id
stringlengths 4
8
| image
imagewidth (px) 4
1.34k
| latex
stringlengths 90
2.83k
|
|---|---|---|
CR-52846 | \begin{tabular}{|c|l|}
\hline
Symbol & Explanation \\
\hline
$L$ & The lumped random variable $L = (W, D)$. \\ \hline
$H$ & The hyper-hidden state of the HMM $H = (L_1, \dots, L_m, U)$. \\ \hline
$V$ & The visible state of the HMM which is the requested DN. \\ \hline
\end{tabular} |
|
CV-31423 | \begin{tabular}{l|cc|c}
\toprule
\multirow{2}{*}{Method} & \scriptsize{CUB-200-2011$\to$} & \scriptsize{CUB-200-Paintings} & \multirow{2}{*}{AVG} \\
& \scriptsize{CUB-200-Paintings} & \scriptsize{$\to$CUB-200-2011} & \\
\hline
ResNet-50 & 47.9 & 36.6 & 42.3 \\
DANN & 57.5 & 43.0 & 50.3 \\
JAN & 62.4 & 40.4 & 51.4 \\
MCD & 63.4 & 43.6 & 53.5 \\
BSP & 63.3 & 46.6 & 55.0 \\
SAFN & 61.4 & 48.9 & 55.2 \\
\hline
CDAN & 63.2 & 45.4 & 54.3 \\
+Ours & 68.3 & 53.8 & 61.1 \\
\hline
PAN & 67.4 & 50.9 & 59.2 \\
+Ours & \textbf{70.5} & \textbf{56.0} & \textbf{63.3} \\
\bottomrule
\end{tabular} |
|
PL-1654 | \begin{tabular}{lcc}
\toprule
Model & Acc$_\text{ex}$ & Execution Errors \\
\midrule
Seq2Seq (2017) & 77.0 & 10.5 \\
Seq2Seq + EG (5) & 77.3 & \phantom{1}6.9 \\
Seq2Seq + EG (10) & 77.9 & \phantom{1}6.3 \\
\bottomrule
\end{tabular} |
|
AI-29081 | \begin{tabular}{ll}
\multicolumn{2}{c}{\textbf{Manifest of supplementary materials package: MultimediaAppendix}} \\
\toprule
Path & Description \\
\midrule
\verb|example_data/| & Example of audio records \\
\quad \verb|radio_corpus/| & 5 West African Radio Corpus file samples \\
\quad \verb|asr/| & 10 West African Virtual Assistant ASR Corpus file samples \\
\quad \verb|lang_id/| & \\
\quad \quad \verb|maninka/| & 2 Examples of audio records contaning Maninka language \\
\quad \quad \verb|pular/| & Examples of audio records contaning Pular language \\
\quad \quad \verb|susu/| & Examples of audio records contaning Susu language \\
\bottomrule
\end{tabular} |
|
CL-1973 | \begin{tabular}{c|c|cc|cc|cc}
\toprule
Model & \multicolumn{1}{c|}{EM} & \multicolumn{2}{c|}{Precision} & \multicolumn{2}{c|}{Recall} & \multicolumn{2}{c}{$F_1$} \\
& & unwgt & wgt & unwgt & wgt & unwgt & wgt \\
\midrule
& \multicolumn{7}{c}{CSU data} \\
\midrule
LSTM & 47.4 & 76.6 & 85.9 & 59.3 & 78.7 & 65.3 & 81.7 \\
BLSTM & 48.2 & 76.1 & 86.0 & 57.6 & 79.4 & 63.5 & 82.2 \\
DeepTag-M & 48.6 & 76.8 & 86.3 & 58.7 & 79.6 & 64.6 & 82.4 \\
\textbf{DeepTag} & \textbf{48.4} & \textbf{79.9} & \textbf{86.1} & \textbf{62.1} & \textbf{79.8} & \textbf{68.0} & \textbf{82.4} \\
\midrule
& \multicolumn{7}{c}{PP data} \\
\midrule
LSTM & 13.8 & 48.1 & 65.7 & 31.8 & 51.9 & 33.8 & 54.4 \\
BLSTM & 13.8 & 47.3 & 66.0 & 35.6 & 57.9 & 36.9 & 58.4 \\
DeepTag-M & 17.1 & 53.4 & 68.0 & 37.9 & 59.9 & 40.6 & 61.1 \\
\textbf{DeepTag} & \textbf{17.4} & \textbf{56.5} & \textbf{70.3} & \textbf{41.4} & \textbf{62.4} & \textbf{43.2} & \textbf{63.4} \\
\bottomrule
\end{tabular} |
|
AI-15640 | \begin{tabular}[c]{@{}l@{}}{[}usr{]}I'dlikeasportsplaceinthecentreplease.\\{[}sys{]}Therearenoresultsmatchingyourquery.\\CanItryadifferentareaortype?\end{tabular} |
|
SE-3104 | \begin{tabular}{lcrrrc}
\hline
\textit{\textbf{cluster ID}} &
\textit{\textbf{size}} & \textit{\textbf{power}} & \textit{\textbf{legitimacy}} & \textit{\textbf{urgency}} & \textit{\textbf{definitive stk.}} \\ \hline
4 ($> Q3$) & 25 & 34.91 & 94.40 & 31.88 & $\mathbf{Def_1}$ \\
3 $(Q2, Q3]$ & 24 & 30.06 & 38.83 & 23.17 & \\
2 $(Q1, Q2]$ & 23 & 12.27 & 25.35 & 19.61 & \\
1 ($\leq Q1]$ & 26 & 2.93 & 7.85 & 11.73 & \\
\hline
\end{tabular} |
|
CV-13858 | \begin{tabular}{lcc}
\toprule
Method & Kinetics & SSv2$^\dagger$ \\ \midrule
$\Omega{=}\{2,3\}$ order reversed & {\bf 85.9} & 51.3 \\
$\Omega{=}\{2,3\}$ & {\bf 85.9} & {\bf 59.1} \\
\bottomrule
\end{tabular} |
|
CR-54999 | \begin{tabular}{@{}cccccc@{}}
\toprule
& IMDB & PROTEINS & COLLAB & MNIST & CIFAR10 \\ \midrule
GST & 3.3 & 7.2 & 7.7 & 65.9 & 113.0 \\
GST UU & 10.0 & 11.0 & 50.0 & 550.0 & 450.0 \\
GST Retrain & 10 & 11 & 50 & 550 & 450 \\
linear-GST & 3.0 & 6.8 & 6.3 & 54.1 & 91.6 \\
linear-GST UU & 10.0 & 11.0 & 49.6 & 532.5 & 450.0 \\ \bottomrule
\end{tabular} |
|
CR-20242 | \begin{tabular}{cccc}
\hline
& & P(M) & T(ms) \\
\hline
\multirow{2}*{Universal} &
GAP & 1.64 & 384 \\
& MTA & \textbf{0.62} & \textbf{137} \\
\cline{1-4}
\multirow{2}*{Per-instance} &
GAP & 3.77 & 342 \\
& MTA & \textbf{1.33} & \textbf{126} \\
\hline
\end{tabular} |
|
SE-16931 | \begin{tabular}{rll}
\toprule
Year & Authors & Topic \\
\midrule
1998 & & Migration towards SPL \\
2001 & & Industry collaboration \\
2008 & & Industry collaboration \\
& & Evolution operators \\
2012 & & Evolution operators \\
2012 & & Change impact analysis \\
2012 & & Change impact analysis \\
2013 & & Change impact analysis \\
2014 & & Evolution operators \\
2015 & & Change impact analysis \\
2016 & & Change impact analysis \\
& & Evolution operators \\
\bottomrule
\end{tabular} |
|
CV-14030 | \begin{tabular}[c]{@{}l@{}}End-to-end+CElossonMLP+learntsigmoidconversion\\+trainedon6000objsignoringsymmetry\\+theoreticallimitsatinference\end{tabular} |
|
SE-1951 | \begin{tabular}{lllll}
\toprule
\textbf{Task} &
\textbf{Authors} &
\textbf{Reference} &
\textbf{Published at} &
\textbf{Notes}
\\
\midrule
& D'Ambros et al. & & ESE'12 & \\
& Tan et al. & & ICSE'15 & \\
& Wang et al. & & ICSE'16 & Cited \\
\multirow{-3}{*}{Defect Prediction} & Kamei et al. & & ESE'16 & Cited \\
\midrule
Program Repair & Lutellier et al. & & ISSTA'20 & \\
\midrule
Bug Localization & Pradel et al. & & ISSTA'20 & \\
\bottomrule
\end{tabular} |
|
AI-40356 | \begin{tabular}{|c|c|}
\hline
Measure & $sim(\mathbf{q},\mathbf{q'})^{2}$ \\ \hline
Euclidean & $(\mathbf{q}-\mathbf{q'})^T(\mathbf{q}-\mathbf{q'})$ \\ \hline
Weighted Euclidean & $(\mathbf{q}-\mathbf{q'})^T\mathbf{W_{q}}(\mathbf{q}-\mathbf{q'}) $ \\ \hline
Mahalanobis & $ (\mathbf{q}-\mathbf{q'})^T\mathbf{\Sigma}^{-1}(\mathbf{q}-\mathbf{q'}) $ \\ \hline
