\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llccccc}
\toprule
 & & $\beta$ & $\pi_{gm}$ & $\pi_{alt}$ & $\pi_{coop}$ & $\pi_{free}$ \\ \midrule
\multirow{3}{*}{$T_1$} & True & 0.600 & 0.188 & 0.281 & 0.375 & 0.156 \\
 & Mean Estimate & 0.601 & 0.257 & 0.286 & 0.312 & 0.145 \\
 & s.d. & 0.027 & 0.184 & 0.145 & 0.196 & 0.107 \\
 & & & & & & \\
\multirow{3}{*}{$T_2$} & True & 0.600 & 0.188 & 0.281 & 0.375 & 0.156 \\
 & Mean Estimate & 0.599 & 0.217 & 0.263 & 0.369 & 0.151 \\
 & s.d. & 0.028 & 0.209 & 0.172 & 0.207 & 0.126 \\
 & & & & & & \\
\multirow{3}{*}{$T_3$} & True & 0.600 & 0.219 & 0.344 & 0.438 & - \\
 & Mean Estimate & 0.596 & 0.195 & 0.342 & 0.464 & - \\
 & s.d. & 0.025 & 0.122 & 0.184 & 0.224 & - \\
 & & & & & & \\ \bottomrule
\end{tabular}
\caption{The Table reports the results from the SFEM simulation for the high noise ($\beta=0.60$). $\pi^{k}$ is the mixing probability (proportion of subjects) of strategy $k$. \(gm\) stands for the G\&M type, \(alt\) for the altruist, \(coop\) for the conditional co-operator, and \(free\) for the free rider. The frequency of types is set to 6 (0.188), 9 (0.281), 12 (0.375) and 5 (0.156) subjects for each type respectively, in $T_1$ and $T_2$, and 7 (0.219), 11 (0.344) and 14 (0.438) subjects in $T_3$ for the G\&M the altruist, and the conditional co-operator.}
\end{table}
\end{document}

q-fin


math

"Transit-Length Distribution for Particle Transport in Binary Markovian Mixed Media" by Brian C. Kiedrowski and Emily H. Vu, arXiv:2412.19359v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Components of the warning score $\mathrm{WS}$ in tabular form for the Sydney rainfall example (Fig.~b), using evaluation weights $(1, 2, 3)$.}
\begin{tabular}{|r|cc|cc|cc|}
\hline
forecast 	& obs not in MOD+	& obs in MOD+	& obs not in SEV+	& obs in SEV+	& obs not in EXT	& obs in EXT \\
\hline
very likely	& $1.5$			& $0$		& $2.9$		& $0$		& $1.4$		& $0$	\\
likely		& $0.1$			& $0.6$		& $0.8$		& $0.9$		& $1.4$		& $0$	\\
possible	& $0.1$			& $0.6$		& $0$		& $2.1$		& $0.2$		& $1.8$	\\
unlikely	& $0$			& $1.5$		& $0$		& $2.1$		& $0$		& $3.6$	\\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Warnings based on risk matrices: a coherent framework with consistent evaluation" by Robert J. Taggart and David J. Wilke, arXiv:2502.08891v1, 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}{llccccc}
\hline
Estimator & Metric & \multicolumn{5}{c}{Sample Size, $n$} \\
\hline
 & & 10 & 25 & 50 & 100 & 500 \\
$\hat{M}_w$ &
$\operatorname{tr}
 \{U_w^{-1}(\hat{M}_w - M_w) V_w^{-1} (\hat{M}_w - M_w)^\top \}$ & 2.19 & 0.46 & 0.30 & 0.14 & 0.10 \\
$\hat{U}_w$ & $d(\hat{U}_w, U_w)$ & 0.47 & 0.27 & 0.19 & 0.15 & 0.06 \\
$\hat{V}_w$ & $d(\hat{V}_w, V_w)$ & 1.16 & 0.72 & 0.55 & 0.32 & 0.15 \\
\end{tabular}
\caption{\small Results of numerical investigation into convergence of parameter estimates with increasing $n$, for the $\operatorname{Sym}_{> 0}(2)$ model in \S }
\end{table}
\end{document}

math

Simon Preston
Karthik Bharath
Pablo Lopez-Custodio
Alfred Kume

"Rolled Gaussian process models for curves on manifolds" by Simon Preston, Karthik Bharath, Pablo Lopez-Custodio, and Alfred Kume, arXiv:2503.21980v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{An Example of Twitter Eating Disorder Data $\mathcal{T}$. Each row represents a data row (as formulated in definition~), corresponding to one user.}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|}

 \hline
 $f_1$ & $f_2$ & $f_3$ & $f_4$ & $f_5$ & $f_6$ & $f_7$ & $f_8$ & $f_9$ & $f_{10}$ & $f_{11}$ & $f_{12}$ & $f_{13}$ & $f_{14}$ & $f_{15}$ & y \\
 \hline
 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
 \hline
 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 \\
 \hline
 26 &	22 & 0 & 0 & 25 & 0 & 0 & 3 & 0 & 0 & 0 & 0 & 29 & 0 & 0 & 2 \\
 \hline
 1 & 39 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 3 \\
 \hline

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

stat

Mehdi Naseriparsa
Suku Sukunesan
Zhen Cai
Osama Alfarraj
Amr Tolba
Saba Fathi Rabooki
Feng Xia

"ED-Filter: Dynamic Feature Filtering for Eating Disorder Classification" by Mehdi Naseriparsa, Suku Sukunesan, Zhen Cai, Osama Alfarraj, Amr Tolba, Saba Fathi Rabooki, and Feng Xia, arXiv:2501.14785v1, 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}{ccccccccc}
 \toprule
 & \multicolumn{4}{c}{ATT} & \multicolumn{4}{c}{ATO} \\ \cmidrule(lr){2-5} \cmidrule(lr){6-9}
 & \multicolumn{2}{c}{True PS} & \multicolumn{2}{c}{Estimated PS} & \multicolumn{2}{c}{True PS} & \multicolumn{2}{c}{Estimated PS} \\ \cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7} \cmidrule(lr){8-9}
 $\phi$ & Power & Size & Power & Size & Power & Size & Power & Size \\ \midrule
 1.00 & 0.956 & 1083 & 0.969 & 1178 & 0.936 & 971 & 0.969 & 1184 \\
 0.98 & 0.910 & 919 & 0.966 & 1197 & 0.934 & 988 & 0.966 & 1194\\
 0.93 & 0.837 & 842 & 0.897 & 1026 & 0.920 & 1006 & 0.972 & 1330 \\
 0.88 & 0.717 & 854 & 0.711 & 854 & 0.889 & 959 & 0.923 & 1082 \\
 0.84 & 0.632 & 965 & 0.641 & 975 & 0.886 & 974 & 0.916 & 1085 \\
 0.81 & 0.593 & 1124 & 0.546 & 1019 & 0.859 & 912 & 0.904 & 1052 \\
 \bottomrule
 \end{tabular}
\caption{Sample size (size) calculated by the proposed method to achieve the nominal power (power) under $r=0.5$ and various degrees of overlap in simulations. The nominal power is the true power of H\'{a}jek estimator for a sample of $N = 1000$ units, with the true and estimated propensity scores, respectively.}
\end{table}
\end{document}

stat

"Sample size and power calculation for propensity score analysis of observational studies" by Bo Liu, Xiaoxiao Zhou, and Fan Li, arXiv:2501.11181v2, 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}{lccc}
\textbf{Label} & \textbf{Accuracy} & \textbf{AUC-ROC} & \textbf{Yes/No} \\ \hline
Pet prod. & 0.83 $\pm$ 0.02 & 0.79 $\pm$ 0.07 & 23/77 \\ \hline
Pet prod.$^1$ & 0.80 $\pm$ 0.02 & 0.95 $\pm$ 0.05 & 27/73 \\ \hline
Damaged & 0.97 $\pm$ 0.03 & 0.50 $\pm$ 0.50 & 2/98 \\ \hline
Damaged$^2$ & 0.96 $\pm$ 0.01 & 0.75 $\pm$ 0.25 & 5/195 \\ \hline
Damaged$^3$ & 0.82 $\pm$ 0.04 & 0.88 $\pm$ 0.12 & 30/70 \\ \hline
\end{tabular}
\caption{Final performance with \textbf{XGBClassifier} after 100 manual labels using our framework. \\ {\small $^1$Done with $k_{top}$ splitted 50/50 on high and low uncertainty. \\ $^2$Done with 200 manual labelings. \\ $^3$Started with 40 pre-labelled texts.}}
\end{table}
\end{document}

stat

"Mining Unstructured Medical Texts With Conformal Active Learning" by Juliano Genari and Guilherme Tegoni Goedert, arXiv:2502.04372v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The results~(\%) of joint training with supervised and self-supervised model in AVSD. The audio encoder is abbreviated AE and the speaker encoder is abbreviated SE. (* represents joint training in the corresponding module).}
\begin{tabular}{lcccc}
\toprule
\hline
Method & MISS & FA & SPKERR & DER \\ \hline
CNN-AE~\&~ivector-SE~ & 4.01 & 5.86 & 3.22 & 13.09 \\
WHU~ & - & - & - & 8.82\\
SJTU~ & 4.44 & 4.82 & 2.10 & 10.82 \\
HuBERT-AE~\&~ResNet-SE & 3.72 & 5.66 & 2.53 & 11.92 \\
HuBERT-AE$^*$~\&~ResNet-SE & \textbf{1.88} & \textbf{4.96} & \textbf{2.41} & \textbf{9.25} \\
HuBERT-AE$^*$~\&~ResNet-SE$^*$ & 2.35 & 5.14 & 2.67 & 10.16 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Huan Zhao
Li Zhang
Yue Li
Yannan Wang
Hongji Wang
Wei Rao
Qing Wang
Lei Xie

"Joint Training or Not: An Exploration of Pre-trained Speech Models in Audio-Visual Speaker Diarization" by Huan Zhao, Li Zhang, Yue Li, Yannan Wang, Hongji Wang, Wei Rao, Qing Wang, and Lei Xie, arXiv:2312.04131v1, 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{Human Evaluation Scores of the Question-Answer Pairs}
\begin{tabular}{lcccc}
 \toprule
 Criteria & Fluency & Answerability & Relevance & Non-ambiguity \\
 \midrule
 Expert 1 & 4.908 & 4.735 & 4.730 & 4.754 \\
 Expert 2 & 4.886 & 4.697 & 4.768 & 4.650 \\
 Expert 3 & 4.638 & 4.636 & 4.636 & 4.586 \\
 Expert 4 & 4.699 & 4.150 & 4.638 & 4.157 \\
 \midrule
 Average & 4.782 & 4.554 & 4.693 & 4.536 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Abdelrahman Abdallah
Mahmoud Kasem
Mahmoud Abdalla
Mohamed Mahmoud
Mohamed Elkasaby
Yasser Elbendary
Adam Jatowt

"ArabicaQA: A Comprehensive Dataset for Arabic Question Answering" by Abdelrahman Abdallah, Mahmoud Kasem, Mahmoud Abdalla, Mohamed Mahmoud, Mohamed Elkasaby, Yasser Elbendary, and Adam Jatowt, arXiv:2403.17848v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{siunitx}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc}
\toprule
 Ablation & Tracking error (sim) (\SI{}{m})\\
\midrule
 No body frame & \textit{failed}\\
 No fixed intial reference & $0.437 \pm 0.08$ \\
 No feedback term & $0.077 \pm 0.011$ \\
 Feedforward horizon 1 ($H = 0.02s$) & \textit{failed} \\
 Feedforward horizon 5 ($H = 0.3s$) & $0.240 \pm 0.008$ \\
 Feedforward horizon 10 ($H = 0.6s$) (used in main experiments) & $0.055 \pm 0.007$ \\
 Feedforward horizon 15 ($H = 0.9s$) & $0.073 \pm 0.010$ \\
 Feedforward horizon 20 ($H = 1.2s$) & $0.101 \pm 0.018$ \\
 Base policy (no ablation) & $0.046$ \\
\bottomrule
\end{tabular}
\caption{ Tracking error (in \SI{}{m}), in simulation, of various ablations after 15M training steps. \textit{Failed} indicates the drone diverges from the reference trajectory. Tracking error is with respect to infeasible zigzag trajectories. The ablations are done without adaptation, and with no disturbances in the environment. 5 runs were attempted for each ablation.}
\end{table}
\end{document}

eess

Kevin Huang
Rwik Rana
Alexander Spitzer
Guanya Shi
Byron Boots

"DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control" by Kevin Huang, Rwik Rana, Alexander Spitzer, Guanya Shi, and Byron Boots, arXiv:2310.09053v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
 \cline{1-1}\cline{3-3}
 {\bf Geographical regions} && {\bf GICS industries}\\\cline{1-1}\cline{3-3}
 {\bf Europe} && {\bf Cyclical industries} \\\cline{1-1}\cline{3-3}
 Europe && % ACWI-
 Materials \\
 France &&% ACWI-
 Industrials \\
 UK && % ACWI-
 Consumer Discretionary\\
 Italy && % ACWI-
 Financial\\
 Germany && % ACWI-
 IT\\\cline{1-1}
 {\bf Asia Pacific} &&% ACWI-
 Real Estate \\\cline{1-1}\cline{3-3}
 Pacific && {\bf Defensive industries}\\\cline{3-3}
 Singapore &&% ACWI-
 Energy\\
 Japan &&% ACWI-
 Consumer Staples\\
 Hong Kong &&% ACWI-
 Health Care\\
 Australia && % ACWI-
 Communication Services\\\cline{1-1}
 {\bf North America} &&% ACWI-
 Utilities \\\cline{1-1}\cline{3-3}
 United States \\
 Canada \\\cline{1-1}
 {\bf Emerging markets} \\\cline{1-1}
 EM Latin America \\
 EM Europe + Middle East + Africa \\
 EM Asia \\\cline{1-1}
 \end{tabular}
\end{table}
\end{document}

