premise string | hypothesis string | label int64 |
|---|---|---|
| Datasets | SPIRAL<br>-kMeans | k-Shape | CLDS | kMeans<br>-DTW |
| --- | --- | --- | --- | --- |
| SwedishLeaf | 0.61 | 0.56 | 0.60 | 0.50 |
| CricketX | 0.32 | 0.31 | 0.26 | 0.22 |
| uWGLX | 0.47 | 0.38 | 0.28 | 0.32 |
| 50words | 0.68 | 0.50 | 0.56 | 0.53 | | | SLC | 0.60 | 0.56 | 0.41 | N/A |
| --- | --- | --- | --- | --- |
| SC | 0.80 | 0.78 | 0.51 | 0.70 |
| ED | 0.35 | 0.23 | 0.2 | 0.24 | | 1 |
| Datasets | SPIRAL<br>-kMeans | k-Shape | CLDS | kMeans<br>-DTW |
| --- | --- | --- | --- | --- |
| SwedishLeaf | 0.61 | 0.56 | 0.60 | 0.50 |
| CricketX | 0.32 | 0.31 | 0.26 | 0.22 |
| uWGLX | 0.47 | 0.38 | 0.28 | 0.32 |
| 50words | 0.68 | 0.50 | 0.56 | 0.53 | | | | σ | 10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Dataset | Method | | | | | | | | |
| TID2008 | K-SVD<br>SURE-GMM<br>BM3D<br>NL-Bayes<br>Ours | 34.74<br>34.79<br>35.17<br>35.06<br>35.11 | 30.87<br>31.22<br>31.46<br>31.51<br>31.43 | 28.97<br>29.23<br>29.28<br>29.31<br>29.26 | 27.89<br>27.91<br>28.02<br>28.04<br>28.07 | 26.04<br>26.53<br>26.66<br>26.67<br>26.74 | 24.92<br>24.89<br>25.39<br>25.43<br>25.38 | 24.45<br>24.78<br>25.14<br>25.11<br>25.19 | 23.80<br>23.81<br>24.66<br>24.51<br>24.71 |
| BSDS500 | K-SVD<br>SURE-GMM<br>BM3D<br>NL-Bayes<br>Ours | 34.24<br>34.22<br>34.72<br>34.69<br>34.71 | 30.69<br>31.01<br>31.19<br>31.09<br>31.11 | 28.66<br>29.02<br>29.11<br>29.09<br>29.06 | 27.69<br>27.68<br>27.51<br>27.53<br>27.85 | 25.93<br>26.23<br>26.56<br>26.48<br>26.61 | 25.10<br>25.22<br>25.68<br>25.66<br>25.64 | 24.22<br>24.56<br>24.92<br>24.79<br>24.94 | 23.46<br>23.58<br>24.39<br>24.41<br>24.47 |
| | | | | | | | | | |
| TID2008 | K-SVD<br>SURE-GMM<br>BM3D<br>NL-Bayes<br>Ours | 0.947<br>0.944<br>0.968<br>0.962<br>0.958 | 0.930<br>0.935<br>0.938<br>0.934<br>0.933 | 0.900<br>0.904<br>0.912<br>0.916<br>0.912 | 0.861<br>0.868<br>0.874<br>0.875<br>0.877 | 0.841<br>0.848<br>0.856<br>0.860<br>0.862 | 0.817<br>0.818<br>0.828<br>0.827<br>0.829 | 0.798<br>0.807<br>0.816<br>0.818<br>0.816 | 0.775<br>0.787<br>0.800<br>0.794<br>0.802 |
| BSDS500 | K-SVD<br>SURE-GMM<br>BM3D<br>NL-Bayes<br>Ours | 0.938<br>0.939<br>0.961<br>0.953<br>0.956 | 0.925<br>0.923<br>0.931<br>0.926<br>0.929 | 0.897<br>0.899<br>0.907<br>0.908<br>0.910 | 0.856<br>0.859<br>0.867<br>0.866<br>0.866 | 0.830<br>0.833<br>0.848<br>0.849<br>0.851 | 0.822<br>0.827<br>0.835<br>0.834<br>0.836 | 0.790<br>0.793<br>0.806<br>0.810<br>0.811 | 0.766<br>0.774<br>0.792<br>0.793<br>0.798 | | 0 |
| Datasets | SPIRAL<br>-kMeans | k-Shape | CLDS | kMeans<br>-DTW |
| --- | --- | --- | --- | --- |
| SwedishLeaf | 0.61 | 0.56 | 0.60 | 0.50 | | | CricketX | 0.32 | 0.31 | 0.26 | 0.22 |
| --- | --- | --- | --- | --- |
| uWGLX | 0.47 | 0.38 | 0.28 | 0.32 |
| 50words | 0.68 | 0.50 | 0.56 | 0.53 |
| SLC | 0.60 | 0.56 | 0.41 | N/A |
| SC | 0.80 | 0.78 | 0.51 | 0.70 |
| ED | 0.35 | 0.23 | 0.2 | 0.24 | | 1 |
| Datasets | SPIRAL<br>-kMeans | k-Shape | CLDS | kMeans<br>-DTW |
| --- | --- | --- | --- | --- |
| SwedishLeaf | 0.61 | 0.56 | 0.60 | 0.50 | | | TID2008 | K-SVD<br>SURE-GMM<br>BM3D<br>NL-Bayes<br>Ours | 0.947<br>0.944<br>0.968<br>0.962<br>0.958 | 0.930<br>0.935<br>0.938<br>0.934<br>0.933 | 0.900<br>0.904<br>0.912<br>0.916<br>0.912 | 0.861<br>0.868<br>0.874<br>0.875<br>0.877 | 0.841<br>0.848<br>0.856<br>0.860<br>0.862 | 0.817<br>0.818<br>0.828<br>0.827<br>0.829 | 0.798<br>0.807<br>0.816<br>0.818<br>0.816 | 0.775<br>0.787<br>0.800<br>0.794<br>0.802 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| BSDS500 | K-SVD<br>SURE-GMM<br>BM3D<br>NL-Bayes<br>Ours | 0.938<br>0.939<br>0.961<br>0.953<br>0.956 | 0.925<br>0.923<br>0.931<br>0.926<br>0.929 | 0.897<br>0.899<br>0.907<br>0.908<br>0.910 | 0.856<br>0.859<br>0.867<br>0.866<br>0.866 | 0.830<br>0.833<br>0.848<br>0.849<br>0.851 | 0.822<br>0.827<br>0.835<br>0.834<br>0.836 | 0.790<br>0.793<br>0.806<br>0.810<br>0.811 | 0.766<br>0.774<br>0.792<br>0.793<br>0.798 | | 0 |
| SOM | Nstep | #init | #database | Case | LOχ/N | NLOχ/N |
| --- | --- | --- | --- | --- | --- | --- |
| 5x5 | 5 | 5 | 100 | 1 | 1.19 | 1.28 |
| 5x5 | 5 | 5 | 100 | 2 | 1.37 | 1.44 |
| 5x5 | 5 | 10 | 100 | 1 | 1.16 | 1.25 |
| 5x5 | 5 | 10 | 100 | 2 | 1.49 | 1.43 |
| 5x5 | 5 | 15 | 100 | 1 | 1.16 | 1.45 |
| 5x5 | 5 | 20 | 100 | 1 | 1.17 | - |
| 5x5 | 10 | 10 | 100 | 1 | 1.16 | 1.30 | | | 5x5 | 40 | 10 | 100 | 1 | 1.20 | - |
| --- | --- | --- | --- | --- | --- | --- |
| 15x15 | 5 | 5 | 900 | 1 | 1.22 | - |
| 15x15 | 5 | 5 | 900 | 2 | 1.31 | - |
| 15x15 | 5 | 30 | 900 | 1 | 1.16 | 1.25 |
| 15x15 | 5 | 30 | 900 | 2 | 1.25 | l.53 | | 1 |
| SOM | Nstep | #init | #database | Case | LOχ/N | NLOχ/N |
| --- | --- | --- | --- | --- | --- | --- |
| 5x5 | 5 | 5 | 100 | 1 | 1.19 | 1.28 |
| 5x5 | 5 | 5 | 100 | 2 | 1.37 | 1.44 |
| 5x5 | 5 | 10 | 100 | 1 | 1.16 | 1.25 |
| 5x5 | 5 | 10 | 100 | 2 | 1.49 | 1.43 |
| 5x5 | 5 | 15 | 100 | 1 | 1.16 | 1.45 |
| 5x5 | 5 | 20 | 100 | 1 | 1.17 | - |
| 5x5 | 10 | 10 | 100 | 1 | 1.16 | 1.30 | | | | N | PSNR(dB) | RelErr | Iter | Time(s) |
| --- | --- | --- | --- | --- | --- |
| Lena | 1 | 28.83 | 0.0827 | 16 | 0.5 |
| 5 | 29.40 | 0.0774 | 23 | 1.2 | |
| 20 | 29.45 | 0.0769 | 26 | 3.1 | |
| 200 | 29.45 | 0.0769 | 36 | 35.5 | |
| 1000 | 29.45 | 0.0769 | 36 | 199.1 | |
| Barbara | 1 | 27.49 | 0.0790 | 9 | 0.3 |
| 5 | 27.80 | 0.0762 | 19 | 1.0 | |
| 20 | 27.82 | 0.0761 | 20 | 2.5 | |
| 200 | 27.82 | 0.0761 | 20 | 20.4 | |
| 1000 | 27.82 | 0.0761 | 20 | 119.2 | | | 0 |
| SOM | Nstep | #init | #database | Case | LOχ/N | NLOχ/N |
| --- | --- | --- | --- | --- | --- | --- |
| 5x5 | 5 | 5 | 100 | 1 | 1.19 | 1.28 |
| 5x5 | 5 | 5 | 100 | 2 | 1.37 | 1.44 |
| 5x5 | 5 | 10 | 100 | 1 | 1.16 | 1.25 | | | 5x5 | 5 | 10 | 100 | 2 | 1.49 | 1.43 |
| --- | --- | --- | --- | --- | --- | --- |
| 5x5 | 5 | 15 | 100 | 1 | 1.16 | 1.45 |
| 5x5 | 5 | 20 | 100 | 1 | 1.17 | - |
| 5x5 | 10 | 10 | 100 | 1 | 1.16 | 1.30 |
| 5x5 | 40 | 10 | 100 | 1 | 1.20 | - |
| 15x15 | 5 | 5 | 900 | 1 | 1.22 | - |
| 15x15 | 5 | 5 | 900 | 2 | 1.31 | - |
| 15x15 | 5 | 30 | 900 | 1 | 1.16 | 1.25 |
| 15x15 | 5 | 30 | 900 | 2 | 1.25 | l.53 | | 1 |
| SOM | Nstep | #init | #database | Case | LOχ/N | NLOχ/N |
| --- | --- | --- | --- | --- | --- | --- |
| 5x5 | 5 | 5 | 100 | 1 | 1.19 | 1.28 |
| 5x5 | 5 | 5 | 100 | 2 | 1.37 | 1.44 |
| 5x5 | 5 | 10 | 100 | 1 | 1.16 | 1.25 | | | 5 | 27.80 | 0.0762 | 19 | 1.0 |
| --- | --- | --- | --- | --- |
| 20 | 27.82 | 0.0761 | 20 | 2.5 |
| 200 | 27.82 | 0.0761 | 20 | 20.4 |
| 1000 | 27.82 | 0.0761 | 20 | 119.2 | | 0 |
| activity | param. | work-<br>aholic | Arnold | retiree |
| --- | --- | --- | --- | --- |
| sleep | earlystart | 23 | 22 | 22 |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | |
| breakfast | earlystart | 7 | 8 | 7 |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | |
| lunch | earlystart | 12 | 12 | 12 |
| | | | | | | | | | | | |
| --- | --- | --- | --- | --- |
| | | | | |
| | | | | |
| | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | |
| dinner | earlystart | 18 | 19 | 18 |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | |
| work | earlystart | 8 | 9 | 8 |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 1,1,1,1,1,0.8,0 | 1,1,0,1,0,0,0 | 0,0,0,0,0,0,0 | |
| workout | earlystart | 19.5 | 16 | 19 |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 0,0,0,0,0,0,0 | 0,0,0,0,0,0,0 | 0,0,0,0,0,0,0 | | | 1 |
