Project acronym AGNOSTIC
Project Actively Enhanced Cognition based Framework for Design of Complex Systems
Researcher (PI) Bjoern Ottersten
Host Institution (HI) UNIVERSITE DU LUXEMBOURG
Country Luxembourg
Call Details Advanced Grant (AdG), PE7, ERC-2016-ADG
Summary Parameterized mathematical models have been central to the understanding and design of communication, networking, and radar systems. However, they often lack the ability to model intricate interactions innate in complex systems. On the other hand, data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of flexibility. These approaches need labelled data, representing all the facets of the system interaction with the environment. With the aforementioned systems becoming increasingly complex with intricate interactions and operating in dynamic environments, the number of system configurations can be rather large leading to paucity of labelled data. Thus there are emerging networks of systems of critical importance whose cognition is not effectively covered by traditional approaches. AGNOSTIC uses the process of exploration through system probing and exploitation of observed data in an iterative manner drawing upon traditional model-based approaches and data-driven discriminative learning to enhance functionality, performance, and robustness through the notion of active cognition. AGNOSTIC clearly departs from a passive assimilation of data and aims to formalize the exploitation/exploration framework in dynamic environments. The development of this framework in three applications areas is central to AGNOSTIC. The project aims to provide active cognition in radar to learn the environment and other active systems to ensure situational awareness and coexistence; to apply active probing in radio access networks to infer network behaviour towards spectrum sharing and self-configuration; and to learn and adapt to user demand for content distribution in caching networks, drastically improving network efficiency. Although these cognitive systems interact with the environment in very different ways, sufficient abstraction allows cross-fertilization of insights and approaches motivating their joint treatment.
Summary
Parameterized mathematical models have been central to the understanding and design of communication, networking, and radar systems. However, they often lack the ability to model intricate interactions innate in complex systems. On the other hand, data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of flexibility. These approaches need labelled data, representing all the facets of the system interaction with the environment. With the aforementioned systems becoming increasingly complex with intricate interactions and operating in dynamic environments, the number of system configurations can be rather large leading to paucity of labelled data. Thus there are emerging networks of systems of critical importance whose cognition is not effectively covered by traditional approaches. AGNOSTIC uses the process of exploration through system probing and exploitation of observed data in an iterative manner drawing upon traditional model-based approaches and data-driven discriminative learning to enhance functionality, performance, and robustness through the notion of active cognition. AGNOSTIC clearly departs from a passive assimilation of data and aims to formalize the exploitation/exploration framework in dynamic environments. The development of this framework in three applications areas is central to AGNOSTIC. The project aims to provide active cognition in radar to learn the environment and other active systems to ensure situational awareness and coexistence; to apply active probing in radio access networks to infer network behaviour towards spectrum sharing and self-configuration; and to learn and adapt to user demand for content distribution in caching networks, drastically improving network efficiency. Although these cognitive systems interact with the environment in very different ways, sufficient abstraction allows cross-fertilization of insights and approaches motivating their joint treatment.
Max ERC Funding
2 499 595 €
Duration
Start date: 2017-10-01, End date: 2022-09-30
Project acronym BeStMo
Project Beyond Static Molecules: Modeling Quantum Fluctuations in Complex Molecular Environments
Researcher (PI) Alexandre TKATCHENKO
Host Institution (HI) UNIVERSITE DU LUXEMBOURG
Country Luxembourg
Call Details Consolidator Grant (CoG), PE4, ERC-2016-COG
Summary We propose focused theory developments and applications, which aim to substantially advance our ability to model and understand the behavior of molecules in complex environments. From a large repertoire of possible environments, we have chosen to concentrate on experimentally-relevant situations, including molecular fluctuations in electric and optical fields, disordered molecular crystals, solvated (bio)molecules, and molecular interactions at/through low-dimensional nanostructures. A challenging aspect of modeling such realistic environments is that both molecular electronic and nuclear fluctuations have to be treated efficiently at a robust quantum-mechanical level of theory for systems with 1000s of atoms. In contrast, the current state of the art in the modeling of complex molecular systems typically consists of Newtonian molecular dynamics employing classical force fields. We will develop radically new approaches for electronic and nuclear fluctuations that unify concepts and merge techniques from quantum-mechanical many-body Hamiltonians, statistical mechanics, density-functional theory, and machine learning. Our developments will be benchmarked using experimental measurements with terahertz (THz) spectroscopy, atomic-force and scanning tunneling microscopy (AFM/STM), time-of-flight (TOF) measurements, and molecular interferometry.
