62,180 research outputs found

    Modelling children's negation errors using probabilistic learning in MOSAIC.

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    Cognitive models of language development have often been used to simulate the pattern of errors in children’s speech. One relatively infrequent error in English involves placing inflection to the right of a negative, rather than to the left. The pattern of negation errors in English is explained by Harris & Wexler (1996) in terms of very early knowledge of inflection on the part of the child. We present data from three children which demonstrates that although negation errors are rare, error types predicted not to occur by Harris & Wexler do occur, as well as error types that are predicted to occur. Data from MOSAIC, a model of language acquisition, is also presented. MOSAIC is able to simulate the pattern of negation errors in children’s speech. The phenomenon is modelled more accurately when a probabilistic learning algorithm is used

    Simulation modelling and visualisation: toolkits for building artificial worlds

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    Simulations users at all levels make heavy use of compute resources to drive computational simulations for greatly varying applications areas of research using different simulation paradigms. Simulations are implemented in many software forms, ranging from highly standardised and general models that run in proprietary software packages to ad hoc hand-crafted simulations codes for very specific applications. Visualisation of the workings or results of a simulation is another highly valuable capability for simulation developers and practitioners. There are many different software libraries and methods available for creating a visualisation layer for simulations, and it is often a difficult and time-consuming process to assemble a toolkit of these libraries and other resources that best suits a particular simulation model. We present here a break-down of the main simulation paradigms, and discuss differing toolkits and approaches that different researchers have taken to tackle coupled simulation and visualisation in each paradigm

    Modelling the Lexicon in Unsupervised Part of Speech Induction

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    Automatically inducing the syntactic part-of-speech categories for words in text is a fundamental task in Computational Linguistics. While the performance of unsupervised tagging models has been slowly improving, current state-of-the-art systems make the obviously incorrect assumption that all tokens of a given word type must share a single part-of-speech tag. This one-tag-per-type heuristic counters the tendency of Hidden Markov Model based taggers to over generate tags for a given word type. However, it is clearly incompatible with basic syntactic theory. In this paper we extend a state-of-the-art Pitman-Yor Hidden Markov Model tagger with an explicit model of the lexicon. In doing so we are able to incorporate a soft bias towards inducing few tags per type. We develop a particle filter for drawing samples from the posterior of our model and present empirical results that show that our model is competitive with and faster than the state-of-the-art without making any unrealistic restrictions.Comment: To be presented at the 14th Conference of the European Chapter of the Association for Computational Linguistic

    Steering in computational science: mesoscale modelling and simulation

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    This paper outlines the benefits of computational steering for high performance computing applications. Lattice-Boltzmann mesoscale fluid simulations of binary and ternary amphiphilic fluids in two and three dimensions are used to illustrate the substantial improvements which computational steering offers in terms of resource efficiency and time to discover new physics. We discuss details of our current steering implementations and describe their future outlook with the advent of computational grids.Comment: 40 pages, 11 figures. Accepted for publication in Contemporary Physic

    An Intuitive Automated Modelling Interface for Systems Biology

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    We introduce a natural language interface for building stochastic pi calculus models of biological systems. In this language, complex constructs describing biochemical events are built from basic primitives of association, dissociation and transformation. This language thus allows us to model biochemical systems modularly by describing their dynamics in a narrative-style language, while making amendments, refinements and extensions on the models easy. We demonstrate the language on a model of Fc-gamma receptor phosphorylation during phagocytosis. We provide a tool implementation of the translation into a stochastic pi calculus language, Microsoft Research's SPiM

    The kinetic MC modelling of reversible pattern formation in initial stages of thin metallic film growth on crystalline substrates

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    The results of kinetic MC simulations of the reversible pattern formation during the adsorption of mobile metal atoms on crystalline substrates are discussed. Pattern formation, simulated for submonolayer metal coverage, is characterized in terms of the joint correlation functions for a spatial distribution of adsorbed atoms. A wide range of situations, from the almost irreversible to strongly reversible regimes, is simulated. We demonstrate that the patterns obtained are defined by a key dimensionless parameter: the ratio of the mutual attraction energy between atoms to the substrate temperature. Our ab initio calculations for the nearest Ag-Ag adsorbate atom interaction on an MgO substrate give an attraction energy as large as 1.6 eV, close to that in a free molecule. This is in contrast to the small Ag adhesion and migration energies (0.23 and 0.05 eV, respectively) on a defect-free MgO substrate. (C) 2003 Elsevier Science Ltd. All rights reserved
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