Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : COBREX (Horizon 2020, appel 2019, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
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 : 2495266.0 €

Exoplanetology has become a major topic in astronomy over the last 30 years. New observing capabilities and modelling have revolutionised the understanding of how planetary systems form and evolve. Yet, the 5-20 au region, where the giant planets formed by accretion of gas on to a solid core are supposed to form predominantly, remains barely explored due to instrumental limitations. GAIA and the E-ELT instruments will be well suited to explore planetary systems at this scale. However, I believe that exploring this region is possible right now, at a small cost, with the use of recent innovative signal processing developments applied to current High Contrast Imaging (HCI) data and/or the use of high-performance, AO-fed medium/high resolution spectrographs. With COBREX, I want to apply such new data processing techniques on the largest library of SPHERE and GPI HCI data to gain at least one order of magnitude (hence a gain of ~3 in mass/separation) on current planet and disk detection limits in the 5-20 au region. This will allow witnessing, for the first time, analogues of our Solar System Giants at early ages, and constraining the distribution of GPs in the 5-20 au. Combining in different ways various data (HCI, GAIA, RV) will allow exploring the demography of young giant planets at all separations. Then, I want to couple HCI with medium/high resolution spectroscopy, to find and study planet properties into exquisite details, and investigate in an unprecedented way the link between disks and planets. Finally, I wish to explore the possibility of imaging remote magmatic super-Earths in the near future thanks to such improvements.

Mots-clés : exoplanets,formation of planetary systems,high contrast imaging,spectroscopy
Projet ERC : BLACKJACK (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
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 : 1489000.0 €

Expensive computer simulations have become routine in the experimental sciences. Astrophysicists design complex models of the evolution of galaxies, biologists develop intricate models of cells, ecologists model the dynamics of ecosystems at a world scale. A single evaluation of such complex models takes minutes or hours on today's hardware. On the other hand, fitting these models to data can require millions of serial evaluations. Monte Carlo methods, for example, are ubiquitous in statistical inference for scientific data, but they scale poorly with the number of model evaluations. Meanwhile, the use of parallel computing architectures for Monte Carlo is often limited to running independent copies of the same algorithm. Blackjack will provide Monte Carlo methods that unlock inference for expensive models in biology by directly addressing the slow rate of convergence and the parallelization of Monte Carlo methods. The key to take down the Monte Carlo rate is to introduce repulsiveness between the quadrature nodes. For instance, we recently proved that determinantal point processes, a prototypal repulsive distribution introduced in physics, improve the Monte Carlo convergence rate, just like electrons lead to low-variance estimation of volumes by efficiently filling a box. Such results lead to open computational and statistical challenges. We propose to solve these challenges, and make repulsive processes a novel tool for applied statisticians, signal processers, and machine learners. Still with repulsiveness as a hammer, we will design the first parallel Markov chain Monte Carlo algorithms that are qualitatively different from running independent copies of known algorithms, i.e., that explicitly improve the order of convergence of the single-machine algorithm. To this end, we will turn mathematical tools such as repulsive particle systems and non-colliding processes into computationally cheap, communication-efficient Monte Carlo schemes with fast convergence.

Mots-clés : monte carlo methods
Projet ERC : Fireworks (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Weizmann Institute of Science — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2461111.0 €

Experimental time-domain astrophysics is on the verge of a new era as technological, computational, and operational progress combine to revolutionise the manner in which we study the time-variable sky. This proposal consolidates previous breakthrough work on wide-field surveys into a coherent program to advance our study of the variable sky on ever decreasing time-scales: from days, through hours, to minutes. We will watch how stars explode in real time in order to study the complex physics of stellar death, build new tools to handle and analyse the uniquely new data sets we are collecting, and shed light on some of the most fundamental questions in modern astrophysics: from the origin of the elements, via the explosions mechanism of supernova explosions, to the feedback processes that drive star formation and galaxy evolution.

