Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : Symmetries-Cosmology (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Humboldt-Universität zu Berlin — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1793550.0 €

The goal is to uncover symmetries and dualities of string theory including \alpha' corrections, to describe massive string states, and to investigate applications in cosmology. This will be done using double field theory and exceptional field theory and generalizing it to the non-local interactions of genuine stringy states and/or an infinite number of higher-derivative \alpha' corrections. So far these frameworks were mainly used to describe the low-energy effective field theories of the massless states in string/M-theory in a manifestly duality invariant way. The anticipated generalizations require a significant deformation or extension, of which we have only recently obtained first glimpses. Specifically, the higher-derivative corrections are constrained by gauge symmetries. These include the Green-Schwarz transformations, but are far more general and determine all corrections to first order in \alpha'. There was also recent progress on the problem of including massive string states: Sen proved that the sub-sector of string theory consisting of massless fields together with their Kaluza-Klein and winding modes provides a consistent truncation. The resulting theory must be governed by L_{\infty} algebras, which are generalizations of Lie algebras that so far have played little role in conventional field theories but now give us a concrete clue of how to construct a `true double field theory'. Various string cosmology scenarios have been suggested that aim to utilize stringy features for, say, the early universe. However, given our ignorance about the precise `stringy' Einstein equations, it has been impossible to test and verify such ideas, even theoretically. In view of the recent and forthcoming PLANCK data it has become particularly urgent to find a useful formulation of string theory in which problems of this type can be addressed and analyzed. If successful, this research program would be ground-breaking in that it would allow us to do precisely this.
Projet ERC : ASENT (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : University of London — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. London
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1469188.0 €

The goal of ASENT is to construct solid conceptual and methodological foundations for the science of animal sentience. The term sentience refers to an animal’s subjective experience of the world and of its own body. In recent years, an interdisciplinary community of animal sentience researchers, drawn from neuroscience, comparative psychology, evolutionary biology, animal welfare science and philosophy, has begun to emerge. However, the field is characterized by foundational controversy over the nature of sentience and the criteria for its attribution, leading to heated debate over the presence or absence of sentience in fish and in invertebrates such as cephalopods and arthropods. ASENT aims to find ways to resolve these debates, enabling researchers to move beyond the impasses that result from deep foundational disagreement. What is needed is a conceptual framework for thinking about sentience as an evolved phenomenon that varies along several dimensions, a deeper understanding of how these dimensions of sentience relate to measurable aspects of animal behaviour and the nervous system, and a richer picture of the links between sentience, welfare and the ethical status of animals. ASENT will answer this need by providing: 1. An account of the basic functional capacities involved in sentience, and an evaluation of which capacities justify us in regarding an animal as a sentience candidate. 2. An overarching conceptual framework for understanding the dimensions along which sentience varies across the animal kingdom, based on distinguishing multiple structural properties of sentience; 3. A scheme of proposed experimental tests for constructing a species’ most likely sentience profile with reference to these dimensions, implemented with scientific collaborators using bees as a test case. 4. An assessment of which dimensions of sentience are most relevant to animal welfare and to the ethical status of animals.

Mots-clés : animal sentience,animal cognition,animal consciousness,animal minds,animal welfare
Projet ERC : CITRES (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Materials Center Leoben (Austria) — Pays : Autriche — Rôle : PI
Acronyme de l'entité : MCL
Type d'entité : Recherche
Financement ERC de l'entité : 1996519.0 €

The goal of CITRES is to provide new energy storage devices with high power and energy density by developing novel multilayer ceramic capacitors (MLCCs) based on relaxor thin films (RTF). Energy storage units for energy autonomous sensor systems for the Internet of Things (IoT) must possess high power and energy density to allow quick charge/recharge and long-time energy supply. Current energy storage devices cannot meet those demands: Batteries have large capacity but long charging/discharging times due to slow chemical reactions and ion diffusion. Ceramic dielectric capacitors – being based on ionic and electronic polarisation mechanisms – can deliver and take up power quickly, but store much less energy due to low dielectric breakdown strength (DBS), high losses, and leakage currents. RTF are ideal candidates: (i) Thin film processing allows obtaining low porosity and defects, thus enhancing the DBS; (ii) slim polarisation hysteresis loops, intrinsic to relaxors, allow reducing the losses. High energy density can be achieved in RTF by maximising the polarisation and minimising the leakage currents. Both aspects are controlled by the amount, type and local distribution of chemical substituents in the RTF lattice, whereas the latter depends also on the chemistry of the electrode metal. In CITRES, we will identify the influence of substituents on electric polarisation from atomic to macroscopic scale by combining multiscale atomistic modelling with advanced structural, chemical and electrical characterizations on several length scales both in the RTF bulk and at interfaces with various electrodes. This will allow for the first time the design of energy storage properties of RTF by chemical substitution and electrode selection. The ground-breaking nature of CITRES resides in the design and realisation of RTF-based dielectric MLCCs with better energy storage performances than supercapacitors and batteries, thus enabling energy autonomy for IoT sensor systems.
Projet ERC : ENDOMICS (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University of London — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. London
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1490950.0 €

