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作者： Tang, Jianhua Quek, Tony Q. S. Chang, Tsung-Hui Shim, Byonghyo Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China Institute of New Media and Communications Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactions of CCC in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CCC. © 1972-2012 IEEE.

关键词： Integer programming

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Generating ultradense pair beams using 400 GeV/c protons

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Physical Review Research 2021年第2期3卷 023103-023103页

作者： C. D. Arrowsmith N. Shukla N. Charitonidis R. Boni H. Chen T. Davenne A. Dyson D. H. Froula J. T. Gudmundsson B. T. Huffman Y. Kadi B. Reville S. Richardson S. Sarkar J. L. Shaw L. O. Silva P. Simon R. M. G. M. Trines R. Bingham G. Gregori Department of Physics University of Oxford Parks Road Oxford OX1 3PU United Kingdom CINECA High-Performance Computing Department Via Magnanelli 6/3 40033 Casalecchio di Reno - Bologna Italy European Organization for Nuclear Research (CERN) CH-1211 Geneva 23 Switzerland University of Rochester Laboratory for Laser Energetics Rochester New York 14623 USA Lawrence Livermore National Laboratory 7000 East Avenue Livermore California 94550 USA Rutherford Appleton Laboratory Chilton Didcot OX11 0QX United Kingdom Science Institute University of Iceland Dunhaga 3 IS-107 Reykjavik Iceland Department of Space and Plasma Physics School of Electrical Engineering and Computer Science KTH Royal Institute of Technology SE-100 44 Stockholm Sweden Max-Planck-Institut für Kernphysik Saupfercheckweg 1 D-69117 Heidelberg Germany Atomic Weapons Establishment Aldermaston Reading Berkshire RG7 4PR United Kingdom GoLP/Instituto de Plasmas e Fusão Nuclear Instituto Superior Técnico Universidade de Lisboa 1049-001 Lisboa Portugal Department of Physics University of Strathclyde Glasgow G4 0NG United Kingdom

An experimental scheme is presented for generating low-divergence, ultradense, relativistic, electron-positron beams using 400 GeV/c protons available at facilities such as HiRadMat and AWAKE at CERN. Preliminary Monte Carlo and particle-in-cell simulations demonstrate the possibility of generating beams containing 1013–1014 electron-positron pairs at sufficiently high densities to drive collisionless beam-plasma instabilities, which are expected to play an important role in magnetic field generation and the related radiation signatures of relativistic astrophysical phenomena. The pair beams are quasineutral, with size exceeding several skin depths in all dimensions, allowing the examination of the effect of competition between transverse and longitudinal instability modes on the growth of magnetic fields. Furthermore, the presented scheme allows for the possibility of controlling the relative density of hadrons to electron-positron pairs in the beam, making it possible to explore the parameter spaces for different astrophysical environments.

关键词： Beam instabilities Cosmic ray acceleration Cosmic ray composition & spectra Cosmic ray sources Gamma ray astronomy Gamma ray bursts High-energy-density plasmas laboratory studies of space & astrophysical plasmas Optical, UV, & IR astronomy plasma instabilities plasma-beam interactions Relativistic multiple-particle dynamics

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Distributed resource scheduling for large-scale MEC systems: A multi-agent ensemble deep reinforcement learning with imitation acceleration

arXiv

引用

arXiv 2020年

作者： Jiang, Feibo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Lvy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Low noise frequency-domain multiplexing of TES bolometers using sub-kelvin SQUIDs

arXiv

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arXiv 2021年

作者： Elleflot, Tucker Suzuki, Aritoki Arnold, Kam Bebek, Chris Cantor, Robin H. Crowley, Kevin T. Groh, John de Haan, Tijmen Hornsby, Amber Joseph, John Lee, Adrian T. Liu, Tiffany Montgomery, Joshua Russell, Megan Yu, Qingyang Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics University of California San Diego San DiegoCA92093 United States STAR Cryoelectronics Santa FeNM87508 United States Department of Physics University of California Berkeley BerkeleyCA94720 United States National Institute of Standards and Technology BoulderCO80305 United States Ibaraki Tsukuba305-0801 Japan Electrical and Computer Engineering Department Princeton University PrincetonNJ08540 United States Physics Department McGill University MontrealQCH3A 0G4 Canada

