premise string | hypothesis string | label int64 |
|---|---|---|
| | NLPR | RGBD135 | | |
| --- | --- | --- | --- | --- |
| Fβ | MAE | Fβ | MAE | |
| S1 | 0.6497 | 0.1978 | 0.6528 | 0.2004 |
| Ddce | 0.6725 | 0.1528 | 0.6785 | 0.1685 | | | Scdcp | 0.6764 | 0.1442 | 0.6889 | 0.1489 |
| --- | --- | --- | --- | --- |
| S | 0.6875 | 0.1206 | 0.6958 | 0.1343 |
| Sf | 0.7056 | 0.0860 | 0.7105 | 0.0794 | | 1 |
| | NLPR | RGBD135 | | |
| --- | --- | --- | --- | --- |
| Fβ | MAE | Fβ | MAE | |
| S1 | 0.6497 | 0.1978 | 0.6528 | 0.2004 |
| Ddce | 0.6725 | 0.1528 | 0.6785 | 0.1685 | | | Methods | MAE | F-<br>measure | Max-P | Min-P | Max-R | Min-R |
| --- | --- | --- | --- | --- | --- | --- |
| FT | 0.2168 | 0.3270 | 0.3894 | 0.1291 | 1 | 0 |
| SIM | 0.3957 | 0.2927 | 0.3847 | 0.1291 | 1 | 0 |
| HS | 0.1909 | 0.6003 | 0.7503 | 0.1291 | 1 | 0.1859 |
| BSCA | 0.1754 | 0.5925 | 0.7616 | 0.1291 | 1 | 0.0525 |
| LPS | 0.1252 | 0.5890 | 0.6831 | 0.1291 | 1 | 0.0166 |
| RGBD1 | 0.3207 | 0.4843 | 0.7771 | 0.1291 | 1 | 0.2228 |
| RGBD2 | 0.1087 | 0.5957 | 0.8148 | 0.1291 | 1 | 0.0070 |
| OURS1 | 0.1043 | 0.5452 | 0.8029 | 0.0150 | 1 | 0 |
| OURS2 | 0.0900 | 0.7025 | 0.8477 | 0.1291 | 1 | 0.2445 |
| OURS | 0.0852 | 0.7190 | 0.8347 | 0.1291 | 1 | 0.4071 | | 0 |
| | NLPR | RGBD135 | | |
| --- | --- | --- | --- | --- |
| Fβ | MAE | Fβ | MAE | |
| S1 | 0.6497 | 0.1978 | 0.6528 | 0.2004 |
| Ddce | 0.6725 | 0.1528 | 0.6785 | 0.1685 | | | Scdcp | 0.6764 | 0.1442 | 0.6889 | 0.1489 |
| --- | --- | --- | --- | --- |
| S | 0.6875 | 0.1206 | 0.6958 | 0.1343 |
| Sf | 0.7056 | 0.0860 | 0.7105 | 0.0794 | | 1 |
| | NLPR | RGBD135 | | |
| --- | --- | --- | --- | --- |
| Fβ | MAE | Fβ | MAE | |
| S1 | 0.6497 | 0.1978 | 0.6528 | 0.2004 |
| Ddce | 0.6725 | 0.1528 | 0.6785 | 0.1685 | | | OURS1 | 0.1043 | 0.5452 | 0.8029 | 0.0150 | 1 | 0 |
| --- | --- | --- | --- | --- | --- | --- |
| OURS2 | 0.0900 | 0.7025 | 0.8477 | 0.1291 | 1 | 0.2445 |
| OURS | 0.0852 | 0.7190 | 0.8347 | 0.1291 | 1 | 0.4071 | | 0 |
| Method | N=1000 | N=5000 | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| FScore | Stdev | Time(sec) | FScore | Stdev | Time(sec) | FScore | |
| SLIC | 0.8584 | 0.0110 | 6.1 | 0.9194 | 0.0075 | 7.0 | 0.9369 | | | ISF-GRID-MEAN | 0.8815 | 0.0129 | 31.8 | 0.9321 | 0.0069 | 30.3 | 0.9459 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| SLIC+ISF(twoiterations) | 0.8686 | 0.0138 | 17.3 | 0.9305 | 0.0072 | 18.0 | 0.9444 | | 1 |
| Method | N=1000 | N=5000 | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| FScore | Stdev | Time(sec) | FScore | Stdev | Time(sec) | FScore | |
| SLIC | 0.8584 | 0.0110 | 6.1 | 0.9194 | 0.0075 | 7.0 | 0.9369 | | | Method | IOUthreshold=0.5 | IOUthreshold=0.7 | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| R | P | F | R | P | F | |
| RR | 0.764 | 0.822 | 0.792 | 0.613 | 0.574 | 0.593 |
| Quad | 0.767 | 0.872 | 0.817 | 0.577 | 0.676 | 0.623 |
| RRMS | 0.766 | 0.875 | 0.817 | 0.569 | 0.623 | 0.594 |
| QuadMS | 0.785 | 0.878 | 0.829 | 0.617 | 0.690 | 0.651 | | 0 |
| Method | N=1000 | N=5000 | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| FScore | Stdev | Time(sec) | FScore | Stdev | Time(sec) | FScore | | | SLIC | 0.8584 | 0.0110 | 6.1 | 0.9194 | 0.0075 | 7.0 | 0.9369 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| ISF-GRID-MEAN | 0.8815 | 0.0129 | 31.8 | 0.9321 | 0.0069 | 30.3 | 0.9459 |
| SLIC+ISF(twoiterations) | 0.8686 | 0.0138 | 17.3 | 0.9305 | 0.0072 | 18.0 | 0.9444 | | 1 |
| Method | N=1000 | N=5000 | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| FScore | Stdev | Time(sec) | FScore | Stdev | Time(sec) | FScore | | | RR | 0.764 | 0.822 | 0.792 | 0.613 | 0.574 | 0.593 |
| --- | --- | --- | --- | --- | --- | --- |
| Quad | 0.767 | 0.872 | 0.817 | 0.577 | 0.676 | 0.623 |
| RRMS | 0.766 | 0.875 | 0.817 | 0.569 | 0.623 | 0.594 |
| QuadMS | 0.785 | 0.878 | 0.829 | 0.617 | 0.690 | 0.651 | | 0 |
| Method | Precision | Recall | F1-score |
| --- | --- | --- | --- |
| CNN(Section??) | 0.9272 | 0.8513 | 0.8999 |
| CNN(highlyactivated) | 0.9112 | 0.8309 | 0.8692 | | | k-means | 0.968 | 0.7559 | 0.8489 |
| --- | --- | --- | --- |
| Randomselection | 0.8812 | 0.8309 | 0.8553 | | 1 |
| Method | Precision | Recall | F1-score |
| --- | --- | --- | --- |
| CNN(Section??) | 0.9272 | 0.8513 | 0.8999 |
| CNN(highlyactivated) | 0.9112 | 0.8309 | 0.8692 | | | Method | Precision | Recall | F1-score |
| --- | --- | --- | --- |
| FastTumorSegmentation(PHP) | 0.8259 | 0.8019 | 0.8137 |
| AccurateTumorSegmentation(PHP+CNN) | 0.8311 | 0.8235 | 0.8273 |
| HyMap | 0.6469 | 0.7228 | 0.6827 |
| ConvNetCNN3 | 0.6927 | 0.8446 | 0.7612 |
| MCTA | 0.7050 | 0.7419 | 0.7229 |
| TVIA | 0.6993 | 0.7240 | 0.7114 | | 0 |
| Method | Precision | Recall | F1-score |
| --- | --- | --- | --- |
| CNN(Section??) | 0.9272 | 0.8513 | 0.8999 |
| CNN(highlyactivated) | 0.9112 | 0.8309 | 0.8692 | | | k-means | 0.968 | 0.7559 | 0.8489 |
| --- | --- | --- | --- |
| Randomselection | 0.8812 | 0.8309 | 0.8553 | | 1 |
| Method | Precision | Recall | F1-score |
| --- | --- | --- | --- |
| CNN(Section??) | 0.9272 | 0.8513 | 0.8999 |
| CNN(highlyactivated) | 0.9112 | 0.8309 | 0.8692 | | | MCTA | 0.7050 | 0.7419 | 0.7229 |
| --- | --- | --- | --- |
| TVIA | 0.6993 | 0.7240 | 0.7114 | | 0 |
| Event | TweetCount |
| --- | --- |
| TrainingData | |
| 2010NFLDivisionChampionship | 109,809 |
| 2012PremierLeagueSoccerGames | 1,064,040 |
| 2014NHLStanleyCupPlayoffs | 2,421,065 |
| 2014NBAPlayoffs | 500,170 |
| 2014KentuckyDerbyHorseRace | 233,172 |
| 2014BelmontStakesHorseRace | 226,160 |
| 2014FIFAWorldCupStagesA+B | 5,867,783 | | | TestingData | |
| --- | --- |
| 2013MLBWorldSeriesGame5 | 1,052,852 |
| 2013MLBWorldSeriesGame6 | 1,026,848 |
| 2013HonshuEarthquake | 444,018 |
| 2014NFLSuperBowl | 1,024,367 |
| 2014FIFAWorldCupThirdPlace | 809,426 |
| 2014FIFAWorldCupFinal | 1,166,767 |
| 2014IwakiEarthquake | 358,966 |
| Total | 16,305,443 | | 1 |
| Event | TweetCount |
| --- | --- |
| TrainingData | |
| 2010NFLDivisionChampionship | 109,809 |
| 2012PremierLeagueSoccerGames | 1,064,040 |
| 2014NHLStanleyCupPlayoffs | 2,421,065 |
| 2014NBAPlayoffs | 500,170 |
| 2014KentuckyDerbyHorseRace | 233,172 |
| 2014BelmontStakesHorseRace | 226,160 |
| 2014FIFAWorldCupStagesA+B | 5,867,783 | | | Sport | KeyMoments |
| --- | --- |
| TrainingData | |
| 2010NFLDivisionChampionship | 13 |
| 2012PremierLeagueSoccerGames | 21 |
| 2014NHLStanleyCupPlayoffs | 24 |
| 2014NBAPlayoffs | 3 |
| 2014KentuckyDerbyHorseRace | 3 |
| 2014BelmontStakesHorseRace | 3 |
| 2014FIFAWorldCupStagesA+B | 80 |
| TestingData | |
| 2013MLBWorldSeriesGame5 | 7 |
| 2013MLBWorldSeriesGame6 | 8 |
| 2014NFLSuperBowl | 13 |
| 2014FIFAWorldCupThirdPlace | 11 |
| 2014FIFAWorldCupFinal | 7 |
| Total | 193 | | 0 |
| Event | TweetCount |
| --- | --- |
| TrainingData | |
| 2010NFLDivisionChampionship | 109,809 |
| 2012PremierLeagueSoccerGames | 1,064,040 |
| 2014NHLStanleyCupPlayoffs | 2,421,065 |
| 2014NBAPlayoffs | 500,170 |
| 2014KentuckyDerbyHorseRace | 233,172 |
| 2014BelmontStakesHorseRace | 226,160 |
| 2014FIFAWorldCupStagesA+B | 5,867,783 |
| TestingData | | | | 2013MLBWorldSeriesGame5 | 1,052,852 |
| --- | --- |
| 2013MLBWorldSeriesGame6 | 1,026,848 |
| 2013HonshuEarthquake | 444,018 |
| 2014NFLSuperBowl | 1,024,367 |
| 2014FIFAWorldCupThirdPlace | 809,426 |
| 2014FIFAWorldCupFinal | 1,166,767 |
| 2014IwakiEarthquake | 358,966 |
| Total | 16,305,443 | | 1 |
| Event | TweetCount |
| --- | --- |
| TrainingData | |
| 2010NFLDivisionChampionship | 109,809 |
| 2012PremierLeagueSoccerGames | 1,064,040 |
| 2014NHLStanleyCupPlayoffs | 2,421,065 |
| 2014NBAPlayoffs | 500,170 |
| 2014KentuckyDerbyHorseRace | 233,172 |
| 2014BelmontStakesHorseRace | 226,160 |
| 2014FIFAWorldCupStagesA+B | 5,867,783 |
| TestingData | | | | 2013MLBWorldSeriesGame5 | 7 |
| --- | --- |
| 2013MLBWorldSeriesGame6 | 8 |
| 2014NFLSuperBowl | 13 |
| 2014FIFAWorldCupThirdPlace | 11 |
| 2014FIFAWorldCupFinal | 7 |
| Total | 193 | | 0 |
| Parameter | Values |
| --- | --- |
| k | 5,25,50,75,100 | | | β | 0.1,0.2,0.3,0.4,0.5 |
| --- | --- |
| N | 100K,250K,500K,750K,1,000K |
| L | 1K,2.5K,5K,7.5K,10K |
| \|U\| | 1M,2M,3M,4M,5M | | 1 |
| Parameter | Values |
| --- | --- |
| k | 5,25,50,75,100 | | | β | 5@NDCG | 10@NDCG | 20@NDCG | 40@NDCG |
| --- | --- | --- | --- | --- |
| 0.5 | 0.3885 | 0.2888 | 0.2139 | 0.1478 |
| 1.5 | 0.3392 | 0.2745 | 0.2035 | 0.1370 |
| 2.5 | 0.3392 | 0.2863 | 0.2036 | 0.1379 |
| 3.5 | 0.3392 | 0.2752 | 0.1988 | 0.1362 | | 0 |
| Parameter | Values |
| --- | --- |
| k | 5,25,50,75,100 |
| β | 0.1,0.2,0.3,0.4,0.5 | | | N | 100K,250K,500K,750K,1,000K |
| --- | --- |
| L | 1K,2.5K,5K,7.5K,10K |
| \|U\| | 1M,2M,3M,4M,5M | | 1 |
| Parameter | Values |
| --- | --- |
| k | 5,25,50,75,100 |
| β | 0.1,0.2,0.3,0.4,0.5 | | | 2.5 | 0.3392 | 0.2863 | 0.2036 | 0.1379 |
| --- | --- | --- | --- | --- |
