2 research outputs found
Engineering bound states in continuum via nonlinearity induced extra dimension
Bound states in continuum (BICs) are localized states of a system possessing
significantly large life times with applications across various branches of
science. In this work, we propose an expedient protocol to engineer BICs which
involves the use of Kerr nonlinearities in the system. The generation of BICs
is a direct artifact of the nonlinearity and the associated expansion in the
dimensionality of the system. In particular, we consider single and two mode
anharmonic systems and provide a number of solutions apposite for the creation
of BICs. In close vicinity to the BIC, the steady state response of the system
is immensely sensitive to perturbations in natural frequencies of the system
and we illustrate its propitious sensing potential in the context of
experimentally realizable setups for both optical and magnetic nonlinearities.Comment: 7 pages, 4 figure
Contrastive Graph Pooling for Explainable Classification of Brain Networks
Functional magnetic resonance imaging (fMRI) is a commonly used technique to
measure neural activation. Its application has been particularly important in
identifying underlying neurodegenerative conditions such as Parkinson's,
Alzheimer's, and Autism. Recent analysis of fMRI data models the brain as a
graph and extracts features by graph neural networks (GNNs). However, the
unique characteristics of fMRI data require a special design of GNN. Tailoring
GNN to generate effective and domain-explainable features remains challenging.
In this paper, we propose a contrastive dual-attention block and a
differentiable graph pooling method called ContrastPool to better utilize GNN
for brain networks, meeting fMRI-specific requirements. We apply our method to
5 resting-state fMRI brain network datasets of 3 diseases and demonstrate its
superiority over state-of-the-art baselines. Our case study confirms that the
patterns extracted by our method match the domain knowledge in neuroscience
literature, and disclose direct and interesting insights. Our contributions
underscore the potential of ContrastPool for advancing the understanding of
brain networks and neurodegenerative conditions