Computer vision, leveraging deep learning in the last decade, has achieved unprecedented progress. However, it is largely relying on datasets of still images, thus using "passive vision". On the contrary, biological vision is a fundamentally active process of exploration to disambiguate objects, and yet, the potential of active vision for robotics remains underexplored. The ENDEAVOR project seeks to redefine traditional static image analysis within fast online robotic applications. This project integrates the computational models of Sensorimotor Contingency Theory (O'Regan and Noe, 2001) with event-driven perception and neuromorphic computing. Sensorimotor contingency represents the dynamic relationship between an agent's sensory inputs and motor actions in the environment. Active sensory data generation naturally aligns with event-driven perception, tracking moving objects via agent-generated events, while neuromorphic computing minimises latency and energy use. The humanoid robot iCub will hold objects and examine them from various perspectives through eye and wrist movements. The project capitalises on bioinspired hardware and software solutions, ultimately aiming to reduce computational demands, power consumption, and latency in intelligent systems. ENDEAVOR offers three significant contributions to computer vision and robotics: (1) It introduces active vision strategies that enhance object perception. (2) It integrates event-based visual sensing with rapid and efficient parallel computation, leveraging neuromorphic computing principles. (3) The project establishes a benchmark that allows for both qualitative and quantitative evaluations, fostering comparisons among various approaches, including frame-based, event-based, and spiking-based systems. The importance of this approach lies in the effort to reduce the storage of massive amounts of data while aiming for mW of power consumption.

Experimental evidence indicates that fluid filtration through unsaturated porous media exhibits a hysteretic behavior, originating at a microscopic level from surface tension at the point of contact between water and air in the pores. As a result, the pressure-saturation constitutive relation turns out to be of hysteresis type, accurately described with the Preisach operator by a thorough fitting procedure. The main objective of the MulPHys project is to expand the knowledge about Preisach hysteresis for fluid filtration and build new mathematical models for unsaturated porous media, employing a multiscale approach. The suitability of the Preisach operator in describing the hysteretic behavior of unsaturated porous solids sparked an intense research effort in the community of experts in PDEs with hysteresis, with the goal of including the Preisach operator in mathematical models. In these endeavors, the presence of a microstructure was neglected, with the approach being directly macroscopic. Another branch of research has considered porous media as objects with a microstructure, and has derived the macroscopic description of fluid flow from local behavior. In this body of research, however, porous media are often assumed to be completely saturated so that no hysteresis can occur. With MulPHys, we will fill the gap between these two research areas, employing homogenization techniques to provide a justification of the Preisach operator as the correct tool for describing filtration. Particular attention will be paid to including gravity effects and understanding solid-liquid interactions at the microscopic level. Numerical simulations and experimental data will be instrumental in achieving these objectives. Potential applications in the preservation of historic buildings are foreseen, thus addressing a European priority and implying significant impact not only for the scientific community, but also for the professional sectors and society as a whole.

The urgent need to combat pollution necessitates a focused reduction in CO2 emissions, positioning nuclear energy as a promising alternative to fossil fuels. The demand for advanced materials in nuclear fission and fusion power plants arises from their challenging operating conditions. While current materials are in use, there is a compelling case for exploring new alternatives that offer improved efficiency, enhanced safety, reduced waste, extended operational lifespans, high-temperature durability, and resistance to high-energy neutron irradiation. High-Entropy Alloys (HEAs) have emerged as a promising solution due to their exceptional properties and untapped compositional possibilities. Their key advantage is sluggish diffusion, which limits the movement of point defects, leading to increased recombination and mitigating hardening and swelling effects. To meet the stringent requirements of reactor environments, HEAs must also exhibit low activation properties. To tackle this challenge, our research focuses on understanding ion-irradiated single-phase HEAs under high-temperature and high-fluence conditions. We aim to uncover the underlying physics of hardening and swelling using a comprehensive suite of experimental and simulation techniques, including TEM, HR-TKD, Astar strain mapping, ACOM, nano-indentation, SIMS, XRD, AFM, micropillar tests, APT, DBS-PALS, DFT, MD, and KMC. The ultimate goal is to develop a robust screening tool with high-throughput capabilities, enabling the efficient evaluation of numerous HEA compositions. This tool will provide a foundation for tailoring HEAs to specific applications. The designed HEAs will not only withstand extreme conditions but also retain balanced mechanical properties, striking an optimal trade-off between hardness and toughness. This progress will pave the way for innovative applications in next-generation nuclear reactors.

Unevaluated science is not worth funding. Gone are the days where a scientific breakthrough could be based on scribbles made on a few loose sheets of paper reviewed by a single attentive reader. Most disciplines rely on experimental data that is collected, analyzed, and presented using powerful computational tools. The scientific adventure hinges on our ability to openly and widely share and reproduce such results. The goal of this PoC is to market a tool, R4R, for non-programmer scientists to make their archival work easily reproducible and offer it to them through a non-expensive licence. Affordable reproducibility is key to independent evaluation of previously published results. We will focus on reproducibility of data analysis pipelines written in R with RMarkdown or Jupyter. Creating a reproducible environment is hard, labor-intensive and error-prone, and requires expertise that data analysts lack. We propose to use dynamic program analysis techniques to track dependencies, data inputs, and other sources of non-determinism needed for reproducibility. R4R will synthesize metadata to generate self-contained, portable, fully reproducible environments, based on Docker images.