62,180 research outputs found
Modelling children's negation errors using probabilistic learning in MOSAIC.
Cognitive models of language development have often been used to simulate the pattern of errors in childrenâs 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 childrenâs speech. The phenomenon is modelled more accurately when a probabilistic learning algorithm is used
Simulation modelling and visualisation: toolkits for building artificial worlds
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
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
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
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
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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