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Improved quantum algorithm for A-optimal projection

Physical Review A 2020年第5期102卷 052402-052402页

作者： Shi-Jie Pan Lin-Chun Wan Hai-Ling Liu Qing-Le Wang Su-Juan Qin Qiao-Yan Wen Fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China State Key Laboratory of Cryptology P.O. Box 5159 Beijing 100878 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518055 China School of Control and Computer Engineering North China Electric Power University Beijing 102206 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei 230026 China

Dimensionality reduction algorithms, which reduce the dimensionality of a given data set whereas preserving the information of the original data set as well as possible, play an important role in machine learning and data mining. Duan et al. proposed a quantum version of the A-optimal projection algorithm (AOP) for dimensionality reduction [Phys. Rev. A 99, 032311 (2019)] and claimed that the algorithm has exponential speedups on the dimensionality of the original feature space n and the dimensionality of the reduced feature space k over the classical algorithm. In this paper, we correct the time complexity of the algorithm of Duan et al. to Oκ4sksεspolylogsmnε, where κ is the condition number of a matrix that related to the original data set, s is the number of iterations, m is the number of data points, and ε is the desired precision of the output state. Since the time complexity has an exponential dependence on s, the quantum algorithm can only be beneficial for high-dimensional problems with a small number of iterations s. To get a further speedup, we propose an improved quantum AOP algorithm with time complexity Osκ6kεpolylognmε+s2κ4εpolylogκkε and space complexity O[log2(nk/ε)+s]. With space complexity slightly worse, our algorithm achieves, at least, a polynomial speedup compared to the algorithm of Duan et al.. Also, our algorithm shows exponential speedups in n and m compared with the classical algorithm when κ,k, and 1/ε are O[polylog(nm)].

关键词： Machine learning Quantum algorithms Quantum computation

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Allosteric DNAzyme-based DNA logic circuit: operations and dynamic analysis

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Nucleic acids research 2019年第3期47卷 1097-1109页

作者： Xuedong Zheng Jing Yang Changjun Zhou Cheng Zhang Qiang Zhang Xiaopeng Wei College of Computer Science Shenyang Aerospace University Shenyang 110136 China. School of Control and Computer Engineering North China Electric Power University Beijing 102206 China. College of Mathematics and Computer sciences Zhejiang Normal University Jinhua 321004 China. School of Electronics Engineering and Computer Science Peking University Key laboratory of High Confidence Software Technologies Ministry of Education Beijing 100871 China. Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China. School of Computer Scicence and Technology Dalian University of Technology Dalian 116024 China.

Recently, due to the dual roles of DNA and enzyme, DNAzyme has been widely used in the field of DNA circuit, which has a wide range of applications in bio-engineered system, information processing and biocomputing. In fact, the activity of DNAzymes was regulated by subunits assembly, pH control and metal ions triggers. However, those regulations required to change the sequences of whole DNAzyme, as separating parts and inserting extra DNA sequence. Inspired by the allosteric regulation of proteins in nature, a new allosteric strategy is proposed to regulate the activity of DNAzyme without DNA sequences changes. In this strategy, DNA strand displacement was used to regulate the DNAzyme structure, through which the activity of DNAzyme was well controlled. The strategy was applied to E6-type DNAzymes, and the operations of DNA logic circuit (YES, OR, AND, cascading and feedback) were established and simulated with the dynamic analyses. The allosteric regulation has potential to construct more complicated molecular systems, which can be applied to bio-sensing and detection.

