Project acronym ACDC
Project Algorithms and Complexity of Highly Decentralized Computations
Researcher (PI) Fabian Daniel Kuhn
Host Institution (HI) ALBERT-LUDWIGS-UNIVERSITAET FREIBURG
Call Details Starting Grant (StG), PE6, ERC-2013-StG
Summary "Many of today's and tomorrow's computer systems are built on top of large-scale networks such as, e.g., the Internet, the world wide web, wireless ad hoc and sensor networks, or peer-to-peer networks. Driven by technological advances, new kinds of networks and applications have become possible and we can safely assume that this trend is going to continue. Often modern systems are envisioned to consist of a potentially large number of individual components that are organized in a completely decentralized way. There is no central authority that controls the topology of the network, how nodes join or leave the system, or in which way nodes communicate with each other. Also, many future distributed applications will be built using wireless devices that communicate via radio.
The general objective of the proposed project is to improve our understanding of the algorithmic and theoretical foundations of decentralized distributed systems. From an algorithmic point of view, decentralized networks and computations pose a number of fascinating and unique challenges that are not present in sequential or more standard distributed systems. As communication is limited and mostly between nearby nodes, each node of a large network can only maintain a very restricted view of the global state of the system. This is particularly true if the network can change dynamically, either by nodes joining or leaving the system or if the topology changes over time, e.g., because of the mobility of the devices in case of a wireless network. Nevertheless, the nodes of a network need to coordinate in order to achieve some global goal.
In particular, we plan to study algorithms and lower bounds for basic computation and information dissemination tasks in such systems. In addition, we are particularly interested in the complexity of distributed computations in dynamic and wireless networks."
Summary
"Many of today's and tomorrow's computer systems are built on top of large-scale networks such as, e.g., the Internet, the world wide web, wireless ad hoc and sensor networks, or peer-to-peer networks. Driven by technological advances, new kinds of networks and applications have become possible and we can safely assume that this trend is going to continue. Often modern systems are envisioned to consist of a potentially large number of individual components that are organized in a completely decentralized way. There is no central authority that controls the topology of the network, how nodes join or leave the system, or in which way nodes communicate with each other. Also, many future distributed applications will be built using wireless devices that communicate via radio.
The general objective of the proposed project is to improve our understanding of the algorithmic and theoretical foundations of decentralized distributed systems. From an algorithmic point of view, decentralized networks and computations pose a number of fascinating and unique challenges that are not present in sequential or more standard distributed systems. As communication is limited and mostly between nearby nodes, each node of a large network can only maintain a very restricted view of the global state of the system. This is particularly true if the network can change dynamically, either by nodes joining or leaving the system or if the topology changes over time, e.g., because of the mobility of the devices in case of a wireless network. Nevertheless, the nodes of a network need to coordinate in order to achieve some global goal.
In particular, we plan to study algorithms and lower bounds for basic computation and information dissemination tasks in such systems. In addition, we are particularly interested in the complexity of distributed computations in dynamic and wireless networks."
Max ERC Funding
1 148 000 €
Duration
Start date: 2013-11-01, End date: 2018-10-31
Project acronym ACROSS
Project 3D Reconstruction and Modeling across Different Levels of Abstraction
Researcher (PI) Leif Kobbelt
Host Institution (HI) RHEINISCH-WESTFAELISCHE TECHNISCHE HOCHSCHULE AACHEN
Call Details Advanced Grant (AdG), PE6, ERC-2013-ADG
Summary "Digital 3D models are gaining more and more importance in diverse application fields ranging from computer graphics, multimedia and simulation sciences to engineering, architecture, and medicine. Powerful technologies to digitize the 3D shape of real objects and scenes are becoming available even to consumers. However, the raw geometric data emerging from, e.g., 3D scanning or multi-view stereo often lacks a consistent structure and meta-information which are necessary for the effective deployment of such models in sophisticated down-stream applications like animation, simulation, or CAD/CAM that go beyond mere visualization. Our goal is to develop new fundamental algorithms which transform raw geometric input data into augmented 3D models that are equipped with structural meta information such as feature aligned meshes, patch segmentations, local and global geometric constraints, statistical shape variation data, or even procedural descriptions. Our methodological approach is inspired by the human perceptual system that integrates bottom-up (data-driven) and top-down (model-driven) mechanisms in its hierarchical processing. Similarly we combine algorithms operating on different levels of abstraction into reconstruction and modeling networks. Instead of developing an individual solution for each specific application scenario, we create an eco-system of algorithms for automatic processing and interactive design of highly complex 3D models. A key concept is the information flow across all levels of abstraction in a bottom-up as well as top-down fashion. We not only aim at optimizing geometric representations but in fact at bridging the gap between reconstruction and recognition of geometric objects. The results from this project will make it possible to bring 3D models of real world objects into many highly relevant applications in science, industry, and entertainment, greatly reducing the excessive manual effort that is still necessary today."
