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     

Search for cosmic γ-ray bursts in the (1÷50) GeV energy range

Summary A search for cosmic gamma-ray bursts in the GeV energy range has been performed by means of the EAS-TOP Extensive Air Shower array at Campo Imperatore (Gran Sasso Laboratories) during the period March–December 1990. In 2566.5 hours of measurement the obtained upper limit to the rate of bursts of amplitude >2% of the cosmic-ray intensity and time duration τ=1 s, isR≤7.9y⁻¹ (90% c.l.). Assuming for γ-rays a differential energy spectrumS(E ₀ )≈E ₀ ^−2.5 , the corresponding upper limit to the energy flux of γ-rays with energy >5 GeV in bursts of duration τ≤1 s is Φ<8.3·10⁻⁵erg cm⁻².     

利用Google Earth标示图片位置

本文采用一种新的方法，利Picasa工具对图片在Google Earth中的位置进行标示，展示了Google Earth的一种新用途。     

机助数据采集与通用平差系统

PC-1500袖珍计算机是理想的野外数据采集工具,IBM系列微机又是比较普及的内业处理机,PC-1500与IBM又可以联机通讯.从而可达到从野外数据采集到内业数据处理的自动化.本文就控制测量的野外数据采集与内业数据处理作了一系列理论研究与实用试验,编制出一整套实用软件.可用于生产实际中.     

高纯金属镨中痕量稀土杂质的电感耦合等离子体原子发射光谱分析及光谱干扰的校正

本文提出了PGS-2型平面光栅光谱仪(色散率0.18纳米,二级光谱)与PlasmaTherm ICP-5000D射频发生器联用,乙醇预去溶方式进样,同时直接测定高纯金属镨中5个痕量稀土杂质元素的方法。并讨论了基体浓度对检出限的影响以及光谱干扰及其校正方法。当样品溶液中镨的浓度为5毫克/毫升时,测定下限分别为镧、钕和钐0.002%,铈0.003%和钇0.0005%。获得了良好的实验结果,其相对标准偏差为1.2-6.2%。     

高纯氧化钆中14种稀土杂质元素的柱色谱分离ICP-AES法测定

用Ｐ５０７萃淋树脂柱色谱法分离高纯氧化钆基体与稀土杂质。用ＩＣＰ－ＡＥＳ法测定高纯氧化钆中微量稀土杂质。选择了柱色谱分离的合适条件。优化了仪器测定参数。使本方法测定稀土总量下限小于１×１０－５％。可以满足９９．９９９９％高纯氧化钆的测定。此方法回收率在８５％－１１０％之间。相对标准偏差小于１８％。     

YAG∶Nd中Pr,Sm,Dy杂质对其光谱的影响

采用溶胶 -凝胶法合成了YAG∶Nd ,Re(Re =Pr,Sm ,Dy)系列化合物 ,研究了它们的光谱性质 .结果表明 :Pr3 + ,Sm3 + ,Dy3 + 对YAG基质中Nd3 + 的发光具有猝灭作用 ,属于有害杂质 ,在激光晶体原料中必须除去     

稀土在鼠肝中的物种分布

采用 ICP-MS、微量蛋白层析分离技术 ,研究了长时间、低剂量混合稀土及短时间、高剂量硝酸镧分别对大鼠及小鼠作用后 ,稀土在鼠肝中的代谢累积及其物种分布 .结果表明 ,鼠肝中各稀土含量随给药剂量的增加而增大 ,其累积速度大小顺序为 La>Ce>Nd>Pr;经柱层析分离未得到与稀土特异结合的鼠肝蛋白 ;稀土可与多种鼠肝蛋白 (包括多种重要的酶 )结合 ,在一定条件下 ,稀土与其结合蛋白可发生解离     

Identification of Earth Pigments by Applying Hierarchical Cluster Analysis to Solid State Voltammetry. Application to Severely Damaged Frescoes

Multivariate chemometric methods are applied for identifying earth pigments from square‐wave voltammetric measurements performed at pigment‐modified paraffin‐impregnated graphite electrodes allowing for a separation between hematite‐based earths, French ochres, Spanish ochres, siennas, umbers and green earths. This methodology is applied to the identification of the pigments in samples from the ceiling frescoes of Antonio Palomino (dated 1707) in the vaulted nave of the Sant Joan del Mercat church in Valencia (Spain). These frescoes suffered considerable damage by fire during the Spanish Civil War in 1936, resulting in severe chemical and chromatic alterations. Electrochemical data, supported by scanning electron microscopy/energy dispersive X‐ray microanalysis and Raman spectroscopy, indicate that thermal stress induced the transformation of goethite‐based yellow ochres into hematite and magnetite.