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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Antoine Legrain; Jérémy Omer; Samuel Rosat;

    International audience; In this paper, we focus on the problem studied in the second international nurse rostering competition: a personalized nurse scheduling problem under uncertainty. The schedules must be computed week by week over a planning horizon of up to eight weeks. We present the work that the authors submitted to this competition and which was awarded the second prize. At each stage, the dynamic algorithm is fed with the staffing demand and nurses preferences for the current week and computes an irrevocable schedule for all nurses without knowledge of future inputs. The challenge is to obtain a feasible and near-optimal schedule at the end of the horizon. The online stochastic algorithm described in this paper draws inspiration from the primal-dual algorithm for online optimization and the sample average approximation, and is built upon an existing static nurse scheduling software. The procedure generates a small set of candidate schedules, rank them according to their performance over a set of test scenarios, and keeps the best one. Numerical results show that this algorithm is very robust, since it has been able to produce feasible and near optimal solutions on most of the proposed instances ranging from 30 to 120 nurses over a horizon of 4 or 8 weeks. Finally, the code of our implementation is open source and available in a public repository.

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    European Journal of Operational Research; PolyPublie
    Other literature type . Article . 2020 . Peer-reviewed
    License: Elsevier TDM
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Tiberga, Marco; de Oliveira, Rodrigo Gonzalez Gonzaga; Cervi, Eric; Blanco, Juan Antonio; +4 Authors

    Verification and validation of multi-physics codes dedicated to fast-spectrum molten salt reactors (MSR) is a very challenging task. Existing benchmarks are meant for single-physics codes, while experimental data for validation are absent. This is concerning, given the importance numerical simulations have in the development of fast MSR designs. Here, we propose the use of a coupled numerical benchmark specifically designed to assess the physics-coupling capabilities of the aforementioned codes. The benchmark focuses on the specific characteristics of fast MSRs and features a step-by-step approach, where physical phenomena are gradually coupled to easily identify sources of error. We collect and compare the results obtained during the benchmarking campaign of four multi-physics tools developed within the SAMOFAR project. Results show excellent agreement for all the steps of the benchmark. The benchmark generality and the broad spectrum of results provided constitute a useful tool for the testing and development of similar multi-physics codes. RST/Reactor Physics and Nuclear Materials

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    ZENODO; Annals of Nuclear Energy
    Other literature type . Article . 2020 . Peer-reviewed
    License: Elsevier TDM
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ziemann, Ingvar; Sandberg, Henrik;

    This article introduces and solves a new privacy-related optimization problem for cyber-physical systems where an adversary tries to learn the system dynamics. In the context of linear quadratic systems, we consider the problem of achieving a small cost while balancing the need for keeping knowledge about the model's parameters private. To this end, we formulate a Fisher information regularized version of the linear quadratic regulator with cheap cost. Here the control operator is allowed to not only control the plant but also mask its state by injecting further noise. Within the class of linear policies with additive noise, we solve this problem and show that the optimal noise distribution is Gaussian with state dependent covariance. Next, we prove that the optimal linear feedback law is the same as without regularization. Finally, to motivate our proposed scheme, we formulate for scalar systems an equivalent maximin problem for the worst-case scenario in which the adversary has full knowledge of all other inputs and outputs. Here, our policies are maximin optimal with respect to maximizing the variance over all asymptotically unbiased estimators.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ HAL - UPEC / UPEM; H...arrow_drop_down
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    Hal-Diderot
    Other literature type . 2020
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    Hyper Article en Ligne
    Other literature type . 2020
    https://doi.org/10.23919/acc45...
    Conference object . 2020 . Peer-reviewed
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ HAL - UPEC / UPEM; H...arrow_drop_down
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      Hal-Diderot
      Other literature type . 2020
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Other literature type . 2020
      https://doi.org/10.23919/acc45...
      Conference object . 2020 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hennequin, A.; Couturier, B.; Gligorov, V.V.; Ponce, S.; +2 Authors

