Multiscale Dictionary Learning for Estimating Conditional Distributions

Nonparametric estimation of the conditional distribution of a response given high-dimensional features is a challenging problem. It is important to allow not only the mean but also the variance and shape of the response density to change flexibly with features, which are massive-dimensional. We propose a multiscale dictionary learning model, which expresses the conditional response density as a convex combination of dictionary densities, with the densities used and their weights dependent on the path through a tree decomposition of the feature space. A fast graph partitioning algorithm is applied to obtain the tree decomposition, with Bayesian methods then used to adaptively prune and average over different sub-trees in a soft probabilistic manner. The algorithm scales efficiently to approximately one million features. State of the art predictive performance is demonstrated for toy examples and two neuroscience applications including up to a million features

Progenitors of Supernovae Type Ia and Chemical Enrichment in Hydrodynamical Simulations -I. The Single Degenerate Scenario

The nature of the Type Ia supernovae (SNIa) progenitors remains still uncertain. This is a major issue for galaxy evolution models since both chemical and energetic feedback play a major role in the gas dynamics, star formation and therefore in the overall stellar evolution. The progenitor models for the SNIa available in the literature propose different distributions for regulating the explosion times of these events. These functions are known as the Delay Time Distributions (DTDs). This work is the first one in a series of papers aiming at studying five different DTDs for SNIa. Here, we implement and analyse the Single Degenerate scenario (SD) in galaxies dominated by a rapid quenching of the star formation, displaying the majority of the stars concentrated in the bulge component. We find a good fit to both the present observed SNIa rates in spheroidal dominated galaxies, and to the [O/Fe] ratios shown by the bulge of the Milky Way. Additionally, the SD scenario is found to reproduce a correlation between the specific SNIa rate and the specific star formation rate, which closely resembles the observational trend, at variance with previous works. Our results suggest that SNIa observations in galaxies with very low and very high specific star formation rates can help to impose more stringent constraints on the DTDs and therefore on SNIa progenitors.Comment: 19 pages, 6 figures, 1 table. Accepted for publication in Ap

Computer aided inspection procedures to support smart manufacturing of injection moulded components

This work presents Reverse Engineering and Computer Aided technologies to improve the inspection of injection moulded electro-mechanical parts. Through a strong integration and automation of these methods, tolerance analysis, acquisition tool-path optimization and data management are performed. The core of the procedure concerns the automation of the data measure originally developed through voxel-based segmentation. This paper discusses the overall framework and its integration made according to Smart Manufacturing requirements. The experimental set-up, now in operative conditions at ABB SACE, is composed of a laser scanner installed on a CMM machine able to measure components with lengths in the range of 5÷250 mm, (b) a tool path optimization procedure and (c) a data management both developed as CAD-based applications

Superconducting Gap and Pseudogap in Bi-2212

We present results of Raman scattering experiments in differently doped Bi-2212 single crystals. Below Tc the spectra show pair-breaking features in the whole doping range. The low frequency power laws confirm the existence of a $d_{x^2-y^2}$-wave order parameter. In the normal state between Tc and T* = 200K we find evidence for a pseudogap in B2g symmetry. Upon doping its effect on the spectra decreases while its energy scale appears to be unchanged.Comment: 2 pages, 1 EPS figure; LT22 Proceedings to appear in Physica

Aggregates relaxation in a jamming colloidal suspension after shear cessation

The reversible aggregates formation in a shear thickening, concentrated colloidal suspension is investigated through speckle visibility spectroscopy, a dynamic light scattering technique recently introduced [P.K. Dixon and D.J. Durian, Phys. Rev. Lett. 90, 184302 (2003)]. Formation of particles aggregates is observed in the jamming regime, and their relaxation after shear cessation is monitored as a function of the applied shear stress. The aggregates relaxation time increases when a larger stress is applied. Several phenomena have been proposed to interpret this behavior: an increase of the aggregates size and volume fraction, or a closer packing of the particles in the aggregates.Comment: 7 pages, 7 figures; added figures included in the pdf versio

Projections of the sphere graph to the arc graph of a surface

Let SS be a compact surface, and MM be the double of a handlebody. Given a homotopy class of maps from SS to MM inducing an isomorphism of fundamental groups, we describe a canonical uniformly Lipschitz retraction of the sphere graph of MM to the arc graph of SS. We also show that this retraction is a uniformly bounded distance from the nearest point projection map

Competitive nucleation in reversible Probabilistic Cellular Automata

The problem of competitive nucleation in the framework of Probabilistic Cellular Automata is studied from the dynamical point of view. The dependence of the metastability scenario on the self--interaction is discussed. An intermediate metastable phase, made of two flip--flopping chessboard configurations, shows up depending on the ratio between the magnetic field and the self--interaction. A behavior similar to the one of the stochastic Blume--Capel model with Glauber dynamics is found