检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Cai, Shangming Wang, Dongsheng Wang, Haixia Lyu, Yongqiang Xu, Guangquan Zheng, Xi Vasilakos, Athanasios V. Department of Computer Science and Technology Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518066 China Big Data School Qingdao Huanghai University Qingdao266427 China College of Intelligence and Computing Tianjin University Tianjin300350 China Department of Computing Macquarie University SydneyNSW2109 Australia College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China School of Electrical and Data Engineering University of Technology Sydney Australia Department of Computer Science Electrical and Space Engineering Lulea University of Technology Lulea97187 Sweden

To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic. Typically, edge devices collaboratively train a shared model using real-time generated data through the Parameter Server framework. Although all the edge devices can share the computing workloads, the distributed training processes over edge networks are still time-consuming due to the parameters and gradients transmission procedures between parameter servers and edge devices. Focusing on accelerating distributed Convolutional Neural Networks (CNNs) training at the network edge, we present DynaComm, a novel scheduler that dynamically decomposes each transmission procedure into several segments to achieve optimal layer-wise communications and computations overlapping during run-time. Through experiments, we verify that DynaComm manages to achieve optimal layer-wise scheduling for all cases compared to competing strategies while the model accuracy remains untouched. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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Web Intelligence (WI) 3.0: in search of a better-connected world to create a future intelligent society

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Artificial Intelligence Review 2025年第9期58卷

作者： Kuai, Hongzhi Huang, Jimmy X. Tao, Xiaohui Pasi, Gabriella Yao, Yiyu Liu, Jiming Zhong, Ning School of Computer Science and Technology/School of Artificial Intelligence Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China School of Information Technology York University North YorkON Canada School of Mathematics Physics and Computing University of Southern Queensland ToowoombaQLD Australia Department of Informatics Systems and Communication University of Milano-Bicocca Milano Italy Department of Computer Science University of Regina Regina Canada Department of Computer Science Hong Kong Baptist University Kowloon Tong Hong Kong Faculty of Engineering Maebashi Institute of Technology Gunma Maebashi Japan

Over the past two decades, Web Intelligence (WI) has emerged as a key field driving the evolution of AI in the connected world, addressing the demands of a future intelligent society. This paper provides a comprehensive review of WI’s contributions since its inception in 2000, spanning three distinct phases: Wisdom World Wide Web (WI 1.0, 2000–2009), Wisdom Web of Things (WI 2.0, 2010–2017), and Wisdom Web of Everything (WI 3.0, since 2018). For each phase, we examine key advancements, challenges, and future directions from the perspectives of both intelligent machines and human experts, highlighting significant societal impacts. To advance WI research, we propose a large language model-based learning framework for topic analysis and trend prediction. Moving beyond single-perspective approaches, we emphasize the Connected Intelligence Ecosystem defined by the high5 scheme comprising one goal, two twins, three fundamentals, four functions, and five services that are realized through WI 3.0. This vision serves as a bridge from localized models to a global reference framework for addressing sustainability challenges in future societies. To illustrate the real-world implications of WI 3.0, we present case studies focusing on brain-inspired research, particularly in the intersection of brain intelligence, brain health, and brainternet-fostering interdisciplinary collaboration across diverse research communities. © The Author(s) 2025.

关键词： Intelligent computing

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The Quantum Internet (Technical Version)

arXiv

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arXiv 2025年

作者： Rohde, Peter P. Huang, Zixin Ouyang, Yingkai Huang, He-Liang Su, Zu-En Devitt, Simon Ramakrishnan, Rohit Mantri, Atul Tan, Si-Hui Liu, Nana Harrison, Scott Radhakrishnan, Chandrashekar Brennen, Gavin K. Baragiola, Ben Q. Dowling, Jonathan P. Byrnes, Tim Munro, William J. Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Hearne Institute for Theoretical Physics Louisiana State University Baton Rouge United States School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia School of Mathematical and Physical Sciences University of Sheffield SheffieldS3 7RH United Kingdom Henan Key Laboratory of Quantum Information and Cryptography Henan Zhengzhou450000 China Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science & Technology of China Hefei China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information & Quantum Physics University of Science & Technology of China Hefei China CAS-Alibaba Quantum Computing Laboratory Shanghai China InstituteQ Aalto University Espoo02150 Finland Indian Institute of Science Bangalore India Singapore University of Technology & Design Singapore Centre for Quantum Technologies National University of Singapore Singapore Horizon Quantum Ireland 24 Fitzwilliam Place Dublin 2 D02 T296 Ireland Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China Shanghai Artificial Intelligence Laboratory Shanghai China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China Leibniz Institute for Research & Information in Education Frankfurt am Main Germany Department of Computer Science and Engineering NYU Shanghai 567 West Yangsi Road Shanghai200124 China Centre of Excellence in Engineered Quantum Systems Macquarie University NSW Australia Centre for Quantum Computation and Communication Technology School of Science RMIT University VIC

