Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : DynAI (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Nvidia (United States) — Pays : Italie — Rôle : PI
Type d'entité : Organismes privés (hors enseignement)
Financement ERC de l'entité : 1500000.0 €

Computer vision has become a powerful technology, able to bring applications such as autonomous vehicles and social robots closer to reality. In order for autonomous vehicles to safely navigate a scene, they need to understand the dynamic objects around it. In other words, we need computer vision algorithms to perform dynamic scene understanding (DSU), i.e., detection, segmentation, and tracking of multiple moving objects in a scene. This is an essential feature for higher-level tasks such as action recognition or decision making for autonomous vehicles. Much of the success of computer vision models for DSU has been driven by the rise of deep learning, in particular, convolutional neural networks trained on large-scale datasets in a supervised way. But the closed-world created by our datasets is not an accurate representation of the real world. If our methods only work on annotated object classes, what happens if a new object appears in front of an autonomous vehicle? We propose to rethink the deep learning models we use, the way we obtain data annotations, as well as the generalization of our models to previously unseen object classes. To bring all the power of computer vision algorithms for DSU to the open-world, we will focus on three lines of research: 1-Models. We will design novel machine learning models to address the shortcomings of convolutional neural networks. A hierarchical (from pixels to objects) image-dependent representation will allow us to capture spatio-temporal dependencies at all levels of the hierarchy. 2-Data. To train our models, we will create a new large-scale DSU synthetic dataset, and propose novel methods to mitigate the annotation costs for video data. 3-Open-World. To bring DSU to the open-world, we will design methods that learn directly from unlabeled video streams. Our models will be able to detect, segment, retrieve, and track dynamic objects coming from classes never previously observed during the training of our models.

Mots-clés : dynamic scene understanding,multiple object tracking,video object segmentation,deep learning
Projet ERC : AI4SPIN (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Catalan Institute of Nanoscience and Nanotechnology — Pays : Espagne — Rôle : PI
Acronyme de l'entité : ICN2
Type d'entité : Recherche
Financement ERC de l'entité : 1078750.0 €

The steady increase in energy consumption per capita and the slow transition toward renewable energy sources is becoming a serious global problem, making energy efficiency paramount for new technologies. Two-dimensional materials offer an encouraging path toward ultra-low-power electronics due to our capability to combine them into Van der Waal heterostructures with tailored quantum properties based on their constituents. The spin-orbit torque (SOT) memories are technological prospects that consume a fraction of conventional memories' power. Still, they offer superior speed and storage capacity and were further improved when using 2D materials as building blocks instead of 3D metallic systems. Recently theoretical efforts demonstrated the existence of thousands of potentially synthesizable 2D materials, opening an exponentially larger pool to mine for optimized heterostructures which brute-force approaches cannot tackle. This project aims at developing artificial intelligence that will propose optimized Van der Waal heterostructures for spin-orbit torques. To this end, we will first construct an automatic material assessment (AUTOMATA) tool based on deep neural networks that will perform numerical modeling and quantum transport simulations autonomously to compute the spin-orbit torque efficiencies. In parallel, we will develop a computer-assisted structure (COMPASS) optimizer that will propose new systems for spin-orbit torques by using an evolutionary strategy. The AUTOMATA tool will rank the candidates generated by the COMPASS optimizer, and we will use those with superior performance to improve the COMPASS optimizer prediction. The successful combination of these tools will accelerate the development of technologies by automatizing the material selection phase through this quantum mechanical optimization process. Although we apply it to spin-orbit torques, it is, with little effort, generalizable to any electrical response functions.

Mots-clés : high throughput,neural networks,2d materials,spin orbit torques,quantum transport,emergent computing
Projet ERC : BRACE (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : University Medical Center Utrecht — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UMC
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499736.0 €

