Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : Stick-n-Sense (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : University of Barcelona — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Stick&Sense aims at making gas monitoring technology accessible to everyone. In a moment when society is more and more aware about the risks related to airborne pollution, the market is failing at providing a gas monitoring solution suitable for a massive, widespread use. Stick&Sense addresses, all-in-one, the main limitations of existing technologies: it is a disruptive one-stop solution capable of offering low cost, low complexity, high selectivity, good durability and broad detection range. Stick&Sense is a new colorimetric-electronic device consisting of a new low-cost optical reader and a new gas-specific colorimetric sticker designed to change its colour in presence of a target gas, that will operate together just attaching the sticker to the reader. With this genuine concept, Stick&Sense will offer an easy-to-use system for a wide range of gas sensing and monitoring applications, enabling a rapid expansion, not only among the traditional industry users, but also towards new, non-exploited mass consumers applications with millions of potential users. In short, Stick&Sense can revolutionize how people know the risks around them and relate with their environment to improve life-quality, safety and health. In this project, we will design and produce the first minimum viable demonstrator of the technology that will serve us to validate the Stick&Sense effectiveness both at a technical and at a commercial level. The team hosts all the expertise needed to implement this PoC from a technical and from a business points of view and counts with an extensive network of industry contacts to partner with in customer discovery, validation and eventual tech transfer agreements. The key innovations of the Stick&Sense solution have been recently discovered in ERC-funded activities and its technological core (the reader chip) patented; thus, all its innovation potential can be captured if the here-proposed test, validation and knowledge transfer activities are carried out now.

Mots-clés : gas sensors,colorimetry,disposable
Projet ERC : HIGGSBNDL (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1794562.0 €

String theory provides a unified description of particle physics and gravity, within a consistent theory of quantum gravity. The goal of this research is to develop both the phenomenological implications as well as conceptual foundations of string theory and its non-perturbative completions, M- and F-theory. Both, seemingly independent, questions are deeply connected to a mathematical structure, the Higgs bundle, which characterizes supersymmetric vacua of dimensionally reduced gauge theories, and insights into the moduli space of Higgs bundles will result in a fruitful cross-connection between these subjects. For string theory to engage in a meaningful dialog with particle physics, it is paramount to gain a universal understanding of the low energy effective theories that can arise from it. Building on the success of studying F-theory vacua in terms of Higgs bundles, we propose to develop the Higgs bundle approach for M-theory on G2-manifolds, leading to a universal characterization of the low energy physics. Methods developed for Higgs bundles of d = 3 N = 2 theories obtained from M5-branes on three-manifolds will be used in this process. Associated to each Higgs bundle is a local G2 manifold and we propose a way (using new results in geometry) to construct compact G2 spaces associated to these, which manifestly ensure the phenomenological soundness of the compactifications. Higgs bundles have recently also played a key role in studying the compactifications of the M5-brane in M-theory. We propose and develop a new duality between a d = 4 theory on a four-manifold X4 and a d = 2, N = (2,0) supersymmetric gauge theory on a two-sphere S2, obtained by considering the M5-brane theory on X4xS2. The supersymmetric vacua have a characterization in terms of Higgs bundles, which can be studied with tools developed for F- theory Higgs bundles on four-manifolds. Furthermore we propose a concrete approach to derive this duality from first principles.
Projet ERC : SAVEBRAIN (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : Maastricht University — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UM
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 51896.25 €

