Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : HiRISE (Horizon 2020, appel 2017, Starting 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 : 1496730.0 €

Atmospheric composition provides essential markers of the most fundamental properties of exoplanets, such as their formation mechanism or internal structure. New-generation exoplanet imagers have been designed to achieve high contrast for the detection of young giant planets in the near-infrared, but they only provide very low spectral resolutions (R<100) for their characterization. For a major breakthrough in the comprehension of young exoplanets and their atmospheres, an increase of a factor 100 to 1000 in spectral resolution is absolutely required. This proposal ambitions to develop a novel demonstrator that will combine the capabilities of two flagship instruments installed on the ESO Very Large Telescope, the high-contrast exoplanet imager SPHERE and the high-resolution spectrograph CRIRES+, with the goal of answering fundamental questions on the formation, composition and evolution of young planets. The work will be organized along two axes interconnected with transverse activities: (i) the astrophysics block will investigate signal extraction from high-resolution data and atmospheric modeling, and (ii) the instrumentation block will develop a demonstrator designed to pick up the near-infrared light in SPHERE and feed CRIRES+ via a dedicated injection module and optical fiber relay. We will explore all the key aspects of the project using a combination of instrumental and astrophysical simulations, as well as laboratory validation of components and methods on our high-contrast imaging testbed. We will use the demonstrator to observe a sample of directly imaged companions and obtain high-resolution spectroscopy of their atmospheres. From the data we will (1) determine their formation mechanism through an accurate determination of the carbon and oxygen abundances in their atmospheres, and (2) map the temporal variability of their photosphere through time-resolved Doppler imaging to study dynamical processes related to the formation and evolution of clouds.

Mots-clés : direct imaging,high contrast imaging,exoplanet characterization,high dispersion spectroscopy,high spectral resolution
Projet ERC : MEMETRE (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : University of Helsinki — Pays : Finlande — Rôle : PI
Acronyme de l'entité : UH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1908652.0 €

Atmospheric concentration of the strong greenhouse gas methane (CH4) is rising with an increased annual growth rate. Biosphere has an important role in the global CH4 budget, but high uncertainties remain in the strength of its different sink and source components. Among the natural sources, the contribution of vegetation to the global CH4 budget is the least well understood. Role of trees to the CH4 budget of forest ecosystems has long been overlooked due to the perception that trees do not play a role in the CH4 dynamics. Methanogenic Archaea were long considered as the sole CH4 producing organisms, while new findings of aerobic CH4 production in terrestrial vegetation and in fungi show our incomplete understanding of the CH4 cycling processes. Enclosure measurements from trees reveal that trees can emit CH4 and may substantially contribute to the net CH4 exchange of forests. The main aim of MEMETRE project is to raise the process-based understanding of CH4 exchange in boreal and temperate forests to the level where we can construct a sound process model for the soil-tree-atmosphere CH4 exchange. We will achieve this by novel laboratory and field experiment focusing on newly identified processes, quantifying CH4 fluxes, seasonal and daily variability and drivers of CH4 at leaf-level, tree and ecosystem level. We use novel CH4 flux measurement techniques to identify the roles of fungal and methanogenic production and transport mechanisms to the CH4 emission from trees, and we synthesize the experimental work to build a process model including CH4 exchange processes within trees and the soil, transport of CH4 between the soil and the trees, and transport of CH4 within the trees. The project will revolutionize our understanding of CH4 flux dynamics in forest ecosystems. It will significantly narrow down the high uncertainties in boreal and temperate forests for their contribution to the global CH4 budget.

Mots-clés : fluxes,process modeling,trees,greenhouse gases,methane,microbes,fungi,fungal decomposers,isotopes,molecular techniques,tree physiology,wood anatomy,methanogens,methanotrophs
Projet ERC : SARLEP (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Forschungszentrum Jülich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : FZJ
Type d'entité : Recherche
Financement ERC de l'entité : 1850000.0 €

