decrease in performance.}
\end{table}
\end{document}

stat

Eric Hanchen Jiang
Yasi Zhang
Zhi Zhang
Yixin Wan
Andrew Lizarraga
Shufan Li
Ying Nian Wu

"Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory" by Eric Hanchen Jiang, Yasi Zhang, Zhi Zhang, Yixin Wan, Andrew Lizarraga, Shufan Li, and Ying Nian Wu, arXiv:2411.17472v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
			\hline
			\small\textbf{Case}& \small\textbf{Conditions}& \small\textbf{$\boldsymbol{O_1}$}& \small\textbf{$\boldsymbol{O_2}$} & \small\textbf{Global} \\
			\hline
			\hline

				\multirow{4}{1cm}{\centering{4.1}}
				&
				$\mu=0$
				&
				L14
				&
				\multirow{2}{3cm}{\centering{Unstable node}}
				&
				\multirow{2}{1cm}{\centering{G84}} \\
				\cline{2-3}

				\multirow{4}{*}
				&
				$c_1=0$, $\mu>-1$, $a_0+c_0\mu>0$
				&
				L29
				&
				\multirow{2}{*}
				&
				\multirow{2}{*} {\phantom{h}}
				\\
				\cline{2-5}

				\multirow{4}{*}
				&
				$c_1=0$, $\mu>-1$, $a_0+c_0\mu<0$
				&
				L30
				&
				Unstable node
				&
				G85\\
				\cline{2-5}

				\multirow{4}{*}
				&
				$c_1=0$, $\mu<-1$
				&
				L33
				&
				Saddle
				&
				G86 \\
				\hline

				\multirow{5}{1cm}{\centering{4.2}}
				&
				$\mu=0$
				&
				L5
				&
				Stable node
				&
			 G87\\
				\cline{2-5}

				\multirow{5}{*}
				&
				$c_1=0$, $\mu>-1$, $a_0+c_0\mu>0$
				&
				L27
				&
			 Stable node
				&
				G88 \\
				\cline{2-5}

				\multirow{5}{*}
				&
				$c_1=0$, $\mu>-1$, $a_0+c_0\mu<0$
				&
				L28
				&
				Stable node
				&
				G89 \\
				\cline{2-5}

				\multirow{5}{*}
				&
				$c_1=0$, $\mu<-1$, $a_0+c_0\mu>0$
				&
				L31
				&
				Saddle
				&
				G90 \\
				\cline{2-5}

				\multirow{5}{*}
				&
				$c_1=0$, $\mu<-1$, $a_0+c_0\mu<0$
				&
				L32
				&
				Saddle
				&
				G91 \\
				\hline

				\multirow{3}{1cm}{\centering{4.3}}
				&
				$\mu=0$
				&
				L14
				&
				\multirow{2}{3cm}{\centering{Unstable node}}
				&
				\multirow{2}{1cm}{\centering{G92}} \\
				\cline{2-3}

				\multirow{3}{*}
				&
				$c_1=0$, $\mu>-1$
				&
				L20
				&
				\multirow{2}{*}
				&
				\multirow{2}{*} {\phantom{h}} \\
				\cline{2-5}

				\multirow{3}{*}
				&
				$c_1=0$, $\mu<-1$
				&
				L22
				&
				Saddle
				&
				G93 \\
				\hline

				\multirow{3}{1cm}{\centering{4.4}}
				&
				$\mu=0$
				&
				L5
				&
				Stable node
				&
				G94 \\
				\cline{2-5}

				\multirow{3}{*}
				&
				$c_1=0$, $\mu>-1$
				&
				L19
				&
				Stable node
				&
				G95 \\
				\cline{2-5}

				\multirow{3}{*}
				&
				$c_1=0$, $\mu<-1$
				&
				L21
				&
				Saddle
				&
				G96 \\
				\hline

				\multirow{3}{1cm}{\centering{5.1}}
				&
				$\mu>0$
				&
				L3
				&
				Stable node
				&
				G97 \\
				\cline{2-5}

				\multirow{3}{*}
				&
				$\mu\in(-1,0)$
				&
				L10
				&
				Stable node
				&
				G98 \\
				\cline{2-5}

				\multirow{3}{*}
				&
				$\mu<-1$
				&
				L11
				&
				Saddle
				&
				G99 \\
				\hline

				5.2
				&

				&
				L12
				&
				Unstable node
				&
				G76 \\
				\hline

					\multirow{3}{1cm}{\centering{5.3}}
				&
				$\mu>0$
				&
				L6
				&
				Unstable node
				&
				G100 \\
				\cline{2-5}

				\multirow{3}{*}
				&
				$\mu<-1$
				&
				L8
				&
				Saddle
				&
				G77 \\
				\cline{2-5}

				\multirow{3}{*}
				&
				$\mu\in(-1,0)$
				&
				L13
				&
				Unstable node
				&
				G78 \\
				\hline

						\multirow{3}{1cm}{\centering{5.4}}
				&
				$\mu>0$
				&
				L3
				&
				Stable node
				&
				G79 \\
				\cline{2-5}

				\multirow{3}{*}
				&
				 $\mu\in(-1,0)$
				&
				L10
				&
				Stable node
				&
				G101 \\
				\cline{2-5}

				\multirow{3}{*}
				&
				$\mu<-1$
				&
				L11
				&
				Saddle
				&
				G102 \\
				\hline

				\multirow{4}{1cm}{\centering{5.5}}
				&
				$\mu<-2$
				&
				L1
				&
				Saddle
				&
				G80 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu\in(-1,0)$
				&
				L4
				&
				Stable node
				&
				G81 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu\in(-2,-1)$
				&
				L15
				&
				Saddle
				&
				G82 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu=-2$
				&
				L17
				&
				Saddle
				&
				G83 \\
				\hline

				\multirow{3}{1cm}{\centering{6.1}}
				&
				$\mu=0$
				&
				L14
				&
				\multirow{2}{3cm}{\centering{Unstable node}}
				&
				\multirow{2}{1cm}{\centering{G92}}
				\\
				\cline{2-3}

				\multirow{3}{*}
				&
				$c_1=0$, $\mu>-1$
				&
				L20
				&
				\multirow{2}{*}
				&
				\multirow{2}{*} {\phantom{h}}
				\\
				\cline{2-5}

				\multirow{3}{*}
				&
				$c_1=0$, $\mu<-1$
				&
				L22
				&
				Saddle
				&
				G93 \\
				\hline

				\multirow{3}{1cm}{\centering{6.2}}
				&
				$\mu=0$
				&
				L5
				&
				Stable node
				&
				G94
				\\
				\cline{2-5}

				\multirow{3}{*}
				&
				$c_1=0$, $\mu>-1$
				&
				L19
				&
				Stable node
				&
				G95
				\\
				\cline{2-5}

				\multirow{3}{*}
				&
				$c_1=0$, $\mu<-1$
				&
				L21
				&
				Saddle
				&
				G96 \\
				\hline
\end{tabular}
\end{adjustbox}
\caption{Classification of global phase portrais of system .}
\end{table}
\end{document}

math

Érika Diz-Pita
Jaume Llibre
M. Victoria Otero-Espinar

"Phase portraits of a family of Kolmogorov systems depending on six parameters" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.13714v1, 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}{lccc}
\hline
\textbf{Model} & \textbf{Precision} & \textbf{Recall} & \textbf{Test ACC} \\
\hline
BERT & 0.886 & 0.885 & 0.886 \\
BiGRUx3 & 0.900 & 0.830 & 0.806 \\
SVM & 0.910 & 0.762 & 0.829 \\
\hline
\end{tabular}
\caption{Offensive language detection system's performance using the TOCAB benchmark}
\end{table}
\end{document}

cs

Yunze Xiao
Houda Bouamor
Wajdi Zaghouani

"Chinese Offensive Language Detection:Current Status and Future Directions" by Yunze Xiao, Houda Bouamor, and Wajdi Zaghouani, arXiv:2403.18314v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
Parameters& $p$ & $m$ & $n$ & $\sigma_k$& $\sigma_\psi$ &$c_\mu^0$& $c_1$ &$c_2$&$c_3^+$&$c_3^-$&$k_{\textrm{min}}$&$\psi_{\textrm{min}}$&$F_{\textrm{wall}}$&$c$& $\nu$&$\nu_{\theta}$\\ \hline
Values for & & & & & & & & & & & & & & & &\\
$k-\varepsilon$ model & 3 & 1.5 & -1 & 1.0 & 1.3 & 0.5544 & 1.44 & 1.92 & 1.0 & -0.518 & $7.6\cdot 10^{-6}$ & $1.0 \cdot 10^{-12}$ & 1.0 & 1 & $5.0 \cdot 10^{-6}$ & $5.0 \cdot 10^{-6}$ \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Parameters for the generic length scale turbulence- $k-\varepsilon$ model and Kantha-Clayson stability functions (see ).}
\end{table}
\end{document}

