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

Efficacy of bendamustine and rituximab as first salvage treatment in chronic lymphocytic leukemia and indirect comparison with ibrutinib: A GIMEMA, ERIC and UK CLL FORUM study

We performed an observational study on the efficacy of bendamustine and rituximab (BR) as first salvage regimen in chronic lymphocytic leukemia (CLL). In an intention-to-treat analysis including 237 patients, the median progression-free survival (PFS) was 25 months. The presence of del (17p), unmutated IGHV and advanced stage were associated with a shorter PFS at multivariate analysis. The median time-to-next treatment was 31.3 months. Front-line treatment with a chemoimmunotherapy regimen was the only predictive factor for a shorter time to next treatment at multivariate analysis. The median overall survival (OS) was 74.5 months. Advanced disease stage (i.e. Rai stage III-IV or Binet stage C) and resistant disease were the only parameters significantly associated with a shorter OS. Grade 3-5 infections were recorded in 6.3% of patients. A matched-adjusted indirect comparison with ibrutinib given second-line within Named Patient Programs in the United Kingdom and in Italy was carried out with OS as objective end point. When restricting the analysis to patients with intact 17p who had received chemoimmunotherapy in first line, there was no difference in OS between patients treated with ibrutinib (63% alive at 36 months) and patients treated with BR (74.4% alive at 36 months). BR is an efficacious first salvage regimen in CLL in a real-life population, including the elderly and unfit patients. BR and ibrutinib may be equally effective in terms of OS when used as first salvage treatment in patients without 17p deletion. (Registered at clinicaltrials.gov identifier: 02491398)

Importance of tropical sheep introduced into the country: productive and reproductive characteristics

Sheep is an animal whose distribution is widespread throughout the world, it is found in all climates and ecologies. Thanks to this species, it has been possible to take advantage of extensive areas of poor pasture for other species, especially cattle. Hair sheep have evolved under the selective influence of nature and man. Their coat is similar to that of cattle and goats. They adapt well to tropical environments. Hair sheep contribute to various production systems in the tropics. Although the characteristics of hair sheep production systems vary from region to region, most systems are subject to the same general restrictions. The three categories of restrictions are in common are ecological, biological and socioeconomic. American Pelo sheep are more similar to the Selva Sabana type than the Sahel type, although they are generally larger than the Selva type. The current Creole sheep retain the characteristics of their ancestors (Merino and Churra sheep), but with a very deteriorated quality. Under national conditions, the Assaf and Blackbelly breeds perform better in productivity and herd efficiency. Birth weight is approximately 10% of slaughter weight. Hair sheep are a genetic resource of considerable potential for meat production in tropical regions, and possibly in temperate zones as well

Generalized Fractional Brownian Texture Dataset

A texture associated with a Generalized Fractional Brownian Field (GFBF) is generated from convolution operations over modulated fractional Brownian fields. Related challenges are: 1) counting the number of interacting modulated fractional Brownian fields and 2) locating the corresponding modulation frequencies, see references given below. Dataset structure: one root folder including 4 subfolders, any folder being associated with the number of modulated fractional Brownian interactions. Any TIF file (2 channels: modulus and phase of complex data) included in the subfolders has 2 channels: one for the modulus and the other for the phase of the complex GFBF texture

Violent Scene Dataset (3 categories)

Collection including non-violent and violent scenes (short video clips either in mp4 or avi formats) from Mediaeval challenge and Technicolor data, in addition with certain UCF-101 combat sports. The dataset can be used for binary or ternary affective computing and classification

Explaining a deep spatiotemporal land cover classifier with attention and redescription mining

International audienceDeep learning-based land cover classifiers learnt from Satellite Image Time Series (SITS) are known to reach high performances. In order to explain, at least partly, the rationale leading to each one of their decisions, attention-based architectures have been proposed to automatically weight the importance of predefined data components in the classification process. Though generated for each decision separately, the informational content conveyed by such explanations can remain insufficient to end-users because of the complex nature of SITS. Moreover, getting a general perspective about the way a classifier works requires merging all explanations for each class and relating them to its mode of operation, which is not always straightforward. A preliminary and complementary approach for automatically identifying the data features detected by a pixel-wise deep spatiotemporal land cover classifier and explaining its behavior at the class level is therefore proposed in this paper. Classified pixels are first described using interpretable features coming under the form of data mining patterns. A redescription mining technique is then employed to automatically select, for each class, the features matching the different activation level configurations of the layer that is assumed to capture the aforementioned patterns. Experiments based on a Sentinel-2 time series and a deep spatiotemporal neural network implementing a channel-separated processing as well as a channel-based attention mechanism show the interest of such a combined approach

Attention spectrale et explicabilité pour la classification de séries temporelles satellitaires par réseaux de neurones profonds

International audienceDeep Neural Networks (DNNs) are getting increasing attention to deal with Land Cover Classification (LCC) relying on Satellite Image Time Series (SITS). Though high performances can be achieved, the rationale of a prediction yielded by a DNN often remains unclear. An architecture expressing predictions with respect to input channels is thus proposed in this paper. It relies on convolutional layers and an attention mechanism weighting the importance of each channel in the final classification decision. The correlation between channels is taken into account to set up shared kernels and lower model complexity. Experiments based on a Sentinel-2 SITS show promising results.Les réseaux profonds de neurones sont de plus en plus plébiscités, à fortiori, en classification d'occupation des sols, à partir de séries temporelles d'images satellites. Malgré leurs performances, ces réseaux souffrent de manque d'explicabilité. Dans ce cadre, nous étudions différentes architectures de réseaux profonds convolutionnels qui extraient les caractéristiques spectro-temporelles des séries pour ensuite classifier chaque pixel. Afin d'expliquer ses décisions, nous y intégrons, de deux manières différentes, un module d'attention donnant la contribution de chaque caractéristique spectrale. Des expériences ont été menées sur des données Sentinel-2 de la Réunion. Les corrélations spectrales, inhérentes à ce type de séries, sont prises en compte en factorisant les convolutions sur des bandes fortement corrélées afin d'obtenir le meilleur compromis complexité/performance du modèle. Les résultats semblant prometteurs en termes d'explicabilité et de performances, des développements sont à l'étude pour étendre les attentions au niveau spectral et temporel (cartes de chaleur) et pour comprendre plus finement le fonctionnement des réseaux "multi-sorties" qui permettent d'entraîner le module d'attention

Multivariate Change Detection on High Resolution Monovariate SAR Image Using Linear Time-Frequency Analysis

International audienceIn this paper, we propose a novel methodology for Change Detection between two monovariate complex SAR images. Linear Time-Frequency tools are used in order to recover a spectral and angular diversity of the scatterers present in the scene. This diversity is used in bi-date change detection framework to develop a detector, whose performances are better than the classic detector on monovariate SAR images