Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : ULTIMATE (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : University of Freiburg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1982938.0 €

Dark matter is a major component of the Universe, outnumbering ordinary baryonic matter by a factor 5. As it has not yet been observed, its detection and subsequent characterization is one of the most important goals in particle physics. ULTIMATE will be the first project world-wide that focuses entirely on cutting-edge research towards the ultimate detector. Using a low-background time projection chamber (TPC) filled with ~40t of liquid xenon (LXe), this instrument will search for Galactic dark matter in the form of Weakly Interacting Massive Particles (WIMPs). It will probe the entire experimentally accessible parameter space above masses of a few GeV/c², which is eventually limited by irreducible neutrino backgrounds. If dark matter will be detected by the next-generation experiments, the ultimate detector will deliver a high statistics WIMP sample to study its properties. To eventually propose and build this detector, which will be also sensitive to many non-WIMP science channels, various fundamental experimental challenges need to be solved now. These include a significant reduction of radioactive backgrounds, which would seriously limit the instrument's sensitivity, and structural aspects related to the TPC size of ~2.5m. ULTIMATE will tackle both, following several orthogonal strategies: Two novel TPC concepts will be developed and operated in LXe for the first time, to reduce background from 222Rn (hermetic TPC) and to optimize background rejection (single-phase TPC). Background neutrons and 222Rn emanation from the important material PTFE will be minimized by the identification of radio-pure PTFE, a systematic study of surface treatments, and by building a full-scale TPC mockup. Such prototype has not been constructed before and will enable detailed design, construction and assembly studies of a TPC with minimal material budget. The combination of all strategies explored in ULTIMATE will represent an optimal concept for the ultimate WIMP detector's TPC.

Mots-clés : astroparticle physics,dark matter,rare event search,liquid noble gas detector
Projet ERC : MapModern (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : Open University of Catalonia — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UOC
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Data mining and big data approaches are changing the ways in which we create knowledge, access information and preserve our cultural heritage. This research applies cutting-edge technology to analyse a neglected aspect of European and non-European social and cultural life of the 20th century: the impact of Hispanic and Lusophone literary networks and cultural mediators in international modernity between 1898 and 1959. The project pursues three central goals: 1) to retrieve the lost history of Iberoamerican mediators in modernist intercultural and multilingual networks and reappraise their role; 2) to narrow the knowledge divide in terms of access and production in the Iberoamerican field by generating and making freely available new and reliable data that addresses the lack of documented cultural heritage, and 3) to offer an innovative and reproducible model that can be applied across periods, languages, and disciplines to analyse cross-border phenomena, under-examined mediators and networks and overshadowed geographical scales in their relations to the wider world. These goals will be achieved by a twofold methodology: i) an open and collaborative research tool providing a data source for quantitative and qualitative analysis on Iberoamerican mediators, and ii) four subprojects on key cultural transformation processes distinctive of modern societies (the institutionalization of Iberoamerican cultures, the rise of translated literature in key Iberoamerican modernist journals, the position of Iberoamerican women in the cultural field, and the role of Iberoamerican mediators in new forms of mass media). By combining computational methods, cultural, literary history, translation, sociology, gender and media studies, I will lead an interdisciplinary team of 6 researchers that will fill the gap in modernist studies and will offer an original, reproducible and empirically tested method for studying social human interaction within a global, cultural and decentred approach

Mots-clés : global literary studies,social networks,cultural mediation,iberoamerican modernisms,data mining,big data approaches applied to literary studies
Projet ERC : SOCCERMATICS (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : Uppsala University — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 125318.75 €

