premise string | hypothesis string | label int64 |
|---|---|---|
| FW | Mult.<br>inF(P) | Occurrences<br>incorpus | p1<br>(cid:101) | p2<br>(cid:101) |
| --- | --- | --- | --- | --- |
| | 173871 | 593619 | 0.29 | 0.29 |
| , | 13062 | 32848 | 0.45 | 0.43 |
| de | 11350 | 38458 | 0.38 | 0.36 |
| que | 7112 | 20608 | 0.43 | 0.42 |
| en | 4193 | 15828 | 0.50 | 0.34 |
| a | 3213 | 11943... | | para | 903 | 4338 | 0.52 | 0.37 |
| --- | --- | --- | --- | --- |
| con | 730 | 4354 | 0.47 | 0.37 |
| no | 716 | 4689 | 0.45 | 0.34 |
| es | 564 | 4390 | 0.41 | 0.34 |
| al | 530 | 3076 | 0.51 | 0.30 |
| ha | 464 | 2643 | 0.41 | 0.31 |
| por | 418 | 4562 | 0.41 | 0.32 |
| su | 276 | 2285 | 0.38 | 0.39 |
| sobre | 26... | 1 |
| FW | Mult.<br>inF(P) | Occurrences<br>incorpus | p1<br>(cid:101) | p2<br>(cid:101) |
| --- | --- | --- | --- | --- |
| | 173871 | 593619 | 0.29 | 0.29 |
| , | 13062 | 32848 | 0.45 | 0.43 |
| de | 11350 | 38458 | 0.38 | 0.36 |
| que | 7112 | 20608 | 0.43 | 0.42 |
| en | 4193 | 15828 | 0.50 | 0.34 |
| a | 3213 | 11943... | | FW | Mult.<br>inF(P) | Occurrences<br>incorpus | p1<br>(cid:101) | p2<br>(cid:101) |
| --- | --- | --- | --- | --- |
| | 151778 | 535729 | 0.29 | 0.29 |
| the | 13113 | 35792 | 0.37 | 0.42 |
| , | 9865 | 27186 | 0.44 | 0.39 |
| to | 5590 | 16807 | 0.40 | 0.38 |
| of | 3526 | 18548 | 0.28 | 0.35 |
| a | 2596 | 8343 |... | 0 |
| FW | Mult.<br>inF(P) | Occurrences<br>incorpus | p1<br>(cid:101) | p2<br>(cid:101) |
| --- | --- | --- | --- | --- |
| | 173871 | 593619 | 0.29 | 0.29 |
| , | 13062 | 32848 | 0.45 | 0.43 |
| de | 11350 | 38458 | 0.38 | 0.36 | | | que | 7112 | 20608 | 0.43 | 0.42 |
| --- | --- | --- | --- | --- |
| en | 4193 | 15828 | 0.50 | 0.34 |
| a | 3213 | 11943 | 0.44 | 0.38 |
| y | 2295 | 13505 | 0.42 | 0.42 |
| del | 1441 | 6602 | 0.45 | 0.27 |
| una | 1426 | 5314 | 0.48 | 0.34 |
| un | 1263 | 5187 | 0.44 | 0.35 |
| se | 1227 | 6498 | 0.44 | 0.30 |
| p... | 1 |
| FW | Mult.<br>inF(P) | Occurrences<br>incorpus | p1<br>(cid:101) | p2<br>(cid:101) |
| --- | --- | --- | --- | --- |
| | 173871 | 593619 | 0.29 | 0.29 |
| , | 13062 | 32848 | 0.45 | 0.43 |
| de | 11350 | 38458 | 0.38 | 0.36 | | | this | 713 | 5176 | 0.33 | 0.39 |
| --- | --- | --- | --- | --- |
| inthe | 627 | 3142 | 0.51 | 0.40 |
| are | 616 | 3825 | 0.35 | 0.42 |
| we | 579 | 4419 | 0.37 | 0.32 |
| which | 570 | 4106 | 0.42 | 0.40 |
| on | 453 | 4640 | 0.39 | 0.28 |
| have | 450 | 3498 | 0.33 | 0.33 |
| an | 432 | 1784 | 0.48 | 0.38 |
| has... | 0 |
| Method | Sampling | Batch | ConstantStep<br>TimeObjective | LineSearch<br>TimeObjective | | |
| --- | --- | --- | --- | --- | --- | --- |
| SAG | RS<br>CS<br>SS<br>RS<br>CS<br>SS | 200<br>1000 | 229.220102<br>66.378361<br>67.867812<br>234.129248<br>63.957239<br>65.908254 | 0.3258410619<br>0.3258375894<br>0.32584108... | | SAAG-II | RS<br>CS<br>SS<br>RS<br>CS<br>SS | 200<br>1000 | 297.134694<br>174.232600<br>176.845275<br>299.840227<br>171.384496<br>172.268358 | 0.3263398874<br>0.3263398971<br>0.3263398982<br>0.3258550019<br>0.3258550023<br>0.3258550036 | 708.881659<br>380.852014<br>338.122579<br>687.638964<br>209.564000<br>213.877001 ... | 1 |
| Method | Sampling | Batch | ConstantStep<br>TimeObjective | LineSearch<br>TimeObjective | | |
| --- | --- | --- | --- | --- | --- | --- |
| SAG | RS<br>CS<br>SS<br>RS<br>CS<br>SS | 200<br>1000 | 229.220102<br>66.378361<br>67.867812<br>234.129248<br>63.957239<br>65.908254 | 0.3258410619<br>0.3258375894<br>0.32584108... | | Method | Sampling | Batch | ConstantStep<br>TimeObjective | LineSearch<br>TimeObjective | | |
| --- | --- | --- | --- | --- | --- | --- |
| SAG | RS<br>CS<br>SS<br>RS<br>CS<br>SS | 200<br>1000 | 84.331058<br>20.571099<br>20.380268<br>86.462106<br>19.652540<br>23.803318 | 0.3759925588<br>0.3759958448<br>0.3759925419... | 0 |
| Method | Sampling | Batch | ConstantStep<br>TimeObjective | LineSearch<br>TimeObjective | | |
| --- | --- | --- | --- | --- | --- | --- |
| SAG | RS<br>CS<br>SS<br>RS<br>CS<br>SS | 200<br>1000 | 229.220102<br>66.378361<br>67.867812<br>234.129248<br>63.957239<br>65.908254 | 0.3258410619<br>0.3258375894<br>0.32584108... | | SAAG-II | RS<br>CS<br>SS<br>RS<br>CS<br>SS | 200<br>1000 | 297.134694<br>174.232600<br>176.845275<br>299.840227<br>171.384496<br>172.268358 | 0.3263398874<br>0.3263398971<br>0.3263398982<br>0.3258550019<br>0.3258550023<br>0.3258550036 | 708.881659<br>380.852014<br>338.122579<br>687.638964<br>209.564000<br>213.877001 ... | 1 |
| Method | Sampling | Batch | ConstantStep<br>TimeObjective | LineSearch<br>TimeObjective | | |
| --- | --- | --- | --- | --- | --- | --- |
| SAG | RS<br>CS<br>SS<br>RS<br>CS<br>SS | 200<br>1000 | 229.220102<br>66.378361<br>67.867812<br>234.129248<br>63.957239<br>65.908254 | 0.3258410619<br>0.3258375894<br>0.32584108... | | SAGA | RS<br>CS<br>SS<br>RS<br>CS<br>SS | 200<br>1000 | 119.419700<br>49.054361<br>50.032764<br>115.240795<br>48.484855<br>53.076179 | 0.3761615578<br>0.3761668056<br>0.3761616154<br>0.3767228974<br>0.3767380851<br>0.3767231692 | 244.424858<br>88.012460<br>125.125252<br>218.120526<br>41.586102<br>47.792283 | 0.375991... | 0 |
| Level-1<br>(Block-size/<br>sparsity) | Level-2<br>(Block-size/<br>sparsity) | Top-1Accuracy | Top-5Accuracy |
| --- | --- | --- | --- |
| 4x1/60 | – | 74.81(-1.32) | 92.26(-0.60) |
| 4x1/63 | 1x1/97 | 75.29(-0.84) | 92.57(-0.29) | | | 4x1/65 | 1x1/95 | 75.38(-0.75) | 92.57(-0.29) |
| --- | --- | --- | --- |
| 4x1/70 | 1x1/90 | 75.48(-0.65) | 92.66(-0.20) |
| 4x1/80 | 1x1/80 | 75.53(-0.60) | 92.77(-0.09) |
| – | 1x1/60 | 75.76(-0.37) | 92.85(-0.01) | | 1 |
| Level-1<br>(Block-size/<br>sparsity) | Level-2<br>(Block-size/<br>sparsity) | Top-1Accuracy | Top-5Accuracy |
| --- | --- | --- | --- |
| 4x1/60 | – | 74.81(-1.32) | 92.26(-0.60) |
