Knowledge graphs (KGs) can help enhance recommendation, especially for the data-sparsity scenario with limited user-item interaction data. Due to strong power of representation learning graph neural networks (GNNs), recent works KG-based recommendation deploy GNN models learn from both knowledge and bipartite graph. However, these have not well considered hierarchical structure graph, leading s...

Data organized into hierarchical structures (e.g., phylogenies or cell types) arises in several biological fields. It is therefore of interest to have data containers that store the structure together with profile data, and provide functions easily access manipulate at different resolutions. Here, we present TreeSummarizedExperiment, a R/S4 class extends commonly used SingleCellExperimen...

Variational autoencoder (VAE) has widely been utilized for modeling data distributions because it is theoretically elegant, easy to train, and nice manifold representations. However, when applied image reconstruction synthesis tasks, VAE shows the limitation that generated sample tends be blurry. We observe a similar problem, in which trajectory located between adjacent lanes, often arises VAE-...