| activity | param. | work-<br>aholic | Arnold | retiree |
| --- | --- | --- | --- | --- |
| sleep | earlystart | 23 | 22 | 22 |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | |
| breakfast | earlystart | 7 | 8 | 7 |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | |
| lunch | earlystart | 12 | 12 | 12 |
| | | | | | | | | kNN | RDF | CNN | CNN+LF |
| --- | --- | --- | --- | --- |
| Chores | 33.10 | 17.24 | 00.69 | 20.00 |
| Driving | 55.07 | 60.87 | 98.55 | 96.62 |
| Cooking | 25.66 | 35.53 | 47.37 | 60.53 |
| Exercising | 44.00 | 63.00 | 69.00 | 73.00 |
| Reading | 68.55 | 49.12 | 30.04 | 53.36 |
| Presentation | 80.00 | 72.35 | 80.59 | 87.06 |
| Dogs | 62.17 | 44.35 | 55.65 | 66.09 |
| Resting | 72.73 | 54.55 | 27.27 | 45.45 |
| Eating | 77.14 | 75.75 | 82.05 | 83.12 |
| Working | 91.10 | 96.42 | 93.49 | 95.19 |
| Chatting | 21.74 | 04.35 | 00.00 | 17.39 |
| TV | 77.38 | 75.79 | 81.75 | 81.75 |
| Meeting | 68.73 | 61.00 | 73.36 | 81.47 |
| Cleaning | 26.56 | 30.47 | 38.28 | 46.09 |
| Socializing | 52.85 | 37.31 | 31.60 | 45.08 |
| Shopping | 40.16 | 27.87 | 63.93 | 64.75 |
| Biking | 19.57 | 23.19 | 78.26 | 81.88 |
| Family | 70.82 | 87.42 | 86.69 | 90.15 |
| Hygiene | 52.36 | 46.85 | 51.57 | 62.60 |
| Avg.ClassAccuracy | 54.72 | 50.71 | 57.38 | 65.87 |
| TotalAccuracy | 73.07 | 76.06 | 78.56 | 83.07 | | 0 |
| activity | param. | work-<br>aholic | Arnold | retiree |
| --- | --- | --- | --- | --- |
| sleep | earlystart | 23 | 22 | 22 |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | |
| breakfast | earlystart | 7 | 8 | 7 |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | | | | lunch | earlystart | 12 | 12 | 12 |
| --- | --- | --- | --- | --- |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | |
| dinner | earlystart | 18 | 19 | 18 |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | |
| work | earlystart | 8 | 9 | 8 |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 1,1,1,1,1,0.8,0 | 1,1,0,1,0,0,0 | 0,0,0,0,0,0,0 | |
| workout | earlystart | 19.5 | 16 | 19 |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 0,0,0,0,0,0,0 | 0,0,0,0,0,0,0 | 0,0,0,0,0,0,0 | | | 1 |
| activity | param. | work-<br>aholic | Arnold | retiree |
| --- | --- | --- | --- | --- |
| sleep | earlystart | 23 | 22 | 22 |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | |
| breakfast | earlystart | 7 | 8 | 7 |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | 1,1,1,1,1,1,1 | | | | Working | 91.10 | 96.42 | 93.49 | 95.19 |
| --- | --- | --- | --- | --- |
| Chatting | 21.74 | 04.35 | 00.00 | 17.39 |
| TV | 77.38 | 75.79 | 81.75 | 81.75 |
| Meeting | 68.73 | 61.00 | 73.36 | 81.47 |
| Cleaning | 26.56 | 30.47 | 38.28 | 46.09 |
| Socializing | 52.85 | 37.31 | 31.60 | 45.08 |
| Shopping | 40.16 | 27.87 | 63.93 | 64.75 |
| Biking | 19.57 | 23.19 | 78.26 | 81.88 |
| Family | 70.82 | 87.42 | 86.69 | 90.15 |
| Hygiene | 52.36 | 46.85 | 51.57 | 62.60 |
| Avg.ClassAccuracy | 54.72 | 50.71 | 57.38 | 65.87 |
| TotalAccuracy | 73.07 | 76.06 | 78.56 | 83.07 | | 0 |
| Deviationcategory | 15.1 | 15.2,15.6 |
| --- | --- | --- |
| HarmedRouting | {1,2,3,4,5,6} | {1,2} |
| AffectedStability | {2,5,6} | {2} | | | Non-vulnerability | {7} | {7} |
| --- | --- | --- |
| Total#offounddeviations | 7 | 3 | | 1 |
| Deviationcategory | 15.1 | 15.2,15.6 |
| --- | --- | --- |
| HarmedRouting | {1,2,3,4,5,6} | {1,2} |
| AffectedStability | {2,5,6} | {2} | | | Model | TripAdvisorData | OpenTableData | | |
| --- | --- | --- | --- | --- |
| | | | | |
| OrdinalAspectBias | -557.08 | 1.00 | -493.79 | 1.03 |
| ContinuousAspectBias | -1050.32 | 3.13 | -560.14 | 2.21 |
| OrdinalNoBias | -689.76 | 1.47 | -546.25 | 1.95 |
| ContinuousNoBias | -1904.64 | 3.52 | -651.16 | 2.39 |
| OrdinalGlobalBias | -2438.52 | 2.85 | -570.28 | 2.37 |
| ContinuousGlobalBias | -2632.95 | 3.91 | -595.62 | 2.41 | | 0 |
| Deviationcategory | 15.1 | 15.2,15.6 |
| --- | --- | --- |
| HarmedRouting | {1,2,3,4,5,6} | {1,2} |
| AffectedStability | {2,5,6} | {2} | | | Non-vulnerability | {7} | {7} |
| --- | --- | --- |
| Total#offounddeviations | 7 | 3 | | 1 |
| Deviationcategory | 15.1 | 15.2,15.6 |
| --- | --- | --- |
| HarmedRouting | {1,2,3,4,5,6} | {1,2} |
| AffectedStability | {2,5,6} | {2} | | | ContinuousAspectBias | -1050.32 | 3.13 | -560.14 | 2.21 |
| --- | --- | --- | --- | --- |
| OrdinalNoBias | -689.76 | 1.47 | -546.25 | 1.95 |
| ContinuousNoBias | -1904.64 | 3.52 | -651.16 | 2.39 |
| OrdinalGlobalBias | -2438.52 | 2.85 | -570.28 | 2.37 |
| ContinuousGlobalBias | -2632.95 | 3.91 | -595.62 | 2.41 | | 0 |
| System | Approach | Rank | Acc | L2 |
| --- | --- | --- | --- | --- |
| Baseline* | Rule | 5 | 0.719 | 0.464 | | | | LambdaMART<br>RNN<br>DNN | 1<br>2<br>3 | 0.784<br>0.768<br>0.750 | 0.735<br>0.346<br>0.416 |
| --- | --- | --- | --- | --- |
| | RealCMBP | 2.5 | 0.762 | 0.436 |
| RPN | RPN | 2.5 | 0.757 | 0.374 | | 1 |
| System | Approach | Rank | Acc | L2 |
| --- | --- | --- | --- | --- |
| Baseline* | Rule | 5 | 0.719 | 0.464 | | | System | Approach | Rank | Acc | L2 |
| --- | --- | --- | --- | --- |
| Baseline* | Rule | 6 | 0.575 | 0.691 |
| | RNN<br>Rule<br>IntCMBP | 1<br>2<br>3 | 0.646<br>0.630<br>0.610 | 0.538<br>0.627<br>0.556 |
| | RealCMBP | 1.5 | 0.634 | 0.579 |
| RPN | RPN | 0.5 | 0.650 | 0.549 | | 0 |
| System | Approach | Rank | Acc | L2 |
| --- | --- | --- | --- | --- |
| Baseline* | Rule | 5 | 0.719 | 0.464 | | | | LambdaMART<br>RNN<br>DNN | 1<br>2<br>3 | 0.784<br>0.768<br>0.750 | 0.735<br>0.346<br>0.416 |
| --- | --- | --- | --- | --- |
| | RealCMBP | 2.5 | 0.762 | 0.436 |
| RPN | RPN | 2.5 | 0.757 | 0.374 | | 1 |
| System | Approach | Rank | Acc | L2 |
| --- | --- | --- | --- | --- |
| Baseline* | Rule | 5 | 0.719 | 0.464 | | | | RealCMBP | 1.5 | 0.634 | 0.579 |
| --- | --- | --- | --- | --- |
| RPN | RPN | 0.5 | 0.650 | 0.549 | | 0 |
| Year | Dataset | Pairs | Source |
| --- | --- | --- | --- |
| 2012<br>2012<br>2012<br>2012<br>2012 | MSRpar<br>MSRvid<br>OnWN<br>SMTnews<br>SMTeuroparl | 1500<br>1500<br>750<br>750<br>750 | newswire<br>videos<br>glosses<br>WMTeval.<br>WMTeval. |
| 2013<br>2013<br>2013<br>2013 | HDL<br>FNWN<br>OnWN<br>SMT | 750<br>189<br>561<br>750 | newswire<br>glosses<br>glosses<br>MTeval. |
| 2014<br>2014<br>2014<br>2014<br>2014<br>2014 | HDL<br>OnWN<br>Deft-forum<br>Deft-news<br>Images<br>Tweet-news | 750<br>750<br>450<br>300<br>750<br>750 | newswireheadlines<br>glosses<br>forumposts<br>newssummary<br>imagedescriptions<br>tweet-newspairs |
| 2015<br>2015<br>2015<br>2015<br>2015 | HDL<br>Images<br>Ans.-student<br>Ans.-forum<br>Belief | 750<br>750<br>750<br>375<br>375 | newswireheadlines<br>imagedescriptions<br>studentanswers<br>Q&Aforumanswers<br>committedbelief | | | 2016<br>2016<br>2016<br>2016<br>2016 | HDL<br>Plagiarism<br>post-editing<br>Ans.-Ans.<br>Quest.-Quest. | 249<br>230<br>244<br>254<br>209 | newswireheadlines<br>short-answerplag.<br>MTpostedits<br>Q&Aforumanswers<br>Q&Aforumquestions |
| --- | --- | --- | --- |
| 2017 | Trial | 23 | MixedSTS2016 | | 1 |
| Year | Dataset | Pairs | Source |
| --- | --- | --- | --- |
| 2012<br>2012<br>2012<br>2012<br>2012 | MSRpar<br>MSRvid<br>OnWN<br>SMTnews<br>SMTeuroparl | 1500<br>1500<br>750<br>750<br>750 | newswire<br>videos<br>glosses<br>WMTeval.<br>WMTeval. |
| 2013<br>2013<br>2013<br>2013 | HDL<br>FNWN<br>OnWN<br>SMT | 750<br>189<br>561<br>750 | newswire<br>glosses<br>glosses<br>MTeval. |
