We introduce a novel concept of coarse extrinsic curvature for Riemannian submanifolds, inspired by Ollivier’s notion of coarse Ricci curvature. This curvature is derived from the Wasserstein 1-distance between probability measures supported in the tubular ...
This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...
The limiting distributions of statistics used to test hypotheses about parameters on the boundary of their domains may provide very poor approximations to the finite-sample behaviour of these statistics, even for very large samples. We review theoretical w ...
We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...
We consider that a network is an observation, and a collection of observed networks forms a sample. In this setting, we provide methods to test whether all observations in a network sample are drawn from a specified model. We achieve this by deriving the j ...
Accurate mapping of the functional interactions between remote brain areas with resting-state functional magnetic resonance imaging requires the quantification of their underlying dynamics. In conventional methodological pipelines, a spatial scale of inter ...
Verifying a person's identity claim by combining multiple biometric systems (fusion) is a promising solution to identity theft and automatic access control. This thesis contributes to the state-of-the-art of multimodal biometric fusion by improving the und ...