Our final goal is to bridge the accuracy of quantum mechanics with the efficiency of force fields, enabling large-scale predictive quantum molecular dynamics simulations for complex systems containing 1000s of atoms, and leading to novel conceptual insights into quantum-mechanical fluctuations in large molecular systems. The project goes well beyond the presently possible applications and once successful will pave the road towards having a suite of first-principles-based modeling tools for a wide range of realistic materials, such as biomolecules, nanostructures, disordered solids, and organic/inorganic interfaces.
Summary
We propose focused theory developments and applications, which aim to substantially advance our ability to model and understand the behavior of molecules in complex environments. From a large repertoire of possible environments, we have chosen to concentrate on experimentally-relevant situations, including molecular fluctuations in electric and optical fields, disordered molecular crystals, solvated (bio)molecules, and molecular interactions at/through low-dimensional nanostructures. A challenging aspect of modeling such realistic environments is that both molecular electronic and nuclear fluctuations have to be treated efficiently at a robust quantum-mechanical level of theory for systems with 1000s of atoms. In contrast, the current state of the art in the modeling of complex molecular systems typically consists of Newtonian molecular dynamics employing classical force fields. We will develop radically new approaches for electronic and nuclear fluctuations that unify concepts and merge techniques from quantum-mechanical many-body Hamiltonians, statistical mechanics, density-functional theory, and machine learning. Our developments will be benchmarked using experimental measurements with terahertz (THz) spectroscopy, atomic-force and scanning tunneling microscopy (AFM/STM), time-of-flight (TOF) measurements, and molecular interferometry.
Our final goal is to bridge the accuracy of quantum mechanics with the efficiency of force fields, enabling large-scale predictive quantum molecular dynamics simulations for complex systems containing 1000s of atoms, and leading to novel conceptual insights into quantum-mechanical fluctuations in large molecular systems. The project goes well beyond the presently possible applications and once successful will pave the road towards having a suite of first-principles-based modeling tools for a wide range of realistic materials, such as biomolecules, nanostructures, disordered solids, and organic/inorganic interfaces.
Max ERC Funding
1 811 650 €
Duration
Start date: 2017-03-01, End date: 2022-08-31
Project acronym CLEANH2
Project Chemical Engineering of Fused MetalloPorphyrins Thin Films for the Clean Production of Hydrogen
Researcher (PI) Nicolas BOSCHER
Host Institution (HI) LUXEMBOURG INSTITUTE OF SCIENCE AND TECHNOLOGY
Country Luxembourg
Call Details Consolidator Grant (CoG), PE8, ERC-2019-COG
Summary This project stands in the general context of the current worldwide energy and environmental crisis. It aims to engineer a new generation of conjugated microporous polymers based on fused metalloporphyrins for the low-cost, clean and efficient production of hydrogen from solar water splitting. The CLEANH2 concept relies on the gas phase reaction of metalloporphyrins to engineer new heterogeneous catalysts with remarkable hydrogen production yields. Metalloporphyrins, selected by Nature to fulfil the main catalytic phenomena allowing life, are attractive molecules for water splitting owing to their highly conjugated structure and central metal ion, which can readily interconvert between different oxidation states to accomplish oxidation and reduction reactions. For efficiency and sustainability considerations, it is highly desirable to employ metalloporphyrins in conductive assemblies for heterogeneous catalysis. Nevertheless, due to the lack of synthetic approach, the design and application of conjugated porphyrin assemblies is a largely unexplored topic in view of the plethora of available porphyrin patterns.
