\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Kolmogorov-Smirnov scores for the distribution of $p$-values for all stations on training and test sets for six models.}%
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}%
\toprule
& Poisson & MEP & SEP & SMEP & SpMEP & GB-MEP \\
\midrule
Training set & $0.2186$ & $0.1610$ & $0.0751$ & $0.0326$ & $0.0734$ & $0.0246$ \\
Test set & $0.2217$ & $0.1584$ & $0.0752$ & $0.0350$ & $0.0738$ & $0.0264$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Francesco Sanna Passino
Yining Che
Carlos Cardoso Correia Perello

"Graph-based mutually exciting point processes for modelling event times in docked bike-sharing systems" by Francesco Sanna Passino, Yining Che, and Carlos Cardoso Correia Perello, arXiv:2311.00595v3, 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{Ablation studies}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 \hline
 CA-Module & AE-Module & center loss & accuracy(\%) \\
\hline
\hline
 $\checkmark$ & & & $86.8$ \\
 & $\checkmark$ & & $86.5$ \\
 $\checkmark$ & & $\checkmark$ & $87.9$ \\
 & $\checkmark$ & $\checkmark$ & $87.5$ \\
 $\checkmark$ & $\checkmark$ & $\checkmark$ & $88.3$ \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Tian Zhang
Dongliang Chang
Zhanyu Ma
Jun Guo

"Progressive Co-Attention Network for Fine-grained Visual Classification" by Tian Zhang, Dongliang Chang, Zhanyu Ma, and Jun Guo, arXiv:2101.08527v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Training time of deep learning-based methods (h)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\toprule
\multicolumn{2}{c}{Methods} & GRU & LSTM & CNN & FNN & Ours \\ \midrule
\multirow{3}{*}{Batch Sizes} & 1 & 5.53 & 5.69 & 3.97 & 4.15 & 10.52 \\
 & 4 & 3.81 & 3.99 & 3.11 & 3.67 & 9.21 \\
 & 8 & 3.67 & 3.74 & 2.87 & 3.25 & 8.35 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Shuhao Ma
Jie Zhang
Chaoyang Shi
Pei Di
Ian D. Robertson
Zhi-Qiang Zhang

"Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals" by Shuhao Ma, Jie Zhang, Chaoyang Shi, Pei Di, Ian D. Robertson, and Zhi-Qiang Zhang, arXiv:2412.04213v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|cc}
 & $\lambda_s>\lambda_u$ & $\lambda_s<\lambda_u$\\
 \hline
$\rho<0$ & Type I (stable) & Type II (mixed) \\
$\rho>0$ & Type III (mixed) & Type IV (unstable)
 \end{tabular}
\caption{Cases of the planar non-central SNICeroclinic loop bifurcation where $\rho$ is the non-central eigenvalue of the saddle-node and $-\lambda_s$, $\lambda_u$ are the eigenvalues of the saddle. In this paper we concentrate on the stable case Type I and the unstable case Type IV which corresponds to time-reversal of Type I.}
\end{table}
\end{document}

math

Kateryna Nechyporenko
Peter Ashwin
Krasimira Tsaneva-Atanasova

"A Novel Route to Oscillations via non-central SNICeroclinic Bifurcation: unfolding the separatrix loop between a saddle-node and a saddle" by Kateryna Nechyporenko, Peter Ashwin, and Krasimira Tsaneva-Atanasova, arXiv:2412.12298v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
 \hline
 Policy & Vehicle & Contract & & \multicolumn{6}{|c|}{Scope Variables} \\
 $i$ & $j$ & $t$ & $n_{i,j,t}$ & $n_{i,j,t-1}$ & $n_{i,j,t-2}$ & $n_{i,j,t-3}$ & $n_{i,\bullet,t-1}$ & $n_{i,\bullet,t-2}$ & $n_{i,\bullet,t-3}$ \\
 \hline
 1 & 1 & 1 & 0 & . & . & . & . & . & . \\
 1 & 1 & 2 & 2 & 0 & . & 0 & 0 & . & . \\
 1 & 1 & 3 & 1 & 2 & 0 & 2 & 4 & 0 & . \\
 1 & 1 & 4 & 0 & 1 & 2 & 0 & 1 & 4 & 0 \\
 1 & 2 & 2 & 2 & . & . & 0 & 0 & . & . \\
 1 & 2 & 3 & 0 & 2 & . & . & 4 & 0 & . \\
 2 & 1 & 1 & 0 & . & . & . & . & . & . \\
 3 & 1 & 1 & 0 & . & . & . & . & . & . \\
 3 & 2 & 1 & 1 & 0 & . & . & 1 & 0 & . \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Illustration of scope variables}
\end{table}
\end{document}

stat

"Bonus-Malus scale premiums for tweedie's compound poisson models" by Jean-Philippe Boucher and Raïssa Coulibaly, arXiv:2311.03222v1, 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{Generalized Hurst Exponents for Nifty 50 P/E Ratio}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
Order $q$ & Generalized Hurst Exponent $H(q)$ \\
\hline
1 & 0.5573 \\
2 & 0.5351 \\
3 & 0.4899 \\
4 & 0.4300 \\
5 & 0.3768 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Multi Scale Analysis of Nifty 50 Return Characteristics Valuation Dynamics and Market Complexity 1990 to 2024" by Chandradew Sharma, arXiv:2509.00697v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"MATES: Multi-view Aggregated Two-Sample Test" by Zexi Cai, Wenbo Fei, and Doudou Zhou, arXiv:2412.16684v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Proximity, Similarity, and Friendship Formation: Theory and Evidence" by A. Arda Gitmez and Román Andrés Zárate, arXiv:2210.06611v1, 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{The performance of unsupervised results (BSCA ) processed by our refinement module.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 Dataset & Metric & BSCA & BSCA w/ ref. & ours w/ ref. \\\hline
 \multirow{2}{*}{ECSSD} & $F_{\beta}\uparrow$ & 0.705 & 0.756 & \textbf{0.858} \\
 \cline{2-5}
 ~ & MAE$\downarrow$ & 0.183 & 0.140 & \textbf{0.108} \\\hline
 \multirow{2}{*}{DUT-O} & $F_{\beta}\uparrow$ & 0.500 & 0.618 & \textbf{0.778} \\
 \cline{2-5}
 ~ & MAE$\downarrow$ & 0.196 & 0.134 & \textbf{0.083} \\\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xiaoyang Zheng
Xin Tan
Jie Zhou
Lizhuang Ma
Rynson W. H. Lau

"Weakly-Supervised Saliency Detection via Salient Object Subitizing" by Xiaoyang Zheng, Xin Tan, Jie Zhou, Lizhuang Ma, and Rynson W. H. Lau, arXiv:2101.00932v1, 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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\toprule
Parameter & AVG & SAC \\
\midrule
Replay Buffer Size & 1 & 1 \\
\midrule
Minibatch Size & 1 & 1 \\
\midrule
Discount factor $(\gamma)$ & 0.95 & 0.99 \\
\midrule
Actor Learning Rate & $3 \times 10^{-4}$ & $3 \times 10^{-4}$ \\
\midrule
Critic Learning Rate & $0.00087$ & $3 \times 10^{-4}$ \\
\midrule
Update Actor Every & 1 & 1 \\
\midrule
Update Critic Every & 1 & 1 \\
\midrule
Update Critic Target Every & N/A & 1 \\
\midrule
Target Smoothing Coefficient $(\tau)$ & N/A & 0.005 \\
\midrule
Target Entropy & N/A & $-\vert \mathcal{A} \vert$ \\
\midrule
Entropy coefficient ($\eta$) & 0.05 & Learnable parameter \\
\midrule
Optimizer & Adam & Adam \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Default parameters for Robot Tasks.}
\end{table}
\end{document}

eess

Gautham Vasan
Mohamed Elsayed
Alireza Azimi
Jiamin He
Fahim Shariar
Colin Bellinger
Martha White
A. Rupam Mahmood

"Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers" by Gautham Vasan, Mohamed Elsayed, Alireza Azimi, Jiamin He, Fahim Shariar, Colin Bellinger, Martha White, and A. Rupam Mahmood, arXiv:2411.15370v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
\toprule
& Full Specification & Repeated Services \\
\midrule
Post Service & 0.218*** & 0.268*** \\
& (0.0032) & (0.0058) \\
Post Bill & -0.109*** & -0.083*** \\
& (0.0030) & (0.0056) \\
\midrule
2$^\text{nd}$ Service * Post Service & & -0.176*** \\
& &(0.0070) \\
2$^\text{nd}$ Service * Post Bill & & 0.118*** \\
& &(0.0071) \\
\midrule
$\overline{\text{spend}_{it}}$ & \$120.49 &\$135.78 \\
Observations & 61,860,735 & 29,344,385 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{ Reduced-Form Evidence of Learning}
\end{table}
\end{document}

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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
\textbf{Project Site - Capacity (MW)} & \textbf{Payback period (Months)} \\ \hline
RI - 30 & 6 \\ \hline
MD - 252 & 3 \\ \hline
MA - 804 & 2 \\ \hline
NY - 888 & 3 \\ \hline
VA - 2640 & 2 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Carbon payback period for the projects of interest}
\end{table}
\end{document}

q-fin

Apoorva Bademi
Miriam Stevens
Isha Sura
Shweta Singh

"Estimating the spatial economic and environmental impact of planned offshore wind energy in the USA using Environmentally Extended Multiregional Input-Output analysis" by Apoorva Bademi, Miriam Stevens, Isha Sura, and Shweta Singh, arXiv:2508.15012v1, 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{Hardware overview of experimental system.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\hline
\multicolumn{2}{c}{\textbf{DGX-2 node}} \\ \hline
GPU & 16 NVIDIA Tesla V100 Tensor Core GPUs \\
GPU Memory & 32GB HBM2 on each GPU \\
CPU & 2 Intel Xeon Platinum 8168 Processors \\
CPU Memory & 1.5TB 2666MHz DDR4 \\
CPU cache & L1, L2, and L3 are 32K, 1M, and 33M, respectively\\
PCIe & bidirectional 32 GBps PCIe
 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jie Ren
Samyam Rajbhandari
Reza Yazdani Aminabadi
Olatunji Ruwase
Shuangyan Yang
Minjia Zhang
Dong Li
Yuxiong He

"ZeRO-Offload: Democratizing Billion-Scale Model Training" by Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, and Yuxiong He, arXiv:2101.06840v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Tsimboy Olga
Yermek Kapushev
Evgeny Burnaev
Ivan Oseledets

"Denoising Score Matching with Random Fourier Features" by Tsimboy Olga, Yermek Kapushev, Evgeny Burnaev, and Ivan Oseledets, arXiv:2101.05239v1, 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{Median identification confidence and improvement}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|r|r}
 Method & ID Confidence & Improvement\\
 \hline
 PCA & 71.2\% & 1.15\\
 KNN & 79.0\% & 1.25\\
 KMeans & 49.9\% & 1.05\\
 \textbf{KNS} & 91.4\% & 1.65\\
 \textit{Annulus} & 87.6\% & 1.38\\
 Global & 38.2\% & \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Scout Jarman
Zigfried Hampel-Arias
Adra Carr
Kevin R. Moon

"Improved Background Estimation for Gas Plume Identification in Hyperspectral Images" by Scout Jarman, Zigfried Hampel-Arias, Adra Carr, and Kevin R. Moon, arXiv:2411.15378v1, 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{MDP3 Transition Function}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c||c}\hline
 State & Action & Next State \\\hline
 0 & 0 & 0 \\
 0 & 1 & 0 \\
 1 & 0 & 0 \\
 1 & 1 & 1 \\
 2 & 0 & 2 \\
 2 & 1 & 3 \\
 3 & 0 & 3 \\
 3 & 1 & 3 \\\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Karine Karine
Susan A. Murphy
Benjamin M. Marlin

"BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings" by Karine Karine, Susan A. Murphy, and Benjamin M. Marlin, arXiv:2412.00308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of the ablation study of the proposed egocentric hand gesture recognition and fingertip detection algorithm}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{Method} & \multicolumn{8}{c|}{Gesture} & \multirow{2}{*}{\shortstack{Overall\\Error\\(px)}} \\ \cline{2-9}
 & SingleOne & SingleTwo & SingleThree & SingleFour & SingleFive & SingleSix & SingleSeven & SingleEight & \\ \hline
\multirow{3}{*}{\shortstack{(1)~Method with \\ an Averaging Layer \\ Included in the CNN \\Architecture}} & \multirow{3}{*}{4.64 $\pm$ 3.17} & \multirow{3}{*}{3.94 $\pm$ 1.86} & \multirow{3}{*}{3.61 $\pm$ 1.86} & \multirow{3}{*}{3.85 $\pm$ 1.95} & \multirow{3}{*}{3.63 $\pm$ 1.53} & \multirow{3}{*}{3.51 $\pm$ 1.5} & \multirow{3}{*}{3.65 $\pm$ 1.46} & \multirow{3}{*}{5.8 $\pm$ 3.77} & \multirow{3}{*}{4.08 $\pm$ 2.14} \\
 & & & & & & & & & \\
 & & & & & & & & & \\ \hline
\multirow{3}{*}{\shortstack{(2)~Method with \\Randomly Sampled \\Output from the \\Ensemble}} & \multirow{3}{*}{5.21 $\pm$ 3.43} & \multirow{3}{*}{4.57 $\pm$ 2.3} & \multirow{3}{*}{4.24 $\pm$ 2.23} & \multirow{3}{*}{4.28 $\pm$ 1.98} & \multirow{3}{*}{4.17 $\pm$ 1.87} & \multirow{3}{*}{3.98 $\pm$ 1.78} & \multirow{3}{*}{4.23 $\pm$ 1.68} & \multirow{3}{*}{6.41 $\pm$ 3.56} & \multirow{3}{*}{4.63 $\pm$ 2.35} \\
 & & & & & & & & & \\
 & & & & & & & & & \\ \hline
\multirow{3}{*}{\shortstack{(3)~Method with \\Direct Regression \\of Fingertip \\Coordinates}} & \multirow{3}{*}{5.97 $\pm$ 3.75} & \multirow{3}{*}{6.23 $\pm$ 2.21} & \multirow{3}{*}{6.01 $\pm$ 2.49} & \multirow{3}{*}{5.94 $\pm$ 2.11} & \multirow{3}{*}{5.12 $\pm$ 1.66} & \multirow{3}{*}{5.45 $\pm$ 2.32} & \multirow{3}{*}{4.88 $\pm$ 2.25} & \multirow{3}{*}{6.88 $\pm$ 3.29} & \multirow{3}{*}{5.81 $\pm$ 2.51} \\
 & & & & & & & & & \\
 & & & & & & & & & \\ \hline

\multirow{2}{*}{\shortstack{Proposed Method}} & \multirow{2}{*}{4.51 $\pm$ 3.14} & \multirow{2}{*}{3.89 $\pm$ 1.91} & \multirow{2}{*}{3.62 $\pm$ 1.80} & \multirow{2}{*}{3.79 $\pm$ 1.89} & \multirow{2}{*}{3.63 $\pm$ 1.46} & \multirow{2}{*}{3.4 $\pm$ 1.48} & \multirow{2}{*}{3.64 $\pm$ 1.51} & \multirow{2}{*}{5.68 $\pm$ 3.51} & \multirow{2}{*}{\textbf{4.02 $\mathbf{\pm}$ 2.09}} \\
 & & & & & & & & & \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mohammad Mahmudul Alam
Mohammad Tariqul Islam
S. M. Mahbubur Rahman

"Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection" by Mohammad Mahmudul Alam, Mohammad Tariqul Islam, and S. M. Mahbubur Rahman, arXiv:2101.02047v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Víctor Blanco
José-Fernando Camacho-Vallejo
Carlos Corpus

"A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology" by Víctor Blanco, José-Fernando Camacho-Vallejo, and Carlos Corpus, arXiv:2503.00561v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c|c|c||}
\hline
$\tilde\kappa$ & 0.1 & \textbf{0.3} & 0.5 & 1\\
\hline
rate & -0.257 & \textbf{-0.408} & -0.456 & -0.433\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Convergence rate for the proposed estimator for difference choice of hyperparameter $\tilde\kappa$.}
\end{table}
\end{document}

stat

Xuhui Zhang
Jose Blanchet
Youssef Marzouk
Viet Anh Nguyen
Sven Wang

"Wasserstein-based Minimax Estimation of Dependence in Multivariate Regularly Varying Extremes" by Xuhui Zhang, Jose Blanchet, Youssef Marzouk, Viet Anh Nguyen, and Sven Wang, arXiv:2312.09862v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Paolo Santini
Giulia Rafaiani
Massimo Battaglioni
Franco Chiaraluce
Marco Baldi

"Optimization of a Reed-Solomon code-based protocol against blockchain data availability attacks" by Paolo Santini, Giulia Rafaiani, Massimo Battaglioni, Franco Chiaraluce, and Marco Baldi, arXiv:2201.08261v1, 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{Assessment of the quality of the uncertainty estimates provided by the models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
 & {GP} & {MoNNE} & {BMNN (VI)} \\
\midrule
DTLZ2 & $0.622 (\pm 0.01)$ & $0.924 (\pm 0.014)$ & $0.855 (\pm 0.019)$ \\
VLMOP3 & $0.625 (\pm 0.01)$ & $0.915 (\pm 0.022)$ & $0.731 (\pm 0.027)$ \\
VehicleSafety & $0.63 (\pm 0.016)$ & $0.863 (\pm 0.045)$ & $0.848 (\pm 0.04)$ \\
OSY & $0.64 (\pm 0.015)$ & $0.878 (\pm 0.022)$ & $0.886 (\pm 0.021)$ \\
CarCabDesign & $0.658 (\pm 0.008)$ & $0.951 (\pm 0.08)$ & $0.911 (\pm 0.02)$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Hanyang Wang
Juergen Branke
Matthias Poloczek

"Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble" by Hanyang Wang, Juergen Branke, and Matthias Poloczek, arXiv:2501.18792v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Fitted average probabilities of observing a payment}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
\toprule
 Probability & $j=2$ & $j = 3$ & $j = 4$\\
\midrule
 $\pi_{j,\text{AB}}$ & 0.2367 & 0.2194 & 0.1267\\
 $\pi_{j,\text{BI}}$ & 0.1426 & 0.1220 & 0.0695\\
 $\pi_{j,\text{VD}}$ & 0.6391 & 0.1030 & 0.0311\\
 $\pi_{j,\text{LoU}}$ & 0.4228 & 0.0368 & 0.0087 \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Marie Michaelides
Mathieu Pigeon
Hélène Cossette

"Individual Claims Reserving using Activation Patterns" by Marie Michaelides, Mathieu Pigeon, and Hélène Cossette, arXiv:2208.08430v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Comparative Evaluation of VaR Models: Historical Simulation, GARCH-Based Monte Carlo, and Filtered Historical Simulation" by Xin Tian, arXiv:2505.05646v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Saptarshi Roy
Raymond K. W. Wong
Yang Ni

"Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data" by Saptarshi Roy, Raymond K. W. Wong, and Yang Ni, arXiv:2310.20537v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage[table]{xcolor}
\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
 \rowcolor[HTML]{C0C0C0}
 \multicolumn{4}{|c||}{LHS Parameter Samples} & Target Variable \\ \hline
 $p_{1}$ & $p_{2}$ & $\cdots$ & $p_{K}$ & $v$ \\ \hline
 $p_{1}^{(1)}$ & $p_{2}^{(1)}$ & $\cdots$ & $p_{K}^{(1)}$ & $v^{(1)}$ \\
 $p_{1}^{(2)}$ & $p_{2}^{(2)}$ & $\cdots$ & $p_{K}^{(2)}$ & $v^{(2)}$ \\
 $\vdots$ & $\vdots$ & $\ddots$ & $\vdots$ & $\vdots$ \\
 $p_{1}^{(\mathcal{S})}$ & $p_{2}^{(\mathcal{S})}$ & $\cdots$ & $p_{K}^{(\mathcal{S})}$ & $v^{(\mathcal{S})}$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Zsolt Vizi
Evans Kiptoo Korir
Norbert Bogya
Csaba Rosztóczy
Géza Makay
Péter Boldog

"Age Group Sensitivity Analysis of Epidemic Models: Investigating the Impact of Contact Matrix Structure" by Zsolt Vizi, Evans Kiptoo Korir, Norbert Bogya, Csaba Rosztóczy, Géza Makay, and Péter Boldog, arXiv:2502.19206v2, 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}{lccc|ccc}
			Data set & \# inst. & \# features & \# categorical & sensitive & unrelated 1 & unrelated 2 \\ \hline
			COMPAS & $ 6172 $ & $ 7 $ & $ 4 $ & \textit{race} & \textit{random1} & \textit{random2} \\
			German & $ 1000 $ & $ 25 $ & $ 15 $ & \textit{gender} & \textit{pctOfIncome} & /\\
			CC & $ 1994 $ & $ 100 $ & $ 0 $ & \textit{racePctWhite} & \textit{random1} & \textit{random2}\\ \hline
		\end{tabular}
\end{adjustbox}
\caption{Basic information and sensitive features in the the used data sets. The target variable is not included in the number of features and is binary for all data sets. The \textit{pctOfIncome} full name is \textit{loanRateAsPercentOfIncome}.}
\end{table}
\end{document}

cs

"Better sampling in explanation methods can prevent dieselgate-like deception" by Domen Vreš and Marko Robnik Šikonja, arXiv:2101.11702v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{F1 scores of all tested methods on NoisyCXR dataset}
\begin{tabular}{|c|c|c|}
		\hline
		Noise & Clean & Noisy \\ \hline
		base & 79.36±0.3 & 62.45±0.53 \\ \hline
		o2u & 80.47±0.4 & 64.84±0.86 \\ \hline
		mixup & 78.75±0.24 & 65.03±0.73 \\ \hline
		co & 80.55±0.24 & 65.38±1.18 \\ \hline
		cdr & 80.02±0.94 & 64.64±0.57 \\ \hline
		self & 79.14±0.91 & 57.61±0.71 \\ \hline
		multi & 79.84±0.42 & 58.89±0.26 \\ \hline
		lnlsr & 79.01±0.19 & 62.73±1.58 \\ \hline
		ours & 80.80±0.33 & 69.53±0.48 \\ \hline
	\end{tabular}
\end{table}
\end{document}

