检索结果-内蒙古大学图书馆

Characterizing human collective behaviours of COVID-19 in Hong Kong

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Du, Zhanwei Zhang, Xiao Wang, Lin Yao, Sidan Bai, Yuan Tan, Qi Xu, Xiaoke Pei, Sen Xiao, Jingyi Tsang, Tim K. Liao, Qiuyan Lau, Eric H.Y. Wu, Peng Gao, Chao Cowling, Benjamin J. WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public Health Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Laboratory of Data Discovery for Health Hong Kong Science and Technology Park New Territories Hong Kong Department of Genetics University of Cambridge CambridgeCB2 3EH United Kingdom Singapore College of Information and Communication Engineering Dalian Minzu University Dalian China Department of Environmental Health Sciences Mailman School of Public Health Columbia University New YorkNY10032 United States Northwestern Polytechnical University Xi'An China

People are likely to engage in collective behaviour online during extreme events, such as the COVID-19 crisis, to express their awareness, actions and concerns. Hong Kong has implemented stringent public health and social measures (PHSMs) to curb COVID-19 epidemic waves since the first COVID-19 case was confirmed on 22 January 2020. People are likely to engage in collective behaviour online during extreme events, such as the COVID-19 crisis, to express their awareness, actions and concerns. Here, we offer a framework to evaluate interactions among individuals' emotions, perception, and online behaviours in Hong Kong during the first two waves (February to June 2020) and found a strong correlation between online behaviours of Google search and the real-time reproduction numbers. To validate the model output of risk perception, we conducted 10 rounds of cross-sectional telephone surveys from February 1 through June 20 in 2020 to quantify risk perception levels over time. Compared with the survey results, the estimates of the risk perception of individuals using our network-based mechanistic model capture 80% of the trend of people's risk perception (individuals who worried about being infected) during the studied period. We may need to reinvigorate the public by engaging people as part of the solution to live their lives with reduced risk. © 2022, CC BY-NC-ND.

关键词： Risk perception

Limitations on sharing bell nonlocality between sequential pairs of observers

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Cheng, Shuming Liu, Lijun Baker, Travis J. Hall, Michael J.W. The Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai201804 China Institute for Advanced Study Tongji University Shanghai200092 China College of Mathematics and Computer Science Shanxi Normal University Linfen041000 China Centre for Quantum Computation and Communication Technology Australian Research Council Centre for Quantum Dynamics Griffith University BrisbaneQLD4111 Australia Department of Theoretical Physics Research School of Physics Australian National University CanberraACT0200 Australia

We give strong analytic and numerical evidence that, under mild measurement assumptions, two qubits cannot both be recycled to generate Bell nonlocality between multiple independent observers on each side. This is surprising, as under the same assumptions it is possible to recycle just one of the qubits an arbitrarily large number of times [P. J. Brown and R. Colbeck, Phys. Rev. Lett. 125, 090401 (2020)]. We derive corresponding 'one-sided monogamy relations' that rule out two-sided recycling for a wide range of parameters, based on a general tradeoff relation between the strengths and maximum reversibilities of qubit measurements. We also show if the assumptions are relaxed to allow sufficiently biased measurement selections, then there is a narrow range of measurement strengths that allows two-sided recycling for two observers on each side, and propose an experimental test. Our methods may be readily applied to other types of quantum correlations, such as steering and entanglement, and hence to general information protocols involving sequential measurements. Copyright © 2021, The Authors. All rights reserved.

关键词： Recycling

Sensing Matrix Design for MMV Compressive Sensing: An MVDR Approach

学校读者我要写书评

暂无评论

Sensing Matrix Design for MMV Compressive Sensing: An MVDR A...

作者： Zhang, Liang Huang, Lei Li, Bo Yin, Jingwei Bao, Weimin School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Nuance Communication Inc. MontrealQCH3A 3S7 Canada Key Laboratory of Marine Information Acquisition and Security Ministry of Industry and Information Technology Harbin150001 China Science and Technology Council China Aerospace Science and Technology Cooperation Beijing100048 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin150001 China

Compressive sensing (CS) has been widely used in vehicular technology including compressive spectrum sensing, sparse channel estimation, and vehicular communications. The complete procedure of CS consists of sparse representation, sparse measurement, and sparse recovery. In the CS, measurement matrix Φ is utilized to sample a sparse signal, while sensing matrix Ψ is exploited for sparse recovery. It is important to properly design the sensing matrix that significantly affects the signal recovery performance. This paper addresses the issue of the sensing matrix design for the CS with multiple measurement vectors (MMV). We first study sufficient conditions for the MMV-CS and then develop three sensing matrix design approaches in different situations. The proposed methods have their roots in the principle of minimum variance distortionless response (MVDR) beamformer. This allows us to optimally determine the sensing matrix in the framework of the MVDR, which amounts to minimizing local cumulative cross-coherence between Φ and Ψ while keeping the columns of Ψ highly correlated to their counterparts of Φ. Simulation results are presented to verify the effectiveness of the proposed methods. © 1967-2012 IEEE.

