651 research outputs found
Transformer and Snowball Graph Convolution Learning for Biomedical Graph Classification
Graph or network has been widely used for describing and modeling complex
systems in biomedicine. Deep learning methods, especially graph neural networks
(GNNs), have been developed to learn and predict with such structured data. In
this paper, we proposed a novel transformer and snowball encoding networks
(TSEN) for biomedical graph classification, which introduced transformer
architecture with graph snowball connection into GNNs for learning whole-graph
representation. TSEN combined graph snowball connection with graph transformer
by snowball encoding layers, which enhanced the power to capture multi-scale
information and global patterns to learn the whole-graph features. On the other
hand, TSEN also used snowball graph convolution as position embedding in
transformer structure, which was a simple yet effective method for capturing
local patterns naturally. Results of experiments using four graph
classification datasets demonstrated that TSEN outperformed the
state-of-the-art typical GNN models and the graph-transformer based GNN models.Comment: Prepared for submitting to TB
Estimation of Poisson-Dirichlet Parameters with Monotone Missing Data
This article considers the estimation of the unknown numerical parameters and the density of the base measure in a Poisson-Dirichlet process prior with grouped monotone missing data. The numerical parameters are estimated by the method of maximum likelihood estimates and the density function is estimated by kernel method. A set of simulations was conducted, which shows that the estimates perform well
Experimental investigation and numerical modeling of oxidative pyrolysis mechanism of mining PVC cable sheath
Experimental study on the spread and burning behaviors of continuously discharge spill fires under different slopes
Experiments and modeling of the temperature profile of turbulent diffusion flames with large ullage heights
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