An extension of Lehman's theorem and ideal set functions

Lehman’s theorem on the structure of minimally nonideal clutters is a fundamental result in polyhedral combinatorics. One approach to extending it has been to give a common generalization with the characterization of minimally imperfect clutters (Sebő, 1998; Gasparyan et al., 2003). We give a new generalization of this kind, which combines two types of covering inequalities and works well with the natural definition of minors. We also show how to extend the notion of idealness to unit-increasing set functions, in a way that is compatible with minors and blocking operations

Temperament in Bach's Well-tempered clavier : a historical survey and a new evaluation according to dissonance theory

After a historical survey of temperament in Bach's Well-Tempered Clavier by Johann Sebastian Bach, an analysis of the work has been made by applying a number of historical good temperaments as well as some recent proposals. The results obtained show that the global dissonance for all preludes and fugues in major keys can be minimized using the Kirnberger II temperament. The method of analysis used for this research is based on the mathematical theories of sensory dissonance, which have been developed by authors such as Hermann Ludwig Ferdinand von Helmholtz, Harry Partch, Reinier Plomp, Willem J. M. Levelt and William A. SetharesDesprés d'una visió històrica sobre el temperament a El clavecí ben temperat de Johann Sebastian Bach, s'ha realitzat una anàlisi de l'obra aplicant divesos bons temperaments històrics a més d'algunes propostes recents. Els resultats obtinguts demostren que la dissonància global per a tots els preludis i fugues en tonalitats majors pot minimitzar-se utilitzant el temperament Kirnberger II. El mètode d'anàlisi utilitzat per a aquesta recerca està basat en les teories matemàtiques de la dissonància sensorial desenvolupades per autors com Hermann Ludwig Ferdinand von Helmholtz, Harry Partch, Reinier Plomp, Willem J. M. Levelt i William A. Sethare

Non Linear Modelling of Financial Data Using Topologically Evolved Neural Network Committees

Most of artificial neural network modelling methods are difficult to use as maximising or minimising an objective function in a non-linear context involves complex optimisation algorithms. Problems related to the efficiency of these algorithms are often mixed with the difficulty of the a priori estimation of a network's fixed topology for a specific problem making it even harder to appreciate the real power of neural networks. In this thesis, we propose a method that overcomes these issues by using genetic algorithms to optimise a network's weights and topology, simultaneously. The proposed method searches for virtually any kind of network whether it is a simple feed forward, recurrent, or even an adaptive network. When the data is high dimensional, modelling its often sophisticated behaviour is a very complex task that requires the optimisation of thousands of parameters. To enable optimisation techniques to overpass their limitations or failure, practitioners use methods to reduce the dimensionality of the data space. However, some of these methods are forced to make unrealistic assumptions when applied to non-linear data while others are very complex and require a priori knowledge of the intrinsic dimension of the system which is usually unknown and very difficult to estimate. The proposed method is non-linear and reduces the dimensionality of the input space without any information on the system's intrinsic dimension. This is achieved by first searching in a low dimensional space of simple networks, and gradually making them more complex as the search progresses by elaborating on existing solutions. The high dimensional space of the final solution is only encountered at the very end of the search. This increases the system's efficiency by guaranteeing that the network becomes no more complex than necessary. The modelling performance of the system is further improved by searching not only for one network as the ideal solution to a specific problem, but a combination of networks. These committces of networks are formed by combining a diverse selection of network species from a population of networks derived by the proposed method. This approach automatically exploits the strengths and weaknesses of each member of the committee while avoiding having all members giving the same bad judgements at the same time. In this thesis, the proposed method is used in the context of non-linear modelling of high-dimensional financial data. Experimental results are'encouraging as both robustness and complexity are concerned.Imperial Users onl

Acquiring data designs from existing data-intensive programs

The problem area addressed in this thesis is extraction of a data design from existing data intensive program code. The purpose of this is to help a software maintainer to understand a software system more easily because a view of a software system at a high abstraction level can be obtained. Acquiring a data design from existing data intensive program code is an important part of reverse engineering in software maintenance. A large proportion of software systems currently needing maintenance is data intensive. The research results in this thesis can be directly used in a reverse engineering tool. A method has been developed for acquiring data designs from existing data intensive programs, COBOL programs in particular. Program transformation is used as the main tool. Abstraction techniques and the method of crossing levels of abstraction are also studied for acquiring data designs. A prototype system has been implemented based on the method developed. This involved implementing a number of program transformations for data abstraction, and thus contributing to the production of a tool. Several case studies, including one case study using a real program with 7000 Hues of source code, are presented. The experiment results show that the Entity-Relationship Attribute Diagrams derived from the prototype can represent the data designs of the original data intensive programs. The original contribution of the thesis is that the approach presented in this thesis can identify and extract data relationships from the existing code by combining analysis of data with analysis of code. The approach is believed to be able to provide better capabilities than other work in the field. The method has indicated that acquiring a data design from existing data intensive program code by program transformation with human assistance is an effective method in software maintenance. Future work is suggested at the end of the thesis including extending the method to build an industrial strength tool

An approach to modelling and describing software evolution processes

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

NASA Tech Briefs, January 1999

Topics include: special coverage sections on sensors and data acquisition and sections on electronic components and circuits, electronic software, materials, mechanics, bio-medical physical sciences, book and reports, and a special section of Photonics Tech Briefs

Bridgewater College Catalog, Session 1920-21

https://digitalcommons.bridgewater.edu/college_catalogs/1032/thumbnail.jp