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MODELLING and SCIENTIFIC COMPUTING

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Title: MODELLING and SCIENTIFIC COMPUTING


1
MODELLING and SCIENTIFIC COMPUTING
  • Complex Systems associated Computational Models
    and Data Analysis

2
COMPUTATIONAL vs COMPUTER SCIENCE
  • Challenges
  • Exploration of models of the natural and
    artificial world, where complexity precludes
    exact solutions.
  • Computational Approach third arm to theory
    and experiment.
  • Complex Systems - highly diverse Features e.g.
    Many parameters, Large Volume of data and need to
    mine it, Many basic elements and events.
  • Key Phases
  • Development of the maths. model or data sourcing.
  • Development / Implementation of the algorithm
    for numeric solution
  • Generation of solutions by e.g. numerical
    simulation of the phenomenon of interest, data
    mining/ matching/ analysis.
  • Visualisation / Interpretation and Validation

3
CURRENT Group Interests
  • Modelling of Stock Markets/ Strategies for
    Financial Auditing, (M. Crane, G. Keogh, S.
    Sharifi / J. Horgan, Y. Bimpeh)
  • Agricultural models / Employment patterns
    (L.Killen et al.)
  • Automatic Spoken Language Identification by
    Genetic Programming (A. McLoughlin)
  • Spatiotemporal Models in Physical, Bio- and
    related systems e.g. immune response, traffic
    flow and finance, cellular networks. Associated
    statistical models. (H. Ruskin, Y. Feng. R. Wang,
    J. Burns, B. Zhu and Y. Liu)
  • Mathematical/ Statistical Models - recognition of
    complex, deformable shapes/ patterns e.g.
    application to sign-language recognition,
    patterns in financial markets. (A. Sutherland, H.
    Wu, A. Shamaie)
  • Aerospace - control systems. Environmental
    Pollution (water quality) (L. Tuohey)

4
COLLABORATIONS
  • INCLUDE
  • Internal Schools (Physics, Chemistry and Science
    Ed.), DCUBS, DCU-
  • based centres, e.g. CDVP also
    HEA PRTLI - centre Nat.
  • Inst. for Cellular Biology.
    Sub-theme in DCU Strategic Plan.
  • National TCD, (Depts. Maths., Physics and
    Economics), UCD, (Depts.
  • Physics, Agr. Econ.), NUIM,
    Teagasc, Dell, Hitachi, CAPTEC,
  • Regional Centre of Dermatology,
    Mater, St. James Hosp.
  • International Porto Uni., EU consortium and COST
    initiative, US,
  • (USM, Naval Research Lab
    (Entropics) and Naval PG School).

5
CONFERENCE TARGETS - last 5 years only
  • INCLUDE - papers given at
  • Europhysics Conf. on Computational Physics,
    International Conference on Computational
    Sciences, American Physical Society Annual
    Meeting, VECPAR, (Vector and Parallel Processing
    -biannual international conference), IASTED
    conferences on Modelling and Simulation, on
    Modelling, Identification and Control, IMACS on
    Scientific Computation, Applied Mathematics and
    Simulation, CASI. World Congress of Pharmacy
    Pharmaceutical Sciences/Int'l Congress of
    FIP,EUROSIM Congress/ (Modelling and Simulation
    in Biology, Medicine and Biomedical Engineering),
    Irish Machine Vision and Image Processing, IEEE
    Workshops on PR and JAVA Optimisation, SPIE
    International Symposium on Multispectral Image
    Processing and Pattern Recognition, IASEL (Irish
    Association of Science Education and Learning).

6
PUBLICATION TARGETS - last 5 years only
  • INCLUDE - papers published in
  • Proceedings - as conferences previously
  • Journals - J. Stat. Phys., Physica A,
    Theor. in Biosciences, Computer Physics
    Communications, Lecture Notes in Comp. Science,
    Computers Education, J. of the South African
    Institute of Computer Scientists and Information
    Technology, J. Acc. And Business Res., J.
    Practice and Theory, Amer. Acc. Asoc., J. Bus.
    And Ec. Stats, JASA, ERCIM Journal, Computers and
    Electronics in Agriculture, Labour, J. of
    Software Quality, J. of the American Society for
    Information Systems and Technology.
  • Other Reports - Technical in-house etc.

