Calibration of RPC-based muon detector at BESⅢ

The calibration algorithm for RPC-based muon detector at BESIII has been developed. The calibration method, calibration error and algorithm performance are studied. The primary results of efficiency and noise at layer, module and strip levels have been calibrated with cosmic ray data. The calibration constants are available for simulation and reconstruction tuning. The results of Monte Carlo and data are also compared to check the validation and reliability of the algorithm.     

Calibration of RPC-based muon detector at BESⅢ

The calibration algorithm for RPC-based muon detector at BESⅢ has been developed. The calibration method, calibration error and algorithm performance are studied. The primary results of efficiency and noise at layer, module and strip levels have been calibrated with cosmic ray data. The calibration constants are available for simulation and reconstruction tuning. The results of Monte Carlo and data are also compared to check the validation and reliability of the algorithm.     

An efficient algorithm for optimal allocation of wavelength converters in wavelength routing optical network

In a wavelength routing optical network (WRON), the optimal allocation of wavelength converters (WCs) is very important to minimize the number of WCs, enhance the fiber utilization, reduce the blocking probability, etc.. In this paper, a novel simplified network model with shared WCs has been proposed. An effective algorithm for optimal allocation of shared WCs has been presented by using a revised Dijkstra algorithm and genetic algorithm (GA). The effectiveness of the revised algorithm was verified through the simulation on Nature and Science Foundation (NSF) net of USA. This revised algorithm can achieve blocking probability 36% less than the algorithm in previous work, and the calculating time of the minimum blocking probability can be reduced dramatically.     

Quantum Query Complexity for Searching Multiple Marked States from an Unsorted Database

An important and usual sort of search problems is to find all marked states from an unsorted database with a large number of states. Grover＇s original quantum search algorithm is for finding single marked state with uncertainty, and it has been generalized to the case of multiple marked states, as well as been modified to find single marked state with certainty. However, the query complexity for finding all multiple marked states has not been addressed. We use a generalized Long＇s algorithm with high precision to solve such a problem. We calculate the approximate query complexity, which increases with the number of marked states and with the precision that we demand. In the end we introduce an algorithm for the problem on a ＂duality computer＂ and show its advantage over other algorithms.     

Iterative blind deconvolution of adaptive optics images

Adaptive optics (AO) technique has been extensively used for large ground-based optical telescopes to overcome the effect of atmospheric turbulence. But the correction is often partial. An iterative blind deconvolution (IBD) algorithm based on maximum-likelihood (ML) method is proposed to restore the details of the object image corrected by AO. IBD algorithm and the procedure are briefly introduced and the experiment results are presented. The results show that IBD algorithm is efficient for the restoration of some useful high-frequency of the image.     

Iterative blind deconvolution of adaptive optics images

Adaptive optics (AO) technique has been extensively used for large ground-based optical telescopes to overcome the effect of atmospheric turbulence. But the correction is often partial. An iterative blind deconvolution (IBD) algorithm based on maximum-likelihood (ML) method is proposed to restore the details of the object image corrected by AO. IBD algorithm and the procedure are briefly introduced and the experiment results are presented. The results show that IBD algorithm is efficient for the restoration of some useful high-frequency of the image.     

Measures of genuine multipartite entanglement for graph states

Graph states are special multipartite entangled states that have been proven useful in a variety of quantum information tasks. We address the issue of characterizing and quantifying the genuine multipartite entanglement of graph states up to eight qubits. The entanglement measures used are the geometric measure, the relative entropy of entanglement, and the logarithmic robustness, have been proved to be equal for the genuine entanglement of a graph state. We provide upper and lower bounds as well as an iterative algorithm to determine the genuine multipartite entanglement.     

Constrained high-order statistical blind deconvolution of spectral data

A constrained high-order statistical algorithm is proposed to blindly deconvolute the measured spectral data and estimate the response function of the instruments simultaneously. In this algorithm, no priorknowledge is necessary except a proper length of the unit-impulse response. This length can be easily set to be the width of the narrowest spectral line by observing the measured data. The feasibility of this method has been demonstrated experimentally by the measured Raman and absorption spectral data.     

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation.It treats the DFT as a black box and models the error through external statistical information.As a demonstration,the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides.Through the AGANN correction,the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set.For comparison,the neural network approach reduces the mean value to 2.56%.And for the common combinational approach of genetic algorithm and neural network,the value drops to 2.15%.The multiple linear regression method almost has no correction effect here.     

A Robust Scheme for Two－Qubit Grover Quantum Search Alogrithm Based on the Motional and Internal States of a Single Cold Trapped Ion

We propose a scheme to implement a two-qubit Grover quantum search algorithm. The novelty in the proposal is that the motional state is introduced into the computation and the internal state within a single cold trapped ion.The motional and internal states of the ion are manipulated as two qubits by the laser pulses to accomplish anexample of a Grover algorithm based on the two qubits. The composite laser pulses that are applied to implementthe Grover algorithm have been designed in detail. The issues concerning measurement and decoherence arediscussed.     

The 0-1 test algorithm for chaos and its applications

To determine whether a given deterministic nonlinear dynamic system is chaotic or periodic,a novel test approach named zero-one (0-1) test has been proposed recently.In this approach,the regular and chaotic motions can be decided by calculating the parameter K approaching asymptotically to zero or one.In this study,we focus on the 0-1 test algorithm and illustrate the selection of parameters of this algorithm by numerical experiments.To validate the reliability and the universality of this algorithm,it is applied to typical nonlinear dynamic systems,including fractional-order dynamic system.     

