4:00pm to 5:00pm - Zoom - https://uci.zoom.us/j/97796361534 - Applied and Computational Mathematics Anne Gelb - (Dartmouth College) Empirical Bayesian inference using a support informed prior Abstract: We develop a new empirical Bayesian inference algorithm for solving a linear inverse problem given multiple measurement vectors (MMV) of noisy observable data. Specifically, by exploiting the joint sparsity across the multiple measurements in the sparse domain of the underlying signal or image, we construct a new support informed prior. While a variety of applications can be modeled using this framework, our prototypical example comes from synthetic aperture radar (SAR) data, from which data are acquired from neighboring aperture windows. Hence a good test case is to consider the observations modeled as noisy Fourier samples. Our numerical experiments demonstrate that using the support informed prior not only improves accuracy of the recovery, but also reduces the uncertainty in the posterior when compared to standard sparsity producing priors. |
4:00pm - ISEB 1200 - Differential Geometry Christos Mantoulidis - (Rice University) A nonlinear spectrum on closed manifolds Abstract: The p-widths of a closed Riemannian manifold are a nonlinear
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2:00pm to 3:00pm - 510R Rowland Hall - Combinatorics and Probability Roman Vershynin - (UCI) Mathematics of synthetic data. I. Differential privacy. This is a series of talks on synthetic data and its privacy. It is meant for beginners. In the first talk we will introduce the notion of differential privacy, construct a private mean estimator, and try (unsuccessfully) to construct a private measure. |
2:00pm to 3:00pm - 440R - Mathematical Physics Shiwen Zhang - (U Minnesota) Approximating the ground state eigenvalue via the effective potential
we study 1-d random Schrödinger operators on a finite interval with Dirichlet boundary conditions. We are interested in the approximation of the ground state energy using the minimum of the effective potential. For the 1-d continuous Anderson Bernoulli model, we show that the ratio of the ground state energy and the minimum of the effective potential approaches π^2/8 |
9:00am to 10:00am - Zoom - Inverse Problems Per Christian Hansen - (Technical University of Denmark) Title: Convergence and Non-Convergence of Algebraic Iterative Reconstruction Methods |
11:00am - RH 306 - Harmonic Analysis Ruixiang Zhang - (UC Berkeley) A stationary set method for estimating oscillatory integrals Given a polynomial $P$ of constant degree in $d$ variables and consider the oscillatory integral $$I_P = \int_{[0,1]^d} e(P(\xi)) \mathrm{d}\xi.$$ Assuming $d$ is also fixed, what is a good upper bound of $|I_P|$? In this talk, I will introduce a ``stationary set'' method that gives an upper bound with simple geometric meaning. The proof of this bound mainly relies on the theory of o-minimal structures. As an application of our bound, we obtain the sharp convergence exponent in the two dimensional Tarry's problem for every degree via additional analysis on stationary sets. Consequently, we also prove the sharp $L^{\infty} \to L^p$ Fourier extension estimates for every two dimensional Parsell-Vinogradov surface whenever the endpoint of the exponent $p$ is even. This is joint work with Saugata Basu, Shaoming Guo and Pavel Zorin-Kranich. |
3:00pm to 4:00pm - RH 306 - Applied and Computational Mathematics Zhimin Zhang - (Wayne State University and Beijing Computational Science Research Center) Efficient spectral methods and error analysis for nonlinear Hamiltonian systems We investigate efficient numerical methods for nonlinear Hamiltonian systems. Three polynomial spectral methods (including spectral Galerkin, Petrov-Galerkin, and collocation methods). Our main results include the energy and symplectic structure preserving properties and error estimates. We prove that the spectral Petrov-Galerkin method preserves the energy exactly and both the spectral Gauss collocation and spectral Galerkin methods are energy conserving up to spectral accuracy. While it is well known that collocation at Gauss points preserves symplectic structure, we prove that the Petrov-Galerkin method preserves the symplectic structure up to a Gauss quadrature error and the spectral Galerkin method preserves the symplectic structure to spectral accuracy. Furthermore, we prove that all three methods converge exponentially (with respect to the polynomial degree) under sufficient regularity assumption. All these aforementioned properties make our methods possible to simulate the long time behavior of the Hamiltonian system. Numerical experiments indicate that our algorithms are efficient. |
4:00pm - MSTB 124 - Graduate Seminar Li-Sheng Tseng - (UCI) What is cohomology? |