Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : Back to the Roots (Horizon 2020, appel 2019, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : KU Leuven — Pays : Belgique — Rôle : PI
Acronyme de l'entité : KUL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2485925.0 €

To obtain data-driven dynamic models for simulation, prediction, monitoring, classification or control tasks, in applications e.g. in Industry 4.0 and eHealth, most identification methods ‘solve’ an optimization problem, relying on some nonlinear iterative algorithm. Undeniably, too many heuristics prevail: What do we mean by ‘solved’? Where did the algorithm converge to? Is the model globally optimal, unique and reproducible? To tackle these scientific deficiencies, we design a framework to deal with inexact data. We solve a longstanding open problem of least squares optimality in system identification: for polynomial dynamical models, the optimal model derives from an eigenvalue problem. Hereto, we generalize notions from Algebraic Geometry (multivariate polynomials), Operator Theory (model spaces), System Theory (multidimensional realization) and Numerical Linear Algebra (matrix computations). The first objective is to develop a mathematically rigorous realization approach that maps data onto new mathematical structures (multi-shift invariant projective subspaces). The second objective is to conceive a ‘misfit-latency’ framework to optimally map inexact data to these mathematical structures. We prove this to be a multiparameter eigenvalue problem. We expect breakthroughs in system theoretic characterizations of optimality (covering all existing methods), in the generalization to multiple input-output and multidimensional models and in finding the global optimum in the linear dynamic H2 model reduction problem. The third objective is to implement matrix computation algorithms for the results of the first two objectives, to root sets of multivariate polynomials, to solve multiparameter eigenvalue problems and to isolate only the minimizing roots. We focus on matrix aspects of large scale, sparsity and structure. Deliverables will be publications, software, graduate course material and science outreach initiatives, in line with the PI’s excellent track record

Mots-clés : system identification and estimation,system and control theory,multi parameter eigenvalue problems,numerical linear algebra,model reduction,multivariate polynomial optimization
Projet ERC : microCrysFact (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : University of Barcelona — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 713147.0 €

To study and understand the aggregation, nucleation, and/or self-assembly processes of crystalline matter is of crucial importance for research and applications in many disciplines. For example, understanding the formation of crystalline amyloid fibres could lead to advances in the treatment and prevention of both Alzheimer’s and Parkinson’s diseases, whereas controlling the process of crystal formation can play a significant role in obtaining chemicals and materials that are important for industry as well as society as a whole (e.g., drugs, superconductors, polarizers and/or frequency modulators). Despite the impressive progress made in molecular engineering during the last few decades, the quest for a general tool-box technology to study, control and monitor crystallisation processes as well as to isolate metastable states (dynamic capture) is still incomplete. That is because crystalline assemblies are frequently investigated in their equilibrium form, driving the system to its minimum energy state. This methodology limits the emergence of new chemicals and crystals with advanced functionalities, and thus hampers advances in the field of materials engineering. µ-CrysFact will develop tool-box technologies where diffusion-limited and kinetically controlled environments will be achieved during crystallisation and where the isolation of non-equilibrium species will be facilitated by pushing crystallisation processes out of equilibrium. In addition, µ-CrysFact’s technologies will be used to localise, integrate and chemically treat crystals with the aim of honing their functionality. This unprecedented approach has the potential to lead to the discovery of new materials with advanced functions and unique properties, thus opening new horizons in materials engineering research.

Mots-clés : technology,crystallisation processes,molecular engineering,metastable states,thermodynamic state,kinetically controlled environments,localisation,integration,and functional matter
Projet ERC : BRAINBELIEFS (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : Vrije Universiteit Amsterdam — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : VU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1597291.0 €

