BARRIER is a proof of concept project with multidisciplinary expertise for demonstrating, from the laboratory to a pilot process, that selected bacteria can protect microalgae when growing in various waters including produced water, seawater or wastewaters containing toxic compounds, providing higher algal resilience, productivity and bioremediation efficiency in saline wastewater treatments. Saline wastewater is a stubborn pollution source representing one of the most serious environmental problems occurring on land formations and in water reservoirs. In BARRIER project, natural microalgae and associated bacteria will be selected on organic and metallic toxic compounds. Microalgae-bacteria assemblages will be built, optimized through modeling and tested in large-scale mass culture processes using industrial wastewaters. Microalgae are promising organisms for producing a wide range of commodities (biofuel, bioplastics, …) including recycling and valuation of liquid and gaseous effluents. However, this is hindered by the difficulty to grow microalgae in contaminated waters, where various toxic may reduce their growth, and can even contribute to dramatic crash of the culture. Recent advances have shown that, when associated with a specific cluster of bacterial species, the resilience of assemblage can be significantly stronger than the microalgae alone. Microalgae-bacteria consortia are shaped by complex interactions. Microalgae stimulate bacterial growth by the release of carbon exudates, whereas bacteria supply algae with vitamins and nutrients. Although the microalgae-bacteria relationships through metabolite exchanges are well studied, little is known however regarding the impact of chemical contaminants on the interactions between both microalgae and bacteria. Further experimental studies are required to understand the algae-bacteria interactions in the context of chemical pollution pressure in order to propose innovative strategies for improving the resilience of microalgae assemblage in contaminated effluents. Four objectives in BARRIER project: • To evidence the role of bacteria in the protection of microalgae against contamination, by analyzing physiological responses of microalgae under controlled exposure to toxic chemicals. • To characterize the fate of toxic chemicals and organic matrix during the biodegradation/immobilization processes. • To model and predict the role of interactions between microalgae and associated bacteria when exposed to combined toxic chemicals. • To demonstrate in realistic outdoor pilot conditions that a selected microalgae-bacteria association provides a better resilience of the mass culture process and thus increases the yearly microalgal productivity and bioremediation processing in saline wastewaters with toxic contaminants. BARRIER will perform complementary laboratory experiments in controlled and outdoor conditions with an upscaling approach using cultures of microalgae species and bacteria isolated from a contaminated environment. BARRIER proposes a multidisciplinary approach relying on a consortium associating five academic laboratories and one industrial company developing bioremediation strategies, in order to obtain competencies in microbial ecology, ecotoxicology, organic and inorganic chemistry, molecular biology, modeling and process engineering and microalgae cultivation on oil and gas wastewater. BARRIER will allow a better understanding of the interactions between microalgae and bacteria. The methodological approach will help in characterizing the role of the bacteria in the protection of microalgae against chemical contamination. Lastly, BARRIER will propose innovative approaches with the manipulation of algae-bacteria consortia to use the effective algae-bacteria interactions, approaches that will be tested in realistic outdoor conditions with the support facilities and competences of the industrial Partner.

New generations of mobile access networks promise low delay and high-speed throughput data connections paired with in-network processing capabilities. IoT data and local information available to users’ devices will feed AI-based applications executed in proximity on edge servers and service composition will routinely include such applications and their microservice components. PARFAIT tackles new resource allocation problems emerging due to the need of distributed edge orchestration of both computing and communication, in a context where the unknown footprint of AI-based applications requires advanced learning capabilities to permit efficient and reliable edge service orchestration. The PARFAIT project develops theoretical foundations for distributed and scalable resource allocation schemes on edge computing infrastructures tailored for AI-based processing tasks. Algorithmic solutions will be developed based on the theory of constrained, delayed, and distributed Markov decision processes to account for edge service orchestration actions and quantify the effect of orchestration policies. Furthermore, using both game and team formulations, the project will pave the way for a theory of decentralized orchestration, a missing building block necessary to match the application quest for data proximity and the synchronization problems that arise when multiple edge orchestrators cooperate under local or partial system view. Finally, to achieve efficient online edge service orchestration, such solutions will be empowered with reinforcement learning techniques to define a suit of orchestration algorithms able to at once adapt over time to the applications’ load and cope with the uncertain information available from AI-based applications’ footprints. Validation activities will be designed to demonstrate real-world solutions for practical orchestration use cases, using both large scale simulation experiments and research testbeds.