| 3.5 | 0.3392 | 0.2752 | 0.1988 | 0.1362 | | 0 |
| | fc6size | Totalsize | Ratio | Method | Error |
| --- | --- | --- | --- | --- | --- |
| default | 150,994,944 | 177,546,176 | NA | NA | 18.21 |
| k=5 | 204,805 | 26,756,037 | 6.64 | Lossy | 20.67 |
| Lossless | 19.23 | | | | |
| k=10 | 409,610 | 26,960,842 | 6.59 | Lossy | 19.25 | | | Lossless | 18.87 | | | | |
| --- | --- | --- | --- | --- | --- |
| k=50 | 2,048,050 | 28,599,282 | 6.21 | Lossy | 19.04 |
| Lossless | 18.67 | | | | |
| k=100 | 4,096,100 | 30,647,332 | 5.79 | Lossy | 18.68 |
| Lossless | 18.21 | | | | | | 1 |
| | fc6size | Totalsize | Ratio | Method | Error |
| --- | --- | --- | --- | --- | --- |
| default | 150,994,944 | 177,546,176 | NA | NA | 18.21 |
| k=5 | 204,805 | 26,756,037 | 6.64 | Lossy | 20.67 |
| Lossless | 19.23 | | | | |
| k=10 | 409,610 | 26,960,842 | 6.59 | Lossy | 19.25 | | | Method | Top1 | Top2 | Top5 | Top10 |
| --- | --- | --- | --- | --- |
| Tebessa-C* | 93.1 | 97.0 | 99.5 | 99.5 |
| Delta-nHinge | 93.4 | NA | NA | 98.4 |
| CS-UMD-a* | 95.1 | 97.7 | 98.6 | 99.1 |
| MultiFeature* | 99.2 | 99.6 | 99.8 | 99.8 |
| SRS-LBPl1out8,4 | 96.6 | 98.0 | 99.6 | 99.8 |
| SRS-LBPmetric* | 96.6 | 97.8 | 98.8 | 99.4 |
| SRS-LBPl1out | 98.4 | 99.2 | 99.4 | 99.8 | | 0 |
| | fc6size | Totalsize | Ratio | Method | Error |
| --- | --- | --- | --- | --- | --- |
| default | 150,994,944 | 177,546,176 | NA | NA | 18.21 |
| k=5 | 204,805 | 26,756,037 | 6.64 | Lossy | 20.67 |
| Lossless | 19.23 | | | | |
| k=10 | 409,610 | 26,960,842 | 6.59 | Lossy | 19.25 |
| Lossless | 18.87 | | | | | | | k=50 | 2,048,050 | 28,599,282 | 6.21 | Lossy | 19.04 |
| --- | --- | --- | --- | --- | --- |
| Lossless | 18.67 | | | | |
| k=100 | 4,096,100 | 30,647,332 | 5.79 | Lossy | 18.68 |
| Lossless | 18.21 | | | | | | 1 |
| | fc6size | Totalsize | Ratio | Method | Error |
| --- | --- | --- | --- | --- | --- |
| default | 150,994,944 | 177,546,176 | NA | NA | 18.21 |
| k=5 | 204,805 | 26,756,037 | 6.64 | Lossy | 20.67 |
| Lossless | 19.23 | | | | |
| k=10 | 409,610 | 26,960,842 | 6.59 | Lossy | 19.25 |
| Lossless | 18.87 | | | | | | | MultiFeature* | 99.2 | 99.6 | 99.8 | 99.8 |
| --- | --- | --- | --- | --- |
| SRS-LBPl1out8,4 | 96.6 | 98.0 | 99.6 | 99.8 |
| SRS-LBPmetric* | 96.6 | 97.8 | 98.8 | 99.4 |
| SRS-LBPl1out | 98.4 | 99.2 | 99.4 | 99.8 | | 0 |
| p | n | k | Upperboundon∆inf | Explicit∆(newresult)f |
| --- | --- | --- | --- | --- |
| 5 | 3 | 2 | 12 | 6 |
| 5 | 5 | 2 | 12 | 6 | | | 5 | 5 | 4 | 24 | 6 |
| --- | --- | --- | --- | --- |
| 7 | 3 | 2 | 24 | 8 |
| 7 | 5 | 2 | 24 | 8 |
| 7 | 5 | 4 | 48 | 8 |
| 7 | 7 | 2 | 24 | 8 |
| 7 | 7 | 4 | 48 | 8 |
| 7 | 7 | 6 | 24 | 8 |
| 11 | 3 | 2 | 60 | 12 | | 1 |
| p | n | k | Upperboundon∆inf | Explicit∆(newresult)f |
| --- | --- | --- | --- | --- |
| 5 | 3 | 2 | 12 | 6 |
| 5 | 5 | 2 | 12 | 6 | | | 16 | 11 | 10 | 16 | 24 | 40 | 51 | 61 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 12 | 12 | 14 | 19 | 26 | 58 | 60 | 55 |
| 14 | 13 | 16 | 24 | 40 | 57 | 69 | 56 |
| 14 | 17 | 22 | 29 | 51 | 87 | 80 | 62 |
| 18 | 22 | 37 | 56 | 68 | 109 | 103 | 77 |
| 24 | 35 | 55 | 64 | 81 | 104 | 113 | 92 |
| 49 | 64 | 78 | 87 | 103 | 121 | 120 | 101 |
| 72 | 92 | 95 | 98 | 112 | 100 | 103 | 99 | | 0 |
| p | n | k | Upperboundon∆inf | Explicit∆(newresult)f |
| --- | --- | --- | --- | --- |
| 5 | 3 | 2 | 12 | 6 |
| 5 | 5 | 2 | 12 | 6 |
| 5 | 5 | 4 | 24 | 6 |
| 7 | 3 | 2 | 24 | 8 | | | 7 | 5 | 2 | 24 | 8 |
| --- | --- | --- | --- | --- |
| 7 | 5 | 4 | 48 | 8 |
| 7 | 7 | 2 | 24 | 8 |
| 7 | 7 | 4 | 48 | 8 |
| 7 | 7 | 6 | 24 | 8 |
| 11 | 3 | 2 | 60 | 12 | | 1 |
| p | n | k | Upperboundon∆inf | Explicit∆(newresult)f |
| --- | --- | --- | --- | --- |
| 5 | 3 | 2 | 12 | 6 |
| 5 | 5 | 2 | 12 | 6 |
| 5 | 5 | 4 | 24 | 6 |
| 7 | 3 | 2 | 24 | 8 | | | 24 | 35 | 55 | 64 | 81 | 104 | 113 | 92 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 49 | 64 | 78 | 87 | 103 | 121 | 120 | 101 |
| 72 | 92 | 95 | 98 | 112 | 100 | 103 | 99 | | 0 |
| Steps | Time(sec) |
| --- | --- |
| Precomputation | 7 | | | NUFFTandFourier-BesselExpansion | 1,438 |
| --- | --- |
| SteerablePCA | 42 |
| Total | 1487 | | 1 |
| Steps | Time(sec) |
| --- | --- |
| Precomputation | 7 | | | Step | Time(sec) |
| --- | --- |
| Fourier-BesselsPCA | 537.7 |
| RotationallyInvariantFeatures | 28.2 |
| InitialNearestNeighborSearch | 13.9 |
| VDMClassification | 57.4 |
| LocalAlignmentandClassAverage | 1081 |
| Total | 1718.3(28.6min) | | 0 |
| Steps | Time(sec) |
| --- | --- |
| Precomputation | 7 | | | NUFFTandFourier-BesselExpansion | 1,438 |
| --- | --- |
| SteerablePCA | 42 |
| Total | 1487 | | 1 |
| Steps | Time(sec) |
| --- | --- |
| Precomputation | 7 | | | LocalAlignmentandClassAverage | 1081 |
| --- | --- |
| Total | 1718.3(28.6min) | | 0 |
| | ECO | MDNet | BACF | ADNet | STECF |
| --- | --- | --- | --- | --- | --- |
| ALL | 0.3599 | 0.3389 | 0.3004 | 0.2544 | 0.5104 |
| SC | 0.7756 | 0.6899 | 0.7021 | 0.6439 | 0.7282 | | | RT | 0.0895 | 0.0893 | 0.0610 | 0.0733 | 0.7075 |
| --- | --- | --- | --- | --- | --- |
| PD | 0.2359 | 0.2614 | 0.1961 | 0.1832 | 0.2784 |
| MB | 0.1540 | 0.1412 | 0.1155 | 0.0829 | 0.1748 |
| OCC | 0.5610 | 0.5307 | 0.4695 | 0.4723 | 0.6991 |
| OV | 0.5421 | 0.5021 | 0.4520 | 0.2158 | 0.5743 |
| UC | 0.1610 | 0.1576 | 0.1064 | 0.1095 | 0.4105 | | 1 |
| | ECO | MDNet | BACF | ADNet | STECF |
| --- | --- | --- | --- | --- | --- |
| ALL | 0.3599 | 0.3389 | 0.3004 | 0.2544 | 0.5104 |
| SC | 0.7756 | 0.6899 | 0.7021 | 0.6439 | 0.7282 | | | LPC-EGEE | PIK-IPLEX | SHARCNET-Whale | RICC | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Avg | St.dev. | Avg | St.dev. | Avg | St.dev. | Avg | St.dev. | |
| | 4511 | 6257 | 242 | 1420 | 404 | 1221 | 10850 | 13773 |
| (N=15) | 562 | 1670 | 1.3 | 7 | 26 | 158 | 771 | 1479 |
| | 410 | 1083 | 0.2 | 1.4 | 60 | 204 | 1808 | 3397 |
| | 575 | 1404 | 2.3 | 12 | 94 | 307 | 2746 | 4070 |
| | 888 | 2101 | 1.2 | 5 | 120 | 344 | 4963 | 6080 |
| | 1082 | 2091 | 2.2 | 11 | 180 | 805 | 5387 | 9083 | | 0 |
| | ECO | MDNet | BACF | ADNet | STECF |
| --- | --- | --- | --- | --- | --- |
| ALL | 0.3599 | 0.3389 | 0.3004 | 0.2544 | 0.5104 |
| SC | 0.7756 | 0.6899 | 0.7021 | 0.6439 | 0.7282 |
| RT | 0.0895 | 0.0893 | 0.0610 | 0.0733 | 0.7075 | | | PD | 0.2359 | 0.2614 | 0.1961 | 0.1832 | 0.2784 |
| --- | --- | --- | --- | --- | --- |
| MB | 0.1540 | 0.1412 | 0.1155 | 0.0829 | 0.1748 |
| OCC | 0.5610 | 0.5307 | 0.4695 | 0.4723 | 0.6991 |
| OV | 0.5421 | 0.5021 | 0.4520 | 0.2158 | 0.5743 |
| UC | 0.1610 | 0.1576 | 0.1064 | 0.1095 | 0.4105 | | 1 |
| | ECO | MDNet | BACF | ADNet | STECF |
| --- | --- | --- | --- | --- | --- |
| ALL | 0.3599 | 0.3389 | 0.3004 | 0.2544 | 0.5104 |
| SC | 0.7756 | 0.6899 | 0.7021 | 0.6439 | 0.7282 |
| RT | 0.0895 | 0.0893 | 0.0610 | 0.0733 | 0.7075 | | | | 4511 | 6257 | 242 | 1420 | 404 | 1221 | 10850 | 13773 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| (N=15) | 562 | 1670 | 1.3 | 7 | 26 | 158 | 771 | 1479 |
| | 410 | 1083 | 0.2 | 1.4 | 60 | 204 | 1808 | 3397 |
| | 575 | 1404 | 2.3 | 12 | 94 | 307 | 2746 | 4070 |
| | 888 | 2101 | 1.2 | 5 | 120 | 344 | 4963 | 6080 |
| | 1082 | 2091 | 2.2 | 11 | 180 | 805 | 5387 | 9083 | | 0 |
| name | AB | FDCS | ABCS | FINDER | FMC | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | #bcktr | #bcktr | #Tbcktr | #bcktr | #Tbcktr | #bcktr | #bcktr | | | oddeven<br>wickedoe | 4<br>64 | 0<br>0 | 0<br>0 | 0<br>0 | 0<br>0 | 1<br>8 | 3<br>52 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| appendlast<br>reverselast<br>nreverselast<br>schedule | 43<br>30<br>190170<br>24 | 24<br>68<br>?<br>13 | 1<br>2<br>?<br>1 | 55<br>303<br>?<br>106 | 1<br>2<br>?<br>1 | 618<br>614<br>7<br>>5.10<br>37 | 110019<br>23445<br>?<br>497 |
| multiseto<br>multisetl | 10<br>3 | 7<br>6 | 0<br>1 | 30<br>21 | 0<br>1 | 0<br>12 | 104<br>469 |
| blockpair2o<br>blockpair3o<br>blockpair2l<br>blockpair3l<br>blocksol | 17<br>56<br>28<br>130<br>3615 | 25<br>25<br>3943<br>4009<br>1396146 | 0<br>0<br>2<br>2<br>385 | 49<br>51<br>2733<br>2737<br>1970544 | 0<br>0<br>2<br>2<br>169 | 262<br>879<br>68<br>366<br>4007523 | 5567<br>?<br>91404<br>?<br>? |
| BOO019-1 | 72 | 4 | 0 | 34 | 0 | 14 | 33 | | 1 |
| name | AB | FDCS | ABCS | FINDER | FMC | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | #bcktr | #bcktr | #Tbcktr | #bcktr | #Tbcktr | #bcktr | #bcktr | | | | | | | | |
| --- | --- | --- | --- | --- | --- |
| frb30-15-1 | t<br>c<br>r<br>n | 22,3<br>16,5M<br>105626<br>3863 | 20,9<br>11,1M<br>70924<br>3858 | 29,3<br>16,4M<br>102724<br>4138 | 14,1<br>7,5M<br>46727<br>2499 |
| frb30-15-2 | t<br>c<br>r<br>n | 84,9<br>45,7M<br>311040<br>15457 | 29,7<br>21,8M<br>149119<br>7935 | 118,9<br>90M<br>624360<br>25148 | 95<br>68,9M<br>472124<br>24467 |
| frb35-17-1 | t<br>c<br>r<br>n | 125,8<br>93,9M<br>533694<br>18587 | 193,7<br>144M<br>836462<br>40698 | 118<br>89,7M<br>514258<br>19167 | 250,9<br>180,9M<br>1,03M<br>50611 |