Weighted Mahalanobis & $(\mathbf{q}-\mathbf{q'})^T\mathbf{W_{q}}\mathbf{\Sigma}^{-1}(\mathbf{q}-\mathbf{q'}) $ \\ \hline
\end{tabular} |
|
AI-1730 | \begin{tabular}{|l|cc|cc|}
\hline
\multirow{2}{*}{Method} & \multicolumn{2}{c|}{LSUN Tower $256^2$} & \multicolumn{2}{c|}{LHQ $256^2$} \\
& FID & Dataset size & FID & Dataset size \\
\hline
$t=1$ & 17.51 & 708k & 23.23 & 90k \\
$t=0.99$ & 8.68 & 168k & 10.18 & 50k \\
$t=0.95$ & 8.85 & 116k & 10.48 & 38k \\
$t=0.7$ & 8.83 & 59k & 11.49 & 19k \\
$t=0.5$ & 9.73 & 41.5k & 14.55 & 13k \\
\hline
\end{tabular} |
|
CR-14345 | \begin{tabular}{ccc}
\hline
Dataset & Adversarial Label \\
\hline\hline
MNIST & Shirts \\
Fashion-MNIST & Digit number 7 \\
GTSRB & Air-planes \\
CIFAR-10 & Flowers \\
\hline
\end{tabular} |
|
CV-12119 | \begin{tabular}{|c|ccc|}
\hline
\multirow{2}{*}{Metric} & \multicolumn{3}{c|}{Upscale by 2 of set5 images. } \\[1pt] \cline{2-4}
& Bicubic-CNN & Bilinear-CNN & NN-CNN \\[1pt]
\hline
PSNR & 29.23 & 29.40 & 30.11 \\[1pt]
SSIM & 0.54 & 0.59 & 0.71 \\[1pt]
\hline
\end{tabular} |
|
CV-13157 | \begin{tabular}{|l|l|l|l|l|l|l|l|l|l|l|}
\hline
\multirow{9}{*}{\textbf{SSIM}} & \textbf{Water Type} & \textbf{U-Net} & \textbf{UIE-DAL (Ours)} \\ \cline{2-4}
& 1 & \multirow{2}{*}{0.8691} & \multirow{2}{*}{\textbf{0.9313}} \\ \cline{2-2}
& 3 & & \\ \cline{2-4}
& 5 & 0.8733 & \textbf{0.9364} \\ \cline{2-4}
& 7 & 0.8687 & \textbf{0.9353} \\ \cline{2-4}
& 9 & 0.8614 & \textbf{0.925} \\ \cline{2-4}
& I & 0.8385 & \textbf{0.9129} \\ \cline{2-4}
& II & \multirow{2}{*}{0.8385} & \multirow{2}{*}{\textbf{0.9235}} \\ \cline{2-2}
& III & & \\ \hline
\multirow{9}{*}{\textbf{PSNR}} & 1 & \multirow{2}{*}{21.6283} & \multirow{2}{*}{\textbf{28.4488}} \\ \cline{2-2}
& 3 & & \\ \cline{2-4}
& 5 & 22.6119 & \textbf{28.6697} \\ \cline{2-4}
& 7 & 22.5754 & \textbf{28.5793} \\ \cline{2-4}
& 9 & 22.5263 & \textbf{27.6551} \\ \cline{2-4}
& I & 22.3236 & \textbf{27.1015} \\ \cline{2-4}
& II & \multirow{2}{*}{21.8279} & \multirow{2}{*}{\textbf{28.1602}} \\ \cline{2-2}
& III & & \\ \hline
\end{tabular} |
|
AI-6624 | \begin{tabular}{|c|c|c|}
\hline
Method & KL & AUC \\
\hline
\hline
HOG detector* & 8.54 & 0.736 \\
Judd et al.* & 11.00 & 0.715 \\
Itti \& Koch & 16.53 & 0.533 \\
central bias & 9.59 & 0.780 \\
human & 6.14 & 0.922 \\
\hline
PDP(without finetuning) & {\bf 7.92} & 0.845 \\
PDP*(with finetuning) & 8.23 & {\bf 0.875} \\
\hline
\end{tabular} |
|
AI-16471 | \begin{tabular}{|p{3.5cm}|p{3.5cm}|}
\toprule
\textbf{Input} & \textbf{Output} \\\midrule
Two of the cast fainted and most of the rest * repaired * to the nearest bar. & repaired = \emph{Self motion} $\vert$ most of the rest = \emph{Self\_mover} $\vert$ to the nearest bar = \emph{Goal} $\vert$ \\ \hline
He blinked , taken aback by the * vigour * of her outburst. & vigour = \emph{Dynamism} $\vert$ of her outburst = \emph{Action} $\vert$ \\ \hline
The rain * dripped * down his neck. & dripped = \emph{Fluidic motion} $\vert$ The rain = \emph{Fluid} $\vert$ down his neck = \emph{Path} $\vert$ \\ \hline
She * adored * shopping for bargains and street markets and would have got on well with Cherry. & adored = \emph{Experiencer focus} $\vert$ She = \emph{Experiencer} $\vert$ shopping for bargains and street markets = \emph{Content} $\vert$ \\ \hline
He * cleared * his throat as the young man looked up. & cleared = \emph{Emptying} $\vert$ He = \emph{Agent} $\vert$ his throat = \emph{Source} $\vert$ \\
\bottomrule
\end{tabular} |
|
CV-2881 | \begin{tabular}{l|ll|l|ll}
\toprule
Category & ${T_\mathrm{min}}$ & $T_\mathrm{max}$ & Category & $T_\mathrm{min}$ & $T_\mathrm{max}$ \\ \hline
aero plane & 0.034 & 0.160 & bicycle & 0.013 & 0.087 \\ \hline
bird & 0.021 & 0.148 & boat & 0.031 & 0.158 \\ \hline
bottle & 0.006 & 0.148 & bus & 0.167 & 0.450 \\ \hline
car & 0.018 & 0.262 & cat & 0.129 & 0.381 \\ \hline
chair & 0.021 & 0.129 & cow & 0.069 & 0.292 \\ \hline
potted plant & 0.079 & 0.286 & sheep & 0.076 & 0.302 \\ \hline
sofa & 0.082 & 0.284 & train & 0.082 & 0.264 \\ \hline
tv/monitor & 0.027 & 0.249 & dining table & 0.012 & 0.109 \\ \hline
dog & 0.039 & 0.297 & horse & 0.084 & 0.262 \\ \hline
motorbike & 0.132 & 0.356 & person & 0.028 & 0.199 \\
\bottomrule
\end{tabular} |
|
CV-25293 | \begin{tabular}{c|c|c|c|c|c|c}
\hline
& No Inter (G=6) & No Intra (G=1) & G=2 & G=4 & G=6 & G=8 \\
\hline
\hline
Parameters & 11.0M & 20.9M & 11.7M & 12.9M & 14.1M & 15.3 M \\
\hline
MAE & 57.00 & 60.11 & 60.04 & 58.85 & {\bf 55.77} & 57.50 \\
MSE & 96.77 & 104.67 & 104.50 & 95.83 & {\bf 90.23} & 98.60 \\
\hline
\end{tabular} |
|
AI-3688 | \begin{tabular}{c|c|c|c|c|c|c|c|c|c}
\multicolumn{2}{c|}{Architecture} & Alexnet & DenseNet-121 & DenseNet-169 & DenseNet-201 & Xception & ResNet-18 & ResNet-34 & ResNet-50 \\ \hline\hline
\multicolumn{2}{c|}{MCNN} & 0.18856 & 0.3574 & 0.35958 & 0.54936 & 0.71128 & 0.2506 & 0.27376 & 0.30304 \\ \hline
\multirow{3}{*}{LCNN} & \textit{avg} & \textbf{0.336} & 0.47392 & 0.49604 & 0.48392 & 0.69468 & 0.29904 & 0.25228 & \textbf{0.3896} \\ \cline{2-10}
& \textit{add} & 0.1 & \textbf{0.55664} & 0.5418 & \textbf{0.60304} & 0.69968 & 0.37636 & 0.37844 & 0.34484 \\ \cline{2-10}
& \textit{sub} & 0.1 & 0.53472 & \textbf{0.54212} & 0.576 & \textbf{0.7122} & \textbf{0.39008} & \textbf{0.39488} & 0.36508
\end{tabular} |
|
CL-3671 | \begin{tabular}{|l|c|c|}
\hline
\bf Method & \bf An-Con & \bf Con-An \\
\hline
Seq-to-Seq & 23.10 & 31.20 \\
+ copying & 26.41 & 35.66 \\
+ Local Attention & \textbf{26.95} & \textbf{36.34} \\
\hline
\end{tabular} |
|
CV-27291 | \begin{tabular}{|c|cccccccc|}
\hline
Component & & & & & Dubox & & & \\
\hline
Batch balance and OHEM? & & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark \\
IoU loss? & & & \checkmark & \checkmark & \checkmark & \checkmark & & \\
Hooks? & & & & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark \\