Organoid are a recent technology very promising in different medical applications(Characterization of molecules effect, drug choice for personalized medicine). Computational tools are now required for fully taking benefit from this approach. With this respect, this project aims at filling this gap in the case of endocrinian disruptors effect understanding. These endocrinian disruptors are of a major concern in european community for taking decisions, notably in terms of agriculture management. This project has two main aims. The first one consists in deriving machine learning tools to draw a phenotypic map of endocrinian perturbators. To achieve this goal we will first consider deep learning approaches for their discriminative preperties by developing an neural network for classifying the different phenotypes. The classes will be represented in the bottleneck space. To improve the classes interpretability and to better characterize the geometrical and topological preperties of the phenotypes we will define a space of graphs as a stratifold where each point represents a given organoid by its graph (the nodes being the cells and edges defining adjacency between cells). We then will define a mapping between the "bottleneck" and the graph spaces. The second goal concerns organoid growth modeling at the local and global scales. This modelling will rely on the obtained phenotypic map. The growth of a given organoid will be assoicated to a trajectory in this space. A classification algorithm will allow grouping the samples with respect to their phenotypic dynamic during growth. The effects of endocrinien disruptors will be characterized by the deviations from the trajectories. Experiments will be done on two models whcih are the prostate organoids and the gastruloids. These two models will ensure several different phenotypes. These claases will be enriched due to the adaptative property of the proposed approach by filling the description of the two definend spaces.

Brain-Computer Interfaces (BCIs) are a fascinating field of investigation that reach the very essence of the human being. Restoring communication and movement are exemplary key applications of BCI. The potential of BCIs to contribute to solve important societal challenges is highlighted by its inclusion within recent H2020 research and innovation calls (e.g. ICT-22-2014). However, it continues to be difficult to transfer BCI technology to social and economic spheres. As in most emerging fields, there is still a huge gap between the prototypes being tested in research laboratories and usable BCIs for daily life. Some manufacturers and media have been casting a deceptive light on the field by hiding inner workings and implying that much can be controlled with little effort. But obviously human subjects are not deterministic machines and fine tuning is needed to adapt a BCI to a specific user. Conversely, it takes time and effort before users can reach their operational objective with a BCI. Our consortium is fully aware of this efficiency challenge and aims to make control of Brain-Computer Interfaces easier and more natural. Our proposal is 1) to adopt a novel user-centered approach, where the user is a learning entity rather than a static one, and 2) to develop trans-disciplinary conception of BCI, allowing diverse areas of expertise (engineering, computer science, neuroscience, medicine, psychology) to interact and to co-design this new generation of BCI.

Biometrics refers to the automated recognition of individuals based on their behavioural or biological characteristics. In spite of their numerous advantages over traditional authentication systems based on PINs or passwords (e.g., biometric characteristics cannot be lost or forgotten), biometric systems are vulnerable to external attacks and can leak privacy. Presentations attacks (PAs) - impostors who manipulate biometric systems by masquerading as other people - are serious threats to security. Privacy concerns involve the use of personal and sensitive biometric information, as classified by the GDPR, for purposes other than those intended. Vulnerabilities to PAs and privacy leakage are unacceptable and have hindered the deployment of biometric technology in commercial applications. The biometrics community has responded with presentation attack detection (PAD) technologies and privacy preservation mechanisms (biometric template protection schemes, BTP). Even though the latest PAD technologies are largely successful in protecting biometrics systems from known forms of PA, they tend to lack generalisation to different forms of attacks. The standard approach to privacy preservation involves some form of encryption or irreversible transformations, though the most recent fully homomorphic algorithms are general computationally prohibitive. Multi-biometric systems, explored extensively as a means of improving recognition reliability, also offer potential to improve PAD generalisation. Multi-biometric systems offer natural protection against spoofing since an impostor is less likely to succeed in fooling multiple systems simultaneously. For the same reason, previously unseen PAs are less likely to fool multi-biometric systems protected by PAD. Unfortunately, each sub-system in a multi-biometric approach to recognition has potential to leak privacy. Multi-biometric systems only compound the need for computationally prohibitive privacy preservation. RESPECT, a Franco-German collaborative project, will explore the potential of using multi-biometrics as a means to defend against diverse PAs and improve generalisation while still preserving privacy. Central to this idea is the use of (i) biometric characteristics that can be captured easily and reliably using ubiquitous smart devices and, (ii) biometric characteristics which facilitate computationally manageable privacy preserving, homomorphic encryption. The research will focus on characteristics readily captured with consumer-grade microphones and video cameras, specifically face, iris and voice. Further advances beyond the current state of the art involve the consideration of dynamic characteristics, namely utterance verification and lip dynamics. The core research objective will be to determine which combination of biometrics characteristics gives the best biometric authentication reliability and PAD generalisation while remaining compatible with computationally efficient privacy preserving BTP schemes.