q-fin

"Risk factor aggregation and stress testing" by Natalie Packham, arXiv:2310.04511v1, 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}{ccccc}
Type & $n_1$ & $n_2$ & $n_3$ & $n_4$ \\ \hline
R &8 &0 &0 &2 \\
S &7 &0 &3 &0 \\
T &7 &1 &1 &1 \\
U &6 &2 &2 &0 \\
V &6 &3 &0 &1 \\
W &5 &4 &1 &0 \\
X &4 &6 &0 &0
\end{tabular}
\caption{A summary of the seven possible transversal types of $L$.}%
\end{table}
\end{document}

math

Curtis Bright
Amadou Keita
Brett Stevens

"Myrvold's Results on Orthogonal Triples of $10 \times 10$ Latin Squares: A SAT Investigation" by Curtis Bright, Amadou Keita, and Brett Stevens, arXiv:2503.10504v1, 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{Whether one needs to load vocabulary embeddings and output layer weights on IsarStep tasks.}
\begin{tabular}{lllllll}
 \toprule
 Model & Top-1 Acc. & Top-10 Acc \\
 \midrule
 No pretrain~ & 20.4 & 33.1\\
 LIME \texttt{Mix} &26.9 &40.4 \\
 LIME \texttt{Mix} + Loading All Weights & 26.7 &40.6 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Yuhuai Wu
Markus Rabe
Wenda Li
Jimmy Ba
Roger Grosse
Christian Szegedy

"LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning" by Yuhuai Wu, Markus Rabe, Wenda Li, Jimmy Ba, Roger Grosse, and Christian Szegedy, arXiv:2101.06223v2, 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{Results for Problem ().}
\begin{tabular}{|r|r|}
\hline
$\ell$ & $\rho_\ell$\hspace{1.5cm} \\
\hline
0 & .6476128469955936\\
1 & 2.799999999999968\\
\hline
\end{tabular}
\end{table}
\end{document}

math

Luigi Brugnano
Gianmarco Gurioli
Felice Iavernaro

"A shooting-Newton procedure for solving fractional terminal value problems" by Luigi Brugnano, Gianmarco Gurioli, and Felice Iavernaro, arXiv:2312.08516v5, 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}{lrrrrrrr} % Left-align first column, right-align others
 \toprule
 Problem & Feeds & Pools & Products & Qualities & Variables & Equations QP & Equations LP \\
 \midrule
 haverly1 & 3 & 1 & 2 & 1 & 10 & 17 & 29 \\
 haverly2 & 3 & 1 & 2 & 1 & 13 & 20 & 38 \\
 haverly3 & 3 & 1 & 2 & 1 & 10 & 17 & 29 \\
 bental4 & 4 & 1 & 2 & 1 & 13 & 21 & 39 \\
 bental5 & 13 & 3 & 5 & 2 & 92 & 121 & 301 \\
 foulds2 & 6 & 2 & 4 & 1 & 36 & 46 & 94 \\
 adhya1 & 5 & 2 & 4 & 4 & 33 & 62 & 122 \\
 adhya2 & 5 & 2 & 4 & 6 & 33 & 70 & 130 \\
 adhya3 & 8 & 3 & 4 & 6 & 52 & 94 & 190 \\
 adhya4 & 8 & 2 & 5 & 4 & 58 & 95 & 215 \\
 \bottomrule
 \end{tabular}
\caption{Model statistics for examined pooling problems}
\end{table}
\end{document}

math

Asimina Marousi
Vassilis M. Charitopoulos

"Global and Robust Optimisation for Non-Convex Quadratic Programs" by Asimina Marousi and Vassilis M. Charitopoulos, arXiv:2503.07310v1, 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
\begin{tabular}{ccccccc}
 \toprule
\multirow{2}{*}[-0.2em]{Metric} & \multicolumn{3}{c}{GDBT} & \multicolumn{3}{c}{AutoML} \\
\cmidrule(lr){2-4} \cmidrule(lr){5-7}
 & (Base) & + BS & + IPB & (Base) & + BS & + IPB
 \\ \midrule
 NLL
 & $4.77$ & $4.33$ & $\mathbf{3.20}$ & $3.60$ & $3.03$ & $\mathbf{2.07}$ \\
 Accuracy
 & $4.87$ & $4.43$ & $\mathbf{3.23}$ & $3.50$ & $2.50$ & $\mathbf{2.47}$ \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"On Uncertainty Quantification for Near-Bayes Optimal Algorithms" by Ziyu Wang and Chris Holmes, arXiv:2403.19381v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Oscar Chang
Dongseong Hwang
Olivier Siohan

"Revisiting the Entropy Semiring for Neural Speech Recognition" by Oscar Chang, Dongseong Hwang, and Olivier Siohan, arXiv:2312.10087v2, 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{Frequency of Special Characters}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

math

Dinesh Parthasarathy
Tommaso Bevilacqua
Martin Lanser
Axel Klawonn
Harald Köstler

"Towards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations" by Dinesh Parthasarathy, Tommaso Bevilacqua, Martin Lanser, Axel Klawonn, and Harald Köstler, arXiv:2412.08186v1, 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{Improvement(\%) for structured datasets. We report the average improvement and standard deviation across the five splits.}
\begin{tabular}{ccccc}
\textbf{Method} & \textbf{Diabetes} & \textbf{Groceries} & \textbf{Spleen} & \textbf{REBOA} \\ \midrule
Regress \& Compare & $2.00\pm 0.25$ & $94.17 \pm 6.25$ & $8.03 \pm 2.24$ & $-17.80 \pm 27.91$ \\
Causal Forest & $ 2.50 \pm 0.69$& $98.68 \pm 5.98$ & $-4.84 \pm 13.14$& $-4.82 \pm 3.18$\\
Optimal Policy Tree & $1.72\pm 1.53$ & $106.58 \pm 5.33$ & $16.41 \pm 3.15$ & $\mathbf{10.79 \pm 4.34}$\\
PNN & $\mathbf{4.01 \pm 0.90} $& $\mathbf{110.88 \pm 5.91}$ & $\mathbf{20.47 \pm 1.68}$& $10.22 \pm 4.55$\\
Mirrored OCT & $3.93\pm 1.21$ & $110.22\pm 6.94$ & $15.72\pm 6.01$ &$10.21\pm 5.03$ \\
\end{tabular}
\end{table}
\end{document}

stat

Multimodal Prescriptive Deep Learning

Dimitris Bertsimas
Lisa Everest
Vasiliki Stoumpou

"Multimodal Prescriptive Deep Learning" by Dimitris Bertsimas, Lisa Everest, and Vasiliki Stoumpou, arXiv:2501.14152v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{DiD State Capacity Analysis: Extensive Margin, Events in Over 60\% of Years}
\begin{tabular}{lccccccc}
\toprule
& Tax & Operational & Ratio Gov. Trans. & Fiscal & Administrative & Rule & \textbf{Anderson} \tabularnewline & Revenue (pc) & Costs (pc) & to Revenue & Performance & Performance & Compliance & \textbf{Index} \tabularnewline
{}&{(1)}&{(2)}&{(3)}&{(4)}&{(5)}&{(6)}&{(7)} \tabularnewline
\midrule
\midrule Ceasefire \( \times \) FARC&--0.009&0.005&--0.050&0.012&--0.058&0.122&0.067 \tabularnewline
&(0.011)&(0.005)&(0.539)&(0.108)&(0.090)&(0.100)&(0.054) \tabularnewline
&&&&&&& \tabularnewline
Treated Municip.&216&216&216&216&216&216&216 \tabularnewline
Control Municip.&41&41&41&41&41&41&41 \tabularnewline
Mean Dependent Var.&0.100&0.113&7.149&--0.065&--0.132&--0.087&0.014 \tabularnewline
Observations&2,822&2,822&2,815&2,823&2,827&2,567&2,827 \tabularnewline
\(R^2\)&0.778&0.813&0.676&0.548&0.483&0.433&0.595 \tabularnewline
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Peace Dividends: The Economic Effects of Colombia's Peace Agreement" by Miguel Fajardo-Steinhäuser, arXiv:2301.01843v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccc}\hline

 {\textbf{Predicted Land Use}} & \multicolumn{4}{c}{\textbf{Official Land Use}}\\ %\cline{2-5}

 & \textbf{Business} & \textbf{Residential} & \textbf{Education} & \textbf{Recreation} \\ \hline

 \multicolumn{5}{l}{\textbf{Melbourne}} \\ %\hline

 Business & \textbf{52.4\%} & - & - & - \\ %\hline

 Residential & - & \textbf{56\%} & - & - \\ %\hline

 Education & - & - & 0\% & - \\ %\hline

 Recreation & - & - & - & \textbf{67.2\%} \\ \hline

 \multicolumn{5}{l}{\textbf{Sydney}} \\ %\hline

 Business & {\textbf{62.5\%}} & - & - & - \\ %\hline

 Residential & - & {\textbf{50\%}} & - & - \\ %\hline

 Education & - & - & {\textbf{53.5\%}} & - \\ %\hline

 Recreation & - & - & - & {\textbf{52.6\%}} \\ \hline
 \end{tabular}
\caption{Overlap between the land use predicted by Twitter activity and the Official land use.}
\end{table}
\end{document}

cs

"Land Use Detection & Identification using Geo-tagged Tweets" by Saeed Khan and Md Shahzamal, arXiv:2101.03337v1, 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|}
 \hline
 Definition in Case \textbf{A} & Definition in Case \textbf{B} & Definition in Case \textbf{C}\\ \hline
 $s(\boldsymbol{p}) = - \boldsymbol{e} \cdot \boldsymbol{p}$ & $s(\boldsymbol{p}) = \boldsymbol{e} \cdot \boldsymbol{p}$ & $s(\boldsymbol{p}) = 0$\\ \hline
 \end{tabular}
\end{table}
\end{document}

math

Positivity sets of hinge functions

Josef Schicho
Ayush Kumar Tewari
Audie Warren

"Positivity sets of hinge functions" by Josef Schicho, Ayush Kumar Tewari, and Audie Warren, arXiv:2503.13512v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Transit-Length Distribution for Particle Transport in Binary Markovian Mixed Media" by Brian C. Kiedrowski and Emily H. Vu, arXiv:2412.19359v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems" by Grzegorz Filcek and Janusz Miroforidis, arXiv:2401.00292v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Effects of Air Pollution on Exam Results}
\begin{tabular}{lccccccccccccc}
				\toprule \toprule
				& \multirow{2}{*}{Localized} & & \multicolumn{3}{c}{Distant Measure} \\
				\cline{4-6}
				& \multirow{2}{*}{Measure} & & The Closest & Two Closest & Three Closest \\
				& & & Station & Stations & Stations \\
				\cline{2-6}
				& (1) & & (2) & (3) & (4) \\
				\midrule