    The upgraded CERN LHCb detector, due to start data taking in 2021, will have to reconstruct 4 TB/s of raw detector data in real time using commodity processors. This is one of the biggest real-time data processing challenges in any scientific domain. We present an intrinsically parallel reconstruction algorithm for the vertex detector of the LHCb experiment designed to optimally exploit multi-core general purpose architectures. We evaluate the algorithm on two high-end architectures from two different vendors and discuss in detail the impact of different SIMD Instruction Set Architecture extensions on the performance. We further compare the algorithm to current state-of-the-art scalar pattern recognition algorithms. We show a factor 2 speedup while achieving similar or better levels of physics performance. The upgraded CERN LHCb detector, due to start data taking in 2021, will have to reconstruct 4 TB/s of raw detector data in real time using commodity processors. This is one of the biggest real-time data processing challenges in any scientific domain. We present an intrinsically parallel reconstruction algorithm for the vertex detector of the LHCb experiment designed to optimally exploit multi-core general purpose architectures. We compare it to previous state-of-the-art scalar pattern recognition algorithms and show significantly faster processing and in some cases increased physics performance over all current alternatives. We evaluate the algorithm on two high-end architectures from two different vendors and discuss in detail the impact of different SIMD Instruction Set Architecture extensions on the performance.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CERN Document Serverarrow_drop_down
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    CERN Document Server
    Other literature type . 2019
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    Journal of Instrumentation
    Other literature type . Article . 2020 . Peer-reviewed
    License: CC BY
    https://doi.org/10.48550/arxiv...
    Article . 2019
    License: CC BY
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CERN Document Serverarrow_drop_down
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      CERN Document Server
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      Journal of Instrumentation
      Other literature type . Article . 2020 . Peer-reviewed
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      https://doi.org/10.48550/arxiv...
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Vianney Debavelaere; Stanley Durrleman; Stéphanie Allassonnière;

    International audience; Given repeated observations of several subjects over time, i.e. a longitudinal data set, this paper introduces a new model to learn a classification of the shapes progression in an unsupervised setting: we automatically cluster a longitudinal data set in different classes without labels. Our method learns for each cluster an average shape trajectory (or representative curve) and its variance in space and time. Representative trajectories are built as the combination of pieces of curves. This mixture model is flexible enough to handle independent trajectories for each cluster as well as fork and merge scenarios. The estimation of such non linear mixture models in high dimension is known to be difficult because of the trapping states effect that hampers the optimisation of cluster assignments during training. We address this issue by using a tempered version of the stochastic EM algorithm. Finally, we apply our algorithm on different data sets. First, synthetic data are used to show that a tempered scheme achieves better convergence. We then apply our method to different real data sets: 1D RECIST score used to monitor tumors growth, 3D facial expressions and meshes of the hippocampus. In particular, we show how the method can be used to test different scenarios of hip-pocampus atrophy in ageing by using an heteregenous population of normal ageing individuals and mild cog-nitive impaired subjects.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Gidaris, Spyros; Bursuc, Andrei; Komodakis, Nikos; Pérez, Patrick; +1 Authors

    Self-supervised representation learning targets to learn convnet-based image representations from unlabeled data. Inspired by the success of NLP methods in this area, in this work we propose a self-supervised approach based on spatially dense image descriptions that encode discrete visual concepts, here called visual words. To build such discrete representations, we quantize the feature maps of a first pre-trained self-supervised convnet, over a k-means based vocabulary. Then, as a self-supervised task, we train another convnet to predict the histogram of visual words of an image (i.e., its Bag-of-Words representation) given as input a perturbed version of that image. The proposed task forces the convnet to learn perturbation-invariant and context-aware image features, useful for downstream image understanding tasks. We extensively evaluate our method and demonstrate very strong empirical results, e.g., our pre-trained self-supervised representations transfer better on detection task and similarly on classification over classes "unseen" during pre-training, when compared to the supervised case. This also shows that the process of image discretization into visual words can provide the basis for very powerful self-supervised approaches in the image domain, thus allowing further connections to be made to related methods from the NLP domain that have been extremely successful so far. Comment: Accepted to CVPR2020

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    Other literature type . 2020
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    http://arxiv.org/pdf/2002.1224...
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    https://doi.org/10.1109/cvpr42...
    Conference object . 2020 . Peer-reviewed
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    Article . 2020
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ arXiv.org e-Print Ar...arrow_drop_down
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      Other literature type . 2020
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      http://arxiv.org/pdf/2002.1224...
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      https://doi.org/10.1109/cvpr42...
      Conference object . 2020 . Peer-reviewed
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      https://doi.org/10.48550/arxiv...
      Article . 2020
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Galland, Stéphane; Rodriguez, Sebastian; Gaud, Nicolas;