Summary
"Digital 3D models are gaining more and more importance in diverse application fields ranging from computer graphics, multimedia and simulation sciences to engineering, architecture, and medicine. Powerful technologies to digitize the 3D shape of real objects and scenes are becoming available even to consumers. However, the raw geometric data emerging from, e.g., 3D scanning or multi-view stereo often lacks a consistent structure and meta-information which are necessary for the effective deployment of such models in sophisticated down-stream applications like animation, simulation, or CAD/CAM that go beyond mere visualization. Our goal is to develop new fundamental algorithms which transform raw geometric input data into augmented 3D models that are equipped with structural meta information such as feature aligned meshes, patch segmentations, local and global geometric constraints, statistical shape variation data, or even procedural descriptions. Our methodological approach is inspired by the human perceptual system that integrates bottom-up (data-driven) and top-down (model-driven) mechanisms in its hierarchical processing. Similarly we combine algorithms operating on different levels of abstraction into reconstruction and modeling networks. Instead of developing an individual solution for each specific application scenario, we create an eco-system of algorithms for automatic processing and interactive design of highly complex 3D models. A key concept is the information flow across all levels of abstraction in a bottom-up as well as top-down fashion. We not only aim at optimizing geometric representations but in fact at bridging the gap between reconstruction and recognition of geometric objects. The results from this project will make it possible to bring 3D models of real world objects into many highly relevant applications in science, industry, and entertainment, greatly reducing the excessive manual effort that is still necessary today."
Max ERC Funding
2 482 000 €
Duration
Start date: 2014-03-01, End date: 2019-02-28
Project acronym ACUITY
Project Algorithms for coping with uncertainty and intractability
Researcher (PI) Nikhil Bansal
Host Institution (HI) TECHNISCHE UNIVERSITEIT EINDHOVEN
Call Details Consolidator Grant (CoG), PE6, ERC-2013-CoG
Summary The two biggest challenges in solving practical optimization problems are computational intractability, and the presence
of uncertainty: most problems are either NP-hard, or have incomplete input data which
makes an exact computation impossible.
Recently, there has been a huge progress in our understanding of intractability, based on spectacular algorithmic and lower bound techniques. For several problems, especially those with only local constraints, we can design optimum
approximation algorithms that are provably the best possible.
However, typical optimization problems usually involve complex global constraints and are much less understood. The situation is even worse for coping with uncertainty. Most of the algorithms are based on ad-hoc techniques and there is no deeper understanding of what makes various problems easy or hard.
This proposal describes several new directions, together with concrete intermediate goals, that will break important new ground in the theory of approximation and online algorithms. The particular directions we consider are (i) extend the primal dual method to systematically design online algorithms, (ii) build a structural theory of online problems based on work functions, (iii) develop new tools to use the power of strong convex relaxations and (iv) design new algorithmic approaches based on non-constructive proof techniques.
The proposed research is at the
cutting edge of algorithm design, and builds upon the recent success of the PI in resolving several longstanding questions in these areas. Any progress is likely to be a significant contribution to theoretical
computer science and combinatorial optimization.
