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arXiv 2018年

作者： Chen, Guorui Jiang, Lili Wu, Shuang Lv, Bosai Li, Hongyuan Chittari, Bheema Lingam Watanabe, Kenji Takashi Taniguchi Shi, Zhiwen Jung, Jeil Zhang, Yuanbo Wang, Feng Department of Physics University of California at Berkeley BerkeleyCA United States State Key Laboratory of Surface Physics Department of Physics Fudan University Shanghai200433 China Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA United States Key Laboratory of Artificial Structures and Quantum Control Ministry of Education School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Collaborative Innovation Center of Advanced Microstructures Nanjing China National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Physics University of Seoul Seoul02504 Korea Republic of Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Kavli Energy NanoSciences Institute University of California Berkeley Lawrence Berkeley National Laboratory BerkeleyCA United States

Mott insulator plays a central role in strongly correlated physics, where the repulsive Coulomb interaction dominates over the electron kinetic energy and leads to insulating states with one electron occupying each unit cell.1,2 Doped Mott insulator is often described by the Hubbard model3, which can give rise to other correlated phenomena such as unusual magnetism and even high temperature superconductivity2,4. A tunable Mott insulator, where the competition between the Coulomb interaction and the kinetic energy can be varied in situ, can provide an invaluable model system for the study of Mott physics. Here we report the realization of such a tunable Mott insulator in the ABC trilayer graphene (TLG) and hexagonal boron nitride (hBN) heterostructure with a moiré superlattice. Unlike massless Dirac electrons in monolayer graphene5,6, electrons in pristine ABC TLG are characterized by quartic energy dispersion and large effective mass7-11 that are conducive for strongly correlated phenomena. The moiré superlattice12-18 in TLG/hBN heterostructures leads to narrow electronic minibands that are gatetunable. Each filled miniband contains 4 electrons in one moiré lattice site due to the spin and valley degeneracy of graphene. The Mott insulator states emerge at 1/4 and 1/2 fillings, corresponding to one electron and two electrons per site, respectively. Moreover, the Mott states in the ABC TLG/hBN heterostructure exhibit unprecedented tunability: The Mott gap can be modulated in situ by a vertical electrical field, and at the mean time the electron doping can be gate-tuned to fill the band from one Mott insulating state to another. Our observation of a tunable Mott insulator opens up exciting opportunities to explore novel strongly correlated phenomena in two-dimensional moiré superlattice heterostructures. Copyright © 2018, The Authors. All rights reserved.

关键词： Mott insulators

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BitID: Easily Add Battery-Free Wireless Sensors to Everyday Objects

BitID: Easily Add Battery-Free Wireless Sensors to Everyday ...

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International Conference on Smart computing (SMARTCOMP)

作者： Tengxiang Zhang Nicholas Becker Yuntao Wang Yuan Zhou Yuanchun Shi Department of Computer Science and Technology Tsinghua University Beijing China Global Innovation eXchange Institute Tsinghua University Beijing China Department of Electrical Engineering University of Washington Seattle USA Chinese Academy of Sciences Institute of Computing Technology Beijing China Tsinzhua National Laboratory for Information Science and Technology

ISBN: (纸本)9781509065189

Radio-Frequency Identification (RFID) systems are becoming increasingly used within smart environments. In this paper, we propose BitID, a passive Ultra-High Frequency (UHF) RFID based sensing technique that can easily be made using off-the-shelf tags. BitID can be added to everyday objects to enable sensing and control capabilities. With a simple shorting mechanism, BitID is able to differentiate between two states of the object to which it is attached (for example, whether a door is open or closed). We explain the working principle of BitID, and demonstrate how to build and apply it to target objects. We also show that by using a three- layered system architecture, BitID can be used for various applications, including event detection, energy monitoring, fitness tracking, human behavior tracking and control.

