Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : CacheMire (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : CentraleSupélec — Pays : France — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149682.86 €

Mobile data traffic sharply increases each year, due to the rich multi-media applications, video streaming, social networks, and billions of connected users and devices. This increasing mobile data traffic is expected to reach by 2018 roughly 60% of total network traffic. In this regard, caching contents at the edge of the network, namely at the base station and user terminals, is a promising way of offloading the backhaul (especially crucial in dense network deployments) and decreasing the end-to-end content access delays, since the requested contents become very close to the users. Therefore, caching has the potential to become the third key technique for wireless systems sustainability. The goal of this proof of concept is to realize a prototype of such an architecture which enables caching at the edge of the network, and called as “CacheMire”. In particular, we shall focus on development of the first version of CacheMire, which aims to 1) provide an application programming interface (API) to website developers (or content providers); 2) build a set of software/hardware tools to track/collect users' content access statistics under privacy constraints and regulations; 3) and design a storage unit/box for caching strategic contents (i.e., images, videos, files, news) at the base stations and access points. In addition, we aim to combine advanced physical layer techniques with caching so that resources in the uplink/downlink of next generation 5G wireless networks can be further optimized.

Mots-clés : edge caching,machine learning,wireless communications
Projet ERC : PEVAP (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1464320.0 €

Modern astronomy has truly entered the exoplanet era. Although our knowledge of what planet formation produces has grown immensely thanks to observational advances, our actual understanding of the physical processes that give rise to planets and planetary systems is limited. We now know most stars are unlike our own Sun, in that they host planets which orbit around their star with periods of months or shorter, yet many have volatile rich atmospheres. These planets must result from a dominant (if not the dominant) mode of planet formation, yet they were completely missing from our planet formation theories a decade ago. Planets which are close to their parent star are extremely vulnerable to mass-loss through evaporation, where UV/X-ray photons can heat their upper atmospheres to close to the escape temperature, causing them to lose-mass. Recently, I have played a leading role in showing that evaporation drives the evolution of the observed exoplanet population. Thus, the observed exoplanet population is not representative of the one at birth; to use it as a probe of planet formation we must understand evaporation. However, the evaporation of highly-irradiated planetary atmospheres is not well understood. This especially true for terrestrial planets where the atmospheres are dominated by heavy elements. My team will use a combination of theory, simulations and observations to build the first global and comprehensive models of exoplanet evaporation. In doing this, my team will use evaporation as a window into planet formation by answering the following key questions: 1 What are the mass-loss rates and evaporative flow structures for the full spectrum of observed planets? 2 How can we use observations of evaporating planets to learn about their compositions and histories? 3 How does evaporation affect and control the evolution of planets and their atmospheres? By understanding how exoplanets evaporate and evolve, my team will unveil the exoplanet population at birth.

Mots-clés : exoplanet evolution,exoplanet atmospheres
Projet ERC : FAFC (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Hebrew University of Jerusalem — Pays : Israël — Rôle : PI
Acronyme de l'entité : HUJI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1307188.0 €

Modern cryptography has successfully followed an "all-or-nothing" design paradigm over the years. For example, the most fundamental task of data encryption requires that encrypted data be fully recoverable using the encryption key, but be completely useless without it. Nowadays, however, this paradigm is insufficient for a wide variety of evolving applications, and a more subtle approach is urgently needed. This has recently motivated the cryptography community to put forward a vision of "functional cryptography'': Designing cryptographic primitives that allow fine-grained access to sensitive data. This proposal aims at making substantial progress towards realizing the premise of functional cryptography. By tackling challenging key problems in both the foundations and the applications of functional cryptography, I plan to direct the majority of our effort towards addressing the following three fundamental objectives, which span a broad and interdisciplinary flavor of research directions: (1) Obtain a better understanding of functional cryptography's building blocks, (2) develop functional cryptographic tools and schemes based on well-studied assumptions, and (3) increase the usability of functional cryptographic systems via algorithmic techniques. Realizing the premise of functional cryptography is of utmost importance not only to the development of modern cryptography, but in fact to our entire technological development, where fine-grained access to sensitive data plays an instrumental role. Moreover, our objectives are tightly related to two of the most fundamental open problems in cryptography: Basing cryptography on widely-believed worst-case complexity assumptions, and basing public-key cryptography on private-key primitives. I strongly believe that meaningful progress towards achieving our objectives will shed new light on these key problems, and thus have a significant impact on our understanding of modern cryptography.

