Project acronym HADES
Project Benthic diagenesis and microbiology of hadal trenches
Researcher (PI) Ronnie N Glud
Host Institution (HI) SYDDANSK UNIVERSITET
Call Details Advanced Grant (AdG), PE10, ERC-2014-ADG
Summary With this project, called HADES, we aim to provide the first detailed, combined analysis of benthic diagenesis and microbial ecology of some of the deepest oceanic trenches on Earth. We argue that deep trenches, some of the most remote, extreme, and scantly explored habitats on Earth, are hotspots of deposition and mineralization of organic material. With the development of novel autonomous in situ instrumentation to overcome large sampling artifacts from decompression, we will i) determine rates of benthic metabolism and the importance of the deep trenches for the marine carbon and nitrogen cycles, ii) explore the unique benthic microbial communities driving these processes, and iii) investigate the proposed great role of virus in regulating microbial performance and carbon cycling in hadal sediments. By comparing trenches from contrasting oceanic settings the project provides a completely novel general analysis of hadal biogeochemistry and the role of deep trenches in the oceans, as well as fundamental new insights into the composition and functioning of microbial communities at extreme pressure.
Summary
With this project, called HADES, we aim to provide the first detailed, combined analysis of benthic diagenesis and microbial ecology of some of the deepest oceanic trenches on Earth. We argue that deep trenches, some of the most remote, extreme, and scantly explored habitats on Earth, are hotspots of deposition and mineralization of organic material. With the development of novel autonomous in situ instrumentation to overcome large sampling artifacts from decompression, we will i) determine rates of benthic metabolism and the importance of the deep trenches for the marine carbon and nitrogen cycles, ii) explore the unique benthic microbial communities driving these processes, and iii) investigate the proposed great role of virus in regulating microbial performance and carbon cycling in hadal sediments. By comparing trenches from contrasting oceanic settings the project provides a completely novel general analysis of hadal biogeochemistry and the role of deep trenches in the oceans, as well as fundamental new insights into the composition and functioning of microbial communities at extreme pressure.
Max ERC Funding
3 185 000 €
Duration
Start date: 2016-01-01, End date: 2020-12-31
Project acronym LASSO
Project Learning, Analysis, SynthesiS and Optimization of Cyber-Physical Systems
Researcher (PI) Kim Guldstrand Larsen
Host Institution (HI) AALBORG UNIVERSITET
Call Details Advanced Grant (AdG), PE6, ERC-2014-ADG
Summary Cyber-physical systems (CPS) are emerging throughout society, e.g. traffic systems, smart grids, smart cities, and medical devices, and brings the promise to society of better solutions in terms of performance, efficiency and usability. However, CPS are often highly safety critical, e.g. cars or medical devices, thus the utmost care must be taken that optimization of performance does not hamper crucial safety conditions. Given the constant growth in complexity of CPS, this task is becoming increasingly demanding for companies to handle with existing methods. The principle barrier for mastering the engineering of complex CPS being both trustworthy and efficient is the lack of a theoretical well-founded framework for CPS engineering supported by powerful tools, that will enable companies to timely meet increasing market demands.
With his extensive contributions to model-driven methodologies, and as provider of one of the “foremost” tools for embedded systems verification, the PI is well prepared to provide the missing framework. The LASSO framework will support the quantitative modeling of both cyber- and physical components, their efficient analysis, the learning of models for unknown components, as well as automatic synthesis and optimization of missing cyber-components from specifications. LASSO will provide the new generation of scalable tools for CPS, allowing trade-offs between safety constraints and performance measure to be made.
LASSO will achieve its objective by ground-breaking and extensive combinations of two different research areas: model checking and machine learning. The framework will develop a complete metric approximation theory for quantitative models, allowing properties to be inferred from reduced or learned models with metric guarantees of their validity in the original system. Further, the applicability of the framework will be validated through a number of CPS case studies, and the tools developed will be made generally available.
Summary
Cyber-physical systems (CPS) are emerging throughout society, e.g. traffic systems, smart grids, smart cities, and medical devices, and brings the promise to society of better solutions in terms of performance, efficiency and usability. However, CPS are often highly safety critical, e.g. cars or medical devices, thus the utmost care must be taken that optimization of performance does not hamper crucial safety conditions. Given the constant growth in complexity of CPS, this task is becoming increasingly demanding for companies to handle with existing methods. The principle barrier for mastering the engineering of complex CPS being both trustworthy and efficient is the lack of a theoretical well-founded framework for CPS engineering supported by powerful tools, that will enable companies to timely meet increasing market demands.
With his extensive contributions to model-driven methodologies, and as provider of one of the “foremost” tools for embedded systems verification, the PI is well prepared to provide the missing framework. The LASSO framework will support the quantitative modeling of both cyber- and physical components, their efficient analysis, the learning of models for unknown components, as well as automatic synthesis and optimization of missing cyber-components from specifications. LASSO will provide the new generation of scalable tools for CPS, allowing trade-offs between safety constraints and performance measure to be made.
LASSO will achieve its objective by ground-breaking and extensive combinations of two different research areas: model checking and machine learning. The framework will develop a complete metric approximation theory for quantitative models, allowing properties to be inferred from reduced or learned models with metric guarantees of their validity in the original system. Further, the applicability of the framework will be validated through a number of CPS case studies, and the tools developed will be made generally available.
