The Luminosity Distribution of Local Group Galaxies

From a rediscussion of Local Group membership, and of distances to individual galaxies, we obtain $M_V$ values for 35 probable and possible Local Group members. The luminosity function of these objects is well fitted by a Schechter function with faint end slope $\alpha = -1.1 \pm 0.1$. The probability that the luminosity distribution of the Local Group is a single Schechter function with $\alpha$ steeper than -1.3 is less than 1 per cent. However, more complicated luminosity functions, such as multi-component Schechter functions with steep faint-end slopes, cannot be ruled out. There is some evidence that the luminosity distribution of dwarf spheroidal galaxies in the Local Group is steeper than that of dwarf irregular galaxies.Comment: 13 pages, 2 figures, accepted for publication in The Astronomical Journal. Figure 2 replaced, conclusion based on this figure change

Competitive exception learning using fuzzy frequency distributions

A competitive exception learning algorithm for finding a non-linear mapping is proposed which puts the emphasis on the discovery of the important exceptions rather than the main rules. To do so,we first cluster the output space using a competitive fuzzy clustering algorithm and derive a fuzzy frequency distribution describing the general, average system's output behavior. Next, we look for a fuzzy partitioning of the input space in such away that the corresponding fuzzy output frequency distributions `deviate at most' from the average one as found in the first step. In this way, the most important `exceptional regions' in the input-output relation are determined. Using the joint input-output fuzzy frequency distributions, the complete input-output function as extracted from the data, can be expressed mathematically. In addition, the exceptions encountered can be collected and described as a set of fuzzy if-then-else-rules. Besides presenting a theoretical description of the new exception learning algorithm, we report on the outcomes of certain practical simulations.competitive learning;exception learning;fuzzy pattern recognition

Noncommutative curves and noncommutative surfaces

In this survey article we describe some geometric results in the theory of noncommutative rings and, more generally, in the theory of abelian categories. Roughly speaking and by analogy with the commutative situation, the category of graded modules modulo torsion over a noncommutative graded ring of quadratic, respectively cubic growth should be thought of as the noncommutative analogue of a projective curve, respectively surface. This intuition has lead to a remarkable number of nontrivial insights and results in noncommutative algebra. Indeed, the problem of classifying noncommutative curves (and noncommutative graded rings of quadratic growth) can be regarded as settled. Despite the fact that no classification of noncommutative surfaces is in sight, a rich body of nontrivial examples and techniques, including blowing up and down, has been developed.Comment: Suggestions by many people (in particular Haynes Miller and Dennis Keeler) have been incorporated. The formulation of some results has been improve

Financial Markets Analysis by Probabilistic Fuzzy Modelling

For successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one???s actions. In this paper, we propose to use probabilistic fuzzy systems for this purpose. We concentrate on Takagi???Sugeno (TS) probabilistic fuzzy systems that combine interpretability of fuzzy systems with the statistical properties of probabilistic systems. We start by recapitulating the general architecture of TS probabilistic fuzzy rule-based systems and summarize the corresponding reasoning schemes. We mention how probabilities can be estimated from a given data set and how a probability distribution can be approximated by a fuzzy histogram. We apply our methodology for financial time series analysis and demonstrate how a probabilistic TS fuzzy system can be identified, assuming that a linguistic term set is given. We illustrate the interpretability of such a system by inspecting the rule bases of our models.time series analysis;data-driven design;fuzzy reasoning;fuzzy rule base;probabilistic fuzzy systems

Relative Distress and Return Distribution Characteristics of Japanese Stocks, a Fuzzy-Probabilistic Approach

In this article, we demonstrate that a direct relation exists between the context of Japanese firms indicating relative distress and conditional return distribution properties. We map cross-sectional vectors with company characteristics on vectors with return feature vectors, using a fuzzy identification technique called Competitive Exception Learning Algorithm (CELA)1. In this study we use company characteristics that follow from capital structure theory and we relate the recognized conditional return properties to this theory. Using the rules identified by this mapping procedure this approachenables us to make conditional predictions regarding the probability of a stock's or a group of stocks' return series for different return distribution classes (actually return indices). Using these findings, one may construct conditional indices that may serve as benchmarks. These would be particularly useful for tracking and portfolio management.capital structure;asset pricing;fuzzy systems;conditional return distribution;heuristic learning

Shear-free perfect fluids with a solenoidal electric curvature

We prove that the vorticity or the expansion vanishes for any shear-free perfect fluid solution of the Einstein field equations where the pressure satisfies a barotropic equation of state and the spatial divergence of the electric part of the Weyl tensor is zero.Comment: 9 page

The Stellar Mass Spectrum in the Young Populous Cluster NGC 1866

The young populous cluster NGC 1866 in the Large Magellanic Cloud LMC), which is probably one of the most massive object formed in the LMC during the last ~ 3 Gyr, appears to have an unexpectedly high mass-to-light ratio. From its velocity dispersion Fischer et al. (1992) find its mass to be (1.35 " 0.25) x 105 Mu. The luminosity of this cluster is MV = -8.93 " 0.13, corresponding to LV = (3.2 " 0.4) x 105 LV (u). This yields M/LV = 0.42 " 0.09 in solar units. For a cluster of age 0.1 Gyr such a relatively high mass-to-light ratio requires a mass spectrum with an exponent x = 1.72 " 0.09; or x = 1.75 " 0.09 if mass loss by evolving stars is taken into account.Comment: To be published in the October 1999 issue of the Publications of the Astronomical Society of the Pacifi