Summary
The two biggest challenges in solving practical optimization problems are computational intractability, and the presence
of uncertainty: most problems are either NP-hard, or have incomplete input data which
makes an exact computation impossible.
Recently, there has been a huge progress in our understanding of intractability, based on spectacular algorithmic and lower bound techniques. For several problems, especially those with only local constraints, we can design optimum
approximation algorithms that are provably the best possible.
However, typical optimization problems usually involve complex global constraints and are much less understood. The situation is even worse for coping with uncertainty. Most of the algorithms are based on ad-hoc techniques and there is no deeper understanding of what makes various problems easy or hard.
This proposal describes several new directions, together with concrete intermediate goals, that will break important new ground in the theory of approximation and online algorithms. The particular directions we consider are (i) extend the primal dual method to systematically design online algorithms, (ii) build a structural theory of online problems based on work functions, (iii) develop new tools to use the power of strong convex relaxations and (iv) design new algorithmic approaches based on non-constructive proof techniques.
The proposed research is at the
cutting edge of algorithm design, and builds upon the recent success of the PI in resolving several longstanding questions in these areas. Any progress is likely to be a significant contribution to theoretical
computer science and combinatorial optimization.
Max ERC Funding
1 519 285 €
Duration
Start date: 2014-05-01, End date: 2019-04-30
Project acronym ALEXANDRIA
Project "Foundations for Temporal Retrieval, Exploration and Analytics in Web Archives"
Researcher (PI) Wolfgang Nejdl
Host Institution (HI) GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVER
Call Details Advanced Grant (AdG), PE6, ERC-2013-ADG
Summary "Significant parts of our cultural heritage are produced on the Web, yet only insufficient opportunities exist for accessing and exploring the past of the Web. The ALEXANDRIA project aims to develop models, tools and techniques necessary to archive and index relevant parts of the Web, and to retrieve and explore this information in a meaningful way. While the easy accessibility to the current Web is a good baseline, optimal access to Web archives requires new models and algorithms for retrieval, exploration, and analytics which go far beyond what is needed to access the current state of the Web. This includes taking into account the unique temporal dimension of Web archives, structured semantic information already available on the Web, as well as social media and network information.
Within ALEXANDRIA, we will significantly advance semantic and time-based indexing for Web archives using human-compiled knowledge available on the Web, to efficiently index, retrieve and explore information about entities and events from the past. In doing so, we will focus on the concurrent evolution of this knowledge and the Web content to be indexed, and take into account diversity and incompleteness of this knowledge. We will further investigate mixed crowd- and machine-based Web analytics to support long- running and collaborative retrieval and analysis processes on Web archives. Usage of implicit human feedback will be essential to provide better indexing through insights during the analysis process and to better focus harvesting of content.
The ALEXANDRIA Testbed will provide an important context for research, exploration and evaluation of the concepts, methods and algorithms developed in this project, and will provide both relevant collections and algorithms that enable further research on and practical application of our research results to existing archives like the Internet Archive, the Internet Memory Foundation and Web archives maintained by European national libraries."
Summary
"Significant parts of our cultural heritage are produced on the Web, yet only insufficient opportunities exist for accessing and exploring the past of the Web. The ALEXANDRIA project aims to develop models, tools and techniques necessary to archive and index relevant parts of the Web, and to retrieve and explore this information in a meaningful way. While the easy accessibility to the current Web is a good baseline, optimal access to Web archives requires new models and algorithms for retrieval, exploration, and analytics which go far beyond what is needed to access the current state of the Web. This includes taking into account the unique temporal dimension of Web archives, structured semantic information already available on the Web, as well as social media and network information.
Within ALEXANDRIA, we will significantly advance semantic and time-based indexing for Web archives using human-compiled knowledge available on the Web, to efficiently index, retrieve and explore information about entities and events from the past. In doing so, we will focus on the concurrent evolution of this knowledge and the Web content to be indexed, and take into account diversity and incompleteness of this knowledge. We will further investigate mixed crowd- and machine-based Web analytics to support long- running and collaborative retrieval and analysis processes on Web archives. Usage of implicit human feedback will be essential to provide better indexing through insights during the analysis process and to better focus harvesting of content.
