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\caption{Forecasting performance of the proposed E-STGCN model in comparison to the temporal-only and spatiotemporal forecasting techniques for 30 30-day ahead forecast horizon of NO$_{2}$ pollutant (best results are \underline{\textbf{highlighted}}).}
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\begin{tabular}{|c|c|cccccc|ccccccc|c|}
\hline Forecast & \multirow{3}{*}{Metric} & \multicolumn{6}{c|}{Temporal-only Model} & \multicolumn{7}{c|}{Spatiotemporal Model} & Proposed \\ \cline{3-15}
 \multirow{2}{*}{Period} & & \multirow{2}{*}{ARIMA} & \multirow{2}{*}{LSTM} & \multirow{2}{*}{TCN} & \multirow{2}{*}{DeepAR} & \multirow{2}{*}{Transformers} & \multirow{2}{*}{NBeats} & \multirow{2}{*}{STARMA} & \multirow{2}{*}{GSTAR} & \multirow{2}{*}{GpGp} & \multirow{2}{*}{{\color{black}STGCN}} & \multirow{2}{*}{STNN} & Modified & \multirow{2}{*}{DeepKrigging} & \multirow{2}{*}{E-STGCN} \\
 & & & & & & & & & & & & & STGCN & & \\ \hline

\multirow{6}{*}{JAN} & MAE & 13.76 & 23.48 & 38.72 & 26.73 & 16.49 & 15.71 & 17.87 & 15.76 & 16.37 & {\color{black} 17.68} & 28.46 & \underline{\textbf{12.28}} & 40.02 & \underline{\textbf{12.28}} \\
 & MASE & 3.33 & 8.21 & 15.68 & 9.85 & 11.36 & 4.58 & 4.99 & \underline{\textbf{2.48}} & 4.48 & {\color{black} 10.59} & 8.15 & 4.74 & 13.43 & 4.74 \\
 & RMSE & 17.01 & 26.50 & 41.37 & 29.63 & 19.45 & 19.34 & 21.80 & 19.37 & 19.92 & {\color{black}20.64} & 36.38 & \underline{\textbf{15.01}} & 48.68 & \underline{\textbf{15.01}} \\
 & SMAPE & 33.70 & 61.60 & 160.40 & 76.20 & 39.50 & 41.50 & 44.20 & 38.70 & 39.70 & {\color{black}40.26} & 62.50 & \underline{\textbf{28.30}} & 109.50 & \underline{\textbf{28.30}} \\
 & {\color{black}	Pinball Loss	} & {\color{black}	7.80	} & {\color{black}	18.36	} & {\color{black}	30.95	} & {\color{black}	21.20	} & {\color{black}	8.07	} & {\color{black}	10.73	} & {\color{black}	13.71	} & {\color{black}	11.03	} & {\color{black}	9.43	} & {\color{black} 9.49} & {\color{black}	13.02	} & {\color{black}	\underline{\textbf{6.50}}	} & {\color{black}	23.98	} & {\color{black}	\underline{\textbf{6.50}}	} \\
 & {\color{black}	CRPS	} & {\color{black}	19.37	} & {\color{black}	24.59	} & {\color{black}	26.04	} & {\color{black}	25.26	} & {\color{black}	20.56	} & {\color{black}	21.88	} & {\color{black}	23.64	} & {\color{black}	22.00	} & {\color{black}	21.54	} & {\color{black} 21.13} & {\color{black}	29.21	} & {\color{black}	\underline{\textbf{18.30}}	} & {\color{black}	32.98	} & {\color{black}	\underline{\textbf{18.30}}	} \\ \hline
\multirow{6}{*}{FEB} & MAE & 14.21 & 25.80 & 39.27 & 27.09 & 16.76 & 13.64 & 20.57 & 18.42 & 16.95 & {\color{black}17.47} & 33.94 & \underline{\textbf{11.92}} & 33.84 & \underline{\textbf{11.92}} \\
 & MASE & \underline{\textbf{2.32}} & 4.85 & 8.16 & 5.18 & 4.47 & 2.65 & 3.61 & 2.71 & 3.58 & {\color{black}6.28} & 7.55 & 2.50 & 6.94 & 2.50 \\
 & RMSE & 17.06 & 28.35 & 41.70 & 29.61 & 19.36 & 16.64 & 23.51 & 21.46 & 20.09 & {\color{black}20.08} & 47.18 & \underline{\textbf{14.56}} & 36.77 & \underline{\textbf{14.56}} \\
 & SMAPE & 34.60 & 70.80 & 158.20 & 76.70 & 40.20 & 37.00 & 56.20 & 47.40 & 41.40 & {\color{black}39.56} & 67.40 & \underline{\textbf{28.50}} & 123.20 & \underline{\textbf{28.50}} \\
 & {\color{black}	Pinball Loss	} & {\color{black}	8.33	} & {\color{black}	20.27	} & {\color{black}	31.39	} & {\color{black}	21.39	} & {\color{black}	8.20	} & {\color{black}	\underline{\textbf{6.95}}	} & {\color{black}	15.70	} & {\color{black}	13.93	} & {\color{black}	10.21	} & {\color{black}9.69} & {\color{black}	14.18	} & {\color{black}	7.05	} & {\color{black}	26.70	} & {\color{black}	7.05	} \\
 & {\color{black}	CRPS	} & {\color{black}	19.39	} & {\color{black}	25.05	} & {\color{black}	26.07	} & {\color{black}	25.30	} & {\color{black}	20.27	} & {\color{black}	19.15	} & {\color{black}	24.85	} & {\color{black}	23.57	} & {\color{black}	22.18	} & {\color{black}20.67} & {\color{black}	32.54	} & {\color{black}	\underline{\textbf{17.85}}	} & {\color{black}	27.05	} & {\color{black}	\underline{\textbf{17.85}}	} \\ \hline
\multirow{6}{*}{MAR} & MAE & 12.39 & 17.26 & 31.02 & 18.48 & 16.78 & 12.68 & \underline{\textbf{8.64}} & 9.94 & 14.91 & {\color{black}14.81} & 21.44 & 8.96 & 30.13 & 8.96 \\
 & MASE & 4.65 & 6.43 & 12.07 & 6.82 & 9.83 & 4.20 & 3.16 & \underline{\textbf{2.63}} & 5.53 & {\color{black}8.44} & 8.56 & 3.53 & 11.56 & 3.53 \\
 & RMSE & 14.36 & 19.22 & 32.85 & 20.42 & 18.54 & 15.22 & \underline{\textbf{10.60}} & 11.92 & 17.07 & {\color{black}16.80} & 27.80 & 11.23 & 33.18 & 11.23 \\
 & SMAPE & 38.90 & 53.90 & 157.50 & 59.50 & 48.30 & 39.00 & \underline{\textbf{27.20}} & 31.40 & 46.50 & {\color{black}43.66} & 58.50 & 27.40 & 144.90 & 27.40 \\
 & {\color{black}	Pinball Loss	} & {\color{black}	3.97	} & {\color{black}	13.15	} & {\color{black}	24.79	} & {\color{black}	14.33	} & {\color{black}	5.30	} & {\color{black}	\underline{\textbf{3.84}}	} & {\color{black}	4.81	} & {\color{black}	5.22	} & {\color{black}	5.26	} & {\color{black}6.20} & {\color{black}	9.37	} & {\color{black}	4.46	} & {\color{black}	23.50	} & {\color{black}	4.46	} \\
 & {\color{black}	CRPS	} & {\color{black}	15.57	} & {\color{black}	18.43	} & {\color{black}	21.04	} & {\color{black}	18.92	} & {\color{black}	17.63	} & {\color{black}	15.99	} & {\color{black}	\underline{\textbf{13.64}}	} & {\color{black}	14.24	} & {\color{black}	17.23	} & {\color{black} 16.92} & {\color{black}	22.17	} & {\color{black}	13.92	} & {\color{black}	21.17	} & {\color{black}	13.92	} \\
 \hline
\multirow{6}{*}{APR} & MAE & 15.70 & 22.31 & 36.47 & 23.05 & 19.74 & 16.72 & 18.32 & 18.19 & 17.41 & {\color{black}19.48} & 25.79 & \underline{\textbf{13.63}} & 37.75 & \underline{\textbf{13.63}} \\
 & MASE & 3.57 & 3.15 & 6.41 & 3.24 & 5.02 & 2.78 & 2.56 & 2.94 & 4.37 & {\color{black}4.62} & 6.08 & \underline{\textbf{2.28}} & 6.95 & \underline{\textbf{2.28}} \\
 & RMSE & 19.05 & 25.77 & 39.24 & 26.49 & 22.29 & 20.62 & 22.58 & 22.24 & 21.18 & {\color{black}22.00} & 33.53 & \underline{\textbf{16.22}} & 40.30 & \underline{\textbf{16.22}} \\
 & SMAPE & 40.50 & 60.90 & 168.20 & 64.30 & 49.60 & 47.70 & 49.20 & 50.00 & 45.20 & {\color{black}48.22} & 64.20 & \underline{\textbf{34.40}} & 196.80 & \underline{\textbf{34.40}} \\
 & {\color{black}	Pinball Loss	} & {\color{black}	8.17	} & {\color{black}	17.26	} & {\color{black}	29.17	} & {\color{black}	17.95	} & {\color{black}	8.13	} & {\color{black}	12.16	} & {\color{black}	14.33	} & {\color{black}	13.75	} & {\color{black}	9.77	} & {\color{black} 9.42} & {\color{black}	13.23	} & {\color{black}	\underline{\textbf{7.24}}	} & {\color{black}	30.20	} & {\color{black}	\underline{\textbf{7.24}}	} \\
 & {\color{black}	CRPS	} & {\color{black}	19.90	} & {\color{black}	22.70	} & {\color{black}	24.22	} & {\color{black}	22.83	} & {\color{black}	21.53	} & {\color{black}	22.09	} & {\color{black}	22.49	} & {\color{black}	22.50	} & {\color{black}	21.36	} & {\color{black}21.03} & {\color{black}	26.38	} & {\color{black}	\underline{\textbf{18.45}}	} & {\color{black}	23.74	} & {\color{black}	\underline{\textbf{18.45}}	} \\
 \hline
\multirow{6}{*}{MAY} & MAE & 14.82 & 19.30 & 32.75 & 19.71 & 20.06 & 18.59 & 14.77 & 13.94 & 16.85 & {\color{black}19.98} & 27.00 & \underline{\textbf{11.33}} & 23.67 & \underline{\textbf{11.33}} \\
 & MASE & 3.37 & 2.59 & 5.80 & 2.59 & 6.46 & 6.02 & \underline{\textbf{2.18}} & 2.62 & 4.64 & {\color{black}7.01} & 7.54 & 2.20 & 8.26 & 2.20 \\
 & RMSE & 18.05 & 22.61 & 35.85 & 23.03 & 22.99 & 22.09 & 18.47 & 17.38 & 20.32 & {\color{black}22.70} & 35.38 & \underline{\textbf{14.35}} & 28.05 & \underline{\textbf{14.35}} \\
 & SMAPE & 41.70 & 56.70 & 155.60 & 58.60 & 52.60 & 55.40 & 41.30 & 40.40 & 47.50 & {\color{black}50.63} & 66.00 & \underline{\textbf{32.40}} & 65.40 & \underline{\textbf{32.40}} \\
 & {\color{black}	Pinball Loss	} & {\color{black}	6.41	} & {\color{black}	14.75	} & {\color{black}	26.16	} & {\color{black}	15.20	} & {\color{black}	7.26	} & {\color{black}	8.62	} & {\color{black}	9.99	} & {\color{black}	8.76	} & {\color{black}	7.63	} & {\color{black}8.46} & {\color{black}	11.37	} & {\color{black}	\underline{\textbf{6.29}}	} & {\color{black}	10.81	} & {\color{black}	\underline{\textbf{6.29}}	} \\
 & {\color{black}	CRPS	} & {\color{black}	18.55	} & {\color{black}	20.24	} & {\color{black}	22.93	} & {\color{black}	20.43	} & {\color{black}	21.12	} & {\color{black}	21.10	} & {\color{black}	18.83	} & {\color{black}	18.30	} & {\color{black}	19.78	} & {\color{black}20.86} & {\color{black}	26.20	} & {\color{black}	\underline{\textbf{16.18}}	} & {\color{black}	23.40	} & {\color{black}	\underline{\textbf{16.18}}	} \\ \hline
\multirow{6}{*}{JUN} & MAE & 13.79 & 12.89 & 23.74 & 13.38 & 24.21 & 11.55 & \underline{\textbf{7.43}} & 10.03 & 13.84 & {\color{black} 19.91} & 25.04 & 8.05 & 26.23 & 8.05 \\
 & MASE & 5.61 & 3.72 & 9.24 & 3.77 & 10.91 & 5.10 & \underline{\textbf{1.84}} & 4.18 & 10.88 & {\color{black}9.26} & 14.84 & 2.71 & 10.78 & 2.71 \\
 & RMSE & 15.80 & 14.59 & 25.68 & 15.09 & 25.40 & 13.58 & \underline{\textbf{9.28}} & 11.79 & 16.15 & {\color{black}21.21} & 33.49 & 9.88 & 27.36 & 9.88 \\
 & SMAPE & 49.80 & 48.50 & 144.50 & 50.70 & 70.10 & 45.60 & \underline{\textbf{29.30}} & 39.80 & 51.30 & {\color{black}60.29} & 70.60 & 31.30 & 197.50 & 31.30 \\
 & {\color{black}	Pinball Loss	} & {\color{black}	4.03	} & {\color{black}	8.73	} & {\color{black}	18.81	} & {\color{black}	9.48	} & {\color{black}	5.76	} & {\color{black}	4.49	} & {\color{black}	5.39	} & {\color{black}	7.27	} & {\color{black}	4.47	} & {\color{black}6.27} & {\color{black}	8.70	} & {\color{black}	\underline{\textbf{3.82}}	} & {\color{black}	20.98	} & {\color{black}	\underline{\textbf{3.82}}	} \\
