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Machine Learning in High Energy Physics Community White Paper
[article]
2019
arXiv
pre-print
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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 this document we discuss promising future research and development areas for machine learning in particle physics. ...
Preface To outline the challenges in computing that high-energy physics will face over the next years and strategies to approach them, the HEP software foundation has organised a Community White Paper ...
arXiv:1807.02876v3
fatcat:k2fjloakhjcd7lg7may5rtm6ya
Machine Learning in High Energy Physics Community White Paper
2018
Journal of Physics, Conference Series
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Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in ...
In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, ...
Software Methodology Presently, there are two machine learning software methodologies in high-energy physics. ...
doi:10.1088/1742-6596/1085/2/022008
fatcat:tqhhxd3c4ngurdackxqwxy3dmi
Long-lived particle signatures at the energy frontier
[article]
2020
Zenodo
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Summarizes the most vital directions of research related to long-lived particles at the Large Hadron Collider at CERN and beyond that will be of high importance for the Snowmass 2021 process. ...
the completion of the white paper and. ...
Thus, it is imperative that LLPs remain within the standard set of signatures considered for the near and far future of high-energy physics, lest this rich avenue for the potential discovery of BSM physics ...
doi:10.5281/zenodo.4274125
fatcat:rnvyepothbd75b6e76tfz4x2gi
Energy Efficiency in 5G Communications – Conventional to Machine Learning Approaches
2020
Journal of Telecommunications and Information Technology
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In this paper, we review state-of-the-art techniques ensuring good energy efficiency in 5G wireless networks. ...
In order to handle ultra-high data exchange rates while offering low latency levels, fifth generation (5G) networks have been proposed. Energy efficiency is one of the key objectives of 5G networks. ...
Energy-efficient physical layer hardware designs are reviewed in Section 4, and machine learning techniques ensuring EE are presented in Section 5. ...
doi:10.26636/jtit.2020.146820
fatcat:kp56hlb3yfhvlerrfau6y2qjva
What can Machine Learning do for Radio Spectrum Management?
2020
Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks
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The opening of the unlicensed radio spectrum creates new opportunities and new challenges for communication technology that can be faced by Machine Learning techniques. ...
We survey Machine learning and Deep Learning algorithms with possible radio applications, and highlight the corresponding challenges. ...
ML models are classi ed to supervised machine learning or unsupervised machine learning. ...
doi:10.1145/3416013.3426443
dblp:conf/mswim/AlmazroueiGAD20
fatcat:2hjionukbjgo5gx6bnknaiymse
6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions
2020
IEEE Open Journal of the Communications Society
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in guaranteeing the QoS. ...
This paper presents the vision of future 6G wireless communication and its network architecture. ...
machine learning. ...
doi:10.1109/ojcoms.2020.3010270
fatcat:6btdopl6l5cz3ediq3rmqueooq
Developing a common approach for classifying building stock energy models
2020
Renewable & Sustainable Energy Reviews
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Given these advancements, a new scheme for classifying building stock energy models is needed to facilitate communication of modeling approaches and the handling of important model dimensions. ...
In this article, we present a new building stock energy model classification framework that leverages international modeling expertise from the participants of the International Energy Agency's Annex 70 ...
Acknowledgments The authors gratefully acknowledge the strong support of Annex 70 from the International Energy Agency Energy in Buildings and Commu- ...
doi:10.1016/j.rser.2020.110276
fatcat:f6z3fu4lbjdvtb3i3s7lmp2mji
Performance modeling for systematic performance tuning
2011
State of the Practice Reports on - SC '11
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for comparison with results from experimental programs in high energy & nuclear physics Bernard, Gottlieb et al.: Studying Quarks and Gluons On Mimd Parallel Computers 1]: Barnes, Rountree, Lowenthal, ...
[1, 2] ): • "Black-box" approach: machine learning, neural networks, statistical learning … • Semi-empirical modeling: • "White box" approach: find asymptotically tight analytic models, parameterize ...