The ALEXANDRIA Testbed will provide an important context for research, exploration and evaluation of the concepts, methods and algorithms developed in this project, and will provide both relevant collections and algorithms that enable further research on and practical application of our research results to existing archives like the Internet Archive, the Internet Memory Foundation and Web archives maintained by European national libraries."
Max ERC Funding
2 493 600 €
Duration
Start date: 2014-03-01, End date: 2019-02-28
Project acronym ALUNIF
Project Algorithms and Lower Bounds: A Unified Approach
Researcher (PI) Rahul Santhanam
Host Institution (HI) THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Call Details Consolidator Grant (CoG), PE6, ERC-2013-CoG
Summary One of the fundamental goals of theoretical computer science is to
understand the possibilities and limits of efficient computation. This
quest has two dimensions. The
theory of algorithms focuses on finding efficient solutions to
problems, while computational complexity theory aims to understand when
and why problems are hard to solve. These two areas have different
philosophies and use different sets of techniques. However, in recent
years there have been indications of deep and mysterious connections
between them.
In this project, we propose to explore and develop the connections between
algorithmic analysis and complexity lower bounds in a systematic way.
On the one hand, we plan to use complexity lower bound techniques as inspiration
to design new and improved algorithms for Satisfiability and other
NP-complete problems, as well as to analyze existing algorithms better.
On the other hand, we plan to strengthen implications yielding circuit
lower bounds from non-trivial algorithms for Satisfiability, and to derive
new circuit lower bounds using these stronger implications.
This project has potential for massive impact in both the areas of algorithms
and computational complexity. Improved algorithms for Satisfiability could lead
to improved SAT solvers, and the new analytical tools would lead to a better
understanding of existing heuristics. Complexity lower bound questions are
fundamental
but notoriously difficult, and new lower bounds would open the way to
unconditionally secure cryptographic protocols and derandomization of
probabilistic algorithms. More broadly, this project aims to initiate greater
dialogue between the two areas, with an exchange of ideas and techniques
which leads to accelerated progress in both, as well as a deeper understanding
of the nature of efficient computation.
Summary
One of the fundamental goals of theoretical computer science is to
understand the possibilities and limits of efficient computation. This
quest has two dimensions. The
theory of algorithms focuses on finding efficient solutions to
problems, while computational complexity theory aims to understand when
and why problems are hard to solve. These two areas have different
philosophies and use different sets of techniques. However, in recent
years there have been indications of deep and mysterious connections
between them.
In this project, we propose to explore and develop the connections between
algorithmic analysis and complexity lower bounds in a systematic way.
On the one hand, we plan to use complexity lower bound techniques as inspiration
to design new and improved algorithms for Satisfiability and other
NP-complete problems, as well as to analyze existing algorithms better.
On the other hand, we plan to strengthen implications yielding circuit
lower bounds from non-trivial algorithms for Satisfiability, and to derive
new circuit lower bounds using these stronger implications.
This project has potential for massive impact in both the areas of algorithms
and computational complexity. Improved algorithms for Satisfiability could lead
to improved SAT solvers, and the new analytical tools would lead to a better
understanding of existing heuristics. Complexity lower bound questions are
fundamental
but notoriously difficult, and new lower bounds would open the way to
unconditionally secure cryptographic protocols and derandomization of
probabilistic algorithms. More broadly, this project aims to initiate greater
dialogue between the two areas, with an exchange of ideas and techniques
which leads to accelerated progress in both, as well as a deeper understanding
of the nature of efficient computation.
Max ERC Funding
1 274 496 €
Duration
Start date: 2014-03-01, End date: 2019-02-28
Project acronym AMD
Project Algorithmic Mechanism Design: Beyond Truthful Mechanisms
Researcher (PI) Michal Feldman
Host Institution (HI) TEL AVIV UNIVERSITY
Call Details Starting Grant (StG), PE6, ERC-2013-StG
Summary "The first decade of Algorithmic Mechanism Design (AMD) concentrated, very successfully, on the design of truthful mechanisms for the allocation of resources among agents with private preferences.