The central idea of CLEANH2 builds upon our recent advance in the gas phase synthesis and deposition of directly fused metalloporphyrins coatings. Progress in our approach is expected to open the way for the construction of powerful catalytic and photocatalytic materials. To achieve this, the key challenging goals of this project are: 1) the engineering of the microstructure and electronic structure of directly fused metalloporphyrins thin films; 2) the use of the full potential of directly fused metalloporphyrins thin films for the unmet, clean and high quantum yield overall water splitting for hydrogen production. The outcomes of CLEANH2 will be foundational for the engineering of directly fused metalloporphyrins systems and their implementation in advanced technological applications related to catalysis and solar energy.
Summary
This project stands in the general context of the current worldwide energy and environmental crisis. It aims to engineer a new generation of conjugated microporous polymers based on fused metalloporphyrins for the low-cost, clean and efficient production of hydrogen from solar water splitting. The CLEANH2 concept relies on the gas phase reaction of metalloporphyrins to engineer new heterogeneous catalysts with remarkable hydrogen production yields. Metalloporphyrins, selected by Nature to fulfil the main catalytic phenomena allowing life, are attractive molecules for water splitting owing to their highly conjugated structure and central metal ion, which can readily interconvert between different oxidation states to accomplish oxidation and reduction reactions. For efficiency and sustainability considerations, it is highly desirable to employ metalloporphyrins in conductive assemblies for heterogeneous catalysis. Nevertheless, due to the lack of synthetic approach, the design and application of conjugated porphyrin assemblies is a largely unexplored topic in view of the plethora of available porphyrin patterns.
The central idea of CLEANH2 builds upon our recent advance in the gas phase synthesis and deposition of directly fused metalloporphyrins coatings. Progress in our approach is expected to open the way for the construction of powerful catalytic and photocatalytic materials. To achieve this, the key challenging goals of this project are: 1) the engineering of the microstructure and electronic structure of directly fused metalloporphyrins thin films; 2) the use of the full potential of directly fused metalloporphyrins thin films for the unmet, clean and high quantum yield overall water splitting for hydrogen production. The outcomes of CLEANH2 will be foundational for the engineering of directly fused metalloporphyrins systems and their implementation in advanced technological applications related to catalysis and solar energy.
Max ERC Funding
1 900 711 €
Duration
Start date: 2020-05-01, End date: 2025-04-30
Project acronym CLOUDMAP
Project Cloud Computing via Homomorphic Encryption and Multilinear Maps
Researcher (PI) Jean-Sebastien Coron
Host Institution (HI) UNIVERSITE DU LUXEMBOURG
Country Luxembourg
Call Details Advanced Grant (AdG), PE6, ERC-2017-ADG
Summary The past thirty years have seen cryptography move from arcane to commonplace: Internet, mobile phones, banking system, etc. Homomorphic cryptography now offers the tantalizing goal of being able to process sensitive information in encrypted form, without needing to compromise on the privacy and security of the citizens and organizations that provide the input data. More recently, cryptographic multilinear maps have revolutionized cryptography with the emergence of indistinguishability obfuscation (iO), which in theory can been used to realize numerous advanced cryptographic functionalities that previously seemed beyond reach. However the security of multilinear maps is still poorly understood, and many iO schemes have been broken; moreover all constructions of iO are currently unpractical.
The goal of the CLOUDMAP project is to make these advanced cryptographic tasks usable in practice, so that citizens do not have to compromise on the privacy and security of their input data. This goal can only be achieved by considering the mathematical foundations of these primitives, working "from first principles", rather than focusing on premature optimizations. To achieve this goal, our first objective will be to better understand the security of the underlying primitives of multilinear maps and iO schemes. Our second objective will be to develop new approaches to significantly improve their efficiency. Our third objective will be to build applications of multilinear maps and iO that can be implemented in practice.
Summary
The past thirty years have seen cryptography move from arcane to commonplace: Internet, mobile phones, banking system, etc. Homomorphic cryptography now offers the tantalizing goal of being able to process sensitive information in encrypted form, without needing to compromise on the privacy and security of the citizens and organizations that provide the input data. More recently, cryptographic multilinear maps have revolutionized cryptography with the emergence of indistinguishability obfuscation (iO), which in theory can been used to realize numerous advanced cryptographic functionalities that previously seemed beyond reach. However the security of multilinear maps is still poorly understood, and many iO schemes have been broken; moreover all constructions of iO are currently unpractical.