Refine module? & & & & & \checkmark & \checkmark & \checkmark & \checkmark \\
Residual dual scale? & & & & & & \checkmark & \checkmark & \checkmark \\
CRPS loss? & & & & & & & \checkmark & \checkmark \\
Reducing redundancy strategy? & & & & & & & & \checkmark \\
\hline
[email protected] & 54.32 & 63.56 & 73.4 & 74.2 & 75.7 & 77.01 & 79.43 & 81.02 \\
\hline
\end{tabular} |
|
CV-13967 | \begin{tabular}[c]{@{}p{22em}@{}}airplane,train,parkingmeter,cat,bear,suitcase,frisbee,snowboard,fork,sandwich,hotdog,toilet,mouse,toaster,hairdrier\end{tabular} |
|
AI-22250 | \begin{tabular}{ll|cccc}
\toprule
\multicolumn{2}{c|}{{\bf Method}} & & & & \textbf{Persona} \\
Model & Profile & \textbf{Fluency} & \textbf{Engagingness} & \textbf{Consistency} &
\textbf{Detection} \\
\midrule
Human & Self & 4.31(1.07) & 4.25(1.06) & 4.36(0.92) & 0.95(0.22) \\
\midrule
{\em Generative PersonaChat Models} & \\
Seq2Seq & None & 3.17(1.10) & 3.18(1.41) & 2.98(1.45) & 0.51(0.50) \\
Profile Memory & Self & 3.08(1.40) & 3.13(1.39) & 3.14(1.26) & 0.72(0.45) \\
\midrule
{\em Ranking PersonaChat Models} & \\
KV Memory & None & 3.81(1.14) & 3.88(0.98) & 3.36(1.37) & 0.59(0.49) \\
KV Profile Memory & Self & 3.97(0.94) & 3.50(1.17) & 3.44(1.30) & 0.81(0.39) \\
\midrule
Twitter LM & None & 3.21(1.54) & 1.75(1.04) & 1.95(1.22) & 0.57(0.50) \\
OpenSubtitles 2018 LM & None & 2.85(1.46) & 2.13(1.07) & 2.15(1.08) & 0.35(0.48) \\
OpenSubtitles 2009 LM & None & 2.25(1.37) & 2.12(1.33) & 1.96(1.22) & 0.38(0.49) \\
OpenSubtitles 2009 KV Memory & None & 2.14(1.20) & 2.22(1.22) & 2.06(1.29) & 0.42(0.49) \\
\bottomrule
\end{tabular} |
|
CR-32902 | \begin{tabular}[c]{@{}c@{}}Walker,Hopper,Humanoid,Ant,\\InvertedPendulum,Reacher,\\RoadRunner,BankHeist,pong,\\Acrobot,andFreeway\end{tabular} |
|
AI-5250 | \begin{tabular}{|c|c|c|c|}
\hline
\textbf{McNemar's Test} & \multicolumn{3}{c|}{\textbf{p-value}} \\ \hline
\textbf{Performed with} & \textbf{Herlev Pap Smear} & \textbf{Mendeley LBC} & \textbf{SIPaKMeD Pap Smear} \\ \hline
ResNet-50 & 0.0046 & 0.0012 & 0.0005 \\ \hline
VGG-16 & 0.0001 & 0.0211 & 0.0007 \\ \hline
DenseNet-121 & 0.0103 & 0.0089 & 0.0315 \\ \hline
Inception v3 & 0.0007 & 0.0061 & 0.0100 \\ \hline
\end{tabular} |
|
PL-182 | \begin{tabular}[c]{@{}l@{}}Therearetwoshapeswhichare\\different(i.e.callsontwo\\differentinventedprimitives)\end{tabular} |
|
CL-5858 | \begin{tabular}{|lrrr|}
\hline\hline
System & val EM/F1 & dev EM/F1 & test EM/F1 \\ [0.5ex]
\hline\hline
Seq2seq Attetion Baseline & & & \\
Match LSTM Baseline & 38.8/ 53.8 & 42.1/55.4 & \\
Coattention + feedforward decoder & 48.6/64.0 & 46.4/60.8 & 46.1/60.1 \\
Coattention + ans-ptr & 49.7/65.2 & & \\
Coattention + ans-ptr + dropout +word lookup & 56.0/69.8 & 61.6/72.3 & \textbf{61.9/72.8} \\[1ex]
\hline
\end{tabular} |
|
AI-22559 | \begin{tabular}{r|r|r|r|r|r}
\hline
Target Feature ID & \multicolumn{5}{c}{Top5 Highly Correlated Feature IDs} \\ \hline
1 & 72 (0.98) & 87 (0.95) & 44 (0.94) & 92 (0.94) & 87 (0.94) \\
32 & 111 (0.96) & 114 (0.95) & 100 (0.95) & 15 (0.94) & 119 (0.94) \\
64 & 127 (0.97) & 15 (0.97) & 119 (0.97) & 114 (0.97) & 57 (0.97) \\ \hline
\end{tabular} |
|
CR-4860 | \begin{tabular}{@{}llcc@{}}
\toprule
Multi-PIE Dataset & & \# of subjects & \# of comparisons \\ \cmidrule(r){1-1} \cmidrule(l){3-4}
\textbf{Session} & & 264 & 81K \\
\textbf{Facial Rotation} & & 337 & 151K \\
\textbf{Lighting} & & 337 & 131K \\
\textbf{Facial Expression} & & 337 & 104K \\ \bottomrule
\end{tabular} |
|
CV-13736 | \begin{tabular}{ccc}
\hline
\multicolumn{3}{c}{Gaussian Kernels of DCRF for Roughness Map} \\
\hline
& $\quad\qquad\mathbf{p}_{i}\quad\qquad$ & $\qquad\mathbf{d}_{i}\qquad$ \\
$\kappa_{1}$ & 0.04 & - \\
$\kappa_{2}$ & 0.06 & 0.2 \\
\hline
\end{tabular} |
|
SE-14248 | \begin{tabular}{|l|r|r|r|}
\hline
Dataset & Precision & Recall & F1 \\ \hline
Accumulo & 0.0399 & \textless0.0001 & \textless0.0001 \\ \hline
Ignite & \textless0.0001 & \textless0.0001 & \textless0.0001 \\ \hline
Isis & 0.4243 & \textless0.0001 & \textless0.0001 \\ \hline
Tika & \textless0.0001 & \textless0.0001 & \textless0.0001 \\ \hline
\end{tabular} |
|
SE-19973 | \begin{tabular}[c]{@{}l@{}}Signature-basedtechniqueisusedtodetect\\refactoringcandidatesinsoftwarerepositoriesandrankthemusing\\similaritymetrics.Therankingofrefactoringcandidatesdiffersdependingon\\thesimilaritymetric.\end{tabular} |
|
CL-1389 | \begin{tabular}{l|c|c|c|c}
\hline
& \multicolumn{1}{c|}{\textbf{PKU}} & \multicolumn{1}{c|}{\textbf{MSR}} & \multicolumn{1}{c|}{\textbf{AS}} & \multicolumn{1}{c}{\textbf{CITYU}} \\
\hline
& 95.8 & 97.1 & 95.3 & 95.6 \\
& 94.3 & 96.0 & 94.6 & 95.6 \\
& 95.7 & 97.3 & - & - \\
& 96.0 & 97.8 & - & - \\
& 96.1 & \textbf{98.1} & 96.2 & 97.2 \\
& 96.1 & 97.5 & - & - \\
& \textbf{96.6} & 97.9 & \textbf{96.6} & \textbf{97.6} \\
\hline
\textbf{Our Method} & 95.5 & 97.7 & 95.7 & 96.4 \\
\hline
\end{tabular} |
|
CL-5720 | \begin{tabular}{l|l|l}
& Development & Test \\
\hline
Average Intent Accuracy & 0.9859 & 0.9482 \\
Requested Slots F1-score & 0.9769 & 0.9847 \\
\end{tabular} |
|
CV-10891 | \begin{tabular}{l}
\Xhline{1pt}
\textbf{Algorithm 1}: Solve the TWSC Model () via ADMM
\\
\hline
\textbf{Input:} $\mathbf{Y},\mathbf{W}_{1},\mathbf{W}_{2},\mathbf{W}_{3}$, $\mu$, $\text{Tol}$, $K_{1}$;
\\
\textbf{Initialization:} $\mathbf{C}_{0}=\mathbf{Z}_{0}=\mathbf{\Delta}_{0}=\mathbf{0}$, $\rho_{0}>0$, $k=0$, \text{T} = \text{False};
\\
\textbf{While} (\text{T} == \text{false}) \textbf{do}
\\
1. Update $\mathbf{C}_{k+1}$ by solving Eq.\ ();
\\
2. Update $\mathbf{Z}_{k+1}$ by soft thresholding ();
\\
3. Update $\mathbf{\Delta}_{k+1}$ by Eq.\ ();
\\
4. Update $\rho_{k+1}$ by $\rho_{k+1}=\mu\rho_{k}$, where $\mu\ge1$;
\\
5. $k \leftarrow k + 1$;
\\
\quad \textbf{if} (Converged) or ($k\ge K_{1}$)
\\
6.\quad\quad \text{T} $\leftarrow$ \text{True};
\\
\quad \textbf{end if}
\\
\textbf{end while}
\\
\textbf{Output:} Matrices $\mathbf{C}$ and $\mathbf{Z}$.