MRI provides the unique opportunity to investigate the relation between brain structure and function non-invasively, which is key to understand the functioning of the brain and unravel disease processes. An important technique is diffusion MRI (dMRI), which allows to reconstruct the structural connections through which brain structures communicate. Conversely, no technique can reconstruct the internal wiring of brain structures such as the cortex in living humans, where cognitive functions are processed, hindering scientific and societal progress. To achieve this, three challenges must be overcome: a spatial gap of an order of magnitude between (d)MRI and the cortical circuitry; insufficient contrast provided by (d)MRI in the cortex; and a lack of appropriate models to characterize cortical circuits.  I aim to bridge the gap between structure and function by mapping the fibers of the brain cortex non-invasively for the first time, which are expected to be affected by neurodegenerative processes, and to be closely related to cognitive decline. To achieve this goal, I will pioneer a synergistic approach combining my expertise (dMRI acquisition and modelling), with unique acquisition hardware designed at our institution. First, I will establish a novel multidimensional acquisition combining dMRI with MR relaxometry, together with a signal model tailored to characterize axonal fibers in the cortex. Next, I will leverage state-of-the-art hardware and super-resolution approaches to push the boundaries of in-vivo MRI by an order of magnitude (to the mesoscale). This will unveil cortical fibers in living humans for the first time, and allow us to explore their properties in patients with Alzheimer’s disease, their changes during ageing, and their relationship with cognitive function. Altogether, we will provide a new asset to understand cognitive dysfunction in neurodegeneration, and generate novel fundamental insights for systems neuroscience.

Mots-clés : brain structure and function,connectivity,fiber tractography,diffusion mri,brain cortex
Projet ERC : DELV (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and I mmunotherapy
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 : 1500000.0 €

The emergence and rapid transmission of viruses pose increasing risks and challenges to modern societies, threatening public health and economic stability. A thorough understanding of basic virology is therefore critical for an informed development of preventive and control strategies. Although for more than 40 years the only known member of deltaviruses was the human Hepatitis Delta virus (HDV), it was recently discovered that HDV-like agents are present in a variety of animal vectors and reservoirs including bats, rats, snakes, birds and insects. Metagenomic data indicate that these satellite viruses possess an unrecognized host shifting capacity enabling them to cross the species barrier. As no efficient antiviral treatment is available against HDV, the emergence of novel deltaviruses poses a significant potential threat to human health. To date, animal deltaviruses have not been functionally characterized and little is known about their basic biology. The proposed project (DELV) aims to generate essential knowledge about the biology of deltaviruses, their interactions with host cells, their zoonotic potential and evolutionary fitness. Using newly generated deltavirus molecular clones coupled to unbiased proteomic and genetic approaches, DELV will identify host factors interacting with deltaviruses in their natural animal and human hosts (Aim 1). Further, it will determine and characterize host factors that are essential for deltavirus replication in the human host when they cross the species barrier (Aim 2). Finally, DELV will discover viral elements favoring deltavirus host shifting and adaptation capacities (Aim 3). DELV aims to shape novel paradigms in virology, RNA biology and host-pathogen interactions. Knowledge generated through DELV will guide the development of novel antiviral strategies and will have profound implications for understanding the ecology and evolution of these newly discovered, yet mysterious, viral elements.

Mots-clés : rna virus,hepatitis d virus,animal deltaviruses,zoonosis,virus adaptation,host factors,restriction factors,proteomics,genetics
Projet ERC : SkiM2Lab (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Groupe ENSAE-ENSAI — Pays : France — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1313131.0 €

The distribution of workers across jobs and across different geographic areas has major implications for growth, social welfare and inequality. It is complex but essential to understand the mechanisms behind the allocation of talents in the economy, that is, how skilled individuals choose their jobs and where they live. However, the existing literature on the distribution of skills faces two major challenges. First, it has largely neglected the role of the marriage market and family constraints, although family formation and partner choice are intimately linked to career and location choices and these choices influence each other. Second, it must go beyond the standard one-dimensional classification of skills based on educational attainment alone. This hierarchy underestimates the inequalities that exist between multidimensional and non-hierarchical skill sets. SkiM2Lab will address this challenge in developing state-of-the-art multidimensional matching models with two specific objectives. In the first objective, I will analyze the interactions between the labor market and the marriage market using equilibrium models of matching where individuals and jobs are associated with multidimensional skill sets and are located in different places. Estimating these structural models on household data will reveal how family and labor markets affect wage disparities and occupational segregation by gender and region. In the second objective, I will leverage big data such as online resumes and online job postings, as well as machine learning and natural language processing technologies, to extract skills at the most granular level, build new relevant combinations of skills and include them in a competitive matching model. This will make it possible to propose a new method to identify rapidly relevant emerging skills and their impact on wages and production.

