The main objective of AERIAL-CORE is the development of core technology modules and an integrated aerial cognitive robotic system that will have unprecedented capabilities on the operational range and safety in the interaction with people, or Aerial Co-Workers (ACW), for applications such as the inspection and maintenance of large infrastructures. The project will integrate aerial robots with different characteristics to meet the requirements of: (1) Long range (several kilometres) and local very accurate (subcentimetre) inspection of the infrastructure capability; (2) Maintenance activities based on aerial manipulation involving force interactions; and (3) Aerial co-working safely and efficiently helping human workers in inspection and maintenance. AERIAL-CORE technology modules will be based on Cognitive Mechatronics and apply cognitive capabilities to aerial morphing in order to combine long range endurance and hovering for local observations, manipulation involving force interactions, and co-working with humans. The project will develop: (1) Cognitive functionalities for aerial robots including perception based on novel sensors, such as event cameras, and data fusion techniques, learning, reactivity, fast on-line planning, and teaming; (2) Aerial platforms with morphing capabilities, to save energy in long range flights and perform a very accurate inspection; (3) Cognitive aerial manipulation capabilities, including manipulation while flying, while holding with one limb, and while hanging or perching to improve accuracy and develop greater forces; (4) Cognitive safe aerial robotic co-workers capable of physical interaction with people; and (5) Integrated aerial robotic system for the inspection and maintenance of large infrastructures. The system will be demonstrated in electrical power system inspection and maintenance, which is an application with a huge economic impact that also has implications in the safety of workers and in wildlife conservation.

The nature of dark matter, which makes up more than 80% of the Universe's matter content, remains unknown. Light axions and axion-like particles (ALPs) are well motivated dark-matter candidates that could be detected through their oscillations into photons in the presence of magnetic fields. Here, complementary laboratory and astrophysical searches for dark-matter axions and ALPs are proposed that will cover more than 10 orders of magnitude of possible axion and ALP masses. The astrophysical searches will focus on high-energy gamma-ray observations with the Fermi Large Area Telescope as well as current and future imaging air Cherenkov telescopes. Photon-ALP oscillations would cause features in the spectra of distant galaxies as well as gamma-ray bursts from core-collapse supernovae. Axion and ALP decay would also increase the opacity of the Universe for gamma rays. These signals will be searched for through novel comparisons of gamma-ray data and model predictions. The laboratory searches will focus on contributions to the Any Light Particle Search (ALPS II) and International Axion Observatory (IAXO) experiments. New analysis and simulation frameworks, as well as trigger concepts, will be developed in order to significantly improve the background rejection for the Transition Edge Sensor (TES) detector employed in the ALPS experiment. These improvements could pave the way for an ALP detection in the laboratory with first data runs at the ALPS II experiment planned in 2021. Monte Carlo simulations will be used to assess whether TES detectors can achieve the low background rates required for IAXO. Such high energy resolution detectors could help to precisely measure the axion/ALP mass through mass-dependent spectral features. Through an unprecedented investigation of axion and ALP signatures and by enhancing the sensitivity of future laboratory experiments, the proposed research will discover or rule out so-far unprobed dark-matter axions and ALPs.

Our vision consists of revolutionizing dental and orthopedic surgery by introducing a new paradigm, model-based theranostics, which consists of an integrative coupling of therapeutics, diagnostics and numerical simulation in order to optimize the performances of the surgical protocol and to predict its clinical outcome. The success of surgical protocols involving endosseous implants is limited by i) the empirical methods employed to assess implant stability, which is a strong determinant of the surgical outcome, ii) the absence of therapeutic approaches to stimulate osseointegration phenomena and iii) the difficulty of predicting the implant outcome. The aim of UltraSimplant is to develop a radically new unified model-based theranostic concept using innovative ideas in the domain of quantitative ultrasound (QUS). The new concept will combine characterization, simulation and stimulation of osseointegration phenomena, leading to the foundation of a revolutionary approach capable of providing a decision support system to the surgeon, to improve osseointegration in a patient specific manner and to predict the surgical outcome, thus leading to a drastic decrease of the implants failure rate. We will conceive and validate (in vitro, in silico, in vivo and in a clinical trial) a minimum viable product and consisting of a medical device using QUS techniques to assess dental implant stability. A validated model of the evolution of the bone-implant system will take into account the complex multiscale nature of the interface in order to validate in silico the QUS device and to predict the effect of ultrasound stimulation and implant outcome. The model will be used in order to optimize the parameters to be employed in the stimulation. UltraSimplant will first focus on dental implants because of the important failure rate and to the easy access of the implant. In the long term, model-based theranostic approaches will be applied to other implants in orthopedic surgery.

Polyhedral techniques allow the powerhouse of linear programming to be used for the discrete problems in combinatorial optimization. Their impact on commercial solvers, the theory of classical algorithms, as well as approximation algorithms, is well established. Nevertheless, these techniques play only a marginal role in mainstream research on parameterized algorithms. Recent advances, many of which are due to the PI, are showing potential for a revolutionary synergy between parameterized algorithms and polyhedral techniques. This project aims to unlock the full power of this synergy and use it to address several pressing open questions in combinatorial optimization. The work plan consists of three complementary directions: I. Exploring new frontiers for polyhedral techniques in parameterized algorithms, in particular, the novel usage of proximity bounds to obtain FPT algorithms. We will specifically investigate the open question of whether bin packing in the number of item sizes is in FPT. II. Closing notorious loose ends in existing research lines, in particular, the lack of polyhedral understanding of algebraic methods in matroid problems. III. Embracing parameterization to strengthen polyhedral techniques in approximation algorithms. Specifically, we will investigate an innovative technique crucial for understanding the approximability of scheduling on unrelated parallel machines, max-min fair allocation, and directed Steiner tree. The topics of the project are relevant for a broad spectrum of researchers from both parameterized algorithms and approximation algorithms and have the potential to bring these fields much closer together.