Clustering of discretely observed diffusion processes

In this paper a new dissimilarity measure to identify groups of assets dynamics is proposed. The underlying generating process is assumed to be a diffusion process solution of stochastic differential equations and observed at discrete time. The mesh of observations is not required to shrink to zero. As distance between two observed paths, the quadratic distance of the corresponding estimated Markov operators is considered. Analysis of both synthetic data and real financial data from NYSE/NASDAQ stocks, give evidence that this distance seems capable to catch differences in both the drift and diffusion coefficients contrary to other commonly used metrics

Bayesian outlier detection in Capital Asset Pricing Model

We propose a novel Bayesian optimisation procedure for outlier detection in the Capital Asset Pricing Model. We use a parametric product partition model to robustly estimate the systematic risk of an asset. We assume that the returns follow independent normal distributions and we impose a partition structure on the parameters of interest. The partition structure imposed on the parameters induces a corresponding clustering of the returns. We identify via an optimisation procedure the partition that best separates standard observations from the atypical ones. The methodology is illustrated with reference to a real data set, for which we also provide a microeconomic interpretation of the detected outliers

Calibration of a visual method for the analysis of the mechanical properties of historic masonry

The conservation and preservation of historic buildings affords many challenges to those who aim to retain our building heritage. In this area, the knowledge of the mechanical characteristics of the masonry material is fundamental. However, mechanical destructive testing is always expensive and time-consuming, especially when applied to masonry historic structures. In order to overcome such kind of problems, the authors of this article, proposed in 2014 a visual method for the estimation of some critical mechanical parameters of the masonry material. Based on the fact that the mechanical behavior of masonry material depends on many factors, such as compressive or shear strength of components (mortar and masonry units), unit shape, volumetric ratio between components and stone arrangement, that is the result of applying a series of construction solutions which form the "rule of art". Taking into account the complexity of the problem due to the great number of variables, and being on-site testing a not-always viable solution, a visual estimate of the mechanical parameters of the walls can be made on the basis of a qualitative criteria evaluation. A revision of this visual method is proposed in this paper. The draft version of new Italian Building Code have been used to re-calibrate this visual method and more tests results have been also considered for a better estimation of the mechanical properties of masonry

Constraints on massive sterile plus active neutrino species in non minimal cosmologies

Cosmological measurements are affected by the energy density of both active and sterile massive neutrinos. We extend here a recent analysis of current cosmological data to non minimal cosmologies. Several possible scenarios are examined: a constant w \neq -1 dark energy equation of state, a non flat universe, a time varying dark energy component and coupled dark matter dark energy universes or modified gravity scenarios. When considering cosmological data only, (3+2) massive neutrino models with ~0.5 eV sterile species are allowed at 95% CL. This scenario has been shown to reconcile reactor, LSND and MiniBooNE positive signals with null results from other searches. Big Bang Nucleosynthesis bounds could compromise the viability of (3+2) models if the two sterile species are fully thermalized states at decoupling.Comment: 8 pages, 5 figure

3D bioprinted human cortical neural constructs derived from induced pluripotent stem cells

Bioprinting techniques use bioinks made of biocompatible non-living materials and cells to build 3D constructs in a controlled manner and with micrometric resolution. 3D bioprinted structures representative of several human tissues have been recently produced using cells derived by differentiation of induced pluripotent stem cells (iPSCs). Human iPSCs can be differentiated in a wide range of neurons and glia, providing an ideal tool for modeling the human nervous system. Here we report a neural construct generated by 3D bioprinting of cortical neurons and glial precursors derived from human iPSCs. We show that the extrusion-based printing process does not impair cell viability in the short and long term. Bioprinted cells can be further differentiated within the construct and properly express neuronal and astrocytic markers. Functional analysis of 3D bioprinted cells highlights an early stage of maturation and the establishment of early network activity behaviors. This work lays the basis for generating more complex and faithful 3D models of the human nervous systems by bioprinting neural cells derived from iPSCs

Categorical Properties of Italian Verbs in Written Word Recognition

The study addresses the issue of lexical representation of inflected Italian verbal forms. Linguistic and experimental data suggest the existence of differences in lexical processing of verbs depending on morphological factors. We aimed at verifying whether lexical organization of verbs in the mental lexicon is affected by information about the grammatical category of mood. Two unmasked priming lexical decision experiments were carried out with different SOAs. Primetarget pairs composed of inflected verbs sharing or not mood information were compared. A number of control conditions were also included. The results show that information about mood becomes available in the early stages of lexical processing of verbs, but it is likely to induce priming effects a few hundred milliseconds after its pre-activation. This pattern provides evidence that mood is represented in the input component(s) and is an organizational criterion for verbal forms in the lexicon

Electrical, mechanical and electromechanical properties of graphene-thermoset polymer composites produced using acetone-DMF solvents

Recently, graphene-polymer composites gained a central role in advanced stress and strain sensing. A fundamental step in the production of epoxy-composites filled with graphene nanoplatelets (GNPs) consists in the exfoliation and dispersion of expanded graphite in a proper solvent, in the mixing of the resulting GNP suspension with the polymer matrix, and in the final removal of the solvent from the composite before curing through evaporation. The effects of traces of residual solvent on polymer curing process are usually overlooked, even if it has been found that even a small amount of residual solvent can affect the mechanical properties of the final composite. In this paper, we show that residual traces of N,N′-Dimethylformamide (DMF) in vinylester epoxy composites can induce relevant variations of the electrical, mechanical and electromechanical properties of the cured GNP-composite. To this purpose, a complete analysis of the morphological and structural characteristics of the composite samples produced using different solvent mixtures (combining acetone and DMF) is performed. Moreover, electrical, mechanical and electromechanical properties of the produced composites are assessed. In particular, the effect on the piezoresistive response of the use of DMF in the solvent mixture is analyzed using an experimental strain dependent percolation law to fit the measured electromechanical data. It is shown that the composites realized using a higher amount of DMF are characterized by a higher electrical conductivity and by a strong reduction of Young’s Modulus

Direct conversion of human pluripotent stem cells into cranial motor neurons using a piggyBac vector

Human pluripotent stem cells (PSCs) are widely used for in vitro disease modeling. One of the challenges in the field is represented by the ability of converting human PSCs into specific disease-relevant cell types. The nervous system is composed of a wide variety of neuronal types with selective vulnerability in neurodegenerative diseases. This is particularly relevant for motor neuron diseases, in which different motor neurons populations show a different susceptibility to degeneration. Here we developed a fast and efficient method to convert human induced Pluripotent Stem Cells into cranial motor neurons of the branchiomotor and visceral motor subtype. These populations represent the motor neuron subgroup that is primarily affected by a severe form of amyotrophic lateral sclerosis with bulbar onset and worst prognosis. This goal was achieved by stable integration of an inducible vector, based on the piggyBac transposon, allowing controlled activation of Ngn2, Isl1 and Phox2a (NIP). The NIP module effectively produced electrophysiologically active cranial motor neurons. Our method can be easily extended to PSCs carrying disease-associated mutations, thus providing a useful tool to shed light on the cellular and molecular bases of selective motor neuron vulnerability in pathological conditions