Gray code based gradient-free optimization algorithm for parameterized quantum circuit

A Gray code based gradient-free optimization(GCO)algorithm is proposed to update the parameters of parameterized quantum circuits(PQCs)in this work.Each parameter of PQCs is encoded as a binary string,named as a gene,and a genetic-based method is adopted to select the offsprings.The individuals in the offspring are decoded in Gray code way to keep Hamming distance,and then are evaluated to obtain the best one with the lowest cost value in each iteration.The algorithm is performed iteratively for all parameters one by one until the cost value satisfies the stop condition or the number of iterations is reached.The GCO algorithm is demonstrated for classification tasks in Iris and MNIST datasets,and their performance are compared by those with the Bayesian optimization algorithm and binary code based optimization algorithm.The simulation results show that the GCO algorithm can reach high accuracies steadily for quantum classification tasks.Importantly,the GCO algorithm has a robust performance in the noise environment.

Two-level Bregmanized method for image interpolation with graph regularized sparse coding

A two-level Bregmanized method with graph regularized sparse coding （TBGSC） is presented for image interpolation. The outer-level Bregman iterative procedure enforces the observation data constraints, while the inner-level Bregmanized method devotes to dictionary updating and sparse represention of small overlapping image patches. The introduced constraint of graph regularized sparse coding can capture local image features effectively, and consequently enables accurate reconstruction from highly undersampled partial data. Furthermore, modified sparse coding and simple dictionary updating applied in the inner minimization make the proposed algorithm converge within a relatively small number of iterations. Experimental results demonstrate that the proposed algorithm can effectively reconstruct images and it outperforms the current state-of-the-art approaches in terms of visual comparisons and quantitative measures.

Effects of Mapping Methods on Accuracy of Protein Coding Regions Prediction

[Objective] To discuss the effects of major mapping methods for DNA sequence on the accuracy of protein coding regions prediction,and to find out the effective mapping methods.[Method] By taking Approximate Correlation（AC） as the full measure of the prediction accuracy at nucleotide level,the windowed narrow pass-band filter（WNPBF） based prediction algorithm was applied to study the effects of different mapping methods on prediction accuracy.[Result] In DNA data sets ALLSEQ and HMR195,the Voss and Z-Curve methods are proved to be more effective mapping methods than paired numeric（PN）,Electron-ion Interaction Potential（EIIP） and complex number methods.[Conclusion] This study lays the foundation to verify the effectiveness of new mapping methods by using the predicted AC value,and it is meaningful to reveal DNA structure by using bioinformatics methods.

Totally Coded Method for Signal Flow Graph Algorithm

After a code-table has been established by means of node association information from signal flow graph, the totally coded method (TCM) is applied merely in the domain of code operation beyond any figure-earching algorithm. The code-series (CS) have the holo-information nature, so that both the content and the sign of each gain-term can be determined via the coded method. The principle of this method is simple and it is suited for computer programming. The capability of the computer-aided analysis for switched current network (SIN) can be enhanced.

Graph Regularized Sparse Coding Method for Highly Undersampled MRI Reconstruction

The imaging speed is a bottleneck for magnetic resonance imaging( MRI) since it appears. To alleviate this difficulty,a novel graph regularized sparse coding method for highly undersampled MRI reconstruction( GSCMRI) was proposed. The graph regularized sparse coding showed the potential in maintaining the geometrical information of the data. In this study, it was incorporated with two-level Bregman iterative procedure that updated the data term in outer-level and learned dictionary in innerlevel. Moreover,the graph regularized sparse coding and simple dictionary updating stages derived by the inner minimization made the proposed algorithm converge in few iterations, meanwhile achieving superior reconstruction performance. Extensive experimental results have demonstrated GSCMRI can consistently recover both real-valued MR images and complex-valued MR data efficiently,and outperform the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

Open-Source Codes of Topology Optimization: A Summary for Beginners to Start Their Research