	$PM_{10}$ ($\mu g/m^{3}$) & -0.011$^{***}$ & & 0.002 & 0.002 & 0.002 \\

	\hspace{3mm} & (0.003) & & (0.002) & (0.003) & (0.003) \\

	AQI & -0.012$^{***}$ & & 0.003 & 0.002 & 0.002\\

	\hspace{3mm} & (0.004) & & (0.002) & (0.003) & (0.003)\\

	High pollution ($PM_{10}$) & -0.181$^{***}$ & & -0.010 & -0.002 & 0.000\\

	\hspace{3mm} & (0.041) & & (0.025) & (0.025) & (0.025) \\

	AQI mean & 8.70 & & 19.99 & 19.95 & 19.99 \\

	Average distance (km) & - & & 16.05 & 21.11 & 25.64 \\
	Number of observations & 4,650 & & 4,650 & 4,650 & 4,650 \\
	\bottomrule
		\end{tabular}
\end{table}
\end{document}

q-fin

"The Effects of Air Pollution on Teenagers' Cognitive Performance: Evidence from School Leaving Examination in Poland" by Agata Galkiewicz, arXiv:2506.19801v1, 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}{lcccc}
 \toprule
 Dataset & Flu. & Coh. & Inf. & Average \\
 \midrule
 ICL & 1.51 & 1.05 & 1.04 & 1.20 \\
 ICL$_{context=1}$ & 1.53 & 1.08 & 0.98 & 1.19 \\
 ICL$_{context=2}$ & 1.48 & 0.91 & 0.84 & 1.08 \\
 ICL$_{context=3}$ & 1.44 & 0.74 & 0.82 & 1.00 \\
 SDA & 1.62 & 1.20 & 1.19 & 1.34 \\
 \bottomrule
 \end{tabular}
\caption{Human evaluation on dialogue model prediction with different data augmentation methods. Flu./Coh./Inf. stands for Fluency/Coherence/Informativeness respectively.}
\end{table}
\end{document}

cs

Zhenhua Liu
Tong Zhu
Jianxiang Xiang
Wenliang Chen

"Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation" by Zhenhua Liu, Tong Zhu, Jianxiang Xiang, and Wenliang Chen, arXiv:2404.00361v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{OpenOOD performance across different preprocessing methods for expectation maximization trained hierarchical DPMM models. The preprocessing methods are the raw ViT features (ViT), marginal covariance whitening followed by a rotation into the average class-covariance eigenspace (W\&R), and PCA. Baselines: Mahalanobis distance score to the closely related Mahalanobis distance score methods, MDS~ and RMDS~. We also compare to maximum softmax probability (MSP)~ and temperature scaled MSP with $T=1000$ (Temp. Scale)~ which is the ODIN~ method without input preprocessing. A single linear layer was trained with gradient descent and supervised cross-entropy loss for the MSP and ODIN methods}
\begin{tabular}{llcccccccc}
\toprule
& & & \multicolumn{3}{c}{Near} & \multicolumn{4}{c}{Far} \\
\cmidrule(lr){4-6}\cmidrule(lr){7-10}
Model & Pre & Accuracy & SSB Hard & NINCO & Avg. & iNaturalist & OpenImage-O & Textures & Avg. \\
\midrule
MSP & ViT & \textbf{80.94} & 73.80 & 82.72 & 78.26 & 92.08 & 88.67 & 88.39 & 89.71 \\
 & W\&R & 80.90 & 71.74 & 79.87 & 75.80 & 88.65 & 85.62 & 84.64 & 86.30 \\
 & PCA & 80.91 & 73.67 & 82.94 & 78.31 & 92.08 & 88.60 & 88.34 & 89.67 \\
\midrule
Temp. & ViT & \textbf{80.94} & \textbf{75.60} & 84.36 & 79.98 & 94.22 & 90.82 & \textbf{90.82} & 91.95 \\
MSP & W\&R & 80.90 & 73.29 & 81.28 & 77.29 & 91.24 & 87.82 & 86.81 & 88.62 \\
T=1000 & PCA & 80.91 & 75.24 & 84.77 & 80.00 & 94.25 & 90.78 & 90.75 & 91.93 \\
\midrule
MDS & ViT & 80.22 & 71.45 & 86.44 & 78.94 & 95.96 & 92.33 & 89.37 & 92.55 \\
 & W\&R & 80.41 & 71.45 & 86.48 & 78.97 & 96.00 & 92.34 & 89.38 & 92.57 \\
 & PCA & 80.41 & 71.45 & 86.48 & 78.97 & 96.00 & 92.34 & 89.38 & 92.57 \\
\midrule
RMDS & ViT & 80.22 & 72.78 & 87.18 & 79.98 & 96.00 & 92.23 & 89.28 & 92.50 \\
 & W\&R & 80.41 & 72.79 & 87.28 & 80.03 & \textbf{96.09} & 92.29 & 89.38 & 92.59 \\
 & PCA & 80.41 & 72.79 & 87.28 & 80.03 & \textbf{96.09} & 92.29 & 89.38 & 92.59 \\
\midrule
\multicolumn{10}{c}{Hierarchical Gaussian DPMMs} \\
\midrule
Tied & ViT & 80.40 & 71.79 & 86.75 & 79.27 & 95.99 & \textbf{92.40} & 89.71 & \textbf{92.70} \\
 & W\&R & 80.41 & 71.80 & 86.76 & 79.28 & 96.00 & \textbf{92.40} & 89.72 & \textbf{92.70} \\
 & PCA & 80.40 & 71.79 & 86.75 & 79.27 & 96.00 & \textbf{92.40} & 89.70 & \textbf{92.70} \\
\midrule
Full & ViT & 76.82 & 62.64 & 78.32 & 70.48 & 85.76 & 84.95 & 88.03 & 86.24 \\
 & W\&R & 76.78 & 62.84 & 78.48 & 70.66 & 85.88 & 85.03 & 88.02 & 86.31 \\
 & PCA & 76.82 & 62.64 & 78.33 & 70.49 & 85.76 & 84.95 & 88.03 & 86.25 \\
\midrule
Diag. & ViT & 75.96 & 72.38 & 85.96 & 79.17 & 94.14 & 90.18 & 87.20 & 90.51 \\
 & W\&R & 76.54 & 73.89 & 87.32 & 80.60 & 95.36 & 90.78 & 86.42 & 90.85 \\
 & PCA & 75.76 & 71.99 & 85.52 & 78.75 & 93.91 & 90.18 & 87.39 & 90.49 \\
\midrule
Coupled & ViT & 75.93 & 72.80 & 86.15 & 79.48 & 94.08 & 90.20 & 87.19 & 90.49 \\
Diag. & W\&R & 76.52 & 74.47 & \textbf{87.48} & \textbf{80.98} & 95.51 & 90.63 & 86.02 & 90.72 \\
 & PCA & 75.76 & 72.40 & 85.97 & 79.19 & 95.02 & 90.92 & 88.09 & 91.34 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Randolph W. Linderman
Yiran Chen
Scott W. Linderman

"A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection" by Randolph W. Linderman, Yiran Chen, and Scott W. Linderman, arXiv:2502.08695v1, 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{McFadden $R^2$ of Model Predicting 2017 Cohort Membership vs. Other Cohorts Using Control Variables}
\begin{tabular}{lcccccc}
\toprule
Population & 2014 & 2015 & 2016 & 2018 & 2019 & 2020 \\
\midrule
General & 0.003*** & 0.001*** & 0.001*** & 0.001*** & 0.003*** & 0.017*** \\
Professional & 0.004*** & 0.001*** & 0.000*** & 0.001*** & 0.003*** & 0.018*** \\
Technological & 0.004*** & 0.002*** & 0.001*** & 0.018*** & 0.020*** & 0.038*** \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Quotas for scholarship recipients: an efficient race-neutral alternative to affirmative action?" by Louis Gleyo, arXiv:2507.17191v4, 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{PPO Hyperparameters}
\begin{tabular}{|c|c|c|c|}
\hline
$\textbf{Hyperparameter}$ & $\textbf{Values}$ & $\textbf{Hyperparameter}$ & $\textbf{Values}$\\
\hline
No. of episodes & 6000 & Entropy coeff.~$(c2)$ & $0.01$ \\
\hline
Minibatch size & $32$ & Discount factor~$(\gamma)$& $0.99$\\
\hline
GAE parameter~$(\lambda)$ & $0.95$ & Clipping parameter~$(\epsilon)$ & $0.2$\\
\hline
Optimizer & Adam & Optimizer epsilon & $10^{-5}$\\
\hline
Activation function & Relu & No. of hidden layers & 1\\
\hline
Actor Size & $256$ & Critic Size & $512$ \\
\hline
Actor learning rate & $10^{-2}$ & Critic learning rate& $10^{-4}$\\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Salwa Mostafa
Mateus P. Mota
Alvaro Valcarce
Mehdi Bennis

"Intent-Aware DRL-Based NOMA Uplink Dynamic Scheduler for IIoT" by Salwa Mostafa, Mateus P. Mota, Alvaro Valcarce, and Mehdi Bennis, arXiv:2403.18364v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 \hline
 Method & Time/s & Fidelity+ \\
 \hline
 GraphEXT & 39.3 $\pm$ 17.1 & \textbf{0.78 }
 \\SubgraphX & 73.5 $\pm$ 39.2 & 0.51
 \\FlowX & 21.0 $\pm$ 4.1 & 0.68 \\GNNExplainer & 1.65 $\pm$ 0.64 & 0.28 \\GradCAM & 0.07 $\pm$ 0.002 & 0.64
 \\PGExplainer & \textbf{0.04 $\pm$ 0.001}(Training 698s) & 0.37
 \\
 \hline
 \end{tabular}
\caption{Efficiency study of different methods}
\end{table}
\end{document}

q-fin

"Explainable Graph Neural Networks via Structural Externalities" by Lijun Wu, Dong Hao, and Zhiyi Fan, arXiv:2507.17848v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
\(\delta\) & \(\|x_1-x(\delta)\|\) & \(\|x_2-x(2\delta)\|\) \\
\hline
1/16 &4.23(-4) & 4.87(-5)\\
1/32 & 1.15(-4) & 6.56(-6)\\
1/64 & 3.00(-4) & 8.50(-7)\\
1/128 &7.62(-6) & 1.08(-7)\\
1/256&1.93(-6)& 1.36(-8)
\end{tabular}
\caption{Errors in the \(x\) variable vs.\ \(\delta\) after one or two timesteps.}
\end{table}
\end{document}

stat

On the unconventional Hug integrator

"On the unconventional Hug integrator" by Christophe Andrieu and J. M. Sanz-Serna, arXiv:2502.10199v1, 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{Simulation results by cases: (1) base case, (2) stronger proxy, (3) noisier outcome, (4) rougher spatial confounding, (a) use posterior parameters and outcome is NO$_2$, (b) use posterior parameters and outcome is PM$_{2.5}$. In case (1), (2), (3), the true spline ratio is 40\%; in case (4), (a), (b), the true spline ratio is 60\%. The columns display the average absolute bias (A.B.) with standard deviation, mean square error (MSE) with standard deviation, coverage probability (C.P.), Watanabe-Akaike Information Criterion (WAIC), and the selected spline ratio (S.R.).}
\begin{tabular}{llrrrrr}
\hline\hline
Case & Method & A.B. & MSE & C.P. & WAIC & S.R. \\
\hline
(1) & Latent Adjustment & 0.003 (0.106) & 0.011 (0.015) & 95 & 1083 & 54 \\
& Outcome Regr with Proxy & 0.245 (0.088) & 0.068 (0.043) & 18 & 1115 & 44 \\
& No Adjustment & 1.197 (0.106) & 1.445 (0.257) & 0 & 1460 & 42 \\
\hline
(2) & Latent Adjustment & 0.003 (0.086) & 0.007 (0.010) & 94 & 1030 & 49 \\
& Outcome Regr with Proxy & 0.135 (0.078)& 0.024 (0.022) & 55 & 1038 & 44 \\
& No Adjustment & 1.198 (0.102) & 1.446 (0.246) & 0 & 1460 & 42 \\
\hline
(3) & Latent Adjustment & 0.010 (0.159) & 0.025 (0.040) & 95 & 1590 & 43 \\
& Outcome Regr with Proxy & 0.228 (0.125) & 0.067 (0.060) & 54 & 1580 & 37 \\
& No Adjustment & 1.170 (0.143) & 1.388 (0.341) & 0 & 1736 & 40 \\
\hline
(4) & Latent Adjustment & 0.012 (0.107) & 0.011 (0.014) & 95 & 110 & 66 \\
& Outcome Regr with Proxy & 0.243 (0.090) & 0.067 (0.041) & 19 & 1144 & 58 \\
& No Adjustment & 1.198 (0.103) & 1.445 (0.249) & 0 & 1485 & 60 \\
\hline
(a) & Latent Adjustment & 0.001 (0.154) & 0.023 (0.036) & 90 & 1874 & 69 \\
& Outcome Regr with Proxy & 0.051 (0.215) & 0.052 (0.079) & 78 & 1905 & 68 \\
& No Adjustment & 0.640 (0.124) & 0.425 (0.158) & 0 & 1967 & 69 \\
\hline
(b) & Latent Adjustment & 0.003 (0.040) & 0.002 (0.002) & 91 & 470 & 69 \\
& Outcome Regr with Proxy & 0.004 (0.063) & 0.004 (0.005) & 73 & 498 & 68 \\
& No Adjustment & 0.048 (0.032) & 0.003 (0.003) & 67 & 478 & 68 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Estimating the Causal Effect of Redlining on Present-day Air Pollution" by Xiaodan Zhou, Shu Yang, and Brian J Reich, arXiv:2501.16958v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Fitted log-normal distribution for the three products.}
\begin{tabular}{lllllll}
\toprule
\multirow{3}{*}{Product } &\multicolumn{6}{c}{Log-normal distribution} \\
&\multicolumn{3}{c}{normal-demand periods} &\multicolumn{3}{c}{ booming-demand periods}\\\cmidrule(lr){2-4}\cmidrule(lr){5-7}
&$\mu$ &$\sigma$ & p-value &$\mu$ &$\sigma$ & p-value\\
\midrule
keyboard &3.66 &0.60 &0.38 &5.79&0.26&0.58\\
mouse &4.13 &0.66 &0.62 & 5.91 &0.33 & 0.66\\
headset &3.54&0.46&0.18 &4.96 &0.18 & 0.96\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"A multi-period multi-product stochastic inventory problem with order-based loan" by Zhen Chen and Ren-qian Zhang, arXiv:2012.04850v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{RMSE values of estimation errors $\tilde{f}_a$ and $\tilde{f}_s$ in several cases}
\begin{tabular}{|c|c|c|c|c|}
\hline
Error & Different methods & \textbf{Case 1} & \textbf{Case 2} & \textbf{Case 3} \\ \hline
\multirow{3}{*}{$\tilde{f}_a$} &FAUIO~ & 0.0076 & 0.0340 & 0.0189 \\ \cline{2-5}& & 0.02 & 0.0596 & 0.04 \\ \cline{2-5}& & 0.196 & 0.116 & 0.121 \\ \hline
\multirow{3}{*}{$\tilde{f}_s$} &FAUIO~ & 0.0078 & 0.0295 & 0.0120 \\ \cline{2-5} & & 0.018 & 0.0315 & 0.05 \\ \cline{2-5} & & 0.110 & 0.105 & 0.102 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"Estimating the Faults/Attacks using a Fast Adaptive Unknown Input Observer: An Enhanced LMI Approach" by Shivaraj Mohite, Adil Sheikh, and S. R. Wagh, arXiv:2312.05026v1, 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{Comparing the accuracy of the results between the solutions obtained using the numerical method and those obtained using the neural network method. }
\begin{tabular}{ccccc}
		\toprule
		\textbf{Method}	& \textbf{$X1(t)$ error}	& \textbf{$X2(t)$ error} & \textbf{$X3(t)$ error} & \textbf{$X4(t)$ error} \\
		\midrule
		Numerical method & $3.16804\times {{10}^{-08}}$ & $5.73393\times {{10}^{-08}}$ & $7.70381\times {{10}^{-10}}$ & $4.67541\times {{10}^{-09}}$ \\
		Neural network & $5.67513\times {{10}^{-09}}$ & $2.43858\times {{10}^{-09}}$ & $7.54885\times {{10}^{-10}}$ & $3.10772\times {{10}^{-09}}$ \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