Truthful mechanisms are ones that incentivize rational users to report their preferences truthfully.
Truthfulness, however, for all its theoretical appeal, suffers from several inherent limitations, mainly its high communication and computation complexities.
It is not surprising, therefore, that practical applications forego truthfulness and use simpler mechanisms instead.
Simplicity in itself, however, is not sufficient, as any meaningful mechanism should also have some notion of fairness; otherwise agents will stop using it over time.
In this project I plan to develop an innovative AMD theoretical framework that will go beyond truthfulness and focus instead on the natural themes of simplicity and fairness, in addition to computational tractability.
One of my primary goals will be the design of simple and fair poly-time mechanisms that perform at near optimal levels with respect to important economic objectives such as social welfare and revenue.
To this end, I will work toward providing precise definitions of simplicity and fairness and quantifying the effects of these restrictions on the performance levels that can be obtained.
A major challenge in the evaluation of non-truthful mechanisms is defining a reasonable behavior model that will enable their evaluation.
The success of this project could have a broad impact on Europe and beyond, as it would guide the design of natural mechanisms for markets of tens of billions of dollars in revenue, such as online advertising, or sales of wireless frequencies.
The timing of this project is ideal, as the AMD field is now sufficiently mature to lead to a breakthrough and at the same time young enough to be receptive to new approaches and themes."
Summary
"The first decade of Algorithmic Mechanism Design (AMD) concentrated, very successfully, on the design of truthful mechanisms for the allocation of resources among agents with private preferences.
Truthful mechanisms are ones that incentivize rational users to report their preferences truthfully.
Truthfulness, however, for all its theoretical appeal, suffers from several inherent limitations, mainly its high communication and computation complexities.
It is not surprising, therefore, that practical applications forego truthfulness and use simpler mechanisms instead.
Simplicity in itself, however, is not sufficient, as any meaningful mechanism should also have some notion of fairness; otherwise agents will stop using it over time.
In this project I plan to develop an innovative AMD theoretical framework that will go beyond truthfulness and focus instead on the natural themes of simplicity and fairness, in addition to computational tractability.
One of my primary goals will be the design of simple and fair poly-time mechanisms that perform at near optimal levels with respect to important economic objectives such as social welfare and revenue.
To this end, I will work toward providing precise definitions of simplicity and fairness and quantifying the effects of these restrictions on the performance levels that can be obtained.
A major challenge in the evaluation of non-truthful mechanisms is defining a reasonable behavior model that will enable their evaluation.
The success of this project could have a broad impact on Europe and beyond, as it would guide the design of natural mechanisms for markets of tens of billions of dollars in revenue, such as online advertising, or sales of wireless frequencies.
The timing of this project is ideal, as the AMD field is now sufficiently mature to lead to a breakthrough and at the same time young enough to be receptive to new approaches and themes."
Max ERC Funding
1 394 600 €
Duration
Start date: 2013-11-01, End date: 2018-10-31
Project acronym AMPLIFY
Project Amplifying Human Perception Through Interactive Digital Technologies
Researcher (PI) Albrecht Schmidt
Host Institution (HI) LUDWIG-MAXIMILIANS-UNIVERSITAET MUENCHEN
Call Details Consolidator Grant (CoG), PE6, ERC-2015-CoG
Summary Current technical sensor systems offer capabilities that are superior to human perception. Cameras can capture a spectrum that is wider than visible light, high-speed cameras can show movements that are invisible to the human eye, and directional microphones can pick up sounds at long distances. The vision of this project is to lay a foundation for the creation of digital technologies that provide novel sensory experiences and new perceptual capabilities for humans that are natural and intuitive to use. In a first step, the project will assess the feasibility of creating artificial human senses that provide new perceptual channels to the human mind, without increasing the experienced cognitive load. A particular focus is on creating intuitive and natural control mechanisms for amplified senses using eye gaze, muscle activity, and brain signals. Through the creation of a prototype that provides mildly unpleasant stimulations in response to perceived information, the feasibility of implementing an artificial reflex will be experimentally explored. The project will quantify the effectiveness of new senses and artificial perceptual aids compared to the baseline of unaugmented perception. The overall objective is to systematically research, explore, and model new means for increasing the human intake of information in order to lay the foundation for new and improved human senses enabled through digital technologies and to enable artificial reflexes. The ground-breaking contributions of this project are (1) to demonstrate the feasibility of reliably implementing amplified senses and new perceptual capabilities, (2) to prove the possibility of creating an artificial reflex, (3) to provide an example implementation of amplified cognition that is empirically validated, and (4) to develop models, concepts, components, and platforms that will enable and ease the creation of interactive systems that measurably increase human perceptual capabilities.
