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Institution

fondazione bruno kessler

FacilityTrento, Italy
About: fondazione bruno kessler is a facility organization based out in Trento, Italy. It is known for research contribution in the topics: Silicon photomultiplier & Machine translation. The organization has 1145 authors who have published 4730 publications receiving 94404 citations. The organization is also known as: Trentino Institute of Culture.


Papers
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Journal ArticleDOI
TL;DR: An overview of the key aspects of graphene and related materials, ranging from fundamental research challenges to a variety of applications in a large number of sectors, highlighting the steps necessary to take GRMs from a state of raw potential to a point where they might revolutionize multiple industries are provided.
Abstract: We present the science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems, targeting an evolution in technology, that might lead to impacts and benefits reaching into most areas of society. This roadmap was developed within the framework of the European Graphene Flagship and outlines the main targets and research areas as best understood at the start of this ambitious project. We provide an overview of the key aspects of graphene and related materials (GRMs), ranging from fundamental research challenges to a variety of applications in a large number of sectors, highlighting the steps necessary to take GRMs from a state of raw potential to a point where they might revolutionize multiple industries. We also define an extensive list of acronyms in an effort to standardize the nomenclature in this emerging field.

2,560 citations

Journal ArticleDOI
TL;DR: This article shows how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario.
Abstract: To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F1 score in evaluating binary classification tasks by all scientific communities.

2,358 citations

Journal ArticleDOI
27 Mar 2014-Nature
TL;DR: For example, the authors mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body.
Abstract: Regulated transcription controls the diversity, developmental pathways and spatial organization of the hundreds of cell types that make up a mammal Using single-molecule cDNA sequencing, we mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body We find that few genes are truly 'housekeeping', whereas many mammalian promoters are composite entities composed of several closely separated TSSs, with independent cell-type-specific expression profiles TSSs specific to different cell types evolve at different rates, whereas promoters of broadly expressed genes are the most conserved Promoter-based expression analysis reveals key transcription factors defining cell states and links them to binding-site motifs The functions of identified novel transcripts can be predicted by coexpression and sample ontology enrichment analyses The functional annotation of the mammalian genome 5 (FANTOM5) project provides comprehensive expression profiles and functional annotation of mammalian cell-type-specific transcriptomes with wide applications in biomedical research

1,715 citations

Book
01 Jan 2006
TL;DR: This chapter discusses Classical Planning and its Applications, as well as Neoclassical and Neo-Classical Techniques, and discusses search procedures and Computational Complexity.
Abstract: 1 Introduction and Overview I Classical Planning 2 Representations for Classical Planning*3 Complexity of Classical Planning*4 State-Space Planning*5 Plan-Space Planning II Neoclassical Planning 6 Planning-Graph Techniques*7 Propositional Satisfiability Techniques*8 Constraint Satisfaction Techniques III Heuristics and Control Strategies 9 Heuristics in Planning*10 Control Rules in Planning*11 Hierarchical Task Network Planning*12 Control Strategies in Deductive Planning IV Planning with Time and Resources 13 Time for Planning*14 Temporal Planning*15 Planning and Resource Scheduling V Planning under Uncertainty 16 Planning based on Markov Decision Processes*17 Planning based on Model Checking*18 Uncertainty with Neo-Classical Techniques VI Case Studies and Applications 19 Space Applications*20 Planning in Robotics*21 Planning for Manufacturability Analysis*22 Emergency Evacuation Planning *23 Planning in the Game of Bridge VII Conclusion 24 Conclusion and Other Topics VIII Appendices A Search Procedures and Computational Complexity*B First Order Logic*C Model Checking

