Multiple scattering of elastic waves by pinned dislocation segments in a continuum

The coherent propagation of elastic waves in a solid filled with a random distribution of pinned dislocation segments is studied to all orders in perturbation theory. It is shown that, within the independent scattering approximation, the perturbation series that generates the mass operator is a geometric series that can thus be formally summed. A divergent quantity is shown to be renormalizable to zero at low frequencies. At higher frequencies said quantity can be expressed in terms of a cut-off with dimensions of length, related to the dislocation length, and physical quantities can be computed in terms of two parameters, to be determined by experiment. The approach used in this problem is compared and contrasted with the scattering of de Broglie waves by delta-function potentials as described by the Schr\"odinger equation

GAN-AE : An anomaly detection algorithm for New Physics search in LHC data

In recent years, interest has grown in alternative strategies for the search for New Physics beyond the Standard Model. One envisaged solution lies in the development of anomaly detection algorithms based on unsupervised machine learning techniques. In this paper, we propose a new Generative Adversarial Network-based auto-encoder model that allows both anomaly detection and model-independent background modeling. This algorithm can be integrated with other model-independent tools in a complete heavy resonance search strategy. The proposed strategy has been tested on the LHC Olympics 2020 dataset with promising results.Comment: 10 pages, 8 figure

Segmentation d'Images solaires en Extrême Ultraviolet par une Approche Classification floue Multispectrale

L'étude de la variabilité de la couronne solaire et le suivi de régions caractéristiques à sa surface (régions actives, trous coronaux) sont d'une importance capitale en astrophysique et pour le développement de la météorologie de l'espace. Dans ce cadre, nous proposons un algorithme de segmentation multispectrale d'images du Soleil acquises en extrême ultraviolet, utilisant un algorithme de classification flou spatialement contraint. L'utilisation de la logique floue permet de prendre en compte les imprécisions et les incertitudes inhérentes à la définition des différentes régions d'intérêt dans l'image. La méthode est appliquée sur des images prises par le téléscope EIT du satellite SoHO, depuis janvier 1997 jusque mai 2005, couvrant ainsi presque l'intégralité d'un cycle solaire. Les résultats en terme de caractérisation géométrique et radiométrique des régions actives et des trous coronaux sont en accord avec d'autres observations menées par ailleurs. La méthode met de plus en évidence des périodes dans la série temporelle étudiée, reliées à des phénomènes de physique solaire connus

Fusion d'images 3D du cerveau : étude de modèles et applications

The collection of various data coming from imagery, expert knowledge or physiological signals is becoming very common for the study of a given pathology. The treatment of these data is performed by a physicist, who analyses and aggregates them according to his knowledge, and generally leads to a medical decision. The aim of this work is to model this aggregation process by means of a fusion technique, in the case of brain studies. The fusion process is divided into three steps:We first model the available information, numerical or symbolic, in a common theoretical frame. The possibilistic logic allows for the management of ambiguities and imprecision that are inherent to medical data. We thus propose to model on the one hand the distribution of cerebral tissues in anatomic (MR) and functional (SPECT and TEP) images by means of a fuzzy clustering algorithm on appropriate feature vectors, and on the other hand the symbolic information coming from expert knowledge.We then aggregate these information with a fusion operator. This operator has to affirm redundancy, manage the complementarities and also take into account conflicts, that often underline the presence of a pathology. We thus propose three models for three precise clinical cases: the fusion of MR images, the fusion of anatomical and functional images and the fusion of MR images with symbolic information.We finally propose a synthetic piece of information that allows to best represent the available data. We define for the threee previous models a resulting image that allows for example to propose a diagnosis, to establich a prognosis or to provide help for a surgical planning.Four clinical applications illustrate these concepts: brain tissue volumes quantification, study of Alzheimer’s disease, study of epilepsy and segmentation of the subthalamic nucleus in the treatment of Parkinson’s disease. For every case, besides the basic model previously described, we propose specific treatments and a clinical validation.An industrial application in collaboration with SEGAMI corporation, that finalizes and industrially increases this work, is finally presented.Le recueil de données diverses issues de l'imagerie, de compétences expertes ou de signaux physiologiques est devenu courant pour l'étude d'une pathologie donnée. Leur exploitation est effectuée par le clinicien qui les analyse et les agrège en fonction de ses connaissances. La motivation de ce travail est de modéliser ce processus d'agrégation à l'aide de techniques empruntées à la fusion de données, dans le cadre d'études portant sur le cerveau. Le processus de fusion est décomposé en trois phases fondamentales.Nous modélisons tout d'abord les informations dans un cadre théorique commun. Le formalisme retenu est celui de la logique possibiliste, permettant de prendre en compte les ambiguïtés inhérentes aux données médicales. Nous proposons de modéliser d'une part la distribution des tissus cérébraux dans les images IRM, TEM et TEP par un algorithme de classification flou sur des vecteurs forme appropriés et d'autre part des informations issues de connaissances expertes.Nous agrégeons ensuite ces différentes informations par un opérateur de fusion. Celui-ci doit affirmer les redondances, gérer les complémentarités et prendre en compte les conflits soulignant souvent la présence d'une pathologie. Nous proposons alors trois modèles d'agrégation : la fusion d'images IRM, la fusion d'images anatomiques et fonctionnelles, et la fusion d'une image IRM et d'informations symboliques.Nous construisons enfin une information synthétique permettant d’exploiter les résultats de la fusion . Nous définissons pour chaque modèle une image permettant par exemple de proposer un diagnostic, d'établir un pronostic ou d'élaborer une aide thérapeutique.Quatre applications cliniques sont proposées en illustration : la quantification de volumes de tissus cérébraux, l'étude de la démence de type Alzheimer, l'étude de l'épilepsie et la localisation du noyau sous-thalamique pour le traitement de la maladie de Parkinson. Pour chacun de ces cas, outre les développements décrits auparavant, des modèles spécifiques à la pathologie étudiée sont proposés et une validation clinique des résultats est effectuée.Enfin, une application réalisée en collaboration avec la société SEGAMI, concrétisant et valorisant de façon industrielle ce travail, est présentée

