\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}{crl}
 \toprule
 $n$ & \textit{found} & \textit{connectivity} \\
 \midrule
 11 & 18 & 1: 11; 2: 7 \\
 12 & 19 & 1: 8; 2: 11 \\
 13 & 21 & 1: 13; 2: 8 \\
 14 & 9 & 1: 4; 2: 5 \\
 15 & 1 & 1: 1 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\small Details about the 1-metric antidimensional graphs found with the Barab\'{a}si–Albert model.}
\end{table}
\end{document}

math

Serafino Cicerone
Gabriele Di Stefano
Sandi Klavžar
Ismael G. Yero

"Burning some myths on privacy properties of social networks against active attacks" by Serafino Cicerone, Gabriele Di Stefano, Sandi Klavžar, and Ismael G. Yero, arXiv:2504.16944v1, 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{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccccc}
			\toprule
			Number of Robots	&	5	&	10	&	20	&	40 & 80 \\
			\midrule
			Additional training time (min) & $\infty$ & 320 & 1132 & 1248 & 2945 \\
			\toprule
	\end{tabular}
\end{adjustbox}
\caption{The additional training time used for a complete communication network to reach the same performance as Star-<5>, evaluated on an NVIDIA Tesla K80 GPU.}
\end{table}
\end{document}

eess

Guangyi Liu
Arash Amini
Martin Takáč
Héctor Muñoz-Avila
Nader Motee

"Distributed Map Classification using Local Observations" by Guangyi Liu, Arash Amini, Martin Takáč, Héctor Muñoz-Avila, and Nader Motee, arXiv:2012.10480v2, 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.


q-fin

Zero-Leverage Puzzle

"Zero-Leverage Puzzle" by Mykola Pinchuk, arXiv:2302.00761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Preprocessing noisy functional data: a multivariate perspective" by Siegfried Hörmann and Fatima Jammoul, arXiv:2012.05824v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Short-term prediction of Time Series based on bounding techniques" by Pedro Cadahía and Jose Manuel Bravo Caro, arXiv:2101.10719v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{pifont}
\usepackage{adjustbox}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccccccccc}
\hline
\multicolumn{2}{c}{\multirow{2}{*}{denoiser}} & \multicolumn{8}{c}{kernel (see )} & \multirow{2}{*}{convergence} \\
\cline{3-10}
 && (a) & (b) & (c) & (d) & (e) & (f) & (g) & (h)\\
\hline
\multicolumn{2}{c}{Observation} & $23.36$ & $22.93$ & $23.43$ & $19.49$ & $23.84$ & $19.85$ & $20.75$ & $20.67$ \\
\multicolumn{2}{c}{RealSN } & $26.24$ & $26.25$ & $26.34$ & $25.89$ & $25.08$ & $25.84$ & $24.81$ & $23.92$ & \checkmark \\
\multicolumn{2}{c}{$\text{prox}_{\mu_{\ell_1}\|\Psi^\dagger \cdot\|_1}$} & $29.44$ & $29.20$ & $29.31$ & $28.87$ & $30.90$ & $30.81$ & $29.40$ & $29.06$ & \checkmark \\
\multicolumn{2}{c}{$\text{prox}_{\mu_\text{TV} \|\cdot\|_\text{TV}}$} & $29.70$ & $29.35$ & $29.43$ & $29.15$ & $30.67$ & $30.62$ & $29.61$ & $29.23$ & \checkmark \\
\multicolumn{2}{c}{DnCNN } & $29.82$ & $29.24$ & $29.26$ & $28.88$ & $30.84$ & $30.95$ & $29.54$ & $29.17$ & \ding{55} \\
\multicolumn{2}{c}{BM3D } & $30.05$ & $29.53$ & $29.93$ & $29.10$ & $31.08$ & $30.78$ & $29.56$ & $29.41$ & \ding{55}\\
\multicolumn{2}{c}{Proposed} & $\mathbf{30.86}$ & $\mathbf{30.33}$ & $\mathbf{30.31}$ & $\mathbf{30.14}$ & $\mathbf{31.72}$ & $\mathbf{31.69}$ & $\mathbf{30.42}$ & $\mathbf{30.09}$ & \checkmark \\
\hline
\end{tabular}
\end{adjustbox}
\caption{\small Average PSNR values obtained by different denoisers plugged in the PnP-FB algorithm , to solve the deblurring problem~ with kernels of (a)-(h) considering the BSD10 test set. The last row provides the average SSIM values for the observed blurred image $y$ in each experimental setting. Each algorithm is stopped after a fixed number of iterations equal to 1000. The best PSNR values are indicated in bold.}
\end{table}
\end{document}

eess

Jean-Christophe Pesquet
Audrey Repetti
Matthieu Terris
Yves Wiaux

"Learning Maximally Monotone Operators for Image Recovery" by Jean-Christophe Pesquet, Audrey Repetti, Matthieu Terris, and Yves Wiaux, arXiv:2012.13247v2, 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 performance of datasets used in FT-Transformer paper, averaged within 5 seeds. As the tree-based models act as an upper bound of performance, we highlight the best performing "deep learning model" in \textbf{\underline{Bold with underline}}, and the "deep learning model" with second performance in \textbf{Bold}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccccccc}
\toprule
 & \textbf{CA ↓} & \textbf{AD ↑} & \textbf{HE ↑} & \textbf{JA ↑} & \textbf{HI ↑} & \textbf{AL ↑} & \textbf{EP ↑} & \textbf{YE ↓} & \textbf{CO ↑} & \textbf{YA ↓} & \textbf{MI ↓} \\
\midrule
\multicolumn{12}{c}{Default} \\
\midrule
DOFEN (ours) & \textbf{\underline{0.4584}} & \textbf{\underline{0.8667}} & \textbf{0.3858} & \textbf{\underline{0.7332}} & \textbf{\underline{0.7311}} & \textbf{0.9567} & OOM & \textbf{\underline{8.7572}} & 0.9104 & \textbf{\underline{0.7470}} & \textbf{\underline{0.7438}} \\
GRANDE & 0.481 & 0.859 & 0.350 & 0.723 & 0.721 & 0.785 & 0.870 & 8.943 & 0.792 & 0.779 & 0.754 \\
Trompt & 0.4793 & \textbf{0.8622} & 0.3665 & \textbf{0.7287} & \textbf{0.7299} & 0.9316 & 0.8941 & \textbf{8.8427} & 0.9035 & \textbf{0.7535} & \textbf{0.7465} \\
FT-Transformer & 0.4677 & 0.8580 & 0.3803 & 0.7237 & 0.7237 & 0.9522 & \textbf{0.8960} & 8.9173 & \textbf{0.9667} & 0.7560 & 0.7471 \\
CatBoost & 0.4303 & 0.8730 & 0.3820 & 0.7207 & 0.7255 & 0.9464 & 0.8882 & 8.9140 & 0.9077 & 0.7510 & 0.7454 \\
XGBoost & 0.4622 & 0.8741 & 0.3479 & 0.7110 & 0.7165 & 0.9242 & 0.8799 & 9.1922 & 0.9640 & 0.7607 & 0.7514 \\
\midrule
\multicolumn{12}{c}{Searched} \\
\midrule
FT-Transformer & \textbf{0.4597} & 0.8583 & \textbf{\underline{0.3916}} & \textbf{\underline{0.7332}} & 0.7292 & \textbf{\underline{0.9608}} & \textbf{\underline{0.8981}} & 8.8589 & \textbf{\underline{0.9695}} & N/A & 0.7462 \\
CatBoost & 0.4317 & 0.8722 & 0.3846 & 0.7231 & 0.7243 & OOM & 0.8881 & 8.8765 & 0.9658 & 0.7429 & 0.7429 \\
XGBoost & 0.4338 & 0.8722 & 0.3749 & 0.7218 & 0.7264 & OOM & 0.8835 & 8.953 & 0.9689 & 0.7357 & 0.7424 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

DOFEN: Deep Oblivious Forest ENsemble

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

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{PCM degradation measure heuristics and references}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
\hline \hline
\textbf{Degradation Measure } & \textbf{References} \\
 \hline \hline
Depth of Discharge (DoD) & \\
State of Charge (SoC) & \\
Charge/Discharge Cycle & \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Degradation-based Energy Management for Microgrids in the Presence of Energy Storage Elements" by Satish Vedula, arXiv:2503.09028v2, 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{\textbf{Ablation study} of Query Preservation Sweeping (default: $\alpha$=0.9).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
& Content & Musical Instrument & Sound FX \\
& Preservation ($\uparrow$) & Style Fit ($\uparrow$) & Style Fit ($\uparrow$) \\
\midrule
$\gamma$ = 0 & 0.242 & 0.144 & 0.161 \\
$\gamma$ = 0.25 & 0.353 & 0.156 & 0.158 \\
$\gamma$ = 0.5 & 0.435 & 0.157 & 0.149 \\
$\gamma$ = 0.75 & 0.486 & \textbf{0.157} & \textbf{0.139} \\
$\gamma$ = 1 & \textbf{0.519} & \textbf{0.158} & 0.133 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Heehwan Wang
Joonwoo Kwon
Sooyoung Kim
Shinjae Yoo
Yuewei Lin
Jiook Cha

"A Training-Free Approach for Music Style Transfer with Latent Diffusion Models" by Heehwan Wang, Joonwoo Kwon, Sooyoung Kim, Shinjae Yoo, Yuewei Lin, and Jiook Cha, arXiv:2411.15913v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the emerging states in 1d swarmalators between the Kuramoto coupling and the Winfree coupling:}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 State \hspace{0.001 cm} & Kuramoto coupling \hspace{0.01 cm} & Winfree coupling\\ \hline

 Static $\pi$ \hspace{0.01 cm} & \checkmark \hspace{0.01 cm} & \checkmark \\

 Static async \hspace{0.01 cm} &\checkmark \hspace{0.01 cm} &\checkmark \\

 Static phase wave \hspace{0.01 cm} &\checkmark \hspace{0.01 cm} &\checkmark \\

 Active async \hspace{0.01 cm} &\checkmark \hspace{0.01 cm} & $\times$ \\

 $x$-(antiphase) sync\hspace{0.01 cm} & $\times$ \hspace{0.01 cm} & \checkmark \\

 $\theta$-(antiphase) sync\hspace{0.01 cm} & $\times$ \hspace{0.01 cm} & \checkmark \\

 Intermediate mixed \hspace{0.01 cm} &$\times$ \hspace{0.01 cm} & \checkmark \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Samali Ghosh
Kevin O'Keeffe
Gourab Kumar Sar
Dibakar Ghosh

"Dynamics of pulsating swarmalators on a ring" by Samali Ghosh, Kevin O'Keeffe, Gourab Kumar Sar, and Dibakar Ghosh, arXiv:2504.14912v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Feature extraction hyperparameters} optimised using the leave-$p$-out cross-validation as described in Section~ } % title name of the table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
			\hline
			\hline
			\textbf{Hyperparameter} & \textbf{Description} & \textbf{Range} \\
			\hline
			\hline
			\multirow{2}{*}{MFCC ($\mathcal{M}$)} & Number of lower-order & $13 \times k_1$, where \\
			& MFCCs to keep & $k_1=1, 2, 3, 4, 5$ \\
			\hline
			\multirow{2}{*}{Frame ($\mathcal{F}$)} & Frame-size in which & $2^{k_2}$ where \\
			& audio is segmented & $k_2=8, \cdots, 12$\\
			\hline
			\multirow{2}{*}{Seg ($\mathcal{S}$)} & Number of frames & $10 \times k_3$, where \\
			& extracted from the audio & $k_3=5, 7, 10, 12, 15$ \\
			\hline
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Madhurananda Pahar
Marisa Klopper
Robin Warren
Thomas Niesler

"COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings" by Madhurananda Pahar, Marisa Klopper, Robin Warren, and Thomas Niesler, arXiv:2012.01926v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 \hline
 \textbf{Exploration parameter} & \textbf{Value} \\
 \hline
 Initial $\epsilon / \tau$/target rate & 1 \\
 Final $\epsilon / \tau$/target rate & 0.01 \\
 Decay period in agent steps & 250k \\
 \hline
 VPD value of $k$ & 5 \\
 SimHash number of bits & 256 \\
 \hline
 \end{tabular}
\caption{Exploration parameters used across all exploration strategies.}
\end{table}
\end{document}

cs

Marius Captari
Remo Sasso
Matthia Sabatelli

"VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts" by Marius Captari, Remo Sasso, and Matthia Sabatelli, arXiv:2403.17542v1, 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}{ll}
\toprule
\textbf{Symbol} & \textbf{Description} \\
\midrule
$V(P,Q)$ & Total variation distance \\
$\chi^2(P,Q)$ & $\chi^2$ distance \\
$\text{KL}(P,Q)$ & Kullback–Leibler divergence \\
$\delta_{j}(B) =
\begin{cases}
	1 & \text{if } j \in B \\
	0 & \text{otherwise}
\end{cases}$ & Dirac delta \\
$\delta_{j,k} = \delta_{j}(\{k\}) =
\begin{cases}
	1 & \text{if } j = k \\
	0 & \text{otherwise}
\end{cases}$ & Dirac delta \\
$B_{j,t}(p) = \binom{t}{j} \cdot p^j \cdot (1-p)^{t-p}, \; 0 \leq p \leq 1$ & $j$-th element of the $t$-order Bernstein basis \\
$b_{j,t}(\pi) = E_{p \sim \pi}[B_{j,t}(p)]$ & Expected $j$-th fingerprint under $\pi$ \\
$P_{\pi}(A) = \int \text{Bin}(t,p)(A)\ d\pi(p) = \sum_{j=1}^t b_j(\pi) \cdot \delta_{j}(A)$ & Measure of set $A$ for binomial mixture \\
$m_l(\pi) = E_{p\sim\pi}[p^l]$ & $l$-moment of distribution $\pi$ \\
$V(\pi) = V_{p\sim\pi}[p]$ & Variance of distribution $\pi$ \\
$\mu_p = p \cdot (1-p)$ & Variance of $\text{Ber}(p)$ \\
$B_t(f)(x) = \sum_{j=0}^t f\left(j/t\right) \cdot B_{j,t}(x)$ & Bernstein polynomial approximation of $f$ \\
$K_m(x,p,t) = \sum_{v=0}^m (-1)^{m-v} \binom{t-x}{m-v}\binom{x}{v}p^{m-v}(1-p)^v$ & $m$-th Kravchuk polynomial \\
$\tilde{K}_m(x,p,t) = \binom{t}{m}^{-1} \cdot K_m(x,p,t)$ & $m$-th normalized Kravchuk polynomial \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Lucas Kania
Larry Wasserman
Sivaraman Balakrishnan

"Testing Random Effects for Binomial Data" by Lucas Kania, Larry Wasserman, and Sivaraman Balakrishnan, arXiv:2504.13977v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Moritz Laber
Peter Klimek
Martin Bruckner
Liuhuaying Yang
Stefan Thurner

"Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability" by Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang, and Stefan Thurner, arXiv:2210.01846v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Mariia Kozlova
Antti Ahola
Pamphile T. Roy
Julian Scott Yeomans

"Simple binning algorithm and SimDec visualization for comprehensive sensitivity analysis of complex computational models" by Mariia Kozlova, Antti Ahola, Pamphile T. Roy, and Julian Scott Yeomans, arXiv:2310.13446v2, 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{Data variable definition and description}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
 \hline
 \textbf{Name} & \textbf{~~~~~~Description~~~~~~} \\ \hline
 abnormal\_ALP & Number of patients with abnormal alkaline phosphatase	\\ \hline
 abnormal\_MPV & Number of patients with abnormal mean platelet volume	\\ \hline
 abnormal\_hematocrit & Number of patients with abnormal hematocrit \\ \hline
 abnormal\_PO2 & Number of patients with abnormal partial pressure of oxygen \\ \hline
 abnormal\_creatinine & Number of patients with abnormal creatinine \\ \hline
 abnormal\_INR	& Number of patients with abnormal international normalized ratio \\ \hline
 abnormal\_MCHb & Number of patients with abnormal mean corpuscular hemoglobin	\\ \hline
 abnormal\_MCHb\_conc & Number of patients with abnormal mean corpuscular hemoglobin concentration \\ \hline
 abnormal\_hb & Number of patients with abnormal hemoglobin \\ \hline
 abnormal\_mcv & Number of patients with abnormal mean corpuscular volume	\\ \hline
 abnormal\_plt & Number of patients with abnormal platelet count	\\ \hline
 abnormal\_redcellwidth & Number of patients with abnormal red cell distribution width	\\ \hline
 abnormal\_wbc & Number of patients with abnormal white cell count	\\ \hline
 abnormal\_ALC & Number of patients with abnormal absolute lymphocyte count \\ \hline
 location\_GeneralMedicine & Number of patients in general medicine \\ \hline
 location\_Hematology & Number of patients in hematology \\ \hline
 location\_IntensiveCare & Number of patients in intensive care \\ \hline
 location\_CardiovascularSurgery & Number of patients in cardiovascular surgery \\ \hline
 location\_Pediatric	& Number of patients in pediatrics \\ \hline
 Monday & Indicating the day of the week	\\ \hline
 Tuesday & Indicating the day of the week \\ \hline
 Wednesday & Indicating the day of the week \\ \hline
 Thursday & Indicating the day of the week \\ \hline
 Friday & Indicating the day of the week	\\ \hline
 Saturday & Indicating the day of the week \\ \hline
 Sunday & Indicating the day of the week	\\ \hline
 lastWeek\_Usage & Number of units transfused in the previous week \\ \hline
 yesterday\_Usage & Number of platelet units transfused in the previous day \\ \hline
 yesterday\_ReceivedUnits & Number of units received by the hospital in the previous day \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Maryam Motamedi
Jessica Dawson
Na Li
Douglas G. Down
Nancy M. Heddle

"Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models" by Maryam Motamedi, Jessica Dawson, Na Li, Douglas G. Down, and Nancy M. Heddle, arXiv:2101.02305v2, 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 architecture of the convolutional encoder used in the experiments. The convolutional layers are parameterized respectively by the number of input channels, the number of output channels, and the filter size. The linear layers are defined by their input and output sizes.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cc|cc|cc|}
\hline
\multicolumn{2}{|c|}{Encoder} & \multicolumn{2}{c|}{Encoder cont'd} & \multicolumn{2}{c|}{Encoder cont'd} \\ \hline
Number & Layer & Number & Layer & Number & Layer \\ \hline
1 & Conv2D(3,8,5) & 6 & Conv2D(16,16,3) & 11 & LeakyReLU(0.1) \\ \hline
2 & Conv2D(3,8,5) & 7 & LeakyReLU(0.1) & 12 & Linear(256,128) \\ \hline
3 & LeakyReLU(0.1) & 8 & MaxPool(2,2) & \multicolumn{2}{c|}{Decoder} \\ \hline
4 & MaxPool(2,2) & 9 & Flatten & 1 & Linear(64,10) \\ \hline
5 & Conv2D(8,16,3) & 10 & Linear(1024,256) & 2 & Softmax \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Milad Sefidgaran
Abdellatif Zaidi
Piotr Krasnowski

"Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior" by Milad Sefidgaran, Abdellatif Zaidi, and Piotr Krasnowski, arXiv:2504.18455v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\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}{lcc}
 \toprule
 \textbf{Augmentations} & $p$ & Parameters
 \\
 \midrule
 \cellcolor{gray!15} &\cellcolor{gray!15} & \cellcolor{gray!15} $\text{degrees} \in [-25, 25]$
 \\
 \cellcolor{gray!15} & \cellcolor{gray!15} & \cellcolor{gray!15} $\text{translation} \in [0, 0.2]$
 \\
 \multirow{-3}{*}{\cellcolor{gray!15}Random Affine} & \multirow{-3}{*}{\cellcolor{gray!15} 0.8} & \cellcolor{gray!15} $\text{scale} \in [0.9, 1.5]$
 \\
 \cellcolor{white} & \cellcolor{white} & \cellcolor{white} $\text{brightness}\in[-0.1, 0.1]$
 \\
 \multirow{-2}{*}{\cellcolor{white}{Brightness Contrast}} & \multirow{-2}{*}{\cellcolor{white}{0.5}} & \cellcolor{white} $\text{contrast}\in[0.5, 1.5]$
 \\
 \cellcolor{gray!15} & \cellcolor{gray!15} & \cellcolor{gray!15} $\alpha\in[1, 10]$
 \\
 \multirow{-2}{*}{\cellcolor{gray!15}Elastic Transform} & \multirow{-2}{*}{\cellcolor{gray!15} 0.8} & \cellcolor{gray!15} $\sigma\in[8,15]$
 \\
 Sharpness & 0.5 & $\text{sharpness} = 5$
 \\
 \cellcolor{gray!15} & \cellcolor{gray!15} & \cellcolor{gray!15} $\sigma\in[0.1, 1.5]$
 \\
 \multirow{-2}{*}{\cellcolor{gray!15}Gaussian Blur} & \multirow{-2}{*}{\cellcolor{gray!15} 0.5} & \cellcolor{gray!15} $k=5$
 \\
 \cellcolor{white} & \cellcolor{white} & \cellcolor{white} $\mu\in[0, 0.05]$
 \\
 \multirow{-2}{*}{\cellcolor{white}Gaussian Noise} & \multirow{-2}{*}{\cellcolor{white} 0.5} & \cellcolor{white} $ \sigma \in [0, 0.05]$
 \\
 Horizontal Flip & 0.5 & None
 \\
 Vertical Flip & 0.5 & None
 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\textbf{Data augmentations for generating synthetic tasks}. Given a set of $m+1$ copies of the same example, we randomly sampled data augmentations for each instance to increase the diversity of examples within the task. Each augmentation is sampled with probability $p$.}
\end{table}
\end{document}

eess

Hallee E. Wong
Jose Javier Gonzalez Ortiz
John Guttag
Adrian V. Dalca

"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance" by Hallee E. Wong, Jose Javier Gonzalez Ortiz, John Guttag, and Adrian V. Dalca, arXiv:2412.15058v2, 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{SMAPE and IEHI Rank by Industry with Spearman Correlation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 Industry & SMAPE (\%) & IEHI Rank \\
 \midrule
 Financial Activities & 13.42 & 1 \\
 Education Services & 13.08 & 2 \\
 Professional Services & 16.94 & 3 \\
 Construction & 21.44 & 4 \\
 Transportation & 32.96 & 5 \\
 Leisure/Hospitality & 18.13 & 6 \\
 Other Services & 33.72 & 7 \\
 Information & 41.09 & 8 \\
 Manufacturing & 51.32 & 9 \\
 Natural Resources & 52.94 & 10 \\
 \midrule
 \multicolumn{3}{l}{\textit{Spearman Rank Correlation}} \\
 \cmidrule(lr){1-2}
 Spearman's $\rho$ & 0.95 & \\
 $p$-value & 2.28e-05 & \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Adam Nelson-Archer
Aleia Sen
Meena Al Hasani
Sofia Davila
Jessica Le
Omar Abbouchi

"Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach" by Adam Nelson-Archer, Aleia Sen, Meena Al Hasani, Sofia Davila, Jessica Le, and Omar Abbouchi, arXiv:2507.01979v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Emmanuel Jordy Menvouta
Jolien Ponnet
Robin Van Oirbeek
Tim Verdonck

"mCube: Multinomial Micro-level reserving Model" by Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek, and Tim Verdonck, arXiv:2212.00101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The table provides an overview of the graph benchmark datasets, including the number of nodes and links, as well as the input attributes and output label classes. For the experiments, we randomly sampled 20 node instances per class as the training set for each graph dataset, and 1,000 random nodes as the testing set.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
\multirow{2}{*}{} & \multicolumn{3}{c|}{\textbf{Graph Datasets}} \\ \cline{2-4}
 & \textbf{Cora} & \textbf{Citeseer} & \textbf{Pubmed} \\ \hline\hline
Node \#	& 2,708 & 3,327 & 19,717 \\ \hline
Link \#	& 10,556 & 9,104 & 88,648 \\ \hline
Input Dim.	& 1,433 & 3,703 & 500 \\ \hline
Output Dim.& 7 & 6 & 3 \\ \hline
Train \#	& 140 & 120 & 60 \\ \hline
Test \#	& 1,000 & 1,000 & 1,000 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer" by Jiawei Zhang, arXiv:2411.11162v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc} \toprule
 CRSP & $\mu^s$ & $\sigma^s$ & $\lambda^s$ & $u^s$ &
 $\eta_1^s$ & $\eta_2^s$ & $\rho_{sb}$ \\ \midrule
 & 0.08912 & 0.1460& 0.3263 & 0.2258 & 4.3625 & 5.5335 & 0.08420\\
 \midrule
 \midrule
30-day T-bill & $\mu^b$ & $\sigma^b$ & $\lambda^b$ & $u^b$ &
 $\eta_1^b$ & $\eta_2^b$ & $\rho_{sb}$ \\ \midrule
 & 0.0046 & 0.0130 & 0.5053 & 0.3958 & 65.801 & 57.793 & 0.08420
\\
\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{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|}
\hline
Config & Setting \\
\hline
\hline
Batch size & 256 \\
Optimizer & Adam \\
Learning rate & $10^{-3}$ \\
Scheduler & StepLR \\
Training iterations & $10^6$ \\
Step rate & $10^4$ \\
Decay rate & 0.9 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Standard optimizer settings.}
\end{table}
\end{document}

math

Jerry Liu
Jessica Grogan
Owen Dugan
Ashish Rao
Simran Arora
Atri Rudra
Christopher Ré

"Towards Learning High-Precision Least Squares Algorithms with Sequence Models" by Jerry Liu, Jessica Grogan, Owen Dugan, Ashish Rao, Simran Arora, Atri Rudra, and Christopher Ré, arXiv:2503.12295v1, 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{Heterogeneous Treatment Effects by Industry AI Readiness and Location Characteristics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
\multirow{2}{*}{Category} & Treatment & Standard & \multirow{2}{*}{P-value} & N & Share of Total \\
& Effect & Error & & Observations & Employment (\%) \\
\midrule
\multicolumn{6}{l}{\textbf{Panel A: By Industry AI Readiness}} \\
High AI Readiness & 0.084** & 0.022 & 0.000 & 1,104 & 28.7 \\
\quad (IT, Finance, Professional) & & & & & \\
Medium AI Readiness & 0.041* & 0.017 & 0.016 & 736 & 34.2 \\
\quad (Manufacturing, Healthcare) & & & & & \\
Low AI Readiness & 0.012 & 0.015 & 0.427 & 1,472 & 37.1 \\
\quad (Retail, Hospitality, Transport) & & & & & \\
\midrule
\multicolumn{6}{l}{\textbf{Panel B: By Infrastructure Quality}} \\
High Infrastructure (Top Quartile) & 0.067** & 0.019 & 0.001 & 828 & 42.3 \\
Medium Infrastructure (Middle 50\%) & 0.038* & 0.016 & 0.018 & 1,656 & 41.8 \\
Low Infrastructure (Bottom Quartile) & 0.019 & 0.018 & 0.292 & 828 & 15.9 \\
\midrule
\multicolumn{6}{l}{\textbf{Panel C: By Human Capital Level}} \\
High Human Capital (Top Tertile) & $0.059^{**}$ & 0.020 & 0.003 & 1,104 & 38.7 \\
Medium Human Capital (Middle Tertile) & $0.043^{*}$ & 0.017 & 0.012 & 1,104 & 35.2 \\
Low Human Capital (Bottom Tertile) & $0.030^\dagger$ & 0.016 & 0.061 & 1,104 & 26.1 \\
\midrule
\multicolumn{6}{l}{\textbf{Panel D: Statistical Tests}} \\
F-test: Industry Groups Equal & \multicolumn{3}{c}{F(2,156) = 8.47, p = 0.000} & & \\
F-test: Infrastructure Groups Equal & \multicolumn{3}{c}{F(2,156) = 3.92, p = 0.022} & & \\
F-test: Human Capital Groups Equal & \multicolumn{3}{c}{F(2,156) = 2.84, p = 0.062} & & \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"AI-Driven Spatial Distribution Dynamics: A Comprehensive Theoretical and Empirical Framework for Analyzing Productivity Agglomeration Effects in Japan's Aging Society" by Tatsuru Kikuchi, arXiv:2507.19911v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Conditional-mean Multiplicative Operator Models for Count Time Series" by Christian H. Weiß and Fukang Zhu, arXiv:2212.05831v2, 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{Same setup as Table~, but using \textbf{dilation} annealing.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
& \multicolumn{5}{c}{sample size $N$}\\
& 100 & 300 & 1000 & 3000 & 10000 \\
\midrule
SBTM (ours) & \textbf{0.037} & 0.12 & 0.10 & 0.096 & 0.11 \\
SDE & 0.062 & \textbf{0.031} & \textbf{0.080} & \textbf{0.054} & \textbf{0.061} \\
SVGD & 0.48 & 0.32 & 1.00 & 1.10 & 0.99 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Score-Based Deterministic Density Sampling" by Vasily Ilin, Peter Sushko, and Jingwei Hu, arXiv:2504.18130v2, 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|}
\hline
Candidate & voter-1 & voter-2 & voter-3 & Borda & $W_{0}^{1}$ & $W_{1}^{1}$ & S \\
 \hline
\hline
A & 5 [1] & -5 [4] & 3 [2] & 3+0+2 & 3.0 &1.84 & 6\\
\hline
B & 2 [2] & 0 [3] & 4 [1] & 2+1+3 & 6.0 & 6.0 & 14\\
\hline
C & 1 [3] & 1 [2] & -2 [4] & 1+2+0 & 0.0 & 0.0 & 6 \\
\hline
D & -1 [4] & 4 [1] & 1 [3] & 0+3+1 & 4.0 & 3.33 & 11\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Negative votes to depolarize politics

"Negative votes to depolarize politics" by Karthik H. Shankar, arXiv:2012.13657v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Directed Hybrid Random Networks Mixing Preferential Attachment with Uniform Attachment Mechanisms" by Tiandong Wang and Panpan Zhang, arXiv:2101.04611v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The mean and standard deviation of the total rewards and harvesting decision epochs under the RL with MR policy starting with different prior information obtained from $J_0=3$ data points.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|lcccc|}
\hline
$k$ & $\widehat{\rho}^c(\pi)$ & $\widehat{\mbox{SD}}^c(\pi)$ & Avg. $T$ & Std. dev. $T$ \\ \hline
$0.25$ & 122.60 & 221.07 & 5.45 & 1.05 \\
$0.5$ & 147.57 & 166.15 & 5.45 & 0.77 \\
 $1$ & 159.16 & 128.76 & 5.42 & 0.82 \\
$2$ & 127.22 & 69.10 & 4.69 & 0.90 \\
$4$ & 34.86 & 23.42 & 1.98 & 0.98 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Bo Wang
Wei Xie
Tugce Martagan
Alp Akcay
Bram van Ravenstein

"Biomanufacturing Harvest Optimization with Small Data" by Bo Wang, Wei Xie, Tugce Martagan, Alp Akcay, and Bram van Ravenstein, arXiv:2101.03735v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications" by Mario Manzo, arXiv:1912.09972v1, 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{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}{llll}\toprule
 \textbf{Disturbance generator set}&&\textbf{Average Daily Cost (€)} & \textbf{$\#$ of Constraint Violations} \\\midrule
 Normal($[-1,1]\times[-1,1]$) & DFMPC & 457.3 & 0 \\
 & NoMPC & 395.2 & 378 \\
 &\textbf{CTMPC1} &\textbf{421.5} & \textbf{0} \\
 &CTMPC1.5 &435.4 & 0 \\
 &CTMPC2 &449.3 & 0 \\ \hline
 Challenging($[-1,-0.5]\times[0.5,1]$) & \textbf{DFMPC} & \textbf{705.2} & \textbf{17} \\
 & NoMPC & 769.3 & 2214 \\
 &CTMPC1 &769.0 & 1769 \\
 &CTMPC1.5 &766.9 & 1723 \\
 &CTMPC2 &763.4 & 1614 \\ \hline
 Extreme($\{-1\}\times\{1\}$) & DFMPC & 755.3 & 2068 \\
 & NoMPC & 764.9 & 2298 \\
 &CTMPC1 &759.4 & 2240\\
 &CTMPC1.5 &756.5 & 2226 \\
 &CTMPC2 & 753.3 & 2230
 \\\bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mirhan Ürkmez
Carsten Kallesøe
Jan Dimon Bendtsen
Eric C. Kerrigan
John Leth

