Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism

Readout errors caused by measurement noise are a significant source of errors in quantum circuits,which severely affect the output results and are an urgent problem to be solved in noisy-intermediate scale quantum(NISQ)computing.In this paper,we use the bit-flip averaging(BFA)method to mitigate frequent readout errors in quantum generative adversarial networks(QGAN)for image generation,which simplifies the response matrix structure by averaging the qubits for each random bit-flip in advance,successfully solving problems with high cost of measurement for traditional error mitigation methods.Our experiments were simulated in Qiskit using the handwritten digit image recognition dataset under the BFA-based method,the Kullback-Leibler(KL)divergence of the generated images converges to 0.04,0.05,and 0.1 for readout error probabilities of p=0.01,p=0.05,and p=0.1,respectively.Additionally,by evaluating the fidelity of the quantum states representing the images,we observe average fidelity values of 0.97,0.96,and 0.95 for the three readout error probabilities,respectively.These results demonstrate the robustness of the model in mitigating readout errors and provide a highly fault tolerant mechanism for image generation models.

Monitoring subsidence rates along road network by persistent scatterer SAR interferometry with high-resolution TerraSAR-X imagery

Ground subsidence is one of the key factors damaging transportation facilities, e.g., road networks consisting of highways and railways. In this paper, we propose to apply the persistent scatterer synthetic aperture radar interferometry （PS-InSAR） approach that uses high- resolution TerraSAR-X （TSX） imagery to extract the regional scale subsidence rates （i.e., average annual sub- sidence in mm/year） along road networks. The primary procedures involve interferometric pair selection, interfer- ogram generation, persistent scatterer （PS） detection, PS networking, phase parameterization, and subsidence rate estimation. The Xiqing District in southwest Tianjin （China） is selected as the study area. This district contains one railway line and several highway lines. A total of 15 TSX images covering this area between April 2009 and June 2010 are utilized to obtain the subsidence rates by using the PS-InSAR （PSI） approach. The subsidence rates derived from PSI range from -68.7 to -1.3 mm/year. These findings show a significantly uneven subsidence pattern along the road network. Comparison between the PSI-derived subsidence rates and the leveling data obtained along the highways shows that the mean and standard deviation （SD） of the discrepancies between the two types of subsidence rates are 0.1 and 4-3.2 mm/year, respectively. The results indicate that the high-resolution TSX PSI is capable of providing comprehensive and detailed subsidence information regarding road networks with millimeter-level accuracy. Further inspections under geo- logical conditions and land-use categories in the study area indicate that the observed subsidence is highly related to aquifer compression due to groundwater pumping. Therefore, measures should be taken to mitigate groundwater extraction for the study area.

Prediction of flow stress of Ti-15-3 alloy with artificial neural network

Hot compression experiments were conducted on Ti 15 3 alloy specimens using Gleeble 1500 Thermal Simulator.These tests were focused to obtain the flow stress data under various conditions of strain,strain rate and temperature. On the basis of these data, the predicting model for the nonlinear relation between flow stress and deformation strain,strain rate and temperature for Ti 15 3 alloy was developed with a back propagation artificial neural network method. Results show that the neural network can reproduce the flow stress in the sampled data and predict the nonsampled data well. Thus the neural network method has been verified to be used to tackle hot deformation problems of Ti 15 3 alloy. [

Cognitive amplify-and-forward dual-hop relay networks over α-μ fading channels

This paper provides an analytic performance evaluation of dual-hop cognitive amplify-and-forward (AF) relaying networks over independent nonidentically distributed (i.n.i.d.) fading channels. Two different transmit power constraint strategies at the secondary network are proposed to investigate the performance of the secondary network. In the case of combined power constraint,the maximum tolerable interference power on the primary network and the maximum transmit power at the secondary network are considered. Closed-form lower bound and its asymptotic expression for the outage probability (OP) are achieved. Utilizing the above results,average symbol error probability (ABEP) at high signal-to-noise ratios (SNRs) are also derived. In order to further study the performance of dual-hop cognitive AF relaying networks,the Closed-form lower bounds and asymptotic expressions for OP with single power constraint of the tolerable interference on the primary network is also obtained. Both analytical and simulation are employed to validate the accuracy of the theoretical analysis. The results show that the secondary network obtains a better performance when higher power constraint is employed.