Data plays an increasingly important role in sport, from monitoring of fitness of athletes to success statistics in baseball. In football (soccer) the team performance is more important than the performance of any one individual. While every team has its star players, it is the ability of the team to function as a unit that is the key to its success. For the data analyst, this presents a challenge: how to measure, understand and improve team performance. My research during an ERC funded action (IDCAB) has revolved around devising exactly this type of analysis methods. Starting with my work on measuring and understanding animal groups in the ERC action, I have gone on to develop a set of tools for looking at the collective action on the football pitch. In May 2016 my popular science book, Soccermatics, will be published by Bloomsbury press. This book details some of the work on football analysis I have done up until now, and will give me, and Soccermatics, significant name recognition. But it is jus a starting point: the idea of this PoC proposal is to develop a set of commercial tools for both clubs and fans to better understand football. I will use data provided by sports performance company Opta to provide consultancy to football clubs, online football analysis for fans and copyright protected applications for visualising football. I will build the business through three initial projects: (1) visualisation for online content; (2) a ‘manage your own team’ app; (3) football club analysis tools. The business will be focussed first on the UK market and scale up to a European and World market. The main innovation highlights of the action are: 1, Methodology for data analysis established during ERC starting-grant action. 2, Visually striking and intuitive approach to football data. Giving fans and clubs what they want. 3, Multiple avenues for commercialisation. 4, Scalability through delivery of copyrighted software.

Mots-clés : football,collective motion,visualisation,online content
Projet ERC : HUNTING (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : University of Amsterdam — Pays : Pays-Bas — Rôle : Partner
Acronyme de l'entité : UvA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 123037.94 €

Data-driven political campaigns are on the rise. Concerns have been voiced that practices like online political microtargeting techniques are harmful for democracy. These concerns grew after the unexpected outcome of the US presidential elections in 2016, the Brexit vote in the UK, and several recent elections in Europe. However, it is unclear if data-driven campaigns using online microtargeting techniques are an actual threat to democracy. The project will focus on the consequences of data-driven targeting and digital persuasion. In light of ongoing political and societal turmoil, investigating how citizens may be persuaded in a changing media landscape has never been of more importance. The overarching objective of this project is to identify the conditions and the extent to which data-driven online political microtargeting affect citizens’ attitudes and opinions, and eventually voting behavior. The project addresses three research questions: To what extent and under which conditions does data-driven political targeting have a beneficial impact, and under which conditions a harmful impact on democracy? And how can the beneficial effects trump the harmful effects? The project is novel as it systematically analyses the impact of data-driving campaigning, providing a theoretical dual-processing model, while using a mixture of research methods and a comparative perspective. As a whole, the project will offer a deeper understanding of the global impact of online data-driven targeting techniques during elections in several countries.

Mots-clés : political communication,political science,microtargeting,data driven campaigns,elections,effects,persuasion,social media
Projet ERC : INFLUENCE (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Delft University of Technology — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : TU Delft
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1475560.0 €

Decision-theoretic sequential decision making (SDM) is concerned with endowing an intelligent agent with the capability to choose actions that optimize task performance. SDM techniques have the potential to revolutionize many aspects of society and recent successes, e.g., agents that play Atari games and beat a world champion in the game of Go, have sparked renewed interest in this field. However, despite these successes, fundamental problems of scalability prevents these methods from addressing other problems with hundreds or thousands of state variables. For instance, there is no principled way of computing an optimal or near-optimal traffic light control plan for an intersection that takes into account the current state of traffic in an entire city. I will develop one in this project. To achieve this, I will develop a new class of influence-based SDM methods that overcome scalability issues for such problems by using novel ways of abstraction. Considered from a decentralized system perspective, the intersection’s local problem is manageable, but the influence that the rest of the network exerts on it is complex. The key idea is that by using (deep) machine learning methods, we can learn sufficiently accurate representations of such influence to facilitate near-optimal decisions. This project will construct a theoretical framework for such approximate influence representations and SDM methods that use them. Scalability of these methods will be demonstrated by rigorous empirical evaluation on two simulated challenge domains: traffic lights control in an entire city, and robotic order picking in a large-scale autonomous warehouse. If successful, INFLUENCE will produce a range of influence-based SDM algorithms that can, in a principled manner, deal with a broad range of very large complex problems consisting of hundreds or thousands of variables, thus making an important step towards realizing the promise of autonomous agent technology.
Projet ERC : DeepSPIN (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Instituto de Telecomunicações — Pays : Portugal — Rôle : PI
Acronyme de l'entité : IT
Type d'entité : Recherche
Financement ERC de l'entité : 1336000.0 €

