Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : PLATINUM (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : University of Naples Federico II — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1485712.0 €

The aim of PLATINUM is to scrutinize Latin texts on papyrus from several points of view in order to highlight their substantial contribution to our knowledge of innovations in ancient Roman literature, language, history, and society, especially in the multilingual and multicultural contexts of the Eastern part of the Empire between the 1st century B.C. and 8th century A.D. The first phase of the project will consist in assembling, updating and publishing critical editions, in order to present a new and more accurate corpus of Latin papyri on an easily accessible online platform. The second phase will be focused on providing the texts with a specific, pluridisciplinary commentary that gives new insights on Roman culture. Coming mainly from Egypt and other Roman provinces (as well as Herculaneum and Ravenna), Latin papyri deserve more scholarly attention not only from papyrologists and paleographers, but also from scholars of Latin language, as well as intellectual and cultural historians of Rome. Latin papyri, tablets, and ostraka (potsherds) are constantly increasing in number through archaeological discoveries. Because they are so rare, they are even more valuable than the Greek papyri, which have garnered much attention. The Latin papyri have hitherto represented a border-line field of study that has not been fully exploited either by papyrologists or by scholars of Latin literature. Moreover, the obsolete bibliography and the considerable number of unpublished texts make the study of Latin papyri (and bilingual Latin-Greek, Latin-Coptic, Latin-Punic texts) - whether literary (e.g. Cicero, Vergil, law), paraliterary (grammar, medicine, magic), or documentary (letters, official registers, receipts) – a pioneering and challenging task. A more through study will reveal the untapped potential of Latin texts on papyrus for renewing our knowledge of the circulation and reception of Latin language and education, as a cultural engine in Mediterranean societies.

Mots-clés : latin philology and literature,papyrology,manuscript tradition and transmission,education in antiquity,latin greek bilingualism,cultural history of the roman empire,latin documentary sources
Projet ERC : PLAT_ACE (Horizon 2020, appel 2018, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Institut Català d'Investigació Química — Pays : Espagne — Rôle : PI
Acronyme de l'entité : ICIQ;ICIQ-CERCA
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

The aim of PLAT_ACE is to study the technical and commercial viability of this novel synthetic methodology and its industrial implementation for the generation of nonacene and other larger acenes supported onto surfaces for use in semi-conducting devices with unprecendeted electronic properties and low bandgap. Our technology will be benchmarked against current industrial methodologies to identify the unique selling points and niche applications. We will demonstrate the application of the new acenes as organic based transistors and for other electronic applications by their controlled deposition of different surfaces.

Mots-clés : acenes   organic synthesis   catalysis   metal surfaces   molecular electronics
Projet ERC : SUSTAIN (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The study of the human past
Entité participante : University of Bristol — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 439826.0 €

The aim of SUSTAIN is to elucidate how early agricultural societies in Europe maintained their capacity to self-generate and persist through time. It examines societies that have only recently settled in new environments and begun to practice mixed agro-pastoral husbandry. This at the time novel way of interaction with the environment involved a different scale of interference with the life cycles of plants and animals, and was of fundamental importance for subsequent developments in Europe. While archaeological research traditionally centres on the question of how societies and livelihoods changed, the project asks what made them capable to carry on, i.e. to remain sustainable. The project weaves together approaches from social anthropology, biogeography, bioarchaeology and complexity science in a manner that has not been attempted previously. We will examine the relationship of domesticates’ past range expansions and climate using correlative species distribution models (SDMs) and will derive past agropastoral practices from isotope signals in plant and animal remains. Novel approaches of quantitative inference will allow us to estimate parameters of crop and livestock productivity and to directly compare agropastoral performance across time and space in Europe. Using this knowledge, we will employ spatially explicit agent-based simulation to explore the social parameters of growth and decline of the first agricultural societies in Europe in previously unconsidered detail. We will examine the role of social webs of lateral insurance in colonising populations, and the extent to which fragmentation of these relationships constrained population expansion into new regions. The project will produce insights that challenge conventional wisdom about farming and environmental sustainability, particularly in regard to social connectedness, relations of sharing, and mobility, which will have value and impact beyond archaeology.

