Location Dependent Dirichlet Processes

Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However, in their basic form they do not directly integrate dependency information among data arising from space and time. In this paper, we propose location dependent Dirichlet processes (LDDP) which incorporate nonparametric Gaussian processes in the DP modeling framework to model such dependencies. We develop the LDDP in the context of mixture modeling, and develop a mean field variational inference algorithm for this mixture model. The effectiveness of the proposed modeling framework is shown on an image segmentation task

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

Ice-sheet bed 3-D tomography

This is the published version. Copyright 2010 International Glaciological SocietyInformation on bed topography and basal conditions is essential to developing the next-generation ice-sheet models needed to generate a more accurate estimate of ice-sheet contribution to sea-level rise. Synthetic aperture radar (SAR) images of the ice–bed can be analyzed to obtain information on bed topography and basal conditions. We developed a wideband SAR, which was used during July 2005 to perform measurements over a series of tracks between the GISP2 and GRIP cores near Summit Camp, Greenland. The wideband SAR included an eight-element receive-antenna array with multiple-phase centers. We applied the MUltiple SIgnal Classification (MUSIC) algorithm, which estimates direction of arrival signals, to single-pass multichannel data collected as part of this experiment to obtain fine-resolution bed topography. This information is used for producing fine-resolution estimates of bed topography over a large swath of 1600m, with a 25m posting and a relative accuracy of approximately 10m. The algorithm-derived estimate of ice thickness is within 10m of the GRIP ice-core length. Data collected on two parallel tracks separated by 500m and a perpendicular track are compared and found to have difference standard deviations of 9.1 and 10.3m for the parallel and perpendicular tracks, respectively