ERC FUNDED PROJECTS

Some of the most fascinating physical phenomena are experimentally observed in strongly correlated electron systems and, on the theoretical side, only poorly understood hitherto. The aim of the ERC project AbinitioDGA is the development, implementation and application of a new, 21th century method for the ab initio calculation of materials with such strong electronic correlations. AbinitioDGA includes strong electronic correlations on all time and length scales and hence is a big step beyond the state-of-the-art methods, such as the local density approximation, dynamical mean field theory, and the GW approach (Green function G times screened interaction W). It has the potential for an extraordinary high impact not only in the field of computational materials science but also for a better understanding of quantum critical heavy fermion systems, high-temperature superconductors, and transport through nano- and heterostructures. These four physical problems and related materials will be studied within the ERC project, besides the methodological development. On the technical side, AbinitioDGA realizes Hedin's idea to include vertex corrections beyond the GW approximation. All vertex corrections which can be traced back to a fully irreducible local vertex and the bare non-local Coulomb interaction are included. This way, AbinitioDGA does not only contain the GW physics of screened exchange and the strong local correlations of dynamical mean field theory but also non-local correlations beyond on all length scales. Through the latter, AbinitioDGA can prospectively describe phenomena such as quantum criticality, spin-fluctuation mediated superconductivity, and weak localization corrections to the conductivity. Nonetheless, the computational effort is still manageable even for realistic materials calculations, making the considerable effort to implement AbinitioDGA worthwhile.

1 491 090 €

Start date: 2013-01-01, End date: 2018-07-31

Communication networks have become a critical infrastructure of our digital society. However, with the explosive growth of data-centric applications and the resulting increasing workloads headed for the world’s datacenter networks, today’s static and demand-oblivious network architectures are reaching their capacity limits. The AdjustNet project proposes a radically different perspective, envisioning demand-aware networks which can dynamically adapt their topology to the workload they currently serve. Such self-adjusting networks hence allow to exploit structure in the demand, and thereby reach higher levels of efficiency and performance. The vision of AdjustNet is timely and enabled by recent innovations in optical technologies which allow to flexibly reconfigure the physical network topology. The goal of AdjustNet is to lay the theoretical foundations for self-adjusting networks. We will identify metrics that serve as yardstick of what can and cannot be achieved in a self-adjusting network for a given demand, devise algorithms for online adaption, and validate our framework through case studies. Our novel methodology is motivated by an intriguing connection of self-adjusting networks to known datastructures and to information theory. AdjustNet comes with significant challenges since, similar to self-driving cars, self-adjusting networks require human network operators to give away control, and since more autonomous network operations may lead to instabilities. AdjustNet will overcome these risks and achieve its objectives by pursuing a rigorous approach, devising a theoretical well-founded framework for self-adjusting networks which come with provable guarantees and incorporate self–protection mechanisms. The PI is well-equipped for this project and recently obtained first promising results. As the community is currently re-architecting communication networks, there is a unique opportunity to bridge the gap between theory and practice, and have impact.

1 670 823 €

Start date: 2020-03-01, End date: 2025-02-28

The goals are 1. To develop a scale assessing the diversity of active ageing with four dimensions that are ability (what people can do), activity (what people do do), ambition (what are the valued activities that people want to do), and autonomy (how satisfied people are with the opportunity to do valued activities); 2. To examine health and physical and psychological functioning as the determinants and social and build environment, resilience and personal skills as modifiers of active ageing; 3. To develop a multicomponent sustainable intervention aiming to promote active ageing (methods: counselling, information technology, help from volunteers); 4. To test the feasibility and effectiveness on the intervention; and 5. To study cohort effects on the phenotypes on the pathway to active ageing. “If You Can Measure It, You Can Change It.” Active ageing assessment needs conceptual progress, which I propose to do. A quantifiable scale will be developed that captures the diversity of active ageing stemming from the WHO definition of active ageing as the process of optimizing opportunities for health and participation in the society for all people in line with their needs, goals and capacities as they age. I will collect cross-sectional data (N=1000, ages 75, 80 and 85 years) and model the pathway to active ageing with state-of-the art statistical methods. By doing this I will create novel knowledge on preconditions for active ageing. The collected cohort data will be compared to a pre-existing cohort data that was collected 25 years ago to obtain knowledge about changes over time in functioning of older people. A randomized controlled trial (N=200) will be conducted to assess the effectiveness of the envisioned intervention promoting active ageing through participation. The project will regenerate ageing research by launching a novel scale, by training young scientists, by creating new concepts and theory development and by producing evidence for active ageing promotion

2 044 364 €

Start date: 2016-09-01, End date: 2021-08-31

Real-world optimization problems pose major challenges to algorithmic research. For instance, (i) many important problems are believed to be intractable (i.e. NP-hard) and (ii) with the growth of data size, modern applications often require a decision making under {\em incomplete and dynamically changing input data}. After several decades of research, central problems in these domains have remained poorly understood (e.g. Is there an asymptotically most efficient binary search trees?) Existing algorithmic techniques either reach their limitation or are inherently tailored to special cases. This project attempts to untangle this gap in the state of the art and seeks new interplay across multiple areas of algorithms, such as approximation algorithms, online algorithms, fixed-parameter tractable (FPT) algorithms, exponential time algorithms, and data structures. We propose new directions from the {\em structural perspectives} that connect the aforementioned algorithmic problems to basic questions in combinatorics. Our approaches fall into one of the three broad schemes: (i) new structural theory, (ii) intermediate problems, and (iii) transfer of techniques. These directions partially build on the PI's successes in resolving more than ten classical problems in this context. Resolving the proposed problems will likely revolutionize our understanding about algorithms and data structures and potentially unify techniques in multiple algorithmic regimes. Any progress is, in fact, already a significant contribution to the algorithms community. We suggest concrete intermediate goals that are of independent interest and have lower risks, so they are suitable for Ph.D students.

1 411 258 €

Start date: 2018-02-01, End date: 2024-01-31