Decentralized Constraint Satisfaction

We show that several important resource allocation problems in wireless networks fit within the common framework of Constraint Satisfaction Problems (CSPs). Inspired by the requirements of these applications, where variables are located at distinct network devices that may not be able to communicate but may interfere, we define natural criteria that a CSP solver must possess in order to be practical. We term these algorithms decentralized CSP solvers. The best known CSP solvers were designed for centralized problems and do not meet these criteria. We introduce a stochastic decentralized CSP solver and prove that it will find a solution in almost surely finite time, should one exist, also showing it has many practically desirable properties. We benchmark the algorithm's performance on a well-studied class of CSPs, random k-SAT, illustrating that the time the algorithm takes to find a satisfying assignment is competitive with stochastic centralized solvers on problems with order a thousand variables despite its decentralized nature. We demonstrate the solver's practical utility for the problems that motivated its introduction by using it to find a non-interfering channel allocation for a network formed from data from downtown Manhattan

Quality development framework for extended services

The Quality Development Framework (QDF) is a flexible self-evaluation tool which supports review and self-assessment, planning and quality assurance. It is a framework for schools, Children’s Centres and multi-agency partners working as Consortia to deliver Extended Services, which recognises the complex and developmental nature of this process. Engagement with the QDF will contribute to the completion of Schools and Children’s Centres’ Self Evaluations Forms and provide an evidence base to support other Self-Evaluation processes and Quality Assurance systems

Divide and conquer identification using Gaussian process priors

We investigate the reconstruction of nonlinear systems from locally identified linear models. It is well known that the equilibrium linearisations of a system do not uniquely specify the global dynamics. Information about the dynamics near to equilibrium provided by the equilibrium linearisations is therefore combined with other information about the dynamics away from equilibrium provided by suitable measured data. That is, a hybrid local/global modelling approach is considered. A non-parametric Gaussian process prior approach is proposed for combining in a consistent manner these two distinct types of data. This approach seems to provide a framework that is both elegant and powerful, and which is potentially in good accord with engineering practice

Extending learning opportunities: a framework for self-evaluation in study support

The Extending Learning Opportunities (ELO) framework for selfevaluation in study support is a tool to use when quality assuring learning opportunities for children and young people outside of normal lesson time. This framework is a revised edition of the ELO (2009) DCSF publication. It provides a framework for all schools, and school consortiums including Complementary Supplementary Schools, Children’s Centres, Playing for Success (PfS) centres, Higher Education Institutions (HEIs) and organisations such as libraries, museums, galleries, theatres, including youth and community/volunteer projects that offer a structured learning programmes to young people, to extend and enhance their provision and so to improve their outcomes. It is based on self–evaluation and sets out criteria against which schools, HEIs and other organisations, can review their extended provision, the planning and practices which support it and the overall ethos which supports learning. The criteria, which take the form of Key Indicators, are at three levels: Emerged, Established and Advanced

A Deep \u3cem\u3eChandra\u3c/em\u3e ACIS Survey of M83

We have obtained a series of deep X-ray images of the nearby galaxy M83 using Chandra, with a total exposure of 729 ks. Combining the new data with earlier archival observations totaling 61 ks, we find 378 point sources within the D25 contour of the galaxy. We find 80 more sources, mostly background active galactic nuclei (AGNs), outside of the D25 contour. Of the X-ray sources, 47 have been detected in a new radio survey of M83 obtained using the Australia Telescope Compact Array. Of the X-ray sources, at least 87 seem likely to be supernova remnants (SNRs), based on a combination of their properties in X-rays and at other wavelengths. We attempt to classify the point source population of M83 through a combination of spectral and temporal analysis. As part of this effort, we carry out an initial spectral analysis of the 29 brightest X-ray sources. The soft X-ray sources in the disk, many of which are SNRs, are associated with the spiral arms, while the harder X-ray sources, mostly X-ray binaries (XRBs), do not appear to be. After eliminating AGNs, foreground stars, and identified SNRs from the sample, we construct the cumulative luminosity function (CLF) of XRBs brighter than 8 × 1035 erg s–1. Despite M83\u27s relatively high star formation rate, the CLF indicates that most of the XRBs in the disk are low mass XRBs