Entanglement and quantum combinatorial designs

We introduce several classes of quantum combinatorial designs, namely quantum Latin squares, cubes, hypercubes and a notion of orthogonality between them. A further introduced notion, quantum orthogonal arrays, generalizes all previous classes of designs. We show that mutually orthogonal quantum Latin arrangements can be entangled in the same way than quantum states are entangled. Furthermore, we show that such designs naturally define a remarkable class of genuinely multipartite highly entangled states called $k$-uniform, i.e. multipartite pure states such that every reduction to $k$ parties is maximally mixed. We derive infinitely many classes of mutually orthogonal quantum Latin arrangements and quantum orthogonal arrays having an arbitrary large number of columns. The corresponding multipartite $k$-uniform states exhibit a high persistency of entanglement, which makes them ideal candidates to develop multipartite quantum information protocols.Comment: 14 pages, 3 figures. Comments are very welcome

A Qualitative Exploration of Emerging Adults’ and Parents’ Perspectives on Communicating Adulthood Status

In this study the authors examine parent - child communication in Emerging Adulthood. Thirty - seven college students and one or both of their parents completed written questionnaires assessing whether the parent had verbally communicated or did some action to acknowledge the Emerging Adult’s maturity. Communication about changes in the parent - child relationship, as well as the Emerging Adult’s decision - making abilities, obligations to the family, and financial responsibilities were also assessed. The responses to the open ended questions were qualitatively analyzed using grounded theory. The findings indicated that the Emerging Adults’ and parents’ responses were very similar, and the overwhelming majority reported that there had indeed been an acknowledgment from the parents to indicate Emerging Adulthood status, although this was not always verbally communicated; sometimes it was indicated through the parents’ behavior.

Fast spatial simulation of extreme high-resolution radar precipitation data using INLA

We develop a methodology for modelling and simulating high-dimensional spatial precipitation extremes, using a combination of the spatial conditional extremes model, latent Gaussian models and integrated nested Laplace approximations (INLA). The spatial conditional extremes model requires data with Laplace marginal distributions, but precipitation distributions contain point masses at zero that complicate necessary standardisation procedures. We propose to model conditional extremes of nonzero precipitation only, while separately modelling precipitation occurrences. The two models are then combined to create a complete model for extreme precipitation. Nonzero precipitation marginals are modelled using a combination of latent Gaussian models with gamma and generalised Pareto likelihoods. Four different models for precipitation occurrence are investigated. New empirical diagnostics and parametric models are developed for describing components of the spatial conditional extremes model. We apply our framework to simulate spatial precipitation extremes over a water catchment in Central Norway, using high-density radar data. Inference on a 6000-dimensional data set is performed within hours, and the simulated data capture the main trends of the observed data well

An Efficient Workflow for Modelling High-Dimensional Spatial Extremes

A successful model for high-dimensional spatial extremes should, in principle, be able to describe both weakening extremal dependence at increasing levels and changes in the type of extremal dependence class as a function of the distance between locations. Furthermore, the model should allow for computationally tractable inference using inference methods that efficiently extract information from data and that are robust to model misspecification. In this paper, we demonstrate how to fulfil all these requirements by developing a comprehensive methodological workflow for efficient Bayesian modelling of high-dimensional spatial extremes using the spatial conditional extremes model while performing fast inference with R-INLA. We then propose a post hoc adjustment method that results in more robust inference by properly accounting for possible model misspecification. The developed methodology is applied for modelling extreme hourly precipitation from high-resolution radar data in Norway. Inference is computationally efficient, and the resulting model fit successfully captures the main trends in the extremal dependence structure of the data. Robustifying the model fit by adjusting for possible misspecification further improves model performance

Feynman graphs and the large dimensional limit of multipartite entanglement

We are interested in the properties of multipartite entanglement of a system composed by $n$ $d$-level parties (qudits). Focussing our attention on pure states we want to tackle the problem of the maximization of the entanglement for such systems. In particular we effort the problem trying to minimize the purity of the system. It has been shown that not for all systems this function can reach its lower bound, however it can be proved that for all values of $n$ a $d$ can always be found such that the lower bound can be reached. In this paper we examine the high-temperature expansion of the distribution function of the bipartite purity over all balanced bipartition considering its optimization problem as a problem of statistical mechanics. In particular we prove that the series characterizing the expansion converges and we analyze the behavior of each term of the series as $d\to \infty$.Comment: 29 pages, 11 figure

Changes in behavioural response of Mediterranean Seabass (Dicenthratus labrax L.) under different feeding distributions

Captive-induced behavioural deviations may involve many aspects of fish behaviour such as swimming activity and enhancement of individual aggressiveness. We studied seabass (Dicentrarchus labrax) behaviour as a function of manual and automatic feeding distribution modes. Under manual mode, the food is distributed over an extended area for a longer period, and its precise location is not always predictable, while with pneumatic automatic feeders, fish receive the same amount of resource, which is concentrated in the same surface area over a shorter period. We compared seabass behaviour under automatic and manual conditions collecting video image recordings before, during, and after feeding distribution, in the morning and in afternoon, on two different days, and analysing data within independent sessions of measurements. Feeding modes significantly affected swimming behaviour: automatically- fed fish were characterised by vertical movements through the water column (towards the surface and bottom) and by horizontal swimming. Manually-fed fish were instead characterised by sharp direction changes during their swimming, mostly towards the surface. Feeding distribution induced changes in collision frequency and elicited aggressive behaviour. In particular, agonistic behaviour (i.e. a fish attacks another fish) was almost exclusively recorded during the feeding under automatic distribution, whereas it was constantly expressed during all the distribution phases under manual mod