Empirical analysis on impact of FDI on the level of urbanization in costal areas

Purpose: There is a close relationship between FDI and the level of urbanization. the objective of this research is to analyze the relationship between FDI and the level of urbanization in Jiangsu and Guangdong provinces in costal areas. Design/methodology/approach: The author uses the modern econometric methods by panel unit root test, cointegration test, random effects models and fixed effects models, and the data of FDI (2000-2012), urbanization rate, industrial structure and regional GDP of Nanjing, Xuzhou, Suzhou, Wuxi and other 13 cities in Jiangsu Province, Guangzhou, Shenzhen and other 19 cities in Guangdong province, researches the relationship between FDI and urbanization rate. Findings: Models show, FDI was closely correlated with urbanization rate in Jiangsu province and Guangdong province, the highly correlated with industrial structure, while it negatively correlated with the growth of GDP, and the degree of correlation is not high in Jiangsu province, but the high negative correlation in Guangdong province. The results shows the industrial structure and the urbanization rate mainly cause FDI growth. Research limitations/implications: There are many provinces in costal areas of China, this paper just research the relationship between FDI and the level of urbanization in Jiangsu province and Guangdong province, there are some limitations in the study areas and results.Peer Reviewe

Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements

Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing and 3D modeling. Recently, motivated by compressive imaging, background subtraction from compressive measurements (BSCM) is becoming an active research task in video surveillance. In this paper, we propose a novel tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework. In this approach, we use 3D total variation (TV) to enhance the spatio-temporal continuity of foregrounds, and Tucker decomposition to model the spatio-temporal correlations of video background. Based on this idea, we design a basic tensor RPCA model over the video frames, dubbed as the holistic TenRPCA model (H-TenRPCA). To characterize the correlations among the groups of similar 3D patches of video background, we further design a patch-group-based tensor RPCA model (PG-TenRPCA) by joint tensor Tucker decompositions of 3D patch groups for modeling the video background. Efficient algorithms using alternating direction method of multipliers (ADMM) are developed to solve the proposed models. Extensive experiments on simulated and real-world videos demonstrate the superiority of the proposed approaches over the existing state-of-the-art approaches.Comment: To appear in IEEE TI

Symmetry-Dependent Spin Transport Properties and Spin-Filter Effects in Zigzag-Edged Germanene Nanoribbons

We performed the first-principles calculations to investigate the spin-dependent electronic transport properties of zigzag-edged germanium nanoribbons (ZGeNRs). We choose of ZGeNRs with odd and even widths of 5 and 6, and the symmetry-dependent transport properties have been found, although the σ mirror plane is absent in ZGeNRs. Furthermore, even-N and odd-N ZGeNRs have very different current-voltage relationships. We find that the even 6-ZGeNR shows a dual spin-filter effect in antiparallel (AP) magnetism configuration, but the odd 5-ZGeNR behaves as conventional conductors with linear current-voltage dependence. It is found that when the two electrodes are in parallel configuration, the 6-ZGeNR system is in a low resistance state, while it can switch to a much higher resistance state when the electrodes are in AP configuration, and the magnetoresistance of 270% can be observed

An Interactive Pedestrian Re-Identification Tool with Semantic Based Re-Identification

Person re-identification is an essential task of recognizing and matching people from non-overlapping cameras. A typical application of person re-identification is identifying a particular person in a gallery of pedestrian images from a camera with one or more given probe images of this person from another camera. This is a chal- lenging and practical task that provides solutions for video-surveillance. In this work, we present a person re-identification software which is called Interactive Pedestrian Re-identification GUI (IPRG), and a semantic-based labelling tool named Reid It (Reidit). According to the growing need for surveillance applications, we develop IPRG to address the person searching and matching problem with the dataset from on-campus security camera videos. From these video frames, we can get semantic in- formation of the candidate such as height, ethnicity, cloth color, etc. By customizing these semantic features in IPRG, we can identify a candidate in the video database rapidly. We also propose a light-labelling tool, Reidit, for labelling pedestrian images with semantic features as the pre-processing for pedestrian recognition. We present an experiment on IPRG with Viewpoint Invariant Pedestrian Recognition (VIPeR) dataset which contains 632 identities. Our experiment shows that our software is more efficient and accurate compared with traditional manual solutions. Moreover, IPRG can handle the situation of missing query person in the database, and it will return the top ten possible individuals. Our software is compatible with different platforms and user-friendly with customizable databases and semantic features.Computer Science, Department o