Rejuvenation and Memory in model Spin Glasses in 3 and 4 dimensions

We numerically study aging for the Edwards-Anderson Model in 3 and 4 dimensions using different temperature-change protocols. In D=3, time scales a thousand times larger than in previous work are reached with the SUE machine. Deviations from cumulative aging are observed in the non monotonic time behavior of the coherence length. Memory and rejuvenation effects are found in a temperature-cycle protocol, revealed by vanishing effective waiting times. Similar effects are reported for the D=3$site-diluted ferromagnetic Ising model (without chaos). However, rejuvenation is reduced if off-equilibrium corrections to the fluctuation-dissipation theorem are considered. Memory and rejuvenation are quantitatively describable in terms of the growth regime of the spin-glass coherence length.Comment: Extended protocols. Accepted in Phys. Rev. B. 10 postscript figure

Helicopter Wake Encounters in the Context of RECAT-EU

This work presents a first attempt to apply the RECAT-EU (European Wake Turbulence Categorisation and Separation Minima) methodology of fixed-wing aircraft separation to helicopters. The approach is based on a classification of helicopters in categories using their rotor diameter and weight combined with wake comparisons between different classes of fixed-wing aircraft and helicopters. Where necessary the upset caused by a wake encounter to a simple helicopter model is used to establish safe separation distances. The work is based on a very limited amount of data for wake strengths but shows that the principles of the RECAT-EU methodology are directly applicable to helicopters at least for landing and take-off. This research calls for further measurements of helicopter wakes with modern methods so that the suggested separation distances can be further ascertained and ultimately refined allowing for better and safer integration of fixed and rotary-wing traffic at airports

Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules

Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In this paper we present an approach that combines a neural embedding model and logic-based modules to accurately divide an input ontology matching task into smaller and more tractable matching (sub)tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed method is adequate in practice and can be integrated within the workflow of systems unable to cope with very large ontologies

Pandemic Through My Eyes

Student generated description: “Drawing I drew featuring a person with a mask on and Coronavirus in their eyes. Words I think of when I think of the pandemic surround and the bottom has a timeline of the months that have passed in the style of a board game many of us spent a lot of time playing during quarantine