Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : GALOP (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1997959.0 €

A period is a complex number defined by the integral of an algebraic differential form over a region defined by polynomial inequalities. Examples include: algebraic numbers, elliptic integrals, and Feynman integrals in high-energy physics. Many problems in mathematics can be cast as a statement involving periods. A deep idea, based on Grothendieck's philosophy of motives, is that there should be a Galois theory of periods, generalising classical Galois theory for algebraic numbers. This reposes on inaccessible conjectures in transcendence theory, but these can be circumvented in many important cases using an elementary notion of motivic periods. This allows one to set up a working Galois theory of periods in many situations of arithmetic and physical interest. These ideas grew out of the PI's recent proof of the Deligne-Ihara conjecture, in which the Galois theory of multiple zeta values was worked out. Multiple zeta values are one of the most fundamental families of periods, and their Galois group plays an important role in mathematics: it is conjecturally equal to Drinfeld's Grothendieck-Teichmuller group, the stable derivation algebra on moduli spaces of curves, and the Galois group of mixed Tate motives over the integers. It occurs in deformation quantization, the homology of the graph complex, and the Kashiwara-Vergne problem, as well as having numerous connections to string theory, and quantum field theory. The goal of this proposal is to generalise this picture. Periods of moduli spaces of curves, multiple L-functions of modular forms, and Feynman amplitudes in quantum field and string theory should each have their own Galois theory which is yet to be worked out. This is completely uncharted territory, and will have numerous applications to number theory, algebraic geometry and physics.

Mots-clés : periods,zeta function,galois theory,fundamental groups,motives,moduli spaces,modular curves,feynman integral,scattering amplitudes,quantum field theory,string perturbation theory
Projet ERC : TreatBrainDamage (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and Immunotherapy
Entité participante : UK Research and Innovation — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : UKRI
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

A proposal to use local IL2 delivery via a viral vector to prevent and reverse the inflammatory damage that happens to the brain during neuroinflammatory diseases as diverse as traumatic brain injury, Multiple Sclerosis and Alzheimer's Disease. In "TissueTregs" we identified and characterised a brain-resident regulatory T cell population, with high expression of anti-inflammatory and reparative proteins. A synthetic circuit for deliver IL2 to the brain expanded this population, without any off-target increases in circulation or the blood. In order to translate these findings beyond the scope of the original "TissueTregs" grant, we have moved to a viral vector system, designed to give brain-specific expression of IL2. This systems demonstrates long-lasting brain-specific expansion of the brain-resident regulatory T cell population, after only a single intravenous injection. Preliminary results demonstrate a striking ability of this treatment to prevent neurological damage in mouse models of traumatic brain injury and Multiple Sclerosis. A provisional patent has been filed to cover this invention. In this PoC grant, we will run the necessary validation and target deconvolution experiments to pave the way for this treatment to move into the clinical sphere.

Mots-clés : regulatory t cells,traumatic brain injury,multiple sclerosis,neuroinflammation,interleukin 2,gene therapy,viral vectors,alzheimer s disease
Projet ERC : INSOMNIA AID (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : Royal Netherlands Academy of Arts and Sciences — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : KNAW
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

A recent surge in consumer products to record sleep and provide feedback on its duration show that a large market exists for sleep improvement. Ensuring high quality sleep is at least as important to promote health as exercise, restricting caloric intake and quitting smoking and alcohol. Consumers are keen to utilize tools that help them attain these aims. Commercially available devices recording movement and physiology provide consumers with an estimate of SLEEP DURATION. However, novel findings from our ERC-AdG project, point out that SLEEP QUALITY matters more for health and well-being than duration. About one-third of the world population report insomnia complaints. In 10%, symptom severity and chronicity justifies a diagnosis of insomnia disorder. Early detection and quantification of poor sleep quality is essential to detect a need for sleep behavioral changes and to evaluate personalized intervention effectiveness. However, NONE OF THE CURRENTLY AVAILABLE CONSUMER SLEEP SYSTEMS INCLUDES A VALIDATED METHOD TO ASSESS SLEEP QUALITY. Our ERC-AdG project revealed readily detectable sleep features with a tremendous impact on health and well-being. These features are highly promising sleep quality biomarkers with unprecedented commercial potential to disrupt and innovate the current market of consumer sleep devices and applications. Combined with sleep training programs, THIS WILL ENABLE END USERS TO IMPROVE BOTH SLEEP QUANTITY AND QUALITY. This PoC project provides automated versions of sleep quality assessment algorithms validated in the host ERC-AdG project. Feature extraction and algorithms are co-developed with companies active in sleep measurement, ensuring implementation of the end product in novel commercial systems. We envision this project to be a double-edged sword: a valuable addition to the toolbox of the clinician treating insomnia, and providing the general public with the ability to measure and improve sleep quality through consumer electronics.

