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\begin{tabular}{c|c|c|c|c|c|c} \hline \multirow{2}{*}{Method} & \multicolumn{2}{c|}{Market1501}& \multicolumn{2}{c|}{DukeMTMC-reID}& \multicolumn{2}{c}{CUHK03}\\ \cline{2-7} &Rank-1&mAP&Rank-1&mAP&Rank-1&mAP\\ \hline\hline CGPN-1 & 94.9 & 87.9 & 89.3 & 78.4 & 82.4 & 79.9\\ CGPN-2 & 95.3 & 89.4 & 90.3 & 80.2 & 85.3 & 82.5\\ CGPN-3 & 94.2 & 86.2 & 88.6 & 76.9 & 84.3 & 81.0\\ CGPN-4 & 95.2 & 89.3 & 90.0 & 79.9 & 83.4 & 80.7\\ CGPN & $\mathbf{96.1}$ & $\mathbf{89.9}$ & $\mathbf{90.4}$ & $\mathbf{80.9}$ & $\mathbf{87.1}$ & $\mathbf{83.6}$\\ \hline \end{tabular}
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\begin{tabular}{llllllll} \toprule \textbf{Dataset} & \textbf{\#user} &\textbf{\#item} & \textbf{\#rating} & \textbf{\#doc/user} & \textbf{\#doc/item} & \textbf{\#word/doc} & \textbf{Density}\\ \hline Musical Instruments & 1,429 & 900 & 10,261 & 10 & 17 & 59 & 0.798\%\\ Office Products & 4905 & 2420 & 53,228 & 7 & 11 & 56 & 0.449\%\\ Grocery and Gourmet Food & 14,681 & 8,713 & 151,254 & 10 & 22 & 89 & 0.118\%\\ Video Games & 24,303 & 10,672 & 231,577 & 9 & 21 & 124 & 0.089\%\\ Sports and Outdoors & 35,598 & 18,357 & 296,337 &8 &14 &99& 0.045\%\\ \bottomrule \end{tabular}
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\begin{tabular}{lccccc} \toprule Variants & \textit{Musical Instruments} & \textit{Office Products} & \textit{Grocery and Gourmet Food} & \textit{Video Games} & \textit{Sports and Outdoors}\\ \hline HTI-wavg & 0.632 & 0.584 & 0.723 & 0.762 & 0.646\\ HTI-wmax & 0.618 & 0.563 & 0.684 & 0.742 & 0.636\\ HTI-davg & 0.669 & 0.635 & 0.788 & 0.782& 0.690\\ HTI-dmax & 0.654 & 0.619 & 0.760 & 0.793&0.669\\ HTI & 0.611* & 0.552* & 0.672* & 0.731* &0.629*\\ \bottomrule \end{tabular}
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\begin{tabular}{l|cc|cc|cc|cc|cc} \toprule \multirow{2}{*}{Models} & \multicolumn{2}{|c|}{\textit{Musical Instruments}} & \multicolumn{2}{c|}{\textit{Office Products}} & \multicolumn{2}{c|}{\textit{Grocery and Gourmet Food}} & \multicolumn{2}{c|}{\textit{Video Games}} & \multicolumn{2}{c}{\textit{Sports and Outdoors}} \\ \cline{2-11} & train & test & train & test & train & test & train & test & train & test \\ \hline PMF & 0.616 & 0.031 & 3.370 & 0.142 & 10.098 & 0.406 & 15.399 & 0.768 & 20.448 & 1.022\\ NeuMF & 0.812 & 0.040 & 4.060 & 0.165 & 11.719 & 0.587 & 18.729 & 0.765 & 26.743 & 1.540\\ CDL & 2.053 & 0.091 & 10.566 & 0.584 & 31.452 & 1.218 & 53.148 & 2.500 & 70.675 & 2.677 \\ ConMF & 2.425 & 0.099 & 12.858 & 0.682 & 42.649 & 1.945 & 53.318 & 2.826 & 70.118 & 3.879\\ DeepCoNN&3.660&0.194&22.484&1.122&63.477&3.182&85.095&4.910&128.966&6.534\\ D-attn&10.118&0.590&59.501&3.128&164.936&9.124&211.647&13.608&338.507&17.912\\ CARL&4.961&0.218&29.619&1.122&86.245&3.340&110.168&4.910&172.168&6.644\\ NARRE & 4.282 & 0.250 & 21.408 & 0.954 & 74.376 & 2.872 & 108.362 & 5.239 & 144.203 & 7.196\\ DAML & 4.983 & 0.193 & 25.379 & 1.212 & 87.672 & 4.525 & 114.934 & 6.484 & 171.536 & 9.489\\ HTI&5.365&0.219&26.825&1.262&77.425&3.853&117.943&5.893&151.441&7.586\\ \bottomrule \end{tabular}
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\begin{tabular}{c|cc} $\phi$ & $\psi$ &\\ \hline -0.0101100286 & 0.0050550143 &+ 0.0087555416j \\ -0.0345177968 & -0.0345177968 &+ 0j \\ 0.0589255650 & -0.0294627825 &+ 0.0510310363j \\ 0.2845177968 & -0.1422588984 &- 0.2463996399j \\ 0.4023689270 & 0.4023689270 &+ 0j \\ 0.2845177968 & -0.1422588984 &+ 0.2463996399j \\ 0.0589255650 & -0.0294627825 &- 0.0510310363j \\ -0.0345177968 & -0.0345177968 &+ 0j \\ -0.0101100286 & 0.0050550143 &- 0.0087555416j \\ \end{tabular}
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\begin{tabular}{|l|l|l|} \hline \textit{Predicting Mean reversion} & Feature-set 1 & Feature-set 2 \\ \hline Intercept & 0.10 *** & 0.10 *** \\ \hline Individual performance & \textbf{0.07 **} & \textbf{0.08 ** (VIF: 1.21)} \\ \hline Team performance & & -0.004 (VIF: 1.21) \\ \hline & \begin{tabular}[c]{@{}l@{}}Log-likelihood: 384.4\\ AIC: -764.8\\ BIC: -754.4\end{tabular} & \begin{tabular}[c]{@{}l@{}}Log-likelihood: 384.4\\ AIC: -762.8\\ BIC: -747.2\end{tabular} \\ \hline \end{tabular}
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\begin{tabular}{|l|l|l|l|l|} \hline \textit{Predicting Persuasiveness} & Feature-set 1 & Feature-set 2 & Feature-set 3 & Feature-set 4 \\ \hline Intercept & 0.13 *** & 0.19 *** & 0.12 *** & 0.12 *** \\ \hline Expertise & \textbf{0.20 ***} & & \textbf{0.17 *** (VIF: 1.05)} & \textbf{0.17 *** (VIF: 1.11)} \\ \hline Confidence & & \textbf{0.14 ***} & \textbf{0.11 *** (VIF: 1.05)} & \textbf{0.12 *** (VIF: 1.06)} \\ \hline Response network out-degree & & & & 0.00 (VIF: 2.63) \\ \hline Sentiment network out-degree & & & & 0.002 ** (VIF: 2.54) \\ \hline & \begin{tabular}[c]{@{}l@{}}Log-likelihood: 162.86\\ AIC: -321.7\\ BIC: -316.3\end{tabular} & \begin{tabular}[c]{@{}l@{}}Log-likelihood: 162.90\\ AIC: -321.8\\ BIC: -316.4\end{tabular} & \begin{tabular}[c]{@{}l@{}}Log-likelihood: 168.12\\ AIC: -330.2\\ BIC: -322.1\end{tabular} & \begin{tabular}[c]{@{}l@{}}Log-likelihood: 170.18\\ AIC: -330.4\\ BIC: -316.8\end{tabular} \\ \hline \end{tabular}
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\begin{tabular}{|l|c|} \hline & $l1$-norm of estimated parameters \\ \hline Previous influence matrix & 0.2137 $\pm$ 0.0027 \\ \hline Expertise & 0.0239 $\pm$ 0.0027 \\ \hline Message content embedding & 0.0111 $\pm$ 0.0003 \\ \hline Message sentiment & 0.0078 $\pm$ 0.0010 \\ \hline Message emotion & 0.0050 $\pm$ 0.0007 \\ \hline Message responsiveness & 0.0041 $\pm$ 0.0004 \\ \hline \end{tabular}
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\begin{tabular}{cc} \hline \hline Range [pad size] &$\Delta \theta_\text{FWHM}$ [deg.] \\ \hline \hline 11(1) & 2.09(4) \\ 20(1) & 0.93(2) \\ 28(1) & 0.53(1) \\ 37(1) & 0.40(1) \\ 46(1) & 0.29(1) \\ 56(1) & 0.26(1) \\ \hline \hline \end{tabular}
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\begin{tabular}{ccc} \hline \hline Algorithm & $\varepsilon$ [\%] &Threshold [pad size] \\ \hline \hline RANSAC & 67 & 1.5 \\ LMedS & 82 & 1.5 \\ MLESAC & 83 & 3.0 \\ J-Linkage & 92 & 2.0 \\ \hline \hline \end{tabular}
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\begin{tabular}{cc} \hline \hline Parameters &Values\\ \hline Path loss exponent $\gamma$ & 2 \\ Path gain, $\forall k $ & $a(\theta _k)\sim\mathcal {C}\mathcal {N}(0, 1)$ \\ UAV height $H$ & 50m\\ Noise power ${\sigma^2_n}$ & -50 dBm\\ Fixed power consumption $\rm{P}_{hov}+\rm{P}_{cir}$ & 30dBm\\ Transmit power budget $\rm{P}_{max}$ & 26 dBm\\ approximation error ${\delta}$ & -20dB\\ \hline \hline \end{tabular}
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\begin{tabular}{|c||cc|cc|} \hline \textbf{Attribute} & \multicolumn{2}{|c|}{\textbf{Transformer}} & \multicolumn{2}{|c|}{\textbf{Conformer}} \\ \hline \hline \multicolumn{1}{|l||}{Code} & (S) & (M) & (S) & (M) \\ \multicolumn{1}{|l||}{Parameters (M)} & 10.9 & 30.5 & 10.3 & 27.9 \\ \hline \hline \multicolumn{1}{|l||}{Num. Layers} & 16 & 16 & 16 & 16 \\ \multicolumn{1}{|l||}{Layer Dim.} & 160 & 288 & 144 & 256 \\ \multicolumn{1}{|l||}{Attention Heads} & 4 & 4 & 4 & 4 \\ \multicolumn{1}{|l||}{Conv. Kernels} & -- & -- & 32 & 32 \\ \hline \end{tabular}
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\begin{tabular}{c|ccc|cccccccc|c} \hline Method & block5 &block4 &block3 & person & rider & car & truck & bus & train & mcycle & bicycle & mAP \\ \hline \multirow{3}{*}{SD-DA} & & &\checkmark &32.3 &44.4 &43.7 &28.8 &42.6 &22.7 &33.3 &38.2 &35.8 \\ \cline{2-13} & &\checkmark &\checkmark & 32.5 &45.7 &43.8 & 26.8 & 49.6 &30.0 & 33.5 &37.5 &37.4 \\ \cline{2-13} & \checkmark&\checkmark&\checkmark & 33.3 & 46.2 & 44.0 & 31.1 & 47.7 & 36.4 & 36.1 &36.4 & {\bf 38.9} \\ \hline \multirow{3}{*}{SA-DA} & \checkmark & & &30.6 &40.0 & 40.5 & 26.4 & 37.4 & 27.3 & 30.0 & 33.4 & 33.2 \\ \cline{2-13} &\checkmark &\checkmark & &32.7 &47.5 & 44.9 & 36.2 & 43.7 & 23.4 & 38.0 & 36.5 & {\bf 37.8} \\ \cline{2-13} & \checkmark&\checkmark&\checkmark &33.9 &47.2 & 44.9 &35.5 &41.2 & 11.9 &40.0 &35.6 & 36.3 \\ \hline \end{tabular}
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\begin{tabular}{ll} \hline $U^0$ & Matrix $ m \times l $ containing the texture image in gray scales\\ $U^n$ & \\ $A$ & Matrix $ m \cdot l \times m \cdot l $ for algebraic linear system\\ $b$ & Array $ m \cdot l \times 1 $ containing independent terms of linear system\\ \hline \end{tabular}
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\begin{tabular}{ll} \hline $\mathrm{size}(U)$ & Return the size (number of rows and columns) of the matrix $A$ \\ $\mathrm{PCG}(A, b, U)$ & Return the solution of the linear system $Ax=b$ by the PCG method with \\ &initial approximation $U$\\ \hline \end{tabular}
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\begin{tabular}{c|cccc} P & 1 & 2 & 3 & 4\\ \hline R & 8 & 16 & 24 & 24\\ \end{tabular}