math

Van Truong Vo
Samad Noeiaghdam
Denis Sidorov
Aliona Dreglea
Liguo Wang

"Solving Nonlinear Energy Supply and Demand System Using Physics-Informed Neural Networks" by Van Truong Vo, Samad Noeiaghdam, Denis Sidorov, Aliona Dreglea, and Liguo Wang, arXiv:2412.17001v1, 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}{lccccc}
\hline \hline
\textbf{Base model} & \textbf{AUC} & \textbf{ACC} & \textbf{SEN} & \textbf{SPC} & \textbf{PPV} \\
\hline
NASNet & 93.81 & 88.00 & 80.00 & 91.43 & 80.00 \\
ResNetV2 & 92.67 & 86.00 & 76.67 & 90.00 & 76.67 \\
Xception & 92.10 & 85.00 & 73.33 & 90.00 & 75.86 \\
DenseNet169 & 86.14 & 85.00 & 76.67 & 88.57 & 74.19 \\
ResNet152 & 85.14 & 83.00 & 70.00 & 88.57 & 72.41 \\
\hline\hline
\end{tabular}
\caption{WM scenario: Classification evaluation metrics reported for SST data set resulted from different base models in the proposed recognition pipeline, including SST mask in the input.}
\end{table}
\end{document}

eess

Mostafa Jahanifar
Neda Zamani Tajeddin
Meisam Hasani
Babak Shekarchi
Kamran Azema

"Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks" by Mostafa Jahanifar, Neda Zamani Tajeddin, Meisam Hasani, Babak Shekarchi, and Kamran Azema, arXiv:2011.11777v1, 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}{lccc}
 \toprule
 & IMDB & QuAC & NarrativeQA \\
 \midrule
 Attention & 84.1 & 27.9 & 45.8 \\
 Mamba & 48.8 & 20.2 & 27.7 \\
 Attention-Mamba & 90.9 & 26.6 & 43.7 \\
 \bottomrule
 \end{tabular}
\caption{Mamba performs poorly on certain datasets, while the Attention-Mamba hybrid performs on par with the Attention model.}
\end{table}
\end{document}

cs

Opher Lieber
Barak Lenz
Hofit Bata
Gal Cohen
Jhonathan Osin
Itay Dalmedigos
Erez Safahi
Shaked Meirom
Yonatan Belinkov
Shai Shalev-Shwartz
Omri Abend
Raz Alon
Tomer Asida
Amir Bergman
Roman Glozman
Michael Gokhman
Avashalom Manevich
Nir Ratner
Noam Rozen
Erez Shwartz
Mor Zusman
Yoav Shoham

"Jamba: A Hybrid Transformer-Mamba Language Model" by Opher Lieber, Barak Lenz, Hofit Bata, Gal Cohen, Jhonathan Osin, Itay Dalmedigos, Erez Safahi, Shaked Meirom, Yonatan Belinkov, Shai Shalev-Shwartz, Omri Abend, Raz Alon, Tomer Asida, Amir Bergman, Roman Glozman, Michael Gokhman, Avashalom Manevich, Nir Ratner, Noam Rozen, Erez Shwartz, Mor Zusman, and Yoav Shoham, arXiv:2403.19887v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|l|}
 \hline
 &\\
 $c = EC(F,G)$ & \\
 \hspace{6.8em}$\Big \Vert $ by Definition & \\
$c_t = \#\{j : \beta_j \in A_t \}$
 & (Expression involving $\alpha$ and $\beta$) \\
 \hspace{4.8em}$\Big \Vert$ Lemma & \\
 $c_t = \# \{j : \overline\# \{x : f(x) = 0 \wedge x > \beta_j\} = m-t\}$ & (Expression involving $\beta$) \\
 \hspace{10.1em}$\Big \Vert$ Lemmas , , , & \\
 $c = \tau(\operatorname*{sign} D(F,G))$ & (Expression involving only $a_{ij}$ and $b_{ij}$) \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Hoon Hong
Daniel Profili
J. Rafael Sendra

"Conditions for eigenvalue configurations of two real symmetric matrices" by Hoon Hong, Daniel Profili, and J. Rafael Sendra, arXiv:2401.00866v3, 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}{llll}
unsigned? & \# of cores & Latency& Time complexity\\
\hline
Yes & Single core & $7GN^{2}$ & $O(N^{2}))$\\
Yes & Multi-core & $2GNlog(N) + 3GN$ & $O(Nlog(N))$ \\
No & Single core & $7GN^{2} + 6GN$ & $O(N^{2}))$\\
No & Multi-core & $2GNlog(N) + 3GN + 2Glog(N) + 3G$ & $O(Nlog(N))$ \\
\end{tabular}
\caption{Multiplier scalability}
\end{table}
\end{document}

cs

"CryptoEmu: An Instruction Set Emulator for Computation Over Ciphers" by Xiaoyang Gong and Dan Negrut, arXiv:2101.03403v1, 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{TeS-AMP SPA message definitions at iteration $t$}
\begin{tabular}{c|c}
\toprule
${{p}}_{\mathbf x \rightarrow [\ell,i,x_i] } (t,\cdot)$ & SPA message from node $\mathsf a_i^{\ell}(x_i)$ to node $p_{\mathsf v_{\mathbf x} | \mathsf u_{\mathbf x} }$ \\
${{p}}_{\mathbf x \leftarrow [\ell,i,x_i] } (t,\cdot)$ & SPA message from node $p_{\mathsf v_{\mathbf x} | \mathsf u_{\mathbf x} }$ to node $\mathsf a_i^{\ell}(x_i)$ \\
${{p}}_{ [\ell,i,x_i] } (t,\cdot)$ & SPA-approximated log posterior pdf of message $\mathsf a_i^{\ell}(x_i)$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"Tensor Generalized Approximate Message Passing" by Yinchuan Li, Guangchen Lan, and Xiaodong Wang, arXiv:2504.00008v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Development set results with a ratio of the leading characters correctly recognized.}
\begin{tabular}{cccc}
		\toprule
		Model & Ratio & CER & SER \\
		\midrule
		\multirow{3}{*}[-1pt]{Conv1d resnet 3} & 0 & 54.8 & 49.6 \\
		& 1/3 & 61.9 & 55.3\\
		& 1/2 & 57.3 & 51.4\\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

Wen-Chin Huang
Chia-Hua Wu
Shang-Bao Luo
Kuan-Yu Chen
Hsin-Min Wang
Tomoki Toda

"Speech Recognition by Simply Fine-tuning BERT" by Wen-Chin Huang, Chia-Hua Wu, Shang-Bao Luo, Kuan-Yu Chen, Hsin-Min Wang, and Tomoki Toda, arXiv:2102.00291v1, 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}{lll}
\hline
\textbf{Output} & \textbf{natbib command} & \textbf{Old command}\\
\hline
 & \verb|\citep| & \verb|\cite| \\
 & \verb|\citealp| & no equivalent \\
 & \verb|\citet| & \verb|\newcite| \\
 & \verb|\citeyearpar| & \verb|\shortcite| \\
\hline
\end{tabular}
\caption{ Citation commands supported by the style file. The style is based on the natbib package and supports all natbib citation commands. It also supports commands defined in previous style files for compatibility.}
\end{table}
\end{document}

cs

Parag Pravin Dakle
Alolika Gon
Sihan Zha
Liang Wang
SaiKrishna Rallabandi
Preethi Raghavan

"Jetsons at FinNLP 2024: Towards Understanding the ESG Impact of a News Article using Transformer-based Models" by Parag Pravin Dakle, Alolika Gon, Sihan Zha, Liang Wang, SaiKrishna Rallabandi, and Preethi Raghavan, arXiv:2404.00386v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Probabilistic Overview of Probabilities of Default for Low Default Portfolios by K. Pluto and D. Tasche" by Andrius Grigutis, arXiv:2303.06148v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\color{black}Count of cycles in the CPDAG predictions without post-processing of removing cycles.}
\begin{tabular}{ccc}
\toprule
Dataset & WS-L-G & SBM-L-G \\
\midrule
Rate of Graphs with Cycles & $0.66 \pm 0.66 \%$&$0.00 \pm 0.00 \%$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Jiaru Zhang
Rui Ding
Qiang Fu
Bojun Huang
Zizhen Deng
Yang Hua
Haibing Guan
Shi Han
Dongmei Zhang

"Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning" by Jiaru Zhang, Rui Ding, Qiang Fu, Bojun Huang, Zizhen Deng, Yang Hua, Haibing Guan, Shi Han, and Dongmei Zhang, arXiv:2502.10883v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems" by Grzegorz Filcek and Janusz Miroforidis, arXiv:2401.00292v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of Algorithm on Example }
\begin{tabular}{cccc}
\hline
Number of& Algorithm &Iterations &CPU time\\
initial points& &(Min, Max, Mean, Median, Mode, SD) &(Min, Max, Mean, Median, $\lceil\text{Mode}\rceil$, SD) \\
\hline
$100$ & QNM&($1,~10,~5,~4.5800
,~5,~1,~1.9719$) & ($2.5750,~23.7773,~14.9322,~16.2000,~2,~10.2129$) \\
 & SD &($1,~11,~10.220,~9,~2,2.4887$) & ($1.2113,~28.1483,~26.1314,~26.1002,~25,~0.2662$) \\
\hline
\end{tabular}
\end{table}
\end{document}

math

Debdas Ghosh
Anshika
Jen-Chih Yao
Xiaopeng Zhao

"Quasi-Newton Method for Set Optimization Problems with Set-Valued Mapping Given by Finitely Many Vector-Valued Functions" by Debdas Ghosh, Anshika, Jen-Chih Yao, and Xiaopeng Zhao, arXiv:2501.04711v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The Causal Effects of Drafting Position on Log Race Rank (2SLS Estimates)}
\begin{tabular}{lccccc}
\toprule
& \multicolumn{2}{c}{\textbf{Capped Drafting Position}} & \multicolumn{3}{c}{\textbf{Uncapped Drafting Position}} \\
\cmidrule(lr){2-3} \cmidrule(lr){4-6}
 & (1) Full FE & (2) +Athlete FE & (3) +Event FE & (4) +Cluster FE & (5) +Athlete FE \\
\midrule
\textbf{Drafting Position (Fit)} & $-0.504^{***}$ & $-4.741^{*}$ & $-0.046^{***}$ & $-0.083^{***}$ & $-0.011^{*}$ \\
 & (0.069) & (2.321) & (0.011) & (0.013) & (0.005) \\
\addlinespace
\textbf{Leader Indicator} & $-1.889^{***}$ & $-6.425^{*}$ & $-0.797^{***}$ & $-0.848^{***}$ & $-0.103^{*}$ \\
 & (0.261) & (3.159) & (0.117) & (0.137) & (0.048) \\
\midrule
Observations & 23,544 & 9,316 & 34,061 & 34,061 & 34,061 \\
RMSE & 1.34 & 1.42 & 3.20 & 5.22 & 0.78 \\
Adjusted $R^2$ & -0.30 & -1.43 & -2.90 & -9.46 & 0.64 \\
Within $R^2$ & -6.06 & -12.70 & -8.52 & -24.80 & -0.94 \\
First-stage $F$ & 87.3 & 8.42 & 400.3 & 129.6 & 1,076.5 \\
Wu–Hausman & 530.9 & 93.0 & 3,755.8 & 3,518.6 & 1,038.0 \\
\midrule
Athlete Fixed Effects & \checkmark & \checkmark & -- & -- & \checkmark \\
Event Fixed Effects & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark \\
Cluster Fixed Effects & -- & -- & -- & \checkmark & \checkmark \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Strategic Effort and Bandwagon Effects in Finite Multi-Stage Games with Non-Linear Externalities: Evidence from Triathlon" by Felix Reichel, arXiv:2505.03247v2, 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}{lcc}
\toprule
{\bf Model} & Pass@1 \\
\midrule
{\it Closed-source LLMs} \\
GPT-4 & {88.4} \\
\midrule
{\it Open-source LLMs} \\
WizardCoder-Python-13B & 60.37 \\
Instruct tuning & 63.26 \\
{\bf CCT-CodeLlama} & {66.1} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Yufan Jiang
Qiaozhi He
Xiaomin Zhuang
Zhihua Wu