| rand-2-30-15 | t<br>c<br>r<br>n | 1240<br>114,5M<br>922251<br>28725 | 74,4<br>53M<br>443792<br>19846 | 98<br>72,5M<br>602582<br>20192 | 108,1<br>78,1M<br>642665<br>28766 |
| geo50-20-d4-75-2 | t<br>c<br>r<br>n | 226,1<br>191,8M<br>778758<br>20069 | 401,8<br>310,3M<br>1,3M<br>60182 | 34,8<br>28,2M<br>117241<br>3735 | 39,5<br>28,8M<br>124163<br>5484 | | 0 |
| name | AB | FDCS | ABCS | FINDER | FMC | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | #bcktr | #bcktr | #Tbcktr | #bcktr | #Tbcktr | #bcktr | #bcktr |
| oddeven<br>wickedoe | 4<br>64 | 0<br>0 | 0<br>0 | 0<br>0 | 0<br>0 | 1<br>8 | 3<br>52 | | | appendlast<br>reverselast<br>nreverselast<br>schedule | 43<br>30<br>190170<br>24 | 24<br>68<br>?<br>13 | 1<br>2<br>?<br>1 | 55<br>303<br>?<br>106 | 1<br>2<br>?<br>1 | 618<br>614<br>7<br>>5.10<br>37 | 110019<br>23445<br>?<br>497 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| multiseto<br>multisetl | 10<br>3 | 7<br>6 | 0<br>1 | 30<br>21 | 0<br>1 | 0<br>12 | 104<br>469 |
| blockpair2o<br>blockpair3o<br>blockpair2l<br>blockpair3l<br>blocksol | 17<br>56<br>28<br>130<br>3615 | 25<br>25<br>3943<br>4009<br>1396146 | 0<br>0<br>2<br>2<br>385 | 49<br>51<br>2733<br>2737<br>1970544 | 0<br>0<br>2<br>2<br>169 | 262<br>879<br>68<br>366<br>4007523 | 5567<br>?<br>91404<br>?<br>? |
| BOO019-1 | 72 | 4 | 0 | 34 | 0 | 14 | 33 | | 1 |
| name | AB | FDCS | ABCS | FINDER | FMC | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | #bcktr | #bcktr | #Tbcktr | #bcktr | #Tbcktr | #bcktr | #bcktr |
| oddeven<br>wickedoe | 4<br>64 | 0<br>0 | 0<br>0 | 0<br>0 | 0<br>0 | 1<br>8 | 3<br>52 | | | frb35-17-1 | t<br>c<br>r<br>n | 125,8<br>93,9M<br>533694<br>18587 | 193,7<br>144M<br>836462<br>40698 | 118<br>89,7M<br>514258<br>19167 | 250,9<br>180,9M<br>1,03M<br>50611 |
| --- | --- | --- | --- | --- | --- |
| rand-2-30-15 | t<br>c<br>r<br>n | 1240<br>114,5M<br>922251<br>28725 | 74,4<br>53M<br>443792<br>19846 | 98<br>72,5M<br>602582<br>20192 | 108,1<br>78,1M<br>642665<br>28766 |
| geo50-20-d4-75-2 | t<br>c<br>r<br>n | 226,1<br>191,8M<br>778758<br>20069 | 401,8<br>310,3M<br>1,3M<br>60182 | 34,8<br>28,2M<br>117241<br>3735 | 39,5<br>28,8M<br>124163<br>5484 | | 0 |
| | MetaSDNet-01 | pyMDNet-30 | pyMDNet-15 | pyMDNet-10 | pyMDNet-05 | pyMDNet-03 | pyMDNet-01 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| bag | 0 | 0.07 | 0 | 0 | 0.07 | 0.13 | 0.2 |
| ball1 | 0.4 | 0.07 | 0.13 | 0 | 0.2 | 0.33 | 1.6 |
| ball2 | 0.67 | 2.27 | 2.33 | 2.67 | 2.67 | 3.07 | 3.33 |
| basketball | 1.27 | 1.07 | 1.27 | 1.33 | 1.8 | 3 | 5.73 |
| birds1 | 0.47 | 2.2 | 1.73 | 1.47 | 1.33 | 2.33 | 3.13 |
| birds2 | 0.4 | 0.27 | 0.4 | 0.13 | 0.13 | 0.47 | 0.6 |
| blanket | 0 | 0 | 0 | 0 | 0.13 | 0.33 | 1 |
| bmx | 0.87 | 0.13 | 0 | 0 | 0.13 | 0.2 | 0.47 | | | bolt1 | 0.13 | 0 | 0.07 | 0.13 | 0.67 | 1.4 | 2.27 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| bolt2 | 0 | 0.27 | 0.73 | 0.6 | 0.4 | 1.4 | 1.87 |
| book | 3.4 | 3.53 | 3.27 | 3.53 | 4.73 | 4.47 | 7.4 |
| butterfly | 0.13 | 0 | 0.13 | 0.13 | 0.6 | 0.8 | 1.6 |
| car1 | 1.73 | 2.27 | 2.33 | 1.87 | 2.07 | 2.93 | 2.87 |
| car2 | 0 | 0 | 0 | 0 | 0.2 | 0.53 | 1 |
| crossing | 0 | 0.07 | 0.07 | 0.07 | 0.07 | 0.13 | 0.47 |
| dinosaur | 0.6 | 0 | 0 | 0 | 0.47 | 3.6 | 4.67 |
| fernando | 0.67 | 0.67 | 1.33 | 0.73 | 0.93 | 1.4 | 3.47 |
| fish1 | 2.73 | 2.8 | 2.67 | 2.8 | 3.13 | 3.2 | 4.47 |
| fish2 | 2.4 | 3 | 2.73 | 3 | 3.2 | 4.8 | 7.2 |
| fish3 | 0.27 | 0.87 | 0.67 | 0.73 | 0.73 | 0.87 | 0.8 |
| fish4 | 0.67 | 0.13 | 0.4 | 0.6 | 0.8 | 1.47 | 1.2 |
| girl | 0.07 | 0.07 | 0.13 | 0 | 0 | 0.33 | 1.53 |
| glove | 2.8 | 2.47 | 2.4 | 2.73 | 3.13 | 3.87 | 4.67 |
| godfather | 0 | 0 | 0 | 0 | 0.47 | 0.47 | 1.47 |
| graduate | 0 | 0.13 | 0.07 | 0.07 | 0.2 | 0.93 | 1.8 |
| gymnastics1 | 1.53 | 0.67 | 0.6 | 0.87 | 0.73 | 1.13 | 2.93 |
| gymnastics2 | 1.87 | 1.4 | 1.73 | 1.67 | 1.53 | 3.07 | 4.27 |
| gymnastics3 | 1.2 | 1.07 | 1.4 | 1.4 | 1.4 | 2.2 | 3.93 |
| gymnastics4 | 0.13 | 0 | 0 | 0.07 | 0.13 | 0.33 | 1.33 |
| hand | 2.53 | 0.8 | 1.53 | 0.93 | 1.2 | 4.07 | 6.6 |
| handball1 | 0.93 | 0.6 | 0.93 | 0.73 | 1.93 | 2.13 | 3 |
| handball2 | 1.93 | 2.27 | 2.47 | 2.67 | 2.73 | 3.67 | 7.53 |
| helicopter | 1 | 0.27 | 0.33 | 0.4 | 0.27 | 0.2 | 0.87 |
| iceskater1 | 0.07 | 0.27 | 0.2 | 0.07 | 0.4 | 0.2 | 1.6 |
| iceskater2 | 0.47 | 0 | 0.07 | 0.4 | 1.2 | 2.93 | 7.8 |
| leaves | 4.4 | 4.4 | 4.67 | 4.73 | 4.2 | 4.27 | 4.67 |
| marching | 0 | 0.13 | 0.2 | 0.33 | 1.27 | 1.6 | 2.27 |
| matrix | 1.8 | 1.53 | 1.4 | 1.4 | 1.87 | 1.6 | 3.53 |
| motocross1 | 0.13 | 0 | 0 | 0 | 0 | 0.13 | 2.07 |
| motocross2 | 0.07 | 0 | 0 | 0 | 0.07 | 0.73 | 0.93 |
| nature | 2.47 | 2.4 | 2.2 | 1.8 | 2.73 | 3.53 | 4.8 |
| octopus | 0 | 0 | 0 | 0 | 0 | 0.07 | 0.27 |
| pedestrian1 | 1.2 | 1.07 | 1 | 1.27 | 1.07 | 1.47 | 2.67 |
| pedestrian2 | 0 | 0 | 0.13 | 0 | 0.07 | 0.13 | 0.67 |
| rabbit | 3.2 | 4.27 | 4.67 | 5.07 | 4.53 | 5.27 | 6.53 |
| racing | 0 | 0 | 0 | 0 | 0.13 | 0.73 | 1.07 |
| road | 0 | 0 | 0 | 0 | 0 | 0.07 | 0.4 |
| shaking | 0.07 | 0.07 | 0.07 | 0.13 | 0.33 | 0.33 | 0.53 |
| sheep | 0.07 | 0 | 0 | 0.27 | 0.53 | 0.73 | 0.87 |
| singer1 | 0 | 0 | 0 | 0 | 0 | 0.2 | 0.8 |
| singer2 | 0.73 | 0.33 | 0.2 | 0.2 | 0.47 | 1.07 | 3.27 |
| singer3 | 0.4 | 0.33 | 0.53 | 0.27 | 0.33 | 0.53 | 1 |
| soccer1 | 0.73 | 1.47 | 0.93 | 0.8 | 1.47 | 1.27 | 1.73 |
| soccer2 | 7.4 | 10.4 | 9.8 | 10.53 | 11.87 | 12.6 | 12.8 |
| soldier | 0.93 | 0.07 | 0.2 | 0.47 | 0.53 | 0.93 | 2 |
| sphere | 0 | 0 | 0 | 0 | 0.07 | 0.4 | 0.87 |
| tiger | 0.6 | 0.07 | 0 | 0 | 0.2 | 0.47 | 2.47 |
| traffic | 0.07 | 0.07 | 0.13 | 0 | 0 | 0.33 | 0.87 |
| tunnel | 0 | 0 | 0 | 0 | 0 | 0.8 | 1.2 |
| wiper | 0.47 | 0.33 | 0.33 | 0.27 | 0.33 | 0.53 | 1.07 |
| mean | 0.93 | 0.94 | 0.98 | 0.99 | 1.2 | 1.7 | 2.73 |
| weightedmean | 0.78 | 0.74 | 0.77 | 0.74 | 0.97 | 1.51 | 2.62 | | 1 |
| | MetaSDNet-01 | pyMDNet-30 | pyMDNet-15 | pyMDNet-10 | pyMDNet-05 | pyMDNet-03 | pyMDNet-01 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| bag | 0 | 0.07 | 0 | 0 | 0.07 | 0.13 | 0.2 |
| ball1 | 0.4 | 0.07 | 0.13 | 0 | 0.2 | 0.33 | 1.6 |
| ball2 | 0.67 | 2.27 | 2.33 | 2.67 | 2.67 | 3.07 | 3.33 |
| basketball | 1.27 | 1.07 | 1.27 | 1.33 | 1.8 | 3 | 5.73 |
| birds1 | 0.47 | 2.2 | 1.73 | 1.47 | 1.33 | 2.33 | 3.13 |
| birds2 | 0.4 | 0.27 | 0.4 | 0.13 | 0.13 | 0.47 | 0.6 |
| blanket | 0 | 0 | 0 | 0 | 0.13 | 0.33 | 1 |
| bmx | 0.87 | 0.13 | 0 | 0 | 0.13 | 0.2 | 0.47 | | | | MetaSDNet-01 | pyMDNet-30 | pyMDNet-15 | pyMDNet-10 | pyMDNet-05 | pyMDNet-03 | pyMDNet-01 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| bag | 0.49 | 0.52 | 0.51 | 0.49 | 0.47 | 0.43 | 0.38 |
| ball1 | 0.77 | 0.79 | 0.78 | 0.79 | 0.78 | 0.77 | 0.75 |
| ball2 | 0.33 | 0.46 | 0.47 | 0.38 | 0.46 | 0.45 | 0.5 |
| basketball | 0.62 | 0.62 | 0.63 | 0.61 | 0.63 | 0.57 | 0.53 |
| birds1 | 0.51 | 0.48 | 0.5 | 0.48 | 0.49 | 0.47 | 0.45 |
| birds2 | 0.35 | 0.33 | 0.34 | 0.35 | 0.34 | 0.31 | 0.33 |
| blanket | 0.6 | 0.58 | 0.56 | 0.56 | 0.51 | 0.54 | 0.49 |
| bmx | 0.19 | 0.41 | 0.43 | 0.43 | 0.37 | 0.4 | 0.35 |
| bolt1 | 0.51 | 0.52 | 0.53 | 0.54 | 0.58 | 0.6 | 0.56 |
| bolt2 | 0.54 | 0.55 | 0.57 | 0.57 | 0.57 | 0.58 | 0.42 |
| book | 0.45 | 0.46 | 0.44 | 0.4 | 0.38 | 0.31 | 0.33 |
| butterfly | 0.36 | 0.29 | 0.34 | 0.3 | 0.39 | 0.38 | 0.29 |
| car1 | 0.75 | 0.77 | 0.77 | 0.77 | 0.76 | 0.75 | 0.61 |
| car2 | 0.71 | 0.75 | 0.75 | 0.75 | 0.72 | 0.7 | 0.5 |
| crossing | 0.67 | 0.69 | 0.67 | 0.67 | 0.65 | 0.67 | 0.56 |
| dinosaur | 0.57 | 0.62 | 0.61 | 0.62 | 0.58 | 0.36 | 0.42 |
| fernando | 0.45 | 0.43 | 0.43 | 0.44 | 0.42 | 0.38 | 0.33 |
| fish1 | 0.46 | 0.44 | 0.44 | 0.43 | 0.43 | 0.42 | 0.37 |
| fish2 | 0.32 | 0.32 | 0.33 | 0.32 | 0.31 | 0.29 | 0.24 |
| fish3 | 0.6 | 0.64 | 0.65 | 0.63 | 0.6 | 0.54 | 0.38 |
| fish4 | 0.36 | 0.37 | 0.38 | 0.41 | 0.32 | 0.33 | 0.32 |
| girl | 0.69 | 0.69 | 0.67 | 0.7 | 0.69 | 0.67 | 0.6 |
| glove | 0.49 | 0.52 | 0.49 | 0.5 | 0.48 | 0.38 | 0.38 |
| godfather | 0.46 | 0.45 | 0.45 | 0.45 | 0.44 | 0.4 | 0.4 |
| graduate | 0.51 | 0.53 | 0.52 | 0.52 | 0.49 | 0.43 | 0.4 |
| gymnastics1 | 0.43 | 0.51 | 0.53 | 0.42 | 0.51 | 0.42 | 0.43 |
| gymnastics2 | 0.44 | 0.49 | 0.49 | 0.46 | 0.45 | 0.46 | 0.42 |
| gymnastics3 | 0.25 | 0.25 | 0.26 | 0.25 | 0.26 | 0.26 | 0.23 |
| gymnastics4 | 0.46 | 0.48 | 0.49 | 0.47 | 0.45 | 0.43 | 0.36 |
| hand | 0.51 | 0.5 | 0.5 | 0.5 | 0.51 | 0.49 | 0.45 |
| handball1 | 0.55 | 0.58 | 0.58 | 0.58 | 0.55 | 0.55 | 0.52 |
| handball2 | 0.55 | 0.57 | 0.56 | 0.56 | 0.55 | 0.53 | 0.51 |
| helicopter | 0.57 | 0.49 | 0.5 | 0.49 | 0.44 | 0.4 | 0.37 |
| iceskater1 | 0.53 | 0.54 | 0.52 | 0.54 | 0.53 | 0.5 | 0.49 |
| iceskater2 | 0.55 | 0.55 | 0.54 | 0.53 | 0.48 | 0.49 | 0.42 |
| leaves | 0.29 | 0.32 | 0.32 | 0.4 | 0.31 | 0.28 | 0.28 |
| marching | 0.72 | 0.72 | 0.7 | 0.71 | 0.59 | 0.52 | 0.43 |
| matrix | 0.5 | 0.54 | 0.55 | 0.54 | 0.54 | 0.56 | 0.55 |