A new algorithm for wireless sensor network based on NS-2

Considering wireless sensor network characteristics,this paper uses network simulator,version2(NS-2)algorithm to improve Ad hoc on-demand distance vector(AODV)routing algorithm,so that it can be applied to wireless sensor networks.After studying AODV routing protocol,a new algorithm called Must is brought up.This paper introduces the background and algorithm theory of Must,and discusses the details about how to implement Must algorithm.At last,using network simulator(NS-2),the performance of Must is evaluated and compared with that of AODV.Simulation results show that the network using Must algorithm has perfect performance.

Chaotic Neural Network Technique for "0-1" Programming Problems

0-1 programming is a special case of the integer programming, which is commonly encountered in many optimization problems. Neural network and its general energy function are presented for 0-1 optimization problem. Then, the 0-1 optimization problems are solved by a neural network model with transient chaotic dynamics (TCNN). Numerical simulations of two typical 0-1 optimization problems show that TCNN can overcome HNN's main drawbacks that it suffers from the local minimum and can search for the global optimal solutions in to solveing 0-1 optimization problems.

FPGA Optimized Accelerator of DCNN with Fast Data Readout and Multiplier Sharing Strategy

With the continuous development of deep learning,Deep Convolutional Neural Network(DCNN)has attracted wide attention in the industry due to its high accuracy in image classification.Compared with other DCNN hard-ware deployment platforms,Field Programmable Gate Array(FPGA)has the advantages of being programmable,low power consumption,parallelism,and low cost.However,the enormous amount of calculation of DCNN and the limited logic capacity of FPGA restrict the energy efficiency of the DCNN accelerator.The traditional sequential sliding window method can improve the throughput of the DCNN accelerator by data multiplexing,but this method’s data multiplexing rate is low because it repeatedly reads the data between rows.This paper proposes a fast data readout strategy via the circular sliding window data reading method,it can improve the multiplexing rate of data between rows by optimizing the memory access order of input data.In addition,the multiplication bit width of the DCNN accelerator is much smaller than that of the Digital Signal Processing(DSP)on the FPGA,which means that there will be a waste of resources if a multiplication uses a single DSP.A multiplier sharing strategy is proposed,the multiplier of the accelerator is customized so that a single DSP block can complete multiple groups of 4,6,and 8-bit signed multiplication in parallel.Finally,based on two strategies of appeal,an FPGA optimized accelerator is proposed.The accelerator is customized by Verilog language and deployed on Xilinx VCU118.When the accelerator recognizes the CIRFAR-10 dataset,its energy efficiency is 39.98 GOPS/W,which provides 1.73×speedup energy efficiency over previous DCNN FPGA accelerators.When the accelerator recognizes the IMAGENET dataset,its energy efficiency is 41.12 GOPS/W,which shows 1.28×−3.14×energy efficiency compared with others.

Design of Distributed Fault-Tolerant Industrial Network

A token-bus-based design method of the distributedfault-tolerant industrial network is presented in this pa-per.The dual-link network is of hot-redundancy.The performance of the network is also discussed.

Parameter Self - Learning of Generalized Predictive Control Using BP Neural Network

This paper describes the self—adjustment of some tuning-knobs of the generalized predictive controller(GPC).A three feedforward neural network was utilized to on line learn two key tuning-knobs of GPC,and BP algorithm was used for the training of the linking-weights of the neural network.Hence it gets rid of the difficulty of choosing these tuning-knobs manually and provides easier condition for the wide applications of GPC on industrial plants.Simulation results illustrated the effectiveness of the method.

