Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : Land Limits (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Study of the Human Past
Entité participante : University College Dublin — Pays : Irlande — Rôle : PI
Acronyme de l'entité : UCD
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499978.0 €

Land Limits is a ground-breaking environmental history that explores the ecological impact of population growth in Eurasia, from the emancipation of the serfs in 1861 to the close of the civil war in the early 1920s: a period of unprecedented mobility and demographic flux. It redefines the field of late imperial Russian and early Soviet history by challenging assumptions that in a sparsely populated political territory stretching across a sixth of the world’s surface, population pressures occurred only in the agrarian provinces of what was then ‘European Russia’. Instead, it proposes relocating to the empire’s borderlands, and conceptualizing the empire as multiple geographically-disparate but ecologically-interconnected regions: an innovative method of analysing a political entity that usually resists holistic critical enquiry. Via a programme of nuanced, critical historical research conducted in libraries and archives across five nation states, the project seeks to understand both intellectual and material dimensions of the relationship between population pressure and anthropogenic environmental change, and then interrogates the implications of these ecological shifts. It suggests that as increased populations created changes in land use and resource exploitation, so these new patterns became both the motor of economic growth via local, national and global networks of labour, capital and commodities, and the fulcrum around which various forms of conflict emerged, as land and resources became limited, contested and politicised. These were vital forces that transformed borderlands and became key factors in the violent collapse of the empire and the evolution of the early Soviet state. In doing so, the project redefines scholarly debates on the nature of economic growth and of state and community violence in the late imperial period, by restoring the environment as a vital category in exposing the complex causalities that connected migration, capital and conflict.

Mots-clés : environmental history history of violence resources and commodities economic history eurasia population history
Projet ERC : FLEXI (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Study of the Human Past
Entité participante : University College Dublin — Pays : Irlande — Rôle : PI
Acronyme de l'entité : UCD
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499999.0 €

FLEXI investigates how the fluid, multifarious law texts were (re)produced and accepted as sources of law in medieval Europe, using four sets of late medieval Irish law texts called the ‘digests’ as its main object of study. The digests contain texts quoted from diverse sources, and organise them into a systematic review of the legal tradition. They represent the law in operation in the form of highly variegated and complex intertextual networks, and therefore are an ideal sample for studying the dynamics between fluidity and unity in medieval law texts. FLEXI focuses on how law texts were (re)produced, studied, and transmitted in medieval Ireland. It is not only the first in-depth study of the Irish digests, but also a ground-breaking attempt to synthesise philology, comparative legal history, network theories, and digital techniques to revolutionise the research of medieval law texts. FLEXI traces and measures the sources used in these digests quantitatively, using linked data model and network analysis to reveal their compilatory principles and text reuse patterns. This is an innovative approach to probe the intellectual universe of the jurists. It evaluates the variation within the digests and examines the interpretative techniques by which the jurists justified and harmonised the variation. FLEXI also looks for possible influences from other legal traditions such as the Canon Law and the Civil Law on the compilatory principles of the digests, and makes comparison with medieval Welsh law on how inherited materials were treated and updated in Ireland and Wales. Finally, FLEXI tests and develops software for automated detection of parallel texts (text reuse) for early Irish texts. The toolkit and software developed by FLEXI will offer a long-needed digital solution to the processing of early Irish texts, because it can quickly identify similar texts from the vast corpus of Irish materials, thus revealing intertextual relationships hidden from the human eye.

