\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
 \toprule
 Method & Frobnability \\
 \midrule
 Theirs & Frumpy \\
 Yours & Frobbly \\
 Ours & Makes one's heart Frob\\
 \bottomrule
 \end{tabular}
\caption{Results. Ours is better.}
\end{table}
\end{document}

cs

Byeongin Joung
Byeong-Uk Lee
Jaesung Choe
Ukcheol Shin
Minjun Kang
Taeyeop Lee
In So Kweon
Kuk-Jin Yoon

"Stable Surface Regularization for Fast Few-Shot NeRF" by Byeongin Joung, Byeong-Uk Lee, Jaesung Choe, Ukcheol Shin, Minjun Kang, Taeyeop Lee, In So Kweon, and Kuk-Jin Yoon, arXiv:2403.19985v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Scaling and shape of financial returns distributions modeled as conditionally independent random variables" by Hernán Larralde and Roberto Mota Navarro, arXiv:2504.20488v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccr|cccc}
\toprule
Setting & $1-\alpha$ & $th$ & & Cov$\uparrow$ & Single$\uparrow$ & $|\text{CQ}|\downarrow$ & Amb \\ \midrule
\midrule
ACID & .99 & 7 & CICC & \underline{1} & \textbf{.77} & \textbf{3.00} & .10 \\
& & & B1 & .98 & .85 & 5 & 0 \\
& & & B2 & .95 & 1 & $-$ & 0 \\
& & & B3 & \underline{.99} & 0 & 5 & 0 \\
\midrule
ATIS & .99 & 7 & CICC & \underline{.99} & \textbf{.98} & \textbf{2.54} & 0 \\
& & & B1 & \underline{.99} & .73 & 5 & 0 \\
& & & B2 & .98 & 1 & $-$ & 0 \\
& & & B3 & \underline{1} & 0 & 5 & 0 \\
\midrule
B77/BERT & .99 & 7 & CICC & \underline{.98} & \textbf{.79} & \textbf{2.90} & .04 \\
& & & B1 & .97& .90 & 5 & 0 \\
& & & B2 & .93 & 1 & $-$ & 0 \\
& & & B3 & \underline{.99} & 0 & 5 & 0 \\
\midrule
B77/DFCX & .99 & 4 & CICC & .97 & 0 & 5 & 1 \\
& & & B1 & .97 & .05 & 5 & .95 \\
& & & B2 & .90 & 1 & $-$ & 0 \\
& & & B3 & .97 & 0 & 5 & 1 \\
\midrule
C150 & .99 & 7 & CICC & \underline{.99} & \textbf{.97} & \textbf{2.66} & 0 \\
& & & B1 & \underline{.99} & .82 & 5 & 0 \\
& & & B2 & .98 & 1 & $-$ & 0 \\
& & & B3 & \underline{1} & 0 & 5 & 0 \\
\midrule
HWU64 & .99 & 7 & CICC & \underline{.99} & \textbf{.25} & \textbf{3.39} & .28 \\
& & & B1 & .98 & .05 & 5 & 0 \\
& & & B2 & .90 & 1 & $-$ & 0 \\
& & & B3 & .98 & 0 & 5 & 0 \\
\midrule
MTOD & .99 & 7 & CICC & \underline{.99} & \textbf{1} & $-$ & 0 \\
& & & B1 & \underline{1} & .98 & 5 & 0 \\
& & & B2 & \underline{.99} & \textbf{1} & $-$ & 0 \\
& & & B3 & \underline{1} & 0 & 5 & 0 \\
\bottomrule
\end{tabular}
\caption{Test set results for $1-\alpha=.99$ where \underline{underline} indicates meeting coverage requirement. \textbf{Bold} denotes best when meeting this requirement, omitted for last column due to missing ground truth for ambiguous.}
\end{table}
\end{document}

cs

Floris den Hengst
Ralf Wolter
Patrick Altmeyer
Arda Kaygan

"Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition" by Floris den Hengst, Ralf Wolter, Patrick Altmeyer, and Arda Kaygan, arXiv:2403.18973v1, 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}

stat

""Medium-n studies" in computing education conferences" by Michael Guerzhoy, arXiv:2311.14679v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c}
 \textbf{Method} & \textbf{MCD$\downarrow$} & \textbf{GPE$\downarrow$} & \textbf{VDE$\downarrow$} & \textbf{FFE$\downarrow$} \\
 \hline
 Conv-1 & \textbf{13.65}& \textbf{28.70} & \textbf{18.04} & \textbf{35.46}\\
 Attention-1 & 14.63 & 30.45 & 19.60 & 37.64\\
 Conv-2 & \textbf{14.15} & \textbf{30.50} & \textbf{19.98} & \textbf{38.26} \\
 Attention-2 & 15.17 & 32.51 & 21.06 & 41.64 \\
 \end{tabular}
\caption{Quantitative metrics on FSM-SS on zero shot approach. Conv: Convolution based normalization in FSM-SS , Attention : Multi head attention based normalization in FSM-SS, 1- VCTK dataset, 2- LibriTTS dataset}
\end{table}
\end{document}

eess

Neeraj Kumar
Srishti Goel
Ankur Narang
Brejesh Lall

"Few Shot Adaptive Normalization Driven Multi-Speaker Speech Synthesis" by Neeraj Kumar, Srishti Goel, Ankur Narang, and Brejesh Lall, arXiv:2012.07252v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Federico Fioravanti
Fernando Delbianco
Fernando Tohmé

"Visitors Out! The Absence of Away Team Supporters as a Source of Home Advantage in Football" by Federico Fioravanti, Fernando Delbianco, and Fernando Tohmé, arXiv:2308.06279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

A. Papanicolaou
H. Fu
P. Krishnamurthy
B. Healy
F. Khorrami

"An Optimal Control Strategy for Execution of Large Stock Orders Using LSTMs" by A. Papanicolaou, H. Fu, P. Krishnamurthy, B. Healy, and F. Khorrami, arXiv:2301.09705v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The experimental data setting of Herlev dataset}
\begin{tabular}{|l|c|c|}
\hline
\multirow{2}{*}{Dataset} & \multicolumn{2}{c|}{Total Number of Images} \\ \cline{2-3}
 & 7-Class & 2-Class \\ \hline
Training & 8190 & 8235 \\ \hline
Validation & 185 & 184 \\ \hline
Test & 186 & 184 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Md Mamunur Rahaman
Chen Li
Yudong Yao
Frank Kulwa
Xiangchen Wu
Xiaoyan Li
Qian Wang

"DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques" by Md Mamunur Rahaman, Chen Li, Yudong Yao, Frank Kulwa, Xiangchen Wu, Xiaoyan Li, and Qian Wang, arXiv:2102.12191v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c}
 \textbf{Method} & \textbf{MCD$\downarrow$} & \textbf{GPE$\downarrow$} & \textbf{VDE$\downarrow$} & \textbf{FFE$\downarrow$} &\textbf{MoS$\uparrow$}\\
 \hline
 BM+P+E & 21.68& 40.45 & 45.76 & 65.87&2.64$\pm$0.14\\
 BM+SE & 20.65& 38.71 & 39.56 & 56.64&2.92$\pm$0.26\\
 BM+SE+P & 16.38 & 33.65 & 29.76 & 45.76&3.32$\pm$0.08\\
 FSM-SS & \textbf{13.65}& \textbf{28.70} & \textbf{18.04} & \textbf{35.46}& \textbf{3.72$\pm$0.24}\\

 \end{tabular}
\caption{Ablation Study of FSM-SS. BM is Base Model without normalization method. SE is speaker embedding in normalization, P and E are pitch and energy values in the normalization network. }
\end{table}
\end{document}

eess


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccc}
\toprule
 {Data} & {MSE}& {MAE} & {SSIM}\\\midrule
Vancouver93 & 1.8e-1 & 3.4e-1 & NA \\
CloudCast & 4.9e-7 & 6.0e-4 & 0.85 \\
\bottomrule
\end{tabular}
\caption{Similarity metrics for ensemble standard deviation.}
\end{table}
\end{document}

math

Siddharth Rout
Eldad Haber
Stéphane Gaudreault

"Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data" by Siddharth Rout, Eldad Haber, and Stéphane Gaudreault, arXiv:2503.12273v1, 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}{c}
\toprule
 \textbf{Ontology} \\
\midrule
 OMOP Extension \\
Medicare Specialty \\
CPT4 \\
CVX \\
ICD9Proc \\
RxNorm \\
SNOMED \\
RxNorm Extension \\
Cancer Modifier \\
ICD10PCS \\
CMS Place of Service \\
Visit \\
Ethnicity \\
Gender \\
ICDO3 \\
Race \\
LOINC \\
HCPCS \\
\bottomrule
\end{tabular}
\caption{OHDSI Athena ontolgies used in our benchmark}
\end{table}
\end{document}

eess

Shih-Cheng Huang
Zepeng Huo
Ethan Steinberg
Chia-Chun Chiang
Matthew P. Lungren
Curtis P. Langlotz
Serena Yeung
Nigam H. Shah
Jason A. Fries

"INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis" by Shih-Cheng Huang, Zepeng Huo, Ethan Steinberg, Chia-Chun Chiang, Matthew P. Lungren, Curtis P. Langlotz, Serena Yeung, Nigam H. Shah, and Jason A. Fries, arXiv:2311.10798v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Matthew West
Colin Magdamo
Lily Cheng
Yingnan He
Sudeshna Das

"Leveraging Pre-trained and Transformer-derived Embeddings from EHRs to Characterize Heterogeneity Across Alzheimer's Disease and Related Dementias" by Matthew West, Colin Magdamo, Lily Cheng, Yingnan He, and Sudeshna Das, arXiv:2404.00464v1, 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 effect of modifying the number of gradient steps using the optimizer configuration reported in the main section for Gradient Descent on the stochastic Lorenz attractor (RK4). Ordered by RMSE.}
\begin{tabular}{lc}
\toprule
\textbf{Method} & \textbf{RMSE} \\
\midrule
Gradient Descent ($K = 100, \eta = 0.05$) & $1.985 \pm 0.011$ \\
Gradient Descent ($K = 1, \eta = 0.05$) & $1.937 \pm 0.282$ \\
Gradient Descent ($K = 50, \eta = 0.05$) & $1.847 \pm 0.010$ \\
Gradient Descent ($K = 25, \eta = 0.05$) & $1.493 \pm 0.009$ \\
Gradient Descent ($K = 10, \eta = 0.05$) & $0.987 \pm 0.008$ \\
Gradient Descent ($K = 5, \eta = 0.05$) & $0.743 \pm 0.010$ \\
Gradient Descent ($K = 3, \eta = 0.05$) & $0.701 \pm 0.018$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Gianluca M. Bencomo
Jake C. Snell
Thomas L. Griffiths

"Implicit Maximum a Posteriori Filtering via Adaptive Optimization" by Gianluca M. Bencomo, Jake C. Snell, and Thomas L. Griffiths, arXiv:2311.10580v1, 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{Dataset statistics used in our experiments.}
\begin{tabular}{lccc}
 \toprule
 Dataset & Train & Validation & Test \\
 \midrule
 ScienceQA & 12726 & 4241 & 4241 \\
 ECQA & 8520 & 1221 & 1221 \\
 LastLetter & 10000 & 5000 & 5000 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

"Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering" by Yexin Wu, Zhuosheng Zhang, and Hai Zhao, arXiv:2403.19167v1, 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}{cccc}
 \hline\hline
 Methods& $\|\nabla f\|$& Error & Time\\
 \hline
 ASK&1.0303e-09&3.6223e-10&4.8347e-03 \\
 GD&9.8228e-07&4.8131e-07&8.0462e-03 \\
 NAG&2.5607e-03&1.0887e-03&6.6670e-02 \\
 HB&9.8054e-07&4.7935e-07&6.4078e-03 \\
 CVX&3.8696e-28&9.6740e-29&2.3438e-01 \\
 \hline\hline
 \end{tabular}
\caption{Rotated Hyper-Ellipsoid, init=$130\times\text{rand}(2,1)-65$.}
\end{table}
\end{document}

math

Mengqi Hu
Bian Li
Yi-An Ma
Yifei Lou
Xiu Yang

"A Gradient-Based Optimization Method Using the Koopman Operator" by Mengqi Hu, Bian Li, Yi-An Ma, Yifei Lou, and Xiu Yang, arXiv:2312.14361v1, 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
\begin{tabular}{c|c|c|ccc}
\hline
\multirow{2}{*}{\textbf{Methods}} & \multirow{2}{*}{\textbf{Dynamics.}} & \multirow{2}{*}{\textbf{MRR}} & \multicolumn{3}{c}{\textbf{Hits} @ } \\
 & & & \textbf{K=1} & \textbf{K=3} & \textbf{K=5} \\ \hline \hline
\multirow{3}*{\shortstack{SGA*\\ \textbf{}}}
& (i) & 97.9 & 96.6 & 99.1 & 99.7 \\
& (ii) & 93.6 & \textbf{90.6} & 96.2 & 97.5 \\
& (iii) & 88.8 &87.1 & 94.2 & 96.2 \\\hline
\multirow{3}*{\shortstack{SG-PGM\\ \small{(ours)}}}
& (i) & \textbf{99.8} & \textbf{99.7} & \textbf{99.9} & \textbf{100} \\
& (ii) & \textbf{94.2} & 90.0 & \textbf{98.2} & \textbf{99.3} \\
& (iii) & \textbf{93.4} & \textbf{88.9} & \textbf{97.7} & \textbf{99.2} \\\hline
\end{tabular}
\caption{\textbf{Alignment of a local 3D scene to a prior 3D map with differences in overlap and changes.}}
\end{table}
\end{document}

cs

"SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks" by Yaxu Xie, Alain Pagani, and Didier Stricker, arXiv:2403.19474v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c}
 \textbf{Shots} & \textbf{MCD$\downarrow$} & \textbf{GPE$\downarrow$} & \textbf{VDE$\downarrow$} & \textbf{FFE$\downarrow$}\\
 \hline
 1-shot &14.12 & 29.44 & 19.39 & 37.11\\
 2-shot & 13.39& 28.66 & 18.06 & 35.38 \\
 3-shot & 12.09 & 26.89 & 18.22 & 33.74 \\
 4-shot & 11.77 & 26.03 & 17.21 & 32.91 \\
 5-shot & 11.18 & 25.91 & 16.12 & 31.23 \\
 \end{tabular}
\caption{Metric for few shot approach for attention based normalization in FSM-SS for VCTK dataset }
\end{table}
\end{document}

eess


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.


q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Atari Adam PPO hyperparameters}
\begin{tabular}{rl}
 \toprule
 \textbf{Hyperparameter} & \textbf{Value} \\
 \midrule
 Learning Rate & 0.00025 \\
 Number of Epochs & 4 \\
 Minibatches & 4 \\
 $\gamma$ & $0.99$ \\
 GAE $\lambda$ & $0.95$ \\
 Normalise Advantages & \texttt{True} \\
 $\epsilon$ & $0.1$ \\
 Value Function Clipping & \texttt{True} \\
 Max Grad Norm & $0.5$ \\
 Number of Environments & $8$ \\
 Number of Rollout Steps & 128 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Benjamin Ellis
Matthew T. Jackson
Andrei Lupu
Alexander D. Goldie
Mattie Fellows
Shimon Whiteson
Jakob Foerster

"Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps" by Benjamin Ellis, Matthew T. Jackson, Andrei Lupu, Alexander D. Goldie, Mattie Fellows, Shimon Whiteson, and Jakob Foerster, arXiv:2412.17113v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of trained networks}
\begin{tabular}{c|cccc}
\hline
Test SNR & \multicolumn{2}{c}{-5 dB} & \multicolumn{2}{c}{0 dB} \\ \hline
Evaluation Metrics & STOI & PESQ & STOI & PESQ \\ \hline
Noisy & 51.4 & 1.03 & 62.6 & 1.24 \\
SEGAN & 63.4 & 1.97 & 77.3 & 2.21 \\
Baseline & 81.3 & 2.35 & 87.9 & 2.94 \\
VSEGAN & \textbf{86.8} & \textbf{2.88 } & \textbf{89.8} & \textbf{3.10} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Xinmeng Xu
Yang Wang
Dongxiang Xu
Yiyuan Peng
Cong Zhang
Jie Jia
Binbin Chen

"VSEGAN: Visual Speech Enhancement Generative Adversarial Network" by Xinmeng Xu, Yang Wang, Dongxiang Xu, Yiyuan Peng, Cong Zhang, Jie Jia, and Binbin Chen, arXiv:2102.02599v2, 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{Training and verification results of the CEGIS loop}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 Iter. & $\epsilon$ & Training (s) & Verif. (s) & Result \\
 \hline
 1 & 0.30 & 836.92 & 1367& Certified \\
 2 & 0.27 & - & 22 & Counterexample found \\
 3 & 0.27 & 181.27 & 2735 & Certified \\
 4 & 0.243 & - & 5537 & Certified \\
 5 & 0.2187 & - & 237& Counterexample found \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Prashant Solanki
Nikolaus Vertovec
Yannik Schnitzer
Jasper Van Beers
Coen de Visser
Alessandro Abate

"Certified Approximate Reachability (CARe): Formal Error Bounds on Deep Learning of Reachable Sets" by Prashant Solanki, Nikolaus Vertovec, Yannik Schnitzer, Jasper Van Beers, Coen de Visser, and Alessandro Abate, arXiv:2503.23912v1, 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{Surrogate Model Accuracy (accuracy is measured as the percentage of correct predictions)}
\begin{tabular}{lccc}
\toprule
 & {GP} & {MoNNE} & {BMNN (VI)}\\
\midrule
DTLZ2 & $0.9$ & $0.92$& $0.9$ \\
VLMOP3 & $0.86$ & $0.92$ & $0.84$\\
VehicleSafety & $0.82$ & $0.86$ & $0.86$\\
OSY & $0.82$ & $0.88$ & $0.88$\\
CarCabDesign & $0.84$ & $0.96$ & $0.94$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Hanyang Wang
Juergen Branke
Matthias Poloczek

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


stat

Frederick Fabre Ferber
Dominique Gay
Jean-Christophe Soulie
Jean Diatta
Odalric-Ambrym Maillard

"Interpolation pour l'augmentation de donnees : Application à la gestion des adventices de la canne a sucre a la Reunion" by Frederick Fabre Ferber, Dominique Gay, Jean-Christophe Soulie, Jean Diatta, and Odalric-Ambrym Maillard, arXiv:2501.12400v1, 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{Comparing the Performance of NOM and YALMIP in solving for System .}
\begin{tabular}{c|c|c|c}
 \hline
 Method & Infeasibility & Violation & CE\\
 & ($\%$) & ($\%$) & (Norm.) \\
 \hline
 NOM & 0 & 0 & 0.188\\
 \hline
 YALMIP & 46.31 & 18.29 & 1 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Anran Li
John P. Swensen
Mehdi Hosseinzadeh

"A Guaranteed-Stable Neural Network Approach for Optimal Control of Nonlinear Systems" by Anran Li, John P. Swensen, and Mehdi Hosseinzadeh, arXiv:2501.17333v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Detecting Mild Traumatic Brain Injury with MEG Scan Data: One-vs-K-Sample Tests" by Jian Zhang and Gary Green, arXiv:2502.04258v1, 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{Comparison of the parameters estimated by the Joint cause-specific Spatiotemporal model with the one estimated by the cause-specific AFT model on PROACT data (analysis datset)}
\begin{tabular}{|l|c|ccc|}
\hline
 &Matched weibull scale & \multicolumn{3}{c|}{Weibull shape}\\
 &Relative difference & Spatiotemporal&Cause-specific&Concordance of \\
 & (\%) & ($\rho_l$)&AFT($\rho_l$)&hazard progression\\
\hline
 NIV & -5.12 & 1.91 &2.1 [1.9, 2.2]&yes\\
 Death & -22.28 & 3.50 &2.3 [2.1, 2.6]&yes\\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

Juliette Ortholand
Stanley Durrleman
Sophie Tezenas du Montcel

"A joint spatiotemporal model for multiple longitudinal markers and competing events" by Juliette Ortholand, Stanley Durrleman, and Sophie Tezenas du Montcel, arXiv:2501.08960v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Shengyu Cao
Simai He
Ruoqing Jiang
Jin Xu
Hongsong Yuan

"Best Arm Identification in Batched Multi-armed Bandit Problems" by Shengyu Cao, Simai He, Ruoqing Jiang, Jin Xu, and Hongsong Yuan, arXiv:2312.13875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"f-Betas and Portfolio Optimization with f-Divergence induced Risk Measures" by Rui Ding, arXiv:2302.00452v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Smart Black Box 2.0: Efficient High-bandwidth Driving Data Collection based on Video Anomalies" by Ryan Feng, Yu Yao, and Ella Atkins, arXiv:2101.00706v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|cccc|}
\hline
 \multirow{ 2}{*}{Tokenizer}& \multirow{ 2}{*}{Model} & \multicolumn{4}{|c|}{Augmentation Probability} \\
 \cline{3-6}
 & & 0.0 & 0.3 & 0.6 & 0.9\\
 \hline
 \multirow{ 4}{*}{Fourier} & Transformer & 0.6268 & 0.6321 & 0.65 & \cellcolor[HTML]{B0B0B0}0.9984\\
 & LSTM & 0.5286 & 0.5571 & \cellcolor[HTML]{F0F0F0} 0.7232 & \cellcolor[HTML]{D0D0D0}0.8857 \\
 & CNN & 0.3286 & 0.4482 & 0.5232 & 0.5732\\
 & MLP & 0.2571 & 0.3482 & 0.2911 & 0.5696 \\
 \hline
 \multirow{ 4}{*}{CNN} & Transformer & \cellcolor[HTML]{F0F0F0} 0.9911 & 0.9893 & 0.9839 & 0.9821 \\
 & LSTM & 0.9661 & \cellcolor[HTML]{D0D0D0}0.9946 & \cellcolor[HTML]{D0D0D0}0.9964 & \cellcolor[HTML]{D0D0D0}0.9946 \\
 & CNN & \cellcolor[HTML]{F0F0F0} 0.9911 & \cellcolor[HTML]{B0B0B0} 0.9982 & \cellcolor[HTML]{F0F0F0} 0.9911 & \cellcolor[HTML]{B0B0B0}0.9982 \\
 & MLP & 0.9054 & \cellcolor[HTML]{B0B0B0} 0.9982 & \cellcolor[HTML]{D0D0D0}0.9964 & \cellcolor[HTML]{D0D0D0}0.9964 \\
 \hline
 \multirow{ 4}{*}{Constant} & Transformer & 0.9107 & 0.9321 & 0.925 & 0.9089 \\
 & LSTM & 0.9929 & \cellcolor[HTML]{F0F0F0} 0.9946 & \cellcolor[HTML]{D0D0D0} 0.9964 & \cellcolor[HTML]{B0B0B0} 0.9982 \\
 & CNN & \cellcolor[HTML]{D0D0D0} 0.9964 & 0.9911 & 0.9929 & 0.9911 \\
 & MLP & 0.8946 & 0.9679 & 0.9875 & \cellcolor[HTML]{F0F0F0} 0.9946 \\
 \hline
\end{tabular}
\caption{\textbf{Baseline Results: } Test accuracies of different models under various tokenizer and augmentation parameters. Top 3 accuracies are highlighted, using darker colors for higher values.}
\end{table}
\end{document}

eess

"FaultFormer: Pretraining Transformers for Adaptable Bearing Fault Classification" by Anthony Zhou and Amir Barati Farimani, arXiv:2312.02380v3, 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}{rccccc}
 \toprule
 & $\sigma$ & HP & NORM & TA & TVA \\
 \midrule
 Baseline & 800 & \textbf{0.92} & 0.66 & 0.66 & 0.67 \\
 Baseline & 1500 & 0.85 & 0.72 & 0.65 & 0.68 \\
 Baseline & 7000 & 0.60 & 0.78 & 0.76 & 0.84 \\
 Multi-resolution & - & 0.89 & \textbf{0.83} & \textbf{0.81} & \textbf{0.87} \\
 \bottomrule
 \end{tabular}
\caption{Comparison of the class BA between the baseline and the proposed multi-resolution approach.}
\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.


