Results 91 to 100 of about 15,373 (263)

Tensor Decompositions in Deep Learning

, 2020
The paper surveys the topic of tensor decompositions in modern machine learning applications. It focuses on three active research topics of significant relevance for the community. After a brief review of consolidated works on multi-way data analysis, we consider the use of tensor decompositions in compressing the parameter space of deep learning ...
Bacciu D., Mandic D. P.
openaire   +3 more sources

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

Advanced Functional Materials, EarlyView.
Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
wiley   +1 more source

High Entropy Wide‐Bandgap Borates with Broadband Luminescence and Large Nonlinear Optical properties

Advanced Functional Materials, EarlyView.
High‐entropy rare‐earth borates exhibit excellent nonlinear optical and broadband luminescence properties arising from multi‐component doping, chemical disorder, increased configurational entropy, and increased lattice and electronic anharmonicity. This formulation enabled us to obtain a large, environmentally stable single crystal with 3X higher laser‐
Saugata Sarker, Yu Wang, Yongwen Sun, Caeli Benyacko, Saeed S.I. Almishal, Tao Zhou, Zhiyu Zhang, Jadupati Nag, Himirkanti Sarkar, Jeffrey Shallenberger, Hemant Yennawar, Yang Yang, Jon‐Paul Maria, Zhiqiang Mao, Venkatraman Gopalan   +14 more
wiley   +1 more source

Objects features extraction by singular projections of data tensor to matrices

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
The problem of multidimensional tensor objects features extraction in a manner of matrices is considered. The tensor’ elements Higher Order Singular Value Decomposition (SVD) is presented as the d-SVD which includes SVD of the tensor reshaped as a ...
Yuriy Bunyak, Roman Kvуetnyy, Olga Sofina, Volodymyr Kotsiubynskyi   +3 more
doaj   +1 more source

Programmable In‐Situ Interactions Between Resins and Photopolymerized Structures for Seamlessly Integrated Optical Manufacturing of Microlenses

Advanced Functional Materials, EarlyView.
This study presents a dynamic interaction between liquid resins and photopolymerized structures enabled by an in situ light‐writing setup. By controlling a three‐phase interface through localized photopolymerization, which provides physical confinement for the remaining uncured resin regions, the approach establishes a programmable pathway that ...
Kibeom Kim, Sangmin Oh, Kahyun Hur, Hyesung Cho   +3 more
wiley   +1 more source

Harnessing the Orientation of Columnar Discotic Liquid Crystals for Narrowband Blue Emission with Enhanced Out‐Coupling Efficiency Toward Improvement of SP‐OLEDs Performance

Advanced Functional Materials, EarlyView.
Solution‐processed OLEDs containing discotic liquid‐crystalline MR‐TADF emitters are reported. Supramolecular self‐assembly induces homeotropic columnar alignment, enforcing preferential horizontal orientation of the emitter transition dipole moment in spin‐coated films, which leads to an enhancement in the device light outcoupling efficiency while ...
Joydip De, Abhishek Kumar Gupta, Džiugas Litvinas, Francisco Tenopala‐Carmona, Matthias Lehmann, Malte C. Gather, Eli Zysman‐Colman   +6 more
wiley   +1 more source

Tensor decompositions

, 2020
Tensors are combinations of several vectors such that a bigger vector space, also calledthe tensor space, emerges. The tensor space has a richer structure that that of theseparate vector spaces. Tensors are universally used in quantum mechanics to modelmutipartite systems. Tensor decompositions aim to write these tensors as combina-tions of elements of
openaire   +1 more source

Clustering Patients with Tensor Decomposition

, 2017
In this paper we present a method for the unsupervised clustering of high-dimensional binary data, with a special focus on electronic healthcare records. We present a robust and efficient heuristic to face this problem using tensor decomposition. We present the reasons why this approach is preferable for tasks such as clustering patient records, to ...
Ruffini, Matteo, Gavaldà Mestre, Ricard, Limón, Esther   +2 more
openaire   +3 more sources

Bio‐Inspired Magnetically Tunable Structural Colors from Elliptical Self‐Assembled Block Copolymer Microparticles

Advanced Functional Materials, EarlyView.
Cephalopod‐inspired photonic microparticles with dynamic structural coloration are fabricated via confined self‐assembly of linear block copolymers into ellipsoids containing stacked lamellae. Embedded superparamagnetic nanoparticles enable rapid magnetic alignment, restoring vivid, angle‐dependent color.
Gianluca Mazzotta, Simone Bertucci, Jose Mendoza‐Carreño, Agustín Mihi, Paola Lova, Davide Comoretto, Ullrich Steiner, Marco Lattuada, Andrea Dodero   +8 more
wiley   +1 more source

Scalable Symmetric Tucker Tensor Decomposition

SIAM Journal on Matrix Analysis and Applications
We study the best low-rank Tucker decomposition of symmetric tensors. The motivating application is decomposing higher-order multivariate moments. Moment tensors have special structure and are important to various data science problems. We advocate for projected gradient descent (PGD) method and higher-order eigenvalue decomposition (HOEVD ...
Ruhui Jin, Joe Kileel, Tamara G. Kolda, Rachel Ward   +3 more
openaire   +3 more sources