SCIENTIFIC PROGRAMS AND ACTIVITIES

March 29, 2024
THE FIELDS INSTITUTE FOR RESEARCH IN MATHEMATICAL SCIENCES

Physics/Fields Colloquium
2014-2015

Organizing Committee
Stephen Morris (Toronto)

Mary Pugh (Toronto)

The goal of the Physics/Fields Colloquium is to feature scientists whose work is of interest to both the physics and the mathematical science community. The series has been running since the Spring of 2007. Usually there is one speaker per semester. Each speaker gives a primary, general talk in the regular physics colloquium venue and, whenever possible, a second, more specialised talk at the Fields Institute.
Index of 2013-14 seminars
No colloquium talks were planned for the 2012-13 academic year.

2014-15 Schedule

November 27, 2014
at 4:10 p.m.
MP102
McLennan Physical Laboratories (MP)
255 Huron Street
(campus map)

Geoffrey Hinton, Computer Science at University of Toronto
Deep Learning

I will give a brief history of deep learning explaining what it is, what kinds of task it should be good for and why it was largely abandoned in the 1990's. I will then describe how ideas from statistical physics were used to make deep learning work much better. Finally I will describe how deep learning is now used by Google for speech recognition and object recognition and how it may soon be used for machine translation.

January 22, 2015
at 4:10 p.m.,
MP102
McLennan Physical Laboratories (MP)
255 Huron Street
(campus map)

Karin Dahmen, University of Illinois at Urbana Champaign
Universal quake statistics: from nanopillars to earthquakes

The deformation of many solid materials is not continuous, but discrete, with intermittent slips similar to earthquakes. Here, we suggest that the statistical distributions of the slips, such as the slip-size distributions, reflect tuned criticality, with approximately the same regular (power-law) functions, and the same tunable exponential cutoffs, for systems spanning 13 decades in length, from tens of nanometers to hundreds of kilometers; for compressed nano-crystals, to amorphous materials, to earthquakes. The similarities are explained by a simple analytic model, which suggests that results are transferable across scales. This study provides a unified understanding of fundamental properties of shear-induced deformation in systems ranging from nanocrystals to earthquakes. It also provides many new predictions for future experiments and simulations. The studies draw on methods from the theory of phase transitions, the renormalization group, and numerical simulations. Connections to other systems with avalanches, such as magnets and neuron firing avalanches in the brain are also discussed.

 


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