Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : SMILE (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : Board of the Swiss Federal Institutes of Technology — Pays : Suisse — Rôle : PI
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 599753.36 €

Computers are now able to recognize people, to tell a dog from a cat, or to process speech so efficiently that they can answer complicated questions. This was still impossible only a decade ago. This progress is largely due to the development of the artificial “deep-learned neural networks”. Nowadays, “deep learning” is revolutionizing our life, prompting an economic battle between internet giants and the creation of a myriad of start-ups. As attractive and performant as it is, however, many agree that deep learning is largely an empirical field that lacks a theoretical understanding of its capacity and limitations. The algorithms used to "train" these networks explore a very complex and non-convex energy landscape that eludes most of the present theoretical methodology in machine learning. The behavior of the dynamics in such complicated "glassy" landscape is, however, similar to those that have been studied for decades in the physics of disordered systems such as molecular and spin glasses. In this project we pursue this analogy and use advanced methods of disordered systems to develop a statistical mechanics approach to deep neural networks. Our first main objective is to create a model for learning features from data via a multi-level neural network. We then regard this model as a kind of a spin glass system amenable to an exact asymptotic analysis via the replica and cavity method. Analyzing its phase diagram and phase transitions shall bring theoretical understanding of the principles behind the empirical success of deep neural networks. This approach will also lead to our second objective: the creation of a new class of fast, efficient, and asymptotically optimal message passing algorithms for deep learning. It is the synergy between the theoretical statistical physics approach and scientific questions from computer science that makes the project’s objectives feasible and enables a leap forward in our understanding of learning from data.

Mots-clés : spin glasses,artificial neural networks,bethe approximation,replica method,cavity method,message passing algorithms,belief propagation,unsupervised learning,graphical models,feature learning
Projet ERC : D-SynMA (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Leicester — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 154997.5 €

Computing is changing from living on our desktops and in dedicated devices to being everywhere. In phones, sensors, appliances, and robots – computers (from now on devices) are everywhere and affecting all aspects of our lives. The techniques to make them safe and reliable are investigated and are starting to emerge and consolidate. However, these techniques enable devices to work in isolation or co-exist. We currently do not have techniques that enable development of real autonomous collaboration between devices. Such techniques will revolutionize all usage of devices and, as consequence, our lives. Manufacturing, supply chain, transportation, infrastructures, and earth- and space exploration would all transform using techniques that enable development of collaborating devices. When considering isolated (and co-existing) devices, reactive synthesis – automatic production of plans from high level specification – is emerging as a viable tool for the development of robots and reactive software. This is especially important in the context of safety-critical systems, where assurances are required and systems need to have guarantees on performance. The techniques that are developed today to support robust, assured, reliable, and adaptive devices rely on a major change in focus of reactive synthesis. The revolution of correct-by-construction systems from specifications is occurring and is being pushed forward. However, to take this approach forward to work also for real collaboration between devices the theoretical frameworks that will enable distributed synthesis are required. Such foundations will enable the correct-by-construction revolution to unleash its potential and allow a multiplicative increase of utility by cooperative computation. d-SynMA will take distributed synthesis to this new frontier by considering novel interaction and communication concepts that would create an adaptable framework of correct-by-construction application of collaborating devices.

Mots-clés : reactive synthesis,two player games,temporal logic
Projet ERC : ConnectToBrain (Horizon 2020, appel 2018, Synergy grants)
Entité participante : University of Tübingen — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2996264.0 €

ConnectToBrain will introduce whole-brain multi-locus transcranial magnetic stimulation (mTMS), in which the brain-stimulating electric-field location, direction, magnitude and timing are controlled electronically based on real-time high-density electroencephalography (hdEEG) information of activity and connectivity in brain networks. The final mTMS apparatus will consist of 50 coils. Superpositions of electric fields produced by the different overlapping coils allow spatiotemporally millimeter- and millisecond-precise stimulus sequences to arbitrary cortical sites without physical movements of the coil set. Spatial targeting of mTMS will be further improved by measuring individual brain conductivity distributions with ultra-low-field MRI. The proposed hdEEG methodology uses a brain–computer interface (BCI) and a computer–brain interface (CBI) in a closed, algorithmically-controlled loop. BCI receives real-time information about brain activity and connectivity from hdEEG, while CBI adapts mTMS to drive brain activity and connectivity into desired directions. ConnectToBrain will allow unprecedented tracking of dynamic changes and reorganization of brain networks in real-time, and network-targeted closed-loop stimulation. This radically novel technology will cause a paradigm shift from current open-loop practice that is only moderately effective in therapy. We will apply ConnectToBrain to reach new levels of efficacy of therapeutic applications. Patients after stroke and with Alzheimer’s disease will be tested and treated as models of network disorders. Our high-risk, high-gain endeavor will reach the ambitious goals only through the Synergy of the 3 PIs, world leaders in their complementary areas of expertise (instrumentation, algorithms, translation). If the project succeeds, we expect the value of societal, health and industrial benefits in Europe to exceed €1 billion annually, not to mention the immense value of alleviating human suffering from brain disorders.