Summary
Current technical sensor systems offer capabilities that are superior to human perception. Cameras can capture a spectrum that is wider than visible light, high-speed cameras can show movements that are invisible to the human eye, and directional microphones can pick up sounds at long distances. The vision of this project is to lay a foundation for the creation of digital technologies that provide novel sensory experiences and new perceptual capabilities for humans that are natural and intuitive to use. In a first step, the project will assess the feasibility of creating artificial human senses that provide new perceptual channels to the human mind, without increasing the experienced cognitive load. A particular focus is on creating intuitive and natural control mechanisms for amplified senses using eye gaze, muscle activity, and brain signals. Through the creation of a prototype that provides mildly unpleasant stimulations in response to perceived information, the feasibility of implementing an artificial reflex will be experimentally explored. The project will quantify the effectiveness of new senses and artificial perceptual aids compared to the baseline of unaugmented perception. The overall objective is to systematically research, explore, and model new means for increasing the human intake of information in order to lay the foundation for new and improved human senses enabled through digital technologies and to enable artificial reflexes. The ground-breaking contributions of this project are (1) to demonstrate the feasibility of reliably implementing amplified senses and new perceptual capabilities, (2) to prove the possibility of creating an artificial reflex, (3) to provide an example implementation of amplified cognition that is empirically validated, and (4) to develop models, concepts, components, and platforms that will enable and ease the creation of interactive systems that measurably increase human perceptual capabilities.
Max ERC Funding
1 925 250 €
Duration
Start date: 2016-07-01, End date: 2021-06-30
Project acronym ANGI
Project Adaptive significance of Non Genetic Inheritance
Researcher (PI) Benoit François Pujol
Host Institution (HI) CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Call Details Consolidator Grant (CoG), LS8, ERC-2015-CoG
Summary Our ability to predict adaptation and the response of populations to selection is limited. Solving this issue is a fundamental challenge of evolutionary ecology with implications for applied sciences such as conservation, and agronomy. Non genetic inheritance (NGI; e.g., ecological niche transmission) is suspected to play a foremost role in adaptive evolution but such hypothesis remains untested. Using quantitative genetics in wild plant populations, experimental evolution, and epigenetics, we will assess the role of NGI in the adaptive response to selection of plant populations. The ANGI project will follow the subsequent research program: (1) Using long-term survey data, we will measure natural selection in wild populations of Antirrhinum majus within its heterogeneous array of micro-habitats. We will calculate the fitness gain provided by multiple traits and stem elongation to plants growing in bushes where they compete for light. Stem elongation is known to depend on epigenetic variation. (2) Using a statistical approach that we developed, we will estimate the quantitative genetic and non genetic heritability of traits. (3) We will identify phenotypic changes caused by fitness that are based on genetic variation and NGI and assess their respective roles in adaptive evolution. (4) In controlled conditions, we will artificially select for increased stem elongation in clonal lineages, thereby excluding DNA variation. We will quantify the non genetic response to selection and test for a quantitative epigenetic signature of selection. (5) We will build on our results to generate an inclusive theory of genetic and non genetic natural selection. ANGI builds on a confirmed expertise in selection experiments, quantitative genetics and NGI. In addition, the availability of survey data provides a solid foundation for the achievement of this project. Our ambition is to shed light on original mechanisms underlying adaptation that are an alternative to genetic selection.
