Perceptions of Multi-Lateral Cross Boundary Organization of Local Governments in China: A Q-Analysis

Most public issues fall beyond the boundaries of any particular local government to solve. Conversely, many of those problems are equally likely to be too localized for national or state/provincial governments to solve. Rather, they scale to a level of governance somewhere between those two levels – often referred to as "the metropolitan region." Such scaling is a ubiquitous and global phenomenon, and governments in virtually every developed country have tried a variety of approaches to balance the desire for centralized coordination and decentralized application. For example, local governments in the United States have seen the emergence of regionally scaled voluntary cross-boundary organizations of local governments to address common public policy problems (Miller and Nelles, 2018; 2020). The issue of regional scaling in China has only recently attracted the attention of Chinese scholars and practitioners. As such, there is scant scholarly research even though China is experiencing steady growth in such organizational designs. The purpose of this thesis is to explore Chinese scholars' and practitioners' subjective understanding of the nature, purpose, and value of these new arrangements that scale to the level of "region." This research used Q-methodology to interview 54 Chinese government officials and scholars who have experience with working in cross-boundary organizations. The researcher used principal components analysis coupled with varimax rotation then generated four factors. These four factors identified illustrate different views toward these cross-boundary organizations in terms of how local government multi-lateral cross-boundary collaboration should be organized. This research has provided a new angle to view regional intergovernmental cooperation in China

Analysis of a stochastic delay competition system driven by Lévy noise under regime switching

This paper is concerned with a stochastic delay competition system driven by Lévy noise under regime switching. Both the existence and uniqueness of the global positive solution are examined. By comparison theorem, sufficient conditions for extinction and non-persistence in the mean are obtained. Some discussions are made to demonstrate that the different environment factors have significant impacts on extinction. Furthermore, we show that the global positive solution is stochastically ultimate boundedness under some conditions, and an important asymptotic property of system is given. In the end, numerical simulations are carried out to illustrate our main results

A Simple Asymmetric Momentum Make SGD Greatest Again

We propose the simplest SGD enhanced method ever, Loss-Controlled Asymmetric Momentum(LCAM), aimed directly at the Saddle Point problem. Compared to the traditional SGD with Momentum, there's no increase in computational demand, yet it outperforms all current optimizers. We use the concepts of weight conjugation and traction effect to explain this phenomenon. We designed experiments to rapidly reduce the learning rate at specified epochs to trap parameters more easily at saddle points. We selected WRN28-10 as the test network and chose cifar10 and cifar100 as test datasets, an identical group to the original paper of WRN and Cosine Annealing Scheduling(CAS). We compared the ability to bypass saddle points of Asymmetric Momentum with different priorities. Finally, using WRN28-10 on Cifar100, we achieved a peak average test accuracy of 80.78\% around 120 epoch. For comparison, the original WRN paper reported 80.75\%, while CAS was at 80.42\%, all at 200 epoch. This means that while potentially increasing accuracy, we use nearly half convergence time. Our demonstration code is available at\\ https://github.com/hakumaicc/Asymmetric-Momentum-LCA

Convolutional Hierarchical Attention Network for Query-Focused Video Summarization

Previous approaches for video summarization mainly concentrate on finding the most diverse and representative visual contents as video summary without considering the user's preference. This paper addresses the task of query-focused video summarization, which takes user's query and a long video as inputs and aims to generate a query-focused video summary. In this paper, we consider the task as a problem of computing similarity between video shots and query. To this end, we propose a method, named Convolutional Hierarchical Attention Network (CHAN), which consists of two parts: feature encoding network and query-relevance computing module. In the encoding network, we employ a convolutional network with local self-attention mechanism and query-aware global attention mechanism to learns visual information of each shot. The encoded features will be sent to query-relevance computing module to generate queryfocused video summary. Extensive experiments on the benchmark dataset demonstrate the competitive performance and show the effectiveness of our approach.Comment: Accepted by AAAI 2020 Conferenc