math

G. Garcia-Sanchez
A. M. Mancho
A. G. Ramos
J. Coca
J. A. Jimenez-Madrid

"Dynamical systems for remote validation of very high-resolution ocean models" by G. Garcia-Sanchez, A. M. Mancho, A. G. Ramos, J. Coca, and J. A. Jimenez-Madrid, arXiv:2501.16026v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mechanism Decomposition by CEO Age}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
 & \multicolumn{2}{c}{Young CEOs (Under 50)} & \multicolumn{2}{c}{Older CEOs (60+)} & \\
 & Effect & \% of Total & Effect & \% of Total & Difference \\
\midrule
Cost Reduction & 0.011*** & 35\% & 0.009** & 50\% & 0.002 \\
 & (0.003) & & (0.004) & & (0.005) \\
Revenue Enhancement & 0.013*** & 42\% & 0.006* & 33\% & 0.007** \\
 & (0.004) & & (0.003) & & (0.003) \\
Innovation Output & 0.007** & 23\% & 0.003 & 17\% & 0.004* \\
 & (0.003) & & (0.002) & & (0.002) \\
\midrule
Total Effect & 0.031*** & 100\% & 0.018** & 100\% & 0.013** \\
 & (0.009) & & (0.009) & & (0.006) \\
\midrule
Observations & 892 & & 697 & & 1,589 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises" by Tatsuru Kikuchi, arXiv:2508.03757v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Konstantinos Perrakis
Dimitris Karlis
Mario Cools
Davy Janssens

"Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures" by Konstantinos Perrakis, Dimitris Karlis, Mario Cools, and Davy Janssens, arXiv:2011.06045v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem" by Stefano Favaro and Sandra Fortini, arXiv:2411.07651v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Test outcomes for Models 1--4 in Section .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 {\bf Model A} & {\bf Model B} & {\bf S-W statistic} & {\bf In favor}\\
 \midrule
1 & 2 & -4.433 &2\\
1 & 3 & -1.827 &-\\
1 & 4 & -1.374 &-\\
2 & 3 & 0.908 &-\\
2 & 4 & 5.802 &2\\
3 & 4 & 1.680 &-\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Itai Dattner
Shota Gugushvili
Oleksandr Laskorunskyi

"Model Selection for Ordinary Differential Equations: a Statistical Testing Approach" by Itai Dattner, Shota Gugushvili, and Oleksandr Laskorunskyi, arXiv:2308.16438v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccc|}
 \hline
 \textbf{beta} & \textbf{0.5} & \textbf{1} & \textbf{2} & \textbf{5} \\
 \hline
 \textbf{N=2} & 0.37\% & 0.32\% & 0.24\% & 0.03\% \\
 \textbf{N=3} & 0.03\% & 0.05\% & 0.10\% & 0.21\% \\
 \textbf{N=5} & 0.00\% & 0.00\% & 0.00\% & 0.00\% \\
 \textbf{N=8} & 0.00\% & 0.00\% & 0.00\% & 0.00\% \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Relative absolute difference for different $N$ and $\beta$ on the option price compared to $N=10$, with $Y_0=D, h_0=1, \alpha=0.1, T_0=2$, strike $K=2$ and the time to maturity $\vartheta=1$. BNS model.}
\end{table}
\end{document}

q-fin

"Pricing Options on Forwards in Function-Valued Affine Stochastic Volatility Models" by Jian He, Sven Karbach, and Asma Khedher, arXiv:2508.14813v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Run times (wallclock seconds) for unos and heart\_failure. Times for the other datasets are given in Table in the Appendix.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
model & unos & heart\_failure \\
\midrule
CoxPH & 22.895 ± 1.257 & 17.920 ± 2.906 \\
AFT & 12.073 ± 0.282 & 9.378 ± 0.201 \\
SATransformer & 5414.365 ± 602.512 & 2100.597 ± 95.863 \\
DRSA & 1157.576 ± 76.178 & 360.696 ± 26.253 \\
RSF & * & * \\
CoxNAM & 657.999 ± 89.798 & 165.809 ± 31.627 \\
PseudoNAM & * & * \\
DyS & 1517.205 ± 86.914 & 783.335 ± 165.016 \\
DNAMite & 2690.676 ± 103.465 & 819.684 ± 78.972 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Mike Van Ness
Billy Block
Madeleine Udell

"DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive Models" by Mike Van Ness, Billy Block, and Madeleine Udell, arXiv:2411.05923v1, 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{Optimal solution for a small-scale example}
\begin{tabular}{ccc|ccc}
 \hline
 Dock & Mode & Truck order & Dock & Mode & Truck order \\
 \hline
 $D_1$ & Unloading-only & 1 $\rightarrow$ 6 & $D_4$ & Loading-only & 13 $\rightarrow$ 15 $\rightarrow$ 17 $\rightarrow$ 12 $\rightarrow$ 10 \\
 $D_2$ & Unloading-only & 7 $\rightarrow$ 2 & $D_5$ & Loading-only & 16 $\rightarrow$ 9 $\rightarrow$ 11 $\rightarrow$ 20 \\
 $D_3$ & Mixed-mode & 3 $\rightarrow$ 5 $\rightarrow$ 8 $\rightarrow$ 4 & $D_6$ & Loading-only & 14 $\rightarrow$ 18 $\rightarrow$ 19 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Yueyi Li
Mehrdad Mohammadi
Xiaodong Zhang
Yunxing Lan
Willem van Jaarsveld

"Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm" by Yueyi Li, Mehrdad Mohammadi, Xiaodong Zhang, Yunxing Lan, and Willem van Jaarsveld, arXiv:2412.09090v1, 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
 \multicolumn{4}{|c|}{Table 1: Percentages of stable simulations (out of 1,000 simulations)} \\
 \hline
 LQR about the origin & Robust control: Eq. & Data-conforming robust control: Eq. \\
 \hline
 0.0\% & 61.8\% & 94.3\% \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Dampening parameter distributional shifts under robust control and gain scheduling" by Mohammad Ramadan and Mihai Anitescu, arXiv:2411.16566v1, 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{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameter search range in numerical experiments.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
		\toprule
		\multirow{2}{*}{Parameter} & \multicolumn{2}{c}{RT} & \multicolumn{2}{c}{PW} \\
		\cmidrule{2-5}
		& Lower & Upper & Lower & Upper \\
		\midrule
		$I_{ph}$ (A) & 0 & 1 & 0 & 2 \\
		$ I_0, I_{01}, I_{02} $ ($ \mu $A) & 0 & 1 & 0 & 50 \\
		$ n, n_1, n_2 $ & 1 & 2 & 1 & 50 \\
		$ R_s $ ($ \Omega $) & 0 & 0.5 & 0 & 2 \\
		$ R_p $ ($ \Omega $) & 0 & 100 & 0 & 2000 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Shuhua Gao
Yunyi Zhao
Cheng Xiang
Yu Ming
Tan Kuan Tak
Tong Heng Lee

"On Solar Photovoltaic Parameter Estimation: Global Optimality Analysis and a Simple Efficient Differential Evolution Method" by Shuhua Gao, Yunyi Zhao, Cheng Xiang, Yu Ming, Tan Kuan Tak, and Tong Heng Lee, arXiv:2011.12114v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Zardad Khan
Naz Gul
Nosheen Faiz
Asma Gul
Werner Adler
Berthold Lausen

"Optimal trees selection for classification via out-of-bag assessment and sub-bagging" by Zardad Khan, Naz Gul, Nosheen Faiz, Asma Gul, Werner Adler, and Berthold Lausen, arXiv:2012.15301v1, 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{ Cost of 1 iteration for each of the methods tested. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|r|r|r|r}
Method & Residuals & Jacobians & Solves & O(n) \\\hline
Dogleg* & 6 & 6 & 0 & O($n^2$)\\
Steihaug* & 6 & 6 & 0 & O($n^2$)\\
Newton (RF) & 1 & 1 & 1 & O($n^3$)\\
L-BFGS-B* & 1 & 3 & 0 & O($n^2$)\\
Broyden & 1 & 0 & 1 & O($n^3$)\\
Broyden (inv) & 1 & 0 & 0 & O($n^2$)\\
ADAM* & 3 & 4 & 0 & O($n^2$)\\
ADAGRAD* & 3 & 4 & 0 & O($n^2$)
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Alberto Cattaneo
Varun Shankar
M. Keith Ballard