Mots-clés : matching models,marriage market,labor market,skills,gender gaps,machine learning tools
Projet ERC : OptInfinite (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Groupe ENSAE-ENSAI — Pays : France — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499632.0 €

Optimization over probability measures has become a powerful approach for solving complex problems that involve probabilistic modeling. Advanced by the PI and collaborators, it extends finite-dimensional optimization to the infinite-dimensional space of probability measures. This framework provides a princi- pled way to address in particular the task of sampling, that refers to the process of drawing samples from a complex probability distribution, either to approximate it or generate new data. In Bayesian machine learning for instance, we can model uncertainty of the predictions by sampling a model’s parameters. Similarly, in generative modeling, sampling is crucial for producing new data such as images or text. Existing methods struggle with complex measures, are difficult to evaluate, and are limited to Euclidean spaces, making them unsuitable for infinite-dimensional ones like functions or operators. Also, their lack of computational efficiency limits their use in sequential sampling tasks. My goal is to create a unified framework to design and evaluate efficient methods for sampling measures over general spaces. Central to my approach is the use of tools from optimal transport and information geometry, which will help compare measures and design optimization dynamics. OptInfinite will advance optimization over measures by addressing two key challenges: 1) developing optimization objectives and geometries suited to the space of measures over general (possibly-infinite-dimensional) spaces to design tractable schemes and metrics, and 2) learning to solve advanced optimization problems, such as sequences of optimization tasks. This framework will yield novel sampling methods, whose efficiency can be evaluated using optimization tools. It will also enable to compare measures and reveal where and how they are different. Ultimately, our work will provide a clear methodology and toolset applicable across multiple domains.

Mots-clés : bayesian inference methods,generative modeling,optimal transport,particle systems
Projet ERC : MoveMeRU (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Centre for East European and International Studies — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : ZOiS
Type d'entité : Recherche
Financement ERC de l'entité : 1500000.0 €

Children of migrants are exposed to two national histories: those told in their country of residence and those relating to their family?s homeland. However, it remains unclear how the intergenerational transmission of historical views shapes the relationship migrants cultivate with ?here? and ?there?. Applying theories of intergenerational transmission and second-generation transnationalism, MoveMeRU addresses this urgent gap and compares the historical memories of migrants and non-migrants across two generations. It studies the Russian migrant population in a favourable, hostile and neutral reception context, looking at Germany, Estonia and Canada. Like many other autocracies, Russia uses historical memories to appeal to the emotions of citizens at home and abroad and strengthen their sense of belonging to Russia. The project will make major academic contributions in three areas: 1) understanding the historical identification of young adults in migrant and non-migrant families with their parents? country of origin; 2) identifying when the memories of migrant and non-migrant families converge or diverge across generations; 3) determining what historical memories contribute to solidarity and pluralistic political attitudes. MoveMeRU entails a groundbreaking triangulation of methods: 1) parent-child opinion surveys on views on history among migrant communities and nationals in the three countries of destination and Russia; 2) cross-generational focus groups in the same countries; 3) analysis of historical narratives in media targeting Russian speakers abroad. The project will refine our understanding of differences and similarities in the intergenerational transmission of memories in migrant and non-migrant families, offering important insights into the drivers of and obstacles to integration. The results have major implications for political decision-making in countries of destination and for public awareness about intergenerational shifts within migrant communities.
Projet ERC : HEPIQ (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : CY Cergy Paris Université — Pays : France — Rôle : PI
Acronyme de l'entité : CYU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1497850.0 €

In the past years, quantum non-equilibrium emerged as a new principal research arena, promising to assist the current development of new quantum technologies and to shine a new light on disparate fields of theoretical and experimental physics, from black holes to condensed matter and statistical physics. As quantum dynamics represents a major challenge for modern computational methods, relevant developments have come from devising new generalised and extended forms of classical hydrodynamic theory to effectively describe its macroscopic features. This effort is nowadays constituting an essential part of contemporary theoretical physics, contributing to a deeper understanding of dynamical phenomena and providing new directions in different experimental areas. This proposal focuses on the interplay between classical non-linear dynamics and quantum evolution, promising to a) unveil a new deep comprehension of how the non-equilibrium dynamics in many-body quantum systems can dissipate or either recover quantum information and how effective non-linear classical behaviour emerges, b) release new efficient theoretical tools to access quantum many-body quantum systems which are strongly interacting and in non-equilibrium settings beyond linear response, far away from known regimes of low-energy, low-temperatures or weak interactions. The outcomes will provide new reliable and much needed theoretical methods as well as a new dictionary to catalogue and relate different non-equilibrium phenomena in quantum and classical physics and deepen our understanding of out-of-equilibrium matter.

