13,527 research outputs found
(k,q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior
Advanced diffusion magnetic resonance imaging (dMRI) techniques, like
diffusion spectrum imaging (DSI) and high angular resolution diffusion imaging
(HARDI), remain underutilized compared to diffusion tensor imaging because the
scan times needed to produce accurate estimations of fiber orientation are
significantly longer. To accelerate DSI and HARDI, recent methods from
compressed sensing (CS) exploit a sparse underlying representation of the data
in the spatial and angular domains to undersample in the respective k- and
q-spaces. State-of-the-art frameworks, however, impose sparsity in the spatial
and angular domains separately and involve the sum of the corresponding sparse
regularizers. In contrast, we propose a unified (k,q)-CS formulation which
imposes sparsity jointly in the spatial-angular domain to further increase
sparsity of dMRI signals and reduce the required subsampling rate. To
efficiently solve this large-scale global reconstruction problem, we introduce
a novel adaptation of the FISTA algorithm that exploits dictionary
separability. We show on phantom and real HARDI data that our approach achieves
significantly more accurate signal reconstructions than the state of the art
while sampling only 2-4% of the (k,q)-space, allowing for the potential of new
levels of dMRI acceleration.Comment: To be published in the 2017 Computational Diffusion MRI Workshop of
MICCA
Systemic Infinitesimal Over-dispersion on General Stochastic Graphical Models
Stochastic models of interacting populations have crucial roles in scientific
fields such as epidemiology and ecology, yet the standard approach to extending
an ordinary differential equation model to a Markov chain does not have
sufficient flexibility in the mean-variance relationship to match data (e.g.
\cite{bjornstad2001noisy}). A previous theory on time-homogeneous dynamics over
a single arrow by \cite{breto2011compound} showed how gamma white noise could
be used to construct certain over-dispersed Markov chains, leading to widely
used models (e.g. \cite{breto2009time,he2010plug}). In this paper, we define
systemic infinitesimal over-dispersion, developing theory and methodology for
general time-inhomogeneous stochastic graphical models. Our approach, based on
Dirichlet noise, leads to a new class of Markov models over general direct
graphs. It is compatible with modern likelihood-based inference methodologies
(e.g. \cite{ionides2006inference,ionides2015inference,king2008inapparent}) and
therefore we can assess how well the new models fit data. We demonstrate our
methodology on a widely analyzed measles dataset, adding Dirichlet noise to a
classical SEIR (Susceptible-Exposed-Infected-Recovered) model. We find that the
proposed methodology has higher log-likelihood than the gamma white noise
approach, and the resulting parameter estimations provide new insights into the
over-dispersion of this biological system.Comment: 47 page
On the momentum-dependence of -nuclear potentials
The momentum dependent -nucleus optical potentials are obtained based
on the relativistic mean-field theory. By considering the quarks coordinates of
meson, we introduced a momentum-dependent "form factor" to modify the
coupling vertexes. The parameters in the form factors are determined by fitting
the experimental -nucleus scattering data. It is found that the real
part of the optical potentials decrease with increasing momenta, however
the imaginary potentials increase at first with increasing momenta up to
MeV and then decrease. By comparing the calculated mean
free paths with those from / scattering data, we suggested that the
real potential depth is MeV, and the imaginary potential parameter
is MeV.Comment: 9 pages, 4 figure
Dynamics of evaporative colloidal patterning
Drying suspensions often leave behind complex patterns of particulates, as
might be seen in the coffee stains on a table. Here we consider the dynamics of
periodic band or uniform solid film formation on a vertical plate suspended
partially in a drying colloidal solution. Direct observations allow us to
visualize the dynamics of the band and film deposition, and the transition in
between when the colloidal concentration is varied. A minimal theory of the
liquid meniscus motion along the plate reveals the dynamics of the banding and
its transition to the filming as a function of the ratio of deposition and
evaporation rates. We also provide a complementary multiphase model of colloids
dissolved in the liquid, which couples the inhomogeneous evaporation at the
evolving meniscus to the fluid and particulate flows and the transition from a
dilute suspension to a porous plug. This allows us to determine the
concentration dependence of the bandwidth and the deposition rate. Together,
our findings allow for the control of drying-induced patterning as a function
of the colloidal concentration and evaporation rate.Comment: 11 pages, 7 figures, 2 table
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