Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter‐parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty‐six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age‐specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow‐up. These data show that PPA is a robust, quantitative and explainable ML‐based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.

The aerospace industry relies on massive collections of complex and technical documents covering system descriptions, manuals or procedures. This paper presents a question answering (QA) system that would help aircraft pilots access information in this documentation by naturally interacting with the system and asking questions in natural language. After describing each module of the dialog system, we present a multi-task based approach for the QA module which enables performance improvement on a Flight Crew Operating Manual (FCOM) dataset. A method to combine scores from the retriever and the QA modules is also presented.

Until humanity succeeds in massively producing clean energy to satisfy its inexhaustible needs, one of its biggest challenges is to save and use its resources as efficiently as possible. With outdoor lighting being responsible for 2% of worldwide electricity consumption, smart urban lighting has recently gained a lot of attention in this respect. As an integrated part of smart cities, smart urban lighting rests on the anal-ysis of sensed data to tackle highly dynamical problems. This sensed data shapes a representation of the environment in which the smart system will have to perform. To reduce problem complexity, distributed solu-tions commonly apply local lighting policies and therefore benefit from the knowledge of the geographical positioning of the relevant streetlights in the environment. In this paper, we propose an adaptive multiagent approach that aims at ensuring the robustness and coherence through time of the smart system's environment representation. Our approach leverages real time series data returned by streetlight sensors informing on vehicles and pedestrians traffic. We exploit this data to perform a structural reconstruction of the streetlight "fleet" topology without any a priori knowledge about its internal structure. We then ensure its cor-rectness through time by handling internal structure changes in order to continuously provide a coherent foundation for the smart lighting system to perform upon.