ERC FUNDED PROJECTS

For the human brain, music is a highly complex and versatile stimulus that is closely linked to speech, executive-motor, emotion, and memory networks. In severe ageing-related neurological disorders, such as post-stroke aphasia and Alzheimer’s disease (AD) dementia, music and singing may provide a valuable alternative route to verbal and emotional expression and to memory and self-awareness. However, the neural processes underlying this are still poorly understood. Moreover, although there is increasing evidence for the efficacy of musical activities in supporting normal neurocognitive ageing and enhancing neurological recovery, the focus has been on individual-level musical activities, overlooking the enormous social potential of music. PREMUS will combine modern behavioural and neuroimaging methods in the unique context of cross-sectional and cohort studies and clinical trials to achieve both fundamental and applied research goals. The fundamental goal of PREMUS is to determine the neural basis of singing, music-evoked emotions and memories, and explicit and implicit musical learning (i) across normal ageing, (ii) in aphasia, and (iii) in different stages of AD. The applied goal of PREMUS is to uncover the rehabilitative potential of social musical activities by (iv) exploring the long-term efficacy of choir singing on neurocognitive, emotional, and social functioning in normal ageing and mild cognitive impairment and (v) determining the rehabilitative efficacy of a novel intervention that utilizes adapted choir singing, melodic intonation therapy, and computer-based singing training on verbal, cognitive, emotional, and social functioning in aphasia, together with uncovering the structural and functional neuroplasticity changes underlying the effects of the singing interventions. The outcome of PREMUS will have major scientific, clinical, and societal value as well as enormous practical impact on promoting healthy ageing, aphasia rehabilitation, and dementia care

1 499 967 €

Start date: 2019-01-01, End date: 2023-12-31

This project exploits the synergy between the trending area of artificial molecular machines and cutting edge computational chemistry approaches. Specific emphasis is placed on photoswitchable catalysts, which respond to external stimuli with a conformational or configurational change. These controllable motions allow catalytic function to be turned ON/OFF in a switch type fashion by opening/hindering access of a substrate to a catalytic site. On one hand, the rich morphology and chemistry of these smart catalysts calls for computational insights and design principles that complement experiment and push the field forward. On the other hand, the inherent complexity of these highly fluxional molecules makes them perfect subjects for driving modern quantum chemistry out of its comfort zone. To benefit from this synergy, the latest innovations in quantum chemistry-based machine learning techniques will be combined with methods capable of thoroughly mapping the intricate chemistry of molecular actuators. Overall, we aim to bridge the gap between the current state-of-the-art, which has reached reasonable quantum chemical accuracy for rigid medium size organic molecules, and more challenging fluxional architectures. The proposed methodological toolbox will be applied to the field of smart catalysis where general strategies for improving the efficiencies and enhancing enantioselectivity will be formulated. Thus, this project involves exploiting a wide range of modern computational approaches to chemical tasks that are broadly relevant to flexible/switchable catalytic systems. The anticipated output will furnish the computational chemistry community with a comprehensive array of novel next-generation approaches with applicability beyond the field of molecular machines.

1 949 385 €

Start date: 2019-10-01, End date: 2024-09-30

QTONE aims at discovering new quantum phenomena involving THz vibrational modes, and at gaining control over them using novel concepts inspired from cavity quantum optomechanics and new techniques developed for nano-plasmonics and molecular break-junctions. The three main goals of the project are: (i) Perform optomechanical quantum information processing with THz phonons in low-dimensional systems, using a combination of ultrafast spectroscopy and time-correlated photon counting to measure quantum correlations mediated by non-classical vibrational states. (ii) Demonstrate the feasibility of dynamical backaction amplification of THz phonons by coupling molecules and nanomaterials to plasmonic cavities and by leveraging exciton-phonon coupling to realize exciton-assisted optomechanics. (iii) Interrogate and drive a single-molecule inside a plasmonic nanocavity using simultaneous inelastic electron tunneling and Raman spectroscopies in a molecular break-junction with engineered plasmonic resonance. I anticipate that this project will have widespread impacts on our understanding of quantum phenomena in molecular-scale oscillators, and will foster the excellence of Europe in fields ranging from fundamental science to quantum technologies and molecular electronics.

2 437 500 €

Start date: 2019-08-01, End date: 2024-07-31

This ERC project pushes the boundary of reliable data-driven decision making in cyber-physical systems (CPS), by bridging reinforcement learning (RL), nonparametric estimation and robust optimization. RL is a powerful abstraction of decision making under uncertainty and has witnessed dramatic recent breakthroughs. Most of these successes have been in games such as Go - well specified, closed environments that - given enough computing power - can be extensively simulated and explored. In real-world CPS, however, accurate simulations are rarely available, and exploration in these applications is a highly dangerous proposition. We strive to rethink Reinforcement Learning from the perspective of reliability and robustness required by real-world applications. We build on our recent breakthrough result on safe Bayesian optimization (SAFE-OPT): The approach allows - for the first time - to identify provably near-optimal policies in episodic RL tasks, while guaranteeing under some regularity assumptions that with high probability no unsafe states are visited - even if the set of safe parameter values is a priori unknown. While extremely promising, this result has several fundamental limitations, which we seek to overcome in this ERC project. To this end we will (1) go beyond low-dimensional Gaussian process models and towards much richer deep Bayesian models; (2) go beyond episodic tasks, by explicitly reasoning about the dynamics and employing ideas from robust control theory and (3) tackle bootstrapping of safe initial policies by bridging simulations and real-world experiments via multi-fidelity Bayesian optimization, and by pursuing safe active imitation learning. Our research is motivated by three real-world CPS applications, which we pursue in interdisciplinary collaboration: Safe exploration of and with robotic platforms; tuning the energy efficiency of photovoltaic powerplants and safely optimizing the performance of a Free Electron Laser.

1 996 500 €

Start date: 2019-01-01, End date: 2023-12-31