\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ABT variables, part 5}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
 \hline
 & & \\
 Nr & Name & Description \\
 & & \\
 \hline
161 & ags12\_Min\_CMaxI\_Due & Min calc. on last 12 mths on max Cust. due for Ins product \\
162 & ags12\_Min\_CMaxA\_Due & Min calc. on last 12 mths on max Cust. due for all product \\
163 & agr12\_Min\_CMaxC\_Days & Min calc. on last 12 mths on unmissing max Cust. days for Css product \\
164 & agr12\_Min\_CMaxI\_Days & Min calc. on last 12 mths on unmissing max Cust. days for Ins product \\
165 & agr12\_Min\_CMaxA\_Days & Min calc. on last 12 mths on unmissing max Cust. days for all product \\
166 & agr12\_Min\_CMaxC\_Due & Min calc. on last 12 mths on unmissing max Cust. due for Css product \\
167 & agr12\_Min\_CMaxI\_Due & Min calc. on last 12 mths on unmissing max Cust. due for Ins product \\
168 & agr12\_Min\_CMaxA\_Due & Min calc. on last 12 mths on unmissing max Cust. due for all product \\
169 & ags3\_Min\_CMaxC\_Days & Min calc. on last 3 mths on max Cust. days for Css product \\
170 & ags3\_Min\_CMaxI\_Days & Min calc. on last 3 mths on max Cust. days for Ins product \\
171 & ags3\_Min\_CMaxA\_Days & Min calc. on last 3 mths on max Cust. days for all product \\
172 & ags3\_Min\_CMaxC\_Due & Min calc. on last 3 mths on max Cust. due for Css product \\
173 & ags3\_Min\_CMaxI\_Due & Min calc. on last 3 mths on max Cust. due for Ins product \\
174 & ags3\_Min\_CMaxA\_Due & Min calc. on last 3 mths on max Cust. due for all product \\
175 & agr3\_Min\_CMaxC\_Days & Min calc. on last 3 mths on unmissing max Cust. days for Css product \\
176 & agr3\_Min\_CMaxI\_Days & Min calc. on last 3 mths on unmissing max Cust. days for Ins product \\
177 & agr3\_Min\_CMaxA\_Days & Min calc. on last 3 mths on unmissing max Cust. days for all product \\
178 & agr3\_Min\_CMaxC\_Due & Min calc. on last 3 mths on unmissing max Cust. due for Css product \\
179 & agr3\_Min\_CMaxI\_Due & Min calc. on last 3 mths on unmissing max Cust. due for Ins product \\
180 & agr3\_Min\_CMaxA\_Due & Min calc. on last 3 mths on unmissing max Cust. due for all product \\
181 & ags6\_Min\_CMaxC\_Days & Min calc. on last 6 mths on max Cust. days for Css product \\
182 & ags6\_Min\_CMaxI\_Days & Min calc. on last 6 mths on max Cust. days for Ins product \\
183 & ags6\_Min\_CMaxA\_Days & Min calc. on last 6 mths on max Cust. days for all product \\
184 & ags6\_Min\_CMaxC\_Due & Min calc. on last 6 mths on max Cust. due for Css product \\
185 & ags6\_Min\_CMaxI\_Due & Min calc. on last 6 mths on max Cust. due for Ins product \\
186 & ags6\_Min\_CMaxA\_Due & Min calc. on last 6 mths on max Cust. due for all product \\
187 & agr6\_Min\_CMaxC\_Days & Min calc. on last 6 mths on unmissing max Cust. days for Css product \\
188 & agr6\_Min\_CMaxI\_Days & Min calc. on last 6 mths on unmissing max Cust. days for Ins product \\
189 & agr6\_Min\_CMaxA\_Days & Min calc. on last 6 mths on unmissing max Cust. days for all product \\
190 & agr6\_Min\_CMaxC\_Due & Min calc. on last 6 mths on unmissing max Cust. due for Css product \\
191 & agr6\_Min\_CMaxI\_Due & Min calc. on last 6 mths on unmissing max Cust. due for Ins product \\
192 & agr6\_Min\_CMaxA\_Due & Min calc. on last 6 mths on unmissing max Cust. due for all product \\
193 & ags9\_Min\_CMaxC\_Days & Min calc. on last 9 mths on max Cust. days for Css product \\
194 & ags9\_Min\_CMaxI\_Days & Min calc. on last 9 mths on max Cust. days for Ins product \\
195 & ags9\_Min\_CMaxA\_Days & Min calc. on last 9 mths on max Cust. days for all product \\
196 & ags9\_Min\_CMaxC\_Due & Min calc. on last 9 mths on max Cust. due for Css product \\
197 & ags9\_Min\_CMaxI\_Due & Min calc. on last 9 mths on max Cust. due for Ins product \\
198 & ags9\_Min\_CMaxA\_Due & Min calc. on last 9 mths on max Cust. due for all product \\
199 & agr9\_Min\_CMaxC\_Days & Min calc. on last 9 mths on unmissing max Cust. days for Css product \\
200 & agr9\_Min\_CMaxI\_Days & Min calc. on last 9 mths on unmissing max Cust. days for Ins product \\
201 & agr9\_Min\_CMaxA\_Days & Min calc. on last 9 mths on unmissing max Cust. days for all product \\
202 & agr9\_Min\_CMaxC\_Due & Min calc. on last 9 mths on unmissing max Cust. due for Css product \\
203 & agr9\_Min\_CMaxI\_Due & Min calc. on last 9 mths on unmissing max Cust. due for Ins product \\
204 & agr9\_Min\_CMaxA\_Due & Min calc. on last 9 mths on unmissing max Cust. due for all product \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Credit acceptance process strategy case studies - the power of Credit Scoring" by Karol Przanowski, arXiv:1403.6531v1, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results on the COIL-100 dataset.}
\begin{tabular}{|c|c|}
\hline
\textbf{Method} & \textbf{Accuracy}\\
\hline
\textbf{BoAW} & \textbf{99.77}\%\\
\hline
\textbf{ARSRG}$emb$ & 99.55\%\\
\hline
BoVW & 51.71\%\\
\hline
gdFil & 32.61\%\\
\hline
VFSR& 91.60\%\\
\hline
APGM & 99.11\%\\
\hline
VEAM & 99.44\%\\
DTROD-AdaBoost & 84.50\%\\
\hline
RSW+Boosting & 89.20\%\\
\hline
Sequential Patterns & 89.80\%\\
\hline
LAF & 99.40\%\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccc}
 \toprule
 Method & PSNR $\uparrow$ & SSIM $\uparrow$ & LPIPS $\downarrow$ & {\# Gaussians} $\downarrow$ \\
 \toprule
 GaussianAvatar~ & 26.53 & 0.9087 & 0.1487 & {98083} \\
 \hline
 Ours (w/o perceptual) & 27.03 & 0.9116 & 0.1447 & \textbf{31875} \\
 Ours (w/o wrinkle reg.) & 28.10 & 0.9216 & 0.1312 & 33998 \\
 Ours (w/o mouth subdivision) & 28.35 & 0.9321 & 0.1244 & 34917 \\
 Ours (w/o Color MLP) & {28.93} & {0.9366} & {0.1235} & 32792 \\
 \hline
 Ours (Full) & \textbf{29.90} & \textbf{0.9495} & \textbf{0.1104} & {32379} \\
 \bottomrule
 \end{tabular}
\caption{Avatar Reconstruction: With fewer Gaussian points, our method achieves superior quality compared to the alternate approaches. Perceptual loss increases the sharpness, wrinkle regularization models dynamic wrinkles, mouth subdivision learns better mouth interior, Color MLP synthesizes sharper colors and accurate dynamic wrinkles. The full avatar initialization with all regularizations achieves the best results.}
\end{table}
\end{document}

eess

Shivangi Aneja
Artem Sevastopolsky
Tobias Kirschstein
Justus Thies
Angela Dai
Matthias Nießner

"GaussianSpeech: Audio-Driven Gaussian Avatars" by Shivangi Aneja, Artem Sevastopolsky, Tobias Kirschstein, Justus Thies, Angela Dai, and Matthias Nießner, arXiv:2411.18675v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
 \cline{2-5}
 & daily & six-hourly & three-hourly & 10min \\
 \hline
 daily&1&$\mathbf{1.64\cdot10^{-1}}$&$3.84\cdot10^{-2}$& $3.61\cdot10^{-2}$ \\ \hline
 six-hourly&&1&$\mathbf{7.21\cdot10^{-1}}$&$\mathbf{6.20\cdot10^{-1}}$ \\ \hline
 three-hourly&&&1& $\mathbf{9.01\cdot10^{-1}}$ \\ \hline
 \end{tabular}
\caption{$p$-values of Kolmogorov-Smirnov test for instantaneous NWTC wind speed distributions. All values where $p \leq 0.05$ are marked \textbf{bold} and the corresponding distributions are not considered to be significantly different.}
\end{table}
\end{document}

stat

Nina Effenberger
Nicole Ludwig
Rachel H. White

"Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts" by Nina Effenberger, Nicole Ludwig, and Rachel H. White, arXiv:2309.09540v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Pearson correlation coefficients between the proposed metrics and human ratings indicate a positive correlation trend.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
\hline
Metric & Random & $\mathbb{TR}/\mathcal{TR}$ & $\mathbb{VC}/\mathcal{VC}$ \\ \hline\hline
\textit{Pearson's r} & 0.02 & 0.47 & 0.53 \\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"TV-Dialogue: Crafting Theme-Aware Video Dialogues with Immersive Interaction" by Sai Wang, Fan Ma, Xinyi Li, Hehe Fan, and Yu Wu, arXiv:2501.18940v1, 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{Friedman Simulation Results - BART Predictions}
\begin{tabular}{cccccc}
\toprule
Type & $M$\textsuperscript{a} & Bias\textsuperscript{b} & RMSE\textsuperscript{b} & Coverage\textsuperscript{b} & Width\textsuperscript{b}\\
\midrule
oracle & & -0.001 (0.001) & 0.040 (0.001) & 0.949 (0.016) & 0.160 (0.000)\\
\midrule
clbart & 5 & -0.001 (0.001) & 0.165 (0.001) & 0.801 (0.028) & 0.431 (0.002)\\
clbart & 10 & -0.001 (0.001) & 0.144 (0.001) & 0.914 (0.020) & 0.502 (0.002)\\
clbart & 25 & -0.001 (0.001) & 0.130 (0.001) & 0.967 (0.013) & 0.568 (0.002)\\
clbart & 50 & -0.001 (0.001) & 0.127 (0.001) & 0.978 (0.010) & 0.596 (0.003)\\
clbart & 100 & -0.001 (0.001) & 0.126 (0.001) & 0.980 (0.010) & 0.600 (0.003)\\
\bottomrule
\multicolumn{6}{l}{\textsuperscript{a} $M$: Number of trees.}\\
\multicolumn{6}{l}{\textsuperscript{b} Monte Carlo mean and standard errors across 200 simulations reported.}\\
\end{tabular}
\end{table}
\end{document}

stat

Jacob Englert
Stefanie Ebelt
Howard Chang

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


\documentclass{article}
\usepackage{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
\hline
Statistics & 1E & 1D & 5E & 5D \\
\hline
\hline
Mean of growth & 1.99\% & 1.59\% & 1.9\% & 1.47\% \\
\hline
Stdev of growth & 28\% & 11\% & 8\% & 5\% \\
\hline
Stdev of $\varepsilon$ & 25\% & 19\% & 18\% & 17\% \\
\hline
Regression $R^2$ & 16.4\% & 12.6\% & 8.0\% & 9.5\% \\
\hline
\hline
Long-term valuation $h$ & 0.32 & 0.11 & 0.35 & 0.23 \\
\hline
Mean-reversion $b$ & 0.67 & 0.75 & 0.84 & 0.81 \\
\hline
Linear trend $c$ & 4.6\% & 5\% & 4.6\% & 5\% \\
\hline
Coefficient $\gamma$ & 0.33 & 0.25 & 0.16 & 0.19 \\
\hline
Student $T(\gamma)$ & 5.4 & 3.4 & 4.5 & 3.8 \\
\hline
Jan 2025 valuation $H$ & 0.062 & -0.022 & 0.196 & 0.123 \\
\hline
Pearson correlation & -73\% & -42\% & -28\% & -26\% \\
\hline
Spearman correlation & -60\% & -40\% & -20\% & -19\% \\
\hline
\hline
SW $p$ & 0.1\% & 29\% & 39\% & 2.3\% \\
\hline
JB $p$ & $< 0.1\%$ & 25\% & 28\% & 0.3\% \\
\hline
O5 $p$ & 82\% & 4.2\% & 10\% & 3.3\% \\
\hline
O10 $p$ & 37\% & 0.3\% & 14\% & 4.7\% \\
\hline
A5 $p$ & 24\% & 5.8\% & 12\% & 11\% \\
\hline
A10 $p$ & 25\% & 10.5\% & 25\% & 15\% \\
\hline
\hline
\end{tabular}
\end{adjustbox}
\caption{Results of regression~ for four versions of fundamentals: 1-year earnings (1E) or dividends (1D), 5-year averaged trailing earnings (5E) or dividends (5D). Only for 5-year averaged trailing earnings (5E) we can conclusively accept that residuals are i.i.d. Gaussian.}
\end{table}
\end{document}

