Amino acid profiles and liposomes: Their role as chemosensory information carriers in the marine environment

Yellowfin tuna enhance their hunting success in the vast pelagic environment by using their sense of smell to detect intact (uninjured) prey that are beyond visual range. However, the olfactory cues that tuna use would normally face huge and rapid dilution in the open ocean. We demonstrate that these prey odors are complexed within biologically derived lipid structures that probably delay the dilution of the amino acids to subthreshold concentrations and provide persistent arousal and search cues for the tuna. This may be the first demonstration of an extracorporeal biological function for liposomes. Tuna may also form chemical search images to maximize feeding efficiency. We demonstrate that the amino acid profiles of various prey species are consistent over time and between schools, which makes the formation of search images feasible.     

A heuristic 0–1 integer programming method

Summary In this paper, we present a novel relaxation method for the integer programming problem:Ax=b,A {–1,0,+1}^m×N,x {0,1}^N, N>m. The report includes experimental results of problems with up tom=100 equations andN=400 variables.     

Parallel depth first search. Part II. Analysis

This paper presents the analysis of a parallel formulation of depth-first search. At the heart of this parallel formulation is a dynamic work-distribution scheme that divides the work between different processors. The effectiveness of the parallel formulation is strongly influenced by the work-distribution scheme and the target architecture. We introduce the concept of isoefficiency function to characterize the effectiveness of different architectures and work-distribution schemes. Many researchers considered the ring architecture to be quite suitable for parallel depth-first search. Our analytical and experimental results show that hypercube and shared-memory architectures are significantly better. The analysis of previously known work-distribution schemes motivated the design of substantially improved schemes for ring and shared-memory architectures. In particular, we present a work-distribution algorithm that guarantees close to optimal performance on a shared-memory/-network-with-message-combining architecture (e.g. RP3). Much of the analysis presented in this paper is applicable to other parallel algorithms in which work is dynamically shared between different processors (e.g., parallel divide-and-conquer algorithms). The concept of isoefficiency is useful in characterizing the scalability of a variety of parallel algorithms.This work was supported by Army Research Office Grant No. DAAG29-84-K-0060 to the Artificial Intelligence Laboratory, and Office of Naval Research Grant N00014-86-K-0763 to the Computer Science Department at the University of Texas at Austin.     

基于绿色证书交易机制的含风电场电力系统动态环境经济调度

为了促进风电消纳以及提高电力系统的经济性,构建基于绿色证书交易机制的含风电场电力系统多目标动态环境经济调度（DEED）模型,提出一种基于反向学习混沌搜索的OLCS-MOEA/D算法的DEED调度求解方法。首先,采用广义反向学习初始化种群提高种群多样性,并引入基于Logistic映射的混沌搜索算法以增强算法局部搜索能力。然后,为了验证算法的性能,采用标准测试函数F1—F7进行测试,同时将该算法对模型进行求解,获得了比其他算法范围更广和分布均匀的Pareto解集前沿。最后,对基于绿色证书交易机制的动态环境经济调度与传统动态环境经济调度进行结果对比分析,通过仿真算例验证该模型和算法的有效性和优越性。     

基于语义分析的设计实体智能搜索

本文提出了在设计联盟组织过程中实现互联网上设计实体智能搜索的方法。根据对互联网上设计实体搜索问题的分析建立了设计实体智能搜索策略；定义了提取设计实体查询向量和描述向量的语义分析规则；提出了基于语义分析的设计实体智能搜索算法；最后编程实现了各项功能。     

快速的层次树集合分划编码算法*

设计了一个改进算法,它仅需对小波分解矩阵进行一遍扫描即可确定层次树集合分划编码(SPIHT)算法所需的所有D(i,j)和L(i,j)重要性并完成对所有(i,j)子系数的编码,使得按照SPIHT的排序方法编码时只需查找存放D(i,j)和L(i,j)的重要性及(i,j)子系数的编码表,从而大大提高了SPIHT的编码速度.     