Mots-clés : cryptography,foundations,applications
Projet ERC : TORI (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Sorbonne Université — Pays : France — Rôle : Partner
Acronyme de l'entité : Sorbonne U
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 212500.0 €

Modern scientific methods heavily rely on large-scale 3D simulations. However, current data production speeds are much higher than storage speeds (5 orders of magnitude on typical supercomputers). This imbalance constitutes a major bottleneck in the scientific computing pipeline, such that most of the data generated by a simulation is not saved to disk, and thus remains unvisualized, unexplored and unanalyzed. TORI addresses this data bottleneck by introducing the next generation data reduction tools for large-scale scientific 3D data. TORI’s angle of attack is based on original and important advances in Topological Data Analysis (TDA), a class of techniques popularized in scientific visualization. TORI addresses data reduction at two levels: (i) at the data level, by deriving an analysis framework for ensembles of topological objects that is inspired by optimal transport, and (ii) at the computation level, by entirely revisiting TDA to adapt it to the context of high-performance in-situ data analysis. To identify informative datasets (i), TORI will introduce efficient methods for distance computations, barycenter evaluations and trajectory analysis. To perform this analysis on-the-fly (ii), TORI will revisit TDA with task parallel algorithms, coarse-to-fine computations and TDA-aware lossy compressors. TORI will be implemented in open-source in the Topology ToolKit, a leading TDA package, and interfaced with standard scientific computing packages (VTK, ParaView). It will be integrated in simulation codes with Catalyst and evaluated on real-life use cases in climatology, geophysics and astrophysics. TORI will have a far reaching impact on all fields of science using large-scale 3D simulations. By bringing together optimal transport and TDA in an innovative coarse-to-fine model, TORI will establish TDA as a standard tool for the analysis of large-scale ensemble datasets and it will initiate a new line of research in high-performance in-situ data analysis.

Mots-clés : scientific visualization,topological data analysis,high performance computing
Projet ERC : Learn (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Milano-Bicocca — Pays : Italie — Rôle : PI
Acronyme de l'entité : UNIMIB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1141875.0 €

Modern software systems must be extremely flexible and easily adaptable to different user needs and environments. However, this flexibility also introduces relevant quality issues. These problems are so common that is sufficient browsing the Web to find millions of reports about failures observed after updates and incompatibilities caused by the interaction of a newly installed component with the existing components. The impact of problems introduced by end-users can be dramatic because end-users can easily modify applications, like developers do, but end-users have neither the knowledge nor the skill of developers, and they cannot debug and fix the problems that they unintentionally introduce. It is thus necessary to timely develop novel solutions that can increase the reliability of the moderns systems, which can be extended and adapted by end-users, with the capability to automatically address problems that are unknown at development-time. The Learn project aims to produce innovative solutions for the development of systems that can work around the problems introduced by end-users when modifying their applications. The three key elements introduced by Learn to automatically produce a (temporary) fix for the software are: (1) the definition of the InternetLearn infrastructure, which is a network infrastructure that enables communication between every individual instance of a same program running at different end-users’ sites, thus augmenting each application with the capability to access a huge amount of information collected in-the-field from other sites; (2) the definition of analysis techniques that can learn the characteristics of successful and failed runs by monitoring executions in the field from a number of instances running at many end-user sites; and (3) the definition of techniques for the automatic generation and actuation of temporary fixes on an Internet (World) scale.

Mots-clés : automatic debugging,self repairing programs,anomaly detection
Projet ERC : ArtMotor (Horizon 2020, appel 2020, Synergy grants)
Entité participante : University of Bayreuth — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 3229883.0 €

