China's Western Development Strategy: Policies, Effects and Prospects

China’s Western Development Strategy (WDS) has been carried out since 1999 with remarkable achievements, whereby Western China also experienced a rapid and stable development during the past decade. This paper analyzes policy actions and effects of WDS. The findings indicate that Western China’s economic development has experienced a dramatic reversion after implementation of WDS, which to a certain extent, proves that WDS has played a significant role in promoting western regions’ development. This paper also reveals some key constraints on Western China’s economic development and then offers a set of policy ideas for the next stage of Western development.Western Development Strategy; Regional Policy; Policy effects; Western China

Background field method in the large NfN_f expansion of scalar QED

Using the background field method, we, in the large $N_f$ approximation, calculate the beta function of scalar quantum electrodynamics at the first nontrivial order in $1/N_f$ by two different ways. In the first way, we get the result by summing all the graphs contributing directly. In the second way, we begin with the Borel transform of the related two point Green's function. The main results are that the beta function is fully determined by a simple function and can be expressed as an analytic expression with a finite radius of convergence, and the scheme-dependent renormalized Borel transform of the two point Green's function suffers from renormalons.Comment: 13 pages, 4 figures, 1 table, to appear in the European Physical Journal

Generating Text Sequence Images for Recognition

Recently, methods based on deep learning have dominated the field of text recognition. With a large number of training data, most of them can achieve the state-of-the-art performances. However, it is hard to harvest and label sufficient text sequence images from the real scenes. To mitigate this issue, several methods to synthesize text sequence images were proposed, yet they usually need complicated preceding or follow-up steps. In this work, we present a method which is able to generate infinite training data without any auxiliary pre/post-process. We tackle the generation task as an image-to-image translation one and utilize conditional adversarial networks to produce realistic text sequence images in the light of the semantic ones. Some evaluation metrics are involved to assess our method and the results demonstrate that the caliber of the data is satisfactory. The code and dataset will be publicly available soon