The goal of the CLOUDMAP project is to make these advanced cryptographic tasks usable in practice, so that citizens do not have to compromise on the privacy and security of their input data. This goal can only be achieved by considering the mathematical foundations of these primitives, working "from first principles", rather than focusing on premature optimizations. To achieve this goal, our first objective will be to better understand the security of the underlying primitives of multilinear maps and iO schemes. Our second objective will be to develop new approaches to significantly improve their efficiency. Our third objective will be to build applications of multilinear maps and iO that can be implemented in practice.
Max ERC Funding
2 491 266 €
Duration
Start date: 2018-10-01, End date: 2023-09-30
Project acronym CRISP
Project Cognitive Aging: From Educational Opportunities to Individual Risk Profiles
Researcher (PI) Anja LEIST
Host Institution (HI) UNIVERSITE DU LUXEMBOURG
Country Luxembourg
Call Details Starting Grant (StG), SH3, ERC-2018-STG
Summary Cognitive impairment and dementia have dramatic individual and social consequences, and create high economic costs for societies. In order to delay cognitive aging of future generations as long as possible, we need evidence about which contextual factors are most supportive for individuals to reach highest cognitive levels relative to their potential. At the same time, for current older generations, we need scalable methods to exactly identify individuals at risk of cognitive impairment. The project intends to apply recent methodological and statistical advancements to reach two objectives. Firstly, contextual influences on cognitive aging will be comparatively assessed, with a focus on inequalities related to educational opportunities and gender inequalities. This will be done using longitudinal, population-representative, harmonized cross-national aging surveys, merged with contextual information. Secondly, the project will quantify the ability of singular and clustered individual characteristics, such as indicators of cognitive reserve and behaviour change, to predict cognitive aging and diagnosis of dementia. Project methodology will rely partly on parametric ‘traditional’ multilevel- or fixed-effects modelling, partly on non-parametric statistical learning approaches, to address objectives both hypothesis- and data-driven. Applying statistical learning techniques in the field of cognitive reserve will open new research avenues for efficient handling of large amounts of data, among which most prominently the accurate prediction of health and disease outcomes. Quantifying the role of contextual inequalities related to education and gender will guide policymaking in and beyond the project. Assessing risk profiles of individuals in relation to cognitive aging will support efficient and scalable risk screening of individuals. Identifying the value of behaviour change to delay cognitive impairment will guide treatment plans for individuals affected by dementia.
Summary
Cognitive impairment and dementia have dramatic individual and social consequences, and create high economic costs for societies. In order to delay cognitive aging of future generations as long as possible, we need evidence about which contextual factors are most supportive for individuals to reach highest cognitive levels relative to their potential. At the same time, for current older generations, we need scalable methods to exactly identify individuals at risk of cognitive impairment. The project intends to apply recent methodological and statistical advancements to reach two objectives. Firstly, contextual influences on cognitive aging will be comparatively assessed, with a focus on inequalities related to educational opportunities and gender inequalities. This will be done using longitudinal, population-representative, harmonized cross-national aging surveys, merged with contextual information. Secondly, the project will quantify the ability of singular and clustered individual characteristics, such as indicators of cognitive reserve and behaviour change, to predict cognitive aging and diagnosis of dementia. Project methodology will rely partly on parametric ‘traditional’ multilevel- or fixed-effects modelling, partly on non-parametric statistical learning approaches, to address objectives both hypothesis- and data-driven. Applying statistical learning techniques in the field of cognitive reserve will open new research avenues for efficient handling of large amounts of data, among which most prominently the accurate prediction of health and disease outcomes. Quantifying the role of contextual inequalities related to education and gender will guide policymaking in and beyond the project. Assessing risk profiles of individuals in relation to cognitive aging will support efficient and scalable risk screening of individuals. Identifying the value of behaviour change to delay cognitive impairment will guide treatment plans for individuals affected by dementia.
