4月29日
研讨会, 演讲, 讲座
OCES Departmental Seminar: Local to Global Drivers of Past and Future Sea-Level and Coastal Environmental Change
Geological proxies provide valuable archives of the sea-level response to past climate variability over periods of more extreme global mean surface temperatures than the brief instrumental period.
4月29日
研讨会, 演讲, 讲座
Department of Mathematics - Seminar on PDE - Anisotropic Dynamical Horizons Arising in Gravitational Collapse
Black holes are predicted by Einstein's theory of general relativity, and now we have ample observational evidence for their existence.
4月29日
研讨会, 演讲, 讲座
Department of Mathematics - Seminar on Statistics - A Learning System in Pandemic Prevention — from macro predictive modeling to small probability estimation
Reproduction number (R
4月27日
研讨会, 演讲, 讲座
Physics Department - Condensed Matter Seminar: Correlations and Topology in a Transition Metal Dichalcogenide Compound
4月27日
研讨会, 演讲, 讲座
Department of Mathematics - Seminar on Applied Mathematics - Riemannian optimization with three metrics for Hermitian PSD fixed rank constraints
Hermitian PSD fixed rank constraint is used in many applications, e.g., it is also used for approximating the Hermitian PSD constraint. We study and compare three methodologies for minimizing f(X) with X being a Hermitian PSD fixed rank matrix.
4月27日
研讨会, 演讲, 讲座
Department of Mathematics - PhD Student Seminar - Free Limit Laws for Block Correlation Matrices
Independence test for the components of a random vector is a classical problem. When the variances of the components are unknown, various statistics constructed from the sample correlation matrices are often used.
4月25日
研讨会, 演讲, 讲座
Department of Mathematics - PhD Student Seminar - Optimal Estimation and Computational Limit of Low-rank Gaussian Mixtures
We propose a low-rank Gaussian mixture model (LrMM) assuming each matrix-valued observation has a planted low-rank structure.
4月22日
研讨会, 演讲, 讲座
Department of Mathematics - Seminar on PDE - Regularity for singular and degenerate PDEs: qualitative vs. sharp estimates
Singular and degenerate partial differential equations are unavoidable in the modelling of several phenomena, like phase transitions and chemotaxis, and are also used in machine learning in the context of semi-supervised learning.