Results 141 to 150 of about 253,625 (236)
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
Advanced Intelligent Discovery, EarlyView.Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser +6 more
wiley +1 more source
Novel temperature-based spectral topological indices for QSPR modeling of polyacenes in predicting physicochemical properties. [PDF]
Sci RepHayat S, Alanazi SJF, Belay MB, Wang S.
europepmc +1 more source
The generalized spectral theory and its application to the Kuramoto conjecture (Recent developments in mathematics of integrable systems)A spectral theory of linear operators based on a Gelfand triplet (rigged Hilbert space) is developed under the assumptions that a linear operator 𝑇 on a Hilbert space 𝐻 is a perturbation of a self-adjoint operator, and the spectral measure of the self-adjoint operator has an analytic continuation near the real axis.
openaire
B. D. Sleeman Multiparameter spectral theory in Hilbert space (Research Notes in Mathematics 22, Pitman, 1978), 118 p. £6·00. [PDF]
Proceedings of the Edinburgh Mathematical Society, 1981 openaire +1 more source
The spectral theory of the Neumann-Poincaré operator on convex domains (Mathematical aspects of quantum fields and related topics)The Neumann-Poincaré operator (abbreviated by NP) is a boundary integral operator naturally arising when solving classical boundary value problems using layer potentials. If the boundary of the domain, on which the NP operator is defined, is C[1, α] smooth, then the NP operator is compact.
openaire
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
Advanced Intelligent Discovery, EarlyView.This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
Optical excited states of archetypical blue light emitter, dimethyl-acridine diphenyl-sulfone derivative (DMAC-DPS) in solid films, studied by electroabsorption spectroscopy. [PDF]
Sci RepPelczarski D +2 more
europepmc +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Rare Events Statistics for Z d Map Lattices Coupled by Collision. [PDF]
Commun Math PhysBahsoun W, Phalempin M.
europepmc +1 more source