eess

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Full list of Codes for Model Interpretability study. Each row lists a code, the rate each code occurs for each verification technique, and the category we place the code in.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrl}
 \toprule
 Code & BMC & BMC+K & SymEx & CEGAR & Category \\ \midrule
 Assert Call & 0.010 & 0.018 & 0.036 & 0.009 & Error Reachability \\
 Assert Condition & 0.115 & 0.182 & 0.155 & 0.118 & Assertion Satisfiability \\
 Assert Function & 0.019 & 0.000 & 0.009 & 0.000 & Error Reachability \\
 Assert Return & 0.010 & 0.009 & 0.036 & 0.009 & Error Reachability \\
 Assume Condition & 0.019 & 0.045 & 0.000 & 0.027 & Assertion Satisfiability \\
 Branch Condition & 0.019 & 0.027 & 0.100 & 0.064 & Branching (No Loops) \\
 Call Of Indirectly Dependent (1) Function & 0.000 & 0.000 & 0.000 & 0.009 & Error Reachability \\
 Call Of Indirectly Dependent (2) Function & 0.000 & 0.009 & 0.000 & 0.000 & Error Reachability \\
 Conditional Statement & 0.010 & 0.036 & 0.009 & 0.018 & Branching (No Loops) \\
 Directly Dependent Function & 0.000 & 0.009 & 0.018 & 0.018 & Error Reachability \\
 Directly Dependent Function Call & 0.000 & 0.009 & 0.000 & 0.000 & Error Reachability \\
 Directly Dependent Function Return & 0.096 & 0.000 & 0.100 & 0.082 & Error Reachability \\
 Dead Code & 0.019 & 0.018 & 0.009 & 0.018 & Miscellaneous \\
 Decl Of Directly Dependent Variable & 0.058 & 0.018 & 0.009 & 0.045 & Assertion Satisfiability \\
 Decl Of Indirectly Dependent (1) Variable & 0.019 & 0.009 & 0.009 & 0.009 & Assertion Satisfiability \\
 Decl Of Indirectly Dependent (2) Variable & 0.010 & 0.000 & 0.009 & 0.000 & Assertion Satisfiability \\
 Decl Of Loop Condition Variable (Large Bound) & 0.010 & 0.000 & 0.000 & 0.000 & Loop (Large Bound) \\
 Error Call & 0.019 & 0.055 & 0.045 & 0.027 & Error Reachability \\
 Error Function & 0.019 & 0.000 & 0.000 & 0.000 & Error Reachability \\
 Error Return & 0.000 & 0.000 & 0.027 & 0.000 & Error Reachability \\
 Indirectly Dependent (1) Function & 0.000 & 0.000 & 0.000 & 0.009 & Error Reachability \\
 Indirectly Dependent (1) Function Return & 0.010 & 0.000 & 0.000 & 0.009 & Error Reachability \\
 Indirectly Dependent (1) Variable Update & 0.000 & 0.000 & 0.000 & 0.027 & Assertion Satisfiability \\
 Input Function & 0.000 & 0.000 & 0.018 & 0.018 & Miscellaneous \\
 Loop Body (Small Bound) & 0.000 & 0.018 & 0.000 & 0.000 & Loop (Small Bound) \\
 Loop Condition (Large Bound) & 0.010 & 0.027 & 0.000 & 0.045 & Loop (Large Bound) \\
 Loop Condition (Nondet Bound) & 0.010 & 0.009 & 0.000 & 0.009 & Loop (Large Bound) \\
 Loop Condition (Small Bound) & 0.038 & 0.027 & 0.027 & 0.000 & Loop (Small Bound) \\
 Loop Header (Large Bound) & 0.010 & 0.009 & 0.000 & 0.000 & Loop (Large Bound) \\
 Loop Header (Small Bound) & 0.010 & 0.000 & 0.009 & 0.000 & Loop (Small Bound) \\
 Recursion Call (Nondet Bound) & 0.000 & 0.000 & 0.000 & 0.018 & Loop (Large Bound) \\
 Recursion Call (Small Bound) & 0.000 & 0.000 & 0.027 & 0.009 & Loop (Small Bound) \\
 Recursion Condition (Nondet Bound) & 0.000 & 0.000 & 0.000 & 0.009 & Loop (Large Bound) \\
 Recursion Condition (Small Bound) & 0.000 & 0.000 & 0.018 & 0.009 & Loop (Small Bound) \\
 Recursion Function (Small Bound) & 0.000 & 0.000 & 0.000 & 0.009 & Loop (Small Bound) \\
 Recursion Function Decl (Nondet Bound) & 0.000 & 0.000 & 0.000 & 0.009 & Loop (Large Bound) \\
 Recursion Function Return (Small Bound) & 0.000 & 0.000 & 0.018 & 0.000 & Loop (Small Bound) \\
 Return Indirectly Dependent (1) Function & 0.010 & 0.000 & 0.000 & 0.000 & Error Reachability \\
 Return Of Recursion Function (Nondet Bound) & 0.000 & 0.000 & 0.000 & 0.027 & Loop (Large Bound) \\
 Type Def & 0.010 & 0.000 & 0.000 & 0.009 & Miscellaneous \\
 Type Of Directly Dependent Variable & 0.010 & 0.000 & 0.000 & 0.000 & Assertion Satisfiability \\
 Type Of Indirectly Dependent (1) Variable & 0.000 & 0.000 & 0.000 & 0.009 & Assertion Satisfiability \\
 Update Loop Condition Variable (Infinite Loop) & 0.000 & 0.000 & 0.064 & 0.009 & Loop (Large Bound) \\
 Update Loop Condition Variable (Nondet Bound) & 0.019 & 0.000 & 0.000 & 0.009 & Loop (Large Bound) \\
 Update Of Directly Dependent Variable & 0.087 & 0.182 & 0.136 & 0.091 & Assertion Satisfiability \\
 Update Of Indirectly Dependent (1) Variable & 0.135 & 0.064 & 0.045 & 0.018 & Assertion Satisfiability \\
 Update Of Indirectly Dependent (2) Variable & 0.048 & 0.045 & 0.009 & 0.000 & Assertion Satisfiability \\
 Update Of Indirectly Dependent (3) Variable & 0.000 & 0.027 & 0.000 & 0.000 & Assertion Satisfiability \\
 Update Of Loop Condition Variable (Infinite Loop) & 0.000 & 0.000 & 0.000 & 0.009 & Loop (Large Bound) \\
 Update Of Loop Condition Variable (Large Bound) & 0.019 & 0.064 & 0.000 & 0.009 & Loop (Large Bound) \\
 Update Of Loop Condition Variable (Nondet Bound) & 0.000 & 0.055 & 0.000 & 0.009 & Loop (Large Bound) \\
 Update Of Loop Condition Variable (Small Bound) & 0.038 & 0.009 & 0.000 & 0.000 & Loop (Small Bound) \\
 Update Of Recursion Condition Variable (Nondet Bound) & 0.000 & 0.000 & 0.000 & 0.055 & Loop (Large Bound) \\
 Update Of Recursion Condition Variable (Small Bound) & 0.000 & 0.000 & 0.055 & 0.036 & Loop (Small Bound) \\
 Use Of Directly Dependent Variable & 0.010 & 0.009 & 0.000 & 0.064 & Assertion Satisfiability \\
 Use Of Indirectly Dependent (1) Variable & 0.029 & 0.000 & 0.000 & 0.000 & Assertion Satisfiability \\
 Use Of Loop Condition Variable (Large Bound) & 0.000 & 0.009 & 0.000 & 0.000 & Loop (Large Bound) \\
 Use Of Loop Condition Variable (Small Bound) & 0.010 & 0.000 & 0.000 & 0.000 & Loop (Small Bound) \\
 Use Of Loop Condition Variable (Small Bound) & 0.038 & 0.000 & 0.000 & 0.000 & Loop (Small Bound) \\
 Use Of Recursion Condition Variable (Nondet Bound) & 0.000 & 0.000 & 0.000 & 0.009 & Loop (Large Bound) \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Algorithm Selection for Software Verification using Graph Neural Networks" by Will Leeson and Matthew B Dwyer, arXiv:2201.11711v3, 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}{lrr}
Method & Setting 1 & Setting 2 \\
\hline
CORAL & 0.744 & 0.709 \\
Global & 0.745 & 0.718 \\
Group-Aware Global & 0.745 & 0.674 \\
Cluster Global & 0.759 & 0.745 \\
Label Shift & 0.695 & 0.702 \\
Our Method & 0.706 & 0.751 \\
\hline
True Clustering & 0.696 & 0.791 \\
\end{tabular}
\end{adjustbox}
\caption{Average AUC calculated on the held-out test set for Amazon datasets in settings 1 and 2.}
\end{table}
\end{document}

stat

Daniel Zeiberg
Shantanu Jain
Predrag Radivojac

"Leveraging Structure for Improved Classification of Grouped Biased Data" by Daniel Zeiberg, Shantanu Jain, and Predrag Radivojac, arXiv:2212.03697v1, 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{Statistics of the datasets showing the no. of sentences with corresponding sentiment polarities of constituent aspect terms.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{Dataset}} & \multicolumn{4}{c|}{\textbf{Train}} & \multicolumn{4}{c|}{\textbf{Test}} \\
\cline{2-9} & \textbf{Positive} & \textbf{Negative} & \textbf{Neutral} & \textbf{Total} & \textbf{Positive} & \textbf{Negative} & \textbf{Neutral} & \textbf{Total} \\
\hline
\textbf{Laptop} & 994 & 870 & 464 & 2328 & 341 & 128 & 169 & 638 \\
\hline
\textbf{Restaurant} & 2164 & 807 & 637 & 3608 & 728 & 196 & 196 & 1120 \\
\hline
\textbf{Men's T-shirt} & 1122 & 699 & 50 & 1871 & 270 & 186 & 16 & 472 \\
\hline
\textbf{Television} & 2540 & 919 & 287 & 3746 & 618 & 257 & 67 & 942 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Rajdeep Mukherjee
Shreyas Shetty
Subrata Chattopadhyay
Subhadeep Maji
Samik Datta
Pawan Goyal

"Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild" by Rajdeep Mukherjee, Shreyas Shetty, Subrata Chattopadhyay, Subhadeep Maji, Samik Datta, and Pawan Goyal, arXiv:2101.09449v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{marvosym}
\usepackage{pifont}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 Method & Metrics & Code \\
 \midrule
 GP & \textbf{AR} & {\color{red}\ding{55}} \\
 AutoAlpha & IC, \textbf{AR}, \textbf{SR} & {\color{red}\ding{55}} \\
 AlphaEvolve & IC, \textbf{SR} &{\color{red}\ding{55}} \\
 AlphaGen & IC, RankIC, \textbf{AR} & {\color{teal}\ding{51}} \\
 RiskMiner & IC, ICIR, RankIC & {\color{red}\ding{55}}\\
 QFR & IC, RankIC & {\color{red}\ding{55}}\\
 $\text{Alpha}^2$ & IC & {\color{red}\ding{55}}\\
 AlphaForge & IC & {\color{teal}\ding{51}} \\
 AlphaQCM & IC & {\color{teal}\ding{51}} \\
 FAMA & RankIC, RankICIR & {\color{red}\ding{55}} \\
 AlphaAgent & IC, ICIR, \textbf{AR}, IR, \textbf{MaxDD} & {\color{red}\ding{55}} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Summary of current alpha mining models. Metrics in \textbf{bold} are based on backtesting.}
\end{table}
\end{document}

q-fin

Hongjun Ding
Binqi Chen
Jinsheng Huang
Taian Guo
Zhengyang Mao
Guoyi Shao
Lutong Zou
Luchen Liu
Ming Zhang

"AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining" by Hongjun Ding, Binqi Chen, Jinsheng Huang, Taian Guo, Zhengyang Mao, Guoyi Shao, Lutong Zou, Luchen Liu, and Ming Zhang, arXiv:2508.13174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Aida Abiad
Ángeles Carmona
Andrés M. Encinas
Maria José Jiménez
Álvaro Samperio

