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\begin{tabular}{lll} \hline\noalign{\smallskip} recording filename & subject gender & subject age (years) \\ \noalign{\smallskip}\hline\noalign{\smallskip} chb01\_01.edf & female & 11 \\ chb02\_01.edf & male & 11 \\ chb03\_01.edf & female & 14 \\ chb04\_01.edf & male & 22 \\ chb05\_01.edf & female & 7 \\ chb06\_01.edf ...
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\begin{tabular}{llll} \hline\noalign{\smallskip} FP1-F7 & F7-T7 & T7-P7 & P7-O1 \\ FP1-F3 & F3-C3 & C3-P3 & P3-O1 \\ FP2-F4 & F4-C4 & C4-P4 & P4-O2 \\ FZ-CZ & CZ-PZ & T7-FT9 & \\ FT9-FT10 & FT10-T8 & & \\ \noalign{\smallskip}\hline \end{tabular}
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\begin{tabular}{ll} \toprule Concepts/Abbreviations & Definition \\ \hline GADGMSS & General-Accumulation Discrete GM(1,1) model in Simplex Space \\ TGMI & Traditional GM(1,1) model with \emph{ilr}-transformation \\ IGADGM & Infused General-Accumulation Discrete GM(1,1) model \\ DE algorithm & Differential Evolution al...
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\begin{tabular}{ccccccccccc} \toprule Region&2010&2011&2012&2013&2014&2015&2016&2017&2018&2019\\ \midrule Arctic nations&10.23\%&12.06\%&13.63\%&12.81\%&13.38\%&15.15\%&16.67\%&19.12\%&19.77\%&18.29\%\\ Middle east &46.77\%&49.50\%&49.75\%&51.76\%&52.79\%&50.38\%&47.01\%&43.56\%&45.75\%&47.34\%\\ Africa&21.92\%&18.53\%...
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\begin{tabular}{ccc} \toprule Name & Abbreviation & Formula \\ \hline Component Vector Percentage Error & CVPE & $\frac{\langle ilr(X_1),ilr(X_2) \rangle}{\langle ilr(X_1),ilr(X_1)\rangle}$ \\ Mean Component Percentage Error & MCPE & $\frac{1}{n}\sum_{i=1}^{n}\frac{\langle ilr(x_{1i}),ilr(x_{2i}) \rangle}{\langle ilr(x...
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\begin{tabular}{ccccccccccc} \toprule Region&2010&2011&2012&2013&2014&2015&2016&2017&2018&2019\\ \midrule America&57.32\%&59.67\%&59.55\%&60.33\%&63.56\%&56.55\%&58.27\%&57.03\%&47.81\%&44.08\%\\ Europe&26.69\%&24.82\%&23.43\%&23.78\%&20.91\%&27.66\%&24.97\%&24.72\%&34.45\%&36.33\%\\ Asia&15.99\%&15.51\%&17.03\%&15.89\...
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\begin{tabular}{cccccccccc} \toprule & \multicolumn{3}{c|}{American} & \multicolumn{3}{c|}{Europe} & \multicolumn{3}{c}{Asia} \\ \cline{2-10} Times & TGMI & GADGMSS & \multicolumn{1}{c|}{IGADGM} & TGMI & GADGMSS & \multicolumn{1}{c|}{IGADGM} & TGMI & GADGMSS & IGADGM \\ \hline 2010 & 0.57 & 0.57 & 0.57 & 0.27 & 0.27 & ...
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\begin{tabular}{ccccccc} \toprule & \multicolumn{3}{c|}{Prediction Error} & \multicolumn{3}{c}{Fitting Error} \\ \cline{2-7} & CVPE & MCPE & MAPE & CVPE & MCPE & MAPE \\ \hline TGMI & 0.215 & 0.213 & 0.132 & 0.032 & 0.008 & 0.150 \\ GADGMSS & 0.307 & 0.283 & \textbf{0.087} & 0.025 & 0.007 & 0.163 \\ IGADGM & \textbf{0....
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\begin{tabular}{ccccccccccc} \toprule Region&2010&2011&2012&2013&2014&2015&2016&2017&2018&2019\\ \midrule Middle East&58.20\%&61.28\%&60.64\%&59.96\%&57.30\%&54.13\%&55.91\%&55.05\%&57.04\%&54.29\%\\ Africa&19.26\%&16.79\%&15.52\%&14.50\%&15.64\%&17.67\%&15.52\%&14.36\%&13.56\%&14.85\%\\ America&8.57\%&8.03\%&12.64\%&1...
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\begin{tabular}{ccccccccccccc} \toprule & \multicolumn{3}{c|}{Middle East} & \multicolumn{3}{c|}{Africa} & \multicolumn{3}{c|}{America} & \multicolumn{3}{c}{Asia\&Pacific} \\ \cline{1-13} Times & TGMI & GADGMSS & \multicolumn{1}{c|}{IGADGM} & TGMI & GADGMSS & \multicolumn{1}{c|}{IGADGM} & TGMI & GADGMSS & \multicolumn{...
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\begin{tabular}{ccccccc} \toprule & \multicolumn{3}{c|}{Prediction Error} & \multicolumn{3}{c}{Fitting Error} \\ \cline{2-7} & \multicolumn{1}{c}{CVPE} & \multicolumn{1}{c}{MCPE} & \multicolumn{1}{c}{MAPE} & \multicolumn{1}{c}{CVPE} & \multicolumn{1}{c}{MCPE} & \multicolumn{1}{c}{MAPE} \\ \hline TGMI & 0.032 & 0.032 & ...
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\begin{tabular}{ccccccc} \toprule & \multicolumn{3}{c|}{Prediction Error} & \multicolumn{3}{c}{Fitting Error} \\ \cline{2-7} Models & CVPE & MCPE & MAPE & CVPE & MCPE & MAPE \\ \hline ARIMA & 0.091 & 0.091 & 0.180 & 0.046 & 0.031 & 0.137 \\ IGADGM & \textbf{0.031} & \textbf{0.031} & \textbf{0.082} & 0.010 & 0.007 & 0.0...
