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Akademický článek
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Autor:
Farrell, Steven, Calafiura, Paolo, Mudigonda, Mayur, Prabhat, Anderson, Dustin, Vlimant, Jean-Roch, Zheng, Stephan, Bendavid, Josh, Spiropulu, Maria, Cerati, Giuseppe, Gray, Lindsey, Kowalkowski, Jim, Spentzouris, Panagiotis, Tsaris, Aristeidis
For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew inspiration from computer vision applications and operated on an image
Externí odkaz:
http://arxiv.org/abs/1810.06111
Autor:
Albertsson, Kim, Altoe, Piero, Anderson, Dustin, Anderson, John, Andrews, Michael, Espinosa, Juan Pedro Araque, Aurisano, Adam, Basara, Laurent, Bevan, Adrian, Bhimji, Wahid, Bonacorsi, Daniele, Burkle, Bjorn, Calafiura, Paolo, Campanelli, Mario, Capps, Louis, Carminati, Federico, Carrazza, Stefano, Chen, Yi-fan, Childers, Taylor, Coadou, Yann, Coniavitis, Elias, Cranmer, Kyle, David, Claire, Davis, Douglas, De Simone, Andrea, Duarte, Javier, Erdmann, Martin, Eschle, Jonas, Farbin, Amir, Feickert, Matthew, Castro, Nuno Filipe, Fitzpatrick, Conor, Floris, Michele, Forti, Alessandra, Garra-Tico, Jordi, Gemmler, Jochen, Girone, Maria, Glaysher, Paul, Gleyzer, Sergei, Gligorov, Vladimir, Golling, Tobias, Graw, Jonas, Gray, Lindsey, Greenwood, Dick, Hacker, Thomas, Harvey, John, Hegner, Benedikt, Heinrich, Lukas, Heintz, Ulrich, Hooberman, Ben, Junggeburth, Johannes, Kagan, Michael, Kane, Meghan, Kanishchev, Konstantin, Karpiński, Przemysław, Kassabov, Zahari, Kaul, Gaurav, Kcira, Dorian, Keck, Thomas, Klimentov, Alexei, Kowalkowski, Jim, Kreczko, Luke, Kurepin, Alexander, Kutschke, Rob, Kuznetsov, Valentin, Köhler, Nicolas, Lakomov, Igor, Lannon, Kevin, Lassnig, Mario, Limosani, Antonio, Louppe, Gilles, Mangu, Aashrita, Mato, Pere, Meenakshi, Narain, Meinhard, Helge, Menasce, Dario, Moneta, Lorenzo, Moortgat, Seth, Neubauer, Mark, Newman, Harvey, Otten, Sydney, Pabst, Hans, Paganini, Michela, Paulini, Manfred, Perdue, Gabriel, Perez, Uzziel, Picazio, Attilio, Pivarski, Jim, Prosper, Harrison, Psihas, Fernanda, Radovic, Alexander, Reece, Ryan, Rinkevicius, Aurelius, Rodrigues, Eduardo, Rorie, Jamal, Rousseau, David, Sauers, Aaron, Schramm, Steven, Schwartzman, Ariel, Severini, Horst, Seyfert, Paul, Siroky, Filip, Skazytkin, Konstantin, Sokoloff, Mike, Stewart, Graeme, Stienen, Bob, Stockdale, Ian, Strong, Giles, Sun, Wei, Thais, Savannah, Tomko, Karen, Upfal, Eli, Usai, Emanuele, Ustyuzhanin, Andrey, Vala, Martin, Vasel, Justin, Vallecorsa, Sofia, Verzetti, Mauro, Vilasís-Cardona, Xavier, Vlimant, Jean-Roch, Vukotic, Ilija, Wang, Sean-Jiun, Watts, Gordon, Williams, Michael, Wu, Wenjing, Wunsch, Stefan, Yang, Kun, Zapata, Omar
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and r
Externí odkaz:
http://arxiv.org/abs/1807.02876
Autor:
Nguyen, Thong Q., Weitekamp III, Daniel, Anderson, Dustin, Castello, Roberto, Cerri, Olmo, Pierini, Maurizio, Spiropulu, Maria, Vlimant, Jean-Roch
Publikováno v:
Comput Softw Big Sci (2019) 3: 12
We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-
Externí odkaz:
http://arxiv.org/abs/1807.00083
We present a lightweight Python framework for distributed training of neural networks on multiple GPUs or CPUs. The framework is built on the popular Keras machine learning library. The Message Passing Interface (MPI) protocol is used to coordinate t
Externí odkaz:
http://arxiv.org/abs/1712.05878
Publikováno v:
Journal of the Intensive Care Society; Aug2024, Vol. 25 Issue 3, p339-345, 7p
Autor:
Anderson, Dustin, author
Publikováno v:
Beckett in the Cultural Field / Beckett dans le champ culturel. 25:75-89
Autor:
Pokharel, Bibek, Duggins, Peter, Misplon, Moses, Lynn, Walter, Hallman, Kevin, Anderson, Dustin, Kapulkin, Arie, Pattanayak, Arjendu K.
We study the dynamical complexity of an open quantum driven double-well oscillator, mapping its dependence on effective Planck's constant $\hbar_{eff}\equiv\beta$ and coupling to the environment, $\Gamma$. We study this using stochastic Schrodinger e
Externí odkaz:
http://arxiv.org/abs/1604.02743
Publikováno v:
In Educational Research Review November 2020 31
Akademický článek
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