502 research outputs found
Cardinality heterogeneities in Web service composition: Issues and solutions
Data exchanges between Web services engaged in a composition raise several
heterogeneities. In this paper, we address the problem of data cardinality
heterogeneity in a composition. Firstly, we build a theoretical framework to
describe different aspects of Web services that relate to data cardinality, and
secondly, we solve this problem by developing a solution for cardinality
mediation based on constraint logic programming
a combined top-down and bottom-up approach
The thesis focuses on the interoperability of autonomous legacy databases with
the idea of meeting the actual requirements of an organization. The
interoperability is resolved by combining the topdown and bottom-up
strategies. The legacy objects are extracted from the existing databases
through a database reverse engineering process. The business objects are
defined by both the organization requirements and the integration of the
legacy objects
Sparse image reconstruction on the sphere: implications of a new sampling theorem
We study the impact of sampling theorems on the fidelity of sparse image
reconstruction on the sphere. We discuss how a reduction in the number of
samples required to represent all information content of a band-limited signal
acts to improve the fidelity of sparse image reconstruction, through both the
dimensionality and sparsity of signals. To demonstrate this result we consider
a simple inpainting problem on the sphere and consider images sparse in the
magnitude of their gradient. We develop a framework for total variation (TV)
inpainting on the sphere, including fast methods to render the inpainting
problem computationally feasible at high-resolution. Recently a new sampling
theorem on the sphere was developed, reducing the required number of samples by
a factor of two for equiangular sampling schemes. Through numerical simulations
we verify the enhanced fidelity of sparse image reconstruction due to the more
efficient sampling of the sphere provided by the new sampling theorem.Comment: 11 pages, 5 figure
A Variational Framework for the Simultaneous Segmentation and Object Behavior Classification of Image Sequences
In this paper, we advance the state of the art in variational image segmentation through the fusion of bottom-up segmentation and top-down classification of object behavior over an image sequence. Such an approach is beneficial for both tasks and is carried out through a joint optimization, which enables the two tasks to cooperate, such that knowledge relevant to each can aid in the resolution of the other, thereby enhancing the final result. In particular, classification offers dynamic probabilistic priors to guide segmentation, while segmentation supplies its results to classification, ensuring that they are consistent with prior knowledge. The prior models are learned from training data and updated dynamically, based on segmentations of earlier images in the sequence. We demonstrate the potential of our approach in a hand gesture recognition application, where the combined use of segmentation and classification improves robustness in the presence of occlusion and background complexity
Implications for compressed sensing of a new sampling theorem on the sphere
A sampling theorem on the sphere has been developed recently, requiring half
as many samples as alternative equiangular sampling theorems on the sphere. A
reduction by a factor of two in the number of samples required to represent a
band-limited signal on the sphere exactly has important implications for
compressed sensing, both in terms of the dimensionality and sparsity of
signals. We illustrate the impact of this property with an inpainting problem
on the sphere, where we show the superior reconstruction performance when
adopting the new sampling theorem compared to the alternative.Comment: 1 page, 2 figures, Signal Processing with Adaptive Sparse Structured
Representations (SPARS) 201
Error and Attack Tolerance of Layered Complex Networks
Many complex systems may be described not by one, but by a number of complex
networks mapped one on the other in a multilayer structure. The interactions
and dependencies between these layers cause that what is true for a distinct
single layer does not necessarily reflect well the state of the entire system.
In this paper we study the robustness of three real-life examples of two-layer
complex systems that come from the fields of communication (the Internet),
transportation (the European railway system) and biology (the human brain). In
order to cover the whole range of features specific to these systems, we focus
on two extreme policies of system's response to failures, no rerouting and full
rerouting. Our main finding is that multilayer systems are much more vulnerable
to errors and intentional attacks than they seem to be from a single layer
perspective.Comment: 5 pages, 3 figure
Dense Deformation Field Estimation for Atlas Registration using the Active Contour Framework
In this paper, we propose a new paradigm to carry outthe registration task with a dense deformation fieldderived from the optical flow model and the activecontour method. The proposed framework merges differenttasks such as segmentation, regularization, incorporationof prior knowledge and registration into a singleframework. The active contour model is at the core of ourframework even if it is used in a different way than thestandard approaches. Indeed, active contours are awell-known technique for image segmentation. Thistechnique consists in finding the curve which minimizesan energy functional designed to be minimal when thecurve has reached the object contours. That way, we getaccurate and smooth segmentation results. So far, theactive contour model has been used to segment objectslying in images from boundary-based, region-based orshape-based information. Our registration technique willprofit of all these families of active contours todetermine a dense deformation field defined on the wholeimage. A well-suited application of our model is theatlas registration in medical imaging which consists inautomatically delineating anatomical structures. Wepresent results on 2D synthetic images to show theperformances of our non rigid deformation field based ona natural registration term. We also present registrationresults on real 3D medical data with a large spaceoccupying tumor substantially deforming surroundingstructures, which constitutes a high challenging problem
Information Theoretic Combination of Classifiers With Application to Multiple SVMs
Combining several classifiers has proved to be an efficient machine learning technique. Two concepts influence clearly the efficiency of an ensemble: the diversity between classifiers and the individual accuracies of the classifiers. We use an information theoretic framework to establish a link between these quantities and as they appear to be contradictory, we propose an information theoretic measure that express a trade-off between individual accuracy and diversity. This technique can be directly adapted for the selection of an ensemble in a pool of classifiers. We then consider the particular case of multiple Support Vector Machines using this new measure. We will cover genetic algorithm optimization as well as a adaptation of the Kernel-Adatron algorithm to online learning of multiple SVMs. The results are compared to standard multiple SVMs techniques
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