Slicing the metric space to provide quick indexing of complex data in the main memory

Searching in a dataset for elements that are similar to a given query element is a core problem in applications that manage complex data, and has been aided by metric access methods (MAMs). A growing number of applications require indices that must be built faster and repeatedly, also providing faster response for similarity queries. The increase in the main memory capacity and its lowering costs also motivate using memory-based MAMs. In this paper, we propose the Onion-tree, a new and robust dynamic memory-based MAM that slices the metric space into disjoint subspaces to provide quick indexing of complex data. It introduces three major characteristics: (i) a partitioning method that controls the number of disjoint subspaces generated at each node; (ii) a replacement technique that can change the leaf node pivots in insertion operations; and (iii) range and k-NN extended query algorithms to support the new partitioning method, including a new visit order of the subspaces in k-NN queries. Performance tests with both real-world and synthetic datasets showed that the Onion-tree is very compact. Comparisons of the Onion-tree with the MM-tree and a memory-based version of the Slim-tree showed that the Onion-tree was always faster to build the index. The experiments also showed that the Onion-tree significantly improved range and k-NN query processing performance and was the most efficient MAM, followed by the MM-tree, which in turn outperformed the Slim-tree in almost all the tests.     

An artificial negotiating agent modeling approach embedding dynamic offer generating and cognitive layer

In this paper, a dynamic offer generating unit and cognitive layer are suggested for artificial agents based negotiation systems. For this purpose, first, adaptive time and behavior dependent tactics are developed taking advantages from time continuity and dynamics aspects (features) integrated in their modeling. Then, a negotiation strategy (bilateral over single issue) based on these two tactics is suggested. Second, a cognitive negotiation model for a negotiator agent is developed using Win-Lose and Win-Win orientations which will be formed based on personality factors. Afterwards, an experimental validation is conducted for testing applicability of time dependent tactics, the effect of offering time, and the effect of cognitive orientations (Win-Lose and Win-Win) on final negotiation outcomes. The results prove the applicability of the suggested time and behavior dependent tactics as well as the proposed cognitive negotiation model.     

The complex local mean decomposition

The local mean decomposition (LMD) has been recently developed for the analysis of time series which have nonlinearity and nonstationarity. The smoothed local mean of the LMD surpasses the cubic spline method used by the empirical mode decomposition (EMD) to extract amplitude and frequency modulated components. To process complex-valued data, we propose complex LMD, a natural and generic extension to the complex domain of the original LMD algorithm. It is shown that complex LMD extracts the frequency modulated rotation and envelope components. Simulations on both artificial and real-world complex-valued signals support the analysis.     

Characterization and humidity sensing properties of Bi0.5Na0.5TiO3-Bi0.5K0.5TiO3 powder synthesized by metal-organic decomposition

Bi_0.5Na_0.5TiO₃-Bi_0.5K_0.5TiO₃ (BNT-BKT) powder is synthesized by a metal-organic decomposition method and characterized by field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD). A humidity sensor, which is consisted of five pairs of Ag-Pd interdigitated electrodes and an Al₂O₃ ceramic substrate, is fabricated by spin-coating the BNT-BKT powder on the substrate. Good humidity sensing properties such as high response value, short response and recovery times, and small hysteresis are observed in the sensing measurement. The impedance changes more than four orders of magnitude within the whole humidity range from 11% to 95% relative humidity (RH) at 100 Hz. The response time and recovery time are about 20 and 60 s, respectively. The maximum hysteresis is around 4% RH. The results indicate that BNT-BKT powder is of potential applications for fabricating high performance humidity sensors.     