关键词： RFID tags Antennas Strips Monitoring Modulation

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Flexible multiple base station association and activation for downlink heterogeneous networks

arXiv

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arXiv 2017年

作者： Shen, Kaiming Liu, Ya-Feng Ding, David Yiwei Yu, Wei Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This letter shows that the flexible association of possibly multiple base stations (BSs) to each user over multiple frequency bands, along with the joint optimization of BS transmit power that encourages the turning-off of the BSs at off-peak time, can significantly improve the performance of a downlink heterogeneous wireless cellular network. We propose a gradient projection algorithm for optimizing BS association and an iteratively reweighting scheme together with a novel proximal gradient method for optimizing power in order to find the optimal tradeoff between network utility and power consumption. Simulation results reveal significant performance improvement as compared to the conventional single-BS association. Copyright © 2017, The Authors. All rights reserved.

关键词： Chemical activation

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A Lite Cellular Generalized Neuron Network for Frequency Prediction of Synchronous Generators in a Multimachine Power System

A Lite Cellular Generalized Neuron Network for Frequency Pre...

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International Joint Conference on Neural Networks

作者： Yawei Wei Ganesh Kumar Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory The Holcombe Department of Electrical and Computer Engineering Clemson University

ISBN: (纸本)9781509006212

Power system protection and emergency control systems require continuous monitoring to prevent grid-wide frequency instability or system islanding. Amongst different methods, cellular computation networks (CCNs) are more suitable for providing dynamic frequency predictions. CCN is a scalable and distributed framework using the concept of cells and intercellular connections to represent a power system topology with multiple synchronous machines, transmission lines and loads. However, from a practical viewpoint, fast computing algorithms are needed for effective monitoring and response. In this paper, a lite CCN is proposed based on generalized neurons (GNs), referred to as the cellular generalized neuron network (CGNN). The performance of CGNN is compared with that of a cellular multilayer perceptron network (CMLPN). The frequency predictions by the CGNN and CMLPN have been validated on a two-area four-machine benchmark power system. Data for the CGNN is provided from a real time digital simulation of the power system with phasor measurement units (PMUs). Based on a performance metric, results obtained show that the CGNN outperforms the CMLPN, especially in terms of least number of trainable parameters, low training time and better prediction accuracy.

关键词： Cellular computational network Frequency prediction Generalized neuron Particle swarm optimization Power systems Synchronous generators

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An Overview of Dynamic Spectrum Sharing: Ongoing Initiatives, Challenges, and a Roadmap for Future Research

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IEEE Transactions on Cognitive Communications and Networking 2016年第2期2卷 110-128页

作者： Bhattarai, Sudeep Park, Jung-Min Gao, Bo Bian, Kaigui Lehr, William Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061-0131 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Institute of Network Computing and Information Systems Peking University Beijing100871 China Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

We are in the midst of a major paradigm shift in how we manage radio spectrum. This paradigm shift is necessitated by the growth of wireless services of all types and the demand pressure imposed on limited spectrum resources under legacy management regimes. The shift is feasible because of advances in radio and networking technologies that make it possible to share spectrum dynamically in all possible dimensions-i.e., across frequencies, time, location, users, uses, and networks. realizing the full potential of this shift to Dynamic Spectrum Sharing will require the co-evolution of wireless technologies, markets, and regulatory policies;a process which is occurring on a global scale. This paper provides a current overview of major technological and regulatory reforms that are leading the way toward a global paradigm shift to more flexible, dynamic, market-based ways to manage and share radio spectrum resources. We focus on current efforts to implement database-driven approaches for managing the shared co-existence of users with heterogeneous access and interference protection rights, and discuss open research challenges. © 2015 IEEE.

关键词： Spectroscopy

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Bounds on the dynamics of periodic quantum walks and emergence of the gapless and gapped Dirac equation

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Physical Review A 2018年第1期97卷 012116-012116页

作者： N. Pradeep Kumar Radhakrishnan Balu Raymond Laflamme C. M. Chandrashekar The Institute of Mathematical Sciences C. I. T Campus Taramani Chennai 600113 India U.S. Army Research Laboratory Computational and Information Sciences Directorate Adelphi Maryland 20783 USA Computer Science and Electrical Engineering University of Maryland Baltimore County 1000 Hilltop Circle Baltimore Maryland 21250 USA Institute for Quantum Computing and Department of Physics and Astronomy University of Waterloo Waterloo N2L 3G1 Ontario Canada Perimeter Institute for Theoretical Physics Waterloo N2L 2Y5 Ontario Canada Homi Bhabha National Institute Training School Complex Anushakti Nagar Mumbai 400094 India