Mots-clés : stable isotope analysis,agent based modelling,bioarchaeology,biomolecular archaeology,model based archaeology,neolithic archaeology,computational social science,sustainability science,environmental humanities
Projet ERC : HYDROPHO-CHEAP (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : National Centre of Scientific Research "Demokritos" — Pays : Grèce — Rôle : Partner
Acronyme de l'entité : NCSR;NCSR-D
Type d'entité : Recherche
Financement ERC de l'entité : 0.0 €

The aim of the HYDROPHO-CHEAP project is to optimize, protect, and commercialize a recently developed methodology that allowed us to achieve “tailor-made” control of the wettability of polymer surfaces, rendering them super water-repellent. What makes our methodology highly innovative is the fast and cheap fabrication process, and most importantly the potential of implementing it in real-life environments. With this unique method we can fabricate ‘superhydrophobic islands’ (with a spatial resolution of 100 μm in the horizontal plane) of any shape i.e. dots, stripes, polygons and interconnected shapes, in any combination. Such functional surfaces can be produced in a single and fast fabrication step, without applying any hydrophobization top coating since we process an inherently hydrophobic material. We plan to demonstrate the capabilities that our method can offer to specific applications (microfluidic chips, fog harvesting, low flow friction surfaces) that we identified as the most prominent, protect the intellectual property that we have produced through our research and to find the optimum route-to-market in order to commercialize our research results.

Mots-clés : water repellent,superhydrophobic surface fabrication,addressable wettability,low flow friction
Projet ERC : LEMAN (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 200000.1 €

The aim of the project is to develop a geometrically meaningful framework that allows generalizing deep learning paradigms to data on non-Euclidean domains. Such geometric data are becoming increasingly important in a variety of fields including computer graphics and vision, sensor networks, biomedicine, genomics, and computational social sciences. Existing methodologies for dealing with geometric data are limited, and a paradigm shift is needed to achieve quantitatively and qualitatively better results. Our project is motivated by the recent dramatic success of deep learning methods in a wide range of applications, which has literally shaken the academic and industrial world. Though these methods have been known for decades, the computational power of modern computers, availability of large datasets, and efficient optimization methods allowed creating and effectively training complex models that made a qualitative breakthrough. In particular, in computer vision, deep neural networks have achieved unprecedented performance on notoriously hard problems such as object recognition. However, so far research has mainly focused on developing deep learning methods for Euclidean data such as acoustic signals, images, and videos. In fields dealing with geometric data, the adoption of deep learning has been lagging behind, primarily since the non-Euclidean nature of objects dealt with makes the very definition of basic operations used in deep networks rather elusive. The ambition of the project is to develop geometric deep learning methods all the way from a mathematical model to an efficient and scalable software implementation, and apply them to some of today’s most important and challenging problems from the domains of computer graphics and vision, genomics, and social network analysis. We expect the proposed framework to lead to a leap in performance on several known tough problems, as well as to allow addressing new and previously unthinkable problems.

Mots-clés : geometric deep learning,convolutional neural networks,intrinsic geometry,shape analysis,shape correspondence,signal processing on graphs,spectral methods,face analysis,social network analysis
Projet ERC : IDIU (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1497271.0 €

The aim of this project is to create the technology needed to understand the content of images in a detailed, human-like manner, significantly superseding the current limitations of automatic image understanding, and enabling new far reaching human-centric applications. The first goal is to substantially broaden the spectrum of visual information that machines can extract from images. For example, where current technology may discover that there is a ``person'' in an image, we would like to produce a description such as ``person wearing a red uniform, tall, brown haired, with a bayonet, and a long black hat.'' The second goal is to do so efficiently, by developing integrated image representations that can share knowledge and computation in multiple computer vision tasks, from detecting edges to recognising and describing thousands of different object types. In order to do so, we will investigate, for the fist time in a systematic manner, the breadth of information that humans can extract from images, from abstract patterns to object parts and attributes, and we will incorporate it in the next generation of machine vision systems. Compared to existing technology, the new algorithms will have a significantly richer and more detailed understanding of the content of images. They will be learned from data building on recent breakthroughs in large scale discriminative and deep machine learning, and will be delivered as general-purpose open-source software for the benefit of the research community and businesses. In order to make these systems future-proof, we will develop methods to extend them automatically, by learning from images downloaded from the Internet with very little human supervision. These new advanced capabilities will be demonstrated in breakthrough applications in large scale image search and visual information retrieval.
Projet ERC : Outgroup (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : University of Bristol — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1971866.0 €

