Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : COST-X (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : Université de Turin — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1389776.0 €

In our digital era, characterised by easy access to both information and misinformation, understanding the mechanisms that encourage truthful communication is more crucial than ever. Despite the social and ethical importance of these issues, however, we lack a unified framework to systematically investigate the social incentives that motivate speakers to communicate truthfully. COST-X will inaugurate a novel, interdisciplinary, empirically-minded approach to studying communicative norms. The project’s overarching goal is to develop a new methodology, grounded in Costly Signalling Theory, to study how norms and reputation underlie truthful communication, offline and online. Adopting this new lens, COST-X will radically change our understanding of a diversity of phenomena relating to truthfulness: (i) the nature and epistemic function of communicative norms; (ii) deniability and indirectness in speech; (iii) testimonial transmission of knowledge; and (iv) online communication. It will inaugurate a new field of study, digital infrastructure epistemology, which investigates how misinformation spreads by attending to how online platforms alter the reputational infrastructure that motivates speakers to be truthful. Methodologically, COST-X will bring rigorous analytic thought into dialogue with empirical research. Theoretical assumptions will be tested empirically, and theory-building will profit from interdisciplinary cross-pollination from linguistics, biology, game theory, epistemology, and psychology. It will be of high scientific value to all disciplines investigating, or building on, human communication and its norms. Its findings on online communications will have practical uses, establishing philosophical foundations to better understand and tackle the spread of fake news – thus bolstering support for democratic institutions, which rely on a healthy information environment to function properly.

Mots-clés : epistemic norms,costly signalling theory,speech act theory,epistemology of testimony,epistemology of digital media,experimental philosophy,philosophy of language,experimental pragmatics,misinformation,truthfulness,deniability,fake news,reputation,trust and credibility,experimental psychology,media studies,digital governance,online communication,social epistemology,philosophy of language,pragmatics,linguistics
Projet ERC : ReMembrane (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and Analytical Chemical Sciences
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 : 1495625.0 €

Our bodies rely on protein driven shaping and remodelling of our cells’ membranes to function. Uncovering the mechanisms of remodelling of the cell membrane is, therefore, essential for understanding biological processes such as fertilization, but also to allow for precise intervention in them when needed. The interplay between protein position, membrane tension, and local curvature is believed to dictate these processes. However, experimental verifications of this hypothesis in specific biological systems are scarce. Here, I propose to apply my expertise in the characterization of mechanical properties and remodelling of membranes to obtain ground-breaking quantitative details of the shaping and remodelling mechanisms in which the Tetraspanin (TSPN) family of proteins are involved. TSPNs provide an ideal case study for several reasons, they are of extreme importance to biological processes such as viral infection, they are well characterized by biochemical, genetic and proteomics approaches, and their mode of action is suspected to depend on membrane tension and curvature. Of specific interest is the role of TSPN in the formation of the newly discovered cellular organelles, called migrasomes, which are a new cell-cell communication paradigm. This proposed project addresses TSPN functions by a bottom-up approach, reconstituting the processes of interest from simple building blocks and characterizing the distinguished roles of membrane tension and curvature. To this end we will use several new assays based on combined optical tweezers, micropipette aspiration and confocal microscopy as well as AFM that will operate on crafted membrane model systems. Our unique experimental approach will allow us to recreate the conditions leading to migrasome formation, egg-sperm, and viral membrane fusion. Revealing the mechanisms underlying these processes will have direct impact on the development of infertility treatments, non-hormonal contraceptives, and novel anti-viral drugs.

