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 & Large Hadron Collider. 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.

Gravitational waves from known pulsars: Results from the initial detector era

Abstract: We present the results of searches for gravitational waves from a large selection of pulsars using data from the most recent science runs (S6, VSR2 and VSR4) of the initial generation of interferometric gravitational wave detectors LIGO (Laser Interferometric Gravitational-wave Observatory) and Virgo. We do not see evidence for gravitational wave emission from any of the targeted sources but produce upper limits on the emission amplitude. We highlight the results from seven young pulsars with large spin-down luminosities. We reach within a factor of five of the canonical spin-down limit for all seven of these, whilst for the Crab and Vela pulsars we further surpass their spin-down limits. We present new or updated limits for 172 other pulsars (including both young and millisecond pulsars). Now that the detectors are undergoing major upgrades, and, for completeness, we bring together all of the most up-to-date results from all pulsars searched for during the operations of the first-generation LIGO, Virgo and GEO600 detectors. This gives a total of 195 pulsars including the most recent results described in this paper.

A Support Vector Domain Description Approach to Supervised Classification of Remote Sensing Images

Abstract: This paper addresses the problem of supervised classification of remote sensing images in the presence of incomplete (nonexhaustive) training sets. The problem is analyzed according to two different perspectives: 1) description and recognition of a specific land-cover class by using single-class classifiers and 2) solution of multiclass problems with single-class classification techniques. In this framework, we analyze different one-class classifiers and introduce in the remote sensing community the support vector domain description method (SVDD). The SVDD is a kernel-based method that exhibits intrinsic regularization ability and robustness versus low numbers of high-dimensional samples. The SVDD technique is compared with other standard single-class methods both in problems focused on the recognition of a single specific land-cover class and in multiclass problems. For the latter, we properly define an easily scalable multiclass architecture capable to deal with incomplete training data. Experimental results, obtained on different kinds of data (synthetic, hyperspectral, and multisensor images), point out the effectiveness of the SVDD technique and provide important indications for driving the choice of the classification technique and architecture in the presence of incomplete training data.

Cleaning Interfaces in Layered Materials Heterostructures

Abstract: Heterostructures formed by stacking layered materials require atomically clean interfaces. However, contaminants are usually trapped between the layers, aggregating into blisters. We report a process to remove such blisters, resulting in clean interfaces. We fabricate blister-free regions of graphene encapsulated in hexagonal boron nitride of$\sim$5000$\mu $m$^{2}$, limited only by the size of the exfoliated flakes. These have mobilities up to$\sim$180000cm$^2$V$^{-1}$s$^{-1}$ at room temperature, and$\sim$1.8$\times$10$^6$cm$^2$V$^{-1}$s$^{-1}$ at 9K. We further demonstrate the effectiveness of our approach by cleaning heterostructures assembled using graphene intentionally exposed to polymers and solvents. After cleaning, these samples reach similar high mobilities. We also showcase the general applicability of our approach to layered materials by cleaning blisters in other heterostructures based on MoS$_{2}$. This demonstrates that exposure of graphene to processing-related contaminants is compatible with the realization of high mobility samples, paving the way to the development of fab-based processes for the integration of layered materials in (opto)-electronic devices.