Let P: ... -> C_2 -> C_1 -> P^1 be a Z_p-cover of the projective line over a finite field of characteristic p which ramifies at exactly one rational point. In this talk, we study the p-adic Newton slopes of L-functions associated to characters of the Galois group of P. It turns out that for covers P such that the genus of C_n is a quadratic polynomial in p^n for n large, the Newton slopes are uniformly distributed in the interval [0,1]. Furthermore, for a large class of such covers P, these slopes behave in an even more regular way. This is joint work with Hui June Zhu.
My goal is to prove Agmon theorem in two talks. In the first talk, I will use the limiting absorption principle for the free Laplacian to prove Agmon theorem. Next Friday, Lili Yan will present the limiting absorption principle for free Laplacian.
Let us take a couple of 2x2 matrices A and B, and consider a long product of matrices, where each multiplier is either A or B, chosen randomly. What should we expect as a typical norm of such a product? This simple question leads to a rich theory of random matrix products. We will discuss some of the classical theorems (e.g. Furstenberg Theorem), as well as the very recent results.
Peer observation of teaching is an excellent way to receive concrete, fact-based feedback on what it's like in your classroom. This spring quarter the math department will run peer observation among 1st and 2nd year grad students, and this meeting will introduce that. It is also open to more advanced grad students. In particular, any student eventually needing a teaching-focused letter of recommendation (which is required for almost all postdocs) is strongly encouraged to attend this session. Our set-up will be based on Danny Mann's talk in fall quarter.
Normal approximation for recovery of structured unknowns in high dimension: Steining the Steiner formula Larry Goldstein, University of Southern California Abstract Intrinsic volumes of convex sets are natural geometric quantities that also play important roles in applications. In particular, the discrete probability distribution L(VC) given by the sequence v0,...,vd of conic intrinsic volumes of a closed convex cone C in Rd summarizes key information about the success of convex programs used to solve for sparse vectors, and other structured unknowns such as low rank matrices, in high dimensional regularized inverse problems. The concentration of VC implies the existence of phase transitions for the probability of recovery of the unknown in the number of observations. Additional information about the probability of recovery success is provided by a normal approximation for VC. Such central limit theorems can be shown by first considering the squared length GC of the projection of a Gaussian vector on the cone C. Applying a second order Poincar´e inequality, proved using Stein’s method, then produces a non-asymptotic total variation bound to the normal for L(GC). A conic version of the classical Steiner formula in convex geometry translates finite sample bounds and a normal limit for GC to that for VC. Joint with Ivan Nourdin and Giovanni Peccati. http://arxiv.org/abs/1411.6265
I shall describe past and current projects on non-convex optimization arising in signal/image recovery and classification. The non-convexity comes from either sparse constraint or objective function constructed from probability or information theory. We shall explore techniques beyond convex relaxations.
We will discuss the exponential map (from a Lie algebra to the corresponding Lie group) in the case of positive characteristic p, and its relation to the Campbell-Baker-Hausdorf formula which expresses the group product via the Lie brackets. If time permits, we will also talk about loops (algebraic structures similar to groups where only a weaker form of associativity holds).