Molecular motors and machines are essential for all cellular processes that together enable life. Built from proteins, with a wide range of properties, functionalities and performance characteristics, biological motors perform complex tasks and can transduce chemical energy into mechanical work more efficiently than human-made combustion engines. Sophisticated studies of biological protein motors have led to much structural and biophysical information and the development of models for motor function. However, from the study of highly evolved, biological motors it remains difficult to discern detailed mechanisms, for example about the relative role of different force generation mechanisms, or how information is communicated across a protein to achieve the necessary coordination. A promising, complementary approach to answering these questions is to build synthetic protein motors from the bottom up. Indeed, much effort has been invested in functional protein design, but so far, the ‘holy grail’ of designing and building a functional synthetic protein motor has not been realized. The purpose of ArtMotor is to design and build functional, synthetic protein motors capable of moving and transducing energy, based on existing, non-motor protein modules of known molecular function. Harnessing the synergy of expertise in computational protein design, structural and molecular biology, and single-molecule detection, we will use a two-pronged approach to (a) construct relatively simple protein motors that will require external control, while (b) construct, step by step, an autonomous protein motor capable of moving along a track. Such a functional, synthetic protein will constitute a ground-breaking advance in synthetic biology, physics and engineering. In addition to gaining new insights into mechanisms of energy transduction in proteins, we will also inspire other, complex protein designs that may lead to advances in fields from enzyme design to nano-engineering.

Mots-clés : protein design,protein engineering,artificial molecular motor,molecular motor,molecular machine,synthetic biology,single molecule approaches
Projet ERC : M-Imm (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University of Helsinki — Pays : Finlande — Rôle : PI
Acronyme de l'entité : UH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2047337.0 €

Molecular pathogenesis of most immune-mediated disorders, such as of autoimmune diseases, is poorly understood. These common maladies carry a heavy burden both on patients and on society. Current therapy is non-targeted and results in significant short- and long-term adverse effects. Large granular lymphocyte (LGL) leukemia is characterized by expansion of cytotoxic T- or NK-cells and represents an intriguing clinical continuum between a neoplastic and an autoimmune disorder. Patients suffer from autoimmune cytopenias and rheumatoid arthritis (RA), which are thought to be mediated by LGL cells targeting host tissues. My group recently discovered that 40-50% of LGL leukemia patients carry in their lymphoid cells acquired, activating mutations in the STAT3 gene – a key regulator of immune and oncogenic processes (Koskela et al, N Engl J Med, 2012). This breakthrough discovery gives insight to the pathogenesis of autoimmune disorders at large. I present here a hypothesis that a strong antigen-induced proliferation is a mutational driver, which causes somatic mutations in lymphoid cells. When mutations hit key activating pathways, autoreactive cells acquire functional advantage and expand. The target antigen of the expanded clone determines the clinical characteristics of the autoimmune disease induced. To prove this hypothesis, we will separate small lymphocyte clones from patients with autoimmune diseases and use sensitive next-generation sequencing methods to characterize the spectrum of somatic mutations in lymphoid cells. Further, we will study the function of mutated lymphocytes and examine the mechanisms of autocytotoxicity and end-organ/tissue damage. Finally, we aim to understand factors, which induce somatic mutations in lymphoid cells, such as the role of viral infections. The results will transform our understanding of molecular pathogenesis of autoimmune diseases and lead to accurate diagnostics and discovery of novel drug targets.

Mots-clés : etiology,somatic mutations,virus,leukemia,aplastic anemia,rheumatoid arthritis
Projet ERC : EMC2 (Horizon 2020, appel 2018, Synergy grants)
Entité participante : Institut national de recherche en sciences et technologies du numérique — Pays : France — Rôle : PI
Acronyme de l'entité : Inria
Type d'entité : Recherche
Financement ERC de l'entité : 1401305.0 €

Molecular simulation has become an instrumental tool in chemistry, condensed matter physics, molecular biology, materials science, and nanosciences. It will allow to propose de novo design of e.g. new drugs or materials provided that the efficiency of underlying software is accelerated by several orders of magnitude. The ambition of the EMC2 project is to achieve scientific breakthroughs in this field by gathering the expertise of a multidisciplinary community at the interfaces of four disciplines: mathematics, chemistry, physics, and computer science. It is motivated by the twofold observation that, i) building upon our collaborative work, we have recently been able to gain efficiency factors of up to 3 orders of magnitude for polarizable molecular dynamics in solution of multi-million atom systems, but this is not enough since ii) even larger or more complex systems of major practical interest (such as solvated biosystems or molecules with strongly-correlated electrons) are currently mostly intractable in reasonable clock time. The only way to further improve the efficiency of the solvers, while preserving accuracy, is to develop physically and chemically sound models, mathematically certified and numerically efficient algorithms, and implement them in a robust and scalable way on various architectures (from standard academic or industrial clusters to emerging heterogeneous and exascale architectures). EMC2 has no equivalent in the world: there is nowhere such a critical number of interdisciplinary researchers already collaborating with the required track records to address this challenge. Under the leadership of the 4 PIs, supported by highly recognized teams from three major institutions in the Paris area, EMC2 will develop disruptive methodological approaches and publicly available simulation tools, and apply them to challenging molecular systems. The project will strongly strengthen the local teams and their synergy enabling decisive progress in the field.