 & {\color{black}	CRPS	} & {\color{black}	14.75	} & {\color{black}	14.52	} & {\color{black}	17.40	} & {\color{black}	14.66	} & {\color{black}	19.25	} & {\color{black}	13.74	} & {\color{black}	\underline{\textbf{11.54}}	} & {\color{black}	13.29	} & {\color{black}	14.83	} & {\color{black}17.15} & {\color{black}	22.43	} & {\color{black}	11.60	} & {\color{black}	16.25	} & {\color{black}	11.60	} \\
 \hline
\multirow{6}{*}{JUL} & MAE & 15.21 & 10.11 & 19.58 & 10.19 & 25.66 & 8.11 & \underline{\textbf{6.74}} & 7.92 & 13.51 & {\color{black}12.63} & 23.31 & 7.56 & 14.86 & 7.56 \\
 & MASE & 6.35 & 3.40 & 6.25 & 3.28 & 10.64 & 2.77 & \underline{\textbf{2.15}} & 2.50 & 6.09 & {\color{black}5.20} & 9.64 & 2.68 & 5.08 & 2.68 \\
 & RMSE & 16.93 & 11.74 & 21.19 & 11.88 & 26.97 & 10.41 & \underline{\textbf{8.48}} & 9.63 & 15.33 & {\color{black}14.46} & 31.45 & 9.95 & 16.94 & 9.95 \\
 & SMAPE & 61.60 & 46.90 & 153.80 & 47.50 & 82.10 & 47.50 & \underline{\textbf{35.00}} & 40.10 & 59.30 & {\color{black}55.29} & 74.90 & 37.20 & 80.00 & 37.20 \\
 & {\color{black}	Pinball Loss	} & {\color{black}	3.68	} & {\color{black}	5.83	} & {\color{black}	15.60	} & {\color{black}	6.30	} & {\color{black}	5.80	} & {\color{black}	5.24	} & {\color{black}	4.50	} & {\color{black}	5.29	} & {\color{black}	3.87	} & {\color{black} 5.12} & {\color{black}	7.51	} & {\color{black}	\underline{\textbf{3.26}}	} & {\color{black}	9.30	} & {\color{black}	\underline{\textbf{3.26}}	} \\
 & {\color{black}	CRPS	} & {\color{black}	13.84	} & {\color{black}	11.39	} & {\color{black}	13.65	} & {\color{black}	11.53	} & {\color{black}	18.77	} & {\color{black}	11.15	} & {\color{black}	\underline{\textbf{9.57}}	} & {\color{black}	10.69	} & {\color{black}	13.27	} & {\color{black}12.78} & {\color{black}	20.03	} & {\color{black}	10.39	} & {\color{black}	13.44	} & {\color{black}	10.39	} \\ \hline
\multirow{6}{*}{AUG} & MAE & 17.45 & 9.59 & 17.26 & 9.62 & 25.21 & 8.56 & 7.89 & 8.40 & 14.07 & {\color{black}19.32} & 19.88 & \underline{\textbf{6.95}} & 12.12 & \underline{\textbf{6.95}} \\
 & MASE & 7.14 & 4.18 & 6.10 & 4.22 & 10.73 & 3.11 & 2.44 & 2.76 & 7.98 & {\color{black}8.73} & 12.51 & \underline{\textbf{2.13}} & 5.74 & \underline{\textbf{2.13}} \\
 & RMSE & 19.36 & 11.41 & 19.24 & 11.45 & 27.10 & 10.80 & 9.92 & 10.40 & 16.21 & {\color{black}20.87} & 28.54 & \underline{\textbf{8.66}} & 13.93 & \underline{\textbf{8.66}} \\
 & SMAPE & 71.50 & 52.90 & 154.50 & 53.10 & 86.80 & 47.50 & 40.80 & 46.60 & 64.80 & {\color{black}77.15} & 75.40 & \underline{\textbf{38.40}} & 74.90 & \underline{\textbf{38.40}} \\
 & {\color{black}	Pinball Loss	} & {\color{black}	4.21	} & {\color{black}	5.21	} & {\color{black}	13.78	} & {\color{black}	5.19	} & {\color{black}	5.72	} & {\color{black}	4.84	} & {\color{black}	4.79	} & {\color{black}	5.84	} & {\color{black}	3.98	} & {\color{black}6.22} & {\color{black}	6.96	} & {\color{black}	\underline{\textbf{3.25}}	} & {\color{black}	7.72	} & {\color{black}	\underline{\textbf{3.25}}	} \\
 & {\color{black}	CRPS	} & {\color{black}	14.78	} & {\color{black}	10.81	} & {\color{black}	12.04	} & {\color{black}	10.80	} & {\color{black}	18.80	} & {\color{black}	10.31	} & {\color{black}	9.91	} & {\color{black}	10.59	} & {\color{black}	13.13	} & {\color{black}15.14} & {\color{black}	17.39	} & {\color{black}	\underline{\textbf{9.04}}	} & {\color{black}	11.65	} & {\color{black}	\underline{\textbf{9.04}}	} \\ \hline
\multirow{6}{*}{SEP} & MAE & 13.27 & 10.27 & 20.46 & 10.32 & 20.50 & 9.90 & 8.50 & 10.48 & 12.52 & {\color{black}19.13} & 15.40 & \underline{\textbf{6.46}} & 22.24 & \underline{\textbf{6.46}} \\
 & MASE & 4.74 & 3.50 & 5.65 & 3.49 & 7.77 & 3.24 & 2.30 & 2.62 & 6.35 & {\color{black}7.36} & 6.97 & \underline{\textbf{1.87}} & 6.37 & \underline{\textbf{1.87}} \\
 & RMSE & 14.91 & 11.93 & 22.27 & 11.98 & 21.82 & 11.92 & 10.62 & 12.61 & 14.62 & {\color{black}20.57} & 19.91 & \underline{\textbf{8.32}} & 23.50 & \underline{\textbf{8.32}} \\
 & SMAPE & 53.50 & 48.50 & 148.30 & 48.80 & 69.10 & 49.60 & 41.00 & 52.20 & 53.30 & {\color{black}65.75} & 66.50 & \underline{\textbf{32.10}} & 195.10 & \underline{\textbf{32.10}} \\
 & {\color{black}	Pinball Loss	} & {\color{black}	3.35	} & {\color{black}	6.40	} & {\color{black}	16.29	} & {\color{black}	6.56	} & {\color{black}	4.72	} & {\color{black}	5.92	} & {\color{black}	6.04	} & {\color{black}	7.93	} & {\color{black}	4.20	} & {\color{black}5.64} & {\color{black}	7.80	} & {\color{black}	\underline{\textbf{2.87}}	} & {\color{black}	17.78	} & {\color{black}	\underline{\textbf{2.87}}	} \\
 & {\color{black}	CRPS	} & {\color{black}	13.11	} & {\color{black}	12.42	} & {\color{black}	15.94	} & {\color{black}	12.47	} & {\color{black}	16.65	} & {\color{black}	12.64	} & {\color{black}	11.63	} & {\color{black}	12.87	} & {\color{black}	13.47	} & {\color{black}16.16} & {\color{black}	15.71	} & {\color{black}	\underline{\textbf{9.69}}	} & {\color{black}	13.97	} & {\color{black}	\underline{\textbf{9.69}}	} \\ \hline
\multirow{6}{*}{OCT} & MAE & 13.92 & 19.79 & 34.59 & 20.17 & 18.54 & 16.77 & 16.12 & 16.93 & 15.79 & {\color{black}18.41} & 24.02 & \underline{\textbf{12.09}} & 32.98 & \underline{\textbf{12.09}} \\
 & MASE & 2.55 & 3.05 & 6.12 & 3.09 & 3.81 & 2.77 & 2.48 & 2.61 & 3.46 & {\color{black}3.64} & 4.22 & \underline{\textbf{1.91}} & 6.01 & \underline{\textbf{1.91}} \\
 & RMSE & 19.20 & 24.99 & 38.91 & 25.41 & 23.56 & 22.57 & 22.37 & 23.21 & 21.30 & {\color{black}23.34} & 30.25 & \underline{\textbf{17.30}} & 37.23 & \underline{\textbf{17.30}} \\
 & SMAPE & 35.40 & 55.90 & 165.90 & 57.50 & 46.20 & 48.50 & 44.40 & 47.60 & 41.90 & {\color{black}45.57} & 79.90 & \underline{\textbf{30.30}} & 136.30 & \underline{\textbf{30.30}} \\
 & {\color{black}	Pinball Loss	} & {\color{black}	7.32	} & {\color{black}	15.22	} & {\color{black}	27.65	} & {\color{black}	15.61	} & {\color{black}	8.05	} & {\color{black}	12.37	} & {\color{black}	12.44	} & {\color{black}	12.73	} & {\color{black}	8.85	} & {\color{black}9.45} & {\color{black}	17.04	} & {\color{black}	\underline{\textbf{7.23}}	} & {\color{black}	26.09	} & {\color{black}	\underline{\textbf{7.23}}	} \\
 & {\color{black}	CRPS	} & {\color{black}	18.13	} & {\color{black}	21.68	} & {\color{black}	23.23	} & {\color{black}	21.77	} & {\color{black}	20.34	} & {\color{black}	21.46	} & {\color{black}	21.09	} & {\color{black}	21.47	} & {\color{black}	19.78	} & {\color{black}20.32} & {\color{black}	23.39	} & {\color{black}	\underline{\textbf{17.00}}	} & {\color{black}	22.84	} & {\color{black}	\underline{\textbf{17.00}}	} \\ \hline
\multirow{6}{*}{NOV} & MAE & 19.56 & 31.39 & 42.55 & 31.40 & 25.12 & 22.51 & 18.01 & 20.86 & 20.54 & {\color{black}26.53} & 34.00 & \underline{\textbf{16.62}} & 46.43 & \underline{\textbf{16.62}} \\
 & MASE & 3.11 & 4.04 & 5.61 & 4.03 & 4.83 & 3.39 & 3.02 & 3.42 & 4.35 & {\color{black}5.75} & 5.18 & \underline{\textbf{2.19}} & 5.95 & \underline{\textbf{2.19}} \\
 & RMSE & 24.01 & 35.90 & 47.07 & 35.92 & 29.31 & 27.62 & 22.48 & 25.60 & 24.75 & {\color{black}30.55} & 42.28 & \underline{\textbf{21.10}} & 49.98 & \underline{\textbf{21.10}} \\
 & SMAPE & 43.80 & 77.40 & 140.20 & 77.40 & 51.40 & 54.70 & 42.20 & 46.30 & 47.60 & {\color{black}51.18} & 84.10 & \underline{\textbf{36.20}} & 190.20 & \underline{\textbf{36.20}} \\
 & {\color{black}	Pinball Loss	} & {\color{black}	11.74	} & {\color{black}	24.45	} & {\color{black}	33.62	} & {\color{black}	24.45	} & {\color{black}	14.70	} & {\color{black}	14.09	} & {\color{black}	11.40	} & {\color{black}	13.20	} & {\color{black}	10.93	} & {\color{black}14.60} & {\color{black}	23.12	} & {\color{black}	\underline{\textbf{10.31}}	} & {\color{black}	37.08	} & {\color{black}	\underline{\textbf{10.31}}	} \\
 & {\color{black}	CRPS	} & {\color{black}	24.64	} & {\color{black}	29.61	} & {\color{black}	31.59	} & {\color{black}	29.59	} & {\color{black}	27.63	} & {\color{black}	28.09	} & {\color{black}	23.75	} & {\color{black}	26.04	} & {\color{black}	25.12	} & {\color{black}28.31} & {\color{black}	31.36	} & {\color{black}	\underline{\textbf{22.93}}	} & {\color{black}	29.92	} & {\color{black}	\underline{\textbf{22.93}}	} \\
 \hline
\multirow{6}{*}{DEC} & MAE & \underline{\textbf{17.57}} & 30.04 & 41.09 & 30.01 & 23.40 & 20.50 & 18.74 & 24.55 & 18.18 & {\color{black}22.21} & 31.54 & 21.44 & 47.41 & 21.44 \\
 & MASE & 2.62 & 4.24 & 6.53 & 4.24 & 3.30 & 3.91 & \underline{\textbf{2.56}} & 3.42 & 2.99 & {\color{black}3.41} & 5.07 & 3.30 & 8.38 & 3.30 \\
 & RMSE & \underline{\textbf{21.30}} & 33.03 & 45.11 & 33.00 & 26.49 & 24.97 & 22.83 & 28.63 & 22.00 & {\color{black}25.41} & 38.10 & 24.77 & 51.95 & 24.77 \\
 & SMAPE & \underline{\textbf{37.40}} & 74.20 & 134.40 & 74.10 & 50.80 & 48.60 & 40.50 & 59.90 & 39.00 & {\color{black}48.59} & 76.00 & 47.90 & 160.60 & 47.90 \\
 & {\color{black}	Pinball Loss	} & {\color{black}	11.59	} & {\color{black}	23.72	} & {\color{black}	32.33	} & {\color{black}	23.68	} & {\color{black}	16.55	} & {\color{black}	\underline{\textbf{9.18}}	} & {\color{black}	14.08	} & {\color{black}	19.20	} & {\color{black}	11.25	} & {\color{black}15.04} & {\color{black}	21.30	} & {\color{black}	14.46	} & {\color{black}	37.07	} & {\color{black}	14.46	} \\
 & {\color{black}	CRPS	} & {\color{black}	\underline{\textbf{23.45}}	} & {\color{black}	28.37	} & {\color{black}	32.10	} & {\color{black}	28.38	} & {\color{black}	26.41	} & {\color{black}	24.65	} & {\color{black}	25.10	} & {\color{black}	27.51	} & {\color{black}	24.04	} & {\color{black}26.07} & {\color{black}	30.23	} & {\color{black}	26.35	} & {\color{black}	30.54	} & {\color{black}	26.35	} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Madhurima Panja
Tanujit Chakraborty
Anubhab Biswas
Soudeep Deb

