A broad range of signal recovery problems can be abstracted into the problem of minimizing the sum of several convex functions in a Hilbert space. We propose a proximal decomposition algorithm which, under mild condit...
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ISBN:
(纸本)9781424423538
A broad range of signal recovery problems can be abstracted into the problem of minimizing the sum of several convex functions in a Hilbert space. We propose a proximal decomposition algorithm which, under mild conditions, provides a solution to such a problem. A significant improvement over the methods currently in use in the area of signal recovery is that it is not limited to two nondifferentiable functions. An application to image restoration is demonstrated.
Supporting high-performance computing pipelines in wide-area networks is crucial to enabling large-scale distributed scientific applications that require minimizing end-to-end delay for fast user interaction or maximi...
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Medical imageprocessing is known as a computationally expensive and data intensive domain. It is thus well suited for Grid computing However, Grid computing usually requires the applications to be designed for parall...
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ISBN:
(纸本)9781607500278
Medical imageprocessing is known as a computationally expensive and data intensive domain. It is thus well suited for Grid computing However, Grid computing usually requires the applications to be designed for parallelprocessing, which is a challenge for medical imaging researches in hospitals that are most often not used to this Making parallel programming methods easier to apply can promote Grid technologies in clinical environments. Readily available, functional tools with an intuitive interface are required to really promote healthgrids Moreover, the tools need to be well integrated with the Grid infrastructure. To facilitate the adoption of Gilds in the Geneva University Hospitals we have set up a develop environment based on the Taverna workflow engine Its usage with a medical imaging application on the hospitals' internal Grid cluster is presented in this paper.
An emerging application field for structure matching is related to in silico studies ofmolecular biology. Considering that protein function is mainly related to its external morphology, the possibility to match macrom...
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An interactive web based system for learning imageprocessing is presented in this paper. The aim of the Interactive Web Based Learning: imageprocessing project was to create a distributed e-learning system for unive...
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We present an algorithm that minimizes asymptotically a sequence of non-negative convex functions over diffusion networks. To account for possible node failures, position changes, and/or reachability problems (because...
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ISBN:
(纸本)9781424423538
We present an algorithm that minimizes asymptotically a sequence of non-negative convex functions over diffusion networks. To account for possible node failures, position changes, and/or reachability problems (because of moving obstacles, jammers, etc), the algorithm can cope with dynamic networks and cost functions, a desirable feature for online algorithms where information arrives sequentially. Many projection-based algorithms can be straightforwardly extended to diffusion networks with the proposed scheme. We use the acoustic source localization problem in sensor networks as an example of a possible application.
Multi-core architectures can deliver high processing power if the multiple levels of parallelism they expose are exploited. However, it is non-trivial to orchestrate the computational and memory resources allocation. ...
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Solving complex convection-diffusion equations is very important to many practical mathematical and physical problems. After the finite difference discretization, most of the time for equations solution is spent on sp...
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Three-dimensional reconstruction of cryo-electron tomography (cryo-ET) has emerged as the leading technique in analyzing structures of complex pleomorphic cellulars. A classical iterative method, simultaneous algebrai...
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A distributed online learning framework for support vector machines (SVMs) is presented and analyzed. First, the generic binary classification problem is decomposed into multiple relaxed subproblems. Then, each of the...
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ISBN:
(纸本)9781424438723
A distributed online learning framework for support vector machines (SVMs) is presented and analyzed. First, the generic binary classification problem is decomposed into multiple relaxed subproblems. Then, each of them is solved iteratively through parallel update algorithms with minimal communication overhead. This computation can be performed by individual processing units, such as separate computers or processor cores, in parallel and possibly having access to only a subset of the data. Convergence properties of continuous-and discrete-time variants of the proposed parallel update schemes are studied. A sufficient condition is derived under which synchronous and asynchronous gradient algorithms converge to the approximate solution. Subsequently, a class of stochastic update algorithms, which may arise due to distortions in the information flow between units, is shown to be globally stable under similar sufficient conditions. Active set methods are utilized to decrease communication and computational overhead. A numerical example comparing centralized and distributed learning schemes indicates favorable properties of the proposed framework such as configurability and fast convergence.
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