Mots-clés : non equilibrium quantum many body,integrable models,tensor networks,non equilibrium statistical mechanics,luttinger liquids,one dimensional systems
Projet ERC : nQUICHE (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
Entité participante : Commissariat à l’énergie atomique et aux énergies alternatives — Pays : France — Rôle : PI
Acronyme de l'entité : CEA
Type d'entité : Recherche
Financement ERC de l'entité : 2485658.0 €

Quantum RAM (QRAM) is a key quantum computing resource featuring in many theoretical proposals that has never been demonstrated in experiment. A QRAM is a quantum memory in which one can read or write quantum information to a superposition of memory cells. Such a device is powerful as a tool for implementing many quantum algorithms, including Grover’s search algorithm, quantum chemistry, quantum cryptography and quantum machine learning, and is regarded by many as essential for a future quantum computer.  Nuclear Quantum Information processing Controlled via Hyperfine interaction with Electrons (nQUICHE) aims to use recently developed techniques in single spin detection, spectroscopy and coherent control to demonstrate a quantum router, the unit cell of a QRAM, using electron and nuclear spins in the solid state coupled to superconducting circuits. The hybrid spin architecture proposed in nQUICHE has a baked-in double controlled-SWAP interaction that efficiently routes quantum information into memory qubits with seconds-long coherence times, making the system particularly well suited to realising a QRAM. Beyond QRAM, nQUICHE will expand the toolbox of techniques for solid state spin quantum information processing and will investigate new crystal hosts and an alterative spin system with a large nuclear spin of 7/2, exploring its 16-dimensional Hilbert space. Architectures for controlling large arrays of spins and interfacing with superconducting circuits in a scalable, modular architecture will be developed. These experiments are relevant not only for constructing a QRAM, but also for quantum computation and networking with this platform. The techniques developed will push the frontiers of electron paramagnetic resonance. In addition to having technological applications, experiments performed during nQUICHE will explore novel effects with single spins that are of interest from a purely fundamental physics perspective.

Mots-clés : spin,hybrid,electron paramagnetic resonance,superconducting devices,circuit qed,circuits,nuclear spin,electron spin,qubit,quantum devices,quantum sensing,quantum ram,qram,quantum memory
Projet ERC : OSCI-PRED (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Compl exity
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 : 1427563.0 €

One of the most exciting yet puzzling questions in Neuroscience is how the brain coordinates the activity between different areas, integrating distinct representations into conscious percepts and thoughts. For decades neuroscientists have investigated how the brain orchestrates diverse regions’ activity, pointing at oscillations as one of the key mechanisms involved in such a process. However, previous research has mainly focused on the temporal aspect of oscillatory dynamics, largely overlooking how oscillations propagate through the brain. Although rhythmic traveling waves have recently gained renewed interest, their functional role and relation to cognitive functions remain largely unknown. In this project, I will address this fundamental question: what is the role of oscillatory traveling waves in brain dynamics? I plan to take on this challenge using a multi-scale computational approach, modeling neural dynamics within and between cortical regions, as well as cortical-thalamic interactions. Importantly, the novelty of this approach consists in framing the model in the light of Predictive Coding principles, to test the compelling yet striking hypothesis that traveling waves encode Predictions and Prediction-Errors. The results of the simulations will be compared against experimental recordings in human participants to validate and assess the model’s predictions. Lastly, some implementations will turn into deep learning architectures, to test their dynamics in visual tasks while improving current models of artificial vision. All in all, this proposal can significantly advance our understanding of the neurophysiological mechanisms involved in sensory and cognitive functions, testing whether and how oscillatory traveling waves are a critical mechanism in neural dynamics, and producing fundamental results in the scientific field and future technological applications.

Mots-clés : predictive coding travelling waves brain oscillations deep learning