Results 191 to 200 of about 294,893 (309)
Initial State Privacy of Nonlinear Systems on Riemannian Manifolds
ABSTRACT In this paper, we investigate initial state privacy protection for discrete‐time nonlinear closed systems. By capturing Riemannian geometric structures inherent in such privacy challenges, we refine the concept of differential privacy through the introduction of an initial state adjacency set based on Riemannian distances.
Le Liu, Yu Kawano, Antai Xie, Ming Cao
wiley +1 more source
Simultaneous clustering and estimation of networks in multiple graphical models. [PDF]
Li G, Wang M.
europepmc +1 more source
ABSTRACT This work presents a non‐geometrical navigation approach based on a purely topological understanding of underground environments. By conceptualizing subterranean scenarios as a set of tunnels that intersect with each other, and taking a navigation approach based on topological instructions, we simplify the navigation problem to the sequential ...
Lorenzo Cano +2 more
wiley +1 more source
Analysis of mean-field models arising from self-attention dynamics in transformer architectures with layer normalization. [PDF]
Burger M +4 more
europepmc +1 more source
ABSTRACT The fifth industrial revolution (I5.0), which is based on the utilization of interconnected data for efficient resource usage in meeting human requirements, proposes efficient solutions to resource constraint situations. However, the transition to I5.0 in the health sector is not easy and has to face several obstacles.
Ajay Jha +5 more
wiley +1 more source
Algebraic Approach to Maximum Likelihood Factor Analysis. [PDF]
Fukasaku R +3 more
europepmc +1 more source
ABSTRACT George Herbert Mead is an oft forgotten or ignored American philosopher who was one of the originators of pragmatism. Today, he is recognised as a creative thinker who has teased out knotty problems that others in the field had not realised were problems. Understanding Mead's analysis has been made difficult because he died prematurely without
Richard Ormerod
wiley +1 more source
Asymmetric canonical correlation analysis of Riemannian and high-dimensional data. [PDF]
Buenfil J, Lila E.
europepmc +1 more source
Enhancing generalized spectral clustering with embedding Laplacian graph regularization
Abstract An enhanced generalised spectral clustering framework that addresses the limitations of existing methods by incorporating the Laplacian graph and group effect into a regularisation term is presented. By doing so, the framework significantly enhances discrimination power and proves highly effective in handling noisy data.
Hengmin Zhang +5 more
wiley +1 more source
Boosted unsupervised feature selection for tumor gene expression profiles
Abstract In an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples.
Yifan Shi +5 more
wiley +1 more source