| 4x1/63 | 1x1/97 | 75.29(-0.84) | 92.57(-0.29) | | | block-size/sparsity | top-10 | top-20 | top-30 | top-40 |
| --- | --- | --- | --- | --- |
| 4x1/60 | 89.31 | 76.95 | 68.21 | 61.73 |
| 4x1/63,1x1/97 | 100 | 88.28 | 74.41 | 65.51 |
| 4x1/65,1x1/95 | 100 | 95.48 | 78.46 | 67.95 |
| 4x1/70,1x1/90 | 100 | 100 | 88.16 | 73.78 |
| 4x1/80,1x1/80 | 100 | 100 | 100 | 84.29 | | 0 |
| Level-1<br>(Block-size/<br>sparsity) | Level-2<br>(Block-size/<br>sparsity) | Top-1Accuracy | Top-5Accuracy |
| --- | --- | --- | --- |
| 4x1/60 | – | 74.81(-1.32) | 92.26(-0.60) |
| 4x1/63 | 1x1/97 | 75.29(-0.84) | 92.57(-0.29) |
| 4x1/65 | 1x1/95 | 75.38(-0.75) | 92.57(-0.29) | | | 4x1/70 | 1x1/90 | 75.48(-0.65) | 92.66(-0.20) |
| --- | --- | --- | --- |
| 4x1/80 | 1x1/80 | 75.53(-0.60) | 92.77(-0.09) |
| – | 1x1/60 | 75.76(-0.37) | 92.85(-0.01) | | 1 |
| Level-1<br>(Block-size/<br>sparsity) | Level-2<br>(Block-size/<br>sparsity) | Top-1Accuracy | Top-5Accuracy |
| --- | --- | --- | --- |
| 4x1/60 | – | 74.81(-1.32) | 92.26(-0.60) |
| 4x1/63 | 1x1/97 | 75.29(-0.84) | 92.57(-0.29) |
| 4x1/65 | 1x1/95 | 75.38(-0.75) | 92.57(-0.29) | | | 4x1/63,1x1/97 | 100 | 88.28 | 74.41 | 65.51 |
| --- | --- | --- | --- | --- |
| 4x1/65,1x1/95 | 100 | 95.48 | 78.46 | 67.95 |
| 4x1/70,1x1/90 | 100 | 100 | 88.16 | 73.78 |
| 4x1/80,1x1/80 | 100 | 100 | 100 | 84.29 | | 0 |
| TypeSize | Seq/STLSeqQS | ForkSURandfork |
| --- | --- | --- |
| 10000000<br>100000000<br>Random1000000000<br>8388607<br>33554431<br>134217727 | 1.4791.620<br>13.31913.742<br>107.080117.963<br>1.4471.580<br>4.8635.265<br>15.88816.617 | 0.3883.81.818<br>2.8914.613.607<br>20.2875.350.679<br>0.7741.91.772<br>0.9035.45.6... | | 10000000<br>100000000<br>Gauss1000000000<br>8388607<br>33554431<br>134217727 | 1.2521.354<br>11.92312.971<br>119.464130.255<br>1.0291.112<br>4.4084.236<br>15.88817.263 | 0.2754.61.621<br>2.5164.714.972<br>22.2885.4106.658<br>0.2474.21.353<br>0.8705.15.492<br>2.7715.719.774 |
| --- | --- | --- |
| 10000000<br>10000000... | 1 |
| TypeSize | Seq/STLSeqQS | ForkSURandfork |
| --- | --- | --- |
| 10000000<br>100000000<br>Random1000000000<br>8388607<br>33554431<br>134217727 | 1.4791.620<br>13.31913.742<br>107.080117.963<br>1.4471.580<br>4.8635.265<br>15.88816.617 | 0.3883.81.818<br>2.8914.613.607<br>20.2875.350.679<br>0.7741.91.772<br>0.9035.45.6... | | TypeSize | Seq/STLSeqQS | ForkSURandfork |
| --- | --- | --- |
| 10000000<br>100000000<br>Random1000000000<br>8388607<br>33554431<br>134217727 | 1.2221.341<br>13.23213.492<br>131.266144.100<br>1.0161.113<br>4.4014.745<br>17.53718.405 | 0.2924.21.200<br>2.5855.18.442<br>24.6985.341.073<br>0.2683.81.126<br>0.7755.73.89... | 0 |
| TypeSize | Seq/STLSeqQS | ForkSURandfork |
| --- | --- | --- |
| 10000000<br>100000000<br>Random1000000000<br>8388607<br>33554431<br>134217727 | 1.4791.620<br>13.31913.742<br>107.080117.963<br>1.4471.580<br>4.8635.265<br>15.88816.617 | 0.3883.81.818<br>2.8914.613.607<br>20.2875.350.679<br>0.7741.91.772<br>0.9035.45.6... | | 10000000<br>100000000<br>Buckets1000000000<br>8388607<br>33554431<br>134217727 | 1.1311.233<br>12.37312.801<br>122.822135.833<br>0.9691.057<br>4.1114.505<br>16.48417.154 | 0.2414.71.517<br>1.8186.815.136<br>19.2146.4121.967<br>0.1865.21.244<br>0.6626.24.774<br>2.0388.117.203 |
| --- | --- | --- |
| 10000000<br>100000... | 1 |
| TypeSize | Seq/STLSeqQS | ForkSURandfork |
| --- | --- | --- |
| 10000000<br>100000000<br>Random1000000000<br>8388607<br>33554431<br>134217727 | 1.4791.620<br>13.31913.742<br>107.080117.963<br>1.4471.580<br>4.8635.265<br>15.88816.617 | 0.3883.81.818<br>2.8914.613.607<br>20.2875.350.679<br>0.7741.91.772<br>0.9035.45.6... | | 10000000<br>100000000<br>Gauss1000000000<br>8388607<br>33554431<br>134217727 | 1.2341.331<br>11.86112.945<br>119.297129.859<br>1.0271.105<br>4.4074.197<br>15.82417.178 | 0.2554.81.481<br>2.4544.89.870<br>22.1495.487.425<br>0.2274.51.045<br>0.8375.34.592<br>2.7045.913.898 |
| --- | --- | --- |
| 10000000<br>100000000<... | 0 |
| Method | Np | MAE | RMSE |
| --- | --- | --- | --- |
| YOLO | - | 102.89 | 110.02 |
| FasterR-CNN | 200 | 103.48 | 110.64 |
| *YOLO | - | 48.89 | 57.55 |
| *FasterR-CNN | 200 | 47.45 | 57.39 | | | *FasterR-CNN(RPN-small) | 200 | 24.32 | 37.62 |
| --- | --- | --- | --- |
| †One-LookRegression | - | 59.46 | 66.84 |
| OurCarCountingCNNModel | 200 | 23.80 | 36.79 | | 1 |
| Method | Np | MAE | RMSE |
| --- | --- | --- | --- |
| YOLO | - | 102.89 | 110.02 |
| FasterR-CNN | 200 | 103.48 | 110.64 |
| *YOLO | - | 48.89 | 57.55 |
| *FasterR-CNN | 200 | 47.45 | 57.39 | | | Method | MAE | MSE |
| --- | --- | --- |
| SquareChnDetector | 20.55 | 439.1 |
| R-FCN | 6.02 | 5.46 |
| FasterR-CNN | 5.91 | 6.60 |
| CountForest | 4.40 | 2.40 |
| ExemplaryDensity | 1.82 | 2.74 |
| BoostingCNN | 2.01 | N/A |
| MoCNN | 2.75 | 13.40 |
| WeightedVLAD | 2.41 | 9.12 |
| DecideNet | 1.52 | 1.90 | | 0 |
| Method | Np | MAE | RMSE |
| --- | --- | --- | --- |
| YOLO | - | 102.89 | 110.02 | | | FasterR-CNN | 200 | 103.48 | 110.64 |
| --- | --- | --- | --- |
| *YOLO | - | 48.89 | 57.55 |
| *FasterR-CNN | 200 | 47.45 | 57.39 |
| *FasterR-CNN(RPN-small) | 200 | 24.32 | 37.62 |
| †One-LookRegression | - | 59.46 | 66.84 |
| OurCarCountingCNNModel | 200 | 23.80 | 36.79 | | 1 |
| Method | Np | MAE | RMSE |
| --- | --- | --- | --- |
| YOLO | - | 102.89 | 110.02 | | | WeightedVLAD | 2.41 | 9.12 |
| --- | --- | --- |
| DecideNet | 1.52 | 1.90 | | 0 |
| | SSC | LRR | MFC10 | MFC20 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| K | T(s) | I | T/I | T(s) | I | T/I | T(s) | I | T/I | T(s) | I | T/I |
| 2 | 1.73 | 107 | 0.02 | 1.73 | 61 | 0.03 | 1.14 | 24 | 0.05 | 0.96 | 24 | 0.04 | | | 4 | 5.69 | 108 | 0.05 | 6.32 | 64 | 0.10 | 2.70 | 25 | 0.11 | 2.19 | 24 | 0.09 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 6 | 12.20 | 109 | 0.11 | 14.19 | 64 | 0.22 | 4.95 | 25 | 0.20 | 3.36 | 24 | 0.14 |
