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arXiv 2024年

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing314001 China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight (LoS) dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom (DoFs) to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent (BCD) algorithm to iteratively optimize the antenna position vector (APV), array rotation vector (ARV), receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Automated Soccer Event Detection and Highlight Generation for Short and Long Views

SSRN

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SSRN 2024年

作者： Afzal, Maira Shah, Jamal Hussain Rehman, Saeed ur Dahan, Fadl Yasmin, Mussarat Kadry, Seifedine Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Department of Management Information Systems College of Business Administration - Hawtat Bani Tamim Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon MEU Research Unit Middle East University Amman11831 Jordan

The computer vision field has wide applications in various areas, including sports. Almost all sports events have been exploiting the best features. Sports videos are structure-based, and due to this characteristic, these videos can be categorized into interesting and non-interesting events. Identifying the view of the video and separating important events from non-interesting events is challenging. However, correct view detection can lead to correct event detection. Various researchers have proposed many strategies for detecting events in sports videos. Significant research shows some gaps while generating highlights due to limited work available in the long or short view;there is still a need for some automated methods. The main purpose of this research work is to detect key events by normalizing the data, extracting features, fusing those features, and classifying them. In this research, events are detected by classification. A new dataset is created for research purposes. The benchmark dataset is divided into two subsets, which can be used separately or as part of a larger dataset. A proposed novel approach for event highlight generation in long and short view is presented using a fusion of AlexNet and VGGNet architectures to explore the model's efficiency in the context of accurate highlight generation deep learning models fused with handcrafted. AlexNet and VGG16 pre-trained deep CNN models are applied to extract deep prior features, which are then combined with HOG to improve the results. The proposed methodology undergoes evaluation on a dataset that we created, resulting in an accuracy of 99.6%. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Learner Action Patterns in the Problem-Solving Process Related to Program Code Composition Based on Tracking System Activities

Learner Action Patterns in the Problem-Solving Process Relat...

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International Conference on Informatics and Computing (ICIC)

作者： Aulia Akhrian Syahidi Ahmad Afif Supianto Tsukasa Hirashima Yutaka Watanobe Smart City Information System Applied Undergraduate Program Politeknik Negeri Banjarmasin Banjarmasin City Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Bandung City Indonesia Learning Engineering Laboratory Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Higashihiroshima Japan Department of Computer Science and Engineering Division of Information Systems University of Aizu Aizu-Wakamatsu City Fukushima Japan

ISBN: (纸本)9798350345728

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the “if-then” rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process.

关键词： Visualization Codes Media data models Recording data mining Problem-solving

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Using image conversion techniques to detect adversarial examples in machine learning models for tabular data

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Applied Soft Computing 2025年

作者： Zahi Kapri Edita Grolman Ikuya Morikawa Toshiya Shimizu Yuval Elovici Asaf Shabtai Department of Software and Information Systems Engineering Ben Gurion University of the Negev Beer Sheva Israel Data & Security Research Laboratory Fujitsu Limited Kawasaki Japan

Most adversarial detection methods are designed for deep neural networks (DNNs) trained on homogeneous data (e.g., images), which leaves machine learning (ML) models for tabular data particularly vulnerable to adversarial attacks. This paper introduces two novel approaches for the detection of adversarial examples in tabular data that transform tabular data into image representations, enabling the use of convolutional neural networks (CNNs) to improve adversarial robustness. The first is an unsupervised framework that exploits explainability techniques to compare model behavior using two distinct data representations: the original tabular form and its transformed image-based counterpart. By analyzing discrepancies in feature attributions and model predictions, this approach detects adversarial examples in a model- and attack-agnostic manner. The second is a supervised method utilizing transfer learning which leverages a large-scale pretrained image classification model to extract adversarial patterns from converted tabular data, significantly enhancing detection accuracy with minimal training overhead. The evaluation of these approaches, which assessed their ability to detect multiple types of attacks, was performed on seven structured datasets. Both approaches were shown to outperform five state-of-the-art adversarial detection methods, achieving near-perfect detection rates in most cases. This work pioneers the integration of multi-view learning, explainable AI (XAI), and transfer learning for adversarial detection in tabular data, providing a novel and highly effective paradigm for securing ML models in real-world applications.

