Results 71 to 80 of about 1,409,223 (260)

Hyperspectral Image Denoising via $L_{0}$ Regularized Low-Rank Tucker Decomposition

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
This article studies the mixed noise removal problem for hyperspectral images (HSIs), which often suffer from Gaussian noise and sparse noise. Conventional denoising models mainly employ the $L_{1}$-norm-based regularizers to remove sparse noise and ...
Xin Tian, Kun Xie, Hanling Zhang
doaj   +1 more source

Sparsistency and agnostic inference in sparse PCA

, 2015
The presence of a sparse "truth" has been a constant assumption in the theoretical analysis of sparse PCA and is often implicit in its methodological development.
Lei, Jing, Vu, Vincent Q.
core   +1 more source

Sparse Square Roots [PDF]

, 2013
We show that it can be decided in polynomial time whether a graph of maximum degree 6 has a square root; if a square root exists, then our algorithm finds one with minimum number of edges. We also show that it is FPT to decide whether a connected n-vertex graph has a square root with at most n − 1 + k edges when this problem is parameterized by k ...
Cochefert, M., Couturier, J-F., Golovach, P.A., Kratsch, D., Paulusma, D.   +4 more
openaire   +2 more sources

Direct Sparse Deblurring [PDF]

Journal of Mathematical Imaging and Vision, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lou, Yifei, Bertozzi, Andrea L., Soatto, Stefano   +2 more
openaire   +1 more source

Patterns of Postictal Abnormalities in Relation to Status Epilepticus in Adults

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Abnormalities on peri‐ictal diffusion‐weighted magnetic resonance imaging (DWI‐PMAs) are well‐established for patients with status epilepticus (SE), but knowledge on patterns of DWI‐PMAs and their prognostic impact is sparse. Methods This systematic review and individual participant data meta‐analysis included observational studies ...
Andrea Enerstad Bolle, Silvana Sarria‐Estrada, Thomas Bonduelle, Bertram Daniel Dynesen, Manuel Toledo, Zhu Chung Che, Kjell Heuser, Anastasios Chatzikonstantinou, Jérôme Aupy, Udaya Seneviratne, Estevo Santamarina Perez, Christoph Patrick Beier   +11 more
wiley   +1 more source

Superresolution of Radar Forward-Looking Imaging Based on Accelerated TV-Sparse Method

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Total variation-sparse (TV-sparse)-based multiconstraint devonvolution method has been used to realize superresolution imaging and preserve target contour information simultaneously of radar forward-looking imaging.
Yin Zhang, Qiping Zhang, Yongchao Zhang, Yulin Huang, Jianyu Yang   +4 more
doaj   +1 more source

Adult‐Onset Subacute Sclerosing Panencephalitis Presenting With Subacute Cognitive Deficits

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT We describe the case of a 41‐year‐old man diagnosed with adult‐onset subacute sclerosing panencephalitis (SSPE). The patient presented with subacute progressive cognitive deficits and a neuropsychological profile indicating predominant frontoparietal dysfunction. MRI showed only mild parietal‐predominant cerebral atrophy.
Dennis Yeow, Nicola McKern, Vincent Oxenham, Kate Ahmad, Omar Ahmad   +4 more
wiley   +1 more source

A New Sparse Collaborative Low-Rank Prior Knowledge Representation for Thick Cloud Removal in Remote Sensing Images

Remote Sensing
Efficiently removing clouds from remote sensing imagery presents a significant challenge, yet it is crucial for a variety of applications. This paper introduces a novel sparse function, named the tri-fiber-wise sparse function, meticulously engineered ...
Dong-Lin Sun, Teng-Yu Ji, Meng Ding
doaj   +1 more source

Regression and Multiclass Classification Using Sparse Extreme Learning Machine via Smoothing Group L_1/2 Regularizer

IEEE Access, 2020
Extreme learning machine (ELM) is a simple feedforward neural network, and it has been extensively used in applications for its extremely fast learning speed and good generalization performance.
Qinwei Fan, Lei Niu, Qian Kang
doaj   +1 more source

Efficient Layer-Wise N:M Sparse CNN Accelerator with Flexible SPEC: Sparse Processing Element Clusters

Micromachines, 2023
Recently, the layer-wise N:M fine-grained sparse neural network algorithm (i.e., every M-weights contains N non-zero values) has attracted tremendous attention, as it can effectively reduce the computational complexity with negligible accuracy loss ...
Xiaoru Xie, Mingyu Zhu, Siyuan Lu, Zhongfeng Wang   +3 more
doaj   +1 more source