Urbanization, Inequality, and Poverty in the People’s Republic of China

Relying on the present literature, official statistics, and household survey data in the People’s Republic of China, this paper summarizes research findings on the relationship between urbanization, urban–rural inequality, and poverty, and provides further empirical evidence on the role of urbanization and government policies in urban poverty. Several conclusions can be drawn from. First, urbanization has a significant effect on reducing both poverty of rural residents and poverty of migrating peasants, and, consequently, has a positive effect on narrowing the rural–urban income or consumption gap. Urban labor markets play an important role in this effect. Second, urbanization is positively correlated with urban poverty. This can be explained by the competition between migrating peasants and urban workers in the labor market, and the failure of the government’s anti-poverty policies in urban areas. Third, the existence of an informal sector has a negative effect on the poverty of urban citizens. Being employed by the informal sector significantly increases the probability of falling into poverty for urban citizens. Fourth, the minimum wage has a positive effect on reducing urban poverty, while the effect of other policies, such as Di Bao and the minimum living standard, is limited

Community Structure Detection in Complex Networks with Partial Background Information

Constrained clustering has been well-studied in the unsupervised learning society. However, how to encode constraints into community structure detection, within complex networks, remains a challenging problem. In this paper, we propose a semi-supervised learning framework for community structure detection. This framework implicitly encodes the must-link and cannot-link constraints by modifying the adjacency matrix of network, which can also be regarded as de-noising the consensus matrix of community structures. Our proposed method gives consideration to both the topology and the functions (background information) of complex network, which enhances the interpretability of the results. The comparisons performed on both the synthetic benchmarks and the real-world networks show that the proposed framework can significantly improve the community detection performance with few constraints, which makes it an attractive methodology in the analysis of complex networks

Secrecy Wireless Information and Power Transfer in OFDMA Systems

In this paper, we consider simultaneous wireless information and power transfer (SWIPT) in orthogonal frequency division multiple access (OFDMA) systems with the coexistence of information receivers (IRs) and energy receivers (ERs). The IRs are served with best-effort secrecy data and the ERs harvest energy with minimum required harvested power. To enhance physical-layer security and yet satisfy energy harvesting requirements, we introduce a new frequency-domain artificial noise based approach. We study the optimal resource allocation for the weighted sum secrecy rate maximization via transmit power and subcarrier allocation. The considered problem is non-convex, while we propose an efficient algorithm for solving it based on Lagrange duality method. Simulation results illustrate the effectiveness of the proposed algorithm as compared against other heuristic schemes.Comment: To appear in Globecom 201