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Keynote Speeches
1. Parallel Problem Solving
in Honeybees: Vision, Navigation and `Cognition'
Mandyam V. Srinivasan
Centre for Visual Sciences, Research School of Biological Sciences,
Australian National University
ABSTRACT:
Anyone who has watched a fly make a flawless landing
on the rim of a teacup, or marvelled at a honeybee speeding home after
collecting nectar from a flower patch several kilometres away, would
know that insects possess visual systems that are fast, reliable and
accurate. Insects cope remarkably well with their world, despite
possessing a brain that carries fewer than 0.01% as many neurons as
ours does. This talk will explore the secrets of their success, by
describing research aimed at understanding the mechanisms underlying
visual perception, navigation, learning, memory and 'cognition' in
honeybees. The application of insect-based principles to the design of
novel, autonomous land-based and aerial vehicles will also be described.
Speaker's Bio Sketch:
Mandyam Srinivasan is currently Professor of Visual Science at
the Australian National University's Research School of Biological
Sciences, and Director of the University's Centre for Visual Science.
He received a B.E. in Electrical Engineering from Bangalore University,
an M.E. in Electrical Engineering from the Indian Institute of Science,
a Ph.D. in Engineering and Applied Science from Yale University, and a
D.Sc. in Neuroethology from the Australian National University. He is a
Fellow of the Australian Academy of Science, a Fellow of the Royal
Society of London, and an Inaugural ARC Federation Fellow.
Srinivasan's research is directed at understanding principles of
vision, navigation and "cognition' in small animals, and in applying
some of these ideas to the design of biologically inspired algorithms
for machine vision and robotics.
2. Constraint Partitioning and its Applications In
Parallel Problem Solving
Benjamin W. Wah
Department of Electrical and Computer Engineering
and the Coordinated Science Laboratory
University of Illinois, Urbana-Champaign
Urbana, IL 61801
http://manip.crhc.uiuc.edu
The solution of mixed-integer constrained optimization problems with
highly nonlinear objectives and/or constraints has long been considered
challenging. These problems are abundant in many engineering
applications, including IC design automation, signal processing,
temporal planning, scheduling, computer aided manufacturing, robotics,
and neural-network learning. In this talk, we present a new approach
that partitions a nonlinear optimization problem by their constraints
into subproblems, that evaluates each subproblem independently, and
that resolves inconsistent global constraints across subproblems. The
evaluation of each subproblem and the resolution of inconsistent global
constraints are based on a formal mathematical foundation of extended
saddle points that is proved to be necessary and sufficient for
characterizing constrained local minima in these problems. The
advantage of our partitioning approach is that it allows complex
problems to be solved as a number of simpler subproblems, each with
significantly fewer constraints, before resolving violated global
constraints that relate the subproblems. We discuss our approach in
parallel problem solving by illustrating applications in temporal
planning, neural-network
learning, signal processing, and nonlinear optimization.
Biography. Benjamin W. Wah is currently the
Franklin W. Woeltge Endowed
Professor of Electrical and Computer Engineering and Professor of the
Coordinated Science Laboratory of the University of Illinois at
Urbana-Champaign, Urbana, IL. He received his Ph.D. degree in computer
science from the University of California, Berkeley, CA, in 1979.
Previously, he had served on the faculty of Purdue University
(1979-85), as a Program Director at the National Science Foundation
(1988-89), as Fujitsu Visiting Chair Professor of Intelligence
Engineering, University of Tokyo (1992), and McKay Visiting Professor
of Electrical Engineering and Computer Science, University of
California, Berkeley (1994). In 1989, he was awarded a University
Scholar of the University of
Illinois; in 1998, he received the IEEE Computer Society Technical
Achievement Award; in 2000, the IEEE Millennium Medal; and in 2003, the
Raymond T. Yeh Lifetime Achievement Award from the Society for Design
and Process Science. Wah's current research interests are in the areas
of nonlinear search and optimization, multimedia signal processing, and
computer networks.
Wah was the Editor-in-Chief of the IEEE
Transactions on Knowledge and Data Engineering between 1993 and
1996, and is the Honorary Editor-in-Chief of Knowledge and Information Systems.
He currently serves on the editorial boards of Information Sciences, International Journal on Artificial
Intelligence Tools, Journal of VLSI Signal Processing, World Wide Web,
and Neural Processing Letters.
He had chaired a number of international conferences and was the
International Program Committee Chair of the IFIP World Congress in
2000. He has served the IEEE Computer Society in various capacities,
including Vice President for Publications (1998 and 1999) and President
(2001). He is a Fellow of the IEEE.
3. Next Generation CiteSeer
Dr. C. Lee Giles
David Reese Professor, School of Information Sciences and Technology
Professor, Computer Science and Engineering
Professor, Supply Chain and Information Systems
The Pennsylvania State University
University Park, PA
giles@ist.psu.edu
CiteSeer, a computer and information science search engine and digital
library, has been a radical departure for scientific document access
and analysis. With nearly 700,000 documents, it has sometimes two
million page views a day making it one of the most popular document
access engines in science. CiteSeer is also portable, having been
extended to ebusiness (eBizSearch) and more recently to academic
business documents (SMEALSearch). CiteSeer is based on two features:
actively acquiring new documents and automatic tagging and linking of
metadata information inherent in an academic document’s syntactic
structure. Why is CiteSeer so popular? We discuss this and methods for
providing new tagged metadata such as institutions and
acknowledgements, new data resources and services and the issues in
automation. We then discuss the next generation of CiteSeer.
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