Events
Department of Mathematics and Statistics
Texas Tech University
abstract pdf
The notion of great antipodal sets of a Riemannian manifold was introduced in [B.-Y. Chen and T. Nagano, Un invariant geometrique riemannien, C.R. Acad. Sci. Paris Math. 295 (1982), 389–391]. Later, great antipodal sets have been studied by a number of mathematicians and was shown that great antipodal sets relate closely with several important areas in mathematics.
In this talk, I will present this geometric notion and explain how this geometric notion can be applied to several important areas in mathematics.
This presentation may be viewed in the TTU Mediasite catalog via eraider login.
A key aspect of quantum theory its insistence that states evolve via unitary transformations. In order to understand the symmetries of higher dimensional quantum field theory, we need to develop higher dimensional analogues of unitarity. The language and theory of higher categories has greatly clarified the way we express these higher symmetries, but unfortunately this language imposes a certain dogma seems to be in conflict with various attempts at describing unitarity. In the nLab for example, there is a great debate over whether or not unitary structures on a (higher) category are evil; at term which is both dogmatic and technically precise.
Various attempts have been made to force these structures to play nice with one another, to varying degrees of success. In this talk I will present our most recent contribution to these efforts: defining the notion of a 3-Hilbert space. Our work aims to encode a kind of evaluation on spheres of every dimension that plays nicely with duality structures that are imposed by the cobordism hypothesis. I will show how this compatibility is stronger than simply having daggers at all levels, thus differentiating our construction from previous attempts at higher unitarity. If time permits, we will discuss a roadmap for unitarity in any dimension via a unitary version of condensation completion.
Abstract. This talk is about kernels. In particular, I will bring up reproducing kernels, Bochner’s theorem, Mercer series, feature maps and random Fourier features. My idea is to bring this into machine learning and large language models. Hopefully I can tie together some of kernel method and to start thinking about better or reduced random features (via “better” kernels) which then should lead to faster and accurate machine learning models.
When: 4:00 pm (Lubbock's local time is GMT -6)
Where: room Math 011 (Math Basement)
ZOOM details:
- Choice #1: use this link
Direct Link that embeds meeting and ID and passcode.
- Choice #2: join meeting using this link
Join Meeting, then you will have to input the ID and Passcode by hand:
* Meeting ID: 979 1333 6658
* Passcode: Applied (Note the capital letter "A")
 | Thursday
Apr. 10
6:30 PM
MA 108
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| Mathematics Education
Math Circle
Stone Fields
Mathematics and Statistics, Texas Tech University
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Math Circle Spring Poster
abstract 2 PM CDT (UT-5)
Zoom link available from Dr. Brent Lindquist upon request.