| 2014<br>2014<br>2014<br>2014<br>2014<br>2014 | HDL<br>OnWN<br>Deft-forum<br>Deft-news<br>Images<br>Tweet-news | 750<br>750<br>450<br>300<br>750<br>750 | newswireheadlines<br>glosses<br>forumposts<br>newssummary<br>imagedescriptions<br>tweet-newspairs |
| 2015<br>2015<br>2015<br>2015<br>2015 | HDL<br>Images<br>Ans.-student<br>Ans.-forum<br>Belief | 750<br>750<br>750<br>375<br>375 | newswireheadlines<br>imagedescriptions<br>studentanswers<br>Q&Aforumanswers<br>committedbelief | | | WE | Dim | Vocab# | TrainCorpus<br>(Toks#) | POS(Acc) | CHUNK(F1) | NER |
| --- | --- | --- | --- | --- | --- | --- |
| | 80 | 82K | Broadcastnews<br>(400M) | 96.97 | 92.53 | 84.69 |
| | 50 | 130K | RCV1+Wiki<br>(221M+631M) | 97.02 | 93.76 | 89.34 |
| | 300 | 3M | Googlenews<br>(10B) | 96.85 | 92.45 | 85.80 |
| | 100 | 1193K | Twitter(27B) | 97.02 | 93.01 | 87.33 |
| BLSTMWE(10m) | 100 | 100K | USnews(10M) | 96.61 | 91.91 | 84.66 |
| BLSTMWE(100m) | 100 | 100K | USnews(100M) | 97.10 | 93.86 | 86.47 |
| BLSTMWE(all) | 100 | 100K | USnews(536M) | 97.26 | 94.44 | 88.38 |
| BLSTMWE(all)+ | 100 | 113K | USnews(536M) | 97.26 | 94.59 | 89.64 |
| RANDOM | 100 | 100K | N/A | 96.61 | 91.71 | 82.52 | | 0 |
| Year | Dataset | Pairs | Source |
| --- | --- | --- | --- |
| 2012<br>2012<br>2012<br>2012<br>2012 | MSRpar<br>MSRvid<br>OnWN<br>SMTnews<br>SMTeuroparl | 1500<br>1500<br>750<br>750<br>750 | newswire<br>videos<br>glosses<br>WMTeval.<br>WMTeval. |
| 2013<br>2013<br>2013<br>2013 | HDL<br>FNWN<br>OnWN<br>SMT | 750<br>189<br>561<br>750 | newswire<br>glosses<br>glosses<br>MTeval. |
| 2014<br>2014<br>2014<br>2014<br>2014<br>2014 | HDL<br>OnWN<br>Deft-forum<br>Deft-news<br>Images<br>Tweet-news | 750<br>750<br>450<br>300<br>750<br>750 | newswireheadlines<br>glosses<br>forumposts<br>newssummary<br>imagedescriptions<br>tweet-newspairs |
| 2015<br>2015<br>2015<br>2015<br>2015 | HDL<br>Images<br>Ans.-student<br>Ans.-forum<br>Belief | 750<br>750<br>750<br>375<br>375 | newswireheadlines<br>imagedescriptions<br>studentanswers<br>Q&Aforumanswers<br>committedbelief | | | 2016<br>2016<br>2016<br>2016<br>2016 | HDL<br>Plagiarism<br>post-editing<br>Ans.-Ans.<br>Quest.-Quest. | 249<br>230<br>244<br>254<br>209 | newswireheadlines<br>short-answerplag.<br>MTpostedits<br>Q&Aforumanswers<br>Q&Aforumquestions |
| --- | --- | --- | --- |
| 2017 | Trial | 23 | MixedSTS2016 | | 1 |
| Year | Dataset | Pairs | Source |
| --- | --- | --- | --- |
| 2012<br>2012<br>2012<br>2012<br>2012 | MSRpar<br>MSRvid<br>OnWN<br>SMTnews<br>SMTeuroparl | 1500<br>1500<br>750<br>750<br>750 | newswire<br>videos<br>glosses<br>WMTeval.<br>WMTeval. |
| 2013<br>2013<br>2013<br>2013 | HDL<br>FNWN<br>OnWN<br>SMT | 750<br>189<br>561<br>750 | newswire<br>glosses<br>glosses<br>MTeval. |
| 2014<br>2014<br>2014<br>2014<br>2014<br>2014 | HDL<br>OnWN<br>Deft-forum<br>Deft-news<br>Images<br>Tweet-news | 750<br>750<br>450<br>300<br>750<br>750 | newswireheadlines<br>glosses<br>forumposts<br>newssummary<br>imagedescriptions<br>tweet-newspairs |
| 2015<br>2015<br>2015<br>2015<br>2015 | HDL<br>Images<br>Ans.-student<br>Ans.-forum<br>Belief | 750<br>750<br>750<br>375<br>375 | newswireheadlines<br>imagedescriptions<br>studentanswers<br>Q&Aforumanswers<br>committedbelief | | | BLSTMWE(100m) | 100 | 100K | USnews(100M) | 97.10 | 93.86 | 86.47 |
| --- | --- | --- | --- | --- | --- | --- |
| BLSTMWE(all) | 100 | 100K | USnews(536M) | 97.26 | 94.44 | 88.38 |
| BLSTMWE(all)+ | 100 | 113K | USnews(536M) | 97.26 | 94.59 | 89.64 |
| RANDOM | 100 | 100K | N/A | 96.61 | 91.71 | 82.52 | | 0 |
| -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 |
| --- |
| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 |
| -classe1820(attendus=169,ramenes=321.00,corrects=41.00)<br>rappel=0.243precision=0.128f-mesure=0.167 |
| -classe1830(attendus=169,ramenes=422.00,corrects=40.00)<br>rappel=0.237precision=0.095f-mesure=0.135 |
| -classe1840(attendus=169,ramenes=83.00,corrects=9.00)<br>rappel=0.053precision=0.108f-mesure=0.071 |
| -classe1850(attendus=169,ramenes=166.00,corrects=13.00)<br>rappel=0.077precision=0.078f-mesure=0.078 |
| -classe1860(attendus=170,ramenes=111.00,corrects=8.00)<br>rappel=0.047precision=0.072f-mesure=0.057 |
| -classe1870(attendus=169,ramenes=82.00,corrects=7.00)<br>rappel=0.041precision=0.085f-mesure=0.056 |
| -classe1880(attendus=169,ramenes=118.00,corrects=13.00)<br>rappel=0.077precision=0.110f-mesure=0.091 | | | -classe1890(attendus=197,ramenes=78.00,corrects=9.00)<br>rappel=0.046precision=0.115f-mesure=0.065 |
| --- |
| -classe1900(attendus=203,ramenes=135.00,corrects=16.00)<br>rappel=0.079precision=0.119f-mesure=0.095 |
| -classe1910(attendus=201,ramenes=67.00,corrects=6.00)<br>rappel=0.030precision=0.090f-mesure=0.045 |
| -classe1920(attendus=200,ramenes=143.00,corrects=18.00)<br>rappel=0.090precision=0.126f-mesure=0.105 |
| -classe1930(attendus=200,ramenes=80.00,corrects=9.00)<br>rappel=0.045precision=0.113f-mesure=0.064 |
| -classe1940(attendus=198,ramenes=620.00,corrects=93.00)<br>rappel=0.470precision=0.150f-mesure=0.227 |
| -surl’ensembledes15classes<br>macrorappel=0.115macroprecision=0.106macroF-mesure=0.110 | | 1 |
| -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 |
| --- |
| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 |
| -classe1820(attendus=169,ramenes=321.00,corrects=41.00)<br>rappel=0.243precision=0.128f-mesure=0.167 |
| -classe1830(attendus=169,ramenes=422.00,corrects=40.00)<br>rappel=0.237precision=0.095f-mesure=0.135 |
| -classe1840(attendus=169,ramenes=83.00,corrects=9.00)<br>rappel=0.053precision=0.108f-mesure=0.071 |
| -classe1850(attendus=169,ramenes=166.00,corrects=13.00)<br>rappel=0.077precision=0.078f-mesure=0.078 |
| -classe1860(attendus=170,ramenes=111.00,corrects=8.00)<br>rappel=0.047precision=0.072f-mesure=0.057 |
| -classe1870(attendus=169,ramenes=82.00,corrects=7.00)<br>rappel=0.041precision=0.085f-mesure=0.056 |
| -classe1880(attendus=169,ramenes=118.00,corrects=13.00)<br>rappel=0.077precision=0.110f-mesure=0.091 | | | -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 |
| --- |
| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 |
| -classe1820(attendus=169,ramenes=321.00,corrects=41.00)<br>rappel=0.243precision=0.128f-mesure=0.167 |
| -classe1830(attendus=169,ramenes=422.00,corrects=40.00)<br>rappel=0.237precision=0.095f-mesure=0.135 |
| -classe1840(attendus=169,ramenes=83.00,corrects=9.00)<br>rappel=0.053precision=0.108f-mesure=0.071 |
| -classe1850(attendus=169,ramenes=166.00,corrects=13.00)<br>rappel=0.077precision=0.078f-mesure=0.078 |
| -classe1860(attendus=170,ramenes=111.00,corrects=8.00)<br>rappel=0.047precision=0.072f-mesure=0.057 |
| -classe1870(attendus=169,ramenes=82.00,corrects=7.00)<br>rappel=0.041precision=0.085f-mesure=0.056 |
| -classe1880(attendus=169,ramenes=118.00,corrects=13.00)<br>rappel=0.077precision=0.110f-mesure=0.091 |
| -classe1890(attendus=197,ramenes=78.00,corrects=9.00)<br>rappel=0.046precision=0.115f-mesure=0.065 |
| -classe1900(attendus=203,ramenes=135.00,corrects=16.00)<br>rappel=0.079precision=0.119f-mesure=0.095 |
| -classe1910(attendus=201,ramenes=67.00,corrects=6.00)<br>rappel=0.030precision=0.090f-mesure=0.045 |
| -classe1920(attendus=200,ramenes=143.00,corrects=18.00)<br>rappel=0.090precision=0.126f-mesure=0.105 |
| -classe1930(attendus=200,ramenes=80.00,corrects=9.00)<br>rappel=0.045precision=0.113f-mesure=0.064 |