q-fin

"A New Stock Market Valuation Measure with Applications to Retirement Planning" by Andrey Sarantsev, arXiv:1905.04603v15, 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}{lccccc} \toprule
 \textbf{Category} & \textbf{Source} & \textbf{\# params} & \textbf{\# Groups} & \textbf{Retex.?} & \textbf{Topology} \\ \hline
 Pottery & Blender & 2 & 1 & No & Fixed \\
 \hline
 Vase$_{0}$ & Blender & 5 & 1 & No & Varies \\
 \hline
 Vase$_{1}$ & Blender & 2 & 1 & No & Fixed \\
 \hline
 Mug & Blender & 1 & 2 & No & Fixed \\
 \hline
 Pumpkin & Blender & 1 & 1 & No & Varies \\
 \hline
 Desk & Blender & 1 & 3 & No & Varies \\
 \hline
 Sofa & Blender & 4 & 4 & No & Varies \\
 \hline
 Cake & Blender & 4 & 4 & Yes & Varies \\
 \hline
 Spaceship & Blender & 4 & 4 & Yes & Fixed \\
 \hline
 Stair & Blender & 6 & 4 & Yes & Varies \\
 \hline
 Door & Blender & 2 & 1 & No & Varies \\
 \hline
 Drawer & Blender & 4 & 4 & No & Varies \\
 \hline
 Cupcake & Blender & 1 & 1 & Yes & Varies \\
 \hline
 Tree & Online & 2 & 7 & No & Fixed \\
 \hline
 Mussel & Infinigen & 2 & 2 & No & Varies \\
 \hline
 Fork & Infinigen & 3 & 2 & No & Varies \\
 \hline
 Mushroom & Infinigen & 2 & 2 & Yes & Varies \\
 \hline
 Rock & Infinigen & 2 & 1 & No & Varies \\
 \hline
 House & Blender & 1 & 4 & Yes & Fixed \\
 \hline
 Chair & Infinigen & 10 & 3 & Yes & Varies \\
 \hline
 Clam & Infinigen & 2 & 2 & No & Varies \\
 \hline
 Starfruit & Infinigen & 2 & 2 & No & Varies \\
 \hline
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Procedural Generators Statistics}
\end{table}
\end{document}

cs

Ruiqi Xu
Zihan Zhu
Ben Ahlbrand
Srinath Sridhar
Daniel Ritchie

"ProcTex: Consistent and Interactive Text-to-texture Synthesis for Part-based Procedural Models" by Ruiqi Xu, Zihan Zhu, Ben Ahlbrand, Srinath Sridhar, and Daniel Ritchie, arXiv:2501.17895v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of model performance across different ML approaches. For LLMs, results are shown as structured string / natural language description. Best performance per metric is shown in bold, and second-best performance is underlined.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccc}
\toprule
\textbf{Model} & \textbf{Model Type} & \textbf{MAE (eV)} & \textbf{RMSE (eV)} & \textbf{$R^2$} \\
\midrule
SVR & Shallow ML & $0.601 \pm 0.010$ & $0.844 \pm 0.008$ & $0.600 \pm 0.008$ \\
Random Forest & Shallow ML & $0.385 \pm 0.006$ & $0.609 \pm 0.006$ & $0.792 \pm 0.005$ \\
XGBoost & Shallow ML & $0.318 \pm 0.005$ & $0.537 \pm 0.005$ & $0.838 \pm 0.004$ \\
RoBERTa\textsubscript{(string)} & LLM (Encoder) & $0.325 \pm 0.006$ & $0.447 \pm 0.005$ & $0.855 \pm 0.004$ \\
RoBERTa\textsubscript{(description)} & LLM (Encoder) & $0.421 \pm 0.007$ & $0.590 \pm 0.006$ & $0.797 \pm 0.006$ \\
T5\textsubscript{(string)} & LLM (Encoder-Decoder) & $0.301 \pm 0.007$ & $0.448 \pm 0.006$ & $0.861 \pm 0.005$ \\
T5\textsubscript{(description)} & LLM (Encoder-Decoder) & $0.446 \pm 0.011$ & $0.615 \pm 0.008$ & $0.759 \pm 0.008$ \\
Llama-3\textsubscript{(string)} & LLM (Decoder) & $\mathbf{0.248 \pm 0.006}$ & $\mathbf{0.345 \pm 0.005}$ & $\mathbf{0.891 \pm 0.004}$ \\
Llama-3\textsubscript{(description)} & LLM (Decoder) & $0.335 \pm 0.008$ & $0.473 \pm 0.006$ & $0.843 \pm 0.054$ \\
MatSciBERT\textsubscript{(string)} & LLM (Encoder) & \underline{$0.288 \pm 0.007$} & \underline{$0.407 \pm 0.006$} & \underline{$0.871 \pm 0.004$} \\
MatSciBERT\textsubscript{(description)} & LLM (Encoder) & $0.366 \pm 0.009$ & $0.503 \pm 0.007$ & $0.808 \pm 0.007$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ying-Ting Yeh
Janghoon Ock
Achuth Chandrasekhar
Shagun Maheshwari
Amir Barati Farimani

"Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap Prediction" by Ying-Ting Yeh, Janghoon Ock, Achuth Chandrasekhar, Shagun Maheshwari, and Amir Barati Farimani, arXiv:2501.03456v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Type I error rates}
\begin{tabular}{ccrr} \hline
MAF & Method & $n = 100$ & $n = 300$ \\ \hline
\multirow{3}{*}{0.12} & MCM & 0.050 & 0.049 \\
 & MMCM & 0.051 & 0.049 \\
 & KW & 0.039 & 0.048 \\ \hline
\multirow{3}{*}{0.25} & MCM & 0.049 & 0.052 \\
 & MMCM & 0.049 & 0.051 \\
 & KW & 0.047 & 0.050 \\ \hline
\multirow{3}{*}{0.33} & MCM & 0.051 & 0.049 \\
 & MMCM & 0.051 & 0.050 \\
 & KW & 0.050 & 0.049 \\ \hline
\multirow{3}{*}{0.50} & MCM & 0.049 & 0.051 \\
 & MMCM & 0.048 & 0.051 \\
 & KW & 0.047 & 0.049 \\ \hline
\multicolumn{4}{l}{\scriptsize \shortstack[l]{\\Abbreviations: MAF, minor allele frequency; MCM,\\ maximum contrast method; MMCM, modified maxi- \\ mum contrast method; KW, Kruskal--Wallis test.}}
\end{tabular}
\end{table}
\end{document}

stat

Kengo Nagashima
Yasunori Sato
Chikuma Hamada

"A modified maximum contrast method for unequal sample sizes in pharmacogenomic studies" by Kengo Nagashima, Yasunori Sato, and Chikuma Hamada, arXiv:2012.03475v1, 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{Finite element spaces used to approximate different fields }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llr}
 \hline
 Field & FE space & \# DoF per element\\
 \hline
 Displacement & linear Lagrange& $2\times 3=6$ \\
 Rotation& linear Lagrange & $2\times 3=6$ \\
 Traction 1-form& constant & $1 \times 3=3$ \\
 Deformation 1-form & constant & 1\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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

"A novel four-field mixed variational approach to Kirchhoff rods implemented with finite element exterior calculus" by Jamun Kumar N, Bensingh Dhas, Arun R Srinivasa, J N Reddy, and Debasish Roy, arXiv:2202.12376v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of tip coordinates for different load levels}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
 \hline
 Authors & 300 & 600\\
 \hline
 Present & 58.80, 22.26, 40.19 & 47.18, 15.70, 53.50\\
 Ghosh and Roy & 58.89, 22.26, 40.08 & 47.29, 15.67, 53.37 \\
 Simo and Vu-Quoc & 58.84, 22.33, 40.08 & 47.23, 15.79, 53.37\\
 M\"akinen &- & 47.01, 15.62, 53.50\\
 Mohan & 58.69, 22.19, 40.33 & 47.14, 15.67, 53.48 \\
 Cardona and Geradin & 58.64, 22.14, 40.35 & 47.04, 15.55, 53.50\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math


\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|}
 \hline &\multicolumn{2}{c|}{ACAT2}&\multicolumn{2}{c|}{WBACAT2}& \multicolumn{2}{c|}{ACAT4} & \multicolumn{2}{c|}{WBACAT4} \\
 Points & Order & Error & Order & Error & Order & Error & Order& Error \\ \hline
 16 & - & 2.00E-1 & - & 1.99E-1 & - & 1.57E-2 & - & 1.61E-2 \\
 \hline
 31 & 1.51 & 7.01E-2 & 1.50 & 7.04E-2 & 3.01 & 1.95E-3 & 2.99 & 2.02E-3\\
 \hline
 61 & 1.85 & 1,94E-2 & 1.86 & 1.94E-2 & 3.62 & 1.58E-4 & 3.60 & 1.67E-4 \\
 \hline
 121 & 2.19 & 4.25E-3 & 2.19 & 4.25E-3 & 8.10 & 5.71E-7 & 8.20 & 5.71E-7 \\
 \hline
 241 & 2.00 & 1.05E-3 & 2.00 & 1.06E-3 & 3.99 & 3.57E-8 & 4.00 & 3.56E-8 \\
 \hline
 481 & 2.00 & 2.64E-4 & 2.00 & 2.64E-4 & 4.00 & 2.22E-9 & 4.00 & 2.22E-9 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Test : Errors in $L^1$ norm and convergence rates for ACAT2, ACAT4, WBACAT2 and WBACAT4 at time $t=1.$}
\end{table}
\end{document}

math

"Well-balanced adaptive compact approximate Taylor methods for systems of balance laws" by H. Carrillo, E. Macca, C. Pares, and G. Russo, arXiv:2202.02068v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|ccc|}
 \hline
 \textbf{Educator} & \textbf{No. of Sentences} & \textbf{Avg. word count per sentence} \\
 \hline
 1 & 39 & 19.59\\
 2 & 20 & 25.15\\
 3 & 15& 36.33\\
 4 & 59 & 24.93\\
 5 & 34 & 21.85 \\
 6 & 27 & 28.52 \\
 7 & 32 & 30.72 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Sentence count and Average word count per sentence}
\end{table}
\end{document}

cs

Abhishek Kaushik
Sargam Yadav
Andrew Browne
David Lillis
David Williams
Jack Mc Donnell
Peadar Grant
Siobhan Connolly Kernan
Shubham Sharma
Mansi Arora

"Exploring the Impact of Generative Artificial Intelligence in Education: A Thematic Analysis" by Abhishek Kaushik, Sargam Yadav, Andrew Browne, David Lillis, David Williams, Jack Mc Donnell, Peadar Grant, Siobhan Connolly Kernan, Shubham Sharma, and Mansi Arora, arXiv:2501.10134v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Interpretable Traffic Event Analysis with Bayesian Networks" by Tong Yuan, Jian Yang, and Zeyi Wen, arXiv:2310.06713v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Value of $\mathcal{K}^{\Pi}(H)$ under optimal upgrade policy~$\Pi$ conditioned on having $N$ upgrades.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
			\hline
			$N$ & $\Pi$ & $\mathcal{K}^\Pi(H)$ \\
			\hline
			0 & \{$\emptyset$\} & 201.715 \\
			1 & \{15\} & 65.8081$ + c_0$ \\
			2 & \{10,20\} & 34.6101$ + 2 c_0$ \\
			5 & \{5,10,15,20,25\} & 33.7322$ + 5 c_0$ \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Fiona Sloothaak
Alp Akçay
Matthieu van der Heijden
Geert-Jan van Houtum

