A large-scale energy transition of society requires efficient electrochemical processes for generating, converting, and storing sustainable energy. Unfortunately, existing electrochemical processes have serious limitations and are inadequate to meet the grand challenges ahead. At present there is insufficient knowledge of the processes occurring in electrochemical systems at the smallest scale to fundamentally improve these processes. In this multidisciplinary fundamental research program, chemists and physicists lay the foundation for new efficient electrochemical technologies designed to dramatically reduce humanitys carbon footprint.

Deep Learning (DL) algorithms are an extremely promising instrument in artificial intelligence, achieving very high performance in numerous recognition, identification, and classification tasks. To foster their pervasive adoption in a vast scope of new applications and markets, a step forward is needed towards the implementation of the on-line classification task (called inference) on low-power embedded systems, enabling a shift to the edge computing paradigm. Nevertheless, when DL is moved at the edge, severe performance requirements must coexist with tight constraints in terms of power/energy consumption, posing the need for parallel and energy-efficient heterogeneous computing platforms. Unfortunately, programming for this kind of architectures requires advanced skills and significant effort, also considering that DL algorithms are designed to improve precision, without considering the limitations of the device that will execute the inference. Thus, the deployment of DL algorithms on heterogeneous architectures is often unaffordable for SMEs and midcaps without adequate support from software development tools. The main goal of ALOHA is to facilitate implementation of DL on heterogeneous low-energy computing platforms. To this aim, the project will develop a software development tool flow, automating: • algorithm design and analysis; • porting of the inference tasks to heterogeneous embedded architectures, with optimized mapping and scheduling; • implementation of middleware and primitives controlling the target platform, to optimize power and energy savings. During the development of the ALOHA tool flow, several main features will be addressed, such as architecture-awareness (the features of the embedded architecture will be considered starting from the algorithm design), adaptivity, security, productivity, and extensibility. ALOHA will be assessed over three different use-cases, involving surveillance, smart industry automation, and medical application domains

Our understanding of cosmology and fundamental physics continues to be challenged by ever more precise experiments. The resulting “standard” model of cosmology describes the data well, but is unable to explain the origin of the main constituents of our Universe, namely dark matter and dark energy. More than an order of magnitude improvement in the quality and quantity of observational data is needed. This has motivated ESA to select Euclid as the second mission of its cosmic vision program, with a scheduled launch in 2020. It is designed to accurately measure the alignments of distant galaxies due to the differential deflection of light-rays by intervening structures, a phenomenon called gravitational lensing. Euclid will measure this signal by imaging 1.5 billion galaxies with a resolution similar to that of the Hubble Space Telescope. Although Euclid is designed to minimize observational systematics the observations are still compromised by two factors. Various instrumental effects need to be corrected for, and the tremendous improvement in precision has to be matched with comparable advances in the modelling of astrophysical effects that affect the signal. The objective of this proposal is to make significant progress on both fronts. To do so, we will (i) quantify the morphology of galaxies using archival HST observations; (ii) carry out a unique narrow-band photometric redshift survey to obtain state-of-the-art constraints on the intrinsic alignments of galaxies that arise due to tidal interactions, and would otherwise contaminate the cosmological signal; (iii) integrate these results into the end-to-end simulation pipeline; (iv) perform a spectroscopic redshift survey to calibrate the photometric redshift technique. The Euclid Consortium has identified these as critical issues, which need to be addressed before launch, in order to maximise the science return of this exciting mission, and enable the dark energy science objectives of Europe.