Mots-clés : transcranial magnetic stimulation,closed loop stimulation,brain connectivity,brain networks,network diseases,therapy,stroke
Projet ERC : ConnectToBrain (Horizon 2020, appel 2018, Synergy grants)
Entité participante : Aalto University — Pays : Finlande — Rôle : CO-PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 3998905.0 €

ConnectToBrain will introduce whole-brain multi-locus transcranial magnetic stimulation (mTMS), in which the brain-stimulating electric-field location, direction, magnitude and timing are controlled electronically based on real-time high-density electroencephalography (hdEEG) information of activity and connectivity in brain networks. The final mTMS apparatus will consist of 50 coils. Superpositions of electric fields produced by the different overlapping coils allow spatiotemporally millimeter- and millisecond-precise stimulus sequences to arbitrary cortical sites without physical movements of the coil set. Spatial targeting of mTMS will be further improved by measuring individual brain conductivity distributions with ultra-low-field MRI. The proposed hdEEG methodology uses a brain–computer interface (BCI) and a computer–brain interface (CBI) in a closed, algorithmically-controlled loop. BCI receives real-time information about brain activity and connectivity from hdEEG, while CBI adapts mTMS to drive brain activity and connectivity into desired directions. ConnectToBrain will allow unprecedented tracking of dynamic changes and reorganization of brain networks in real-time, and network-targeted closed-loop stimulation. This radically novel technology will cause a paradigm shift from current open-loop practice that is only moderately effective in therapy. We will apply ConnectToBrain to reach new levels of efficacy of therapeutic applications. Patients after stroke and with Alzheimer’s disease will be tested and treated as models of network disorders. Our high-risk, high-gain endeavor will reach the ambitious goals only through the Synergy of the 3 PIs, world leaders in their complementary areas of expertise (instrumentation, algorithms, translation). If the project succeeds, we expect the value of societal, health and industrial benefits in Europe to exceed €1 billion annually, not to mention the immense value of alleviating human suffering from brain disorders.

Mots-clés : transcranial magnetic stimulation,closed loop stimulation,brain connectivity,brain networks,network diseases,therapy,stroke
Projet ERC : ConquerIons (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : KTH Royal Institute of Technology — Pays : Suède — Rôle : Partner
Acronyme de l'entité : KTH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 321788.75 €

ConquerIons is seeking a definitive solution for the detection of ions in real scenarios by innovatively addressing the limitations of traditional ion-sensing concepts in terms of effectiveness, sensitiveness, robustness and downscaled platforms. The project proposes a new generation of voltammetric, amperometric and optical ion sensors based on calibration-free selective membranes inspired by conventional membranes used in the well-established potentiometry field but with a thickness at the nanoscale. The uniqueness of the dynamic electrochemical tuning of ion-transfer processes across these nano-membranes opens up a revolutionary approach that allows for the monitoring of ion concentration in a sample with unprecedented analytical performance. Thus, the project combines efforts from the synthetic chemistry, material science, nanoscience, electrochemistry and analytical chemistry disciplines resulting in a universal concept for the reliable detection of ions. ConquerIons is based on a research approach carefully designed mainly considering the following challenges: (i) the use of novel materials to afford a robust and universal detection; (ii) the implementation of thin-layer concepts of electrochemical and/or optical-sensing platforms to achieve reduced limits of detection and calibration-free methodology; (iii) the translation of the developed concepts to the single-entity scale (i.e. single nanoparticles) towards the study of ion-transfer processes employing, for the first time, ‘nanocoulometry’ readout; and (iv) exploitation of sensors from the laboratory context based on relevant cases as the next step towards the final use as daily smart informers operating in real scenarios. This latter purpose will lead to a series of extraordinary analytical applications from a long-term perspective related to very distinct fields, such as cell-scale therapies and speciation of trace ion levels in environmental analysis.