"After-sales services during an asset's lifetime: collaborative planning of system upgrades" by Fiona Sloothaak, Alp Akçay, Matthieu van der Heijden, and Geert-Jan van Houtum, arXiv:2202.12128v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llll}
	\toprule
	{\textbf{Dataset/Model}} & \textbf{Value} & \textbf{Min} & \textbf{Max}\\
	\midrule
	Ground truth & $0.045\pm0.043$ & 0.002 & 0.305\\
	3-tier MTCRNN & $0.038\pm0.052$ & 0.002 & 0.298\\
	1-tier MTCRNN & $0.295\pm0.144$ & 0.034 & 0.603\\
	\bottomrule
\end{tabular}
\caption{Spectral flatness of the results from a standard MTCRNN compared to a 1-tier variant and the ground truth (lower better).}
\end{table}
\end{document}

stat

"MTCRNN: A multi-scale RNN for directed audio texture synthesis" by M. Huzaifah and L. Wyse, arXiv:2011.12596v1, 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{FVA Wasserstein Radii}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
Percentage of $\delta^u$ & 0.50 & 0.60 & 0.70 & 0.80 & 0.90 & 1.0 \\
 \hline
W Radius delta & 1.935 & 2.322 & 2.709 & 3.096 & 3.483 & 3.87 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\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
 20 & 1.14E$-2$ & $-$ & 1.12E$-1$ & $-$ \\
 \hline
 40 & 4.82E$-3$ & 1.56 & 4.54E$-2$ & 1.40 \\
 \hline
 80 & 8.33E$-6$ & 9.18 & 2.25E$-4$ & 7.66 \\
 \hline
 160 & 7.53E$-8$ & 6.79 & 3.00E$-6$ & 6.23 \\
 \hline
 320 & 2.14E$-9$ & 5.14 & 1.01E$-7$ & 4.89 \\
 \hline
 640 & 6.59E$-11$ & 5.02 & 3.57E$-9$ & 4.82 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Error table for problem - , Complex domain, WLS-UW.}
\end{table}
\end{document}

cs

"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Anass Aghbalou
François Portier
Anne Sabourin

"On the bias of K-fold cross validation with stable learners" by Anass Aghbalou, François Portier, and Anne Sabourin, arXiv:2202.10211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Voltages for points $A$ and $B$ for the 5-bus test system in~.}
\begin{tabular}{|c|c|c|}
\hline
 & Point A & Point B\tabularnewline
\hline
\hline
1 & 1.0467 - 0.0000i & 1.0500 - 0.0000i\tabularnewline
\hline
2 & 0.9550 - 0.0578i & 0.9838 - 0.1183i\tabularnewline
\hline
3 & 0.9485 - 0.0533i & 0.9796 - 0.1196i\tabularnewline
\hline
4 & 0.7791 + 0.6011i & 0.9931 - 0.1650i\tabularnewline
\hline
5 & 0.7362 + 0.7487i & 1.0317 - 0.1032i\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

eess

Mohammad Rasoul Narimani
Katherine R. Davis
Daniel K. Molzahn

"Certifying the Nonexistence of Feasible Path Between Power System Operating Points" by Mohammad Rasoul Narimani, Katherine R. Davis, and Daniel K. Molzahn, arXiv:2509.05935v1, 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{\textsc{Accuracy of differential ambiguity estimation}}
\begin{tabular}{lcccc}
\toprule
& \multicolumn{3}{c}{\textbf{Transmit Power [dBm]}} \\
\cmidrule(lr){2-4}
\textbf{Failure Probability} & --20 & --10 & 0 \\
\midrule
$p_f = 0$ & 98.46\% & 99.21\% & 99.43\% \\
$p_f = 10^{-3}$ & 97.81\% & 98.43\% & 98.88\% \\
$p_f = 10^{-2}$ & 97.17\% & 97.99\% & 98.23\% \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Fatih Ayten
Mehmet C. Ilter
Akshay Jain
Ossi Kaltiokallio
Jukka Talvitie
Elena Simona Lohan
Henk Wymeersch
Mikko Valkama

"Failure Tolerant Phase-Only Indoor Positioning via Deep Learning" by Fatih Ayten, Mehmet C. Ilter, Akshay Jain, Ossi Kaltiokallio, Jukka Talvitie, Elena Simona Lohan, Henk Wymeersch, and Mikko Valkama, arXiv:2508.14739v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The statistics of graph datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccc|}
\hline
Graphs & Nodes & Edges & Features & \#Classes \\
\hline
\textbf{Cora} & 2,708 & 5,429 & 1,433 & 7 \\
\textbf{Citeseer} & 3,327 & 4,732 & 3,703 & 6 \\
\textbf{Pubmed} & 19,717 & 44,338 & 500 & 3 \\ \hline
\textbf{Coauthor-CS} & 18,333 & 163,788 & 6,805 & 15 \\
\textbf{Coauthor-Physics} & 34,493 & 495,924 & 8,415 & 3 \\
\textbf{Polblogs} & 1,490 & 19,025 & index & 2\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mengxi Yang
Dai Shi
Xuebin Zheng
Jie Yin
Junbin Gao

"Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution" by Mengxi Yang, Dai Shi, Xuebin Zheng, Jie Yin, and Junbin Gao, arXiv:2201.04728v2, 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{Clothing Datasets}
\begin{tabular}{lr} \toprule

 Fashionista dataset & \\
 Clothing Attributes Dataset & \\
 Paper Doll & \\
 Color-Fashion Dataset & \\
 MVC Dataset & \\
 Deep Fashion Dataset & \\
 Fashion Mnist & \\
 imat fashion & \\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sample table title}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\bf PART &\bf DESCRIPTION
\\ \hline \\
Dendrite &Input terminal \\
Axon &Output terminal \\
Soma &Cell body (contains cell nucleus) \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yuxuan Hu
Jing Zhang
Xiaodong Chen
Zhe Zhao
Cuiping Li
Hong Chen

"LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model" by Yuxuan Hu, Jing Zhang, Xiaodong Chen, Zhe Zhao, Cuiping Li, and Hong Chen, arXiv:2501.08582v1, 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{CDS Spreads}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
CDX Index & IG & HY \\
 \hline
CDS Spread & 53 & 323 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Distributionally Robust XVA via Wasserstein Distance Part 2: Wrong Way Funding Risk" by Derek Singh and Shuzhong Zhang, arXiv:1910.03993v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Garrett Honke
Irina Higgins
Nina Thigpen
Vladimir Miskovic
Katie Link
Sunny Duan
Pramod Gupta
Julia Klawohn
Greg Hajcak

"Representation learning for improved interpretability and classification accuracy of clinical factors from EEG" by Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Sunny Duan, Pramod Gupta, Julia Klawohn, and Greg Hajcak, arXiv:2010.15274v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc|cc}
 \toprule
 \multirow{2}{*}{Batch Size} & \multicolumn{2}{c}{Ego Only} & \multicolumn{2}{c}{Exo+Ego} \\ \cline{2-5}
 & Acc & F1@0.1 & Acc & F1@0.1 \\ \midrule
 4 & 52.14 & 52.09 & 53.25 & 53.81 \\
 16 & 55.82 & 54.78 & 54.25 & 54.85 \\
 32 & \textbf{56.53} & \textbf{55.67} & \textbf{56.07} & \textbf{56.89} \\
 128 & 54.69 & 54.67 & 54.97 & 55.79 \\
 512 & 54.99 & 54.75 & 49.86 & 49.50 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Ablation on batch sizes. Performances of the EdgeConv, SAGEConv architecture are reported on one train/val split.}
\end{table}
\end{document}

cs

Julia Lee Romero
Kyle Min
Subarna Tripathi
Morteza Karimzadeh

"Graph-Based Multimodal and Multi-view Alignment for Keystep Recognition" by Julia Lee Romero, Kyle Min, Subarna Tripathi, and Morteza Karimzadeh, arXiv:2501.04121v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Reward Comparison}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
		& \multicolumn{2}{c|}{Wind Speed}			& \multicolumn{2}{c|}{Wind Direction} \\
\hline
 & MAE & RMSE & MAE & RMSE \\
\hline
float32 	& $0.0403$ & $ 0.0573 $ 	 & $0.0596$ & $ 0.1151 $ \\
\hline
float16 	& $0.0396$ & $ 0.0554 $ & $0.0609 $ & $ 0.1192$ \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Hwei-Ming Chung
Sabita Maharjan
Yan Zhang
Frank Eliassen
Tingting Yuan

"Edge Intelligence Empowered UAVs for Automated Wind Farm Monitoring in Smart Grids" by Hwei-Ming Chung, Sabita Maharjan, Yan Zhang, Frank Eliassen, and Tingting Yuan, arXiv:2009.11256v1, 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{Number of series (among the 15) for which each method selects an order that is within 1 compared to the order selected by the full sample. The maximum lag is set to 15. The largest value is marked in boldface.}
\begin{tabular}{lrrrrrrr}
\toprule
$\pi$ & Linear & iSSA & Kalman & ScalarF & Mean & TWI & $k$-TWI \\ \midrule
0.1 & 9 & {\bf 10} & 8 & 7 & 9 & {\bf 10} & 9 \\
0.2 & {\bf 9} & 8 & 7 & 8 & 5 & 8 & 8 \\
0.3 & 7 & 6 & 6 & 7 & 6 & 6 & {\bf 8} \\
0.4 & {\bf 7} & 2 & {\bf 7} & 5 & 4 & 5 & {\bf 7} \\
0.5 & 7 & 5 & 4 & {\bf 8} & 4 & 6 & {\bf 8} \\
0.6 & {\bf 6} & 2 & 4 & 4 & 5 & 4 & 5 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Shuo-Chieh Huang
Tengyuan Liang
Ruey S. Tsay

"Temporal Wasserstein Imputation: A Versatile Method for Time Series Imputation" by Shuo-Chieh Huang, Tengyuan Liang, and Ruey S. Tsay, arXiv:2411.02811v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Roberto Camassa
Lingyun Ding
Richard M. McLaughlin
Robert Overman
Richard Parker
Ashwin Vaidya

