Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : POLYBIOLUB (Horizon 2020, appel 2014, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Board of the Swiss Federal Institutes of Technology — Pays : Suisse — Rôle : PI
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2456570.0 €

Lubrication in nature is based on water, but only functions due to the presence of a host of biomolecules. In articular (e.g. hip) joints, it appears that the lubrication system is even more complex than previously believed, involving multiple gradients in structures and properties. Gels and brushes appear to play an important role in biolubrication, while highly confined, highly hydrated charged polymers are key to the important mechanical properties of cartilage. Components such as hyaluronic acid, glycoproteins, and lipids all appear to act synergistically to yield the extraordinary tribological properties of the cartilage-synovial-fluid system. POLYBIOLUB seeks to mimic the mechanisms revealed by the latest studies of cartilage, by means of the synthesis of functionally similar polymeric structures. This is a completely novel approach to the problem, which has the promise not only of revealing structural dependencies of cartilage function, but also of yielding radically new, biomimetic, lubricious, low-wear materials that could find applications in either industrial or clinical environments. The principal synthetic tool will be controlled radical polymerization in a flow environment, involving in situ growth monitoring, followed by a series of postmodification and grafting steps, to yield structures that are lubricious, wear resistant, and tough. These efforts will be accompanied by extensive characterization of composition, structure and mechanical/tribological properties at each stage. Our group has extensive experience in controlled radical polymerization in a flow system, postmodification of polymers, brush-gel and layered polymer systems,. We also have over a decade of experience in water-based lubrication, natural lubrication, synovial-joint studies, and multidimensional gradient systems. The challenge now is to combine these skills to fabricate a completely new biomimetic material.

Mots-clés : multilayer structures,polymer brushes,gels,lubrication,gradients,biomimetic materials
Projet ERC : HIDDeN (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Chalmers University of Technology — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2496319.0 €

Luminous infrared galaxies (LIRGs) emit most of their bolometric luminosity in the far-infrared. They are mainly powered by extreme bursts of star formation and/or Active Galactic Nuclei (AGNs; accreting supermassive black holes (SMBHs)) in their centres. LIRGs are the closest examples of rapid evolution in galaxies and a detailed study of LIRGs is critical for our understanding of the cosmic evolution of galaxies and SMBHs. Centres of some LIRGs are deeply obscured and unreachable at optical, IR and even X-ray wavelengths. These hidden nuclei therefore represent a largely unexplored phase of the growth of central regions with their SMBHs. Large growth spurts are suspected to occur when the SMBHs are deeply embedded. Obscured AGNs thus can provide new constraints on the AGN duty cycle, give the full range of environments and astrophysical processes that drive the growth of SMBHs, and help to complete the picture of connections between the host galaxy and SMBH. Many dust embedded AGNs are still to be discovered as studies suggest that a significant fraction of SMBHs may be obscured in the local and more distant Universe. In the HIDDeN project we use mm and submm observational methods to reach behind the curtain of dust in the most embedded centres of LIRGs, allowing us to undertake ground-breaking studies of heretofore hidden rapid evolutionary phases of nearby galaxy nuclei. HIDDeN takes advantage of emerging opportunities to address the nature of near-field, and redshift z=1-2, obscured AGNs/starbursts and their associated molecular inflows and outflows in the context of their evolution and the starburst-AGN connection. In particular we use the ALMA and NOEMA telescopes, supported by JVLA, LOFAR, HST and future JWST observations, to address four interconnected goals: A. Probing the Dusty Interiors of Compact Obscured Nuclei (CONs), B. The cold winds of change - Molecular Outflows from LIRGs and AGNs, C. The Co-Evolution of Starbursts and AGNs and D. Are there hidden CONs at z=1-2

Mots-clés : galaxy nuclei,molecules,outflows,dust
Projet ERC : RE-STORE (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : IRBM Science Park — Pays : Italie — Rôle : Partner
Type d'entité : Organismes privés (hors enseignement)
Financement ERC de l'entité : 30000.0 €

