epshteyn (at) math.utah.edu)
January 22. Special joint Applied Math and Stochastics Seminar. Note
Time 3pm and Place LCB 219.
Speaker: Wenjia Jing, Department of Mathematics, The University of Chicago
Title: Imaging in random media: modeling and sifting noisy signals
Abstract: In imaging problems, waves propagating through a medium are recorded and used to locate reflectors, sources, etc. in the medium. The recorded signals are often incoherent due to multiple scattering by inhomogeneities in the medium. In optics, additional incoherencies appear due to the loss of phase information in the measurements. Imaging with such incoherent signals is challenging. In this talk, through the examples of passive array imaging in random waveguides and so-called "transient opto-elastography," I will discuss how to model such noisy signals and how to "sift" them, namely using correlation-based techniques, to extract useful information for imaging purposes.
This talk is based mainly on joint works with Habib Ammari and Josselin Garnier.
January 27 (Wednesday). Special joint Statistics/Stochastics
and Applied Math Seminar.
Note Time 3pm and Place LCB 219.
Speaker: Giovanni Motta, Department of Statistics, Columbia University
Title: Local polynomials estimation of time-varying matrices for multivariate locally stationary processes
Abstract: In this paper we propose two novel non-parametric approaches to estimate, respectively, the time-varying covariance and the time-varying correlation matrix of a multivariate locally stationary process. They both improve our previous estimators of the time-varying covariance and the time-varying correlation matrices. Our previous approach to estimate a time-varying covariance matrix is based on a Kernel-type smoother with the same bandwidth for all the entries of the matrix. This estimator is positive definite by construction. However, the use of one common bandwidth for all the entries of the covariance matrix appears to be restrictive, as these curves are in general characterized by different degrees of smoothness. A possible approach is to use a kernel-type estimator based on different smoothing parameters. However, this estimator is not in general positive definite, unless some severe restrictions are imposed on the bandwidths. Our novel estimator adapts to the different degrees of smoothness of the entries of the covariance matrix and, at the same time, is positive definite by construction. Our previous approach to estimate a time-varying correlation uses the same bandwidth for both numerator (covariance) and denominator (variances). This approach guarantees that the resulting estimator is a well defined correlation matrix. However, the use of one common bandwidth for both numerator and denominator does not allow the covariance and the variances to have different degrees of smoothness. On the other hand, a kernel-type estimator based on different smoothing parameters for numerator and denominator might deliver unbounded correlations and the resulting estimated correlation matrix is not necessarily positive semi-definite. Moreover, the estimated bandwidths that are optimal for estimating the covariance and the variances are not necessarily optimal for estimating the ratio. The estimator we propose in this paper is a local average of the signs of the cross-products: it does not require distinguishing between numerator and denominator and, at the same time, is positive definite by construction.
January 29. Commutative Algebra Seminar, 3:10pm - 4:00pm, LCB
Speaker: Graeme Milton, Department of Mathematics, The University of Utah
Title: Superfunctions and the algebra of subspace collections
Abstract: A natural connection between rational functions of several real or complex variables, and subspace collections is explored. A new class of function, superfunctions, are introduced which are the counterpart to functions at the level of subspace collections. Operations on subspace collections are found to correspond to various operations on rational functions, such as addition, multiplication and substitution. It is established that every rational matrix valued function which is homogeneous of degree 1 can be generated from an appropriate, but not necessarily unique, subspace collection: the mapping from subspace collections to rational functions is onto, but not one to one. For some applications superfunctions may be more important than functions, as they incorporate more information about the physical problem, yet can be manipulated in much the same way as functions. Orthogonal subspace collections occur in many physical problems, but we'll show an example of the use of non-orthogonal ones, which when substituted in orthogonal ones greatly accelerate the convergence of Fast Fourier transform methods.
February 5. Special Applied Math Seminar. Note
Time 3:55pm and Place LCB 225.
Speaker: Francois Monard, Department of Mathematics, University of Michigan
Title: Geodesic X-ray transforms on surfaces and tensor tomography
Abstract: In this talk, we will study what can be reconstructed about a function (or a tensor) on a surface, from knowledge of its integrals along a given family of geodesic curves, that is, its X-ray transform. The "straight-line" version of this question was first answered by J. Radon in 1917 and its solution forms the theoretical backbone of Computerized Tomography since the 1960's. In practice, variations of the refractive index do occur and bend photon paths in optics-based imaging, and this requires that the problem be studied for general curves.
In a geometric setting beyond that of the Radon transform, examples of situations impacting the qualitative invertibility and stability of these transforms are (i) the case of "simple" surfaces, (ii) the presence of conjugate points/caustics, and (iii) the presence of trapped geodesics. We will discuss positive and negative theoretical results occurring when one considers each of the scenarios above, with numerical illustrations throughout.