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\begin{tabular}{cccccc} \toprule Years & Arctic field & Middle East & Africa & Americas & Southeast Asia \\ \hline 2020 & 0.195 & 0.454 & 0.117 & 0.116 & 0.118 \\ 2021 & 0.196 & 0.442 & 0.121 & 0.118 & 0.123 \\ 2022 & 0.202 & 0.436 & 0.117 & 0.121 & 0.124 \\ 2023 & 0.210 & 0.430 & 0.113 & 0.123 & 0.124 \\ 2024 & 0.217 ...
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\begin{tabular}{ccc} \toprule Name&Route&Voyage(sea mile)\\ \midrule Middle East route 1& Kuwait port-strait of Hormuz-Singapore-Lianyungang&6371\\ Middle East route 2&Suez-Mander strait-Singapore-Lianyungang&7362\\ West Africa&Lagos-Cape Town-Singapore-Lianyungang&10588\\ The South America&the port of Maracaibo-panama...
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\begin{tabular}{lll} \hline Parameter & Description & Prior\\ \hline\hline $\xi_{r}$ & Fraction of recycled NS in low-mass Gaussian & $U(0,1)$ \\ \hline $\mu_{r1}$ & Mean of low-mass Gaussian in $m_r$ distribution & $U(1.1M_{\odot},1.5M_{\odot}$) \\ \hline $\sigma_{r1}$ & Standard deviation of low-mass Gaussian in $m_r...
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\begin{tabular}{ccc} \hline $k$ ($\unit[]{km\, s^{-1} M_\odot^{-1}}$)& $f_\text{radio}$ & $f_\text{GW}$ \\ \hline\hline 100 & 0.084 & 0.083\\ 200 & 0.084 & 0.083 \\ 300 & 0.082 & 0.083 \\ 400 & 0.059 & 0.083 \\ 500 & 0.042 & 0.083 \\ 600 & 0.02 & 0.081 \\ 700 & 0.004 & 0.06 \\ 800 & 0 & 0.034 \\ \hline \end{tabular}
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\begin{tabular}{lcc} \toprule & $\phi$ explicitly known & $\phi$ relation to \textit{data fidelity} \\ \cline{1-3} Colorization & \ding{55} & Luminance and edge related \\ Inpainting & \ding{51} & Binary mask based \\ Denoising & \ding{55} & Noise-level adaptive \\\bottomrule \\ \end{tabular}
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\begin{tabular}{|c|ccc|l} \cline{1-4} i & & Bob & & \\ \cline{1-4} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \ \ \ \ 0 \ \ \ \ & \\ \cline{2-4} Alice & \multicolumn{1}{c|}{ \ \ 0 \ \ } & \multicolumn{1}{c|}{0} & 0 & \\ \cline{2-4} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & 1 & \\ \cline{1-4} \end{tabular...
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\begin{tabular}{lll} \hline Datasets & Entity type & Number of annotations \\ \hline NCBI Disease & Disease & 6881 \\ BC5CDR & Disease & 12694 \\ BC5CDR & Drug/Chem. & 15411 \\ BC4CHEMD & Drug/Chem. & 79824 \\ BC2GM & Gene/Protein & 20703 \\ JNLPBA & Gene/Protein & 35460 \\ LINNAEUS & Species & 4077 \\ Species-800 & Sp...
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\begin{tabular}{||c|ccccc||} \hline Name & $a$ (au) & $e$ & $i$ (deg) & $q$ (au) & $Q$ (au) \\ [0.5ex] \hline\hline 29P/Schwassmann-Wachmann 1 & 5.99 & 0.045 & 9.391 & 5.72 & 6.26 \\ \hline 39P/Oterma & 7.251 & 0.246 & 1.943 & 5.471 & 9.032 \\ \hline 2000 GM137 & 7.862 & 0.121 & 15.865 & 6.907 & 8.816 \\ \hline 2004 VP...
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\begin{tabular}{||c|cccccc||} \hline Population & Reservoir & \multicolumn{2}{c}{Total Number} & \multicolumn{2}{c}{Time in Region (kyr)} & Near Centaur Limit \\ [0.5ex] & & OJFCs & NCs & OJFCs & NCs & \\ \hline\hline Classical & 4800 & 62 & 102 & 24.3$\pm$6.3 & 10.9$\pm$1.7 & 261$\pm$90 \\ \hline Resonant & 4800 & 84 ...
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\begin{tabular}{|c|rr|rr|rr|rr|} \hline \multirow{3}{*}{Models} & \multicolumn{2}{c|}{\multirow{2}{*}{ Full}} & \multicolumn{6}{c|}{Bilinear projection (BP)} \\ \cline{4-9} & & & \multicolumn{2}{c|}{$\alpha=1$} & \multicolumn{2}{c|}{$\alpha=2$} & \multicolumn{2}{c|}{$\alpha=3$} \\ \cline{2-9} & \multicolumn{1}{c}{EXC} ...
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\begin{tabular}{|c|c|c|c|c|} \hline \multirow{2}{*}{Models} & \multirow{2}{*}{Full} & \multicolumn{3}{c|}{BP} \\ \cline{3-5} & & $\alpha=1$ & $\alpha=2$ & $\alpha=3$\\ \hline Top-1 error & 28.5 & 36.7 & 32.2 & 28.9 \\ Top-5 error & 9.4 & 14.3 & 11.9 & 10.2 \\ \hline \end{tabular}
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\begin{tabular}{|c|c|c|c|c|c|c|} \hline \multirow{2}{*}{Models} & \multirow{2}{*}{Full} & \multicolumn{3}{c|}{BP} &\multirow{2}{*}{CP} &\multirow{2}{*}{LR} \\ \cline{3-5} & & $\alpha=1$ & $\alpha=2$ & $\alpha=3$ & &\\ \hline S-VGG & 82.9 & 68.5 & 74.3 & {\bf 83.1} & 81.7 & 81.8 \\ ResNet-56 & 92.7 & 83.4 & 89.9 & {\bf ...
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\begin{tabular}{|c|c|c|c|c|c|c|} \hline \multirow{2}{*}{Models} & \multirow{2}{*}{Full} & \multicolumn{3}{c|}{BP} &\multirow{2}{*}{CP} &\multirow{2}{*}{LR} \\ \cline{3-5} & & $\alpha=1$ & $\alpha=2$ & $\alpha=3$ & &\\ \hline S-VGG & 93.6 & 87.3 & 89.1 & {\bf 93.7} & 92.3 & 92.2 \\ ResNet-56 & 97.3 & 93.7 & 95.4 & {\bf ...