"Code Comparison Tuning for Code Large Language Models" by Yufan Jiang, Qiaozhi He, Xiaomin Zhuang, and Zhihua Wu, arXiv:2403.19121v2, 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}{lll}
 \toprule
 Loss Function & $\ell(y,u)$ & $\hat{\ell}(y,a) = \max_{u} \: ua - \ell(u,a)$ \\
 \midrule
 Ordinary Least Squares (OLS) & $\tfrac{1}{2} (y - u)^2$ & $y a + \tfrac{1}{2} a^2$ \\
 Pinball Loss & $\max\{q (y - u),\ (1 - q)(u - y)\}$ & $\begin{cases}
 y a & \text{if } -q \leq a \leq 1 - q, \\
 +\infty & \text{otherwise},
 \end{cases}$ \\
 Logistic Loss & $\log \left(1 + e^{-y u}\right)$ & $-H(-y\alpha)$, for $y\alpha \in [-1, 0]$ \\
 \midrule
 \end{tabular}
\caption{Examples of loss functions and their Fenchel conjugates. Here, $H(x) = -x\log x - (1 - x)\log(1 - x)$.}
\end{table}
\end{document}

stat

"Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age" by Marcos Matabuena, arXiv:2501.06868v1, 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{Key Device Parameters of SOT-Device Adopted for Simulation}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 \bf{$RA $} & Resistance area product & $\Omega\cdot m^2 $ & 1.51 $\times 10^{-9} $ \\
 \hline
 \bf{$t_{sl} $} & Free layer thickness & nm & 2.5 \\
 \hline
 \bf{$t{ox} $} & MgO barrier thickness & nm & 1.7 \\
 \hline
 \bf{$V_h $} & Voltage bias when TMR(V) = 0.5$\times $TMR(0) & V & 0.75 \\
 \hline
 \bf{$TMR $} & TMR ratio under zero bias voltage & \% & 156 \\
 \hline
 \bf{$a $} & MTJ pillar length & nm & 600 \\
 \hline
 \bf{$b $} & MTJ pillar width & nm & 300 \\
 \hline
 \bf{$d $} & HM-strip thickness & nm & 6 \\
 \hline
 \bf{$\rho $} & HM-strip resistivity & $\Omega\cdot $m & 195 $\times 10^{-8} $ \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Yu Gu
Puyang Huang
Tianhao Chen
Chenyi Fu
Aitian Chen
Shouzhong Peng
Xixiang Zhang
Xufeng Kou

"A noise-tolerant, resource-saving probabilistic binary neural network implemented by the SOT-MRAM compute-in-memory system" by Yu Gu, Puyang Huang, Tianhao Chen, Chenyi Fu, Aitian Chen, Shouzhong Peng, Xixiang Zhang, and Xufeng Kou, arXiv:2403.19374v1, 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}{lccccc}
 \toprule
 & Point mass & Mean & $\ell_{Z|_{\{Z<1\}}}$ & $\ell_Z$ & AIC \\
 \midrule
 Empirical density (Blue) & 0.034 & 0.339 & - & - & - \\
 MLE of the standard problem (Green) & 0.020 & 0.337 &32 682 &13 475 & -26 947 \\
 MLE of the extended problem (Red) & 0.034 & 0.336 &33 257 &14 587 & -29 168 \\
 \bottomrule
 \end{tabular}
\caption{The MBBEFD example: results.}
\end{table}
\end{document}

stat

"Modeling lower-truncated and right-censored insurance claims with an extension of the MBBEFD class" by Selim Gatti and Mario V. Wüthrich, arXiv:2310.11471v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Xueqing Liu
Tianchen Qian
Lauren Bell
Bibhas Chakraborty

"Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes" by Xueqing Liu, Tianchen Qian, Lauren Bell, and Bibhas Chakraborty, arXiv:2310.18905v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c||ccccc}
 \hline
 Interval Length & XY-only & PPI & PPI++ & RePPI & Reduced Samples (\%) \\
\hline
0.0015 & NA & 22613 & 18267 & 13843 & 24.21\% \\
0.0016 & NA & 18493 & 15724 & 11853 & 24.61\% \\
0.0017 & NA & 15575 & 13623 & 10197 & 25.15\% \\
0.0018 & 25728 & 13408 & 12000 & 8929 & 25.59\% \\
0.0019 & 23102 & 11712 & 10615 & 7864 & 25.91\% \\
0.0020 & 20902 & 10301 & 9431 & 7076 & 24.99\% \\
\hline
 \end{tabular}
\caption{The required sample size to achieve a given interval length on US Census data. The last column gives the sample size reduction of RePPI compared to PPI++. Here ``NA'' means the target length cannot be reached within the range of sample sizes we consider.}
\end{table}
\end{document}

stat

"Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI" by Wenlong Ji, Lihua Lei, and Tijana Zrnic, arXiv:2501.09731v1, 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{The masking parameters.}
\begin{tabular}{ccccc}
 \toprule
 $F$ & $m_{F}$ & $T$ & $m_{T}$ & $p$ \\
 \midrule
 10 & 2 & 45 & 2 & 0.1\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Chengxi Lei
Satwinder Singh
Feng Hou
Xiaoyun Jia
Ruili Wang

"PhasePerturbation: Speech Data Augmentation via Phase Perturbation for Automatic Speech Recognition" by Chengxi Lei, Satwinder Singh, Feng Hou, Xiaoyun Jia, and Ruili Wang, arXiv:2312.08571v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

David Swanson
Alexander Sherry
Chad Tang

"Variance component mixture modelling for longitudinal T-cell receptor clonal dynamics" by David Swanson, Alexander Sherry, and Chad Tang, arXiv:2502.04553v1, 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{Measuring condition adherence with CLIP.}
\begin{tabular}{lcc}
\toprule
\textbf{Condition} & \textbf{Low Temp.} & \textbf{Base} \\ \hline
{Man with black hair} & 0.75 & 0.71 \\
{Blonde woman, lipstick} & 0.63 & 0.53 \\
{Smiling old man} & 0.95 & 0.85 \\
{Young woman, no hair} & 0.97 & 0.91 \\
{Man with make-up} & 0.40 & 0.35 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

James Thornton
Louis Bethune
Ruixiang Zhang
Arwen Bradley
Preetum Nakkiran
Shuangfei Zhai

"Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo" by James Thornton, Louis Bethune, Ruixiang Zhang, Arwen Bradley, Preetum Nakkiran, and Shuangfei Zhai, arXiv:2502.12786v1, 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{amsmath}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small Trigger Patterns Ablation. The trigger patterns are in Figure~, and the injected backdoor objective is ${\sf Trap}$. The white and gray rows are the statistics in the training-control and data-poisoning settings, respectively.}
\begin{tabular}{l|rrrrr}\toprule
 Trigger & \textbf{Succ. Decay } & \textbf{Path Len. Incr.} & \textbf{Trigger Rate} & \textbf{P.C.S Rate} \\\midrule
 \multirow{2}{*}{Square} & 0.48\% & 1.13\% & 99.01\% & 97.29\% \\
 & \cellcolor[HTML]{A8A8A8}0.73\% & \cellcolor[HTML]{A8A8A8}2.76\% & \cellcolor[HTML]{A8A8A8}97.99\% & \cellcolor[HTML]{A8A8A8}98.28\% \\
 \multirow{2}{*}{Diamond} & 0.38\% & 1.32\% & 98.11\% & 97.77\% \\
 & \cellcolor[HTML]{A8A8A8}0.49\% & \cellcolor[HTML]{A8A8A8}2.19\% & \cellcolor[HTML]{A8A8A8}97.27\% & \cellcolor[HTML]{A8A8A8}98.52\% \\
 \multirow{2}{*}{Triangle} & 1.68\% & 1.28\% & 98.51\% & 96.98\% \\
 & \cellcolor[HTML]{A8A8A8}1.93\% & \cellcolor[HTML]{A8A8A8}1.96\% & \cellcolor[HTML]{A8A8A8}96.93\% & \cellcolor[HTML]{A8A8A8}97.38\% \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

"Manipulating Neural Path Planners via Slight Perturbations" by Zikang Xiong and Suresh Jagannathan, arXiv:2403.18256v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Babooshka Shavazipour
Theodor J. Stewart

"A novel multi-stage multi-scenario multi-objective optimisation framework for adaptive robust decision-making under deep uncertainty" by Babooshka Shavazipour and Theodor J. Stewart, arXiv:2312.11745v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The performance as function of the hypothesis class size }
\begin{tabular}{cccccc}
\toprule
\# Tuned Layers \ \ \ & Acc. & \ \ Loss avg \ & \ \ Loss STD \ \ & Regret\\
\hline
0 layers & 0.920 & 0.203 & 0.87 & 0.0 \\
2 layers & 0.921 & 0.173 & 0.30 & 0.14 \\
7 layers & 0.913 & 0.244 & 0.27 & 0.23 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data" by Koby Bibas, arXiv:2412.07520v1, 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}{r|cccccc}
 \toprule
 & \multicolumn{6}{c}{Patch scale $\sigma$ [\textmu m]}\\
 \midrule
 Type & 100 & 800 & 1500 & 4000 & 7000 & 8000 \\
 \midrule
 BA (6-class) & 0.40 & 0.45 & \textbf{0.46} & 0.41 & 0.37 & 0.38 \\
 \midrule
 NORM & 0.70 & 0.66 & 0.72 & 0.76 & 0.78 & 0.71\\
 HP & 0.81 & {\bfseries 0.92} & 0.85 & 0.70 & 0.60 & 0.69 \\
 TA (HG+LG) & 0.65 & 0.66 & 0.65 & 0.71 & {\bfseries 0.76} & 0.70 \\
 TVA (HG+LG) & 0.64 & 0.67 & 0.68 & 0.74 & {\bfseries 0.84} & 0.76 \\
 \bottomrule
 \end{tabular}
\caption{Preliminary experiments: overall BA for all of the six classes (first row) and BA for each polyp type, plus normal tissue.}
\end{table}
\end{document}

eess

Carlo Alberto Barbano
Daniele Perlo
Enzo Tartaglione
Attilio Fiandrotti
Luca Bertero
Paola Cassoni
Marco Grangetto

"UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading" by Carlo Alberto Barbano, Daniele Perlo, Enzo Tartaglione, Attilio Fiandrotti, Luca Bertero, Paola Cassoni, and Marco Grangetto, arXiv:2101.09991v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|c|c}
\hline
\textbf{Model} & \textbf{Train Set} & \textbf{Test F1} & \textbf{Test ACC} \\
\hline
XLM-R$_{\text{large}}^*$ & COLD+KOLD & 0.798 & 0.825 \\
COLDET & COLD & 0.810 & 0.810 \\
TJIGDET & Jigsaw dataset & 0.620 & 0.600 \\
BAIDUTC & Unknown & 0.540 & 0.630 \\
PSELFDET & BERT & 0.580 & 0.590 \\
\hline
\end{tabular}
\caption{State-of-the-art offensive language detection system's performance using the COLD benchmark}
\end{table}
\end{document}

cs

Yunze Xiao
Houda Bouamor
Wajdi Zaghouani

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|c|c}
\hline
\multirow{1}{*}{} & {\footnotesize{}Nodes} & {\footnotesize{}Edges} & {\footnotesize{}Nodes + Edges}\tabularnewline
\hline
{\footnotesize{}Shapley} & {\scriptsize{}$0.891\pm0.049$} & {\scriptsize{}$0.927\pm0.072$} & {\scriptsize{}$0.909\pm0.061$}\tabularnewline
{\footnotesize{}Sampling} & {\scriptsize{}$0.890\pm0.050$} & {\scriptsize{}$0.925\pm0.074$} & {\scriptsize{}$0.908\pm0.062$}\tabularnewline
{\footnotesize{}Permut.} & {\scriptsize{}$0.892\pm0.049$} & {\scriptsize{}$0.926\pm0.072$} & {\scriptsize{}$0.909\pm0.061$}\tabularnewline
{\footnotesize{}Naive} & {\scriptsize{}$0.856\pm0.055$} & {\scriptsize{}$0.915\pm0.091$} & {\scriptsize{}$0.886\pm0.073$}\tabularnewline
{\footnotesize{}BIGEN} & {\scriptsize{}$0.890\pm0.052$} & {\scriptsize{}$\mathbf{0.980\pm0.045}$} & \textbf{\scriptsize{}$\mathbf{0.935\pm0.048}$}\tabularnewline
\hline
\end{tabular}
\caption{NDCG@$k$ for RCA in a supply chain.}
\end{table}
\end{document}

stat

Phuoc Nguyen
Truyen Tran
Sunil Gupta
Thin Nguyen
Svetha Venkatesh

"Root Cause Explanation of Outliers under Noisy Mechanisms" by Phuoc Nguyen, Truyen Tran, Sunil Gupta, Thin Nguyen, and Svetha Venkatesh, arXiv:2312.11818v1, 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{Average Absolute Denormalized Error}
\begin{tabular}{clcll}
\toprule
 Case& $V^m$ (p.u.) & $\theta$ (\textit{deg})& $P$ (MW) & $Q$ (Mvar) \\ \midrule
14 & $0.0008 \pm 0.0006$ & $0.09 \pm 0.07$ & $0.02 \pm 0.03$ & $0.13 \pm 0.16$ \\
118 & $0.0022 \pm 0.0018$ & $0.64 \pm 0.54$ & $0.18 \pm 0.36$ & $1.20 \pm 1.47$ \\
6470rte & $0.0072 \pm 0.0061$ & $10.2 \pm 7.89$ & $0.23 \pm 0.42$ & $0.12 \pm 6.46$ \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Nan Lin
Stavros Orfanoudakis
Nathan Ordonez Cardenas
Juan S. Giraldo
Pedro P. Vergara

