Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Shah Rukh Qasim"'
Autor:
Shah Rukh Qasim, Nadezda Chernyavskaya, Jan Kieseler, Kenneth Long, Oleksandr Viazlo, Maurizio Pierini, Raheel Nawaz
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 82, Iss 8, Pp 1-15 (2022)
Abstract We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a
Externí odkaz:
https://doaj.org/article/60ff6221e4bd498fb980ca4017e6f044
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 79, Iss 7, Pp 1-11 (2019)
Abstract We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while n
Externí odkaz:
https://doaj.org/article/94c59c4caee2427fb162ef11d2f53b7b
Autor:
Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler, Vladimir Loncar, Maurizio Pierini, Shah Rukh Qasim, Marcel Rieger, Sioni Summers, Gerrit Van Onsem, Kinga Anna Wozniak, Jennifer Ngadiuba, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Zhenbin Wu
Publikováno v:
Frontiers in Big Data, Vol 3 (2021)
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGP
Externí odkaz:
https://doaj.org/article/c0766df513ec43ff8f6284d42859ca59
Publikováno v:
EPJ Web of Conferences, Vol 251, p 03072 (2021)
The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events with up to 200 simultaneous protonproton interactions. The planned CMS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b8613fa0a66acddca47a4444c82477c7
http://cds.cern.ch/record/2775923
http://cds.cern.ch/record/2775923
Autor:
Verzetti, Mauro, Pierini, Maurizio, Kieseler, Jan, Swapneel Sundeep Mehta, Shah Rukh Qasim, Yutaro Iiyama
We use Graph Networks to learn representations of irregular detector geometries and perform on it typical tasks such as cluster segmentation or pattern recognition. Thanks to the flexibility and generality of the graph architecture, this kind of netw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d31d35aa8a71cc811a4f5cd63fdd65d4
Publikováno v:
ICDAR
Document structure analysis, such as zone segmentation and table recognition, is a complex problem in document processing and is an active area of research. The recent success of deep learning in solving various computer vision and machine learning p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c807179ff9a995805069133d3b1299b
http://arxiv.org/abs/1905.13391
http://arxiv.org/abs/1905.13391
Publikováno v:
ICDAR
Table detection is a crucial step in many document analysis applications as tables are used for presenting essential information to the reader in a structured manner. It is a hard problem due to varying layouts and encodings of the tables. Researcher
Autor:
Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler, Vladimir Loncar, Maurizio Pierini, Shah Rukh Qasim, Marcel Rieger, Sioni Summers, Gerrit Van Onsem, Kinga Anna Wozniak, Jennifer Ngadiuba, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Zhenbin Wu
Publikováno v:
Frontiers in Big Data
Frontiers in Big Data, Vol 3 (2021)
Frontiers in Big Data, Vol 3 (2021)
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGP
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 79, Iss 7, Pp 1-11 (2019)
European Physical Journal
The European Physical Journal C
European Physical Journal
The European Physical Journal C
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while natively m
Autor:
'Shah Rukh Qasim
Publikováno v:
Nadezda Chernyavskaya