Une version modifiée de l'Ensemble Tracking

National audienceConsidérant le suivi comme un problème de classification binaire, l'algorithme Ensemble Tracking de Shaï Avidan permet de localiser un objet dans une séquence vidéo grâce à un classifieur entrainé pour distinguer les pixels du fond des pixels de l'objet. Nous introduisons ici une nouvelle approche pour la sélection des exemples d'apprentissage ainsi qu'une technique de modularisation de l'algorithme permettant au système de travailler sur des espaces de caractéristiques homogènes

pyBumpHunter: A model independent bump hunting tool in Python for High Energy Physics analyses

The BumpHunter algorithm is widely used in the search for new particles in High Energy Physics analysis. This algorithm offers the advantage of evaluating the local and global p-values of a localized deviation in the observed data without making any hypothesis on the supposed signal. The increasing popularity of the Python programming language motivated the development of a new public implementation of this algorithm in Python, called pyBumpHunter, together with several improvements and additional features. It is the first public implementation of the BumpHunter algorithm to be added to Scikit-HEP. This paper presents in detail the BumpHunter algorithm as well as all the features proposed in this implementation. All these features have been tested in order to demonstrate their behaviour and performance.Comment: 14 pages, 9 figure

A combined voxel and surface based method for topology correction of brain surfaces

Brain surfaces provide a reliable representation for cortical mapping. The construction of correct surfaces from magnetic resonance images (MRI) segmentation is a challenging task, especially when genus zero surfaces are required for further processing such as parameterization, partial inflation and registration. The generation of such surfaces has been approached either by correcting a binary image as part of the segmentation pipeline or by modifying the mesh representing the surface. During this task, the preservation of the structure may be compromised because of the convoluted nature of the brain and noisy/imperfect segmentations. In this paper, we propose a combined, voxel and surfacebased, topology correction method which preserves the structure of the brain while yielding genus zero surfaces. The topology of the binary segmentation is first corrected using a set of topology preserving operators applied sequentially. This results in a white matter/gray matter binary set with correct sulci delineation, homotopic to a filled sphere. Using the corrected segmentation, a marching cubes mesh is then generated and the tunnels and handles resulting from the meshing are finally removed with an algorithm based on the detection of nonseparating loops. The approach was validated using 20 young individuals MRI from the OASIS database, acquired at two different time-points. Reproducibility and robustness were evaluated using global and local criteria such as surface area, curvature and point to point distance. Results demonstrated the method capability to produce genus zero meshes while preserving geometry, two fundamental properties for reliable and accurate cortical mapping and further clinical studies

Dipolar ordering in Fe8?

We show that the low-temperature physics of molecular nanomagnets, contrary to the prevailing one-molecule picture, must be determined by the long-range magnetic ordering due to many-body dipolar interactions. The calculations here performed, using Ewald's summation, suggest a ferromagnetic ground state with a Curie temperature of about 130 mK. The energy of this state is quite close to those of an antiferromagnetic state and to a glass of frozen spin chains. The latter may be realized at finite temperature due to its high entropy.Comment: 7 pages, no figures, submitted to EP