Lysosomal storage disorders (LSDs) are a family of inherited genetic diseases characterized by lysosomal dysfunction, with a consequent block in the degradative capacity of the cell. The skeleton is one of the most affected organs in LSD patients and current therapies are largely ineffective for the treatment of bone and cartilage manifestations. Thanks to the support of the ERC-starting grant BONEPHAGY we demonstrated that the degradation of the endoplasmic reticulum fragments via lysosome/autophagy pathway (ER-phagy) exerts ER quality control functions that are essential for secretion of procollagens. This process is impaired in LSD chondrocytes and osteoblasts, the cartilage and bone forming cells, respectively and accounts, at least in part, for the skeletal growth retardation observed in LSD mouse models. Prompted by these exciting findings, RE-STORE proposes to develop a novel cell-based integrated screening platform to identify selective ER-phagy inducers to target the skeletal phenotype of LSDs and contribute significantly to their development into innovative therapeutics. The access to proprietary library, through a collaboration with an industrial partner, and the set-up of a rapid hit confirmation in disease animal models will allow RE-STORE to establish a complete drug development pipeline for the selected hits. RE-STORE is an ambitious project that aims to develop an innovative substrate-to-lysosome screening approach for the study of selective autophagy and to identify new therapies for the so-called “ER-storage disorders” characterized by accumulation of misfolded polypeptides in the lumen of the ER.

Mots-clés : er phagy,selective autophagy,lysosome,small molecules,therapy,skeleton,lysosomal storage disorders
Projet ERC : MESG (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : University of Aberdeen — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 323157.94 €

MESG explores how the notion of a collective European-North African cultural memory has been articulated through music for different sociopolitical ends in colonial and postcolonial contexts. Based on the notion of convivencia (the alleged coexistence between Christians, Jews and Muslims in Islamic Spain), music has been employed as a means of social control and representation during French-Spanish colonialism in North Africa (1912–56) and as a model for multiculturalism among North African communities in Europe today. Current scholarship on musical exchange between Europe and North Africa is fragmented, often focusing on isolated geographical case studies. There is limited understanding of how a collective cultural memory has shaped musical practice and discourse in the colonial past and the postcolonial present. In contrast, MESG offers a comparative study of music and colonialism in the Maghreb. By examining colonial music scholarship, policy and education, and musical encounters between different cultural groups, MESG probes the social dynamics of musical interaction at this time, framed by issues of race, imperialism and cultural memory. Second, MESG explores how the idea of a collective cultural memory is invoked through musical collaboration today, by focusing on various genres such as Arab-Andalusian music and flamenco. Rather than separating these historical periods, however, MESG analyses how modern-day practices of musical exchange in the region are shaped by discourses and networks formed during colonialism. Musical exchange will be read against the wider context of multiculturalism, immigration and cultural diplomacy that underpins postcolonial relations between Europe and North Africa. Combining archival and ethnographic research, this groundbreaking project brings together for the first time different geographical, linguistic and musical specialisms, leading towards a fuller understanding of musical exchange in the region.

Mots-clés : ethnomusicology,music and multiculturalism,music and postcolonial studies,flamenco,arab andalusian music,al andalus,cultural memory,colonialism
Projet ERC : MESG (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : University of Manchester — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 135239.5 €

MESG explores how the notion of a collective European-North African cultural memory has been articulated through music for different sociopolitical ends in colonial and postcolonial contexts. Based on the notion of convivencia (the alleged coexistence between Christians, Jews and Muslims in Islamic Spain), music has been employed as a means of social control and representation during French-Spanish colonialism in North Africa (1912–56) and as a model for multiculturalism among North African communities in Europe today. Current scholarship on musical exchange between Europe and North Africa is fragmented, often focusing on isolated geographical case studies. There is limited understanding of how a collective cultural memory has shaped musical practice and discourse in the colonial past and the postcolonial present. In contrast, MESG offers a comparative study of music and colonialism in the Maghreb. By examining colonial music scholarship, policy and education, and musical encounters between different cultural groups, MESG probes the social dynamics of musical interaction at this time, framed by issues of race, imperialism and cultural memory. Second, MESG explores how the idea of a collective cultural memory is invoked through musical collaboration today, by focusing on various genres such as Arab-Andalusian music and flamenco. Rather than separating these historical periods, however, MESG analyses how modern-day practices of musical exchange in the region are shaped by discourses and networks formed during colonialism. Musical exchange will be read against the wider context of multiculturalism, immigration and cultural diplomacy that underpins postcolonial relations between Europe and North Africa. Combining archival and ethnographic research, this groundbreaking project brings together for the first time different geographical, linguistic and musical specialisms, leading towards a fuller understanding of musical exchange in the region.

