Results 61 to 70 of about 154,546 (314)

Sparse connectivity enables efficient information processing in cortex-like artificial neural networks

Frontiers in Neural Circuits
Neurons in cortical networks are very sparsely connected; even neurons whose axons and dendrites overlap are highly unlikely to form a synaptic connection. What is the relevance of such sparse connectivity for a network’s function?
Rieke Fruengel, Rieke Fruengel, Marcel Oberlaender, Marcel Oberlaender   +3 more
doaj   +1 more source

Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features

Shock and Vibration, 2016
Sparse signal representations attract much attention in the community of signal processing because only a few coefficients are required to represent a signal and these coefficients make the signal understandable.
Wei Peng, Dong Wang, Changqing Shen, Dongni Liu   +3 more
doaj   +1 more source

Epigenetic heterogeneity and plasticity in therapy‐induced tumor states through single‐cell multi‐omics

Molecular Oncology, EarlyView.
Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim, Hwiyeong Lee, Jin Hong, Daechan Park   +3 more
wiley   +1 more source

Cluster-Sparse Proportionate NLMS Algorithm With the Hybrid Norm Constraint

IEEE Access, 2018
In this paper, an enhanced proportionate normalized least mean square (PNLMS) algorithm with the hybrid l2,0-norm constraint is proposed for block-sparse signal processing.
Yingsong Li, Zhengxiong Jiang, Zhan Jin, Xiao Han, Jingwei Yin   +4 more
doaj   +1 more source

Analysing the significance of small conformational changes and low occupancy states in serial crystallographic data

FEBS Open Bio, EarlyView.
This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill, Yelyzaveta Pulnova, Elke De Zitter, Helen M. Ginn, Briony A. Yorke   +4 more
wiley   +1 more source

Sparse regularization method combining SVA for feature enhancement of SAR images

Electronics Letters, 2022
Sparse signal processing has been widely used in synthetic aperture radar imaging and feature enhancement of images in the recent decade. Sparse regularization ℓ1 can reduce the imaging noise level and suppress sidelobes.
Zhongqiu Xu, Guoru Zhou, Bingchen Zhang, Zhe Zhang, Yirong Wu   +4 more
doaj   +1 more source

Streaming sparse Gaussian process approximations

, 2017
Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractabilities to be sidestepped. However, the field lacks a principled method to handle streaming data in which both the posterior distribution over function values and the ...
Bui, TD, Nguyen, CV, Turner, RE
openaire   +3 more sources

A unified approach to sparse signal processing [PDF]

EURASIP Journal on Advances in Signal Processing, 2012
A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly.
Farokh Marvasti, Arash Amini, Farzan Haddadi, Mehdi Soltanolkotabi, Babak Hossein Khalaj, Akram Aldroubi, Saeid Sanei, Jonathon A. Chambers   +7 more
openaire   +4 more sources

Actually Sparse Variational Gaussian Processes

CoRR, 2023
Gaussian processes (GPs) are typically criticised for their unfavourable scaling in both computational and memory requirements. For large datasets, sparse GPs reduce these demands by conditioning on a small set of inducing variables designed to summarise the data.
Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth   +4 more
openaire   +3 more sources

The cosparse analysis model and algorithms

, 2013
After a decade of extensive study of the sparse representation synthesis model, we can safely say that this is a mature and stable field, with clear theoretical foundations, and appealing applications.
Gribonval, Rémi, Davies, M. E.; id_orcid, Elad, M., Davies, Mike E., Gribonval, R., Nam, S., Nam, Sangnam, Elad, Michael, Davies, M.E.   +8 more
core   +1 more source