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On Stochastic Error and Computational Efficiency of the Markov Chain Monte Carlo Method

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Communications in Computational Physics 2014年第7期16卷 467-490页

作者： Jun li Philippe Vignal Shuyu Sun victor M.Calo Center for Numerical Porous Media King Abdullah University of Science and TechnologyThuwalSaudi Arabia Material Science and Engineering King Abdullah University of Science and TechnologyThuwalSaudi Arabia AppliedMathematics and Computational Science Earth Sciences and EngineeringKing Abdullah University of Science and TechnologyThuwalSaudi Arabia.

InMarkov ChainMonte Carlo(MCMC)simulations,thermal equilibria quantities are estimated by ensemble average over a sample set containing a large number of correlated *** samples are selected in accordance with the probability distribution function,known from the partition function of equilibrium *** the stochastic error of the simulation results is significant,it is desirable to understand the variance of the estimation by ensemble average,which depends on the sample size(i.e.,the total number of samples in the set)and the sampling interval(i.e.,cycle number between two consecutive samples).Although large sample sizes reduce the variance,they increase the computational cost of the *** a given CPU time,the sample size can be reduced greatly by increasing the sampling interval,while having the corresponding increase in variance be negligible if the original sampling interval is very *** this work,we report a few general rules that relate the variance with the sample size and the sampling *** results are observed and confirmed *** variance rules are derived for theMCMCmethod but are also valid for the correlated samples obtained using other Monte Carlo *** main contribution of this work includes the theoretical proof of these numerical observations and the set of assumptions that lead to them.

关键词： Phase coexistence Gibbs ensemble molecular simulation Markov Chain Monte Carlo method variance estimation blocking method

Novel DDoS Feature Representation Model Combining Deep Belief Network and Canonical Correlation Analysis

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Computers, Materials & Continua 2019年第8期61卷 657-675页

作者： Chen Zhang Jieren Cheng Xiangyan Tang victor SSheng Zhe Dong Junqi li College of Information Science&Technology Hainan UniversityHaikou570228China State Key Laboratory of Marine Resource Utilization in South China Sea Haikou570228China Key Laboratory of Internet Information Retrieval of Hainan Province Hainan UniversityHaikouChina Department of Computer Science University of Central ArkansasConwayAR 72035USA

Distributed denial of service(DDoS)attacks launch more and more frequently and are more *** representation as an important part of DDoS defense technology directly affects the efficiency of *** DDoS feature extraction methods cannot fully utilize the information of the original data,resulting in the extracted features losing useful *** this paper,a DDoS feature representation method based on deep belief network(DBN)is *** quantify the original data by the size of the network flows,the distribution of IP addresses and ports,and the diversity of packet sizes of different protocols and train the DBN in an unsupervised manner by these quantified *** feedforward neural networks(FFNN)are initialized by the trained deep belief network,and one of the feedforward neural networks continues to be trained in a supervised *** canonical correlation analysis(CCA)method is used to fuse the features extracted by two feedforward neural networks per *** show that compared with other methods,the proposed method can extract better features.

关键词： Deep belief network DDoS feature representation canonical correlation analysis

Few-Shot Object Detection Based on the Transformer and High-Resolution Network

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Computers, Materials & Continua 2023年第2期74卷 3439-3454页

作者： Dengyong Zhang Huaijian Pu Feng li Xiangling Ding victor S.Sheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China Department of Computer Science Texas Tech UniversityLubbock79409TXUSA

Now object detection based on deep learning tries different *** uses fewer data training networks to achieve the effect of large dataset ***,the existing methods usually do not achieve the balance between network parameters and training *** makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection *** improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the *** and spatial attention are used to make the network focus on features that are more useful to the *** addition,the recently popular transformer is used to fuse the features of the existing *** compensates for the previous network failure by making full use of existing object *** on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.

