Geometric deep learning is an umbrella term for emerging techniques attempting to
generalize (structured) deep neural models to non-Euclidean domains, such as graphs and …

We propose a novel point signature based on the properties of the heat diffusion process on
a shape. Our signature, called the Heat Kernel Signature (or HKS), is obtained by restricting …

Cell membranes are vital to shield a cell's interior from the environment. At the same time
they determine to a large extent the cell's mechanical resistance to external forces. In recent …

In recent years a considerable amount of work in graphics and geometric optimization used
tools based on the Laplace-Beltrami operator on a surface. The applications of the …

We study data-driven representations for three-dimensional triangle meshes, which are one
of the prevalent objects used to represent 3D geometry. Recent works have developed …

Scientists study trajectory data to understand trends in movement patterns, such as human
mobility for traffic analysis and urban planning. In this paper, we introduce a novel trajectory …

This paper outlines and qualitatively compares the implementations of seven different
methods for solving Poisson's equation on the disk. The methods include two classical finite …

We present a volume-preserving scheme for two-phase immiscible incompressible flows
using an immersed boundary method (IBM) in a three-dimensional space. The two-phase …

We construct an extension of spectral and diffusion geometry to multiple modalities through
simultaneous diagonalization of Laplacian matrices. This naturally extends classical data …

We consider the class of curves of finite total curvature, as introduced by Milnor. This is a
natural class for variational problems and geometric knot theory, and since it includes both …