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\begin{tabular}{c|c|c} \normalsize Device & \normalsize OpenIPMC size (kiB) & \normalsize Total size (kiB) \\ \hline \normalsize ZynqUS+ & \normalsize 55 & \normalsize 218 \\ \normalsize ESP32 & \normalsize 33 & \normalsize 93 \\ \normalsize STM32 & \normalsize 40 & \normalsize 75 \end{tabular}
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\begin{tabular}{l|c|c|c|c} & Ultra96-Pulsar2b & Trenz-Serenity & ESP32-Pulsar2b & STM32-Pulsar2b \\ \hline \multicolumn{1}{l|}{SoC/microcontroller} & ZynqUS+ ZU3EG & ZynqUS+ ZU4EG & ESP32 & STM32H745 \\ \hline \multicolumn{1}{l|}{Architecture} & ARM Cortex-R5 & ARM Cortex-R5 & Tensilica Xtensa Lx6 & ARM Cortex-M7 \\ \hline \multicolumn{1}{l|}{IPMB-A} & I$^2$C-PS & I$^2$C-PS & I$^2$C & I$^2$C \\ \hline \multicolumn{1}{l|}{IPMB-B} & I$^2$C-PS & I$^2$C-PS & I$^2$C & I$^2$C \\ \hline \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}Hardware \\ Address (bit 0)\end{tabular}} & GPIO-PL & {\begin{tabular}[c]{@{}c@{}}PCA9557 using software-emulated \\ I$^2$C on GPIO-PS\end{tabular}} & GPIO & GPIO \\ \hline \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}Hardware \\ Address (bits 1-7)\end{tabular}} & GPIO-PS & {\begin{tabular}[c]{@{}c@{}}PCA9557 using software-emulated \\ I$^2$C on GPIO-PS\end{tabular}} & GPIO & GPIO \\ \hline \multicolumn{1}{l|}{Blue\_LED} & GPIO-PL & {\begin{tabular}[c]{@{}c@{}}PCA9557 using software-emulated \\ I$^2$C on GPIO-PS\end{tabular}} & GPIO & GPIO \\ \hline \multicolumn{1}{l|}{Handle\_Switch} & GPIO-PL & {\begin{tabular}[c]{@{}c@{}}PCA9557 using software-emulated\\ I$^2$C on GPIO-PS\end{tabular}} & GPIO & GPIO \\ \hline \multicolumn{1}{l|}{Soft\_Reset} & - & GPIO-PS & - & - \\ \hline \multicolumn{1}{l|}{12V\_Enable} & - & {\begin{tabular}[c]{@{}c@{}}PCA9557 using software-emulated \\ I$^2$C on GPIO-PS\end{tabular}} & - & - \\ \end{tabular}
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\begin{tabular}{|c|l|l|l|l|l|l|} \hline \multirow{2}{*}{} & \multicolumn{1}{c|}{\multirow{2}{*}{SVM$^{1}$\hspace{0.2cm}}} & \multirow{2}{*}{MLP$^{2}$\hspace{0.2cm}} & \multicolumn{1}{c|}{\multirow{2}{*}{RF$^{3}$\hspace{0.2cm}}} & \multicolumn{3}{c|}{Random Forest-MLP} \\ \cline{5-7} & \multicolumn{1}{c|}{} & & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{Majority Vote} & \multicolumn{1}{c|}{Equiprobability} & \multicolumn{1}{c|}{Weighted probability} \\ \hline Iris Dataset & 64.1 & 66.6 & 73.4 & 75.6 & 76.7 & \bf79.2 \\ \hline Digits Dataset & 47.3 & 42.4 & 51.4 & 54.5 & 52.3 & 54.1 \\ \hline Wine Dataset & 45.1 & 48.5 & 57.4 & 62.4 & 63.1 & \bf65.8 \\ \hline breast cancer Dataset & 53.7 & 56.1 & 59.2 & 64.9 & 66.1 & \bf68.2 \\ \hline \end{tabular}
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\begin{tabular}{|l|c|c||l|c|c|} \hline Operator & P & T & Operator & P & T \\ \hline $\vec{v}$ & + & - & $\vec{j}$ & + & - \\ $\hat{e}_3$ & - & + & $\vec{r}$ & - & + \\ \hline \end{tabular}
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\begin{tabular}{|c|ccccc||cccc||cc||c|} \hline \multirow{2}{*}{Nucleus} & \multicolumn{5}{c||}{$A$ {[}keV{]}} & \multicolumn{4}{c||}{$A_1$ {[}keV{]}} & \multicolumn{2}{c||}{$B_1$ {[}keV{]}} & $B$ {[}keV{]} \\ & LO & NLO & N$^2$LO & N$^3$LO & N$^4$LO & NLO & N$^2$LO & N$^3$LO & N$^4$LO & N$^3$LO & N$^4$LO & N$^4$LO \\\hline $^{99}$\textbf{Tc} & 89.92 & 89.92 & 71.40 & 57.65 & 79.02 & 32.22 & 50.74 & 83.39 & 60.31 & -6.874 & -3.454 & -1.710 \\ $^{167}$\textbf{Er} & 13.43 & 13.43 & 11.18 & 11.14 & 11.24 & 5.59 & 7.84 & 7.94 & 7.83 & -0.021 & -0.005 & -0.008 \\ $^{169}$\textbf{Er} & 13.30 & 13.30 & 11.76 & 11.73 & 11.78 & 8.22 & 9.75 & 9.83 & 9.77 & -0.015 & -0.007 & -0.004 \\ $^{167}$\textbf{Tm} & 13.43 & 13.43 & 12.35 & 12.40 & 12.50 & -9.96 & -8.88 & -8.99 & -9.10 & 0.023 & 0.039 & -0.008 \\ $^{169}$\textbf{Tm} & 13.30 & 13.30 & 12.38 & 12.41 & 12.47 & -10.50 & -9.58 & -9.65 & -9.72 & 0.016 & 0.026 & -0.005 \\ $^{183}$\textbf{W} & 16.68 & 16.68 & 13.01 & 12.99 & 12.84 & -1.19 & 2.49 & 2.52 & 2.69 & -0.008 & -0.033 & 0.013 \\ $^{235}$\textbf{U} & 7.25 & 7.25 & 6.03 & 6.02 & 6.05 & -2.93 & -1.71 & -1.69 & -1.73 & -0.003 & 0.002 & -0.003 \\ $^{239}$\textbf{Pu} & 7.34 & 7.34 & 6.25 & 6.25 & 6.27 & -4.72 & -3.63 & -3.64 & -3.66 & 0.001 & 0.004 & -0.002 \\ \hline \end{tabular}
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\begin{tabular}{|c|cccc||cc||c|} \hline \multirow{2}{*}{Nucleus} & \multicolumn{4}{c||}{$A$ {[}keV{]}} & \multicolumn{2}{c||}{$A_3$ {[}keV{]}} & {$B$ {[}keV{]}} \\ & LO & N$^2$LO & N$^3$LO & N$^4$LO & N$^3$LO & N$^4$LO & N$^4$LO \\\hline ${}^{159}$\textbf{Dy} & 16.48 & 11.32 & 11.35 & 11.44 & -0.005 & -0.009 & -0.005 \\ \hline \end{tabular}
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\begin{tabular}{|l||c|c|c|c|c|c|c|} \hline Method &\cellcolor{red!25} Abs Rel &\cellcolor{red!25} Sq Rel &\cellcolor{red!25} RMSE &\cellcolor{red!25} RMSE log &\cellcolor{blue!25}$\sigma<1.25$&\cellcolor{blue!25}$\sigma<1.25^{2}$&\cellcolor{blue!25}$\sigma< 1.25^{3}$ \\ \hline \hline M & 0.151 & 2.110 & 2.961 & 0.205 & 0.913 & \textbf{0.978} & \textbf{0.989} \\ M* & 0.150 & 2.118 & \textbf{2.934} & \textbf{0.204} & \textbf{0.915} & \textbf{0.978} & \textbf{0.989} \\ S & 0.231 & 1.716 & 5.632 & 0.646 & 0.665 & 0.905 & 0.919 \\ S* & 0.235 & 1.737 & 5.650 & 0.644 & 0.657 & 0.900 & 0.919 \\ M+S & \textbf{0.116} & 0.747 & 3.147 & 0.221 & 0.886 & 0.929 & 0.963 \\ M+S* & \textbf{0.116} & \textbf{0.742} & 3.165 & 0.222 & 0.885 & 0.929 & 0.962 \\ \hline \end{tabular}
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\begin{tabular}{|l|l||c|c|c|c|c|c|} \hline \multicolumn{2}{|c||}{\textbf{OpenVSLAM}} & \multicolumn{6}{c|}{ATEs estimated on Dataset Rosario} \\ \hline Setting & Train & 01& 02& 03& 04& 05& 06\\ \hline \hline Mono+D${}_{GT}$ & - &8.32 &4.94 &5.70 &4.31 &5.92 &13.60 \\ Mono+D${}_{CNN}$ & M &X &X &X &X &X &X \\ Mono+D${}_{CNN}$ & M* &10.99 &12.46 &16.98 &14.52 &13.58 &33.35 \\ Mono+D${}_{CNN}$ & S &\textbf{5.21} &3.80 &2.79 &3.01 &3.26 &8.40 \\ Mono+D${}_{CNN}$ & S* &5.25 &3.78 &2.73 &2.96 &2.90 &8.62 \\ Mono+D${}_{CNN}$ & MS &5.44 &3.57 &\textbf{2.54} &\textbf{2.71} &2.95 &\textbf{7.52} \\ Mono+D${}_{CNN}$ & MS* &5.37 &\textbf{3.41} &2.62 &2.94 &\textbf{2.63} &7.79 \\ \hline \hline Mono+D${}_{GT}^{scaled}$ & - &7.44 &2.03 &0.678 &\textbf{0.25} &2.39 &\textbf{5.78}\\ Mono+D${}_{CNN}^{scaled}$ & M &X &X &X &X &X &X\\ Mono+D${}_{CNN}^{scaled}$ & M* &9.30 &2.91 &1.04 &0.72 &3.60 &10.30\\ Mono+D${}_{CNN}^{scaled}$ & S &\textbf{2.31}&1.40 &0.59 &0.28 &2.37 &6.35\\ Mono+D${}_{CNN}^{scaled}$ & S* &2.71 &1.35 &0.59 &0.29 &1.98 &6.95\\ Mono+D${}_{CNN}^{scaled}$ & MS &3.61 &1.35 &0.60 &0.26 &2.24 &6.14\\ Mono+D${}_{CNN}^{scaled}$ & MS* &3.29 &\textbf{1.26}&\textbf{0.57} &0.26 &\textbf{1.75} &6.18\\ \hline \hline Stereo \textbf{(baseline)} & - &1.35 &1.95 &1.75 &1.48 &1.65 &3.41\\ \hline \end{tabular}
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\begin{tabular}{lccc} \hline & RG & GVG & SFG \\ Total & 218 & 81 & 230 \\ E & 122 & 7 & 2 \\ S0 & 63 & 4 & 6\\ Sa & 21 & 23 & 9 \\ Sb & 9 & 42 & 88 \\ Sc & 1 & 3 & 69 \\ Sd & 2 & 2 & 56 \\ \end{tabular}
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\begin{tabular}{lcccccr} \hline Source &Flare &LCCF &Timelag &Significance &$\beta_{\text{original}}$ &$\beta_{\text{simulations}}$\\ &Peak &Peak &[hours] &[$\%$] & & \\ \hline 3C 454.3 &Dec 2009 &0.4$\pm$0.2 &11.5$\pm$3.3 &$\geq$68 &1.05 $\pm$ 0.01 &1.11 $\pm$ 0.20 \\ 3C 454.3 &Nov 2010 &0.9$\pm$0.1 &0.1$\pm$1.3 &$\geq$99 &1.22 $\pm$ 0.01 &1.27 $\pm$ 0.28 \\ CTA 102 &Sept 2012 &0.8$\pm$0.2 &-13.9$\pm$1.7 &$\geq$99 &0.67 $\pm$ 0.03 &0.73 $\pm$ 0.36\\ CTA 102 &Feb 2016 &0.7$\pm$0.4 &5.6$\pm$7.2 &$\geq$95 &1.28 $\pm$ 0.02 &1.32 $\pm$ 0.31\\ PKS 1510-089 &Nov 2011 &0.8$\pm$0.2 &17.5$\pm$2.4 &$\geq$99 &0.71 $\pm$ 0.01 &0.74 $\pm$ 0.22 \\ PKS 1510-089 &Feb 2012 &0.3$\pm$0.2 &25.5$\pm$8.0 &$\geq$68 &1.05 $\pm$ 0.01 &1.01 $\pm$ 0.17\\ PKS 1502+106 &Feb 2009 &0.3$\pm$0.2 &10.6$\pm$4.1 &$\geq$68 &1.13 $\pm$ 0.02 &1.20 $\pm$ 0.28 \\ PKS 1502+106 &July 2015 &0.6$\pm$0.4 &0.5$\pm$2.7 &$\geq$95 &0.86 $\pm$ 0.01 &0.75 $\pm$ 0.19 \\ 3C 279 &June 2015 &0.7$\pm$0.3 &0.9$\pm$2.8 &$\geq$99 &0.76 $\pm$ 0.01 &0.77 $\pm$ 0.16 \\ 4C 21.35 &June 2010 &0.9$\pm$0.2 &-5.8$\pm$0.4 &$\geq$99 &0.97 $\pm$ 0.02 &1.08 $\pm$ 0.25 \\ \hline \end{tabular}
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\begin{tabular}{ll} \hline \multicolumn{1}{c}{\textbf{Representation levels}} & \multicolumn{1}{c}{\textbf{Encoded text}} \\ \hline Input text & Hot3YoGirlOnBeach \\ Digit \& special char. & Hot\$YoGirlOnBeach \\ Binary representation & 101 \\ Orthographic representation & CccNCcCcccCcCcccc \\ Output text & \begin{tabular}[c]{@{}l@{}}Hot\$YoGirlOnBeach 101\\ CccNCcCcccCcCcccc\end{tabular} \\ \hline Input text & !!!!yoB0yXX \\ Digit \& special char. & \#\#\#\#yoB\$yXX \\ Binary representation & 0101 \\ Orthographic representation & PPPPccCNcCC \\ Output text & \begin{tabular}[c]{@{}l@{}}\#\#\#\#yoB\$yXX 0101\\PPPPccCNcCC\end{tabular} \\ \hline Input text & FG5F44GDSdfs234DG \\ Digit \& special char. & FG\$\$F\$\$GDSdfs\$\$DG \\ Binary representation & 1010101 \\ Orthographic representation & CCNCNNCCCcccNNNCC \\ Output text & \begin{tabular}[c]{@{}l@{}}FG\$\$F\$\$GDSdfs\$\$DG 1010101 \\ CCNCNNCCCcccNNNCC\end{tabular} \\ \hline \end{tabular}
