Project acronym HOPE
Project Humans On Planet Earth - Long-term impacts on biosphere dynamics
Researcher (PI) Harry John Betteley BIRKS
Host Institution (HI) UNIVERSITETET I BERGEN
Call Details Advanced Grant (AdG), PE10, ERC-2016-ADG
Summary A critical question in Earth system science is what was the impact of prehistoric people on the biosphere and climate? There is much information about human impact through clearance, agriculture, erosion, and modifying water and nutrient budgets. Humans have greatly changed the Earth in the last 8000 years, but did humans modify the major ecological processes (e.g. assembly rules) that shape community assembly and dynamics? Did inter-relationships between processes change in response to human impact? Lyons et al. & Dietl (2016 Nature) suggest that human activities in the last 6000 years had such impacts. Dietl proposes that using past ‘natural experiments’ to predict future changes is “flawed” and “out is the use of uniformitarianism”. As using natural experiments is a common strategy and uniformitarianism is the major working concept in Earth sciences, it is imperative to test whether prehistoric human activity changed major ecological processes determining community development. To test this hypothesis, patterns in pollen-stratigraphical data for the past 11,500 years from over 2000 sites across the globe will be explored consistently using numerical techniques to discern changes in 25 ecosystem properties (richness, evenness, and diversity; turnover; rates of change; taxon co-occurrences, etc.). Patterns in these properties will be compared statistically at sites within biomes, between biomes, within continents, and between continents to test the hypotheses that prehistoric human activities changed the basic ecological processes of community assembly and that their inter-relationships changed through time. These areas provide major contrasts in human prehistory and biomes. HOPE is interdisciplinary: pollen analysis, databases, multivariate analysis, ecology, new statistical methods, numerical simulations, statistical modelling. HOPE’s impact goes beyond human effects on the biosphere and extends to the very core of Earth science’s basic conceptual framework.
Summary
A critical question in Earth system science is what was the impact of prehistoric people on the biosphere and climate? There is much information about human impact through clearance, agriculture, erosion, and modifying water and nutrient budgets. Humans have greatly changed the Earth in the last 8000 years, but did humans modify the major ecological processes (e.g. assembly rules) that shape community assembly and dynamics? Did inter-relationships between processes change in response to human impact? Lyons et al. & Dietl (2016 Nature) suggest that human activities in the last 6000 years had such impacts. Dietl proposes that using past ‘natural experiments’ to predict future changes is “flawed” and “out is the use of uniformitarianism”. As using natural experiments is a common strategy and uniformitarianism is the major working concept in Earth sciences, it is imperative to test whether prehistoric human activity changed major ecological processes determining community development. To test this hypothesis, patterns in pollen-stratigraphical data for the past 11,500 years from over 2000 sites across the globe will be explored consistently using numerical techniques to discern changes in 25 ecosystem properties (richness, evenness, and diversity; turnover; rates of change; taxon co-occurrences, etc.). Patterns in these properties will be compared statistically at sites within biomes, between biomes, within continents, and between continents to test the hypotheses that prehistoric human activities changed the basic ecological processes of community assembly and that their inter-relationships changed through time. These areas provide major contrasts in human prehistory and biomes. HOPE is interdisciplinary: pollen analysis, databases, multivariate analysis, ecology, new statistical methods, numerical simulations, statistical modelling. HOPE’s impact goes beyond human effects on the biosphere and extends to the very core of Earth science’s basic conceptual framework.
Max ERC Funding
2 278 884 €
Duration
Start date: 2018-01-01, End date: 2022-12-31
Project acronym INNOSTOCH
Project INNOVATIONS IN STOCHASTIC ANALYSIS AND APPLICATIONS with emphasis on STOCHASTIC CONTROL AND INFORMATION
Researcher (PI) Bernt Karsten Øksendal
Host Institution (HI) UNIVERSITETET I OSLO
Call Details Advanced Grant (AdG), PE1, ERC-2008-AdG
Summary "For almost all kinds of dynamic systems modeling real processes in nature or society, most of the mathematical models we can formulate are - at best - inaccurate, and subject to random fluctuations and other types of ""noise"". Therefore it is important to be able to deal with such noisy models in a mathematically rigorous way. This rigorous theory is stochastic analysis. Theoretical progress in stochastic analysis will lead to new and improved applications in a wide range of fields.
