Abstract not found

We suggest a solution for iterative processing in data flow diagrams based on the notion of a conditional data flow switch, and a specialized iterative construct based on pattern matching for vectors and matrices. Both of these constructs can be seamlessly incorporated into a data flow visual programming language. We demonstrate how these constructs may be used to reveal the spatial/temporal dualism of data streams.

The goal of this paper is to provide a uniform overview of the unification problem in algebras capable of describing sets. The problem has been tackled, directly and indirectly, by many researchers and it can find important applications in various research areas - e.g., deductive databases, theorem proving, static analysis, rapid software prototyping. The problem has been explored in depth, but the various solutions proposed are spread across a large literature, and some of the approaches have been ignored and/or rediscovered by different researchers. In this

The goal of this paper is to provide a uniform overview of the unification problem in the algebras capable of describing sets. The problem has been tackled, directly and indirectly, by many researchers and finds important applications in various research areas -- e.g., deductive databases, theorem proving, static analysis, rapid software prototyping. The problem has been explored in depth, but the various solutions proposed are spread across a large literature, and some of the approaches have been ignored and/or rediscovered by different researchers. In this paper we provide a uniform presentation of unification of sets, starting with the simpler instances of the problem -- e.g., matching of completely ground terms -- and proceeding to progressively more complex problems -- e.g., unification between general ACI terms. The algorithms presented are partly drawn from the literature -- and properly revisited and analyzed -- and partly novel proposals by the authors. The major contribution of this work is to provide the first uniform presentation of the problem, covering all its different instances, surveying at the same time the different solutions developed.

Abstract- SequenceL is a concise, high-level language with a simple semantic that provides for the automatic derivation of many iterative and parallel control structures. The semantic repeatedly applies a “Normalize-Transpose ” operation to functions and operators until base cases are discovered, which include the grounding of variables and the application of built-in operators to operands of appropriate types. This paper introduces the language from an intuitive point of view, indicating the scalability of the normalize-transpose, the different forms of recursion supported by the language and its abstraction, and new shorthand methods for specifying additional iterative/recursive problem solutions.

Abstract. We present a high level language called SequenceL. The language is relationships between their inputs and outputs, rather than in terms of algorithms for carrying out computations. The semantics of the language are capable of automatically discovering and implementing the required algorithms, including iterative and parallel control structures in many cases. Current implementations are prototypes only, and do not produce code of comparable efficiency to that of a good human programmer. Current implementations can, however, be used as a tool to guide human programmers in discovering and comparing options for parallelizing their solutions. This paper describes the language and approach, and demonstrates the use of SequenceL to discover options for parallelizing matrix computations. 1 Introduction and Related Work SequenceL is a compact, Turing-complete, high-level language, in which algorithms for implementing a solution are automatically derived from a high level description of

hereby granted, provided that this notice and the reference

Abstract not found

SequenceL is a concise, high-level language with a simple semantic that provides for the automatic derivation of many iterative and parallel control structures. The semantic repeatedly applies a “Normalize-Transpose-Distribute ” operation to functions and operators until base cases are discovered. Base cases include the grounding of variables and the application of built-in operators to operands of appropriate types. This paper introduces the results of a 24-month effort to reduce the language to a very small set of primitives. Included are comparisons with other languages, the formal syntax and semantics, and the traces of several example problems run with a prototype interpreter developed in 2006.

Satellite images already produce huge amounts of data, which makes their processing a serious computational challenge. This problem will become even more complicated with the launch of multi-spectral Earth-imaging satellites that will increase the amount of information by at least two orders of magnitude. With such a huge amount of information, it is necessary to come up with data processing methods that are as fast as possible. In particular, we show that for fuzzy processing techniques, this leads to the necessity to use integer-valued fuzzy sets. Keywords--- Satellite imaging, multi-spectral, computational complexity, integer-valued fuzzy sets I. Hyper-Spectral Satellite Imaging: Challenges A. Satellite Imaging Nowadays, satellite imaging is one of the most important sources of geographical, geophysical, and environmental information. Satellite images can determine the amount and type of vegetation, the geological type of the underlying soils (and often, of the minerals below), e...