"PowerFlowNet: Power Flow Approximation Using Message Passing Graph Neural Networks" by Nan Lin, Stavros Orfanoudakis, Nathan Ordonez Cardenas, Juan S. Giraldo, and Pedro P. Vergara, arXiv:2311.03415v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Christian Chan
Xiaotian Dai
Thierry Chekouo
Quan Long
Xuewen Lu

"Broken Adaptive Ridge Method for Variable Selection in Generalized Partly Linear Models with Application to the Coronary Artery Disease Data" by Christian Chan, Xiaotian Dai, Thierry Chekouo, Quan Long, and Xuewen Lu, arXiv:2311.00210v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Risk-Adaptive Local Decision Rules

"Risk-Adaptive Local Decision Rules" by Johannes O. Royset and Miguel A. Lejeune, arXiv:2310.09844v2, 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{Venezuelan Migrants with Permits -- Extensive Margin, Events in Over 60\% of Years}
\begin{tabular}{c|ccc|ccc}
\toprule
& \multicolumn{3}{c|}{\textbf{Flow}} & \multicolumn{3}{c}{\textbf{Stock}} \tabularnewline & FARC & ELN & p-value Diff. & FARC & ELN & p-value Diff. \tabularnewline
{Year}&{(1)}&{(2)}&{(3)}&{(4)}&{(5)}&{(6)} \tabularnewline
\midrule
\midrule 2017&1.41&2.02&&1.41&2.02& \tabularnewline
2018&17.51&29.29&&18.9&31.27& \tabularnewline
2019&1.78&1.92&&20.41&32.17& \tabularnewline
&&&&&& \tabularnewline
\textbf{\textbf{All Years}}&6.9&11.08&0.088&13.57&21.82&0.014 \tabularnewline
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Peace Dividends: The Economic Effects of Colombia's Peace Agreement" by Miguel Fajardo-Steinhäuser, arXiv:2301.01843v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameters of DPMamba XS, S, M, and L. $R \times 2$ means $R$ DP blocks and each block consists of one intra-chunk and one inter-chunk BiMamba unit.\\}
\begin{tabular}{c|cccc}
 \toprule
 Model & Dimension $D$ & \#Layers & \#Params (M) \\ \hline \hline
 DPMamba (XS) & 128 & 8 $\times$ 2 & 2.3 \\
 DPMamba (S) & 256 & 8 $\times$ 2 & 8.1 \\
 DPMamba (M) & 256 & 16 $\times$ 2 & 15.9 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

"Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation" by Xilin Jiang, Cong Han, and Nima Mesgarani, arXiv:2403.18257v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c||ccccc}
 \hline
 Interval Length & XY-only & PPI & PPI++ & RePPI & Reduced Samples (\%) \\
\hline
0.7 & NA & NA & 1524 & 1438 & 5.64\% \\
0.8 & 1426 & NA & 1143 & 1085 & 5.08\% \\
0.9 & 1129 & 1274 & 887 & 830 & 6.42\% \\
1.0 & 907 & 973 & 700 & 650 & 7.14\% \\
1.1 & 756 & 772 & 579 & 551 & 4.84\% \\
\hline
 \end{tabular}
\caption{The required sample size to achieve a given interval length on the politeness data. The last column gives the sample size reduction of RePPI compared to PPI++. Here ``NA'' means the target length cannot be reached within the range of sample sizes we consider.}
\end{table}
\end{document}

stat


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Computation time [s] comparison of Offline Koopman-based Model with Retraining and Online Update with RLS.}
\begin{tabular}{ccccc}
 \toprule
 Prediction Horizon [s] &50s& 20s&10s&5s \\
 \midrule
 Offline Model with Retraining & 4.48&4.71&4.37&4.33\\
 \hline
 Online Model&0.04&0.04&0.03&0.04 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Mehmet Fatih Ozkan
Jeff Chrstos
Marcello Canova
Stephanie Stockar

"Modeling Driver Behavior in Speed Advisory Systems: Koopman-based Approach with Online Update" by Mehmet Fatih Ozkan, Jeff Chrstos, Marcello Canova, and Stephanie Stockar, arXiv:2502.20347v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cc|cc|cc|cc}
\multirow{2}{*}{Structure} & \multicolumn{2}{c|}{5V No Cycle} & \multicolumn{2}{c|}{5V Cycle} & \multicolumn{2}{c|}{6V No Cycle} & \multicolumn{2}{c}{7V Cycle} \\ \cline{2-9}
 & No Noise & Noise & No Noise & Noise & No Noise & Noise & No Noise & Noise \\ \hline
tsFCI & 7 & 4 & 6 & 8 & 10 & 11 & 10 & 10 \\
VARLiNGAM & 6 & 6 & 12 & 12 & 9 & 11 & 16 & 15 \\
Granger & 6 & 6 & 12 & 12 & 11 & 12 & 15 & 15 \\
PCMCI & 5 & 5 & 5 & \textbf{2} & 11 & \textbf{3} & 11 & 10 \\
DYNOTEARS & 8 & 15 & 11 & 12 & 19 & 18 & 16 & 22 \\
SLARAC & 16 & 16 & 18 & 18 & 25 & 27 & 21 & 25 \\ \hline
MXMap & \textbf{1} & \textbf{1} & \textbf{0} & \textbf{2} & \textbf{2} & 4 & \textbf{4} & \textbf{6}
\end{tabular}
\caption{SHD scores of MXMap and baselines for 5V-7V settings on simulated no-noise and noisy dynamical systems.}
\end{table}
\end{document}

stat

Elise Zhang
François Mirallès
Raphaël Rousseau-Rizzi
Arnaud Zinflou
Di Wu
Benoit Boulet

"MXMap: A Multivariate Cross Mapping Framework for Causal Discovery in Dynamical Systems" by Elise Zhang, François Mirallès, Raphaël Rousseau-Rizzi, Arnaud Zinflou, Di Wu, and Benoit Boulet, arXiv:2502.03802v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Niall Taylor
Dan Schofield
Andrey Kormilitzin
Dan W Joyce
Alejo Nevado-Holgado

"Developing Healthcare Language Model Embedding Spaces" by Niall Taylor, Dan Schofield, Andrey Kormilitzin, Dan W Joyce, and Alejo Nevado-Holgado, arXiv:2403.19802v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Unemployment and Endogenous Reallocation over the Business Cycle" by Carlos Carrillo-Tudela and Ludo Visschers, arXiv:2304.00544v1, 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|r|r|r}
 \textbf{Cloud Resource} & \textbf{\# Monitored} & \textbf{\# Abuses} & \textbf{\% Abuses} \\
 \hline
 Azure Web Application & 690,779 & 8,347 & 1.21 \\
 Azure VM & 565,684 & 983 & 0.17\\
 Azure Blob & 20,389 & - & - \\
 AWS Elasticbeanstalk & 138,523 & 668 & 0.48\\
 Azure Traffic Manager & 140,183 & 2,980 & 0.21\\
 Azure Cloud Service & 299,494 & 1,060 & 0.35\\
 Azure API & 17,100 & - & - \\
 Azure FrontDoor & 14,183 & - & - \\
 Heroku App & 30,532 & 146 & 0.48\\
 Azure CDN & 37,360 & 461 & 1.23\\
 Azure Service Bus & 10,152 & - & - \\
 AWS S3 & 1,137,613 & 5,876 & 0.52
 \end{tabular}
\caption{Abused cloud services among domains monitored.}
\end{table}
\end{document}

cs

Jens Frieß
Tobias Gattermayer
Nethanel Gelernter
Haya Schulmann
Michael Waidner

"Cloudy with a Chance of Cyberattacks: Dangling Resources Abuse on Cloud Platforms" by Jens Frieß, Tobias Gattermayer, Nethanel Gelernter, Haya Schulmann, and Michael Waidner, arXiv:2403.19368v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Bayesian Despeckling of Structured Sources" by Ali Zafari and Shirin Jalali, arXiv:2501.11860v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the results obtained for $(5,g)$-spectra}
\begin{tabular}{|c|c|c|c|}
 			\hline
 			Girth $g$ & $n(5,g)$ & Orders to be investigated & $N(5,g)$ \\
 			\hline
 			3 & 6 & -- & 6 \\
 			\hline
 			4 & 10 & -- & 10 \\
 			\hline
 			5 & 30 & 34 & $ \leq$ 36 \\
 			\hline
 			6 & 42 & -- & 46\\
 			\hline
 7 & 152 & 170--258 & 260\\
 			\hline
 		\end{tabular}
\end{table}
\end{document}

math

L. C. Eze
R. Jajcay
T. Jajcayová
D. Závacká

"Theoretical and Computational Approaches to Determining Sets of Orders for $(k,g)$-Graphs" by L. C. Eze, R. Jajcay, T. Jajcayová, and D. Závacká, arXiv:2503.06466v1, 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{\small List of functions explored from MachSuite, grouped by benchmark. The table reports: benchmark name, function name, and size of the configuration space ($|CS|$).}
\begin{tabular}{|l|l|l|}
\hline
{\textbf{Benchmark}} & {\textbf{Function name}} & {\textbf{$|$\textbf{CS}$|$}} \\
\hline
spmv ellpack & ellpack & 1600\\
\hline
bfs bulk & bulk &2352 \\
\hline
md knn & md\_kernel & 1600\\
\hline
viterbi & viterbi & 1152\\
\hline
gemm ncubed & gemm & 2744\\
gemm blocked & bbgemm & 1600\\
\hline
fft strided & fft & 64\\
\hline
\multirow{2}{*}{sort merge} & ms\_mergesort & 4096\\
& merge & 4096\\
\hline
stencil stencil2d & stencil & 1344\\
stencil stencil3d & stencil3d & 1536\\
\hline
\multirow{7}{*}{radix sort} & update & 2400 \\
& hist & 4704\\
& init & 484 \\
& sum\_scan & 1280\\
& last\_step\_scan & 800\\
& local\_scan & 704\\
& ss\_sort & 1792 \\
\hline
\multirow{7}{*}{aes} & aes\_addRoundKey & 500\\
& aes\_subBytes & 50\\
& aes\_addRoundKey\_cpy & 625\\
& aes\_shiftRows & 20\\
& aes\_mixColumns & 18 \\
& aes\_expandEncKey & 216 \\
& aes256\_encrypt\_ecb & 1944\\
\hline
\multirow{13}{*}{backprop} & get\_delta\_matrix\_weights1 & 21952 \\
& get\_delta\_matrix\_weights2 & 31213 \\
& get\_delta\_matrix\_weights3 & 21952 \\
& get\_oracle\_activations1 & 2401 \\
& get\_oracle\_activations2 & 1372 \\
& product\_with\_bias\_input\_layer & 1372\\
& product\_with\_bias\_second\_layer & 686\\
& product\_with\_bias\_output\_layer & 392\\
& backprop & 2048 \\
& add\_bias\_to\_activations & 1372\\
& soft\_max & 64\\
& take\_difference & 512\\
& update\_weights & 1024\\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Lorenzo Ferretti
Jihye Kwon
Giovanni Ansaloni
Giuseppe Di Guglielmo
Luca Carloni
Laura Pozzi

"DB4HLS: A Database of High-Level Synthesis Design Space Explorations" by Lorenzo Ferretti, Jihye Kwon, Giovanni Ansaloni, Giuseppe Di Guglielmo, Luca Carloni, and Laura Pozzi, arXiv:2101.00587v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{$C_3$ Part 2 Summary}
\begin{tabular}{|l|l|l|l|} \hline
ID & Term & Symmetric Terms & Table Name \\ \hline
 1 & $- f (X)_{i_0,i_0} \cdot f(X)_{i_0,i_2} \cdot w(X)_{i_0,j_2} \cdot h(X)_{j_0,i_0} \cdot w(X)_{i_0,j_1}$ & Yes & N/A \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Yichuan Deng
Zhao Song
Shenghao Xie
Chiwun Yang

"Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights" by Yichuan Deng, Zhao Song, Shenghao Xie, and Chiwun Yang, arXiv:2310.12462v1, licensed under CC BY-NC-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{Bits/value Result of Lossless Compression}
\begin{tabular}{lcccccc}
		\toprule
		\multirow{2}{*}{\textbf{Dataset}} & \textbf{Gorilla} & \textbf{Chimp} & \multicolumn{2}{c}{\textbf{VW}} & \multicolumn{2}{c}{\textbf{Cameo}} \\
		\cmidrule[\heavyrulewidth]{2-7}
		 & Bits/v & Bits/v & Bits/v & $\epsilon$ & Bits/v & $\epsilon$ \\
		\toprule
		ElecPower & 63.73 & 55.52 & 23.88 & 3e-3 & 16.03 & 1e-3 \\
		MinTemp & 59.76 & 22.52 & 16.56 & 7e-3 & 16.31 & 3e-3 \\
		Pedestrian & 16.63 & 27.45 & 14.45 & 7e-3 & 14.52 & 1e-3 \\
		UKElecDem & 18.33 & 28.95 & 17.76 & 5e-3 & 7.4 & 1e-3 \\
		\midrule
		AUSElecDem & 56.56 & 53.52 & 49.92 & 1e-4 & 26.63 & 1e-4 \\
		Humidity & 52.64 & 22.16 & 21.6 & 2e-5 & 1.51 & 1e-5 \\
		IRBioTemp & 52.40 & 20.33 & 15.13 & 5e-5 & 11.4 & 5e-5 \\
		Solar & 3.55 & 9.87 & 2.31 & 1e-4 & 0.14	 & 1e-4 \\
		\toprule
	\end{tabular}
\end{table}
\end{document}

math

Carlos Enrique Muñiz-Cuza
Matthias Boehm
Torben Bach Pedersen

"CAMEO: Autocorrelation-Preserving Line Simplification for Lossy Time Series Compression" by Carlos Enrique Muñiz-Cuza, Matthias Boehm, and Torben Bach Pedersen, arXiv:2501.14432v1, 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
\caption{Impact of the \textit{T} values on accuracy }
\begin{tabular}{c||c|c|c|c|c}
 Clauses & T & Training & Testing & Validation & Better Classification \\
 \hline
 \hline
 30 & 2 & 83.5 \% & 80.5 \% & 83.8 \% & \checkmark\\
 30 & 23 & 74.9 \% & 71.1 \% & 76.1 \% & \\
 \hline
 450 & 2 & 89.7 \% & 86.1 \% & 84.9 \% & \\
 450 & 23 & 96.8 \% & 92.5 \% & 92.7 \% & \checkmark\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Jie Lei
Tousif Rahman
Rishad Shafik
Adrian Wheeldon
Alex Yakovlev
Ole-Christoffer Granmo
Fahim Kawsar
Akhil Mathur

"Low-Power Audio Keyword Spotting using Tsetlin Machines" by Jie Lei, Tousif Rahman, Rishad Shafik, Adrian Wheeldon, Alex Yakovlev, Ole-Christoffer Granmo, Fahim Kawsar, and Akhil Mathur, arXiv:2101.11336v1, 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}{lll}
\hline
\textbf{} & \textbf{Our Method} & \textbf{}\\
\hline
train set & 14964 & 14067 \\
\hline
validation set & 1500 & 1432 \\
\hline
test set & 1358 & 1300 \\
\hline
\end{tabular}
\caption{ Statistics of relabeled PDTB data.}
\end{table}
\end{document}

cs

"Automatic Alignment of Discourse Relations of Different Discourse Annotation Frameworks" by Yingxue Fu, arXiv:2403.20196v2, 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{Classification accuracies of surrogate CNNs for building the ensemble in the training data and manipulation parameter mismatch scenario. }
\begin{tabular}{|l|ccc|}
	\hline
 \textbf{CNN Architect.} & \textbf{Detection} & \textbf{Classfication} &\textbf{Parameterization} \\\hline
MISLNet& 99.02\%& 99.28\%&90.88\%\\
Zhan et. al &99.27\%&97.99\%&68.13\%\\
PHNet&99.44\%&98.86\%&90.44\%\\
SRNet&99.65\%&98.72\%& 88.31\%\\
DenseNet\textunderscore BC &94.80\%& 96.58\%& 73.64\%\\
VGG-19 & 99.97\% & 99.26\% & 88.09\%\\
\textbf{Avg.} & \textbf{98.69\%}& \textbf{98.45\%} &\textbf{83.25\%}\\\hline
\end{tabular}
\end{table}
\end{document}

eess

"A Transferable Anti-Forensic Attack on Forensic CNNs Using A Generative Adversarial Network" by Xinwei Zhao, Chen Chen, and Matthew C. Stamm, arXiv:2101.09568v1, 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{Values of parameters in Algorithm~ that are used in Sec. for multiple split cases with 3 CPHXS}
\begin{tabular}{cccc}
\toprule
param & value & param & value \\ \hline
$n_{\mathrm{pop}}$ & 400 & $d_{\mathrm{range}}$ & $[4,16]$kW \\
$n_{\mathrm{nodes}}$ & 3 & ${n_{\mathrm{conf}}}$ & 3 \\
$n_{\mathrm{f}}$ & 3 & $D$ & $d_i/\Sigma d_i$ \\
$n_{\mathrm{train}}$ & 350 & $n_{\mathrm{test}}$ & 50 \\
Sampling method & LHS & & \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Saeid Bayat
Nastaran Shahmansouri
Satya RT Peddada
Alex Tessier
Adrian Butscher
James T Allison

"Extracting Design Knowledge from Optimization Data: Enhancing Engineering Design in Fluid Based Thermal Management Systems" by Saeid Bayat, Nastaran Shahmansouri, Satya RT Peddada, Alex Tessier, Adrian Butscher, and James T Allison, arXiv:2310.16324v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{12 selected locations with the corresponding results of monthly SSTA and MHW forecasts (Part 4).}
\begin{tabular}{ll}
\textbf{Location} & \textbf{Six lead months} \\ \hline
\multirow{3}{*}{BOP} & Huber, WMSE \\
 & \\
 & SWMSE2 \\ \hline
\multirow{3}{*}{BP} & Huber \\
 & SWMSE3 \\
 & SWMSE2, SWMSE3 \\ \hline
\multirow{3}{*}{CI} & Huber, WMSE, SWMSE1, SWMSE2 \\
 & \\
 & Huber \\ \hline
\multirow{3}{*}{CR} & Huber, SWMSE1, SWMSE2 \\
 & \\
 & SWMSE1, SWMSE2 \\ \hline
\multirow{3}{*}{CS} & Huber, SWMSE2 \\
 & SWMSE3 \\
 & SWMSE2, SWMSE3 \\ \hline
\multirow{3}{*}{F} & Huber, SWMSE1 \\
 & \\
 & \\ \hline
\multirow{3}{*}{HG} & Huber \\
 & \\
 & \\ \hline
\multirow{3}{*}{OP} & Huber, SWMSE2 \\
 & SWMSE2, SWMSE3 \\
 & Huber, SWMSE2, SWMSE3 \\ \hline
\multirow{3}{*}{R} & Huber, SWMSE1, SWMSE2 \\
 & \\
 & SWMSE2 \\ \hline
\multirow{3}{*}{SI} & Huber, SWMSE1, SWMSE2 \\
 & SWMSE1, SWMSE2, SWMSE3 \\
 & SWMSE1, SWMSE2, SWMSE3 \\ \hline
\multirow{3}{*}{T} & WMSE \\
 & \\
 & \\ \hline
W & Huber, WMSE, SWMSE1, SWMSE2 \\
 & SWMSE3
\end{tabular}
\end{table}
\end{document}

stat

Ding Ning
Varvara Vetrova
Sébastien Delaux
Rachael Tappenden
Karin R. Bryan
Yun Sing Koh

"A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models" by Ding Ning, Varvara Vetrova, Sébastien Delaux, Rachael Tappenden, Karin R. Bryan, and Yun Sing Koh, arXiv:2502.13495v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Detection Results of $AVTENet_{asf}$ (Average\\ Score fusion).}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\textbf {Test-Set type} & \textbf {Class} & \textbf {Precision} & \textbf {Recall} & \textbf {F1-Score} & \textbf {Accuracy} \\
\hline \hline
\multirow{2}{*}{Testset-I} & Real & 0.92 & 1.00 & 0.96 & \multirow{2}{*}{0.96} \\
\cline{2-5}
& Fake & 1.00 & 0.91 & 0.96 &\\
\hline
\multirow{2}{*}{Testset-II} & Real & 1.00 & 1.00 & 1.00 & \multirow{2}{*}{1.00} \\
\cline{2-5}
& Fake & 1.00 & 1.00 & 1.00 & \\
\hline
\multirow{2}{*}{faceswap} & Real & 0.79 & 1.00 & 0.88 & \multirow{2}{*}{0.86} \\
\cline{2-5}
& Fake & 1.00 & 0.73 & 0.84 & \\
\hline
\multirow{2}{*}{faceswap-wav2lip} & Real & 1.00 & 1.00 & 1.00 & \multirow{2}{*}{1.00} \\
\cline{2-5}
& Fake & 1.00 & 1.00 & 1.00 & \\
\hline
\multirow{2}{*}{fsgan} & Real & 0.88 & 1.00 & 0.93 & \multirow{2}{*}{0.93} \\
\cline{2-5}
& Fake & 1.00 & 0.86 & 0.92 & \\
\hline
\multirow{2}{*}{fsgan-wav2lip} & Real & 1.00 & 1.00 & 1.00 & \multirow{2}{*}{ 1.00} \\
\cline{2-5}
& Fake & 1.00 & 1.00 & 1.00 & \\
\hline
\multirow{2}{*}{RTVC} & Real & 0.96 & 1.00 & 0.98 & \multirow{2}{*}{0.98} \\
\cline{2-5}
& Fake & 1.00 & 0.96 & 0.98 & \\
\hline
\multirow{2}{*}{wav2lip} & Real & 1.00 & 0.99 & 0.99 & \multirow{2}{*}{0.99} \\
\cline{2-5}
& Fake & 0.99 & 1.00 & 0.99 & \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Ammarah Hashmi
Sahibzada Adil Shahzad
Chia-Wen Lin
Yu Tsao
Hsin-Min Wang

"AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection" by Ammarah Hashmi, Sahibzada Adil Shahzad, Chia-Wen Lin, Yu Tsao, and Hsin-Min Wang, arXiv:2310.13103v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{Month}} & \multicolumn{2}{c|}{\textbf{Growth Rates}} & \multicolumn{2}{c|}{\textbf{Employment Level}} \\
\cline{2-5}
& \textbf{with shock} & \textbf{without shock} & \textbf{with shock} & \textbf{without shock} \\
\hline
$1$ & $g_{1} + \beta_1$ & $g_{1}$ & $y_t(g_1 + \beta_1)$ & $y_t g_1$ \\
\hline
$2$ & $g_{2} + \beta_2$ & $g_{2}$ & $y_t(g_2 + \beta_2)$ & $y_t g_2$ \\
\hline
$3$ & $g_{3} + \beta_3$ & $g_{3}$ & $y_t(g_3 + \beta_3)$ & $y_t g_3$ \\
\hline
\end{tabular}
\caption{Growth rates and employment levels with and without shock}
\end{table}
\end{document}

q-fin

Fires and Local Labor Markets

"Fires and Local Labor Markets" by Raphaelle G. Coulombe and Akhil Rao, arXiv:2308.02739v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccccc}
\hline
Method & Count & min & 25\% & Median & 75\% & max\\
\hline
Integration & 22 (16.67\%) & 0 & $6.94\cdot 10^{-16}$ & $3.66\cdot 10^{-15}$ & $1.76\cdot 10^{-15}$ & $5.98\cdot 10^{-14}$\\
Series & 7 (5.30\%) & $4.44\cdot 10^{-16}$ & $6.11\cdot 10^{-16}$ & $2.00\cdot 10^{-15}$ & $5.39\cdot 10^{-13}$ & $3.22\cdot 10^{-12}$\\
Asymptotic & 100 (75.76\%) & $8.88\cdot 10^{-16}$ & $1.35\cdot 10^{-14}$ & $3.00\cdot 10^{-14}$ & $5.57\cdot 10^{-14}$ & $1.30\cdot 10^{-13}$\\
Asymptotic & 3 (2.27\%) & $5.76\cdot 10^{-13}$ & $8.84\cdot 10^{-13}$ & $1.19\cdot 10^{-12}$ & $1.42\cdot 10^{-12}$ & $1.64\cdot 10^{-12}$\\
	\hline
	\end{tabular}
\caption{Precision metrics of the numerical methods used for computing in the large (hard) region. The errors are the absolute relative errors compared to the reference solutions obtained using mpmath. Percentiles: 25, 50 (median), 75.}
\end{table}
\end{document}

stat

"On the computation of the cumulative distribution function of the Normal Inverse Gaussian distribution" by Guillermo Navas-Palencia, arXiv:2502.16015v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"GAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restoration" by Youssef Mansour and Reinhard Heckel, arXiv:2404.00807v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Defense via Reconstructing Inputs}
\begin{tabular}{l|cccc}\toprule
 \multirow{2}{*}{Specs} & \multicolumn{3}{c}{\textbf{Reconstruct Inputs}} \\\cmidrule{2-4}
 & \textbf{Trigger Rate Decay} & \textbf{Succ. Decr.} & \textbf{Path.Len. Incr.} \\\midrule
 {\sf Hide} & 71.92\% & 23.51\% & 19.29\% \\
 {\sf Misguide} & 74.11\% & 23.16\% & 25.38\% \\
 {\sf Trap} & 72.84\% & 25.42\% & 14.09\% \\
 {\sf Waste Energy} & 84.13\% & 24.19\% & 25.83\% \\
 {\sf Branch} & 85.27\% & 26.41\% & 17.09\% \\
 {\sf Camouflage} & 88.52\% & 33.31\% & 18.83\% \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

