73,445 research outputs found
Accelerated Parameter Estimation with DALE
We consider methods for improving the estimation of constraints on a
high-dimensional parameter space with a computationally expensive likelihood
function. In such cases Markov chain Monte Carlo (MCMC) can take a long time to
converge and concentrates on finding the maxima rather than the often-desired
confidence contours for accurate error estimation. We employ DALE (Direct
Analysis of Limits via the Exterior of ) for determining confidence
contours by minimizing a cost function parametrized to incentivize points in
parameter space which are both on the confidence limit and far from previously
sampled points. We compare DALE to the nested sampling algorithm
implemented in MultiNest on a toy likelihood function that is highly
non-Gaussian and non-linear in the mapping between parameter values and
. We find that in high-dimensional cases DALE finds the same
confidence limit as MultiNest using roughly an order of magnitude fewer
evaluations of the likelihood function. DALE is open-source and available
at https://github.com/danielsf/Dalex.git
Task-based Augmented Contour Trees with Fibonacci Heaps
This paper presents a new algorithm for the fast, shared memory, multi-core
computation of augmented contour trees on triangulations. In contrast to most
existing parallel algorithms our technique computes augmented trees, enabling
the full extent of contour tree based applications including data segmentation.
Our approach completely revisits the traditional, sequential contour tree
algorithm to re-formulate all the steps of the computation as a set of
independent local tasks. This includes a new computation procedure based on
Fibonacci heaps for the join and split trees, two intermediate data structures
used to compute the contour tree, whose constructions are efficiently carried
out concurrently thanks to the dynamic scheduling of task parallelism. We also
introduce a new parallel algorithm for the combination of these two trees into
the output global contour tree. Overall, this results in superior time
performance in practice, both in sequential and in parallel thanks to the
OpenMP task runtime. We report performance numbers that compare our approach to
reference sequential and multi-threaded implementations for the computation of
augmented merge and contour trees. These experiments demonstrate the run-time
efficiency of our approach and its scalability on common workstations. We
demonstrate the utility of our approach in data segmentation applications
Image-Dependent Spatial Shape-Error Concealment
Existing spatial shape-error concealment techniques are broadly based upon either parametric curves that exploit geometric information concerning a shape's contour or object shape statistics using a combination of Markov random fields and maximum a posteriori estimation. Both categories are to some extent, able to mask errors caused by information loss, provided the shape is considered independently of the image/video. They palpably however, do not afford the best solution in applications where shape is used as metadata to describe image and video content. This paper presents a novel image-dependent spatial shape-error concealment (ISEC) algorithm that uses both image and shape information by employing the established rubber-band contour detecting function, with the novel enhancement of automatically determining the optimal width of the band to achieve superior error concealment. Experimental results corroborate both qualitatively and numerically, the enhanced performance of the new ISEC strategy compared with established techniques
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