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Achieving data utility-privacy tradeoff in internet of medical things: A machine learning approach

arXiv

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arXiv 2019年

作者： Guan, Zhitao Lv, Zefang Du, Xiaojiang Wu, Longfei Guizani, Mohsen School of Control and Computer Engineering North China Electric Power University China School of Mathematics and Physics North China Electric Power University China Department of Computer and Information Science Temple University PhiladelphiaPA United States Department of Mathematics and Computer Science Fayetteville State University FayettevilleNC United States Electrical and Computer Engineering Department University of Idaho MoscowID United States

The emergence and rapid development of the Internet of Medical Things (IoMT), an application of the Internet of Things into the medical and healthcare systems, have brought many changes and challenges to modern medical and healthcare systems. Particularly, machine learning technology can be used to process the data involved in IoMT for medical analysis and disease diagnosis. However, in this process, the disclosure of personal privacy information must receive considerable attentions especially for sensitive medical data. Cluster analysis is an important technique for medical analysis and disease diagnosis. To enable privacy-preserving cluster analysis in IoMT, this paper proposed an Efficient Differentially Private Data Clustering scheme (EDPDCS) based on MapReduce framework. In EDPDCS, we optimize the allocation of privacy budgets and the selection of initial centroids to improve the accuracy of differentially private Kmeans clustering algorithm. Specifically, the number of iterations of the K-means algorithm is set to a fixed value according to the total privacy budget and the minimal privacy budget of each iteration. In addition, an improved initial centroids selection method is proposed to increase the accuracy and efficiency of the clustering algorithm. Finally, we prove that the proposed EDPDCS can improve the accuracy of the differentially private k-means algorithm by comparing the Normalized Intra-Cluster Variance (NICV) produced by our algorithm on two datasets with two other algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： MapReduce

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Inference and prediction of malaria transmission dynamics using time series data

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Infectious Diseases of Poverty 2020年第4期9卷 84-96页

作者： Benyun Shi Shan Lin Qi Tan Jie Cao Xiaohong Zhou Shang Xia Xiao-Nong Zhou Jiming Liu School of Computer Science and Technology Nanjing Tech UniversityNanjing 211800JiangsuChina College of Information Engineering Nanjing University of Finance&EconomicsNanjing 210003JiangsuChina Department of Computer Science Hong Kong Baptist UniversityKowloonHong Kong Department of Pathogen Biology School of Public HealthSouthern Medical UniversityGuangzhou 510515GuangdongChina National Institute of Parasitic Diseases Chinese Center for Diseases Control and PreventionShanghai 200025China Key Laboratory of Parasite and Vector Biology National Health Commission of the People Republic of ChinaShanghai 200025China Chinese Center for Tropical Disease Research Shanghai 200025China World Health Organization Collaborating Centre for Tropical Diseases Shanghai 200025China

Background:Disease surveillance systems are essential for effective disease intervention and control by monitoring disease prevalence as time *** evaluate the severity of an epidemic,statistical methods are widely used to forecast the trend,seasonality,and the possible number of infections of a ***,most statistical methods are limited in revealing the underlying dynamics of disease transmission,which may be affected by various impact factors,such as environmental,meteorological,and physiological *** this study,we focus on investigating malaria transmission dynamics based on time series ***:A data-driven nonlinear stochastic model is proposed to infer and predict the dynamics of malaria transmission based on the time series of prevalence ***,the dynamics of malaria transmission is modeled based on the notion of vectorial capacity(VCAP)and entomological inoculation rate(EIR).A particle Markov chain Monte Carlo(PMCMC)method is employed to estimate the model ***,a one-step-ahead prediction method is proposed to project the number of future malaria ***,two case studies are carried out on the inference and prediction of Plasmodium vivax transmission in Tengchong and Longling,Yunnan province,***:The results show that the trained data-driven stochastic model can well fit the historical time series of *** prevalence data in both counties from 2007 to ***,with well-trained model parameters,the proposed one-step-ahead prediction method can achieve better performances than that of the seasonal autoregressive integrated moving average model with respect to predicting the number of future malaria ***:By involving dynamically changing impact factors,the proposed data-driven model together with the PMCMC method can successfully(i)depict the dynamics of malaria transmission,and(ii)achieve accurate one-step-ahead prediction about malaria *** a data-

