Magnetic properties and anisotropy of orthorhombic DyMnO3 single crystal

An orthorhombic DyMnO3 single crystal has been studied in magnetic fields up to 14 T and between 3 K and room temperature. The field dependent ordering temperature of Dy moments is deduced. The paramagnetic Curie Weiss behavior is related mainly to the Dy3+sublattice whereas the Mn sublattice contribution plays a secondary role. DC magnetization measurements show marked anisotropic features, related to the anisotropic structure of a cubic system stretched along a body diagonal, with a magnetic easy axis parallel to the crystallographic b axis. A temperature and field dependent spin flop transition is observed below 9 K, when relatively weak magnetocrystalline anisotropy is overcome by magnetic fields up to 1.6 T. © 2013 Elsevier B.V.

Automatic construction and natural-language description of nonparametric regression models

Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This paper presents the beginnings of an automatic statistician, focusing on regression problems. Our system explores an open-ended space of statistical models to discover a good explanation of a data set, and then produces a detailed report with figures and natural- language text. Our approach treats unknown regression functions non- parametrically using Gaussian processes, which has two important consequences. First, Gaussian processes can model functions in terms of high-level properties (e.g. smoothness, trends, periodicity, changepoints). Taken together with the compositional structure of our language of models this allows us to automatically describe functions in simple terms. Second, the use of flexible nonparametric models and a rich language for composing them in an open-ended manner also results in state- of-the-art extrapolation performance evaluated over 13 real time series data sets from various domains.

Analysis and retrofitting design of a single-span R.C. bridge

This paper deals with the case history of a damaged one-span prestressed concrete bridge on a crucial artery near the city of Cagliari (Sardinia), along the sea-side. After being involved in a disastrous flood, attention has arisen on the worrying safety state of the deck, submitted to an intense daily traffic load. Evident signs of this severe condition were the deterioration of the beams concrete and the corrosion, the lack of tension and even the rupture of the prestressing cables. After performing a limited in situ test campaign, consisting of sclerometer, pull out and carbonation depth tests, a first evaluation of the safety of the structure was performed. After collecting the data of dynamic and static load tests as well, a comprehensive analysis have been carried out, also by means of a properly calibrated F.E. model. Finally the retrofitting design is presented, consisting of the reparation and thickening of the concrete cover, providing flexural and shear FRP external reinforcements and an external prestressing system, capable of restoring a satisfactory bearing capacity, according to the current national codes. The intervention has been calibrated by the former F.E. model with respect to transversal effects and influence of local and overall deformation of reinforced elements. © 2012 Taylor & Francis Group.

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.