We explore some foundational issues in the development of a theory of intensional semantics. A programming language may be given a variety of semantics, differing in the level of abstraction; one generally chooses the semantics at an abstraction level appropriate for reasoning about a particular kind of program property. Extensional semantics are typically appropriate for proving properties such as partial correctness, but an intensional semantics at a lower abstraction level is required in order to reason about computation strategy and thereby support reasoning about intensional aspects of behavior such as order of evaluation and efficiency. It is obviously desirable to be able to establish sensible relationships between two semantics for the same language, and we seek a general category-theoretic framework that permits this. Beginning with an "extensional" category, whose morphisms we can think of as functions of some kind, we model a notion of computation as a comonad with certain e...

ing from syntax. Conventionally an equation for algebra ' is just a pair of terms built from variables, the constituent operations of ' , and some fixed standard operations. An equation is valid if the two terms are equal for all values of the variables. We are going to model a syntactic term as a morphism that has the values of the variables as source. For example, the two terms ` x ' and ` x join x ' (with variable x of type tree ) are modeled by morphisms id and id \Delta id ; join of type tree ! tree . So, an equation for ' is modeled by a pair of terms (T '; T 0 ') , T and T 0 being mappings of morphisms which we call `transformers'. This faces us with the following problem: what properties must we require of an arbitrary mapping T in order that it model a classical syntactic Datatype Laws without Signatures 7 term? Or, rather, what properties of classical syntactic terms are semantically essential, and how can we formalise these as properties of a transformer T ? Of course...

We propose to standardize object-oriented metrics definitions using the Object Constraint Language (OCL), a part of the Unified Modeling Language (UML) standard, and a meta-model of the modeling formalism. OCL allows specifying invariants, preconditions, postconditions and other types of constraints. To illustrate this approach, we describe the MOOD2 metrics in OCL, based upon the meta-model of our object design modeling formalism – the GOODLY language. The outcome is, we believe, an elegant, precise and straightforward way to define metrics that may help to overcome several current problems. Besides, it is a natural approach since we are using object technology to define metrics on object technology itself. 1.

We present ReactiveML, a programming language dedicated to the implementation of complex reactive systems as found in graphical user interfaces, video games or simulation problems. The language is based on the reactive model introduced by Boussinot. This model combines the so-called synchronous model found in Esterel which provides instantaneous communication and parallel composition with classical features found in asynchronous models like dynamic creation of processes. The language comes as a conservative extension of an existing call-by-value ML language and it provides additional constructs for describing the temporal part of a system. The language receives a behavioral semantics à la Esterel and a transition semantics describing precisely the interaction between ML values and reactive constructs. It is statically typed through a Milner type inference system and programs are compiled into regular ML programs. The language has been used for programming several complex simulation problems (e.g., routing protocols in mobile ad-hoc networks).

Unification, or two-way pattern matching, is the process of solving an equation involving two first-order terms with variables. Unification is used in type inference in many programming languages and in the execution of logic programs. This means that unification algorithms have to be written over and over again for different termtypes. Many other functions also make sense for a large class of datatypes - examples are pretty printers, equality checks, maps etc. They can be defined by induction on the structure of userdefined datatypes. Implementations of these functions for different datatypes are closely related to the structure of the datatypes. We call such functions polytypic. This paper describes a unification algorithm parametrised on the type of the terms and shows how to use polytypism to obtain a unification algorithm that works for all regular term types.

Object-oriented computing is influencing many areas of computer science including software engineering, user interfaces, operating systems, programming languages and database systems. The appeal of object-orientation is attributed to its higher levels of abstraction for modeling real world concepts, its support for incremental development and its potential for interoperability. Despite many advances, object-oriented computing is still in its infancy and a universally acceptable definition of an object-oriented data model is virtually nonexistent, although some standardization efforts are underway. This report presents the TIGUKAT 1 object model definition that is the result of an investigation of object-oriented modeling features which are common among earlier proposals, along with some distinctive qualities that extend the power and expressibility of this model beyond others. The literature recognizes two perspectives of an object model: the structural view and the behavioral view. ...

In this paper we first formal ly define the notions of data fusion andde3W#3I fusion.Thenwe formulate a theorem that decision fusion is a special case of data fusion. We show the meaning of this theorem on a simple example of edge detection. Edge detection can be done in two ways: by first fusing the original images and then detecting edges in the fused image (data fusion) or by first detecting edges in each image separately and then fusing the results (decision fusion) of edge detection in the decision fusion block. We show, first in general and then on the edge detection example, that decision fusion can be viewed as a special case of data fusion. To the designer of an information fusion system this means that the choice of the decision fusion approach over data fusion in any specific case needs to be supported by some additional consideration, for instance the computational complexity of the fusion algorithm.

We investigate the semantics of concurrent constraint programming and of various sublanguages, with particular emphasis on nondeterminism and infinite behavior. The aim is to find out what is the minimal structure which a domain must have in order to capture these two aspects. We show that a notion of observables, obtained by the upward-closure of the results of computations, is relatively easy to model even in presence of synchronization. On the contrary modeling the exact set of results is problematic, even for the simple sublanguage of constraint logic programming. We show that most of the standard topological techniques fail in capturing this more precise notion of observables. The analysis of these failed attempts leads us to consider a categorical approach.

. This paper is about the relationship between the theory of monadic types and the practice of concurrent functional programming. We present a typed functional programming language CMML, with a type system based on Moggi's monadic metalanguage, and concurrency based on Reppy's Concurrent ML. We present an operational and denotational semantics for the language, and show that the denotational semantics is fully abstract for may-testing. We show that a fragment of CML can be translated into CMML, and that the translation is correct up to weak bisimulation. Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 Mathematical preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1 Categories and monads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Partial orders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....

A comprehensive approach to the theory of higher spin gauge fields is proposed. By explicitly separating out details of implementation from general principles, it becomes possible to focus on the bare minimum of requirements that such a theory must satisfy. The abstraction is based on a survey of the progress that has been achieved since relativistic wave equations for higher spin fields were first considered in the nineteen thirties. As a byproduct, a formalism is obtained that is abstract enough to describe a wide class of classical field theories. The formalism, viewed as syntax, can then be semantically mapped to a category of homotopy Lie algebras, thus showing that the theory in some sense exists, at least as an abstract mathematical structure. Still, a concrete physics-like, implementation remains to be constructed. Lacking deep physical insight into the problem, an implementation in terms of generalized vertex operators is set up within which a brute force iterative determination of the first few orders in the interaction can be attempted.