"A Spectral Approach to Kemeny's Constant" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The sample standard deviation of the MESES for each nonparametric estimator fitted to the simulated continuous data with a jump in the regression function.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|r|rrr|rrr|rrr|}
 \hline
 & & $n=400$ & & & $n=800$ & & & $n=1600$ & \\
 \hline
$\sigma$ & LC & ALC & ALCT & LC & ALC & ALCT & LC & ALC & ALCT \\
 0.1 & 0.00109 & 0.00043 & 0.00030 & 0.00123 & 0.00033 & 0.00015 & 0.00071 & 0.00118 & 0.00008 \\
 0.5 & 0.00735 & 0.01848 & 0.005 & 0.00494 & 0.00995 & 0.00222 & 0.00281 & 0.00597 & 0.00155 \\
 1 & 0.03002 & 0.03732 & 0.01904 & 0.01328 & 0.01753 & 0.00843 & 0.00816 & 0.00956 & 0.00515 \\
 2 & 0.08068 & 0.08431 & 0.07131 & 0.03541 & 0.04431 & 0.02913 & 0.02699 & 0.02902 & 0.01662 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Anisotropic local constant smoothing for change-point regression function estimation" by John R. J. Thompson and W. John Braun, arXiv:2012.00180v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccc}
\multicolumn{6}{c}{Inferred (MAP estimate)}\\
\hline
$\mu_\ell$ [mPa.s] & $\alpha$ & $\beta$ & $x_c$ [cm] & $c$ [cm] & $d_{s_o}$ [cm]\\
4.2 $\pm$ 0.15 & 0.722 $\pm$ 0.193 & 19.2 $\pm$ 12.6 & 3.25 $\pm$ 2.87 & 7.32 $\pm$ 2.44 & 0.159 $\pm$ 0.0881\\
\multicolumn{6}{c}{Prior}\\
\hline
$\mu_\ell$ [mPa.s] & $\alpha$ & $\beta$ & $x_c$ [cm] & $c$ [cm] & $d_{s_o}$ [cm]\\
4.2 $\pm$ 0.15 & 2 $\pm$ 6 & 10 $\pm$ 15 & 0 $\pm$ 30 & 8.5 $\pm$ 30 & 0 $\pm$ 15
\end{tabular}
\caption{Compound mixing length parameters.}
\end{table}
\end{document}

math

A. Kontogiannis
P. Nair
M. Loecher
D. B. Ennis
A. Marsden
M. P. Juniper

"Bayesian inference of mean velocity fields and turbulence models from flow MRI" by A. Kontogiannis, P. Nair, M. Loecher, D. B. Ennis, A. Marsden, and M. P. Juniper, arXiv:2412.11266v1, 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|ccccccccccccccccccccccccc}
\hline
 No.& 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 & 16 & 17 & 18 & 19 & 20 & 21 & 22 & 23 & 24 & 25 \\
\hline
$\mu_4$ & $\times$ & & & & & & $\times$ & & $\times$ & & & $\times$ & $\times$ & & & & & & & & & & & & \\
$\mu_6$ & $\times$ & $\times$ & & $\times$ & & & $\times$ & & $\times$ & & & $\times$ & $\times$ & & $\times$ & & & & & & & & & & \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Test of normal i.i.d. for $\mu_4$ and $\mu_6$. The rejection of the null hypothesis is $\times$. }
\end{table}
\end{document}

q-fin

"Deformation of semi-circle law for the correlated time series and Phase transition" by Masato Hisakado and Takuya Kaneko, arXiv:2508.07192v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Christoph Adam
Jorge Castelo Mourelle
Alberto García Martín-Caro
Andrzej Wereszczynski

"Spinning boson stars in nonlinear sigma models and Universal Relations" by Christoph Adam, Jorge Castelo Mourelle, Alberto García Martín-Caro, and Andrzej Wereszczynski, arXiv:2502.20923v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Thien-Minh Le
Ping-Shou Zhong
Chenlei Leng

"Testing the Graph of a Gaussian Graphical Model" by Thien-Minh Le, Ping-Shou Zhong, and Chenlei Leng, arXiv:2212.06931v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Giannis Papagiannopoulos
Genly Leon
Andronikos Paliathanasis

"Avoiding Big Rip Singularities in Phantom Scalar Field theory with Gauss-Bonnet term" by Giannis Papagiannopoulos, Genly Leon, and Andronikos Paliathanasis, arXiv:2501.18392v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Amine Allouah
Omar Besbes
Josué D Figueroa
Yash Kanoria
Akshit Kumar

"What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce" by Amine Allouah, Omar Besbes, Josué D Figueroa, Yash Kanoria, and Akshit Kumar, arXiv:2508.02630v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Property-Preserving Hashing for $\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks" by Hassan Asghar, Chenhan Zhang, and Dali Kaafar, arXiv:2504.16355v1, 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{Detection cost function (DCF) on development and evaluation sets of FS-2 speech activity detection challenge for raw and postprocessed classifications comparing two and thee-layer MLP trained with or without semi-supervised (A11) data.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|rr|c}
\toprule
\multirow{2}{*}{N\textsuperscript{\underline{o}}} & \multirow{2}{*}{Model size} & \multirow{2}{*}{Train data} & \multicolumn{2}{c|}{DCF Dev, \%} & DCF Eval, \% \\
& & & Raw & Post. & Post. \\
\midrule
1 & 2 x 256 & FS-2 & 1.68 & 1.25 & 1.87 \\
2 & 2 x 256 & FS-2 + A11 & 3.03 & 1.23 & 2.03 \\
3 & 3 x 400 & FS-2 + A11 & 2.01 & 0.92 & 1.56 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Arseniy Gorin
Daniil Kulko
Steven Grima
Alex Glasman

""This is Houston. Say again, please". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)" by Arseniy Gorin, Daniil Kulko, Steven Grima, and Alex Glasman, arXiv:2008.01504v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Thomas Guilmeau
Emilie Chouzenoux
Víctor Elvira

"On variational inference and maximum likelihood estimation with the λ-exponential family" by Thomas Guilmeau, Emilie Chouzenoux, and Víctor Elvira, arXiv:2310.05781v2, 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{F1 Scores (in\%) for different features with TDNN Classifier}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\hline
\textbf{Features} & \textbf{Repetition} & \textbf{Filled-pause} & \textbf{Prolongation} \\
\hline
MFCC & 77.34 & 73.32 & 72.24 \\
PLP Features & 80.12 & 80.36 & 78.74 \\
ZTWCC & 81.22 & 80.85 & 79.20 \\
PE-SFCC& 76.09 & 73.33 & 74.21 \\
PE-ZTWCC& \textbf{83.22} & \textbf{81.23} & \textbf{81.15} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Priyanka Kommagouni
Vamshiraghusimha Narasinga
Purva Barche
Sai Akarsh C
Anil Vuppala

"Typical vs. Atypical Disfluency Classification: Introducing the IIITH-TISA Corpus and Temporal Context-Based Feature Representations" by Priyanka Kommagouni, Vamshiraghusimha Narasinga, Purva Barche, Sai Akarsh C, and Anil Vuppala, arXiv:2411.17149v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{For each \( L \geq 6 \), the minimum value of \( P \), denoted as \( P_{\text{min}} \), for which Theorem holds with \( P < P_0 = 2^{L+1} \).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccc}
 \( L \) & 6 & 8 & 10 & 12 & 14 & 16 \\ \hline\hline
 \( P_0 \) & 128 & 512 & 2048 & 8192 & 32768 & 131072 \\ \hline
 \( P_{\text{min}} \) & 49 & 138 & 281 & 355 & 609 & 821 \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Explicit Construction of Quantum Quasi-Cyclic Low-Density Parity-Check Codes with Column Weight 2 and Girth 12" by Daiki Komoto and Kenta Kasai, arXiv:2501.13444v2, 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}{|cc|}
 \hline
 \textbf{Model} & \textbf{Weights}\\
 \hline
 \textbf{DenseNet201} & 0.22 \\
 \textbf{Xception} & 0.29\\
 \textbf{InceptionResNet} & 0.18\\
 \textbf{ResNet152V2} & 0.17\\
 \textbf{MobileNetV2} & 0.15\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Weighted Average Ensemble Model Weights}
\end{table}
\end{document}

eess

"An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images" by Sagar Kora Venu, arXiv:2011.05543v1, 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{Block counts according to the swap counts and arbitrage swap counts in the block}
\begin{tabular}{ccccccccccc}
 \toprule
 & \multicolumn{5}{c}{\textbf{Arbitrage swaps count}} && \multicolumn{4}{c}{\textbf{Swaps count}} \\\cmidrule{2-6}\cmidrule{8-11} & \textbf{0} & \textbf{1} & \textbf{2} & \textbf{$\ge$ 3} &\textbf{Total}&& \textbf{0} & \textbf{1} & \textbf{2} & \textbf{$\ge$ 3}\\
 \midrule
 \textbf{Arbitrage block count} &3,575 & 4,114& 584 & 104 &8,377&&2,376 &4,165&1,403&433\\
 \textbf{Non-arbitrage block count} &241,265 & --& -- & -- &241,265&&168,286 &57,619&12,780&2,580\\
 \midrule
 \textbf{Arbitrage blocks proportion} &1.460$\%$&--&--&--&3.356$\%$&&1.392$\%$	& 6.741$\%$	 & 9.892$\%$	 & 14.371$\%$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

Arbitrage on Decentralized Exchanges

"Arbitrage on Decentralized Exchanges" by Xue Dong He, Chen Yang, and Yutian Zhou, arXiv:2507.08302v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll} \hline
 \textbf{Notation} & \textbf{Description}\\ \hline
 $\mathcal{D}$ & network data \\
 $\mathcal{V}$ and $\mathcal{E}$ & sets of nodes and edges, respectively\\
 $m_{v_i}$ & denotes the network of a user $v_i$\\
 $fr_{v_i}$ and $fl_{v_i}$ & denote sets of friends and followers of user $v_i$\\
 $\kappa \in m_{v_i} $ & set of reciprocal ties of user $v_i$ \\
 $\mathcal{A}_f$ & set of all possible attributes of a node\\
 $\mathcal{X}_{f}$ & set of features inducing reciprocity\\
 $a \succ b$ & a binary relation between $a$ and $b$ \\
 $\exp{x}$ or $e^x$ & base of a natural logarithm, $ln$, $\exp{(lnx)}=x$; $e \approx 2.71828$ \\
 $d_i, p_i, q_i, u_i$ & respective $ith$ component of vectors, $\mathbf{D, P,Q,U}$ \\
 $\tau$ & a predefined threshold for comparison, e.g. $\tau \geq 0.5$ \\
 $\mathcal{S}_r$ and $\mathcal{T}_r$ & sets of structurally-related and textually-related nodes, respectively \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Isa Inuwa-Dutse
Mark Liptrott
Yannis Korkontzelos