"Critical density triplets for the arrestment of a sphere falling in a sharply stratified fluid" by Roberto Camassa, Lingyun Ding, Richard M. McLaughlin, Robert Overman, Richard Parker, and Ashwin Vaidya, arXiv:2202.09435v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\usepackage{tikz}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
& & & \\
Series & $\mathcal{O}_q(G)$ & Crossed node & $\mathcal{O}_q(G/L_S)$ \\
& & & \\
\hline
& & & \\
$A_{n}$ & $\mathcal{O}_q(\mathrm{SU}_{n+1})$ &
\begin{tikzpicture}[scale=.5]
\draw
(0,0) circle [radius=.25]
(8,0) circle [radius=.25]
(2,0) circle [radius=.25]
(6,0) circle [radius=.25] ;
\draw[fill=black]
(4,0) circle [radius=.25] ;
\draw[thick,dotted]
(2.25,0) -- (3.75,0)
(4.25,0) -- (5.75,0);
\draw[thick]
(.25,0) -- (1.75,0)
(6.25,0) -- (7.75,0);
\end{tikzpicture} & $\mathcal{O}_q(\mathrm{Gr}_{n+1,m})$\\
& & & \\
$B_n$ & $\mathcal{O}_q(\mathrm{Spin}_{2n+1})$ &
\begin{tikzpicture}[scale=.5]
\draw
(4,0) circle [radius=.25]
(2,0) circle [radius=.25]
(6,0) circle [radius=.25]
(8,0) circle [radius=.25] ;
\draw[fill=black]
(0,0) circle [radius=.25];
\draw[thick]
(.25,0) -- (1.75,0);
\draw[thick,dotted]
(2.25,0) -- (3.75,0)
(4.25,0) -- (5.75,0);
\draw[thick]
(6.25,-.06) --++ (1.5,0)
(6.25,+.06) --++ (1.5,0);
\draw[thick]
(7,0.15) --++ (-60:.2)
(7,-.15) --++ (60:.2);
\end{tikzpicture} & $\mathcal{O}_q(\mathbf{Q}_{2n+1})$ \\
& & & \\
$C_n$ & $\mathcal{O}_q(\mathrm{Sp}_{n})$ &
\begin{tikzpicture}[scale=.5]
\draw
(0,0) circle [radius=.25]
(2,0) circle [radius=.25]
(4,0) circle [radius=.25]
(6,0) circle [radius=.25] ;
\draw[fill=black]
(8,0) circle [radius=.25];
\draw[thick]
(.25,0) -- (1.75,0);
\draw[thick,dotted]
(2.25,0) -- (3.75,0)
(4.25,0) -- (5.75,0);
\draw[thick]
(6.25,-.06) --++ (1.5,0)
(6.25,+.06) --++ (1.5,0);
\draw[thick]
(7,0) --++ (60:.2)
(7,0) --++ (-60:.2);
\end{tikzpicture} & $\mathcal{O}_q(\mathbf{L}_{n})$ \\
& & & \\
$D_n$ & $\mathcal{O}_q(\mathrm{Spin}_{2n})$ &
\begin{tikzpicture}[scale=.5]
\draw[fill=black]
(0,0) circle [radius=.25] ;
\draw
(2,0) circle [radius=.25]
(4,0) circle [radius=.25]
(6,.5) circle [radius=.25]
(6,-.5) circle [radius=.25];
\draw[thick]
(.25,0) -- (1.75,0)
(4.25,0.1) -- (5.75,.5)
(4.25,-0.1) -- (5.75,-.5);
\draw[thick,dotted]
(2.25,0) -- (3.75,0);
\end{tikzpicture} & $\mathcal{O}_q(\mathbf{Q}_{2n})$ \\
& & & \\
$D_n$ & $\mathcal{O}_q(\mathrm{Spin}_{2n})$ &
\begin{tikzpicture}[scale=.5]
\draw
(0,0) circle [radius=.25]
(2,0) circle [radius=.25]
(4,0) circle [radius=.25] ;
\draw[fill=black]
(6,.5) circle [radius=.25];
\draw%[fill=black]
(6,-.5) circle [radius=.25];
\draw[thick]
(.25,0) -- (1.75,0)
(4.25,0.1) -- (5.75,.5)
(4.25,-0.1) -- (5.75,-.5);
\draw[thick,dotted]
(2.25,0) -- (3.75,0);
\end{tikzpicture} & $\mathcal{O}_q(\textbf{S}_{n})$ \\
$E_6$ & $\mathcal{O}_q(E_6)$ & \begin{tikzpicture}[scale=.5]
\draw
(2,0) circle [radius=.25]
(4,0) circle [radius=.25]
(4,1) circle [radius=.25]
(6,0) circle [radius=.25] ;
\draw%[fill=black]
(0,0) circle [radius=.25];
\draw[fill=black]
(8,0) circle [radius=.25];
\draw[thick]
(.25,0) -- (1.75,0)
(2.25,0) -- (3.75,0)
(4.25,0) -- (5.75,0)
(6.25,0) -- (7.75,0)
(4,.25) -- (4, .75);
\end{tikzpicture}
& $\mathcal{O}_q(\mathbb{OP}^2)$ \\
& & & \\
$E_7$ & $\mathcal{O}_q(E_7)$ &
\begin{tikzpicture}[scale=.5]
\draw
(0,0) circle [radius=.25]
(2,0) circle [radius=.25]
(4,0) circle [radius=.25]
(4,1) circle [radius=.25]
(6,0) circle [radius=.25]
(8,0) circle [radius=.25];
\draw[fill=black]
(10,0) circle [radius=.25];
\draw[thick]
(.25,0) -- (1.75,0)
(2.25,0) -- (3.75,0)
(4.25,0) -- (5.75,0)
(6.25,0) -- (7.75,0)
(8.25, 0) -- (9.75,0)
(4,.25) -- (4, .75);
\end{tikzpicture} & $\mathcal{O}_q(\textbf{F})$
 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Alessandro Carotenuto
Réamonn Ó Buachalla

"Bimodule Connections for Relative Line Modules over the Irreducible Quantum Flag Manifolds" by Alessandro Carotenuto and Réamonn Ó Buachalla, arXiv:2202.09842v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Simos G. Meintanis
Bojana Milošević
Marko Obradović

"Bahadur efficiency for certain goodness--of--fit tests based on the empirical characteristic function" by Simos G. Meintanis, Bojana Milošević, and Marko Obradović, arXiv:2201.08604v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Geoff K. Nicholls
Jeong Eun Lee
Nicholas Karn
David Johnson
Rukuang Huang
Alexis Muir-Watt

"Bayesian inference for partial orders from random linear extensions: power relations from 12th Century Royal Acta" by Geoff K. Nicholls, Jeong Eun Lee, Nicholas Karn, David Johnson, Rukuang Huang, and Alexis Muir-Watt, arXiv:2212.05524v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{dsfont}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Payoff functions and their transforms for a variety of financial contingency claims. $\mathds{1}$ represents an indicator function and $n<\infty$ is any positive integer. The singularity always exits at $y=0$ in the transformed payoff function when $y=x+\chi-\log K.$} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|ccc|} % centered columns (4 columns)
\hline
Financial Contingency Claim&Payoff Function&Transformed Payoff Function\\
&$U(S_T,K)$&$U(e^{x+\chi-\log K},K)$\\
\hline

Call&$\max(S_T-K,0)$&$K\max(e^{x+\chi-\log K}-1,0)$\\
Put&$\max(K-S_T,0)$&$K\max(1-e^{x+\chi-\log K},0)$\\
Covered Call&$\min(S_T,K)$&$K\min(e^{x+\chi-\log K}-1,0)+K$\\
Cash-or-Nothing Call&$\mathds{1}_{S_T\geq K}$&$\mathds{1}_{e^{x+\chi-\log K}\geq 1}$\\
Cash-or-Nothing Put&$\mathds{1}_{S_T\leq K}$&$\mathds{1}_{e^{x+\chi-\log K}\leq 1}$\\
Asset-or-Nothing Call&$S_T\mathds{1}_{S_T\geq K}$&$e^{x+\chi}\mathds{1}_{e^{x+\chi-\log K}\geq 1}$\\
Asset-or-Nothing Put&$S_T\mathds{1}_{S_T\leq K}$&$e^{x+\chi}\mathds{1}_{e^{x+\chi-\log K}\leq 1}$\\
Asymmetric Call&$(S_T^n-K^n)\mathds{1}_{S_T\geq K}$&$K^n(e^{n(x+\chi-\log K)}-1)\mathds{1}_{e^{x+\chi-\log K}\geq 1}$\\
Asymmetric Put&$(K^n-S_T^n)\mathds{1}_{S_T\leq K}$&$K^n(1-e^{n(x+\chi-\log K)})\mathds{1}_{e^{x+\chi-\log K}\leq 1}$\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Hedging and Pricing European-type, Early-Exercise and Discrete Barrier Options using Algorithm for the Convolution of Legendre Series" by Tat Lung Chan and Nicholas Hale, arXiv:1811.09257v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The crustal root model. Relative Model Error and Relative Misfit Function of $W_2$ with the operators $\mathcal{P}_{2}$ and $\mathcal{P}_{3}$ in $40$, $80$ and $160$ iteration steps, respectively.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
	\toprule
	\multirow{2}{*}{Iteration Steps} & \multicolumn{2}{c}{Relative Model Error} & \multicolumn{2}{c}{Relative Misfit Function} \\
	\cline{2-5}
	 & $W_2$ with $P_2$ & $W_2$ with $P_3$ & $W_2$ with $P_2$ & $W_2$ with $P_3$ \\
 \midrule
	 $40$ & $6.43 \times 10^{-1}$ & $5.59 \times 10^{-1}$ & $5.47 \times 10^{-3}$ & $6.35 \times 10^{-4}$ \\
	 $80$ & $5.37 \times 10^{-1}$ & $4.68 \times 10^{-1}$ & $7.83 \times 10^{-4}$ & $1.74 \times 10^{-4}$ \\
	 $160$ & $ 4.32 \times 10^{-1}$ & $3.99 \times 10^{-1}$ & $1.33 \times 10^{-4}$ & $6.11 \times 10^{-5}$ \\
	 \bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Zhengyang Li
Yijia Tang
Jing Chen
Hao Wu

"The Quadratic Wasserstein Metric With Squaring Scaling For Seismic Velocity Inversion" by Zhengyang Li, Yijia Tang, Jing Chen, and Hao Wu, arXiv:2201.11305v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Numerical PDE results for $\eta=0$.}
\begin{tabular}{c|c|c|c|c|c|c|c}\hline
 $w_0$ & $1$ & $5$ & $10$ & $20$ & $35$ & $50$ & $75$ \\ \hline
 PDE & $85.7$ & $93.0$ & $96.8$ & $100.6$ & $103.4$ & $105.1$ & $106.4 $ \\ \hline
 \multicolumn{8}{c}{WDT range mean $\pm$ std$=100.5\pm 9.6.$ }\\ \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"Retirement spending problem under Habit Formation Model" by S. Kirusheva, H. Huang, and T. S. Salisbury, arXiv:2210.06255v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Deep learning architectures for inference of AC-OPF solutions" by Thomas Falconer and Letif Mones, arXiv:2011.03352v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Performance evaluation of DMUs using hybrid fuzzy multi-objective data envelopment analysis" by Awadh Pratap Singh and Shiv Prasad Yadav, arXiv:2202.01585v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Application of machine learning regression models to inverse eigenvalue problems" by Nikolaos Pallikarakis and Andreas Ntargaras, arXiv:2212.04279v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|c|c|c}
 \toprule
 Category & Train & Valid & Test & Total \\
 \midrule
 Duration (hours) & 320 & 7.4 & 7.2 & 334.6 \\
 \# Utterances & 147,641 & 3,292 & 3,336 & 154,269 \\
 \# Words & 1.61M & 36.1k & 36.4k & 1.68M \\
 \# Unique Words & 157,616 & 13,790 & 14,209 & 160,570 \\
 \# Device IDs & 1,577 & 29 & 29 & 1,635 \\
 \# Speakers & - & 29 & 29 & - \\
 \bottomrule
 \end{tabular}
\caption{The KSC database specifications.}
\end{table}
\end{document}

eess

Yerbolat Khassanov
Saida Mussakhojayeva
Almas Mirzakhmetov
Alen Adiyev
Mukhamet Nurpeiissov
Huseyin Atakan Varol

"A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline" by Yerbolat Khassanov, Saida Mussakhojayeva, Almas Mirzakhmetov, Alen Adiyev, Mukhamet Nurpeiissov, and Huseyin Atakan Varol, arXiv:2009.10334v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
coefficient & point estimate & $p$-value & [2.5\%, 97.5\%] CI \\
\hline
$\alpha$ & 0.1 & 0.2\% & [0.038, 0.162] \\
\hline
$\beta$ & 0.0071 & 0.1\% & [0.003, 0.011] \\
\hline
$\gamma$ & 0.1569 & 0.1\% & [0.069, 0.245] \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Regression Results~ when fundamentals are 5-year trailing averaged earnings. We see all coefficients are significantly different from zero.}
\end{table}
\end{document}

q-fin

"A New Stock Market Valuation Measure with Applications to Retirement Planning" by Andrey Sarantsev, arXiv:1905.04603v15, 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{\textbf{Standard deviations from ANOVA model.}. Summary for standard deviation between all average scores and standard deviation for review scores within a paper.}
\begin{tabular}{lcccc}
\bf Year &\bf Std. Between &\bf Std. Within
\\ \hline \\
\texttt{2017} & 2.3334 & 1.0307\\
\texttt{2018} & 2.1080 & 1.1947 \\
\texttt{2019} & 2.1038 & 1.1036 \\
\texttt{2020} & 2.9595 & 1.7209 \\
\end{tabular}
\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{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Absolute error convergence for the COS method for calls under the Heston model with $T=1$ and $T=10.$ Reference values are 5.785155450\ldots ($T=1$) and 22.318945791\ldots ($T=10$).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cc||cc|} % centered columns (4 columns)
\hline
$T=1$&&$T=10$&\\
\hline
$N$& Error & $N$&Error\\
\hline
64& 4.92e-03&32&7.40e-03\\
96& 2.99e-04&64&5.02e-05\\
128&1.94e-05&96&1.40e-07 \\
160&2.99e-06&128&4.92e-10\\
192&3.17e-07&160&1.85e-10\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Singular Fourier-Padé Series Expansion of European Option Prices" by Tat Lung Chan, arXiv:1706.06709v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Garyfallos Konstantinoudis
Anthony Hauser
Julien Riou

