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Seminar on Mathematics for various disciplines
Seminar information archive ～04/30｜Next seminar｜Future seminars 05/01～
| Date, time & place | Tuesday 10:30 - 11:30 056Room #056 (Graduate School of Math. Sci. Bldg.) |
|---|---|
| Organizer(s) | Yoshikazu Giga, Naoyuki Ishimura, Norikazu Saito, Masahiro Yamamoto, Hiroyoshi Mitake |
| URL | https://www.math.sci.hokudai.ac.jp/coe/sympo/various/index_en.html |
Seminar information archive
2005/11/22
16:30-17:30 Room #128 (Graduate School of Math. Sci. Bldg.)
Hans Heesterbeek (University of Utrecht)
Mathematics in the epidemiology and control of infectious diseases
http://coe.math.sci.hokudai.ac.jp/sympo/various/index.html
Hans Heesterbeek (University of Utrecht)
Mathematics in the epidemiology and control of infectious diseases
[ Abstract ]
In this lecture I will give examples of the way in which mathematics helps in getting insight into the spread and control of infectious diseases. I will do this by discussing the population phenomena that are observed after an infectious agent enters a population (invasion, epidemic, recurrent epidemic, endemic, regulation, control). Along the way I will also give insight into the historical development of mathematical modelling in infectious disease epidemiology. Examples will be taken from human and animal infections. Special topics treated in some detail are threshold quantities such as the basic reproduction number R_0, the importance of understanding the structure of contacts in a population, the use of R_0 to estimate control effort with vaccines. In the last part of the lecture a number of important epidemiological problems will be discussed where input of new mathematical theory is needed.
[ Reference URL ]In this lecture I will give examples of the way in which mathematics helps in getting insight into the spread and control of infectious diseases. I will do this by discussing the population phenomena that are observed after an infectious agent enters a population (invasion, epidemic, recurrent epidemic, endemic, regulation, control). Along the way I will also give insight into the historical development of mathematical modelling in infectious disease epidemiology. Examples will be taken from human and animal infections. Special topics treated in some detail are threshold quantities such as the basic reproduction number R_0, the importance of understanding the structure of contacts in a population, the use of R_0 to estimate control effort with vaccines. In the last part of the lecture a number of important epidemiological problems will be discussed where input of new mathematical theory is needed.
http://coe.math.sci.hokudai.ac.jp/sympo/various/index.html
2005/10/18
16:30-17:30 Room #128 (Graduate School of Math. Sci. Bldg.)
Rainer Kress (Goettingen 大学)
Hybrid methods for inverse boundary problems
[ Reference URL ]
http://coe.math.sci.hokudai.ac.jp/sympo/various/index.html
Rainer Kress (Goettingen 大学)
Hybrid methods for inverse boundary problems
[ Reference URL ]
http://coe.math.sci.hokudai.ac.jp/sympo/various/index.html
2005/06/22
16:30-17:30 Room #056 (Graduate School of Math. Sci. Bldg.)
Y. H. Richard Tsai (University of Texas)
Threshold Dynamics for the Piecewise Constant
http://coe.math.sci.hokudai.ac.jp/sympo/various/index.html
Y. H. Richard Tsai (University of Texas)
Threshold Dynamics for the Piecewise Constant
[ Abstract ]
We propose an efficient algorithm for minimizing the piecewise constant Mumford-Shah functional of image segmentation. It is based on the threshold dynamics of Merriman, Bence, and Osher for evolving an interface by its mean curvature. We show that a very fast minimization can be achieved by alternating the solution of a linear parabolic partial differential equation and simple thresholding. We discuss our current work of extending this line of work to higher order accuracy and to applications involving Willmore flow.
[ Reference URL ]We propose an efficient algorithm for minimizing the piecewise constant Mumford-Shah functional of image segmentation. It is based on the threshold dynamics of Merriman, Bence, and Osher for evolving an interface by its mean curvature. We show that a very fast minimization can be achieved by alternating the solution of a linear parabolic partial differential equation and simple thresholding. We discuss our current work of extending this line of work to higher order accuracy and to applications involving Willmore flow.
http://coe.math.sci.hokudai.ac.jp/sympo/various/index.html