Stroke represents currently one of the largest – if not the largest - unmet medical need: It is the second leading cause of death and the leading cause of disability. In contrast to this high demand for effective treatments, there is only one drug available, a blood-clot dissolving agent. However, this drug is marginally effective has over 30 contra-indications, and bears an extremely high risk of causing fatal bleeding that 85% of all stroke patients are not treated with it. The needed innovation would be a drug which is broadly applicable, i.e. has no contraindication, can be given in all forms of stroke and already in the ambulance, is safe, i.e. bears no risk to cause secondary bleeding, is effective, i.e. reduces the brain infarct, increases survival and improves neurological outcomes for patients, has a different mechanism of action, i.e. is directly neuroprotective. Our therapeutic principle, discovered in the course of the ERC-ADG RadMed fulfils all above criteria and is also commercially an innovative approach because we reduce the risk by developing only a single compound by here combining three compounds that target the same disease mechanism but at different points. We thereby expect to dramatically reduce the likelihood risk of failure in our post-proof of concept (PoC) clinical development and commercialisation process. We here propose to conduct the final step, the PoC, before a first-in-man study in stroke. Our PoC plan has three major objectives: To complete an IPR FTPO and Foreground analysis, to chose the optimal compounds and complete phase III pre-clinical to provide PoC for a first-in-man trial in stroke, to then allow the work-out of a complete business plan including competitive analysis, development strategy, and commercial/business development strategy.

Mots-clés : drug development,spin off company,out licencing
Projet ERC : WASCOSYS (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : RWTH Aachen University — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : RWTH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

Strongly correlated quantum systems, which are at the heart of many open problems in condensed matter, quantum chemistry, or high-energy physics, are challenging to understand due to their intricate entanglement structure. Quantum information theory provides the right framework to characterize highly entangled states and has given rise to the class of Tensor Network States, which capture the entanglement structure of strongly correlated systems by building the global wavefunction from local tensors and provide an efficient description of their low-energy states. In this project, we will develop a framework for the systematic study of strongly correlated systems using exact wavefunctions based on Tensor Network States. It will give us the tools to construct controlled families of states by encoding the relevant structure of the system directly into the wavefunction, rather than a Hamiltonian, and to study their behavior. Since the tensor describing the wavefunction also gives rise to an associated Hamiltonian, this establishes a framework for building solvable models with the tensor as the new central object. The novelty of our approach lies in the fact that quantum information gives us the tools to systematically construct wavefunctions for general strongly correlated systems, while at the same time, encoding the structure of the problem directly into the wavefunction results in small families of states with a direct physical interpretation of the parameters, unlike for fully variational approaches. We will apply our framework to study the physics of a range of strongly correlated models, in particular frustrated fermionic and spin systems, in order to understand the possible physics they can exhibit. This will enhance our understanding of the physics of strongly correlated systems, and, together with numerical results, experimental findings, and quantum simulations, ultimately lead to new applications and materials based on strongly correlated matter.

Mots-clés : many body entanglement theory,strongly correlated systems,tensor network methods
Projet ERC : WASCOSYS (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 1338500.0 €

Strongly correlated quantum systems, which are at the heart of many open problems in condensed matter, quantum chemistry, or high-energy physics, are challenging to understand due to their intricate entanglement structure. Quantum information theory provides the right framework to characterize highly entangled states and has given rise to the class of Tensor Network States, which capture the entanglement structure of strongly correlated systems by building the global wavefunction from local tensors and provide an efficient description of their low-energy states. In this project, we will develop a framework for the systematic study of strongly correlated systems using exact wavefunctions based on Tensor Network States. It will give us the tools to construct controlled families of states by encoding the relevant structure of the system directly into the wavefunction, rather than a Hamiltonian, and to study their behavior. Since the tensor describing the wavefunction also gives rise to an associated Hamiltonian, this establishes a framework for building solvable models with the tensor as the new central object. The novelty of our approach lies in the fact that quantum information gives us the tools to systematically construct wavefunctions for general strongly correlated systems, while at the same time, encoding the structure of the problem directly into the wavefunction results in small families of states with a direct physical interpretation of the parameters, unlike for fully variational approaches. We will apply our framework to study the physics of a range of strongly correlated models, in particular frustrated fermionic and spin systems, in order to understand the possible physics they can exhibit. This will enhance our understanding of the physics of strongly correlated systems, and, together with numerical results, experimental findings, and quantum simulations, ultimately lead to new applications and materials based on strongly correlated matter.