| motocross1 | 0.47 | 0.48 | 0.48 | 0.47 | 0.47 | 0.46 | 0.39 |
| motocross2 | 0.49 | 0.58 | 0.58 | 0.59 | 0.56 | 0.45 | 0.42 |
| nature | 0.49 | 0.44 | 0.43 | 0.39 | 0.43 | 0.4 | 0.42 |
| octopus | 0.57 | 0.59 | 0.58 | 0.58 | 0.59 | 0.56 | 0.47 |
| pedestrian1 | 0.71 | 0.71 | 0.71 | 0.71 | 0.66 | 0.61 | 0.65 |
| pedestrian2 | 0.37 | 0.44 | 0.48 | 0.51 | 0.45 | 0.5 | 0.4 |
| rabbit | 0.3 | 0.33 | 0.3 | 0.28 | 0.33 | 0.27 | 0.25 |
| racing | 0.48 | 0.45 | 0.45 | 0.45 | 0.43 | 0.41 | 0.34 |
| road | 0.45 | 0.47 | 0.47 | 0.45 | 0.48 | 0.47 | 0.45 |
| shaking | 0.6 | 0.58 | 0.54 | 0.58 | 0.59 | 0.59 | 0.57 |
| sheep | 0.53 | 0.54 | 0.53 | 0.53 | 0.48 | 0.45 | 0.47 |
| singer1 | 0.58 | 0.57 | 0.6 | 0.56 | 0.58 | 0.59 | 0.5 |
| singer2 | 0.59 | 0.64 | 0.64 | 0.65 | 0.64 | 0.57 | 0.48 |
| singer3 | 0.28 | 0.26 | 0.27 | 0.26 | 0.28 | 0.28 | 0.26 |
| soccer1 | 0.53 | 0.57 | 0.59 | 0.58 | 0.57 | 0.54 | 0.56 |
| soccer2 | 0.59 | 0.62 | 0.58 | 0.59 | 0.62 | 0.53 | 0.49 |
| soldier | 0.45 | 0.52 | 0.51 | 0.52 | 0.48 | 0.46 | 0.34 |
| sphere | 0.5 | 0.55 | 0.54 | 0.54 | 0.55 | 0.56 | 0.52 |
| tiger | 0.68 | 0.66 | 0.67 | 0.65 | 0.62 | 0.59 | 0.57 |
| traffic | 0.78 | 0.8 | 0.79 | 0.8 | 0.77 | 0.58 | 0.54 |
| tunnel | 0.84 | 0.84 | 0.83 | 0.84 | 0.84 | 0.74 | 0.39 |
| wiper | 0.71 | 0.7 | 0.7 | 0.7 | 0.68 | 0.6 | 0.44 |
| mean | 0.52 | 0.54 | 0.53 | 0.53 | 0.52 | 0.49 | 0.44 |
| weightedmean | 0.54 | 0.55 | 0.55 | 0.54 | 0.53 | 0.5 | 0.45 | | 0 |
| | MetaSDNet-01 | pyMDNet-30 | pyMDNet-15 | pyMDNet-10 | pyMDNet-05 | pyMDNet-03 | pyMDNet-01 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| bag | 0 | 0.07 | 0 | 0 | 0.07 | 0.13 | 0.2 |
| ball1 | 0.4 | 0.07 | 0.13 | 0 | 0.2 | 0.33 | 1.6 |
| ball2 | 0.67 | 2.27 | 2.33 | 2.67 | 2.67 | 3.07 | 3.33 |
| basketball | 1.27 | 1.07 | 1.27 | 1.33 | 1.8 | 3 | 5.73 |
| birds1 | 0.47 | 2.2 | 1.73 | 1.47 | 1.33 | 2.33 | 3.13 |
| birds2 | 0.4 | 0.27 | 0.4 | 0.13 | 0.13 | 0.47 | 0.6 |
| blanket | 0 | 0 | 0 | 0 | 0.13 | 0.33 | 1 |
| bmx | 0.87 | 0.13 | 0 | 0 | 0.13 | 0.2 | 0.47 |
| bolt1 | 0.13 | 0 | 0.07 | 0.13 | 0.67 | 1.4 | 2.27 |
| bolt2 | 0 | 0.27 | 0.73 | 0.6 | 0.4 | 1.4 | 1.87 |
| book | 3.4 | 3.53 | 3.27 | 3.53 | 4.73 | 4.47 | 7.4 |
| butterfly | 0.13 | 0 | 0.13 | 0.13 | 0.6 | 0.8 | 1.6 |
| car1 | 1.73 | 2.27 | 2.33 | 1.87 | 2.07 | 2.93 | 2.87 |
| car2 | 0 | 0 | 0 | 0 | 0.2 | 0.53 | 1 |
| crossing | 0 | 0.07 | 0.07 | 0.07 | 0.07 | 0.13 | 0.47 |
| dinosaur | 0.6 | 0 | 0 | 0 | 0.47 | 3.6 | 4.67 |
| fernando | 0.67 | 0.67 | 1.33 | 0.73 | 0.93 | 1.4 | 3.47 |
| fish1 | 2.73 | 2.8 | 2.67 | 2.8 | 3.13 | 3.2 | 4.47 |
| fish2 | 2.4 | 3 | 2.73 | 3 | 3.2 | 4.8 | 7.2 |
| fish3 | 0.27 | 0.87 | 0.67 | 0.73 | 0.73 | 0.87 | 0.8 |
| fish4 | 0.67 | 0.13 | 0.4 | 0.6 | 0.8 | 1.47 | 1.2 |
| girl | 0.07 | 0.07 | 0.13 | 0 | 0 | 0.33 | 1.53 |
| glove | 2.8 | 2.47 | 2.4 | 2.73 | 3.13 | 3.87 | 4.67 |
| godfather | 0 | 0 | 0 | 0 | 0.47 | 0.47 | 1.47 |
| graduate | 0 | 0.13 | 0.07 | 0.07 | 0.2 | 0.93 | 1.8 |
| gymnastics1 | 1.53 | 0.67 | 0.6 | 0.87 | 0.73 | 1.13 | 2.93 |
| gymnastics2 | 1.87 | 1.4 | 1.73 | 1.67 | 1.53 | 3.07 | 4.27 |
| gymnastics3 | 1.2 | 1.07 | 1.4 | 1.4 | 1.4 | 2.2 | 3.93 |
| gymnastics4 | 0.13 | 0 | 0 | 0.07 | 0.13 | 0.33 | 1.33 |
| hand | 2.53 | 0.8 | 1.53 | 0.93 | 1.2 | 4.07 | 6.6 |
| handball1 | 0.93 | 0.6 | 0.93 | 0.73 | 1.93 | 2.13 | 3 |
| handball2 | 1.93 | 2.27 | 2.47 | 2.67 | 2.73 | 3.67 | 7.53 |
| helicopter | 1 | 0.27 | 0.33 | 0.4 | 0.27 | 0.2 | 0.87 |
| iceskater1 | 0.07 | 0.27 | 0.2 | 0.07 | 0.4 | 0.2 | 1.6 |
| iceskater2 | 0.47 | 0 | 0.07 | 0.4 | 1.2 | 2.93 | 7.8 |
| leaves | 4.4 | 4.4 | 4.67 | 4.73 | 4.2 | 4.27 | 4.67 |
| marching | 0 | 0.13 | 0.2 | 0.33 | 1.27 | 1.6 | 2.27 |
| matrix | 1.8 | 1.53 | 1.4 | 1.4 | 1.87 | 1.6 | 3.53 |
| motocross1 | 0.13 | 0 | 0 | 0 | 0 | 0.13 | 2.07 |
| motocross2 | 0.07 | 0 | 0 | 0 | 0.07 | 0.73 | 0.93 |
| nature | 2.47 | 2.4 | 2.2 | 1.8 | 2.73 | 3.53 | 4.8 |
| octopus | 0 | 0 | 0 | 0 | 0 | 0.07 | 0.27 |
| pedestrian1 | 1.2 | 1.07 | 1 | 1.27 | 1.07 | 1.47 | 2.67 |
| pedestrian2 | 0 | 0 | 0.13 | 0 | 0.07 | 0.13 | 0.67 |
| rabbit | 3.2 | 4.27 | 4.67 | 5.07 | 4.53 | 5.27 | 6.53 | | | racing | 0 | 0 | 0 | 0 | 0.13 | 0.73 | 1.07 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| road | 0 | 0 | 0 | 0 | 0 | 0.07 | 0.4 |
| shaking | 0.07 | 0.07 | 0.07 | 0.13 | 0.33 | 0.33 | 0.53 |
| sheep | 0.07 | 0 | 0 | 0.27 | 0.53 | 0.73 | 0.87 |
| singer1 | 0 | 0 | 0 | 0 | 0 | 0.2 | 0.8 |
| singer2 | 0.73 | 0.33 | 0.2 | 0.2 | 0.47 | 1.07 | 3.27 |
| singer3 | 0.4 | 0.33 | 0.53 | 0.27 | 0.33 | 0.53 | 1 |
| soccer1 | 0.73 | 1.47 | 0.93 | 0.8 | 1.47 | 1.27 | 1.73 |
| soccer2 | 7.4 | 10.4 | 9.8 | 10.53 | 11.87 | 12.6 | 12.8 |
| soldier | 0.93 | 0.07 | 0.2 | 0.47 | 0.53 | 0.93 | 2 |
| sphere | 0 | 0 | 0 | 0 | 0.07 | 0.4 | 0.87 |
| tiger | 0.6 | 0.07 | 0 | 0 | 0.2 | 0.47 | 2.47 |
| traffic | 0.07 | 0.07 | 0.13 | 0 | 0 | 0.33 | 0.87 |
| tunnel | 0 | 0 | 0 | 0 | 0 | 0.8 | 1.2 |
| wiper | 0.47 | 0.33 | 0.33 | 0.27 | 0.33 | 0.53 | 1.07 |
| mean | 0.93 | 0.94 | 0.98 | 0.99 | 1.2 | 1.7 | 2.73 |
| weightedmean | 0.78 | 0.74 | 0.77 | 0.74 | 0.97 | 1.51 | 2.62 | | 1 |
| | MetaSDNet-01 | pyMDNet-30 | pyMDNet-15 | pyMDNet-10 | pyMDNet-05 | pyMDNet-03 | pyMDNet-01 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| bag | 0 | 0.07 | 0 | 0 | 0.07 | 0.13 | 0.2 |
| ball1 | 0.4 | 0.07 | 0.13 | 0 | 0.2 | 0.33 | 1.6 |
| ball2 | 0.67 | 2.27 | 2.33 | 2.67 | 2.67 | 3.07 | 3.33 |
| basketball | 1.27 | 1.07 | 1.27 | 1.33 | 1.8 | 3 | 5.73 |
| birds1 | 0.47 | 2.2 | 1.73 | 1.47 | 1.33 | 2.33 | 3.13 |
| birds2 | 0.4 | 0.27 | 0.4 | 0.13 | 0.13 | 0.47 | 0.6 |
| blanket | 0 | 0 | 0 | 0 | 0.13 | 0.33 | 1 |
| bmx | 0.87 | 0.13 | 0 | 0 | 0.13 | 0.2 | 0.47 |
| bolt1 | 0.13 | 0 | 0.07 | 0.13 | 0.67 | 1.4 | 2.27 |
| bolt2 | 0 | 0.27 | 0.73 | 0.6 | 0.4 | 1.4 | 1.87 |
| book | 3.4 | 3.53 | 3.27 | 3.53 | 4.73 | 4.47 | 7.4 |
| butterfly | 0.13 | 0 | 0.13 | 0.13 | 0.6 | 0.8 | 1.6 |
| car1 | 1.73 | 2.27 | 2.33 | 1.87 | 2.07 | 2.93 | 2.87 |
| car2 | 0 | 0 | 0 | 0 | 0.2 | 0.53 | 1 |
| crossing | 0 | 0.07 | 0.07 | 0.07 | 0.07 | 0.13 | 0.47 |
| dinosaur | 0.6 | 0 | 0 | 0 | 0.47 | 3.6 | 4.67 |
| fernando | 0.67 | 0.67 | 1.33 | 0.73 | 0.93 | 1.4 | 3.47 |
| fish1 | 2.73 | 2.8 | 2.67 | 2.8 | 3.13 | 3.2 | 4.47 |
| fish2 | 2.4 | 3 | 2.73 | 3 | 3.2 | 4.8 | 7.2 |
| fish3 | 0.27 | 0.87 | 0.67 | 0.73 | 0.73 | 0.87 | 0.8 |
| fish4 | 0.67 | 0.13 | 0.4 | 0.6 | 0.8 | 1.47 | 1.2 |
| girl | 0.07 | 0.07 | 0.13 | 0 | 0 | 0.33 | 1.53 |
| glove | 2.8 | 2.47 | 2.4 | 2.73 | 3.13 | 3.87 | 4.67 |
| godfather | 0 | 0 | 0 | 0 | 0.47 | 0.47 | 1.47 |
| graduate | 0 | 0.13 | 0.07 | 0.07 | 0.2 | 0.93 | 1.8 |
| gymnastics1 | 1.53 | 0.67 | 0.6 | 0.87 | 0.73 | 1.13 | 2.93 |
| gymnastics2 | 1.87 | 1.4 | 1.73 | 1.67 | 1.53 | 3.07 | 4.27 |
| gymnastics3 | 1.2 | 1.07 | 1.4 | 1.4 | 1.4 | 2.2 | 3.93 |
| gymnastics4 | 0.13 | 0 | 0 | 0.07 | 0.13 | 0.33 | 1.33 |
| hand | 2.53 | 0.8 | 1.53 | 0.93 | 1.2 | 4.07 | 6.6 |
| handball1 | 0.93 | 0.6 | 0.93 | 0.73 | 1.93 | 2.13 | 3 |
| handball2 | 1.93 | 2.27 | 2.47 | 2.67 | 2.73 | 3.67 | 7.53 |
| helicopter | 1 | 0.27 | 0.33 | 0.4 | 0.27 | 0.2 | 0.87 |
| iceskater1 | 0.07 | 0.27 | 0.2 | 0.07 | 0.4 | 0.2 | 1.6 |
| iceskater2 | 0.47 | 0 | 0.07 | 0.4 | 1.2 | 2.93 | 7.8 |
| leaves | 4.4 | 4.4 | 4.67 | 4.73 | 4.2 | 4.27 | 4.67 |
| marching | 0 | 0.13 | 0.2 | 0.33 | 1.27 | 1.6 | 2.27 |
| matrix | 1.8 | 1.53 | 1.4 | 1.4 | 1.87 | 1.6 | 3.53 |
| motocross1 | 0.13 | 0 | 0 | 0 | 0 | 0.13 | 2.07 |
| motocross2 | 0.07 | 0 | 0 | 0 | 0.07 | 0.73 | 0.93 |
| nature | 2.47 | 2.4 | 2.2 | 1.8 | 2.73 | 3.53 | 4.8 |
| octopus | 0 | 0 | 0 | 0 | 0 | 0.07 | 0.27 |
| pedestrian1 | 1.2 | 1.07 | 1 | 1.27 | 1.07 | 1.47 | 2.67 |
| pedestrian2 | 0 | 0 | 0.13 | 0 | 0.07 | 0.13 | 0.67 |
| rabbit | 3.2 | 4.27 | 4.67 | 5.07 | 4.53 | 5.27 | 6.53 | | | book | 0.45 | 0.46 | 0.44 | 0.4 | 0.38 | 0.31 | 0.33 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| butterfly | 0.36 | 0.29 | 0.34 | 0.3 | 0.39 | 0.38 | 0.29 |
| car1 | 0.75 | 0.77 | 0.77 | 0.77 | 0.76 | 0.75 | 0.61 |
| car2 | 0.71 | 0.75 | 0.75 | 0.75 | 0.72 | 0.7 | 0.5 |
| crossing | 0.67 | 0.69 | 0.67 | 0.67 | 0.65 | 0.67 | 0.56 |
| dinosaur | 0.57 | 0.62 | 0.61 | 0.62 | 0.58 | 0.36 | 0.42 |
| fernando | 0.45 | 0.43 | 0.43 | 0.44 | 0.42 | 0.38 | 0.33 |
| fish1 | 0.46 | 0.44 | 0.44 | 0.43 | 0.43 | 0.42 | 0.37 |
| fish2 | 0.32 | 0.32 | 0.33 | 0.32 | 0.31 | 0.29 | 0.24 |
| fish3 | 0.6 | 0.64 | 0.65 | 0.63 | 0.6 | 0.54 | 0.38 |
| fish4 | 0.36 | 0.37 | 0.38 | 0.41 | 0.32 | 0.33 | 0.32 |
| girl | 0.69 | 0.69 | 0.67 | 0.7 | 0.69 | 0.67 | 0.6 |
| glove | 0.49 | 0.52 | 0.49 | 0.5 | 0.48 | 0.38 | 0.38 |
| godfather | 0.46 | 0.45 | 0.45 | 0.45 | 0.44 | 0.4 | 0.4 |
| graduate | 0.51 | 0.53 | 0.52 | 0.52 | 0.49 | 0.43 | 0.4 |
| gymnastics1 | 0.43 | 0.51 | 0.53 | 0.42 | 0.51 | 0.42 | 0.43 |
| gymnastics2 | 0.44 | 0.49 | 0.49 | 0.46 | 0.45 | 0.46 | 0.42 |
