Principles of Pitch Organization in Béla Bartók's 'Duke Bluebeard's Castle'

The topic of my doctoral thesis is to demonstrate the usefulness of incorporating tonal and modal elements into a pitch-web square analysis of Béla Bartók's (1881-1945) opera, 'A kékszakállú herceg vára' ('Duke Bluebeard's Castle'). My specific goal is to demonstrate that different musical materials, which exist as foreground melodies or long-term key progressions, are unified by the unordered pitch set {0,1,4}, which becomes prominent in different sections of Bartók's opera. In Bluebeard's Castle, the set {0,1,4} is also found as a subset of several tetrachords: {0,1,4,7}, {0,1,4,8}, and {0,3,4,7}. My claim is that {0,1,4} serves to link music materials between themes, between sections, and also between scenes. This study develops an analytical method, drawn from various theoretical perspectives, for conceiving superposed diatonic spaces within a hybrid pitch-space comprised of diatonic and chromatic features. The integrity of diatonic melodic lines is retained, which allows for a non-reductive understanding of diatonic superposition, without appealing to pitch centers or specifying complete diatonic collections. Through combining various theoretical insights of the Hungarian scholar Ernő Lendvai, and the American theorists Elliott Antokoletz, Paul Wilson and Allen Forte, as well as the composer himself, this study gives a detailed analysis of the opera's pitch material in a way that combines, complements, and expands upon the studies of those scholars. The analyzed pitch sets are represented on Aarre Joutsenvirta's note-web square, which adds a new aspect to the field of Bartók analysis. Keywords: Bartók, Duke Bluebeard's Castle (Op. 11), Ernő Lendvai, axis system, Elliott Antokoletz, intervallic cycles, intervallic cells, Allen Forte, set theory, interval classes, interval vectors, Aarre Joutsenvirta, pitch-web square, pitch-web analysis.

Inverse Problem for Fractional Brownian Motion with Discrete Data

The problem of recovering information from measurement data has already been studied for a long time. In the beginning, the methods were mostly empirical, but already towards the end of the sixties Backus and Gilbert started the development of mathematical methods for the interpretation of geophysical data. The problem of recovering information about a physical phenomenon from measurement data is an inverse problem. Throughout this work, the statistical inversion method is used to obtain a solution. Assuming that the measurement vector is a realization of fractional Brownian motion, the goal is to retrieve the amplitude and the Hurst parameter. We prove that under some conditions, the solution of the discretized problem coincides with the solution of the corresponding continuous problem as the number of observations tends to infinity. The measurement data is usually noisy, and we assume the data to be the sum of two vectors: the trend and the noise. Both vectors are supposed to be realizations of fractional Brownian motions, and the goal is to retrieve their parameters using the statistical inversion method. We prove a partial uniqueness of the solution. Moreover, with the support of numerical simulations, we show that in certain cases the solution is reliable and the reconstruction of the trend vector is quite accurate.

Computationally Efficient Methods for MDL-Optimal Density Estimation and Data Clustering

The Minimum Description Length (MDL) principle is a general, well-founded theoretical formalization of statistical modeling. The most important notion of MDL is the stochastic complexity, which can be interpreted as the shortest description length of a given sample of data relative to a model class. The exact definition of the stochastic complexity has gone through several evolutionary steps. The latest instantation is based on the so-called Normalized Maximum Likelihood (NML) distribution which has been shown to possess several important theoretical properties. However, the applications of this modern version of the MDL have been quite rare because of computational complexity problems, i.e., for discrete data, the definition of NML involves an exponential sum, and in the case of continuous data, a multi-dimensional integral usually infeasible to evaluate or even approximate accurately. In this doctoral dissertation, we present mathematical techniques for computing NML efficiently for some model families involving discrete data. We also show how these techniques can be used to apply MDL in two practical applications: histogram density estimation and clustering of multi-dimensional data.

Maximum Loss Calculation using Scenario Analysis, Heavy Tails and Implied Volatility Patterns

The objective of this paper is to improve option risk monitoring by examining the information content of implied volatility and by introducing the calculation of a single-sum expected risk exposure similar to the Value-at-Risk. The figure is calculated in two steps. First, there is a need to estimate the value of a portfolio of options for a number of different market scenarios, while the second step is to summarize the information content of the estimated scenarios into a single-sum risk measure. This involves the use of probability theory and return distributions, which confronts the user with the problems of non-normality in the return distribution of the underlying asset. Here the hyperbolic distribution is used to describe one alternative for dealing with heavy tails. Results indicate that the information content of implied volatility is useful when predicting future large returns in the underlying asset. Further, the hyperbolic distribution provides a good fit to historical returns enabling a more accurate definition of statistical intervals and extreme events.

On the principles of Japanese forest policy since 1950.

XVIII IUFRO World Congress, Ljubljana 1986.

Distributed algorithms for edge dominating sets

An edge dominating set for a graph G is a set D of edges such that each edge of G is in D or adjacent to at least one edge in D. This work studies deterministic distributed approximation algorithms for finding minimum-size edge dominating sets. The focus is on anonymous port-numbered networks: there are no unique identifiers, but a node of degree d can refer to its neighbours by integers 1, 2, ..., d. The present work shows that in the port-numbering model, edge dominating sets can be approximated as follows: in d-regular graphs, to within 4 − 6/(d + 1) for an odd d and to within 4 − 2/d for an even d; and in graphs with maximum degree Δ, to within 4 − 2/(Δ − 1) for an odd Δ and to within 4 − 2/Δ for an even Δ. These approximation ratios are tight for all values of d and Δ: there are matching lower bounds.