The patterns discussed in this section are some of the most common, basic and important design patterns one can find in the areas of object-oriented design and programming. Some of these fundamental design patterns, such as the Interface, Abstract Parent, Private Methods, etc., are used extensively

In 1970, Knuth, Pratt, and Morris [1] showed how to do basic pattern matching in linear time. Related problems, such as those discussed in [4], have previously been solved by efficient but sub-optimal algorithms. In this paper, we introduce an interesting data structure called a bi-tree. A linear

This paper presents the view that (1) a significant proportion of the thousands of UFO reports analyzed by the authors come from witnesses who have really observed an object in the sky or at ground level; (2) the objects these witnesses have seen have characteristics very different from all identifiable objects and phenomena; (3) the phenomenon is of high scientific interest; and (4) a systematic research approach can be defined. These conclusions are based on many years of research into the phenomenon, in the course of which the authors have had at their disposal the official Air Force files of the United States and of France, as well as files of several reliable private organizations. The paper presents the results of computer statistics obtained independently, and it describes some correlations between geomagnetic disturbances and UFO observations.

A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data

In a previous work ("Abstract Data Type Systems", TCS 173(2), 1997), the leI two authors presented a combined lmbined made of a (strongl normal3zG9 alrmal rewrite system and a typed #-calA#Ik enriched by pattern-matching definitions folnitio a certain format,calat the "

, small balanced separators can be found efficiently. Faithful low-dimensional representations of statistical data allow for meaningful and efficient clustering, which is one of the most basic tasks in pattern-recognition. For the (mostly heuristic) methods used

of digital data. These theories and tools are the subject of the emerging field of knowledge discovery in databases (KDD). At an abstract level, the KDD field is concerned with the development of methods and techniques for making sense of data. The basic problem addressed by the KDD process is one of mapping

comparisons and linear regressions to describe spending on health and how it is financed. The data cover all sources — out-of-pocket spending, social insurance contributions, financing from government general revenues and voluntary and employment-related private insurance — classified according to their completeness and reliability. Total health spending rises from around 2–3 % of gross domestic product (GDP) at low incomes (<US $ 1000 per capita) to typically 8–9 % at high incomes (>US $ 7000). Surprisingly, there is as much relative variation in the share for poor countries as for rich ones, and even more relative variation in amounts in US$. Poor countries and poor people that most need protection from financial catastrophe are the least protected by any form of prepayment or risk-sharing. At low incomes, out-of-pocket spending is high on average and varies from 20–80 % of the total; at high incomes that share drops sharply and the variation narrows. Absolute out-of-pocket expenditure nonetheless increases with income. Public financing increases faster, and as a share of GDP, and converges at high incomes. Health takes an increasing share of total public expenditure as income rises, from 5–6 % to around 10%. This is arguably the opposite of the relation between total health needs and need for public spending, for any given combination of services. Within public spending, there is no convergence in the type of finance — general revenue versus social insurance. Private insurance is usually insignificant except in some rich countries.

during search and its most distinguishing feature is the augmentation of basic backtracking search with a powerfil confzict analysis procedure. Analyzing confzicts to determine their cawes enables GRASP to backtrack non-chronologically to earlier levels in the search tree, potentially pruning large

Korean natural language queries are composed of a number of basic building blocks. This paper describes the process to identify the basic patterns considered to be basic building blocks constructing Korean queries. Two sets of Korean queries generated by two groups of senior-level students