Summary
Our ability to predict adaptation and the response of populations to selection is limited. Solving this issue is a fundamental challenge of evolutionary ecology with implications for applied sciences such as conservation, and agronomy. Non genetic inheritance (NGI; e.g., ecological niche transmission) is suspected to play a foremost role in adaptive evolution but such hypothesis remains untested. Using quantitative genetics in wild plant populations, experimental evolution, and epigenetics, we will assess the role of NGI in the adaptive response to selection of plant populations. The ANGI project will follow the subsequent research program: (1) Using long-term survey data, we will measure natural selection in wild populations of Antirrhinum majus within its heterogeneous array of micro-habitats. We will calculate the fitness gain provided by multiple traits and stem elongation to plants growing in bushes where they compete for light. Stem elongation is known to depend on epigenetic variation. (2) Using a statistical approach that we developed, we will estimate the quantitative genetic and non genetic heritability of traits. (3) We will identify phenotypic changes caused by fitness that are based on genetic variation and NGI and assess their respective roles in adaptive evolution. (4) In controlled conditions, we will artificially select for increased stem elongation in clonal lineages, thereby excluding DNA variation. We will quantify the non genetic response to selection and test for a quantitative epigenetic signature of selection. (5) We will build on our results to generate an inclusive theory of genetic and non genetic natural selection. ANGI builds on a confirmed expertise in selection experiments, quantitative genetics and NGI. In addition, the availability of survey data provides a solid foundation for the achievement of this project. Our ambition is to shed light on original mechanisms underlying adaptation that are an alternative to genetic selection.
Max ERC Funding
1 999 970 €
Duration
Start date: 2016-03-01, End date: 2021-02-28
Project acronym AOC
Project Adversary-Oriented Computing
Researcher (PI) Rachid Guerraoui
Host Institution (HI) ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Call Details Advanced Grant (AdG), PE6, ERC-2013-ADG
Summary "Recent technological evolutions, including the cloud, the multicore, the social and the mobiles ones, are turning computing ubiquitously distributed. Yet, building high-assurance distributed programs is notoriously challenging. One of the main reasons is that these systems usually seek to achieve several goals at the same time. In short, they need to be efficient, responding effectively in various average-case conditions, as well as reliable, behaving correctly in severe, worst-case conditions. As a consequence, they typically intermingle different strategies: each to cope with some specific condition, e.g., with or without node failures, message losses, time-outs, contention, cache misses,
over-sizing, malicious attacks, etc. The resulting programs end up hard to design, prove, verify, implement, test and debug. Not surprisingly, there are anecdotal evidences of the fragility of the most celebrated distributed systems.
The goal of this project is to contribute to building high-assurance distributed programs by introducing a new dimension for separating and isolating their concerns, as well as a new scheme for composing and reusing them in a modular manner. In short, the project will explore the inherent power and limitations of a novel paradigm, Adversary-Oriented Computing (AOC). Sub-programs, each implementing a specific strategy to cope with a given adversary, modelling a specific working condition, are designed, proved, verified, implemented, tested and debugged independently. They are then composed, possibly dynamically, as black-boxes within the same global program. The AOC project is ambitious and it seeks to fundamentally revisit the way distributed algorithms are designed and distributed systems are implemented. The gain expected in comparison with today's approaches is substantial, and I believe it will be proportional to the degree of difficulty of the distributed problem at hand."
Summary
"Recent technological evolutions, including the cloud, the multicore, the social and the mobiles ones, are turning computing ubiquitously distributed. Yet, building high-assurance distributed programs is notoriously challenging. One of the main reasons is that these systems usually seek to achieve several goals at the same time. In short, they need to be efficient, responding effectively in various average-case conditions, as well as reliable, behaving correctly in severe, worst-case conditions. As a consequence, they typically intermingle different strategies: each to cope with some specific condition, e.g., with or without node failures, message losses, time-outs, contention, cache misses,
over-sizing, malicious attacks, etc. The resulting programs end up hard to design, prove, verify, implement, test and debug. Not surprisingly, there are anecdotal evidences of the fragility of the most celebrated distributed systems.
