638 research outputs found

    Sampling per mode simulation for switching diffusions

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    We consider the problem of rare event estimation in switching diffusions using an Interacting Particle Systems (IPS) based Monte Carlo simulation approach \cite{DelMoral}. While in theory the IPS approach is virtually applicable to any strong Markov process, in practice the straightforward application of this approach to switching diffusions may fail to produce reasonable estimates within a reasonable amount of simulation time. The reason is that there may be few if no particles in modes with small probabilities (i.e.\ "light" modes). This happens because each resampling step tends to sample more "heavy" particles from modes with higher probabilities, thus, "light" particles in the "light" modes tend to be discarded. This badly affects IPS estimation performance. By increasing the number of particles the IPS estimates should improve but only at the cost of substantially increased simulation time which makes the performance of IPS approach in switching diffusions similar to one of the standard Monte Carlo. To avoid this, a conditional "sampling per mode" algorithm has been proposed in \cite{Krystul}; instead of starting the algorithm with N particles randomly distributed, we draw in each mode j, a fixed number Nj particles and at each resampling step, the same number of particles is sampled for each visited mode. Using the techniques introduced in \cite{LeGland}, we recently established a Law of Large Number theorem as well as a Central Limit Theorem for the estimate of the rare event probability

    Minimal Sparsity for Second-Order Moment-SOS Relaxations of the AC-OPF Problem

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    AC-OPF (Alternative Current Optimal Power Flow)aims at minimizing the operating costs of a power gridunder physical constraints on voltages and power injections.Its mathematical formulation results in a nonconvex polynomial optimizationproblem which is hard to solve in general,but that can be tackled by a sequence of SDP(Semidefinite Programming) relaxationscorresponding to the steps of the moment-SOS (Sums-Of-Squares) hierarchy.Unfortunately, the size of these SDPs grows drastically in the hierarchy,so that even second-order relaxationsexploiting the correlative sparsity pattern of AC-OPFare hardly numerically tractable for largeinstances -- with thousands of power buses.Our contribution lies in a new sparsityframework, termed minimal sparsity, inspiredfrom the specific structure of power flowequations.Despite its heuristic nature, numerical examples show that minimal sparsity allows the computation ofhighly accurate second-order moment-SOS relaxationsof AC-OPF, while requiring far less computing time and memory resources than the standard correlative sparsity pattern. Thus, we manage to compute second-order relaxations on test caseswith about 6000 power buses, which we believe to be unprecedented

    FAIR semantics and the NVS

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    The FAIR principles provide guidelines for the publication of digital resources such as datasets, code, workflows, and research objects aiming at making them Findable, Accessible, Interoperable, and Reusable(1). Amongst them, the I of the FAIR promotes interoperability and more specifically principle I2 suggests that metadata should use vocabularies that themselves follow the FAIR principles. Recently, FAIRsFAIR1 project officially published a first iteration of recommendations for making vocabularies FAIR (2). These recommendations include 17 general recommendations aligned with the different FAIR Principles and 10 Best Practice recommendations. The main objective of these recommendations is to provide a set of guidelines for creating a harmonised and interoperable semantic landscape easing the use and reuse of semantic artefacts from multiple different scientific domains

    Converging on a Semantic Interoperability Framework for the European Data Space for Science, Research and Innovation (EOSC)

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    L’interopérabilité sémantique (IS) est au cœur des principes FAIR et de la conception à grande échelle des infrastructures interdisciplinaires. L’European Open Science Cloud (EOSC) est un effort à l’échelle européenne vers une telle infrastructure, visant à approfondir la collaboration régionale en matière de recherche et à construire un espace de données partagé pour la science, la recherche et l’innovation

    Controlling Microgrids Without External Data: A Benchmark of Stochastic Programming Methods

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    Microgrids are local energy systems that integrate energy production, demand, and storage units. They are generally connected to the regional grid to import electricity when local production and storage do not meet the demand. In this context, Energy Management Systems (EMS) are used to ensure the balance between supply and demand, while minimizing the electricity bill, or an environmental criterion. The main implementation challenges for an EMS come from the uncertainties in the consumption, the local renewable energy production, and in the price and the carbon intensity of electricity. Model Predictive Control (MPC) is widely used to implement EMS but is particularly sensitive to the forecast quality, and often requires a subscription to expensive third-party forecast services. We introduce four Multistage Stochastic Control Algorithms relying only on historical data obtained from on-site measurements. We formulate them under the shared framework of Multistage Stochastic Programming and benchmark them against two baselines in 61 different microgrid setups using the EMSx dataset. Our most effective algorithm produces notable cost reductions compared to an MPC that utilizes the same uncertainty model to generate predictions, and it demonstrates similar performance levels to an ideal MPC that relies on perfect forecasts

    Gender-based violence-supportive cognitions in adolescent girls and boys: The function of violence exposure and victimisation

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    Violence against women and girls is widespread in the Caribbean, which may be due to heightened acceptance of such acts in this specific social context. In spite of this, studies investigating attitudes towards violence and their correlates among participants drawn from the region are missing. In order to address this void in the literature, we examined associations between violence exposure and victimisation and two gender-based violence-related cognitions (attitudes towards male physical domestic violence and social norms regarding physical violence against girls) as well as general beliefs about violence, using structural equation modelling. Participants were a sample of adolescent girls (n = 661; M age = 13.15) and boys (n = 639; M age = 13.22) from two Eastern Caribbean countries, Barbados and Grenada, recruited from 10 primary schools, nine secondary schools, and two youth offender centres. In considering that girls and boys were previously demonstrated to differ in their experiences as well as tolerance of violence, structural models were specified and tested separately for the two sexes. Results indicated that violence victimisation was positively strongly associated with attitudes towards male physical domestic violence and social norms regarding physical violence against girls among boys. Increased violence victimisation among girls, in turn, correlated with increased acceptance of social norms regarding physical violence against girls, but this relationship was weak. Violence exposure did not have any significant associations with any of the attitudinal variables included in the study. We discuss the importance of these findings for the development of appropriate gender-based violence prevention strategies for youths from the Eastern Caribbean
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