The goal of ENDOMICS is to drive forward a new paradigm of Raman endoscopic technology that enables proteomic and lipidomic analysis for diagnosis of bladder cancers in vivo. Raman endoscopy is a label-free optical technique that can provide a point-wise vibrational molecular fingerprint of tissue “optical biopsy” for cancer diagnosis in vivo. State-of-the-art Raman endoscopy, however, does not offer specific compositional analysis or insights into molecular biology of tissue. This is because the vibrational Raman bands are overlapping and cannot be deciphered into the myriad of biomolecules in complex tissue. We will introduce a ground-breaking new methodology to enable Raman proteomic and lipidomic analysis in vivo. To this end, heterospectral co-registered Raman and mass spectrometry imaging will be used to develop a multivariate regression model “Rosetta Stone” for translating vibrational structural information (Raman spectroscopy) into compositional information. To meet the unmet clinical needs in urology we will tailor the first fibre-optic Raman endoscopic technology that can measure depth-dependent molecular profiles to simultaneously enable detection, grading and staging of bladder cancers. We will finally conduct a clinical trial by applying the technique to measure a comprehensive molecular database of bladder pathologies in vivo. The latter will allow for the identification of proteomic and lipidomic biomarkers to develop novel algorithms for real-time diagnosis of bladder cancers. The synergy between scientific and technological advances in ENDOMICS will break ground for shedding new light on the molecular biology of bladder cancer in vivo including new insights into clinical diversity and identification of biomarkers for diagnostics, prognostics and novel therapeutic targets.

Mots-clés : raman proteomics,raman lipidomics,biomedical spectroscopy,bladder cancer,cystoscopy,in vivo diagnosis
Projet ERC : INTEGRATOR (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Bocconi University — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498937.0 €

The goal of INTEGRATOR is to develop novel data sets, theories, and algorithms to incorporate demographic factors into language technology. This will improve performance of existing tools for all users, reduce demographic bias, and enable completely new applications. Language reflects demographic factors like our age, gender, etc. People actively use this information to make inferences, but current language technology (NLP) fails to account for demographics, both in language understanding (e.g., sentiment analysis) and generation (e.g., chatbots). This failure prevents us from reaching human-like performance, limits possible future applications, and introduces systematic bias against underrepresented demographic groups. Solving demographic bias is one of the greatest challenges for current language technology. Failing to do so will limit the field and harm public trust in it. Bias in AI systems recently emerged as a severe problem for privacy, fairness, and ethics of AI. It is especially prevalent in language technology, due to language's rich demographic information. Since NLP is ubiquitous (translation, search, personal assistants, etc.), demographically biased models creates uneven access to vital technology. Despite increased interest in demographics in NLP, there are no concerted efforts to integrate it: no theory, data sets, or algorithmic solutions. INTEGRATOR will address these by identifying which demographic factors affect NLP systems, devising a bias taxonomy and metrics, and creating new data. These will enable us to use transfer and reinforcement learning methods to build demographically aware input representations and systems that incorporate demographics to improve performance and reduce bias. Demographically aware NLP will lead to high-performing, fair systems for text analysis and generation. This ground-breaking research advances our understanding of NLP, algorithmic fairness, and bias in AI, and creates new research resources and avenues.

Mots-clés : natural language processing,demographic factors,bias,fairness
Projet ERC : NEFERTITI (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : University of Florence — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1180813.0 €

The goal of NEFERTITI is to make a major step forward in our understanding of the first stars and galaxies by catching the stellar fossils from the early Universe in our Galactic neighborhood. To move beyond the state-of-the-art and study many of these precious fossils, I will adopt a novel approach that integrates theoretical and observational research and that will allow me to fully exploit: i) the huge data-flow from upcoming stellar surveys, ii) my cosmological models, which uniquely link Local data and early cosmic star-formation. The first stars profoundly influenced the primordial Universe, affecting subsequent stellar generations and the build-up of the first galaxies. In spite of extraordinary progress in theoretical modeling and observational techniques little is known about their properties, not even their typical mass. A direct exploration of their formation epochs is a tremendous challenge. Even JWST will not see the faint dwarf galaxies where the first stars formed more than 13 billion years ago. In the Local Group, the living relics of the first stars can be directly observed and used to re-trace the chemical evolution and star-formation of the gas during those “invisible” times. Yet, these early Universe survivors are very rare and difficult to catch. In the present era of wide and deep Local surveys, such as DES, Gaia-ESO, and WEAVE, the total number of stars observed is dramatically increasing. Combining semi-numerical models with radiative transfer codes, I will fully exploit these novel data flow to catch the local stellar fossils and to: 1) constrain the mass distribution of the first stars, 2) uncover the physical processes driving the build-up of the first galaxies. NEFERTITI will link Near and Far-field cosmology, give new insights into the formation of the Local Group, guide the interpretation of data from future surveys, and pave the way for the exploitation of new generation spectrographs on the E-ELT (MOSAIC, HIRES).
Projet ERC : PARTICIPATE (Horizon 2020, appel 2020, Starting 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 : 1404215.0 €