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\begin{tabular}{|c|c|c|c|} \hline Model & $\alpha=1$ & $\alpha=2$ & $\alpha=3$ \\ \hline S-VGG & 34K & 91K & 159K \\ \hline ResNet-56 & 153K & 612K & 1.4M \\ \hline SqueezeNet & 313K & 1.2M & 2.7M \\ \hline MobileNet v2 & 353K & 4.7M & 23.0M \\ \hline \end{tabular}
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\begin{tabular}{|c|c|c|c|} \hline Model & $\alpha=1$ & $\alpha=2$ & $\alpha=3$ \\ \hline S-VGG & 0.02G & 0.06G & 0.14G \\ \hline ResNet-56 & 0.52G &2.09G & 4.71G \\ \hline SqueezeNet & 0.03G & 0.01G & 0.32G \\ \hline MobileNet v2 & 0.06G &0.96G & 0.48G \\ \hline \end{tabular}
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\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c|c} \toprule \multicolumn{6}{c|}{CASIA-B} & \multicolumn{6}{c}{OUMVLP} \\ \hline Layer Name & In\_C & Out\_C & Kernel & Global & N-part & Layer Name & In\_C & Out\_C & Kernel & Global & N-part \\ \midrule \multirow{2}[2]{*}{Conv3d} & \multirow{2}[2]{*}{\checkmark} & \multirow{2}[2]{...
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\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c} \toprule \multirow{2}[2]{*}{\textbf{Method}} & \multicolumn{14}{c|}{\textbf{Probe View}} & \multicolumn{1}{c}{\multirow{2}[2]{*}{\textbf{Mean}}} \\ \cline{2-15} \multicolumn{1}{c|}{} & $0^{\circ}$ & $15^{\circ}$ & $30^{\circ}$ & $45^{\circ}$ & $60^{\circ}$ & $75^{\circ}$...
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\begin{tabular}{c|c|c|c|c|c|c|c|c} \toprule \multicolumn{2}{c|}{GLConvA-1} & \multicolumn{2}{c|}{GLConvA-2} & \multicolumn{2}{c|}{GLConvB} & \multirow{2}[2]{*}{NM} & \multirow{2}[2]{*}{BG} & \multirow{2}[2]{*}{CL} \\ \cline{1-6} Global & N-parts & Global & N-parts & Global & N-parts & & & \\ \midrule \checkmark & $\tim...
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\begin{tabular}{c|c|c|c|c|c|c} \toprule \multicolumn{3}{c|}{\textbf{Spatial Feature Mapping}} & \multirow{2}[2]{*}{NM} & \multirow{2}[2]{*}{BG} & \multirow{2}[2]{*}{CL} & \multirow{2}[2]{*}{Mean} \\ \cline{1-3} $F_{Max}^{1\times1\times W_{2}}$ & $F_{Avg}^{1\times1\times W_{2}}$ & $GeM$ & & & & \\ \midrule \checkmark & ...
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\begin{tabular}{c|c|c|c|c} \toprule \multicolumn{2}{c|}{Downsampling} & \multirow{2}[2]{*}{NM} & \multirow{2}[2]{*}{BG} & \multirow{2}[2]{*}{CL} \\ \cline{1-2} 1st pooling layer & 2nd pooling layer & & & \\ \midrule SP & SP & 96.3 & 90.9 & 77.7 \\ \hline SP & LTA & 97.0 & 93.9 & 82.2 \\ \hline LTA & SP & \textbf{97.4} ...
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\begin{tabular}{lllll} \toprule \multicolumn{1}{r}{} & \multicolumn{2}{c}{Validation Accuracy} & \multicolumn{2}{c}{Test Accuracy} \\ \cmidrule(r){2-5} Model Name & 1-img prediction & 4-rot prediction & 1-img prediction & 4-rot prediction \\ \midrule Baseline & 63.20 & \ & 63.04 & \ \\ Reg with loss & 69.34 & 71.18 & 6...
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\begin{tabular}{|c||cc|cc|} \hline \multirow{2}{*}{Parameter settings} & \multicolumn{2}{c|}{COVID-19} & \multicolumn{2}{c|}{Robotic Instrument} \\ \cline{2-5} & Loss & IoU &Loss & mIoU \\ \hline $\tau=1,\upsilon=10$ & 0.034 & 72.8 & 0.086 & 71.2 \\ $\tau=1,\upsilon=1$ & 0.033 & 73.4 & 0.086 & 71.5 \\ $\tau=10,\upsilon...
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\begin{tabular}{|c||cc|cc|} \hline \multirow{2}{*}{Method} & \multicolumn{2}{c|}{Part 1} & \multicolumn{2}{c|}{Part 2} \\ \cline{2-5} & Pixel Acc. & IoU & Pixel Acc. & IoU \\ \hline Cross-entropy & 81.1 & 69.6 & 86.6 & 76.8 \\ Dice loss & 80.7 & 70.0 & 85.5 & 76.1 \\ Tversky loss & 80.7 & 68.1 & {\bf 89.1} & 76.0 \\ Fo...
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\begin{tabular}{|c||cc|cc|} \hline \multirow{2}{*}{Method} & \multicolumn{2}{c|}{Binary} & \multicolumn{2}{c|}{Multi-class} \\ \cline{2-5} & Pixel Acc. & IoU & Pixel Acc. & mIoU \\ \hline Cross-entropy & 94.6 & 86.0 & 81.1 & 66.2 \\ Dice loss & {\bf 97.7} & 84.7 & 78.5 & 67.6 \\ Tversky loss & 96.4 & 85.9 & 79.7 & 68.6...
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\begin{tabular}{|c||cccc|} \hline Method & Shaft & Wrist & Claspers & Probe \\ \hline Cross-entropy & 81.1 & 55.3 & 55.5 & 72.9 \\ Dice loss & 83.8 & 61.8 & 54.2 & 70.5 \\ Tversky loss & 84.9 & 64.8 & 55.4 & 69.3 \\ Focal loss & 86.5 & 62.9 & 56.5 & 72.0 \\ lov{\'a}sz-softmax & 86.3 & 64.4 & 55.5 & 69.3 \\ Ours & {\bf ...