We study the dynamics of discrete-time quantum walk using quantum coin operations, Ĉ(θ1) and Ĉ(θ2), in time-dependent periodic sequence. For the two-period quantum walk with the parameters θ1 and θ2 in the coin operations we show that the standard deviation [σθ1,θ2(t)] is the same as the minimum of standard deviation obtained from one of the one-period quantum walks with coin operations θ1 or θ2, σθ1,θ2(t)=min{σθ1(t),σθ2(t)}. Our numerical result is analytically corroborated using the dispersion relation obtained from the continuum limit of the dynamics. Using the dispersion relation for one- and two-period quantum walks, we present the bounds on the dynamics of three- and higher-period quantum walks. We also show that the bounds for the two-period quantum walk will hold good for the split-step quantum walk which is also defined using two coin operators using θ1 and θ2. Unlike the previous known connection of discrete-time quantum walks with the massless Dirac equation where coin parameter θ=0, here we show the recovery of the massless Dirac equation with nonzero θ parameters contributing to the intriguing interference in the dynamics in a totally nonrelativistic situation. We also present the effect of periodic sequence on the entanglement between coin and position space.

关键词： Quantum algorithms Quantum entanglement Quantum simulation Quantum walks

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Pattern Recognition for Electric Energy Consumption Prediction in a laboratory Environment

Pattern Recognition for Electric Energy Consumption Predicti...

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IEEE Symposium Series on Computational Intelligence

作者： Guneet Bedi Ganesh Kumar Venayagamoorthy Rajendra Singh Holcombe Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory School of Engineering University of Kwazulu-Natal Durban south Africa Department of Automotive Engineering Clemson University Clemson SC USA

Particle Swarm Optimization (PSO), a computational intelligence (CI) technique, is applied in a laboratory environment to recognize existence of a pattern between the net energy consumption by the electric loads in the building and the ambient temperature along with the occupancy state of the building;and use the detected pattern to predict energy consumption in the near-future. The electric loads under consideration include lighting and heating, ventilation and air conditioning (HVAC) units with intelligent monitoring and control capabilities using internet of things (IoT) devices and technologies. Having this prediction capability is extremely useful to ensure sufficient energy is generated to meet the demands of the electric loads at any time. This, in turn, reduces energy waste due to excess generation.

关键词： Computational intelligence Electric energy consumption prediction Internet of things Particle swarm optimization

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Implementation vulnerabilities in general quantum cryptography

arXiv

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arXiv 2018年

作者： Huang, Anqi Barz, Stefanie Andersson, Erika Makarov, Vadim Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Institute for Functional Matter and Quantum Technologies University of Stuttgart Stuttgart70569 Germany Center for Integrated Quantum Science and Technology University of Stuttgart Iqst Stuttgart70569 Germany Institute of Photonics and Quantum Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom Russian Quantum Center and Misis University Moscow Russia Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada

Quantum cryptography is information-theoretically secure owing to its solid basis in quantum mechanics. However, generally, initial implementations with practical imperfections might open loopholes, allowing an eavesdropper to compromise the security of a quantum cryptographic system. This has been shown to happen for quantum key distribution (QKD). Here we apply experience from implementation security of QKD to several other quantum cryptographic primitives. We survey quantum digital signatures, quantum secret sharing, source-independent quantum random number generation, quantum secure direct communication, and blind quantum computing. We propose how the eavesdropper could in principle exploit the loopholes to violate assumptions in these protocols, breaking their security properties. Applicable countermeasures are also discussed. It is important to consider potential implementation security issues early in protocol design, to shorten the path to future applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum cryptography

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Empirical data analysis: A new tool for data analytics

Empirical data analysis: A new tool for data analytics

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2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016

作者： Angelov, Plamen Gu, Xiaowei Kangin, Dmitry Principe, Jose Data Science Group School of Computing and Communications Lancaster University Lancaster United Kingdom Computational Neuro Engineering Laboratory Department of Electrical and Computer Engineering University of Florida GrainsvilleFL United States