"E-STGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting" by Madhurima Panja, Tanujit Chakraborty, Anubhab Biswas, and Soudeep Deb, arXiv:2411.12258v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
Neural Network & TP & FP & TN & FN & Accuracy & AUC & Confidence Interval (95\%) \\ \hline
ResNet18 & 90.2\% & 0.3\% & 8.4\% & 1\% & 98.6\% & 0.99908 & 84.69 – 91.82 \\
ResNet50 & 90.9\% & 0.7\% & 8.0\% & 0.3\% & 99.0\% & 0.98751& 86.53 – 93.12 \\
DenseNet201 & 89.9\% & 0.7\% & 8.0\% & 1.5\% & 97.9\% & 0.98958 & 86.98 – 93.69\\
Ensemble NN & 91.3\% & 0.3\% & 8.4\% & 0\% & 99.7\% & 0.99954 & 87.75 – 94.22 \\
\end{tabular}
\end{adjustbox}
\caption{Confusion Matrix Values of Each Neural Network (k = 1-5)}
\end{table}
\end{document}

eess

"Fused Deep Convolutional Neural Network for Precision Diagnosis of COVID-19 Using Chest X-Ray Images" by Hussin K. Ragb, Ian T. Dover, and Redha Ali, arXiv:2009.08831v1, 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.


stat

"datadriftR: An R Package for Concept Drift Detection in Predictive Models" by Ugur Dar and Mustafa Cavus, arXiv:2412.11308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rccccc}
\toprule
\textbf{$p$} & \textbf{Mean} & \textbf{Median} & \textbf{Lower Quartile} & \textbf{Upper Quartile} \\ \midrule
$1$ & 57.19 & 57.33 & 53.00 & 61.33 \\
$2$ & 55.25 & 55.33 & 52.00 & 58.67 \\
$5$ & 56.40 & 57.33 & 52.00 & 60.00 \\
$10$ & 56.53 & 56.67 & 53.33 & 60.00 \\
$15$ & 56.35 & 56.00 & 53.33 & 60.00 \\
$20$ & 56.39 & 56.00 & 53.33 & 60.00 \\
$\infty$ & 56.39 & 56.67 & 53.33 & 60.00 \\
$1$ and $\infty$ & \textbf{59.32} & \textbf{60.00} & \textbf{56.00} & \textbf{64.00} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Mean, median, and the first and third quartile of the accuracies over the $100$ experiments for different values of $p$ in our \textbf{point cloud classification} problem. The last column represents accuracies from combining the persistence images for $1$-Rips and $\infty$-Rips.}
\end{table}
\end{document}

math

Nello Blaser
Morten Brun
Odin Hoff Gardaa
Lars M. Salbu

"Monoidal Rips: Stable Multiparameter Filtrations of Directed Networks" by Nello Blaser, Morten Brun, Odin Hoff Gardaa, and Lars M. Salbu, arXiv:2503.14019v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Forecasting Accuracy under Different Bits for Quantization}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
\multicolumn{2}{|c|}{}		& \multicolumn{2}{c|}{Wind Speed}			& \multicolumn{2}{c|}{Wind Direction} \\
\hline
$\omega_{w}$ & $\omega_{f}$ & MAE & RMSE & MAE & RMSE \\
\hline
float32 	& float32 & $0.0403$ & $ 0.0573 $ 	 & $0.0596$ & $ 0.1151 $ \\
\hline
16 & 16	& $0.0396$ & $ 0.0554 $ & $0.0609 $ & $ 0.1192$ \\
\hline
8 & 8	& $0.0392$ & $ 0.0579 $ & $ 0.0608 $ & $ 0.1191 $ \\
\hline
4 & 4	& $0.0401$ & $ 0.0571 $ & $0.0598 $ & $ 0.1183 $ \\
\hline
2 & 2	& $0.0431$ & $ 0.0614 $ & $ 0.0807 $ & $ 0.1308 $ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

"Edge Intelligence Empowered UAVs for Automated Wind Farm Monitoring in Smart Grids" by Hwei-Ming Chung, Sabita Maharjan, Yan Zhang, Frank Eliassen, and Tingting Yuan, arXiv:2009.11256v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
\hline
\textbf{Feat.} & \textbf{M1-JV} & \textbf{M1-V4} & \textbf{M2-V4} \\
\hline
PCE & 3.21 ± 0.26 & 3.34 ± 0.22 & 2.66 ± 0.66 \\
\hline
PR & 46.95 ± 9.16 & 53.07 ± 11.23 & 33.24 ± 12.54 \\
\hline
P & 5.11 ± 1.06 & 3.03 ± 2.17 & 3.71 ± 1.65 \\
\hline
NoP & 29.49 ± 9.43 & 38.41 ± 10.55 & 13.56 ± 7.37 \\
\hline
PS & 0.80 ± 0.07 & 0.77 ± 0.09 & 0.86 ± 0.10 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Comparison of musical features between Model 1 trained on the JAZZVAR dataset (M1-JV) and the VAR4000 dataset (M1-V4), and Model 2 trained on the VAR4000 dataset (M2-V4) with mean and standard deviation.}
\end{table}
\end{document}

eess

"Exploring Transformer-Based Music Overpainting for Jazz Piano Variations" by Eleanor Row, Ivan Shanin, and György Fazekas, arXiv:2412.04610v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Inverse problem CPU time comparison for the analytical benchmark case.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
 \hline
 & Alifanov's reg. & \multicolumn{2}{c|}{Parameterized heat flux} \\
 \hline
 & & offline & online \\
 \hline
 CPU time & $18.8~s$ & $7.21~s$ & $0.0056~s$\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Umberto Emil Morelli
Patricia Barral
Peregrina Quintela
Gianluigi Rozza
Giovanni Stabile

"A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation" by Umberto Emil Morelli, Patricia Barral, Peregrina Quintela, Gianluigi Rozza, and Giovanni Stabile, arXiv:2101.11985v1, 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{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc|cc}
\Xhline{2\arrayrulewidth} \hline
\multirow{2}{*}{} & \multicolumn{3}{c}{Fed-EF-SGD} & \multicolumn{2}{c}{No EF} \\ \hline
 & Sign & TopK & Hv-Sign & Stoc & Full-precision \\ \hline
MNIST & 90.87 ($\pm$0.84) & 91.04 ($\pm$1.05) & 91.18 ($\pm$1.10) & 90.16 ($\pm$0.96) & 90.85 ($\pm$0.89) \\
FMNIST & 71.13 ($\pm$0.68) & 71.16 ($\pm$0.77) & 71.07 ($\pm$0.83) & 71.26 ($\pm$0.87) & 71.20 ($\pm$0.71) \\
\hline \Xhline{2\arrayrulewidth}
\end{tabular}
\end{adjustbox}
\caption{Test accuracy (\%) with client participation rate $p=0.5$, of Fed-EF-SGD with \textbf{Sign}, \textbf{TopK} and \textbf{heavy-Sign} compressor and \textbf{Stoc} (stochastic quantization) without EF. The compression parameters (i.e., $k$ and $b$) of the compressors are consistent with Figure~.}
\end{table}
\end{document}

stat

"Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression" by Xiaoyun Li and Ping Li, arXiv:2211.14292v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\footnotesize Calibration of the HW and rHW model: parameters determined in swaption calibration. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c||c|c}
&\multicolumn{2}{c||}{$\text{Hull-White}$}&\multicolumn{2}{c}{$\text{RAnD Hull-White}$}\\\hline
$T$, expiry &$\eta$&$\lambda$&$\eta$&$\lambda$\\\hline\hline
$1y$&0.0094&0.0090&0.0091&$\lambda\sim\mathcal{N}(0.1,0.45^2)$\\
$2y$&0.0082 &0.0035 & 0.0080&$\lambda\sim\mathcal{N}(0.1,0.33^2)$\\
$5y$&0.0069&0.0020&0.0079&$\lambda\sim\mathcal{N}(0.1,0.16^2)$\\
$8y$&0.0067&0.0095&0.0080&$\lambda\sim\mathcal{N}(0.1,0.12^2)$\\
$10y$&0.0067&0.0090&0.0082&$\lambda\sim\mathcal{N}(0.1,0.11^2)$\\
$15y$&0.0064&0.0080&0.0085&$\lambda\sim\mathcal{N}(0.1,0.09^2)$\\
$20y$&0.0060&0.0080&0.0086&$\lambda\sim\mathcal{N}(0.1,0.08^2)$
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Randomization of Short-Rate Models, Analytic Pricing and Flexibility in Controlling Implied Volatilities" by Lech A. Grzelak, arXiv:2211.05014v2, 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}

cs

"No-Regret Caching via Online Mirror Descent" by T. Si Salem, G. Neglia, and S. Ioannidis, arXiv:2101.12588v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
\hline
Model & Output & RGB (px) & Depth (m) \\
\hline\hline
Base & Depth & n/a & 8.02 \\
Base & RGBD & 21.13 & 8.41 \\
Slice-10 & Depth & n/a & 7.23 \\
Slice-10 & RGBD & 20.71 & 7.01 \\
Slice-32 & Depth & n/a & \textbf{6.31} \\
Slice-32 & RGBD & 20.39 & 6.97 \\
Slice-64 & Depth & n/a & 6.64 \\
Slice-64 & RGBD & \textbf{20.37} & 7.1 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Base vs Slice models on the Synthetic dataset. The input is 6Dof-Quat, so it is omitted.}
\end{table}
\end{document}

eess

"Pose2RGBD. Generating Depth and RGB images from absolute positions" by Mihai Cristian Pîrvu, arXiv:2007.07013v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccccc}
\toprule
\textbf{Metrics} & \textbf{Method} & \textbf{Time=0} & \textbf{Time=8} & \textbf{Time=16} & \textbf{Time=24} & \textbf{Time=32} & \textbf{Avg.} \\
\midrule
\multirow{2}{*}{SSIM↑}
 & Baseline & 0.9466 & 0.9668 & 0.9706 & 0.9396 & 0.8909 & 0.9429 \\
 & \textbf{Ours} & 0.9443 & 0.9848 & 0.9864 & 0.9865 & 0.9224 & 0.9649 \\
\midrule
\multirow{2}{*}{PSNR↑}
 & Baseline & 31.4973 & 32.4322 & 34.4965 & 28.6187 & 24.5761 & 30.7242 \\
 & \textbf{Ours} & 31.0204 & 38.3950 & 39.0909 & 39.0871 & 26.9297 & 34.9046 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Comparison between Baseline (previous frame GT) and Our Method on testing timestamps.}
\end{table}
\end{document}