Conclusions and Future Work • Models in use for predictions and optimizations • First successes: ~10-20% improved performance [1] • Simple strategy enables application team models • Better chance to ...
doi:10.1145/2063348.2063356
dblp:conf/sc/HoeflerGKS11
fatcat:6lsq5a65mbgz7lgea2qwzbnlxq
Smart Wireless Communication is the Cornerstone of Smart Infrastructures
[article]
2017
arXiv
pre-print
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In real time, using machine learning, patterns and relationships in the data over space, time, and application can be detected and predictions can be made; on the basis of these, resources can be managed ...
We are at the beginning of a revolution in how we live with technology, resulting from a convergence of machine learning (ML), the Internet-of-Things (IoT), and robotics. ...
This white paper is part of a series of white papers on Intelligent Infrastructure, written by community members. ...
arXiv:1706.07363v1
fatcat:4wyj35qrardmheubqoubtuilyq
Is there any gender/race bias in hep-lat primary publication? Machine-Learning Evaluation of Author Ethnicity and Gender
[article]
2021
arXiv
pre-print
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In this work, we analyze papers that are classified as primary hep-lat to study whether there is any race or gender bias in the journal-publication process. ...
We implement machine learning to predict the race and gender of authors based on their names and look for measurable differences between publication outcomes based on author classification. ...
Acknowledgments We thank Etsuko Itou, Meifeng Lin, Liuming Liu, Shigemi Ohta, Andreas Kronfeld, Pavel Nadolsky and Saul Cohen for their help in identifying authors with first names missing from the metadata ...
arXiv:2107.12991v1
fatcat:4n5muxg7kbfa7buklx3twnmpua
Symmetry Group Equivariant Architectures for Physics
[article]
2022
arXiv
pre-print
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In this report, we argue that both the physics community and the broader machine learning community have much to understand and potentially to gain from a deeper investment in research concerning symmetry ...
group equivariant machine learning architectures. ...
In this White Paper we focus on a particular subset of applications in particle physics where the data typically contain the energy-momentum 4-vectors of particles produced in collision events at high-energy ...
arXiv:2203.06153v1
fatcat:scm4hmmhlncydjx3cnszuppddi
The Optimal Power Management System for Chinthalapudi Engineering College using Neural Algorithmic Techniques with Embedded System Approach
2016
International Journal of Engineering Research and
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This paper is very useful for power saving efficient with high speed mechanism. ...
In this paper we are going to employ new strategic methods for power saving in real time for Chinthalapudi Engineering College. ...
This paper is very useful for power saving efficient with high speed mechanism. ...
doi:10.17577/ijertv5is110144
fatcat:rgxv4uqcfjcrli6uecy5qzmgei
Arch2030: A Vision of Computer Architecture Research over the Next 15 Years
[article]
2016
arXiv
pre-print
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Five years ago, we released a white paper, 21st Century Computer Architecture, which influenced funding programs in both academia and industry. ...
The computer architecture community engaged in several visioning exercises over the years. ...
Five years ago, we released a white paper, 21st Century Computer Architecture, which influenced funding programs in both academia and industry. ...
arXiv:1612.03182v1
fatcat:d36sywwubnh6dnirw2icyyciom
Editorial Energy Efficiency of Machine-Learning-Based Designs for Future Wireless Systems and Networks
2021
IEEE Transactions on Green Communications and Networking
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The use of Digital Object Identifier 10.1109/TGCN.2021.3099580 ML in 6G was discussed in the white paper [3] where the authors discussed the advances of ML in wireless communications in various layers ...
MACHINE LEARNING PARADIGMS, RESOURCE CONSTRAINTS, AND ENERGY EFFICIENCY
A. ...
doi:10.1109/tgcn.2021.3099580
fatcat:uvqwn3o7hfesvmmgvmgmrol6oi
6G White Paper on Edge Intelligence
[article]
2020
arXiv
pre-print
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In this white paper we provide a vision for 6G Edge Intelligence. ...
In this white paper, we focus in the domains of edge computing infrastructure and platforms, data and edge network management, software development for edge, and real-time and distributed training of ML ...
6G white papers to be published in their final format in June 2020. ...
arXiv:2004.14850v1
fatcat:2gt2fnyqsjemxdxbuclxnrqzwu
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