math

Charly Robinson La Rocca
Jean-François Cordeau
Emma Frejinger

"Supervised Large Neighbourhood Search for MIPs" by Charly Robinson La Rocca, Jean-François Cordeau, and Emma Frejinger, arXiv:2501.10778v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Architectural ablations on the MusicSlots for unsupervisd note discovery task. `Default' here refers to the MusicSlots model with the setup used for object discovery in , where stride = (1, 1) in the convolutional encoder layers and the spatial broadcast decoder outputs softmax alpha masks.}
\begin{tabular}{cccc}
\toprule
Dataset & Model & Note MSE $\downarrow$ & mIoU $\uparrow$ \\ \midrule
\multirow{5}{*}{JazzNet-multi} & Default & 70.05 $\scriptstyle {\pm 23.61}$ & 0.70 $\scriptstyle {\pm 0.07}$ \\
 & Default + stride\_length = (1, 2) & 51.31 $\scriptstyle {\pm 1.89}$ & 0.76 $\scriptstyle {\pm 0.04}$ \\
 & Default - Softmax Alpha Mask & 39.08 $\scriptstyle {\pm 6.35}$ & 0.79 $\scriptstyle {\pm 0.02}$ \\
 & Default + Implicit Differentiation & 32.56 $\scriptstyle {\pm 9.84}$ & 0.83 $\scriptstyle {\pm 0.00}$ \\
 & MusicSlots & \textbf{19.95} $\scriptstyle {\pm 1.89}$ & \textbf{0.90} $\scriptstyle {\pm 0.01}$ \\ \midrule
\multirow{5}{*}{JSB-multi} & Default & 100.77 $\scriptstyle {\pm 52.91}$ & 0.60 $\scriptstyle {\pm 0.15}$ \\
 & Default + stride\_length = (1, 2) & 60.22 $\scriptstyle {\pm 14.56}$ & 0.76 $\scriptstyle {\pm 0.04}$ \\
 & Default - Softmax Alpha Mask & 40.06 $\scriptstyle {\pm 14.60}$ & 0.78 $\scriptstyle {\pm 0.04}$ \\
 & Default + Implicit Differentiation & 59.34 $\scriptstyle {\pm 22.01}$ & 0.79 $\scriptstyle {\pm 0.04}$ \\
 & MusicSlots & \textbf{13.47} $\scriptstyle {\pm 0.90}$ & \textbf{0.91} $\scriptstyle {\pm 0.01}$ \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Joonsu Gha
Vincent Herrmann
Benjamin Grewe
Jürgen Schmidhuber
Anand Gopalakrishnan

"Unsupervised Musical Object Discovery from Audio" by Joonsu Gha, Vincent Herrmann, Benjamin Grewe, Jürgen Schmidhuber, and Anand Gopalakrishnan, arXiv:2311.07534v2, 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{Overall and class-specific prediction rates with 95\% prediction intervals (car versus other modes)}
\begin{tabular}{lccc}
		\toprule
		Mode & Mean Accuracy & 95\% PI Lower & 95\% PI Upper \\
		\midrule
		\textbf{Overall} & \textbf{0.7897} & \textbf{0.7828} & \textbf{0.7967} \\
		Car & 0.818 & 0.807 & 0.829 \\
		Other modes & 0.763 & 0.752 & 0.775 \\
		\hline
	\end{tabular}
\end{table}
\end{document}

q-fin

"Predicting Children's Travel Modes for School Journeys in Switzerland: A Machine Learning Approach Using National Census Data" by Hannes Wallimann and Noah Balthasar, arXiv:2504.09947v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"HyperNOs: Automated and Parallel Library for Neural Operators Research" by Massimiliano Ghiotto, arXiv:2503.18087v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

A. Felipe
M. Jaenada
P. Miranda
L. Pardo

"Robust parameter estimation of the log-logistic distribution based on density power divergence estimators" by A. Felipe, M. Jaenada, P. Miranda, and L. Pardo, arXiv:2312.02662v1, 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{Model Data with Inspection Times, Stress Factor, Failures, and Devices.}
\begin{tabular}{|c|c|c|c|c|}
		\hline
		\textbf{Condition} & \textbf{Inspection Time (\( \tau_i \))} & \textbf{Stress Factor (\( x_i \))} & \textbf{Failures (\( n_i \))} & \textbf{Devices (\( K_i \))} \\
		\hline
		1 & 1 & 0 & $n_1$ & 100 \\
		2 & 1 & 0.5 & $n_2$ & 100 \\
		3 & 1 & 1 & $n_3$ & 100 \\
		4 & 1.5 & 0 & $n_4$ & 100 \\
		5 & 1.5 & 0.5 & $n_5$ & 100 \\
		6 & 1.5 & 1 & $n_6$ & 100 \\
		7 & 2.5 & 0 & $n_7$ & 100 \\
		8 & 2.5 & 0.5 & $n_8$ & 100 \\
		9 & 2.5 & 1 & $n_9$ & 100 \\
		\hline
	\end{tabular}
\end{table}
\end{document}

math

María González-Calderón
María Jaenada
Leandro Pardo

"Robust statistical inference for accelerated life-tests with one-shot devices under log-logistic distributions" by María González-Calderón, María Jaenada, and Leandro Pardo, arXiv:2502.20467v1, 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{Accuracy and weighted F1 score on Tamil Code-Mixed Text}
\begin{tabular}{lccc}
 \toprule
 Method & Accuracy & weighted F1\\
 \midrule
 Fine Tuned BERT & 0.65 & 0.53\\
 fastText + Tranformer& 0.66 & 0.57\\
 fastText + ELMO + Transformer& 0.66 & 0.57\\
 \textbf{fastText + ELMO + TF-IDF + Transformer}& \textbf{0.67} & \textbf{0.58} \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"CMSAOne@Dravidian-CodeMix-FIRE2020: A Meta Embedding and Transformer model for Code-Mixed Sentiment Analysis on Social Media Text" by Suman Dowlagar and Radhika Mamidi, arXiv:2101.09004v1, 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
 & Validation phase & Testing phase \\
\midrule
PSNR & 31.8201 & 31.7784 \\
SSIM & 0.8494 & 0.8655 \\
\bottomrule
\end{tabular}
\caption{NTIRE 2024 Challenge Results with x4 SR in terms of PSNR and SSIM on validation phase and testing phase.}
\end{table}
\end{document}

cs

Chih-Chung Hsu
Chia-Ming Lee
Yi-Shiuan Chou

"DRCT: Saving Image Super-resolution away from Information Bottleneck" by Chih-Chung Hsu, Chia-Ming Lee, and Yi-Shiuan Chou, arXiv:2404.00722v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Marc Chen
Mohammad Shirazi
Peter A. Forsyth
Yuying Li

"Machine Learning and Hamilton-Jacobi-Bellman Equation for Optimal Decumulation: a Comparison Study" by Marc Chen, Mohammad Shirazi, Peter A. Forsyth, and Yuying Li, arXiv:2306.10582v1, 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{The video-based binary and 3-class classification accuracy and testing time. \dag Using the ground truth view label directly. *The key-frame annotation in the training set is used for the aggregation module training.}
\begin{tabular}{l|c|c|c|c}
\hline\hline
&Accuracy&AUC&Accuracy& Testing time\\
&(Binary)&(Binary)&(3-class)& (Titan X)\\\hline
Frame-independent & 0.908 & 0.894 & 0.893 & 98ms\\
Frame-independent\dag & 0.913 & 0.898& 0.901 & 42ms\\
Frame-independent* & 0.915 & 0.902& 0.903 & 98ms\\\hline
RNN & 0.918& 0.909 & 0.912 & 1537ms\\
RNN\dag & 0.921 & 0.912 & 0.916 & 716ms\\
RNN* & 0.931 & 0.917& 0.918 & 1537ms\\\hline
Non-local & 0.933 &0.918 & 0.914 & 294ms\\
Non-local\dag & 0.935 &0.920 & 0.918 & 187ms\\\hline
Temporal convolution & 0.939 & 0.922 & 0.921 & 132ms\\
Temporal convolution\dag & 0.944& 0.926 & 0.923 & 60ms\\
Temporal convolution* & 0.947& 0.928 & 0.925 & 132ms\\\hline\hline
\end{tabular}
\end{table}
\end{document}

eess

Jing Wang
Xiaofeng Liu
Fangyun Wang
Lin Zheng
Fengqiao Gao
Hanwen Zhang
Xin Zhang
Wanqing Xie
Binbin Wang

"Automated interpretation of congenital heart disease from multi-view echocardiograms" by Jing Wang, Xiaofeng Liu, Fangyun Wang, Lin Zheng, Fengqiao Gao, Hanwen Zhang, Xin Zhang, Wanqing Xie, and Binbin Wang, arXiv:2311.18788v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Yuqin Yang
Saber Salehkaleybar
Negar Kiyavash

"Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data" by Yuqin Yang, Saber Salehkaleybar, and Negar Kiyavash, arXiv:2312.06091v2, 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}{rrrr}
 \hline
 & uni$<$0 & uni=0 & uni$>$0 \\
 \hline
lasso$<$0 & 33 & 13 & 0 \\
 lasso=0 & 1 & 9901 & 0 \\
 lasso$>$0 & 0 & 13 & 39 \\
 \hline
\end{tabular}
\caption{\em GWAS example: signs of Lasso and UniLasso coefficients in final chosen models.}
\end{table}
\end{document}

stat

Univariate-Guided Sparse Regression

Sourav Chatterjee
Trevor Hastie
Robert Tibshirani

"Univariate-Guided Sparse Regression" by Sourav Chatterjee, Trevor Hastie, and Robert Tibshirani, arXiv:2501.18360v7, 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}{c|cccccc}

 \toprule
 Input Modality & Method & ACCEL$\downarrow$ & MPJPE$\downarrow$ & PA-MPJPE$\downarrow$ & PVE$\downarrow$ & PCK0.3$\uparrow$ \\
 \midrule
 \multirow{3}*{LiDAR}
 ~ & P4Tranformer~ & 66.33 & 172.04 & 150.65 & 206.75 & 0.51 \\
 ~ & PCT~ & 59.19 & 144.40 & 116.99 & 174.33 & 0.67 \\
 ~ & LiDARCap~ & 54.42 & 144.51 & 106.20 & 176.98 & 0.67 \\
 \midrule
 \multirow{3}*{RGB}
 & HybrIK~ & 58.39 & 249.34 & 163.91 & 255.98 & 0.53 \\
 & NIKI~ & 55.62 & 196.68 & 142.48 & 198.10 & 0.61 \\
 & SMPLer-X~ & 50.15 & 171.97 & 128.02 & 185.83 & 0.66 \\
 \midrule

 \multirow{2}*{Event} & EventHPE~ & - & 193.7 & 115.72 & 224.59 & 0.52 \\
 & EventPointPose~ & - & 16.24(2D) & 10.91(2D) & - & 0.69(2D) \\
 \midrule
 \multirow{2}{1cm}{\centering RGB+ \\ LiDAR}
 & ImmFusion~ & 49.19 & 175.00 & 159.62 & 187.31 & 0.67 \\
 & FusionPose~ & 44.89 & 136.15 & 110.19 & 166.94 & 0.75 \\
 \bottomrule
 \end{tabular}
\caption{Comparison of SOTA HPE methods on different modals in RELI11D. Unit: $mm$}
\end{table}
\end{document}

cs

Ming Yan
Yan Zhang
Shuqiang Cai
Shuqi Fan
Xincheng Lin
Yudi Dai
Siqi Shen
Chenglu Wen
Lan Xu
Yuexin Ma
Cheng Wang

