Transfer Learning across Networks for Collective Classification

This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.Comment: Published in the proceedings of IEEE ICDM 201

Hospital Treatment Rates and Spill-Over Effects: Does Ownership Matter?

This paper studies the effect of hospital ownership on treatment rates allowing for spatial correlation among hospitals. Competition among hospitals and knowledge spillovers generate significant externalities which we try to capture using the spatial Durbin model. Using a panel of 2342 hospitals in the 48 continental states observed over the period 2005 to 2008, we find significant spatial correlation of medical service treatment rates among hospitals. The paper also shows mixed results on the effect of hospital ownership on treatment rates that depends upon the market structure where the hospital is located and which varies by treatment type

4D-MRI in Radiotherapy

Four-dimensional (4D) imaging provides a useful estimation of tissue motion pattern and range for radiation therapy of moving targets. 4D-CT imaging has been a standard care of practice for stereotactic body radiation therapy of moving targets. Recently, 4D-MRI has become an emerging developmental area in radiotherapy. In comparison with 4D-CT imaging, 4D-MRI provides better spatial rendering of radiotherapy targets in abdominal and pelvis regions with improved visualization of soft tissue motion. Successful implementation of 4D-MRI requires an integration of optimized acquisition protocols, advanced image reconstruction techniques, and sufficient hardware capabilities. The proposed chapter intends to introduce basic theories, current research, development, and applications of 4D-MRI in radiotherapy