Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Shah Rukh"'
Autor:
Shah Rukh Qasim, Nadezda Chernyavskaya, Jan Kieseler, Kenneth Long, Oleksandr Viazlo, Maurizio Pierini, Raheel Nawaz
Publikováno v:
European Physical Journal
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 distance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ca06924cc1a438d23eb8287bf1ed429
http://arxiv.org/abs/2204.01681
http://arxiv.org/abs/2204.01681
Recently, a new window to explore tweet data has been opened in TExVis tool through visualizing the relations between the frequent keywords. However, timeline exploration of tweet data, not present in TExVis, could play a critical factor in understan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72e6c8906bee1c5d22b4e671bb1d80c4
http://arxiv.org/abs/2107.04799
http://arxiv.org/abs/2107.04799
Autor:
Alex Endert, Shah Rukh Humayoun, Michael Gleicher, Florian Heimerl, Shenyu Xu, Remco Chang, Dylan Cashman, Cong Liu, Subhajit Das
Most visual analytics systems assume that all foraging for data happens before the analytics process; once analysis begins, the set of data attributes considered is fixed. Such separation of data construction from analysis precludes iteration that ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b445d3b2b573d0ae0757c57a56b7f919
http://arxiv.org/abs/2009.02865
http://arxiv.org/abs/2009.02865
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
Recent advancements in mobile devices encourage researchers to utilize them in collaborative environments as a medium to interact with large shared wall-displays. In this paper, we focus on a semi-controlled user study that we conducted to measure th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e1d3ba0b6880828acd060608e933cf40
http://arxiv.org/abs/1904.13364
http://arxiv.org/abs/1904.13364
Autor:
Abigail Mosca, Kendall Park, Subhajit Das, Shah Rukh Humayoun, John Thompson, Florian Heimerl, Michael Gleicher, John Stasko, Bahador Saket, Remco Chang, Alex Endert, Dylan Cashman
Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7029c367c16b97a8890600c7a036724
In this position paper, we present our approach of utilizing mobile devices (i.e., mobile phones and tablets) for assisting engineers and experts in understanding and maintaining the factory pipelines. For this, we present a platform, called assistME
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ff594faaef367383de24738ca7a460c
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