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Customizing and Deploying data Science Roadmapping: A Case Study of an Oil and Gas Company

Customizing and Deploying Data Science Roadmapping: A Case S...

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IEEE International Technology Management Conference

作者： Kerem Kayabay Mert Onuralp Gökalp Ebru Gökalp Serhat Murat Alagöz P. Erhan Eren Altan Koçyiğit Department TÜBİTAK ULAKBİM Data Informatics Middle East Technical University Network Technologies Ankara Türkiye Data Analytics Center TÜPRAŞ Ankara Türkiye Computer Engineering Department Hacettepe University Ankara Türkiye Data Analytics Center TÜPRAŞ İstanbul Türkiye Information Systems Middle East Technical University Ankara Türkiye

ISBN: (数字)9781665470278

ISBN: (纸本)9781665470285

Many organizations still face challenges leveraging data science in production and need strategic planning for organization-wide data science efforts and assets. data Science Roadmapping (DSR) customizes the widely used Technology Roadmapping to facilitate such planning in organizations, aligning market, business, data, technology, and organization perspectives. This paper employs the action research method to customize and apply DSR in an oil and gas company. The approach mostly met the expectations regarding objectives and outcomes, the first step toward generalizability. The case study provides researchers and practitioners insights into roadmapping customization, remote and agile roadmap development process, and strategic planning for technology adoption in a large organization. Lastly, we identify the future research directions using the recent roadmapping literature.

关键词： Technology management Oils Companies Production data science Strategic planning Faces

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Highlights from the Tibet ASγ experiment 38

Highlights from the Tibet ASγ experiment

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38th International Cosmic Ray Conference, ICRC 2023

