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Asymmetric scattering behaviors of spin wave dependent on magnetic vortex chirality

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arXiv 2020年

作者： Zhang, Xue-Feng Shim, Je-Ho Ma, Xiao-Ping Song, Cheng Yu, Haiming Piao, Hong-Guang Hubei Engineering Research Center of Weak Magnetic-Field Detection China Three Gorges University Yichang443002 China Department of Physics College of Science Yanbian University Yanji133002 China School of Materials Science and Engineering Tsinghua University Beijing100084 China Fert Beijing Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

In this letter, an asymmetric spin wave scattering behaviors caused by vortex chirality are investigated in cross-shaped ferromagnetic system. In the system, four scattering behaviors are found, 1) asymmetric skew scattering, depending on the polarity of vortex core, 2) back scattering (reflection), depending on the vortex core stiffness, 3) side deflection scattering, depending on structural symmetry of the vortex circulation, and 4) geometrical scattering, depending on waveguide structure. The first and second scattering behaviors are attributed to nonlinear topological magnon spin Hall effect related to magnon spin-transfer torque effect, which has value for magnonic exploration and application. Copyright © 2020, The Authors. All rights reserved.

关键词： Chirality

Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

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Science China(Information Sciences) 2021年第1期64卷 5-78页

作者： Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

Pay attention to evolution: Time series forecasting with deep graph-evolution learning

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arXiv 2020年

作者： Spadon, Gabriel Hong, Shenda Brandoli, Bruno Matwin, Stan Rodrigues, Jose F. Sun, Jimeng University of Sao Paulo Institute of Mathematics and Computer Sciences SP Sao Carlos13566-590 Brazil Georgia Institute of Technology College of Computing AtlantaGA30332-0365 United States Dalhousie University Institute for Big Data Analytics HalifaxNSB3H 1W5 Canada National Institute of Health Data Science Peking University Beijing100191 China Institute of Medical Technology Peking University Beijing100191 China Institute of Computer Science of the Polish Academy of Sciences Warsaw525-000-94-01 Poland Université Grenoble Alpes Faculty of Science Saint-Martin-d'Hères 38400 France University of Illinois Urbana-Champaign Department of Computer Science ChampaignIL61801-2302 United States

Time-series forecasting is one of the most active research topics in artificial intelligence. It has the power to bring light to problems in several areas of knowledge, such as epidemiological studies, healthcare inference, and climate change analysis. Applications in real-world time series should consider two factors for achieving reliable predictions: modeling dynamic dependencies among multiple variables and adjusting the model's intrinsic hyperparameters. An open gap in the literature is that statistical and ensemble learning approaches systematically present lower predictive performance than deep learning methods. The existing applications consistently disregard the data sequence aspect entangled with multivariate data represented in more than one time series. Conversely, this work presents a novel neural network architecture for time-series forecasting that combines the power of graph evolution with deep recurrent learning on distinct data distributions, named after Recurrent Graph Evolution Neural Network (REGENN). The idea is to infer multiple multivariate relationships between co-occurring time-series by assuming that the temporal data depends not only on inner variables and intra-temporal relationships (i.e., observations from itself) but also on outer variables and inter-temporal relationships (i.e., observations from other-selves). An extensive set of experiments was conducted comparing REGENN with tens of ensemble methods and classical statistical ones. The results outperformed both statistical and ensemble-learning approaches, showing an improvement of 64.87% over the competing algorithms on the SARS-CoV-2 dataset of the renowned John Hopkins University for 188 countries simultaneously. For further validation, we tested our architecture in two other public datasets of different domains, the PhysioNet computing in Cardiology Challenge 2012 and Brazilian Weather datasets. We also analyzed the Evolution Weights arising from the hidden layers of REGENN to

关键词： Time series

Weighing neutrinos with 21cm Intensity Mapping at the SKAO

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arXiv 2025年

作者： Autieri, Gabriele Berti, Maria Spinelli, Marta Haridasu, Balakrishna Sandeep Viel, Matteo SISSA- International School for Advanced Studies Via Bonomea 265 Trieste34136 Italy INFN National Institute for Nuclear Physics Via Valerio 2 TriesteI-34127 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut2 Trieste34151 Italy Département de Physique Théorique Center for Astroparticle Physics Université de Genève Quai E. Ansermet 24 Genève 4CH-1211 Switzerland Observatoire de la Côte d’Azur Laboratoire Lagrange Bd de l’Observatoire CS 34229 Nice06304 cedex 4 France Department of Physics and Astronomy University of the Western Cape Robert Sobukwe Road Cape Town7535 South Africa INAF Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34131 Italy Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy

We explore the constraining power of future 21cm intensity mapping (IM) observations at the SKAO, focusing primarily on the sum of neutrino masses, Σmν. We forecast observations of the 21cm IM auto-power spectrum as well as the 21cm IM and galaxy surveys cross-correlation power spectrum. We construct different synthetic data sets of observations for the 21cm IM observables in the redshift range 0 ν = 0.06, 0.1, 0.4 eV, in the synthetic data sets. To investigate the constraining power of the forecasted 21cm observations, we build a likelihood code that will be made publicly available upon publication. The results of the analysis, obtained through Markov Chain Monte Carlo techniques, are promising. We find that the 21cm auto-power spectrum alone could provide an upper limit on the sum of neutrino masses of Σmν ν. This result is comparable to the upper limits provided by cosmic microwave background (CMB) observations alone. When combining the 21cm auto-power spectrum synthetic data set with Planck 2018 CMB measurements, we find a tighter upper limit of Σmν ν ν Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

Localization of Deep Inpainting Using High-Pass Fully Convolutional Network

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Localization of Deep Inpainting Using High-Pass Fully Convol...

International Conference on Computer Vision (ICCV)

作者： Haodong Li Jiwu Huang Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Image inpainting has been substantially improved with deep learning in the past years. Deep inpainting can fill image regions with plausible contents, which are not visually apparent. Although inpainting is originally designed to repair images, it can even be used for malicious manipulations, e.g., removal of specific objects. Therefore, it is necessary to identify the presence of inpainting in an image. This paper presents a method to locate the regions manipulated by deep inpainting. The proposed method employs a fully convolutional network that is based on high-pass filtered image residuals. Firstly, we analyze and observe that the inpainted regions are more distinguishable from the untouched ones in the residual domain. Hence, a high-pass pre-filtering module is designed to get image residuals for enhancing inpainting traces. Then, a feature extraction module, which learns discriminative features from image residuals, is built with four concatenated ResNet blocks. The learned feature maps are finally enlarged by an up-sampling module, so that a pixel-wise inpainting localization map is obtained. The whole network is trained end-to-end with a loss addressing the class imbalance. Extensive experimental results evaluated on both synthetic and realistic images subjected to deep inpainting have shown the effectiveness of the proposed method.

关键词： Forensics Feature extraction Convolution Kernel Machine learning Computer vision Probability

Blockchain and IoT data Analytics for Fine-Grained Transportation Insurance 24

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Blockchain and IoT Data Analytics for Fine-Grained Transport...

24th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2018

作者： Li, Zengxiang Xiao, Zhe Xu, Quanqing Sotthiwat, Ekanut Mong Goh, Rick Siow Liang, Xueping Institute of High Performance Computing ASTAR Singapore Big Data Experience Center Computer Engineering Department King Mongkut's University of Technology Thonburi Thailand Old Dominion University NorfolkVA United States

ISBN: (纸本)9781538673089

Innovations such as the Cloud, Internet of Things (IoT), and data analytics have already dramatically altered the customer experience in many, if not all, industries. Blockchain, as another emerging technology, is expected to be the next generation infrastructure to established trusted multiparty collaborations. In this paper, we investigated the convergence of aforementioned technologies, by presenting a prototype of fine-grained transportation insurance. Insurance premium were assessed based on vehicle's usage and driver's behavior, which were deduced from streaming IoT data collected from mobile sensors. This incentive mechanism promotes fairness among drivers and encourages safer driving style. The prototype takes advantage of both private blockchain (e.g., high transaction rate in Hyperledger) and public blockchain (e.g., inbuilt cryptocurrency). Besides system architecture and implementation details, preliminary performance evaluations are presented and discussed. © 2018 IEEE.

关键词： Insurance

Determination factors of internet advertising affecting internet users’ behavior using canonical correlation analysis

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AIP Conference Proceedings 2024年第1期2895卷

作者： Farah Nabilah Zabidi Nurain Ibrahim Nur Izzah Atiqah Nurul Athirah Norzaidi Salsabilla Kamaludin Adina Najwa Kamarudin School of Mathematical Sciences College of Computing Informatics and Media Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Regional Department Suu Balm Malaysia Livemed Sdn Bhd Aurora Place Plaza No 1 Persiaran Bukit Jalil 57000 Kuala Lumpur Malaysia Federal Government Administrative Centre Block C6 Complex C Lebuh Perdana Selatan Presint 1 62514 Putrajaya Wilayah Persekutuan Putrajaya Malaysia Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

