Results 31 to 40 of about 724,853 (235)
IEEE Access, 2019 Opportunistic network enables users to form an instant network for data sharing, which is a type of Ad-hoc network in nature, thus depends on cooperation between nodes to complete message transmission.
Peng Zheng +3 more
doaj +1 more source
Decomposition results for Gram matrix determinants
, 2010 We study the Gram matrix determinants for the groups $S_n,O_n,B_n,H_n$, for their free versions $S_n^+,O_n^+,B_n^+,H_n^+$, and for the half-liberated versions $O_n^*,H_n^*$.
Banica, Teodor, Curran, Stephen
core +1 more source
RMPD: Method for Enhancing the Robustness of Recommendations With Attack Environments
IEEE Access, 2021 Personalized item recommendation has become a hot topic research among academic and industry community. But lots of purposeful fraudsters maybe perform different attacks on the recommender system to insert fake ratings, which could reduce the ...
Qi Ding +4 more
doaj +1 more source
Supertropical matrix algebra III: Powers of matrices and generalized eigenspaces [PDF]
, 2010 We investigate powers of supertropical matrices, with special attention to the role of the coefficients of the supertropical characteristic polynomial (especially the supertropical trace) in controlling the rank of a power of a matrix.
Izhakian, Zur, Rowen, Louis
core +3 more sources
Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition
, 2016 We present a natural generalization of the recent low rank + sparse matrix decomposition and consider the decomposition of matrices into components of multiple scales.
Lustig, Michael, Ong, Frank
core +1 more source
Decompose Boolean Matrices with Correlation Clustering
Entropy, 2021 One of the tasks of data science is the decomposition of large matrices in order to understand their structures. A special case of this is when we decompose relations, i.e., logical matrices.
László Aszalós
doaj +1 more source
Fast Spectral Clustering Using Autoencoders and Landmarks
, 2017 In this paper, we introduce an algorithm for performing spectral clustering efficiently. Spectral clustering is a powerful clustering algorithm that suffers from high computational complexity, due to eigen decomposition.
A Choromanska +5 more
core +1 more source
Decrypting cancer's spatial code: from single cells to tissue niches
Molecular Oncology, EarlyView.Spatial transcriptomics maps gene activity across tissues, offering powerful insights into how cancer cells are organised, switch states and interact with their surroundings. This review outlines emerging computational, artificial intelligence (AI) and geospatial approaches to define cell states, uncover tumour niches and integrate spatial data with ...
Cenk Celik +4 more
wiley +1 more source
Molecular Oncology, EarlyView.Combining PTEN protein assessment and transcriptomic profiling of prostate tumors, we uncovered a network enriched in senescence and extracellular matrix (ECM) programs associated with PTEN loss and conserved in a mouse model. We show that PTEN‐deficient cells trigger paracrine remodeling of the surrounding stroma and this information could help ...
Ivana Rondon‐Lorefice +16 more
wiley +1 more source
IEEE Access, 2020 Infrared small target detection is a crucial and challenging topic for various applications. In recent years, the spectrum scale space (SSS) algorithm has shown considerable potential in the field of target detection.
Zujing Yan +4 more
doaj +1 more source