Results 171 to 180 of about 35,609 (308)
Multimodal subspace independent vector analysis effectively captures latent relationships between brain structure and function. [PDF]
Li X +4 more
europepmc +1 more source
Cluster Evaluation of Density Based Subspace Clustering
Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach.
Jasni, Mohamad Zain +1 more
core
A smart design strategy for NIR‐II organic fluorophores is proposed by combining self‐driven Iterative evolution, deep Learning, and fragment‐based assembly. This work establishes a broadly applicable approach for molecular design, accelerating the discovery of NIR‐II fluorophores and extending to optoelectronic materials and therapeutic compounds ...
Yu Zhang +6 more
wiley +1 more source
Efficient Techniques Based on Sparse Representation for Classifying High-dimensional Multiclass Microarray Data. [PDF]
Miri M, Sadeghi MT, Abootalebi V.
europepmc +1 more source
Krylov Subspace Methods on Parallel Computers
The aspects of implementing Krylov subspace methods on parallel computers are investigated. It is shown how to increase the parallel performance by restructuring standard sequential versions of the algorithms, with some trade-off in stability.
Patrik Skogqvist
core
Abstract This paper presents a numerical and experimental study aimed at the modeling and dynamic characterization of the reinforced concrete structure of the Palazzetto dello Sport in Rome, designed and by Pier Luigi Nervi with Annibale Vitellozzi, and built by Nervi & Bartoli contractors in 1956‐57.
Jacopo Ciambella +2 more
wiley +1 more source
Gated subspace alignment with drift compensation for parameter-efficient Class-Incremental Learning. [PDF]
Gu J, Huang S, Li T, Zhang S, Li M.
europepmc +1 more source
Subspace methods in the eigenvalue problem
The eigenvalue problem plays an important role in many areas of mathematics, science, and engineering. In many situations, we are confronted with the problem of finding the lowest or highest, few eigenpairs of a large, sparse, and symmetric matrix ...
Yasmine F Balkis
core
DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection
Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley +1 more source
Direct Cardiac T1 Mapping with Subspace Modeling and Free-breathing Data Acquisition. [PDF]
Marin T +8 more
europepmc +1 more source