"RELI11D: A Comprehensive Multimodal Human Motion Dataset and Method" by Ming Yan, Yan Zhang, Shuqiang Cai, Shuqi Fan, Xincheng Lin, Yudi Dai, Siqi Shen, Chenglu Wen, Lan Xu, Yuexin Ma, and Cheng Wang, arXiv:2403.19501v1, 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{Average dice score performance (\%) of the GCN refinement compared with the CNN prediction. The CNN model was trained with 10 samples for the pancreas and 9 for the spleen. Statistical significance is indicated by (*) for a p-value $<0.05$, and (**) for a p-value $< 0.01$ with respect to the CNN prediction.}
\begin{tabular}{l|c|c|c}
			\hline
			Task & CNN & CRF & GCN \\
			 & 2D U-Net & refinement & Refinement (ours) \\
			\hline
			Pancreas-10 & $52.14 \pm 22.6$ & $52.20 \pm 22.6$ & $\mathbf{54.55 \pm 22.2}$* \\
			\hline
			Spleen-9 & $78.89 \pm 28.4$ & $78.80 \pm 28.4$ & $\mathbf{81.15 \pm 28.9}$** \\
			\hline
			\end{tabular}
\end{table}
\end{document}

eess

Roger D. Soberanis-Mukul
Nassir Navab
Shadi Albarqouni

"An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation" by Roger D. Soberanis-Mukul, Nassir Navab, and Shadi Albarqouni, arXiv:2012.03352v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Water Cells in Compositions of 1s and 2s" by Brian Hopkins and Aram Tangboonduangjit, arXiv:2412.11528v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
 \textbf{ML}& \multicolumn{2}{c|}{\textbf{$Ts^{C_0}$}}& \multicolumn{2}{c|}{\textbf{$D_b$}}& \multicolumn{2}{c|}{\textbf{$D_g$}} \\
 \cline{2-7}
 \textbf{algorithm}& \textbf{Mal} & \textbf{Ben} & \textbf{Mal} & \textbf{Ben} & \textbf{Mal} & \textbf{Ben}\\
 \hline
 RF & 0.66 & 0.99 & 0.0 & 0.99 & 0.54 & 0.99\\
 XG Boost & 0.70 & 0.99 & 0.003 & 0.99 & 0.60 & 0.99 \\
 DT & 0.71 & 0.99 & 0.04 & 0.98 & 0.67 & 0.99\\
 ADaBoost & 0.51 & 0.99 & 0.001 & 0.99 & 0.56 & 0.99\\
 Grad. Boost. & 0.72 & 0.99 & 0.006 & 0.99 & 0.38 & 0.99 \\
 ETC & 0.70 & 0.99 & 0.0 & 0.99 & 0.70 & 0.99 \\
 \textbf{NN} & \textbf{0.83} & \textbf{0.94} & \textbf{0.25} & \textbf{0.89} & \textbf{0.95} & \textbf{0.90} \\
 \hline
 \end{tabular}
\caption{Recall score obtained when different ML algorithms are trained on $Tr^{C_0}$. Here `Mal' represents malicious class, `Ben' represents the benign class, `RF' represents RandomForest classifier, `DT' represents Decision Trees, `Grad. Boost.' represent Gradient Boosting, `ETC' represents ExtraTrees classifier, and `NN' represent Neural Networks.}%, and `GCN' represents Graph Convolutional Networks.} % Since GCN did not provide better results for $Ts^{C_0}$, we did not execute it on $D_b$. For $D_g$, as this is generated data we do not have the graph available. Thus, we could not execute GCN.}
\end{table}
\end{document}

cs

Rachit Agarwal
Tanmay Thapliyal
Sandeep K. Shukla

"Detecting Malicious Accounts showing Adversarial Behavior in Permissionless Blockchains" by Rachit Agarwal, Tanmay Thapliyal, and Sandeep K. Shukla, arXiv:2101.11915v1, 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{Hyperlinks to the files with makespans.}
\begin{tabular}{|l|l|}
 \hline
 dataset & link to file \\ \hline
 j60 & old.math.nsc.ru/LBRT/k4/j60-hrs-Goncharov.txt \\ \hline
 j90 & old.math.nsc.ru/LBRT/k4/j90-hrs-Goncharov.txt \\ \hline
 j120 & old.math.nsc.ru/LBRT/k4/j120-hrs-Goncharov.txt \\ \hline
 \end{tabular}
\end{table}
\end{document}

math

"A hybrid heuristic algorithm for the resource-constrained project scheduling problem" by Evgenii Goncharov, arXiv:2502.18330v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llll}
			\toprule
\multirow{2}{*}{\textbf{Model}} & \multicolumn{3}{c}{\textbf{CNN/DM}} \\
			 & RG-1 & RG-2 & RG-L \\
			\midrule
			SSR-base & \bf 43.53$^{*}$ & \bf 20.79$^{*}$ & \bf 40.47$^{*}$ \\
			\quad No curriculum & 43.26 & 20.53 & 40.14 \\
			\quad Anti-curriculum & 43.09 & 20.48 & 40.01 \\
			\quad Loss-only curriculum & 43.40 & 20.67 & 40.25 \\
 \quad Length-only curriculum & 43.43 & 20.71 & 40.35 \\
			\bottomrule
	\end{tabular}
\caption{Ablation study results on CNN/DailyMail for SSR-base with different curriculum learning strategies. $^{*}$The asterisk denotes statistically significant improvement with p-value < 0.05 upon all compared ablation.}
\end{table}
\end{document}

cs

Wangchunshu Zhou
Tao Ge
Canwen Xu
Ke Xu
Furu Wei

"Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting" by Wangchunshu Zhou, Tao Ge, Canwen Xu, Ke Xu, and Furu Wei, arXiv:2101.00416v2, 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{\footnotesize \textbf{Importance scores and ranks for the 3 important components.}}
\begin{tabular}{c|c|c|c|c}
\hline
Components & Monotone & No Beard & Wearing Lipstick \\ \hline \hline
Score (Rank) & X & 0.794 (3) & 0.465 (4) \\ \hline
Score (Rank) & O & 0.757 (6) & 0.738 (7) \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Seokhun Park
Insung Kong
Yongchan Choi
Chanmoo Park
Yongdai Kim

"Tensor Product Neural Networks for Functional ANOVA Model" by Seokhun Park, Insung Kong, Yongchan Choi, Chanmoo Park, and Yongdai Kim, arXiv:2502.15215v3, 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|c|c}
			\hline
			\multirow{2}{*}{rule}
			& training & test	&number
			&\multicolumn{3}{c}{suboptimality}\\
			\cline{5-7}
			& data & data & feasible & min &avg & max\\
			\hline
			$B^\nu,b^\nu$ & unif & unif & 92 &0.000 &0.009 &0.023\\
			MDR & unif & unif & 100 &0.003 &0.011 &0.023\\
			AMDR & unif & unif & 100 &0.000 &0.002 &0.013\\
			\hline
			$B^\nu,b^\nu$ & unif & beta & 70 &0.000 &0.016 &0.034\\
			MDR & unif & beta & 100 &0.001 &0.018 &0.042\\
			AMDR & unif & beta & 100 &0.000 &0.008 &0.033\\
			\hline
			$B^\nu,b^\nu$ & beta & unif & 67 &0.000 &0.008 &0.018\\
			MDR & beta & unif & 100 &0.003 &0.011 &0.023\\
			AMDR & beta & unif & 100 &0.000 &0.001 &0.013\\
			\hline
			$B^\nu,b^\nu$ & beta & beta & 65 &0.000 &0.015 &0.032\\
			MDR & beta & beta & 100 &0.001 &0.018 &0.042\\
			AMDR & beta & beta & 100 &0.000 &0.006 &0.023\\
 \hline
		\end{tabular}
\caption{Performance of decision rules obtain from (W-SP1)$^\nu$. Suboptimality statistics are computed over feasible decisions.}
\end{table}
\end{document}

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
\begin{tabular}{lll}
\toprule
 & Volatility Clustering & Leverage Effect \\
\midrule
In-sample & 53.88 [0.69, 174.34]& 4.69 [0.0, 14.65]\\
Out-of-sample &3.60 [0.0, 42.94]&2.08 [0.0, 8.77]\\
Market Generator & 14.66 [0.0, 63.77]	& 1.16 [0.0, 4.84]\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Adil Rengim Cetingoz
Charles-Albert Lehalle

"Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chance" by Adil Rengim Cetingoz and Charles-Albert Lehalle, arXiv:2501.03993v4, 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{Distributional impacts - Passenger}
\begin{tabular}{llllll}
\hline
Profiles & All & Group 1 & Group 2 & Group 3 & Group 4 \\
\hline
\multicolumn{6}{l}{Distance} \\
Range of daily surplus (USD/weekday) & {[}-13.1,9.2{]} & {[}-13.1,-1.3) & {[}-1.3,0) & {[}0,1.3) & {[}1.3,9.2{]} \\
Percentage in population & 100\% & 22.8\% & 37.1\% & 33.3\% & 6.8\% \\
Avg daily surplus (USD/weekday) & -0.84 & -4.73 & -0.33 & 0.28 & 4.1 \\
Avg number of car trips with either OD in toll area during toll periods & 0.554 & 0.725 & 0.561 & 0.454 & 0.431 \\
Avg distance traveled by car within toll area during toll periods (km) & 5.19 & 7.27 & 5.24 & 4.00 & 3.82 \\
Proportion of households with work/education location in toll area & 0.683 & 0.933 & 0.623 & 0.524 & 0.953 \\
Proportion of work activities among all daily activities & 0.272 & 0.238 & 0.257 & 0.282 & 0.420 \\
Avg household income (thousand \$/year) & 86.31 & 79.24 & 86.51 & 87.15 & 105 \\
\hline
\multicolumn{6}{l}{Cordon} \\
Range of daily surplus (USD/weekday) & {[}-14.7,3.75{]} & {[}-14.7,-1.3) & {[}-1.3,0) & {[}0,1.3) & {[}1.3,3.75{]} \\
Percentage in population & 100\% & 8.1\% & 34.2\% & 51.3\% & 6.3\% \\
Avg daily surplus (USD/weekday) & -0.25 & -6.0 & -0.28 & 0.41 & 2.0 \\
Avg number of car trips with either OD in toll area during toll periods & 0.561 & 1.16 & 0.425 & 0.397 & 1.87 \\
Avg number of car trips entering toll area during toll periods & 0.0953 & 1.13 & 0.0108 & 0.000100 & 0.000179 \\
Avg distance traveled by car within toll area during toll periods (km) & 5.50 & 11.1 & 3.38 & 4.37 & 19.1 \\
Proportion of households with work/education location in toll area & 0.683 & 1.000 & 0.613 & 0.648 & 0.950 \\
Proportion of work activities among all daily activities & 0.272 & 0.181 & 0.262 & 0.276 & 0.416 \\
Avg household income (thousand \$/year) & 86.3 & 78.0 & 84.5 & 86.7 & 105 \\
\hline
\multicolumn{6}{l}{Area} \\
Range of daily surplus (USD/weekday) & {[}-6.9,10.1{]} & {[}-6.9,-1.3) & {[}-1.3,0) & {[}0,1.3) & {[}1.3,10.1{]} \\
Percentage in population & 100\% & 31.5\% & 27.9\% & 28.9\% & 11.7\% \\
Avg daily surplus change (USD/weekday) & -0.93 & -4.34 & -0.33 & 0.34 & 3.63 \\
Avg number of car trips with either OD in toll area during toll periods & 0.820 & 1.16 & 0.304 & 0.348 & 2.30 \\
Avg distance traveled by car within toll area during toll periods (km) & 6.76 & 10.7 & 1.69 & 1.74 & 20.6 \\
Proportion of households with work/education location in toll area & 0.683 & 0.853 & 0.551 & 0.551 & 0.865 \\
Proportion of work activities among all daily activities & 0.272 & 0.254 & 0.270 & 0.268 & 0.334 \\
Avg household income (thousand \$/year) & 86.3 & 82.5 & 86.3 & 88.4 & 91.5 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Peiyu Jing
Ravi Seshadri
Takanori Sakai
Ali Shamshiripour
Andre Romano Alho
Antonios Lentzakis
Moshe E. Ben-Akiva

"Evaluating congestion pricing schemes using agent-based passenger and freight microsimulation" by Peiyu Jing, Ravi Seshadri, Takanori Sakai, Ali Shamshiripour, Andre Romano Alho, Antonios Lentzakis, and Moshe E. Ben-Akiva, arXiv:2305.07318v1, 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
Group & CDR & MMSE \\
\hline
Normal Control (NC) & $ 0 < $ & $ 25-30 $ \\
Alzheimer's Group (AD) & $ 0.5 < $ & $ < 24 $ \\
\hline
\end{tabular}
\caption{\bf Criteria for Subdivision into AD and NC }
\end{table}
\end{document}

eess

"ADiag: Graph Neural Network Based Diagnosis of Alzheimer's Disease" by Vishnu Ram Sampathkumar, arXiv:2101.02870v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Complexity for the Optimization Method}
\begin{tabular}{c|c}
\hline
\bf Alg. & \bf Complexity \\
\hline
\bf & \small $\mathcal{O}(I_1(KN^2 + \hat{I}_1(2KN^2+\bar{I}_1 C^{\{i\}})))$\\
\bf & \small $\mathcal{O}(3I_2KN^2)$ \\
\bf & \small $\mathcal{O}(I_3( (I_1(KN^2 + \hat{I}_1(2KN^2+\bar{I}_1 C^{\{i\}}))) + 3I_2KN^2))$\\
\hline
\end{tabular}
\end{table}
\end{document}

math

"Energy Efficiency Maximization for Intelligent Surfaces Aided Massive MIMO with Zero" by Wilson de Souza Junior and Taufik Abrao, arXiv:2312.13563v1, 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|l|}
\hline
Method & HMDB51 \\ \hline
Average & 33.1 $\pm$ 2.9 \\
Dot Product & 33.9 $\pm$ 3.2 \\
Weighted Average & 35.3 $\pm$ 3.6 \\
Concatenation & 43.2 $\pm$ 1.9 \\
\hline
\end{tabular}
\caption{Results on different aggregation options for the semantic and visual embeddings.}
\end{table}
\end{document}

cs

Shreyank N Gowda
Laura Sevilla-Lara
Frank Keller
Marcus Rohrbach

"CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition" by Shreyank N Gowda, Laura Sevilla-Lara, Frank Keller, and Marcus Rohrbach, arXiv:2101.07042v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Theoretical Characterization of Effect of Masks in Snapshot Compressive Imaging" by Mengyu Zhao and Shirin Jalali, arXiv:2501.06653v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Comparing different subgradient methods for solving convex optimization problems with functional constraints" by Thi Lan Dinh and Ngoc Hoang Anh Mai, arXiv:2101.01045v2, 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.