Mots-clés : smart informers,ion detection,selective membranes,calibration free,thin layer samples,single entity,nanocoulometry
Projet ERC : COR-RAND (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Université Libre de Bruxelles — Pays : Belgique — Rôle : Partner
Acronyme de l'entité : ULB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 332500.0 €

Consider a partial differential equation (PDE) with random coefficients as in engineering or applied physics: When combined with a spatial scale separation, the randomness and the differential operator interact to give rise to some effective behavior. The recent growing mathematical activity in this domain has led to a ``seemingly'' complete theory of stochastic homogenization of linear elliptic operators. Central to this theory is the so-called corrector equation, a degenerate elliptic equation posed on the (infinite-dimensional) probability space. The context of linear elliptic operators yields the simplest such equation. Time-dependent and/or nonlinear PDEs also involve corrector equations (or a family thereof), albeit with a significantly more complex structure. Their study and use to characterize the large-scale/time behavior of solutions of PDEs with random coefficients are at the heart of this project. Whereas the relevance of corrector equations is clear in problems such as diffusion in random media, sedimentation of randomly placed particles in a fluid, or water waves on a rough bottom, it is less obvious for the long-time behavior of waves in disordered media. The latter is related to the spectrum of the associated random elliptic operator, the characterization of which still remains a largely open question today. We propose to relate the long-time behavior of waves to the properties of a family of corrector equations. These corrector equations are widely unstudied and offer many analytical challenges. They constitute the first half of the project. Even in the ``well-understood'' setting of linear elliptic operators, this requires to revisit the corrector equation in the light of much weaker topologies than considered before. The second half of the project aims at using correctors to establish the large-scale behavior of solutions as random objects. This may involve surprising quantities such as the recently introduced ``homogenization commutator".

Mots-clés : pdes with random coefficients,homogenization,effective behavior,fluctuations,waves
Projet ERC : NBEB-SSP (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Collegio Carlo Alberto — Pays : Italie — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 472500.0 €

Consider a population of individuals belonging to different species with unknown proportions. Given an initial (observable) random sample from the population, how do we estimate the number of species in the population, or the probability of discovering a new species in one additional sample, or the number of hitherto unseen species that would be observed in additional unobservable samples? These are archetypal examples of a broad class of statistical problems referred to as species sampling problems (SSP), namely: statistical problems in which the objects of inference are functionals involving the unknown species proportions and/or the species frequency counts induced by observable and unobservable samples from the population. SSPs first appeared in ecology, and their importance has grown considerably in the recent years driven by challenging applications in a wide range of leading scientific disciplines, e.g., biosciences and physical sciences, engineering sciences, machine learning, theoretical computer science and information theory, etc. The objective of this project is the introduction and a thorough investigation of new nonparametric Bayes and empirical Bayes methods for SSPs. The proposed advances will include: i) addressing challenging methodological open problems in classical SSPs under the nonparametric empirical Bayes framework, which is arguably the most developed (currently most implemented by practitioners) framework do deal with classical SSPs; fully exploiting and developing the potential of tools from mathematical analysis, combinatorial probability and Bayesian nonparametric statistics to set forth a coherent modern approach to classical SSPs, and then investigating the interplay between this approach and its empirical counterpart; extending the scope of the above studies to more challenging SSPs, and classes of generalized SSPs, that have emerged recently in the fields of biosciences and physical sciences, machine learning and information theory.

Mots-clés : bayesian nonparametrics,empirical bayes,species sampling problems,uncertainty quantification,bayesian asymptotics,reinforcement learning,feature sampling problems,differential privacy
Projet ERC : FOOD CITIZENS (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : Leiden University — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999761.0 €

