Robust Adaptive Detection of Buried Pipes using GPR

International audienceDetection of buried objects such as pipes using a Ground Penetrating Radar (GPR) is intricate for three main reasons. First, noise is important in the resulting image because of the presence of several rocks and/or layers in the ground, highly influencing the Probability of False Alarm (PFA) level. Also, wave speed and object responses are unknown in the ground and depend on the relative permit-tivity, which is not directly measurable. Finally, the depth of the pipes leads to strong attenuation of the echoed signal, leading to poor SNR scenarios. In this paper, we propose a detection method: (1) enhancing the signal of interest while reducing the noise and layer contributions, and (2) giving a local estimate of the relative permittivity. We derive an adaptive detector where the signal of interest is parametrised by the wave speed in the ground. For this detector, noise is assumed to follow a Spherically Invariant Random Vector (SIRV) distribution in order to obtain a robust detection. We use robust maximum likelihood-type covariance matrix estimators called M-estimators. To handle the significant amount of data, we consider regularised versions of said estimators. Simulation will allow to estimate the relation PFA-Threshold. Comparison is performed with standard GPR processing methods, showing the aptitude of the method in detecting pipes having low response levels with a reasonable PFA

Map Style Formalization: Rendering Techniques Extension for Cartography

International audienceCartographic design requires controllable methods and tools to produce maps that are adapted to users' needs and preferences. The formalized rules and constraints for cartographic representation come mainly from the conceptual framework of graphic semiology. Most current Geographical Information Systems (GIS) rely on the Styled Layer Descriptor and Semiology Encoding (SLD/SE) specifications which provide an XML schema describing the styling rules to be applied on geographic data to draw a map. Although this formalism is relevant for most usages in cartography, it fails to describe complex cartographic and artistic styles. In order to overcome these limitations, we propose an extension of the existing SLD/SE specifications to manage extended map stylizations, by the means of controllable expressive methods. Inspired by artistic and cartographic sources (Cassini maps, mountain maps, artistic movements, etc.), we propose to integrate into our system three main expressive methods: linear stylization, patch-based region filling and vector texture generation. We demonstrate how our pipeline allows to personalize map rendering with expressive methods in several examples

Cancer risk and tumour spectrum in 172 patients with a germline SUFU pathogenic variation : a collaborative study of the SIOPE Host Genome Working Group

Background Little is known about risks associated with germline SUFU pathogenic variants (PVs) known as a cancer predisposition syndrome. Methods To study tumour risks, we have analysed data of a large cohort of 45 unpublished patients with a germline SUFU PV completed with 127 previously published patients. To reduce the ascertainment bias due to index patient selection, the risk of tumours was evaluated in relatives with SUFU PV (89 patients) using the Nelson-Aalen estimator. Results Overall, 117/172 (68%) SUFU PV carriers developed at least one tumour: medulloblastoma (MB) (86 patients), basal cell carcinoma (BCC) (25 patients), meningioma (20 patients) and gonadal tumours (11 patients). Thirty-three of them (28%) had multiple tumours. Median age at diagnosis of MB, gonadal tumour, first BCC and first meningioma were 1.5, 14, 40 and 44 years, respectively. Follow-up data were available for 160 patients (137 remained alive and 23 died). The cumulative incidence of tumours in relatives was 14.4% (95% CI 6.8 to 21.4), 18.2% (95% CI 9.7 to 25.9) and 44.1% (95% CI 29.7 to 55.5) at the age of 5, 20 and 50 years, respectively. The cumulative risk of an MB, gonadal tumour, BCC and meningioma at age 50 years was: 13.3% (95% CI 6 to 20.1), 4.6% (95% CI 0 to 9.7), 28.5% (95% CI 13.4 to 40.9) and 5.2% (95% CI 0 to 12), respectively. Sixty-four different PVs were reported across the entire SUFU gene and inherited in 73% of cases in which inheritance could be evaluated. Conclusion Germline SUFU PV carriers have a life-long increased risk of tumours with a spectrum dominated by MB before the age of 5, gonadal tumours during adolescence and BCC and meningioma in adulthood, justifying fine-tuned surveillance programmes.Peer reviewe

Etude théorique de la structure de bande du graphite par une méthode de potentiels effectifs et par une méthode E. H. T. modifiée

Le calcul de la bande d'énergie d'un plan graphitique par une méthode d'hamilthoniens effectifs et par une méthode de type EHT conduit à des résultats satisfaisants et compte-tenu de leur simplicité permet d'envisager leur utilisation pour des systèmes plus complexes tels que les composés d’insertion du graphite

Estimateur de Tyler régularisé dans le cas sous-déterminé. Application à la détection d'objets enfouis

International audienceAmong the various covariance matrix estimators, the regularised Tyler estimator performs independently from the data distribution and is robust to data outlier corruption. However, the shrinkage parameter value selection depends on the target application and data configuration, and have a direct influence on the estimator performance results. Thus finding a generic rule optimal for every criterion is not straightforward. This paper proposes a new regularistaion parameter selection based on a subspace approach. The performances of this method are investigated both in simulation and application to the adaptive buried objects detection problem