Atmospheric pollutants emitted by natural and anthropogenic sources influence significantly the quality of life on our planet. Their removal in the atmosphere is controlled by their reactions with photochemically produced hydroxyl radicals. Recent findings from experimental studies and quantum-chemical calculations suggest that an important part of atmospheric radical chemistry, which is directly linked to the self-cleansing ability of our atmosphere, has been overlooked. This causes considerable uncertainty in our understanding of the couplings between the biosphere, atmospheric chemistry and climate. The greatest impact of this lack of understanding has been found for regions with large emissions of organic compounds from plants in remote or rural areas. Within this project, the oxidation of organic compounds will be comprehensively investigated for the most important, biogenic organic compounds. The innovative experimental approach will quantify the radical destruction and production rates in experiments in the unique atmosphere simulation chamber SAPHIR at the host institution. These experiments aim to close the gap between laboratory and field studies. The advantages are: (1) Experiments will be conducted under atmospherically relevant conditions. (2) Radical recycling efficiency will be quantified for the entire chemical system, not just for single reactions. (3) The complexity of the chemical system studied will be increased from single compounds to natural plant emissions. New innovative instrumentation will be developed for accurate and precise measurements of radical species and oxidized organic compounds. These are also of great interest beyond this project. The results of this project will improve our understanding of atmospheric radical chemistry required for accurately predicting the atmospheric radical budget, the formation of harmful secondary pollutants such as ozone, acids and aerosol and the lifetime of greenhouse gases affecting climate change.
Projet ERC : Unicorn (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Universität Innsbruck — Pays : Autriche — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1960392.0 €

Atmospheric turbulence exerts a dominant control on the exchange of heat, CO2, water vapor, pollutants and momentum between the surface and the atmosphere, and therefore drives phenomena as diverse as climate, storm systems, air pollution, and glacial melt. Existing turbulence theory was developed for horizontally homogenous flat terrain, and fails in more complex terrain. Thus, for the majority of our planetary surface no viable theory of turbulence is available, and approaches that are known to be inadequate are nevertheless applied. The time is ripe to close this fundamental knowledge gap and formulate a theory universally applicable in complex terrain. Unicorn addresses this using a synergy of measurements, numerical modelling and theory to create a novel framework extending the existing theory of near-surface turbulence to complex terrain. Based on the ground-breaking hypothesis that including the directionality of turbulent exchange (anisotropy) can encode the boundary conditions, Unicorn will identify the key physical processes that cause anisotropy in complex terrain to differ from that over flat terrain. Thus Unicorn will systematically explore the parameter space of different sources of complexity, such as topography, flow conditions and heterogeneity, using unprecedented analysis of over sixty measurement datasets over flat and complex terrain coupled with machine learning approaches, sensitivity studies using state-of-the-art high resolution numerical simulations, and reduced order theoretical derivations. This synergistic approach incorporating the effects of complex terrain into a framework based on turbulence anisotropy will bring a much-needed breakthrough for understanding turbulence in complex terrain. Findings will revolutionize near-surface turbulence representation in numerical models, leading to better predictive capability in numerous societally and scientifically relevant topics, such as climate, extreme weather and air pollution.

Mots-clés : atmospheric turbulence,boundary layer,complex terrain,mountain meteorology,similarity scaling
Projet ERC : MAP (Horizon 2020, appel 2018, Proof of concept grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : University of Reading — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterised by atypical social communication and interaction, and repetitive and restricted behaviours, activities and interests, affecting around 1% of the general population worldwide. It is estimated that about 30% of children with ASD do not develop functional speech, and remain non-verbal or minimally verbal even after years of speech, language and educational interventions. Although a wide range of programmes have been developed for treating language impairments in ASD, none has been effective in eliciting functional speech in ASD children. Building on the research findings of the CAASD project, this proof of concept grant aims to develop a set of multifaceted, individualized, easily implemented, music-assisted intervention programmes (MAP) to increase spoken language ability in 2-4-year-old, nonverbal or minimally verbal children with ASD. Specifically, we will develop a structured training protocol, delivered through naturalistic strategies and interactive activities, to teach language to ASD children through songs and music making. Over a period of 12 weeks, randomised controlled trials will be run on two groups of ASD children, randomly assigned to the treatment (undertaking music-assisted language interventions) or the control group (receiving traditional speech and language therapy). The efficacy of MAP will be assessed through the learning of 36 target words that relate to naturalistic, everyday activities that the children spontaneously engage in. Ultimately, an app will be built and developed for parents or carers of children with ASD to learn the songs and implement the associated activities wherever there is a need. Addressing the urgent need for language interventions in ASD, MAP has the potential to open the neural pathways to language in the autistic brain and to create genuine and lasting outcomes to resolve the socio-neuro-constructionist issues in ASD.

Mots-clés : autism spectrum disorder,language,music,intervention,communication,verbal
Projet ERC : MoViS (Horizon 2020, appel 2017, Proof of concept grants)
Entité participante : University of Trento — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 144186.0 €

Autism spectrum disorders (ASD) comprise an early-onset neurodevelopmental syndrome primarily characterized by impairments in social perception, cognition and communication, and a restricted pattern of interests and behavior. Without any doubt, infants and children with ASD greatly benefit from early behavioral intervention, therefore an early detection and treatment of ASD has been placed as a major health care priority. Unfortunately, nowadays, ASD diagnosis occurs at 2.5-4 years of age. In my previous ERC AdG Project, I proved that inborn predispositions to visual social stimuli (i.e., face-like patterns, eye-gaze and biological motion) is different between newborns at high-risk for ASD (HR, younger siblings of affected children) and newborns at low-risk (LR). Significant predictors for HR newborns were obtained and an accurate biomarker was identified. Starting from this, the goal of MoViS Project (Mobile Visual Stimulation) will be to develop, for the very first time, a simple and innovative tool for early screening and monitoring of neonates social behaviour by means of visual stimulation. This is an innovative and a promising project because, behind providing an important contribution in expanding our current knowledge about the ontogenesis of the ASD, the possibility of targeting high-risk infants during the first days of their life, via a behavioral marker for social orienting, could allow for a new generation of early therapeutic interventions for rehabilitation, starting in a time when brain plasticity allows reorganization of cortical circuits.

Mots-clés : autism spectrum disorders,human newborn,screening protocol,social,predispositions impairments,visual preference,behavioral biomarker
Projet ERC : MorSR (Horizon 2020, appel 2018, Proof of concept grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149919.0 €

Automatic Speech Recognition (ASR) is considered to represent the most natural man-machine interface across the spectrum of technological space. Current commercial ASR systems rely on a ‘rich’ representation of an acoustic signal for words and their variants, resulting in major challenges in the deployment of ASR systems in areas where it could have substantial social impact. Our central goal is to translate research results from the ERC funded project MORPHON into a novel ASR system to remove such barriers. We have previously demonstrated that the use of a universal set of phonological features delivers an isolated word recognition system (FlexSR) with enhanced phoneme recognition accuracy. It is more robust under conditions of non-standard speech, dialect variation and can be easily adapted to new languages. These aspects are problematic for current ASR systems which rely on the probabilistic sequencing of whole words in their language model (LM) based on large written text corpora for training. Obtaining sufficient training data for a new LM is prohibitively expensive. Instead, MorSR will incorporate linguistic information about word-structure to reject improbable words. This reduces the search space and increases the probability of identifying correct words. A major outcome will be an innovative LM based on linguistic principles. Unlike existing approaches, it is based on speech data to capture crucial regularities that are lost in text corpora. Combined with FlexSR's key strengths in identifying subtle phonological contrasts, MorSR will not only enable improved predictions of word sequences in running speech, but also dramatically reduce the requirement for training data when adapting the system to a new language. MorSR's strengths include: (a) prediction of fine-grained possibilities of word sequences based on grammatical principles; (b) requiring considerably less training data; (c) easily adaptable to new languages; and (d) will be fast, secure and accurate.

Mots-clés : automatic speech recognition,language model,morpho phonological predictions
Projet ERC : NATURAL (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Luxembourg — Pays : Luxembourg — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1495988.0 €