| 8 | 21.33 | 110 | 0.19 | 25.14 | 64 | 0.39 | 7.62 | 27 | 0.28 | 5.48 |... | 1 |
| | SSC | LRR | MFC10 | MFC20 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| K | T(s) | I | T/I | T(s) | I | T/I | T(s) | I | T/I | T(s) | I | T/I |
| 2 | 1.73 | 107 | 0.02 | 1.73 | 61 | 0.03 | 1.14 | 24 | 0.05 | 0.96 | 24 | 0.04 | | | | SSC | LRR | MFC10 | MFC20 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| K | T(s) | I | T/I | T(s) | I | T/I | T(s) | I | T/I | T(s) | I | T/I |
| 2 | 0.120 | 92 | 0.001 | 0.105 | 57 | 0.002 | 0.191 | 22 | 0.009 | 0.218 | 24 | 0.009 |
| 3 | 0.196 | 99 | ... | 0 |
| | SSC | LRR | MFC10 | MFC20 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| K | T(s) | I | T/I | T(s) | I | T/I | T(s) | I | T/I | T(s) | I | T/I |
| 2 | 1.73 | 107 | 0.02 | 1.73 | 61 | 0.03 | 1.14 | 24 | 0.05 | 0.96 | 24 | 0.04 |
| 4 | 5.69 | 108 | 0.05 | ... | | 6 | 12.20 | 109 | 0.11 | 14.19 | 64 | 0.22 | 4.95 | 25 | 0.20 | 3.36 | 24 | 0.14 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 8 | 21.33 | 110 | 0.19 | 25.14 | 64 | 0.39 | 7.62 | 27 | 0.28 | 5.48 | 32 | 0.17 |
| 10 | 37.54 | 110 | 0.34 | 40.82 | 64 | 0.64 | 11.17 | 35 | 0.32 | 7.... | 1 |
| | SSC | LRR | MFC10 | MFC20 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| K | T(s) | I | T/I | T(s) | I | T/I | T(s) | I | T/I | T(s) | I | T/I |
| 2 | 1.73 | 107 | 0.02 | 1.73 | 61 | 0.03 | 1.14 | 24 | 0.05 | 0.96 | 24 | 0.04 |
| 4 | 5.69 | 108 | 0.05 | ... | | 10 | 1.196 | 93 | 0.013 | 1.368 | 57 | 0.024 | 1.630 | 25 | 0.065 | 1.598 | 24 | 0.067 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 11 | 1.480 | 94 | 0.016 | 1.380 | 58 | 0.024 | 1.760 | 28 | 0.063 | 1.765 | 24 | 0.074 |
| Avg. | 0.670 | 93 | 0.007 | 0.679 | 57 | 0.012 | 0.899 |... | 0 |
| Aliases | Characters |
| --- | --- |
| Concepts | Developers |
| DateAdded | DateLastUpdated |
| Description | ShortDescription |
| Platforms | Publishers |
| Franchises | Genres |
| KilledCharacters | DebutedCharacters |
| Locations | DebutedObjects |
| DebutedLocations | Objects |
| DebutedConcepts | People |
| Deb... | | ExpectedReleaseDate | OriginalReleaseDate |
| --- | --- |
| Themes | Re-ReleaseDates |
| Rating | SimilarGames | | 1 |
| Aliases | Characters |
| --- | --- |
| Concepts | Developers |
| DateAdded | DateLastUpdated |
| Description | ShortDescription |
| Platforms | Publishers |
| Franchises | Genres |
| KilledCharacters | DebutedCharacters |
| Locations | DebutedObjects |
| DebutedLocations | Objects |
| DebutedConcepts | People |
| Deb... | | Movie | ReleaseDate |
| --- | --- |
| Armored | 2009-12-04 |
| Avatar | 2009-12-18 |
| TheBlindSide | 2009-11-20 |
| TheBookofEli | 2010-01-15 |
| Daybreakers | 2010-01-08 |
| DearJohn | 2010-02-05 |
| DidYouHearAboutTheMorgans | 2009-12-18 |
| EdgeOfDarkness | 2010-01-29 |
| ExtraordinaryMeasures | 2010-01-22 |
| Fr... | 0 |
| Aliases | Characters |
| --- | --- |
| Concepts | Developers |
| DateAdded | DateLastUpdated |
| Description | ShortDescription |
| Platforms | Publishers |
| Franchises | Genres | | | KilledCharacters | DebutedCharacters |
| --- | --- |
| Locations | DebutedObjects |
| DebutedLocations | Objects |
| DebutedConcepts | People |
| DebutedPeople | Videos |
| MainImage | AllImages |
| UserReviews | StaffReviews |
| ExpectedReleaseDate | OriginalReleaseDate |
| Themes | Re-ReleaseDates |
| Rating | Simil... | 1 |
| Aliases | Characters |
| --- | --- |
| Concepts | Developers |
| DateAdded | DateLastUpdated |
| Description | ShortDescription |
| Platforms | Publishers |
| Franchises | Genres | | | TheCrazies | 2010-02-26 |
| --- | --- |
| ToothFairy | 2010-01-22 |
| Transylmania | 2009-12-04 |
| WhenInRome | 2010-01-29 |
| YouthInRevolt | 2010-01-08 | | 0 |
| Term | TermsthatConflictwithit |
| --- | --- |
| New | NewYork,NewYorkCity |
| York | NewYork,YorkCity,NewYorkCity |
| City | YorkCity,NewYorkCity |
| NewYork | New,York,YorkCity,NewYorkCity | | | YorkCity | York,City,NewYork,NewYorkCity |
| --- | --- |
| NewYorkCity | New,York,City,NewYork,YorkCity | | 1 |
| Term | TermsthatConflictwithit |
| --- | --- |
| New | NewYork,NewYorkCity |
| York | NewYork,YorkCity,NewYorkCity |
| City | YorkCity,NewYorkCity |
| NewYork | New,York,YorkCity,NewYorkCity | | | MethodNameDescription |
| --- |
| ExtractSynonymWordreturnssynonymsofaword |
| ExtractHypernymWordreturnshyponymsofaword |
| ExtractSynonymSentencereturnssynonymsofwordsinasentence |
| ExtractSynonymFilereturnssynonymsofwordsinafile | | 0 |
| Term | TermsthatConflictwithit |
| --- | --- |
| New | NewYork,NewYorkCity | | | York | NewYork,YorkCity,NewYorkCity |
| --- | --- |
| City | YorkCity,NewYorkCity |
| NewYork | New,York,YorkCity,NewYorkCity |
| YorkCity | York,City,NewYork,NewYorkCity |
| NewYorkCity | New,York,City,NewYork,YorkCity | | 1 |
| Term | TermsthatConflictwithit |
| --- | --- |
| New | NewYork,NewYorkCity | | | ExtractSynonymSentencereturnssynonymsofwordsinasentence |
| --- |
| ExtractSynonymFilereturnssynonymsofwordsinafile | | 0 |
| Method | nodrop | 30percent | 40percent | 50percent |
| --- | --- | --- | --- | --- |
| Baseline | 81.28 | 76.75 | 74.62 | 69.11 |
| HardNegative | - | 74.68 | 73.99 | 71.41 |
| OverlapbasedSoftSampling | - | 78.24 | 76.78 | 73.5 | | | ScorebasedSoftSampling | - | 77.99 | 75.91 | 72.55 |
| --- | --- | --- | --- | --- |
| Upperbound | - | 79.26 | 77.5 | 74.54 | | 1 |
| Method | nodrop | 30percent | 40percent | 50percent |
| --- | --- | --- | --- | --- |
| Baseline | 81.28 | 76.75 | 74.62 | 69.11 |
| HardNegative | - | 74.68 | 73.99 | 71.41 |
| OverlapbasedSoftSampling | - | 78.24 | 76.78 | 73.5 | | | Methodno. | Rank/Score | F-Measure | pseudoF-Measure | PSNR | DRD |
