The cultural, ethnic and linguistic classification of populations and neighbourhoods using personal names

There are growing needs to understand the nature and detailed composition of ethnicgroups in today?s increasingly multicultural societies. Ethnicity classifications areoften hotly contested, but still greater problems arise from the quality and availabilityof classifications, with knock on consequences for our ability meaningfully tosubdivide populations. Name analysis and classification has been proposed as oneefficient method of achieving such subdivisions in the absence of ethnicity data, andmay be especially pertinent to public health and demographic applications. However,previous approaches to name analysis have been designed to identify one or a smallnumber of ethnic minorities, and not complete populations.This working paper presents a new methodology to classify the UK population andneighbourhoods into groups of common origin using surnames and forenames. Itproposes a new ontology of ethnicity that combines some of its multidimensionalfacets; language, religion, geographical region, and culture. It uses data collected atvery fine temporal and spatial scales, and made available, subject to safeguards, at thelevel of the individual. Such individuals are classified into 185 independentlyassigned categories of Cultural Ethnic and Linguistic (CEL) groups, based on theprobable origins of names. We include a justification for the need of classifyingethnicity, a proposed CEL taxonomy, a description of how the CEL classification wasbuilt and applied, a preliminary external validation, and some examples of current andpotential applications

Front propagation in stochastic neural fields

We analyse the effects of extrinsic multiplicative noise on front propagation in a scalar neural field with excitatory connections. Using a separation of time scales, we represent the fluctuating front in terms of a diffusive–like displacement (wandering) of the front from its uniformly translating position at long time scales, and fluctuations in the front profile around its instantaneous position at short time scales. One major result of our analysis is a comparison between freely propagating fronts and fronts locked to an externally moving stimulus. We show that the latter are much more robust to noise, since the stochastic wandering of the mean front profile is described by an Ornstein–Uhlenbeck process rather than a Wiener process, so that the variance in front position saturates in the long time limit rather than increasing linearly with time. Finally, we consider a stochastic neural field that supports a pulled front in the deterministic limit, and show that the wandering of such a front is now subdiffusive

A unit commitment study of the application of energy storage toward the integration of renewable generation

To examine the potential benefits of energy storage in the electric grid, a generalized unit commitment model of thermal generating units and energy storage facilities is developed. Three different storage scenarios were tested—two without limits to total storage assignment and one with a constrained maximum storage portfolio. Given a generation fleet based on the City of Austin’s renewable energy deployment plans, results from the unlimited energy storage deployment scenarios studied show that if capital costs are ignored, large quantities of seasonal storage are preferred. This operational approach enables storage of plentiful wind generation during winter months that can then be dispatched during high cost peak periods in the summer. These two scenarios yielded $70 million and$94 million in yearly operational cost savings but would cost hundreds of billions to implement. Conversely, yearly cost reductions of $40 million can be achieved with one compressed air energy storage facility and a small set of electrochemical storage devices totaling 13GWh of capacity. Similarly sized storage fleets with capital costs, service lifetimes, and financing consistent with these operational cost savings can yield significant operational benefit by avoiding dispatch of expensive peaking generators and improving utilization of renewable generation throughout the year. Further study using a modified unit commitment model can help to clarify optimal storage portfolios, reveal appropriate market participation approaches, and determine the optimal siting of storage within the grid.Mechanical Engineerin

A Positive-Weight Next-to-Leading-Order Monte Carlo for e+e- Annihilation to Hadrons

We apply the positive-weight Monte Carlo method of Nason for simulating QCD processes accurate to Next-To-Leading Order to the case of e+e- annihilation to hadrons. The method entails the generation of the hardest gluon emission first and then subsequently adding a `truncated' shower before the emission. We have interfaced our result to the Herwig++ shower Monte Carlo program and obtained better results than those obtained with Herwig++ at leading order with a matrix element correction.Comment: 21 pages, 11 figures, 2 tables Reason for replacement: minor corrections, typos and 1 changed referenc

The effects of newly measured cross sections in hydrogen on the production of secondary nuclei during the propagation of cosmic rays through interstellar H

The cross sections of six important cosmic ray source nuclei in hydrogen at several energies between 300 and 1800 MeV/nuc were measured. Significant differences, sometimes exceeding 50%, exist between these new measurements and the earlier semiempirical predictions, and a new set of semiempirical formulae are being determined that better describe this fragmentation. New cross sections were obtained so that the systematics of their effects on cosmic ray propagation through interstellar hydrogen can be examined

Recurrence Quantification Analysis and Principal Components in the Detection of Short Complex Signals

Recurrence plots were introduced to help aid the detection of signals in complicated data series. This effort was furthered by the quantification of recurrence plot elements. We now demonstrate the utility of combining recurrence quantification analysis with principal components analysis to allow for a probabilistic evaluation for the presence of deterministic signals in relatively short data lengths.Comment: 10 pages, 3 figures; Elsevier preprint, elsart style; programs used for analysis available for download at http://homepages.luc.edu/~cwebbe

Structured and Unstructured Cache Models for SMT Domain Adaptation

We present a French to English translation system for Wikipedia biography articles. We use training data from out- of-domain corpora and adapt the system for biographies. We propose two forms of domain adaptation. The first biases the system towards words likely in biographies and encourages repetition of words across the document. Since biographies in Wikipedia follow a regular structure, our second model exploits this structure as a sequence of topic segments, where each segment discusses a narrower subtopic of the biography domain. In this structured model, the system is encouraged to use words likely in the current segment’s topic rather than in biographies as a whole. We implement both systems using cache based translation techniques. We show that a system trained on Europarl and news can be adapted for biographies with 0.5 BLEU score improvement using our models. Further the structure-aware model out performs the system which treats the entire document as a single segment