作者： Takita, M. Amenomori, M. Bao, Y.W. Bi, X.J. Chen, D. Chen, T.L. Chen, W.Y. Chen, Xu Chen, Y. Cirennima Cui, S.W. Danzengluobu Ding, L.K. Fang, J.H. Fang, K. Feng, C.F. Feng, Zhaoyang Feng, Z.Y. Gao, Qi Gou, Q.B. Guo, Y.Q. Guo, Y.Y. Hayashi, Y. He, H.H. He, Z.T. Hibino, K. Hotta, N. Hu, Haibing Hu, H.B. Hu, K.Y. Huang, J. Jia, H.Y. Jiang, L. Jiang, P. Jin, H.B. Kasahara, K. Katayose, Y. Kato, C. Kato, S. Kawahara, I. Kawashima, T. Kawata, K. Kozai, M. Labaciren Le, G.M. Li, A.F. Li, H.J. Li, W.J. Li, Y. Lin, Y.H. Liu, B. Liu, C. Liu, J.S. Liu, L.Y. Liu, M.Y. Liu, W. Lu, H. Makishima, T. Masuda, Y. Matsuhashi, S. Matsumoto, M. Meng, X.R. Meng, Y. Mizuno, A. Munakata, K. Nakamura, Y. Nanjo, H. Ning, C.C. Nishizawa, M. Noguchi, R. Ohnishi, M. Okukawa, S. Ozawa, S. Qian, X. Qian, X.L. Qu, X.B. Saito, T. Sakata, M. Sako, T. Sako, T.K. Sasaki, T. Shao, J. Shibasaki, T. Shibata, M. Shiomi, A. Sugimoto, H. Takano, W. Tan, Y.H. Tateyama, N. Torii, S. Tsuchiya, H. Udo, S. Usui, R. Wang, H. Wang, S.F. Wang, Y.P. Wangdui Wu, H.R. Wu, Q. Xu, J.L. Xue, L. Yang, Z. Yao, Y.Q. Yin, J. Yokoe, Y. Yu, Y.L. Yuan, A.F. Zhai, L.M. Zhang, H.M. Zhang, J.L. Zhang, X. Zhang, X.Y. Zhang, Y. Zhang, Yi Zhang, Ying Zhao, S.P. Zhaxisangzhu Zhou, X.X. Zou, Y.H. Department of Physics Hirosaki University Hirosaki036-8561 Japan School of Astronomy and Space Science Nanjing University Nanjing210093 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China National Astronomical Observatories Chinese Academy of Sciences Beijing1 00101 China Department of Mathematics and Physics Tibet University Lhasa850000 China Department of Physics Hebei Normal University Shijiazhuang050016 China University of Chinese Academy of Sciences Beijing100049 China Shandong University Qingdao26 6237 China Institute of Modern Physics SouthWest Jiaotong University Chengdu610031 China Department of Physics Shinshu University Matsumoto390-8621 Japan Faculty of Engineering Kanagawa University Yokohama221-8686 Japan Faculty of Education Utsunomiya University Utsunomiya321-8505 Japan Faculty of Systems Engineering Shibaura Institute of Technology Omiya330-8570 Japan Faculty of Engineering Yokohama National University Yokohama240-8501 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa277-8582 Japan Polar Environment Data Science Center Joint Support-Center for Data Sci ence Research Research Organization of Information and Systems Tachikawa190- 0014 Japan National Center for Space Weather China Meteorological Administration Beijing100081 China School of Information Science and Engineering Shandong Agriculture Univ ersity Taian271018 China Department of Astronomy School of Physical Sciences University of Scie nce and Technology of China Hefei230026 China National Institute of Informatics Tokyo101-8430 Japan National Institute of Information and Communications Technology Tokyo1 84-8795 Japan Department of Mechanical and Electrical Engineering Shangdong Managemen t University Jinan250357 China College of Science China University of Petroleum Qingdao266555 China Tokyo Metropolitan College of Industrial Technology Tokyo116-8523 Japa

The Tibet ASγ experiment, observing cosmic rays/gamma rays above a few TeV, is located at 4,300m above sea level, in Tibet, China. The experiment is composed of a 65,700 m2 surface air shower array and 3,400 m2 underground water Cherenkov muon detectors. The surface air shower array is used for reconstructing the primary particle energy and direction, while the underground muon detectors are used for discriminating gamma-ray induced muon-poor air showers from cosmic-ray (proton, helium,...) induced muon-rich air showers. Furthermore, the underground muon detectors turn out to be effective to select the proton component in cosmic rays. We present the recent progress on analysis method for the proton energy spectrum in the hundred TeV region by means of proton selection by the underground muon detector information, and on the recent 100 TeV gamma-ray observation. In addition, we discuss the modelling of the cosmic-ray anisotropy observed in the TeV region. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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Observation of gamma rays from the northern celestial sky up to the sub-PeV range with the Tibet air shower array and its underground muon detector array 38