\\
\Xhline{1pt}
\end{tabular} |
|
CR-8999 | \begin{tabular}{c|c|c|c}
\hline
\textbf{Program} & \textbf{Vulnerability} & \textbf{Benign\ Gadget\ Chains} & \textbf{Malicious\ Gadget\ Chains} \\ \hline
Adobe flash 11.2.202.33 & CVE-2014-0502 & 201097 & 167580 \\
Nginx 1.4.0 & CVE-2013-2028 & 124476 & 103980 \\
Proftpd 1.3.0a & CVE-2006-6563 & 92042 & 60035 \\
Firefox 3.5.10 & CVE-2010-1214 & 211509 & 195920 \\ \hline
\end{tabular} |
|
AI-30964 | \begin{tabular}{cc}
\hline
\textbf{Hyperparameter} & \textbf{Value} \\
\hline\hline
Preamble length M: & $512$ \\
Initial log($\sigma$): & $-1.0$ \\
Noise power density $N_0$: & $0.01$ \\
Power loss factor $\lambda_p$: & $0.09$ \\
k in kNN: & $3$ \\
Step size: & $0.00245$ \\
Hidden units: & $40$ \\
Training iterations: & $2000$
\end{tabular} |
|
SE-19954 | \begin{tabular}{llll}
\toprule
\textbf{Refactoring-related Categories} \\
\midrule
Internal & Inheritance, Abstraction, Complexity, Composition, Coupling, Encapsulation \\
& Design Size, Polymorphism, Cohesion, Messaging, Concern Separation, Dependency \\
External & Functionality, Performance, Compatibility, Readability, Stability, Usability \\
& Flexibility, Extensibility, Efficiency, Accuracy, Accessibility, Robustness, Testability \\
& Correctness, Scalability, Configurability, Simplicity, Reusability, Reliability, Modularity \\
& Maintainability, Traceability, Interoperability, Fault-tolerance, Repeatability, Understandability \\
& Effectiveness, Productivity, Modifiability, Reproducibility, Adaptability, Manageability \\
Code Smell & Duplicate Code, Dead Code, Data Class, Long Method, Switch Statement, Lazy Class \\
& Too Many Parameters, Primitive Obsession, Feature Envy, Blob Class, Blob Operation \\
& Redundancy, Useless class, Code style, Antipattern, Design Flaw, Code Smell \\
& Temporary Field, Old Comment \\
\bottomrule
\end{tabular} |
|
CL-4367 | \begin{tabular}{p{3.4cm}|c|c|c}
\hline
& \textbf{\#PPs} & \textbf{MAP} & \textbf{P@1} \\
\hline
Glava\v{s}$_{(n=95)}$ & --- & 22.8 & 13.5 \\
WordNet$_{(n=82)}$ & 6.63 & 62.2 & 50.6 \\
Kauchak$_{(n=48)}$ & 4.39 & \textbf{76.4}$^\dagger$ & 68.9 \\
SimplePPDB$_{(n=100)}$ & 8.77 & 67.8 & 78.0 \\
\hline
\hline
SimplePPDB++$_{(n=100)}$ & \textbf{9.52} & 69.1 & \textbf{80.2} \\
\hline
\end{tabular} |
|
CV-17372 | \begin{tabular}{lccc}
\textbf{Method} & \textbf{\#Params} & \textbf{CIFAR10} & \textbf{CIFAR100} \\ \hline
VGGNet(16L) /Enhanced & 138m & 91.4 / 92.45 & - \\
ResNet-110L / 1202L * & 1.7/10.2m & 93.57 / 92.07 & 74.84/72.18 \\
SD-110L / 1202L & 1.7/10.2m & 94.77 / 95.09 & 75.42 / - \\
WRN-(16/8)/(28/10) & 11/36m & 95.19 / 95.83 & 77.11/79.5 \\
DenseNet & 27.2m & 96.26 & 80.75 \\
Highway Network & N/A & 92.40 & 67.76 \\
FitNet & 1M & 91.61 & 64.96 \\
FMP* (1 tests) & 12M & 95.50 & 73.61 \\
Max-out(k=2) & 6M & 90.62 & 65.46 \\
Network in Network & 1M & 91.19 & 64.32 \\
DSN & 1M & 92.03 & 65.43 \\
Max-out NIN & - & 93.25 & 71.14 \\
LSUV & N/A & 94.16 & N/A \\
SimpNet & 5.48M & 95.49/95.56 & 78.08 \\
SimpNet & 8.9M & 95.89 & 79.17 \\ \hline
\end{tabular} |
|
AI-19156 | \begin{tabular}[c]{@{}l@{}}Depot$-c_2-c_4-c_5-c_7-c_8-c_1-c_6-c_{10}-c_{15}-c_{11}-$\\$c_{3}-c_{9}-c_{13}-c_{18}-c_{12}-c_{16}-c_{14}-$Depot\end{tabular} |
|
CL-3703 | \begin{tabular}{p{8cm}|l|l}
\toprule
{\bf Template Candidates} & {\bf Positive Example Queries} & {\bf Negative Example Queries} \\
\midrule
table $:=$ $v : \text{value}$ $t : \text{table}$ & ``Mexican restaurants'' & ``4.5 hotels'' \\
\hline
table $:=$ $t : \text{table}$ ``in'' $v : \text{value}$ & ``hotels in Florida'' & ``restaurants in Mexican '' \\
\hline
table $:=$ $t : \text{table}$ ``with'' $v : \text{value}$ & ``hotels with fitness center'' & ``restaurants with Mexican'' \\
\hline
table $:=$ $t : \text{table}$ ``containing'' $v : \text{value}$ & ``hotels containing fitness center'' & ``restaurants containing Mexican'' \\
\hline
table $:=$ $t : \text{table}$ $p : \text{(vb | vb\_prefix)}$ $v : \text{value}$ & ``person works for Google'' & ``restaurants serves Mexican'' \\
\hline
table $:=$ $v : \text{value}$ $p : \text{(vb\_suffix | nnp)}$ $t : \text{table}$ & ``Mexican cuisine restaurants'' & ``Nobel prize award person'' \\
\hline
table $:=$ $t : \text{table}$ ``with'' $v : \text{value}$ $p : \text{(vb\_suffix | nnp)}$ & ``restaurants with Mexican cuisine'' & ``hotels with Florida state'' \\
\hline
table $:=$ $v : \text{value}$ $p : \text{jj\_suffix}$ $p : \text{(jj | jj\_prefix)}$ $t : \text{table}$ & ``5 star rated restaurants'' & ``Nobel prize award received person'' \\
\bottomrule
\end{tabular} |
|
CR-43373 | \begin{tabular}{l*{6}c}
\cmidrule[1pt]{1-7}
& \textbf{Min.} & \textbf{Max.} & \textbf{Mean} & \textbf{Mode} & \textbf{Median} & \textbf{Sum} \\
\cmidrule[0.5pt]{1-7}
BD & 0.01 & 1.13 & 0.5 & 0.11 & 0.11 & 3.5 \\
ID & 0.004 & 6.41 & 1.06 & 0.1 & 0.33 & 17.02 \\
SESL & 0.584 & 4.24 & 1.59 & 1.0 & 1.23 & 9.57 \\
TDO & - & - & - & - & - & - \\
US & 0.009 & 1.1 & 0.46 & 0.1 & 0.38 & 6.44 \\
HSU & 0.00002 & 11.96 & 1.44 & 0.1 & 1.02 & 171.22 \\
HT & 1.009 & 1.1 & 1.05 & 1.0 & 1.05 & 2.11 \\
SMC & 0.399 & 4.94 & 1.76 & 2.0 & 1.99 & 47.39 \\
\cmidrule[0.5pt]{1-7}
Overall & 0.00002 & 11.96 & 1.35 & 1.0 & 1.01 & 257.25 \\
\cmidrule[1pt]{1-7}
\end{tabular} |
|
CV-31304 | \begin{tabular}{c|cc|cc}
\hline
\multirow{2}[3]{*}{Methods} & \multicolumn{2}{c|}{DukeMTMC-ReID} & \multicolumn{2}{c}{Market-1501} \\
\cline{2-5} & R-1 & mAP & R-1 & mAP \\
\hline
IDE$^{*}$ & 80.1 & 64.2 & 89.0 & 73.9 \\
SENet50$^{*}$ & 81.2 & 64.8 & 90.0 & 75.6 \\
HA-CNN & 80.5 & 63.8 & 91.2 & 75.7 \\
SpaAtt+Q$^{*}$ & 84.7 & 69.6 & 91.6 & 77.4 \\
CASN+IDE & 84.5 & 67.0 & 92.0 & 78.0 \\
MHN-6 (IDE) & \textbf{87.5} & \textbf{75.2} & \textbf{ 93.6} & \textbf{83.6} \\
\hline
\end{tabular} |
|
CV-11491 | \begin{tabular}{lcc}
\toprule
& Not learned & Learned \\
\midrule
1 iteration & 46.7 & 49.5 \\
5 iterations & 51.3 & 55.4 \\
10 iterations & 52.4 & 59.4 \\
20 iterations & 53.6 & {\bf 60.7} \\
50 iterations & 59.2 & {\bf 60.9} \\
100 iterations & 59.4 & 60.7 \\
\bottomrule
\end{tabular} |
|
AI-40261 | \begin{tabular}{cccccccccc}
\toprule
\textbf{Dataset} & \multicolumn{5}{c}{\textbf{SCAN}} & \textbf{SCAN-ext} & \textbf{MiniSCAN} \\
& Simple & Add Jump & Around Right & Length & MCD\,($1$/$2$/$3$) & Extend & Limit \\
\midrule
Train Size & $16728$ & $14670$ & $15225$ & $16990$ & $8365$ & $20506$ & $14$ \\
Test Size & $4182$ & $7706$ & $4476$ & $3920$ & $1045$ & $4000$ & $8$ \\
\bottomrule
\end{tabular} |
|
CR-36858 | \begin{tabular}{|lllllll|}
\hline
\multicolumn{7}{|l|}{\textbf{$K$-anonymous counterfactual instance}} \\ \hline
\multicolumn{1}{|l|}{Identifier} &
\multicolumn{3}{|l|}{\textbf{Quasi-Identifiers}} & \multicolumn{2}{l|}{\textbf{Private attributes}} &
\multicolumn{1}{l|}{\textbf{Model prediction}} \\ \hline
\multicolumn{1}{|l|}{Name} &
\multicolumn{1}{|l|}{Age} & \multicolumn{1}{l|}{Gender} & \multicolumn{1}{l|}{City} & \multicolumn{1}{l|}{Salary} & \multicolumn{1}{l|}{Relationship status}
& \multicolumn{1}{l|}{Credit decision} \\ \hline
\multicolumn{1}{|l|}{\textit{*}} &