The main purpose of this proposal is to establish a research environment which enhances the creation of new ideas and methods in the research of stochastic analysis and its applications. The emphasis is more on innovation, new models and challenges in the research frontiers, rather than small variations and minor improvements of already established theories and results. We will concentrate on applications in finance and biology, but the theoretical results may as well apply to several other areas.
Utilizing recent results and achievements by PI and a large group of distinguished coworkers, the natural extensions from the present knowledge is to concentrate on the mathematical theory of the interplay between stochastic analysis, stochastic control and information. More precisely, we have ambitions to make fundamental progress in the general theory of stochastic control of random systems and applications in finance and biology, and the explicit relation between the optimal performance and the amount of information available to the controller. Explicit examples of special interest include optimal control under partial or delayed information, and optimal control under inside or advanced information. A success of the present proposal will represent a substantial breakthrough, and in turn bring us a significant step forward in our attempts to understand various aspects of the world better, and it will help us to find optimal, sustainable ways to influence it."
Summary
"For almost all kinds of dynamic systems modeling real processes in nature or society, most of the mathematical models we can formulate are - at best - inaccurate, and subject to random fluctuations and other types of ""noise"". Therefore it is important to be able to deal with such noisy models in a mathematically rigorous way. This rigorous theory is stochastic analysis. Theoretical progress in stochastic analysis will lead to new and improved applications in a wide range of fields.
The main purpose of this proposal is to establish a research environment which enhances the creation of new ideas and methods in the research of stochastic analysis and its applications. The emphasis is more on innovation, new models and challenges in the research frontiers, rather than small variations and minor improvements of already established theories and results. We will concentrate on applications in finance and biology, but the theoretical results may as well apply to several other areas.
Utilizing recent results and achievements by PI and a large group of distinguished coworkers, the natural extensions from the present knowledge is to concentrate on the mathematical theory of the interplay between stochastic analysis, stochastic control and information. More precisely, we have ambitions to make fundamental progress in the general theory of stochastic control of random systems and applications in finance and biology, and the explicit relation between the optimal performance and the amount of information available to the controller. Explicit examples of special interest include optimal control under partial or delayed information, and optimal control under inside or advanced information. A success of the present proposal will represent a substantial breakthrough, and in turn bring us a significant step forward in our attempts to understand various aspects of the world better, and it will help us to find optimal, sustainable ways to influence it."
Max ERC Funding
1 864 800 €
Duration
Start date: 2009-09-01, End date: 2014-08-31
Project acronym PaPaAlg
Project Pareto-Optimal Parameterized Algorithms
Researcher (PI) Daniel LOKSHTANOV
Host Institution (HI) UNIVERSITETET I BERGEN
Call Details Starting Grant (StG), PE6, ERC-2016-STG
Summary In this project we revise the foundations of parameterized complexity, a modern multi-variate approach to algorithm design. The underlying question of every algorithmic paradigm is ``what is the best algorithm?'' When the running time of algorithms is measured in terms of only one variable, it is easy to compare which one is the fastest. However, when the running time depends on more than one variable, as is the case for parameterized complexity:
**It is not clear what a ``fastest possible algorithm'' really means.**
The previous formalizations of what a fastest possible parameterized algorithm means are one-dimensional, contrary to the core philosophy of parameterized complexity. These one-dimensional approaches to a multi-dimensional algorithmic paradigm unavoidably miss the most efficient algorithms, and ultimately fail to solve instances that we could have solved.
We propose the first truly multi-dimensional framework for comparing the running times of parameterized algorithms. Our new definitions are based on the notion of Pareto-optimality from economics. The new approach encompasses all existing paradigms for comparing parameterized algorithms, opens up a whole new world of research directions in parameterized complexity, and reveals new fundamental questions about parameterized problems that were considered well-understood.
In this project we will develop powerful algorithmic and complexity theoretic tools to answer these research questions. The successful completion of this project will take parameterized complexity far beyond the state of the art, make parameterized algorithms more relevant for practical applications, and significantly advance adjacent subfields of theoretical computer science and mathematics.
Summary
In this project we revise the foundations of parameterized complexity, a modern multi-variate approach to algorithm design. The underlying question of every algorithmic paradigm is ``what is the best algorithm?'' When the running time of algorithms is measured in terms of only one variable, it is easy to compare which one is the fastest. However, when the running time depends on more than one variable, as is the case for parameterized complexity:
**It is not clear what a ``fastest possible algorithm'' really means.**
The previous formalizations of what a fastest possible parameterized algorithm means are one-dimensional, contrary to the core philosophy of parameterized complexity. These one-dimensional approaches to a multi-dimensional algorithmic paradigm unavoidably miss the most efficient algorithms, and ultimately fail to solve instances that we could have solved.