The desire to share and unite remote digital assets motivated the development of the classical internet, the enabler of the entire 21st century economy and our modern way of life. As we enter the quantum era, it is to be expected there will be a similar demand for networking quantum assets, motivating a global quantum internet for bringing together the world's quantum resources, leveraging off their exponential trajectory in capability. We present models for quantum networking, how they might be applied in the future, and the implications they will have. Socially, economically, politically and geostrategically, the upcoming era of quantum supremacy will be as significant for the 21st century as the transistor was for the 20th. The inherently different scaling in the computational power of quantum computers fundamentally changes the dynamics of how they will operate in the future. Given their high expected initial cost, a client/server model for outsourcing computation will be essential for enabling the accessibility and proliferation of this technology, and ensuring its economic viability. We therefore anticipate the emergence of cloud quantum computing, a model for outsourcing quantum computations to the network. We argue that economic efficiency will mandate that all future quantum computers be united into a single global virtual quantum computer, offering exponentially more power to all network participants than if they were to keep their resources to themselves. This model for the allocation of computational resources is uniquely quantum, with no classical analogue, completely altering the economic landscape for the future of computation. Given the sensitivity of much of the data to which future quantum computers are going to foreseeably be applied, protocols for encrypted quantum computation will be essential - the outsourcing of computations that neither an eavesdropper nor even the server performing the computation can spy upon. This will enable new models fo

关键词： Outsourcing

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In-situ data scheduling optimization based on rainbow DQN for IIoT

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Future Generation computer Systems 2026年 174卷

作者： Peiying Zhang Siyi Li Lizhuang Tan Neeraj Kumar Jian Wang Kai Liu Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala 147004 India Department of Computer Science University of Economics and Human Sciences Warszawa 01-043 Poland Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia College of Science China University of Petroleum (East China) Qingdao 266580 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Space Network and Communications Tsinghua University Beijing 100084 China

In industrial systems, in-situ computing refers to a mode of local or proximate data processing within the cloud–edge-terminal architecture. In the Industrial Internet of Things (IIoT) environment, servers are typically deployed near terminal devices to coordinate and manage the scheduling of in-situ data, forming a comprehensive in-situ server system. With the rapid growth of IIoT terminal devices, challenges such as high processing delays and severe demand congestion arise in the in-situ data scheduling process. To address this challenge, this paper proposes a scheduling optimization scheme based on Rainbow DQN. The scheme designs a scheduling buffer and classification strategy to distinguish in-situ data based on storage demand characteristics, thereby optimizing the scheduling process. At the same time, a multi-level storage structure is introduced to store in-situ data classified according to the scheduling strategy. Additionally, the Rainbow DQN algorithm is utilized to assist in the scheduling decision-making of in-situ data retrieval, effectively alleviating system congestion while ensuring certain throughput performance. Extensive simulation experiments show that the Rainbow DQN algorithm achieves a reduction of 60.82% in average waiting delay, 62.94% in average unprocessed demand, and a 58.27% reduction in average completion delay.

