Open Access
Issue
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
Article Number 01341
Number of page(s) 8
DOI https://doi.org/10.1051/epjconf/202533701341
Published online 07 October 2025
  1. J. Zurawski, et al., High energy physics network requirements review: Two-year update, Tech. Rep. OSTI ID:2405935, Energy Sciences Network (2023), https://doi.org/10.2172/2405935 [Google Scholar]
  2. E. Fajardo, D. Weitzel, M. Rynge, M. Zvada, J. Hicks, M. Selmeci, B. Lin, P. Paschos, B. Bockelman, A. Hanushevsky et al., Creating a content delivery network for general science on the internet backbone using XCaches, EPJ Web of Conferences 245, 04041 (2020) [CrossRef] [EDP Sciences] [Google Scholar]
  3. C. Sim, K. Wu, A. Sim, I. Monga, C. Guok, D. Hazen, F. Wurthwein, D. Davila, H. Newman, J. Balcas, Predicting Resource Utilization Trends with Southern California Petabyte Scale Cache, in 26th International Conference on Computing in High Energy & Nuclear Physics (CHEP2023) (2023) [Google Scholar]
  4. A. Dorigo, P. Elmer, F. Furano, A. Hanushevsky, Xrootd - a highly scalable architecture for data access, WSEAS Transactions on Computers 4, 348 (2005) [Google Scholar]
  5. L. Bauerdick, D. Benjamin, K. Bloom, B. Bockelman, D. Bradley, S. Dasu, M. Ernst, R. Gardner, A. Hanushevsky, H. Ito et al., Using xrootd to federate regional storage, Journal of Physics: Conference Series 396, 042009 (2012) [CrossRef] [Google Scholar]
  6. C. Sim, K. Wu, A. Sim, I. Monga, C. Guok, F. Wurthwein, D. Davila, H. Newman, J. Balcas, Effectiveness and predictability of in-network storage cache for Scientific Workflows, in IEEE International Conference on Computing, Networking and Communication (ICNC2023) (2023) [Google Scholar]
  7. A. Sherstinsky, Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network, Physica D: Nonlinear Phenomena 404, 132306 (2020) [NASA ADS] [CrossRef] [Google Scholar]
  8. K. Greff, R.K. Srivastava, J. Koutník, B.R. Steunebrink, J. Schmidhuber, LSTM: A search space odyssey, in IEEE transactions on neural networks and learning systems (2016), Vol. 28, pp. 2222–2232 [Google Scholar]
  9. J. Yosinski, J. Clune, Y. Bengio, H. Lipson, How transferable are features in deep neural networks?, in Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS’14) (2014) [Google Scholar]
  10. F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong, Q. He, A comprehensive survey on transfer learning, Proceedings of the IEEE 109, 43 (2021) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.