Mots-clés : membrane biophysics,membrane physical chemistry,membrane fusion,membrane remodeling,optical tweezers
Projet ERC : ALGOCONTRACT (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
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 : 936250.0 €

No algorithm is an island -- algorithms constantly interact with self-interested players. Algorithmic Game Theory (AGT) has thus far concentrated on one aspect of this: the algorithm's input is reported by such players. Incentivizing truthful reports is the focus of mechanism design in economics, and in AGT, algorithmic mechanism design became a hugely successful research area. We propose to apply the algorithmic lens to a different but no less important field in economics called contract design, recognized by the 2016 Nobel Prize. The essence of a contract is to incentivize players' actions (rather than reports). It is thus extremely relevant to another way in which algorithms interact with players -- the algorithm's output is carried out through their actions. We refer to the new research area that will emerge as algorithmic contract design (ACD).  We aim to lay the theoretical foundations for ACD. Typically, computational environments are more complex than traditional economics ones. Key complexities are: 1) A rich choice of actions makes computing an optimal contract nontrivial; 2) The optimal contract can be unintuitive and brittle; 3) A one-size-fits-all contract is suboptimal for a diverse player population; 4) Multiple contracts can undermine each other; 5) Traditional contract formats can be too weak. We will tackle these complexities, designing the next generation of algorithmic incentive schemes for strategic action -- tractable, simple/robust, personalized and coordinated -- and develop new contract formats en route. The potential impact of ACD is far-reaching: First, it will prevent traditional algorithms from failing due to selfish action choices. Second, given the current influence of algorithms on behavior, it will help achieve a more socially-efficient allocation of effort. Applications include traditional contracts moving to online platforms, like freelancing, as well as novel data-driven incentive schemes for domains like digital healthcare.

Mots-clés : algorithmic game theory,algorithmic mechanism design,algorithmic contract theory,moral hazard and adverse selection,optimal mechanism design,algorithmic delegation,simple versus optimal mechanisms,strategic classification,beyond worst case analysis,combinatorial auctions
Projet ERC : OPTGEN (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
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 : 1494375.0 €

Recent advances in the field of machine learning (ML) are revolutionizing an ever-growing variety of domains, ranging from statistical learning algorithms in computer vision and natural language processing all the way to reinforcement learning algorithms in autonomous driving and conversational AI. However, many of these breakthroughs demonstrate phenomena that lack explanations, and sometimes even contradict conventional wisdom. Perhaps the greatest mystery of modern ML---and arguably, one of the greatest mysteries of all of modern computer science---is the question of generalization: why do these immensely complex prediction rules successfully apply to future unseen instances? Apart from the pure scientific curiosity it stimulates, I believe that this lack of understanding poses a significant obstacle to widening the applicability of ML to critical applications, like in healthcare or autonomous driving, where the cost of error is disastrous. The broad goal of this project is to tackle the generalization mystery in the context of both statistical learning and reinforcement learning, focusing on optimization algorithms being the de facto contemporary standard in training learning models. Our methodology points out to inherent shortcomings of widely accepted viewpoints with regard to generalization of optimization-based learning algorithms, and takes a crucially different approach that targets the optimization algorithm itself; building bottom-up from fundamental and tractable optimization models, we identify intrinsic properties and develop algorithmic methodologies that enable optimization to effectively generalize in modern statistical- and reinforcement-learning scenarios. A successful outcome would not only lead to a timely and crucial shift in the way the research community approaches generalization of contemporary optimization-based ML, but it may also significantly transform the way we develop practical, efficient and reliable learning systems.

Mots-clés : statistical learning theory,convex optimization,reinforcement learning,generalization,online learning,regret minimization
Projet ERC : COeXISTENCE (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Université Jagellonne — Pays : Pologne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1494405.0 €