To successfully complete secondary education, persistent learning behavior is essential. Why are some adolescents more resilient to setbacks at school than others? In addition to actual ability, students’ implicit beliefs about the nature of their abilities have major impact on their motivation and achievements. Ability beliefs range from viewing abilities as “entities” that cannot be improved much by effort (entity beliefs), to believing that they are incremental with effort and time (incremental beliefs). Importantly, ability beliefs shape which goals a student pursues at school; proving themselves (performance goals) or improving themselves (learning goals). The central aims of the proposal are to unravel 1) the underlying processing mechanisms of how beliefs and goals shape resilience to setbacks at school and 2) how to influence these mechanisms to stimulate persistent learning behavior. Functional brain research, including my own, has revealed the profound top-down influence of goals on selective information processing. Goals may thus determine which learning-related information is attended. Project 1 jointly investigates the essential psychological and neurobiological processes to unravel the longitudinal effects of beliefs and goals on how the brain prioritizes information during learning, and how this relates to school outcomes. Project 2 reveals how to influence this interplay with the aim to long-lastingly stimulate persistent learning behavior. I will move beyond existing approaches by introducing a novel intervention in which students experience their own learning-related brain activity and its malleability. The results will demonstrate how ability beliefs and goals shape functional brain development and school outcomes during adolescence, and how we can optimally stimulate this interplay. The research has high scientific impact as it bridges multiple disciplines and thereby provides a strong impulse to the emerging field of educational neuroscience.

Mots-clés : developmental cognitive neuroscience,educational neuroscience,implicit beliefs,feedback processing,fmri,neurofeedback
Projet ERC : MobiliSense (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Institut national de la santé et de la recherche médicale — Pays : France — Rôle : PI
Acronyme de l'entité : Inserm
Type d'entité : Recherche
Financement ERC de l'entité : 2000000.0 €

To support policies at the European and national levels, MobiliSense aims to explore the effects of air pollution and noise exposure related to personal transport habits on respiratory and cardiovascular health. Building on methods from Epidemiology, Geography, and Transport sciences, the objectives of MobiliSense are to quantify the contribution of personal transport to the air pollution and noise exposure of individuals; to compare the air pollution and noise exposure in the different transport modes; to investigate whether total and transport-related personal exposure to air pollutants and noise are associated with short-term and two-year changes in respiratory and cardiovascular health; and to examine whether transport-related exposures mediate socioeconomic disparities in health. The MobiliSense project will use passive and active sensors of location, behaviour, environmental nuisances, and health in a representative sample of 1000 participants followed-up over two years. It addresses a gap in knowledge: (i) by assessing transport behaviour over 6 days with GPS receivers and an electronic mobility survey; (ii) by considering the personal exposure to both air pollution and noise and improving its characterisation (inhaled doses, noise frequency components, impulsive noise, and interactions with subjective annoyance); (iii) by measuring a wide range of respiratory and cardiovascular outcomes (smartphone-assessed respiratory symptoms, lung function assessed by spirometry, resting blood pressure, ambulatory brachial / central blood pressure, arterial stiffness, and heart rate variability); and (iv) by investigating short-term and longer-term effects of transport. To assist policy-makers, the final aim is to deliver a simulation tool permitting to determine the extent to which scenarios (i) of changes in personal transport behaviour and (ii) of changes in exposure levels during transport affect individual exposure and respiratory / cardiovascular health.

Mots-clés : transportation behaviour,mobility patterns,socioeconomic status,cardiovascular health,respiratory health,air pollution,noise
Projet ERC : NVS (Horizon 2020, appel 2014, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Weizmann Institute of Science — Pays : Israël — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 175000.0 €

To understand how the brain works, tools need to be developed that will allow neuroscientists to investigate how interactions between individual neurons lead to emergent networks. Towards this goal, we will develop targetable voltage sensing nanorods that self-insert into the cell membrane and optically and non-invasively record action potentials at the single particle and nanoscale level, at multiple sites and across a large field-of-view. In semiconductors, absorption and emission band edges are modulated by an external electric field, even more so when optically excited electron-hole pairs are confined, giving rise to the quantum confined Stark effect. The physical origin of this effect is in the separation of photoexcited charges, creating a dipole that opposes the external field. The proposed sensors will optically record action potential with unique advantages not offered by other methods: much larger voltage sensitivity, high brightness, and hence single-particle voltage sensitivity, large spectral shift (affording noise-immune ratiometric measurements), fast temporal response, minimal photobleaching, large Stokes shifts, large two-photon excitation cross sections, excellent performance in the NIR, and compatibility with lifetime imaging. The proposed sensors could afford, for example, the recording of pre- and post-synaptic membrane potentials, sub-threshold events, ultrafast spiking, individual ion channel activity, or a release of ions from single Ca+2 stores. In addition, deep tissue imaging could be afforded by two photon microscopy and far-field non-linear temporal focusing combined with lifetime imaging. Here we seek to optimize all aspects of the sensors’ synthesis, functionalization, delivery, targeting and detection, in order to provide neuroscientists and physiologists a viable and user-friendly technology that will be generally useful for the study of action potential signals in the brain and in healthy or diseased heart and muscle tissues.

