Embracing the future: embedding digital repositories in the University of London. Briefing paper

This briefing paper captures the key findings and recommendations of a study commissioned by the Joint Information Systems Committee (JISC) on aspects of the strategic commitment of institutions to repository sustainability.1 This project, labelled EMBRACE (EMBedding Repositories And Consortial Enhancement) is aimed at enhancing the functionality, inter-operability and extensibility of the SHERPA-LEAP repository service, which currently supports the repositories of thirteen University of London institutions. This briefing paper aims to clarify the different motivations to use and invest in digital repositories, and potential ways to address the challenges to embedding these repositories in institutional strategy and daily operation are highlighted. It is designed for use by Higher Education Institutions (HEIs), who are encouraged to adapt the recommendations to their specific context

Embracing the future: embedding digital repositories in the University of London

Digital repositories can help Higher Education Institutions (HEIs) to develop coherent and coordinated approaches to capture, identify, store and retrieve intellectual assets such as datasets, course material and research papers. With the advances of technology, an increasing number of Higher Education Institutions are implementing digital repositories. The leadership of these institutions, however, has been concerned about the awareness of and commitment to repositories, and their sustainability in the future. This study informs a consortium of thirteen London institutions with an assessment of current awareness and attitudes of stakeholders regarding digital repositories in three case study institutions. The report identifies drivers for, and barriers to, the embedding of digital repositories in institutional strategy. The findings therefore should be of use to decision-makers involved in the development of digital repositories. Our approach was entirely based on consultations with specific groups of stakeholders in three institutions through interviews with specific individuals. The research in this report was prepared for the SHERPA-LEAP Consortium and conducted by RAND Europe

Adaptive Polar Sampling with an Application to a Bayes Measure of Value-at-Risk

Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlo method for Bayesian analysis of models with ill-behaved posterior distributions. In order to sample efficiently from such a distribution, a location-scale transformation and a transformation to polar coordinates are used. After the transformation to polar coordinates, a Metropolis-Hastings algorithm is applied to sample directions and, conditionally on these, distances are generated by inverting the CDF. A sequential procedure is applied to update the location and scale. Tested on a set of canonical models that feature near non-identifiability, strong correlation, and bimodality, APS compares favourably with the standard Metropolis-Hastings sampler in terms of parsimony and robustness. APS is applied within a Bayesian analysis of a GARCH-mixture model which is used for the evaluation of the Value-at-Risk of the return of the Dow Jones stock index.Markov Chain Monte Carlo;simulation;polar coordinates;GARCH;ill-behaved posterior;value-at-risk

Phase mapping of ultrashort pulses in bimodal photonic structures: A window on local group velocity dispersion

The amplitude and phase evolution of ultrashort pulses in a bimodal waveguide structure has been studied with a time-resolved photon scanning tunneling microscope (PSTM). When waveguide modes overlap in time intriguing phase patterns are observed. Phase singularities, arising from interference between different modes, are normally expected at equidistant intervals determined by the difference in effective index for the two modes. However, in the pulsed experiments the distance between individual singularities is found to change not only within one measurement frame, but even depends strongly on the reference time. To understand this observation it is necessary to take into account that the actual pulses generating the interference signal change shape upon propagation through a dispersive medium. This implies that the spatial distribution of phase singularities contains direct information on local dispersion characteristics. At the same time also the mode profiles, wave vectors, pulse lengths, and group velocities of all excited modes in the waveguide are directly measured. The combination of these parameters with an analytical model for the time-resolved PSTM measurements shows that the unique spatial phase information indeed gives a direct measure for the group velocity dispersion of individual modes. As a result interesting and useful effects, such as pulse compression, pulse spreading, and pulse reshaping become accessible in a local measuremen

Explaining Adaptive Radial-Based Direction Sampling

In this short paper we summarize the computational steps of Adaptive Radial-Based Direction Sampling (ARDS), which can be used for Bayesian analysis of ill behaved target densities. We consider one simulation experiment in order to illustrate the good performance of ARDS relative to the independence chain MH algorithm and importance sampling.importance sampling;Markov Chain Monte Carlo;radial coordinates

Adaptive radial-based direction sampling; Some flexible and robust Monte Carlo integration methods

Adaptive radial-based direction sampling (ARDS) algorithms are specified for Bayesian analysis of models with nonelliptical, possibly, multimodal target distributions.A key step is a radial-based transformation to directions and distances. After the transformations a Metropolis-Hastings method or, alternatively, an importance sampling method is applied to evaluate generated directions. Next, distances are generated from the exact target distribution by means of the numerical inverse transformation method. An adaptive procedure is applied to update the initial location and covariance matrix in order to sample directions in an efficient way. Tested on a set of canonical mixture models that feature multimodality, strong correlation, and skewness, the ARDS algorithms compare favourably with the standard Metropolis-Hastings and importance samplers in terms of flexibility and robustness. The empirical examples include a regression model with scale contamination and a mixture model for economic growth of the USA.Markov chain Monte Carlo;importance sampling;radial coordinates

Does public service broadcasting serve the public? The future of television in the changing media landscape

The media landscape is subject to substantial technological change. In this Discussion Paper, we analyse how technological trends affect the economic rationale for PSB. After identifying the aims and nature of PSB, we derive eight possible market failures from the specific economic characteristics of information. The changing relevance of these market failures is subsequently discussed in the light of the technological changes. Based on this analysis, we argue that public service broadcasting (PSB) for the digital age should be light in the sense that it has a much smaller mandate. The main reason for this conclusion is that, due to technological developments, many market failures in the broadcasting industry are no longer relevant. The broadcasting market thus functions more and more like a normal market. This implies that the allocation tends to the efficient outcome, as long as consumer valuation is properly accounted for. This is not the case when there are externalities and possibly not when it comes to valuing quality. In the presence of these market failures, an efficient allocation is not warranted in the broadcasting industry. It is these remaining market failures that give a future PSB a right to exist.