| gymnastics3 | 0.25 | 0.25 | 0.26 | 0.25 | 0.26 | 0.26 | 0.23 |
| gymnastics4 | 0.46 | 0.48 | 0.49 | 0.47 | 0.45 | 0.43 | 0.36 |
| hand | 0.51 | 0.5 | 0.5 | 0.5 | 0.51 | 0.49 | 0.45 |
| handball1 | 0.55 | 0.58 | 0.58 | 0.58 | 0.55 | 0.55 | 0.52 |
| handball2 | 0.55 | 0.57 | 0.56 | 0.56 | 0.55 | 0.53 | 0.51 |
| helicopter | 0.57 | 0.49 | 0.5 | 0.49 | 0.44 | 0.4 | 0.37 |
| iceskater1 | 0.53 | 0.54 | 0.52 | 0.54 | 0.53 | 0.5 | 0.49 |
| iceskater2 | 0.55 | 0.55 | 0.54 | 0.53 | 0.48 | 0.49 | 0.42 |
| leaves | 0.29 | 0.32 | 0.32 | 0.4 | 0.31 | 0.28 | 0.28 |
| marching | 0.72 | 0.72 | 0.7 | 0.71 | 0.59 | 0.52 | 0.43 |
| matrix | 0.5 | 0.54 | 0.55 | 0.54 | 0.54 | 0.56 | 0.55 |
| motocross1 | 0.47 | 0.48 | 0.48 | 0.47 | 0.47 | 0.46 | 0.39 |
| motocross2 | 0.49 | 0.58 | 0.58 | 0.59 | 0.56 | 0.45 | 0.42 |
| nature | 0.49 | 0.44 | 0.43 | 0.39 | 0.43 | 0.4 | 0.42 |
| octopus | 0.57 | 0.59 | 0.58 | 0.58 | 0.59 | 0.56 | 0.47 |
| pedestrian1 | 0.71 | 0.71 | 0.71 | 0.71 | 0.66 | 0.61 | 0.65 |
| pedestrian2 | 0.37 | 0.44 | 0.48 | 0.51 | 0.45 | 0.5 | 0.4 |
| rabbit | 0.3 | 0.33 | 0.3 | 0.28 | 0.33 | 0.27 | 0.25 |
| racing | 0.48 | 0.45 | 0.45 | 0.45 | 0.43 | 0.41 | 0.34 |
| road | 0.45 | 0.47 | 0.47 | 0.45 | 0.48 | 0.47 | 0.45 |
| shaking | 0.6 | 0.58 | 0.54 | 0.58 | 0.59 | 0.59 | 0.57 |
| sheep | 0.53 | 0.54 | 0.53 | 0.53 | 0.48 | 0.45 | 0.47 |
| singer1 | 0.58 | 0.57 | 0.6 | 0.56 | 0.58 | 0.59 | 0.5 |
| singer2 | 0.59 | 0.64 | 0.64 | 0.65 | 0.64 | 0.57 | 0.48 |
| singer3 | 0.28 | 0.26 | 0.27 | 0.26 | 0.28 | 0.28 | 0.26 |
| soccer1 | 0.53 | 0.57 | 0.59 | 0.58 | 0.57 | 0.54 | 0.56 |
| soccer2 | 0.59 | 0.62 | 0.58 | 0.59 | 0.62 | 0.53 | 0.49 |
| soldier | 0.45 | 0.52 | 0.51 | 0.52 | 0.48 | 0.46 | 0.34 |
| sphere | 0.5 | 0.55 | 0.54 | 0.54 | 0.55 | 0.56 | 0.52 |
| tiger | 0.68 | 0.66 | 0.67 | 0.65 | 0.62 | 0.59 | 0.57 |
| traffic | 0.78 | 0.8 | 0.79 | 0.8 | 0.77 | 0.58 | 0.54 |
| tunnel | 0.84 | 0.84 | 0.83 | 0.84 | 0.84 | 0.74 | 0.39 |
| wiper | 0.71 | 0.7 | 0.7 | 0.7 | 0.68 | 0.6 | 0.44 |
| mean | 0.52 | 0.54 | 0.53 | 0.53 | 0.52 | 0.49 | 0.44 |
| weightedmean | 0.54 | 0.55 | 0.55 | 0.54 | 0.53 | 0.5 | 0.45 | | 0 |
| | | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | -6 | -4 | -2 | 0 | 2 | 4 |
| r=2 | (55,59.13) | (72,61.46) | (67,59.78) | (N/A,N/A) | (73,61.53) | (70,58.69) |
| r=3 | (99,48.46) | (100,41.94) | (100,41.22) | (100,37.18) | (95,50.4) | (56,62.98) |
| r=4 | (100,40.83) | (100,41.04) | (100,40.24) | (100,34.3) | (95,49.4) | (58,64.98) | | | r=5 | (100,41.35) | (100,41.78) | (100,40.8) | (100,32.7) | (96,49.7) | (73,61.11) |
| --- | --- | --- | --- | --- | --- | --- |
| r=6 | (100,42.18) | (100,43.28) | (100,42.65) | (100,32.45) | (98,52.02) | (71,59.92) |
| DDT(r=2,X=0)withspeed/powertradeoff | | | | | | |
| max\|LAT\|−2/NFusedtoanneali,jij | | | | | | | | 1 |
| | | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | -6 | -4 | -2 | 0 | 2 | 4 |
| r=2 | (55,59.13) | (72,61.46) | (67,59.78) | (N/A,N/A) | (73,61.53) | (70,58.69) |
| r=3 | (99,48.46) | (100,41.94) | (100,41.22) | (100,37.18) | (95,50.4) | (56,62.98) |
| r=4 | (100,40.83) | (100,41.04) | (100,40.24) | (100,34.3) | (95,49.4) | (58,64.98) | | | | AU | AP | VP-25 | SF |
| --- | --- | --- | --- | --- |
| N=60 | 0.600 | 0.569 | 0.722 | 0.920 |
| N=70 | 0.612 | 0.655 | 0.801 | 0.992 |
| N=80 | 0.645 | 0.657 | 0.814 | 0.989 |
| N=90 | 0.642 | 0.678 | 0.816 | 0.986 |
| N=100 | 0.663 | 0.685 | 0.823 | 0.983 | | 0 |
| | | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | -6 | -4 | -2 | 0 | 2 | 4 |
| r=2 | (55,59.13) | (72,61.46) | (67,59.78) | (N/A,N/A) | (73,61.53) | (70,58.69) | | | r=3 | (99,48.46) | (100,41.94) | (100,41.22) | (100,37.18) | (95,50.4) | (56,62.98) |
| --- | --- | --- | --- | --- | --- | --- |
| r=4 | (100,40.83) | (100,41.04) | (100,40.24) | (100,34.3) | (95,49.4) | (58,64.98) |
| r=5 | (100,41.35) | (100,41.78) | (100,40.8) | (100,32.7) | (96,49.7) | (73,61.11) |
| r=6 | (100,42.18) | (100,43.28) | (100,42.65) | (100,32.45) | (98,52.02) | (71,59.92) |
| DDT(r=2,X=0)withspeed/powertradeoff | | | | | | |
| max\|LAT\|−2/NFusedtoanneali,jij | | | | | | | | 1 |
| | | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | -6 | -4 | -2 | 0 | 2 | 4 |
| r=2 | (55,59.13) | (72,61.46) | (67,59.78) | (N/A,N/A) | (73,61.53) | (70,58.69) | | | N=70 | 0.612 | 0.655 | 0.801 | 0.992 |
| --- | --- | --- | --- | --- |
| N=80 | 0.645 | 0.657 | 0.814 | 0.989 |
| N=90 | 0.642 | 0.678 | 0.816 | 0.986 |
| N=100 | 0.663 | 0.685 | 0.823 | 0.983 | | 0 |
| Algorithm | ζ=0T | 0<ζ<nT | |
| --- | --- | --- | --- |
| nData=5 | nData=10 | nData=5 | nData=10 | | | He-Geng | 28,342±2248 | 54,454±3959 | 24,714±3336 | 52,042±3010 |
| --- | --- | --- | --- | --- |
| Baseline | 2,568±303 | 4,983±528 | 1,805±486 | 3,837±598 |
| MIMB | 1,390±196 | 2,400±747 | 1,332±179 | 2,166±307 | | 1 |
| Algorithm | ζ=0T | 0<ζ<nT | |
| --- | --- | --- | --- |
| nData=5 | nData=10 | nData=5 | nData=10 | | | Algorithm | ζ=0T | 0<ζ<nT | | |
| --- | --- | --- | --- | --- |
| nData=5 | nData=10 | nData=5 | nData=10 | |
| He-Geng | 28,375±2280 | 55,287±4347 | 24,574±3231 | 53,525±2705 |
| Baseline | 2,584±126 | 3,483±518 | 1,308±432 | 3,174±675 |
| MIMB | 1,102±85 | 1,843±210 | 922±204 | 1,738±184 | | 0 |
| Algorithm | ζ=0T | 0<ζ<nT | | |
| --- | --- | --- | --- | --- |
| nData=5 | nData=10 | nData=5 | nData=10 | |
| He-Geng | 28,342±2248 | 54,454±3959 | 24,714±3336 | 52,042±3010 | | | Baseline | 2,568±303 | 4,983±528 | 1,805±486 | 3,837±598 |
| --- | --- | --- | --- | --- |
| MIMB | 1,390±196 | 2,400±747 | 1,332±179 | 2,166±307 | | 1 |
| Algorithm | ζ=0T | 0<ζ<nT | | |
| --- | --- | --- | --- | --- |
| nData=5 | nData=10 | nData=5 | nData=10 | |
| He-Geng | 28,342±2248 | 54,454±3959 | 24,714±3336 | 52,042±3010 | | | Baseline | 2,584±126 | 3,483±518 | 1,308±432 | 3,174±675 |
| --- | --- | --- | --- | --- |
| MIMB | 1,102±85 | 1,843±210 | 922±204 | 1,738±184 | | 0 |
| VNV-Kmeans | VNV-Forest | VNV-AASC | VNV-MSC-Forest-hard | VT-MSC-Forest | VNV-MSC-Forest |
| --- | --- | --- | --- | --- | --- |
| 79.48 | 87.91 | 32.47 | 48.51 | 81.25 | 57.43 |
| 39.50 | 19.33 | 30.25 | 45.80 | 41.60 | 70.17 |
| 94.41 | 59.38 | 65.46 | 79.77 | 70.07 | 91.45 |
| 74.82 | 44.30 | 45.77 | 84.93 | 60.48 | 79.96 |
| 92.97 | 46.25 | 41.25 | 96.88 | 84.22 | 99.22 |
| 82.74 | 16.71 | 33.15 | 89.40 | 82.88 | 90.08 | | | 0.00 | 0.00 | 13.70 | 21.15 | 10.82 | 0.00 |
| --- | --- | --- | --- | --- | --- |
| 60.94 | 9.77 | 33.59 | 38.87 | 47.85 | 43.75 |
| 65.61 | 35.45 | 36.96 | 63.16 | 59.89 | 66.50 | | 1 |
| VNV-Kmeans | VNV-Forest | VNV-AASC | VNV-MSC-Forest-hard | VT-MSC-Forest | VNV-MSC-Forest |
| --- | --- | --- | --- | --- | --- |
| 79.48 | 87.91 | 32.47 | 48.51 | 81.25 | 57.43 |
| 39.50 | 19.33 | 30.25 | 45.80 | 41.60 | 70.17 |
| 94.41 | 59.38 | 65.46 | 79.77 | 70.07 | 91.45 |
| 74.82 | 44.30 | 45.77 | 84.93 | 60.48 | 79.96 |
| 92.97 | 46.25 | 41.25 | 96.88 | 84.22 | 99.22 |
| 82.74 | 16.71 | 33.15 | 89.40 | 82.88 | 90.08 | | | | bird | car | cat | deer | dog | frog | horse | plane | truck |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Nodefense | 1.94 | 0.31 | 0.74 | 4.72 | 7.99 | 3.66 | 9.22 | 0.75 | 1.32 |
| Defensivedistill | 6.55 | 0.70 | 13.78 | 2.54 | 13.90 | 2.56 | 11.36 | 0.66 | 3.54 |
| Adv.retraining | 2.58 | 0.31 | 0.75 | 6.08 | 0.75 | 9.01 | 6.06 | 0.31 | 4.08 |
| RobustOpt.+BReLU | 17.11 | 1.02 | 4.07 | 13.50 | 7.09 | 15.34 | 7.15 | 2.08 | 17.57 |
| RSE(ours) | 12.87 | 2.61 | 12.47 | 21.47 | 31.90 | 19.09 | 9.45 | 10.21 | 22.15 | | 0 |
| VNV-Kmeans | VNV-Forest | VNV-AASC | VNV-MSC-Forest-hard | VT-MSC-Forest | VNV-MSC-Forest |
| --- | --- | --- | --- | --- | --- |
| 79.48 | 87.91 | 32.47 | 48.51 | 81.25 | 57.43 |
| 39.50 | 19.33 | 30.25 | 45.80 | 41.60 | 70.17 |
| 94.41 | 59.38 | 65.46 | 79.77 | 70.07 | 91.45 |
| 74.82 | 44.30 | 45.77 | 84.93 | 60.48 | 79.96 |
| 92.97 | 46.25 | 41.25 | 96.88 | 84.22 | 99.22 | | | 82.74 | 16.71 | 33.15 | 89.40 | 82.88 | 90.08 |
| --- | --- | --- | --- | --- | --- |
| 0.00 | 0.00 | 13.70 | 21.15 | 10.82 | 0.00 |
| 60.94 | 9.77 | 33.59 | 38.87 | 47.85 | 43.75 |
| 65.61 | 35.45 | 36.96 | 63.16 | 59.89 | 66.50 | | 1 |
| VNV-Kmeans | VNV-Forest | VNV-AASC | VNV-MSC-Forest-hard | VT-MSC-Forest | VNV-MSC-Forest |
| --- | --- | --- | --- | --- | --- |
| 79.48 | 87.91 | 32.47 | 48.51 | 81.25 | 57.43 |
| 39.50 | 19.33 | 30.25 | 45.80 | 41.60 | 70.17 |
| 94.41 | 59.38 | 65.46 | 79.77 | 70.07 | 91.45 |
| 74.82 | 44.30 | 45.77 | 84.93 | 60.48 | 79.96 |
| 92.97 | 46.25 | 41.25 | 96.88 | 84.22 | 99.22 | | | Defensivedistill | 6.55 | 0.70 | 13.78 | 2.54 | 13.90 | 2.56 | 11.36 | 0.66 | 3.54 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Adv.retraining | 2.58 | 0.31 | 0.75 | 6.08 | 0.75 | 9.01 | 6.06 | 0.31 | 4.08 |
| RobustOpt.+BReLU | 17.11 | 1.02 | 4.07 | 13.50 | 7.09 | 15.34 | 7.15 | 2.08 | 17.57 |
| RSE(ours) | 12.87 | 2.61 | 12.47 | 21.47 | 31.90 | 19.09 | 9.45 | 10.21 | 22.15 | | 0 |