Considerable attention goes to ‘smart’ urban food procurement, with little notice of the cultural diversity within Europe. For a growing urban population (80% by 2050), food is a mediator of relations within social networks, not only a commodity or nutrient. Eaters are not just consumers but social actors whose meaning-making depend on faith, gender, age, income, or kinship. How we procure and share food is thus central to cultural understandings of citizenship: the project studies in-depth nine cases of collective food procurement across three European cities, asking if collective food procurement networks indicate emerging forms of ‘food citizenship’, or if they concomitantly co-produce hegemonic notions of participation and belonging – and either way, how. Challenging stereotypical imaginaries of European urbanites, multilevel comparison in Rotterdam, Turin and Gdańsk will investigate three types of collective food procurement networks (a. urban foraging; b. short food chains; c. local food governance) in post-industrial cities, considering the dimensions of solidarity, diversity, skill and scale of action. Ethnographically, we investigate how collective food procurement networks engage with and through food: how do they interpret and articulate solidarity? Which skills do they acquire or lack? How do they operate across and within diverse communities? Do they scale ‘up’ or ‘out’, and how? Conceptually, we deliver a critical theory of food citizenship, adding a ‘meso’ level of sociocultural analysis to food scenarios, which mostly focus on the ‘macro’ (food systems) or ‘micro’ (individual deliberations and habituated reflexes) scale. Methodologically, we match in-depth fieldwork observation with participants’ narratives, using pioneering digital visual media to deliver collaborative and immersive ‘thick descriptions’ of their experiences and trajectories. Societal and local government stakeholders have granted access and will benefit from comparative insights.

Mots-clés : cultural anthropology,ethnography,anthropology of food,visual anthropology,alternative food networks,ethnographic documentary,food and politics,anthropology of skill and enskilment
Projet ERC : CONSULTATIONEFFECTS (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Bergen — Pays : Norvège — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1424856.0 €

Consultations with stakeholders (citizens and interest organizations) are frequently used by executive bureaucracies to design policies and formulate legislation. National ministries, regulatory agencies and the European Commission employ a variety of consultation designs that combine different practices - public consultations, public hearings, workshops, expert groups, advisory committees. Consultations are key to European economic growth strategies such as the Lisbon Agenda and Europe 2020. Despite their near ubiquitous use and legitimising rhetoric, there is currently no systematic analysis assessing empirically the assumption that stakeholders’ participation in policymaking via consultations improves policymaking and results in better outcomes and more legitimate governance. This project aims to address this gap and to systematically investigate and explain the effects of stakeholder consultation designs on policy inputs, processes and outcomes of executive policymaking in 29 political systems: all 28 EU Member States and the EU polity. The project pioneers a path-breaking conceptualisation of consultation designs as representative institutions similar to electoral systems. They play a key instrumental role in the institutional balance of power and constitute a new source of bureaucratic reputation, autonomy and power. The project elaborates an original theory explaining consultation effects on policymaking that accounts for the intrinsic challenges of democratising twenty-first century bureaucracies, and the inherent trade-offs of democratic and technocratic policymaking. Empirically, the project breaks new ground by designing an ambitious data collection strategy aimed to construct an unprecedented, cross-national, comparative dataset on stakeholder consultation designs and characteristics of inputs, processes, outputs and outcomes observed at policy proposal level, across policy areas and political systems.
Projet ERC : TopATLAS (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : Delft University of Technology — Pays : Pays-Bas — Rôle : Partner
Acronyme de l'entité : TU Delft
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 583204.76 €

Contemporary borders operate in ways that are more complex than in the past. They have been variously conceptualised as porous, shifting and solidified. Where a border may be open for some, for others it is an impenetrable wall. Combined with the mobility of geopolitical territorial formations that operate beyond legal frameworks, such as the formation of ISIS and the situation in Europe where borders are being opened and closed against agreed treaties, the very concept of the border is being radically questioned. We need new ways to make sense of these increasingly complex spaces. This proposal aims to develop a transdisciplinary research programme for mapping, analysing and intervening in border areas in the form of a digital atlas. Topological Atlas is developed as a methodology for producing visual counter-geographies at border sites. It is ground breaking in its use of digital technologies combined with a participative approach that attends to those who are at the margins of traditional geopolitical inquiry. The project uses topology as conceptual framework and methodology to make maps that produce ‘seamless transitions’ from the space of the migrant to that of the security apparatus that creates barriers to her movement. In doing so it seeks to disrupt the cartographic norms that are being reinforced through the prevalence of GIS technology and mapping platforms such as Google Earth. It investigates forms of visual and co-produced research adapted to situations of crisis and proposes a new model for researching border areas beyond the current top-down international relations or security perspective. At the same time it acknowledges the intertwined relationship between the practice of academic inquiry, the knowledge it produces and what such knowledge can do. The project is organised around the following research question: How can mapping be used to represent borders as topological entities through the experience of those who encounter them?

Mots-clés : mapping,borders