Results 11 to 20 of about 10,035 (101)
Invariant Measure and Universality of the 2D Yang–Mills Langevin Dynamic
ABSTRACT We prove that the Yang–Mills (YM) measure for the trivial principal bundle over the two‐dimensional torus, with any connected, compact structure group, is invariant for the associated renormalised Langevin dynamic. Our argument relies on a combination of regularity structures, lattice gauge‐fixing and Bourgain's method for invariant measures ...
Ilya Chevyrev, Hao Shen
wiley +1 more source
Abstract Objective This study was undertaken to develop and validate an artificial intelligence (AI) diagnostic tool using hybrid electroencephalographic (EEG)–video signals for automatic epileptic spasms (ES) detection. Methods This retrospective cohort study with internal cross‐validation and multicenter external validation was conducted from July ...
Lin Wan +15 more
wiley +1 more source
On Geometric Phase Model in the Theory of Curves With Myller Configuration
ABSTRACT In this paper, we introduce a linearly polarized light wave in an optical fiber and rotation of the polarization plane through the Frenet‐type frame with Myller configuration. Since the geometric evaluation and interpretations of a polarized light wave are associated with geometric phase, a new type of geometric phase model has been ...
Zehra İşbilir +2 more
wiley +1 more source
Geometric flows on warped product manifold
We derive one unified formula for Ricci curvature tensor on arbitrary warped product manifold by introducing a new notation for the lift vector and the Levi-Civita connection.This formula is helpful to further consider Ricci flow (RF) and hyperbolic ...
Lu, Wei-Jun
core +1 more source
Initial State Privacy of Nonlinear Systems on Riemannian Manifolds
ABSTRACT In this paper, we investigate initial state privacy protection for discrete‐time nonlinear closed systems. By capturing Riemannian geometric structures inherent in such privacy challenges, we refine the concept of differential privacy through the introduction of an initial state adjacency set based on Riemannian distances.
Le Liu, Yu Kawano, Antai Xie, Ming Cao
wiley +1 more source
SDFs from Unoriented Point Clouds using Neural Variational Heat Distances
We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from unoriented point clouds. We first compute a small time step of heat flow (middle) and then use its gradient directions to solve for a neural SDF (right). Abstract We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from ...
Samuel Weidemaier +5 more
wiley +1 more source
Survey on differential estimators for 3d point clouds
Abstract Recent advancements in 3D scanning technologies, including LiDAR and photogrammetry, have enabled the precise digital replication of real‐world objects. These methods are widely used in fields such as GIS, robotics, and cultural heritage. However, the point clouds generated by such scans are often noisy and unstructured, posing challenges for ...
Léo Arnal–Anger +4 more
wiley +1 more source
Non‐Rigid 3D Shape Correspondences: From Foundations to Open Challenges and Opportunities
Abstract Estimating correspondences between deformed shape instances is a long‐standing problem in computer graphics; numerous applications, from texture transfer to statistical modelling, rely on recovering an accurate correspondence map. Many methods have thus been proposed to tackle this challenging problem from varying perspectives, depending on ...
A. Zhuravlev +14 more
wiley +1 more source
Establishing Shape Correspondences: A Survey
Abstract Shape correspondence between surfaces in 3D is a central problem in geometry processing, concerned with establishing meaningful relations between surfaces. While all correspondence problems share this goal, specific formulations can differ significantly: Downstream applications require certain properties that correspondences must satisfy ...
A. Heuschling, H. Meinhold, L. Kobbelt
wiley +1 more source
Density‐Valued ARMA Models by Spline Mixtures
ABSTRACT This paper proposes a novel framework for modeling time series of probability density functions by extending autoregressive moving average (ARMA) models to density‐valued data. The method is based on a transformation approach, wherein each density function on a compact domain [0,1]d$$ {\left[0,1\right]}^d $$ is approximated by a B‐spline ...
Yasumasa Matsuda, Rei Iwafuchi
wiley +1 more source

