After Housing Policy: Housing and the UK General Election 2001

This paper describes the results of a review of the housing content of UK General Election 2001 manifestos. Housing policy was of little importance during the election campaign. The main British political parties had, essentially, a shared housing agenda - to promote and facilitate home ownership, support area and community regeneration, tackle homelessness, improve the private rented sector, and prevent building on greenfield sites. Many issues of importance to housing specialists received little or no attention, most notably that of low demand. Some policy variations within the UK were evident, for example in attitudes towards greenfield development, home ownership and stock transfer. The paper concludes that differences in housing policy are emerging within the UK as part of a new politics of devolution and that the days of a single housing policy approach for the UK are over.

Devolving Housing Policy and Practice in Northern Ireland 1998 - 2002

This paper reviews the effect of devolution on housing policy and practice in Northern Ireland. It outlines the history and context of devolution and housing policy in Northern Ireland, including the legacy and persistence of intense social conflict. Current devolution arrangements are reviewed, including the implications of enforced coalition for policy governance. The paper focuses on three dimensions of housing and housing-related policy development and implementation: social housing, especially the distinctive history and changing organisation of social housing provision; policies affecting the housing market, including the changing regime for spatial planning; and, regeneration and tenant participation. The paper argues that housing policy has tended to converge with policies in England, rather than moving towards a distinctively local agenda. Local political agendas remain dominated by disagreements over constitutional status, thus policy formulation is determined more by officials than by elected politicians.

On the Diversity-Multiplexing Tradefoff for Wireless Cooperative Multiple Access Systems

A spectrally efficient strategy is proposed for cooperative multiple access (CMA) channels in a centralized communication environment with $N$ users. By applying superposition coding, each user will transmit a mixture containing its own information as well as the other users', which means that each user shares parts of its power with the others. The use of superposition coding in cooperative networks was first proposed in , which will be generalized to a multiple-user scenario in this paper. Since the proposed CMA system can be seen as a precoded point-to-point multiple-antenna system, its performance can be best evaluated using the diversity-multiplexing tradeoff. By carefully categorizing the outage events, the diversity-multiplexing tradeoff can be obtained, which shows that the proposed cooperative strategy can achieve larger diversity/multiplexing gain than the compared transmission schemes at any diversity/multiplexing gain. Furthermore, it is demonstrated that the proposed strategy can achieve optimal tradeoff for multiplexing gains $0leq r leq 1$ whereas the compared cooperative scheme is only optimal for $0leq r leq ({1}/{N})$. As discussed in the paper, such superiority of the proposed CMA system is due to the fact that the relaying transmission does not consume extra channel use and, hence, the deteriorating effect of cooperative communication on the data rate is effectively limited.

Gene selection by cooperative competition clustering

Clustering analysis of data from DNA microarray hybridization studies is an essential task for identifying biologically relevant groups of genes. Attribute cluster algorithm (ACA) has provided an attractive way to group and select meaningful genes. However, ACA needs much prior knowledge about the genes to set the number of clusters. In practical applications, if the number of clusters is misspecified, the performance of the ACA will deteriorate rapidly. In fact, it is a very demanding to do that because of our little knowledge. We propose the Cooperative Competition Cluster Algorithm (CCCA) in this paper. In the algorithm, we assume that both cooperation and competition exist simultaneously between clusters in the process of clustering. By using this principle of Cooperative Competition, the number of clusters can be found in the process of clustering. Experimental results on a synthetic and gene expression data are demonstrated. The results show that CCCA can choose the number of clusters automatically and get excellent performance with respect to other competing methods.