"Rootfinding and Optimization Techniques for Solving Nonlinear Systems of Equations Arising from Cohesive Zone Models" by Alberto Cattaneo, Varun Shankar, and M. Keith Ballard, arXiv:2502.19583v1, 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||c|}
 \hline
 Problem & Bases & PtO & Jacobian & Total & Train (GPU) \\ \hline
 2D-CDR & 437 & 6,554 & 492 & 7,482 & 145 \\ \hline
 3D-CDR & 31,241 & 52,224 & 10,076 & 93,541 & 413 \\ \hline
 \end{tabular}
\caption{Offline time (in seconds) in computing the input and output projection bases, PtO maps, Jacobians, and training (GPU) the neural networks, averaged over 5 times.}
\end{table}
\end{document}

math

"Accurate, scalable, and efficient Bayesian optimal experimental design with derivative-informed neural operators" by Jinwoo Go and Peng Chen, arXiv:2312.14810v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{makecell}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
 \hline
 Use case & Prompt \\
 \Xhline{2pt}
 \multirow{7}{*}{\parbox{2cm}{Zephyr \\ summarization}} & \multirow{7}{*}[-1ex]{\parbox{4cm}{<|system|>\\
You are a tool providing a short text summary.\\
<|user|>\\
Write a short summary of the following text: {context}\\
<|assistant|>}} \\
 & \\
 & \\
 & \\
 & \\
 & \\
 & \\
 & \\
 \hline
 \multirow{6}{*}{\parbox{2cm}{FLAN-T5 \\ summarization \\ Comparative \\ Assessment}} & \multirow{6}{*}[-1ex]{\parbox{4cm}{Passage: \{passage\}\\ Summary A: \{summary 1\}\\ Summary B: \{summary 2\}\\ Between Summary A and Summary B, which text summarises the passage better?}} \\
 & \\
 & \\
 & \\
 & \\
 & \\
 & \\
 \hline
 \multirow{6}{*}{\parbox{2cm}{FLAN-T5 \\ translation \\ Comparative \\ Assessment}} & \multirow{6}{*}[-1ex]{\parbox{4cm}{Original text: \{context\}\\ Translation A: \{translation 1\}\\ Translation B: \{translation 2\}\\ Between Translation A and Translation B, which is the better translation of original text?}} \\
 & \\
 & \\
 & \\
 & \\
 & \\
 & \\
 \hline
 \end{tabular}
\caption{prompts used for experiments.}
\end{table}
\end{document}

cs

Piotr Molenda
Adian Liusie
Mark J. F. Gales

"WaterJudge: Quality-Detection Trade-off when Watermarking Large Language Models" by Piotr Molenda, Adian Liusie, and Mark J. F. Gales, arXiv:2403.19548v1, 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}{lccccccc}
\hline\hline
Model &
\multicolumn{3}{c}{TF-IDF} &
 &
\multicolumn{3}{c}{TF-IDF-VADER}
\\
\cline{2-4} \cline{6-8}
 & Accuracy & Merit & F1 & & Accuracy & Merit & F1 \\ \hline
LR & 67.37\% & 15.41\% & 78.00\% & & 63.96\% & ~~8.52\% & 76.80\% \\
SVM & 67.30\% & 13.24\% & 78.24\% & & 63.72\% & ~~7.29\% & 76.65\% \\
GB & 64.61\% & 16.43\% & 76.03\% & & 62.75\% & 11.31\% & 75.44\% \\
MLP & 58.08\% & 36.04\% & 71.22\% & & 56.50\% & 34.96\% & 66.38\% \\
RF & 65.56\% & ~~9.80\% & 77.64\% & & 60.67\% & 18.71\% & 73.28\% \\
\hline
\end{tabular}
\caption{Average classification accuracy, merit, and F1 scores for 500 random splits of training and testing sets under the TF-IDF and TF-IDF-VADER featurizations.}
\end{table}
\end{document}

q-fin

Peiheng Gao
Ning Sun
Xuefeng Wang
Chen Yang
Ričardas Zitikis

"NLP-based detection of systematic anomalies among the narratives of consumer complaints" by Peiheng Gao, Ning Sun, Xuefeng Wang, Chen Yang, and Ričardas Zitikis, arXiv:2308.11138v3, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccc|ccc|ccc}
 \hline
 \multirow{2}{*}{Method} & \multicolumn{3}{c|}{COCO} & \multicolumn{3}{c|}{CrowdPose} & \multicolumn{3}{c}{OCHuman}\\
 & $\text{AP}$ & $\text{AP}^{50}$ & $\text{AP}^{75}$ & $\text{AP}$ & $\text{AP}^{50}$ & $\text{AP}^{75}$ & $\text{AP}$ & $\text{AP}^{50}$ & $\text{AP}^{75}$ \\
 \hline
 CSM+SCARB [2] & 73.8 & 91.7 & 81.4 & - & - & - & - & - & - \\
 MDN [4] & 52.3 & 77.2 & 58.0 & - & - & - & - & - & - \\
 MaskRCNN & 64.8 & - & - & 57.2 & 83.5 & 60.3 & 20.2 & 33.2 & 24.5 \\
 SBL & 73.7 & 91.9 & 81.8 &60.8 & 81.4 & 65.7 & 24.1 & 37.4 & 26.8 \\
 AlphaPose & 70.1 & - & - & 61.0 & 81.3 & 66.0 & - & - & - \\
 CrowdPose[5] & 70.9 & - & - & 66.0 & 81.4 & 65.7 & - & - & - \\
 AlphaPose+ & 72.2 & 90.1 & 79.3 & 68.5 & 86.7 & 73.2 & 27.5 & 40.8 & 29.9 \\
 OPEC-Net[6] & 73.9 & 91.9 & \textbf{82.2} & \textbf{70.6} & \textbf{86.8} & \textbf{75.6} & 29.1 & 41.3 & 31.4\\
 MIPNet (Ours) & \textbf{74.2} & \textbf{92.4} & 81.9 & 68.1 & 86.3 & 73.4 & \textbf{40.0} & \textbf{51.0} & \textbf{44.0} \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{For a fair comparison, all methods use ResNet-101 backbone (except MDN) and YOLO-v3 detector (except CSM+SCARB and MDN).}
\end{table}
\end{document}

cs

Rawal Khirodkar
Visesh Chari
Amit Agrawal
Ambrish Tyagi

"Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation" by Rawal Khirodkar, Visesh Chari, Amit Agrawal, and Ambrish Tyagi, arXiv:2101.11223v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Anastasia Mantziou
Kerstin Hotte
Mihai Cucuringu
Gesine Reinert

"GDP nowcasting with large-scale inter-industry payment data in real time -- A network approach" by Anastasia Mantziou, Kerstin Hotte, Mihai Cucuringu, and Gesine Reinert, arXiv:2411.02029v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of Accuracy, AUC, and Weighted Average F1 Between Gemma 2-2B and Gemma 2-9B}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 \textbf{Model}
 & \textbf{Accuracy}
 & \textbf{AUC}
 & \textbf{F1} \\
 \midrule
 Last Hidden State + LR
 & 0.761
 & 0.628
 & 0.737 \\
 Last Hidden State + MLP
 & 0.770
 & 0.634
 & 0.731 \\
 \textbf{SAE-FiRE (Gemma2-2B)}
 & \textbf{0.793}
 & \textbf{0.657}
 & \textbf{0.743} \\
 \textbf{SAE-FiRE (Gemma2-9B)}
 & \textbf{0.801}
 & \textbf{0.668}
 & \textbf{0.757} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection" by Huopu Zhang, Yanguang Liu, and Mengnan Du, arXiv:2505.14420v1, 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}{r|r|r|r|r|r}
Water Year & no assim. & SWE update & $\Delta$(\%) & Joint SWE \& Stream & $\Delta$(\%) \\
\hline
2006 & 1.08 & 0.02 & -98 & 0.04 & -96 \\
2011 & 1.97 & 0.05 & -97 & 0.05 & -98 \\
2014 & 0.20 & 0.01 & -93 & 0.01 & -95 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Experiments validation in terms of basin-mean SWE RMSE.}
\end{table}
\end{document}