Max ERC Funding
1 148 290 €
Duration
Start date: 2019-01-01, End date: 2023-12-31
Project acronym DEEP PURPLE
Project DEEP PURPLE: darkening of the Greenland Ice Sheet
Researcher (PI) Martyn TRANTER, Alexandre Barbosa Anesio, Liane Benning
Host Institution (HI) AARHUS UNIVERSITET
Country Denmark
Call Details Synergy Grants (SyG), SyG, ERC-2019-SyG
Summary The stability of the Greenland Ice Sheet (GrIS) is a threat to coastal communities worldwide. The PIs have changed our understanding of why it darkens during the melt season, becoming increasingly deep purple due to pigmented ice algal blooms in the ice surface, producing more melt and accelerating the GrIS towards its tipping point, and increasing sea level. The next step jump in our understanding of biological darkening will be provided by DEEP PURPLE, which will establish the factors that control ice algal blooms. These factors are essential for modelling of future melting, which require a process-based understanding of blooming. DEEP PURPLE will quantify the synergies between the biology, chemistry and physics of ice algae micro-niches in rotting, melting ice, and examine the combination of factors which stabilise them. State-of-the-science analytical and observational methods will be employed to characterise the complex mosaic of wet ice habitats, dependent on factors such as the hydrology, nutrient status, particulate content and light fields within these continually evolving ice-water-particulate-microbe systems. We will quantitatively assess why and how the fine light mineral dust particulates contained within the melting ice amplify the growth of ice algae. The particulate content and composition of different layers in the GrIS is dependent on age, and so the algae that the melting ice can support may fundamentally change over time. We look back to understand if the ice biome has changed through the Anthropocene via analyse of fjord sediments. The first draft genome of ice algae will show their key adaptations to glacier surface habitats. DEEP PURPLE looks forward by providing the critical field data sets and conceptual models of ice algal growth that will facilitate the next generation of predictive models of sea level rise due to biologically enhanced melting of the GrIS.
Summary
The stability of the Greenland Ice Sheet (GrIS) is a threat to coastal communities worldwide. The PIs have changed our understanding of why it darkens during the melt season, becoming increasingly deep purple due to pigmented ice algal blooms in the ice surface, producing more melt and accelerating the GrIS towards its tipping point, and increasing sea level. The next step jump in our understanding of biological darkening will be provided by DEEP PURPLE, which will establish the factors that control ice algal blooms. These factors are essential for modelling of future melting, which require a process-based understanding of blooming. DEEP PURPLE will quantify the synergies between the biology, chemistry and physics of ice algae micro-niches in rotting, melting ice, and examine the combination of factors which stabilise them. State-of-the-science analytical and observational methods will be employed to characterise the complex mosaic of wet ice habitats, dependent on factors such as the hydrology, nutrient status, particulate content and light fields within these continually evolving ice-water-particulate-microbe systems. We will quantitatively assess why and how the fine light mineral dust particulates contained within the melting ice amplify the growth of ice algae. The particulate content and composition of different layers in the GrIS is dependent on age, and so the algae that the melting ice can support may fundamentally change over time. We look back to understand if the ice biome has changed through the Anthropocene via analyse of fjord sediments. The first draft genome of ice algae will show their key adaptations to glacier surface habitats. DEEP PURPLE looks forward by providing the critical field data sets and conceptual models of ice algal growth that will facilitate the next generation of predictive models of sea level rise due to biologically enhanced melting of the GrIS.
Max ERC Funding
11 007 344 €
Duration
Start date: 2020-01-01, End date: 2025-12-31
Project acronym DISCOVERER
Project A novel chemical discovery platform enabled by machine learning
Host Institution (HI) UNIVERSITE DU LUXEMBOURG
Country Luxembourg
Call Details Proof of Concept (PoC), ERC-2020-PoC
Summary Computational design and discovery of molecules and materials relies on the exploration of increasingly growing chemical spaces. The discovery and formulation of new drugs, antivirals, antibiotics, catalysts, battery materials, and in general chemicals with tailored properties, require a fundamental paradigm shift to search in unchartered swaths of the vast chemical space. This is in stark contrast to current approaches, which start from (commercially available) libraries of compounds from various suppliers. Within the ERC Consolidator grant BeStMo (grant agreement ID 725291) we aimed to substantially advance our ability to model and understand the behaviour of molecules in complex environments. As a result, we successfully developed a set of machine learning and physics-based methods for covalent and non-covalent interactions that now allow an accurate and efficient modelling of molecules of increasing size (from 10 to 1000 atoms). These methods now enable routine calculations of quantum-mechanical properties of molecules throughout chemical compound space, provided that enough reference data is produced as a starting point for training. Within DISCOVERER, we aim to promote a paradigm shift in chemical discovery by inverting the selection pyramid by starting with pre-defined parameters from which new chemical entities are designed through machine learning and AI-enabled algorithms. We can do so by integrating these modules into a commercial platform: “Chemical Space Machine”. DISCOVERER’s main goal is to finalize the development of a commercial alpha version of “Chemical Space Machine” and setting up its commercialisation strategy.