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Experimental Evidence of Topological Surface States in Mg3Bi2 Films Grown by Molecular Beam Epitaxy

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Chinese Physics Letters 2019年第11期36卷 65-69页

作者： Tong Zhou Xie-Gang Zhu Mingyu Tong Yun Zhang Xue-Bing Luo Xiangnan Xie Wei Feng Qiuyun Chen Shiyong Tan Zhen-Yu Wang Tian Jiang Yuhua Tang Xin-Chun Lai Xuejun Yang State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073 Science and Technology on Surface Physics and Chemistry Laboratory Jiangyou 621908 National Innovation Institute of Defense Technology Academy of Military Sciences PLA ChinaBeijing 100010 Institute of Materials China Academy of Engineering PhysicsMianyang 621700 College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangsha 410073 Academy of Military Sciences PLA China Beijing 100010 Beijing Academy of Quantum Information Sciences Beijing 100084

Nodal line semimetal(NLS) is a new quantum state hosting one-dimensional closed loops formed by the crossing of two bands. The so-called type-Ⅱ NLS means that these two crossing bands have the same sign in their slopes along the radial direction of the loop, which requires that the crossing bands are either right-tilted or left-tilted at the same time. According to the theoretical prediction, Mg3Bi2 is an ideal candidate for studying the type-Ⅱ NLS by tuning its spin-orbit coupling(SOC). high-quality Mg3 Bi2 films are grown by molecular beam epitaxy(MBE). By in-situ angle resolved photoemission spectroscopy(ARPES), a pair of surface resonance bands around theГ point are clearly seen. This shows that Mg3Bi2 films grown by MBE are Mg(1)-terminated by comparing the ARPES spectra with the first principles calculations results. Moreover, the temperature dependent weak anti-localization effect in Mg3Bi2 films is observed under magneto-transport measurements, which shows clear two-dimensional(2 D) e-e scattering characteristics by fitting with the Hikami–Larkin–Nagaoka model. Therefore, by combining with ARPES, magneto-transport measurements and the first principles calculations, this work proves that Mg3Bi2 is a semimetal with topological surface states. This paves the way for Mg3Bi2 to be used as an ideal material platform to study the exotic features of type-Ⅱ nodal line semimetals and the topological phase transition by tuning its SOC.

关键词： bands topological terminated

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Intelligent non-invasive elderly fall monitoring by designing software defined radio frequency sensing system

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Digital Communications and Networks 2024年

作者： Adeel Akram Muhammad Bilal Khan Najah Abed Abu Ali Qixing Zhang Awais Ahmad Muhammad Shahid Iqbal Syed Atif Moqurrab Laboratory for Intelligent Sensing and Computing Institute of Advanced Technology University of Science and Technology of China Hefei 230031 Anhui China State Key Laboratory of Fire Science University of Science and Technology of China Hefei 230026 Anhui China College of IT United Arab Emirates University Al-Ain 15551 United Arab Emirates Department of Electrical and Computer Engineering COMSATS University Islamabad Attock 43600 Pakistan College of Computer and Information Sciences Imam Muhammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Kingdom of Saudi Arabia Department of Computer Science and Information Technology Women University of AJ&K Bagh Pakistan School of Computing Gachon University 1342 Seongnam-daero Sujeong-gu Seongnam-si 13120 Korea

The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population, especially in relation to falls. While falls can lead to significant cognitive impairments, timely intervention can mitigate their adverse effects. In this context, the need for non-invasive, efficient monitoring systems becomes paramount. Although wearable sensors have gained traction for monitoring health activities, they may cause discomfort during prolonged use, especially for the elderly. To address this issue, we present an intelligent, non-invasive Software-Defined Radio Frequency (SDRF) sensing system, tailored red for monitoring elderly people's falls during routine activities. Harnessing the power of deep learning and machine learning, our system processes the Wireless Channel State information (WCSI) generated during regular and fall activities. By employing sophisticated signal processing techniques, the system captures unique patterns that distinguish falls from normal activities. In addition, we use statistical features to streamline data processing, thereby optimizing the computational efficiency of the system. Our experiments, conducted for a typical home environment while using treadmill, demonstrate the robustness of the system. The results show high classification accuracies of 92.5%, 95.1%, and 99.8% for three Artificial Intelligence (AI) algorithms. Notably, the SDRF-based approach offers flexibility, cost-effectiveness, and adaptability through software modifications, circumventing the need for hardware overhaul. This research attempts to bridge the gap in RF-based sensing for elderly fall monitoring, providing a solution that combines the benefits of non-invasiveness with the precision of deep learning and machine learning.

关键词： AI Elderly falls Intelligent learning SDRF WCSI

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Optimizing Data Placement on Hierarchical Storage Architecture via Machine Learning 16th

Optimizing Data Placement on Hierarchical Storage Architectu...

