Category Theory and Model-Driven Engineering: From Formal Semantics to Design Patterns and Beyond

There is a hidden intrigue in the title. CT is one of the most abstract mathematical disciplines, sometimes nicknamed "abstract nonsense". MDE is a recent trend in software development, industrially supported by standards, tools, and the status of a new "silver bullet". Surprisingly, categorical patterns turn out to be directly applicable to mathematical modeling of structures appearing in everyday MDE practice. Model merging, transformation, synchronization, and other important model management scenarios can be seen as executions of categorical specifications. Moreover, the paper aims to elucidate a claim that relationships between CT and MDE are more complex and richer than is normally assumed for "applied mathematics". CT provides a toolbox of design patterns and structural principles of real practical value for MDE. We will present examples of how an elementary categorical arrangement of a model management scenario reveals deficiencies in the architecture of modern tools automating the scenario.Comment: In Proceedings ACCAT 2012, arXiv:1208.430

Determinants of Long-term Economic Development: An Empirical Cross-country Study Involving Rough Sets Theory and Rule Induction

Empirical findings on determinants of long-term economic growth are numerous, sometimes inconsistent, highly exciting and still incomplete. The empirical analysis was almost exclusively carried out by standard econometrics. This study compares results gained by cross-country regressions as reported in the literature with those gained by the rough sets theory and rule induction. The main advantages of using rough sets are being able to classify classes and to discretize. Thus, we do not have to deal with distributional, independence, (log-)linearity, and many other assumptions, but can keep the data as they are. The main difference between regression results and rough sets is that most education and human capital indicators can be labeled as robust attributes. In addition, we find that political indicators enter in a non-linear fashion with respect to growth.Economic growth, Rough sets, Rule induction

Shapes of Molecular Cloud Cores and the Filamentary Mode of Star Formation

Using recent dust continuum data, we generate the intrinsic ellipticity distribution of dense, starless molecular cloud cores. Under the hypothesis that the cores are all either oblate or prolate randomly-oriented spheroids, we show that a satisfactory fit to observations can be obtained with a gaussian prolate distribution having a mean intrinsic axis ratio of 0.54. Further, we show that correlations exist between the apparent axis ratio and both the peak intensity and total flux density of emission from the cores, the sign of which again favours the prolate hypothesis. The latter result shows that the mass of a given core depends on its intrinsic ellipticity. Monte Carlo simulations are performed to find the best-fit power law of this dependence. Finally, we show how these results are consistent with an evolutionary scenario leading from filamentary parent clouds to increasingly massive, condensed, and roughly spherical embedded cores.Comment: 16 pages, incl. 11 Postscript figures. Accepted by Ap

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

Rational physical agent reasoning beyond logic

The paper addresses the problem of defining a theoretical physical agent framework that satisfies practical requirements of programmability by non-programmer engineers and at the same time permitting fast realtime operation of agents on digital computer networks. The objective of the new framework is to enable the satisfaction of performance requirements on autonomous vehicles and robots in space exploration, deep underwater exploration, defense reconnaissance, automated manufacturing and household automation