Comment faciliter la prise en charge, ainsi que l'échange d'informations, des usagers de Caritas à Genève par le biais de l'informatique

De nos jours, l’informatique est l’un des outils principaux de l’économie contemporaine. Ces deux secteurs sont désormais indissociables l’un de l’autre. En effet, les informations tendent à être informatisées, ceci tant afin d’améliorer une productivité en amoindrissant les coûts qu’en permettant de supprimer les informations redondantes, dans le but d’être le plus rapidement rentable. Selon Wikipédia1, cette informatisation « peut conduire à d'importants gains de productivité, mais aussi à une amélioration de la qualité, éventuellement après une dématérialisation des documents. En fonction des modèles employés, l'informatisation peut conduire à certaines dérives productivistes ». Mais qu’en est-il de la relation entre l’informatique et le social ? L’informatique ne peut-elle pas être aussi utile dans un milieu à relations humaines que pour de simples chiffres ? En d’autres termes, au lieu de servir à développer le chiffre d’affaire d’une entreprise, l’informatique ne peut-elle pas développer et faciliter l’organisation d’une association à but non lucratif ? A travers l’association Caritas Genève, je souhaiterais analyser son fonctionnement interne, la manière dont les informations transitent afin de vérifier si certaines solutions existantes sur le marché pourraient permettre un soulagement dans le travail des collaborateurs de Caritas aussi bien qu’une meilleure interactivité avec les personnes faisant appel aux services de l’association

Lipid profile and antioxidant activity of macadamia nuts (Macadamia integrifolia) cultivated in Venezuela

Macadamia nuts (Macadamia integrifolia) grown in Venezuela have showed an average total fat content of 70%. Oleic acid (18:1) was the main monounsaturated fatty acid (MUFA) (51.3%), followed by palmitoleic acid (16:1, 22.6%). The content of polyunsaturated fatty acids (PUFAs), C18:2 and C18:3 represented 5.4%. Thus, MUFAs and PUFAs together constituted more than 80% of the total fatty acids present. Trans-vaccenic acid was also present (3%). As regards to other phytochemical compounds, tocopherols and tocotrienols were not found in the sample, but the presence of squalene was detected. The antioxidant activity (44.2%) of the extract was produced by the phytochemicals present. (Résumé d'auteur

Learning the weight matrix for sparsity averaging in compressive imaging

We propose to map the fast iterative shrinkage-thresholding algorithm to a deep neural network (DNN), with a sparsity prior in a concatenation of wavelet bases, in the context of compressive imaging. We exploit the DNN architecture to learn the optimal weight matrix of the corresponding reweighted l1-minimization problem. We later use the learned weight matrix for the image reconstruction process, which is recast as a simple l1-minimization problem. The approach, denoted as learned extended FISTA, shows promising results in terms of image quality, compared to state-of-the-art algorithms, and significantly reduces the reconstruction time required to solve the reweighted l1-minimization problem

Pulse-Stream Models In Time-Of-Flight Imaging

This paper considers the problem of reconstructing raw signals from random projections in the context of time-of-flight imaging with an array of sensors. It presents a new signal model, coined as multi-channel pulse-stream model, which exploits pulse-stream models and accounts for additional structure induced by inter-sensor dependencies. We propose a sampling theorem and a reconstruction algorithm, based on l1-minimization, for signals belonging to such a model. We demonstrate the benefits of the proposed approach by means of numerical simulations and on a real nondestructive- evaluation application where the peak-signal-to-noise ratio is increased by 3 dB compared to standard compressed-sensing strategies

Beamforming-deconvolution: A novel concept of deconvolution for ultrasound imaging

In ultrasound (US) imaging, beamforming is usually separated from the deconvolution or some other post-processing techniques. The former processes raw data to build radio-frequency (RF) images while the latter restore high-resolution images, denoted as tissue reflectivity function (TRF), from RF images. This work is the very first trial to perform deconvolution directly with raw data, bridging the gap between beamforming and deconvolution, and thus reducing the estimation errors from two separate steps. The proposed approach retrieves both high quality RF and TRF images and exhibits better RF image quality than a classical beamforming approach

A Deep Learning Approach to Ultrasound Image Recovery

Based on the success of deep neural networks for image recovery, we propose a new paradigm for the compression and decompression of ultrasound~(US) signals which relies on stacked denoising autoencoders. The first layer of the network is used to compress the signals and the remaining layers perform the reconstruction. We train the network on simulated US signals and evaluate its quality on images of the publicly available PICMUS dataset. We demonstrate that such a simple architecture outperforms state-of-the art methods, based on the compressed sensing framework, both in terms of image quality and computational complexity

Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging

We investigate the benefits of known partial support for the recovery of joint-sparse signals and demonstrate that it is advantageous in terms of recovery performance for both rank-blind and rank-aware algorithms. We suggest extensions of several joint-sparse recovery algorithms, e.g. simultaneous normalized iterative hard thresholding, subspace greedy methods and subspace-augmented multiple signal classification (MUSIC) techniques. We describe a direct application of the proposed methods for compressive multiplexing of ultrasound (US) signals. The technique exploits the compressive multiplexer architecture for signal compression and relies on joint-sparsity of US signals in the frequency domain for signal reconstruction. We validate the proposed algorithms on numerical experiments and show their superiority against state-of-the-art approaches in rank-defective cases. We also demonstrate that the techniques lead to a significant increase of the image quality on in vivo carotid images compared to reconstruction without partially known support. The supporting code is available on https://github.com/AdriBesson/ spl2018_joint_sparse

What Factors Shape Spatial Distribution of Biomass in Riparian Forests? Insights from a LiDAR Survey over a Large Area

Riparian ecosystems are home to a remarkable biodiversity, but have been degraded in many regions of the world. Vegetation biomass is central to several key functions of riparian systems. It is influenced by multiple factors, such as soil waterlogging, sediment input, flood, and human disturbance. However, knowledge is lacking on how these factors interact to shape spatial distribution of biomass in riparian forests. In this study, LiDAR data were used in an individual tree approach to map the aboveground biomass in riparian forests along 200 km of rivers in the Meuse catchment, in southern Belgium (Western Europe). Two approaches were tested, relying either on a LiDAR Canopy Height Model alone or in conjunction with a LiDAR point cloud. Cross-validated biomass relative mean square errors for 0.3 ha plots were, respectively, 27% and 22% for the two approaches. Spatial distribution of biomass patterns were driven by parcel history (and particularly vegetation age), followed by land use and topographical or geomorphological variables. Overall, anthropogenic factors were dominant over natural factors. However, vegetation patches located in the lower parts of the riparian zone exhibited a lower biomass than those in higher locations at the same age, presumably due to a combination of a more intense disturbance regime and more limiting growing conditions in the lower parts of the riparian zone. Similar approaches to ours could be deployed in other regions in order to better understand how biomass distribution patterns vary according to the climatic, geological or cultural contexts.Les écosystèmes riverains abritent une biodiversité remarquable, mais ils ont été dégradés dans de nombreuses régions du monde. La biomasse végétale est essentielle à plusieurs fonctions clés des systèmes riverains. Elle est influencée par de multiples facteurs, tels que l'engorgement du sol, l'apport de sédiments, les inondations et les perturbations humaines. Cependant, les connaissances concernant la façon dont ces facteurs interagissent pour façonner la distribution spatiale de la biomasse dans les forêts riveraines sont fragmentaires. Dans cette étude, les données LiDAR ont été utilisées dans une approche à l’échelle de l’arbre pour cartographier la biomasse aérienne dans les forêts riveraines le long de 200 km de rivières dans le bassin versant de la Meuse, dans le sud de la Belgique (Europe occidentale). Deux approches ont été testées, s'appuyant sur un modèle numérique de hauteur LiDAR seul ou en conjonction avec un nuage de points LiDAR. Les erreurs quadratiques moyennes relatives de la biomasse pour des parcelles de 0,3 ha étaient respectivement de 27 % et 22 % pour les deux approches. La distribution spatiale des modèles de biomasse était surtout influencée par l'historique des parcelles (et en particulier l'âge de la végétation), suivie par l'utilisation des terres et les variables topographiques ou géomorphologiques. Dans l'ensemble, les facteurs anthropiques étaient dominants par rapport aux facteurs naturels. Cependant, les parcelles de végétation situées dans les parties inférieures de la zone riveraine présentaient une biomasse plus faible que celles situées dans les parties supérieures au même âge, probablement en raison de la combinaison d'un régime de perturbation plus intense et de conditions de croissance plus limitantes dans les parties inférieures de la zone riveraine. Des approches similaires à la nôtre pourraient être déployées dans d'autres régions afin de mieux comprendre comment les schémas de distribution de la biomasse varient en fonction des contextes climatiques, géologiques ou culturels.Peer reviewe