Mots-clés : early irish law late medieval digests comparative legal history text reuse intellectual network
Projet ERC : A-B-C-Deep (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Technion - Institut de technologie d'Israël — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Deep Learning (DL) has reached unparalleled performance in many domains. However, this impressive performance typically comes at the cost of gathering large datasets and training massive models, requiring extended time and prohibitive costs. Significant research efforts are being invested in improving DL training efficiency, i.e., the amount of time, data, and resources required to train these models, by changing the model (e.g., architecture, numerical precision) or the training algorithm (e.g., parallelization). Other modifications aim to address critical issues, such as credibility and over-confidence, which hinder the implementation of DL in the real world. However, such modifications often cause an unexplained degradation in the generalization performance of DL to unseen data. Recent findings suggest that this degradation is caused by changes to the hidden algorithmic bias of the training algorithm and model. This bias selects a specific solution from all solutions which fit the data. After years of trial-and-error, this bias in DL is often at a "sweet spot" which implicitly allows ANNs to learn well, due to unknown key design choices. But performance typically degrades when these choices change. Therefore, understanding and controlling algorithmic bias is the key to unlocking the true potential of deep learning.  Our goal is to develop a rigorous theory of algorithmic bias in DL and to apply it to alleviate critical practical bottlenecks that prevent such models from scaling up or implemented in real-world applications.  Our approach has three objectives: (1) identify the algorithmic biases affecting DL; (2) understand how these biases affect the functional capabilities and generalization performance; and (3) control these biases to alleviate critical practical bottlenecks. To demonstrate the feasibility of this challenging project, we describe how recent advances and concrete preliminary results enable us to effectively approach all these objectives.

Mots-clés : deep learning,theory,optimization
Projet ERC : BAYES-RL (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Technion - Institut de technologie d'Israël — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

For robots to assist humanity in homes, or hospitals, the capability to manipulate diverse objects is imperative. So far, however, robotic manipulation technology has struggled in managing the uncertainty and unstructuredness that characterize human environments. Machine learning is a natural approach -- the robot can adapt to a given scenario, even if it was not programmed to handle it beforehand. Indeed, Deep Reinforcement Learning (deep RL), which has recently led to AI breakthroughs in computer games, has been publicized as the learning-based approach to robotics. To date, however, deep RL studies focused on known and observable systems, where uncertainty was resolved by lengthy trial and error. Quickly learning to act in novel environments, as required for robotics, is not yet within our reach. The crux of the matter is the tight coupling between perception and control under high uncertainty -- the robot must actively reduce uncertainty while also trying to solve the task; for complex and high-dimensional systems, we do not have a suitable algorithmic framework for this.  In this proposal, our overarching goal is to: Develop the algorithmic framework of using deep learning in problems that tightly couple perception, planning, and control, for advancing robotic AI to reliably manipulate general objects in unstructured environments.  Towards this end, we shall develop neural network representations of uncertainty, and algorithms that estimate uncertainty from data. We will develop theory and algorithms for decision making under uncertainty, bringing in a fresh perspective to the problem based on Bayesian reinforcement learning (Bayes-RL). These advances will allow us to study safety certificates for deep RL, and develop a general and practical methodology for learning-based robotic manipulation under uncertainty, validated on real robot experiments. Aside from robotic manipulation, we expect impact on various fields where decision making plays an important role.

Mots-clés : reinforcement learning,robotic manipulation,deep learning,representation learning
Projet ERC : MyFirstBody (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Compl exity
Entité participante : Université de Turin — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1237060.0 €

The representation of one’s own body as a distinct entity from the environment (i.e., bodily-self representation, BSR) is a fundamental component of our sense of self. Neuropsychological literature has provided an important contribution, revealing that brain damage can selectively disrupt BSR. MyFirstBody starts from my well-grounded expertise in BSR pathological alterations, and aims at providing the first comprehensive account of the ontogenetic development of BSR, by translating from a neuropsychological to a developmental perspective. First, I will look for implicit signatures of the BSR emergence in prenatal and postnatal life, by describing the maturation of the crucial components identified through the study of neurological patients (WP1). Then, I will move to a causative level, by challenging i) the neural mechanism that underpins the BSR emergence (WP2) and b) the developmental context that leads to its normal and pathological growth (WP3). I expect to describe a clear picture of BSR development (WP1) and its underlying network dynamics (WP2), starting with a primitive coding of the bodily-self in space, which likely emerges in the maternal womb, and proceeding to further specializations along post-natal life until the maturation of a more abstract knowledge of the bodily-self. From the comparison between congenital and acquired motor deprivations (WP3), I expect to provide the proof that early motor experience represents the crucial context for BSR development. MyFirstBody pioneers a new area of research at the intersection between neuropsychological and developmental research, by addressing different levels of analysis (cognitive and neural) in foetuses, infants, and clinical populations, all while combining advanced neuroimaging techniques (foetal fMRI, EEG, fNIRS). The final outcome will result in original theoretical insights, innovative methods and translational impacts that will represent the optimal foundation for future investigation in the field.