Observation of gamma rays from the northern celestial sky up...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Kato, S. Amenomori, M. Bao, Y.W. Bi, X.J. Chen, D. Chen, T.L. Chen, W.Y. Chen, Xu Chen, Y. Cirennima Cui, S.W. Danzengluobu Ding, L.K. Fang, J.H. Fang, K. Feng, C.F. Feng, Zhaoyang Feng, Z.Y. Gao, Qi Gou, Q.B. Guo, Y.Q. Guo, Y.Y. Hayashi, Y. He, H.H. He, Z.T. Hibino, K. Hotta, N. Hu, Haibing Hu, H.B. Hu, K.Y. Huang, J. Jia, H.Y. Jiang, L. Jiang, P. Jin, H.B. Kasahara, K. Katayose, Y. Kato, C. Kato, S. Kawahara, I. Kawashima, T. Kawata, K. Kozai, M. Labaciren Le, G.M. Li, A.F. Li, H.J. Li, W.J. Li, Y. Lin, Y.H. Liu, B. Liu, C. Liu, J.S. Liu, L.Y. Liu, M.Y. Liu, W. Lu, H. Makishima, T. Masuda, Y. Matsuhashi, S. Matsumoto, M. Meng, X.R. Meng, Y. Mizuno, A. Munakata, K. Nakamura, Y. Nanjo, H. Ning, C.C. Nishizawa, M. Noguchi, R. Ohnishi, M. Okukawa, S. Ozawa, S. Qian, X. Qian, X.L. Qu, X.B. Saito, T. Sakata, M. Sako, T. Sako, T.K. Sasaki, T. Shao, J. Shibasaki, T. Shibata, M. Shiomi, A. Sugimoto, H. Takano, W. Takita, M. Tan, Y.H. Tateyama, N. Torii, S. Tsuchiya, H. Udo, S. Usui, R. Wang, H. Wang, S.F. Wang, Y.P. Wangdui Wu, H.R. Wu, Q. Xu, J.L. Xue, L. Yang, Z. Yao, Y.Q. Yin, J. Yokoe, Y. Yu, Y.L. Yuan, A.F. Zhai, L.M. Zhang, H.M. Zhang, J.L. Zhang, X. Zhang, X.Y. Zhang, Y. Zhang, Yi Zhang, Ying Zhao, S.P. Zhaxisangzhu Zhou, X.X. Zou, Y.H. Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa277-8582 Japan Department of Physics Hirosaki University Hirosaki036-8561 Japan School of Astronomy and Space Science Nanjing University Nanjing210093 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Department of Mathematics and Physics Tibet University Lhasa850000 China Department of Physics Hebei Normal University Shijiazhuang050016 China University of Chinese Academy of Sciences Beijing100049 China Shandong University Qingdao266237 China Institute of Modern Physics SouthWest Jiaotong University Chengdu610031 China Department of Physics Shinshu University Matsumoto390-8621 Japan Faculty of Engineering Kanagawa University Yokohama221-8686 Japan Faculty of Education Utsunomiya University Utsunomiya321-8505 Japan Faculty of Systems Engineering Shibaura Institute of Technology Omiya330-8570 Japan Faculty of Engineering Yokohama National University Yokohama240-8501 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa277-8582 Japan Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems Tachikawa190-0014 Japan National Center for Space Weather China Meteorological Administration Beijing100081 China School of Information Science and Engineering Shandong Agriculture University Taian271018 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China National Institute of Informatics Tokyo101-8430 Japan National Institute of Information and Communications Technology Tokyo184-8795 Japan Department of Mechanical and Electrical Engineering Shangdong Management University Jinan250357 China College of Science China University of Petroleum Qingdao266555

Gamma-ray observation in the sub-PeV range helps us search for Galactic PeV cosmic-ray accelerators, PeVatrons. Measurement of the energy spectrum of gamma rays from astrophysical sources is especially crucial to discuss their hadronic or leptonic origin. In this study, gamma rays from the sky region of HESS J1843−033 are observed by the Tibet air shower array equipped with the underground muon detector array. A new gamma-ray source TASG J1844−038 is detected above 25 TeV with a 6.2 σ level in the region of interest, the position of which is consistent with HESS J1843−033, eHWC J1842−035, and LHAASO J1843−0338. The gamma-ray energy spectrum of TASG J1844−038 is successfully measured in 25−130 TeV for the first time;it smoothly connects to that of HESS J1843−033 and gives consistent flux levels with those of eHWC J1842−035 and LHAASO J1843−0338 at 56 and 100 TeV, respectively. The analysis of the combined gamma-ray spectra between the H.E.S.S., LHAASO, and TASG sources indicates the cutoff at about 50 TeV. The distribution of the molecular clouds around TASG J1844−038 is investigated by analyzing public CO line-emission data;our study finds the clouds in the velocity range of 75−95 km s−1 lying flat over the region with a 50 K km s−1 level. The association of the observed gamma rays with a nearby supernova remnant SNR G28.6−0.1 is discussed, and it is proposed that cosmic rays accelerated by SNR G28.6−0.1 up to about 500 TeV generate the observed gamma rays from the collisions with the ambient gas found in the analysis. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic ray detectors