| --- | --- | --- | --- | --- | --- |
| 1 | 1/51.3792 | 88.55 | 92.25 | 18.28 | 5.57 |
| 2 | 3/50.2433 | 86.79 | 86.29 | 17.64 | 6.08 |
| 3 | 2/50.7329 | 87.30 | 89.50 | 17.95 | 5.87 |
| Otsu | - | 77.75 | 79.98 | 15.42 | 31.11 |
| GridbasedSauvola |... | 0 |
| Method | nodrop | 30percent | 40percent | 50percent |
| --- | --- | --- | --- | --- |
| Baseline | 81.28 | 76.75 | 74.62 | 69.11 |
| HardNegative | - | 74.68 | 73.99 | 71.41 |
| OverlapbasedSoftSampling | - | 78.24 | 76.78 | 73.5 | | | ScorebasedSoftSampling | - | 77.99 | 75.91 | 72.55 |
| --- | --- | --- | --- | --- |
| Upperbound | - | 79.26 | 77.5 | 74.54 | | 1 |
| Method | nodrop | 30percent | 40percent | 50percent |
| --- | --- | --- | --- | --- |
| Baseline | 81.28 | 76.75 | 74.62 | 69.11 |
| HardNegative | - | 74.68 | 73.99 | 71.41 |
| OverlapbasedSoftSampling | - | 78.24 | 76.78 | 73.5 | | | PC | - | 90.19 | 91.35 | 19.89 | 5.23 |
| --- | --- | --- | --- | --- | --- |
| Howe | - | 89.58 | 91.88 | 18.53 | 4.11 | | 0 |
| | 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 | | | 1 |
| | 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 | | | | | SDR(dB) | SIR(dB) | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | WMDLD | MDLD | GS | WMDLD | MDLD | GS | WMDLD | MDLD |
| Dev3Female4 | 11.77 | 6.02 | 16.93 | 22.26 | 23.84 | 22.43 | 12.23 | 6.17 |
| Example3×5 | 8.41 | 3.91 | 9.94 | 17.58 | 17.92 | 15.21 | 9.10 | 4.17 |
| Example... | 0 |
| | 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 | | | 1 |
| | 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 | | | | Dev3Female4 | 11.77 | 6.02 | 16.93 | 22.26 | 23.84 | 22.43 | 12.23 | 6.17 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Example3×5 | 8.41 | 3.91 | 9.94 | 17.58 | 17.92 | 15.21 | 9.10 | 4.17 |
| Example4×8 | 5.29 | 2.24 | -18.63 | 13.72 | 16.4 | -17.58 | 6.23 | 2.52 | | 0 |
| RealityMining |
| --- |
| 0.44 | | | 0.64 |
| --- |
| 0.75 |
| 0.77 |
| 0.78 |
| 0.80 |
| 0.81 |
| 0.83 |
| 0.84 |
| 0.85 | | 1 |
| RealityMining |
| --- |
| 0.44 | | | 0.44 |
| --- |
| 0.46 |
| 0.44 |
| 0.45 |
| 0.40 | | 0 |
| RealityMining |
| --- |
| 0.44 |
| 0.64 |
| 0.75 | | | 0.77 |
| --- |
| 0.78 |
| 0.80 |
| 0.81 |
| 0.83 |
| 0.84 |
| 0.85 | | 1 |
| RealityMining |
| --- |
| 0.44 |
| 0.64 |
| 0.75 | | | 0.45 |
| --- |
| 0.40 | | 0 |
| | 1cm | 2cm | 4cm | 8cm |
| --- | --- | --- | --- | --- |
| DNN-ILD | 3.01±.044 | 3.03±.046 | 3.10±.047 | 3.23±.049 |
| Linear-ILD | 0.92±.017 | 2.21±.036 | 3.18±.049 | 3.43±.052 | | | DNN-IPD | 0.46±.008 | 0.48±.009 | 0.50±.009 | 0.54±.009 |
| --- | --- | --- | --- | --- |
| Linear-IPD | 0.17±.005 | 0.38±.008 | 0.53±.009 | 0.57±.010 | | 1 |
| | 1cm | 2cm | 4cm | 8cm |
| --- | --- | --- | --- | --- |
| DNN-ILD | 3.01±.044 | 3.03±.046 | 3.10±.047 | 3.23±.049 |
| Linear-ILD | 0.92±.017 | 2.21±.036 | 3.18±.049 | 3.43±.052 | | | | 30dB | 20dB | 10dB |
| --- | --- | --- | --- |
| DNN-ILD | 3.03±.046 | 3.03±.046 | 3.05±.046 |
| Linear-ILD | 2.31±.036 | 2.32±.036 | 2.36±.037 |
| DNN-IPD | 0.48±.009 | 0.48±.009 | 0.48±.009 |
| Linear-IPD | 0.40±.008 | 0.40±.008 | 0.41±.008 | | 0 |
| | 1cm | 2cm | 4cm | 8cm |
| --- | --- | --- | --- | --- |
| DNN-ILD | 3.01±.044 | 3.03±.046 | 3.10±.047 | 3.23±.049 | | | Linear-ILD | 0.92±.017 | 2.21±.036 | 3.18±.049 | 3.43±.052 |
| --- | --- | --- | --- | --- |
| DNN-IPD | 0.46±.008 | 0.48±.009 | 0.50±.009 | 0.54±.009 |
| Linear-IPD | 0.17±.005 | 0.38±.008 | 0.53±.009 | 0.57±.010 | | 1 |
| | 1cm | 2cm | 4cm | 8cm |
| --- | --- | --- | --- | --- |
| DNN-ILD | 3.01±.044 | 3.03±.046 | 3.10±.047 | 3.23±.049 | | | DNN-IPD | 0.48±.009 | 0.48±.009 | 0.48±.009 |
| --- | --- | --- | --- |
| Linear-IPD | 0.40±.008 | 0.40±.008 | 0.41±.008 | | 0 |
| Similar | | | | |
| --- | --- | --- | --- | --- |
| | K | N.TFT | B. | C. |
| SBV<br>Basic<br>Bayesian | 148.7<br>141.5<br>142.3 | 164.3<br>162.5<br>165.2 | 139.4<br>145.4<br>144.0 | 61.0<br>62.8<br>61.7 |
| AverageSimilarity | | | | |
| | K | N.TFT | B. | C. |
| SBV<br>Basic<br>Bayesian | 155.1<br>153.1<br... | | | K | N.TFT | B. | C. |
| --- | --- | --- | --- | --- |
| SBV<br>Basic<br>Bayesian | 163.8<br>162.2<br>163.7 | 175.7<br>176.6<br>177.2 | 156.6<br>160.1<br>160.5 | 73.4<br>87.7<br>88.0 | | 1 |
| Similar | | | | |
| --- | --- | --- | --- | --- |
| | K | N.TFT | B. | C. |
| SBV<br>Basic<br>Bayesian | 148.7<br>141.5<br>142.3 | 164.3<br>162.5<br>165.2 | 139.4<br>145.4<br>144.0 | 61.0<br>62.8<br>61.7 |
| AverageSimilarity | | | | |
| | K | N.TFT | B. | C. |
| SBV<br>Basic<br>Bayesian | 155.1<br>153.1<br... | | Similar | | | | |
| --- | --- | --- | --- | --- |
| | K | N.TFT | B. | C. |
| Averse<br>Neutral<br>Mix<br>Seeker<br>SBV | 0.241<br>0.259<br>0.250<br>0.169<br>0.181 | 0.160<br>0.164<br>0.216<br>0.248<br>0.150 | 0.238<br>0.224<br>0.267<br>0.283<br>0.184 | 0.372<br>0.557<br>0.520<br>0.615<br>0.552 |
| AverageSimila... | 0 |
| Similar | | | | |
| --- | --- | --- | --- | --- |
| | K | N.TFT | B. | C. |
| SBV<br>Basic<br>Bayesian | 148.7<br>141.5<br>142.3 | 164.3<br>162.5<br>165.2 | 139.4<br>145.4<br>144.0 | 61.0<br>62.8<br>61.7 |
| AverageSimilarity | | | | |
| | K | N.TFT | B. | C. |
| SBV<br>Basic<br>Bayesian | 155.1<br>153.1<br... | | | K | N.TFT | B. | C. |
| --- | --- | --- | --- | --- |
| SBV<br>Basic<br>Bayesian | 163.8<br>162.2<br>163.7 | 175.7<br>176.6<br>177.2 | 156.6<br>160.1<br>160.5 | 73.4<br>87.7<br>88.0 | | 1 |
| Similar | | | | |
| --- | --- | --- | --- | --- |
| | K | N.TFT | B. | C. |
| SBV<br>Basic<br>Bayesian | 148.7<br>141.5<br>142.3 | 164.3<br>162.5<br>165.2 | 139.4<br>145.4<br>144.0 | 61.0<br>62.8<br>61.7 |
| AverageSimilarity | | | | |
| | K | N.TFT | B. | C. |
| SBV<br>Basic<br>Bayesian | 155.1<br>153.1<br... | | Dissimilar | | | | |
| --- | --- | --- | --- | --- |
