Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : AdjustNet (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Technische Universität Berlin — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : TUB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1115260.49 €

Communication networks have become a critical infrastructure of our digital society. However, with the explosive growth of data-centric applications and the resulting increasing workloads headed for the world’s datacenter networks, today’s static and demand-oblivious network architectures are reaching their capacity limits. The AdjustNet project proposes a radically different perspective, envisioning demand-aware networks which can dynamically adapt their topology to the workload they currently serve. Such self-adjusting networks hence allow to exploit structure in the demand, and thereby reach higher levels of efficiency and performance. The vision of AdjustNet is timely and enabled by recent innovations in optical technologies which allow to flexibly reconfigure the physical network topology. The goal of AdjustNet is to lay the theoretical foundations for self-adjusting networks. We will identify metrics that serve as yardstick of what can and cannot be achieved in a self-adjusting network for a given demand, devise algorithms for online adaption, and validate our framework through case studies. Our novel methodology is motivated by an intriguing connection of self-adjusting networks to known datastructures and to information theory. AdjustNet comes with significant challenges since, similar to self-driving cars, self-adjusting networks require human network operators to give away control, and since more autonomous network operations may lead to instabilities. AdjustNet will overcome these risks and achieve its objectives by pursuing a rigorous approach, devising a theoretical well-founded framework for self-adjusting networks which come with provable guarantees and incorporate self–protection mechanisms. The PI is well-equipped for this project and recently obtained first promising results. As the community is currently re-architecting communication networks, there is a unique opportunity to bridge the gap between theory and practice, and have impact.

Mots-clés : communication networks,network algorithms,routing,online algorithms,datacenter networks,optical networks,information theory,self adjusting datastructures
Projet ERC : AdjustNet (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Ben-Gurion University of the Negev — Pays : Israël — Rôle : Partner
Acronyme de l'entité : BGU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 326250.0 €

Communication networks have become a critical infrastructure of our digital society. However, with the explosive growth of data-centric applications and the resulting increasing workloads headed for the world’s datacenter networks, today’s static and demand-oblivious network architectures are reaching their capacity limits. The AdjustNet project proposes a radically different perspective, envisioning demand-aware networks which can dynamically adapt their topology to the workload they currently serve. Such self-adjusting networks hence allow to exploit structure in the demand, and thereby reach higher levels of efficiency and performance. The vision of AdjustNet is timely and enabled by recent innovations in optical technologies which allow to flexibly reconfigure the physical network topology. The goal of AdjustNet is to lay the theoretical foundations for self-adjusting networks. We will identify metrics that serve as yardstick of what can and cannot be achieved in a self-adjusting network for a given demand, devise algorithms for online adaption, and validate our framework through case studies. Our novel methodology is motivated by an intriguing connection of self-adjusting networks to known datastructures and to information theory. AdjustNet comes with significant challenges since, similar to self-driving cars, self-adjusting networks require human network operators to give away control, and since more autonomous network operations may lead to instabilities. AdjustNet will overcome these risks and achieve its objectives by pursuing a rigorous approach, devising a theoretical well-founded framework for self-adjusting networks which come with provable guarantees and incorporate self–protection mechanisms. The PI is well-equipped for this project and recently obtained first promising results. As the community is currently re-architecting communication networks, there is a unique opportunity to bridge the gap between theory and practice, and have impact.

Mots-clés : communication networks,network algorithms,routing,online algorithms,datacenter networks,optical networks,information theory,self adjusting datastructures
Projet ERC : FlexNets (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Technical University of Munich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : TUM
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1931250.0 €