计及风电置信风险的源网协调多目标优化调度

为了更为合理灵活地评估风电高估/低估给电力系统优化调度带来的风险性以及降低调度决策的保守性,基于风电机会约束提出风电高估/低估置信风险功率偏差量化计算方法,并在决策变量中引入变压器变比调节和无功补偿容量优化,构建计及风电置信风险和源网协调运行的经济/风险多目标优化调度模型。提出一种基于可行性和非劣性综合排序回溯搜索算法,从而实现对调度模型的高效准确求解。IEEE 30节点系统算例结果验证了所提方法的有效性和优越性。     

Exploiting temporal information in Web search

Time plays important roles in Web search, because most Web pages contain temporal information and a lot of Web queries are time-related. How to integrate temporal information in Web search engines has been a research focus in recent years. However, traditional search engines have little support in processing temporal-textual Web queries. Aiming at solving this problem, in this paper, we concentrate on the extraction of the focused time for Web pages, which refers to the most appropriate time associated with Web pages, and then we used focused time to improve the search efficiency for time-sensitive queries. In particular, three critical issues are deeply studied in this paper. The first issue is to extract implicit temporal expressions from Web pages. The second one is to determine the focused time among all the extracted temporal information, and the last issue is to integrate focused time into a search engine. For the first issue, we propose a new dynamic approach to resolve the implicit temporal expressions in Web pages. For the second issue, we present a score model to determine the focused time for Web pages. Our score model takes into account both the frequency of temporal information in Web pages and the containment relationship among temporal information. For the third issue, we combine the textual similarity and the temporal similarity between queries and documents in the ranking process. To evaluate the effectiveness and efficiency of the proposed approaches, we build a prototype system called Time-Aware Search Engine (TASE). TASE is able to extract both the explicit and implicit temporal expressions for Web pages, and calculate the relevant score between Web pages and each temporal expression, and re-rank search results based on the temporal-textual relevance between Web pages and queries. Finally, we conduct experiments on real data sets. The results show that our approach has high accuracy in resolving implicit temporal expressions and extracting focused time, and has better ranking effectiveness for time-sensitive Web queries than its competitor algorithms.     

An architectural design for effective information retrieval in semantic web

The current web IR system retrieves relevant information only based on the keywords which is inadequate for that vast amount of data. It provides limited capabilities to capture the concepts of the user needs and the relation between the keywords. These limitations lead to the idea of the user conceptual search which includes concepts and meanings. This study deals with the Semantic Based Information Retrieval System for a semantic web search and presented with an improved algorithm to retrieve the information in a more efficient way.This architecture takes as input a list of plain keywords provided by the user and the query is converted into semantic query. This conversion is carried out with the help of the domain concepts of the pre-existing domain ontologies and a third party thesaurus and discover semantic relationship between them in runtime. The relevant information for the semantic query is retrieved and ranked according to the relevancy with the help of an improved algorithm. The performance analysis shows that the proposed system can improve the accuracy and effectiveness for retrieving relevant web documents compared to the existing systems.     

Reactive Search strategies using Reinforcement Learning,local search algorithms and Variable Neighborhood Search

Optimization techniques known as metaheuristics have been applied successfully to solve different problems, in which their development is characterized by the appropriate selection of parameters (values) for its execution. Where the adjustment of a parameter is required, this parameter will be tested until viable results are obtained. Normally, such adjustments are made by the developer deploying the metaheuristic. The quality of the results of a test instance [The term instance is used to refer to the assignment of values to the input variables of a problem.] will not be transferred to the instances that were not tested yet and its feedback may require a slow process of “trial and error” where the algorithm has to be adjusted for a specific application. Within this context of metaheuristics the Reactive Search emerged defending the integration of machine learning within heuristic searches for solving complex optimization problems. Based in the integration that the Reactive Search proposes between machine learning and metaheuristics, emerged the idea of putting Reinforcement Learning, more specifically the Q-learning algorithm with a reactive behavior, to select which local search is the most appropriate in a given time of a search, to succeed another local search that can not improve the current solution in the VNS metaheuristic. In this work we propose a reactive implementation using Reinforcement Learning for the self-tuning of the implemented algorithm, applied to the Symmetric Travelling Salesman Problem.