q-fin

"On Finding Bi-objective Pareto-optimal Fraud Prevention Rule Sets for Fintech Applications" by Chengyao Wen and Yin Lou, arXiv:2311.00964v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Predicting Realized Variance Out of Sample: Can Anything Beat The Benchmark?" by Austin Pollok, arXiv:2506.07928v1, 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}{|r|l|}
 \hline
 \textbf{Notation}& {\textbf{Meaning}}\\
 \hline
 $D_a$& Dataset\\
 $M$ & Set of malicious activities present in $D_a$ \\
 $M_i$ & An element in $M$\\
 $A_{M_i}$ & Set of accounts in $D_a$ associated with $M_i$\\
 \multirow{2}{*}{$CS_{i,j}$} & Cosine similarity between accounts $i$\\
 & and $j$\\
 $p(CS_{i,j}\geq 0)$ & probability of $CS_{i,j}$ to be $>0$\\
 $k$ & Hyperparameter of K-Means algorithm\\
 $C$ & Set of sub-datasets of $D_a$\\
 $C_i$ & an element in $C$\\
 $Tr^{C_i}$ & Training dataset created from $C_i$\\
 $Ts^{C_i}$ & Testing dataset created from $C_i$\\
 \multirow{2}{*}{$A_{M_1}^{S_1}$} & Set of accounts associated with $M_1$ and\\
 & in $Ts^{C_1}$\\
 $D_b$ & New malicious accounts dataset\\
 $D_g$ & Adversarial dataset\\
 \multirow{2}{*}{$Ts^{D_g}$} & Testing dataset created from $D_g$ and \\
 & benign accounts in $Ts^{C_1}$.\\
 $F$ & Feature Vector\\
 \hline

 \end{tabular}
\caption{List of Notations used in our work.}
\end{table}
\end{document}

cs


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of All Configurations in Cross-Validation}
\begin{tabular}{lcccc}
\hline
\hline
Configuration & ICC & R$^2$ & MAE & MAPE \\
\hline
\textbf{Visceral Adipose Tissue (VAT) in L} &&&& \\
Least squares instance & 0.996 & 0.992 & 0.150 & 5.2\\
Mean-variance instance & 0.997 & 0.993 & 0.134 & 4.6\\
Least squares ensemble & 0.997 & 0.993 & 0.133 & 4.6\\
Mean-variance ensemble & 0.997 & 0.994 & 0.122 & 4.2 \\
&&&& \\
\textbf{Abdominal Subcutaneous Adipose Tissue (SAT) in L} &&&& \\
Least squares instance & 0.995 & 0.991 & 0.222 & 3.3 \\
Mean-variance instance & 0.996 & 0.992 & 0.209 & 3.1 \\
Least squares ensemble & 0.996 & 0.992 & 0.202 & 3.0 \\
Mean-variance ensemble & 0.996 & 0.993 & 0.191 & 2.8 \\
&&&& \\
\textbf{Total Adipose Tissue (TAT) in L} &&&& \\
Least squares instance & 0.997 & 0.993 & 0.420 & 2.1\\
Mean-variance instance & 0.997 & 0.994 & 0.390 & 1.9\\
Least squares ensemble & 0.997 & 0.994 & 0.377 & 1.9\\
Mean-variance ensemble & 0.997 & 0.995 & 0.358 & 1.8 \\
&&&& \\
\textbf{Total Lean Tissue (TLT) in L} &&&& \\
Least squares instance & 0.981 & 0.963 & 0.650 & 2.8\\
Mean-variance instance & 0.981 & 0.962 & 0.632 & 2.7\\
Least squares ensemble & 0.983 & 0.966 & 0.594 & 2.6\\
Mean-variance ensemble & 0.983 & 0.966 & 0.579 & 2.5 \\
&&&& \\
\textbf{Total Thigh Muscle (TTM) in L} &&&& \\
Least squares instance & 0.996 & 0.991 & 0.182 & 1.8 \\
Mean-variance instance & 0.996 & 0.992 & 0.176 & 1.8 \\
Least squares ensemble & 0.996 & 0.993 & 0.163 & 1.6 \\
Mean-variance ensemble & 0.996 & 0.993 & 0.162 & 1.6 \\
&&&& \\
\textbf{Liver Fat Fraction (LFF) in \%} &&&& \\
Least squares instance & 0.977 & 0.956 & 0.706 & 28.1\\
Mean-variance instance & 0.977 & 0.954 & 0.702 & 26.6\\
Least squares ensemble & 0.979 & 0.960 & 0.671 & 27.0\\
Mean-variance ensemble & 0.979 & 0.959 & 0.666 & 25.7 \\
\hline
\hline
\multicolumn{5}{l}{* Results for all configurations in 10-fold cross-validation on dataset \textit{$D_{cv}$}.} \\
\multicolumn{5}{l}{ N: Number of subjects, ICC: Intraclass correlation coefficient, R$^2$: Coefficient of determination, } \\
\multicolumn{5}{l}{ MAE: Mean absolute error, MAPE: Mean absolute percentage error.} \\
\end{tabular}
\end{table}
\end{document}

eess

Taro Langner
Fredrik K. Gustafsson
Benny Avelin
Robin Strand
Håkan Ahlström
Joel Kullberg

"Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI" by Taro Langner, Fredrik K. Gustafsson, Benny Avelin, Robin Strand, Håkan Ahlström, and Joel Kullberg, arXiv:2101.06963v3, 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{Kolmogorov-Smirnov test on actual and modelled returns of SBU during COVID 19 (significance level$=0.01$) }
\begin{tabular}{ccccc}
			\hline
			& \text{MM-gBm} & \text{gBm} & \text{MM-XOU} & \text{XOU} \\ \hline
			p-value & 0.0198 & $3.61e^{-6}$ & 0.0133 & $2.69e^{-8}$ \\
			Decision & Similar & Different & Similar & Different \\ \hline
	\end{tabular}
\end{table}
\end{document}

q-fin

Erina Nanyonga
Juma Kasozi
Fred Mayambala
Hassan W. Kayondo
Matt Davison

"A Stochastic Model for Illiquid Stock Prices and its Conclusion about Correlation Measurement" by Erina Nanyonga, Juma Kasozi, Fred Mayambala, Hassan W. Kayondo, and Matt Davison, arXiv:2509.10553v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Luke Marris
Siqi Liu
Ian Gemp
Georgios Piliouras
Marc Lanctot

"Deviation Ratings: A General, Clone-Invariant Rating Method" by Luke Marris, Siqi Liu, Ian Gemp, Georgios Piliouras, and Marc Lanctot, arXiv:2502.11645v1, 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}

stat

""Medium-n studies" in computing education conferences" by Michael Guerzhoy, arXiv:2311.14679v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Randomized directional search for nonconvex optimization" by Yuxuan Zhang and Wenxun Xing, arXiv:2501.00469v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Green Shields: The Role of ESG in Uncertain Time" by Fatih Kansoy and Dominykas Stasiulaitis, arXiv:2506.02143v1, 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}{lrr}
\toprule
Scenario & $\delta$ (s) & Runtime (ms) \\
\midrule
Paris & 0.1 & 13.65 \\
 & 0.2 & 0.01 \\
New York & 0.1 & 92.50 \\
Singapore & 0.1 & 33.33 \\
 & 0.2 & 23.01 \\
\bottomrule
\end{tabular}
\caption{Booktabs table}
\end{table}
\end{document}

cs

Enze Xie
Wenjia Wang
Wenhai Wang
Peize Sun
Hang Xu
Ding Liang
Ping Luo

"Segmenting Transparent Object in the Wild with Transformer" by Enze Xie, Wenjia Wang, Wenhai Wang, Peize Sun, Hang Xu, Ding Liang, and Ping Luo, arXiv:2101.08461v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Channel Choice and Customer Value

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"Channel Choice and Customer Value" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2508.00208v1, 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|ll|ll}
\hline
Problems&Classification&&Regression\\
\cline{2-5}
Metrics&ACC&AUC&MSE&MAE\\
\hline
\hline
LSTM&0.5222&0.5154&1.7299&0.9591\\
LSTM+GAT&0.5283&0.5023&1.3874&0.8633\\
GRU&0.5139&0.5027 &1.4963& 0.8944\\
GRU+GAT&0.5150&0.4805&1.2083& 0.8031\\
Corr-Cos&0.5328&\bf{0.5290}&1.1850&0.7940\\
AD-GAT& 0.5189& 0.5056&1.2143&0.8183\\
GAT-AGNN&\bf{0.5350}&0.5190&\bf{1.1585}& \bf{0.7823}\\
\hline
\end{tabular}
\caption{Comparison of classification and regression prediction results across models.}
\end{table}
\end{document}

q-fin

"Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)" by Tzu-Ya Lai, Wen Jung Cheng, and Jun-En Ding, arXiv:2301.10153v1, 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}{ccc}
 \toprule
 Model (backbone) & Precision (\%) & Recall (\%) \\
 \midrule
 YOLO (DarkNet-53) & 93.22 & 92.58 \\
 SSD (VGG-16) & 92.47 & 92.53 \\
 Faster R-CNN (ResNet-50) & 94.50 & 92.67 \\
 Faster R-CNN (ConvNeXt) & 95.03 & 93.70 \\
 Faster R-CNN (Swin Tranformer) & 95.09 & 93.96\\
 Faster R-CNN (ResNet-50) + FPN & 97.98 & 99.11 \\
 Proposed & \textbf{98.87} & \textbf{99.33} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Lvyang Yang
Jiankang Zhang
Huaiqiang Li
Longfei Ren
Chen Yang
Jingyu Wang
Dongyuan Shi

"A Comprehensive End-to-End Computer Vision Framework for Restoration and Recognition of Low-Quality Engineering Drawings" by Lvyang Yang, Jiankang Zhang, Huaiqiang Li, Longfei Ren, Chen Yang, Jingyu Wang, and Dongyuan Shi, arXiv:2312.13620v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Prasanna Vijayaraghavan
Jeffrey Frederic Queisser
Sergio Verduzco Flores
Jun Tani

"Development of Compositionality and Generalization through Interactive Learning of Language and Action of Robots" by Prasanna Vijayaraghavan, Jeffrey Frederic Queisser, Sergio Verduzco Flores, and Jun Tani, arXiv:2403.19995v2, 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}{c|c|c|c}
 \hline
 Proj. Head & Number of Layers & Backbone & Projection Head \\ \hline
 \multirow{2}{*}{FFN}
 & 1 & \textbf{42.51\%} & 39.46\%\\
 & 3 & 39.10\% & 36.25\% \\ \hline
 \multirow{2}{*}{TF}
 & 1 & \textbf{46.12\%} & 44.16\%\\
 & 3 & 41.85\% & 30.34\%\\ \hline
 \end{tabular}
\caption{Ablation on different setups to measure supervised accuracy on CIFAR100 with ResNet18 and small projection head.}
\end{table}
\end{document}

cs

"Deep Fusion: Capturing Dependencies in Contrastive Learning via Transformer Projection Heads" by Huanran Li and Daniel Pimentel-Alarcón, arXiv:2403.18681v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Sulaiman Vesal
Indrani Bhattacharya
Hassan Jahanandish
Xinran Li
Zachary Kornberg
Steve Ran Zhou
Elijah Richard Sommer
Moon Hyung Choi
Richard E. Fan
Geoffrey A. Sonn
Mirabela Rusu