\multicolumn{1}{|l|}{\textit{24-27}} & \multicolumn{1}{l|}{\textit{F}} & \multicolumn{1}{l|}{\textit{Antwerp}} & \multicolumn{1}{l|}{\textit{\$60K}} & \multicolumn{1}{l|}{\textit{Single}} &
\multicolumn{1}{l|}{\textit{Accept}} \\ \hline
\end{tabular} |
|
SE-10266 | \begin{tabular}{{|l|c|c|c|c|c|}}
\hline
\multicolumn{6}{|l|}{\textbf{Tester 1}} \\
\hline
\textbf{Demands} & \textbf{Task 1} & \textbf{Task 2} & \textbf{Task 3} & \textbf{Task 4} & \textbf{Weight} \\
\hline
\hline
Mental Demand & 30 & 5 & 65 & 10 & 3 \\
\hline
Physical Demand & 5 & 5 & 5 & 5 & 0 \\
\hline
Temporal Demand & 75 & 15 & 65 & 35 & 4 \\
\hline
Performance & 25 & 20 & 15 & 5 & 2 \\
\hline
Effort & 40 & 10 & 70 & 10 & 3 \\
\hline
Frustration & 60 & 20 & 30 & 25 & 3 \\
\hline
\hline
Product Sum & 740 & 205 & 785 & 285 & - \\
\hline
Score & 49 & 13 & 52 & 19 & - \\
\hline
\end{tabular} |
|
CR-21972 | \begin{tabular}{|p{5cm}|m{5cm}|m{5cm}|}
\hline
Group & Scalability & Maturity \\
\hline
List Based & high & high \\
List Based Hidden & high & high \\
Compressed List & high & high \\
Cryptographic Accumulators & low & medium- low \\
Credential Update & high-medium & medium \\
LVVC & high & medium \\
\hline
\end{tabular} |
|
PL-2952 | \begin{tabular}{llrrr}
\hline
\multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{3}{c}{\textbf{Metrics: Exact Match}} \\ \cline{3-5}
\multicolumn{1}{c}{\multirow{-2}{*}{\textbf{Localization}}} & \multicolumn{1}{c}{\multirow{-2}{*}{\textbf{Transformation}}} & \multicolumn{1}{r}{\textbf{P@1}} & \multicolumn{1}{r}{\textbf{P@3}} & \multicolumn{1}{r}{\textbf{P@50}} \\ \hline
None & None & \cellcolor[HTML]{FFFFFF}0.47 & \cellcolor[HTML]{FFFFFF}0.68 & \cellcolor[HTML]{FFFFFF}0.84 \\
Compiler & None & \cellcolor[HTML]{F7FBF5}0.53 & \cellcolor[HTML]{F2F8EE}0.75 & \cellcolor[HTML]{E7F2E1}0.87 \\
Compiler & Fixed Example & \cellcolor[HTML]{88CD97}0.70 & \cellcolor[HTML]{E2EFDA}0.82 & \cellcolor[HTML]{63BE7B}0.89 \\
Compiler & Smart Selection & \cellcolor[HTML]{63BE7B}0.71 & \cellcolor[HTML]{63BE7B}0.85 & \cellcolor[HTML]{E2EFDA}0.87 \\
Abstracted Compiler & None & \cellcolor[HTML]{F8FBF6}0.53 & \cellcolor[HTML]{EEF6E9}0.77 & \cellcolor[HTML]{ECF5E7}0.86 \\
Abstracted Compiler & Fixed Example & \cellcolor[HTML]{63BE7B}0.71 & \cellcolor[HTML]{63BE7B}0.85 & \cellcolor[HTML]{63BE7B}0.89 \\
Abstracted Compiler & Smart Selection & \cellcolor[HTML]{E2EFDA}0.68 & \cellcolor[HTML]{96D2A1}0.84 & \cellcolor[HTML]{E2EFDA}0.87
\end{tabular} |
|
CV-7335 | \begin{tabular}{lcc}
\hline
$\alpha$ & OA & Average F1
\\
\hline
NA & \textbf{0.846} & 0.694 \\
1.1 & 0.820 & 0.703 \\
\textbf{1.2} & 0.822 & \textbf{0.707} \\
1.3 & 0.829 & 0.697 \\
1.4 & 0.828 & 0.701 \\
1.5 & 0.834 & 0.695 \\
\hline
\end{tabular} |
|
AI-2666 | \begin{tabular}{lrrrrr}
\toprule
Datasets & \# Relations & \# Entities & \# Train & \# Test & \# Valid \\
\midrule
Kinship & 25 & 104 & 8544 & 1074 & 1068 \\[5pt]
UMLS & 46 & 135 & 5216 & 661 & 652 \\[5pt]
WN18RR & 11 & 40943 & 86835 & 3134 & 3034 \\[5pt]
FB15k-237 & 237 & 14541 & 272115 & 20466 & 17535 \\[5pt]
YAGO3-10 & 37 & 123182 & 1079040 & 5000 & 5000 \\[2pt]
\bottomrule
\end{tabular} |
|
AI-9528 | \begin{tabular}{p{1.5cm}p{2.5cm}p{2.5cm}}
\hline
\textsc{Category} & \textsc{Obstacle} & \textsc{Examples} \\
\hline
\emph{Repetition} & Cannot identify a surface
parameter & What did you say? \\
\hline
\emph{Clausal}
& Uncertain value for a clausal dialogue history
parameter & Are you asking if BO SMITH left? \\
\hline
\emph{Intended} & The hearer can find no value for a
parameter
& Who is Bo? \\
\hline
\emph{Correction} & The hearer thinks that the speaker made a mistake and offers an alternative realization & Did you mean to say `Bro'? \\
\hline
\end{tabular} |
|
CR-23621 | \begin{tabular}{l|lllll}
\hline
Header length & 16 & 24 & 32 & 40 \\
\hline
Trapezoid Area & 0.44 & 5.176 & 10.432 & 10.864 \\
\hline
\end{tabular} |
|
CR-51387 | \begin{tabular}{@{\quad}ll@{\quad\quad\quad}ll}
569 & ijlt & 1251 & ELTZ \\
683 & Ifr & 1366 & BCR \\
797 & Js & 1479 & ADGHKNOQUVXYw \\
909 & acez & 1593 & m \\
1024 & bdghknopquvxy & 1821 & M \\
1139 & FPS & 1933 & W
\end{tabular} |
|
CR-20138 | \begin{tabular}[b]{@{}c@{}}\underline{\ding{182}\ding{183}\ding{185}\ding{186}\ding{187}\ding{188}\ding{189}\hspace{6pt}}\end{tabular} |
|
CR-23293 | \begin{tabular}{cc|cccc}
\hline
\multirow{2}{*}{Dataset} & Model & \multirow{2}{*}{Method} & Time & Time & Time \\
& Type & & Secure & Non-Sec. & Ratio \\
\hline
\multirow{4}{*}{MNIST} & \multirow{4}{*}{FCN} & SFE & 54 & 2.2 & 24.5 \\
& & LTFE & 337 & 2.8 & 120.3 \\
& & CTFE & 207 & 0.8 & 258.7 \\
\hline
\multirow{4}{*}{MNIST} & \multirow{4}{*}{LeNet-5} & SFE & 814 & 2.4 & 339.1 \\
& & LTFE & 2163 & 5.3 & 408.1 \\
& & CTFE & 3344 & 3.1 & 1078.7 \\
\hline
\multirow{4}{*}{Synthetic} & \multirow{4}{*}{FCN} & SFE & 45 & 1.7 & 26.4 \\
& & LTFE & 206 & 2.2 & 93.6 \\
& & CTFE & 171 & 0.5 & 342 \\
\hline
\multirow{4}{*}{Fraud} & \multirow{4}{*}{FCN} & SFE & 20 & 0.33 & 60 \\
& & LTFE & 80.9 & 0.06 & 1348.34 \\
& & CTFE & 70.7 & 0.08 & 883.75 \\
\hline
\end{tabular} |
|
AI-6505 | \begin{tabular}{l|ccc}
\toprule
& CIFAR & SVHN & Tiny-ImageNet \\
\bottomrule \toprule
Optimizer & SGD & SGD & SGD \\
\midrule
Momentum & 0.9 & 0.9 & 0.9 \\
\midrule
Weight decay & 1e-4 & 1e-4 & 1e-4 \\
\midrule
Epochs & 200 & 200 & 90 \\
\midrule
LR & 0.1 & 0.01 & 0.1 \\
\midrule
LR decay & (100, 150) & (100, 150) & (30, 60) \\
\bottomrule
\end{tabular} |
|
PL-1938 | \begin{tabular}{l}
Inferred code \\
\toprule
int \_\_main\_\_(int var0, int var1) \\
\quad vars: int var2, int var3, int var4 \\
\quad var2 = min(var0, var1) \\
\quad var3 = ((var0 + var1) / 3) \\
\quad return min(var2, var3) \\
\bottomrule
\end{tabular} |
|
SE-5255 | \begin{tabular}{p{0,5cm}p{0,5cm}p{0,5cm}p{0,5cm}p{0,5cm}p{0,5cm}p{0,5cm}p{0,5cm}p{0,5cm}p{0,5cm}}
\hline
\textbf{} & \textbf{2014} & \textbf{2015} & \textbf{2016} & \textbf{2017} & \textbf{2018} & \textbf{2019} & \textbf{2020} & \textbf{2021} & \textbf{2022} \\
\hline
Posts & 37 & 381 & 1688 & 2559 & 3976 & 5490 & 7107 & 8607 & 531 \\
Bugs & 9 & 121 & 663 & 1054 & 1712 & 2482 & 3378 & 3879 & 251 \\
\hline
\end{tabular} |
|
CV-32275 | \begin{tabular}{|p{4.5cm}|c|c|}
\hline
Approach & MCA & MPCA \\ \hline \hline
\textit{G-GFU} & 58.9 & 58.7 \\ \hline
\textit{G} & 61.3 & 60.5 \\ \hline \hline
\textit{cGAN-(GFU and $\hat z$)} & 88.4 & 87.7 \\ \hline
\textit{cGAN-GFU} & 89.5 & 88.3 \\ \hline
\textit{MLS-GAN- $\hat z$} & 91.2 & 90.8 \\ \hline \hline
\cellcolor[HTML]{C0C0C0}MLS-GAN & \cellcolor[HTML]{C0C0C0}\textbf{91.7} & \cellcolor[HTML]{C0C0C0}\textbf{91.2} \\ \hline
\end{tabular} |
|
CR-7810 | \begin{tabular}{|c|c|c|c|c|c|c|}
\hline
Model & Training & Testing & Prec. & Recall & F1 & AUC \\
& Scenarios & Scenario & & & & \\
\hline
Logistic & 2,9 & 1 & 0.90 & 0.90 & 0.90 & 0.94 \\
Regression & 1,9 & 2 & 0.98 & 0.95 & 0.97 & {\bf 0.99} \\
& 1,2 & 9 & 0.97 & 0.87 & 0.92 & 0.96 \\
\hline
Random & 2,9 & 1 & {\bf 0.99} & {\bf 0.97} & {\bf 0.98} & {\bf 0.99} \\
Forest & 1,9 & 2 & 0.95 & {\bf 0.96} & 0.95 & 0.98 \\
& 1,2 & 9 & {\bf 1} & {\bf 0.90} & {\bf 0.94} & {\bf 0.96} \\
\hline