Speaker: Gunilla Kreiss, Department of Information Technology, Uppsala University
Title: Super convergence of finite difference solutions of PDEs
Abstract: A stable finite difference method applied to a problem with a smooth solution allows for a straightforward estimate of convergence rate based on the convergence rate of the local truncation error. In many high order cases the local truncation error at a few points near boundaries is significantly larger than at interior points. The straight forward estimat will predict a rate determined by the slowest converging local truncation error. Convergence in numerical computations is often faster than this prediction. We explore ways to improve our understanding of such super-convergence. We will in particular consider the second order wave equation.
February 10 (Wednesday). Special joint Statistics/Stochastics
and Applied Math Seminar.
Note Time 4pm and Place LCB 219.
Speaker: Lizhen Lin, Department of Statistics and Data Sciences, University of Texas at Austin
Title: Nonparametric Statistical Inference of non-Euclidean data
Abstract: Over the last few decades data represented in various non-conventional forms have become increasingly more prevalent. Typical examples include diffusion matrices in diffusion tensor imaging (DTI) of neuroimaging, and various digital image data arising in biology, medicine, machine vision and other fields of science and engineering. One may also encounter data that are stored in the forms of subspaces, orthonormal frames, surfaces, and networks. Statistical analysis of such data requires rigorous formulation and characterization of the underlying space, and inference is heavily dependent on the geometry of the space. For a majority of the cases considered, the underlying spaces fall into the general category of manifolds. This talk focuses on nonparametric inference on manifolds and other non-Euclidean spaces. Appropriate notion of means (e.g., Frechet means) and variations are defined, and inference is based on the asymptotic distributions of their sample counterparts. In particular, we present omnibus central limit theorems for Frechet means for inference, from which many of the existing CLTs follows immediately. Applications are provided to some stratified spaces of recent interest, and to the space of symmetric positive definite matrices arising in diffusion tensor imaging. In addition to inferring i.i.d data, we also consider nonparametric regression problems where predictors or responses lying on manifolds.
February 12. Note Time is
3:55pm and Place LCB 225.
Speaker: Mikyoung Lim, Department of Mathematical Sciences, KAIST
Title: Spectrum of the Neumann-Poincaré operator and plasmon resonance
Abstract: In this talk we consider spectral properties of the Neumann-Poincaré(NP) operator on planar domains with corners. Recently there is rapidly growing interest in the spectral properties of the NP operator due to its relation to plasmonics and cloaking by anomalous localized resonance: Plasmon resonance occurs at eigenvalues of the NP operator and anomalous localized resonance occurs at the accumulation point of eigenvalues, respectively. We show that the rate of resonance at continuous spectrum is different from that at eigenvalues, and then derive a method to distinguish continuous spectrum from eigenvalues. We analyse the spectrum of intersecting disks which has two corners and show computational experiments which provide the spectral properties of domains with corners. For the computations we use a modification of the Nyström method which makes it possible to construct high-order convergent discretizations of the NP operator on domains with corners.
February 19. Special joint Statistics/Stochastics
and Applied Math Seminar.
Note Time 3pm and Place LCB 219.
Speaker: Shuyang Bai, Department of Mathematics and Statistics, Boston University
Title: Self-normalized resampling of long-memory time series
Abstract: Statistical inference of long-memory time series faces two challenges due to the special behavior of the sample sum 1) a non-standard fluctuation rate which is typically unknown 2) a family of non-Gaussian scaling limits arise and it is difficult to statistically determine which one. We introduce a procedure which combines two strategies: self-normalization and resampling. Such combination successfully bypasses the aforementioned challenges. To establish the validity of the procedure, a key result involving bounding the maximal correlation between two blocks of a long-memory sequence is derived. Furthermore, the same procedure also works under short memory or heavy tails. It thus provides a unified treatment for various different situations.
February 22. Special joint Statistics/Stochastics
and Applied Math Seminar. Note Room is LCB 219.
Speaker: Veniamin Morgenshtern, Department of Statistics, Stanford University
Speaker: Dima Pesin, Department of Physics, The University of Utah
Speaker: Lajos Horvath, Department of Mathematics, The University of Utah
April 11 (Student Talk)
Speaker: Todd Harry Reeb, Department of Mathematics, The University of Utah
epshteyn (at) math.utah.edu).
Past lectures: Fall 2015, Spring 2015, Fall 2014, Spring 2014, Fall 2013, Spring 2013, Fall 2012, Spring 2012, Fall 2011, Spring 2011, Fall 2010, Spring 2010, Fall 2009, Spring 2009, Fall 2008, Spring 2008, Fall 2007, Spring 2007, Fall 2006, Spring 2006, Fall 2005, Spring 2005, Fall 2004, Spring 2004, Fall 2003, Spring 2003, Fall 2002, Spring 2002, Fall 2001, Spring 2001, Fall 2000, Spring 2000, Fall 1999, Spring 1999, Fall 1998, Spring 1998, Winter 1998, Fall 1997, Spring 1997, Winter 1997, Fall 1996, Spring 1996, Winter 1996, Fall 1995.