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Network representation learning can effectively solve the problem of data sparsity in recommendation. In this paper, LINE and DeepWalk in network representation learning were improved, and a hybrid recommendation algorithm was proposed to be applied to movie recommendation scene. The new algorithm generates three low dimensional feature vectors by learning user preference feature, user aversion feature and similar user feature. Three low dimensional feature vectors are linearly combined to form a user representation vector, and cosine similarity is used as the similarity index to recommend the movies associated with similar users to target users. Experimental results show that, compared with the suboptimal algorithm, the accuracy and Fl index of the proposed algorithm are improved by 12% and 7% respectively on MovieLens dataset, and 16% and 18% respectively on MovieTweetings dataset. The recommendation algorithm based on multi-dimensional feature representation learning proposed in this paper has significant advantages in movie recommendation scenes. [ABSTRACT FROM AUTHOR]

Since the left ventricle pressure waveform（LVPW）estimation is based on the model with numerous patient specific parameters, there is a great need to reduce the computation cost. A parameter subset selection strategy based on sensitivity analysis is proposed to solve these problems. LVPW features are chosen as outputs of a systemic circulation model. The meta model is created by adaptive sparse polynomial chaos expansion algorithm, and then Sobol sensitivity index is computed. Finally, the parameters which have large impact on LVPW features are selected as parameter subset. The parameter subset selection proposed in this paper can provide help for applying model to patient specific situations in clinical applications. Meantime, the results indicate that the reducing size of parameter subset can decrease the complex computation of parameter optimization significantly. Furthermore, LVPW estimation based on the proposed method has high correlation with that based on full model parameters. [ABSTRACT FROM AUTHOR]