Gradient & 2,9 & 1 & {\bf 1} & {\bf 0.97} & {\bf 0.98} & {\bf 0.99} \\
boosting & 1,9 & 2 & {\bf 1} & 0.92 & {\bf 0.96} & {\bf 0.99} \\
& 1,2 & 9 & {\bf 1} & 0.87 & 0.93 & 0.95 \\
\hline
\end{tabular} |
|
AI-6477 | \begin{tabular}{cll}
\toprule
$V$ & vertex set including the depot and the $n$ customers, $V=\{v_0,\ldots,v_n\}$ \\
$V'$ & vertex set excluding the depot, $V' = V \backslash \{v_0\}$ \\
$A$ & arc set \\
$D$ & set of possible drone paths $(i,k,j)$ formed by two arcs, $(i,k)$ and $(k,j)$ \\ & that respects the drone's maximum endurance \\
$L$ & set of possible moments for truck visits, $L=\{0,\ldots,n\}$ \\
\midrule
$e$ & drone flight endurance time \\
$s^L$ & setup time for launching the drone \\
$s^R$ & setup time for returning the drone \\
$\tau_{i,j}$ & time required by the truck to traverse arc $(i,j)$ \\
$\tau^D_{i,k,j}$ & time required by the drone to traverse arcs $(i,k)$ and $(k,j)$ \\
$M$ & upper bound for the time required by the truck to visit all customers \\
\bottomrule
\end{tabular} |
|
PL-1579 | \begin{tabular}{|l|l|l|}\hline
Qubits & M & N \\ \hline \hline
8 & 2 & 4 \\ \hline
16 & 2 & 8 \\ \hline
32 & 4 & 8 \\ \hline
64 & 8 & 8 \\ \hline
128 & 8 & 16 \\ \hline
256 & 16 & 16 \\ \hline
\end{tabular} |
|
CR-7278 | \begin{tabular}{|llll|}
\hline
& & & \\
$\langle 1,A$ & $\longrightarrow$ & $B:$ & $\{N_a.A\}_{k_b}\rangle,$ \\
$\langle 2,B$ & $\longrightarrow$ & $A:$ & $\{B.\hbar ash(B.N_a)\}_{k_a}.\{B.N_b\}_{k_a}\rangle,$ \\
$\langle 3,A$ & $\longrightarrow$ & $B:$ & $A.\{\hbar ash(A.N_b)\}_{k_b}\rangle. $ \\
& & & \\
\hline
\end{tabular} |
|
SE-17868 | \begin{tabular}{m{1.7cm}|m{2.2cm}|m{2.3cm}|m{2.3cm}|m{1.7cm}|m{1.5cm}}\hline
\textbf{Architecture}
& \textbf{Example application}
& \textbf{Fault-detection techniques}
& \textbf{Fault-detection expressiveness}
& \textbf{Cross-layer interaction}
& \textbf{Code intertwined}
\\\hline
RIAPS
& Discovery service
& Heartbeats, timestamps
& Low
& None
& Yes
\\\hline
iLand
& Video surveillance
& Not the focus
& Not the focus
& None
& Unknown
\\\hline
For IEC~61499
& Baggage handling system
& Monitor events and data and using ontology
& Low
& None
& No
\\\hline
For IEC~61499
& Injection moulding machine
& Monitor events and data
& Low
& None
& No
\\\hline
CLAIR (this~paper)
& Factory assembly line sorter
& Basic heartbeats and timestamps. Expressive observers that are implemented using finite state machines, timed automata and hybrid automata.
& High
& Yes
& No
\\\hline
\end{tabular} |
|
AI-21861 | \begin{tabular}{l|c|c|c|c|c|c|c|c|c}
\hline
\multirow{2}{*}{\bf Data} & \multirow{2}{*}{\bf Model} & \multicolumn{2}{|c}{{\bf Es$\Rightarrow$En}} & \multicolumn{2}{|c}{{\bf En$\Rightarrow$Es}} & \multicolumn{2}{|c}{{\bf De$\Rightarrow$En}} & \multicolumn{2}{|c}{\bf En$\Rightarrow$De} \\
\cline{3-10}
& & dev & test & dev & test & dev & test & dev & test \\
\hline
\hline
\multirow{3}{*}{\shortstack[l]{1/4 bilingual + \\4/4 monolingual}}&Ours&61.46&61.02&57.86&57.40&56.77&56.54&51.11& 51.58\\
& BT & 62.47 & 61.99 & 60.28 & 59.59 & 57.75 & 58.20 & 52.47 & 52.96 \\
& Ours+BT & \textbf{65.98} & \textbf{65.51} & \textbf{62.48} & \textbf{62.22} & \textbf{62.22} & \textbf{61.79} & \textbf{56.75} & \textbf{56.50} \\
\hline\hline
\multirow{3}{*}{\shortstack[l]{2/4 bilingual + \\4/4 monolingual}}&Ours&65.17&64.69&61.31&61.01&61.43&61.19&55.55&55.35\\
& BT & 63.82 & 63.10 & 61.59 & 60.83 & 59.17 & 59.26 & 54.18 & 54.29 \\
& Ours+BT & \textbf{66.95} & \textbf{66.38} & \textbf{63.22} & \textbf{62.90} & \textbf{63.68} & \textbf{63.10} & \textbf{57.69} & \textbf{57.40} \\
\hline
\end{tabular} |
|
AI-35460 | \begin{tabular}{c|l}
\toprule[2pt]
Notations & Explanations \\
\midrule[1pt]
$\mathcal{U},\mathcal{I}, \mathcal{C}$ & User, item and category ID sets \\
$U, I, C$ & User, item and category embedding sets \\
$\mathcal{L}^u_{t-1}, \mathcal{S}^u_t, \mathcal{L}^u_t$ & Long-term, short-term and all sequential behaviors at time $t$ \\
$L^u_{t-1}, S^u_t$ & Long-term and short-term sequential behavior embeddings at time $t$ \\
$H^u_{t-1}$ & Time-aware history representation \\
$P^u$ & Personalized time position embedding for user $u$ \\
$W_*, b_*$ & Trainable weight matrix and bias vector \\
$L_s$ & Long-term sequence length \\
$d_f$ & Embedding size \\
$\gamma$ & Trainable parameter for adjusting the order of magnitude \\
$c^u_t$ & Dynamic user category ID at time $t$ \\
$u_{e,t}$ & User embedding at time $t$ for user $u$ \\
$u_{t-1}, u_{t}$ & User long-term and current preferences representation for user $u$ \\
$y_j$ & Label of item $j$ \\
$\lambda$ & L2-loss weight \\
\bottomrule[2pt]
\end{tabular} |
|
CR-30269 | \begin{tabular}{|l|l|l|}
\hline
Type & Common Name & FSolidM Plugin \\
\hline\hline
\multirow{2}{*}{Vulnerabilities} & reentrancy & locking \\
\cline{2-3}
& transaction ordering & transition counter \\
\hline
\multirow{2}{*}{Patterns} & time constraint & timed transitions \\
\cline{2-3}
& authorization & access control \\
\hline
\end{tabular} |
|
SE-21684 | \begin{tabular}{|l|c|c|c|}
\hline
\textbf{Method} & \textbf{Precision} & \textbf{Recall} & \textbf{F-Measure} \\ \hline
No filter & 0.5160 & 0.5117 & 0.4883 \\
Removal & 0.5451 & 0.5414 & 0.5194 \\
Subtract & 0.5407 & 0.5371 & 0.5147 \\
Single & 0.5187 & 0.5148 & 0.4910 \\
GCF & 0.5172 & 0.5129 & 0.4889 \\ \hline
\end{tabular} |
|
AI-34516 | \begin{tabular}{lll}
$reach(d_1,g_1) \gets edge(d_1,g_1,1)$ & $reach(d_2,a_1) \gets edge(d_2,a_1,1)$ \\
$reach(d_2,a_2) \gets edge(d_2,a_2,1)$ & $reach(a_1,g_1) \gets edge(a_1,g_1,1)$ \\
$reach(a_1,g_2) \gets edge(a_1,g_2,1)$ & $reach(a_2,g_2) \gets edge(a_2,g_2,1)$ \\
\end{tabular} |
|
AI-31553 | \begin{tabular}{c|cccc|cccc}
\toprule
Model & $F_E$ & $F_A$ & $F_D$ & $G_P$ & SSIM$\uparrow$ & PSNR$\uparrow$ & FSIM$\uparrow$ & LPIPS$\downarrow$ \\
\hline
"No $G_P$" & + & + & + & - & 0.5338 & 20.2712 & 0.7399 & 0.4482 \\
"Linear $F$" & * & - & - & + & 0.4585 & 18.8164 & 0.7006 & 0.4845 \\
"E Only" & * & - & - & + & 0.4761 & 19.0881 & 0.7146 & 0.4604 \\
"ED Only" & + & - & + & + & 0.5414 & 20.2488 & 0.7392 & 0.4340 \\
"A Only" & - & + & - & + & 0.5280 & 20.0312 & 0.7196 & 0.4418 \\
\hline
"EA64" & + & + & - & + & \textbf{0.5954} & \textbf{21.2050} & 0.7586 & 0.4053 \\
"EAD" & + & + & + & + & 0.5848 & 21.1291 & \textbf{0.7592} & \textbf{0.3985} \\
\bottomrule
\end{tabular} |
|
SE-23212 | \begin{tabular}{lll}
Speaker & Search Function & Dialogue Act \\ \hline
\multirow{10}{*}{User} & \multirow{2}{*}{Semantic Search} & \texttt{provideQuery} \\
& & \texttt{provideKeyword} \\ \cline{2-3}
& \multirow{3}{*}{User Critique} & \texttt{rejectKeywords} \\
& & \texttt{rejectComponents} \\
& & \texttt{unsure} \\ \cline{2-3}
& \multirow{5}{*}{Standard Navigation} & \texttt{elicitInfoAPI} \\
& & \texttt{elicitInfoAllAPI} \\
& & \texttt{elicitSuggAPI} \\
& & \texttt{elicitListResults} \\
& & \texttt{changePage} \\ \cline{2-3}
& General & \texttt{END} \\ \hline
\multirow{8}{*}{System} & \multirow{2}{*}{Query Refinement} & \texttt{requestQuery} \\
& & \texttt{suggKeywords} \\ \cline{2-3}
& \multirow{2}{*}{API Recommendation} & \texttt{suggAPI} \\