"A multilevel clustering technique for community detection" by Isa Inuwa-Dutse, Mark Liptrott, and Yannis Korkontzelos, arXiv:2101.06551v1, 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
\textbf{Metric} & \textbf{$\boldsymbol{O_{NCD}=0.09}$} & \textbf{$\boldsymbol{O_{NCD}=0.18}$} \\
\hline
\textbf{SS} &0.85 &0.50 \\
\hline
\textbf{ARI} &0.64 &0.44 \\
\hline
\textbf{MI} &0.94 &0.83 \\
\hline
\textbf{ACC (\%)} &79.34 &55.68 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Clustering Comparison for Different Levels of Openness Eq:~ ($O_{NCD}$ )}
\end{table}
\end{document}

eess

Parampreet Singh
Adwik Gupta
Aakarsh Mishra
Vipul Arora

"Identification and Clustering of Unseen Ragas in Indian Art Music" by Parampreet Singh, Adwik Gupta, Aakarsh Mishra, and Vipul Arora, arXiv:2411.18611v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Table II: Compared Methods}
\begin{tabular}{lcccc}
\toprule
Segmentation Method & Whole & Core & Enh. & Mean \\
\midrule
U-Net & 84.72 & 86.4 & 77.42 & 82.85 \\
ROI & 88.65 & 89.11 & 82 & 86.83 \\
ROI+SC & 88.92 & \textbf{89.15} & 84.53 & 87.53 \\
ROI+SC+I-Norm & \textbf{89.13} & 88.97 & \textbf{87.47} & \textbf{88.52} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction" by Zachary Schwehr and Sriman Achanta, arXiv:2401.00587v2, 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}{l|cc|cc}
			\hline
			n & $I_R(2)$ & $I_{\Sigma}(2)$ & $I_R(3)$ & $I_{\Sigma}(3)$\\
			\hline
			10 & 36.31 & 59.94 & 40.70 & 55.07\\
			20 & 90.52 & 87.20 & 80.18 & 83.71\\
			50 & 92.34 & 96.67 & 82.94 & 94.46\\
			100 & 98.73 & 99.16 & 86.88 & 96.38\\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Bayesian semiparametric modelling of covariance matrices for multivariate longitudinal data" by Georgios Papageorgiou, arXiv:2012.09833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|ccc|}
 \hline
 Models & Volatility MAPE (\%) & Call Price MAPE (\%) \\
 \hline\hline
 PINN & 16.4823 & 13.5132 \\
 \hline
 ConvLSTM & 8.4060 & 6.0774 \\
 \hline
 SA-ConvLSTM & 8.4218 & 6.1265 \\
 \hline
 ConvTF & 5.7891 & 4.5944 \\
 \hline
 PI-ConvTF & 4.9174 & 3.8457 \\
 \hline
 VAR & 8.8043 & 8.5821 \\
 \hline
 ARIMA & 28.2453 & 32.5921 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Average MAPE values across test dates for all models trained with outlier data. Call price MAPE values are determined after excluding outlier values below the $20^{th}$ percentile.}
\end{table}
\end{document}

q-fin

Soohan Kim
Seok-Bae Yun
Hyeong-Ohk Bae
Muhyun Lee
Youngjoon Hong

"Physics-Informed Convolutional Transformer for Predicting Volatility Surface" by Soohan Kim, Seok-Bae Yun, Hyeong-Ohk Bae, Muhyun Lee, and Youngjoon Hong, arXiv:2209.10771v3, 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
 $l$ & $2000$ & $500$ & $200$ & $50$\\
 \midrule
 Time (s) & $122.66$ & $4.31$ & $1.15$ & $0.63$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Time (seconds) required to construct the calibration bands on the mean of binned independent normal responses $(Y_i)_{i=1}^{n}$ for different amounts of bins.}
\end{table}
\end{document}

math

Łukasz Delong
Selim Gatti
Mario V. Wüthrich

"Calibration Bands for Mean Estimates within the Exponential Dispersion Family" by Łukasz Delong, Selim Gatti, and Mario V. Wüthrich, arXiv:2503.18896v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Anna Pandolfi
Ignacio Romero
Michael Ortiz

"An optimal-transport finite-particle method for driven mass diffusion" by Anna Pandolfi, Ignacio Romero, and Michael Ortiz, arXiv:2503.02813v1, 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}{l|ccc|ccc}
 \toprule
 &\multicolumn{3}{c|}{Aircraft} &\multicolumn{3}{c}{Cars} \\%& \multicolumn{3}{c|}{Dogs}\\
 {Base CNN} & {\#9} & {\#27} & {\#36} & {\#9} & {\#27} & {\#36} \\% & {9} & {27} & {36}\\
 \toprule
 ResNet-50 &85.9 &94.9 &91.2 &92.9 &94.9 & 91.9 \\% &90.5 &95.8 &92.1 \\
 Xception &87.8 &94.1 &90.0 &93.9 &95.7 &92.6 \\% &95.3 &96.1 &95.2\\
 NASNet-M &92.7 &93.8 &90.3 &92.4 &93.7 &90.9 \\% &91.7 &96.0 &93.3\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Accuracy (\%) of our model with a varying number of integral regions. More results in the supplementary in the end.}
\end{table}
\end{document}

cs

Ardhendu Behera
Zachary Wharton
Pradeep Hewage
Asish Bera

"Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification" by Ardhendu Behera, Zachary Wharton, Pradeep Hewage, and Asish Bera, arXiv:2101.06635v1, 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}{cccccc}
 \hline
 Model & $F1_{micro}$ & $A$ & $D$ & $N$ \\
 \hline
 {LR$_{ArgF}$} & 53.5 & 22.4 & 57.2 & 56.3 \\
 {LR$_{ArgF+SarcF}$} & \textbf{56.4}$^{\alpha{^{*}}}$ &	31.0 & {58.4} & {58.9}\\
\hline
{LSTM$_{Attn}$} & 51.8 & 28.0 & 49.4 & 59.2\\
{LSTM$_{MT}$} & 53.1 & 30.0 & 53.2 & 56.5 \\
{LSTM$_{{MT}_{uncert}}$} & \textbf{54.6} $^{\alpha{^{*}}}$ & 33.1 & 54.5 & 58.5 \\
 \hline
 {BERT$_{orig}$} & 62.2 &	41.8 &	63.3 & 64.4\\
{BERT$_{MT}$} & 63.2 & {44.5} & {64.1} & 65.4 \\
{BERT$_{{MT}_{uncert}}$} & \textbf{65.3}$^{\alpha{^{*}}}$ & {44.6} & {66.2} & {67.5} \\
\hline
{BERT$_{ALT}$} & 63.4 & 40.1 & 62.2 & {66.9} \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Results for argumentative relation detection ($F1_{micro}$ and F1 scores/category) on the $test$ set of $IAC_{orig}$. $^{\alpha{^{*}}}$ depict significance on $p\le 0.05$ (measured via Mcnemar's test) against the corresponding individual model (e.g., LR$_{ArgF}$, LSTM$_{Attn}$, BERT$_{orig}$, respectively). Highest scores per group of models are in \textbf{bold}.}%, showing the benefits of joint modelling.}
\end{table}
\end{document}

cs

Debanjan Ghosh
Ritvik Shrivastava
Smaranda Muresan

""Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space" by Debanjan Ghosh, Ritvik Shrivastava, and Smaranda Muresan, arXiv:2101.10952v1, 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|cc}
Task & Flare (500K) & RAD (500K) & Flare (1M) & RAD (1M)\\ \hline
Quadruped Walk & $\mathbf{296}\pm139$ & $206\pm112$ & $\mathbf{488}\pm221$ & $322\pm229$ \\
Pendulum Swingup & $\mathbf{242}\pm152$ & $79\pm73$ & $\mathbf{809}\pm31$ & $520\pm321$\\
Hopper Hop & $\mathbf{90}\pm55$ & $40\pm41$ & $\mathbf{217}\pm59$ & $211\pm27$ \\
Finger Turn hard & $\mathbf{282}\pm67$ & $137\pm98$ & $\mathbf{661}\pm315$ & $249\pm98$ \\
Walker Run & $426\pm33$ & $\mathbf{547}\pm48$ & $556\pm93$ & $\mathbf{628}\pm39$ \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Reinforcement Learning with Latent Flow

Wenling Shang
Xiaofei Wang
Aravind Srinivas
Aravind Rajeswaran
Yang Gao
Pieter Abbeel
Michael Laskin

"Reinforcement Learning with Latent Flow" by Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, and Michael Laskin, arXiv:2101.01857v1, 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{\textbf{Visibility on arXiv during the review period, CMU, MIT, and Cornell} at ICLR 2020. Logistic regression predicting paper acceptance as a function of mean reviewer score and an indicator demarcating if the paper was on arXiv up to one week after the submission date. The accuracy of the classifier was 94\% on a hold-out set containing 30\% of 2020 papers.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\bf Variable &\bf Coefficient &\bf Std. Error &\bf Z-score &\bf p-value
\\ \hline \\
\texttt{mean reviewer score} & 2.7144 & 0.372 & 7.295 & 0.000\\% 1.985 3.444
\texttt{visible on arXiv?} & 1.8285 & 0.551 & 3.317 & 0.001\\% 0.748 2.909
\texttt{constant} & -14.4988 & 1.967 & -7.372 & 0.000\\% -18.353 -10.644
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

David Tran
Alex Valtchanov
Keshav Ganapathy
Raymond Feng
Eric Slud
Micah Goldblum
Tom Goldstein

"Analyzing the Machine Learning Conference Review Process" by David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, and Tom Goldstein, arXiv:2011.12919v2, 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{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
 Notation & Definition\\
 \midrule
 $\mathcal{V}$ & Set of timetabled trips\\
 $\mathcal{D}$ & Set of depots\\
 $\mathcal{H}$ & Set of charging stations\\
 $\mathcal{H}^C$ & Set of charging nodes\\
 $\mathcal{H}^W$ & Set of waiting nodes\\
 $\mathcal{R}$ & Set of time intervals\\
 $\mathcal{S}$ & Set of all feasible vehicle schedules\\
 $\mathcal{S}^d$ & Set of all feasible vehicle schedules hosted in depot $d$\\
 $y_s$ & Binary variable equal to 1 if schedule $s$ is selected\\
 $a_{is}$ & Binary parameter equal to 1 if schedule $s$ covers trip $i$\\
 $w_s^{hr}$ & Binary parameter equal to 1 if schedule $s$ includes a charging activity at station $h$ in time interval $r$\\
 $\tau$ & Minimum layover time \\
 $\rho$ & Duration of each time interval\\
 $\sigma^{low}$ & Lower bound on the recommended SoC range\\
 $\sigma^{up}$ & Upper bound on the recommended SoC range\\
 $\sigma^{init}$ & Initial SoC at the beginning of the planning period\\
 $\sigma^{min}$ & Minimum authorized SoC (e.g., 0\%)\\
 $\sigma^{max}$ & Maximum authorized SoC (e.g., 100\%)\\
 $b_d$ & Number of available EBs at depot $d$\\
 $g_h$ & Number of available chargers at charging station $h$\\
 $\epsilon$ & Chance constraint threshold\\
 $e_i(\mu)$ & PMF with finite supports of the energy consumption of trip $i$ (relative to the total battery capacity)\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Overview of the notation of the E-VSP with battery degradation and stochastic energy consumption}
\end{table}
\end{document}

math

Léa Ricard
Guy Desaulniers
Andrea Lodi
Louis-Martin Rousseau

"Chance-constrained battery management strategies for the electric bus scheduling problem" by Léa Ricard, Guy Desaulniers, Andrea Lodi, and Louis-Martin Rousseau, arXiv:2503.19853v1, 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}{ll|lll}
\hline
Model & LASA & PSNR~(dB) $\uparrow$ & SSIM $\uparrow$ \\\hline
Base& - & 15.485 & 0.569 \\
Base+SC&-& 28.702 & 0.784 \\
Base+SC+GRL&-& 28.907&0.785 \\
Base+SC+GRL+LRL&-& 28.673 & 0.784 \\\hline
Base+SC+GRL& $\surd$ & 30.075 & \textbf{0.794} \\
Base+SC+GRL+LRL& $\surd$ &\textbf{30.170}&0.793 \\\hline
\end{tabular}
\end{adjustbox}
\caption{Ablation study on the design of Linear Array Network.}
\end{table}
\end{document}

cs

Keqi Wang
Ziteng Cui
Jieru Jia
Hao Xu
Ge Wu
Yin Zhuang
Lu Chen
Zhiguo Hu
Yuhua Qian

"Linear Array Network for Low-light Image Enhancement" by Keqi Wang, Ziteng Cui, Jieru Jia, Hao Xu, Ge Wu, Yin Zhuang, Lu Chen, Zhiguo Hu, and Yuhua Qian, arXiv:2201.08996v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Robust iterative methods for linear systems with saddle point structure" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, 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{Optimal cost estimates obtained as a ratio of shadow prices from four equivalent formulations of the original problem () -- () where alternative equation formulations (17.a) and (18.a) were optionally replacing the original formulations () and (). \\ }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccr}
\toprule
	\textbf{Equations formulation} & ~~~ & \textbf{Cost estimate $-\xi_1/\lambda_1$} \\
\midrule
	(17), (18) & & 0.794 \\ % file: apples.lst.95.ok.bak
	(17), (18.a) & & 0.767 \\ % file: apples-3rd-formulation.lst.95.ok.bak
	(17.a), (18) & & 1.027 \\ % file: apples-4th-formulation.lst.95.ok.bak
	(17.a), (18.a) & & 1.000 \\ % file: apples-2nd-formulation.lst.95.ok.bak
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Nikolay Khabarov
Alexey Smirnov
Michael Obersteiner

"Shadow prices and optimal cost in economic applications" by Nikolay Khabarov, Alexey Smirnov, and Michael Obersteiner, arXiv:2211.03591v2, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc}
 \hline
 \hline
 & MusicFM & MERT\\
 \hline
 FT & 180M & 51M\\
 Probing & 0 & 0\\
 \hline
 Adapter & 657696 & 322752\\
 \hline
 Prompt & 65536 & 49152\\
 Prefix & 9649152 & 7385088\\
 \hline
 BitFit & 116736 & 50688\\
 SSF & 190464 & 82944\\
 LoRA & 405504 & 165888\\
 \hline
 \hline

 \end{tabular}
\end{adjustbox}
\caption{Comparison of trainable parameters of different systems.}
\end{table}
\end{document}

eess

"Parameter-Efficient Transfer Learning for Music Foundation Models" by Yiwei Ding and Alexander Lerch, arXiv:2411.19371v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Deep Learning Models Meet Financial Data Modalities" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2504.13521v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 $n$ & Error $\|\cdot\|_1$ & Order $\|\cdot\|_1$ & Error
 $\|\cdot\|_{\infty}$ & Order $\|\cdot\|_{\infty}$ \\
 \hline
 40 & 8.73E$-6$ & $-$ & 2.44E$-5$ & $-$ \\
 \hline
 80 & 2.70E$-7$ & 5.01 & 7.35E$-7$ & 5.05 \\
 \hline
 160 & 8.45E$-9$ & 5.00 & 2.31E$-8$ & 4.99 \\
 \hline
 320 & 2.64E$-10$ & 5.00 & 6.95E$-10$ & 5.06 \\
 \hline
 640 & 8.26E$-12$ & 5.00 & 2.13E$-11$ & 5.03 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Lagrange extrapolation (without filter).}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation Parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc} % 去掉了竖线
			\toprule % 使用 top rule
			\textbf{Parameter} & \textbf{Value} \\
			\midrule % 使用 mid rule
			Number of MEC servers $M$ & 5 \\
			Plateau factors of MZipf $q_m$ & [100, 200, 90, 40, 80] \\
			Zipf factors of MZipf $k_m$ & [0.60, 0.60, 0.75, 0.90, 0.90] \\
			Data size of contents $\eta_c$ & 1$\sim$8 GB \\
			Downloading payment of contents $\phi_c$ & 0.05$\sim$0.5 HKD \\
			Number of DNN layers & 6 \\
			Number of neurons per hidden layer & 128 \\
			Learning rate $\xi$ & 0.002 \\
			Update coefficient $\tau$ & 0.005 \\
			Discounted factor $\gamma$ & 0.99 \\
			\bottomrule % 使用 bottom rule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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


\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}{cccccc}
\hline\hline
& \multicolumn{5}{c}{\hspace{3ex} \textbf{State}}\\
\cline{2-6}
 & $S_{1}$ & $S_{2}$ & $S_{3}$ & $S_{4}$ & $S_{5+}$\\
\hline
 &586.20 & -4045.03& -657.02 & 0.00 & 0.00 \\
 & 1,247.06 & -1,169.81& 1,179.08 & 965.04 & 611.74 \\
& 2,584.57& 1,669.23& 3,615.08 & 3,576.90 & 2,596.18 \\
 & 7,955.07& 3,805.08& 4,775.55 & 5,070.10 & 3,501.23 \\
 \hline\hline
\end{tabular}
\end{adjustbox}
\caption{Splitting points for the previous payment (deltPay) in each data set.}
\end{table}
\end{document}

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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|ccccccc|}
\hline
$p$ & $0.01$ & $0.1$ & $0.3$ & $0.5$ & $0.6$ & $0.8$ & $0.9$ \\
\hline
$1/\lambda$ &$10^{139}$ &$6\times10^{11}$ & $558.46$ &$11.99$ &$4.9$ &$1.73$ &$1.26$\\
\hline
\end{tabular}
\end{table}
\end{document}

math

Sergey Foss
Takis Konstantopoulos
Bastien Mallein
Sanjay Ramassamy

"Last passage percolation and limit theorems in Barak-Erdős directed random graphs and related models" by Sergey Foss, Takis Konstantopoulos, Bastien Mallein, and Sanjay Ramassamy, arXiv:2312.02884v2, 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{Test SROCC and Pairwise Ranking Accuracy (Pairwise) for lecture ranking models (task 2) with standard error (higher values are better).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
\hline
Model & \multicolumn{2}{c}{GBM} & \multicolumn{2}{c}{RF} \\
Feature Set & SROCC & Pairwise & SROCC & Pairwise \\ \hline
Content-based & \textbf{.6241$\pm$.0291} & \textbf{.7221$\pm$.0102} & .6190$\pm$.0237 & .7202$\pm$.0086 \\
+ Wiki-based & .6245$\pm$.0339 & .7224$\pm$.0115 & \textbf{.6251$\pm$.0322} & \textbf{.7225$\pm$.0123} \\
+ Video-specific & \textbf{.6761$\pm$.0434} & \textbf{.7446$\pm$.0183} & .6758$\pm$.0458 & .7446$\pm$.0197 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Sahan Bulathwela
Maria Perez-Ortiz
Emine Yilmaz
John Shawe-Taylor

"VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement" by Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, and John Shawe-Taylor, arXiv:2011.02273v1, 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{\textbf{Top \& Bottom 5 UK Geographies by Growth in Overall Card Spending \\ (November 2020, Year-on-Year \% Changes)}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|cc} \hline
		Top 5 & \% Change & Bottom 5 & \% Change \\
		\hline
		Outer London - South & 16.8\% & Southern Scotland & 3.9\% \\
		Outer London - East & 15.8\% & Cumbria & 3.9\% \\
		Gloucestershire, Wiltshire & 14.6\% & Derbyshire \& Nottinghamshire & 4.3\% \\
		Berkshire, Bucks \& Oxfordshire & 13.5\% & West Wales & 5.2\% \\
		Hampshire \& Isle of Wight & 12.1\% & Lancashire & 5.3\% \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

John Gathergood
Fabian Gunzinger
Benedict Guttman-Kenney
Edika Quispe-Torreblanca
Neil Stewart

"Levelling Down and the COVID-19 Lockdowns: Uneven Regional Recovery in UK Consumer Spending" by John Gathergood, Fabian Gunzinger, Benedict Guttman-Kenney, Edika Quispe-Torreblanca, and Neil Stewart, arXiv:2012.09336v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Maxime Ryckewaert
Diego Marcos
Christophe Botella
Maximilien Servajean
Pierre Bonnet
Alexis Joly

"Applying the maximum entropy principle to neural networks enhances multi-species distribution models" by Maxime Ryckewaert, Diego Marcos, Christophe Botella, Maximilien Servajean, Pierre Bonnet, and Alexis Joly, arXiv:2412.19217v3, 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{\\Sample size (2011-14)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 & Black & Non-Black & Total \\
 \midrule
 \multicolumn{4}{c}{A. FairFace} \\
 No.\ of players & 178 & 69 & 247 \\
 \quad Mean L* value & 47.45 & 64.92 & 52.33 \\
 \quad \quad (SD) & (10.59) & (7.62) & (12.59) \\
 \quad \quad Min–max & 20.52–71.26 & 41.85–79.4813 & 20.52–79.4813 \\
 \addlinespace
 No.\ of referees & 17 & 24 & 41 \\
 \quad Mean L* value & 43.01 & 57.88 & 51.71 \\
 \quad \quad (SD) & (9.40) & (7.47) & (11.07) \\
 \quad \quad Min–max & 30.06–60.75 & 43.21–72.92 & 30.06–72.92 \\
 \addlinespace
 No.\ of games & 816 & 813 & 816 \\
 No.\ of player–games & 12,377 & 3,332 & 15,709 \\
 No.\ of player–minutes & 265,311 & 64,516 & 329,827 \\
 \multicolumn{4}{c}{B. Human raters} \\
 No.\ of players & 178 & 69 & 247 \\
 \quad Mean L* value & 47.50 & 64.79 & 52.33 \\
 \quad \quad (SD) & (10.64) & (7.77) & (12.59) \\
 \quad \quad Min–max & 20.52–71.26 & 41.85–79.4813 & 20.52–79.4813 \\
 \addlinespace
 No.\ of referees & 20 & 21 & 41 \\
 \quad Mean L* value & 44.94 & 58.17 & 51.71 \\
 \quad \quad (SD) & (10.06) & (7.69) & (11.07) \\
 \quad \quad Min–max & 30.06–63.32 & 43.21–72.92 & 30.06–72.92 \\
 \addlinespace
 No.\ of games & 816 & 813 & 816 \\
 No.\ of player–games & 12,267 & 3,442 & 15,709 \\
 No.\ of player–minutes & 262,005 & 67,822 & 329,827 \\
 \multicolumn{4}{c}{C. Referee crew compositions} \\
 No.\ of unique crews & & & 666 \\
 \quad Mean abs.\ L* diff. & & & 7.87 \\
 \quad \quad (SD) & & & (3.91) \\
 \quad \quad Min–max & & & 0.414–25.06 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Kenneth Colombe
Alex Krumer
Rosa Lavelle-Hill
Tim Pawlowski

"Racial bias, colorism, and overcorrection" by Kenneth Colombe, Alex Krumer, Rosa Lavelle-Hill, and Tim Pawlowski, arXiv:2508.10585v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Known classification of weighing matrices of order $n$ and weight $9$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
\noalign{\hrule height1pt}
$n$ & $N(n,9)$ & References\\
\hline
$1,2,\ldots,9,11$ & $0$ & \\
$10$ & $9$ & \\
$12$ & $4$ & \\
$13$ &$8$ & \\
$14$ & $7$ & \\
$15$ & $37$& \\
$16$ & $704$ & \\
$17$ & $2360$ & \\
$18$ &$11891$ & \\
\noalign{\hrule height1pt}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Makoto Araya
Masaaki Harada
Hadi Kharaghani
Sho Suda
Wei-Hsuan Yu

