This paper presents , a curriculum vitae class for LaTeX2e, in a progressive approach going from a first contact with the class, through concrete examples of customization, and some aspects of advanced usage.

This paper presents some first results on how to perform uniform random walks (where every trace has the same probability to occur) in very large models. The models considered here are described in a succinct way as a set of communicating reactive modules. The method relies upon techniques for counting and drawing uniformly at random words in regular languages. Each module is considered as an automaton defining such a language. It is shown how it is possible to combine local uniform drawings of traces, and to obtain some global uniform random sampling, without construction of the global model.

This paper presents an ongoing research on the behavior and performance of Lisp with respect to C in the context of scientific numerical computing. Several simple image processing algorithms are used to evaluate the performance of pixel access and arithmetic operations in both languages. We demonstrate that the behavior of equivalent Lisp and C code is similar with respect to the choice of data structures and types, and also to external parameters such as hardware optimization. We further demonstrate that properly typed and optimized Lisp code runs as fast as the equivalent C code, or even faster in some cases.

The problem of person recognition and verification based on their hand images has been addressed. The system is based on the images of the right hands of the subjects, captured by a flatbed scanner in an unconstrained pose at 45 dpi. In a preprocessing stage of the algorithm, the silhouettes of hand images are registered to a fixed pose, which involves both rotation and translation of the hand and, separately, of the individual fingers. Two feature sets have been comparatively assessed, Hausdorff distance of the hand contours and independent component features of the hand silhouette images. Both the classification and the verification performances are found to be very satisfactory as it was shown that, at least for groups of about five hundred subjects, hand-based recognition is a viable secure access control scheme.

Contrary to popular belief, Lisp code can be very efficient today: it can run as fast as equivalent C code or even faster in some cases. In this paper, we explain how to tune Lisp code for performance by introducing the proper type declarations, using the appropriate data structures and compiler information. We also explain how efficiency is achieved by the compilers. These techniques are applied to simple image processing algorithms in order to demonstrate the announced performance on pixel access and arithmetic operations in both languages.

We investigate the decomposition property of posterior restoration energies on level sets in a discrete Markov Random Field framework. This leads us to the concept of ’levelable’ potentials (which TV is shown to be the paradigm of). We prove that convex levelable posterior energies can be minimized exactly with level-independant binary graph cuts. We extend this scheme to the case of non-convex levelable energies, and present convincing restoration results for images degraded by impulsive noise.

Sensor networks are networks consisting of miniature and low-cost systems with limited computation power and energy. Thanks to the low cost of the devices, one can spread a huge number of sensors into a given area to monitor, for example, physical change of the environment. Typical applications are in defense, environment, and design of ad-hoc networks areas. In this paper, we address the problem of verifying the correctness of such networks through a case study. We modelize a simple sensor network whose aim is to detect the apparition of an event in a bounded area (such as a fire in a forest). The behaviour of the network is probabilistic, so we use APMC, a tool that allows to approximately check the correctness of extremely large probabilistic systems, to verify it.

We present a vectorial self dual morphological filter. Contrary to many methods, our approach does not require the use of an ordering on vectors. It relies on the minimization of the total variation with $L^1$ norm as data fidelity on each channel. We further constraint this minimization in order not to create new values. It is shown that this minimization yields a self-dual and contrast invariant filter. Although the above minimization is not a convex problem, we propose an algorithm which computes a global minimizer. This algorithm relies on minimum cost cut-based optimizations.