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

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

Identification and neuromodulation of brain states to promote recovery of consciousness

Experimental and clinical studies of consciousness identify brain states (i.e., transient, relevant features of the brain associated with the state of consciousness) in a non-systematic manner and largely independent from the research into the induction of state changes. In this narrative review with a focus on patients with a disorder of consciousness (DoC), we synthesize advances on the identification of brain states associated with consciousness in animal models and physiological (sleep), pharmacological (anesthesia) and pathological (DoC) states of altered consciousness in human. We show that in reduced consciousness the frequencies in which the brain operates are slowed down and that the pattern of functional communication in the brain is sparser, less efficient, and less complex. The results also highlight damaged resting state networks, in particular the default mode network, decreased connectivity in long-range connections and in the thalamocortical loops. Next, we show that therapeutic approaches to treat DoC, through pharmacology (e.g., amantadine, zolpidem), and (non-)invasive brain stimulation (e.g., transcranial current stimulation, deep brain stimulation) have shown some effectiveness to promote consciousness recovery. It seems that these deteriorated features of conscious brain states may improve in response to these neuromodulation approaches, yet, targeting often remains non-specific and does not always lead to (behavioral) improvements. Furthermore, in silico model-based approaches allow the development of personalized assessment of the effect of treatment on brain-wide dynamics. Although still in infancy, the fields of brain state identification and neuromodulation of brain states in relation to consciousness are showing fascinating developments that, when united, might propel the development of new and better targeted techniques for DoC. For example, brain states could be identified in a predictive setting, and the theoretical and empirical testing (i.e., in animals, under anesthesia and patients with a DoC) of neuromodulation techniques to promote consciousness could be investigated. This review further helps to identify where challenges and opportunities lay for the maturation of brain state research in the context of states of consciousness. Finally, it aids in recognizing possibilities and obstacles for the clinical translation of these diagnostic techniques and neuromodulation treatment options across both the multi-modal and multi-species approaches outlined throughout the review. This paper presents interactive figures, supported by the Live Paper initiative of the Human Brain Project, enabling the interaction with data and figures illustrating the concepts in the paper through EBRAINS (go to https://wiki.ebrains.eu/bin/view/Collabs/live-paper-states-altered-consciousness and get started with an EBRAINS account).NA is research fellow, OG is Research Associate, and SL is research director at FRS-FNRS. JA is postdoctoral fellow at the FWO. The study was further supported by the University and University Hospital of Liège, the BIAL Foundation, the Belgian National Funds for Scientific Research (FRS-FNRS), the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3), the FNRS PDR project (T.0134.21), the ERA-Net FLAG-ERA JTC2021 project ModelDXConsciousness (Human Brain Project Partnering Project), the fund Generet, the King Baudouin Foundation, the Télévie Foundation, the European Space Agency (ESA) and the Belgian Federal Science Policy Office (BELSPO) in the framework of the PRODEX Programme, the Public Utility Foundation 'Université Européenne du Travail', "Fondazione Europea di Ricerca Biomedica", the BIAL Foundation, the Mind Science Foundation, the European Commission, the Fondation Leon Fredericq, the Mind-Care foundation, the DOCMA project (EU-H2020-MSCA–RISE–778234), the National Natural Science Foundation of China (Joint Research Project 81471100) and the European Foundation of Biomedical Research FERB Onlus

PhylArray: phylogenetic probe design algorithm for microarray

International audienceMOTIVATION: Microbial diversity is still largely unknown in most environments, such as soils. In order to get access to this microbial 'black-box', the development of powerful tools such as microarrays are necessary. However, the reliability of this approach relies on probe efficiency, in particular sensitivity, specificity and explorative power, in order to obtain an image of the microbial communities that is close to reality. RESULTS: We propose a new probe design algorithm that is able to select microarray probes targeting SSU rRNA at any phylogenetic level. This original approach, implemented in a program called 'PhylArray', designs a combination of degenerate and non-degenerate probes for each target taxon. Comparative experimental evaluations indicate that probes designed with PhylArray yield a higher sensitivity and specificity than those designed by conventional approaches. Applying the combined PhyArray/GoArrays strategy helps to optimize the hybridization performance of short probes. Finally, hybridizations with environmental targets have shown that the use of the PhylArray strategy can draw attention to even previously unknown bacteria