Research on the International Relevance of China’s Economic Policy Uncertainty

Currently facing a complex international environment, the world faces instability and uncertainty, and the world ’s economic growth momentum is inadequate. Facing environmental instability in various aspects, in order to promote sustainable economic development, governments of various countries are also implementing policies. There is a large uncertainty. Based on the economic policy uncertainty index, this article deeply studies the correlation between the uncertainty of China's economic policies and the uncertainty of economic policies of the world's important economies, and draws the following conclusions: In terms of correlation, when major historical events occur, the economic policy uncertainty index of each country has increased significantly. Secondly, in terms of targeted correlation, the economic policy uncertainty of each country is not completely independent and will be affected by other countries. Among them, the United States and China have the highest autonomy index and have the greatest impact on other countries. France and Germany have less influence on other countries and are more affected by other countries

Neural Control of Parametric Solutions for High-dimensional Evolution PDEs

We develop a novel computational framework to approximate solution operators of evolution partial differential equations (PDEs). By employing a general nonlinear reduced-order model, such as a deep neural network, to approximate the solution of a given PDE, we realize that the evolution of the model parameter is a control problem in the parameter space. Based on this observation, we propose to approximate the solution operator of the PDE by learning the control vector field in the parameter space. From any initial value, this control field can steer the parameter to generate a trajectory such that the corresponding reduced-order model solves the PDE. This allows for substantially reduced computational cost to solve the evolution PDE with arbitrary initial conditions. We also develop comprehensive error analysis for the proposed method when solving a large class of semilinear parabolic PDEs. Numerical experiments on different high-dimensional evolution PDEs with various initial conditions demonstrate the promising results of the proposed method.Comment: Accepted for publication SIAM Journal on Scientific Computing (To Appear

Self-Adaptive On Demand Geographic Routing Protocols for Mobile Ad Hoc Networks

Abstract — It has been a big challenge to develop routing protocol that can meet different application needs and optimize routing paths according to the topology change in mobile ad hoc networks. Basing their forwarding decisions only on the local topology, geographic routing protocols have drawn a lot of attentions in recent years. However, inaccurate local topology knowledge and the outdated destination position information can lead to inefficient geographic forwarding and even routing failure. Proactive local position distribution can hardly adapt to the traffic demand. It is also difficult to pre-set protocol parameters correctly to fit in different environments. We have developed two self-adaptive on-demand geographic routing schemes. The local topology is updated in a timely manner according to network dynamics and traffic demands. Our route optimization scheme adapts the routing path according to both topology changes and actual data traffic requirements. Each node can determine and adjust the protocol parameter values independently according to different network environments, data traffic conditions and node’s own requirements. Our simulation studies have shown that the proposed routing protocols are more robust and outperform the existing geographic routing protocol. Specifically, the packet delivery latency is reduced almost four times as compared to GPSR at high mobility

Breaking from Tradition: Experimental Poems by Four Contemporary Asian American Women Poets

This paper examines the themes and technical strategies in the experimental poems by three 20th-Century Asian American women poets —Cathy Song, Trinh T. Minh-ha, and Kimiko Hahn— through close reading. While challenging Bloom’s theory of “poetic influence” and Anglo-American feminist theories for constructing an alternative tradition of women writers, my analysis considers the importance of the subject positionalities of gender, race, and ethnicity in shaping the poetics of Asian American women poets