MODELLING and SCIENTIFIC COMPUTING

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

2. 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 diverseFeatures 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 COMPUTATIONAL vs COMPUTER SCIENCE

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 GeneticProgramming(A. McLoughlin) • Spatiotemporal Models in Physical, Bio- and related systemse.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 EXAMPLESSTOCK 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 EXAMPLESSTRATEGIES FOR FINANCIAL AUDITING(J. Horgan, Y. Bimpeh) • The Issue:Developing 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 EXAMPLESSOCIO-EONOMIC and AGRICULTURAL STUDIES (L.Killen) The Issue: Non-standard Employment Patterns Approach: Comparative Cross-sectional statistical analyses incorporating trends over time. Need: Methodologies fordetermining 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 EXAMPLESFUNDAMENTAL 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 EXAMPLESModels 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 EXAMPLESAEROSPACE 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) waterquality via remote sensing.Aim = viable model of water quality from optical measurement. • Approach: Solution of so-called “inverse problem”. • Plans: Application torelated problems in geophysics

14. THE FUTURENEW 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.