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\begin{tabular}{|c||cc|cc|} \hline \multirow{2}{*}{Method} & \multicolumn{2}{c|}{COVID-19} & \multicolumn{2}{c|}{Robotic Instrument} \\ \cline{2-5} & Part 1 & Part 2 & Binary & Multi-class \\ \hline Ours + Tversky loss & 71.6 & 78.5 & 87.7 & 73.1 \\ Ours + Dice loss & 71.5 & 78.0 & 87.8 & 73.2 \\ \hline \end{tabular}
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\begin{tabular}{ll} \hline Parameter & Description \\ \hline \hline $\beta (t)$ & Rate at which susceptible individuals become infected due to \\ & contacts with infectious individuals at time $t$; $\beta(t)$ is a function \\ & of the mobility variables $\mathbf{m}(t)$ at time $t$ \\ $\gamma_A$ & Weight representing lo...
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\begin{tabular}{|l|l|l|} \hline & \multicolumn{1}{c|}{\textbf{Number of Scenarios}} & \multicolumn{1}{c|}{\textbf{How are they used}} \\ \hline \textbf{\begin{tabular}[c]{@{}l@{}}Virtual \\ Testing\end{tabular}} & \begin{tabular}[c]{@{}l@{}}High. Any number of \\test scenarios can be \\ generated in the given \\logical...
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\begin{tabular}{lll} \textbf{Layer} & \textbf{Output Shape} & \textbf{\# Parameters} \\ \hline conv1(3,3) & (224, 224, 32) & 896 \\ batch normalization & (224, 224, 32) & 896 \\ max pooling(2,2) & ( 112, 112, 32) & 0 \\ conv2(3,3) & ( 112, 112, 64) & 18496 \\ batch normalization & (112, 112, 64) & 448 \\ max pooling(2,...
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\begin{tabular}{|clcccc|} \hline No.& Name & Objectives & Variables & Constraints & Best known $f(x,y)$ \\ \hline 1 & Cassini1 & 1 & 6 & 4 & 4.9307 \\ 2 & Cassini2 & 1 & 22 & 0 & 8.3830 \\ 3 & Messenger (reduced) & 1 & 18 & 0 & 8.6299 \\ 4 & Messenger (full) & 1 & 26 & 0 & 1.9579 \\ 5 & GTOC1 & 1 & 8 & 6 & -1581950.0 \...
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\begin{tabular}{lcl} C/C++ &~~~:~~~& gtopx( benchmark, f, g, x ); \\ Python &~~~:~~~& [ f, g ] = gtopx( benchmark, x, o,n,m ) \\ Matlab &~~~:~~~& [ f, g ] = gtopxmex( benchmark, x ) \\ \end{tabular}
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\begin{tabular}{cc} \hline ~~~Value~~~ & ~~~~~~Planet~~~~~~ \\ \hline 1 & Mercury \\ 2 & Venus \\ 3 & Earth \\ 4 & Mars \\ 5 & Jupiter \\ 6 & Saturn \\ 7 & Uranus \\ 8 & Neptune \\ 9 & Pluto \\ \hline \end{tabular}
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\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|} \hline $\frac{\sigma_t}{t}$ & 1.5 & 1.4 & 1.3 & 1.2 & 1.1 & 1.0 & 0.9 & 0.8 & 0.7 & 0.6 & 0.5 & 0.4 & 0.3 \\ \hline $\frac{\sigma_q}{q}$ & any & 0.6 & 0.8 & 1.01 & 1.3 & 1.65 & 2.13 & 3.25 & 5.5 & 8.0 & 20.0 & 70.0 & 1000.0 \\ \hline $\delta(\mathcal{T})$ & 0.50 & 0.48 & 0...
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\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|} \hline $\frac{\sigma_t}{t}$ & 1.5 & 1.4 & 1.3 & 1.2 & 1.1 & 1.0 & 0.9 & 0.8 & 0.7 & 0.6 & 0.5 & 0.4 & 0.3 \\ \hline $\frac{\sigma_q}{q}$ & any & 0.39 & 0.6 & 0.85 & 1.2 & 1.54 & 2.1 & 3.2 & 5.9 & 8.0 & 20.0 & 80.0 & 1000.0 \\ \hline $\delta(\mathcal{T})$ & 0.50 & 0.48 & 0....
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\begin{tabular}{|c|ccc|}\hline & \multicolumn{3}{c|}{Estimated order of convergence} \\ & $r$ & $p$ & $x$ \\\hline Optimally compatible & 1.0067 & 1.0076 & 1.0021 \\ Compatible & 1.0066 & 1.0055 & 0.5351 \\\hline \end{tabular}
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\begin{tabular}{c|c|c} \hline Calibrations (Distance) & $f_x$ (pixel) & $f_y$ (pixel) \\ \hline Reference (0km/0mi) & 1810.4 & 1840.1 \\ \hline Stop1 (2.63km/1.63mi) & 1870.1 & 1906.3 \\ \hline Stop2 (1.85km/1.15mi) & 1918.7 & 1939.0 \\ \hline Stop3 (7.54km/4.69mi) & 1935.2 & 1965.8 \\ \hline Relative Variation & 6.625...
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\begin{tabular}{l|c|c} & Symmetrized & {\bf Rebalanced}\\\hline Inverted W & $(85 \pm 6)$\% & {\bf $\mathbf{(66 \pm 5)}$\%} \\ Grover & $(78 \pm 5)$\% & {\bf $\mathbf{(58 \pm 5)}$\%} \\ Gaussian $(\mu = -0.11)$ & $(98 \pm 6)$\% & {\bf $\mathbf{(56 \pm 5)}$\%} \\ Gaussian $(\mu = 0.78)$ & $(80 \pm 6)$\% & {\bf $\mathbf{...
