A quantum algorithm for Toeplitz matrix-vector multiplication

Toeplitz matrix-vector multiplication is widely used in various fields,including optimal control,systolic finite field multipliers,multidimensional convolution,etc.In this paper,we first present a non-asymptotic quantum algorithm for Toeplitz matrix-vector multiplication with time complexity O(κpolylogn),whereκand 2n are the condition number and the dimension of the circulant matrix extended from the Toeplitz matrix,respectively.For the case with an unknown generating function,we also give a corresponding non-asymptotic quantum version that eliminates the dependency on the L_(1)-normρof the displacement of the structured matrices.Due to the good use of the special properties of Toeplitz matrices,the proposed quantum algorithms are sufficiently accurate and efficient compared to the existing quantum algorithms under certain circumstances.

Code Design and Latency Analysis of Distributed Matrix Multiplication with Straggling Servers in Fading Channels

This paper exploits coding to speed up computation offloading in a multi-server mobile edge computing(MEC)network with straggling servers and channel fading.The specific task we consider is to compute the product between a user-generated input data matrix and a large-scale model matrix that is stored distributively across the multiple edge nodes.The key idea of coding is to introduce computation redundancy to improve robustness against straggling servers and to create communication redundancy to improve reliability against channel fading.We utilize the hybrid design of maximum distance separable(MDS)coding and repetition coding.Based on the hybrid coding scheme,we conduct theoretical analysis on the average task uploading time,average edge computing time,and average output downloading time,respectively and then obtain the end-to-end task execution time.Numerical results demonstrate that when the task uploading phase or the edge computing phase is the performance bottleneck,the hybrid coding reduces to MDS coding;when the downlink transmission is the bottleneck,the hybrid coding reduces to repetition coding.The hybrid coding also outperforms the entangled polynomial coding that causes higher uplink and downlink communication loads.

Research and design of matrix operation accelerator based on reconfigurable array

In the case of massive data,matrix operations are very computationally intensive,and the memory limitation in standalone mode leads to the system inefficiencies.At the same time,it is difficult for matrix operations to achieve flexible switching between different requirements when implemented in hardware.To address this problem,this paper proposes a matrix operation accelerator based on reconfigurable arrays in the context of the application of recommender systems(RS).Based on the reconfigurable array processor(APR-16)with reconfiguration,a parallelized design of matrix operations on processing element(PE)array is realized with flexibility.The experimental results show that,compared with the proposed central processing unit(CPU)and graphics processing unit(GPU)hybrid implementation matrix multiplication framework,the energy efficiency ratio of the accelerator proposed in this paper is improved by about 35×.Compared with blocked alternating least squares(BALS),its the energy efficiency ratio has been accelerated by about 1×,and the switching of matrix factorization(MF)schemes suitable for different sparsity can be realized.

Research and Design of Reconfigurable Matrix Multiplication over Finite Field in VLIW Processor

Matrix multiplication plays a pivotal role in the symmetric cipher algorithms, but it is one of the most complex and time consuming units, its performance directly affects the efficiency of cipher algorithms. Combined with the characteristics of VLIW processor and matrix multiplication of symmetric cipher algorithms, this paper extracted the reconfigurable elements and analyzed the principle of matrix multiplication, then designed the reconfigurable architecture of matrix multiplication of VLIW processor further, at last we put forward single instructions for matrix multiplication between 4×1 and 4×4 matrix or two 4×4 matrix over GF(2~8), through the instructions extension, the instructions could support larger dimension operations. The experiment shows that the instructions we designed supports different dimensions matrix multiplication and improves the processing speed of multiplication greatly.

Performance Prediction Based on Statistics of Sparse Matrix-Vector Multiplication on GPUs

As one of the most essential and important operations in linear algebra, the performance prediction of sparse matrix-vector multiplication (SpMV) on GPUs has got more and more attention in recent years. In 2012, Guo and Wang put forward a new idea to predict the performance of SpMV on GPUs. However, they didn’t consider the matrix structure completely, so the execution time predicted by their model tends to be inaccurate for general sparse matrix. To address this problem, we proposed two new similar models, which take into account the structure of the matrices and make the performance prediction model more accurate. In addition, we predict the execution time of SpMV for CSR-V, CSR-S, ELL and JAD sparse matrix storage formats by the new models on the CUDA platform. Our experimental results show that the accuracy of prediction by our models is 1.69 times better than Guo and Wang’s model on average for most general matrices.