| -classe1940(attendus=198,ramenes=620.00,corrects=93.00)<br>rappel=0.470precision=0.150f-mesure=0.227 |
| -surl’ensembledes15classes<br>macrorappel=0.115macroprecision=0.106macroF-mesure=0.110 | | 0 |
| -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 |
| --- |
| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 | | | -classe1820(attendus=169,ramenes=321.00,corrects=41.00)<br>rappel=0.243precision=0.128f-mesure=0.167 |
| --- |
| -classe1830(attendus=169,ramenes=422.00,corrects=40.00)<br>rappel=0.237precision=0.095f-mesure=0.135 |
| -classe1840(attendus=169,ramenes=83.00,corrects=9.00)<br>rappel=0.053precision=0.108f-mesure=0.071 |
| -classe1850(attendus=169,ramenes=166.00,corrects=13.00)<br>rappel=0.077precision=0.078f-mesure=0.078 |
| -classe1860(attendus=170,ramenes=111.00,corrects=8.00)<br>rappel=0.047precision=0.072f-mesure=0.057 |
| -classe1870(attendus=169,ramenes=82.00,corrects=7.00)<br>rappel=0.041precision=0.085f-mesure=0.056 |
| -classe1880(attendus=169,ramenes=118.00,corrects=13.00)<br>rappel=0.077precision=0.110f-mesure=0.091 |
| -classe1890(attendus=197,ramenes=78.00,corrects=9.00)<br>rappel=0.046precision=0.115f-mesure=0.065 |
| -classe1900(attendus=203,ramenes=135.00,corrects=16.00)<br>rappel=0.079precision=0.119f-mesure=0.095 |
| -classe1910(attendus=201,ramenes=67.00,corrects=6.00)<br>rappel=0.030precision=0.090f-mesure=0.045 |
| -classe1920(attendus=200,ramenes=143.00,corrects=18.00)<br>rappel=0.090precision=0.126f-mesure=0.105 |
| -classe1930(attendus=200,ramenes=80.00,corrects=9.00)<br>rappel=0.045precision=0.113f-mesure=0.064 |
| -classe1940(attendus=198,ramenes=620.00,corrects=93.00)<br>rappel=0.470precision=0.150f-mesure=0.227 |
| -surl’ensembledes15classes<br>macrorappel=0.115macroprecision=0.106macroF-mesure=0.110 | | 1 |
| -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 |
| --- |
| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 | | | -classe1830(attendus=169,ramenes=422.00,corrects=40.00)<br>rappel=0.237precision=0.095f-mesure=0.135 |
| --- |
| -classe1840(attendus=169,ramenes=83.00,corrects=9.00)<br>rappel=0.053precision=0.108f-mesure=0.071 |
| -classe1850(attendus=169,ramenes=166.00,corrects=13.00)<br>rappel=0.077precision=0.078f-mesure=0.078 |
| -classe1860(attendus=170,ramenes=111.00,corrects=8.00)<br>rappel=0.047precision=0.072f-mesure=0.057 |
| -classe1870(attendus=169,ramenes=82.00,corrects=7.00)<br>rappel=0.041precision=0.085f-mesure=0.056 |
| -classe1880(attendus=169,ramenes=118.00,corrects=13.00)<br>rappel=0.077precision=0.110f-mesure=0.091 |
| -classe1890(attendus=197,ramenes=78.00,corrects=9.00)<br>rappel=0.046precision=0.115f-mesure=0.065 |
| -classe1900(attendus=203,ramenes=135.00,corrects=16.00)<br>rappel=0.079precision=0.119f-mesure=0.095 |
| -classe1910(attendus=201,ramenes=67.00,corrects=6.00)<br>rappel=0.030precision=0.090f-mesure=0.045 |
| -classe1920(attendus=200,ramenes=143.00,corrects=18.00)<br>rappel=0.090precision=0.126f-mesure=0.105 |
| -classe1930(attendus=200,ramenes=80.00,corrects=9.00)<br>rappel=0.045precision=0.113f-mesure=0.064 |
| -classe1940(attendus=198,ramenes=620.00,corrects=93.00)<br>rappel=0.470precision=0.150f-mesure=0.227 |
| -surl’ensembledes15classes<br>macrorappel=0.115macroprecision=0.106macroF-mesure=0.110 | | 0 |
| Parameter | Value |
| --- | --- |
| Simulationruns<br>hlineAPs | 1000<br>1 |
| BTSs | 1 |
| APpeakthroughput | 36Mbps | | | BTSpeakthroughput | 20Mbps |
| --- | --- |
| SLC1airitmebid | 1.4 |
| SLC2airitmebid | 0.6 |
| SlicesperAP | 2 |
| SlicesperBTS | 2 | | 1 |
| Parameter | Value |
| --- | --- |
| Simulationruns<br>hlineAPs | 1000<br>1 |
| BTSs | 1 |
| APpeakthroughput | 36Mbps | | | Parameter | Value |
| --- | --- |
| L2CacheSize | 512KB |
| L1CacheSize | 16KB |
| L2CachelineSize | 32bytes |
| L1CachelineSize | 32bytes |
| BMissPenalty2 | 110ns |
| BMissPenalty1 | 16.25ns |
| TLBEntries | 64 |
| CompCostNode | 30ns |
| W(MemoryBandwidth)1 | 647MB/s |
| W(NetworkBandwidth)2 | 138MB/s | | 0 |
| Parameter | Value |
| --- | --- |
| Simulationruns<br>hlineAPs | 1000<br>1 |
| BTSs | 1 |
| APpeakthroughput | 36Mbps |
| BTSpeakthroughput | 20Mbps | | | SLC1airitmebid | 1.4 |
| --- | --- |
| SLC2airitmebid | 0.6 |
| SlicesperAP | 2 |
| SlicesperBTS | 2 | | 1 |
| Parameter | Value |
| --- | --- |
| Simulationruns<br>hlineAPs | 1000<br>1 |
| BTSs | 1 |
| APpeakthroughput | 36Mbps |
| BTSpeakthroughput | 20Mbps | | | L1CachelineSize | 32bytes |
| --- | --- |
| BMissPenalty2 | 110ns |
| BMissPenalty1 | 16.25ns |
| TLBEntries | 64 |
| CompCostNode | 30ns |
| W(MemoryBandwidth)1 | 647MB/s |
| W(NetworkBandwidth)2 | 138MB/s | | 0 |
| Dataset | Size | Dimensions | Classes |
| --- | --- | --- | --- |
| COIL-20 | 1,440 | 1,024 | 20 |
| PenDigits | 7,494 | 16 | 10 | | | USPS | 9,298 | 256 | 10 |
| --- | --- | --- | --- |
| MNIST | 70,000 | 784 | 10 |
| RCV1 | 193,844 | 47,236 | 103 | | 1 |
| Dataset | Size | Dimensions | Classes |
| --- | --- | --- | --- |
| COIL-20 | 1,440 | 1,024 | 20 |
| PenDigits | 7,494 | 16 | 10 | | | Dataset | N | K | D | d |
| --- | --- | --- | --- | --- |
| Hopkins-155 | 39-556 | 2-3 | 30-200 | 3 |
| Yale | 2432 | 38 | 2016 | 9 |
| COIL-20 | 1440 | 20 | 1024 | 9 |
| COIL-100 | 7200 | 100 | 1024 | 9 |
| USPS | 9298 | 10 | 256 | 15 |
| MNIST-10k | 10000 | 10 | 500 | 3 | | 0 |
| Dataset | Size | Dimensions | Classes |
| --- | --- | --- | --- |
| COIL-20 | 1,440 | 1,024 | 20 | | | PenDigits | 7,494 | 16 | 10 |
| --- | --- | --- | --- |
| USPS | 9,298 | 256 | 10 |
| MNIST | 70,000 | 784 | 10 |
| RCV1 | 193,844 | 47,236 | 103 | | 1 |
| Dataset | Size | Dimensions | Classes |
| --- | --- | --- | --- |
| COIL-20 | 1,440 | 1,024 | 20 | | | COIL-20 | 1440 | 20 | 1024 | 9 |
| --- | --- | --- | --- | --- |
| COIL-100 | 7200 | 100 | 1024 | 9 |
| USPS | 9298 | 10 | 256 | 15 |
| MNIST-10k | 10000 | 10 | 500 | 3 | | 0 |
| 7.1 | 6.3 | 4.2 | 6.2 | 3.1 | 2.6 | 4.9 | 6.8 | 2.6 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 6.3 | 5.5 | 9.9 | 12.2 | 132.8 | 2.8 | 4.9 | 6.5 | 2.6 |
| 5.4 | 5.0 | 4.1 | 6.0 | 3.5 | 2.7 | 4.0 | 5.4 | 2.5 |
| 5.1 | 5.0 | 4.5 | 6.7 | 3.8 | 2.5 | 3.5 | 5.4 | 2.2 | | | 6.0 | 5.6 | 7.6 | 9.8 | 11.4 | 2.5 | 5.1 | 7.3 | 2.5 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 10.8 | 12.2 | 6.6 | 10.0 | 4.3 | 4.4 | 8.2 | 11.0 | 4.3 | | 1 |
| 7.1 | 6.3 | 4.2 | 6.2 | 3.1 | 2.6 | 4.9 | 6.8 | 2.6 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 6.3 | 5.5 | 9.9 | 12.2 | 132.8 | 2.8 | 4.9 | 6.5 | 2.6 |
| 5.4 | 5.0 | 4.1 | 6.0 | 3.5 | 2.7 | 4.0 | 5.4 | 2.5 |
| 5.1 | 5.0 | 4.5 | 6.7 | 3.8 | 2.5 | 3.5 | 5.4 | 2.2 | | | 20.6 | 18.0 | 10.8 | 16.0 | 8.3 | 7.1 | 14.1 | 18.1 | 6.9 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 18.0 | 6.8 | 21.2 | 26.5 | 193.7 | 7.9 | 13.3 | 18.1 | 6.8 |
| 15.1 | 14.0 | 10.7 | 15.8 | 9.2 | 6.8 | 11.1 | 14.2 | 6.3 |
| 14.4 | 13.6 | 14.7 | 20.2 | 17.7 | 6.5 | 9.7 | 14.4 | 5.8 |
| 19.1 | 17.1 | 15.4 | 20.7 | 18.6 | 7.1 | 15.7 | 22.3 | 6.7 | | 0 |
| 7.1 | 6.3 | 4.2 | 6.2 | 3.1 | 2.6 | 4.9 | 6.8 | 2.6 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 6.3 | 5.5 | 9.9 | 12.2 | 132.8 | 2.8 | 4.9 | 6.5 | 2.6 |
| 5.4 | 5.0 | 4.1 | 6.0 | 3.5 | 2.7 | 4.0 | 5.4 | 2.5 |
| 5.1 | 5.0 | 4.5 | 6.7 | 3.8 | 2.5 | 3.5 | 5.4 | 2.2 | | | 6.0 | 5.6 | 7.6 | 9.8 | 11.4 | 2.5 | 5.1 | 7.3 | 2.5 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 10.8 | 12.2 | 6.6 | 10.0 | 4.3 | 4.4 | 8.2 | 11.0 | 4.3 | | 1 |