Communication networks have emerged to become the basic infrastructure for all areas of our society with application areas ranging from social media to industrial production and healthcare. New requirements include the need for dynamic changes of the required resources, for example, to react to social events or to shifts of demands. Existing networks and, in particular, the Internet cannot meet those requirements mainly due to their ossification and hence limitation in resource allocation, i.e., lack of flexibility to adapt the available resources to changes of demands on a small time-scale and in an efficient way. In recent years, several concepts have emerged in networking research to provide more flexibility in networks through virtualization and control plane programmability. In particular, the split between data plane and a centralized control plane as defined by Software Defined Networking (SDN) is regarded as the basic concept to allow flexibility in networks. However, a deeper understanding of what flexibility means remains open. In this project, flexibility focuses on the dynamic changes in time and size of a network that is characterized by its resources (link rate and node capacities) and connectivity (network graph). It is the objective of this research to analyse the fundamental design space for flexibility in SDN-based networks with respect to cost such as resource usage, traffic overhead and delay. The outcome will be a set of quantitative arguments pro and contra certain design choices. An analytical cost model to quantitatively assess the trade-off for flexibility vs. cost will be developed. To assess flexibility with respect to general graph properties a graph model will be designed. The detailed analysis is based on three use cases: dynamic resource allocation, QoS control, and resilience. In the state of the art, selected aspects of flexibility have been explored for certain network scenarios, a fundamental and comprehensive analysis is missing.

Mots-clés : software defined networking,network virtualization,network scalability,performance analysis
Projet ERC : SMART DESIGN (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : Commissariat à l’énergie atomique et aux énergies alternatives — Pays : France — Rôle : Partner
Acronyme de l'entité : CEA
Type d'entité : Recherche
Financement ERC de l'entité : 100000.0 €

Compared to existing Random Access Memories, the Magnetic RAM (MRAM) has the advantage of being non-volatile. Though the basic requirements for reading and writing a single memory element are fulfilled, the present approach based on Spin Transfer Torque (STT) suffers from an innate lack of flexibility. The solution that I propose is based on the discovery of a novel phenomenon, where instead of transferring spin angular momentum from a neighbouring layer, magnetization reversal is achieved by angular momentum transfer directly from the crystal lattice. There is a long list of advantages that this novel approach has compared to STT, but the goal of this project is to focus only on their most generic difference: flexibility. The singularity of spin-orbit torque is that the in-plane current injection geometry decouples the “read” and “write” mechanisms. The disconnection is essential, as unlike STT where the pillar shape of the magnetic trilayer sets the current path, in the case of SOT the composing elements may be shaped separately. The liberty of shaping the current distribution allows to spatially modulate the torque exerted on the local magnetization. The central goal of my project is to explore the new magnetization dynamics, specific to the Spin-Orbit Torque (SOT) geometry, and design novel magnetization switching schemes. I will begin by tackling the fundamental questions about the origin of SOT and try to control it by mastering its dependence on the layer structure. Materials with on-demand SOT will serve as playground for the testing of a broad range of magnetization reversal techniques. The most successful among them will become the building-blocks of complex magnetic objects whose switching behaviour is tightly related to their shape. To study their magnetization dynamics I plan to build a time-resolved near-field magneto-optical microscope, a unique tool for the ultimate spatial and temporal resolution.

Mots-clés : magnetization dynamics,spin orbit interaction,rashba effect,spin hall effect,spin transfer torque
Projet ERC : NanoComSol (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : University of Natural Resources and Life Sciences, Vienna — Pays : Autriche — Rôle : PI
Acronyme de l'entité : BOKU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Composite polymer materials are a rapidly growing market. These materials are also strongly driving device and product innovation by allowing creation of multifunctional, light-weight and moldable components for various products from airplanes to electronics and textiles. We have invented new methods for scalable production of inorganic nanomaterials that allow us to control their distribution and properties in polymer materials. In short, we can mask functional nano- or microparticles by a thin surface coating such that it assumes the properties of the polymer (or environment) in which it should be processed. Thereby, they can be controllably mixed and organized into the polymer, which is essential to give the polymer material better or additional e.g. mechanical and optical properties. Our methods are nearly universal and cost effective; they incorporate an innovation that allows us to modify the surface of quantum dots and other nanoparticles with very precise optic, electric and magnetic properties without deleterious effect on those properties. Industrial partners from the polymer materials industry have shown great interest in these developments. In NanoComSol we will develop industrially relevant application demonstrators that show how these innovations can further be used to create composite materials that have qualitatively new properties produced at industrial scale. Successful such demonstrations will lead to manufacturing of polymer composite materials as active instead of only passive optical, electrical and magnetic components, while reducing costs, environmental impact and materials use in production. NanoComSol thus applies ERC-funded innovations in nanomaterial synthesis to develop industrial scale production of advanced functional materials.