"Bayesian ensemble modelling to monitor excess deaths during summer 2022 in Switzerland" by Garyfallos Konstantinoudis, Anthony Hauser, and Julien Riou, arXiv:2308.15251v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lllllllll}
 \toprule
 Study & Group & Dist. & $n$ & $a$ & $q_1$ & $m$ & $q_3$ & $b$ \\ \midrule
 1 & Cont. & LN$(0.75, 1)$ & 75 & 0.23 & 1.36 & 2.47 & 4.23 & 23.37 \\
 & Trt. & LN$(1.5, 0.35)$ & 100 & 2.29 & 3.64& 4.24& 5.28& 10.05\\
 2 & Cont. & GLD$(6, 0.1, 1.1, 1.8)$ & 50 & -2.03 & 0.87& 4.35& 8.81& 11.36 \\
 & Trt. & GLD$(7, 0.15, 1.3, 1.8)$ & 100 & 1.89 & 4.27 &6.40 &8.93& 10.66\\
 3 & Cont. & N$(2.5, 1)$ & 150 & -0.09 & 2.05 &2.59 &3.26 & 5.15 \\
 & Trt. & N$(7.5, 1.5)$ & 50 & 2.99& 6.49& 7.09& 8.45& 10.34\\
 \bottomrule
 \end{tabular}
\caption{Five-point summary results from one simulation run for three studies, each with two arms (control and treatment) where Study 1 samples from lognormal distributions, Study 2 from the GLD and Study 3 from the normal, with varying parameters and sample sizes ($n$).}
\end{table}
\end{document}

stat

Alysha M De Livera
Luke Prendergast
Udara Kumaranathunga

"A novel density-based approach for estimating unknown means, distribution visualisations and meta-analyses of quantiles" by Alysha M De Livera, Luke Prendergast, and Udara Kumaranathunga, arXiv:2411.10971v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Topological inference on brain networks across subtypes of post-stroke aphasia" by Yuan Wang, Jian Yin, and Rutvik H. Desai, arXiv:2311.01625v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|l|l|l|l|l|l}
\hline
\textbf{Regions} & \textbf{1} & \textbf{2} & \textbf{3} & \textbf{4} &
\textbf{5} & \textbf{6} \\
\hline \hline
\textbf{OLr} & 14.3138 & 10.7826 & 23.8861 & 10.2356 & 18.4429 & 18.6336 \\
\hline
\textbf{ORr} & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
\textbf{OLrf} & 15.0862 & 12.6854 & 10.8883 & 14.1512 & 9.0053 & 22.3374 \\
\hline
\textbf{ORrf} & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
\textbf{OLrm} & 13.6813 & 23.3288 & 19.7852 & 18.2779 & 23.2627 & 21.5179 \\
\textbf{ORrm} & 0 & 0 & 0 & 0 & 0 & 0 \\
\hline \hline
\end{tabular}
\caption{Outliers in Sciences grades by region and year}
\end{table}
\end{document}

stat

Ousmane Saine
Soumaila Dembélé
Gane Samb Lo
Mohamed Cheikh Haidara

"Overview description of the Gambian GABECE Educational Data and associated algorithms and unsupervized learning process" by Ousmane Saine, Soumaila Dembélé, Gane Samb Lo, and Mohamed Cheikh Haidara, arXiv:2012.08488v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Heterogeneous Response of Investment to $\epsilon^{X}_t$ \\ \footnotesize S.T. Leverage \& Alt. Specification}
\begin{tabular}{lcccc}
 & \multicolumn{4}{c}{Firm investment - $\Delta \log k_{i,t}$ } \\
 & (1) & (2) & (3) & (4) \\ \hline \hline
 & & & & \\
$\left(l_{i,t-1} - \mathbb{E}_{i} \left[ l_{t}\right] \right) \epsilon^{X}_t$ & -0.207* & -0.209* & -0.160 & -0.156 \\
 & (0.106) & (0.107) & (0.131) & (0.130) \\
 & & & & \\
Firm FE & yes & yes & yes & yes \\
Sector-Time FE & yes & yes & yes & yes \\
Control for Lagged Inv. & no & yes & yes & yes \\
Firm-Controls & no & no & yes & yes \\
Aggregate-Controls & no & no & no & yes \\
 & & & & \\
Observations & 12,617 & 12,448 & 11,849 & 11,843 \\ \hline
$R^{2}$ & 0.056 & 0.064 & 0.075 & 0.081 \\ \hline \hline
\multicolumn{5}{c}{ Clustered standard errors in parentheses} \\
\multicolumn{5}{c}{ *** p$<$0.01, ** p$<$0.05, * p$<$0.1} \\
\end{tabular}
\end{table}
\end{document}

q-fin

Santiago Camara
Sebastian Ramirez Venegas

"The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity" by Santiago Camara and Sebastian Ramirez Venegas, arXiv:2209.11150v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l}
\toprule
\multirow{2}{*}{Method} & \multirow{2}{*}{VCR} & \multirow{2}{*}{SNLI-VE} \\
 & & \\
\midrule
LLaVA-13b-v0 & 28.3 & 40.3 \\
\midrule
LLaVA-7b-v1.5 & 25.06 & 0.06 \\
\midrule
LLaVA-13b-v1.5 & 25.18 & 45.78 \\
\midrule
IdealGPT & 50.7 & 55.3 \\
\midrule
\textbf{SQ-7b} & 32.66 & 48.98 \\
\midrule
\textbf{SQ-13b} & 45.78 & 61.44 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Comparison experiments: Like the paper of IdealGPT did, we sampled 5000 data from each of the VCR and SNLI-VE. \textbf{SQ-7b} is LLaVA-7b-v1.5 fine-tuned by our proposed SQ framework with extra CapQA dataset and \textbf{SQ-13b} is that of LLaVA-13b-v1.5 similarly.}
\end{table}
\end{document}

cs

Wanpeng Hu
Haodi Liu
Lin Chen
Feng Zhou
Changming Xiao
Qi Yang
Changshui Zhang

"Socratic Questioning: Learn to Self-guide Multimodal Reasoning in the Wild" by Wanpeng Hu, Haodi Liu, Lin Chen, Feng Zhou, Changming Xiao, Qi Yang, and Changshui Zhang, arXiv:2501.02964v2, 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{\bf{ADF t-statistics test for financial data}} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
		Stocks & Test statistics & P-value \\
		\hline
		Bank of America & -14.873 & 0.01 \\
		Discover & -13.955 & 0.01 \\
		Intel & -21.314 & 0.01\\
		IAG & -13.830 & 0.01 \\
\hline %inserts single line
\hline %inserts single line
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Maria C Mariani
Md Al Masum Bhuiyan
Osei K Tweneboah
Hector Gonzalez-Huizar
Ionut Florescu

"Volatility Models Applied to Geophysics and High Frequency Financial Market Data" by Maria C Mariani, Md Al Masum Bhuiyan, Osei K Tweneboah, Hector Gonzalez-Huizar, and Ionut Florescu, arXiv:1901.09145v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc}\hline
\textbf{$p$}&$11$&$12$&$13$&$14$&$15$\\\hline
\textbf{$|\Delta_N(2^p,t_0)|$}&$0.00004$&$0.00002$&$0.00001$
 &$0.000005$&$0.0000026$\\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Evaluation of the Gauss integral

"Evaluation of the Gauss integral" by Dmitri Martila and Stefan Groote, arXiv:2202.12394v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Oriana Presacan
Alexandru Dorobantiu
Vajira Thambawita
Michael A. Riegler
Mette H. Stensen
Mario Iliceto
Alexandru C. Aldea
Akriti Sharma

"Merging synthetic and real embryo data for advanced AI predictions" by Oriana Presacan, Alexandru Dorobantiu, Vajira Thambawita, Michael A. Riegler, Mette H. Stensen, Mario Iliceto, Alexandru C. Aldea, and Akriti Sharma, arXiv:2412.01255v2, 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{Measuring the CONLeg method in error convergence and CPU time for pricing European at/around-the-money call prices, Delta $\Delta_t$ and Gamma $\Gamma_t$ under the BSM model with parameters taken from \textbf{GBM2}. 1000 call prices are computed in a range of $K$ from 80 to 120.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|cc|cc|c|} % centered columns (4 columns)
\hline
&\multicolumn{2}{|c}{Price}& \multicolumn{2}{|c}{Delta\,$\Delta_t$}& \multicolumn{2}{|c|}{Gamma\,$\Gamma_t$}&\\
&$R_\infty$&$R_2$&$R_\infty$&$R_2$&$R_\infty$&$R_2$&Time\\
\hline
$T=50$&2.842e-14& 2.821e-13& 2.220e-16 & 1.005e-15 & 7.170e-17& 5.007e-16&8.34e-02\\
\hline
\multicolumn{8}{c}{}\\
\hline
&$R_\infty$&$R_2$&$R_\infty$&$R_2$&$R_\infty$&$R_2$&Time\\
\hline
$T=100$&2.842e-14& 2.874e-13& 3.841e-16& 2.123e-16& 6.126e-19& 3.776e-18&9.12e-02\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Hedging and Pricing European-type, Early-Exercise and Discrete Barrier Options using Algorithm for the Convolution of Legendre Series" by Tat Lung Chan and Nicholas Hale, arXiv:1811.09257v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Differences between Webster for families and Webster for states.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
State&Quota&\multicolumn{2}{c}{Seats}\\
&& Webster for Families& Webster for States\\
\hline
North Dakota	&	1.024	&		1	&	1\\
South Dakota	&	1.166	&		1	&	1\\
Delaware &	1.302	&		1	&	1\\
Montana	&	1.426	&		1	&	1	\\
Rhode Island	&	1.443	&		2	&	1\\
Maine	&	1.791	&			2	&	2	\\
New Hampshire	&	1.812	&		2	&	2		\\
Hawaii	&	1.918 &	2	&	2\\
\hline
1 Family & 11.883	&	12 & 11\\
\hline
Louisiana	&	6.124	&		6	&	6		\\
Alabama	&	6.608	&			6	&	7		\\
South Carolina	&	6.733	&		7	&	7	\\
\hline
6 Family & 19.465	&	19	& 20\\
\hline
Minnesota	&	7.501	&		7	&	8		\\
Colorado	&	7.596	&		8	&	8		\\
Wisconsin	&	7.748	&		8	&	8		\\
\hline
7 Family & 22.846	&	23	& 24\\
\hline
Tennessee		&	9.087	&		9	&	9		\\
Massachusetts	&	9.240	&		9	&	9		\\
Arizona		&	9.405	&		10	&	9		\\
\hline
9 Family & 27.733	&	28	& 27\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A New Perspective on Impartial and Unbiased Apportionment" by Ross Hyman and Nicolaus Tideman, arXiv:2201.11787v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c||c|c|c|c|c}
 $\log^k(n)$ & $n^\epsilon$ & yes & yes & no & no & no \\
 $n^k$ & $c^n$ & yes & yes & no & no & no \\
 $\sqrt{n}$ & $n^{\sin n}$ & no & no & no & no & no \\
 $2^n$ & $2^{n/2}$ & no & no & yes & yes & no \\
 $n^{\log c}$ & $c^{\log n}$ & yes & no & yes & no & yes \\
 $\log(n!)$ & $\log(n^n)$ & yes & no & yes & no & yes
 \end{tabular}
\end{table}
\end{document}

stat

"Improved Algorithms for Adversarial Bandits with Unbounded Losses" by Mingyu Chen and Xuezhou Zhang, arXiv:2310.01756v1, 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{Funding Spreads}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
Funding Tenor & 1y & 2y & 3y & 5y & 7y & 10y \\
 \hline
IG Spread & 0.54\% & 0.81\% & 0.81\% & 0.88\% & 1.01\% & 1.14\% \\
 \hline
HY Spread & N/A & N/A & 8.02\% & 6.72\% & 7.08\% & 6.36\% \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


q-fin

"The Composition of Wage Differentials between Migrants and Natives" by Panagiotis Nanos and Christian Schluter, arXiv:1306.1781v3, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{rotating}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{multirow}
\usepackage{graphicx}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccccccccc}%c}
		&

		&\multicolumn{2}{c}{Bitcoin}&& \multicolumn{2}{c}{Ethereum}&& \multicolumn{2}{c}{Monero}\\
		&Social Signal &\tiny $R$ & \tiny $DC$ &&\tiny $R$ & \tiny $DC$ &&\tiny $R$ & \tiny $DC$ \\
		\hline
		\multirow{8}{0.5em}{\rotatebox{90}{GitHub}}&

		Watch&{\cellcolor{teal!87}0.87 } & {\cellcolor{teal!86} 	0.86}
		& &
		{\cellcolor{teal!68 }0.68 } & {\cellcolor{teal!73} 	0.73	}
		&	 &
		{\cellcolor{teal!72 }0.72 } & {\cellcolor{teal!68} 	0.68	}
		\\%[15pt]

		&
		Fork&{\cellcolor{teal!75}0.75 } & {\cellcolor{teal!72} 	0.72}
		& &
		{\cellcolor{teal!40 }0.40 } & {\cellcolor{teal!38} 	0.38	}
		&&
		{\cellcolor{teal!41 }0.41 } & {\cellcolor{teal!48} 	0.48	}
		\\%[15pt]

