Results 61 to 70 of about 710,592 (303)

Tensors and canonic tensor decompositions: Tucker decomposition [PDF]

, 2017
Soudobý vývoj v aplikované a numerické lineární algebře stále více směřuje, mimo jiné, od maticových výpočtů směrem k výpočtům tenzorovým. Tenzorové úlohy se přirozeně vyskytují např. ve výpočtech v kvantové chemii, ale i v jednodušších problémech, např.
Žáková, Jana
core   +1 more source

Decomposition of Fundamental Function of Finsler Space & Some Tensors by Lie-Derivative in GBK- 5RFn

Al-Kitab Journal for Pure Sciences
This paper discuses the decomposition of Fundamental Function of Finsler space by Lie-derivative in generalized fifth recurrent Finsler space for Cartan's fourth curvature tensor K_jkh^i in sense of Berwald.
Adel Alqashbri
doaj   +1 more source

A Hybrid Norm for Guaranteed Tensor Recovery

Frontiers in Physics, 2022
Benefiting from the superiority of tensor Singular Value Decomposition (t-SVD) in excavating low-rankness in the spectral domain over other tensor decompositions (like Tucker decomposition), t-SVD-based tensor learning has shown promising performance and
Yihao Luo, Andong Wang, Andong Wang, Guoxu Zhou, Guoxu Zhou, Qibin Zhao   +5 more
doaj   +1 more source

High‐Entropy Alloy Interlayers Toward Advanced Joining for High‐Performance Structural Applications: Current Progress and Emerging Challenges

Advanced Engineering Materials, EarlyView.
HEA interlayers offer a versatile route for joining high‐performance structural materials. Their compositional and structural design regulates interfacial reactions, suppresses brittle IMCs, and improves metallurgical bonding. Sandwich interlayers further integrate defect healing with precipitation strengthening, enabling improved strength–ductility ...
Lin Yuan, Jiafen Song, Jinglong Li, Jiangtao Xiong, Tao Yang   +4 more
wiley   +1 more source

Orthogonal Tensor Recovery Based on Non-Convex Regularization and Rank Estimation

IEEE Access
In this paper, a method for orthogonal tensor recovery based on non-convex regularization and rank estimation (OTRN-RE) is proposed, which aims to accurately recover the low-rank and sparse components of the tensor.
Xixiang Chen, Jingjing Zheng, Li Zhao, Wei Jiang, Xiaoqin Zhang   +4 more
doaj   +1 more source

Portfolio strategy based on nonnegative tensor decomposition

Nantong Daxue xuebao. Ziran kexue ban, 2023
Effective extraction of the inter dependence between the stock pairs from stock price time series can improve the return rate of portfolio investment. This study uses non-negative tensor decomposition technology based on block coordinate descent method ...
XU Xiangjian; MA Haiyang; ZHAO Weihua
doaj   +1 more source

Clustering Patients with Tensor Decomposition

CoRR, 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   +4 more sources

Supporting AI Readiness Through Digital Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns, Lateef Abdul, Stephan Barth, Martin Glauer, Manuel Günther, Fabian Neuhaus, Jan‐Ole Perschewski, Thomas Bjarsch, Henrik Abel, Johannes Bosch, Niklas Meyer, Peter Hofmann, Marc Drechsler, Alexander Dyck, Dhanunjaya Kumar Nerella, Marvin Tegeler, Oleg Shchyglo, Ingo Steinbach, Timo Klecker, Henk Birkholz, Ehsan Borzabadi Farahani, Bernard Fedelich, Cetin Haftaoglu, Tarakeshwar Lakshmipathy, Elena Garcia Trelles, Christoph Schweizer, Reza Darvishi Kamachali, Lukas Koschmieder, Janin Eiken, Markus Apel, Sung‐Min Wi, Jiangdong Zhao, Andreas Fezer, Nicolas Christ, Patrick Schneider, Eldor Urinov, Veit Königer, Volker Knoblauch, Janik Harter, Thomas Seifert, Thitichai Janpheng, Florian Fuchs, Fabian Teichert, Jörg Schuster, Anke Bardehle, Andreas Limper, Ulrich Giese, Carlos Lucke, Mario Beiner, Christoph Gögelein, Lisa Leuchtenberger‐Engel, Christian Hopmann, Felix Wentzien, Heike Wittek, Benjamin Klie, Alexander Röhrs, Ludger Overmeyer, Achraf Atila, Marcel Sadowski, Leopold Talirz, Jan Janssen, Jesper R. Pedersen, Peter Beck, Tejs Vegge, Ivano E. Castelli, Wolfgang Wenzel, Tilmann Hickel, Jörg Schaarschmidt   +67 more
wiley   +1 more source

A video watermark algorithm based on tensor decomposition

, 2019
Since most of the previous video watermark algorithms regard a video as a series of consecutive images, the embedding and extraction of watermark are performed on these images, and the correlation and redundancy among frames of a video are not considered.
Xu, Xianghua, Li, Li, Chang, Chin-Chen, Zhang, Shanqing, Guo, Xiaoyun   +4 more
core   +1 more source

Accelerated Low-Rank Tensor Completion via Projected Tensor Block Coordinate Descent

IEEE Access
The low-rank tensor completion problem aims to find a low-rank approximation of a tensor by filling in missing entries from partially observed entries to enhance the accuracy of the tensor data analysis.
Geunseop Lee
doaj   +1 more source