| 7.1 | 6.3 | 4.2 | 6.2 | 3.1 | 2.6 | 4.9 | 6.8 | 2.6 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 6.3 | 5.5 | 9.9 | 12.2 | 132.8 | 2.8 | 4.9 | 6.5 | 2.6 |
| 5.4 | 5.0 | 4.1 | 6.0 | 3.5 | 2.7 | 4.0 | 5.4 | 2.5 |
| 5.1 | 5.0 | 4.5 | 6.7 | 3.8 | 2.5 | 3.5 | 5.4 | 2.2 | | | 15.1 | 14.0 | 10.7 | 15.8 | 9.2 | 6.8 | 11.1 | 14.2 | 6.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 14.4 | 13.6 | 14.7 | 20.2 | 17.7 | 6.5 | 9.7 | 14.4 | 5.8 |
| 19.1 | 17.1 | 15.4 | 20.7 | 18.6 | 7.1 | 15.7 | 22.3 | 6.7 | | 0 |
| Foresttoagri. | Agri.toforest | Agri.tobuilt-up |
| --- | --- | --- |
| conv-128 | conv-128 | conv-256 |
| spatialweight | | |
| maxpooling | | |
| conv-256 | conv-256 | conv-512 |
| spatialweight | | |
| maxpooling | | |
| conv-512 | conv-512 | conv-1024 | | | spatialweight | | |
| --- | --- | --- |
| conv-1024 | conv-1024 | conv-2048 |
| spatialweight | | |
| globalaveragepooling | | |
| dense-1036<br>dense-400<br>dense-80<br>dense-7 | dense-1036<br>dense-400<br>dense-80<br>dense-7 | dense-2048<br>dense-800<br>dense-300<br>dense-120<br>dense-60 |
| Sigmoid | | | | 1 |
| Foresttoagri. | Agri.toforest | Agri.tobuilt-up |
| --- | --- | --- |
| conv-128 | conv-128 | conv-256 |
| spatialweight | | |
| maxpooling | | |
| conv-256 | conv-256 | conv-512 |
| spatialweight | | |
| maxpooling | | |
| conv-512 | conv-512 | conv-1024 | | | Method | Size | Oxf5k | Oxf105k | Oxf1M | Paris6k | Paris1M |
| --- | --- | --- | --- | --- | --- | --- |
| Max-pooling | 256 | 53.3 | - | - | 67.0 | - |
| SPoC | 256 | 53.1 | 50.1 | - | - | - |
| MAC | 256 | 56.9 | 47.8 | - | 72.4 | - |
| NetVLAD | 256 | 63.5 | - | - | 73.5 | - |
| CroW | 256 | 65.4 | 59.3 | - | 77.9 | - |
| Ngetal | 128 | 59.3 | - | - | 59.0 | - |
| MAC* | 128 | 76.8 | 70.8 | 60.1 | 78.8 | 62.5 |
| SUMPool | 128 | 72.6 | 67.7 | 57.9 | 78.4 | 62.4 |
| SIAM-FV | 128 | 77.3 | 71.8 | 62.5 | 78.9 | 63.2 | | 0 |
| Foresttoagri. | Agri.toforest | Agri.tobuilt-up |
| --- | --- | --- |
| conv-128 | conv-128 | conv-256 |
| spatialweight | | |
| maxpooling | | |
| conv-256 | conv-256 | conv-512 |
| spatialweight | | |
| maxpooling | | | | | conv-512 | conv-512 | conv-1024 |
| --- | --- | --- |
| spatialweight | | |
| conv-1024 | conv-1024 | conv-2048 |
| spatialweight | | |
| globalaveragepooling | | |
| dense-1036<br>dense-400<br>dense-80<br>dense-7 | dense-1036<br>dense-400<br>dense-80<br>dense-7 | dense-2048<br>dense-800<br>dense-300<br>dense-120<br>dense-60 |
| Sigmoid | | | | 1 |
| Foresttoagri. | Agri.toforest | Agri.tobuilt-up |
| --- | --- | --- |
| conv-128 | conv-128 | conv-256 |
| spatialweight | | |
| maxpooling | | |
| conv-256 | conv-256 | conv-512 |
| spatialweight | | |
| maxpooling | | | | | CroW | 256 | 65.4 | 59.3 | - | 77.9 | - |
| --- | --- | --- | --- | --- | --- | --- |
| Ngetal | 128 | 59.3 | - | - | 59.0 | - |
| MAC* | 128 | 76.8 | 70.8 | 60.1 | 78.8 | 62.5 |
| SUMPool | 128 | 72.6 | 67.7 | 57.9 | 78.4 | 62.4 |
| SIAM-FV | 128 | 77.3 | 71.8 | 62.5 | 78.9 | 63.2 | | 0 |
| | CNN | DailyMail | | |
| --- | --- | --- | --- | --- |
| | Train | Test | Train | Test |
| Frequency | 33.2 | 35.0 | 33.4 | 32.4 |
| WA | 47.3 | 50.6 | 54.3 | 53.4 |
| nBOW | 40.3 | 43.5 | 48.7 | 47.8 | | | WMD | 41.2 | 44.0 | 49.3 | 48.5 |
| --- | --- | --- | --- | --- |
| FS | 18.9 | 22.0 | 25.2 | 24.3 | | 1 |
| | CNN | DailyMail | | |
| --- | --- | --- | --- | --- |
| | Train | Test | Train | Test |
| Frequency | 33.2 | 35.0 | 33.4 | 32.4 |
| WA | 47.3 | 50.6 | 54.3 | 53.4 |
| nBOW | 40.3 | 43.5 | 48.7 | 47.8 | | | | CNN | Dailymail | | |
| --- | --- | --- | --- | --- |
| #Training | Val | Test | Val | Test |
| 10 | 35.2 | 41.6 | 45.7 | 43.6 |
| 20 | 57.4 | 55.1 | 57.2 | 56.2 |
| 30 | 56.7 | 60.2 | 61.4 | 60.3 |
| 40 | 57 | 60 | 62.3 | 61.3 |
| 50 | 60.3 | 63.1 | 63.5 | 62.5 |
| 100 | 61.5 | 63.9 | 65.4 | 64.6 |
| 200 | 62.5 | 64.9 | 67.3 | 65.2 |
| 500 | 62.8 | 65 | 67.5 | 66.3 |
| 1000 | 62.9 | 65.2 | 68.3 | 66.7 |
| 2000 | 63.2 | 65.2 | 69.0 | 67.3 |
| 5000 | 64.3 | 65.8 | 69.1 | 67.3 | | 0 |
| | CNN | DailyMail | | |
| --- | --- | --- | --- | --- |
| | Train | Test | Train | Test |
| Frequency | 33.2 | 35.0 | 33.4 | 32.4 |
| WA | 47.3 | 50.6 | 54.3 | 53.4 | | | nBOW | 40.3 | 43.5 | 48.7 | 47.8 |
| --- | --- | --- | --- | --- |
| WMD | 41.2 | 44.0 | 49.3 | 48.5 |
| FS | 18.9 | 22.0 | 25.2 | 24.3 | | 1 |
| | CNN | DailyMail | | |
| --- | --- | --- | --- | --- |
| | Train | Test | Train | Test |
| Frequency | 33.2 | 35.0 | 33.4 | 32.4 |
| WA | 47.3 | 50.6 | 54.3 | 53.4 | | | 2000 | 63.2 | 65.2 | 69.0 | 67.3 |
| --- | --- | --- | --- | --- |
| 5000 | 64.3 | 65.8 | 69.1 | 67.3 | | 0 |
| LCSeg | | | |
| --- | --- | --- | --- |
| Sampling | R-1 | R-2 | R-1 |
| Weight<br>Resampling<br>SMOTE | 0.660<br>0.666<br>0.673 | 0.141<br>0.142<br>0.145 | 0.663<br>0.674<br>0.675 | | | Weight<br>Resampling<br>SMOTE | 0.679<br>0.705<br>0.694 | 0.147<br>0.158<br>0.152 | 0.702<br>0.703<br>0.695 |
| --- | --- | --- | --- |
| Weight<br>Resampling<br>SMOTE | 0.490<br>0.563<br>0.525 | 0.114<br>0.143<br>0.123 | 0.473<br>0.556<br>0.567 | | 1 |
| LCSeg | | | |
| --- | --- | --- | --- |
| Sampling | R-1 | R-2 | R-1 |
| Weight<br>Resampling<br>SMOTE | 0.660<br>0.666<br>0.673 | 0.141<br>0.142<br>0.145 | 0.663<br>0.674<br>0.675 | | | LCSeg | | | | | |
| --- | --- | --- | --- | --- | --- |
| Sampling | Pre | Rec | F | Pre | Rec |
| Weight<br>Resampling<br>SMOTE | 0.792<br>0.796<br>0.831 | 0.787<br>0.790<br>0.804 | 0.786<br>0.789<br>0.811 | 0.788<br>0.791<br>0.830 | 0.784<br>0.787<br>0.807 |
| Weight<br>Resampling<br>SMOTE | 0.726<br>0.892<br>0.817 | 0.722<br>0.889<br>0.821 | 0.721<br>0.888<br>0.819 | 0.730<br>0.880<br>0.817 | 0.727<br>0.877<br>0.820 |
| Weight<br>Resampling<br>SMOTE | 0.623<br>0.752<br>0.660 | 0.591<br>0.741<br>0.694 | 0.563<br>0.739<br>0.667 | 0.601<br>0.690<br>0.691 | 0.584<br>0.689<br>0.711 | | 0 |
| LCSeg | | | |
| --- | --- | --- | --- |
| Sampling | R-1 | R-2 | R-1 |
| Weight<br>Resampling<br>SMOTE | 0.660<br>0.666<br>0.673 | 0.141<br>0.142<br>0.145 | 0.663<br>0.674<br>0.675 | | | Weight<br>Resampling<br>SMOTE | 0.679<br>0.705<br>0.694 | 0.147<br>0.158<br>0.152 | 0.702<br>0.703<br>0.695 |
| --- | --- | --- | --- |
| Weight<br>Resampling<br>SMOTE | 0.490<br>0.563<br>0.525 | 0.114<br>0.143<br>0.123 | 0.473<br>0.556<br>0.567 | | 1 |
| LCSeg | | | |
| --- | --- | --- | --- |
| Sampling | R-1 | R-2 | R-1 |
| Weight<br>Resampling<br>SMOTE | 0.660<br>0.666<br>0.673 | 0.141<br>0.142<br>0.145 | 0.663<br>0.674<br>0.675 | | | Weight<br>Resampling<br>SMOTE | 0.726<br>0.892<br>0.817 | 0.722<br>0.889<br>0.821 | 0.721<br>0.888<br>0.819 | 0.730<br>0.880<br>0.817 | 0.727<br>0.877<br>0.820 |
| --- | --- | --- | --- | --- | --- |
| Weight<br>Resampling<br>SMOTE | 0.623<br>0.752<br>0.660 | 0.591<br>0.741<br>0.694 | 0.563<br>0.739<br>0.667 | 0.601<br>0.690<br>0.691 | 0.584<br>0.689<br>0.711 | | 0 |
| n | degree | closeness | betweenness | random-walk |
| --- | --- | --- | --- | --- |
| 1 | {10} | {35} | {35} | {10} | | | 2 | {10,25} | {10,25} | {25,35} | {10,25} |
| --- | --- | --- | --- | --- |
| 3 | {10,25,39} | {10,25,39} | {10,25,35} | {10,25,39} |
| 4 | {10,17,25,39},...(87) | {10,17,25,39},...(87) | {10,17,25,35} | {10,17,25,39} | | 1 |
| n | degree | closeness | betweenness | random-walk |
| --- | --- | --- | --- | --- |
| 1 | {10} | {35} | {35} | {10} | | | n | degree | closeness | betweenness | random-walk |
| --- | --- | --- | --- | --- |