& & \texttt{suggInfoAPI} \\ \cline{2-3}
& \multirow{3}{*}{Standard Navigation} & \texttt{infoAPI} \\
& & \texttt{infoAllAPI} \\
& & \texttt{listResults} \\
& & \texttt{changePage} \\ \cline{2-3}
& General & \texttt{START}
\end{tabular} |
|
SE-2352 | \begin{tabular}{|m{8em}|m{14em}|}
\hline
\textbf{Category} & \textbf{Example Functions} \\ [0.5ex]
\hline
\#1 - Input & \verb_scanf_ and its family of functions, \verb_fread_, \verb_getc_, \verb_gets_ \\
\hline
\#2 - Memory & \verb_memcpy_, \verb_memmove_, \verb_strcat_ and its family of functions \\
\hline
\#3 - Output & \verb_printf_ family of functions, \verb_putc_ and related functions, other output functions defined in \verb_stdio.h_ \\
\hline
\#4 - Utility & \verb_realpath_, \verb_getwd_, \verb_getopt_, and \verb_getpass_ \\
\hline
\#5 - Buffer Access & Any type of buffer (array) read or write \\
\hline
\end{tabular} |
|
CV-11228 | \begin{tabular}{lccc}
\toprule[1.5pt]
& mIoU(\
Baseline (RGB only) & 35.1 & 44.1 \\
Depth-aware conv & 38.1 & 48.0 \\
3DN-Conv (this paper) & {\bf 39.2} & {\bf 50.8} \\
\bottomrule[1.5pt]
\end{tabular} |
|
CV-26942 | \begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
\thead{} & \thead{head} & \thead{neck} & \thead{right \\shoulder} & \thead{right \\elbow} & \thead{right \\wrist} & \thead{left \\shoulder} & \thead{left \\elbow} & \thead{left \\wrist} \\ [0.5ex]
\hline\hline
\thead{Before \\fine-tuning} & 1.00 & 1.00 & 0.99 & 1.00 & 0.96 & 1.00 & 1.00 & 0.94\\
\thead{After \\fine-tuning} & 1.00 & 1.00 & 0.99 & 1.00 & 0.97 & 1.00 & 1.00 & 0.96\\
\hline
\end{tabular} |
|
CR-56514 | \begin{tabular}{@{}lcccc@{}}
Grid Search & 43.712 & 72.260 & \textbf{89.133} & 0.683 \\
Evolutionary & 5.250 & 72.615 & 73.745 & \textbf{0.175} \\
Bayesian & \textbf{2.853} & 73.385 & 81.562 & 0.349 \\
Reinforcement & 31.165 & \textbf{74.906} & 75.022 & 0.240 \\ \bottomrule
\end{tabular} |
|
CR-35361 | \begin{tabular}{c|c|c|c|c|c|c}
\specialrule{1pt}{0pt}{0pt}
Scheme & Operation & $\tau$ & $U$ & $2 \tau U$ & $\mathrm{M}$ & $r$ \\ \hline\hline
\multirow{4}{*}{Dilithium2} & $c\mathbf{s}_{1}$ & 39 & 2 & 156 & $2^{8}$ & 4 \\\cline{2-7}
& $c\mathbf{s}_{2}$ & 39 & 2 & 156 & $2^{8}$ & 4 \\\cline{2-7}
& $c\mathbf{t}_{0}$ & 39 & $2^{12}$ & 319488 & $2^{19}$ & 4 \\ \cline{2-7}
& $c\mathbf{t}_{1}$ & 39 & $2^{10}$ & 79872 & $2^{17}$ & 4 \\ \hline
\multirow{4}{*}{Dilithium3} & $c\mathbf{s}_{1}$ & 49 & 4 & 392 & $2^{9}$ & 5 \\\cline{2-7}
& $c\mathbf{s}_{2}$ & 49 & 4 & 392 & $2^{9}$ & 6 \\\cline{2-7}
& $c\mathbf{t}_{0}$ & 49 & $2^{12}$ & 401408 & $2^{19}$ & 6 \\\cline{2-7}
& $c\mathbf{t}_{1}$ & 49 & $2^{10}$ & 100352 & $2^{17}$ & 6 \\\hline
\multirow{4}{*}{Dilithium5} & $c\mathbf{s}_{1}$ & 60 & 2 & 240 & $2^{8}$ & 7 \\\cline{2-7}
& $c\mathbf{s}_{2}$ & 60 & 2 & 240 & $2^{8}$ & 8 \\\cline{2-7}
& $c\mathbf{t}_{0}$ & 60 & $2^{12}$ & 491520 & $2^{19}$ & 8 \\\cline{2-7}
& $c\mathbf{t}_{1}$ & 60 & $2^{10}$ & 122880 & $2^{17}$ & 8 \\\specialrule{1pt}{0pt}{0pt}
\end{tabular} |
|
CR-45484 | \begin{tabular}{ccc}
\hline
Algorithm & Time & Space \\ \hline
CWC on plaintext & $O(kmn+k\log k)$ & $O(kmn)$ \\
secure CWC (baseline) & $O(kmn\log k+k\log^2 k)$ & $O(kmn)$ \\
improved & $O(kmn+k\log ^2k+k\log k\log mn)$ & $O(kmn)$\; \\
\hline
\end{tabular} |
|
SE-10691 | \begin{tabular}[c]{@{}l@{}}Doingworkbeyondexpectationsandcompletingtasksbeforethedeadlines\\Keepontryingtoimprove,resultinginimprovingthequalityofthework\\Gettingdetailedrequirementsfromdifficulttohandlecustomers\\Committedtodoingthework,resultinginsuccessfulprojectcompletion\end{tabular} |
|
AI-28367 | \begin{tabular}{|l|l||c|c|c|c|c|c|}
\hline
\textbf{Data Set} & \textbf{Embedding Model} & \textbf{Book} & \textbf{Film} & \textbf{Actor} & \textbf{Company} & \textbf{University} & \textbf{Writer} \\ \hline
\multirow{5}{*} {Wikipedia }
& Skip-gram (avg) & 0.6 & 0.2 & 0.0 & 0.2 & 0.9 & 0.1 \\
& Skip-gram (sum) & 0.6 & 0.2 & 0.0 & 0.2 & 0.9 & 0.1 \\
& CBOW (avg) & 0.0 & 0.1 & 0.2 & 0.6 & 0.8 & 0.4 \\
& CBOW (sum) & 0.0 & 0.1 & 0.2 & 0.6 & 0.8 & 0.4 \\
& Glove (avg) & 0.3 & 0.8 & 0.5 & 0.7 & 0.6 & 0.4 \\
& Glove (sum) & 0.3 & 0.8 & 0.5 & 0.7 & 0.4 & 0.4 \\
\hline
\multirow{2}{*} {DBpedia } & RDF2vec (skip-gram) & 0.4 & 0.1 & 0.4 & 0.3 & 0.1 & 0.3 \\
& RDF2vec (CBOW) & 0.2 & 0.0 & 0.4 & 0.4 & 0.1 & 0.5 \\
& RDF (Glove) & 0.2 & 0 & 0.1 & 0.2 & 0 & 0 \\ \hline
\end{tabular} |
|
SE-5199 | \begin{tabular}
{ >{\centering\arraybackslash}m{1.4cm}|
>{\centering\arraybackslash}m{2.8cm} |
>{\centering\arraybackslash}m{2.8cm} |
>{\centering\arraybackslash}m{0.6cm} |
>{\centering\arraybackslash}m{1.85cm}|
>{\centering\arraybackslash}m{0.6cm} |
>{\centering\arraybackslash}m{0.6cm}}
\toprule
\textbf{Dataset} & \textbf{Ground Truth} & \textbf{Predicted Code} & \textbf{\textit{SC}} & \textbf{\textit{ROUGE-4 (F$_1$)}} & \textbf{\textit{ED}} & \textbf{\textit{EM}} \\ \midrule
& \texttt{add EAX, EBX} & \texttt{add EAX, EBX} & $1.0$ & $0.0$ & $1.0$ & $1.0$ \\ \cmidrule{2-7}
\textit{Assembly} & \texttt{xor ECX, ECX \textbackslash{n} mul ECX} & \texttt{xor ECX, ECX \textbackslash{n} mul \textcolor{red}{EBX}} & $0.0$ & $0.66$ & $0.95$ & $0.0$ \\ \cmidrule{2-7}
& \texttt{jmp decode} & \texttt{jmp decode} & $1.0$ & $0.0$ & $1.0$ & $1.0$ \\ \bottomrule
& \texttt{break} & \texttt{\textcolor{red}{sys.exit()}} & $0.0$ & $0.0$ & $0.1$ & $0.0$ \\ \cmidrule{2-7}
\textit{Python} & \texttt{for byte in encoder:} & \texttt{for bytes in encoder:} & $1.0$ & $0.0$ & 0.95 & $0.0$ \\ \cmidrule{2-7}
& \texttt{encoded = "\textbackslash \textbackslash x"} & \texttt{encoded = ‘\textbackslash \textbackslash x'} & $1.0$ & $0.0$ & $0.87$ & $0.0$ \\ \midrule
\end{tabular} |
|
CR-13081 | \begin{tabular}{p{0.04\textwidth}p{0.52\textwidth}p{0.1\textwidth}p{0.15\textwidth}p{0.15\textwidth}}
\toprule
\textbf{\#} & \textbf{Company} & \textbf{Patent Count} & \textbf{Value} & \textbf{Impact} \\
\midrule
1 & International Business Machines Corporation & 7314 & 0.479355 & 3,488,180 \\
2 & Microsoft Technology Licensing, LLC & 2644 & 0.815053 & 3,157,710 \\
3 & Amazon Technologies, Inc. & 2133 & 0.487107 & 1,632,140 \\
4 & Cisco Technology, Inc & 1040 & 0.779808 & 1,532,470 \\
5 & Advanced New Technologies Co, Ltd & 518 & 0.486486 & 1,453,670 \\
6 & Intel Corporation & 2788 & 0.457317 & 1,283,470 \\
7 & EMC IP Holding Company LLC & 1235 & 0.733603 & 1,265,040 \\
8 & Apple Inc. & 2568 & 0.54595 & 1,132,630 \\
9 & AS America, Inc. & 496 & 0.59879 & 941,869 \\
10 & Google LLC & 1621 & 0.676126 & 921,388 \\
\bottomrule
\end{tabular} |
|
CR-56609 | \begin{tabular}{|c|c|c|c|c|}
\hline
Country & Total pop & 4/1 cases & 5/1 cases \\
\hline
UK & 65.8m & 29.9k & 178.7k \\
\hline
USA & 332.6m & 213.2k & 1.1m \\
\hline
Europe & 741.m & 429k & 1.4m \\
\hline
\end{tabular} |
|
PL-2865 | \begin{tabular}{ccclll}
\hline
BLEU-1 & BLEU-2 & BLEU-3 & BLEU-4 & ROUGE-L & METEOR \\ \hline
0.3472 & 0.0946 & 0.0608 & \multicolumn{1}{c}{0.0144} & \multicolumn{1}{c}{0.3784} & \multicolumn{1}{c}{0.0808} \\ \hline
\end{tabular} |
|
CR-39477 | \begin{tabular}[c]{@{}l@{}}ThismoduleinstantiatesaperipheralwithinaSoCusingasignaltodistinguishbetweentrustedanduntrusted\\entities.