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\begin{tabular}{ll} \hline \multicolumn{1}{c}{\textbf{File Path}} & \multicolumn{1}{c}{\textbf{File Name}} \\ \hline l:\textbackslash{}Modeling\textbackslash{}Silver Models\textbackslash{}Sarah\textbackslash{}Silver Starlets\textbackslash{}Pink skirt 5\textbackslash{} & pinkskirt-1-028.JPG \\ l:\textbackslash{}Modeling\textbackslash{}Silver Models\textbackslash{}Sarah\textbackslash{}Silver Starlets\textbackslash{}Pink skirt 5\textbackslash{} & pinkskirt-1-029.JPG \\ l:\textbackslash{}Modeling\textbackslash{}Silver Models\textbackslash{}Sarah\textbackslash{}Silver Starlets\textbackslash{}Pink skirt 5\textbackslash{} & pinkskirt-1-030.JPG \\ l:\textbackslash{}Modeling\textbackslash{}BD Modeling\textbackslash{}Charming Models\textbackslash{} & GX8E5675.jpg \\ l:\textbackslash{}Modeling\textbackslash{}BD Modeling\textbackslash{}Charming Models\textbackslash{} & GX8E5676.jpg \\ \hline \end{tabular}
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\begin{tabular}{lrrr} \hline & \multicolumn{1}{l}{\textbf{CSEM}} & \multicolumn{1}{l}{\textbf{Non-CSEM}} & \multicolumn{1}{l}{\textbf{Total}} \\ \hline Before pre-processing & 90,000 & 800,000 & 890,000 \\ Training set size & 72,000 & 640,000 & 712,000 \\ Testing set size & 18,000 & 160,000 & 178,000 \\ \hline After pre-processing & 37,648 & 537,807 & 575,455 \\ Training set size & 30,161 & 421,175 & 451,336 \\ Testing set size & 7,487 & 116,632 & 124,119 \\ \hline \end{tabular}
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\begin{tabular}{llll} \hline & \textbf{CSEM} & \textbf{Non-CSEM} & \textbf{Total} \\ \hline Before pre-processing & 2,864,105 & 3,031,802 & 5,895,907 \\ Training set size & 1,971,585 & 1,971,585 & 3,943,170 \\ Testing set size & 892,520 & 1,060,217 & 1,952,737 \\ \hline After pre-processing & 924,445 & 1,141,145 & 2,065,590 \\ Training set size & 31,925 & 80,928 & 112,853 \\ Testing set size & 892,520 & 1,060,217 & 1,952,737 \\ \hline \end{tabular}
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\begin{tabular}{ccrrrr} \hline \textbf{Model Name} & \textbf{Category} & \multicolumn{1}{c}{\textbf{Precision}} & \multicolumn{1}{c}{\textbf{Recall}} & \multicolumn{1}{c}{\textbf{F1}} & \multicolumn{1}{l}{\textbf{Support}} \\ \hline \multicolumn{1}{c|}{\multirow{3}{*}{\textbf{FNC}}} & Non-CSEM & 0.99 & 0.99 & 0.99 & 116,632 \\ \multicolumn{1}{c|}{} & CSEM & 0.90 & 0.97 & 0.93 & 7,487 \\ \multicolumn{1}{c|}{} & Average & 0.95 & 0.98 & 0.96 & 124,119 \\ \hline \multicolumn{1}{c|}{\multirow{3}{*}{\textbf{FPC}}} & Non-CSEM & 0.96 & 1.00 & 0.98 & 1,060,217 \\ \multicolumn{1}{c|}{} & CSEM & 1.00 & 0.95 & 0.97 & 892,520 \\ \multicolumn{1}{c|}{} & Average & 0.97 & 0.97 & 0.97 & 1,952,737 \\ \hline \end{tabular}
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\begin{tabular}{|c|c|c|c|c|c|c|} \hline Nb. cells & DoFs & $\tau=h$ & $E_h$ & $D_h$ & GF Iter & Schur Iter \\ \hline 64 & 1920 & $\sqrt{2}/2$ & -1.002E-2 & 1.062E-2 & 11 & [60,65] \\ \hline 256 & 7680 & $\sqrt{2}/4$ & -9.709E-3 & 5.967E-3 & 17 & [85,101] \\ \hline 1024 & 30720 & $\sqrt{2}/8$ & -8.762E-3 & 2.962E-3 & 28 & [118,148] \\ \hline \end{tabular}
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\begin{tabular}{|c|c|c|c|c|c|c|c|c|} \cline{2-9} \multicolumn{1}{c|}{ } & \multicolumn{4}{c|}{LDG} & \multicolumn{4}{c|}{SIPG} \\ \hline $\tau=h$ & $E_h$ & $D_h$ & GF Iter & Schur Iter & $E_h$ & $D_h$ & GF Iter & Schur Iter \\ \hline $\sqrt{2}/2$ & -8.28E-3 & 7.71E-3 & 7 & [302,321] & -8.30E-3 & 7.72E-3 & 7 & [284,307] \\ \hline $\sqrt{2}/4$ & -6.63E-3 & 3.45E-3 & 14 & [557,605] & -6.64E-3 & 3.46E-3 & 13 & [556,600] \\ \hline $\sqrt{2}/8$ & -4.88E-3 & 1.34E-3 & 37 & [788,831] & -4.90E-3 & 1.34E-3 & 35 & [787,833] \\ \hline \end{tabular}
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\begin{tabular}{|l|c|c|c|} \cline{2-4} \multicolumn{1}{c|}{ } & \multicolumn{2}{c|}{LDG} & \multicolumn{1}{c|}{SIPG} \\ \hline $\sharp$ ref. & $\qquad\gamma_0=\gamma_1=1\qquad$ & $\gamma_0=5000,\gamma_1=1100$ & $\gamma_0=5000,\gamma_1=1100$ \\ \hline $l=3$ & 0.0478 & 0.0311 & 0.0312 \\ \hline $l=4$ & 0.0443 & 0.0211 & 0.0213 \\ \hline $l=5$ & 0.0365 & 0.0118 & 0.0119 \\ \hline \end{tabular}
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\begin{tabular}{|c|c|c|c|c|} \cline{2-5} \multicolumn{1}{c|}{ } & Initial & BC PP & Metric PP & Final \\ \hline $E_h$ & 120.3590 & 1.1951 & 2.5464 & 1.7707 \\ \hline $D_h$ & 9.8696 & 3.2899 & 9.8609E-2 & 9.5183E-2 \\ \hline \end{tabular}
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\begin{tabular}{|c|c|c|c|c|} \cline{2-5} \multicolumn{1}{c|}{ } & Initial & BC PP & Metric PP & Final \\ \hline $E_h$ & 413.7400 & 5.5344 & 28.9184 & 13.0706 \\ \hline $D_h$ & 25.2909 & 26.1854 & 9.9997E-2 & 1.0178E-1 \\ \hline \end{tabular}
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\begin{tabular}{|c|c|c|c|c|c|c|} \cline{2-7} \multicolumn{1}{c}{ } & \multicolumn{2}{|c|}{$\widetilde{tol}=0.1$} & \multicolumn{2}{|c|}{$\widetilde{tol}=0.025$} & \multicolumn{2}{|c|}{$\widetilde{tol}=0.01$} \\ \cline{2-7} \multicolumn{1}{c|}{ } & Algo 2 & Algo 1 & Algo 2 & Algo 1 & Algo 2 & Algo 1\\ \hline $E_h$ & 36.9461 & 4.01094 & 103.838 & 7.42946 & 146.215 & 8.78622\\ \hline $D_h$ & 2.62428 & 3.19839 & 1.36864 & 2.69258 & 0.853431 & 1.83427\\ \hline \end{tabular}
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\begin{tabular}{|c|c|c|c|c|} \cline{2-5} \multicolumn{1}{c|}{ } & Initial & BC PP & Metric PP & Final \\ \hline $E_h$ & 138020 & 0.658342 & 202.144 & 7.7461 \\ \hline $D_h$ & 5.17664 & 5.16565 & 0.248419 & 1.15764 \\ \hline \end{tabular}
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\begin{tabular}{cc} \toprule \textbf{Decoder Layer Index} & \textbf{mean $\pm$ std} \\ \midrule 0 & $1.9 \pm 0.3$\\ 1 & $1.0 \pm 0.2$\\ 2 & $1.1 \pm 0.4$\\ 3 & $2.8 \pm 1.9$\\ 4 & $11.1 \pm 6.3$\\ 5 & $11.6 \pm 7.4$\\ \bottomrule \end{tabular}
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\begin{tabular}{lcc} \toprule \textbf{WER} & \textbf{dev} & \textbf{test} \\ \midrule \quad Baseline & 9.3 & 8.1 \\ \midrule {\sl Proposed} \\ \quad $\lambda=0.1$ & {\bf 8.7} & 7.8 \\ \quad $\lambda=0.2$ & 8.8 & {\bf 7.7}\\ \quad $\lambda=0.3$ & 9.6 & 8.0 \\ \bottomrule \end{tabular}
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\begin{tabular}{lcccc} \toprule \textbf{WER} & \textbf{dev\_clean} & \textbf{dev\_other} & \textbf{test\_clean} & \textbf{test\_other} \\ \midrule \quad Baseline & {\bf 3.7} & 9.8 & {\bf 4.0} & 10.0 \\ \midrule {\sl Proposed} \\ \quad $\lambda=0.1$ & 3.8 & {\bf 8.6} & 4.1 & {\bf 8.7} \\ \bottomrule \end{tabular}
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\begin{tabular}{|r|lll|} \hline & Syntax & BERT & BERT+syntax \\ Mean score & 63.9 $\pm$1.21 & 66.5 $\pm$1.21 & 66.3 $\pm$1.19 \\ \hline \end{tabular}
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\begin{tabular}{|c|ccc|} \hline & Not rejected &Rejected &Total \\ \hline True nulls&$U$ &$V$ &$m_0$ \\ True alternatives&$T$ &$S$ &$m_1$ \\ Total& $m-R$&$R$ &$m$ \\ \hline \end{tabular}
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\begin{tabular}{lccccc} \\[-1.8ex]\hline \hline \\[-1.8ex] & Holm & IHW & weighted Bonferroni & CAMT.fwer & Improve \\ \hline \\[-1.8ex] Balding Type I & $836_*$ & $836_*$ & $836_*$ & 833 & -0.4\% \\ BMI & 1287 & 1287 & 1347 & $1364_*$ & 6\% \\ Heel T Score & 2104 & 2104 & 2144 & $2146_*$ & 2\% \\ Height & 3463 & 3460 & $3555_*$ & 3550 & 2.5\% \\ Waist-hip Ratio & 909 & 909 & 937 & $952_*$ & 4.7\% \\ Eosinophil Count & 1750 & 1750 & $1817_*$ & 1797 & 2.7\% \\ Mean Corpular Hemoglobin & 1913 & 1913 & $1953_*$ & 1925 & 0.6\% \\ Red Blood Cell Count & 1570 & 1570 & 1609 & $1633_*$ & 4\% \\ Red Blood Cell Distribution Width & 1470 & 1470 & $1493_*$ & 1470 & 0\% \\ White Blood Cell Count & 1393 & 1393 & 1430 & $1462_*$ & 5\% \\ Auto Immune Traits & 179 & 179 & $180_*$ & 138 & -22.9\% \\ Cardiovascular Diseases & 512 & 512 & 529 & $540_*$ & 5.5\% \\ Eczema & 423 & 423 & 426 & $431_*$ & 1.9\% \\ Hypothyroidism & 373 & 373 & 377 & $424_*$ & 13.7\% \\ Respiratory and Ear-nose-throat Diseases & 228 & 228 & 231 & $236_*$ & 3.5\% \\ Type 2 Diabetes & 156 & 156 & 158 & $160_*$ & 2.6\% \\ Age at Menarche & 634 & 634 & 648 & $652_*$ & 2.8\% \\ Age at Menopause & 200 & 200 & 201 & $203_*$ & 1.5\% \\ FEV1-FVC Ratio & 1537 & 1537 & 1575 & $1599_*$ & 4\% \\ Forced Vital Capacity (FVC) & 867 & 867 & 924 & $947_*$ & 9.2\% \\ Hair Color & 1606 & 1606 & 1616 & $1629_*$ & 1.4\% \\ Morning Person & 204 & 204 & 217 & $229_*$ & 12.3\% \\ Neuroticism & 176 & 115 & 189 & $198_*$ & 12.5\% \\ Smoking Status & 221 & 159 & 232 & $254_*$ & 14.9\% \\ Sunburn Occasion & 232 & 232 & 232 & $237_*$ & 2.2\% \\ Systolic Blood Pressure & 1108 & 1108 & 1148 & $1157_*$ & 4.4\% \\ Years of Education & 383 & 383 & 416 & $447_*$ & 16.7\% \\ \hline \\[-1.8ex] \end{tabular}
