Results 41 to 50 of about 96,220 (224)
Orthogonal tucker decomposition using factor priors for 2D+3D facial expression recognition
IET Biometrics, 2021 In this article, an effective approach is proposed to recognise the 2D+3D facial expression automatically based on orthogonal Tucker decomposition using factor priors (OTDFPFER).
Yunfang Fu +4 more
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 +5 more
doaj +1 more source
Peroxidasin enables melanoma immune escape by inhibiting natural killer cell cytotoxicity
Molecular Oncology, EarlyView.Peroxidasin (PXDN) is secreted by melanoma cells and binds the NK cell receptor NKG2D, thereby suppressing NK cell activation and cytotoxicity. PXDN depletion restores NKG2D signaling and enables effective NK cell–mediated melanoma killing. These findings identify PXDN as a previously unrecognized immune evasion factor and a potential target to improve
Hsu‐Min Sung +17 more
wiley +1 more source
Tensor Completion Using Kronecker Rank-1 Tensor Train With Application to Visual Data Inpainting
IEEE Access, 2018 The problem of data reconstruction with partly sampled elements under a tensor structure, which is referred to as tensor completion, is addressed in this paper.
Weize Sun, Yuan Chen, Hing Cheung So
doaj +1 more source
Nonconvex Tensor Relative Total Variation for Image Completion
Mathematics, 2023 Image completion, which falls to a special type of inverse problems, is an important but challenging task. The difficulties lie in that (i) the datasets usually appear to be multi-dimensional; (ii) the unavailable or corrupted data entries are randomly ...
Yunqing Bai, Jihong Pei, Min Li
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A Riemannian trust-region method for low-rank tensor completion
, 2017 The goal of tensor completion is to fill in missing entries of a partially known tensor (possibly including some noise) under a low-rank constraint. This may be formulated as a least-squares problem.
Heidel, Gennadij, Schulz, Volker
core +1 more source
RIPK4 function interferes with melanoma cell adhesion and metastasis
Molecular Oncology, EarlyView.RIPK4 promotes melanoma growth and spread. RIPK4 levels increase as skin lesions progress to melanoma. CRISPR/Cas9‐mediated deletion of RIPK4 causes melanoma cells to form less compact spheroids, reduces their migratory and invasive abilities and limits tumour growth and dissemination in mouse models.
Norbert Wronski +9 more
wiley +1 more source
Covid-19 pandemic data analysis using tensor methods [PDF]
Computational Algorithms and Numerical DimensionsIn this paper, we use tensor models to analyze the Covid-19 pandemic data. First, we use tensor models, canonical polyadic, and higher-order Tucker decompositions to extract patterns over multiple modes. Second, we implement a tensor completion algorithm
Dipak Dulal +2 more
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Hippo pathway at the crossroads of stemness and therapeutic resistance in breast cancer
Molecular Oncology, EarlyView.Dysregulation of the Hippo pathway drives nuclear accumulation of YAP/TAZ, activating stemness‐related transcriptional programs that sustain breast cancer stemness and fuel therapeutic resistance across subtypes, underscoring Hippo signaling as a targetable vulnerability. Figure created and edited with BioRender.com.
Giulia Schiavoni +11 more
wiley +1 more source
Low-Rank Tensor Completion via Tensor Nuclear Norm With Hybrid Smooth Regularization
IEEE Access, 2019 As a convex surrogate of tensor multi rank, recently the tensor nuclear norm (TNN) obtains promising results in the tensor completion. However, only considering the low-tubal-rank prior is not enough for recovering the target tensor, especially when the ...
Xi-Le Zhao +4 more
doaj +1 more source