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System Network Analytics: Evolution and Stable Rules of a State Series

arXiv

引用

arXiv 2022年

作者： Chaturvedi, Animesh Tiwari, Aruna Spyratos, Nicolas Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Karnataka Dharwad India Computer Science and Engineering Indian Institute of Technology Indore Indore India Computer Science Department LISN Lab University of Paris-Saclay Paris France

System Evolution Analytics on a system that evolves is a challenge because it makes a State Series SS = {S1, S2… SN} (i.e., a set of states ordered by time) with several inter-connected entities changing over time. We present stability characteristics of interesting evolution rules occurring in multiple states. We defined an evolution rule with its stability as the fraction of states in which the rule is interesting. Extensively, we defined stable rule as the evolution rule having stability that exceeds a given threshold minimum stability (minStab). We also defined persistence metric, a quantitative measure of persistent entity-connections. We explain this with an approach and algorithm for System Network Analytics (SysNet-Analytics), which uses minStab to retrieve Network Evolution Rules (NERs) and Stable NERs (SNERs). The retrieved information is used to calculate a proposed System Network Persistence (SNP) metric. This work is automated as a SysNet-Analytics Tool to demonstrate application on real world systems including: software system, natural-language system, retail market system, and IMDb system. We quantified stability and persistence of entity-connections in a system state series. This results in evolution information, which helps in system evolution analytics based on knowledge discovery and data mining. © 2022, CC BY.

关键词： Stability

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Measurement of the primary cosmic-ray proton spectrum between 40 TeV and a few hundred TeV with the Tibet hybrid experiment (Tibet-III + MD) 38

Measurement of the primary cosmic-ray proton spectrum betwee...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Katayose, Y. Amenomori, M. Bao, Y.W. Bi, X.J. Chen, D. Chen, T.L. Chen, W.Y. Chen, Xu Chen, Y. Cirennima Cui, S.W. Danzengluobu Ding, L.K. Fang, J.H. Fang, K. Feng, C.F. Feng, Zhaoyang Feng, Z.Y. Gao, Qi Gou, Q.B. Guo, Y.Q. Guo, Y.Y. Hayashi, Y. He, H.H. He, Z.T. Hibino, K. Hotta, N. Hu, Haibing Hu, H.B. Hu, K.Y. Huang, J. Jia, H.Y. Jiang, L. Jiang, P. Jin, H.B. Kasahara, K. Katayose, Y. Kato, C. Kato, S. Kawahara, I. Kawashima, T. Kawata, K. Kozai, M. Labaciren Le, G.M. Li, A.F. Li, H.J. Li, W.J. Li, Y. Lin, Y.H. Liu, B. Liu, C. Liu, J.S. Liu, L.Y. Liu, M.Y. Liu, W. Lu, H. Makishima, T. Masuda, Y. Matsuhashi, S. Matsumoto, M. Meng, X.R. Meng, Y. Mizuno, A. Munakata, K. Nakamura, Y. Nanjo, H. Ning, C.C. Nishizawa, M. Noguchi, R. Ohnishi, M. Okukawa, S. Ozawa, S. Qian, X. Qian, X.L. Qu, X.B. Saito, T. Sakata, M. Sako, T. Sako, T.K. Sasaki, T. Shao, J. Shibasaki, T. Shibata, M. Shiomi, A. Sugimoto, H. Takano, W. Takita, M. Tan, Y.H. Tateyama, N. Torii, S. Tsuchiya, H. Udo, S. Usui, R. Wang, H. Wang, S.F. Wang, Y.P. Wangdui Wu, H.R. Wu, Q. Xu, J.L. Xue, L. Yang, Z. Yao, Y.Q. Yin, J. Yokoe, Y. Yu, Y.L. Yuan, A.F. Zhai, L.M. Zhang, H.M. Zhang, J.L. Zhang, X. Zhang, X.Y. Zhang, Y. Zhang, Yi Zhang, Ying Zhao, S.P. Zhaxisangzhu Zhou, X.X. Zou, Y.H. Faculty of Engineering Yokohama National University Yokohama240-8501 Japan Department of Physics Hirosaki University Hirosaki036-8561 Japan School of Astronomy and Space Science Nanjing University Nanjing210093 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Department of Mathematics and Physics Tibet University Lhasa850000 China Department of Physics Hebei Normal University Shijiazhuang050016 China University of Chinese Academy of Sciences Beijing100049 China Shandong University Qingdao266237 China Institute of Modern Physics SouthWest Jiaotong University Chengdu610031 China Department of Physics Shinshu University Matsumoto390-8621 Japan Faculty of Engineering Kanagawa University Yokohama221-8686 Japan Faculty of Education Utsunomiya University Utsunomiya321-8505 Japan Faculty of Systems Engineering Shibaura Institute of Technology Omiya330-8570 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa277-8582 Japan Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems Tachikawa190-0014 Japan National Center for Space Weather China Meteorological Administration Beijing100081 China School of Information Science and Engineering Shandong Agriculture University Taian271018 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China National Institute of Informatics Tokyo101-8430 Japan National Institute of Information and Communications Technology Tokyo184-8795 Japan Department of Mechanical and Electrical Engineering Shangdong Management University Jinan250357 China College of Science China University of Petroleum Qingdao266555 China Tokyo Metropolitan College of Industrial Technology Tokyo116-8523 Japan Depart

