scispace - formally typeset
Search or ask a question
Institution

University of Trento

EducationTrento, Italy
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.


Papers
More filters
Journal ArticleDOI
TL;DR: In this article, the authors argue that current levels of youth unemployment need to be understood in the context of increased labor market flexibility, an expansion of higher education, youth migration, and family legacies of long-term unemployment.
Abstract: Current levels of youth unemployment need to be understood in the context of increased labor market flexibility, an expansion of higher education, youth migration, and family legacies of long-term unemployment. Compared with previous recessions, European-wide policies and investments have significantly increased with attempts to support national policies. By mapping these developments and debates, we illustrate the different factors shaping the future of European labor markets. We argue that understanding youth unemployment requires a holistic approach that combines an analysis of changes in the economic sphere around labor market flexibility, skills attainment, and employer demand, as well as understanding the impact of family legacies affecting increasingly polarized trajectories for young people today. The success of EU policy initiatives and investments will be shaped by the ability of national actors to implement these effectively.

289 citations

Proceedings Article
27 May 2017
TL;DR: In this paper, the authors provide the key atomic components for complex-valued deep neural networks and apply them to convolutional feed-forward networks, and demonstrate that such complexvalued models are competitive with their real-valued counterparts.
Abstract: At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and representations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capacity and could also facilitate noise-robust memory retrieval mechanisms. Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models. In this work, we provide the key atomic components for complex-valued deep neural networks and apply them to convolutional feed-forward networks. More precisely, we rely on complex convolutions and present algorithms for complex batch-normalization, complex weight initialization strategies for complex-valued neural nets and we use them in experiments with end-to-end training schemes. We demonstrate that such complex-valued models are competitive with their real-valued counterparts. We test deep complex models on several computer vision tasks, on music transcription using the MusicNet dataset and on Speech spectrum prediction using TIMIT. We achieve state-of-the-art performance on these audio-related tasks.

288 citations

Journal ArticleDOI
TL;DR: In this article, the second-order and third-order azimuthal anisotropy harmonics of unidentified charged particles, as well as v2v2 of View the MathML sourceKS0 and ViewTheMathML sourceΛ/Λ ǫ particles, are extracted from long-range two-particle correlations as functions of particle multiplicity and transverse momentum.

288 citations

Journal ArticleDOI
TL;DR: In this paper, the authors argue that there is a close connection between knowing and learning in practice and sensible knowledge, by referring to field research conducted in a variety of workplaces: a sawmill, a roofing firm and a secretarial office.
Abstract: The article shows that there is a close connection between knowing and learning in practice and sensible knowledge. It does so by referring to field research conducted in a variety of workplaces: a sawmill, a roofing firm and a secretarial office. The concluding remarks argue for the re-construction of organizational discourse through aesthetic understanding, because this is an approach which brings out the ‘don't-know-what’ characteristic of a large part of practical experience in organizations.

288 citations

Journal ArticleDOI
B. P. Abbott1, Richard J. Abbott1, T. D. Abbott2, M. R. Abernathy1  +1619 moreInstitutions (220)
TL;DR: In this article, the sky localization of the first observed compact binary merger is presented, where the authors describe the low-latency analysis of the LIGO data and present a sky localization map.
Abstract: A gravitational-wave (GW) transient was identified in data recorded by the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) detectors on 2015 September 14. The event, initially designated G184098 and later given the name GW150914, is described in detail elsewhere. By prior arrangement, preliminary estimates of the time, significance, and sky location of the event were shared with 63 teams of observers covering radio, optical, near-infrared, X-ray, and gamma-ray wavelengths with ground- and space-based facilities. In this Letter we describe the low-latency analysis of the GW data and present the sky localization of the first observed compact binary merger. We summarize the follow-up observations reported by 25 teams via private Gamma-ray Coordinates Network circulars, giving an overview of the participating facilities, the GW sky localization coverage, the timeline, and depth of the observations. As this event turned out to be a binary black hole merger, there is little expectation of a detectable electromagnetic (EM) signature. Nevertheless, this first broadband campaign to search for a counterpart of an Advanced LIGO source represents a milestone and highlights the broad capabilities of the transient astronomy community and the observing strategies that have been developed to pursue neutron star binary merger events. Detailed investigations of the EM data and results of the EM follow-up campaign are being disseminated in papers by the individual teams.

288 citations


Authors

Showing all 10758 results

NameH-indexPapersCitations
Yi Chen2174342293080
Jie Zhang1784857221720
Richard B. Lipton1762110140776
Jasvinder A. Singh1762382223370
J. N. Butler1722525175561
Andrea Bocci1722402176461
P. Chang1702154151783
Bradley Cox1692150156200
Marc Weber1672716153502
Guenakh Mitselmakher1651951164435
Brian L Winer1621832128850
J. S. Lange1602083145919
Ralph A. DeFronzo160759132993
Darien Wood1602174136596
Robert Stone1601756167901
Network Information
Related Institutions (5)
ETH Zurich
122.4K papers, 5.1M citations

94% related

University of Maryland, College Park
155.9K papers, 7.2M citations

93% related

University of California, Santa Barbara
80.8K papers, 4.6M citations

93% related

Sapienza University of Rome
155.4K papers, 4.3M citations

92% related

Centre national de la recherche scientifique
382.4K papers, 13.6M citations

92% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023158
2022340
20212,399
20202,286
20192,129
20181,943