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.

Awareness requirements for adaptive systems

Abstract: Recently, there has been a growing interest in self-adaptive systems Roadmap papers in this area point to feedback loops as a promising way of operationalizing adaptivity in such systems In this paper, we define a new type of requirement - called Awareness Requirement - that can refer to other requirements and their success/failures We propose a way to elicit and formalize such requirements and offer a requirements monitoring framework to support them

Two uses of anaphora resolution in summarization

Abstract: We propose a new method for using anaphoric information in Latent Semantic Analysis (LSA), and discuss its application to develop an LSA-based summarizer which achieves a significantly better performance than a system not using anaphoric information, and a better performance by the rouge measure than all but one of the single-document summarizers participating in DUC-2002. Anaphoric information is automatically extracted using a new release of our own anaphora resolution system, GUITAR, which incorporates proper noun resolution. Our summarizer also includes a new approach for automatically identifying the dimensionality reduction of a document on the basis of the desired summarization percentage. Anaphoric information is also used to check the coherence of the summary produced by our summarizer, by a reference checker module which identifies anaphoric resolution errors caused by sentence extraction.

Semisupervised Transfer Component Analysis for Domain Adaptation in Remote Sensing Image Classification

Abstract: In this paper, we study the problem of feature extraction for knowledge transfer between multiple remotely sensed images in the context of land-cover classification. Several factors such as illumination, atmospheric, and ground conditions cause radiometric differences between images of similar scenes acquired on different geographical areas or over the same scene but at different time instants. Accordingly, a change in the probability distributions of the classes is observed. The purpose of this work is to statistically align in the feature space an image of interest that still has to be classified (the target image) to another image whose ground truth is already available (the source image). Following a specifically designed feature extraction step applied to both images, we show that classifiers trained on the source image can successfully predict the classes of the target image despite the shift that has occurred. In this context, we analyze a recently proposed domain adaptation method aiming at reducing the distance between domains, Transfer Component Analysis, and assess the potential of its unsupervised and semisupervised implementations. In particular, with a dedicated study of its key additional objectives, namely the alignment of the projection with the labels and the preservation of the local data structures, we demonstrate the advantages of Semisupervised Transfer Component Analysis. We compare this approach with other both linear and kernel-based feature extraction techniques. Experiments on multi- and hyperspectral acquisitions show remarkable cross- image classification performances for the considered strategy, thus confirming its suitability when applied to remotely sensed images.

Spectrophotometric properties of the nucleus of comet 67P/Churyumov-Gerasimenko from the OSIRIS instrument onboard the ROSETTA spacecraft

Abstract: The Rosetta mission of the European Space Agency has been orbiting the comet 67P/Churyumov-Gerasimenko (67P) since August 2014 and is now in its escort phase. A large complement of scientific experiments designed to complete the most detailed study of a comet ever attempted are onboard Rosetta. We present results for the photometric and spectrophotometric properties of the nucleus of 67P derived from the OSIRIS imaging system, which consists of a Wide Angle Camera (WAC) and a Narrow Angle Camera (NAC). The disk-averaged phase function of the nucleus of 67P shows a strong opposition surge with a G parameter value of -0.13$\pm$0.01 in the HG system formalism and an absolute magnitude $H_v(1,1,0)$ = 15.74$\pm$0.02 mag. The integrated spectrophotometry in 20 filters covering the 250-1000 nm wavelength range shows a red spectral behavior, without clear absorption bands except for a potential absorption centered at $\sim$ 290 nm that is possibly due to SO$_2$ ice. The nucleus shows strong phase reddening, with disk-averaged spectral slopes increasing from 11\%/(100 nm) to 16\%/(100 nm) in the 1.3$^{\circ}$--54$^{\circ}$ phase angle range. The geometric albedo of the comet is 6.5$\pm$0.2\% at 649 nm, with local variations of up to $\sim$ 16\% in the Hapi region. From the disk-resolved images we computed the spectral slope together with local spectrophotometry and identified three distinct groups of regions (blue, moderately red, and red). The Hapi region is the brightest, the bluest in term of spectral slope, and the most active surface on the comet. Local spectrophotometry shows an enhancement of the flux in the 700-750 nm that is associated with coma emissions.