Residential Status and Satisfaction with China’s Public Health Services

This thesis examined the joint effect of hukou (household registration) status and residence location on people’s satisfaction with public health services in China. It also examines the role of education level, media use (official and unofficial), perception of equality, self–rated social status, self–rated health status, public health insurance participation in the relationship between residential status and satisfaction with public health services. This thesis found that hukou status and residence location have significantly joint effect on satisfaction. Satisfaction score is the highest among those with rural residence and rural hukou, followed by urban individuals with rural hukou, with urban individuals with urban hukou having the lowest satisfaction score. Official media and self-rated social status significantly suppress the effect of residential status on satisfaction while unofficial media, perception of equality and self-rated health status significantly mediate the effect. Findings from this study provide a better understanding of inequalities in health services across hukou status and residence location and provide insights on how to utilize information on public satisfaction appropriately in formulating and evaluating health policies. The expectations–experience competing effect model used in this thesis is not fully supported by the data. More research is needed to examine whether hukou status and residence location influence expectations of health service. In addition, factors other than higher expectations might explain urban residents’ lower levels of satisfaction with public health services needs to be identified

Oriented Response Networks

Deep Convolution Neural Networks (DCNNs) are capable of learning unprecedentedly effective image representations. However, their ability in handling significant local and global image rotations remains limited. In this paper, we propose Active Rotating Filters (ARFs) that actively rotate during convolution and produce feature maps with location and orientation explicitly encoded. An ARF acts as a virtual filter bank containing the filter itself and its multiple unmaterialised rotated versions. During back-propagation, an ARF is collectively updated using errors from all its rotated versions. DCNNs using ARFs, referred to as Oriented Response Networks (ORNs), can produce within-class rotation-invariant deep features while maintaining inter-class discrimination for classification tasks. The oriented response produced by ORNs can also be used for image and object orientation estimation tasks. Over multiple state-of-the-art DCNN architectures, such as VGG, ResNet, and STN, we consistently observe that replacing regular filters with the proposed ARFs leads to significant reduction in the number of network parameters and improvement in classification performance. We report the best results on several commonly used benchmarks.Comment: Accepted in CVPR 2017. Source code available at http://yzhou.work/OR

Existence and continuous dependence of mild solutions for fractional abstract differential equations with infinite delay

In this paper, we prove the existence, uniqueness, and continuous dependence of the mild solutions for a class of fractional abstract differential equations with infinite delay. The results are obtained by using the Krasnoselskii's fixed point theorem and the theory of resolvent operators for integral equations