Cache performance optimization of irregular sparse matrix multiplication on modern multi-core CPU and GPU

This paper focuses on how to optimize the cache performance of sparse matrix-matrix multiplication(SpGEMM).It classifies the cache misses into two categories;one is caused by the irregular distribution pattern of the multiplier-matrix,and the other is caused by the multiplicand.For each of them,the paper puts forward an optimization method respectively.The first hash based method removes cache misses of the 1 st category effectively,and improves the performance by a factor of 6 on an Intel 8-core CPU for the best cases.For cache misses of the 2nd category,it proposes a new cache replacement algorithm,which achieves a cache hit rate much higher than other historical knowledge based algorithms,and the algorithm is applicable on CELL and GPU.To further verify the effectiveness of our methods,we implement our algorithm on GPU,and the performance perfectly scales with the size of on-chip storage.

Study on Multiplication, Rooting and Transplanting of Tissue Culture Plantlets of Rhododendron chrysanthum Pall

[Objective] This study aimed to shorten the multiplication culture and root- ing culture periods of Rh. chrysanthum Pall. [Method] The Rh. chrysanthum Pall tis- sue culture plantlets collected from Changbai Mountain were used as material, and the effects of different hormone combinations and coconut milk on the proliferation and differentiation of Rh. chrysanthum Pall tissue culture plantlets were investigated. In addition, the rooting medium and transplanting matrix for Rh. chrysanthum Pall tissue culture plantlets were explored. [Result] The medium composed of modified MS, iBA （3 mg/L） and ZT （1.5 mg/L） was the optimum medium for subculture mul- tiplication of Rh. chrysanthum Pall tissue culture plantlets. The multiplication multiple and average plant height were significantly improved by adding coconut milk into the medium （150 mg/L）. [Conclusion] For Rh. chrysanthum Pall tissue culture plantlets, the optimum rooting culture medium was composed of modified MS （1/4） and IBA （5.0 mg/L）, and the tissue culture plantlets began to root 8 d after the inoculation. The root induction treatment was carried out after a 15-d sand culture, and the suitable matrix was composed of tufty soil, humus soil and perlite （2：1：1） with a survival rate of 95.66%.

Method for multiple attribute decision making based on incomplete linguistic judgment matrix

With respect to the multiple attribute decision making problems with linguistic preference relations on alternatives in the form of incomplete linguistic judgment matrix, a method is proposed to analyze the decision problem. The incomplete linguistic judgment matrix is transformed into incomplete fuzzy judgment matrix and an optimization model is developed on the basis of incomplete fuzzy judgment matrix provided by the decision maker and the decision matrix to determine attribute weights by Lagrange multiplier method. Then the overall values of all alternatives are calculated to rank them. A numerical example is given to illustrate the feasibility and practicality of the proposed method.

Multiple extended target tracking algorithm based on Gaussian surface matrix

In this paper, we consider the problem of irregular shapes tracking for multiple extended targets by introducing the Gaussian surface matrix（GSM） into the framework of the random finite set（RFS） theory. The Gaussian surface function is constructed first by the measurements, and it is used to define the GSM via a mapping function. We then integrate the GSM with the probability hypothesis density（PHD） filter, the Bayesian recursion formulas of GSM-PHD are derived and the Gaussian mixture implementation is employed to obtain the closed-form solutions. Moreover, the estimated shapes are designed to guide the measurement set sub-partition, which can cope with the problem of the spatially close target tracking. Simulation results show that the proposed algorithm can effectively estimate irregular target shapes and exhibit good robustness in cross extended target tracking.

The role of fibronectin in multiple sclerosis and the effect of drug delivery across the blood-brain barrier

Remyelination failure is one of the main characteristics of multiple sclerosis and is potentially correlated with disease progression.Previous research has shown that the extracellular matrix is associated with remyelination failure because remodeling of the matrix often fails in both chronic and progressive multiple sclerosis.Fibronectin aggregates are assembled and persistently exist in chronic multiple sclerosis,thus inhibiting remyelination.Although many advances have been made in the mechanisms and treatment of multiple sclerosis,it remains very difficult for drugs to reach pathological brain tissues;this is due to the complexity of brain structure and function,especially the existence of the blood-brain barrier.Therefore,herein,we review the effects of fibronectin aggregates on multiple sclerosis and the efficacy of different forms of drug delivery across the blood-brain barrier in the treatment of this disease.