| 1 | {29} | {9} | {9} | {29} |
| 2 | {26,29} | {26,29} | {9,29} | {26,29} |
| 3 | {10,12,26} | {10,12,26} | {9,26,29} | {10,12,26} |
| 4 | {10,12,26,32},...(4) | {10,12,24,26},...(4) | {10,12,26,29} | {10,12,26,29} | | 0 |
| n | degree | closeness | betweenness | random-walk |
| --- | --- | --- | --- | --- |
| 1 | {10} | {35} | {35} | {10} | | | 2 | {10,25} | {10,25} | {25,35} | {10,25} |
| --- | --- | --- | --- | --- |
| 3 | {10,25,39} | {10,25,39} | {10,25,35} | {10,25,39} |
| 4 | {10,17,25,39},...(87) | {10,17,25,39},...(87) | {10,17,25,35} | {10,17,25,39} | | 1 |
| n | degree | closeness | betweenness | random-walk |
| --- | --- | --- | --- | --- |
| 1 | {10} | {35} | {35} | {10} | | | 3 | {10,12,26} | {10,12,26} | {9,26,29} | {10,12,26} |
| --- | --- | --- | --- | --- |
| 4 | {10,12,26,32},...(4) | {10,12,24,26},...(4) | {10,12,26,29} | {10,12,26,29} | | 0 |
| Model | KL-weight | D-ScoreMNIST | D-ScoreChairs |
| --- | --- | --- | --- |
| Ours | 1 | 6.71 | 1.79 |
| Ours | 2 | 6.74 | 1.73 |
| Ours | 4 | 6.82 | 1.81 |
| Slow | 1 | 4.70 | 1.19 |
| Slow | 2 | 5.78 | 1.57 | | | Slow | 4 | 6.38 | 1.39 |
| --- | --- | --- | --- |
| VAE | 1 | 1.08 | 1.27 |
| VAE | 2 | 1.70 | 1.24 |
| VAE | 4 | 1.71 | 1.35 | | 1 |
| Model | KL-weight | D-ScoreMNIST | D-ScoreChairs |
| --- | --- | --- | --- |
| Ours | 1 | 6.71 | 1.79 |
| Ours | 2 | 6.74 | 1.73 |
| Ours | 4 | 6.82 | 1.81 |
| Slow | 1 | 4.70 | 1.19 |
| Slow | 2 | 5.78 | 1.57 | | | Model | KL-weight | Acc4 | Acc2 |
| --- | --- | --- | --- |
| Ours | 1 | 0.83 | 0.71 |
| Ours | 2 | 0.88 | 0.64 |
| Ours | 4 | 0.82 | 0.69 |
| Slow | 1 | 0.58 | 0.11 |
| Slow | 2 | 0.65 | 0.12 |
| Slow | 4 | 0.66 | 0.13 |
| VAE | 1 | 0.80 | 0.13 |
| VAE | 2 | 0.52 | 0.12 |
| VAE | 4 | 0.51 | 0.13 | | 0 |
| Model | KL-weight | D-ScoreMNIST | D-ScoreChairs |
| --- | --- | --- | --- |
| Ours | 1 | 6.71 | 1.79 |
| Ours | 2 | 6.74 | 1.73 | | | Ours | 4 | 6.82 | 1.81 |
| --- | --- | --- | --- |
| Slow | 1 | 4.70 | 1.19 |
| Slow | 2 | 5.78 | 1.57 |
| Slow | 4 | 6.38 | 1.39 |
| VAE | 1 | 1.08 | 1.27 |
| VAE | 2 | 1.70 | 1.24 |
| VAE | 4 | 1.71 | 1.35 | | 1 |
| Model | KL-weight | D-ScoreMNIST | D-ScoreChairs |
| --- | --- | --- | --- |
| Ours | 1 | 6.71 | 1.79 |
| Ours | 2 | 6.74 | 1.73 | | | Ours | 2 | 0.88 | 0.64 |
| --- | --- | --- | --- |
| Ours | 4 | 0.82 | 0.69 |
| Slow | 1 | 0.58 | 0.11 |
| Slow | 2 | 0.65 | 0.12 |
| Slow | 4 | 0.66 | 0.13 |
| VAE | 1 | 0.80 | 0.13 |
| VAE | 2 | 0.52 | 0.12 |
| VAE | 4 | 0.51 | 0.13 | | 0 |
| Entity/Calculations | Buyer | Seller |
| --- | --- | --- |
| Exponentiation | 1+log(p) | p |
| Division | log(p) | 0 | | | Signing | 0 | log(p) |
| --- | --- | --- |
| Total | 1+2log(p) | p+log(p) | | 1 |
| Entity/Calculations | Buyer | Seller |
| --- | --- | --- |
| Exponentiation | 1+log(p) | p |
| Division | log(p) | 0 | | | Model | Realpurchases | Predictedpurchases |
| --- | --- | --- |
| with12-hourobservationbaseline1 | 129 | 32 |
| with12-hourobservationbaseline2 | 129 | 245 |
| with12-hourobservationSP | 129 | 110 |
| Model | Realpurchases | Predictedpurchases |
| with12-hourobservationbaseline1 | 75 | 28 |
| with12-hourobservationbaseline2 | 75 | 147 |
| with12-hourobservationSP | 75 | 110 | | 0 |
| Entity/Calculations | Buyer | Seller |
| --- | --- | --- |
| Exponentiation | 1+log(p) | p | | | Division | log(p) | 0 |
| --- | --- | --- |
| Signing | 0 | log(p) |
| Total | 1+2log(p) | p+log(p) | | 1 |
| Entity/Calculations | Buyer | Seller |
| --- | --- | --- |
| Exponentiation | 1+log(p) | p | | | with12-hourobservationbaseline1 | 75 | 28 |
| --- | --- | --- |
| with12-hourobservationbaseline2 | 75 | 147 |
| with12-hourobservationSP | 75 | 110 | | 0 |
| | HR | s=3 | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| LR-3 | Non-Joint-3 | Joint-3 | Joint-OA | LR-4 | Non-Joint-4 | Joint-4 | | |
| RMSE | 0.146 | 0.142 | 0.121 | 0.132 | 0.129 | 0.155 | 0.123 | 0.127 |
| CC | 0.430 | 0.392 | 0.363 | 0.396 | 0.399 | 0.381 | 0.354 | 0.380 |
| CCC | 0.325 | 0.302 | 0.293 | 0.323 | 0.328 | 0.283 | 0.281 | 0.319 |
| | HR | s=6 | | | | | | |
| LR-6 | Non-Joint-6 | Joint-6 | Joint-OA | LR-8 | Non-Joint-8 | Joint-8 | | |
| RMSE | 0.146 | 0.149 | 0.128 | 0.127 | 0.134 | 0.161 | 0.129 | 0.134 |
| CC | 0.430 | 0.300 | 0.344 | 0.325 | 0.375 | 0.323 | 0.317 | 0.320 |
| CCC | 0.325 | 0.263 | 0.280 | 0.274 | 0.309 | 0.238 | 0.265 | 0.266 | | | | HR | s=12 | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| LR-12 | Non-Joint-12 | Joint-12 | Joint-OA | LR-16 | Non-Joint-16 | Joint-16 | | |
| RMSE | 0.146 | 0.143 | 0.126 | 0.125 | 0.132 | 0.137 | 0.124 | 0.137 |
| CC | 0.430 | 0.291 | 0.287 | 0.246 | 0.308 | 0.316 | 0.244 | 0.219 |
| CCC | 0.325 | 0.224 | 0.235 | 0.204 | 0.223 | 0.212 | 0.191 | 0.192 | | 1 |
| | HR | s=3 | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| LR-3 | Non-Joint-3 | Joint-3 | Joint-OA | LR-4 | Non-Joint-4 | Joint-4 | | |
| RMSE | 0.146 | 0.142 | 0.121 | 0.132 | 0.129 | 0.155 | 0.123 | 0.127 |
| CC | 0.430 | 0.392 | 0.363 | 0.396 | 0.399 | 0.381 | 0.354 | 0.380 |
| CCC | 0.325 | 0.302 | 0.293 | 0.323 | 0.328 | 0.283 | 0.281 | 0.319 |
| | HR | s=6 | | | | | | |
| LR-6 | Non-Joint-6 | Joint-6 | Joint-OA | LR-8 | Non-Joint-8 | Joint-8 | | |
| RMSE | 0.146 | 0.149 | 0.128 | 0.127 | 0.134 | 0.161 | 0.129 | 0.134 |
| CC | 0.430 | 0.300 | 0.344 | 0.325 | 0.375 | 0.323 | 0.317 | 0.320 |
| CCC | 0.325 | 0.263 | 0.280 | 0.274 | 0.309 | 0.238 | 0.265 | 0.266 | | | | m2 | m3 | m4 | m5 | m6 |
| --- | --- | --- | --- | --- | --- |
| Nsw | 9530 | 9530 | 9530 | 9530 | 9530 |
| N | 9159 | 9159 | 9159 | 9159 | 9159 |
| Ksw | 22305 | 43894 | 64161 | 83192 | 101104 |
| K | 14627 | 28494 | 41472 | 53596 | 64840 |
| Lsw | 3.59 | 2.92 | 2.70 | 2.55 | 2.45 |
| L | 6.42 | 4.73 | 4.12 | 3.79 | 3.58 |
| Dsw | 16 | 9 | 7 | 6 | 6 |
| D | 26 | 15 | 11 | 10 | 8 |
| Csw | 0.15 | 0.55 | 0.63 | 0.66 | 0.68 |
| C | 0.01 | 0.47 | 0.56 | 0.60 | 0.64 |
| ωsw | 5 | 5 | 5 | 5 | 5 |
| ω | 15 | 15 | 15 | 15 | 15 | | 0 |
| | HR | s=3 | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| LR-3 | Non-Joint-3 | Joint-3 | Joint-OA | LR-4 | Non-Joint-4 | Joint-4 | | |
| RMSE | 0.146 | 0.142 | 0.121 | 0.132 | 0.129 | 0.155 | 0.123 | 0.127 |
| CC | 0.430 | 0.392 | 0.363 | 0.396 | 0.399 | 0.381 | 0.354 | 0.380 |
| CCC | 0.325 | 0.302 | 0.293 | 0.323 | 0.328 | 0.283 | 0.281 | 0.319 |
| | HR | s=6 | | | | | | |
| LR-6 | Non-Joint-6 | Joint-6 | Joint-OA | LR-8 | Non-Joint-8 | Joint-8 | | | | | RMSE | 0.146 | 0.149 | 0.128 | 0.127 | 0.134 | 0.161 | 0.129 | 0.134 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| CC | 0.430 | 0.300 | 0.344 | 0.325 | 0.375 | 0.323 | 0.317 | 0.320 |
| CCC | 0.325 | 0.263 | 0.280 | 0.274 | 0.309 | 0.238 | 0.265 | 0.266 |
| | HR | s=12 | | | | | | |
| LR-12 | Non-Joint-12 | Joint-12 | Joint-OA | LR-16 | Non-Joint-16 | Joint-16 | | |
| RMSE | 0.146 | 0.143 | 0.126 | 0.125 | 0.132 | 0.137 | 0.124 | 0.137 |
| CC | 0.430 | 0.291 | 0.287 | 0.246 | 0.308 | 0.316 | 0.244 | 0.219 |
| CCC | 0.325 | 0.224 | 0.235 | 0.204 | 0.223 | 0.212 | 0.191 | 0.192 | | 1 |
| | HR | s=3 | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| LR-3 | Non-Joint-3 | Joint-3 | Joint-OA | LR-4 | Non-Joint-4 | Joint-4 | | |
| RMSE | 0.146 | 0.142 | 0.121 | 0.132 | 0.129 | 0.155 | 0.123 | 0.127 |
| CC | 0.430 | 0.392 | 0.363 | 0.396 | 0.399 | 0.381 | 0.354 | 0.380 |
| CCC | 0.325 | 0.302 | 0.293 | 0.323 | 0.328 | 0.283 | 0.281 | 0.319 |
