Results 91 to 100 of about 10,692 (245)

Polyharmonic hypersurfaces into pseudo-Riemannian space forms. [PDF]

Ann Mat Pura Appl, 2023
Branding V, Montaldo S, Oniciuc C, Ratto A.   +3 more
europepmc   +1 more source

Deep Learning Unlocks Behavioral Prediction and Neurobehavioral Decoding

Med Research, Volume 2, Issue 2, Page 371-387, June 2026.
This review evaluates deep learning frameworks that surmount conventional limitations through high‐dimensional nonlinear modeling, spatiotemporal dependency capture, and multimodal information integration. Focusing on biological behavior forecasting and neural mechanism decoding, we delineate cutting‐edge applications, including real‐time action ...
Tianzhe Han, Yunhua Jia, Xiaomin Hu, Shuyue Wang, Ruiwen Wang, Zhijing Zhu   +5 more
wiley   +1 more source

On the Computation of Tensor Functions under Tensor‐Tensor Multiplications with Linear Maps

Numerical Linear Algebra with Applications, Volume 33, Issue 3, June 2026.
ABSTRACT In this paper, we study the computation of both algebraic and non‐algebraic tensor functions under the tensor‐tensor multiplication with linear maps. In the case of algebraic tensor functions, we prove that the asymptotic exponent of both the tensor‐tensor multiplication and the tensor polynomial evaluation problem under this multiplication is
Jeong‐Hoon Ju, Susana López‐Moreno
wiley   +1 more source

Counting Degrees of Freedom: A Method Applicable From Scalars to f(Q)$f(\mathbb {Q})$ Gravity and Beyond

Fortschritte der Physik, Volume 74, Issue 6, June 2026.
ABSTRACT We present a clear, step‐by‐step method for counting degrees of freedom and identifying constraints in general field theories. This approach, grounded in the works of Einstein, Hilbert, Cartan, Kuranishi, and, more recently, Seiler, is neither Lagrangian nor Hamiltonian in nature. Instead, it applies directly to the field equations. We offer a
Lavinia Heisenberg
wiley   +1 more source

Minimal homogeneous submanifolds in euclidean spaces [PDF]

, 2002
We prove that minimal (extrinsically) homogeneous submanifolds of the Euclidean space are totally geodesic. As an application, we obtain that a complex (intrisecally) homogeneous submanifold of a complex Euclidean space must be totally ...
Di Scala, Antonio Jose'
core   +1 more source

Covariance Estimation for Wide Data

WIREs Computational Statistics, Volume 18, Issue 2, June 2026.
Covariance matrix estimation is fundamental to multivariate analysis, with applications spanning finance, genomics, climate science, and signal processing. This review synthesizes recent advances in high‐dimensional covariance estimation‐thresholding, linear and nonlinear shrinkage, graphical models, and random matrix theory‐under a unifying framework ...
Eran Raviv
wiley   +1 more source

Geometry of conformal vector fields

Arab Journal of Mathematical Sciences, 2017
It is well known that the Euclidean space (Rn,〈,〉), the n-sphere Sn(c) of constant curvature c and Euclidean complex space form (Cn,J,〈,〉) are examples of spaces admitting conformal vector fields and therefore conformal vector fields are used in ...
Sharief Deshmukh
doaj   +1 more source

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

Studies 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

Integrable Equations and Their Evolutions Based on Intrinsic Geometry of Riemann Spaces

International Journal of Mathematics and Mathematical Sciences, 2009
The intrinsic geometry of surfaces and Riemannian spaces will be investigated. It is shown that many nonlinear partial differential equations with physical applications and soliton solutions can be determined from the components of the relevant metric ...
Paul Bracken
doaj   +1 more source

Spatial depth for data in metric spaces

Scandinavian 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