On the direct searches for non-smooth stochastic optimization problems

Many difficult engineering problems cannot be solved by the conventional optimization techniques in practice. Direct searches that need no recourse to explicit derivatives are revived and become popular since the new century. In order to get a deep insight into this field, some notes on the direct searches for non-smooth optimization problems are made. The global convergence vs. local convergence and their influences on expected solutions for simulation-based stochastic optimization are pointed out. The sufficient and simple decrease criteria for step acceptance are analyzed, and why simple decrease is enough for globalization in direct searches is identified. The reason to introduce the positive spanning set and its usage in direct searches is explained. Other topics such as the generalization of direct searches to bound, linear and non-linear constraints are also briefly discussed.

Chinese Film Searches for Its “Missing Gun”

THE ceremonious premiere was recently held of a Chinese film whose theme is the grim search by a policeman in a small town for his missing gun. The film is the maiden work of a young director. It starred Jiang Wen, cur-

Verification of Substring Searches on the Untrusted Cloud

Ensuring the correctness of answers to substring queries has not been a concern for consumers working within the traditional confines of their own organisational infrastructure. This is due to the fact that organisations generally trust their handling of their own data hosted on their own servers and networks. With cloud computing however, where both data and processing are delegated to unknown servers, guarantees of the correctness of queries need to be available. The verification of the results of substring searches has not been given much focus to date within the wider scope of data and query, verification. We present a verification scheme for existential substring searc, hes on text files, which is the first of its kind to satisfy the desired properties of authenticity, completeness, and freshness. The scheme is based on suffix arrays, Merkle hash trees and cryptographic hashes to provide strong guarantees of correctness for the consumer, even in fully untrusted environments. We provide a description of our scheme, along with the results of experiments conducted on a fully-working prototype.

Information searches by consumers of miniature automobiles

We studied the information search behaviors of Chinese consumers of miniature automobiles. First, we identified the main sources where consumers acquire or seek information about miniature automobiles and discussed their extent of information search. Then, based on logistic regression and optimal scaling regression of statistics, we studied the influences of characteristics of consumers of miniature automobiles on the extent of information search and on Internet usage. The results indicate that consumers often utilize four sources to obtain information about miniature automobiles. The dominant information source for consumers is their friends/family, followed by dealers, newspapers, and TV. Age, occupation, education and income significantly affect the extent of information search, but gender and city of residence do not have significant impacts. Age, city of residence, occupation, education and income produce significant influences on Internet usage. Gender has an insignificant influence on whether a consumer uses the Internet to search for information.

Keyword Searches in Data-Centric XML Documents Using Tree Partitioning

This paper presents an effective keyword search method for data-centric extensive markup language （XML） documents. The method divides an XML document into compact connected integral subtrees, called self-integral trees （SI-Trees）, to capture the structural information in the XML document. The SI-Trees are generated based on a schema guide. Meaningful self-integral trees （MSI-Trees） are identified, which contain all or some of the input keywords for the keyword search in the XML documents. Indexing is used to accelerate the retrieval of MSI-Trees related to the input keywords. The MSI-Trees are ranked to identify the top-k results with the highest ranks. Extensive tests demonstrate that this method costs 10-100 ms to answer a keyword query, and outperforms existing approaches by 1-2 orders of magnitude.

SEARCHES FOR MONOPOLES,SUPERSYMMETRY,TECHNICOLOR,COMPOSITENESS,EXTRA DIMENSIONS,etc.

Magnetic Monopole SearchesIsolated supermassive monopole candidate events have not been confirmed. The most sensitive experiments obtain negative results.

AN ADAPTIVE NONMONOTONIC TRUST REGION METHOD WITH CURVILINEAR SEARCHES

In this paper, an algorithm for unconstrained optimization that employs both trust region techniques and curvilinear searches is proposed. At every iteration, we solve the trust region subproblem whose radius is generated adaptively only once. Nonmonotonic backtracking curvilinear searches are performed when the solution of the subproblem is unacceptable. The global convergence and fast local convergence rate of the proposed algorithms are established under some reasonable conditions. The results of numerical ＇experiments are reported to show the effectiveness of the proposed algorithms.

Empirical Research on User Acceptance of Mobile Searches

The mobile search, a combination of a web search engine and a mobile communication system, is viewed as the most influential application in the 3G era. Therefore, mobile search service providers are eager to know which factors most influence user acceptance of mobile searches. Based on the characteristics of mobile searches and a review of previous information technology acceptance research, this study integrates the task technology fit model and the unified theory of acceptance and use of technology model to develop a mobile search acceptance model and empirically tests this model. This study finds that, for mobile searches, the performance expectancy, social influence, and perceived cost all significantly influence use intention and the performance expectancy increases with the increasing user＇s experience and higher tasktechnology fit degree. The effort expectancy is found to not affect the use intention of mobile searches and the users＇ gender does not have a significant moderating effect on the use intention. The results are then used to develop suggestions for mobile search providers to promote their application and development.