关键词： malaria prediction dynamics

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Master equations and quantum trajectories for squeezed wave packets

arXiv

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arXiv 2021年

作者： Gross, Jonathan A. Baragiola, Ben Stace, T.M. Combes, Joshua Center for Quantum Information and Control University of New Mexico AlbuquerqueNM87131-0001 United States Institut Quantique Université de Sherbrooke SherbrookeQCJ1K 2R1 Canada Centre for Quantum Computation & Communication Technology School of Science RMIT University MelbourneVIC3000 Australia ARC Centre for Engineered Quantum System Department of Physics University of Queensland BrisbaneQLD4072 Australia Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO80309 United States

The interaction between matter and squeezed light has mostly been treated within the approximation that the field correlation time is small. Methods for treating squeezed light with more general correlations currently involve explicitly modeling the systems producing the light. We develop a general purpose input-output theory for a particular form of narrowband squeezed light-a squeezed wave-packet mode-that only cares about the statistics of the squeezed field and the shape of the wave packet. This formalism allows us to derive the input-output relations and the master equation. We also consider detecting the scattered field using photon counting and homodyne measurements which necessitates the derivation of the stochastic master equation. The non Markovian nature of the field manifests itself in the master equation as a coupled hierarchy of equations. We illustrate these with consequences for the decay and resonance fluorescence of two-level atoms in the presence of such fields. Copyright © 2021, The Authors. All rights reserved.

关键词： Wave packets

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Evaluation and Diagnosis of Brain Death: a Non-Invasive Pilot Study Using Functional Near-Infrared Spectroscopy (fNIRS)

Evaluation and Diagnosis of Brain Death: a Non-Invasive Pilo...

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Iranian Conference on Electrical engineering (ICEE)

作者： Abolfazl Arabkoohi Seyed Kamaledin Setarehdan Farahnaz Sadegh Beigee Alireza Jahangirifard Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Organ Procurement Unit Lung Transplantation Research Center National Research Institute of Tuberculosis and Lung Diseases Shahid Beheshti University of Medical Sciences Tehran Iran Lung Transplantation Research Center National Research Institute of Tuberculosis and Lung Diseases Shahid Beheshti University of Medical Sciences Tehran Iran

ISBN: (数字)9781728172965

ISBN: (纸本)9781728172972

Brain death is an irreversible event for the brain that the brain and brainstem functions are destroyed. Conventional methods of diagnosing and evaluating brain death are time-consuming, costly and in many cases dangerous. Alternative monitoring systems also have several limitations. The functional near-infrared spectroscopy (fNIRS) can track oxyhemoglobin and deoxyhemoglobin changes of the tissue using scattering and light absorption properties of the brain and blood components. The spatially resolved spectroscopy (SRS) method can additionally measure tissue oxygen index (TOI). We have collected data from several brain death patients using our non-invasive fNIRS portable system. Initially, we have observed that in brain dead patients, oxyhemoglobin and deoxyhemoglobin changes are quasiperiodic. Moreover, their TOI change has a very low standard deviation and was quasi-periodic. These results can be biomarkers for diagnosis, evaluation, and validation of brain death.

关键词： Spectroscopy Spatial resolution Standards Monitoring Indexes Electroencephalography Detectors

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Distribution system state estimation: an overview of recent developments

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Frontiers of Information Technology & Electronic engineering 2019年第1期20卷 4-17页

作者： Wang Gang Giannakis Georgios B Chen Jie Sun Jian Department of Electrical and Computer Engineering and Digital Technology Center University of Minnesota USA Minneapolis MN 55455 School of Automation Beijing Institute of Technology Beijing 100081 Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081

In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of (industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate, fast, and robust power system state estimation (PSSE). Nonetheless, most real-time data available in the current and upcoming transmission/distribution systems are nonlinear in power system states (i.e., nodal voltage phasors). Scalable approaches to dealing with PSSE tasks undergo a paradigm shift toward addressing the unique modeling and computational challenges associated with those nonlinear measurements. In this study, we provide a contemporary overview of PSSE and describe the current state of the art in the nonlinear weighted least-squares and least-absolutevalue PSSE. To benchmark the performance of unbiased estimators, the Cramer-Rao lower bound is developed. Accounting for cyber attacks, new corruption models are introduced, and robust PSSE approaches are outlined as well. Finally, distribution system state estimation is discussed along with its current challenges. Simulation tests corroborate the effectiveness of the developed algorithms as well as the practical merits of the theory.