| | Virtual | Virtual-Gray | Amazon | DSLR | PASCAL |
| --- | --- | --- | --- | --- | --- |
| Virtual | 30.8(0.1) | 16.5(1.0) | 24.1(0.6) | 28.3(0.2) | 10.7(0.5) |
| Virtual-Gray | 32.3(0.6) | 32.3(0.5) | 27.3(0.8) | 32.7(0.6) | 17.9(0.7) | | | Amazon | 39.9(0.4) | 30.0(1.0) | 39.2(0.4) | 37.9(0.4) | 18.6(0.6) |
| --- | --- | --- | --- | --- | --- |
| DSLR | 68.2(0.2) | 62.1(1.0) | 68.1(0.6) | 66.5(0.1) | 37.7(0.5) | | 1 |
| | Virtual | Virtual-Gray | Amazon | DSLR | PASCAL |
| --- | --- | --- | --- | --- | --- |
| Virtual | 30.8(0.1) | 16.5(1.0) | 24.1(0.6) | 28.3(0.2) | 10.7(0.5) |
| Virtual-Gray | 32.3(0.6) | 32.3(0.5) | 27.3(0.8) | 32.7(0.6) | 17.9(0.7) | | | network | RM | NM | CM | AvgC | ARL | AvgS |
| --- | --- | --- | --- | --- | --- | --- |
| amazon0302 | 0.25 | 0.82 | 0.79 | 0.18 | 5.86 | 19.95 |
| amazon0312 | 0.31 | 0.8 | 0.73 | 0.24 | 8.77 | 26.29 |
| amazon0505 | 0.31 | 0.76 | 0.73 | 0.24 | 8.86 | 26.42 |
| amazon0601 | 0.32 | 0.74 | 0.73 | 0.24 | 8.95 | 26.96 |
| citarnetminer | 0.06 | 0.65 | 0.63 | 0.42 | 6.63 | 16.8 |
| cithepph | 0.25 | 0.56 | 0.55 | 0.48 | 18.5 | 28.7 |
| cithepth | 0.27 | 0.53 | 0.59 | 0.42 | 18.2 | 31.6 |
| citpatents | 0.08 | 0.76 | 0.59 | 0.45 | 8.95 | 18.6 |
| colastroph | 0.34 | 0.51 | 0.63 | 0.39 | 14 | 22.7 |
| colcondmat | 0.29 | 0.64 | 0.74 | 0.32 | 6.75 | 18.3 |
| colgrqc | 0.31 | 0.79 | 0.83 | 0.33 | 5 | 17.7 |
| colhepph | 0.42 | 0.58 | 0.71 | 0.33 | 11 | 19.9 |
| colhepth | 0.19 | 0.69 | 0.75 | 0.33 | 5.4 | 16.7 |
| emailenron | 0.18 | 0.5 | 0.57 | 0.47 | 9.34 | 49.1 |
| emaileuall | 0.01 | 0.73 | 0.66 | 0.46 | 3.98 | 409 |
| football | 0.17 | 0.57 | 0.88 | 0.14 | 7.2 | 20.9 |
| livejournal | 0.19 | -1 | 0.53 | 0.48 | 15.2 | 19.3 |
| p2p4 | 0.007 | 0.38 | 0.53 | 0.6 | 8.2 | 11.9 |
| p2p5 | 0.006 | 0.4 | 0.54 | 0.59 | 8.1 | 12.6 |
| p2p6 | 0.006 | 0.39 | 0.54 | 0.59 | 8.1 | 12.5 |
| p2p8 | 0.015 | 0.46 | 0.58 | 0.54 | 7.4 | 12.7 |
| p2p9 | 0.014 | 0.46 | 0.58 | 0.54 | 7.3 | 12.5 |
| p2p24 | 0.002 | 0.47 | 0.62 | 0.48 | 5.9 | 11.1 |
| p2p25 | 0.005 | 0.49 | 0.63 | 0.47 | 5.8 | 11.5 |
| p2p30 | 0.005 | 0.5 | 0.62 | 0.46 | 5.8 | 11.3 |
| p2p31 | 0.003 | 0.5 | 0.63 | 0.46 | 5.7 | 11 |
| roadnetca | 0.04 | 0.99 | 0.93 | 0.087 | 3.67 | 26.7 |
| roadnetpa | 0.04 | 0.99 | 0.93 | 0.087 | 3.68 | 26.9 |
| roadnettx | 0.04 | 0.99 | 0.93 | 0.1 | 3.63 | 26.2 |
| vikivote | 0.002 | -1 | 0.58 | 0.5 | 4.89 | 496 |
| webberkstan | 0.54 | 0.91 | 0.65 | 0.32 | 9 | 44.4 |
| webgoogle | 0.39 | 0.92 | 0.79 | 0.17 | 6.68 | 30.3 |
| webnotredame | 0.35 | 0.93 | 0.76 | 0.16 | 5 | 88.2 |
| webstanford | 0.47 | 0.88 | 0.65 | 0.35 | 7.9 | 40.8 | | 0 |
| | Virtual | Virtual-Gray | Amazon | DSLR | PASCAL |
| --- | --- | --- | --- | --- | --- |
| Virtual | 30.8(0.1) | 16.5(1.0) | 24.1(0.6) | 28.3(0.2) | 10.7(0.5) |
| Virtual-Gray | 32.3(0.6) | 32.3(0.5) | 27.3(0.8) | 32.7(0.6) | 17.9(0.7) | | | Amazon | 39.9(0.4) | 30.0(1.0) | 39.2(0.4) | 37.9(0.4) | 18.6(0.6) |
| --- | --- | --- | --- | --- | --- |
| DSLR | 68.2(0.2) | 62.1(1.0) | 68.1(0.6) | 66.5(0.1) | 37.7(0.5) | | 1 |
| | Virtual | Virtual-Gray | Amazon | DSLR | PASCAL |
| --- | --- | --- | --- | --- | --- |
| Virtual | 30.8(0.1) | 16.5(1.0) | 24.1(0.6) | 28.3(0.2) | 10.7(0.5) |
| Virtual-Gray | 32.3(0.6) | 32.3(0.5) | 27.3(0.8) | 32.7(0.6) | 17.9(0.7) | | | roadnetpa | 0.04 | 0.99 | 0.93 | 0.087 | 3.68 | 26.9 |
| --- | --- | --- | --- | --- | --- | --- |
| roadnettx | 0.04 | 0.99 | 0.93 | 0.1 | 3.63 | 26.2 |
| vikivote | 0.002 | -1 | 0.58 | 0.5 | 4.89 | 496 |
| webberkstan | 0.54 | 0.91 | 0.65 | 0.32 | 9 | 44.4 |
| webgoogle | 0.39 | 0.92 | 0.79 | 0.17 | 6.68 | 30.3 |
| webnotredame | 0.35 | 0.93 | 0.76 | 0.16 | 5 | 88.2 |
| webstanford | 0.47 | 0.88 | 0.65 | 0.35 | 7.9 | 40.8 | | 0 |
| | Kosarak | Retail | Q148 | Nasa |
| --- | --- | --- | --- | --- |
| Count | 8019015 | 908576 | 234954 | 284170 |
| Distinctitems | 41270 | 16470 | 11824 | 2116 |
| Min | 1 | 0 | 0 | 0 |
| Max | 41270 | 16469 | 149464496 | 28474 | | | Mean | 2387.2 | 3264.7 | 3392.9 | 353.9 |
| --- | --- | --- | --- | --- |
| Median | 640 | 1564 | 63 | 120 |
| Std.deviation | 4308.5 | 4093.2 | 309782.5 | 778.1 |
| Skewness | 3.5 | 1.5 | 478.1 | 6.5 | | 1 |
| | Kosarak | Retail | Q148 | Nasa |
| --- | --- | --- | --- | --- |
| Count | 8019015 | 908576 | 234954 | 284170 |
| Distinctitems | 41270 | 16470 | 11824 | 2116 |
| Min | 1 | 0 | 0 | 0 |
| Max | 41270 | 16469 | 149464496 | 28474 | | | | Kosarak | Retail | Q148 | Webdocs |
| --- | --- | --- | --- | --- |
| Count | 8019015 | 908576 | 234954 | 299887139 |
| Distinctitems | 41270 | 16470 | 11824 | 5267656 |
| Min | 1 | 0 | 0 | 1 |
| Max | 41270 | 16469 | 149464496 | 5267656 |
| Mean | 2387.2 | 3264.7 | 3392.9 | 122715 |
| Median | 640 | 1564 | 63 | 1988 |
| Std.deviation | 4308.5 | 4093.2 | 309782.5 | 549736 |
| Skewness | 3.5 | 1.5 | 478.1 | 6.1 | | 0 |
| | Kosarak | Retail | Q148 | Nasa |
| --- | --- | --- | --- | --- |
| Count | 8019015 | 908576 | 234954 | 284170 |
| Distinctitems | 41270 | 16470 | 11824 | 2116 |
| Min | 1 | 0 | 0 | 0 |
| Max | 41270 | 16469 | 149464496 | 28474 |
| Mean | 2387.2 | 3264.7 | 3392.9 | 353.9 |
| Median | 640 | 1564 | 63 | 120 | | | Std.deviation | 4308.5 | 4093.2 | 309782.5 | 778.1 |
| --- | --- | --- | --- | --- |
| Skewness | 3.5 | 1.5 | 478.1 | 6.5 | | 1 |
| | Kosarak | Retail | Q148 | Nasa |
| --- | --- | --- | --- | --- |
| Count | 8019015 | 908576 | 234954 | 284170 |
| Distinctitems | 41270 | 16470 | 11824 | 2116 |
| Min | 1 | 0 | 0 | 0 |
| Max | 41270 | 16469 | 149464496 | 28474 |
| Mean | 2387.2 | 3264.7 | 3392.9 | 353.9 |
| Median | 640 | 1564 | 63 | 120 | | | Std.deviation | 4308.5 | 4093.2 | 309782.5 | 549736 |
| --- | --- | --- | --- | --- |
| Skewness | 3.5 | 1.5 | 478.1 | 6.1 | | 0 |
| Strategy | RMSE±STD | Av.Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.0728±0.0030 | 277.03±4.30 | 420 | - | | | GKKM | 0.0729±0.0030 | 25.52±0.18 | 36 | 0.7760 |
| --- | --- | --- | --- | --- |
| M=5,µ=0.25,σ=0.25480 | | | | |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0748±0.0031<br>0.0749±0.0031<br>0.0764±0.0032<br>0.0776±0.0033 | 2.99±0.10<br>3.12±0.18<br>2.85±0.06<br>2.99±0.15 | 10<br>10<br>8<br>7.13±0.97 | 0.2482<br>0.2482<br>0.2482<br>0.2331 |
| M=10,µ=0.1111,σ=0.13200 | | | | |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0746±0.0031<br>0.0745±0.0031<br>0.0757±0.0032<br>0.0757±0.0031 | 2.85±0.19<br>2.84±0.14<br>2.57±0.04<br>2.64±0.10 | 16<br>16<br>16<br>13.09±1.10 | 0.1108<br>0.1108<br>0.1104<br>0.0996 |
| M=20,µ=0.0526,σ=0.09650 | | | | |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0735±0.0029<br>0.0737±0.0029<br>0.0753±0.0031<br>0.0753±0.0031 | 2.87±0.12<br>2.96±0.17<br>2.55±0.03<br>2.56±0.04 | 21<br>21<br>20<br>15.95±1.13 | 0.0520<br>0.0520<br>0.0525<br>0.0467 |
| M=30,µ=0.0345,σ=0.05030 | | | | |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0740±0.0029<br>0.0740±0.0029<br>0.0737±0.0029<br>0.0742±0.0030 | 5.41±0.18<br>5.62±0.19<br>3.24±0.04<br>2.74±0.07 | 42<br>42<br>41<br>33.39±1.43 | 0.0339<br>0.0339<br>0.0344<br>0.0326 | | 1 |
| Strategy | RMSE±STD | Av.Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.0728±0.0030 | 277.03±4.30 | 420 | - | | | Strategy | RMSE±STD | Av.Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.0839±0.0035 | 14.6747±0.3073 | 103 | - |
| GKKM | 0.0878±0.0038 | 5.31±0.02 | 5 | 0.5347 |
| M=5,µ=0.25,σ=0.23850 | | | | |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0861±0.0037<br>0.0861±0.0037<br>0.0877±0.0038<br>0.0882±0.0039 | 4.34±0.04<br>4.34±0.05<br>4.17±0.02<br>4.56±0.23 | 6<br>6<br>6<br>4.89±0.37 | 0.2402<br>0.2395<br>0.2338<br>0.1812 |
| M=10,µ=0.1111,σ=0.10 | | | | |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0835±0.0035<br>0.0835±0.0035<br>0.0852±0.0035<br>0.0857±0.0036 | 3.27±0.03<br>3.25±0.04<br>3.32±0.01<br>3.38±0.08 | 12<br>12<br>12<br>9.58±0.75 | 0.1098<br>0.1098<br>0.1080<br>0.0907 |
| M=20,µ=0.0526,σ=0.04980 | | | | |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0817±0.0034<br>0.0817±0.0034<br>0.0817±0.0034<br>0.0828±0.0035 | 3.22±0.04<br>3.23±0.05<br>3.27±0.02<br>3.24±0.05 | 20<br>20<br>20<br>16.55±0.88 | 0.0383<br>0.0437<br>0.0499<br>0.0408 |
| M=30,µ=0.0345,σ=0.03530 | | | | |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0804±0.0033<br>0.0803±0.0033<br>0.0806±0.0033<br>0.0810±0.0034 | 3.43±0.05<br>3.45±0.03<br>3.48±0.05<br>3.33±0.06 | 25<br>25<br>25<br>21.09±1.02 | 0.0314<br>0.0311<br>0.0300<br>0.0282 | | 0 |
| Strategy | RMSE±STD | Av.Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.0728±0.0030 | 277.03±4.30 | 420 | - |
| GKKM | 0.0729±0.0030 | 25.52±0.18 | 36 | 0.7760 |
| M=5,µ=0.25,σ=0.25480 | | | | |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0748±0.0031<br>0.0749±0.0031<br>0.0764±0.0032<br>0.0776±0.0033 | 2.99±0.10<br>3.12±0.18<br>2.85±0.06<br>2.99±0.15 | 10<br>10<br>8<br>7.13±0.97 | 0.2482<br>0.2482<br>0.2482<br>0.2331 |