Mots-clés : bodily self representation,motor control,movement deprivation,multisensory integration,peripersonal space
Projet ERC : FIREPOL (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Université de Turin — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1209993.84 €

Extreme wildfires have been on the rise across the globe due to climate change and other human actions. Despite the abundant literature on fire ecology and a recognition of the linkages of wildfires to global warming, a patchy understanding of the relationship between fire and anthropogenic actions and their political drivers persists. Social sciences scholarship has mostly focused on fire mitigation and management in the Global North, while complex relationships between societies and fires in the Global South remain broadly unexplored. These two gaps in the academic literature in terms of scope and geography are reflected in a narrow public understanding of wildfires as ‘risks’ and ‘natural disasters’.  FIREPOL goes beyond the state of the art by leading the most rigorous, cross-continental study of the political drivers of wildfires in the Global South. Through a radical multi-methods approach and a comparative perspective, my team and I will combine and analyse newly compiled fire policy and remote sensing data with qualitative case studies and ethnographic research. The aim will be to understand and explain how a range of political factors linked to formal policies and institutions, actor-driven power dynamics, and social contention shape the geographical distribution and social impact of wildfires, and public narratives about them. FIREPOL is an ambitious, high-risk/high-gain project that will deliver a new framework to understand and explain the connections between politics and wildfires, at a timely moment when wildfires have been identified as crucial socio-ecological challenges within the global climate change agenda. It will develop a new theoretical framework around the concept of ‘wildfire commons’ as a way of engaging the academic community, policy stakeholders and the general public in the co-production of alternative pathways for the sustainable, equitable and politically engaged management of wildfires.

Mots-clés : wildfire politics political drivers of wildfires wildfire commons global south mixed methods
Projet ERC : Lo-Rig (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Compl exity
Entité participante : Centre for bhutan and gnh studies — Pays : Bhoutan — Rôle : Partner
Type d'entité : Recherche
Financement ERC de l'entité : 207445.0 €

Gongduk and Mönpa are two Sino-Tibetan languages of Bhutan with no known relatives within the language family. Despite their unique morphosyntactic feature of biactantial verbal agreement, both languages have been largely overlooked in the literature. Gongduk and Mönpa are also at a high risk of extinction. Therefore, it is of absolute necessity that we write their descriptive grammars, based on a natural speech corpus, before they disappear forever. When a people lose mastery of their mother tongue, they often suffer from a loss of identity, and subsequently a plethora of emotional, mental, and social problems. Realising that the description of the world’s languages can provide indigenous communities with the basic set of materials to promote their linguistic heritage and counteract such negative developments, the United Nations has declared 2023-2032 the International Decade of Indigenous Languages. At the same time, linguistic description offers science the opportunity to conduct ground-breaking typological and historical linguistic research. The exhaustive description of Gongduk and Mönpa will enable us to compare their verbal agreement systems to those of other Sino-Tibetan languages and infer the implications for the origin and development of verbal agreement and the language family as a whole. Bhutan’s unique cultural and linguistic heritage forms one of the cornerstones of its national identity and development philosophy. Technological advances present both a risk and an opportunity for safeguarding this heritage. The Lo-Rig project team will apply and develop innovative methods that speed up and simplify the transcription of recorded texts, using technologies such as Automatic Speech Recognition, and software applications for the subsequent analysis and dissemination of under-resourced, endangered languages. They will also train Bhutanese linguists, contributing to a more sustainable future of Bhutan’s languages, and produce outputs for language promotion.

