Results 51 to 60 of about 4,857 (288)
Advanced Electronic Materials, EarlyView.The BiSbF2 monolayer is a 2D topological insulator with a large bandgap (252 meV) hosting topological edge states and giant valley‐contrasted spin‐splitting (478 meV). The sizable gap enables room‐temperature quantum spin Hall effects, while its coexisting topological and valleytronic features provide a versatile platform for exploring topological ...
Bin Geng +4 more
wiley +1 more source
Counting the number of master integrals for sunrise diagrams via the Mellin-Barnes representation
Journal of High Energy Physics, 2017 A number of irreducible master integrals for L-loop sunrise and bubble Feynman diagrams with generic values of masses and external momenta are explicitly evaluated via the Mellin-Barnes representation.
Mikhail Yu. Kalmykov, Bernd A. Kniehl
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Applied Economic Perspectives and Policy, EarlyView.Abstract This paper employs machine learning to determine which preferential trade agreement (PTA) provisions are relevant to agricultural trade patterns and the factors that may influence their adoption. Utilizing the three‐way gravity model, we apply plug‐in Lasso regularized regression to pinpoint predictive PTA provisions for agricultural trade ...
Stepan Gordeev +3 more
wiley +1 more source
Orthogonal bases in specific generalized symmetry classes of tensors [PDF]
Journal of Mahani Mathematical ResearchLet $V$ be a unitary vector space. Suppose $G$ is a permutation group of degree $m$ and $\Lambda$ is an irreducible unitary representation of $G$. We denote by $V_{\Lambda}(G)$ the generalized symmetry class of tensors associated with $G$ and $\Lambda ...
Gholamreza Rafatneshan, Yousef Zamani
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Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Connections with Irreducible Holonomy Representations
Advances in Mathematics, 2001 3 Irreducible Berger algebras 17 3.1 Real Berger algebras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2 Examples of Berger algebras . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2.1 Conformal Lie algebras . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2.2 Symmetric connections . . . . . . . . . . . . . . . .
openaire +2 more sources
Irreducible representations of the Chinese monoid
Journal of Algebra, 2016 Comment: 20 ...
Łukasz Kubat, Jan Okniński
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Deep Learning‐Assisted Coherent Raman Scattering Microscopy
Advanced Intelligent Discovery, EarlyView.The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu +4 more
wiley +1 more source
A Characterization for non-DCC Lattices
Journal of Mathematical Extension, 2009 Join-irreducible elements in a lattice have an important role. They act like blocks of a lattice. In DCC lattices each element of the lattice has a unique finite representation as a join of join-irreducible elements.
M. Hosseinyazdi, A. Ghanbarnezhad
doaj
Predicting Performance of Hall Effect Ion Source Using Machine Learning
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source