| | HR | s=6 | | | | | | |
| LR-6 | Non-Joint-6 | Joint-6 | Joint-OA | LR-8 | Non-Joint-8 | Joint-8 | | | | | ωsw | 5 | 5 | 5 | 5 | 5 |
| --- | --- | --- | --- | --- | --- |
| ω | 15 | 15 | 15 | 15 | 15 | | 0 |
| Method | mAP |
| --- | --- |
| HoG | 0.584 | | | Loci | 0.419 |
| --- | --- |
| Graph-based | 0.565 |
| FFT | 0.771 |
| ProposedRLF | 0.783 | | 1 |
| Method | mAP |
| --- | --- |
| HoG | 0.584 | | | Method | mAP |
| --- | --- |
| SIFT | 0.115 |
| SURF | 0.106 |
| BRISK | 0.035 |
| ORB | 0.098 |
| KAZE | 0.283 |
| DoLF | 0.517 |
| ProposedRLF | 0.490 | | 0 |
| Method | mAP |
| --- | --- |
| HoG | 0.584 | | | Loci | 0.419 |
| --- | --- |
| Graph-based | 0.565 |
| FFT | 0.771 |
| ProposedRLF | 0.783 | | 1 |
| Method | mAP |
| --- | --- |
| HoG | 0.584 | | | SURF | 0.106 |
| --- | --- |
| BRISK | 0.035 |
| ORB | 0.098 |
| KAZE | 0.283 |
| DoLF | 0.517 |
| ProposedRLF | 0.490 | | 0 |
| 8 | 16 | 32 | 64 | 128 | 256 |
| --- | --- | --- | --- | --- | --- |
| conv(1,8)<br>conv(8,8)<br>conv(8,8) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) |
| | conv(8,16)<br>conv(16,16)<br>conv(16,16) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) |
| | | conv(16,24)<br>conv(24,24)<br>conv(24,24) | conv(16,24)<br>conv(24,24)<br>conv(24,24)<br>maxpool(2) | conv(16,24)<br>conv(24,24)<br>conv(24,24)<br>maxpool(2) | conv(16,24)<br>conv(24,24)<br>conv(24,24)<br>maxpool(2) |
| | | | conv(24,32)<br>conv(32,32)<br>conv(32,32) | conv(24,32)<br>conv(32,32)<br>conv(32,32)<br>maxpool(2) | conv(24,32)<br>conv(32,32)<br>conv(32,32)<br>maxpool(2) | | | | | | | conv(32,40)<br>conv(40,40)<br>conv(40,40) | conv(32,40)<br>conv(40,40)<br>conv(40,40)<br>maxpool(2) |
| --- | --- | --- | --- | --- | --- |
| | | | | | conv(40,48)<br>conv(48,48)<br>conv(48,48) |
| Dropout(0.5) | | | | | |
| fully-connected(1024) | | | | | |
| fully-connected(4) | | | | | |
| Normalize | | | | | | | 1 |
| 8 | 16 | 32 | 64 | 128 | 256 |
| --- | --- | --- | --- | --- | --- |
| conv(1,8)<br>conv(8,8)<br>conv(8,8) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) |
| | conv(8,16)<br>conv(16,16)<br>conv(16,16) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) |
| | | conv(16,24)<br>conv(24,24)<br>conv(24,24) | conv(16,24)<br>conv(24,24)<br>conv(24,24)<br>maxpool(2) | conv(16,24)<br>conv(24,24)<br>conv(24,24)<br>maxpool(2) | conv(16,24)<br>conv(24,24)<br>conv(24,24)<br>maxpool(2) |
| | | | conv(24,32)<br>conv(32,32)<br>conv(32,32) | conv(24,32)<br>conv(32,32)<br>conv(32,32)<br>maxpool(2) | conv(24,32)<br>conv(32,32)<br>conv(32,32)<br>maxpool(2) | | | 8 | 16 | 32 | 64 | 128 | 256 |
| --- | --- | --- | --- | --- | --- |
| conv(1,8)<br>conv(8,8) | conv(1,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>maxpool(2) |
| | conv(8,16)<br>conv(16,16) | conv(8,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>maxpool(2) |
| | | conv(16,24)<br>conv(24,24) | conv(16,24)<br>conv(24,24)<br>maxpool(2) | conv(16,24)<br>conv(24,24)<br>maxpool(2) | conv(16,24)<br>conv(24,24)<br>maxpool(2) |
| | | | conv(24,32)<br>conv(32,32) | conv(24,32)<br>conv(32,32)<br>maxpool(2) | conv(24,32)<br>conv(32,32)<br>maxpool(2) |
| | | | | conv(32,40)<br>conv(40,40) | conv(32,40)<br>conv(40,40)<br>maxpool(2) |
| | | | | | conv(40,48)<br>conv(48,48) |
| Dropout(0.5) | | | | | |
| fully-connected(1024) | | | | | |
| fully-connected(4) | | | | | |
| Normalize | | | | | | | 0 |
| 8 | 16 | 32 | 64 | 128 | 256 |
| --- | --- | --- | --- | --- | --- |
| conv(1,8)<br>conv(8,8)<br>conv(8,8) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) |
| | conv(8,16)<br>conv(16,16)<br>conv(16,16) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) |
| | | conv(16,24)<br>conv(24,24)<br>conv(24,24) | conv(16,24)<br>conv(24,24)<br>conv(24,24)<br>maxpool(2) | conv(16,24)<br>conv(24,24)<br>conv(24,24)<br>maxpool(2) | conv(16,24)<br>conv(24,24)<br>conv(24,24)<br>maxpool(2) |
| | | | conv(24,32)<br>conv(32,32)<br>conv(32,32) | conv(24,32)<br>conv(32,32)<br>conv(32,32)<br>maxpool(2) | conv(24,32)<br>conv(32,32)<br>conv(32,32)<br>maxpool(2) | | | | | | | conv(32,40)<br>conv(40,40)<br>conv(40,40) | conv(32,40)<br>conv(40,40)<br>conv(40,40)<br>maxpool(2) |
| --- | --- | --- | --- | --- | --- |
| | | | | | conv(40,48)<br>conv(48,48)<br>conv(48,48) |
| Dropout(0.5) | | | | | |
| fully-connected(1024) | | | | | |
| fully-connected(4) | | | | | |
| Normalize | | | | | | | 1 |
| 8 | 16 | 32 | 64 | 128 | 256 |
| --- | --- | --- | --- | --- | --- |
| conv(1,8)<br>conv(8,8)<br>conv(8,8) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) | conv(1,8)<br>conv(8,8)<br>conv(8,8)<br>maxpool(2) |
| | conv(8,16)<br>conv(16,16)<br>conv(16,16) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) | conv(8,16)<br>conv(16,16)<br>conv(16,16)<br>maxpool(2) |
| | | conv(16,24)<br>conv(24,24)<br>conv(24,24) | conv(16,24)<br>conv(24,24)<br>conv(24,24)<br>maxpool(2) | conv(16,24)<br>conv(24,24)<br>conv(24,24)<br>maxpool(2) | conv(16,24)<br>conv(24,24)<br>conv(24,24)<br>maxpool(2) |
| | | | conv(24,32)<br>conv(32,32)<br>conv(32,32) | conv(24,32)<br>conv(32,32)<br>conv(32,32)<br>maxpool(2) | conv(24,32)<br>conv(32,32)<br>conv(32,32)<br>maxpool(2) | | | | | | | | conv(40,48)<br>conv(48,48) |
| --- | --- | --- | --- | --- | --- |
| Dropout(0.5) | | | | | |
| fully-connected(1024) | | | | | |
| fully-connected(4) | | | | | |
| Normalize | | | | | | | 0 |
| Breakout(724frames) | Enduro(2914frames) | Phoenix(575frames) | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Model | NSS | ROC | NSS | ROC | NSS | ROC | | | Softattention | 1.326 | 0.787 | 0.699 | 0.689 | 0.187 | 0.529 |
| --- | --- | --- | --- | --- | --- | --- |
| GBVS | -0.074 | 0.489 | -0.083 | 0.578 | -0.143 | 0.485 |
| Itti-Koch | -0.112 | 0.453 | -0.048 | 0.593 | 0.267 | 0.599 | | 1 |
| Breakout(724frames) | Enduro(2914frames) | Phoenix(575frames) | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Model | NSS | ROC | NSS | ROC | NSS | ROC | | | Model | AUC-ROC | AUC-PR | Specificity | Sensitivity | PPV | Accuracy |
| --- | --- | --- | --- | --- | --- | --- |
| 5-day | 0.4247 | 0.2114 | 0.0256 | 0.9009 | 0.0833 | 0.6733 |
| 10-day | 0.7339 | 0.6884 | 0.3077 | 1.0 | 1.0 | 0.820 |
| 15-day | 0.7710 | 0.5808 | 0.3333 | 0.9009 | 0.5417 | 0.7533 |
| 20-day | 0.7533 | 0.6880 | 0.5385 | 0.9820 | 0.9130 | 0.8667 | | 0 |
| Breakout(724frames) | Enduro(2914frames) | Phoenix(575frames) | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Model | NSS | ROC | NSS | ROC | NSS | ROC | | | Softattention | 1.326 | 0.787 | 0.699 | 0.689 | 0.187 | 0.529 |
| --- | --- | --- | --- | --- | --- | --- |
| GBVS | -0.074 | 0.489 | -0.083 | 0.578 | -0.143 | 0.485 |
| Itti-Koch | -0.112 | 0.453 | -0.048 | 0.593 | 0.267 | 0.599 | | 1 |
| Breakout(724frames) | Enduro(2914frames) | Phoenix(575frames) | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Model | NSS | ROC | NSS | ROC | NSS | ROC | | | 15-day | 0.7710 | 0.5808 | 0.3333 | 0.9009 | 0.5417 | 0.7533 |
| --- | --- | --- | --- | --- | --- | --- |
| 20-day | 0.7533 | 0.6880 | 0.5385 | 0.9820 | 0.9130 | 0.8667 | | 0 |
| λDecoderSLMiteration | F0 | F1 | F2 | F3 | F4 | F5 | FX | overall |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1.0Viterbi | 13.0 | 30.8 | 42.1 | 31.0 | 22.8 | 52.3 | 53.9 | 33.1 |
| ∗<br>0.0A0 | 13.3 | 31.7 | 44.5 | 32.0 | 25.1 | 54.4 | 54.8 | 34.4 |
