Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Mark S. Neubauer"'
Autor:
E. A. Huerta, Ben Blaiszik, L. Catherine Brinson, Kristofer E. Bouchard, Daniel Diaz, Caterina Doglioni, Javier M. Duarte, Murali Emani, Ian Foster, Geoffrey Fox, Philip Harris, Lukas Heinrich, Shantenu Jha, Daniel S. Katz, Volodymyr Kindratenko, Christine R. Kirkpatrick, Kati Lassila-Perini, Ravi K. Madduri, Mark S. Neubauer, Fotis E. Psomopoulos, Avik Roy, Oliver Rübel, Zhizhen Zhao, Ruike Zhu
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-10 (2023)
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles w
Externí odkaz:
https://doaj.org/article/e815e307fdb8458fa4bef94640bad47f
Autor:
Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Dongning Guo, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belina von Krosigk, Shen Wang, Thomas K. Warburton
Publikováno v:
Frontiers in Big Data, Vol 6 (2023)
Externí odkaz:
https://doaj.org/article/fb1f7518123f425abdb4bab7ccb196f6
Autor:
Yifan Chen, E. A. Huerta, Javier Duarte, Philip Harris, Daniel S. Katz, Mark S. Neubauer, Daniel Diaz, Farouk Mokhtar, Raghav Kansal, Sang Eon Park, Volodymyr V. Kindratenko, Zhizhen Zhao, Roger Rusack
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-10 (2022)
Abstract To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) mo
Externí odkaz:
https://doaj.org/article/d67a4ff4df854871839ca49644de7ebb
Autor:
Johan Bonilla, Grigorios Chachamis, Barry M. Dillon, Sergei V. Chekanov, Robin Erbacher, Loukas Gouskos, Andreas Hinzmann, Stefan Höche, B. Todd Huffman, Ashutosh. V. Kotwal, Deepak Kar, Roman Kogler, Clemens Lange, Matt LeBlanc, Roy Lemmon, Christine McLean, Benjamin Nachman, Mark S. Neubauer, Tilman Plehn, Salvatore Rappoccio, Debarati Roy, Jennifer Roloff, Giordon Stark, Nhan Tran, Marcel Vos, Chih-Hsiang Yeh, Shin-Shan Yu
Publikováno v:
Frontiers in Physics, Vol 10 (2022)
Even though jet substructure was not an original design consideration for the Large Hadron Collider (LHC) experiments, it has emerged as an essential tool for the current physics program. We examine the role of jet substructure on the motivation for
Externí odkaz:
https://doaj.org/article/4a0594272a0b4c129a1748ae411fdc1f
Autor:
Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Dongning Guo, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belina von Krosigk, Shen Wang, Thomas K. Warburton
Publikováno v:
Frontiers in Big Data, Vol 5 (2022)
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery
Externí odkaz:
https://doaj.org/article/9e1a26011d554b649834394385e92870
Autor:
Kyle Cranmer, Sabine Kraml, Harrison Prosper, Philip Bechtle, Florian Bernlochner, Itay M. Bloch, Enzo Canonero, Marcin Chrzaszcz, Andrea Coccaro, Jan Conrad, Glen Cowan, Matthew Feickert, Nahuel Ferreiro, Andrew Fowlie, Lukas A. Heinrich, Alexander Held, Thomas Kuhr, Anders Kvellestad, Maeve Madigan, Farvah Nazila Mahmoudi, Knut Dundas Morå, Mark S. Neubauer, Maurizio Pierini, Juan Rojo, Sezen Sekmen, Luca Silvestrini, Veronica Sanz, Giordon H. Stark, Riccardo Torre, Robert Thorne, Wolfgang Waltenberger, Nicholas Wardle, Jonas Wittbrodt
Publikováno v:
SciPost Physics, Vol 12, Iss 1, p 037 (2022)
The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8866c20eee9650479e702b1399fe4736
http://cds.cern.ch/record/2780990
http://cds.cern.ch/record/2780990
Autor:
Javier Duarte, Haoyang Li, Avik Roy, Ruike Zhu, E A Huerta, Daniel Diaz, Philip Harris, Raghav Kansal, Daniel S Katz, Ishaan H Kavoori, Volodymyr V Kindratenko, Farouk Mokhtar, Mark S Neubauer, Sang Eon Park, Melissa Quinnan, Roger Rusack, Zhizhen Zhao
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 4, p 045062 (2023)
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and ot
Externí odkaz:
https://doaj.org/article/edd2fcfe5d994d0f84af824d77a1d18a
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 3, p 035003 (2023)
Recent developments in the methods of explainable artificial intelligence (XAI) allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about input–output relationships and realizing how data co
Externí odkaz:
https://doaj.org/article/9b4c47fef1984b699e7785acce9be66b
Publikováno v:
SciPost Physics, Vol 12, Iss 1, p 037 (2022)
The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishi
Externí odkaz:
https://doaj.org/article/5f087502309c43c3b3087f92801d9179