"A Robust Predictive Control Method for Pump Scheduling in Water Distribution Networks" by Mirhan Ürkmez, Carsten Kallesøe, Jan Dimon Bendtsen, Eric C. Kerrigan, and John Leth, arXiv:2507.18492v1, 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{Hyper-parameters for pre-training 2B, 8B and 137B models. All models were trained with 256K tokens per batch.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
 \toprule
 Parameters & Layers & Units & Heads & pre-train steps & pre-train chips & pre-train time (days) & fine-tune chips & fine-tune time (hours) \\
 \midrule
 2B & 10 & 2560 & 40 & 501k & 64 & 1.5 & 16 & 3 \\
 8B & 16 & 4096 & 64 & 521k & 64 & 23 & 16 & 6 \\
 137B & 64 & 8192 & 128 & 3M & 1024 & 57.7 & 64 & 36 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Romal Thoppilan
Daniel De Freitas
Jamie Hall
Noam Shazeer
Apoorv Kulshreshtha
Heng-Tze Cheng
Alicia Jin
Taylor Bos
Leslie Baker
Yu Du
YaGuang Li
Hongrae Lee
Huaixiu Steven Zheng
Amin Ghafouri
Marcelo Menegali
Yanping Huang
Maxim Krikun
Dmitry Lepikhin
James Qin
Dehao Chen
Yuanzhong Xu
Zhifeng Chen
Adam Roberts
Maarten Bosma
Vincent Zhao
Yanqi Zhou
Chung-Ching Chang
Igor Krivokon
Will Rusch
Marc Pickett
Pranesh Srinivasan
Laichee Man
Kathleen Meier-Hellstern
Meredith Ringel Morris
Tulsee Doshi
Renelito Delos Santos
Toju Duke
Johnny Soraker
Ben Zevenbergen
Vinodkumar Prabhakaran
Mark Diaz
Ben Hutchinson
Kristen Olson
Alejandra Molina
Erin Hoffman-John
Josh Lee
Lora Aroyo
Ravi Rajakumar
Alena Butryna
Matthew Lamm
Viktoriya Kuzmina
Joe Fenton
Aaron Cohen
Rachel Bernstein
Ray Kurzweil
Blaise Aguera-Arcas
Claire Cui
Marian Croak
Ed Chi
Quoc Le

"LaMDA: Language Models for Dialog Applications" by Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Vincent Zhao, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Pranesh Srinivasan, Laichee Man, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, and Quoc Le, arXiv:2201.08239v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Adaptive Rank-based Tests for High Dimensional Mean Problems" by Yu Zhang and Long Feng, arXiv:2401.00255v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

"Efficient Data-Driven Leverage Score Sampling Algorithm for the Minimum Volume Covering Ellipsoid Problem in Big Data" by Elizabeth Harris, Ali Eshragh, Bishnu Lamichhane, Jordan Shaw-Carmody, and Elizabeth Stojanovski, arXiv:2411.03617v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Causal graph learning comparison on different sparsity length $l_m$. The $SC_t$ is the computation time of the PCMCI skeleton structure learning.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|}
\hline
\textbf{$\mathbf{l_m}$} & \textbf{Compressed Data Size} & \textbf{$\mathbf{SC_t}$ (sec)}$\downarrow$ & \textbf{Link Pruning Adjustment} & \textbf{APRC}$\uparrow$ & \textbf{SHD}$\downarrow$ \\ \hline
\multirow{2}{*}{10} & \multirow{2}{*}{911} & \multirow{2}{*}{18.969} & False & - & - \\ \cline{4-6}
 & & & True & - & - \\ \hline
\multirow{2}{*}{15} & \multirow{2}{*}{1274} & \multirow{2}{*}{21.625} & False & 0.741 & 29 \\ \cline{4-6}
 & & & True & \textbf{0.748} & \textbf{12 } \\ \hline
\multirow{2}{*}{20} & \multirow{2}{*}{1629} & \multirow{2}{*}{30.219} & False & \textbf{0.731 } & 31 \\ \cline{4-6}
 & & & True & 0.723 & \textbf{14 } \\ \hline
\multirow{2}{*}{25} & \multirow{2}{*}{1974} & \multirow{2}{*}{30.703} & False & 0.692 & 35 \\ \cline{4-6}
 & & & True & \textbf{0.748 } & \textbf{12} \\ \hline
\multirow{2}{*}{30} & \multirow{2}{*}{2319} & \multirow{2}{*}{34.625} & False & 0.711 & 38 \\ \cline{4-6}
 & & & True & \textbf{0.777} & \textbf{11} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Mulugeta Weldezgina Asres
Christian Walter Omlin
The CMS-HCAL Collaboration

"Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data" by Mulugeta Weldezgina Asres, Christian Walter Omlin, and The CMS-HCAL Collaboration, arXiv:2412.11800v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Selected Training Parameters for the Classification Model}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llr}
\hline
\textbf{Parameter} & \textbf{Definition} & \textbf{Value} \\ \hline
Learning Rate - Step 1 & Learning rate to fine-tune classifcation layer & 10-3 \\
Learning Rate - Step 2 & Learning rate to fine-tune whole model & 10-4 \\
Early Stopping Criteria & Training epochs without improvement before stopping & 1 \\
Learning Rate Reduction Criteria & Training steps without improvement before reducing the learning rate by a factor of 0.1 & 5 \\ \hline
Maximum Sequence Length & Maximum input sequence lenght for DistilBERT & 512 \\
Batch Size & Batch size used for training and validation & 32 \\
Loss Funcion & \multicolumn{2}{l}{Model optimization based on minimizing: Sparse categorical cross-entropy loss} \\
Evaluation Metric & \multicolumn{2}{l}{Model performance evaluation metric: Accuracy} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, 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{Bribery in traditional and modern sectors in selected countries in Asia in 2012-2015.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
 \hline
 \hline
 {Country}& {Year} & {Traditional firms'} & {Modern firms'} & {$\frac{\tau_0}{\tau_1}$} & {Growth rate}\\
 & & {{bribe ($\tau_0$)} } & {bribe ($\tau_1$)} & & {in 2012-2015}\\
 \hline
{China} & {2012} & 5.2\% & 2.9\% & 1.8 & 7.5\%\\
{Pakistan} & {2013} & 6.1\% & 5.0\% & 1.2 & 4.2\%\\
{Philippines} & {2015} & 2.4\% & 2.4\% & 1.0 & 6.5\%\\
{Nepal} & {2013} & 5.3\% & 6.9\% & 0.8 & 4.3\%\\
{India} & {2014} & 2.4\% & 4.2\% & 0.6 & 6.5\%\\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Jafar M. Olimov
Yi-Chan Tsai
Hao-Yu Yang

"Heterogeneous Bribery, Technology Choice, and Capital Accumulation" by Jafar M. Olimov, Yi-Chan Tsai, and Hao-Yu Yang, arXiv:2507.13124v2, 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}{lcccc}
\toprule
\textbf{Acronym} & \textbf{Inference Algorithm} & \shortstack{\textbf{Beat} \\ \textbf{F-measure}} & \shortstack{\textbf{Beat} \\ \textbf{AML$_t$}} & \shortstack{\textbf{Downbeat} \\ \textbf{F-measure}} \\
\midrule
BP-HMM & Viterbi Algorithm & 0.718 & 0.722 & 0.44 \\
BP-AMPF & Auxiliary Mixed Particle Filter & 0.825 & 0.906 & 0.574 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Baseline meter tracking performance on $\mathrm{CMR}_f$ using Bar Pointer model}
\end{table}
\end{document}

eess

"Revisiting Meter Tracking in Carnatic Music using Deep Learning Approaches" by Satyajeet Prabhu, arXiv:2509.11241v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
\textbf{Data detection strategy} & \textbf{Computational complexity} \\
\hline
RML with NN search & $\mathcal{O}(N_{\mathrm{NN}}K \max\{{N_{\mathrm{NN}},M}\})$ \\
\hline
JD & $\mathcal{O}(L^K)$ \\
\hline
$N$-JD & $\mathcal{O}(N^K)$ \\
\hline
H-SUD & $\mathcal{O}(L)$ \\
\hline
E-SUD & $\mathcal{O}(L^K)$ \\
\hline
Genie-aided data detection & $\mathcal{O}(L)$ \\
\hline
\end{tabular}
\caption{Computational complexity.}
\end{table}
\end{document}

math

"Enhanced Uplink Data Detection for Massive MIMO with 1-Bit ADCs: Analysis and Joint Detection" by Amin Radbord, Italo Atzeni, and Antti Tolli, arXiv:2312.04183v2, 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{Noise Robustness Evaluation on GPRNet with two metrics.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\hline
& \multicolumn{2}{|c|}{CD distance} & \multicolumn{2}{|c|}{\emph{L1} distance} \\
\hline
\hline
Variance \& Noise density = 0.01 & \multicolumn{2}{|c|}{6.350} & \multicolumn{2}{|c|}{23.029} \\
Variance \& Noise density = 0.05 & \multicolumn{2}{|c|}{6.715} & \multicolumn{2}{|c|}{24.851} \\
Variance \& Noise density = 0.1 & \multicolumn{2}{|c|}{7.374} & \multicolumn{2}{|c|}{25.639} \\
Variance \& Noise density = 0.2 & \multicolumn{2}{|c|}{7.765} & \multicolumn{2}{|c|}{26.589} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jinglun Feng
Liang Yang
Ejup Hoxha
Diar Sanakov
Stanislav Sotnikov
Jizhong Xiao

"GPR-based Model Reconstruction System for Underground Utilities Using GPRNet" by Jinglun Feng, Liang Yang, Ejup Hoxha, Diar Sanakov, Stanislav Sotnikov, and Jizhong Xiao, arXiv:2011.02635v3, 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|l|l|l|l|}
\hline
\textbf{Number of clusters} & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline
\textbf{Silhouette score} & 0.5233 & 0.5730 & 0.5134 & 0.4960 & 0.4916 & 0.4873 & 0.4758 & 0.5030 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{The table shows the Silhouette scored for number of clusters $n=3,\cdots ,10$, for the cluster analysis using median of primary indices.}
\end{table}
\end{document}

math

"A Topological Data Analysis Framework for Quantifying Necrosis in Glioblastomas" by Francisco Tellez and Enrique Torres-Giese, arXiv:2503.17331v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Kernel compensation method for Maxwell eigenproblem with mimetic finite difference discretization" by Chenhao Jin, Yinhua Xia, and Yan Xu, arXiv:2503.19379v1, 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
 & \multicolumn{2}{c|}{\textbf{Mean error}} \\
 \hline
 \textbf{Experiment} & \textbf{CCMLE} & \textbf{Classical estimation} \\
 \hline
 (a) & 0.03 & 0.11 \\
 \hline
 (b) & 0.18 & 0.41 \\
 \hline
 (c) & 0.06 & 0.12 \\
 \hline
 (d) & 0.1 & 0.16 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Mean errors for experiments described in Figure . The mean error in (d) only considers time steps from $t=21$ to $t=70$, as there are no errors before the change at $t=20$.}
\end{table}
\end{document}

eess

"Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation" by Melkior Ornik and Ufuk Topcu, arXiv:1911.12976v2, 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{MSE of the CART student as a function of to the percentage of randomly added data. The original training set $D$ contains 3,866 observations and 1\% of the recently labeled dataset $D"$ corresponds to 2,368 observations.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\toprule
\% added data & Resulting MSE \\ \midrule
0 & 24.73 \\
2 & 13.66 \\
4 & 10.31 \\
\textbf{5} & \textbf{10.26} \\
6 & 10.48 \\
8 & 10.67 \\
10 & 11.40 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Maxime Biehler
Mohamed Guermazi
Célim Starck

