Results 71 to 80 of about 2,327 (208)
Quasiconformal harmonic functions between convex domains [PDF]
Publications de l'Institut Mathematique, 2004 Summary: We generalize \textit{O. Martio}'s paper [Ann. Acad. Sci. Fenn., Ser. A I 425, 10 p. (1968; Zbl 0162.37902)]. Indeed the problem studied in this paper is under which conditions on a homeomorphism \(f\) between the unit circle \(S^1:=\{z: |z|=1\}\) and a fixed convex Jordan curve \(\gamma\) the harmonic extension of \(f\) is a quasiconformal ...
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Advanced Intelligent Discovery, EarlyView.A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi +5 more
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Dark Solitons with Majorana Fermions in Spin-Orbit-Coupled Fermi Gases
, 2014 We show that a single dark soliton can exist in a spin-orbit-coupled Fermi gas with a high spin imbalance, where spin-orbit coupling favors uniform superfluids over non-uniform Fulde-Ferrell-Larkin-Ovchinnikov states, leading to dark soliton excitations ...
Mao, Li +3 more
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A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws
Advanced Intelligent Discovery, EarlyView.The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows +7 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
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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
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Entanglement detection in hybrid optomechanical systems
, 2011 We study a device formed by a Bose Einstein condensate (BEC) coupled to the field of a cavity with a moving end-mirror and find a working point such that the mirror-light entanglement is reproduced by the BEC-light quantum correlations.
D. F. Walls +4 more
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Harmonically M-convex Set-valued Function
Abstract This research aimed to introduce the concept of harmonically m-convex set-valued functions, which is obtained from the combination of two definitions: harmonically m-convex functions and setvalued functions. In this work some properties and characteristics are developed, as well as a inequality of the Hermite-Hadamard type for such ...
Santana, Gabriel, Valera-López, Maira
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ON A SUBCLASS OF CERTAIN CONVEX HARMONIC FUNCTIONS
Journal of the Korean Mathematical Society, 2006 We deflne and investigate a subclass of complex val- ued harmonic convex functions that are univalent and sense pre- serving in the open unit disk. We obtain coe-cient conditions, extreme points, distortion bounds, convolution conditions for the above family of harmonic functions.
Yalcin, Sibel, Ozturk, Metin
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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
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