‘Governing (Through) Rights: Statistics as Technologies of Governmentality’

An increasing amount of attention is being given to the use of human rights measurement indicators in monitoring ‘progress’ in rights and there is consequently a growing focus on statistics and information. This article concentrates on the use of statistics in rights discourse, with reference to the new human rights institution for the European Union: the Fundamental Rights Agency. The article has two main objectives: first, to show that statistics operate as technologies of governmentality – by explaining that statistics both govern rights and govern through rights. Second, the article discusses the implications that this has for rights discourse – rights become a discourse of governmentality, that is a normalizing and regulating discourse. In doing so, the article stresses the importance of critique and questioning new socio-legal methodologies, which involve the collection and dissemination of information and data (statistics), in rights discourse.

Laplace transforms of probability distributions and their inversions are easy on logarithmic scales

It is shown that, when expressing arguments in terms of their logarithms, the Laplace transform of a function is related to the antiderivative of this function by a simple convolution. This allows efficient numerical computations of moment generating functions of positive random variables and their inversion. The application of the method is straightforward, apart from the necessity to implement it using high-precision arithmetics. In numerical examples the approach is demonstrated to be particularly useful for distributions with heavy tails, Such as lognormal, Weibull, or Pareto distributions, which are otherwise difficult to handle. The computational efficiency compared to other methods is demonstrated for an M/G/1 queueing problem.

NON-GAUSSIAN STATISTICS OF OIL PRICING TIME-SERIES: A CASE STUDY

The stochastic nature of oil price fluctuations is investigated over a twelve-year period, borrowing feedback from an existing database (USA Energy Information Administration database, available online). We evaluate the scaling exponents of the fluctuations by employing different statistical analysis methods, namely rescaled range analysis (R/S), scale windowed variance analysis (SWV) and the generalized Hurst exponent (GH) method. Relying on the scaling exponents obtained, we apply a rescaling procedure to investigate the complex characteristics of the probability density functions (PDFs) dominating oil price fluctuations. It is found that PDFs exhibit scale invariance, and in fact collapse onto a single curve when increments are measured over microscales (typically less than 30 days). The time evolution of the distributions is well fitted by a Levy-type stable distribution. The relevance of a Levy distribution is made plausible by a simple model of nonlinear transfer. Our results also exhibit a degree of multifractality as the PDFs change and converge toward to a Gaussian distribution at the macroscales.

A similarity measure for graphs with low computational complexity

We present and analyze an algorithm to measure the structural similarity of generalized trees, a new graph class which includes rooted trees. For this, we represent structural properties of graphs as strings and define the similarity of two Graphs as optimal alignments of the corresponding property stings. We prove that the obtained graph similarity measures are so called Backward similarity measures. From this we find that the time complexity of our algorithm is polynomial and, hence, significantly better than the time complexity of classical graph similarity methods based on isomorphic relations. (c) 2006 Elsevier Inc. All rights reserved.

Information theoretic measures of UHG graphs with low computational complexity

We introduce a novel graph class we call universal hierarchical graphs (UHG) whose topology can be found numerously in problems representing, e.g., temporal, spacial or general process structures of systems. For this graph class we show, that we can naturally assign two probability distributions, for nodes and for edges, which lead us directly to the definition of the entropy and joint entropy and, hence, mutual information establishing an information theory for this graph class. Furthermore, we provide some results under which conditions these constraint probability distributions maximize the corresponding entropy. Also, we demonstrate that these entropic measures can be computed efficiently which is a prerequisite for every large scale practical application and show some numerical examples. (c) 2007 Elsevier Inc. All rights reserved.

Computational Investigation of Inlet Distortion at High Angles of Attack

Lip separation is one of the primary sources of inlet distortion, which can result in a loss in fan stability. High angles of incidence are one of several critical causes of lip separation. There have been many studies into inlet performance at high incidence, including the resulting distortion levels when lip separation occurs. However, the vast majority of these investigations have been carried out experimentally, with little in the way of computational results for inlet performance at high incidence. The flow topology within an inlet when lip separation has occurred is also not well understood. This work aims to demonstrate a suitable model for the prediction of inlet flows at high incidence using ANSYS CFX, looking at both the performance of the inlet and the separated flow topology within the inlet. The attenuating effect of the fan is also investigated, with particular emphasis on the flow redistribution ahead of the fan. The results show that the model used is suitable for predicting inlet performance in adverse operating conditions, showing good agreement with experimental results. In addition, the attenuation of the distortion by the fan is also captured by the numerical model.