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16th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2019

作者： Cheng, Peng Lu, Yutong Du, Yunfei Chen, Zhiguang Liu, Yang College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing Changsha China National Supercomputer Center in Guangzhou School of Data and Computer Science Sun Yat-Sen University Guangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783030307080

As storage hierarchies are getting deeper on modern high-performance computing systems, intelligent data placement strategies that can choose the optimal storage tier dynamically is the key to realize the potential of hierarchical storage architecture. However, providing a general solution that can be applied in different storage architectures and diverse applications is challenging. In this paper, we propose adaptive storage learner (ASL), which explores the idea of using machine learning techniques to mine the relationship between data placement strategies and I/O performance under varied workflow characteristics and system status, and uses the learned model to choose the optimal storage tier intelligently. We implement a prototype and integrate it into an existing data management system. Empirical comparison based on real scientific workflows tests shows that ASL is capable of combining workflow characteristics and real-time system status to make optimal data placement decisions. © 2019, IFIP International Federation for information Processing.

关键词： Real time systems

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Observation of perfect diamagnetism and interfacial effect on the electronic structures in Nd0.8Sr0.2NiO2 superconducting infinite layers

arXiv

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arXiv 2021年

作者： Zeng, S.W. Yin, X.M. Li, C.J. Chow, L.E. Tang, C.S. Han, K. Huang, Z. Cao, Y. Wan, D.Y. Zhang, Z.T. Lim, Z.S. Diao, C.Z. Yang, P. Wee, A.T.S. Pennycook, S.J. Ariando, A. Department of Physics Faculty of Science National University of Singapore Singapore117551 Singapore National University of Singapore 5 Research Link Singapore117603 Singapore Shanghai Key Laboratory of High Temperature Superconductors Physics Department Shanghai University Shanghai200444 China Department of Materials Science and Engineering National University of Singapore Singapore117575 Singapore Department of Materials Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China 2 Fusionopolis Way Singapore138634 Singapore Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Anhui Hefei230601 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Nickel-based complex oxides have served as a playground for decades in the quest for a copper-oxide analog of the high-temperature superconductivity They may provide clues towards understanding the mechanism and an alternative route for high-temperature superconductors The recent discovery of superconductivity in the infinite-layer nickelate thin films has fulfilled this pursuit However, material synthesis remains challenging, direct demonstration of perfect diamagnetism is still missing, and understanding of the role of the interface and bulk to the superconducting properties is still lacking Here, we show high-quality Nd0.8Sr0.2NiO2 thin films with different thicknesses and demonstrate the interface and strain effects on the electrical, magnetic and optical properties Perfect diamagnetism is achieved, confirming the occurrence of superconductivity in the films Unlike the thick films in which the normal-state Hall-coefficient changes signs as the temperature decreases, the Hall-coefficient of films thinner than 5 5 nm remains negative, suggesting a thickness-driven band structure modification Moreover, X-ray absorption spectroscopy reveals the Ni-O hybridization nature in doped infinite-layer nickelates, and the hybridization is enhanced as the thickness decreases Consistent with band structure calculations on the nickelate/SrTiO3 heterostructure, the interface and strain effect induce a dominating electron-like band in the ultrathin film, thus causing the sign-change of the Hall-coefficient. Copyright © 2021, The Authors. All rights reserved.

关键词： Nickel oxide

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SiNED-ancillary services for reliable power grids in times of progressive German energiewende and digital transformation

SiNED-ancillary services for reliable power grids in times o...

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ETG-Kongress 2021: Von Komponenten bis zum Gesamtsystem fur die Energiewende - ETG Congress 2021: From Components to the Overall System for the Energy Turnaround