Identifying multiple influential spreaders in complex networks based on spectral graph theory

One of the hot research topics in propagation dynamics is identifying a set of critical nodes that can influence maximization in a complex network.The importance and dispersion of critical nodes among them are both vital factors that can influence maximization.We therefore propose a multiple influential spreaders identification algorithm based on spectral graph theory.This algorithm first quantifies the role played by the local structure of nodes in the propagation process,then classifies the nodes based on the eigenvectors of the Laplace matrix,and finally selects a set of critical nodes by the constraint that nodes in the same class are not adjacent to each other while different classes of nodes can be adjacent to each other.Experimental results on real and synthetic networks show that our algorithm outperforms the state-of-the-art and classical algorithms in the SIR model.

Multiple Regression and Big Data Analysis for Predictive Emission Monitoring Systems

Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple regression is one of the fundamental statistical techniques to describe the relationship between dependent and independent variables. This model can be effectively used to develop a PEMS, to estimate the amount of pollution emitted by industrial sources, where the fuel composition and other process-related parameters are available. It often makes them sufficient to predict the emission discharge with acceptable accuracy. In cases where PEMS are accepted as an alternative method to CEMS, which use gas analyzers, they can provide cost savings and substantial benefits for ongoing system support and maintenance. The described mathematical concept is based on the matrix algebra representation in multiple regression involving multiple precision arithmetic techniques. Challenging numerical examples for statistical big data analysis, are investigated. Numerical examples illustrate computational accuracy and efficiency of statistical analysis due to increasing the precision level. The programming language C++ is used for mathematical model implementation. The data for research and development, including the dependent fuel and independent NOx emissions data, were obtained from CEMS software installed on a petrochemical plant.

一种基于模型概率单调性变化的自适应IMM-UKF改进算法

针对现有交互式多模型(IMM)算法模型间切换迟滞和转换速率慢的缺点,提出一种基于模型概率单调性变化的自适应交互式多模型无迹卡尔曼滤波改进算法(mIMM-UKF)。该算法利用后验信息模型概率的单调性,对马尔可夫转移概率矩阵及模型估计概率进行二次修正,加快了匹配模型的切换速度及转换速率。仿真结果表明,与现有算法相比,该算法通过快速切换匹配模型,有效提高了水下目标跟踪精度。

乘型模糊判断矩阵排序向量的递推方法

文章首先在模糊判断矩阵乘型一致性以及矩阵元素和权重之间关系的基础上,结合模糊判断矩阵的上三角矩阵元素,构建了一个关于权重和矩阵上三角元素的方程组,并证明了该方程组存在唯一的正解。随后指出方程组的证明过程就是模糊判断矩阵排序向量的求解过程,从而给出了乘型一致性模糊判断矩阵排序向量的一种递推方法。然后,在偏差函数基础上,通过构造并求解一个优化模型,求出了非乘型一致性模糊判断矩阵的排序向量,结果显示,其解的形式与采用乘型一致性模糊判断矩阵递推方法得到的排序向量完全一样。最后,通过实例以及相关方法对比说明排序向量递推方法是可行有效的。

An Evaluation of the Critical Success Factors in Sustainable Food Supply Chains in Developing Countries

Food is one of the biggest industries in developed and underdeveloped countries. Supply chain sustainability is essential in established and emerging economies because of the rising acceptance of cost-based outsourcing and the growing technological, social, and environmental concerns. The food business faces serious sustainability and growth challenges in developing countries. A comprehensive analysis of the critical success factors (CSFs) influencing the performance outcome and the sustainable supply chain management (SSCM) process. A theoretical framework is established to explain how they are used to examine the organizational aspect of the food supply chain life cycle analysis. This study examined the CSFs and revealed the relationships between them using a methodology that included a review of literature, interpretative structural modeling (ISM), and cross-impact matrix multiplication applied in classification (MICMAC) tool analysis of soil liquefaction factors. The findings of this research demonstrate that the quality and safety of food are important factors and have a direct effect on other factors. To make sustainable food supply chain management more adequate, legislators, managers, and experts need to pay attention to this factor. In this work. It also shows that companies aiming to create a sustainable business model must make sustainability a fundamental tenet of their organization. Practitioners and managers may devise effective long-term plans for establishing a sustainable food supply chain utilizing the recommended methodology.