A subspace approach for shrinkage parameter selection in undersampled configuration for Regularised Tyler Estimators

International audienceRegularized Tyler Estimator's (RTE) have raised attention over the past years due to their attractive performance over a wide range of noise distributions and their natural robustness to outliers. Developing adaptive methods for the selection of the regularisation parameter α is currently an active topic of research. Indeed, the bias-performance compromise of RTEs highly depends on the considered application. Thus, finding a generic rule that is optimal for every criterion and/or data configurations is not straightforward. This issue is addressed in this paper for undersampled configurations (number of samples lower than the dimension of the data). The paper proposes a new regularisation parameter selection based on a subspace reduction approach. The performance of this method is investigated in terms of estimation accuracy and for adaptive detection purposes, both on simulation and real data

Estimateur de Tyler régularisé dans le cas sous-déterminé. Application à la détection d'objets enfouis

International audienceAmong the various covariance matrix estimators, the regularised Tyler estimator performs independently from the data distribution and is robust to data outlier corruption. However, the shrinkage parameter value selection depends on the target application and data configuration, and have a direct influence on the estimator performance results. Thus finding a generic rule optimal for every criterion is not straightforward. This paper proposes a new regularistaion parameter selection based on a subspace approach. The performances of this method are investigated both in simulation and application to the adaptive buried objects detection problem

Constrained Palette-Space Exploration

International audienceColor palettes are widely used by artists to define colors of artworks and explore color designs. In general, artists select the colors of a palette by following a set of rules, eg contrast or relative luminance. Existing interactive palette exploration tools explore palette spaces following limited constraints defined as geometric configurations in color space eg{} harmony rules on the color wheel.Palette search algorithms sample palettes from color relations learned from an input dataset, however they cannot provide interactive user edits and palette refinement.We introduce in this work a new versatile formulation enabling the creation of constraint-based interactive palette exploration systems. Our technical contribution is a graph-based palette representation, from which we define palette exploration as a minimization problem that can be solved efficiently and provide real-time feedback. Based on our formulation, we introduce two interactive palette exploration strategies: constrained palette exploration, and for the first time, constrained palette interpolation. We demonstrate the performances of our approach on various application cases and evaluate how it helps users finding trade-offs between concurrent constraints

Robust Adaptive Detection of Buried Pipes using GPR

International audienceThe Ground Penetrating Radar (GPR) consists in an electromagnetic signal which is transmitted at different positions through the ground in order to obtain an image of the subsoil. In particular, the GPR is used to detect buried objects like pipes. Their detection and localisation are intricate for three main reasons. First, the noise is important in the resulting image due to the presence of several rocks and/or layers. Second, the wave speed and the response of the pipe depend on the characteristics of the different layers. Finally, the signal attenuation could be important because of the depth of pipes. In this paper, we propose to derive an adaptive detector where the steering vector is parametrised by the wave speed in the ground and the noise follows a Spherically Invariant Random Vector (SIRV) distribution in order to obtain a robust detector. To estimate the covariance matrix, we propose to use robust maximum likelihood-type estimators called M-estimators. To handle the large size of data, we consider regularised versions of such M-estimators. Simulations will allow to estimate the relation Probability of False Alarm (PFA)-Threshold. Application on real datasets will show the relevancy of the proposed analysis for detecting buried objects like pipes

Mol Clin Oncol

Targeted immunotherapy of high-grade cervical intra-epithelial neoplasia (CIN) has been developed as an alternative to conization, to preserve future reproductive outcomes and avoid human papillomavirus (HPV) persistence. The objectives of the review are to present drugs according to their process of development and to examine their potential future use. A search for key words associated with CIN and targeted immunotherapy was carried out in the Cochrane library, Pubmed, Embase, and ClinicalTrials.gov from 1990 to 2016. Publications (randomized, prospective and retrospective studies) in any language were eligible for inclusion, as well as ongoing trials registered on the ClinicalTrials.gov website. Targeted immunotherapy includes peptide/protein-based vaccines, nucleic acid-based vaccines (DNA), and live vector-based vaccines (bacterial or viral). A total of 18 vaccines were identified for treatment of CIN at various stages of development, and the majority were well-tolerated. Adverse effects were primarily injection site reactions and flu-like symptoms under grade 2. The efficacy of vaccines defined by regression of CIN2/3 to no CIN or CIN1 ranged from 17 to 59% following a minimum of a 12-week follow-up. In the majority of studies, there was no association demonstrated between histological response and HPV clearance, or between histological or virological response and immune T cell response. Given that the spontaneous regression of CIN2/3 is 20-25% at 6 months, targeted immunotherapy occurs an additional value, which never reaches 50%, with one trial an exception to this. However, research and development on HPV eradication drugs needs to be encouraged, due to HPV-associated disease burden