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\begin{tabular}{|c|c|c|c|c|} \hline & \multicolumn{2}{c|}{Test Accuracy ($\%$): $f{=}5$} & \multicolumn{2}{c|}{Test Accuracy ($\%$): $f{=}17$} \\ \hline & OracleSGD & DiverseFL & OracleSGD & DiverseFL \\ \hline Gaussian & 81.0 & 80.8 & 28.5 & 28.5 \\ \hline \end{tabular}
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\begin{tabular}{||c|c|c|c|c||} \hline Index & Parameter & Description & Nominal Value & Units \\ [0.5ex] \hline\hline $1$ & $a$ & Number of resting microglia & $1000$ & cells\\ \hline $2$ & $k_{M1}$ & Activation rate of microglia to $M1$ & $0.44$ & $\frac{1}{\text{hours}}$ \\ \hline $3$ & $k_{M2}$ & Activation rate of ...
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\begin{tabular}{||c|c|c|c|c||} \hline Index & Parameter & Description & Nominal Value & Units \\ [0.5ex] \hline\hline $18$ & $b$ & Minimum value of $N_1$ & $0.3$ & unitless \\ \hline $19$ & $\tau_1$ & Steepness of $N_1$ & $1$ & unitless \\ \hline $20$ & $\tau_2$ & Steepness of $N_2$ & $1$ & unitless \\ \hline $21$ & $L...
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\begin{tabular}{ccccc} \toprule Dataset & Nodes & Edges & Classes & Features\\ \midrule Cora& 2,708 & 5,429 & 7 & 1,433\\ PubMed& 19,711 & 44,338 & 3 & 500\\ \midrule Flickr & 89,250 & 899,756 & 7 & 500\\ Reddit & 232,965 & 11,606,919 & 41 & 602\\ \bottomrule \end{tabular}
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\begin{tabular}{c|cccc|cccc} \toprule \multirow{2}{*}{Networks} & \multicolumn{4}{c|}{Cora} & \multicolumn{4}{c}{PubMed}\\ \cline{2-9} & Accuracy & M.S. & D.S. & C.O. & Accuracy & M.S. & D.S. & C.O.\\ \midrule GCN & \textbf{81.4 ± 0.4} & 360K & 14.8M & 2.50e8 & 79.0 ± 0.3 & 125.75K & 37.6M & 6.38e8\\ Bi-GCN(binarize fe...
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\begin{tabular}{c|cccc|cccc} \toprule \multirow{2}{*}{Networks} & \multicolumn{4}{c|}{Reddit} & \multicolumn{4}{c}{Flickr}\\ \cline{2-9} & F1-micro & M.S. & D.S. & C.O. & F1-micro & M.S. & D.S. & C.O.\\ \midrule inductiveGCN & \textbf{93.8 ± 0.1} &643.00K& 534.99M & 4.18e10 &\textbf{50.9 ± 0.3} &507.00K&170.23M & 1.18e...
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\begin{tabular}{|l|l|l|l|l|} \hline Characteristic & Early/No disease & Moderate disease & Advanced disease & Overall \\ \hline \# SDOCT-SAP pairs & 27216 & 5387 & 5831 & 38434 \\ \hline \# SDOCT & 26860 & 5289 & 5752 & 37901 \\ \hline \# SAP & 24055 & 4603 & 4920 & 33578 \\ \hline \# Eyes & 16856 & 3638 & 3885 & 23171...
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\begin{tabular}{|l|l|l|l|l|} \hline Method & Early/No disease & Moderate disease & Advanced disease & Overall \\ \hline Linear Regression & $4.11 \; (0.01)$ & $6.36 \; (0.03)$ & $13.34 \; (0.04)$ & $5.54 \; (0.01)$\\ \hline Fully connected model & $3.38 \; (0.01)$ & $6.47 \; (0.03)$ & $12.65 \; (0.04)$ & $5.00 \; (0.01...
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\begin{tabular}{|l|l|l|l|l|l|l|l|} \hline Method & Central & Temporal & Inferior & Inferior & Superior & Superior & MD \\ & & & & Nasal & & Nasal & \\ \hline Linear Regression & $4.99 \; (0.01)$ & $5.46 \; (0.01)$ & $5.67 \; (0.01)$ & $5.29 \; (0.01)$ & $5.87 \; (0.01)$ & $5.59 \; (0.01)$ & $4.31 \; (0.01)$ \\ \hline F...
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\begin{tabular}{cccccccccc} \hline \multirow{2}{*}{Case} &\multicolumn{4}{c}{inflated state}& &\multicolumn{4}{c}{deflated state} \\ \cline{2-5} \cline{7-10} & $V_{u}$ [cc] & $V_{l}$ [cc] & $N_{j}$ & $N_{e}$ & & $V_{u}$ [cc] & $V_{l}$ [cc] & $N_{j}$ & $N_{e}$ \\ \hline \hline 1 & 173.5 & 242.9 & 58 & 62 & & 82.7 & 96.9...
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\begin{tabular}{cccccc} \hline Error metric & Rigid & AF & PWA & LSM & Proposed \\ \hline \hline MD [mm] & 10.09 $\pm$ 2.14 & 4.13 $\pm$ 1.22 & 1.49 $\pm$ 0.20 & 0.24 $\pm$ 0.05 & 0.24 $\pm$ 0.06 \\ HD [mm] & 29.65 $\pm$ 5.72 & 15.97 $\pm$ 4.84 & 6.35 $\pm$ 1.85 & 0.74 $\pm$ 0.18 & 0.78 $\pm$ 0.17 \\ CD (mean) [mm] & 4...
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\begin{tabular}{ccc} \hline Case & Bronchus & Parenchyma\\ \hline \hline 1 & 0.315 $\pm$ 0.064 & 0.286 $\pm$ 0.194 \\ 2 & 0.372 $\pm$ 0.176 & 0.616 $\pm$ 0.506 \\ 3 & 0.320 $\pm$ 0.209 & 0.329 $\pm$ 0.162 \\ 4 & 0.275 $\pm$ 0.099 & 0.430 $\pm$ 0.398 \\ 5 & 0.247 $\pm$ 0.138 & 0.499 $\pm$ 0.330 \\ 6 & 0.230 $\pm$ 0.109 ...
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\begin{tabular}{cccccc} \hline \multicolumn{3}{c}{Feature extraction module} & \multicolumn{3}{c}{DI reconstruction module} \\ Layer & K-Size & K-Num & Layer & K-Size & K-Num \\ \hline DConv1 & 3$\times$3 & 64 & TConv6 & 2$\times$2 & 512 \\ Pooling1 & 2$\times$2 & - & DConv6 & 3$\times$3 & 512 \\ DConv2 & 3$\times$3 & ...
