Results 81 to 90 of about 154,546 (314)
Monte Carlo methods for adaptive sparse approximations of time-series
, 2007 This paper deals with adaptive sparse approximations of time-series. The work is based on a Bayesian specification of the shift-invariant sparse coding model.
Michael E. Davies +3 more
core +1 more source
Uncovering G Protein‐Coupled Receptors: Novel Targets and Biomarkers for Predicting Glioma Prognosis
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Low‐grade gliomas (LGG) exhibit significant heterogeneity and recurrence risk. G protein‐coupled receptors (GPCR) contribute to glioma malignant progression, but their prognostic value remains unclear. This work attempts to formulate a GPCR‐based outcome‐predicting model for LGG. Methods Based on TCGA LGG data, the enrichment scores
Jun Yang +4 more
wiley +1 more source
Stable sparse encoding for predictive processing [PDF]
2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017 Hierarchical Predictive Coding systems that have adopted prediction as their primary goal, are heavily reliant on the stable sparse coding of sensory input. Furthermore, such systems will require their spatial coding function to be adaptive and able to reform to reflect changes within the environment.
Linda Main, John Thornton 0001
openaire +1 more source
Sparse and shift-invariant representations of music
, 2006 Redundancy reduction has been proposed as the main computational process in the primary sensory pathways in the mammalian brain. This idea has led to the development of sparse coding techniques, which are exploited in this article to extract salient ...
Mike Davies +3 more
core +1 more source
Semi-blind sparse image reconstruction with application to MRFM [PDF]
, 2012 We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known.
Hero, Alfred O. +2 more
core +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
Arthritis Care &Research, EarlyView.Objective We aimed to validate the Pediatric Arthritis Ultrasound Scoring System (PAUSS) for upper extremity joints in children with juvenile idiopathic arthritis (JIA). Methods Children with JIA were evaluated for elbow, wrist, or finger arthritis by clinical examination (CE) and musculoskeletal ultrasound (MSUS) with images scored according to the ...
Patricia Vega‐Fernandez +12 more
wiley +1 more source
Sparse-view irradiation processing volumetric additive manufacturing
International Journal of Extreme ManufacturingVolumetric additive manufacturing (VAM) transforms traditional 2D light pattern projection into spatial light field energy superposition, maximizing the utilization of radiated light and allowing for ultra-fast, support-free printing, which has specific ...
Huiyuan Wang +8 more
doaj +1 more source
Sparse Gaussian Process Variational Autoencoders
CoRR, 2020 Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering. An effective framework for handling such data are Gaussian process deep generative models (GP-DGMs), which employ GP priors over the latent variables of DGMs.
Matthew Ashman +5 more
openaire +2 more sources
Identification of Matrices Having a Sparse Representation
, 2008 We consider the problem of recovering a matrix from its action on a known vector in the setting where the matrix can be represented efficiently in a known matrix dictionary.
Pfander, Goetz E. +6 more
core +1 more source