200 research outputs found

    Aiding the conservation of two wooden Buddhist sculptures with 3D imaging and spectroscopic techniques

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    The conservation of Buddhist sculptures that were transferred to Europe at some point during their lifetime raises numerous questions: while these objects historically served a religious, devotional purpose, many of them currently belong to museums or private collections, where they are detached from their original context and often adapted to western taste. A scientific study was carried out to address questions from Museo d'Arte Orientale of Turin curators in terms of whether these artifacts might be forgeries or replicas, and how they may have transformed over time. Several analytical techniques were used for materials identification and to study the production technique, ultimately aiming to discriminate the original materials from those added within later interventions

    Efficient finite element methods for solving high-frequency time-harmonic acoustic wave problems in heterogeneous media

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    This thesis focuses on the efficient numerical solution of frequency-domain wave propagation problems using finite element methods. In the first part of the manuscript, the development of domain decomposition methods is addressed, with the aim of overcoming the limitations of state-of-the art direct and iterative solvers. To this end, a non-overlapping substructured domain decomposition method with high-order absorbing conditions used as transmission conditions (HABC DDM) is first extended to deal with cross-points, where more than two subdomains meet. The handling of cross-points is a well-known issue for non-overlapping HABC DDMs. Our methodology proposes an efficient solution for lattice-type domain partitions, where the domains meet at right angles. The method is based on the introduction of suitable relations and additional transmission variables at the cross-points, and its effectiveness is demonstrated on several test cases. A similar non-overlapping substructured DDM is then proposed with Perfectly Matched Layers instead of HABCs used as transmission conditions (PML DDM). The proposed approach naturally considers cross-points for two-dimensional checkerboard domain partitions through Lagrange multipliers used for the weak coupling between subproblems defined on rectangular subdomains and the surrounding PMLs. Two discretizations for the Lagrange multipliers and several stabilization strategies are proposed and compared. The performance of the HABC and PML DDM is then compared on test cases of increasing complexity, from two-dimensional wave scattering in homogeneous media to three-dimensional wave propagation in highly heterogeneous media. While the theoretical developments are carried out for the scalar Helmholtz equation for acoustic wave propagation, the extension to elastic wave problems is also considered, highlighting the potential for further generalizations to other physical contexts. The second part of the manuscript is devoted to the presentation of the computational tools developed during the thesis and which were used to produce all the numerical results: GmshFEM, a new C++ finite element library based on the application programming interface of the open-source finite element mesh generator Gmsh; and GmshDDM, a distributed domain decomposition library based on GmshFEM.Cette thèse porte sur la résolution numérique efficace de problèmes de propagation d'ondes dans le domaine fréquentiel avec la méthode des éléments finis. Dans la première partie du manuscrit, le développement de méthodes de décomposition de domaine est abordé, dans le but de surmonter les limitations des solveurs directs et itératifs de l'état de l'art. À cette fin, une méthode de décomposition de domaine sous-structurée sans recouvrement avec des conditions absorbante d'ordre élevé utilisées comme conditions de transmission (HABC DDM) est d'abord étendue pour traiter les points de jonction, où plus de deux sous-domaines se rencontrent. Le traitement des points de jonction est un problème bien connu pour les HABC DDM sans recouvrement. La méthodologie proposée mène à une solution efficace pour les partitions en damier, où les domaines se rencontrent à angle droit. La méthode est basée sur l'introduction de variables de transmission supplémentaires aux points de jonction, et son efficacité est démontrée sur plusieurs cas-tests. Une DDM sans recouvrement similaire est ensuite proposée avec des couches parfaitement adaptées au lieu des HABC (DDM PML). L'approche proposée prend naturellement en compte les points de jonction des partitions de domaine en damier par le biais de multiplicateurs de Lagrange couplant les sous-domaines et les couches PML adjacentes. Deux discrétisations pour les multiplicateurs de Lagrange et plusieurs stratégies de stabilisation sont proposées et comparées. Les performances des DDM HABC et PML sont ensuite comparées sur des cas-tests de complexité croissante, allant de la diffraction d'ondes dans des milieux homogènes bidimensionnelles à la propagation d'ondes tridimensionnelles dans des milieux hautement hétérogènes. Alors que les développements théoriques sont effectués pour l'équation scalaire de Helmholtz pour la simulation d'ondes acoustiques, l'extension aux problèmes d'ondes élastiques est également considérée, mettant en évidence le potentiel de généralisation des méthodes développées à d'autres contextes physiques. La deuxième partie du manuscrit est consacrée à la présentation des outils de calcul développés au cours de la thèse et qui ont été utilisés pour produire tous les résultats numériques : GmshFEM, une nouvelle bibliothèque d'éléments finis C++ basée sur le générateur de maillage open-source Gmsh ; et GmshDDM, une bibliothèque de décomposition de domaine distribuée basée sur GmshFEM

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    LIPIcs, Volume 277, GIScience 2023, Complete Volume

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    LIPIcs, Volume 277, GIScience 2023, Complete Volum

    12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

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    Superconducting flux circuits for coherent quantum annealing