"Using Knowledge Distillation to improve interpretable models in a retail banking context" by Maxime Biehler, Mohamed Guermazi, and Célim Starck, arXiv:2209.15496v1, 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{\textbf{Dependent variable: Log(structural diversity index)}. The regression models elucidate the correlations between the structural diversity index and network characteristics as illustrated in Figure : (i) the correlations with degree-heterogeneity, the Wiener index, and edge density are robust. (ii) The correlation with clustering fades when accounting for degree-heterogeneity, Wiener index, size and edge density. (iii) The correlation with size reverses when factoring in other network characteristics. All variables are standardized for a direct comparison between regression coefficients. See main text for interpretations of these results and Methods for technical details about the regressions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 \toprule
 \textbf{Variable} & \textbf{Model 1} & \textbf{Model 2} & \textbf{Model 3} & \textbf{Model 4} & \textbf{Model 5}\\
 \midrule
 Log(degree heterogeneity) & -0.67*** & & & & -0.56*** \\
 & (0.07) & & & & (0.06) \\
 Log(wiener index) & & 0.03 & & & 0.37*** \\
 & & (0.09) & & & (0.08) \\
 Log(edge density) & & & 0.65*** & & 1.38***\\
 & & & (0.07) & & (0.12) \\
 Log(clustering) & & & & 0.84*** & 0.02 \\
 & & & & (0.05) & (0.06) \\
 Log(size) & & & & & 0.70*** \\
 & & & & & (0.07) \\
 \midrule
 $\#$ observations & 119 & 119 & 119 & 119 & 119 \\
 $R^{2}$ & 0.45 & 0.0 & 0.43 & 0.72 & 0.93 \\
 Adjusted $R^{2}$ & 0.44 & 0.0 & 0.42 & 0.71 & 0.92 \\
 \bottomrule

 \multicolumn{3}{l}{\textsuperscript{***}$p<0.001$,
 \textsuperscript{**}$p<0.01$, \textsuperscript{*}$p<0.1$}
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"How networks shape diversity for better or worse" by Andrea Musso and Dirk Helbing, arXiv:2201.09254v4, 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{ASR (\%) of Targeted Soft-Label Attacks. $\sigma$ = 0.1.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|ccc}

		\toprule
		Dataset & Attacks %& & ASR (\%) &\\ &
		& No defense & $\theta_1$ = 0.5 & $\theta_2$ = 0.7 \\
		\midrule
		 & AZ & 100 & 8 & 8 \\
		 MNIST & GA & 100 & 0 & 0\\
		 & SimBA & 97 & 3 & 0 \\
		 \midrule
		 & AZ & 100 & 9 & 9 \\
		 CIFAR10 & GA & 98.76 & 0 & 0 \\
		 & SimBA & 97.14 & 23 & 15\\
		 \midrule \midrule
		 Dataset & Attacks %& &ASR (\%) &\\
		 & No Defense & $\theta_1$ = 0.1 & $\theta_2$ = 0.3 \\
		 \midrule
		 & AZ & 100 & 0 & 0 \\
		 IMAGENET & NES-QL & 100 & 69 & 8 \\
		 & GA & 100 & 0 & 0\\
		 & SimBA & 96.5 & 6 & 2 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Boundary Defense Against Black-box Adversarial Attacks" by Manjushree B. Aithal and Xiaohua Li, arXiv:2201.13444v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Medical Bill Shock and Imperfect Moral Hazard" by Alex Hoagland, David M. Anderson, and Ed Zhu, arXiv:2211.01116v2, 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{DiD Additional Economic Outcomes: Extensive Margin, Events in Over 60\% of Years}
\begin{tabular}{lccccccc}
\toprule
{}&{(1)}&{(2)}&{(3)}&{(4)}&{(5)}&{(6)}&{(7)} \tabularnewline
\midrule
\textbf{Panel A. Robustness}&Nighttime Light&GDP per Capita&Agr. Production&Agr. Productivity&Urban Built Up&Human Dev. Index& \tabularnewline
\midrule Ceasefire \( \times \) FARC&--0.008&--0.346&0.222&0.301&--0.010***&--0.001& \tabularnewline
&(0.021)&(0.402)&(0.138)&(0.493)&(0.002)&(0.001)& \tabularnewline
&&&&&&& \tabularnewline
Mean Dep. Var.&--0.314&6.364&1.693&6.361&0.025&0.716& \tabularnewline
Observations&2,827&2,822&2,827&2,827&2,827&2,056& \tabularnewline
&&&&&&& \tabularnewline
\midrule \textbf{Panel B. Agricultural Trade}&\# Products Supplied&\# Markets Supplied&\# Deliveries&Avr. Price&Total Value&Total Quantity& \tabularnewline
\midrule Ceasefire \( \times \) FARC&--0.510&0.452*&--11.841&0.026&675.025&3.488& \tabularnewline
&(0.782)&(0.252)&(15.600)&(0.167)&(653.091)&(2.241)& \tabularnewline
&&&&&&& \tabularnewline
Mean Dep. Var.&14.420&2.907&86.224&1.564&3887.102&6.771& \tabularnewline
Observations&1,799&1,799&1,799&1,605&1,799&1,799& \tabularnewline
&&&&&&& \tabularnewline
\midrule \textbf{Panel C. Tourism \& Migration}&\# Beds&Acc. Employees (pc)&Tourists (Total)&Tourists (pc)&Travel Expenses&Immigrated&Immigrated (20+) \tabularnewline
\midrule Ceasefire \( \times \) FARC&--3.882&1.643&1.853***&1.112***&--1.598&7.502&--12.977 \tabularnewline
&(31.143)&(1.413)&(0.497)&(0.372)&(33.771)&(203.868)&(139.428) \tabularnewline
&&&&&&& \tabularnewline
Mean Dep. Var.&129.053&4.990&4.319&1.791&142.614&460.222&251.603 \tabularnewline
Observations&2,827&2,827&1,028&1,028&1,028&514&514 \tabularnewline
&&&&&&& \tabularnewline
\midrule \textbf{Panel D. Health \& Education}&Birth Weight&Birth Size&Apgar Score (5 Min.)&Child Mortality&\# Off. Schools&\# Teachers&\# Students \tabularnewline
\midrule Ceasefire \( \times \) FARC&2.447&--0.178&--0.027&--0.001&--0.075*&--0.337**&--8.836* \tabularnewline
&(5.833)&(0.113)&(0.021)&(0.007)&(0.044)&(0.158)&(5.192) \tabularnewline
&&&&&&& \tabularnewline
Mean Dep. Var.&3193.500&50.772&9.588&0.050&3.212&11.345&231.496 \tabularnewline
Observations&2,827&2,827&2,827&2,827&2,827&2,827&2,827 \tabularnewline
&&&&&&& \tabularnewline
\midrule \textbf{Panel E. GEIH Survey Data}&Min. House Price&Asset Index&Weeks Job Search&Months Current Job&Months Emp. Past Year&Weekly Work Hours&Monthly Salary \tabularnewline
\midrule Ceasefire \( \times \) FARC&--3.316&0.025&--1.294&--3.173&--0.071&--0.360&0.218 \tabularnewline
&(4.624)&(0.025)&(1.080)&(2.451)&(0.057)&(0.469)&(0.215) \tabularnewline
&&&&&&& \tabularnewline
Mean Dep. Var.&32.994&--0.521&17.194&81.379&11.076&46.138&7.561 \tabularnewline
Observations&112,268&295,070&50,642&382,977&382,977&382,974&158,039 \tabularnewline
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Peace Dividends: The Economic Effects of Colombia's Peace Agreement" by Miguel Fajardo-Steinhäuser, arXiv:2301.01843v2, 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}{cc}
\hline
Symbol & Meaning \\
\hline
$k_{lev}$& Leverage Ratio.\\
\hline
$K(x)$ & Internal Ratings Based (IRB) Capital Requirement for the portfolio $x$. \\
\hline
$\delta$ & Opportunity cost of the capital (equity). \\
\hline
$X_{x}$ & Realizations for the loan portfolio $x$. \\
\hline
$\rho(x)$ & Risk measure for the portfolio $x$ (EL, UL etc.)\\
\hline
$\mu$ & Lower-bound on Expected Return \\
\hline
$\theta$ & Upper-bound on the risk. \\
\hline
\end{tabular}
\end{adjustbox}
\caption{List of symbols used in the model}
\end{table}
\end{document}

q-fin

Deb Narayan Barik
Siddhartha P. Chakrabarty

"Does limited liability reduce leveraged risk?: The case of loan portfolio management" by Deb Narayan Barik and Siddhartha P. Chakrabarty, arXiv:2209.12636v1, 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{Sample rows from the pre-processed dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\toprule
\texttt{ID} & \texttt{HY} & $Y_{i,t}$ & $v_{i,t}$ & $\widehat{\mu}_{i,t}$ \\ \midrule
2460002101 & 2019H1 & 1209.00 & 1 & 1750.443 \\
2460002101 & 2019H2 & 13712.17 & 4 & 2011.496 \\
2460002101 & 2020H1 & 0 & 0 & - \\
2460002101 & 2020H2 & 0 & 0 & - \\
$\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jae Youn Ahn
Himchan Jeong
Mario V. Wüthrich

"An Observation-Driven State-Space Model for Claims Size Modeling" by Jae Youn Ahn, Himchan Jeong, and Mario V. Wüthrich, arXiv:2412.21099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Melanie F. Pradier
Javier Zazo
Sonali Parbhoo
Roy H. Perlis
Maurizio Zazzi
Finale Doshi-Velez

"Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible" by Melanie F. Pradier, Javier Zazo, Sonali Parbhoo, Roy H. Perlis, Maurizio Zazzi, and Finale Doshi-Velez, arXiv:2101.05360v1, 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{Economic problems and role type selection in EconGym.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
Economic Problems & Government Type & Individual Type & Firm Type & Bank Type \\
\midrule
\textbf{Pension Issues} &&&&\\
Q1: How does delayed retirement affect the economy? & Pension & OLG & Perfect & Non-profit \\
Q2: Do personal pensions improve security? & Pension & OLG & Perfect & Non-profit \\
Q3: How does aging impact the macroeconomy? & Pension & OLG & Perfect & Non-profit \\
Q4: How to close pension funding gaps? & Pension & OLG & Perfect & Non-profit \\
Q5: How do pension systems vary across countries? & Pension & OLG & Perfect & Non-profit \\
\midrule
\textbf{Fiscal Policy Issues} &&&&\\
Q6: Can consumption taxes boost growth and fairness? & Fiscal & Ramsey & Perfect & Non-profit \\
Q7: How does inheritance tax affect wealth distribution? & Fiscal & OLG & Perfect & Non-profit \\
Q8: Does universal basic income enhance equity? & Fiscal & OLG & Perfect & Non-profit \\
Q9: How to design optimal tax policies? & Fiscal & Ramsey & Perfect & Non-profit \\
Q10: How does wealth tax impact wealth concentration? & Fiscal & Ramsey & Perfect & Non-profit \\
\midrule
\textbf{Monetary Policy Issues} &&&&\\
Q11: How effective are negative interest rates? & Central Bank & Ramsey & Perfect & Commercial \\
Q12: How to control inflation via monetary policy? & Central Bank & Ramsey & Perfect & Commercial \\
Q13: What are the long-term effects of quantitative easing? & Central Bank & Ramsey & Perfect & Commercial \\
Q14: How to set optimal bank rate spreads? & Central Bank & Ramsey & Perfect & Commercial \\
Q15: How to coordinate monetary and fiscal policies? & Central Bank + Fiscal & Ramsey & Perfect & Commercial \\
\midrule
\textbf{Market Competition Issues} &&&&\\
Q16: How does technology drive long-term growth? & Fiscal & OLG & Perfect & Non-profit \\
Q17: How do monopolies affect resources and welfare? & Fiscal & Ramsey & Monopoly & Non-profit \\
Q18: What is algorithmic collusion in oligopolies? & Fiscal & Ramsey & Oligopoly & Non-profit \\
Q19: How does product diversity affect welfare? & Fiscal & Ramsey & Monopolistic & Non-profit \\
\midrule
\textbf{Individual Decision Issues} &&&&\\
Q20: Does “996” work culture improve utility and efficiency? & * & OLG & Perfect & Non-profit \\
Q21: How does age affect consumption patterns? & * & OLG & Perfect & Commercial \\
Q22: How does asset allocation affect wealth? & * & Ramsey & Perfect & Commercial \\
Q23: How does work-life balance impact well-being? & * & OLG & Perfect & Non-profit \\
Q24: How does over-leveraged consumption impact the economy? & * & OLG & Perfect & Commercial \\
Q25: How do market structures shape consumer behavior? & * & Ramsey & All & Non-profit \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Qirui Mi
Qipeng Yang
Zijun Fan
Wentian Fan
Heyang Ma
Chengdong Ma
Siyu Xia
Bo An
Jun Wang
Haifeng Zhang

