Results 61 to 70 of about 384,280 (237)

Initial State Privacy of Nonlinear Systems on Riemannian Manifolds

International Journal of Robust and Nonlinear Control, EarlyView.
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

The Proximal Gradient Method for Composite Optimization Problems on Riemannian Manifolds

Mathematics
In this paper, the composite optimization problem is studied on Riemannian manifolds. To tackle this problem, the proximal gradient method to solve composite optimization problems is proposed on Riemannian manifolds. Under some reasonable conditions, the
Xiaobo Li
doaj   +1 more source

On the Geometry of Three-dimensional Pseudo-Riemannian Homogeneous Spaces. II [PDF]

Известия Саратовского университета. Новая серия: Математика. Механика. Информатика, 2020
The problem of establishing links between the curvature and the topological structure of a manifold is one of the important problems of the geometry. In general, the purpose of the research of manifolds of various types is rather complicated.
Mozhey, Natal’ya Pavlovna
doaj   +1 more source

A Comprehensive Review of Golden Riemannian Manifolds

Axioms
In differential geometry, the concept of golden structure represents a compelling area with wide-ranging applications. The exploration of golden Riemannian manifolds was initiated by C. E. Hretcanu and M.
Bang-Yen Chen, Majid Ali Choudhary, Afshan Perween   +2 more
doaj   +1 more source

Curvature based triangulation of metric measure spaces [PDF]

, 2010
We prove that a Ricci curvature based method of triangulation of compact Riemannian manifolds, due to Grove and Petersen, extends to the context of weighted Riemannian manifolds and more general metric measure spaces.
Saucan, Emil
core  

On semi-slant $\xi^\perp-$Riemannian submersions

, 2017
The aim of the present paper to define and study semi-slant $\xi^\perp-$Riemannian submersions from Sasakian manifolds onto Riemannian manifolds as a generalization of anti-invariant $\xi^\perp-$Riemannian submersions, semi-invariant $\xi^\perp ...
Akyol, Mehmet Akif, Sarı, Ramazan
core   +1 more source

SDFs from Unoriented Point Clouds using Neural Variational Heat Distances

Computer Graphics Forum, EarlyView.
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, Florine Hartwig, Josua Sassen, Sergio Conti, Mirela Ben‐Chen, Martin Rumpf   +5 more
wiley   +1 more source

Density‐Valued ARMA Models by Spline Mixtures

Journal of Time Series Analysis, EarlyView.
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

Biharmonic maps on V-manifolds

International Journal of Mathematics and Mathematical Sciences, 2001
We generalize biharmonic maps between Riemannian manifolds into the case of the domain being V-manifolds. We obtain the first and second variations of biharmonic maps on V-manifolds.
Yuan-Jen Chiang, Hongan Sun
doaj   +1 more source

Spatial depth for data in metric spaces

Scandinavian Journal of Statistics, EarlyView.
Abstract We propose a novel measure of statistical depth, the metric spatial depth, for data residing in an arbitrary metric space. The measure assigns high (low) values for points located near (far away from) the bulk of the data distribution, allowing quantifying their centrality/outlyingness.
Joni Virta
wiley   +1 more source