Effects of information transmission delay and channel blocking on synchronization in scale-free Hodgkin-Huxley neuronal networks

In this paper,we investigate the evolution of spatiotemporal patterns and synchronization transitions in dependence on the information transmission delay and ion channel blocking in scale-free neuronal networks.As the underlying model of neuronal dynamics,we use the Hodgkin-Huxley equations incorporating channel blocking and intrinsic noise.It is shown that delays play a significant yet subtle role in shaping the dynamics of neuronal networks.In particular,regions of irregular and regular propagating excitatory fronts related to the synchronization transitions appear intermittently as the delay increases.Moreover,the fraction of working sodium and potassium ion channels can also have a significant impact on the spatiotemporal dynamics of neuronal networks.As the fraction of blocked sodium channels increases,the frequency of excitatory events decreases,which in turn manifests as an increase in the neuronal synchrony that,however,is dysfunctional due to the virtual absence of large-amplitude excitations.Expectedly,we also show that larger coupling strengths improve synchronization irrespective of the information transmission delay and channel blocking.The presented results are also robust against the variation of the network size,thus providing insights that could facilitate understanding of the joint impact of ion channel blocking and information transmission delay on the spatiotemporal dynamics of neuronal networks.

Integrating network pharmacology and pharmacological evaluation for deciphering the mechanism of(-)-epigallocatechin-3-gallate alleviating ethanol-induced endothelial cells injury

Objective To investigate the potential therapeutic targets and pharmacological mechanism of(-)-epigallocatechin-3-gallate(EGCG)based on network pharmacology and experimental verification.METHODS The druggability of EGCG was measured by the traditional Chinese medicine systems pharmacology(TCMSP)server,and potential targets of EGCG were identified by Pharm Mapper and Drug Repositioning and Adverse drug Reaction via Chemical-Protein Interactome(DRAR-CPI).The potential targets were imported into GeneMANIA database to obtain the protein-protein direct interaction network,and target physical interaction,co-expression,prediction,genetic interaction,and shared protein domains.The biological process,molecular functions,cellular components and KEGG signaling pathways of potential targets were analyzed using DAVID database.For further study,ethanol was used to establish a model of endothelial injury in vitro.The cell viability was assayed by MTT method,the cellular apoptosis was stained by Annexin V/PI,and the expression levels of Bcl-2,Bax and cleved-caspase-3 were tested by Western blotting.Then,JC-1 and nuclear translocation of NF-κB experiments were used to study the mitochondrial membrane potential and nuclear translocation.RESULTS The oral availability of EGCG was 55.09%(≥30%)and drug-like index was 0.77(≥0.18),which were considered pharmacokinetically active.17 potential targetable proteins of EGCG were predicted by Pharm Mapper and DRAR-CPI.Further research showed that 68.13%displayed similar co-expression characteristics,26.11%physical interactions,and 2.74%shared the same protein domain.The depth network analysis results showed that the biofunctions of EGCG were mainly by regulating glutathione derivative biosynthetic process,glutathione metabolic process,nitrogen compound metabolic process etc..via drug binding,catalytic activity,glutathione transferase activity,anion binding etc..in sarcoplasmic reticulum,spindle pole,microtubule cytoskeleton and cytoplasm.KEGG enrichment analysis showed that Glutathione metabolism,IL^(-1)7 signaling pathway,EGFR tyrosine kinase inhibitor resistance,PI3K-Akt signaling pathway and other pathways were involves in the biofunction of EGCG.The above analyses indicated that EGCG exerts its biofunction through antioxidant and anti-inflammatory mechanisms.The experimental results showed that ethanol 20.0 mmol·L^(-1) decreased cell viability,Bcl-2 expression,and increased cell apoptosis,the intracellular ROS,as well as the expression of Bax and cleaved-caspase-3 of human endothelial cells.However,treatment of the cells with EGCG can significantly alleviate ethanol induced endothelial cells injury.Further study showed that EGCG significantly alleviates ethanol induced mitochondrial depolarization and nuclear translocation of NF-κB.CONCLUSIONS EGCG exerts pharmacological efficacies on ethanol induced endothelial cell injury through multi-target,multi-function and multi-pathway mode.Protective effect of EGCG on ethanol induced cell injury was mainly through alteration of mitochondrial function and NF-κB translocation.Therefore,EGCG have great potential in protecting against endothelial dysfunction of the persons who are chronically abuse of ethanol.This study also provides a new understanding of EGCG in clinical application on cardiovascular and cerebrovascular diseases.

