Reflections on Transparency and Monitoring under the 1993 United States-Russian Federation Highly Enriched Uranium Purchase Agreement

ABSTRACT

The 1993 Highly Enriched Uranium (HEU) Purchase Agreement between the United States and Russian Federation is often described as one of the world's most successful nuclear nonproliferation programs. In 2013, the two states achieved the agreement's major goals of downblending 500 metric tons of Russian weapon-origin HEU to low enriched uranium (LEU) and delivering all resultant LEU to the United States. At one time, the LEU delivered under the agreement generated nearly 10 percent of all electricity in the United States. The agreement achieved its nonproliferation goals through a unique government/industry partnership. Commercial executive agents contracted for the annual sale and delivery of downblended LEU, while the US and Russian governments exercised reciprocal transparency monitoring measures to demonstrate that all LEU delivered under the agreement was derived from Russian weapons-origin HEU, and that the same LEU was used for exclusively peaceful purposes in the United States. The commercial development, negotiations, and implementation of the agreement have been well documented. This article describes the scope of US transparency monitoring activities in Russian HEU processing facilities, as well as Russian monitoring in the United States. In addition, it discusses the results of twenty years of reciprocal transparency monitoring and data analysis, and outlines lessons learned that are potentially applicable to future transparency monitoring and verification regimes and similar cooperative efforts.     

基于FCM与粗糙集的连续数据知识挖掘方法

对连续数据知识挖掘可采用模糊C均值聚类将其离散化,再应用粗糙集进行约简.文中针对该过程中样本数据离散化后出现多个聚类中心隶属度值较接近的情况,提出了隶属度重叠度的方法来扩展对应离散类别的选取.离散类别确定后再应用粗糙集理论对其进行知识挖掘,可得到连续数据的本质特性.此方法应用于汽轮机轴系振动数据的知识挖掘.与采用最大隶属度确定对应类别的方法相比,该方法不影响对连续数据的知识挖掘,并能真实反映连续数据的特性.     

多媒体数据挖掘中数据间的相似性度量研究

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入侵检测中频繁模式的有效挖掘算法

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基于数据仓库的数据挖掘技术在指挥控制系统中的应用

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A novel facial emotion recognition scheme based on graph mining

Recent years have seen an explosion in graph data from a variety of scientific, social and technological fields. From these fields, emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human, driver safety during driving, pain monitoring during surgery etc. A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region, where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion. To reduce the number of generated sub-graphs, overlap ratio metric is utilized for this purpose. After encoding the final selected sub-graphs, binary classification is then applied to classify the emotion of the queried input facial image using six levels of classification. Binary cat swarm intelligence is applied within each level of classification to select proper sub-graphs that give the highest accuracy in that level. Different experiments have been conducted using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the final system accuracy was 90.00%. The results show significant accuracy improvements (about 2%) by the proposed system in comparison to current published works in SAVEE database.     

Reactor-grade plutonium and nuclear weapons: ending the debate

The claim that reactor-grade plutonium cannot or will not be used to produce nuclear weapons has been used to justify non-nuclear-weapon states’ large stockpiles of plutonium that has been separated from highly radioactive spent fuel. However, by using reduced-mass plutonium cores, it is possible to manufacture reliable nuclear weapons with reactor-grade plutonium. These weapons can have the same design, size, weight, and predetonation probability as weapons using weapon-grade plutonium and would require no special cooling. The increased radiation from reactor-grade plutonium could be easily managed by shielding and operational procedures. Weapons using plutonium routinely produced by pressurized-water reactors could have a lethal area between 40 percent and 75 percent that of weapons using weapon-grade plutonium. In the past, both Sweden and Pakistan considered using reactor-grade plutonium to produce nuclear weapons, and India may be using reactor-grade plutonium in its arsenal today. Despite claims to the contrary, the United States used what was truly reactor-grade plutonium in a successful nuclear test in 1962. The capability of reactor-grade plutonium to produce highly destructive nuclear weapons leads to the conclusion that the separation of plutonium, plutonium stockpiling, and the use of plutonium-based fuels must be phased out and banned.     

基于数据挖掘的网络化教学研究

基于传统网络化教学系统存在的问题,应用数据挖掘的相关技术,对以往在网络化教学系统中不能对学生练习结果进行科学、客观分析的实际问题给出了初步解决方案。基于数据挖掘技术的网络化教学系统,其目的就是在一定程度上提高教师的教学效率。．     

数据挖掘在图像信息隐写分析中的应用

提出数据挖掘在图像信息隐写分析中的应用。首先计算原始图像和隐写图像在通用图像质量指标下的失真测度,然后利用数据挖掘技术建立一个预测模型,完成失真测度到已知隐写概率的映射,模型的输出即为预测隐写概率。实验结果表明,使用该方法得到的预测隐写概率和已知隐写概率的相关度,当使用训练样本时达到0.951以上,使用测试样本时达到0.943以上,达到了很好的预测效果。     

支持度门限改变下的频繁闭项集增量挖掘

针对CLOSET算法每次改变支持度门限时需要重新对所有数据进行挖掘而没有充分利用前一次挖掘结果的问题,提出了基于支持度门限改变下的频繁闭项集增量挖掘算法,利用前一次发现的频繁闭项集作为本次挖掘的约束条件,极大地减少了频繁闭项集的搜索空间。实验结果表明:在支持度门限减小不大的情况下,算法效率提高显著,支持度门限增加时,只需几十个毫秒的时间就可以发现全部频繁闭项集。