| | K | N.TFT | B. | C. |
| Averse<br>Neutral<br>Mix<br>Seeker<br>SBV | 0.203<br>0.209<br>0.228<br>0.060<br>0.07 | 0.167<br>0.196<br>0.183<br>0.247<br>0.16 | 0.245<br>0.271<br>0.265<br>0.294<br>0.128 | 0.373<br>0.475<br>0.452<br>0.566<br>0.257 | | 0 |
| D1 | D2 | X1 | X2 | Function |
| --- | --- | --- | --- | --- |
| A | A | 0 | B | A·B(AND) |
| A | A | 0 | B | A+B(NOR) | | | A | A | B | B | A⊕B(XOR) |
| --- | --- | --- | --- | --- |
| A | A | B | B | A(cid:12)B(XNOR) | | 1 |
| D1 | D2 | X1 | X2 | Function |
| --- | --- | --- | --- | --- |
| A | A | 0 | B | A·B(AND) |
| A | A | 0 | B | A+B(NOR) | | | | | |
| --- | --- | --- |
| | | |
| d1<br>d2<br>d3 | 0,27×104480,25×10<br>291761203628673<br>666<br>0,50×100,45×100,30×10 | 2,58×10752,58×10<br>66<br>4,12×103474,11×10<br>666<br>24,45×1019,62×103,46×10 |
| total | 0,80×100,46×100,58×10 | 31,15×1019,62×1010,16×10 |
| DBLP | Netflix | |
| | | |
| d1<br>d2<br>d... | 0 |
| D1 | D2 | X1 | X2 | Function |
| --- | --- | --- | --- | --- |
| A | A | 0 | B | A·B(AND) |
| A | A | 0 | B | A+B(NOR) | | | A | A | B | B | A⊕B(XOR) |
| --- | --- | --- | --- | --- |
| A | A | B | B | A(cid:12)B(XNOR) | | 1 |
| D1 | D2 | X1 | X2 | Function |
| --- | --- | --- | --- | --- |
| A | A | 0 | B | A·B(AND) |
| A | A | 0 | B | A+B(NOR) | | | d1<br>d2<br>d3 | 0,79×102370,55×10<br>66<br>2,62×10424070,97×10<br>666<br>3,27×101,61×101,61×10 | 15,54×1025612,47×10<br>966<br>0,19×100,14×1020,30×10<br>966<br>0,20×1044,52×100,92×10 |
| --- | --- | --- |
| total | 6,68×101,61×102,13×10 | 0,39×1044,66×1033,69×10 | | 0 |
| Input | KMC2 | Kmerlight |
| --- | --- | --- |
| 23billion<br>21-mers | 2350sec-<br>(1GBRAM,20GBHDD) | 1395sec(120MBRAM) | | | 9.2billion<br>63-mers | Abort-(1GBRAM)<br>3150sec-<br>(2GBRAM,42GBHDD) | 995sec(120MBRAM)<br>1200sec(500MBRAM)<br>1291sec(960MBRAM) |
| --- | --- | --- |
| 144billion<br>63-mers | 4.5hr-<br>(32GBRAM,264GBHDD) | 2hr10min(120MBRAM) |
| 61billion<br>85-mers | Abort-(32GBRAM)<br>2hr20min-<br>(64GBRAM,250GBHDD) | 1hr10min... | 1 |
| Input | KMC2 | Kmerlight |
| --- | --- | --- |
| 23billion<br>21-mers | 2350sec-<br>(1GBRAM,20GBHDD) | 1395sec(120MBRAM) | | | | a | b | r |
| --- | --- | --- | --- |
| 256MB:512MBRatio | 0.5829 | -0.2517 | -0.9984 |
| 512MB:768MBRatio | 4.89 | -0.1292 | -0.9748 |
| 768MB:1GBRatio | 0.3821 | -0.1709 | -0.9801 |
| 1GB:1.5GBRatio | 3.98 | -0.1367 | -0.9833 |
| 1.5GB:2GBRatio | 1.51 | -0.0925 | -0.9897 |
| 2GB:4GBRatio | 4.951 | -0.1008 | -0.9... | 0 |
| Input | KMC2 | Kmerlight |
| --- | --- | --- |
| 23billion<br>21-mers | 2350sec-<br>(1GBRAM,20GBHDD) | 1395sec(120MBRAM) |
| 9.2billion<br>63-mers | Abort-(1GBRAM)<br>3150sec-<br>(2GBRAM,42GBHDD) | 995sec(120MBRAM)<br>1200sec(500MBRAM)<br>1291sec(960MBRAM) | | | 144billion<br>63-mers | 4.5hr-<br>(32GBRAM,264GBHDD) | 2hr10min(120MBRAM) |
| --- | --- | --- |
| 61billion<br>85-mers | Abort-(32GBRAM)<br>2hr20min-<br>(64GBRAM,250GBHDD) | 1hr10min(120MBRAM) | | 1 |
| Input | KMC2 | Kmerlight |
| --- | --- | --- |
| 23billion<br>21-mers | 2350sec-<br>(1GBRAM,20GBHDD) | 1395sec(120MBRAM) |
| 9.2billion<br>63-mers | Abort-(1GBRAM)<br>3150sec-<br>(2GBRAM,42GBHDD) | 995sec(120MBRAM)<br>1200sec(500MBRAM)<br>1291sec(960MBRAM) | | | 512MB:768MBRatio | 4.89 | -0.1292 | -0.9748 |
| --- | --- | --- | --- |
| 768MB:1GBRatio | 0.3821 | -0.1709 | -0.9801 |
| 1GB:1.5GBRatio | 3.98 | -0.1367 | -0.9833 |
| 1.5GB:2GBRatio | 1.51 | -0.0925 | -0.9897 |
| 2GB:4GBRatio | 4.951 | -0.1008 | -0.9880 | | 0 |
| | SIMCCsAUCAUCNSSEMD |
| --- | --- |
| DSCLRCN | 0.680.800.720.872.352.17 |
| SAM-ResNet | 0.680.780.700.872.342.15 |
| SAM-VGG | 0.670.770.710.872.302.14 |
| DeepFix | 0.670.780.710.872.262.04 |
| SALICON | 0.600.740.740.872.122.62 |
| PDP | 0.600.700.730.852.052.58 |
| ML-Net | 0.590.670.700.852.052.63 |
| SalGAN(... | | Mr-CNN | 0.480.480.690.791.373.71 |
| --- | --- |
| DeepGazeII | 0.460.520.720.881.293.98 |
| GBVS | 0.480.480.630.811.243.51 |
| eDN | 0.410.450.620.821.144.56 | | 1 |
| | SIMCCsAUCAUCNSSEMD |
| --- | --- |
| DSCLRCN | 0.680.800.720.872.352.17 |
| SAM-ResNet | 0.680.780.700.872.342.15 |
| SAM-VGG | 0.670.770.710.872.302.14 |
| DeepFix | 0.670.780.710.872.262.04 |
| SALICON | 0.600.740.740.872.122.62 |
| PDP | 0.600.700.730.852.052.58 |
| ML-Net | 0.590.670.700.852.052.63 |
| SalGAN(... | | | CCsAUCAUCNSS |
| --- | --- |
| SAM-ResNet | 0.8420.7790.8833.204 |
| DSCLRCN | 0.8310.7760.8843.157 |
| SAM-VGG | 0.8250.7740.8813.143 |
| ML-Net | 0.7430.7680.8662.789 |
| MixNet | 0.7300.7710.8612.767 |
| SU | 0.7800.7600.8802.610 |
| SalGAN(*) | 0.7810.7720.7812.459 |
| SalNet | 0.6220.7240.8581.859 |
| DeepGaz... | 0 |
| | SIMCCsAUCAUCNSSEMD |
| --- | --- |
| DSCLRCN | 0.680.800.720.872.352.17 |
| SAM-ResNet | 0.680.780.700.872.342.15 |
| SAM-VGG | 0.670.770.710.872.302.14 |
| DeepFix | 0.670.780.710.872.262.04 |
| SALICON | 0.600.740.740.872.122.62 |
| PDP | 0.600.700.730.852.052.58 |
| ML-Net | 0.590.670.700.852.052.63 |
| SalGAN(... | | SalNet | 0.520.580.690.831.513.31 |
| --- | --- |
| BMS | 0.510.550.650.831.413.35 |
| Mr-CNN | 0.480.480.690.791.373.71 |
| DeepGazeII | 0.460.520.720.881.293.98 |
| GBVS | 0.480.480.630.811.243.51 |
| eDN | 0.410.450.620.821.144.56 | | 1 |
| | SIMCCsAUCAUCNSSEMD |
| --- | --- |
| DSCLRCN | 0.680.800.720.872.352.17 |
| SAM-ResNet | 0.680.780.700.872.342.15 |
| SAM-VGG | 0.670.770.710.872.302.14 |
| DeepFix | 0.670.780.710.872.262.04 |
| SALICON | 0.600.740.740.872.122.62 |
| PDP | 0.600.700.730.852.052.58 |
| ML-Net | 0.590.670.700.852.052.63 |
| SalGAN(... | | SalGAN(*) | 0.7810.7720.7812.459 |
| --- | --- |
| SalNet | 0.6220.7240.8581.859 |
| DeepGazeII | 0.5090.7610.8851.336 | | 0 |
| (b,b,b,b)0123 | Typeoffunction | Querycomplexity |
| --- | --- | --- |
| 0000 | Constantfunction | 0 |