| M=10,µ=0.1111,σ=0.13200 | | | | | | | CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0746±0.0031<br>0.0745±0.0031<br>0.0757±0.0032<br>0.0757±0.0031 | 2.85±0.19<br>2.84±0.14<br>2.57±0.04<br>2.64±0.10 | 16<br>16<br>16<br>13.09±1.10 | 0.1108<br>0.1108<br>0.1104<br>0.0996 |
| --- | --- | --- | --- | --- |
| M=20,µ=0.0526,σ=0.09650 | | | | |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0735±0.0029<br>0.0737±0.0029<br>0.0753±0.0031<br>0.0753±0.0031 | 2.87±0.12<br>2.96±0.17<br>2.55±0.03<br>2.56±0.04 | 21<br>21<br>20<br>15.95±1.13 | 0.0520<br>0.0520<br>0.0525<br>0.0467 |
| M=30,µ=0.0345,σ=0.05030 | | | | |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0740±0.0029<br>0.0740±0.0029<br>0.0737±0.0029<br>0.0742±0.0030 | 5.41±0.18<br>5.62±0.19<br>3.24±0.04<br>2.74±0.07 | 42<br>42<br>41<br>33.39±1.43 | 0.0339<br>0.0339<br>0.0344<br>0.0326 | | 1 |
| Strategy | RMSE±STD | Av.Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.0728±0.0030 | 277.03±4.30 | 420 | - |
| GKKM | 0.0729±0.0030 | 25.52±0.18 | 36 | 0.7760 |
| M=5,µ=0.25,σ=0.25480 | | | | |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0748±0.0031<br>0.0749±0.0031<br>0.0764±0.0032<br>0.0776±0.0033 | 2.99±0.10<br>3.12±0.18<br>2.85±0.06<br>2.99±0.15 | 10<br>10<br>8<br>7.13±0.97 | 0.2482<br>0.2482<br>0.2482<br>0.2331 |
| M=10,µ=0.1111,σ=0.13200 | | | | | | | M=10,µ=0.1111,σ=0.10 | | | | |
| --- | --- | --- | --- | --- |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0835±0.0035<br>0.0835±0.0035<br>0.0852±0.0035<br>0.0857±0.0036 | 3.27±0.03<br>3.25±0.04<br>3.32±0.01<br>3.38±0.08 | 12<br>12<br>12<br>9.58±0.75 | 0.1098<br>0.1098<br>0.1080<br>0.0907 |
| M=20,µ=0.0526,σ=0.04980 | | | | |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0817±0.0034<br>0.0817±0.0034<br>0.0817±0.0034<br>0.0828±0.0035 | 3.22±0.04<br>3.23±0.05<br>3.27±0.02<br>3.24±0.05 | 20<br>20<br>20<br>16.55±0.88 | 0.0383<br>0.0437<br>0.0499<br>0.0408 |
| M=30,µ=0.0345,σ=0.03530 | | | | |
| CCBS<br>CCBS(r)<br>GCBS<br>GCBS(r) | 0.0804±0.0033<br>0.0803±0.0033<br>0.0806±0.0033<br>0.0810±0.0034 | 3.43±0.05<br>3.45±0.03<br>3.48±0.05<br>3.33±0.06 | 25<br>25<br>25<br>21.09±1.02 | 0.0314<br>0.0311<br>0.0300<br>0.0282 | | 0 |
| N | Int32 | Int64 | Float | Double |
| --- | --- | --- | --- | --- |
| 32M | 24.1 | 12.4 | 24.5 | 12.6 | | | 64M | 24.8 | 12.6 | 25.1 | 12.9 |
| --- | --- | --- | --- | --- |
| 128M | 25.3 | 12.7 | 25.5 | 13.0 |
| 256M | 25.5 | 12.7 | 25.6 | 13.0 |
| 512M | 25.5 | 12.8 | 25.7 | 13.0 | | 1 |
| N | Int32 | Int64 | Float | Double |
| --- | --- | --- | --- | --- |
| 32M | 24.1 | 12.4 | 24.5 | 12.6 | | | N | exp | s,i1 | s,i2 | s,s,i | f,i |
| --- | --- | --- | --- | --- | --- |
| 64 | 1 | 0.4 | 2 | 7 | 9 |
| 128 | 5 | 0.7 | 3 | 30 | 39 |
| 256 | 30 | 1.3 | 7 | 139 | 206 |
| 512 | 344 | 4.2 | 19 | 611 | 1200 | | 0 |
| N | Int32 | Int64 | Float | Double |
| --- | --- | --- | --- | --- |
| 32M | 24.1 | 12.4 | 24.5 | 12.6 | | | 64M | 24.8 | 12.6 | 25.1 | 12.9 |
| --- | --- | --- | --- | --- |
| 128M | 25.3 | 12.7 | 25.5 | 13.0 |
| 256M | 25.5 | 12.7 | 25.6 | 13.0 |
| 512M | 25.5 | 12.8 | 25.7 | 13.0 | | 1 |
| N | Int32 | Int64 | Float | Double |
| --- | --- | --- | --- | --- |
| 32M | 24.1 | 12.4 | 24.5 | 12.6 | | | 128 | 5 | 0.7 | 3 | 30 | 39 |
| --- | --- | --- | --- | --- | --- |
| 256 | 30 | 1.3 | 7 | 139 | 206 |
| 512 | 344 | 4.2 | 19 | 611 | 1200 | | 0 |
| Class | F1-scoreinitial | F1-scorefiltered | Newsensible<br>trigrams |
| --- | --- | --- | --- |
| org:founded-by | 19.30 | 13.33 | 2 |
| org:members | 14.86 | 13.64 | 0 |
| org:top-members/employees | 44.77 | 40.49 | 1 |
| per:alternate-names | 24.72 | 31.17 | 1 |
| per:cities-of-residence | 52.82 | 54.73 | 0 |
| per:countries-of-residence | 9.33 | 13.20 | 0 |
| per:country-of-birth | 25.83 | 26.86 | 0 |
| per:employee-of | 43.39 | 39.91 | 3 |
| per:spouse | 43.56 | 36.00 | 1 | | | per:stateorprovinces-of-residence | 43.95 | 45.49 | 1 |
| --- | --- | --- | --- |
| per:title | 87.45 | 87.55 | 2 | | 1 |
| Class | F1-scoreinitial | F1-scorefiltered | Newsensible<br>trigrams |
| --- | --- | --- | --- |
| org:founded-by | 19.30 | 13.33 | 2 |
| org:members | 14.86 | 13.64 | 0 |
| org:top-members/employees | 44.77 | 40.49 | 1 |
| per:alternate-names | 24.72 | 31.17 | 1 |
| per:cities-of-residence | 52.82 | 54.73 | 0 |
| per:countries-of-residence | 9.33 | 13.20 | 0 |
| per:country-of-birth | 25.83 | 26.86 | 0 |
| per:employee-of | 43.39 | 39.91 | 3 |
| per:spouse | 43.56 | 36.00 | 1 | | | Rule1 | Rule2 | Rule3 | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| IG | CC | IG | CC | IG | CC | |
| Age | 0 | 0.04 | 0 | 0 | 0 | -0.10 |
| Gender | 0.06 | 0.14 | 0.06 | 0.13 | 0 | -0.06 |
| Job | 0.08 | -0.18 | 0.08 | -0.17 | 0.04 | 0.11 |
| Studies | 0 | 0.18 | 0 | 0.16 | 0 | 0.017 |
| Freq.ofuse | 0.03 | 0.05 | 0.06 | 0.08 | 0.02 | -0.07 |
| #Accounts | 0 | 0.14 | 0 | 0.16 | 0 | -0.05 |
| Priv.Concern | 0 | 0.13 | 0 | 0.16 | 0 | 0.04 | | 0 |
| Class | F1-scoreinitial | F1-scorefiltered | Newsensible<br>trigrams |
| --- | --- | --- | --- |
| org:founded-by | 19.30 | 13.33 | 2 |
| org:members | 14.86 | 13.64 | 0 |
| org:top-members/employees | 44.77 | 40.49 | 1 |
| per:alternate-names | 24.72 | 31.17 | 1 |
| per:cities-of-residence | 52.82 | 54.73 | 0 |
| per:countries-of-residence | 9.33 | 13.20 | 0 | | | per:country-of-birth | 25.83 | 26.86 | 0 |
| --- | --- | --- | --- |
| per:employee-of | 43.39 | 39.91 | 3 |
| per:spouse | 43.56 | 36.00 | 1 |
| per:stateorprovinces-of-residence | 43.95 | 45.49 | 1 |
| per:title | 87.45 | 87.55 | 2 | | 1 |
| Class | F1-scoreinitial | F1-scorefiltered | Newsensible<br>trigrams |
| --- | --- | --- | --- |
| org:founded-by | 19.30 | 13.33 | 2 |
| org:members | 14.86 | 13.64 | 0 |
| org:top-members/employees | 44.77 | 40.49 | 1 |
| per:alternate-names | 24.72 | 31.17 | 1 |
| per:cities-of-residence | 52.82 | 54.73 | 0 |
| per:countries-of-residence | 9.33 | 13.20 | 0 | | | Studies | 0 | 0.18 | 0 | 0.16 | 0 | 0.017 |
| --- | --- | --- | --- | --- | --- | --- |
| Freq.ofuse | 0.03 | 0.05 | 0.06 | 0.08 | 0.02 | -0.07 |
| #Accounts | 0 | 0.14 | 0 | 0.16 | 0 | -0.05 |
| Priv.Concern | 0 | 0.13 | 0 | 0.16 | 0 | 0.04 | | 0 |
| input(1,96,1360) | |
| --- | --- |
| Conv2dandMax-Pooling | (32,(3,3))and(2,4) |
| Conv2dandMax-Pooling | (32,(3,3))and(4,4) |
| Conv2dandMax-Pooling | (32,(3,3))and(4,5) |
| Conv2dandMax-Pooling | (32,(3,3))and(2,4) |
| Conv2dandMax-Pooling | (32,(3,3))and(4,4) | | | Fully-connectedlayer | (50) |
| --- | --- |
| output(50) | | | 1 |
| input(1,96,1360) | |
| --- | --- |
| Conv2dandMax-Pooling | (32,(3,3))and(2,4) |
| Conv2dandMax-Pooling | (32,(3,3))and(4,4) |
| Conv2dandMax-Pooling | (32,(3,3))and(4,5) |
| Conv2dandMax-Pooling | (32,(3,3))and(2,4) |
| Conv2dandMax-Pooling | (32,(3,3))and(4,4) | | | input(1,96,1360) | |
| --- | --- |
| Conv2dandMax-Pooling | (32,(3,3))and(2,4) |
| Batchnormalization-ELUactivation | |
| Conv2dandMax-Pooling | (32,(3,3))and(4,4) |
| Batchnormalization-ELUactivation | |
| Conv2dandMax-Pooling | (32,(3,3))and(4,5) |
| Batchnormalization-ELUactivation | |
| Conv2dandMax-Pooling | (32,(3,3))and(2,4) |
| Batchnormalization-ELUactivation | |
| Conv2dandMax-Pooling | (32,(3,3))and(4,4) |
| Batchnormalization-ELUactivation | |
| Fully-connectedlayer | (50) |
| output(50) | | | 0 |
| input(1,96,1360) | |
| --- | --- |
| Conv2dandMax-Pooling | (32,(3,3))and(2,4) |
| Conv2dandMax-Pooling | (32,(3,3))and(4,4) |
| Conv2dandMax-Pooling | (32,(3,3))and(4,5) | | | Conv2dandMax-Pooling | (32,(3,3))and(2,4) |
| --- | --- |
| Conv2dandMax-Pooling | (32,(3,3))and(4,4) |
| Fully-connectedlayer | (50) |
| output(50) | | | 1 |
| input(1,96,1360) | |
| --- | --- |
| Conv2dandMax-Pooling | (32,(3,3))and(2,4) |
| Conv2dandMax-Pooling | (32,(3,3))and(4,4) |
| Conv2dandMax-Pooling | (32,(3,3))and(4,5) | | | Conv2dandMax-Pooling | (32,(3,3))and(2,4) |
| --- | --- |
| Batchnormalization-ELUactivation | |
| Conv2dandMax-Pooling | (32,(3,3))and(4,4) |
| Batchnormalization-ELUactivation | |
| Fully-connectedlayer | (50) |
| output(50) | | | 0 |
| n | Definitional | Pruning | PredictedRatio | ActualRatio |
| --- | --- | --- | --- | --- |
| 4 | 120 | 44 | 0.25 | 0.36 |
| 5 | 1850 | 210 | 0.125 | 0.113 |
| 6 | 63,981 | 2,040 | 0.0625 | 0.0318 |
| 7 | 989,751 | 6,272 | 0.03125 | 0.00634 | | | 8 | 58,155,904 | 39,808 | 0.015625 | 0.000684 |
| --- | --- | --- | --- | --- |
| 9 | | 198,300 | 0.0078125 | |
| 10 | | 1,933,147 | 0.00390625 | | | 1 |
| n | Definitional | Pruning | PredictedRatio | ActualRatio |
| --- | --- | --- | --- | --- |
| 4 | 120 | 44 | 0.25 | 0.36 |
| 5 | 1850 | 210 | 0.125 | 0.113 |
| 6 | 63,981 | 2,040 | 0.0625 | 0.0318 |
| 7 | 989,751 | 6,272 | 0.03125 | 0.00634 | | | n | Definitional | Pruning | PredictedRatio | ActualRatio |
| --- | --- | --- | --- | --- |
| 3 | 36 | 29 | 0.5 | 0.72 |