Monte Carlo simulations of systems with complex energy landscapes

Non-traditional Monte Carlo simulations are a powerful approach to the study of systems with complex energy landscapes. After reviewing several of these specialized algorithms we shall describe the behavior of typical systems including spin glasses, lattice proteins, and models for “real” proteins. In the Edwards-Anderson spin glass it is now possible to produce probability distributions in the canonical ensemble and thermodynamic results of high numerical quality. In the hydrophobic-polar (HP) lattice protein model Wang-Landau sampling with an improved move set (pull-moves) produces results of very high quality. These can be compared with the results of other methods of statistical physics. A more realistic membrane protein model for Glycophorin A is also examined. Wang-Landau sampling allows the study of the dimerization process including an elucidation of the nature of the process.     

Software execution processes as an evolving complex network

Inspired by the surprising discovery of several recurring structures in various complex networks, in recent years a number of related works treated software systems as a complex network and found that software systems might expose the small-world effects and follow scale-free degree distributions. Different from the research perspectives adopted in these works, the work presented in this paper treats software execution processes as an evolving complex network for the first time. The concept of software mirror graph is introduced as a new model of complex networks to incorporate the dynamic information of software behavior. The experimentation paradigm with statistical repeatability was applied to three distinct subject programs to conduct several software experiments. The corresponding experimental results are analyzed by treating the software execution processes as an evolving directed topological graph as well as an evolving software mirror graph. This results in several new findings. While the software execution processes may demonstrate as a small-world complex network in the topological sense, they no longer expose the small-world effects in the temporal sense. Further, the degree distributions of the software execution processes may follow a power law. However, they may also follow an exponential function or a piecewise power law.     

Robust synchronization in an array of fuzzy delayed cellular neural networks with stochastically hybrid coupling

In this paper, the robust synchronization control problem of an array of fuzzy cellular neural networks with uncertain stochastically coupling is investigated, which involves constant coupling, discrete time-varying delay coupling and distributed time-varying delay coupling. By using adaptive feedback control scheme and exploiting some stochastic analysis techniques, several sufficient conditions are developed to ensure the synchronization of stochastically hybrid coupled fuzzy neural networks with all admissible uncertainties in mean square. Finally, a numerical example illustrated by scale-free complex networks is provided to show the effectiveness and the applicability of the proposed method.     

复杂适应系统的多层级建模研究

针对复杂系统仿真普遍缺乏层级思想,局限于广度模拟的情况,对复杂适应系统（CAS）层级建模进行研究,协同进化理论,从主体平均适应度的角度,分析中间子系统的生成、过渡和演化,得到层级结构产生的内在动因,提出层级CAS演化的数学模型和层级框架。该模型应用于多人重复囚徒博弈,结果证明其能在高N值时形成稳定高合作率。     

相容关系下的分割知识约简算法的研究

粗糙集理论是一个能有效地删除冗余特征的工具。由于实际应用的数据往往是连续的,并且结构复杂、特征多,现有的粗糙集知识约简方法对真实复杂的数据计算效率较低。为此,首先将相容关系应用于粗糙集的知识约简,再将复杂的信息表纵向分割成简单的缩减表和小规模信息表,然后把缩减表和小规模信息表连接起来进行知识约简。实例表明,提出的方法能够有效提高粗糙集对复杂数据的计算效率。     

一种基于局部拓展的并行重叠社区发现算法

处理海量级数据的有效途径之一是将算法分解为一系列互不依赖的任务,然后利用开源工具并行地执行算法。而在重叠社区发现算法中,基于局部拓展的方法在拓展阶段往往仅需要局部社区及其相应的邻居结点的信息,因而具备可并行执行的可能性。提出了一种可并行化执行的局部拓展算法,并借助开源工具Spark将其实现。算法分为4个阶段。首先,挑选出一组不相关的中心结点并使用其对应的局部网络作为种子;其次,通过删除本身连接不是很紧密的局部网络来过滤选出的种子;然后,采用一种批量式的拓展策略来拓展种子,即一次向局部社区中添加一批邻居结点或从社区中删除一批结点;最后,融合相似的社区。在人工生成的网络以及真实世界中的网络上的实验结果显示 ,所提算法既准确又高效。