[ont.events] U of Toronto Computer Science activities, June 1-5

clarke@utcsri.UUCP (05/25/87)

         (SF = Sandford Fleming Building, 10 King's College Road)
              (GB = Galbraith Building, 35 St. George Street)

SUMMARY:

SYSTEMS SEMINAR, Tuesday, June 2, 11 am, SF1105 -- Yair Wand:
     "A System Theory Model for Formalizing Information Systems Design"

COLLOQUIUM, Tuesday, June 2, 11 am, SF1101 -- David M. Regan:
     "Recovery and unconfounding of visual by human brain neurons"

A.I. SEMINAR, Tuesday, June 2, 3 pm, SF1101 -- Eric L. Grimson:
     "Model Based Object Recognition and Localization"

COMBINATORICS SEMINAR, Thursday, June 4, 3 pm, GB120 -- J. Fonlupt:
     "POLYNOMIAL ALGORITHM for solving the traveling salesman
                problem on certain classes of graphs"

---------------

              SYSTEMS SEMINAR, Tuesday, June 2, 11 am, SF1105

                            Professor Yair Wand
                      University of British Columbia

   ``A System Theory Model for Formalizing Information Systems Design"

Methodologies and  techniques for systems analysis and design are based on
practice and experience rather than being anchored in a  theory. This work
is based  on the  premise that  a theoretical  foundation for systems
design can be found in general systems theory using ontological concepts.
The idea is that an information system is a representation of the real sys-
tem. The process of analysis and design is a transformation from user per-
ceptions into a working information system.  There must be some invariants
of the  transformation that will capture the semantics of  the  real  sys-
tem  for  the  information  system  to  be  a  'good' representation. The
identification of these invariants can be the basis for the analysis and
design process.

               COLLOQUIUM, Tuesday, June 2, 11 am, SF1101

                         Professor David M. Regan
                          University of Toronto
                           and York University

      ``Recovery and unconfounding of visual by human brain neurons"

In order to achieve precise eye-limb coordination, the brain must solve the
problem of recovering the 3-D structive and 3-D motion of objects from the
2-D retinal image, and must unconfound different visual parameters.  But
any given neuron in primary visual cortex responds to several visual param-
eters.  I will describe psychophysical evidence that the human brain con-
tains hardwired elements that use strongly nonlinear operations to recover
and unconfound features of the external world, and that this process can
more easily be understood in terms of achieving visually - guided motor
action than in terms of extracting a veridical representation of the exter-
nal world. Following these psychophysical findings, neurons with
corresponding properties were found in the visual pathways of animals.
There is evidence that spatial aspects of images are analysed by strongly
nonlinear operations that allow the 25 sec arc sampling of the retinal
image to be transcended, while unconfounding visual parameters.  A new
method for experimentally characterizing nonlinearities of human image pro-
cessing will be described, providing a means of sharply distinguishing
between nonlinear neural models of form and motion processing.

                A.I. SEMINAR, Tuesday, June 2, 3 pm, SF1101

                         Professor Eric L. Grimson
                                  M.I.T.

            ``Model Based Object Recognition and Localization"

For the past several years,  Tomas Lozano-Perez and I have been developing
a framework for model-based object recognition from sensory data.  The
method is intended to be applicable to a wide range of sensing modalities,
and assumes that the data is noisy, possibly sparse, and that the objects
being sensed can be heavily occluded.  The key to the method is the
development of simple constraints on the relative shapes of the object
models that can be used to rapidly reduce the search space of possible
interpretations of the data. The method has been successfully applied to
interpretation of tactile, laser range, sonar and visual data.  Further-
more, it can be extended to deal with parameterized families of objects,
and can be amplified with a technique for automatically predicting optimal
positions for obtaining additional sensory data.  Examples of all of these
areas will be presented.

           COMBINATORICS SEMINAR, Thursday, June 4, 3 pm, GB120

                           Professor J. Fonlupt
                          University of Grenoble
                         and University of Toronto

         ``POLYNOMIAL ALGORITHM for solving the traveling salesman
                   problem on certain classes of graphs"

We study a certain class of graphs for which the traveling salesman problem
can be solved by a polynomial algorithm.  These graphs are the graphs which
cannot be reduced by deletion or contraction of edges to a well specified
graph on six vertices.

This algorithm generalizes results obtained by D. Ratliff and A. Rosenthal,
G. Cornuejols, J. Fonlupt and D. Naddef, and includes results concerning
the traveling salesman perfect graphs introduced by J. Fonlupt and D. Nad-
def.  This work is a joint work with A. Nachef.
-- 

Jim Clarke -- Dept. of Computer Science, Univ. of Toronto, Canada M5S 1A4
              (416) 978-4058
{allegra,cornell,decvax,linus,utzoo}!utcsri!clarke