eess

Sami A. Malek
Alexandre M. Bayen
Steven D. Glaser

"Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter" by Sami A. Malek, Alexandre M. Bayen, and Steven D. Glaser, arXiv:1912.03861v1, 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|c|c}
 & 1 m & 3 m & 6 m & 12 m & 18 m \\
\hline
SPX & 99.82\% & 99.74\% & 99.79\% & 99.94\% & 99.89\% \\
Eurostoxx & 99.15\% & 99.38\% & 99.12\% & 98.97\% & 98.25\% \\
UKX & 99.52\% & 99.51\% & 99.41\% & 99.17\% & 98.67\% \\
Nikkei & 98.24\% & 98.00\% & 97.32\% & 97.38\% & 97.36\%
\end{tabular}
\end{adjustbox}
\caption{Correlation between the time series of the square root of the Variance Swap and the At The Money Volatility.}
\end{table}
\end{document}

q-fin

"Estimation of Historical volatility and Allocation strategies using Variance Swaps" by Lucio Fiorin, arXiv:2208.03164v1, 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
 \textbf{Technique Used} &$T_{tot} (ms)$&$T_{inv}(ms)$ & $N$\\
 \hline
 Algorithm
 ($\varrho$ initialised with $\varrho_{off}$) & $0.3236$ & $0.0036$ & $ 33$ \\
 \hline
 Proposed Algorithm & $0.2913$ & $0.0026$ & $33$ \\
 \hline
\multicolumn{4}{|c|}{ms=milliseconds} \\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Comparison of different approaches}
\end{table}
\end{document}

math

"Choosing Augmentation Parameters in OSQP- A New Approach based on Conjugate Directions" by Avinash Kumar, arXiv:2503.05941v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Kilani Ghoudi
Bouchra R. Nasri
Bruno N. Remillard

"On testing for independence between generalized error models of several time series" by Kilani Ghoudi, Bouchra R. Nasri, and Bruno N. Remillard, arXiv:2410.24003v2, 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}{|r|lllr|}
 \hline
rank & ROPE & posterior CI & combination rule & AUC \\
 \hline
1 & 80\% ETI & 80\% HDI & option 2 & 0.890 \\
2 & 80\% ETI & 80\% ETI & option 2 & 0.873 \\
3 & 80\% ETI & 80\% ETI & option 3 & 0.838 \\
4 & 80\% ETI & 80\% HDI & option 3 & 0.834 \\
5 & 75\% ETI & 75\% HDI & option 3 & 0.823 \\
6 & 75\% ETI & 75\% ETI & option 3 & 0.802 \\
7 & 85\% ETI & 85\% HDI & option 2 & 0.693 \\
8 & 85\% ETI & 85\% ETI & option 2 & 0.691 \\
9 & 90\% ETI & 90\% HDI & option 1 & 0.653 \\
10 & 85\% ETI & 85\% HDI & option 1 & 0.643 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Ranking of top ten BPgWSP test specifications according to AUC averaged over all sample scenarios given a correct prior belief.}
\end{table}
\end{document}

stat

"The BPgWSP test: a Bayesian Weibull Shape Parameter signal detection test for adverse drug reactions" by Julia Dyck and Odile Sauzet, arXiv:2412.05463v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Xi Chen
Yu Guan
Jian Qing Shi
Xiu-Li Du
Janet Eyre

"Designing Compact Features for Remote Stroke Rehabilitation Monitoring using Wearable Accelerometers" by Xi Chen, Yu Guan, Jian Qing Shi, Xiu-Li Du, and Janet Eyre, arXiv:2009.08798v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{rotating}
\usepackage{graphicx}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\specialrule{1.5pt}{0pt}{0pt}
\multirow{4}{*}[-12ex]{\rotatebox{90}{Risk Certificate}} & & \multicolumn{4}{c}{Contrastive 0-1 Risk} \\
\cmidrule(lr){3-6}
 & & $\tau=1 $ & $\tau=0.7$ & $\tau=0.5$ & $\tau=0.2$ \\
\specialrule{1.5pt}{0pt}{0pt}
& Test Loss & 0.0571 & 0.0529 & 0.0478 & 0.0357 \\
 \midrule
 & kl bound (iid) & 0.408 & 0.327 & 0.324 & 0.319 \\
 & Catoni's bound (iid) & 0.419 & 0.333 & 0.331 & 0.309 \\
 & Classic bound (iid) & 0.466 & 0.396 & 0.394 & 0.399 \\
 & Nozawa et al. & 2.535 & 1.95 & 2.097 & 2.097 \\
 & Th. 4 (ours) & 0.356 & 0.347 & 0.343 & 0.333 \\
 & Th. 5 (ours) & 0.113 & 0.101 & 0.098 & 0.088 \\
\specialrule{1.5pt}{0pt}{0pt}
\end{tabular}
\end{adjustbox}
\caption{Comparison of risk certificates for the contrastive zero-one risk on the MNIST dataset. }
\end{table}
\end{document}

stat

"Tight PAC-Bayesian Risk Certificates for Contrastive Learning" by Anna Van Elst and Debarghya Ghoshdastidar, arXiv:2412.03486v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average PS and ACR for M1 and M2 models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Model} & \textbf{Average PS} & \textbf{Average ACR} \\
\midrule
M1 & 61.405091 & 0.552943 \\
M2 & 29.420010 & 0.550404 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Temitope Adeyeha
Chetraj Pandey
Berkay Aydin

"Large Scale Evaluation of Deep Learning-based Explainable Solar Flare Forecasting Models with Attribution-based Proximity Analysis" by Temitope Adeyeha, Chetraj Pandey, and Berkay Aydin, arXiv:2411.18070v1, 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{Demographics of VOTE400 Dialog Speech}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcr}
\hline
Region(\texttt{R}) & No. Participants & Age ($\mu/\sigma$) \\ \hline \hline
Seoul-si(SE) & 251(F:210,M:41) & 78.98/5.13 \\ \hline
Daegu-si(DG) & 108(F:95,M:13) & 80.33/6.08 \\ \hline
Gyoungki-do(GG) & 110(F:83,M:27) & 80.17/5.41 \\ \hline
Chungcheongnam-do(CN) & 6(F:6,M:0) & 77.00/3.69 \\ \hline
Jeollanam-do(JN) & 70(F:56,M:14) & 80.76/4.90 \\ \hline
Busan-si(PS) & 160(F:137,M:23) & 78.70/5.51 \\ \hline
Daejeon-si(DJ) & 96(F:72,M:24) & 78.81/5.24 \\ \hline
Gangwon-do(GW) & 109(F:94,M:15) & 80.07/5.50 \\ \hline
Gyeongsangbuk-do(GB) & 98(F:95,M:3) & 80.87/4.48 \\ \hline
Gwangju-si(GJ) & 87(F:70,M:17) & 79.39/5.77 \\ \hline
Chungcheongbuk-do(CB) & 17(F:17,M:0) & 80.47/5.51 \\ \hline
Ulsan-si(WS) & 58(F:49,M:9) & 76.97/4.48 \\ \hline \hline
Total & 1,170(F:984,M:186) & 79.47/5.37 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Minsu Jang
Sangwon Seo
Dohyung Kim
Jaeyeon Lee
Jaehong Kim
Jun-Hwan Ahn

"VOTE400(Voide Of The Elderly 400 Hours): A Speech Dataset to Study Voice Interface for Elderly-Care" by Minsu Jang, Sangwon Seo, Dohyung Kim, Jaeyeon Lee, Jaehong Kim, and Jun-Hwan Ahn, arXiv:2101.11469v1, 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{siunitx}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Resource utilization of target ZYNQ device XC7Z045 for different \mbox{BinArray} configurations in \%. [1,32,2] means $N_{SA}=1$, $D_{arch}=32$, $M_{arch}=2$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
		\hline
	 & & \multicolumn{4}{c}{BinArray} \\
		 & Total & [1,8,2] & [1,32,2] & [4,32,4] & [16,32,4] \\
		\hline
		LUT 		& 218,600 & 0.78 & 1.68 & 13.32 & 52.74 \\
		FF 		& 437,200 & 0.53 & 1.22 & 8.11 & 32.01 \\
		BRAM CNN-A	& \SI{19.2}{\mega\bit} & 1.15 & 1.15 & 6.19 & 24.2 \\
		BRAM CNN-B	& \SI{19.2}{\mega\bit} & 23.72 & 23.94 & 28.85 & 46.90 \\
		DSP 		& 900 & 0.22 & 0.22 & 1.78 & 7.11 \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"BinArray: A Scalable Hardware Accelerator for Binary Approximated CNNs" by Mario Fischer and Juergen Wassner, arXiv:2012.03481v1, 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{AUC Comparison between LoRaFlow and NELoRa}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\hline
SF & LoRaFlow AUC & NELoRa AUC & Improvement over NELoRa \\
\hline
7 & \textbf{2.922} & 2.227 & 31.2\% \\
8 & \textbf{3.143} & 2.409 & 30.5\% \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"LoRaFlow: High-Quality Signal Reconstruction using Rectified Flow" by Mohamed Osman and Tamer Nadeem, arXiv:2501.00024v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 Element & Rows & Nonzeros per row \\ \hline
 Morley & 289 & 10.63 \\
 PS6 & 243 & 18.41 \\
 PS12 & 451 & 22.73 \\
 HCT-red & 243 & 18.41 \\
 HCT3 & 451 & 22.73 \\
 HCT4 & 995 & 32.56 \\
 Bell & 486 & 36.81 \\
 Argyris & 694 & 41.02
 \end{tabular}
\caption{The biharmonic equation was discretized on an $8 \times 8$ mesh, divided into right triangles. This table shows the total number of degrees of freedom (rows in the matrix) and the average sparsity over all rows of the matrix.}
\end{table}
\end{document}