Mots-clés : biophysics,electrophysiology,neurobiology,single molecule spectroscopy,physical chemistry,analytical chemistry,method development in chemistry,physical chemistry of biological systems
Projet ERC : NVS (Horizon 2020, appel 2014, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Institut national de la santé et de la recherche médicale — Pays : France — Rôle : Partner
Acronyme de l'entité : Inserm
Type d'entité : Recherche
Financement ERC de l'entité : 225000.0 €

To understand how the brain works, tools need to be developed that will allow neuroscientists to investigate how interactions between individual neurons lead to emergent networks. Towards this goal, we will develop targetable voltage sensing nanorods that self-insert into the cell membrane and optically and non-invasively record action potentials at the single particle and nanoscale level, at multiple sites and across a large field-of-view. In semiconductors, absorption and emission band edges are modulated by an external electric field, even more so when optically excited electron-hole pairs are confined, giving rise to the quantum confined Stark effect. The physical origin of this effect is in the separation of photoexcited charges, creating a dipole that opposes the external field. The proposed sensors will optically record action potential with unique advantages not offered by other methods: much larger voltage sensitivity, high brightness, and hence single-particle voltage sensitivity, large spectral shift (affording noise-immune ratiometric measurements), fast temporal response, minimal photobleaching, large Stokes shifts, large two-photon excitation cross sections, excellent performance in the NIR, and compatibility with lifetime imaging. The proposed sensors could afford, for example, the recording of pre- and post-synaptic membrane potentials, sub-threshold events, ultrafast spiking, individual ion channel activity, or a release of ions from single Ca+2 stores. In addition, deep tissue imaging could be afforded by two photon microscopy and far-field non-linear temporal focusing combined with lifetime imaging. Here we seek to optimize all aspects of the sensors’ synthesis, functionalization, delivery, targeting and detection, in order to provide neuroscientists and physiologists a viable and user-friendly technology that will be generally useful for the study of action potential signals in the brain and in healthy or diseased heart and muscle tissues.

Mots-clés : biophysics,electrophysiology,neurobiology,single molecule spectroscopy,physical chemistry,analytical chemistry,method development in chemistry,physical chemistry of biological systems
Projet ERC : GRAPHFITI (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Delft University of Technology — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : TU Delft
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Today, antimicrobial resistance is emerging as a global burden, endangering our lives. Drug-resistant pathogens kill about 25,000 infected people solely in the European Union every year. In the fight against this crisis, rapid Antimicrobial Susceptibility Testing (AST) plays a crucial role. Although effective in discerning the right concentration of antibiotics required to inhibit the growth of a pathogen, conventional AST techniques are not fit for point-of-care, since they are slow and require large microbial inocula. The advent of graphene, and the ability to fabricate one-atom thick membranes, has made it possible to make miniaturised sensors that are extremely sensitive to nanoscale forces. During the research project ENIGMA carried out in the framework of the ERC Starting Grant no. 802093, we have developed a methodology that can probe nanomechanical motion of graphene membranes in the presence of bacterial cells. Here, I propose to further validate this technology and realize a proof-of-concept demonstrator that offers automated high throughput AST using massively parallelized graphene membranes. By eliminating the need for culturing large number of bacterial cells, GRAPHFITI has the potential to shift the current paradigm of AST technologies towards the use of atomically thin membranes with single bacterial cell sensitivity, thus enabling diagnostic tools for fast point-of-care screening of antimicrobial resistance.

Mots-clés : graphene,antimicrobial susceptibility test,nanomotion detection
Projet ERC : ADIX (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2500000.0 €

