A Survey of Contextual Optimization Methods for Decision Making under Uncertainty

Recently there has been a surge of interest in operations research (OR) and the machine learning (ML) community in combining prediction algorithms and optimization techniques to solve decision-making problems in the face of uncertainty. This gave rise to the field of contextual optimization, under which data-driven procedures are developed to prescribe actions to the decision-maker that make the best use of the most recently updated information. A large variety of models and methods have been presented in both OR and ML literature under a variety of names, including data-driven optimization, prescriptive optimization, predictive stochastic programming, policy optimization, (smart) predict/estimate-then-optimize, decision-focused learning, (task-based) end-to-end learning/forecasting/optimization, etc. Focusing on single and two-stage stochastic programming problems, this review article identifies three main frameworks for learning policies from data and discusses their strengths and limitations. We present the existing models and methods under a uniform notation and terminology and classify them according to the three main frameworks identified. Our objective with this survey is to both strengthen the general understanding of this active field of research and stimulate further theoretical and algorithmic advancements in integrating ML and stochastic programming

Distributed Model Predictive Operation Control of Interconnected Microgrids

The upward trends in renewable energy deployment in recent years brings new challengesto the development of electrical networks. Interconnected microgrids appear as a novelbottom-up approach to the production and integration of renewable energy.Using model predictive control (MPC), the energy management of several interconnectedmicrogrids is investigated. An optimisation problem is formulated and distributed ontothe individual units using the alternating direction method of multipliers (ADMM). Themicrogrids cooperate to reach a global optimum using neighbour-to-neighbour communications.The benefits of using distributed operation control for microgrids are analysed and a controlarchitecture is proposed. Two algorithms are implemented to solve the optimisationproblem and their advantages or differences are confronted.Förnybara energikällor har ökat under senaste åren. Det innebär nya utmaningar förevolutionen av elektriska nät. Microgrids är en bottom-up ansats för produktion ochintegrering av förnybar energi.Energiförsörjning av flera sammankoppladeMicrogrids studeras in detta arbete genommodellbaserad prediktiv kontroll (MPC). Ett optimeringsproblem formuleras på de enskildaenheterna med Alternating DirectionMethod ofMultipliers (ADMM) och parallellberäkningar härledas.Microgrids samarbetar för att nå en global lösning av neighbourto-neighbour kommunikation.Distribuerad energiförsörjning av microgrids analyseras och två kontroll algorithmerutformas

Distributed Model Predictive Operation Control of Interconnected Microgrids

The upward trends in renewable energy deployment in recent years brings new challengesto the development of electrical networks. Interconnected microgrids appear as a novelbottom-up approach to the production and integration of renewable energy.Using model predictive control (MPC), the energy management of several interconnectedmicrogrids is investigated. An optimisation problem is formulated and distributed ontothe individual units using the alternating direction method of multipliers (ADMM). Themicrogrids cooperate to reach a global optimum using neighbour-to-neighbour communications.The benefits of using distributed operation control for microgrids are analysed and a controlarchitecture is proposed. Two algorithms are implemented to solve the optimisationproblem and their advantages or differences are confronted.Förnybara energikällor har ökat under senaste åren. Det innebär nya utmaningar förevolutionen av elektriska nät. Microgrids är en bottom-up ansats för produktion ochintegrering av förnybar energi.Energiförsörjning av flera sammankoppladeMicrogrids studeras in detta arbete genommodellbaserad prediktiv kontroll (MPC). Ett optimeringsproblem formuleras på de enskildaenheterna med Alternating DirectionMethod ofMultipliers (ADMM) och parallellberäkningar härledas.Microgrids samarbetar för att nå en global lösning av neighbourto-neighbour kommunikation.Distribuerad energiförsörjning av microgrids analyseras och två kontroll algorithmerutformas

Robust Counterfactual Explanations for Random Forests

Counterfactual explanations describe how to modify a feature vector in order to flip the outcome of a trained classifier. Several heuristic and optimal methods have been proposed to generate these explanations. However, the robustness of counterfactual explanations when the classifier is re-trained has yet to be studied. Our goal is to obtain counterfactual explanations for random forests that are robust to algorithmic uncertainty. We study the link between the robustness of ensemble models and the robustness of base learners and frame the generation of robust counterfactual explanations as a chance-constrained optimization problem. We develop a practical method with good empirical performance and provide finite-sample and asymptotic guarantees for simple random forests of stumps. We show that existing methods give surprisingly low robustness: the validity of naive counterfactuals is below $50\%$ on most data sets and can fall to $20\%$ on large problem instances with many features. Even with high plausibility, counterfactual explanations often exhibit low robustness to algorithmic uncertainty. In contrast, our method achieves high robustness with only a small increase in the distance from counterfactual explanations to their initial observations. Furthermore, we highlight the connection between the robustness of counterfactual explanations and the predictive importance of features

N° 11. — Spectres infrarouges de 4 000 à 600 cm

Les spectres infrarouges des composés d’addition (Et2O)2 — MgBr2, Et2O — ZnCl2, Et2O — AlX3 et (CH3CD2)2O — AlX3 (X = Cl ou Br) ont été enregistrés et analysés entre 4 000 et 600 cm–1. Une attribution basée sur la comparaison des spectres des complexes avec celui de l’éther libre vitreux est proposée. Elle montre que les vibrations les plus perturbées par la complexation sont les vibrations de valence mettant en jeu les liaisons CH des groupements CH2 et CO

Impact of dexamethasone on the incidence of ventilator-associated pneumonia and blood stream infections in COVID-19 patients requiring invasive mechanical ventilation: a multicenter retrospective study

International audienceAbstract Background Dexamethasone decreases mortality in patients with severe coronavirus disease 2019 (COVID-19) and has become the standard of care during the second wave of pandemic. Dexamethasone is an immunosuppressive treatment potentially increasing the risk of secondary hospital acquired infections in critically ill patients. We conducted an observational retrospective study in three French intensive care units (ICUs) comparing the first and second waves of pandemic to investigate the role of dexamethasone in the occurrence of ventilator-associated pneumonia (VAP) and blood stream infections (BSI). Patients admitted from March to November 2020 with a documented COVID-19 and requiring mechanical ventilation (MV) for ≥ 48 h were included. The main study outcomes were the incidence of VAP and BSI according to the use of dexamethasone. Secondary outcomes were the ventilator-free days (VFD) at day-28 and day-60, ICU and hospital length of stay and mortality. Results Among the 151 patients included, 84 received dexamethasone, all but one during the second wave. VAP occurred in 63% of patients treated with dexamethasone (DEXA+) and 57% in those not receiving dexamethasone (DEXA−) ( p = 0.43). The cumulative incidence of VAP, considering death, duration of MV and late immunosuppression as competing factors was not different between groups ( p = 0.59). A multivariate analysis did not identify dexamethasone as an independent risk factor for VAP occurrence. The occurrence of BSI was not different between groups (29 vs. 30%; p = 0.86). DEXA+ patients had more VFD at day-28 (9 (0–21) vs. 0 (0–11) days; p = 0.009) and a reduced ICU length of stay (20 (11–44) vs. 32 (17–46) days; p = 0.01). Mortality did not differ between groups. Conclusions In this cohort of COVID-19 patients requiring invasive MV, dexamethasone was not associated with an increased incidence of VAP or BSI. Dexamethasone might not explain the high rates of VAP and BSI observed in critically ill COVID-19 patients