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\begin{tabular}{ccc} \centering Model & Input Features & Accuracy \\ \hline FCNN & IQ & 92.25\%\\ FCNN & Frequency & 92.42\% \\ CNN & IQ & 98.92\% \\ CNN & Frequency & 99.19\% \\ RNN & IQ & 93.78\%\\ RNN & Frequency & 92.67\% \\ CRNN & IQ & 98.28\% \\ CRNN & Frequency & 99.03\% \\ \hline \end{tabular}
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\begin{tabular}{l|ccccccccc} \hline Coefficient & $\sigma_{k1}$ & $\sigma_{k1}$ & $\sigma_{\omega1}$ & $\sigma_{\omega2}$ & $\beta_1$ & $\beta_2$ & $\beta^*$ & $\kappa$ & $a_1$ \\ \hline Nominal value & 0.85 & 1.0 & 0.5 & 0.856 & 0.075 & 0.828 & 0.09 & 0.41 & 0.31 \\ \hline \end{tabular}
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\begin{tabular}{c|c} \textbf{Parameter} & $\mathrm{GHz}$ \\ \hline $E_C/h $ & $0.124 $ \\ $E_J/h$ & $1600$ \\ $E_J^{\mathrm{eff}}/h$ & $760$ \\ $E_L/h$ & $704$ \end{tabular}
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\begin{tabular}{cc} \hline \hline \textbf{Quantity} & \textbf{Used Value} \\ \hline Excited state lifetime & $1.13 \times 10^{-19}$~s \\ Resonance energy & 1457.8--1469.8~keV \\ Beam mass excess & 14.087~MeV\\ Recoil mass excess & 12.051~MeV\\ $\alpha$ partial width & $5.82$~keV\\ $\gamma$ partial width & 0.1114~eV\\ $\gamma$ branching ratios & 82\% (to the ground state) \\ & 18\% (to the first excited)\\ $\gamma$ angular distributions & \\ $2 \rightarrow 3$ (ground state) & $W(\theta) = 0.86 + 0.21 \sin^2(\theta)$ \\ $2 \rightarrow 1$ (first excited) & $W(\theta) = 0.50 + 0.75 \sin^2(\theta)$ \\ \hline \hline \end{tabular}
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\begin{tabular}{lcc} \hline \hline \textbf{Quantity} & \textbf{Measured} & \textbf{Relative}\\ ~ & \textbf{Value} & \textbf{Uncertainty} \\ \hline $\eta_{CSF}$ & $0.523(8)$ & 1.5\% \\ $N_{beam}$ & $3.252(53) \times 10^{14}$ & 1.64\% \\ $\eta_{BGO}$ & $0.332(38)$ & 11.4\% \\ $\eta_{separator}$ & $0.122(5)$ & 4.1\% \\ $\epsilon$ (eV cm$^2$) & $24.63(136) \times 10^{15}$ & 5.5\% \\ $\eta_{livetime}$ & $0.91573(8) $ & 0.009\% \\ $E_{beam}$ (A MeV) & $0.612(1)$ & 0.16\% \\ \hline \textbf{Total systematic} & ~ & 13.49\%\\ \textbf{uncertainty} & \\ \hline \hline \end{tabular}
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\begin{tabular}{c|c|c} \hline \rule[-1ex]{0pt}{3.5ex} Polarization & $\delta$ & $\partial\delta/\partial z$\\ \hline\hline \rule[-1ex]{0pt}{3.5ex} s & $E_t$ & $\partial E_t/\partial z = -ikH_n$\\ \rule[-1ex]{0pt}{3.5ex} p & $H_t$ & $\partial H_t/\partial z = ikE_n$\\ \hline \end{tabular}
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\begin{tabular}{c|ccccc} $f(x)$&$g(\cos k_{\rm F})$&$g^{(1)}(\cos k_{\rm F})$&$g^{(2)}(\cos k_{\rm F})$&$g^{(3)}(\cos k_{\rm F})$&$g^{(4)}(\cos k_{\rm F})$\\ \hline $1$&$1-\frac{k_{\rm F}}{\pi}$&$\frac{1}{\pi \sin k_{\rm F}}$&$\frac{\cos k_{\rm F}}{\pi \sin^3 k_{\rm F}}$&$\frac{2+\cos(2 k_{\rm F})}{\pi\sin^5 k_{\rm F}}$&$\frac{3\cos k_{\rm F}(4+\cos(2 k_{\rm F}))}{\pi\sin^7 k_{\rm F}}$\\ $x$&$-\frac{\sin k_{\rm F}}{\pi}$&$\frac{1}{\pi \tan k_{\rm F}}$&$\frac{1}{\pi \sin^3 k_{\rm F}}$&$\frac{3\cos k_{\rm F}}{\pi\sin^5 k_{\rm F}}$&$\frac{3(3+2\cos (2k_{\rm F}))}{\pi \sin^7 k_{\rm F}}$ \end{tabular}
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\begin{tabular}{l|ll|l|l} \hline \multicolumn{5}{c}{\textbf{Task A} } \\ \hline & \textbf{Eval. } & & \multicolumn{2}{c}{\textbf{Tst. f-avg} } \\ \hline \multicolumn{5}{c}{\textbf{Not-preprocessed} } \\ \hline \textbf{Emd\#} & \textbf{\%} & \textbf{f-avg} & \textit{T\%80} & \textit{T\%100} \\ \hline \textbf{\textit{BERT$^*$ }} & 0.480 & \textbf{0.532} & 0.533$^*$ & \textbf{0.601} \\ \textbf{\textit{SVs}} & \textbf{0.518} & \textbf{0.548} & \textbf{0.589} & 0.532 \\ \textbf{\textit{TWITA300 }} & \textbf{0.482} & 0.526 & \textbf{0.578} & 0.551 \\ \textbf{\textit{TWITA100}} & 0.480 & 0.521 & 0.494 & 0.551 \\ \textbf{\textit{Fasttext}} & 0.485 & 0.521 & 0.479 & 0.482 \\ \textbf{\textit{GloVe}} & 0.445 & 0.308 & 0.401 & 0.401 \\ \hline \hline \multicolumn{5}{c}{\textbf{Preprocessed} } \\ \hline \textbf{\textit{SVs}} & \textbf{0.515} & \textbf{0.556} & 0.524 & 0.566 \\ \textbf{\textit{TWITA100}} & \textbf{0.513} & \textbf{0.543} & 0.560$^*$ & 0.566 \\ \textbf{\textit{FastText}} & 0.485 & 0.489 & 0.532 & 0.528 \\ \textbf{\textit{TWITA300}} & 0.447 & 0.490 & 0.541 & 0.506 \\ \textbf{\textit{GloVe}} & 0.445 & 0.308 & 0.401 & 0.401 \\ \textbf{\textit{BERT }} & 0.475 & 0.445 & 0.512 & 0.213 \\ \hline \textit{Baseline} & & & \textit{0.578} & \textit{0.578} \\ \hline \end{tabular}
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\begin{tabular}{|c|c|c|} \hline Network layers & Constraints & $R_{\infty}$ \\ \hline \hline Work \& Household & $-$ & $32 \%$ \\ & Color (\emph{Optimized}) & $12 \%$ \\ \hline Work \& Household \& Social & $-$ & $81 \%$ \\ & Memory & $78 \%$ \\ & Color (\emph{Optimized}) & $71\%$ \\ & Color (\emph{Optimized}) + Memory & $64 \%$ \\ \hline \end{tabular}
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\begin{tabular}{lcccc|c} \toprule {Method} &base & $\ell^2$ & $\ell^2$-CO & IAPE & $\infty$-levels \\ \midrule $R$ train & \textbf{9.74$\pm$.09} & 9.56$\pm$.14& 9.36$\pm$.15 & 9.54$\pm$.13 & 9.14$\pm$.14 \\ $D^i_{kl}({\pi_{\infty}}|{\pi})$ train & 0.57$\pm$0.39 & 0.2$\pm$0.16 & 0.15$\pm$0.12 & \textbf{0.11$\pm$0.12} & - \\ $\Delta T_{base}|$R=10 train & - & 1.34$\pm$7.94 & 3.3$\pm$8.79 & 2.73$\pm$8.15 & 4.65$\pm$8.39 \\ \midrule $R$ test & 7.47$\pm$.25 & 7.49$\pm$.22 & 7.99$\pm$.20 & \textbf{8.23$\pm$.18} & \textbf{9.05$\pm$.23} \\ $D^i_{kl}({\pi_{\infty}}|{\pi})$ test & 0.57$\pm$0.39 & 0.19$\pm$0.15 & 0.15$\pm$0.11 & \textbf{0.1$\pm$0.09} & -\\ $\Delta T_{base}|$R=10 test & - & 1.73$\pm$12.71 & 0.45$\pm$11.54 & 1.14$\pm$12.28 & 1.51$\pm$12.49 \\ \bottomrule \end{tabular}
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\begin{tabular}{lcccc|c} \toprule {Method} &base & $\ell^2$ & $\ell^2$-CO & IAPE & $\infty$-levels \\ \midrule $\gamma$ &$.99$&$.99$&$.99$&$.99$&$.99$\\ bootstrap rollout length &$256$ &$256$ &$256$ &$256$ &$256$ \\ $\underline{w}$ &$2$ &$2$ &$2$ &$2$ &$2$\\ $\overline{w}$ &$\frac{1}{2}$ &$\frac{1}{2}$ &$\frac{1}{2}$ &$\frac{1}{2}$ &$\frac{1}{2}$\\ minibatch size &$8$ &$8$ &$8$ &$8$ &$8$ \\ ADAM learning rate &$2\times 10^{-4}$ &$2\times 10^{-4}$ &$2\times 10^{-4}$ &$2\times 10^{-4}$ &$2\times 10^{-4}$\\ $\ell_2$ penalty &$0$ &$2\times 10^{-5}$ &$2\times 10^{-5}$ &$2\times 10^{-5}$ &$2\times 10^{-5}$\\ \# of training levels &$500$ &$500$ &$500$ &$500$ &$\infty$\\ uses Cutout &No &No &Yes &No &No\\ uses Batchnorm &Yes &Yes &Yes &Yes &Yes\\ \# of ensembles &- &- &- &$10$ &-\\ \bottomrule \end{tabular}
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\begin{tabular}{l|cccc|cccc|cccc} \toprule \multirow{2}{*}{Model} & \multicolumn{4}{c|}{PTB} & \multicolumn{4}{c|}{Yelp15} & \multicolumn{4}{c}{Yahoo}\\ & NLL$\downarrow$ & PPL$\downarrow$ & MI$\uparrow$ & KL & NLL$\downarrow$ & PPL$\downarrow$ & MI$\uparrow$ & KL & NLL$\downarrow$ & PPL$\downarrow$ & MI$\uparrow$ & KL \\ \midrule VAE-LSTM & 101.2 & 101.4 & 0.0 & 0.0 & 357.9& 40.6 & 0.0 & 0.0 & 328.6& 61.2& 0.0 & 0.0 \\ SA-VAE & 101.0 & 100.7 & 0.8 & 1.3 & 355.9 & 39.7 & 2.8 & 1.7 & 327.2& 60.2& 2.7 & 5.2 \\ Cyc-VAE & 102.8 & 109.0 & 1.3 & 1.4 & 359.5 & 41.3 & 1.0 & 2.0 & 330.6 & 65.3 & 2.0 & 2.1 \\ Lag-VAE & 100.9 & 99.8 & 0.8 & 0.9 & 355.9 & 39.7 & 2.4 & 3.8 & 326.7& 59.8& 2.9 & 5.7 \\ BN-VAE (0.7) & 100.2 & 96.9 & \textbf{5.5} & 7.2 & 355.9 & 39.7 & \textbf{7.4} & 9.1 & 327.4 & 60.2 & \textbf{7.4} & 8.8 \\ \midrule TWR-VAE$_\text{sum}$ & 96.7 & 63.2 & 3.7 & 5.9 & 378.3 & 47.4 & 3.8 & 3.9 & 345.6 & 71.1 & 3.7 & 3.8\\ TWR-VAE$_\text{mean}$& 95.6 & 60.4 & 3.9 & 4.9 & 361.7 & 40.0 & 3.9& 3.5 & 324.8 & 55.0 & 4.1 & 4.8 \\ TWR-VAE & \textbf{86.6} & \textbf{40.9} & 4.1 & 5.0 & \textbf{344.3} & \textbf{33.5} &4.1& 3.1 & \textbf{317.3} & \textbf{50.2} & 4.1 & 3.3\\ \bottomrule \end{tabular}
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\begin{tabular}{l|cccc|cccc} \toprule \multicolumn{1}{c|}{\multirow{2}{*}{Model}} & \multicolumn{4}{c|}{Yelp15} & \multicolumn{4}{c}{Yahoo}\\ & NLL$\downarrow$ & PPL$\downarrow$ & MI$\uparrow$ & KL & NLL$\downarrow$ & PPL$\downarrow$ & MI$\uparrow$ & KL \\ \midrule Basic-VAE$_{\text{RNN}}$ & 399.2 & 58.7 & 0.0 & 0.0 & 363.9 & 89.1 & 0.0 & 0.1\\ TWR-VAE$_{\text{RNN}}$ & 395.4& 56.4 & 3.9 & 0.5 &363.0 & 88.2 & 4.1 & 0.6 \\ Basic-VAE$_{\text{GRU}}$ & 389.6 & 53.2 & 0.6 & 0.6 & 355.0 & 79.9 & 2.3 & 2.6\\ TWR-VAE$_{\text{GRU}}$ & 360.9 & 39.7 & \textbf{4.2} & 3.3 & 336.9 & 63.9 & \textbf{4.2} & 3.7 \\\midrule TWR-VAE$_{\text{LSTM-last25}}$ & 360.4 & 39.5 & 4.1 & 8.3 & 338.2 & 64.9 & \textbf{4.2} & 8.4 \\ TWR-VAE$_{\text{LSTM-last50}}$ & 356.2 & 37.9 & 4.1 & 5.1 & 331.7 & 59.9 & \textbf{4.2} & 5.3 \\ TWR-VAE$_{\text{LSTM-last75}}$ & 352.6 & 36.5 & 4.1 & 3.7 & 321.0 & 52.5 & 4.1 & 4.1 \\ TWR-VAE & \textbf{344.3} & \textbf{33.5} & 4.1& 3.1 & \textbf{317.3} & \textbf{50.2} & 4.1 & 3.3 \\ \bottomrule \end{tabular}
