A necessary condition for H∞H^\infty well-posedness of p-evolution equations

We consider p-evolution equations, for $p\geq2$, with complex valued coefficients. We prove that a necessary condition for $H^\infty$ well-posedness of the associated Cauchy problem is that the imaginary part of the coefficient of the subprincipal part (in the sense of Petrowski) satisfies a decay estimate as x goes to infinity

Large quadratic programs in training gaussian support vector machines

We consider the numerical solution of the large convex quadratic program arising in training the learning machines named support vector machines. Since the matrix of the quadratic form is dense and generally large, solution approaches based on explicitstorage of this matrix are not practicable. Well known strategies for this quadratic program are based on decomposition techniques that split the problem into a sequence of smaller quadratic programming subproblems. For the solution of these subproblems we present an iterative projection-type method suited for the structure of the constraints and very eective in case of Gaussian support vector machines. We develop an appropriate decomposition technique designed to exploit the high performance of the proposed inner solver on medium or large subproblems. Numerical experiments on large-scale benchmark problems allow to compare this approach with another widelyused decomposition technique. Finally, a parallel extension of the proposed strategy is described

Limited-memory scaled gradient projection methods for real-time image deconvolution in microscopy

Gradient projection methods have given rise to effective tools for image deconvolution in several relevant areas, such as microscopy, medical imaging and astronomy. Due to the large scale of the optimization problems arising in nowadays imaging applications and to the growing request of real-time reconstructions, an interesting challenge to be faced consists in designing new acceleration techniques for the gradient schemes, able to preserve the simplicity and low computational cost of each iteration. In this work we propose an acceleration strategy for a state of the art scaled gradient projection method for image deconvolution in microscopy. The acceleration idea is derived by adapting a step-length selection rule, recently introduced for limited-memory steepest descent methods in unconstrained optimization, to the special constrained optimization framework arising in image reconstruction. We describe how important issues related to the generalization of the step-length rule to the imaging optimization problem have been faced and we evaluate the improvements due to the acceleration strategy by numerical experiments on large-scale image deconvolution problems

Shearlet-based regularized reconstruction in region-of-interest computed tomography

Region of interest (ROI) tomography has gained increasing attention in recent years due to its potential to reducing radiation exposure and shortening the scanning time. However, tomographic reconstruction from ROI-focused illumination involves truncated projection data and typically results in higher numerical instability even when the reconstruction problem has unique solution. To address this problem, both ad hoc analytic formulas and iterative numerical schemes have been proposed in the literature. In this paper, we introduce a novel approach for ROI tomographic reconstruction, formulated as a convex optimization problem with a regularized term based on shearlets. Our numerical implementation consists of an iterative scheme based on the scaled gradient projection method and it is tested in the context of fan-beam CT. Our results show that our approach is essentially insensitive to the location of the ROI and remains very stable also when the ROI size is rather small.Peer reviewe

Improving the angular resolution of coded aperture instruments using a modified Lucy-Richardson algorithm for deconvolution

A problem with coded-mask telescopes is the achievable angular resolution. For example, with the standard cross-correlation (CC) analysis, the INTEGRAL IBIS/ISGRI angular resolution is about 13'. We are currently investigating an iterative Lucy-Richardson (LR) algorithm. The LR algorithm can be used effectively when the PSF is known, but little or no information is available for the noise. This algorithm maximizes the probability of the restored image, under the assumption that the noise is Poisson distributed, which is appropriate for photon noise in the data, and converges to the maximum likelihood solution. We have modified the classical LR algorithm, adding non-negative constraints. It doesn't take into account of the features leading to a difference in PSF depending on position in the field of view (dead pixels, gaps between modules etc), which are easily corrected for in the classical CC analysis, so we must correct for these either after the restoration of the image or by modifing the data before the sky reconstruction. We present some results using real IBIS data indicating the power of the proposed reconstruction algorithm

An ontology enhanced parallel SVM for scalable spam filter training

This is the post-print version of the final paper published in Neurocomputing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have been proposed for spam filter training and classification. However, SVM training is a computationally intensive process. This paper presents a MapReduce based parallel SVM algorithm for scalable spam filter training. By distributing, processing and optimizing the subsets of the training data across multiple participating computer nodes, the parallel SVM reduces the training time significantly. Ontology semantics are employed to minimize the impact of accuracy degradation when distributing the training data among a number of SVM classifiers. Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart

A Resource Aware MapReduce Based Parallel SVM for Large Scale Image Classifications

Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them support vector machines (SVMs) are used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. This paper presents RASMO, a resource aware MapReduce based parallel SVM algorithm for large scale image classifications which partitions the training data set into smaller subsets and optimizes SVM training in parallel using a cluster of computers. A genetic algorithm based load balancing scheme is designed to optimize the performance of RASMO in heterogeneous computing environments. RASMO is evaluated in both experimental and simulation environments. The results show that the parallel SVM algorithm reduces the training time significantly compared with the sequential SMO algorithm while maintaining a high level of accuracy in classifications.National Basic Research Program (973) of China under Grant 2014CB34040

Equazioni alle derivate parziali- Analisi microlocale analitica e Gevrey.

(PRIN 2002) Stime per le soluzioni di un'equazione ausiliaria collegata all'equazione di Eulero. Buona positura in H-infinito ed in spazi di Gevrey per equazioni iperboliche di molteplicità costante, quasilineari, con coefficienti poco regolari nella variabile tempo. Connessioni tra globale ipoellitticità e globale risolubilità sul toro. Uniforme distribuzione connessa al problema degli zeri della funzione L di Dirichlet e alla distribuzione dei primi nelle progressioni geometriche