Professor Fred Wan will describe some highlight of his 50 years long journey in applied mathematics research at four institutions. He will touch on scientific topics such as mechanics, economics, and the biology of Morphogens that guide tissue development.
We consider random Dirac operators on a strip of width 2L of the form $J\partial+V$ where J is the $2L \times 2L $ symplectic form and V a hermitian matrix-valued random potential satisfying a time reversal symmetry property.
The operator can be analyzed using transfer matrices. The time reversal symmetry forces the transfer matrices to be in the group $SO^*(2L)$. This leads to symmetry and Kramer's degeneracy for the Lyapunov spectrum which forces two Lyapunov exponents to be zero if L is odd. Adopting a criterion
by Goldsheid and Margulis one proves that these are the only vanishing Lyapunov exponents under sufficient randomness. Adopting Kotani theory one obtains a.c. spectrum of multiplicity two on the whole real line. If moreover the random potential includes i.i.d., a.c. distributed matrix Diracpeaks on a lattice in $\RR$, we can adopt the work of Jaksic and Last to prove that the a.c. spectrum is pure. This is a big contrast to the case where L is even and no Lyapunov exponent vanishes for sufficient randomness. There one expects to get pure
point spectrum using similar techniques as in the one dimensional Anderson model. (joint work with H. Schulz-Baldes)
In this talk, we consider the family of pseudo differential operators $\{\Delta+ b \Delta^{\alpha/2}; b\in [0, 1]\}$ that evolves continuously from $\Delta$ to $\Delta + \Delta^{\alpha/2}$. We establish a uniform boundary Harnack principle with explicit boundary decay rate for nonnegative functions which are harmonic with respect to $\Delta +b = \Delta^{\alpha/2}$ (or equivalently, the sum of a Brownian motion and an independent symmetric $\alpha$-stable process with constant multiple $b^{1/\alpha}$) in $C^{1, 1}$ open sets.
Adding a column of numbers produces `carries' along the way. We show that random digits produce a pattern of carries with a neat probabilistic description: the carries form a one-dependent determinantal point process. This makes it easy to answer natural questions: How many carries are typical? Where are they located? (Many further examples, from combinatorics, algebra and group theory, have essentially the same neat formulae.) The examples give a gentle introduction to the emerging fields of one-dependent and determinantal point processes. This work is joint with Alexei Borodin and Persi Diaconis.
Ergodic theory of dispersing billiards was developed in 1970s-1980s. An important part of the theory is the analysis of the structure of the sets where the billiard map is discontinuous. They were assumed to be smooth manifolds till recently, when a new pathological type of behavior of these sets was found. Thus a reconsideration of earlier arguments was needed.
I'll review the recent work which recover the ergodicity results, explain the main difficulties and some further progress.
Major outstanding questions regarding vertebrate limb development
concern how the numbers of skeletal
elements along the proximodistal (P-D) and anteroposterior (A-P) axes
are determined and how the shape of
a growing limb affects skeletal element formation. Recently [Alber etal., The morphostatic limit for a
model of skeletal pattern formation in the vertebrate limb, Bulletin of Mathematical Biology, 2008, v70, pp. 460-483], a simplified
two-equation reaction-diffusion system
was developed to describe the interaction of two of the key morphogens: the activator and an activator-dependent
inhibitor of precartilage condensation formation. In this talk, I will present a discontinuous Galerkin (DG) finite element method to solve this nonlinear system on complex domains
to study the effects of domain geometry on the pattern generated. Moreover, recently we have extended these previous results and developed a DG finite element model in a moving and deforming domain for skeletal
pattern formation in the vertebrate limb. Simulations reflect the actual dynamics of limb development and
indicate the important role played
by the geometry of the undifferentiated apical zone. This computational model can also be applied to
simulate various fossil limbs.
Jayne's maximum entropy principle is a widely used method for learning probabilistic models of data. Learning the parameters of such models is computationally intractable for most problems of interest in machine learning. As a result one has to resort to severe approximations. However, by "appropriately tweaking" the standard learning rules, one can define a nonlinear dynamical system without fixed points or even periodic orbits.This system is related to a family of weakly chaotic systems known as "piecewise isometries" which have vanishing topological entropy. The symbolic sequences of the very simplest 1 dimensional system areequivalent to Sturmian sequences. The averages over the symbolic sequences of many coupled variables can be shown to capture the relevant correlations present in the data. In this sense, we use this system to learn from data and make new predictions.