O(t^(-β))-SYNCHRONIZATION AND ASYMPTOTIC SYNCHRONIZATION OF DELAYED FRACTIONAL ORDER NEURAL NETWORKS

This article explores the O(t^(-β))synchronization and asymptotic synchronization for fractional order BAM neural networks(FBAMNNs)with discrete delays,distributed delays and non-identical perturbations.By designing a state feedback control law and a new kind of fractional order Lyapunov functional,a new set of algebraic sufficient conditions are derived to guarantee the O(t^(-β))Synchronization and asymptotic synchronization of the considered FBAMNNs model;this can easily be evaluated without using a MATLAB LMI control toolbox.Finally,two numerical examples,along with the simulation results,illustrate the correctness and viability of the exhibited synchronization results.

Optimization of Process Parameters of Continuous Microwave Drying Raspberry Puree Based on RSM and ANN-GA

To improve drying uniformity and anthocyanin content of the raspberry puree dried in a continuous microwave dryer,the effects of process parameters(microwave intensity,air velocity,and drying time)on evaluation indexes(average temperature,average moisture content,average retention rate of the total anthocyanin content,temperature contrast value,and moisture dispersion value)were investigated via the response surface method(RSM)and the artificial neural network(ANN)with genetic algorithm(GA).The results showed that the microwave intensity and drying time dominated the changes of evaluation indexes.Overall,the ANN model was superior to the RSM model with better estimation ability,and higher drying uniformity and anthocyanin retention rate were achieved for the ANN-GA model compared with RSM.The optimal parameters were microwave intensity of 5.53 W•g^(-1),air velocity of 1.22 m·s^(-1),and drying time of 5.85 min.This study might provide guidance for process optimization of microwave drying berry fruits.

ICNSC 2008 CALL FOR PAPERS 5th IEEE International Conference on Networking, Sensing and Control Sanya China April 6-8, 2008

Conference Theme Advanced Technologies for Emergency Planning and ResponseThe 2008 IEEE International Conference on Networking, Sensing and Control will be held in Sanya,China. The main theme of the conference is advanced technologies for emergency planning and re-

Intelligent method to develop constitutive relationship of Ti-6Al-2Zr-1Mo-1V alloy

The isothermal compression tests were carried out in the Thermecmastor-Z thermo-simulator at temperatures of 800, 850, 900, 950, 1000 and 1050 ℃ and the strain rates of 0.01, 0.1, 1 and 10 s-1. The influence of deformation temperature and strain rate on the flow stress of Ti-6Al-2Zr-IMo-IV alloy was studied. Based on the experimental data sets, the high temperature deformation behavior of Ti-6A1-2Zr-IMo-IV alloy was presented using the intelligent method of artificial neural network （ANN）. The results indicate that the predicted flow stress values by ANN model is quite consistent with the experimental results, which implies that the artificial neural network is an effective tool for studying the hot deformation behavior of the present alloy. In addition, the development of graphical user interface is implemented using Visual Basic programming language.

基于混合双层自组织径向基函数神经网络的优化学习算法

针对传统方法采用先训练后测试两阶段学习机制极易导致的过拟合或欠拟合问题,提出一种基于混合双层自组织径向基函数神经网络的优化学习(hybrid bilevel self-organizing radial basis function neural network optimization learning,Hb-SRBFNN-OL)算法。首先,将训练过程和测试过程集成到一个统一的框架中,规避过拟合或欠拟合问题。其次,基于进化学习机制,提出上下2层的交互式优化学习算法,上层基于网络复杂度和测试误差自组织调整网络结构,下层采用列文伯格-马夸尔特(Levenberg Marquardt,LM)算法作为优化器对自组织径向基函数神经网络(self-organizing radial basis function neural network,SO-RBFNN)的连接权值进行优化。最后,利用来自多个子网络的综合信息生成模型的最终输出,加速网络全局收敛。为验证所提方法的可行性,分别在多个分类和预测任务中进行了测试实验。结果表明,在与传统神经网络结构相似甚至更好的测试和分类精度下,该方法不仅能实现更快的训练收敛,而且能进化成更精简紧凑的径向基函数神经网络(radial basis function neural network,RBFNN)模型。尤其在污水处理过程中总磷的质量浓度预测实验中,测试集中均方根误差(root mean squared error,RMSE)最高可降低48.90%,实际场景实验结果验证了所提算法的精确性更佳且泛化能力更强。