Today’s AI landscape is permeated by plentiful data and dominated by powerful methods with the potential to impact a wide range of human sectors, including healthcare and the practice of law. Yet, this potential is hindered by the opacity of most data-centric AI methods and it is widely acknowledged that AI cannot fully benefit society without addressing its widespread inability to explain its outputs, causing human mistrust and doubts regarding its regulatory and ethical compliance. Extensive research efforts are currently being devoted towards explainable AI, but they are mostly focused on engineering shallow, static explanations providing little transparency on how the explained outputs are obtained and limited opportunities for human insight. ADIX aims to define a novel scientific paradigm of deep, interactive explanations that can be deployed alongside a variety of data-centric AI methods to explain their outputs by providing justifications in their support. These can be progressively questioned by humans and the outputs of the AI methods refined as a result of human feedback, within explanatory exchanges between humans and machines. This ambitious paradigm will be realised using computational argumentation as the underpinning, unifying theoretical foundation: I will define argumentative abstractions of the inner workings of a variety of data-centric AI methods from which various explanation types, providing argumentative grounds for outputs, can be drawn, generate explanatory exchanges between humans and machines from interaction patterns instantiated on the argumentative abstractions and explanation types, and develop argumentative wrappers from human feedback. The novel paradigm will be theoretically defined and informed and tested by experiments and empirical evaluation, and it will lead to a radical re-thinking of explainable AI that can work in synergy with humans within a human-centred but AI-supported society.

Mots-clés : explanation,computational argumentation
Projet ERC : MARCAN (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : New Mexico Institute of Mining and Technology — Pays : Etats-Unis — Rôle : Partner
Acronyme de l'entité : NMIMT;NMT
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 45472.5 €

Topographically-driven meteoric (TDM) recharge is a key driver of offshore groundwater systems because sea level has been lower than at present for 80% of the last 2.6 million years. Groundwater has been implicated as an important agent in the geomorphic evolution of passive continental margins and the canyons that incise them. However, the geomorphic efficacy of groundwater remains dubious, and a diagnostic link between landscape form and groundwater processes remains poorly quantified, especially for bedrock and cohesive sediments. Obstacles that prevent going beyond the current state-of-knowledge include: (i) a focus on terrestrial contexts and a lack of mechanistic understanding of groundwater erosion/weathering; (ii) limited information on offshore groundwater architecture, history and dynamics. By addressing the role of TDM offshore groundwater in the geomorphic evolution of the most prevalent types of continental margins, MARCAN is expected to open new scientific horizons in continental margin research and bring about a step-change in our understanding of some of the most widespread and significant landforms on Earth. The project’s methodology is rooted in an innovative, multi-scale and multidisciplinary approach that incorporates: (i) the most detailed 3D characterisation of TDM offshore groundwater systems and their evolution during an integral glacial cycle, based on state-of-the-art marine data and hydrogeologic models, and (ii) the development of a comprehensive continental margin geomorphic evolution model, based on realistic laboratory simulations, accurate field measurements and advanced numerical solutions. By placing better constraints on past fluid migration histories, MARCAN will also have strong applied relevance, primarily by improving assessment and exploitation of offshore freshwater as a source of drinking water.

Mots-clés : geomorphology,groundwater,weathering,erosion,canyons,landscape evolution,continental margins
Projet ERC : TopSupra (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : University of Basel — Pays : Suisse — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2497577.0 €

Topological matter is a new research focus with great perspectives. These are insulators with an inverted “negative” bandgap and a conducting surface state. While the surface state in a topological insulator (TI) is composed of chiral fermions carrying charge and spin, in topological superconductors it is pinned to zero energy due to particle-hole symmetry and composed of fermions that carry neither charge nor spin. In-stead, they are non-abelian fermions, Majorana and parafermions (MF/PF), that have been proposed for topological quantum computing. Evidence for MFs have been found in nanowires. However, the scaling-up challenge requires a platform in which networks of MFs can be realized. Here, we propose to use graphene-based van der Waals heterostructure for this purpose. The unprecedented versatility is enabled by combining high-mobility graphene with other layered materials, such as transition-metal dichalcogenide, few-layer ferromagnets and superconductors (SCs). This allows to design topological systems, e.g. the quantum spin, anomalous and valley Hall effect, by combining Zeeman energy, spin-orbit and pairing interaction. We will design 2D quantum matter using different approaches, including strain tuning and the dressing of the bandstructure by photon-fields (Floquet TI), and couple it to SCs to induce topological superconductivity. We will use our expertise from studies of Cooper-pair splitters to not only add pairing in a single edge-state, but also between different edge-states, beneficial in obtaining MFs and more exotic quasiparticles. We will apply advanced high-frequency techniques, e.g. emission and noise - in addition to local tunneling spectroscopy - to characterize the in-gap states and to prove their topological nature. We will deliver a versatile technology with which new states of matter can be obtained in a platform which can be engineered in a top-down manner into networks allowing for quantum-state manipulation of MFs and PFs.

Mots-clés : topological superconductivity,graphene based heterostructures,superconducting 2d systems