Mots-clés : sleep,prevention,mental health
Projet ERC : EMPIRE (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Heidelberg University — Pays : Allemagne — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 248150.56 €

A thorough understanding of the processes regulating the conversion of gas into stars is key to understand structure formation in the universe and the evolution of galaxies through cosmic time. Despite significant progress over the past years, the properties of the actual dense, star forming gas across normal disk galaxies remain largely unknown. This will be changed with EMPIRE, a comprehensive 500hr large program led by the PI at the IRAM 30m mm-wave telescope. EMPIRE will provide for the first time extended maps of a suite of dense gas tracers (e.g., HCN, HCO+, HNC) for a sample of nearby, star-forming, disk galaxies. By means of detailed analysis, including radiative transfer and chemical modelling, we will constrain a variety of physical quantities (in particular gas densities). We will relate these directly to the local star formation efficiency and to a variety of other dynamical, stellar and local ISM properties from existing pan-chromatic mapping of these galaxies (HI, IR, CO, UV, optical) to answer the question: ``how is star formation regulated across galaxy disks?''. By determining true abundance variations, we will contribute key constraints to the nascent field of galaxy-scale astrochemistry. Detailed comparisons to data for star forming regions in the Milky Way will link core, cloud and galactic scales towards a coherent view of dense gas and star formation. These results will provide an essential anchor point to Milky Way and high redshift observations alike. Analysis, interpretation and modelling of this complex data set requires a team of two postdocs and two PhD students. The PI has demonstrated his ability to successfully lead a research group through his current position as a DFG funded Emmy-Noether group leader. In combination with his widely recognized previous work and his expertise in mm-wave astronomy and ISM/star formation studies, the PI and the proposed group are uniquely positioned to make significant impact during this ERC grant.
Projet ERC : XAI (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Scuola Normale Superiore — Pays : Italie — Rôle : PI
Acronyme de l'entité : SNS
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 915500.0 €

A wealthy friend of mine asks for a vacation credit card to his bank, to discover that the credit he is offered is very low. The bank teller cannot explain why. My stubborn friend continues his quest for explanation up to the bank executives, to discover that an algorithm lowered his credit score. Why? After a long investigation, it turns out that the reason is: bad credit by the former owner of my friend’s house. Black box AI systems for automated decision making, often based on ML over (big) data, map a user’s features into a class or a score without explaining why. This is problematic for lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artefacts hidden in the training data, which may lead to unfair or wrong decisions. I strive for solutions of the urgent challenge of how to construct meaningful explanations of opaque AI/ML systems, introducing the local-to-global framework for black box explanation, articulated along 3 lines: a) the language for explanations in terms of expressive logic rules, with statistical and causal interpretation; b) the inference of local explanations for revealing the decision rationale for a specific case; c), the bottom-up generalization of many local explanations into simple global ones. An intertwined line of research will investigate both causal explanations, i.e., models that capture the causal relationships among the features and the decision, and mechanistic/physical models of complex system physics, that capture the data generation mechanism behind specific deep learning models. I will also develop: an infrastructure for benchmarking, for the users' assessment of the explanations and the crowdsensing of observational decision data; an ethical-legal framework, for compliance and impact of our results on legal standards and on the “right of explanation” provisions of the GDPR; case studies in explanation-by-design, with a priority in health and fraud detection.

