Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : ATOMICAR (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Technical University of Denmark — Pays : Danemark — Rôle : PI
Acronyme de l'entité : DTU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1496000.0 €

The goal of ATOMICAR is to achieve the ultimate sensitivity limit in heterogeneous catalysis: Quantitative measurement of chemical turnover on a single catalytic nanoparticle. Most heterogeneous catalysis occurs on metal nanoparticle in the size range of 3 nm - 10 nm. Model studies have established that there is often a strong coupling between nanoparticle size & shape - and catalytic activity. The strong structure-activity coupling renders it probable that “super-active” nanoparticles exist. However, since there is no way to measure catalytic activity of less than ca 1 million nanoparticles at a time, any super-activity will always be hidden by “ensemble smearing” since one million nanoparticles of exactly identical size and shape cannot be made. The state-of-the-art in catalysis benchmarking is microfabricated flow reactors with mass-spectrometric detection, but the sensitivity of this approach cannot be incrementally improved by six orders of magnitude. This calls for a new measurement paradigm where the activity of a single nanoparticle can be benchmarked – the ultimate limit for catalytic measurement. A tiny batch reactor is the solution, but there are three key problems: How to seal it; how to track catalytic turnover inside it; and how to see the nanoparticle inside it? Graphene solves all three problems: A microfabricated cavity with a thin SixNy bottom window, a single catalytic nanoparticle inside, and a graphene seal forms a gas tight batch reactor since graphene has zero gas permeability. Catalysis is then tracked as an internal pressure change via the stress & deflection of the graphene seal. Crucially, the electron-transparency of graphene and SixNy enables subsequent transmission electron microscope access with atomic resolution so that active nanoparticles can be studied in full detail. ATOMICAR will re-define the experimental limits of catalyst benchmarking and lift the field of basic catalysis research into the single-nanoparticle age.

Mots-clés : single nanoparticle,microcavity,cavity reactor,batch reactor,array reactor,graphene,ultra sensitive,ambient pressure tem,structure activity relation,super activity,super active nanoparticles
Projet ERC : GRIFFIN (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Universidad de Sevilla — Pays : Espagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499750.0 €

The goal of GRIFFIN is the derivation of a unified framework with methods, tools and technologies for the development of flying robots with dexterous manipulation capabilities. The robots will be able to fly minimizing energy consumption, to perch on curved surfaces and to perform dexterous manipulation. Flying will be based on foldable wings with flapping capabilities. They will be able to safely operate in sites where rotorcrafts cannot do it and physically interact with people. Dexterous manipulation will be performed maintaining fixed contact with a surface, such as a pole or a pipe, by means of one or more limbs and manipulating with others overcoming the limitations of dexterous manipulation in free flying of existing aerial manipulators. Compliance will play an important role in these robots and in their flight and manipulation control methods. The control systems will be based on appropriate kinematic, dynamic and aerodynamic models. The GRIFFIN robots will have autonomous perception, reactivity and planning based on these models. They will be also able to associate with others to perform cooperative manipulation tasks. New software tools will be developed to facilitate the design and implementation of these complex robotic systems. Thus, configurations with different complexity could be derived depending on the requirements of flight endurance and manipulation tasks from simple grasping to more complex dexterous manipulation. The implementation will be based on additive and shape deposition manufacturing to fabricate multi-material parts and parts with embedded electronics and sensors. In GRIFFIN we will develop a small flapping wings proof of concept prototype which will be able to land autonomously on a small surface by using computer vision, a manipulation system with the body attached to a pole, and finally full size prototypes which will demonstrate flying, landing and manipulation, including cooperative manipulation, by maintaining the equilibrium.