eess

"InnerGS: Internal Scenes Rendering via Factorized 3D Gaussian Splatting" by Shuxin Liang, Yihan Xiao, and Wenlu Tang, arXiv:2508.13287v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
{\cellcolor{gray!20}{\textbf{Parameter}}} & {\cellcolor{gray!20}{\textbf{Value}}} \\ \hline
System & 3GPP Release 16\\ \hline
Bandwidth & 10 MHz\\ \hline
Duplex mode & FDD\\ \hline
CP mode & Normal\\ \hline
Modulation & QPSK\\ \hline
\# Rx antennas & 2\\ \hline
Delay profile & Extended Vehicular A model (EVA) \\ \hline
Doppler frequency & 500 Hz\\ \hline
Fading & Rayleigh\\ \hline
Equalization & MMSE\\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Environment-Adaptive Multiple Access for Distributed V2X Network: A Reinforcement Learning Framework" by Seungmo Kim, Byung-Jun Kim, and B. Brian Park, arXiv:2101.10447v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
& & \multicolumn{4}{c}{Testing accuracy(\%)}{Convergence time(s)}\\\cmidrule{3-6}
Method & Sample size (S) & Fixed & Variate& Fixed & Variate \\ \midrule
BBVI & 200 & $74.46\pm 1.74$ & $74.11\pm 1.28$ & 1247 & 1860\\
& 500 & $74.28\pm 0.91$ & $74.64\pm 0.56$ & 1359 & 2061\\
& 1000 & $75.89\pm 0.56$ & $75.12\pm 0.80$&1652 & 2194\\
BBVI-CV & 200 & $75.71\pm0. 61$ & $73.93\pm 5.95 $ & 1201 & 1268\\
 & 500 & $75.35\pm 0.56$ & $74.11\pm 5.95$& 1263 & 1255\\
& 1000 & $75.00 \pm0.00$ & $74.28\pm6.00$& 1735 & 1466\\\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Performance for BBVI and BBVI-CV.}
\end{table}
\end{document}

stat

Shrijita Bhattacharya
Zihuan Liu
Tapabrata Maiti

"Variational Bayes Neural Network: Posterior Consistency, Classification Accuracy and Computational Challenges" by Shrijita Bhattacharya, Zihuan Liu, and Tapabrata Maiti, arXiv:2011.09592v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Values of test setup variables for the statistical analysis of the drifting balloon problem.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 Variable & Tested Values \\
 \hline
 Parameter process noise ($q_p$) & [1e-10, 1e-8, 1e-6, 1e-4, 1e-2] \\
 Measurement Noise ($r$) & [1e-6, 1e-5, 5e-5, 1e-4] \\
 Static Bias Parameter ($A$) & [0, 0.001, 0.01, 0.1, 0.5] \\
 Linear Bias Parameter ($B$)& [0, 0.001, 0.01, 0.1, 2.0] \\
 Quadratic Bias Parameter ($C$)& [0, 0.001, 0.01, 0.1, 2.0] \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Artem Mustaev
Nicholas Galioto
Matt Boler
John D. Jakeman
Cosmin Safta
Alex Gorodetsky

"A switching Kalman filter approach to online mitigation and correction of sensor corruption for inertial navigation" by Artem Mustaev, Nicholas Galioto, Matt Boler, John D. Jakeman, Cosmin Safta, and Alex Gorodetsky, arXiv:2412.06601v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccc}
			\toprule
			 & COCO & VG & SBU & CC3M & CC12M & LAION \\
			\midrule
			\# image & 113K & 100K & 860K & 3M & 10M & 115M\\
			\# text & 567K & 769K & 860K & 3M & 10M & 115M\\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Junnan Li
Dongxu Li
Caiming Xiong
Steven Hoi

"BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation" by Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi, arXiv:2201.12086v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Inverse problem CPU time comparison for the industrial benchmark case.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
 \hline
 & Alifanov's reg. & \multicolumn{2}{c|}{Parameterized heat flux} \\
 \hline
 & & offline & online \\
 \hline
 CPU time & $221~s$ & $121.4~s$ & $0.15~s$\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc} \toprule
\textbf{Method} & 100 uncrpt & 100 crpt \\ \midrule
FC3, KIP & \textbf{43.09$\pm$0.20} & \textit{37.71$\pm$0.38} \\
FC3, Natural & 24.48$\pm$0.15 & 18.92$\pm$0.61 \\
\midrule
Conv2, KIP & \textbf{43.68$\pm$0.46} & \textit{37.08$\pm$0.48} \\
Conv2, Natural & 26.23 $\pm$0.69 & 17.10$\pm$1.33 \\
\midrule
WideResNet, KIP & \textbf{33.29$\pm$1.14} & 23.89$\pm$1.30 \\
WideResNet, Natural & \textit{27.93$\pm$0.75} & 19.00$\pm$1.01 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Timothy Nguyen
Zhourong Chen
Jaehoon Lee

"Dataset Meta-Learning from Kernel Ridge-Regression" by Timothy Nguyen, Zhourong Chen, and Jaehoon Lee, arXiv:2011.00050v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccc|}
 \hline
 & $T_1$ & $T_2$ & $T_3$ & $T_4$ \\\hline
 $\theta^*$ & $\langle (0.87,0.13);0.86 \rangle$ & $\langle (0.66,0.34);0.34 \rangle$& $\langle (0.84,0.16);0.82 \rangle$ & $\langle (0.65,0.35);0.35 \rangle$ \\ \hline
 $\theta^-$ & $\langle (0.53,0.47);0.51 \rangle$ & $\langle (0.32,0.68);0.68 \rangle$& $\langle (0.44,0.56);0.44 \rangle$ & $\langle (0.42,0.58);0.58 \rangle$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{C-IF positive and negative ideal solutions}
\end{table}
\end{document}

math

"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Top-1 ImageNet validation accuracy of architectures used throughout the text, with the references indicating the source for the listed accuracy. * are models found by us using OFA~ for a specific target complexity metric.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
Architecture & ImageNet Top-1 [$\%$] & Reference \\
\hline
EfficientNet-B0 & 77.7 & Ours, using \\
SPNASNet-100 & 74.084 & From \\
MNasNet-B1-1.0$\times$ & 74.658 & From \\
MNasNet-A1-1.0$\times$ & 75.448 & From \\
MNasNet-A1-1.4$\times$ & 77.2 & From \\
FBNet-C-100 & 78.124 & From \\
MobileNetV2 (1.0x) & 72.970 & From \\
MobileNetV2 (1.4x) & 76.516 & From \\
MobileNetV3 (Large) & 75.766 & From \\
ProxyLessNas CPU & 75.3 & From \\
ProxyLessNas GPU & 75.1 & From \\
ProxyLessNas Mobile & 74.6 & From \\
ResNet34 & 75.1 & From \\
ResNet50 & 79.0 & From \\
OFA/Scratch & 77.5 & Ours, with \\
OFA-flops-A* & 77.3 & Ours, with \\
OFA-flops-B* & 77.5 & Ours, with \\
OFA-flops-C* & 78.6 & Ours, with \\
OFA-sim-A* & 77.1 & Ours, with \\
OFA-sim-B* & 78.1 & Ours, with \\
OFA-sim-C* & 78.5 & Ours, with \\
DNA-A & 77.1 & From \\
DNA-B & 77.5 & From \\
DNA-C & 77.8 & From \\
DNA-D & 78.4 & From \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Bert Moons
Parham Noorzad
Andrii Skliar
Giovanni Mariani
Dushyant Mehta
Chris Lott
Tijmen Blankevoort

"Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces" by Bert Moons, Parham Noorzad, Andrii Skliar, Giovanni Mariani, Dushyant Mehta, Chris Lott, and Tijmen Blankevoort, arXiv:2012.08859v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|}
\hline
 Country & Initial reports & Intervention & Population&$n$& $I(0)$\\
\hline
USA &January 20 &March 22& 331,002,651 & 485 &5 \\
Brazil &February 26 &March 24& 212,559,417& 448& 5\\
Mexico &February 28 &March 23& 128,932,753& 446& 6\\
Argentina & March 03&March 19& 45,195,774 & 442& 5\\
Chile &March 03&March 24 & 19,116,201 & 442& 5\\
Colombia &March 06&March 25 & 50,882,891 & 439&5\\
Peru &March 07&March 16& 32,971,854 & 438&9\\
Panama &March 10&March 24 & 4, 314,767 & 435& 5\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"A modified Susceptible-Infected-Recovered model for observed under-reported incidence data" by Imelda Trejo and Nicolas Hengartner, arXiv:2012.05294v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Detailed information on domain split of Bacterial and Viral datasets.}
\begin{tabular}{ccccccc}
		\toprule
		Dataset & Domain & Sympotom & \#normal & \#pneumonia & \#COVID-19 & \#Total \\ \hline
		\multirow{3}{*}{Bacterial} & source & Pneumonia & 1,660 & 3,001 & 0 & 4,661 \\
		& target & COVID-19 & 1,610 & 0 & 1,281 & 2,891 \\ \hline
		\multirow{3}{*}{Viral} & source & Pneumonia & 1,660 & 1,656 & 0 & 3,316 \\
		& target & COVID-19 & 1,610 & 0 & 1,281 & 2,891 \\ \bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

Jindong Wang
Wenjie Feng
Chang Liu
Chaohui Yu
Mingxuan Du
Renjun Xu
Tao Qin
Tie-Yan Liu

"Learning Invariant Representations across Domains and Tasks" by Jindong Wang, Wenjie Feng, Chang Liu, Chaohui Yu, Mingxuan Du, Renjun Xu, Tao Qin, and Tie-Yan Liu, arXiv:2103.05114v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|r|r}
 \hline \textbf{Method} & \textbf{input} & \textbf{Points} & \textbf{Accuracy} \\
 \hline
 PointNet & P & $1 \mathrm{K }$ & $89.2 \%$ \\
 SO-Net & P, N & $2 \mathrm{K}$ & $90.9 \%$ \\
 A-CNN & P, N & $1 \mathrm{K}$ & $92.6 \%$ \\
 PointNet++ & P & $1 \mathrm{K}$ & $90.7 \%$ \\
 P2Sequence & P & $1 \mathrm{K}$ & $92.6 \%$ \\
 Kd-Net & P & $32 \mathrm{K}$ & $91.8 \%$ \\
 PCNN & P & $1 \mathrm{K}$ & $92.3 \%$ \\
 PointCNN & P & $1 \mathrm{K}$ & $92.5 \%$ \\
 PointConv & P, N & $1 \mathrm{K}$ & $92.5 \%$ \\
 RS-CNN & P & $1 \mathrm{K}$ & $92.9 \%$ \\

 KPConv & P & $7 \mathrm{K}$ & $92.9 \%$ \\
 DG-CNN & P & $1 \mathrm{K}$ & $92.9 \%$ \\
 PointGrid & P & $1 \mathrm{K}$ & $92.0 \%$ \\

 DC-GNN & P & $1 \mathrm{K}$ & $93. 64 \%$ \\
 \hline
 \textbf{Ours} & P & $1 \mathrm{K}$ & $\mathbf{92 . 71 \%}$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Md Meraz
Md Afzal Ansari
Mohammed Javed
Pavan Chakraborty

"Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation" by Md Meraz, Md Afzal Ansari, Mohammed Javed, and Pavan Chakraborty, arXiv:2412.03052v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
Batch Size & Accuracy (\%) & Recall (\%) & Precision (\%) & F1-Measure (\%)\\
\hline
3	&97.73	&95.17	&94.41	&94.57\\
10	&95.29	&91.51	&88.64	&88.84\\
20	&97.97	&94.95	&95.98	&95.40\\
30	&97.82	&95.01	&95.70	&95.30\\
40	&97.93	&94.91	&95.96	&95.34\\
50	&95.66	&91.62	&92.83	&91.50\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Obtained classification accuracies with different batch size.}
\end{table}
\end{document}

eess

"Effect of Different Batch Size Parameters on Predicting of COVID19 Cases" by Ali Narin and Ziynet Pamuk, arXiv:2012.05534v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison with the recent top-five SotA approaches on each dataset. Methods marked with * involve transfer/joint learning strategy for objects/patches/regions consisting more than one dataset (primary and secondary)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc|lc}
 \toprule
 \multicolumn{2}{c}{\textbf{Stanford Dogs}}&
 \multicolumn{2}{c}{\textbf{Oxford Flowers}} \\
 {Method} & {Accuracy (\%)} & {Method} & {Accuracy (\%)} \\
 \midrule
 FCANs (Liu et al. 2016) %
 &	89.0 & InterAct (Xie et al. 2016) %
 & 96.4 \\
 SJFT$^*$ (Ge and Yu 2017)%\cite {ge2017borrowing}
 & 90.3 & SJFT$^*$ (Ge and Yu 2017) %
 & 97.0 \\
 DAN (Hu et al. 2019)%\cite {hu2019see}
 &92.2 &OPAM$^*$ (Peng, He, and Zhao 2018) %
 & 97.1 \\
 WARN (Rodríguez et al. 2020) %\cite {lopez2020pay}
 &92.9 &DSTL$^*$ (Cui et al. 2018) %
 & 97.6 \\
 CPM$^*$ (Ge, Lin, and Yu 2019) %
 & \textbf{97.1}& MC$_{Loss}*$
 (Chang et al. 2020) %
 & \textbf{97.7} \\
 \hline
 \textbf{Proposed} & {96.1} &\textbf{Proposed} & \textbf{97.7} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ardhendu Behera
Zachary Wharton
Pradeep Hewage
Asish Bera

"Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification" by Ardhendu Behera, Zachary Wharton, Pradeep Hewage, and Asish Bera, arXiv:2101.06635v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{arydshln}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{MISE per method on dry bean dataset} for $\beta = \frac{3}{4}$ and varying levels of $\alpha$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 $\beta = \frac{3}{4}$\\
 \hline \\
 \hline \\
 & \multicolumn{4}{c}{$\alpha$}\\
 \cdashline{2-5}\\
 Model & 1.0 & 2.0 & 3.0 & 4.0 \\
 \hline \\
 Linear Regression & 2.91 \scriptsize{± 0.01} & 2.92 \scriptsize{± 0.02} & 3.01 \scriptsize{± 0.03} & 3.01 \scriptsize{± 0.02} \\
 CART & 1.24 \scriptsize{± 0.11} & 1.31 \scriptsize{± 0.08} & 1.32 \scriptsize{± 0.10} & 1.43 \scriptsize{± 0.11} \\
 xgboost & 1.04 \scriptsize{± 0.05} & 1.04 \scriptsize{± 0.08} & 1.00 \scriptsize{± 0.06} & 1.09 \scriptsize{± 0.06} \\
 MLP & 1.19 \scriptsize{± 0.09} & 1.19 \scriptsize{± 0.1} & 1.64 \scriptsize{± 0.11} & 1.57 \scriptsize{± 0.15} \\
 \hdashline\\
 DRNet & 0.60 \scriptsize{± 0.07} & 0.59 \scriptsize{± 0.06} & 0.71 \scriptsize{± 0.05} & 0.70 \scriptsize{± 0.08} \\
 VCNet & 0.55 \scriptsize{± 0.10} & 0.62 \scriptsize{± 0.07} & 0.60 \scriptsize{± 0.05} & 0.66 \scriptsize{± 0.07} \\
 \hdashline\\
 CBRNet(MMD$_{lin}$) & \textit{0.35} \scriptsize{± 0.07} & \textit{0.39} \scriptsize{± 0.11} & \textbf{0.39} \scriptsize{± 0.09} & \textbf{0.45} \scriptsize{± 0.06} \\
 CBRNet(MMD$_{rbf}$) & 0.43 \scriptsize{± 0.11} & 0.49 \scriptsize{± 0.18} & \textit{0.42} \scriptsize{± 0.06} & \textit{0.51} \scriptsize{± 0.13} \\
 CBRNet(Wass) & \textbf{0.34} \scriptsize{± 0.06} & \textbf{0.38} \scriptsize{± 0.10} & \textit{0.42} \scriptsize{± 0.08} & \textbf{0.45} \scriptsize{± 0.07} \\
 \hline \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Christopher Bockel-Rickermann
Toon Vanderschueren
Jeroen Berrevoets
Tim Verdonck
Wouter Verbeke

"Using representation balancing to learn conditional-average dose responses from clustered data" by Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets, Tim Verdonck, and Wouter Verbeke, arXiv:2309.03731v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results for Constant Radial Strain Patch Test}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
Strain Component & Computed Average & Expected Value & Absolute Error \\
\midrule
\( \varepsilon_r \) (Radial strain) & 0.010000 & 0.010000 & \(1.73\times10^{-18}\) \\
\( \varepsilon_z \) (Axial strain) & \(-0.000000\) & 0.000000 & \(1.69\times10^{-21}\) \\
\( \varepsilon_\theta \) (Hoop strain) & 0.003247 & 0.000000 & \(3.25\times10^{-3}\) \\
\( \gamma_{rz} \) (Shear strain) & \(-0.000000\) & 0.000000 & \(8.67\times10^{-19}\) \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Virtual Element Method Applied to Two Dimensional Axisymmetric Elastic Problems" by Paulo Akira F. Enabe and Rodrigo Provasi, arXiv:2504.21305v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Information-theoretic subset selection of multivariate Markov chains via submodular optimization" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|} \hline
		\multirow{2}{*}{\parbox[c]{1.8cm}{\centering{Number of crossings}}} & \multicolumn{4}{c|}{Kh width} \\ \cline{2-5}
		& 2 & 3 & 4 & 5 \\ \hline
		13 & 4554 ($\sim$73\%)& 1678 ($\sim$27\%)& 4 & 0 \\ \hline
		14 & 23691 ($\sim$70\%) & 9928 ($\sim$30\%)& 53 & 0 \\ \hline
		15 & 135054 ($\sim$67\%) & 66101($\sim$33\%) & 547 & 0 \\ \hline
		16 & 767345 ($\sim$63\%)& 437781 ($\sim$36\%)& 5480 & 0 \\ \hline
		17 & 4481503 ($\sim$60\%) & 2953589 ($\sim$40\%)& 58896 ($\sim$1\%)& 20 \\ \hline
	\end{tabular}
\end{adjustbox}
\caption{The distribution of width of even Khovanov homology of all non-alternating knots from 13 to 17 crossings.}
\end{table}
\end{document}

math

Data Driven Perspectives on Knot Theory

Pawel Dlotko
Davide Gurnari
Radmila Sazdanovic

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 Resolution & Error & Order \\
 \hline
 $h_0$ & 3.97E$-$2 & $-$ \\
 \hline
 $h_1$ & 2.00E$-$2 & $0.99$ \\
 \hline
 $h_2$ & 1.00E$-$2 & $1.00$ \\
 \hline
 $h_3$ & 5.00E$-$3 & $1.00$ \\
 \hline
 $h_4$ & 2.50E$-$3 & $1.00$ \\
 \hline
 $h_5$ & 1.25E$-$3 & $1.00$ \\
 \hline
 $h_6$ & 6.25E$-$4 & $1.00$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Improved weights (), example 2.}
\end{table}
\end{document}

math

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


stat

"Generalized M-Estimation in Censored Regression Model under Endogeneity" by Swati Shukla, Subhra Sankar Dhar, and Shalabh, arXiv:2312.10690v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \textbf{N} & DRT3D & PPFFT3D \\
 \hline
 4 & 10.7983 & 5.8997\\
 8 & 24.1062 & 13.1113 \\
 16 & 67.1549 & 37.7967 %\phantom{54.7688}
\end{tabular}
\end{adjustbox}
\caption{\color{blue} $L_2$ norm condition numbers for DRT and Pseudo Polar Fast Fourier based Radon transform}
\end{table}
\end{document}

math

José Marichal-Hernández
Óscar Gómez-Cárdenes
Fernando Rosa
Do Hyung Kim
José M. Rodríguez-Ramos

"Three-dimensional multiscale discrete Radon and John transforms" by José Marichal-Hernández, Óscar Gómez-Cárdenes, Fernando Rosa, Do Hyung Kim, and José M. Rodríguez-Ramos, arXiv:2501.13664v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Unemployment and Endogenous Reallocation over the Business Cycle" by Carlos Carrillo-Tudela and Ludo Visschers, arXiv:2304.00544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{amsfonts}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
			\toprule
			$\mathcal{D}_{\theta^*} - train$ & $\mathcal{D}_{\theta^*} - test$ & Metric & SIDD & OOD Avg. \\
			\midrule\midrule
			\multirow{7}{*}{NAFNet~} & \multirow{2}{*}{Restormer~} & PSNR & 39.19 & 39.75 \\
			 & & SSIM & 0.9567 & 0.9820 \\
			\cmidrule{2-5}
			 & \multirow{2}{*}{KBNet~} & PSNR & 39.15 & 39.84 \\
			 & & SSIM & 0.9565 & 0.9819 \\
			\cmidrule{2-5}
			 & \multirow{2}{*}{Xformer~} & PSNR & 39.02 & 39.84 \\
			 & & SSIM & 0.9555 & 0.9822 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Inju Ha
Donghun Ryou
Seonguk Seo
Bohyung Han

"Learning to Translate Noise for Robust Image Denoising" by Inju Ha, Donghun Ryou, Seonguk Seo, and Bohyung Han, arXiv:2412.04727v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
Graphs & Algorithm & Max. Coverage & Wt. Precision & F-Score\\ \hline
\multirow{3}{*}{\parbox[c]{2cm}{\begin{center}$k\geq 2$\\(100\%)\end{center}}} & Unitigs & 0.42 & 1.00 & 0.58\\
& ExtUnitigs & 0.59 & 1.00 & 0.73\\
& Safe\&Comp & 0.71 & 1.00 & 0.82\\
& Greedy & 0.89 & 0.92 & 0.89\\
\hline
\multirow{3}{*}{\parbox[c]{2cm}{\begin{center}$2\le k \le 10$\\(98\%)\end{center}}} & Unitigs & 0.42 & 1.00 & 0.58\\
& ExtUnitigs & 0.59 & 1.00 & 0.74\\
& Safe\&Comp & 0.71 & 1.00 & 0.83\\
& Greedy & 0.89 & 0.93 & 0.90\\
\hline
\multirow{3}{*}{\parbox[c]{2cm}{\begin{center}$k>10$\\(2\%)\end{center}}} & Unitigs & 0.24 & 1.00 & 0.38\\
& ExtUnitigs & 0.42 & 1.00 & 0.59\\
& Safe\&Comp & 0.58 & 1.00 & 0.74\\
& Greedy & 0.82 & 0.49 & 0.56\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Summary of evaluation metrics for the Catfish dataset, computed relative to nodes.}
\end{table}
\end{document}

cs

Shahbaz Khan
Milla Kortelainen
Manuel Cáceres
Lucia Williams
Alexandru I. Tomescu

"Safety and Completeness in Flow Decompositions for RNA Assembly" by Shahbaz Khan, Milla Kortelainen, Manuel Cáceres, Lucia Williams, and Alexandru I. Tomescu, arXiv:2201.10372v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Microscopic Traffic Models, Accidents, and Insurance Losses" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Run time, in seconds, and (number of iterations until convergence) for different applications, averaged over images in Set12, on an NVIDIA RTX 8000 GPU.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 \toprule
 Inpainting $30\times 30$ & Missing pixels 10\% & SISR 4:1 & Deblurring 10\% & CS 10\%\\
 \midrule
 15.7 (647) & 7.8 (313)& 7.6 (301) & 8.4 (332) & 32.5 (317) \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser" by Zahra Kadkhodaie and Eero P. Simoncelli, arXiv:2007.13640v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Tianyu Qiu
Eric Ouano
Fernando Palafox
Christian Ellis
David Fridovich-Keil

"PSN Game: Game-theoretic Planning via a Player Selection Network" by Tianyu Qiu, Eric Ouano, Fernando Palafox, Christian Ellis, and David Fridovich-Keil, arXiv:2505.00213v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Ze-yu Liu
Jian-wei Liu
Xin Zuo
Ming-fei Hu

"Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection" by Ze-yu Liu, Jian-wei Liu, Xin Zuo, and Ming-fei Hu, arXiv:2201.09574v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation" by Alexandre Rame and Matthieu Cord, arXiv:2101.05544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Zardad Khan
Naz Gul
Nosheen Faiz
Asma Gul
Werner Adler
Berthold Lausen

"Optimal trees selection for classification via out-of-bag assessment and sub-bagging" by Zardad Khan, Naz Gul, Nosheen Faiz, Asma Gul, Werner Adler, and Berthold Lausen, arXiv:2012.15301v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Sophie Wharrie
Lisa Eick
Lotta Mäkinen
Andrea Ganna
Samuel Kaski
FinnGen

"Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal Mechanisms" by Sophie Wharrie, Lisa Eick, Lotta Mäkinen, Andrea Ganna, Samuel Kaski, and FinnGen, arXiv:2310.12595v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{DG spaces using complete polynomials}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
{$k$} & {$\mathcal{V}_h^-$}
 & {$\mathcal{V}_h$}
& {$\mathcal{V}_h^+$} \\
\hline
 0 & N/A & $\mathcal{P}_{r+1}(\mathcal{T}_h)$ & $[\mathcal{P}_r(\mathcal{T}_h)]^3$\\
 1 & $\mathcal{P}_{r+1}(\mathcal{T}_h)$ & $[\mathcal{P}_r(\mathcal{T}_h)]^3$ & $[\mathcal{P}_{r-1}(\mathcal{T}_h)]^3$ \\
 2 & $[\mathcal{P}_{r+1}(\mathcal{T}_h)]^3$ & $[\mathcal{P}_r(\mathcal{T}_h)]^3$ & $\mathcal{P}_{r-1}(\mathcal{T}_h)$ \\
 3 & $[\mathcal{P}_{r+1}(\mathcal{T}_h)]^3$ & $\mathcal{P}_r(\mathcal{T}_h)$ & N/A \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"An Extended Galerkin analysis in finite element exterior calculus" by Qingguo Hong, Yuwen Li, and Jinchao Xu, arXiv:2101.09735v1, 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}{lrrrr}
\toprule
\textbf{language} & \textbf{\#doc} & \textbf{\#ent} & \textbf{\#rel} & \textbf{\#attr} \\ \midrule
de & 118 & 3,487 & 2,163 & 1,141 \\
fr & 100 & 1,939 & 1,129 & 537 \\
ja & 619 & 9,464 & 5,083 & 2,364 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Lisa Raithel
Hui-Syuan Yeh
Shuntaro Yada
Cyril Grouin
Thomas Lavergne
Aurélie Névéol
Patrick Paroubek
Philippe Thomas
Tomohiro Nishiyama
Sebastian Möller
Eiji Aramaki
Yuji Matsumoto
Roland Roller
Pierre Zweigenbaum

"A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages" by Lisa Raithel, Hui-Syuan Yeh, Shuntaro Yada, Cyril Grouin, Thomas Lavergne, Aurélie Névéol, Patrick Paroubek, Philippe Thomas, Tomohiro Nishiyama, Sebastian Möller, Eiji Aramaki, Yuji Matsumoto, Roland Roller, and Pierre Zweigenbaum, arXiv:2403.18336v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\large Table 1: Connectedness Table / Network Adjacency Matrix, $D$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 			\toprule
 			& \bf $x_1$ & \bf $x_2$ & \bf $...$ & \bf $x_N$ & From Others to $i$ \\
 			&&&&& (In-Degrees) \\ \midrule
 			\bf $x_1$ & $ d_{11}^H$ & $d_{12}^H$ & $\cdots$ & $d_{1N}^H$ & $\Sigma_{j=2}^N d_{1j}^H$ \\
 			\bf $x_2$ & $d_{21}^H$ & $d_{22}^H$ & $\cdots$ & $d_{2N}^H$ & $\Sigma_{j=1, j \ne 2}^N d_{2j}^H$ \\
 			\bf $\vdots$ & $\vdots$& $\vdots$ & $\ddots$ & $\vdots$ & \vdots \\
 			\bf $x_N$ & $d_{N1}^H$ & $d_{N2}^H$ & $\cdots$ & $ d_{NN}^H$ & $\Sigma_{j=1}^{N-1} d_{Nj}^H$ \\ \midrule
 			To Others From $j$ & {$\Sigma_{i=2}^N d_{i1}^H$} & {$\Sigma_{i=1, i \ne 2}^N d_{i2}^H$} & {$\cdots$} & {$\Sigma_{i=1}^{N-1} d_{iN}^H$} & {$\Sigma_{i,j=1; i \ne j}^N d_{ij}^H$} \\
 			(Out-Degrees) & & & {} & & \\
 			\bottomrule
 		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"On the Past, Present, and Future of the Diebold-Yilmaz Approach to Dynamic Network Connectedness" by Francis X. Diebold and Kamil Yilmaz, arXiv:2211.04184v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Model} & \textbf{SIG} & \textbf{BAK} & \textbf{OVRL} & \textbf{NISQA} \\
 \midrule
 Voicefixer (before) & 2.657 & 3.080 & 2.295 & 2.919 \\
 Voicefixer (after) & \textbf{3.096} & \textbf{3.745} & \textbf{2.756} & \textbf{3.312} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Evaluation on real-world singing-voice recordings before and after DPO model's annotation}
\end{table}
\end{document}

eess

Junan Zhang
Xueyao Zhang
Jing Yang
Yuancheng Wang
Fan Fan
Zhizheng Wu

"Multi-Metric Preference Alignment for Generative Speech Restoration" by Junan Zhang, Xueyao Zhang, Jing Yang, Yuancheng Wang, Fan Fan, and Zhizheng Wu, arXiv:2508.17229v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Hongfei Wang
Binghui Liu
Long Feng
Yanyuan Ma

"Fisher's combined probability test for cross-sectional independence in panel data models with serial correlation" by Hongfei Wang, Binghui Liu, Long Feng, and Yanyuan Ma, arXiv:2309.08543v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Linear evaluation accuracy (\%) of contrastive learning methods with the original and sparse projectors on CIFAR-10, CIFAR-100, and ImageNet-100.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccc}
 \toprule
 Dataset & Framework & Baseline & Sparse Projector & Gains \\ \midrule
 \multirow{2}{*}{CIFAR-10} & SimCLR & 87.47 & 88.16 & +0.69 \\
 & Barlow Twins & 89.00 & 89.48 & +0.48 \\ \midrule
 \multirow{2}{*}{CIFAR-100} & SimCLR & 58.12 & 61.99 & +3.87\\
 & Barlow Twins & 64.16 & 68.15 & +3.99 \\ \midrule
 \multirow{2}{*}{ImageNet-100} & SimCLR & 67.36 & 68.48 & +1.12 \\
 & Barlow Twins & 69.96 & 71.86 & +1.90 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Zhuo Ouyang
Kaiwen Hu
Qi Zhang
Yifei Wang
Yisen Wang

"Projection Head is Secretly an Information Bottleneck" by Zhuo Ouyang, Kaiwen Hu, Qi Zhang, Yifei Wang, and Yisen Wang, arXiv:2503.00507v2, 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{Success rates of the CBO method on the non-convex function with heart-shaped constraint.}
\begin{tabular}{|c|r|r|r|r|}
 \hline
 \multirow{2}{*}{$K$} & \multicolumn{4}{c|}{${N}$} \\
 \cline{2-5}
 & \textbf{10} & \textbf{20} & \textbf{50} & \textbf{100} \\
 \hline
 5 & 0.29 & 0.54 & 0.90 & 0.95 \\
 10 & 0.46 & 0.69 & 0.88 & 0.99 \\
 20 & 0.54 & 0.81 & 0.95 & 1.00 \\
 50 & 0.59 & 0.78 & 0.98 & 0.99 \\
 100 & 0.57 & 0.81 & 0.97 & 1.00 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

"Well-posedness and approximation of reflected McKean-Vlasov SDEs with applications" by P. D. Hinds, A. Sharma, and M. V. Tretyakov, arXiv:2412.20247v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|r|r|r|r}
 \hline
 \multicolumn{2}{c}{} & \multicolumn{2}{|c}{End item demand only} & \multicolumn{2}{|c}{Component demand} \\
 \hline
 Utilization & $\lambda$ & Runtime (s) & $\Delta$ (\%) & Runtime (s) & $\Delta$ (\%)\\
 \hline
 \multirow{4}{*}{50\%}
 & 0.1 & 809.2 & 5.22 & 1651.4 & 7.83 \\
 & 1 & 324.4 & \textbf{1.35} & 533.4 & \textbf{2.69} \\
 & 10 & 260.7 & 2.40 & 354.4 & 3.03 \\
 & 100 & 288.9 & 2.53 & 377.3 & 3.06 \\
 \hline
 \multirow{4}{*}{90\%}
 & 0.1 & 905.4 & 7.79 & 1950.4 & 5.31\\
 & 1 & 394.5 & \textbf{4.35} & 1012.4 & \textbf{2.48}\\
 & 10 & 436.3 & 4.63 & 600.0 & 2.50\\
 & 100 & 329.2 & 5.91 & 768.8 & \textbf{2.48} \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Majority voting consensus calculation: Evaluation of the $\lambda$-multiple in terms of average runtime and cost delta to optimal cost on a scenario tree with $|\Omega| = 2$. Results are reported for 96 test instances grouped by utilization and component type.}
\end{table}
\end{document}

math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccccccccc|c} \hline \hline
Manual& \multicolumn{3}{c}{BL Label} & \multicolumn{3}{c}{LM Label} & \multicolumn{3}{c}{MPQA Label} &\\
Label & {Pos} & {Neg} & {Neu} & {Pos} & {Neg} & {Neu} & {Pos} & {Neg} & {Neu} & {Total} \\\hline
{Pos} & 56 & 4 & 1 & 41 & 12 & 8 & 61 & 0 & 0 & 61\\
{Neg} & 9 & 2 & 1 & 0 & 9 & 3 & 9 & 2 & 1 & 12\\
{Neu} & 22 & 5 & 0 & 10 & 15 & 2 & 26 & 0 & 1 & 27\\\hline
{Total} & 87 & 11 & 2 & 51 & 36 & 13 & 96 & 2 & 2 & 100\\
		\hline
 \hline
	\end{tabular}
\end{adjustbox}
\caption{Sentiment Classification Results for 100 Randomly Selected Articles}
\end{table}
\end{document}

q-fin

Junni L. Zhang
Wolfgang Karl Härdle
Cathy Y. Chen
Elisabeth Bommes

"Distillation of News Flow into Analysis of Stock Reactions" by Junni L. Zhang, Wolfgang Karl Härdle, Cathy Y. Chen, and Elisabeth Bommes, arXiv:2009.10392v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Image reconstruction normalized mean-squared error computed for each reconstruction approach within the abnormality regions. The metric is reported per compartment and was computed on the (A) 4$\times$ and (B) 8$\times$ retrospectively undersampled MRIs from the Fast-MRI knee validation set. `All' refers to the average NMSE on all the bounding boxes. \textbf{Bold} values represent statistically significant difference when a model's performance was compared against the relative baseline reconstruction method (e.g. (FNAF U-Net vs U-Net), (FNAF I-RIM vs I-RIM)). A two-sided Wilcoxon rank-sum test was conducted using $p\leq \alpha$, with $\alpha$ set to $0.05$ as the significance level. Per-abnormality summaries of the NMSE computed in all the samples are reported in Fig.~-~. Column 2- titled N- reports the count of occurrences of each abnormality, in the validation-set.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c|c|c}
\hline\hline
\multicolumn{7}{c}{AF=4$\times$}\\
Compartment & N & U-Net & FNAF U-Net & B-Box U-Net & I-RIM & FNAF I-RIM & B-Box I-RIM \\
\hline
Cart Med Fem& 19 & 0.04720 & 0.04771 & 0.04695 & 0.04003 & 0.03989 & \textbf{0.03685} \\
Cart Lat Fem& 20 & 0.05818 & 0.06046 & 0.06353 & 0.04588 & 0.04574 & \textbf{0.04365} \\
Cart Med Tib& 5 & 0.08705 & 0.08843 & 0.09137 & 0.09260 & 0.08821 & 0.08289 \\
Cart Lat Tib& 7 & 0.06148 & 0.06145 & \textbf{0.06959} & 0.05064 & 0.05035 & \textbf{0.04595} \\
BML Med Fem & 24 & 0.04153 & 0.04142 & \textbf{0.04298} & 0.03880 & \textbf{0.03790} & \textbf{0.03440} \\
BML Lat Fem & 29 & 0.05803 & 0.05847 & \textbf{0.06009} & 0.05245 & \textbf{0.05099} & \textbf{0.04690} \\
BML Med Tib & 10 & 0.06031 & 0.06091 & 0.06315 & 0.05905 & \textbf{0.05744} & \textbf{0.05201} \\
BML Lat Tib & 16 & 0.04905 & 0.04920 & 0.04730 & 0.04777 & \textbf{0.04661} & \textbf{0.04389} \\
Med Men & 11 & 0.04902 & 0.04964 & 0.05014 & 0.04139 & 0.04151 & \textbf{0.0392} \\
Lat Men & 11 & 0.04314 & 0.04421 & \textbf{0.04838} & 0.03321 & 0.03335 & \textbf{0.03158} \\
Cyst & 5 & 0.03557 & 0.03585 & 0.03665 & 0.03494 & 0.03496 & 0.02945 \\
\hline
All & & 0.05213 & 0.05277 & 0.05437 & 0.04649 & 0.04569 & 0.04232 \\
\hline\hline
 \\
\multicolumn{7}{c}{(A)} \\
\\
\hline\hline
\multicolumn{7}{c}{AF=8$\times$}\\
Compartment & N& U-Net & FNAF U-Net & B-Box U-Net & I-RIM & FNAF I-RIM & B-Box I-RIM \\
\hline
Cart Med Fem & 19 & 0.08435 & 0.08399 & \textbf{0.07794} & 0.06181 & 0.06418 & \textbf{0.05611} \\
Cart Lat Fem & 20 & 0.08809 & 0.08767 & 0.08742 & 0.06375 & 0.06402 & 0.06216 \\
Cart Med Tib & 5 & 0.15370 & 0.15350 & 0.13490 & 0.13850 & 0.13500 & 0.11910 \\
Cart Lat Tib & 7 & 0.11830 & 0.11790 & 0.13470 & 0.09268 & 0.09446 & \textbf{0.08259} \\
BML Med Fem & 24 & 0.08110 & 0.08178 & 0.07576 & 0.06020 & 0.06065 & \textbf{0.05374} \\
BML Lat Fem & 29 & 0.10280 & 0.10050 & \textbf{0.09199} & 0.08084 & 0.08059 & \textbf{0.06936} \\
BML Med Tib & 10 & 0.10990 & \textbf{0.10610} & 0.10840 & 0.08304 & \textbf{0.08070} & 0.07396 \\
BML Lat Tib & 16 & 0.07505 & 0.073440 & 0.07519 & 0.06799 & 0.06768 & \textbf{0.06196} \\
Med Men & 11 & 0.0828 & 0.08043 & 0.08265 & 0.06721 & 0.06654 & 0.06524 \\
Lat Men & 11 & 0.09097 & 0.08793 & 0.09225 & 0.05334 & 0.05439 & 0.05393 \\
Cyst & 5 & 0.07787 & 0.07586 & 0.07092 & 0.05993 & 0.06199 & 0.04969 \\
\hline
All & & 0.09228 & 0.091 & 0.08852 & 0.07085 & 0.07108 & 0.06417 \\
\hline\hline
 \\
\multicolumn{7}{c}{(B)} \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Francesco Calivá
Kaiyang Cheng
Rutwik Shah
Valentina Pedoia

