1,793 research outputs found

    Somalia and the Pirates. ESF Working Paper No. 33, 18 December 2009

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    Piracy is defined by The Hague Centre for Strategic Studies as an "act of boarding or attempting to board any ship with the apparent intent to commit theft or any other crime and with the apparent intent or capability to use force in furtherance of that act." And it is estimated that from 1995 to 2009, around 730 persons were killed or are presumed dead, approximately 3,850 seafarers were held hostage, around 230 were kidnapped and ransomed, nearly 800 were seriously injured and hundreds more were threatened with guns and knives. (See paper by Rob de Wijk). In November 2009, CEPS held a European Security Forum seminar, in collaboration with the Institute for Strategic Studies, the Centre for the Democratic Control of Armed Forces and the Geneva Centre for Security Policy, to focus on the issue of Somalia and the Pirates, chaired by Francois Heisbourg. Four eminent specialists in this field: David Anderson, Rob de Wijk, Steven Haines and Jonathon Stevenson looked at the links with Somalia, and the historical, legal, political and security dimensions of the troubling success of piracy in today’s world. Their conclusions and recommendations for future action are brought together in this ESF 33 Working Paper

    Adapting to climate change--reply.

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    Dissecting Trait Heterogeneity: a Comparison of Three Clustering Methods Applied to Genotypic Data

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    Background: Trait heterogeneity, which exists when a trait has been defined with insufficient specificity such that it is actually two or more distinct traits, has been implicated as a confounding factor in traditional statistical genetics of complex hu man disease. In the absence of de tailed phenotypic data collected consistently in combination with genetic data, unsupervised computational methodologies offer the potential for discovering underlying trait heteroge neity. The performance of three such methods – Bayesian Classification, Hyperg raph-Based Clustering, and Fuzzy k -Modes Clustering – appropriate for categorical data were comp ared. Also tested was the ability of these methods to detect trait heterogeneity in the presence of locus heteroge neity and/or gene-gene interaction , which are two other complicating factors in discovering genetic models of complex human disease. To dete rmine the efficacy of applying the Bayesian Classification method to re al data, the reliability of its intern al clustering metr ics at finding good clusterings was evaluated using permutation testing. Results: Bayesian Classifica tion outperformed the other two method s, with the exception that the Fuzzy k -Modes Clustering performed best on the most comp lex genetic model. Bayesian Classificati on achieved excellent recovery for 75% of the da tasets simulated under the simplest genetic model, while it achieved moderate recovery for 56% of datase ts with a sample size of 500 or more (across all simulated models) and for 86% of datasets with 10 or fewer nonfuncti onal loci (across all si mulated models). Neither Hypergraph Clustering nor Fuzzy k -Modes Clustering achieved good or excellent cluster recovery for a majority of datasets even under a re stricted set of conditions. When usin g the average log of class strength as the internal clustering metric, th e false positive rate was controlled very well, at three percent or less for all three significance levels (0. 01, 0.05, 0.10), and the false negative rate was acceptably low (18 percent) for the least stringent sign ificance level of 0.10. Conclusion: Bayesian Classificati on shows promise as an unsuper vised computational method for dissecting trait hetero geneity in genotypic data. Its control of fa lse positive and false negative rates lends confidence to the validity of its results. Further investigation of how differ ent parameter settings may improve the performance of Bayesian Classification, especi ally under more comp lex genetic models, is ongoing

    The medieval regulars and their book collections: St. Augustine's Abbey, Canterbury, and The Abbey of St. Mary de Pratis, Leicester

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    The recent editing of many monastic house library catalogues in the Corpus of British Medieval Library Catalogues has opened up the opportunity for new studies into the book collections of the regulars. The catalogues can be problematic, so an attempt has been made to tackle these issues before exploring them further as sources. This thesis demonstrates the use of these catalogues and extant manuscripts to explore further a monastic culture of learning that grew within both St. Augustine’s Abbey, Canterbury, and St. Mary de Pratis, Leicester. There is a particular focus on denoting not only the acquisition of these collections but their use as well, using the surviving evidence to demonstrate two communities thoroughly involved with their book collections. This includes the catalogue as a text, its order, the selection of authors, the location of books within the monastery, the donation and borrowing records of volumes, the contents of volumes and the surviving manuscripts
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