Mots-clés : polymer composite,nanoparticle,hybrid functional material,core shell nanoparticle,polymer processing
Projet ERC : NEWTON (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Istituto Nazionale di Geofisica e Vulcanologia — Pays : Italie — Rôle : Partner
Acronyme de l'entité : INGV
Type d'entité : Recherche
Financement ERC de l'entité : 230000.0 €

Comprehensive seismic programs undertaken in the past few years, combined with emerging new numerical technologies now provide the potential, for the first time, to explore in detail the Earth’s interior. However, such an integrated approach is currently not contemplated, which produces physical inconsistencies among the different studies that strongly bias our understanding of the Earth’s internal structure and dynamics. Of particular concern are nowadays apparent thermo-petrological anomalies in tomographic images that are generated by the unaccounted-for anisotropic structure of the mantle and that are commonly confused with real thermo-petrological features. Given the diffuse mantle seismic anisotropy, apparent thermo-petrological anomalies contaminate most tomographic models against which tectono-magmatic models are validated, representing a critical issue for the present-day window. Here we aim to develop a new methodology that combines state-of-the-art geodynamic modelling and seismological methods. The new methodology will allow building robust anisotropic tomographic models that will exploit anisotropy predictions from petrological-thermomechanical modelling to decompose velocity anomalies into isotropic (true thermo-petrological) and anisotropic (mechanically-induced) components. As a major outcome, we expect to provide a new, geodynamically and seismologically constrained perspective of the current deep structure and tectono-magmatic evolution of different tectonic settings. This new methodology will be applied to the Mediterranean and the Cascadia subduction zone where, despite the abundant seismological observations, large uncertainties about the subsurface structure and tectono-magmatic evolution persist. Furthermore, we plan to develop a new inversion technique for seismic anisotropy, and release an open source, sophisticated code for mantle fabric modelling, which will allow coupling geodynamic and seismological modelling in other tectonic settings.

Mots-clés : seismic tomography,seismic anisotropy,apparent anomalies,geodynamic modelling,subduction
Projet ERC : ELVER (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Cambridge — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. Cambridge
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2473844.0 €

Computer systems have become critical to modern society, but they are pervasively subject to security flaws and malicious attacks, with large-scale exposures of confidential data, denial-of-service and ransom attacks, and the threat of nation-state attackers: they are trusted, but are far from trustworthy. This is especially important for the major pan-industry components of our information infrastructure: processors, programming languages, operating systems, etc. The basic problem is that conventional engineering techniques suffice only to make systems that *usually* work. The usual test-and-debug development methods, with poorly specified abstractions described in prose, lack the mathematical rigour of other engineering disciplines - yet the huge investment in legacy systems and skills makes it hard to improve. ELVER will develop *mathematically rigorous* methods for specifying, testing, and reasoning about *real systems*, focussed on the core mechanisms used by hardware and software to enforce security boundaries. It will establish mathematical models for the industry ARM architecture, used pervasively in mobile phones and embedded devices, and the CHERI research architecture, which protects against many attacks. Using these, ELVER will build tools for analysis of system software, develop techniques for mathematical proof of safety and security properties, and explore improved systems programming languages. ELVER will build on successful collaborations with ARM, IBM, and the C/C++ ISO standards committees. It will directly impact mainstream processor architectures, languages, and development methods, smoothly complementing existing methods while simultaneously enabling longer-term research towards the gold standard of provably secure systems. ELVER will thus demonstrate the feasibility and benefits of a more rigorous approach to system engineering, putting future systems on more solid foundations, and hence making them safer and more secure

Mots-clés : applying formal semantic modelling,testing,and verification to computer systems engineering  for architecture,operating systems,computer languages,and security
Projet ERC : CC-MEM (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Uppsala University — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1610000.0 €