Image reconstruction from few views by l_0-norm optimization

In the medical computer tomography(CT) field, total variation(TV), which is the ?1-norm of the discrete gradient transform(DGT), is widely used as regularization based on the compressive sensing(CS) theory. To overcome the TV model’s disadvantageous tendency of uniformly penalizing the image gradient and over smoothing the low-contrast structures, an iterative algorithm based on the ?0-norm optimization of the DGT is proposed. In order to rise to the challenges introduced by the ?0-norm DGT, the algorithm uses a pseudo-inverse transform of DGT and adapts an iterative hard thresholding(IHT) algorithm, whose convergence and effective efficiency have been theoretically proven. The simulation demonstrates our conclusions and indicates that the algorithm proposed in this paper can obviously improve the reconstruction quality.     

A general phase retrieval algorithm based on a ptychographical iterative engine for coherent diffractive imaging

Coherent diffractive imaging (CDI) is a lensless imaging technique and can achieve a resolution beyond the Rayleigh or Abbe limit. The ptychographical iterative engine (PIE) is a CDI phase retrieval algorithm that uses multiple diffraction patterns obtained through the scan of a localized illumination on the specimen, which has been demonstrated successfully at optical and X-ray wavelengths. In this paper, a general PIE algorithm (gPIE) is presented and demonstrated with an He-Ne laser light diffraction dataset. This algorithm not only permits the removal of the accurate model of the illumination function in PIE, but also provides improved convergence speed and retrieval quality.     

Twin image removal in X-ray fluorescence holography with two energies

In the last decade, X-ray fluorescence holography has been developed for the study of 3D atomic arrangements in solids. However, it encounters the twin image problem which may disturb the reconstructed atomic images. In this paper, the formation of twin image is discussed and we propose a modified two-energy algorithm to remove the twin image. The simulation shows that the method is valid and more efficient than the multiple-energy algorithm proposed by Barton.     

Detecting Overlapping Communities Based on Community Cores in Complex Networks

The identification of communities is significant for the understanding of network structures and functions. Since some nodes naturally belong to several communities, the study of overlapping community structures has attracted increasing attention recently, and many algorithms have been designed to detect overlapping communities. We propose a new algorithm. The main idea is first to find the core of a community by detecting maximal cliques and then merging some tight community cores to form the community. Experimental results on two real networks demonstrate that the present algorithm is more accurate for detecting overlapping community structures, compared with some well-known results and methods.     

Lagrange multiplier method used in BESⅢ kinematic fitting

In this paper,the kinematic fitting with the Lagrange multiplier method has been studied for BESⅢ experiment.First we introduce the Lagrange multiplier method and implement kinematic constraints.Then we present the performance of the kinematic fitting algorithm.With the kinematic fitting,we can improve the resolution of track parameters and reduce the background.     

Lagrange multiplier method used in BESⅢ

In this paper, the kinematic fitting with the Lagrange multiplier method has been studied for BESⅢ experiment. First we introduce the Lagrange multiplier method and implement kinematic constraints. Then we present the performance of the kinematic fitting algorithm. With the kinematic fitting, we can improve the resolution of track parameters and reduce the background.     

Detecting community structure using label propagation with consensus weight in complex network

Community detection is a fundamental work to analyse the structural and functional properties of complex networks.The label propagation algorithm(LPA) is a near linear time algorithm to find a good community structure. Despite various ubsequent advances, an important issue of this algorithm has not yet been properly addressed. Random update orders within the algorithm severely hamper the stability of the identified community structure. In this paper, we executed the asic label propagation algorithm on networks multiple times, to obtain a set of consensus partitions. Based on these onsensus partitions, we created a consensus weighted graph. In this consensus weighted graph, the weight value of the dge was the proportion value that the number of node pairs allocated in the same cluster was divided by the total number f partitions. Then, we introduced consensus weight to indicate the direction of label propagation. In label update steps,y computing the mixing value of consensus weight and label frequency, a node adopted the label which has the maximum mixing value instead of the most frequent one. For extending to different networks, we introduced a proportion parameter o adjust the proportion of consensus weight and label frequency in computing mixing value. Finally, we proposed an pproach named the label propagation algorithm with consensus weight(LPAcw), and the experimental results showed that he LPAcw could enhance considerably both the stability and the accuracy of community partitions.     

Lagrange multiplier method used in BESⅢ kinematic fitting

In this paper,the kinematic fitting with the Lagrange multiplier method has been studied for BESⅢ experiment.First we introduce the Lagrange multiplier method and implement kinematic constraints.Then we present the performance of the kinematic fitting algorithm.With the kinematic fitting,we can improve the resolution of track parameters and reduce the background.     

A hybrid quantum encoding algorithm of vector quantization for image compression

Many classical encoding algorithms of vector quantization (VQ) of image compression that can obtain global optimal solution have computational complexity O(N). A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45\sqrt{N} times approximately. In this paper, a hybrid quantum VQ encoding algorithm between the classical method and the quantum algorithm is presented. The number of its operations is less than \sqrt{N} for most images, and it is more efficient than the pure quantum algorithm.