Summary
Computational design and discovery of molecules and materials relies on the exploration of increasingly growing chemical spaces. The discovery and formulation of new drugs, antivirals, antibiotics, catalysts, battery materials, and in general chemicals with tailored properties, require a fundamental paradigm shift to search in unchartered swaths of the vast chemical space. This is in stark contrast to current approaches, which start from (commercially available) libraries of compounds from various suppliers. Within the ERC Consolidator grant BeStMo (grant agreement ID 725291) we aimed to substantially advance our ability to model and understand the behaviour of molecules in complex environments. As a result, we successfully developed a set of machine learning and physics-based methods for covalent and non-covalent interactions that now allow an accurate and efficient modelling of molecules of increasing size (from 10 to 1000 atoms). These methods now enable routine calculations of quantum-mechanical properties of molecules throughout chemical compound space, provided that enough reference data is produced as a starting point for training. Within DISCOVERER, we aim to promote a paradigm shift in chemical discovery by inverting the selection pyramid by starting with pre-defined parameters from which new chemical entities are designed through machine learning and AI-enabled algorithms. We can do so by integrating these modules into a commercial platform: “Chemical Space Machine”. DISCOVERER’s main goal is to finalize the development of a commercial alpha version of “Chemical Space Machine” and setting up its commercialisation strategy.
Max ERC Funding
150 000 €
Duration
Start date: 2021-09-01, End date: 2023-02-28
Project acronym DREAM
Project Demonstration of a Radar Enabled weArable platforM
Researcher (PI) Bjoern Ottersten
Host Institution (HI) UNIVERSITE DU LUXEMBOURG
Country Luxembourg
Call Details Proof of Concept (PoC), ERC-2020-PoC
Summary Sport research and procedures that aim to increase performance or assist recovery after an injury often require capture of athlete’s motion. However, implementing a motion capture system in routine practice takes significant effort. Capture volume, weather conditions, motion dynamics and athletes' timing (where timing defines coincide movements in relation to external factors) are the key parameters for outdoor sport activities (e.g. track and field or football), yet no available motion capture system is suitable for much needed high accuracy absolute position measurements of body segments in outdoor conditions with minimal setup effort for large capture volumes, which is capable of measuring the athletes' timing. Therefore, there is a significant unmet need for a portable motion capture system that can perform accurate infield measurements on a large volume, even in diverse weather conditions (e.g. sunlight, fog, rain) with the capability of the athletes' timing analysis, which is a combination of reaction time, decision -making and co-ordination in relation to external factors.
DREAM aims to generate a motion capture and mapping system (minimum viable product) that creates a complete orientation and absolute position measurement with no external position reference, provides reliable orientation of body segments even for high dynamic motions, and has a small form factor, which is minimally invasive for the athlete. Further, the offered solution is complementary to existing solutions and operates in real time. DREAM’s value proposition is a cost-effective, robust and accurate infield motion capture and mapping system with minimal setup effort, for large field coverage in any place and in any condition. The uniqueness of the product stems from the complementarity between radar and IMU sensors, enhanced athletes timing analysis, all enabled through an innovative
algorithm developed in the AGNOSTIC ERC Advanced grant (ID 742648).
