Results 101 to 110 of about 320,618 (278)

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

f-Biharmonic Maps Between Riemannian Manifold

, 2012
We show that if ψ is an f-biharmonic map from a compact Riemannian manifold into a Riemannian manifold with non-positive curvature satisfying a condition, then ψ is an f-harmonic map.
Chiang, Yuan-Jen
core   +1 more source

A curvature identity on a 6-dimensional Riemannian manifold and its applications [PDF]

, 2017
summary:We derive a curvature identity that holds on any 6-dimensional Riemannian manifold, from the Chern-Gauss-Bonnet theorem for a 6-dimensional closed Riemannian manifold. Moreover, some applications of the curvature identity are given.
Euh, Yunhee, Park, Jeong Hyeong, Jeong Hyeong Park, Kouei Sekigawa, Sekigawa, Kouei, Yunhee Euh   +5 more
core   +1 more source

Two theorems on (ϵ)-Sasakian manifolds

International Journal of Mathematics and Mathematical Sciences, 1998
In this paper, We prove that every (ϵ)-sasakian manifold is a hypersurface of an indefinite kaehlerian manifold, and give a necessary and sufficient condition for a Riemannian manifold to be an (ϵ)-sasakian manifold.
Xu Xufeng, Chao Xiaoli
doaj   +1 more source

The left-invariant contact metric structure on the Sol manifold

Учёные записки Казанского университета: Серия Физико-математические науки, 2020
Among the known eight-dimensional Thurston geometries, there is a geometry of the Sol manifold – a Lie group consisting of real special matrices. For a left-invariant Riemannian metric on the Sol manifold, the left shift group is a maximal simple ...
V.I. Pan’zhenskii, A.O. Rastrepina
doaj   +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

Characterizing Affine Vector Fields on Pseudo-Riemannian Manifolds

Axioms
We study the characteristics of affine vector fields on pseudo-Riemannian manifolds and provide tensorial formulas that characterize these vector fields.
Norah Alshehri, Mohammed Guediri
doaj   +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

Riemannian Maps To Almost Hermitian Manifolds

, 2017
In this chapter, we study Riemannian maps from Riemannian manifolds to almost Hermitian manifolds. In section 1, we study invariant Riemannian maps, that is, the image of derivative map is invariant under the almost complex structure of the base manifold.
Sahin, Bayram, Bayram Şahin
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