Results 71 to 80 of about 1,952,092 (277)
An efficient tensor completion method via truncated nuclear norm
Tensor completion aims to recover missing entries from partial observations for multi-dimensional data. Traditional tensor completion algorithms process the dimensional data by unfolding the tensor into matrices, which breaks the inherent correlation and
Chen, Xi +5 more
core +1 more source
The physical realization of artificial neurons is a critical challenge for energy‐efficient neuromorphic computing. This review presents a comprehensive analysis of the evolution of artificial neuron implementations from conventional CMOS to emerging post‐CMOS technologies.
Kannan Udaya Mohanan +4 more
wiley +1 more source
Joint Weighted Tensor Schatten
In the traditional non-local similar patch-based denoising algorithms, the image patches are firstly flatted into a vector. The structure information within the image patches is ignored; however, the spatial layout information can be used for improving ...
Xiaoqin Zhang +4 more
doaj +1 more source
Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy
Small target detection is a crucial technique that restricts the performance of many infrared imaging systems. In this paper, a novel detection model of infrared small target via non-convex tensor rank surrogate joint local contrast energy (NTRS) is ...
Xuewei Guan +3 more
doaj +1 more source
Cell therapies typically rely on cold‐chain logistics and cryopreservation, limiting access and compromising cell quality. Here, a dual‐chamber device separates an oxygen‐supplying chamber from a hyaluronic acid cargo chamber, sustaining oxygen delivery for over 70 h and enabling ambient‐temperature shipment.
Daniel A. Domingo‐Lopez +9 more
wiley +1 more source
On Tensor Completion via Nuclear Norm Minimization
Many problems can be formulated as recovering a low-rank tensor. Although an increasingly common task, tensor recovery remains a challenging problem because of the delicacy associated with the decomposition of higher order tensors.
core
Robust Tensor Recovery Using Second-Order Difference-Induced Adaptive Nuclear Norm
Tensor data, such as hyperspectral images and videos, are often degraded by mixed noise, including Gaussian noise, sparse corruption, and outliers. In this paper, we propose a robust tensor recovery model based on second-order difference-induced adaptive
Wenqin Li, Jingyao Hou
doaj +1 more source
An integrated pulmonary mRNA delivery platform combining novel biodegradable syringic acid‐derived ionizable lipids, design‐of‐experiments formulation optimization, and vibrating‐mesh nebulizer engineering enabled stable aerosolization and efficient lung delivery.
Neha Kaushal +21 more
wiley +1 more source
Nonconvex Nonlinear Transformation of Low-Rank Approximation for Tensor Completion
Recovering incomplete high-dimensional data to create complete and valuable datasets is the main focus of tensor completion research, which lies at the intersection of mathematics and information science.
Yifan Mei +3 more
doaj +1 more source
We demonstrate that halide composition determines phase morphology, governing distinct operating mechanisms in mixed halide perovskites. Phase segregating 75% Br compositions enhance solar cell performance, whereas intrinsically phase‐separated 95% Br compositions provide stable light emitting diodes.
Tal Binyamin +6 more
wiley +1 more source

