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 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 NATURAL
Project Natural Program Repair
Researcher (PI) Tegawende F. Bissyande
Host Institution (HI) UNIVERSITE DU LUXEMBOURG
Country Luxembourg
Call Details Starting Grant (StG), PE6, ERC-2020-STG
Summary Automatic bug fixing, i.e., the idea of having programs that fix other programs, is a long-standing dream that is increasingly embraced by the software engineering community. Indeed, despite the significant effort that humans put into reviewing code and running software test campaigns, programming mistakes slip by, with severe consequences. Fixing those mistakes automatically has recently been the focus of a number of potentially promising techniques. Proposed approaches are however recurrently criticized as being shallow (i.e., they mostly address unit test failures, which are often neither hard nor important problems).
Initial successes in automatic bug fixing are based on scenarios such as the following: when a bug is localized, patches are generated repetitively and automatically, through trial and error, until a valid patch is produced. The produced patch could then be later revised by developers. While the reported achievements are certainly worthwhile, they do not address what we believe is a more comprehensive challenge of software engineering: to systematically fix features of a software system based on end-user requirements.
The ambition of NATURAL is to develop a methodology for yielding an intelligent agent that is capable of receiving a natural language description of a problem that a user faces with a software feature, and then synthesizing code to address this problem so that it meets the user's expectations. Such a repair bot would be a trustworthy software contributor that is 1) first, targeting real bugs in production via exploiting bug reports, which remain largely under-explored, 2) second, aligning with the conversational needs of collaborative work via generating explanations for patch suggestions, 3) third, shifting the repair paradigm towards the design of self-improving systems via yielding novel algorithms that iteratively integrate feedback from humans. Ultimately, NATURAL will be transformative in the practice of software engineering.
Summary
Automatic bug fixing, i.e., the idea of having programs that fix other programs, is a long-standing dream that is increasingly embraced by the software engineering community. Indeed, despite the significant effort that humans put into reviewing code and running software test campaigns, programming mistakes slip by, with severe consequences. Fixing those mistakes automatically has recently been the focus of a number of potentially promising techniques. Proposed approaches are however recurrently criticized as being shallow (i.e., they mostly address unit test failures, which are often neither hard nor important problems).
Initial successes in automatic bug fixing are based on scenarios such as the following: when a bug is localized, patches are generated repetitively and automatically, through trial and error, until a valid patch is produced. The produced patch could then be later revised by developers. While the reported achievements are certainly worthwhile, they do not address what we believe is a more comprehensive challenge of software engineering: to systematically fix features of a software system based on end-user requirements.
The ambition of NATURAL is to develop a methodology for yielding an intelligent agent that is capable of receiving a natural language description of a problem that a user faces with a software feature, and then synthesizing code to address this problem so that it meets the user's expectations. Such a repair bot would be a trustworthy software contributor that is 1) first, targeting real bugs in production via exploiting bug reports, which remain largely under-explored, 2) second, aligning with the conversational needs of collaborative work via generating explanations for patch suggestions, 3) third, shifting the repair paradigm towards the design of self-improving systems via yielding novel algorithms that iteratively integrate feedback from humans. Ultimately, NATURAL will be transformative in the practice of software engineering.
Max ERC Funding
1 495 988 €
Duration
Start date: 2021-02-01, End date: 2026-01-31
Project acronym RealTCut
Project Towards real time multiscale simulation of cutting in non-linear materials
with applications to surgical simulation and computer guided surgery
Researcher (PI) Stephane Pierre Alain Bordas
Host Institution (HI) UNIVERSITE DU LUXEMBOURG
Country Luxembourg
Call Details Starting Grant (StG), PE8, ERC-2011-StG_20101014
Summary "Surgeons are trained as apprentices. Some conditions are rarely encountered and surgeons will only be trained in the specific skills associated with a given situation if they come across it. At the end of their residency, it is hoped that they will have faced sufficiently many cases to be competent. This can be dangerous to the patients.
If we were able to reproduce faithfully, in a virtual environment, the audio, visual and haptic experience of a surgeon as they prod, pull and incise tissue, then, surgeons would not have to train on cadavers, phantoms, or on the patients themselves.
Only a few researchers in the Computational Mechanics community have attacked the mechanical problems related to surgical simulation, so that mechanical faithfulness is not on par with audiovisual. This lack of fidelity in the reproduction of surgical acts such as cutting may explain why most surgeons who tested existing simulators report that the ""sensation"" fed back to them remains unrealistic. To date, the proposers are not aware of Computational Mechanics solutions addressing, at the same time, geometrical faithfulness, material realism, evolving cuts and quality control of the solution.
The measurable objectives for this research are as follows:
O1:Significantly alleviate the mesh generation and regeneration burden to represent organs’ geometries, underlying tissue microstructure and cuts with sufficient accuracy but minimal user intervention
O2:Move away from simplistic coarse-scale material models by deducing tissue rupture at the organ level from constitutive (e.g. damage) and contact models designed at the meso and micro scales
O3:Ensure real-time results through model order reduction coupled with the multi-scale fracture tools of O2
O4:Control solution accuracy and validate against a range of biomechanics problems including real-life brain surgery interventions with the available at our collaborators’"
Summary
"Surgeons are trained as apprentices. Some conditions are rarely encountered and surgeons will only be trained in the specific skills associated with a given situation if they come across it. At the end of their residency, it is hoped that they will have faced sufficiently many cases to be competent. This can be dangerous to the patients.
If we were able to reproduce faithfully, in a virtual environment, the audio, visual and haptic experience of a surgeon as they prod, pull and incise tissue, then, surgeons would not have to train on cadavers, phantoms, or on the patients themselves.
Only a few researchers in the Computational Mechanics community have attacked the mechanical problems related to surgical simulation, so that mechanical faithfulness is not on par with audiovisual. This lack of fidelity in the reproduction of surgical acts such as cutting may explain why most surgeons who tested existing simulators report that the ""sensation"" fed back to them remains unrealistic. To date, the proposers are not aware of Computational Mechanics solutions addressing, at the same time, geometrical faithfulness, material realism, evolving cuts and quality control of the solution.
The measurable objectives for this research are as follows:
O1:Significantly alleviate the mesh generation and regeneration burden to represent organs’ geometries, underlying tissue microstructure and cuts with sufficient accuracy but minimal user intervention
O2:Move away from simplistic coarse-scale material models by deducing tissue rupture at the organ level from constitutive (e.g. damage) and contact models designed at the meso and micro scales
O3:Ensure real-time results through model order reduction coupled with the multi-scale fracture tools of O2
O4:Control solution accuracy and validate against a range of biomechanics problems including real-life brain surgery interventions with the available at our collaborators’"
Max ERC Funding
1 343 955 €
Duration
Start date: 2012-01-01, End date: 2016-12-31