Deep learning is revolutionizing the field of Natural Language Processing (NLP), with breakthroughs in machine translation, speech recognition, and question answering. New language interfaces (digital assistants, messenger apps, customer service bots) are emerging as the next technologies for seamless, multilingual communication among humans and machines. From a machine learning perspective, many problems in NLP can be characterized as structured prediction: they involve predicting structurally rich and interdependent output variables. In spite of this, current neural NLP systems ignore the structural complexity of human language, relying on simplistic and error-prone greedy search procedures. This leads to serious mistakes in machine translation, such as words being dropped or named entities mistranslated. More broadly, neural networks are missing the key structural mechanisms for solving complex real-world tasks requiring deep reasoning. This project attacks these fundamental problems by bringing together deep learning and structured prediction, with a highly disruptive and cross-disciplinary approach. First, I will endow neural networks with a "planning mechanism" to guide structural search, letting decoders learn the optimal order by which they should operate. This makes a bridge with reinforcement learning and combinatorial optimization. Second, I will develop new ways of automatically inducing latent structure inside the network, making it more expressive, scalable and interpretable. Synergies with probabilistic inference and sparse modeling techniques will be exploited. To complement these two innovations, I will investigate new ways of incorporating weak supervision to reduce the need for labeled data. Three highly challenging applications will serve as testbeds: machine translation, quality estimation, and dependency parsing. To maximize technological impact, a collaboration is planned with a start-up company in the crowd-sourcing translation industry.

Mots-clés : statistical machine translation,neural networks
Projet ERC : BREAK (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : University of Oslo — Pays : Norvège — Rôle : PI
Acronyme de l'entité : UiO
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2575970.0 €

Deformation in Earth’s crust localizes onto faults that may rupture rapidly producing earthquakes or undergo slow aseismic slip. The detailed mechanisms that control the transition between the seismic and aseismic regimes and the onset of earthquakes remain unknown. These mechanisms control the geophysical processes preceding catastrophic failure, such as fracture development and strain localization on faults and in the rock volumes surrounding them. Our goal is to provide the first quantitative laboratory observations of the full displacement field in rocks before and during fault slip, and separate the aseismic and seismic components of it. We will develop novel experimental techniques based primarily on simultaneous dynamic synchrotron X-ray microtomography imaging and acoustic emission data acquisition and analysis. The data will reveal how slow and fast deformations develop and interact with each other in dry and wet crustal rocks under the stress, fluid pressure and temperature conditions at depths up to 10 km, and characterize the production of fractures during earthquake nucleation and rupture propagation. We will search for weak signals before dynamic rupture and develop ways to predict the time to failure from these signals. If we can demonstrate that the joint analysis of acoustic emission signals and X-ray microtomography data can be used to predict dynamic rupture in our experiments, we will have discovered an important lead towards earthquake prediction, which we will pursue in follow-up projects. We will compare the deformation microstructures produced in laboratory experiments with those of natural rock samples collected in California and Norway, where earthquakes occurred. The overarching goal is to progress toward a general model of the path to brittle failure in rocks by advancing knowledge of how fractures accumulate before and during both slow and fast earthquakes, under dry conditions and in the presence of water.

Mots-clés : laboratory earthquakes,rock physics,synchrotron imaging
Projet ERC : BREAK (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : European synchrotron radiation facility — Pays : France — Rôle : Partner
Acronyme de l'entité : ESRF
Type d'entité : Recherche
Financement ERC de l'entité : 845875.0 €

Deformation in Earth’s crust localizes onto faults that may rupture rapidly producing earthquakes or undergo slow aseismic slip. The detailed mechanisms that control the transition between the seismic and aseismic regimes and the onset of earthquakes remain unknown. These mechanisms control the geophysical processes preceding catastrophic failure, such as fracture development and strain localization on faults and in the rock volumes surrounding them. Our goal is to provide the first quantitative laboratory observations of the full displacement field in rocks before and during fault slip, and separate the aseismic and seismic components of it. We will develop novel experimental techniques based primarily on simultaneous dynamic synchrotron X-ray microtomography imaging and acoustic emission data acquisition and analysis. The data will reveal how slow and fast deformations develop and interact with each other in dry and wet crustal rocks under the stress, fluid pressure and temperature conditions at depths up to 10 km, and characterize the production of fractures during earthquake nucleation and rupture propagation. We will search for weak signals before dynamic rupture and develop ways to predict the time to failure from these signals. If we can demonstrate that the joint analysis of acoustic emission signals and X-ray microtomography data can be used to predict dynamic rupture in our experiments, we will have discovered an important lead towards earthquake prediction, which we will pursue in follow-up projects. We will compare the deformation microstructures produced in laboratory experiments with those of natural rock samples collected in California and Norway, where earthquakes occurred. The overarching goal is to progress toward a general model of the path to brittle failure in rocks by advancing knowledge of how fractures accumulate before and during both slow and fast earthquakes, under dry conditions and in the presence of water.