关键词： Object detection few shot object detection transformer high-resolution

Small Object Detection via Precise Region-Based Fully Convolutional Networks

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Computers, Materials & Continua 2021年第11期69卷 1503-1517页

作者： Dengyong Zhang Jiawei Hu Feng li Xiangling Ding Arun Kumar Sangaiah victor SSheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China School of Computing Science and Engineering Vellore Institute of Technology(VIT)Vellore632014India Department of Computer Science Texas Tech UniversityLubbock79409TXUSA

In the past several years,remarkable achievements have been made in the field of object *** performance is generally improving,the accuracy of small object detection remains low compared with that of large object *** addition,localization misalignment issues are common for small objects,as seen in GoogLeNets and residual networks(ResNets).To address this problem,we propose an improved region-based fully convolutional network(R-FCN).The presented technique improves detection accuracy and eliminates localization misalignment by replacing positionsensitive region of interest(PS-RoI)pooling with position-sensitive precise region of interest(PS-Pr-RoI)pooling,which avoids coordinate quantization and directly calculates two-order integrals for position-sensitive score maps,thus preventing a loss of spatial precision.A validation experiment was conducted in which the Microsoft common objects in context(MS COCO)training dataset was *** showed an accuracy improvement of 3.7%for object detection tasks and an increase of 6.0%for small objects.

关键词： Small object detection precise R-FCN PS-Pr-RoI pooling two-stage detector

Application and evaluation of payment channel in hybrid decentralized ethereum token exchange

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Blockchain(Research and Applications) 2020年第1期1卷 1-17页

作者： Xuan Luo Zehua Wang Wei Cai Xiuhua li victor CM.Leung Department of Electrical and Computer Engineering The University of British ColumbiaVancouverV6T 1Z4Canada Blockchain@UBC The University of British ColumbiaVancouverV6T 1Z4Canada The Chinese University of Hong Kong Shenzhen518172China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518100China Chongqing University Chongqing400044China Shenzhen University Shenzhen518061China

Traditional centralized token exchange(CEX)has been suffering from hacking due to the centralized management of users’*** contrast,decentralized token exchange(DEX)maintains users’assets by smart contracts in a decentralized manner,but introduces additional overhead in terms of gas fee and transaction confirmation *** decentralized token exchange(HEX)has been proposed to combine the benefits of CEX and ***,existing HEX is criticized for two ***,trading transactions are time-consuming and expensive for frequent token ***,excessive simultaneous transactions might cause the pending transaction congestion in the Ethereum *** this paper,we propose a payment channel based HEX,which extends existing solutions by adding a new payment channel layer to benefit frequent traders and alleviate the pending transaction ***,we propose the very first gas-price ***-latency function to guide Ethereum transaction issuers to choose an optimal gas price that minimizes the overall *** simulations are conducted to compare the cost in the proposed HEX with that in the conventional *** results demonstrate the effectiveness of our proposed mechanism in terms of reducing gas fees and transaction confirmation latency for frequent traders as well as the pending transaction congestion in Ethereum.

关键词： Blockchain Payment channel Ethereum Smart contract Token exchange Optimal gas price

Temperature-induced Evolution of Micro-structure and Chain Dynamics in Thermoplastic Polyurethane with Low Hard Segment Content as Studied by In Situ Synchrotron SAXS and Time-Domain NMR Experiments

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Chinese Journal of Polymer Science 2023年第12期41卷 1902-1911,I0008页

作者： Hong-Ru Wang victor litvinov Wei Yu Kar Wee Eddie Peh Qian-li Yu Yong-Feng Men State Key Laboratory of Polymer Physics and Chemistry Changchun Institute of Applied ChemistryChinese Academy of SciencesChangchun 130022China School of Applied Chemistry and Engineering University of Science and Technology of ChinaHefei 230026China BASF Polyurethane Specialties(China)Co.Ltd. Shanghai 200137China V.lit.Consult Gozewijnstraat 46191 WV Beekthe Netherlands

The microstructural evolution of a thermoplastic polyurethane(TPU)with low hard segment content has been monitored utilizing in situ real-time synchrotron small angle X-ray scattering(SAXS)and time-domain nuclear magnetic resonance(NMR)*** TPU is composed of 23 wt% of[4,4-methylenediphenyl diisocyanate(MDI)]-[1,4-butanediol(BD)]chain segments,which form hard domains,as[polytetrahydrofuran(PTHF)]forming soft *** number and distribution of monomer units in hard blocks is determined by the successive self-nucleation and annealing thermal fractionation *** situ SAXS method reveals heating-induced increase in the spacing of hard and soft domains,while time-domain ^(1)H-NMR characterizes the changes in the phase composition and chain dynamics in these domains.A glassy fraction of short MDI-BD chain segments in hard domains passes through T_(g) above ambient *** higher temperatures,MDI-BD nanocrystals start to *** length distribution of MDI-BD chain segments causes a distribution in crystal sizes and wide melting temperature *** melting is accompanied by the mixing of MDI-BD with PTHF segments in soft domains,and by increase in segmental mobility in these *** 180℃,the TPU melt is homogeneous on the scale above nanometers according to SAXS data.