Mots-clés : many body entanglement theory,strongly correlated systems,tensor network methods
Projet ERC : PRIDISP (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Fondation Jean-Jacques Laffont - Toulouse sciences économiques — Pays : France — Rôle : PI
Acronyme de l'entité : JJL-TSE
Type d'entité : Recherche
Financement ERC de l'entité : 911812.5 €

Structural models of demand and supply in differentiated product markets are widely used for policy evaluation and in antitrust. They typically consider firms set uniform prices and fail to explain price dispersion within products. Yet, it is common that the same good is purchased at different prices and this has major consequences for consumers that standard models cannot analyze. This proposal extends standard models to incorporate price dispersion. These new structural models are used to shed lights on the sources of price dispersion, their importance and evaluate the distributional impacts and profitability of price dispersion on important markets (automobile, grocery items and online markets). We model and analyze specific sources of price dispersion. First, we consider price dispersion within product lines and the pricing of quality with a model of second degree price discrimination. Second, we develop a model of third degree price discrimination with limited arbitrage opportunity. When price discrimination is not explicit, consumers are not locked in and some individuals are able to arbitrage and pay the lowest price. The third project considers individualized pricing and purchase history and develops a structural model in which firms learn about consumers’ preferences by observing their previous purchase patterns and leverage this information by setting individualized prices. Finally, we consider a framework with spatial price dispersion to analyze price dispersion for grocery items across locations. As a transversal project, we provide a reliable method to test alternative models of market equilibrium and apply them to confront our new models to the standard ones.

Mots-clés : structural model,industrial organization,market equilibrium,price discrimination,welfare effects
Projet ERC : STRUCTURE (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
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 : 1499632.0 €

Structure holds the key to many of the physical world’s most intriguing secrets. Diffraction from single crystals has revolutionized our knowledge of crystalline matter by providing atomic-scale images of countless samples and leading to landmark achievements in science. However, when crystals of sufficient dimensions cannot be grown, structure can hardly be retrieved using currently available methodologies. This hampers our understanding of the physico-chemical behavior of numerous samples, such as functional organic powders (FOP), hence precluding the design of new materials with tailored properties. Solid-state NMR (SSNMR) has the potential to be the key to access the structure of powders for applications in energy or pharmacy. However, the inherently low sensitivity of NMR constitutes the main barrier to retrieve valuable constraints such as interatomic distances and torsional angles from spin-spin couplings involving rare nuclei (e.g. C-13, N-15) on organic samples at natural isotopic abundance (NA), for which chemical shifts are certainly easier to access but less structurally relevant. The project will capitalize on Dynamic Nuclear Polarization (DNP) to enhance the sensitivity of SSNMR and obtain unique structural constraints on NA FOPs. Specifically: (i) intra and intermolecular distances, torsional/bond angles and H bonds will be measured for the first time via DNP SSNMR; (ii) together with powder X-ray data, these constraints will be integrated within modern computational algorithms to assist the generation of physically meaningful 3D structures with minimized risk of false positives. The protocol will be applied to time-resolved in situ/ex situ investigation of self-assembly to gain control into polymorph production. We will create an integrated experimental/in silico tool that will extend the proficiency of crystallography in de novo structure elucidation of FOPs of increasing complexity, opening new avenues in chemistry and materials science.

Mots-clés : solid,nuclear magnetic resonance,dynamic nuclear polarization,dipolar coupling,j coupling,diffraction,computation,crystal structure,structure prediction,structure search,self assembly
Projet ERC : GeoSub (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : Partner
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 0.0 €

Sub-Riemannian spaces are geometrical structures that model constrained systems, and constitute a vast generalization of Riemannian geometry. They arise in control theory, harmonic and complex analysis, subelliptic PDEs, geometric measure theory, calculus of variations, optimal transport, and potential analysis. In the last 10 years, a surge of interest in the study of geometric and functional inequalities on sub-Riemannian spaces revealed unexpected behaviours and intriguing phenomena that failed to fit into the classical schemes inspired by Riemannian geometry. In this project, I aim to develop a framework of geometric and functional interpolation inequalities adapted to sub-Riemannian manifolds, and to use this theory to tackle old and new problems concerning the geometric analysis of these structures. The project focuses on the following interconnected topics: (i) the development of a unifying theory of curvature bounds including sub-Riemannian structures, (ii) the study of measure contraction properties of Carnot groups, (iii) applications to isoperimetric-type problems, and (iv) applications to the regularity of the sub-Riemannian heat kernel at the cut locus. The project adopts a unique approach combining methods from geometric control theory, optimal transport and comparison geometry that I developed in recent years, and which already allowed me and my collaborators to obtain important results in the field. The project aims to achieve an ambitious unification program, solve long-standing problems, and explore new research directions in sub-Riemannian geometry, with an impact in several neighbouring areas, including geometric analysis on non-smooth spaces, analysis of hypoelliptic operators, geometric measure theory, spectral geometry. My long-term purpose is to build a leading research group in sub-Riemannian geometry, to significantly advance our understanding of Geometry under non-holonomic constraints.