"Adversarial Robust Training of Deep Learning MRI Reconstruction Models" by Francesco Calivá, Kaiyang Cheng, Rutwik Shah, and Valentina Pedoia, arXiv:2011.00070v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ASR results with various pooling methods under the fixed-length setting.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\hline
\textbf{Method} & \textbf{WER (\%)} & \textbf{CER (\%)} \\ \hline
Entropy-guided (medium, 15Hz) & 5.6 & 2.9 \\
Fixed-length max pooling (2, 25Hz) & 6.4 & 3.5 \\
Fixed-length average pooling (2, 25Hz) & 6.7 & 3.8 \\
Fixed-length attention pooling (2, 25Hz) & 6.2 & 3.4 \\
Fixed-length max pooling (4, 12.5Hz) & 6.8 & 3.6 \\
Fixed-length average pooling (4, 12.5Hz) & 7.2 & 3.9 \\
Fixed-length attention pooling (4, 12.5Hz) & 6.6 & 3.5 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jialong Zuo
Guangyan Zhang
Minghui Fang
Shengpeng Ji
Xiaoqi Jiao
Jingyu Li
Yiwen Guo
Zhou Zhao

"Entropy-based Coarse and Compressed Semantic Speech Representation Learning" by Jialong Zuo, Guangyan Zhang, Minghui Fang, Shengpeng Ji, Xiaoqi Jiao, Jingyu Li, Yiwen Guo, and Zhou Zhao, arXiv:2509.00503v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{arydshln}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ASR finetuning results for Librispeech measured in WER(\%). The ``finetuning'' column indicates the total duration of finetuning data used. With the exception of WavLM, the baseline results were directly retrieved from their respective papers.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}

 \toprule
 Model & Finetuning (hrs) & test-clean	& test-other \\
 \midrule
 MERaLiON-SpeechEncoder & 100 & 3.3 & 6.1\\
 MERaLiON-SpeechEncoder & 960& 2.1& 4.3\\
 \noalign{\vskip 1mm}
 \hdashline
 \noalign{\vskip 1mm}
 Wav2Vec 2.0 large~ & 100 & 3.1 & 6.3 \\
 Wav2Vec 2.0 large~ & 960 & 2.2 & 4.5 \\
 HuBERT large & 100 & 2.9& 6.0\\
 HuBERT large & 960 & 2.1& 4.3\\
 WavLM large (finetuned in-house) & 960 & 2.5 & 4.6\\
 Whisper large v2 (zero-shot) & -& 2.7 & 5.2 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Muhammad Huzaifah
Geyu Lin
Tianchi Liu
Hardik B. Sailor
Kye Min Tan
Tarun K. Vangani
Qiongqiong Wang
Jeremy H. M. Wong
Jinyang Wu
Nancy F. Chen
Ai Ti Aw

"MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond" by Muhammad Huzaifah, Geyu Lin, Tianchi Liu, Hardik B. Sailor, Kye Min Tan, Tarun K. Vangani, Qiongqiong Wang, Jeremy H. M. Wong, Jinyang Wu, Nancy F. Chen, and Ai Ti Aw, arXiv:2412.11538v3, 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|}
\hline
\textbf{Card} & \textbf{Value Assigned}\\
\hline
2,3,4,5,6 & +1\\
\hline
7,8,9 & 0 \\
\hline
10,J,Q,K,A & -1\\
\hline
\end{tabular}
\caption{Under the Hi-Lo system, each card is assigned a value of either -1,+1 or 0. The lower cards (2-6) in the deck are given a value of +1. The ``neutral cards” (7-9) are given a zero value and the high cards (10,K,Q,J,A) are all given a -1 value. Using the true count, this system allows a player to know his edge and helps him to size his bet at the start of every round.}
\end{table}
\end{document}

q-fin

"Variations on the Reinforcement Learning performance of Blackjack" by Avish Buramdoyal and Tim Gebbie, arXiv:2308.07329v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
 \hline
 \textbf{Polynomials $f$} & \textbf{Generator Polynomial} & \textbf{Parameters} \\
 \hline
 $x^{15} + x^3 + x^2 + \xi$ & $x^3 + x^2 + \xi^2$ & $[15, 12, 3]_4$ \\
 \hline
 $x^{15} + \xi x^3 + x^2 + \xi$ & $x^5 + \xi^2 x^3 + x + \xi^2$ & $[15, 10, 4]_4$ \\
 \hline
 $x^{15} + \xi x^3 + x^2 + \xi$ & $x^4 + x^3 + \xi x^2 + \xi x + \xi^2$ & $[15, 11, 4]_4$ \\
 \hline
 $x^{15} + x^6 + x^2 + \xi$ & $x^{10} + x^8 + \xi^2 x^7 + \xi x^6 + \xi x^5 + \xi^2 x^4 + \xi^2 x^3 + \xi x^2 + \xi x + 1$ & $[15, 5, 8]_4$ \\
 \hline
 $x^{15} + \xi x^{12} + x^2 + 1$ & $x^6 + \xi x^5 + x^4 + \xi x^3 + \xi x^2 + \xi x + \xi^2$ & $[15, 9, 5]_4$ \\
 \hline
 $x^{15} + \xi^2 x^{12} + x + \xi^2$ & $x^4 + x^3 + \xi^2 x^2 + \xi^2 x + \xi$ & $[15, 11, 4]_4$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Optimal polycyclic codes with best known parameters of length $n=15$ over $\mathbb{F}_{4} $.}
\end{table}
\end{document}

math

"Equivalence of Families of Polycyclic Codes over Finite Fields" by Hassan Ou-azzou and Anna-Lena Horlemann, arXiv:2503.04498v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study of components in the proposed audio caption generation pipeline for audio-text retrieval tasks on the AudioCaps test set.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\multirow{2}{*}{\textbf{Settings}} & \textbf{T2A} & \textbf{A2T} \\ \cmidrule(lr){2-3}
 & \textbf{R@1} & \textbf{R@1} \\ \midrule
AudioCaps & $39.7$ & $51.1$ \\
AudioCaps (AudioSetCaps pipeline) & $39.7$ & $50.9$ \\ \midrule
\quad w/o Fine-grained speech and music content & $38.4$ & $48.3$ \\
\quad w/o Prompt chaining for Qwen-Audio & $39.6$ & $49.2$ \\
\quad w/o Caption refinement & $38.7$ & $48.9$ \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jisheng Bai
Haohe Liu
Mou Wang
Dongyuan Shi
Wenwu Wang
Mark D. Plumbley
Woon-Seng Gan
Jianfeng Chen

"AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Large Audio and Language Models" by Jisheng Bai, Haohe Liu, Mou Wang, Dongyuan Shi, Wenwu Wang, Mark D. Plumbley, Woon-Seng Gan, and Jianfeng Chen, arXiv:2411.18953v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Microscopic Traffic Models, Accidents, and Insurance Losses" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Maciej K Wozniak
Mattias Hansson
Marko Thiel
Patric Jensfelt

"UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps" by Maciej K Wozniak, Mattias Hansson, Marko Thiel, and Patric Jensfelt, arXiv:2403.17633v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Examples of circular and axial densities.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccc}
 \toprule
 Name & Range & Density & Location & Concentration\\ \midrule
 Circular von Mises & $[0, 2\pi)$&$\frac{\exp(\kappa \cos(x-\mu))}{2\pi I_0(\kappa)}$ & $0 \leq \mu < 2\pi$ & $\kappa \geq 0$\\ \\
 Circular wrapped Cauchy & $[0, 2\pi)$ & $\frac{1-\kappa^2}{2\pi(1+\kappa^2 - 2\kappa\cos(x-\mu))}$ &$0 \leq \mu < 2\pi$ & $0 \leq \kappa <1$\\ \\
 Axial von Mises & $[0, \pi)$ &$\frac{\exp(\kappa \cosh(y-\mu))}{\pi I_0(\kappa)}$ &$0 \leq \mu < \pi$&$\kappa \geq 0$\\ \\
 Axial wrapped Cauchy & $[0, \pi)$& $\frac{1}{\pi}\frac{1-\kappa^4}{1+\kappa^4-2\kappa^2\cos(2(y-\mu))}$ & $0 \leq \mu < \pi$ &$0 \leq \kappa <1$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Marco Mingione
Francesco Lagona
Priyanka Nagar
Francois von Holtzhausen
Andriette Bekker
Janine Schoombie
Peter C. le Roux

"Does wind affect the orientation of vegetation stripes? A copula-based mixture model for axial and circular data" by Marco Mingione, Francesco Lagona, Priyanka Nagar, Francois von Holtzhausen, Andriette Bekker, Janine Schoombie, and Peter C. le Roux, arXiv:2411.11461v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Error profile for computing .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|rr|rr}
 \hline
$G_i$ & % \multicolumn{2}{c|}{$\|\b u-\b u_h\|_{0}$ $O(h^r)$} &
 $\|\b u-\b u_h\|_{0}$&$O(h^r)$ & $\|\nabla_w(\b u-\b u_h)\|_0$ & $O(h^r)$ \\ \hline
 & \multicolumn{4}{c}{By the $P_1$ WG element on Figure grids} \\
 4& 0.523E+01 & 3.1& 0.395E+03 & 1.7 \\
 5& 0.744E+00 & 2.8& 0.116E+03 & 1.8 \\
 6& 0.125E+00 & 2.6& 0.397E+02 & 1.5 \\
 \hline
 & \multicolumn{4}{c}{By the $P_2$ WG element on Figure grids} \\
 4& 0.225E+01 & 4.1& 0.310E+03 & 2.9 \\
 5& 0.140E+00 & 4.0& 0.395E+02 & 3.0 \\
 6& 0.883E-02 & 4.0& 0.502E+01 & 3.0 \\
 \hline
 & \multicolumn{4}{c}{By the $P_3$ WG element on Figure grids} \\
 4& 0.567E+00 & 5.0& 0.127E+03 & 4.0 \\
 5& 0.176E-01 & 5.0& 0.797E+01 & 4.0 \\
 6& 0.548E-03 & 5.0& 0.499E+00 & 4.0 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"An Auto-Stabilized Weak Galerkin Method for Elasticity Interface Problems on Nonconvex Meshes" by Chunmei Wang and Shangyou Zhang, arXiv:2501.13822v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 $d$ & $\widetilde{E}_{d,\lambda}$ \\ \hline
 $2$ & $y^2=x(1-x)(x-\lambda)$ \\ \hline
 $3$ & $y^2+xy+\frac{\lambda}{27}y = x^3$ \\ \hline
 $4$ & $y^2=x(x^2+x+\frac{\lambda}{4})$ \\ \hline
 $6$ & $y^2+xy=x^3-\frac{\lambda}{432}$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{}
\end{table}
\end{document}

math

"Hypergeometric Distributions and Joint Families of Elliptic Curves" by Brian Grove and Hasan Saad, arXiv:2501.13330v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{First Stage Results: Executive Demographics and AI Investment}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
 & \multicolumn{4}{c}{Dependent Variable: AI Investment} \\
 & (1) & (2) & (3) & (4) \\
\midrule
CEO Age & -0.018*** & & & -0.015*** \\
 & (0.005) & & & (0.006) \\
Female CEO & & 0.124** & & 0.108** \\
 & & (0.052) & & (0.049) \\
Technical Education & & 0.247*** & & 0.223*** \\
 & & (0.067) & & (0.071) \\
Technology Experience & & & 0.185*** & 0.162** \\
 & & & (0.058) & (0.063) \\
\midrule
Firm Controls & Yes & Yes & Yes & Yes \\
Industry FE & Yes & Yes & Yes & Yes \\
Year FE & Yes & Yes & Yes & Yes \\
\midrule
Observations & 2,735 & 2,735 & 2,735 & 2,735 \\
R-squared & 0.342 & 0.361 & 0.348 & 0.374 \\
F-statistic & 12.8 & 15.7 & 10.2 & 26.3 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises" by Tatsuru Kikuchi, arXiv:2508.03757v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\textbf{ZOO} & \textbf{1} & \textbf{2} & \textbf{3} & \textbf{4} & \textbf{5}\\
\hline
Success Rate (\%) & 0 & 100 & 100 & 100 & 100\\
\hline
\% of Unrealistic Values & - & 1.6 & 0 & 0 & 0\\
\hline
\# Checked Fields & - & - & 159 & 133 & 153\\
\hline
\# Non-Editable Fields & - & - & - & 37 & 0 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Results obtained with each experiment configuration for ZOO attack}
\end{table}
\end{document}

cs

Francesco Cartella
Orlando Anunciacao
Yuki Funabiki
Daisuke Yamaguchi
Toru Akishita
Olivier Elshocht

"Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data" by Francesco Cartella, Orlando Anunciacao, Yuki Funabiki, Daisuke Yamaguchi, Toru Akishita, and Olivier Elshocht, arXiv:2101.08030v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\multicolumn{2}{|c|}{Angle $\varphi$}&$\varphi_1=1$&$\varphi_2=0.5$&$\varphi_3=0.1$&$\varphi_4=0.005$ \\
\hline
{\bf Case 1}&$\mathcal{M}_i(\varphi)$&$2.5236$ &$1.3580$&$0.3221$&$0.0175$ \\
\hline
{\bf Case 2}&$\mathcal{M}_i(\varphi)$&$62.473$ &$21.693$&$1.8607$&$0.0175$\\
\hline
\end{tabular}
\caption{Stress values corresponding to different angles of twist for the two cases ({\bf Example 1})}
\end{table}
\end{document}

math

"Simultaneous identification of the parameters in the plasticity function for power hardening materials : A Bayesian approach" by Salih Tatar and Mohamed BenSalah, arXiv:2412.05241v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameters for experiments in Section }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \toprule
 Environment/task & Number of bins ($n$) & BCQ $\tau$ & CQL $\alpha$ & IQL $\beta$, $\lambda$ & OneStep $\lambda$ \\
 \midrule
 Maze & 3 & 0.5 & 1 & 0.5, 20 & 50 \\
 Maze & 5 & 0.5 & 0.25 & 0.5, 20 & 20 \\
 Maze & 7 & 0.5 & 0.5 & 0.5, 50 & 20 \\
 Maze & 10 & 0.5 & 0.5 & 0.5, 50 & 20 \\
 Maze & 12 & 0.5 & 0.5 & 0.5, 50 & 50 \\
 Maze & 15 & 0.5 & 0.5 & 0.5, 20 & 20 \\
 FingerSpin & 3 & 0.5 & 0.25 & 0.5, 1 & 1 \\
 FishSwim & 3 & 0.25 & 0.25 & 0.5, 1 & 1 \\
 CheetahRun & 3 & 0.05 & 0.25 & 0.5, 1 & 1 \\
 QuadrupedWalk & 3 & 0.25 & 0.25 & 0.5, 2 & 1 \\
 HumanoidStand & 3 & 0.5 & 0.25 & 0.5, 2 & 2 \\
 DogTrot & 3 & 0.25 & 1 & 0.5, 5 & 5 \\
 DogTrot & 10 & 0.25 & 0.25 & 0.5, 5 & 2 \\
 DogTrot & 30 & 0.75 & 1 & 0.5, 2 & 2 \\
 DogTrot & 50 & 0.5 & 0.5 & 0.5, 2 & 2 \\
 DogTrot & 75 & 0.7 & 0.5 & 0.5, 2 & 2 \\
 DogTrot & 100 & 0.5 & 2 & 0.5, 5 & 5 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Alex Beeson
David Ireland
Giovanni Montana

"An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces" by Alex Beeson, David Ireland, and Giovanni Montana, arXiv:2411.11088v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|ccc}
 \hline
 $N$ & Sc & Clusters & ARI & NFD \\
 \hline
 \multirow{3}{3em}{300} & Sc 1 & 3.91 (0.46) & 0.74 (0.13) & 0.09 (0.06) \\
 & Sc 2 & 4.01 (0.46) & 0.77 (0.16) & 0.08 (0.05) \\
 & Sc 3 & 3.03 (0.36) & 0.75 (0.16) & 0.09 (0.07) \\
 \hline
 \multirow{3}{3em}{450} & Sc 1 & 4.04 (0.20) & 0.81 (0.03) & 0.06 (0.01) \\
 & Sc 2 & 4.02 (0.20) & 0.84 (0.07) & 0.05 (0.02) \\
 & Sc 3 & 3.09 (0.32) & 0.83 (0.05) & 0.06 (0.02) \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Clustering performance for PAM evaluated according to the number of total detected clusters (truth = 4 clusters for Sc 1 and Sc 2; 3 clusters for Sc 3) based on the estimated optimal clustering, the adjusted Rand index (ARI), and the normalized Frobenius distance (NFD). The entries are Mean (SD) over 100 datasets.}
\end{table}
\end{document}

stat

Dehua Bi
Tianjian Zhou
Wei Zhong
Yuan Ji

"PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data" by Dehua Bi, Tianjian Zhou, Wei Zhong, and Yuan Ji, arXiv:2310.20087v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
 Model & Activation & Valence & Dominance \\
 \midrule
ASR features &	0.649& 0.393 &	0.544\\
CPC-based pre-traing &	\textbf{0.706}&	0.377&	\textbf{0.639}\\
SA2SR features&	0.679&	\textbf{0.412} &	0.564\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{SA2SR produces good features for enhancing the CCC metric}
\end{table}
\end{document}

cs

Ayoub Ghriss
Bo Yang
Viktor Rozgic
Elizabeth Shriberg
Chao Wang

"Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition" by Ayoub Ghriss, Bo Yang, Viktor Rozgic, Elizabeth Shriberg, and Chao Wang, arXiv:2201.11826v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Total number of parameters in the identified input-output systems using T-ERA and ERA at different singular tuple/value truncation levels.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
\multirow{1}{*}{Truncation Level \( k \)} & \multicolumn{2}{c}{Number of Parameters} \\
& T-ERA & ERA \\
\hline
0 & 10725 & 32175 \\
10 & 7425 & 22275 \\
20 & 4725 & 14175 \\
30 & 2625 & 7875 \\
40 & 1125 & 3375 \\
50 & 225 & 675 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Shenghan Mei
Ziqin He
Yidan Mei
Xin Mao
Anqi Dong
Ren Wang
Can Chen

"Data-driven model order reduction for T-Product-Based dynamical systems" by Shenghan Mei, Ziqin He, Yidan Mei, Xin Mao, Anqi Dong, Ren Wang, and Can Chen, arXiv:2504.14721v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l}
\toprule
\bf Setting & \bf Unlabeled MSA& \bf Unlabeled DA \\ \hline
ST, $\tau=0.80$ & 43.88 & 44.46 \\
ST, $\tau=0.90$ & 44.69 & 47.83 \\
ST, $\tau=0.95$ & 43.43 & 46.87 \\
\hline
Avg & 43.67 & \textbf{46.34} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{ Ablation experiment with MSA unlabeled data for zero-shot NER. Development set macro F1 is shown when using both unlabeled MSA and DA data with the same size. Average performance with DA unlabeled data is higher showing the effect of unlabeled DA on model final performance.}
\end{table}
\end{document}

cs

Muhammad Khalifa
Muhammad Abdul-Mageed
Khaled Shaalan

"Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling" by Muhammad Khalifa, Muhammad Abdul-Mageed, and Khaled Shaalan, arXiv:2101.04758v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{arydshln}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Effectiveness of the click-adaptive teacher.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 Teacher & AUC$_{\text{CTCVR}}$ & AUC$_{\text{CVR}}$ & D-AUC$_{\text{CVR}}$\\
 \midrule
 Click-adaptive Model & \textbf{0.7485} & \textbf{0.6664} & \textbf{0.6689} \\
 SingleCVR & 0.7462 & 0.6633 & 0.6657 \\
 \hdashline[2pt/1.2pt]
 No (i.e., \textit{Joint}) & 0.7445 & 0.6584 & 0.6582 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zixuan Xu
Penghui Wei
Weimin Zhang
Shaoguo Liu
Liang Wang
Bo Zheng

"UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation" by Zixuan Xu, Penghui Wei, Weimin Zhang, Shaoguo Liu, Liang Wang, and Bo Zheng, arXiv:2201.08024v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Taban Baghfalaki
Reza Hashemi
Christophe Tzourio
Catherine Helmer
Helene Jacqmin-Gadda

"A Two-stage Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers and Competing Risk Outcomes" by Taban Baghfalaki, Reza Hashemi, Christophe Tzourio, Catherine Helmer, and Helene Jacqmin-Gadda, arXiv:2412.03797v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccc}
 \toprule
 \multirow{2}{*}{\textbf{Model}} & \multirow{2}{*}{\shortstack{\textbf{Number of}\\\textbf{parameters}}} & \multicolumn{2}{c}{\textbf{Learning rates}} \\
 & & \textbf{Fast} & \textbf{Slow} \\
 \midrule
 PPMI & \multicolumn{3}{c}{N/A} \\
 GloVe & \multicolumn{3}{c}{N/A} \\
 \midrule
 CBOW & 1.7M & \multirow{2}{*}{$1\mathrm{e}{-3}$} & \multirow{2}{*}{$1\mathrm{e}{-4}$} \\
 Skip-Gram & 7.5M & & \\
 \midrule
 BERT & 108M & \multirow{2}{*}{$1\mathrm{e}{-4}$} & \multirow{2}{*}{$1\mathrm{e}{-5}$} \\
 ALBERT & 12M & & \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Model parameter size comparison and default learning rates.}
\end{table}
\end{document}

cs

"An Assessment of the Impact of OCR Noise on Language Models" by Konstantin Todorov and Giovanni Colavizza, arXiv:2202.00470v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 \textbf{Model} & \textbf{Modality} & \textbf{Hours} & \textbf{WER (\%)} \\
 \midrule
 \multicolumn{4}{l}{\textit{Ours}} \\
 \quad \textsc{Speech-only (SpiritLM)} & S,T & 30 & 4.43 \\
 \quad \textsc{AVLM (Q-Former Prefix)} & S,T & 433 & 3.50 \\
 \midrule
 \multicolumn{4}{l}{\textit{Prior Work (Lip-only)}} \\
 \quad AV-HuBERT (Large) & T & 433 & 4.20 \\
 \quad MMS-LLaMa & T & 433 & 0.92 \\
 \quad LLaMa-AVSR & T & 433 & 0.95 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{WER (\%) on clean speech. “Modality” indicates support for speech (S) or text (T) generation.}
\end{table}
\end{document}

eess

Weiting Tan
Jiachen Lian
Hirofumi Inaguma
Paden Tomasello
Philipp Koehn
Xutai Ma

"Seeing is Believing: Emotion-Aware Audio-Visual Language Modeling for Expressive Speech Generation" by Weiting Tan, Jiachen Lian, Hirofumi Inaguma, Paden Tomasello, Philipp Koehn, and Xutai Ma, arXiv:2508.16188v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|r|c|r|r|}
\hline
\textbf{Facies} & \textbf{Mean Porosity (\%)} & \textbf{Oil Content} & \textbf{Resistivity (ohm-m)} & \textbf{Reward} \\
\hline
Channel Sand & 20 & Oil-saturated & 171.0 & 1.0 \\
Crevasse Splay Sand & 12 & Partially & 55.0 & 0.5 \\
Floodplain Shale & 2 & No & 4.0 & 0.0 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Mean porosity, oil content, resistivity, and "per-pixel" reward $C$ for different facies types.}
\end{table}
\end{document}

math

Sergey Alyaev
Kristian Fossum
Hibat Errahmen Djecta
Jan Tveranger
Ahmed H. Elsheikh

"DISTINGUISH Workflow: A New Paradigm of Dynamic Well Placement Using Generative Machine Learning" by Sergey Alyaev, Kristian Fossum, Hibat Errahmen Djecta, Jan Tveranger, and Ahmed H. Elsheikh, arXiv:2503.08509v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yangxinyu Xie
Xiang Li
Tanwi Mallick
Weijie J. Su
Ruixun Zhang

"Debiasing Watermarks for Large Language Models via Maximal Coupling" by Yangxinyu Xie, Xiang Li, Tanwi Mallick, Weijie J. Su, and Ruixun Zhang, arXiv:2411.11203v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.