Convex and non-convex optimization methods in image processing
In this talk, we discuss some results of convex and non-convex optimization methods in image processing. Examples including image colorization, blind decovolution and impulse noise removal are presented to demonstrate these methods. Their a...
Category수학강연회소속Hong Kong Baptist University강연자Michael Ng
The lace expansion in the past, present and future
The lace expansion is one of the few methods to rigorously prove critical behavior for various models in high dimensions. It was initiated by David Brydges and Thomas Spencer in 1985 to show degeneracy of the critical behavior for weakly se...
Creation of concepts for prediction models and quantitative trading
Modern mathematics with axiomatic systems has been developed to create a complete reasoning system. This was one of the most exciting mathematical experiments. However, even after the failure of the experiment, mathematical research is still...
Topological aspects in the theory of aperiodic solids and tiling spaces
After a review of various types of tilings and aperiodic materials, the notion of tiling space (or Hull) will be defined. The action of the translation group makes it a dynamical system. Various local properties, such as the notion of "Finit...
Category수학강연회소속Georgia Institute of Technology, School of Mathematics and School of Physics강연자Jean V. Bellissard
Quantum Dynamics in the Mean-Field and Semiclassical Regime
The talk will review a new approach to the limits of the quantum N-body dynamics leading to the Hartree equation (in the large N limit) and to the Liouville equation (in the small Planck constant limit). This new strategy for studying both l...
We consider different growth rates associated with the geometry (distance, volume, heat kernel) on a cover of a compact Riemannian manifold. We present general inequalities. We discuss the rigidity results and questions in the case of negati...
A new view of Fokker-Planck equations in finite and Infinite dimensional spaces
Fokker-Planck and Kolmogorov (backward) equations can be interpreted as linearisations of the underlying stochastic differential equations (SDE). It turns out that, in particular, on infinite dimensional spaces (i.e. for example if the SDE i...
