We introduce TestU01, a software library implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical testing of uniform random number generators (RNGs). It provides general implementations of the classical statistical tests for RNGs, as well as several others tests proposed in the literature, and some original ones. Predefined tests suites for sequences of uniform random numbers over the interval (0, 1) and for bit sequences are available. Tools are also offered to perform systematic studies of the interaction between a specific test and the structure of the point sets produced by a given family of RNGs. That is, for a given kind of test and a given class of RNGs, to determine how large should be the sample size of the test, as a function of the generator's period length, before the generator starts to fail the test systematically. Finally, the library provides various types of generators implemented in generic form, as well as many specific generators proposed in the literature or found in widely used software. The tests can be applied to instances of the generators predefined in the library, or to user-defined generators, or to streams of random numbers produced by any kind of device or stored in files. Besides introducing TestU01, the article provides a survey and a classification of statistical tests for RNGs. It also applies batteries of tests to a long list of widely used RNGs.

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TestU01: A C library for empirical testing of random number generators

Proteins are highly dynamic molecules, whose function is intrinsically linked to their molecular motions. Despite the pivotal role of protein dynamics, their computational simulation cost has led to most structure-based approaches for assessing the impact of mutations on protein structure and function relying upon static structures. Here we present DynaMut, a web server implementing two distinct, well established normal mode approaches, which can be used to analyze and visualize protein dynamics by sampling conformations and assess the impact of mutations on protein dynamics and stability resulting from vibrational entropy changes. DynaMut integrates our graph-based signatures along with normal mode dynamics to generate a consensus prediction of the impact of a mutation on protein stability. We demonstrate our approach outperforms alternative approaches to predict the effects of mutations on protein stability and flexibility (P-value < 0.001), achieving a correlation of up to 0.70 on blind tests. DynaMut also provides a comprehensive suite for protein motion and flexibility analysis and visualization via a freely available, user friendly web server at http://biosig.unimelb.edu.au/dynamut/.

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/216409/DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability.pdf

DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability

The Monte Carlo method.- Sampling from known distributions.- Pseudorandom number generators.- Variance reduction techniques.- Quasi-Monte Carlo constructions.- Using quasi-Monte Carlo constructions.- Using quasi-Monte Carlo in practice.- Financial applications.- Beyond numerical integration.- Review of algebra.- Error and variance analysis for Halton sequences.- References.- Index.

Monte Carlo and Quasi-Monte Carlo Sampling

In telecommunication networks, as in many other areas of science and engineering, the proliferation of computers as research tools has resulted in the adoption of computer simulation as the most commonly used paradigm of scientific investigations. This, together with a plethora of existing simulation languages and packages, has created a popular opinion that simulation is mainly an exercise in computer programming. In new computing environments, programming can be minimized, or even fully replaced, by the manipulation of icons (representing prebuilt programming objects containing basic functional blocks of simulated systems) on a computer monitor. One can say that we have witnessed another success of modern science and technology: the emergence of wonderful and powerful tools for exploring and predicting the behavior of such complex stochastic dynamic systems as telecommunication networks. But this enthusiasm is not shared by all researchers in this area. An opinion is spreading that one cannot rely on the majority of the published results on performance evaluation studies of telecommunication networks based on stochastic simulation, since they lack credibility. Indeed, the spread of this phenomenon is so wide that one can speak about a deep crisis of credibility. In this article this claim is supported by the results of a survey of over 2200 publications on telecommunication networks in proceedings of IEEE INFOCOM and such journals as IEEE Transactions on Communications, IEEE/ACM Transactions on Networking, and Performance Evaluation Journal. The discussion focuses on two important necessary conditions of a credible simulation study: use of appropriate pseudo-random generators of independent uniformly distributed numbers, and appropriate analysis of simulation output data. Having considered their perils and pitfalls, we formulate guidelines that, if observed, could help to ensure a basic level of credibility of simulation studies of telecommunication networks.

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On credibility of simulation studies of telecommunication networks

Thank you very much for reading nonparametrics statistical methods based on ranks. Maybe you have knowledge that, people have look hundreds times for their favorite novels like this nonparametrics statistical methods based on ranks, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some malicious virus inside their desktop computer.

Nonparametrics Statistical Methods Based On Ranks

Point 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. We 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 (Δ
ΔG) 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. MAESTRO 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
 .

https://link.springer.com/content/pdf/10.1186%2Fs12859-015-0548-6.pdf

MAESTRO - multi agent stability prediction upon point mutations

This review paper deals with nonlinear methods for the generation of uniform pseudorandom numbers in the unit interval. The emphasis is on results of the theoretical analysis of quadratic congruential and (recursive) inversive congruential generators, which are scattered over a fairly large number of articles. Additionally, empirical results of some sample generators in a two—level overlapping serial test are given.

A survey of quadratic and inversive congruential pseudorandom numbers

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 Maurer's Universal Test.

Empirical evidence concerning AES

We present results from a series of empirical tests of pseudorandom number generators. The tests cover a broad range of designs due to bit-oriented, efficient test statistics and a testing procedure inwhich we vary the sample size, dimension, and the statistics' resolution within vast bounds. Inversive generation methods pass the tests for a broader range of thest parameters thatn linear generators with equal period legth. The results exemplify how the lattice structure of linear generatorsf can affect a stochastic simulation and suggest the use of inversive generators for cross-checking the results.

Inversive and linear congruential pseudorandom number generators in empirical tests

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.

/pdf/sleep-in-patients-with-disorders-of-consciousness-1olf45pumb.pdf

Stefan Wegenkittl

Papers

MAESTRO - multi agent stability prediction upon point mutations

A survey of quadratic and inversive congruential pseudorandom numbers

Empirical evidence concerning AES

Inversive and linear congruential pseudorandom number generators in empirical tests

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