Digital Frequency-Domain Multiplexing (DfMux) is a technique that uses MHz superconducting resonators and Superconducting Quantum Interference Device (SQUID) arrays to read out sets of Transition Edge Sensors. DfMux has been used by several Cosmic Microwave Background experiments, including most recently POLARBEAR-2 and SPT-3G with multiplexing factors as high as 68, and is the baseline readout technology for the planned satellite mission LiteBIRD. Here, we present recent work focused on improving DfMux readout noise, reducing parasitic impedance, and improving sensor operation. We have achieved a substantial reduction in stray impedance by integrating the sensors, resonators, and SQUID array onto a single carrier board operated at 250 mK. This also drastically simplifies the packaging of the cryogenic components and leads to better-controlled crosstalk. We demonstrate a low readout noise level of 8.6 pA/Hz1/2, which was made possible by operating the SQUID array at a reduced temperature and with a low dynamic impedance. This is a factor of two improvement compared to the achieved readout noise level in currently operating Cosmic Microwave Background experiments using DfMux and represents a critical step toward maturation of the technology for the next generation of instruments. © 2021, CC BY.

关键词： Frequency domain analysis

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Promoting diversity for end-to-end conversation response generation

arXiv

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arXiv 2019年

作者： Ruan, Yu-Ping Ling, Zhen-Hua Liu, Quan Gu, Jia-Chen Zhu, Xiaodan National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei China iFLYTEK Research Hefei China Department of Electrical and Computer Engineering Queen's University Kingston Canada

We present our work on Track 2 in the Dialog System technology Challenges 7 (DSTC7). The DSTC7-Track 2 aims to evaluate the response generation of fully data-driven conversation models in knowledge-grounded settings, which provides the contextual-relevant factual texts. The Sequence-to-Sequence models have been widely used for end-to-end generative conversation modelling and achieved impressive results. However, they tend to output dull and repeated responses in previous studies. Our work aims to promote the diversity for end-to-end conversation response generation, which follows a two-stage pipeline: 1) Generate multiple responses. At this stage, two different models are proposed, i.e., a variational generative (VariGen) model and a retrieval based (Retrieval) model. 2) Rank and return the most related response by training a topic coherence discrimination (TCD) model for the ranking process. According to the official evaluation results, our proposed Retrieval and VariGen systems ranked first and second respectively on objective diversity metrics, i.e., Entropy, among all participant systems. And the VariGen system ranked second on NIST and METEOR metrics. Copyright © 2019, The Authors. All rights reserved.

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Exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge

arXiv

引用

arXiv 2019年

作者： Ruan, Yu-Ping Zhu, Xiaodan Ling, Zhen-Hua Shi, Zhan Liu, Quan Wei, Si National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei China Department of Electrical and Computer Engineering Queen's University KingstonON Canada iFLYTEK Research Hefei China

Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing computers' intelligence on common sense representation and reasoning. This paper presents the new state-of-theart on WSC, achieving an accuracy of 71.1%. We demonstrate that the leading performance benefits from jointly modelling sentence structures, utilizing knowledge learned from cutting-edge pretraining models, and performing fine-tuning. We conduct detailed analyses, showing that fine-tuning is critical for achieving the performance, but it helps more on the simpler associative problems. Modelling sentence dependency structures, however, consistently helps on the harder non-associative subset of WSC. Analysis also shows that larger fine-tuning datasets yield better performances, suggesting the potential benefit of future work on annotating more Winograd schema sentences. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Localized-delocalized crossover of spin-carriers and magnetization reversal in Co2VO4

arXiv

引用

arXiv 2021年

作者： Kademane, Abhijit Bhat Bhandari, Churna Paudyal, Durga Cottrell, Stephen Das, Pinaki Liu, Yong Yiu, Yuen Naveen Kumar, C.M. Siemensmeyer, Konrad Hoser, Andreas Quintero-Castro, Diana Lucia Vaknin, David Toft-Petersen, Rasmus Department of Mathematics and Physics Universitetet i Stavanger Stavanger4036 Norway Ames Laboratory Iowa State University AmesIA50011 United States Department of Electrical and Computer Engineering Iowa State University AmesIA50011 United States ISIS Facility STFC Rutherford Appleton Laboratory Harwell Science and Innovation Campus Chilton Didcot OxonOX11 0QX United Kingdom Institute of Solid State Physics Vienna University of Technology Wiedner Hauptstrae 810 Vienna1040 Austria AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków30-059 Poland Helmholtz Zentrum Berlin für Materialien und Energie BerlinD-14109 Germany Department of Physics and Astronomy Iowa State University AmesIA50011 United States Department of Physics Technical University of Denmark Kongens LyngbyDK-2800 Denmark

