Foreign exchange policy and banking reform in China

Foreign exchange ; Dollar

The Ringel--Hall Lie algebra of a spherical object

For an integer $w$, let \cs_w be the algebraic triangulated category generated by a $w$-spherical object. We determine the Picard group of \cs_w and show that each orbit category of \cs_w is triangulated and is triangle equivalent to a certain orbit category of the bounded derived category of a standard tube. When $n=2$, the orbit category \cs_w/\Sigma^2 is 2-periodic triangulated, and we characterize the associated Ringel--Hall Lie algebra in the sense of Peng and Xiao.Comment: 26page

A Deep Learning Reconstruction Framework for Differential Phase-Contrast Computed Tomography with Incomplete Data

Differential phase-contrast computed tomography (DPC-CT) is a powerful analysis tool for soft-tissue and low-atomic-number samples. Limited by the implementation conditions, DPC-CT with incomplete projections happens quite often. Conventional reconstruction algorithms are not easy to deal with incomplete data. They are usually involved with complicated parameter selection operations, also sensitive to noise and time-consuming. In this paper, we reported a new deep learning reconstruction framework for incomplete data DPC-CT. It is the tight coupling of the deep learning neural network and DPC-CT reconstruction algorithm in the phase-contrast projection sinogram domain. The estimated result is the complete phase-contrast projection sinogram not the artifacts caused by the incomplete data. After training, this framework is determined and can reconstruct the final DPC-CT images for a given incomplete phase-contrast projection sinogram. Taking the sparse-view DPC-CT as an example, this framework has been validated and demonstrated with synthetic and experimental data sets. Embedded with DPC-CT reconstruction, this framework naturally encapsulates the physical imaging model of DPC-CT systems and is easy to be extended to deal with other challengs. This work is helpful to push the application of the state-of-the-art deep learning theory in the field of DPC-CT

Industry specialization, diversification, churning, and unemployment in Chinese cities

This paper studies how industry specialization, diversification, and churning affect unemployment rates in Chinese cities. Using a city level panel data set from 1997 to 2006, we find that the specialization of wholesale and retail industry can significantly decrease unemployment rate; however, specializing in finance industry increases unemployment rate. In contrast to the evidence from developed countries, industry diversity is positively and significantly associated with unemployment rates in Chinese cities, possibly due to the higher degree of industry churning during the sample period. We also find that urban economic growth, market maturity measured by the proportion of private sector employment, and human capital can decrease unemployment rate. Industry diversity does not stabilize unemployment; wholesale and retail industry increases unemployment fluctuations; but market maturity and human capital stabilize unemployment.Industry structure; specialization; industry diversity; unemployment; churning