Time Series Prediction Based on Chaotic Attractor

A new prediction technique is proposed for chaotic time series. The usefulness of the technique is thatit can kick off some false neighbor points which are not suitable for the local estimation of the dynamics systems. Atime-delayed embedding is used to reconstruct the underlying attractor, and the prediction model is based on the timeevolution of the topological neighboring in the phase space. We use a feedforward neural network to approximate thelocal dominant Lyapunov exponent, and choose the spatial neighbors by the Lyapunov exponent. The model is testedfor the Mackey-Glass equation and the convection amplitude of lorenz systems. The results indicate that this predictiontechnique can improve the prediction of chaotic time series.     

On effectiveness of wiretap programs in mapping social networks

Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost. Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php     

Fiber-Optic Chemical Sensor for Detection of NO2 Using Poly (3-Octylthiophene)

A fiber-optic chemical sensor (FOCS) for detection of nitrogen dioxide (NO₂) molecules is reported. The FOCS presents an optropode structure because of the transmission properties of the sensitive material. The NO₂ FOCS is activated by using the semiconductor polymer: regioregular head-to-tail poly(3-octylthiophene-2,5-diyl). The operation wavelength of the sensor is 543.5 nm such that a simple LED and detector can be used for the design of this device. The sensor response decreases after each exposure, demonstrating the reduction in sensitivity as well as irreversibility lower than 5%. However, its properties such as rapid response, high selectivity, high sensitivity (0.43 ± 0.01 muW/ppm), hygroscopic properties, and its operation at room temperature make this kind of FOCS a good alternative for NO₂ toxic gas detection.     

Synthesis and Crystal Structure of a Novel Organic Adduct Nonlinear Optical Material: 3,5-Dinitrobenzonic Acid·ethanolamine (3,5-DNBEA)

The title compound (C2H7NO·C7H4N2O6) has been obtained by the reaction of etha- nolamine with 3,5-dinitrobenzoic acid in deionized water at room temperature. The crystal crystal- lizes in orthorhombic, space group P212121 with a = 6.048(2), b = 9.146(3), c = 21.955(7)(A), C9H11N3O7, Mr = 273.21, Z = 4, V = 1214.3(7)(A)3, Dc = 1.494 g/cm3, F(000) = 568, μ(MoKα) = 0.131 mm-1, R1 = 0.0338 and wR2 = 0.0497. The new organic adduct is composed of one ethanolamine and one 3,5-dinitrobenzoic acid, which are linked up by O-H…O and N-H…O types of hydrogen bonds to form a nine-membered ring and an eleven-membered ring, extending into a one-dimensional network structure.     

一种新的WDM光网中的共享链路保护策略

研究了WDM光网中的链路保护问题.提出了一种新的共享链路保护策略--基于SRLG的共享链路保护策略(SRLG-SLP).SRLG(共享风险链路组)定义了对一条工作光通道分配保护资源时的资源可用性的约束.它规定任意两条有着同样故障风险或者说处于同一个SRLG的工作光通道不能利用同样的保护资源.另外,还提出了一种更加符合实际的单链路故障模型,其中链路故障间隔时间和链路故障保持时间被考虑作为两个独立的变量.基于该链路故障模型,通过大量的仿真试验,比较了专用链路保护(DLP)、共享链路保护(SLP)和提出的SRLG-SLP保护策略的资源利用率、保护效率以及业务中断率.结果显示,我们提出的SRLG-SLP在保护效率和业务中断率方面的保护性能远好于DLP和SLP,但会牺牲一些资源利用率.     

Single to 16-Channel Wavelength Conversion at 10 Gb/s Based on Cross-Gain Modulation of ASE Spectrum in SOA

A simple scheme for single to multi-channel wavelength conversion based on cross-gain modulation of amplified spontaneous emission (ASE) spectrum in semiconductor optical amplifier (SOA) is described. Single to 16-channel wavelength conversion at 10 Gb/s is first demonstrated without any additional probe lights, the modulation information carried by input signal could be converted into arbitrary many channels if only the demultiplexer with enough channels is exploited. Output performance and pattern effects are investigated experimentally.     

Mid-infrared generation based on a periodically poled LiNbO3 optical parametric oscillator

We report a nanosecond Nd:YVO_4-pumped optical parametric oscillator (OPO) based on periodically poled LiNbO_3 (PPLN). Tuning is achieved in this experiment by varying the temperature and period of the PPLN. The design of double-pass singly resonant oscillator (DSRO) and confocal cavity enables the OPO threshold to be lowered considerably, resulting in a simple, compact, all-solid-state configuration with the mid-infrared idler powers of up to 466mW at 3.41μm.     

具有可调紫外滤光性能的富勒烯胺/壳聚糖共混膜的研究

通过溶液共混及流涎成膜法制备了一系列不同C60乙醇胺含量的壳聚糖共混膜。以及含Cu（Ⅱ）的C60乙醇胺/壳聚糖共混膜。研究表明，C60乙醇胺/壳聚糖共混膜具有优异的可调紫外滤光性能，随着C60乙醇胺含量的增加，其紫外滤光波长从200nm红移至400nm左右。在该复合体系中引入Cu（Ⅱ）能有效改善膜的紫外光学性能，同时还初步探讨了这类复合材料的紫外滤光机制。     

网络股泡沫大小测度研究

网络股泡沫是最能反映网络泡沫本质的表现形式，本文利用理性预期理论，构建了网络股泡沫大小的测度模型，说明网络股泡沫的存在，在此基础上确定了网络股泡沫大小的测度指标，并以雅虎公司股票为例对泡沫的大小进行了测度，结果符合网络泡沫的实际情况。     

农户信用评估系统的设计与运用研究

农户信用评估的研究对推动农村消费信用的发展，促进农村经济良好运行十分重要。本在构造农户信用评估的指标体系的基础上，提出了农户信用评估神经网络模型的算法，利用实际搜集到的农户资料进一步建立了农户信用评估模型，继而构造了农户信用评估系统，并举例说明了该系统的实际运用，以期能为农村经济发展中的农户信用评价及相关研究提供一丝基础性启发。