Parallel and Scalable Heat Methods for Geodesic Distance Computation

Project: Heat kernel construction on cortical surface using geodesic distance

Geodesic Distance Function Learning via Heat Flow on Vector Fields

Learning a distance function or metric on a given data manifold is of great importance in machine learning and pattern recognition. Many of the previous works first embed the manifold to Euclidean space and then learn the distance function. However, such a scheme might not faithfully preserve the distance function if the original manifold is not Euclidean. In this paper, we propose to learn the...

Parallel computation framework for optimizing trailer routes in bulk transportation

We consider a rich tanker trailer routing problem with stochastic transit times for chemicals and liquid bulk orders. A typical route of the tanker trailer comprises of sourcing a cleaned and prepped trailer from a pre-wash location, pickup and delivery of chemical orders, cleaning the tanker trailer at a post-wash location after order delivery and prepping for the next order. Unlike traditiona...

Geodesic Computation for Adaptive Remeshing

This video presents an application of geodesic computation on 3D meshes to surface remeshing. The farthest point paradigm [1] is a powerful greedy algorithm that allows fast and accurate sampling of the underlying surface. The use of a varying speed function for front propagation enables an adaptive placement of the seed points. The connectivity of the resulting mesh is computed using a geodesi...

A Scalable Parallel Approach for Subgraph Census Computation

Counting the occurrences of small subgraphs in large networks is a fundamental graph mining metric with several possible applications. Computing frequencies of those subgraphs is also known as the subgraph census problem, which is a computationally hard task. In this paper we provide a parallel multicore algorithm for this purpose. At its core we use FaSE, an efficient network-centric sequentia...