Divergence-free Wavelets for Navier-Stokes

In this paper, we investigate the use of compactly supported divergence-free wavelets for the representation of the Navier-Stokes solution. After reminding the theoretical construction of divergence-free wavelet vectors, we present in detail the bases and corresponding fast algorithms for 2D and 3D incompressible flows. In order to compute the nonlinear term, we propose a new method which provides in practice with the Hodge decomposition of any flow: this decomposition enables us to separate the incompressible part of the flow from its orthogonal complement, which corresponds to the gradient component of the flow. Finally we show numerical tests to validate our approach.Comment: novembre 200

Craya decomposition using compactly supported biorthogonal wavelets

Special Issue on Continuous Wavelet Transform in Memory of Jean Morlet, Part IIInternational audienceWe present a new local Craya--Herring decomposition of three-dimensional vector fields using compactly supported biorthogonal wavelets. Therewith vector-valued function spaces are split into two orthogonal components, i.e., curl-free and divergence-free spaces. The latter is further decomposed into toroidal and poloidal parts to decorrelate horizontal from vertical contributions which are of particular interest in geophysical turbulence. Applications are shown for isotropic, rotating and stratified turbulent flows. A comparison between isotropic and anisotropic orthogonal Craya--Herring wavelets, built in Fourier space and thus not compactly supported, is also given

Impact de l'apprentissage d'activités créatives en arts visuels sur le développement de la personne aînée

La vieillesse n'est plus aujourd'hui synonyme de pertes et de déclin; il est reconnu que le développement de la personne aînée peut se poursuivre jusqu'à la fin de sa vie s'il est stimulé, notamment par l'apprentissage de nouveaux savoirs. Cette recherche complémentaire de l'Étude longitudinale québécoise sur le vieillissement (ELQUEV) a pour objectif premier de vérifier, dans son volet expérimental, si le niveau d'actualisation du potentiel de personnes aînées faiblement et moyennement actualisées peut être augmenté à la faveur de l'apprentissage d'activités créatives en arts visuels. Le second objectif, celui du volet exploratoire, est d'identifier différents aspects de l'enrichissement de la vie procuré par l'apprentissage d'activités créatives en arts visuels. Ces deux objectifs ont été atteints en utilisant un protocole de recherche quasi expérimental et une stratégie de recherche mixte. L'hypothèse du volet expérimental supposait que l'apprentissage d'activités créatives en arts visuels, sous forme d'un cours structuré de deux heures par semaine pendant 14 semaines aurait pour effet d'augmenter la performance obtenue à la mesure de l'actualisation du potentiel (MAP) de personnes âgées de 65 ans et plus, faiblement et moyennement actualisées."--Résumé abrégé par UMI

Información de alumnos universitarios sobre el Consejo Profesional de Ciencias Económicas

La investigación que se presenta comienza a gestarse en el año 2012 a partir de prácticas profesionales de la Licenciatura en Administración en el área de Calidad. Las mismas se llevaron a cabo en el Consejo Profesional de Ciencias Económicas de Mendoza donde se conformó un equipo para relevar tres de los principales procesos. El autor de este trabajo desconocía aspectos del Consejo Profesional que son fundamentales a saber por los futuros profesionales. Es decir, que la experiencia en el campo formuló interrogantes acerca del trabajo realizado por el Consejo Profesional en los alumnos de las Universidades de Mendoza. Se descubrió cuan importante es que el Consejo trabaje en sus clientes potenciales, siendo estos los alumnos de las carreras en Ciencias Económicas. En dicha organización, el autor de este trabajo, detectó que no hay un programa que se dirija a la capacitación de los alumnos que están próximos a recibirse y que por lo tanto están próximos a matricularse; siendo la matriculación obligatoria por ley. Tampoco hay programas en los que se les informe sobre el contenido y aplicación del Código de Ética de la Profesión. Es por esto que se plantea como interrogante ¿Cuánto conocen los alumnos en Ciencias Económicas sobre el Consejo Profesional en Ciencias Económicas de Mendoza? Para responder a esta pregunta es que se diseñó este trabajo de investigación en el que seguidamente se presentan los objetivos, hipótesis y pregunta de investigación de modo que sirvan de guía para el resto del trabajo.Fil: Deriaz, Gustavo Rafael. Universidad Nacional de Cuyo. Facultad de Ciencias Económicas

Wavelet Helmholtz decomposition for weak lensing mass map reconstruction

To derive the convergence field from the gravitational shear (gamma) of the background galaxy images, the classical methods require a convolution of the shear to be performed over the entire sky, usually expressed thanks to the Fast Fourier transform (FFT). However, it is not optimal for an imperfect geometry survey. Furthermore, FFT implicitly uses periodic conditions that introduce errors to the reconstruction. A method has been proposed that relies on computation of an intermediate field u that combines the derivatives of gamma and on convolution with a Green kernel. In this paper, we study the wavelet Helmholtz decomposition as a new approach to reconstructing the dark matter mass map. We show that a link exists between the Helmholtz decomposition and the E/B mode separation. We introduce a new wavelet construction, that has a property that gives us more flexibility in handling the border problem, and we propose a new method of reconstructing the dark matter mass map in the wavelet space. A set of experiments based on noise-free images illustrates that this Wavelet Helmholtz decomposition reconstructs the borders better than all other existing methods.Comment: Accepted for publication in A&A (16 pages, 12 figures

Dark Matter from Lorentz Invariance and the LHC

International audienceIn extra dimensional models, a Dark Matter candidate can be present thanks to a Kaluza-Klein parity which makes the lightest resonances stable. However, compactifications considered sofar need the symmetry to be imposed by hand on the model: here we propose the unique orbifold in 6 dimensions where such parity arises naturally as part of the unbroken 6D Lorentz invariance. As an example we introduce a model of universal extra dimensions where all standard model fields propagate in the extra dimensions. The dark matter candidate is a scalar photon and its preferred mass range lies below 300 GeV. Due to the small splitting between states in the same Kaluza-Klein tier, discovery of the lightest tiers is challenging at the LH

Service Recommendations with Deep Learning: A Study on Neural Collaborative Engines

Background: The present paper aims to investigate the adoption of Neural Networks for recommendation systems and to propose Deep Learning architectures as advanced frameworks for designing Collaborative Filtering engines. Recommendation systems are data-driven infrastructures which are widely adopted to create effective and cutting-edge smart services, allowing to personalize the value proposition and adapt it to changes and variations in customers’ preferences. Method: Our research represents an exploratory investigation on the adoption of Neural Networks for Recommendation Systems, inspired by the findings of a recent study on service science that highlighted the suitability of those models for designing cutting-edge recommenders capable of overcoming stable traditional benchmarks like the Singular Value Decomposition and the k-Nearest Neighbors algorithms. Following this study, we designed a more “complex” Feed-Forward Neural Network, trained on the “Movielens 100K” dataset using the Mean-Squared Error function to approximate the model loss generated and the Adaptive Moment Estimation algorithm (Adam) for the parameters optimization. Results: The results of this study demonstrate the primary role of Feed-Forward Neural Networks for designing advanced Collaborative recommenders, consolidating and even improving the outcomes of the work that inspired our research. Conclusion: Given these assumptions, we confirm the suitability of Feed-Forward Neural Networks as effective recommendation algorithms, laying the foundations for further studies in neural-based recommendation science