1,652 research outputs found
Truncated decompositions and filtering methods with Reflective/Anti-Reflective boundary conditions: a comparison
The paper analyzes and compares some spectral filtering methods as truncated
singular/eigen-value decompositions and Tikhonov/Re-blurring regularizations in
the case of the recently proposed Reflective [M.K. Ng, R.H. Chan, and W.C.
Tang, A fast algorithm for deblurring models with Neumann boundary conditions,
SIAM J. Sci. Comput., 21 (1999), no. 3, pp.851-866] and Anti-Reflective [S.
Serra Capizzano, A note on anti-reflective boundary conditions and fast
deblurring models, SIAM J. Sci. Comput., 25-3 (2003), pp. 1307-1325] boundary
conditions. We give numerical evidence to the fact that spectral decompositions
(SDs) provide a good image restoration quality and this is true in particular
for the Anti-Reflective SD, despite the loss of orthogonality in the associated
transform. The related computational cost is comparable with previously known
spectral decompositions, and results substantially lower than the singular
value decomposition. The model extension to the cross-channel blurring
phenomenon of color images is also considered and the related spectral
filtering methods are suitably adapted.Comment: 22 pages, 10 figure
Finsler geometry on higher order tensor fields and applications to high angular resolution diffusion imaging.
We study 3D-multidirectional images, using Finsler geometry. The application considered here is in medical image analysis, specifically in High Angular Resolution Diffusion Imaging (HARDI) (Tuch et al. in Magn. Reson. Med. 48(6):1358–1372, 2004) of the brain. The goal is to reveal the architecture of the neural fibers in brain white matter. To the variety of existing techniques, we wish to add novel approaches that exploit differential geometry and tensor calculus. In Diffusion Tensor Imaging (DTI), the diffusion of water is modeled by a symmetric positive definite second order tensor, leading naturally to a Riemannian geometric framework. A limitation is that it is based on the assumption that there exists a single dominant direction of fibers restricting the thermal motion of water molecules. Using HARDI data and higher order tensor models, we can extract multiple relevant directions, and Finsler geometry provides the natural geometric generalization appropriate for multi-fiber analysis. In this paper we provide an exact criterion to determine whether a spherical function satisfies the strong convexity criterion essential for a Finsler norm. We also show a novel fiber tracking method in Finsler setting. Our model incorporates a scale parameter, which can be beneficial in view of the noisy nature of the data. We demonstrate our methods on analytic as well as simulated and real HARDI data
Parallel Hierarchical Affinity Propagation with MapReduce
The accelerated evolution and explosion of the Internet and social media is
generating voluminous quantities of data (on zettabyte scales). Paramount
amongst the desires to manipulate and extract actionable intelligence from vast
big data volumes is the need for scalable, performance-conscious analytics
algorithms. To directly address this need, we propose a novel MapReduce
implementation of the exemplar-based clustering algorithm known as Affinity
Propagation. Our parallelization strategy extends to the multilevel
Hierarchical Affinity Propagation algorithm and enables tiered aggregation of
unstructured data with minimal free parameters, in principle requiring only a
similarity measure between data points. We detail the linear run-time
complexity of our approach, overcoming the limiting quadratic complexity of the
original algorithm. Experimental validation of our clustering methodology on a
variety of synthetic and real data sets (e.g. images and point data)
demonstrates our competitiveness against other state-of-the-art MapReduce
clustering techniques
Colloquium: Mechanical formalisms for tissue dynamics
The understanding of morphogenesis in living organisms has been renewed by
tremendous progressin experimental techniques that provide access to
cell-scale, quantitative information both on theshapes of cells within tissues
and on the genes being expressed. This information suggests that
ourunderstanding of the respective contributions of gene expression and
mechanics, and of their crucialentanglement, will soon leap forward.
Biomechanics increasingly benefits from models, which assistthe design and
interpretation of experiments, point out the main ingredients and assumptions,
andultimately lead to predictions. The newly accessible local information thus
calls for a reflectionon how to select suitable classes of mechanical models.
We review both mechanical ingredientssuggested by the current knowledge of
tissue behaviour, and modelling methods that can helpgenerate a rheological
diagram or a constitutive equation. We distinguish cell scale ("intra-cell")and
tissue scale ("inter-cell") contributions. We recall the mathematical framework
developpedfor continuum materials and explain how to transform a constitutive
equation into a set of partialdifferential equations amenable to numerical
resolution. We show that when plastic behaviour isrelevant, the dissipation
function formalism appears appropriate to generate constitutive equations;its
variational nature facilitates numerical implementation, and we discuss
adaptations needed in thecase of large deformations. The present article
gathers theoretical methods that can readily enhancethe significance of the
data to be extracted from recent or future high throughput
biomechanicalexperiments.Comment: 33 pages, 20 figures. This version (26 Sept. 2015) contains a few
corrections to the published version, all in Appendix D.2 devoted to large
deformation
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