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Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction
To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers.
Aharonov-Bohm Effect in Three-dimensional Higher-order Topological Insulator
Unlike AB interferometer of 3D topological insulator(TI), we find that there are different AB oscillation frequencies for a given direction of magnetic field in 3D HOTI.
Mesoscale and Nanoscale Physics
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning
Firstly, a deep Gaussian process (DGP) model is built on the historical data to learn empirical knowledge.
VLSI Mask Optimization: From Shallow To Deep Learning
VLSI mask optimization is one of the most critical stages in manufacturability aware design, which is costly due to the complicated mask optimization and lithography simulation.