| 0001 | AND3 | 3 | | | 0010<br>0011<br>0100<br>0101<br>0110 | EXACT3<br>2<br>Th3<br>1<br>EXACT3<br>PARITY3<br>NAE3 | 2<br>2<br>2<br>2<br>2 |
| --- | --- | --- |
| 0111 | IsomorphictoAND3 | 3 |
| 1000 | IsomorphictoAND3 | 3 |
| 1001<br>1010<br>1011<br>1100<br>1101 | IsomorphictoNAE3<br>IsomorphictoPARITY3<br>1<br>IsomorphictoEXACT3<br>2<br>... | 1 |
| (b,b,b,b)0123 | Typeoffunction | Querycomplexity |
| --- | --- | --- |
| 0000 | Constantfunction | 0 |
| 0001 | AND3 | 3 | | | - | - |
| --- | --- |
| 3x3<br>3x3<br>2x2<br>3x3 | 1<br>1<br>2<br>1 |
| 3x3<br>3x3<br>2x2<br>3x3 | 1<br>1<br>2<br>1 |
| 3x3<br>3x3<br>2x2<br>3x3 | 1<br>1<br>2<br>1 |
| 3x3<br>3x3<br>2x2<br>3x3 | 1<br>1<br>2<br>1 |
| 3x3<br>3x3<br>2x2<br>3x3 | 1<br>1<br>2<br>1 |
| - | - | | 0 |
| (b,b,b,b)0123 | Typeoffunction | Querycomplexity |
| --- | --- | --- |
| 0000 | Constantfunction | 0 |
| 0001 | AND3 | 3 |
| 0010<br>0011<br>0100<br>0101<br>0110 | EXACT3<br>2<br>Th3<br>1<br>EXACT3<br>PARITY3<br>NAE3 | 2<br>2<br>2<br>2<br>2 |
| 0111 | IsomorphictoAND3 | 3 |
| 1000 | IsomorphictoAND3 | 3 |
| 1001<br>1... | | 1110 | IsomorphictoAND3 | 3 |
| --- | --- | --- |
| 1111 | Constantfunction | 0 | | 1 |
| (b,b,b,b)0123 | Typeoffunction | Querycomplexity |
| --- | --- | --- |
| 0000 | Constantfunction | 0 |
| 0001 | AND3 | 3 |
| 0010<br>0011<br>0100<br>0101<br>0110 | EXACT3<br>2<br>Th3<br>1<br>EXACT3<br>PARITY3<br>NAE3 | 2<br>2<br>2<br>2<br>2 |
| 0111 | IsomorphictoAND3 | 3 |
| 1000 | IsomorphictoAND3 | 3 |
| 1001<br>1... | | 3x3<br>3x3<br>2x2<br>3x3 | 1<br>1<br>2<br>1 |
| --- | --- |
| 3x3<br>3x3<br>2x2<br>3x3 | 1<br>1<br>2<br>1 |
| 3x3<br>3x3<br>2x2<br>3x3 | 1<br>1<br>2<br>1 |
| - | - | | 0 |
| No.ofnodes | NameofTheNeurons | NeuronLabelNo. |
| --- | --- | --- |
| 1 | IL2DL | 1 |
| 2 | IL2DR | 6 |
| 3 | ASIL | 76 | | | 4 | ASIR | 90 |
| --- | --- | --- |
| 5 | AINL | 120 |
| 6 | SDQR | 179 |
| 7 | DB05 | 204 |
| 8 | AS08 | 220 |
| 9 | PVDR | 227 |
| 10 | DVB | 253 |
| 11 | PLNR | 260 |
| 12 | PHCR | 274 |
| 13 | PLML | 279 | | 1 |
| No.ofnodes | NameofTheNeurons | NeuronLabelNo. |
| --- | --- | --- |
| 1 | IL2DL | 1 |
| 2 | IL2DR | 6 |
| 3 | ASIL | 76 | | | | Performance | Highfitness | Neurons | Connectivity |
| --- | --- | --- | --- | --- |
| HPvsPEO | 0.009 | 0.033 | 0.318 | 0.983 |
| HPvsLIN | 0.009 | 0.106 | 0.601 | 0.349 |
| HPvsGA | 0.023 | 0.091 | 0.699 | 0.859 |
| PEOvsLIN | 0.763 | 0.009 | 0.130 | 0.171 |
| PEOvsGA | 0.027 | 0.044 | 0.107 | 0.781 |
| LINvsGA |... | 0 |
| No.ofnodes | NameofTheNeurons | NeuronLabelNo. |
| --- | --- | --- |
| 1 | IL2DL | 1 |
| 2 | IL2DR | 6 |
| 3 | ASIL | 76 |
| 4 | ASIR | 90 |
| 5 | AINL | 120 |
| 6 | SDQR | 179 |
| 7 | DB05 | 204 | | | 8 | AS08 | 220 |
| --- | --- | --- |
| 9 | PVDR | 227 |
| 10 | DVB | 253 |
| 11 | PLNR | 260 |
| 12 | PHCR | 274 |
| 13 | PLML | 279 | | 1 |
| No.ofnodes | NameofTheNeurons | NeuronLabelNo. |
| --- | --- | --- |
| 1 | IL2DL | 1 |
| 2 | IL2DR | 6 |
| 3 | ASIL | 76 |
| 4 | ASIR | 90 |
| 5 | AINL | 120 |
| 6 | SDQR | 179 |
| 7 | DB05 | 204 | | | PEOvsLIN | 0.763 | 0.009 | 0.130 | 0.171 |
| --- | --- | --- | --- | --- |
| PEOvsGA | 0.027 | 0.044 | 0.107 | 0.781 |
| LINvsGA | 0.019 | 0.684 | 0.762 | 0.289 | | 0 |
| Name | Abbr. | Description |
| --- | --- | --- |
| Bar´abasi-Albert | BA | Originalpreferentialattachmentmodel | | | HeuristicallyOptimizedTrade-offs | FKP | Localoptimizationmodel |
| --- | --- | --- |
| GeneralizedLinearPreference | GLP | Modifiedpreferentialattachmentmodel |
| UnivariateHeuristicallyOptimizedTrade-offs | UFKP | Modifiedlocaloptimizationmodel |
| BivariateHeuristicallyOptimizedTrade-offs | BFKP | Modifiedlocaloptimizat... | 1 |
| Name | Abbr. | Description |
| --- | --- | --- |
| Bar´abasi-Albert | BA | Originalpreferentialattachmentmodel | | | Symbol | Meaning |
| --- | --- |
| n | Numberofpatterns |
| p | Numberofinputs(features)inapattern |
| m | Numberofcolumns |
| q | Numberofproximalsynapsespercolumn |
| φ+ | Permanenceincrementamount |
| φ− | Permanencedecrementamount |
| φσ | Windowofpermanenceinitialization |
| ρs | Proximalsynapseactivationtreshol... | 0 |
| Name | Abbr. | Description |
| --- | --- | --- |
| Bar´abasi-Albert | BA | Originalpreferentialattachmentmodel |
| HeuristicallyOptimizedTrade-offs | FKP | Localoptimizationmodel |
| GeneralizedLinearPreference | GLP | Modifiedpreferentialattachmentmodel |
| UnivariateHeuristicallyOptimizedTrade-offs | UFKP | Modifiedloc... | | InteractiveGrowth | IG | Modifiedpreferentialattachmentmodel |
| --- | --- | --- |
| PositiveFeedbackPreference(1) | PFP1 | ModifiedIGmodel |
| PositiveFeedbackPreference(2) | PFP1 | ModifiedPFP1model |
| ASIM | ASIM | Agentbasedtopologygenerator | | 1 |
| Name | Abbr. | Description |
| --- | --- | --- |
| Bar´abasi-Albert | BA | Originalpreferentialattachmentmodel |
| HeuristicallyOptimizedTrade-offs | FKP | Localoptimizationmodel |
| GeneralizedLinearPreference | GLP | Modifiedpreferentialattachmentmodel |
| UnivariateHeuristicallyOptimizedTrade-offs | UFKP | Modifiedloc... | | β0 | Maximumboost |
| --- | --- |
| τ | Dutycycleperiod | | 0 |
| Strategy | RMSE±STD | Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.1249±0.0131 | 0.3467 | 211 | - |
| GKKM | 0.2322±0.0508 | 0.7728 | 8 | 0.8841 |
| M=5,µ=0.2500,σ=0.05380 | | | | |
| CCBS<br>GCBS | 0.2112±0.0426<br>0.2251±0.0491 | 0.0690<br>0.0237 | 8<br>6 | 0.2396<br>0.2357 | | | M=10,µ=0.1111,σ=0.02760 | | | | |
| --- | --- | --- | --- | --- |
| CCBS<br>GCBS | 0.1802±0.0290<br>0.1839±0.0304 | 0.0682<br>0.0176 | 14<br>13 | 0.1095<br>0.1104 |
| M=20,µ=0.0526,σ=0.01550 | | | | |
| CCBS<br>GCBS | 0.1534±0.0150<br>0.1655±0.0198 | 0.0851<br>0.0195 | 23<br>20 | 0.0521<br>0.0507 |
| M=30,µ=0... | 1 |
| Strategy | RMSE±STD | Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.1249±0.0131 | 0.3467 | 211 | - |