"Unbiased weighing matrices of weight $9$" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, 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}{llllll}\toprule
Dataset & \#R & \#E & Train & Test & Val. \\ \midrule
FB15K237	& 237	& 14,541 &	272,115	& 17,535 &	20,466 \\
WN18RR	& 11	& 40,943 &86,835& 3,134&3,034\\
FB13	& 13	& 75,043 &	316,232	& 23,733 &	5,908 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Statistics of the datasets}
\end{table}
\end{document}

cs

Danushka Bollegala
Huda Hakami
Yuichi Yoshida
Ken-ichi Kawarabayashi

"RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding" by Danushka Bollegala, Huda Hakami, Yuichi Yoshida, and Ken-ichi Kawarabayashi, arXiv:2101.10070v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sensitivity analysis for expected purchasing time}
\begin{tabular}{cccccccccc}
		\toprule %添加表格头部粗线
		 Parameter	& base case & $\gamma=0.6$ & $\gamma=0.9$ & $m=0.026$ &
		 $m=0.028$ & $\rho=0.04$ &$\rho=0.05$ \\
		 \midrule
	$\mathbb{E}[\eta(z;b)]$	&1.87 &3.58 &0.03& 1.56 &1.08&0.1 & 0.5 \\
	\midrule
	Parameter	&$B=1000$&$B=1500$ &$r=0.02$& $r=0.03$ &$y=200$&$y=250$ & 0.5 \\
	\midrule
	$\mathbb{E}[\eta(z;b)]$	&0.22 &0.01 &0.03& 0.8 &0.02&0.1 & 0.5 \\			\bottomrule
	\end{tabular}
\end{table}
\end{document}

q-fin

"Striking the Balance: Life Insurance Timing and Asset Allocation in Financial Planning" by An Chen, Giorgio Ferrari, and Shihao Zhu, arXiv:2312.02943v1, 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{A quantitative comparison of the SOTA SR models pre-trained on the ImageNet dataset ($\times 2$) and fine-tuned on the AID dataset ($\times 2$, and $\times 4$) in terms of FID, PSNR, SSIM, SAM, SRE, NIQE, and AG. The tests are conducted on the dev split test data. The best values are highlighted }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccccc}
\toprule
\multicolumn{10}{c}{ImageNet} \\\midrule
Category & Method & Scale & FID $\downarrow$ & PSNR$\uparrow$ & SSIM$\uparrow$ & SAM $\downarrow$ & SRE$\downarrow$ & NIQE $\downarrow$ & AG $\uparrow$ \\
\multirow{2}{*}{GAN} & LE-GAN & \multirow{9}{*}{$\times 2$} & 48.39 & 48.27 & 0.85 & \textbf{5.61} & \textbf{5.16} & 12.82 & 4.51 \\
 & ESRGAN-DP & & 53.26 & 48.02 & 0.84 & 6.97 & 8.35 & 12.24 & 4.81 \\
\multirow{2}{*}{VAE} & RefVAE & & 47.15 & 45.61 & 0.8 & 7.66 & 9.04 & 13.27 & 4.98 \\
 & LSR-SR & & 47.44 & 45.49 & 0.84 & 7.84 & 8.28 & 12.84 & 5.12 \\
\multirow{5}{*}{Diffusion} & SR3 & & 40.52 & 50.47 & \textbf{0.95} & 6.55 & 7.32 & 10.48 & 4.82 \\
 & IRSDE & & 45.03 & 48.56 & 0.85 & 6.62 & 7.94 & 12.42 & 5.36 \\
 & EDiffSR & & \textbf{40.41} & 49.85 & 0.88 & 6.68 & 7.17 & 11.04 & 5.62 \\
 & LWTDM & & 43.98 & 46.3 & 0.77 & 7.8 & 9.07 & 14.27 & 4.33 \\
 & Proposed & & 40.45 & \textbf{52.97} & \textbf{0.95} & 5.79 & 5.65 & \textbf{9.86} & \textbf{5.85} \\
\midrule
\multicolumn{10}{c}{AID} \\
\midrule
Category & Method & Scale & FID $\downarrow$ & PSNR$\uparrow$ & SSIM$\uparrow$ & SAM $\downarrow$ & SRE$\downarrow$ & NIQE $\downarrow$ & AG $\uparrow$ \\
\multirow{2}{*}{GAN} & LE-GAN & \multirow{9}{*}{$\times 2$} & 53.13 & 39.8 & 0.71 & 8.09 & \textbf{7.68} & 14.3 & 3.9 \\
 & ESRGAN-DP & & 55.2 & 39.62 & 0.7 & 9.66 & 10.71 & 16.08 & 3.91 \\
\multirow{2}{*}{VAE} & RefVAE & & 59.76 & 37.7 & 0.67 & 10.39 & 11.46 & 17.01 & 3.73 \\
 & LSR-SR & & 60.21 & 37.22 & 0.7 & 10.58 & 11.37 & 17.06 & 3.65 \\
\multirow{5}{*}{Diffusion} & SR3 & & 51.43 & 40.8 & 0.67 & 10.07 & 10.31 & 13.2 & 3.98 \\
 & IRSDE & & 53.8 & 40.41 & 0.71 & 9.35 & 10.46 & 15.7 & 4.06 \\
 & EDiffSR & & 51.49 & \textbf{41.49} & 0.73 & 9.11 & 9.99 & 14.98 & 4.07 \\
 & LWTDM & & 55.06 & 39.56 & 0.64 & 10.72 & 12.03 & 18.04 & 3.65 \\
 & Proposed & & \textbf{50.9} & 41.48 & \textbf{0.75} & \textbf{8.07} & 7.71 & \textbf{12.03} & \textbf{4.24} \\
\midrule
\multirow{2}{*}{GAN} & LE-GAN & \multirow{9}{*}{$\times 4$} & 56.19 & 35.14 & 0.7 & 10.14 & 9.12 & 16.32 & 3.46 \\
 & ESRGAN-DP & & 61.05 & 35.64 & 0.63 & 11.31 & 12.75 & 18.67 & 2.82 \\
\multirow{2}{*}{VAE} & RefVAE & & 66.04 & 33.82 & 0.6 & 12.01 & 13.24 & 19.26 & 3.35 \\
 & LSR-SR & & 66.25 & 32.97 & 0.63 & 11.79 & 13.12 & 19.04 & 3.17 \\
\multirow{5}{*}{Diffusion} & SR3 & & 66.02 & 35.85 & 0.6 & 11.51 & 11.84 & 14.86 & 3.4 \\
 & IRSDE & & 59.58 & 35.75 & 0.64 & 11.22 & 11.82 & 17.36 & 2.98 \\
 & EDiffSR & & 56.67 & 37.17 & 0.66 & 10.06 & 11.05 & 16.8 & 3.55 \\
 & LWTDM & & 67.34 & 32.23 & 0.58 & 12.63 & 14.11 & 20.11 & 2.52 \\
 & Proposed & & \textbf{53.18} & \textbf{38.64} & \textbf{0.71} & \textbf{8.97} & \textbf{8.47} & \textbf{13.75} & \textbf{4.14}\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yue Shi
Liangxiu Han
Darren Dancy
Lianghao Han

"WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images" by Yue Shi, Liangxiu Han, Darren Dancy, and Lianghao Han, arXiv:2412.18996v1, 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{Account Balance Scenario Test}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|}
\hline
& \textbf{Quantile-Attention LSTM} & \textbf{FutureQuant Transformer} \\ \hline
Initial funds in account: & \$1,000,000 & \$1,000,000 \\ \hline
Account balance after one month: & \$1,122,540 & \$1,143,160 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Wenhao Guo
Yuda Wang
Zeqiao Huang
Changjiang Zhang
Shumin ma

"Trading Under Uncertainty: A Distribution-Based Strategy for Futures Markets Using FutureQuant Transformer" by Wenhao Guo, Yuda Wang, Zeqiao Huang, Changjiang Zhang, and Shumin ma, arXiv:2505.05595v1, 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{\small Definition of MPC baselines.}
\begin{tabular}{lll}
 \toprule
 \textbf{Name} & \textbf{Formulation} & \textbf{Solver} \\
 \midrule
 MPC($N$) & Problem~, translated to QP via~-. & OSQP~ \\
 MPC-T($N$) & \begin{minipage}{8cm}Problem~ with terminal cost $\rho\| x_N \|^2$ added to the objective, translated to QP similarly to~-. Weight $\rho$ grid-searched over $\{ 1, 10, 100\}$, picking the best-performing one.\end{minipage} & OSQP~ \\
 Tube & \begin{minipage}{8cm} Tube MPC implemented by open-sourced code of , simplified for linear systems. Horizon $N=16$ and terminal weight $\rho = 10$. Tube size grid-searched over $\{ 0.05, 0.1, 0.2, 0.3 \}$, since using the actual upper bound on disturbance magnitude may result in infeasibility.\end{minipage} & Mosek~ \\
 Scenario & \begin{minipage}{8cm}Scenario-based MPC implemented by the do-mpc toolbox~. Horizon $N = 16$ and terminal weight $\rho = 10$. Three scenarios are generated for each of $A, B, w$. \end{minipage} & IPOPT~ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Yiwen Lu
Zishuo Li
Yihan Zhou
Na Li
Yilin Mo

"MPC-Inspired Reinforcement Learning for Verifiable Model-Free Control" by Yiwen Lu, Zishuo Li, Yihan Zhou, Na Li, and Yilin Mo, arXiv:2312.05332v5, 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{TL nominal parameters.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|}
\hline
Parameter & Zero Sequence & Positive Sequence\\ \hline
R ($\Omega$/$(km)$) & $0.3864$ & $0.01273$\\
L ($mH$/$(km)$) & $4.1264$ & $0.9337$ \\
C ($\mu$F/$(km)$) & $7.751\times 10^{-3}$ & $12.74\times 10^{-3}$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Fatemeh Mohammadi Shakiba
Milad Shojaee
S. Mohsen Azizi
Mengchu Zhou

"Transfer Learning for Fault Diagnosis of Transmission Lines" by Fatemeh Mohammadi Shakiba, Milad Shojaee, S. Mohsen Azizi, and Mengchu Zhou, arXiv:2201.08018v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Data Driven Perspectives on Knot Theory

Pawel Dlotko
Davide Gurnari
Radmila Sazdanovic

"Data Driven Perspectives on Knot Theory" by Pawel Dlotko, Davide Gurnari, and Radmila Sazdanovic, arXiv:2503.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Antonio Cabrales
Manu García
David Ramos Muñoz
Angel Sánchez

"The Interactions of Social Norms about Climate Change: Science, Institutions and Economics" by Antonio Cabrales, Manu García, David Ramos Muñoz, and Angel Sánchez, arXiv:2208.09239v1, 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{Proportion of cases in which 95\% confidence intervals contain 1 for $\theta$ parameter, for $\sigma^2=5$ in the scenario 2. \\ $\theta_0$ means $\theta$ when $x=0$ and $\theta_1$ means $\theta$ when $x=1$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|ccccccccccc}
\hline
 & & \multicolumn{11}{c}{$n$}\\
\hline
Parameter & Model & 100 & 200 & 400 & 800 & 1000 & 2000 & 5000 & 8000 & 10000 & 12000 & 15000 \\
\hline
\multirow{2}{*}{$\theta_0$} & DD model & 0.001	& 0.002	& 0 &	0 &	0 &	0 &	0 &	0 &	0 &	0	& 0 \\
 & DD-PVF model & 0.089	& 0.042 &	0.019 &	0.028 &	0.033 &	0.026 &	0.032 &	0.036	& 0.039 &	0.041 &	0.039
 \\
\hline
\multirow{2}{*}{$\theta_1$} & DD model & 0.56	& 0.68 & 	0.766 & 	0.83 & 	0.793 & 	0.547 & 	0.055 & 	0.001 & 	0	& 0	& 0
 \\
 & DD-PVF model & 0.73	& 0.797 &	0.862 &	0.927 &	0.935 &	0.954	& 0.969 &	0.963 &	0.965 &	0.967 &	0.962
 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Long-Term Dagum-PVF Frailty Regression Model: Application in Health Studies" by Agatha Rodrigues and Patrick Borges, arXiv:2311.06415v1, 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{Accuracies of average human raters and the best LLM for 24 skills in BIG-bench Lite; information from Figure 4 of . ``NA'' represents that the accuracy is unclear from the figure.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Skill} & \textbf{Accruacy of average human rater} & \textbf{Accuracy of the best LLM} \\ \hline
auto\_debugging & 0.15 & NA \\ \hline
bbq\_lite\_json & 0.73 & 0.73 \\ \hline
code\_line\_description & 0.6 & 0.46 \\ \hline
conceptual\_combinations & 0.83 & 0.48 \\ \hline
conlang\_translation & NA & 0.5 \\ \hline
emoji\_movie & 0.94 & 0.9 \\ \hline
formal\_fallacies & 0.55 & 0.53 \\ \hline
hindu\_knowledge & NA & 0.75 \\ \hline
known\_unknowns & 0.8 & 0.68 \\ \hline
language\_identification & NA & 0.36 \\ \hline
linguistics\_puzzles & NA & NA \\ \hline
logic\_grid\_puzzle & 0.4 & 0.36 \\ \hline
logical\_deduction & 0.4 & 0.36 \\ \hline
misconceptions\_russian & NA & 0.68 \\ \hline
novel\_concepts & 0.65 & 0.57 \\ \hline
operators & 0.46 & 0.36 \\ \hline
parsinlu\_reading\_comprehension & NA & 0 \\ \hline
play\_dialog\_same\_or\_different & NA & 0.63 \\ \hline
repeat\_copy\_logic & 0.39 & 0.12 \\ \hline
strange\_stories & 0.8 & 0.63 \\ \hline
strategyqa & 0.62 & 0.6 \\ \hline
symbol\_interpretation & 0.38 & 0.25 \\ \hline
vitamin\_fact\_verification & 0.63 & 0.57 \\ \hline
winowhy & NA & 0.59 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

L. Elisa Celis
Lingxiao Huang
Nisheeth K. Vishnoi

"A Mathematical Framework for AI-Human Integration in Work" by L. Elisa Celis, Lingxiao Huang, and Nisheeth K. Vishnoi, arXiv:2505.23432v2, 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{The mean test accuracy (\%) on 5-way Omniglot classification across 3 different runs. The 95\% confidence intervals are displayed as $\pm x$, and calculated over all runs and meta-test tasks (600 per run). The plain LSTM is outperformed by MAML. All methods (except LSTM) used a fully-connected feed-forward classifier. The best performances are dislpayed in bold font.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
 \toprule
 Technique & parameters & 1-shot & 5-shot \\
 \midrule
 MAML & $247\,621$ & 84.1 $\pm$ 0.90 & \textbf{93.5} $\pm$ 0.30 \\
 ProtoNet & $247\,621$ & 83.6 $\pm$ 0.88 & 93.4 $\pm$ 0.29 \\
 \midrule
 LSTM & $13\,530\,097$ & 72.6 $\pm$ 0.90 & 84.8 $\pm$ 0.50 \\
 OP-LSTM (ours) & $249\,167$ &\textbf{84.3} $\pm$ 0.90 & 91.8 $\pm$ 0.30 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Are LSTMs Good Few-Shot Learners?

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

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


\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
Model & Precision & Recall & F1 \\
\hline
{LSTM$_{Attn}$} & 52.9 &	52.8&	 52.9 \\
{LSTM$_{MT}$} & 59.5 &	59.3&	\textbf{59.4} \\
\hline

{BERT$_{orig}$} & 57.4 & 57.4 & 57.4\\
{BERT$_{MT}$} & 61.8 & 61.7 & \textbf{61.8} \\
{BERT$_{{MT}_{uncert}}$} & 64.1 & 63.5 & \textbf{64.0} \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Evaluations of sarcasm detection on the $test$ set of $IAC_{orig}$.}
\end{table}
\end{document}

cs

Debanjan Ghosh
Ritvik Shrivastava
Smaranda Muresan

""Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space" by Debanjan Ghosh, Ritvik Shrivastava, and Smaranda Muresan, arXiv:2101.10952v1, 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{Data statistics of synthetic traffic dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcrrrr}
 \toprule
 \multirow{3}{*}{\textbf{Dataset}}
 & \multirow{3}{*}{\textbf{Total vehicles}}
 & \multicolumn{4}{c}{\textbf{Arrival rate (vehicles/300s)}} \\
 \cmidrule(lr){3-6}
 & & \textbf{Mean} & \textbf{Std} & \textbf{Max} & \textbf{Min} \\
 \midrule
 {mixed$_{l}$} & 2570 & 214.17 & 198.41 & 600 & 60 \\
 {mixed$_{h}$} & 4770 & 397.50 & 420.75 & 1200 & 60 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Liwen Zhu
Peixi Peng
Zongqing Lu
Xiangqian Wang
Yonghong Tian

"MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control" by Liwen Zhu, Peixi Peng, Zongqing Lu, Xiangqian Wang, and Yonghong Tian, arXiv:2101.00746v5, 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}{l|c}
 \hline
 Training data & miniImageNet 5way1shot \\
 \hline
 Support set only & $56.37 \pm 0.68$\\
 Support set + 1 feature from the nearest class & $62.39 \pm 0.49$\\
 Support set + 5 features from the nearest class & $59.73 \pm 0.42$\\
 Support set + 10 features from the nearest class & $58.93 \pm 0.49$\\
 Support set + 100 features from the nearest class & $57.33 \pm 0.48$\\
 Support set + 100 features sampled from calibrated distribution & $\textbf{68.53} \pm \textbf{0.32}$\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{The comparison with nearest class feature augmentation.}
\end{table}
\end{document}

cs

"Free Lunch for Few-shot Learning: Distribution Calibration" by Shuo Yang, Lu Liu, and Min Xu, arXiv:2101.06395v3, 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 main producers of USVs}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \toprule
	 Company & Country \\
	 \midrule
	 Kongsberg Maritime & Norway \\
	 Teledyne Technologies & USA \\
	 ECA Group & France \\
 Elbit Systems Ltd. & USA \\
 Textron Systems & USA \\
 ASV Global & UK \\
 Atlas Elektronik & UK \\
 Rafael Advanced Defense Systems & Israel \\
 L3 Harries Technologies & USA \\
 Saab & Sweden \\
 Fugro & Netherlands \\
 \bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jiajie Xu
Xabier Irigoien
Mohamed-Slim Alouini

"State-of-the-Art Underwater Vehicles and Technologies Enabling Smart Ocean: Survey and Classifications" by Jiajie Xu, Xabier Irigoien, and Mohamed-Slim Alouini, arXiv:2412.18667v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Hanhan Zhou
Tian Lan
Guru Venkataramani
Wenbo Ding

"On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning" by Hanhan Zhou, Tian Lan, Guru Venkataramani, and Wenbo Ding, arXiv:2201.11803v2, 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}
 & Success \% & Distance \% \\
 \hline
 Without Setup Policies & 1.9 & 36.3 \\
 With Setup Policies & \textbf{71.2} & \textbf{80.2} \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Success rate and percentage of the total terrain length travelled from 1000 episodes of all 5 terrain types, randomly shuffled each episode.}
\end{table}
\end{document}

cs

Brendan Tidd
Nicolas Hudson
Akansel Cosgun
Jurgen Leitner

"Learning Setup Policies: Reliable Transition Between Locomotion Behaviours" by Brendan Tidd, Nicolas Hudson, Akansel Cosgun, and Jurgen Leitner, arXiv:2101.09391v2, 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[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{DNSMOS scores on the Interspeech 2020 DNS Challenge blind test set. Discriminative models are shown with a white background; generative models are highlighted with a gray background. UniFlow variants correspond to different generative objectives.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
\toprule
\textbf{Model} & \multicolumn{3}{c}{\textbf{With Reverb}} & \multicolumn{3}{c}{\textbf{No Reverb}} \\
\cmidrule(lr){2-4} \cmidrule(lr){5-7}
& SIG & BAK & OVRL & SIG & BAK & OVRL \\
\midrule
Noisy & 1.76 & 1.50 & 1.39 & 3.39 & 2.62 & 2.48 \\
\midrule
Conv-TasNet & 2.42 & 2.71 & 2.01 & 3.09 & 3.34 & 3.00 \\
DEMUCS & 2.86 & 3.90 & 2.55 & 3.58 & 4.15 & 3.35 \\
\rowcolor{gray!20} SELM & 3.16 & 3.58 & 2.70 & 3.51 & 4.10 & 3.26 \\
\rowcolor{gray!20} AnyEnhance & 3.50 & 4.04 & 3.20 & 3.64 & 4.18 & 3.42 \\
\rowcolor{gray!20} LLaSE-G1 & 3.59 & 4.10 & 3.33 & 3.66 & 4.17 & 3.42 \\
\rowcolor{gray!20} FlowSE & \textbf{3.61} & 4.11 & \textbf{3.34} & 3.69 & 4.20 & 3.45 \\
\midrule
\rowcolor{gray!20} UniFlow$_\text{DDPM}$ & 3.59 & \textbf{4.12} & 3.32 & \textbf{3.72} & \textbf{4.21} & \textbf{3.48} \\
\rowcolor{gray!20} UniFlow$_\text{FM}$ & 3.54 & 4.07 & 3.28 & 3.68 & 4.17 & 3.45 \\
\rowcolor{gray!20} UniFlow$_\text{MF}$ & 3.50 & 4.02 & 3.24 & 3.64 & 4.22 & 3.43 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

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


q-fin

Soohan Kim
Seok-Bae Yun
Hyeong-Ohk Bae
Muhyun Lee
Youngjoon Hong

"Physics-Informed Convolutional Transformer for Predicting Volatility Surface" by Soohan Kim, Seok-Bae Yun, Hyeong-Ohk Bae, Muhyun Lee, and Youngjoon Hong, arXiv:2209.10771v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Introducing the $σ$-Cell: Unifying GARCH, Stochastic Fluctuations and Evolving Mechanisms in RNN-based Volatility Forecasting" by German Rodikov and Nino Antulov-Fantulin, arXiv:2309.01565v1, 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}{|l|l|l|}
 \hline
 Controller & Parameters System & Parameters Specification \\ \hline
 P (no additional FCR-Control) & $D_i$ & $D, H$ \\ \hline
 PI & $D_i, K_i$ & $D, K, H$ \\ \hline
 PLI & $D_i, G_i, T_i$ & $D, G, T, H$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Summary of the tuneable parameters for the system and the specification.}
\end{table}
\end{document}

eess

"Probabilistic Behavioral Aggregation: A Case Study on the Nordic Power Grid" by Anna Büttner and Frank Hellmann, arXiv:2412.11899v1, 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}{r|c|c|c|}
 & \multicolumn{3}{c|}{\textbf{Fifty-year mobility type}} \\
 & $20 \to 50$ & $50 \to 80$ & $20 \to 80$ \\
 \hline \hline
 Curved $R(p)$ with randomness & 3.54\% & 1.92\% & 0.005\% \\
 Flat $R(p)$ with randomness & 16.05\% & 9.78\% & 1.13\%
 \end{tabular}
\caption{Mobility results for the given models. The 50-year $(P_1 \to P_2)$-Mobility is the probability that a member of the $P_1$-percentile at time zero has an income level above the $P_2$-percentile at a final time $T$. Results are from using the parameter choices listed in .}
\end{table}
\end{document}

math

"Inequality and Mobility in a Minimal Model for Evolving Income Distributions" by Scott A. McKinley and Gary A. Hoover, arXiv:2503.17375v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.