    SARL is a general-purpose agent-oriented programming language. This language aims at providing the fundamental abstractions for dealing with concurrency, distribution, interaction, decentralization, reactivity, autonomy and dynamic reconfiguration that are usually considered as essential for implementing agent-based applications. Every programming language specifies an execution model. For SARL, this run-time model is supported by a SARL run-time environment. The goals of this paper are to highlight the key principles for creating a SARL run-time environment, and its concrete implementation into the Janus agent platform. Fil: Galland, Stéphane. Universite de Bourgogne; Francia Fil: Rodriguez, Sebastian Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Tecnológica Nacional; Argentina Fil: Gaud, Nicolas. Universite de Bourgogne; Francia

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    CONICET Digital
    Article . 2017
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Future Generation Computer Systems
    Article . 2020 . Peer-reviewed
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    Article . 2020
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    Authors: Gheisari, Marzieh; Furon, Teddy; Amsaleg, Laurent;

    International audience; This paper proposes a framework for group membership protocols preventing the curious but honest server from reconstructing the enrolled biometric signatures and inferring the identity of querying clients. This framework learns the embedding parameters, group representations and assignments simultaneously. Experiments show the trade-off between se-curity/privacy and verification/identification performances.

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    http://arxiv.org/pdf/2002.1036...
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    https://doi.org/10.48550/arxiv...
    Article . 2020
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    Authors: Pariente, Manuel; Cornell, Samuele; Deleforge, Antoine; Vincent, Emmanuel;

    Single-channel speech separation has recently made great progress thanks to learned filterbanks as used in ConvTasNet. In parallel, parameterized filterbanks have been proposed for speaker recognition where only center frequencies and bandwidths are learned. In this work, we extend real-valued learned and parameterized filterbanks into complex-valued analytic filterbanks and define a set of corresponding representations and masking strategies. We evaluate these filterbanks on a newly released noisy speech separation dataset (WHAM). The results show that the proposed analytic learned filterbank consistently outperforms the real-valued filterbank of ConvTasNet. Also, we validate the use of parameterized filterbanks and show that complex-valued representations and masks are beneficial in all conditions. Finally, we show that the STFT achieves its best performance for 2ms windows. Comment: ICASSP 2020

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    http://arxiv.org/pdf/1910.1040...
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    Other literature type . 2020
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    https://doi.org/10.1109/icassp...
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    https://doi.org/10.48550/arxiv...
    Article . 2019
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    Authors: Cherni, Afef; Anthoine, Sandrine; Chaux, Caroline;

    International audience; In Nuclear Magnetic Resonance (NMR) spectroscopy, an efficient analysis and a relevant extraction of different molecule properties from a given chemical mixture are important tasks, especially when processing bidimensional NMR data. To that end, using a blind source separation approach based on a variational formulation seems to be a good strategy. However, the poor resolution of NMR spectra and their large dimension require a new and modern blind source separation method. In this work, we propose a new variational formulation for blind source separation (BSS) based on a β-divergence data fidelity term combined with sparsity promoting regularization functions. An application to 2D HSQC NMR experiments illustrates the interest and the effectiveness of the proposed method whether in simulated or real cases.

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    https://doi.org/10.1109/icassp...
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Antoine Legrain; Jérémy Omer; Samuel Rosat;

    International audience; In this paper, we focus on the problem studied in the second international nurse rostering competition: a personalized nurse scheduling problem under uncertainty. The schedules must be computed week by week over a planning horizon of up to eight weeks. We present the work that the authors submitted to this competition and which was awarded the second prize. At each stage, the dynamic algorithm is fed with the staffing demand and nurses preferences for the current week and computes an irrevocable schedule for all nurses without knowledge of future inputs. The challenge is to obtain a feasible and near-optimal schedule at the end of the horizon. The online stochastic algorithm described in this paper draws inspiration from the primal-dual algorithm for online optimization and the sample average approximation, and is built upon an existing static nurse scheduling software. The procedure generates a small set of candidate schedules, rank them according to their performance over a set of test scenarios, and keeps the best one. Numerical results show that this algorithm is very robust, since it has been able to produce feasible and near optimal solutions on most of the proposed instances ranging from 30 to 120 nurses over a horizon of 4 or 8 weeks. Finally, the code of our implementation is open source and available in a public repository.

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    European Journal of Operational Research; PolyPublie
    Other literature type . Article . 2020 . Peer-reviewed
    License: Elsevier TDM
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Tiberga, Marco; de Oliveira, Rodrigo Gonzalez Gonzaga; Cervi, Eric; Blanco, Juan Antonio; +4 Authors

    Verification and validation of multi-physics codes dedicated to fast-spectrum molten salt reactors (MSR) is a very challenging task. Existing benchmarks are meant for single-physics codes, while experimental data for validation are absent. This is concerning, given the importance numerical simulations have in the development of fast MSR designs. Here, we propose the use of a coupled numerical benchmark specifically designed to assess the physics-coupling capabilities of the aforementioned codes. The benchmark focuses on the specific characteristics of fast MSRs and features a step-by-step approach, where physical phenomena are gradually coupled to easily identify sources of error. We collect and compare the results obtained during the benchmarking campaign of four multi-physics tools developed within the SAMOFAR project. Results show excellent agreement for all the steps of the benchmark. The benchmark generality and the broad spectrum of results provided constitute a useful tool for the testing and development of similar multi-physics codes. RST/Reactor Physics and Nuclear Materials

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    ZENODO; Annals of Nuclear Energy
    Other literature type . Article . 2020 . Peer-reviewed
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    Authors: Ziemann, Ingvar; Sandberg, Henrik;

    This article introduces and solves a new privacy-related optimization problem for cyber-physical systems where an adversary tries to learn the system dynamics. In the context of linear quadratic systems, we consider the problem of achieving a small cost while balancing the need for keeping knowledge about the model's parameters private. To this end, we formulate a Fisher information regularized version of the linear quadratic regulator with cheap cost. Here the control operator is allowed to not only control the plant but also mask its state by injecting further noise. Within the class of linear policies with additive noise, we solve this problem and show that the optimal noise distribution is Gaussian with state dependent covariance. Next, we prove that the optimal linear feedback law is the same as without regularization. Finally, to motivate our proposed scheme, we formulate for scalar systems an equivalent maximin problem for the worst-case scenario in which the adversary has full knowledge of all other inputs and outputs. Here, our policies are maximin optimal with respect to maximizing the variance over all asymptotically unbiased estimators.

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    https://doi.org/10.23919/acc45...
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ HAL - UPEC / UPEM; H...arrow_drop_down
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      https://doi.org/10.23919/acc45...
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    Authors: Hennequin, A.; Couturier, B.; Gligorov, V.V.; Ponce, S.; +2 Authors

    The upgraded CERN LHCb detector, due to start data taking in 2021, will have to reconstruct 4 TB/s of raw detector data in real time using commodity processors. This is one of the biggest real-time data processing challenges in any scientific domain. We present an intrinsically parallel reconstruction algorithm for the vertex detector of the LHCb experiment designed to optimally exploit multi-core general purpose architectures. We evaluate the algorithm on two high-end architectures from two different vendors and discuss in detail the impact of different SIMD Instruction Set Architecture extensions on the performance. We further compare the algorithm to current state-of-the-art scalar pattern recognition algorithms. We show a factor 2 speedup while achieving similar or better levels of physics performance. The upgraded CERN LHCb detector, due to start data taking in 2021, will have to reconstruct 4 TB/s of raw detector data in real time using commodity processors. This is one of the biggest real-time data processing challenges in any scientific domain. We present an intrinsically parallel reconstruction algorithm for the vertex detector of the LHCb experiment designed to optimally exploit multi-core general purpose architectures. We compare it to previous state-of-the-art scalar pattern recognition algorithms and show significantly faster processing and in some cases increased physics performance over all current alternatives. We evaluate the algorithm on two high-end architectures from two different vendors and discuss in detail the impact of different SIMD Instruction Set Architecture extensions on the performance.

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    Journal of Instrumentation
    Other literature type . Article . 2020 . Peer-reviewed
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    https://doi.org/10.48550/arxiv...
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      Journal of Instrumentation
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    Authors: Vianney Debavelaere; Stanley Durrleman; Stéphanie Allassonnière;

    International audience; Given repeated observations of several subjects over time, i.e. a longitudinal data set, this paper introduces a new model to learn a classification of the shapes progression in an unsupervised setting: we automatically cluster a longitudinal data set in different classes without labels. Our method learns for each cluster an average shape trajectory (or representative curve) and its variance in space and time. Representative trajectories are built as the combination of pieces of curves. This mixture model is flexible enough to handle independent trajectories for each cluster as well as fork and merge scenarios. The estimation of such non linear mixture models in high dimension is known to be difficult because of the trapping states effect that hampers the optimisation of cluster assignments during training. We address this issue by using a tempered version of the stochastic EM algorithm. Finally, we apply our algorithm on different data sets. First, synthetic data are used to show that a tempered scheme achieves better convergence. We then apply our method to different real data sets: 1D RECIST score used to monitor tumors growth, 3D facial expressions and meshes of the hippocampus. In particular, we show how the method can be used to test different scenarios of hip-pocampus atrophy in ageing by using an heteregenous population of normal ageing individuals and mild cog-nitive impaired subjects.

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    Authors: Gidaris, Spyros; Bursuc, Andrei; Komodakis, Nikos; Pérez, Patrick; +1 Authors

    Self-supervised representation learning targets to learn convnet-based image representations from unlabeled data. Inspired by the success of NLP methods in this area, in this work we propose a self-supervised approach based on spatially dense image descriptions that encode discrete visual concepts, here called visual words. To build such discrete representations, we quantize the feature maps of a first pre-trained self-supervised convnet, over a k-means based vocabulary. Then, as a self-supervised task, we train another convnet to predict the histogram of visual words of an image (i.e., its Bag-of-Words representation) given as input a perturbed version of that image. The proposed task forces the convnet to learn perturbation-invariant and context-aware image features, useful for downstream image understanding tasks. We extensively evaluate our method and demonstrate very strong empirical results, e.g., our pre-trained self-supervised representations transfer better on detection task and similarly on classification over classes "unseen" during pre-training, when compared to the supervised case. This also shows that the process of image discretization into visual words can provide the basis for very powerful self-supervised approaches in the image domain, thus allowing further connections to be made to related methods from the NLP domain that have been extremely successful so far. Comment: Accepted to CVPR2020

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    http://arxiv.org/pdf/2002.1224...
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    https://doi.org/10.1109/cvpr42...
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      http://arxiv.org/pdf/2002.1224...
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      https://doi.org/10.1109/cvpr42...
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    Authors: Galland, Stéphane; Rodriguez, Sebastian; Gaud, Nicolas;

    SARL is a general-purpose agent-oriented programming language. This language aims at providing the fundamental abstractions for dealing with concurrency, distribution, interaction, decentralization, reactivity, autonomy and dynamic reconfiguration that are usually considered as essential for implementing agent-based applications. Every programming language specifies an execution model. For SARL, this run-time model is supported by a SARL run-time environment. The goals of this paper are to highlight the key principles for creating a SARL run-time environment, and its concrete implementation into the Janus agent platform. Fil: Galland, Stéphane. Universite de Bourgogne; Francia Fil: Rodriguez, Sebastian Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Tecnológica Nacional; Argentina Fil: Gaud, Nicolas. Universite de Bourgogne; Francia

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    Future Generation Computer Systems
    Article . 2020 . Peer-reviewed
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    Authors: Gheisari, Marzieh; Furon, Teddy; Amsaleg, Laurent;

    International audience; This paper proposes a framework for group membership protocols preventing the curious but honest server from reconstructing the enrolled biometric signatures and inferring the identity of querying clients. This framework learns the embedding parameters, group representations and assignments simultaneously. Experiments show the trade-off between se-curity/privacy and verification/identification performances.

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    http://arxiv.org/pdf/2002.1036...
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    https://doi.org/10.48550/arxiv...
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    Authors: Pariente, Manuel; Cornell, Samuele; Deleforge, Antoine; Vincent, Emmanuel;

    Single-channel speech separation has recently made great progress thanks to learned filterbanks as used in ConvTasNet. In parallel, parameterized filterbanks have been proposed for speaker recognition where only center frequencies and bandwidths are learned. In this work, we extend real-valued learned and parameterized filterbanks into complex-valued analytic filterbanks and define a set of corresponding representations and masking strategies. We evaluate these filterbanks on a newly released noisy speech separation dataset (WHAM). The results show that the proposed analytic learned filterbank consistently outperforms the real-valued filterbank of ConvTasNet. Also, we validate the use of parameterized filterbanks and show that complex-valued representations and masks are beneficial in all conditions. Finally, we show that the STFT achieves its best performance for 2ms windows. Comment: ICASSP 2020

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    https://doi.org/10.1109/icassp...
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    Authors: Cherni, Afef; Anthoine, Sandrine; Chaux, Caroline;

    International audience; In Nuclear Magnetic Resonance (NMR) spectroscopy, an efficient analysis and a relevant extraction of different molecule properties from a given chemical mixture are important tasks, especially when processing bidimensional NMR data. To that end, using a blind source separation approach based on a variational formulation seems to be a good strategy. However, the poor resolution of NMR spectra and their large dimension require a new and modern blind source separation method. In this work, we propose a new variational formulation for blind source separation (BSS) based on a β-divergence data fidelity term combined with sparsity promoting regularization functions. An application to 2D HSQC NMR experiments illustrates the interest and the effectiveness of the proposed method whether in simulated or real cases.

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    https://doi.org/10.1109/icassp...
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