"EconGym: A Scalable AI Testbed with Diverse Economic Tasks" by Qirui Mi, Qipeng Yang, Zijun Fan, Wentian Fan, Heyang Ma, Chengdong Ma, Siyu Xia, Bo An, Jun Wang, and Haifeng Zhang, arXiv:2506.12110v1, 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}{lcccccccccccc}
\toprule
\textbf{Gen} & \textbf{1} & \textbf{2} & \textbf{3} & \textbf{4} & \textbf{5} & \textbf{6} & \textbf{7} & \textbf{8} & \textbf{9} & \textbf{10} & \textbf{11} & \textbf{12} \\
\midrule
$\boldsymbol{\alpha}$ & 1.33 & 1.25 & 1.22 & 0.61 & 2.7 & 1.83 & 0.61 & 1.79 & 1.16 & 0.65 & 0.67 & 1.08 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Dirichlet concentration parameters ($\boldsymbol{\alpha}$) for transfer share sampling to find Pareto-improving policies.}
\end{table}
\end{document}

q-fin

Felix Kübler
Simon Scheidegger
Oliver Surbek

"Using Machine Learning to Compute Constrained Optimal Carbon Tax Rules" by Felix Kübler, Simon Scheidegger, and Oliver Surbek, arXiv:2507.01704v1, 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 Marginal Effects for October 1, 2024}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
 \toprule
 & & Marginal Effects & \\
 Variable & Prob. In First Quartile & Gas Equivalent Cost & Cost in USD \\
 \midrule
 \midrule
 max fee per gas & 0.02\% & 53 & \$0.0032 \\
 to DEX & 17.61\% & 979 & \$0.0571 \\
 to MEV & 36.91\% & 2,051 & \$0.1196 \\
 from DEX & 31.18\% & 1,733 & \$0.1010 \\
 from MEV & -40.54\% & 2,253 & \$0.1314 \\
 front run & 82.33\% & 4,575 & \$0.2668 \\
 back run & 78.05\% & 4,337 & \$0.2529 \\

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

q-fin

"The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering" by Bruce Mizrach and Nathaniel Yoshida, arXiv:2508.04003v1, 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{Margins and print area specifications.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
\rule[-1ex]{0pt}{3.5ex} Margin & A4 & Letter \\
\hline
\rule[-1ex]{0pt}{3.5ex} Top margin & 2.54 cm & 1.0 in. \\
\hline
\rule[-1ex]{0pt}{3.5ex} Bottom margin & 4.94 cm & 1.25 in. \\
\hline
\rule[-1ex]{0pt}{3.5ex} Left, right margin & 1.925 cm & .875 in. \\
\hline
\rule[-1ex]{0pt}{3.5ex} Printable area & 17.15 x 22.23 cm & 6.75 x 8.75 in. \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Han Liu
Kathryn L. Holloway
Dario J. Englot
Benoit M. Dawant

"A Multi-rater Comparative Study of Automatic Target Localization Methods for Epilepsy Deep Brain Stimulation Procedures" by Han Liu, Kathryn L. Holloway, Dario J. Englot, and Benoit M. Dawant, arXiv:2201.11002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Yixin Zhang
Nicholas Konz
Kevin Kramer
Maciej A. Mazurowski

"Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects" by Yixin Zhang, Nicholas Konz, Kevin Kramer, and Maciej A. Mazurowski, arXiv:2412.04243v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cccc|}
\hline
\bf{coefficient} & \bf{estimate} & \bf{Student} $p$-\bf{value} & \bf{stderr} \\
\hline
$\alpha$ & 0.023893 & 51.6\% & 0.037\\
$\beta$ & 0.008608 & 0.3\% & 0.003\\
$\gamma$ & 0.190133 & 0.2\% & 0.060\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Regression results for~. Coefficients $\beta$ and $\gamma$ are statistically significant. Innovations $U$ are IID but not Gaussian.}
\end{table}
\end{document}

q-fin

Andrey Sarantsev
Angel Piotrowski
Ian Anderson

"A Time Series Model for Three Asset Classes used in Financial Simulator" by Andrey Sarantsev, Angel Piotrowski, and Ian Anderson, arXiv:2508.06010v1, 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|}
\hline
Parameter & Value \\ \hline
$\eta$ & 0.0003 \\ \hline
MaxIters & 500 \\ \hline
FPIters & 10 \\ \hline
SQIters & 10 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Parameters used when performing the maximum likelihood estimation.}
\end{table}
\end{document}

cs

Matthew Werenski
Ruijie Jiang
Abiy Tasissa
Shuchin Aeron
James M. Murphy

"Measure Estimation in the Barycentric Coding Model" by Matthew Werenski, Ruijie Jiang, Abiy Tasissa, Shuchin Aeron, and James M. Murphy, arXiv:2201.12195v2, 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}{cccccc}
Consistency & (0.4,0.5] & (0.5,0.6] & (0.6,0.7] & (0.7,0.8] & (0.8,1] \\ \hline
Rating & 1.4 & 1.24 & 1.20 & 1.16 & 1.04
\end{tabular}
\end{adjustbox}
\caption{Average audit rating vs. average F1 consistency score on our Kinetics-GEBD Val set.}
\end{table}
\end{document}

cs

Mike Zheng Shou
Stan Weixian Lei
Weiyao Wang
Deepti Ghadiyaram
Matt Feiszli

"Generic Event Boundary Detection: A Benchmark for Event Segmentation" by Mike Zheng Shou, Stan Weixian Lei, Weiyao Wang, Deepti Ghadiyaram, and Matt Feiszli, arXiv:2101.10511v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Yu Shao
Jiawen Bai
Yingze Hou
Xia'an Zhou
Zhanhao Pan

"A Novel Methodology in Credit Spread Prediction Based on Ensemble Learning and Feature Selection" by Yu Shao, Jiawen Bai, Yingze Hou, Xia'an Zhou, and Zhanhao Pan, arXiv:2412.09769v1, 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{Japan:Performance Comparison of GMV Portfolios: Quantile Loss and Joint Loss.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccc}
 \toprule
 Portfolio Size & & QLoss$_{1\%}$ & QLoss$_{5\%}$ & JointLoss$_{1\%}$ & JointLoss$_{5\%}$ \\
 \midrule
 \multirow{3}{*}{100} & DCC & \textbf{7.725} & \textbf{31.698} & \textbf{2.397} & \textbf{1.676} \\
 & Scalar BEKK & 10.992 & 33.243 & 3.107 & 2.014 \\
 & LSTM-BEKK & 12.328 & 35.190 & 3.333 & 2.147 \\
 \midrule
 \multirow{3}{*}{175} & DCC & 9.019 & 30.624 & \textbf{2.186} & 1.617 \\
 & Scalar BEKK & \textbf{7.414} & 25.543 & 2.346 & \textbf{1.544} \\
 & LSTM-BEKK & 7.865 & \textbf{25.180} & 2.491 & 1.605 \\
 \midrule
 \multirow{3}{*}{250} & DCC & 9.935 & 30.143 & 2.131 & 1.608 \\
 & Scalar BEKK & \textbf{6.485} & 25.712 & \textbf{2.083} & \textbf{1.466} \\
 & LSTM-BEKK & 7.341 & \textbf{24.794} & 2.168 & 1.488 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Haoyuan Wang
Chen Liu
Minh-Ngoc Tran
Chao Wang

"Deep Learning Enhanced Multivariate GARCH" by Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, and Chao Wang, arXiv:2506.02796v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ List of Variables Used in the Analysis.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \hline
Symbol& Description \\
 \hline
 $\Omega$& similarity region \\
 $n$& URLLC modulation order\\
 $m$& eMBB modulation order\\
 $L$& BS downlink frequency resources \\
 $l$& average URLLC traffic \\
 $\gamma_{\rm e}$& eMBB Signal to Noise Ratio \\
 $\gamma_{\rm u}$& URLLC Signal to Noise Ratio \\
 $L_m$& eMBB frequency resources with modulation order $m$ \\
 $l_{n,m}$& {punctured} eMBB symbols of modulation order $m$ \\
 &by URLLC traffic of modulation order $n$\\
 $\mathcal{L}_{n,m}$& effectively {punctured} eMBB symbols of modulation order $m$ \\
 &by URLLC traffic of modulation order $n$\\
 $\overline{\mathcal{L}}_{n,m}$& non-effectively {punctured} eMBB symbols of modulation order $m$\\
 &by URLLC traffic of modulation order $n$\\
 $p_m$& the probability of encoding eMBB with modulation order $m$ \\
 $P(.)$& eMBB traffic symbol SER \\
 $\mathcal{P}(.)$& URLLC traffic symbol SER \\
 $\zeta$& URLLC block size \\
 $U_{n,m}(.)$& average similar symbols\\
 ${K}$& similarity search space\\
 $s_u$& URLLC symbol \\
 $s_e$& eMBB symbol \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mohammed AL-Mekhlafi
Mohaned Chraiti
Mohamed Amine Arfaoui
Chadi Assi
Ali Ghrayeb
Amira Alloum