关键词： Compressed sensing

Comparison of Different Machine Learning Methods to Forecast Air Quality Index 6th

学校读者我要写书评

暂无评论

Comparison of Different Machine Learning Methods to Forecast...

6th International Conference on Frontier Computing, FC 2018

作者： Liu, Bo Shi, Chao Li, Jianqiang Li, Yong Lang, Jianlei Gu, Rentao Beijing Advanced Innovation Center for Future Internet Technology Beijing University of Technology Beijing100124 China Faculty of Information Technology School of Software Engineering Beijing University of Technology Beijing100124 China Key Laboratory of Beijing on Regional Air Pollution Control Beijing University of Technology Beijing100124 China College of Environmental and Energy Engineering Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789811336478

In recent years, more and more people have been plagued by respiratory diseases. The air quality, which is characterized by inhalable particles and fine particles, has attracted increasing attention. Accurately monitor and forecast the quality of air could not only help the government conduct interventions to alleviate the air pollution earlier, but also alert relevant people who suffer from respiratory diseases. In order to develop effective Air Quality Index (AQI) prediction models, this paper compared the performance of different Machine Learning (ML) methods and feature selection methods. First the air quality data in Beijing from 2016 to 2017 were collected. Then Multi-Linear Regression (MLR), Random forest Regression (RFR), BP Neural Network (BPNN) and Support Vector Regression (SVR) algorithm were trained on 10-fold cross validation. Correlation coefficient (R), mean absolute error (MAE) and root mean square error (RMSE) were used as evaluation metrics. The experimental results showed that the performance of SVR and BPNN were similarly well. MLR had the worst performance, which was possibly caused by a small feature dimension, and RFR had higher accuracy and better generalization capability than the other models, probably because the algorithm of regression tree in random forest included the interaction of variables. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Decision trees

SigNet: A novel deep learning framework for radio signal classification

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Chen, Zhuangzhi Cui, Hui Xiang, Jingyang Qiu, Kunfeng Huang, Liang Zheng, Shilian Chen, Shichuan Xuan, Qi Yang, Xiaoniu The Institute of Cyberspace Security The College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The College of Computer Science and Technology Zhejiang University of Technology Hangzhou China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518000 China The Utron Technology Co. Ltd. [as Hangzhou Qianjiang Distinguished Expert Hangzhou310056 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China

Deep learning methods achieve great success in many areas due to their powerful feature extraction capabilities and end-to-end training mechanism, and recently they are also introduced for radio signal modulation classification. In this paper, we propose a novel deep learning framework called SigNet, where a signal-to-matrix (S2M) operator is adopted to convert the original signal into a square matrix first and is co-trained with a follow-up CNN architecture for classification. This model is further accelerated by integrating 1D convolution operators, leading to the upgraded model SigNet2.0. The simulations on two signal datasets show that both SigNet and SigNet2.0 outperform a number of well-known baselines. More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided. They can achieve a relatively high accuracy even when 1% training data are kept, while other baseline models may lose their effectiveness much more quickly as the datasets get smaller. Such result suggests that SigNet/SigNet2.0 could be extremely useful in the situations where labeled signal data are difficult to obtain. The visualization of the output features of our models demonstrates that our model can well divide different modulation types of signals in the feature hyper-space. Copyright © 2020, The Authors. All rights reserved.

关键词： Matrix algebra

Tree-based feature transformation for purchase behavior prediction

学校读者我要写书评

暂无评论

Tree-based feature transformation for purchase behavior pred...

作者： Hou, Chunyan Chen, Chen Wang, Jinsong School of Computer and Communication Engineering Tianjin University of Technology Tianjin China College of Computer and Control Engineering Nankai University Tianjin China

In the era of e-commerce, purchase behavior prediction is one of the most important issues to promote both online companies' sales and the consumers' experience. The previous researches usually use the feature engineering and ensemble machine learning algorithms for the prediction. The performance really depends on designed features and the scalability of algorithms because the large-scale data and a lot of categorical features lead to huge samples and the high-dimensional feature. In this study, we explore an alternative to use tree-based Feature Transformation (FT) and simple machine learning algorithms (e.g. Logistic Regression). Random Forest (RF) and Gradient Boosting decision tree (GB) are used for FT. Then, the simple algorithm, rather than ensemble algorithms, is used to predict purchase behavior based on transformed features. Tree-based FT regards the leaves of trees as transformed features, and can learn high-order interactions among original features. Compared with RF, if GB is used for FT, simple algorithms are enough to achieve better performance. © 2018 The Institute of Electronics, Information and communication Engineers.

