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Auroral breakup detection in all-sky images by unsupervised learning
No full textInternational audienceAbstract. Due to a large number of automatic auroral camera systems on the ground, image data analysis requires more efficiency than what human expert visual inspection can provide. Furthermore, there is no solid consensus on how many different types or shapes exist in auroral displays. We report the first attempt to classify auroral morphological forms by an unsupervised learning method on an image set that contains both nightside and dayside aurora. We used 6 months of full-colour auroral all-sky images captured at a high-Arctic observatory on Svalbard, Norway, in 2019–2020. The selection of images containing aurora was performed manually. These images were then input into a convolutional neural network called SimCLR for feature extraction. The clustered and fused features resulted in 37 auroral morphological clusters. In the clustering of auroral image data with two different time resolutions, we found that the occurrence of 8 clusters strongly increased when the image cadence was high (24 s), while the occurrence of 14 clusters experienced little or no change with changes in input image cadence. We therefore investigated the temporal evolution of a group of eight “active aurora” clusters. Time periods for which this active aurora persisted for longer than two consecutive images with a maximum cadence of 6 min coincided with ground-magnetic deflections, and their occurrence was found to maximize around magnetic midnight. The active aurora onsets typically included vortical auroral structures and equivalent current patterns typical for substorms. Our findings therefore suggest that our unsupervised image clustering method can be used to detect auroral breakups in ground-based image datasets with a temporal accuracy determined by the image cadence
A direct multimode method for the reduction of vibration induced oscillations on force signals during “pseudo-rigid” water impact experiments
No full textInternational audienceA new experimental method is proposed to improve the force measurements during water impact experiments (e.g. water entry or wave impact tests) carried out with “pseudo-rigid” mock-ups. Despite the efforts of making the mock-up as stiff as possible, the impulsive nature of water impact loads may induce a transient response of the mock-up with a broad frequency content and a perturbation of the force measurements. Using the principles of momentum conservation, it can be shown that the load cell signal is the sum of the hydrodynamic forcing term and of an additional inertial term directly related to the vibrations of the structure. In the present paper, we suggest to estimate the inertial term using several accelerometers which record the response of the structure at different locations. Assuming that the structure response can be approximated by a set of natural modes, we show that it is possible to estimate the inertial term by a linear combination of the acceleration signals. The coefficients of the linear combination may be identified a priori by performing hammer tests, but in certain cases they can also be identified a posteriori using a segment of the signal time series. The performance of the method is demonstrated by considering two experimental test cases of increasing complexity. The first test case is a hydrofoil of constant section impacting water at constant speed which exhibits two-dimensional beam-like vibrations. The second test case is a segmented model of a vertical cylinder impacted by a breaking wave whose segments exhibit three-dimensional vibrations. The proposed method is efficient, conceptually simple and very simple to implement from a signal processing point of view, which makes it promising not only for water impact problems but also for other unstationary fluid–structure interaction problems
On the road toward daphnepapytones: total synthesis of oleodaphnone, daphnenicillata W and diarthroncha C
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Tandem Hock and Friedel-Crafts reactions allowing an expedient synthesis of a cyclolignan-type scaffold
No full textInternational audienceThe Hock cleavage is an acid-catalyzed oxydative reaction involving allylic or benzylic hydroperoxides. Since it generates an electrophilic oxocarbenium species, it could be used in tandem processes in the presence of nucleophiles. Here, starting from benzyl(prenyl)malonate substrates, the prenyl moiety was first photooxygenated. The resulting hydroperoxide was directly engaged in a Hock cleavage by adding a Lewis acid in presence of an aromatic nucleophile to promote tandem Friedel-Crafts reactions, also involving th
HeROcache: Storage-Aware Scheduling in Heterogeneous Serverless Edge - The Case of IDS
No full textInternational audienceIntrusion Detection Systems (IDS) are time-sensitive applications that aim to classify potentially malicious network traffic. IDSs are part of a class of applications that rely on short-lived functions that can be run reactively and, as such, could be deployed on edge resources, to offload processing from energy-constrained battery-backed devices. The serverless service model could fit the needs of such applications, given that the platform allows adequate levels of Quality of Service (QoS) for a variety of users, since the criticality of IDS applications depends on several parameters.Deploying serverless functions on unreserved edge resources requires to pay particular attention to (1) initialization delays that could be significant on low resources platforms, (2) inter-function communication between edge nodes, and (3) heterogeneous devices.In this paper, we propose both a storage-aware allocation and scheduling policy that seek to minimize task placement costs for service providers on edge devices while optimizing QoS for IDS users.To do so, we propose a caching and consolidation strategy that minimizes cold starts and inter-function communication delays while satisfying QoS by leveraging heterogeneous edge resources.We evaluated our platform in a simulation environment using characterization data from real-world IDS tasks and execution platforms and compared it with a vanilla Knative orchestrator and a storage-agnostic policy. Our strategy achieves 18% fewer QoS penalties while consolidating applications across 80% fewer edge nodes
Temporal characterization of laser pulses using an air-based knife-edge technique
No full textInternational audienceThe characterization of ultrashort laser pulses is presented by using the plasma-induced frequency-resolved optical switching technique, implemented in ambient air. The recently developed method allows for a temporal reconstruction of a pulse at its focal spot by utilizing a moderately intense pump laser pulse for generating a ionization-induced ultrafast defocusing lens. When propagating through the produced plasma lens, the probe beam to characterize experiences an increase of its size in the far field. The spectrum of the defocused probe field, measured as a function of the pump-probe delay, allows for a comprehensive characterization of the temporal and spectral attributes of the pulse. Herein, the ability of this technique, initially designed for use in rare gases, is reported to operate in ambient air conditions with similar performance. The method is remarkably straightforward to implement and requires no additional optical component other than a focusing mirror while delivering laser pulse reconstructions of high reliability
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
No full textInternational audienceUncertainty quantification is critical for deploying deep neural networks (DNNs) in real-world applications.An Auxiliary Uncertainty Estimator (AuxUE) is one of the most effective means to estimate the uncertainty of the main task prediction without modifying the main task model.To be considered robust, an AuxUE must be capable of maintaining its performance and triggering higher uncertainties while encountering Out-of-Distribution (OOD) inputs, i.e., to provide robust aleatoric and epistemic uncertainty. However, for vision regression tasks, current AuxUE designs are mainly adopted for aleatoric uncertainty estimates, and AuxUE robustness has not been explored. In this work, we propose a generalized AuxUE scheme for more robust uncertainty quantification on regression tasks. Concretely, to achieve a more robust aleatoric uncertainty estimation, different distribution assumptions are considered for heteroscedastic noise, and Laplace distribution is finally chosen to approximate the prediction error.For epistemic uncertainty, we propose a novel solution named Discretization-Induced Dirichlet pOsterior (DIDO), which models the Dirichlet posterior on the discretized prediction error.Extensive experiments on age estimation, monocular depth estimation, and super-resolution tasks show that our proposed method can provide robust uncertainty estimates in the face of noisy inputs and that it can be scalable to both image-level and pixel-wise tasks
LOTUS: Learning from Operational Teaming with Unmanned Systems
No full textInternational audienceThe LOTUS project aims at improving maritime surveillance. In this context, this position paper presents ongoingcontributions, including novel machine learning algorithms for multi-agent systems to be applied to groups of underwater drones involved in surveillance missions. It emphasises incorporating human-machine teaming to bolster decision-making in maritime scenarios. The expected outcomes of this project comprise the robust control of groups of autonomous vehicles, adaptable to environmental changes, as well as an effective reporting method. Mission summaries will be delivered to human operators by way of narratives about the relevant events detected thanks to drones. The integration of this narrative construction poweredby machine learning will enhance the overall effectiveness of the team, constituting a significant breakthrough
Introduction aux équations aux dérivées partielles hyperboliques et à leur approximation numérique
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Direct parametrisation of invariant manifolds for non-autonomous forced systems including superharmonic resonances
No full textInternational audienceThe direct parametrisation method for invariant manifold is a model-order reduction technique that can be applied to nonlinear systems described by PDEs and discretised e.g. witha finite element procedure in order to derive efficient reduced-order models (ROMs). In non-linear vibrations, it has already been applied to autonomous and non-autonomous problemsto propose ROMs that can compute backbone and frequency-response curves of structures with geometric nonlinearity. While previous developments used a first-order expansion tocope with the non-autonomous term, this assumption is here relaxed by proposing a different treatment. The key idea is to enlarge the dimension of the parametrising coordinateswith additional entries related to the forcing. A new algorithm is derived with this starting assumption and, as a key consequence, the resonance relationships appearing throughthe homological equations involve multiple occurrences of the forcing frequency, showing that with this new development, ROMs for systems exhibiting a superharmonic resonance,can be derived. The method is implemented and validated on academic test cases involving beams and arches. It is numerically demonstrated that the method generates efficient ROMsfor problems involving 3:1 and 2:1 superharmonic resonances, as well as converged results for systems where the first-order truncation on the non-autonomous term showed a clear limitation