AIM: In order to determine the reliability between two of these methodologically different method, this study evaluated the systematic and random errors of the method proposed by Tanaka and Johnston, ...

Abstract: Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on heterogeneous graphs. Typical HGNNs require repetitive message passing during training, limiting ...

Abstract: For the online distributed estimation problem of time-varying parameters, we study a linear regression model with measurement noises over time-varying random graphs. We propose a distributed ...

Background incidence rates (BIRs) are essential for contextualizing adverse event rates in vaccine safety monitoring, particularly through observed-to-expected (O/E) analyses. The unprecedented rapid ...