Possibilistic uncertainty handling for answer set programming

In this work, we introduce a new framework able to deal with a reasoning that is at the same time non monotonic and uncertain. In order to take into account a certainty level associated to each piece of knowledge, we use possibility theory to extend the non monotonic semantics of stable models for logic programs with default negation. By means of a possibility distribution we define a clear semantics of such programs by introducing what is a possibilistic stable model. We also propose a syntactic process based on a fix-point operator to compute these particular models representing the deductions of the program and their certainty. Then, we show how this introduction of a certainty level on each rule of a program can be used in order to restore its consistency in case of the program has no model at all. Furthermore, we explain how we can compute possibilistic stable models by using available softwares for Answer Set Programming and we describe the main lines of the system that we have developed to achieve this goal.     

Nanoparticle detachment: a possible link between macro- and nanotribology?

Although particle detachment is a common phenomenon associated with most tribological processes, it seldom occurs that each piece of elemental debris can be considered as the result of a single event. Such an association has been revealed by the systematic study of a specific system, where a pin of graphite is made to rub against thoroughly polished steel. While the discontinuous nature of the transfer film allows a quantitative assessment of the volume of transfer h _e to be made by 3D optical-profilometry, the linear dependence of the rate of particle detachment dh _e/dn (n=number of rubbing cycles) with the logarithm of sliding speed v strongly suggests the existence of a particular type of stick–slip, where each stick may lead to the detachment of a debris particle. The variations in size of these debris with environment as revealed by AFM, further suggest that the global rate of particle detachment is of the form: dh _e/dn=Nx _i , where N is the number of stick–slip events per rubbing cycle, x the proportion of stick events leading to a cohesive rupture, and _i the mean volume of an elemental particle. While this relation is apparently supported by most experimental results, its actual validation can only be made by experiments at the level of single (nanoscale) asperities, carried out under well-controlled experimental conditions.     

High-Efficiency Light Coupling in a Submicrometric Silicon-on-Insulator Waveguide

Light coupling into a sub-micrometer-thick waveguide is usually done through a grating coupler. Coupling efficiency is strongly enhanced by addition of a mirror above the grating. This new kind of coupler can be designed to achieve efficiencies as great as 80%. Numerical calculations for a high-angular-spread Gaussian incident beam are compared with experimental results obtained for a standard silicon-on-insulator waveguide.     

Improvement of conical corrugated horn analysis using Gaussianbeams

The classical analysis of a conical corrugated horn is extended out of the balanced hybrid conditions using a vectorial multimodal Gaussian beam expansion. An experimental validation is also presented     

Paraxial approximation effect on a dielectric interface analysis

We are presenting a fast new method of analysing a dielectric interface which is assumed to be cylindrically symmetrical. It is based on a multimode Gaussian beam development. Then two different cases are considered and we show the kind of results we can expect from this process. Finally, we explain in detail the relationships between the paraxial approximation and further justified simplifications which lead to an important calculation time decrease.     

Near-field optical patterning and structuring based on local-field enhancement at the extremity of a metal tip

We present a particular approach and the associated results allowing the nanostructuration of a thin photosensitive polymer film. This approach based on a scanning near-field optical microscopy configuration uses the field-enhancement (FE) effect, a so-called lightning-rod effect appearing at the extremity of a metallic tip when illuminated with an incident light polarized along the tip axis. The local enhancement of the electromagnetic field straight below the tip's apex is observed directly through a photoisomerization reaction, inducing the growth of a topographical nanodot characterized in situ by atomic-force microscopy using the same probe. From a survey of the literature, we first review the different experimental approaches offered to nanostructure materials by near-field optical techniques. We describe more particularly the FE effect approach. An overview of the theoretical approach of this effect is then given before presenting some experimental results so as theoretical results using the finite-element method. These results show the influence on the nanostructuration of the polymer of a few experimental parameters such as the polarization state, the illumination mode and the tip's geometry. Finally, the potentiality of this technique for some applications in the field of lithography and high-density data storage is shown via the fabrication of nano-patterns.     

Learning eigenfunctions links spectral embedding and kernel PCA

In this letter, we show a direct relation between spectral embedding methods and kernel principal components analysis and how both are special cases of a more general learning problem: learning the principal eigenfunctions of an operator defined from a kernel and the unknown data-generating density. Whereas spectral embedding methods provided only coordinates for the training points, the analysis justifies a simple extension to out-of-sample examples (the Nystr?m formula) for multidimensional scaling (MDS), spectral clustering, Laplacian eigenmaps, locally linear embedding (LLE), and Isomap. The analysis provides, for all such spectral embedding methods, the definition of a loss function, whose empirical average is minimized by the traditional algorithms. The asymptotic expected value of that loss defines a generalization performance and clarifies what these algorithms are trying to learn. Experiments with LLE, Isomap, spectral clustering, and MDS show that this out-of-sample embedding formula generalizes well, with a level of error comparable to the effect of small perturbations of the training set on the embedding.     

Authentication in wireless networks: state of the art and integration with smart card

Wirelesslan have spread very quickly over the past few years. Demand for wireless access tolans has raised due to new mobile computing devices, such as laptops and personal digital assistants, and a desire for seamless and permanent connections to networks. Nevertheless a lot of security issues remain and stop its deployment in corporations. One of the most important issues is the authentication of a terminal to an Access Point. We propose an interface to integrate the Extensible Authentication Protocol into smart cards and will show that smart cards could constitute the de-facto device for authentication in Wirelesslan as they are forgsm and will be forumts (Universal Mobile Telecommunication System).