A Chinese Way of Democratization?

With an equal social structure, China seems to be better prepared for a functioning democracy than other developing countries. It has stayed authoritarian because the CCP has successfully diverted the demand for democratization through tactics of economic growth, expansion of civil liberty, and selective accountability. However, the results of these tactics inevitably bring about forces and elements arguing and even fighting for democratization. As a result, there are more democratic elements in China than people usually believe and these elements are growing. Chinas path to democratization may prove to be appropriate taking into account Chinas recent history and cultural heritage.democracy, social structure, economic growth, expansion of civil liberty, and selective accountability

The Disinterested Government: An Interpretation of China's Economic Success in the Reform Era

In the last 30 years, China has achieved high economic growth and successfully transformed its economy from a planned economy to a market-based system. The country, to a large extent, has attained success through the recommendations proposed by standard economic theory. However, the role of political economy has been omitted from the literature: how did China adopt the right economic policies and the appropriate road to reform? This paper attempts to answer this question. The central assumption of the paper is that China achieved success because the Chinese government has been a disinterested party, i.e., a government that does not favour any particular sections of the population and prioritizes the long-term welfare of the whole society. In this paper, we first define and analyse the concept of disinterested governments, and then proceed to provide several examples to demonstrate that China has been characterized by a disinterested government. Based on a theoretical model, we also discuss the reasons of theDisinterested governments, the China miracle, econimic reform

Poisson Matrix Completion

We extend the theory of matrix completion to the case where we make Poisson observations for a subset of entries of a low-rank matrix. We consider the (now) usual matrix recovery formulation through maximum likelihood with proper constraints on the matrix $M$, and establish theoretical upper and lower bounds on the recovery error. Our bounds are nearly optimal up to a factor on the order of $\mathcal{O}(\log(d_1 d_2))$. These bounds are obtained by adapting the arguments used for one-bit matrix completion \cite{davenport20121} (although these two problems are different in nature) and the adaptation requires new techniques exploiting properties of the Poisson likelihood function and tackling the difficulties posed by the locally sub-Gaussian characteristic of the Poisson distribution. Our results highlight a few important distinctions of Poisson matrix completion compared to the prior work in matrix completion including having to impose a minimum signal-to-noise requirement on each observed entry. We also develop an efficient iterative algorithm and demonstrate its good performance in recovering solar flare images.Comment: Submitted to IEEE for publicatio

Population-based incremental learning with associative memory for dynamic environments

Copyright © 2007 IEEE. Reprinted from IEEE Transactions on Evolutionary Computation. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In recent years there has been a growing interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) due to its importance in real world applications. Several approaches, such as the memory and multiple population schemes, have been developed for EAs to address dynamic problems. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of EAs, for DOPss. A PBIL-specific associative memory scheme, which stores best solutions as well as corresponding environmental information in the memory, is investigated to improve its adaptability in dynamic environments. In this paper, the interactions between the memory scheme and random immigrants, multi-population, and restart schemes for PBILs in dynamic environments are investigated. In order to better test the performance of memory schemes for PBILs and other EAs in dynamic environments, this paper also proposes a dynamic environment generator that can systematically generate dynamic environments of different difficulty with respect to memory schemes. Using this generator a series of dynamic environments are generated and experiments are carried out to compare the performance of investigated algorithms. The experimental results show that the proposed memory scheme is efficient for PBILs in dynamic environments and also indicate that different interactions exist between the memory scheme and random immigrants, multi-population schemes for PBILs in different dynamic environments