1,612 citations

Journal ArticleDOI
M. Punturo, M. R. Abernathy1, Fausto Acernese2, Benjamin William Allen3, Nils Andersson4, K. G. Arun5, Fabrizio Barone2, B. Barr1, M. Barsuglia6, M. G. Beker7, N. Beveridge1, S. Birindelli8, Suvadeep Bose9, L. Bosi, S. Braccini, C. Bradaschia, Tomasz Bulik10, Enrico Calloni, G. Cella, E. Chassande Mottin6, Simon Chelkowski11, Andrea Chincarini, John A. Clark12, E. Coccia13, C. N. Colacino, J. Colas, A. Cumming1, L. Cunningham1, E. Cuoco, S. L. Danilishin14, Karsten Danzmann3, G. De Luca, R. De Salvo15, T. Dent12, R. De Rosa, L. Di Fiore, A. Di Virgilio, M. Doets7, V. Fafone13, Paolo Falferi16, R. Flaminio17, J. Franc17, F. Frasconi, Andreas Freise11, Paul Fulda11, Jonathan R. Gair18, G. Gemme, A. Gennai11, A. Giazotto, Kostas Glampedakis19, M. Granata6, Hartmut Grote3, G. M. Guidi20, G. D. Hammond1, Mark Hannam21, Jan Harms22, D. Heinert23, Martin Hendry1, Ik Siong Heng1, Eric Hennes7, Stefan Hild1, J. H. Hough, Sascha Husa24, S. H. Huttner1, Gareth Jones12, F. Y. Khalili14, Keiko Kokeyama11, Kostas D. Kokkotas19, Badri Krishnan24, M. Lorenzini, Harald Lück3, Ettore Majorana, Ilya Mandel25, Vuk Mandic22, I. W. Martin1, C. Michel17, Y. Minenkov13, N. Morgado17, Simona Mosca, B. Mours26, H. Müller–Ebhardt3, P. G. Murray1, Ronny Nawrodt1, John Nelson1, Richard O'Shaughnessy27, Christian D. Ott15, C. Palomba, A. Paoli, G. Parguez, A. Pasqualetti, R. Passaquieti28, D. Passuello, L. Pinard17, Rosa Poggiani28, P. Popolizio, Mirko Prato, P. Puppo, D. S. Rabeling7, P. Rapagnani29, Jocelyn Read24, Tania Regimbau8, H. Rehbein3, Stuart Reid1, Luciano Rezzolla24, F. Ricci29, F. Richard, A. Rocchi, Sheila Rowan1, Albrecht Rüdiger3, Benoit Sassolas17, Bangalore Suryanarayana Sathyaprakash12, Roman Schnabel3, C. Schwarz, Paul Seidel, Alicia M. Sintes24, Kentaro Somiya15, Fiona C. Speirits1, Kenneth A. Strain1, S. E. Strigin14, P. J. Sutton12, S. P. Tarabrin14, Andre Thüring3, J. F. J. van den Brand7, C. van Leewen7, M. van Veggel1, C. Van Den Broeck12, Alberto Vecchio11, John Veitch11, F. Vetrano20, A. Viceré20, Sergey P. Vyatchanin14, Benno Willke3, Graham Woan1, P. Wolfango30, Kazuhiro Yamamoto3 
TL;DR: The third-generation ground-based observatory Einstein Telescope (ET) project as discussed by the authors is currently in its design study phase, and it can be seen as the first step in this direction.
Abstract: Advanced gravitational wave interferometers, currently under realization, will soon permit the detection of gravitational waves from astronomical sources. To open the era of precision gravitational wave astronomy, a further substantial improvement in sensitivity is required. The future space-based Laser Interferometer Space Antenna and the third-generation ground-based observatory Einstein Telescope (ET) promise to achieve the required sensitivity improvements in frequency ranges. The vastly improved sensitivity of the third generation of gravitational wave observatories could permit detailed measurements of the sources' physical parameters and could complement, in a multi-messenger approach, the observation of signals emitted by cosmological sources obtained through other kinds of telescopes. This paper describes the progress of the ET project which is currently in its design study phase.

1,497 citations


Authors

Showing all 1174 results

NameH-indexPapersCitations
Stefano Merler4718013272
Maurizio Ferrari455229157
Jochen Wambach452707534
Marco Pistore441509727
Beniamino Di Girolamo44936214
Ravinder Dahiya433068110
Davide Brunelli432517705
Luca Faes432364944
Marcello Federico4221512232
Marco Ajelli421358342
Luciano Serafini412407119
Bruno Lepri412275732
Graziano Guella403095961
Claudio Piemonte392896720
Francesca Bovolo392085624
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202317
202244
2021405
2020502
2019410
2018373