The goal of this project is to contribute to building high-assurance distributed programs by introducing a new dimension for separating and isolating their concerns, as well as a new scheme for composing and reusing them in a modular manner. In short, the project will explore the inherent power and limitations of a novel paradigm, Adversary-Oriented Computing (AOC). Sub-programs, each implementing a specific strategy to cope with a given adversary, modelling a specific working condition, are designed, proved, verified, implemented, tested and debugged independently. They are then composed, possibly dynamically, as black-boxes within the same global program. The AOC project is ambitious and it seeks to fundamentally revisit the way distributed algorithms are designed and distributed systems are implemented. The gain expected in comparison with today's approaches is substantial, and I believe it will be proportional to the degree of difficulty of the distributed problem at hand."
Max ERC Funding
2 147 012 €
Duration
Start date: 2014-06-01, End date: 2019-05-31
Project acronym APEG
Project Algorithmic Performance Guarantees: Foundations and Applications
Researcher (PI) Susanne ALBERS
Host Institution (HI) TECHNISCHE UNIVERSITAET MUENCHEN
Call Details Advanced Grant (AdG), PE6, ERC-2015-AdG
Summary Optimization problems are ubiquitous in computer science. Almost every problem involves the optimization of some objective function. However a major part of these problems cannot be solved to optimality. Therefore, algorithms that achieve provably good performance guarantees are of immense importance. Considerable progress has already been made, but great challenges remain: Some fundamental problems are not well understood. Moreover, for central problems arising in new applications, no solutions are known at all.
The goal of APEG is to significantly advance the state of the art on algorithmic performance guarantees. Specifically, the project has two missions: First, it will develop new algorithmic techniques, breaking new ground in the areas of online algorithms, approximations algorithms and algorithmic game theory. Second, it will apply these techniques to solve fundamental problems that are central in these algorithmic disciplines. APEG will attack long-standing open problems, some of which have been unresolved for several decades. Furthermore, it will formulate and investigate new algorithmic problems that arise in modern applications. The research agenda encompasses a broad spectrum of classical and timely topics including (a) resource allocation in computer systems, (b) data structuring, (c) graph problems, with relations to Internet advertising, (d) complex networks and (e) massively parallel systems. In addition to basic optimization objectives, the project will also study the new performance metric of energy minimization in computer systems.
Overall, APEG pursues cutting-edge algorithms research, focusing on both foundational problems and applications. Any progress promises to be a breakthrough or significant contribution.
Summary
Optimization problems are ubiquitous in computer science. Almost every problem involves the optimization of some objective function. However a major part of these problems cannot be solved to optimality. Therefore, algorithms that achieve provably good performance guarantees are of immense importance. Considerable progress has already been made, but great challenges remain: Some fundamental problems are not well understood. Moreover, for central problems arising in new applications, no solutions are known at all.
The goal of APEG is to significantly advance the state of the art on algorithmic performance guarantees. Specifically, the project has two missions: First, it will develop new algorithmic techniques, breaking new ground in the areas of online algorithms, approximations algorithms and algorithmic game theory. Second, it will apply these techniques to solve fundamental problems that are central in these algorithmic disciplines. APEG will attack long-standing open problems, some of which have been unresolved for several decades. Furthermore, it will formulate and investigate new algorithmic problems that arise in modern applications. The research agenda encompasses a broad spectrum of classical and timely topics including (a) resource allocation in computer systems, (b) data structuring, (c) graph problems, with relations to Internet advertising, (d) complex networks and (e) massively parallel systems. In addition to basic optimization objectives, the project will also study the new performance metric of energy minimization in computer systems.
Overall, APEG pursues cutting-edge algorithms research, focusing on both foundational problems and applications. Any progress promises to be a breakthrough or significant contribution.
Max ERC Funding
2 404 250 €
Duration
Start date: 2016-10-01, End date: 2021-09-30