不相关多子集零相关区序列集的构造

在多小区环境下的准同步码分多址(quasi-synchronous code-division multiple-access,QS-CDMA)系统中,不相关多子集零相关区(zero correlation zone,ZCZ)序列集不仅可以消除一定时延内来自相同小区的信号干扰,而且可以完全消除来自相邻小区的信号干扰。针对如何基于仿酉矩阵构造多子集ZCZ序列集的问题,设计了两种构造方法。具体地,通过构造具有一定特性的多个初始矩阵,再结合仿酉矩阵,进而利用不同的初始矩阵产生不同的ZCZ序列集,且序列集间互不相关。所提方法的核心是多个初始矩阵的设计,方法1通过对离散傅里叶变换(discrete Fourier transform,DFT)矩阵进行分块与叠加实现,方法2利用已有的不相关多子集ZCZ序列集实现。由两种方法得到的构造结果,其参数趋近于最优,为解决多小区环境下的干扰抑制问题提供了新的序列设计方法。

密室逃脱类场所人员疏散影响因素研究

准确识别火灾等突发事件时影响密室逃脱类场所人员疏散的因素,并客观系统分析因素间的层次关系和重要程度,对密室逃脱类场所的应急疏散至关重要。基于密室逃脱类场所行业规范和现状,从人员、建筑结构、环境、消防管理4个维度提取了26个影响因素,采用AISM(对抗解释结构模型)及MICMAC(交叉影响矩阵相乘法)构建密室逃脱类场所人员疏散影响因素3级递阶结构模型,综合分析因素间作用关系,并分析因素与管理层、运营层、玩家层3类主体对应责任关系,然后联合ANP(网络层次分析法)确定各影响因素的权重和排序。结果表明:影响密室逃脱类场所人员疏散的深层根本因素主要为制度与规程编制完备性、密室剧情类型、空间组合形式、空间嵌套层数等,其可通过驱动密室场所经营位置、门锁联动性、空间内部昏暗度、道具种类与数量、NPC(非玩家角色)疏散引导能力等中层特性影响因素优化,规范消防设施完备性、应急预案编制情况、安全教育与培训情况等关键共性影响因素,最终提升玩家应急疏散能力。

基于改进松弛嵌入空间的多视图聚类

针对传统聚类方法缺乏统一特征表示,存在保守性的缺陷,提出一种基于改进松弛嵌入空间的多视图聚类方法。在统一的框架下联合学习一个综合的潜在嵌入表示矩阵、全局相似矩阵和一个精确指标矩阵。进一步放松全局相似矩阵的约束,并在此基础上提出一种改进的松弛多视图聚类嵌入空间,使得该方法具有更低的计算复杂度和更多的数据点对之间的相关性。实验结果表明,该方法能够获得鲁棒性更强、准确度更高的聚类结果。

基于改进自适应IMM算法的高速列车组合定位

针对列车高精度定位问题,该文提出基于改进自适应交互多模型(IMM)的高速列车高精度组合定位方法。首先,根据列车定位需求和各传感器特点,设计了卫星接收器、轮轴测速传感器、测速雷达以及单轴陀螺仪4种传感器的组合定位方案。然后,针对IMM融合滤波算法因先验信息不准导致固定参数设置不当的问题,引入Sage-Husa自适应滤波和转移概率矩阵(TPM)自适应更新集成为自适应IMM算法。针对多模型切换的滞后问题,利用子模型似然函数值能快速反映模型变化趋势的特点,将似然函数值设为判定标志,并引入判定窗对TPM矩阵元素进行修正,有效提升了模型的切换速度。最后,基于改进自适应IMM算法对4种传感器定位信息进行融合滤波,实现高速列车的高精度组合定位。仿真结果表明:改进后的算法相比其他自适应IMM算法提升定位精度1.6%~14.7%,并且能通过提高模型间切换速度来有效降低位置误差峰值,同时具备较好的抗噪性能。

NM-SpMM:面向国产异构向量处理器的半结构化稀疏矩阵乘算法

深度神经网络在自然语言处理、计算机视觉等领域取得了优异的成果,由于智能应用处理数据规模的增长和大模型的快速发展,对深度神经网络的推理性能要求越来越高,N∶M半结构化稀疏化技术成为平衡算力需求和应用效果的热点技术之一。国产异构向量处理器FT-M7032为智能模型处理中的数据并行和指令并行开发提供了较大空间。针对N∶M半结构化稀疏模型计算稀疏模式多样性,提出了一种面向FT-M7032的可灵活配置的稀疏矩阵乘算法NM-SpMM。NM-SpMM设计了一种高效的压缩偏移地址稀疏编码格式COA,避免了半结构化参数配置对稀疏数据访存计算的影响。基于COA编码,NM-SpMM对不同维度稀疏矩阵计算进行了细粒度优化。在FT-M7032单核上的实验结果表明,相较于稠密矩阵乘,NM-SpMM能获得1.73~21.00倍的加速,相较于采用CuSPARSE稀疏计算库的NVIDIA V100 GPU,能获得0.04~1.04倍的加速。