		&
		Issues&{\cellcolor{teal!5 }0.05 } & {\cellcolor{teal!9} 	0.09}
		& &
		{\cellcolor{teal!22 }0.22 } & {\cellcolor{teal!5} 	0.05	}
		& &
		{\cellcolor{teal!00 }0.00 } & {\cellcolor{teal!36} 	0.36	}
		\\%[15pt]

		&
		IssueComment&{\cellcolor{teal!13 }0.13 } & {\cellcolor{teal!14} 	0.14	}
		&&
		{\cellcolor{teal!36}0.36 } & {\cellcolor{teal!29} 	0.29	}
		& &
		{\cellcolor{teal!25 }0.25} & {\cellcolor{teal!54} 0.54	}
		\\%[15pt]

		&
		Push&{\cellcolor{teal!06 }0.06 } & {\cellcolor{teal!9} 0.09}
		& &
		{\cellcolor{teal!06}0.06 } & {\cellcolor{teal!17} 	0.17	}
		& &
		{\cellcolor{teal!07 }0.07 } & {\cellcolor{teal!	11	} 	0.11	}
		\\%[15pt]

		&
		CommitComment&{\cellcolor{teal!06 }0.06 } & {\cellcolor{teal!	9	} 	0.09	}
		& &
		{\cellcolor{teal!04 }0.04 } & {\cellcolor{teal!	12	} 	0.12	}
		&&
		{\cellcolor{teal!08 }0.08 } & {\cellcolor{teal!	8	} 	0.08	}
		\\%[15pt]

		&
		PullRequest (PR)&{\cellcolor{teal!18}0.18} & {\cellcolor{teal!	20	} 	0.20	}
		&&
		{\cellcolor{teal!14 }0.14 } & {\cellcolor{teal!	22	} 	0.22	}
		& &
		{\cellcolor{teal!15 }0.15 } & {\cellcolor{teal!	21	} 	0.21	}
		\\%[15pt]

		&
		PRReviewComment&{\cellcolor{teal!39 }0.39} &{\cellcolor{teal!	37	} 	0.37	}
		&&
		{\cellcolor{teal!29 }0.29 } &{\cellcolor{teal!	36	} 	0.36	}
		&&
		{\cellcolor{teal!22 }0.22 } &{\cellcolor{teal!	44	} 	0.44	}
		\\%[15pt]

		\hline

		\multirow{4}{0.5em}{\rotatebox{90}{Reddit}}&
		Comment Volume & {\cellcolor{teal!58} 0.58 	} & {\cellcolor{teal!	62	} 	0.62	}
		&
		& {\cellcolor{teal!48} 0.48 } & {\cellcolor{teal!	51	} 	0.51	}
		&
		& {\cellcolor{teal!67} 0.67 } & {\cellcolor{teal!	78	} 	0.78	}
		\\
		&
		Subjectivity & --- 	& {\cellcolor{teal!	0	} 	0.00	}
		&
		& ---				& {\cellcolor{teal!	5	} 	0.05	}
		&
		& {\cellcolor{teal!16} 0.16 } & {\cellcolor{teal!	25	} 	0.25	}
		\\
		&
		Polarity & --- 		& {\cellcolor{teal!	2	} 	0.02	}
		&
		& {\cellcolor{teal!13} 0.13 } & {\cellcolor{teal!	15	} 	0.15	}
		&
		& {\cellcolor{teal!31} 0.31 } & {\cellcolor{teal!	43	} 	0.43	}
		\\
		&
		Score & {\cellcolor{teal!34} 0.34} & {\cellcolor{teal!	37	} 	0.37	}
		&
		& {\cellcolor{teal!47} 0.47 } & {\cellcolor{teal!	59	} 	0.59	}
		&
		& {\cellcolor{teal!69} 0.69 } &{\cellcolor{teal!	79	} 	0.79	}
		\\
		\hline
		\\
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Maria Glenski
Tim Weninger
Svitlana Volkova

"Improved Forecasting of Cryptocurrency Price using Social Signals" by Maria Glenski, Tim Weninger, and Svitlana Volkova, arXiv:1907.00558v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Bahadur efficiency for certain goodness--of--fit tests based on the empirical characteristic function" by Simos G. Meintanis, Bojana Milošević, and Marko Obradović, arXiv:2201.08604v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"An Event Study of the Ethereum Transition to Proof-of-Stake" by Elie Kapengut and Bruce Mizrach, arXiv:2210.13655v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of preprocessing overhead for METIS and RCM (Units: second).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc}
 \toprule
 Dataset & Reddit & Orkut & Twitter & Friendster & papers100M \\
 \midrule
 METIS & 342 & 1009 & 23473 & 32490 & 13660 \\
 RCM & 4.25 & 9.39 & 175 & 209 & 201 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Cheng Wan
Runkai Tao
Zheng Du
Yang Katie Zhao
Yingyan Celine Lin

"MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators" by Cheng Wan, Runkai Tao, Zheng Du, Yang Katie Zhao, and Yingyan Celine Lin, arXiv:2501.01951v3, 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{Taxonomy}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \hline
 Variable & Description\\
 \hline
 MARKET & Market data, i.e., data from other assets' t.s. \\
 TECH & Technical indicators \\
 FUND & Fundamental corporate finance metrics \\
 MACRO & Macroeconomic data \\
 OTHER\{SPECIFY\} & Various \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey" by Lukas Ryll and Sebastian Seidens, arXiv:1906.07786v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{r|c|c|c|c}
 \midrule
 \textbf{Evidence type} & \textbf{Protocols} & \textbf{Publications} & \textbf{Dimensions} & \textbf{Evidence strength} \\ \midrule
 Anecdote & one & one & one & none \\
 Robustness checks & few & one & few & weak \\
 Internal paths & many & one & many & sustained \\
 External confirmation & many & many & one & sustained \\
 Exhaustive documentation & many & many & many & strong \\ \midrule
 \end{tabular}
\caption{\textbf{Classification of empirical conclusions}. \small{The number of protocols (i.e., paths) can come either from several publications that treat the same topic, or, as in the present paper, from one publication which generates and reports many results internally. By ``\textit{few}'', we mean a dozen at most, whereas by ``\textit{many}'', we mean a hundred at least. } }
\end{table}
\end{document}

q-fin

Forking paths in financial economics

"Forking paths in financial economics" by Guillaume Coqueret, arXiv:2401.08606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results on the Oxford-IIIT Pet 2-task transmission (semantic segmentation and classification)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
		\toprule
		\multirow{2}{*}{Models} & \multicolumn{2}{c}{Semantic Seg.} & Classif. & \multicolumn{3}{c}{Relative (\%)} \\ \cmidrule(lr){2-3} \cmidrule(lr){4-4} \cmidrule(lr){5-7}
		& mIoU ↑ & Pixel Acc. ↑ & Acc. ↑ & Seg. & Cls. & Ave. \\ \midrule
		Single Model & \textbf{64.9} & 86.2 & 68.2 & - & - & - \\
		Multi-Task & 59.3 & 83.6 & 64.8 & -5.8 & -5.2 &-5.3 \\
		Dynashare & 62.8 & 84.8 & 70.7& -2.4 & 3.7 & 0.6 \\
		GAI (ours) & 63.3 & \textbf{85.0} & \textbf{71.0} & -1.8 & \textbf{4.1} & \textbf{1.2} \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xi Yu
Tiejun Lv
Weicai Li
Wei Ni
Dusit Niyato
Ekram Hossain

"Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction" by Xi Yu, Tiejun Lv, Weicai Li, Wei Ni, Dusit Niyato, and Ekram Hossain, arXiv:2501.02006v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llllll}
 \toprule
 Name & Objective & Approximation & Surrogate $\log\lambda_P$ & Number of data points & Counts and Exposures \\
 \midrule
 Part I & $\Lambda_0(\mathcal X)$ & $\exp\overline{\log\Lambda}_0(\mathcal X)$ & $\log\Lambda_0(\mathcal X)$ & 1 & $c_i = 0$, $e_i = 1$ \\ \\
 Part II & $\sum_{h=1}^n\sum_{i = 1}^{B_h} \Lambda_h(b_{i, h})$ & $\sum_{h=1}^n\sum_{i = 1}^{B_h} \exp\overline{\log\Lambda}_h(b_{i,h})$ & $\log\Lambda_h(b_{i,h})$ & $\sum_h B_h$ & $c_i = 0$, $e_i = 1$ \\ \\
 Part III & $\sum_{h=1}^n\log\lambda(\mathbf x_h)$ & $\sum_{h=1}^n \exp\overline{\log\lambda}(\mathbf x_h)$ & $\log\lambda(\mathbf x)$ & $n$ & $c_i = 1$, $e_i = 0$ \\ \\
 \bottomrule
 \end{tabular}
\caption{Hawkes process log-likelihood components approximation}
\end{table}
\end{document}

stat

Mark Naylor
Francesco Serafini
Finn Lindgren
Ian Main

"Bayesian modelling of the temporal evolution of seismicity using the ETAS.inlabru R-package" by Mark Naylor, Francesco Serafini, Finn Lindgren, and Ian Main, arXiv:2212.06077v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Dengming Zhang
Weitao You
Ziheng Liu
Lingyun Sun
Pei Chen

"Personalized Dynamic Music Emotion Recognition with Dual-Scale Attention-Based Meta-Learning" by Dengming Zhang, Weitao You, Ziheng Liu, Lingyun Sun, and Pei Chen, arXiv:2412.19200v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf{GARCH statistics for IAG stock exchange}} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
Parameter & Estimate & Error & t-statistic & p-value \\ % inserts table
		\hline
	$a_0$ & 1.31E-07 & 1.59E-08 & 8.278 & 2.2E-16 \\
	$a_1$ & 2.77E-01 & 4.03E-02 & 6.867 & 6.6E-12\\
	$b_1$ & 4.83E-01 & 5.24E-02 & 9.208 & $<$2E-16 \\
\hline \hline %inserts single line
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Volatility Models Applied to Geophysics and High Frequency Financial Market Data" by Maria C Mariani, Md Al Masum Bhuiyan, Osei K Tweneboah, Hector Gonzalez-Huizar, and Ionut Florescu, arXiv:1901.09145v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textsc{Evaluation Metric Scores of 21 test images given in TNO}}
\begin{tabular}{|c|c|c|c|}
 \hline
 Metrics & Qw & Qe & Qe*Qw \\
 \hline
 DLF & 0.67280 & 0.62082 & 0.41769 \\
 \hline
 DeepFuse & 0.72468 & 0.70971 & 0.51431 \\
 \hline
 Hybrid\_MSD & 0.68894 & 0.71466 & 0.49236 \\
 \hline
 Dual\_Branch & 0.68242 & 0.65022 & 0.44372 \\
 \hline
 Rfn-Nest & 0.66136 & 0.65125 & 0.43071 \\
 \hline
 Proposed & \textbf{0.75285} & \textbf{0.74463} &\textbf{0.56059} \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

