Boundary algebras and Kac modules for logarithmic minimal models

Virasoro Kac modules were initially introduced indirectly as representations whose characters arise in the continuum scaling limits of certain transfer matrices in logarithmic minimal models, described using Temperley-Lieb algebras. The lattice transfer operators include seams on the boundary that use Wenzl-Jones projectors. If the projectors are singular, the original prescription is to select a subspace of the Temperley-Lieb modules on which the action of the transfer operators is non-singular. However, this prescription does not, in general, yield representations of the Temperley-Lieb algebras and the Virasoro Kac modules have remained largely unidentified. Here, we introduce the appropriate algebraic framework for the lattice analysis as a quotient of the one-boundary Temperley-Lieb algebra. The corresponding standard modules are introduced and examined using invariant bilinear forms and their Gram determinants. The structures of the Virasoro Kac modules are inferred from these results and are found to be given by finitely generated submodules of Feigin-Fuchs modules. Additional evidence for this identification is obtained by comparing the formalism of lattice fusion with the fusion rules of the Virasoro Kac modules. These are obtained, at the character level, in complete generality by applying a Verlinde-like formula and, at the module level, in many explicit examples by applying the Nahm-Gaberdiel-Kausch fusion algorithm.Comment: 71 pages. v3: version published in Nucl. Phys.

Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures

Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (DPM) models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data

Minor parties and independents in times of change: Scottish local elections 1974 to 2007

This article explores the electoral performance of minor party and Independent candidates in Scottish local elections from 1974 to 2007. This is a period which began with a major restructuring of local government and ended with a change in the electoral system from first-past-the-post to the single transferable vote. It encompasses a second restructuring in the 1990s, the consolidation of the Scottish National Party as an electoral force, and the creation of the Scottish Parliament. Throughout the period, while there have been ebbs and flows, Independents and minor parties have remained significant players in local electoral politics in Scotland

Looking Good With Flickr Faves: Gaussian Processes for Finding Difference Makers in Personality Impressions

Flickr allows its users to generate galleries of "faves", i.e., pictures that they have tagged as favourite. According to recent studies, the faves are predictive of the personality traits that people attribute to Flickr users. This article investigates the phenomenon and shows that faves allow one to predict whether a Flickr user is perceived to be above median or not with respect to each of the Big-Five Traits (accuracy up to 79\% depending on the trait). The classifier - based on Gaussian Processes with a new kernel designed for this work - allows one to identify the visual characteristics of faves that better account for the prediction outcome

Inferring surface time of Minke whales from inter-surfacing interval data using a hidden Markov model

Surfacing rate data of Minke whales is an important factor used in the abundance estimates of Minke whale (Balaenoptera acutorostrata) stocks, both in aerial and vessel based surveys. Today, most abundance estimates of Minke whales rely on VHF-transmitters data rather than visual data. Visual data collected from land has the advantage of being relatively cheap to collect, which allows data to be collected from a larger number of individuals while causing no effect on the surfacing rates of the animals being studied, hence limiting biases. In this study, individual follows of Minke whales were conducted from a land-based station in Faxaflói bay, Iceland, and data on inter-breath intervals (IBI) were collected. Two distinct dive types were present within the surfacing data, which we defined as regular dives and deep dives. Those emerged from two different biological processes: whales spending time at the surface and whales engaging in foraging activities. A hidden Markov model was used to identify and define the density distribution of IBI as the observation state of these two hidden diving processes. Regular dives had a mean surfacing interval of 43 seconds (SD=44.8) and deep dives had a mean surfacing interval of 155 seconds (SD=115.1). The transition probabilities between the two dive types were estimated, from which the relative proportion spent in each dive type could be inferred. Minke whales perform regular dives during 62% and deep dives during 38% of their time. The relative proportions spent in each dive type can be used as estimates of how much time a whale will be typically at the surface available to be detected during cue counting surveys and to estimate the odds that a whale is in a long dive and therefore unlikely to be detected. Data was also collected from commercial whalewatching boats in the same bay, and were analysed together with the land based data to measure the effect of whalewatching boat interaction on Minke whale surface intervals. The proportion of time spent in deep dives decreased from 38% to 14% during interactions with whalewatching boats, while regular dives increased from 62% to 86%. The inter-surfacing interval used in abundance estimates of Minke whales in the North Atlantic today is derived from VHF-transmitter data and is about 77 seconds. Our mean values of surface intervals lies below and above this mean, which raises the question if a single mean value of surfacing interval can be used to make reliable abundance estimates of Minke whales, as both the dive type and the presence of vessels is likely to affect this value

Sociotechnical systems as applied to knowledge work

This study examines the logic behind choosing variances and the design of forums during the planning of deliberations in non-routine work environments using a Sociotechnical System design approach. This study was accomplished through review and comparison of literature on sociotechnical applications of non-routine, knowledge work environments. The traditional sociotechnical application applied to factory settings with linear and routine work tasks analyzes unit operations within an open system, identifying technical variances that contribute to problems and social roles that control the variances. A new sociotechnical approach has been developed for systems involved in non-routine, knowledge work environments. This approach focuses on deliberations formed around topics, establishes variances that lead to poor deliberations, designs forums that minimize variances and gives control of variances to discretionary coalitions. These results generally support that variances contributing to poor deliberations are well established and that organizations need only identify the key variances that contribute to problems in their system. Organizations need to understand how the key variances affect the development of knowledge and how forums can be designed to enhance deliberations. This study places specific focus on the design of information technology forums that enhance knowledge developmenthttp://www.archive.org/details/sociotechnicalsy00oswaLieutenant, United States NavyApproved for public release; distribution is unlimited

What can you do with 0.1× genome coverage? A case study based on a genome survey of the scuttle fly Megaselia scalaris (Phoridae)

<p>Abstract</p> <p>Background</p> <p>The declining cost of DNA sequencing is making genome sequencing a feasible option for more organisms, including many of interest to ecologists and evolutionary biologists. While obtaining high-depth, completely assembled genome sequences for most non-model organisms remains challenging, low-coverage genome survey sequences (GSS) can provide a wealth of biologically useful information at low cost. Here, using a random pyrosequencing approach, we sequence the genome of the scuttle fly <it>Megaselia scalaris </it>and evaluate the utility of our low-coverage GSS approach.</p> <p>Results</p> <p>Random pyrosequencing of the <it>M. scalaris </it>genome provided a depth of coverage (0.05x0.1x) much lower than typical GSS studies. We demonstrate that, even with extremely low-coverage sequencing, bioinformatics approaches can yield extensive information about functional and repetitive elements. We also use our GSS data to develop genomic resources such as a nearly complete mitochondrial genome sequence and microsatellite markers for <it>M. scalaris</it>.</p> <p>Conclusion</p> <p>We conclude that low-coverage genome surveys are effective at generating useful information about organisms currently lacking genomic sequence data.</p