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\begin{tabular}{l|cccc|c|cccc|c} \toprule \multirow{2}{*}{Metrics} & \multicolumn{5}{c|}{Switchboard} & \multicolumn{5}{c}{Dailydialog}\\ & SeqGAN & CVAE & WAE & iVAE & TWR-VAE & SeqGAN & CVAE & WAE & iVAE & TWR-VAE\\ \midrule BLEU-R$\uparrow$ & 0.282 & 0.295 & 0.394 & \textbf{0.427} & 0.395 & 0.270 & 0.265 &0.341 & 0.355 & \textbf{0.407}\\ BLEU-P$\uparrow$ & \textbf{0.282} & 0.258 & 0.254 & 0.254 & 0.258 & 0.270 & 0.222 & 0.278 & 0.239 & \textbf{0.281}\\ BLEU-F1$\uparrow$ & 0.282 & 0.275 & 0.309 & \textbf{0.319} & 0.312 & 0.270 & 0.242 & 0.306 & 0.285 & \textbf{0.333}\\ BOW-A$\uparrow$ & 0.817 & 0.836 & 0.897 & \textbf{0.930} & 0.921 & 0.918 & 0.923 & 0.948 & 0.951 & \textbf{0.952}\\ BOW-E$\uparrow$ & 0.515 & 0.572 & 0.627 & \textbf{0.670} & 0.654 & 0.495 & 0.543 & 0.578 & \textbf{0.609} & 0.603\\ BOW-G$\uparrow$ & 0.748 & 0.846 & 0.887 & \textbf{0.900} & \textbf{0.900} & 0.774 & 0.811 & 0.846 & \textbf{0.872} & 0.865\\ Intra-dist1$\uparrow$ & 0.705 & 0.803 & 0.713 & 0.828 & \textbf{0.860} & 0.747 & \textbf{0.938} & 0.830 & 0.897 & 0.921\\ Intra-dist2$\uparrow$ & 0.521 & 0.415 & 0.651 & 0.692 & \textbf{0.849} & 0.806 & 0.973 & 0.940 & 0.975 & \textbf{0.990}\\ Inter-dist1$\uparrow$ & 0.070 & 0.112 & 0.245 & 0.391 & \textbf{0.470} & 0.075 & 0.177 & 0.327 & \textbf{0.501} & 0.497\\ Inter-dist2$\uparrow$ & 0.052 & 0.102 & 0.413 & 0.668 & \textbf{0.766} & 0.081 & 0.222 & 0.583 & \textbf{0.868} & 0.817\\ \bottomrule \end{tabular}
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\begin{tabular}{|c|c|c|} \hline Fit | Model & Halo & Disk\\ \hline e-fit & 203 (0.53) & 17.85 (0.52) \\ \hline combined fit & 3.8 (2.1) & 1.48 (1.20) \\ \hline \end{tabular}
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\begin{tabular}{l|cc|cc|cc} \toprule \hline \multirow{3}{*}{\textbf{Models}} & \multicolumn{2}{c|}{\textbf{Conc.}} & \multicolumn{2}{c|}{\textbf{SQuAD}} & \multicolumn{2}{c}{\textbf{WikiText-2}} \\ & \multirow{2}{*}{CLS} & \multirow{2}{*}{NSP} & SQuAD1.1 & SQuAD2.0 & SQuAD1.1 & SQuAD2.0 \\ & & & EM / F1 & EM / F1 & EM / F1 & EM / F1 \\ \hline \hline Pre-trained BERT & - & - & 75.9 / 85.4 & 71.1 / 73.8 & - & - \\ \hline \hline \multirow{3}{*} {BERT} & - & - & 49.5 / 60.6 & 54.8 / 57.4 & 48.1 / 60.5 & 54.7 / 57.9 \\ & + & - & 48.1 / 59.1 & \textbf{56.7} / \textbf{59.7} & 48.8 / 60.6 & \textbf{55.0} / \textbf{58.0} \\ & - & + & 48.7 / 60.2 & 55.5 / 58.5 & 48.2 / 59.7 & 51.2 / 53.2 \\ \hline \multirow{3}{*} {Lower NSP} & - & - & 47.2 / 58.3 & 56.3 / 58.8 & \textbf{51.4} / \textbf{62.7} & 54.1 / 55.9 \\ & + & - & 47.0 / 58.5 & 55.8 / 58.3 & 46.8 / 57.6 & 53.6 / 56.6 \\ & - & + & \textbf{52.1} / \textbf{63.4} & 54.8 / 57.3 & 46.0 / 57.6 & 54.7 / 57.8 \\ \hline \multirow{3}{*} {Lower Mask} & - & - & 27.5 / 34.0 & 48.1 / 48.8 & 41.8 / 52.2 & 47.1 / 47.4 \\ & + & - & 43.3 / 53.5 & 47.6 / 48.2 & 25.0 / 31.8 & 46.9 / 47.4 \\ & - & + & 45.5 / 56.3 & 51.2 / 53.4 & 23.7 / 30.2 & 48.1 / 48.6 \\ \hline Lower NSP-Freeze & - & - & 40.9 / 51.4 & 51.7 / 53.4 & 39.7 / 49.5 & 50.2 / 52.3 \\ \hline Without NSP & - & - & 30.2 / 37.5 & 46.9 / 47.5 & - & - \\ \hline Bigram Shift & - & - & 47.0 / 57.6 & 55.6 / 58.0 & 34.5 / 45.7 & 46.0 / 48.4 \\ \hline BiLSTM & - & - & 22.9 / 29.3 & 46.4 / 46.7 & - & - \\ Without Pre-training & - & - & 7.4 / 12.1 & 51.0 / 51.0 & - & - \\ \hline \bottomrule \end{tabular}
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\begin{tabular}{l|cc|cc} \toprule \hline \multirow{2}{*}{\textbf{Models}} & \multicolumn{2}{c|}{\textbf{Pre-training}} & \multicolumn{2}{c}{\textbf{Accuracy}} \\ & CLS & NSP & Matched & Mismatched \\ \hline \hline Pre-trained BERT & - & - & 84.4 & 84.9 \\ \hline \hline \multirow{3}{*} {BERT} & - & - & \textbf{71.7} & \textbf{72.5} \\ & + & - & 70.2 & 70.8 \\ & - & + & 71.1 & 71.3 \\ \hline \multirow{3}{*}{Lower Mask} & - & - & 69.9 & 70.7 \\ & + & - & 68.5 & 69.2 \\ & - & + & 67.1 & 67.9 \\ \hline \multirow{3}{*}{Lower NSP} & - & - & 69.1 & 70.2 \\ & + & - & 70.3 & 71.9 \\ & - & + & 70.5 & 71.3 \\ \hline Lower NSP-Freeze & - & - & 45.2 & 45.3 \\ \hline Bigram Shift & - & - & 69.8 & 70.7 \\ \hline BiLSTM & - & - & 66.8 & 67.3 \\ Without Pre-training & - & - & 61.2 & 61.4 \\ \hline \bottomrule \end{tabular}
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\begin{tabular}{l|cc|cc|cc|cc} \toprule \hline \multirow{3}{*}{\textbf{Models}} & \multicolumn{2}{c|}{\textbf{PT}} & \multicolumn{2}{c|}{\textbf{FT}} & \multicolumn{2}{c|}{\textbf{SQuAD}} & \multicolumn{2}{c}{\textbf{WikiText-2}} \\ & CLS & NSP & CLS & NSP & SQuAD1.1 & SQuAD2.0 & SQuAD1.1 & SQuAD2.0 \\ & & & & & EM / F1 & EM / F1 & EM / F1 & EM / F1 \\ \hline \hline \multirow{5}{*} {BERT} & - & - & - & - & 49.5 / 60.6 & 54.8 / 57.4 & 48.1 / 60.5 & 54.7 / 57.9 \\ \cline{2-9} & - & - & + & - & 50.0 / 61.6 & 55.1 / 58.4 & 48.0 / 60.7 & 54.9 / 58.2 \\ & - & - & - & + & 50.4 / 61.7 & 56.5 / 59.5 & 47.4 / 60.1 & \textbf{55.6} / \textbf{58.9} \\ \cline{2-9} & + & - & + & - & 48.1 / 59.1 & \textbf{56.7} / \textbf{59.7} & 48.8 / 60.6 & 55.0 / 58.0 \\ & - & + & - & + & 48.7 / 60.2 & 55.5 / 58.5 & 48.2 / 59.7 & 51.2 / 53.2 \\ \hline \hline \multirow{7}{*} {Lower NSP} & - & - & - & - & 47.2 / 58.3 & 56.3 / 58.8 & \textbf{51.4} / \textbf{62.7} & 54.1 / 55.9 \\ \cline{2-9} & - & - & + & - & 47.9 / 59.5 & 56.7 / 59.6 & 50.7 / 62.1 & 54.0 / 57.1\\ & - & - & - & + & 48.3 / 59.4 & 56.3 / 59.3 & 50.6 / 62.3 & 54.6 / 57.7 \\ \cline{2-9} & + & - & + & - & 47.0 / 58.5 & 55.8 / 58.3 & 46.8 / 57.6 & 53.6 / 56.6 \\ & - & + & - & + & \textbf{52.1} / \textbf{63.4} & 54.8 / 57.3 & 46.0 / 57.6 & 54.7 / 57.8 \\ \cline{2-9} & + & - & - & - & 47.1 / 58.3 & 55.6 / 58.3 & 46.9 / 58.0 & 53.4 / 56.3 \\ & - & + & - & - & 51.0 / 62.8 & 54.2 / 56.9 & 46.5 / 58.1 & 55.7 / 58.4 \\ \hline \bottomrule \end{tabular}
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\begin{tabular}{l|cc|cccccccccc} \toprule \hline \multirow{2}{*}{\textbf{Models}} & \multicolumn{2}{c|}{\textbf{PT}} & \multirow{2}{*}{\textbf{SL}} & \multirow{2}{*}{\textbf{WC}} & \multirow{2}{*}{ \textbf{TD}} & \multirow{2}{*}{ \textbf{BS}} & \multirow{2}{*}{\textbf{TC}} & \multirow{2}{*}{\textbf{T}} & \multirow{2}{*}{\textbf{SN}} & \multirow{2}{*}{\textbf{ON}} & \multirow{2}{*}{\textbf{OM}} & \multirow{2}{*}{\textbf{CI}} \\ & CLS & NSP \\ \hline \hline PT. BERT & - & - & 68.3 & 32.4 & 34.3 & 86.5 & 75.2 & 88.7 & 83.0 & 77.8 & 64.3 & 74.4 \\ \hline \hline \multirow{3}{*}{BERT} & - & - & 83.8 & 9.6 & 36.0 & 62.5 & 70.1 & \textbf{75.9} & 74.7 & 69.9 & 49.7 & \textbf{59.8} \\ & + & - & 75.7 & 10.1 & 34.6 & 57.9 & 63.6 & 70.2 & 73.8 & 68.9 & 50.5 & 55.6 \\ & - & + & 84.9 & \textbf{12.4} & 34.9 & 60.4 & 60.7 & 72.2 & 73.2 & 69.2 & 49.9 & 58.6 \\ \hline \multirow{3}{*}{Lower Mask} & - & - & 73.2 & 2.2 & 29.0 & 56.1 & 61.3 & 73.6 & 73.0 & 67.1 & 50.2 & 56.8 \\ & + & - & 43.8 & 1.8 & 23.5 & 54.1 & 30.7 & 70.5 & 62.2 & 58.2 & 49.8 & 51.5 \\ & - & + & 41.7 & 0.8 & 21.3 & 52.1 & 26.7 & 66.6 & 62.3 & 58.0 & 49.9 & 51.7 \\ \hline \multirow{3}{*}{Lower NSP} & - & - & 91.0 & 5.6 & 31.7 & 52.9 & 57.5 & 71.7 & 71.9 & 66.4 & \textbf{51.1} & 56.0 \\ & + & - & 86.1 & 11.1 & 34.5 & 60.0 & 67.9 & 74.9 & \textbf{75.4} & \textbf{71.5} & 50.8 & 57.2 \\ & - & + & 90.1 & 1.1 & 31.0 & 57.6 & 71.8 & 70.9 & 72.7 & 65.5 & 50.3 & 57.6 \\ \hline L. NSP-Freeze & - & - & \textbf{93.2} & 1.5 & 33.7 & 53.8 & \textbf{72.3} & \textbf{75.9} & 75.2 & 68.8 & 50.3 & 57.2 \\ \hline Bigram Shift & - & - & 88.6 & 3.8 & \textbf{37.2} & \textbf{70.3} & 65.6 & 70.5 & 72.7 & 68.2 & 49.9 & \textbf{59.8} \\ \hline \bottomrule \end{tabular}
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\begin{tabular}{|c|l||c|c|c|c|c|c|c|c|c|c|} \hline $j$ & {\bf ROI, $\mathcal{R}_j$} & MD & FA & CL & CP & DV & ASD & DE & CVD & LR & {\bf DNN} \\ \hline \hline 1 & lh-SFW & 0.69 & 0.62 & {\bf 0.72} & 0.59 & 0.67 & 0.69 & 0.67 & 0.54 & {\bf 0.72} & {\bf 0.97} \\ 2 & rh-SFW & 0.69 & 0.54 & 0.54 & 0.59 & 0.69 & 0.69 & 0.67 & 0.49 & {\bf 0.72} & {\bf 0.97} \\ 3 & lh-CBW & 0.46 & 0.64 & 0.64 & 0.64 & 0.54 & 0.46 & 0.49 & 0.64 & {\bf 0.72} & {\bf 1.00} \\ 4 & rh-CBW & 0.54 & 0.64 & {\bf 0.77} & 0.51 & 0.54 & 0.54 & 0.54 & 0.69 & 0.62 & {\bf 1.00} \\ 5 & lh-PreCW & 0.69 & {\bf 0.82} & 0.79 & 0.51 & 0.69 & 0.69 & 0.69 & 0.72 & {\bf 0.82} & {\bf 1.00} \\ 6 & rh-PreCW & {\bf 0.69} & 0.67 & 0.67 & 0.62 & 0.72 & {\bf 0.69} & 0.64 & 0.62 & 0.67 & {\bf 0.97} \\ 7 & lh-IPW & 0.74 & 0.67 & 0.74 & 0.59 & {\bf 0.77} & 0.74 & {\bf 0.77} & 0.64 & 0.72 & {\bf 0.95} \\ 8 & rh-IPW & 0.79 & 0.74 & 0.74 & 0.64 & 0.79 & 0.79 & {\bf 0.82} & 0.69 & 0.77 & {\bf 0.97} \\ 9 & lh-MFWros & 0.72 & 0.64 & 0.74 & 0.59 & 0.69 & 0.72 & {\bf 0.77} & 0.62 & 0.59 & {\bf 0.97} \\ 10 & rh-MFWros & {\bf 0.69} & 0.59 & 0.49 & 0.64 & {\bf 0.69} & {\bf 0.69} & 0.67 & 0.54 & 0.67 & {\bf 1.00} \\ 11 & lh-PCUNW & 0.72 & 0.59 & 0.59 & 0.44 & {\bf 0.74} & 0.72 & {\bf 0.74} & 0.44 & 0.56 & {\bf 0.92} \\ 12 & rh-PCUNW & 0.79 & 0.54 & 0.49 & 0.46 & 0.82 & 0.79 & {\bf 0.85} & 0.54 & 0.62 & {\bf 0.95} \\ 13 & lh-SPW & 0.56 & 0.64 & 0.62 & 0.49 & 0.54 & 0.56 & 0.56 & 0.54 & {\bf 0.72} & {\bf 0.87} \\ 14 & rh-SPW & {\bf 0.77} & 0.67 & 0.69 & 0.56 & 0.74 & {\bf 0.77} & 0.72 & 0.67 & {\bf 0.77} & {\bf 0.92} \\ \hline \end{tabular}
