We study robust PCA for the fully observed setting, which is about separating a low rank matrix L and a sparse matrix S from their sum D=L+S.

Latent variable models lay the statistical foundation for data science problems with unstructured, incomplete and heterogeneous information. Spectral methods extract low-dimensional geometric structures for downstream tasks in a computationally efficient way.

In this talk, we consider the large time behavior of strong solutions to the one-dimensional, compressible Navier-Stokes system for a viscous and heat conducting ideal polytropic gas, when the viscosity is constant and the heat conductivity is proportional to a positive power of the temperature.

The chiral de Rham complex is a sheaf of vertex algebra on a complex manifold.