"ProsDectNet: Bridging the Gap in Prostate Cancer Detection via Transrectal B-mode Ultrasound Imaging" by Sulaiman Vesal, Indrani Bhattacharya, Hassan Jahanandish, Xinran Li, Zachary Kornberg, Steve Ran Zhou, Elijah Richard Sommer, Moon Hyung Choi, Richard E. Fan, Geoffrey A. Sonn, and Mirabela Rusu, arXiv:2312.05334v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
zone structure & rotation number & Hill monodromy \\ \hline
band & $\rho \in \mathbb{Z}_{>0} $ & hyperbolic \\ \hline
gap & $\rho\notin\mathbb{Z}$ & elliptic \\ \hline
band-gap edge & --- & parabolic \\ \hline
\end{tabular}
\caption{Correspondence between band-gap structure, rotation number and Hill equation monodromies.}
\end{table}
\end{document}

math

Artem Alexandrov
Alexey Glutsyuk
Alexander Gorsky

"Phase-locking in dynamical systems and quantum mechanics" by Artem Alexandrov, Alexey Glutsyuk, and Alexander Gorsky, arXiv:2504.20181v1, 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|c|c|c|}
\hline
& \textbf{Easy} & \textbf{Medium} & \textbf{Difficult} \\ \hline
\textbf{Epoch 0-4}& 0.7 & 0.2 & 0.1 \\ \hline
\textbf{Epoch 5-9}& 0.15 & 0.7 & 0.15 \\ \hline
\textbf{Epoch 10-14}& 0.1 & 0.2 & 0.7 \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

T. Y. S. S Santosh
Kristina Kaiser
Matthias Grabmair

"CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval" by T. Y. S. S Santosh, Kristina Kaiser, and Matthias Grabmair, arXiv:2404.00590v1, licensed under CC BY 4.0. 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{Effect of Recommendation on Fund Search Effort}
\begin{tabular}{lccc}
 \midrule
 Dependent Variable: & (1) & (2) & (3)\\
 & Total Time & Unique \# of Funds & Avg Time Per Fund \\
 \midrule
 Recommended & -1332.860* & -0.048 & -65.471** \\
 & (755.645) & (0.376) & (30.841) \\
 Polynomial Degree & 2 & 2 & 2 \\
 Investor Group Dummies & Yes & Yes & Yes \\
 Fund Type Dummies & Yes & Yes & Yes \\
 Month Dummies & Yes & Yes & Yes \\
 Day of Week Dummies & Yes & Yes & Yes \\
 Control Variables & Yes & Yes & Yes \\
 Number of Observations & 5,339 & 5,339 & 5,339 \\
 \textcolor[rgb]{ .133, .133, .133}{\textit{$R^2$}} & 0.055 & 0.050 & 0.067 \\
 \midrule
 \multicolumn{4}{p{36em}}{\scriptsize Notes: {***} $p<0.01$, {**} $p<0.05$, {*} $p<0.1$. The dependent variables in Columns (1) - (3) are the search time spent on all funds, the average search time per fund, and the unique number of funds viewed. Control variables include management fees, purchase fees, custodial fees, asset size, dummy for valuable fund brand, fund past returns, fund risk level, and fund tenure. Robust standard errors are in parentheses.} \\
 \end{tabular}
\end{table}
\end{document}

q-fin

"The Effect of Product Recommendations on Online Investor Behaviors" by Ruiqi Rich Zhu, Cheng He, and Yu Jeffrey Hu, arXiv:2303.14263v2, 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{Training signal count distribution before and after slicing over 5 classes.}
\begin{tabular}{c|c|c}
\hline
\textbf{Event Type} & \textbf{Event Sessions} & \textbf{Sliced Windows} \\ \hline
HB & 13 & 104 \\ \hline
AA & 12 & 108 \\ \hline
HL & 16 & 126 \\ \hline
HR & 15 & 121 \\ \hline
RD & 14 & 113 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Nima Taherifard
Murat Simsek
Charles Lascelles
Burak Kantarci

"Prior Knowledge Input to Improve LSTM Auto-encoder-based Characterization of Vehicular Sensing Data" by Nima Taherifard, Murat Simsek, Charles Lascelles, and Burak Kantarci, arXiv:2101.01259v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Energy stable Runge-Kutta discontinuous Galerkin schemes for fourth order gradient flows" by Hailiang Liu and Peimeng Yin, arXiv:2101.00152v1, 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{Contingency table for all words assigned to each pair of clusters (DKM rows, SDKM columns).}
\begin{tabular}{c|ccc}
\hline
\textbf{DKM $\backslash$ SDKM} & 1 & 2 & 3\\
\hline
1 & 201 & 0 & 0 \\
2 & 0 & 295 & 143 \\
3 & 256 & 125 & 185 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Ilaria Bombelli
Domenica Fioredistella Iezzi
Emiliano Seri
Maurizio Vichi

"Spherical Double K-Means: a co-clustering approach for text data analysis" by Ilaria Bombelli, Domenica Fioredistella Iezzi, Emiliano Seri, and Maurizio Vichi, arXiv:2501.04562v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Robust MCVaR Portfolio Optimization with Ellipsoidal Support and Reproducing Kernel Hilbert Space-based Uncertainty" by Rupendra Yadav and Aparna Mehra, arXiv:2509.00447v1, 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}{ll}
		Parameters & values\\
		\toprule
		$\delta$ & 0.01\\
		$(\alpha_d, \phi_d, \sigma_d)$ & ( -0.02, 8, 0.016)\\
		$(\alpha_g, \phi_g, \sigma_g)$ & ( -0.02, 8, 0.016) \\
		$(\alpha_\lambda, \varphi, \sigma_\lambda)$ & (0, 0.01, 0.016)\\
		$A_d$ & 0.12\\
		$(A_g, A_g')$ & (0.10, 0.15)\\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

q-fin

Michael Barnett
William Brock
Lars Peter Hansen
Ruimeng Hu
Joseph Huang

"A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty" by Michael Barnett, William Brock, Lars Peter Hansen, Ruimeng Hu, and Joseph Huang, arXiv:2310.13200v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{WER impact of cleaning speech signals between certain SNR ranges, using a music source separator. End-to-end ConvNet GLU model is used without LM, and percentage of cleaned samples are reported.}
\begin{tabular}{lcccc}
 \toprule
 \multirow{1}{*}{SNR} & \multicolumn{2}{c}{Cleaned Samples [\%] } &
 \multicolumn{2}{c}{Test WER [\%]} \\
 & 2018 & 2020 & 2018 & 2020 \\

 \midrule
 \multirow{1}{*}{$(-\infty, \infty)$} & \multirow{1}{*}{100} & 100 & 37.50 & 53.53\\
 \multirow{1}{*}{$(-\infty, 10)$} & 25.97 & 34.22 & -0.05 & -0.87\\
 \multirow{1}{*}{($-5$, $10$)} & 25.84 & 31.33 & -0.05 & -0.88\\
 \multirow{1}{*}{($-5$, $5$)} & 5.14 & 11.88 & -0.07 & \textbf{-1.03}\\
 \multirow{1}{*}{($-5$, $8$)} & 14.95 & 22.11 & \textbf{-0.08} & -0.97\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Martin Kocour
Guillermo Cámbara
Jordi Luque
David Bonet
Mireia Farrús
Martin Karafiát
Karel Veselý
Jan ''Honza'' Ĉernocký

"BCN2BRNO: ASR System Fusion for Albayzin 2020 Speech to Text Challenge" by Martin Kocour, Guillermo Cámbara, Jordi Luque, David Bonet, Mireia Farrús, Martin Karafiát, Karel Veselý, and Jan ''Honza'' Ĉernocký, arXiv:2101.12729v1, 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{Randomly generated UBQP problems, relaxation order $\omega=2$: Loraine, MOSEK and ADMM}
\begin{tabular}{c|rrr|rr|rr}\toprule
			&		\multicolumn{3}{c|}{Loraine for }& \multicolumn{2}{c|}{{\sc mosek} for }	&\multicolumn{2}{c}{{\sc admm} for }\\
			UBQP size & iter &	CG iter &	time & iter & time&	iter & time \\\midrule
			10 &	 17 &	178 &	0.2 & 6 & 0.2& 1006& 0.4\\
			15 &	 17 &	291 &	1.1 & 5 & 1.1& 1966& 3.0\\
			20 &	 18 &	447 &	4.0 & 6 & 9.3& 3063& 12\\
			25 &	 18 &	501 &	10 & 7 & 81 & 5843& 63\\
			30 &	 19 &	577 &	 22 & 7 & 496& 8018& 174\\
			35 &	 21 &	650 &	 46 & \multicolumn{2}{c}{memory}& 11493& 511\\
			40 &	 23 &	1057 &	121 & & & 17532& 1318\\
			45 &	 22 &	2368 &	334 & & & 45030& 4258\\
			50 &	 21 & 1809 &	410 & & & 21407& 3316\\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

math

Soodeh Habibi
Michal Kocvara
Michael Stingl

"On the numerical solution of Lasserre relaxations of unconstrained binary quadratic optimization problem" by Soodeh Habibi, Michal Kocvara, and Michael Stingl, arXiv:2412.19776v1, 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{Welch's T-test: Inference of Linguistically Represented Goal,\\ Training Ratio: 60\%}
\begin{tabular}{cccc}
 \hline
 \bf{Group 1} &\bf{Group 2} &\bf{T-statistic} &\bf{P-value} \\
 \hline
 \bf{A2} &\bf{B2} &13.59 &9.73 x $10^{-7}$ \\
 \bf{A2} &\bf{C2} &11.12 &1.34 x $10^{-5}$ \\
 \bf{A2} &\bf{D2} &17.59 &1.13 x $10^{-7}$ \\
 \bf{B2} &\bf{C2} &1.50 &1.80 x $10^{-1}$ \\
 \bf{B2} &\bf{D2} &7.30 &9.20 x $10^{-5}$ \\
 \bf{C2} &\bf{D2} &3.75 &7.50 x $10^{-3}$ \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average dice score performance (\%) of the GCN refinement at $\tau=0.5$. The table shows the drop in the performance of $w_2$ compared with the U-Net prediction and $w_1$. Statistical significance is indicated by (*) for a p-value $<0.05$, and (**) for a p-value $< 0.01$ with respect to the CNN prediction.}
\begin{tabular}{l|c|c|c}
			\hline
			Task & CNN & GCN & GCN \\
			 & 2D U-Net & $w_{1,(0.5,1)}$ & $w_{2,(1,1)}$ \\
			\hline
			Pancreas & $76.89 \pm 6.6$ & $77.77 \pm 6.3$* & $63.27 \pm 9.9$** \\
			\hline
			Pancreas-10 & $52.14 \pm 22.6$ & $54.15 \pm 22.2$ & $43.05 \pm 22.1$** \\
			\hline
			\hline
			Spleen & $93.17 \pm 2.5$ & $94.98 \pm 1.4$** & $87.15 \pm 3.1$** \\
			\hline
			Spleen-9 & $78.89 \pm 28.4$ & $80.94 \pm 28.8$** & $75.47 \pm 26.8$** \\
			\hline
			\end{tabular}
\end{table}
\end{document}

eess

Roger D. Soberanis-Mukul
Nassir Navab
Shadi Albarqouni

"An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation" by Roger D. Soberanis-Mukul, Nassir Navab, and Shadi Albarqouni, arXiv:2012.03352v1, licensed under CC BY 4.0. 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{\textcolor{black}{Performance comparison between fixed length Huffman tree and proposed approach on `12 Angry Men' dataset. We set $\theta_{cos}$=0.88 and $\alpha$=1e-04 for this experiment.}}
\begin{tabular}{ccc|cccc}
\toprule
\multicolumn{3}{c|}{Fixed Length Tree} & \multicolumn{4}{c}{Variable Length Tree} \\ \hline
N & Replaced & AUC(\%) & AUC(\%) & avg. N & Merged & $\theta_{merge}$ \\ \hline
9 & 266 & 61.23 & 80.28 & 14 &64 & 0.96 \\
11 & 151 & \textbf{75.48} &81.34& 16 &44 & 0.97 \\
13 &101 & 69.38 & \textbf{83.61} &17 & 22 & 0.98 \\
15 & 57 & 66.61 & 75.44 & 21 &5 & 0.99 \\
17 & 39 & 62.47&&&&\\
19 & 32&74.90 &&&&\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"Anomaly Detection in Audio with Concept Drift using Adaptive Huffman Coding" by Pratibha Kumari and Mukesh Saini, arXiv:2102.10515v2, 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{Parameters of different detection in signal space}
\begin{tabular}{c|c|c|c}
		\hline\hline
		Detection & Splitting ratio $\rho$ &DoFs & Distance \\
		\hline\hline
		CD & 1 & 1 ($x$ axis) & 2 \\
		\hline
		ED & 0 & 1 ($y$ axis) & 2 \\
		\hline
		Splitting Detection (SDSD and SDJD) & $0<\rho<1$ & 2 & $2\sqrt{1+\frac{(1-\rho)^2}{\rho}}$ \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Qianqian Wang
Quansheng Guan
Julian Cheng
Fei Ji