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\begin{tabular}{|c|l||c|c|c|c|c|c|c|c|c|c|} \hline $j$ & {\bf ROI, $\mathcal{R}_j$} & MD & FA & CL & CP & DV & ASD & DE & CVD & LR & {\bf DNN} \\ \hline \hline 1 & lh-SFW & 0.73 & 0.37 & 0.31 & 0.61 & 0.73 & 0.73 & 0.72 & 0.46 & {\bf 0.83} & {\bf 0.97} \\ 2 & rh-SFW & {\bf 0.74} & 0.38 & 0.39 & 0.45 & {\bf 0.74} & {\bf 0.74} & 0.73 & 0.45 & {\bf 0.74} & {\bf 0.98} \\ 3 & lh-CBW & 0.54 & 0.36 & 0.39 & 0.31 & 0.54 & 0.54 & 0.53 & 0.39 & {\bf 0.74} & {\bf 1.00} \\ 4 & rh-CBW & 0.46 & 0.31 & 0.30 & 0.48 & 0.44 & 0.46 & 0.43 & 0.31 & {\bf 0.65} & {\bf 1.00} \\ 5 & lh-PreCW & 0.82 & 0.19 & 0.18 & 0.44 & 0.82 & 0.82 & 0.80 & 0.27 & {\bf 0.89} & {\bf 1.00} \\ 6 & rh-PreCW & 0.72 & 0.28 & 0.26 & 0.47 & 0.72 & 0.72 & 0.72 & 0.34 & {\bf 0.79} & {\bf 0.99} \\ 7 & lh-IPW & 0.84 & 0.31 & 0.21 & 0.55 & 0.84 & 0.84 & 0.84 & 0.39 & {\bf 0.85} & {\bf 0.98} \\ 8 & rh-IPW & 0.84 & 0.29 & 0.21 & 0.44 & {\bf 0.86} & 0.84 & 0.87 & 0.36 & 0.84 & {\bf 0.99} \\ 9 & lh-MFWros & {\bf 0.76} & 0.32 & 0.33 & 0.47 & {\bf 0.76} & {\bf 0.76} & 0.75 & 0.36 & 0.69 & {\bf 0.97} \\ 10 & rh-MFWros & {\bf 0.74} & 0.47 & 0.54 & 0.38 & {\bf 0.74} & {\bf 0.74} & {\bf 0.74} & 0.47 & 0.71 & {\bf 1.00} \\ 11 & lh-PCUNW & 0.81 & 0.41 & 0.38 & 0.49 & 0.83 & 0.81 & {\bf 0.84} & 0.48 & 0.63 & {\bf 0.98} \\ 12 & rh-PCUNW & 0.85 & 0.45 & 0.42 & 0.62 & {\bf 0.87} & 0.84 & {\bf 0.87} & 0.50 & 0.57 & {\bf 0.97} \\ 13 & lh-SPW & 0.74 & 0.36 & 0.31 & 0.44 & 0.74 & 0.74 & 0.73 & 0.40 & {\bf 0.77} & {\bf 0.93} \\ 14 & rh-SPW & 0.82 & 0.27 & 0.23 & 0.54 & 0.82 & 0.82 & 0.82 & 0.31 & {\bf 0.83} & {\bf 0.94} \\ \hline \end{tabular}
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\begin{tabular}{lccl} \hline Model & Layer & Threshold & Accuracy \\ \hline \hline \textbf{Baseline} & - & - & 50.0\%\\ \hline \hline \textbf{Before clipping}\\ BERT & 7 & 0.7 & 67.5\%\\ RoBERTa & 10 & 0.9 & 69.0\%\\ \hline \hline \textbf{After clipping}\\ BERT-clip & 10 & 0.5 & \textbf{68.4\%}\\ RoBERTa-clip & 11 & 0.6 & \textbf{69.9\%}\\ \hline \end{tabular}
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\begin{tabular}{llllllll} \hline Dataset & STS-B & SICK-R & STS-12 & STS-13 & STS-14 & STS-15 & STS-16 \\ \hline \hline \textbf{Baseline}\\ Avg. GloVe & 58.02 & 53.76 & 55.14 & 70.66 & 59.73 & 68.25 & 63.66\\ \hline \hline \textbf{Before clipping}\\ BERT & 58.61(3) & 60.78(2) & 48.00(1) & 61.19(12) & 50.10(12) & 61.15(1) & 62.38(12)\\ RoBERTa & 56.60(11) & 64.68(11) & 40.00(1) & 58.33(11) & 49.79(8) & 64.39(9) & 64.82(11)\\ \hline \hline \textbf{After clipping}\\ BERT-clip & \textbf{63.06(2)} & \textbf{61.74(2)} & \textbf{50.40(1)} & \textbf{61.44(1)} & \textbf{54.52(2)} & \textbf{67.00(2)} & \textbf{64.18(2)}\\ RoBERTa-clip & \textbf{60.61(11)} & \textbf{64.82(11)} & \textbf{43.44(1)} & \textbf{59.72(11)}& \textbf{51.92(3)}& \textbf{66.15(3)} & \textbf{67.14(11)}\\ \hline \end{tabular}
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\begin{tabular}{lccc} \hline Model & SST-2 & IMDB & SST-5\\ \hline \hline \textbf{Before clipping}\\ BERT & 85.9\%(12) & 86.8\%(10) & 46.2\%(10)\\ RoBERTa & 88.4\%(8) & 91.5\%(9) & 46.9\%(7)\\ \hline \hline \textbf{After clipping}\\ BERT-clip & 85.4\%(12) & 86.4\%(10) & 46.1\%(12)\\ RoBERTa-clip & 88.7\%(8) & 91.6\%(9) & 47.0\%(7)\\ \hline \end{tabular}
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\begin{tabular}{lc} \hline \textbf{Hyper-parameter} & Our RoBERTa-base \\ \hline \hline Number of Layers & 12\\ Hidden size & 768\\ FNN inner hidden size & 3072\\ Attention Heads & 12 \\ Attention Head size & 64 \\ Dropout & 0.1\\ Warmup Steps & 10k\\ Max Steps & 200k\\ Learning Rates & 1e-4\\ Batch Size & 256\\ Weight Decay & 0.01\\ Learning Rate Decay & Polynomial\\ Adam ($\epsilon$, $\beta_1$, $\beta_2$) & (1e-6, 0.9, 0.98)\\ Gradient Clipping & 0.5\\ \hline \end{tabular}
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\begin{tabular}{lccl} \hline Model & Layer & Threshold & Acc. \\ \hline \hline \textbf{Baseline} & - & - & 50.0\%\\ \hline \hline \textbf{Before clipping}\\ BERT-distil & 5 & 0.9 & 66.5\%\\ RoBERTa-distil & 5 & 0.9 & 63.7\%\\ BERT-large & 12 & 0.7 & 70.2\%\\ RoBERTa-large & 10 & 0.9 & 70.4\%\\ \hline \hline \textbf{After clipping}\\ BERT-distil-clip & 6 & 0.6 & \textbf{67.3\%}\\ RoBERTa-distil-clip & 5 & 0.6 & \textbf{66.7\%}\\ BERT-large-clip & 12 & 0.6 & \textbf{70.3\%}\\ RoBERTa-large-clip & 16 & 0.6 & \textbf{71.3\%}\\ \hline \end{tabular}
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\begin{tabular}{lllll} \toprule \textbf{Hyperparameter} & \textbf{Value} & \textbf{Lower} & \textbf{Upper} & \textbf{Scale} \\ \midrule Dropout & 0.4 & 0.0 & 0.5 & Linear \\ Batch Size & 8 & 4 & 512 & log$_{2}$ \\ Kernel Size & 4 & 2 & 5 & Linear \\ Learning Rate & 0.00977 & 0.001 & 0.01 & log$_{10}$ \\ Number of Layers & 5 & 5 & 10 & Linear \\ Pool Size & 3 & 3 & 4 & Linear\\ Convolution Filters & 11 & 6 & 12 & Linear\\ \bottomrule \end{tabular}
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\begin{tabular}{lll} \toprule \textbf{Location} & \textbf{Normal} & \textbf{Melanoma} \\ \midrule Torso & 17106 & 257 \\ Lower extremity & 8293 & 124 \\ Upper extremity & 4872 & 111 \\ Head/neck & 1781 & 74 \\ Palms/soles & 370 & 5 \\ Oral/genital & 120 & 4 \\ \bottomrule \end{tabular}
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\begin{tabular}{ll} IV$_1$ & conventional IV approach using all six IVs;\\ IV$_2$ & conventional IV approach using five IVs $(Z_1,\ldots, Z_5)$ and treating $(X_6,Z_6)$ as covariates;\\ Aux$_1$ &the proposed auxiliary variables approach assuming two factors and using all six IVs;\\ Aux$_2$ & the auxiliary variables approach assuming two factors,\\ & using $(Z_5,Z_6)$ as IVs and $(Z_1,\ldots, Z_4)$ as covariates;\\ Aux$_3$ & the auxiliary variables approach with one factor, using $Z_6$ as an IV and $(Z_1,\ldots, Z_5)$ as covariates;\\ PI$_1$ & proximal inference using $(Z_5,Z_6)$ and $(W_1,W_2)$ as the treatment- and outcome-inducing \\ & confounder proxies, respectively and $(Z_1,\ldots, Z_4)$ as covariates;\\ PI$_2$ & proximal inference using $Z_6$ and $W_1$ as the treatment- and outcome-inducing confounder proxy, \\ & respectively and $(Z_1,\ldots, Z_5)$ as covariates;\\ OLS& ordinary least squares estimation by regression $Y$ on $X$ and $Z$. \end{tabular}
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\begin{tabular}{cccccccc} &&$\beta_1$&$\beta_2$&$\beta_3$&$\beta_4$&$\beta_5$&$\beta_6$\\ \multirow{2}{*}{Aux$_1$}&&0.948 &0.940& 0.940& 0.970& 0.933 &0.958\\ &&0.943 &0.949& 0.954& 0.950& 0.933 &0.949\\ \multirow{2}{*}{Aux$_2$}&&0.946 &0.966& 0.970& 0.963& 0.972 &0.960\\ &&0.942 &0.959& 0.958& 0.953& 0.950 &0.942 \end{tabular}
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\begin{tabular}{ccccccccc} \toprule \midrule & Initial key & $C{Z_{0,1}}$ & $C{Z_{0,2}}$ & $C{Z_{1,2}}$ & ${H_{0,1,2}}$ & ${H_2}$ & ${H_2}$ & $CNO{T_{1,2}}$\\ \midrule ${q_0}$ & $\left\{ {1,1} \right\}$ & $\left\{ {1,0} \right\}$ & $\left\{ {1,0} \right\}$ & & $\left\{ {0,1} \right\}$ & & & \\ ${q_1}$ & $\left\{ {1,0} \right\}$ & $\left\{ {1,1} \right\}$ & & $\left\{ {1,1} \right\}$ & $\left\{ {1,1} \right\}$ & & &$\left\{ {1,1} \right\}$\\ ${q_2}$ & $\left\{ {0,1} \right\}$ & & $\left\{ {0,0} \right\}$ & $\left\{ {0,1} \right\}$ & $\left\{ {1,0} \right\}$ & $\left\{ {0,1} \right\}$ & $\left\{ {1,0} \right\}$ & $\left\{ {0,0} \right\}$\\ \bottomrule & $T_2^\dag $ & $CNO{T_{0,2}}$ & ${T_2}$ & $CNO{T_{1,2}}$ & $T_2^\dag $ & $CNO{T_{0,2}}$ & $T_1^\dag $ & $CNO{T_{0,1}}$ \\ \midrule ${q_0}$ & & $\left\{ {0,0} \right\}$ & & & & $\left\{ {0,1} \right\}$ & $\left\{ {0,1} \right\}$ \\ ${q_1}$ & & & & $\left\{ {1,1} \right\}$ & & & $\left\{ {1,0} \right\}$ & $\left\{ {1,0} \right\}$ \\ ${q_2}$ & $\left\{ {1,1} \right\}$ & $\left\{ {1,1} \right\}$ & $\left\{ {1,0} \right\}$ & $\left\{ {0,0} \right\}$ & $\left\{ {1,1} \right\}$ & $\left\{ {1,1} \right\}$ & & \\ \toprule & ${T_2}$ & $T_1^\dag $ & $CNO{T_{0,1}}$ & ${H_2}$ & ${T_1}$ & ${S_1}$ & ${H_2}$ & ${H_{0,1,2}}$ \\ \midrule ${q_0}$ & & & $\left\{ {0,0} \right\}$ & & $\left\{ {0,1} \right\}$ & & & $\left\{ {1,0} \right\}$ \\ ${q_1}$ & & $\left\{ {1,1} \right\}$ & $\left\{ {1,1} \right\}$ & & & $\left\{ {1,0} \right\}$ & & $\left\{ {0,1} \right\}$\\ ${q_2}$ & $\left\{ {1,0} \right\}$ & & & $\left\{ {0,1} \right\}$ & & & $\left\{ {1,0} \right\}$ & $\left\{ {0,1} \right\}$ \\ \midrule \bottomrule \end{tabular}
