of the language semantics. The approach easily extends from polymorphic functional languages to imperative languages that provide references, exceptions, continuations, and similar features. We illustrate the technique with a type soundness theorem for the core of Standard ML, which includes the first type

A useful starting point for designing advanced graphical user interjaces is the Visual lnformation-Seeking Mantra: overview first, zoom and filter, then details on demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been

of distortions are distinguished. The first type are those which are the source of misregistration, i.e., they are the cause of the misalignment between the two images. Distortions which are the source of misregistration determine the transformation class which will optimally align the two images

types of sensors. The first type is capable of nique allows correlation of a asurement with Its a based on sending information which can be used to infer the numsubsequent, as well as previous, data. To keep the number of hypotheses ber of targets within the area of coverage of the sensor reasonable

There are two major ways of structuring data in programming languages. The first and common one, used for example in Pascal, can be said to derive from standard branches of mathematics. Data is organized as cartesian products (i.e. record types), disjoint sums (i.e. unions or variant types

, and Wadler) and give a detailed proof of type safety. The extended system formalizes for the first time some of the key features

Schema matching is a critical step in many applications, such as XML message mapping, data warehouse loading, and schema integration. In this paper, we investigate algorithms for generic schema matching, outside of any particular data model or application. We first present a taxonomy for past

Although initially introduced and studied in the late 1960s and early 1970s, statistical methods of Markov source or hidden Markov modeling have become increasingly popular in the last several years. There are two strong reasons why this has occurred. First the models are very rich in mathematical

definitions are given first. They are then commented upon, and supplemented by additional definitions, which address the threats to dependability and security (faults, errors, failures), their attributes, and the means for their achievement (fault prevention, fault tolerance, fault removal, fault forecasting

parties, officials, and scientists; complex, redundant, and layered institutions; a mix of institu-tional types; and designs that facilitate experimentation, learning, and change. In 1968, Hardin (1) drew attention to two human factors that drive environmental change. The first factor is the increasing de