Mots-clés : ethnomusicology,music and multiculturalism,music and postcolonial studies,flamenco,arab andalusian music,al andalus,cultural memory,colonialism
Projet ERC : MICOLL (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Padua — Pays : Italie — Rôle : PI
Acronyme de l'entité : UNIPD
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1610593.0 €

MICOLL will analyze the development of commercial law by means of a tool almost ignored in this field: historical linguistics. The borrowing and transfer of legal terms will be carried out through a comprehensive and systematic investigation of medieval and early modern legal sources, in particular commercial letters, contracts and statutes. Even though legal historians tend to deny the effectiveness of a body of customary laws uniformly adopted across medieval and modern Europe, the “myth” of the ancient lex mercatoria continues to provide historical legitimacy to the supporters of corporate self-regulation. According to a widespread historiographical topos, merchants all over the world “spoke the same language” when it came to what was important for them: to make profits. As legal institutions are represented by technical legal words, an analysis of the terms merchants actually used is a powerful and never attempted way to verify the impact of merchants’ migrations on the development of commercial law, which had, in its turn, tremendous effects on social and economic history. The center of this project will be Venice, for several centuries the mandatory stop for merchandise coming from the East and directed both to northern Europe and to Genoa, from where men and goods would reach other trading centers (e.g., France and the Iberian peninsula). Our time-frame spans from the “commercial revolution” of the 11th century to the beginning of the modern period, when the new dynamics of transoceanic trade left Venice at the periphery of a world that was changing its very dimensions. Did merchants actually use the same legal terms in different geographical areas? And, above all, did they grant these words the same legal meaning? MICOLL will answer these questions by means of two main tools: a “Glossary of medieval commercial law terms” and an interactive digital map of the land routes connecting Venice with northern Europe and the Tyrrhenian Sea.

Mots-clés : lex mercatoria,commercial law history,historical linguistics,medieval merchants,trade routes,migration,multilingualism,multiculturalism,maritime republics,hansa
Projet ERC : MUTE (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : National Hellenic Research Foundation — Pays : Grèce — Rôle : PI
Acronyme de l'entité : NHRF
Type d'entité : Recherche
Financement ERC de l'entité : 1662495.22 €

MUTE’s main objective is to investigate for the first time empirically and theoretically the use of music and sound in situations of confinement and displacement from the Cold War to contemporary times. It explores their weaponization in light of transnational developments in technologies of terror. At the same time, it carefully attends to how music/sound can become a tool of survival, even in the same setting in which they are weaponized. Also explored are ethical challenges of such research, and of music programmes for prisoners and refugees, highlighting their shortcomings and providing alternative models. MUTE innovates through a theoretical framework that investigates the interlocking of politics, ethics, and aesthetics, focusing on the ethics of sound and ethics of witnessing. Its comparative approach extends to (post)colonial Cyprus, Greece, Serbia, Germany, Soviet Union and contemporary Russia, Iraq, Argentina, Chile, Uruguay and Paraguay. MUTE will critically analyse this complex phenomenon across the disciplines of (ethno)musicology, social anthropology, history, critical theory, human rights law and sound art, since it cannot be fully grasped through the methods of any one alone. The PI’s extensive immersion into all disciplinary components over the last decade, evident in her novel approach, ensures the successful implementation of research objectives. MUTE’s findings and results will transform scientific discourse, changing current perceptions about music’s social function. This historical recovery is important in revisiting and assessing detention-related policies and current definitions of torture. Given recent mass asylum seeking in Europe and the growing number of music research projects with refugees, it will offer the needed ethical, methodological and theoretical foundations for present and future research, ensuring the well-being of participants and researchers, research excellence, and critically nuanced scholarship.

Mots-clés : musicology,ethnomusicology,trauma studies,sound studies,social anthropology,ethics,witnessing,trauma memory,detention,displacement,acoustic violence,torture,aesthetics,research based art
Projet ERC : SOLARIS (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Institut national de recherche en sciences et technologies du numérique — Pays : France — Rôle : PI
Acronyme de l'entité : Inria
Type d'entité : Recherche
Financement ERC de l'entité : 1498465.0 €

