Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : FGR PODS (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Amsterdam University Medical Centers — Pays : Pays-Bas — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1700000.0 €

Early-onset foetal growth restriction <32 weeks’ gestation is a severe pregnancy disorder that affects an estimated 15,000 European pregnancies annually and puts the babies at risk of severe morbidity and mortality. The underlying mechanism of the disorder is placental insufficiency leading to failure to meet the foetal metabolic and respiratory demands (the first leading to the growth disorder, the latter leading to fetal demise). Birth of the foetus prevents further damage from the intrauterine environment, but exposes the neonate to the challenges of severe prematurity. When women are admitted to the hospital with this complication of pregnancy, the obstetrician monitors the progressive signs of placental insufficiency and decides to expedite birth when the risks of foetal demise outweigh the risks of prematurity. Currently, there is lacking evidence how to weigh all prognosticators in this balance, therefore it takes place in the black box of the obstetrician's mind. In FGR PODS (Fetal Growth Restriction Perinatal Outcome Decision Support), I aim to develop and validate a ground-breaking decision support tool that will aid the obstetrician with objectively determining the optimal timing of birth. The decision support tool will integrate a combination of foetal and maternal prognosticators, and can be implemented in electronic patient charts. Provided as freeware, centres that have implemented the tool will feed back pseudonymized patient data in order to continuously refine the model. Also, within FGR PODS, I will perform innovative analysis (including machine learning) of fetal heart rate signals and of serially measured biomarkers to pick up placental respiratory failure. These studies will further our understanding of the foetal pathophysiological mechanisms resulting from placental insufficiency and will potentially identify targets for therapeutic interventions.

Mots-clés : fetal growth restriction cardiotocography perinatal outcomes clinical decision support tool maternal biomarkers fetal heart monitoring syncytiotrophoblast machine learning deep learning
Projet ERC : Foundation (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : Université technique du Danemark — Pays : Danemark — Rôle : PI
Acronyme de l'entité : DTU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999024.0 €

Planetary climate models are essential to understanding the climate on Earth while also being windows into the many climates that may exist throughout the Universe. However, current models often fail to simulate planets that diverged from Earth-like conditions as they rely on Earth-centric formulations and suffer a shortage of first principle representations. This severely impacts our ability to understand and predict climate change and evolution, as the physical accuracy of the simulations is compromised. To solve this current gap in our knowledge, I will lead the development of the first planet climate simulator, Foundation. My central role in developing unprecedented 3D planetary atmospheric models from scratch sets me in an advantageous position to successfully lead this ambitious project. Our goal is to use the building blocks of physical processes we know occur in atmospheres, such as fluid flow equations, moist physics and cloud formation, and build up climate physics in a 3D model that achieves accurate simulations. Our novel model will address climate phenomena that remain unsolved in the Solar System due to current model limitations, namely the nature of Jupiter's chaotic atmosphere, Venus's deep atmospheric circulation and Titan's methane cycle. These are major gaps in our knowledge, even with more than 50 years of spacecraft data.  A model based on Universal physics that can reproduce the most challenging climates of the Solar System is extremely valuable to evaluate Earth's climate model predictions. Our approach can strongly impact the robustness of Earth's changing climate simulations and the prediction of extreme weather events, which are becoming increasingly more critical to our living environment.  Foundation's greater climate prediction capabilities will also revolutionise exoplanet atmospheric characterisation and provide a thorough theory on the climate stability of terrestrial planets, essential to our understanding of climate diversity.

Mots-clés : global circulation models,solar system planets,planetary sciences,venus,jupiter,titan,exoplanets,earth climate,climate physics
Projet ERC : NoKnow (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université technique du Danemark — Pays : Danemark — Rôle : PI
Acronyme de l'entité : DTU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999114.0 €

