Asymétries intersectionnelles dans le processus d’enquête. Réflexions sur la place d’une anthropologue française auprès de Sénégalaises transmigrantes dans la Médina de Casablanca (Maroc)

A partir d’une approche anthropologique, cet article explore les relations d’enquête développées avec des Sénégalaises migrantes qui travaillent en tant que vendeuses ambulantes sur le marché de la Médina de Casablanca. L’article étudie les assignations identitaires de la chercheuse formulées par ses interlocutrices et l’ensemble des groupes sociaux présents sur ce terrain d’étude. Ces assignations procèdent d’un croisement d’asymétries sociales, raciales et genrées non choisies et peu maîtrisables. Elles soulignent une intersectionnalité des privilèges de l’anthropologue face à l’intrication de discriminations vécues par ses interlocutrices dans l’espace casablancais. A travers les choix ethnographiques opérés pour contourner certaines catégorisations et difficultés d’accès au terrain, l’article interroge les processus relationnels intersubjectifs à l’œuvre, le contexte qui les nourrit et leur valeur heuristique. Cette analyse réflexive questionne enfin les tensions inhérentes à l’enquête entre méthodologie et engagement moral, implication et distanciation, subjectivité et intérêt professionnel.Based on anthropological research, this article explores the relationships developed during fieldwork investigation with Senegalese women who are migrants and work as street vendors in the Medina of Casablanca. The paper analyses the identities assigned to the researcher by the participants of her research and by other social groups involved in this fieldwork. These identities are at the crossroad of social, racial and gender asymmetries which are not chosen or controlled by the researcher. They underline the intersectionality of the anthropologist's privileges and mirror the discriminations experienced by the participants of her research in Casablanca. Revealing ethnographic choices to avoid or face categorizations and difficulties in accessing the fieldwork, the article questions the intersubjective and relational processes at stake, as well as the framework which nourishes these issues and their heuristic value. Finally, this reflexive analysis highlights the tensions underlying this academic investigation, between methodology and moral commitment, implication and distancing, subjectivity and professional interest

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

Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data

6p.International audienceDespite remarkable successes achieved by Differential InSAR, estimations of low tectonic strain rates remain challenging in areas where deformation and topography are correlated, mainly because of the topography‐related atmospheric phase screen (APS). In areas of high relief, empirical removal of the stratified component of the APS may lead to biased estimations of tectonic deformation rates. Here we describe a method to correct interferograms from the effects of the spatial and temporal variations in tropospheric stratification by computing tropospheric delay maps coincident with SAR acquisitions using the ERA‐ Interim global meteorological model. The modeled phase delay is integrated along vertical profiles at the ERA‐I grid nodes and interpolated at the spatial sampling of the interferograms above the elevation of each image pixel. This approach is validated on unwrapped interferograms. We show that the removal of the atmospheric signal before phase unwrapping reduces the risk of unwrapping errors in areas of rough topography

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

The human HIP gene, overexpressed in primary liver cancer encodes for a C-type carbohydrate binding protein with lactose binding activity

AbstractHIP was originally identified as a gene expressed in primary liver cancers, and in normal tissues such as pancreas and small intestine. Based on gene data base homologies, the HIP protein should consist of a signal peptide linked to a single carbohydrate recognition domain. To test this hypothesis HIP and the putative carbohydrate recognition domain encoded by the last 138 C-terminal amino acids, were expressed as glutathione-S -transferase proteins (GST-HIP and GST-HIP-142, respectively). Both recombinant proteins were purified by a single affinity purification step from bacterial lysates and their ability to bind saccharides coupled to trisacryl GF 2000M were tested. Our results show that HIP and HIP-142 proteins bind to lactose, moreover the binding requires divalent cations. Thus the HIP protein is a lactose-binding lectin with the characteristics of a C-type carbohydrate recognition domain of 138 amino acids in the C-terminal region

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