The CALYPSO methodology for structure prediction

Structure prediction methods have been widely used as a state-of-the-art tool for structure searches and materials discovery, leading to many theory-driven breakthroughs on discoveries of new materials. These methods generally involve the exploration of the potential energy surfaces of materials through various structure sampling techniques and optimization algorithms in conjunction with quantum mechanical calculations. By taking advantage of the general feature of materials potential energy surface and swarm-intelligence-based global optimization algorithms, we have developed the CALYPSO method for structure prediction, which has been widely used in fields as diverse as computational physics, chemistry, and materials science. In this review, we provide the basic theory of the CALYPSO method, placing particular emphasis on the principles of its various structure dealing methods. We also survey the current challenges faced by structure prediction methods and include an outlook on the future developments of CALYPSO in the conclusions.     

Nonparametric Causal Structure Learning in High Dimensions

The PC and FCI algorithms are popular constraint-based methods for learning the structure of directed acyclic graphs (DAGs) in the absence and presence of latent and selection variables, respectively. These algorithms (and their order-independent variants, PC-stable and FCI-stable) have been shown to be consistent for learning sparse high-dimensional DAGs based on partial correlations. However, inferring conditional independences from partial correlations is valid if the data are jointly Gaussian or generated from a linear structural equation model—an assumption that may be violated in many applications. To broaden the scope of high-dimensional causal structure learning, we propose nonparametric variants of the PC-stable and FCI-stable algorithms that employ the conditional distance covariance (CdCov) to test for conditional independence relationships. As the key theoretical contribution, we prove that the high-dimensional consistency of the PC-stable and FCI-stable algorithms carry over to general distributions over DAGs when we implement CdCov-based nonparametric tests for conditional independence. Numerical studies demonstrate that our proposed algorithms perform nearly as good as the PC-stable and FCI-stable for Gaussian distributions, and offer advantages in non-Gaussian graphical models.     

The quantum adiabatic algorithm applied to random optimization problems: The quantum spin glass perspective

Among various algorithms designed to exploit the specific properties of quantum computers with respect to classical ones, the quantum adiabatic algorithm is a versatile proposition to find the minimal value of an arbitrary cost function (ground state energy). Random optimization problems provide a natural testbed to compare its efficiency with that of classical algorithms. These problems correspond to mean field spin glasses that have been extensively studied in the classical case. This paper reviews recent analytical works that extended these studies to incorporate the effect of quantum fluctuations, and presents also some original results in this direction.     

Dual adaption based evolutionary algorithm for optimized the smart healthcare communication service of the Internet of Things in smart city

The Internet of Things (IoT) is a revolutionary technique of sharing data for smart devices that generates huge amounts of data from smart healthcare systems. Therefore, healthcare systems utilize the convergence power and traffic analysis of sensors that cannot be satisfactorily handled by the IoT. In this article, a novel mutation operator is devised and incorporated with the differential evolution (DE) algorithm. Two tests have been conducted in the validation process. Firstly, the newly dual adaption-based operators incorporated with the differential evolution algorithm are being proposed. The proposed approach provides sufficient diversity and enhances the search speed of nature’s local and global search environments in the problem. The proposed method incorporates the application of IoT-based smart healthcare. Second, an application-based test has been conducted, in which the proposed approach is applied to the application in the smart healthcare system. Therefore, IoT sensor deployment is an optimization problem to minimize service time, delay, and energy loss by considering the communication constraint between sensors(objects). The proposed algorithm is applied in this article to solve this optimization problem. Further, in the experimentation and comparative study, the proposed method is superior to the standard evolutionary algorithms in IoT applications concerning the minimum number of function evaluations and minimization of traffic services. The proposed approach also achieves efficiency in the minimum loss of energy in each service and reduces load and delay.     

