Seminar Series


Upcoming Seminars

Date and Time: Friday, April 5, 2019, at 9 AM

Location: PB 138

Title: On the Non-hypercyclicity of Scalar Type Spectral Operators and Collections of Their Exponentials

Speaker: Marat Markin

Abstract: We give a straightforward proof of the non-hypercyclicity of scalar type spectral operators and certain collections of their exponentials. The important particular case of normal operators follows immediately.

 

If you need a disability-related accommodation or wheelchair access information, please contact the Mathematics Department at 559.278.2992 or e-mail mathsa@csufresno.edu. Requests should be made at least one week in advance of the event.

 

Recent Seminars

SPRING 2019

Date and Time: Friday, February 8, 2019, at 9 AM

Location: PB 138

Title:Modified commutation relationships from the Berry-Keating program

Speaker: Michael Bishop

Abstract: Current approaches to quantum gravity suggest there should be a modification of the standard quantum mechanical commutator, [^x; ^p] = i. Typical modifications are phenomenological and designed to result in a minimal length scale. As a motivating principle for the modification of the position and momentum commutator, we assume the validity of a version of the Bender-Brody-Muller symmetry operator in their suggestive approach to the Riemann hypothesis. We arrive at a family of modified position and momentum operators, and their associated commutator, which lead to a minimal length scale. Additionally, this larger family generalizes the Bender-Brody-Muller approach.

 

FALL 2018

Date and Time: Friday, November 20, 2018 at 11AM 

Location: PB 136

Title: On Hypercyclicity for Unbounded Linear Operator

Speaker: Marat Markin

Abstract:

Date and Time: Friday, November 2, 2018 at 9 AM

Location: PB 136

Title: The Quantum Harmonic Oscillator and a Step Toward GNS Construction

Speaker: Michael Bishop

Abstract: The quantum harmonic oscillator is a standard example of a  quantum variation of a classical system.   It inspires many of the elements of quantum field theory.  I will introduce this model along with the ladder method for solving for all the energy eigenvalues and associated eigenfunctions.  From there, I will introduce the Gelfand-Naimark-Segal construction which is a method to recover a Hilbert space of states from a Banach Algebra.  We will return to the oscillator and apply a similar method to this specific model which, as far as I can tell, has not been done.  This is preliminary work with Gerardo Munoz from the Physics department.

Date and Time:  Friday, October 26, 2018 at 9 AM

Location: PB 136

Title: A Space-Time Double-Hurdle for Marine Bird Count Data 

Speaker: Earvin Balderama

Abstract: The spatial distribution and relative abundance of marine birds along the US Northeast and Mid-Atlantic coastlines are of special interest to ocean planners. However, marine bird count data often exhibits excessive zero-inflation and over-dispersion. Modeling of such data typically involves a truncation or censoring technique to avoid the extremely large counts. Our modeling effort incorporates a spatial-temporal double-hurdle model specifically tailored to look at extreme abundances, which is especially important for assessing potential risks of offshore activities to sea ducks and other highly aggregative species. The double-hurdle model includes negative binomial and generalized Pareto distribution components to handle the extreme right tails, and is compared to several single-hurdle specifications using Bayesian model selection criterions. Spatial heterogeneity is modeled using a conditional auto-regressive (CAR) prior, and a Fourier basis was used for seasonal variation. Model parameters are estimated in a Bayesian hierarchical framework, using an MCMC algorithm with auto-tune parameters, all written and run in R. We demonstrate our model by creating predictive maps that show areas of high probability of aggregation and persistence for several species of marine bird.

 

Date and Time: Friday, October 12, 2018 at 9 AM

Title: Investigating the Gerchberg-Saxton algorithm: Some updates

Speaker: Comlan de Souza

Date and Time:  Friday, September 28, 2018 at 9 AM

Location: PB 136

Title: The sum of polynomials with three recurrence

Speaker: Khang Tran

Abstract: 

Date and Time: Friday, September 14, 2018 at 9 AM

Location: PB 136

Title: Deep Learning - Motivation and Applications

Speaker: Mario Banuelos

Abstract:  As a state-of-the-art machine learning tool, deep learning is often viewed as a black-box tool for applications such as image classification, speech recognition, and signal processing. In this talk, we will explore how linear and logistic regression can be viewed as a neural network mathematically as well as an overview of the different optimization techniques used to minimize error. An important part of optimizing performance in a deep learning model is the choice of an activation function, since activation functions allow deep learning models to approximate functions of complex, nonlinear data.

To further explore this idea, we study the importance of choosing an activation function. We designed and carried out experiments to measure the effectiveness of specific activation functions in multiple architectures. We propose a two-parameter, trainable activation function which we call TAct. We briefly explore the performance of Taylor Series approximations of TAct fitted to popular activation functions, including Rectified Linear Unit (ReLU), in a convolutional neural network (CNN) to reconstruct noisy representations of handwritten digits.

 

Date and Time: Friday, August 31, 2018 at 9 AM

Location: PB 136

Title: Eigenvalue techniques in graph theory and combinatorics

Speaker: Morgan Rodgers

Abstract:  When we are dealing with a finite mathematical structure, such as a graph, a finite geometry, or a collection of subsets from some fixed set, it is often possible to encode relevant information about the structure under consideration in the form of a matrix. This opens up the possibility of applying linear algebra techniques to the matrix to assist in studying properties of the related structure. In particular, looking at the eigenvalues and eigenspaces of a matrix related to a mathematical structure often provides deep information about the structure; this is often loosely referred to as applying “eigenvalue techniques” to study the structure in question.

One common example of the application of eigenvalue techniques arises in graph theory.  The adjacency matrix of a graph is a 0-1 graph that contains all of the information about which vertices are joined by edges. While a graph is not completely determined by the eigenvalues and multiplicities of this matrix, we can obtain information about whether the graph is regular, the diameter of the graph, even strong bounds on the size of the largest cliques and independent sets.

In this talk we will look at some different settings where eigenvalue techniques have been used to solve difficult combinatorial problems. 

Past Seminars

Archived Seminars