math

"FIAT: enabling classical and modern macroelements" by Pablo D. Brubeck and Robert C. Kirby, arXiv:2501.14599v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Adopters Sample Size by Adoption Time - By Product Category}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\toprule
Adoption Month & Dry Dog Food & Dry Cat Food & Dog Hygiene & Cat Hygiene \\
\midrule
2019-07 & 85 & 53 & 52 & 36 \\
2019-08 & 111 & 46 & 71 & 41 \\
2019-09 & 100 & 34 & 96 & 44 \\
2019-10 & 110 & 42 & 82 & 47 \\
2019-11 & 174 & 51 & 143 & 67 \\
2019-12 & 144 & 78 & 120 & 58 \\
2020-01 & 138 & 57 & 107 & 47 \\
2020-02 & 143 & 52 & 106 & 55 \\
2020-03 & 334 & 160 & 382 & 190 \\
2020-04 & 438 & 208 & 442 & 241 \\
2020-05 & 357 & 143 & 353 & 209 \\
2020-06 & 276 & 113 & 260 & 157 \\ \midrule
Total & 2410 & 1037 & 2214 & 1192 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"How does online shopping affect offline price sensitivity?" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average token costs for each topic.}
\begin{tabular}{|c|cc|cc|}
\hline
\multirow{2}{*}{\textbf{Datasets}} & \multicolumn{2}{c|}{\textbf{Ours}} & \multicolumn{2}{c|}{\textbf{LLM-TM}} \\
 & \textbf{\#Input} & \textbf{\#Output} & \textbf{\#Input} & \textbf{\#Output} \\ \hline
\textit{Tweet} & 1591.01 & 274.90 & 4958.73 & 1214.07 \\
\textit{AGNews} & 1588.60 & 264.22 & 33382.61 & 7541.41 \\
\textit{TagMyNews} & 1593.70 & 275.83 & 61872.93 & 14448.79 \\
\textit{YahooAnswer} & 1578.43 & 231.50 & 27358.56 & 5913.36 \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

Shuyu Chang
Rui Wang
Peng Ren
Qi Wang
Haiping Huang

"A Large Language Model Guided Topic Refinement Mechanism for Short Text Modeling" by Shuyu Chang, Rui Wang, Peng Ren, Qi Wang, and Haiping Huang, arXiv:2403.17706v2, 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
			Method & Training Set & AP on Image-test & AP on Video-test \\
			\hline
			Base & I & 0.770 & 0.631 \\
			MixupDet & I + V & 0.778 & 0.714 \\ % TODO
			TCR & I + V & 0.774 & 0.654 \\
			MixupDet + TCR & I + V & \textbf{0.791} & \textbf{0.717} \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Zhi-Qin Zhan
Huazhu Fu
Yan-Yao Yang
Jingjing Chen
Jie Liu
Yu-Gang Jiang

"Colonoscopy Polyp Detection: Domain Adaptation From Medical Report Images to Real-time Videos" by Zhi-Qin Zhan, Huazhu Fu, Yan-Yao Yang, Jingjing Chen, Jie Liu, and Yu-Gang Jiang, arXiv:2012.15531v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison between training strategies with Thin ResNet-34 on VoxCeleb1-H.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 \textbf{Training strategy} & \textbf{EER{[}\%{]}} & \textbf{minDCF} \\
 \midrule
 AAMSoftmax & 3.17 & 0.1882 \\
 SupCon & 3.42 & 0.2107 \\
 H-SCL & 3.25 & 0.2006 \\
 SupCon + CHNS (ours) & 2.94 & 0.1845 \\
 H-SCL + CHNS (ours) & \textbf{2.88} & \textbf{0.1784} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Piotr Masztalski
Michał Romaniuk
Jakub Żak
Mateusz Matuszewski
Konrad Kowalczyk

"Clustering-based hard negative sampling for supervised contrastive speaker verification" by Piotr Masztalski, Michał Romaniuk, Jakub Żak, Mateusz Matuszewski, and Konrad Kowalczyk, arXiv:2507.17540v1, 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{Result from synthetic experiment. Accuracy is in \% (chance level is 50\%), z-sim is the average absolute value of cosine similarity between feature vectors $z$ for different pairs of $z$. True Cluster: pairs of $z$ are from different ground truth clusters. Found Clusters: pairs of $z$ are from two different found clusters. Within Cluster: pairs of $z$ are randomly picked from the same found cluster, averaged between two found clusters.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|llll}
\bf \small Dataset &\bf \small Accuracy &\bf \small z-sim: True Cluster &\bf \small z-sim: Found Clusters &\bf \small z-sim: Within Cluster
\\ \hline \\
Identifiable &100.0 &0.017 &0.017 &0.503 \\
Not-identifiable &69.8 &0.717 &0.287 &0.770 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zengyi Li
Yubei Chen
Yann LeCun
Friedrich T. Sommer

"Neural Manifold Clustering and Embedding" by Zengyi Li, Yubei Chen, Yann LeCun, and Friedrich T. Sommer, arXiv:2201.10000v1, 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}
		& MAFDS \\
		computer model 1 & $ 0.00138 $ \\
		computer model 2 & $ 0.00326 $ \\
	\end{tabular}
\end{adjustbox}
\caption{ Median of the bootstrap estimates of the $ 0.95 $-quantiles of the $ n=100 $ maximal model errors for both computer models of the MAFDS.}
\end{table}
\end{document}