Automatic bug fixing, i.e., the idea of having programs that fix other programs, is a long-standing dream that is increasingly embraced by the software engineering community. Indeed, despite the significant effort that humans put into reviewing code and running software test campaigns, programming mistakes slip by, with severe consequences. Fixing those mistakes automatically has recently been the focus of a number of potentially promising techniques. Proposed approaches are however recurrently criticized as being shallow (i.e., they mostly address unit test failures, which are often neither hard nor important problems). Initial successes in automatic bug fixing are based on scenarios such as the following: when a bug is localized, patches are generated repetitively and automatically, through trial and error, until a valid patch is produced. The produced patch could then be later revised by developers. While the reported achievements are certainly worthwhile, they do not address what we believe is a more comprehensive challenge of software engineering: to systematically fix features of a software system based on end-user requirements. The ambition of NATURAL is to develop a methodology for yielding an intelligent agent that is capable of receiving a natural language description of a problem that a user faces with a software feature, and then synthesizing code to address this problem so that it meets the user's expectations. Such a repair bot would be a trustworthy software contributor that is 1) first, targeting real bugs in production via exploiting bug reports, which remain largely under-explored, 2) second, aligning with the conversational needs of collaborative work via generating explanations for patch suggestions, 3) third, shifting the repair paradigm towards the design of self-improving systems via yielding novel algorithms that iteratively integrate feedback from humans. Ultimately, NATURAL will be transformative in the practice of software engineering.

Mots-clés : program repair,software maintenance,bug fixing,bug reports,debugging,software practice,user requirements
Projet ERC : MultiMT (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Sheffield — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 483257.33 €

Automatically translating human language has been a long sought-after goal in the field of Natural Language Processing (NLP). Machine Translation (MT) can significantly lower communication barriers, with enormous potential for positive social and economic impact. The dominant paradigm is Statistical Machine Translation (SMT), which learns to translate from human-translated examples. Human translators have access to a number of contextual cues beyond the actual segment to translate when performing translation, for example images associated with the text and related documents. SMT systems, however, completely disregard any form of non-textual context and make little or no reference to wider surrounding textual content. This results in translations that miss relevant information or convey incorrect meaning. Such issues drastically affect reading comprehension and may make translations useless. This is especially critical for user-generated content such as social media posts -- which are often short and contain non-standard language -- but applies to a wide range of text types. The novel and ambitious idea in this proposal is to devise methods and algorithms to exploit global multi-modal information for context modelling in SMT. This will require a significantly disruptive approach with new ways to acquire multilingual multi-modal representations, and new machine learning and inference algorithms that can process rich context models. The focus will be on three context types: global textual content from the document and related texts, visual cues from images and metadata including topic, date, author, source. As test beds, two challenging user-generated datasets will be used: Twitter posts and product reviews. This highly interdisciplinary research proposal draws expertise from NLP, Computer Vision and Machine Learning and claims that appropriate modelling of multi-modal context is key to achieve a new breakthrough in SMT, regardless of language pair and text type.

Mots-clés : statistical machine translation,multi modal natural language processing,context models,vision and language
Projet ERC : Autophagy in vitro (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: Biological Mechanisms, Structures and Functions
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 314668.5 €

Autophagy is a catabolic pathway that delivers cytoplasmic material to lysosomes for degradation. Under vegetative conditions, the pathway serves as quality control system, specifically targeting damaged or superfluous organelles and protein-aggregates. Cytotoxic stresses and starvation, however, induces the formation of larger autophagosomes that capture cargo unselectively. Autophagosomes are being generated from a cup-shaped precursor membrane, the isolation membrane, which expands to engulf cytoplasmic components. Sealing of this structure gives rise to the double-membrane surrounded autophagosomes. Two interconnected ubiquitin (Ub)-like conjugation systems coordinate the expansion of autophagosomes by conjugating the autophagy related (Atg)-protein Atg8 to the isolation membrane. In an effort to unravel the function of Atg8, we reconstituted the system on model membranes in vitro and found that Atg8 forms together with the Atg12–Atg5-Atg16 complex a membrane scaffold which is required for productive autophagy in yeast. Humans possess seven Atg8-homologs and two mutually exclusive Atg16-variants. Here, we propose to investigate the function of the human Ub-like conjugation system using a fully reconstituted in vitro system. The spatiotemporal organization of recombinant fluorescent-labeled proteins with synthetic model membranes will be investigated using confocal and TIRF-microscopy. Structural information will be obtained by atomic force and electron microscopy. Mechanistic insights, obtained from the in vitro work, will be tested in vivo in cultured human cells. We belief that revealing 1) the function of the human Ub-like conjugation system in autophagy, 2) the functional differences of Atg8-homologs and the two Atg16-variants Atg16L1 and TECPR1 and 3) how Atg16L1 coordinates non-canonical autophagy will provide essential insights into the pathophysiology of cancer, neurodegenerative, and autoimmune diseases.

Mots-clés : autophagy,in vitro reconstitution,model membranes