Slow-Time Code Design for Space-Time Adaptive Processing in Airborne Radar

Space-time adaptive processing (STAP) techniques have been motivated as a key enabling technology for advanced airborne radar applications. In this paper, a slow-time code design is considered for the STAP technique in airborne radar, and the principle for improving signal-to-clutter and noise ratio (SCNR) based on slow-time coding is given. We present two algorithms for the optimization of transmitted codes under the energy constraint on a predefined area of spatial-frequency and Doppler-frequency plane. The proposed algorithms are constructed based on convex optimization (CVX) and alternating direction (AD), respectively. Several criteria regarding parameter selection are also given for the optimization process. Numerical examples show the feasibility and effectiveness of the proposed methods.     

A genetic algorithm-based approach for solving the target Q-coverage problem in over and under provisioned directional sensor networks

Target coverage and network lifetime extension have been addressed as two major research topics over the last two decades. This paper focuses on “target $Q$-Coverage” in Directional Sensor Networks (DSNs) where coverage requirement of each target in the environment differs from that of the others. In such network, how to achieve the coverage requirement and simultaneously prolong the network lifetime is a major problem. In this study, two target-oriented genetic-based algorithms were developed to solve the problem. The first algorithm was developed to cover the targets in an over-provisioned environment, and the second algorithm was developed in an under-provisioned environment. The main objective of the first algorithm is satisfying the coverage requirement of targets by activating minimal sensors, while the second algorithm was developed to achieve a maximum balanced coverage for all the targets in the network. To evaluate the performance of the developed algorithms, they were compared with some state-of-the-art algorithms presented in recent studies. In this regard, several parameters, including Distance Index, Q-Balancing Index, Coverage Quality, Power Consumption, and Activate Sensors were taken into account. The comparative results indicated that the developed algorithms performed efficiently in solving the $Q$-coverage problem in both environments.     

半透射高光谱成像技术与支持向量机的马铃薯空心病无损检测研究

针对马铃薯空心病的难以检测问题,提出了一种基于半透射高光谱成像技术结合支持向量机(support vector machine,SVM)的马铃薯空心病无损检测方法。选取224个马铃薯样本(合格149个,空心75个)作为研究对象,搭建了马铃薯半透射高光谱图像采集系统,采集了马铃薯样本半透射高光谱图像(390~1 040 nm),对感兴趣区域内的光谱进行平均和光谱特征分析。采用变量标准化(normalize)对原始光谱进行光谱预处理,建立了全波段的SVM判别模型,模型对测试集样本的识别准确率仅为87.5%。为了提高模型性能,采用竞争性自适应重加权算法(competitive adaptive reweighed sampling algorithm, CARS)结合连续投影算法(successive projection algorithm, SPA)对光谱全波段520个变量进行变量选择,最终确定了8个光谱特征变量(454,601,639,664,748,827,874和936 nm),所选8个光谱变量建立的SVM模型对马铃薯测试集的识别率为94.64%。分别采用人工鱼群算法(artificial fish swarm algorithm,AFSA)、遗传算法(genetic algorithm,GA)和网格搜索法(grid search algorithm)对SVM模型的惩罚参数c和核参数g进行优化。经过建模比较分析,确定AFSA为最优优化算法,最优模型参数为c=10.659 1,g=0.349 7,确定AFSA-SVM模型为马铃薯空心病的最优识别模型,该模型总体识别率达到100%。试验结果表明：基于半透射高光谱成像技术结合CARS-SPA与AFSA-SVM方法能够对马铃薯空心病进行准确的检测,也为马铃薯空心病的快速无损检测提供技术支持。     

Role-Aware Information Spread in Online Social Networks

Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.     

Influence of rounding errors on the quality of heuristic optimization algorithms

Search space smoothing and related heuristic optimization algorithms provide an alternative approach to simulated annealing and its variants: while simulated annealing traverses barriers in the energy landscape at finite temperatures, search space smoothing intends to remove these barriers, so that a greedy algorithm is sufficient to find the global minimum. Several formulas for smoothing the energy landscape have already been applied, one of them making use of the finite numerical precision on a computer. In this paper, we thoroughly investigate the effect of finite numerical accuracy on the quality of results achieved with heuristic optimization algorithms. We present computational results for the traveling salesman problem.     