| GKKM | 0.2322±0.0508 | 0.7728 | 8 | 0.8841 |
| M=5,µ=0.2500,σ=0.05380 | | | | |
| CCBS<br>GCBS | 0.2112±0.0426<br>0.2251±0.0491 | 0.0690<br>0.0237 | 8<br>6 | 0.2396<br>0.2357 | | | Strategy | RMSE±STD | Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.1320±0.0161 | 0.3034 | 211 | - |
| GKKM | 0.1325±0.0150 | 1.8930 | 15 | 0.7694 |
| M=5,µ=0.2500,σ=0.10840 | | | | |
| CCBS<br>GCBS | 0.1358±0.0154<br>0.1585±0.0229 | 0.0550<br>0.0227 | 7<br>5 | 0.2441<br>0.2212 |
| M=10,µ=0.1111,σ... | 0 |
| Strategy | RMSE±STD | Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.1249±0.0131 | 0.3467 | 211 | - |
| GKKM | 0.2322±0.0508 | 0.7728 | 8 | 0.8841 |
| M=5,µ=0.2500,σ=0.05380 | | | | |
| CCBS<br>GCBS | 0.2112±0.0426<br>0.2251±0.0491 | 0.0690<br>0.0237 | 8<br>6 | 0.2396<br>0.2357 |
| M=10,µ=0.1111,σ=... | | CCBS<br>GCBS | 0.1534±0.0150<br>0.1655±0.0198 | 0.0851<br>0.0195 | 23<br>20 | 0.0521<br>0.0507 |
| --- | --- | --- | --- | --- |
| M=30,µ=0.0345,σ=0.01110 | | | | |
| CCBS<br>GCBS | 0.1399±0.0105<br>0.1426±0.0111 | 0.0866<br>0.0167 | 30<br>27 | 0.0325<br>0.0296 | | 1 |
| Strategy | RMSE±STD | Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.1249±0.0131 | 0.3467 | 211 | - |
| GKKM | 0.2322±0.0508 | 0.7728 | 8 | 0.8841 |
| M=5,µ=0.2500,σ=0.05380 | | | | |
| CCBS<br>GCBS | 0.2112±0.0426<br>0.2251±0.0491 | 0.0690<br>0.0237 | 8<br>6 | 0.2396<br>0.2357 |
| M=10,µ=0.1111,σ=... | | M=30,µ=0.0345,σ=0.02420 | | | | |
| --- | --- | --- | --- | --- |
| CCBS<br>GCBS | 0.1123±0.0131<br>0.1166±0.0143 | 0.1679<br>0.0248 | 29<br>28 | 0.0339<br>0.0323 | | 0 |
| DataSet | #classes | #instances | #dim. |
| --- | --- | --- | --- |
| Wdbc | 2 | 569 | 31 |
| Ionosphere | 2 | 351 | 34 | | | Spectf | 2 | 267 | 44 |
| --- | --- | --- | --- |
| Spambase | 2 | 4,601 | 57 |
| Colon | 2 | 62 | 2,000 |
| Prostate | 2 | 102 | 6,033 |
| Leukemia | 2 | 72 | 7,129 |
| Lungcancer | 2 | 181 | 12,533 | | 1 |
| DataSet | #classes | #instances | #dim. |
| --- | --- | --- | --- |
| Wdbc | 2 | 569 | 31 |
| Ionosphere | 2 | 351 | 34 | | | Dataset | #Instances | #Dimensions |
| --- | --- | --- |
| Digits08 | 1500 | 16 |
| Credit | 1000 | 61 |
| Cancer | 699 | 10 |
| Qsar | 1055 | 41 |
| Sonar | 208 | 60 |
| Theorem | 3060 | 51 |
| Diabetes | 768 | 8 |
| Spambase | 4600 | 57 |
| KDD99 | 494021 | 41 |
| CAPTCHA | 1885 | 26 | | 0 |
| DataSet | #classes | #instances | #dim. |
| --- | --- | --- | --- |
| Wdbc | 2 | 569 | 31 |
| Ionosphere | 2 | 351 | 34 |
| Spectf | 2 | 267 | 44 |
| Spambase | 2 | 4,601 | 57 | | | Colon | 2 | 62 | 2,000 |
| --- | --- | --- | --- |
| Prostate | 2 | 102 | 6,033 |
| Leukemia | 2 | 72 | 7,129 |
| Lungcancer | 2 | 181 | 12,533 | | 1 |
| DataSet | #classes | #instances | #dim. |
| --- | --- | --- | --- |
| Wdbc | 2 | 569 | 31 |
| Ionosphere | 2 | 351 | 34 |
| Spectf | 2 | 267 | 44 |
| Spambase | 2 | 4,601 | 57 | | | Diabetes | 768 | 8 |
| --- | --- | --- |
| Spambase | 4600 | 57 |
| KDD99 | 494021 | 41 |
| CAPTCHA | 1885 | 26 | | 0 |
| Network | nC | Smin | R@50 | R@100 | Model | Speed | Network | nC | Smin | R@50 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| PNet | 1 | 16 | 0.9604 | 0.9712 | 28KB | 25.17FPS | APN24 | 3 | 16 | 0.9920 |
| PNet | 1 | 20 | 0.9557 | 0.9700 | 28KB | 33.75FPS | APN24 | 3 | 20 | 0.9898 |
| PNet |... | | APN24 | 2 | 20 | 0.9888 | 0.9921 | 360KB | 29.68FPS | APN24(S) | 2 | 20 | 0.9604 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| APN24 | 2 | 24 | 0.9859 | 0.9901 | 360KB | 38.02FPS | APN24(S) | 2 | 24 | 0.9564 | | 1 |
| Network | nC | Smin | R@50 | R@100 | Model | Speed | Network | nC | Smin | R@50 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| PNet | 1 | 16 | 0.9604 | 0.9712 | 28KB | 25.17FPS | APN24 | 3 | 16 | 0.9920 |
| PNet | 1 | 20 | 0.9557 | 0.9700 | 28KB | 33.75FPS | APN24 | 3 | 20 | 0.9898 |
| PNet |... | | | GradualandSharp | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Video | TP | FP | FN | P | R | F |
| 01811a | 60 | 7 | 4 | 0.896 | 0.938 | 0.916 |
| 6011 | 40 | 96 | 81 | 0.294 | 0.331 | 0.311 |
| 8024 | 85 | 22 | 21 | 0.794 | 0.802 | 0.798 |
| 8386 | 113 | 10 | 5 | 0.919 | 0.958 | 0.938 |
| 8401 | ... | 0 |
| Network | nC | Smin | R@50 | R@100 | Model | Speed | Network | nC | Smin | R@50 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| PNet | 1 | 16 | 0.9604 | 0.9712 | 28KB | 25.17FPS | APN24 | 3 | 16 | 0.9920 |
| PNet | 1 | 20 | 0.9557 | 0.9700 | 28KB | 33.75FPS | APN24 | 3 | 20 | 0.9898 |
| PNet |... | | APN24 | 1 | 20 | 0.9860 | 0.9882 | 360KB | 32.63FPS | APN12 | 2 | 20 | 0.9484 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| APN24 | 1 | 24 | 0.9825 | 0.9856 | 360KB | 43.39FPS | APN12 | 2 | 24 | 0.9354 |
| APN24 | 2 | 16 | 0.9907 | 0.9946 | 360KB | 20.82FPS | APN24(S) | 2 | 16 | 0.9760 |
| A... | 1 |
| Network | nC | Smin | R@50 | R@100 | Model | Speed | Network | nC | Smin | R@50 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| PNet | 1 | 16 | 0.9604 | 0.9712 | 28KB | 25.17FPS | APN24 | 3 | 16 | 0.9920 |
| PNet | 1 | 20 | 0.9557 | 0.9700 | 28KB | 33.75FPS | APN24 | 3 | 20 | 0.9898 |
| PNet |... | | UGS09 | 169 | 12 | 24 | 0.934 | 0.876 | 0.904 |
| --- | --- | --- | --- | --- | --- | --- |
| Total | 1756 | 285 | 244 | 0.86 | 0.878 | 0.869 | | 0 |
| | Yahoo!<br>TopT10T100Undis | | | |
| --- | --- | --- | --- | --- |
| LNLS5 | 3.2 | 1.6 | 1.6 | 93.5 |
| LNLS7 | 1.3 | 1.3 | 0 | 97.4 |
| LS5-LNLS5 | 68.3 | 7.8 | 2.3 | 21.7 | | | LS5-LNLS7 | 67.6 | 8.1 | 2.3 | 22.0 |
| --- | --- | --- | --- | --- |
| LS7-LNLS5 | 67.3 | 4.9 | 1.9 | 25.9 |
| LS7-LNLS7 | 66.7 | 4.9 | 1.9 | 26.5 |
| TI-LNLS5 | 64.7 | 8.1 | 0.6 | 26.5 |
| TI-LNLS7 | 64.1 | 8.7 | 0.6 | 26.5 | | 1 |
| | Yahoo!<br>TopT10T100Undis | | | |
| --- | --- | --- | --- | --- |