Pore-scale study of the pressure-sensitive effect of sandstone and its influence on multiphase flows

The pressure-sensitive effect on the pore structure of sandstone was investigated using X-ray computed micro-tomography and QEMSCAN quantitative mineral analysis. In a physical simulation study, we extracted the pore network model from digital cores at different confining pressures and evaluated the effect of pressure sensitivity on the multiphase displacement process. In both the pore network model and QEMSCAN scanning, the pore structure was observed to be damaged under a high confining pressure. Due to their different scales, the pores and throats exhibited inhomogeneous changes; further, the throats exhibited a significant variation compared to that exhibited by the pores. Meanwhile, the heterogeneity of the pore structure under the two aforementioned activities was aggravated by the elastic-plastic deformation of the pore structure.The pressure-sensitive effect increased the proportion of mineral particles, such as quartz(the main component of the core skeleton), and reduced the proportion of clay minerals. The clay minerals were originally attached to the pore walls or interspersed in the pores; however, as the pressure increased, the clay minerals accumulated in the pores resulting in blockage of the pores. While simulating the multiphase displacement process, increasing the confining pressure was observed to severely restrict the flowability of oil and water. This study promises to improve the efficiency of reservoir development in terms of oil and gas exploitation.

Aviation Accident Causation Analysis Based on Complex Network Theory

Accident causation analysis is of great importance for accident prevention.In order to improve the aviation safety,a new analysis method of aviation accident causation based on complex network theory is proposed in this paper.Through selecting 257 accident investigation reports,45 causative factors and nine accident types are obtained by the three-level coding process of the grounded theory,and the interaction of these factors is analyzed based on the“2-4”model.Accordingly,the aviation accident causation network is constructed based on complex network theory which has scale-free characteristics and small-world properties,the characteristics of causative factors are analyzed by the topology of the network,and the key causative factors of the accidents are identified by the technique for order of preference by similarity to ideal solution(TOPSIS)method.The comparison results show that the method proposed in this paper has the advantages of independent of expert experience,quantitative analysis of accident causative factors and statistical analysis of a lot of accident data,and it has better applicability and advancement.

MicroRNA regulatory pattern in spinal cord ischemia-reperfusion injury

After spinal cord injury, dysregulated miRNAs appear and can participate in inflammatory responses, as well as the inhibition of apoptosis and axon regeneration through multiple pathways. However, the functions of miRNAs in spinal cord ischemia-reperfusion injury progression remain unclear. miRCURY LNATM Arrays were used to analyze miRNA expression profiles of rats after 90 minutes of ischemia followed by reperfusion for 24 and 48 hours. Furthermore, subsequent construction of aberrantly expressed miRNA regulatory patterns involved cell survival, proliferation, and apoptosis. Remarkably, the mitogen-activated protein kinase(MAPK) signaling pathway was the most significantly enriched pathway among 24-and 48-hour groups. Bioinformatics analysis and quantitative reverse transcription polymerase chain reaction confirmed the persistent overexpression of miR-22-3 p in both groups. These results suggest that the aberrant miRNA regulatory network is possibly regulated MAPK signaling and continuously affects the physiological and biochemical status of cells, thus participating in the regulation of spinal cord ischemia-reperfusion injury. As such, miR-22-3 p may play sustained regulatory roles in spinal cord ischemia-reperfusion injury. All experimental procedures were approved by the Animal Ethics Committee of Jilin University, China [approval No. 2020(Research) 01].

Semi-solid Pressing Bonding Strength between Steel and Cu-graphite Composite

The pressing bonding of steel plate with QTi3.5-3.5graphite slurry was studied. The relationship among preheating temperature of steel plate, preheating temperature of dies, solid fraction of QTi3.5-3.5graphite slurry, and interfacial shear strength of bonding plate could be established with artificial neural networks perfectly. This model could be optimized with a genetic algorithm. The results show that the optimum bonding parameters are: 618℃ for preheating temperature of steel plate, 526℃ for preheating temperature of dies and 46.2% for solid fraction of QTi3.5-3.5graphite slurry, and the largest interfacial shear strength of bonding plate is 128.3 MPa.