Mots-clés : aerial robotics,aerial manipulation
Projet ERC : OsciLang (Horizon 2020, appel 2017, Proof of concept grants)
Entité participante : Basque Center on Cognition, Brain and Language — Pays : Espagne — Rôle : PI
Acronyme de l'entité : BCBL
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

The goal of OsciLang is to provide an affordable, lightweight, wearable brain-computer-interface neurofeedback system that can facilitate the detection and treatment of language disorders such as dyslexia and specific language impairment (SLI). To date there is no established neurofeedback protocols for treating language or reading impairments. In essence, this tool will (a) diagnose/measure and (b) improve/rehabilitate an individual’s ability to synchronize their brain’s activity with changes in a speech signal. This novel type of neurofeedback, based on phase coherence of the neural oscillatory activity with speech, will allow us to detect and enhance language function. It capitalizes on our recent research on neural oscillations during speech processing, where we found reduced auditory tracking in delta band in dyslexic readers compared with controls, advocating impaired auditory processing in dyslexia. Thus, our work and related findings suggest that literacy skills in dyslexics will improve by means of neurofeedback training protocols that enhance brain-speech tracking capabilities, especially in noisy environments. OsciLang will help to sustainably enhance brain-speech synchronization strength by allowing participants to shape their neural activity while tracking speech. This will be accomplished by presenting participants with a simple and friendly interface that quantifies basic parameters of their brain activity in real time, measured via EEG. This information can then be used by participants to facilitate top-down control on specific activation patterns. Based on our prior findings, OsciLang’ algorithms will be monitoring phase coherence at theta and delta bands, an innovative approach that has yet to be implemented in a neurofeedback system. Oscilang will also lay the foundations for extending this novel approach to provide a general framework and technology for enhancing any individual’s ability to learn to read.

Mots-clés : dyslexia,neurofeedback,speech processing,oscillations,brain activity
Projet ERC : SEED (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Lund University — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999412.0 €

The goal of SEED is to fundamentally advance the methodology of computer vision by exploiting a dynamic analysis perspective in order to acquire accurate, yet tractable models, that can automatically learn to sense our visual world, localize still and animate objects (e.g. chairs, phones, computers, bicycles or cars, people and animals), actions and interactions, as well as qualitative geometrical and physical scene properties, by propagating and consolidating temporal information, with minimal system training and supervision. SEED will extract descriptions that identify the precise boundaries and spatial layout of the different scene components, and the manner they move, interact, and change over time. For this purpose, SEED will develop novel high-order compositional methodologies for the semantic segmentation of video data acquired by observers of dynamic scenes, by adaptively integrating figure-ground reasoning based on bottom-up and top-down information, and by using weakly supervised machine learning techniques that support continuous learning towards an open-ended number of visual categories. The system will be able not only to recover detailed models of dynamic scenes, but also forecast future actions and interactions in those scenes, over long time horizons, by contextual reasoning and inverse reinforcement learning. Two demonstrators are envisaged, the first corresponding to scene understanding and forecasting in indoor office spaces, and the second for urban outdoor environments. The methodology emerging from this research has the potential to impact fields as diverse as automatic personal assistance for people, video editing and indexing, robotics, environmental awareness, augmented reality, human-computer interaction, or manufacturing.
Projet ERC : SKILLS4ROBOTS (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Technical University of Darmstadt — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1405572.5 €

The goal of SKILLS4ROBOTS is to develop a autonomous skill learning system that enables humanoid robots to acquire and improve a rich set of motor skills. This robot skill learning system will allow scaling of motor abilities up to fully anthropomorphic robots while overcoming the current limitations of skill learning systems to only few degrees of freedom. To achieve this goal, it will decompose complex motor skills into simpler elemental movements – called movement primitives – that serve as building blocks for the higher-level movement strategy and the resulting architecture will be able to address arbitrary, highly complex tasks – up to robot table tennis for a humanoid robot. Learned primitives will be superimposed, sequenced and blended. Four recent breakthroughs in the PI’s research will make this project possible due to successes on the representation of the parametric probabilistic representations of the elementary movements, on probabilistic imitation learning, on relative entropy policy search-based reinforcement learning and on the modular organization of the representation. These breakthroughs will allow create a general, autonomous skill learning system that can learn many different skills in the exact same framework without changing a single line of programmed code. To accomplish this goal, our skill learning system will autonomously extract the necessary movement primitives out of observed trajectories, learn to generalize these primitives to different situations and select, sequence or combine them such that complex behavior can be synthesized out of the primitive building blocks. We will evaluate our autonomous learning framework on a real humanoid robot platform with 60 degrees of freedom and show that it can learn a large variety of new skills.
Projet ERC : SPHERES (Horizon 2020, appel 2019, Synergy grants)
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 : 210462.08 €