The aim of this project is to determine the proximate and ultimate consequences of a fundamental but neglected aspect of sociality: out-group conflict. In a wide range of social species, from ants to humans, group members invest considerable defensive effort against individual intruders and rival groups. The lasting impacts of these conflicts with conspecifics are poorly understood. I will integrate empirical and theoretical approaches to uncover the effect of out-group conflict on: (i) individual behaviour, within-group interactions and group decision-making; (ii) steroid hormones that underlie stress, social behaviour and reproduction; (iii) variation in reproductive success arising from maternal investment and offspring care; and (iv) the evolution of societal structure, cooperation and punishment among group-mates, and weaponry and fortification. I will achieve these ambitious objectives using proven experimental paradigms, innovative non-invasive sampling, long-term monitoring, and state-of-the-art analytical methods to collect data from two highly tractable model systems that I have established over the last 4 years: a captive-breeding population of the cichlid fish Neolamprologus pulcher and a habituated wild population of dwarf mongooses (Helogale parvula). I will use some of the same data to inform the assumptions of mathematical models and evolutionary simulations in the development of a rigorous, predictive framework on out-group conflict, which I will test using both my model systems and phylogenetically controlled meta-analyses across species. The management and consequences of conflict are of major importance to science, human society and global politics. My novel and inter-disciplinary proposal will not only significantly advance our understanding of the evolution of sociality, but will invigorate a variety of existing research programmes across biology, anthropology, economics, psychology, and the social and political sciences.

Mots-clés : conflict,behaviour,hormones,reproductive success,evolution
Projet ERC : ITHACA (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2494117.0 €

The aim of this project is to develop a new synergy between climate and computer science to increase the accuracy and hence reliability of comprehensive weather and climate models. The scientific basis for this project lies in the PI’s pioneering research on stochastic sub-grid parametrisations for climate models. These parametrisations provide estimates of irreducible uncertainty in weather and climate models, and will be used to determine where numerical precision for model variables can be reduced without degradation. By identifying those bits that carry negligible information – typically in high-wavenumber components of the dynamical core and within parametrisation and Earth-System modules – computational resources can be reinvested into areas (resolution, process representation, ensemble size) where they are sorely needed. This project will determine scale-dependent estimates of information content as rigorously as possible based on a variety of new tools, which include information-theoretic diagnostics and emulators of imprecision, and in a variety of models, from idealised to comprehensive. The project will contribute significantly to the development of next-generation weather and climate models and is well timed for the advent of exascale supercomputing where energy efficiency is paramount and where movement of bits, being the single biggest determinant of power consumption, must be minimised. The ideas will be tested on emerging hardware capable of exploiting the benefits of mixed-precision arithmetic. A testable scientific hypothesis is presented: a proposed increase in forecast reliability arising from an increase in the forecast model’s vertical resolution, the cost being paid for by a reduction in precision of small-scale variables. This project can be expected to provide new scientific understanding of how different scales interact in the nonlinear climate system, for example in maintaining persistent atmospheric flow regimes.
Projet ERC : FORTITUDE (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Leicester — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 313916.51 €