"A Splitting-Detection Joint-Decision Receiver for Ultrasonic Intra-Body Communications" by Qianqian Wang, Quansheng Guan, Julian Cheng, and Fei Ji, arXiv:2101.01369v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{makecell}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Dataset used for the experiment. The datasets with $*$ indicate that these datasets used the validation set as the test set, as stated in Appendix~.}
\begin{tabular}{l|cccc}
\Xhline{3\arrayrulewidth}
\textbf{Dataset} & Task & $N_{Class}$ & $N_{Train}$ & $N_{Test}$ \\ \Xhline{2\arrayrulewidth}
SST2 & Sentiment & 2 & 6,919 & 1,820 \\
SST5 & Sentiment & 5 & 8,544 & 2,210 \\
TREC & Question Type & 6 & 5,452 & 500 \\
CoLA & Acceptability & 2 & 8,551 & 527 \\
RTE$^{*}$ & NLI & 2 & 2,490 & 277 \\
MNLI-matched$^{*}$ & NLI & 3 & 392,702 & 9,815 \\
MNLI-mismatched$^{*}$ & NLI & 3 & 392,702 & 9,832 \\
QNLI$^{*}$ & NLI & 2 & 104,743 & 5,463 \\ \Xhline{3\arrayrulewidth}
\end{tabular}
\end{table}
\end{document}

cs

"Adverb Is the Key: Simple Text Data Augmentation with Adverb Deletion" by Juhwan Choi and YoungBin Kim, arXiv:2403.20015v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|}
				\hline
				\textbf{SNR in dB} & \multicolumn{2}{c|}{$\gamma=3$} & \multicolumn{2}{c|}{$\gamma=6$} & \multicolumn{2}{c|}{$\gamma=9$} & \multicolumn{2}{c|}{$\gamma=12$} & \multicolumn{2}{c|}{$\gamma=15$} \\ \hline
				\textbf{Method} &\textbf{M2} & \textbf{B2} & \textbf{M2} & \textbf{B2} & \textbf{M2} & \textbf{B2} & \textbf{M2} & \textbf{B2} & \textbf{M2} & \textbf{B2} \\ \hline
				\textbf{Feasibility\%} & $0.07$ & $1.00$ & $0.06$ & $1.00$ & $0.06$ & $1.00$ & $0.06$ & $1.00$ & $0.04$ & $1.00$ \\ \hline
				\textbf{Rank One\%} & $1.00$ & $1.00$ & $1.00$ & $1.00$ & $1.00$ & $1.00$ & $1.00$ & $1.00$ & $1.00$ & $1.00$ \\ \hline
		\end{tabular}
\end{table}
\end{document}

cs

Sissi Xiaoxiao Wu
Sherry Xue-Ying Ni
Jiaying Li
Anthony Man-Cho So

"Quartic Perturbation-based Outage-constrained Robust Design in Two-hop One-way Relay Networks" by Sissi Xiaoxiao Wu, Sherry Xue-Ying Ni, Jiaying Li, and Anthony Man-Cho So, arXiv:2101.06907v1, 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}{ccccccccccc}
 \toprule
 \textbf{Parameters} & $\varphi_0$ & $\varphi_1$ & $\varphi_2$ & $\varphi_3$ & $\eta$ & $h_1$ & $h_2$ & $h_3$ & $\lambda_1$ & $\lambda_2$ \\ \hline
 \textbf{Value} & $10^{-4}$ & $0.02$ & $0.99$ & $0.02$ & $10^{-3}$ & $10^{-2}$ & $10^{-2}$ & $10^{-2}$ & $10^{-4}$ & $10^{-4}$ \\
 \bottomrule
 \end{tabular}
\caption{Choice of hyperparameters.}
\end{table}
\end{document}

math

"Mean-field underdamped Langevin dynamics and its spacetime discretization" by Qiang Fu and Ashia Wilson, arXiv:2312.16360v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cccc}
 \toprule
 \textbf{\#Samples}&\textbf{MNLI}&\textbf{SVAMP}&\textbf{QNLI}&\textbf{RTE}\\ \midrule
 0&56.6&0&88.3&68.5\\
 1000&60.0&17.3&91.5&69.3\\
 2000&\textbf{63.4}&22.7&90.4&69.3\\
 3000&62.5&25.3&\textbf{91.7}&\textbf{69.6}\\
 4000&62.5&25.3&90.9&68.9\\
 5000&62.9&\textbf{28.3}&89.4&69.3\\
 \bottomrule
\end{tabular}
\caption{Ablation on the number of generated samples.}
\end{table}
\end{document}

cs

Mohsen Gholami
Mohammad Akbari
Cindy Hu
Vaden Masrani
Z. Jane Wang
Yong Zhang

"GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation" by Mohsen Gholami, Mohammad Akbari, Cindy Hu, Vaden Masrani, Z. Jane Wang, and Yong Zhang, arXiv:2403.19754v1, 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}{lrrrrrrr}
 \hline
 $N$ & 1 & 2 & 4 & 8 & 16 & 32 & 64 \\
 \hline
 Exec time (s) & 4.1 & 10.6 & 21.6 & 42.6 & 83.0 & 144.0 & 251.0 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Jean-Philippe Chancelier
Michel De Lara
François Pacaud
Teemu Pennanen
Ari-Pekka Perkkiö

"Optimal Operation and Valuation of Electricity Storages" by Jean-Philippe Chancelier, Michel De Lara, François Pacaud, Teemu Pennanen, and Ari-Pekka Perkkiö, arXiv:2504.14292v1, 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}{cccccc}
\toprule
Problem & $\theta_1$ & $\theta_2$ & $\theta_3$ & Interpolated & Target \\
\midrule
Airy	 & 6.74 & 6.76 & 6.83 & 7.46 & 6.78 \\
Fractional Laplacian & 7.52 & 7.55 & 7.48 & 7.58 & 7.49 \\
Advection Diffusion & 6.63 & 6.61 & 6.69 & 6.65 & 6.66 \\
\bottomrule
\end{tabular}
\caption{Summary of the empirical error for the Airy, Fractional Laplacian, and Advection-Diffusion problems, in the case where the models are learned and tested on a noisy dataset.}
\end{table}
\end{document}

math

Harshwardhan Praveen
Jacob Brown
Christopher Earls

"chebgreen: Learning and Interpolating Continuous Empirical Green's Functions from Data" by Harshwardhan Praveen, Jacob Brown, and Christopher Earls, arXiv:2501.18715v5, 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{Computational Cost to achieve MMD $<$ 0.1 in the SIR Model (Mean $\pm$ Standard Deviation)}
\begin{tabular}{c|ccc|c}
 Method & Simulation Time (min) & Network Training Time (min) & Total & Number of Simulations \\
 \hline
 SMC-ABC & 115.38 $\pm$ 1.51 & - & 115.38 $\pm$ 1.51 & 82.85 $\pm$ 1.03 \\
 SPPE & \textbf{2.65 $\pm$ 1.03} & \textbf{15.73 $\pm$ 7.67} & \textbf{18.38 $\pm$ 8.63} & \textbf{2.12 $\pm$ 0.71} \\
 SPLE & 7.11 $\pm$ 1.01 & 45.98 $\pm$ 14.46 & 53.09 $\pm$ 15.26 & 5.40 $\pm$ 0.77
\end{tabular}
\end{table}
\end{document}

stat

Yifei Xiong
Nianqiao Phyllis Ju
Sanguo Zhang

"Simulation-based Bayesian Inference from Privacy Protected Data" by Yifei Xiong, Nianqiao Phyllis Ju, and Sanguo Zhang, arXiv:2310.12781v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Human evaluation of our model based on: (A) Quality and (B) Intelligibility.}
\begin{tabular}{lcccccc}
 \hline
 Data & Metric & Noisy & ~ & ~ & AO & Ours \\
 \hline

 \multirow{2}{*}{LRS2} & (A) & 1.23 & 2.02 & 2.38 & 3.45 & \textbf{3.73}\\
 & (B) & 2.81 & 3.05 & 3.27 & 3.96 & \textbf{4.12}\\

 \hline

 \multirow{2}{*}{LRS3} & (A) & 1.25 & 2.14 & 2.49 & 3.52 & \textbf{3.84} \\
 & (B) & 2.85 & 3.12 & 3.36 & 3.98 & \textbf{4.25} \\

 \hline

 Curated real & (A) & 1.49 & 1.92 & 2.27 & 3.31 & \textbf{3.83} \\
 world samples & (B) & 2.66 & 2.91 & 3.08 & 4.01 & \textbf{4.31} \\

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

eess

Sindhu B Hegde
K R Prajwal
Rudrabha Mukhopadhyay
Vinay Namboodiri
C. V. Jawahar

"Visual Speech Enhancement Without A Real Visual Stream" by Sindhu B Hegde, K R Prajwal, Rudrabha Mukhopadhyay, Vinay Namboodiri, and C. V. Jawahar, arXiv:2012.10852v1, 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{EXTRACTED SOIL NUTRITION DATA}
\begin{tabular}{|c|c|}
 \hline
 \textbf{Parameter} & \textbf{Value} \\
 \hline
 Agro-Meteoro Zone & Rangpur \\
 \hline
 Latitude & 25.740580°N \\
 \hline
 Longitude & 89.261139°E \\
 \hline
 pH & 5.6-6.5 \\
 \hline
 Phosphorus & VH \\
 \hline
 Potassium & M \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Forkan Uddin Ahmed
Annesha Das
Md Zubair

"A Machine Learning Approach for Crop Yield and Disease Prediction Integrating Soil Nutrition and Weather Factors" by Forkan Uddin Ahmed, Annesha Das, and Md Zubair, arXiv:2403.19273v1, 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{hhline}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{AHM Signal: Performance of the Extension Methods and the Associated Boundary-Free STFT}
\begin{tabular}{|c||c|c||c|}
 \hline
 \multirow{2}{60pt}{\centering Extension method} & \multirow{2}{30pt}{\centering CPU time (s)} & \multirow{2}{50pt}{\centering MSE\\ (mean $\pm$ SD)} & \multirow{2}{50pt}{\centering Index $D$ of the STFT}\\
 & & & \\
 \hhline{|=#=|=#=|}
 {\sf SigExt ($M=100$)} & $0.008$ & $1.133 \pm 0.077$ & $0.0091 \pm 0.0019$\\
 \hline
 {\sf SigExt ($M=750$)} & $0.122$ & $0.479 \pm 0.166$ & $0.0056 \pm 0.0019$\\
 \hline
 {\sf SigExt ($M\!=\!1500$)} & $0.634$ & $0.907 \pm 0.377$ & $0.0065 \pm 0.0023$\\
 \hline
 Symmetric & $<0.001$ & $5.452 \pm 0.002$ & $1.8209 \pm 0.0025$\\
 \hline
 EDMD & $1.587$ & $0.983 \pm 0.004$ & $0.1145 \pm 0.0018$\\
 \hline
 GPR & $515.787$ & $0.479 \pm 0.166$ & $0.0119 \pm 0.0037$\\
 \hline
 TBATS & $1776.778$ & $0.686 \pm 0.467$ & $0.3847 \pm 0.1397$\\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