Topology optimization(TO),a numerical technique to find the optimalmaterial layoutwith a given design domain,has attracted interest from researchers in the field of structural optimization in recent years.For beginners,opensource codes are undoubtedly the best alternative to learning TO,which can elaborate the implementation of a method in detail and easily engage more people to employ and extend the method.In this paper,we present a summary of various open-source codes and related literature on TO methods,including solid isotropic material with penalization(SIMP),evolutionary method,level set method(LSM),moving morphable components/voids(MMC/MMV)methods,multiscale topology optimization method,etc.Simultaneously,we classify the codes into five levels,fromeasy to difficult,depending on their difficulty,so that beginners can get started and understand the form of code implementation more quickly.

The Research on Methodology for Material Code Standardization in the Implementation of MRP－Ⅱ

This paper analyses the current common problems of material code standardization in the implementation of MRP Ⅱ, and puts forward the basic ideas and methods for solving the problems, which has some reference value for the popularization of application o

Pseudo-Random Coding Side-Lobe Suppression Method Based on CLEAN Algorithm

A pseudo-random coding side-lobe suppression method based on CLEAN algorithm is introduced.The CLEAN algorithm mainly processes pulse compression results of a pseudo-random coding,and estimates a target＇s distance by a method named interpolation method,so that we can get an ideal pulse compression result of the target,and then use the adjusted ideal pulse compression side-lobe to cut the actual pulse compression result,so as to achieve the remarkable performance of side-lobe suppression for large targets,and let the adjacent small targets appear.The computer simulations by MATLAB with this method analyze the effect of side-lobe suppression in an ideal or noisy environment.It is proved that this method can effectively solve the problem due to the side-lobe of pseudo-random coding being too high,and can enhance the radar＇s multi-target detection ability.

GOAT:a simulation code for high-intensity beams

A simulation code,GOAT,is developed to simulate single-bunch intensity-dependent effects and their interplay in the proton ring of the Electron-Ion Collider in China(EicC)project.GOAT is a scalable and portable macroparticle tracking code written in Python and coded by object-oriented programming technology.It allows for transverse and longitudinal tracking,including impedance,space charge effect,electron cloud effect,and beam-beam interaction.In this paper,physical models and numerical approaches for the four types of high-intensity effects,together with the benchmark results obtained through other simulation codes or theories,are presented and discussed.In addition,a numerical application of the cross-talk simulation between the beam-beam interaction and transverse impedance is shown,and a dipole instability is observed below the respective instability threshold.Different mitigation measures implemented in the code are used to suppress the instability.The flexibility,completeness,and advancement demonstrate that GOAT is a powerful tool for beam dynamics studies in the EicC project or other high-intensity accelerators.

Two-Level Bregman Method for MRI Reconstruction with Graph Regularized Sparse Coding

In this paper, a two-level Bregman method is presented with graph regularized sparse coding for highly undersampled magnetic resonance image reconstruction. The graph regularized sparse coding is incorporated with the two-level Bregman iterative procedure which enforces the sampled data constraints in the outer level and updates dictionary and sparse representation in the inner level. Graph regularized sparse coding and simple dictionary updating applied in the inner minimization make the proposed algorithm converge with a relatively small number of iterations. Experimental results demonstrate that the proposed algorithm can consistently reconstruct both simulated MR images and real MR data efficiently, and outperforms the current state-of-the-art approaches in terms of visual comparisons and quantitative measures.