Machine learning has become a key part of scientific fields that produce a massive amount of data and that are in dire need of scalable tools to automatically make sense of it. Unfortunately, classical statistical modeling has often become impractical due to recent shifts in the amount of data to process, and in the high complexity and large size of models that are able to take advantage of massive data. The promise of SOLARIS is to invent a new generation of machine learning models that fulfill the current needs of large-scale data analysis: high scalability, ability to deal with huge-dimensional models, fast learning, easiness of use, and adaptivity to various data structures. To achieve the expected breakthroughs, our angle of attack consists of novel optimization techniques for solving large-scale problems and a new learning paradigm called deep kernel machine. This paradigm marries two schools of thought that have been considered so far to have little overlap: kernel methods and deep learning. The former is associated with a well-understood theory and methodology but lacks scalability, whereas the latter has obtained significant success on large-scale prediction problems, notably in computer vision. Deep kernel machines will lead to theoretical and practical breakthroughs in machine learning and related fields. For instance, convolutional neural networks were invented more than two decades ago and are today’s state of the art for image classification. Yet, theoretical foundations and principled methodology for these deep networks are nowhere to be found. The project will address such fundamental issues, and its results are expected to make deep networks simpler to design, easier to use, and faster to train. It will also leverage the ability of kernels to model invariance and work with a large class of structured data such as graphs and sequences, leading to a broad scope of applications with potentially groundbreaking advances in diverse scientific fields.

Mots-clés : deep learning,kernel methods,large scale optimization
Projet ERC : HOLI (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Tel Aviv University — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1932500.0 €

Machine learning has rapidly evolved in the last decade, significantly improving accuracy on tasks such as image classification. Much of this success can be attributed to the re-emergence of neural nets. However, learning algorithms are still far from achieving the capabilities of human cognition. In particular, humans can rapidly organize an input stream (e.g., textual or visual) into a set of entities, and understand the complex relations between those. In this project I aim to create a general methodology for semantic interpretation of input streams. Such problems fall under the structured-prediction framework, to which I have made numerous contributions. The proposal identifies and addresses three key components required for a comprehensive and empirically effective approach to the problem. First, we consider the holistic nature of semantic interpretations, where a top-down process chooses a coherent interpretation among the vast number of options. We argue that deep-learning architectures are ideally suited for modeling such coherence scores, and propose to develop the corresponding theory and algorithms. Second, we address the complexity of the semantic representation, where a stream is mapped into a variable number of entities, each having multiple attributes and relations to other entities. We characterize the properties a model should satisfy in order to produce such interpretations, and propose novel models that achieve this. Third, we develop a theory for understanding when such models can be learned efficiently, and how well they can generalize. To achieve this, we address key questions of non-convex optimization, inductive bias and generalization. We expect these contributions to have a dramatic impact on AI systems, from machine reading of text to image analysis. More broadly, they will help bridge the gap between machine learning as an engineering field, and the study of human cognition.

Mots-clés : structured prediction,graphical models,deep learning,approximate inference
Projet ERC : SEQUOIA (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Institut national de recherche en sciences et technologies du numérique — Pays : France — Rôle : PI
Acronyme de l'entité : Inria
Type d'entité : Recherche
Financement ERC de l'entité : 1998750.0 €

Machine learning is needed and used everywhere, from science to industry, with a growing impact on many disciplines. While first successes were due at least in part to simple supervised learning algorithms used primarily as black boxes on medium-scale problems, modern data pose new challenges. Scalability is an important issue of course: with large amounts of data, many current problems far exceed the capabilities of existing algorithms despite sophisticated computing architectures. But beyond this, the core classical model of supervised machine learning, with the usual assumptions of independent and identically distributed data, or well-defined features, outputs and loss functions, has reached its theoretical and practical limits. Given this new setting, existing optimization-based algorithms are not adapted. The main objective of this proposal is to push the frontiers of supervised machine learning, in terms of (a) scalability to data with massive numbers of observations, features, and tasks, (b) adaptability to modern computing environments, in particular for parallel and distributed processing, (c) provable adaptivity and robustness to problem and hardware specifications, and (d) robustness to non-convexities inherent in machine learning problems. To achieve the expected breakthroughs, we will design a novel generation of learning algorithms amenable to a tight convergence analysis with realistic assumptions and efficient implementations. They will help transition machine learning algorithms towards the same wide-spread robust use as numerical linear algebra libraries. Outcomes of the research described in this proposal will include algorithms that come with strong convergence guarantees and are well-tested on real-life benchmarks coming from computer vision, bioinformatics, audio processing and natural language processing. For both distributed and non-distributed settings, we will release open-source software, adapted to widely available computing platforms.