Stefan Wegenkittl

Empirical evidence concerning AES

AES, the Advanced Encryption Standard, is one of the most important algorithms in modern cryptography. Certain randomness properties of AES are of vital importance for its security. At the same time, these properties make AES an interesting candidate for a fast nonlinear random number generator for stochastic simulation. In this article, we address both of these two aspects of AES. We study the performance of AES in a series of statistical tests that are related to cryptographic notions like confusion and diffusion. At the same time, these tests provide empirical evidence for the suitability of AES in stochastic simulation. A substantial part of this article is devoted to the strategy behind our tests and to their relation to other important test statistics like Maurers Universal Test.

MAESTRO - multi agent stability prediction upon point mutations

BackgroundPoint mutations can have a strong impact on protein stability. A change in stability may subsequently lead to dysfunction and finally cause diseases. Moreover, protein engineering approaches aim to deliberately modify protein properties, where stability is a major constraint. In order to support basic research and protein design tasks, several computational tools for predicting the change in stability upon mutations have been developed. Comparative studies have shown the usefulness but also limitations of such programs.ResultsWe aim to contribute a novel method for predicting changes in stability upon point mutation in proteins called MAESTRO. MAESTRO is structure based and distinguishes itself from similar approaches in the following points: (i) MAESTRO implements a multi-agent machine learning system. (ii) It also provides predicted free energy change (ΔΔG) values and a corresponding prediction confidence estimation. (iii) It provides high throughput scanning for multi-point mutations where sites and types of mutation can be comprehensively controlled. (iv) Finally, the software provides a specific mode for the prediction of stabilizing disulfide bonds. The predictive power of MAESTRO for single point mutations and stabilizing disulfide bonds is comparable to similar methods.ConclusionsMAESTRO is a versatile tool in the field of stability change prediction upon point mutations. Executables for the Linux and Windows operating systems are freely available to non-commercial users from http://biwww.che.sbg.ac.at/MAESTRO.

Linear Congruential Generators for Parallel Monte Carlo: the Leap-Frog Case

In this paper we consider parallel streams of pseudorandom numbers (PRNs) which are obtained by splitting linear congruential generators (LCGs) using the leap-frog technique. We employ the spectral test to compute an a priori figure of merit which rates the amount of correlation that is present in such sequences for given step size and dimension. It is shown that for some widely used LCGs there exist practically relevant splitting parameters such that the according parallel streams have poor quality. As can be seen from a sample Monte Carlo integration study, these theoretical findings have high practical importance.

Linear and inversive pseudorandom numbers for parallel and distributed simulation

We discuss the use and possible abuse of linear and inversive pseudorandom numbers (PRNs) in parallel and distributed environments. After an investigation of properties of PRNs which determine how these may be applied in such environments, we introduce a software package which provides a unified and easy to use approach to the generating and handling of parallel streams of such PRNs. Experimental results are conducted which describe the features of the software package and compare the performance of two selected types of pseudorandom number generators.

Towards a framework for agent-based image analysis of remote-sensing data

Object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects’ properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA).

Getting rid of correlations among pseudorandom numbers: discarding versus tempering

We consider the impact of discarding and tempering on modern huge period high speed linear generators, and illustrate how a simple strategy yields unexpected &mdashh; and unwanted — success in a fair coin gambling which is simulated by a recently proposed generator. It becomes clear that discarding is no general rule to get rid of unwanted correlations.

Parallel Random Number Generation: Long-Range Correlations Among Multiple Processors

We use an empirical study based on simple Monte Carlo integrations to exhibit the well known long-range correlations between linear congruential random numbers. In contrast to former studies, our long-range correlation test is carried out to assess more than only two parallel streams. In addition we perform our test also with explicit inversive generators which from the theoretical point of view have to be stable against long-range correlations.

Entropy estimators and serial tests for ergodic chains

Maurer (1992, A universal statistical test for random bit generators) discussed a statistic whose value is closely related to the per-bit-entropy of an ergodic stationary source. Here we derive an entropy estimate from a class of generalized serial tests and discuss its relationship to return-time-based entropy estimators and frequency-based goodness-of-fit tests. Our setup extends Kullbacks I-divergence approach for independent stationary sequences to the class of ergodic Markov chains. The effects caused by the order of the source are examined theoretically and by an empirical study.

Monkeys, gambling, and return times: assessing pseudorandomness

We present a general construction kit for empirical tests of pseudorandom number generators which comprises a wide range of well-known standard tests. Within our setup we identify two important families of tests and check for connections between them. This leads us to query the existence of universal tests which claim to be able to detect any possible defect of a generator.

Sleep in patients with disorders of consciousness characterized by means of machine learning

Sleep has been proposed to indicate preserved residual brain functioning in patients suffering from disorders of consciousness (DOC) after awakening from coma. However, a reliable characterization of sleep patterns in this clinical population continues to be challenging given severely altered brain oscillations, frequent and extended artifacts in clinical recordings and the absence of established staging criteria. In the present study, we try to address these issues and investigate the usefulness of a multivariate machine learning technique based on permutation entropy, a complexity measure. Specifically, we used long-term polysomnography (PSG), along with video recordings in day and night periods in a sample of 23 DOC; 12 patients were diagnosed as Unresponsive Wakefulness Syndrome (UWS) and 11 were diagnosed as Minimally Conscious State (MCS). Eight hour PSG recordings of healthy sleepers (N = 26) were additionally used for training and setting parameters of supervised and unsupervised model, respectively. In DOC, the supervised classification (wake, N1, N2, N3 or REM) was validated using simultaneous videos which identified periods with prolonged eye opening or eye closure.The supervised classification revealed that out of the 23 subjects, 11 patients (5 MCS and 6 UWS) yielded highly accurate classification with an average F1-score of 0.87 representing high overlap between the classifier predicting sleep (i.e. one of the 4 sleep stages) and closed eyes. Furthermore, the unsupervised approach revealed a more complex pattern of sleep-wake stages during the night period in the MCS group, as evidenced by the presence of several distinct clusters. In contrast, in UWS patients no such clustering was found. Altogether, we present a novel data-driven method, based on machine learning that can be used to gain new and unambiguous insights into sleep organization and residual brain functioning of patients with DOC.