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\begin{tabular}{cccccc} \hline \multirow{2}{*}{Dataset} & \multirow{2}{*}{Metrics} & \multicolumn{4}{c}{Size of filter} \\ & & 3 & 5 & 7 & 9 \\ \hline \multirow{3}{*}{S1} & Pr(\%) & 27.34 & \textbf{47.10} & 41.91 & 22.94 \\ & Rc(\%) & \textbf{41.59} & 31.93 & 27.97 & 36.85 \\ & F1(\%) & 32.99 & \textbf{38.06} & 33.55 &...
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\begin{tabular}{lccll} \hline \hline Datasets & Number & Resolution & Data type & Public \\ \hline DRIVE & 40 & $565 \times 584$ & Fundus & Public \\ STARE & 20 & $605 \times 700$ & Fundus & Public \\ IOSTAR & 30 & $1024 \times 1024$ & Fundus & Public \\ CORN-1 & 1698 & $384 \times 384$ & CCM & Public \\ OCTA & 30 & $1...
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\begin{tabular}{l|ccccc} \hline \hline \textbf{Methods} & \textbf{TPR} $\uparrow$ & \textbf{FNR} $\downarrow$ & \textbf{FPR} $\downarrow$ & \textbf{OR} $\downarrow$ & \textbf{UR} $\downarrow$\\ \hline Backbone & 0.9517 & 0.0493 & 0.0103 & 0.0877 & 0.0808 \\ Backbone+CAB & 0.9663 & 0.0310 & 0.0024 & 0.1082 & 0.0341 \\ B...
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\begin{tabular}{l|l} \toprule Symbol & Meaning\\ \midrule $\bm{X}^{(i)}$ & The $i$-th view of multi-view data. \\ \midrule $\bm{H}$ & Latent representation of multiple views. \\ \midrule $\bm{x}_a^{(i)}$ & Sample $a$ in the $i$-th view. \\ \midrule $\bm{h}_a^{(i)}$ & Sample $a$ in latent representation. \\ \midrule $n$...
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\begin{tabular}{l|c|c|c} \toprule Datasets & \# Views & \# Samples & \# Clusters\\ \midrule 100leaves & 3 & 1600 & 100 \\ \midrule BBCSport & 2 & 544 & 5 \\ \midrule Caltech101 & 6 & 9144 & 102 \\ \midrule Football & 9 & 248 & 20 \\ \midrule NottingHill & 3 & 4660 & 5 \\ \midrule Olympics & 9 & 464 & 28 \\ \midrule ORL...
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\begin{tabular}{l|ccccccccc} \toprule ACC & 100leaves & BBCSport & Caltech101 & Football & NottingHill & Olympics & ORL & Politicsie & UCI\\ \midrule k-means & 0.617 & 0.408 & 0.258 & 0.651 & 0.822 & 0.569 & 0.729 & 0.621 & 0.732 \\ \midrule LRR (TPAMI'13) & 0.462 & 0.797 & 0.241 & 0.834 & 0.794 & 0.780 & 0.812 & 0.681...
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\begin{tabular}{l|ccccccccc} \toprule NMI & 100leaves & BBCSport & Caltech101 & Football & NottingHill & Olympics & ORL & Politicsie & UCI\\ \midrule k-means & 0.818 & 0.136 & 0.496 & 0.717 & 0.723 & 0.713 & 0.876 & 0.623 & 0.738 \\ \midrule LRR (TPAMI'13) & 0.736 & 0.700 & 0.495 & 0.850 & 0.579 & 0.867 & \underline{0....
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\begin{tabular}{l|ccccccccc} \toprule F-measure & 100leaves & BBCSport & Caltech101 & Football & NottingHill & Olympics & ORL & Politicsie & UCI\\ \midrule k-means & 0.505 & 0.409 & 0.224 & 0.452 & 0.753 & 0.302 & 0.645 & 0.542 & 0.681 \\ \midrule LRR (TPAMI'13) & 0.352 & 0.761 & 0.179 & 0.745 & 0.653 & 0.710 & 0.765 &...
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\begin{tabular}{l|c|c|c} \toprule Datasets & True \# Clusters & Closed \# Clusters & NMI \\ \midrule 100leaves & 100 & 114 & 0.647\\ \midrule BBCSport & 5 & 5 & 0.890\\ \midrule Caltech101 & 102 & 56 & 0.473 \\ \midrule Football & 20 & 17 & 0.855 \\ \midrule NottingHill & 5 & 3 & 0.721 \\ \midrule Olympics & 28 & 29 & ...
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\begin{tabular}{l|c|c|c} \toprule Datasets & ~~BMVC~~ & ~~LVMSC~~ & ~~MHC~~ \\ \midrule 100leaves & 0.665 & 9.417 & 1.220 \\ \midrule BBCSport & 0.082 & 1.385 & 0.548 \\ \midrule Caltech101 & 8.157 & 139.75 & 26.628 \\ \midrule Football & 0.061 & 2.306 & 1.007 \\ \midrule NottingHill & 2.532 & 9.08 & 60.431 \\ \midrule...
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\begin{tabular}{clc} \textbf{Symbol} & \textbf{Description} & \textbf{Units}\\ \hline $\theta$ & Hopper's angle & \textit{degrees}\\ $\beta$ & Surface slope & \textit{degrees}\\ $l$ & Spike's length & $m$\\ $I_f$ & Flywheel's rotational inertia & $kg \cdot m^2$\\ $I_p$ & Platform's rotational inertia & $kg \cdot m^2$\\...
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\begin{tabular}{clc} \textbf{Symbol} & \textbf{Description} & \textbf{Value}\\ \hline $g$ & Itokawa's mean gravity acceleration & 77 $\mu m/s^2$\\ $\beta$ & Surface inclination & -30º$< \beta <$30º\\ $\alpha$ & Half the angle between spikes & 45º\\ $l$ & Spike's length & 0.071 $m$\\ $m_p$ & \textit{Hopper}'s mass & 1.5...