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    Quantum annealing is a method with the potential to solve hard optimization problems faster than any classical method. In the near term, quantum annealing is particularly appealing due to its low control requirement, relative to gate-based quantum computation. However, despite the fact that large-scale quantum annealers containing more than 5000 qubits have been made commercially available, identifying a quantum advantage for practical problems has remained an elusive target. Amongst other issues, poor coherence is considered the main prohibitive factor for these annealers to take on the quest for quantum advantage. In this thesis, we make progress in realizing a highly coherent quantum annealer, based on superconducting capacitively-shunted flux qubits (CSFQ). First, we are met with the challenge of crosstalk calibration when implementing individual control of the qubits and couplers in the annealer, which is important for exploring novel annealing protocols. Two different methods, relying on the symmetries of the superconducting circuits, are proposed and successfully implemented to tackle this challenge. Second, we experimentally demonstrate long-range correlation in a chain of couplers, which enables effective coupling of qubits over large distances. The coupler chain could be expanded to a coupler network to support high qubit connectivity, a highly desirable feature when embedding practical-scale optimization problems into the annealer hardware. Finally, we evaluate the noise properties of the CSFQ. Coherence time measurements reveal that the dominant noise in the system is intrinsic flux noise in the two control loops of the qubit. Landau-Zener transition, a toy model for quantum annealing, is investigated in a CSFQ, revealing a crossover from the weak to strong coupling to the environment. This crossover regime was not studied before in either theory or experiment, and we present a phenomenological spin bath model to elucidate this regime

    Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

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    This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book

    Advances in Superconducting Circuit Quantum Electrodynamics

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    The topics of this thesis are based on circuit quantum electrodynamics (cQED), a theoretical and experimental platform allowing the study of light--matter interaction. This platform is rich both in observable physical phenomena and future practical applications. A "circuit" in cQED may comprise various elements, with the two main types being electromagnetic quantum harmonic oscillators, or resonators, and superconducting Josephson quantum bits, qubits. Because of the relative ease to fabricate and control quantum circuits—especially when compared to the more traditional cavity quantum electrodynamics—cQED has quickly grown in popularity in research labs across the world and is regarded as one of the major contenders for quantum computing. The advances referred to in the title of this thesis address three significant challenges to practical applications of cQED; they are relevant not only to quantum computing, but also to other applications, such as simulations of physical systems. The first advance is related to control scalability. Practical applications require large circuits, and the current approaches used to send control signals to those circuits will not scale indefinitely. A solution to this challenge, the quantum socket, is presented and evaluated in depth. The second advance concerns calibration. Any application of cQED requires knowing the precise parameters defining the interactions between the various components of a circuit. Two cutting edge methods for the calibration of interaction parameters are explained and benchmarked; they show a remarkable improvement over existing, inefficient, methods. The third advance involves the physics of dielectric defects in the samples on which circuits are fabricated. These unwanted defects are modeled as two-level systems (TLS) that interact with circuit elements such as qubits. Experimental measurements and novel simulations conclusively demonstrate that interactions between TLS are responsible for the stochastic relaxation-time fluctuations observed in superconducting qubits

    LIPIcs, Volume 244, ESA 2022, Complete Volume

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    LIPIcs, Volume 244, ESA 2022, Complete Volum

    Scalable and Reliable Sparse Data Computation on Emergent High Performance Computing Systems

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    Heterogeneous systems with both CPUs and GPUs have become important system architectures in emergent High Performance Computing (HPC) systems. Heterogeneous systems must address both performance-scalability and power-scalability in the presence of failures. Aggressive power reduction pushes hardware to its operating limit and increases the failure rate. Resilience allows programs to progress when subjected to faults and is an integral component of large-scale systems, but incurs significant time and energy overhead. The future exascale systems are expected to have higher power consumption with higher fault rates. Sparse data computation is the fundamental kernel in many scientific applications. It is suitable for the studies of scalability and resilience on heterogeneous systems due to its computational characteristics. To deliver the promised performance within the given power budget, heterogeneous computing mandates a deep understanding of the interplay between scalability and resilience. Managing scalability and resilience is challenging in heterogeneous systems, due to the heterogeneous compute capability, power consumption, and varying failure rates between CPUs and GPUs. Scalability and resilience have been traditionally studied in isolation, and optimizing one typically detrimentally impacts the other. While prior works have been proved successful in optimizing scalability and resilience on CPU-based homogeneous systems, simply extending current approaches to heterogeneous systems results in suboptimal performance-scalability and/or power-scalability. To address the above multiple research challenges, we propose novel resilience and energy-efficiency technologies to optimize scalability and resilience for sparse data computation on heterogeneous systems with CPUs and GPUs. First, we present generalized analytical and experimental methods to analyze and quantify the time and energy costs of various recovery schemes, and develop and prototype performance optimization and power management strategies to improve scalability for sparse linear solvers. Our results quantitatively reveal that each resilience scheme has its own advantages depending on the fault rate, system size, and power budget, and the forward recovery can further benefit from our performance and power optimizations for large-scale computing. Second, we design a novel resilience technique that relaxes the requirement of synchronization and identicalness for processes, and allows them to run in heterogeneous resources with power reduction. Our results show a significant reduction in energy for unmodified programs in various fault situations compared to exact replication techniques. Third, we propose a novel distributed sparse tensor decomposition that utilizes an asynchronous RDMA-based approach with OpenSHMEM to improve scalability on large-scale systems and prove that our method works well in heterogeneous systems. Our results show our irregularity-aware workload partition and balanced-asynchronous algorithms are scalable and outperform the state-of-the-art distributed implementations. We demonstrate that understanding different bottlenecks for various types of tensors plays critical roles in improving scalability
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