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Institution

University of Milan

EducationMilan, Italy
About: University of Milan is a education organization based out in Milan, Italy. It is known for research contribution in the topics: Population & Medicine. The organization has 58413 authors who have published 139784 publications receiving 4636354 citations. The organization is also known as: Università degli Studi di Milano & Statale.


Papers
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Journal ArticleDOI
S. Schael1, R. Barate2, R. Brunelière2, D. Buskulic2  +1672 moreInstitutions (143)
TL;DR: In this paper, the results of the four LEP experiments were combined to determine fundamental properties of the W boson and the electroweak theory, including the branching fraction of W and the trilinear gauge-boson self-couplings.

684 citations

Journal ArticleDOI
TL;DR: In this article, the authors provide an updated recommendation for the usage of sets of parton distribution functions (PDFs) and the assessment of PDF and PDF+$\alpha_s$ uncertainties suitable for applications at the LHC Run II.
Abstract: We provide an updated recommendation for the usage of sets of parton distribution functions (PDFs) and the assessment of PDF and PDF+$\alpha_s$ uncertainties suitable for applications at the LHC Run II. We review developments since the previous PDF4LHC recommendation, and discuss and compare the new generation of PDFs, which include substantial information from experimental data from the Run I of the LHC. We then propose a new prescription for the combination of a suitable subset of the available PDF sets, which is presented in terms of a single combined PDF set. We finally discuss tools which allow for the delivery of this combined set in terms of optimized sets of Hessian eigenvectors or Monte Carlo replicas, and their usage, and provide some examples of their application to LHC phenomenology.

683 citations

Proceedings ArticleDOI
01 Jun 2019
TL;DR: The paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter, and provides an analysis and discussion about the participant systems and the results they achieved in both subtasks.
Abstract: The paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter. The task is organized in two related classification subtasks: a main binary subtask for detecting the presence of hate speech, and a finer-grained one devoted to identifying further features in hateful contents such as the aggressive attitude and the target harassed, to distinguish if the incitement is against an individual rather than a group. HatEval has been one of the most popular tasks in SemEval-2019 with a total of 108 submitted runs for Subtask A and 70 runs for Subtask B, from a total of 74 different teams. Data provided for the task are described by showing how they have been collected and annotated. Moreover, the paper provides an analysis and discussion about the participant systems and the results they achieved in both subtasks.

682 citations

Journal ArticleDOI
TL;DR: This study explored the hypothesis that patients suffering from dementia of the Alzheimer type (DAT) are particularly impaired in the functioning of the Central Executive component of working memory, and that this will be reflected in the capacity of patients to perform simultaneously two concurrent tasks.
Abstract: This study explored the hypothesis that patients suffering from dementia of the Alzheimer type (DAT) are particularly impaired in the functioning of the Central Executive component of working memory, and that this will be reflected in the capacity of patients to perform simultaneously two concurrent tasks. DAT patients, age-matched controls and young controls were required to combine performance on a tracking task with each of three concurrent tasks, articulatory suppression, simple reaction time to a tone and auditory digit span. The difficulty of the tracking task and length of digit sequence were both adjusted so as to equate performance across the three groups when the tasks were performed alone. When digit span or concurrent RT were combined with tracking, the deterioration in performance shown by the DAT patients was particularly marked.

682 citations

Proceedings ArticleDOI
18 Nov 2002
TL;DR: This work proposes a self-regulating system where the P2P network is used to implement a robust reputation mechanism, and a distributed polling algorithm by which resource requestors can assess the reliability of a resource offered by a participant before initiating the download.
Abstract: Peer-to-peer (P2P) applications have seen an enormous success, and recently introduced P2P services have reached tens of millions of users. A feature that significantly contributes to the success of many P2P applications is user anonymity. However, anonymity opens the door to possible misuses and abuses, exploiting the P2P network as a way to spread tampered with resources, including Trojan Horses, viruses, and spam. To address this problem we propose a self-regulating system where the P2P network is used to implement a robust reputation mechanism. Reputation sharing is realized through a distributed polling algorithm by which resource requestors can assess the reliability of a resource offered by a participant before initiating the download. This way, spreading of malicious contents will be reduced and eventually blocked. Our approach can be straightforwardly piggybacked on existing P2P protocols and requires modest modifications to current implementations.

681 citations


Authors

Showing all 58902 results

NameH-indexPapersCitations
Yi Cui2201015199725
Peter J. Barnes1941530166618
Thomas C. Südhof191653118007
Charles A. Dinarello1901058139668
Alberto Mantovani1831397163826
John J.V. McMurray1781389184502
Giuseppe Remuzzi1721226160440
Russel J. Reiter1691646121010
Jean Louis Vincent1611667163721
Tobin J. Marks1591621111604
Tomas Hökfelt158103395979
José Baselga156707122498
Naveed Sattar1551326116368
Silvia Franceschi1551340112504
Frederik Barkhof1541449104982
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023240
2022777
20219,390
20209,000
20197,475
20186,804