Mots-clés : high performance algorithms,mathematics of molecular simulations,error analysis in numerical simulations
Projet ERC : BEAT (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University of Turin — Pays : Italie — Rôle : PI
Acronyme de l'entité : UNITO
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1793421.0 €

Monoclonal antibodies against the EGF receptor (EGFR) provide substantive benefit to colorectal cancer (CRC) patients. However, no genetic lesions that robustly predict ‘addiction’ to the EGFR pathway have been yet identified. Further, even in tumours that regress after EGFR blockade, subsets of drug-tolerant cells often linger and foster ‘minimal residual disease’ (MRD), which portends tumour relapse. Our preliminary evidence suggests that reliance on EGFR activity, as opposed to MRD persistence, could be assisted by genetically-based variations in transcription factor partnerships and activities, gene expression outputs, and biological fates controlled by the WNT/beta-catenin pathway. On such premises, BEAT (Beta-catenin and EGFR Abrogation Therapy) will elucidate the mechanisms of EGFR dependency, and escape from it, with the goal to identify biomarkers for more efficient clinical management of CRC and develop new therapies for MRD eradication. A multidisciplinary approach will be pursued spanning from integrative gene regulation analyses to functional genomics in vitro, pharmacological experiments in vivo, and clinical investigation, to address whether: (i) specific genetic alterations of the WNT pathway affect anti-EGFR sensitivity; (ii) combined neutralisation of EGFR and WNT signals fuels MRD deterioration; (iii) data from analysis of this synergy can lead to the discovery of clinically meaningful biomarkers with predictive and prognostic significance. This proposal capitalises on a unique proprietary platform for high-content studies based on a large biobank of viable CRC samples, which ensures strong analytical power and unprecedented biological flexibility. By providing fresh insight into the mechanisms whereby WNT/beta-catenin signalling differentially sustains EGFR dependency or drug tolerance, the project is expected to put forward an innovative reinterpretation of CRC molecular bases and advance the rational application of more effective therapies.

Mots-clés : colorectal cancer,targeted therapy
Projet ERC : LEOpowver (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Saarland University — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

More than 17,000 satellites are announced for launch into Low Earth Orbit (LEO) before 2027, for the purpose of real-time Earth observation and worldwide connectivity. The multitude of mission initiatives is a sign of democratization of space and induces enormous business opportunities. LEOpowver will harvest results of the ERC Advanced Grant POWVER for the LEO market to arrive at orbit-proof software for the continuous, fully automated, power-optimal and profit maximising dynamic operation of Low Earth Satellite Constellations. The software will solve the pivotal challenge in the software-driven orchestration of satellite constellations, namely the management of the severely limited in-orbit electric power budget. + At any point in time a highly accurate power model approximates the actual distribution of the state-of-charge of the on-board batteries very tightly. This enables tracking and extrapolation of the battery state with unprecedented accuracy. + Data transfer needs within large satellite constellations induce critical interdependencies between the individual satellites' power budgets. The LEOpowver software determines the optimal data transfer in the form of a contact plan, enabling the constellation-wide best utilisation of resources while provably minimizing battery depletion risks. + In-orbit battery measurements are transmitted to ground at the earliest possible moments, where they are fed into a machine learning mechanism rectifying the on-ground models of the satellites, which in turn is the basis for perpetuating the computation of always safe and best-to-follow receding-horizon schedules in a self-adaptive manner. In this way, the quality of the constellation management is continually improved. The central activities of LEOpowver are (i) in-orbit validation of this software on a LEO satellite constellation and (ii) in-depth exploration of commercialisation opportunities for the software product.

Mots-clés : low earth orbit,networked satellite constellations,contact plans,kinetic battery model,optimisation,in orbit validation,commercialisation