Programming Logic Modeling and Cross-Program Defect Detection Method for Object-Oriented Code

Code defects can lead to software vulnerability and even produce vulnerability risks.Existing research shows that the code detection technology with text analysis can judge whether object-oriented code files are defective to some extent.However,these detection techniques are mainly based on text features and have weak detection capabilities across programs.Compared with the uncertainty of the code and text caused by the developer’s personalization,the programming language has a stricter logical specification,which reflects the rules and requirements of the language itself and the developer’s potential way of thinking.This article replaces text analysis with programming logic modeling,breaks through the limitation of code text analysis solely relying on the probability of sentence/word occurrence in the code,and proposes an object-oriented language programming logic construction method based on method constraint relationships,selecting features through hypothesis testing ideas,and construct support vector machine classifier to detect class files with defects and reduce the impact of personalized programming on detection methods.In the experiment,some representative Android applications were selected to test and compare the proposed methods.In terms of the accuracy of code defect detection,through cross validation,the proposed method and the existing leading methods all reach an average of more than 90%.In the aspect of cross program detection,the method proposed in this paper is superior to the other two leading methods in accuracy,recall and F1 value.

Resin Matrix/Fiber Reinforced Composite Material, Ⅱ: Method of Solution and Computer Code

According to a mathematical model which describes the curing process of composites constructed from continuous fiber-reinforced, thermosetting resin matrix prepreg materials, and the consolidation of the composites, the solution method to the model is made and a computer code is developed, which for flat-plate composites cured by a specified cure cycle, provides the variation of temperature distribution, the cure reaction process in the resin, the resin flow and fibers stress inside the composite, the void variation and the residual stress distribution.

A Codebook Design Method for Robust VQ-Based Face Recognition Algorithm

In this paper, we present a theoretical codebook design method for VQ-based fast face recognition algorithm to im-prove recognition accuracy. Based on the systematic analysis and classification of code patterns, firstly we theoretically create a systematically organized codebook. Combined with another codebook created by Kohonen’s Self-Organizing Maps (SOM) method, an optimized codebook consisted of 2×2 codevectors for facial images is generated. Experimental results show face recognition using such a codebook is more efficient than the codebook consisted of 4×4 codevector used in conventional algorithm. The highest average recognition rate of 98.6% is obtained for 40 persons’ 400 images of publicly available face database of AT&T Laboratories Cambridge containing variations in lighting, posing, and expressions. A table look-up (TLU) method is also proposed for the speed up of the recognition processing. By applying this method in the quantization step, the total recognition processing time achieves only 28 msec, enabling real-time face recognition.

On the Computing of the Minimum Distance of Linear Block Codes by Heuristic Methods

The evaluation of the minimum distance of linear block codes remains an open problem in coding theory, and it is not easy to determine its true value by classical methods, for this reason the problem has been solved in the literature with heuristic techniques such as genetic algorithms and local search algorithms. In this paper we propose two approaches to attack the hardness of this problem. The first approach is based on genetic algorithms and it yield to good results comparing to another work based also on genetic algorithms. The second approach is based on a new randomized algorithm which we call 'Multiple Impulse Method (MIM)', where the principle is to search codewords locally around the all-zero codeword perturbed by a minimum level of noise, anticipating that the resultant nearest nonzero codewords will most likely contain the minimum Hamming-weight codeword whose Hamming weight is equal to the minimum distance of the linear code.

Hierarchical Symbolic Analysis of Large Analog Circuits with Totally Coded Method

Symbolic analysis has many applications in the design of analog circuits. Existing approaches rely on two forms of symbolic-expression representation: expanded sum-of-product form and arbitrarily nested form. Expanded form suffers the problem that the number of product terms grows exponentially with the size of a circuit. Nested form is neither canonical nor amenable to symbolic manipulation. In this paper, we present a new approach to exact and canonical symbolic analysis by exploiting the sparsity and sharing of product terms. This algorithm, called totally coded method (TCM), consists of representing the symbolic determinant of a circuit matrix by code series and performing symbolic analysis by code manipulation. We describe an efficient code-ordering heuristic and prove that it is optimum for ladder-structured circuits. For practical analog circuits, TCM not only covers all advantages of the algorithm via determinant decision diagrams (DDD) but is more simple and efficient than DDD method.

