Offshore Neopycnodonte oyster reefs in the Mediterranean Sea

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Angeletti, L., & Taviani, M. Offshore Neopycnodonte oyster reefs in the Mediterranean Sea. Diversity, 12(3), (2020): 92, doi:10.3390/d12030092.Oysters are important ecosystem engineers best known to produce large bioconstructions at shallow depth, whilst offshore deep-subtidal oyster reefs are less widely known. Oyster reefs engineered by Neopycnodonte cochlear (family Gryphaeidae) occur at various sites in the Mediterranean Sea, between 40 and 130 m water depths. Remotely Operated Vehicle surveys provide new insights on this rather neglected reef types with respect to their shape, dimensions and associated biodiversity. We suggest that these little contemplated reefs should be taken in due consideration for protection.This work was partly supported by the EU FP-VI and VII HERMES and HERMIONE, by the ‘Convenzione MATTM-CNR per i Programmi di Monitoraggio per la Direttiva sulla Strategia Marina (MSFD, Art. 11, Dir. 2008/56/CE), and is part of the DG Environment programme IDEM (grant agreement no. 11.0661/2017/750680/SUB/EN V.C2)

Adaptive Deep Learning through Visual Domain Localization

A commercial robot, trained by its manufacturer to recognize a predefined number and type of objects, might be used in many settings, that will in general differ in their illumination conditions, background, type and degree of clutter, and so on. Recent computer vision works tackle this generalization issue through domain adaptation methods, assuming as source the visual domain where the system is trained and as target the domain of deployment. All approaches assume to have access to images from all classes of the target during training, an unrealistic condition in robotics applications. We address this issue proposing an algorithm that takes into account the specific needs of robot vision. Our intuition is that the nature of the domain shift experienced mostly in robotics is local. We exploit this through the learning of maps that spatially ground the domain and quantify the degree of shift, embedded into an end-to-end deep domain adaptation architecture. By explicitly localizing the roots of the domain shift we significantly reduce the number of parameters of the architecture to tune, we gain the flexibility necessary to deal with subset of categories in the target domain at training time, and we provide a clear feedback on the rationale behind any classification decision, which can be exploited in human-robot interactions. Experiments on two different settings of the iCub World database confirm the suitability of our method for robot vision

Statistics of sums of correlated variables described by a matrix product ansatz

We determine the asymptotic distribution of the sum of correlated variables described by a matrix product ansatz with finite matrices, considering variables with finite variances. In cases when the correlation length is finite, the law of large numbers is obeyed, and the rescaled sum converges to a Gaussian distribution. In constrast, when correlation extends over system size, we observe either a breaking of the law of large numbers, with the onset of giant fluctuations, or a generalization of the central limit theorem with a family of nonstandard limit distributions. The corresponding distributions are found as mixtures of delta functions for the generalized law of large numbers, and as mixtures of Gaussian distributions for the generalized central limit theorem. Connections with statistical physics models are emphasized.Comment: 6 pages, 1 figur

Matrix product representation and synthesis for random vectors: Insight from statistical physics

Inspired from modern out-of-equilibrium statistical physics models, a matrix product based framework permits the formal definition of random vectors (and random time series) whose desired joint distributions are a priori prescribed. Its key feature consists of preserving the writing of the joint distribution as the simple product structure it has under independence, while inputing controlled dependencies amongst components: This is obtained by replacing the product of distributions by a product of matrices of distributions. The statistical properties stemming from this construction are studied theoretically: The landscape of the attainable dependence structure is thoroughly depicted and a stationarity condition for time series is notably obtained. The remapping of this framework onto that of Hidden Markov Models enables us to devise an efficient and accurate practical synthesis procedure. A design procedure is also described permitting the tuning of model parameters to attain targeted properties. Pedagogical well-chosen examples of times series and multivariate vectors aim at illustrating the power and versatility of the proposed approach and at showing how targeted statistical properties can be actually prescribed.Comment: 10 pages, 4 figures, submitted to IEEE Transactions on Signal Processin

Matrix products for the synthesis of stationary time series with a priori prescribed joint distributions

Inspired from non-equilibrium statistical physics models, a general framework enabling the definition and synthesis of stationary time series with a priori prescribed and controlled joint distributions is constructed. Its central feature consists of preserving for the joint distribution the simple product struc- ture it has under independence while enabling to input con- trolled and prescribed dependencies amongst samples. To that end, it is based on products of d-dimensional matrices, whose entries consist of valid distributions. The statistical properties of the thus defined time series are studied in details. Having been able to recast this framework into that of Hidden Markov Models enabled us to obtain an efficient synthesis procedure. Pedagogical well-chosen examples (time series with the same marginal distribution, same covariance function, but different joint distributions) aim at illustrating the power and potential of the approach and at showing how targeted statistical prop- erties can be actually prescribed.Comment: 4 pages, 2 figures, conference publication published in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 201

Convergence of large deviation estimators

We study the convergence of statistical estimators used in the estimation of large deviation functions describing the fluctuations of equilibrium, nonequilibrium, and manmade stochastic systems. We give conditions for the convergence of these estimators with sample size, based on the boundedness or unboundedness of the quantity sampled, and discuss how statistical errors should be defined in different parts of the convergence region. Our results shed light on previous reports of 'phase transitions' in the statistics of free energy estimators and establish a general framework for reliably estimating large deviation functions from simulation and experimental data and identifying parameter regions where this estimation converges.Comment: 13 pages, 6 figures. v2: corrections focusing the paper on large deviations; v3: minor corrections, close to published versio

Scorched Earth

Economic Warfare - storia dell'arma economic

Ropeway roller batteries dynamics. Modeling, identification, and full-scale validation

A parametric mechanical model based on a Lagrangian formulation is here proposed to predict the dynamic response of roller batteries during the vehicles transit across the so-called compression towers in ropeways transportation systems. The model describes the dynamic interaction between the ropeway substructures starting from the modes and frequencies of the system to the forced dynamic response caused by the vehicles transit. The analytical model is corroborated and validated via an extensive experimental campaign devoted to the dynamic characterization of the roller battery system. The data acquired on site via a custom-design sensor network allowed to identify the frequencies and damping ratios by employing the Frequency Domain Decomposition (FDD) method. The high fidelity modeling and the system identification procedure are discussed