Mots-clés : automated decision making,black box models,machine learning,data mining,statistical,logic,rules,rule learning,transparency,right to explanation,participatory platform
Projet ERC : Vis-a-Vis (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : University of Valencia — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UV
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1969821.0 €

A widely accepted view in virology is that virions function as independent infectious units. However, recent work by us and others indicates that viruses are often transmitted as more complex structures, such as virion aggregates, lipid vesicles or protein matrices harbouring multiple infectious particles. This demonstrates that viruses can be transmitted as “collective infectious units”, in sharp contrast with the current paradigm. Critically, these recent discoveries now set the stage for the evolution of social interactions, a previously unappreciated facet of viruses. I propose to investigate how collective infectious units drive virus social evolution using state-of-the-art tools from the fields of virology, genetics, structural biology, and nanotechnology. The effects of collective infectivity on viral fitness will be tested directly using experimental evolution and genetic engineering, and confirmed in vivo. Three widely different viruses will be used to achieve generality: human enteroviruses, a vector-borne rhabdovirus, and a baculovirus. Furthermore, the implications of virus social interactions for the maintenance of genetic diversity, evolvability, virulence evolution, and the emergence of drug resistance will be investigated. Radically new processes such as the putative extracellular fusion of viral particles will be also explored. I expect that infectious units constituted by viruses from different species will be uncovered as well, with far-reaching implications for epidemiology. It is becoming increasingly recognized that parasite sociality is a disease determinant, and our results may therefore inspire new antiviral strategies. In sum, this project aims at laying the foundations of virus sociality from a mechanistically-informed, bottom-up approach. Importantly, beyond their practical importance viruses will also provide a simple and tractable system that will help us to establish more general principles of social evolution.

Mots-clés : social evolution,virus host interactions,systems biology,experimental evolution,molecular genetics
Projet ERC : AGEnTh (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Scuola Internazionale Superiore di Studi Avanzati — Pays : Italie — Rôle : PI
Acronyme de l'entité : SISSA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1055317.0 €

AGEnTh is an interdisciplinary proposal which aims at theoretically investigating atomic many-body systems (cold atoms and trapped ions) in close connection to concepts from quantum information, condensed matter, and high energy physics. The main goals of this programme are to: I) find to scalable schemes for the measurements of entanglement properties, and in particular entanglement spectra, by proposing a shifting paradigm to access entanglement focused on entanglement Hamiltonians and field theories instead of probing density matrices; II) show how atomic gauge theories (including dynamical gauge fields) are ideal candidates for the realization of long-sought, highly-entangled states of matter, in particular topological superconductors supporting parafermion edge modes, and novel classes of quantum spin liquids emerging from clustering; III) develop new implementation strategies for the realization of gauge symmetries of paramount importance, such as discrete and SU(N)xSU(2)xU(1) groups, and establish a theoretical framework for the understanding of atomic physics experiments within the light-from-chaos scenario pioneered in particle physics. These objectives are at the cutting-edge of fundamental science, and represent a coherent effort aimed at underpinning unprecedented regimes of strongly interacting quantum matter by addressing the basic aspects of probing, many-body physics, and implementations. The results are expected to (i) build up and establish qualitatively new synergies between the aforementioned communities, and (ii) stimulate an intense theoretical and experimental activity focused on both entanglement and atomic gauge theories. In order to achieve those, AGEnTh builds: (1) on my background working at the interface between atomic physics and quantum optics from one side, and many-body theory on the other, and (2) on exploratory studies which I carried out to mitigate the conceptual risks associated with its high-risk/high-gain goals.

Mots-clés : topological matter,low dimensional systems,gauge theories
Projet ERC : ALGOSOC (Horizon 2020, appel 2019, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Durham University — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2150686.0 €

ALGOSOC develops a new approach to understanding and responding to the consequences of machine learning algorithms for contemporary societies. Rapid advancements in machine learning technologies are transforming social and political life in ways that uniquely challenge how we live in relation to others. The life chances of a person are now intimately connected to the attributes that an algorithm has learned from the data patterns of unknown others. From judgements in the criminal justice system to decisions on treatment pathways in health, the outputs of algorithms have become pivotal to the decisions and adjudications on the probable futures of individuals. While there is substantial academic and public emphasis on defining ethical codes of conduct for algorithmic decisions, there is a lack of attention to how machine learning algorithms remake the ethical relations that define a society. In short, existing research is focused on limiting the harms of the actions of algorithms, whereas ALGOSOC focuses on how algorithms are redefining the thresholds of what harmful, good, bad, or risky behaviour means in a society. The ALGOSOC project will examine how 21st century machine learning algorithms are learning to recognize, to attribute, and to infer the characteristics of entities (people, groups, and objects). In order to do this, the project will conduct a series of path-defining studies of societal domains of machine learning that, though they share algorithms in common, have not previously been researched in combination: behavioural biometrics and biomedical object recognition; consumer recommendation and criminal justice scoring; oncology treatment pathways and anomaly detection for security. The ALGOSOC project will provide new social science knowledge of what is taking place as machine learning algorithms travel across different domains and sites, and how precisely they learn by their exposure to different worlds of data.