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\begin{tabular}{lc} \hline Code & $(i,j,k,\ell, m)$ \\ \hline $C6 =~ \{ {\bf 123}, {\bf 125}, {\bf 145}, {\bf 234}, 12, 15, 23, 4, \emptyset\}$ & $(3,2,1,5,4)$ \\ $C10 = \{ {\bf 134}, {\bf 135}, {\bf 234}, {\bf 245}, {\bf 12}, 13, 24, 34, 1,2,5, \emptyset \}$ & $(1,3,4,2,5)$ \\ $C15 = \{ {\bf 123}, {\bf 125}, {\bf 145} ,{\bf 234}, {\bf 345}, 12,15,23,34,45, \emptyset \}$ & $(1,2,3,4,5)$ \\ \hline \end{tabular}
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\begin{tabular}{llllllll} \hline\hline Dataset & Definition & Non-def & Majority & Total & Common & Common & Common\\ & sentences & sentences & vote &words& W2V& FT& BERT\\ \hline WCL & 1,871 & 2,847 & 0.603 & 21,843 & 14,368 & 16,937& 10,740 \\ W00 & 731 & 1,454 & 0.665 & 7,478 & 5,307 & 6,077 & 4,329\\ WFMALL & 1,934 & 4,206 & 0.685 & 9,759 & 6,052 & 7,366 & 5,025\\ MULTI & 4,536 & 8,507 & 0.652 & 30,791 & 18,037 & 22,155 & 12,678 \\ \hline \end{tabular}
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\begin{tabular}{lllllllll} \hline Method & W2V & FT & BERT & self & W2V & FT & BERT & self \\ & acc & acc & acc & acc & F1 & F1 & F1 & F1 \\ \hline RF & 0.779 & 0.807 & 0.878 & 0.721 & 0.773 & 0.800 & 0.875 & 0.600 \\ SL & 0.817 & 0.859 & \textbf{0.935} & 0.775 & 0.817 & 0.859 & 0.934 & 0.771 \\ SVM & 0.824 & 0.756 & 0.934 & 0.780 & 0.824 & 0.863 & \textbf{0.936} & 0.776 \\ DefMiner & 0.797 & 0.797 & 0.797 & 0.797 & 0.741 & 0.741 & 0.741 & 0.741 \\ \hline & & & & \\ $\mathrm{CNN_{m}}$ & 0.889 & 0.916 & 0.925 & 0.909 & 0.889 & 0.916 & 0.925 & 0.908 \\ $\mathrm{CNN_{ml}}$ & 0.909 & 0.930 & 0.925 & 0.934 & 0.909 & 0.930 & 0.925 & 0.934 \\ $\mathrm{CNN_{mld}}$ & 0.915 & 0.929 & 0.925 & 0.917 & 0.915 & 0.929 & 0.925 & 0.917 \\ $\mathrm{CBLSTM_{m}}$ & 0.892 & 0.903 & 0.825 & 0.931 & 0.891 & 0.902 & 0.824 & 0.931 \\ $\mathrm{CBLSTM_{ml}}$ & 0.921 & 0.929 & 0.825 & \textbf{0.935} & 0.921 & 0.929 & 0.824 & \textbf{0.935} \\ $\mathrm{CBLSTM_{mld}}$ & 0.914 & 0.926 & 0.825 & 0.916 & 0.914 & 0.926 & 0.824 & 0.917 \\ $\mathrm{LSTM_{m}}$ & 0.858 & 0.871 & 0.618 & 0.913 & 0.858 & 0.871 & 0.506 & 0.914 \\ $\mathrm{LSTM_{ml}}$ & 0.876 & 0.890 & 0.618 & 0.914 & 0.876 & 0.889 & 0.506 & 0.914 \\ $\mathrm{LSTM_{mld}}$ & 0.905 & 0.919 & 0.618 & 0.933 & 0.905 & 0.920 & 0.506 & 0.933 \\ $\mathrm{BLSTMCNN_{m}}$ & 0.889 & 0.912 & 0.922 & 0.915 & 0.889 & 0.912 & 0.922 & 0.915 \\ $\mathrm{BLSTMCNN_{ml}}$ & \textbf{0.923} & 0.916 & 0.922 & 0.913 & \textbf{0.927} & 0.915 & 0.922 & 0.912 \\ $\mathrm{BLSTMCNN_{mld}}$ & 0.919 & \textbf{0.933} & 0.922 & 0.913 & 0.920 & \textbf{0.933} & 0.922 & 0.913 \\ \hline \end{tabular}
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\begin{tabular}{lllllllll} \hline Method & W2V & FT & BERT & self & W2V & FT & BERT & self \\ & acc & acc & acc & acc & F1 & F1 & F1 & F1 \\ \hline RF & 0.717 & 0.705 & 0.702 & 0.678 & 0.659 & 0.637 & 0.622 & 0.591 \\ SL & 0.703 & 0.721 & 0.722 & 0.690 & 0.677 & 0.697 & 0.716 & 0.640 \\ SVM & 0.707 & 0.717 & 0.701 & 0.682 & 0.645 & 0.665 & 0.700 & 0.575 \\ DefMiner & \textbf{0.819} & \textbf{0.819} & \textbf{0.819} & \textbf{0.819} & 0.644 & 0.644 & 0.644 & 0.644 \\ \hline $\mathrm{CNN_{m}}$ & 0.696 & 0.698 & 0.769 & 0.652 & 0.622 & 0.607 & \textbf{0.766} & 0.636 \\ $\mathrm{CNN_{ml}}$ & 0.691 & 0.698 & 0.769 & 0.714 & 0.617 & 0.610 & \textbf{0.766} & 0.668 \\ $\mathrm{CNN_{mld}}$ & 0.698 & 0.700 & 0.769 & 0.696 & 0.631 & 0.617 & \textbf{0.766} & 0.685 \\ $\mathrm{CBLSTM_{m}}$ & 0.696 & 0.709 & 0.684 & 0.698 & 0.656 & 0.677 & 0.556 & 0.696 \\ $\mathrm{CBLSTM_{ml}}$ & 0.712 & 0.716 & 0.684 & 0.732 & 0.676 & 0.668 & 0.556 & \textbf{0.707} \\ $\mathrm{CBLSTM_{mld}}$ & 0.735 & 0.705 & 0.684 & 0.707 & 0.732 & 0.698 & 0.556 & 0.702 \\ $\mathrm{LSTM_{m}}$ & 0.684 & 0.698 & 0.684 & 0.686 & 0.649 & 0.679 & 0.556 & 0.654 \\ $\mathrm{LSTM_{ml}}$ & 0.689 & 0.696 & 0.684 & 0.675 & 0.649 & 0.643 & 0.556 & 0.618 \\ $\mathrm{LSTM_{mld}}$ & 0.723 & 0.730 & 0.684 & 0.698 & 0.706 & \textbf{0.719} & 0.556 & 0.697 \\ $\mathrm{BLSTMCNN_{m}}$ & 0.712 & 0.728 & 0.769 & 0.657 & 0.689 & 0.700 & \textbf{0.766} & 0.660 \\ $\mathrm{BLSTMCNN_{ml}}$ & 0.700 & 0.716 & 0.769 & 0.705 & 0.706 & 0.707 & \textbf{0.766} & 0.701 \\ $\mathrm{BLSTMCNN_{mld}}$ & 0.719 & 0.696 & 0.769 & 0.689 & 0.721 & 0.691 & \textbf{0.766} & 0.683 \\ \hline \end{tabular}
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\begin{tabular}{lllllllll} \hline Method & W2V & FT & BERT & self & W2V & FT & BERT & self \\ & acc & acc & acc & acc & F1 & F1 & F1 & F1 \\ \hline RF & 0.720 & 0.766 & 0.776 & 0.705 & 0.665 & 0.733 & 0.623 & 0.740 \\ SL & 0.756 & 0.788 & 0.841 & 0.721 & 0.737 & 0.780 & 0.660 & 0.840 \\ SVM & 0.759 & 0.792 & \textbf{0.844} & 0.700 & 0.736 & 0.780 & 0.700 & \textbf{0.844} \\ DefMiner & 0.704 & 0.704 & 0.704 & 0.704 & 0.134 & 0.134 & 0.134 & 0.134 \\ \hline $\mathrm{CNN_{m}}$ & 0.834 & 0.856 & 0.827 & 0.717 & 0.830 & 0.856 & 0.821 & 0.679 \\ $\mathrm{CNN_{ml}}$ & 0.832 & 0.859 & 0.827 & 0.780 & 0.831 & 0.858 & 0.821 & 0.781 \\ $\mathrm{CNN_{mld}}$ & \textbf{0.841} & \textbf{0.867} & 0.827 & 0.785 & 0.836 & \textbf{0.866} & 0.821 & 0.782 \\ $\mathrm{CBLSTM_{m}}$ & 0.835 & 0.860 & 0.702 & 0.751 & 0.836 & 0.861 & 0.646 & 0.752 \\ $\mathrm{CBLSTM_{ml}}$ & 0.840 & 0.864 & 0.702 & 0.744 & 0.835 & 0.865 & 0.646 & 0.742 \\ $\mathrm{CBLSTM_{mld}}$ & 0.835 & 0.855 & 0.702 & 0.789 & 0.831 & 0.857 & 0.646 & 0.784 \\ $\mathrm{LSTM_{m}}$ & 0.832 & 0.853 & 0.673 & 0.742 & 0.829 & 0.847 & 0.576 & 0.731 \\ $\mathrm{LSTM_{ml}}$ & 0.828 & 0.860 & 0.673 & 0.746 & 0.828 & 0.858 & 0.576 & 0.734 \\ $\mathrm{LSTM_{mld}}$ & \textbf{0.841} & 0.849 & 0.673 & \textbf{0.801} & \textbf{0.839} & 0.850 & 0.576 & 0.801 \\ $\mathrm{BLSTMCNN_{m}}$ & 0.829 & 0.849 & 0.833 & 0.751 & 0.823 & 0.850 & \textbf{0.831} & 0.748 \\ $\mathrm{BLSTMCNN_{ml}}$ & 0.833 & 0.851 & 0.833 & 0.781 & 0.831 & 0.849 & \textbf{0.831} & 0.778 \\ $\mathrm{BLSTMCNN_{mld}}$ & 0.828 & 0.859 & 0.833 & 0.779 & 0.828 & 0.858 & \textbf{0.831} & 0.782 \\ \hline \end{tabular}
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\begin{tabular}{lllllllll} \hline Method & W2V & FT & BERT & self & W2V & FT & BERT & self \\ & acc & acc & acc & acc & F1 & F1 & F1 & F1 \\ \hline RF & 0.726 & 0.750 & 0.777 & 0.695 & 0.690 & 0.722 & 0.752 & 0.626 \\ SL & 0.749 & 0.784 & 0.841 & 0.711 & 0.734 & 0.775 & 0.840 & 0.675 \\ SVM & 0.749 & 0.776 & \textbf{0.851} & 0.698 & 0.727 & 0.763 & \textbf{0.850} & 0.636 \\ DefMiner & 0.766 & 0.766 & 0.766 & 0.766 & 0.549 & 0.549 & 0.549 & 0.549 \\ \hline $\mathrm{CNN_{m}}$ & 0.839 & 0.859 & 0.843 & 0.713 & 0.838 & 0.860 & 0.837 & 0.705 \\ $\mathrm{CNN_{ml}}$ & 0.844 & \textbf{0.870} & 0.843 & 0.787 & 0.844 & \textbf{0.870} & 0.837 & 0.785 \\ $\mathrm{CNN_{mld}}$ & 0.847 & 0.859 & 0.843 & 0.774 & 0.847 & 0.860 & 0.837 & 0.775 \\ $\mathrm{CBLSTM_{m}}$ & 0.841 & \textbf{0.870} & 0.765 & 0.732 & 0.840 & 0.868 & 0.761 & 0.735 \\ $\mathrm{CBLSTM_{ml}}$ & 0.850 & 0.861 & 0.765 & 0.732 & 0.851 & 0.857 & 0.761 & 0.735 \\ $\mathrm{CBLSTM_{mld}}$ & 0.841 & 0.865 & 0.765 & 0.785 & 0.842 & 0.864 & 0.761 & 0.782 \\ $\mathrm{LSTM_{m}}$ & \textbf{0.856} & 0.865 & 0.667 & 0.752 & \textbf{0.853} & 0.863 & 0.572 & 0.750 \\ $\mathrm{LSTM_{ml}}$ & 0.796 & 0.861 & 0.667 & 0.617 & 0.790 & 0.860 & 0.572 & 0.569 \\ $\mathrm{LSTM_{mld}}$ & 0.851 & 0.861 & 0.667 & 0.785 & 0.850 & 0.861 & 0.572 & 0.781 \\ $\mathrm{BLSTMCNN_{m}}$ & 0.853 & 0.849 & 0.829 & 0.749 & \textbf{0.853} & 0.847 & 0.818 & 0.746 \\ $\mathrm{BLSTMCNN_{ml}}$ & 0.844 & 0.860 & 0.829 & \textbf{0.800} & 0.843 & 0.859 & 0.818 & \textbf{0.798} \\ $\mathrm{BLSTMCNN_{mld}}$ & 0.842 & 0.864 & 0.829 & 0.787 & 0.840 & 0.863 & 0.818 & 0.783 \\ \hline \end{tabular}
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\begin{tabular}{llllll} \hline Dataset & Domain & accuracy & recall & precision & F1 \\ \hline WCL & in-domain & \textbf{0.964} & 0.964 & 0.948 & \textbf{0.956} \\ WFMALL & in-domain & 0.844 & 0.864 & 0.627 & 0.727 \\ W00 & in-domain & 0.804 & 0.854 & 0.489 & 0.622 \\ MULTI & multi-domain & 0.804 & 0.854 & 0.489 & 0.622 \\ WFMALL$=>$WCL & cross-domain & \textbf{0.929} & 0.876 & 0.955 & \textbf{0.914} \\ WFMALL$=>$W00 & cross-domain & 0.666 & 0.000 & 0.000 & 0.000 \\ WCL$=>$WFMALL & cross-domain & 0.516 & 0.391 & 0.960 & 0.555 \\ W00$=>$WFMALL & cross-domain & 0.675 & 0.402 & 0.067 & 0.114 \\ \hline \end{tabular}