Max ERC Funding
2 493 750 €
Duration
Start date: 2015-11-01, End date: 2021-10-31
Project acronym MPCPRO
Project Better MPC Protocols in Theory and in Practice
Researcher (PI) Ivan Bjerre Damgård
Host Institution (HI) AARHUS UNIVERSITET
Call Details Advanced Grant (AdG), PE6, ERC-2014-ADG
Summary Multiparty computation (MPC) is a cryptographic technique allowing us to build distributed computer systems for handling confidential data. We can control exactly what information is released from the system, and privacy of the input data is maintained, even if an adversary breaks into several of the machines in the system. The efficiency of MPC protocols has been significantly improved in recent years. There are countless applications and the techniques are just now entering the commercial domain. However, the theory of the area has in several respects failed to keep up with this development, and we are still very far from being able to apply MPC to large-scale applications. In this project, we propose that state of the art for MPC protocols can be dramatically advanced by
1) Developing a completely new theory for the performance of MPC protocols based on a more detailed model that better reflects what happens when protocols are executed on real platforms.
2) Use the new theory to guide development and implementation of new MPC protocols that will perform much better in practice.
3) Explore the limits of what we can achieve by showing new lower bounds for MPC protocols, attacking a number of long-standing open problems. This will enable us to focus our attention to where improvements are possible.
Summary
Multiparty computation (MPC) is a cryptographic technique allowing us to build distributed computer systems for handling confidential data. We can control exactly what information is released from the system, and privacy of the input data is maintained, even if an adversary breaks into several of the machines in the system. The efficiency of MPC protocols has been significantly improved in recent years. There are countless applications and the techniques are just now entering the commercial domain. However, the theory of the area has in several respects failed to keep up with this development, and we are still very far from being able to apply MPC to large-scale applications. In this project, we propose that state of the art for MPC protocols can be dramatically advanced by
1) Developing a completely new theory for the performance of MPC protocols based on a more detailed model that better reflects what happens when protocols are executed on real platforms.
2) Use the new theory to guide development and implementation of new MPC protocols that will perform much better in practice.
3) Explore the limits of what we can achieve by showing new lower bounds for MPC protocols, attacking a number of long-standing open problems. This will enable us to focus our attention to where improvements are possible.
Max ERC Funding
2 421 995 €
Duration
Start date: 2015-10-01, End date: 2020-09-30
Project acronym SCALE
Project Scalable Quantum Photonic Networks
Researcher (PI) Peter Lodahl
Host Institution (HI) KOBENHAVNS UNIVERSITET
Call Details Advanced Grant (AdG), PE2, ERC-2014-ADG
Summary It is an outstanding challenge in quantum physics of today to scale small proof-of-concept experimental demonstrations into larger quantum networks. In the last decade, solid-state photonic systems have matured significantly, and an ambitious research project on such scaling seems viable. With the present proposal we intend to take up this challenge and exploit single quantum dots in photonic-crystal nanostructures as a deterministic photon-emitter interface for scalable quantum architectures.
The project objectives are threefold. We will explore: 1) Deterministic single-photon sources for quantum simulations, 2) A giant photon nonlinearity for quantum-information processing, 3) The deterministic interfacing of multiple quantum dots.
In 1) we will exploit our recently developed deterministic single-photon source to produce a spatially multiplexed array of single photons (prospectively of 10 photons or more). This source will be used for quantum simulations. Area 2) exploits a single quantum dot in a photonic-crystal waveguide as a giant nonlinearity. The quantum dot will be operated either as a passive nonlinear scatterer or actively controlled. The nonlinearity will enable constructing a deterministic CNOT gate for photons or a single-photon transistor. Finally, 3) concerns the coupling of two or more quantum dots by an extended dipole-dipole interaction that is mediated by the photonic-crystal waveguide. The fundamental limits for the size and complexity of such a quantum photonic network will be explored.
The present project focus on overcoming the fundamental obstacles that photonic quantum-information processing applications have been suffering from, i.e., probabilistic single-photon emission and weak nonlinearities. The successful accomplishment of the project could elevate quantum photonics to a frontrunner technology for scalable quantum-information processing.
Summary
It is an outstanding challenge in quantum physics of today to scale small proof-of-concept experimental demonstrations into larger quantum networks. In the last decade, solid-state photonic systems have matured significantly, and an ambitious research project on such scaling seems viable. With the present proposal we intend to take up this challenge and exploit single quantum dots in photonic-crystal nanostructures as a deterministic photon-emitter interface for scalable quantum architectures.
The project objectives are threefold. We will explore: 1) Deterministic single-photon sources for quantum simulations, 2) A giant photon nonlinearity for quantum-information processing, 3) The deterministic interfacing of multiple quantum dots.
In 1) we will exploit our recently developed deterministic single-photon source to produce a spatially multiplexed array of single photons (prospectively of 10 photons or more). This source will be used for quantum simulations. Area 2) exploits a single quantum dot in a photonic-crystal waveguide as a giant nonlinearity. The quantum dot will be operated either as a passive nonlinear scatterer or actively controlled. The nonlinearity will enable constructing a deterministic CNOT gate for photons or a single-photon transistor. Finally, 3) concerns the coupling of two or more quantum dots by an extended dipole-dipole interaction that is mediated by the photonic-crystal waveguide. The fundamental limits for the size and complexity of such a quantum photonic network will be explored.
The present project focus on overcoming the fundamental obstacles that photonic quantum-information processing applications have been suffering from, i.e., probabilistic single-photon emission and weak nonlinearities. The successful accomplishment of the project could elevate quantum photonics to a frontrunner technology for scalable quantum-information processing.
Max ERC Funding
2 499 981 €
Duration
Start date: 2015-12-01, End date: 2020-11-30