7
BRIEF EXAMPLES STOCK MARKETS (M. Crane, G.
Keogh, S. Sharifi)
The Issue Market Crashes. Crash detection using
eigenvalues and eigenvectors of correlation
matrices. Use of Random Matrix Theory to
identify noisy and non-noisy parts of correlation
matrix. Identification and tracking of market
movements. Approach Fractional Calculus in
Finance Use of models with fractional derivative
powers (previously used in memory processes).
Identification of volatile periods and their
signatures using FC Models Applications of FC to
financial correlation matrices to model time and
frequency effects.
8
BRIEF EXAMPLES STRATEGIES FOR FINANCIAL
AUDITING (J. Horgan, Y. Bimpeh)
  • The IssueDeveloping sampling and estimation
    procedures for rare incidence, skewed accounting
    populations
  • Need Classical statistical theory does not apply
  • Approach Bayesian, non-parametric and computer
    intensive methods of estimation.

9
BRIEF EXAMPLES SOCIO-EONOMIC and AGRICULTURAL
STUDIES (L.Killen)
The Issue Non-standard Employment
Patterns Approach Comparative Cross-sectional
statistical analyses incorporating trends over
time. Need Methodologies for determining
Characteristic Lactation Curves in Dairy
cattle. Approach Multivariate Analyses of
constituent factors for general data sets,
incorporating milk constituents, breed info. etc.
10
BRIEF EXAMPLES AUTOMATIC SPOKEN LANGUAGE ID (A.
McLoughlin)
  • The Problem Creation of Automatic
    Spoken-Language Identification Programme.
  • The Issues No specialised linguistic expertise,
    No labour-intensive labelling of training data,
    addition of new languages without extensive
    re-training, large-vocabulary continuous speech
    recognition. Uses Telephone call routing,
    International enterprises, emergency services,
    law enforcement and intelligence, real-time
    systems.
  • The Approach Evolutionary Computation, (modelled
    on biological evolution and mutation), Genetic
    Programming.

11
BRIEF EXAMPLES FUNDAMENTAL CELLULAR SYSTEMS
(H. Ruskin, Y. Feng, R. Wang, B. Zhu,
Y.Liu)
  • The Problems Fundamental Spatiotemporal
    Processes e.g. complexity as for disordered
    cellular networks (froth coarsening), many
    cell-types (models of human immune-response,
    vehicle units in traffic flow.
  • Principal Issues Quantification of
  • behaviour. Prediction.
  • Approaches Microscopic Models,
  • statistical estimation/physical laws, robust
  • simulations. Links Bioinformatics

12
BRIEF EXAMPLES Models in recognition of complex,
deformable shapes/ patterns (A. Sutherland, H.
Wu, A. Shamaie)
  • The Issues Nature of Model Choice - definition
    of shapes, boundaries distributions of shapes
    texture and feature extraction types of
    classifier, etc.
  • Applications Pattern matching in finance, hand
    and other-gesture recognition - (best models and
    estimates), ecological competition, security
    systems and so on.
  • Approach Mechanics may include real-time video
    need to define spatio-temporal process and
    template matching, or e,g, generate training data
    for statistical models, such as the Discrete
    Hidden Markov Model.

13
BRIEF EXAMPLES AEROSPACE CONTROL SYSTEMS /
ENVIRONMENTAL SCIENCE (L. Tuohey)
  • Experience Several European Space Agency
    missions (development of on-board spacecraft
    control SW, payload calibration). Quality aspects
    of comms. SW for civil aviation. Software
    Engineering processes and quality.
  • Current Concerns Modelling, simulation, and
    numerical techniques in estimation of (lake)
    water quality via remote sensing. Aim viable
    model of water quality from optical measurement.
  • Approach Solution of so-called inverse
    problem.
  • Plans Application to related problems in
    geophysics

14
THE FUTURE NEW PROJECTS and DIRECTIONS/ The
FUNDING?
  • PAST FUNDING EXPERIENCE EI Basic (submitted
    since 1997), ATRP(2001), RIF (2002), SFI
    (2001), other minor EI schemes, Teagasc - few
    specific targets however - record variable
  • WHY? - ?? Quality of Research ?? (see pub.
    records and of good Impact Factor conferences
    and journals).
  • SCHEMES - FEW for area to date large-scale
    less appropriate, joint/non-specialist panels not
    seen as favouring inter-disciplinary work unless
    specified areas. Inter-disciplinarity ? fewer
    beans typically? Non-networkers? Product - not
    immediately commercial.
  • REMEDY- target specialists - (e.g. Teagasc 2002
    -success current discussions with Goldman-Sachs,
    EU consortium on COST and Asia-Link - more
    favourable focus, ?? OTHERS ?? - to be explored.
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