Mots-clés : supernovae,time domain astrophysics,wide field surveys,cosmic explosions,massive stars
Projet ERC : HYPERDIVERSE (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : Muséum national d'histoire naturelle — Pays : France — Rôle : PI
Acronyme de l'entité : MNHN
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1949788.0 €

Explaining why some taxa are more diversified than their closest relatives remains one of the major challenges in evolutionary biology. However, much of Earth’s biodiversity is concentrated in few diverse non-vertebrate animal lineages, whose analysis of diversification dynamics is hampered by the lack of empirical data. The NEOME project will focus on one hyperdiverse and poorly-known taxon of molluscs, the marine predators Neogastropoda. Their high species diversity (>15,000 species), largely undescribed, and the key innovations they evolved, linked to their feeding habits and dispersal abilities, make them a perfect model system to test hypotheses related to the diversification of hyperdiverse taxa. The first objective of the project will be to reconstruct a robust, precise and complete phylogeny of the group by using cutting-edge omic technologies. The second goal will be to test correlations between two traits – the exogenome diversity as proxy of feeding habits, and the type of larval development as a proxy of dispersal capabilities, which are keys for understanding diversification dynamics, using genomic, transcriptomic and proteomic approaches. The same data will also be used to fulfil the last objective: identifying the genetic determinants of the two traits, the molecular mechanisms at the origin of their diversity, and the processes by which these traits interact with the environment to mediate the diversification of the neogastropods. The major breakthrough of the project will thus rely on the joint analysis of the correlation between traits and diversification and of the causal factors at the origin of this correlation. Furthermore, the project will also impact other fields of research, with the production of unique data on many culturally, economically and scientifically important species, including the first complete genomes for the group, and the characterization of numerous compounds potentially promising in human therapeutics and biotechnologies.

Mots-clés : evolutionary systematics,population genomics,exogenomic,comparative transcriptomics and proteomics
Projet ERC : DEXIM (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Tübingen — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Explanations are valuable because they scaffold the kind of learning that supports adaptive behaviour, e.g. explanations enable users to adapt themselves to the situations that are about to arise. Explanations allow us to attain a stable environment and have the possibility to control it, e.g. explanations put us in a better position to control the future. Explanations in the medical domain can help patients identify and monitor the abnormal behaviour of their ailment. In the domain of self-driving vehicles they can warn the user of some critical state and collaborate with her to prevent a wrong decision. In the domain of satellite imagery, an explanatory monitoring system justifying the evidence of a future hurricane can save millions of lives. Hence, a learning machine that a user can trust and easily operate need to be fashioned with the ability of explanation. Moreover, according to GDPR, an automatic decision maker is required to be transparent by law. As decision makers, humans can justify their decisions with natural language and point to the evidence in the visual world which led to their decisions. In contrast, artificially intelligent systems are frequently seen as opaque and are unable to explain their decisions. This is particularly concerning as ultimately such systems fail in building trust with human users. In this proposal, the goal is to build a fully transparent end-to-end trainable and explainable deep learning approach for visual scene understanding. To achieve this goal, we will make use of the positive interactions between multiple data modalities, incorporate uncertainty and temporal continuity constraints, as well as memory mechanisms. The output of this proposal will have direct consequences for many practical applications, most notably in mobile robotics and intelligent vehicles industry. This project will therefore strengthen the user’s trust in a very competitive market.

Mots-clés : zero shot learning,explainable ai
Projet ERC : EXTORT (Horizon 2020, appel 2019, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : University of St. Gallen — Pays : Suisse — Rôle : Partner
Acronyme de l'entité : HSG
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 66251.25 €

Extortion had come to occupy a quintessential position in the global imaginary of mafia-type-criminal organizations. Yet it is also increasingly present beyond clandestine criminal networks and is becoming a normalised profitable source of livelihood and governance and a key visible social relation in many parts of the world. Systematic research on and explanatory models for the apparent expansion and rountinisation of extortion in everyday life (including cyber-extortion) are currently lacking. Timely, the project sets up extortion as an object of anthropological inquiry and charts the first comprehensive cross-cultural account of extortion in social life across South and East Asia, the Americas, Africa and Europe. The proposed program of work consists of 5 main components: 1) Primary research on ‘offers that cannot be refused' across different domains of life in and across 22 settings; 2) Analytical and theoretical development in the study of power, consent and organized crime; 3) Methodological innovation by conducting a simultaneous cross-national ethnographic research and adding a South-to-South comparative angle in an area dominated by single case studies and a focus on the Global North; 4) Capability enhancement by forging an international network of scholars in the emerging field of the anthropology of crime and supporting the careers of the project's 5 Post-docs; 5) Policy relevant research in the fields of violence and consent, organized crime, informal economy and development. The study capitalises on previous research and skills of the PI in the cross-cultural study of bossism, politics and kinship and 'mafias' in the global South. Research will be embedded in anthropology but informed by empirical and analytical questions relevant to criminology, political science, economics and development studies.