Mots-clés : laboratory earthquakes,rock physics,synchrotron imaging
Projet ERC : NEW-AGE (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : University of Dundee — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149961.0 €

Degenerative diseases of old age, including cardiovascular diseases, type II diabetes, and chronic respiratory conditions, have emerged as the major causes of death in EU countries. Of special concern are neurodegenerative conditions including Alzheimer’s and Parkinson’s, which have been described as the greatest unmet need facing modern medicine. Developing new therapies to slow, halt, or reverse these pathologies is regarded as a key element in addressing this global health priority. No such therapies have been successfully developed to date. A major unsolved challenge faced by drug discovery organisations is that for most degenerative conditions, there are no prognostic markers that can be used to predict whether a candidate therapy is likely to improve clinical outcome. Our solution to this problem exploits the fact that the pathogenesis and progression of all degenerative diseases is associated with oxidative stress. Oxidative stress thus makes an excellent potential prognostic biomarker, but it has been difficult to measure in vivo. Now, under the REDOX ERC Advanced Investigator Award, we have developed a portfolio of next-generation mouse models in which oxidative stress can be monitored at single-cell resolution across all organs. Moreover, the measurements can also be performed non-invasively and in real-time. We believe that these mice provides a new platform with unprecedented utility that can be used by drug-discovery organisations to accelerate, reduce the costs, and increase the predictive power of their pre-clinical pipelines for degenerative conditions. To support commercial exploitation of these models, we will: a) Show that our oxidative stress biomarkers are elevated in mouse models for two degenerative conditions, Alzheimer’s and Hutchinson-Gilford Progeria Syndrome. b) Demonstrate that the biomarker signals are reduced in mice administered therapies believed to be disease-modifying.

Mots-clés : oxidative stress,in vivo biomarkers,drug development,degenerative disease
Projet ERC : AEONS (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : University of Amsterdam — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UvA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2425000.0 €

Densities in neutron star (NS) cores can reach up to ten times the density of a normal atomic nucleus, and the stabilising effect of gravitational confinement permits long-timescale weak interactions. This generates nucleonic matter that is extremely neutron-rich, and the exciting possibility of stable states of strange matter (hyperons or deconfined quarks). Our uncertainty about the nature of cold ultradense matter is encoded in the Equation of State (EOS), which can be mapped via the stellar structure equations to quantities like mass M and radius R that determine the exterior space-time. One very promising technique for measuring the EOS exploits hotspots that form on the NS surface due to the pulsar mechanism, accretion streams, or during thermonuclear explosions in the stellar ocean. As the NS rotates, the hotspot gives rise to a pulsation and relativistic effects encode information about the EOS into the pulse profile. Pulse Profile Modelling (PPM), which employs relativistic ray-tracing and Bayesian inference codes to measure M-R and the EOS, is being pioneered by NASA’s NICER telescope, which is poised to deliver its first results in 2019. Complexities, that have only become apparent with exposure to real data, mean that there is work to be done if we are to have confidence in the nominal 5-10% accuracy of NICER’s M-R results. AEONS will deliver this. The project will also look ahead to the next generation of large-area X-ray timing telescopes, since it is only then that PPM will place tight constraints on dense matter models. The sources these missions target, accreting neutron stars, pose challenges for PPM such as variability, surface pattern uncertainty, and polarimetric signatures. AEONS will develop a robust pipeline for accreting NS PPM and embed it in a multi-messenger EOS inference framework with radio and gravitational wave constraints. This will ensure that PPM delivers major advances in our understanding of the nature of matter.

Mots-clés : neutron stars,dense matter,bayesian inference,relativistic ray tracing,thermonuclear bursts,burst oscillations,accreting pulsars