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\begin{tabular}{llrrrrrr} \textbf{Distance} & $\mathbf{d_h}\textbf{[m]}$ & \textbf{5} & \textbf{10} & \textbf{30} & \textbf{50} & \textbf{70} & \textbf{100}\\ \hline \textbf{Angular speed} & $\omega_f$[rad/s] & 81.3 & 115.0 & 198.9 & 256.7 & 303.7 & 363.0\\ \textbf{Speed-up time} & $T_{min}$[s] & 375.0 & 531.0 & 919.0 ...
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\begin{tabular}{clrc} \bf{Symbol} & \bf{Description} & \bf{Value} & \bf{Units}\\ \hline $J$ & Moment of inertia of the flywheel & 3.42e-5 & $kg\ m^2$\\ $b$ & Friction coefficient of the rotor & 2.20e-5 & $N\ m\ s$\\ $K$ & Back electro-motive force constant & 47.96e-3 & $\frac{V}{rad/s}$\\ $L$ & Motor winding inductance...
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\begin{tabular}{crrrr} \textbf{Test} & $\omega_f\ [rad/s]$ & $\beta\ [degrees]$ & $\Delta t\ [s]$ & $d_h\ [m]$\\ \hline 2 & 81.16 & 0 & 0.00 & 5\\ 3 & 114.88 & 10 & 0.98 & 10\\ 4 & 198.76 & 5 & 0.26 & 30\\ 5 & 199.49 & 30 & 1.37 & 30\\ 6 & 256.67 & 20 & 0.80 & 50\\ 7 & 389.98 & -15 & 0.00 & 100 \end{tabular}
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\begin{tabular}{ccrr|r} \textbf{Slope} & \textbf{Parameter} & \textbf{Exp. Response} & \textbf{Real Response} & \textbf{Error}\\ \hline 0º & $d_h\ [m]$ & \textbf{5} & \textbf{3.00} & 40.00 \%\\ & $\omega_f\ [rad/s]$ & 81.16 & 62.83\\ & $\Delta t\ [s]$ & 0.00 & 0.06\\ \hline 10º & $d_h\ [m]$ & \textbf{10} & \textbf{8.29...
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\begin{tabular}{crrr} \textbf{Target Distance} & \textbf{Mean Distance} & \textbf{Std Deviation} & \textbf{Relative Error}\\ \hline 5 $m$ & 3.6 $m$ & $\pm0.2\ m$ & \textbf{27.2\%}\\ 10 $m$ & 8.5 $m$ & $\pm0.2\ m$ & \textbf{15.5\%}\\ 30 $m$ & 35.7 $m$ & $\pm1.6\ m$ & \textbf{19.1\%}\\ 50 $m$ & 51.9 $m$ & $\pm0.4\ m$ & \...
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\begin{tabular}{crrr} \textbf{Target Distance} & \textbf{Mean Distance} & \textbf{Std Deviation} & \textbf{Relative Error}\\ \hline 5 $m$ & 3.8 $m$ & $\pm0.2\ m$ & \textbf{23.0\%}\\ 10 $m$ & 8.5 $m$ & $\pm0.2\ m$ & \textbf{14.8\%}\\ 30 $m$ & 31.4 $m$ & $\pm1.9\ m$ & \textbf{4.8\%}\\ 50 $m$ & 51.4 $m$ & $\pm0.5\ m$ & \t...
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\begin{tabular}{crrr} \textbf{Target Distance} & \textbf{Mean Distance} & \textbf{Std Deviation} & \textbf{Relative Error}\\ \hline 5 $m$ & 3.9 $m$ & $\pm0.1\ m$ & \textbf{23.1\%}\\ 10 $m$ & 8.4 $m$ & $\pm0.3\ m$ & \textbf{16.1\%}\\ 30 $m$ & 34.1 $m$ & $\pm2.2\ m$ & \textbf{13.7\%}\\ 50 $m$ & 49.6 $m$ & $\pm1.2\ m$ & \...
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\begin{tabular}{cr|r} \textbf{Target Distance $[m]$} & \textbf{Resultant Distance $[m]$} & \textbf{Error} [\%]\\ \hline 100 & 102.41 & 2.41\\ 100 & 102.18 & 2.18\\ 100 & 105.67 & 5.67\\ 75 & 73.95 & 1.40\\ \textbf{385} & \textbf{384.33} & 0.17 \end{tabular}
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\begin{tabular}{c|c|c|ccccc} \hline \multirow{2}{*}{Dataset}& \multirow{2}{*}{T}& \multirow{2}{*}{Metric}& \multicolumn{5}{c}{Hidden size} \\ \cline{4-8} &&&8&16&32&64&128\\ \hline\hline \multirow{5}{*}{SZ-taxi}& \multirow{5}{*}{60min} &RMSE& 4.0630 & 4.0490& 4.0510& \textbf{4.0270}& 4.1090\\ &&MAE& 2.7220 & 2.7000& \t...
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\begin{tabular}{c|c|c|ccccc} \hline \multirow{2}{*}{Dataset}& \multirow{2}{*}{T}& \multirow{2}{*}{Metric}& \multicolumn{5}{c}{Monte Carlo dropout probability} \\ \cline{4-8} &&&0.1&0.3&0.5&0.7&0.9\\ \hline\hline \multirow{5}{*}{SZ-taxi}& \multirow{5}{*}{60min} &RMSE& \textbf{4.0270}& 4.0350& 4.0470 & 4.1020& 4.6620\\ &...
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\begin{tabular}{c|c|c|cc} \hline \multirow{2}{*}{Dataset}& \multirow{2}{*}{T}& \multirow{2}{*}{Metric}& \multicolumn{2}{c}{Activation function} \\ \cline{4-5} &&&Identity&Sigmoid\\ \hline\hline \multirow{5}{*}{SZ-taxi}& \multirow{5}{*}{60min} &RMSE&4.0280&\textbf{4.0270}\\ &&MAE&2.7020&\textbf{2.6860}\\ &&ACC&0.7194&\t...
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\begin{tabular}{c|ccc|ccc|ccc} \hline & \multicolumn{3}{c|}{ Case 1} & \multicolumn{3}{c|}{ Case 2} & \multicolumn{3}{c}{ Case 3} \\ \hline $\vert g_t \vert$, $v_t$ & \multicolumn{3}{c|}{S} & \multicolumn{3}{c|}{L} & \multicolumn{3}{c}{L} \\ \hline $\vert g_t - g_{t-1} \vert$, $s_t$ & \multicolumn{3}{c|}{S} & \multicol...