作者： Wussow, Jonas Babazadeh, Davood Beutel, Vanessa Buchholz, Sebastian Geissendörfer, Stefan Gerlach, Jana Majumdar, Neelopal von Maydell, Karsten Narayan, Anand Hoffmann, Melanie Kahl, Lily Leveringhaus, Thomas Lotz, Marc René Scheunert, Alexandra Baboli, Payam Teimourzadeh Tiemann, Paul Hendrik Huxoll, Nils Werth, Oliver Agert, Carsten Breitner, Michael H. Engel, Bernd Hofmann, Lutz Könemund, Martin Kurrat, Michael Lehnhoff, Sebastian Nieße, Astrid Weyer, Hartmut Technische Universität Braunschweig Elenia Institute for High Voltage Technology and Power Systems Braunschweig Germany Carl von Ossietzky Universität Oldenburg Department of Computing Science Digitalized Energy Systems Group Oldenburg Germany German Aerospace Center Institute of Networked Energy Systems Oldenburg Germany Leibniz Universität Hannover Institute of Computer Science for Business Administration Hannover Germany Leibniz Universität Hannover Institute of Electric Power Systems Electric Power Engineering Section Hannover Germany OFFIS Institute for Information Technology Oldenburg Germany Ostfalia - University of Applied Science Institute of Electrical Systems and Automation Technology Wolfenbüttel Germany Technische Universität Clausthal Institute of German and International Mining and Energy Law Clausthal-Zellerfeld Germany

ISBN: (纸本)9783800755493

Within SiNED research project, several members of the Energy Research Centre of Lower Saxony (Energieforschungszentrum Niedersachsen, EFZN) are working on various issues relating to the future provision of ancillary services and to future congestion management. The questions include energy technology, economic and energy law aspects as well as information and communications technology (ICT) and data. The investigations are based on Lower Saxony and the framework conditions there. The temporal focus of the investigations is the year 2030. © VDE VERLAG GMBH. Berlin. Offenbach

关键词： Electric power transmission networks

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The association between bone density of lumbar spines and different daily protein intake in different renal function

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Renal Failure 2024年第1期46卷 2298080页

作者： Lee, Chia-Lin Chen, Kun-Hui Liu, Wei‑Ju Chen, Ching-Hsien Tsai, Shang-Feng Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan School of Medicine National Yang-Ming University Taipei Taiwan Division of Endocrinology and Metabolism Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan Intelligent data mining laboratory Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Public Health College of Public Health China Medical University Taichung Taiwan Department of Orthopedic Surgery Taichung Veterans General Hospital Taichung Taiwan Department of Computer Science & Information Engineering College of Computing and Informatics Providence University Taichung Taiwan Divisions of Nephrology and Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine University of California at Davis Davis CA United States Division of Nephrology Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Life Science Tunghai University Taichung Taiwan

Background: Low protein intake (LPI) has been suggested as a treatment for chronic kidney disease (CKD). However, protein intake is essential for bone health. Methods: We studied the database of the National Health and Nutrition Examination Survey, 2005–2010. Basic variables, metabolic diseases, and bone density of different femoral areas were stratified into four subgroups according to different protein intake (DPI) (that is, <0.8, 0.8–1.0, 1.0–1.2, and >1.2 g/kg/day). Results: Significant differences were found among all lumbar area bone mineral density (BMD) and T-scores (p < 0.0001). There was an apparent trend between a decreasing BMD in the CKD groups with increasing DPI in all single lumbar spines (L1, L2, L3, and L4) and all L spines (L1-L4). Compared with DPI (0.8–1.0 g/day/kg), higher risks of osteoporosis were noticed in the subgroup of >1.2 g/day/kg over L2 (relative risk (RR)=1.326, 95% confidence interval (CI)=1.062–1.656), subgroup >1.2 g/day/kg over L3 (RR = 1.31, 95%CI = 1.057–1.622), subgroup <0.8 g/day/kg over L4 (RR = 1.276, 95%CI = 1.015–1.605), subgroup <0.8 g/day/kg over all L spines (RR = 11.275, 95%CI = 1.051–1.548), and subgroup >1.2 g/day/kg over all L spines (RR = 0.333, 95%CI = 1.098–1.618). However, a higher risk of osteoporosis was observed only in the non-CKD group. There was an apparent trend of higher DPI coexisting with lower BMD and T scores in patients with CKD. For osteoporosis (reference:0.8–1.0 g/day/kg), lower (<0.8 g/day/kg) or higher DPI (>1.2 g/day/kg) was associated with higher risks in the non-CKD group, but not in the CKD group. Conclusions: In the CKD group, LPI for renal protection was safe without threatening L spine bone density and without causing a higher risk of osteoporosis. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Bone density chronic kidney disease lumbar spine osteoporosis protein diet

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