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arXiv 2021年

作者： Arruda, Edilson F. Alexandre, Rodrigo e A. Fragoso, Marcelo D. do Val, João B.R. Thomas, Sinnu S. Department of Decision Analytics and Risk Southampton Business School University of Southampton 12 University Rd SouthamptonSO17 1BJ United Kingdom Alberto Luiz Coimbra Institute-Graduate School and Research in Engineering Federal University of Rio de Janeiro. CP 68507 Rio de Janeiro21941-972 Brazil National Laboratory for Scientific Computation Av. Gettúlio Vargas 333 Quitandinha Petrópolis RJ25651-075 Brazil School of Electrical Engineering University of Campinas Av. Albert Einstein 400 Cidade Universitária Campinas SP13083-852 Brazil School of Computer Science and Engineering Kerala University of Digital Sciences Innovation and Technology Technocity Mangalapuram Thonnakkal PO ThiruvananthapuramKerala695317 India

MSC Codes 9010, 93E20In this paper we propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a set up under general latency and infectious period distributions. To some extent, queuing systems with infinitely many servers and a Markov chain with time-varying transition rate are the very technical underpinning of the paper. Although more general, the Markov chain is as tractable as previous models for exponentially distributed latency and infection periods. It is also significantly simpler and more tractable than semi-Markov models with a similar level of generality. Based on the notion of stochastic stability, we derive a sufficient condition for a shrinking epidemic in terms of the queuing system’s occupation rate that drives the dynamics. Relying on this condition, we propose a class of ad-hoc stabilising mitigation strategies that seek to keep a balanced occupation rate after a prescribed mitigation-free period. We validate the approach in the light of recent data on the COVID-19 epidemic and assess the effect of different stabilising strategies. The results suggest that it is possible to curb the epidemic with various occupation rate levels, as long as the mitigation is not excessively procrastinated. © 2021, CC BY-NC-ND.

关键词： Queueing theory

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Linear precoding for fading cognitive multiple access wiretap channel with finite-alphabet inputs

arXiv

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arXiv 2020年

作者： Jin, Juening Xiao, Chengshan Tao, Meixia Chen, Wen Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO65409 United States Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University Shanghai200240 China School of Electronics Engineering and Automation Guilin University of Electronics Technology Guilin541004 China

We investigate the fading cognitive multiple access wiretap channel (CMAC-WT), in which two secondary-user transmitters (STs) send secure messages to a secondary-user receiver (SR) in the presence of an eavesdropper (ED) and subject to interference threshold constraints at multiple primaryuser receivers (PRs). We design linear precoders to maximize the average secrecy sum rate for multiple-input multiple-output (MIMO) fading CMAC-WT under finite-alphabet inputs and statistical channel state information (CSI) at STs. For this nondeterministic polynomial time (NP)-hard problem, we utilize an accurate approximation of the average secrecy sum rate to reduce the computational complexity, and then present a two-layer algorithm by embedding the convex-concave procedure into an outer approximation framework. The idea behind this algorithm is to reformulate the approximated average secrecy sum rate as a difference of convex functions, and then generate a sequence of simpler relaxed sets to approach the non-convex feasible set. Subsequently, we maximize the approximated average secrecy sum rate over the sequence of relaxed sets by using the convexconcave procedure. Numerical results indicate that our proposed precoding algorithm is superior to the conventional Gaussian precoding method in the medium and high signal-to-noise ratio (SNR) regimes. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Semiconductor UV photonics: feature introduction

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Photonics Research 2019年第12期7卷 I0003-I0004页

作者： XIAOHANG LI RUSSELL D.DUPUIS TIM WERNICKE King Abdullah University of Science and Technology(KAUST) Advanced Semiconductor LaboratoryThuwal 23955-6900Saudi Arabia School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGeorgia 30332USA Technische Universitat Berlin Institute of Solid State PhysicsBerlin D-10623Germany

Semiconductor UV photonics research has emerged as one of the most heavily invested areas among semiconductor photonics research due to numerous crucial applications such as sterilization,sensing,curing,and *** feature issue disseminates nine timely original research and two review papers from leading research groups and companies,covering most frontiers of the semiconductor UV photonics research,from epitaxy,device physics and design,nanostructures,fabrication,packaging,reliability,and application for light-emitting diodes,laser diodes,and photodetectors.