Neutron diffraction, magnetization and muon spin relaxation measurements, supplemented by density functional theory (DFT) calculations are employed to unravel temperature-driven magnetization reversal (MR) in inverse spinel Co2VO4. All measurements show a second order magnetic phase transition at TC = 168 K to a collinear ferrimagnetic phase. The DFT results suggest the moments in the ferrimagnetic phase are delocalized and undergo gradual localization as the temperature is lowered below TC. The delocalized-localized crossover gives rise to a maximum magnetization at TNC = 138 K and the continuous decrease in magnetization produces sign-change at TMR ∼ 65 K. Muon spectroscopy results support the DFT, as a strong T1-relaxation is observed around TNC, indicating highly delocalized spin-carriers gradually tend to localization upon cooling. The magnetization reversal determined at zero field is found to be highly sensitive to applied magnetic field, such that above B ∼ 0.25 T instead of a reversal a broad minimum in the magnetization is apparent at TMR. Analysis of the neutron diffraction measurements shows two antiparallel magnetic sub-lattice-structure, each belonging to magnetic ions on two distinct crystal lattice sites. The relative balance of these two structural components in essence determines the magnetization. Indeed, the order parameter of the magnetic phase on one site develops moderately more than that on the other site. Unusual tipping of the magnetic balance, caused by such site-specific magnetic fluctuation, gives rise to a spontaneous flipping of the magnetization as the temperature is lowered. © 2021, CC BY.

关键词： Magnetization reversal

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The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

arXiv

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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Stacked auto encoder based Deep Reinforcement Learning for online resource scheduling in large-scale MEC networks

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Newly established autonomous adaptive low-power instrument platform(AAL-PIP) chain on East Antarctic Plateau and operation

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Advances in Polar Science 2019年第4期30卷 362-374页

作者： Zhonghua XU Michael DHARTINGER Robert CLAUER Daniel WEIMER Kshitija DESHPANDE Hyomin KIM Stephen MUSKO Anna WILLER Thomas EDWARDS Shane COYLE Yuxiang PENG Dong LIN John BOWMAN Bradley Department of Electrical and Computer Engineering Virginia TechBlacksburgVA 24061USA Center for Space Plasma Physics Space Science InstituteBoulderCO 80301USA Department of Physical Sciences Embry-Riddle Aeronautical UniversityDaytona BeachFL 32114USA Center for Solar-Terrestrial Research New Jersey Institute of TechnologyNewarkNJ 07102USA Space Physics Research Laboratory College of EngineeringUniversity of MichiganAnn ArborMI48109USA DTU Space National Space InstituteTechnical University of DenmarkKongens LyngbyDenmark

An autonomous adaptive low-power instrument platform(AAL-PIP)chain of six stations has been newly established on East Antarctic Plateau along the 40°geomagnetic meridian,to investigate interhemispheric geomagnetically conjugate current systems,waves,and other space weather phenomena in Polar *** six stations,PG0 to PG5,which host low-power magnetometers(Fluxgate and Searchcoil),dual frequency GPS receivers,HF radio experiment,and run autonomously with solar power and two-way satellite communication,are designated at the geomagnetically conjugate(based on the International Geomagnetic Reference Field)locations of the West Greenland geomagnetic chain covering magnetic latitudes from 70°to 80°.We present the development,deployment,and operation of this chain,as well as the data collected by the chain and some preliminary scientific results showing evidence of interhemispheric asymmetries,which are important to better understand Solar Wind–Magnetosphere–Ionosphere(SWMI)coupling in Polar *** investigations focus on magnetic impulse(MI)events,traveling convection vortices(TCVs),and ultra-low frequency(ULF)waves in the coupled southern and northern hemispheres.

关键词： magnetometers magnetic perturbation magnetic conjugate

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