关键词： State estimation Cramer-Rao bound Feasible point pursuit Semidefinite relaxation Proximal linear algorithm Composite optimization Cyber attack Bad data detection

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A case-study of NIRS application for infant cerebral hemodynamic monitoring: A report of data analysis for feature extraction and infant classification into healthy and unhealthy

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Informatics in Medicine Unlocked 2018年 11卷 44-50页

作者： Rahimpour, Ali Noubari, Hosein Ahmadi Kazemian, Mohammad Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Neonatal Health Research Center Research Institute for Children Health Shahid Beheshti University of Medical Sciences Tehran Iran Electrical and Computer Engineering University of British Colombia Canada

This paper reports the results of cerebral hemodynamic data analysis gathered from infant foreheads in selected case-studies. These studies were utilized for extracting relevant features aimed for discriminatory classification of infants into healthy and unhealthy cases. The principal objective of the study was to examine the effectiveness and accuracy of using near-infrared spectroscopy for measuring cerebral blood flow in infants and for health monitoring purposes. A total of 41 infants from several age groups varying from 2 h to several days since birth were participated for experimental data recordings. Both healthy and unhealthy infants of similar age groups were selected for the study. Selection was made without consideration as to the type of disorder in unhealthy infants, or any consideration as to being under any particular monitoring action. Data were collected during the rest state with no external stimulus. Several data analysis approaches were applied including temporal and time-frequency analyses, which were used for feature extraction of hemodynamic data and for identifying selective features to be used during data classification. We utilized SVM for pattern recognition and feature extraction aimed at discriminatory classification of healthy infants from unhealthy cases. This was followed by the application of the t-test for statistical analysis and accuracy evaluation. The results show a 94% accuracy in classification. A clear relationship was also found between oxy- and deoxy-hemoglobin concentration data belonging to healthy infants as shown in 2D data clustering illustration that can be used for infant classification. Similar correlation results were also observed with other physiological data. © 2018

关键词： Brain hemodynamic response Functional near-infrared spectroscopy Oxy- and deoxy hemoglobin concentration Support vector machine T-test

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Comprehensive and Comparative Study of Drowning Person Detection and Rescue Systems

Comprehensive and Comparative Study of Drowning Person Detec...

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International Conference on Information and Communication Technologies (ICICT)

作者： Muhammad Aftab Hayat Goutian Yang Atif Iqbal Adeel Saleem Muhammad Mateen School of Control & Computer Engineering North China Electric power University Beijing China School of Renewable Energy & Clean Energy North China Electric Power University Beijing China University of Lahore Lahore Pakistan

ISBN: (数字)9781728123349

ISBN: (纸本)9781728123356

Drowning is the leading cause of death for the past few years because of this. It has become a severe problem for progressive countries. This article is the extensive surveys of current progress made towards the swimming pool rescue systems by using different techniques. Different aspects of human drowning systems are discussed here with their core technologies and applications. Various methodologies have been adopted for autonomous swimming pool safety with their intelligent control systems. Different detection and rescue techniques like motion detection, pressure sensing, image processing, heartbeat sensing, video-based detection, HSV based detection, and LASER-LDR mechanism have been used. Our analysis, which aims to provide a broad state-of-the-art review of the field which also addresses different challenges related to these systems and applications. Additionally, in this evaluation, several applications are debated in excessive detail. This article is the scrutiny of used approaches and an intermingling which can quickly and accurately implemented. Finally, after surveying the territory, we have also mentioned the future scope of research which can be fulfilled the requirements of the domestic swimming pools. At the end we also proposed our model which can satisfy all the flaws present in the existing pool rescue systems.

关键词： Cameras Monitoring Biomedical monitoring Heart beat Radiofrequency identification Feature extraction

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