关键词： Phase separation Thermoplastic polyurethane Melting behavior Time-domain NMR SAXS

Efficient sampling methods for characterizing POIs on maps based on road networks

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Frontiers of Computer Science 2018年第3期12卷 582-592页

作者： Ziting ZHOU Pengpeng ZHAO victor S. SHENG Jiajie XU Zhixu li Jian WU Zhiming CUI School of Computer Science and Technology Soochow University Suzhou 215006 China Department of Computer Science University of Central Arkansas Conway AR 72035 USA Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing 210044 China

With the rapid development of location-based services, a particularly important aspect of start-up marketing research is to explore and characterize points of interest （PoIs） such as restaurants and hotels on maps. However, due to the lack of direct access to PoI databases, it is necessary to rely on existing APIs to query Pols within a region and calculate PoI statistics. Unfortunately, public APIs generally im- pose a limit on the maximum number of queries. Therefore, we propose effective and efficient sampling methods based on road networks to sample PoIs on maps and provide unbiased estimators for calculating PoI statistics. In general, the more intense the roads, the denser the distribution of PoIs is within a region. Experimental results show that compared with state-of-the-art methods, our sampling methods improve the efficiency of aggregate statistical estimations.

关键词： sampling aggregate statistical estimation roadnetworks

Image Inpainting Detection Based on High-Pass Filter Attention Network

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Computer Systems Science & Engineering 2022年第12期43卷 1145-1154页

作者： Can Xiao Feng li Dengyong Zhang Pu Huang Xiangling Ding victor S.Sheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China Department of Computer Science Texas Tech UniversityLubbock79409USA

Image inpainting based on deep learning has been greatly *** original purpose of image inpainting was to repair some broken photos, suchas inpainting artifacts. However, it may also be used for malicious operations,such as destroying evidence. Therefore, detection and localization of imageinpainting operations are essential. Recent research shows that high-pass filteringfull convolutional network (HPFCN) is applied to image inpainting detection andachieves good results. However, those methods did not consider the spatial location and channel information of the feature map. To solve these shortcomings, weintroduce the squeezed excitation blocks (SE) and propose a high-pass filter attention full convolutional network (HPACN). In feature extraction, we apply concurrent spatial and channel attention (scSE) to enhance feature extraction and obtainmore information. Channel attention (cSE) is introduced in upsampling toenhance detection and localization. The experimental results show that the proposed method can achieve improvement on ImageNet.

关键词： Image inpainting detection spatial attention channel attention full convolutional network high-pass filter

An Improved BPNN Prediction Method Based on Multi-Strategy Sparrow Search Algorithm

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Computers, Materials & Continua 2023年第2期74卷 2789-2802页

作者： Xiangyan Tang Dengfang Feng KeQiu li Jingxin liu Jinyang Song victor S.Sheng College of Intelligence and Computing Tianjin UniversityTianjin300350China School of Computer Science and Technology Hainan UniversityHaikou570228China School of Cyberspace Security(School of Cryptology) Hainan UniversityHaikou570228China Department of Computer Science Texas Tech University TX 79409USA

Data prediction can improve the science of decision-making by making predictions about what happens in daily life based on natural law *** propagation(BP)neural network is a widely used prediction *** reduce its probability of falling into local optimum and improve the prediction accuracy,we propose an improved BP neural network prediction method based on a multi-strategy sparrow search algorithm(MSSA).The weights and thresholds of the BP neural network are optimized using the sparrow search algorithm(SSA).Three strategies are designed to improve the SSA to enhance its optimization-seeking ability,leading to the MSSA-BP prediction *** MSSA algorithm was tested with nine different types of benchmark functions to verify the optimization performance of the *** different datasets were selected for comparison experiments on three groups of *** the same conditions,the mean absolute error(MAE),root mean square error(RMSE),andmean absolute percentage error(MAPE)of the prediction results of MSSA-BPwere significantly reduced,and the convergence speed was significantly ***-BP can effectively improve the prediction accuracy and has certain application value.

关键词： Prediction parrow search algorithm back propagation neural network