Mots-clés : bhutan biactantial agreement descriptive linguistics polypersonal agreement verbal agreement trans himalayan sino tibetan tibeto burman direct inverse marking language preservation language promotion
Projet ERC : NeutrinoNucleai (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

The ability of current and next generation accelerator based neutrino oscillation measurements to reach their desired sensitivity and provide new insight into the nature of our Universe, requires a high-level of understanding of the neutrino-nucleus interactions. These include precise estimation of the relevant cross sections and the reconstruction of the incident neutrino energy from the measured final state particles. Incomplete understanding of these interactions can skew the reconstructed neutrino spectrum and thereby bias the extraction of fundamental oscillation parameters and searches for new physics. This proposal exploit the similarity of electron- and neutrino-nucleus interactions. It leverages wide phase-space exclusive electron scattering data with known beam energies as well as neutrino scattering data in the same energy region and on similar nuclear targets, to test energy reconstruction methods and interaction models. The ERC NeutrinoNuclei project will deliver well constrained simulation environment and uncertainties of the neutrino-nuclei interaction, to meet the requirements of future high-precision experiments.

Mots-clés : neutrino,nuclear,lartpc,microboone,clas,oscillations,cross sections,simulation
Projet ERC : CORE (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems E ngineering
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498750.0 €

Living cells have evolved to provide subcellular microenvironments that accommodate and control specific (bio)catalytic processes. These compartments include membrane-bound and membraneless organelles, or biomolecular condensates. New breakthroughs in studying and characterizing cellular biomolecular condensates have facilitated researchers to design synthetic condensates that are formed by liquid-liquid phase separation of engineered proteins, polypeptides, peptides, or nucleic acids. Yet, a precise regulation of designed condensates’ properties and their utilization for nanobiotechnological applications remains a major challenge. I propose to utilize my expertise in peptide design to develop libraries of synthetic condensates with systematically tunable chemical composition using minimalistic LLPS-promoting peptide building blocks. I will analyze how the chemical composition of condensates affect their physical and material properties and harness this knowledge to regulate organic reactions and drug synthesis in engineered microenvironments and develop condensates with emergent collective catalytic capacity. The proposed research will elucidate how the chemical composition, physical, and material properties of designed condensates affect reaction rate, conversion, and condensate reactivity. The designed condensates will go beyond the state of the art of synthetic nano- and microreactors and provide the next generation of reaction regulation for sustainable drug synthesis in organic solvent-free, aqueous environment. These findings will pave the way to establish novel design principles for the fabrication of synthetic condensates from peptide building blocks with both ordered and disordered motifs, leading to precise controlling, and balancing of these motifs to regulate reactivity effectively. I expect that this research will promote the LLPS, self-assembly, and nanobiotechnology communities for the development of condensates for emerging nanotechnologies.

Mots-clés : liquid liquid phase separation biomolecular condensates designed condensates coacervates peptides self assembly reactions compartmentalization
Projet ERC : StemMemo (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Université Jagellonne — Pays : Pologne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2500000.0 €

Human organism produces over a million new blood cells each second. The hematopoietic system dynamically reacts to environmental stress and forms adaptive immunity to memorize and effectively fight the encounter pathogens. However, based on our recent studies and preliminary results, we propose that the adaptive capabilities and memory of the hematopoietic system reach far beyond the classical adaptive antigen-specific immunity. We hypothesize that hematopoietic stress induces clonal expansion of lineage-biased hematopoietic stem cells (HSCs) with epigenetic memory that faster and more effectively respond to secondary stimulation with a given stress factor. We propose that lineage-biased HSCs accumulating during aging provide a broad adaptive memory of the hematopoietic system, that is not restricted to immune cells, but includes all blood cell lineages. The aim of our proposal is to understand how the HSC-stored memory is created, maintained and recalled, and to clarify the underlying mechanisms. First, we will selectively stimulate granulopoiesis, erythropoiesis and thrombopoiesis to define the specificity and physiological role of hematopoietic memory provided by HSCs, both in vitro and in vivo. Second, we will use single-cell level fate mapping and sequencing to investigate clonal and epigenetic mechanisms driving the HSCs-based memory in mice. Third, we will test if aging pool of human HSCs consists of lineage-biased HSCs that store memory of the previously encountered stimuli. The humanized mice models will determine the clonal expansion of human HSCs upon primary and secondary stimulation with stress factors. Altogether, the expected outcome of the project is to understand how and why hematopoietic system adjusts to the environmental challenges upon aging. The proposed novel concept of the hematopoietic system adaptivity may help to design strategies to train patients' hematopoiesis and improve the transplantations of HSCs.

Mots-clés : aging,hematopoietic stem cells,bone marrow transplantation,hematology,