| 0.4A0 | 12.5 | 30.5 | 42.2 | 31.0 | 23.0 | 52.9 | 53.9 | 33.0 | | | 1.0A0 | 12.9 | 30.7 | 42.1 | 31.0 | 22.8 | 52.3 | 53.9 | 33.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0.0A2 | 14.8 | 31.7 | 46.3 | 31.6 | 27.5 | 54.3 | 54.8 | 35.1 |
| 0.4A2 | 12.2 | 30.7 | 42.0 | 31.1 | 22.5 | 53.1 | 54.4 | 33.0 |
| 1.0A2 | 12.9 | 30.7 | 42.1 | 31.0 | 22.8 | 52.3 | 53.9 | 33.1 | | 1 |
| λDecoderSLMiteration | F0 | F1 | F2 | F3 | F4 | F5 | FX | overall |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1.0Viterbi | 13.0 | 30.8 | 42.1 | 31.0 | 22.8 | 52.3 | 53.9 | 33.1 |
| ∗<br>0.0A0 | 13.3 | 31.7 | 44.5 | 32.0 | 25.1 | 54.4 | 54.8 | 34.4 |
| 0.4A0 | 12.5 | 30.5 | 42.2 | 31.0 | 23.0 | 52.9 | 53.9 | 33.0 | | | λDecoderSLMiteration | F0 | F1 | F2 | F3 | F4 | F5 | FX | overall |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1.0Viterbi | 14.5 | 32.5 | 44.9 | 33.3 | 25.7 | 54.9 | 56.1 | 35.2 |
| ∗<br>0.0A0 | 14.6 | 32.9 | 44.6 | 33.1 | 26.3 | 54.4 | 56.9 | 35.4 |
| 0.4A0 | 14.1 | 32.2 | 44.4 | 33.0 | 25.0 | 54.2 | 56.1 | 34.9 |
| 1.0A0 | 14.5 | 32.4 | 44.9 | 33.3 | 25.7 | 54.9 | 56.1 | 35.2 |
| 0.0A2 | 13.7 | 32.4 | 44.7 | 32.9 | 26.1 | 54.3 | 56.3 | 35.0 |
| 0.4A2 | 13.4 | 32.2 | 44.1 | 31.9 | 25.3 | 54.2 | 56.2 | 34.7 |
| 1.0A2 | 14.5 | 32.4 | 44.9 | 33.3 | 25.7 | 54.9 | 56.1 | 35.2 | | 0 |
| λDecoderSLMiteration | F0 | F1 | F2 | F3 | F4 | F5 | FX | overall |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1.0Viterbi | 13.0 | 30.8 | 42.1 | 31.0 | 22.8 | 52.3 | 53.9 | 33.1 |
| ∗<br>0.0A0 | 13.3 | 31.7 | 44.5 | 32.0 | 25.1 | 54.4 | 54.8 | 34.4 | | | 0.4A0 | 12.5 | 30.5 | 42.2 | 31.0 | 23.0 | 52.9 | 53.9 | 33.0 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1.0A0 | 12.9 | 30.7 | 42.1 | 31.0 | 22.8 | 52.3 | 53.9 | 33.1 |
| 0.0A2 | 14.8 | 31.7 | 46.3 | 31.6 | 27.5 | 54.3 | 54.8 | 35.1 |
| 0.4A2 | 12.2 | 30.7 | 42.0 | 31.1 | 22.5 | 53.1 | 54.4 | 33.0 |
| 1.0A2 | 12.9 | 30.7 | 42.1 | 31.0 | 22.8 | 52.3 | 53.9 | 33.1 | | 1 |
| λDecoderSLMiteration | F0 | F1 | F2 | F3 | F4 | F5 | FX | overall |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1.0Viterbi | 13.0 | 30.8 | 42.1 | 31.0 | 22.8 | 52.3 | 53.9 | 33.1 |
| ∗<br>0.0A0 | 13.3 | 31.7 | 44.5 | 32.0 | 25.1 | 54.4 | 54.8 | 34.4 | | | 0.4A0 | 14.1 | 32.2 | 44.4 | 33.0 | 25.0 | 54.2 | 56.1 | 34.9 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1.0A0 | 14.5 | 32.4 | 44.9 | 33.3 | 25.7 | 54.9 | 56.1 | 35.2 |
| 0.0A2 | 13.7 | 32.4 | 44.7 | 32.9 | 26.1 | 54.3 | 56.3 | 35.0 |
| 0.4A2 | 13.4 | 32.2 | 44.1 | 31.9 | 25.3 | 54.2 | 56.2 | 34.7 |
| 1.0A2 | 14.5 | 32.4 | 44.9 | 33.3 | 25.7 | 54.9 | 56.1 | 35.2 | | 0 |
| Architecture | Dice |
| --- | --- |
| Blacktop-hat+CC | 0.77 |
| Blacktop-hat | 0.85 | | | U-Net | 0.92 |
| --- | --- |
| U-Net+CC | 0.89 |
| SiameseU-Net | 0.95 |
| SiameseU-Net+CC | 0.98 | | 1 |
| Architecture | Dice |
| --- | --- |
| Blacktop-hat+CC | 0.77 |
| Blacktop-hat | 0.85 | | | Architecture | Catheter | Vessels |
| --- | --- | --- |
| U-Net | 0.49 | 0.30 |
| U-Net+CC | 0.44 | 0.36 |
| SiameseU-Net+CC | 0.51 | 0.50 |
| SiameseU-Net+Augm1 | 0.61 | 0.49 |
| SiameseU-Net+Augm2 | 0.69 | 0.54 | | 0 |
| Architecture | Dice |
| --- | --- |
| Blacktop-hat+CC | 0.77 |
| Blacktop-hat | 0.85 |
| U-Net | 0.92 | | | U-Net+CC | 0.89 |
| --- | --- |
| SiameseU-Net | 0.95 |
| SiameseU-Net+CC | 0.98 | | 1 |
| Architecture | Dice |
| --- | --- |
| Blacktop-hat+CC | 0.77 |
| Blacktop-hat | 0.85 |
| U-Net | 0.92 | | | SiameseU-Net+Augm1 | 0.61 | 0.49 |
| --- | --- | --- |
| SiameseU-Net+Augm2 | 0.69 | 0.54 | | 0 |
| System+Band | Abbr. | Date | SpatialResolution | Dimensions |
| --- | --- | --- | --- | --- |
| RADARSAT-2C | SF-L | Apr2008 | 8(m) | 1380×1800 |
| RADARSAT-2C | Flevo-C | Apr2008 | 8(m) | 1024×900 | | | AIRSARL | SF-L | Aug1989 | 10(m) | 1635×2375 |
| --- | --- | --- | --- | --- |
| AIRSARL | Flevo-L | Aug1989 | 10(m) | 1024×750 | | 1 |
| System+Band | Abbr. | Date | SpatialResolution | Dimensions |
| --- | --- | --- | --- | --- |
| RADARSAT-2C | SF-L | Apr2008 | 8(m) | 1380×1800 |
| RADARSAT-2C | Flevo-C | Apr2008 | 8(m) | 1024×900 | | | Block<br>Size | Freq(GHz) | Area(GE) |
| --- | --- | --- |
| TG | TG | |
| 1 | 3.84 | 28101772 |
| 4 | 3.42.7 | 29552471 |
| 8 | 3.62.3 | 37633575 |
| 16 | 3.61.7 | 38415768 | | 0 |
| System+Band | Abbr. | Date | SpatialResolution | Dimensions |
| --- | --- | --- | --- | --- |
| RADARSAT-2C | SF-L | Apr2008 | 8(m) | 1380×1800 | | | RADARSAT-2C | Flevo-C | Apr2008 | 8(m) | 1024×900 |
| --- | --- | --- | --- | --- |
| AIRSARL | SF-L | Aug1989 | 10(m) | 1635×2375 |
| AIRSARL | Flevo-L | Aug1989 | 10(m) | 1024×750 | | 1 |
| System+Band | Abbr. | Date | SpatialResolution | Dimensions |
| --- | --- | --- | --- | --- |
| RADARSAT-2C | SF-L | Apr2008 | 8(m) | 1380×1800 | | | 8 | 3.62.3 | 37633575 |
| --- | --- | --- |
| 16 | 3.61.7 | 38415768 | | 0 |
| V | H | E | Data | Train,Val,Test |
| --- | --- | --- | --- | --- |
| 10 | 128 | 200 | 9k,1k,10k | 0.9362,0.8732,0.8731 |
| 100 | 128 | 200 | 9k,1k,10k | 0.3967,0.1884,0.1883 |
| 100 | 128 | 200 | 135k,15k,10k | 0.9690,0.9613,0.9623 | | | 100 | 256 | 81 | 135k,150k,10k | 0.9904,0.9784,0.9787 |
| --- | --- | --- | --- | --- |
| 1000 | 256 | 133 | 135k,15k,10k | 0.9410,0.9151,0.9155 | | 1 |
| V | H | E | Data | Train,Val,Test |
| --- | --- | --- | --- | --- |
| 10 | 128 | 200 | 9k,1k,10k | 0.9362,0.8732,0.8731 |
| 100 | 128 | 200 | 9k,1k,10k | 0.3967,0.1884,0.1883 |
| 100 | 128 | 200 | 135k,15k,10k | 0.9690,0.9613,0.9623 | | | V | H | E | Data | Train,Val,Test |
| --- | --- | --- | --- | --- |
| 10 | 128 | 180 | 9k,1k,10k | 0.9635,0.9172,0.9150 |
| 100 | 128 | 200 | 9k,1k,10k | 0.7392,0.5472,0.5488 |
| 100 | 128 | 61 | 135k,15k,10k | 0.9927,0.9911,0.9912 |
| 100 | 256 | 40 | 135k,15k,10k | 0.9974,0.9997,0.9975 |
| 1000 | 256 | 75 | 135k,15k,10k | 0.9868,0.9884,0.9885 | | 0 |
| V | H | E | Data | Train,Val,Test |
| --- | --- | --- | --- | --- |
| 10 | 128 | 200 | 9k,1k,10k | 0.9362,0.8732,0.8731 |
| 100 | 128 | 200 | 9k,1k,10k | 0.3967,0.1884,0.1883 | | | 100 | 128 | 200 | 135k,15k,10k | 0.9690,0.9613,0.9623 |
| --- | --- | --- | --- | --- |
| 100 | 256 | 81 | 135k,150k,10k | 0.9904,0.9784,0.9787 |
| 1000 | 256 | 133 | 135k,15k,10k | 0.9410,0.9151,0.9155 | | 1 |
| V | H | E | Data | Train,Val,Test |
| --- | --- | --- | --- | --- |
| 10 | 128 | 200 | 9k,1k,10k | 0.9362,0.8732,0.8731 |
| 100 | 128 | 200 | 9k,1k,10k | 0.3967,0.1884,0.1883 | | | 100 | 256 | 40 | 135k,15k,10k | 0.9974,0.9997,0.9975 |
| --- | --- | --- | --- | --- |
| 1000 | 256 | 75 | 135k,15k,10k | 0.9868,0.9884,0.9885 | | 0 |
| PSNR |
| --- |
| 28.59 |
| 30.14 | | | 29.25 |
| --- |
| 30.88 |
| 30.60 |
| 31.57 | | 1 |
| PSNR |
| --- |
| 28.59 |
| 30.14 | | | | N | PSNR(dB) | RelErr | Iter | Time(s) |
| --- | --- | --- | --- | --- | --- |
| Lena | 1 | 28.83 | 0.0827 | 16 | 0.5 |
| 5 | 29.40 | 0.0774 | 23 | 1.2 | |
| 20 | 29.45 | 0.0769 | 26 | 3.1 | |
| 200 | 29.45 | 0.0769 | 36 | 35.5 | |
| 1000 | 29.45 | 0.0769 | 36 | 199.1 | |
| Barbara | 1 | 27.49 | 0.0790 | 9 | 0.3 |
| 5 | 27.80 | 0.0762 | 19 | 1.0 | |
| 20 | 27.82 | 0.0761 | 20 | 2.5 | |
| 200 | 27.82 | 0.0761 | 20 | 20.4 | |
| 1000 | 27.82 | 0.0761 | 20 | 119.2 | | | 0 |
| PSNR |
| --- |
| 28.59 |
| 30.14 | | | 29.25 |
| --- |
| 30.88 |
| 30.60 |
| 31.57 | | 1 |
| PSNR |
| --- |
| 28.59 |
| 30.14 | | | 200 | 27.82 | 0.0761 | 20 | 20.4 |
| --- | --- | --- | --- | --- |
| 1000 | 27.82 | 0.0761 | 20 | 119.2 | | 0 |
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