"Visible and Infrared Image Fusion Using Encoder-Decoder Network" by Ferhat Can Ataman and Gözde Bozdaği Akar, arXiv:2412.08073v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{makecell}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Test MSE of all models in all four seasons.}
\begin{tabular}{lllll}
 \Xhline{3\arrayrulewidth}
 &\multicolumn{4}{c}{\textbf{Hours ahead}} \\
 \Xhline{3\arrayrulewidth}
 \multirow{2}{*}{\textbf{Model}}&
 \multirow{2}{*}{\textbf{12h}} &
 \multirow{2}{*}{\textbf{24h}} &
 \multirow{2}{*}{\textbf{48h}} &
 \multirow{2}{*}{\textbf{72h}}\\
 & & & & \\\Xhline{3\arrayrulewidth}
 \textbf{3DDR-UNet} & 5.40e-02	 & 7.78e-02	 & 1.21e-01	& 1.69e-01	\\
 \textbf{Res-3DDR-UNet} & 6.34e-02	& 7.99e-02	& 1.42e-01	& 1.77e-01	\\
 \textbf{InceptionRes-3DDR-UNet} & 5.19e-02	 & \underline{7.08e-02}	& 1.20e-01	 & 1.47e-01	 \\
 \textbf{AsymmInceptionRes-3DDR-UNet} & \underline{5.15e-02} & 7.56e-02	& \underline{1.17e-01}	& \underline{1.41e-01}	\\
 \Xhline{3\arrayrulewidth}
 \end{tabular}
\end{table}
\end{document}

eess

Jesús García Fernández
Ismail Alaoui Abdellaoui
Siamak Mehrkanoon

"Deep coastal sea elements forecasting using U-Net based models" by Jesús García Fernández, Ismail Alaoui Abdellaoui, and Siamak Mehrkanoon, arXiv:2011.03303v2, 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{ Absolute errors for $ m = 5 $ and for different values of $ \alpha $ }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{{|c|ccc|}}
\hline
$x$ & $\alpha =0.7$ & $\alpha =0.8$ & $\alpha =1$ \\
 \hline
$0.1$ & $\allowbreak 5.\,\allowbreak 583\,9\times 10^{-2}$ & $\allowbreak
2.\,\allowbreak 825\,1\times 10^{-2}$ & $3.\,\allowbreak 834\,8\times 10^{-5}
$ \\
 \hline
$0.2$ & $4.\,\allowbreak 923\,8\times 10^{-2}$ & $\allowbreak
2.\,\allowbreak 367\,8\times 10^{-2}$ & $\allowbreak 3.\,\allowbreak
476\,4\times 10^{-5}$ \\ \hline
$0.3$ & $\allowbreak 4.\,\allowbreak 577\,6\times 10^{-2}$ & $\allowbreak
2.\,\allowbreak 066\,5\times 10^{-2}$ & $\allowbreak 3.\,\allowbreak
1270\times 10^{-5}$ \\
 \hline
$0.4$ & $\allowbreak 4.\,\allowbreak 324\,2\times 10^{-2}$ & $\allowbreak
1.\,\allowbreak 855\,0\times 10^{-2}$ & $\allowbreak 2.\,\allowbreak
983\,1\times 10^{-5}$ \\ \hline
$0.5$ & $\allowbreak 4.\,\allowbreak 065\,4\times 10^{-2}$ & $\allowbreak
1.\,\allowbreak 719\,5\times 10^{-2}$ & $\allowbreak 3.\,\allowbreak
236\,2\times 10^{-5}$ \\ \hline
$0.6$ & $\allowbreak 3.\,\allowbreak 751\,0\times 10^{-2}$ & $\allowbreak
1.\,\allowbreak 660\,5\times 10^{-2}$ & $\allowbreak 4.\,\allowbreak
072\,1\times 10^{-5}$ \\ \hline
$0.7$ & $\allowbreak 3.\,\allowbreak 352\,8\times 10^{-2}$ & $1.6\,810\times
10^{-2}$ & $\allowbreak 5.\,\allowbreak 671\,6\times 10^{-5}$ \\
 \hline
$0.8$ & $\allowbreak 2.\,\allowbreak 853\,9\times 10^{-2}$ & $\allowbreak
1.\,\allowbreak 782\,4\times 10^{-2}$ & $\allowbreak 8.\,\allowbreak
2100\times 10^{-5}$ \\
\hline
$0.9$ & $\allowbreak 2.\,\allowbreak 242\,8\times 10^{-2}$ & $\allowbreak
1.\,\allowbreak 961\,9\times 10^{-2}$ & $1.\,\allowbreak 185\,7\times 10^{-4}
$ \\
 \hline
$1.0$ & $\allowbreak 1.\,\allowbreak 509\,9\times 10^{-2}$ & $\allowbreak
2.\,\allowbreak 212\,5\times 10^{-2}$ & $\allowbreak 1.\,\allowbreak
677\,8\times 10^{-4}$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A new operational matrix based on Boubaker polynomials for solving fractional Emden-Fowler problem" by Abdelkrim Bencheikh and Lakhdar Chiter, arXiv:2202.09787v2, 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{Objective evaluation of different CI implementations}
\begin{tabular}{lcccc}
 \toprule
 & \multicolumn{3}{c}{} \\ %\cmidrule(lr){5-5}
 Method & Configuration & STOI${\tiny \uparrow}$ & ESTOI${\tiny \uparrow}$ & NCM${\tiny \uparrow}$ \\
 \midrule
 ACE & - & 0.4870 & 0.2067 & 0.3262\\
 ECS & - & 0.4870 & 0.2073 & 0.3258 \\
 ASE-ECS & Pre-trained & 0.5943 & 0.3557 & 0.4914 \\
 AVSE-ECS & Pre-trained & 0.6141 & 0.3672 & 0.5067 \\
 AVSE-ECS & Joint training & \textbf{0.6305} & \textbf{0.3899} & \textbf{0.5211} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Meng-Ping Lin
Enoch Hsin-Ho Huang
Shao-Yi Chien
Yu Tsao

"End-to-End Audio-Visual Learning for Cochlear Implant Sound Coding in Noisy Environments" by Meng-Ping Lin, Enoch Hsin-Ho Huang, Shao-Yi Chien, and Yu Tsao, arXiv:2508.13576v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccccc}
 \toprule
 Model variants & Params & \# multimodal blocks \(N_1\) & \# single-modal blocks \(N_2\) & Hidden dim \(h\) & Latent dim & Time (s) \\
 \midrule
 S-16kHz & 157M & 4 & 8 & 448 & 20 & 1.23 \\
 S-44.1kHz & 157M & 4 & 8 & 448 & 40 & 1.30 \\
 M-44.1kHz & 621M & 4 & 8 & 896 & 40 & 1.35 \\
 L-44.1kHz & 1.03B & 7 & 14 & 896 & 40 & 1.96 \\
 \midrule
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Ho Kei Cheng
Masato Ishii
Akio Hayakawa
Takashi Shibuya
Alexander Schwing
Yuki Mitsufuji

"MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis" by Ho Kei Cheng, Masato Ishii, Akio Hayakawa, Takashi Shibuya, Alexander Schwing, and Yuki Mitsufuji, arXiv:2412.15322v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters determining the coefficient of exposition}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
			\toprule
			Category of exposition & $k$ & $z_0$ (m) & $z_{\text{min}}$ (m) \\
			\midrule
			I & $0.17$ & $0.01$ & $2$ \\
			II & $0.19$ & $0.05$ & $4$ \\
			III & $0.20$ & $0.10$ & $5$ \\
			IV & $0.22$ & $0.30$ & $8$ \\
			V & $0.23$ & $0.70$ & $12$ \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Various IEEE precisions and their units roundoff. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
		\hline
		Precision & Unit Roundoff \\ \hline
		fp16 (half) & $4.88\cdot 10^{-4}$ \\ \hline
		fp32 (single) & $5.96\cdot 10^{-8}$ \\ \hline
		fp64 (double) & $1.11\cdot 10^{-16}$ \\ \hline
		fp128 (quad) & $9.63\cdot 10^{-35}$ \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Mixed Precision GMRES-based Iterative Refinement with Recycling" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, 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}{r|r|r|r}
 \toprule
 Category & \# traces & $n$ (distinct pages) & Source \\
 \midrule
 \texttt{WikiMedia} & 3 & [461, 480, 455] & \\
 \texttt{Bright Kite} & 1 & 66 & \\
 \texttt{WikiBench} & 4 & [476, 466, 466, 455] & \\
 \texttt{WebSearch} & 3 & [500, 497, 494] & \\
 \texttt{Financial} & 2 & [128, 380] & \\
 \texttt{Zipf} & 5 & [985, 427, 205, 114, 79] & Synthetic\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Description of the datasets used.}
\end{table}
\end{document}

cs

Keerthana Gurushankar
Noah G. Singer
Bernardo Subercaseaux

"Latency Guarantees for Caching with Delayed Hits" by Keerthana Gurushankar, Noah G. Singer, and Bernardo Subercaseaux, arXiv:2501.16535v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Abnormal component analysis

Romain Valla
Pavlo Mozharovskyi
Florence d'Alché-Buc

"Abnormal component analysis" by Romain Valla, Pavlo Mozharovskyi, and Florence d'Alché-Buc, arXiv:2312.16139v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ASR and SER results on different datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc|c|c|c}
\toprule
\multirow{2}{*}{} & \multicolumn{2}{c|}{\textbf{LibriSpeech}} & \multicolumn{2}{c|}{\textbf{AlpacaTTS}} & {\textbf{IEMOCAP}} \\
 & \textbf{WER} $\downarrow$ & \textbf{CER} $\downarrow$ & \textbf{WER} $\downarrow$ & \textbf{ACC} $\uparrow$ & \textbf{ACC} $\uparrow$ \\
\midrule
LPE w/ stage1 & 1.357 & 1.491 & 0.611 & 42.79 & 45.31\\
\quad + w/ stage2 & 1.359 & 1.594 & 0.600 & 70.39 & 73.64\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jingran Xie
Shun Lei
Yue Yu
Yang Xiang
Hui Wang
Xixin Wu
Zhiyong Wu

"Leveraging Chain of Thought towards Empathetic Spoken Dialogue without Corresponding Question-Answering Data" by Jingran Xie, Shun Lei, Yue Yu, Yang Xiang, Hui Wang, Xixin Wu, and Zhiyong Wu, arXiv:2501.10937v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Amol Salunkhe
Georgios Georgalis
Abani Patra
Varun Chandola

"An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds" by Amol Salunkhe, Georgios Georgalis, Abani Patra, and Varun Chandola, arXiv:2211.14098v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Full results of uncertainty quantification. Bayesian predictive uncertainty outperforms other uncertainty algorithms on all class regions.}
\begin{tabular}{l|cccc|cccc}
\toprule
\multirow{2}{*}{\textbf{Uncertainty Algorithm}} & \multicolumn{4}{c|}{\textbf{AUC (\%) $\uparrow$}} & \multicolumn{4}{c}{\textbf{ECE (\%) $\downarrow$}} \\ \cline{2-9}
 & All & Head & Med & Tail & All & Head & Med & Tail \\ \midrule
MCP~ & 80.15 & 87.56 & 80.78 & 62.15 & 19.39 & 11.52 & 18.30 & 36.84 \\
Evidential~ & 78.16 & 84.51 & 79.62 & \textbf{67.63} & 21.88 & 10.27 & 22.01 & 34.53 \\
Bayesian (RF-DLC) & \textbf{80.62} & \textbf{88.47} & \textbf{81.82} & 63.05 & \textbf{10.87} & \textbf{6.37} & \textbf{11.09} & \textbf{16.71} \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Making Reliable and Flexible Decisions in Long-tailed Classification" by Bolian Li and Ruqi Zhang, arXiv:2501.14090v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c}
		Landmark & ANS & Bregma & CFM & Or (L) & Po (L) \\ \hline
		Mean$\pm$SD(mm) & 1.2 $\pm$ 0.45 & 1.9 $\pm$ 0.53 & 2.32 $\pm$ 0.88 & 1.6 $\pm$ 0.335 & 2.21 $\pm$ 0.87 \\ \hline
		Landmark & Na & Or (R) & Po (R) & ML (L) & ML (R) \\ \hline
		Mean$\pm$SD(mm) & 1.73 $\pm$ 0.565 & 1.3 $\pm$ 0.38 & 1.63 $\pm$ 1.045 & 1.96 $\pm$ 0.65 & 1.72 $\pm$ 0.77 \\