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\begin{tabular}{c|c|c|c|c|c|c|c} \hline AdaBelief & SGD & AdaBound & Yogi & Adam & MSVAG & RAdam & AdamW \\ \hline \textbf{70.08} & 70.23$^\dag$ & 68.13$^\dag$ & 68.23$^\dag$ & 63.79$^\dag$ (66.54$^\ddag$) & 65.99 & 67.62$^\ddag$ & 67.93$^\dag$ \\ \hline \end{tabular}
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\begin{tabular}{c|c} \hline Generator & Discriminator \\ \hline \hline ConvTranspose ({[}inchannel = 100, outchannel = 512, kernel = 4$\times$4, stride = 1{]}) & Conv2D({[}inchannel=3, outchannel=64, kernel = 4$\times$4, stride=2{]}) \\ \hline BN-ReLU & LeakyReLU \\ \hline ConvTranspose ({[}inchannel = 512, outchannel ...
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\begin{tabular}{ccccc} \hline Cases & \multicolumn{4}{c}{Service Type} \\ & 1 & 2 & 3 & 4 \\ \hline Case a & 0 & 0 & 40 & 40 \\ Case b & 10 & 10 & 30 & 30 \\ Case c & 20 & 20 & 20 & 20 \\ Case d & 30 & 30 & 10 & 10 \\ Case e & 40 & 40 & 0 & 0 \\ \hline \end{tabular}
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\begin{tabular}{c|c|c|c} \hline \multirow{3}{2.1cm}{The maximum $\langle M_{1}M_{2} \rangle$ at the first $n.n.$} & \multicolumn{3}{c}{Host semiconductors} \\ \cline{2-4} &InSb &InAs &GaSb \\ &(gap=0.17eV) &(gap=0.42eV) &(gap=0.75eV) \\ \hline \multirow{2}*{Fe} &0.07 (p-type) &0.14 (p-type) &0.1 (p-type) \\ &0.09 (n-ty...
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\begin{tabular}{c|c|c|c|c} \hline the $i$-th $n.n.$ &first &second &third &fourth \\ \hline $\langle M_1^zM_2^z \rangle$ (10$^{-2}$ $\mu_B^2$) &0.467 &0.231 &0.155 &0.124\\ \hline $J_i/k_B$ (K) &6.757 &3.334 &2.235 &1.788\\ \hline \end{tabular}
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\begin{tabular}{c|c|c|c} \hline doping concentration &12.5$\%$ &25$\%$ &37.5$\%$ \\ \hline Tc (K) &120 &245 &379\\ \hline \end{tabular}
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\begin{tabular}{c|c|c|c|c} \hline &doping concentration &12.5$\%$ &25$\%$ &37.5$\%$ \\ \hline \multirow{3}*{Tc (K)} &(In,Cr)Sb &62 &214 &406 \\ \cline{2-5} &(In,Cr)As &17 &129 &264 \\ \cline{2-5} &(Ga,Cr)Sb &242 &534 &851 \\ \hline \end{tabular}
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\begin{tabular}{lccc} \hline Datasets &NYT10&CoNLL04&ADE\\ \hline Relation types &29&5&2 \\ Entity types &7&4&3 \\ Training set &70339&910&3416 \\ Training triplets &87739&1273&5650 \\ Test set &4006&288&854 \\ Test triplets &5859&422&1450 \\ \hline \end{tabular}
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\begin{tabular}{||c|c|c|c||} \hline \hline Domains sequence & Burgers vectors & $N_{Si}$ & E per Si atom (meV) \\ \hline \hline $abcda$ & $\vec{b}_+ \times 2$, $\vec{b}_- \times 2 $ &104 & 0 \\ $abcda$, Adatoms at the boundaries & $\vec{b}_+ \times 2$, $\vec{b}_- \times 2 $ & 108 & 66.9 \\ $abcda$, Adatoms at the domai...
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\begin{tabular}{cc} \hline\hline Total COM energy T$_{c}$ (GeV) & Total cross section $\sigma$ (mb) \\ [0.5ex] \hline 0.01 & 1.33 \\ 0.02 & 1.06 \\ 0.03 & 0.852 \\ 0.1 & 0.158 \\ 0.19 & 0.00886 \\ 0.2 & 0.00570 \\ \hline \end{tabular}
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\begin{tabular}{cc} \hline\hline Total COM energy T$_{c}$ (GeV) & Total cross section $\sigma$ (mb) \\ [0.5ex] \hline 0.02 & 0.15 \\ 0.04 & 0.11 \\ 0.1 & 0.07 \\ 0.2 & 0.03 \\ 0.3 & 0.01 \\ 0.5 & 0 \\ \hline \end{tabular}
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\begin{tabular}{cc} \hline\hline Total COM energy T$_{c}$ (GeV) & Total cross section $\sigma$ (mb) \\ [0.5ex] \hline 0.01 & 0.282 \\ 0.02 & 0.269 \\ 0.03 & 0.244 \\ 0.04 & 0.222 \\ 0.1 & 0.121 \\ 0.2 & 0.0472 \\ 0.3 & 0.0173 \\ 0.4 & 0.00544 \\ 0.5 & 0.00121 \\ \hline \end{tabular}
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\begin{tabular}{ccc} \toprule Term & \# of tweets & \# of tweets localized in U.S. \\ \midrule \#dinner & 6,007,037 & 1,520,573 \\ \#breakfast & 5,736,525 & 1,335,432 \\ \#lunch & 5,514,638 & 1,213,171 \\ \#brunch & 2,160,015 & 768,577 \\ \#snack & 898,178 & 246,842 \\ \#meal & 553,214 & 115,101 \\ \#supper & 138,658 &...
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\begin{tabular}{cc} \toprule Year (March-May) & \# of tweets localized in U.S. \\ \midrule 2020 & 63,898 \\ 2019 & 81,036 \\ 2018 & 131,418 \\ 2017 & 135,734 \\ 2016 & 174,792 \\ 2015 & 185,264 \\ \bottomrule \end{tabular}