法典化背景下教育法学研究方法的反思与重构

研究方法的确立事关教育法学的学科建设与学科发展,对教育法典的编纂具有重要的支撑作用。教育法典的编纂从形体和实质两个方面提出了对教育法学研究方法的要求。通过对我国教育法学研究范式的变迁分析,可以发现教育法学的研究方法在整体上受到法学研究方法的影响,主要存在立法法学研究、法教义学研究、社科法学研究三种不同的研究路径。这些不同类型的研究方法对教育法典的编纂各有其效用与优势,但同时也存在一些有待改进的问题。为了更好地支持和服务于教育法典的编纂,教育法学研究应在对上述研究方法进行分析批判的基础之上进行重构:以中国教育法治实践为基本立场,以创新教育法学学科知识体系为主要目标,着力加强和改进不同研究方法的运用,促进规范研究与实证研究的有机融合,以更好地提升教育法学研究的解释力。

教育法典编纂的“公因式元素”及其逻辑主线

编纂契合新时代需求的教育法典,需要运用提取公因式的立法技术。运用该技术提取“公因式元素”,契合法典总分模式的体例要求,符合司法裁判的规范适用要求,得到国内外法典编纂的有效验证,符合中国教育国情的实际需求。教育法典提取“公因式元素”的过程分为三步:首先确定提取范围,其次通过因式分解法以相似性确定提取类别,最后通过逻辑涵摄方法以共有性提取特定“公因式元素”。教育法基本原则具有天然“公因式元素”属性;以法律效果和构成要件为双重标准,可进一步提取其他“公因式元素”,大致包含受教育权、教育主体、教育客体、教育内容等。基于逆向逻辑检视,可以洞察教育法典的各“公因式元素”之间存在一条以“权力-权利”为核心紧密联结的逻辑主线,其以保障受教育权为逻辑起点,保障教育主体权利、规范教育活动和呈现合理教育内容为联结中介,以国家教育权为外部保障,最终指向人的全面发展这一终极目标。

正交-响应面法在PBM细观参数标定中的应用

数值模拟作为研究岩石力学特性、再现细观裂纹演化的主要途径,已受到大量关注。现有的数值模拟参数标定方法主要为试错法及正交试验法,但二者都未能充分考虑细观参数交互作用的影响,模拟精度欠佳且宏观破坏形态与室内试验存在较大差异。因此,采用正交-响应面法相结合的数值分析方法,首先通过正交试验筛选出具有显著影响的平行黏结模型(PBM)细观参数,其次应用响应面法(RSM)研究其交互作用对模型试样宏观参量的影响规律,最后结合岩石宏观破坏形态提出一套PBM参数标定流程。结果表明:有效模量E*与刚度比k n/k s对弹性模量E影响显著;k n/k s、接触摩擦系数μ、最小颗粒半径R_(min)对泊松比ν影响显著;黏聚力c与法向黏结强度σc及其交互作用对单轴抗压强度UCS影响显著,应用响应面法计算分析得出的细观参数的模拟值与试验值误差绝对值小于7%,且二者应力应变曲线力学特征相似,宏观破坏形态相同,证明所提出的PBM细观参数标定流程具备科学性和可靠性。

论作为数字行政执法方式的行政行为码

数字信息技术与行政权力的融合为以法治为中心的政府治理带来挑战,而行政行为码作为数字行政执法方式的应用是数治与法治结合的重要体现,并蕴含着整体智治下对行政执法体制一体化塑造的制度逻辑、行政自制论对数字赋能与赋权关系重塑的理论逻辑以及行政效能对行政执法全流程覆盖的实践逻辑。行政行为码以统一赋码、一码关联、一码查询与一码追踪的实践运行机理,兼具政府信息公开与政府数据开放的法律性质,也为拓展应用奠定了理论基础。与此同时作为数字新兴执法方式的行政行为码由于规制理念的不明确、规则体系不健全以及功能应用程度不足,限制其应用场景与空间。在未来,行政行为码应从理念更新、规范控制和功能拓展三个方面进行塑形,推动行政行为码的功能实现与广泛应用。