Computer systems today are power limited. As a result, efficiency gains can be translated into performance. Over the past decade we have been so effective at making computation more efficient that we are now at the point where we spend as much energy moving data (from memory to cache to processor) as we do computing the results. And this trend is only becoming worse as we demand more bandwidth for more powerful processors. To improve performance we need to revisit the way we design memory systems from an energy-first perspective, both at the hardware level and by coordinating data movement between hardware and software. CC-MEM will address memory system efficiency by redesigning low-level hardware and high-level hardware/software integration for energy efficiency. The key novelty is in developing a framework for creating efficient memory systems. This framework will enable researchers and designers to compose solutions to different memory system problems (through a shared exchange of metadata) and coordinate them towards high-level system efficiency goals (through a shared policy framework). Central to this framework is a bilateral exchange of metadata and policy between hardware and software components. This novel communication will open new challenges and opportunities for fine-grained optimizations, system-level efficiency metrics, and more effective divisions of responsibility between hardware and software components. CC-MEM will change how researchers and designers approach memory system design from today’s ad hoc development of local solutions to one wherein disparate components can be integrated (composed) and driven (coordinated) by system-level metrics. As a result, we will be able to more intelligently manage data, leading to dramatically lower memory system energy and increased performance, and open new possibilities for hardware and software optimizations.

Mots-clés : memory system efficiency,data management,prefetching,cache,dvfs,hardware,software integration
Projet ERC : RSM (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : TU Dresden — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : TUD
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 448745.12 €

Computer vision has gained considerable momentum in recent years – both in industry and academia. There seems to be a spirit that the time is ripe to realize grand goals and to bring computer vision from the lab into real life. But is a vision system already as good as a human is? The answer is: “Unfortunately, not yet.” Given a single image, a child can describe the objects and their relationships in a much more detailed manner than any computer can. Also, humans can quite effortlessly “visually extract” an object from its background, even in the presence of fine details such as hair. Computers cannot yet achieve this automatically. But, for many real-world applications it is absolutely necessary to reach such levels of rich output, accuracy, quality, robustness, and system autonomy. In this proposal we try to get closer to this overarching goal. We believe that the key to success is a richer representation. Here “rich” stands for rich, detailed output, modelling rich, physical and semantic constraints, and learning rich, statistical relations between different aspects of a scene. Towards this end we propose the Rich Scene Model (RSM), which is one joint statistical, structured model of many physical and semantic scene aspects that can take full advantage of the synergy effect between all its components. This effort goes beyond previous attempts, in many respects. However, it is simple to say “We will build the best ever joint, rich scene model”. Accordingly, the crux of this proposal is to design novel models, learning and inference techniques to make the RSM a reality. This proposal addresses not only theoretical questions such as, “What can we infer from a few images of a dynamically changing 3D scene?”, and “Is our RSM rich enough to make statistical learning “work better” than deterministic learning?” we also propose a model that can give new forms of output, better deal with challenging real world scenarios, and can adapt nicely to human and application needs
Projet ERC : RSM (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Heidelberg University — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1549535.88 €

Computer vision has gained considerable momentum in recent years – both in industry and academia. There seems to be a spirit that the time is ripe to realize grand goals and to bring computer vision from the lab into real life. But is a vision system already as good as a human is? The answer is: “Unfortunately, not yet.” Given a single image, a child can describe the objects and their relationships in a much more detailed manner than any computer can. Also, humans can quite effortlessly “visually extract” an object from its background, even in the presence of fine details such as hair. Computers cannot yet achieve this automatically. But, for many real-world applications it is absolutely necessary to reach such levels of rich output, accuracy, quality, robustness, and system autonomy. In this proposal we try to get closer to this overarching goal. We believe that the key to success is a richer representation. Here “rich” stands for rich, detailed output, modelling rich, physical and semantic constraints, and learning rich, statistical relations between different aspects of a scene. Towards this end we propose the Rich Scene Model (RSM), which is one joint statistical, structured model of many physical and semantic scene aspects that can take full advantage of the synergy effect between all its components. This effort goes beyond previous attempts, in many respects. However, it is simple to say “We will build the best ever joint, rich scene model”. Accordingly, the crux of this proposal is to design novel models, learning and inference techniques to make the RSM a reality. This proposal addresses not only theoretical questions such as, “What can we infer from a few images of a dynamically changing 3D scene?”, and “Is our RSM rich enough to make statistical learning “work better” than deterministic learning?” we also propose a model that can give new forms of output, better deal with challenging real world scenarios, and can adapt nicely to human and application needs