Mots-clés : violence  force,intimidation,consent,protection,power,informal,criminal economies,governance,organized crime
Projet ERC : ExocyTher (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
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 : 1496094.0 €

Extracellular vesicles (EVs) from stem cells (SCs) may recapitulate the therapeutic effect of their mother cells in regenerative medicine while mitigating risks of uncontrolled replication, differentiation and vascular occlusion, offering “off-the-shelf”, storage and shelf-life gains. Yet, the clinical translation of SC EV therapy is hampered by production, engineering and administration challenges. Building on my preliminary results, ExocyTher aims at developing SC EV therapy through: (1) standardized scalable high-yield EV production: I propose the concept of turbulence vesiculation to induce EV release from adipose SCs (ASCs) by a controlled turbulence shear stress integrated to large-scale cell culture in bioreactors; (2) optimised delivery: I propose EV administration in a synergic fistula-occlusive companion gel (injectable at <20°C and gelling at body temperature) in the attempt to fill the entire fistula tract and retain EVs at the site of interest; ExocyTher will tackle the therapy of fistulas, a major health burden related to Crohn's disease or secondary to surgery, cancer therapy or trauma, affecting millions of people in Europe with a high morbidity rate. All-in-one turbulence-triggered vesiculation and priming are expected to provide ASC EVs in high yield and with improved immunomodulatory properties specially for a more efficient therapy of inflammatory Crohn’s disease fistulas. Capitalizing on the use of a gel medical device authorized in Europe as an off-label fistula occlusive EV vehicle and considering key regulatory issues, ExocyTher will set the basis for the first clinical trial on ASC EV fistula therapy. ExocyTher concepts may be extended to other EV parent cell types or therapy indications, foreseeing a high scientific and technological impact. Start date is requested due to the COVID-19 pandemic and this is necessary to minimise any disruption could be caused by the COVID-19 pandemic.

Mots-clés : extracellular vesicle production,turbulence,extracellular vesicle engineering,fistula therapy,thermoresponsive hydrogel
Projet ERC : ReservoirDOCs (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : University of California System — Pays : Etats-Unis — Rôle : Partner
Acronyme de l'entité : UC
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149100.0 €

Extracted evolutionary and epidemiological information from pathogen genomes has grown into an important instrument across infectious disease research. By harnessing such information, molecular epidemiologists aim to shed light on the origin and epidemic history of pathogens, from reservoir dynamics to emergence and adaptation to new hosts, and their spatiotemporal spread. However, despite the revolution in genome sequencing technologies and advances in statistical methodology, key questions about pathogen emergence and establishment in human populations remain unresolved for major viral epidemics. When confronted with new viral outbreaks, such as the recent devastating Ebola virus epidemic, we also struggle to deploy these technologies in a systematic and concerted way despite a critical need to support public health interventions. In this project, we propose to unravel crucial steps in the emergence and establishment of key viral pathogens. We will scrutinise the reservoir dynamics of HCV by sequencing complete hepacivirus genomes from infected samples emerging from a large-scale screening of African rodents, and analyze the cross-species transmission history using novel evolutionary methods that accommodate spatial and temporal variability in selective pressures. To test hypotheses about the early establishment of HIV-1, we will carve a genomic window into the past epidemic history of the virus by integrating molecular work on archival samples from Central Africa and on samples representative of the current HIV-1 diversity, with the development of ancestral recombination graphs that accommodate dated tips and spatial diffusion, as well as population dynamic models that incorporate epidemiological information. Finally, we will take the recent Ebola epidemic in West Africa as a model to develop high-performance statistical approaches for extracting practical and timely epidemiological information from virus genome sequences during epidemics as they unfold.