"An Efficient Forecasting Approach to Reduce Boundary Effects in Real-Time Time-Frequency Analysis" by Adrien Meynard and Hau-Tieng Wu, arXiv:2102.07226v2, 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{An example of calculating the empirical PD term-structure across horizon $v$ for a reference period of $r=3$ months. From .}
\begin{tabular}{ccccc}
\toprule
\textbf{Horizon} & \textbf{Cohorts} & \textbf{Sum of Performing Accounts} & \textbf{Sum of Defaults} & \textbf{Empirical PD} \\
\midrule
1 & 201505 : 201507 & 2250 & 45 & 2.000\% \\
2 & 201504 : 201506 & 2100 & 18 & 0.857\% \\
3 & 201503 : 201505 & 1950 & 20 & 1.026\% \\
4 & 201502 : 201504 & 1800 & 12 & 0.667\% \\
5 & 201501 : 201503 & 1650 & 19 & 1.152\% \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Approaches for modelling the term-structure of default risk under IFRS 9: A tutorial using discrete-time survival analysis" by Arno Botha and Tanja Verster, arXiv:2507.15441v1, 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}{|c||c|c|}
 \hline
\emph{method} & $\varepsilon$ rule & $C$-\textbf{schedule} \\
 \hline
 \hline
Baratt-Boyd (\texttt{BB}) & $\varepsilon = \mathbf{1}_{N}$ & / \\
\hline
Alternating-Minimization (\texttt{AM})& $\varepsilon = \mathbf{0}_{N}$& / \\
\hline
Max-Min (\texttt{MM}) & \multirow{2}{*}{Lemma } & \multirow{2}{*}{$C_k = \frac{2}{\sqrt{k-1}+3}$} \\
(Norm.) Softmin (\texttt{SM}) & & \\
\hline
\end{tabular}
\caption{Specifications of methods.}
\end{table}
\end{document}

math

"Global and local approaches for the minimization of a sum of pointwise minima of convex functions" by Guillaume Van Dessel and François Glineur, arXiv:2501.19372v1, 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{Evolution of scholarship and non-scholarship student numbers from 2014 to 2020}
\begin{tabular}{lccccccc}
\hline
\textbf{Session} & \textbf{2014} & \textbf{2015} & \textbf{2016} & \textbf{2017} & \textbf{2018} & \textbf{2019} & \textbf{2020} \\
\hline
\textbf{Scholarship} & & & & & & & \\
Value N & 46,626 & 49,622 & 52,212 & 57,419 & 64,676 & 67,977 & 73,883 \\
Growth (\%) & & 6.23 & 5.09 & 9.51 & 11.90 & 4.98 & 8.33 \\
\hline
\textbf{Non-scholarship} & & & & & & & \\
Value N & 271,424 & 279,947 & 289,446 & 300,725 & 315,238 & 307,973 & 305,493 \\
Growth (\%) & & 3.14 & 3.39 & 3.90 & 4.83 & -2.30 & -0.81 \\
\hline
\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{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
\toprule
\multirow{2}{*}{Embeddings} & Word2Vec 		& GloVe 						& fastText \\
 	& Dom/All 	& Dom/All 					& Dom/All \\ \midrule
definition 	 			& 83.4/\textbf{83.9} & \textbf{83.9}/83.4 & 92.5/\textbf{93.2} \\
stereotype 			& 12.7/\textbf{12.3} & 11.8/\textbf{11.4} & 4.8/\textbf{4.3} \\
none 			& \textbf{3.9}/\textbf{3.9} & \textbf{4.3}/5.2 & 2.7/\textbf{2.5} \\ \midrule
sub-definition 			& 55.0/\textbf{57.5} & \textbf{67.5}/\textbf{67.5} & 77.5/\textbf{85.0} \\
sub-stereotype 			& 35.0/\textbf{32.5} & \textbf{27.5}/\textbf{27.5} & 12.5/\textbf{10.0} \\
sub-none 			& \textbf{10.0}/\textbf{10.0} & \textbf{5.0}/\textbf{5.0} & 10.0/\textbf{5.0} \\
\bottomrule
\end{tabular}
\caption{Performance obtained when using only the dominant gloss (Dom) or all glosses (All) on SemBias.}
\end{table}
\end{document}

cs

"Dictionary-based Debiasing of Pre-trained Word Embeddings" by Masahiro Kaneko and Danushka Bollegala, arXiv:2101.09525v1, 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&($1,~22,~19.1700,~19,~21,~2.4621$) & ($3.2973,~44.5769,~37.5000,~37.6309,~3,~4.7245$) \\
 & SD &($1,~33,~32.4600,~32,~32,~0.5009$) & ($2.1389,~32.6268,~31.5719,~31.3467,~30,~0.5165$) \\
\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{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Variations on BLEU score relative to the model trained with all losses.}
\begin{tabular}{ccccc}
 \toprule
 &\multicolumn{2}{c}{Without interference}&\multicolumn{2}{c}{With interference}\\
 &SNR=6&SNR=15&SNR=6&SNR=15\\
 \midrule
 Without $\mathcal{L}_{distill}(\boldsymbol{\hat{s}}^\mathcal{T},\boldsymbol{\hat{s}}^\mathcal{S})$& -5.16\%& -2.25\%& -5.11\%& -1.07\%\\
 Without $\mathcal{L}_{distill}(\boldsymbol{x}^\mathcal{T},\boldsymbol{x}^\mathcal{S})$& \multirow{2}{*}{-6.08\%} & \multirow{2}{*}{-4.62\%}&\multirow{2}{*}{-5.73\%} & \multirow{2}{*}{-1.45\%}\\and $\mathcal{L}_{distill}(\boldsymbol{\hat{t}}^\mathcal{T},\boldsymbol{\hat{t}}^\mathcal{S})$& & \\
 Without $\mathcal{L}_{distill}(\boldsymbol{p}^\mathcal{T},\boldsymbol{p}^\mathcal{S})$ & \multirow{2}{*}{-5.08\%} & \multirow{2}{*}{-6.35\%} &\multirow{2}{*}{-0.89\%} & \multirow{2}{*}{-1.18\%}\\and $\mathcal{L}_{distill}(\boldsymbol{\hat{p}}^\mathcal{T},\boldsymbol{\hat{p}}^\mathcal{S})$& &\\
 Without $\mathcal{L}_{hard}(\boldsymbol{s}^\mathcal{S},\boldsymbol{\hat{s}}^\mathcal{S})$ & -18.63\%& -12.59\%&-11.71\% &-6.44\%\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Chenguang Liu
Yuxin Zhou
Yunfei Chen
Shuang-Hua Yang

"Knowledge Distillation Based Semantic Communications For Multiple Users" by Chenguang Liu, Yuxin Zhou, Yunfei Chen, and Shuang-Hua Yang, arXiv:2311.13789v1, 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{Unicorns recommendation model}
\begin{tabular}{|c|c|c|}
 \hline
 \textbf{Name} & \textbf{Enter Series} & \textbf{Last Series Value} \\
 \hline
 Coursera & series\_c & 2 500 000 000 \\
 H2O.ai & series\_b & 1 700 000 000 \\
 Freee & series\_c & \\
 Cybereason & series\_c & 3 100 000 000 \\
 Honest Buildings & series\_b & \\
 Reserve & series\_unknown & \\
 Qumulo & series\_c & 1 200 000 000 \\
 Silent Circle & series\_c & \\
 Vlocity & series\_b & 1 000 000 000 \\
 Aura Financial & series\_c & \\
 MoneyLion & series\_a & \\
 Databricks & series\_c & 2 750 000 000 \\
 OakNorth & series\_b & 2 800 000 000 \\
 ProtectWise & series\_b & \\
 Earnest & series\_c & \\
 Cohesity & secondary\_market & 2 500 000 000 \\
 Spring & series\_c & \\
 Darktrace & series\_d & 1 650 000 000 \\
 Bread & series\_unknown & \\
 Guideline & series\_c & 1 150 000 000 \\
 Nexxiot & series\_b & \\
 Cato Networks & series\_c & 2 500 000 000 \\
 CloudMinds & series\_b & \\
 Stash Financial & series\_e & 1 400 000 000 \\
 Branch International & series\_c & \\
 Fungible & series\_c & 700 000 000 \\
 BlueVoyant & series\_b & \\
 Reali & series\_b & \\
 AMD Pensando & series\_c & \\
 IntSights & series\_d & \\
 BigID & series\_c & 1 250 000 000 \\
 Flutterwave & series\_b & 1 000 000 000 \\
 K Health & series\_c & 1 400 000 000 \\
 Doma & series\_c & \\
 BlockFi & series\_a & \\
 Tomorrow.io & series\_c & \\
 Kin Insurance & series\_b & \\
 TrueLayer & series\_c & 1 000 000 000 \\
 bitkey Japan & series\_unknown & \\
 MegazoneCloud & series\_b & \\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

Mark Potanin
Andrey Chertok
Konstantin Zorin
Cyril Shtabtsovsky

"Startup success prediction and VC portfolio simulation using CrunchBase data" by Mark Potanin, Andrey Chertok, Konstantin Zorin, and Cyril Shtabtsovsky, arXiv:2309.15552v1, 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 correlation of unembedding neurons. Neurons that correspond to the same coset are averaged together in the unembedding, leading to the unembedding vectors being highly correlated.}
\begin{tabular}{lcc}
 \toprule
 & Mean Correlation & Std Dev Correlation \\
 \midrule
 Within Coset & 0.814 & 0.445 \\
 \midrule
 Within Subgroup Conjugacy Class & -0.002 & 0.222 \\
 \midrule
 Baseline & -0.003 & 0.163 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Dashiell Stander
Qinan Yu
Honglu Fan
Stella Biderman

"Grokking Group Multiplication with Cosets" by Dashiell Stander, Qinan Yu, Honglu Fan, and Stella Biderman, arXiv:2312.06581v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Zhiwei Xu
Ziming Gan
Doudou Zhou
Shuting Shen
Junwei Lu
Tianxi Cai

"Inference of Dependency Knowledge Graph for Electronic Health Records" by Zhiwei Xu, Ziming Gan, Doudou Zhou, Shuting Shen, Junwei Lu, and Tianxi Cai, arXiv:2312.15611v1, 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{Computed RI, DDI, and MRDI measures for the Example 1 and Example 2.}
\begin{tabular}{ccccc}
\hline
\hline
\multirow{1}{*}{}&cluster&\multicolumn{3}{c}{measure}\\
\cline{3-5}
 & model&RI&DDI&MRDI\\
\hline
 \multirow{2}{*}{first Example} &$M_{1}$ & 0.822& 0.708& 0.708 \\
\cline{3-5}
 				 &$M_{2}$ & 0.888& 0.821& 0.821 \\
 				 \cline{1-5}
 \multirow{2}{*}{second Example} &$M_{1}$& 0.678 &0.593 &0.593 \\
\cline{3-5}
 				 &$M_{2}$ &0.642 &0.572 & 0.572\\
\hline
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Soccer Goalkeeper Performance Evaluation: Clustering Approach" by Mahdi Teimouri, arXiv:2502.05548v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Ternary near-extremal self-dual codes of lengths $36$, $48$ and $60$" by Masaaki Harada, arXiv:2412.04795v2, 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{Performance on CS test data with parameters optimized on 1000 TIMIT sentences, or on 1000 CS train utterances, both with (1/F-score) as cost function.}
\begin{tabular}{|c|c|c|c|}
 \hline
 \textbf{Training} & \textbf{Corr Coef} & \textbf{SR error} & \textbf{F-score} \\
 \textbf{corpus} & \textbf{of \#syl} & \textbf{rate (\%)} & \textbf{} \\ \hline \hline
 TIMIT & 0.959 & 9.9 & 87.56 \\ \hline
 CS & 0.980 & 7.3 & 93.74 \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

Kamini Sabu
Syomantak Chaudhuri
Preeti Rao
Mahesh Patil

"An Optimized Signal Processing Pipeline for Syllable Detection and Speech Rate Estimation" by Kamini Sabu, Syomantak Chaudhuri, Preeti Rao, and Mahesh Patil, arXiv:2103.04346v1, 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}{|c|cccc|c|cccc|c|}
 \hline
 &\multicolumn{5}{c|}{Dice} &\multicolumn{5}{c|}{Average Symmetric Surface Distance} \\ \hline
 $\omega$-SFS &AA &LAC &LVC &MYO &Average &AA &LAC &LVC &MYO &Average \\ \hline
 \textbf{1-SFS} &86.2 &83.5 &75.4 &70.9 &79.0 &11.1 &5.0 &10.8 &3.6 &9.8 \\
 \hline
 \textbf{3-SFS} &88.0 &83.7 &81.0 &72.5 &81.3 &6.3 &7.2 &4.7 &6.1 &6.1 \\
 \hline
 \textbf{5-SFS} &88.0 &83.8 &81.9 &73.3 &81.7 &6.2 &7.4 &4.8 &5.7 &6.0 \\
 \hline
 \textbf{7-SFS} &86.8 &84.8 &82.0 &73.5 &81.8 &4.8 &7.2 &4.4 &5.6 &5.9 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

"Domain Adaptation for the Segmentation of Confidential Medical Images" by Serban Stan and Mohammad Rostami, arXiv:2101.00522v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c|c|c|c||}
 \hline
 & 5min & 15min & 1hour \\
 \hline\hline
 mean & 714.237kW & 1495.218kW & 7286.616kW \\
 \hline
 median & 581.408kW & 966.566kW & 6518.619kW \\
 \hline
 max & 3905.480kW & 4708.951kW & 17035.819kW \\
 \hline
\end{tabular}
\caption{Statistics on the energy paid from grid relative to the real-time decision using adaptive mode at a given 4 kW threshold for each day of January 2023}
\end{table}
\end{document}

q-fin

Tom Nonnenmacher
Jenny Nelson
Benedict Winchester

"Maximum Return on Investment for a Domestic Photovoltaic Installation" by Tom Nonnenmacher, Jenny Nelson, and Benedict Winchester, arXiv:2310.09323v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A tutorial on optimal dynamic treatment regimes" by Chunyu Wang and Brian DM Tom, arXiv:2502.16988v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.