Application Based Construction and Optimization of Substitution Boxes Over 2D Mixed Chaotic Maps

Chaotic maps play a vital role in the development of cryptographic techniques being used in today’s world. Efficient and highly secure algorithms can be constructed based on chaotic maps. Chaotic maps have the intrinsic property of being highly sensitive to initial conditions. In this paper, we have presented a novel scheme for construction and optimization of substitution boxes (S-boxes) based on mixed two dimensional (2D) chaotic maps in which cryptographic properties of S-boxes are optimized based on initial conditions of their parent 2D chaotic map. The proposed scheme and the resulting substitution boxes are analyzed with existing cryptanalysis techniques and their results have been compared with some other algorithms available in literature. The proposed scheme has been found to be more efficacious than other algorithms. The outcomes of security analysis indicate that our proposed technique and resulting optimized non-linear component in the current era of information technology.

     

参数寻优支持向量机在基于光声光谱法的变压器故障诊断中的应用

为了解决变压器气相色谱分析法故障诊断中存在的操作繁琐、消耗待测气体和载气、检测周期长等缺点,提出了利用光声光谱技术检测变压器油中CH₄,C₂H₂,C₂H₄,C₂H₆,H₂五种特征气体的含量并计算C₂H₂/C₂H₄,CH₄/H₂,C₂H₄/C₂H₆三对比值数据。将五种SVM类型和四种核函数采用交叉组合建立20种不同的支持向量机模型,并采用启发式算法对于惩罚因子c和g的取值进行参数寻优,以建立变压器故障诊断准确率最高、最快运行速度的支持向量机模型。启发式算法主要对比研究了粒子群算法和遗传算法在寻优精度与速度上的效果。仿真实验结果表明C-SVC模型、RBF核函数、遗传算法寻优构成的支持向量机模型对变压器故障的诊断准确率最高,测试集达到97.5%,训练集达到98.333 3%,并且遗传算法的寻优速度快于粒子群算法2倍左右。该方法具有操作简单、非接触性测量、不消耗载气、检测周期短、稳定性和灵敏度高等优点。可以代替传统的气相色谱分析法进行变压器故障诊断,满足变压器故障诊断的实际工程需要。     

基于稀疏优化的计算机断层成像图像不完全角度重建综述

计算机断层成像(computed tomography,CT)技术在医学和工业无损检测中都具有非常广泛的应用,CT重建算法是其中的核心,而不完全角度重建问题则是实际应用中重建算法研究领域的一个热点和难点问题.近年来,随着稀疏优化理论与算法的飞速发展,基于稀疏优化的重建算法已经在不完全角度重建问题中得到了较广泛的应用,且表现出了良好的精度与速度性能.本文首先对稀疏优化的基本理论结论与常用算法进行了介绍;而后对稀疏优化理论在CT图像不完全角度重建中的应用进行归纳,分类介绍了其主要研究成果及稀疏优化所发挥的作用;最后对基于稀疏优化的不完全角度重建研究进行了展望.     

An analysis on convergence and convergence rate estimate of elitist genetic algorithms in noisy environments

Random noise perturbs objective functions in many practical problems, and genetic algorithms (GAs) have been widely proposed as an effective optimization tool for dealing with noisy objective functions. However, little papers for convergence and convergence speed of genetic algorithms in noisy environments (GA-NE) have been published. In this paper, a Markov chain that models elitist genetic algorithms in noisy environments (EGA-NE) was constructed under the circumstance that objective function is perturbed only by additive random noise, and it was proved to be an absorbing state Markov chain. The convergence of EGA-NE was proved on the basis of the character of the absorbing state Markov chain, its convergence rate was analyzed, and its upper and lower bounds for the iteration number expectation were derived when EGA-NE first gets a globally optimal solution.     

Hybrid bio-inspired lateral inhibition and Imperialist Competitive Algorithm for complicated image matching

Bio-inspired intelligent algorithms, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), have been applied to solve image matching problems. However, due to high computational complexity and premature convergence problems associated with these methods, they have limitations in defining the global optimal matcher efficiently and accurately. To address these problems, we proposed a hybrid bio-inspired optimization approach, coupling the lateral inhibition mechanism and Imperialist Competitive Algorithm (ICA), to solve complicated image matching problems. With the adoption of the lateral inhibition mechanism, the global convergence of conventional ICA algorithms has been greatly improved. We demonstrate the efficiency and feasibility of the proposed approach by extensive comparative experiments.     

