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资源简介：
SUMO, Adaptive dynamic programming, Robustness
应用场景：
创建时间：
2021-10-04
相关数据集
Dataset 4U.6: Solutions files, sigma = 500, part 6 (Nam et al., Robustness and parameter geography in post-translational modification systems)
All proper solutions files for Paramotopy runs with \sigma = 500 on ILR-sampled parameter points in [10^{-p}, 10^p]^8, for p = 2, 3, 4, 5.
NIAID Data Ecosystem70
Results for noise-influenced models on (, ) samples with , , and , , , noise sequences.
The ratio of noise sequences is set at , and , ‘’ indicates that the real motifs were not in the top 25. The number after each run time is the ranking number of a true planted motif among the top 25
NIAID Data Ecosystem40
The effect of noise on spectral clustering f1-measures is illustrated.
The proposed sub-graph affinity model performs as well as existing spectral clustering methods when PSNR is high. When the PSNR is low, the sub-graph affinity model outperforms f1-measures for the Ng-
NIAID Data Ecosystem20
Dataset 5A.13: Certification files, sigma = 1, part 13 (Nam et al., Robustness and parameter geography in post-translational modification systems)
All composite output files from certifying solutions from Paramotopy runs with \sigma = 1.0 on VEGAS-sampled parameter points at \sigma = 50.
NIAID Data Ecosystem60
Dataset 4O.3: Solutions files, sigma = 10, part 3 (Nam et al., Robustness and parameter geography in post-translational modification systems)
All solutions statistics files for Paramotopy runs with \sigma = 10.
NIAID Data Ecosystem30