关键词： companies photonics frontier

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Time Series Forecasting of New Cases and New Deaths Rate for COVID-19 using Deep Learning Methods

arXiv

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arXiv 2021年

作者： Ayoobi, Nooshin Sharifrazi, Danial Alizadehsani, Roohallah Shoeibi, Afshin Gorriz, Juan M. Moosaei, Hossein Khosravi, Abbas Nahavandi, Saeid Chofreh, Abdoulmohammad Gholamzadeh Goni, Feybi Ariani Klemeš, Jiří Jaromír Mosavi, Amir Department of Mathematics Savitribai Phule Pune University Pune411007 India Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain Department of Mathematics Faculty of Science University of Bojnord Iran Sustainable Process Integration Laboratory - SPIL NETME Centre Faculty of Mechanical Engineering Brno University of Technology - VUT Brno Technická 2896/2 Brno616 69 Czech Republic Department of Management Faculty of Business and Management Brno University of Technology VUT Brno Kolejní 2906/4 Brno612 00 Czech Republic Faculty of Civil Engineering Technische Universität Dresden Dresden01069 Germany John von Neumann Faculty of Informatics Obuda University Budapest1034 Hungary School of Economics and Business Norwegian University of Life Sciences 1430 Norway

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. Several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organizations working against COVID-19 and determining their long-term plans. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

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Federated variance-reduced stochastic gradient descent with robustness to Byzantine attacks

arXiv

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arXiv 2019年

作者： Wu, Zhaoxian Ling, Qing Chen, Tianyi Giannakis, Georgios B. School of Data and Computer Science Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University GuangzhouGuangdong510006 China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical and Computer Engineering Digital Technology Center University of Minnesota MinneapolisMN55455 United States

This paper deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks. To cope with such attacks, most resilient approaches so far combine stochastic gradient descent (SGD) with different robust aggregation rules. However, the sizeable SGD-induced stochastic gradient noise makes it challenging to distinguish malicious messages sent by the Byzantine attackers from noisy stochastic gradients sent by the 'honest' workers. This motivates us to reduce the variance of stochastic gradients as a means of robustifying SGD in the presence of Byzantine attacks. To this end, the present work puts forth a Byzantine attack resilient distributed (Byrd-) SAGA approach for learning tasks involving finite-sum optimization over networks. Rather than the mean employed by distributed SAGA, the novel Byrd-SAGA relies on the geometric median to aggregate the corrected stochastic gradients sent by the workers. When less than half of the workers are Byzantine attackers, the robustness of geometric median to outliers enables Byrd-SAGA to attain provably linear convergence to a neighborhood of the optimal solution, with the asymptotic learning error determined by the number of Byzantine workers. Numerical tests corroborate the robustness to various Byzantine attacks, as well as the merits of Byrd-SAGA over Byzantine attack resilient distributed SGD. Copyright © 2019, The Authors. All rights reserved.

关键词： Optimization

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Generalized Quadratic Matrix Programming: A Unified Framework for Linear Precoding With Arbitrary Input Distributions

arXiv

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arXiv 2020年

作者： Jin, Juening Zheng, Yahong Rosa Chen, Wen Xiao, Chengshan Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO65409 United States Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University Shanghai200240 China School of Electronics Engineering and Automation Guilin University of Electronics Technology Guilin541004 China

