La voix marginale de Nicolas Gouguenot, huguenot, maître-écrivain, dramaturge et théoricien du théâtre, dans les années Louis XIII.

Les Cahiers du CEIMA, n°8, décembre 2012, "Voix défendues"National audienceFrançois Lasserre présente une figure exceptionnelle de la littérature française du début du XVIIe siècle, le protestant dijonnais Nicolas Gougenot, voixlittéraire inventive mais marginalisée, en dépit de l’estime que lui portait Corneille, par les partisans du classicisme à l’époque et encore aujourd’hui,mais aussi auto-marginalisée par sa valorisation d’une autre compétence, celle de calligraphe

The mining industry in Canada north of the 55th parallel : a maritime traffic generator?

This paper reviews and assesses the state of the mining industry in Canada north of the 55th parallel. It aims to describe and monitor to what extent the development of mining projects in the Canadian Arctic are likely to trigger and expand commercial shipping in Canadian Arctic waters. Based on a literature and statistical review of publicly available information, the results show that only 3 actives mines out of 10 rely on a shipping logistics through Canadian Arctic waters to export raw materials. Once active and in operation, seven other mining projects will likely increase commercial shipping activities through Canadian Arctic waters, while it remains difficult to quantify precisely. However, this paper argues that the viability of northern mineral development is related to a wide variety of conditions including access to capital and foreign direct investment for the development and construction of infrastructure, international market conditions, and shifting demand which largely determines commodity prices and the profitability of a project, harsh environmental conditions, and high operating costs in northern latitudes. In this context, there is no Arctic mining rush and all these factors contribute to increasing the cost of doing business in the north

Bayesian Sparse Fourier Representation of Off-Grid Targets

We consider the problem of estimating a finite sum of cisoids via the use of a sparsifying Fourier dictionary (problem that may be of use in many radar applications). Numerous signal sparse representation (SSR) techniques can be found in the literature regarding this problem. However, they are usually very sensitive to grid mismatch. In this paper, we present a new Bayesian model robust towards grid mismatch. Synthetic and experimental radar data are used to assess the ability of the proposed approach to robustify the SSR towards grid mismatch

Bayesian sparse Fourier representation of off-grid targets with application to experimental radar data

The problem considered is the estimation of a finite number of cisoids embedded in white noise, using a sparse signal representation (SSR) approach, a problem which is relevant in many radar applications. Many SSR algorithms have been developed in order to solve this problem, but they usually are sensitive to grid mismatch. In this paper, two Bayesian algorithms are presented, which are robust towards grid mismatch: a first method uses a Fourier dictionary directly parametrized by the grid mismatch while the second one employs a first-order Taylor approximation to relate linearly the grid mismatch and the sparse vector. The main strength of these algorithms lies in the use of a mixed-type distribution which decorrelates sparsity level and target power. Besides, both methods are implemented through a Monte-Carlo Markov chain algorithm. They are successfully evaluated on synthetic and experimental radar data, and compared to a benchmark algorith

Velocity ambiguity mitigation of off-grid range migrating targets via Bayesian sparse recovery

Within the scope of sparse signal representation, we consider the problem of velocity ambiguity mitigation for wideband radar signal. We present a Bayesian robust algorithm based on a new sparsifying dictionary suited for range-migrating targets possibly straddling range-velocity bins. Numerical simulations on experimental data demonstrate the ability of the proposed algorithm in mitigating velocity ambiguity

New Sparse-Promoting Prior for the Estimation of a Radar Scene with Weak and Strong Targets

In this paper, we consider the problem of estimating a signal of interest embedded in noise using a sparse signal representation (SSR) approach. This problem is relevant in many radar applications. In particular, estimating a radar scene consisting of targets with wide amplitude range can be challenging since the sidelobes of a strong target can disrupt the estimation of a weak one. Within a Bayesian framework, we present a new sparse-promoting prior designed to estimate this specific type of radar scene. The main strength of this new prior lies in its mixed-type structure which decorrelates sparsity level and target power, as well as in its subdivided support which enables the estimation process to span the whole target power range. This algorithm is implemented through a Monte-Carlo Markov chain. It is successfully evaluated on synthetic and semiexperimental radar data and compared to state-of-the-art algorithms

Unambiguous Sparse Recovery of Migrating Targets with a Robustified Bayesian Model

The problem considered is that of estimating unambiguously migrating targets observed with a wideband radar. We extend a previously described sparse Bayesian algorithm to the presence of diffuse clutter and off-grid targets. A hybrid-Gibbs sampler is formulated to jointly estimate the sparse target amplitude vector, the grid mismatch and the (assumed) autoregressive noise. Results on synthetic and fully experimental data show that targets can be actually unambiguously estimated even if located in blind speeds

An unambiguous radar mode with a single PRF wideband waveform

In this paper, we consider the problem of unambiguously estimating targets, including in blind velocities, using a single-low-PRF wideband radar signal. We present a Bayesian sparse recovery algorithm able to estimate the amplitude and location of range-migrating targets possibly straddling range-velocity bins embedded in colored noise. Numerical simulations on synthetic data and experimental data show that the proposed algorithm is able to mitigate velocity ambiguity and estimate targets in blind velocities

Uniform distribution of three Candida albicans microsatellite markers in two French ICU populations supports a lack of nosocomial cross-contamination

BACKGROUND: The nosocomial acquisition of Candida albicans is a growing concern in intensive care units (ICUs) and understanding the route of contamination is relevant for infection control guidelines. METHODS: To analyze whether there is a specific ecology for any given hospital, we genotyped C. albicans isolates of the ICU of Versailles hospital (Hospital A) and compared the results with those previously obtained in another ICU in Henri Mondor hospital (Hospital B) using three polymorphic microsatellite markers (PMM). RESULTS: Among 36 patients with at least one positive culture for C. albicans, 26 had a specific multilocus genotype, two shared a common multilocus genotype, and 8 had the most common multilocus genotype found in the general population. The time interval between periods of hospitalization between patients with common genotypes differed by 13 to 78 days, thus supporting a lack of direct contamination. To confirm this hypothesis, the multilocus genotypic distributions of the three PMM were compared between the two hospitals. No statistically significant difference was observed. Multiple correspondences analysis did not indicate the association of a multilocus genotypic distribution with any given hospital. CONCLUSION: The present epidemiological study supports the conclusions that each patient harbours his/her own isolate, and that nosocomial transmission is not common in any given ICU. This study also supports the usefulness and practicability of PMM for studying the epidemiology of C. albicans

Bayesian Sparse Estimation of a Radar Scene with Weak and Strong Targets

We consider the problem of estimating a finite number of atoms of a dictionary embedded in white noise, using a sparse signal representation (SSR) approach, a problem which is relevant in many radar applications. In particular, the estimation of a radar scene consisting of targets with wide amplitude range can be challenging since the sidelobes of a strong target can disrupt the estimation of a weak one. In this paper, we present a Bayesian algorithm able to estimate weak targets possibly hidden by strong ones. The main strength of this algorithm lies in a novel sparse-promoting prior distribution which decorrelates sparsity level and target power and makes the estimation process span the whole target power range. This algorithm is implemented through a Monte-Carlo Markov chain. It is successfully evaluated on synthetic and semiexperimental radar data