AI-driven technologies are ready to enter urban mobility. They promise relief to the notoriously congested transport systems in pursuing sustainability goals. Since AI already outperforms humans in the most complex games (chess and Go) it is likely to win the urban mobility games as well, outperforming us e.g. in: route choices (to arrive faster), mode choices (to reduce costs), pricing strategies and fleet management (to increase market shares and profits). Tempting us and policymakers to gradually hand over our decisions to intelligent machines. The consequences of this ongoing revolution are challenging to predict and largely unknown. While the abundance of previous studies proves the positive potential of AI in urban mobility (from autonomous vehicles via optimal routing up to fleet management), the negative impact is overlooked. Conversely, our scenario of interest is the machine-dominated urban mobility system, where (collective) decisions of machine intelligence improve system-wide performance, yet at the cost of humans, now facing e.g. longer travel times, greater monetary costs or being nudged to change natural travel habits into the optimal ones - desired by the machine-centred system.  Such scenarios, however, need to be discovered. To this end, COeXISTENCE embarks on the interdisciplinary expedition inside the virtual environment of urban mobility, where machines and humans play the game for limited resources. In the four pre-identified games I will explore the conflict scenarios, demonstrate them on reproducible case-studies, quantify with proposed measures and finally mitigate with a proposed multi-objective reinforcement learning framework, where machines learn to mitigate conflicts while simultaneously reaching their inherently selfish objectives. Reaching the projects' objectives will be ground-breaking when new phenomena are discovered and lead to breakthrough when they are mitigated pushing the system towards the synergy of COeXISTENCE.

Mots-clés : urban mobility travel demand travel behaviour reinforcement learning agent based modelling traffic microsimulation discrete choice two sided mobility platforms traffic assignment
Projet ERC : MedPlaceboEffect (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Study of the Human Past
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 : 1487500.0 €

This project sets out to recover how hope and expectation for cure, belief, and trust configured late medieval medicine and the healing encounter. The therapeutic power of these sensibilities is recognized today by the modern clustering concept, placebo effect. This integrative project will identify the intellectual, social, and cultural forces in the medieval past that articulated and applied the idea that health may be improved by evoking certain sensibilities. In a triangular approach that examines ideas, practices, and organizing structures of healing activity, I analyze the medieval placebo effect as a paradigmatic attribute of the healing of the period. The project brings into dialogue current debates on the curative role of “symbolic aspects of a therapeutic intervention” with medieval perceptions of the soul’s impact on the body. By so doing it will shed new light on the salient engagement of medieval healing practices with hope, expectations, belief, and trust as critical elements of therapy. It will demonstrate the pervasiveness of placebo effect sensibilities. It will view the breadth of the engagement with the soul/mind as a formative aspect of healing theory and technique, and will show the dynamics between ideas, practices, institutions, and networks in procuring hope, belief, and trust in late medieval Southern European healing. By looking beyond the term to the components that link the phenomena, this historical project will enable the examination of a health system that is simultaneously alien to and part of the underlying legacy of European medicine and will offer a new path for thinking about these issues, essential for effective healthcare. MedPlaceboEffect will thus lay the groundwork for examining the ways these sensibilities are negotiated in the 21st century as healing practices face substantial challenges such as the growing distrust of medical services, and the increasing difficulty of overcoming placebos in pharmaceutical research.

Mots-clés : medieval medical theory and practice,medieval ritual and miracles,medieval emotions
Projet ERC : QuantumCUSP (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
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 : 1433750.0 €

The theory of electrical, thermoelectric and magnetoelectric transport lies at the core of condensed matter research, because it allows to probe many ground state properties as well as dynamical and temperature-dependent features and harness them for advancing technology. Due to the progress in precision and resolution of experimental techniques, formerly inaccessible nonlinear and nonlocal effects take an increasingly central role. However, this pace is not matched in theory, where the physical understanding of response phenomena beyond the local and linear approximation remains fragmented. The advent of new quantum materials makes it necessary to take our understanding of transport theory to the next level.  Here, I propose a universal and intuitive approach to quantum transport based on wavepacket deformations which allows the organization and prediction of complex transport phenomena with the help of geometric band structure properties. The goal of this research program is to develop this concept into a powerful and applicable tool in order to accelerate our comprehension of novel transport phenomena and prediction of new ones. To this end, using diagrammatic methods I will generalize the concept of the anomalous quasiparticle motion to all orders in the response, thereby significantly expanding on the concept of Berry curvatures. Connecting wavepacket deformations with gravitational phenomena, I will recast the theory of quantum transport in terms of a motion in curved spacetime. I will apply these concepts to novel flatband platforms by employing an innovative mapping of the quasiparticle flow between real and momentum space. Such powerful technology will enable the creation and investigation of dynamical gravitational fields in a condensed matter setting. The geometric formulation of quantum transport will reshape our understanding of condensed matter physics and lead to the discovery of new phenomena like gravitational anomalies in quantum materials.
Projet ERC : DAPHNE (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Queen Mary University of London — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : QMUL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499550.0 €