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\begin{tabular}{llllllllllllll} \hline \multicolumn{13}{c}{Miami Heat @ Houston Rockets, November 12, 2012} \\ $y_{t_k}^{\text{James},1}$ & 0 & 1 & 0 & 1 & 1 & 0 & 1 & 1 & - & - & - & - & - \\ $t_k$ (in min.) & 11.01 & 11.01 & 23.48 & 23.48 & 46.68 & 46.68 & 47.09 & 47.09 & - & - & - & - & - \\ \hline \multicolumn{13}{c}{Miami Heat @ Los Angeles Clippers, November 14, 2012} \\ $y_{t_k}^{\text{James},2}$ & 0 & 1 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & - & - & - & - \\ $t_k$ (in min.) & 9.47 & 23.08 & 23.08 & 24.73 & 36.95 & 36.95 & 41 & 41 & 42.77 & - & - & - & - \\ \hline \multicolumn{13}{c}{Milwaukee Bucks @ Miami Heat, November 21, 2012} \\ $y_{t_k}^{\text{James},3}$ & 0 & 1 & 0 & 1 & 0 & 1 & 1 & 0 & - & - & - & - & - \\ $t_k$ (in min.) & 7.5 & 7.5 & 10.02 & 10.02 & 35.62 & 35.62 & 42.68 & 42.68 & - & - & - & - & - \\ \hline \multicolumn{13}{c}{Cleveland Cavaliers @ Miami Heat, November 24, 2012} \\ $y_{t_k}^{\text{James},4}$ & 1 & 1 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & - & - & - & - \\ $t_k$ (in min.) & 11.62 & 21.07 & 21.07 & 21.38 & 21.38 & 31.95 & 31.95 & 32.68 & 32.68 & - & - & - & - \\ \hline \multicolumn{13}{c}{Los Angeles Lakers @ Miami Heat, January 23, 2014} \\ $y_{t_k}^{\text{James},5}$ & 0 & 1 & 0 & 1 & 1 & 0 & 0 & 1 & 1 & 0 & 1 & 0 & 1 \\ $t_k$ (in min.) & 9.28 & 9.28 & 20.53 & 25.97 & 25.97 & 31.57 & 31.57 & 33.43 & 33.43 & 42.1 & 42.1 & 47.62 & 47.62 \\ \hline \end{tabular}
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\begin{tabular}{llcc} \\[-1.8ex]\hline \hline \\[-1.8ex] \multicolumn{2}{l}{parameter} & estimate & 95\% CI \\ \hline \\[-1.8ex] $\theta$ & (drift) & 0.042 & [0.016; 0.109] \\ $\sigma$ & (diffusion) & 0.101 & [0.055; 0.185] \\ $\beta_1$ & (\textit{home}) & 0.023 & [-0.009; 0.055] \\ $\beta_2$ & (\textit{scorediff}) & 0.030 & [0.011; 0.048] \\ $\beta_3$ & (\textit{last30}) & 0.003 & [-0.051; 0.058] \\ $\beta_4$ & (\textit{ft2}) & 0.223 & [0.192; 0.254] \\ $\beta_5$ & (\textit{ft3}) & 0.421 & [0.279; 0.563] \\ \hline \\[-1.8ex] \end{tabular}
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\begin{tabular}{|l|l|l|l|} \hline & Cleared ($\$$bn) & Uncleared ($\$$bn) & total ($\$$bn)\\ \hline Variational Margin & $260.8$ & $870.4$ & $1,131.2$\\ Initial Margin & $173.4$ & $107.1$ & $280.5$\\ Total & $434.2$ & $977.5$ & $1,411.7$ \\ \hline \end{tabular}
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\begin{tabular}{|l||c|} \hline APS Journal & number of papers \\ \hline \textit{Phys.\ Rev.\ A} & 5826 \\ \textit{Phys.\ Rev.\ B} & 18028 \\ \textit{Phys.\ Rev.\ C} & 3131 \\ \textit{Phys.\ Rev.\ D} & 7589 \\ \textit{Phys.\ Rev.\ E} & 8137 \\ \textit{Phys.\ Rev.\ Lett.} & 12163 \\ \hline \end{tabular}
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\begin{tabular}{ll} \toprule \multicolumn{1}{c}{Amsterdam (1972)} & \multicolumn{1}{c}{Gallup (1970)} \\ \midrule \multicolumn{2}{c}{Assessment criteria} \\ Recognition of mirror image & Combination of changing behaviors \\ \quad Touch spot of rouge & \quad Decrease social responses \\ \quad Turns head and observes nose & \quad Increase self-directed responses \\ \quad Labels self (verbal) & Touches to mark \\ \quad Points to self & \quad Confirms self-directed touches \\ & \quad More when mirror present than absent \end{tabular}
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\begin{tabular}{llll} \hline $N$&$B_N$&$j_{\frac{N}{2}-1,1}$&$A_{1,N}$\\ \hline \hline $2$& [3.1415926535, 3.1415926536]& [2.4048255576, 2.4048255577]& [18.1684145355, 18.1684145356] \\ $3$& [4.1887902047, 4.1887902048]& [3.1415926535, 3.1415926536]& [25.6463452794, 25.6463452795] \\ $4$& [4.9348022005, 4.9348022006]& [3.8317059702, 3.8317059703]& [32.6151384322, 32.6151384323] \\ $5$& [5.2637890139, 5.2637890140]& [4.4934094579, 4.4934094580]& [39.2347942529, 39.2347942530] \\ \hline \end{tabular}
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\begin{tabular}{l||c|c|c|c} & $E_{\textrm{max}}$ {[}eV] & $\hbar\Gamma_p${[}eV] & $E_p(q=0)${[}eV] & $\Delta E_p$ {[}meV] \\ \hline \hline bright SB segment & $(15.218 \pm 0.020)$ & $(2.796 \pm 0.062)$ & $(15.282 \pm 0.02)$ & $(37.7 \pm 20.5)$ \\ \begin{tabular}[c]{@{}l@{}} Adjacent matrix (averaged) \\bright SB segment \end{tabular} & $(15.185 \pm 0.004)$ & $(2.688 \pm 0.017)$ & $(15.244 \pm 0.004)$ & - \\ dark SB segment & $(15.194 \pm 0.017)$ & $(2.63 \pm 0.021)$ & $(15.251 \pm 0.018)$ & $(13.8 \pm 18.5)$ \\ \begin{tabular}[c]{@{}l@{}} Adjacent matrix (averaged) \\dark SB segment \end{tabular} & $(15.179 \pm 0.006)$ & $(2.679 \pm 0.009)$ & $(15.238 \pm 0.006)$ & - \end{tabular}
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\begin{tabular}{l||c|c|c|c} & $E_{\textrm{max}}$ {[}eV] & $\hbar\Gamma_p${[}eV] & $E_p(q=0)${[}eV] & $\Delta E_p$ {[}meV] \\ \hline \hline bright SB segment & $(15.247 \pm 0.020)$ & $(2.833 \pm 0.036)$ & $(15.313 \pm 0.02)$ & $(33.2 \pm 20.6)$ \\ \begin{tabular}[c]{@{}l@{}} Adjacent matrix (averaged) \\bright SB segment \end{tabular} & $(15.218 \pm 0.005)$ & $(2.737 \pm 0.016)$ & $(15.279 \pm 0.005)$ & - \\ dark SB segment & $(15.215 \pm 0.019)$ & $(2.648 \pm 0.033)$ & $(15.272 \pm 0.019)$ & $(10.9 \pm 19.4)$ \\ \begin{tabular}[c]{@{}l@{}} Adjacent matrix (averaged) \\dark SB segment \end{tabular} & $(15.202 \pm 0.004)$ & $(2.694 \pm 0.017)$ & $(15.262 \pm 0.004)$ & - \end{tabular}
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\begin{tabular}{|c|c|c|} \hline Alternative & Density & Notation \\ \hline Exponential & $\theta \exp(-\theta x)$ & $Exp(\theta)$ \\ Gamma & $\left(\Gamma(\theta)\right)^{-1}x^{\theta-1}\exp(-x)$ & $\Gamma(\theta)$\\ Weibull & $\theta x^{\theta-1}\exp(-x^{\theta})$ & $W(\theta)$ \\ Lognormal & $\left(\theta x \sqrt{2\pi}\right)^{-1} \exp\left(-{\log^2(x)}\left(2\theta^2\right)^{-1}\right)$ & $LN(\theta)$\\ Chi square & $\left(2^{\theta/2}\Gamma(\theta/2)\right)^{-1}x^{\theta/2-1}\exp(-x/2)$ & $\chi^2(\theta)$ \\ Beta & ${x^{\alpha-1}(1-x)^{\theta-1}\Gamma(\alpha+\theta)}\left(\Gamma(\alpha)\Gamma(\theta)\right)^{-1}$ & $\beta(\alpha,\theta)$\\ \hline \end{tabular}
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\begin{tabular}{|c|c|c|c|c|c|c||c|c|} \hline & $q_{L_{i}}$ & $u_{R_{i}}$ & $d_{R_{i}}$ & $\ell_{L_{i}}$ & $e_{R_{i}}$ & $N_{R_{\alpha}}$ & $H$ & $ \Phi $ \\ \hline\hline SU$(3)_{\rm{C}}$ & ${\bf 3}$ & ${\bf 3}$ & ${\bf 3}$ & ${\bf 1}$ & ${\bf 1}$ & ${\bf 1}$ & ${\bf 1}$ & ${\bf 1}$ \\ \hline \hline SU$(2)_{\rm{L}}$ & ${\bf 2}$ & ${\bf 1}$ & ${\bf 1}$ & ${\bf 2}$ & ${\bf 1}$ & ${\bf 1}$ & ${\bf 2}$ & ${\bf 1}$ \\ \hline \hline U$(1)_{\rm{Y}}$ & $1/6$ & $2/3$ & $-1/3$ & $-1/2$ & $-1$ & $0$ & $1/2$ & $0$ \\ \hline \hline U$(1)_X$ & $\frac{1}{6}x_H + \frac{1}{3}x_\Phi$ & $\frac{2}{3}x_H+\frac{1}{3}x_\Phi$ & $-\frac{1}{3}x_H+\frac{1}{3}x_\Phi$ & $-\frac{1}{2}x_H-x_\Phi$ & $-x_H-x_\Phi$ & $-x_\Phi$ & $\frac{1}{2}x_H$ & $2x_\Phi$ \\ \hline \end{tabular}
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\begin{tabular}{|c|c|c|c|c|c|} \hline & $u_i$ & $d_i$ & $\ell_i$ & $\nu_i$ & $N_i$ \\ \hline \hline $n_V$& $\frac{ 5 }{ 12 } x_H + \frac{ 1 }{ 3 } x_\Phi$ & $-\frac{ 1 }{ 12 } x_H + \frac{ 1 }{ 3 } x_\Phi$ & $-\frac{ 3 }{ 4 } x_H - x_\Phi$ & 0 & 0 \\ \hline \hline $n_A$ & $\frac{ 1 }{ 4 } x_H$ & $ - \frac{ 1 }{ 4 } x_H $ & $- \frac{ 1 }{ 4 } x_H$ & $\frac{ 1 }{ 2 } x_H + x_\Phi$ & $ - x_\Phi$ \\ \hline \hline \end{tabular}
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\begin{tabular}{lc}\toprule & time (seconds)\\\midrule $V_\mathrm{fkr}$ & 225.4\\ $M_\mathrm{fkr}$ & 2516.1\\ \bottomrule \end{tabular}
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\begin{tabular}{lc}\toprule No of targets & Target clustering in seconds ($\pm$ std)\\\midrule 183 & 0.104 ($\pm$ 0.01)\\ 268 & 0.209 ($\pm$ 0.04) \\ 2211 & 15.887 ($\pm$ 0.06)\\ \bottomrule \end{tabular}
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\begin{tabular}{lc}\toprule No of targets & Base and hole tour finding in seconds ($\pm$ std)\\\midrule 183 & 0.919 ($\pm$ 0.01)\\ 268 & 3.804 ($\pm$ 0.06)\\ \bottomrule \end{tabular}
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\begin{tabular}{l|c} Parameter & Value \\ \hline $N_{tour}$ & 5\\ $P_{tour}$ & 0.4\\ $N_{cross}$ & 1\\ $P_{mutate}$ & 0.2 \\ $N_{subpop}^{generations}$ & 90 \\ $N_{subpop}$ & 5\\ $N_{cull}$ & 50\\ $N_{elite}$ & 25\\ \hline \end{tabular}
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\begin{tabular}{c|c|c} Method & $\bar{R}$ & $\sigma_{R}$ \\ \hline GD & 85.85 & 143.7 \\ GS & 3.29 & 3.89 \\ RNG & 3.11 & 3.44 \\ PSO & 0.00057 & 0.00030 \\ GA & 0.0014 & 0.0021 \\ \hline \end{tabular}
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\begin{tabular}{c|c} Parameter & Default value\\ \hline \emph{num-boost-round} & 10\\ \emph{learning-rate} & 0.3\\ \emph{max-depth} & 6\\ \emph{gamma} & 0\\ \emph{min-child-weight} & 1.0\\ \emph{subsample} & 1.0\\ \emph{colsample-bytree} & 1.0\\ \hline \end{tabular}
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\begin{tabular}{l|c} Parameter & Value \\ \hline $N_{tour}$ & 5 \\ $P_{tour}$ & 0.4 \\ $N_{cross}$ & 1 \\ $P_{mutate}$ & 0.2 \\ $N_{subpop}^{generations}$ & 90 \\ $N_{subpop}$ & 5\\ $N_{cull}$ & 40\\ $N_{elite}$ & 7\\ \hline \end{tabular}
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