Chinese Online Unemployment-Related Searches and Macroeconomic Indicators

Official monthly unemployment data is unavailable in China, while intense public interest in unemployment requires timely and accurate information. Using data on web queries from lead search engines in China, Baidu and Google, I build two indices measuring intensity of online unemployment-related searches. The unemployment-related search indices identify a structural break in the time series between October and November 2008, which corresponds to a turning point indicated by some macroeconomic indicators. The unemployment- related search indices are proven to have significant correlation with Purchasing Managers＇ Employment Indices and a set of macroeconomic indicators that are closely related to changes in unemployment in China. The results of Granger causality analysis show that the unemployment-related search indices can improve predictions of the c indicators. It suggests that unemploy- ment-related searches can potentially provide valuable, timely, and low-cost information for macroeconomic monitoring.

Normalized MEDLINE Distance in Context-Aware Life Science Literature Searches

Literature searches on the Web result in great volumes of query results. A model is presented here to refine the search process using user interests. User interests are analyzed to calculate semantic similarity among the interest terms to refine the query. Traditional general purpose similarity measures may not always fit a domain specific context. This paper presents a similarity method for medical literature searches based on the biomedical literature knowledge source ＂MEDLINE＂, the normalized MEDLINE distance, to more reasonably reflect the relevance between medical terms. This measure gives more accurate user interest descriptions through calculating the similarities of user interest terms to rerank the interest term list. The accurate user interest descriptions can be used for query refinement in keyword searches to give more personalized results for the user. This measure also improves the search results for personalization through controlling the return number of results on each topic of interest.

Efficient and Low-Latency Systolic Array Architecture for Full Searches in Block-Matching Motion Estimation

This paper describes an efficient, low latency systolic array architecture for full searches in block matching motion estimation. Conventional one dimensional systolic array architecture is used to develop a novel ring like systolic array architecture through operator rescheduling considering the symmetry of the data flow. High latency delay due to stuffing of the array pipeline in the conventional architecture was eliminated. The new architecture delivers a higher throughput rate, achieves higher processor utilization, and has low power consumption. In addition, the minimum memory bandwidth of the conventional architecture is preserved.

Conjugate Gradient Methods with Armijo-type Line Searches

Two Armijo-type line searches are proposed in this paper for nonlinear conjugate gradient methods. Under these line searches, global convergence results are established for several famous conjugate gradient methods, including the Fletcher-Reeves method, the Polak-Ribiere-Polyak method, and the conjugate descent method.

Robust Distributed Model Predictive Control for Formation Tracking of Nonholonomic Vehicles

Dear Editor,This letter proposes a robust distributed model predictive control(MPC) strategy for formation tracking of a group of wheeled vehicles subject to constraints and disturbances. Formation control has attracted significant interest because of its applications in searching and exploration [1], [2].

Sequestration of helium and xenon via iron-halide compounds in early Earth

The terrestrial abundance anomalies of helium and xenon suggest the presence of deep-Earth reservoirs of these elements,which has led to great interest in searching for materials that can host these usually unreactive elements.Here,using an advanced crystal structure search approach in conjunction with first-principles calculations,we show that several Xe/He-bearing iron halides are thermodynamically stable in a broad region of P–T phase space below 60 GPa.Our results present a compelling case for sequestration of He and Xe in the early Earth and may suggest their much wider distribution in the present Earth than previously believed.These findings offer insights into key material-based and physical mechanisms for elucidating major geological phenomena.

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

Cooperative UAV search strategy based on DMPC-AACO algorithm in restricted communication scenarios

Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference(EMI)has become a contentious research problem.In certain mission environments,due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations,the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages.Furthermore,the data interaction between unmanned aerial vehicles(UAVs)can only be performed in specific communication-available stages.Traditional cooperative search algorithms cannot handle such situations well.To solve this problem,this study constructed a distributed model predictive control(DMPC)architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor.An attention mechanism ant-colony optimization(AACO)algorithm is proposed for UAV search-control decision planning.The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information,a priori information,and emergent information of the mission to satisfy different search expectations to the maximum extent.Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.

Quafu-RL:The cloud quantum computers based quantum reinforcement learning

With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.

一种基于CatBoost优化的光伏阵列故障诊断模型

大部分光伏电站地处偏僻、地形复杂的区域,受到外界环境的影响,易发生各种故障.而传统的光伏阵列故障诊断方法存在精度不高以及光伏数据利用率低等问题.针对以上问题,本文先是通过引入Levy飞行策略和步长因子动态调整策略改进麻雀搜索算法(Sparrow Search Algorithm,SSA),降低SSA算法陷入局部最优的风险,提升SSA算法的寻优能力.然后采用改进的Levy步长调整麻雀搜索算法(Levy Adjustment Sparrow Search Algorithm,LASSA)对CatBoost模型关键超参数进行寻优,提出了一种基于CatBoost并以LASSA为优化策略的光伏阵列故障诊断模型LASSA-CatBoost,以实现光伏阵列的短路、开路、老化和阴影遮挡故障的精确诊断.实验结果表明,LASSA-CatBoost模型的故障诊断准确率为99.7%,相较于优化前的CatBoost模型,准确率提高了3.6%.与现有的光伏阵列故障诊断模型相比,LASSA-CatBoost模型的准确性和稳定性更高.

AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.