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© 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license Background Continuous electronic fetal heart-rate monitoring is widely used during labour, and computerised interpretation could increase its usefulness. We aimed to establish whether the addition of decision-support software to assist in the interpretation of cardiotocographs affected the number of poor neonatal outcomes. Methods In this unmasked randomised controlled trial, we recruited women in labour aged 16 years or older having continuous electronic fetal monitoring, with a singleton or twin pregnancy, and at 35 weeks' gestation or more at 24 maternity units in the UK and Ireland. They were randomly assigned (1:1) to decision support with the INFANT system or no decision support via a computer-generated stratified block randomisation schedule. The primary outcomes were poor neonatal outcome (intrapartum stillbirth or early neonatal death excluding lethal congenital anomalies, or neonatal encephalopathy, admission to the neonatal unit within 24 h for ≥48 h with evidence of feeding difficulties, respiratory illness, or encephalopathy with evidence of compromise at birth), and developmental assessment at age 2 years in a subset of surviving children. Analyses were done by intention to treat. This trial is completed and is registered with the ISRCTN Registry, number 98680152. Findings Between Jan 6, 2010, and Aug 31, 2013, 47 062 women were randomly assigned (23 515 in the decision-support group and 23 547 in the no-decision-support group) and 46 042 were analysed (22 987 in the decision-support group and 23 055 in the no-decision-support group). We noted no difference in the incidence of poor neonatal outcome between the groups—172 (0·7%) babies in the decision-support group compared with 171 (0·7%) babies in the no-decision-support group (adjusted risk ratio 1·01, 95% CI 0·82–1·25). At 2 years, no significant differences were noted in terms of developmental assessment. Interpretation Use of computerised interpretation of cardiotocographs in women who have continuous electronic fetal monitoring in labour does not improve clinical outcomes for mothers or babies. Funding National Institute for Health Research.

International audience; In the framework of the Chemistry-Aerosol Mediterranean Experiment (ChArMEx; http://charmex.lsce.ipsl.fr/) initiative, a field campaign took place in the western Mediterranean Basin between 10 June and 5 July 2013 within the ADRIMED (Aerosol Direct Radiative Impact on the regional climate in the MEDiterranean region) project. The scientific objectives of ADRIMED are the characterization of the most common ‘Mediterranean aerosols’ and their direct radiative forcing (column closure and regional scale). During 15–24 June a multi-intrusion dust event took place over the western and central Mediterranean Basin. Extra measurements were carried out by some EARLINET/ACTRIS (European Aerosol Research Lidar Network /Aerosols, Clouds, and Trace gases Research InfraStructure Network, http://www.actris.net/) lidar stations in Spain and Italy, in particular on 22 June in support to the flight over southern Italy of the Falcon 20 aircraft involved in the campaign. This article describes the physical and optical properties of dust observed at the different lidar stations in terms of dust plume centre of mass, optical depth, lidar ratio, and particle depolarization ratio. To link the differences found in the origin of dust plumes, the results are discussed on the basis of back-trajectories and air- and space-borne lidars. This work puts forward the collaboration between a European research infrastructure (ACTRIS) and an international project (ChArMEx) on topics of interest for both parties, and more generally for the atmospheric community.

International audience; The massive alcoholization of young people does not seem to weaken in spite of an important number of actions organized to curb this phenomenon. Setting out from this premise, this research aims at identifying better the real-life experience of young people during student nights and their perception of alcohol risk factors. The main result concerns the highlighting of a group effect with its social hierarchy which contributes to strengthen a falsified perception of the incurred risks.

Domain adaptation techniques in transfer learning try to reduce the amount of training data required for classification by adapting a classifier trained on samples from a source domain to a new data set (target domain) where the features may have different distributions. In this paper, we propose a new technique for domain adaptation based on logistic regression. Starting with a classifier trained on training data from the source domain, we iteratively include target domain samples for which class labels have been obtained from the current state of the classifier, while at the same time removing source domain samples. In each iteration the classifier is re-trained, so that the decision boundaries are slowly transferred to the distribution of the target features. To make the transfer procedure more robust we introduce weights as a function of distance from the decision boundary and a new way of regularisation. Our methodology is evaluated using a benchmark data set consisting of aerial images and digital surface models. The experimental results show that in the majority of cases our domain adaptation approach can lead to an improvement of the classification accuracy without additional training data, but also indicate remaining problems if the difference in the feature distributions becomes too large.

Figure-ground image segmentation has been a challenging problem in computer vision. Apart from the difficulties in establishing an effective framework to divide the image pixels into meaningful groups, the notions of figure and ground often need to be properly defined by providing either user inputs or object models. In this paper, we propose a novel graph-based segmentation framework, called superpixel cut. The key idea is to formulate foreground segmentation as finding a subset of superpixels that partitions a graph over superpixels. The problem is formulated as Min-Cut. Therefore, we propose a novel cost function that simultaneously minimizes the inter-class similarity while maximizing the intra-class similarity. This cost function is optimized using parametric programming. After a small learning step, our approach is fully automatic and fully bottom-up, which requires no high-level knowledge such as shape priors and scene content. It recovers coherent components of images, providing a set of multiscale hypotheses for high-level reasoning. We evaluate our proposed framework by comparing it to other generic figure-ground segmentation approaches. Our method achieves improved performance on state-of-the-art benchmark databases.

The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.

Matching and fusion of 3D point clouds, such as close range laser scans, is important for creating an integrated 3D model data infrastructure. The Iterative Closest Point algorithm for alignment of point clouds is one of the most commonly used algorithms for matching of rigid bodies. Evidently, scans are acquired from different positions and might present different data characterization and accuracies, forcing complex data-handling issues. The growing demand for near real-time applications also introduces new computational requirements and constraints into such processes. This research proposes a methodology to solving the computational and processing complexities in the ICP algorithm by introducing specific performance enhancements to enable more efficient analysis and processing. An Octree data structure together with the caching of localized Delaunay triangulation-based surface meshes is implemented to increase computation efficiency and handling of data. Parallelization of the ICP process is carried out by using the Single Instruction, Multiple Data processing scheme – based on the Divide and Conquer multi-branched paradigm – enabling multiple processing elements to be performed on the same operation on multiple data independently and simultaneously. When compared to the traditional non-parallel list processing the Octree-based SIMD strategy showed a sharp increase in computation performance and efficiency, together with a reliable and accurate alignment of large 3D point clouds, contributing to a qualitative and efficient application.

The massive alcoholization of young people does not seem to weaken in spite of an important number of actions organized to curb this phenomenon. Setting out from this premise, this research aims at identifyingbetter the real-life experience of young people during student nights and their perception of alcohol risk factors. The main result concerns the highlighting of a group effect with its social hierarchy which contributes tostrengthen a falsified perception of the incurred risks., L’alcoolisation massive des jeunes continue de progresser malgré les campagnes de prévention mises en place ces dernières années. Partant de ce constat, cette recherche vise à mieux cerner le vécu des jeunes lors des soirées étudiantes et leur perception du risque alcool. Le principal résultat concerne la mise en évidence d’un effet de groupe dont la hiérarchisation des liens contribue à renforcer une perception faussée des risques encourus.

The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.

In this paper we address the problem of classification of remote sensing images in the framework of transfer learning with a focus on domain adaptation. The main novel contribution is a method for transductive transfer learning in remote sensing on the basis of logistic regression. Logistic regression is a discriminative probabilistic classifier of low computational complexity, which can deal with multiclass problems. This research area deals with methods that solve problems in which labelled training data sets are assumed to be available only for a source domain, while classification is needed in the target domain with different, yet related characteristics. Classification takes place with a model of weight coefficients for hyperplanes which separate features in the transformed feature space. In term of logistic regression, our domain adaptation method adjusts the model parameters by iterative labelling of the target test data set. These labelled data features are iteratively added to the current training set which, at the beginning, only contains source features and, simultaneously, a number of source features are deleted from the current training set. Experimental results based on a test series with synthetic and real data constitutes a first proof-of-concept of the proposed method.