A Standard Model-Theoretic Approach to Operational Semantics of Recursive Programs

In this paper we try to introduce a new approach to operational semantics of recursive programsby using ideas in the“priority method”which is a fundamental tool in Recursion Theory.In lieu ofmodelling partial functions by introducing undefined values in a traditional approach,we shall define apriority derivation tree for every term,and by respecting the rule“attacking the subterm of thehighest priority first”we define transition relations,computation sequences etc.directly based on astandard interpretation which includes no undefined value in its domain.Finally,we prove that ournew approach generates the same operational semantics as the traditional one.It is also pointed outthat we can use our strategy to refute a claim of Loeckx and Sieber that the opperational semanticsof recursive programs cannot be built based on predicate logic.     

难选氧化锰矿的选别工艺特征

氧化锰矿的泥化现象一般较严重，有的矿石很容易在机械作用下碎裂泥化，并且锰在粗、细粒级中的含量差别较大，因此在选别过程中避免过碎、强搅拌、强擦洗，并利用合理的分级工序，往往能获得十分理想的选别指标。     

Incremental wrapper-based gene selection from microarray data for cancer classification

Gene expression microarray is a rapidly maturing technology that provides the opportunity to assay the expression levels of thousands or tens of thousands of genes in a single experiment. We present a new heuristic to select relevant gene subsets in order to further use them for the classification task. Our method is based on the statistical significance of adding a gene from a ranked-list to the final subset. The efficiency and effectiveness of our technique is demonstrated through extensive comparisons with other representative heuristics. Our approach shows an excellent performance, not only at identifying relevant genes, but also with respect to the computational cost.     

基于语法的元程序设计系统的自动生成技术

本文提出一种基于语法的元程序设计系统的自动生成技术，并开发了一个元程序设计系统的自动生成系统Ａ－ＭＰＳ。元程序设计系统是一种以程序作为操作对象的有效的程序设计工具，并具有广泛的应用。     

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.     

快中子零功率反应堆(DF-VI)的临界事故概率分析

本文应用故障树分析方法,对快中子零功率堆(DF-Ⅵ)在启动过程中发生临界事故的可能性做了初步的定量估计。结果表明,该堆每次启动发生临界事故的概率约为10~(-6)量级,得出确保装置启动安全的首要因素是选配合格的操作人员的结论,并对设备的改进提出了一些建议.     

斜井提升钢丝绳磨损“失效树”分析

介绍了引起斜井提升钢丝绳失效的各种因素 ,并建立“失效树” ,为煤矿生产中判断其失效原因及预防提供可靠依据。     

Tree-based partitioning of date for association rule mining

The most computationally demanding aspect of Association Rule Mining is the identification and counting of support of the frequent sets of items that occur together sufficiently often to be the basis of potentially interesting rules. The task increases in difficulty with the scale of the data and also with its density. The greatest challenge is posed by data that is too large to be contained in primary memory, especially when high data density and/or low support thresholds give rise to very large numbers of candidates that must be counted. In this paper, we consider strategies for partitioning the data to deal effectively with such cases. We describe a partitioning approach which organises the data into tree structures that can be processed independently. We present experimental results that show the method scales well for increasing dimensions of data and performs significantly better than alternatives, especially when dealing with dense data and low support thresholds. Shakil Ahmed received a first class BSc (Hons) degree from Dhaka University, Bangladesh, in 1990; and an MSc (first class), also Dhaka University, in 1992. He received his PhD from The University of Liverpool, UK, in 2005. From 2000 onwards he is a member of the Data Mining Group at the Department of Computer Science of the University of Liverpool, UK. His research interests include data mining, Association Rule Mining and pattern recognition. Frans Coenen has been working in the field of Data Mining for many years and has written widely on the subject. He received his PhD from Liverpool Polytechnic in 1989, after which he took up a post as a RA within the Department of Computer Science at the University of Liverpool. In 1997, he took up a lecturing post within the same department. His current Data Mining research interests include Association rule Mining, Classification algorithms and text mining. He is on the programme committee for ICDM'05 and was the chair for the UK KDD symposium (UKKDD'05). Paul Leng is professor of e-Learning at the University of Liverpool and director of the e-Learning Unit, which is responsible for overseeing the University's online degree programmes, leading to degrees of MSc in IT and MBA. Along with e-Learning, his main research interests are in Data Mining, especially in methods of discovering Association Rules. In collaboration with Frans Coenen, he has developed efficient new algorithms for finding frequent sets and is exploring applications in text mining and classification.     

以数字搜索树表示的二值图象的几何变换

本文介绍以数字搜索树表示的二值图象的一种几何变换算法，文中讨论了四分区与正四边区的基本关系，输出ＤＳＴ结点成立的充分条件，给出了完整的几何变换算法并对算法的复杂性作了分析。     

基于Sql Server BI平台和决策树技术的CET-4成绩分析

文章首先对Sql Server 2008商业智能平台及决策树技术相关理论作了介绍,然后对挖掘数据源数据进行了一系列预处理。利用Sql Server 2008商业智能平台下的决策树技术并采用数据挖掘扩展语言即DMX语言创建了CET-4成绩分析决策树模型。分类矩阵和挖掘提升图分别对该模型的评估结果表明了模型具有较高的可靠性和分类准确度,同时模型对应的一些规则可作为英语教学管理和改革的重要参考依据。