Spatially resolved genomic technologies have allowed us to study the physical organization
of cells and tissues, and promise an understanding of local interactions between cells. …

Current tools for multivariate density estimation struggle when the density is concentrated
near a non-linear subspace or manifold. Most approaches require the choice of a kernel, with …

Dimension reduction is useful for exploratory data analysis. In many applications, it is of
interest to discover variation that is enriched in a "foreground" dataset relative to a "background" …

Spatially-resolved genomic technologies have allowed us to study the physical organization
of cells and tissues, and promise an understanding of the local interactions between cells. …

Many statistical and machine learning approaches rely on pairwise distances between data
points. The choice of distance metric has a fundamental impact on performance of these …

We recapitulate the achievement about the Dubins path as well as some precise proofs which
are important but omitted by Dubins. Then we prove that the shortest path ( -geodesic) in …

… Some covariance inequality tests have attempted to address the problem of highdimensional
data by using estimators of the distance between covariance matrices based on the …

In statistical dimensionality reduction, it is common to rely on the assumption that high
dimensional data tend to concentrate near a lower dimensional manifold. There is a rich literature …

Gaussian processes are widely employed as versatile modelling and predictive tools in
spatial statistics, functional data analysis, computer modelling and diverse applications of …

Spatial navigation and orientation are emerging as promising markers for altered cognition
in prodromal Alzheimer’s disease, and even in cognitively normal individuals at risk for …