Répétabilité des mesures de T1 issues du MP2RAGE dans les lésions SEP du cerveau et de la moelle épinière cervicale

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Immerstar, une plateforme de 25 ans, encore en évolution

International audienceThe two platforms Immersia and Immermove offer an immersive collaborative space called ImmerStar, intended for the scientific and industrial community, for international research projects.Immersia is a 3D virtual reality platform which, thanks to its exceptional dimensions, offers an environment for experimentation, particularly in the context of real-time and multi-modal interaction (vision, sound, haptics, brain-computer interface) between humans and virtual models.Immermove is a technological platform dedicated to motion capture, extended by a virtual reality space. It enables the precise capture of rapid movements (sports for example) or of a group of people, in order to study human behavior such as crowd-movement or sport gestures.This paper presents the history of the evolution of these two joint platforms, an overview of their technical specifications, associated research projects and perspective of evolutions.Les deux plateformes Immersia et Immermove offrent un espace collaboratif immersif appelé ImmerStar, destiné à la communauté scientifique et industrielle, pour des projets de recherche internationaux.Immersia est une plateforme de réalité virtuelle 3D qui, grâce à ses dimensions exceptionnelles, offre un environnement d'expérimentation, notamment dans le cadre de l'interaction en temps réel et multimodale (vision, son, haptique, interface cerveau-ordinateur) entre l'homme et les modèles virtuels.Immermove est une plateforme technologique dédiée à la capture de mouvements, prolongée par un espace de réalité virtuelle. Elle permet la capture précise de mouvements rapides (sportifs par exemple) ou d'un groupe de personnes, afin d'étudier les comportements humains tels que les mouvements de foule ou les gestes sportifs.Cet article présente l'historique de l'évolution de ces deux plateformes communes, un aperçu de leurs spécifications techniques, les projets de recherche associés et les perspectives d'évolution

Any theory that admits a Wigner's Friend type multi-agent paradox is logically contextual

39+16 pages. Both authors contributed equally to this work. Initial versions of some of these results were included in NN's PhD thesis (ETH Zurich, 2023)Wigner's Friend scenarios push the boundaries of quantum theory by modeling agents, along with their memories storing measurement outcomes, as physical quantum systems. Extending these ideas beyond quantum theory, we ask: in which physical theories, and under what assumptions, can agents who are reasoning logically about each other's measurement outcomes encounter apparent paradoxes? To address this, we prove a link between Wigner's Friend type multi-agent paradoxes and contextuality in general theories: if agents who are modeled within a physical theory come to a contradiction when reasoning using that theory (under certain assumptions on how they reason and describe measurements), then the theory must admit contextual correlations of a logical form. This also yields a link between the distinct fundamental concepts of Heisenberg cuts and measurement contexts in general theories, and in particular, implies that the quantum Frauchiger-Renner paradox is a proof of logical contextuality. Moreover, we identify structural properties of such paradoxes in general theories and specific to quantum theory. For instance, we demonstrate that theories admitting behaviors corresponding to extremal vertices of n-cycle contextuality scenarios admit Wigner's Friend type paradoxes without post-selection, and that any quantum Wigner's Friend paradox based on the n-cycle scenario must necessarily involve post-selection. Further, we construct a multi-agent paradox based on a genuine contextuality scenario involving sequential measurements on a single system, showing that Bell non-local correlations between distinct subsystems are not necessary for Wigner's Friend paradoxes. Our work offers an approach to investigate the structure of physical theories and their information-theoretic resources by means of deconstructing the assumptions underlying multi-agent physical paradoxes

Hybrid high-order methods for elasto-acoustic wave propagation in the time domain

We devise a Hybrid High-Order (HHO) method for the coupling between the acoustic and elastic wave equations in the time domain. A first-order formulation in time is considered. The HHO method can use equal-order and mixed-order settings, as well as O(1)-and O(1/h)-stabilizations. An energy-error estimate is established in the time-continuous case. A numerical spectral analysis is performed, showing that O(1)-stabilization is required to avoid excessive CFL limitations for explicit time discretizations. Moreover, the spectral radius of the stiffness matrix is fairly independent of the geometry of the mesh cells. For analytical solutions on general meshes, optimal convergence rates of order (k + 1) are shown in both equal-and mixed-order settings using O(1)-stabilization, whereas order (k + 2) is achieved in the mixed-order setting using O(1/h)-stabilization. Test cases with a Ricker wavelet as an initial condition showcase the relevance of the proposed method for the simulation of elasto-acoustic wave propagation across media with contrasted material properties

Acceptability of Overground Wearable Powered Exoskeletons for People with Spinal Cord Injury: A Multicenter Qualitative Study