Summary
Sport research and procedures that aim to increase performance or assist recovery after an injury often require capture of athlete’s motion. However, implementing a motion capture system in routine practice takes significant effort. Capture volume, weather conditions, motion dynamics and athletes' timing (where timing defines coincide movements in relation to external factors) are the key parameters for outdoor sport activities (e.g. track and field or football), yet no available motion capture system is suitable for much needed high accuracy absolute position measurements of body segments in outdoor conditions with minimal setup effort for large capture volumes, which is capable of measuring the athletes' timing. Therefore, there is a significant unmet need for a portable motion capture system that can perform accurate infield measurements on a large volume, even in diverse weather conditions (e.g. sunlight, fog, rain) with the capability of the athletes' timing analysis, which is a combination of reaction time, decision -making and co-ordination in relation to external factors.
DREAM aims to generate a motion capture and mapping system (minimum viable product) that creates a complete orientation and absolute position measurement with no external position reference, provides reliable orientation of body segments even for high dynamic motions, and has a small form factor, which is minimally invasive for the athlete. Further, the offered solution is complementary to existing solutions and operates in real time. DREAM’s value proposition is a cost-effective, robust and accurate infield motion capture and mapping system with minimal setup effort, for large field coverage in any place and in any condition. The uniqueness of the product stems from the complementarity between radar and IMU sensors, enhanced athletes timing analysis, all enabled through an innovative
algorithm developed in the AGNOSTIC ERC Advanced grant (ID 742648).
Max ERC Funding
150 000 €
Duration
Start date: 2020-10-01, End date: 2022-03-31
Project acronym ELWar
Project Electoral Legacies of War: Political Competition in Postwar Southeast Europe
Researcher (PI) Josip GLAURDIC
Host Institution (HI) UNIVERSITE DU LUXEMBOURG
Country Luxembourg
Call Details Starting Grant (StG), SH2, ERC-2016-STG
Summary We know remarkably little about the impact of war on political competition in postwar societies in spite of the fact that postwar elections have garnered tremendous interest from researchers in a variety of fields. That interest, however, has been limited to establishing the relationship between electoral democratization and the incidence of conflict. Voters’ and parties’ electoral behaviour after the immediate post‐conflict period have remained largely neglected by researchers. The proposed project will fill this gap in our understanding of electoral legacies of war by analysing the evolution of political competition over the course of more than two decades in the six postwar states of Southeast Europe: Bosnia-Herzegovina, Croatia, Kosovo, Macedonia, Montenegro, and Serbia. Organised around three thematic areas/levels of analysis – voters, parties, communities – the project will lead to a series of important contributions. Through a combination of public opinion research, oral histories, and the innovative method of matching of individual census entries, the project will answer to which extent postwar elections are decided by voters’ experiences and perceptions of the ended conflict, as opposed to their considerations of the parties’ peacetime economic platforms and performance in office. In-depth study of party documents and platforms, party relations with the organisations of the postwar civil sector, as well as interviews with party officials and activists will shed light on the influence of war on electoral strategies, policy preferences, and recruitment methods of postwar political parties. And a combination of large-N research on the level of the region’s municipalities and a set of paired comparisons of several communities in the different postwar communities in the region will help expose the mechanisms through which war becomes embedded into postwar political competition and thus continues to exert its influence even decades after the violence has ended.
Summary
We know remarkably little about the impact of war on political competition in postwar societies in spite of the fact that postwar elections have garnered tremendous interest from researchers in a variety of fields. That interest, however, has been limited to establishing the relationship between electoral democratization and the incidence of conflict. Voters’ and parties’ electoral behaviour after the immediate post‐conflict period have remained largely neglected by researchers. The proposed project will fill this gap in our understanding of electoral legacies of war by analysing the evolution of political competition over the course of more than two decades in the six postwar states of Southeast Europe: Bosnia-Herzegovina, Croatia, Kosovo, Macedonia, Montenegro, and Serbia. Organised around three thematic areas/levels of analysis – voters, parties, communities – the project will lead to a series of important contributions. Through a combination of public opinion research, oral histories, and the innovative method of matching of individual census entries, the project will answer to which extent postwar elections are decided by voters’ experiences and perceptions of the ended conflict, as opposed to their considerations of the parties’ peacetime economic platforms and performance in office. In-depth study of party documents and platforms, party relations with the organisations of the postwar civil sector, as well as interviews with party officials and activists will shed light on the influence of war on electoral strategies, policy preferences, and recruitment methods of postwar political parties. And a combination of large-N research on the level of the region’s municipalities and a set of paired comparisons of several communities in the different postwar communities in the region will help expose the mechanisms through which war becomes embedded into postwar political competition and thus continues to exert its influence even decades after the violence has ended.