The aim of this project is to improve the legal capability of children. This focus on children breaks new ground because other research into legal capability focuses on adults. On paper, children are both empowered and protected by numerous laws and international rights instruments. In practice, however, we know that millions of children live at risk of violence, abuse and neglect in the UK alone. The proposed research seeks to create a range of resources that will empower and enable children who are at risk to enforce their legal rights independently; so increasing the likelihood of them accessing support and securing protection from harm. As well as targeting children at risk, this project seeks to strengthen all children’s capability to deal effectively with the many law-related issues that they encounter in their day to day lives. This is based on the view that children are competent social actors, whose views should be taken seriously. The project aim will be achieved through three work packages: 1. We will draw on the experiences of children to co-create a child-centred framework of attributes of legal capability. 2. We will co-create a range of game-based interventions that will both measure and improve these attributes, drawing on theories of play. 3. We will optimise the effectiveness of these interventions by determining what works best, in what circumstances, for improving attributes of legal capability among children. We will also develop a specification of the process by which such interventions can be created and optimised. The project will provide a range of game-based resources that will improve the lives of children by providing them with opportunities to improve their legal capabilities, as well as providing a framework for the development of these resources in other populations, such as EU states and developing countries. This provides the basis for opening up further lines of research for our team internationally.
Projet ERC : MNEMOSYNE (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
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 : 1998250.0 €

The aim of this project is to study the influence of sleep in the establishment of aversive memory and to propose a proof of concept for the use of brain-computer interfaces during sleep to treat pathologies associated with fear or anxiety such as post-traumatic stress disorders. It is now accepted that sleep plays a crucial role in memory consolidation processes that allow the conversion of newly encoded memory traces into more stable information. Numerous studies have shown that some of the positive effect of sleep on consolidation relies on the reactivations of previous experiences. We recently showed that spontaneous sleep reactivations can be used to create an artificial memory during sleep. We made electrical stimulations of a brain reward center contingent on the spontaneous reactivations of a hippocampal place cell during sleep. After this procedure, mice developed a place preference for the location of the place field of the trigger cell, showing that a place/reward association had been created. Our experiment, as most of the studies on place cells or reactivations, was done with appetitive learning and we now intend to tackle this issue in the context of aversive learning. In this project, we will use our closed-loop device to address long lasting controversies such as the existence of reactivations in deep SWS or REM sleep. We will also investigate how avoided places are represented in the hippocampus and whether they are more reactivated during subsequent sleep. Additionally, we will show whether aversive-related wake preplays can be actively modulated, suggesting that “intrusive memories” or rumination can be addressed in animal models. Finally, we will test whether an aversive association performed during wake can be modified by our closed-loop-driven appetitive association during sleep. This would bring the proof of concept that sleep could be used to erase aversive memories in pathological situations such as post-traumatic stress disorders.

Mots-clés : close loop,brain computer interface,sleep reactivation,sleep replay
Projet ERC : EVOLOR (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems Engineering
Entité participante : Eötvös Loránd University — Pays : Hongrie — Rôle : PI
Acronyme de l'entité : ELTE
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1202500.0 €

The aim of this project is to understand the causal factors contributing to the cognitive decline during senescence and to develop sensitive and standardized behaviour tests for early detection in order to increase the welfare of affected species. With the rapidly ageing population of Europe, related research is a priority in the European Union. We will focus both on characterising the ageing phenotype and the underlying biological processes in dogs as a well-established natural animal model. We develop a reliable and valid test battery applying innovative multidisciplinary methods (e.g. eye-tracking, motion path analysis, identification of behaviour using inertial sensors, EEG, fMRI, candidate gene, and epigenetics) in both longitudinal and cross-sectional studies. We expect to reveal specific environmental risk factors which hasten ageing and also protective factors which may postpone it. We aim to provide objective criteria (behavioural, physiological and genetic biomarkers) to assess and predict the ageing trajectory for specific individual dogs. This would help veterinarians to recognise the symptoms early, and initiate necessary counter actions. This approach establishes the framework for answering the broad question that how we can extend the healthy life of ageing dogs which indirectly also contributes to the welfare of the owner and decreases veterinary expenses. The detailed description of the ageing phenotype may also facilitate the use of dogs as a natural model for human senescence, including the development and application of pharmaceutical interventions. We expect that our approach offers the scientific foundation to delay the onset of cognitive ageing in dog populations by 1-2 years, and also increase the proportion of dogs that enjoy healthy ageing.

Mots-clés : fmri,epigenetics,candidate gene