eess

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


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{rotating}
\usepackage{graphicx}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|l|c|c}
 & TripletWeight & $P^{con}$ & $P^{lin}$ \\
\hline
 & \cellcolor[HTML]{FFFFFF}$T^{con}$ & \cellcolor[HTML]{FCFDFF}75.5 $\pm$ 0.2 & \cellcolor[HTML]{7FACF8}77.8 $\pm$ 0.9 \\
 & \cellcolor[HTML]{FFFFFF}$T^{cos}$ & \cellcolor[HTML]{EFF5FF}75.8 $\pm$ 0.2 & \cellcolor[HTML]{8EB6F9}77.5 $\pm$ 0.5 \\
 & \cellcolor[HTML]{FFFFFF}$T^{cos}$ \& $T^{sc1}$ & \cellcolor[HTML]{AAC8FB}77.0 $\pm$ 0.2 & \cellcolor[HTML]{4587F5}78.8 $\pm$ 0.6 \\
 & \cellcolor[HTML]{FFFFFF}$T^{cos}$ \& $T^{sc2}$ & \cellcolor[HTML]{A0C2FA}77.2 $\pm$ 0.5 & \cellcolor[HTML]{5B95F6}78.4 $\pm$ 0.5 \\
 & \cellcolor[HTML]{FFFFFF}$T^{cir}$ & \cellcolor[HTML]{FFFFFF}75.5 $\pm$ 0.3 & \cellcolor[HTML]{6098F6}78.3 $\pm$ 0.2 \\
 & \cellcolor[HTML]{FFFFFF}$T^{cir}$ \& $T^{sc1}$ & \cellcolor[HTML]{A9C8FA}77.0 $\pm$ 0.4 & \cellcolor[HTML]{6EA1F7}78.1 $\pm$ 0.6 \\
\multirow{-7}{*}{\rotatebox[origin=c]{90}{CAR}} & \cellcolor[HTML]{FFFFFF}$T^{cir}$ \& $T^{sc2}$ & \cellcolor[HTML]{BFD6FC}76.6 $\pm$ 0.8 & \cellcolor[HTML]{4285F4}78.8 $\pm$ 0.3 \\
\hline
 & \cellcolor[HTML]{FFFFFF}$T^{con}$ & \cellcolor[HTML]{C9DDFC}85.2 $\pm$ 0.1 & \cellcolor[HTML]{5D97F6}87.3 $\pm$ 0.2 \\
 & \cellcolor[HTML]{FFFFFF}$T^{cos}$ & \cellcolor[HTML]{A4C4FA}86.0 $\pm$ 0.3 & \cellcolor[HTML]{4285F4}87.9 $\pm$ 0.4 \\
 & \cellcolor[HTML]{FFFFFF}$T^{cos}$ \& $T^{sc1}$ & \cellcolor[HTML]{D0E1FD}85.1 $\pm$ 0.3 & \cellcolor[HTML]{5E97F6}87.3 $\pm$ 0.3 \\
 & \cellcolor[HTML]{FFFFFF}$T^{cos}$ \& $T^{sc2}$ & \cellcolor[HTML]{EFF5FF}84.5 $\pm$ 0.2 & \cellcolor[HTML]{76A7F7}86.9 $\pm$ 0.2 \\
 & \cellcolor[HTML]{FFFFFF}$T^{cir}$ & \cellcolor[HTML]{A0C2FA}86.0 $\pm$ 0.1 & \cellcolor[HTML]{4D8CF5}87.7 $\pm$ 0.2 \\
 & \cellcolor[HTML]{FFFFFF}$T^{cir}$ \& $T^{sc1}$ & \cellcolor[HTML]{DCE9FD}84.9 $\pm$ 0.2 & \cellcolor[HTML]{649BF6}87.2 $\pm$ 0.1 \\
\multirow{-7}{*}{\rotatebox[origin=c]{90}{In-shop}} & \cellcolor[HTML]{FFFFFF}$T^{cir}$ \& $T^{sc2}$ & \cellcolor[HTML]{FFFFFF}84.2 $\pm$ 0.1 & \cellcolor[HTML]{73A5F7}86.9 $\pm$ 0.2
\end{tabular}
\end{adjustbox}
\caption{Comparing recall@1 performance of different triplet weights with constant and linear pair weight on CAR and In-shop dataset}
\end{table}
\end{document}

cs

"Dissecting the impact of different loss functions with gradient surgery" by Hong Xuan and Robert Pless, arXiv:2201.11307v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Local Eviction Moratoria and the Spread of COVID-19" by Julia Hatamyar and Christopher F. Parmeter, arXiv:2307.00251v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
\textbf{Method} & \textbf{Train Accuracy} & \textbf{Test Accuracy} \\ \hline
MLP & 49.98\% & 50.00\% \\
CNN & 55.28\% & 52.98\% \\
ResNet & 77.35\% & 50.64\% \\
RNN & 53.70\% & 50.72\% \\
GRU & 49.65\% & 50.00\% \\
LSTM & 49.80\% & 50.00\% \\ \
Autoencoder & 50.12\% & 50.00\% \\
t-CNN & 55.08\% & 52.29\% \\
m-CNN & 96.01\% & 50.67\% \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Average results of the complementary experiment using stock price time series. The average test accuracy in all machine learning models is approximately 50\%.}
\end{table}
\end{document}

q-fin

"Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks" by Yu Zhang, Zelin Wu, and Claudio Tessone, arXiv:2504.12771v1, 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{Llama3 MT-Bench results on Helpsteer2 and Zephyr settings.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
 & \multicolumn{2}{c}{\textbf{Helpsteer2}} & \multicolumn{2}{c}{\textbf{Zephyr}} \\
\cmidrule(lr){2-3} \cmidrule(lr){4-5}
 & \textbf{Llama3-8B-Base} & \textbf{Llama3-8B-Instruct} & \textbf{Llama3-8B-Base} & \textbf{Llama3-8B-Instruct} \\
\midrule
\textbf{Method} & {GPT-4 Score} & {GPT-4 Score} & {GPT-4 Score} & {GPT-4 Score} \\
\midrule
Initial Model & 4.9 & 8.3 & 6.1 & {8.3} \\
\midrule
DPO & 5.6 & \textbf{8.2} & \underline{7.0} & \textbf{8.3} \\
DPO+SFT & 5.5 & \underline{8.1} & \underline{7.0} & 8.0 \\
cDPO & 5.8 & \underline{8.1} & 6.9 & \underline{8.2} \\
R-DPO & 5.4 & \textbf{8.2} & 6.9 & 8.1 \\
IPO & 4.9 & 8.0 & 6.3 & 8.0 \\
$\chi$PO & 5.5 & 8.0 & 6.9 & 8.1 \\
SPPO & 5.8 & \textbf{8.2} & \textbf{7.1} & \underline{8.2} \\
CPO & \underline{5.9} & 7.2 & 6.3 & 8.0 \\
RRHF & 5.7 & 7.9 & 6.2 & 8.1 \\
SLiC-HF & \underline{5.9} & 7.8 & 6.8 & 7.9 \\
ORPO & 5.7 & \underline{8.1} & 6.6 & \textbf{8.3} \\
SimPO & \textbf{6.0} & 8.0 & 6.8 & \underline{8.2} \\
ROPO & 5.8 & \underline{8.1} & \textbf{7.1} & \underline{8.2} \\
\midrule
POWER-DL & \textbf{6.0} & \textbf{8.2} & \textbf{7.1} & \underline{8.2} \\
POWER & \underline{5.9} & \textbf{8.2} & \underline{7.0} & \underline{8.2} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Sail into the Headwind: Alignment via Robust Rewards and Dynamic Labels against Reward Hacking" by Paria Rashidinejad and Yuandong Tian, arXiv:2412.09544v1, 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{Energy analysis for single MAC and data-memory access.}
\begin{tabular}{lllll}
 \toprule
 \cmidrule(r){2-3}
 Operation & Notation & Energy (pJ) \\
 \midrule
 $k_b$ bit Memory access & $E_{A,k_b}$ & 2.5$k_b$ \\
 32 bit MULT 32 bit & $E_{M,I}$ & 3.1 \\
 32 bit ADD 32 bit & $E_{Add,I}$ & 0.1 \\
 $k_b$ bit MAC INT & $E_{C,k_b}$ & (3.1 * $k_b$)/ 32) + 0.1 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Rachel Sterneck
Abhishek Moitra
Priyadarshini Panda

"Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks" by Rachel Sterneck, Abhishek Moitra, and Priyadarshini Panda, arXiv:2101.01543v2, 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}{lcccccccc}
 & \multicolumn{4}{c}{micro (label average)} & \multicolumn{4}{c}{macro (instance average)} \\
	\cmidrule(lr){2-5} \cmidrule(lr){6-9}
 & P@N & R@N & F1@N & mAP & P@N & R@N & F1@N & mAP \\
	\toprule
 ($b=128$) & 0.701 & 0.711 & 0.699 & 0.676 & - & - & - & - \\
 (simplified, $b=64$) & 0.704 & 0.701 & 0.696 & 0.665 & 0.430 & 0.480 & 0.429 & 0.385 \\
 ($b=64$) & 0.717 & 0.737 & - & 0.685 & 0.450 & 0.550 & - & 0.444 \\
 DeepSphere (equiangular $b=64$) & 0.709 & 0.700 & 0.698 & 0.665 & 0.439 & 0.489 & 0.439 & 0.403 \\
 DeepSphere (HEALPix $N_{side}=32$) & 0.725 & 0.717 & 0.715 & 0.686 & 0.475 & 0.508 & 0.468 & 0.428\\
	\bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Official metrics from the SHREC'17 object retrieval competition.}
\end{table}
\end{document}

stat

DeepSphere: a graph-based spherical CNN

Michaël Defferrard
Martino Milani
Frédérick Gusset
Nathanaël Perraudin

"DeepSphere: a graph-based spherical CNN" by Michaël Defferrard, Martino Milani, Frédérick Gusset, and Nathanaël Perraudin, arXiv:2012.15000v1, 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{Computation Time (seconds)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
 & {GP} & {MoNNE} & {BMNN (VI)}\\
\midrule
DTLZ2 & $0.39 (\pm 0.02)$ & $3.53 (\pm 0.91)$& $61.34 (\pm 0.17)$ \\
VLMOP3 & $0.42 (\pm 0.02)$ & $4.19 (\pm 0.91)$ & $60.66 (\pm 0.01)$\\
VehicleSafety & $0.49 (\pm 0.04)$ & $5.58 (\pm 1.05)$ & $60.88 (\pm 0.02)$\\
OSY & $0.49 (\pm 0.03)$ & $3.83 (\pm 0.8)$ & $123.59 (\pm 0.04)$\\
CarCabDesign & $0.28(\pm 0.01)$ & $4.92 (\pm 0.98)$ & $124.32 (\pm 0.11)$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Hanyang Wang
Juergen Branke
Matthias Poloczek

"Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble" by Hanyang Wang, Juergen Branke, and Matthias Poloczek, arXiv:2501.18792v1, 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{Comparison between deformed and real images - mean squared error (MSE) and Dice coefficient of different tissues. For our model, we report error between prescribed $a_{FNIRT}$ and the computed atrophy $\rm{det}(\textbf{F)}$. We report average values and corresponding standard deviation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
 \toprule
 Method & Input & MSE$_{atrophy}$ & MSE$_{Image}$ & Dice$_{CSF}$ & Dice$_{WM}$ & Dice$_{GM}$ & Dice$_{DGM}$ \\
 \midrule
 FNIRT &- & - & \( 9.0\times10^{-4}\) & 0.878 & 0.922 & 0.839 & 0.780 \\

 & & & ($4.8\times10^{-4}$) & (0.021) & (0.008) & (0.012) & (0.028)\\
 Ours & a$_{FNIRT}$ & \(5.1\times10^{-5}\) & \( 1.0\times10^{-3} \) & 0.877 & 0.921 & 0.837 & 0.775 \\

 & & \( (1.8\times10^{-5})\) & \( (5.1\times10^{-4}) \) & $ (0.021)$ & $(0.009)$ & $(0.013)$ & $(0.032)$\\
 & a$_{FNIRT}$ & \(8.2\times10^{-4}\) & \(1.2\times10^{-3}\) & 0.874 & 0.920 & 0.836 & 0.767 \\
 & brain only & \( (3.9\times10^{-5})\) & \( (6.9\times10^{-4})\) & $ (0.022)$ & $(0.009)$ & $(0.014)$ & $(0.044)$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Mariana da Silva
Kara Garcia
Carole H. Sudre
Cher Bass
M. Jorge Cardoso
Emma Robinson

"Biomechanical modelling of brain atrophy through deep learning" by Mariana da Silva, Kara Garcia, Carole H. Sudre, Cher Bass, M. Jorge Cardoso, and Emma Robinson, arXiv:2012.07596v1, 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{Simulation Parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc} % 去掉了竖线
			\toprule % 使用 top rule
			\textbf{Parameter} & \textbf{Value} \\
			\midrule % 使用 mid rule
			Number of MEC servers $M$ & 5 \\
			Plateau factors of MZipf $q_m$ & [100, 200, 90, 40, 80] \\
			Zipf factors of MZipf $k_m$ & [0.60, 0.60, 0.75, 0.90, 0.90] \\
			Data size of contents $\eta_c$ & 1$\sim$8 GB \\
			Downloading payment of contents $\phi_c$ & 0.05$\sim$0.5 HKD \\
			Number of DNN layers & 6 \\
			Number of neurons per hidden layer & 128 \\
			Learning rate $\xi$ & 0.002 \\
			Update coefficient $\tau$ & 0.005 \\
			Discounted factor $\gamma$ & 0.99 \\
			\bottomrule % 使用 bottom rule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Personalized Federated Deep Reinforcement Learning for Heterogeneous Edge Content Caching Networks" by Zhen Li, Tan Li, Hai Liu, and Tse-Tin Chan, arXiv:2412.12543v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Price of Disaster: Estimating the Impact of Hurricane Harvey on the Texas Construction Labor Market" by Kartik Ganesh, arXiv:2509.11501v1, 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{CART Simulation Results - BART Predictions}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
Type & $M$\textsuperscript{a} & Bias\textsuperscript{b} & RMSE\textsuperscript{b} & Coverage\textsuperscript{b} & Width\textsuperscript{b}\\
\midrule
oracle & & 0.002 (0.001) & 0.036 (0.001) & 0.940 (0.017) & 0.144 (0.000)\\
\midrule
clbart & 1 & 0.000 (0.001) & 0.067 (0.001) & 0.819 (0.027) & 0.187 (0.003)\\
clbart & 5 & 0.002 (0.001) & 0.056 (0.001) & 0.933 (0.018) & 0.211 (0.002)\\
clbart & 10 & 0.002 (0.001) & 0.058 (0.001) & 0.952 (0.015) & 0.235 (0.002)\\
clbart & 25 & 0.002 (0.001) & 0.063 (0.001) & 0.960 (0.014) & 0.266 (0.001)\\
clbart & 50 & 0.002 (0.001) & 0.069 (0.001) & 0.958 (0.014) & 0.286 (0.001)\\
\bottomrule
\multicolumn{6}{l}{\textsuperscript{a} $M$: Number of trees.}\\
\multicolumn{6}{l}{\textsuperscript{b} Monte Carlo mean and standard errors across 200 simulations reported.}\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

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{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
 \toprule \midrule
 Category & Percentage (\# speakers) \\
 \midrule
 \multirow{2}{*}{Gender} & male: 69\% (20) \\
 & female: 31\% (9) \\
 \midrule
 \multirow{3}{*}{Severity} & mild: 24.1\% (7) \\
 & moderate: 37.9\% (11) \\
 & severe: 37.9\% (11) \\
 \midrule
 Etiology & ALS: 41.4\% (12) \\
 (top-3) & Cerebral Palsy: 17.2\% (5) \\
 & Vocal Cord Paralysis: 6.9\% (2) \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Diversity of our \textbf{conversational speech test set}.}
\end{table}
\end{document}

eess

Jimmy Tobin
Katrin Tomanek
Subhashini Venugopalan

"Towards a Single ASR Model That Generalizes to Disordered Speech" by Jimmy Tobin, Katrin Tomanek, and Subhashini Venugopalan, arXiv:2412.19315v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
{Set} & {$b$} & Exact entropy or bounds\\ \hline
$S_0$ & $\left(4,\frac{14}{3}\right)$ & $\left[0.14717,0.28888\right]$ \\ \hline
$T_0$ & $\left[\frac{14}{3},\frac{21}{4}\right]$ & $0.20844$ \\ \hline
$U_0$ & $\left(\frac{21}{4},\frac{11}{2}\right)$ & $\left[0.14717,023031\right]$ \\ \hline
$V_0$ & $\left[\frac{11}{2},\frac{20}{3}\right]$ & $0.18600$ \\ \hline
$S_1$ & $\left(\frac{20}{3},\frac{34}{5}\right)$ & $\left[0.11977,0.21132\right]$ \\ \hline
$T_1$ & $\left[\frac{34}{5},\frac{85}{12}\right]$ & $ 0.16389$ \\ \hline
$U_1$ & $\left(\frac{85}{12},\frac{43}{6}\right)$ & $\left[0.11977,0.18155\right]$ \\ \hline
$V_1$ & $\left[\frac{43}{6},\frac{84}{11}\right]$ & $0.15051$ \\ \hline
$S_2$ & $\left(\frac{84}{11},\frac{682}{89}\right)$ & $\left[0.10238,0.17042\right]$ \\ \hline
$T_2$ & $\left[\frac{682}{89},\frac{31}{4}\right]$ & $0.13698$ \\ \hline
$U_2$ & $\left(\frac{31}{4},\frac{171}{22}\right)$ & $\left[0.10238,0.15186\right]$ \\ \hline
$V_2$ & $\left[\frac{171}{22},\frac{340}{43}\right]$ & $0.12795$ \\ \hline
\end{tabular}
\end{adjustbox}
\caption{First sets of the partition of the parameter interval $(4,8)$ and exact value (with five significant digits) or bounds of the entropy according to Proposition .}
\end{table}
\end{document}

math

Anna Cima
Armengol Gasull
Víctor Mañosa
Francesc Mañosas

"Further results for a family of continuous piecewise linear planar maps" by Anna Cima, Armengol Gasull, Víctor Mañosa, and Francesc Mañosas, arXiv:2503.02411v1, 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{Forward star representation of a network with more nodes.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rc|c|crc|c|c|}
		Node &
		 &
		Point &
		 &
		Link &
		 &
		tail &
		head \\
		\cline{1-1}
		\cline{3-3}
		\cline{5-5}
		\cline{7-8}
		1 & & 1 & & 1 & & 1 & 2 \\
		\cline{3-3}
		\cline{7-8}
		2 & & 3 & & 2 & & 1 & 4 \\
		\cline{3-3}
		\cline{7-8}
		3 & & 4 & & 3 & & 2 & 5 \\
		\cline{3-3}
		\cline{7-8}
		4 & & 6 & & 4 & & 3 & 5 \\
		\cline{3-3}
		\cline{7-8}
		5 & & 8 & & 5 & & 3 & 6 \\
		\cline{3-3}
		\cline{7-8}
		6 & & 8 & & 6 & & 4 & 5 \\
		\cline{3-3}
		\cline{7-8}
		7 & & 9 & & 7 & & 4 & 7 \\
		\cline{3-3}
		\cline{7-8}
		8 & & 10 & & 8 & & 6 & 8 \\
		\cline{3-3}
		\cline{7-8}
		9 & & 12 & & 9 & & 7 & 5 \\
		\cline{3-3}
		\cline{7-8}
		 & & & & 10 & & 8 & 3 \\
		\cline{7-8}
		 & & & & 11 & & 8 & 5 \\
		\cline{7-8}
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Stephen D. Boyles
Nicholas E. Lownes
Avinash Unnikrishnan

