Read many books for many years
(Image above from Unsplash collection by Spentys )
Our new project within the Graphene Flagship ( Flagera Call JTC 2019) officially started !
MARGO stands for MAxillofacial bone Regeneration by 3D-printed laser-activated Graphene Oxide Scaffolds
MARGO is an exciting data-driven interdisciplinary research on our previous results on the Antibacterial coating and stem cell replication by Graphene Oxide, following our ERC PoC Project VANGUARD!
See also
We propose the use of artificial neural networks to design and characterize photonic topological insulators. As a hallmark, the band structures of these systems show the key feature of the emergence of edge states, with energies lying within the energy gap of the bulk materials and localized at the boundary between regions of distinct topological invariants. We consider different structures such as one-dimensional photonic crystals, PT-symmetric chains and cylindrical systems and show how, through a machine learning application, one can identify the parameters of a complex topological insulator to obtain protected edge states at target frequencies. We show how artificial neural networks can be used to solve the long standing quest of inverse-problems solution and apply it to the cutting edge topic of topological nanophotonics.
Pilozzi et al 2020 Nanotechnology https://doi.org/10.1088/1361-6528/abd508
Combinatorial optimization problems are crucial for widespread applications but remain difficult to solve on a large scale with conventional hardware. Novel optical platforms, known as coherent or photonic Ising machines, are attracting considerable attention as accelerators on optimization tasks formulable as Ising models. Annealing is a well-known technique based on adiabatic evolution for finding optimal solutions in classical and quantum systems made by atoms, electrons, or photons. Although various Ising machines employ annealing in some form, adiabatic computing on optical settings has been only partially investigated. Here, we realize the adiabatic evolution of frustrated Ising models with 100 spins programmed by spatial light modulation. We use holographic and optical control to change the spin couplings adiabatically, and exploit experimental noise to explore the energy landscape. Annealing enhances the convergence to the Ising ground state and allows to find the problem solution with probability close to unity. Our results demonstrate a photonic scheme for combinatorial optimization in analogy with adiabatic quantum algorithms and classical annealing methods but enforced by optical vector-matrix multiplications and scalable photonic technology.
https://www.osapublishing.org/optica/fulltext.cfm?uri=optica-7-11-1535&id=442147
See also https://arxiv.org/abs/2005.08690
The website of the PRIN 2017 project “Photonic Extreme Learning Machine” is online !