"Manipulating Neural Path Planners via Slight Perturbations" by Zikang Xiong and Suresh Jagannathan, arXiv:2403.18256v1, 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{Enhanced Rebates Scheme for Long Tail / New Markets}
\begin{tabular}{|l|l|l|}
 \hline
 ~ & Maker Volume (\% of 30D Market Volume) & Enhanced Rebates \\ \hline
 Tier 1 & $\geq$ 5\% & -0.0125\% \\ \hline
 Tier 2 & $\geq$ 10\% & -0.0150\% \\ \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"dYdX: Liquidity Providers' Incentive Programme Review" by Colin Chan, arXiv:2307.03935v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ {Accuracy for clean test dataset and attack successful rate for backdoor test dataset.}}
\begin{tabular}{c|cc|cc|cc|cc}
\toprule
\midrule
\multirow{2}{*}{Dataset} & \multicolumn{2}{c|}{Train Clean} & \multicolumn{2}{c|}{Train Backdoor} & \multicolumn{2}{c|}{Add Backdoor} & \multicolumn{2}{c}{Remove Backdoor} \\
 & Clean & Backdoor & Clean & Backdoor & Clean & Backdoor & Clean & Backdoor \\\midrule[0.8pt]
Optdigits & 96.21\% & 8.91\% & 96.27\% & 100\% & 95.94\% & 100\% & 95.82\% & 9.69\% \\
Pendigits & 96.11\% & 3.97\% & 96.43\% & 100\% & 96.48\% & 100\% & 96.51\% & 5.55\% \\
Letter & 93.9\% & 1.38\% & 94.08\% & 100\% & 93.62\% & 100\% & 93.78\% & 3.48\% \\
Covtype & 78.4\% & 47.83\% & 78.32\% & 100\% & 78.38\% & 100\% & 78.38\% & 51.71\% \\
\midrule
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Huawei Lin
Jun Woo Chung
Yingjie Lao
Weijie Zhao

"Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data" by Huawei Lin, Jun Woo Chung, Yingjie Lao, and Weijie Zhao, arXiv:2502.01634v1, 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{Trace Summary}
\begin{tabular}{llllll}
 \hline
 Trace & \# Batches &$T$ &$N$& $R$ & $h$ \\
 \hline
 Fixed Popularity & $1.5 \times 10^5$ & $1.5 \times 10^5$& $10^4$ &1 & 1\\
 Batched Fixed Popularity (1) & $10^4$ & $10^4$&$ 10^4$& $5\times10^3$ & 2\\
 Batched Fixed Popularity (2) & $10^4$ & $10^4$&$ 10^4$& $5\times10^3$ & 5\\
 Batched Fixed Popularity (3) & $10^4$ & $10^4$&$ 10^4$& $5\times10^3$ & 87\\
 Partial Popularity Change (1) & $5 \times 10^3$ & $10^3$&$10^4$& $5\times10^3$ & 2\\
 Partial Popularity Change (2) & $5 \times 10^3$ & $10^3$&$10^4$& $5\times10^3$ & 6\\
 Partial Popularity Change (3) & $5 \times 10^3$ & $10^3$&$10^4$& $5\times10^3$ & 10\\
 Global Popularity Change & $1.5 \times 10^5$ & $1.5 \times 10^5$& $10^4$&1 & 1\\
 Downscaled Global Popularity Change & $9 \times 10^3$ & $9 \times 10^3$& $25$&1 & 1\\
 CDN Trace & $1.7\times 10^4$ & $10^2$ & $10^3$ & $5 \times 10^3$ & $380$ \\
 \hline
\end{tabular}
\end{table}
\end{document}

cs

"No-Regret Caching via Online Mirror Descent" by T. Si Salem, G. Neglia, and S. Ioannidis, arXiv:2101.12588v5, 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}{lrrrrrr}
 \toprule
 & \multicolumn{6}{c}{\em Nord Stream}\\
 lang & {\sc PL} & {\sc CS} & {\sc RU} & {\sc BG} & {\sc SL} & {\sc UK}\\
 \midrule
 {\sc PER} & 692 & 681 & 454 & 404 & 814 & 87 \\
 {\sc LOC} & 2107 & 2174 & 1885 & 1574 & 2110 & 655 \\
 {\sc ORG} & 1066 & 638 & 1115 & 747 & 834 & 777 \\
 {\sc PRO} & 1150 & 616 & 750 & 654 & 370 & 8 \\
 {\sc EVT} & 17 & 19 & 5 & 8 & 57 & 17 \\
 \midrule
 Total & 5032 & 4129 & 4210 & 3387 & 4185 & 1544\\
 \midrule
 forms & 845 & 770 & 892 & 504 & 902 & 336\\
 lemmas & 634 & 550 & 583 & 448 & 600 & 244\\
 entity IDs & 441 & 392 & 320 & 305 & 461 & 175\\
 \bottomrule
 \end{tabular}
\caption{Distribution of named-entity types across languages and statistics on surface forms, lemmas, and unique entities IDs for the domain {\em Nord Stream}.}
\end{table}
\end{document}

cs

Jakub Piskorski
Michał Marcińczuk
Roman Yangarber

"Cross-lingual Named Entity Corpus for Slavic Languages" by Jakub Piskorski, Michał Marcińczuk, and Roman Yangarber, arXiv:2404.00482v2, 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
\caption{The LBE and LFE Productivity Effect Estimates}
\begin{tabular}{lcccc|c}
			\toprule[1pt]
			& \multicolumn{4}{c}{\it Point Estimates} & Stat.~Signif. \\
			& Mean & 1st Qu. & Median & 3rd Qu. & (\% Obs.) \tabularnewline
			\midrule
			\multicolumn{6}{c}{\textbf{---Learning by Exporting---}} \\
			All Firms & 0.363 & 0.323 & 0.390 & 0.451 & 92.7 \\
			& (0.189, 0.555) & (0.139, 0.531) & (0.207, 0.603) & (0.250, 0.675) & \\
			Exporters & 0.158 & --0.000 & 0.257 & 0.353 & 68.3 \\
			& (0.068, 0.254) & (--0.072, 0.067) & (0.117, 0.419) & (0.184, 0.546) & \\
			Non-exporters & 0.418 & 0.356 & 0.408 & 0.465 & 99.2 \\
			& (0.234, 0.643) & (0.170, 0.572) & (0.220, 0.628) & (0.259, 0.695) & \\

			\multicolumn{6}{c}{\textbf{---Learning from Exporters---}} \\
			All Firms & 0.324 & 0.141 & 0.296 & 0.457 & 68.5 \\
			& (0.116, 0.545) & (--0.059, 0.363) & (0.069, 0.520) & (0.166, 0.729) & \\
			Exporters & 0.508 & 0.182 & 0.399 & 0.778 & 73.9 \\
			& (0.302, 0.76) & (--0.013, 0.432) & (0.189, 0.618) & (0.508, 1.148) & \\
			Non-exporters & 0.275 & 0.132 & 0.28 & 0.42 & 67.1 \\
			& (0.027, 0.489) & (--0.088, 0.34) & (0.027, 0.497) & (0.124, 0.701) & \\
			\midrule
			\multicolumn{6}{p{14.4cm}}{\small \textit{Notes}: Reported is a summary of point estimates of the LBE and LFE effects tabulated by the firm's exporter status, with 95\% bootstrap percentile confidence intervals in parentheses. The far right column reports the share of (sub)sample for which the observation-specific estimates are statistically significant at the 5\% level.}\\
			\bottomrule[1pt]
		\end{tabular}
\end{table}
\end{document}

q-fin

"Detecting Learning by Exporting and from Exporters" by Jingfang Zhang and Emir Malikov, arXiv:2302.13427v1, 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{Semmantic similarity of the generated images with different methods evaluated against different metrics: LPIPS and FID. Some of the results are from the work GESCO~ (Smaller value is better). We present different choices of $n$ for our proposed Diff-GO. }
\begin{tabular}{|c||c||c|c|}
\hline
Method & LPIPS$\downarrow$ & FID$\downarrow$ \\
\hline
SPADE~& 0.546 & 103.24\\
\hline
CC-FPSE~& 0.546 & 245.9\\
\hline
SMIS~& 0.546 & 87.58\\
\hline
OASIS~& 0.561 & 104.03\\
\hline
SDM~& 0.549 & 98.99\\
\hline
OD& 0.2191 & 55.85\\
\hline
GESCO& 0.591 & 83.74\\
\hline
RN & 0.3448 & 96.409\\
\hline
Diff-GO (n=20)& 0.3206 & 74.09\\
\hline
Diff-GO (n=50)& 0.2697 & 72.95\\
\hline
Diff-GO (n=100)& 0.2450 & 68.59\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Achintha Wijesinghe
Songyang Zhang
Suchinthaka Wanninayaka
Weiwei Wang
Zhi Ding

"Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High Spectrum Efficiency" by Achintha Wijesinghe, Songyang Zhang, Suchinthaka Wanninayaka, Weiwei Wang, and Zhi Ding, arXiv:2312.02984v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|c|}
 \hline
 Variable & QLOMA & WOMA & No. of times stat. significant$^*$ \\ \hline
 \multicolumn{4}{|c|}{Add-on and visit interaction with:} \\ \hline
 Intensive group & -0.1 & -0.4 & 1 \\ \hline
 Age & -0.0 & -0.1 & 7 \\ \hline
 Female sex & 0.3 & -0.5 & 3 \\ \hline
 Race Black & \multicolumn{3}{|c|}{Reference} \\ \hline
 Race Hispanic & 0.5 & 0.6 & 1 \\ \hline
 Race White & -1.4 & -1.8 & 17 \\ \hline
 \hspace{0.2cm} Other & 1.3 & 2.5 & 5 \\ \hline
 Smoking (ever) & 0.7 & -0.7 & 1 \\ \hline
 BMI & 0.0 & 0.1 & 5 \\ \hline
 HDL & -0.0 & -0.0 & 2 \\ \hline
 SBP Baseline & -0.0 & -0.0 & 6 \\ \hline
 SBP Current month & 0.0 & 0.0 & 5 \\ \hline
 CVD & 0.3 & 0.1 & 0 \\ \hline
 Aspirin use & 0.6 & 0.3 & 2 \\ \hline
 Statin use & -0.1 & -0.3 & 3 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Janie Coulombe
Dany El-Riachi
Fanxing Du
Tianze Jiao

"Constructing optimal dynamic monitoring and treatment regimes: An application to hypertension care" by Janie Coulombe, Dany El-Riachi, Fanxing Du, and Tianze Jiao, arXiv:2501.08274v1, 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{Example of visiting order}
\begin{tabular}{|c|c|c|c|c|c|c|}
			\hline
			Patients & 1 (3) & 3 (1) & 4 (3) & 2 (1) & 6 (2) & 5 (2) \\
			\hline
			Caregivers & & & & & & \\
			\hline
		\end{tabular}
\end{table}
\end{document}

math

"Mathematical models and heuristics for the home health care routing and scheduling problem" by Mohammed Bazirha, arXiv:2312.08032v1, 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{Evaluation results of Llama 3.2 1B FT experiments.}
\begin{tabular}{l|c}
\toprule
Optimizer & GSM-8K$\uparrow$\\
\midrule
Adam & 48.90\% \\
C-Adam & \underline{49.81\%} \\
Grams (ours) & \textbf{51.02\%} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"Grams: Gradient Descent with Adaptive Momentum Scaling" by Yang Cao, Xiaoyu Li, and Zhao Song, arXiv:2412.17107v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|cccc|}
 \hline
 Method & \textsf{Beds} & \textsf{Laboratory Tests} & \textsf{Medications} \\
 \hline
 Constant & 0.93 (0) & 129 (0) & 6.9 (0) \\
 Hold Last & 0.72 (23) & 85 (34) & 6.2 (10) \\
 Model & 0.45 (52) & 73 (43) & 4.3 (38) \\
 \hline
 \end{tabular}
\caption{Absolute error of sequence length prediction results. Values are the average absolute error per episode and values in parenthesis are percentage improvement over the Constant baseline approach.}
\end{table}
\end{document}

cs

Alan D. Kaplan
Priyadip Ray
John D. Greene
Vincent X. Liu

"Sequential Inference of Hospitalization Electronic Health Records Using Probabilistic Models" by Alan D. Kaplan, Priyadip Ray, John D. Greene, and Vincent X. Liu, arXiv:2403.19011v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.