Results 111 to 120 of about 320,618 (278)

A new class of almost complex structures on tangent bundle of a Riemannian manifold [PDF]

open access: yes, 2018
summary:In this paper, the standard almost complex structure on the tangent bunle of a Riemannian manifold will be generalized. We will generalize the standard one to the new ones such that the induced $(0,2)$-tensor on the tangent bundle using these ...
Baghban, Amir, Abedi, Esmaeil
core   +1 more source

Selective Convergence of Followers to Multiple Leaders With Repulsion and Cohesion on Riemannian Manifolds

open access: yesStudies in Applied Mathematics, Volume 156, Issue 6, June 2026.
ABSTRACT We study the long‐term dynamics of followers that selectively follow one of multiple leaders on Riemannian manifolds, where the leaders interact through repulsive forces while remaining cohesively bounded. We propose a multileader–follower multiagent system defined on Riemannian manifolds. In our model, each follower chooses exactly one leader
Hyunjin Ahn
wiley   +1 more source

LARGE SOLUTIONS FOR YAMABE AND SIMILAR PROBLEMS ON DOMAINS IN RIEMANNIAN MANIFOLDS

open access: yes, 2011
We present a unified approach to study large positive solutions (i.e., u(x) -> infinity as x -> partial derivative Omega) of the equation Delta u + hu - k psi(u) = -f in an arbitrary domain Omega.
Martin Dindoš, Dindos, Martin; id_orcid
core   +1 more source

Conformal quasi-hemi-slant $\xi^{\perp}$-Riemannian submersions from Sasakian manifolds [PDF]

open access: yesMathematica Bohemica
We introduce some geometric properties of a horizontally conformal quasi-hemi-slant Riemannian submersion from a Sasakian manifold, normal to the characteristic vector field, supported by an example.
Fortuné Massamba, Pontsho Moile
doaj   +1 more source

Spatial depth for data in metric spaces

open access: yesScandinavian Journal of Statistics, Volume 53, Issue 2, Page 684-711, June 2026.
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

PARTIAL ISOMETRIES OF A SUB-RIEMANNIAN MANIFOLD

open access: yes, 2012
In this article, we obtain the following generalization of isometric C1-immersion theorem of Nash and Kuiper. Let M be a smooth manifold of dimension m and H a rank k subbundle of the tangent bundle TM with a Riemannian metric gH.
MAHUYA DATTA
core   +1 more source

Subgraph entropy

open access: yesBulletin of the London Mathematical Society, Volume 58, Issue 6, June 2026.
Abstract Given r⩾3$r \geqslant 3$, we prove that there exists λ>0$\lambda >0$ depending only on r$r$ so that if G$G$ is a metric graph of rank r$r$ with metric entropy 1, then there exists a proper subgraph H$H$ of G$G$ with metric entropy at least λ$\lambda$. This answers a question of the second two authors together with Rieck. We interpret this as a
Tawfiq Hamed, Tarik Aougab, Matt Clay
wiley   +1 more source

On the tightness of left‐invariant contact structures

open access: yesBulletin of the London Mathematical Society, Volume 58, Issue 6, June 2026.
Abstract We prove that all left‐invariant contact structures on three‐dimensional Lie groups are tight. The argument is based on Riemannian methods and establishes a unique factorization property for any Lie group admitting a left‐invariant contact structure, other than SU(2)$\mathrm{SU}(2)$. We then make use of such factorization property to construct
Eugenio Bellini
wiley   +1 more source

Martingales on manifolds and geometric Ito calculus [PDF]

open access: yes
This work studies properties of stochastic processes taking values in a differential manifold M with a linear connection Γ, or in a Riemannian manifold with a metric connection.
Darling, R. W. R.
core  

Riemannian manifold learning for nonlinear dimensionality reduction

open access: yes, 2006
In recent years, nonlinear dimensionality reduction (NLDR) techniques have attracted much attention in visual perception and many other areas of science. We propose an efficient algorithm called Riemannian manifold learning (RML).
Hongbin Zha   +8 more
core   +1 more source

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