Resistive MHD Transport Model for an RFP: Part II - Results

No. DE-FG02-91ER-54109. Reproduction, translation, publication, use and disposal, in whole or in part, by or for the United States government is permitted. Submitted for publication to Physics ofPlasmas

Resistive MHD Transport Model for an RFP: Part I - The Model

No. DE-FG02-91ER-54109. Reproduction, translation, publication, use and disposal, in whole or in part, by or for the United States government is permitted. Submitted for publication to Physics ofPlasmas

An experiment with association rules and classification: post-bagging and conviction

In this paper we study a new technique we call post-bagging, which consists in resampling parts of a classification model rather then the data. We do this with a particular kind of model: large sets of classification association rules, and in combination with ordinary best rule and weighted voting approaches. We empirically evaluate the effects of the technique in terms of classification accuracy. We also discuss the predictive power of different metrics used for association rule mining, such as confidence, lift, conviction and X². We conclude that, for the described experimental conditions, post-bagging improves classification results and that the best metric is conviction.Programa de Financiamento Plurianual de Unidades de I & D.Comunidade Europeia (CE). Fundo Europeu de Desenvolvimento Regional (FEDER).Fundação para a Ciência e a Tecnologia (FCT) - POSI/SRI/39630/2001/Class Project

Statistical M-Estimation and Consistency in Large Deformable Models for Image Warping

The problem of defining appropriate distances between shapes or images and modeling the variability of natural images by group transformations is at the heart of modern image analysis. A current trend is the study of probabilistic and statistical aspects of deformation models, and the development of consistent statistical procedure for the estimation of template images. In this paper, we consider a set of images randomly warped from a mean template which has to be recovered. For this, we define an appropriate statistical parametric model to generate random diffeomorphic deformations in two-dimensions. Then, we focus on the problem of estimating the mean pattern when the images are observed with noise. This problem is challenging both from a theoretical and a practical point of view. M-estimation theory enables us to build an estimator defined as a minimizer of a well-tailored empirical criterion. We prove the convergence of this estimator and propose a gradient descent algorithm to compute this M-estimator in practice. Simulations of template extraction and an application to image clustering and classification are also provided

Long‐term trends in the distribution, abundance and impact of native “injurious” weeds

Questions: How can we quantify changes in the distribution and abundance of injurious weed species (Senecio jacobaea, Cirsium vulgare, Cirsium arvense, Rumex obtusifolius, Rumex crispus and Urtica dioica), over long time periods at wide geographical scales? What impact do these species have on plant communities? To what extent are changes driven by anthropogenically induced drivers such as disturbance, eutrophication and management? Location: Great Britain. Methods: Data from national surveys were used to assess changes in the frequency and abundance of selected weed species between 1978 and 2007. This involved novel method development to create indices of change, and to relate changes in distribution and abundance of these species to plant community diversity and inferred changes in resource availability, disturbance and management. Results: Three of the six weed species became more widespread in GB over this period and all of them increased in abundance (in grasslands, arable habitats, roadsides and streamsides). Patterns were complex and varied by landscape context and habitat type. For most of the species, there were negative relationships between abundance, total plant species richness, grassland, wetland and woodland indicators. Each individual species responds to a different combination of anthropogenic drivers but disturbance, fertility and livestock management significantly influenced most species. Conclusions: The increase in frequency and abundance of weeds over decades has implications for landscape‐scale plant diversity, fodder yield and livestock health. This includes reductions in plant species richness, loss of valuable habitat specialists and homogenisation of vegetation communities. Increasing land‐use intensity, excessive nutrient input, overgrazing, sward damage, poaching and bare ground in fields and undermanagement or too frequent cutting on linear features may have led to increases in weeds. These weeds do have conservation value so we are not advocating eradication, rather co‐existence, without dominance. Land management policy needs to adapt to benefit biodiversity and agricultural productivity

Local linear regression with adaptive orthogonal fitting for the wind power application

Short-term forecasting of wind generation requires a model of the function for the conversion of me-teorological variables (mainly wind speed) to power production. Such a power curve is nonlinear and bounded, in addition to being nonstationary. Local linear regression is an appealing nonparametric ap-proach for power curve estimation, for which the model coefficients can be tracked with recursive Least Squares (LS) methods. This may lead to an inaccurate estimate of the true power curve, owing to the assumption that a noise component is present on the response variable axis only. Therefore, this assump-tion is relaxed here, by describing a local linear regression with orthogonal fit. Local linear coefficients are defined as those which minimize a weighted Total Least Squares (TLS) criterion. An adaptive es-timation method is introduced in order to accommodate nonstationarity. This has the additional benefit of lowering the computational costs of updating local coefficients every time new observations become available. The estimation method is based on tracking the left-most eigenvector of the augmented covari-ance matrix. A robustification of the estimation method is also proposed. Simulations on semi-artificial datasets (for which the true power curve is available) underline the properties of the proposed regression and related estimation methods. An important result is the significantly higher ability of local polynomia