This paper investigates a new class of non-convex optimization, which provides a unified framework for linear precoding in single/multi-user multiple-input multiple-output (MIMO) channels with arbitrary input distributions. The new optimization is called generalized quadratic matrix programming (GQMP). Due to the nondeterministic polynomial time (NP)hardness of GQMP problems, instead of seeking globally optimal solutions, we propose an efficient algorithm which is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) point. The idea behind this algorithm is to construct explicit concave lower bounds for non-convex objective and constraint functions, and then solve a sequence of concave maximization problems until convergence. In terms of application, we consider a downlink underlay secure cognitive radio (CR) network, where each node has multiple antennas. We design linear precoders to maximize the average secrecy (sum) rate with finite-alphabet inputs and statistical channel state information (CSI) at the transmitter. The precoding problems under secure multicast/broadcast scenarios are GQMP problems, and thus they can be solved efficiently by our proposed algorithm. Several numerical examples are provided to show the efficacy of our algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Cognitive radio

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Participant Recruitment for Coverage-Aware Mobile Crowdsensing with Location Differential Privacy

Participant Recruitment for Coverage-Aware Mobile Crowdsensi...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Liang Li Xinyue Zhang Ronghui Hou Hao Yue Hui Li Miao Pan School of Cyber Engineering Xidian University P. R. China Science and Technology on Communication Networks Laboratory Department of Electrical and Computer Engineering University of Houston Houston TX Department of Computer Science San Francisco State University San Francisco CA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Mobile crowdsensing is recognized to be a promising paradigm wherein location-based sensing tasks are outsourced to participants carrying mobile devices. A prominent issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participating devices, which requires the disclosure of participants' locations and leads to potential location privacy threats. In this paper, we aim to develop a privacy-preserving participant recruiting scheme for mobile crowdsensing, which guarantees the crowdsensing coverage while preserving participants' location differential privacy against a semi-honest crowdsensing aggregator. Briefly, based on the differential private geo-indistinguishability method, we enable candidate participants to locally perturb their location data. With the obfuscated location information, we formulate the crowdsensing coverage optimization as an Integer Program (IP), and develop a 1-(1 - 1/f) f -approximation algorithm, which yields a near-optimal participant recruiting solution. Through extensive simulations, we demonstrate the tradeoff between privacy preservation and crowdsensing utility, and show that satisfactory crowdsensing coverage can be achieved while preserving the participants' differential location privacy.

关键词： Privacy Sensors Task analysis Recruitment Approximation algorithms Monitoring

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EEG microstates: Functional significance and short-term test-retest reliability

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Neuroimage: Reports 2022年第2期2卷 100089-100089页

作者： Antonova, Elena Holding, Martin Suen, Ho Chak Sumich, Alex Maex, Reinoud Nehaniv, Chrystopher Division of Psychology Department of Life Sciences College of Health Medicine and Life Sciences & Centre for Cognitive Neuroscience Brunel University London Kingston Lane Uxbridge UB8 3PH United Kingdom Department of Psychology Institute of Psychiatry Psychology & Neuroscience King's College London De Crespigny Park London SE5 8AF United Kingdom Department of Psychology School of Social Sciences Nottingham Trent University 50 Shakespeare Street Nottingham NG1 4FQ United Kingdom Royal Society Wolfson Biocomputation Research Laboratory Centre for Computer Science and Informatics Research University of Hertfordshire College Lane Hatfield AL10 9AB United Kingdom Departments of Systems Design Engineering and Electrical & Computer Engineering University of Waterloo 200 University Avenue W Waterloo N2L 3G1 ON Canada