International audienceBackground: Exoskeletons are used in rehabilitation centers for people with spinal cord injuries (SCI) due to the potential benefits they offer for locomotor rehabilitation. The acceptability of exoskeletons is crucial to promote rehabilitation and to ensure a successful implementation of this technology. The objective was to explore the acceptability of overground wearable powered exoskeleton used in rehabilitation among people with SCI. Methods: Fourteen individuals with SCI (9 men, mean [SD] age 47 years [14.8], a majority with traumatic and thoracic lesion (T6-T12)) who had utilized an exoskeleton in Canada or in France during their rehabilitation participated in a semi-structured interview. A thematic analysis using the theoretical framework of acceptability was carried out. Results: Participants were motivated to use an exoskeleton during their rehabilitation. They reported several perceived benefits to its use, including better walking pattern, increased endurance, and greater muscle mass. They also experienced mild pain, notable concentration demands, and fatigue. Most participants reported that using exoskeletons in their rehabilitation process was appropriate and relevant to them. Conclusions: Exoskeletons are generally well accepted by participants in this study. Adjustments in their use, such as conducting training sessions in obstacle-free environment and technological improvements to address the device’s restrictive characteristics, heaviness, and massiveness are however still needed

Second order kinematic surface fitting in anatomical structures

International audienceSymmetry detection and morphological classification of anatomical structures play pivotal roles in medical image analysis. The application of kinematic surface fitting, a method for characterizing shapes through parametric stationary velocity fields, has shown promising results in computer vision and computer-aided design. However, existing research has predominantly focused on first order rotational velocity fields, which may not adequately capture the intricate curved and twisted nature of anatomical structures. To address this limitation, we propose an innovative approach utilizing a second order velocity field for kinematic surface fitting. This advancement accommodates higher rotational shape complexity and improves the accuracy of symmetry detection in anatomical structures. We introduce a robust fitting technique and validate its performance through testing on synthetic shapes and real anatomical structures. Our method not only enables the detection of curved rotational symmetries (core lines) but also facilitates morphological classification by deriving intrinsic shape parameters related to curvature and torsion. We illustrate the usefulness of our technique by categorizing the shape of human cochleae in terms of the intrinsic velocity field parameters. The results showcase the potential of our method as a valuable tool for medical image analysis, contributing to the assessment of complex anatomical shapes

Empirical dataset generation for AI-optimized IoT infrastructure placement

International audienceThe strategic placement of nodes in Wireless IoT Networks (WIoTs) is crucial for ensuring optimal coverage, connectivity, and energy efficiency. Traditionally, node placement has relied on heuristic and manual methods, which often result in inefficiencies and suboptimal network performance. In this paper, we focus on optimizing the coverage performance of WIoTs, which play a pivotal role in environmental monitoring and event detection. In particular, we first develop a tool that allows IoT designers to simulate and generate datasets for multiple sensor deployment options. Then, we empirically generate a dataset that can contribute to the growing field of optimized sensor placement strategies by bridging algorithmic simulations with predictive modeling. Finally, we use the generated dataset to train a decision tree model for sensor node placement predictions. The prototype implementation of our tool and the generated datasets are publicly available for exploitation from the research community

Decoding Algorithms for Tensor Codes

Tensor codes are a generalisation of matrix codes. Such codes are defined as subspaces of order-$r$ tensors for which the ambient space is endowed with the tensor-rank as a metric. A class of these codes was introduced by Roth who outlined a decoding algorithm for low tensor-rank errors for particular cases. They may be viewed as a generalisation of the well-known Delsarte-Gabidulin-Roth maximum rank distance codes. We study a generalised class of these codes. We investigate the properties of these codes and outline decoding techniques for different metrics that leverage their tensor structure. We first consider a fibre-wise decoding approach, as each fibre of a codeword corresponds to a Gabidulin codeword. We then give a generalisation of Loidreau's decoding method that corrects errors with properties constrained by the dimensions of the slice spaces and fibre spaces. The metrics we consider are upper bounded by the tensor-rank metric, and therefore these algorithms also decode tensor-rank weight errors

Alpha Mesh Swc: automatic and robust surface mesh generation from the skeleton description of brain cells

In recent years, there has been a significant increase in publicly available skeleton descriptions of real brain cells from laboratories all over the world. In theory, this should make it possible to perform large scale realistic simulations on brain cells. However, currently there is still a gap between the skeleton descriptions and high quality simulation-ready surface and volume meshes of brain cells. We propose and implement a tool called {\it Alpha\_Mesh\_Swc} to generate automatically and efficiently triangular surface meshes that are optimized for finite elements simulations. We use an Alpha Wrapping method with an offset parameter on component surface meshes to efficiently generate a global watertight mesh. Then mesh simplification and re-meshing are used to produce an optimal surface mesh. Our methodology limits the number of surface triangles while preserving geometrical accuracy, permits cutting and gluing of cell components, is robust to imperfect skeleton descriptions, and allows mixed cell descriptions (surface meshes combined with skeletons). We compared the robustness, performance and accuracy of {\it Alpha\_Mesh\_Swc} against existing tools and found significant improvement in terms of mesh accuracy. We show, on average, we can generate fully automatically a brain cell (neurons or glia) surface mesh in a couple of minutes on a laptop computer resulting in a simplified surface mesh with only around 10k nodes. The resulting meshes were used to perform diffusion MRI simulations in neurons and microglia. The code and a number of sample brain cell surface meshes have been made publicly available

Détection et segmentation des anomalies cérébrales post-AVC

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