| 4 | 1,000 | 288 | 0.25 | 0.288 |
| 5 | 50,625 | 2,424 | 0.125 | 0.0478 |
| 6 | 4,084,101 | 18,479 | 0.0625 | 0.00452 |
| 7 | 481,890,304 | 145,134 | 0.03125 | 0.000301 |
| 8 | 78,364,164,096 | 1,150,386 | 0.015625 | 0.0000147 | | 0 |
| n | Definitional | Pruning | PredictedRatio | ActualRatio |
| --- | --- | --- | --- | --- |
| 4 | 120 | 44 | 0.25 | 0.36 |
| 5 | 1850 | 210 | 0.125 | 0.113 |
| 6 | 63,981 | 2,040 | 0.0625 | 0.0318 |
| 7 | 989,751 | 6,272 | 0.03125 | 0.00634 |
| 8 | 58,155,904 | 39,808 | 0.015625 | 0.000684 | | | 9 | | 198,300 | 0.0078125 | |
| --- | --- | --- | --- | --- |
| 10 | | 1,933,147 | 0.00390625 | | | 1 |
| n | Definitional | Pruning | PredictedRatio | ActualRatio |
| --- | --- | --- | --- | --- |
| 4 | 120 | 44 | 0.25 | 0.36 |
| 5 | 1850 | 210 | 0.125 | 0.113 |
| 6 | 63,981 | 2,040 | 0.0625 | 0.0318 |
| 7 | 989,751 | 6,272 | 0.03125 | 0.00634 |
| 8 | 58,155,904 | 39,808 | 0.015625 | 0.000684 | | | 7 | 481,890,304 | 145,134 | 0.03125 | 0.000301 |
| --- | --- | --- | --- | --- |
| 8 | 78,364,164,096 | 1,150,386 | 0.015625 | 0.0000147 | | 0 |
| T1 | T2 | T3 | T4 | T5 |
| --- | --- | --- | --- | --- |
| obama | obama | romney | romney | obama |
| romney | romney | obama | obama | romney |
| debate | debate | tax | mitt | mitt |
| mitt | president | mitt | like | not |
| tonight | gt | plan | not | like |
| president | ryan | debate | debate | jobs |
| amp | paul | trillion | women | candy |
| not | poll | details | get | crowley | | | would | won | president | vote | right |
| --- | --- | --- | --- | --- |
| plan | mitt | don | president | said |
| back | amp | like | election | president |
| one | election | cut | tonight | debate |
| point | tonight | taxes | don | amp |
| last | voters | amp | amp | libya |
| win | vote | know | get | | | 1 |
| T1 | T2 | T3 | T4 | T5 |
| --- | --- | --- | --- | --- |
| obama | obama | romney | romney | obama |
| romney | romney | obama | obama | romney |
| debate | debate | tax | mitt | mitt |
| mitt | president | mitt | like | not |
| tonight | gt | plan | not | like |
| president | ryan | debate | debate | jobs |
| amp | paul | trillion | women | candy |
| not | poll | details | get | crowley | | | T1 | T2 | T3 | T4 | T5 |
| --- | --- | --- | --- | --- |
| romney | obama | obama | obama | romney |
| obama | romney | romney | romney | obama |
| mitt | debate | mitt | not | mitt |
| debate | mitt | barack | like | class |
| poll | tonight | debate | mitt | like |
| election | gt | president | vote | middle |
| won | presidential | michelle | don | tax |
| cnn | president | not | people | not |
| amp | vote | amp | voting | cut |
| president | election | video | amp | says |
| street | first | anniversary | election | taxes |
| wins | get | would | know | president |
| y | watch | election | debate | fucked |
| de | amp | party | president | debate |
| | like | tonight | get | big | | 0 |
| T1 | T2 | T3 | T4 | T5 |
| --- | --- | --- | --- | --- |
| obama | obama | romney | romney | obama |
| romney | romney | obama | obama | romney |
| debate | debate | tax | mitt | mitt |
| mitt | president | mitt | like | not |
| tonight | gt | plan | not | like | | | president | ryan | debate | debate | jobs |
| --- | --- | --- | --- | --- |
| amp | paul | trillion | women | candy |
| not | poll | details | get | crowley |
| would | won | president | vote | right |
| plan | mitt | don | president | said |
| back | amp | like | election | president |
| one | election | cut | tonight | debate |
| point | tonight | taxes | don | amp |
| last | voters | amp | amp | libya |
| win | vote | know | get | | | 1 |
| T1 | T2 | T3 | T4 | T5 |
| --- | --- | --- | --- | --- |
| obama | obama | romney | romney | obama |
| romney | romney | obama | obama | romney |
| debate | debate | tax | mitt | mitt |
| mitt | president | mitt | like | not |
| tonight | gt | plan | not | like | | | street | first | anniversary | election | taxes |
| --- | --- | --- | --- | --- |
| wins | get | would | know | president |
| y | watch | election | debate | fucked |
| de | amp | party | president | debate |
| | like | tonight | get | big | | 0 |
| Method | MAE | MSE |
| --- | --- | --- |
| Headdetection | 655.7 | 697.8 |
| Densitymap+MESA | 493.4 | 487.1 | | | Multi-sourcefeatures | 419.5 | 541.6 |
| --- | --- | --- |
| CrowdCNN | 467.0 | 498.5 |
| Multi-columnCNN | 377.6 | 509.1 |
| ConvLSTM-nt | 284.5 | 297.1 |
| Shangetal. | 270.3 | - | | 1 |
| Method | MAE | MSE |
| --- | --- | --- |
| Headdetection | 655.7 | 697.8 |
| Densitymap+MESA | 493.4 | 487.1 | | | Method | MAE | MSE |
| --- | --- | --- |
| Gaussianprocessregression | 2.24 | 7.97 |
| Ridgeregression | 2.25 | 7.82 |
| Cumulativeattributeregression | 2.07 | 6.90 |
| Densitymap+MESA | 1.70 | - |
| Countforest | 1.60 | 4.40 |
| CrowdCNN | 1.60 | 3.31 |
| Multi-columnCNN | 1.07 | 1.35 |
| HydraCNN | 1.65 | - |
| CNNboosting | 1.10 | - |
| ConvLSTM-nt | 1.73 | 3.52 |
| ConvLSTM | 1.30 | 1.79 |
| BidirectionalConvLSTM | 1.13 | 1.43 | | 0 |
| Method | MAE | MSE |
| --- | --- | --- |
| Headdetection | 655.7 | 697.8 |
| Densitymap+MESA | 493.4 | 487.1 |
| Multi-sourcefeatures | 419.5 | 541.6 | | | CrowdCNN | 467.0 | 498.5 |
| --- | --- | --- |
| Multi-columnCNN | 377.6 | 509.1 |
| ConvLSTM-nt | 284.5 | 297.1 |
| Shangetal. | 270.3 | - | | 1 |
| Method | MAE | MSE |
| --- | --- | --- |
| Headdetection | 655.7 | 697.8 |
| Densitymap+MESA | 493.4 | 487.1 |
| Multi-sourcefeatures | 419.5 | 541.6 | | | Densitymap+MESA | 1.70 | - |
| --- | --- | --- |
| Countforest | 1.60 | 4.40 |
| CrowdCNN | 1.60 | 3.31 |
| Multi-columnCNN | 1.07 | 1.35 |
| HydraCNN | 1.65 | - |
| CNNboosting | 1.10 | - |
| ConvLSTM-nt | 1.73 | 3.52 |
| ConvLSTM | 1.30 | 1.79 |
| BidirectionalConvLSTM | 1.13 | 1.43 | | 0 |
| | c | l | Train | Test |
| --- | --- | --- | --- | --- |
| MR | 2 | 20 | 10,662 | 10-foldcrossvalidation |
| SUBJ | 2 | 23 | 10,000 | 10-foldcrossvalidation | | | TREC | 6 | 10 | 5,952 | 500 |
| --- | --- | --- | --- | --- |
| SST | 5 | 18 | 11,855 | 2,210 |
| IMDb | 2 | 100 | 25,000 | 25,000 | | 1 |
| | c | l | Train | Test |
| --- | --- | --- | --- | --- |
| MR | 2 | 20 | 10,662 | 10-foldcrossvalidation |
| SUBJ | 2 | 23 | 10,000 | 10-foldcrossvalidation | | | Phase | Train | Validation | Test | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Experiment | Diag | Nondiag | Diag | Nondiag | Diag | Nondiag |
| Fold1 | 4626 | 4822 | 1542 | 1607 | 2055 | 2143 |
| Fold2 | 4625 | 4822 | 1542 | 1607 | 2056 | 2143 |
| Fold3 | 4625 | 4822 | 1542 | 1607 | 2056 | 2143 |
| Fold4 | 4625 | 4822 | 1542 | 1607 | 2056 | 2143 | | 0 |
| | c | l | Train | Test |
| --- | --- | --- | --- | --- |
| MR | 2 | 20 | 10,662 | 10-foldcrossvalidation | | | SUBJ | 2 | 23 | 10,000 | 10-foldcrossvalidation |
| --- | --- | --- | --- | --- |
| TREC | 6 | 10 | 5,952 | 500 |
| SST | 5 | 18 | 11,855 | 2,210 |
| IMDb | 2 | 100 | 25,000 | 25,000 | | 1 |
| | c | l | Train | Test |
| --- | --- | --- | --- | --- |
| MR | 2 | 20 | 10,662 | 10-foldcrossvalidation | | | Fold2 | 4625 | 4822 | 1542 | 1607 | 2056 | 2143 |
| --- | --- | --- | --- | --- | --- | --- |
| Fold3 | 4625 | 4822 | 1542 | 1607 | 2056 | 2143 |
| Fold4 | 4625 | 4822 | 1542 | 1607 | 2056 | 2143 | | 0 |
| Algorithm | 30 | 50 | 100 |
| --- | --- | --- | --- |
| StandardPCA | 0.4667 | 0.4600 | 0.3750 |
| mixPPCA | 0.5417±0.0126 | 0.6290±0.0387 | 0.5845±0.0336 | | | GLRAM | 0.5167 | 0.5600 | 0.4950 |
| --- | --- | --- | --- |
| TBV-DR | 0.7617±0.0294 | 0.6790±0.0251 | 0.6480±0.0155 | | 1 |
| Algorithm | 30 | 50 | 100 |
| --- | --- | --- | --- |
| StandardPCA | 0.4667 | 0.4600 | 0.3750 |
| mixPPCA | 0.5417±0.0126 | 0.6290±0.0387 | 0.5845±0.0336 | | | Algorithm | m=5000 | m=50 | | |
| --- | --- | --- | --- | --- |
| | | | | |
| MMPC | 1±0 | 1±0 | 0.9714±0.0904 | 0.9200±0.1033 |
| HITON-PC | 1±0 | 1±0 | 0.9714±0.0904 | 0.9200±0.1033 |
| MRMR(k=PC) | 1±0 | 1±0 | 0.8600±0.0966 | 0.8600±0.0966 |
| CMIM(k=PC) | 1±0 | 1±0 | 0.5800±0.1989 | 0.5800±0.1989 |
| JMI(k=PC) | 1±0 | 1±0 | 0.8000±0.0943 | 0.8000±0.0943 | | 0 |
| Algorithm | 30 | 50 | 100 |
| --- | --- | --- | --- |
| StandardPCA | 0.4667 | 0.4600 | 0.3750 |
| mixPPCA | 0.5417±0.0126 | 0.6290±0.0387 | 0.5845±0.0336 | | | GLRAM | 0.5167 | 0.5600 | 0.4950 |
| --- | --- | --- | --- |
| TBV-DR | 0.7617±0.0294 | 0.6790±0.0251 | 0.6480±0.0155 | | 1 |
| Algorithm | 30 | 50 | 100 |
| --- | --- | --- | --- |
| StandardPCA | 0.4667 | 0.4600 | 0.3750 |
| mixPPCA | 0.5417±0.0126 | 0.6290±0.0387 | 0.5845±0.0336 | | | MMPC | 1±0 | 1±0 | 0.9714±0.0904 | 0.9200±0.1033 |
| --- | --- | --- | --- | --- |
| HITON-PC | 1±0 | 1±0 | 0.9714±0.0904 | 0.9200±0.1033 |
| MRMR(k=PC) | 1±0 | 1±0 | 0.8600±0.0966 | 0.8600±0.0966 |
| CMIM(k=PC) | 1±0 | 1±0 | 0.5800±0.1989 | 0.5800±0.1989 |
| JMI(k=PC) | 1±0 | 1±0 | 0.8000±0.0943 | 0.8000±0.0943 | | 0 |
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