8,705 research outputs found

    Status report of the baseline collimation system of CLIC. Part II

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    Important efforts have recently been dedicated to the characterisation and improvement of the design of the post-linac collimation system of the Compact Linear Collider (CLIC). This system consists of two sections: one dedicated to the collimation of off-energy particles and another one for betatron collimation. The energy collimation system is further conceived as protection system against damage by errant beams. In this respect, special attention is paid to the optimisation of the energy collimator design. The material and the physical parameters of the energy collimators are selected to withstand the impact of an entire bunch train. Concerning the betatron collimation section, different aspects of the design have been optimised: the transverse collimation depths have been recalculated in order to reduce the collimator wakefield effects while maintaining a good efficiency in cleaning the undesired beam halo; the geometric design of the spoilers has been reviewed to minimise wakefields; in addition, the optics design has been optimised to improve the collimation efficiency. This report presents the current status of the the post-linac collimation system of CLIC. Part II is mainly dedicated to the study of the betatron collimation system and collimator wakefield effects.Comment: 25 pages, 13 figure

    LEARNFCA: A FUZZY FCA AND PROBABILITY BASED APPROACH FOR LEARNING AND CLASSIFICATION

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    Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering. This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems. We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success. Adviser: Jitender Deogu

    LearnFCA: A Fuzzy FCA and Probability Based Approach for Learning and Classification

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    Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering. This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems. We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success. Adviser: Dr Jitender Deogu

    The zero-temperature phase diagram of soft-repulsive particle fluids

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    Effective pair interactions with a soft-repulsive component are a well-known feature of polymer solutions and colloidal suspensions, but they also provide a key to interpret the high-pressure behaviour of simple elements. We have computed the zero-temperature phase diagram of four different model potentials with various degrees of core softness. Among the reviewed crystal structures, there are also a number of non-Bravais lattices, chosen among those observed in real systems. Some of these crystals are indeed found to be stable for the selected potentials. We recognize an apparently universal trend for unbounded potentials, going from high- to low-coordinated crystal phases and back upon increasing the pressure. Conversely, a bounded repulsion may lead to intermittent appearance of compact structures with compression and no eventual settling down in a specific phase. In both cases, the fluid phase repeatedly reenters at intermediate pressures, as suggested by a cell-theory treatment of the solids. These findings are of relevance for soft matter in general, but they also offer fresh insight into the mechanisms subtended to solid polymorphism in elemental substances.Comment: 16 pages, 5 figures, to be published on Soft Matte

    Simplicial Gravity Coupled to Scalar Matter

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    A model for quantized gravity coupled to matter in the form of a single scalar field is investigated in four dimensions. For the metric degrees of freedom we employ Regge's simplicial discretization, with the scalar fields defined at the vertices of the four-simplices. We examine how the continuous phase transition found earlier, separating the smooth from the rough phase of quantized gravity, is influenced by the presence of scalar matter. A determination of the critical exponents seems to indicate that the effects of matter are rather small, unless the number of scalar flavors is large. Close to the critical point where the average curvature approaches zero, the coupling of matter to gravity is found to be weak. The nature of the phase diagram and the values for the critical exponents suggest that gravitational interactions increase with distance. \vspace{24pt} \vfillComment: (34 pages + 8 figures
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