Comparative analysis of photoluminescence and Raman enhancement by metal nanoparticles

We present a comparative study on the enhancement of photoluminescence and Raman processes by a single metal nanoparticle. Taking an analytical approach, we show the physics behind strikingly different orders of magnitude in enhancement that have been observed, provide fundamental explanation for not observing quenching of Raman processes, and outline the path to optimization of both photoluminescence and Raman enhancement.     

Stochastic global optimization as a filtering problem

We present a reformulation of stochastic global optimization as a filtering problem. The motivation behind this reformulation comes from the fact that for many optimization problems we cannot evaluate exactly the objective function to be optimized. Similarly, we may not be able to evaluate exactly the functions involved in iterative optimization algorithms. For example, we may only have access to noisy measurements of the functions or statistical estimates provided through Monte Carlo sampling. This makes iterative optimization algorithms behave like stochastic maps. Naive global optimization amounts to evolving a collection of realizations of this stochastic map and picking the realization with the best properties. This motivates the use of filtering techniques to allow focusing on realizations that are more promising than others. In particular, we present a filtering reformulation of global optimization in terms of a special case of sequential importance sampling methods called particle filters. The increasing popularity of particle filters is based on the simplicity of their implementation and their flexibility. We utilize the flexibility of particle filters to construct a stochastic global optimization algorithm which can converge to the optimal solution appreciably faster than naive global optimization. Several examples of parametric exponential density estimation are provided to demonstrate the efficiency of the approach.     

Towards Secure Big Data Analysis via Fully Homomorphic Encryption Algorithms

Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without first applying the decryption process. Homomorphic encryption algorithms provide innovative solutions to support computations on encrypted data while preserving the content of private information. However, these algorithms have some limitations, such as computational cost as well as the need for modifications for each case study. In this paper, we present a comprehensive overview of various homomorphic encryption tools for Big Data analysis and their applications. We also discuss a security framework for Big Data analysis while preserving privacy using homomorphic encryption algorithms. We highlight the fundamental features and tradeoffs that should be considered when choosing the right approach for Big Data applications in practice. We then present a comparison of popular current homomorphic encryption tools with respect to these identified characteristics. We examine the implementation results of various homomorphic encryption toolkits and compare their performances. Finally, we highlight some important issues and research opportunities. We aim to anticipate how homomorphic encryption technology will be useful for secure Big Data processing, especially to improve the utility and performance of privacy-preserving machine learning.     

Comparison of beaked whale detection algorithms

Due to recent advances in passive acoustic monitoring techniques, beaked whales are now more effectively detected acoustically than visually during vessel-based (e.g. line-transect) surveys. Beaked whales signals can be discriminated from those of other cetaceans by the unique characteristics of their echolocation clicks (e.g. duration >175 μs, center frequencies between 30 and 40 kHz, inter-click intervals between 0.2 and 0.4 s and frequency upsweeps). Furthermore, these same characteristics make these signals ideal candidates for testing automated detection and classification algorithms. There are several different beaked whale automated detectors currently available for use. However, no comparative analysis of detectors exists. Therefore, comparison between studies and datasets is difficult. The purpose of this study was to test, validate, and compare algorithms for detection of beaked whales in acoustic line-transect survey data. Six different detection algorithms (XBAT, Ishmael, PAMGUARD, ERMA, GMM and FMCD) were evaluated and compared. Detection trials were run on three sample days of towed-hydrophone array recordings collected by NOAA Southwest Fisheries Science Center (SWFSC) during which were confirmed visual sightings of beaked whales (Ziphius cavirostris and Mesoplodon peruvianus). Detections also were compared to human verified acoustic detections for a subset of these data. In order to measure the probabilities of false detection, each detector was also run on three sample recordings containing clicks from another species: Risso’s dolphin (Grampus griseus). Qualitative and quantitative comparisons and the detection performance of the different algorithms are discussed.