We propose the first truly multi-dimensional framework for comparing the running times of parameterized algorithms. Our new definitions are based on the notion of Pareto-optimality from economics. The new approach encompasses all existing paradigms for comparing parameterized algorithms, opens up a whole new world of research directions in parameterized complexity, and reveals new fundamental questions about parameterized problems that were considered well-understood.
In this project we will develop powerful algorithmic and complexity theoretic tools to answer these research questions. The successful completion of this project will take parameterized complexity far beyond the state of the art, make parameterized algorithms more relevant for practical applications, and significantly advance adjacent subfields of theoretical computer science and mathematics.
Max ERC Funding
1 499 557 €
Duration
Start date: 2017-02-01, End date: 2022-01-31
Project acronym Waterscales
Project Mathematical and computational foundations for modeling cerebral fluid flow.
Researcher (PI) Marie Elisabeth ROGNES
Host Institution (HI) SIMULA RESEARCH LABORATORY AS
Call Details Starting Grant (StG), PE1, ERC-2016-STG
Summary Your brain has its own waterscape: whether you are reading or sleeping, fluid flows through the brain tissue and clears waste in the process. These physiological processes are crucial for the well-being of the brain. In spite of their importance we understand them but little. Mathematics and numerics could play a crucial role in gaining new insight. Indeed, medical doctors express an urgent need for multiscale modeling of water transport through the brain, to overcome limitations in traditional techniques. Surprisingly little attention has been paid to the numerics of the brain's waterscape however, and fundamental knowledge is missing.
In response, the Waterscales ambition is to establish the mathematical and computational foundations for predictively modeling fluid flow and solute transport through the brain across scales -- from the cellular to the organ level. The project aims to bridge multiscale fluid mechanics and cellular electrophysiology to pioneer new families of mathematical models that couple macroscale, mesoscale and microscale flow with glial cell dynamics. For these models, we will design numerical discretizations that preserve key properties and that allow for whole organ simulations. To evaluate predictability, we will develop a new computational platform for model adaptivity and calibration. The project is multidisciplinary combining mathematics, mechanics, scientific computing, and physiology.
If successful, this project enables the first in silico studies of the brain's waterscape across scales. The new models would open up a new research field within computational neuroscience with ample opportunities for further mathematical and more applied study. The processes at hand are associated with neurodegenerative diseases e.g. dementia and with brain swelling caused by e.g. stroke. The Waterscales project will provide the field with a sorely needed, new avenue of investigation to understand these conditions, with tremendous long-term impact.
Summary
Your brain has its own waterscape: whether you are reading or sleeping, fluid flows through the brain tissue and clears waste in the process. These physiological processes are crucial for the well-being of the brain. In spite of their importance we understand them but little. Mathematics and numerics could play a crucial role in gaining new insight. Indeed, medical doctors express an urgent need for multiscale modeling of water transport through the brain, to overcome limitations in traditional techniques. Surprisingly little attention has been paid to the numerics of the brain's waterscape however, and fundamental knowledge is missing.
In response, the Waterscales ambition is to establish the mathematical and computational foundations for predictively modeling fluid flow and solute transport through the brain across scales -- from the cellular to the organ level. The project aims to bridge multiscale fluid mechanics and cellular electrophysiology to pioneer new families of mathematical models that couple macroscale, mesoscale and microscale flow with glial cell dynamics. For these models, we will design numerical discretizations that preserve key properties and that allow for whole organ simulations. To evaluate predictability, we will develop a new computational platform for model adaptivity and calibration. The project is multidisciplinary combining mathematics, mechanics, scientific computing, and physiology.
If successful, this project enables the first in silico studies of the brain's waterscape across scales. The new models would open up a new research field within computational neuroscience with ample opportunities for further mathematical and more applied study. The processes at hand are associated with neurodegenerative diseases e.g. dementia and with brain swelling caused by e.g. stroke. The Waterscales project will provide the field with a sorely needed, new avenue of investigation to understand these conditions, with tremendous long-term impact.
Max ERC Funding
1 500 000 €
Duration
Start date: 2017-04-01, End date: 2022-03-31