"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Juan Zhang
Junyue Luo
Fangfang Zhang
Xiaoqiang Yue

"Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs" by Juan Zhang, Junyue Luo, Fangfang Zhang, and Xiaoqiang Yue, arXiv:2504.14930v1, 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|ccc}
\hline
$\delta$ & & \textsc{ME} & \textsc{Smart-ME} \\
\hline\hline
0.5 & 2.66 & 377.21 & 279.24 \\
1.0 & 3.95 & 313.92 & 160.79 \\
2.0 & 5.19 & 265.27 & 107.84 \\
4.0 & 37.93 & 276.15 & 107.62 \\
\hline
\end{tabular}
\caption{\centering Relative deviation cost difference (in \%) for three benchmark policies vs. \textsc{Off}, for $\delta \in \{0.5, 1, 2, 4\}$.}
\end{table}
\end{document}

math

Chamsi Hssaine
Huseyin Topaloglu
Garrett van Ryzin

"Target-Following Online Resource Allocation Using Proxy Assignments" by Chamsi Hssaine, Huseyin Topaloglu, and Garrett van Ryzin, arXiv:2412.12321v1, 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}{lccc}
 \toprule
 Method & F1-Score & Deviation & Duration\\
 \midrule
 \textbf{CNN} & 0.89 & \textbf{9} ms & 267 s\\
 \textbf{RNN} & 0.89 & 10 ms & 266 s\\
 \textbf{HFC} & 0.94 & 17 ms & \textbf{7} s\\
 \textbf{Complex} & \textbf{0.95} & 21 ms & 10 s\\
 \midrule
 \textbf{CNN-SF} & 0.89 & \textbf{6} ms & -\\
 \textbf{RNN-SF} & 0.88 & 9 ms & -\\
 \textbf{HFC-SF} & 0.93 & 11 ms & -\\
 \textbf{Complex-SF} & \textbf{0.94} & 11 ms & -\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Final results of the onset detection study for vocal percussion. The '-SF' suffix indicates the inclusion of spectral flux refinement in the process. From left to right, it is displayed the F1-Score, the mean absolute deviation of the predicted onsets with respect to the real ones, and the duration of the analysis for all onsets using the same laptop.}
\end{table}
\end{document}

eess

Alejandro Delgado
SKoT McDonald
Ning Xu
Mark Sandler

"A New Dataset for Amateur Vocal Percussion Analysis" by Alejandro Delgado, SKoT McDonald, Ning Xu, and Mark Sandler, arXiv:2009.11737v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Sound Classification of Four Insect Classes" by Yinxuan Wang and Sudip Vhaduri, arXiv:2412.12395v1, 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{Dataset-2: Segmentation performance metrics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\hline
\textbf{Metric} & \textbf{Mean} & \textbf{Std} \\
\hline
Dice & 0.814431 & 0.078670 \\
IoU & 0.694019 & 0.106993 \\
Accuracy & 0.872015 & 0.096104 \\
Precision & 0.803413 & 0.112562 \\
Recall & 0.851245 & 0.124639 \\
F1 Score & 0.814431 & 0.078670 \\
SSIM & 0.502930 & 0.245851 \\
Hausdorff & 57.287395 & 34.920233 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"GUI Based Fuzzy Logic and Spatial Statistics for Unsupervised Microscopy Segmentation" by Surajit Das and Pavel Zun, arXiv:2508.15979v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The Number of Articles by Field and Year in arVix}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrrrr}
\hline
\hline
Year & Maths & Phys & CS & EE\&SS & Stats & Bio & Econ & Fin & Total \\
\hline
2021 & 36280 & 69824 & 60467 & 8185 & 5103 & 2187 & 1251 & 931 & 184228 \\
2022 & 38578 & 69928 & 65631 & 8970 & 5233 & 1979 & 1331 & 874 & 192524 \\
2023 & 49210 & 84966 & 92830 & 10731 & 7028 & 2655 & 2054 & 1158 & 250632 \\
\hline
Total & 124068 & 224718 & 218928 & 27886 & 17364 & 6821 & 4636 & 2963 & 627384 \\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing" by Cong William Lin and Wu Zhu, arXiv:2504.13629v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Aleix Alcacer
Irene Epifanio
Ximo Gual-Arnau

"Biarchetype analysis: simultaneous learning of observations and features based on extremes" by Aleix Alcacer, Irene Epifanio, and Ximo Gual-Arnau, arXiv:2311.11153v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Beyond Volatility: Common Factors in Idiosyncratic Quantile Risks" by Jozef Barunik and Matej Nevrla, arXiv:2208.14267v4, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameter Setup in HJM Model}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccccccccc}
 \hline
 \textbf{Mode} & $\sigma_0$ & $\alpha_\sigma$ & $f_0$ & $c_f$ & $\alpha_f$ & $R$ & $C$ & $t'_0$ & $\Delta t'$ & $n_p$ & $\Delta t$ & $T^*$ \\
 \hline
 $N_{\text{train}}=3,000,000$ & $[0.01, 0.03]$ & $[0.001, 0.9]$ & $[0.01, 0.03]$ & $[0.01, 0.05]$ & $[0.001, 0.9]$ &
 \multirow{2}{*}{$\exp(-\frac{\sum_{i=0}^{n-1} f(0, t'_i)}{T^* - t'_0})$} &
 \multirow{2}{*}{$100$} &
 \multirow{2}{*}{$5$} &
 \multirow{2}{*}{$1$} &
 \multirow{2}{*}{$20$} &
 \multirow{2}{*}{$1/52$} &
 \multirow{2}{*}{$25$} \\
 evaluation & $0.0015$ & $100$ & $0.02$ & $3.0$ & $0.5$ & & & & & & & \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Fengpei Li
Haoxian Chen
Jiahe Lin
Arkin Gupta
Xiaowei Tan
Honglei Zhao
Gang Xu
Yuriy Nevmyvaka
Agostino Capponi
Henry Lam

"Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate" by Fengpei Li, Haoxian Chen, Jiahe Lin, Arkin Gupta, Xiaowei Tan, Honglei Zhao, Gang Xu, Yuriy Nevmyvaka, Agostino Capponi, and Henry Lam, arXiv:2412.11257v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study" by Yilie Huang, Yanwei Jia, and Xun Yu Zhou, arXiv:2412.16175v1, 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|ccc|c|}
 \hline
 Criteria & $E_1$ & $E_2$ & $E_3$& Aggregated results \\ \hline
 $T_1$ & $\langle (0.8,0.2);0.8\rangle$ & $\langle (0.7,0.3);0.7\rangle$ &$\langle (0.5,0.5);0.5\rangle$& $\langle (0.72,0.28);0.69 \rangle$ \\
 $T_2$ & $\langle (0.6,0.4);0.6\rangle$ & $\langle (0.6,0.4);0.6\rangle$ &$\langle (0.4,0.6);0.4\rangle$&$\langle (0.57,0.43);0.55 \rangle$ \\
 $T_3$ & $\langle (0.6,0.4);0.6\rangle$ & $\langle (0.5,0.5);0.5\rangle$ &$\langle (0.3,0.7);0.3\rangle$&$\langle (0.51,0.49);0.49 \rangle$ \\
 $T_4$ & $\langle (0.5,0.5);0.5\rangle$ & $\langle (0.4,0.6);0.4\rangle$ &$\langle (0.2,0.8);0.2\rangle$&$\langle (0.41,0.59);0.38 \rangle$ \\ \hline

 \end{tabular}
\end{adjustbox}
\caption{The importance weights matrix of criteria and its aggregated results}
\end{table}
\end{document}

math

"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.