The Internet of Things connects physical devices offering sensing or actuating with their vicinity. The ever-growing capabilities of devices allow to imagine new architectures including them as first class citizens. New added-value applications can then be envisioned in smart agriculture, smart buildings, smart cities, energy and water management, e-health and ageing well... The Web of Things (WoT) allows to describe the devices semantics, bridging the gap between the different domain and service descriptions. In today WoT architectures, physical devices can be located at distance from systems that perform reasoning. A centralised approach does not take advantage of the devices capabilities and induces suboptimal data transfers as well as server overload. Besides, many devices are now smart enough to discover each other, exchange data, and collectively make decisions. CoSWoT objectives are to propose a distributed WoT-enabled software architecture embedded on constrained devices with two main characteristics: (1) it will use ontologies to specify declaratively the application logic of devices and the semantics of the exchanged messages; (2) it will add reasoning functionalities to devices, so as to distribute processing tasks among them. Doing so, the development of applications including devices of the WoT will be highly simplified: our platform will enable the development and execution of intelligent and decentralised smart WoT applications despite the heterogeneity of devices. In CoSWoT, WoT applications will rely on a platform hosting the base services. Besides traditional services, it will host extensions that correspond to two scientific barriers: (1) the use of ontologies as a generalised model for exchanges between heterogeneous devices. A joint statement from AIOTI WG3, IEEE P2413, oneM2M, W3C positions ontologies as key enablers for semantic interoperability on the WoT. However research questions remain concerning (i) the adequation of existing ontologies to the target application domains; (ii) the applicability of theoretical principles developed in a variety of protocols and standards, in the context of data streams; (iii) the discovery of heterogeneous devices, their services and how to solicit them. (2) distributed and embedded incremental reasoning. Devices become powerful enough to offer storage and processing; new architectures appear, based on edge computing including devices such as sensors and actuators. The data streams provided by sensors require to perform incremental reasoning tasks. Research questions remain on (i) how to embed reasoning in devices with various capacities, it requires specific optimisations; (ii) how to efficiently distribute reasoning tasks among devices. Smart agriculture is a typical application domain of such WoT architectures, where the surveillance of cultivated fields requires various sensors that push streaming data, which must be collected and reasoned upon to take decisions executed by actuators. Smart buildings is another such typical application domain where added-value application services involve other verticals such as energy management, e-health, or ageing well. We will define use cases and requirements for smart agriculture and smart buildings, run simulations, and then lead real experiments. The CoSWoT platform will foster the decoupling of the development of software and the development of hardware, so as to ease the emergence of a new economic sector in the digital industry around WoT applications development, disconnected from the development of the smart devices themselves.

Dry eye syndrome (DES) is a worldwide disease that affects the quality of life of millions of individuals to varying degrees. If the positive diagnosis is generally very easy, the objective and reliable determination of its severity (grading) remains the main diagnostic problem for decades. In practice, two very different situations must be distinguished: routine clinical practice where grading is necessary to personalize treatment and clinical trials where grading is essential to demonstrate the efficacy of a new treatment: 1/ In routine clinical practice, the clinician collects the symptoms, observes the ocular surface stained with fluorescein to assess superficial punctate keratitis and tear film break-up time and estimates the quantity of tear using the Schirmer test. 2/ During clinical trials, the very high imprecision and/or the lack of reliability (precision, accuracy, repeatability) of the 3 tests forces the addition of other evaluation criteria, like different biomarkers but none of which has ever been imposed or transferred to routine use. FLUOSICCA aims to revolutionize the diagnosis of the severity and monitoring of dry eye syndrome by developing a process combining fluorescence imaging and biology. We will quantify in situ a pair of biomarkers, directly on the ocular surface of patients in a sensitive and specific manner: a marker of constant or little variation in the pathology and serves to normalize the signal and a marker that is overexpressed in dry eye syndrome. The measurement of a ratio will reduce inter and intra individual variability. The ligands of the two biomarkers (Partner 3) will be synthetic, non-immunogenic, stable and easily customizable proteins. They will be coupled to functionalized BODIPY fluorophores (Partner 2), excitable in a non-dazzling wavelength range (>650 nm, easy to tolerate) while maintaining brightness and photostability. The imaging device (Partner 4) will be sensitive and can be industrialized on a large scale. It will allow the detection and discrimination of the two fluorophores in order to calculate their ratio. The whole system will first be developed in vitro on cells expressing the two biomarkers, then tested on primary ocular epithelial cells and on human corneas preserved in partner 1's bioreactor, using an innovative dry eye ex vivo model. This integrated solution will then be transposable to other biomarkers and other pathologies of the ocular surface.