关键词： Decision trees

Computer-aided diagnosis for Diabetic Retinopathy based on Firefly algorithm

学校读者我要写书评

暂无评论

Computer-aided diagnosis for Diabetic Retinopathy based on F...

International Conference on Advanced Computing, ICAC

作者： B. Keerthiveena T. Veerakumar S. Esakkirajan Badri Narayan Subudhi Department of Instrumentation and Control Systems Engineering PSG College of Technology Coimbatore India Department of Electronics and Communication Engineering National Institute of Technology Goa India Department of Electrical Engineering Indian Institute of Technology Jammu India

ISBN: (数字)9781728152868

ISBN: (纸本)9781728152875

Diabetic retinopathy is a diabetes complication that has been a leading cause of blindness worldwide. Segmentation of blood vessels and detection of exudates in fundus images through an automated system will help the ophthalmologist to provide a proper treatment that may cure or decrease the severity of the retinal diseases. Realizing its significance, a diabetic retinopathy screening system was proposed in this paper which classifies normal from the abnormal fundus image. Segmentation of blood vessel is based on match filter followed by fuzzy c-means clustering. The small and thin vessels were obtained from directional filter bank by incorporating the use of line-like directional features. In this study a combination of both statistical and geometric features were extracted from image regions. A firefly algorithm for discriminative feature selection for the early detection of diabetic retinopathy was proposed. Publically available dataset such as DRIVE, HRF, DIAREDB1, MESSIDOR and one local dataset are used to validate the suggested system. Moreover, the proposed algorithm has been compared with few state-of-the-art techniques such as particle swarm optimization, genetic algorithm and ant colony optimization. The results demonstrated that the proposed method improves the classification accuracy with a minimized feature set for the early detection of diabetic retinopathy.

关键词： Diabetes Blood vessels Biomedical imaging Retinopathy Feature extraction Retina Filter banks

Robust Design of Transmit/Receive Beamforming for Multiple-input Multiple-output Radar

学校读者我要写书评

暂无评论

Robust Design of Transmit/Receive Beamforming for Multiple-i...

2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019

作者： Qian, Junhui Luo, Yu Fu, Ning Liu, Ran Feng, Zeyu School of Microelectronic and Communication Engineering Chongqing University Chongqing China Harbin Institute of Technology Department of Automatic Test and Control Harbin China College of Computer Science Chongqing University Chongqing China Chengdu Surveying Geotechnical Research Institute Co. Ltd. of MCC Chengdu China

ISBN: (纸本)9781728123455

The transmit/receive robust adaptive beamformer design of a multiple-input multiple-output (MIMO) radar system is studied in this paper. We first construct the interference covariance matrix via a shrink-based approach. Then, the desired transmit-receive steering vector (SV) is estimated through maximizing the output power upon the cooperative constraint and norm constraints under the presumed SV. An iterative procedure is designed to deal with the original nonconvex problem, whose convergence is analytically proven with the estimated SV. Simulation results show the merits of the proposed design in various scenarios. © 2019 IEEE.

关键词： MIMO radar

3D facial geometry recovery from a depth view with attention guided generative adversarial network

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Cai, Xiaoxu Yu, Hui Lou, Jianwen Zhang, Xuguang Li, Gongfa Dong, Junyu School of Creative Technologies University of Portsmouth PortsmouthPO1 2DJ United Kingdom School of Communication Engineering Hangzhou Dianzi University Hangzhou310018 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Information Science and Engineering Ocean University of China Qingdao266100 China

We present to recover the complete 3D facial geometry from a single depth view by proposing an Attention Guided Generative Adversarial Networks (AGGAN). In contrast to existing work which normally requires two or more depth views to recover a full 3D facial geometry, the proposed AGGAN is able to generate a dense 3D voxel grid of the face from a single unconstrained depth view. Specifically, AGGAN encodes the 3D facial geometry within a voxel space and utilizes an attention-guided GAN to model the ill-posed 2.5D depth-3D mapping. Multiple loss functions, which enforce the 3D facial geometry consistency, together with a prior distribution of facial surface points in voxel space are incorporated to guide the training process. Both qualitative and quantitative comparisons show that AGGAN recovers a more complete and smoother 3D facial shape, with the capability to handle a much wider range of view angles and resist to noise in the depth view than conventional methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks