This project will develop a decision-support system (DSS) for disaster risk management by considering multiple interacting natural hazards and cascading impacts using a novel resilient-informed and service-oriented approach that accounts for forecasted modifications in the hazard (e.g., climate change), vulnerability/resilience (e.g., aging structures and populations) and exposure (e.g., population decrease/increase). The primary deliverable from MEDiate will be a decision support framework in the form of service-orientated web tool and accompanying disaster risk management framework providing end users (local authorities, businesses etc) with the ability to build accurate scenarios to model the potential impact of their mitigation and adaptation risk management actions. The scenarios, which can be customised to reflect local conditions and needs (e.g., demographics, deprivation, natural resources etc), will be based on a combination of the historical record and future climate change projections to forecast the location and intensity of climate related disaster events and to predict their impacts, including cascading impacts, on the vulnerability of the local physical, economic and social systems. The scenarios will allow end users to evaluate the potential impact of different risk management strategies to reduce vulnerability and enhance community resilience. The project will consist of analysis of relevant data and co-development with testbed decision-makers of a DSS to enable more reliable resilience assessments, accounting for risk mitigation and adaptive capabilities, to be made, therefore reducing losses (human, financial, environmental etc) from future climate-related and geophysical disasters. The project will involve a multi-disciplinary team of geophysical and meteorological scientists, risk engineers, social scientists, information technologists and end-users, working together to ensure that the system is user-led and supported by appropriate technology.

PHENOmenon will develop and validate an integral manufacturing approach (material, process and technology) for large area direct laser writing of 2&3D optical structures, targeting high speed production of optical surfaces with subwavelength resolution, using NonLinear Absorption. Developments in photochemistry and laser beam forming will allow to produce structures at different scales (100 nm to 10 microns). An unedited productivity in freeform fabrication of 3D structures will trigger the manufacturing of new and powerful optostructures with applications in lighting, displays, sensing, etc. The novelty focuses on the combination of ultrasensitive nonlinear photocurable materials, and the laser projection of up to 1 million simultaneous laser spots. The photochemistry relies on new types of ultrasensitive photoinitiators and groundbreaking nonlinear sensitized resins for CW [Continuous Wave] laser writing. The developments in beam forming are based in modulation with SLMs [Spatial Light Modulators] and hybrid diffractive optics for massive 3D parallelization by imaging and holographic projection. The enabled optical structures (hybrid microlenses, waveguides, polarizers, metasurfaces and holograms) will be modelled at the micro and macroscale, to develop application oriented simulation and design methodologies. Selected demonstrators will show the capability to produce 3D optical micro-nanostructured components with unique optical characteristics, offering differential advantages in many products: advanced security holograms, efficient lighting, high performance optics, backlighting units for displays, holographic HMIs [Human Machine Interface] and planar concentrator microlenses. These components will contribute to address societal challenges like energy efficiency or security while reinforcing EU industry competitiveness. A consortium comprising 4 top Research Institutions and 8 Industrial partners (4 SMEs) covering the complete value chain, will develop this project clearly driven by user needs.

The long term vision in BAYFLEX is to create a radically new technology that uses low cost, green organic electronics for probabilistic computing in order to allow continuous and private monitoring of bio-signals on flexible substrates. The vision of flexible green AI sensors with on chip classification extends well beyond biomedical devices and the democratization of health care, with the possibility to transform sensor data at the edge of large networks. To achieve our goal, BAYFLEX will demonstrate a patch using active physiological sensors based on organic materials that interface with the soft human body and that also includes classification circuits (~ 100 transistors) fabricated using Thin Organic Large Area Electronics (TOLAE) processes. These circuits use spiking neurons realized in Organic Thin Film Transistors (OTFTs) to transform the non-stationary electrical signals from the sensors into stochastic bit streams. Bayesian inference is then used to classify the data using circuits of cascaded Muller C-elements. Taking advantage of the unique properties of organic electrochemical transistors (OECTs), low transistor count dynamic Muller C-elements are targeted. The patch will be tested on a simple task using healthy humans. The project brings together an interdisciplinary consortium with expertise in modeling emerging devices, biologically inspired circuit design, experts in machine learning involving electrophysiological data (including an SME) and teams with expertise in OTFT and OECT fabrication. BAYFLEX targets dissemination to a variety of publics including: scientists via publications in (open access) high impact journals and conferences; industrials and end-users through an industrial advisory board, a workshop and demonstrations at targeted conferences; the general public with the creation of a transferable workshop for non-scientific communities and training the next generation of experts through specialized schools and workshops.

The main objective of the project is to propose a novel approach of distributed, scalable, dynamic and energy efficient algorithms for managing resources in a mobile network. This new approach relies on the design of an orchestration mechanism of a portfolio of algorithms. The ultimate goal of the proposed mechanism is to enhance the user experience, while at the same time to better utilize the operator resources. User mobility and new services are key elements to take into account if the operator wants to improve the user quality of experience. Future autonomous network management and control algorithms will thus have to deal with a real-time dynamicity due to user mobility and to traffic variations resulting from various usages. To achieve this goal, we focus on two central aspects of mobile networks and intend to design distributed learning mechanisms in non-stationary environments, as well as an orchestration mechanism that applies the best algorithms depending on the situation. The first main aspect to be addressed is the management of radio resources at the RAN (Radio Access Network) level. In current (LTE) and future (LTE-A) cellular systems, interference appears as a bottleneck for providing high data rates and seamless connectivity to the end-user. To reduce interference it is possible either to coordinate the transmissions of neighboring base stations (BS) so as to avoid simultaneous transmissions on the same radio resources or to allow BSs to cooperate: two or more BSs combine their transmissions towards a single user in order to increase its data rate. Both cases require distributed learning algorithms. The second aspect is the management of the popular contents users want to get access to. In a Content Delivery Network (CDN), popular content is disseminated and stored in cache servers as close as possible to the demand to avoid delay in access. How to place servers in the network and replicated contents in the servers are traditional issues in CDNs. In mobile CDNs, things are exacerbated because of the changing and unpredictable environment characterized by spatial and temporal changes in the traffic demand, user mobility and variable channel conditions. The way the project intends to tackle these problems is based on a “learn to learn approach”. If we think about BSs and cache servers as autonomous entities seeking to optimize a global objective function and able to take decisions based on incomplete information, the notion of distributed learning arises naturally. There are numerous approaches along these lines and each mechanism has its own characteristics in terms of needed information, type of achieved equilibrium, convergence speed, and stability. Each mechanism can also be tuned thanks via an array of parameters. The problem is exacerbated in non-stationary situations due to mobility, traffic demand or radio channel variations. The originality of the project thus lies in its objective of building a portfolio of distributed learning algorithms that are then to be orchestrated. To account for learning in the presence of non-stationary processes, we intend to use the theory of stochastic approximation in order to develop robust versions of existing learning schemes. Orchestrating a portfolio of learning algorithms is, in many regards, similar to the literature on “learning with expert advice”, so our goal will be to devise adaptive learning schemes that select dynamically between different learning schemes so that their long-term learning power exceeds the regret of any individual “expert”. Bringing together experts form both network and learning, NETLEARN ultimately intends to propose architecture and protocol adaptations for implementing our resource management algorithms.