Training multi-source domain adaptation network by mutual information estimation and minimization.

We address the problem of Multi-Source Domain Adaptation (MSDA), which trains a neural network using multiple labeled source datasets and an unlabeled target dataset, and expects the trained network to well classify the unlabeled target data. The main challenge in this problem is that the datasets are generated by relevant but different joint distributions. In this paper, we propose to address this challenge by estimating and minimizing the mutual information in the network latent feature space, which leads to the alignment of the source joint distributions and target joint distribution simultaneously.

[Fabrication of alginate microsphere for controlled release and investigation of its release characteristics in vitro].

This study sought to producte alginate sodium microsphere for controlled release bovine serum albumin(BSA) and to investigate the protein release profile of the BSA-alginate sodium microsphere in vitro, which threw some light on the angiogenesis of tissue engineering bone with vascular endothelial growth factor (VEGF) controlled release under stress. The BSA-alginate sodium microsphere was fabricated with W/O emulsification and ion cross-linking method using alginate sodium. The appearance, microsphere diameter and envelopment rate were detected, and the release characteristics of the BSA-alginate sodium microsphere in vitro was investigated.