One of the most important and fundamental parts of today’s business world is advertising. Advertising is necessary for both business and consumers. We can now observe that the advertising industry has experienced substantial changes and is now employed globally. An advertising company has found this platform to be quite useful for increasing revenue by influencing the online users’ behaviour, which may change in either a positive or bad way. Therefore, the primary topic of this study paper is on how internet advertising affects consumers’ online behaviour in UiTM Shah Alam. Through surveys, we gathered information about two sets of variables: Internet Users’ Behaviour (Purchase Intention, Effective Medium, Frequency of Browsing, and Demographic Variable), and sets of variables Internet Advertising (Webpage Content, Informativeness and Interactivity). To determine the aspects of internet advertising influencing internet users’ behaviour, Canonical Correlation Analysis was applied to the data that was gathered. Internet advertising and internet users’ behaviour are significantly correlated, according to the results analysis. As a result, Internet advertising plays a big role in influencing consumers’ behaviour because of its close connection to their purchasing decisions. Based on findings, it can be concluded that internet advertising plays significant roles in examining internet users’ behaviour, which may be advantageous for many industries globally, particularly in current era of digitalization. With information on consumer behaviour both now and in the future, these findings can assist marketers and advertisers in developing more effective online advertising strategies that better engage young adults’ attention and influence their behaviour, particularly with regard to purchase intention, leading to a misalignment. It will then be able to create more successful online advertising.

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Laser pulse induced efficient terahertz emission from Co/Al heterostructures

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Physical Review B 2020年第2期102卷 024435-024435页

作者： Hui Zhang Zheng Feng Jine Zhang He Bai Huaiwen Yang Jianwang Cai Weisheng Zhao Wei Tan Fengxia Hu Baogen Shen Jirong Sun Fert Beijing Institute School of Microelectronics Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijng 100191 People's Republic of China Microsystem and Terahertz Research Center China Academy of Engineering Physics Chengdu Sichuan 610200 People's Republic of China Institute of Electronic Engineering China Academy of Engineering Physics Mianyang Sichuan 621999 People's Republic of China Beijing National Laboratory for Condensed Matter Physics & Institute of Physics Chinese Academy of Sciences Beijing 100190 People's Republic of China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 Peoples’ Republic of China Songshan Lake Materials Laboratory Dongguan Guangdong 523808 People's Republic of China Spintronics Institute University of Jinan Jinan Shandong 250022 People's Republic of China

Investigation on terahertz (THz) emission is important not only for fundamental research but also for practical application. A rapidly developing approach to generate THz emission is using a heterostructure composed of ferromagnetic metal (FM) and nonmagnetic metal (NM) which is pumped by femtosecond laser pulse. Previous works in this regard mainly focused on bilayer with heavy NM (such as Pt) with a large spin Hall angle. Here we present a comprehensive investigation on THz emission from Co/Al heterostructures stemming from the inverse spin Hall effect. It is surprising to find that although the spin Hall angle of Al is two orders of magnitude smaller than that of Pt, the measured THz signals are close to one-third of that of the typical Co/Pt heterostructures. To explore the underlying physics, theoretical models are employed to investigate the spin-related properties of Al. We obtain that the upper limit of the spin Hall angle of Al is 0.55% and the spin-diffusion length is 2.2 ± 0.2 nm; the diffusion length is comparable to that of Pt (2.3 ± 0.1 nm). This work shows that it is worth revisiting light metals for the spintronic THz emitter.

关键词： Spin-orbit coupling Spintronics Ultrafast magnetic effects Metals Terahertz techniques

Better approximations of high dimensional smooth functions by deep neural networks with rectified power units

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arXiv 2019年

作者： Li, Bo Tang, Shanshan Yu, Haijun NCMIS & LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China China Justice Big Data Institute Beijing100043 China

MSC Codes 65D15, 65M12, 65M15Deep neural networks with rectified linear units (ReLU) are getting more and more popular due to their universal representation power and successful applications. Some theoretical progress regarding the approximation power of deep ReLU network for functions in Sobolev space and Korobov space have recently been made by [D. Yarotsky, Neural Network, 94:103-114, 2017] and [H. Montanelli and Q. Du, SIAM J Math. data Sci., 1:78-92, 2019], etc. In this paper, we show that deep networks with rectified power units (RePU) can give better approximations for smooth functions than deep ReLU networks. Our analysis bases on classical polynomial approximation theory and some efficient algorithms proposed in this paper to convert polynomials into deep RePU networks of optimal size with no approximation error. Comparing to the results on ReLU networks, the sizes of RePU networks required to approximate functions in Sobolev space and Korobov space with an error tolerance Ε, by our constructive proofs, are in general O(log1Ε ) times smaller than the sizes of corresponding ReLU networks constructed in most of the existing literature. Comparing to the classical results of Mhaskar [Mhaskar, Adv. Comput. Math. 1:61-80, 1993], our constructions use less number of activation functions and numerically more stable, they can be served as good initials of deep RePU networks and further trained to break the limit of linear approximation theory. The functions represented by RePU networks are smooth functions, so they naturally fit in the places where derivatives are involved in the loss function. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks