608 research outputs found
Bayesian Fused Lasso regression for dynamic binary networks
We propose a multinomial logistic regression model for link prediction in a
time series of directed binary networks. To account for the dynamic nature of
the data we employ a dynamic model for the model parameters that is strongly
connected with the fused lasso penalty. In addition to promoting sparseness,
this prior allows us to explore the presence of change points in the structure
of the network. We introduce fast computational algorithms for estimation and
prediction using both optimization and Bayesian approaches. The performance of
the model is illustrated using simulated data and data from a financial trading
network in the NYMEX natural gas futures market. Supplementary material
containing the trading network data set and code to implement the algorithms is
available online
QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data
Optical coherence tomography (OCT) enables high-resolution and non-invasive
3D imaging of the human retina but is inherently impaired by speckle noise.
This paper introduces a spatio-temporal denoising algorithm for OCT data on a
B-scan level using a novel quantile sparse image (QuaSI) prior. To remove
speckle noise while preserving image structures of diagnostic relevance, we
implement our QuaSI prior via median filter regularization coupled with a Huber
data fidelity model in a variational approach. For efficient energy
minimization, we develop an alternating direction method of multipliers (ADMM)
scheme using a linearization of median filtering. Our spatio-temporal method
can handle both, denoising of single B-scans and temporally consecutive
B-scans, to gain volumetric OCT data with enhanced signal-to-noise ratio. Our
algorithm based on 4 B-scans only achieved comparable performance to averaging
13 B-scans and outperformed other current denoising methods.Comment: submitted to MICCAI'1
Differential Phase-contrast Interior Tomography
Differential phase contrast interior tomography allows for reconstruction of
a refractive index distribution over a region of interest (ROI) for
visualization and analysis of internal structures inside a large biological
specimen. In this imaging mode, x-ray beams target the ROI with a narrow beam
aperture, offering more imaging flexibility at less ionizing radiation.
Inspired by recently developed compressive sensing theory, in numerical
analysis framework, we prove that exact interior reconstruction can be achieved
on an ROI via the total variation minimization from truncated differential
projection data through the ROI, assuming a piecewise constant distribution of
the refractive index in the ROI. Then, we develop an iterative algorithm for
the interior reconstruction and perform numerical simulation experiments to
demonstrate the feasibility of our proposed approach
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