University of Trento

About: University of Trento is a education organization based out in Trento, Italy. It is known for research contribution in the topics: Population & Context (language use). The organization has 10527 authors who have published 30978 publications receiving 896614 citations. The organization is also known as: Universitá degli Studi di Trento & Universita degli Studi di Trento.

On the boundary‐layer structure over highly complex terrain: Key findings from MAP

Abstract: Within MAP, one of the scientific projects was devoted to 'Boundary Layers in Complex Terrain'. In a number of subprojects, boundary-layer issues were addressed and detailed high-resolution multi-sensor observations were combined with simulation by models allowing for adequate parametrization of turbulence processes. In this contribution, the projects are briefly introduced and an attempt is made to summarize their key findings and to put them into a joint perspective. Spatial variability is found to be large but strictly related to topography and therefore allowing for possible parametrization. Traditional boundary-layer scaling approaches cannot simply be applied over highly complex topography, but some of the MAP findings suggest the potential for suitable extensions of those scaling relations to cover various cases of complex terrain. The mean boundary-layer structure and thermally driven flows in narrow valleys are found not to be generally in line with previous results from larger valleys elsewhere. Furthermore, local circulations are reported to contribute considerably to exchange between valley and free troposphere. In particular, the range of their effects on the lower atmosphere seems to be larger than just turbulent transport within the planetary boundary layer would suggest. Thus in larger-scale numerical models where the topography is not resolved, possible sub-grid parametrizations for local exchange seem to be in order. Copyright  2007 Royal Meteorological Society

Web Image Annotation Via Subspace-Sparsity Collaborated Feature Selection

Abstract: The number of web images has been explosively growing due to the development of network and storage technology. These images make up a large amount of current multimedia data and are closely related to our daily life. To efficiently browse, retrieve and organize the web images, numerous approaches have been proposed. Since the semantic concepts of the images can be indicated by label information, automatic image annotation becomes one effective technique for image management tasks. Most existing annotation methods use image features that are often noisy and redundant. Hence, feature selection can be exploited for a more precise and compact representation of the images, thus improving the annotation performance. In this paper, we propose a novel feature selection method and apply it to automatic image annotation. There are two appealing properties of our method. First, it can jointly select the most relevant features from all the data points by using a sparsity-based model. Second, it can uncover the shared subspace of original features, which is beneficial for multi-label learning. To solve the objective function of our method, we propose an efficient iterative algorithm. Extensive experiments are performed on large image databases that are collected from the web. The experimental results together with the theoretical analysis have validated the effectiveness of our method for feature selection, thus demonstrating its feasibility of being applied to web image annotation.

Study of the rare decays of B s 0 and B 0 mesons into muon pairs using data collected during 2015 and 2016 with the ATLAS detector

Abstract: A study of the decays B0 s ! + and B0 ! + has been performed using 26 : 3 fb of 13TeV LHC proton-proton collision data collected with the ATLAS detector in 2015 and 2016. Since the detector resolut ...

A scalable realization of local U(1) gauge invariance in cold atomic mixtures

Abstract: In the fundamental laws of physics, gauge fields mediate the interaction between charged particles. An example is the quantum theory of electrons interacting with the electromagnetic field, based on U(1) gauge symmetry. Solving such gauge theories is in general a hard problem for classical computational techniques. Although quantum computers suggest a way forward, large-scale digital quantum devices for complex simulations are difficult to build. We propose a scalable analog quantum simulator of a U(1) gauge theory in one spatial dimension. Using interspecies spin-changing collisions in an atomic mixture, we achieve gauge-invariant interactions between matter and gauge fields with spin- and species-independent trapping potentials. We experimentally realize the elementary building block as a key step toward a platform for quantum simulations of continuous gauge theories.