Results 51 to 60 of about 66,822 (262)
Sparse Recovery Using Sparse Random Matrices [PDF]
, 2010 Over the recent years, a new linear method for compressing high-dimensional data (e.g., images) has been discovered. For any high-dimensional vector x, its sketch is equal to Ax, where A is an m×n matrix (possibly chosen at random). Although typically the sketch length m is much smaller than the number of dimensions n, the sketch contains enough ...
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Spatial biology in cancer epigenetics
Molecular Oncology, EarlyView.Spatial epigenomics combines molecular profiling with tissue architecture to reveal how gene regulation is organized within intact tissues. In cancer, these technologies uncover the mechanisms driving tumor heterogeneity and microenvironmental interactions, opening new opportunities for biomarker discovery and precision medicine.
Eva Crespo‐García, Manel Esteller
wiley +1 more source
Nonconvex Penalized Regularization for Robust Sparse Recovery in the Presence of
IEEE Access, 2018 Nonconvex penalties have recently received considerable attention in sparse recovery based on Gaussian assumptions. However, many sparse recovery problems occur in the presence of impulsive noises. This paper is concerned with the analysis and comparison
Yunyi Li +5 more
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Considerable efforts have been dedicated to developing effective treatments for post‐stroke executive impairment (PSEI), among which repetitive transcranial magnetic stimulation (rTMS) has shown great potential. This study aimed to investigate the therapeutic effects of high‐frequency rTMS on working memory (WM) and response ...
Mengting Lao +6 more
wiley +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Down syndrome regression disorder is a syndrome characterized by subacute loss of cognitive, behavioral, and functional abilities in individuals with Down syndrome. Electroencephalography abnormalities are frequently observed during evaluation, but it remains unclear whether these findings represent a dynamic marker of disease ...
Jonathan D. Santoro +14 more
wiley +1 more source
Robust Sparse Recovery in Impulsive Noise via M-Estimator and Non-Convex Regularization
IEEE Access, 2019 Robust sparse recovery aims at recovering a sparse signal or image from its compressed and contaminated measurements. Under the impulsive noise condition, the performance of traditional sparse recovery algorithms may deteriorate seriously for exploiting &
Le Gao +5 more
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Treatment of disorders of consciousness (DoC) remains a major clinical challenge, and noninvasive, targeted modulation of deep brain structures has emerged as a promising therapeutic strategy. We aimed to evaluate the feasibility/safety and preliminary effects of thalamic temporal interference stimulation (TIS) targeting centromedian‐
Gengyao Hu +7 more
wiley +1 more source
IEEE Access, 2018 In the past decade, sparse and low-rank recovery has drawn much attention in many areas such as signal/image processing, statistics, bioinformatics, and machine learning.
Fei Wen +3 more
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Arthritis Care &Research, EarlyView.Objective We aimed to estimate the prevalence and cumulative incidence of hydroxychloroquine retinopathy (HCQ‐R) and its risk factors among patients receiving long‐term HCQ with rheumatic diseases through a systematic review and meta‐analysis of observational studies that used spectral‐domain optical coherence tomography (SD‐OCT) for screening ...
Narsis Daftarian +4 more
wiley +1 more source
Approximate sparse recovery [PDF]
Proceedings of the forty-second ACM symposium on Theory of computing, 2010 An approximate sparse recovery system consists of parameters $k,N$, an $m$-by-$N$ measurement matrix, $Φ$, and a decoding algorithm, $\mathcal{D}$. Given a vector, $x$, the system approximates $x$ by $\widehat x =\mathcal{D}(Φx)$, which must satisfy $\| \widehat x - x\|_2\le C \|x - x_k\|_2$, where $x_k$ denotes the optimal $k$-term approximation to $x$
Anna C. Gilbert +3 more
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