The theoretical analysis of deep neural networks (DNN) is arguably among the most challenging research directions in machine learning right now, as it requires scientists to lay novel statistical learning foundations to explain their behavior in practice. In this proposal, we aim to explore the interplay between DNNs and game theory by considering the widely studied class of congestion games with the goal of relating them to both linear and non-linear DNNs and to the properties of their loss surface. Beyond retrieving the state-of-the-art results from the literature in a principally new way, we expect that our proposal will provide a very promising novel tool for analyzing DNNs and will allow solving concrete open problems related to 1) characterizing the DNNs optimization inefficiency depending on the algorithmic choices, such as their architecture, activation, and loss function used and 2) proposing new optimization strategies with strong convergence guarantees.

Air pollution is the leading environmental cause of mortality. This worrying situation is not improving at the moment, since the emission levels of the main pollutants (nitrogen oxides NOx, ozone O3, volatile organic compounds VOCs and small particles SP) remain stable in the majority of major European cities. Beyond the environmental consequences reducing and monitoring air pollution is a real public health issue. Considering this serious conclusion it appears imperative to ensure a continuous measurement of the presence of these pollutants in ambient air in order to be able to implement means of remediation and methods of treatment, destruction or trapping of the effluent gases. The CAPTAIN project (Optical Sensors for Air Quality Monitoring with regard to NO2, O3, pollutants), aims to develop a new generation of highly sensitivity, highly selective, robust, miniature, low-cost and energy-efficient optical micro sensors, dedicated to the monitoring of gaseous pollutants for the control of indoor and / or outdoor air quality. The project partners aim at an experimental demonstration (TRL 4 or 5) of sensors with a performance (sensitivity, sub-ppb detection, high selectivity, robustness to noise ...) beyond the state of the art. The targeted pollutants are the NO2 and O3. While targeting sub-ppb detection thresholds, the solutions proposed by the CAPTAIN project in terms of selectivity and time drift will challenge the main limitations of currently available technologies (electrochemical, optical, Mox ...). They will eventually lead to dedicated micro sensors for indicative measurement of air pollution, thus meeting the requirements of Directive 2008/50 EC of the European Parliament. In this project an optical signal transfer based on plasmon excitation (SPR-Surface Plasmon Resonance) will be used, combined with functional layers which will interact with the pollutants. Several devices based on plasmon excitation have already shown their applicability for the detection of gaseous pollutants or chemical contaminants with high sensitivity and partial selectivity. However, their size, complexity and cost limit their use on a large scale. The innovative approach protected by a patent and proposed in the CAPTAIN project is also based on the excitation of plasmon modes but exploits a new effect of energy switching or the commutation of energy between propagated reflected orders. This effect allows to better exploit the plasmon modes (SPR) detection compared to existing sensors since it allows i) to increase the sensitivity by a factor of at least 2, ii) to simplify considerably the implementation of the sensor and iii) to overcome the problems of common mode variations. The CAPTAIN project is very multidisciplinary since it requires skills in material chemistry, physico-chemical characterization, optical signal transfer, optical modeling, micro-structuring, thin films, environmental metrology and implicitly sensors. This is why it involves 4 partners: 2 academic partners (Hubert Curien Laboratory and Pascal Institute) and 2 industrial partners (SILSEF and Environnement SA). This project has a direct impact on the environment and health, but also on the industry (automotive, process engineering, Smart City development, innovative industrial sensor networks and building automation ...). It focuses on the design of a new generation of potentially low-cost micro-sensors allowing their large-scale deployment thanks to their miniaturization and integration capability (on a miniature hybrid platform) with multi-gas detection potential (or a cocktail of pollutants).

The project deals with the development of nanostructured polymeric composites with an ultra-high absorption performance of electromagnetic radiation (EMR). The strategy relies on tailoring their internal architecture leading to an in-situ morphological structuration with local electrical/magnetic properties. Such proof of concept can be elaborated by an innovative micro/nanolayer coextrusion and emerging injection molding process such In-Mold Electronics (IME). A specific attention will be devoted to the eco-design and recyclability of the obtained smart materials. In this regard, flexible films containing conductive and/or magnetic fillers will be investigated as support for Plastronic devices. The challenge is to obtain high-efficiency, robust/alternative 3D electrical/magnetic filler network with high orientation/ordered distribution. Dual experiments and simulations will be performed for tailoring and modelling the dielectric/magnetic properties. The results will offer some new enlightenment for fundamental understanding of layer confinement, the triggered interphases and induced structure in the developed nanostructured smart materials.