Machine learning and artificial intelligence techniques are progressing at a tremendous pace and impressive results appear across scientific fields. However, as machine learning models grow in capacity, they become increasingly ‘black box’, and it becomes harder for humans to reason about the patterns discovered by the machine. A root cause of this difficulty is that most machine learning models can express the same pattern in infinitely many, equally good, ways within their internal representations of the world. This is known as an identifiability problem.  Today we lack a general solution to identifiability problems, and either give up on understanding the patterns discovered by the machine or reduce model complexity to lessen the problem. The latter also reduces the fidelity and applicability of the model. NoKnow rephrase the question of identifiability to be concerned with tasks solved by the representation rather than the representation itself. Using tools from differential geometry and Bayesian inference, we develop the theoretical tools to ensure that tasks solved in the representation have an identifiable outcome even if the representations themselves are not identifiable.  To turn theory into practice, we develop state-of-the-art algorithms for assessing the uncertainty of learned representations in order to indirectly estimate the topology of the representation manifold. We further develop novel, high-fidelity predictive models that have identifiable outcomes when trained on learned representations.  NoKnow provides the fundamental tools needed to engage with learned representations to guarantee identifiable outcomes. This, in turn, increase trust in findings as they are not dependent on arbitrariness in learned representations. As society increasingly automates decisions, this trust in machine learning becomes ever more important.

Mots-clés : identifiability,unsupervised representation learning,unsupervised learning,bayesian inference,approximate bayesian inference,geometric statistics
Projet ERC : MAGNIFY (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : Université technique du Danemark — Pays : Danemark — Rôle : PI
Acronyme de l'entité : DTU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999933.0 €

Magnetic fields are ubiquitous and have a substantial impact on galactic, stellar and planetary evolution and on life. Theoretical models imply that magnetic fields can affect stars from the deep interior to the outermost layers at each stage of evolution. Yet the origin, geometry and evolution of magnetic fields often remain a mystery. Consequently, standard stellar evolution models do not consider the interaction of magnetic fields with other physical processes. Extensive surveys show that only 10% of the intermediate-mass A&F-type main-sequence stars have detectable magnetic fields. However, studies show that up to 60% of red giant stars that evolve from these stars have strong internal magnetic fields. Stellar evolution dictates that if the magnetic fields exist in these red giants, they must also exist in younger stars. This discrepancy raises many open questions: Have surface magnetic fields in A&F stars been below the detection threshold of modern instruments? Are their magnetic fields confined to stellar interiors and never penetrate the surface? If so, how may we detect them? Are there physical processes that prevent A and F stars from producing and maintaining stable magnetic fields? MAGNIFY aims at answering these questions by 1) investigating the existence of internal and surface magnetic fields in A&F stars using asteroseismology and 2) exploring the mechanisms producing magnetic fields by studying stellar spots. With asteroseismology, we can use stellar pulsations to probe deep into the stellar interior to detect the presence of hidden magnetic fields. We will use data from the Kepler, TESS and Gaia space missions to study A&F type pulsators and use stellar evolution and pulsation models to disentangle the signatures of magnetic fields from other physical processes. Finally, we will use measurements of stellar spots in a large number of A&F stars to broaden our understanding of magnetic field generation and how it varies with stellar mass and age.

Mots-clés : asteroseismology,magnetic fields,intermediate mass stars,stellar spots
Projet ERC : SECRET (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Life Science
Panel : Integrative B iology : from Genes and Genomes to S ystems
Entité participante : Université de l'est de la Finlande — Pays : Finlande — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death worldwide. Aside from asymptomatic manifestations, the first sign of clinically significant ASCVD is often a severe clinical event, such as stroke or myocardial infarction. Thus, identification of people at high risk is central to battle the deadly consequences of ASCVD. The usefulness of current risk prediction models such as SCORE2 is unsatisfactory most likely since the score is built on prevalent risk factors rather than mechanistic changes occuring along the disease path. Especially, genetic risk factors acting already early in life and diverse longitudinal exposures accumulating during the lifetime of a person, lead to disturbance of gene regulatory networks which are not considered in the current risk models. In addition, the current models predict the combined risk of coronary and peripheral artery disease and ischemic stroke despite mounting evidence of ASCVD heterogeneity. To capture these missing aspects of ASCVD risk, we leverage the predictive ability of genetic variation provided to us by the world’s largest meta-analysis of GWAS for ASCVD and introduce a new disease mechanism-based stratification. In work package (WP) 1, we will map the transcriptomic and epigenetic effects of risk variants using single cell multiomics profiling of 500 human atherosclerotic tissue samples. In WP2, we infer disease associated genes, gene-gene interactions and gene regulatory networks using an innovative CRISPR-based experimental approach. In WP3, we will make use of the generated information to develop novel functionally informed polygenic risk models which are benchmarked against the conventional risk prediction models for predictive accuracy. Ultimately, this information will provide us with a mechanistic understanding of the genetic basis of disease while allowing construction of new gold standard polygenic risk prediction models for prevention of ASCVD events.