Mots-clés : algorithms,technology,data,politics,society,ethics,governance
Projet ERC : ALPI (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Fondazione hub innovazione trentino — Pays : Italie — Rôle : Partner
Acronyme de l'entité : Hit
Type d'entité : Recherche
Financement ERC de l'entité : 25000.0 €

ALPI aims at the integration of a photonic neural network within an optical transceiver to increase the transmission capacity of the optical link. Based on a deep learning approach, the new compact device provides real time compensation of fiber nonlinearities which degrade optical signals. In fact, the tremendous growth of transmission bandwidth both in optical networks as well as in data centers is baffled by the optical fiber nonlinear Shannon capacity limit. Nowadays, computational intensive approaches based on power hungry software are commonly used to mitigate fiber nonlinearities. Here, we propose to integrate in the optical link the neuromorphic photonic circuits which we are currently developing in the ERC-AdG BACKUP project. Specifically, the proposed error-correction circuit implements a small all-optical complex-valued neural network which is able to recover distortion on the optical transmitted data caused by the Kerr nonlinearities in multiwavelength optical fibers. Network training is realized by means of efficient gradient-free methods using a properly designed data-preamble. A new neuromorphic transceiver demonstrator realized in active hybrid Si/InP technology will be designed, developed and tested on a 100 Gbps 80 km long optical link with multiple-levels symbols. The integrated neural network will mitigate the nonlinearities either by precompensation/autoencoding at the transmitter TX side or by data correction at the receiver RX side or by concurrently acting on both the TX and RX sides. This achievement will bear to the second ALPI’s goal: moving from the demonstrator to the industrialization of the improved transceiver. For this purposes, patents will be filed and a business plan will be developed in partnership with semiconductor, telecom and IT companies where a path to the commercialization will be individuated. The foreseen market is the big volume market of optical interconnection in large data centers or metro networks.

Mots-clés : neural network,deep learning,optical communication,optical transceiver,integrated optics,photonics
Projet ERC : ARO-MAT (Horizon 2020, appel 2019, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2491625.0 €

ARO-MAT will target emergent cooperative electronic and magnetic phenomena in molecules with dimensions of 5–25 nm (i.e. as big as many proteins). The project will develop supramolecular architectures with large pi-systems and well-defined geometries, in which the frontier orbitals coherently delocalize charge over the whole nanostructure. Aromaticity is a key emergent phenomenon; it can be defined as the ability of a cyclic molecule to sustain a ring current when placed in a magnetic field. Until recently, it was thought that aromaticity is restricted to small molecules, with circuits of less than about 22 pi-electrons. Anderson has shown that circuits of more than 160 pi-electrons (circumference > 15 nm) can exhibit strong aromatic ring currents. Testing even larger rings will elucidate the link between aromaticity and the persistent currents found in non-molecular mesoscopic rings (diameter 50–500 nm). ARO-MAT will explore the effects of molecular size and topology on nanoscale aromaticity. Other emergent phenomena to be addressed include the formation of open-shell singlet polyradical ground states, magnetic bistability in systems with many paramagnetic metal centers, and the control of charge transport through single-molecule devices by quantum interference. This multidisciplinary project combines organic synthesis, supramolecular chemistry, theory, electronic structure calculations, NMR and EPR spectroscopy, magnetochemistry, molecular electronics and low-temperature charge transport experiments. The core objective is to create low band gap materials with unprecedented electronic and magnetic properties, and to understand the structure-property relationships governing the behavior of these new materials. Most of the target structures are based on metalloporphyrins because of their redox activity, stability, structural versatility, suitability for template-directed synthesis and ability to position multiple strongly coupled paramagnetic metal centers.

Mots-clés : supramolecular,templates,cooperativity,self assembly,macrocycles,porphyrins,molecular materials,pi systems