However, this signal depicting the security level is incorrectly grounded.\end{tabular} |
|
CR-14293 | \begin{tabular}{ll}
\toprule
\textbf{Letter} & \multicolumn{1}{c}{\textbf{Observation}} \\ \midrule
A & Assumes or learns properties of adversarial examples \\
B & Relies on the assumption that the attacker does not have full information \\
C & Relies on the assumption that the attacker cannot deal with randomness \\
D & Requires genuine data \\
E & Explicit role of loss \\
F & Implicit loss constraint (effect on loss or accuracy) \\
G & Provides an explicit accuracy-robustness trade-off \\
H & A non-trivial part of the computation is performed before the input is known \\
I & Problem statements are not directly comparable \\
J & Partially broken \\
K & Classifies \emph{sets} of inputs \\
L & Analysis is heavily influenced by what is defined to be the success condition \\
\bottomrule
\end{tabular} |
|
AI-34118 | \begin{tabular}{l|l|p{7.5cm}}
\bf type & \bf subtype & \bf tags \\ \hline
\bf \em pass & cross, simple pass & accurate, not accurate, key pass, opportunity, assist, (goal) \\
\bf \em foul & & no card, yellow, red, 2nd yellow \\
\bf \em shot & & accurate, not accurate, block, opportunity, assist, (goal) \\
\bf \em duel & air duel, dribbles, tackles, ground loose ball & accurate, not accurate \\
\bf \em free kick & corner, shot, goal kick, throw in, penalty, simple kick \quad \ & accurate, not accurate, key pass, opportunity, assist, (goal) \\
\bf \em offside & & \\
\bf \em touch & acceleration, clearance, simple touch & counter attack, dangerous ball lost, missed ball, interception, opportunity, assist, (goal) \\
\hline
\end{tabular} |
|
CR-29235 | \begin{tabular}{|c|c|c|c|c|c|}
\hline
\multicolumn{6}{|c|}{\textbf{Untimed CPN Model Statistics}} \\ \hline
\textbf{Places} & \textbf{Count} & \textbf{Sum} & \textbf{Average} & \textbf{Min} & \textbf{Max} \\ \hline
Inputs & 51 & 204 & 4.000000 & 4 & 4 \\ \hline
Point Store & 51 & 834 & 16.352941 & 4 & 28 \\ \hline
Provers' Distance Store & 51 & 696 & 13.647059 & 1 & 27 \\ \hline
\end{tabular} |
|
PL-3542 | \begin{tabular}{lcc}
\toprule
Example & LC & FPLC \\
\midrule
{\tt hypercube } & 100 & 1140 \\
{\tt coupon } & 27 & 184 \\
{\tt vertex-cover } & 30 & 61 \\
{\tt pairwise-indep } & 30 & 231 \\
{\tt private-sums } & 22 & 80 \\
{\tt poly-id-test } & 22 & 32 \\
{\tt random-walk } & 16 & 42 \\
{\tt dice-sampling } & 10 & 64 \\
{\tt matrix-prod-test} & 20 & 75 \\
\bottomrule
\end{tabular} |
|
CR-51044 | \begin{tabular}{ccccc}
\toprule
\multirow{2}[4]{*}{\textbf{Method}} & \multicolumn{3}{c}{\textbf{Equivalent Transformation}} & \multicolumn{1}{c}{\multirow{2}[4]{*}{\textbf{Our Unified Attack}}} \\
\cmidrule{2-4} & \multicolumn{1}{c}{\textit{PathScale}} & \multicolumn{1}{c}{\textit{LayerShuffle}} & \multicolumn{1}{c}{\textit{SignFLip}} & \multicolumn{1}{c}{} \\
\midrule
Uchida's & \ding{51} & \ding{51} & \ding{51} & \ding{51} \\
\midrule
RIGA & \ding{51} & \ding{51} & \ding{51} & \ding{51} \\
\midrule
Greedy Residuals & \ding{51} & \ding{51} & \ding{51} & \ding{51} \\
\midrule
IPR-GAN & \ding{55} & \ding{51} & \ding{51} & \ding{51} \\
\midrule
Lottery Verification & \ding{55} & \ding{51} & \ding{55} & \ding{51} \\
\midrule
DeepSigns & \ding{51} & \ding{51} & \ding{51} & \ding{51} \\
\midrule
IPR-IC & \ding{51} & \ding{51} & \ding{51} & \ding{51} \\
\midrule
DeepIPR & \ding{51} & \ding{51} & \ding{51} & \ding{51} \\
\midrule
Passport-aware & \ding{51} & \ding{51} & \ding{51} & \ding{51} \\
\bottomrule
\end{tabular} |
|
CV-26137 | \begin{tabular}{|l|p{1.5cm}|p{1.8cm}|p{1.7cm}|p{1.7cm}|}
\hline
Classifier & Grasp Type (PIP) & Opposition Type & Grasped Dimension & Grasp Information(All) \\
\hline
\hline
Multi-class decision forest & 0.6460 & 0.6532 & 0.6508 & \bf{0.6966} \\
Multi-class neural network & 0.6810 & 0.6820 & 0.6474 & \bf{0.6929} \\
Locally deep SVM (Binary) & 0.6688 & 0.6908 & 0.6677 & \bf{0.6943} \\
SVMs (Binary) & 0.6789 & \bf{0.6912} & 0.6644 & 0.6854 \\
Neural network (Binary) & 0.6973 & 0.7041 & 0.6508 & \bf{0.7085} \\
\hline
\end{tabular} |
|
CV-27497 | \begin{tabular}{|c|c|c|c|c|c|}
\hline
method & network & box encoding & train data & mAP & FPS \\
\hline\hline
Faster RCNN & AlexNet & 2D center offset encoding & 07+12 & 62.1 & 18 \\
Faster RCNN & VGG16 & 2D center offset encoding & 07+12 & 73.2 & 7 \\
\hline
YOLO & DarkNet & 2D center offset encoding & 07+12 & 63.4 & 45 \\
YOLO & VGG16 & 2D center offset encoding & 07+12 & 66.4 & 21 \\
\hline
\end{tabular} |
|
CV-28890 | \begin{tabular}{lrrrrr}
\toprule
\multirow{2}{*}{Methods} & \multicolumn{2}{c}{Market-1501} & \multicolumn{2}{c}{MSMT17} \\
\cmidrule{2-5}
& rank-1 & mAP & rank-1 & mAP \\
\midrule
Feature Dropping Branch & 93.6 & 83.3 & - & - \\
Base+Random Erasing & 92.5 & 82.9 & 72.8 & 48.1 \\
Base+Grad-CAM Erasing & 94.1 & 84.7 & 74.6 & 49.4 \\
Base+Feature Dropping Branch & 93.7 & 83.2 & 73.1 & 47.0 \\
\midrule
Base+BESM & 94.3 & 85.1 & 75.4 & 49.9 \\
ES-Net(Ours) & \textbf{95.7} & \textbf{88.6} & \textbf{80.5} & \textbf{57.3} \\
\bottomrule
\end{tabular} |
|
CV-26691 | \begin{tabular}{c|cc}\hline
& cifar10 & cifar100 \\ \hline
ReLU & 0.927 & 0.653 \\
selu & 0.899 & 0.572 \\
ELU & 0.903 & 0.550 \\
softplus & 0.908 & 0.598 \\
Leaky ReLU & 0.918 & \underline{0.673} \\
SiL & 0.919 & 0.638 \\
PReLU & \underline{0.935} & \underline{0.678} \\
Swish & \underline{0.935} & \underline{0.689} \\
WiG (Pro.) & {\bf \underline{0.949} } & {\bf \underline{0.742} } \\ \hline
\end{tabular} |
|
AI-22967 | \begin{tabular}{cc}
\toprule
\textbf{Dataset} & \textbf{SARIMAX(p,d,q)(P,D,Q,m)} \\
\midrule
AEC-DS & (1,0,0)(1,0,0,7) \\
HPC-DS & (1,0,1)(1,1,1,7) \\
SHWI-DS & (1,0,0)(2,0,1,7) \\
\bottomrule
\end{tabular} |