Mots-clés : emerging infectious diseases,molecular epidemiology,bayesian inference
Projet ERC : LEARNATTEND (Horizon 2020, appel 2018, Advanced 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 : 2499875.0 €

Extracting statistical regularities from the environment is one of the most fundamental abilities of any living organism. This type of learning is largely unconscious, unintentional, and implicit; it runs "in the background", both seeking and giving structure to the world around us; making it coherent, predictable and quickly manageable. Even though a lot is known about how statistical learning affects language acquisition, object recognition, motor learning, and decision making, only recently it became apparent that it plays a key role in attentional selection. Visual perception must be selective, as we are confronted with the massive amount of available sensory input. Statistical learning occurring often beneath the level of awareness provides structure to the environment uncovering the relations between objects in space and time. The proposed research program investigates the mechanisms underlying visual statistical learning (VSL) focusing on how, when and what information is extracted by the visual system. Through brain imaging we seek to understand how learning taking place in the medial temporal lobe (hippocampus), affects attentional representations within putative priority maps across the visual hierarchy. By means of EEG, we seek to connect hippocampal activity to the activations within the spatial priority map which ultimately controls attentional selection. By means of single cell recording in humans we determine at a cell level how statistical learning develops over time. To understand the mechanism, we analyse individual differences in VSL and relate this to visual working memory capacity and attentional selection in psychopathy. The proposed research will have a large impact on the study of cognition, learning, and memory.

Mots-clés : attentional selection,statistical learning
Projet ERC : FACETS (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : Polytechnic University of Turin — Pays : Italie — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 125000.0 €

FACETS studies the meaning of the face in contemporary visual cultures. There are two complementary research foci: widespread practices of face exhibition in social networks like Facebook, Instagram, Snapchat, and Tinder; and minority practices of occultation, including the mask in anti-establishment political activism (e.g., Anonymous) and in anti-surveillance artistic provocation (e.g., Leonardo Selvaggio). Arguably, the meaning of the human face is currently changing on a global scale: through the invention and diffusion of new visual technologies (e.g., digital photography, visual filters, as well as software for automatic face recognition); through the creation and establishment of novel genres of face representation (e.g., the selfie); and through new approaches to face perception, reading, and memorization (e.g., the ‘scrolling’ of faces on Tinder). Cognitions, emotions, and actions that people attach to the interaction with one’s and others’ faces might soon be undergoing dramatic shifts. In FACETS, an interdisciplinary but focused approach combines visual history, semiotics, phenomenology, visual anthropology, but also face perception studies and collection, analysis, and social contextualization of big data, so as to study the cultural and technological causes of these changes and their effects in terms of alterations in self-perception and communicative interaction. In the tension between, on the one hand, political and economic agencies pressing for increasing disclosure, detection, and marketing of the human face (for reasons of security and control, for commercial or bureaucratic purposes) and, on the other hand, the counter-trends of face occultation (writers and artists like Banksy, Ferrante, Sia, or Christopher Sievey / Frank Sidebottom choosing not to reveal their faces), the visual syntax, the semantics, and the pragmatics of the human face are rapidly evolving. FACETS carries on an innovative, cross-disciplinary survey of this phenomenon.

Mots-clés : face,digital sphere,visual history,visual studies,face perception studies,visual semiotics,visual anthropology,digital social networks studies,art history,art theory,phenomenology
Projet ERC : FERMIcQED (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : École polytechnique — Pays : France — Rôle : PI
Acronyme de l'entité : X
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499133.0 €

FERMIcQED aims at interfacing novel quantum materials with microwave light at the level of the single photon and fermion. To achieve this ambitious goal, I plan to use low-dimensional quantum conductors – such as carbon nanotubes or semiconducting nanowires – combined with state-of-the-art architectures and techniques of circuit Quantum Electrodynamics. The idea consists in isolating an individual fermionic degree of freedom within a hybrid Josephson junction – a quantum dot connected to two superconductors. Due to the superconducting proximity effect, entangled electron-hole states – called the Andreev bound states – form in the quantum dot and depend on the superconducting phase difference. By enclosing the hybrid Josephson junction inside a superconducting photonic cavity, one can couple these fermionic states to microwave light and probe their quantum properties in a well-controlled environment. Specifically, FERMIcQED will tackle three key experiments. First, we will detect the spin degree of freedom of the Andreev bound states and manipulate it coherently as a superconducting spin qubit. We will demonstrate strong coupling with cavity photons, which will enable quantum logic operations and long-range qubit interactions. Second, we will operate the hybrid Josephson junction in the topological regime in order to observe and manipulate Majorana fermions, thus implementing a topological qubit. At last, we will probe the joint entangled dynamics of bosonic and fermionic modes that coexist in hybrid Josephson junctions and simulate the spin-boson problem.

Mots-clés : hybrid quantum circuits