Laplace spectra as fingerprints for image recognition

In the area of image retrieval from data bases and for copyright protection of large image collections there is a growing demand for unique but easily computable fingerprints for images. These fingerprints can be used to quickly identify every image within a larger set of possibly similar images. This paper introduces a novel method to automatically obtain such fingerprints from an image. It is based on a reinterpretation of an image as a Riemannian manifold. This representation is feasible for gray value images and color images. We discuss the use of the spectrum of eigenvalues of different variants of the Laplace operator as a fingerprint and show the usability of this approach in several use cases. Contrary to existing works in this area we do not only use the discrete Laplacian, but also with a particular emphasis the underlying continuous operator. This allows better results in comparing the resulting spectra and deeper insights in the problems arising. We show how the well known discrete Laplacian is related to the continuous Laplace-Beltrami operator. Furthermore, we introduce the new concept of solid height functions to overcome some potential limitations of the method.     

Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data

The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub-shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7.8% for palustrine wetland/upland models and 17.0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents.     

Nonparametric estimation from length-biased data under competing risks

A new model for cross-sectional lifetime data is presented. The model is based on the length-bias assumption, and it is adapted to situations in which several types of censoring may occur. The NPMLE of the survival function is derived. An EM-algorithm to approximate the NPMLE is devised. The performance of the introduced estimator is investigated through simulations. A real set of data collected as part of a study on unemployment duration in Spain is used for illustration purposes.     

Networks for sorting multitonic sequences

Lee and Batcher have designed networks that efficiently merge k separately provided sorted sequences of known lengths totalling n. We show that the design is still possible, and in fact easier to describe, if we do not make use of the lengths, or even the directions of monotonicity, of the individual sequences—the sequences can be provided in a single undelimited concatenation of length n. The depth of the simplest resulting network to sort sequences that are “k-tonic” and of length n is , generalizing Batcher's 1968 results for the extreme values of k (k=2 corresponding to merging, and k=n/2 corresponding to general sorting).The exposition is self-contained and can serve even as an introduction to sorting networks and Batcher's results.     

包钢医疗保险信息管理系统的构建

本文论述了包钢医疗保险信息管理系统的网络设计、应用程序功能及主要技术,并说明了应用情况及使用价值.     

一种基于时间分层决策矩阵的综合评价算法

综合评价系统用四元总体（A，Z，X^(k)，D）表示，其中决策矩阵X^(k)是具有对象维i,指标维j,时间维k的三维矩阵，将三维矩阵X（k)按时间划分形成m个二维矩阵，每个二维矩阵分别代表每个年度n个评价对象对应于p个指标的属性集，讨论了按时间分层决策矩阵的多指标综合评价问题，描述了按时间分层的决策矩阵的求解算法，并采用Visual Foxpro数据库语言编程实现。     

Adherence clustering: an efficient method for mining market-basket clusters

We explore in this paper the efficient clustering of market-basket data. Different from those of the traditional data, the features of market-basket data are known to be of high dimensionality and sparsity. Without explicitly considering the presence of the taxonomy, most prior efforts on clustering market-basket data can be viewed as dealing with items in the leaf level of the taxonomy tree. Clustering transactions across different levels of the taxonomy is of great importance for marketing strategies as well as for the result representation of the clustering techniques for market-basket data. In view of the features of market-basket data, we devise in this paper a novel measurement, called the category-based adherence, and utilize this measurement to perform the clustering. With this category-based adherence measurement, we develop an efficient clustering algorithm, called algorithm k-todes, for market-basket data with the objective to minimize the category-based adherence. The distance of an item to a given cluster is defined as the number of links between this item and its nearest tode. The category-based adherence of a transaction to a cluster is then defined as the average distance of the items in this transaction to that cluster. A validation model based on information gain is also devised to assess the quality of clustering for market-basket data. As validated by both real and synthetic datasets, it is shown by our experimental results, with the taxonomy information, algorithm k-todes devised in this paper significantly outperforms the prior works in both the execution efficiency and the clustering quality as measured by information gain, indicating the usefulness of category-based adherence in market-basket data clustering.     

基于微机的X射线分析自动测量系统

本文介绍了一个基于微机的X射线分析自动测量系统。该系统有三个基本功能:自动获取数据、自动换样和处理谱数据。     

TiAl基合金粉末爆炸烧结特性常数的测算

本文测算了自蔓延高温合成TiAl基合金粉末的爆炸烧结特性常数值：γ＝1．44，lgβ＝25．25，并为试验证实，这为获取大尺寸TiAl基合金提供了理论依据。     

天然汞的全套中子数据评价

在１０￣（－５）＿ｅＶ－２０ＭｅＶ中子能量范围内对天然汞的中子核数据进行评价。评价内容包括总的、弹性、去弹、总非弹、３０条分立能级、连续非弹、（ｎ，２ｎ）、（ｎ，３ｎ，）、（ｎ，ｎ＇Ｐ）、（ｎ，ｎ＇α）、（ｎ，Ｐ）、（ｎ，α）、（ｎ，ｄ）、（ｎ，ｔ）和（ｎ，γ）截面；次级中子角分布；双微分截面（ＤＤＣＳ）；γ射线产生数据等。评价根据有效实验数据（至１９９３年）和ＵＮＦ－９２￣［１］理论计算。评价数据以ＥＮＤＦ／Ｂ６格式录入中国评价核数据库ＣＥＮＤＬ－２．１版（ＭＡＴ＝１８００），并提供核工程有关部门使用。