Results 51 to 60 of about 224,871 (275)
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 +9 more
wiley +1 more source
Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint
Sensors, 2018 Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks.
Zhi Gao +5 more
doaj +1 more source
On the Sample Complexity of Predictive Sparse Coding [PDF]
, 2012 The goal of predictive sparse coding is to learn a representation of examples as sparse linear combinations of elements from a dictionary, such that a learned hypothesis linear in the new representation performs well on a predictive task.
Gray, Alexander G., Mehta, Nishant A.
core
Scalable Online Convolutional Sparse Coding
, 2017 Convolutional sparse coding (CSC) improves sparse coding by learning a shift-invariant dictionary from the data. However, existing CSC algorithms operate in the batch mode and are expensive, in terms of both space and time, on large datasets.
Kwok, James T. +3 more
core +1 more source
Molecular Oncology, EarlyView.Here, we demonstrate that HS1BP3 interacts with Cortactin through a proline‐rich region (PRR3.1) and show that this interaction, and HS1BP3 itself, promote cancer cell proliferation and invasion. Inhibition of this interaction leads to build‐up of TKS5 in multivesicular endosomes and altered secretion of CD63 and CD9, providing an explanation for the ...
Arja Arnesen Løchen +9 more
wiley +1 more source
Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences [PDF]
, 2014 This paper introduces sparse coding and dictionary learning for Symmetric Positive Definite (SPD) matrices, which are often used in machine learning, computer vision and related areas.
Harandi, Mehrtash +3 more
core
Variational Sparse Coding [PDF]
, 2019 Unsupervised discovery of interpretable features and controllable generation with highdimensional data are currently major challenges in machine learning, with applications\ud in data visualisation, clustering and artificial\ud data synthesis. We propose a model based\ud on variational auto-encoders (VAEs) in which\ud interpretation is induced through ...
Tonolini, Francesco +2 more
openaire +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To investigate the value of constructing models based on habitat radiomics and pathomics for predicting the risk of progression in high‐grade gliomas. Methods This study conducted a retrospective analysis of preoperative magnetic resonance (MR) images and pathological sections from 72 patients diagnosed with high‐grade gliomas (52 ...
Yuchen Zhu +14 more
wiley +1 more source
Von Economo Neuron Loss in Frontotemporal Dementia: A Meta‐Analysis of Neuropathological Studies
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Von Economo neurons (VENs) have been reported to be vulnerable to neurodegeneration in frontotemporal dementia (FTD), particularly the behavioral variant (bvFTD), but these findings have not been systematically assessed across independent brain banks.
Daniel Talmasov +2 more
wiley +1 more source
Recursive Sparse, Spatiotemporal Coding [PDF]
2009 11th IEEE International Symposium on Multimedia, 2009 We present a new approach to learning sparse, spatiotemporal codes in which the number of basis vectors, their orientations, velocities and the size of their receptive fields change over the duration of unsupervised training. The algorithm starts with a relatively small, initial basis with minimal temporal extent. This initial basis is obtained through
Thomas L. Dean +2 more
openaire +1 more source