Max ERC Funding
1 499 788 €
Duration
Start date: 2017-04-01, End date: 2022-03-31
Project acronym ExpoBiome
Project Deciphering the impact of exposures from the gut microbiome-derived molecular complex in human health and disease
Researcher (PI) Paul WILMES
Host Institution (HI) UNIVERSITE DU LUXEMBOURG
Country Luxembourg
Call Details Consolidator Grant (CoG), LS2, ERC-2019-COG
Summary The human gut microbiome is a complex ecosystem, which contributes essential functions to human physiology. Changes to the microbiome are associated with several chronic diseases characterised by inflammation, including neurodegenerative and autoimmune diseases. Microbiome-derived effector molecules comprising nucleic acids, (poly)peptides and metabolites are present at high levels in the gut but have so far eluded systematic study. This gap in knowledge is limiting mechanistic understanding of the microbiome’s functional impact on chronic diseases such as Parkinson’s disease (PD) and rheumatoid arthritis (RA). Here, I will for the first time integrate a combination of advanced high-resolution methodologies to comprehensively identify the constituents of this molecular complex and their impact on the human immune system. First, I will perform a quantitative, integrated multi-omic analysis on microbiome samples collected from healthy individuals and patients with newly diagnosed PD or RA. I will integrate and analyse the data using a newly developed knowledge base. Using contextualised prior knowledge (ExpoBiome Map) and machine learning methods, I will identify microbial molecules associated with condition-specific immunophenotypes. Second, I will validate and track the biomarker signature during a model clinical intervention (therapeutic fasting) to predict treatment outcomes. Third, microbes and molecules will be screened in personalised HuMiX gut-on-chip models to identify novel anti-inflammatory compounds. By providing mechanistic insights into the molecular basis of human-microbiome interactions, the project will generate essential new knowledge about causal relationships between the gut microbiome and the immune system in health and disease. By facilitating the elucidation of currently unknown microbiome-derived molecules, it will identify new genes, proteins, metabolites and host pathways for the development of future diagnostic and therapeutic applications.
Summary
The human gut microbiome is a complex ecosystem, which contributes essential functions to human physiology. Changes to the microbiome are associated with several chronic diseases characterised by inflammation, including neurodegenerative and autoimmune diseases. Microbiome-derived effector molecules comprising nucleic acids, (poly)peptides and metabolites are present at high levels in the gut but have so far eluded systematic study. This gap in knowledge is limiting mechanistic understanding of the microbiome’s functional impact on chronic diseases such as Parkinson’s disease (PD) and rheumatoid arthritis (RA). Here, I will for the first time integrate a combination of advanced high-resolution methodologies to comprehensively identify the constituents of this molecular complex and their impact on the human immune system. First, I will perform a quantitative, integrated multi-omic analysis on microbiome samples collected from healthy individuals and patients with newly diagnosed PD or RA. I will integrate and analyse the data using a newly developed knowledge base. Using contextualised prior knowledge (ExpoBiome Map) and machine learning methods, I will identify microbial molecules associated with condition-specific immunophenotypes. Second, I will validate and track the biomarker signature during a model clinical intervention (therapeutic fasting) to predict treatment outcomes. Third, microbes and molecules will be screened in personalised HuMiX gut-on-chip models to identify novel anti-inflammatory compounds. By providing mechanistic insights into the molecular basis of human-microbiome interactions, the project will generate essential new knowledge about causal relationships between the gut microbiome and the immune system in health and disease. By facilitating the elucidation of currently unknown microbiome-derived molecules, it will identify new genes, proteins, metabolites and host pathways for the development of future diagnostic and therapeutic applications.
Max ERC Funding
1 998 620 €
Duration
Start date: 2020-11-01, End date: 2025-10-31