We have been observing extensive air showers using the Tibet-III air shower array and the underground water-Cherenkov muon detector array (MD) to determine the chemical composition of cosmic rays with energies corresponding to the knee energy region. In previous studies, we developed a method to acquire the proton spectrum in the energy range from 40 to 630 TeV via a hybrid experiment using the Tibet-III and MD and investigated its performance. We conducted Monte Carlo simulations showing that this method can separate protons with a purity of 90% and that the maximum total systematic error of the proton flux depending on interaction models in air-shower development is ±37%. In the present study, analysis results for data acquired over a period of 12 days in 2014 are presented. The abundances of proton-like showers to whole well-reconstructed showers, including model dependence, are 9.1%–14.5% at ∼35 TeV and 1.8%–3.1% at ∼450 TeV. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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Cooperation for Scalable Supervision of Autonomy in Mixed Traffic

arXiv

引用

arXiv 2021年

作者： Hickert, Cameron Li, Sirui Wu, Cathy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States Laboratory for Information & Decision Systems The Institute for Data Systems and Society United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Advances in autonomy offer the potential for dramatic positive outcomes in a number of domains, yet enabling their safe deployment remains an open problem. This work’s motivating question is: In safety-critical settings, can we avoid the need to have one human supervise one machine at all times? The work formalizes this scalable supervision problem by considering remotely-located human supervisors and investigates how autonomous agents can cooperate to achieve safety. The paper focuses on the safety-critical context of autonomous vehicles (AVs) merging into traffic consisting of a mixture of AVs and human drivers. The analysis establishes high reliability upper bounds on human supervision requirements. It further shows that AV cooperation can improve supervision reliability by orders of magnitude and counterintuitively requires fewer supervisors (per AV) as more AVs are adopted. These analytical results leverage queuing-theoretic analysis, order statistics, and a conservative, reachability-based approach. A key takeaway is the potential value of cooperation in enabling the deployment of autonomy at scale. While this work focuses on AVs, the scalable supervision framework may be of independent interest to a broader array of autonomous control challenges. Copyright © 2021, The Authors. All rights reserved.

关键词： Autonomous agents

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