Results 81 to 90 of about 114,572 (313)

Convolutional sparse coding network for sparse seismic time-frequency representation

Artificial Intelligence in Geosciences
Seismic time-frequency (TF) transforms are essential tools in reservoir interpretation and signal processing, particularly for characterizing frequency variations in non-stationary seismic data.
Qiansheng Wei, Zishuai Li, Haonan Feng, Yueying Jiang, Yang Yang, Zhiguo Wang   +5 more
doaj   +1 more source

Parametric Sparse Representation and Its Applications to Radar Sensing

Leida xuebao, 2016
Sparse signal processing has been utilized to the area of radar sensing. Due to the presence of unknown factors such as the motion of the targets of interest and the error of the radar trajectory, a predesigned dictionary cannot provide the optimally ...
Li Gang, Xia Xiang-Gen
doaj   +1 more source

A Novel Spatial-Spectral Sparse Representation for Hyperspectral Image Classification Based on Neighborhood Segmentation [PDF]

, 2016
Traditional hyperspectral image classification algorithms focus on spectral' information application, however, with the increase of spatial resolution of hyperspectral remote sensing images, hyperspectral imaging presents clustering properties on ...
Wang, HW, Wen, J, Hu Bing-liang, Xu, J, Wen Jia, Li Xiang-juan, Wang, CL, Hu, BL, Xu Jun, Wang Hong-wei, Wang Cai-ling, Li, XJ   +11 more
core   +1 more source

Interaction of HS1BP3 with cortactin modulates TKS5 localisation, cell secretion and cancer malignancy

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, Kristiane Søreng, Chiara Veroni, Laura Trachsel‐Moncho, Nagham Asp, Robin Gaupset, Lars Gustav Lyckander, Helene Knævelsrud, Lars Eftang, Anne Simonsen   +9 more
wiley   +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

Fast sparse representation with prototypes [PDF]

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010
Sparse representation has found applications in numerous domains and recent developments have been focused on the convex relaxation of the lo-norm minimization for sparse coding (i.e., the l\-norm minimization). Nevertheless, the time and space complexities of these algorithms remain significantly high for large-scale problems.
Jia-Bin Huang 0001, Ming-Hsuan Yang 0001
openaire   +1 more source

SMALLbox - An Evaluation Framework for Sparse Representations and Dictionary Learning Algorithms [PDF]

, 2010
International ...
Plumbley, Mark D., Plumbley, MD, Mark D. Plumbley, Davies, Matthew, Matthew E. P. Davies, Damnjanovic, Ivan, Davies, MEP, Ivan Damnjanovic, Damnjanovic, I   +8 more
core   +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

Vehicle recognition in acoustic sensor networks using multiple kernel sparse representation over learned dictionaries

International Journal of Distributed Sensor Networks, 2017
Sparse representation–based classification and kernel methods have emerged as important methods for pattern recognition. In this work, we study the problem of vehicle recognition using acoustic sensor networks in real-world applications.
Rui Wang, Wenming Cao, Zhihai He
doaj   +1 more source

Sparse representations, inference and learning

Journal of Statistical Mechanics: Theory and Experiment
Abstract In recent years statistical physics has proven to be a valuable tool to probe into large dimensional inference problems such as the ones occurring in machine learning. Statistical physics provides analytical tools to study fundamental limitations in their solutions and proposes algorithms to solve individual instances.
Lauditi, Clarissa, Troiani, Emanuele, Mezard, Marc   +2 more
openaire   +2 more sources