EEG microstates, reflecting discrete topographical organization of the EEG signal power, may have clinical relevance;however, their functional significance and test-retest reliability remain unclear. To investigate the functional significance of the canonical EEG microstate classes and their pairwise transitions, and to establish their within-session test-retest reliability, we recorded 36-channel EEGs in 20 healthy volunteers during three eyes-closed conditions: mind-wandering, verbalization (silently repeating the word ‘square’ every 2 s), and visualization (visualizing a square every 2 s). Each condition lasted 3 min and the sequence of three conditions was repeated four times (two runs of two sequence repetitions). The participants' alertness and their sense of effort during the experiment were rated using visual-analogue scales. The EEG data were 2–20 Hz bandpass-filtered and analysed into the four canonical microstate classes: A, B, C, and D. EEG microstate classes C and D were persistently more dominant than classes A and B in all conditions. Of the first-order microstate parameters, average microstate duration was most reliable. The duration of class D microstate was longer during mind-wandering (106.8 ms) than verbalization (102.2 ms) or visualization (99.8 ms), with a concomitantly higher coverage (36.4% vs. 34.7% and 35.2%), but otherwise there was no clear association of the four microstate classes to particular mental states. The test-retest reliability was higher at the beginning of each run, together with higher average alpha power and subjective ratings of alertness. Only the transitions between classes C and D (from C to D in particular) were significantly higher than what would be expected from the respective microstates' occurrences. The transition probabilities, however, did not distinguish between conditions, and their test-retest reliability was overall lower than that of the first-order parameters such as duration and coverage. Further studies

关键词： Biomarkers EEG microstates Mind-wandering Neuroimaging Resting-state EEG Resting-state networks

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Localized heat diffusion in topological thermal materials

arXiv

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arXiv 2021年

作者： Qi, Minghong Wang, Dong Cao, Pei-Chao Zhu, Xue-Feng Qiu, Cheng-Wei Chen, Hongsheng Li, Ying Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China School of Physics Innovation Institute Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Various unusual behaviors of artificial materials are governed by their topological properties, among which the edge state at the boundary of a photonic or phononic lattice has been captivated as a popular notion. However, this remarkable bulk-boundary correspondence and the related phenomena are missing in thermal materials. One reason is that heat diffusion is described in a non-Hermitian framework because of its dissipative nature. The other is that the relevant temperature field is mostly composed of modes that extend over wide ranges, making it difficult to be rendered within the tight-binding theory as commonly employed in wave physics. Here, we overcome the above challenges and perform systematic studies on heat diffusion in thermal lattices. Based on a continuum model, we introduce a state vector to link the Zak phase with the existence of the edge state, and thereby analytically prove the thermal bulk-boundary correspondence. We experimentally demonstrate the predicted edge states with a topologically protected and localized heat dissipation capacity. Our finding sets up a solid foundation to explore the topology in novel heat transfer manipulations. © 2021, CC BY.

关键词： Topology

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Hadoop perfect file: A fast access container for small files with direct in disc metadata access

arXiv

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arXiv 2019年

作者： Tchaye-Kondi, Jude Zhai, Yanlong Lin, Kwei-Jay Zhu, Liehuang Tao, Wenjun Yang, Kai School of Computer Science Beijing Institute of Technology Beijing100081 China Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China

Storing and processing massive small files is one of the major challenges for the Hadoop Distributed File System (HDFS). In order to provide fast data access, the NameNode (NN) in HDFS maintains the metadata of all files in its main-memory. Hadoop performs well with a small number of large files that require relatively little metadata in the NN’s memory. But for a large number of small files, Hadoop has problems such as NN memory overload caused by the huge metadata size of these small files. We present a new type of archive file, Hadoop Perfect File (HPF), to solve HDFS’s small files problem by merging small files into a large file on HDFS. Existing archive files offer limited functionality and have poor performance when accessing a file in the merged file due to the fact that during metadata lookup it is necessary to read and process the entire index file(s). In contrast, HPF file can directly access the metadata of a particular file from its index file without having to process it entirely. The HPF index system uses two hash functions: file’s metadata are distributed through index files by using a dynamic hash function and, for each index file, we build an order preserving perfect hash function that preserves the position of each file’s metadata in the index file. The HPF design will only read the part of the index file that contains the metadata of the searched file during its access. HPF file also supports the file appending functionality after its creation. Our experiments show that HPF can be more than 40% faster file’s access from the original HDFS. If we don’t consider the caching effect, HPF’s file access is around 179% faster than MapFile and 11294% faster than HAR file. If we consider caching effect, HPF is around 35% faster than MapFile and 105% faster than HAR file. Copyright © 2019, The Authors. All rights reserved.

关键词： File organization

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