Mots-clés : single cell,atherosclerosis,functional genomics,polygenic risk score
Projet ERC : SHIPs (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Study of the Human Past
Entité participante : Université de Haïfa — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2723617.5 €

Until the emergence of the modern container ship, vessels spent a significant part of their lifespan in a harbour. The time period when a ship sets anchor in a harbour is key as the entire harbour was designed to offer mooring and all the facilities required for her stay. Yet, a harbour was not merely the accumulation of standings structures made of stone, concrete, or wood that helped ships make fast in Roman harbours. Associated activities extended beyond their principal basin, taking place on a far larger scale and in an agglomeration of different areas than previously conceived by scholars and archaeologists. These together constituted a harbour system, one of the most significant foundations of ancient economies. The project’s aims are 1) to identify the various operational parts of harbours, 2) to consider how the functions carried out there were combined into a larger area, and 3) to consolidate a set of tools that together comprise a unified methodology, which can be applied to examples elsewhere. Beyond these larger concerns, the project explores specific issues regarding ships when mooring in harbours: what type of vessels did they include? What were their needs and what structures could fulfil them? Did they change according to the different seasons of the year? The project comprises an investigation of what we term a “harbour system,” relying on the three case studies of Caesarea, Terracina, and Port-Vendres, and considers the specificities of each site and their individual environmental context. The end goal is that the interdisciplinary methodology developed by our project at the above sites explored within a comparative framework can be later adapted and applied to similar harbour sites elsewhere.

Mots-clés : harbour archaeology ship archaeology maritime trade ancient economy ancient society
Projet ERC : DIWA (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université de Haïfa — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1997259.0 €

Social science explanations for rising inequality are incomplete in two significant ways. First, they commonly study income inequality through the narrow prism of earnings, overlooking key sources originating in the labor market, such as employee benefits and corporate profits. Second, and related to the first, research on rising inequality is still frequently conducted at the individual or national levels, neglecting workplaces, and in particular power relations at work. Conflicts arise and compromises are forged not primarily at the national level but within specific institutional settings, including the workplace. As a consequence of these two limitations, social science explanations of rising inequality neglect the complex fabric of workers’ and employers’ relative bargaining power, their multidimensionality and interdependence, and their resulting incomes.  I propose to formulate an original account of income inequality that is new in three ways. First, by constructing what I refer to as “distributional workplace accounts” (DIWA), I plan to bring together for the first time all components of income – earnings, fringe benefits, and capital – of both employees and employers, at the workplace level. Second, based on DIWA, I will estimate the first statistics on the scope and dynamics of the division of earnings, fringe benefits, capital, and total workplace income to different social or income classes. Third, I will develop a novel theory explaining the institutional, structural, and status-based mechanisms of workers’ and employers’ relative bargaining power. Based on the new theory and an innovative empirical approach I will analyze the relations between relative bargaining power and the income shares of different social and income classes. The proposed project will make a significant contribution to the social science disciplines debating the upswing in inequality, and to public policy seeking to confront the resurgence of income inequality.

Mots-clés : income inequality,bargaining power,workplaces,rising inequality,institutions
Projet ERC : ULU (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : National Institute for Astrophysics — Pays : Italie — Rôle : PI
Acronyme de l'entité : INAF
Type d'entité : Recherche
Financement ERC de l'entité : 1996250.0 €

