Online Kernel Sliced Inverse Regression

Online dimension reduction is a common method for high-dimensional streaming data processing. Online principal component analysis, online sliced inverse regression, online kernel principal component analysis and other methods have been studied in depth, but as far as we know, online supervised nonlinear dimension reduction methods have not been fully studied. In this article, an online kernel sliced inverse regression method is proposed. By introducing the approximate linear dependence condition and dictionary variable sets, we address the problem of increasing variable dimensions with the sample size in the online kernel sliced inverse regression method, and propose a reduced-order method for updating variables online. We then transform the problem into an online generalized eigen-decomposition problem, and use the stochastic optimization method to update the centered dimension reduction directions. Simulations and the real data analysis show that our method can achieve close performance to batch processing kernel sliced inverse regression

A study of the anisotropy of stress in a fluid confined in a nanochannel

We present molecular dynamics simulations of planar Poiseuille flow of a Lennard-Jones fluid at various temperatures and body forces. Local thermostatting is used close to the walls to reach steady-state up to a limit body force. Macroscopic fields are obtained from microscopic data by time- and space-averaging and smoothing the data with a self-consistent coarse-graining method based on kernel interpolation. Two phenomena make the system interesting: (i) strongly confined fluids show layering, i.e., strong oscillations in density near the walls, and (ii) the stress deviates from the Newtonian fluid assumption, not only in the layered regime, but also much further away from the walls. Various scalar, vectorial, and tensorial fields are analyzed and related to each other in order to understand better the effects of both the inhomogeneous density and the anisotropy on the flow behavior and rheology. The eigenvalues and eigendirections of the stress tensor are used to quantify the anisotropy in stress and form the basis of a newly proposed objective, inherently anisotropic constitutive model that allows for non-collinear stress and strain gradient by construction

Breaking the mass / anisotropy degeneracy in the Coma cluster

We provide the first direct lifting of the mass/anisotropy degeneracy for a cluster of galaxies, by jointly fitting the line of sight velocity dispersion and kurtosis profiles of the Coma cluster, assuming an NFW tracer density profile, a generalized-NFW dark matter profile and a constant anisotropy profile. We find that the orbits in Coma must be quasi-isotropic, and find a mass consistent with previous analyses, but a concentration parameter 50% higher than expected in cosmological N-body simulations. We then test the accuracy of our method on realistic non-spherical systems with substructure and streaming motions, by applying it to the ten most massive structures in a cosmological N-body simulation. We find that our method yields fairly accurate results on average (within 20%), although with a wide variation (factor 1.7 at 1 sigma) for the concentration parameter, with decreased accuracy and efficiency when the projected mean velocity is not constant with radius.Comment: 5 pages, oral presentation at IAU Colloquium 195, Outskirts of Galaxy Clusters: intense life in the suburbs, ed. A. Diaferi