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.

Search for top-squark pair production in the single-lepton final state in pp collisions at √s=8 TeV

Abstract: This paper presents a search for the pair production of top squarks in events with a single isolated electron or muon, jets, large missing transverse momentum, and large transverse mass. The data sample corresponds to an integrated luminosity of 19.5 inverse femtobarns of pp collisions collected in 2012 by the CMS experiment at the LHC at a center-of-mass energy of sqrt(s) = 8 TeV. No significant excess in data is observed above the expectation from standard model processes. The results are interpreted in the context of supersymmetric models with pair production of top squarks that decay either to a top quark and a neutralino or to a bottom quark and a chargino. For small mass values of the lightest supersymmetric particle, top-squark mass values up to around 650 GeV are excluded.

Climate change response in Europe: What's the reality? Analysis of adaptation and mitigation plans from 200 urban areas in 11 countries

Abstract: Urban areas are pivotal to global adaptation and mitigation efforts. But how do cities actually perform in terms of climate change response? This study sheds light on the state of urban climate change adaptation and mitigation planning across Europe. Europe is an excellent test case given its advanced environmental policies and high urbanization. We performed a detailed analysis of 200 large and medium-sized cities across 11 European countries and analysed the cities’ climate change adaptation and mitigation plans. We investigate the regional distribution of plans, adaptation and mitigation foci and the extent to which planned greenhouse gas (GHG) reductions contribute to national and international climate objectives. To our knowledge, it is the first study of its kind as it does not rely on self-assessment (questionnaires or social surveys). Our results show that 35 % of European cities studied have no dedicated mitigation plan and 72 % have no adaptation plan. No city has an adaptation plan without a mitigation plan. One quarter of the cities have both an adaptation and a mitigation plan and set quantitative GHG reduction targets, but those vary extensively in scope and ambition. Furthermore, we show that if the planned actions within cities are nationally representative the 11 countries investigated would achieve a 37 % reduction in GHG emissions by 2050, translating into a 27 % reduction in GHG emissions for the EU as a whole. However, the actions would often be insufficient to reach national targets and fall short of the 80 % reduction in GHG emissions recommended to avoid global mean temperature rising by 2 °C above pre-industrial levels.

Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.

Abstract: In the version of this article initially published, some reference citations were incorrect. The three references to Jupyter Notebooks should have cited Kluyver et al. instead of Gonzalez et al. The reference to Qiita should have cited Gonzalez et al. instead of Schloss et al. The reference to mothur should have cited Schloss et al. instead of McMurdie & Holmes. The reference to phyloseq should have cited McMurdie & Holmes instead of Huber et al. The reference to Bioconductor should have cited Huber et al. instead of Franzosa et al. And the reference to the biobakery suite should have cited Franzosa et al. instead of Kluyver et al. The errors have been corrected in the HTML and PDF versions of the article.

Animating Arbitrary Objects via Deep Motion Transfer

Abstract: This paper introduces a novel deep learning framework for image animation. Given an input image with a target object and a driving video sequence depicting a moving object, our framework generates a video in which the target object is animated according to the driving sequence. This is achieved through a deep architecture that decouples appearance and motion information. Our framework consists of three main modules: (i) a Keypoint Detector unsupervisely trained to extract object keypoints, (ii) a Dense Motion prediction network for generating dense heatmaps from sparse keypoints, in order to better encode motion information and (iii) a Motion Transfer Network, which uses the motion heatmaps and appearance information extracted from the input image to synthesize the output frames. We demonstrate the effectiveness of our method on several benchmark datasets, spanning a wide variety of object appearances, and show that our approach outperforms state-of-the-art image animation and video generation methods.