"A Downlink Puncturing Scheme for Simultaneous Transmission of URLLC and eMBB Traffic by Exploiting Data Similarity" by Mohammed AL-Mekhlafi, Mohaned Chraiti, Mohamed Amine Arfaoui, Chadi Assi, Ali Ghrayeb, and Amira Alloum, arXiv:2012.13923v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{siunitx}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 \textbf{Method} & $\text{F1}_\text{any}$ & $\text{P}_\text{any}$ & $\text{R}_\text{any}$ \\
 \midrule
 Spectral Flux & $\SI{79.49}{\percent}$ & $\SI{78.53}{\percent}$ & $\SI{81.86}{\percent}$ \\
 Super Flux & $\SI{74.36}{\percent}$ & $\SI{77.04}{\percent}$ & $\SI{73.36}{\percent}$ \\
 CD-ODF & $\SI{79.32}{\percent}$ & $\SI{68.50}{\percent}$ & $\SI{98.15}{\percent}$ \\
 \midrule
 \textbf{Ours} & \textbf{$\SI{97.60}{\percent}$} & \textbf{$\SI{96.54}{\percent}$} & \textbf{$\SI{98.73}{\percent}$} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Strumming detection results on pickup audio.}
\end{table}
\end{document}

eess

Sebastian Murgul
Johannes Schimper
Michael Heizmann

"Joint Transcription of Acoustic Guitar Strumming Directions and Chords" by Sebastian Murgul, Johannes Schimper, and Michael Heizmann, arXiv:2508.07973v1, 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{}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 & \multicolumn{3}{l}{\textbf{2.5\% of capital retained/yr}} \\
 & \textbf{Deposits =} & \textbf{Deposits =} & \textbf{Deposits =} \\
\textbf{Period} & \textbf{capital} & \textbf{3 x capital} & \textbf{7$\times$ capital} \\ \hline
1397-1420 & 45.62\% & 27.19\% & 12.35\% \\
1420-1435 & 53.04\% & 31.61\% & 14.35\% \\
1435-1450 & 55.72\% & 33.21\% & 15.08\% \\
 & \multicolumn{3}{l}{\textbf{5\% of capital retained/yr}} \\
 & \textbf{Deposits =} & \textbf{Deposits =} & \textbf{Deposits =} \\
\textbf{Period} & \textbf{capital} & \textbf{3 x capital} & \textbf{7$\times$ capital} \\ \hline
1397-1420 & 46.25\% & 27.37\% & 12.42\% \\
1420-1435 & 53.50\% & 31.66\% & 14.37\% \\
1435-1450 & 55.72\% & 32.98\% & 14.96\% \\
 & \multicolumn{3}{l}{\textbf{10\% of capital retained/yr}} \\
 & \textbf{Deposits =} & \textbf{Deposits =} & \textbf{Deposits =} \\
\textbf{Period} & \textbf{capital} & \textbf{3 x capital} & \textbf{7$\times$ capital} \\ \hline
1397-1420 & 39.68\% & 23.14\% & 10.48\% \\
1420-1435 & 36.29\% & 21.17\% & 9.59\% \\
1435-1450 & 33.79\% & 19.71\% & 8.93\%
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Cancellation of principal in banking: Four radical ideas emerge from deep examination of double entry bookkeeping in banking" by Brian P. Hanley, arXiv:2010.10703v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\infty$) processes and applications" by Yingli Wang and Zhenyu Cui, arXiv:2503.18259v3, 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{Panel B: Full-sample performance (Jan 2010–Jun 2025).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrr}
 \toprule
 Metric & LTT & MKT & STT+LTT & STT & MKT+STT+LTT & MKT+STT & SGCTAT \\
 \midrule
 Cumulative Return (\%) & 259.1 & 246.7 & 267.0 & 105.6 & 313.1 & 308.9 & 55.6 \\
 Annual Return (\%) & 8.6 & 8.4 & 8.8 & 4.8 & 9.6 & 9.5 & 2.9 \\
 Volatility (\%) & 10.3 & 11.4 & 9.6 & 9.3 & 10.3 & 10.1 & 11.0 \\
 Sharpe Ratio & 0.70 & 0.61 & 0.76 & 0.37 & 0.80 & 0.80 & 0.14 \\
 Max Drawdown (\%) & 18.8 & 20.3 & 16.7 & 15.2 & 17.7 & 14.9 & 23.0 \\
 Return/Max-DD & 0.46 & 0.41 & 0.53 & 0.31 & 0.54 & 0.64 & 0.13 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Eric Benhamou
Jean-Jacques Ohana
Alban Etienne
Béatrice Guez
Ethan Setrouk
Thomas Jacquot

"Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach" by Eric Benhamou, Jean-Jacques Ohana, Alban Etienne, Béatrice Guez, Ethan Setrouk, and Thomas Jacquot, arXiv:2507.15876v1, 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{Material parameters (taken from table 4 in~).}%
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}\toprule
 & Mortar matrix & Aggregates\\ \midrule
	 Young's modulus & $E_{\mathrm{m}} = 30\,000\,\mathrm{MPa}$ & $E_{\mathrm{a}}=60\,000\,\mathrm{MPa}$ \\
	 Poisson ratio & $\nu_{\mathrm{m}} = 0.2$ & $\nu_{\mathrm{a}}=0.2$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Philipp Diercks
Karen Veroy
Annika Robens-Radermacher
Jörg F. Unger

"Multiscale modeling of linear elastic heterogeneous structures via localized model order reduction" by Philipp Diercks, Karen Veroy, Annika Robens-Radermacher, and Jörg F. Unger, arXiv:2201.10374v4, 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}{lccc}
\toprule
Method & Translations on x & Translations on y & Rotations \\ \hline
Random & $25.57\%_{\pm0.028}$ & $25.54\%_{\pm0.028}$ & $13.49\%_{\pm0.009}$ \\
CapsNet & $83.78\%_{\pm0.006}$ & $79.82\%_{\pm0.009}$ & $88.01\%_{\pm0.006}$ \\
Efficient-CapsNet & $89.69\%_{\pm0.005}$ & $87.28\%_{\pm0.008}$ & $88.75\%_{\pm0.005}$ \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Average percentage of variance captured by the first component of PCA performed on the output capsule vectors of the different transformations applied to test set images.}
\end{table}
\end{document}

cs

Vittorio Mazzia
Francesco Salvetti
Marcello Chiaberge

"Efficient-CapsNet: Capsule Network with Self-Attention Routing" by Vittorio Mazzia, Francesco Salvetti, and Marcello Chiaberge, arXiv:2101.12491v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Test collection statistics for the CLEF and HC4 newswire collections.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccccccc}
\toprule
\multirow{2}{*}{Collection} & HC4 & HC4 & CLEF & CLEF & CLEF & CLEF & CLEF\\
 & Chinese & Persian & French & German & Italian & Russian & Spanish \\ \midrule
\#documents & 646K & 486K & 129k & 294k & 157k & 16k & 454k \\
\#passages & 3.6M & 3.1M & 0.7M & 1.6M & 0.8M & 0.1M & 2.7M \\
\#queries & 50 & 50 & 200 & 200 & 200 & 62 & 160 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Suraj Nair
Eugene Yang
Dawn Lawrie
Kevin Duh
Paul McNamee
Kenton Murray
James Mayfield
Douglas W. Oard

"Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models" by Suraj Nair, Eugene Yang, Dawn Lawrie, Kevin Duh, Paul McNamee, Kenton Murray, James Mayfield, and Douglas W. Oard, arXiv:2201.08471v1, 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 preferential tie scores: the fraction of sales and volume (in USD) from a seller to their preferential buyer (columns 1 and 2), and from a buyer to their preferential seller (columns 3 and 4).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
 & \multicolumn{2}{c}{\textbf{Seller to buyer}} & \multicolumn{2}{c}{\textbf{Buyer to seller}} \\
 & \textit{Sales} & \textit{Volume} & \textit{Sales} & \textit{Volume} \\
SuperRare & 79\% & 83\% & 80\% & 85\% \\
Async Art & 89\% & 90\% & 96\% & 96\% \\
KnownOrigin & 89\% & 91\% & 90\% & 93\% \\
Foundation & 94\% & 95\% & 94\% & 96\% \\
Makersplace & 84\% & 87\% & 91\% & 93\% \\
Zora & 94\% & 94\% & 96\% & 97\% \\
\textbf{Overall} & \textbf{82\%} & \textbf{86\%} & \textbf{86\%} & \textbf{89\%}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Seller-buyer networks in NFT art are driven by preferential ties" by Giovanni Colavizza, arXiv:2210.04339v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Forecasting realized volatility in the stock market: a path-dependent perspective" by Xiangdong Liu, Sicheng Fu, and Shaopeng Hong, arXiv:2503.00851v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

A Benchmark Study on Calibration

Linwei Tao
Younan Zhu
Haolan Guo
Minjing Dong
Chang Xu

"A Benchmark Study on Calibration" by Linwei Tao, Younan Zhu, Haolan Guo, Minjing Dong, and Chang Xu, arXiv:2308.11838v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A PINN-enriched finite element method for linear elliptic problems" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, 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}{lcc|cc}
\toprule
\textbf{Model}
 & \multicolumn{2}{c|}{\textbf{Beat}}
 & \multicolumn{2}{c}{\textbf{Downbeat}} \\
\cmidrule(lr){2-3} \cmidrule(lr){4-5}
 & \textbf{F-measure} & \textbf{AMLt}
 & \textbf{F-measure} & \textbf{AMLt} \\
\midrule
\textit{Beat This!} & \textbf{89.1 ± 0.3} & 89.8 ± 0.4 & \textbf{78.3 ± 0.4} & 79.1 ± 0.6 \\
TCN + DBN & 88.5 & \textbf{93.1} & 67.2 & \textbf{83.2} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Comparison of TCN and Beat This! performance on the GTZAN dataset.}
\end{table}
\end{document}

eess

"Revisiting Meter Tracking in Carnatic Music using Deep Learning Approaches" by Satyajeet Prabhu, arXiv:2509.11241v1, 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{Overview of Asset4 ESG Scores}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrr}
\hline
\textbf{} & \textbf{Count} & \textbf{Average Score} \\ \hline
\textbf{Companies} & 3,343 & \\
\textbf{Companies} & 3,343 & \\
\textbf{Market Capitalization} & 3,309 & \\ \hline
\textbf{Scores 2018} & 2,842 & 35 \\
\textbf{Scores 2019} & 3,078 & 37 \\
\textbf{Scores 2020} & 3,075 & 38 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Similarity measures for the different XAI methods using the XGB model and the \textit{maximum} set of features.}
\begin{tabular}{ccccccc}
\toprule
 & MDI-XGB & MDA-XGB & SHAP-XGB & LIME-XGB & Guidelines & Experts \\
\cmidrule(lr){1-1}\cmidrule(lr){2-5}\cmidrule(lr){6-7}
MDI-XGB & \cellcolor[gray]
 {0.8}1.00 & 0.24 & 0.60 & 0.26 & 0.43 & 0.40 \\
MDA-XGB & 0.24 & \cellcolor[gray]
 {0.8}1.00 & 0.38 & 0.26 & 0.26 & 0.29 \\
SHAP-XGB & 0.60 & 0.38 & \cellcolor[gray]
 {0.8}1.00 & 0.35 & 0.40 & 0.40 \\
LIME-XGB & 0.26 & 0.26 & 0.35 & \cellcolor[gray]
 {0.8}1.00 & 0.19 & 0.18 \\
\midrule
Guidelines & 0.43 & 0.26 & 0.40 & 0.19 & \cellcolor[gray]
 {0.8}1.00 & 0.63 \\
Experts & 0.40 & 0.29 & 0.40 & 0.18 & 0.63 & \cellcolor[gray]
 {0.8}1.00 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

José Bobes-Bascarán
Eduardo Mosqueira-Rey
Ángel Fernández-Leal
Elena Hernández-Pereira
David Alonso-Ríos
Vicente Moret-Bonillo
Israel Figueirido-Arnoso
Yolanda Vidal-Ínsua

"Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach" by José Bobes-Bascarán, Eduardo Mosqueira-Rey, Ángel Fernández-Leal, Elena Hernández-Pereira, David Alonso-Ríos, Vicente Moret-Bonillo, Israel Figueirido-Arnoso, and Yolanda Vidal-Ínsua, arXiv:2403.19820v1, 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
 Type & Affine & Fine-scale& Difference\\
 \hline
 $\sigma=0.0$ & 0.76 & 0.83 & 0.070\\
 $\sigma=0.1$ & 0.72 & 0.80 & 0.076\\
 $\sigma=0.2$ & 0.62 & 0.71 & 0.086\\
 $\sigma=0.3$ & 0.51 & 0.60 & 0.091\\
 $\sigma=0.4$ & 0.41 & 0.50 & 0.085\\
 $\sigma=0.5$ & 0.33 & 0.40 & 0.070\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"A Structurally Coherent Spatial Phase Estimate" by Brian Knight and Naoki Saito, arXiv:2412.08070v2, 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}{lllll}
\toprule
\textbf{Parameters} & \textbf{Description} & \textbf{Age group} & \textbf{Value} &\textbf{Ref.} \\ \midrule
$r$ & relative infectiousness of asymptomatic individuals & - & 50\% & $\dagger$ \\ \midrule
$k$ & proportion of pre-symptomatic transmission & - & 50\% & \\ \midrule
$\epsilon$ & incubation period & - & 5 days & \\ \midrule
$p_a$ & symptomatic probability & 0-9 & 0.181 & \\
 & & 10-19 & 0.181 & \\
 & & 20-29 & 0.225 & \\
 & & 30-39 & 0.225 & \\
 & & 40-49 & 0.300 & \\
 & & 50-59 & 0.300 & \\
 & & 60-69 & 0.360 & \\
 & & 70-79 & 0.360 & \\
 & & $\geq 80$ & 0.646 & \\ \midrule
$\chi$ & susceptibility & 0-18 & 0.56 & \\
& & $\geq 19$ & 1.00 & \\ \midrule
$\gamma$ & pre-symptomatic period & - & 2 days & \\ \midrule
$\mu$ & time to isolation & - & 2.5 days & \\ \midrule
$\delta$ & days from isolation to death & - & 12.5 & \\ \midrule
IFR & infection fatality ratio & 0-9 & 0.00161\% & \ddag \\
 & & 10-19 & 0.00695\% & \\
 & & 20-29 & 0.0309\% & \\
 & & 30-39 & 0.0844\% & \\
 & & 40-49 & 0.161\% & \\
 & & 50-59 & 0.595\% & \\
 & & 60-69 & 1.93\% & \\
 & & 70-79 & 4.28\% & \\
 & & $\geq 80$ & 7.80\% & \\ \midrule
$T_n$ & Notification of death & - & 7 days & \\ \midrule
$\theta$ & outdoor transmissibility & - & 0.05 & \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Baseline set of parameters. $\dagger$: assumed ;$*$: calibrated to the generation time $T_g$; $\ddag$ Only applied to symptomatic individuals. As such, a correction factor of 1/p is applied to all age groups.}
\end{table}
\end{document}

q-fin

Marco Pangallo
Alberto Aleta
R. Maria del Rio Chanona
Anton Pichler
David Martín-Corral
Matteo Chinazzi
François Lafond
Marco Ajelli
Esteban Moro
Yamir Moreno
Alessandro Vespignani
J. Doyne Farmer

"The unequal effects of the health-economy tradeoff during the COVID-19 pandemic" by Marco Pangallo, Alberto Aleta, R. Maria del Rio Chanona, Anton Pichler, David Martín-Corral, Matteo Chinazzi, François Lafond, Marco Ajelli, Esteban Moro, Yamir Moreno, Alessandro Vespignani, and J. Doyne Farmer, arXiv:2212.03567v1, 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}{lrrrrr} \hline
 Number of runs, $n$ & 10 & 20 & 30 & 40 & 50 \\ \hline
 $\hat{U}_G(\bar{X}_B)/\hat{U}_G(\bar{X}_G)$ & 0.037 & 0.061 & 0.131 & 0.159 & 0.175 \\
 $\hat{U}_B(\bar{X}_G)/\hat{U}_B(\bar{X}_B)$ & 0.270 & 0.510 & 0.600 & 0.650 & 0.680 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Estimated expected utility efficiency of the Gibbs and Bayesian optimal designs under the alternative utility.}
\end{table}
\end{document}

stat

Gibbs optimal design of experiments

Antony M. Overstall
Jacinta Holloway-Brown
James M. McGree

"Gibbs optimal design of experiments" by Antony M. Overstall, Jacinta Holloway-Brown, and James M. McGree, arXiv:2310.17440v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Katherine Tsai
Mladen Kolar
Sanmi Koyejo

"High-Dimensional Markov-switching Ordinary Differential Processes" by Katherine Tsai, Mladen Kolar, and Sanmi Koyejo, arXiv:2501.00087v1, 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{Experimental results on dev set with Whisper large v3 Turbo as encoder, EuroLLM 1.7B Instruct as LLM tuned with different LoRA parameters.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\hline
\textbf{Experimental Setting} & \textbf{Dev Set (WER)} \\ \hline
LoRA ($\alpha = 8, r = 8$) & 21.14\% \\
LoRA ($\alpha = 8, r = 16$) & 17.80\% \\
LoRA ($\alpha = 16, r = 16$) & 20.04\% \\
LoRA ($\alpha = 32, r = 16$) & 15.88\% \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Lorenzo Concina
Jordi Luque
Alessio Brutti
Marco Matassoni
Yuchen Zhang

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|}
 \hline

 Solar Panels & $SD^q$ &$SD^p$ \\ \hline
 $P_1$ &$\langle (0.619,0.380);0.717\rangle$&$\langle (0.619,0.380);0.741\rangle$\\
 $P_2$ &$\langle (0.601,0.399);0.596\rangle$&$\langle (0.601,0.399);0.620\rangle$\\
 $P_3$ &$\langle (0.553,0.447);0.420\rangle$&$\langle (0.553,0.447);0.429\rangle$\\
 $P_4$ &$\langle (0.593,0.407);0.651\rangle$&$\langle (0.593,0.407);0.668\rangle$\\
 $P_5$ &$\langle (0.553,0.447);0.695\rangle$&$\langle (0.553,0.447);0.702\rangle$\\ \hline
 \end{tabular}
\end{adjustbox}
\caption{The results of significance degrees $SD_i$}
\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.


q-fin

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cc|cc|cc}
			\toprule
			 & \multicolumn{2}{c}{\textbf{2018-21}} & \multicolumn{2}{c}{\textbf{2021-23}} & \multicolumn{2}{c}{\textbf{2018-23}}\\
			\cmidrule(rl){2-3} \cmidrule(rl){4-5} \cmidrule(rl){6-7}
			 & \textbf{3-OU} & \textbf{4-OU}& \textbf{3-OU} & \textbf{4-OU} & \textbf{3-OU} & \textbf{4-OU}\\
			\midrule
			Number of Iterations & 10 Mio. & 10 Mio. & 10 Mio. & 10 Mio. & 10 Mio. & 10 Mio.\\
			Burn-In-Period & 9 Mio. & 9 Mio. & 9 Mio. & 9 Mio. & 9 Mio. & 9 Mio.\\
			Number of Loops for Birth-Death step & 5 & 5 & 5 & 1 & 5 & 5\\
			Number of Loops for Multiplicative Update & 5 & 5 & 5 & 1 & 5 & 5\\
			Number of Loops for Latent OU-Variable & - & 30 & - & 200 & - & 30\\
			Number of New Increments per Loop & - & 100 & - & 1 & - & 1\\
			Number of Permutations Increments per Loop & - & 100 & - & 2 & - & 2\\
			Runtime (days) & 3.9958 & 3.3188 & 4.0901 & 5.4426 & 9.6146 & 8.2958\\
			\bottomrule
		\end{tabular}
\caption{Information about the updates of the hidden variables.}
\end{table}
\end{document}

q-fin

Christian Laudagé
Florian Aichinger
Sascha Desmettre

"A Comparative Study of Factor Models for Different Periods of the Electricity Spot Price Market" by Christian Laudagé, Florian Aichinger, and Sascha Desmettre, arXiv:2306.07731v3, 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{Diarization results for multichannel LibriCSS evaluation set. Late fusion using DL achieved better performance compared to early fusion based on dereverberation and beamforming.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\textbf{Method} & \textbf{MS} & \textbf{FA} & \textbf{Conf.} & \textbf{DER} \\ \midrule
7-channel avg. & \textbf{2.58} & 0.96 & 5.86 & 9.40 \\
7-channel best & 2.59 & 0.99 & 5.53 & 9.11 \\
\midrule
WPE + Beamforming & 2.91 & 0.96 & 5.86 & 9.33 \\
DL & 3.60 & \textbf{0.66} & \textbf{4.76} & \textbf{9.02} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Desh Raj
Leibny Paola Garcia-Perera
Zili Huang
Shinji Watanabe
Daniel Povey
Andreas Stolcke
Sanjeev Khudanpur

"DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs" by Desh Raj, Leibny Paola Garcia-Perera, Zili Huang, Shinji Watanabe, Daniel Povey, Andreas Stolcke, and Sanjeev Khudanpur, arXiv:2011.01997v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems" by Grzegorz Filcek and Janusz Miroforidis, arXiv:2401.00292v1, 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{Results on the ETH-80 dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
\textbf{Method} & \textbf{Accuracy}\\
\hline
\textbf{BoAW} & \textbf{89.29}\%\\
\hline
\textbf{ARSRG}$emb$ & 89.26\%\\
\hline
BoW & 58.83\%\\
\hline
{gdFil}& 47.59\%\\
\hline
{APGM} & 84.39\%\\
\hline
{VEAM} & 82.68\%\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications" by Mario Manzo, arXiv:1912.09972v1, 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{Scaling Factor} & \textbf{Time Step} & \textbf{Total No. of Mosquitoes Released} & \textbf{Initial Fraction of Infection $p_{thres}$} \\ \hline
$\Delta t$ & 0.017 & 1,072,640 & 0.357 \\ \hline
$\Delta t/2$ & 0.008 & 1,047,040 & 0.349 \\ \hline
$\Delta t/4$ & 0.004 & 1,032,140 & 0.344 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Daniela Florez
Ricardo Cortez
James M. Hyman
Zhuolin Qu

"Improving Wolbachia-Based Control Programs in Urban Settings: Insights from Spatial Modeling" by Daniela Florez, Ricardo Cortez, James M. Hyman, and Zhuolin Qu, arXiv:2503.12262v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 Model & Best hyperparameter settings \\
 \midrule
 LSTM & batch size=4, dropout=0.2, hidden layer=64, layers=8 \\
 Transformer (V) & batch size=4, dropout=0.2, heads=4, layers=2\\
 Synthesizer (R) & batch size=4, dropout=0.2, heads=4, layers=4\\
 Synthesizer (FR) & batch size=4, dropout=0.2, heads=4, layers=8\\
 Synthesizer (D) & batch size=4, dropout=0.2, heads=8, layers=4\\
 Synthesizer (FD) & batch size=4, dropout=0.2, heads=4, layers=4\\
 Synthesizer (MD) & batch size=4, dropout=0.1, heads=2, layers=4\\
 Synthesizer (MR) & batch size=4, dropout=0.1, heads=8, layers=2\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{The best hyperparameters based on the validation set, for the different Transformer models. We use R for random, F for factorised, M for mixed, D for dense, and V for Vanilla models.}
\end{table}
\end{document}

q-fin

"Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models" by Dorien Herremans and Kah Wee Low, arXiv:2211.08281v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.