Mots-clés : sub riemannian geometry,carnot carath odory spaces,ricci curvature bounds,isoperimetric problems,carnot groups,optimal transport
Projet ERC : DEEP-trigger (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Institut polytechnique de Grenoble — Pays : France — Rôle : Partner
Acronyme de l'entité : Grenoble INP
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 33185.0 €

Subduction zones host the world’s largest earthquakes and tsunamis. Understanding how such earthquakes initiate and interact is a first-order challenge in earth sciences. A puzzling and unexplained observation is that megathrust earthquakes seem to be clustering at the scale of the plate boundary. This suggests that the subducting slab plays an important role, albeit downplayed, in the triggering of megathrust earthquakes. I propose to study subduction zones recently affected by megathrust events or earthquakes sequences, with an enlarged perspective, considering the mechanisms of deformation in the larger subduction system, including the slab, and their potential role in pushing the megathrust to failure: - At the scale of the seismic asperity, how aseismic slip can trigger earthquakes and how this triggering depends on depth, duration, migration, periodicity, and amplitude will be examined, notably by integrating new observations of small, short or long-lived slow slip events. - At the scale of the subduction zone, how distant changes transfer into large-scale deformation, potentially initiate slow slip and eventual rupture on the plate interface will be analyzed. The role of metamorphic fluids in the softening of the mantle surrounding the slab, which may contribute to distant triggering of earthquakes, and recently observed deep-shallow interactions will be explored. Multi-scale observations of deformation and seismicity will be extracted from the great amount of geophysical data available in South America, Japan and Sumatra, complemented by in-situ GPS monitoring at a promising area in south Peru. Machine Learning will serve to systematize these complementary observables, to characterize how their empirical relationships evolve with time and space, and to isolate the key processes hidden in these large datasets. Physical mechanisms driving the plate interface destabilization will be explored through mechanical and fluid modeling, and tested against the data.

Mots-clés : subduction,earthquakes,slow slip events,geodesy,seismology,machine learning,fluids,plate tectonics
Projet ERC : SUBMODULAR (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Technion – Israel Institute of Technology — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1442690.0 €

Submodularity is a fundamental mathematical notion that captures the concept of diminishing returns, and is prevalent in many areas of science and technology. From pollution detection and gang violence reduction, to active influence spreading in social networks (as recent controversial events suggest), submodular maximization directly affects multiple aspects of our daily lives. With a constantly growing number of real-world applications, it is no surprise that combinatorial maximization problems with a submodular objective have been the focus of intense theoretical and practical research for more than a decade. Unfortunately, many of the fundamental submodular maximization problems remain unresolved and lack fast and simple to implement algorithms that can be used in practice. A main reason for this state is that the current algorithmic toolkit used to solve submodular maximization problems is insufficient. This research proposal addresses the theoretical foundations of submodular maximization. Our overarching goal is to enrich the algorithmic toolkit and devise new algorithmic approaches that can be broadly applied to fundamental problems in submodular maximization and combinatorial optimization. A successful outcome of this research project will be a new theoretical algorithmic foundation for submodular maximization, that will enable us to completely resolve basic optimization problems and facilitate the design of fast and simple to implement algorithms that can be used in practice. The combination of the latter with ongoing practical research on the numerous uses of submodularity, could lead to a significant revolution in our ability to identify and solve the many real-world applications of submodular maximization.

Mots-clés : submodular,approximation algorithms,combinatorial optimization,randomized algorithms