	\end{tabular}
\caption{Detection error evaluation of 10 reference landmarks. Most of the landmarks are annotated almost within clinical requirements.}
\end{table}
\end{document}

eess

Hye Sun Yun
Chang Min Hyun
Seong Hyeon Baek
Sang-Hwy Lee
Jin Keun Seo

"Automated 3D cephalometric landmark identification using computerized tomography" by Hye Sun Yun, Chang Min Hyun, Seong Hyeon Baek, Sang-Hwy Lee, and Jin Keun Seo, arXiv:2101.05205v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
 \hline
 Dataset & Videos & Actions & Atomic \\
 \hline
 KTH~ & 600 & 6 & \checkmark \\
 Weizmann~ & 90 & 10 & \checkmark \\
 UCF Sports~ & 150 & 10 & \\
 Hollywood-2~ & 1,707 & 12 & \\
 HMDB51~ & 7,000 & 51 & \\
 UCF101~ & 13,320 & 101 & \\
 DALY~ & 510 & 10 & \\
 AVA~ & 387,000 & 80 & \checkmark \\
 Kinetics 700~ & 650,317 & 700 & \\
 HACS~ & 1,550,000 & 200 & \checkmark \\
 Moments in Time~ & 1,000,000 & 339 & \checkmark\\
 FineGym~ & 32,687 & 530 & \checkmark\\
 \textbf{HAA500} & \textbf{10,000} & \textbf{500} & \checkmark \\ \hline
 \end{tabular}
\caption{Summary of representative action recognition datasets.}
\end{table}
\end{document}

eess

Jihoon Chung
Cheng-hsin Wuu
Hsuan-ru Yang
Yu-Wing Tai
Chi-Keung Tang

"HAA500: Human-Centric Atomic Action Dataset with Curated Videos" by Jihoon Chung, Cheng-hsin Wuu, Hsuan-ru Yang, Yu-Wing Tai, and Chi-Keung Tang, arXiv:2009.05224v2, 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{Deepfake Classification Results (Task 1) on the validation split (\emph{Val}) and \emph{TestA} of AV-Deepfake1M++ dataset. The validation results for the \textit{Audio} and \textit{Video} models are presented on their respective modality scores only}
\begin{tabular}{lcc}
 \toprule
 Method & Val, AUC & TestA, AUC\\
 \midrule
 \multicolumn{2}{l}{\emph{Video models} (See Section~ for Features)} & \\
 TCN with Features 1,2 & 84.29 & 69.31 \\
 TCN with Features 1,2,3 & 85.48 & 72.00 \\
 TCN with Features 1,2,3,4 & \textbf{88.41} & \textbf{73.11} \\
 \midrule
 \emph{Audio models} & & \\
 Baseline Wav2Vec-AASIST-1 & 73.48 & 60.53 \\ % trained on ASV19
 Baseline Wav2Vec-AASIST-2 & 73.34 & --- \\ % trained on ASV5
 Wav2Vec-AASIST-RTPG & 99.35 & 82.14\\
 Wav2Vec-AASIST-BP & 99.46 & 82.57\\
 Wav2Vec-AASIST-codecs & \textbf{99.71} & \textbf{82.91} \\
 Wav2Vec-AASIST-all-aug & 98.20 & 80.58 \\
 Wav2Vec-AASIST-ensemble & 99.60 & 81.89 \\
 \midrule
 \emph{Multimodal fusion} & & \\
 W2V-AASIST-codecs + Video, AVG & 97.86 & 91.97 \\
 KLASSify & \textbf{98.04} & \textbf{92.78} \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"KLASSify to Verify: Audio-Visual Deepfake Detection Using SSL-based Audio and Handcrafted Visual Features" by Ivan Kukanov and Jun Wah Ng, arXiv:2508.07337v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{dsfont}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Payoff functions and their transforms for a variety of financial contingency claims} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|ccc|} % centered columns (4 columns)
\hline
Financial Contingency Claim&Payoff Function&Transformed Payoff Function\\
&$G(S_T,K)$&$G(e^{x+\chi},K)$\\
\hline

Call&$\max(S_T-K,0)$&$K\max(e^{x+\chi-\log K}-1,0)$\\
Put&$\max(K-S_T,0)$&$-K\max(e^{x+\chi-\log K}-1,0)$\\
Covered Call&$\min(S_T,K)$&$K\min(e^{x+\chi-\log K}-1,0)+K$\\
Cash-or-Nothing Call&$\mathds{1}_{S_T\geq K}$&$\mathds{1}_{e^{x+\chi-\log K}\geq 1}$\\
Cash-or-Nothing Put&$\mathds{1}_{S_T\leq K}$&$\mathds{1}_{e^{x+\chi-\log K}\leq 1}$\\
Asset-or-Nothing Call&$S_T\mathds{1}_{S_T\geq K}$&$e^{x+\chi}\mathds{1}_{e^{x+\chi-\log K}\geq 1}$\\
Asset-or-Nothing Put&$S_T\mathds{1}_{S_T\leq K}$&$e^{x+\chi}\mathds{1}_{e^{x+\chi-\log K}\leq 1}$\\
Asymmetric Call&$(S_T^n-K^n)\mathds{1}_{S_T\geq K}$&$K^n(e^{n(x+\chi-\log K)}-1)\mathds{1}_{e^{x+\chi-\log K}\geq 1}$\\
Asymmetric Put&$(K^n-S_T^n)\mathds{1}_{S_T\leq K}$&$-K^n(e^{n(x+\chi-\log K)}-1)\mathds{1}_{e^{x+\chi-\log K}\leq 1}$\\
Symmetric Call&$(S_T-K)^n\mathds{1}_{S_T\geq K}$&$\sum\limits_{j=0}^n \binom{n}{j} (-1)^{(n-j)}e^{j(x+\chi)+(n-j)\log K}\mathds{1}_{e^{x+\chi-\log K}\geq 1}$\\
Symmetric Put&$(K-S_T)^n\mathds{1}_{S_T\leq K}$&$\sum\limits_{j=0}^n \binom{n}{j}(-1)^{(n-j)}e^{(n-j)(x+\chi)+j\log K}\mathds{1}_{e^{x+\chi-\log K}\leq 1}$\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Singular Fourier-Padé Series Expansion of European Option Prices" by Tat Lung Chan, arXiv:1706.06709v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf{KPSS t-statistics test for financial data}} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
		Stocks & Test statistics & P-value \\
			\hline
			Bank of America & 0.1733 & 0.1 \\
			Discover & 0.1221 & 0.1 \\
			Intel & 0.0069 & 0.1\\
			IAG & 0.3412& 0.1 \\
\hline %inserts single line
\hline %inserts single line
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Volatility Models Applied to Geophysics and High Frequency Financial Market Data" by Maria C Mariani, Md Al Masum Bhuiyan, Osei K Tweneboah, Hector Gonzalez-Huizar, and Ionut Florescu, arXiv:1901.09145v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Published t-stats are much larger than 2.0.}
\begin{tabular}{lrrrrrrr}
\toprule
 & \multicolumn{7}{c}{t-stat minimum} \\
 & 2.0 & 3.0 & 4.0 & 5.0 & 6.0 & 7.0 & 8.0 \\
\cmidrule{2-8} & & & & & & & \\
 & \multicolumn{7}{c}{(a) Number of Predictors that Meet Minimum} \\
\cmidrule{2-8}Published and Replicated & 183 & 121 & 74 & 48 & 26 & 18 & 12 \\
Systematically Data-Mined & 5,464 & 2,837 & 1,522 & 832 & 374 & 185 & 76 \\
 & & & & & & & \\
 & \multicolumn{7}{c}{(b) Percent of Signals that Meet Minimum} \\
\cmidrule{2-8}Published and Replicated & 88.4058 & 58.4541 & 35.7488 & 23.1884 & 12.5604 & 8.6957 & 5.7971 \\
Systematically Data-Mined & 30.1662 & 15.6628 & 8.4028 & 4.5934 & 2.0648 & 1.0214 & 0.4196 \\
\midrule
 & & & & & & & \\
 & \multicolumn{7}{c}{(c) Percent of Signals Implied by Null (Significance Level)} \\
\cmidrule{2-8}Standard Normal & 4.5500 & 0.2700 & 0.0063 & 0.0001 & 0.0000 & 0.0000 & 0.0000 \\
Bootstrap & 4.6115 & 0.3034 & 0.0107 & 0.0002 & 0.0000 & 0.0000 & 0.0000 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Publication Bias in Asset Pricing Research" by Andrew Y. Chen and Tom Zimmermann, arXiv:2209.13623v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Corresponding Voltages for points $A$ and $B$ for the 5-bus test system in~.}
\begin{tabular}{|c|c|c|}
\hline
Bus & Point A & Point B\tabularnewline
\hline
\hline
1 & 0.9184 - 0.1354i & 0.8463 - 0.3785i\tabularnewline
\hline
2 & 0.9030 - 0.1643i & 0.8436 - 0.4049i\tabularnewline
\hline
3 & 0.9878 + 0.0000i & 1.0878 + 0.0000i\tabularnewline
\hline
4 & 0.9040 - 0.1524i & 0.8370 - 0.3940i\tabularnewline
\hline
5 & 1.0495 + 0.2700i & 0.9428 - 0.2943i\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

eess

Mohammad Rasoul Narimani
Katherine R. Davis
Daniel K. Molzahn

"Certifying the Nonexistence of Feasible Path Between Power System Operating Points" by Mohammad Rasoul Narimani, Katherine R. Davis, and Daniel K. Molzahn, arXiv:2509.05935v1, 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{ Rank aggregation over different working correlation structures for the separate presence models at different ages, and overall across all ages. }
\begin{tabular}{rccccccl}
Age & Rank 1 & Rank 2 & Rank 3 & Rank 4 \\
 \hline
Age 9 & A.1 & A.2 & A.4 & A.3 \\
 Age 13 & A.3 & A.4 & A.1 & A.2 \\
 Age 17 & A.4 & A.1 & A.2 & A.3 \\
 Age 23 & A.3 & A.2 & A.1 & A.4 \\
 Overall & A.3 & A.1 & A.2 & A.4 \\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

Shoumi Sarkar
Anish Mukherjee
Jeremy T. Gaskins
Steven Levy
Peihua Qiu
Somnath Datta

"Analyzing zero-inflated clustered longitudinal ordinal outcomes using GEE-type models with an application to dental fluorosis studies" by Shoumi Sarkar, Anish Mukherjee, Jeremy T. Gaskins, Steven Levy, Peihua Qiu, and Somnath Datta, arXiv:2412.11348v4, 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{Days of school closure and educational score}
\begin{tabular}{lcccccc}

					\hline

					& \multicolumn{5}{c}{\textbf{Panel}} & \textbf{Cross-section} (year=2021) \\
					Model & \multicolumn{3}{c}{\textbf{OLS with FE}} & Low \& Middle School & High School & \textbf{2SLS-IV}\\ \hline

					VARIABLES & \textbf{Math (std.)} & \textbf{Math (std.)} & \textbf{Italian (std.)} & \multicolumn{3}{c}{\textbf{Math (std.)}} \\
					& (1) & (2) & (3) & (4) & (5) & (6) \\
					\hline
					& & & & & & \\
					\textbf{Days of school closure} & -0.006*** & -0.060*** & -0.038*** & -0.027*** & -0.083*** & -0.271*** \\
				 & (0.002) & (0.003) & (0.002) & (0.004) & (0.003) & (0.062) \\ \hline

					\textbf{Days of school closure} X \textbf{Peers' score in math test} & & -0.073*** & & -0.055*** & -0.080*** & \\
					 & & (0.003) & & (0.004) & (0.003) & \\
					\textbf{Days of school closure} X \textbf{Years of parents' education} & & 0.010*** & 0.006*** & 0.021*** & -0.000 & \\
				 & & (0.001) & (0.001) & (0.002) & (0.002) & \\

					\textbf{Days of school closure} X \textbf{Peers' score in Italian test} & & & -0.070*** & & & \\
					& & & (0.002) & & & \\ \hline

					& & & & & & \\
					\textbf{Other controls} & Yes & Yes & Yes & Yes & Yes & Yes \\
					\textbf{Student FEs} & Yes & Yes & Yes & Yes & No & No \\
					\textbf{Relative time dummies} & Yes & Yes & Yes & No & No & No \\
					\textbf{Grade dummies} & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline
					Observations & 334,366 & 334,366 & 334,366 & 230,332 & 104,034 & 86,140 \\
					R-squared & 0.564 & 0.567 & 0.412 & 0.554 & 0.603 & 0.317 \\ \hline

				 & \multicolumn{6}{c}{\textbf{First stage results and statistics}} \\ \hline

				 Covid-19 cases per person & & & & & & 0.028*** \\
				 & & & & & & (0.002) \\
				 Variance of Covid-19 cases	& & & & & & -0.000*** \\
				 & & & & & & (0.000) \\

					F-test (P-value) & & & & & & 1293.29 (0.000) \\
					Hansen overid. test (P-value) & & & & & & 1.482 (0.223) \\ \hline

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

q-fin

"Will the last be the first? School closures and educational outcomes" by Michele Battisti and Giuseppe Maggio, arXiv:2208.11606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

On Lie algebras associated with a spray

"On Lie algebras associated with a spray" by Manelo Anona and Hasina Ratovoarimanana, arXiv:2201.10867v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Test MSEs of models with different metrics in the training process.}
\begin{tabular}{ccccc}
\toprule
\multirow{2}{*}{$n_O (\times 5)$} & \multicolumn{4}{c}{Metrics used in training} \\
\cmidrule{2-5}
 & MSE & MAE & RMSPE & RMSE \\
\midrule
1 & 0.632 & 0.825 & 0.680 & 1.472 \\
2 & 0.626 & 0.653 & 0.679 & 0.623 \\
3 & 0.619 & 0.637 & 0.645 & 0.619 \\
4 & 0.611 & 0.612 & 0.633 & 0.595 \\
5 & 0.609 & 0.625 & 0.643 & 0.668 \\
10 & 0.601 & 0.612 & 0.647 & 0.641 \\
15 & 0.647 & 0.621 & 0.630 & 0.613 \\
20 & 0.592 & 0.581 & 0.619 & 0.600 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

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