ISBN: (纸本)9781509018970

In this paper, a novel empirical data analysis approach (abbreviated as EDA) is introduced which is entirely data-driven and free from restricting assumptions and pre-defined problem- or user-specific parameters and thresholds. It is well known that the traditional probability theory is restricted by strong prior assumptions which are often impractical and do not hold in real problems. Machine learning methods, on the other hand, are closer to the real problems but they usually rely on problem- or user-specific parameters or thresholds making it rather art than science. In this paper we introduce a theoretically sound yet practically unrestricted and widely applicable approach that is based on the density in the data space. Since the data may have exactly the same value multiple times we distinguish between the data points and unique locations in the data space. The number of data points, k is larger or equal to the number of unique locations, l and at least one data point occupies each unique location. The number of different data points that have exactly the same location in the data space (equal value), f can be seen as frequency. Through the combination of the spatial density and the frequency of occurrence of discrete data points, a new concept called multimodal typicality, τMM is proposed in this paper. It offers a closed analytical form that represents ensemble properties derived entirely from the empirical observations of data. Moreover, it is very close (yet different) from the histograms, from the probability density function (pdf) as well as from fuzzy set membership functions. Remarkably, there is no need to perform complicated pre-processing like clustering to get the multimodal representation. Moreover, the closed form for the case of Euclidean, Mahalanobis type of distance as well as some other forms (e.g. cosine-based dissimilarity) can be expressed recursively making it applicable to data streams and online algorithms. Inference/estimation of the ty

关键词： Membership functions

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Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

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Journal of medical Internet research 2021年第11期23卷 e34625页

作者： Griffin M Weber Harrison G Zhang Sehi L'Yi Clara-Lea Bonzel Chuan Hong Paul Avillach Alba Gutiérrez-Sacristán Nathan P Palmer Amelia Li Min Tan Xuan Wang William Yuan Nils Gehlenborg Anna Alloni Danilo F Amendola Antonio Bellasi Riccardo Bellazzi Michele Beraghi Mauro Bucalo Luca Chiovato Kelly Cho Arianna Dagliati Hossein Estiri Robert W Follett Noelia García Barrio David A Hanauer Darren W Henderson Yuk-Lam Ho John H Holmes Meghan R Hutch Ramakanth Kavuluru Katie Kirchoff Jeffrey G Klann Ashok K Krishnamurthy Trang T Le Molei Liu Ne Hooi Will Loh Sara Lozano-Zahonero Yuan Luo Sarah Maidlow Adeline Makoudjou Alberto Malovini Marcelo Roberto Martins Bertrand Moal Michele Morris Danielle L Mowery Shawn N Murphy Antoine Neuraz Kee Yuan Ngiam Marina P Okoshi Gilbert S Omenn Lav P Patel Miguel Pedrera Jiménez Robson A Prudente Malarkodi Jebathilagam Samayamuthu Fernando J Sanz Vidorreta Emily R Schriver Petra Schubert Pablo Serrano Balazote Byorn Wl Tan Suzana E Tanni Valentina Tibollo Shyam Visweswaran Kavishwar B Wagholikar Zongqi Xia Daniela Zöller Isaac S Kohane Tianxi Cai Andrew M South Gabriel A Brat Department of Biomedical Informatics Harvard Medical School Boston MA United States. BIOMERIS (BIOMedical Research Informatics Solutions) Pavia Italy. Clinical Research Unit Botucatu Medical School São Paulo State University Botucatu Brazil. Division of Nephrology Department of Medicine Ente Ospedaliero Cantonale Lugano Switzerland. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Information Technology Department Azienda Socio-Sanitaria Territoriale di Pavia Pavia Italy. Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Pavia Italy. Massachusetts Veterans Epidemiology Research and Information Center Veterans Affairs Boston Healthcare System Boston MA United States. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Department of Medicine Massachusetts General Hospital Boston MA United States. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States. Health Informatics Hospital Universitario 12 de Octubre Madrid Spain. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI United States. Department of Biomedical Informatics University of Kentucky Lexington KY United States. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Institute for Biomedical Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Department of Preventive Medicine Northwestern University Chicago IL United States. Institute for Biomedical Informatics University of Kentucky Lexington KY United States. Medical University of South Carolina Charleston SC United States. Department of Computer Science Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill NC United States. Departm

[This corrects the article DOI: 10.2196/31400.].

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