The goal of SPHERES is to understand the dynamics and consequences of adipocyte hypertrophy (enlargement) through investigation of its large lipid droplet (LD). The adipocyte LD is a unique organelle specialized in storing energy in triglycerides (TGs). Its surface is composed of a phospholipid monolayer and specific LD-associated proteins (such as perilipins, CIDEs and lipases), which jointly regulate LD stability and TG turnover. Adipocyte hypertrophy due to an increase in LD size may, irrespective of body fat mass, cause a wide range of pathological conditions, notably cardiometabolic diseases. SPHERES PIs (Langin, Rydén, Antonny) postulate that disturbances in the interactions between LD proteins and LD lipid composition lead to adipocyte hypertrophy and its deleterious consequences. We have identified three fundamental unanswered questions: what determines the unique structure and dynamics of large LD; why does increased LD size alter the functional phenotype of the adipocyte; which factors cause inter-individual variations in LD size. To address these questions, SPHERES gathers expertise in clinical and cellular studies on human adipocytes, in/ex vivo investigations in mouse models, and biophysical analyses of LDs. At the core of this application is the development of beyond-state-of-the-art models and methods (spheroid cultures, native large LD preparation and reconstitution, proximity labelling of LD proteins, gene editing in cell and mouse models, and advanced LD imaging), only achievable through joint integrated effort of the PIs and co-workers. Spanning from molecular, cellular to the whole-body level, SPHERES will link new knowledge on the formation and maintenance of large adipocyte LDs to the deleterious impact of adipocyte hypertrophy. Altogether, SPHERES has a strong potential to discover novel pathogenic mechanisms, leading to a better understanding of highly prevalent diseases and identification of therapeutic strategies targeting adipocytes.
Projet ERC : SOWELL (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Fondation nationale des sciences politiques — Pays : France — Rôle : PI
Acronyme de l'entité : FNSP
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1569820.0 €

The goal of my project is to develop advanced research into the foundations of social preferences and well-being. If high value is placed on social cooperation and well-being for human development, then it becomes an urgent task to elaborate appropriate theories and measures, to understand their foundations, and to identify policies that will enhance them. I will advance this research in three steps: 1) The first stage will break new ground in the theory and measurement of social preferences and well-being, by exploiting the "Big Data" revolution and exporting behavioral economics into the field with online representative samples of societies and organizations. 2) The next stage will exploit those new large-scale behavioral measures to analyze the foundations of social preferences, sorting out the role of social cognition, individual life experience and social norms. 3) The third step will be to evaluate how policy affects social preferences and well-being, and in particular in the realms of education, employment and institutions. This project will yield proposals for a new agenda in the assessment of policies, by integrating criteria based on their impact on social cooperation and happiness. I will propose cutting-edge methods to carry out this research. First I will use the revolutionary possibilities of Big Data to test theories of happiness by exploiting high-frequency behavioral measurements of well-being from Web 2.0. Second I will use computational sciences to develop an online laboratory aimed at studying social behaviors on representative samples of the population and how they relate with real world production and policy, thus addressing the lack of external validity that currently hampers experimental economics. Last, I will combine these new measurements of behavior with randomized trials, in order to assess policy within a new paradigm based on social preferences and well-being. My research is both theoretical, empirical and trans-disciplinary.

Mots-clés : social preferences,well being,behavioral economics,psychology,big data,organizations,policy
Projet ERC : MULTIPROSMM (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Board of the Swiss Federal Institutes of Technology — Pays : Suisse — Rôle : Partner
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 122500.0 €

The goal of the MULTIPROSMM project is to design systems able to present magnetic bistabilities under different stimuli (temperature, magnetic field or light) on an unprecedented large temperature range, i.e. very low temperature with Single Molecule Magnet (SMM) behaviour, intermediate temperature with Light Induced Excited State Trapping (LIESST) and high temperature with SpinCrossOver (SCO). On one hand, as a photography of the energy-splitting of the spectroscopic states, the lanthanide luminescence will be used as a key tool for the understanding of the magnetic properties of lanthanide ions. On the other hand, Circularly Polarized Luminescence (CPL) combines the sensitivity of the luminescence with crucial information on the chiral environment. A step by step synthetic strategy will be used to elaborate molecular systems in which the coexistence of i) SMM and SCO; ii) SMM and CPL and iii) SMM, SCO and CPL are operating. The enhancement of the magnetic properties is needed to step forward towards applications. To reach such optimizations, the quantum regime of the SMM and the internal magnetic field must be vanished playing with the hyperfine coupling and magnetic dilutions. Both isotopic enrichment and shaping (i.e. decoration of both mesoporous silica and nanoparticle surfaces) of the designed systems could allow high magnetic performance in multiple properties SMM. The final result could be a system suitable for very high density data storage on a wide temperature range (from cryogenic to room temperature).
Projet ERC : MULTIPROSMM (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1382500.0 €

The goal of the MULTIPROSMM project is to design systems able to present magnetic bistabilities under different stimuli (temperature, magnetic field or light) on an unprecedented large temperature range, i.e. very low temperature with Single Molecule Magnet (SMM) behaviour, intermediate temperature with Light Induced Excited State Trapping (LIESST) and high temperature with SpinCrossOver (SCO). On one hand, as a photography of the energy-splitting of the spectroscopic states, the lanthanide luminescence will be used as a key tool for the understanding of the magnetic properties of lanthanide ions. On the other hand, Circularly Polarized Luminescence (CPL) combines the sensitivity of the luminescence with crucial information on the chiral environment. A step by step synthetic strategy will be used to elaborate molecular systems in which the coexistence of i) SMM and SCO; ii) SMM and CPL and iii) SMM, SCO and CPL are operating. The enhancement of the magnetic properties is needed to step forward towards applications. To reach such optimizations, the quantum regime of the SMM and the internal magnetic field must be vanished playing with the hyperfine coupling and magnetic dilutions. Both isotopic enrichment and shaping (i.e. decoration of both mesoporous silica and nanoparticle surfaces) of the designed systems could allow high magnetic performance in multiple properties SMM. The final result could be a system suitable for very high density data storage on a wide temperature range (from cryogenic to room temperature).
Projet ERC : QUISM (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Weizmann Institute of Science — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

The goal of the QuISM project is to develop an add-on module for confocal microscopes which will enhance the spatial resolution of the microscope by up to a factor of 4 and provide quantitative fluorescence imaging, taking advantage of the Quantum-assisted Image Scanning Confocal method recently developed within the frame of the ColloQuantO ERC-CoG project. Image-scanning confocal microscopes (ISM), proposed several decades ago and demonstrated for the first time in 2010 are already a commercial reality (led by the Zeiss "Airyscan" system). Our recently developed method takes advantage of the inherent quantum properties of fluorescence emission to achieve an up to a 2-fold improvement of resolution in ISM without compromising any of the advantageous properties of the standard ISM system. The only significant hardware changes required for QuISM are the operation of the detector in single-photon counting (“Geiger”) mode and the addition of an electronic correlator element which performs preliminary data analysis on the readout of the stream of photons from the detector array. Within this configuration, “standard” ISM data is readily obtained even when a microscope is operated in QuISM mode. The QuISM project will set the basis for commercializing QuISM by substantiating the already commercialized CMOScompatible single photon avalanche photodiode array detectors are suitable for this method, proving that the method is applicable for conventional fluorophores used in biology experiments, as well as by developing proper image analysis tools which can fully exploit the potantial of QuISM in terms of acquisition time and quantitative assessment of samples.

Mots-clés : confocal microscopy,superresolution,organic dyes,antibunching,quantitative fluorescence,microscopy