The goal of PARTICIPATE is to develop a comprehensive approach to the study of campaign finance, information and influence, using new individual-level data and investigating recent changes in participation behaviors. What are the motives behind small campaign contributions? Does tax policy affect political giving? Why are so few politicians from the working class and can this change? What is the ability of the media to induce citizens to make electoral decisions they would not make if reporting were unbiased? While there is evidence at the macro level on the flow of money in elections, news consumption, or the extent of charitable giving, relatively little is known at the micro level, e.g. on individual-level behaviours such as the motivations of small donors, the tax-price elasticity of political donations, or the exposure to competing information flows. PARTICIPATE will help fill this gap. By providing groundbreaking evidence on new forms of participation, it will lead to the reassessment of influential theories of special interest groups and policy formation. One distinguishing feature of the proposal is to combine comprehensive individual-level datasets and the use of computer sciences tools (such as natural language processing techniques and machine learning). PARTICIPATE will advance the existing research in three steps. First, I will propose a unified analysis of political contributions. This will include a groundbreaking assessment of the importance of small campaign donations, and a combined study of charitable giving and political contributions investigating the impact of tax policy on donations. Fundraising success can lead to the emergence of new candidates. I will then consider citizens’ decision to run for elections, and investigate the role played by network in political selection. Finally, given the importance of media organizations in shaping participation, I will study the changing patterns of information propagation and political influence.

Mots-clés : campaign finance,campaign donations,charitable giving,tax price elasticity,news consumption,information propagation,twitter,media power,political participation,big data,natural language processing,machine learning,dynamic multidimensional graphs
Projet ERC : PETRA (Horizon 2020, appel 2014, Proof of concept grants)
Entité participante : Consorzio Nazionale Interuniversitario per le Telecomunicazioni — Pays : Italie — Rôle : PI
Acronyme de l'entité : CNIT
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

The goal of PETRA (Photonic Environment moniToring & Risk Assessment) is the design and the IPR transfer of a radically new precommercial photonics based prototype for noninvasive continuous remote monitoring system for geo-technical risk assessment. PETRA will step from the ERC-funded project PHODIR that successfully carried out the realization and the field trial of the first photonic based radar prototype, and from the contacts that were made between CNIT and industrial partners during the PoC PREPaRE, that focused on the preindustrial design and IPR selling of an optics based radar for integrated traffic control in airports. The specific application on which PETRA will focus is the high precision real time analysis and mapping of potential risk areas for health protection, disaster prevention and for new civil engineering buildings (as bridges or power plants) where the advances of photonics will be of practical interest in a short-medium term, and it will: i) study the technical requirements and the current limitations of earth monitoring systems, to define the technical specifications for a commercial photonics based integrated radar system; ii) accordingly modify the radar structure and the data processing tools defined during the PHODIR project, allowing a short/medium-term industrial realization and commercialization with an attractive cost/performance ratio; iii) boost the IP transfer of each single subsystem preindustrial design to companies either through already filed patent selling or by encouraging companies to patent the presented design.

Mots-clés : environment monitoring,radar,microwave photonics
Projet ERC : SPHERES (Horizon 2020, appel 2019, Synergy grants)
Entité participante : Karolinska Institutet — Pays : Suède — Rôle : PI
Acronyme de l'entité : KI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 3004881.0 €

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 : UFSD (Horizon 2020, appel 2014, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : National Institute for Nuclear Physics — Pays : Italie — Rôle : PI
Acronyme de l'entité : INFN
Type d'entité : Recherche
Financement ERC de l'entité : 1743312.5 €

The goal of our project is to empower a broad range of research fields with a completely new particle detector, able to concurrently deliver time resolutions of the order of ~10 picoseconds and position resolutions of ~30 microns. In so doing, we will remove the constrains that many applications such as particle tracking, medical PET, mass spectroscopy, and beam monitoring have due to the lack of precise information on all 4 dimensions. Our analysis of state-of-the-art particle detectors has shown a dichotomy: specialized sensors measure very accurately either time or position, but not both: the ambitious goal of UFSD is to create a new family of detectors, based on controlled charge multiplication in silicon, which will remove this limitation. We will have to tackle significant challenges along this research path, but our simulations and prototypes indicate that this approach has the potentiality to radically transform present detectors and to enable many applications to reach their peak performances. Our ultimate goal -- a highly segmented detector with a space resolution of ~30 microns and a time resolution of ~10 picoseconds -- can be achieved only by developing full custom Very Large Scale Integrated chips that, matching the size of the read-out to the area of each pixel sensor, will deliver unprecedented timing resolution at the pixel level. The PI, N. Cartiglia, is well known in the field of particle physics and detector development. He is currently in charge of several projects involving both new directions in physics, among which the LPCC forward physics group and the CT-PPS project at CERN, as well as new developments in instrumentation. He will lead the project leveraging on his past experience in detector development and group/grant management, his knowledge of the silicon research foundries and laboratories, and the expertise available in his home institution (INFN, Torino, Italy).

Mots-clés : ultra fast particle tracking,4dimensional tracking,silicon sensor,quartic gauge coupling