| LNLS5 | 3.2 | 1.6 | 1.6 | 93.5 |
| LNLS7 | 1.3 | 1.3 | 0 | 97.4 |
| LS5-LNLS5 | 68.3 | 7.8 | 2.3 | 21.7 | | | | Gradual | Sharp | | | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Video | #T | TP | FP | FN | P | R | F | #T | TP | FP | FN | P |
| 1004ABCa | 203 | 166 | 13 | 37 | 0.927 | 0.818 | 0.869 | 224 | 213 | 22 | 11 | 0.906 |
| 1012CNNa | 170 | 136 | 13 | 34... | 0 |
| | Yahoo!<br>TopT10T100Undis | | | |
| --- | --- | --- | --- | --- |
| LNLS5 | 3.2 | 1.6 | 1.6 | 93.5 |
| LNLS7 | 1.3 | 1.3 | 0 | 97.4 |
| LS5-LNLS5 | 68.3 | 7.8 | 2.3 | 21.7 | | | LS5-LNLS7 | 67.6 | 8.1 | 2.3 | 22.0 |
| --- | --- | --- | --- | --- |
| LS7-LNLS5 | 67.3 | 4.9 | 1.9 | 25.9 |
| LS7-LNLS7 | 66.7 | 4.9 | 1.9 | 26.5 |
| TI-LNLS5 | 64.7 | 8.1 | 0.6 | 26.5 |
| TI-LNLS7 | 64.1 | 8.7 | 0.6 | 26.5 | | 1 |
| | Yahoo!<br>TopT10T100Undis | | | |
| --- | --- | --- | --- | --- |
| LNLS5 | 3.2 | 1.6 | 1.6 | 93.5 |
| LNLS7 | 1.3 | 1.3 | 0 | 97.4 |
| LS5-LNLS5 | 68.3 | 7.8 | 2.3 | 21.7 | | | 1123CNNa | 126 | 93 | 29 | 33 | 0.762 | 0.738 | 0.75 | 236 | 214 | 10 | 22 | 0.955 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1126ABCa | 189 | 168 | 12 | 21 | 0.933 | 0.889 | 0.911 | 273 | 261 | 23 | 12 | 0.919 |
| 1208CNNa | 137 | 112 | 15 | 25 | 0.882 | 0.818 | 0.848 | 212 |... | 0 |
| Method | Year | P | R | F |
| --- | --- | --- | --- | --- |
| CCTN | – | 0.90 | 0.83 | 0.86 |
| TextFlowetal. | 2015 | 0.85 | 0.76 | 0.80 |
| Zhangetal. | 2015 | 0.88 | 0.74 | 0.80 |
| Luetal. | 2015 | 0.89 | 0.70 | 0.78 |
| NeumannandMatas | 2015 | 0.82 | 0.72 | 0.77 | | | FASText | 2015 | 0.84 | 0.69 | 0.77 |
| --- | --- | --- | --- | --- |
| iwrr2014 | 2014 | 0.86 | 0.70 | 0.77 |
| USTBTexStar | 2014 | 0.88 | 0.66 | 0.76 |
| TextSpotter | 2012 | 0.88 | 0.65 | 0.75 | | 1 |
| Method | Year | P | R | F |
| --- | --- | --- | --- | --- |
| CCTN | – | 0.90 | 0.83 | 0.86 |
| TextFlowetal. | 2015 | 0.85 | 0.76 | 0.80 |
| Zhangetal. | 2015 | 0.88 | 0.74 | 0.80 |
| Luetal. | 2015 | 0.89 | 0.70 | 0.78 |
| NeumannandMatas | 2015 | 0.82 | 0.72 | 0.77 | | | | Year | R | P | F |
| --- | --- | --- | --- | --- |
| MSRA-TD500 | | | | |
| CCTN | – | 0.65 | 0.79 | 0.71 |
| Yinetal. | 2015 | 0.63 | 0.81 | 0.71 |
| Yinetal. | 2014 | 0.61 | 0.71 | 0.66 |
| Yaoetal. | 2014 | 0.62 | 0.64 | 0.61 |
| Kangetal. | 2014 | 0.62 | 0.71 | 0.66 |
| Yaoetal. | 2012 | 0.63 | 0.63 | 0.60... | 0 |
| Method | Year | P | R | F |
| --- | --- | --- | --- | --- |
| CCTN | – | 0.90 | 0.83 | 0.86 |
| TextFlowetal. | 2015 | 0.85 | 0.76 | 0.80 |
| Zhangetal. | 2015 | 0.88 | 0.74 | 0.80 |
| Luetal. | 2015 | 0.89 | 0.70 | 0.78 |
| NeumannandMatas | 2015 | 0.82 | 0.72 | 0.77 |
| FASText | 2015 | 0.84 | 0.69 | 0.77 | | | iwrr2014 | 2014 | 0.86 | 0.70 | 0.77 |
| --- | --- | --- | --- | --- |
| USTBTexStar | 2014 | 0.88 | 0.66 | 0.76 |
| TextSpotter | 2012 | 0.88 | 0.65 | 0.75 | | 1 |
| Method | Year | P | R | F |
| --- | --- | --- | --- | --- |
| CCTN | – | 0.90 | 0.83 | 0.86 |
| TextFlowetal. | 2015 | 0.85 | 0.76 | 0.80 |
| Zhangetal. | 2015 | 0.88 | 0.74 | 0.80 |
| Luetal. | 2015 | 0.89 | 0.70 | 0.78 |
| NeumannandMatas | 2015 | 0.82 | 0.72 | 0.77 |
| FASText | 2015 | 0.84 | 0.69 | 0.77 | | | Yaoetal. | 2014 | 0.62 | 0.64 | 0.61 |
| --- | --- | --- | --- | --- |
| Kangetal. | 2014 | 0.62 | 0.71 | 0.66 |
| Yaoetal. | 2012 | 0.63 | 0.63 | 0.60 |
| ICDAR2011 | | | | |
| CCTN | – | 0.79 | 0.88 | 0.84 |
| Yinetal. | 2015 | 0.66 | 0.84 | 0.74 |
| ICDAR2013 | | | | |
| CCTN | – | 0.83 | 0.90 | 0.86 |
| Y... | 0 |
| k/paa | 0.2 | 0.4 | 0.6 | 0.8 | 1 | 1.2 | 1.4 | 1.6 | 1.8 | 2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Min | 0.0 | 0.0 | 5.9 | 13.0 | 21.7 | 27.3 | 29.4 | 29.4 | 29.4 | 29.4 |
| Avg | 3.9 | 10.5 | 20.0 | 29.1 | 38.3 | 48.8 | 60.4 | 72.5 | 85.6 | 98.0 | | | Max | 10.0 | 18.2 | 50.0 | 62.5 | 87.5 | 112.5 | 137.5 | 150.0 | 175.0 | 187.5 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| #Worse | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| #Better | 31 | 49 | 50 | 50 | 50 | 50 | 50 | 50 | 50 | 50 | | 1 |
| k/paa | 0.2 | 0.4 | 0.6 | 0.8 | 1 | 1.2 | 1.4 | 1.6 | 1.8 | 2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Min | 0.0 | 0.0 | 5.9 | 13.0 | 21.7 | 27.3 | 29.4 | 29.4 | 29.4 | 29.4 |
| Avg | 3.9 | 10.5 | 20.0 | 29.1 | 38.3 | 48.8 | 60.4 | 72.5 | 85.6 | 98.0 | | | R | Optimalvalue | iter | CPU |
| --- | --- | --- | --- |
| 0.0001 | 0.000186 | 2 | 0.235 |
| 0.0002 | 0.000189 | 2 | 0.234 |
| 0.0003 | 0.000193 | 2 | 0.218 |
| 0.0004 | 0.000182 | 3 | 0.266 |
| 0.0005 | 0.000174 | 3 | 0.266 |
| 0.0006 | 0.000173 | 4 | 0.312 |
| 0.0007 | 0.000170 | 4 | 0.313 |
| 0.0008 | 0.000167 | ... | 0 |
| k/paa | 0.2 | 0.4 | 0.6 | 0.8 | 1 | 1.2 | 1.4 | 1.6 | 1.8 | 2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Min | 0.0 | 0.0 | 5.9 | 13.0 | 21.7 | 27.3 | 29.4 | 29.4 | 29.4 | 29.4 |
| Avg | 3.9 | 10.5 | 20.0 | 29.1 | 38.3 | 48.8 | 60.4 | 72.5 | 85.6 | 98.0 |
| Max | 10.0 | 18.2 | 50.0 | 62.5 ... | | #Worse | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| #Better | 31 | 49 | 50 | 50 | 50 | 50 | 50 | 50 | 50 | 50 | | 1 |
| k/paa | 0.2 | 0.4 | 0.6 | 0.8 | 1 | 1.2 | 1.4 | 1.6 | 1.8 | 2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Min | 0.0 | 0.0 | 5.9 | 13.0 | 21.7 | 27.3 | 29.4 | 29.4 | 29.4 | 29.4 |
| Avg | 3.9 | 10.5 | 20.0 | 29.1 | 38.3 | 48.8 | 60.4 | 72.5 | 85.6 | 98.0 |
| Max | 10.0 | 18.2 | 50.0 | 62.5 ... | | 0.0003 | 0.000193 | 2 | 0.218 |
| --- | --- | --- | --- |
| 0.0004 | 0.000182 | 3 | 0.266 |
| 0.0005 | 0.000174 | 3 | 0.266 |
| 0.0006 | 0.000173 | 4 | 0.312 |
| 0.0007 | 0.000170 | 4 | 0.313 |
| 0.0008 | 0.000167 | 3 | 0.266 |
| 0.0009 | 0.000167 | 4 | 0.313 |
| 0.001 | 0.000167 | 4 | 0.312 |
| 0.002 | 0.000156 | 2 ... | 0 |
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