16,137 research outputs found
Modeling Covariate Effects in Group Independent Component Analysis with Applications to Functional Magnetic Resonance Imaging
Independent component analysis (ICA) is a powerful computational tool for
separating independent source signals from their linear mixtures. ICA has been
widely applied in neuroimaging studies to identify and characterize underlying
brain functional networks. An important goal in such studies is to assess the
effects of subjects' clinical and demographic covariates on the spatial
distributions of the functional networks. Currently, covariate effects are not
incorporated in existing group ICA decomposition methods. Hence, they can only
be evaluated through ad-hoc approaches which may not be accurate in many cases.
In this paper, we propose a hierarchical covariate ICA model that provides a
formal statistical framework for estimating and testing covariate effects in
ICA decomposition. A maximum likelihood method is proposed for estimating the
covariate ICA model. We develop two expectation-maximization (EM) algorithms to
obtain maximum likelihood estimates. The first is an exact EM algorithm, which
has analytically tractable E-step and M-step. Additionally, we propose a
subspace-based approximate EM, which can significantly reduce computational
time while still retain high model-fitting accuracy. Furthermore, to test
covariate effects on the functional networks, we develop a voxel-wise
approximate inference procedure which eliminates the needs of computationally
expensive covariance estimation. The performance of the proposed methods is
evaluated via simulation studies. The application is illustrated through an
fMRI study of Zen meditation.Comment: 36 pages, 5 figure
Rational Solutions of the H3 and Q1 Models in the ABS Lattice List
In the paper we present rational solutions for the H3 and Q1 models in the
Adler-Bobenko-Suris lattice list. These solutions are in Casoratian form and
are generated by considering difference equation sets satisfied by the basic
Casoratian column vector
Photovoltaic effect in multi-domain ferroelectric perovskite oxides
We propose a device model that elucidates the role of domain walls in the
photovoltaic effect in multi-domain ferroelectric perovskites. The model
accounts for the intricate interplay between ferroelectric polarization, space
charges, photo-generation and electronic transport. When applied to bismuth
ferrite, results show a significant electric potential step across both
71-degree and 109-degree domain walls, which in turn contributes to the
photovoltaic (PV) effect. We also find a strong correlation between
polarization and oxygen octahedra tilts, which indicates the nontrivial role of
the latter in the PV effect. The domain wall-based PV effect is further shown
to be additive in nature, allowing for the possibility of generating
above-bandgap voltag
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