Tissues are piezoelectric materials, generating electrical signals in response to mechanical stress, and yet this important aspect has not yet been incorporated into the design of disease research models, particularly in conditions like idiopathic pulmonary fibrosis (IPF). Piezoelectricity affects cellular migration, growth, and differentiation. However, most of our understanding of cell-tissue interactions comes from studying cells under ‘static’ mechanical conditions. Cells interact with their microenvironment by applying forces and receiving bioelectric feedback through ion channels, which are overexpressed in fibrotic tissues (16-fold). Because macrophages and fibroblasts are involved in IPF-related fibrogenesis, we hypothesize that piezoelectricity plays a critical role in their differentiation states during disease progression. Current IPF research models do not replicate the electro-mechanical cellular environment, widening the gap between predictive in vitro models and clinical outcomes. Bridging this gap requires a paradigm shift toward models that incorporate electro-mechanical coupling. With DAPHNE, the applicant will develop a state-of-the-art piezoelectric platform providing both electrical and mechanical signals, enhancing the physiological relevance of current models. This innovation will accelerate drug development by enabling the testing of novel anti-fibrotic therapies targeting ion channel activity and creating the first statistically powerful library of cell-ECM electro-mechanical interactions. At the core of DAPHNE is the ReShape platform, which integrates three key functions: • Force: Applying dynamic forces to modulate piezoelectricity and cellular activity. • Visualize: Using real-time, label-free Fluorescence Lifetime Imaging Microscopy (FLIM) to track cellular ‘metabolic state’ changes at relevant biological timescale. • ReShape: Evaluating tissue-wide responses to cellular activity at high throughput.

Mots-clés : piezoelectricity,fibrosis,fak,mechanobiology,ion channels
Projet ERC : SPARTA (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Université d'Oslo — Pays : Norvège — Rôle : PI
Acronyme de l'entité : UiO
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499368.0 €

High-energy physics is headed for an impasse: the next particle collider will cost several billion euros, and while designs have been ready for a decade, they are so expensive that no host country has come forward—a problem that will soon impact progress in the field.  Plasma acceleration is a novel technology promising to fix this issue—with accelerating fields 1000 times larger than in conventional machines, the size and cost of future accelerators can be drastically reduced. However, there is a gap between what current plasma accelerators can do and what the next collider requires. Therefore, a recent R&D roadmap (European Strategy for Particle Physics) calls for intensified plasma-accelerator research, as well as an intermediate demonstrator facility.  SPARTA tackles two basic problems in plasma acceleration: to reach high energy by connecting multiple accelerator stages without degrading the accelerated beam, and to do so in a stable manner. Access to stable, high-energy electron beams at a fraction of today’s cost will enable ground-breaking advances in strong-field quantum electrodynamics (SFQED), an important near-term experiment that doubles as a demo facility.  I have proposed two concepts for overcoming these problems: nonlinear plasma lenses for transport between stages, and a new mechanism for self-stabilization. Can these concepts be realized in practice?  Making use of numerical simulations and beam-based experiments at international accelerator labs, this project has 3 objectives:  1. Develop nonlinear plasma lenses experimentally; 2. Investigate self-stabilization, theoretically and experimentally; 3. Design a plasma-accelerator facility for SFQED.  Reaching this goal will not only impact high-energy physics, producing advances in SFQED and as a major step toward realizing a collider, but also society at large: applications of high-energy electrons, from bright x-ray beams to advanced cancer treatments, will all become significantly more affordable.

Mots-clés : plasma accelerators,plasma lenses,particle beam optics,strong field quantum electrodynamics