The ULU project will explore the largest scales of our Universe mapping one of the last uncharted observational windows of the cosmic electromagnetic spectrum: the ultra-low radio frequencies (10-100 MHz). Although these frequencies are crucial to unveil the nature and evolution of galaxy clusters, the complexity of such observations prevented their exploitation until now. Techniques I developed to analyse Low Frequency Array (LOFAR) data were able to overcome this limitation. With ULU we will survey the northern sky producing ultra-low frequency images that are 100 times more sensitive than the state-of-the-art. The legacy of ULU will be long lasting with a far reaching scientific outcome: from galaxy evolution to ionospheric science, from exoplanet habitability to the detection of the first galaxies at cosmic dawn.  Within the ULU project, I will combine the survey with my expertise on the study of galaxy clusters. My group will adopt an innovative approach based on unveiling the full picture of the cosmic ray (CR) life-cycle in the intracluster medium, effectively combining diverse topics historically studied independently, such as the growth of structures, AGN activity, and galaxy evolution. While conventional radio frequencies are sensitive to emission generated by highly energetic CRs, with ULU we will explore the far larger domain of low-energy CRs that can be accelerated by still unexplored microphysical mechanisms. Furthermore, their emission can be observed over Gyr time scales, revealing the long-term actions and interactions of radio sources in cluster environments. With a full picture of the CR life-cycle we will 1. unveil how cluster merger energy is deposited in the intracluster medium through shocks, turbulence and other mechanisms, 2. measure the long-term effect of AGN feedback up to the age of cluster formation, and 3. make a realistic attempt at the characterisation of the WHIM properties and constrain the origin of cosmological magnetic fields.

Mots-clés : radio astronomy,astronomical surveys,galaxy clusters
Projet ERC : VEGA (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
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 : 1999490.0 €

In this project, we propose to develop a theoretical and practical framework for transfer learning with geometric 3D data. Most existing learning-based approaches, aimed at analyzing 3D data, are based on training neural networks from scratch for each data modality and application. This means that such methods, first, ignore the wider information overlap that might exist across different tasks and object or scene categories, and, second, tend to generalize poorly beyond the specific scenarios for which they are trained. Even more fundamentally, the majority of existing techniques are limited to problem settings in which sufficient amount of training data is available, making them ill-adapted in many practical applications with limited supervision.  In this project, we suggest to take a fundamentally different approach to geometric data analysis: rather than designing independent application or class-specific solutions, we propose to develop a theoretical and practical framework for geometric transfer learning. Our main goal will be to develop universally-applicable methods by combining powerful pre-trainable modules with effective multi-scale analysis and fine-tuning, given minimal task-specific data. The overall key to our study will be analyzing rigorous ways, both theoretically and in practice, in which solutions can be transferred and adapted across problems, semantic categories and geometric data types.  Such an approach will open the door to fundamentally new tasks and modeling tools, applicable to any geometric data analysis scenario, regardless of the amount of training data available. This would allow, for example, to track the evolution of biological systems, by studying the underlying complex 3D shape dynamics, or to analyze variability in object and scene collections consisting of 3D scans of previously unseen shape categories, crucial in cultural preservation and life science applications, among myriad others.

Mots-clés : 3d deep learning,transfer learning for geometric data,3d shape analysis,3d computer vision,geometry processing,shape matching
Projet ERC : ASTROCYTES-REWARD (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Université hébraïque de Jérusalem — Pays : Israël — Rôle : PI
Acronyme de l'entité : HUJI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2340575.0 €

Astrocytes are currently investigated in a range of questions extending far beyond their traditional 'housekeeping' functions. We have contributed to expanding the horizon of functions attributable to astrocytes, and have recently identified a novel and unexpected role for hippocampal astrocytes in signaling the location of reward. This pioneering ERC project, will drive considerable progress in an important and completely unknown facet of reward-related information processing: the roles of hippocampal astrocytes in signaling reward, and their interaction with dopamine, the main player in the reward system. In Aim 1 we will define the role of hippocampal astrocytes in the perception of reward. We will test whether astrocytes can encode the presences of reward irrespective of its location, based on non-spatial (auditory) cues. By simultaneously imaging both astrocytes and neurons, we will delineate the spatio-temporal interplay between them in reward representation, and examine the projection-specific effect of astrocytes in rewarded behavior. Work on Aim 2 will delineate the effects of the dopaminergic system on astrocytes. We will comprehensively analyse the distribution of dopamine receptors on astrocytes and the astrocytic response to tonic vs. phasic dopaminergic signals. We will also perform simultaneous imaging of dopamine levels and astrocyte Ca2+ dynamics in different behavioral states. Aim 3 will determine how astrocytes mediate the effects of the dopaminergic system. We will do that by manipulating astrocytic activity measure pre-synaptic dopamine axonal release in the hippocampus and its influence on the poorly understood phenomenon of over-representation of reward locations by neuronal place cells.  Our published work and our new (unpublished) results generate the conceptual basis for this proposal and demonstrate its technical feasibility. Achieving our goals will provide fundamental insights into the roles of astrocytes in information processing.