Results 61 to 70 of about 20,444,531 (305)

Network divergence analysis identifies adaptive gene modules and two orthogonal vulnerability axes in pancreatic cancer

Molecular Oncology, EarlyView.
Tumors contain diverse cellular states whose behavior is shaped by context‐dependent gene coordination. By comparing gene–gene relationships across biological contexts, we identify adaptive transcriptional modules that reorganize into distinct vulnerability axes.
Brian Nelson, Lyanne Delgado‐Coka, Natalia Marchenko, Luisa F. Escobar‐Hoyos, Kenneth R. Shroyer, Alisa Yurovsky, Trey Ideker, Gabor Balázsi, Thomas MacCarthy, Scott Powers   +9 more
wiley   +1 more source

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

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

Weighted SPICE: A Unifying Approach for Hyperparameter-Free Sparse Estimation

, 2014
In this paper we present the SPICE approach for sparse parameter estimation in a framework that unifies it with other hyperparameter-free methods, namely LIKES, SLIM and IAA.
Li, Jian, Stoica, Petre, Zachariah, Dave
core   +1 more source

Sparse Communication for Distributed Gradient Descent [PDF]

Conference on Empirical Methods in Natural Language Processing, 2017
We make distributed stochastic gradient descent faster by exchanging sparse updates instead of dense updates. Gradient updates are positively skewed as most updates are near zero, so we map the 99% smallest updates (by absolute value) to zero then ...
Alham Fikri Aji, Kenneth Heafield
semanticscholar   +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

Xampling: Signal Acquisition and Processing in Union of Subspaces

, 2011
We introduce Xampling, a unified framework for signal acquisition and processing of signals in a union of subspaces. The main functions of this framework are two.
Eldar, Yonina C., Elron, Asaf, Mishali, Moshe   +2 more
core   +1 more source

Sparse Additive Gaussian Process Regression

, 2019
In this paper we introduce a novel model for Gaussian process (GP) regression in the fully Bayesian setting. Motivated by the ideas of sparsification, localization and Bayesian additive modeling, our model is built around a recursive partitioning (RP) scheme. Within each RP partition, a sparse GP (SGP) regression model is fitted.
Luo, Hengrui, Nattino, Giovanni, Pratola, Matthew T.   +2 more
openaire   +3 more sources

SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction [PDF]

Computer Vision and Pattern Recognition
Vision-based perception for autonomous driving requires an explicit modeling of a 3D space, where 2D latent representations are mapped and subsequent 3D operators are applied.
Pin Tang, Zhongdao Wang, Guoqing Wang, Jilai Zheng, Xiangxuan Ren, Bailan Feng, Chao Ma   +6 more
semanticscholar   +1 more source